fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

790da02a1f4ea3a9e0f830edc861126ed46cf2b6

18,657

py

Python

benchmarks/f3_wrong_hints/scaling_ltl_timed_transition_system/15-sender_receiver_10.py

EnricoMagnago/F3

c863215c318d7d5f258eb9be38c6962cf6863b52

[ "MIT" ]

3

2021-04-23T23:29:26.000Z

2022-03-23T10:00:30.000Z

benchmarks/f3_wrong_hints/scaling_ltl_timed_transition_system/15-sender_receiver_10.py

EnricoMagnago/F3

c863215c318d7d5f258eb9be38c6962cf6863b52

[ "MIT" ]

null

benchmarks/f3_wrong_hints/scaling_ltl_timed_transition_system/15-sender_receiver_10.py

EnricoMagnago/F3

c863215c318d7d5f258eb9be38c6962cf6863b52

[ "MIT" ]

1

2021-11-17T22:02:56.000Z

from typing import FrozenSet from collections import Iterable from math import log, ceil from mathsat import msat_term, msat_env from mathsat import msat_make_constant, msat_declare_function from mathsat import msat_get_integer_type, msat_get_rational_type, msat_get_bool_type from mathsat import msat_make_and, msat_make_not, msat_make_or, msat_make_iff from mathsat import msat_make_leq, msat_make_equal, msat_make_true from mathsat import msat_make_number, msat_make_plus, msat_make_times from pysmt.environment import Environment as PysmtEnv import pysmt.typing as types from ltl.ltl import TermMap, LTLEncoder from utils import name_next, symb_to_next from hint import Hint, Location delta_name = "delta" def decl_consts(menv: msat_env, name: str, c_type) -> tuple: assert not name.startswith("_"), name s = msat_declare_function(menv, name, c_type) s = msat_make_constant(menv, s) x_s = msat_declare_function(menv, name_next(name), c_type) x_s = msat_make_constant(menv, x_s) return s, x_s def make_enum(menv, v_name: str, enum_size: int): bool_type = msat_get_bool_type(menv) num_bits = ceil(log(enum_size, 2)) b_vars = [] for idx in range(num_bits): c_name = "{}{}".format(v_name, idx) b_vars.append(tuple(decl_consts(menv, c_name, bool_type))) vals = [] x_vals = [] for enum_val in range(enum_size): bit_val = format(enum_val, '0{}b'.format(num_bits)) assert len(bit_val) == num_bits assert all(c in {'0', '1'} for c in bit_val) assign = [b_vars[idx] if c == '1' else (msat_make_not(menv, b_vars[idx][0]), msat_make_not(menv, b_vars[idx][1])) for idx, c in enumerate(reversed(bit_val))] pred = assign[0][0] x_pred = assign[0][1] for it in assign[1:]: pred = msat_make_and(menv, pred, it[0]) x_pred = msat_make_and(menv, x_pred, it[1]) vals.append(pred) x_vals.append(x_pred) assert len(vals) == enum_size assert len(x_vals) == enum_size return b_vars, vals, x_vals def msat_make_minus(menv: msat_env, arg0: msat_term, arg1: msat_term): m_one = msat_make_number(menv, "-1") arg1 = msat_make_times(menv, arg1, m_one) return msat_make_plus(menv, arg0, arg1) def msat_make_lt(menv: msat_env, arg0: msat_term, arg1: msat_term): geq = msat_make_geq(menv, arg0, arg1) return msat_make_not(menv, geq) def msat_make_geq(menv: msat_env, arg0: msat_term, arg1: msat_term): return msat_make_leq(menv, arg1, arg0) def msat_make_gt(menv: msat_env, arg0: msat_term, arg1: msat_term): leq = msat_make_leq(menv, arg0, arg1) return msat_make_not(menv, leq) def msat_make_impl(menv: msat_env, arg0: msat_term, arg1: msat_term): n_arg0 = msat_make_not(menv, arg0) return msat_make_or(menv, n_arg0, arg1) def diverging_symbs(menv: msat_env) -> frozenset: real_type = msat_get_rational_type(menv) delta = msat_declare_function(menv, delta_name, real_type) delta = msat_make_constant(menv, delta) return frozenset([delta]) def check_ltl(menv: msat_env, enc: LTLEncoder) -> (Iterable, msat_term, msat_term, msat_term): assert menv assert isinstance(menv, msat_env) assert enc assert isinstance(enc, LTLEncoder) int_type = msat_get_integer_type(menv) real_type = msat_get_rational_type(menv) r2s, x_r2s = decl_consts(menv, "r2s", int_type) s2r, x_s2r = decl_consts(menv, "s2r", int_type) delta, x_delta = decl_consts(menv, delta_name, real_type) sender = Sender("s", menv, enc, r2s, x_r2s, s2r, x_s2r, delta) receiver = Receiver("r", menv, enc, s2r, x_s2r, r2s, x_r2s, delta) curr2next = {r2s: x_r2s, s2r: x_s2r, delta: x_delta} for comp in [sender, receiver]: for s, x_s in comp.symb2next.items(): curr2next[s] = x_s zero = msat_make_number(menv, "0") init = msat_make_and(menv, receiver.init, sender.init) trans = msat_make_and(menv, receiver.trans, sender.trans) # invar delta >= 0 init = msat_make_and(menv, init, msat_make_geq(menv, delta, zero)) trans = msat_make_and(menv, trans, msat_make_geq(menv, x_delta, zero)) # delta > 0 -> (r2s' = r2s & s2r' = s2r) lhs = msat_make_gt(menv, delta, zero) rhs = msat_make_and(menv, msat_make_equal(menv, x_r2s, r2s), msat_make_equal(menv, x_s2r, s2r)) trans = msat_make_and(menv, trans, msat_make_impl(menv, lhs, rhs)) # (G F !s.stutter) -> G (s.wait_ack -> F s.send) lhs = enc.make_G(enc.make_F(msat_make_not(menv, sender.stutter))) rhs = enc.make_G(msat_make_impl(menv, sender.wait_ack, enc.make_F(sender.send))) ltl = msat_make_impl(menv, lhs, rhs) return TermMap(curr2next), init, trans, ltl class Module: def __init__(self, name: str, menv: msat_env, enc: LTLEncoder, *args, **kwargs): self.name = name self.menv = menv self.enc = enc self.symb2next = {} true = msat_make_true(menv) self.init = true self.trans = true def _symb(self, v_name, v_type): v_name = "{}_{}".format(self.name, v_name) return decl_consts(self.menv, v_name, v_type) def _enum(self, v_name: str, enum_size: int): c_name = "{}_{}".format(self.name, v_name) return make_enum(self.menv, c_name, enum_size) class Sender(Module): def __init__(self, name: str, menv: msat_env, enc: LTLEncoder, in_c, x_in_c, out_c, x_out_c, delta): super().__init__(name, menv, enc) bool_type = msat_get_bool_type(menv) int_type = msat_get_integer_type(menv) real_type = msat_get_rational_type(menv) loc, x_loc = self._symb("l", bool_type) evt, x_evt = self._symb("evt", bool_type) msg_id, x_msg_id = self._symb("msg_id", int_type) timeout, x_timeout = self._symb("timeout", real_type) c, x_c = self._symb("c", real_type) self.move = evt self.stutter = msat_make_not(menv, evt) self.x_move = x_evt self.x_stutter = msat_make_not(menv, x_evt) self.send = loc self.wait_ack = msat_make_not(menv, loc) self.x_send = x_loc self.x_wait_ack = msat_make_not(menv, x_loc) self.symb2next = {loc: x_loc, evt: x_evt, msg_id: x_msg_id, timeout: x_timeout, c: x_c} zero = msat_make_number(menv, "0") one = msat_make_number(menv, "1") base_timeout = one # send & c = 0 & msg_id = 0 self.init = msat_make_and(menv, msat_make_and(menv, self.send, msat_make_equal(menv, c, zero)), msat_make_equal(menv, msg_id, zero)) # invar: wait_ack -> c <= timeout self.init = msat_make_and( menv, self.init, msat_make_impl(menv, self.wait_ack, msat_make_leq(menv, c, timeout))) self.trans = msat_make_impl(menv, self.x_wait_ack, msat_make_leq(menv, x_c, x_timeout)) # delta > 0 | stutter -> l' = l & msg_id' = msg_id & timeout' = timeout & # c' = c + delta & out_c' = out_c lhs = msat_make_or(menv, msat_make_gt(menv, delta, zero), self.stutter) rhs = msat_make_and( menv, msat_make_and(menv, msat_make_iff(menv, x_loc, loc), msat_make_equal(menv, x_msg_id, msg_id)), msat_make_and(menv, msat_make_equal(menv, x_timeout, timeout), msat_make_equal(menv, x_c, msat_make_plus(menv, c, delta)))) rhs = msat_make_and(menv, rhs, msat_make_equal(menv, x_out_c, out_c)) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) disc_t = msat_make_and(menv, self.move, msat_make_equal(menv, delta, zero)) # (send & send') -> # (msg_id' = msg_id & timeout' = base_timeout & c' = 0 & out_c' = out_c) lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.send, self.x_send)) rhs = msat_make_and( menv, msat_make_and(menv, msat_make_equal(menv, x_msg_id, msg_id), msat_make_equal(menv, x_timeout, base_timeout)), msat_make_and(menv, msat_make_equal(menv, x_c, zero), msat_make_equal(menv, x_out_c, out_c))) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # (send & wait_ack') -> # (msg_id' = msg_id + 1 & timeout' = base_timeout & c' = 0 & out_c' = out_c) lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.send, self.x_wait_ack)) rhs = msat_make_and( menv, msat_make_and(menv, msat_make_equal(menv, x_msg_id, msat_make_plus(menv, msg_id, one)), msat_make_equal(menv, x_timeout, base_timeout)), msat_make_and(menv, msat_make_equal(menv, x_c, zero), msat_make_equal(menv, x_out_c, out_c))) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # (wait_ack) -> (c' = 0 & out_c' = out_c & # (wait_ack' <-> (in_c != msg_id & c > timeout)) lhs = msat_make_and(menv, disc_t, self.wait_ack) rhs_iff = msat_make_and(menv, msat_make_not(menv, msat_make_equal(menv, in_c, msg_id)), msat_make_geq(menv, c, timeout)) rhs_iff = msat_make_iff(menv, self.x_wait_ack, rhs_iff) rhs = msat_make_and(menv, msat_make_and(menv, msat_make_equal(menv, x_c, zero), msat_make_equal(menv, x_out_c, out_c)), rhs_iff) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # (wait_ack & wait_ack') -> (timeout' > timeout) lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.wait_ack, self.x_wait_ack)) rhs = msat_make_gt(menv, x_timeout, timeout) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # (wait_ack) -> (send' <-> (in_c = msg_id & c < timeout)) lhs = msat_make_and(menv, disc_t, self.wait_ack) rhs = msat_make_iff(menv, self.x_send, msat_make_and(menv, msat_make_equal(menv, in_c, msg_id), msat_make_lt(menv, c, timeout))) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # (wait_ack & send') -> (timeout' = base_timeout) lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.wait_ack, self.x_send)) rhs = msat_make_equal(menv, x_timeout, base_timeout) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) class Receiver(Module): def __init__(self, name: str, menv: msat_env, enc: LTLEncoder, in_c, x_in_c, out_c, x_out_c, delta): super().__init__(name, menv, enc) bool_type = msat_get_bool_type(menv) loc, x_loc = self._symb("l", bool_type) self.wait = loc self.work = msat_make_not(menv, loc) self.x_wait = x_loc self.x_work = msat_make_not(menv, x_loc) self.symb2next = {loc: x_loc} zero = msat_make_number(menv, "0") # wait self.init = self.wait # delta > 0 -> loc' = loc & out_c' = out_c lhs = msat_make_gt(menv, delta, zero) rhs = msat_make_and(menv, msat_make_iff(menv, x_loc, loc), msat_make_equal(menv, x_out_c, out_c)) self.trans = msat_make_impl(menv, lhs, rhs) disc_t = msat_make_equal(menv, delta, zero) # wait -> (wait' <-> in_c = out_c) lhs = msat_make_and(menv, disc_t, self.wait) rhs = msat_make_iff(menv, self.x_wait, msat_make_equal(menv, in_c, out_c)) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # (wait & wait') -> (out_c' = out_c) lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.wait, self.x_wait)) rhs = msat_make_equal(menv, x_out_c, out_c) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # (wait & work') -> out_c' = in_c lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.wait, self.x_work)) rhs = msat_make_equal(menv, x_out_c, in_c) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # work -> out_c' = out_c lhs = msat_make_and(menv, disc_t, self.work) rhs = msat_make_equal(menv, x_out_c, out_c) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) def hints(env: PysmtEnv) -> FrozenSet[Hint]: assert isinstance(env, PysmtEnv) mgr = env.formula_manager delta = mgr.Symbol(delta_name, types.REAL) r2s = mgr.Symbol("r2s", types.INT) s2r = mgr.Symbol("r2s", types.INT) s_l = mgr.Symbol("s_l", types.BOOL) s_evt = mgr.Symbol("s_evt", types.BOOL) s_msg_id = mgr.Symbol("s_msg_id", types.INT) s_timeout = mgr.Symbol("s_timeout", types.REAL) s_c = mgr.Symbol("s_c", types.REAL) r_l = mgr.Symbol("r_l", types.BOOL) symbs = frozenset([delta, r2s, s2r, s_l, s_evt, s_msg_id, s_timeout, s_c, r_l]) x_delta = symb_to_next(mgr, delta) x_r2s = symb_to_next(mgr, r2s) x_s2r = symb_to_next(mgr, s2r) x_s_l = symb_to_next(mgr, s_l) x_s_evt = symb_to_next(mgr, s_evt) x_s_msg_id = symb_to_next(mgr, s_msg_id) x_s_timeout = symb_to_next(mgr, s_timeout) x_s_c = symb_to_next(mgr, s_c) x_r_l = symb_to_next(mgr, r_l) res = [] r0 = mgr.Real(0) r1 = mgr.Real(1) i0 = mgr.Int(0) i1 = mgr.Int(1) loc0 = Location(env, mgr.Equals(delta, r0)) loc0.set_progress(0, mgr.Equals(x_delta, r0)) hint = Hint("h_delta0", env, frozenset([delta]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.Equals(s2r, i0)) loc0.set_progress(0, mgr.Equals(x_s2r, i0)) hint = Hint("h_s2r0", env, frozenset([s2r]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, s_evt) loc0.set_progress(0, x_s_evt) hint = Hint("h_s_evt0", env, frozenset([s_evt]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.Equals(s_msg_id, i0)) loc0.set_progress(0, mgr.Equals(x_s_msg_id, i0)) hint = Hint("h_s_msg_id0", env, frozenset([s_msg_id]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.Equals(s_c, r0)) loc0.set_progress(0, mgr.Equals(x_s_c, r0)) hint = Hint("h_s_c0", env, frozenset([s_c]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, r_l) loc0.set_progress(0, x_r_l) hint = Hint("h_r_l0", env, frozenset([r_l]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.GE(s2r, i0)) loc0.set_progress(0, mgr.Equals(x_s2r, i1)) hint = Hint("h_s2r1", env, frozenset([s2r]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.GE(r2s, i0)) loc0.set_progress(0, mgr.Equals(x_r2s, i1)) hint = Hint("h_r2s1", env, frozenset([r2s]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, s_l) loc0.set_progress(1, mgr.Not(x_s_l)) loc1 = Location(env, mgr.Not(s_l)) loc1.set_progress(0, x_s_l) hint = Hint("h_s_l1", env, frozenset([s_l]), symbs) hint.set_locs([loc0, loc1]) res.append(hint) loc0 = Location(env, s_evt) loc0.set_progress(1, mgr.Not(x_s_evt)) loc1 = Location(env, mgr.Not(s_evt)) loc1.set_progress(0, x_s_evt) hint = Hint("h_s_evt1", env, frozenset([s_evt]), symbs) hint.set_locs([loc0, loc1]) res.append(hint) loc0 = Location(env, mgr.GE(s_msg_id, i0)) loc0.set_progress(0, mgr.Equals(x_s_msg_id, mgr.Plus(s_msg_id, i1))) hint = Hint("h_s_msg_id1", env, frozenset([s_msg_id]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.GE(s_timeout, r0)) loc0.set_progress(0, mgr.Equals(x_s_timeout, mgr.Plus(s_timeout, r1))) hint = Hint("h_s_timeout1", env, frozenset([s_timeout]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, r_l) loc0.set_progress(1, mgr.Not(x_r_l)) loc1 = Location(env, mgr.Not(r_l)) loc1.set_progress(0, x_r_l) hint = Hint("h_r_l1", env, frozenset([r_l]), symbs) hint.set_locs([loc0, loc1]) res.append(hint) loc0 = Location(env, mgr.GE(delta, r0)) loc0.set_progress(0, mgr.Equals(x_delta, mgr.Plus(delta, r1))) hint = Hint("h_delta2", env, frozenset([delta]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.GE(s2r, i0)) loc0.set_progress(0, mgr.Equals(x_s2r, mgr.Plus(s2r, i1))) hint = Hint("h_s2r2", env, frozenset([s2r]), symbs) hint.set_locs([loc0]) res.append(hint) return frozenset(res)

38.388889

89

0.577049

from typing import FrozenSet from collections import Iterable from math import log, ceil from mathsat import msat_term, msat_env from mathsat import msat_make_constant, msat_declare_function from mathsat import msat_get_integer_type, msat_get_rational_type, msat_get_bool_type from mathsat import msat_make_and, msat_make_not, msat_make_or, msat_make_iff from mathsat import msat_make_leq, msat_make_equal, msat_make_true from mathsat import msat_make_number, msat_make_plus, msat_make_times from pysmt.environment import Environment as PysmtEnv import pysmt.typing as types from ltl.ltl import TermMap, LTLEncoder from utils import name_next, symb_to_next from hint import Hint, Location delta_name = "delta" def decl_consts(menv: msat_env, name: str, c_type) -> tuple: assert not name.startswith("_"), name s = msat_declare_function(menv, name, c_type) s = msat_make_constant(menv, s) x_s = msat_declare_function(menv, name_next(name), c_type) x_s = msat_make_constant(menv, x_s) return s, x_s def make_enum(menv, v_name: str, enum_size: int): bool_type = msat_get_bool_type(menv) num_bits = ceil(log(enum_size, 2)) b_vars = [] for idx in range(num_bits): c_name = "{}{}".format(v_name, idx) b_vars.append(tuple(decl_consts(menv, c_name, bool_type))) vals = [] x_vals = [] for enum_val in range(enum_size): bit_val = format(enum_val, '0{}b'.format(num_bits)) assert len(bit_val) == num_bits assert all(c in {'0', '1'} for c in bit_val) assign = [b_vars[idx] if c == '1' else (msat_make_not(menv, b_vars[idx][0]), msat_make_not(menv, b_vars[idx][1])) for idx, c in enumerate(reversed(bit_val))] pred = assign[0][0] x_pred = assign[0][1] for it in assign[1:]: pred = msat_make_and(menv, pred, it[0]) x_pred = msat_make_and(menv, x_pred, it[1]) vals.append(pred) x_vals.append(x_pred) assert len(vals) == enum_size assert len(x_vals) == enum_size return b_vars, vals, x_vals def msat_make_minus(menv: msat_env, arg0: msat_term, arg1: msat_term): m_one = msat_make_number(menv, "-1") arg1 = msat_make_times(menv, arg1, m_one) return msat_make_plus(menv, arg0, arg1) def msat_make_lt(menv: msat_env, arg0: msat_term, arg1: msat_term): geq = msat_make_geq(menv, arg0, arg1) return msat_make_not(menv, geq) def msat_make_geq(menv: msat_env, arg0: msat_term, arg1: msat_term): return msat_make_leq(menv, arg1, arg0) def msat_make_gt(menv: msat_env, arg0: msat_term, arg1: msat_term): leq = msat_make_leq(menv, arg0, arg1) return msat_make_not(menv, leq) def msat_make_impl(menv: msat_env, arg0: msat_term, arg1: msat_term): n_arg0 = msat_make_not(menv, arg0) return msat_make_or(menv, n_arg0, arg1) def diverging_symbs(menv: msat_env) -> frozenset: real_type = msat_get_rational_type(menv) delta = msat_declare_function(menv, delta_name, real_type) delta = msat_make_constant(menv, delta) return frozenset([delta]) def check_ltl(menv: msat_env, enc: LTLEncoder) -> (Iterable, msat_term, msat_term, msat_term): assert menv assert isinstance(menv, msat_env) assert enc assert isinstance(enc, LTLEncoder) int_type = msat_get_integer_type(menv) real_type = msat_get_rational_type(menv) r2s, x_r2s = decl_consts(menv, "r2s", int_type) s2r, x_s2r = decl_consts(menv, "s2r", int_type) delta, x_delta = decl_consts(menv, delta_name, real_type) sender = Sender("s", menv, enc, r2s, x_r2s, s2r, x_s2r, delta) receiver = Receiver("r", menv, enc, s2r, x_s2r, r2s, x_r2s, delta) curr2next = {r2s: x_r2s, s2r: x_s2r, delta: x_delta} for comp in [sender, receiver]: for s, x_s in comp.symb2next.items(): curr2next[s] = x_s zero = msat_make_number(menv, "0") init = msat_make_and(menv, receiver.init, sender.init) trans = msat_make_and(menv, receiver.trans, sender.trans) init = msat_make_and(menv, init, msat_make_geq(menv, delta, zero)) trans = msat_make_and(menv, trans, msat_make_geq(menv, x_delta, zero)) lhs = msat_make_gt(menv, delta, zero) rhs = msat_make_and(menv, msat_make_equal(menv, x_r2s, r2s), msat_make_equal(menv, x_s2r, s2r)) trans = msat_make_and(menv, trans, msat_make_impl(menv, lhs, rhs)) lhs = enc.make_G(enc.make_F(msat_make_not(menv, sender.stutter))) rhs = enc.make_G(msat_make_impl(menv, sender.wait_ack, enc.make_F(sender.send))) ltl = msat_make_impl(menv, lhs, rhs) return TermMap(curr2next), init, trans, ltl class Module: def __init__(self, name: str, menv: msat_env, enc: LTLEncoder, *args, **kwargs): self.name = name self.menv = menv self.enc = enc self.symb2next = {} true = msat_make_true(menv) self.init = true self.trans = true def _symb(self, v_name, v_type): v_name = "{}_{}".format(self.name, v_name) return decl_consts(self.menv, v_name, v_type) def _enum(self, v_name: str, enum_size: int): c_name = "{}_{}".format(self.name, v_name) return make_enum(self.menv, c_name, enum_size) class Sender(Module): def __init__(self, name: str, menv: msat_env, enc: LTLEncoder, in_c, x_in_c, out_c, x_out_c, delta): super().__init__(name, menv, enc) bool_type = msat_get_bool_type(menv) int_type = msat_get_integer_type(menv) real_type = msat_get_rational_type(menv) loc, x_loc = self._symb("l", bool_type) evt, x_evt = self._symb("evt", bool_type) msg_id, x_msg_id = self._symb("msg_id", int_type) timeout, x_timeout = self._symb("timeout", real_type) c, x_c = self._symb("c", real_type) self.move = evt self.stutter = msat_make_not(menv, evt) self.x_move = x_evt self.x_stutter = msat_make_not(menv, x_evt) self.send = loc self.wait_ack = msat_make_not(menv, loc) self.x_send = x_loc self.x_wait_ack = msat_make_not(menv, x_loc) self.symb2next = {loc: x_loc, evt: x_evt, msg_id: x_msg_id, timeout: x_timeout, c: x_c} zero = msat_make_number(menv, "0") one = msat_make_number(menv, "1") base_timeout = one self.init = msat_make_and(menv, msat_make_and(menv, self.send, msat_make_equal(menv, c, zero)), msat_make_equal(menv, msg_id, zero)) self.init = msat_make_and( menv, self.init, msat_make_impl(menv, self.wait_ack, msat_make_leq(menv, c, timeout))) self.trans = msat_make_impl(menv, self.x_wait_ack, msat_make_leq(menv, x_c, x_timeout)) # c' = c + delta & out_c' = out_c lhs = msat_make_or(menv, msat_make_gt(menv, delta, zero), self.stutter) rhs = msat_make_and( menv, msat_make_and(menv, msat_make_iff(menv, x_loc, loc), msat_make_equal(menv, x_msg_id, msg_id)), msat_make_and(menv, msat_make_equal(menv, x_timeout, timeout), msat_make_equal(menv, x_c, msat_make_plus(menv, c, delta)))) rhs = msat_make_and(menv, rhs, msat_make_equal(menv, x_out_c, out_c)) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) disc_t = msat_make_and(menv, self.move, msat_make_equal(menv, delta, zero)) # (send & send') -> lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.send, self.x_send)) rhs = msat_make_and( menv, msat_make_and(menv, msat_make_equal(menv, x_msg_id, msg_id), msat_make_equal(menv, x_timeout, base_timeout)), msat_make_and(menv, msat_make_equal(menv, x_c, zero), msat_make_equal(menv, x_out_c, out_c))) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # (msg_id' = msg_id + 1 & timeout' = base_timeout & c' = 0 & out_c' = out_c) lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.send, self.x_wait_ack)) rhs = msat_make_and( menv, msat_make_and(menv, msat_make_equal(menv, x_msg_id, msat_make_plus(menv, msg_id, one)), msat_make_equal(menv, x_timeout, base_timeout)), msat_make_and(menv, msat_make_equal(menv, x_c, zero), msat_make_equal(menv, x_out_c, out_c))) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # (wait_ack) -> (c' = 0 & out_c' = out_c & # (wait_ack' <-> (in_c != msg_id & c > timeout)) lhs = msat_make_and(menv, disc_t, self.wait_ack) rhs_iff = msat_make_and(menv, msat_make_not(menv, msat_make_equal(menv, in_c, msg_id)), msat_make_geq(menv, c, timeout)) rhs_iff = msat_make_iff(menv, self.x_wait_ack, rhs_iff) rhs = msat_make_and(menv, msat_make_and(menv, msat_make_equal(menv, x_c, zero), msat_make_equal(menv, x_out_c, out_c)), rhs_iff) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.wait_ack, self.x_wait_ack)) rhs = msat_make_gt(menv, x_timeout, timeout) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) lhs = msat_make_and(menv, disc_t, self.wait_ack) rhs = msat_make_iff(menv, self.x_send, msat_make_and(menv, msat_make_equal(menv, in_c, msg_id), msat_make_lt(menv, c, timeout))) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) # (wait_ack & send') -> (timeout' = base_timeout) lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.wait_ack, self.x_send)) rhs = msat_make_equal(menv, x_timeout, base_timeout) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) class Receiver(Module): def __init__(self, name: str, menv: msat_env, enc: LTLEncoder, in_c, x_in_c, out_c, x_out_c, delta): super().__init__(name, menv, enc) bool_type = msat_get_bool_type(menv) loc, x_loc = self._symb("l", bool_type) self.wait = loc self.work = msat_make_not(menv, loc) self.x_wait = x_loc self.x_work = msat_make_not(menv, x_loc) self.symb2next = {loc: x_loc} zero = msat_make_number(menv, "0") # wait self.init = self.wait # delta > 0 -> loc' = loc & out_c' = out_c lhs = msat_make_gt(menv, delta, zero) rhs = msat_make_and(menv, msat_make_iff(menv, x_loc, loc), msat_make_equal(menv, x_out_c, out_c)) self.trans = msat_make_impl(menv, lhs, rhs) disc_t = msat_make_equal(menv, delta, zero) # wait -> (wait' <-> in_c = out_c) lhs = msat_make_and(menv, disc_t, self.wait) rhs = msat_make_iff(menv, self.x_wait, msat_make_equal(menv, in_c, out_c)) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.wait, self.x_wait)) rhs = msat_make_equal(menv, x_out_c, out_c) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) lhs = msat_make_and(menv, disc_t, msat_make_and(menv, self.wait, self.x_work)) rhs = msat_make_equal(menv, x_out_c, in_c) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) lhs = msat_make_and(menv, disc_t, self.work) rhs = msat_make_equal(menv, x_out_c, out_c) self.trans = msat_make_and(menv, self.trans, msat_make_impl(menv, lhs, rhs)) def hints(env: PysmtEnv) -> FrozenSet[Hint]: assert isinstance(env, PysmtEnv) mgr = env.formula_manager delta = mgr.Symbol(delta_name, types.REAL) r2s = mgr.Symbol("r2s", types.INT) s2r = mgr.Symbol("r2s", types.INT) s_l = mgr.Symbol("s_l", types.BOOL) s_evt = mgr.Symbol("s_evt", types.BOOL) s_msg_id = mgr.Symbol("s_msg_id", types.INT) s_timeout = mgr.Symbol("s_timeout", types.REAL) s_c = mgr.Symbol("s_c", types.REAL) r_l = mgr.Symbol("r_l", types.BOOL) symbs = frozenset([delta, r2s, s2r, s_l, s_evt, s_msg_id, s_timeout, s_c, r_l]) x_delta = symb_to_next(mgr, delta) x_r2s = symb_to_next(mgr, r2s) x_s2r = symb_to_next(mgr, s2r) x_s_l = symb_to_next(mgr, s_l) x_s_evt = symb_to_next(mgr, s_evt) x_s_msg_id = symb_to_next(mgr, s_msg_id) x_s_timeout = symb_to_next(mgr, s_timeout) x_s_c = symb_to_next(mgr, s_c) x_r_l = symb_to_next(mgr, r_l) res = [] r0 = mgr.Real(0) r1 = mgr.Real(1) i0 = mgr.Int(0) i1 = mgr.Int(1) loc0 = Location(env, mgr.Equals(delta, r0)) loc0.set_progress(0, mgr.Equals(x_delta, r0)) hint = Hint("h_delta0", env, frozenset([delta]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.Equals(s2r, i0)) loc0.set_progress(0, mgr.Equals(x_s2r, i0)) hint = Hint("h_s2r0", env, frozenset([s2r]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, s_evt) loc0.set_progress(0, x_s_evt) hint = Hint("h_s_evt0", env, frozenset([s_evt]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.Equals(s_msg_id, i0)) loc0.set_progress(0, mgr.Equals(x_s_msg_id, i0)) hint = Hint("h_s_msg_id0", env, frozenset([s_msg_id]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.Equals(s_c, r0)) loc0.set_progress(0, mgr.Equals(x_s_c, r0)) hint = Hint("h_s_c0", env, frozenset([s_c]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, r_l) loc0.set_progress(0, x_r_l) hint = Hint("h_r_l0", env, frozenset([r_l]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.GE(s2r, i0)) loc0.set_progress(0, mgr.Equals(x_s2r, i1)) hint = Hint("h_s2r1", env, frozenset([s2r]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.GE(r2s, i0)) loc0.set_progress(0, mgr.Equals(x_r2s, i1)) hint = Hint("h_r2s1", env, frozenset([r2s]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, s_l) loc0.set_progress(1, mgr.Not(x_s_l)) loc1 = Location(env, mgr.Not(s_l)) loc1.set_progress(0, x_s_l) hint = Hint("h_s_l1", env, frozenset([s_l]), symbs) hint.set_locs([loc0, loc1]) res.append(hint) loc0 = Location(env, s_evt) loc0.set_progress(1, mgr.Not(x_s_evt)) loc1 = Location(env, mgr.Not(s_evt)) loc1.set_progress(0, x_s_evt) hint = Hint("h_s_evt1", env, frozenset([s_evt]), symbs) hint.set_locs([loc0, loc1]) res.append(hint) loc0 = Location(env, mgr.GE(s_msg_id, i0)) loc0.set_progress(0, mgr.Equals(x_s_msg_id, mgr.Plus(s_msg_id, i1))) hint = Hint("h_s_msg_id1", env, frozenset([s_msg_id]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.GE(s_timeout, r0)) loc0.set_progress(0, mgr.Equals(x_s_timeout, mgr.Plus(s_timeout, r1))) hint = Hint("h_s_timeout1", env, frozenset([s_timeout]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, r_l) loc0.set_progress(1, mgr.Not(x_r_l)) loc1 = Location(env, mgr.Not(r_l)) loc1.set_progress(0, x_r_l) hint = Hint("h_r_l1", env, frozenset([r_l]), symbs) hint.set_locs([loc0, loc1]) res.append(hint) loc0 = Location(env, mgr.GE(delta, r0)) loc0.set_progress(0, mgr.Equals(x_delta, mgr.Plus(delta, r1))) hint = Hint("h_delta2", env, frozenset([delta]), symbs) hint.set_locs([loc0]) res.append(hint) loc0 = Location(env, mgr.GE(s2r, i0)) loc0.set_progress(0, mgr.Equals(x_s2r, mgr.Plus(s2r, i1))) hint = Hint("h_s2r2", env, frozenset([s2r]), symbs) hint.set_locs([loc0]) res.append(hint) return frozenset(res)

true

790da3fb01143d681327600c538468b2c14e75f6

392

py

Python

test_validate_input_file.py

cszelesbbs/servicecatalogenabler2

f3ece5e49f047b45e796a7656816d7603877ec70

[ "Apache-2.0" ]

null

test_validate_input_file.py

cszelesbbs/servicecatalogenabler2

f3ece5e49f047b45e796a7656816d7603877ec70

[ "Apache-2.0" ]

null

test_validate_input_file.py

cszelesbbs/servicecatalogenabler2

f3ece5e49f047b45e796a7656816d7603877ec70

[ "Apache-2.0" ]

null

import json import yaml from jsonschema import validate import os configuration_file = os.environ['SC_ENABLER_CONF'] with open(configuration_file, 'r') as conf_file: input_config = yaml.safe_load(conf_file) with open("./input_schema_validator.json", 'r') as schema_file: schema = json.load(schema_file) def test_input_params(): validate(instance=input_config, schema=schema)

23.058824

63

0.772959

import json import yaml from jsonschema import validate import os configuration_file = os.environ['SC_ENABLER_CONF'] with open(configuration_file, 'r') as conf_file: input_config = yaml.safe_load(conf_file) with open("./input_schema_validator.json", 'r') as schema_file: schema = json.load(schema_file) def test_input_params(): validate(instance=input_config, schema=schema)

true

790da45c9689d6ffd0b636d581f5ce7ab96fe34b

3,532

py

Python

gtpython/gt/annotationsketch/image_info.py

ggonnella/genometools

48103b35c99920179fae697086efdf6d0548a1fe

[ "BSD-2-Clause" ]

1

2020-02-19T14:10:38.000Z

gtpython/gt/annotationsketch/image_info.py

ggonnella/genometools

48103b35c99920179fae697086efdf6d0548a1fe

[ "BSD-2-Clause" ]

null

gtpython/gt/annotationsketch/image_info.py

ggonnella/genometools

48103b35c99920179fae697086efdf6d0548a1fe

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (c) 2014 Daniel Standage <daniel.standage@gmail.com> # Copyright (c) 2008 Sascha Steinbiss <steinbiss@zbh.uni-hamburg.de> # Copyright (c) 2008 Center for Bioinformatics, University of Hamburg # # Permission to use, copy, modify, and distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. # from gt.dlload import gtlib from gt.annotationsketch.rec_map import RecMap import math class ImageInfo: def __init__(self): self.ii = gtlib.gt_image_info_new() self._as_parameter_ = self.ii self.hotspots = None def __del__(self): try: gtlib.gt_image_info_delete(self.ii) except AttributeError: pass def from_param(cls, obj): if not (isinstance(obj, ImageInfo) or obj == None): raise TypeError, "argument must be an ImageInfo" if obj == None: return None return obj._as_parameter_ from_param = classmethod(from_param) def get_height(self): return gtlib.gt_image_info_get_height(self.ii) def num_of_rec_maps(self): return gtlib.gt_image_info_num_of_rec_maps(self.ii) def compare_hotspots(cls, hs1, hs2): if hs1[2] - hs1[0] + 1 > hs2[2] - hs2[0] + 1: return 1 elif hs1[2] - hs1[0] + 1 == hs2[2] - hs2[0] + 1: if hs1[3] > hs2[3]: return 1 elif hs1[3] == hs2[3]: return 0 else: return -1 else: return -1 compare_hotspots = classmethod(compare_hotspots) def each_hotspot(self): if not self.hotspots: self.hotspots = [] for i in range(self.num_of_rec_maps()): rm = RecMap(gtlib.gt_image_info_get_rec_map(self.ii, i)) self.hotspots.append([math.floor(rm.get_northwest_x()), math.floor(rm.get_northwest_y()), math.floor(rm.get_southeast_x()), math.floor(rm.get_southeast_y()), rm.get_genome_feature()]) self.hotspots.sort(ImageInfo.compare_hotspots) for hs in self.hotspots: yield (hs[0], hs[1], hs[2], hs[3], hs[4]) def register(cls, gtlib): from ctypes import c_void_p, c_ulong, c_uint gtlib.gt_image_info_delete.restype = None gtlib.gt_image_info_delete.argtypes = [c_void_p] gtlib.gt_image_info_get_rec_map.restype = c_void_p gtlib.gt_image_info_get_rec_map.argtypes = [c_void_p, c_ulong] gtlib.gt_image_info_num_of_rec_maps.restype = c_ulong gtlib.gt_image_info_num_of_rec_maps.argtypes = [c_void_p] gtlib.gt_image_info_get_height.restype = c_uint gtlib.gt_image_info_get_height.argtypes = [c_void_p] gtlib.gt_image_info_new.restype = c_void_p gtlib.gt_image_info_new.argtypes = [] register = classmethod(register)

37.178947

91

0.656569

from gt.dlload import gtlib from gt.annotationsketch.rec_map import RecMap import math class ImageInfo: def __init__(self): self.ii = gtlib.gt_image_info_new() self._as_parameter_ = self.ii self.hotspots = None def __del__(self): try: gtlib.gt_image_info_delete(self.ii) except AttributeError: pass def from_param(cls, obj): if not (isinstance(obj, ImageInfo) or obj == None): raise TypeError, "argument must be an ImageInfo" if obj == None: return None return obj._as_parameter_ from_param = classmethod(from_param) def get_height(self): return gtlib.gt_image_info_get_height(self.ii) def num_of_rec_maps(self): return gtlib.gt_image_info_num_of_rec_maps(self.ii) def compare_hotspots(cls, hs1, hs2): if hs1[2] - hs1[0] + 1 > hs2[2] - hs2[0] + 1: return 1 elif hs1[2] - hs1[0] + 1 == hs2[2] - hs2[0] + 1: if hs1[3] > hs2[3]: return 1 elif hs1[3] == hs2[3]: return 0 else: return -1 else: return -1 compare_hotspots = classmethod(compare_hotspots) def each_hotspot(self): if not self.hotspots: self.hotspots = [] for i in range(self.num_of_rec_maps()): rm = RecMap(gtlib.gt_image_info_get_rec_map(self.ii, i)) self.hotspots.append([math.floor(rm.get_northwest_x()), math.floor(rm.get_northwest_y()), math.floor(rm.get_southeast_x()), math.floor(rm.get_southeast_y()), rm.get_genome_feature()]) self.hotspots.sort(ImageInfo.compare_hotspots) for hs in self.hotspots: yield (hs[0], hs[1], hs[2], hs[3], hs[4]) def register(cls, gtlib): from ctypes import c_void_p, c_ulong, c_uint gtlib.gt_image_info_delete.restype = None gtlib.gt_image_info_delete.argtypes = [c_void_p] gtlib.gt_image_info_get_rec_map.restype = c_void_p gtlib.gt_image_info_get_rec_map.argtypes = [c_void_p, c_ulong] gtlib.gt_image_info_num_of_rec_maps.restype = c_ulong gtlib.gt_image_info_num_of_rec_maps.argtypes = [c_void_p] gtlib.gt_image_info_get_height.restype = c_uint gtlib.gt_image_info_get_height.argtypes = [c_void_p] gtlib.gt_image_info_new.restype = c_void_p gtlib.gt_image_info_new.argtypes = [] register = classmethod(register)

false

true

790da6152f12e012efa9bdc9399809ed616980f1

77,846

py

Python

pysnmp/HUAWEI-RSVPTE-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

11

2021-02-02T16:27:16.000Z

2021-08-31T06:22:49.000Z

pysnmp/HUAWEI-RSVPTE-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

75

2021-02-24T17:30:31.000Z

2021-12-08T00:01:18.000Z

pysnmp/HUAWEI-RSVPTE-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

10

2019-04-30T05:51:36.000Z

2022-02-16T03:33:41.000Z

# # PySNMP MIB module HUAWEI-RSVPTE-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/HUAWEI-RSVPTE-MIB # Produced by pysmi-0.3.4 at Mon Apr 29 19:36:34 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueRangeConstraint, ConstraintsIntersection, SingleValueConstraint, ValueSizeConstraint, ConstraintsUnion = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "ConstraintsIntersection", "SingleValueConstraint", "ValueSizeConstraint", "ConstraintsUnion") hwDatacomm, = mibBuilder.importSymbols("HUAWEI-MIB", "hwDatacomm") ifIndex, = mibBuilder.importSymbols("IF-MIB", "ifIndex") BitRate, MessageSize, QosService, BurstSize, SessionType = mibBuilder.importSymbols("INTEGRATED-SERVICES-MIB", "BitRate", "MessageSize", "QosService", "BurstSize", "SessionType") ModuleCompliance, ObjectGroup, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "ObjectGroup", "NotificationGroup") Integer32, Unsigned32, MibIdentifier, Counter64, Counter32, TimeTicks, IpAddress, ModuleIdentity, iso, Gauge32, MibScalar, MibTable, MibTableRow, MibTableColumn, ObjectIdentity, NotificationType, Bits = mibBuilder.importSymbols("SNMPv2-SMI", "Integer32", "Unsigned32", "MibIdentifier", "Counter64", "Counter32", "TimeTicks", "IpAddress", "ModuleIdentity", "iso", "Gauge32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ObjectIdentity", "NotificationType", "Bits") DisplayString, TruthValue, TimeStamp, TimeInterval, TextualConvention, RowStatus = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TruthValue", "TimeStamp", "TimeInterval", "TextualConvention", "RowStatus") hwRsvpTe = ModuleIdentity((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148)) hwRsvpTe.setRevisions(('2014-10-25 17:36', '2014-06-16 14:55', '2013-08-28 17:55',)) if mibBuilder.loadTexts: hwRsvpTe.setLastUpdated('201410251736Z') if mibBuilder.loadTexts: hwRsvpTe.setOrganization('Huawei Technologies Co.,Ltd.') hwRsvpTeObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1)) hwRsvpTeSessionTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1), ) if mibBuilder.loadTexts: hwRsvpTeSessionTable.setStatus('current') hwRsvpTeSessionEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber")) if mibBuilder.loadTexts: hwRsvpTeSessionEntry.setStatus('current') hwRsvpTeSessionNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeSessionNumber.setStatus('current') hwRsvpTeSessionType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 2), SessionType()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionType.setStatus('current') hwRsvpTeSessionDestAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionDestAddr.setStatus('current') hwRsvpTeSessionDestAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 4), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionDestAddrLength.setStatus('current') hwRsvpTeSessionSenders = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 5), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionSenders.setStatus('current') hwRsvpTeSessionReceivers = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 6), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionReceivers.setStatus('current') hwRsvpTeSessionRequests = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 7), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionRequests.setStatus('current') hwRsvpTeSessionTunnelId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 8), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionTunnelId.setStatus('current') hwRsvpTeSessionTunnelExtId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 9), IpAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionTunnelExtId.setStatus('current') hwRsvpTeSessionLspsNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 10), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionLspsNumber.setStatus('current') hwRsvpTeSessionStyle = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 11), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(10, 17, 18))).clone(namedValues=NamedValues(("ff", 10), ("wf", 17), ("se", 18)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionStyle.setStatus('current') hwRsvpTeSenderTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2), ) if mibBuilder.loadTexts: hwRsvpTeSenderTable.setStatus('current') hwRsvpTeSenderEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderNumber")) if mibBuilder.loadTexts: hwRsvpTeSenderEntry.setStatus('current') hwRsvpTeSenderNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeSenderNumber.setStatus('current') hwRsvpTeSenderType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 2), SessionType()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderType.setStatus('current') hwRsvpTeSenderDestAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderDestAddr.setStatus('current') hwRsvpTeSenderAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAddr.setStatus('current') hwRsvpTeSenderDestAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 5), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderDestAddrLength.setStatus('current') hwRsvpTeSenderAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 6), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAddrLength.setStatus('current') hwRsvpTeSenderHopAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 7), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderHopAddr.setStatus('current') hwRsvpTeSenderHopLih = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 8), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderHopLih.setStatus('current') hwRsvpTeSenderInterface = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 9), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderInterface.setStatus('current') hwRsvpTeSenderTSpecRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 10), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTSpecRate.setStatus('current') hwRsvpTeSenderTSpecPeakRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 11), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTSpecPeakRate.setStatus('current') hwRsvpTeSenderTSpecBurst = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 12), BurstSize()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTSpecBurst.setStatus('current') hwRsvpTeSenderTSpecMinTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 13), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTSpecMinTu.setStatus('current') hwRsvpTeSenderTSpecMaxTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 14), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTSpecMaxTu.setStatus('current') hwRsvpTeSenderInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 15), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderInterval.setStatus('current') hwRsvpTeSenderRsvpHop = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 16), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderRsvpHop.setStatus('current') hwRsvpTeSenderPolicy = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 17), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 65532))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderPolicy.setStatus('current') hwRsvpTeSenderAdspecBreak = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 18), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecBreak.setStatus('current') hwRsvpTeSenderAdspecHopCount = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 19), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecHopCount.setStatus('current') hwRsvpTeSenderAdspecPathBw = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 20), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecPathBw.setStatus('current') hwRsvpTeSenderAdspecMinLatency = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 21), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecMinLatency.setStatus('current') hwRsvpTeSenderAdspecMtu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 22), Integer32()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecMtu.setStatus('current') hwRsvpTeSenderAdspecGuaranteedSvc = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 23), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedSvc.setStatus('current') hwRsvpTeSenderAdspecGuaranteedBreak = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 24), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedBreak.setStatus('current') hwRsvpTeSenderAdspecGuaranteedCtot = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 25), Integer32()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedCtot.setStatus('current') hwRsvpTeSenderAdspecGuaranteedDtot = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 26), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedDtot.setStatus('current') hwRsvpTeSenderAdspecGuaranteedCsum = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 27), Integer32()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedCsum.setStatus('current') hwRsvpTeSenderAdspecGuaranteedDsum = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 28), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedDsum.setStatus('current') hwRsvpTeSenderAdspecGuaranteedHopCount = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 29), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedHopCount.setStatus('current') hwRsvpTeSenderAdspecGuaranteedPathBw = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 30), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedPathBw.setStatus('current') hwRsvpTeSenderAdspecGuaranteedMinLatency = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 31), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedMinLatency.setStatus('current') hwRsvpTeSenderAdspecGuaranteedMtu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 32), Integer32()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedMtu.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadSvc = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 33), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadSvc.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadBreak = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 34), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadBreak.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadHopCount = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 35), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadHopCount.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadPathBw = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 36), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadPathBw.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadMinLatency = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 37), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadMinLatency.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadMtu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 38), Integer32()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadMtu.setStatus('current') hwRsvpTeSenderTtl = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 39), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTtl.setStatus('current') hwRsvpTeLspId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 40), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeLspId.setStatus('current') hwRsvpTeSenderMsgIdSndFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 41), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdSndFlag.setStatus('current') hwRsvpTeSenderMsgIdSndEpoch = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 42), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdSndEpoch.setStatus('current') hwRsvpTeSenderMsgIdSndNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 43), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdSndNumber.setStatus('current') hwRsvpTeSenderMsgIdRcvFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 44), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdRcvFlag.setStatus('current') hwRsvpTeSenderMsgIdRcvEpoch = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 45), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdRcvEpoch.setStatus('current') hwRsvpTeSenderMsgIdRcvNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 46), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdRcvNumber.setStatus('current') hwRsvpTeSenderClassType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 47), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderClassType.setStatus('current') hwRsvpTeSenderLabelRequestCtype = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 48), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("withoutLabelRange", 1), ("withAtmLabelRange", 2), ("withFrameRelayLabelRange", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestCtype.setStatus('current') hwRsvpTeSenderLabelRequestL3pid = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 49), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestL3pid.setStatus('current') hwRsvpTeSenderLabelRequestAtmMinVpi = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 50), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestAtmMinVpi.setStatus('current') hwRsvpTeSenderLabelRequestAtmMinVci = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 51), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestAtmMinVci.setStatus('current') hwRsvpTeSenderLabelRequestAtmMaxVpi = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 52), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestAtmMaxVpi.setStatus('current') hwRsvpTeSenderLabelRequestAtmMaxVci = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 53), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestAtmMaxVci.setStatus('current') hwRsvpTeSenderLabelRequestFrMinDlci = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 54), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestFrMinDlci.setStatus('current') hwRsvpTeSenderLabelRequestFrMaxDlci = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 55), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestFrMaxDlci.setStatus('current') hwRsvpTeSenderSessionAttrType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 56), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 7))).clone(namedValues=NamedValues(("withRa", 1), ("withoutRa", 7)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrType.setStatus('current') hwRsvpTeSenderSessionAttrSetupPrio = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 57), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrSetupPrio.setStatus('current') hwRsvpTeSenderSessionAttrHoldPrio = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 58), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrHoldPrio.setStatus('current') hwRsvpTeSenderSessionAttrFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 59), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrFlag.setStatus('current') hwRsvpTeSenderSessionAttrName = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 60), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 64))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrName.setStatus('current') hwRsvpTeSenderSessionAttrExcludeAny = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 61), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrExcludeAny.setStatus('current') hwRsvpTeSenderSessionAttrIncludeAny = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 62), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrIncludeAny.setStatus('current') hwRsvpTeSenderSessionAttrIncludeAll = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 63), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrIncludeAll.setStatus('current') hwRsvpTeSenderFrrSetupPrio = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 64), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrSetupPrio.setStatus('current') hwRsvpTeSenderFrrHoldPrio = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 65), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrHoldPrio.setStatus('current') hwRsvpTeSenderFrrHopLimit = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 66), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrHopLimit.setStatus('current') hwRsvpTeSenderFrrFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 67), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("oneToOneDesired", 1), ("facilityDesired", 2), ("noBackupDesired", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrFlag.setStatus('current') hwRsvpTeSenderFrrBandwidth = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 68), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrBandwidth.setStatus('current') hwRsvpTeSenderFrrExcludeAny = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 69), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrExcludeAny.setStatus('current') hwRsvpTeSenderFrrIncludeAny = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 70), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrIncludeAny.setStatus('current') hwRsvpTeSenderFrrIncludeAll = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 71), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrIncludeAll.setStatus('current') hwRsvpTeSenderFrrInuseFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 72), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5))).clone(namedValues=NamedValues(("normal", 1), ("plrInUse", 2), ("mpInUse", 3), ("plrAndMpInUse", 4), ("underProtection", 5)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrInuseFlag.setStatus('current') hwRsvpTeSenderDiffServPsc = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 73), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderDiffServPsc.setStatus('current') hwRsvpTeResvTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3), ) if mibBuilder.loadTexts: hwRsvpTeResvTable.setStatus('current') hwRsvpTeResvEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeResvNumber")) if mibBuilder.loadTexts: hwRsvpTeResvEntry.setStatus('current') hwRsvpTeResvNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeResvNumber.setStatus('current') hwRsvpTeResvType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 2), SessionType()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvType.setStatus('current') hwRsvpTeResvDestAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvDestAddr.setStatus('current') hwRsvpTeResvSenderAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvSenderAddr.setStatus('current') hwRsvpTeResvDestAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 5), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvDestAddrLength.setStatus('current') hwRsvpTeResvSenderAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 6), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvSenderAddrLength.setStatus('current') hwRsvpTeResvHopAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 7), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvHopAddr.setStatus('current') hwRsvpTeResvHopLih = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 8), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvHopLih.setStatus('current') hwRsvpTeResvInterface = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 9), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvInterface.setStatus('current') hwRsvpTeResvService = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 10), QosService()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvService.setStatus('current') hwRsvpTeResvTSpecRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 11), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTSpecRate.setStatus('current') hwRsvpTeResvTSpecPeakRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 12), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTSpecPeakRate.setStatus('current') hwRsvpTeResvTSpecBurst = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 13), BurstSize()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTSpecBurst.setStatus('current') hwRsvpTeResvTSpecMinTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 14), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTSpecMinTu.setStatus('current') hwRsvpTeResvTSpecMaxTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 15), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTSpecMaxTu.setStatus('current') hwRsvpTeResvRSpecRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 16), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvRSpecRate.setStatus('current') hwRsvpTeResvRSpecSlack = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 17), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvRSpecSlack.setStatus('current') hwRsvpTeResvInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 18), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvInterval.setStatus('current') hwRsvpTeResvScope = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 19), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvScope.setStatus('current') hwRsvpTeResvShared = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 20), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvShared.setStatus('current') hwRsvpTeResvExplicit = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 21), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvExplicit.setStatus('current') hwRsvpTeResvRsvpHop = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 22), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvRsvpHop.setStatus('current') hwRsvpTeResvPolicy = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 23), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvPolicy.setStatus('current') hwRsvpTeResvTtl = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 24), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTtl.setStatus('current') hwRsvpTeResvConfirm = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 25), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvConfirm.setStatus('current') hwRsvpTeResvFwdTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4), ) if mibBuilder.loadTexts: hwRsvpTeResvFwdTable.setStatus('current') hwRsvpTeResvFwdEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdNumber")) if mibBuilder.loadTexts: hwRsvpTeResvFwdEntry.setStatus('current') hwRsvpTeResvFwdNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeResvFwdNumber.setStatus('current') hwRsvpTeResvFwdType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 2), SessionType()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdType.setStatus('current') hwRsvpTeResvFwdDestAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdDestAddr.setStatus('current') hwRsvpTeResvFwdSenderAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdSenderAddr.setStatus('current') hwRsvpTeResvFwdDestAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 5), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdDestAddrLength.setStatus('current') hwRsvpTeResvFwdSenderAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 6), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdSenderAddrLength.setStatus('current') hwRsvpTeResvFwdHopAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 7), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdHopAddr.setStatus('current') hwRsvpTeResvFwdHopLih = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 8), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdHopLih.setStatus('current') hwRsvpTeResvFwdInterface = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 9), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdInterface.setStatus('current') hwRsvpTeResvFwdService = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 10), QosService()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdService.setStatus('current') hwRsvpTeResvFwdTSpecRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 11), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTSpecRate.setStatus('current') hwRsvpTeResvFwdTSpecPeakRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 12), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTSpecPeakRate.setStatus('current') hwRsvpTeResvFwdTSpecBurst = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 13), BurstSize()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTSpecBurst.setStatus('current') hwRsvpTeResvFwdTSpecMinTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 14), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTSpecMinTu.setStatus('current') hwRsvpTeResvFwdTSpecMaxTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 15), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTSpecMaxTu.setStatus('current') hwRsvpTeResvFwdRSpecRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 16), BitRate()).setUnits('bytes per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdRSpecRate.setStatus('current') hwRsvpTeResvFwdRSpecSlack = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 17), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdRSpecSlack.setStatus('current') hwRsvpTeResvFwdInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 18), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdInterval.setStatus('current') hwRsvpTeResvFwdScope = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 19), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdScope.setStatus('current') hwRsvpTeResvFwdShared = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 20), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdShared.setStatus('current') hwRsvpTeResvFwdExplicit = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 21), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdExplicit.setStatus('current') hwRsvpTeResvFwdRsvpHop = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 22), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdRsvpHop.setStatus('current') hwRsvpTeResvFwdPolicy = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 23), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdPolicy.setStatus('current') hwRsvpTeResvFwdTtl = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 24), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTtl.setStatus('current') hwRsvpTeResvFwdMsgIdFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 25), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdMsgIdFlag.setStatus('current') hwRsvpTeResvFwdMsgIdEpoch = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 26), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdMsgIdEpoch.setStatus('current') hwRsvpTeResvFwdMsgIdNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 27), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdMsgIdNumber.setStatus('current') hwRsvpTeIfTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5), ) if mibBuilder.loadTexts: hwRsvpTeIfTable.setStatus('current') hwRsvpTeIfEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1), ).setIndexNames((0, "IF-MIB", "ifIndex")) if mibBuilder.loadTexts: hwRsvpTeIfEntry.setStatus('current') hwRsvpTeIfUdpNbrs = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 1), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfUdpNbrs.setStatus('current') hwRsvpTeIfIpNbrs = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 2), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfIpNbrs.setStatus('current') hwRsvpTeIfNbrs = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 3), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfNbrs.setStatus('current') hwRsvpTeIfRefreshBlockadeMultiple = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 4), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRefreshBlockadeMultiple.setStatus('current') hwRsvpTeIfRefreshMultiple = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 5), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRefreshMultiple.setStatus('current') hwRsvpTeIfTtl = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 6), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfTtl.setStatus('current') hwRsvpTeIfRefreshInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 7), TimeInterval()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRefreshInterval.setStatus('current') hwRsvpTeIfRouteDelay = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 8), TimeInterval()).setUnits('hundredths of a second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRouteDelay.setStatus('current') hwRsvpTeIfEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 9), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfEnabled.setStatus('current') hwRsvpTeIfUdpRequired = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 10), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfUdpRequired.setStatus('current') hwRsvpTeIfStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 11), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: hwRsvpTeIfStatus.setStatus('current') hwRsvpTeIfHelloEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 12), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfHelloEnabled.setStatus('current') hwRsvpTeIfSrefreshEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 13), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfSrefreshEnabled.setStatus('current') hwRsvpTeIfSrefreshInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 14), TimeInterval()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfSrefreshInterval.setStatus('current') hwRsvpTeIfRetranIncDelta = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 15), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRetranIncDelta.setStatus('current') hwRsvpTeIfRetranInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 16), TimeInterval()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRetranInterval.setStatus('current') hwRsvpTeIfAuthEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 17), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfAuthEnabled.setStatus('current') hwRsvpTeIfAuthEncrypted = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 18), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfAuthEncrypted.setStatus('current') hwRsvpTeIfAuthHandshake = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 19), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfAuthHandshake.setStatus('current') hwRsvpTeIfAuthLifeTime = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 20), TimeInterval()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfAuthLifeTime.setStatus('current') hwRsvpTeIfAuthKey = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 21), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 392))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfAuthKey.setStatus('current') hwRsvpTeIfWindowSize = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 22), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfWindowSize.setStatus('current') hwRsvpTeNbrTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6), ) if mibBuilder.loadTexts: hwRsvpTeNbrTable.setStatus('current') hwRsvpTeNbrEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrAddress")) if mibBuilder.loadTexts: hwRsvpTeNbrEntry.setStatus('current') hwRsvpTeNbrAddress = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 1), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))) if mibBuilder.loadTexts: hwRsvpTeNbrAddress.setStatus('current') hwRsvpTeNbrProtocol = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("ip", 1), ("udp", 2), ("both", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrProtocol.setStatus('current') hwRsvpTeNbrStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 3), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: hwRsvpTeNbrStatus.setStatus('current') hwRsvpTeNbrSendersNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 4), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrSendersNumber.setStatus('current') hwRsvpTeNbrReceiversNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 5), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrReceiversNumber.setStatus('current') hwRsvpTeNbrHelloEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 6), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrHelloEnabled.setStatus('current') hwRsvpTeNbrHelloSrcInstance = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 7), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrHelloSrcInstance.setStatus('current') hwRsvpTeNbrHelloDstInstance = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 8), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrHelloDstInstance.setStatus('current') hwRsvpTeNbrHelloLostCounter = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 9), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrHelloLostCounter.setStatus('current') hwRsvpTeNbrHelloType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 10), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("request", 1), ("ack", 2), ("none", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrHelloType.setStatus('current') hwRsvpTeNbrGrCapability = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 11), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrGrCapability.setStatus('current') hwRsvpTeNbrGrRestartTime = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 12), TimeStamp()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrGrRestartTime.setStatus('current') hwRsvpTeNbrGrRecoveryTime = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 13), TimeStamp()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrGrRecoveryTime.setStatus('current') hwRsvpTeNbrGrStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 14), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6))).clone(namedValues=NamedValues(("normal", 1), ("supporting", 2), ("restarting", 3), ("restartTimerRunning", 4), ("recoveryTimerRunning", 5), ("grEnd", 6)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrGrStatus.setStatus('current') hwRsvpTeNbrAuthKeyId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 15), OctetString().subtype(subtypeSpec=ConstraintsUnion(ValueSizeConstraint(0, 0), ValueSizeConstraint(6, 6), ))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrAuthKeyId.setStatus('current') hwRsvpTeNbrReductionEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 16), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrReductionEnabled.setStatus('current') hwRsvpTeNbrReliabilityEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 17), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrReliabilityEnabled.setStatus('current') hwRsvpTeMessageIdTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 7), ) if mibBuilder.loadTexts: hwRsvpTeMessageIdTable.setStatus('current') hwRsvpTeMessageIdEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 7, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrAddress"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeMessageIdEpoch"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeMessageIdNumber")) if mibBuilder.loadTexts: hwRsvpTeMessageIdEntry.setStatus('current') hwRsvpTeMessageIdEpoch = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 7, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeMessageIdEpoch.setStatus('current') hwRsvpTeMessageIdNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 7, 1, 2), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeMessageIdNumber.setStatus('current') hwRsvpTeMessageIdFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 7, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5))).clone(namedValues=NamedValues(("senderIncoming", 1), ("senderOutgoing", 2), ("resv", 3), ("resvFwd", 4), ("rtBuff", 5)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeMessageIdFlag.setStatus('current') hwRsvpTeFilterSpecTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8), ) if mibBuilder.loadTexts: hwRsvpTeFilterSpecTable.setStatus('current') hwRsvpTeFilterSpecEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeResvNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeFilterSpecNumber")) if mibBuilder.loadTexts: hwRsvpTeFilterSpecEntry.setStatus('current') hwRsvpTeFilterSpecNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeFilterSpecNumber.setStatus('current') hwRsvpTeFilterSpecLspId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8, 1, 2), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeFilterSpecLspId.setStatus('current') hwRsvpTeFilterSpecIngressLsrId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8, 1, 3), OctetString()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeFilterSpecIngressLsrId.setStatus('current') hwRsvpTeFilterSpecLabel = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8, 1, 4), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeFilterSpecLabel.setStatus('current') hwRsvpTeRroTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9), ) if mibBuilder.loadTexts: hwRsvpTeRroTable.setStatus('current') hwRsvpTeRroEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeRroNumber")) if mibBuilder.loadTexts: hwRsvpTeRroEntry.setStatus('current') hwRsvpTeRroNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeRroNumber.setStatus('current') hwRsvpTeRroType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("ipv4", 1), ("ipv6", 2), ("label", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeRroType.setStatus('current') hwRsvpTeRroIpAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 3), OctetString()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeRroIpAddr.setStatus('current') hwRsvpTeRroIpPrefixLen = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 4), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeRroIpPrefixLen.setStatus('current') hwRsvpTeRroLabel = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 5), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeRroLabel.setStatus('current') hwRsvpTeRroFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 6), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeRroFlag.setStatus('current') hwRsvpTeEroTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10), ) if mibBuilder.loadTexts: hwRsvpTeEroTable.setStatus('current') hwRsvpTeEroEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeEroNumber")) if mibBuilder.loadTexts: hwRsvpTeEroEntry.setStatus('current') hwRsvpTeEroNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeEroNumber.setStatus('current') hwRsvpTeEroType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("ipv4", 1), ("ipv6", 2)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeEroType.setStatus('current') hwRsvpTeEroIpAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10, 1, 3), OctetString()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeEroIpAddr.setStatus('current') hwRsvpTeEroIpPrefixLen = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10, 1, 4), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeEroIpPrefixLen.setStatus('current') hwRsvpTeExtendObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 11)) hwRsvpTeExtendTrap = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12)) hwRsvpTeTrapObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1)) hwRsvpTeNbr = MibScalar((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1, 1), IpAddress()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: hwRsvpTeNbr.setStatus('current') hwRsvpTeIfNbrCurrentCount = MibScalar((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1, 2), Integer32()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: hwRsvpTeIfNbrCurrentCount.setStatus('current') hwRsvpTeIfNbrThreshold = MibScalar((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1, 3), Integer32()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: hwRsvpTeIfNbrThreshold.setStatus('current') hwRsvpTeIfNbrTotalCount = MibScalar((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1, 4), Integer32()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: hwRsvpTeIfNbrTotalCount.setStatus('current') hwRsvpTeIfName = MibScalar((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1, 5), DisplayString()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: hwRsvpTeIfName.setStatus('current') hwRsvpTeTrap = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2)) hwRsvpTeHelloLost = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 1)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbr")) if mibBuilder.loadTexts: hwRsvpTeHelloLost.setStatus('current') hwRsvpTeHelloLostRecovery = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 2)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbr")) if mibBuilder.loadTexts: hwRsvpTeHelloLostRecovery.setStatus('current') hwRsvpTeAuthFail = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 3)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbr")) if mibBuilder.loadTexts: hwRsvpTeAuthFail.setStatus('current') hwRsvpTeAuthSuccess = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 4)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbr")) if mibBuilder.loadTexts: hwRsvpTeAuthSuccess.setStatus('current') hwRsvpTeIfNbrThresholdExceed = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 5)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfName"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrCurrentCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrThreshold"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrTotalCount")) if mibBuilder.loadTexts: hwRsvpTeIfNbrThresholdExceed.setStatus('current') hwRsvpTeIfNbrThresholdExceedClear = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 6)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfName")) if mibBuilder.loadTexts: hwRsvpTeIfNbrThresholdExceedClear.setStatus('current') hwRsvpTeIfNbrTotalCountExceed = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 7)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfName"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrTotalCount")) if mibBuilder.loadTexts: hwRsvpTeIfNbrTotalCountExceed.setStatus('current') hwRsvpTeIfNbrTotalCountExceedClear = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 8)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfName")) if mibBuilder.loadTexts: hwRsvpTeIfNbrTotalCountExceedClear.setStatus('current') hwRsvpTeConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2)) hwRsvpTeGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1)) hwRsvpTeSessionGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 1)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionDestAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionDestAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionSenders"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionReceivers"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionRequests"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionTunnelId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionTunnelExtId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionLspsNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionStyle")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeSessionGroup = hwRsvpTeSessionGroup.setStatus('current') hwRsvpTeSenderGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 2)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderDestAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderDestAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderHopAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderHopLih"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderInterface"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTSpecRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTSpecPeakRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTSpecBurst"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTSpecMinTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTSpecMaxTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderRsvpHop"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderPolicy"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecBreak"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecHopCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecPathBw"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecMinLatency"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecMtu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedSvc"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedBreak"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedCtot"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedDtot"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedCsum"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedDsum"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedHopCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedPathBw"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedMinLatency"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedMtu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadSvc"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadBreak"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadHopCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadPathBw"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadMinLatency"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadMtu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTtl"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeLspId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdSndFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdSndEpoch"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdSndNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdRcvFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdRcvEpoch"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdRcvNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderClassType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestCtype"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestL3pid"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestAtmMinVpi"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestAtmMinVci"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestAtmMaxVpi"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestAtmMaxVci"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestFrMinDlci"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestFrMaxDlci"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrSetupPrio"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrHoldPrio"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrName"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrExcludeAny"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrIncludeAny"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrIncludeAll"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrSetupPrio"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrHoldPrio"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrHopLimit"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrBandwidth"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrExcludeAny"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrIncludeAny"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrIncludeAll"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrInuseFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderDiffServPsc")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeSenderGroup = hwRsvpTeSenderGroup.setStatus('current') hwRsvpTeResvGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 3)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvDestAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvSenderAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvDestAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvSenderAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvHopAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvHopLih"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvInterface"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvService"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTSpecRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTSpecPeakRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTSpecBurst"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTSpecMinTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTSpecMaxTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvRSpecRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvRSpecSlack"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvScope"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvShared"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvExplicit"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvRsvpHop"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvPolicy"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTtl"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvConfirm")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeResvGroup = hwRsvpTeResvGroup.setStatus('current') hwRsvpTeResvFwdGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 4)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdDestAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdSenderAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdDestAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdSenderAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdHopAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdHopLih"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdInterface"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdService"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTSpecRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTSpecPeakRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTSpecBurst"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTSpecMinTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTSpecMaxTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdRSpecRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdRSpecSlack"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdScope"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdShared"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdExplicit"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdPolicy"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTtl"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdMsgIdFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdMsgIdEpoch"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdMsgIdNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdRsvpHop")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeResvFwdGroup = hwRsvpTeResvFwdGroup.setStatus('current') hwRsvpTeIfGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 5)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfUdpNbrs"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfIpNbrs"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrs"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRefreshBlockadeMultiple"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRefreshMultiple"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfTtl"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRefreshInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRouteDelay"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfUdpRequired"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfStatus"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfHelloEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfSrefreshEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfSrefreshInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRetranIncDelta"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRetranInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfAuthEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfAuthEncrypted"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfAuthHandshake"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfAuthKey"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfWindowSize"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfAuthLifeTime")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeIfGroup = hwRsvpTeIfGroup.setStatus('current') hwRsvpTeNbrGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 6)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrHelloSrcInstance"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrHelloDstInstance"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrHelloLostCounter"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrHelloType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrHelloEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrSendersNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrReceiversNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrGrCapability"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrGrRestartTime"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrGrRecoveryTime"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrGrStatus"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrAuthKeyId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrReductionEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrReliabilityEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrProtocol"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeNbrGroup = hwRsvpTeNbrGroup.setStatus('current') hwRsvpTeMessageIdGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 7)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeMessageIdFlag")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeMessageIdGroup = hwRsvpTeMessageIdGroup.setStatus('current') hwRsvpTeFilterSpecGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 8)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeFilterSpecLspId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeFilterSpecIngressLsrId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeFilterSpecLabel")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeFilterSpecGroup = hwRsvpTeFilterSpecGroup.setStatus('current') hwRsvpTeRroGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 9)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroIpAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroIpPrefixLen"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroLabel"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroFlag")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeRroGroup = hwRsvpTeRroGroup.setStatus('current') hwRsvpTeEroGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 10)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeEroType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeEroIpAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeEroIpPrefixLen")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeEroGroup = hwRsvpTeEroGroup.setStatus('current') hwRsvpTeTrapObjectsGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 11)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrCurrentCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrThreshold"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrTotalCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfName")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeTrapObjectsGroup = hwRsvpTeTrapObjectsGroup.setStatus('current') hwRsvpTeTrapGroup = NotificationGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 12)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeHelloLost"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeHelloLostRecovery"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeAuthFail"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeAuthSuccess"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrThresholdExceed"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrThresholdExceedClear"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrTotalCountExceed"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrTotalCountExceedClear")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeTrapGroup = hwRsvpTeTrapGroup.setStatus('current') hwRsvpTeCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 2)) hwRsvpTeCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 2, 1)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeFilterSpecGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeEroGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeTrapObjectsGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeTrapGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeMessageIdGroup")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeCompliance = hwRsvpTeCompliance.setStatus('current') mibBuilder.exportSymbols("HUAWEI-RSVPTE-MIB", hwRsvpTeIfIpNbrs=hwRsvpTeIfIpNbrs, hwRsvpTeResvFwdTSpecMaxTu=hwRsvpTeResvFwdTSpecMaxTu, hwRsvpTeSenderAdspecGuaranteedHopCount=hwRsvpTeSenderAdspecGuaranteedHopCount, hwRsvpTeMessageIdFlag=hwRsvpTeMessageIdFlag, hwRsvpTeResvFwdSenderAddr=hwRsvpTeResvFwdSenderAddr, hwRsvpTeCompliances=hwRsvpTeCompliances, hwRsvpTeIfRefreshInterval=hwRsvpTeIfRefreshInterval, hwRsvpTeIfGroup=hwRsvpTeIfGroup, hwRsvpTeSenderSessionAttrIncludeAny=hwRsvpTeSenderSessionAttrIncludeAny, hwRsvpTeResvFwdTable=hwRsvpTeResvFwdTable, hwRsvpTeGroups=hwRsvpTeGroups, hwRsvpTeRroIpPrefixLen=hwRsvpTeRroIpPrefixLen, hwRsvpTeResvFwdSenderAddrLength=hwRsvpTeResvFwdSenderAddrLength, hwRsvpTeEroTable=hwRsvpTeEroTable, hwRsvpTeIfRefreshBlockadeMultiple=hwRsvpTeIfRefreshBlockadeMultiple, hwRsvpTeNbrGrRecoveryTime=hwRsvpTeNbrGrRecoveryTime, hwRsvpTeEroIpAddr=hwRsvpTeEroIpAddr, hwRsvpTeIfRouteDelay=hwRsvpTeIfRouteDelay, hwRsvpTeSenderAdspecCtrlLoadMtu=hwRsvpTeSenderAdspecCtrlLoadMtu, hwRsvpTeSessionRequests=hwRsvpTeSessionRequests, hwRsvpTeSessionSenders=hwRsvpTeSessionSenders, hwRsvpTeSenderEntry=hwRsvpTeSenderEntry, hwRsvpTeSenderRsvpHop=hwRsvpTeSenderRsvpHop, hwRsvpTeTrapGroup=hwRsvpTeTrapGroup, hwRsvpTeIfNbrCurrentCount=hwRsvpTeIfNbrCurrentCount, hwRsvpTeNbrProtocol=hwRsvpTeNbrProtocol, hwRsvpTeMessageIdTable=hwRsvpTeMessageIdTable, hwRsvpTeRroNumber=hwRsvpTeRroNumber, hwRsvpTeSenderLabelRequestFrMinDlci=hwRsvpTeSenderLabelRequestFrMinDlci, hwRsvpTeResvFwdDestAddr=hwRsvpTeResvFwdDestAddr, hwRsvpTeIfStatus=hwRsvpTeIfStatus, hwRsvpTeResvType=hwRsvpTeResvType, hwRsvpTeSessionDestAddr=hwRsvpTeSessionDestAddr, hwRsvpTeResvEntry=hwRsvpTeResvEntry, hwRsvpTeIfAuthEncrypted=hwRsvpTeIfAuthEncrypted, hwRsvpTeRroGroup=hwRsvpTeRroGroup, hwRsvpTeSenderType=hwRsvpTeSenderType, hwRsvpTeSenderFrrIncludeAny=hwRsvpTeSenderFrrIncludeAny, hwRsvpTeSenderSessionAttrType=hwRsvpTeSenderSessionAttrType, hwRsvpTeMessageIdNumber=hwRsvpTeMessageIdNumber, hwRsvpTeSenderLabelRequestAtmMaxVpi=hwRsvpTeSenderLabelRequestAtmMaxVpi, hwRsvpTeFilterSpecIngressLsrId=hwRsvpTeFilterSpecIngressLsrId, hwRsvpTeRroEntry=hwRsvpTeRroEntry, hwRsvpTeResvFwdRSpecRate=hwRsvpTeResvFwdRSpecRate, hwRsvpTe=hwRsvpTe, hwRsvpTeResvFwdHopLih=hwRsvpTeResvFwdHopLih, hwRsvpTeNbrHelloDstInstance=hwRsvpTeNbrHelloDstInstance, hwRsvpTeSessionNumber=hwRsvpTeSessionNumber, hwRsvpTeSessionEntry=hwRsvpTeSessionEntry, hwRsvpTeSenderMsgIdSndNumber=hwRsvpTeSenderMsgIdSndNumber, hwRsvpTeIfUdpNbrs=hwRsvpTeIfUdpNbrs, hwRsvpTeResvShared=hwRsvpTeResvShared, hwRsvpTeSenderAdspecPathBw=hwRsvpTeSenderAdspecPathBw, hwRsvpTeIfRetranInterval=hwRsvpTeIfRetranInterval, hwRsvpTeFilterSpecTable=hwRsvpTeFilterSpecTable, hwRsvpTeResvScope=hwRsvpTeResvScope, hwRsvpTeNbrGroup=hwRsvpTeNbrGroup, hwRsvpTeCompliance=hwRsvpTeCompliance, hwRsvpTeSessionTable=hwRsvpTeSessionTable, hwRsvpTeNbrHelloSrcInstance=hwRsvpTeNbrHelloSrcInstance, hwRsvpTeEroType=hwRsvpTeEroType, hwRsvpTeSenderAdspecGuaranteedMinLatency=hwRsvpTeSenderAdspecGuaranteedMinLatency, hwRsvpTeAuthFail=hwRsvpTeAuthFail, hwRsvpTeSenderFrrInuseFlag=hwRsvpTeSenderFrrInuseFlag, hwRsvpTeSenderMsgIdRcvFlag=hwRsvpTeSenderMsgIdRcvFlag, hwRsvpTeResvFwdTSpecPeakRate=hwRsvpTeResvFwdTSpecPeakRate, hwRsvpTeResvService=hwRsvpTeResvService, hwRsvpTeResvPolicy=hwRsvpTeResvPolicy, hwRsvpTeNbrAuthKeyId=hwRsvpTeNbrAuthKeyId, hwRsvpTeRroLabel=hwRsvpTeRroLabel, hwRsvpTeSenderFrrIncludeAll=hwRsvpTeSenderFrrIncludeAll, hwRsvpTeSenderClassType=hwRsvpTeSenderClassType, hwRsvpTeSenderSessionAttrExcludeAny=hwRsvpTeSenderSessionAttrExcludeAny, hwRsvpTeIfAuthKey=hwRsvpTeIfAuthKey, hwRsvpTeSenderTSpecBurst=hwRsvpTeSenderTSpecBurst, hwRsvpTeIfNbrTotalCount=hwRsvpTeIfNbrTotalCount, hwRsvpTeIfNbrTotalCountExceedClear=hwRsvpTeIfNbrTotalCountExceedClear, hwRsvpTeSenderFrrExcludeAny=hwRsvpTeSenderFrrExcludeAny, hwRsvpTeResvConfirm=hwRsvpTeResvConfirm, hwRsvpTeResvDestAddr=hwRsvpTeResvDestAddr, hwRsvpTeResvFwdShared=hwRsvpTeResvFwdShared, hwRsvpTeHelloLostRecovery=hwRsvpTeHelloLostRecovery, hwRsvpTeResvTSpecRate=hwRsvpTeResvTSpecRate, hwRsvpTeSenderNumber=hwRsvpTeSenderNumber, hwRsvpTeSenderAdspecHopCount=hwRsvpTeSenderAdspecHopCount, hwRsvpTeSessionDestAddrLength=hwRsvpTeSessionDestAddrLength, hwRsvpTeSenderTable=hwRsvpTeSenderTable, hwRsvpTeSenderPolicy=hwRsvpTeSenderPolicy, hwRsvpTeSenderAdspecGuaranteedCtot=hwRsvpTeSenderAdspecGuaranteedCtot, hwRsvpTeResvFwdType=hwRsvpTeResvFwdType, hwRsvpTeNbrEntry=hwRsvpTeNbrEntry, hwRsvpTeSenderHopAddr=hwRsvpTeSenderHopAddr, hwRsvpTeSenderMsgIdSndEpoch=hwRsvpTeSenderMsgIdSndEpoch, hwRsvpTeSenderFrrBandwidth=hwRsvpTeSenderFrrBandwidth, hwRsvpTeSenderTSpecPeakRate=hwRsvpTeSenderTSpecPeakRate, hwRsvpTeSenderAddr=hwRsvpTeSenderAddr, hwRsvpTeSenderFrrHopLimit=hwRsvpTeSenderFrrHopLimit, hwRsvpTeSenderSessionAttrName=hwRsvpTeSenderSessionAttrName, hwRsvpTeResvSenderAddrLength=hwRsvpTeResvSenderAddrLength, hwRsvpTeResvInterface=hwRsvpTeResvInterface, hwRsvpTeResvRsvpHop=hwRsvpTeResvRsvpHop, hwRsvpTeResvFwdExplicit=hwRsvpTeResvFwdExplicit, hwRsvpTeIfTtl=hwRsvpTeIfTtl, hwRsvpTeResvFwdDestAddrLength=hwRsvpTeResvFwdDestAddrLength, hwRsvpTeResvTSpecBurst=hwRsvpTeResvTSpecBurst, hwRsvpTeRroIpAddr=hwRsvpTeRroIpAddr, hwRsvpTeNbrGrRestartTime=hwRsvpTeNbrGrRestartTime, hwRsvpTeResvTSpecMaxTu=hwRsvpTeResvTSpecMaxTu, hwRsvpTeNbr=hwRsvpTeNbr, hwRsvpTeSessionType=hwRsvpTeSessionType, hwRsvpTeIfAuthEnabled=hwRsvpTeIfAuthEnabled, hwRsvpTeFilterSpecLabel=hwRsvpTeFilterSpecLabel, PYSNMP_MODULE_ID=hwRsvpTe, hwRsvpTeResvFwdNumber=hwRsvpTeResvFwdNumber, hwRsvpTeExtendObjects=hwRsvpTeExtendObjects, hwRsvpTeIfName=hwRsvpTeIfName, hwRsvpTeIfSrefreshInterval=hwRsvpTeIfSrefreshInterval, hwRsvpTeSessionLspsNumber=hwRsvpTeSessionLspsNumber, hwRsvpTeSenderAdspecGuaranteedDsum=hwRsvpTeSenderAdspecGuaranteedDsum, hwRsvpTeSenderSessionAttrSetupPrio=hwRsvpTeSenderSessionAttrSetupPrio, hwRsvpTeSenderTSpecRate=hwRsvpTeSenderTSpecRate, hwRsvpTeSenderAdspecGuaranteedDtot=hwRsvpTeSenderAdspecGuaranteedDtot, hwRsvpTeSenderAdspecCtrlLoadSvc=hwRsvpTeSenderAdspecCtrlLoadSvc, hwRsvpTeResvGroup=hwRsvpTeResvGroup, hwRsvpTeSessionGroup=hwRsvpTeSessionGroup, hwRsvpTeRroFlag=hwRsvpTeRroFlag, hwRsvpTeResvExplicit=hwRsvpTeResvExplicit, hwRsvpTeIfNbrThreshold=hwRsvpTeIfNbrThreshold, hwRsvpTeRroTable=hwRsvpTeRroTable, hwRsvpTeRroType=hwRsvpTeRroType, hwRsvpTeSenderDestAddr=hwRsvpTeSenderDestAddr, hwRsvpTeEroEntry=hwRsvpTeEroEntry, hwRsvpTeSenderAdspecCtrlLoadPathBw=hwRsvpTeSenderAdspecCtrlLoadPathBw, hwRsvpTeResvFwdGroup=hwRsvpTeResvFwdGroup, hwRsvpTeTrapObjectsGroup=hwRsvpTeTrapObjectsGroup, hwRsvpTeResvTable=hwRsvpTeResvTable, hwRsvpTeIfRefreshMultiple=hwRsvpTeIfRefreshMultiple, hwRsvpTeSenderGroup=hwRsvpTeSenderGroup, hwRsvpTeFilterSpecGroup=hwRsvpTeFilterSpecGroup, hwRsvpTeEroGroup=hwRsvpTeEroGroup, hwRsvpTeResvSenderAddr=hwRsvpTeResvSenderAddr, hwRsvpTeNbrReceiversNumber=hwRsvpTeNbrReceiversNumber, hwRsvpTeNbrReliabilityEnabled=hwRsvpTeNbrReliabilityEnabled, hwRsvpTeNbrHelloEnabled=hwRsvpTeNbrHelloEnabled, hwRsvpTeNbrGrCapability=hwRsvpTeNbrGrCapability, hwRsvpTeResvTtl=hwRsvpTeResvTtl, hwRsvpTeSenderSessionAttrFlag=hwRsvpTeSenderSessionAttrFlag, hwRsvpTeResvTSpecMinTu=hwRsvpTeResvTSpecMinTu, hwRsvpTeSenderMsgIdRcvEpoch=hwRsvpTeSenderMsgIdRcvEpoch, hwRsvpTeIfWindowSize=hwRsvpTeIfWindowSize, hwRsvpTeSenderDiffServPsc=hwRsvpTeSenderDiffServPsc, hwRsvpTeMessageIdEpoch=hwRsvpTeMessageIdEpoch, hwRsvpTeNbrTable=hwRsvpTeNbrTable, hwRsvpTeNbrGrStatus=hwRsvpTeNbrGrStatus, hwRsvpTeSenderLabelRequestFrMaxDlci=hwRsvpTeSenderLabelRequestFrMaxDlci, hwRsvpTeSessionReceivers=hwRsvpTeSessionReceivers, hwRsvpTeResvFwdScope=hwRsvpTeResvFwdScope, hwRsvpTeSenderAdspecMtu=hwRsvpTeSenderAdspecMtu, hwRsvpTeSenderMsgIdSndFlag=hwRsvpTeSenderMsgIdSndFlag, hwRsvpTeSenderAdspecGuaranteedBreak=hwRsvpTeSenderAdspecGuaranteedBreak, hwRsvpTeResvTSpecPeakRate=hwRsvpTeResvTSpecPeakRate, hwRsvpTeIfRetranIncDelta=hwRsvpTeIfRetranIncDelta, hwRsvpTeSenderFrrFlag=hwRsvpTeSenderFrrFlag, hwRsvpTeResvFwdInterface=hwRsvpTeResvFwdInterface, hwRsvpTeSenderTtl=hwRsvpTeSenderTtl, hwRsvpTeSenderAdspecMinLatency=hwRsvpTeSenderAdspecMinLatency, hwRsvpTeResvFwdTtl=hwRsvpTeResvFwdTtl, hwRsvpTeSenderLabelRequestAtmMinVci=hwRsvpTeSenderLabelRequestAtmMinVci, hwRsvpTeResvFwdService=hwRsvpTeResvFwdService, hwRsvpTeSenderInterface=hwRsvpTeSenderInterface, hwRsvpTeSenderInterval=hwRsvpTeSenderInterval, hwRsvpTeResvFwdRsvpHop=hwRsvpTeResvFwdRsvpHop, hwRsvpTeEroIpPrefixLen=hwRsvpTeEroIpPrefixLen, hwRsvpTeResvFwdEntry=hwRsvpTeResvFwdEntry, hwRsvpTeLspId=hwRsvpTeLspId, hwRsvpTeResvFwdRSpecSlack=hwRsvpTeResvFwdRSpecSlack, hwRsvpTeResvRSpecSlack=hwRsvpTeResvRSpecSlack, hwRsvpTeResvFwdInterval=hwRsvpTeResvFwdInterval, hwRsvpTeResvFwdHopAddr=hwRsvpTeResvFwdHopAddr, hwRsvpTeSenderAdspecCtrlLoadBreak=hwRsvpTeSenderAdspecCtrlLoadBreak, hwRsvpTeResvFwdPolicy=hwRsvpTeResvFwdPolicy, hwRsvpTeConformance=hwRsvpTeConformance, hwRsvpTeSenderAdspecBreak=hwRsvpTeSenderAdspecBreak, hwRsvpTeResvFwdTSpecBurst=hwRsvpTeResvFwdTSpecBurst, hwRsvpTeResvFwdMsgIdNumber=hwRsvpTeResvFwdMsgIdNumber, hwRsvpTeExtendTrap=hwRsvpTeExtendTrap, hwRsvpTeAuthSuccess=hwRsvpTeAuthSuccess, hwRsvpTeFilterSpecNumber=hwRsvpTeFilterSpecNumber, hwRsvpTeIfNbrTotalCountExceed=hwRsvpTeIfNbrTotalCountExceed, hwRsvpTeSenderFrrSetupPrio=hwRsvpTeSenderFrrSetupPrio, hwRsvpTeResvHopLih=hwRsvpTeResvHopLih, hwRsvpTeIfEnabled=hwRsvpTeIfEnabled, hwRsvpTeIfTable=hwRsvpTeIfTable, hwRsvpTeIfHelloEnabled=hwRsvpTeIfHelloEnabled, hwRsvpTeIfAuthLifeTime=hwRsvpTeIfAuthLifeTime, hwRsvpTeSenderMsgIdRcvNumber=hwRsvpTeSenderMsgIdRcvNumber, hwRsvpTeResvFwdTSpecRate=hwRsvpTeResvFwdTSpecRate, hwRsvpTeSenderAdspecGuaranteedPathBw=hwRsvpTeSenderAdspecGuaranteedPathBw, hwRsvpTeResvDestAddrLength=hwRsvpTeResvDestAddrLength, hwRsvpTeNbrHelloLostCounter=hwRsvpTeNbrHelloLostCounter, hwRsvpTeSenderAdspecCtrlLoadHopCount=hwRsvpTeSenderAdspecCtrlLoadHopCount, hwRsvpTeHelloLost=hwRsvpTeHelloLost, hwRsvpTeIfUdpRequired=hwRsvpTeIfUdpRequired, hwRsvpTeNbrReductionEnabled=hwRsvpTeNbrReductionEnabled, hwRsvpTeSessionStyle=hwRsvpTeSessionStyle, hwRsvpTeNbrAddress=hwRsvpTeNbrAddress, hwRsvpTeNbrHelloType=hwRsvpTeNbrHelloType, hwRsvpTeSessionTunnelId=hwRsvpTeSessionTunnelId, hwRsvpTeIfSrefreshEnabled=hwRsvpTeIfSrefreshEnabled, hwRsvpTeEroNumber=hwRsvpTeEroNumber, hwRsvpTeSenderAdspecGuaranteedCsum=hwRsvpTeSenderAdspecGuaranteedCsum, hwRsvpTeSenderSessionAttrHoldPrio=hwRsvpTeSenderSessionAttrHoldPrio, hwRsvpTeSenderLabelRequestAtmMaxVci=hwRsvpTeSenderLabelRequestAtmMaxVci, hwRsvpTeSenderHopLih=hwRsvpTeSenderHopLih, hwRsvpTeFilterSpecLspId=hwRsvpTeFilterSpecLspId, hwRsvpTeSenderSessionAttrIncludeAll=hwRsvpTeSenderSessionAttrIncludeAll, hwRsvpTeSenderLabelRequestL3pid=hwRsvpTeSenderLabelRequestL3pid, hwRsvpTeSenderAdspecGuaranteedMtu=hwRsvpTeSenderAdspecGuaranteedMtu, hwRsvpTeResvNumber=hwRsvpTeResvNumber, hwRsvpTeTrapObjects=hwRsvpTeTrapObjects, hwRsvpTeResvFwdMsgIdEpoch=hwRsvpTeResvFwdMsgIdEpoch, hwRsvpTeSenderDestAddrLength=hwRsvpTeSenderDestAddrLength, hwRsvpTeIfAuthHandshake=hwRsvpTeIfAuthHandshake, hwRsvpTeSenderTSpecMaxTu=hwRsvpTeSenderTSpecMaxTu, hwRsvpTeSenderLabelRequestCtype=hwRsvpTeSenderLabelRequestCtype, hwRsvpTeObjects=hwRsvpTeObjects, hwRsvpTeIfNbrThresholdExceed=hwRsvpTeIfNbrThresholdExceed, hwRsvpTeResvFwdMsgIdFlag=hwRsvpTeResvFwdMsgIdFlag, hwRsvpTeResvInterval=hwRsvpTeResvInterval, hwRsvpTeSessionTunnelExtId=hwRsvpTeSessionTunnelExtId, hwRsvpTeMessageIdGroup=hwRsvpTeMessageIdGroup, hwRsvpTeSenderTSpecMinTu=hwRsvpTeSenderTSpecMinTu, hwRsvpTeResvRSpecRate=hwRsvpTeResvRSpecRate, hwRsvpTeSenderFrrHoldPrio=hwRsvpTeSenderFrrHoldPrio, hwRsvpTeResvFwdTSpecMinTu=hwRsvpTeResvFwdTSpecMinTu, hwRsvpTeNbrSendersNumber=hwRsvpTeNbrSendersNumber, hwRsvpTeIfEntry=hwRsvpTeIfEntry, hwRsvpTeSenderAdspecGuaranteedSvc=hwRsvpTeSenderAdspecGuaranteedSvc, hwRsvpTeMessageIdEntry=hwRsvpTeMessageIdEntry, hwRsvpTeFilterSpecEntry=hwRsvpTeFilterSpecEntry, hwRsvpTeTrap=hwRsvpTeTrap, hwRsvpTeNbrStatus=hwRsvpTeNbrStatus, hwRsvpTeSenderAdspecCtrlLoadMinLatency=hwRsvpTeSenderAdspecCtrlLoadMinLatency, hwRsvpTeIfNbrs=hwRsvpTeIfNbrs, hwRsvpTeIfNbrThresholdExceedClear=hwRsvpTeIfNbrThresholdExceedClear, hwRsvpTeResvHopAddr=hwRsvpTeResvHopAddr, hwRsvpTeSenderLabelRequestAtmMinVpi=hwRsvpTeSenderLabelRequestAtmMinVpi, hwRsvpTeSenderAddrLength=hwRsvpTeSenderAddrLength)

149.703846

12,243

0.770675

ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueRangeConstraint, ConstraintsIntersection, SingleValueConstraint, ValueSizeConstraint, ConstraintsUnion = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "ConstraintsIntersection", "SingleValueConstraint", "ValueSizeConstraint", "ConstraintsUnion") hwDatacomm, = mibBuilder.importSymbols("HUAWEI-MIB", "hwDatacomm") ifIndex, = mibBuilder.importSymbols("IF-MIB", "ifIndex") BitRate, MessageSize, QosService, BurstSize, SessionType = mibBuilder.importSymbols("INTEGRATED-SERVICES-MIB", "BitRate", "MessageSize", "QosService", "BurstSize", "SessionType") ModuleCompliance, ObjectGroup, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "ObjectGroup", "NotificationGroup") Integer32, Unsigned32, MibIdentifier, Counter64, Counter32, TimeTicks, IpAddress, ModuleIdentity, iso, Gauge32, MibScalar, MibTable, MibTableRow, MibTableColumn, ObjectIdentity, NotificationType, Bits = mibBuilder.importSymbols("SNMPv2-SMI", "Integer32", "Unsigned32", "MibIdentifier", "Counter64", "Counter32", "TimeTicks", "IpAddress", "ModuleIdentity", "iso", "Gauge32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ObjectIdentity", "NotificationType", "Bits") DisplayString, TruthValue, TimeStamp, TimeInterval, TextualConvention, RowStatus = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TruthValue", "TimeStamp", "TimeInterval", "TextualConvention", "RowStatus") hwRsvpTe = ModuleIdentity((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148)) hwRsvpTe.setRevisions(('2014-10-25 17:36', '2014-06-16 14:55', '2013-08-28 17:55',)) if mibBuilder.loadTexts: hwRsvpTe.setLastUpdated('201410251736Z') if mibBuilder.loadTexts: hwRsvpTe.setOrganization('Huawei Technologies Co.,Ltd.') hwRsvpTeObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1)) hwRsvpTeSessionTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1), ) if mibBuilder.loadTexts: hwRsvpTeSessionTable.setStatus('current') hwRsvpTeSessionEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber")) if mibBuilder.loadTexts: hwRsvpTeSessionEntry.setStatus('current') hwRsvpTeSessionNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeSessionNumber.setStatus('current') hwRsvpTeSessionType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 2), SessionType()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionType.setStatus('current') hwRsvpTeSessionDestAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionDestAddr.setStatus('current') hwRsvpTeSessionDestAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 4), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionDestAddrLength.setStatus('current') hwRsvpTeSessionSenders = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 5), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionSenders.setStatus('current') hwRsvpTeSessionReceivers = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 6), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionReceivers.setStatus('current') hwRsvpTeSessionRequests = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 7), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionRequests.setStatus('current') hwRsvpTeSessionTunnelId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 8), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionTunnelId.setStatus('current') hwRsvpTeSessionTunnelExtId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 9), IpAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionTunnelExtId.setStatus('current') hwRsvpTeSessionLspsNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 10), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionLspsNumber.setStatus('current') hwRsvpTeSessionStyle = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 1, 1, 11), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(10, 17, 18))).clone(namedValues=NamedValues(("ff", 10), ("wf", 17), ("se", 18)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSessionStyle.setStatus('current') hwRsvpTeSenderTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2), ) if mibBuilder.loadTexts: hwRsvpTeSenderTable.setStatus('current') hwRsvpTeSenderEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderNumber")) if mibBuilder.loadTexts: hwRsvpTeSenderEntry.setStatus('current') hwRsvpTeSenderNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeSenderNumber.setStatus('current') hwRsvpTeSenderType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 2), SessionType()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderType.setStatus('current') hwRsvpTeSenderDestAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderDestAddr.setStatus('current') hwRsvpTeSenderAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAddr.setStatus('current') hwRsvpTeSenderDestAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 5), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderDestAddrLength.setStatus('current') hwRsvpTeSenderAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 6), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAddrLength.setStatus('current') hwRsvpTeSenderHopAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 7), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderHopAddr.setStatus('current') hwRsvpTeSenderHopLih = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 8), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderHopLih.setStatus('current') hwRsvpTeSenderInterface = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 9), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderInterface.setStatus('current') hwRsvpTeSenderTSpecRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 10), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTSpecRate.setStatus('current') hwRsvpTeSenderTSpecPeakRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 11), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTSpecPeakRate.setStatus('current') hwRsvpTeSenderTSpecBurst = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 12), BurstSize()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTSpecBurst.setStatus('current') hwRsvpTeSenderTSpecMinTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 13), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTSpecMinTu.setStatus('current') hwRsvpTeSenderTSpecMaxTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 14), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTSpecMaxTu.setStatus('current') hwRsvpTeSenderInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 15), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderInterval.setStatus('current') hwRsvpTeSenderRsvpHop = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 16), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderRsvpHop.setStatus('current') hwRsvpTeSenderPolicy = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 17), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 65532))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderPolicy.setStatus('current') hwRsvpTeSenderAdspecBreak = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 18), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecBreak.setStatus('current') hwRsvpTeSenderAdspecHopCount = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 19), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecHopCount.setStatus('current') hwRsvpTeSenderAdspecPathBw = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 20), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecPathBw.setStatus('current') hwRsvpTeSenderAdspecMinLatency = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 21), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecMinLatency.setStatus('current') hwRsvpTeSenderAdspecMtu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 22), Integer32()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecMtu.setStatus('current') hwRsvpTeSenderAdspecGuaranteedSvc = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 23), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedSvc.setStatus('current') hwRsvpTeSenderAdspecGuaranteedBreak = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 24), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedBreak.setStatus('current') hwRsvpTeSenderAdspecGuaranteedCtot = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 25), Integer32()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedCtot.setStatus('current') hwRsvpTeSenderAdspecGuaranteedDtot = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 26), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedDtot.setStatus('current') hwRsvpTeSenderAdspecGuaranteedCsum = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 27), Integer32()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedCsum.setStatus('current') hwRsvpTeSenderAdspecGuaranteedDsum = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 28), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedDsum.setStatus('current') hwRsvpTeSenderAdspecGuaranteedHopCount = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 29), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedHopCount.setStatus('current') hwRsvpTeSenderAdspecGuaranteedPathBw = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 30), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedPathBw.setStatus('current') hwRsvpTeSenderAdspecGuaranteedMinLatency = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 31), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedMinLatency.setStatus('current') hwRsvpTeSenderAdspecGuaranteedMtu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 32), Integer32()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecGuaranteedMtu.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadSvc = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 33), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadSvc.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadBreak = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 34), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadBreak.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadHopCount = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 35), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadHopCount.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadPathBw = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 36), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadPathBw.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadMinLatency = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 37), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadMinLatency.setStatus('current') hwRsvpTeSenderAdspecCtrlLoadMtu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 38), Integer32()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderAdspecCtrlLoadMtu.setStatus('current') hwRsvpTeSenderTtl = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 39), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderTtl.setStatus('current') hwRsvpTeLspId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 40), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeLspId.setStatus('current') hwRsvpTeSenderMsgIdSndFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 41), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdSndFlag.setStatus('current') hwRsvpTeSenderMsgIdSndEpoch = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 42), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdSndEpoch.setStatus('current') hwRsvpTeSenderMsgIdSndNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 43), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdSndNumber.setStatus('current') hwRsvpTeSenderMsgIdRcvFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 44), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdRcvFlag.setStatus('current') hwRsvpTeSenderMsgIdRcvEpoch = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 45), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdRcvEpoch.setStatus('current') hwRsvpTeSenderMsgIdRcvNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 46), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderMsgIdRcvNumber.setStatus('current') hwRsvpTeSenderClassType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 47), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderClassType.setStatus('current') hwRsvpTeSenderLabelRequestCtype = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 48), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("withoutLabelRange", 1), ("withAtmLabelRange", 2), ("withFrameRelayLabelRange", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestCtype.setStatus('current') hwRsvpTeSenderLabelRequestL3pid = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 49), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestL3pid.setStatus('current') hwRsvpTeSenderLabelRequestAtmMinVpi = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 50), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestAtmMinVpi.setStatus('current') hwRsvpTeSenderLabelRequestAtmMinVci = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 51), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestAtmMinVci.setStatus('current') hwRsvpTeSenderLabelRequestAtmMaxVpi = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 52), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestAtmMaxVpi.setStatus('current') hwRsvpTeSenderLabelRequestAtmMaxVci = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 53), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestAtmMaxVci.setStatus('current') hwRsvpTeSenderLabelRequestFrMinDlci = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 54), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestFrMinDlci.setStatus('current') hwRsvpTeSenderLabelRequestFrMaxDlci = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 55), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderLabelRequestFrMaxDlci.setStatus('current') hwRsvpTeSenderSessionAttrType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 56), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 7))).clone(namedValues=NamedValues(("withRa", 1), ("withoutRa", 7)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrType.setStatus('current') hwRsvpTeSenderSessionAttrSetupPrio = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 57), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrSetupPrio.setStatus('current') hwRsvpTeSenderSessionAttrHoldPrio = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 58), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrHoldPrio.setStatus('current') hwRsvpTeSenderSessionAttrFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 59), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrFlag.setStatus('current') hwRsvpTeSenderSessionAttrName = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 60), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 64))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrName.setStatus('current') hwRsvpTeSenderSessionAttrExcludeAny = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 61), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrExcludeAny.setStatus('current') hwRsvpTeSenderSessionAttrIncludeAny = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 62), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrIncludeAny.setStatus('current') hwRsvpTeSenderSessionAttrIncludeAll = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 63), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderSessionAttrIncludeAll.setStatus('current') hwRsvpTeSenderFrrSetupPrio = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 64), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrSetupPrio.setStatus('current') hwRsvpTeSenderFrrHoldPrio = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 65), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrHoldPrio.setStatus('current') hwRsvpTeSenderFrrHopLimit = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 66), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrHopLimit.setStatus('current') hwRsvpTeSenderFrrFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 67), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("oneToOneDesired", 1), ("facilityDesired", 2), ("noBackupDesired", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrFlag.setStatus('current') hwRsvpTeSenderFrrBandwidth = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 68), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrBandwidth.setStatus('current') hwRsvpTeSenderFrrExcludeAny = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 69), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrExcludeAny.setStatus('current') hwRsvpTeSenderFrrIncludeAny = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 70), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrIncludeAny.setStatus('current') hwRsvpTeSenderFrrIncludeAll = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 71), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrIncludeAll.setStatus('current') hwRsvpTeSenderFrrInuseFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 72), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5))).clone(namedValues=NamedValues(("normal", 1), ("plrInUse", 2), ("mpInUse", 3), ("plrAndMpInUse", 4), ("underProtection", 5)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderFrrInuseFlag.setStatus('current') hwRsvpTeSenderDiffServPsc = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 2, 1, 73), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeSenderDiffServPsc.setStatus('current') hwRsvpTeResvTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3), ) if mibBuilder.loadTexts: hwRsvpTeResvTable.setStatus('current') hwRsvpTeResvEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeResvNumber")) if mibBuilder.loadTexts: hwRsvpTeResvEntry.setStatus('current') hwRsvpTeResvNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeResvNumber.setStatus('current') hwRsvpTeResvType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 2), SessionType()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvType.setStatus('current') hwRsvpTeResvDestAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvDestAddr.setStatus('current') hwRsvpTeResvSenderAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvSenderAddr.setStatus('current') hwRsvpTeResvDestAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 5), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvDestAddrLength.setStatus('current') hwRsvpTeResvSenderAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 6), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvSenderAddrLength.setStatus('current') hwRsvpTeResvHopAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 7), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvHopAddr.setStatus('current') hwRsvpTeResvHopLih = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 8), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvHopLih.setStatus('current') hwRsvpTeResvInterface = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 9), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvInterface.setStatus('current') hwRsvpTeResvService = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 10), QosService()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvService.setStatus('current') hwRsvpTeResvTSpecRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 11), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTSpecRate.setStatus('current') hwRsvpTeResvTSpecPeakRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 12), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTSpecPeakRate.setStatus('current') hwRsvpTeResvTSpecBurst = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 13), BurstSize()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTSpecBurst.setStatus('current') hwRsvpTeResvTSpecMinTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 14), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTSpecMinTu.setStatus('current') hwRsvpTeResvTSpecMaxTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 15), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTSpecMaxTu.setStatus('current') hwRsvpTeResvRSpecRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 16), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvRSpecRate.setStatus('current') hwRsvpTeResvRSpecSlack = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 17), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvRSpecSlack.setStatus('current') hwRsvpTeResvInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 18), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvInterval.setStatus('current') hwRsvpTeResvScope = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 19), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvScope.setStatus('current') hwRsvpTeResvShared = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 20), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvShared.setStatus('current') hwRsvpTeResvExplicit = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 21), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvExplicit.setStatus('current') hwRsvpTeResvRsvpHop = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 22), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvRsvpHop.setStatus('current') hwRsvpTeResvPolicy = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 23), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvPolicy.setStatus('current') hwRsvpTeResvTtl = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 24), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvTtl.setStatus('current') hwRsvpTeResvConfirm = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 3, 1, 25), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvConfirm.setStatus('current') hwRsvpTeResvFwdTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4), ) if mibBuilder.loadTexts: hwRsvpTeResvFwdTable.setStatus('current') hwRsvpTeResvFwdEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdNumber")) if mibBuilder.loadTexts: hwRsvpTeResvFwdEntry.setStatus('current') hwRsvpTeResvFwdNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeResvFwdNumber.setStatus('current') hwRsvpTeResvFwdType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 2), SessionType()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdType.setStatus('current') hwRsvpTeResvFwdDestAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdDestAddr.setStatus('current') hwRsvpTeResvFwdSenderAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdSenderAddr.setStatus('current') hwRsvpTeResvFwdDestAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 5), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdDestAddrLength.setStatus('current') hwRsvpTeResvFwdSenderAddrLength = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 6), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdSenderAddrLength.setStatus('current') hwRsvpTeResvFwdHopAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 7), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdHopAddr.setStatus('current') hwRsvpTeResvFwdHopLih = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 8), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdHopLih.setStatus('current') hwRsvpTeResvFwdInterface = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 9), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdInterface.setStatus('current') hwRsvpTeResvFwdService = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 10), QosService()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdService.setStatus('current') hwRsvpTeResvFwdTSpecRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 11), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTSpecRate.setStatus('current') hwRsvpTeResvFwdTSpecPeakRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 12), BitRate()).setUnits('bits per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTSpecPeakRate.setStatus('current') hwRsvpTeResvFwdTSpecBurst = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 13), BurstSize()).setUnits('bytes').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTSpecBurst.setStatus('current') hwRsvpTeResvFwdTSpecMinTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 14), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTSpecMinTu.setStatus('current') hwRsvpTeResvFwdTSpecMaxTu = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 15), MessageSize()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTSpecMaxTu.setStatus('current') hwRsvpTeResvFwdRSpecRate = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 16), BitRate()).setUnits('bytes per second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdRSpecRate.setStatus('current') hwRsvpTeResvFwdRSpecSlack = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 17), Integer32()).setUnits('microseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdRSpecSlack.setStatus('current') hwRsvpTeResvFwdInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 18), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdInterval.setStatus('current') hwRsvpTeResvFwdScope = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 19), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdScope.setStatus('current') hwRsvpTeResvFwdShared = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 20), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdShared.setStatus('current') hwRsvpTeResvFwdExplicit = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 21), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdExplicit.setStatus('current') hwRsvpTeResvFwdRsvpHop = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 22), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdRsvpHop.setStatus('current') hwRsvpTeResvFwdPolicy = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 23), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 65535))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdPolicy.setStatus('current') hwRsvpTeResvFwdTtl = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 24), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdTtl.setStatus('current') hwRsvpTeResvFwdMsgIdFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 25), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdMsgIdFlag.setStatus('current') hwRsvpTeResvFwdMsgIdEpoch = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 26), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdMsgIdEpoch.setStatus('current') hwRsvpTeResvFwdMsgIdNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 4, 1, 27), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeResvFwdMsgIdNumber.setStatus('current') hwRsvpTeIfTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5), ) if mibBuilder.loadTexts: hwRsvpTeIfTable.setStatus('current') hwRsvpTeIfEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1), ).setIndexNames((0, "IF-MIB", "ifIndex")) if mibBuilder.loadTexts: hwRsvpTeIfEntry.setStatus('current') hwRsvpTeIfUdpNbrs = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 1), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfUdpNbrs.setStatus('current') hwRsvpTeIfIpNbrs = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 2), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfIpNbrs.setStatus('current') hwRsvpTeIfNbrs = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 3), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfNbrs.setStatus('current') hwRsvpTeIfRefreshBlockadeMultiple = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 4), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRefreshBlockadeMultiple.setStatus('current') hwRsvpTeIfRefreshMultiple = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 5), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRefreshMultiple.setStatus('current') hwRsvpTeIfTtl = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 6), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfTtl.setStatus('current') hwRsvpTeIfRefreshInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 7), TimeInterval()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRefreshInterval.setStatus('current') hwRsvpTeIfRouteDelay = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 8), TimeInterval()).setUnits('hundredths of a second').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRouteDelay.setStatus('current') hwRsvpTeIfEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 9), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfEnabled.setStatus('current') hwRsvpTeIfUdpRequired = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 10), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfUdpRequired.setStatus('current') hwRsvpTeIfStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 11), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: hwRsvpTeIfStatus.setStatus('current') hwRsvpTeIfHelloEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 12), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfHelloEnabled.setStatus('current') hwRsvpTeIfSrefreshEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 13), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfSrefreshEnabled.setStatus('current') hwRsvpTeIfSrefreshInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 14), TimeInterval()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfSrefreshInterval.setStatus('current') hwRsvpTeIfRetranIncDelta = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 15), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRetranIncDelta.setStatus('current') hwRsvpTeIfRetranInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 16), TimeInterval()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfRetranInterval.setStatus('current') hwRsvpTeIfAuthEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 17), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfAuthEnabled.setStatus('current') hwRsvpTeIfAuthEncrypted = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 18), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfAuthEncrypted.setStatus('current') hwRsvpTeIfAuthHandshake = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 19), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfAuthHandshake.setStatus('current') hwRsvpTeIfAuthLifeTime = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 20), TimeInterval()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfAuthLifeTime.setStatus('current') hwRsvpTeIfAuthKey = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 21), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 392))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfAuthKey.setStatus('current') hwRsvpTeIfWindowSize = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 5, 1, 22), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeIfWindowSize.setStatus('current') hwRsvpTeNbrTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6), ) if mibBuilder.loadTexts: hwRsvpTeNbrTable.setStatus('current') hwRsvpTeNbrEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrAddress")) if mibBuilder.loadTexts: hwRsvpTeNbrEntry.setStatus('current') hwRsvpTeNbrAddress = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 1), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 16))) if mibBuilder.loadTexts: hwRsvpTeNbrAddress.setStatus('current') hwRsvpTeNbrProtocol = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("ip", 1), ("udp", 2), ("both", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrProtocol.setStatus('current') hwRsvpTeNbrStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 3), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: hwRsvpTeNbrStatus.setStatus('current') hwRsvpTeNbrSendersNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 4), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrSendersNumber.setStatus('current') hwRsvpTeNbrReceiversNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 5), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrReceiversNumber.setStatus('current') hwRsvpTeNbrHelloEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 6), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrHelloEnabled.setStatus('current') hwRsvpTeNbrHelloSrcInstance = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 7), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrHelloSrcInstance.setStatus('current') hwRsvpTeNbrHelloDstInstance = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 8), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrHelloDstInstance.setStatus('current') hwRsvpTeNbrHelloLostCounter = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 9), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrHelloLostCounter.setStatus('current') hwRsvpTeNbrHelloType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 10), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("request", 1), ("ack", 2), ("none", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrHelloType.setStatus('current') hwRsvpTeNbrGrCapability = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 11), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrGrCapability.setStatus('current') hwRsvpTeNbrGrRestartTime = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 12), TimeStamp()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrGrRestartTime.setStatus('current') hwRsvpTeNbrGrRecoveryTime = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 13), TimeStamp()).setUnits('milliseconds').setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrGrRecoveryTime.setStatus('current') hwRsvpTeNbrGrStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 14), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6))).clone(namedValues=NamedValues(("normal", 1), ("supporting", 2), ("restarting", 3), ("restartTimerRunning", 4), ("recoveryTimerRunning", 5), ("grEnd", 6)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrGrStatus.setStatus('current') hwRsvpTeNbrAuthKeyId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 15), OctetString().subtype(subtypeSpec=ConstraintsUnion(ValueSizeConstraint(0, 0), ValueSizeConstraint(6, 6), ))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrAuthKeyId.setStatus('current') hwRsvpTeNbrReductionEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 16), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrReductionEnabled.setStatus('current') hwRsvpTeNbrReliabilityEnabled = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 6, 1, 17), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeNbrReliabilityEnabled.setStatus('current') hwRsvpTeMessageIdTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 7), ) if mibBuilder.loadTexts: hwRsvpTeMessageIdTable.setStatus('current') hwRsvpTeMessageIdEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 7, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrAddress"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeMessageIdEpoch"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeMessageIdNumber")) if mibBuilder.loadTexts: hwRsvpTeMessageIdEntry.setStatus('current') hwRsvpTeMessageIdEpoch = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 7, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeMessageIdEpoch.setStatus('current') hwRsvpTeMessageIdNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 7, 1, 2), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeMessageIdNumber.setStatus('current') hwRsvpTeMessageIdFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 7, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5))).clone(namedValues=NamedValues(("senderIncoming", 1), ("senderOutgoing", 2), ("resv", 3), ("resvFwd", 4), ("rtBuff", 5)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeMessageIdFlag.setStatus('current') hwRsvpTeFilterSpecTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8), ) if mibBuilder.loadTexts: hwRsvpTeFilterSpecTable.setStatus('current') hwRsvpTeFilterSpecEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeResvNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeFilterSpecNumber")) if mibBuilder.loadTexts: hwRsvpTeFilterSpecEntry.setStatus('current') hwRsvpTeFilterSpecNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeFilterSpecNumber.setStatus('current') hwRsvpTeFilterSpecLspId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8, 1, 2), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeFilterSpecLspId.setStatus('current') hwRsvpTeFilterSpecIngressLsrId = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8, 1, 3), OctetString()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeFilterSpecIngressLsrId.setStatus('current') hwRsvpTeFilterSpecLabel = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 8, 1, 4), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeFilterSpecLabel.setStatus('current') hwRsvpTeRroTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9), ) if mibBuilder.loadTexts: hwRsvpTeRroTable.setStatus('current') hwRsvpTeRroEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeRroNumber")) if mibBuilder.loadTexts: hwRsvpTeRroEntry.setStatus('current') hwRsvpTeRroNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeRroNumber.setStatus('current') hwRsvpTeRroType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("ipv4", 1), ("ipv6", 2), ("label", 3)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeRroType.setStatus('current') hwRsvpTeRroIpAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 3), OctetString()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeRroIpAddr.setStatus('current') hwRsvpTeRroIpPrefixLen = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 4), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeRroIpPrefixLen.setStatus('current') hwRsvpTeRroLabel = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 5), Gauge32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeRroLabel.setStatus('current') hwRsvpTeRroFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 9, 1, 6), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeRroFlag.setStatus('current') hwRsvpTeEroTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10), ) if mibBuilder.loadTexts: hwRsvpTeEroTable.setStatus('current') hwRsvpTeEroEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10, 1), ).setIndexNames((0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderNumber"), (0, "HUAWEI-RSVPTE-MIB", "hwRsvpTeEroNumber")) if mibBuilder.loadTexts: hwRsvpTeEroEntry.setStatus('current') hwRsvpTeEroNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10, 1, 1), Gauge32()) if mibBuilder.loadTexts: hwRsvpTeEroNumber.setStatus('current') hwRsvpTeEroType = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("ipv4", 1), ("ipv6", 2)))).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeEroType.setStatus('current') hwRsvpTeEroIpAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10, 1, 3), OctetString()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeEroIpAddr.setStatus('current') hwRsvpTeEroIpPrefixLen = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 10, 1, 4), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: hwRsvpTeEroIpPrefixLen.setStatus('current') hwRsvpTeExtendObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 11)) hwRsvpTeExtendTrap = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12)) hwRsvpTeTrapObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1)) hwRsvpTeNbr = MibScalar((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1, 1), IpAddress()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: hwRsvpTeNbr.setStatus('current') hwRsvpTeIfNbrCurrentCount = MibScalar((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1, 2), Integer32()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: hwRsvpTeIfNbrCurrentCount.setStatus('current') hwRsvpTeIfNbrThreshold = MibScalar((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1, 3), Integer32()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: hwRsvpTeIfNbrThreshold.setStatus('current') hwRsvpTeIfNbrTotalCount = MibScalar((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1, 4), Integer32()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: hwRsvpTeIfNbrTotalCount.setStatus('current') hwRsvpTeIfName = MibScalar((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 1, 5), DisplayString()).setMaxAccess("accessiblefornotify") if mibBuilder.loadTexts: hwRsvpTeIfName.setStatus('current') hwRsvpTeTrap = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2)) hwRsvpTeHelloLost = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 1)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbr")) if mibBuilder.loadTexts: hwRsvpTeHelloLost.setStatus('current') hwRsvpTeHelloLostRecovery = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 2)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbr")) if mibBuilder.loadTexts: hwRsvpTeHelloLostRecovery.setStatus('current') hwRsvpTeAuthFail = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 3)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbr")) if mibBuilder.loadTexts: hwRsvpTeAuthFail.setStatus('current') hwRsvpTeAuthSuccess = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 4)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbr")) if mibBuilder.loadTexts: hwRsvpTeAuthSuccess.setStatus('current') hwRsvpTeIfNbrThresholdExceed = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 5)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfName"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrCurrentCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrThreshold"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrTotalCount")) if mibBuilder.loadTexts: hwRsvpTeIfNbrThresholdExceed.setStatus('current') hwRsvpTeIfNbrThresholdExceedClear = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 6)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfName")) if mibBuilder.loadTexts: hwRsvpTeIfNbrThresholdExceedClear.setStatus('current') hwRsvpTeIfNbrTotalCountExceed = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 7)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfName"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrTotalCount")) if mibBuilder.loadTexts: hwRsvpTeIfNbrTotalCountExceed.setStatus('current') hwRsvpTeIfNbrTotalCountExceedClear = NotificationType((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 1, 12, 2, 8)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfName")) if mibBuilder.loadTexts: hwRsvpTeIfNbrTotalCountExceedClear.setStatus('current') hwRsvpTeConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2)) hwRsvpTeGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1)) hwRsvpTeSessionGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 1)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionDestAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionDestAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionSenders"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionReceivers"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionRequests"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionTunnelId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionTunnelExtId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionLspsNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionStyle")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeSessionGroup = hwRsvpTeSessionGroup.setStatus('current') hwRsvpTeSenderGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 2)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderDestAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderDestAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderHopAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderHopLih"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderInterface"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTSpecRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTSpecPeakRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTSpecBurst"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTSpecMinTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTSpecMaxTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderRsvpHop"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderPolicy"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecBreak"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecHopCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecPathBw"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecMinLatency"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecMtu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedSvc"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedBreak"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedCtot"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedDtot"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedCsum"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedDsum"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedHopCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedPathBw"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedMinLatency"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecGuaranteedMtu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadSvc"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadBreak"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadHopCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadPathBw"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadMinLatency"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderAdspecCtrlLoadMtu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderTtl"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeLspId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdSndFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdSndEpoch"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdSndNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdRcvFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdRcvEpoch"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderMsgIdRcvNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderClassType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestCtype"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestL3pid"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestAtmMinVpi"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestAtmMinVci"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestAtmMaxVpi"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestAtmMaxVci"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestFrMinDlci"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderLabelRequestFrMaxDlci"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrSetupPrio"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrHoldPrio"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrName"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrExcludeAny"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrIncludeAny"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderSessionAttrIncludeAll"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrSetupPrio"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrHoldPrio"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrHopLimit"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrBandwidth"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrExcludeAny"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrIncludeAny"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrIncludeAll"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderFrrInuseFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderDiffServPsc")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeSenderGroup = hwRsvpTeSenderGroup.setStatus('current') hwRsvpTeResvGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 3)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvDestAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvSenderAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvDestAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvSenderAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvHopAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvHopLih"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvInterface"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvService"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTSpecRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTSpecPeakRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTSpecBurst"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTSpecMinTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTSpecMaxTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvRSpecRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvRSpecSlack"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvScope"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvShared"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvExplicit"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvRsvpHop"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvPolicy"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvTtl"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvConfirm")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeResvGroup = hwRsvpTeResvGroup.setStatus('current') hwRsvpTeResvFwdGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 4)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdDestAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdSenderAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdDestAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdSenderAddrLength"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdHopAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdHopLih"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdInterface"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdService"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTSpecRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTSpecPeakRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTSpecBurst"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTSpecMinTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTSpecMaxTu"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdRSpecRate"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdRSpecSlack"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdScope"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdShared"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdExplicit"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdPolicy"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdTtl"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdMsgIdFlag"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdMsgIdEpoch"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdMsgIdNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdRsvpHop")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeResvFwdGroup = hwRsvpTeResvFwdGroup.setStatus('current') hwRsvpTeIfGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 5)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfUdpNbrs"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfIpNbrs"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrs"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRefreshBlockadeMultiple"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRefreshMultiple"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfTtl"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRefreshInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRouteDelay"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfUdpRequired"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfStatus"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfHelloEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfSrefreshEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfSrefreshInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRetranIncDelta"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfRetranInterval"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfAuthEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfAuthEncrypted"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfAuthHandshake"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfAuthKey"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfWindowSize"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfAuthLifeTime")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeIfGroup = hwRsvpTeIfGroup.setStatus('current') hwRsvpTeNbrGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 6)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrHelloSrcInstance"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrHelloDstInstance"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrHelloLostCounter"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrHelloType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrHelloEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrSendersNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrReceiversNumber"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrGrCapability"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrGrRestartTime"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrGrRecoveryTime"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrGrStatus"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrAuthKeyId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrReductionEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrReliabilityEnabled"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrProtocol"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeNbrGroup = hwRsvpTeNbrGroup.setStatus('current') hwRsvpTeMessageIdGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 7)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeMessageIdFlag")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeMessageIdGroup = hwRsvpTeMessageIdGroup.setStatus('current') hwRsvpTeFilterSpecGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 8)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeFilterSpecLspId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeFilterSpecIngressLsrId"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeFilterSpecLabel")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeFilterSpecGroup = hwRsvpTeFilterSpecGroup.setStatus('current') hwRsvpTeRroGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 9)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroIpAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroIpPrefixLen"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroLabel"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroFlag")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeRroGroup = hwRsvpTeRroGroup.setStatus('current') hwRsvpTeEroGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 10)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeEroType"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeEroIpAddr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeEroIpPrefixLen")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeEroGroup = hwRsvpTeEroGroup.setStatus('current') hwRsvpTeTrapObjectsGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 11)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbr"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrCurrentCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrThreshold"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrTotalCount"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfName")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeTrapObjectsGroup = hwRsvpTeTrapObjectsGroup.setStatus('current') hwRsvpTeTrapGroup = NotificationGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 1, 12)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeHelloLost"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeHelloLostRecovery"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeAuthFail"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeAuthSuccess"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrThresholdExceed"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrThresholdExceedClear"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrTotalCountExceed"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfNbrTotalCountExceedClear")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeTrapGroup = hwRsvpTeTrapGroup.setStatus('current') hwRsvpTeCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 2)) hwRsvpTeCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 2011, 5, 25, 148, 2, 2, 1)).setObjects(("HUAWEI-RSVPTE-MIB", "hwRsvpTeSessionGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeSenderGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeIfGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeNbrGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeFilterSpecGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeRroGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeEroGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeTrapObjectsGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeTrapGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeResvFwdGroup"), ("HUAWEI-RSVPTE-MIB", "hwRsvpTeMessageIdGroup")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): hwRsvpTeCompliance = hwRsvpTeCompliance.setStatus('current') mibBuilder.exportSymbols("HUAWEI-RSVPTE-MIB", hwRsvpTeIfIpNbrs=hwRsvpTeIfIpNbrs, hwRsvpTeResvFwdTSpecMaxTu=hwRsvpTeResvFwdTSpecMaxTu, hwRsvpTeSenderAdspecGuaranteedHopCount=hwRsvpTeSenderAdspecGuaranteedHopCount, hwRsvpTeMessageIdFlag=hwRsvpTeMessageIdFlag, hwRsvpTeResvFwdSenderAddr=hwRsvpTeResvFwdSenderAddr, hwRsvpTeCompliances=hwRsvpTeCompliances, hwRsvpTeIfRefreshInterval=hwRsvpTeIfRefreshInterval, hwRsvpTeIfGroup=hwRsvpTeIfGroup, hwRsvpTeSenderSessionAttrIncludeAny=hwRsvpTeSenderSessionAttrIncludeAny, hwRsvpTeResvFwdTable=hwRsvpTeResvFwdTable, hwRsvpTeGroups=hwRsvpTeGroups, hwRsvpTeRroIpPrefixLen=hwRsvpTeRroIpPrefixLen, hwRsvpTeResvFwdSenderAddrLength=hwRsvpTeResvFwdSenderAddrLength, hwRsvpTeEroTable=hwRsvpTeEroTable, hwRsvpTeIfRefreshBlockadeMultiple=hwRsvpTeIfRefreshBlockadeMultiple, hwRsvpTeNbrGrRecoveryTime=hwRsvpTeNbrGrRecoveryTime, hwRsvpTeEroIpAddr=hwRsvpTeEroIpAddr, hwRsvpTeIfRouteDelay=hwRsvpTeIfRouteDelay, hwRsvpTeSenderAdspecCtrlLoadMtu=hwRsvpTeSenderAdspecCtrlLoadMtu, hwRsvpTeSessionRequests=hwRsvpTeSessionRequests, hwRsvpTeSessionSenders=hwRsvpTeSessionSenders, hwRsvpTeSenderEntry=hwRsvpTeSenderEntry, hwRsvpTeSenderRsvpHop=hwRsvpTeSenderRsvpHop, hwRsvpTeTrapGroup=hwRsvpTeTrapGroup, hwRsvpTeIfNbrCurrentCount=hwRsvpTeIfNbrCurrentCount, hwRsvpTeNbrProtocol=hwRsvpTeNbrProtocol, hwRsvpTeMessageIdTable=hwRsvpTeMessageIdTable, hwRsvpTeRroNumber=hwRsvpTeRroNumber, hwRsvpTeSenderLabelRequestFrMinDlci=hwRsvpTeSenderLabelRequestFrMinDlci, hwRsvpTeResvFwdDestAddr=hwRsvpTeResvFwdDestAddr, hwRsvpTeIfStatus=hwRsvpTeIfStatus, hwRsvpTeResvType=hwRsvpTeResvType, hwRsvpTeSessionDestAddr=hwRsvpTeSessionDestAddr, hwRsvpTeResvEntry=hwRsvpTeResvEntry, hwRsvpTeIfAuthEncrypted=hwRsvpTeIfAuthEncrypted, hwRsvpTeRroGroup=hwRsvpTeRroGroup, hwRsvpTeSenderType=hwRsvpTeSenderType, hwRsvpTeSenderFrrIncludeAny=hwRsvpTeSenderFrrIncludeAny, hwRsvpTeSenderSessionAttrType=hwRsvpTeSenderSessionAttrType, hwRsvpTeMessageIdNumber=hwRsvpTeMessageIdNumber, hwRsvpTeSenderLabelRequestAtmMaxVpi=hwRsvpTeSenderLabelRequestAtmMaxVpi, hwRsvpTeFilterSpecIngressLsrId=hwRsvpTeFilterSpecIngressLsrId, hwRsvpTeRroEntry=hwRsvpTeRroEntry, hwRsvpTeResvFwdRSpecRate=hwRsvpTeResvFwdRSpecRate, hwRsvpTe=hwRsvpTe, hwRsvpTeResvFwdHopLih=hwRsvpTeResvFwdHopLih, hwRsvpTeNbrHelloDstInstance=hwRsvpTeNbrHelloDstInstance, hwRsvpTeSessionNumber=hwRsvpTeSessionNumber, hwRsvpTeSessionEntry=hwRsvpTeSessionEntry, hwRsvpTeSenderMsgIdSndNumber=hwRsvpTeSenderMsgIdSndNumber, hwRsvpTeIfUdpNbrs=hwRsvpTeIfUdpNbrs, hwRsvpTeResvShared=hwRsvpTeResvShared, hwRsvpTeSenderAdspecPathBw=hwRsvpTeSenderAdspecPathBw, hwRsvpTeIfRetranInterval=hwRsvpTeIfRetranInterval, hwRsvpTeFilterSpecTable=hwRsvpTeFilterSpecTable, hwRsvpTeResvScope=hwRsvpTeResvScope, hwRsvpTeNbrGroup=hwRsvpTeNbrGroup, hwRsvpTeCompliance=hwRsvpTeCompliance, hwRsvpTeSessionTable=hwRsvpTeSessionTable, hwRsvpTeNbrHelloSrcInstance=hwRsvpTeNbrHelloSrcInstance, hwRsvpTeEroType=hwRsvpTeEroType, hwRsvpTeSenderAdspecGuaranteedMinLatency=hwRsvpTeSenderAdspecGuaranteedMinLatency, hwRsvpTeAuthFail=hwRsvpTeAuthFail, hwRsvpTeSenderFrrInuseFlag=hwRsvpTeSenderFrrInuseFlag, hwRsvpTeSenderMsgIdRcvFlag=hwRsvpTeSenderMsgIdRcvFlag, hwRsvpTeResvFwdTSpecPeakRate=hwRsvpTeResvFwdTSpecPeakRate, hwRsvpTeResvService=hwRsvpTeResvService, hwRsvpTeResvPolicy=hwRsvpTeResvPolicy, hwRsvpTeNbrAuthKeyId=hwRsvpTeNbrAuthKeyId, hwRsvpTeRroLabel=hwRsvpTeRroLabel, hwRsvpTeSenderFrrIncludeAll=hwRsvpTeSenderFrrIncludeAll, hwRsvpTeSenderClassType=hwRsvpTeSenderClassType, hwRsvpTeSenderSessionAttrExcludeAny=hwRsvpTeSenderSessionAttrExcludeAny, hwRsvpTeIfAuthKey=hwRsvpTeIfAuthKey, hwRsvpTeSenderTSpecBurst=hwRsvpTeSenderTSpecBurst, hwRsvpTeIfNbrTotalCount=hwRsvpTeIfNbrTotalCount, hwRsvpTeIfNbrTotalCountExceedClear=hwRsvpTeIfNbrTotalCountExceedClear, hwRsvpTeSenderFrrExcludeAny=hwRsvpTeSenderFrrExcludeAny, hwRsvpTeResvConfirm=hwRsvpTeResvConfirm, hwRsvpTeResvDestAddr=hwRsvpTeResvDestAddr, hwRsvpTeResvFwdShared=hwRsvpTeResvFwdShared, hwRsvpTeHelloLostRecovery=hwRsvpTeHelloLostRecovery, hwRsvpTeResvTSpecRate=hwRsvpTeResvTSpecRate, hwRsvpTeSenderNumber=hwRsvpTeSenderNumber, hwRsvpTeSenderAdspecHopCount=hwRsvpTeSenderAdspecHopCount, hwRsvpTeSessionDestAddrLength=hwRsvpTeSessionDestAddrLength, hwRsvpTeSenderTable=hwRsvpTeSenderTable, hwRsvpTeSenderPolicy=hwRsvpTeSenderPolicy, hwRsvpTeSenderAdspecGuaranteedCtot=hwRsvpTeSenderAdspecGuaranteedCtot, hwRsvpTeResvFwdType=hwRsvpTeResvFwdType, hwRsvpTeNbrEntry=hwRsvpTeNbrEntry, hwRsvpTeSenderHopAddr=hwRsvpTeSenderHopAddr, hwRsvpTeSenderMsgIdSndEpoch=hwRsvpTeSenderMsgIdSndEpoch, hwRsvpTeSenderFrrBandwidth=hwRsvpTeSenderFrrBandwidth, hwRsvpTeSenderTSpecPeakRate=hwRsvpTeSenderTSpecPeakRate, hwRsvpTeSenderAddr=hwRsvpTeSenderAddr, hwRsvpTeSenderFrrHopLimit=hwRsvpTeSenderFrrHopLimit, hwRsvpTeSenderSessionAttrName=hwRsvpTeSenderSessionAttrName, hwRsvpTeResvSenderAddrLength=hwRsvpTeResvSenderAddrLength, hwRsvpTeResvInterface=hwRsvpTeResvInterface, hwRsvpTeResvRsvpHop=hwRsvpTeResvRsvpHop, hwRsvpTeResvFwdExplicit=hwRsvpTeResvFwdExplicit, hwRsvpTeIfTtl=hwRsvpTeIfTtl, hwRsvpTeResvFwdDestAddrLength=hwRsvpTeResvFwdDestAddrLength, hwRsvpTeResvTSpecBurst=hwRsvpTeResvTSpecBurst, hwRsvpTeRroIpAddr=hwRsvpTeRroIpAddr, hwRsvpTeNbrGrRestartTime=hwRsvpTeNbrGrRestartTime, hwRsvpTeResvTSpecMaxTu=hwRsvpTeResvTSpecMaxTu, hwRsvpTeNbr=hwRsvpTeNbr, hwRsvpTeSessionType=hwRsvpTeSessionType, hwRsvpTeIfAuthEnabled=hwRsvpTeIfAuthEnabled, hwRsvpTeFilterSpecLabel=hwRsvpTeFilterSpecLabel, PYSNMP_MODULE_ID=hwRsvpTe, hwRsvpTeResvFwdNumber=hwRsvpTeResvFwdNumber, hwRsvpTeExtendObjects=hwRsvpTeExtendObjects, hwRsvpTeIfName=hwRsvpTeIfName, hwRsvpTeIfSrefreshInterval=hwRsvpTeIfSrefreshInterval, hwRsvpTeSessionLspsNumber=hwRsvpTeSessionLspsNumber, hwRsvpTeSenderAdspecGuaranteedDsum=hwRsvpTeSenderAdspecGuaranteedDsum, hwRsvpTeSenderSessionAttrSetupPrio=hwRsvpTeSenderSessionAttrSetupPrio, hwRsvpTeSenderTSpecRate=hwRsvpTeSenderTSpecRate, hwRsvpTeSenderAdspecGuaranteedDtot=hwRsvpTeSenderAdspecGuaranteedDtot, hwRsvpTeSenderAdspecCtrlLoadSvc=hwRsvpTeSenderAdspecCtrlLoadSvc, hwRsvpTeResvGroup=hwRsvpTeResvGroup, hwRsvpTeSessionGroup=hwRsvpTeSessionGroup, hwRsvpTeRroFlag=hwRsvpTeRroFlag, hwRsvpTeResvExplicit=hwRsvpTeResvExplicit, hwRsvpTeIfNbrThreshold=hwRsvpTeIfNbrThreshold, hwRsvpTeRroTable=hwRsvpTeRroTable, hwRsvpTeRroType=hwRsvpTeRroType, hwRsvpTeSenderDestAddr=hwRsvpTeSenderDestAddr, hwRsvpTeEroEntry=hwRsvpTeEroEntry, hwRsvpTeSenderAdspecCtrlLoadPathBw=hwRsvpTeSenderAdspecCtrlLoadPathBw, hwRsvpTeResvFwdGroup=hwRsvpTeResvFwdGroup, hwRsvpTeTrapObjectsGroup=hwRsvpTeTrapObjectsGroup, hwRsvpTeResvTable=hwRsvpTeResvTable, hwRsvpTeIfRefreshMultiple=hwRsvpTeIfRefreshMultiple, hwRsvpTeSenderGroup=hwRsvpTeSenderGroup, hwRsvpTeFilterSpecGroup=hwRsvpTeFilterSpecGroup, hwRsvpTeEroGroup=hwRsvpTeEroGroup, hwRsvpTeResvSenderAddr=hwRsvpTeResvSenderAddr, hwRsvpTeNbrReceiversNumber=hwRsvpTeNbrReceiversNumber, hwRsvpTeNbrReliabilityEnabled=hwRsvpTeNbrReliabilityEnabled, hwRsvpTeNbrHelloEnabled=hwRsvpTeNbrHelloEnabled, hwRsvpTeNbrGrCapability=hwRsvpTeNbrGrCapability, hwRsvpTeResvTtl=hwRsvpTeResvTtl, hwRsvpTeSenderSessionAttrFlag=hwRsvpTeSenderSessionAttrFlag, hwRsvpTeResvTSpecMinTu=hwRsvpTeResvTSpecMinTu, hwRsvpTeSenderMsgIdRcvEpoch=hwRsvpTeSenderMsgIdRcvEpoch, hwRsvpTeIfWindowSize=hwRsvpTeIfWindowSize, hwRsvpTeSenderDiffServPsc=hwRsvpTeSenderDiffServPsc, hwRsvpTeMessageIdEpoch=hwRsvpTeMessageIdEpoch, hwRsvpTeNbrTable=hwRsvpTeNbrTable, hwRsvpTeNbrGrStatus=hwRsvpTeNbrGrStatus, hwRsvpTeSenderLabelRequestFrMaxDlci=hwRsvpTeSenderLabelRequestFrMaxDlci, hwRsvpTeSessionReceivers=hwRsvpTeSessionReceivers, hwRsvpTeResvFwdScope=hwRsvpTeResvFwdScope, hwRsvpTeSenderAdspecMtu=hwRsvpTeSenderAdspecMtu, hwRsvpTeSenderMsgIdSndFlag=hwRsvpTeSenderMsgIdSndFlag, hwRsvpTeSenderAdspecGuaranteedBreak=hwRsvpTeSenderAdspecGuaranteedBreak, hwRsvpTeResvTSpecPeakRate=hwRsvpTeResvTSpecPeakRate, hwRsvpTeIfRetranIncDelta=hwRsvpTeIfRetranIncDelta, hwRsvpTeSenderFrrFlag=hwRsvpTeSenderFrrFlag, hwRsvpTeResvFwdInterface=hwRsvpTeResvFwdInterface, hwRsvpTeSenderTtl=hwRsvpTeSenderTtl, hwRsvpTeSenderAdspecMinLatency=hwRsvpTeSenderAdspecMinLatency, hwRsvpTeResvFwdTtl=hwRsvpTeResvFwdTtl, hwRsvpTeSenderLabelRequestAtmMinVci=hwRsvpTeSenderLabelRequestAtmMinVci, hwRsvpTeResvFwdService=hwRsvpTeResvFwdService, hwRsvpTeSenderInterface=hwRsvpTeSenderInterface, hwRsvpTeSenderInterval=hwRsvpTeSenderInterval, hwRsvpTeResvFwdRsvpHop=hwRsvpTeResvFwdRsvpHop, hwRsvpTeEroIpPrefixLen=hwRsvpTeEroIpPrefixLen, hwRsvpTeResvFwdEntry=hwRsvpTeResvFwdEntry, hwRsvpTeLspId=hwRsvpTeLspId, hwRsvpTeResvFwdRSpecSlack=hwRsvpTeResvFwdRSpecSlack, hwRsvpTeResvRSpecSlack=hwRsvpTeResvRSpecSlack, hwRsvpTeResvFwdInterval=hwRsvpTeResvFwdInterval, hwRsvpTeResvFwdHopAddr=hwRsvpTeResvFwdHopAddr, hwRsvpTeSenderAdspecCtrlLoadBreak=hwRsvpTeSenderAdspecCtrlLoadBreak, hwRsvpTeResvFwdPolicy=hwRsvpTeResvFwdPolicy, hwRsvpTeConformance=hwRsvpTeConformance, hwRsvpTeSenderAdspecBreak=hwRsvpTeSenderAdspecBreak, hwRsvpTeResvFwdTSpecBurst=hwRsvpTeResvFwdTSpecBurst, hwRsvpTeResvFwdMsgIdNumber=hwRsvpTeResvFwdMsgIdNumber, hwRsvpTeExtendTrap=hwRsvpTeExtendTrap, hwRsvpTeAuthSuccess=hwRsvpTeAuthSuccess, hwRsvpTeFilterSpecNumber=hwRsvpTeFilterSpecNumber, hwRsvpTeIfNbrTotalCountExceed=hwRsvpTeIfNbrTotalCountExceed, hwRsvpTeSenderFrrSetupPrio=hwRsvpTeSenderFrrSetupPrio, hwRsvpTeResvHopLih=hwRsvpTeResvHopLih, hwRsvpTeIfEnabled=hwRsvpTeIfEnabled, hwRsvpTeIfTable=hwRsvpTeIfTable, hwRsvpTeIfHelloEnabled=hwRsvpTeIfHelloEnabled, hwRsvpTeIfAuthLifeTime=hwRsvpTeIfAuthLifeTime, hwRsvpTeSenderMsgIdRcvNumber=hwRsvpTeSenderMsgIdRcvNumber, hwRsvpTeResvFwdTSpecRate=hwRsvpTeResvFwdTSpecRate, hwRsvpTeSenderAdspecGuaranteedPathBw=hwRsvpTeSenderAdspecGuaranteedPathBw, hwRsvpTeResvDestAddrLength=hwRsvpTeResvDestAddrLength, hwRsvpTeNbrHelloLostCounter=hwRsvpTeNbrHelloLostCounter, hwRsvpTeSenderAdspecCtrlLoadHopCount=hwRsvpTeSenderAdspecCtrlLoadHopCount, hwRsvpTeHelloLost=hwRsvpTeHelloLost, hwRsvpTeIfUdpRequired=hwRsvpTeIfUdpRequired, hwRsvpTeNbrReductionEnabled=hwRsvpTeNbrReductionEnabled, hwRsvpTeSessionStyle=hwRsvpTeSessionStyle, hwRsvpTeNbrAddress=hwRsvpTeNbrAddress, hwRsvpTeNbrHelloType=hwRsvpTeNbrHelloType, hwRsvpTeSessionTunnelId=hwRsvpTeSessionTunnelId, hwRsvpTeIfSrefreshEnabled=hwRsvpTeIfSrefreshEnabled, hwRsvpTeEroNumber=hwRsvpTeEroNumber, hwRsvpTeSenderAdspecGuaranteedCsum=hwRsvpTeSenderAdspecGuaranteedCsum, hwRsvpTeSenderSessionAttrHoldPrio=hwRsvpTeSenderSessionAttrHoldPrio, hwRsvpTeSenderLabelRequestAtmMaxVci=hwRsvpTeSenderLabelRequestAtmMaxVci, hwRsvpTeSenderHopLih=hwRsvpTeSenderHopLih, hwRsvpTeFilterSpecLspId=hwRsvpTeFilterSpecLspId, hwRsvpTeSenderSessionAttrIncludeAll=hwRsvpTeSenderSessionAttrIncludeAll, hwRsvpTeSenderLabelRequestL3pid=hwRsvpTeSenderLabelRequestL3pid, hwRsvpTeSenderAdspecGuaranteedMtu=hwRsvpTeSenderAdspecGuaranteedMtu, hwRsvpTeResvNumber=hwRsvpTeResvNumber, hwRsvpTeTrapObjects=hwRsvpTeTrapObjects, hwRsvpTeResvFwdMsgIdEpoch=hwRsvpTeResvFwdMsgIdEpoch, hwRsvpTeSenderDestAddrLength=hwRsvpTeSenderDestAddrLength, hwRsvpTeIfAuthHandshake=hwRsvpTeIfAuthHandshake, hwRsvpTeSenderTSpecMaxTu=hwRsvpTeSenderTSpecMaxTu, hwRsvpTeSenderLabelRequestCtype=hwRsvpTeSenderLabelRequestCtype, hwRsvpTeObjects=hwRsvpTeObjects, hwRsvpTeIfNbrThresholdExceed=hwRsvpTeIfNbrThresholdExceed, hwRsvpTeResvFwdMsgIdFlag=hwRsvpTeResvFwdMsgIdFlag, hwRsvpTeResvInterval=hwRsvpTeResvInterval, hwRsvpTeSessionTunnelExtId=hwRsvpTeSessionTunnelExtId, hwRsvpTeMessageIdGroup=hwRsvpTeMessageIdGroup, hwRsvpTeSenderTSpecMinTu=hwRsvpTeSenderTSpecMinTu, hwRsvpTeResvRSpecRate=hwRsvpTeResvRSpecRate, hwRsvpTeSenderFrrHoldPrio=hwRsvpTeSenderFrrHoldPrio, hwRsvpTeResvFwdTSpecMinTu=hwRsvpTeResvFwdTSpecMinTu, hwRsvpTeNbrSendersNumber=hwRsvpTeNbrSendersNumber, hwRsvpTeIfEntry=hwRsvpTeIfEntry, hwRsvpTeSenderAdspecGuaranteedSvc=hwRsvpTeSenderAdspecGuaranteedSvc, hwRsvpTeMessageIdEntry=hwRsvpTeMessageIdEntry, hwRsvpTeFilterSpecEntry=hwRsvpTeFilterSpecEntry, hwRsvpTeTrap=hwRsvpTeTrap, hwRsvpTeNbrStatus=hwRsvpTeNbrStatus, hwRsvpTeSenderAdspecCtrlLoadMinLatency=hwRsvpTeSenderAdspecCtrlLoadMinLatency, hwRsvpTeIfNbrs=hwRsvpTeIfNbrs, hwRsvpTeIfNbrThresholdExceedClear=hwRsvpTeIfNbrThresholdExceedClear, hwRsvpTeResvHopAddr=hwRsvpTeResvHopAddr, hwRsvpTeSenderLabelRequestAtmMinVpi=hwRsvpTeSenderLabelRequestAtmMinVpi, hwRsvpTeSenderAddrLength=hwRsvpTeSenderAddrLength)

true

790da6315d54ab39b87b78c2b6aab8546c002052

72,148

py

Python

chia/full_node/full_node_api.py

AppleOfEnlightenment/chia-blockchain

d3f2ae367d00cf20360c7d7a177f941ea53ecbcb

[ "Apache-2.0" ]

null

chia/full_node/full_node_api.py

AppleOfEnlightenment/chia-blockchain

d3f2ae367d00cf20360c7d7a177f941ea53ecbcb

[ "Apache-2.0" ]

null

chia/full_node/full_node_api.py

AppleOfEnlightenment/chia-blockchain

d3f2ae367d00cf20360c7d7a177f941ea53ecbcb

[ "Apache-2.0" ]

null

import asyncio import dataclasses import time import traceback from secrets import token_bytes from typing import Dict, List, Optional, Tuple, Set from blspy import AugSchemeMPL, G2Element from chiabip158 import PyBIP158 import chia.server.ws_connection as ws from chia.consensus.block_creation import create_unfinished_block from chia.consensus.block_record import BlockRecord from chia.consensus.pot_iterations import calculate_ip_iters, calculate_iterations_quality, calculate_sp_iters from chia.full_node.bundle_tools import best_solution_generator_from_template, simple_solution_generator from chia.full_node.full_node import FullNode from chia.full_node.mempool_check_conditions import get_puzzle_and_solution_for_coin from chia.full_node.signage_point import SignagePoint from chia.protocols import farmer_protocol, full_node_protocol, introducer_protocol, timelord_protocol, wallet_protocol from chia.protocols.full_node_protocol import RejectBlock, RejectBlocks from chia.protocols.protocol_message_types import ProtocolMessageTypes from chia.protocols.wallet_protocol import ( PuzzleSolutionResponse, RejectHeaderBlocks, RejectHeaderRequest, CoinState, RespondSESInfo, ) from chia.server.outbound_message import Message, make_msg from chia.types.blockchain_format.coin import Coin, hash_coin_list from chia.types.blockchain_format.pool_target import PoolTarget from chia.types.blockchain_format.program import Program from chia.types.blockchain_format.sized_bytes import bytes32 from chia.types.blockchain_format.sub_epoch_summary import SubEpochSummary from chia.types.coin_record import CoinRecord from chia.types.end_of_slot_bundle import EndOfSubSlotBundle from chia.types.full_block import FullBlock from chia.types.generator_types import BlockGenerator from chia.types.mempool_inclusion_status import MempoolInclusionStatus from chia.types.mempool_item import MempoolItem from chia.types.peer_info import PeerInfo from chia.types.transaction_queue_entry import TransactionQueueEntry from chia.types.unfinished_block import UnfinishedBlock from chia.util.api_decorators import api_request, peer_required, bytes_required, execute_task, reply_type from chia.util.generator_tools import get_block_header from chia.util.hash import std_hash from chia.util.ints import uint8, uint32, uint64, uint128 from chia.util.merkle_set import MerkleSet class FullNodeAPI: full_node: FullNode def __init__(self, full_node) -> None: self.full_node = full_node @property def server(self): return self.full_node.server @property def log(self): return self.full_node.log @property def api_ready(self): return self.full_node.initialized @peer_required @api_request @reply_type([ProtocolMessageTypes.respond_peers]) async def request_peers(self, _request: full_node_protocol.RequestPeers, peer: ws.WSChiaConnection): if peer.peer_server_port is None: return None peer_info = PeerInfo(peer.peer_host, peer.peer_server_port) if self.full_node.full_node_peers is not None: msg = await self.full_node.full_node_peers.request_peers(peer_info) return msg @peer_required @api_request async def respond_peers( self, request: full_node_protocol.RespondPeers, peer: ws.WSChiaConnection ) -> Optional[Message]: self.log.debug(f"Received {len(request.peer_list)} peers") if self.full_node.full_node_peers is not None: await self.full_node.full_node_peers.respond_peers(request, peer.get_peer_info(), True) return None @peer_required @api_request async def respond_peers_introducer( self, request: introducer_protocol.RespondPeersIntroducer, peer: ws.WSChiaConnection ) -> Optional[Message]: self.log.debug(f"Received {len(request.peer_list)} peers from introducer") if self.full_node.full_node_peers is not None: await self.full_node.full_node_peers.respond_peers(request, peer.get_peer_info(), False) await peer.close() return None @execute_task @peer_required @api_request async def new_peak(self, request: full_node_protocol.NewPeak, peer: ws.WSChiaConnection) -> Optional[Message]: """ A peer notifies us that they have added a new peak to their blockchain. If we don't have it, we can ask for it. """ # this semaphore limits the number of tasks that can call new_peak() at # the same time, since it can be expensive waiter_count = len(self.full_node.new_peak_sem._waiters) if waiter_count > 0: self.full_node.log.debug(f"new_peak Waiters: {waiter_count}") if waiter_count > 20: return None async with self.full_node.new_peak_sem: return await self.full_node.new_peak(request, peer) @peer_required @api_request async def new_transaction( self, transaction: full_node_protocol.NewTransaction, peer: ws.WSChiaConnection ) -> Optional[Message]: """ A peer notifies us of a new transaction. Requests a full transaction if we haven't seen it previously, and if the fees are enough. """ # Ignore if syncing if self.full_node.sync_store.get_sync_mode(): return None if not (await self.full_node.synced()): return None # Ignore if already seen if self.full_node.mempool_manager.seen(transaction.transaction_id): return None if self.full_node.mempool_manager.is_fee_enough(transaction.fees, transaction.cost): # If there's current pending request just add this peer to the set of peers that have this tx if transaction.transaction_id in self.full_node.full_node_store.pending_tx_request: if transaction.transaction_id in self.full_node.full_node_store.peers_with_tx: current_set = self.full_node.full_node_store.peers_with_tx[transaction.transaction_id] if peer.peer_node_id in current_set: return None current_set.add(peer.peer_node_id) return None else: new_set = set() new_set.add(peer.peer_node_id) self.full_node.full_node_store.peers_with_tx[transaction.transaction_id] = new_set return None self.full_node.full_node_store.pending_tx_request[transaction.transaction_id] = peer.peer_node_id new_set = set() new_set.add(peer.peer_node_id) self.full_node.full_node_store.peers_with_tx[transaction.transaction_id] = new_set async def tx_request_and_timeout(full_node: FullNode, transaction_id, task_id): counter = 0 try: while True: # Limit to asking to a few peers, it's possible that this tx got included on chain already # Highly unlikely that the peers that advertised a tx don't respond to a request. Also, if we # drop some transactions, we don't want to refetch too many times if counter == 5: break if transaction_id not in full_node.full_node_store.peers_with_tx: break peers_with_tx: Set = full_node.full_node_store.peers_with_tx[transaction_id] if len(peers_with_tx) == 0: break peer_id = peers_with_tx.pop() assert full_node.server is not None if peer_id not in full_node.server.all_connections: continue peer = full_node.server.all_connections[peer_id] request_tx = full_node_protocol.RequestTransaction(transaction.transaction_id) msg = make_msg(ProtocolMessageTypes.request_transaction, request_tx) await peer.send_message(msg) await asyncio.sleep(5) counter += 1 if full_node.mempool_manager.seen(transaction_id): break except asyncio.CancelledError: pass finally: # Always Cleanup if transaction_id in full_node.full_node_store.peers_with_tx: full_node.full_node_store.peers_with_tx.pop(transaction_id) if transaction_id in full_node.full_node_store.pending_tx_request: full_node.full_node_store.pending_tx_request.pop(transaction_id) if task_id in full_node.full_node_store.tx_fetch_tasks: full_node.full_node_store.tx_fetch_tasks.pop(task_id) task_id: bytes32 = bytes32(token_bytes(32)) fetch_task = asyncio.create_task( tx_request_and_timeout(self.full_node, transaction.transaction_id, task_id) ) self.full_node.full_node_store.tx_fetch_tasks[task_id] = fetch_task return None return None @api_request @reply_type([ProtocolMessageTypes.respond_transaction]) async def request_transaction(self, request: full_node_protocol.RequestTransaction) -> Optional[Message]: """Peer has requested a full transaction from us.""" # Ignore if syncing if self.full_node.sync_store.get_sync_mode(): return None spend_bundle = self.full_node.mempool_manager.get_spendbundle(request.transaction_id) if spend_bundle is None: return None transaction = full_node_protocol.RespondTransaction(spend_bundle) msg = make_msg(ProtocolMessageTypes.respond_transaction, transaction) return msg @peer_required @api_request @bytes_required async def respond_transaction( self, tx: full_node_protocol.RespondTransaction, peer: ws.WSChiaConnection, tx_bytes: bytes = b"", test: bool = False, ) -> Optional[Message]: """ Receives a full transaction from peer. If tx is added to mempool, send tx_id to others. (new_transaction) """ assert tx_bytes != b"" spend_name = std_hash(tx_bytes) if spend_name in self.full_node.full_node_store.pending_tx_request: self.full_node.full_node_store.pending_tx_request.pop(spend_name) if spend_name in self.full_node.full_node_store.peers_with_tx: self.full_node.full_node_store.peers_with_tx.pop(spend_name) if self.full_node.transaction_queue.qsize() % 100 == 0 and not self.full_node.transaction_queue.empty(): self.full_node.log.debug(f"respond_transaction Waiters: {self.full_node.transaction_queue.qsize()}") if self.full_node.transaction_queue.full(): self.full_node.dropped_tx.add(spend_name) return None # Higher fee means priority is a smaller number, which means it will be handled earlier await self.full_node.transaction_queue.put( (0, TransactionQueueEntry(tx.transaction, tx_bytes, spend_name, peer, test)) ) return None @api_request @reply_type([ProtocolMessageTypes.respond_proof_of_weight]) async def request_proof_of_weight(self, request: full_node_protocol.RequestProofOfWeight) -> Optional[Message]: if self.full_node.weight_proof_handler is None: return None if not self.full_node.blockchain.contains_block(request.tip): self.log.error(f"got weight proof request for unknown peak {request.tip}") return None if request.tip in self.full_node.pow_creation: event = self.full_node.pow_creation[request.tip] await event.wait() wp = await self.full_node.weight_proof_handler.get_proof_of_weight(request.tip) else: event = asyncio.Event() self.full_node.pow_creation[request.tip] = event wp = await self.full_node.weight_proof_handler.get_proof_of_weight(request.tip) event.set() tips = list(self.full_node.pow_creation.keys()) if len(tips) > 4: # Remove old from cache for i in range(0, 4): self.full_node.pow_creation.pop(tips[i]) if wp is None: self.log.error(f"failed creating weight proof for peak {request.tip}") return None # Serialization of wp is slow if ( self.full_node.full_node_store.serialized_wp_message_tip is not None and self.full_node.full_node_store.serialized_wp_message_tip == request.tip ): return self.full_node.full_node_store.serialized_wp_message message = make_msg( ProtocolMessageTypes.respond_proof_of_weight, full_node_protocol.RespondProofOfWeight(wp, request.tip) ) self.full_node.full_node_store.serialized_wp_message_tip = request.tip self.full_node.full_node_store.serialized_wp_message = message return message @api_request async def respond_proof_of_weight(self, request: full_node_protocol.RespondProofOfWeight) -> Optional[Message]: self.log.warning("Received proof of weight too late.") return None @api_request @reply_type([ProtocolMessageTypes.respond_block, ProtocolMessageTypes.reject_block]) async def request_block(self, request: full_node_protocol.RequestBlock) -> Optional[Message]: if not self.full_node.blockchain.contains_height(request.height): reject = RejectBlock(request.height) msg = make_msg(ProtocolMessageTypes.reject_block, reject) return msg header_hash: Optional[bytes32] = self.full_node.blockchain.height_to_hash(request.height) if header_hash is None: return make_msg(ProtocolMessageTypes.reject_block, RejectBlock(request.height)) block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(header_hash) if block is not None: if not request.include_transaction_block and block.transactions_generator is not None: block = dataclasses.replace(block, transactions_generator=None) return make_msg(ProtocolMessageTypes.respond_block, full_node_protocol.RespondBlock(block)) return make_msg(ProtocolMessageTypes.reject_block, RejectBlock(request.height)) @api_request @reply_type([ProtocolMessageTypes.respond_blocks, ProtocolMessageTypes.reject_blocks]) async def request_blocks(self, request: full_node_protocol.RequestBlocks) -> Optional[Message]: if request.end_height < request.start_height or request.end_height - request.start_height > 32: reject = RejectBlocks(request.start_height, request.end_height) msg: Message = make_msg(ProtocolMessageTypes.reject_blocks, reject) return msg for i in range(request.start_height, request.end_height + 1): if not self.full_node.blockchain.contains_height(uint32(i)): reject = RejectBlocks(request.start_height, request.end_height) msg = make_msg(ProtocolMessageTypes.reject_blocks, reject) return msg if not request.include_transaction_block: blocks: List[FullBlock] = [] for i in range(request.start_height, request.end_height + 1): header_hash_i: Optional[bytes32] = self.full_node.blockchain.height_to_hash(uint32(i)) if header_hash_i is None: reject = RejectBlocks(request.start_height, request.end_height) return make_msg(ProtocolMessageTypes.reject_blocks, reject) block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(header_hash_i) if block is None: reject = RejectBlocks(request.start_height, request.end_height) return make_msg(ProtocolMessageTypes.reject_blocks, reject) block = dataclasses.replace(block, transactions_generator=None) blocks.append(block) msg = make_msg( ProtocolMessageTypes.respond_blocks, full_node_protocol.RespondBlocks(request.start_height, request.end_height, blocks), ) else: blocks_bytes: List[bytes] = [] for i in range(request.start_height, request.end_height + 1): header_hash_i = self.full_node.blockchain.height_to_hash(uint32(i)) if header_hash_i is None: reject = RejectBlocks(request.start_height, request.end_height) return make_msg(ProtocolMessageTypes.reject_blocks, reject) block_bytes: Optional[bytes] = await self.full_node.block_store.get_full_block_bytes(header_hash_i) if block_bytes is None: reject = RejectBlocks(request.start_height, request.end_height) msg = make_msg(ProtocolMessageTypes.reject_blocks, reject) return msg blocks_bytes.append(block_bytes) respond_blocks_manually_streamed: bytes = ( bytes(uint32(request.start_height)) + bytes(uint32(request.end_height)) + len(blocks_bytes).to_bytes(4, "big", signed=False) ) for block_bytes in blocks_bytes: respond_blocks_manually_streamed += block_bytes msg = make_msg(ProtocolMessageTypes.respond_blocks, respond_blocks_manually_streamed) return msg @api_request async def reject_block(self, request: full_node_protocol.RejectBlock): self.log.debug(f"reject_block {request.height}") @api_request async def reject_blocks(self, request: full_node_protocol.RejectBlocks): self.log.debug(f"reject_blocks {request.start_height} {request.end_height}") @api_request async def respond_blocks(self, request: full_node_protocol.RespondBlocks) -> None: self.log.warning("Received unsolicited/late blocks") return None @api_request @peer_required async def respond_block( self, respond_block: full_node_protocol.RespondBlock, peer: ws.WSChiaConnection, ) -> Optional[Message]: """ Receive a full block from a peer full node (or ourselves). """ self.log.warning(f"Received unsolicited/late block from peer {peer.get_peer_logging()}") return None @api_request async def new_unfinished_block( self, new_unfinished_block: full_node_protocol.NewUnfinishedBlock ) -> Optional[Message]: # Ignore if syncing if self.full_node.sync_store.get_sync_mode(): return None block_hash = new_unfinished_block.unfinished_reward_hash if self.full_node.full_node_store.get_unfinished_block(block_hash) is not None: return None # This prevents us from downloading the same block from many peers if block_hash in self.full_node.full_node_store.requesting_unfinished_blocks: return None msg = make_msg( ProtocolMessageTypes.request_unfinished_block, full_node_protocol.RequestUnfinishedBlock(block_hash), ) self.full_node.full_node_store.requesting_unfinished_blocks.add(block_hash) # However, we want to eventually download from other peers, if this peer does not respond # Todo: keep track of who it was async def eventually_clear(): await asyncio.sleep(5) if block_hash in self.full_node.full_node_store.requesting_unfinished_blocks: self.full_node.full_node_store.requesting_unfinished_blocks.remove(block_hash) asyncio.create_task(eventually_clear()) return msg @api_request @reply_type([ProtocolMessageTypes.respond_unfinished_block]) async def request_unfinished_block( self, request_unfinished_block: full_node_protocol.RequestUnfinishedBlock ) -> Optional[Message]: unfinished_block: Optional[UnfinishedBlock] = self.full_node.full_node_store.get_unfinished_block( request_unfinished_block.unfinished_reward_hash ) if unfinished_block is not None: msg = make_msg( ProtocolMessageTypes.respond_unfinished_block, full_node_protocol.RespondUnfinishedBlock(unfinished_block), ) return msg return None @peer_required @api_request @bytes_required async def respond_unfinished_block( self, respond_unfinished_block: full_node_protocol.RespondUnfinishedBlock, peer: ws.WSChiaConnection, respond_unfinished_block_bytes: bytes = b"", ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None await self.full_node.respond_unfinished_block( respond_unfinished_block, peer, block_bytes=respond_unfinished_block_bytes ) return None @api_request @peer_required async def new_signage_point_or_end_of_sub_slot( self, new_sp: full_node_protocol.NewSignagePointOrEndOfSubSlot, peer: ws.WSChiaConnection ) -> Optional[Message]: # Ignore if syncing if self.full_node.sync_store.get_sync_mode(): return None if ( self.full_node.full_node_store.get_signage_point_by_index( new_sp.challenge_hash, new_sp.index_from_challenge, new_sp.last_rc_infusion, ) is not None ): return None if self.full_node.full_node_store.have_newer_signage_point( new_sp.challenge_hash, new_sp.index_from_challenge, new_sp.last_rc_infusion ): return None if new_sp.index_from_challenge == 0 and new_sp.prev_challenge_hash is not None: if self.full_node.full_node_store.get_sub_slot(new_sp.prev_challenge_hash) is None: collected_eos = [] challenge_hash_to_request = new_sp.challenge_hash last_rc = new_sp.last_rc_infusion num_non_empty_sub_slots_seen = 0 for _ in range(30): if num_non_empty_sub_slots_seen >= 3: self.log.debug("Diverged from peer. Don't have the same blocks") return None # If this is an end of sub slot, and we don't have the prev, request the prev instead # We want to catch up to the latest slot so we can receive signage points full_node_request = full_node_protocol.RequestSignagePointOrEndOfSubSlot( challenge_hash_to_request, uint8(0), last_rc ) response = await peer.request_signage_point_or_end_of_sub_slot(full_node_request, timeout=10) if not isinstance(response, full_node_protocol.RespondEndOfSubSlot): self.full_node.log.debug(f"Invalid response for slot {response}") return None collected_eos.append(response) if ( self.full_node.full_node_store.get_sub_slot( response.end_of_slot_bundle.challenge_chain.challenge_chain_end_of_slot_vdf.challenge ) is not None or response.end_of_slot_bundle.challenge_chain.challenge_chain_end_of_slot_vdf.challenge == self.full_node.constants.GENESIS_CHALLENGE ): for eos in reversed(collected_eos): await self.respond_end_of_sub_slot(eos, peer) return None if ( response.end_of_slot_bundle.challenge_chain.challenge_chain_end_of_slot_vdf.number_of_iterations != response.end_of_slot_bundle.reward_chain.end_of_slot_vdf.number_of_iterations ): num_non_empty_sub_slots_seen += 1 challenge_hash_to_request = ( response.end_of_slot_bundle.challenge_chain.challenge_chain_end_of_slot_vdf.challenge ) last_rc = response.end_of_slot_bundle.reward_chain.end_of_slot_vdf.challenge self.full_node.log.warning("Failed to catch up in sub-slots") return None if new_sp.index_from_challenge > 0: if ( new_sp.challenge_hash != self.full_node.constants.GENESIS_CHALLENGE and self.full_node.full_node_store.get_sub_slot(new_sp.challenge_hash) is None ): # If this is a normal signage point,, and we don't have the end of sub slot, request the end of sub slot full_node_request = full_node_protocol.RequestSignagePointOrEndOfSubSlot( new_sp.challenge_hash, uint8(0), new_sp.last_rc_infusion ) return make_msg(ProtocolMessageTypes.request_signage_point_or_end_of_sub_slot, full_node_request) # Otherwise (we have the prev or the end of sub slot), request it normally full_node_request = full_node_protocol.RequestSignagePointOrEndOfSubSlot( new_sp.challenge_hash, new_sp.index_from_challenge, new_sp.last_rc_infusion ) return make_msg(ProtocolMessageTypes.request_signage_point_or_end_of_sub_slot, full_node_request) @api_request @reply_type([ProtocolMessageTypes.respond_signage_point, ProtocolMessageTypes.respond_end_of_sub_slot]) async def request_signage_point_or_end_of_sub_slot( self, request: full_node_protocol.RequestSignagePointOrEndOfSubSlot ) -> Optional[Message]: if request.index_from_challenge == 0: sub_slot: Optional[Tuple[EndOfSubSlotBundle, int, uint128]] = self.full_node.full_node_store.get_sub_slot( request.challenge_hash ) if sub_slot is not None: return make_msg( ProtocolMessageTypes.respond_end_of_sub_slot, full_node_protocol.RespondEndOfSubSlot(sub_slot[0]), ) else: if self.full_node.full_node_store.get_sub_slot(request.challenge_hash) is None: if request.challenge_hash != self.full_node.constants.GENESIS_CHALLENGE: self.log.info(f"Don't have challenge hash {request.challenge_hash}") sp: Optional[SignagePoint] = self.full_node.full_node_store.get_signage_point_by_index( request.challenge_hash, request.index_from_challenge, request.last_rc_infusion, ) if sp is not None: assert ( sp.cc_vdf is not None and sp.cc_proof is not None and sp.rc_vdf is not None and sp.rc_proof is not None ) full_node_response = full_node_protocol.RespondSignagePoint( request.index_from_challenge, sp.cc_vdf, sp.cc_proof, sp.rc_vdf, sp.rc_proof, ) return make_msg(ProtocolMessageTypes.respond_signage_point, full_node_response) else: self.log.info(f"Don't have signage point {request}") return None @peer_required @api_request async def respond_signage_point( self, request: full_node_protocol.RespondSignagePoint, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None async with self.full_node.timelord_lock: # Already have signage point if self.full_node.full_node_store.have_newer_signage_point( request.challenge_chain_vdf.challenge, request.index_from_challenge, request.reward_chain_vdf.challenge, ): return None existing_sp = self.full_node.full_node_store.get_signage_point( request.challenge_chain_vdf.output.get_hash() ) if existing_sp is not None and existing_sp.rc_vdf == request.reward_chain_vdf: return None peak = self.full_node.blockchain.get_peak() if peak is not None and peak.height > self.full_node.constants.MAX_SUB_SLOT_BLOCKS: next_sub_slot_iters = self.full_node.blockchain.get_next_slot_iters(peak.header_hash, True) sub_slots_for_peak = await self.full_node.blockchain.get_sp_and_ip_sub_slots(peak.header_hash) assert sub_slots_for_peak is not None ip_sub_slot: Optional[EndOfSubSlotBundle] = sub_slots_for_peak[1] else: sub_slot_iters = self.full_node.constants.SUB_SLOT_ITERS_STARTING next_sub_slot_iters = sub_slot_iters ip_sub_slot = None added = self.full_node.full_node_store.new_signage_point( request.index_from_challenge, self.full_node.blockchain, self.full_node.blockchain.get_peak(), next_sub_slot_iters, SignagePoint( request.challenge_chain_vdf, request.challenge_chain_proof, request.reward_chain_vdf, request.reward_chain_proof, ), ) if added: await self.full_node.signage_point_post_processing(request, peer, ip_sub_slot) else: self.log.debug( f"Signage point {request.index_from_challenge} not added, CC challenge: " f"{request.challenge_chain_vdf.challenge}, RC challenge: {request.reward_chain_vdf.challenge}" ) return None @peer_required @api_request async def respond_end_of_sub_slot( self, request: full_node_protocol.RespondEndOfSubSlot, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None msg, _ = await self.full_node.respond_end_of_sub_slot(request, peer) return msg @peer_required @api_request async def request_mempool_transactions( self, request: full_node_protocol.RequestMempoolTransactions, peer: ws.WSChiaConnection, ) -> Optional[Message]: received_filter = PyBIP158(bytearray(request.filter)) items: List[MempoolItem] = await self.full_node.mempool_manager.get_items_not_in_filter(received_filter) for item in items: transaction = full_node_protocol.RespondTransaction(item.spend_bundle) msg = make_msg(ProtocolMessageTypes.respond_transaction, transaction) await peer.send_message(msg) return None # FARMER PROTOCOL @api_request @peer_required async def declare_proof_of_space( self, request: farmer_protocol.DeclareProofOfSpace, peer: ws.WSChiaConnection ) -> Optional[Message]: """ Creates a block body and header, with the proof of space, coinbase, and fee targets provided by the farmer, and sends the hash of the header data back to the farmer. """ if self.full_node.sync_store.get_sync_mode(): return None async with self.full_node.timelord_lock: sp_vdfs: Optional[SignagePoint] = self.full_node.full_node_store.get_signage_point( request.challenge_chain_sp ) if sp_vdfs is None: self.log.warning(f"Received proof of space for an unknown signage point {request.challenge_chain_sp}") return None if request.signage_point_index > 0: assert sp_vdfs.rc_vdf is not None if sp_vdfs.rc_vdf.output.get_hash() != request.reward_chain_sp: self.log.debug( f"Received proof of space for a potentially old signage point {request.challenge_chain_sp}. " f"Current sp: {sp_vdfs.rc_vdf.output.get_hash()}" ) return None if request.signage_point_index == 0: cc_challenge_hash: bytes32 = request.challenge_chain_sp else: assert sp_vdfs.cc_vdf is not None cc_challenge_hash = sp_vdfs.cc_vdf.challenge pos_sub_slot: Optional[Tuple[EndOfSubSlotBundle, int, uint128]] = None if request.challenge_hash != self.full_node.constants.GENESIS_CHALLENGE: # Checks that the proof of space is a response to a recent challenge and valid SP pos_sub_slot = self.full_node.full_node_store.get_sub_slot(cc_challenge_hash) if pos_sub_slot is None: self.log.warning(f"Received proof of space for an unknown sub slot: {request}") return None total_iters_pos_slot: uint128 = pos_sub_slot[2] else: total_iters_pos_slot = uint128(0) assert cc_challenge_hash == request.challenge_hash # Now we know that the proof of space has a signage point either: # 1. In the previous sub-slot of the peak (overflow) # 2. In the same sub-slot as the peak # 3. In a future sub-slot that we already know of # Checks that the proof of space is valid quality_string: Optional[bytes32] = request.proof_of_space.verify_and_get_quality_string( self.full_node.constants, cc_challenge_hash, request.challenge_chain_sp ) assert quality_string is not None and len(quality_string) == 32 # Grab best transactions from Mempool for given tip target aggregate_signature: G2Element = G2Element() block_generator: Optional[BlockGenerator] = None additions: Optional[List[Coin]] = [] removals: Optional[List[Coin]] = [] async with self.full_node._blockchain_lock_high_priority: peak: Optional[BlockRecord] = self.full_node.blockchain.get_peak() if peak is not None: # Finds the last transaction block before this one curr_l_tb: BlockRecord = peak while not curr_l_tb.is_transaction_block: curr_l_tb = self.full_node.blockchain.block_record(curr_l_tb.prev_hash) try: mempool_bundle = await self.full_node.mempool_manager.create_bundle_from_mempool( curr_l_tb.header_hash ) except Exception as e: self.log.error(f"Traceback: {traceback.format_exc()}") self.full_node.log.error(f"Error making spend bundle {e} peak: {peak}") mempool_bundle = None if mempool_bundle is not None: spend_bundle = mempool_bundle[0] additions = mempool_bundle[1] removals = mempool_bundle[2] self.full_node.log.info(f"Add rem: {len(additions)} {len(removals)}") aggregate_signature = spend_bundle.aggregated_signature if self.full_node.full_node_store.previous_generator is not None: self.log.info( f"Using previous generator for height " f"{self.full_node.full_node_store.previous_generator}" ) block_generator = best_solution_generator_from_template( self.full_node.full_node_store.previous_generator, spend_bundle ) else: block_generator = simple_solution_generator(spend_bundle) def get_plot_sig(to_sign, _) -> G2Element: if to_sign == request.challenge_chain_sp: return request.challenge_chain_sp_signature elif to_sign == request.reward_chain_sp: return request.reward_chain_sp_signature return G2Element() def get_pool_sig(_1, _2) -> Optional[G2Element]: return request.pool_signature prev_b: Optional[BlockRecord] = self.full_node.blockchain.get_peak() # Finds the previous block from the signage point, ensuring that the reward chain VDF is correct if prev_b is not None: if request.signage_point_index == 0: if pos_sub_slot is None: self.log.warning("Pos sub slot is None") return None rc_challenge = pos_sub_slot[0].reward_chain.end_of_slot_vdf.challenge else: assert sp_vdfs.rc_vdf is not None rc_challenge = sp_vdfs.rc_vdf.challenge # Backtrack through empty sub-slots for eos, _, _ in reversed(self.full_node.full_node_store.finished_sub_slots): if eos is not None and eos.reward_chain.get_hash() == rc_challenge: rc_challenge = eos.reward_chain.end_of_slot_vdf.challenge found = False attempts = 0 while prev_b is not None and attempts < 10: if prev_b.reward_infusion_new_challenge == rc_challenge: found = True break if prev_b.finished_reward_slot_hashes is not None and len(prev_b.finished_reward_slot_hashes) > 0: if prev_b.finished_reward_slot_hashes[-1] == rc_challenge: # This block includes a sub-slot which is where our SP vdf starts. Go back one more # to find the prev block prev_b = self.full_node.blockchain.try_block_record(prev_b.prev_hash) found = True break prev_b = self.full_node.blockchain.try_block_record(prev_b.prev_hash) attempts += 1 if not found: self.log.warning("Did not find a previous block with the correct reward chain hash") return None try: finished_sub_slots: Optional[ List[EndOfSubSlotBundle] ] = self.full_node.full_node_store.get_finished_sub_slots( self.full_node.blockchain, prev_b, cc_challenge_hash ) if finished_sub_slots is None: return None if ( len(finished_sub_slots) > 0 and pos_sub_slot is not None and finished_sub_slots[-1] != pos_sub_slot[0] ): self.log.error("Have different sub-slots than is required to farm this block") return None except ValueError as e: self.log.warning(f"Value Error: {e}") return None if prev_b is None: pool_target = PoolTarget( self.full_node.constants.GENESIS_PRE_FARM_POOL_PUZZLE_HASH, uint32(0), ) farmer_ph = self.full_node.constants.GENESIS_PRE_FARM_FARMER_PUZZLE_HASH else: farmer_ph = request.farmer_puzzle_hash if request.proof_of_space.pool_contract_puzzle_hash is not None: pool_target = PoolTarget(request.proof_of_space.pool_contract_puzzle_hash, uint32(0)) else: assert request.pool_target is not None pool_target = request.pool_target if peak is None or peak.height <= self.full_node.constants.MAX_SUB_SLOT_BLOCKS: difficulty = self.full_node.constants.DIFFICULTY_STARTING sub_slot_iters = self.full_node.constants.SUB_SLOT_ITERS_STARTING else: difficulty = uint64(peak.weight - self.full_node.blockchain.block_record(peak.prev_hash).weight) sub_slot_iters = peak.sub_slot_iters for sub_slot in finished_sub_slots: if sub_slot.challenge_chain.new_difficulty is not None: difficulty = sub_slot.challenge_chain.new_difficulty if sub_slot.challenge_chain.new_sub_slot_iters is not None: sub_slot_iters = sub_slot.challenge_chain.new_sub_slot_iters required_iters: uint64 = calculate_iterations_quality( self.full_node.constants.DIFFICULTY_CONSTANT_FACTOR, quality_string, request.proof_of_space.size, difficulty, request.challenge_chain_sp, ) sp_iters: uint64 = calculate_sp_iters(self.full_node.constants, sub_slot_iters, request.signage_point_index) ip_iters: uint64 = calculate_ip_iters( self.full_node.constants, sub_slot_iters, request.signage_point_index, required_iters, ) # The block's timestamp must be greater than the previous transaction block's timestamp timestamp = uint64(int(time.time())) curr: Optional[BlockRecord] = prev_b while curr is not None and not curr.is_transaction_block and curr.height != 0: curr = self.full_node.blockchain.try_block_record(curr.prev_hash) if curr is not None: assert curr.timestamp is not None if timestamp <= curr.timestamp: timestamp = uint64(int(curr.timestamp + 1)) self.log.info("Starting to make the unfinished block") unfinished_block: UnfinishedBlock = create_unfinished_block( self.full_node.constants, total_iters_pos_slot, sub_slot_iters, request.signage_point_index, sp_iters, ip_iters, request.proof_of_space, cc_challenge_hash, farmer_ph, pool_target, get_plot_sig, get_pool_sig, sp_vdfs, timestamp, self.full_node.blockchain, b"", block_generator, aggregate_signature, additions, removals, prev_b, finished_sub_slots, ) self.log.info("Made the unfinished block") if prev_b is not None: height: uint32 = uint32(prev_b.height + 1) else: height = uint32(0) self.full_node.full_node_store.add_candidate_block(quality_string, height, unfinished_block) foliage_sb_data_hash = unfinished_block.foliage.foliage_block_data.get_hash() if unfinished_block.is_transaction_block(): foliage_transaction_block_hash = unfinished_block.foliage.foliage_transaction_block_hash else: foliage_transaction_block_hash = bytes32([0] * 32) assert foliage_transaction_block_hash is not None message = farmer_protocol.RequestSignedValues( quality_string, foliage_sb_data_hash, foliage_transaction_block_hash, ) await peer.send_message(make_msg(ProtocolMessageTypes.request_signed_values, message)) # Adds backup in case the first one fails if unfinished_block.is_transaction_block() and unfinished_block.transactions_generator is not None: unfinished_block_backup = create_unfinished_block( self.full_node.constants, total_iters_pos_slot, sub_slot_iters, request.signage_point_index, sp_iters, ip_iters, request.proof_of_space, cc_challenge_hash, farmer_ph, pool_target, get_plot_sig, get_pool_sig, sp_vdfs, timestamp, self.full_node.blockchain, b"", None, G2Element(), None, None, prev_b, finished_sub_slots, ) self.full_node.full_node_store.add_candidate_block( quality_string, height, unfinished_block_backup, backup=True ) return None @api_request @peer_required async def signed_values( self, farmer_request: farmer_protocol.SignedValues, peer: ws.WSChiaConnection ) -> Optional[Message]: """ Signature of header hash, by the harvester. This is enough to create an unfinished block, which only needs a Proof of Time to be finished. If the signature is valid, we call the unfinished_block routine. """ candidate_tuple: Optional[Tuple[uint32, UnfinishedBlock]] = self.full_node.full_node_store.get_candidate_block( farmer_request.quality_string ) if candidate_tuple is None: self.log.warning(f"Quality string {farmer_request.quality_string} not found in database") return None height, candidate = candidate_tuple if not AugSchemeMPL.verify( candidate.reward_chain_block.proof_of_space.plot_public_key, candidate.foliage.foliage_block_data.get_hash(), farmer_request.foliage_block_data_signature, ): self.log.warning("Signature not valid. There might be a collision in plots. Ignore this during tests.") return None fsb2 = dataclasses.replace( candidate.foliage, foliage_block_data_signature=farmer_request.foliage_block_data_signature, ) if candidate.is_transaction_block(): fsb2 = dataclasses.replace( fsb2, foliage_transaction_block_signature=farmer_request.foliage_transaction_block_signature ) new_candidate = dataclasses.replace(candidate, foliage=fsb2) if not self.full_node.has_valid_pool_sig(new_candidate): self.log.warning("Trying to make a pre-farm block but height is not 0") return None # Propagate to ourselves (which validates and does further propagations) request = full_node_protocol.RespondUnfinishedBlock(new_candidate) try: await self.full_node.respond_unfinished_block(request, None, True) except Exception as e: # If we have an error with this block, try making an empty block self.full_node.log.error(f"Error farming block {e} {request}") candidate_tuple = self.full_node.full_node_store.get_candidate_block( farmer_request.quality_string, backup=True ) if candidate_tuple is not None: height, unfinished_block = candidate_tuple self.full_node.full_node_store.add_candidate_block( farmer_request.quality_string, height, unfinished_block, False ) # All unfinished blocks that we create will have the foliage transaction block and hash assert unfinished_block.foliage.foliage_transaction_block_hash is not None message = farmer_protocol.RequestSignedValues( farmer_request.quality_string, unfinished_block.foliage.foliage_block_data.get_hash(), unfinished_block.foliage.foliage_transaction_block_hash, ) await peer.send_message(make_msg(ProtocolMessageTypes.request_signed_values, message)) return None # TIMELORD PROTOCOL @peer_required @api_request async def new_infusion_point_vdf( self, request: timelord_protocol.NewInfusionPointVDF, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None # Lookup unfinished blocks async with self.full_node.timelord_lock: return await self.full_node.new_infusion_point_vdf(request, peer) @peer_required @api_request async def new_signage_point_vdf( self, request: timelord_protocol.NewSignagePointVDF, peer: ws.WSChiaConnection ) -> None: if self.full_node.sync_store.get_sync_mode(): return None full_node_message = full_node_protocol.RespondSignagePoint( request.index_from_challenge, request.challenge_chain_sp_vdf, request.challenge_chain_sp_proof, request.reward_chain_sp_vdf, request.reward_chain_sp_proof, ) await self.respond_signage_point(full_node_message, peer) @peer_required @api_request async def new_end_of_sub_slot_vdf( self, request: timelord_protocol.NewEndOfSubSlotVDF, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None if ( self.full_node.full_node_store.get_sub_slot(request.end_of_sub_slot_bundle.challenge_chain.get_hash()) is not None ): return None # Calls our own internal message to handle the end of sub slot, and potentially broadcasts to other peers. full_node_message = full_node_protocol.RespondEndOfSubSlot(request.end_of_sub_slot_bundle) msg, added = await self.full_node.respond_end_of_sub_slot(full_node_message, peer) if not added: self.log.error( f"Was not able to add end of sub-slot: " f"{request.end_of_sub_slot_bundle.challenge_chain.challenge_chain_end_of_slot_vdf.challenge}. " f"Re-sending new-peak to timelord" ) await self.full_node.send_peak_to_timelords(peer=peer) return None else: return msg @api_request async def request_block_header(self, request: wallet_protocol.RequestBlockHeader) -> Optional[Message]: header_hash = self.full_node.blockchain.height_to_hash(request.height) if header_hash is None: msg = make_msg(ProtocolMessageTypes.reject_header_request, RejectHeaderRequest(request.height)) return msg block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(header_hash) if block is not None: tx_removals, tx_additions, _ = await self.full_node.blockchain.get_tx_removals_and_additions(block) header_block = get_block_header(block, tx_additions, tx_removals) msg = make_msg( ProtocolMessageTypes.respond_block_header, wallet_protocol.RespondBlockHeader(header_block), ) return msg return None @api_request async def request_additions(self, request: wallet_protocol.RequestAdditions) -> Optional[Message]: if request.header_hash is None: header_hash: Optional[bytes32] = self.full_node.blockchain.height_to_hash(request.height) else: header_hash = request.header_hash if header_hash is None: raise ValueError(f"Block at height {request.height} not found") block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(header_hash) # We lock so that the coin store does not get modified if ( block is None or block.is_transaction_block() is False or self.full_node.blockchain.height_to_hash(block.height) != request.header_hash ): reject = wallet_protocol.RejectAdditionsRequest(request.height, header_hash) msg = make_msg(ProtocolMessageTypes.reject_additions_request, reject) return msg assert block is not None and block.foliage_transaction_block is not None # Note: this might return bad data if there is a reorg in this time additions = await self.full_node.coin_store.get_coins_added_at_height(block.height) if self.full_node.blockchain.height_to_hash(block.height) != request.header_hash: raise ValueError(f"Block {block.header_hash} no longer in chain") puzzlehash_coins_map: Dict[bytes32, List[Coin]] = {} for coin_record in additions: if coin_record.coin.puzzle_hash in puzzlehash_coins_map: puzzlehash_coins_map[coin_record.coin.puzzle_hash].append(coin_record.coin) else: puzzlehash_coins_map[coin_record.coin.puzzle_hash] = [coin_record.coin] coins_map: List[Tuple[bytes32, List[Coin]]] = [] proofs_map: List[Tuple[bytes32, bytes, Optional[bytes]]] = [] if request.puzzle_hashes is None: for puzzle_hash, coins in puzzlehash_coins_map.items(): coins_map.append((puzzle_hash, coins)) response = wallet_protocol.RespondAdditions(block.height, block.header_hash, coins_map, None) else: # Create addition Merkle set addition_merkle_set = MerkleSet() # Addition Merkle set contains puzzlehash and hash of all coins with that puzzlehash for puzzle, coins in puzzlehash_coins_map.items(): addition_merkle_set.add_already_hashed(puzzle) addition_merkle_set.add_already_hashed(hash_coin_list(coins)) assert addition_merkle_set.get_root() == block.foliage_transaction_block.additions_root for puzzle_hash in request.puzzle_hashes: result, proof = addition_merkle_set.is_included_already_hashed(puzzle_hash) if puzzle_hash in puzzlehash_coins_map: coins_map.append((puzzle_hash, puzzlehash_coins_map[puzzle_hash])) hash_coin_str = hash_coin_list(puzzlehash_coins_map[puzzle_hash]) result_2, proof_2 = addition_merkle_set.is_included_already_hashed(hash_coin_str) assert result assert result_2 proofs_map.append((puzzle_hash, proof, proof_2)) else: coins_map.append((puzzle_hash, [])) assert not result proofs_map.append((puzzle_hash, proof, None)) response = wallet_protocol.RespondAdditions(block.height, block.header_hash, coins_map, proofs_map) msg = make_msg(ProtocolMessageTypes.respond_additions, response) return msg @api_request async def request_removals(self, request: wallet_protocol.RequestRemovals) -> Optional[Message]: block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(request.header_hash) # We lock so that the coin store does not get modified peak_height = self.full_node.blockchain.get_peak_height() if ( block is None or block.is_transaction_block() is False or block.height != request.height or (peak_height is not None and block.height > peak_height) or self.full_node.blockchain.height_to_hash(block.height) != request.header_hash ): reject = wallet_protocol.RejectRemovalsRequest(request.height, request.header_hash) msg = make_msg(ProtocolMessageTypes.reject_removals_request, reject) return msg assert block is not None and block.foliage_transaction_block is not None # Note: this might return bad data if there is a reorg in this time all_removals: List[CoinRecord] = await self.full_node.coin_store.get_coins_removed_at_height(block.height) if self.full_node.blockchain.height_to_hash(block.height) != request.header_hash: raise ValueError(f"Block {block.header_hash} no longer in chain") all_removals_dict: Dict[bytes32, Coin] = {} for coin_record in all_removals: all_removals_dict[coin_record.coin.name()] = coin_record.coin coins_map: List[Tuple[bytes32, Optional[Coin]]] = [] proofs_map: List[Tuple[bytes32, bytes]] = [] # If there are no transactions, respond with empty lists if block.transactions_generator is None: proofs: Optional[List] if request.coin_names is None: proofs = None else: proofs = [] response = wallet_protocol.RespondRemovals(block.height, block.header_hash, [], proofs) elif request.coin_names is None or len(request.coin_names) == 0: for removed_name, removed_coin in all_removals_dict.items(): coins_map.append((removed_name, removed_coin)) response = wallet_protocol.RespondRemovals(block.height, block.header_hash, coins_map, None) else: assert block.transactions_generator removal_merkle_set = MerkleSet() for removed_name, removed_coin in all_removals_dict.items(): removal_merkle_set.add_already_hashed(removed_name) assert removal_merkle_set.get_root() == block.foliage_transaction_block.removals_root for coin_name in request.coin_names: result, proof = removal_merkle_set.is_included_already_hashed(coin_name) proofs_map.append((coin_name, proof)) if coin_name in all_removals_dict: removed_coin = all_removals_dict[coin_name] coins_map.append((coin_name, removed_coin)) assert result else: coins_map.append((coin_name, None)) assert not result response = wallet_protocol.RespondRemovals(block.height, block.header_hash, coins_map, proofs_map) msg = make_msg(ProtocolMessageTypes.respond_removals, response) return msg @api_request async def send_transaction(self, request: wallet_protocol.SendTransaction, *, test=False) -> Optional[Message]: spend_name = request.transaction.name() await self.full_node.transaction_queue.put( (0, TransactionQueueEntry(request.transaction, None, spend_name, None, test)) ) # Waits for the transaction to go into the mempool, times out after 45 seconds. status, error = None, None sleep_time = 0.01 for i in range(int(45 / sleep_time)): await asyncio.sleep(sleep_time) for potential_name, potential_status, potential_error in self.full_node.transaction_responses: if spend_name == potential_name: status = potential_status error = potential_error break if status is not None: break if status is None: response = wallet_protocol.TransactionAck(spend_name, uint8(MempoolInclusionStatus.PENDING), None) else: error_name = error.name if error is not None else None if status == MempoolInclusionStatus.SUCCESS: response = wallet_protocol.TransactionAck(spend_name, uint8(status.value), error_name) else: # If if failed/pending, but it previously succeeded (in mempool), this is idempotence, return SUCCESS if self.full_node.mempool_manager.get_spendbundle(spend_name) is not None: response = wallet_protocol.TransactionAck( spend_name, uint8(MempoolInclusionStatus.SUCCESS.value), None ) else: response = wallet_protocol.TransactionAck(spend_name, uint8(status.value), error_name) msg = make_msg(ProtocolMessageTypes.transaction_ack, response) return msg @api_request async def request_puzzle_solution(self, request: wallet_protocol.RequestPuzzleSolution) -> Optional[Message]: coin_name = request.coin_name height = request.height coin_record = await self.full_node.coin_store.get_coin_record(coin_name) reject = wallet_protocol.RejectPuzzleSolution(coin_name, height) reject_msg = make_msg(ProtocolMessageTypes.reject_puzzle_solution, reject) if coin_record is None or coin_record.spent_block_index != height: return reject_msg header_hash: Optional[bytes32] = self.full_node.blockchain.height_to_hash(height) if header_hash is None: return reject_msg block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(header_hash) if block is None or block.transactions_generator is None: return reject_msg block_generator: Optional[BlockGenerator] = await self.full_node.blockchain.get_block_generator(block) assert block_generator is not None error, puzzle, solution = get_puzzle_and_solution_for_coin( block_generator, coin_name, self.full_node.constants.MAX_BLOCK_COST_CLVM ) if error is not None: return reject_msg pz = Program.to(puzzle) sol = Program.to(solution) wrapper = PuzzleSolutionResponse(coin_name, height, pz, sol) response = wallet_protocol.RespondPuzzleSolution(wrapper) response_msg = make_msg(ProtocolMessageTypes.respond_puzzle_solution, response) return response_msg @api_request async def request_header_blocks(self, request: wallet_protocol.RequestHeaderBlocks) -> Optional[Message]: if request.end_height < request.start_height or request.end_height - request.start_height > 32: return None header_hashes: List[bytes32] = [] for i in range(request.start_height, request.end_height + 1): header_hash: Optional[bytes32] = self.full_node.blockchain.height_to_hash(uint32(i)) if header_hash is None: reject = RejectHeaderBlocks(request.start_height, request.end_height) msg = make_msg(ProtocolMessageTypes.reject_header_blocks, reject) return msg header_hashes.append(header_hash) blocks: List[FullBlock] = await self.full_node.block_store.get_blocks_by_hash(header_hashes) header_blocks = [] for block in blocks: added_coins_records = await self.full_node.coin_store.get_coins_added_at_height(block.height) removed_coins_records = await self.full_node.coin_store.get_coins_removed_at_height(block.height) added_coins = [record.coin for record in added_coins_records if not record.coinbase] removal_names = [record.coin.name() for record in removed_coins_records] header_block = get_block_header(block, added_coins, removal_names) header_blocks.append(header_block) msg = make_msg( ProtocolMessageTypes.respond_header_blocks, wallet_protocol.RespondHeaderBlocks(request.start_height, request.end_height, header_blocks), ) return msg @api_request async def respond_compact_proof_of_time(self, request: timelord_protocol.RespondCompactProofOfTime): if self.full_node.sync_store.get_sync_mode(): return None await self.full_node.respond_compact_proof_of_time(request) @execute_task @peer_required @api_request @bytes_required async def new_compact_vdf( self, request: full_node_protocol.NewCompactVDF, peer: ws.WSChiaConnection, request_bytes: bytes = b"" ): if self.full_node.sync_store.get_sync_mode(): return None if len(self.full_node.compact_vdf_sem._waiters) > 20: self.log.debug(f"Ignoring NewCompactVDF: {request}, _waiters") return name = std_hash(request_bytes) if name in self.full_node.compact_vdf_requests: self.log.debug(f"Ignoring NewCompactVDF: {request}, already requested") return self.full_node.compact_vdf_requests.add(name) # this semaphore will only allow a limited number of tasks call # new_compact_vdf() at a time, since it can be expensive async with self.full_node.compact_vdf_sem: try: await self.full_node.new_compact_vdf(request, peer) finally: self.full_node.compact_vdf_requests.remove(name) @peer_required @api_request @reply_type([ProtocolMessageTypes.respond_compact_vdf]) async def request_compact_vdf(self, request: full_node_protocol.RequestCompactVDF, peer: ws.WSChiaConnection): if self.full_node.sync_store.get_sync_mode(): return None await self.full_node.request_compact_vdf(request, peer) @peer_required @api_request async def respond_compact_vdf(self, request: full_node_protocol.RespondCompactVDF, peer: ws.WSChiaConnection): if self.full_node.sync_store.get_sync_mode(): return None await self.full_node.respond_compact_vdf(request, peer) @peer_required @api_request async def register_interest_in_puzzle_hash( self, request: wallet_protocol.RegisterForPhUpdates, peer: ws.WSChiaConnection ): if peer.peer_node_id not in self.full_node.peer_puzzle_hash: self.full_node.peer_puzzle_hash[peer.peer_node_id] = set() if peer.peer_node_id not in self.full_node.peer_sub_counter: self.full_node.peer_sub_counter[peer.peer_node_id] = 0 hint_coin_ids = [] # Add peer to the "Subscribed" dictionary max_items = self.full_node.config.get("max_subscribe_items", 200000) for puzzle_hash in request.puzzle_hashes: ph_hint_coins = await self.full_node.hint_store.get_coin_ids(puzzle_hash) hint_coin_ids.extend(ph_hint_coins) if puzzle_hash not in self.full_node.ph_subscriptions: self.full_node.ph_subscriptions[puzzle_hash] = set() if ( peer.peer_node_id not in self.full_node.ph_subscriptions[puzzle_hash] and self.full_node.peer_sub_counter[peer.peer_node_id] < max_items ): self.full_node.ph_subscriptions[puzzle_hash].add(peer.peer_node_id) self.full_node.peer_puzzle_hash[peer.peer_node_id].add(puzzle_hash) self.full_node.peer_sub_counter[peer.peer_node_id] += 1 # Send all coins with requested puzzle hash that have been created after the specified height states: List[CoinState] = await self.full_node.coin_store.get_coin_states_by_puzzle_hashes( include_spent_coins=True, puzzle_hashes=request.puzzle_hashes, min_height=request.min_height ) if len(hint_coin_ids) > 0: hint_states = await self.full_node.coin_store.get_coin_states_by_ids( include_spent_coins=True, coin_ids=hint_coin_ids, min_height=request.min_height ) states.extend(hint_states) response = wallet_protocol.RespondToPhUpdates(request.puzzle_hashes, request.min_height, states) msg = make_msg(ProtocolMessageTypes.respond_to_ph_update, response) return msg @peer_required @api_request async def register_interest_in_coin( self, request: wallet_protocol.RegisterForCoinUpdates, peer: ws.WSChiaConnection ): if peer.peer_node_id not in self.full_node.peer_coin_ids: self.full_node.peer_coin_ids[peer.peer_node_id] = set() if peer.peer_node_id not in self.full_node.peer_sub_counter: self.full_node.peer_sub_counter[peer.peer_node_id] = 0 max_items = self.full_node.config.get("max_subscribe_items", 200000) for coin_id in request.coin_ids: if coin_id not in self.full_node.coin_subscriptions: self.full_node.coin_subscriptions[coin_id] = set() if ( peer.peer_node_id not in self.full_node.coin_subscriptions[coin_id] and self.full_node.peer_sub_counter[peer.peer_node_id] < max_items ): self.full_node.coin_subscriptions[coin_id].add(peer.peer_node_id) self.full_node.peer_coin_ids[peer.peer_node_id].add(coin_id) self.full_node.peer_sub_counter[peer.peer_node_id] += 1 states: List[CoinState] = await self.full_node.coin_store.get_coin_states_by_ids( include_spent_coins=True, coin_ids=request.coin_ids, min_height=request.min_height ) response = wallet_protocol.RespondToCoinUpdates(request.coin_ids, request.min_height, states) msg = make_msg(ProtocolMessageTypes.respond_to_coin_update, response) return msg @api_request async def request_children(self, request: wallet_protocol.RequestChildren) -> Optional[Message]: coin_records: List[CoinRecord] = await self.full_node.coin_store.get_coin_records_by_parent_ids( True, [request.coin_name] ) states = [record.coin_state for record in coin_records] response = wallet_protocol.RespondChildren(states) msg = make_msg(ProtocolMessageTypes.respond_children, response) return msg @api_request async def request_ses_hashes(self, request: wallet_protocol.RequestSESInfo): """Returns the start and end height of a sub-epoch for the height specified in request""" ses_height = self.full_node.blockchain.get_ses_heights() start_height = request.start_height end_height = request.end_height ses_hash_heights = [] ses_reward_hashes = [] for idx, ses_start_height in enumerate(ses_height): if idx == len(ses_height) - 1: break next_ses_height = ses_height[idx + 1] # start_ses_hash if ses_start_height <= start_height < next_ses_height: ses_hash_heights.append([ses_start_height, next_ses_height]) ses: SubEpochSummary = self.full_node.blockchain.get_ses(ses_start_height) ses_reward_hashes.append(ses.reward_chain_hash) if ses_start_height < end_height < next_ses_height: break else: if idx == len(ses_height) - 2: break # else add extra ses as request start <-> end spans two ses next_next_height = ses_height[idx + 2] ses_hash_heights.append([next_ses_height, next_next_height]) nex_ses: SubEpochSummary = self.full_node.blockchain.get_ses(next_ses_height) ses_reward_hashes.append(nex_ses.reward_chain_hash) break response = RespondSESInfo(ses_reward_hashes, ses_hash_heights) msg = make_msg(ProtocolMessageTypes.respond_ses_hashes, response) return msg

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import asyncio import dataclasses import time import traceback from secrets import token_bytes from typing import Dict, List, Optional, Tuple, Set from blspy import AugSchemeMPL, G2Element from chiabip158 import PyBIP158 import chia.server.ws_connection as ws from chia.consensus.block_creation import create_unfinished_block from chia.consensus.block_record import BlockRecord from chia.consensus.pot_iterations import calculate_ip_iters, calculate_iterations_quality, calculate_sp_iters from chia.full_node.bundle_tools import best_solution_generator_from_template, simple_solution_generator from chia.full_node.full_node import FullNode from chia.full_node.mempool_check_conditions import get_puzzle_and_solution_for_coin from chia.full_node.signage_point import SignagePoint from chia.protocols import farmer_protocol, full_node_protocol, introducer_protocol, timelord_protocol, wallet_protocol from chia.protocols.full_node_protocol import RejectBlock, RejectBlocks from chia.protocols.protocol_message_types import ProtocolMessageTypes from chia.protocols.wallet_protocol import ( PuzzleSolutionResponse, RejectHeaderBlocks, RejectHeaderRequest, CoinState, RespondSESInfo, ) from chia.server.outbound_message import Message, make_msg from chia.types.blockchain_format.coin import Coin, hash_coin_list from chia.types.blockchain_format.pool_target import PoolTarget from chia.types.blockchain_format.program import Program from chia.types.blockchain_format.sized_bytes import bytes32 from chia.types.blockchain_format.sub_epoch_summary import SubEpochSummary from chia.types.coin_record import CoinRecord from chia.types.end_of_slot_bundle import EndOfSubSlotBundle from chia.types.full_block import FullBlock from chia.types.generator_types import BlockGenerator from chia.types.mempool_inclusion_status import MempoolInclusionStatus from chia.types.mempool_item import MempoolItem from chia.types.peer_info import PeerInfo from chia.types.transaction_queue_entry import TransactionQueueEntry from chia.types.unfinished_block import UnfinishedBlock from chia.util.api_decorators import api_request, peer_required, bytes_required, execute_task, reply_type from chia.util.generator_tools import get_block_header from chia.util.hash import std_hash from chia.util.ints import uint8, uint32, uint64, uint128 from chia.util.merkle_set import MerkleSet class FullNodeAPI: full_node: FullNode def __init__(self, full_node) -> None: self.full_node = full_node @property def server(self): return self.full_node.server @property def log(self): return self.full_node.log @property def api_ready(self): return self.full_node.initialized @peer_required @api_request @reply_type([ProtocolMessageTypes.respond_peers]) async def request_peers(self, _request: full_node_protocol.RequestPeers, peer: ws.WSChiaConnection): if peer.peer_server_port is None: return None peer_info = PeerInfo(peer.peer_host, peer.peer_server_port) if self.full_node.full_node_peers is not None: msg = await self.full_node.full_node_peers.request_peers(peer_info) return msg @peer_required @api_request async def respond_peers( self, request: full_node_protocol.RespondPeers, peer: ws.WSChiaConnection ) -> Optional[Message]: self.log.debug(f"Received {len(request.peer_list)} peers") if self.full_node.full_node_peers is not None: await self.full_node.full_node_peers.respond_peers(request, peer.get_peer_info(), True) return None @peer_required @api_request async def respond_peers_introducer( self, request: introducer_protocol.RespondPeersIntroducer, peer: ws.WSChiaConnection ) -> Optional[Message]: self.log.debug(f"Received {len(request.peer_list)} peers from introducer") if self.full_node.full_node_peers is not None: await self.full_node.full_node_peers.respond_peers(request, peer.get_peer_info(), False) await peer.close() return None @execute_task @peer_required @api_request async def new_peak(self, request: full_node_protocol.NewPeak, peer: ws.WSChiaConnection) -> Optional[Message]: waiter_count = len(self.full_node.new_peak_sem._waiters) if waiter_count > 0: self.full_node.log.debug(f"new_peak Waiters: {waiter_count}") if waiter_count > 20: return None async with self.full_node.new_peak_sem: return await self.full_node.new_peak(request, peer) @peer_required @api_request async def new_transaction( self, transaction: full_node_protocol.NewTransaction, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None if not (await self.full_node.synced()): return None if self.full_node.mempool_manager.seen(transaction.transaction_id): return None if self.full_node.mempool_manager.is_fee_enough(transaction.fees, transaction.cost): if transaction.transaction_id in self.full_node.full_node_store.pending_tx_request: if transaction.transaction_id in self.full_node.full_node_store.peers_with_tx: current_set = self.full_node.full_node_store.peers_with_tx[transaction.transaction_id] if peer.peer_node_id in current_set: return None current_set.add(peer.peer_node_id) return None else: new_set = set() new_set.add(peer.peer_node_id) self.full_node.full_node_store.peers_with_tx[transaction.transaction_id] = new_set return None self.full_node.full_node_store.pending_tx_request[transaction.transaction_id] = peer.peer_node_id new_set = set() new_set.add(peer.peer_node_id) self.full_node.full_node_store.peers_with_tx[transaction.transaction_id] = new_set async def tx_request_and_timeout(full_node: FullNode, transaction_id, task_id): counter = 0 try: while True: # Limit to asking to a few peers, it's possible that this tx got included on chain already # drop some transactions, we don't want to refetch too many times if counter == 5: break if transaction_id not in full_node.full_node_store.peers_with_tx: break peers_with_tx: Set = full_node.full_node_store.peers_with_tx[transaction_id] if len(peers_with_tx) == 0: break peer_id = peers_with_tx.pop() assert full_node.server is not None if peer_id not in full_node.server.all_connections: continue peer = full_node.server.all_connections[peer_id] request_tx = full_node_protocol.RequestTransaction(transaction.transaction_id) msg = make_msg(ProtocolMessageTypes.request_transaction, request_tx) await peer.send_message(msg) await asyncio.sleep(5) counter += 1 if full_node.mempool_manager.seen(transaction_id): break except asyncio.CancelledError: pass finally: if transaction_id in full_node.full_node_store.peers_with_tx: full_node.full_node_store.peers_with_tx.pop(transaction_id) if transaction_id in full_node.full_node_store.pending_tx_request: full_node.full_node_store.pending_tx_request.pop(transaction_id) if task_id in full_node.full_node_store.tx_fetch_tasks: full_node.full_node_store.tx_fetch_tasks.pop(task_id) task_id: bytes32 = bytes32(token_bytes(32)) fetch_task = asyncio.create_task( tx_request_and_timeout(self.full_node, transaction.transaction_id, task_id) ) self.full_node.full_node_store.tx_fetch_tasks[task_id] = fetch_task return None return None @api_request @reply_type([ProtocolMessageTypes.respond_transaction]) async def request_transaction(self, request: full_node_protocol.RequestTransaction) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None spend_bundle = self.full_node.mempool_manager.get_spendbundle(request.transaction_id) if spend_bundle is None: return None transaction = full_node_protocol.RespondTransaction(spend_bundle) msg = make_msg(ProtocolMessageTypes.respond_transaction, transaction) return msg @peer_required @api_request @bytes_required async def respond_transaction( self, tx: full_node_protocol.RespondTransaction, peer: ws.WSChiaConnection, tx_bytes: bytes = b"", test: bool = False, ) -> Optional[Message]: assert tx_bytes != b"" spend_name = std_hash(tx_bytes) if spend_name in self.full_node.full_node_store.pending_tx_request: self.full_node.full_node_store.pending_tx_request.pop(spend_name) if spend_name in self.full_node.full_node_store.peers_with_tx: self.full_node.full_node_store.peers_with_tx.pop(spend_name) if self.full_node.transaction_queue.qsize() % 100 == 0 and not self.full_node.transaction_queue.empty(): self.full_node.log.debug(f"respond_transaction Waiters: {self.full_node.transaction_queue.qsize()}") if self.full_node.transaction_queue.full(): self.full_node.dropped_tx.add(spend_name) return None await self.full_node.transaction_queue.put( (0, TransactionQueueEntry(tx.transaction, tx_bytes, spend_name, peer, test)) ) return None @api_request @reply_type([ProtocolMessageTypes.respond_proof_of_weight]) async def request_proof_of_weight(self, request: full_node_protocol.RequestProofOfWeight) -> Optional[Message]: if self.full_node.weight_proof_handler is None: return None if not self.full_node.blockchain.contains_block(request.tip): self.log.error(f"got weight proof request for unknown peak {request.tip}") return None if request.tip in self.full_node.pow_creation: event = self.full_node.pow_creation[request.tip] await event.wait() wp = await self.full_node.weight_proof_handler.get_proof_of_weight(request.tip) else: event = asyncio.Event() self.full_node.pow_creation[request.tip] = event wp = await self.full_node.weight_proof_handler.get_proof_of_weight(request.tip) event.set() tips = list(self.full_node.pow_creation.keys()) if len(tips) > 4: for i in range(0, 4): self.full_node.pow_creation.pop(tips[i]) if wp is None: self.log.error(f"failed creating weight proof for peak {request.tip}") return None if ( self.full_node.full_node_store.serialized_wp_message_tip is not None and self.full_node.full_node_store.serialized_wp_message_tip == request.tip ): return self.full_node.full_node_store.serialized_wp_message message = make_msg( ProtocolMessageTypes.respond_proof_of_weight, full_node_protocol.RespondProofOfWeight(wp, request.tip) ) self.full_node.full_node_store.serialized_wp_message_tip = request.tip self.full_node.full_node_store.serialized_wp_message = message return message @api_request async def respond_proof_of_weight(self, request: full_node_protocol.RespondProofOfWeight) -> Optional[Message]: self.log.warning("Received proof of weight too late.") return None @api_request @reply_type([ProtocolMessageTypes.respond_block, ProtocolMessageTypes.reject_block]) async def request_block(self, request: full_node_protocol.RequestBlock) -> Optional[Message]: if not self.full_node.blockchain.contains_height(request.height): reject = RejectBlock(request.height) msg = make_msg(ProtocolMessageTypes.reject_block, reject) return msg header_hash: Optional[bytes32] = self.full_node.blockchain.height_to_hash(request.height) if header_hash is None: return make_msg(ProtocolMessageTypes.reject_block, RejectBlock(request.height)) block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(header_hash) if block is not None: if not request.include_transaction_block and block.transactions_generator is not None: block = dataclasses.replace(block, transactions_generator=None) return make_msg(ProtocolMessageTypes.respond_block, full_node_protocol.RespondBlock(block)) return make_msg(ProtocolMessageTypes.reject_block, RejectBlock(request.height)) @api_request @reply_type([ProtocolMessageTypes.respond_blocks, ProtocolMessageTypes.reject_blocks]) async def request_blocks(self, request: full_node_protocol.RequestBlocks) -> Optional[Message]: if request.end_height < request.start_height or request.end_height - request.start_height > 32: reject = RejectBlocks(request.start_height, request.end_height) msg: Message = make_msg(ProtocolMessageTypes.reject_blocks, reject) return msg for i in range(request.start_height, request.end_height + 1): if not self.full_node.blockchain.contains_height(uint32(i)): reject = RejectBlocks(request.start_height, request.end_height) msg = make_msg(ProtocolMessageTypes.reject_blocks, reject) return msg if not request.include_transaction_block: blocks: List[FullBlock] = [] for i in range(request.start_height, request.end_height + 1): header_hash_i: Optional[bytes32] = self.full_node.blockchain.height_to_hash(uint32(i)) if header_hash_i is None: reject = RejectBlocks(request.start_height, request.end_height) return make_msg(ProtocolMessageTypes.reject_blocks, reject) block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(header_hash_i) if block is None: reject = RejectBlocks(request.start_height, request.end_height) return make_msg(ProtocolMessageTypes.reject_blocks, reject) block = dataclasses.replace(block, transactions_generator=None) blocks.append(block) msg = make_msg( ProtocolMessageTypes.respond_blocks, full_node_protocol.RespondBlocks(request.start_height, request.end_height, blocks), ) else: blocks_bytes: List[bytes] = [] for i in range(request.start_height, request.end_height + 1): header_hash_i = self.full_node.blockchain.height_to_hash(uint32(i)) if header_hash_i is None: reject = RejectBlocks(request.start_height, request.end_height) return make_msg(ProtocolMessageTypes.reject_blocks, reject) block_bytes: Optional[bytes] = await self.full_node.block_store.get_full_block_bytes(header_hash_i) if block_bytes is None: reject = RejectBlocks(request.start_height, request.end_height) msg = make_msg(ProtocolMessageTypes.reject_blocks, reject) return msg blocks_bytes.append(block_bytes) respond_blocks_manually_streamed: bytes = ( bytes(uint32(request.start_height)) + bytes(uint32(request.end_height)) + len(blocks_bytes).to_bytes(4, "big", signed=False) ) for block_bytes in blocks_bytes: respond_blocks_manually_streamed += block_bytes msg = make_msg(ProtocolMessageTypes.respond_blocks, respond_blocks_manually_streamed) return msg @api_request async def reject_block(self, request: full_node_protocol.RejectBlock): self.log.debug(f"reject_block {request.height}") @api_request async def reject_blocks(self, request: full_node_protocol.RejectBlocks): self.log.debug(f"reject_blocks {request.start_height} {request.end_height}") @api_request async def respond_blocks(self, request: full_node_protocol.RespondBlocks) -> None: self.log.warning("Received unsolicited/late blocks") return None @api_request @peer_required async def respond_block( self, respond_block: full_node_protocol.RespondBlock, peer: ws.WSChiaConnection, ) -> Optional[Message]: self.log.warning(f"Received unsolicited/late block from peer {peer.get_peer_logging()}") return None @api_request async def new_unfinished_block( self, new_unfinished_block: full_node_protocol.NewUnfinishedBlock ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None block_hash = new_unfinished_block.unfinished_reward_hash if self.full_node.full_node_store.get_unfinished_block(block_hash) is not None: return None if block_hash in self.full_node.full_node_store.requesting_unfinished_blocks: return None msg = make_msg( ProtocolMessageTypes.request_unfinished_block, full_node_protocol.RequestUnfinishedBlock(block_hash), ) self.full_node.full_node_store.requesting_unfinished_blocks.add(block_hash) async def eventually_clear(): await asyncio.sleep(5) if block_hash in self.full_node.full_node_store.requesting_unfinished_blocks: self.full_node.full_node_store.requesting_unfinished_blocks.remove(block_hash) asyncio.create_task(eventually_clear()) return msg @api_request @reply_type([ProtocolMessageTypes.respond_unfinished_block]) async def request_unfinished_block( self, request_unfinished_block: full_node_protocol.RequestUnfinishedBlock ) -> Optional[Message]: unfinished_block: Optional[UnfinishedBlock] = self.full_node.full_node_store.get_unfinished_block( request_unfinished_block.unfinished_reward_hash ) if unfinished_block is not None: msg = make_msg( ProtocolMessageTypes.respond_unfinished_block, full_node_protocol.RespondUnfinishedBlock(unfinished_block), ) return msg return None @peer_required @api_request @bytes_required async def respond_unfinished_block( self, respond_unfinished_block: full_node_protocol.RespondUnfinishedBlock, peer: ws.WSChiaConnection, respond_unfinished_block_bytes: bytes = b"", ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None await self.full_node.respond_unfinished_block( respond_unfinished_block, peer, block_bytes=respond_unfinished_block_bytes ) return None @api_request @peer_required async def new_signage_point_or_end_of_sub_slot( self, new_sp: full_node_protocol.NewSignagePointOrEndOfSubSlot, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None if ( self.full_node.full_node_store.get_signage_point_by_index( new_sp.challenge_hash, new_sp.index_from_challenge, new_sp.last_rc_infusion, ) is not None ): return None if self.full_node.full_node_store.have_newer_signage_point( new_sp.challenge_hash, new_sp.index_from_challenge, new_sp.last_rc_infusion ): return None if new_sp.index_from_challenge == 0 and new_sp.prev_challenge_hash is not None: if self.full_node.full_node_store.get_sub_slot(new_sp.prev_challenge_hash) is None: collected_eos = [] challenge_hash_to_request = new_sp.challenge_hash last_rc = new_sp.last_rc_infusion num_non_empty_sub_slots_seen = 0 for _ in range(30): if num_non_empty_sub_slots_seen >= 3: self.log.debug("Diverged from peer. Don't have the same blocks") return None # If this is an end of sub slot, and we don't have the prev, request the prev instead full_node_request = full_node_protocol.RequestSignagePointOrEndOfSubSlot( challenge_hash_to_request, uint8(0), last_rc ) response = await peer.request_signage_point_or_end_of_sub_slot(full_node_request, timeout=10) if not isinstance(response, full_node_protocol.RespondEndOfSubSlot): self.full_node.log.debug(f"Invalid response for slot {response}") return None collected_eos.append(response) if ( self.full_node.full_node_store.get_sub_slot( response.end_of_slot_bundle.challenge_chain.challenge_chain_end_of_slot_vdf.challenge ) is not None or response.end_of_slot_bundle.challenge_chain.challenge_chain_end_of_slot_vdf.challenge == self.full_node.constants.GENESIS_CHALLENGE ): for eos in reversed(collected_eos): await self.respond_end_of_sub_slot(eos, peer) return None if ( response.end_of_slot_bundle.challenge_chain.challenge_chain_end_of_slot_vdf.number_of_iterations != response.end_of_slot_bundle.reward_chain.end_of_slot_vdf.number_of_iterations ): num_non_empty_sub_slots_seen += 1 challenge_hash_to_request = ( response.end_of_slot_bundle.challenge_chain.challenge_chain_end_of_slot_vdf.challenge ) last_rc = response.end_of_slot_bundle.reward_chain.end_of_slot_vdf.challenge self.full_node.log.warning("Failed to catch up in sub-slots") return None if new_sp.index_from_challenge > 0: if ( new_sp.challenge_hash != self.full_node.constants.GENESIS_CHALLENGE and self.full_node.full_node_store.get_sub_slot(new_sp.challenge_hash) is None ): full_node_request = full_node_protocol.RequestSignagePointOrEndOfSubSlot( new_sp.challenge_hash, uint8(0), new_sp.last_rc_infusion ) return make_msg(ProtocolMessageTypes.request_signage_point_or_end_of_sub_slot, full_node_request) # Otherwise (we have the prev or the end of sub slot), request it normally full_node_request = full_node_protocol.RequestSignagePointOrEndOfSubSlot( new_sp.challenge_hash, new_sp.index_from_challenge, new_sp.last_rc_infusion ) return make_msg(ProtocolMessageTypes.request_signage_point_or_end_of_sub_slot, full_node_request) @api_request @reply_type([ProtocolMessageTypes.respond_signage_point, ProtocolMessageTypes.respond_end_of_sub_slot]) async def request_signage_point_or_end_of_sub_slot( self, request: full_node_protocol.RequestSignagePointOrEndOfSubSlot ) -> Optional[Message]: if request.index_from_challenge == 0: sub_slot: Optional[Tuple[EndOfSubSlotBundle, int, uint128]] = self.full_node.full_node_store.get_sub_slot( request.challenge_hash ) if sub_slot is not None: return make_msg( ProtocolMessageTypes.respond_end_of_sub_slot, full_node_protocol.RespondEndOfSubSlot(sub_slot[0]), ) else: if self.full_node.full_node_store.get_sub_slot(request.challenge_hash) is None: if request.challenge_hash != self.full_node.constants.GENESIS_CHALLENGE: self.log.info(f"Don't have challenge hash {request.challenge_hash}") sp: Optional[SignagePoint] = self.full_node.full_node_store.get_signage_point_by_index( request.challenge_hash, request.index_from_challenge, request.last_rc_infusion, ) if sp is not None: assert ( sp.cc_vdf is not None and sp.cc_proof is not None and sp.rc_vdf is not None and sp.rc_proof is not None ) full_node_response = full_node_protocol.RespondSignagePoint( request.index_from_challenge, sp.cc_vdf, sp.cc_proof, sp.rc_vdf, sp.rc_proof, ) return make_msg(ProtocolMessageTypes.respond_signage_point, full_node_response) else: self.log.info(f"Don't have signage point {request}") return None @peer_required @api_request async def respond_signage_point( self, request: full_node_protocol.RespondSignagePoint, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None async with self.full_node.timelord_lock: # Already have signage point if self.full_node.full_node_store.have_newer_signage_point( request.challenge_chain_vdf.challenge, request.index_from_challenge, request.reward_chain_vdf.challenge, ): return None existing_sp = self.full_node.full_node_store.get_signage_point( request.challenge_chain_vdf.output.get_hash() ) if existing_sp is not None and existing_sp.rc_vdf == request.reward_chain_vdf: return None peak = self.full_node.blockchain.get_peak() if peak is not None and peak.height > self.full_node.constants.MAX_SUB_SLOT_BLOCKS: next_sub_slot_iters = self.full_node.blockchain.get_next_slot_iters(peak.header_hash, True) sub_slots_for_peak = await self.full_node.blockchain.get_sp_and_ip_sub_slots(peak.header_hash) assert sub_slots_for_peak is not None ip_sub_slot: Optional[EndOfSubSlotBundle] = sub_slots_for_peak[1] else: sub_slot_iters = self.full_node.constants.SUB_SLOT_ITERS_STARTING next_sub_slot_iters = sub_slot_iters ip_sub_slot = None added = self.full_node.full_node_store.new_signage_point( request.index_from_challenge, self.full_node.blockchain, self.full_node.blockchain.get_peak(), next_sub_slot_iters, SignagePoint( request.challenge_chain_vdf, request.challenge_chain_proof, request.reward_chain_vdf, request.reward_chain_proof, ), ) if added: await self.full_node.signage_point_post_processing(request, peer, ip_sub_slot) else: self.log.debug( f"Signage point {request.index_from_challenge} not added, CC challenge: " f"{request.challenge_chain_vdf.challenge}, RC challenge: {request.reward_chain_vdf.challenge}" ) return None @peer_required @api_request async def respond_end_of_sub_slot( self, request: full_node_protocol.RespondEndOfSubSlot, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None msg, _ = await self.full_node.respond_end_of_sub_slot(request, peer) return msg @peer_required @api_request async def request_mempool_transactions( self, request: full_node_protocol.RequestMempoolTransactions, peer: ws.WSChiaConnection, ) -> Optional[Message]: received_filter = PyBIP158(bytearray(request.filter)) items: List[MempoolItem] = await self.full_node.mempool_manager.get_items_not_in_filter(received_filter) for item in items: transaction = full_node_protocol.RespondTransaction(item.spend_bundle) msg = make_msg(ProtocolMessageTypes.respond_transaction, transaction) await peer.send_message(msg) return None # FARMER PROTOCOL @api_request @peer_required async def declare_proof_of_space( self, request: farmer_protocol.DeclareProofOfSpace, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None async with self.full_node.timelord_lock: sp_vdfs: Optional[SignagePoint] = self.full_node.full_node_store.get_signage_point( request.challenge_chain_sp ) if sp_vdfs is None: self.log.warning(f"Received proof of space for an unknown signage point {request.challenge_chain_sp}") return None if request.signage_point_index > 0: assert sp_vdfs.rc_vdf is not None if sp_vdfs.rc_vdf.output.get_hash() != request.reward_chain_sp: self.log.debug( f"Received proof of space for a potentially old signage point {request.challenge_chain_sp}. " f"Current sp: {sp_vdfs.rc_vdf.output.get_hash()}" ) return None if request.signage_point_index == 0: cc_challenge_hash: bytes32 = request.challenge_chain_sp else: assert sp_vdfs.cc_vdf is not None cc_challenge_hash = sp_vdfs.cc_vdf.challenge pos_sub_slot: Optional[Tuple[EndOfSubSlotBundle, int, uint128]] = None if request.challenge_hash != self.full_node.constants.GENESIS_CHALLENGE: # Checks that the proof of space is a response to a recent challenge and valid SP pos_sub_slot = self.full_node.full_node_store.get_sub_slot(cc_challenge_hash) if pos_sub_slot is None: self.log.warning(f"Received proof of space for an unknown sub slot: {request}") return None total_iters_pos_slot: uint128 = pos_sub_slot[2] else: total_iters_pos_slot = uint128(0) assert cc_challenge_hash == request.challenge_hash # Now we know that the proof of space has a signage point either: # 1. In the previous sub-slot of the peak (overflow) # 2. In the same sub-slot as the peak # 3. In a future sub-slot that we already know of # Checks that the proof of space is valid quality_string: Optional[bytes32] = request.proof_of_space.verify_and_get_quality_string( self.full_node.constants, cc_challenge_hash, request.challenge_chain_sp ) assert quality_string is not None and len(quality_string) == 32 # Grab best transactions from Mempool for given tip target aggregate_signature: G2Element = G2Element() block_generator: Optional[BlockGenerator] = None additions: Optional[List[Coin]] = [] removals: Optional[List[Coin]] = [] async with self.full_node._blockchain_lock_high_priority: peak: Optional[BlockRecord] = self.full_node.blockchain.get_peak() if peak is not None: # Finds the last transaction block before this one curr_l_tb: BlockRecord = peak while not curr_l_tb.is_transaction_block: curr_l_tb = self.full_node.blockchain.block_record(curr_l_tb.prev_hash) try: mempool_bundle = await self.full_node.mempool_manager.create_bundle_from_mempool( curr_l_tb.header_hash ) except Exception as e: self.log.error(f"Traceback: {traceback.format_exc()}") self.full_node.log.error(f"Error making spend bundle {e} peak: {peak}") mempool_bundle = None if mempool_bundle is not None: spend_bundle = mempool_bundle[0] additions = mempool_bundle[1] removals = mempool_bundle[2] self.full_node.log.info(f"Add rem: {len(additions)} {len(removals)}") aggregate_signature = spend_bundle.aggregated_signature if self.full_node.full_node_store.previous_generator is not None: self.log.info( f"Using previous generator for height " f"{self.full_node.full_node_store.previous_generator}" ) block_generator = best_solution_generator_from_template( self.full_node.full_node_store.previous_generator, spend_bundle ) else: block_generator = simple_solution_generator(spend_bundle) def get_plot_sig(to_sign, _) -> G2Element: if to_sign == request.challenge_chain_sp: return request.challenge_chain_sp_signature elif to_sign == request.reward_chain_sp: return request.reward_chain_sp_signature return G2Element() def get_pool_sig(_1, _2) -> Optional[G2Element]: return request.pool_signature prev_b: Optional[BlockRecord] = self.full_node.blockchain.get_peak() # Finds the previous block from the signage point, ensuring that the reward chain VDF is correct if prev_b is not None: if request.signage_point_index == 0: if pos_sub_slot is None: self.log.warning("Pos sub slot is None") return None rc_challenge = pos_sub_slot[0].reward_chain.end_of_slot_vdf.challenge else: assert sp_vdfs.rc_vdf is not None rc_challenge = sp_vdfs.rc_vdf.challenge # Backtrack through empty sub-slots for eos, _, _ in reversed(self.full_node.full_node_store.finished_sub_slots): if eos is not None and eos.reward_chain.get_hash() == rc_challenge: rc_challenge = eos.reward_chain.end_of_slot_vdf.challenge found = False attempts = 0 while prev_b is not None and attempts < 10: if prev_b.reward_infusion_new_challenge == rc_challenge: found = True break if prev_b.finished_reward_slot_hashes is not None and len(prev_b.finished_reward_slot_hashes) > 0: if prev_b.finished_reward_slot_hashes[-1] == rc_challenge: # This block includes a sub-slot which is where our SP vdf starts. Go back one more # to find the prev block prev_b = self.full_node.blockchain.try_block_record(prev_b.prev_hash) found = True break prev_b = self.full_node.blockchain.try_block_record(prev_b.prev_hash) attempts += 1 if not found: self.log.warning("Did not find a previous block with the correct reward chain hash") return None try: finished_sub_slots: Optional[ List[EndOfSubSlotBundle] ] = self.full_node.full_node_store.get_finished_sub_slots( self.full_node.blockchain, prev_b, cc_challenge_hash ) if finished_sub_slots is None: return None if ( len(finished_sub_slots) > 0 and pos_sub_slot is not None and finished_sub_slots[-1] != pos_sub_slot[0] ): self.log.error("Have different sub-slots than is required to farm this block") return None except ValueError as e: self.log.warning(f"Value Error: {e}") return None if prev_b is None: pool_target = PoolTarget( self.full_node.constants.GENESIS_PRE_FARM_POOL_PUZZLE_HASH, uint32(0), ) farmer_ph = self.full_node.constants.GENESIS_PRE_FARM_FARMER_PUZZLE_HASH else: farmer_ph = request.farmer_puzzle_hash if request.proof_of_space.pool_contract_puzzle_hash is not None: pool_target = PoolTarget(request.proof_of_space.pool_contract_puzzle_hash, uint32(0)) else: assert request.pool_target is not None pool_target = request.pool_target if peak is None or peak.height <= self.full_node.constants.MAX_SUB_SLOT_BLOCKS: difficulty = self.full_node.constants.DIFFICULTY_STARTING sub_slot_iters = self.full_node.constants.SUB_SLOT_ITERS_STARTING else: difficulty = uint64(peak.weight - self.full_node.blockchain.block_record(peak.prev_hash).weight) sub_slot_iters = peak.sub_slot_iters for sub_slot in finished_sub_slots: if sub_slot.challenge_chain.new_difficulty is not None: difficulty = sub_slot.challenge_chain.new_difficulty if sub_slot.challenge_chain.new_sub_slot_iters is not None: sub_slot_iters = sub_slot.challenge_chain.new_sub_slot_iters required_iters: uint64 = calculate_iterations_quality( self.full_node.constants.DIFFICULTY_CONSTANT_FACTOR, quality_string, request.proof_of_space.size, difficulty, request.challenge_chain_sp, ) sp_iters: uint64 = calculate_sp_iters(self.full_node.constants, sub_slot_iters, request.signage_point_index) ip_iters: uint64 = calculate_ip_iters( self.full_node.constants, sub_slot_iters, request.signage_point_index, required_iters, ) # The block's timestamp must be greater than the previous transaction block's timestamp timestamp = uint64(int(time.time())) curr: Optional[BlockRecord] = prev_b while curr is not None and not curr.is_transaction_block and curr.height != 0: curr = self.full_node.blockchain.try_block_record(curr.prev_hash) if curr is not None: assert curr.timestamp is not None if timestamp <= curr.timestamp: timestamp = uint64(int(curr.timestamp + 1)) self.log.info("Starting to make the unfinished block") unfinished_block: UnfinishedBlock = create_unfinished_block( self.full_node.constants, total_iters_pos_slot, sub_slot_iters, request.signage_point_index, sp_iters, ip_iters, request.proof_of_space, cc_challenge_hash, farmer_ph, pool_target, get_plot_sig, get_pool_sig, sp_vdfs, timestamp, self.full_node.blockchain, b"", block_generator, aggregate_signature, additions, removals, prev_b, finished_sub_slots, ) self.log.info("Made the unfinished block") if prev_b is not None: height: uint32 = uint32(prev_b.height + 1) else: height = uint32(0) self.full_node.full_node_store.add_candidate_block(quality_string, height, unfinished_block) foliage_sb_data_hash = unfinished_block.foliage.foliage_block_data.get_hash() if unfinished_block.is_transaction_block(): foliage_transaction_block_hash = unfinished_block.foliage.foliage_transaction_block_hash else: foliage_transaction_block_hash = bytes32([0] * 32) assert foliage_transaction_block_hash is not None message = farmer_protocol.RequestSignedValues( quality_string, foliage_sb_data_hash, foliage_transaction_block_hash, ) await peer.send_message(make_msg(ProtocolMessageTypes.request_signed_values, message)) # Adds backup in case the first one fails if unfinished_block.is_transaction_block() and unfinished_block.transactions_generator is not None: unfinished_block_backup = create_unfinished_block( self.full_node.constants, total_iters_pos_slot, sub_slot_iters, request.signage_point_index, sp_iters, ip_iters, request.proof_of_space, cc_challenge_hash, farmer_ph, pool_target, get_plot_sig, get_pool_sig, sp_vdfs, timestamp, self.full_node.blockchain, b"", None, G2Element(), None, None, prev_b, finished_sub_slots, ) self.full_node.full_node_store.add_candidate_block( quality_string, height, unfinished_block_backup, backup=True ) return None @api_request @peer_required async def signed_values( self, farmer_request: farmer_protocol.SignedValues, peer: ws.WSChiaConnection ) -> Optional[Message]: candidate_tuple: Optional[Tuple[uint32, UnfinishedBlock]] = self.full_node.full_node_store.get_candidate_block( farmer_request.quality_string ) if candidate_tuple is None: self.log.warning(f"Quality string {farmer_request.quality_string} not found in database") return None height, candidate = candidate_tuple if not AugSchemeMPL.verify( candidate.reward_chain_block.proof_of_space.plot_public_key, candidate.foliage.foliage_block_data.get_hash(), farmer_request.foliage_block_data_signature, ): self.log.warning("Signature not valid. There might be a collision in plots. Ignore this during tests.") return None fsb2 = dataclasses.replace( candidate.foliage, foliage_block_data_signature=farmer_request.foliage_block_data_signature, ) if candidate.is_transaction_block(): fsb2 = dataclasses.replace( fsb2, foliage_transaction_block_signature=farmer_request.foliage_transaction_block_signature ) new_candidate = dataclasses.replace(candidate, foliage=fsb2) if not self.full_node.has_valid_pool_sig(new_candidate): self.log.warning("Trying to make a pre-farm block but height is not 0") return None # Propagate to ourselves (which validates and does further propagations) request = full_node_protocol.RespondUnfinishedBlock(new_candidate) try: await self.full_node.respond_unfinished_block(request, None, True) except Exception as e: # If we have an error with this block, try making an empty block self.full_node.log.error(f"Error farming block {e} {request}") candidate_tuple = self.full_node.full_node_store.get_candidate_block( farmer_request.quality_string, backup=True ) if candidate_tuple is not None: height, unfinished_block = candidate_tuple self.full_node.full_node_store.add_candidate_block( farmer_request.quality_string, height, unfinished_block, False ) # All unfinished blocks that we create will have the foliage transaction block and hash assert unfinished_block.foliage.foliage_transaction_block_hash is not None message = farmer_protocol.RequestSignedValues( farmer_request.quality_string, unfinished_block.foliage.foliage_block_data.get_hash(), unfinished_block.foliage.foliage_transaction_block_hash, ) await peer.send_message(make_msg(ProtocolMessageTypes.request_signed_values, message)) return None # TIMELORD PROTOCOL @peer_required @api_request async def new_infusion_point_vdf( self, request: timelord_protocol.NewInfusionPointVDF, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None # Lookup unfinished blocks async with self.full_node.timelord_lock: return await self.full_node.new_infusion_point_vdf(request, peer) @peer_required @api_request async def new_signage_point_vdf( self, request: timelord_protocol.NewSignagePointVDF, peer: ws.WSChiaConnection ) -> None: if self.full_node.sync_store.get_sync_mode(): return None full_node_message = full_node_protocol.RespondSignagePoint( request.index_from_challenge, request.challenge_chain_sp_vdf, request.challenge_chain_sp_proof, request.reward_chain_sp_vdf, request.reward_chain_sp_proof, ) await self.respond_signage_point(full_node_message, peer) @peer_required @api_request async def new_end_of_sub_slot_vdf( self, request: timelord_protocol.NewEndOfSubSlotVDF, peer: ws.WSChiaConnection ) -> Optional[Message]: if self.full_node.sync_store.get_sync_mode(): return None if ( self.full_node.full_node_store.get_sub_slot(request.end_of_sub_slot_bundle.challenge_chain.get_hash()) is not None ): return None # Calls our own internal message to handle the end of sub slot, and potentially broadcasts to other peers. full_node_message = full_node_protocol.RespondEndOfSubSlot(request.end_of_sub_slot_bundle) msg, added = await self.full_node.respond_end_of_sub_slot(full_node_message, peer) if not added: self.log.error( f"Was not able to add end of sub-slot: " f"{request.end_of_sub_slot_bundle.challenge_chain.challenge_chain_end_of_slot_vdf.challenge}. " f"Re-sending new-peak to timelord" ) await self.full_node.send_peak_to_timelords(peer=peer) return None else: return msg @api_request async def request_block_header(self, request: wallet_protocol.RequestBlockHeader) -> Optional[Message]: header_hash = self.full_node.blockchain.height_to_hash(request.height) if header_hash is None: msg = make_msg(ProtocolMessageTypes.reject_header_request, RejectHeaderRequest(request.height)) return msg block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(header_hash) if block is not None: tx_removals, tx_additions, _ = await self.full_node.blockchain.get_tx_removals_and_additions(block) header_block = get_block_header(block, tx_additions, tx_removals) msg = make_msg( ProtocolMessageTypes.respond_block_header, wallet_protocol.RespondBlockHeader(header_block), ) return msg return None @api_request async def request_additions(self, request: wallet_protocol.RequestAdditions) -> Optional[Message]: if request.header_hash is None: header_hash: Optional[bytes32] = self.full_node.blockchain.height_to_hash(request.height) else: header_hash = request.header_hash if header_hash is None: raise ValueError(f"Block at height {request.height} not found") block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(header_hash) # We lock so that the coin store does not get modified if ( block is None or block.is_transaction_block() is False or self.full_node.blockchain.height_to_hash(block.height) != request.header_hash ): reject = wallet_protocol.RejectAdditionsRequest(request.height, header_hash) msg = make_msg(ProtocolMessageTypes.reject_additions_request, reject) return msg assert block is not None and block.foliage_transaction_block is not None # Note: this might return bad data if there is a reorg in this time additions = await self.full_node.coin_store.get_coins_added_at_height(block.height) if self.full_node.blockchain.height_to_hash(block.height) != request.header_hash: raise ValueError(f"Block {block.header_hash} no longer in chain") puzzlehash_coins_map: Dict[bytes32, List[Coin]] = {} for coin_record in additions: if coin_record.coin.puzzle_hash in puzzlehash_coins_map: puzzlehash_coins_map[coin_record.coin.puzzle_hash].append(coin_record.coin) else: puzzlehash_coins_map[coin_record.coin.puzzle_hash] = [coin_record.coin] coins_map: List[Tuple[bytes32, List[Coin]]] = [] proofs_map: List[Tuple[bytes32, bytes, Optional[bytes]]] = [] if request.puzzle_hashes is None: for puzzle_hash, coins in puzzlehash_coins_map.items(): coins_map.append((puzzle_hash, coins)) response = wallet_protocol.RespondAdditions(block.height, block.header_hash, coins_map, None) else: # Create addition Merkle set addition_merkle_set = MerkleSet() # Addition Merkle set contains puzzlehash and hash of all coins with that puzzlehash for puzzle, coins in puzzlehash_coins_map.items(): addition_merkle_set.add_already_hashed(puzzle) addition_merkle_set.add_already_hashed(hash_coin_list(coins)) assert addition_merkle_set.get_root() == block.foliage_transaction_block.additions_root for puzzle_hash in request.puzzle_hashes: result, proof = addition_merkle_set.is_included_already_hashed(puzzle_hash) if puzzle_hash in puzzlehash_coins_map: coins_map.append((puzzle_hash, puzzlehash_coins_map[puzzle_hash])) hash_coin_str = hash_coin_list(puzzlehash_coins_map[puzzle_hash]) result_2, proof_2 = addition_merkle_set.is_included_already_hashed(hash_coin_str) assert result assert result_2 proofs_map.append((puzzle_hash, proof, proof_2)) else: coins_map.append((puzzle_hash, [])) assert not result proofs_map.append((puzzle_hash, proof, None)) response = wallet_protocol.RespondAdditions(block.height, block.header_hash, coins_map, proofs_map) msg = make_msg(ProtocolMessageTypes.respond_additions, response) return msg @api_request async def request_removals(self, request: wallet_protocol.RequestRemovals) -> Optional[Message]: block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(request.header_hash) # We lock so that the coin store does not get modified peak_height = self.full_node.blockchain.get_peak_height() if ( block is None or block.is_transaction_block() is False or block.height != request.height or (peak_height is not None and block.height > peak_height) or self.full_node.blockchain.height_to_hash(block.height) != request.header_hash ): reject = wallet_protocol.RejectRemovalsRequest(request.height, request.header_hash) msg = make_msg(ProtocolMessageTypes.reject_removals_request, reject) return msg assert block is not None and block.foliage_transaction_block is not None # Note: this might return bad data if there is a reorg in this time all_removals: List[CoinRecord] = await self.full_node.coin_store.get_coins_removed_at_height(block.height) if self.full_node.blockchain.height_to_hash(block.height) != request.header_hash: raise ValueError(f"Block {block.header_hash} no longer in chain") all_removals_dict: Dict[bytes32, Coin] = {} for coin_record in all_removals: all_removals_dict[coin_record.coin.name()] = coin_record.coin coins_map: List[Tuple[bytes32, Optional[Coin]]] = [] proofs_map: List[Tuple[bytes32, bytes]] = [] # If there are no transactions, respond with empty lists if block.transactions_generator is None: proofs: Optional[List] if request.coin_names is None: proofs = None else: proofs = [] response = wallet_protocol.RespondRemovals(block.height, block.header_hash, [], proofs) elif request.coin_names is None or len(request.coin_names) == 0: for removed_name, removed_coin in all_removals_dict.items(): coins_map.append((removed_name, removed_coin)) response = wallet_protocol.RespondRemovals(block.height, block.header_hash, coins_map, None) else: assert block.transactions_generator removal_merkle_set = MerkleSet() for removed_name, removed_coin in all_removals_dict.items(): removal_merkle_set.add_already_hashed(removed_name) assert removal_merkle_set.get_root() == block.foliage_transaction_block.removals_root for coin_name in request.coin_names: result, proof = removal_merkle_set.is_included_already_hashed(coin_name) proofs_map.append((coin_name, proof)) if coin_name in all_removals_dict: removed_coin = all_removals_dict[coin_name] coins_map.append((coin_name, removed_coin)) assert result else: coins_map.append((coin_name, None)) assert not result response = wallet_protocol.RespondRemovals(block.height, block.header_hash, coins_map, proofs_map) msg = make_msg(ProtocolMessageTypes.respond_removals, response) return msg @api_request async def send_transaction(self, request: wallet_protocol.SendTransaction, *, test=False) -> Optional[Message]: spend_name = request.transaction.name() await self.full_node.transaction_queue.put( (0, TransactionQueueEntry(request.transaction, None, spend_name, None, test)) ) # Waits for the transaction to go into the mempool, times out after 45 seconds. status, error = None, None sleep_time = 0.01 for i in range(int(45 / sleep_time)): await asyncio.sleep(sleep_time) for potential_name, potential_status, potential_error in self.full_node.transaction_responses: if spend_name == potential_name: status = potential_status error = potential_error break if status is not None: break if status is None: response = wallet_protocol.TransactionAck(spend_name, uint8(MempoolInclusionStatus.PENDING), None) else: error_name = error.name if error is not None else None if status == MempoolInclusionStatus.SUCCESS: response = wallet_protocol.TransactionAck(spend_name, uint8(status.value), error_name) else: # If if failed/pending, but it previously succeeded (in mempool), this is idempotence, return SUCCESS if self.full_node.mempool_manager.get_spendbundle(spend_name) is not None: response = wallet_protocol.TransactionAck( spend_name, uint8(MempoolInclusionStatus.SUCCESS.value), None ) else: response = wallet_protocol.TransactionAck(spend_name, uint8(status.value), error_name) msg = make_msg(ProtocolMessageTypes.transaction_ack, response) return msg @api_request async def request_puzzle_solution(self, request: wallet_protocol.RequestPuzzleSolution) -> Optional[Message]: coin_name = request.coin_name height = request.height coin_record = await self.full_node.coin_store.get_coin_record(coin_name) reject = wallet_protocol.RejectPuzzleSolution(coin_name, height) reject_msg = make_msg(ProtocolMessageTypes.reject_puzzle_solution, reject) if coin_record is None or coin_record.spent_block_index != height: return reject_msg header_hash: Optional[bytes32] = self.full_node.blockchain.height_to_hash(height) if header_hash is None: return reject_msg block: Optional[FullBlock] = await self.full_node.block_store.get_full_block(header_hash) if block is None or block.transactions_generator is None: return reject_msg block_generator: Optional[BlockGenerator] = await self.full_node.blockchain.get_block_generator(block) assert block_generator is not None error, puzzle, solution = get_puzzle_and_solution_for_coin( block_generator, coin_name, self.full_node.constants.MAX_BLOCK_COST_CLVM ) if error is not None: return reject_msg pz = Program.to(puzzle) sol = Program.to(solution) wrapper = PuzzleSolutionResponse(coin_name, height, pz, sol) response = wallet_protocol.RespondPuzzleSolution(wrapper) response_msg = make_msg(ProtocolMessageTypes.respond_puzzle_solution, response) return response_msg @api_request async def request_header_blocks(self, request: wallet_protocol.RequestHeaderBlocks) -> Optional[Message]: if request.end_height < request.start_height or request.end_height - request.start_height > 32: return None header_hashes: List[bytes32] = [] for i in range(request.start_height, request.end_height + 1): header_hash: Optional[bytes32] = self.full_node.blockchain.height_to_hash(uint32(i)) if header_hash is None: reject = RejectHeaderBlocks(request.start_height, request.end_height) msg = make_msg(ProtocolMessageTypes.reject_header_blocks, reject) return msg header_hashes.append(header_hash) blocks: List[FullBlock] = await self.full_node.block_store.get_blocks_by_hash(header_hashes) header_blocks = [] for block in blocks: added_coins_records = await self.full_node.coin_store.get_coins_added_at_height(block.height) removed_coins_records = await self.full_node.coin_store.get_coins_removed_at_height(block.height) added_coins = [record.coin for record in added_coins_records if not record.coinbase] removal_names = [record.coin.name() for record in removed_coins_records] header_block = get_block_header(block, added_coins, removal_names) header_blocks.append(header_block) msg = make_msg( ProtocolMessageTypes.respond_header_blocks, wallet_protocol.RespondHeaderBlocks(request.start_height, request.end_height, header_blocks), ) return msg @api_request async def respond_compact_proof_of_time(self, request: timelord_protocol.RespondCompactProofOfTime): if self.full_node.sync_store.get_sync_mode(): return None await self.full_node.respond_compact_proof_of_time(request) @execute_task @peer_required @api_request @bytes_required async def new_compact_vdf( self, request: full_node_protocol.NewCompactVDF, peer: ws.WSChiaConnection, request_bytes: bytes = b"" ): if self.full_node.sync_store.get_sync_mode(): return None if len(self.full_node.compact_vdf_sem._waiters) > 20: self.log.debug(f"Ignoring NewCompactVDF: {request}, _waiters") return name = std_hash(request_bytes) if name in self.full_node.compact_vdf_requests: self.log.debug(f"Ignoring NewCompactVDF: {request}, already requested") return self.full_node.compact_vdf_requests.add(name) # this semaphore will only allow a limited number of tasks call # new_compact_vdf() at a time, since it can be expensive async with self.full_node.compact_vdf_sem: try: await self.full_node.new_compact_vdf(request, peer) finally: self.full_node.compact_vdf_requests.remove(name) @peer_required @api_request @reply_type([ProtocolMessageTypes.respond_compact_vdf]) async def request_compact_vdf(self, request: full_node_protocol.RequestCompactVDF, peer: ws.WSChiaConnection): if self.full_node.sync_store.get_sync_mode(): return None await self.full_node.request_compact_vdf(request, peer) @peer_required @api_request async def respond_compact_vdf(self, request: full_node_protocol.RespondCompactVDF, peer: ws.WSChiaConnection): if self.full_node.sync_store.get_sync_mode(): return None await self.full_node.respond_compact_vdf(request, peer) @peer_required @api_request async def register_interest_in_puzzle_hash( self, request: wallet_protocol.RegisterForPhUpdates, peer: ws.WSChiaConnection ): if peer.peer_node_id not in self.full_node.peer_puzzle_hash: self.full_node.peer_puzzle_hash[peer.peer_node_id] = set() if peer.peer_node_id not in self.full_node.peer_sub_counter: self.full_node.peer_sub_counter[peer.peer_node_id] = 0 hint_coin_ids = [] # Add peer to the "Subscribed" dictionary max_items = self.full_node.config.get("max_subscribe_items", 200000) for puzzle_hash in request.puzzle_hashes: ph_hint_coins = await self.full_node.hint_store.get_coin_ids(puzzle_hash) hint_coin_ids.extend(ph_hint_coins) if puzzle_hash not in self.full_node.ph_subscriptions: self.full_node.ph_subscriptions[puzzle_hash] = set() if ( peer.peer_node_id not in self.full_node.ph_subscriptions[puzzle_hash] and self.full_node.peer_sub_counter[peer.peer_node_id] < max_items ): self.full_node.ph_subscriptions[puzzle_hash].add(peer.peer_node_id) self.full_node.peer_puzzle_hash[peer.peer_node_id].add(puzzle_hash) self.full_node.peer_sub_counter[peer.peer_node_id] += 1 # Send all coins with requested puzzle hash that have been created after the specified height states: List[CoinState] = await self.full_node.coin_store.get_coin_states_by_puzzle_hashes( include_spent_coins=True, puzzle_hashes=request.puzzle_hashes, min_height=request.min_height ) if len(hint_coin_ids) > 0: hint_states = await self.full_node.coin_store.get_coin_states_by_ids( include_spent_coins=True, coin_ids=hint_coin_ids, min_height=request.min_height ) states.extend(hint_states) response = wallet_protocol.RespondToPhUpdates(request.puzzle_hashes, request.min_height, states) msg = make_msg(ProtocolMessageTypes.respond_to_ph_update, response) return msg @peer_required @api_request async def register_interest_in_coin( self, request: wallet_protocol.RegisterForCoinUpdates, peer: ws.WSChiaConnection ): if peer.peer_node_id not in self.full_node.peer_coin_ids: self.full_node.peer_coin_ids[peer.peer_node_id] = set() if peer.peer_node_id not in self.full_node.peer_sub_counter: self.full_node.peer_sub_counter[peer.peer_node_id] = 0 max_items = self.full_node.config.get("max_subscribe_items", 200000) for coin_id in request.coin_ids: if coin_id not in self.full_node.coin_subscriptions: self.full_node.coin_subscriptions[coin_id] = set() if ( peer.peer_node_id not in self.full_node.coin_subscriptions[coin_id] and self.full_node.peer_sub_counter[peer.peer_node_id] < max_items ): self.full_node.coin_subscriptions[coin_id].add(peer.peer_node_id) self.full_node.peer_coin_ids[peer.peer_node_id].add(coin_id) self.full_node.peer_sub_counter[peer.peer_node_id] += 1 states: List[CoinState] = await self.full_node.coin_store.get_coin_states_by_ids( include_spent_coins=True, coin_ids=request.coin_ids, min_height=request.min_height ) response = wallet_protocol.RespondToCoinUpdates(request.coin_ids, request.min_height, states) msg = make_msg(ProtocolMessageTypes.respond_to_coin_update, response) return msg @api_request async def request_children(self, request: wallet_protocol.RequestChildren) -> Optional[Message]: coin_records: List[CoinRecord] = await self.full_node.coin_store.get_coin_records_by_parent_ids( True, [request.coin_name] ) states = [record.coin_state for record in coin_records] response = wallet_protocol.RespondChildren(states) msg = make_msg(ProtocolMessageTypes.respond_children, response) return msg @api_request async def request_ses_hashes(self, request: wallet_protocol.RequestSESInfo): ses_height = self.full_node.blockchain.get_ses_heights() start_height = request.start_height end_height = request.end_height ses_hash_heights = [] ses_reward_hashes = [] for idx, ses_start_height in enumerate(ses_height): if idx == len(ses_height) - 1: break next_ses_height = ses_height[idx + 1] # start_ses_hash if ses_start_height <= start_height < next_ses_height: ses_hash_heights.append([ses_start_height, next_ses_height]) ses: SubEpochSummary = self.full_node.blockchain.get_ses(ses_start_height) ses_reward_hashes.append(ses.reward_chain_hash) if ses_start_height < end_height < next_ses_height: break else: if idx == len(ses_height) - 2: break # else add extra ses as request start <-> end spans two ses next_next_height = ses_height[idx + 2] ses_hash_heights.append([next_ses_height, next_next_height]) nex_ses: SubEpochSummary = self.full_node.blockchain.get_ses(next_ses_height) ses_reward_hashes.append(nex_ses.reward_chain_hash) break response = RespondSESInfo(ses_reward_hashes, ses_hash_heights) msg = make_msg(ProtocolMessageTypes.respond_ses_hashes, response) return msg

true

790da6c31447b466d9cc6aace31cb537caffffd7

2,357

py

Python

apps/amcm/migrations/0032_auto_20220104_0437.py

agsneutron/asociacion_mexicana_cuarto_milla

4657e1f494eb572e9b40b2804e012cdfd6193c51

[ "MIT" ]

null

apps/amcm/migrations/0032_auto_20220104_0437.py

agsneutron/asociacion_mexicana_cuarto_milla

4657e1f494eb572e9b40b2804e012cdfd6193c51

[ "MIT" ]

null

apps/amcm/migrations/0032_auto_20220104_0437.py

agsneutron/asociacion_mexicana_cuarto_milla

4657e1f494eb572e9b40b2804e012cdfd6193c51

[ "MIT" ]

null

# Generated by Django 3.2.8 on 2022-01-04 10:37 import datetime from django.db import migrations, models import django.db.models.deletion from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('amcm', '0031_auto_20220104_0431'), ] operations = [ migrations.AddField( model_name='eventoelegibles', name='evento', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to='amcm.evento'), preserve_default=False, ), migrations.AlterField( model_name='credito', name='fecha_pago', field=models.DateField(blank=True, default=datetime.datetime(2022, 1, 4, 10, 37, 3, 977886, tzinfo=utc), null=True, verbose_name='Fecha de pago'), ), migrations.AlterField( model_name='credito', name='fecha_registro', field=models.DateField(default=datetime.datetime(2022, 1, 4, 10, 37, 3, 977861, tzinfo=utc), verbose_name='Fecha de registro'), ), migrations.AlterField( model_name='cuentaspago', name='fecha_registro', field=models.DateField(default=datetime.datetime(2022, 1, 4, 10, 37, 3, 961284, tzinfo=utc), verbose_name='Fecha de Registro'), ), migrations.AlterField( model_name='elegible', name='fecha_registro', field=models.DateField(default=datetime.datetime(2022, 1, 4, 10, 37, 3, 962608, tzinfo=utc), verbose_name='Fecha de registro'), ), migrations.AlterField( model_name='pago', name='fechaPago', field=models.DateField(blank=True, default=datetime.datetime(2022, 1, 4, 10, 37, 3, 959833, tzinfo=utc), null=True, verbose_name='Fecha del Pago'), ), migrations.AlterField( model_name='pago', name='fechaRegistro', field=models.DateField(default=datetime.datetime(2022, 1, 4, 10, 37, 3, 959863, tzinfo=utc), verbose_name='Fecha de Registro'), ), migrations.AlterField( model_name='recibo', name='fecha_registro', field=models.DateField(default=datetime.datetime(2022, 1, 4, 10, 37, 3, 976856, tzinfo=utc), verbose_name='Fecha de registro'), ), ]

40.637931

159

0.614765

import datetime from django.db import migrations, models import django.db.models.deletion from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('amcm', '0031_auto_20220104_0431'), ] operations = [ migrations.AddField( model_name='eventoelegibles', name='evento', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to='amcm.evento'), preserve_default=False, ), migrations.AlterField( model_name='credito', name='fecha_pago', field=models.DateField(blank=True, default=datetime.datetime(2022, 1, 4, 10, 37, 3, 977886, tzinfo=utc), null=True, verbose_name='Fecha de pago'), ), migrations.AlterField( model_name='credito', name='fecha_registro', field=models.DateField(default=datetime.datetime(2022, 1, 4, 10, 37, 3, 977861, tzinfo=utc), verbose_name='Fecha de registro'), ), migrations.AlterField( model_name='cuentaspago', name='fecha_registro', field=models.DateField(default=datetime.datetime(2022, 1, 4, 10, 37, 3, 961284, tzinfo=utc), verbose_name='Fecha de Registro'), ), migrations.AlterField( model_name='elegible', name='fecha_registro', field=models.DateField(default=datetime.datetime(2022, 1, 4, 10, 37, 3, 962608, tzinfo=utc), verbose_name='Fecha de registro'), ), migrations.AlterField( model_name='pago', name='fechaPago', field=models.DateField(blank=True, default=datetime.datetime(2022, 1, 4, 10, 37, 3, 959833, tzinfo=utc), null=True, verbose_name='Fecha del Pago'), ), migrations.AlterField( model_name='pago', name='fechaRegistro', field=models.DateField(default=datetime.datetime(2022, 1, 4, 10, 37, 3, 959863, tzinfo=utc), verbose_name='Fecha de Registro'), ), migrations.AlterField( model_name='recibo', name='fecha_registro', field=models.DateField(default=datetime.datetime(2022, 1, 4, 10, 37, 3, 976856, tzinfo=utc), verbose_name='Fecha de registro'), ), ]

true

790da6f58ba37152bebfc637af2c6cf00f701207

1,715

py

Python

gautools/__init__.py

thompcinnamon/QM-calc-scripts

60b06e14b2efd307d419201079bb24152ab0bd3c

[ "Apache-2.0" ]

null

gautools/__init__.py

thompcinnamon/QM-calc-scripts

60b06e14b2efd307d419201079bb24152ab0bd3c

[ "Apache-2.0" ]

2

2018-07-18T19:53:08.000Z

2019-02-25T23:25:51.000Z

gautools/__init__.py

theavey/QM-calc-scripts

60b06e14b2efd307d419201079bb24152ab0bd3c

[ "Apache-2.0" ]

1

2017-01-04T20:50:21.000Z

"""This is a set of tools built up over time for working with Gaussian and QChem input and output.""" ######################################################################## # # # # # This script was written by Thomas Heavey in 2017. # # theavey@bu.edu thomasjheavey@gmail.com # # # # Copyright 2017 Thomas J. Heavey IV # # # # Licensed under the Apache License, Version 2.0 (the "License"); # # you may not use this file except in compliance with the License. # # You may obtain a copy of the License at # # # # http://www.apache.org/licenses/LICENSE-2.0 # # # # Unless required by applicable law or agreed to in writing, software # # distributed under the License is distributed on an "AS IS" BASIS, # # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # # implied. # # See the License for the specific language governing permissions and # # limitations under the License. # # # ######################################################################## pass

61.25

74

0.348105

true

790da99041289e9e42219eef39722619d6f10b57

3,222

py

Python

stanCode_Projects/weather_master/weather_master.py

clairejrlin/stanCode_projects

452a93f9db2de610d0580faecca80b3c3d311395

[ "MIT" ]

null

stanCode_Projects/weather_master/weather_master.py

clairejrlin/stanCode_projects

452a93f9db2de610d0580faecca80b3c3d311395

[ "MIT" ]

null

stanCode_Projects/weather_master/weather_master.py

clairejrlin/stanCode_projects

452a93f9db2de610d0580faecca80b3c3d311395

[ "MIT" ]

null

""" File: weather_master.py Name: Claire Lin ----------------------- This program should implement a console program that asks weather data from user to compute the average, highest, lowest, cold days among the inputs. Output format should match what is shown in the sample run in the Assignment 2 Handout. """ EXIT = -100 def main(): """ To find the highest and lowest temperature, cold days and the average. """ print('stanCode \"Weather Master 4.0\"!') # my friend told me the maximum and minimum variable can set like this. maximum = -100000000 minimum = 100000000 total = 0 count = 0 cold_day = 0 while True: temperature = int(input('Next Temperature: (or '+str(EXIT) + ' to quit)? ')) # To jump out from the program when no temperature were entered. if temperature == EXIT and count == 0: print('No temperatures were entered.') break # To exclude the temperature not exist. if temperature > 90 or temperature < -100: print('>>> The temperature \"'+str(temperature)+'\" not exist, so we exclude and stop it.') break if temperature == EXIT: break else: count += 1 # count the total days. if temperature < 16: cold_day += 1 # count the cold days which temperature below 16. total += temperature # To plus all temperature. if temperature > maximum: maximum = temperature if temperature < minimum: minimum = temperature else: pass if count != 0: avg = total / count print("") print('Highest temperature = ' + str(maximum)) print('Lowest temperature = ' + str(minimum)) print('Average = '+str(avg)) print(str(cold_day) + ' cold day(s)') # For checking # print(total) # print(count) """ My note: This is the first try, when I debug I found the calculation logic is wrong. The first variable I type will disappear when it enter into the while loop. And the count of total days would include the EXIT constant. """ # if temperature == EXIT: # print('No temperatures were entered.') # # else: # while True: # # if temperature < 16: # # cold_day += 1 # # temperature = int(input('Next Temperature: (or '+str(EXIT) + ' to quit)? ')) # # # count the total days. # count += 1 # # if temperature == EXIT: # break # # total += temperature # if temperature > maximum: # maximum = temperature # elif temperature < minimum: # minimum = temperature # else: # pass # # avg = total / count # print('Highest temperature = ' + str(maximum)) # print('Lowest temperature = ' + str(minimum)) # print('Average = '+str(avg)) # print(str(cold_day) + ' cold day(s)') ###### DO NOT EDIT CODE BELOW THIS LINE ###### if __name__ == "__main__": main()

28.513274

103

0.543451

EXIT = -100 def main(): print('stanCode \"Weather Master 4.0\"!') maximum = -100000000 minimum = 100000000 total = 0 count = 0 cold_day = 0 while True: temperature = int(input('Next Temperature: (or '+str(EXIT) + ' to quit)? ')) if temperature == EXIT and count == 0: print('No temperatures were entered.') break if temperature > 90 or temperature < -100: print('>>> The temperature \"'+str(temperature)+'\" not exist, so we exclude and stop it.') break if temperature == EXIT: break else: count += 1 if temperature < 16: cold_day += 1 total += temperature if temperature > maximum: maximum = temperature if temperature < minimum: minimum = temperature else: pass if count != 0: avg = total / count print("") print('Highest temperature = ' + str(maximum)) print('Lowest temperature = ' + str(minimum)) print('Average = '+str(avg)) print(str(cold_day) + ' cold day(s)')

true

790da9f977709c1b0764594562bf1b2cb0f52777

9,682

py

Python

tracker/tracker/user_tracker.py

PuffyPuffin/LO_user

c7cafc2045b027aad0098d034cbe2b70126c8379

[ "MIT" ]

null

tracker/tracker/user_tracker.py

PuffyPuffin/LO_user

c7cafc2045b027aad0098d034cbe2b70126c8379

[ "MIT" ]

null

tracker/tracker/user_tracker.py

PuffyPuffin/LO_user

c7cafc2045b027aad0098d034cbe2b70126c8379

[ "MIT" ]

null

""" Code for particle tracking, designed for ROMS output. This new version makes extensive use of nearest-neighbor KDTree algorithms for interpolation. This results is significantly (36x) faster runtimes compared with old version. PERFORMANCE: about 3 minutes per day for a 3D cas6 experiment with 10k particles. NOTE: You have to have run make_KDTrees.py for the grid (e.g. cas6) before running. NOTE: There is some issue, perhaps with garbage collection, which causes the loading of NetCDF files to happen slower after running a few times interactively from ipython. It appears that this can be avoided by running from the terminal as: python tracker.py [args]. This program is a driver where you specify: - an experiment (ROMS run + release locations + other choices) - a release or set of releases within that experiment (start day, etc.) The main argument you provide is -exp, which is the experiment name, and is used by experiments.get_exp_info() and .get_ic() to get the gtagex and initial particle locations. Other possible commmand line arguments and their defaults are explained in the argparse section below. NOTE: To improve usefulness for people other than me, this driver will first look for: - LiveOcean_user/tracker/user_trackfun.py before loading my versions. This allows you to create your own modifications to the tracking (e.g. for diurnal depth behavior) while still being able to use git pull to update the main code. It can be run on its own, or with command line arguments to facilitate large, automated jobs, for example in python: Examples: python tracker.py -clb True the same command, with all the argmuents typed, instead of getting the as defaults: python tracker.py -gtx cas6_v3_lo8b -ro 2 -d 2019.07.04 -exp jdf0 -clb True """ import sys from datetime import datetime, timedelta from time import time import argparse import numpy as np from lo_tools import Lfun, zfun Ldir = Lfun.Lstart() from importlib import reload pth = Ldir['LOu'] / 'tracker' if str(pth) not in sys.path: sys.path.append(str(pth)) import experiments as exp reload(exp) import trackfun_nc as tfnc reload(tfnc) # The import of trackfun or user_trackfun is done later in this program, # about 100 lines down. # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # command line arguments, can be input in any order parser = argparse.ArgumentParser() # Set the experiment name # (details set in experiments.py, or, if it exists, user_experiments.py) parser.add_argument('-gtx', '--gtagex', default='cas6_v0_live', type=str) parser.add_argument('-ro', '--roms_out_num', default=2, type=int) # 1 = Ldir['roms_out1'], etc. # this is the first starting day parser.add_argument('-d', '--date_string', default='2021.10.15', type=str) parser.add_argument('-exp', '--exp_name', default='elb', type=str) parser.add_argument('-clb', '--clobber', default=False, type=zfun.boolean_string) # overwrite existing output folder if clobber == True parser.add_argument('-sub_tag', default='', type=str) # append an optional tag to the end of the output folder name # These are False unless the flags are used with the argument True # so if you do NOT use these flags the run will be: # - trapped to the surface # - no vertical turbulent diffusion parser.add_argument('-3d', default=False, type=zfun.boolean_string) # do 3d tracking parser.add_argument('-laminar', default=False, type=zfun.boolean_string) # no turbulence parser.add_argument('-no_advection', default=False, type=zfun.boolean_string) # no advection parser.add_argument('-sink', default=0, type=float) # particle sinking speed (m per day, e.g. 40) # windage = a small number: 0 <= windage << 1 (e.g. 0.03) # fraction of windspeed added to advection, only for 3d=False parser.add_argument('-wnd', '--windage', default=0, type=float) # You can make multiple releases using: # number_of_start_days > 1 & days_between_starts, and which hour (UTC) to start on parser.add_argument('-nsd', '--number_of_start_days', default=1, type=int) parser.add_argument('-dbs', '--days_between_starts', default=1, type=int) parser.add_argument('-dtt', '--days_to_track', default=1, type=int) parser.add_argument('-sh', '--start_hour', default=0, type=int) # number of divisions to make between saves for the integration # e.g. if ndiv = 12 and we have hourly saves, we use a 300 sec step # for the integration. 300 s seems like a good default value, # based on Banas et al. (2009, CSR RISE paper). parser.add_argument('-ndiv', default=12, type=int) parser.add_argument('-sph', default=1, type=int) # sph = saves per hour, a new argument to allow more frequent writing of output. args = parser.parse_args() TR = args.__dict__ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # set where to look for model output if args.roms_out_num == 0: TR['roms_out'] = Ldir['roms_out'] elif args.roms_out_num > 0: TR['roms_out'] = Ldir['roms_out' + str(args.roms_out_num)] # set dependent and default fields TR['turb'] = False # make sure sph is no greater than ndiv TR['sph'] = np.min((TR['sph'],TR['ndiv'])) # overrides if TR['3d']: TR['windage'] = 0 TR['turb'] = True # default is that 3d is always turbulent if TR['laminar']: TR['turb'] = False # get experiment info TR['gridname'], TR['tag'], TR['ex_name'] = TR['gtagex'].split('_') # pass some info to Ldir Ldir['gtagex'] = TR['gtagex'] Ldir['roms_out'] = TR['roms_out'] # get the full path to a valid history file fn00 = Ldir['roms_out'] / TR['gtagex'] / ('f' + TR['date_string']) / 'ocean_his_0001.nc' TR['fn00'] = fn00 # set the name of the output folder out_name = TR['exp_name'] # modify the output folder name, based on other choices if TR['3d']: out_name += '_3d' elif not TR['3d']: out_name += '_surf' if TR['laminar']: out_name += '_laminar' if TR['windage'] > 0: out_name += '_wind' + str(int(100*TR['windage'])) if TR['start_hour'] > 0: out_name += '_sh' + str(int(TR['start_hour'])) if TR['sink'] > 0: out_name += '_sink' + str(int(TR['sink'])) if TR['no_advection'] == True: out_name += '_nadv' if TR['ndiv'] != 12: # only mention ndiv if it is NOT 12 out_name += '_ndiv' + str(TR['ndiv']) if len(TR['sub_tag']) > 0: out_name += '_' + TR['sub_tag'] # make the list of start days (datetimes) for separate releases idt_list = [] dt = datetime.strptime(TR['date_string'], '%Y.%m.%d') for nic in range(TR['number_of_start_days']): idt_list.append(dt) dt = dt + timedelta(TR['days_between_starts']) # make the output directory (empty) outdir0 = Ldir['LOo'] / 'tracks' outdir1 = out_name outdir = outdir0 / outdir1 if outdir.is_dir(): if args.clobber: pass # continue and overwrite if clobber is True else: print('Warning: output directory exists - rename if you want to keep it!!') print('-- tracker run not started --') sys.exit() Lfun.make_dir(outdir, clean=True) print(50*'*' + '\nWriting to ' + str(outdir)) sys.stdout.flush() # Write some info to outdir0 for use by trackfun.py Lfun.dict_to_csv(TR, outdir0 / 'exp_info.csv') # and write the same info to outdir as part of the archived run output Lfun.dict_to_csv(TR, outdir / 'exp_info.csv') # Load the trackfun module. # NOTE: we have to load this module AFTER we write [outdir0]/exp_info.csv # because it uses that information to decide which KDTrees to load. Crude. if (Ldir['LOu'] / 'tracker' / 'user_trackfun.py').is_file(): sys.path.append(str(Ldir['LOu'] / 'tracker')) import user_trackfun as tfun else: import trackfun as tfun reload(tfun) # get the initial particle location vectors EI = exp.get_exp_info(TR['exp_name']) plon00, plat00, pcs00 = exp.get_ic(EI, TR['fn00']) # step through the releases, one for each start day write_grid = True for idt0 in idt_list: tt0 = time() # monitor integration time # name the release file by start day idt0_str = datetime.strftime(idt0,'%Y.%m.%d') outname = ('release_' + idt0_str + '.nc') print('-- ' + outname) sys.stdout.flush() out_fn = outdir / outname # we do the calculation in one-day segments, but write complete # output for a release to a single NetCDF file. for nd in range(TR['days_to_track']): # get or replace the history file list for this day idt = idt0 + timedelta(days=nd) idt_str = datetime.strftime(idt,'%Y.%m.%d') print(' - working on ' + idt_str) sys.stdout.flush() fn_list = tfun.get_fn_list(idt, Ldir) # write the grid file (once per experiment) for plotting if write_grid == True: g_infile = fn_list[0] g_outfile = outdir / 'grid.nc' tfnc.write_grid(g_infile, g_outfile) write_grid = False # DO THE TRACKING if nd == 0: # first day # set IC plon0 = plon00.copy() plat0 = plat00.copy() pcs0 = pcs00.copy() # do the tracking if TR['start_hour'] > 0: fn_list = fn_list[TR['start_hour']:] P = tfun.get_tracks(fn_list, plon0, plat0, pcs0, TR, trim_loc=True) # save the results to NetCDF tfnc.start_outfile(out_fn, P) else: # subsequent days # set IC plon0 = P['lon'][-1,:] plat0 = P['lat'][-1,:] pcs0 = P['cs'][-1,:] # do the tracking P = tfun.get_tracks(fn_list, plon0, plat0, pcs0, TR) tfnc.append_to_outfile(out_fn, P) print(' - Took %0.1f sec for %s day(s)' % (time() - tt0, str(TR['days_to_track']))) print(50*'=') print(50*'*' + '\nWrote to ' + str(outdir))

35.465201

97

0.673001

import sys from datetime import datetime, timedelta from time import time import argparse import numpy as np from lo_tools import Lfun, zfun Ldir = Lfun.Lstart() from importlib import reload pth = Ldir['LOu'] / 'tracker' if str(pth) not in sys.path: sys.path.append(str(pth)) import experiments as exp reload(exp) import trackfun_nc as tfnc reload(tfnc) parser = argparse.ArgumentParser() parser.add_argument('-gtx', '--gtagex', default='cas6_v0_live', type=str) parser.add_argument('-ro', '--roms_out_num', default=2, type=int) parser.add_argument('-d', '--date_string', default='2021.10.15', type=str) parser.add_argument('-exp', '--exp_name', default='elb', type=str) parser.add_argument('-clb', '--clobber', default=False, type=zfun.boolean_string) parser.add_argument('-sub_tag', default='', type=str) parser.add_argument('-3d', default=False, type=zfun.boolean_string) parser.add_argument('-laminar', default=False, type=zfun.boolean_string) parser.add_argument('-no_advection', default=False, type=zfun.boolean_string) parser.add_argument('-sink', default=0, type=float) parser.add_argument('-wnd', '--windage', default=0, type=float) parser.add_argument('-nsd', '--number_of_start_days', default=1, type=int) parser.add_argument('-dbs', '--days_between_starts', default=1, type=int) parser.add_argument('-dtt', '--days_to_track', default=1, type=int) parser.add_argument('-sh', '--start_hour', default=0, type=int) parser.add_argument('-ndiv', default=12, type=int) parser.add_argument('-sph', default=1, type=int) args = parser.parse_args() TR = args.__dict__ if args.roms_out_num == 0: TR['roms_out'] = Ldir['roms_out'] elif args.roms_out_num > 0: TR['roms_out'] = Ldir['roms_out' + str(args.roms_out_num)] TR['turb'] = False TR['sph'] = np.min((TR['sph'],TR['ndiv'])) if TR['3d']: TR['windage'] = 0 TR['turb'] = True if TR['laminar']: TR['turb'] = False TR['gridname'], TR['tag'], TR['ex_name'] = TR['gtagex'].split('_') Ldir['gtagex'] = TR['gtagex'] Ldir['roms_out'] = TR['roms_out'] fn00 = Ldir['roms_out'] / TR['gtagex'] / ('f' + TR['date_string']) / 'ocean_his_0001.nc' TR['fn00'] = fn00 out_name = TR['exp_name'] if TR['3d']: out_name += '_3d' elif not TR['3d']: out_name += '_surf' if TR['laminar']: out_name += '_laminar' if TR['windage'] > 0: out_name += '_wind' + str(int(100*TR['windage'])) if TR['start_hour'] > 0: out_name += '_sh' + str(int(TR['start_hour'])) if TR['sink'] > 0: out_name += '_sink' + str(int(TR['sink'])) if TR['no_advection'] == True: out_name += '_nadv' if TR['ndiv'] != 12: out_name += '_ndiv' + str(TR['ndiv']) if len(TR['sub_tag']) > 0: out_name += '_' + TR['sub_tag'] idt_list = [] dt = datetime.strptime(TR['date_string'], '%Y.%m.%d') for nic in range(TR['number_of_start_days']): idt_list.append(dt) dt = dt + timedelta(TR['days_between_starts']) outdir0 = Ldir['LOo'] / 'tracks' outdir1 = out_name outdir = outdir0 / outdir1 if outdir.is_dir(): if args.clobber: pass else: print('Warning: output directory exists - rename if you want to keep it!!') print('-- tracker run not started --') sys.exit() Lfun.make_dir(outdir, clean=True) print(50*'*' + '\nWriting to ' + str(outdir)) sys.stdout.flush() Lfun.dict_to_csv(TR, outdir0 / 'exp_info.csv') Lfun.dict_to_csv(TR, outdir / 'exp_info.csv') if (Ldir['LOu'] / 'tracker' / 'user_trackfun.py').is_file(): sys.path.append(str(Ldir['LOu'] / 'tracker')) import user_trackfun as tfun else: import trackfun as tfun reload(tfun) EI = exp.get_exp_info(TR['exp_name']) plon00, plat00, pcs00 = exp.get_ic(EI, TR['fn00']) write_grid = True for idt0 in idt_list: tt0 = time() idt0_str = datetime.strftime(idt0,'%Y.%m.%d') outname = ('release_' + idt0_str + '.nc') print('-- ' + outname) sys.stdout.flush() out_fn = outdir / outname for nd in range(TR['days_to_track']): idt = idt0 + timedelta(days=nd) idt_str = datetime.strftime(idt,'%Y.%m.%d') print(' - working on ' + idt_str) sys.stdout.flush() fn_list = tfun.get_fn_list(idt, Ldir) if write_grid == True: g_infile = fn_list[0] g_outfile = outdir / 'grid.nc' tfnc.write_grid(g_infile, g_outfile) write_grid = False if nd == 0: plon0 = plon00.copy() plat0 = plat00.copy() pcs0 = pcs00.copy() if TR['start_hour'] > 0: fn_list = fn_list[TR['start_hour']:] P = tfun.get_tracks(fn_list, plon0, plat0, pcs0, TR, trim_loc=True) tfnc.start_outfile(out_fn, P) else: plon0 = P['lon'][-1,:] plat0 = P['lat'][-1,:] pcs0 = P['cs'][-1,:] P = tfun.get_tracks(fn_list, plon0, plat0, pcs0, TR) tfnc.append_to_outfile(out_fn, P) print(' - Took %0.1f sec for %s day(s)' % (time() - tt0, str(TR['days_to_track']))) print(50*'=') print(50*'*' + '\nWrote to ' + str(outdir))

true

790daa7e42b3981224910e6c988de58eb9912933

38,591

py

Python

pandas/core/base.py

BryanRacic/pandas

21c299194a2b59a715fa7264bd6b44787deafc7a

[ "BSD-3-Clause" ]

null

pandas/core/base.py

BryanRacic/pandas

21c299194a2b59a715fa7264bd6b44787deafc7a

[ "BSD-3-Clause" ]

null

pandas/core/base.py

BryanRacic/pandas

21c299194a2b59a715fa7264bd6b44787deafc7a

[ "BSD-3-Clause" ]

null

""" Base and utility classes for pandas objects. """ from __future__ import annotations import textwrap from typing import ( TYPE_CHECKING, Any, Generic, Hashable, Literal, TypeVar, cast, final, ) import numpy as np import pandas._libs.lib as lib from pandas._typing import ( ArrayLike, DtypeObj, FrameOrSeries, IndexLabel, Shape, npt, ) from pandas.compat import PYPY from pandas.compat.numpy import function as nv from pandas.errors import AbstractMethodError from pandas.util._decorators import ( cache_readonly, doc, ) from pandas.core.dtypes.common import ( is_categorical_dtype, is_dict_like, is_extension_array_dtype, is_object_dtype, is_scalar, ) from pandas.core.dtypes.generic import ( ABCDataFrame, ABCIndex, ABCSeries, ) from pandas.core.dtypes.missing import ( isna, remove_na_arraylike, ) from pandas.core import ( algorithms, ops, ) from pandas.core.accessor import DirNamesMixin from pandas.core.algorithms import ( duplicated, unique1d, value_counts, ) from pandas.core.arraylike import OpsMixin from pandas.core.arrays import ExtensionArray from pandas.core.construction import ( create_series_with_explicit_dtype, ensure_wrapped_if_datetimelike, extract_array, ) import pandas.core.nanops as nanops if TYPE_CHECKING: from pandas._typing import ( NumpySorter, NumpyValueArrayLike, ) from pandas import Categorical _shared_docs: dict[str, str] = {} _indexops_doc_kwargs = { "klass": "IndexOpsMixin", "inplace": "", "unique": "IndexOpsMixin", "duplicated": "IndexOpsMixin", } _T = TypeVar("_T", bound="IndexOpsMixin") class PandasObject(DirNamesMixin): """ Baseclass for various pandas objects. """ # results from calls to methods decorated with cache_readonly get added to _cache _cache: dict[str, Any] @property def _constructor(self): """ Class constructor (for this class it's just `__class__`. """ return type(self) def __repr__(self) -> str: """ Return a string representation for a particular object. """ # Should be overwritten by base classes return object.__repr__(self) def _reset_cache(self, key: str | None = None) -> None: """ Reset cached properties. If ``key`` is passed, only clears that key. """ if not hasattr(self, "_cache"): return if key is None: self._cache.clear() else: self._cache.pop(key, None) def __sizeof__(self) -> int: """ Generates the total memory usage for an object that returns either a value or Series of values """ memory_usage = getattr(self, "memory_usage", None) if memory_usage: mem = memory_usage(deep=True) return int(mem if is_scalar(mem) else mem.sum()) # no memory_usage attribute, so fall back to object's 'sizeof' return super().__sizeof__() class NoNewAttributesMixin: """ Mixin which prevents adding new attributes. Prevents additional attributes via xxx.attribute = "something" after a call to `self.__freeze()`. Mainly used to prevent the user from using wrong attributes on an accessor (`Series.cat/.str/.dt`). If you really want to add a new attribute at a later time, you need to use `object.__setattr__(self, key, value)`. """ def _freeze(self): """ Prevents setting additional attributes. """ object.__setattr__(self, "__frozen", True) # prevent adding any attribute via s.xxx.new_attribute = ... def __setattr__(self, key: str, value): # _cache is used by a decorator # We need to check both 1.) cls.__dict__ and 2.) getattr(self, key) # because # 1.) getattr is false for attributes that raise errors # 2.) cls.__dict__ doesn't traverse into base classes if getattr(self, "__frozen", False) and not ( key == "_cache" or key in type(self).__dict__ or getattr(self, key, None) is not None ): raise AttributeError(f"You cannot add any new attribute '{key}'") object.__setattr__(self, key, value) class DataError(Exception): pass class SpecificationError(Exception): pass class SelectionMixin(Generic[FrameOrSeries]): """ mixin implementing the selection & aggregation interface on a group-like object sub-classes need to define: obj, exclusions """ obj: FrameOrSeries _selection: IndexLabel | None = None exclusions: frozenset[Hashable] _internal_names = ["_cache", "__setstate__"] _internal_names_set = set(_internal_names) @final @property def _selection_list(self): if not isinstance( self._selection, (list, tuple, ABCSeries, ABCIndex, np.ndarray) ): return [self._selection] return self._selection @cache_readonly def _selected_obj(self): if self._selection is None or isinstance(self.obj, ABCSeries): return self.obj else: return self.obj[self._selection] @final @cache_readonly def ndim(self) -> int: return self._selected_obj.ndim @final @cache_readonly def _obj_with_exclusions(self): if self._selection is not None and isinstance(self.obj, ABCDataFrame): return self.obj[self._selection_list] if len(self.exclusions) > 0: # equivalent to `self.obj.drop(self.exclusions, axis=1) # but this avoids consolidating and making a copy return self.obj._drop_axis( self.exclusions, axis=1, consolidate=False, only_slice=True ) else: return self.obj def __getitem__(self, key): if self._selection is not None: raise IndexError(f"Column(s) {self._selection} already selected") if isinstance(key, (list, tuple, ABCSeries, ABCIndex, np.ndarray)): if len(self.obj.columns.intersection(key)) != len(key): bad_keys = list(set(key).difference(self.obj.columns)) raise KeyError(f"Columns not found: {str(bad_keys)[1:-1]}") return self._gotitem(list(key), ndim=2) elif not getattr(self, "as_index", False): if key not in self.obj.columns: raise KeyError(f"Column not found: {key}") return self._gotitem(key, ndim=2) else: if key not in self.obj: raise KeyError(f"Column not found: {key}") subset = self.obj[key] ndim = subset.ndim return self._gotitem(key, ndim=ndim, subset=subset) def _gotitem(self, key, ndim: int, subset=None): """ sub-classes to define return a sliced object Parameters ---------- key : str / list of selections ndim : {1, 2} requested ndim of result subset : object, default None subset to act on """ raise AbstractMethodError(self) def aggregate(self, func, *args, **kwargs): raise AbstractMethodError(self) agg = aggregate class IndexOpsMixin(OpsMixin): """ Common ops mixin to support a unified interface / docs for Series / Index """ # ndarray compatibility __array_priority__ = 1000 _hidden_attrs: frozenset[str] = frozenset( ["tolist"] # tolist is not deprecated, just suppressed in the __dir__ ) @property def dtype(self) -> DtypeObj: # must be defined here as a property for mypy raise AbstractMethodError(self) @property def _values(self) -> ExtensionArray | np.ndarray: # must be defined here as a property for mypy raise AbstractMethodError(self) def transpose(self: _T, *args, **kwargs) -> _T: """ Return the transpose, which is by definition self. Returns ------- %(klass)s """ nv.validate_transpose(args, kwargs) return self T = property( transpose, doc=""" Return the transpose, which is by definition self. """, ) @property def shape(self) -> Shape: """ Return a tuple of the shape of the underlying data. """ return self._values.shape def __len__(self) -> int: # We need this defined here for mypy raise AbstractMethodError(self) @property def ndim(self) -> int: """ Number of dimensions of the underlying data, by definition 1. """ return 1 def item(self): """ Return the first element of the underlying data as a Python scalar. Returns ------- scalar The first element of %(klass)s. Raises ------ ValueError If the data is not length-1. """ if len(self) == 1: return next(iter(self)) raise ValueError("can only convert an array of size 1 to a Python scalar") @property def nbytes(self) -> int: """ Return the number of bytes in the underlying data. """ return self._values.nbytes @property def size(self) -> int: """ Return the number of elements in the underlying data. """ return len(self._values) @property def array(self) -> ExtensionArray: """ The ExtensionArray of the data backing this Series or Index. Returns ------- ExtensionArray An ExtensionArray of the values stored within. For extension types, this is the actual array. For NumPy native types, this is a thin (no copy) wrapper around :class:`numpy.ndarray`. ``.array`` differs ``.values`` which may require converting the data to a different form. See Also -------- Index.to_numpy : Similar method that always returns a NumPy array. Series.to_numpy : Similar method that always returns a NumPy array. Notes ----- This table lays out the different array types for each extension dtype within pandas. ================== ============================= dtype array type ================== ============================= category Categorical period PeriodArray interval IntervalArray IntegerNA IntegerArray string StringArray boolean BooleanArray datetime64[ns, tz] DatetimeArray ================== ============================= For any 3rd-party extension types, the array type will be an ExtensionArray. For all remaining dtypes ``.array`` will be a :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray stored within. If you absolutely need a NumPy array (possibly with copying / coercing data), then use :meth:`Series.to_numpy` instead. Examples -------- For regular NumPy types like int, and float, a PandasArray is returned. >>> pd.Series([1, 2, 3]).array <PandasArray> [1, 2, 3] Length: 3, dtype: int64 For extension types, like Categorical, the actual ExtensionArray is returned >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a'])) >>> ser.array ['a', 'b', 'a'] Categories (2, object): ['a', 'b'] """ raise AbstractMethodError(self) def to_numpy( self, dtype: npt.DTypeLike | None = None, copy: bool = False, na_value=lib.no_default, **kwargs, ) -> np.ndarray: """ A NumPy ndarray representing the values in this Series or Index. Parameters ---------- dtype : str or numpy.dtype, optional The dtype to pass to :meth:`numpy.asarray`. copy : bool, default False Whether to ensure that the returned value is not a view on another array. Note that ``copy=False`` does not *ensure* that ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that a copy is made, even if not strictly necessary. na_value : Any, optional The value to use for missing values. The default value depends on `dtype` and the type of the array. .. versionadded:: 1.0.0 **kwargs Additional keywords passed through to the ``to_numpy`` method of the underlying array (for extension arrays). .. versionadded:: 1.0.0 Returns ------- numpy.ndarray See Also -------- Series.array : Get the actual data stored within. Index.array : Get the actual data stored within. DataFrame.to_numpy : Similar method for DataFrame. Notes ----- The returned array will be the same up to equality (values equal in `self` will be equal in the returned array; likewise for values that are not equal). When `self` contains an ExtensionArray, the dtype may be different. For example, for a category-dtype Series, ``to_numpy()`` will return a NumPy array and the categorical dtype will be lost. For NumPy dtypes, this will be a reference to the actual data stored in this Series or Index (assuming ``copy=False``). Modifying the result in place will modify the data stored in the Series or Index (not that we recommend doing that). For extension types, ``to_numpy()`` *may* require copying data and coercing the result to a NumPy type (possibly object), which may be expensive. When you need a no-copy reference to the underlying data, :attr:`Series.array` should be used instead. This table lays out the different dtypes and default return types of ``to_numpy()`` for various dtypes within pandas. ================== ================================ dtype array type ================== ================================ category[T] ndarray[T] (same dtype as input) period ndarray[object] (Periods) interval ndarray[object] (Intervals) IntegerNA ndarray[object] datetime64[ns] datetime64[ns] datetime64[ns, tz] ndarray[object] (Timestamps) ================== ================================ Examples -------- >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a'])) >>> ser.to_numpy() array(['a', 'b', 'a'], dtype=object) Specify the `dtype` to control how datetime-aware data is represented. Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp` objects, each with the correct ``tz``. >>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET")) >>> ser.to_numpy(dtype=object) array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'), Timestamp('2000-01-02 00:00:00+0100', tz='CET')], dtype=object) Or ``dtype='datetime64[ns]'`` to return an ndarray of native datetime64 values. The values are converted to UTC and the timezone info is dropped. >>> ser.to_numpy(dtype="datetime64[ns]") ... # doctest: +ELLIPSIS array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'], dtype='datetime64[ns]') """ if is_extension_array_dtype(self.dtype): # error: Too many arguments for "to_numpy" of "ExtensionArray" return self.array.to_numpy( # type: ignore[call-arg] dtype, copy=copy, na_value=na_value, **kwargs ) elif kwargs: bad_keys = list(kwargs.keys())[0] raise TypeError( f"to_numpy() got an unexpected keyword argument '{bad_keys}'" ) result = np.asarray(self._values, dtype=dtype) # TODO(GH-24345): Avoid potential double copy if copy or na_value is not lib.no_default: result = result.copy() if na_value is not lib.no_default: result[self.isna()] = na_value return result @property def empty(self) -> bool: return not self.size def max(self, axis=None, skipna: bool = True, *args, **kwargs): """ Return the maximum value of the Index. Parameters ---------- axis : int, optional For compatibility with NumPy. Only 0 or None are allowed. skipna : bool, default True Exclude NA/null values when showing the result. *args, **kwargs Additional arguments and keywords for compatibility with NumPy. Returns ------- scalar Maximum value. See Also -------- Index.min : Return the minimum value in an Index. Series.max : Return the maximum value in a Series. DataFrame.max : Return the maximum values in a DataFrame. Examples -------- >>> idx = pd.Index([3, 2, 1]) >>> idx.max() 3 >>> idx = pd.Index(['c', 'b', 'a']) >>> idx.max() 'c' For a MultiIndex, the maximum is determined lexicographically. >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)]) >>> idx.max() ('b', 2) """ nv.validate_minmax_axis(axis) nv.validate_max(args, kwargs) return nanops.nanmax(self._values, skipna=skipna) @doc(op="max", oppose="min", value="largest") def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int: """ Return int position of the {value} value in the Series. If the {op}imum is achieved in multiple locations, the first row position is returned. Parameters ---------- axis : {{None}} Dummy argument for consistency with Series. skipna : bool, default True Exclude NA/null values when showing the result. *args, **kwargs Additional arguments and keywords for compatibility with NumPy. Returns ------- int Row position of the {op}imum value. See Also -------- Series.arg{op} : Return position of the {op}imum value. Series.arg{oppose} : Return position of the {oppose}imum value. numpy.ndarray.arg{op} : Equivalent method for numpy arrays. Series.idxmax : Return index label of the maximum values. Series.idxmin : Return index label of the minimum values. Examples -------- Consider dataset containing cereal calories >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0, ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}}) >>> s Corn Flakes 100.0 Almond Delight 110.0 Cinnamon Toast Crunch 120.0 Cocoa Puff 110.0 dtype: float64 >>> s.argmax() 2 >>> s.argmin() 0 The maximum cereal calories is the third element and the minimum cereal calories is the first element, since series is zero-indexed. """ delegate = self._values nv.validate_minmax_axis(axis) skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs) if isinstance(delegate, ExtensionArray): if not skipna and delegate.isna().any(): return -1 else: return delegate.argmax() else: # error: Incompatible return value type (got "Union[int, ndarray]", expected # "int") return nanops.nanargmax( # type: ignore[return-value] delegate, skipna=skipna ) def min(self, axis=None, skipna: bool = True, *args, **kwargs): """ Return the minimum value of the Index. Parameters ---------- axis : {None} Dummy argument for consistency with Series. skipna : bool, default True Exclude NA/null values when showing the result. *args, **kwargs Additional arguments and keywords for compatibility with NumPy. Returns ------- scalar Minimum value. See Also -------- Index.max : Return the maximum value of the object. Series.min : Return the minimum value in a Series. DataFrame.min : Return the minimum values in a DataFrame. Examples -------- >>> idx = pd.Index([3, 2, 1]) >>> idx.min() 1 >>> idx = pd.Index(['c', 'b', 'a']) >>> idx.min() 'a' For a MultiIndex, the minimum is determined lexicographically. >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)]) >>> idx.min() ('a', 1) """ nv.validate_minmax_axis(axis) nv.validate_min(args, kwargs) return nanops.nanmin(self._values, skipna=skipna) @doc(argmax, op="min", oppose="max", value="smallest") def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int: delegate = self._values nv.validate_minmax_axis(axis) skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs) if isinstance(delegate, ExtensionArray): if not skipna and delegate.isna().any(): return -1 else: return delegate.argmin() else: # error: Incompatible return value type (got "Union[int, ndarray]", expected # "int") return nanops.nanargmin( # type: ignore[return-value] delegate, skipna=skipna ) def tolist(self): """ Return a list of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) Returns ------- list See Also -------- numpy.ndarray.tolist : Return the array as an a.ndim-levels deep nested list of Python scalars. """ if not isinstance(self._values, np.ndarray): # check for ndarray instead of dtype to catch DTA/TDA return list(self._values) return self._values.tolist() to_list = tolist def __iter__(self): """ Return an iterator of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) Returns ------- iterator """ # We are explicitly making element iterators. if not isinstance(self._values, np.ndarray): # Check type instead of dtype to catch DTA/TDA return iter(self._values) else: return map(self._values.item, range(self._values.size)) @cache_readonly def hasnans(self) -> bool: """ Return if I have any nans; enables various perf speedups. """ return bool(isna(self).any()) def isna(self): return isna(self._values) def _reduce( self, op, name: str, *, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds, ): """ Perform the reduction type operation if we can. """ func = getattr(self, name, None) if func is None: raise TypeError( f"{type(self).__name__} cannot perform the operation {name}" ) return func(skipna=skipna, **kwds) @final def _map_values(self, mapper, na_action=None): """ An internal function that maps values using the input correspondence (which can be a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series The input correspondence object na_action : {None, 'ignore'} If 'ignore', propagate NA values, without passing them to the mapping function Returns ------- Union[Index, MultiIndex], inferred The output of the mapping function applied to the index. If the function returns a tuple with more than one element a MultiIndex will be returned. """ # we can fastpath dict/Series to an efficient map # as we know that we are not going to have to yield # python types if is_dict_like(mapper): if isinstance(mapper, dict) and hasattr(mapper, "__missing__"): # If a dictionary subclass defines a default value method, # convert mapper to a lookup function (GH #15999). dict_with_default = mapper mapper = lambda x: dict_with_default[x] else: # Dictionary does not have a default. Thus it's safe to # convert to an Series for efficiency. # we specify the keys here to handle the # possibility that they are tuples # The return value of mapping with an empty mapper is # expected to be pd.Series(np.nan, ...). As np.nan is # of dtype float64 the return value of this method should # be float64 as well mapper = create_series_with_explicit_dtype( mapper, dtype_if_empty=np.float64 ) if isinstance(mapper, ABCSeries): # Since values were input this means we came from either # a dict or a series and mapper should be an index if is_categorical_dtype(self.dtype): # use the built in categorical series mapper which saves # time by mapping the categories instead of all values cat = cast("Categorical", self._values) return cat.map(mapper) values = self._values indexer = mapper.index.get_indexer(values) new_values = algorithms.take_nd(mapper._values, indexer) return new_values # we must convert to python types if is_extension_array_dtype(self.dtype) and hasattr(self._values, "map"): # GH#23179 some EAs do not have `map` values = self._values if na_action is not None: raise NotImplementedError map_f = lambda values, f: values.map(f) else: values = self._values.astype(object) if na_action == "ignore": map_f = lambda values, f: lib.map_infer_mask( values, f, isna(values).view(np.uint8) ) elif na_action is None: map_f = lib.map_infer else: msg = ( "na_action must either be 'ignore' or None, " f"{na_action} was passed" ) raise ValueError(msg) # mapper is a function new_values = map_f(values, mapper) return new_values def value_counts( self, normalize: bool = False, sort: bool = True, ascending: bool = False, bins=None, dropna: bool = True, ): """ Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default. Parameters ---------- normalize : bool, default False If True then the object returned will contain the relative frequencies of the unique values. sort : bool, default True Sort by frequencies. ascending : bool, default False Sort in ascending order. bins : int, optional Rather than count values, group them into half-open bins, a convenience for ``pd.cut``, only works with numeric data. dropna : bool, default True Don't include counts of NaN. Returns ------- Series See Also -------- Series.count: Number of non-NA elements in a Series. DataFrame.count: Number of non-NA elements in a DataFrame. DataFrame.value_counts: Equivalent method on DataFrames. Examples -------- >>> index = pd.Index([3, 1, 2, 3, 4, np.nan]) >>> index.value_counts() 3.0 2 1.0 1 2.0 1 4.0 1 dtype: int64 With `normalize` set to `True`, returns the relative frequency by dividing all values by the sum of values. >>> s = pd.Series([3, 1, 2, 3, 4, np.nan]) >>> s.value_counts(normalize=True) 3.0 0.4 1.0 0.2 2.0 0.2 4.0 0.2 dtype: float64 **bins** Bins can be useful for going from a continuous variable to a categorical variable; instead of counting unique apparitions of values, divide the index in the specified number of half-open bins. >>> s.value_counts(bins=3) (0.996, 2.0] 2 (2.0, 3.0] 2 (3.0, 4.0] 1 dtype: int64 **dropna** With `dropna` set to `False` we can also see NaN index values. >>> s.value_counts(dropna=False) 3.0 2 1.0 1 2.0 1 4.0 1 NaN 1 dtype: int64 """ return value_counts( self, sort=sort, ascending=ascending, normalize=normalize, bins=bins, dropna=dropna, ) def unique(self): values = self._values if not isinstance(values, np.ndarray): result: ArrayLike = values.unique() if self.dtype.kind in ["m", "M"] and isinstance(self, ABCSeries): # GH#31182 Series._values returns EA, unpack for backward-compat if getattr(self.dtype, "tz", None) is None: result = np.asarray(result) else: result = unique1d(values) return result def nunique(self, dropna: bool = True) -> int: """ Return number of unique elements in the object. Excludes NA values by default. Parameters ---------- dropna : bool, default True Don't include NaN in the count. Returns ------- int See Also -------- DataFrame.nunique: Method nunique for DataFrame. Series.count: Count non-NA/null observations in the Series. Examples -------- >>> s = pd.Series([1, 3, 5, 7, 7]) >>> s 0 1 1 3 2 5 3 7 4 7 dtype: int64 >>> s.nunique() 4 """ uniqs = self.unique() if dropna: uniqs = remove_na_arraylike(uniqs) return len(uniqs) @property def is_unique(self) -> bool: """ Return boolean if values in the object are unique. Returns ------- bool """ return self.nunique(dropna=False) == len(self) @property def is_monotonic(self) -> bool: """ Return boolean if values in the object are monotonic_increasing. Returns ------- bool """ from pandas import Index return Index(self).is_monotonic @property def is_monotonic_increasing(self) -> bool: """ Alias for is_monotonic. """ # mypy complains if we alias directly return self.is_monotonic @property def is_monotonic_decreasing(self) -> bool: """ Return boolean if values in the object are monotonic_decreasing. Returns ------- bool """ from pandas import Index return Index(self).is_monotonic_decreasing def _memory_usage(self, deep: bool = False) -> int: """ Memory usage of the values. Parameters ---------- deep : bool, default False Introspect the data deeply, interrogate `object` dtypes for system-level memory consumption. Returns ------- bytes used See Also -------- numpy.ndarray.nbytes : Total bytes consumed by the elements of the array. Notes ----- Memory usage does not include memory consumed by elements that are not components of the array if deep=False or if used on PyPy """ if hasattr(self.array, "memory_usage"): # https://github.com/python/mypy/issues/1424 # error: "ExtensionArray" has no attribute "memory_usage" return self.array.memory_usage(deep=deep) # type: ignore[attr-defined] v = self.array.nbytes if deep and is_object_dtype(self) and not PYPY: values = cast(np.ndarray, self._values) v += lib.memory_usage_of_objects(values) return v @doc( algorithms.factorize, values="", order="", size_hint="", sort=textwrap.dedent( """\ sort : bool, default False Sort `uniques` and shuffle `codes` to maintain the relationship. """ ), ) def factorize(self, sort: bool = False, na_sentinel: int | None = -1): return algorithms.factorize(self, sort=sort, na_sentinel=na_sentinel) _shared_docs[ "searchsorted" ] = """ Find indices where elements should be inserted to maintain order. Find the indices into a sorted {klass} `self` such that, if the corresponding elements in `value` were inserted before the indices, the order of `self` would be preserved. .. note:: The {klass} *must* be monotonically sorted, otherwise wrong locations will likely be returned. Pandas does *not* check this for you. Parameters ---------- value : array-like or scalar Values to insert into `self`. side : {{'left', 'right'}}, optional If 'left', the index of the first suitable location found is given. If 'right', return the last such index. If there is no suitable index, return either 0 or N (where N is the length of `self`). sorter : 1-D array-like, optional Optional array of integer indices that sort `self` into ascending order. They are typically the result of ``np.argsort``. Returns ------- int or array of int A scalar or array of insertion points with the same shape as `value`. See Also -------- sort_values : Sort by the values along either axis. numpy.searchsorted : Similar method from NumPy. Notes ----- Binary search is used to find the required insertion points. Examples -------- >>> ser = pd.Series([1, 2, 3]) >>> ser 0 1 1 2 2 3 dtype: int64 >>> ser.searchsorted(4) 3 >>> ser.searchsorted([0, 4]) array([0, 3]) >>> ser.searchsorted([1, 3], side='left') array([0, 2]) >>> ser.searchsorted([1, 3], side='right') array([1, 3]) >>> ser = pd.Series(pd.to_datetime(['3/11/2000', '3/12/2000', '3/13/2000'])) >>> ser 0 2000-03-11 1 2000-03-12 2 2000-03-13 dtype: datetime64[ns] >>> ser.searchsorted('3/14/2000') 3 >>> ser = pd.Categorical( ... ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True ... ) >>> ser ['apple', 'bread', 'bread', 'cheese', 'milk'] Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk'] >>> ser.searchsorted('bread') 1 >>> ser.searchsorted(['bread'], side='right') array([3]) If the values are not monotonically sorted, wrong locations may be returned: >>> ser = pd.Series([2, 1, 3]) >>> ser 0 2 1 1 2 3 dtype: int64 >>> ser.searchsorted(1) # doctest: +SKIP 0 # wrong result, correct would be 1 """ @doc(_shared_docs["searchsorted"], klass="Index") def searchsorted( self, value: NumpyValueArrayLike, side: Literal["left", "right"] = "left", sorter: NumpySorter = None, ) -> npt.NDArray[np.intp] | np.intp: return algorithms.searchsorted(self._values, value, side=side, sorter=sorter) def drop_duplicates(self, keep="first"): duplicated = self._duplicated(keep=keep) # error: Value of type "IndexOpsMixin" is not indexable return self[~duplicated] # type: ignore[index] @final def _duplicated( self, keep: Literal["first", "last", False] = "first" ) -> npt.NDArray[np.bool_]: return duplicated(self._values, keep=keep) def _arith_method(self, other, op): res_name = ops.get_op_result_name(self, other) lvalues = self._values rvalues = extract_array(other, extract_numpy=True, extract_range=True) rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape) rvalues = ensure_wrapped_if_datetimelike(rvalues) with np.errstate(all="ignore"): result = ops.arithmetic_op(lvalues, rvalues, op) return self._construct_result(result, name=res_name) def _construct_result(self, result, name): """ Construct an appropriately-wrapped result from the ArrayLike result of an arithmetic-like operation. """ raise AbstractMethodError(self)

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from __future__ import annotations import textwrap from typing import ( TYPE_CHECKING, Any, Generic, Hashable, Literal, TypeVar, cast, final, ) import numpy as np import pandas._libs.lib as lib from pandas._typing import ( ArrayLike, DtypeObj, FrameOrSeries, IndexLabel, Shape, npt, ) from pandas.compat import PYPY from pandas.compat.numpy import function as nv from pandas.errors import AbstractMethodError from pandas.util._decorators import ( cache_readonly, doc, ) from pandas.core.dtypes.common import ( is_categorical_dtype, is_dict_like, is_extension_array_dtype, is_object_dtype, is_scalar, ) from pandas.core.dtypes.generic import ( ABCDataFrame, ABCIndex, ABCSeries, ) from pandas.core.dtypes.missing import ( isna, remove_na_arraylike, ) from pandas.core import ( algorithms, ops, ) from pandas.core.accessor import DirNamesMixin from pandas.core.algorithms import ( duplicated, unique1d, value_counts, ) from pandas.core.arraylike import OpsMixin from pandas.core.arrays import ExtensionArray from pandas.core.construction import ( create_series_with_explicit_dtype, ensure_wrapped_if_datetimelike, extract_array, ) import pandas.core.nanops as nanops if TYPE_CHECKING: from pandas._typing import ( NumpySorter, NumpyValueArrayLike, ) from pandas import Categorical _shared_docs: dict[str, str] = {} _indexops_doc_kwargs = { "klass": "IndexOpsMixin", "inplace": "", "unique": "IndexOpsMixin", "duplicated": "IndexOpsMixin", } _T = TypeVar("_T", bound="IndexOpsMixin") class PandasObject(DirNamesMixin): _cache: dict[str, Any] @property def _constructor(self): return type(self) def __repr__(self) -> str: return object.__repr__(self) def _reset_cache(self, key: str | None = None) -> None: if not hasattr(self, "_cache"): return if key is None: self._cache.clear() else: self._cache.pop(key, None) def __sizeof__(self) -> int: memory_usage = getattr(self, "memory_usage", None) if memory_usage: mem = memory_usage(deep=True) return int(mem if is_scalar(mem) else mem.sum()) return super().__sizeof__() class NoNewAttributesMixin: def _freeze(self): object.__setattr__(self, "__frozen", True) # prevent adding any attribute via s.xxx.new_attribute = ... def __setattr__(self, key: str, value): # _cache is used by a decorator # We need to check both 1.) cls.__dict__ and 2.) getattr(self, key) # because # 1.) getattr is false for attributes that raise errors # 2.) cls.__dict__ doesn't traverse into base classes if getattr(self, "__frozen", False) and not ( key == "_cache" or key in type(self).__dict__ or getattr(self, key, None) is not None ): raise AttributeError(f"You cannot add any new attribute '{key}'") object.__setattr__(self, key, value) class DataError(Exception): pass class SpecificationError(Exception): pass class SelectionMixin(Generic[FrameOrSeries]): obj: FrameOrSeries _selection: IndexLabel | None = None exclusions: frozenset[Hashable] _internal_names = ["_cache", "__setstate__"] _internal_names_set = set(_internal_names) @final @property def _selection_list(self): if not isinstance( self._selection, (list, tuple, ABCSeries, ABCIndex, np.ndarray) ): return [self._selection] return self._selection @cache_readonly def _selected_obj(self): if self._selection is None or isinstance(self.obj, ABCSeries): return self.obj else: return self.obj[self._selection] @final @cache_readonly def ndim(self) -> int: return self._selected_obj.ndim @final @cache_readonly def _obj_with_exclusions(self): if self._selection is not None and isinstance(self.obj, ABCDataFrame): return self.obj[self._selection_list] if len(self.exclusions) > 0: return self.obj._drop_axis( self.exclusions, axis=1, consolidate=False, only_slice=True ) else: return self.obj def __getitem__(self, key): if self._selection is not None: raise IndexError(f"Column(s) {self._selection} already selected") if isinstance(key, (list, tuple, ABCSeries, ABCIndex, np.ndarray)): if len(self.obj.columns.intersection(key)) != len(key): bad_keys = list(set(key).difference(self.obj.columns)) raise KeyError(f"Columns not found: {str(bad_keys)[1:-1]}") return self._gotitem(list(key), ndim=2) elif not getattr(self, "as_index", False): if key not in self.obj.columns: raise KeyError(f"Column not found: {key}") return self._gotitem(key, ndim=2) else: if key not in self.obj: raise KeyError(f"Column not found: {key}") subset = self.obj[key] ndim = subset.ndim return self._gotitem(key, ndim=ndim, subset=subset) def _gotitem(self, key, ndim: int, subset=None): raise AbstractMethodError(self) def aggregate(self, func, *args, **kwargs): raise AbstractMethodError(self) agg = aggregate class IndexOpsMixin(OpsMixin): __array_priority__ = 1000 _hidden_attrs: frozenset[str] = frozenset( ["tolist"] ) @property def dtype(self) -> DtypeObj: raise AbstractMethodError(self) @property def _values(self) -> ExtensionArray | np.ndarray: raise AbstractMethodError(self) def transpose(self: _T, *args, **kwargs) -> _T: nv.validate_transpose(args, kwargs) return self T = property( transpose, doc=""" Return the transpose, which is by definition self. """, ) @property def shape(self) -> Shape: return self._values.shape def __len__(self) -> int: raise AbstractMethodError(self) @property def ndim(self) -> int: return 1 def item(self): if len(self) == 1: return next(iter(self)) raise ValueError("can only convert an array of size 1 to a Python scalar") @property def nbytes(self) -> int: return self._values.nbytes @property def size(self) -> int: return len(self._values) @property def array(self) -> ExtensionArray: raise AbstractMethodError(self) def to_numpy( self, dtype: npt.DTypeLike | None = None, copy: bool = False, na_value=lib.no_default, **kwargs, ) -> np.ndarray: if is_extension_array_dtype(self.dtype): return self.array.to_numpy( dtype, copy=copy, na_value=na_value, **kwargs ) elif kwargs: bad_keys = list(kwargs.keys())[0] raise TypeError( f"to_numpy() got an unexpected keyword argument '{bad_keys}'" ) result = np.asarray(self._values, dtype=dtype) if copy or na_value is not lib.no_default: result = result.copy() if na_value is not lib.no_default: result[self.isna()] = na_value return result @property def empty(self) -> bool: return not self.size def max(self, axis=None, skipna: bool = True, *args, **kwargs): nv.validate_minmax_axis(axis) nv.validate_max(args, kwargs) return nanops.nanmax(self._values, skipna=skipna) @doc(op="max", oppose="min", value="largest") def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int: delegate = self._values nv.validate_minmax_axis(axis) skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs) if isinstance(delegate, ExtensionArray): if not skipna and delegate.isna().any(): return -1 else: return delegate.argmax() else: return nanops.nanargmax( delegate, skipna=skipna ) def min(self, axis=None, skipna: bool = True, *args, **kwargs): nv.validate_minmax_axis(axis) nv.validate_min(args, kwargs) return nanops.nanmin(self._values, skipna=skipna) @doc(argmax, op="min", oppose="max", value="smallest") def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int: delegate = self._values nv.validate_minmax_axis(axis) skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs) if isinstance(delegate, ExtensionArray): if not skipna and delegate.isna().any(): return -1 else: return delegate.argmin() else: return nanops.nanargmin( delegate, skipna=skipna ) def tolist(self): if not isinstance(self._values, np.ndarray): return list(self._values) return self._values.tolist() to_list = tolist def __iter__(self): if not isinstance(self._values, np.ndarray): return iter(self._values) else: return map(self._values.item, range(self._values.size)) @cache_readonly def hasnans(self) -> bool: return bool(isna(self).any()) def isna(self): return isna(self._values) def _reduce( self, op, name: str, *, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds, ): func = getattr(self, name, None) if func is None: raise TypeError( f"{type(self).__name__} cannot perform the operation {name}" ) return func(skipna=skipna, **kwds) @final def _map_values(self, mapper, na_action=None): if is_dict_like(mapper): if isinstance(mapper, dict) and hasattr(mapper, "__missing__"): dict_with_default = mapper mapper = lambda x: dict_with_default[x] else: # convert to an Series for efficiency. # we specify the keys here to handle the # possibility that they are tuples # The return value of mapping with an empty mapper is # expected to be pd.Series(np.nan, ...). As np.nan is # of dtype float64 the return value of this method should # be float64 as well mapper = create_series_with_explicit_dtype( mapper, dtype_if_empty=np.float64 ) if isinstance(mapper, ABCSeries): # Since values were input this means we came from either # a dict or a series and mapper should be an index if is_categorical_dtype(self.dtype): # use the built in categorical series mapper which saves # time by mapping the categories instead of all values cat = cast("Categorical", self._values) return cat.map(mapper) values = self._values indexer = mapper.index.get_indexer(values) new_values = algorithms.take_nd(mapper._values, indexer) return new_values # we must convert to python types if is_extension_array_dtype(self.dtype) and hasattr(self._values, "map"): # GH#23179 some EAs do not have `map` values = self._values if na_action is not None: raise NotImplementedError map_f = lambda values, f: values.map(f) else: values = self._values.astype(object) if na_action == "ignore": map_f = lambda values, f: lib.map_infer_mask( values, f, isna(values).view(np.uint8) ) elif na_action is None: map_f = lib.map_infer else: msg = ( "na_action must either be 'ignore' or None, " f"{na_action} was passed" ) raise ValueError(msg) # mapper is a function new_values = map_f(values, mapper) return new_values def value_counts( self, normalize: bool = False, sort: bool = True, ascending: bool = False, bins=None, dropna: bool = True, ): return value_counts( self, sort=sort, ascending=ascending, normalize=normalize, bins=bins, dropna=dropna, ) def unique(self): values = self._values if not isinstance(values, np.ndarray): result: ArrayLike = values.unique() if self.dtype.kind in ["m", "M"] and isinstance(self, ABCSeries): # GH#31182 Series._values returns EA, unpack for backward-compat if getattr(self.dtype, "tz", None) is None: result = np.asarray(result) else: result = unique1d(values) return result def nunique(self, dropna: bool = True) -> int: uniqs = self.unique() if dropna: uniqs = remove_na_arraylike(uniqs) return len(uniqs) @property def is_unique(self) -> bool: return self.nunique(dropna=False) == len(self) @property def is_monotonic(self) -> bool: from pandas import Index return Index(self).is_monotonic @property def is_monotonic_increasing(self) -> bool: # mypy complains if we alias directly return self.is_monotonic @property def is_monotonic_decreasing(self) -> bool: from pandas import Index return Index(self).is_monotonic_decreasing def _memory_usage(self, deep: bool = False) -> int: if hasattr(self.array, "memory_usage"): # https://github.com/python/mypy/issues/1424 # error: "ExtensionArray" has no attribute "memory_usage" return self.array.memory_usage(deep=deep) # type: ignore[attr-defined] v = self.array.nbytes if deep and is_object_dtype(self) and not PYPY: values = cast(np.ndarray, self._values) v += lib.memory_usage_of_objects(values) return v @doc( algorithms.factorize, values="", order="", size_hint="", sort=textwrap.dedent( """\ sort : bool, default False Sort `uniques` and shuffle `codes` to maintain the relationship. """ ), ) def factorize(self, sort: bool = False, na_sentinel: int | None = -1): return algorithms.factorize(self, sort=sort, na_sentinel=na_sentinel) _shared_docs[ "searchsorted" ] = """ Find indices where elements should be inserted to maintain order. Find the indices into a sorted {klass} `self` such that, if the corresponding elements in `value` were inserted before the indices, the order of `self` would be preserved. .. note:: The {klass} *must* be monotonically sorted, otherwise wrong locations will likely be returned. Pandas does *not* check this for you. Parameters ---------- value : array-like or scalar Values to insert into `self`. side : {{'left', 'right'}}, optional If 'left', the index of the first suitable location found is given. If 'right', return the last such index. If there is no suitable index, return either 0 or N (where N is the length of `self`). sorter : 1-D array-like, optional Optional array of integer indices that sort `self` into ascending order. They are typically the result of ``np.argsort``. Returns ------- int or array of int A scalar or array of insertion points with the same shape as `value`. See Also -------- sort_values : Sort by the values along either axis. numpy.searchsorted : Similar method from NumPy. Notes ----- Binary search is used to find the required insertion points. Examples -------- >>> ser = pd.Series([1, 2, 3]) >>> ser 0 1 1 2 2 3 dtype: int64 >>> ser.searchsorted(4) 3 >>> ser.searchsorted([0, 4]) array([0, 3]) >>> ser.searchsorted([1, 3], side='left') array([0, 2]) >>> ser.searchsorted([1, 3], side='right') array([1, 3]) >>> ser = pd.Series(pd.to_datetime(['3/11/2000', '3/12/2000', '3/13/2000'])) >>> ser 0 2000-03-11 1 2000-03-12 2 2000-03-13 dtype: datetime64[ns] >>> ser.searchsorted('3/14/2000') 3 >>> ser = pd.Categorical( ... ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True ... ) >>> ser ['apple', 'bread', 'bread', 'cheese', 'milk'] Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk'] >>> ser.searchsorted('bread') 1 >>> ser.searchsorted(['bread'], side='right') array([3]) If the values are not monotonically sorted, wrong locations may be returned: >>> ser = pd.Series([2, 1, 3]) >>> ser 0 2 1 1 2 3 dtype: int64 >>> ser.searchsorted(1) # doctest: +SKIP 0 # wrong result, correct would be 1 """ @doc(_shared_docs["searchsorted"], klass="Index") def searchsorted( self, value: NumpyValueArrayLike, side: Literal["left", "right"] = "left", sorter: NumpySorter = None, ) -> npt.NDArray[np.intp] | np.intp: return algorithms.searchsorted(self._values, value, side=side, sorter=sorter) def drop_duplicates(self, keep="first"): duplicated = self._duplicated(keep=keep) # error: Value of type "IndexOpsMixin" is not indexable return self[~duplicated] # type: ignore[index] @final def _duplicated( self, keep: Literal["first", "last", False] = "first" ) -> npt.NDArray[np.bool_]: return duplicated(self._values, keep=keep) def _arith_method(self, other, op): res_name = ops.get_op_result_name(self, other) lvalues = self._values rvalues = extract_array(other, extract_numpy=True, extract_range=True) rvalues = ops.maybe_prepare_scalar_for_op(rvalues, lvalues.shape) rvalues = ensure_wrapped_if_datetimelike(rvalues) with np.errstate(all="ignore"): result = ops.arithmetic_op(lvalues, rvalues, op) return self._construct_result(result, name=res_name) def _construct_result(self, result, name): raise AbstractMethodError(self)

true

790dabad8750b692755b533b3315b84491588b56

3,339

py

Python

app/modules/core/decorators.py

Clivern/Kraven

5d8d2de26e170d853d7d5f2b1f2d453ab07e4401

[ "Apache-2.0" ]

3

2018-07-22T22:36:09.000Z

2019-05-31T10:29:54.000Z

app/modules/core/decorators.py

Clivern/Kraven

5d8d2de26e170d853d7d5f2b1f2d453ab07e4401

[ "Apache-2.0" ]

41

2018-07-22T22:07:52.000Z

2018-11-14T11:07:48.000Z

app/modules/core/decorators.py

Clivern/Kraven

5d8d2de26e170d853d7d5f2b1f2d453ab07e4401

[ "Apache-2.0" ]

1

2020-04-24T12:55:27.000Z

""" Custom Decorators """ # Django from django.shortcuts import redirect, reverse from django.http import JsonResponse from django.utils.translation import gettext as _ from django.http import Http404 # local Django from app.modules.util.helpers import Helpers from app.modules.core.response import Response from app.modules.entity.option_entity import Option_Entity def redirect_if_authenticated(function): def wrap(controller, request, *args, **kwargs): if request.user and request.user.is_authenticated: if "redirect" in request.GET: return redirect(request.GET["redirect"]) return redirect("app.web.admin.dashboard") return function(controller, request, *args, **kwargs) return wrap def login_if_not_authenticated(function): def wrap(controller, request, *args, **kwargs): if not request.user or not request.user.is_authenticated: return redirect(reverse("app.web.login") + "?redirect=" + request.get_full_path()) return function(controller, request, *args, **kwargs) return wrap def stop_request_if_authenticated(function): def wrap(controller, request, *args, **kwargs): if request.user and request.user.is_authenticated: response = Response() return JsonResponse(response.send_private_failure([{ "type": "error", "message": _("Error! Access forbidden for authenticated users.") }])) return function(controller, request, *args, **kwargs) return wrap def redirect_if_not_installed(function): def wrap(controller, request, *args, **kwargs): installed = False if Option_Entity().get_one_by_key("app_installed") is False else True if not installed: return redirect("app.web.install") return function(controller, request, *args, **kwargs) return wrap def protect_metric_with_auth_key(function): def wrap(controller, request, *args, **kwargs): if kwargs["type"] == "prometheus": prometheus_token = Option_Entity().get_one_by_key("prometheus_token") if prometheus_token.value != "" and ("HTTP_AUTHORIZATION" not in request.META or prometheus_token.value != request.META["HTTP_AUTHORIZATION"]): raise Http404("Host not found.") return function(controller, request, *args, **kwargs) return wrap def stop_request_if_installed(function): def wrap(controller, request, *args, **kwargs): installed = False if Option_Entity().get_one_by_key("app_installed") is False else True if installed: response = Response() return JsonResponse(response.send_private_failure([{ "type": "error", "message": _("Error! Application is already installed.") }])) return function(controller, request, *args, **kwargs) return wrap def log_request_data(function): def wrap(controller, request, *args, **kwargs): _helper = Helpers() _logger = _helper.get_logger(__name__) _logger.debug(_("Request Method: %s") % request.method) _logger.debug(_("Request URL: %s") % request.path) _logger.debug(_("Request Body: %s") % request.body) return function(controller, request, *args, **kwargs) return wrap

37.943182

155

0.668763

from django.shortcuts import redirect, reverse from django.http import JsonResponse from django.utils.translation import gettext as _ from django.http import Http404 from app.modules.util.helpers import Helpers from app.modules.core.response import Response from app.modules.entity.option_entity import Option_Entity def redirect_if_authenticated(function): def wrap(controller, request, *args, **kwargs): if request.user and request.user.is_authenticated: if "redirect" in request.GET: return redirect(request.GET["redirect"]) return redirect("app.web.admin.dashboard") return function(controller, request, *args, **kwargs) return wrap def login_if_not_authenticated(function): def wrap(controller, request, *args, **kwargs): if not request.user or not request.user.is_authenticated: return redirect(reverse("app.web.login") + "?redirect=" + request.get_full_path()) return function(controller, request, *args, **kwargs) return wrap def stop_request_if_authenticated(function): def wrap(controller, request, *args, **kwargs): if request.user and request.user.is_authenticated: response = Response() return JsonResponse(response.send_private_failure([{ "type": "error", "message": _("Error! Access forbidden for authenticated users.") }])) return function(controller, request, *args, **kwargs) return wrap def redirect_if_not_installed(function): def wrap(controller, request, *args, **kwargs): installed = False if Option_Entity().get_one_by_key("app_installed") is False else True if not installed: return redirect("app.web.install") return function(controller, request, *args, **kwargs) return wrap def protect_metric_with_auth_key(function): def wrap(controller, request, *args, **kwargs): if kwargs["type"] == "prometheus": prometheus_token = Option_Entity().get_one_by_key("prometheus_token") if prometheus_token.value != "" and ("HTTP_AUTHORIZATION" not in request.META or prometheus_token.value != request.META["HTTP_AUTHORIZATION"]): raise Http404("Host not found.") return function(controller, request, *args, **kwargs) return wrap def stop_request_if_installed(function): def wrap(controller, request, *args, **kwargs): installed = False if Option_Entity().get_one_by_key("app_installed") is False else True if installed: response = Response() return JsonResponse(response.send_private_failure([{ "type": "error", "message": _("Error! Application is already installed.") }])) return function(controller, request, *args, **kwargs) return wrap def log_request_data(function): def wrap(controller, request, *args, **kwargs): _helper = Helpers() _logger = _helper.get_logger(__name__) _logger.debug(_("Request Method: %s") % request.method) _logger.debug(_("Request URL: %s") % request.path) _logger.debug(_("Request Body: %s") % request.body) return function(controller, request, *args, **kwargs) return wrap

true

790dacbafc49042cca1ce842ab68f6c32c98f502

10,765

py

Python

sdk/python/pulumi_aws/signer/_inputs.py

chivandikwa/pulumi-aws

19c08bf9dcb90544450ffa4eec7bf6751058fde2

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_aws/signer/_inputs.py

chivandikwa/pulumi-aws

19c08bf9dcb90544450ffa4eec7bf6751058fde2

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_aws/signer/_inputs.py

chivandikwa/pulumi-aws

19c08bf9dcb90544450ffa4eec7bf6751058fde2

[ "ECL-2.0", "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = [ 'SigningJobDestinationArgs', 'SigningJobDestinationS3Args', 'SigningJobRevocationRecordArgs', 'SigningJobSignedObjectArgs', 'SigningJobSignedObjectS3Args', 'SigningJobSourceArgs', 'SigningJobSourceS3Args', 'SigningProfileRevocationRecordArgs', 'SigningProfileSignatureValidityPeriodArgs', ] @pulumi.input_type class SigningJobDestinationArgs: def __init__(__self__, *, s3: pulumi.Input['SigningJobDestinationS3Args']): """ :param pulumi.Input['SigningJobDestinationS3Args'] s3: A configuration block describing the S3 Destination object: See S3 Destination below for details. """ pulumi.set(__self__, "s3", s3) @property @pulumi.getter def s3(self) -> pulumi.Input['SigningJobDestinationS3Args']: """ A configuration block describing the S3 Destination object: See S3 Destination below for details. """ return pulumi.get(self, "s3") @s3.setter def s3(self, value: pulumi.Input['SigningJobDestinationS3Args']): pulumi.set(self, "s3", value) @pulumi.input_type class SigningJobDestinationS3Args: def __init__(__self__, *, bucket: pulumi.Input[str], prefix: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] bucket: Name of the S3 bucket. :param pulumi.Input[str] prefix: An Amazon S3 object key prefix that you can use to limit signed objects keys to begin with the specified prefix. """ pulumi.set(__self__, "bucket", bucket) if prefix is not None: pulumi.set(__self__, "prefix", prefix) @property @pulumi.getter def bucket(self) -> pulumi.Input[str]: """ Name of the S3 bucket. """ return pulumi.get(self, "bucket") @bucket.setter def bucket(self, value: pulumi.Input[str]): pulumi.set(self, "bucket", value) @property @pulumi.getter def prefix(self) -> Optional[pulumi.Input[str]]: """ An Amazon S3 object key prefix that you can use to limit signed objects keys to begin with the specified prefix. """ return pulumi.get(self, "prefix") @prefix.setter def prefix(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "prefix", value) @pulumi.input_type class SigningJobRevocationRecordArgs: def __init__(__self__, *, reason: Optional[pulumi.Input[str]] = None, revoked_at: Optional[pulumi.Input[str]] = None, revoked_by: Optional[pulumi.Input[str]] = None): if reason is not None: pulumi.set(__self__, "reason", reason) if revoked_at is not None: pulumi.set(__self__, "revoked_at", revoked_at) if revoked_by is not None: pulumi.set(__self__, "revoked_by", revoked_by) @property @pulumi.getter def reason(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "reason") @reason.setter def reason(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "reason", value) @property @pulumi.getter(name="revokedAt") def revoked_at(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "revoked_at") @revoked_at.setter def revoked_at(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "revoked_at", value) @property @pulumi.getter(name="revokedBy") def revoked_by(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "revoked_by") @revoked_by.setter def revoked_by(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "revoked_by", value) @pulumi.input_type class SigningJobSignedObjectArgs: def __init__(__self__, *, s3s: Optional[pulumi.Input[Sequence[pulumi.Input['SigningJobSignedObjectS3Args']]]] = None): """ :param pulumi.Input[Sequence[pulumi.Input['SigningJobSignedObjectS3Args']]] s3s: A configuration block describing the S3 Destination object: See S3 Destination below for details. """ if s3s is not None: pulumi.set(__self__, "s3s", s3s) @property @pulumi.getter def s3s(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['SigningJobSignedObjectS3Args']]]]: """ A configuration block describing the S3 Destination object: See S3 Destination below for details. """ return pulumi.get(self, "s3s") @s3s.setter def s3s(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['SigningJobSignedObjectS3Args']]]]): pulumi.set(self, "s3s", value) @pulumi.input_type class SigningJobSignedObjectS3Args: def __init__(__self__, *, bucket: Optional[pulumi.Input[str]] = None, key: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] bucket: Name of the S3 bucket. :param pulumi.Input[str] key: Key name of the object that contains your unsigned code. """ if bucket is not None: pulumi.set(__self__, "bucket", bucket) if key is not None: pulumi.set(__self__, "key", key) @property @pulumi.getter def bucket(self) -> Optional[pulumi.Input[str]]: """ Name of the S3 bucket. """ return pulumi.get(self, "bucket") @bucket.setter def bucket(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "bucket", value) @property @pulumi.getter def key(self) -> Optional[pulumi.Input[str]]: """ Key name of the object that contains your unsigned code. """ return pulumi.get(self, "key") @key.setter def key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "key", value) @pulumi.input_type class SigningJobSourceArgs: def __init__(__self__, *, s3: pulumi.Input['SigningJobSourceS3Args']): """ :param pulumi.Input['SigningJobSourceS3Args'] s3: A configuration block describing the S3 Destination object: See S3 Destination below for details. """ pulumi.set(__self__, "s3", s3) @property @pulumi.getter def s3(self) -> pulumi.Input['SigningJobSourceS3Args']: """ A configuration block describing the S3 Destination object: See S3 Destination below for details. """ return pulumi.get(self, "s3") @s3.setter def s3(self, value: pulumi.Input['SigningJobSourceS3Args']): pulumi.set(self, "s3", value) @pulumi.input_type class SigningJobSourceS3Args: def __init__(__self__, *, bucket: pulumi.Input[str], key: pulumi.Input[str], version: pulumi.Input[str]): """ :param pulumi.Input[str] bucket: Name of the S3 bucket. :param pulumi.Input[str] key: Key name of the object that contains your unsigned code. :param pulumi.Input[str] version: Version of your source image in your version enabled S3 bucket. """ pulumi.set(__self__, "bucket", bucket) pulumi.set(__self__, "key", key) pulumi.set(__self__, "version", version) @property @pulumi.getter def bucket(self) -> pulumi.Input[str]: """ Name of the S3 bucket. """ return pulumi.get(self, "bucket") @bucket.setter def bucket(self, value: pulumi.Input[str]): pulumi.set(self, "bucket", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: """ Key name of the object that contains your unsigned code. """ return pulumi.get(self, "key") @key.setter def key(self, value: pulumi.Input[str]): pulumi.set(self, "key", value) @property @pulumi.getter def version(self) -> pulumi.Input[str]: """ Version of your source image in your version enabled S3 bucket. """ return pulumi.get(self, "version") @version.setter def version(self, value: pulumi.Input[str]): pulumi.set(self, "version", value) @pulumi.input_type class SigningProfileRevocationRecordArgs: def __init__(__self__, *, revocation_effective_from: Optional[pulumi.Input[str]] = None, revoked_at: Optional[pulumi.Input[str]] = None, revoked_by: Optional[pulumi.Input[str]] = None): if revocation_effective_from is not None: pulumi.set(__self__, "revocation_effective_from", revocation_effective_from) if revoked_at is not None: pulumi.set(__self__, "revoked_at", revoked_at) if revoked_by is not None: pulumi.set(__self__, "revoked_by", revoked_by) @property @pulumi.getter(name="revocationEffectiveFrom") def revocation_effective_from(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "revocation_effective_from") @revocation_effective_from.setter def revocation_effective_from(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "revocation_effective_from", value) @property @pulumi.getter(name="revokedAt") def revoked_at(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "revoked_at") @revoked_at.setter def revoked_at(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "revoked_at", value) @property @pulumi.getter(name="revokedBy") def revoked_by(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "revoked_by") @revoked_by.setter def revoked_by(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "revoked_by", value) @pulumi.input_type class SigningProfileSignatureValidityPeriodArgs: def __init__(__self__, *, type: pulumi.Input[str], value: pulumi.Input[int]): pulumi.set(__self__, "type", type) pulumi.set(__self__, "value", value) @property @pulumi.getter def type(self) -> pulumi.Input[str]: return pulumi.get(self, "type") @type.setter def type(self, value: pulumi.Input[str]): pulumi.set(self, "type", value) @property @pulumi.getter def value(self) -> pulumi.Input[int]: return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[int]): pulumi.set(self, "value", value)

32.820122

186

0.636693

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = [ 'SigningJobDestinationArgs', 'SigningJobDestinationS3Args', 'SigningJobRevocationRecordArgs', 'SigningJobSignedObjectArgs', 'SigningJobSignedObjectS3Args', 'SigningJobSourceArgs', 'SigningJobSourceS3Args', 'SigningProfileRevocationRecordArgs', 'SigningProfileSignatureValidityPeriodArgs', ] @pulumi.input_type class SigningJobDestinationArgs: def __init__(__self__, *, s3: pulumi.Input['SigningJobDestinationS3Args']): pulumi.set(__self__, "s3", s3) @property @pulumi.getter def s3(self) -> pulumi.Input['SigningJobDestinationS3Args']: return pulumi.get(self, "s3") @s3.setter def s3(self, value: pulumi.Input['SigningJobDestinationS3Args']): pulumi.set(self, "s3", value) @pulumi.input_type class SigningJobDestinationS3Args: def __init__(__self__, *, bucket: pulumi.Input[str], prefix: Optional[pulumi.Input[str]] = None): pulumi.set(__self__, "bucket", bucket) if prefix is not None: pulumi.set(__self__, "prefix", prefix) @property @pulumi.getter def bucket(self) -> pulumi.Input[str]: return pulumi.get(self, "bucket") @bucket.setter def bucket(self, value: pulumi.Input[str]): pulumi.set(self, "bucket", value) @property @pulumi.getter def prefix(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "prefix") @prefix.setter def prefix(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "prefix", value) @pulumi.input_type class SigningJobRevocationRecordArgs: def __init__(__self__, *, reason: Optional[pulumi.Input[str]] = None, revoked_at: Optional[pulumi.Input[str]] = None, revoked_by: Optional[pulumi.Input[str]] = None): if reason is not None: pulumi.set(__self__, "reason", reason) if revoked_at is not None: pulumi.set(__self__, "revoked_at", revoked_at) if revoked_by is not None: pulumi.set(__self__, "revoked_by", revoked_by) @property @pulumi.getter def reason(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "reason") @reason.setter def reason(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "reason", value) @property @pulumi.getter(name="revokedAt") def revoked_at(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "revoked_at") @revoked_at.setter def revoked_at(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "revoked_at", value) @property @pulumi.getter(name="revokedBy") def revoked_by(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "revoked_by") @revoked_by.setter def revoked_by(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "revoked_by", value) @pulumi.input_type class SigningJobSignedObjectArgs: def __init__(__self__, *, s3s: Optional[pulumi.Input[Sequence[pulumi.Input['SigningJobSignedObjectS3Args']]]] = None): if s3s is not None: pulumi.set(__self__, "s3s", s3s) @property @pulumi.getter def s3s(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['SigningJobSignedObjectS3Args']]]]: return pulumi.get(self, "s3s") @s3s.setter def s3s(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['SigningJobSignedObjectS3Args']]]]): pulumi.set(self, "s3s", value) @pulumi.input_type class SigningJobSignedObjectS3Args: def __init__(__self__, *, bucket: Optional[pulumi.Input[str]] = None, key: Optional[pulumi.Input[str]] = None): if bucket is not None: pulumi.set(__self__, "bucket", bucket) if key is not None: pulumi.set(__self__, "key", key) @property @pulumi.getter def bucket(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "bucket") @bucket.setter def bucket(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "bucket", value) @property @pulumi.getter def key(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "key") @key.setter def key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "key", value) @pulumi.input_type class SigningJobSourceArgs: def __init__(__self__, *, s3: pulumi.Input['SigningJobSourceS3Args']): pulumi.set(__self__, "s3", s3) @property @pulumi.getter def s3(self) -> pulumi.Input['SigningJobSourceS3Args']: return pulumi.get(self, "s3") @s3.setter def s3(self, value: pulumi.Input['SigningJobSourceS3Args']): pulumi.set(self, "s3", value) @pulumi.input_type class SigningJobSourceS3Args: def __init__(__self__, *, bucket: pulumi.Input[str], key: pulumi.Input[str], version: pulumi.Input[str]): pulumi.set(__self__, "bucket", bucket) pulumi.set(__self__, "key", key) pulumi.set(__self__, "version", version) @property @pulumi.getter def bucket(self) -> pulumi.Input[str]: return pulumi.get(self, "bucket") @bucket.setter def bucket(self, value: pulumi.Input[str]): pulumi.set(self, "bucket", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: return pulumi.get(self, "key") @key.setter def key(self, value: pulumi.Input[str]): pulumi.set(self, "key", value) @property @pulumi.getter def version(self) -> pulumi.Input[str]: return pulumi.get(self, "version") @version.setter def version(self, value: pulumi.Input[str]): pulumi.set(self, "version", value) @pulumi.input_type class SigningProfileRevocationRecordArgs: def __init__(__self__, *, revocation_effective_from: Optional[pulumi.Input[str]] = None, revoked_at: Optional[pulumi.Input[str]] = None, revoked_by: Optional[pulumi.Input[str]] = None): if revocation_effective_from is not None: pulumi.set(__self__, "revocation_effective_from", revocation_effective_from) if revoked_at is not None: pulumi.set(__self__, "revoked_at", revoked_at) if revoked_by is not None: pulumi.set(__self__, "revoked_by", revoked_by) @property @pulumi.getter(name="revocationEffectiveFrom") def revocation_effective_from(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "revocation_effective_from") @revocation_effective_from.setter def revocation_effective_from(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "revocation_effective_from", value) @property @pulumi.getter(name="revokedAt") def revoked_at(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "revoked_at") @revoked_at.setter def revoked_at(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "revoked_at", value) @property @pulumi.getter(name="revokedBy") def revoked_by(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "revoked_by") @revoked_by.setter def revoked_by(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "revoked_by", value) @pulumi.input_type class SigningProfileSignatureValidityPeriodArgs: def __init__(__self__, *, type: pulumi.Input[str], value: pulumi.Input[int]): pulumi.set(__self__, "type", type) pulumi.set(__self__, "value", value) @property @pulumi.getter def type(self) -> pulumi.Input[str]: return pulumi.get(self, "type") @type.setter def type(self, value: pulumi.Input[str]): pulumi.set(self, "type", value) @property @pulumi.getter def value(self) -> pulumi.Input[int]: return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[int]): pulumi.set(self, "value", value)

true

790dad6bf0d7a8a279a293c04eee935f54123d69

3,344

py

Python

grr/client/grr_response_client/client_utils.py

billstackpole/grr

203a0a99990a2d4004aed84a5cd822cbda2b418c

[ "Apache-2.0" ]

1

2019-03-28T07:09:41.000Z

grr/client/grr_response_client/client_utils.py

gingogo/grr

203a0a99990a2d4004aed84a5cd822cbda2b418c

[ "Apache-2.0" ]

null

grr/client/grr_response_client/client_utils.py

gingogo/grr

203a0a99990a2d4004aed84a5cd822cbda2b418c

[ "Apache-2.0" ]

1

2018-08-30T14:50:24.000Z

#!/usr/bin/env python """Client utilities.""" import logging import sys from grr_response_core.lib import utils from grr_response_core.lib.rdfvalues import client_fs as rdf_client_fs from grr_response_core.lib.rdfvalues import paths as rdf_paths # pylint: disable=g-import-not-at-top if sys.platform == "win32": from grr_response_client import client_utils_windows as _client_utils elif sys.platform == "darwin": from grr_response_client import client_utils_osx as _client_utils else: from grr_response_client import client_utils_linux as _client_utils # pylint: enable=g-import-not-at-top # pylint: disable=g-bad-name CanonicalPathToLocalPath = _client_utils.CanonicalPathToLocalPath FindProxies = _client_utils.FindProxies GetExtAttrs = _client_utils.GetExtAttrs GetRawDevice = _client_utils.GetRawDevice KeepAlive = _client_utils.KeepAlive LocalPathToCanonicalPath = _client_utils.LocalPathToCanonicalPath MemoryRegions = _client_utils.MemoryRegions NannyController = _client_utils.NannyController OpenProcessForMemoryAccess = _client_utils.OpenProcessForMemoryAccess TransactionLog = _client_utils.TransactionLog VerifyFileOwner = _client_utils.VerifyFileOwner # pylint: enable=g-bad-name def StatEntryFromPath(path, pathspec, ext_attrs=True): """Builds a stat entry object from a given path. Args: path: A path (string value) to stat. pathspec: A `PathSpec` corresponding to the `path`. ext_attrs: Whether to include extended file attributes in the result. Returns: `StatEntry` object. """ try: stat = utils.Stat(path) except (IOError, OSError) as error: logging.error("Failed to obtain stat for '%s': %s", pathspec, error) return rdf_client_fs.StatEntry(pathspec=pathspec) return StatEntryFromStat(stat, pathspec, ext_attrs=ext_attrs) def StatEntryFromStat(stat, pathspec, ext_attrs=True): """Build a stat entry object from a given stat object. Args: stat: A `Stat` object. pathspec: A `PathSpec` from which `stat` was obtained. ext_attrs: Whether to include extended file attributes in the result. Returns: `StatEntry` object. """ result = rdf_client_fs.StatEntry(pathspec=pathspec) for attr in _STAT_ATTRS: value = getattr(stat.GetRaw(), attr, None) if value is None: continue # TODO(hanuszczak): Why are we doing this? value = int(value) if value < 0: value &= 0xFFFFFFFF setattr(result, attr, value) result.st_flags_linux = stat.GetLinuxFlags() result.st_flags_osx = stat.GetOsxFlags() if ext_attrs: # TODO(hanuszczak): Can we somehow incorporate extended attribute getter to # the `Stat` class? That would make the code a lot prettier but would force # `utils` to depend on `xattrs`. result.ext_attrs = list(GetExtAttrs(stat.GetPath())) return result def StatEntryFromStatPathSpec(stat, ext_attrs): pathspec = rdf_paths.PathSpec( pathtype=rdf_paths.PathSpec.PathType.OS, path=LocalPathToCanonicalPath(stat.GetPath()), path_options=rdf_paths.PathSpec.Options.CASE_LITERAL) return StatEntryFromStat(stat, pathspec, ext_attrs=ext_attrs) _STAT_ATTRS = [ "st_mode", "st_ino", "st_dev", "st_nlink", "st_uid", "st_gid", "st_size", "st_atime", "st_mtime", "st_ctime", "st_blocks", "st_blksize", "st_rdev", ]

29.078261

79

0.746112

import logging import sys from grr_response_core.lib import utils from grr_response_core.lib.rdfvalues import client_fs as rdf_client_fs from grr_response_core.lib.rdfvalues import paths as rdf_paths if sys.platform == "win32": from grr_response_client import client_utils_windows as _client_utils elif sys.platform == "darwin": from grr_response_client import client_utils_osx as _client_utils else: from grr_response_client import client_utils_linux as _client_utils CanonicalPathToLocalPath = _client_utils.CanonicalPathToLocalPath FindProxies = _client_utils.FindProxies GetExtAttrs = _client_utils.GetExtAttrs GetRawDevice = _client_utils.GetRawDevice KeepAlive = _client_utils.KeepAlive LocalPathToCanonicalPath = _client_utils.LocalPathToCanonicalPath MemoryRegions = _client_utils.MemoryRegions NannyController = _client_utils.NannyController OpenProcessForMemoryAccess = _client_utils.OpenProcessForMemoryAccess TransactionLog = _client_utils.TransactionLog VerifyFileOwner = _client_utils.VerifyFileOwner def StatEntryFromPath(path, pathspec, ext_attrs=True): try: stat = utils.Stat(path) except (IOError, OSError) as error: logging.error("Failed to obtain stat for '%s': %s", pathspec, error) return rdf_client_fs.StatEntry(pathspec=pathspec) return StatEntryFromStat(stat, pathspec, ext_attrs=ext_attrs) def StatEntryFromStat(stat, pathspec, ext_attrs=True): result = rdf_client_fs.StatEntry(pathspec=pathspec) for attr in _STAT_ATTRS: value = getattr(stat.GetRaw(), attr, None) if value is None: continue value = int(value) if value < 0: value &= 0xFFFFFFFF setattr(result, attr, value) result.st_flags_linux = stat.GetLinuxFlags() result.st_flags_osx = stat.GetOsxFlags() if ext_attrs: result.ext_attrs = list(GetExtAttrs(stat.GetPath())) return result def StatEntryFromStatPathSpec(stat, ext_attrs): pathspec = rdf_paths.PathSpec( pathtype=rdf_paths.PathSpec.PathType.OS, path=LocalPathToCanonicalPath(stat.GetPath()), path_options=rdf_paths.PathSpec.Options.CASE_LITERAL) return StatEntryFromStat(stat, pathspec, ext_attrs=ext_attrs) _STAT_ATTRS = [ "st_mode", "st_ino", "st_dev", "st_nlink", "st_uid", "st_gid", "st_size", "st_atime", "st_mtime", "st_ctime", "st_blocks", "st_blksize", "st_rdev", ]

true

790dae2213573bb04aeb653ea71d00b40d4cde44

4,045

py

Python

01-Login/webappexample/settings.py

alexisluque/auth0-django-web-app

4c6a530fac04e2b48f2dc85cc8ef414e2b03c599

[ "MIT" ]

null

01-Login/webappexample/settings.py

alexisluque/auth0-django-web-app

4c6a530fac04e2b48f2dc85cc8ef414e2b03c599

[ "MIT" ]

1

2018-07-09T14:23:54.000Z

01-Login/webappexample/settings.py

alexisluque/auth0-django-web-app

4c6a530fac04e2b48f2dc85cc8ef414e2b03c599

[ "MIT" ]

null

""" Django settings for webappexample project. Generated by 'django-admin startproject' using Django 1.11.4. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ from dotenv import load_dotenv, find_dotenv import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '*dn4z%$4b6-d1+epmb=hd1m3g#$*1*%&%x+4m_8*cvakee%=7q' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'social_django', 'auth0login' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'webappexample.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'webappexample.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' ENV_FILE = find_dotenv() if ENV_FILE: load_dotenv(ENV_FILE) # SOCIAL AUTH AUTH0 BACKEND CONFIG SOCIAL_AUTH_TRAILING_SLASH = False SOCIAL_AUTH_AUTH0_KEY = os.environ.get('AUTH0_CLIENT_ID') SOCIAL_AUTH_AUTH0_SECRET = os.environ.get('AUTH0_CLIENT_SECRET') SOCIAL_AUTH_AUTH0_SCOPE = [ 'openid', 'profile' ] SOCIAL_AUTH_AUTH0_DOMAIN = os.environ.get('AUTH0_DOMAIN') AUDIENCE = None if os.environ.get('AUTH0_AUDIENCE'): AUDIENCE = os.environ.get('AUTH0_AUDIENCE') else: if SOCIAL_AUTH_AUTH0_DOMAIN: AUDIENCE = 'https://' + SOCIAL_AUTH_AUTH0_DOMAIN + '/userinfo' if AUDIENCE: SOCIAL_AUTH_AUTH0_AUTH_EXTRA_ARGUMENTS = {'audience': AUDIENCE} AUTHENTICATION_BACKENDS = { 'auth0login.auth0backend.Auth0', 'django.contrib.auth.backends.ModelBackend' } LOGIN_URL = '/login/auth0' LOGIN_REDIRECT_URL = '/dashboard'

26.096774

91

0.710507

from dotenv import load_dotenv, find_dotenv import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = '*dn4z%$4b6-d1+epmb=hd1m3g#$*1*%&%x+4m_8*cvakee%=7q' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'social_django', 'auth0login' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'webappexample.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'webappexample.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' ENV_FILE = find_dotenv() if ENV_FILE: load_dotenv(ENV_FILE) # SOCIAL AUTH AUTH0 BACKEND CONFIG SOCIAL_AUTH_TRAILING_SLASH = False SOCIAL_AUTH_AUTH0_KEY = os.environ.get('AUTH0_CLIENT_ID') SOCIAL_AUTH_AUTH0_SECRET = os.environ.get('AUTH0_CLIENT_SECRET') SOCIAL_AUTH_AUTH0_SCOPE = [ 'openid', 'profile' ] SOCIAL_AUTH_AUTH0_DOMAIN = os.environ.get('AUTH0_DOMAIN') AUDIENCE = None if os.environ.get('AUTH0_AUDIENCE'): AUDIENCE = os.environ.get('AUTH0_AUDIENCE') else: if SOCIAL_AUTH_AUTH0_DOMAIN: AUDIENCE = 'https://' + SOCIAL_AUTH_AUTH0_DOMAIN + '/userinfo' if AUDIENCE: SOCIAL_AUTH_AUTH0_AUTH_EXTRA_ARGUMENTS = {'audience': AUDIENCE} AUTHENTICATION_BACKENDS = { 'auth0login.auth0backend.Auth0', 'django.contrib.auth.backends.ModelBackend' } LOGIN_URL = '/login/auth0' LOGIN_REDIRECT_URL = '/dashboard'

true

790daea48156492a628ca355400ac2fc6d76bdbc

2,288

py

Python

daiquiri/oai/utils.py

agy-why/daiquiri

4d3e2ce51e202d5a8f1df404a0094a4e018dcb4d

[ "Apache-2.0" ]

14

2018-12-23T18:35:02.000Z

2021-12-15T04:55:12.000Z

daiquiri/oai/utils.py

agy-why/daiquiri

4d3e2ce51e202d5a8f1df404a0094a4e018dcb4d

[ "Apache-2.0" ]

40

2018-12-20T12:44:05.000Z

2022-03-21T11:35:20.000Z

daiquiri/oai/utils.py

agy-why/daiquiri

4d3e2ce51e202d5a8f1df404a0094a4e018dcb4d

[ "Apache-2.0" ]

5

2019-05-16T08:03:35.000Z

2021-08-23T20:03:11.000Z

import logging from django.conf import settings from daiquiri.core.utils import import_class from .adapter import OaiAdapter from .models import Record logger = logging.getLogger(__name__) def get_metadata_format(metadata_prefix): return next(metadata_format for metadata_format in settings.OAI_METADATA_FORMATS if metadata_format['prefix'] == metadata_prefix) def get_renderer(metadata_prefix): renderer_class = get_metadata_format(metadata_prefix)['renderer_class'] return import_class(renderer_class)() def update_records(resource_type, resource): logger.debug('update_records %s %s', resource_type, resource) adapter = OaiAdapter() try: resource_id, identifier, datestamp, set_spec, public = adapter.get_record(resource_type, resource) except TypeError: raise RuntimeError('Could not obtain record for %s %s' % (resource_type, resource)) if public is True: for metadata_prefix in adapter.resource_types[resource_type]: try: record = Record.objects.get(identifier=identifier, metadata_prefix=metadata_prefix) except Record.DoesNotExist: record = Record(identifier=identifier, metadata_prefix=metadata_prefix) record.datestamp = datestamp record.set_spec = set_spec record.deleted = False record.resource_type = resource_type record.resource_id = resource_id record.save() else: delete_records(resource_type, resource) def delete_records(resource_type, resource): logger.debug('delete_records %s %s', resource_type, resource) adapter = OaiAdapter() try: resource_id, identifier, datestamp, set_spec, public = adapter.get_record(resource_type, resource) except TypeError: raise RuntimeError('Could not obtain record for %s %s' % (resource_type, resource)) for metadata_prefix in adapter.resource_types[resource_type]: try: record = Record.objects.get(identifier=identifier, metadata_prefix=metadata_prefix) record.datestamp = datestamp record.set_spec = set_spec record.deleted = True record.save() except Record.DoesNotExist: pass

33.15942

106

0.697115

import logging from django.conf import settings from daiquiri.core.utils import import_class from .adapter import OaiAdapter from .models import Record logger = logging.getLogger(__name__) def get_metadata_format(metadata_prefix): return next(metadata_format for metadata_format in settings.OAI_METADATA_FORMATS if metadata_format['prefix'] == metadata_prefix) def get_renderer(metadata_prefix): renderer_class = get_metadata_format(metadata_prefix)['renderer_class'] return import_class(renderer_class)() def update_records(resource_type, resource): logger.debug('update_records %s %s', resource_type, resource) adapter = OaiAdapter() try: resource_id, identifier, datestamp, set_spec, public = adapter.get_record(resource_type, resource) except TypeError: raise RuntimeError('Could not obtain record for %s %s' % (resource_type, resource)) if public is True: for metadata_prefix in adapter.resource_types[resource_type]: try: record = Record.objects.get(identifier=identifier, metadata_prefix=metadata_prefix) except Record.DoesNotExist: record = Record(identifier=identifier, metadata_prefix=metadata_prefix) record.datestamp = datestamp record.set_spec = set_spec record.deleted = False record.resource_type = resource_type record.resource_id = resource_id record.save() else: delete_records(resource_type, resource) def delete_records(resource_type, resource): logger.debug('delete_records %s %s', resource_type, resource) adapter = OaiAdapter() try: resource_id, identifier, datestamp, set_spec, public = adapter.get_record(resource_type, resource) except TypeError: raise RuntimeError('Could not obtain record for %s %s' % (resource_type, resource)) for metadata_prefix in adapter.resource_types[resource_type]: try: record = Record.objects.get(identifier=identifier, metadata_prefix=metadata_prefix) record.datestamp = datestamp record.set_spec = set_spec record.deleted = True record.save() except Record.DoesNotExist: pass

true

790dafb3cd44c4622f4bde96ce06a13b96b35e6e

2,003

py

Python

classifier/src/model_lgb.py

banboooo044/natural-language-sentiment-anaysis

e18d7c0373d9f0a00d5a3cc14abf671081bc940b

[ "DOC" ]

null

classifier/src/model_lgb.py

banboooo044/natural-language-sentiment-anaysis

e18d7c0373d9f0a00d5a3cc14abf671081bc940b

[ "DOC" ]

null

classifier/src/model_lgb.py

banboooo044/natural-language-sentiment-anaysis

e18d7c0373d9f0a00d5a3cc14abf671081bc940b

[ "DOC" ]

null

import os,sys sys.path.append('../') import os import numpy as np import pandas as pd import lightgbm as lgb from src.model import Model from src.util import Util from sklearn.metrics import log_loss, accuracy_score, f1_score, classification_report class ModelLGB(Model): def __init__(self, run_fold_name, **params): super().__init__(run_fold_name, params) def train(self, tr_x, tr_y, va_x=None, va_y=None): validation = va_x is not None dtrain = lgb.Dataset(tr_x, label=tr_y) if validation: dvalid = lgb.Dataset(va_x, label=va_y) params = dict(self.params) num_round = params.pop('num_boost_round') if validation: # バリデーションデータが存在する場合, Eearly Stoppingを行う early_stopping_rounds = params.pop('early_stopping_rounds') watchlist = [dtrain, dvalid ] self.model = lgb.train(params, dtrain, num_round, valid_sets=watchlist, valid_names=['train','eval'], early_stopping_rounds=early_stopping_rounds) else: watchlist = [(dtrain, 'train')] self.model = lgb.train(params, dtrain, num_round, evals=watchlist) def predict(self, te_x): return self.model.predict(te_x, ntree_limit=self.model.best_iteration) def score(self, te_x, te_y): pred_prob = self.predict(te_x) y_pred = np.argmax(pred_prob, axis=1) # print(classification_report(te_y, y_pred)) return f1_score(np.identity(5)[te_y], np.identity(5)[y_pred], average='samples') def save_model(self, feature): model_path = os.path.join(f'../model/model/{feature}', f'{self.run_fold_name}.model') os.makedirs(os.path.dirname(model_path), exist_ok=True) Util.dump(self.model, model_path) def load_model(self, feature): model_path = os.path.join(f'../model/model/{feature}', f'{self.run_fold_name}.model') self.model = Util.load(model_path)

36.418182

93

0.64653

import os,sys sys.path.append('../') import os import numpy as np import pandas as pd import lightgbm as lgb from src.model import Model from src.util import Util from sklearn.metrics import log_loss, accuracy_score, f1_score, classification_report class ModelLGB(Model): def __init__(self, run_fold_name, **params): super().__init__(run_fold_name, params) def train(self, tr_x, tr_y, va_x=None, va_y=None): validation = va_x is not None dtrain = lgb.Dataset(tr_x, label=tr_y) if validation: dvalid = lgb.Dataset(va_x, label=va_y) params = dict(self.params) num_round = params.pop('num_boost_round') if validation: early_stopping_rounds = params.pop('early_stopping_rounds') watchlist = [dtrain, dvalid ] self.model = lgb.train(params, dtrain, num_round, valid_sets=watchlist, valid_names=['train','eval'], early_stopping_rounds=early_stopping_rounds) else: watchlist = [(dtrain, 'train')] self.model = lgb.train(params, dtrain, num_round, evals=watchlist) def predict(self, te_x): return self.model.predict(te_x, ntree_limit=self.model.best_iteration) def score(self, te_x, te_y): pred_prob = self.predict(te_x) y_pred = np.argmax(pred_prob, axis=1) return f1_score(np.identity(5)[te_y], np.identity(5)[y_pred], average='samples') def save_model(self, feature): model_path = os.path.join(f'../model/model/{feature}', f'{self.run_fold_name}.model') os.makedirs(os.path.dirname(model_path), exist_ok=True) Util.dump(self.model, model_path) def load_model(self, feature): model_path = os.path.join(f'../model/model/{feature}', f'{self.run_fold_name}.model') self.model = Util.load(model_path)

true

790db0a494b66f67346f0144d8df455628407ad8

9,231

py

Python

airflow/sensors/base_sensor_operator.py

joshowen/airflow

d0cf232919839d0e338dcc38a5c7a1841077eaae

[ "Apache-2.0" ]

3

2015-08-25T13:56:44.000Z

2020-03-21T10:26:58.000Z

airflow/sensors/base_sensor_operator.py

joshowen/airflow

d0cf232919839d0e338dcc38a5c7a1841077eaae

[ "Apache-2.0" ]

37

2020-07-21T07:50:02.000Z

2022-03-29T22:31:28.000Z

airflow/sensors/base_sensor_operator.py

santecapital/airflow

7f02e56c9cb8b548624d13e9c2c2b89d753f996b

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

4

2020-07-17T14:02:28.000Z

2022-02-23T04:29:58.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import hashlib import os from datetime import timedelta from time import sleep from typing import Any, Dict, Iterable from airflow.exceptions import ( AirflowException, AirflowRescheduleException, AirflowSensorTimeout, AirflowSkipException, ) from airflow.models import BaseOperator, SkipMixin, TaskReschedule from airflow.ti_deps.deps.ready_to_reschedule import ReadyToRescheduleDep from airflow.utils import timezone from airflow.utils.decorators import apply_defaults class BaseSensorOperator(BaseOperator, SkipMixin): """ Sensor operators are derived from this class and inherit these attributes. Sensor operators keep executing at a time interval and succeed when a criteria is met and fail if and when they time out. :param soft_fail: Set to true to mark the task as SKIPPED on failure :type soft_fail: bool :param poke_interval: Time in seconds that the job should wait in between each tries :type poke_interval: float :param timeout: Time, in seconds before the task times out and fails. :type timeout: float :param mode: How the sensor operates. Options are: ``{ poke | reschedule }``, default is ``poke``. When set to ``poke`` the sensor is taking up a worker slot for its whole execution time and sleeps between pokes. Use this mode if the expected runtime of the sensor is short or if a short poke interval is required. Note that the sensor will hold onto a worker slot and a pool slot for the duration of the sensor's runtime in this mode. When set to ``reschedule`` the sensor task frees the worker slot when the criteria is not yet met and it's rescheduled at a later time. Use this mode if the time before the criteria is met is expected to be quite long. The poke interval should be more than one minute to prevent too much load on the scheduler. :type mode: str :param exponential_backoff: allow progressive longer waits between pokes by using exponential backoff algorithm :type exponential_backoff: bool """ ui_color = '#e6f1f2' # type: str valid_modes = ['poke', 'reschedule'] # type: Iterable[str] @apply_defaults def __init__(self, poke_interval: float = 60, timeout: float = 60 * 60 * 24 * 7, soft_fail: bool = False, mode: str = 'poke', exponential_backoff: bool = False, *args, **kwargs) -> None: super().__init__(*args, **kwargs) self.poke_interval = poke_interval self.soft_fail = soft_fail self.timeout = timeout self.mode = mode self.exponential_backoff = exponential_backoff self._validate_input_values() def _validate_input_values(self) -> None: if not isinstance(self.poke_interval, (int, float)) or self.poke_interval < 0: raise AirflowException( "The poke_interval must be a non-negative number") if not isinstance(self.timeout, (int, float)) or self.timeout < 0: raise AirflowException( "The timeout must be a non-negative number") if self.mode not in self.valid_modes: raise AirflowException( "The mode must be one of {valid_modes}," "'{d}.{t}'; received '{m}'." .format(valid_modes=self.valid_modes, d=self.dag.dag_id if self.dag else "", t=self.task_id, m=self.mode)) def poke(self, context: Dict) -> bool: """ Function that the sensors defined while deriving this class should override. """ raise AirflowException('Override me.') def execute(self, context: Dict) -> Any: started_at = timezone.utcnow() try_number = 1 log_dag_id = self.dag.dag_id if self.has_dag() else "" if self.reschedule: # If reschedule, use first start date of current try task_reschedules = TaskReschedule.find_for_task_instance(context['ti']) if task_reschedules: started_at = task_reschedules[0].start_date try_number = len(task_reschedules) + 1 while not self.poke(context): if (timezone.utcnow() - started_at).total_seconds() > self.timeout: # If sensor is in soft fail mode but will be retried then # give it a chance and fail with timeout. # This gives the ability to set up non-blocking AND soft-fail sensors. if self.soft_fail and not context['ti'].is_eligible_to_retry(): self._do_skip_downstream_tasks(context) raise AirflowSkipException( f"Snap. Time is OUT. DAG id: {log_dag_id}") else: raise AirflowSensorTimeout( f"Snap. Time is OUT. DAG id: {log_dag_id}") if self.reschedule: reschedule_date = timezone.utcnow() + timedelta( seconds=self._get_next_poke_interval(started_at, try_number)) raise AirflowRescheduleException(reschedule_date) else: sleep(self._get_next_poke_interval(started_at, try_number)) try_number += 1 self.log.info("Success criteria met. Exiting.") def _do_skip_downstream_tasks(self, context: Dict) -> None: downstream_tasks = context['task'].get_flat_relatives(upstream=False) self.log.debug("Downstream task_ids %s", downstream_tasks) if downstream_tasks: self.skip(context['dag_run'], context['ti'].execution_date, downstream_tasks) def _get_next_poke_interval(self, started_at, try_number): """ Using the similar logic which is used for exponential backoff retry delay for operators. """ if self.exponential_backoff: min_backoff = int(self.poke_interval * (2 ** (try_number - 2))) current_time = timezone.utcnow() run_hash = int(hashlib.sha1("{}#{}#{}#{}".format( self.dag_id, self.task_id, started_at, try_number ).encode("utf-8")).hexdigest(), 16) modded_hash = min_backoff + run_hash % min_backoff delay_backoff_in_seconds = min( modded_hash, timedelta.max.total_seconds() - 1 ) new_interval = min(self.timeout - int((current_time - started_at).total_seconds()), delay_backoff_in_seconds) self.log.info("new %s interval is %s", self.mode, new_interval) return new_interval else: return self.poke_interval @property def reschedule(self): """Define mode rescheduled sensors.""" return self.mode == 'reschedule' # pylint: disable=no-member @property def deps(self): """ Adds one additional dependency for all sensor operators that checks if a sensor task instance can be rescheduled. """ if self.reschedule: return BaseOperator.deps.fget(self) | {ReadyToRescheduleDep()} return BaseOperator.deps.fget(self) def poke_mode_only(cls): """ Class Decorator for child classes of BaseSensorOperator to indicate that instances of this class are only safe to use poke mode. Will decorate all methods in the class to assert they did not change the mode from 'poke'. :param cls: BaseSensor class to enforce methods only use 'poke' mode. :type cls: type """ def decorate(cls_type): def mode_getter(_): return 'poke' def mode_setter(_, value): if value != 'poke': raise ValueError( f"cannot set mode to 'poke'.") if not issubclass(cls_type, BaseSensorOperator): raise ValueError(f"poke_mode_only decorator should only be " f"applied to subclasses of BaseSensorOperator," f" got:{cls_type}.") cls_type.mode = property(mode_getter, mode_setter) return cls_type return decorate(cls) if 'BUILDING_AIRFLOW_DOCS' in os.environ: # flake8: noqa: F811 # Monkey patch hook to get good function headers while building docs apply_defaults = lambda x: x

41.769231

96

0.638609

import hashlib import os from datetime import timedelta from time import sleep from typing import Any, Dict, Iterable from airflow.exceptions import ( AirflowException, AirflowRescheduleException, AirflowSensorTimeout, AirflowSkipException, ) from airflow.models import BaseOperator, SkipMixin, TaskReschedule from airflow.ti_deps.deps.ready_to_reschedule import ReadyToRescheduleDep from airflow.utils import timezone from airflow.utils.decorators import apply_defaults class BaseSensorOperator(BaseOperator, SkipMixin): ui_color = '#e6f1f2' valid_modes = ['poke', 'reschedule'] @apply_defaults def __init__(self, poke_interval: float = 60, timeout: float = 60 * 60 * 24 * 7, soft_fail: bool = False, mode: str = 'poke', exponential_backoff: bool = False, *args, **kwargs) -> None: super().__init__(*args, **kwargs) self.poke_interval = poke_interval self.soft_fail = soft_fail self.timeout = timeout self.mode = mode self.exponential_backoff = exponential_backoff self._validate_input_values() def _validate_input_values(self) -> None: if not isinstance(self.poke_interval, (int, float)) or self.poke_interval < 0: raise AirflowException( "The poke_interval must be a non-negative number") if not isinstance(self.timeout, (int, float)) or self.timeout < 0: raise AirflowException( "The timeout must be a non-negative number") if self.mode not in self.valid_modes: raise AirflowException( "The mode must be one of {valid_modes}," "'{d}.{t}'; received '{m}'." .format(valid_modes=self.valid_modes, d=self.dag.dag_id if self.dag else "", t=self.task_id, m=self.mode)) def poke(self, context: Dict) -> bool: raise AirflowException('Override me.') def execute(self, context: Dict) -> Any: started_at = timezone.utcnow() try_number = 1 log_dag_id = self.dag.dag_id if self.has_dag() else "" if self.reschedule: task_reschedules = TaskReschedule.find_for_task_instance(context['ti']) if task_reschedules: started_at = task_reschedules[0].start_date try_number = len(task_reschedules) + 1 while not self.poke(context): if (timezone.utcnow() - started_at).total_seconds() > self.timeout: if self.soft_fail and not context['ti'].is_eligible_to_retry(): self._do_skip_downstream_tasks(context) raise AirflowSkipException( f"Snap. Time is OUT. DAG id: {log_dag_id}") else: raise AirflowSensorTimeout( f"Snap. Time is OUT. DAG id: {log_dag_id}") if self.reschedule: reschedule_date = timezone.utcnow() + timedelta( seconds=self._get_next_poke_interval(started_at, try_number)) raise AirflowRescheduleException(reschedule_date) else: sleep(self._get_next_poke_interval(started_at, try_number)) try_number += 1 self.log.info("Success criteria met. Exiting.") def _do_skip_downstream_tasks(self, context: Dict) -> None: downstream_tasks = context['task'].get_flat_relatives(upstream=False) self.log.debug("Downstream task_ids %s", downstream_tasks) if downstream_tasks: self.skip(context['dag_run'], context['ti'].execution_date, downstream_tasks) def _get_next_poke_interval(self, started_at, try_number): if self.exponential_backoff: min_backoff = int(self.poke_interval * (2 ** (try_number - 2))) current_time = timezone.utcnow() run_hash = int(hashlib.sha1("{}#{}#{}#{}".format( self.dag_id, self.task_id, started_at, try_number ).encode("utf-8")).hexdigest(), 16) modded_hash = min_backoff + run_hash % min_backoff delay_backoff_in_seconds = min( modded_hash, timedelta.max.total_seconds() - 1 ) new_interval = min(self.timeout - int((current_time - started_at).total_seconds()), delay_backoff_in_seconds) self.log.info("new %s interval is %s", self.mode, new_interval) return new_interval else: return self.poke_interval @property def reschedule(self): return self.mode == 'reschedule' @property def deps(self): if self.reschedule: return BaseOperator.deps.fget(self) | {ReadyToRescheduleDep()} return BaseOperator.deps.fget(self) def poke_mode_only(cls): def decorate(cls_type): def mode_getter(_): return 'poke' def mode_setter(_, value): if value != 'poke': raise ValueError( f"cannot set mode to 'poke'.") if not issubclass(cls_type, BaseSensorOperator): raise ValueError(f"poke_mode_only decorator should only be " f"applied to subclasses of BaseSensorOperator," f" got:{cls_type}.") cls_type.mode = property(mode_getter, mode_setter) return cls_type return decorate(cls) if 'BUILDING_AIRFLOW_DOCS' in os.environ: apply_defaults = lambda x: x

true

790db0fe89a04beb0224baffd456de9a966428fd

12,782

py

Python

topi/python/topi/cuda/conv2d_nhwc_tensorcore.py

retamia/tvm

5d25dc54d874bf2ddf0e8cf34c4748e9e2656fd8

[ "Apache-2.0" ]

9

2019-12-17T08:03:54.000Z

2022-01-19T02:34:23.000Z

topi/python/topi/cuda/conv2d_nhwc_tensorcore.py

retamia/tvm

5d25dc54d874bf2ddf0e8cf34c4748e9e2656fd8

[ "Apache-2.0" ]

2

2020-07-08T12:34:59.000Z

2020-07-11T15:54:47.000Z

topi/python/topi/cuda/conv2d_nhwc_tensorcore.py

retamia/tvm

5d25dc54d874bf2ddf0e8cf34c4748e9e2656fd8

[ "Apache-2.0" ]

3

2020-10-04T20:30:18.000Z

2022-01-24T18:03:52.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # pylint: disable=invalid-name, too-many-locals, too-many-function-args # pylint: disable=too-many-statements, unused-argument, too-many-arguments """Tensorcore template for cuda backend""" import numpy as np import tvm from tvm import te from tvm import autotvm from ..util import get_const_tuple, traverse_inline, simplify from ..nn.pad import pad from ..nn.util import get_pad_tuple from .tensor_intrin import intrin_wmma_load_matrix_A from .tensor_intrin import intrin_wmma_load_matrix_W from .tensor_intrin import intrin_wmma_store_matrix from .tensor_intrin import intrin_wmma_gemm def nhwc_tensorcore_cuda(cfg, Input, Filter, stride, padding, dilation, out_dtype): """Compute declaration for tensorcore""" assert isinstance(stride, int) or len(stride) == 2 assert isinstance(dilation, int) or len(dilation) == 2 if isinstance(stride, int): stride_h = stride_w = stride else: stride_h, stride_w = stride if isinstance(dilation, int): dilation_h = dilation_w = dilation else: dilation_h, dilation_w = dilation batch, in_height, in_width, in_channel = get_const_tuple(Input.shape) kernel_h, kernel_w, _, num_filter = get_const_tuple(Filter.shape) assert (batch % 16 == 0 and in_channel % 16 == 0 and num_filter % 16 == 0) or \ (batch % 8 == 0 and in_channel % 16 == 0 and num_filter % 32 == 0) or \ (batch % 32 == 0 and in_channel % 16 == 0 and num_filter % 8 == 0), \ "The shape of (batch, in_channel, num_filter) "\ "must be multiple of (16, 16, 16) or (32, 16, 8) or (8, 16, 32) for now" # compute the output shape dilated_kernel_h = (kernel_h - 1) * dilation_h + 1 dilated_kernel_w = (kernel_w - 1) * dilation_w + 1 pad_top, pad_left, pad_down, pad_right = get_pad_tuple( padding, (dilated_kernel_h, dilated_kernel_w)) out_channel = num_filter out_height = simplify((in_height - dilated_kernel_h + pad_top + pad_down) // stride_h + 1) out_width = simplify((in_width - dilated_kernel_w + pad_left + pad_right) // stride_w + 1) pad_before = [0, pad_top, pad_left, 0] pad_after = [0, pad_down, pad_right, 0] PaddedInput = pad(Input, pad_before, pad_after, name="PaddedInput") rc = te.reduce_axis((0, in_channel), name='rc') ry = te.reduce_axis((0, kernel_h), name='ry') rx = te.reduce_axis((0, kernel_w), name='rx') # convert data type of input feature maps and weights TransPaddedInput = te.compute( PaddedInput.shape, lambda n, h, w, c: PaddedInput[n, h, w, c].astype('float16')) TransFilter = te.compute( Filter.shape, lambda h, w, i, o: Filter[h, w, i, o].astype('float16')) Output = te.compute( (batch, out_height, out_width, out_channel), lambda nn, yy, xx, ff: te.sum( TransPaddedInput[nn, yy * stride_h + ry * dilation_h, xx * stride_w + rx * dilation_w, rc].astype(out_dtype) * TransFilter[ry, rx, rc, ff].astype(out_dtype), axis=[ry, rx, rc]), name="Conv2dOutput", tag="conv2d_nhwc_tensorcore") return Output def schedule_nhwc_tensorcore_cuda(cfg, s, Conv): """Schedule tensorcore template""" kh, kw, ic = s[Conv].op.reduce_axis out_dtype = Conv.dtype trans_paddata, kernel = s[Conv].op.input_tensors in_dtype = trans_paddata.dtype batch, _, _, _ = get_const_tuple(Conv.shape) _, _, _, out_channels = get_const_tuple(kernel.shape) paddata = s[trans_paddata].op.input_tensors # inline the pad and dtype transform s[trans_paddata].compute_inline() s[kernel].compute_inline() s[paddata[0]].compute_inline() # Designate the memory hierarchy AS = s.cache_read(trans_paddata, 'shared', [Conv]) WS = s.cache_read(kernel, 'shared', [Conv]) AF = s.cache_read(AS, 'wmma.matrix_a', [Conv]) WF = s.cache_read(WS, 'wmma.matrix_b', [Conv]) ConvF = s.cache_write(Conv, 'wmma.accumulator') if Conv.op in s.outputs: output = Conv ConvS = s.cache_read(ConvF, 'shared', [Conv]) OL = ConvS else: output = s.outputs[0].output(0) s[Conv].set_scope('shared') OL = Conv # Schedule for autotvm cfg.define_knob("block_row_warps", [1, 2, 4]) cfg.define_knob("block_col_warps", [1, 2, 4]) cfg.define_knob("warp_row_tiles", [1, 2, 4]) cfg.define_knob("warp_col_tiles", [1, 2, 4]) cfg.define_knob("chunk", [1, 2, 4, 8]) cfg.define_knob("offset", [0, 8]) cfg.define_knob("vector_width", [1, 2, 4, 8]) if (batch % 16 == 0 and out_channels % 16 == 0): cfg.define_knob("wmma_m", [16, 8, 32]) elif (batch % 8 == 0 and out_channels % 32 == 0): cfg.define_knob("wmma_m", [8, 16, 32]) elif (batch % 32 == 0 and out_channels % 8 == 0): cfg.define_knob("wmma_m", [32, 16, 8]) # fallback support target = tvm.target.Target.current() if cfg.is_fallback: ref_log = autotvm.tophub.load_reference_log( target.target_name, target.model, 'conv2d_nhwc_tensorcore.cuda') cfg.fallback_with_reference_log(ref_log) block_row_warps = cfg["block_row_warps"].val block_col_warps = cfg["block_col_warps"].val warp_row_tiles = cfg["warp_row_tiles"].val warp_col_tiles = cfg["warp_col_tiles"].val chunk = cfg["chunk"].val offset = cfg["offset"].val wmma_m = cfg["wmma_m"].val vector_width = cfg["vector_width"].val wmma_k = 16 if wmma_m == 16: wmma_n = 16 elif wmma_m == 8: wmma_n = 32 elif wmma_m == 32: wmma_n = 8 warp_size = 32 block_x = te.thread_axis('blockIdx.x') block_y = te.thread_axis('blockIdx.y') block_z = te.thread_axis('blockIdx.z') thread_x = te.thread_axis('threadIdx.x') thread_y = te.thread_axis('threadIdx.y') thread_z = te.thread_axis('threadIdx.z') # Define the intrin strides def get_strides(extents): return [np.prod(extents[i:]).tolist() for i in range(len(extents))] AS_align = chunk * wmma_k + offset WS_align = warp_col_tiles * block_col_warps * wmma_n + offset block_factor_n = wmma_m * warp_row_tiles * block_row_warps block_factor_o = wmma_n * warp_col_tiles * block_col_warps CS_align = block_factor_o + offset AS_strides = get_strides([1, 1, AS_align, 1]) AL_strides = get_strides([1, 1, wmma_k, 1]) WS_strides = get_strides([WS_align, 1]) WL_strides = get_strides([wmma_n * warp_col_tiles, 1]) CL_strides = get_strides([1, 1, wmma_n * warp_col_tiles, 1]) CS_strides = get_strides([1, 1, CS_align, 1]) # Schedule for output nc, hc, wc, oc = output.op.axis block_k = s[output].fuse(hc, wc) s[output].bind(block_k, block_z) block_i, nc = s[output].split(nc, factor=block_factor_n) block_j, oc = s[output].split(oc, factor=block_factor_o) s[output].reorder(block_k, block_i, block_j, nc, oc) t = s[output].fuse(nc, oc) t, ti = s[output].split(t, factor=vector_width) t, tx = s[output].split(t, factor=warp_size) t, ty = s[output].split(t, factor=block_row_warps) t, tz = s[output].split(t, factor=block_col_warps) s[output].bind(block_i, block_x) s[output].bind(block_j, block_y) s[output].bind(tz, thread_z) s[output].bind(ty, thread_y) s[output].bind(tx, thread_x) s[output].vectorize(ti) # Schedule wmma store s[OL].compute_at(s[output], block_j) nc, hc, wc, oc = OL.op.axis s[OL].reorder(hc, wc, nc, oc) s[OL].storage_align(wc, CS_align - 1, CS_align) oc, ooc = s[OL].split(oc, factor=wmma_n) oc, oci = s[OL].split(oc, factor=warp_col_tiles) _, oc = s[OL].split(oc, factor=block_col_warps) nc, nnc = s[OL].split(nc, factor=wmma_m) nc, nci = s[OL].split(nc, factor=warp_row_tiles) _, nc = s[OL].split(nc, factor=block_row_warps) s[OL].reorder(nc, oc, nci, oci, nnc, ooc) s[OL].bind(nc, thread_y) s[OL].bind(oc, thread_z) # Schedule wmma computation s[ConvF].compute_at(s[OL], oc) n, h, w, o = ConvF.op.axis n, nnf = s[ConvF].split(n, factor=wmma_m) o, oof = s[ConvF].split(o, factor=wmma_n) ic, ii = s[ConvF].split(ic, factor=wmma_k) ko, ki = s[ConvF].split(ic, factor=chunk) s[ConvF].reorder(kh, kw, ko, ki, n, o, nnf, oof, ii) s[AF].compute_at(s[ConvF], ki) s[WF].compute_at(s[ConvF], ki) # Schedule wmma load n, h, w, i = AF.op.axis n, nn = s[AF].split(n, factor=wmma_m) i, ii = s[AF].split(i, factor=wmma_k) s[AF].reorder(n, i, nn, ii) kh, kw, i, o = WF.op.axis i, ii = s[WF].split(i, factor=wmma_k) o, oo = s[WF].split(o, factor=wmma_n) s[WF].reorder(o, i, oo) s[WF].reorder(i, o, ii, oo) s[WS].compute_at(s[ConvF], ko) s[AS].compute_at(s[ConvF], ko) # Schedule for data's share memory n, h, w, i = AS.op.axis s[AS].reorder(h, w, n, i) s[AS].storage_align(w, AS_align - 1, AS_align) t = s[AS].fuse(n, i) t, ti = s[AS].split(t, factor=vector_width) t, tx = s[AS].split(t, factor=warp_size) t, ty = s[AS].split(t, factor=block_row_warps) _, tz = s[AS].split(t, factor=block_col_warps) s[AS].bind(ty, thread_y) s[AS].bind(tz, thread_z) s[AS].bind(tx, thread_x) s[AS].vectorize(ti) # Schedule for kernel's share memory kh, kw, ic, o = WS.op.axis t = s[WS].fuse(ic, o) s[WS].storage_align(ic, WS_align - 1, WS_align) t, ti = s[WS].split(t, factor=vector_width) t, tx = s[WS].split(t, factor=warp_size) t, ty = s[WS].split(t, factor=block_row_warps) _, tz = s[WS].split(t, factor=block_col_warps) s[WS].bind(ty, thread_y) s[WS].bind(tz, thread_z) s[WS].bind(tx, thread_x) s[WS].vectorize(ti) shape = (wmma_m, wmma_n, wmma_k) # tensorize the wmma process AS_shape = (wmma_m, 1, 1, wmma_k) AL_shape = (wmma_m, 1, 1, wmma_k) WS_shape = (wmma_k, wmma_n) WL_shape = (wmma_k, wmma_n) CL_shape = (wmma_m, 1, 1, wmma_n) CS_shape = (wmma_m, 1, 1, wmma_n) AL_gemm = te.placeholder(AL_shape, name='A', dtype=in_dtype) WL_gemm = te.placeholder(WL_shape, name='B', dtype=in_dtype) k_gemm = te.reduce_axis((0, wmma_k), name="k") CL_compute = te.compute(CL_shape, lambda ii, t0, t1, jj: te.sum(AL_gemm[ii, t0, t1, k_gemm].astype(out_dtype) * \ WL_gemm[k_gemm, jj].astype(out_dtype), axis=k_gemm), name='C') s[AF].tensorize(nn, intrin_wmma_load_matrix_A(AL_strides, AS_strides, shape, "row_major", AS_shape, AL_shape, in_dtype)) s[WF].tensorize(ii, intrin_wmma_load_matrix_W(WL_strides, WS_strides, shape, "row_major", WS_shape, WL_shape, in_dtype)) s[OL].tensorize(nnc, intrin_wmma_store_matrix(CS_strides, CL_strides, shape, out_dtype, CL_shape, CS_shape)) s[ConvF].tensorize(nnf, intrin_wmma_gemm(AL_gemm, WL_gemm, CL_compute, AL_strides, WL_strides, CL_strides, shape)) N, OH, OW, CO = get_const_tuple(output.shape) KH, KW, CI, _ = get_const_tuple(kernel.shape) cfg.add_flop(2 * N * OH * OW * CO * CI * KH * KW) @autotvm.register_topi_compute("conv2d_nhwc_tensorcore.cuda") def conv2d_nhwc_tensorcore(cfg, data, kernel, strides, padding, dilation, out_dtype): """Compute conv2d with tensorcore for NCHW layout""" return nhwc_tensorcore_cuda(cfg, data, kernel, strides, padding, dilation, out_dtype) @autotvm.register_topi_schedule("conv2d_nhwc_tensorcore.cuda") def schedule_conv2d_nhwc_tensorcore(cfg, outs): """TOPI schedule callback""" s = te.create_schedule([x.op for x in outs]) def _callback(op): if 'conv2d_nhwc_tensorcore' in op.tag: schedule_nhwc_tensorcore_cuda(cfg, s, op.output(0)) traverse_inline(s, outs[0].op, _callback) return s

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import numpy as np import tvm from tvm import te from tvm import autotvm from ..util import get_const_tuple, traverse_inline, simplify from ..nn.pad import pad from ..nn.util import get_pad_tuple from .tensor_intrin import intrin_wmma_load_matrix_A from .tensor_intrin import intrin_wmma_load_matrix_W from .tensor_intrin import intrin_wmma_store_matrix from .tensor_intrin import intrin_wmma_gemm def nhwc_tensorcore_cuda(cfg, Input, Filter, stride, padding, dilation, out_dtype): assert isinstance(stride, int) or len(stride) == 2 assert isinstance(dilation, int) or len(dilation) == 2 if isinstance(stride, int): stride_h = stride_w = stride else: stride_h, stride_w = stride if isinstance(dilation, int): dilation_h = dilation_w = dilation else: dilation_h, dilation_w = dilation batch, in_height, in_width, in_channel = get_const_tuple(Input.shape) kernel_h, kernel_w, _, num_filter = get_const_tuple(Filter.shape) assert (batch % 16 == 0 and in_channel % 16 == 0 and num_filter % 16 == 0) or \ (batch % 8 == 0 and in_channel % 16 == 0 and num_filter % 32 == 0) or \ (batch % 32 == 0 and in_channel % 16 == 0 and num_filter % 8 == 0), \ "The shape of (batch, in_channel, num_filter) "\ "must be multiple of (16, 16, 16) or (32, 16, 8) or (8, 16, 32) for now" dilated_kernel_h = (kernel_h - 1) * dilation_h + 1 dilated_kernel_w = (kernel_w - 1) * dilation_w + 1 pad_top, pad_left, pad_down, pad_right = get_pad_tuple( padding, (dilated_kernel_h, dilated_kernel_w)) out_channel = num_filter out_height = simplify((in_height - dilated_kernel_h + pad_top + pad_down) // stride_h + 1) out_width = simplify((in_width - dilated_kernel_w + pad_left + pad_right) // stride_w + 1) pad_before = [0, pad_top, pad_left, 0] pad_after = [0, pad_down, pad_right, 0] PaddedInput = pad(Input, pad_before, pad_after, name="PaddedInput") rc = te.reduce_axis((0, in_channel), name='rc') ry = te.reduce_axis((0, kernel_h), name='ry') rx = te.reduce_axis((0, kernel_w), name='rx') TransPaddedInput = te.compute( PaddedInput.shape, lambda n, h, w, c: PaddedInput[n, h, w, c].astype('float16')) TransFilter = te.compute( Filter.shape, lambda h, w, i, o: Filter[h, w, i, o].astype('float16')) Output = te.compute( (batch, out_height, out_width, out_channel), lambda nn, yy, xx, ff: te.sum( TransPaddedInput[nn, yy * stride_h + ry * dilation_h, xx * stride_w + rx * dilation_w, rc].astype(out_dtype) * TransFilter[ry, rx, rc, ff].astype(out_dtype), axis=[ry, rx, rc]), name="Conv2dOutput", tag="conv2d_nhwc_tensorcore") return Output def schedule_nhwc_tensorcore_cuda(cfg, s, Conv): kh, kw, ic = s[Conv].op.reduce_axis out_dtype = Conv.dtype trans_paddata, kernel = s[Conv].op.input_tensors in_dtype = trans_paddata.dtype batch, _, _, _ = get_const_tuple(Conv.shape) _, _, _, out_channels = get_const_tuple(kernel.shape) paddata = s[trans_paddata].op.input_tensors s[trans_paddata].compute_inline() s[kernel].compute_inline() s[paddata[0]].compute_inline() AS = s.cache_read(trans_paddata, 'shared', [Conv]) WS = s.cache_read(kernel, 'shared', [Conv]) AF = s.cache_read(AS, 'wmma.matrix_a', [Conv]) WF = s.cache_read(WS, 'wmma.matrix_b', [Conv]) ConvF = s.cache_write(Conv, 'wmma.accumulator') if Conv.op in s.outputs: output = Conv ConvS = s.cache_read(ConvF, 'shared', [Conv]) OL = ConvS else: output = s.outputs[0].output(0) s[Conv].set_scope('shared') OL = Conv cfg.define_knob("block_row_warps", [1, 2, 4]) cfg.define_knob("block_col_warps", [1, 2, 4]) cfg.define_knob("warp_row_tiles", [1, 2, 4]) cfg.define_knob("warp_col_tiles", [1, 2, 4]) cfg.define_knob("chunk", [1, 2, 4, 8]) cfg.define_knob("offset", [0, 8]) cfg.define_knob("vector_width", [1, 2, 4, 8]) if (batch % 16 == 0 and out_channels % 16 == 0): cfg.define_knob("wmma_m", [16, 8, 32]) elif (batch % 8 == 0 and out_channels % 32 == 0): cfg.define_knob("wmma_m", [8, 16, 32]) elif (batch % 32 == 0 and out_channels % 8 == 0): cfg.define_knob("wmma_m", [32, 16, 8]) target = tvm.target.Target.current() if cfg.is_fallback: ref_log = autotvm.tophub.load_reference_log( target.target_name, target.model, 'conv2d_nhwc_tensorcore.cuda') cfg.fallback_with_reference_log(ref_log) block_row_warps = cfg["block_row_warps"].val block_col_warps = cfg["block_col_warps"].val warp_row_tiles = cfg["warp_row_tiles"].val warp_col_tiles = cfg["warp_col_tiles"].val chunk = cfg["chunk"].val offset = cfg["offset"].val wmma_m = cfg["wmma_m"].val vector_width = cfg["vector_width"].val wmma_k = 16 if wmma_m == 16: wmma_n = 16 elif wmma_m == 8: wmma_n = 32 elif wmma_m == 32: wmma_n = 8 warp_size = 32 block_x = te.thread_axis('blockIdx.x') block_y = te.thread_axis('blockIdx.y') block_z = te.thread_axis('blockIdx.z') thread_x = te.thread_axis('threadIdx.x') thread_y = te.thread_axis('threadIdx.y') thread_z = te.thread_axis('threadIdx.z') def get_strides(extents): return [np.prod(extents[i:]).tolist() for i in range(len(extents))] AS_align = chunk * wmma_k + offset WS_align = warp_col_tiles * block_col_warps * wmma_n + offset block_factor_n = wmma_m * warp_row_tiles * block_row_warps block_factor_o = wmma_n * warp_col_tiles * block_col_warps CS_align = block_factor_o + offset AS_strides = get_strides([1, 1, AS_align, 1]) AL_strides = get_strides([1, 1, wmma_k, 1]) WS_strides = get_strides([WS_align, 1]) WL_strides = get_strides([wmma_n * warp_col_tiles, 1]) CL_strides = get_strides([1, 1, wmma_n * warp_col_tiles, 1]) CS_strides = get_strides([1, 1, CS_align, 1]) nc, hc, wc, oc = output.op.axis block_k = s[output].fuse(hc, wc) s[output].bind(block_k, block_z) block_i, nc = s[output].split(nc, factor=block_factor_n) block_j, oc = s[output].split(oc, factor=block_factor_o) s[output].reorder(block_k, block_i, block_j, nc, oc) t = s[output].fuse(nc, oc) t, ti = s[output].split(t, factor=vector_width) t, tx = s[output].split(t, factor=warp_size) t, ty = s[output].split(t, factor=block_row_warps) t, tz = s[output].split(t, factor=block_col_warps) s[output].bind(block_i, block_x) s[output].bind(block_j, block_y) s[output].bind(tz, thread_z) s[output].bind(ty, thread_y) s[output].bind(tx, thread_x) s[output].vectorize(ti) s[OL].compute_at(s[output], block_j) nc, hc, wc, oc = OL.op.axis s[OL].reorder(hc, wc, nc, oc) s[OL].storage_align(wc, CS_align - 1, CS_align) oc, ooc = s[OL].split(oc, factor=wmma_n) oc, oci = s[OL].split(oc, factor=warp_col_tiles) _, oc = s[OL].split(oc, factor=block_col_warps) nc, nnc = s[OL].split(nc, factor=wmma_m) nc, nci = s[OL].split(nc, factor=warp_row_tiles) _, nc = s[OL].split(nc, factor=block_row_warps) s[OL].reorder(nc, oc, nci, oci, nnc, ooc) s[OL].bind(nc, thread_y) s[OL].bind(oc, thread_z) s[ConvF].compute_at(s[OL], oc) n, h, w, o = ConvF.op.axis n, nnf = s[ConvF].split(n, factor=wmma_m) o, oof = s[ConvF].split(o, factor=wmma_n) ic, ii = s[ConvF].split(ic, factor=wmma_k) ko, ki = s[ConvF].split(ic, factor=chunk) s[ConvF].reorder(kh, kw, ko, ki, n, o, nnf, oof, ii) s[AF].compute_at(s[ConvF], ki) s[WF].compute_at(s[ConvF], ki) n, h, w, i = AF.op.axis n, nn = s[AF].split(n, factor=wmma_m) i, ii = s[AF].split(i, factor=wmma_k) s[AF].reorder(n, i, nn, ii) kh, kw, i, o = WF.op.axis i, ii = s[WF].split(i, factor=wmma_k) o, oo = s[WF].split(o, factor=wmma_n) s[WF].reorder(o, i, oo) s[WF].reorder(i, o, ii, oo) s[WS].compute_at(s[ConvF], ko) s[AS].compute_at(s[ConvF], ko) n, h, w, i = AS.op.axis s[AS].reorder(h, w, n, i) s[AS].storage_align(w, AS_align - 1, AS_align) t = s[AS].fuse(n, i) t, ti = s[AS].split(t, factor=vector_width) t, tx = s[AS].split(t, factor=warp_size) t, ty = s[AS].split(t, factor=block_row_warps) _, tz = s[AS].split(t, factor=block_col_warps) s[AS].bind(ty, thread_y) s[AS].bind(tz, thread_z) s[AS].bind(tx, thread_x) s[AS].vectorize(ti) # Schedule for kernel's share memory kh, kw, ic, o = WS.op.axis t = s[WS].fuse(ic, o) s[WS].storage_align(ic, WS_align - 1, WS_align) t, ti = s[WS].split(t, factor=vector_width) t, tx = s[WS].split(t, factor=warp_size) t, ty = s[WS].split(t, factor=block_row_warps) _, tz = s[WS].split(t, factor=block_col_warps) s[WS].bind(ty, thread_y) s[WS].bind(tz, thread_z) s[WS].bind(tx, thread_x) s[WS].vectorize(ti) shape = (wmma_m, wmma_n, wmma_k) AS_shape = (wmma_m, 1, 1, wmma_k) AL_shape = (wmma_m, 1, 1, wmma_k) WS_shape = (wmma_k, wmma_n) WL_shape = (wmma_k, wmma_n) CL_shape = (wmma_m, 1, 1, wmma_n) CS_shape = (wmma_m, 1, 1, wmma_n) AL_gemm = te.placeholder(AL_shape, name='A', dtype=in_dtype) WL_gemm = te.placeholder(WL_shape, name='B', dtype=in_dtype) k_gemm = te.reduce_axis((0, wmma_k), name="k") CL_compute = te.compute(CL_shape, lambda ii, t0, t1, jj: te.sum(AL_gemm[ii, t0, t1, k_gemm].astype(out_dtype) * \ WL_gemm[k_gemm, jj].astype(out_dtype), axis=k_gemm), name='C') s[AF].tensorize(nn, intrin_wmma_load_matrix_A(AL_strides, AS_strides, shape, "row_major", AS_shape, AL_shape, in_dtype)) s[WF].tensorize(ii, intrin_wmma_load_matrix_W(WL_strides, WS_strides, shape, "row_major", WS_shape, WL_shape, in_dtype)) s[OL].tensorize(nnc, intrin_wmma_store_matrix(CS_strides, CL_strides, shape, out_dtype, CL_shape, CS_shape)) s[ConvF].tensorize(nnf, intrin_wmma_gemm(AL_gemm, WL_gemm, CL_compute, AL_strides, WL_strides, CL_strides, shape)) N, OH, OW, CO = get_const_tuple(output.shape) KH, KW, CI, _ = get_const_tuple(kernel.shape) cfg.add_flop(2 * N * OH * OW * CO * CI * KH * KW) @autotvm.register_topi_compute("conv2d_nhwc_tensorcore.cuda") def conv2d_nhwc_tensorcore(cfg, data, kernel, strides, padding, dilation, out_dtype): return nhwc_tensorcore_cuda(cfg, data, kernel, strides, padding, dilation, out_dtype) @autotvm.register_topi_schedule("conv2d_nhwc_tensorcore.cuda") def schedule_conv2d_nhwc_tensorcore(cfg, outs): s = te.create_schedule([x.op for x in outs]) def _callback(op): if 'conv2d_nhwc_tensorcore' in op.tag: schedule_nhwc_tensorcore_cuda(cfg, s, op.output(0)) traverse_inline(s, outs[0].op, _callback) return s

true

790db224486fee9e2e6acac2ef44531a3d016a9c

44,167

py

Python

shell/control.py

dromero1452/shellsploit-framework

38ce78542fd2dd2ac30f6567972d695ede1e4709

[ "MIT" ]

2

2019-12-23T15:47:02.000Z

2020-01-06T09:51:57.000Z

shell/control.py

badfish5150/shellsploit-framework

22bb910d33379ca29ddd10ba93a63e9ff1eab99d

[ "MIT" ]

null

shell/control.py

badfish5150/shellsploit-framework

22bb910d33379ca29ddd10ba93a63e9ff1eab99d

[ "MIT" ]

1

2021-12-23T16:35:24.000Z

#------------------Bombermans Team---------------------------------# # Author : B3mB4m # Concat : b3mb4m@protonmail.com # Project : https://github.com/b3mb4m/Shellsploit # LICENSE : https://github.com/b3mb4m/Shellsploit/blob/master/LICENSE #------------------------------------------------------------------# import sys import os from .core.color import * from re import findall from .core.Comp import tab from lib.base.framework import ShellsploitFramework if sys.version_info.major >= 3: raw_input = input class B3mB4m(ShellsploitFramework): def __init__(self): ShellsploitFramework.__init__(self) self.argvlist = ["None", "None", "None", "None"] self.disassembly = "None" self.mycache = "None" def control(self, string): bash = bcolors.OKBLUE + bcolors.UNDERLINE + "ssf" + bcolors.ENDC bash += ":" bash += bcolors.RED + string + bcolors.ENDC bash += bcolors.OKBLUE + " > " + bcolors.ENDC try: terminal = raw_input(bash) except KeyboardInterrupt: B3mB4m.exit("\n[*] (Ctrl + C ) Detected, Trying To Exit ...") # Injectors if string[:9] == "injectors": tab.completion("injectors") if terminal[:4] == "help": from .core.help import injectorhelp injectorhelp() self.control(string) elif terminal[:4] == "back": self.argvlist = ["None", "None", "None", "None"] pass # elif terminal[:9] == "need help": # import XX # print youtubelink for this module elif terminal[:4] == "exit": B3mB4m.exit("\nThanks for using shellsploit !\n") elif terminal[:4] == "pids": B3mB4m.pids("wholelist") self.control(string) elif terminal[:6] == "getpid": B3mB4m.pids(None, terminal[7:]) self.control(string) elif terminal[:5] == "clear": B3mB4m.clean() self.control(string) elif terminal[:5] == "unset": if string in B3mB4m.bfdlist(): if terminal[6:] == "exe" or terminal[6:] == "file": self.argvlist[0] = "None" elif terminal[6:] == "host": self.argvlist[1] = "None" elif terminal[6:] == "port": self.argvlist[2] = "None" else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) elif string == "injectors/Windows/x86/tLsInjectorDLL": if terminal[6:] == "exe": self.argvlist[0] = "None" elif terminal[6:] == "dll": self.argvlist[1] = "None" else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) elif string == "injectors/Windows/x86/CodecaveInjector": if terminal[6:] == "exe": self.argvlist[0] = "None" elif terminal[6:] == "shellcode": self.argvlist[1] = "None" else: if terminal[6:] == "pid": self.argvlist[0] = "None" elif terminal[6:] == "shellcode": self.argvlist[1] = "None" else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string) elif terminal[:3] == "set": if string in B3mB4m.bfdlist(): if terminal[4:7] == "exe" or terminal[4:8] == "file": self.argvlist[0] = terminal[9:] elif terminal[4:8] == "host": self.argvlist[1] = terminal[9:] elif terminal[4:8] == "port": self.argvlist[2] = terminal[9:] else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) elif string == "injectors/Windows/x86/tLsInjectorDLL": if terminal[4:7] == "exe": self.argvlist[0] = terminal[8:] elif terminal[4:7] == "dll": self.argvlist[1] = terminal[8:] else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) elif string == "injectors/Windows/x86/CodecaveInjector": if terminal[4:7] == "exe": self.argvlist[0] = terminal[8:] elif terminal[4:13] == "shellcode": self.argvlist[1] = terminal[14:] else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) else: if terminal[4:7] == "pid": self.argvlist[0] = terminal[8:] elif terminal[4:13] == "shellcode": if ".txt" in terminal[14:]: if os.path.isfile(terminal[14:]): with open(terminal[14:], "r") as shellcode: cache = shellcode.readlines() db = "" for x in database: db += x.strip().replace('"', "").replace('+', "").strip() self.argvlist[1] = db else: print(bcolors.RED + bcolors.BOLD + "\nFile can't find, please try with full path.\n" + bcolors.ENDC) self.control(string) else: self.argvlist[1] = terminal[14:] else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string) elif terminal[:14] == "show shellcode": if string in B3mB4m.bfdlist(): print("This option not available for this module.") self.control(string) elif string == "injectors/Windowsx86/tLsInjectorDLL": self.control(string) else: if self.argvlist[1] != "None": B3mB4m.prettyout(self.argvlist[1]) else: print("\nYou must set shellcode before this ..\b") self.control(string) elif terminal[:12] == "show options": from .core.Injectoroptions import controlset if string in B3mB4m.bfdlist(): controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) self.control(string) else: if string != "injectors/Windows/x86/tLsInjectorDLL": if self.argvlist[1] != "None": self.mycache = "process" controlset(string, self.argvlist[0], self.mycache) self.control(string) controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif terminal[:5] == "clear": B3mB4m.clean() self.control(string) elif terminal[:2] == "os": B3mB4m.oscommand(terminal[3:]) self.control(string) elif terminal[:6] == "inject": if self.argvlist[0] == None or self.argvlist[1] == None: print("\nYou must set pid/shellcode before inject !\n") self.control(string) if string == "injectors/Linux86/ptrace": from .inject.menager import linux86ptrace linux86ptrace(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Linux64/ptrace": from .inject.menager import linux64ptrace linux64ptrace(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Windows/byteman": from .inject.menager import windows windows(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Windows/x86/tLsInjectorDLL": from .inject.menager import winx86tLsDLL winx86tLsDLL(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Windows/x86/CodecaveInjector": from .inject.menager import winx86Codecave winx86Codecave(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Windows/Dllinjector": from .inject.menager import winDLL winDLL(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Windows/BFD/Patching": from .inject.menager import winBFD winBFD(self.argvlist[0], self.argvlist[1], int(self.argvlist[2])) # elif string == "injectors/MacOSX/BFD/Patching": # from .inject.menager import MacBFD # MacBFD( FILE, HOST, PORT) # elif string == "injectors/Linux/BFD/Patching": # from .inject.menager import LinuxBFD # LinuxBFD( FILE, HOST, PORT) # elif string == "injectors/Linux/ARM/x86/BFD/Patching": # from .inject.menager import LinuxARMx86BFD # LinuxARMx86BFD( FILE, HOST, PORT) # elif string == "FreeBSD/x86/BFD/Patching": # from .inject.menager import FreeBSDx86 # FreeBSDx86( FILE, HOST, PORT) self.control(string) # elif terminal[:7] == "extract": # Future option # Make it executable (Dynamic virus land) # from bla bla import executable # generator() elif terminal[:4] == "back": self.argvlist = ["None", "None", "None", "None"] pass else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string) # Backdoors elif string[:9] == "backdoors": tab.completion("backdoors") if terminal[:4] == "help": from .core.help import backdoorshelp backdoorshelp() self.control(string) elif terminal[:4] == "exit": B3mB4m.exit("\nThanks for using shellsploit !\n") elif terminal[:2] == "os": B3mB4m.oscommand(terminal[3:]) self.control(string) elif terminal[:12] == "show options": from .core.SHELLoptions import controlset controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif terminal[:5] == "unset": if terminal[6:] == "lhost": self.argvlist[0] = "None" elif terminal[6:] == "lport": self.argvlist[1] = "None" # elif terminal[6:] == "encoder": # self.argvlist[2] = "None" else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string) elif terminal[:3] == "set": if terminal[4:9].lower() == "lhost": self.argvlist[0] = terminal[10:] elif terminal[4:9].lower() == "lport": self.argvlist[1] = terminal[10:] # elif terminal[4:11].lower() == "encoder" # self.argvlist[2] = terminal[11:] else: print(bcolors.RED + bcolors.BOLD + "This option is not available." + bcolors.ENDC) self.control(string) elif terminal[:8] == "generate": from .Session.generator import process # Custom output path will be add .. if self.argvlist[0] == "None" or self.argvlist[1] == "None": print("\nSet options before generate payload.\n") self.control(string) else: process(data=string, HOST=self.argvlist[0], PORT=self.argvlist[1], ENCODER=False, logger=True) self.control(string) elif terminal[:5] == "clear": B3mB4m.clean() self.control(string) elif terminal[:4] == "back": self.argvlist = ["None", "None", "None", "None"] pass else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string) # Shellcodes else: tab.completion("shellcodes") if terminal[:4] == "help": # if terminal[5:11] == "output": # from Outputs.exehelp import help # print help() # self.control( string) from .core.help import shellcodehelp shellcodehelp() self.control(string) elif terminal[:2] == "os": B3mB4m.oscommand(terminal[3:]) self.control(string) elif terminal[:4] == "back": self.argvlist = ["None", "None", "None", "None"] pass elif terminal[:4] == "exit": B3mB4m.exit("\nThanks for using shellsploit !\n") elif terminal[:10] == "whatisthis": from .core.whatisthis import whatisthis if "egg" in string: message = "Egg-hunt" elif "tcp" in string or "reverse" in string or "netcat" in string: message = "Remote" elif "download" in string: message = "Download and execute" else: message = "Local" # Add special part for particul whatisthis(message) self.control(string) elif terminal[:5] == "unset": if terminal[6:] == "encoder": self.argvlist[0] = "None" elif terminal[6:] == "iteration": self.argvlist[1] = "None" elif terminal[6:] == "file": if string in B3mB4m.readlist(): self.argvlist[2] = "None" else: B3mB4m.invalidcommand() elif terminal[6:] == "port": if string in B3mB4m.tcpbindlist() or string in B3mB4m.reversetcplist(): self.argvlist[2] = "None" else: Base.invalidcommand() elif terminal[6:] == "command": if string in B3mB4m.execlist(): self.argvlist[2] = "None" else: B3mB4m.invalidcommand() elif terminal[6:] == "link": if string in B3mB4m.downloadandexecutelist(): self.argvlist[2] = "None" else: B3mB4m.invalidcommand() elif terminal[6:] == "filename": if string in B3mB4m.downloadandexecutelist(): self.argvlist[3] = "None" else: B3mB4m.invalidcommand() elif terminal[6:] == "host": if string in B3mB4m.reversetcplist(): self.argvlist[3] = "None" else: B3mB4m.invalidcommand() else: B3mB4m.invalidcommand() self.control(string) elif terminal[:3] == "set": if terminal[4:8] == "file": if string in B3mB4m.readlist(): self.argvlist[2] = terminal[9:] else: B3mB4m.invalidcommand() elif terminal[4:8] == "port": if string in B3mB4m.tcpbindlist() or string in B3mB4m.reversetcplist(): self.argvlist[2] = terminal[9:] else: B3mB4m.invalidcommand() elif terminal[4:11] == "command": if string in B3mB4m.execlist(): self.argvlist[2] = terminal[12:] else: B3mB4m.invalidcommand() elif terminal[4:8] == "link": if string in B3mB4m.downloadandexecutelist(): self.argvlist[2] = terminal[9:] else: B3mB4m.invalidcommand() elif terminal[4:11] == "message": if string in B3mB4m.messageboxlist(): self.argvlist[2] = terminal[12:] else: B3mB4m.invalidcommand() elif terminal[4:8] == "host": if string in B3mB4m.reversetcplist(): self.argvlist[3] = terminal[9:] else: B3mB4m.invalidcommand() elif terminal[4:12] == "filename": if string in B3mB4m.downloadandexecutelist(): self.argvlist[3] = terminal[13:] else: B3mB4m.invalidcommand() elif terminal[4:11] == "encoder": from .core.lists import encoders if terminal[12:] not in encoders(): print("This encoder not in list !") self.control(string) self.argvlist[0] = terminal[12:] elif terminal[4:13] == "iteration": self.argvlist[1] = terminal[14:] else: B3mB4m.invalidcommand() self.control(string) elif terminal[:12] == "show options": from .core.SHELLoptions import controlset if string[:7] == "linux86": if string == "linux86/read": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux86/chmod": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux86/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux86/reverse_tcp": controlset(string, self.argvlist[3], self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux86/download&exec": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux86/exec": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:10] == "solarisx86": if string == "solarisx86/read": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "solarisx86/reverse_tcp": controlset(string, self.argvlist[3], self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "solarisx86/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:7] == "linux64": if string == "linux64/read": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux64/mkdir": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux64/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux64/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[1], self.argvlist[0]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:5] == "linux": if string == "linux/read": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:5] == "osx86": if string == "osx86/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "osx86/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[1], self.argvlist[0]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:5] == "osx64": if string == "osx64/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "osx64/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:11] == "freebsd_x86": if string == "freebsd_x86/reverse_tcp2": controlset(string, self.argvlist[3], self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "freebsd_x86/reverse_tcp": controlset(string, self.argvlist[3], self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "freebsd_x86/read": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "freebsd_x86/exec": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "freebsd_x86/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:11] == "freebsd_x64": if string == "freebsd_x64/tcp_bind": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2], self.argvlist[3]) elif string == "freebsd_x64/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) elif string == "freebsd_x64/exec": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:9] == "linux_arm": if string == "linux_arm/chmod": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) elif string == "linux_arm/exec": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) elif string == "linux_arm/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:10] == "linux_mips": if string == "linux_mips/chmod": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) elif string == "linux_mips/reverse_tcp": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2], self.argvlist[3]) elif string == "linux_mips/tcp_bind": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:7] == "windows": if string == "windows/messagebox": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "windows/exec": controlset(string, self.argvlist[1], self.argvlist[0], self.argvlist[2]) elif string == "windows/download&execute": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2], self.argvlist[3]) elif string == "windows/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) elif string == "windows/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) self.control(string) elif terminal[:8] == "generate": from .database.generator import generator if string[:7] == "linux86": if string == "linux86/binsh_spawn": self.disassembly = generator("linux86", "binsh_spawn") elif string == "linux86/read": if self.argvlist[2] == "None": print("\nFile name must be declared.\n") self.control(string) self.disassembly = generator("linux86", "read", FILE=self.argvlist[2]) elif string == "linux86/exec": if self.argvlist[2] == "None": print("\nCommand must be declared.\n") self.control(string) self.disassembly = generator("linux86", "exec", COMMAND=self.argvlist[2]) elif string == "linux86/download&exec": if self.argvlist[2] == "None": print("\nLink must be declared.\n") self.control(string) elif "/" not in self.argvlist[2]: print("\nWrong url format example : 127.0.0.1/X\n") self.control(string) elif len(self.argvlist[2].split("/")[-1]) != 1: print("\nYour filename must be one lenght ..\n") self.control(string) if "http" in self.argvlist[2] or "https" in self.argvlist[2] or "www." in self.argvlist: try: edit = self.argvlist[2].replace("http://", "").replace("https://", "").replace("www.", "") self.argvlist[2] = edit except: pass self.disassembly = generator("linux86", "download&exec", URL=self.argvlist[2]) elif string == "linux86/chmod": if self.argvlist[2] == "None": print("\nFile name must be declared.\n") self.control(string) self.disassembly = generator("linux86", "chmod", FILE=self.argvlist[2]) elif string == "linux86/tcp_bind": if self.argvlist[2] == "None": print("\nPORT must be declared.\n") self.control(string) self.disassembly = generator("linux86", "tcp_bind", port=self.argvlist[2]) elif string == "linux86/reverse_tcp": if self.argvlist[2] == "None" or self.argvlist[3] == "None": print("\nHost&Port must be declared.\n") self.control(string) self.disassembly = generator("linux86", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string[:7] == "linux64": if string == "linux64/binsh_spawn": self.disassembly = generator("linux64", "binsh_spawn") elif string == "linux64/tcp_bind": self.disassembly = generator("linux64", "tcp_bind", port=self.argvlist[2]) elif string == "linux64/reverse_tcp": self.disassembly = generator("linux64", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "linux64/read": self.disassembly = generator("linux64", "read", FILE=self.argvlist[2]) if string[:5] == "linux": if string == "linux/read": if self.argvlist[2] == "None": print("\nFile name must be declared.\n") self.control(string) self.disassembly = generator("linux", "read", FILE=self.argvlist[2]) elif string == "linux/binsh_spawn": self.disassembly = generator("linux", "binsh_spawn") elif string == "linux/tcp_bind": self.disassembly = generator("linux", "tcp_bind", port=self.argvlist[2]) elif string == "linux/reverse_tcp": self.disassembly = generator("linux", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string[:5] == "osx86": if string == "osx86/tcp_bind": self.disassembly = generator("osx86", "tcp_bind", port=self.argvlist[2]) elif string == "osx86/binsh_spawn": self.disassembly = generator("osx86", "binsh_spawn") elif string == "osx86/reverse_tcp": self.disassembly = generator("osx86", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string[:5] == "osx64": if string == "osx64/binsh_spawn": self.disassembly = generator("osx64", "binsh_spawn") elif string == "osx64/tcp_bind": self.disassembly = generator("osx64", "tcp_bind", port=self.argvlist[2]) elif string == "osx64/reverse_tcp": self.disassembly = generator("osx64", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string[:11] == "freebsd_x86": if string == "freebsd_x86/binsh_spawn": self.disassembly = generator("freebsdx86", "binsh_spawn") elif string == "freebsd_x86/read": self.disassembly = generator("freebsdx86", "read", FILE=self.argvlist[2]) elif string == "freebsd_x86/reverse_tcp": self.disassembly = generator("freebsdx86", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "freebsd_x86/reverse_tcp2": self.disassembly = generator("freebsdx86", "reverse_tcp2", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "freebsd_x86/exec": self.disassembly = generator("freebsdx86", "exec", COMMAND=self.argvlist[2]) elif string == "freebsd_x86/tcp_bind": self.disassembly = generator("freebsdx86", "tcp_bind", port=self.argvlist[2]) elif string[:11] == "freebsd_x64": if string == "freebsd_x64/binsh_spawn": self.disassembly = generator("freebsdx64", "binsh_spawn") elif string == "freebsd_x64/tcp_bind": self.disassembly = generator("freebsdx64", "tcp_bind", port=self.argvlist[2], PASSWORD=self.argvlist[3]) elif string == "freebsd_x64/reverse_tcp": self.disassembly = generator("freebsdx64", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "freebsd_x64/exec": self.disassembly = generator("freebsdx64", "exec", COMMAND=self.argvlist[2]) elif string[:9] == "linux_arm": if string == "linux_arm/chmod": self.disassembly = generator("linux_arm", "chmod", FILE=self.argvlist[2]) elif string == "linux_arm/binsh_spawn": self.disassembly = generator("linux_arm", "binsh_spawn") elif string == "linux_arm/reverse_tcp": self.disassembly = generator("linux_arm", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "linux_arm/exec": self.disassembly = generator("linux_arm", "exec", COMMAND=self.argvlist[2]) elif string[:10] == "linux_mips": if string == "linux_mips/reverse_tcp": self.disassembly = generator("linux_mips", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "linux_mips/binsh_spawn": self.disassembly = generator("linux_mips", "binsh_spawn") elif string == "linux_mips/chmod": self.disassembly = generator("linux_mips", "chmod", FILE=self.argvlist[2]) elif string == "linux_mips/tcp_bind": self.disassembly = generator("linux_mips", "tcp_bind", port=self.argvlist[2]) elif string[:7] == "windows": if string == "windows/messagebox": self.disassembly = generator("windows", "messagebox", MESSAGE=self.argvlist[2]) elif string == "windows/download&execute": self.disassembly = generator("windows", "downloandandexecute", URL=self.argvlist[2], FILENAME=self.argvlist[3]) elif string == "windows/exec": self.disassembly = generator("windows", "exec", COMMAND=self.argvlist[2]) elif string == "windows/reverse_tcp": self.disassembly = generator("windows", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "windows/tcp_bind": self.disassembly = generator("windows", "tcp_bind", port=self.argvlist[2]) elif string[:10] == "solarisx86": if string == "solarisx86/binsh_spawn": self.disassembly = generator("solarisx86", "binsh_spawn") elif string == "solarisx86/read": if self.argvlist[2] == "None": print("\nFile name must be declared.\n") self.control(string) self.disassembly = generator("solarisx86", "read", FILE=self.argvlist[2]) elif string == "solarisx86/reverse_tcp": self.disassembly = generator("solarisx86", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "solarisx86/tcp_bind": self.disassembly = generator("solarisx86", "tcp_bind", port=self.argvlist[2]) if self.argvlist[0] == "x86/xor_b3m": from .encoders.shellcode.xor_b3m import prestart if self.argvlist[1] == "None": self.argvlist[1] = 1 elif self.argvlist[1] == 0: self.argvlist[1] = 1 self.disassembly = prestart(self.disassembly.replace("\\x", ""), int(self.argvlist[1])) elif self.argvlist[0] == "x86/xor": from .encoders.shellcode.xor import prestart if self.argvlist[1] == "None": self.argvlist[1] = 1 elif self.argvlist[1] == 0: self.argvlist[1] = 1 self.disassembly = prestart(self.disassembly.replace("\\x", ""), int(self.argvlist[1])) else: self.disassembly = self.disassembly # print "\n"+"Shellcode Lenght : %d" % len(str(bytearray(self.disassembly.replace("\\x", "").decode("hex")))) B3mB4m.prettyout(self.disassembly) self.control(string) elif terminal[:6] == "output": if self.disassembly == "None": print("Please generate shellcode before save it.") self.control(string) # I'm not sure about this option, should I get this option with params # Or directly inputs ? .. if terminal[7:10].lower() == "exe": # Will be add missing parts .. if "linux86" in terminal.lower(): OS = "linux86" elif "linux64" in terminal.lower(): OS = "linux64" elif "windows" in terminal.lower(): OS = "windows" elif "freebsdx86" in terminal.lower(): OS = "freebsdx86" elif "freebsdx64" in terminal.lower(): OS = "freebsdx64" elif "openbsdx86" in terminal.lower(): OS = "openbsdx86" elif "solarisx86" in terminal.lower(): OS = "solarisx86" elif "linuxpowerpc" in terminal.lower(): OS = "linuxpowerpc" elif "openbsdpowerpc" in terminal.lower(): OS = "openbsdpowerpc" elif "linuxsparc" in terminal.lower(): OS = "linuxsparc" elif "freebsdsparc" in terminal.lower(): OS = "freebsdsparc" elif "openbsdsparc" in terminal.lower(): OS = "openbsdsparc" elif "solarissparc" in terminal.lower(): OS = "solarissparc" elif "linuxarm" in terminal.lower(): OS = "linuxarm" elif "freebsdarm" in terminal.lower(): OS = "freebsdarm" elif "openbsdarm" in terminal.lower(): OS = "openbsdarm" else: OS = None from .Outputs.exe import ExeFile ExeFile(self.disassembly, OS) self.control(string) elif terminal[7:10].lower() == "c++" or terminal[7:10].lower() == "cpp": from .Outputs.Cplusplus import CplusplusFile if "windows" in string: CplusplusFile(self.disassembly, True) else: CplusplusFile(self.disassembly) elif terminal[7:8].lower() == "c": if "windows" in string: from .Outputs.Cplusplus import CplusplusFile CplusplusFile(self.disassembly, True) else: from .Outputs.C import CFile CFile(self.disassembly) elif terminal[7:9].lower() == "py" or terminal[7:13].lower() == "python": from .Outputs.python import PyFile PyFile(self.disassembly) elif terminal[7:10].lower() == "txt": from .Outputs.txt import TxtFile TxtFile(self.disassembly) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown output type: {0}".format(terminal) + bcolors.ENDC) self.control(string) elif terminal[:5] == "clear": B3mB4m.clean() self.control(string) elif terminal[:2].lower() == "ip": B3mB4m.IP() self.control(string) elif terminal[:13] == "show encoders": from .core.lists import encoderlist encoderlist() self.control(string) elif terminal[:5] == "disas": B3mB4m().startdisas( self.disassembly, string) self.control(string) else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string)

50.883641

140

0.466683

import sys import os from .core.color import * from re import findall from .core.Comp import tab from lib.base.framework import ShellsploitFramework if sys.version_info.major >= 3: raw_input = input class B3mB4m(ShellsploitFramework): def __init__(self): ShellsploitFramework.__init__(self) self.argvlist = ["None", "None", "None", "None"] self.disassembly = "None" self.mycache = "None" def control(self, string): bash = bcolors.OKBLUE + bcolors.UNDERLINE + "ssf" + bcolors.ENDC bash += ":" bash += bcolors.RED + string + bcolors.ENDC bash += bcolors.OKBLUE + " > " + bcolors.ENDC try: terminal = raw_input(bash) except KeyboardInterrupt: B3mB4m.exit("\n[*] (Ctrl + C ) Detected, Trying To Exit ...") if string[:9] == "injectors": tab.completion("injectors") if terminal[:4] == "help": from .core.help import injectorhelp injectorhelp() self.control(string) elif terminal[:4] == "back": self.argvlist = ["None", "None", "None", "None"] pass elif terminal[:4] == "exit": B3mB4m.exit("\nThanks for using shellsploit !\n") elif terminal[:4] == "pids": B3mB4m.pids("wholelist") self.control(string) elif terminal[:6] == "getpid": B3mB4m.pids(None, terminal[7:]) self.control(string) elif terminal[:5] == "clear": B3mB4m.clean() self.control(string) elif terminal[:5] == "unset": if string in B3mB4m.bfdlist(): if terminal[6:] == "exe" or terminal[6:] == "file": self.argvlist[0] = "None" elif terminal[6:] == "host": self.argvlist[1] = "None" elif terminal[6:] == "port": self.argvlist[2] = "None" else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) elif string == "injectors/Windows/x86/tLsInjectorDLL": if terminal[6:] == "exe": self.argvlist[0] = "None" elif terminal[6:] == "dll": self.argvlist[1] = "None" else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) elif string == "injectors/Windows/x86/CodecaveInjector": if terminal[6:] == "exe": self.argvlist[0] = "None" elif terminal[6:] == "shellcode": self.argvlist[1] = "None" else: if terminal[6:] == "pid": self.argvlist[0] = "None" elif terminal[6:] == "shellcode": self.argvlist[1] = "None" else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string) elif terminal[:3] == "set": if string in B3mB4m.bfdlist(): if terminal[4:7] == "exe" or terminal[4:8] == "file": self.argvlist[0] = terminal[9:] elif terminal[4:8] == "host": self.argvlist[1] = terminal[9:] elif terminal[4:8] == "port": self.argvlist[2] = terminal[9:] else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) elif string == "injectors/Windows/x86/tLsInjectorDLL": if terminal[4:7] == "exe": self.argvlist[0] = terminal[8:] elif terminal[4:7] == "dll": self.argvlist[1] = terminal[8:] else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) elif string == "injectors/Windows/x86/CodecaveInjector": if terminal[4:7] == "exe": self.argvlist[0] = terminal[8:] elif terminal[4:13] == "shellcode": self.argvlist[1] = terminal[14:] else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) else: if terminal[4:7] == "pid": self.argvlist[0] = terminal[8:] elif terminal[4:13] == "shellcode": if ".txt" in terminal[14:]: if os.path.isfile(terminal[14:]): with open(terminal[14:], "r") as shellcode: cache = shellcode.readlines() db = "" for x in database: db += x.strip().replace('"', "").replace('+', "").strip() self.argvlist[1] = db else: print(bcolors.RED + bcolors.BOLD + "\nFile can't find, please try with full path.\n" + bcolors.ENDC) self.control(string) else: self.argvlist[1] = terminal[14:] else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string) elif terminal[:14] == "show shellcode": if string in B3mB4m.bfdlist(): print("This option not available for this module.") self.control(string) elif string == "injectors/Windowsx86/tLsInjectorDLL": self.control(string) else: if self.argvlist[1] != "None": B3mB4m.prettyout(self.argvlist[1]) else: print("\nYou must set shellcode before this ..\b") self.control(string) elif terminal[:12] == "show options": from .core.Injectoroptions import controlset if string in B3mB4m.bfdlist(): controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) self.control(string) else: if string != "injectors/Windows/x86/tLsInjectorDLL": if self.argvlist[1] != "None": self.mycache = "process" controlset(string, self.argvlist[0], self.mycache) self.control(string) controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif terminal[:5] == "clear": B3mB4m.clean() self.control(string) elif terminal[:2] == "os": B3mB4m.oscommand(terminal[3:]) self.control(string) elif terminal[:6] == "inject": if self.argvlist[0] == None or self.argvlist[1] == None: print("\nYou must set pid/shellcode before inject !\n") self.control(string) if string == "injectors/Linux86/ptrace": from .inject.menager import linux86ptrace linux86ptrace(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Linux64/ptrace": from .inject.menager import linux64ptrace linux64ptrace(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Windows/byteman": from .inject.menager import windows windows(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Windows/x86/tLsInjectorDLL": from .inject.menager import winx86tLsDLL winx86tLsDLL(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Windows/x86/CodecaveInjector": from .inject.menager import winx86Codecave winx86Codecave(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Windows/Dllinjector": from .inject.menager import winDLL winDLL(self.argvlist[0], self.argvlist[1]) elif string == "injectors/Windows/BFD/Patching": from .inject.menager import winBFD winBFD(self.argvlist[0], self.argvlist[1], int(self.argvlist[2])) # elif string == "injectors/MacOSX/BFD/Patching": # from .inject.menager import MacBFD # MacBFD( FILE, HOST, PORT) # elif string == "injectors/Linux/BFD/Patching": # from .inject.menager import LinuxBFD # LinuxBFD( FILE, HOST, PORT) # elif string == "injectors/Linux/ARM/x86/BFD/Patching": # from .inject.menager import LinuxARMx86BFD # LinuxARMx86BFD( FILE, HOST, PORT) # elif string == "FreeBSD/x86/BFD/Patching": # from .inject.menager import FreeBSDx86 # FreeBSDx86( FILE, HOST, PORT) self.control(string) # elif terminal[:7] == "extract": # Future option # Make it executable (Dynamic virus land) # from bla bla import executable # generator() elif terminal[:4] == "back": self.argvlist = ["None", "None", "None", "None"] pass else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string) # Backdoors elif string[:9] == "backdoors": tab.completion("backdoors") if terminal[:4] == "help": from .core.help import backdoorshelp backdoorshelp() self.control(string) elif terminal[:4] == "exit": B3mB4m.exit("\nThanks for using shellsploit !\n") elif terminal[:2] == "os": B3mB4m.oscommand(terminal[3:]) self.control(string) elif terminal[:12] == "show options": from .core.SHELLoptions import controlset controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif terminal[:5] == "unset": if terminal[6:] == "lhost": self.argvlist[0] = "None" elif terminal[6:] == "lport": self.argvlist[1] = "None" # elif terminal[6:] == "encoder": # self.argvlist[2] = "None" else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string) elif terminal[:3] == "set": if terminal[4:9].lower() == "lhost": self.argvlist[0] = terminal[10:] elif terminal[4:9].lower() == "lport": self.argvlist[1] = terminal[10:] # elif terminal[4:11].lower() == "encoder" # self.argvlist[2] = terminal[11:] else: print(bcolors.RED + bcolors.BOLD + "This option is not available." + bcolors.ENDC) self.control(string) elif terminal[:8] == "generate": from .Session.generator import process # Custom output path will be add .. if self.argvlist[0] == "None" or self.argvlist[1] == "None": print("\nSet options before generate payload.\n") self.control(string) else: process(data=string, HOST=self.argvlist[0], PORT=self.argvlist[1], ENCODER=False, logger=True) self.control(string) elif terminal[:5] == "clear": B3mB4m.clean() self.control(string) elif terminal[:4] == "back": self.argvlist = ["None", "None", "None", "None"] pass else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string) # Shellcodes else: tab.completion("shellcodes") if terminal[:4] == "help": # if terminal[5:11] == "output": # from Outputs.exehelp import help # print help() # self.control( string) from .core.help import shellcodehelp shellcodehelp() self.control(string) elif terminal[:2] == "os": B3mB4m.oscommand(terminal[3:]) self.control(string) elif terminal[:4] == "back": self.argvlist = ["None", "None", "None", "None"] pass elif terminal[:4] == "exit": B3mB4m.exit("\nThanks for using shellsploit !\n") elif terminal[:10] == "whatisthis": from .core.whatisthis import whatisthis if "egg" in string: message = "Egg-hunt" elif "tcp" in string or "reverse" in string or "netcat" in string: message = "Remote" elif "download" in string: message = "Download and execute" else: message = "Local" # Add special part for particul whatisthis(message) self.control(string) elif terminal[:5] == "unset": if terminal[6:] == "encoder": self.argvlist[0] = "None" elif terminal[6:] == "iteration": self.argvlist[1] = "None" elif terminal[6:] == "file": if string in B3mB4m.readlist(): self.argvlist[2] = "None" else: B3mB4m.invalidcommand() elif terminal[6:] == "port": if string in B3mB4m.tcpbindlist() or string in B3mB4m.reversetcplist(): self.argvlist[2] = "None" else: Base.invalidcommand() elif terminal[6:] == "command": if string in B3mB4m.execlist(): self.argvlist[2] = "None" else: B3mB4m.invalidcommand() elif terminal[6:] == "link": if string in B3mB4m.downloadandexecutelist(): self.argvlist[2] = "None" else: B3mB4m.invalidcommand() elif terminal[6:] == "filename": if string in B3mB4m.downloadandexecutelist(): self.argvlist[3] = "None" else: B3mB4m.invalidcommand() elif terminal[6:] == "host": if string in B3mB4m.reversetcplist(): self.argvlist[3] = "None" else: B3mB4m.invalidcommand() else: B3mB4m.invalidcommand() self.control(string) elif terminal[:3] == "set": if terminal[4:8] == "file": if string in B3mB4m.readlist(): self.argvlist[2] = terminal[9:] else: B3mB4m.invalidcommand() elif terminal[4:8] == "port": if string in B3mB4m.tcpbindlist() or string in B3mB4m.reversetcplist(): self.argvlist[2] = terminal[9:] else: B3mB4m.invalidcommand() elif terminal[4:11] == "command": if string in B3mB4m.execlist(): self.argvlist[2] = terminal[12:] else: B3mB4m.invalidcommand() elif terminal[4:8] == "link": if string in B3mB4m.downloadandexecutelist(): self.argvlist[2] = terminal[9:] else: B3mB4m.invalidcommand() elif terminal[4:11] == "message": if string in B3mB4m.messageboxlist(): self.argvlist[2] = terminal[12:] else: B3mB4m.invalidcommand() elif terminal[4:8] == "host": if string in B3mB4m.reversetcplist(): self.argvlist[3] = terminal[9:] else: B3mB4m.invalidcommand() elif terminal[4:12] == "filename": if string in B3mB4m.downloadandexecutelist(): self.argvlist[3] = terminal[13:] else: B3mB4m.invalidcommand() elif terminal[4:11] == "encoder": from .core.lists import encoders if terminal[12:] not in encoders(): print("This encoder not in list !") self.control(string) self.argvlist[0] = terminal[12:] elif terminal[4:13] == "iteration": self.argvlist[1] = terminal[14:] else: B3mB4m.invalidcommand() self.control(string) elif terminal[:12] == "show options": from .core.SHELLoptions import controlset if string[:7] == "linux86": if string == "linux86/read": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux86/chmod": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux86/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux86/reverse_tcp": controlset(string, self.argvlist[3], self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux86/download&exec": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux86/exec": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:10] == "solarisx86": if string == "solarisx86/read": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "solarisx86/reverse_tcp": controlset(string, self.argvlist[3], self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "solarisx86/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:7] == "linux64": if string == "linux64/read": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux64/mkdir": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux64/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux64/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[1], self.argvlist[0]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:5] == "linux": if string == "linux/read": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "linux/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:5] == "osx86": if string == "osx86/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "osx86/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[1], self.argvlist[0]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:5] == "osx64": if string == "osx64/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "osx64/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:11] == "freebsd_x86": if string == "freebsd_x86/reverse_tcp2": controlset(string, self.argvlist[3], self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "freebsd_x86/reverse_tcp": controlset(string, self.argvlist[3], self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "freebsd_x86/read": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "freebsd_x86/exec": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "freebsd_x86/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:11] == "freebsd_x64": if string == "freebsd_x64/tcp_bind": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2], self.argvlist[3]) elif string == "freebsd_x64/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) elif string == "freebsd_x64/exec": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:9] == "linux_arm": if string == "linux_arm/chmod": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) elif string == "linux_arm/exec": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) elif string == "linux_arm/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:10] == "linux_mips": if string == "linux_mips/chmod": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) elif string == "linux_mips/reverse_tcp": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2], self.argvlist[3]) elif string == "linux_mips/tcp_bind": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2]) else: controlset(string, self.argvlist[0], self.argvlist[1]) self.control(string) elif string[:7] == "windows": if string == "windows/messagebox": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) elif string == "windows/exec": controlset(string, self.argvlist[1], self.argvlist[0], self.argvlist[2]) elif string == "windows/download&execute": controlset(string, self.argvlist[0], self.argvlist[1], self.argvlist[2], self.argvlist[3]) elif string == "windows/reverse_tcp": controlset(string, self.argvlist[2], self.argvlist[3], self.argvlist[0], self.argvlist[1]) elif string == "windows/tcp_bind": controlset(string, self.argvlist[2], self.argvlist[0], self.argvlist[1]) self.control(string) elif terminal[:8] == "generate": from .database.generator import generator if string[:7] == "linux86": if string == "linux86/binsh_spawn": self.disassembly = generator("linux86", "binsh_spawn") elif string == "linux86/read": if self.argvlist[2] == "None": print("\nFile name must be declared.\n") self.control(string) self.disassembly = generator("linux86", "read", FILE=self.argvlist[2]) elif string == "linux86/exec": if self.argvlist[2] == "None": print("\nCommand must be declared.\n") self.control(string) self.disassembly = generator("linux86", "exec", COMMAND=self.argvlist[2]) elif string == "linux86/download&exec": if self.argvlist[2] == "None": print("\nLink must be declared.\n") self.control(string) elif "/" not in self.argvlist[2]: print("\nWrong url format example : 127.0.0.1/X\n") self.control(string) elif len(self.argvlist[2].split("/")[-1]) != 1: print("\nYour filename must be one lenght ..\n") self.control(string) if "http" in self.argvlist[2] or "https" in self.argvlist[2] or "www." in self.argvlist: try: edit = self.argvlist[2].replace("http://", "").replace("https://", "").replace("www.", "") self.argvlist[2] = edit except: pass self.disassembly = generator("linux86", "download&exec", URL=self.argvlist[2]) elif string == "linux86/chmod": if self.argvlist[2] == "None": print("\nFile name must be declared.\n") self.control(string) self.disassembly = generator("linux86", "chmod", FILE=self.argvlist[2]) elif string == "linux86/tcp_bind": if self.argvlist[2] == "None": print("\nPORT must be declared.\n") self.control(string) self.disassembly = generator("linux86", "tcp_bind", port=self.argvlist[2]) elif string == "linux86/reverse_tcp": if self.argvlist[2] == "None" or self.argvlist[3] == "None": print("\nHost&Port must be declared.\n") self.control(string) self.disassembly = generator("linux86", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string[:7] == "linux64": if string == "linux64/binsh_spawn": self.disassembly = generator("linux64", "binsh_spawn") elif string == "linux64/tcp_bind": self.disassembly = generator("linux64", "tcp_bind", port=self.argvlist[2]) elif string == "linux64/reverse_tcp": self.disassembly = generator("linux64", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "linux64/read": self.disassembly = generator("linux64", "read", FILE=self.argvlist[2]) if string[:5] == "linux": if string == "linux/read": if self.argvlist[2] == "None": print("\nFile name must be declared.\n") self.control(string) self.disassembly = generator("linux", "read", FILE=self.argvlist[2]) elif string == "linux/binsh_spawn": self.disassembly = generator("linux", "binsh_spawn") elif string == "linux/tcp_bind": self.disassembly = generator("linux", "tcp_bind", port=self.argvlist[2]) elif string == "linux/reverse_tcp": self.disassembly = generator("linux", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string[:5] == "osx86": if string == "osx86/tcp_bind": self.disassembly = generator("osx86", "tcp_bind", port=self.argvlist[2]) elif string == "osx86/binsh_spawn": self.disassembly = generator("osx86", "binsh_spawn") elif string == "osx86/reverse_tcp": self.disassembly = generator("osx86", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string[:5] == "osx64": if string == "osx64/binsh_spawn": self.disassembly = generator("osx64", "binsh_spawn") elif string == "osx64/tcp_bind": self.disassembly = generator("osx64", "tcp_bind", port=self.argvlist[2]) elif string == "osx64/reverse_tcp": self.disassembly = generator("osx64", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string[:11] == "freebsd_x86": if string == "freebsd_x86/binsh_spawn": self.disassembly = generator("freebsdx86", "binsh_spawn") elif string == "freebsd_x86/read": self.disassembly = generator("freebsdx86", "read", FILE=self.argvlist[2]) elif string == "freebsd_x86/reverse_tcp": self.disassembly = generator("freebsdx86", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "freebsd_x86/reverse_tcp2": self.disassembly = generator("freebsdx86", "reverse_tcp2", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "freebsd_x86/exec": self.disassembly = generator("freebsdx86", "exec", COMMAND=self.argvlist[2]) elif string == "freebsd_x86/tcp_bind": self.disassembly = generator("freebsdx86", "tcp_bind", port=self.argvlist[2]) elif string[:11] == "freebsd_x64": if string == "freebsd_x64/binsh_spawn": self.disassembly = generator("freebsdx64", "binsh_spawn") elif string == "freebsd_x64/tcp_bind": self.disassembly = generator("freebsdx64", "tcp_bind", port=self.argvlist[2], PASSWORD=self.argvlist[3]) elif string == "freebsd_x64/reverse_tcp": self.disassembly = generator("freebsdx64", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "freebsd_x64/exec": self.disassembly = generator("freebsdx64", "exec", COMMAND=self.argvlist[2]) elif string[:9] == "linux_arm": if string == "linux_arm/chmod": self.disassembly = generator("linux_arm", "chmod", FILE=self.argvlist[2]) elif string == "linux_arm/binsh_spawn": self.disassembly = generator("linux_arm", "binsh_spawn") elif string == "linux_arm/reverse_tcp": self.disassembly = generator("linux_arm", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "linux_arm/exec": self.disassembly = generator("linux_arm", "exec", COMMAND=self.argvlist[2]) elif string[:10] == "linux_mips": if string == "linux_mips/reverse_tcp": self.disassembly = generator("linux_mips", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "linux_mips/binsh_spawn": self.disassembly = generator("linux_mips", "binsh_spawn") elif string == "linux_mips/chmod": self.disassembly = generator("linux_mips", "chmod", FILE=self.argvlist[2]) elif string == "linux_mips/tcp_bind": self.disassembly = generator("linux_mips", "tcp_bind", port=self.argvlist[2]) elif string[:7] == "windows": if string == "windows/messagebox": self.disassembly = generator("windows", "messagebox", MESSAGE=self.argvlist[2]) elif string == "windows/download&execute": self.disassembly = generator("windows", "downloandandexecute", URL=self.argvlist[2], FILENAME=self.argvlist[3]) elif string == "windows/exec": self.disassembly = generator("windows", "exec", COMMAND=self.argvlist[2]) elif string == "windows/reverse_tcp": self.disassembly = generator("windows", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "windows/tcp_bind": self.disassembly = generator("windows", "tcp_bind", port=self.argvlist[2]) elif string[:10] == "solarisx86": if string == "solarisx86/binsh_spawn": self.disassembly = generator("solarisx86", "binsh_spawn") elif string == "solarisx86/read": if self.argvlist[2] == "None": print("\nFile name must be declared.\n") self.control(string) self.disassembly = generator("solarisx86", "read", FILE=self.argvlist[2]) elif string == "solarisx86/reverse_tcp": self.disassembly = generator("solarisx86", "reverse_tcp", ip=self.argvlist[3], port=self.argvlist[2]) elif string == "solarisx86/tcp_bind": self.disassembly = generator("solarisx86", "tcp_bind", port=self.argvlist[2]) if self.argvlist[0] == "x86/xor_b3m": from .encoders.shellcode.xor_b3m import prestart if self.argvlist[1] == "None": self.argvlist[1] = 1 elif self.argvlist[1] == 0: self.argvlist[1] = 1 self.disassembly = prestart(self.disassembly.replace("\\x", ""), int(self.argvlist[1])) elif self.argvlist[0] == "x86/xor": from .encoders.shellcode.xor import prestart if self.argvlist[1] == "None": self.argvlist[1] = 1 elif self.argvlist[1] == 0: self.argvlist[1] = 1 self.disassembly = prestart(self.disassembly.replace("\\x", ""), int(self.argvlist[1])) else: self.disassembly = self.disassembly # print "\n"+"Shellcode Lenght : %d" % len(str(bytearray(self.disassembly.replace("\\x", "").decode("hex")))) B3mB4m.prettyout(self.disassembly) self.control(string) elif terminal[:6] == "output": if self.disassembly == "None": print("Please generate shellcode before save it.") self.control(string) # I'm not sure about this option, should I get this option with params # Or directly inputs ? .. if terminal[7:10].lower() == "exe": # Will be add missing parts .. if "linux86" in terminal.lower(): OS = "linux86" elif "linux64" in terminal.lower(): OS = "linux64" elif "windows" in terminal.lower(): OS = "windows" elif "freebsdx86" in terminal.lower(): OS = "freebsdx86" elif "freebsdx64" in terminal.lower(): OS = "freebsdx64" elif "openbsdx86" in terminal.lower(): OS = "openbsdx86" elif "solarisx86" in terminal.lower(): OS = "solarisx86" elif "linuxpowerpc" in terminal.lower(): OS = "linuxpowerpc" elif "openbsdpowerpc" in terminal.lower(): OS = "openbsdpowerpc" elif "linuxsparc" in terminal.lower(): OS = "linuxsparc" elif "freebsdsparc" in terminal.lower(): OS = "freebsdsparc" elif "openbsdsparc" in terminal.lower(): OS = "openbsdsparc" elif "solarissparc" in terminal.lower(): OS = "solarissparc" elif "linuxarm" in terminal.lower(): OS = "linuxarm" elif "freebsdarm" in terminal.lower(): OS = "freebsdarm" elif "openbsdarm" in terminal.lower(): OS = "openbsdarm" else: OS = None from .Outputs.exe import ExeFile ExeFile(self.disassembly, OS) self.control(string) elif terminal[7:10].lower() == "c++" or terminal[7:10].lower() == "cpp": from .Outputs.Cplusplus import CplusplusFile if "windows" in string: CplusplusFile(self.disassembly, True) else: CplusplusFile(self.disassembly) elif terminal[7:8].lower() == "c": if "windows" in string: from .Outputs.Cplusplus import CplusplusFile CplusplusFile(self.disassembly, True) else: from .Outputs.C import CFile CFile(self.disassembly) elif terminal[7:9].lower() == "py" or terminal[7:13].lower() == "python": from .Outputs.python import PyFile PyFile(self.disassembly) elif terminal[7:10].lower() == "txt": from .Outputs.txt import TxtFile TxtFile(self.disassembly) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown output type: {0}".format(terminal) + bcolors.ENDC) self.control(string) elif terminal[:5] == "clear": B3mB4m.clean() self.control(string) elif terminal[:2].lower() == "ip": B3mB4m.IP() self.control(string) elif terminal[:13] == "show encoders": from .core.lists import encoderlist encoderlist() self.control(string) elif terminal[:5] == "disas": B3mB4m().startdisas( self.disassembly, string) self.control(string) else: if not terminal: self.control(string) else: print(bcolors.RED + bcolors.BOLD + "[-] Unknown command: {0}".format(terminal) + bcolors.ENDC) self.control(string)

true

790db22589642ac8d0bb393e746a8f9ce6546756

2,603

py

Python

gscripts/general/venn_matrix.py

YeoLab/gscripts

ae653d29d0ce82d342f7f6ff5bbeedd27a2e062b

[ "MIT" ]

12

2015-07-10T09:36:49.000Z

2021-07-06T03:25:04.000Z

gscripts/general/venn_matrix.py

YeoLab/gscripts

ae653d29d0ce82d342f7f6ff5bbeedd27a2e062b

[ "MIT" ]

43

2015-01-21T20:01:38.000Z

2021-04-13T17:50:38.000Z

gscripts/general/venn_matrix.py

YeoLab/gscripts

ae653d29d0ce82d342f7f6ff5bbeedd27a2e062b

[ "MIT" ]

19

2015-05-02T09:33:17.000Z

2022-02-12T17:08:06.000Z

from matplotlib import pyplot as plt from matplotlib_venn import venn2 import glob import compare_two_zlists as cv import math from scipy.stats import hypergeom from decimal import Decimal from math import log def make_venn_matrix(filename_list): fig1 = plt.figure(1) fig1.suptitle('Differentially Expressed Genes Overlap', fontsize=24) subplot_counter = 1 print len(filename_list)*len(filename_list) for zlist1 in filename_list: for zlist2 in filename_list: plt.subplot(len(filename_list), len(filename_list), subplot_counter) offset = math.ceil(float(subplot_counter)/float(len(filename_list))) position = int(subplot_counter - 1) % len(filename_list) + 1 if zlist1 == zlist2: plt.subplot(len(filename_list), len(filename_list), subplot_counter) up_A, up_B, all_changing = cv.get_changing(zlist1) plt.text(0, 0, '{}'.format(zlist1)) plt.text(0, .4, 'Higher in A: {}'.format(str(len(up_A)))) plt.text(0, .2, 'Higher in B: {}'.format(str(len(up_B)))) plt.axis('off') plt.plot() print 'working {}'.format(subplot_counter) subplot_counter+=1 else: plt.subplot(len(filename_list), len(filename_list), subplot_counter) (venn_values, all_union) = cv.compare_two(zlist1, zlist2) color1 = '' color2 = '' if position > offset: color1 = 'MediumVioletRed' color2 = 'OrangeRed' union = venn_values['up_A']['union'] in_common = venn_values['up_A']['common'] unique_1 = venn_values['up_A']['up_1'] unique_2 = venn_values['up_A']['up_2'] if position < offset: color1 = 'LimeGreen' color2 = 'DodgerBlue' union = venn_values['up_B']['union'] in_common = venn_values['up_B']['common'] unique_1 = venn_values['up_B']['up_1'] unique_2 = venn_values['up_B']['up_2'] total_genes = len(all_union) total_1 = unique_1 + in_common total_2 = unique_2 + in_common try: log_prob = Decimal(log(hypergeom.sf(in_common, total_genes, total_1, total_2))) except: log_prob = '-inf' plt.plot(cv.draw_venn(union, in_common, unique_1, unique_2, color1, color2)) if log_prob != '-inf': plt.annotate('log p-value: %2.3f'%log_prob, xy=(0,0), xycoords='axes fraction') else: plt.annotate('log p-value: -inf', xy=(0,0), xycoords='axes fraction') print 'working {}'.format(subplot_counter) print str(total_genes) print str(log_prob) subplot_counter+=1 plt.show() return if __name__ == '__main__': venn_filelist = glob.glob('*zlist') venn_filelist.sort() make_venn_matrix(venn_filelist)

26.835052

84

0.676143

from matplotlib import pyplot as plt from matplotlib_venn import venn2 import glob import compare_two_zlists as cv import math from scipy.stats import hypergeom from decimal import Decimal from math import log def make_venn_matrix(filename_list): fig1 = plt.figure(1) fig1.suptitle('Differentially Expressed Genes Overlap', fontsize=24) subplot_counter = 1 print len(filename_list)*len(filename_list) for zlist1 in filename_list: for zlist2 in filename_list: plt.subplot(len(filename_list), len(filename_list), subplot_counter) offset = math.ceil(float(subplot_counter)/float(len(filename_list))) position = int(subplot_counter - 1) % len(filename_list) + 1 if zlist1 == zlist2: plt.subplot(len(filename_list), len(filename_list), subplot_counter) up_A, up_B, all_changing = cv.get_changing(zlist1) plt.text(0, 0, '{}'.format(zlist1)) plt.text(0, .4, 'Higher in A: {}'.format(str(len(up_A)))) plt.text(0, .2, 'Higher in B: {}'.format(str(len(up_B)))) plt.axis('off') plt.plot() print 'working {}'.format(subplot_counter) subplot_counter+=1 else: plt.subplot(len(filename_list), len(filename_list), subplot_counter) (venn_values, all_union) = cv.compare_two(zlist1, zlist2) color1 = '' color2 = '' if position > offset: color1 = 'MediumVioletRed' color2 = 'OrangeRed' union = venn_values['up_A']['union'] in_common = venn_values['up_A']['common'] unique_1 = venn_values['up_A']['up_1'] unique_2 = venn_values['up_A']['up_2'] if position < offset: color1 = 'LimeGreen' color2 = 'DodgerBlue' union = venn_values['up_B']['union'] in_common = venn_values['up_B']['common'] unique_1 = venn_values['up_B']['up_1'] unique_2 = venn_values['up_B']['up_2'] total_genes = len(all_union) total_1 = unique_1 + in_common total_2 = unique_2 + in_common try: log_prob = Decimal(log(hypergeom.sf(in_common, total_genes, total_1, total_2))) except: log_prob = '-inf' plt.plot(cv.draw_venn(union, in_common, unique_1, unique_2, color1, color2)) if log_prob != '-inf': plt.annotate('log p-value: %2.3f'%log_prob, xy=(0,0), xycoords='axes fraction') else: plt.annotate('log p-value: -inf', xy=(0,0), xycoords='axes fraction') print 'working {}'.format(subplot_counter) print str(total_genes) print str(log_prob) subplot_counter+=1 plt.show() return if __name__ == '__main__': venn_filelist = glob.glob('*zlist') venn_filelist.sort() make_venn_matrix(venn_filelist)

false

true

790db4e57c8d5c2412f1dad6e329136609500df2

286

py

Python

products/urls.py

Nenu1985/blog

df94ae3243314d43e16c33d0150a980ce34535a3

[ "MIT" ]

null

products/urls.py

Nenu1985/blog

df94ae3243314d43e16c33d0150a980ce34535a3

[ "MIT" ]

13

2019-12-04T23:32:05.000Z

2022-02-10T12:07:30.000Z

products/urls.py

Nenu1985/blog

df94ae3243314d43e16c33d0150a980ce34535a3

[ "MIT" ]

null

from django.urls import path from . import views # Wire up our API using automatic URL routing. # Additionally, we include login URLs for the browsable API. urlpatterns = [ path('settings', views.project_settings, name='settings'), path('envs', views.os_envs, name='envs'), ]

26

62

0.723776

from django.urls import path from . import views urlpatterns = [ path('settings', views.project_settings, name='settings'), path('envs', views.os_envs, name='envs'), ]

true

790db547c7710bee45f75044da74e8e17d906927

1,459

py

Python

sagas/nlu/pipes/cat.py

samlet/stack

47db17fd4fdab264032f224dca31a4bb1d19b754

[ "Apache-2.0" ]

3

2020-01-11T13:55:38.000Z

2020-08-25T22:34:15.000Z

sagas/nlu/pipes/cat.py

samlet/stack

47db17fd4fdab264032f224dca31a4bb1d19b754

[ "Apache-2.0" ]

null

sagas/nlu/pipes/cat.py

samlet/stack

47db17fd4fdab264032f224dca31a4bb1d19b754

[ "Apache-2.0" ]

1

2021-01-01T05:21:44.000Z

from typing import Text, Any, Dict, List, Union from blinker import NamedSignal, signal import rx from rx import operators as ops from dataclasses import dataclass from sagas.nlu.pipes import pred_cond, filter_path, to_token from sagas.util.collection_util import wrap, to_obj import logging logger = logging.getLogger(__name__) cat_sig=signal('cat') @cat_sig.connect def cat_proc(sender, **kwargs): from sagas.nlu.utils import predicate from sagas.nlu.translator import trans_axis results=[] source = rx.of(*kwargs['rs']) lang = kwargs['lang'] cond:pred_cond=kwargs['data'] logger.debug(f"pred pos: {cond}") kind=cond.cond logger.debug(f"lang: {lang}, cond: {cond}") source.pipe( filter_path(cond.part), ops.map(lambda t: to_obj({'word': t.text if t.upos.lower() in ['adj'] else t.lemma, **t})), ops.map(lambda t: to_obj({'trans': trans_axis(t.word, lang, t.upos), **t})), ops.filter(lambda t: predicate(kind, t.trans, 'en', '*')), ops.map(lambda t: {'path':t.path, 'word': t.word, 'trans': t.trans, 'cat': kind, 'value': kind, 'pos': t.upos.lower()}), ).subscribe( on_next=lambda value: results.append({**value}), on_error=lambda e: logger.error(e), ) logger.debug(f"result: {results}") return results

31.042553

99

0.59767

from typing import Text, Any, Dict, List, Union from blinker import NamedSignal, signal import rx from rx import operators as ops from dataclasses import dataclass from sagas.nlu.pipes import pred_cond, filter_path, to_token from sagas.util.collection_util import wrap, to_obj import logging logger = logging.getLogger(__name__) cat_sig=signal('cat') @cat_sig.connect def cat_proc(sender, **kwargs): from sagas.nlu.utils import predicate from sagas.nlu.translator import trans_axis results=[] source = rx.of(*kwargs['rs']) lang = kwargs['lang'] cond:pred_cond=kwargs['data'] logger.debug(f"pred pos: {cond}") kind=cond.cond logger.debug(f"lang: {lang}, cond: {cond}") source.pipe( filter_path(cond.part), ops.map(lambda t: to_obj({'word': t.text if t.upos.lower() in ['adj'] else t.lemma, **t})), ops.map(lambda t: to_obj({'trans': trans_axis(t.word, lang, t.upos), **t})), ops.filter(lambda t: predicate(kind, t.trans, 'en', '*')), ops.map(lambda t: {'path':t.path, 'word': t.word, 'trans': t.trans, 'cat': kind, 'value': kind, 'pos': t.upos.lower()}), ).subscribe( on_next=lambda value: results.append({**value}), on_error=lambda e: logger.error(e), ) logger.debug(f"result: {results}") return results

true

790db59cd76ff0c3662434ff66d9e89df5351087

6,263

py

Python

tests/test_graph.py

MenEnger/autokeras

8d96979c49623f7bb56f053ed5d47b3b81f498c0

[ "MIT" ]

1

2018-08-06T03:57:51.000Z

tests/test_graph.py

MenEnger/autokeras

8d96979c49623f7bb56f053ed5d47b3b81f498c0

[ "MIT" ]

null

tests/test_graph.py

MenEnger/autokeras

8d96979c49623f7bb56f053ed5d47b3b81f498c0

[ "MIT" ]

null

from autokeras.generator import DefaultClassifierGenerator from autokeras.graph import * from autokeras.net_transformer import legal_graph from tests.common import get_conv_data, get_add_skip_model, get_conv_dense_model, get_pooling_model, \ get_concat_skip_model def test_conv_deeper_stub(): graph = get_conv_dense_model() layer_num = graph.n_layers graph.to_conv_deeper_model(5, 3) assert graph.n_layers == layer_num + 4 def test_conv_deeper(): graph = get_conv_dense_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_conv_deeper_model(5, 3) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-1 def test_dense_deeper_stub(): graph = get_conv_dense_model() graph.weighted = False layer_num = graph.n_layers graph.to_dense_deeper_model(10) assert graph.n_layers == layer_num + 3 def test_dense_deeper(): graph = get_conv_dense_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_dense_deeper_model(10) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-4 def test_conv_wider_stub(): graph = get_add_skip_model() graph.weighted = False layer_num = graph.n_layers graph.to_wider_model(9, 3) assert graph.n_layers == layer_num def test_conv_wider(): graph = get_concat_skip_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_wider_model(5, 3) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-1 def test_dense_wider_stub(): graph = get_add_skip_model() graph.weighted = False layer_num = graph.n_layers graph.to_wider_model(32, 3) assert graph.n_layers == layer_num def test_dense_wider(): graph = get_add_skip_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_wider_model(32, 3) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-4 def test_skip_add_over_pooling_stub(): graph = get_pooling_model() graph.weighted = False layer_num = graph.n_layers graph.to_add_skip_model(1, 10) assert graph.n_layers == layer_num + 6 def test_skip_add_over_pooling(): graph = get_pooling_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_add_skip_model(1, 10) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-4 def test_skip_concat_over_pooling_stub(): graph = get_pooling_model() graph.weighted = False layer_num = graph.n_layers graph.to_concat_skip_model(1, 14) assert graph.n_layers == layer_num + 6 def test_skip_concat_over_pooling(): graph = get_pooling_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_concat_skip_model(5, 10) graph.to_concat_skip_model(5, 10) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-4 def test_extract_descriptor_add(): descriptor = get_add_skip_model().extract_descriptor() assert descriptor.n_conv == 5 assert descriptor.n_dense == 2 assert descriptor.skip_connections == [(2, 3, NetworkDescriptor.ADD_CONNECT), (3, 4, NetworkDescriptor.ADD_CONNECT)] def test_extract_descriptor_concat(): descriptor = get_concat_skip_model().extract_descriptor() assert descriptor.n_conv == 5 assert descriptor.n_dense == 2 assert descriptor.skip_connections == [(2, 3, NetworkDescriptor.CONCAT_CONNECT), (3, 4, NetworkDescriptor.CONCAT_CONNECT)] def test_deep_layer_ids(): graph = get_conv_dense_model() assert len(graph.deep_layer_ids()) == 3 def test_wide_layer_ids(): graph = get_conv_dense_model() assert len(graph.wide_layer_ids()) == 2 def test_skip_connection_layer_ids(): graph = get_conv_dense_model() assert len(graph.skip_connection_layer_ids()) == 1 def test_long_transform(): graph = DefaultClassifierGenerator(10, (32, 32, 3)).generate() history = [('to_wider_model', 1, 256), ('to_conv_deeper_model', 1, 3), ('to_concat_skip_model', 6, 11)] for args in history: getattr(graph, args[0])(*list(args[1:])) graph.produce_model() assert legal_graph(graph) def test_node_consistency(): graph = DefaultClassifierGenerator(10, (32, 32, 3)).generate() assert graph.layer_list[6].output.shape == (16, 16, 64) for layer in graph.layer_list: assert layer.output.shape == layer.output_shape graph.to_wider_model(6, 64) assert graph.layer_list[6].output.shape == (16, 16, 128) for layer in graph.layer_list: assert layer.output.shape == layer.output_shape graph.to_conv_deeper_model(6, 3) assert graph.layer_list[19].output.shape == (16, 16, 128) for layer in graph.layer_list: assert layer.output.shape == layer.output_shape graph.to_add_skip_model(6, 19) assert graph.layer_list[23].output.shape == (16, 16, 128) for layer in graph.layer_list: assert layer.output.shape == layer.output_shape graph.to_concat_skip_model(6, 19) assert graph.layer_list[25].output.shape == (16, 16, 128) for layer in graph.layer_list: assert layer.output.shape == layer.output_shape

26.99569

120

0.695513

from autokeras.generator import DefaultClassifierGenerator from autokeras.graph import * from autokeras.net_transformer import legal_graph from tests.common import get_conv_data, get_add_skip_model, get_conv_dense_model, get_pooling_model, \ get_concat_skip_model def test_conv_deeper_stub(): graph = get_conv_dense_model() layer_num = graph.n_layers graph.to_conv_deeper_model(5, 3) assert graph.n_layers == layer_num + 4 def test_conv_deeper(): graph = get_conv_dense_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_conv_deeper_model(5, 3) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-1 def test_dense_deeper_stub(): graph = get_conv_dense_model() graph.weighted = False layer_num = graph.n_layers graph.to_dense_deeper_model(10) assert graph.n_layers == layer_num + 3 def test_dense_deeper(): graph = get_conv_dense_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_dense_deeper_model(10) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-4 def test_conv_wider_stub(): graph = get_add_skip_model() graph.weighted = False layer_num = graph.n_layers graph.to_wider_model(9, 3) assert graph.n_layers == layer_num def test_conv_wider(): graph = get_concat_skip_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_wider_model(5, 3) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-1 def test_dense_wider_stub(): graph = get_add_skip_model() graph.weighted = False layer_num = graph.n_layers graph.to_wider_model(32, 3) assert graph.n_layers == layer_num def test_dense_wider(): graph = get_add_skip_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_wider_model(32, 3) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-4 def test_skip_add_over_pooling_stub(): graph = get_pooling_model() graph.weighted = False layer_num = graph.n_layers graph.to_add_skip_model(1, 10) assert graph.n_layers == layer_num + 6 def test_skip_add_over_pooling(): graph = get_pooling_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_add_skip_model(1, 10) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-4 def test_skip_concat_over_pooling_stub(): graph = get_pooling_model() graph.weighted = False layer_num = graph.n_layers graph.to_concat_skip_model(1, 14) assert graph.n_layers == layer_num + 6 def test_skip_concat_over_pooling(): graph = get_pooling_model() model = graph.produce_model() graph = deepcopy(graph) graph.to_concat_skip_model(5, 10) graph.to_concat_skip_model(5, 10) new_model = graph.produce_model() input_data = torch.Tensor(get_conv_data()) model.eval() new_model.eval() output1 = model(input_data) output2 = new_model(input_data) assert (output1 - output2).abs().sum() < 1e-4 def test_extract_descriptor_add(): descriptor = get_add_skip_model().extract_descriptor() assert descriptor.n_conv == 5 assert descriptor.n_dense == 2 assert descriptor.skip_connections == [(2, 3, NetworkDescriptor.ADD_CONNECT), (3, 4, NetworkDescriptor.ADD_CONNECT)] def test_extract_descriptor_concat(): descriptor = get_concat_skip_model().extract_descriptor() assert descriptor.n_conv == 5 assert descriptor.n_dense == 2 assert descriptor.skip_connections == [(2, 3, NetworkDescriptor.CONCAT_CONNECT), (3, 4, NetworkDescriptor.CONCAT_CONNECT)] def test_deep_layer_ids(): graph = get_conv_dense_model() assert len(graph.deep_layer_ids()) == 3 def test_wide_layer_ids(): graph = get_conv_dense_model() assert len(graph.wide_layer_ids()) == 2 def test_skip_connection_layer_ids(): graph = get_conv_dense_model() assert len(graph.skip_connection_layer_ids()) == 1 def test_long_transform(): graph = DefaultClassifierGenerator(10, (32, 32, 3)).generate() history = [('to_wider_model', 1, 256), ('to_conv_deeper_model', 1, 3), ('to_concat_skip_model', 6, 11)] for args in history: getattr(graph, args[0])(*list(args[1:])) graph.produce_model() assert legal_graph(graph) def test_node_consistency(): graph = DefaultClassifierGenerator(10, (32, 32, 3)).generate() assert graph.layer_list[6].output.shape == (16, 16, 64) for layer in graph.layer_list: assert layer.output.shape == layer.output_shape graph.to_wider_model(6, 64) assert graph.layer_list[6].output.shape == (16, 16, 128) for layer in graph.layer_list: assert layer.output.shape == layer.output_shape graph.to_conv_deeper_model(6, 3) assert graph.layer_list[19].output.shape == (16, 16, 128) for layer in graph.layer_list: assert layer.output.shape == layer.output_shape graph.to_add_skip_model(6, 19) assert graph.layer_list[23].output.shape == (16, 16, 128) for layer in graph.layer_list: assert layer.output.shape == layer.output_shape graph.to_concat_skip_model(6, 19) assert graph.layer_list[25].output.shape == (16, 16, 128) for layer in graph.layer_list: assert layer.output.shape == layer.output_shape

true

790db5e4ea2ac9b7b978a97d923cc49dca6e6b37

1,837

py

Python

src/util.py

lukamaletin/multi-gan

53b37c840d74ed0a9db888a03a5bed59ad33bc8e

[ "MIT" ]

null

src/util.py

lukamaletin/multi-gan

53b37c840d74ed0a9db888a03a5bed59ad33bc8e

[ "MIT" ]

null

src/util.py

lukamaletin/multi-gan

53b37c840d74ed0a9db888a03a5bed59ad33bc8e

[ "MIT" ]

null

import os import matplotlib.pyplot as plt import numpy as np from PIL import Image def make_trainable(net, val): net.trainable = val for layer in net.layers: layer.trainable = val def plot_loss(losses): plt.figure(figsize=(10, 8)) plt.plot(losses['g'], label='generative loss') plt.plot(losses['d'], label='discriminitive loss') plt.legend() plt.show() def render_bboxes(bboxes_batch, labels_batch, shape): renders = [] for i in range(len(bboxes_batch)): bboxes = bboxes_batch[i] labels = labels_batch[i] canvas = np.zeros(shape, dtype=np.float32) canvas += 255 for j in range(len(bboxes)): bbox = bboxes[j] top, left, bottom, right = bbox label = labels[j] color = (np.where(label==1)[0][0] + 1) * 10 canvas[top:bottom, left:right, 0] = color canvas /= 255 renders.append(canvas) return np.array(renders) def save_batch(images, epoch, path, suffix=''): samples_path = os.path.join(path, 'samples') if not os.path.exists(samples_path): os.makedirs(samples_path) num_images = images.shape[0] num_rows = images.shape[1] num_cols = images.shape[2] canvas = np.zeros((num_rows, num_images * num_cols, 1), dtype=images.dtype) for i in range(num_images): canvas[0:num_rows, i * num_cols:(i + 1) * num_cols] = images[i] img = canvas img *= 255 img = Image.fromarray(np.squeeze(img)) img = img.convert('L') img.save(samples_path + f'/{epoch}_{suffix}.png') def load_model(model, path, name): model_path = os.path.join(path, name + '.h5') model.load_weights(model_path) def save_model(model, path, name): model_path = os.path.join(path, name + '.h5') model.save_weights(model_path)

25.513889

79

0.623299

import os import matplotlib.pyplot as plt import numpy as np from PIL import Image def make_trainable(net, val): net.trainable = val for layer in net.layers: layer.trainable = val def plot_loss(losses): plt.figure(figsize=(10, 8)) plt.plot(losses['g'], label='generative loss') plt.plot(losses['d'], label='discriminitive loss') plt.legend() plt.show() def render_bboxes(bboxes_batch, labels_batch, shape): renders = [] for i in range(len(bboxes_batch)): bboxes = bboxes_batch[i] labels = labels_batch[i] canvas = np.zeros(shape, dtype=np.float32) canvas += 255 for j in range(len(bboxes)): bbox = bboxes[j] top, left, bottom, right = bbox label = labels[j] color = (np.where(label==1)[0][0] + 1) * 10 canvas[top:bottom, left:right, 0] = color canvas /= 255 renders.append(canvas) return np.array(renders) def save_batch(images, epoch, path, suffix=''): samples_path = os.path.join(path, 'samples') if not os.path.exists(samples_path): os.makedirs(samples_path) num_images = images.shape[0] num_rows = images.shape[1] num_cols = images.shape[2] canvas = np.zeros((num_rows, num_images * num_cols, 1), dtype=images.dtype) for i in range(num_images): canvas[0:num_rows, i * num_cols:(i + 1) * num_cols] = images[i] img = canvas img *= 255 img = Image.fromarray(np.squeeze(img)) img = img.convert('L') img.save(samples_path + f'/{epoch}_{suffix}.png') def load_model(model, path, name): model_path = os.path.join(path, name + '.h5') model.load_weights(model_path) def save_model(model, path, name): model_path = os.path.join(path, name + '.h5') model.save_weights(model_path)

true

790db5f7909e370ab4ab9fd0569746d419b73c10

7,068

py

Python

pyod/models/sod.py

GBR-613/pyod

bfbb297ac067c47488bcade77669c99de5a4838a

[ "BSD-2-Clause" ]

5,126

2018-11-09T06:05:38.000Z

2022-03-31T14:25:14.000Z

pyod/models/sod.py

durgeshsamariya/pyod

dfafc57f74dc3d49d0166f21ab2ddb97e3d1d898

[ "BSD-2-Clause" ]

325

2018-11-14T20:02:39.000Z

2022-03-30T22:49:38.000Z

pyod/models/sod.py

durgeshsamariya/pyod

dfafc57f74dc3d49d0166f21ab2ddb97e3d1d898

[ "BSD-2-Clause" ]

1,049

2018-11-09T06:12:12.000Z

2022-03-31T06:21:28.000Z

# -*- coding: utf-8 -*- """Subspace Outlier Detection (SOD) """ # Author: Yahya Almardeny <almardeny@gmail.com> # License: BSD 2 clause import numpy as np import numba as nb from sklearn.neighbors import NearestNeighbors from sklearn.utils import check_array from ..utils.utility import check_parameter from .base import BaseDetector @nb.njit(parallel=True) def _snn_imp(ind, ref_set_): """Internal function for fast snn calculation Parameters ---------- ind : int Indices return by kNN. ref_set_ : int, optional (default=10) specifies the number of shared nearest neighbors to create the reference set. Note that ref_set must be smaller than n_neighbors. """ n = ind.shape[0] _count = np.zeros(shape=(n, ref_set_), dtype=np.uint32) for i in nb.prange(n): temp = np.empty(n, dtype=np.uint32) test_element_set = set(ind[i]) for j in nb.prange(n): temp[j] = len(set(ind[j]).intersection(test_element_set)) temp[i] = np.iinfo(np.uint32).max _count[i] = np.argsort(temp)[::-1][1:ref_set_ + 1] return _count class SOD(BaseDetector): """Subspace outlier detection (SOD) schema aims to detect outlier in varying subspaces of a high dimensional feature space. For each data object, SOD explores the axis-parallel subspace spanned by the data object's neighbors and determines how much the object deviates from the neighbors in this subspace. See :cite:`kriegel2009outlier` for details. Parameters ---------- n_neighbors : int, optional (default=20) Number of neighbors to use by default for k neighbors queries. ref_set: int, optional (default=10) specifies the number of shared nearest neighbors to create the reference set. Note that ref_set must be smaller than n_neighbors. alpha: float in (0., 1.), optional (default=0.8) specifies the lower limit for selecting subspace. 0.8 is set as default as suggested in the original paper. contamination : float in (0., 0.5), optional (default=0.1) The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Used when fitting to define the threshold on the decision function. Attributes ---------- decision_scores_ : numpy array of shape (n_samples,) The outlier scores of the training data. The higher, the more abnormal. Outliers tend to have higher scores. This value is available once the detector is fitted. threshold_ : float The threshold is based on ``contamination``. It is the ``n_samples * contamination`` most abnormal samples in ``decision_scores_``. The threshold is calculated for generating binary outlier labels. labels_ : int, either 0 or 1 The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies. It is generated by applying ``threshold_`` on ``decision_scores_``. """ def __init__(self, contamination=0.1, n_neighbors=20, ref_set=10, alpha=0.8): super(SOD, self).__init__(contamination=contamination) if isinstance(n_neighbors, int): check_parameter(n_neighbors, low=1, param_name='n_neighbors') else: raise ValueError( "n_neighbors should be int. Got %s" % type(n_neighbors)) if isinstance(ref_set, int): check_parameter(ref_set, low=1, high=n_neighbors, param_name='ref_set') else: raise ValueError("ref_set should be int. Got %s" % type(ref_set)) if isinstance(alpha, float): check_parameter(alpha, low=0.0, high=1.0, param_name='alpha') else: raise ValueError("alpha should be float. Got %s" % type(alpha)) self.n_neighbors_ = n_neighbors self.ref_set_ = ref_set self.alpha_ = alpha self.decision_scores_ = None def fit(self, X, y=None): """Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features) The input samples. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object Fitted estimator. """ # validate inputs X and y (optional) X = check_array(X) self._set_n_classes(y) self.decision_scores_ = self.decision_function(X) self._process_decision_scores() return self def decision_function(self, X): """Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detector algorithms. For consistency, outliers are assigned with larger anomaly scores. Parameters ---------- X : numpy array of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- anomaly_scores : numpy array of shape (n_samples,) The anomaly score of the input samples. """ return self._sod(X) def _snn(self, X): """This function is called internally to calculate the shared nearest neighbors (SNN). SNN is reported to be more robust than k nearest neighbors. Returns ------- snn_indices : numpy array of shape (n_shared_nearest_neighbors,) The indices of top k shared nearest neighbors for each observation. """ knn = NearestNeighbors(n_neighbors=self.n_neighbors_) knn.fit(X) # Get the knn index ind = knn.kneighbors(return_distance=False) return _snn_imp(ind, self.ref_set_) def _sod(self, X): """This function is called internally to perform subspace outlier detection algorithm. Returns ------- anomaly_scores : numpy array of shape (n_samples,) The anomaly score of the input samples. """ ref_inds = self._snn(X) anomaly_scores = np.zeros(shape=(X.shape[0],)) for i in range(X.shape[0]): obs = X[i] ref = X[ref_inds[i,],] means = np.mean(ref, axis=0) # mean of each column # average squared distance of the reference to the mean var_total = np.sum(np.sum(np.square(ref - means))) / self.ref_set_ var_expect = self.alpha_ * var_total / X.shape[1] var_actual = np.var(ref, axis=0) # variance of each attribute var_inds = [1 if (j < var_expect) else 0 for j in var_actual] rel_dim = np.sum(var_inds) if rel_dim != 0: anomaly_scores[i] = np.sqrt( np.dot(var_inds, np.square(obs - means)) / rel_dim) return anomaly_scores

35.164179

79

0.621817

import numpy as np import numba as nb from sklearn.neighbors import NearestNeighbors from sklearn.utils import check_array from ..utils.utility import check_parameter from .base import BaseDetector @nb.njit(parallel=True) def _snn_imp(ind, ref_set_): n = ind.shape[0] _count = np.zeros(shape=(n, ref_set_), dtype=np.uint32) for i in nb.prange(n): temp = np.empty(n, dtype=np.uint32) test_element_set = set(ind[i]) for j in nb.prange(n): temp[j] = len(set(ind[j]).intersection(test_element_set)) temp[i] = np.iinfo(np.uint32).max _count[i] = np.argsort(temp)[::-1][1:ref_set_ + 1] return _count class SOD(BaseDetector): def __init__(self, contamination=0.1, n_neighbors=20, ref_set=10, alpha=0.8): super(SOD, self).__init__(contamination=contamination) if isinstance(n_neighbors, int): check_parameter(n_neighbors, low=1, param_name='n_neighbors') else: raise ValueError( "n_neighbors should be int. Got %s" % type(n_neighbors)) if isinstance(ref_set, int): check_parameter(ref_set, low=1, high=n_neighbors, param_name='ref_set') else: raise ValueError("ref_set should be int. Got %s" % type(ref_set)) if isinstance(alpha, float): check_parameter(alpha, low=0.0, high=1.0, param_name='alpha') else: raise ValueError("alpha should be float. Got %s" % type(alpha)) self.n_neighbors_ = n_neighbors self.ref_set_ = ref_set self.alpha_ = alpha self.decision_scores_ = None def fit(self, X, y=None): X = check_array(X) self._set_n_classes(y) self.decision_scores_ = self.decision_function(X) self._process_decision_scores() return self def decision_function(self, X): return self._sod(X) def _snn(self, X): knn = NearestNeighbors(n_neighbors=self.n_neighbors_) knn.fit(X) ind = knn.kneighbors(return_distance=False) return _snn_imp(ind, self.ref_set_) def _sod(self, X): ref_inds = self._snn(X) anomaly_scores = np.zeros(shape=(X.shape[0],)) for i in range(X.shape[0]): obs = X[i] ref = X[ref_inds[i,],] means = np.mean(ref, axis=0) var_total = np.sum(np.sum(np.square(ref - means))) / self.ref_set_ var_expect = self.alpha_ * var_total / X.shape[1] var_actual = np.var(ref, axis=0) var_inds = [1 if (j < var_expect) else 0 for j in var_actual] rel_dim = np.sum(var_inds) if rel_dim != 0: anomaly_scores[i] = np.sqrt( np.dot(var_inds, np.square(obs - means)) / rel_dim) return anomaly_scores

true

790db68297454438cd4748af30d715a7558e6fe2

7,042

py

Python

test/functional/rpc_getblockstats.py

blinkhash/blinkhash-core

e05662019c2fa4cb2dc3736f38e48492712c23b1

[ "MIT" ]

3

2021-07-27T16:59:47.000Z

2021-12-31T20:55:46.000Z

test/functional/rpc_getblockstats.py

blinkhash/blinkhash-core

e05662019c2fa4cb2dc3736f38e48492712c23b1

[ "MIT" ]

null

test/functional/rpc_getblockstats.py

blinkhash/blinkhash-core

e05662019c2fa4cb2dc3736f38e48492712c23b1

[ "MIT" ]

1

2021-12-31T12:58:23.000Z

#!/usr/bin/env python3 # Copyright (c) 2017-2021 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # # Test getblockstats rpc call # from test_framework.blocktools import COINBASE_MATURITY from test_framework.test_framework import BlinkhashTestFramework from test_framework.util import ( assert_equal, assert_raises_rpc_error, ) import json import os TESTSDIR = os.path.dirname(os.path.realpath(__file__)) class GetblockstatsTest(BlinkhashTestFramework): start_height = 101 max_stat_pos = 2 def add_options(self, parser): parser.add_argument('--gen-test-data', dest='gen_test_data', default=False, action='store_true', help='Generate test data') parser.add_argument('--test-data', dest='test_data', default='data/rpc_getblockstats.json', action='store', metavar='FILE', help='Test data file') def set_test_params(self): self.num_nodes = 1 self.setup_clean_chain = True self.supports_cli = False def get_stats(self): return [self.nodes[0].getblockstats(hash_or_height=self.start_height + i) for i in range(self.max_stat_pos+1)] def generate_test_data(self, filename): mocktime = 1525107225 self.nodes[0].setmocktime(mocktime) self.nodes[0].createwallet(wallet_name="testwallet") self.nodes[0].generatetoaddress(COINBASE_MATURITY + 1, self.nodes[0].getnewaddress()) address = self.nodes[0].get_deterministic_priv_key().address self.nodes[0].sendtoaddress(address=address, amount=10, subtractfeefromamount=True) self.generate(self.nodes[0], 1) self.nodes[0].sendtoaddress(address=address, amount=10, subtractfeefromamount=True) self.nodes[0].sendtoaddress(address=address, amount=10, subtractfeefromamount=False) self.nodes[0].settxfee(amount=0.003) self.nodes[0].sendtoaddress(address=address, amount=1, subtractfeefromamount=True) self.sync_all() self.generate(self.nodes[0], 1) self.expected_stats = self.get_stats() blocks = [] tip = self.nodes[0].getbestblockhash() blockhash = None height = 0 while tip != blockhash: blockhash = self.nodes[0].getblockhash(height) blocks.append(self.nodes[0].getblock(blockhash, 0)) height += 1 to_dump = { 'blocks': blocks, 'mocktime': int(mocktime), 'stats': self.expected_stats, } with open(filename, 'w', encoding="utf8") as f: json.dump(to_dump, f, sort_keys=True, indent=2) def load_test_data(self, filename): with open(filename, 'r', encoding="utf8") as f: d = json.load(f) blocks = d['blocks'] mocktime = d['mocktime'] self.expected_stats = d['stats'] # Set the timestamps from the file so that the nodes can get out of Initial Block Download self.nodes[0].setmocktime(mocktime) self.sync_all() for b in blocks: self.nodes[0].submitblock(b) def run_test(self): test_data = os.path.join(TESTSDIR, self.options.test_data) if self.options.gen_test_data: self.generate_test_data(test_data) else: self.load_test_data(test_data) self.sync_all() stats = self.get_stats() # Make sure all valid statistics are included but nothing else is expected_keys = self.expected_stats[0].keys() assert_equal(set(stats[0].keys()), set(expected_keys)) assert_equal(stats[0]['height'], self.start_height) assert_equal(stats[self.max_stat_pos]['height'], self.start_height + self.max_stat_pos) for i in range(self.max_stat_pos+1): self.log.info('Checking block %d\n' % (i)) assert_equal(stats[i], self.expected_stats[i]) # Check selecting block by hash too blockhash = self.expected_stats[i]['blockhash'] stats_by_hash = self.nodes[0].getblockstats(hash_or_height=blockhash) assert_equal(stats_by_hash, self.expected_stats[i]) # Make sure each stat can be queried on its own for stat in expected_keys: for i in range(self.max_stat_pos+1): result = self.nodes[0].getblockstats(hash_or_height=self.start_height + i, stats=[stat]) assert_equal(list(result.keys()), [stat]) if result[stat] != self.expected_stats[i][stat]: self.log.info('result[%s] (%d) failed, %r != %r' % ( stat, i, result[stat], self.expected_stats[i][stat])) assert_equal(result[stat], self.expected_stats[i][stat]) # Make sure only the selected statistics are included (more than one) some_stats = {'minfee', 'maxfee'} stats = self.nodes[0].getblockstats(hash_or_height=1, stats=list(some_stats)) assert_equal(set(stats.keys()), some_stats) # Test invalid parameters raise the proper json exceptions tip = self.start_height + self.max_stat_pos assert_raises_rpc_error(-8, 'Target block height %d after current tip %d' % (tip+1, tip), self.nodes[0].getblockstats, hash_or_height=tip+1) assert_raises_rpc_error(-8, 'Target block height %d is negative' % (-1), self.nodes[0].getblockstats, hash_or_height=-1) # Make sure not valid stats aren't allowed inv_sel_stat = 'asdfghjkl' inv_stats = [ [inv_sel_stat], ['minfee' , inv_sel_stat], [inv_sel_stat, 'minfee'], ['minfee', inv_sel_stat, 'maxfee'], ] for inv_stat in inv_stats: assert_raises_rpc_error(-8, 'Invalid selected statistic %s' % inv_sel_stat, self.nodes[0].getblockstats, hash_or_height=1, stats=inv_stat) # Make sure we aren't always returning inv_sel_stat as the culprit stat assert_raises_rpc_error(-8, 'Invalid selected statistic aaa%s' % inv_sel_stat, self.nodes[0].getblockstats, hash_or_height=1, stats=['minfee' , 'aaa%s' % inv_sel_stat]) # Mainchain's genesis block shouldn't be found on regtest assert_raises_rpc_error(-5, 'Block not found', self.nodes[0].getblockstats, hash_or_height='000000000019d6689c085ae165831e934ff763ae46a2a6c172b3f1b60a8ce26f') # Invalid number of args assert_raises_rpc_error(-1, 'getblockstats hash_or_height ( stats )', self.nodes[0].getblockstats, '00', 1, 2) assert_raises_rpc_error(-1, 'getblockstats hash_or_height ( stats )', self.nodes[0].getblockstats) if __name__ == '__main__': GetblockstatsTest().main()

41.916667

121

0.629225

from test_framework.blocktools import COINBASE_MATURITY from test_framework.test_framework import BlinkhashTestFramework from test_framework.util import ( assert_equal, assert_raises_rpc_error, ) import json import os TESTSDIR = os.path.dirname(os.path.realpath(__file__)) class GetblockstatsTest(BlinkhashTestFramework): start_height = 101 max_stat_pos = 2 def add_options(self, parser): parser.add_argument('--gen-test-data', dest='gen_test_data', default=False, action='store_true', help='Generate test data') parser.add_argument('--test-data', dest='test_data', default='data/rpc_getblockstats.json', action='store', metavar='FILE', help='Test data file') def set_test_params(self): self.num_nodes = 1 self.setup_clean_chain = True self.supports_cli = False def get_stats(self): return [self.nodes[0].getblockstats(hash_or_height=self.start_height + i) for i in range(self.max_stat_pos+1)] def generate_test_data(self, filename): mocktime = 1525107225 self.nodes[0].setmocktime(mocktime) self.nodes[0].createwallet(wallet_name="testwallet") self.nodes[0].generatetoaddress(COINBASE_MATURITY + 1, self.nodes[0].getnewaddress()) address = self.nodes[0].get_deterministic_priv_key().address self.nodes[0].sendtoaddress(address=address, amount=10, subtractfeefromamount=True) self.generate(self.nodes[0], 1) self.nodes[0].sendtoaddress(address=address, amount=10, subtractfeefromamount=True) self.nodes[0].sendtoaddress(address=address, amount=10, subtractfeefromamount=False) self.nodes[0].settxfee(amount=0.003) self.nodes[0].sendtoaddress(address=address, amount=1, subtractfeefromamount=True) self.sync_all() self.generate(self.nodes[0], 1) self.expected_stats = self.get_stats() blocks = [] tip = self.nodes[0].getbestblockhash() blockhash = None height = 0 while tip != blockhash: blockhash = self.nodes[0].getblockhash(height) blocks.append(self.nodes[0].getblock(blockhash, 0)) height += 1 to_dump = { 'blocks': blocks, 'mocktime': int(mocktime), 'stats': self.expected_stats, } with open(filename, 'w', encoding="utf8") as f: json.dump(to_dump, f, sort_keys=True, indent=2) def load_test_data(self, filename): with open(filename, 'r', encoding="utf8") as f: d = json.load(f) blocks = d['blocks'] mocktime = d['mocktime'] self.expected_stats = d['stats'] self.nodes[0].setmocktime(mocktime) self.sync_all() for b in blocks: self.nodes[0].submitblock(b) def run_test(self): test_data = os.path.join(TESTSDIR, self.options.test_data) if self.options.gen_test_data: self.generate_test_data(test_data) else: self.load_test_data(test_data) self.sync_all() stats = self.get_stats() expected_keys = self.expected_stats[0].keys() assert_equal(set(stats[0].keys()), set(expected_keys)) assert_equal(stats[0]['height'], self.start_height) assert_equal(stats[self.max_stat_pos]['height'], self.start_height + self.max_stat_pos) for i in range(self.max_stat_pos+1): self.log.info('Checking block %d\n' % (i)) assert_equal(stats[i], self.expected_stats[i]) blockhash = self.expected_stats[i]['blockhash'] stats_by_hash = self.nodes[0].getblockstats(hash_or_height=blockhash) assert_equal(stats_by_hash, self.expected_stats[i]) for stat in expected_keys: for i in range(self.max_stat_pos+1): result = self.nodes[0].getblockstats(hash_or_height=self.start_height + i, stats=[stat]) assert_equal(list(result.keys()), [stat]) if result[stat] != self.expected_stats[i][stat]: self.log.info('result[%s] (%d) failed, %r != %r' % ( stat, i, result[stat], self.expected_stats[i][stat])) assert_equal(result[stat], self.expected_stats[i][stat]) some_stats = {'minfee', 'maxfee'} stats = self.nodes[0].getblockstats(hash_or_height=1, stats=list(some_stats)) assert_equal(set(stats.keys()), some_stats) tip = self.start_height + self.max_stat_pos assert_raises_rpc_error(-8, 'Target block height %d after current tip %d' % (tip+1, tip), self.nodes[0].getblockstats, hash_or_height=tip+1) assert_raises_rpc_error(-8, 'Target block height %d is negative' % (-1), self.nodes[0].getblockstats, hash_or_height=-1) inv_sel_stat = 'asdfghjkl' inv_stats = [ [inv_sel_stat], ['minfee' , inv_sel_stat], [inv_sel_stat, 'minfee'], ['minfee', inv_sel_stat, 'maxfee'], ] for inv_stat in inv_stats: assert_raises_rpc_error(-8, 'Invalid selected statistic %s' % inv_sel_stat, self.nodes[0].getblockstats, hash_or_height=1, stats=inv_stat) # Make sure we aren't always returning inv_sel_stat as the culprit stat assert_raises_rpc_error(-8, 'Invalid selected statistic aaa%s' % inv_sel_stat, self.nodes[0].getblockstats, hash_or_height=1, stats=['minfee' , 'aaa%s' % inv_sel_stat]) assert_raises_rpc_error(-5, 'Block not found', self.nodes[0].getblockstats, hash_or_height='000000000019d6689c085ae165831e934ff763ae46a2a6c172b3f1b60a8ce26f') assert_raises_rpc_error(-1, 'getblockstats hash_or_height ( stats )', self.nodes[0].getblockstats, '00', 1, 2) assert_raises_rpc_error(-1, 'getblockstats hash_or_height ( stats )', self.nodes[0].getblockstats) if __name__ == '__main__': GetblockstatsTest().main()

true

790db7299eb21cb742977befe834ae4e20ae0093

4,040

py

Python

transformer/transformers/map_keys.py

santunioni/transformer

a34b8b40cba81382c8483d590050c3e36cee5bff

[ "MIT" ]

1

2022-02-21T22:15:08.000Z

transformer/transformers/map_keys.py

santunioni/Transformer

a34b8b40cba81382c8483d590050c3e36cee5bff

[ "MIT" ]

null

transformer/transformers/map_keys.py

santunioni/Transformer

a34b8b40cba81382c8483d590050c3e36cee5bff

[ "MIT" ]

null

from typing import Any, Dict, Mapping, Optional, Set from pydantic import validator from transformer.transformers.abstract import ExtraHashableModel, Transformer from transformer.transformers.flatters import Flatter, FlatterConfig, Unflatter class ReportMissingData(Exception): def __init__(self, keys: Set[str]): self.keys = keys self.message = f"The keys f{self.keys} are missing in the payload." class MapKeysConfig(ExtraHashableModel): """ This is the configuration for the MapKeys transformer. In order to call this transformer pass the name "map-keys" and a mapping dict. """ mapping: Mapping[str, str] preserve_unmapped: bool = True ignore_missing_data: bool = True level_separator: str = "." return_plain: bool = False @validator("mapping") def backwards_compatibility(cls, mapping: Mapping[str, str]): return { key.replace(".$[", "["): value.replace(".$[", "[") for key, value in mapping.items() } class MapKeys(Transformer[MapKeysConfig]): """ The MapKeys is a complete dict re-designer. It lets you rename the keys and also restructure the entire dict. Creating new nested data where there wasn't and also flattening data that was previously nested is possible, all that preserving the data from the input dictionary. """ def __init__(self, config: MapKeysConfig) -> None: super().__init__(config) self.__flatters_config = FlatterConfig(level_separator=config.level_separator) self.__flatter = Flatter(self.__flatters_config) self.__unflatter = Unflatter(self.__flatters_config) def transform( self, payload: Dict[str, Any], metadata: Optional[Dict[str, Any]] = None ): """ The mapping is done in 4 major steps: 1. Flattens the data. 2. Metadata Replacers: Some key mapping parameters are specified in the metadata. Keys that have placeholders like ${metadata_key} will be substituted by values on the specified metadata key. 3. Map Data. In this moment the keys of the mapping inside config match the keys of the flat payload. That is, the payload and self._config.mapping have matching keys. Maybe not all keys in payload are in self._config.mapping, in which case we choose what to do with those extra keys with the config self._config.preserve_unmapped. If the opposite happens, the self._config.mapping have keys not present in the payload, the configuration self._config.ignore_missing_data chooses what should be done. 4. Unflattens the data. :return: transformed and restructured data. """ flat_data = self.__flatter.transform(payload) translated_dict: Dict = {} map_keys_set = set(self._config.mapping.keys()) for map_key in map_keys_set.intersection(flat_data.keys()): map_value = self._config.mapping[map_key] if metadata is not None: for meta_key, meta_value in metadata.items(): map_key = map_key.replace("@{" + meta_key + "}", str(meta_value)) map_value = map_value.replace( "@{" + meta_key + "}", str(meta_value) ) translated_dict[map_value] = flat_data[map_key] if not self._config.ignore_missing_data: missing_keys = map_keys_set - flat_data.keys() if missing_keys: raise ReportMissingData(missing_keys) if self._config.preserve_unmapped: for unmapped_key in flat_data.keys() - self._config.mapping.keys(): translated_dict[unmapped_key] = flat_data[unmapped_key] if self._config.return_plain: return translated_dict, metadata if metadata is None: return self.__unflatter.transform(translated_dict) return self.__unflatter.transform(translated_dict, metadata)

40.4

117

0.661634

from typing import Any, Dict, Mapping, Optional, Set from pydantic import validator from transformer.transformers.abstract import ExtraHashableModel, Transformer from transformer.transformers.flatters import Flatter, FlatterConfig, Unflatter class ReportMissingData(Exception): def __init__(self, keys: Set[str]): self.keys = keys self.message = f"The keys f{self.keys} are missing in the payload." class MapKeysConfig(ExtraHashableModel): mapping: Mapping[str, str] preserve_unmapped: bool = True ignore_missing_data: bool = True level_separator: str = "." return_plain: bool = False @validator("mapping") def backwards_compatibility(cls, mapping: Mapping[str, str]): return { key.replace(".$[", "["): value.replace(".$[", "[") for key, value in mapping.items() } class MapKeys(Transformer[MapKeysConfig]): def __init__(self, config: MapKeysConfig) -> None: super().__init__(config) self.__flatters_config = FlatterConfig(level_separator=config.level_separator) self.__flatter = Flatter(self.__flatters_config) self.__unflatter = Unflatter(self.__flatters_config) def transform( self, payload: Dict[str, Any], metadata: Optional[Dict[str, Any]] = None ): flat_data = self.__flatter.transform(payload) translated_dict: Dict = {} map_keys_set = set(self._config.mapping.keys()) for map_key in map_keys_set.intersection(flat_data.keys()): map_value = self._config.mapping[map_key] if metadata is not None: for meta_key, meta_value in metadata.items(): map_key = map_key.replace("@{" + meta_key + "}", str(meta_value)) map_value = map_value.replace( "@{" + meta_key + "}", str(meta_value) ) translated_dict[map_value] = flat_data[map_key] if not self._config.ignore_missing_data: missing_keys = map_keys_set - flat_data.keys() if missing_keys: raise ReportMissingData(missing_keys) if self._config.preserve_unmapped: for unmapped_key in flat_data.keys() - self._config.mapping.keys(): translated_dict[unmapped_key] = flat_data[unmapped_key] if self._config.return_plain: return translated_dict, metadata if metadata is None: return self.__unflatter.transform(translated_dict) return self.__unflatter.transform(translated_dict, metadata)

true

790db8137d4ea765b0aab062d890ecbd67994b6d

2,571

py

Python

tests/providers/microsoft/azure/transfers/test_local_to_wasb.py

ChaseKnowlden/airflow

6b71eac1997a7c0db3b8e3aed6b4e65d01871440

[ "Apache-2.0" ]

15,947

2019-01-05T13:51:02.000Z

2022-03-31T23:33:16.000Z

tests/providers/microsoft/azure/transfers/test_local_to_wasb.py

ChaseKnowlden/airflow

6b71eac1997a7c0db3b8e3aed6b4e65d01871440

[ "Apache-2.0" ]

14,603

2019-01-05T09:43:19.000Z

2022-03-31T23:11:59.000Z

tests/providers/microsoft/azure/transfers/test_local_to_wasb.py

ChaseKnowlden/airflow

6b71eac1997a7c0db3b8e3aed6b4e65d01871440

[ "Apache-2.0" ]

8,429

2019-01-05T19:45:47.000Z

2022-03-31T22:13:01.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # import datetime import unittest from unittest import mock from airflow.models.dag import DAG from airflow.providers.microsoft.azure.transfers.local_to_wasb import LocalFilesystemToWasbOperator class TestLocalFilesystemToWasbOperator(unittest.TestCase): _config = { 'file_path': 'file', 'container_name': 'container', 'blob_name': 'blob', 'wasb_conn_id': 'wasb_default', 'retries': 3, } def setUp(self): args = {'owner': 'airflow', 'start_date': datetime.datetime(2017, 1, 1)} self.dag = DAG('test_dag_id', default_args=args) def test_init(self): operator = LocalFilesystemToWasbOperator(task_id='wasb_operator_1', dag=self.dag, **self._config) assert operator.file_path == self._config['file_path'] assert operator.container_name == self._config['container_name'] assert operator.blob_name == self._config['blob_name'] assert operator.wasb_conn_id == self._config['wasb_conn_id'] assert operator.load_options == {} assert operator.retries == self._config['retries'] operator = LocalFilesystemToWasbOperator( task_id='wasb_operator_2', dag=self.dag, load_options={'timeout': 2}, **self._config ) assert operator.load_options == {'timeout': 2} @mock.patch('airflow.providers.microsoft.azure.transfers.local_to_wasb.WasbHook', autospec=True) def test_execute(self, mock_hook): mock_instance = mock_hook.return_value operator = LocalFilesystemToWasbOperator( task_id='wasb_sensor', dag=self.dag, load_options={'timeout': 2}, **self._config ) operator.execute(None) mock_instance.load_file.assert_called_once_with('file', 'container', 'blob', timeout=2)

40.171875

105

0.710618

import datetime import unittest from unittest import mock from airflow.models.dag import DAG from airflow.providers.microsoft.azure.transfers.local_to_wasb import LocalFilesystemToWasbOperator class TestLocalFilesystemToWasbOperator(unittest.TestCase): _config = { 'file_path': 'file', 'container_name': 'container', 'blob_name': 'blob', 'wasb_conn_id': 'wasb_default', 'retries': 3, } def setUp(self): args = {'owner': 'airflow', 'start_date': datetime.datetime(2017, 1, 1)} self.dag = DAG('test_dag_id', default_args=args) def test_init(self): operator = LocalFilesystemToWasbOperator(task_id='wasb_operator_1', dag=self.dag, **self._config) assert operator.file_path == self._config['file_path'] assert operator.container_name == self._config['container_name'] assert operator.blob_name == self._config['blob_name'] assert operator.wasb_conn_id == self._config['wasb_conn_id'] assert operator.load_options == {} assert operator.retries == self._config['retries'] operator = LocalFilesystemToWasbOperator( task_id='wasb_operator_2', dag=self.dag, load_options={'timeout': 2}, **self._config ) assert operator.load_options == {'timeout': 2} @mock.patch('airflow.providers.microsoft.azure.transfers.local_to_wasb.WasbHook', autospec=True) def test_execute(self, mock_hook): mock_instance = mock_hook.return_value operator = LocalFilesystemToWasbOperator( task_id='wasb_sensor', dag=self.dag, load_options={'timeout': 2}, **self._config ) operator.execute(None) mock_instance.load_file.assert_called_once_with('file', 'container', 'blob', timeout=2)

true

790db84293b6d95fe47f418cd8e8dee9afcd0519

461

py

Python

Exercises/Exercises_01/07_exercise.py

Szymon-Budziak/ASD_exercises_solutions

36ccbdae03a6c7e4ad141a2b7b01bef9353574ee

[ "MIT" ]

7

2021-12-28T23:38:42.000Z

2022-03-29T16:36:16.000Z

Exercises/Exercises_01/07_exercise.py

Szymon-Budziak/ASD_exercises_solutions

36ccbdae03a6c7e4ad141a2b7b01bef9353574ee

[ "MIT" ]

null

Exercises/Exercises_01/07_exercise.py

Szymon-Budziak/ASD_exercises_solutions

36ccbdae03a6c7e4ad141a2b7b01bef9353574ee

[ "MIT" ]

4

2021-06-29T20:21:52.000Z

2022-03-12T10:04:17.000Z

# Dana jest posortowana tablica A[1, ..., n] oraz liczba x. Proszę napisać program, który stwierdza # czy istnieją indeksy i oraz j takie, że A[i] + A[j] = x. def sum_search(T, x): l = 0 r = len(T) - 1 while l <= r: if T[l] + T[r] == x: return True elif T[l] + T[r] > x: r -= 1 else: l += 1 return False T = [2, 5, 8, 12, 16, 19, 20, 25, 34, 55, 81] x = 37 print(sum_search(T, x))

21.952381

99

0.488069

def sum_search(T, x): l = 0 r = len(T) - 1 while l <= r: if T[l] + T[r] == x: return True elif T[l] + T[r] > x: r -= 1 else: l += 1 return False T = [2, 5, 8, 12, 16, 19, 20, 25, 34, 55, 81] x = 37 print(sum_search(T, x))

true

790db85459ceb8b54c79ae5345c445b6c1fb5bd1

21,310

py

Python

matchms/old/ms_similarity_classical.py

matchms/old-iomega-spec2vec

216b8f8b5e4ffd320b4575326a05fb6c7cd28223

[ "Apache-2.0" ]

null

matchms/old/ms_similarity_classical.py

matchms/old-iomega-spec2vec

216b8f8b5e4ffd320b4575326a05fb6c7cd28223

[ "Apache-2.0" ]

null

matchms/old/ms_similarity_classical.py

matchms/old-iomega-spec2vec

216b8f8b5e4ffd320b4575326a05fb6c7cd28223

[ "Apache-2.0" ]

1

2020-07-04T23:28:55.000Z

# # Spec2Vec # # Copyright 2019 Netherlands eScience Center # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numba import numpy as np from scipy.optimize import linear_sum_assignment from scipy import spatial # Add multi core parallelization from concurrent.futures import ThreadPoolExecutor #, as_completed # TODO better use joblib ? or dask? def mol_sim_matrix(fingerprints1, fingerprints2, method='cosine', filename=None, max_size=1000, print_progress=True): """Create Matrix of all molecular similarities (based on molecular fingerprints). If filename is not None, the result will be saved as npy. To create molecular fingerprints see mol_fingerprints() function from MS_functions. Args: ---- fingerprints1: list List of molecular fingerprints (numpy arrays). fingerprints2: list List of molecular fingerprints (numpy arrays). method: str Method to compare molecular fingerprints. Can be 'cosine', 'dice' etc. (see scipy.spatial.distance.cdist). filename: str Filename to save results to. OR: If file already exists it will be loaded instead. max_size: int Maximum size of (sub) all-vs-all matrix to handle in one go. Will split up larger matrices into max_size x max_size matrices. print_progress: bool, optional If True, print phase of the run to indicate progress. Default = True. """ if filename is not None: try: molecular_similarities = np.load(filename) print("Molecular similarity scores found and loaded.") collect_new_data = False except FileNotFoundError: print("Could not find file ", filename) print("Molecular scores will be calculated from scratch.") collect_new_data = True else: collect_new_data = True if collect_new_data: # Create array of all finterprints fingerprints_arr1 = np.array(fingerprints1) fingerprints_arr2 = np.array(fingerprints2) # Calculate all-vs-all similarity matrix (similarity here= 1-distance ) matrix_size = (fingerprints_arr1.shape[0], fingerprints_arr2.shape[0]) molecular_similarities = np.zeros(matrix_size) # Split large matrices up into smaller ones to track progress splits = int(np.ceil(matrix_size[0]/max_size) * np.ceil(matrix_size[1]/max_size)) count_splits = 0 for i in range(int(np.ceil(matrix_size[0]/max_size))): low1 = i * max_size high1 = min((i + 1) * max_size, matrix_size[0]) for j in range(int(np.ceil(matrix_size[1]/max_size))): low2 = j * max_size high2 = min((j + 1) * max_size, matrix_size[1]) molecular_similarities[low1:high1, low2:high2] = 1 - spatial.distance.cdist( fingerprints_arr1[low1:high1], fingerprints_arr2[low2:high2], method ) # Track progress: count_splits += 1 if print_progress: print('\r', "Calculated submatrix {} out of {}".format(count_splits, splits), end="") if print_progress: print(20 * '--') print("Succesfully calculated matrix with all-vs-all molecular similarity values.") if filename is not None: np.save(filename, molecular_similarities) print("Matrix was saved under:", filename) return molecular_similarities # -------------------------------------------------------------------------------------------------- # ---------------------------- classical spectra similarity measures ------------------------------- # -------------------------------------------------------------------------------------------------- def cosine_score_greedy(spec1, spec2, mass_shift, tol, min_intens=0, use_numba=True): """Calculate cosine score between spectrum1 and spectrum2. If mass_shifted = True it will shift the spectra with respect to each other by difference in their parentmasses. Args: ---- spec1: Spectrum peaks and intensities as numpy array. spec2: Spectrum peaks and intensities as numpy array. tol: float Tolerance value to define how far two peaks can be apart to still count as match. min_intens: float Minimum intensity (relative to max.intensity peak in spectrum). Peaks with lower intensity will be ignored --> higher min_intens is faster, but less precise. """ if spec1.shape[0] == 0 or spec2.shape[0] == 0: return 0.0, [] # normalize intensities: spec1[:, 1] = spec1[:, 1]/max(spec1[:, 1]) spec2[:, 1] = spec2[:, 1]/max(spec2[:, 1]) # filter, if wanted: spec1 = spec1[spec1[:, 1] > min_intens, :] spec2 = spec2[spec2[:, 1] > min_intens, :] if use_numba: zero_pairs = find_pairs_numba(spec1, spec2, tol, shift=0.0) else: zero_pairs = find_pairs(spec1, spec2, tol, shift=0.0) if mass_shift is not None \ and mass_shift != 0.0: if use_numba: nonzero_pairs = find_pairs_numba(spec1, spec2, tol, shift=mass_shift) else: nonzero_pairs = find_pairs(spec1, spec2, tol, shift=mass_shift) matching_pairs = zero_pairs + nonzero_pairs else: matching_pairs = zero_pairs matching_pairs = sorted(matching_pairs, key=lambda x: x[2], reverse=True) used1 = set() used2 = set() score = 0.0 used_matches = [] for m in matching_pairs: if not m[0] in used1 and not m[1] in used2: score += m[2] used1.add(m[0]) used2.add(m[1]) used_matches.append(m) # Normalize score: score = score/max(np.sum(spec1[:, 1]**2), np.sum(spec2[:, 1]**2)) return score, used_matches def cosine_score_hungarian(spec1, spec2, mass_shift, tol, min_intens=0): """Taking full care of weighted bipartite matching problem. Use Hungarian algorithm (slow...) Args: -------- spec1: Spectrum peaks and intensities as numpy array. spec2: Spectrum peaks and intensities as numpy array. mass_shift: float Difference in parent mass of both spectra to account for. Set to 'None' when no shifting is desired --> back to normal cosine score. tol: float Tolerance value to define how far two peaks can be apart to still count as match. min_intens: float Minimum intensity (relative to max.intensity peak in spectrum). Peaks with lower intensity will be ignored --> higher min_intens is faster, but less precise. """ if spec1.shape[0] == 0 or spec2.shape[0] == 0: return 0.0, [] # Normalize intensities: spec1[:, 1] = spec1[:, 1]/max(spec1[:, 1]) spec2[:, 1] = spec2[:, 1]/max(spec2[:, 1]) # Filter, if wanted: spec1 = spec1[spec1[:, 1] > min_intens, :] spec2 = spec2[spec2[:, 1] > min_intens, :] zero_pairs = find_pairs_numba(spec1, spec2, tol, shift=0.0) if mass_shift is not None \ and mass_shift != 0.0: nonzero_pairs = find_pairs_numba(spec1, spec2, tol, shift=mass_shift) matching_pairs = zero_pairs + nonzero_pairs else: matching_pairs = zero_pairs matching_pairs = sorted(matching_pairs, key=lambda x: x[2], reverse=True) # Use Hungarian_algorithm: used_matches = [] list1 = list(set([x[0] for x in matching_pairs])) list2 = list(set([x[1] for x in matching_pairs])) matrix_size = (len(list1), len(list2)) matrix = np.ones(matrix_size) if len(matching_pairs) > 0: for m in matching_pairs: matrix[list1.index(m[0]), list2.index(m[1])] = 1 - m[2] # Use hungarian agorithm to solve the linear sum assignment problem row_ind, col_ind = linear_sum_assignment(matrix) score = len(row_ind) - matrix[row_ind, col_ind].sum() used_matches = [(list1[x], list2[y]) for (x, y) in zip(row_ind, col_ind)] # Normalize score: score = score/max(np.sum(spec1[:, 1]**2), np.sum(spec2[:, 1]**2)) else: score = 0.0 return score, used_matches def cosine_matrix_fast(spectra, tol, max_mz, min_mz=0): """Calculates cosine similarity matrix. Be careful! Binning is here done by creating one-hot vectors. It is hence really actual "bining" and different from the tolerance-based approach used for the cosine_matrix or molnet_matrix! Also: tol here is about tol/2 when compared to cosine_matrix or molnet_matrix... """ for i, spectrum in enumerate(spectra): spec = np.array(spectrum.peaks.copy(), dtype=float) # Normalize intensities: spec[:, 1] = spec[:, 1]/np.max(spec[:, 1]) if i == 0: vector = one_hot_spectrum(spec, tol, max_mz, shift=0, min_mz=min_mz, method='max') spec_vectors = np.zeros((len(spectra), vector.shape[0])) spec_vectors[0, :] = vector else: spec_vectors[i, :] = one_hot_spectrum(spec, tol, max_mz, shift=0, min_mz=min_mz, method='max') Cdist = spatial.distance.cdist(spec_vectors, spec_vectors, 'cosine') return 1 - Cdist def cosine_score_matrix(spectra, tol, max_mz=1000.0, # min_mz=0, min_intens=0, mass_shifting=False, method='hungarian', num_workers=4, filename=None, safety_points=None): """Create Matrix of all modified cosine similarities. Takes some time to calculate, so better only do it once and save as npy. Now implemented: parallelization of code using concurrent.futures and numba options. spectra: list List of spectra (of Spectrum class) tol: float Tolerance to still count peaks a match (mz +- tolerance). max_mz: float Maxium m-z mass to take into account #min_mz: float # Minimum m-z mass to take into account min_intens: float Sets the minimum relative intensity peaks must have to be looked at for potential matches. mass_shifting: bool Set to 'True' if mass difference between spectra should be accounted for --> "modified cosine" score Set to 'False' for --> "normal cosine" score method: 'greedy', 'greedy-numba', 'hungarian' "greedy" will use Simon's molnet scoring which is faster than hungarian, but not 100% accurate regarding the weighted bipartite matching problem. "hungarian" will use the Hungarian algorithm, which is more accurate. Since its slower, numba is used here to compile in time. "greedy-numba" will use a (partly) numba compiled version of greedy. Much faster, but needs numba. num_workers: int Number of threads to use for calculation. filename: str/ None Filename to look for existing npy-file with molent matrix. Or, if not found, to use to save the newly calculated matrix. safety_points: int Number of safety points, i.e. number of times the modcos-matrix is saved during process. Set to 'None' to avoid saving matrix on the way. """ if filename is not None: if filename[-4:] != '.npy': filename = filename + '.npy' # Try loading saved data try: print("Loading similarity scores from", filename) modcos_sim = np.load(filename) print("Loading min_match values from", filename[:-4]+ "_matches.npy") modcos_matches = np.load(filename[:-4] + "_matches.npy") # Check if matrix was calculated to the end: diagonal = modcos_sim.diagonal() if np.min(diagonal) == 0: print("Uncomplete cosine similarity scores found and loaded.") missing_scores = np.where(diagonal == 0)[0].astype(int) print("Missing cosine scores will be calculated.") counter_total = int((len(spectra)**2)/2) counter_init = counter_total - np.sum(len(spectra) - missing_scores) print("About ", 100*(counter_init/counter_total), "% of the values already completed.") collect_new_data = True else: print("Complete cosine similarity scores found and loaded.") missing_scores = [] counter_init = 0 collect_new_data = False except FileNotFoundError: print("Could not find file ", filename, "or file", filename[:-4] + "_matches.npy") if mass_shifting: print("Modified cosine scores will be calculated from scratch.") else: print("Cosine scores will be calculated from scratch.") collect_new_data = True missing_scores = np.arange(0, len(spectra)) counter_init = 0 else: collect_new_data = True missing_scores = np.arange(0, len(spectra)) counter_init = 0 if collect_new_data: if counter_init == 0: modcos_sim = np.zeros((len(spectra), len(spectra))) modcos_matches = np.zeros((len(spectra), len(spectra))) counter = counter_init if safety_points is not None: # Save modcos-matrix along process safety_save = int(((len(spectra)**2)/2)/safety_points) print("Calculate pairwise scores by", num_workers, "number of workers.") for i in missing_scores: #range(n_start, len(spectra)): spec1 = np.array(spectra[i].peaks, dtype=float) spec1 = spec1[spec1[:, 0] < max_mz, :] parameter_collection = [] for j in range(i, len(spectra)): spec2 = np.array(spectra[j].peaks, dtype=float) spec2 = spec2[spec2[:, 0] < max_mz, :] if mass_shifting: mass_shift = spectra[i].parent_mz - spectra[j].parent_mz else: mass_shift = None parameter_collection.append([spec1, spec2, i, j, mass_shift, tol, min_intens, method, counter]) counter += 1 # Create a pool of processes. For instance one for each CPU in your machine. modcos_pairs = [] with ThreadPoolExecutor(max_workers=num_workers) as executor: futures = [executor.submit(modcos_pair, X, len(spectra)) for X in parameter_collection] modcos_pairs.append(futures) for m, future in enumerate(modcos_pairs[0]): _, _, ind_i, ind_j, _, _, _, _, counting = parameter_collection[m] modcos_sim[ind_i, ind_j] = future.result()[0] modcos_matches[ind_i, ind_j] = future.result()[1] if filename is not None \ and safety_points is not None: if (counting+1) % safety_save == 0: np.save(filename, modcos_sim) np.save(filename[:-4] + "_matches.npy", modcos_matches) # Symmetric matrix --> fill for i in range(1, len(spectra)): for j in range(i): modcos_sim[i, j] = modcos_sim[j, i] modcos_matches[i, j] = modcos_matches[j, i] # Save final results if filename is not None: np.save(filename, modcos_sim) np.save(filename[:-4]+ "_matches.npy", modcos_matches) return modcos_sim, modcos_matches def modcos_pair(X, len_spectra): """Single molnet pair calculation """ spectra_i, spectra_j, i, j, mass_shift, tol, min_intens, method, counter = X if method == 'greedy': molnet_pair, used_matches = cosine_score_greedy(spectra_i, spectra_j, mass_shift, tol, min_intens=min_intens, use_numba=False) elif method == 'greedy-numba': molnet_pair, used_matches = cosine_score_greedy(spectra_i, spectra_j, mass_shift, tol, min_intens=min_intens, use_numba=True) elif method == 'hungarian': molnet_pair, used_matches = cosine_score_hungarian(spectra_i, spectra_j, mass_shift, tol, min_intens=min_intens) else: print("Given method does not exist...") if (counter+1) % 1000 == 0 or counter == len_spectra-1: print('\r', ' Calculated MolNet for pair {} -- {}'.format(i, j), '. ( ', np.round(200*(counter+1)/len_spectra**2, 2), ' % done).', end="") return molnet_pair, len(used_matches) def one_hot_spectrum(spec, tol, max_mz, shift=0, min_mz=0, method='max'): """Convert spectrum peaks into on-hot-vector method: str 'max' take highest intensity peak within every bin. 'sum' take sum of all peaks within every bin. """ dim_vector = int((max_mz - min_mz)/tol) one_hot_spec = np.zeros((dim_vector)) idx = ((spec[:, 0] + shift)*1/tol).astype(int) idx[idx >= dim_vector] = 0 idx[idx < 0] = 0 if method == 'max': for id1 in set(idx): one_hot_spec[id1] = np.max(spec[(idx == id1), 1]) elif method == 'sum': for id1 in set(idx): one_hot_spec[id1] = np.sum(spec[(idx == id1), 1]) else: print("Method not known...") return one_hot_spec @numba.njit def find_pairs_numba(spec1, spec2, tol, shift=0): """Find matching pairs between two spectra. Args ---- spec1 : list of tuples List of (mz, intensity) tuples. spec2 : list of tuples List of (mz, intensity) tuples. tol : float Tolerance. Peaks will be considered a match when < tol appart. shift : float, optional Shift spectra peaks by shift. The default is 0. Returns ------- matching_pairs : list List of found matching peaks. """ matching_pairs = [] for idx in range(len(spec1)): intensity = spec1[idx, 1] matches = np.where((np.abs(spec2[:, 0] - spec1[idx, 0] + shift) <= tol))[0] for match in matches: matching_pairs.append((idx, match, intensity*spec2[match][1])) return matching_pairs def find_pairs(spec1, spec2, tol, shift=0): """Find matching pairs between two spectra. Args ---- spec1 : list of tuples List of (mz, intensity) tuples. spec2 : list of tuples List of (mz, intensity) tuples. tol : float Tolerance. Peaks will be considered a match when < tol appart. shift : float, optional Shift spectra peaks by shift. The default is 0. Returns ------- matching_pairs : list List of found matching peaks. """ # Sort peaks and losses by m/z spec1 = spec1[np.lexsort((spec1[:, 1], spec1[:, 0])), :] spec2 = spec2[np.lexsort((spec2[:, 1], spec2[:, 0])), :] matching_pairs = [] spec2lowpos = 0 spec2length = len(spec2) for idx in range(len(spec1)): mz = spec1[idx, 0] intensity = spec1[idx, 1] # Do we need to increase the lower idx? while spec2lowpos < spec2length and spec2[spec2lowpos][0] + shift < mz - tol: spec2lowpos += 1 if spec2lowpos == spec2length: break spec2pos = spec2lowpos while(spec2pos < spec2length and spec2[spec2pos][0] + shift < mz + tol): matching_pairs.append((idx, spec2pos, intensity * spec2[spec2pos][1])) spec2pos += 1 return matching_pairs

37.783688

103

0.57114

import numba import numpy as np from scipy.optimize import linear_sum_assignment from scipy import spatial from concurrent.futures import ThreadPoolExecutor def mol_sim_matrix(fingerprints1, fingerprints2, method='cosine', filename=None, max_size=1000, print_progress=True): if filename is not None: try: molecular_similarities = np.load(filename) print("Molecular similarity scores found and loaded.") collect_new_data = False except FileNotFoundError: print("Could not find file ", filename) print("Molecular scores will be calculated from scratch.") collect_new_data = True else: collect_new_data = True if collect_new_data: fingerprints_arr1 = np.array(fingerprints1) fingerprints_arr2 = np.array(fingerprints2) matrix_size = (fingerprints_arr1.shape[0], fingerprints_arr2.shape[0]) molecular_similarities = np.zeros(matrix_size) splits = int(np.ceil(matrix_size[0]/max_size) * np.ceil(matrix_size[1]/max_size)) count_splits = 0 for i in range(int(np.ceil(matrix_size[0]/max_size))): low1 = i * max_size high1 = min((i + 1) * max_size, matrix_size[0]) for j in range(int(np.ceil(matrix_size[1]/max_size))): low2 = j * max_size high2 = min((j + 1) * max_size, matrix_size[1]) molecular_similarities[low1:high1, low2:high2] = 1 - spatial.distance.cdist( fingerprints_arr1[low1:high1], fingerprints_arr2[low2:high2], method ) count_splits += 1 if print_progress: print('\r', "Calculated submatrix {} out of {}".format(count_splits, splits), end="") if print_progress: print(20 * '--') print("Succesfully calculated matrix with all-vs-all molecular similarity values.") if filename is not None: np.save(filename, molecular_similarities) print("Matrix was saved under:", filename) return molecular_similarities def cosine_score_greedy(spec1, spec2, mass_shift, tol, min_intens=0, use_numba=True): if spec1.shape[0] == 0 or spec2.shape[0] == 0: return 0.0, [] spec1[:, 1] = spec1[:, 1]/max(spec1[:, 1]) spec2[:, 1] = spec2[:, 1]/max(spec2[:, 1]) spec1 = spec1[spec1[:, 1] > min_intens, :] spec2 = spec2[spec2[:, 1] > min_intens, :] if use_numba: zero_pairs = find_pairs_numba(spec1, spec2, tol, shift=0.0) else: zero_pairs = find_pairs(spec1, spec2, tol, shift=0.0) if mass_shift is not None \ and mass_shift != 0.0: if use_numba: nonzero_pairs = find_pairs_numba(spec1, spec2, tol, shift=mass_shift) else: nonzero_pairs = find_pairs(spec1, spec2, tol, shift=mass_shift) matching_pairs = zero_pairs + nonzero_pairs else: matching_pairs = zero_pairs matching_pairs = sorted(matching_pairs, key=lambda x: x[2], reverse=True) used1 = set() used2 = set() score = 0.0 used_matches = [] for m in matching_pairs: if not m[0] in used1 and not m[1] in used2: score += m[2] used1.add(m[0]) used2.add(m[1]) used_matches.append(m) score = score/max(np.sum(spec1[:, 1]**2), np.sum(spec2[:, 1]**2)) return score, used_matches def cosine_score_hungarian(spec1, spec2, mass_shift, tol, min_intens=0): if spec1.shape[0] == 0 or spec2.shape[0] == 0: return 0.0, [] spec1[:, 1] = spec1[:, 1]/max(spec1[:, 1]) spec2[:, 1] = spec2[:, 1]/max(spec2[:, 1]) spec1 = spec1[spec1[:, 1] > min_intens, :] spec2 = spec2[spec2[:, 1] > min_intens, :] zero_pairs = find_pairs_numba(spec1, spec2, tol, shift=0.0) if mass_shift is not None \ and mass_shift != 0.0: nonzero_pairs = find_pairs_numba(spec1, spec2, tol, shift=mass_shift) matching_pairs = zero_pairs + nonzero_pairs else: matching_pairs = zero_pairs matching_pairs = sorted(matching_pairs, key=lambda x: x[2], reverse=True) used_matches = [] list1 = list(set([x[0] for x in matching_pairs])) list2 = list(set([x[1] for x in matching_pairs])) matrix_size = (len(list1), len(list2)) matrix = np.ones(matrix_size) if len(matching_pairs) > 0: for m in matching_pairs: matrix[list1.index(m[0]), list2.index(m[1])] = 1 - m[2] row_ind, col_ind = linear_sum_assignment(matrix) score = len(row_ind) - matrix[row_ind, col_ind].sum() used_matches = [(list1[x], list2[y]) for (x, y) in zip(row_ind, col_ind)] score = score/max(np.sum(spec1[:, 1]**2), np.sum(spec2[:, 1]**2)) else: score = 0.0 return score, used_matches def cosine_matrix_fast(spectra, tol, max_mz, min_mz=0): for i, spectrum in enumerate(spectra): spec = np.array(spectrum.peaks.copy(), dtype=float) spec[:, 1] = spec[:, 1]/np.max(spec[:, 1]) if i == 0: vector = one_hot_spectrum(spec, tol, max_mz, shift=0, min_mz=min_mz, method='max') spec_vectors = np.zeros((len(spectra), vector.shape[0])) spec_vectors[0, :] = vector else: spec_vectors[i, :] = one_hot_spectrum(spec, tol, max_mz, shift=0, min_mz=min_mz, method='max') Cdist = spatial.distance.cdist(spec_vectors, spec_vectors, 'cosine') return 1 - Cdist def cosine_score_matrix(spectra, tol, max_mz=1000.0, min_intens=0, mass_shifting=False, method='hungarian', num_workers=4, filename=None, safety_points=None): if filename is not None: if filename[-4:] != '.npy': filename = filename + '.npy' try: print("Loading similarity scores from", filename) modcos_sim = np.load(filename) print("Loading min_match values from", filename[:-4]+ "_matches.npy") modcos_matches = np.load(filename[:-4] + "_matches.npy") diagonal = modcos_sim.diagonal() if np.min(diagonal) == 0: print("Uncomplete cosine similarity scores found and loaded.") missing_scores = np.where(diagonal == 0)[0].astype(int) print("Missing cosine scores will be calculated.") counter_total = int((len(spectra)**2)/2) counter_init = counter_total - np.sum(len(spectra) - missing_scores) print("About ", 100*(counter_init/counter_total), "% of the values already completed.") collect_new_data = True else: print("Complete cosine similarity scores found and loaded.") missing_scores = [] counter_init = 0 collect_new_data = False except FileNotFoundError: print("Could not find file ", filename, "or file", filename[:-4] + "_matches.npy") if mass_shifting: print("Modified cosine scores will be calculated from scratch.") else: print("Cosine scores will be calculated from scratch.") collect_new_data = True missing_scores = np.arange(0, len(spectra)) counter_init = 0 else: collect_new_data = True missing_scores = np.arange(0, len(spectra)) counter_init = 0 if collect_new_data: if counter_init == 0: modcos_sim = np.zeros((len(spectra), len(spectra))) modcos_matches = np.zeros((len(spectra), len(spectra))) counter = counter_init if safety_points is not None: safety_save = int(((len(spectra)**2)/2)/safety_points) print("Calculate pairwise scores by", num_workers, "number of workers.") for i in missing_scores: spec1 = np.array(spectra[i].peaks, dtype=float) spec1 = spec1[spec1[:, 0] < max_mz, :] parameter_collection = [] for j in range(i, len(spectra)): spec2 = np.array(spectra[j].peaks, dtype=float) spec2 = spec2[spec2[:, 0] < max_mz, :] if mass_shifting: mass_shift = spectra[i].parent_mz - spectra[j].parent_mz else: mass_shift = None parameter_collection.append([spec1, spec2, i, j, mass_shift, tol, min_intens, method, counter]) counter += 1 modcos_pairs = [] with ThreadPoolExecutor(max_workers=num_workers) as executor: futures = [executor.submit(modcos_pair, X, len(spectra)) for X in parameter_collection] modcos_pairs.append(futures) for m, future in enumerate(modcos_pairs[0]): _, _, ind_i, ind_j, _, _, _, _, counting = parameter_collection[m] modcos_sim[ind_i, ind_j] = future.result()[0] modcos_matches[ind_i, ind_j] = future.result()[1] if filename is not None \ and safety_points is not None: if (counting+1) % safety_save == 0: np.save(filename, modcos_sim) np.save(filename[:-4] + "_matches.npy", modcos_matches) for i in range(1, len(spectra)): for j in range(i): modcos_sim[i, j] = modcos_sim[j, i] modcos_matches[i, j] = modcos_matches[j, i] if filename is not None: np.save(filename, modcos_sim) np.save(filename[:-4]+ "_matches.npy", modcos_matches) return modcos_sim, modcos_matches def modcos_pair(X, len_spectra): spectra_i, spectra_j, i, j, mass_shift, tol, min_intens, method, counter = X if method == 'greedy': molnet_pair, used_matches = cosine_score_greedy(spectra_i, spectra_j, mass_shift, tol, min_intens=min_intens, use_numba=False) elif method == 'greedy-numba': molnet_pair, used_matches = cosine_score_greedy(spectra_i, spectra_j, mass_shift, tol, min_intens=min_intens, use_numba=True) elif method == 'hungarian': molnet_pair, used_matches = cosine_score_hungarian(spectra_i, spectra_j, mass_shift, tol, min_intens=min_intens) else: print("Given method does not exist...") if (counter+1) % 1000 == 0 or counter == len_spectra-1: print('\r', ' Calculated MolNet for pair {} -- {}'.format(i, j), '. ( ', np.round(200*(counter+1)/len_spectra**2, 2), ' % done).', end="") return molnet_pair, len(used_matches) def one_hot_spectrum(spec, tol, max_mz, shift=0, min_mz=0, method='max'): dim_vector = int((max_mz - min_mz)/tol) one_hot_spec = np.zeros((dim_vector)) idx = ((spec[:, 0] + shift)*1/tol).astype(int) idx[idx >= dim_vector] = 0 idx[idx < 0] = 0 if method == 'max': for id1 in set(idx): one_hot_spec[id1] = np.max(spec[(idx == id1), 1]) elif method == 'sum': for id1 in set(idx): one_hot_spec[id1] = np.sum(spec[(idx == id1), 1]) else: print("Method not known...") return one_hot_spec @numba.njit def find_pairs_numba(spec1, spec2, tol, shift=0): matching_pairs = [] for idx in range(len(spec1)): intensity = spec1[idx, 1] matches = np.where((np.abs(spec2[:, 0] - spec1[idx, 0] + shift) <= tol))[0] for match in matches: matching_pairs.append((idx, match, intensity*spec2[match][1])) return matching_pairs def find_pairs(spec1, spec2, tol, shift=0): spec1 = spec1[np.lexsort((spec1[:, 1], spec1[:, 0])), :] spec2 = spec2[np.lexsort((spec2[:, 1], spec2[:, 0])), :] matching_pairs = [] spec2lowpos = 0 spec2length = len(spec2) for idx in range(len(spec1)): mz = spec1[idx, 0] intensity = spec1[idx, 1] while spec2lowpos < spec2length and spec2[spec2lowpos][0] + shift < mz - tol: spec2lowpos += 1 if spec2lowpos == spec2length: break spec2pos = spec2lowpos while(spec2pos < spec2length and spec2[spec2pos][0] + shift < mz + tol): matching_pairs.append((idx, spec2pos, intensity * spec2[spec2pos][1])) spec2pos += 1 return matching_pairs

true

790db8ba305a6bee2517597e27a90ff727edd601

7,725

py

Python

pymc3/distributions/mixture.py

rsumner31/pymc3-2

e824294ddfb45610536cad07394b8c290904c38d

[ "Apache-2.0" ]

null

pymc3/distributions/mixture.py

rsumner31/pymc3-2

e824294ddfb45610536cad07394b8c290904c38d

[ "Apache-2.0" ]

null

pymc3/distributions/mixture.py

rsumner31/pymc3-2

e824294ddfb45610536cad07394b8c290904c38d

[ "Apache-2.0" ]

null

import numpy as np import theano.tensor as tt from pymc3.util import get_variable_name from ..math import logsumexp from .dist_math import bound from .distribution import Discrete, Distribution, draw_values, generate_samples from .continuous import get_tau_sd, Normal def all_discrete(comp_dists): """ Determine if all distributions in comp_dists are discrete """ if isinstance(comp_dists, Distribution): return isinstance(comp_dists, Discrete) else: return all(isinstance(comp_dist, Discrete) for comp_dist in comp_dists) class Mixture(Distribution): R""" Mixture log-likelihood Often used to model subpopulation heterogeneity .. math:: f(x \mid w, \theta) = \sum_{i = 1}^n w_i f_i(x \mid \theta_i) ======== ============================================ Support :math:`\cap_{i = 1}^n \textrm{support}(f_i)` Mean :math:`\sum_{i = 1}^n w_i \mu_i` ======== ============================================ Parameters ---------- w : array of floats w >= 0 and w <= 1 the mixture weights comp_dists : multidimensional PyMC3 distribution (e.g. `pm.Poisson.dist(...)`) or iterable of one-dimensional PyMC3 distributions the component distributions :math:`f_1, \ldots, f_n` Example ------- .. code-block:: python # 2-Mixture Poisson distribution with pm.Model() as model: lam = pm.Exponential('lam', lam=1, shape=(2,)) # `shape=(2,)` indicates two mixtures. # As we just need the logp, rather than add a RV to the model, we need to call .dist() components = pm.Poisson.dist(mu=lam, shape=(2,)) w = pm.Dirichlet('w', a=np.array([1, 1])) # two mixture component weights. like = pm.Mixture('like', w=w, comp_dists=components, observed=data) # 2-Mixture Poisson using iterable of distributions. with pm.Model() as model: lam1 = pm.Exponential('lam1', lam=1) lam2 = pm.Exponential('lam2', lam=1) pois1 = pm.Poisson.dist(mu=lam1) pois2 = pm.Poisson.dist(mu=lam2) w = pm.Dirichlet('w', a=np.array([1, 1])) like = pm.Mixture('like', w=w, comp_dists = [pois1, pois2], observed=data) """ def __init__(self, w, comp_dists, *args, **kwargs): shape = kwargs.pop('shape', ()) self.w = w = tt.as_tensor_variable(w) self.comp_dists = comp_dists defaults = kwargs.pop('defaults', []) if all_discrete(comp_dists): dtype = kwargs.pop('dtype', 'int64') else: dtype = kwargs.pop('dtype', 'float64') try: self.mean = (w * self._comp_means()).sum(axis=-1) if 'mean' not in defaults: defaults.append('mean') except AttributeError: pass try: comp_modes = self._comp_modes() comp_mode_logps = self.logp(comp_modes) self.mode = comp_modes[tt.argmax(w * comp_mode_logps, axis=-1)] if 'mode' not in defaults: defaults.append('mode') except AttributeError: pass super(Mixture, self).__init__(shape, dtype, defaults=defaults, *args, **kwargs) def _comp_logp(self, value): comp_dists = self.comp_dists try: value_ = value if value.ndim > 1 else tt.shape_padright(value) return comp_dists.logp(value_) except AttributeError: return tt.stack([comp_dist.logp(value) for comp_dist in comp_dists], axis=1) def _comp_means(self): try: return tt.as_tensor_variable(self.comp_dists.mean) except AttributeError: return tt.stack([comp_dist.mean for comp_dist in self.comp_dists], axis=1) def _comp_modes(self): try: return tt.as_tensor_variable(self.comp_dists.mode) except AttributeError: return tt.stack([comp_dist.mode for comp_dist in self.comp_dists], axis=1) def _comp_samples(self, point=None, size=None, repeat=None): try: samples = self.comp_dists.random(point=point, size=size, repeat=repeat) except AttributeError: samples = np.column_stack([comp_dist.random(point=point, size=size, repeat=repeat) for comp_dist in self.comp_dists]) return np.squeeze(samples) def logp(self, value): w = self.w return bound(logsumexp(tt.log(w) + self._comp_logp(value), axis=-1).sum(), w >= 0, w <= 1, tt.allclose(w.sum(axis=-1), 1), broadcast_conditions=False) def random(self, point=None, size=None, repeat=None): def random_choice(*args, **kwargs): w = kwargs.pop('w') w /= w.sum(axis=-1, keepdims=True) k = w.shape[-1] if w.ndim > 1: return np.row_stack([np.random.choice(k, p=w_) for w_ in w]) else: return np.random.choice(k, p=w, *args, **kwargs) w = draw_values([self.w], point=point)[0] w_samples = generate_samples(random_choice, w=w, broadcast_shape=w.shape[:-1] or (1,), dist_shape=self.shape, size=size).squeeze() comp_samples = self._comp_samples(point=point, size=size, repeat=repeat) if comp_samples.ndim > 1: return np.squeeze(comp_samples[np.arange(w_samples.size), w_samples]) else: return np.squeeze(comp_samples[w_samples]) class NormalMixture(Mixture): R""" Normal mixture log-likelihood .. math:: f(x \mid w, \mu, \sigma^2) = \sum_{i = 1}^n w_i N(x \mid \mu_i, \sigma^2_i) ======== ======================================= Support :math:`x \in \mathbb{R}` Mean :math:`\sum_{i = 1}^n w_i \mu_i` Variance :math:`\sum_{i = 1}^n w_i^2 \sigma^2_i` ======== ======================================= Parameters ---------- w : array of floats w >= 0 and w <= 1 the mixture weights mu : array of floats the component means sd : array of floats the component standard deviations tau : array of floats the component precisions Note: You only have to pass in sd or tau, but not both. """ def __init__(self, w, mu, *args, **kwargs): _, sd = get_tau_sd(tau=kwargs.pop('tau', None), sd=kwargs.pop('sd', None)) distshape = np.broadcast(mu, sd).shape self.mu = mu = tt.as_tensor_variable(mu) self.sd = sd = tt.as_tensor_variable(sd) if not distshape: distshape = np.broadcast(mu.tag.test_value, sd.tag.test_value).shape super(NormalMixture, self).__init__(w, Normal.dist(mu, sd=sd, shape=distshape), *args, **kwargs) def _repr_latex_(self, name=None, dist=None): if dist is None: dist = self mu = dist.mu w = dist.w sd = dist.sd name = r'\text{%s}' % name return r'${} \sim \text{{NormalMixture}}(\mathit{{w}}={},~\mathit{{mu}}={},~\mathit{{sigma}}={})$'.format(name, get_variable_name(w), get_variable_name(mu), get_variable_name(sd))

34.486607

119

0.535016

import numpy as np import theano.tensor as tt from pymc3.util import get_variable_name from ..math import logsumexp from .dist_math import bound from .distribution import Discrete, Distribution, draw_values, generate_samples from .continuous import get_tau_sd, Normal def all_discrete(comp_dists): if isinstance(comp_dists, Distribution): return isinstance(comp_dists, Discrete) else: return all(isinstance(comp_dist, Discrete) for comp_dist in comp_dists) class Mixture(Distribution): def __init__(self, w, comp_dists, *args, **kwargs): shape = kwargs.pop('shape', ()) self.w = w = tt.as_tensor_variable(w) self.comp_dists = comp_dists defaults = kwargs.pop('defaults', []) if all_discrete(comp_dists): dtype = kwargs.pop('dtype', 'int64') else: dtype = kwargs.pop('dtype', 'float64') try: self.mean = (w * self._comp_means()).sum(axis=-1) if 'mean' not in defaults: defaults.append('mean') except AttributeError: pass try: comp_modes = self._comp_modes() comp_mode_logps = self.logp(comp_modes) self.mode = comp_modes[tt.argmax(w * comp_mode_logps, axis=-1)] if 'mode' not in defaults: defaults.append('mode') except AttributeError: pass super(Mixture, self).__init__(shape, dtype, defaults=defaults, *args, **kwargs) def _comp_logp(self, value): comp_dists = self.comp_dists try: value_ = value if value.ndim > 1 else tt.shape_padright(value) return comp_dists.logp(value_) except AttributeError: return tt.stack([comp_dist.logp(value) for comp_dist in comp_dists], axis=1) def _comp_means(self): try: return tt.as_tensor_variable(self.comp_dists.mean) except AttributeError: return tt.stack([comp_dist.mean for comp_dist in self.comp_dists], axis=1) def _comp_modes(self): try: return tt.as_tensor_variable(self.comp_dists.mode) except AttributeError: return tt.stack([comp_dist.mode for comp_dist in self.comp_dists], axis=1) def _comp_samples(self, point=None, size=None, repeat=None): try: samples = self.comp_dists.random(point=point, size=size, repeat=repeat) except AttributeError: samples = np.column_stack([comp_dist.random(point=point, size=size, repeat=repeat) for comp_dist in self.comp_dists]) return np.squeeze(samples) def logp(self, value): w = self.w return bound(logsumexp(tt.log(w) + self._comp_logp(value), axis=-1).sum(), w >= 0, w <= 1, tt.allclose(w.sum(axis=-1), 1), broadcast_conditions=False) def random(self, point=None, size=None, repeat=None): def random_choice(*args, **kwargs): w = kwargs.pop('w') w /= w.sum(axis=-1, keepdims=True) k = w.shape[-1] if w.ndim > 1: return np.row_stack([np.random.choice(k, p=w_) for w_ in w]) else: return np.random.choice(k, p=w, *args, **kwargs) w = draw_values([self.w], point=point)[0] w_samples = generate_samples(random_choice, w=w, broadcast_shape=w.shape[:-1] or (1,), dist_shape=self.shape, size=size).squeeze() comp_samples = self._comp_samples(point=point, size=size, repeat=repeat) if comp_samples.ndim > 1: return np.squeeze(comp_samples[np.arange(w_samples.size), w_samples]) else: return np.squeeze(comp_samples[w_samples]) class NormalMixture(Mixture): def __init__(self, w, mu, *args, **kwargs): _, sd = get_tau_sd(tau=kwargs.pop('tau', None), sd=kwargs.pop('sd', None)) distshape = np.broadcast(mu, sd).shape self.mu = mu = tt.as_tensor_variable(mu) self.sd = sd = tt.as_tensor_variable(sd) if not distshape: distshape = np.broadcast(mu.tag.test_value, sd.tag.test_value).shape super(NormalMixture, self).__init__(w, Normal.dist(mu, sd=sd, shape=distshape), *args, **kwargs) def _repr_latex_(self, name=None, dist=None): if dist is None: dist = self mu = dist.mu w = dist.w sd = dist.sd name = r'\text{%s}' % name return r'${} \sim \text{{NormalMixture}}(\mathit{{w}}={},~\mathit{{mu}}={},~\mathit{{sigma}}={})$'.format(name, get_variable_name(w), get_variable_name(mu), get_variable_name(sd))

true

790db9c95aa81be22336050a55df0924b1114b92

2,080

py

Python

Clients/ParaView/Testing/Python/AppendAttributes.py

xj361685640/ParaView

0a27eef5abc5a0c0472ab0bc806c4db881156e64

[ "Apache-2.0", "BSD-3-Clause" ]

815

2015-01-03T02:14:04.000Z

2022-03-26T07:48:07.000Z

Clients/ParaView/Testing/Python/AppendAttributes.py

xj361685640/ParaView

0a27eef5abc5a0c0472ab0bc806c4db881156e64

[ "Apache-2.0", "BSD-3-Clause" ]

9

2015-04-28T20:10:37.000Z

2021-08-20T18:19:01.000Z

Clients/ParaView/Testing/Python/AppendAttributes.py

xj361685640/ParaView

0a27eef5abc5a0c0472ab0bc806c4db881156e64

[ "Apache-2.0", "BSD-3-Clause" ]

328

2015-01-22T23:11:46.000Z

2022-03-14T06:07:52.000Z

#/usr/bin/env python from paraview.simple import * import sys wavelet1 = Wavelet() wavelet2 = Wavelet() pythonCalculator1 = PythonCalculator(Input=wavelet2) pythonCalculator1.ArrayName = 'RTData' pythonCalculator1.Expression = 'RTData+200' pythonCalculator1.CopyArrays = 0 # this one should be ignored in the output since it has a different # amount of points and cells than the first one sphereSource = Sphere() appendAttributes1 = AppendAttributes(Input=[wavelet1, sphereSource, pythonCalculator1]) appendAttributes1.UpdatePipeline() if appendAttributes1.PointData.GetNumberOfArrays() != 2: # should have RTData and RTData_input_1 print("ERROR: wrong number of arrays ", appendAttributes1.PointData.GetNumberOfArrays()) sys.exit(1) arrayRange = appendAttributes1.PointData['RTData'].GetRange() if arrayRange[0] < 37 or arrayRange[0] > 38 or arrayRange[1] < 276 or arrayRange[0] > 277: print("ERROR: RTData has wrong array range ", arrayRange) sys.exit(1) arrayRange = appendAttributes1.PointData['RTData_input_2'].GetRange() if arrayRange[0] < 237 or arrayRange[0] > 238 or arrayRange[1] < 476 or arrayRange[0] > 477: print("ERROR: RTData_input_2 has wrong array range ", arrayRange) sys.exit(1) # now try with the can.ex2 exodus file for multiblock testing for i, arg in enumerate(sys.argv): if arg == "-D" and i+1 < len(sys.argv): dataFile = sys.argv[i+1] + '/Testing/Data/can.ex2' canex2 = ExodusIIReader(FileName=[dataFile]) canex2.ElementVariables = ['EQPS'] canex2.PointVariables = ['DISPL', 'VEL', 'ACCL'] canex2.GlobalVariables = ['KE', 'XMOM', 'YMOM', 'ZMOM', 'NSTEPS', 'TMSTEP'] calculator1 = Calculator(Input=canex2) calculator1.AttributeType = 'Point Data' calculator1.CoordinateResults = 0 calculator1.ResultNormals = 0 calculator1.ResultTCoords = 0 calculator1.ReplaceInvalidResults = 1 calculator1.ReplacementValue = 0.0 calculator1.ResultArrayName = 'VEL_X' calculator1.Function = 'VEL_X+100' appendAttributes2 = AppendAttributes(Input=[canex2, calculator1]) appendAttributes2.UpdatePipeline() print("success")

35.862069

92

0.755288

from paraview.simple import * import sys wavelet1 = Wavelet() wavelet2 = Wavelet() pythonCalculator1 = PythonCalculator(Input=wavelet2) pythonCalculator1.ArrayName = 'RTData' pythonCalculator1.Expression = 'RTData+200' pythonCalculator1.CopyArrays = 0 sphereSource = Sphere() appendAttributes1 = AppendAttributes(Input=[wavelet1, sphereSource, pythonCalculator1]) appendAttributes1.UpdatePipeline() if appendAttributes1.PointData.GetNumberOfArrays() != 2: print("ERROR: wrong number of arrays ", appendAttributes1.PointData.GetNumberOfArrays()) sys.exit(1) arrayRange = appendAttributes1.PointData['RTData'].GetRange() if arrayRange[0] < 37 or arrayRange[0] > 38 or arrayRange[1] < 276 or arrayRange[0] > 277: print("ERROR: RTData has wrong array range ", arrayRange) sys.exit(1) arrayRange = appendAttributes1.PointData['RTData_input_2'].GetRange() if arrayRange[0] < 237 or arrayRange[0] > 238 or arrayRange[1] < 476 or arrayRange[0] > 477: print("ERROR: RTData_input_2 has wrong array range ", arrayRange) sys.exit(1) for i, arg in enumerate(sys.argv): if arg == "-D" and i+1 < len(sys.argv): dataFile = sys.argv[i+1] + '/Testing/Data/can.ex2' canex2 = ExodusIIReader(FileName=[dataFile]) canex2.ElementVariables = ['EQPS'] canex2.PointVariables = ['DISPL', 'VEL', 'ACCL'] canex2.GlobalVariables = ['KE', 'XMOM', 'YMOM', 'ZMOM', 'NSTEPS', 'TMSTEP'] calculator1 = Calculator(Input=canex2) calculator1.AttributeType = 'Point Data' calculator1.CoordinateResults = 0 calculator1.ResultNormals = 0 calculator1.ResultTCoords = 0 calculator1.ReplaceInvalidResults = 1 calculator1.ReplacementValue = 0.0 calculator1.ResultArrayName = 'VEL_X' calculator1.Function = 'VEL_X+100' appendAttributes2 = AppendAttributes(Input=[canex2, calculator1]) appendAttributes2.UpdatePipeline() print("success")

true

790dbb66da0d930c4aacd463d876583994ead967

182

py

Python

python_ex/01ex.py

llinmeng/PythonStudy

68c27eaa302b95aa4fb35d794f0d645f98b832dd

[ "MIT" ]

null

python_ex/01ex.py

llinmeng/PythonStudy

68c27eaa302b95aa4fb35d794f0d645f98b832dd

[ "MIT" ]

null

python_ex/01ex.py

llinmeng/PythonStudy

68c27eaa302b95aa4fb35d794f0d645f98b832dd

[ "MIT" ]

null

print "Hello World!" print "Hello Again" print "I like typing this." print "This is fun." print "Yay! Printing" print "Id much rather you 'not'." print 'I "said" do not touch this.'

22.75

35

0.697802

print "Hello World!" print "Hello Again" print "I like typing this." print "This is fun." print "Yay! Printing" print "Id much rather you 'not'." print 'I "said" do not touch this.'

false

true

790dbc3fe1067ad92b9c1bc56af87986404f11fa

202

py

Python

services/resource/project/utils/enums.py

spruce-cq/sblog

287571bffcf19c224d3b4ad4e4e9347225245350

[ "MIT" ]

null

services/resource/project/utils/enums.py

spruce-cq/sblog

287571bffcf19c224d3b4ad4e4e9347225245350

[ "MIT" ]

7

2020-09-07T15:06:12.000Z

2022-02-26T19:09:01.000Z

services/resource/project/utils/enums.py

spruce-cq/sblog

287571bffcf19c224d3b4ad4e4e9347225245350

[ "MIT" ]

null

# services/resource/project/utils/enums.py from enum import Enum class Status(Enum): normal = 0 delete = 1 other = 2 class Scope(Enum): user = 'UserScope' admin = 'AdminScope'

12.625

42

0.643564

from enum import Enum class Status(Enum): normal = 0 delete = 1 other = 2 class Scope(Enum): user = 'UserScope' admin = 'AdminScope'

true

790dbc43e17a08ab4288c2517635a930ec91e743

1,594

py

Python

quora/pyfm/generate_interaction.py

zonemercy/Kaggle

35ecb08272b6491f5e6756c97c7dec9c46a13a43

[ "MIT" ]

17

2017-10-01T00:10:19.000Z

2022-02-07T12:11:01.000Z

quora/pyfm/generate_interaction.py

zonemercy/Kaggle

35ecb08272b6491f5e6756c97c7dec9c46a13a43

[ "MIT" ]

null

quora/pyfm/generate_interaction.py

zonemercy/Kaggle

35ecb08272b6491f5e6756c97c7dec9c46a13a43

[ "MIT" ]

1

2019-08-15T03:58:51.000Z

import os import numpy as np import pandas as pd from sklearn.preprocessing import OneHotEncoder,LabelEncoder,StandardScaler from sklearn.decomposition import TruncatedSVD,PCA from sklearn.metrics.pairwise import cosine_similarity,pairwise_distances from sklearn.feature_extraction.text import TfidfVectorizer SEED = 2048 np.random.seed(SEED) PATH = os.path.expanduser("~") + "/data/quora/" train = pd.read_csv(PATH + "train_porter.csv")#, nrows=5000) test = pd.read_csv(PATH + "test_porter.csv")#, nrows=5000) test['is_duplicated'] = [-1]*test.shape[0] len_train = train.shape[0] data_all = pd.concat([train,test]) def calc_set_intersection(obj,target): a = set(obj.split()) b = set(target.split()) return (len(a.intersection(b))*1.0) / (len(a)*1.0) print('Generate intersection') train_interaction = train.astype(str).apply(lambda x: calc_set_intersection(x['question1'],x['question2']),axis=1) test_interaction = test.astype(str).apply(lambda x: calc_set_intersection(x['question1'],x['question2']),axis=1) pd.to_pickle(train_interaction,PATH+"train_interaction.pkl") pd.to_pickle(test_interaction,PATH+"test_interaction.pkl") print('Generate porter intersection') train_porter_interaction = train.astype(str).apply(lambda x:calc_set_intersection(x['question1_porter'],x['question2_porter']),axis=1) test_porter_interaction = test.astype(str).apply(lambda x:calc_set_intersection(x['question1_porter'],x['question2_porter']),axis=1) pd.to_pickle(train_porter_interaction, PATH+"train_porter_interaction.pkl") pd.to_pickle(test_porter_interaction, PATH+"test_porter_interaction.pkl")

45.542857

134

0.788582

import os import numpy as np import pandas as pd from sklearn.preprocessing import OneHotEncoder,LabelEncoder,StandardScaler from sklearn.decomposition import TruncatedSVD,PCA from sklearn.metrics.pairwise import cosine_similarity,pairwise_distances from sklearn.feature_extraction.text import TfidfVectorizer SEED = 2048 np.random.seed(SEED) PATH = os.path.expanduser("~") + "/data/quora/" train = pd.read_csv(PATH + "train_porter.csv") test = pd.read_csv(PATH + "test_porter.csv") test['is_duplicated'] = [-1]*test.shape[0] len_train = train.shape[0] data_all = pd.concat([train,test]) def calc_set_intersection(obj,target): a = set(obj.split()) b = set(target.split()) return (len(a.intersection(b))*1.0) / (len(a)*1.0) print('Generate intersection') train_interaction = train.astype(str).apply(lambda x: calc_set_intersection(x['question1'],x['question2']),axis=1) test_interaction = test.astype(str).apply(lambda x: calc_set_intersection(x['question1'],x['question2']),axis=1) pd.to_pickle(train_interaction,PATH+"train_interaction.pkl") pd.to_pickle(test_interaction,PATH+"test_interaction.pkl") print('Generate porter intersection') train_porter_interaction = train.astype(str).apply(lambda x:calc_set_intersection(x['question1_porter'],x['question2_porter']),axis=1) test_porter_interaction = test.astype(str).apply(lambda x:calc_set_intersection(x['question1_porter'],x['question2_porter']),axis=1) pd.to_pickle(train_porter_interaction, PATH+"train_porter_interaction.pkl") pd.to_pickle(test_porter_interaction, PATH+"test_porter_interaction.pkl")

true

790dbd0078150244cae8de4b721c0c6c27361515

779

py

Python

src/classification/predict_with_umap.py

menchelab/UMAPanalysis

09f9b4a7823f6eceb6b40e25ee21412f3bf1c7fe

[ "MIT" ]

2

2022-02-27T19:19:36.000Z

2022-03-15T10:38:36.000Z

src/classification/predict_with_umap.py

menchelab/UMAPanalysis

09f9b4a7823f6eceb6b40e25ee21412f3bf1c7fe

[ "MIT" ]

null

src/classification/predict_with_umap.py

menchelab/UMAPanalysis

09f9b4a7823f6eceb6b40e25ee21412f3bf1c7fe

[ "MIT" ]

null

import sys import re import pandas as pd network_filename = sys.argv[1] m = re.match("networks/(?P<dataset>.*?)_similarity", network_filename) dataset = m.groupdict()['dataset'] G=nx.read_gml(network_filename) labels=pd.read_csv(f"munged_data/{dataset}/labels.csv", index_col=0) metadata = pd.read_csv(f"data/intermediate/{dataset}/metadata.csv", index_col=0) features = pd.read_csv(f"data/intermediate/{dataset}/features.csv", index_col=0) train = pd.read_csv(f"data/intermediate/{dataset}/train.csv", header = None)[0].values testing = pd.Series({i:(i in test) for i in labels.index}) labels = labels.mask(testing, other=0) propagator,nodes=make_propagator(G) df,df_time=propagate(propagator, nodes, moas) df.to_csv(f"predictions/{dataset}/predicted_by_propagation.csv")

33.869565

86

0.762516

import sys import re import pandas as pd network_filename = sys.argv[1] m = re.match("networks/(?P<dataset>.*?)_similarity", network_filename) dataset = m.groupdict()['dataset'] G=nx.read_gml(network_filename) labels=pd.read_csv(f"munged_data/{dataset}/labels.csv", index_col=0) metadata = pd.read_csv(f"data/intermediate/{dataset}/metadata.csv", index_col=0) features = pd.read_csv(f"data/intermediate/{dataset}/features.csv", index_col=0) train = pd.read_csv(f"data/intermediate/{dataset}/train.csv", header = None)[0].values testing = pd.Series({i:(i in test) for i in labels.index}) labels = labels.mask(testing, other=0) propagator,nodes=make_propagator(G) df,df_time=propagate(propagator, nodes, moas) df.to_csv(f"predictions/{dataset}/predicted_by_propagation.csv")

true

790dbdcf0a9d7aaa327e40cf4253ecb288613544

596

py

Python

keyword_relation/migrations/0011_keyword_grouping.py

rohanjsuresh/extracted_keyword_validation

94e56c645c066d9d20097433b1716b3e76625b3d

[ "MIT" ]

null

keyword_relation/migrations/0011_keyword_grouping.py

rohanjsuresh/extracted_keyword_validation

94e56c645c066d9d20097433b1716b3e76625b3d

[ "MIT" ]

null

keyword_relation/migrations/0011_keyword_grouping.py

rohanjsuresh/extracted_keyword_validation

94e56c645c066d9d20097433b1716b3e76625b3d

[ "MIT" ]

1

2021-05-18T16:40:55.000Z

# Generated by Django 3.0.8 on 2021-07-07 22:33 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('keyword_relation', '0010_auto_20210322_2049'), ] operations = [ migrations.CreateModel( name='Keyword_Grouping', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('keyword', models.CharField(max_length=512)), ('group', models.IntegerField(default=-1)), ], ), ]

27.090909

114

0.588926

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('keyword_relation', '0010_auto_20210322_2049'), ] operations = [ migrations.CreateModel( name='Keyword_Grouping', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('keyword', models.CharField(max_length=512)), ('group', models.IntegerField(default=-1)), ], ), ]

true

790dbe9c2575b584fcde93e4a4489e80e4e28895

1,455

py

Python

xlsxwriter/test/comparison/test_chart_scatter03.py

Rippling/XlsxWriter-1

be8d1cb8f8b156cf87bbe5d591f1f5475804be44

[ "BSD-2-Clause" ]

null

xlsxwriter/test/comparison/test_chart_scatter03.py

Rippling/XlsxWriter-1

be8d1cb8f8b156cf87bbe5d591f1f5475804be44

[ "BSD-2-Clause" ]

null

xlsxwriter/test/comparison/test_chart_scatter03.py

Rippling/XlsxWriter-1

be8d1cb8f8b156cf87bbe5d591f1f5475804be44

[ "BSD-2-Clause" ]

null

############################################################################### # # Tests for XlsxWriter. # # SPDX-License-Identifier: BSD-2-Clause # Copyright (c), 2013-2021, John McNamara, jmcnamara@cpan.org # from ..excel_comparison_test import ExcelComparisonTest from ...workbook import Workbook class TestCompareXLSXFiles(ExcelComparisonTest): """ Test file created by XlsxWriter against a file created by Excel. """ def setUp(self): self.set_filename('chart_scatter03.xlsx') def test_create_file(self): """Test the creation of a simple XlsxWriter file.""" workbook = Workbook(self.got_filename) worksheet = workbook.add_worksheet() chart = workbook.add_chart({'type': 'scatter', 'subtype': 'straight'}) chart.axis_ids = [54010624, 45705856] data = [ [1, 2, 3, 4, 5], [2, 4, 6, 8, 10], [3, 6, 9, 12, 15], ] worksheet.write_column('A1', data[0]) worksheet.write_column('B1', data[1]) worksheet.write_column('C1', data[2]) chart.add_series({'categories': '=Sheet1!$A$1:$A$5', 'values': '=Sheet1!$B$1:$B$5'}) chart.add_series({'categories': '=Sheet1!$A$1:$A$5', 'values': '=Sheet1!$C$1:$C$5', }) worksheet.insert_chart('E9', chart) workbook.close() self.assertExcelEqual()

26.454545

79

0.538832

true

790dbf8c0033ff8b410ae68d704d1f7f043f9f53

1,090

py

Python

rules/javascript/CVI_3003.py

dahua966/Kunlun-M

978dd0650b555a677d2c5d74fc86ff66319a2d57

[ "MIT" ]

1

2021-06-25T01:44:45.000Z

rules/javascript/CVI_3003.py

dahua966/Kunlun-M

978dd0650b555a677d2c5d74fc86ff66319a2d57

[ "MIT" ]

null

rules/javascript/CVI_3003.py

dahua966/Kunlun-M

978dd0650b555a677d2c5d74fc86ff66319a2d57

[ "MIT" ]

2

2020-12-09T08:26:45.000Z

2021-04-12T03:24:34.000Z

# -*- coding: utf-8 -*- """ auto rule template ~~~~ :author: LoRexxar <LoRexxar@gmail.com> :homepage: https://github.com/LoRexxar/Kunlun-M :license: MIT, see LICENSE for more details. :copyright: Copyright (c) 2017 LoRexxar. All rights reserved """ from utils.api import * class CVI_3003(): """ rule class """ def __init__(self): self.svid = 3003 self.language = "javascript" self.author = "LoRexxar" self.vulnerability = "RCE" self.description = "remote? code exec" # status self.status = True # 部分配置 self.match_mode = "function-param-regex" self.match = r"eval|setTimeout" # for solidity self.match_name = None self.black_list = None # for chrome ext self.keyword = None # for regex self.unmatch = None self.vul_function = None def main(self, regex_string): """ regex string input :regex_string: regex match string :return: """ pass

20.185185

64

0.555046

from utils.api import * class CVI_3003(): def __init__(self): self.svid = 3003 self.language = "javascript" self.author = "LoRexxar" self.vulnerability = "RCE" self.description = "remote? code exec" self.status = True self.match_mode = "function-param-regex" self.match = r"eval|setTimeout" self.match_name = None self.black_list = None self.keyword = None self.unmatch = None self.vul_function = None def main(self, regex_string): pass

true

790dc041534631695a4a573017e279551b20bf64

24,594

py

Python

tests/test_georaster_tiling.py

SimoneDeGasperis/telluric

2fe4388f4a69a5a939078a876943c5f4620693ca

[ "MIT" ]

81

2018-04-12T12:29:06.000Z

2022-03-17T09:41:55.000Z

tests/test_georaster_tiling.py

SimoneDeGasperis/telluric

2fe4388f4a69a5a939078a876943c5f4620693ca

[ "MIT" ]

283

2018-04-09T11:32:25.000Z

2022-03-25T22:16:38.000Z

tests/test_georaster_tiling.py

SimoneDeGasperis/telluric

2fe4388f4a69a5a939078a876943c5f4620693ca

[ "MIT" ]

22

2018-04-09T10:53:52.000Z

2022-02-09T10:38:33.000Z

import os import rasterio import mercantile import numpy as np import pytest from tempfile import NamedTemporaryFile, TemporaryDirectory from affine import Affine from unittest import TestCase from unittest.mock import patch from datetime import datetime from shapely.geometry import Polygon from rasterio.enums import Resampling from rasterio.windows import Window from rasterio.crs import CRS from telluric import GeoRaster2, GeoVector from telluric.constants import WEB_MERCATOR_CRS, WGS84_CRS from telluric.georaster import MERCATOR_RESOLUTION_MAPPING, GeoRaster2Error, GeoRaster2IOError from telluric.util.general import convert_resolution_from_meters_to_deg import sys import logging import tempfile log = logging.getLogger('rasterio._gdal') log.setLevel(logging.DEBUG) ch = logging.StreamHandler(sys.stdout) ch.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(message)s') ch.setFormatter(formatter) log.addHandler(ch) manualtest = pytest.mark.skipif("TEST_MANUAL" not in os.environ, reason="skip on auto testing") window_data = pytest.mark.skip('pending decission of consistency in results between rasterio read and reproject') framing = pytest.mark.skip('witing for framing and get_window with boundless false') tiles = { 10: (579, 394, 10), 11: (1159, 789, 11), 12: (2319, 1578, 12), 14: (9277, 6312, 14), 15: (18554, 12624, 15), 17: (74216, 50496, 17), 18: (148433, 100994, 18) } class GeoRaster2TilesTestGeneral(TestCase): """GeoRaster2 Tiles general tests.""" def test_raise_exception_on_bad_file_path(self): vr = GeoRaster2.open('stam') with self.assertRaises(GeoRaster2IOError): vr.get_tile(1, 2, 3) def test_raise_exception_on_bad_raster_url(self): vr = GeoRaster2.open('http://stam') with self.assertRaises(GeoRaster2IOError): vr.get_tile(1, 2, 3) def test_raise_exception_on_bad_file_path_save_cog(self): vr = GeoRaster2.open('stam') with self.assertRaises(GeoRaster2IOError): vr.save_cloud_optimized('dest_file') def test_raise_exception_on_bad_raster_url_save_cog(self): vr = GeoRaster2.open('http://stam') with self.assertRaises(GeoRaster2IOError): vr.save_cloud_optimized('dest_file') class BaseGeoRasterTestCase(TestCase): @classmethod def setUpClass(cls): path = "./tests/data/raster/raster_for_test.tif" cls.read_only_vgr = GeoRaster2.open(path) path = "./tests/data/raster/raster_wgs84.tif" cls.read_only_vgr_wgs84 = GeoRaster2.open(path) def read_only_virtual_geo_raster(self): return self.read_only_vgr def read_only_virtual_geo_raster_wgs84(self): return self.read_only_vgr_wgs84 class GeoRaster2TestGetTile(BaseGeoRasterTestCase): """GeoRaster2 get tile tests.""" def test_geo_bounding_tile(self): gr = self.read_only_virtual_geo_raster() gv = gr.footprint().reproject(WGS84_CRS) bounding_tile = mercantile.bounding_tile(*gv.get_shape(gv.crs).bounds) self.assertEqual(bounding_tile, (37108, 25248, 16)) @patch.object(GeoRaster2, 'crop') def test_fails_with_empty_raster_for_tile_out_of_raster_area(self, mock__crop): for raster in [self.read_only_virtual_geo_raster(), self.read_only_virtual_geo_raster_wgs84()]: r = raster.get_tile(16384, 16383, 15) self.assertTrue((r.image.data == 0).all()) self.assertTrue((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 256, 256)) self.assertEqual(r.crs, WEB_MERCATOR_CRS) mock__crop.assert_not_called() def test_get_all_raster_in_a_single_tile(self): for raster in [self.read_only_virtual_geo_raster(), self.read_only_virtual_geo_raster_wgs84()]: p = raster.footprint().reproject(WGS84_CRS).centroid r = raster.get_tile(*mercantile.tile(lng=p.x, lat=p.y, zoom=11)) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 256, 256)) self.assertEqual(r.crs, WEB_MERCATOR_CRS) def test_get_tile_for_different_zoom_levels(self): for raster in [self.read_only_virtual_geo_raster(), self.read_only_virtual_geo_raster_wgs84()]: for zoom in tiles: r = raster.get_tile(*tiles[zoom]) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 256, 256)) def test_get_tile_from_different_crs_tile_is_not_tilted(self): raster = self.read_only_virtual_geo_raster_wgs84() r = raster.get_tile(*tiles[18]) self.assertEqual(1, len(np.unique(r.image.mask))) def test_get_tile_from_different_crs_tile_is_not_tilted_with_different_buffer(self): raster = self.read_only_virtual_geo_raster_wgs84() os.environ["TELLURIC_GET_TILE_BUFFER"] = "0" try: r = raster.get_tile(*tiles[18]) except Exception: del os.environ["TELLURIC_GET_TILE_BUFFER"] self.assertEqual(2, len(np.unique(r.image.mask))) def test_get_entire_all_raster(self): vr = self.read_only_virtual_geo_raster() roi = GeoVector.from_xyz(37108, 25248, 16) r = vr.crop(roi) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.shape, (3, 612, 612)) def test_fails_with_empty_raster_for_tile_out_of_raster_area_with_no_tile_size(self): vr = self.read_only_virtual_geo_raster() roi = GeoVector.from_xyz(16384, 16383, 15) r = vr.crop(roi) self.assertTrue((r.image.data == 0).all()) self.assertTrue((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 1223, 1223)) def test_get_window_of_full_resolution(self): vr = self.read_only_virtual_geo_raster() win = Window(0, 0, 300, 300) r = vr.get_window(win) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 300, 300)) def test_get_window_resize_to_256(self): vr = self.read_only_virtual_geo_raster() win = Window(0, 0, 300, 300) r = vr.get_window(win, xsize=256, ysize=256) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 256, 256)) def test_get_window_of_non_square_resize_to_256(self): vr = self.read_only_virtual_geo_raster() win = Window(0, 0, 300, 400) r = vr.get_window(win, xsize=256, ysize=256) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 256, 256)) def test_get_window_of_non_square_keeps_size_proportions_for_give_xsize(self): vr = self.read_only_virtual_geo_raster() win = Window(0, 0, 300, 400) r = vr.get_window(win, xsize=150) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 200, 150)) def test_get_window_of_non_square_keeps_size_proportions_for_give_ysize(self): vr = self.read_only_virtual_geo_raster() win = Window(0, 0, 300, 400) r = vr.get_window(win, ysize=200) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 200, 150)) def test_get_window_width_height_correctness(self): # See https://publicgitlab.satellogic.com/telluric/telluric/issues/58 vr = self.read_only_virtual_geo_raster() expected_height = 200 win = Window(0, vr.height - expected_height, 1, expected_height) r = vr.get_window(win) self.assertEqual(r.image.shape, (3, expected_height, 1)) class GeoRasterCropTest(BaseGeoRasterTestCase): metric_affine = Affine(1, 0.0, 2653750, 0.0, -1, 4594461) def test_crop_in_memory_and_off_memory_without_resizing_are_the_same(self): coords = mercantile.xy_bounds(*tiles[18]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster2 = GeoRaster2.open(rf.name) off_memory_crop = raster2.crop(shape) # load the image data raster2.image in_memory_crop = raster2.crop(shape) self.assertEqual(off_memory_crop, in_memory_crop) @window_data def test_crop_and_get_tile_do_the_same(self): coords = mercantile.xy_bounds(*tiles[15]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster2 = GeoRaster2.open(rf.name) tile15 = raster2.get_tile(*tiles[15]) # load the image data raster2.image cropped15 = raster2.crop(shape, MERCATOR_RESOLUTION_MAPPING[15]) self.assertEqual(tile15, cropped15) @window_data def test_crop_and_get_tile_do_the_same_when_image_is_populated(self): coords = mercantile.xy_bounds(*tiles[15]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster = GeoRaster2.open(rf.name) tile15 = raster.get_tile(*tiles[15]) raster._populate_from_rasterio_object(read_image=True) cropped_15 = raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[15]) self.assertEqual(tile15, cropped_15) @window_data def test_crop_image_from_and_get_win_do_the_same_with_resize(self): bounds = (2, 3, 4, 5) win = rasterio.windows.Window(bounds[0], bounds[1], bounds[2] - bounds[0], bounds[3] - bounds[1]) xsize = round((bounds[2] - bounds[0]) / 2) ysize = round((bounds[3] - bounds[1]) / 2) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster.save('area.tif', tags={'AREA_OR_POINT': 'Area'}) raster.save('point.tif', tags={'AREA_OR_POINT': 'Point'}) saved_raster = GeoRaster2.open(rf.name) cropped_win = saved_raster.get_window(win, xsize=xsize, ysize=ysize) saved_raster_area = GeoRaster2.open('area.tif') cropped_win_area = saved_raster_area.get_window(win, xsize=xsize, ysize=ysize) saved_raster_point = GeoRaster2.open('point.tif') cropped_win_point = saved_raster_point.get_window(win, xsize=xsize, ysize=ysize) cropped_image = raster._crop(bounds, xsize=xsize, ysize=ysize) print('cropped_win_area pixels\n', cropped_win_area.image) print('cropped_win_point pixels\n', cropped_win_point.image) print('cropped_win pixels\n', cropped_win.image) print('cropped_image pixels\n', cropped_image.image) if (cropped_win_point == cropped_win_area): print('point == area') if (cropped_image == cropped_win_area): print('image == area') if (cropped_image == cropped_win_point): print('image == point') if (cropped_win == cropped_win_area): print('win == area') if (cropped_win == cropped_win_point): print('win == point') self.assertEqual(cropped_image, cropped_win) @framing def test_crop_and_get_tile_do_the_same_when_image_is_populated_first_high_zoom(self): coords = mercantile.xy_bounds(*tiles[17]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster = GeoRaster2.open(rf.name) raster._populate_from_rasterio_object(read_image=True) tile17 = raster.get_tile(*tiles[17]) cropped_17 = raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[17]) self.assertEqual(tile17, cropped_17) @framing def test_crop_and_get_tile_do_the_same_when_image_is_populated_first_mid_zoom(self): coords = mercantile.xy_bounds(*tiles[15]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster = GeoRaster2.open(rf.name) raster._populate_from_rasterio_object(read_image=True) tile15 = raster.get_tile(*tiles[15]) cropped_15 = raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[15]) self.assertEqual(tile15, cropped_15) @framing def test_crop_and_get_tile_do_the_same_when_image_is_populated_first_for_low_zoom(self): coords = mercantile.xy_bounds(*tiles[11]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster = GeoRaster2.open(rf.name) raster._populate_from_rasterio_object(read_image=True) tile11 = raster.get_tile(*tiles[11]) cropped_11 = raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[11]) self.assertEqual(tile11, cropped_11) def test_crop_image_from_and_get_win_do_the_same_full_resolution(self): bounds = (20, 13, 40, 15) win = rasterio.windows.Window(bounds[0], bounds[1], bounds[2] - bounds[0], bounds[3] - bounds[1]) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) saved_raster = GeoRaster2.open(rf.name) cropped_win = saved_raster.get_window(win) cropped_image = raster._crop(bounds) self.assertEqual(cropped_image, cropped_win) @patch.object(GeoRaster2, '_crop') def test_crop_use_crop_image_for_a_loaded_image(self, mock__crop): coords = mercantile.xy_bounds(*tiles[15]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[15]) assert mock__crop.called_once @patch.object(GeoRaster2, 'get_window') def test_crop_use_get_window_for_a_not_loaded_image(self, mock_get_window): coords = mercantile.xy_bounds(*tiles[15]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster = GeoRaster2.open(rf.name) raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[15]) assert mock_get_window.called_once def test_crop_returns_full_resolution_as_default(self): coords = mercantile.xy_bounds(*tiles[17]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() _, win = raster._vector_to_raster_bounds(shape) cropped = raster.crop(shape) self.assertEqual(cropped.shape, (raster.num_bands, round(win.height), round(win.width))) self.assertEqual(cropped.affine[0], raster.affine[0]) def test_memory_crop_returns_resized_resolution(self): coords = mercantile.xy_bounds(*tiles[18]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() cropped = raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[18]) self.assertEqual(cropped.shape, (raster.num_bands, 256, 256)) self.assertAlmostEqual(cropped.affine[0], MERCATOR_RESOLUTION_MAPPING[18], 2) def test_geographic_crop(self): raster = self.read_only_virtual_geo_raster_wgs84() rhombus_on_image = Polygon([[0, 2], [1, 1], [2, 2], [1, 3]]) # in pixels rhombus_world = raster.to_world(rhombus_on_image) cropped = raster.crop(rhombus_world) r = raster[0:2, 1:3] assert cropped == r def test_geographic_crop_with_resize(self): coords = mercantile.xy_bounds(*tiles[17]) raster = self.read_only_virtual_geo_raster_wgs84() vector = GeoVector(Polygon.from_bounds(*coords), crs=WEB_MERCATOR_CRS) x_ex_res, y_ex_res = convert_resolution_from_meters_to_deg( self.metric_affine[6], MERCATOR_RESOLUTION_MAPPING[17]) cropped = raster.crop(vector, (x_ex_res, y_ex_res)) self.assertAlmostEqual(cropped.affine[0], x_ex_res) self.assertAlmostEqual(abs(cropped.affine[4]), y_ex_res, 6) def test_crop_raises_error_for_impossible_transformation(self): raster = self.read_only_virtual_geo_raster() vector = GeoVector(Polygon.from_bounds(-180, -90, 180, 90), crs=WGS84_CRS) with self.assertRaises(GeoRaster2Error): raster.crop(vector) def test_crop_of_rasters_with_opposite_affine_and_data_return_the_same(self): array = np.arange(0, 20).reshape(1, 4, 5) array2 = np.arange(19, -1, -1).reshape(1, 4, 5) array2.sort() image1 = np.ma.array(array, mask=False) image2 = np.ma.array(array2, mask=False) aff2 = Affine.translation(0, -8) * Affine.scale(2, 2) aff = Affine.scale(2, -2) r1 = GeoRaster2(image=image1, affine=aff, crs=WEB_MERCATOR_CRS) r2 = GeoRaster2(image=image2, affine=aff2, crs=WEB_MERCATOR_CRS) # r1 == r2 # doesn't work, see https://github.com/satellogic/telluric/issues/79 roi = GeoVector(Polygon.from_bounds(0, 0, 3, -3), crs=WEB_MERCATOR_CRS) r1c = r1.crop(roi) r2c = r2.crop(roi) # r1c == r2c # doesn't work, see https://github.com/satellogic/telluric/issues/79 # currently this is the only way to test the result is the same assert np.all(np.flip(r1c.image, axis=1) == r2c.image) class GeoRasterMaskedTest(TestCase): @classmethod def setUpClass(cls): cls.dir = TemporaryDirectory() path = os.path.join(cls.dir.name, 'test_masked_raster.tif') cls.masked_raster().save(path) cls.read_only_vgr = GeoRaster2.open(path) @classmethod def tearDownClass(cls): cls.dir.cleanup() @classmethod def masked_raster(cls): data = np.array([ [0, 1, 1, 1], [0, 2, 0, 2], [0, 3, 3, 3], ], dtype=np.uint8) mask = np.array([ [True, False, False, False], [True, False, False, False], [True, False, False, False], ], dtype=bool) image = np.ma.array( np.repeat(data[np.newaxis, :, :], 3, 0), mask=np.repeat(mask[np.newaxis, :, :], 3, 0) ) # Don't use exactly -1.0 for the affine for rasterio < 1.0a13, see # https://github.com/mapbox/rasterio/issues/1272 affine = Affine.scale(1, -1.0001) * Affine.translation(0, -3) crs = WGS84_CRS return GeoRaster2( image, affine=affine, crs=crs, ) def read_only_virtual_geo_raster(self): return self.read_only_vgr def test_get_smaller_window_respects_mask(self): window = Window(1, 0, 3, 3) raster = self.read_only_virtual_geo_raster() cropped = raster.get_window(window, masked=True) assert (~cropped.image.mask).all() def test_get_bigger_window_respects_mask(self): window = Window(1, 0, 4, 3) raster = self.read_only_virtual_geo_raster() cropped = raster.get_window(window, masked=True) assert cropped.image[:, :, -1].mask.all() # This line of pixels is masked assert (~cropped.image[:, :, :-1].mask).all() # The rest is not masked def test_small_read_only_virtual_geo_raster_wgs84_crop(): # See https://github.com/satellogic/telluric/issues/61 roi = GeoVector.from_bounds(xmin=0, ymin=0, xmax=2, ymax=2, crs=WGS84_CRS) resolution = 1.0 # deg / px raster = GeoRaster2.empty_from_roi(roi, resolution) assert raster.crop(roi) == raster.crop(roi, raster.resolution()) @manualtest class GeoRaster2ManualTest(TestCase): """manual testing To be run manually only.""" files = { 'original': 'original2.tif', 'cloudoptimized aligned': 'original2_aligned_cloudoptimized-2.tif', 'mrf aligned': 'original2_aligned.mrf', 'cloudoptimized': 'original2_cloudoptimized-2.tif', 'mrf': 'original2.mrf', 'not aligned cloudoptimized': 'not_aligned_cloudoptimized_2.tif', 'not aligned mrf': 'not_aligned.mrf', 'not aligned mrf split': 'not_aligned_split.mrf', 'aligned mrf split': 'original2_aligned_split.mrf', 'original mrf split': 'original2_split.mrf', } resamplings = { # 'avarage': Resampling.average, # 'nearest': Resampling.nearest, # 'bilinear': Resampling.bilinear, 'cubic': Resampling.cubic } def random_string(self): import hashlib now = '%s' % datetime.now() return hashlib.md5(now.encode('utf-8')).hexdigest() def run_test_on_real_rasters(self, zoom, resampling, local): results_arr = np.empty(shape=(len(self.files)), dtype=object) # with rasterio.Env(CPL_DEBUG=True, GDAL_CACHEMAX=0): # with rasterio.Env(CPL_DEBUG=False): print('*' * 80) print(zoom) print('*' * 80) print('#' * 80) print(resampling.name) print('#' * 80) for i, (file_type, file_url) in enumerate(self.files.items()): if local or 'split' in file_type: base_url = './notebooks/' else: base_url = 'https://ariel.blob.core.windows.net/rastersfortest/' file_url = base_url + file_url if local and 'mrf' not in file_type: new_file = file_url + self.random_string() os.system("cp %s %s" % (file_url, new_file)) else: new_file = file_url print('file type: %s' % file_type) print('-' * 80) print('file_url: %s' % file_url) print('new_file: %s' % new_file) print('-' * 80) vr = GeoRaster2.open(new_file) start = datetime.now() rasterio_ops = { 'CPL_DEBUG': True, 'GDAL_DISABLE_READDIR_ON_OPEN': 'YES' } if 'mrf' not in file_type: rasterio_ops['CPL_VSIL_CURL_ALLOWED_EXTENSIONS'] = '.tif' with rasterio.Env(**rasterio_ops): vr.get_tile(*tiles[zoom], resampling=resampling) end = datetime.now() tt = (end - start).total_seconds() * 1000 print("stars time : %s end time: %s total: %s ms" % (start, end, tt)) results_arr[i] = "type: %s, zoom: %i, resampling: %s time: %s msec" % (file_type, zoom, resampling.name, tt) if local and 'mrf' not in file_type: os.system("rm -f %s" % (new_file)) print('=' * 80) print(results_arr) def test_zoom_remote_11_resampling_cubic(self): self.run_test_on_real_rasters(11, Resampling.cubic, False) def test_zoom_remote_12_resampling_cubic(self): self.run_test_on_real_rasters(12, Resampling.cubic, False) def test_zoom_remote_14_resampling_cubic(self): self.run_test_on_real_rasters(14, Resampling.cubic, False) def test_zoom_remote_15_resampling_cubic(self): self.run_test_on_real_rasters(15, Resampling.cubic, False) def test_zoom_remote_17_resampling_cubic(self): self.run_test_on_real_rasters(17, Resampling.cubic, False) def test_zoom_remote_18_resampling_cubic(self): self.run_test_on_real_rasters(18, Resampling.cubic, False)

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import os import rasterio import mercantile import numpy as np import pytest from tempfile import NamedTemporaryFile, TemporaryDirectory from affine import Affine from unittest import TestCase from unittest.mock import patch from datetime import datetime from shapely.geometry import Polygon from rasterio.enums import Resampling from rasterio.windows import Window from rasterio.crs import CRS from telluric import GeoRaster2, GeoVector from telluric.constants import WEB_MERCATOR_CRS, WGS84_CRS from telluric.georaster import MERCATOR_RESOLUTION_MAPPING, GeoRaster2Error, GeoRaster2IOError from telluric.util.general import convert_resolution_from_meters_to_deg import sys import logging import tempfile log = logging.getLogger('rasterio._gdal') log.setLevel(logging.DEBUG) ch = logging.StreamHandler(sys.stdout) ch.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(message)s') ch.setFormatter(formatter) log.addHandler(ch) manualtest = pytest.mark.skipif("TEST_MANUAL" not in os.environ, reason="skip on auto testing") window_data = pytest.mark.skip('pending decission of consistency in results between rasterio read and reproject') framing = pytest.mark.skip('witing for framing and get_window with boundless false') tiles = { 10: (579, 394, 10), 11: (1159, 789, 11), 12: (2319, 1578, 12), 14: (9277, 6312, 14), 15: (18554, 12624, 15), 17: (74216, 50496, 17), 18: (148433, 100994, 18) } class GeoRaster2TilesTestGeneral(TestCase): def test_raise_exception_on_bad_file_path(self): vr = GeoRaster2.open('stam') with self.assertRaises(GeoRaster2IOError): vr.get_tile(1, 2, 3) def test_raise_exception_on_bad_raster_url(self): vr = GeoRaster2.open('http://stam') with self.assertRaises(GeoRaster2IOError): vr.get_tile(1, 2, 3) def test_raise_exception_on_bad_file_path_save_cog(self): vr = GeoRaster2.open('stam') with self.assertRaises(GeoRaster2IOError): vr.save_cloud_optimized('dest_file') def test_raise_exception_on_bad_raster_url_save_cog(self): vr = GeoRaster2.open('http://stam') with self.assertRaises(GeoRaster2IOError): vr.save_cloud_optimized('dest_file') class BaseGeoRasterTestCase(TestCase): @classmethod def setUpClass(cls): path = "./tests/data/raster/raster_for_test.tif" cls.read_only_vgr = GeoRaster2.open(path) path = "./tests/data/raster/raster_wgs84.tif" cls.read_only_vgr_wgs84 = GeoRaster2.open(path) def read_only_virtual_geo_raster(self): return self.read_only_vgr def read_only_virtual_geo_raster_wgs84(self): return self.read_only_vgr_wgs84 class GeoRaster2TestGetTile(BaseGeoRasterTestCase): def test_geo_bounding_tile(self): gr = self.read_only_virtual_geo_raster() gv = gr.footprint().reproject(WGS84_CRS) bounding_tile = mercantile.bounding_tile(*gv.get_shape(gv.crs).bounds) self.assertEqual(bounding_tile, (37108, 25248, 16)) @patch.object(GeoRaster2, 'crop') def test_fails_with_empty_raster_for_tile_out_of_raster_area(self, mock__crop): for raster in [self.read_only_virtual_geo_raster(), self.read_only_virtual_geo_raster_wgs84()]: r = raster.get_tile(16384, 16383, 15) self.assertTrue((r.image.data == 0).all()) self.assertTrue((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 256, 256)) self.assertEqual(r.crs, WEB_MERCATOR_CRS) mock__crop.assert_not_called() def test_get_all_raster_in_a_single_tile(self): for raster in [self.read_only_virtual_geo_raster(), self.read_only_virtual_geo_raster_wgs84()]: p = raster.footprint().reproject(WGS84_CRS).centroid r = raster.get_tile(*mercantile.tile(lng=p.x, lat=p.y, zoom=11)) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 256, 256)) self.assertEqual(r.crs, WEB_MERCATOR_CRS) def test_get_tile_for_different_zoom_levels(self): for raster in [self.read_only_virtual_geo_raster(), self.read_only_virtual_geo_raster_wgs84()]: for zoom in tiles: r = raster.get_tile(*tiles[zoom]) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 256, 256)) def test_get_tile_from_different_crs_tile_is_not_tilted(self): raster = self.read_only_virtual_geo_raster_wgs84() r = raster.get_tile(*tiles[18]) self.assertEqual(1, len(np.unique(r.image.mask))) def test_get_tile_from_different_crs_tile_is_not_tilted_with_different_buffer(self): raster = self.read_only_virtual_geo_raster_wgs84() os.environ["TELLURIC_GET_TILE_BUFFER"] = "0" try: r = raster.get_tile(*tiles[18]) except Exception: del os.environ["TELLURIC_GET_TILE_BUFFER"] self.assertEqual(2, len(np.unique(r.image.mask))) def test_get_entire_all_raster(self): vr = self.read_only_virtual_geo_raster() roi = GeoVector.from_xyz(37108, 25248, 16) r = vr.crop(roi) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.shape, (3, 612, 612)) def test_fails_with_empty_raster_for_tile_out_of_raster_area_with_no_tile_size(self): vr = self.read_only_virtual_geo_raster() roi = GeoVector.from_xyz(16384, 16383, 15) r = vr.crop(roi) self.assertTrue((r.image.data == 0).all()) self.assertTrue((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 1223, 1223)) def test_get_window_of_full_resolution(self): vr = self.read_only_virtual_geo_raster() win = Window(0, 0, 300, 300) r = vr.get_window(win) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 300, 300)) def test_get_window_resize_to_256(self): vr = self.read_only_virtual_geo_raster() win = Window(0, 0, 300, 300) r = vr.get_window(win, xsize=256, ysize=256) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 256, 256)) def test_get_window_of_non_square_resize_to_256(self): vr = self.read_only_virtual_geo_raster() win = Window(0, 0, 300, 400) r = vr.get_window(win, xsize=256, ysize=256) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 256, 256)) def test_get_window_of_non_square_keeps_size_proportions_for_give_xsize(self): vr = self.read_only_virtual_geo_raster() win = Window(0, 0, 300, 400) r = vr.get_window(win, xsize=150) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 200, 150)) def test_get_window_of_non_square_keeps_size_proportions_for_give_ysize(self): vr = self.read_only_virtual_geo_raster() win = Window(0, 0, 300, 400) r = vr.get_window(win, ysize=200) self.assertFalse((r.image.data == 0).all()) self.assertFalse((r.image.mask).all()) self.assertEqual(r.image.shape, (3, 200, 150)) def test_get_window_width_height_correctness(self): vr = self.read_only_virtual_geo_raster() expected_height = 200 win = Window(0, vr.height - expected_height, 1, expected_height) r = vr.get_window(win) self.assertEqual(r.image.shape, (3, expected_height, 1)) class GeoRasterCropTest(BaseGeoRasterTestCase): metric_affine = Affine(1, 0.0, 2653750, 0.0, -1, 4594461) def test_crop_in_memory_and_off_memory_without_resizing_are_the_same(self): coords = mercantile.xy_bounds(*tiles[18]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster2 = GeoRaster2.open(rf.name) off_memory_crop = raster2.crop(shape) raster2.image in_memory_crop = raster2.crop(shape) self.assertEqual(off_memory_crop, in_memory_crop) @window_data def test_crop_and_get_tile_do_the_same(self): coords = mercantile.xy_bounds(*tiles[15]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster2 = GeoRaster2.open(rf.name) tile15 = raster2.get_tile(*tiles[15]) raster2.image cropped15 = raster2.crop(shape, MERCATOR_RESOLUTION_MAPPING[15]) self.assertEqual(tile15, cropped15) @window_data def test_crop_and_get_tile_do_the_same_when_image_is_populated(self): coords = mercantile.xy_bounds(*tiles[15]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster = GeoRaster2.open(rf.name) tile15 = raster.get_tile(*tiles[15]) raster._populate_from_rasterio_object(read_image=True) cropped_15 = raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[15]) self.assertEqual(tile15, cropped_15) @window_data def test_crop_image_from_and_get_win_do_the_same_with_resize(self): bounds = (2, 3, 4, 5) win = rasterio.windows.Window(bounds[0], bounds[1], bounds[2] - bounds[0], bounds[3] - bounds[1]) xsize = round((bounds[2] - bounds[0]) / 2) ysize = round((bounds[3] - bounds[1]) / 2) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster.save('area.tif', tags={'AREA_OR_POINT': 'Area'}) raster.save('point.tif', tags={'AREA_OR_POINT': 'Point'}) saved_raster = GeoRaster2.open(rf.name) cropped_win = saved_raster.get_window(win, xsize=xsize, ysize=ysize) saved_raster_area = GeoRaster2.open('area.tif') cropped_win_area = saved_raster_area.get_window(win, xsize=xsize, ysize=ysize) saved_raster_point = GeoRaster2.open('point.tif') cropped_win_point = saved_raster_point.get_window(win, xsize=xsize, ysize=ysize) cropped_image = raster._crop(bounds, xsize=xsize, ysize=ysize) print('cropped_win_area pixels\n', cropped_win_area.image) print('cropped_win_point pixels\n', cropped_win_point.image) print('cropped_win pixels\n', cropped_win.image) print('cropped_image pixels\n', cropped_image.image) if (cropped_win_point == cropped_win_area): print('point == area') if (cropped_image == cropped_win_area): print('image == area') if (cropped_image == cropped_win_point): print('image == point') if (cropped_win == cropped_win_area): print('win == area') if (cropped_win == cropped_win_point): print('win == point') self.assertEqual(cropped_image, cropped_win) @framing def test_crop_and_get_tile_do_the_same_when_image_is_populated_first_high_zoom(self): coords = mercantile.xy_bounds(*tiles[17]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster = GeoRaster2.open(rf.name) raster._populate_from_rasterio_object(read_image=True) tile17 = raster.get_tile(*tiles[17]) cropped_17 = raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[17]) self.assertEqual(tile17, cropped_17) @framing def test_crop_and_get_tile_do_the_same_when_image_is_populated_first_mid_zoom(self): coords = mercantile.xy_bounds(*tiles[15]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster = GeoRaster2.open(rf.name) raster._populate_from_rasterio_object(read_image=True) tile15 = raster.get_tile(*tiles[15]) cropped_15 = raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[15]) self.assertEqual(tile15, cropped_15) @framing def test_crop_and_get_tile_do_the_same_when_image_is_populated_first_for_low_zoom(self): coords = mercantile.xy_bounds(*tiles[11]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster = GeoRaster2.open(rf.name) raster._populate_from_rasterio_object(read_image=True) tile11 = raster.get_tile(*tiles[11]) cropped_11 = raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[11]) self.assertEqual(tile11, cropped_11) def test_crop_image_from_and_get_win_do_the_same_full_resolution(self): bounds = (20, 13, 40, 15) win = rasterio.windows.Window(bounds[0], bounds[1], bounds[2] - bounds[0], bounds[3] - bounds[1]) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) saved_raster = GeoRaster2.open(rf.name) cropped_win = saved_raster.get_window(win) cropped_image = raster._crop(bounds) self.assertEqual(cropped_image, cropped_win) @patch.object(GeoRaster2, '_crop') def test_crop_use_crop_image_for_a_loaded_image(self, mock__crop): coords = mercantile.xy_bounds(*tiles[15]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[15]) assert mock__crop.called_once @patch.object(GeoRaster2, 'get_window') def test_crop_use_get_window_for_a_not_loaded_image(self, mock_get_window): coords = mercantile.xy_bounds(*tiles[15]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() with NamedTemporaryFile(mode='w+b', suffix=".tif") as rf: raster.save(rf.name) raster = GeoRaster2.open(rf.name) raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[15]) assert mock_get_window.called_once def test_crop_returns_full_resolution_as_default(self): coords = mercantile.xy_bounds(*tiles[17]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() _, win = raster._vector_to_raster_bounds(shape) cropped = raster.crop(shape) self.assertEqual(cropped.shape, (raster.num_bands, round(win.height), round(win.width))) self.assertEqual(cropped.affine[0], raster.affine[0]) def test_memory_crop_returns_resized_resolution(self): coords = mercantile.xy_bounds(*tiles[18]) shape = GeoVector(Polygon.from_bounds(*coords), WEB_MERCATOR_CRS) raster = self.read_only_virtual_geo_raster() cropped = raster.crop(shape, MERCATOR_RESOLUTION_MAPPING[18]) self.assertEqual(cropped.shape, (raster.num_bands, 256, 256)) self.assertAlmostEqual(cropped.affine[0], MERCATOR_RESOLUTION_MAPPING[18], 2) def test_geographic_crop(self): raster = self.read_only_virtual_geo_raster_wgs84() rhombus_on_image = Polygon([[0, 2], [1, 1], [2, 2], [1, 3]]) rhombus_world = raster.to_world(rhombus_on_image) cropped = raster.crop(rhombus_world) r = raster[0:2, 1:3] assert cropped == r def test_geographic_crop_with_resize(self): coords = mercantile.xy_bounds(*tiles[17]) raster = self.read_only_virtual_geo_raster_wgs84() vector = GeoVector(Polygon.from_bounds(*coords), crs=WEB_MERCATOR_CRS) x_ex_res, y_ex_res = convert_resolution_from_meters_to_deg( self.metric_affine[6], MERCATOR_RESOLUTION_MAPPING[17]) cropped = raster.crop(vector, (x_ex_res, y_ex_res)) self.assertAlmostEqual(cropped.affine[0], x_ex_res) self.assertAlmostEqual(abs(cropped.affine[4]), y_ex_res, 6) def test_crop_raises_error_for_impossible_transformation(self): raster = self.read_only_virtual_geo_raster() vector = GeoVector(Polygon.from_bounds(-180, -90, 180, 90), crs=WGS84_CRS) with self.assertRaises(GeoRaster2Error): raster.crop(vector) def test_crop_of_rasters_with_opposite_affine_and_data_return_the_same(self): array = np.arange(0, 20).reshape(1, 4, 5) array2 = np.arange(19, -1, -1).reshape(1, 4, 5) array2.sort() image1 = np.ma.array(array, mask=False) image2 = np.ma.array(array2, mask=False) aff2 = Affine.translation(0, -8) * Affine.scale(2, 2) aff = Affine.scale(2, -2) r1 = GeoRaster2(image=image1, affine=aff, crs=WEB_MERCATOR_CRS) r2 = GeoRaster2(image=image2, affine=aff2, crs=WEB_MERCATOR_CRS) ERCATOR_CRS) r1c = r1.crop(roi) r2c = r2.crop(roi) # r1c == r2c # doesn't work, see https://github.com/satellogic/telluric/issues/79 assert np.all(np.flip(r1c.image, axis=1) == r2c.image) class GeoRasterMaskedTest(TestCase): @classmethod def setUpClass(cls): cls.dir = TemporaryDirectory() path = os.path.join(cls.dir.name, 'test_masked_raster.tif') cls.masked_raster().save(path) cls.read_only_vgr = GeoRaster2.open(path) @classmethod def tearDownClass(cls): cls.dir.cleanup() @classmethod def masked_raster(cls): data = np.array([ [0, 1, 1, 1], [0, 2, 0, 2], [0, 3, 3, 3], ], dtype=np.uint8) mask = np.array([ [True, False, False, False], [True, False, False, False], [True, False, False, False], ], dtype=bool) image = np.ma.array( np.repeat(data[np.newaxis, :, :], 3, 0), mask=np.repeat(mask[np.newaxis, :, :], 3, 0) ) # https://github.com/mapbox/rasterio/issues/1272 affine = Affine.scale(1, -1.0001) * Affine.translation(0, -3) crs = WGS84_CRS return GeoRaster2( image, affine=affine, crs=crs, ) def read_only_virtual_geo_raster(self): return self.read_only_vgr def test_get_smaller_window_respects_mask(self): window = Window(1, 0, 3, 3) raster = self.read_only_virtual_geo_raster() cropped = raster.get_window(window, masked=True) assert (~cropped.image.mask).all() def test_get_bigger_window_respects_mask(self): window = Window(1, 0, 4, 3) raster = self.read_only_virtual_geo_raster() cropped = raster.get_window(window, masked=True) assert cropped.image[:, :, -1].mask.all() # This line of pixels is masked assert (~cropped.image[:, :, :-1].mask).all() # The rest is not masked def test_small_read_only_virtual_geo_raster_wgs84_crop(): # See https://github.com/satellogic/telluric/issues/61 roi = GeoVector.from_bounds(xmin=0, ymin=0, xmax=2, ymax=2, crs=WGS84_CRS) resolution = 1.0 # deg / px raster = GeoRaster2.empty_from_roi(roi, resolution) assert raster.crop(roi) == raster.crop(roi, raster.resolution()) @manualtest class GeoRaster2ManualTest(TestCase): files = { 'original': 'original2.tif', 'cloudoptimized aligned': 'original2_aligned_cloudoptimized-2.tif', 'mrf aligned': 'original2_aligned.mrf', 'cloudoptimized': 'original2_cloudoptimized-2.tif', 'mrf': 'original2.mrf', 'not aligned cloudoptimized': 'not_aligned_cloudoptimized_2.tif', 'not aligned mrf': 'not_aligned.mrf', 'not aligned mrf split': 'not_aligned_split.mrf', 'aligned mrf split': 'original2_aligned_split.mrf', 'original mrf split': 'original2_split.mrf', } resamplings = { # 'avarage': Resampling.average, # 'nearest': Resampling.nearest, # 'bilinear': Resampling.bilinear, 'cubic': Resampling.cubic } def random_string(self): import hashlib now = '%s' % datetime.now() return hashlib.md5(now.encode('utf-8')).hexdigest() def run_test_on_real_rasters(self, zoom, resampling, local): results_arr = np.empty(shape=(len(self.files)), dtype=object) # with rasterio.Env(CPL_DEBUG=True, GDAL_CACHEMAX=0): # with rasterio.Env(CPL_DEBUG=False): print('*' * 80) print(zoom) print('*' * 80) print(' print(resampling.name) print(' for i, (file_type, file_url) in enumerate(self.files.items()): if local or 'split' in file_type: base_url = './notebooks/' else: base_url = 'https://ariel.blob.core.windows.net/rastersfortest/' file_url = base_url + file_url if local and 'mrf' not in file_type: new_file = file_url + self.random_string() os.system("cp %s %s" % (file_url, new_file)) else: new_file = file_url print('file type: %s' % file_type) print('-' * 80) print('file_url: %s' % file_url) print('new_file: %s' % new_file) print('-' * 80) vr = GeoRaster2.open(new_file) start = datetime.now() rasterio_ops = { 'CPL_DEBUG': True, 'GDAL_DISABLE_READDIR_ON_OPEN': 'YES' } if 'mrf' not in file_type: rasterio_ops['CPL_VSIL_CURL_ALLOWED_EXTENSIONS'] = '.tif' with rasterio.Env(**rasterio_ops): vr.get_tile(*tiles[zoom], resampling=resampling) end = datetime.now() tt = (end - start).total_seconds() * 1000 print("stars time : %s end time: %s total: %s ms" % (start, end, tt)) results_arr[i] = "type: %s, zoom: %i, resampling: %s time: %s msec" % (file_type, zoom, resampling.name, tt) if local and 'mrf' not in file_type: os.system("rm -f %s" % (new_file)) print('=' * 80) print(results_arr) def test_zoom_remote_11_resampling_cubic(self): self.run_test_on_real_rasters(11, Resampling.cubic, False) def test_zoom_remote_12_resampling_cubic(self): self.run_test_on_real_rasters(12, Resampling.cubic, False) def test_zoom_remote_14_resampling_cubic(self): self.run_test_on_real_rasters(14, Resampling.cubic, False) def test_zoom_remote_15_resampling_cubic(self): self.run_test_on_real_rasters(15, Resampling.cubic, False) def test_zoom_remote_17_resampling_cubic(self): self.run_test_on_real_rasters(17, Resampling.cubic, False) def test_zoom_remote_18_resampling_cubic(self): self.run_test_on_real_rasters(18, Resampling.cubic, False)

true

790dc05f4c0d5872bd7a197900cdb588ebac477b

6,501

py

Python

client-autosense/sense/sqlite_syn.py

zxypic/PublicPic

8bec621e38955fb061220bf56c2961122651ff9d

[ "MIT" ]

null

client-autosense/sense/sqlite_syn.py

zxypic/PublicPic

8bec621e38955fb061220bf56c2961122651ff9d

[ "MIT" ]

null

client-autosense/sense/sqlite_syn.py

zxypic/PublicPic

8bec621e38955fb061220bf56c2961122651ff9d

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os import sqlite3 import logging logger = logging.getLogger("xtc") class sqlite_handle(object): def __init__(self): self.dbname = "Xsense.db" self.conn = None def db_init(self): # 初始化db task_info、apps、scripts、run_tasks self.db_table_all() conn = sqlite3.connect(self.dbname) try: for cre in self.create_dic: conn.execute(cre) # logger.info(cre) except Exception as e: logger.info("Create table failed: {}".format(e)) return False finally: conn.close() def insert_task(self,taskdict): # 插入任务信息 for conn = sqlite3.connect(self.dbname) for task in taskdict: conn.execute( 'INSERT INTO task_Info VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?)',task ) conn.commit() conn.close() def insert_script_one(self,scriptOne): # 插入脚本信息 conn = sqlite3.connect(self.dbname) conn.execute( 'INSERT INTO scripts VALUES (?,?,?,?,?,?,?,?)',scriptOne ) conn.commit() conn.close() def insert_task_many(self,script_data): # 插入任务信息多项 conn = sqlite3.connect(self.dbname) conn.executemany( 'INSERT INTO scripts VALUES (?,?,?,?,?,?,?,?)',script_data ) conn.commit() conn.close() def db_table_all(self): crt_task_info = '''CREATE TABLE IF NOT EXISTS task_info ( taskId INT, testTaskName TEXT, optType int,scriptId INT,scriptUrl TEXT, startDate int, endDate int, exeBeginTime TEXT, exeEndTime TEXT, exeType int, interval int, iterationNum int, startIterationNumber int );''' crt_scripts = '''CREATE TABLE IF NOT EXISTS scripts ( scriptId INT, scriptName TEXT, scriptType int,scriptUrl TEXT, uploadDate int, scriptMaxRunTime int, scriptVersion int, scriptCacheUrl TEXT );''' crt_apps = '''CREATE TABLE IF NOT EXISTS apps ( scriptId INT, appCheck int, appPackageName TEXT, appUrl TEXT, appMd5 TEXT, appVersion TEXT, appVersionCode TEXT, appLastUpdateTime TEXT, appCacheUrl TEXT );''' run_tasks = '''CREATE TABLE IF NOT EXISTS run_tasks ( taskId INT, testTaskName TEXT, optType int,scriptId INT,scriptUrl TEXT, startDate int, endDate int, exeBeginTime TEXT, exeEndTime TEXT, exeType int, interval int, iterationNum int, startIterationNumber int );''' create_dic = [] create_dic.append(crt_task_info) create_dic.append(crt_scripts) create_dic.append(crt_apps) create_dic.append(run_tasks) # 保存需要运行的任务有必要么 self.create_dic = create_dic def query_runtask(self): conn = sqlite3.connect(self.dbname) taskrows = [] #元素为tuple，(205937, 'pyclient-test', 1, 107864, 'http://202.105.193....69910.zip', 20191006000000, 20201231235959, '000000', '235959', 2, 1, 1, 1) # 获取未完成的按次任务不含重复项新增+启动, exeType=2按次执行 exeType=1按时执行 # optType 1`=新增任务；`2`=暂停任务；`3`=启动任务；`4`=删除任务 for row in conn.execute('SELECT DISTINCT * FROM task_info WHERE optType=3 OR optType=1 AND exeType=2 AND startIterationNumber<=iterationNum'): taskrows.append(row) conn.close() return taskrows def dele_table(self): pass def query(self, sql, sqlstring=False): conn = sqlite3.connect(self.dbname) cursor = conn.cursor() # cursor = self.conn.cursor() if sqlstring: cursor.executemany(sql, sqlstring) else: cursor.execute(sql) data = cursor.fetchall() cursor.close() return data def update(self, sql, sqlstring=False): conn = sqlite3.connect(self.dbname) cursor = conn.cursor() # cursor = self.conn.cursor() if sqlstring: cursor.executemany(sql, sqlstring) else: cursor.execute(sql) conn.commit() cursor.close() def _update(self, sql, value=None, querymany=True): ret = True try: if querymany: self.update(sql, value) else: self.update(sql) #except SqliteException: except Exception as e: logger.info("error('执行sqlite: {} 时出错：{}')".format(sql, e)) ret = False return ret def del_task_byid(self, taskid): conn = sqlite3.connect(self.dbname) cursor = conn.cursor() sql = 'DELETE FROM task_info WHERE taskid={}'.format(taskid) cursor.execute(sql) logger.info("刪除taskid={} cursor.rowcount={}".format(taskid, str(cursor.rowcount))) conn.commit() cursor.close() conn.close() def update_task_run_status(self, taskid, status): conn = sqlite3.connect(self.dbname) cursor = conn.cursor() cursor.execute("UPDATE task_info SET optType={} WHERE taskid={}".format(status, taskid)) logger.info("更新taskid={}，设置optType={}，cursor.rowcount={}".format(taskid, status, str(cursor.rowcount))) conn.commit() cursor.close() conn.close() def update_task_run_count(self, taskid, run_count): conn = sqlite3.connect(self.dbname) cursor = conn.cursor() cursor.execute("UPDATE task_info SET startIterationNumber={} WHERE taskid={}".format(run_count, taskid)) logger.info("更新taskid={}，startIterationNumber={}，cursor.rowcount={}".format(taskid, run_count, str(cursor.rowcount))) conn.commit() cursor.close() conn.close() def updata_table(self): pass if __name__ == "__main__": handle = sqlite_handle() if not os.path.isfile(handle.dbname): handle.db_init() #taskrows = handle.query_runtask() #print("taskrows=" + str(taskrows)) #handle.del_task_byid("1235") handle.update_task_run_count("206266", 60) #handle.update_task_run_status("206266", "5") # 更新/删除单条任务、更新脚本信息 # 下载前查询数据库，如果脚本id已经存在，且更新时间一致则不下载，否则下载-->入库 # 任务运行，先检查是否有新任务，如果有新任务，则入库， # 没有新任务，则查询数据库，任务id运行信息是否达到rm条件（过期、完成等） # 如果运行轮次达到总轮次则del # 如果结束时间超过当前时间则del # 此处需要增加 id 排序后再运行 # 运行完成后，更新 id对应的轮次信息 # 今天搞定脚本运行和结果文件，然后做db update 和 remove

36.318436

167

0.591601

import os import sqlite3 import logging logger = logging.getLogger("xtc") class sqlite_handle(object): def __init__(self): self.dbname = "Xsense.db" self.conn = None def db_init(self): self.db_table_all() conn = sqlite3.connect(self.dbname) try: for cre in self.create_dic: conn.execute(cre) except Exception as e: logger.info("Create table failed: {}".format(e)) return False finally: conn.close() def insert_task(self,taskdict): conn = sqlite3.connect(self.dbname) for task in taskdict: conn.execute( 'INSERT INTO task_Info VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?)',task ) conn.commit() conn.close() def insert_script_one(self,scriptOne): conn = sqlite3.connect(self.dbname) conn.execute( 'INSERT INTO scripts VALUES (?,?,?,?,?,?,?,?)',scriptOne ) conn.commit() conn.close() def insert_task_many(self,script_data): conn = sqlite3.connect(self.dbname) conn.executemany( 'INSERT INTO scripts VALUES (?,?,?,?,?,?,?,?)',script_data ) conn.commit() conn.close() def db_table_all(self): crt_task_info = '''CREATE TABLE IF NOT EXISTS task_info ( taskId INT, testTaskName TEXT, optType int,scriptId INT,scriptUrl TEXT, startDate int, endDate int, exeBeginTime TEXT, exeEndTime TEXT, exeType int, interval int, iterationNum int, startIterationNumber int );''' crt_scripts = '''CREATE TABLE IF NOT EXISTS scripts ( scriptId INT, scriptName TEXT, scriptType int,scriptUrl TEXT, uploadDate int, scriptMaxRunTime int, scriptVersion int, scriptCacheUrl TEXT );''' crt_apps = '''CREATE TABLE IF NOT EXISTS apps ( scriptId INT, appCheck int, appPackageName TEXT, appUrl TEXT, appMd5 TEXT, appVersion TEXT, appVersionCode TEXT, appLastUpdateTime TEXT, appCacheUrl TEXT );''' run_tasks = '''CREATE TABLE IF NOT EXISTS run_tasks ( taskId INT, testTaskName TEXT, optType int,scriptId INT,scriptUrl TEXT, startDate int, endDate int, exeBeginTime TEXT, exeEndTime TEXT, exeType int, interval int, iterationNum int, startIterationNumber int );''' create_dic = [] create_dic.append(crt_task_info) create_dic.append(crt_scripts) create_dic.append(crt_apps) create_dic.append(run_tasks) self.create_dic = create_dic def query_runtask(self): conn = sqlite3.connect(self.dbname) taskrows = [] for row in conn.execute('SELECT DISTINCT * FROM task_info WHERE optType=3 OR optType=1 AND exeType=2 AND startIterationNumber<=iterationNum'): taskrows.append(row) conn.close() return taskrows def dele_table(self): pass def query(self, sql, sqlstring=False): conn = sqlite3.connect(self.dbname) cursor = conn.cursor() if sqlstring: cursor.executemany(sql, sqlstring) else: cursor.execute(sql) data = cursor.fetchall() cursor.close() return data def update(self, sql, sqlstring=False): conn = sqlite3.connect(self.dbname) cursor = conn.cursor() if sqlstring: cursor.executemany(sql, sqlstring) else: cursor.execute(sql) conn.commit() cursor.close() def _update(self, sql, value=None, querymany=True): ret = True try: if querymany: self.update(sql, value) else: self.update(sql) except Exception as e: logger.info("error('执行sqlite: {} 时出错：{}')".format(sql, e)) ret = False return ret def del_task_byid(self, taskid): conn = sqlite3.connect(self.dbname) cursor = conn.cursor() sql = 'DELETE FROM task_info WHERE taskid={}'.format(taskid) cursor.execute(sql) logger.info("刪除taskid={} cursor.rowcount={}".format(taskid, str(cursor.rowcount))) conn.commit() cursor.close() conn.close() def update_task_run_status(self, taskid, status): conn = sqlite3.connect(self.dbname) cursor = conn.cursor() cursor.execute("UPDATE task_info SET optType={} WHERE taskid={}".format(status, taskid)) logger.info("更新taskid={}，设置optType={}，cursor.rowcount={}".format(taskid, status, str(cursor.rowcount))) conn.commit() cursor.close() conn.close() def update_task_run_count(self, taskid, run_count): conn = sqlite3.connect(self.dbname) cursor = conn.cursor() cursor.execute("UPDATE task_info SET startIterationNumber={} WHERE taskid={}".format(run_count, taskid)) logger.info("更新taskid={}，startIterationNumber={}，cursor.rowcount={}".format(taskid, run_count, str(cursor.rowcount))) conn.commit() cursor.close() conn.close() def updata_table(self): pass if __name__ == "__main__": handle = sqlite_handle() if not os.path.isfile(handle.dbname): handle.db_init() handle.update_task_run_count("206266", 60)

true

790dc1204f7d88fa8e7a6bfc76e42000069a6612

1,009

py

Python

grid/migrations/0002_image.py

greatdaniels/gallery-app

e4749ca4ab02b0715e707856aa9d28cc66b7ebc5

[ "MIT" ]

null

grid/migrations/0002_image.py

greatdaniels/gallery-app

e4749ca4ab02b0715e707856aa9d28cc66b7ebc5

[ "MIT" ]

4

2020-06-06T01:10:11.000Z

2021-09-08T02:04:23.000Z

grid/migrations/0002_image.py

greatdaniels/gallery-app

e4749ca4ab02b0715e707856aa9d28cc66b7ebc5

[ "MIT" ]

null

# Generated by Django 3.0.6 on 2020-05-24 13:43 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('grid', '0001_initial'), ] operations = [ migrations.CreateModel( name='Image', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('img_name', models.CharField(max_length=30)), ('img_description', models.TextField()), ('photo', models.ImageField(default='', upload_to='images/')), ('category', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='grid.Category')), ('editor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='grid.Editor')), ('location', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='grid.Location')), ], ), ]

37.37037

114

0.60555

from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('grid', '0001_initial'), ] operations = [ migrations.CreateModel( name='Image', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('img_name', models.CharField(max_length=30)), ('img_description', models.TextField()), ('photo', models.ImageField(default='', upload_to='images/')), ('category', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='grid.Category')), ('editor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='grid.Editor')), ('location', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='grid.Location')), ], ), ]

true

790dc142b9a7987a252d5c15f3f5739223b31b94

9,171

py

Python

segnet_v7.py

vietnamican/Deep-Image-Matting

436487e680027f07387700fb8ee1486635b82335

[ "MIT" ]

null

segnet_v7.py

vietnamican/Deep-Image-Matting

436487e680027f07387700fb8ee1486635b82335

[ "MIT" ]

null

segnet_v7.py

vietnamican/Deep-Image-Matting

436487e680027f07387700fb8ee1486635b82335

[ "MIT" ]

null

import tensorflow.keras.backend as K import tensorflow as tf from tensorflow.keras.layers import Input, Conv2D, UpSampling2D, BatchNormalization, ZeroPadding2D, MaxPooling2D, Reshape, \ Concatenate, Lambda from tensorflow.keras.models import Model from tensorflow.keras.utils import multi_gpu_model from tensorflow.keras.utils import plot_model from custom_layers.unpooling_layer import Unpooling ATROUS_RATES = [6, 12, 18] # Conv-MaxPool SPP 24M def build_encoder_decoder(): # Encoder input_tensor = Input(shape=(320, 320, 4)) x = ZeroPadding2D((1, 1))(input_tensor) x = Conv2D(64, (3, 3), activation='relu', name='conv1_1')(x) x = BatchNormalization()(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(64, (3, 3), activation='relu', name='conv1_2')(x) x = BatchNormalization()(x) orig_1 = x x = MaxPooling2D((2, 2), strides=(2, 2))(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(128, (3, 3), activation='relu', name='conv2_1')(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(128, (3, 3), activation='relu', name='conv2_2')(x) orig_2 = x x = MaxPooling2D((2, 2), strides=(2, 2))(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(256, (3, 3), activation='relu', name='conv3_1')(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(256, (3, 3), activation='relu', name='conv3_2')(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(256, (3, 3), activation='relu', name='conv3_3')(x) orig_3 = x x = MaxPooling2D((2, 2), strides=(2, 2))(x) inputs_size = x.get_shape()[1:3] conv_4_1x1 = Conv2D(512, (1, 1), activation='relu', padding='same', name='conv4_1x1')(x) conv_4_3x3_1 = Conv2D(512, (3, 3), activation='relu', padding='same', dilation_rate=ATROUS_RATES[0], name='conv4_3x3_1')(x) conv_4_3x3_2 = Conv2D(512, (3, 3), activation='relu', padding='same', dilation_rate=ATROUS_RATES[1], name='conv4_3x3_2')(x) conv_4_3x3_3 = Conv2D(512, (3, 3), activation='relu', padding='same', dilation_rate=ATROUS_RATES[2], name='conv4_3x3_3')(x) # Image average pooling image_level_features = Lambda(lambda x: tf.reduce_mean(x, [1, 2], keepdims=True), name='global_average_pooling')(x) image_level_features = Conv2D(512, (1, 1), activation='relu', padding='same', name='image_level_features_conv_1x1')(image_level_features) image_level_features = Lambda(lambda x: tf.image.resize(x, inputs_size), name='upsample_1')(image_level_features) # Concat x = Concatenate(axis=3)([conv_4_1x1, conv_4_3x3_1, conv_4_3x3_2, conv_4_3x3_3, image_level_features]) x = Conv2D(512, (1,1), activation='relu', padding='same', name='conv_1x1_1_concat')(x) x = Conv2D(512, (1,1), activation='relu', padding='same', name='conv_1x1_2_concat')(x) orig_4 = x x = MaxPooling2D((2, 2), strides=(2, 2))(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(512, (3, 3), activation='relu', name='conv5_1')(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(512, (3, 3), activation='relu', name='conv5_2')(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(512, (3, 3), activation='relu', name='conv5_3')(x) orig_5 = x x = MaxPooling2D((2, 2), strides=(2, 2))(x) # Decoder # x = UpSampling2D(size=(2, 2))(x) the_shape = K.int_shape(orig_5) shape = (1, the_shape[1], the_shape[2], the_shape[3]) origReshaped = Reshape(shape)(orig_5) xReshaped = Reshape(shape)(x) together = Concatenate(axis=1)([origReshaped, xReshaped]) x = Unpooling()(together) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='deconv5_1', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='deconv5_2', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='deconv5_3', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = UpSampling2D(size=(2, 2))(x) the_shape = K.int_shape(orig_4) shape = (1, the_shape[1], the_shape[2], the_shape[3]) origReshaped = Reshape(shape)(orig_4) xReshaped = Reshape(shape)(x) together = Concatenate(axis=1)([origReshaped, xReshaped]) x = Unpooling()(together) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='deconv4_1', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='deconv4_2', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='deconv4_3', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = UpSampling2D(size=(2, 2))(x) the_shape = K.int_shape(orig_3) shape = (1, the_shape[1], the_shape[2], the_shape[3]) origReshaped = Reshape(shape)(orig_3) xReshaped = Reshape(shape)(x) together = Concatenate(axis=1)([origReshaped, xReshaped]) x = Unpooling()(together) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='deconv3_1', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='deconv3_2', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='deconv3_3', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = UpSampling2D(size=(2, 2))(x) the_shape = K.int_shape(orig_2) shape = (1, the_shape[1], the_shape[2], the_shape[3]) origReshaped = Reshape(shape)(orig_2) xReshaped = Reshape(shape)(x) together = Concatenate(axis=1)([origReshaped, xReshaped]) x = Unpooling()(together) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='deconv2_1', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='deconv2_2', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = UpSampling2D(size=(2, 2))(x) the_shape = K.int_shape(orig_1) shape = (1, the_shape[1], the_shape[2], the_shape[3]) origReshaped = Reshape(shape)(orig_1) xReshaped = Reshape(shape)(x) together = Concatenate(axis=1)([origReshaped, xReshaped]) x = Unpooling()(together) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='deconv1_1', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='deconv1_2', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(1, (3, 3), activation='sigmoid', padding='same', name='pred', kernel_initializer='he_normal', bias_initializer='zeros')(x) model = Model(inputs=input_tensor, outputs=x) return model def build_refinement(encoder_decoder): input_tensor = encoder_decoder.input input = Lambda(lambda i: i[:, :, :, 0:3])(input_tensor) x = Concatenate(axis=3)([input, encoder_decoder.output]) x = Conv2D(64, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(64, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(64, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(1, (3, 3), activation='sigmoid', padding='same', name='refinement_pred', kernel_initializer='he_normal', bias_initializer='zeros')(x) model = Model(inputs=input_tensor, outputs=x) return model if __name__ == '__main__': with tf.device("/cpu:0"): encoder_decoder = build_encoder_decoder() print(encoder_decoder.summary()) plot_model(encoder_decoder, to_file='encoder_decoder.svg', show_layer_names=True, show_shapes=True) with tf.device("/cpu:0"): refinement = build_refinement(encoder_decoder) print(refinement.summary()) plot_model(refinement, to_file='refinement.svg', show_layer_names=True, show_shapes=True) parallel_model = multi_gpu_model(refinement, gpus=None) print(parallel_model.summary()) plot_model(parallel_model, to_file='parallel_model.svg', show_layer_names=True, show_shapes=True) K.clear_session()

44.736585

141

0.637771

import tensorflow.keras.backend as K import tensorflow as tf from tensorflow.keras.layers import Input, Conv2D, UpSampling2D, BatchNormalization, ZeroPadding2D, MaxPooling2D, Reshape, \ Concatenate, Lambda from tensorflow.keras.models import Model from tensorflow.keras.utils import multi_gpu_model from tensorflow.keras.utils import plot_model from custom_layers.unpooling_layer import Unpooling ATROUS_RATES = [6, 12, 18] def build_encoder_decoder(): input_tensor = Input(shape=(320, 320, 4)) x = ZeroPadding2D((1, 1))(input_tensor) x = Conv2D(64, (3, 3), activation='relu', name='conv1_1')(x) x = BatchNormalization()(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(64, (3, 3), activation='relu', name='conv1_2')(x) x = BatchNormalization()(x) orig_1 = x x = MaxPooling2D((2, 2), strides=(2, 2))(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(128, (3, 3), activation='relu', name='conv2_1')(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(128, (3, 3), activation='relu', name='conv2_2')(x) orig_2 = x x = MaxPooling2D((2, 2), strides=(2, 2))(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(256, (3, 3), activation='relu', name='conv3_1')(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(256, (3, 3), activation='relu', name='conv3_2')(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(256, (3, 3), activation='relu', name='conv3_3')(x) orig_3 = x x = MaxPooling2D((2, 2), strides=(2, 2))(x) inputs_size = x.get_shape()[1:3] conv_4_1x1 = Conv2D(512, (1, 1), activation='relu', padding='same', name='conv4_1x1')(x) conv_4_3x3_1 = Conv2D(512, (3, 3), activation='relu', padding='same', dilation_rate=ATROUS_RATES[0], name='conv4_3x3_1')(x) conv_4_3x3_2 = Conv2D(512, (3, 3), activation='relu', padding='same', dilation_rate=ATROUS_RATES[1], name='conv4_3x3_2')(x) conv_4_3x3_3 = Conv2D(512, (3, 3), activation='relu', padding='same', dilation_rate=ATROUS_RATES[2], name='conv4_3x3_3')(x) image_level_features = Lambda(lambda x: tf.reduce_mean(x, [1, 2], keepdims=True), name='global_average_pooling')(x) image_level_features = Conv2D(512, (1, 1), activation='relu', padding='same', name='image_level_features_conv_1x1')(image_level_features) image_level_features = Lambda(lambda x: tf.image.resize(x, inputs_size), name='upsample_1')(image_level_features) x = Concatenate(axis=3)([conv_4_1x1, conv_4_3x3_1, conv_4_3x3_2, conv_4_3x3_3, image_level_features]) x = Conv2D(512, (1,1), activation='relu', padding='same', name='conv_1x1_1_concat')(x) x = Conv2D(512, (1,1), activation='relu', padding='same', name='conv_1x1_2_concat')(x) orig_4 = x x = MaxPooling2D((2, 2), strides=(2, 2))(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(512, (3, 3), activation='relu', name='conv5_1')(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(512, (3, 3), activation='relu', name='conv5_2')(x) x = ZeroPadding2D((1, 1))(x) x = Conv2D(512, (3, 3), activation='relu', name='conv5_3')(x) orig_5 = x x = MaxPooling2D((2, 2), strides=(2, 2))(x) x = UpSampling2D(size=(2, 2))(x) the_shape = K.int_shape(orig_5) shape = (1, the_shape[1], the_shape[2], the_shape[3]) origReshaped = Reshape(shape)(orig_5) xReshaped = Reshape(shape)(x) together = Concatenate(axis=1)([origReshaped, xReshaped]) x = Unpooling()(together) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='deconv5_1', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='deconv5_2', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='deconv5_3', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = UpSampling2D(size=(2, 2))(x) the_shape = K.int_shape(orig_4) shape = (1, the_shape[1], the_shape[2], the_shape[3]) origReshaped = Reshape(shape)(orig_4) xReshaped = Reshape(shape)(x) together = Concatenate(axis=1)([origReshaped, xReshaped]) x = Unpooling()(together) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='deconv4_1', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='deconv4_2', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='deconv4_3', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = UpSampling2D(size=(2, 2))(x) the_shape = K.int_shape(orig_3) shape = (1, the_shape[1], the_shape[2], the_shape[3]) origReshaped = Reshape(shape)(orig_3) xReshaped = Reshape(shape)(x) together = Concatenate(axis=1)([origReshaped, xReshaped]) x = Unpooling()(together) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='deconv3_1', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='deconv3_2', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='deconv3_3', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = UpSampling2D(size=(2, 2))(x) the_shape = K.int_shape(orig_2) shape = (1, the_shape[1], the_shape[2], the_shape[3]) origReshaped = Reshape(shape)(orig_2) xReshaped = Reshape(shape)(x) together = Concatenate(axis=1)([origReshaped, xReshaped]) x = Unpooling()(together) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='deconv2_1', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='deconv2_2', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = UpSampling2D(size=(2, 2))(x) the_shape = K.int_shape(orig_1) shape = (1, the_shape[1], the_shape[2], the_shape[3]) origReshaped = Reshape(shape)(orig_1) xReshaped = Reshape(shape)(x) together = Concatenate(axis=1)([origReshaped, xReshaped]) x = Unpooling()(together) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='deconv1_1', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='deconv1_2', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(1, (3, 3), activation='sigmoid', padding='same', name='pred', kernel_initializer='he_normal', bias_initializer='zeros')(x) model = Model(inputs=input_tensor, outputs=x) return model def build_refinement(encoder_decoder): input_tensor = encoder_decoder.input input = Lambda(lambda i: i[:, :, :, 0:3])(input_tensor) x = Concatenate(axis=3)([input, encoder_decoder.output]) x = Conv2D(64, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(64, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(64, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', bias_initializer='zeros')(x) x = BatchNormalization()(x) x = Conv2D(1, (3, 3), activation='sigmoid', padding='same', name='refinement_pred', kernel_initializer='he_normal', bias_initializer='zeros')(x) model = Model(inputs=input_tensor, outputs=x) return model if __name__ == '__main__': with tf.device("/cpu:0"): encoder_decoder = build_encoder_decoder() print(encoder_decoder.summary()) plot_model(encoder_decoder, to_file='encoder_decoder.svg', show_layer_names=True, show_shapes=True) with tf.device("/cpu:0"): refinement = build_refinement(encoder_decoder) print(refinement.summary()) plot_model(refinement, to_file='refinement.svg', show_layer_names=True, show_shapes=True) parallel_model = multi_gpu_model(refinement, gpus=None) print(parallel_model.summary()) plot_model(parallel_model, to_file='parallel_model.svg', show_layer_names=True, show_shapes=True) K.clear_session()

true

790dc17972f0343866fcf1c4348d39c89d4aaf8e

358

py

Python

app/recipe/urls.py

Webins/recipe-app-api

9972d634e3d09969331d48180b6ae24e1dee3d6b

[ "MIT" ]

1

2020-07-22T16:29:21.000Z

app/recipe/urls.py

Webins/recipe-app-api

9972d634e3d09969331d48180b6ae24e1dee3d6b

[ "MIT" ]

null

app/recipe/urls.py

Webins/recipe-app-api

9972d634e3d09969331d48180b6ae24e1dee3d6b

[ "MIT" ]

null

from django.urls import path, include from rest_framework.routers import DefaultRouter from recipe import views router = DefaultRouter() router.register('tags', views.TagViewSet) router.register('ingredients', views.IngredientViewSet) router.register('recipe', views.RecipeViewSet) app_name = 'recipe' urlpatterns = [ path('', include(router.urls)) ]

23.866667

55

0.77933

from django.urls import path, include from rest_framework.routers import DefaultRouter from recipe import views router = DefaultRouter() router.register('tags', views.TagViewSet) router.register('ingredients', views.IngredientViewSet) router.register('recipe', views.RecipeViewSet) app_name = 'recipe' urlpatterns = [ path('', include(router.urls)) ]

true

790dc1ec4b631e70e3b7f9f8d7c83a86604e4e4a

4,903

py

Python

src/octopus/image/source.py

gar-syn/congo-lab

dc50af4e35903556bc8bc34dc23a7a708c1f5422

[ "MIT" ]

1

2021-02-02T11:27:25.000Z

src/octopus/image/source.py

gar-syn/congo-lab

dc50af4e35903556bc8bc34dc23a7a708c1f5422

[ "MIT" ]

18

2021-02-01T11:35:15.000Z

2021-08-03T14:23:38.000Z

src/octopus/image/source.py

gar-syn/congo-lab

dc50af4e35903556bc8bc34dc23a7a708c1f5422

[ "MIT" ]

null

# System Imports import cv2 import json from typing import Optional # Library imports import numpy # Twisted Import from twisted.internet import reactor, defer, threads, protocol from twisted.internet.endpoints import TCP4ClientEndpoint from twisted.internet.interfaces import IAddress # Package Imports from .data import Image, ColorSpace class cv_webcam (object): def __init__ (self, device, img_width, img_height): self.device_index = device self.img_width = img_width self.img_height = img_height self.name = "cv_webcam(%s)" % device self.camera = None @defer.inlineCallbacks def connect (self, _protocolFactory): if self.camera is None: self.camera = yield threads.deferToThread(cv2.VideoCapture, self.device_index) # Set picture capture dimensions self.camera.set(3, self.img_width) self.camera.set(4, self.img_height) defer.returnValue(self) @defer.inlineCallbacks def image (self): """ Get an image from the camera. Returns an Image object. """ try: flag, img_array = yield threads.deferToThread(self.camera.read) except SystemError: return if flag is False: print ("No image") return defer.returnValue(Image(img_array, ColorSpace.BGR)) def disconnect (self): threads.deferToThread(self.camera.release) class _camera_proxy_protocol (protocol.Protocol): _state: str _buffer: bytes = b'' _image_callback: Optional[defer.Deferred] = None _camera_id: Optional[bytes] = None def setCameraId(self, camera_id: int): self._camera_id = str(camera_id).encode() self.requestFormat() # def connectionMade(self): # if self._camera_id is not None: # self.requestFormat() def dataReceived(self, data: bytes): """ Byte 1: command Byte 2-5: length Byte 6+: data """ self._buffer += data if len(self._buffer) > 5: command = chr(self._buffer[0]) length = int.from_bytes(self._buffer[1:5], byteorder = 'big') if len(self._buffer) >= length + 5: data = self._buffer[5 : 5 + length] self._buffer = self._buffer[5 + length : ] if command == 'F': self.formatReceived(data) elif command == 'I': self.imageReceived(data) def formatReceived (self, data: bytes): image_format = json.loads(data.decode()) if image_format['channels'] == 1: self._image_shape = (image_format['height'], image_format['width']) else: self._image_shape = ( image_format['height'], image_format['width'], image_format['channels'] ) self._image_colorspace = image_format['colorspace'] def imageReceived (self, data: bytes): try: img_data = numpy.reshape( numpy.frombuffer(data, dtype = numpy.uint8), newshape = self._image_shape ) self._image_callback.callback(img_data) except (AttributeError, defer.AlreadyCalledError) as e: # No callback, or callback already done. (Unexpected image data). pass except Exception as e: try: self._image_callback.errback(e) except defer.AlreadyCalledError: pass def requestFormat (self): self.transport.write(b'F' + self._camera_id + b'\n') def requestImage (self): self._image_callback = defer.Deferred() self.transport.write(b'I' + self._camera_id + b'\n') return self._image_callback class camera_proxy (object): def __init__ (self, host, port, camera_id): self.point = TCP4ClientEndpoint(reactor, host, port) self.name = f"camera_proxy({host!s}, {port!s})" self.camera_id = camera_id @defer.inlineCallbacks def connect (self, _protocolFactory): self._protocol = yield self.point.connect( protocol.Factory.forProtocol(_camera_proxy_protocol) ) self._protocol.setCameraId(self.camera_id) # yield self._protocol._get_format_information() defer.returnValue(self) @defer.inlineCallbacks def image (self): """ Get an image from the camera. Returns a SimpleCV Image. """ try: img_array = yield self._protocol.requestImage() except Exception as e: print('Exception fetching image', e) return defer.returnValue(Image(img_array, ColorSpace.BGR)) def disconnect (self): threads.deferToThread(self.camera.release)

29.011834

90

0.598409

import cv2 import json from typing import Optional import numpy from twisted.internet import reactor, defer, threads, protocol from twisted.internet.endpoints import TCP4ClientEndpoint from twisted.internet.interfaces import IAddress from .data import Image, ColorSpace class cv_webcam (object): def __init__ (self, device, img_width, img_height): self.device_index = device self.img_width = img_width self.img_height = img_height self.name = "cv_webcam(%s)" % device self.camera = None @defer.inlineCallbacks def connect (self, _protocolFactory): if self.camera is None: self.camera = yield threads.deferToThread(cv2.VideoCapture, self.device_index) self.camera.set(3, self.img_width) self.camera.set(4, self.img_height) defer.returnValue(self) @defer.inlineCallbacks def image (self): try: flag, img_array = yield threads.deferToThread(self.camera.read) except SystemError: return if flag is False: print ("No image") return defer.returnValue(Image(img_array, ColorSpace.BGR)) def disconnect (self): threads.deferToThread(self.camera.release) class _camera_proxy_protocol (protocol.Protocol): _state: str _buffer: bytes = b'' _image_callback: Optional[defer.Deferred] = None _camera_id: Optional[bytes] = None def setCameraId(self, camera_id: int): self._camera_id = str(camera_id).encode() self.requestFormat() def dataReceived(self, data: bytes): self._buffer += data if len(self._buffer) > 5: command = chr(self._buffer[0]) length = int.from_bytes(self._buffer[1:5], byteorder = 'big') if len(self._buffer) >= length + 5: data = self._buffer[5 : 5 + length] self._buffer = self._buffer[5 + length : ] if command == 'F': self.formatReceived(data) elif command == 'I': self.imageReceived(data) def formatReceived (self, data: bytes): image_format = json.loads(data.decode()) if image_format['channels'] == 1: self._image_shape = (image_format['height'], image_format['width']) else: self._image_shape = ( image_format['height'], image_format['width'], image_format['channels'] ) self._image_colorspace = image_format['colorspace'] def imageReceived (self, data: bytes): try: img_data = numpy.reshape( numpy.frombuffer(data, dtype = numpy.uint8), newshape = self._image_shape ) self._image_callback.callback(img_data) except (AttributeError, defer.AlreadyCalledError) as e: pass except Exception as e: try: self._image_callback.errback(e) except defer.AlreadyCalledError: pass def requestFormat (self): self.transport.write(b'F' + self._camera_id + b'\n') def requestImage (self): self._image_callback = defer.Deferred() self.transport.write(b'I' + self._camera_id + b'\n') return self._image_callback class camera_proxy (object): def __init__ (self, host, port, camera_id): self.point = TCP4ClientEndpoint(reactor, host, port) self.name = f"camera_proxy({host!s}, {port!s})" self.camera_id = camera_id @defer.inlineCallbacks def connect (self, _protocolFactory): self._protocol = yield self.point.connect( protocol.Factory.forProtocol(_camera_proxy_protocol) ) self._protocol.setCameraId(self.camera_id) defer.returnValue(self) @defer.inlineCallbacks def image (self): try: img_array = yield self._protocol.requestImage() except Exception as e: print('Exception fetching image', e) return defer.returnValue(Image(img_array, ColorSpace.BGR)) def disconnect (self): threads.deferToThread(self.camera.release)

true

790dc1f7c437946d2beb40380f654fcc078627c4

742

py

Python

projects/g3h2-algorithm/practice1/4.py

keybrl/xdu-coursework

9d0e905bef28c18d87d3b97643de0d32f9f08ee0

[ "MIT" ]

null

projects/g3h2-algorithm/practice1/4.py

keybrl/xdu-coursework

9d0e905bef28c18d87d3b97643de0d32f9f08ee0

[ "MIT" ]

null

projects/g3h2-algorithm/practice1/4.py

keybrl/xdu-coursework

9d0e905bef28c18d87d3b97643de0d32f9f08ee0

[ "MIT" ]

null

def get_the_ith_largest(s1: list, s2: list, i: int): m = len(s1) n = len(s2) if i > m + n: raise IndexError('list index out of range') i -= 1 l1 = 0 r1 = i if m - 1 >= i else m - 1 while l1 <= r1: c1 = (l1 + r1) // 2 c1_f = i - c1 - 1 c1_b = i - c1 if c1_f >= 0 and (c1_f >= n or s2[c1_f] > s1[c1]): l1 = c1 + 1 elif 0 <= c1_b < n and s2[c1_b] < s1[c1]: r1 = c1 - 1 else: return s1[c1] return get_the_ith_largest(s2, s1, i + 1) if __name__ == '__main__': s1_test = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] s2_test = [2, 3, 4, 6, 10, 20, 100] print(get_the_ith_largest(s2_test, s1_test, 8))

25.586207

65

0.467655

def get_the_ith_largest(s1: list, s2: list, i: int): m = len(s1) n = len(s2) if i > m + n: raise IndexError('list index out of range') i -= 1 l1 = 0 r1 = i if m - 1 >= i else m - 1 while l1 <= r1: c1 = (l1 + r1) // 2 c1_f = i - c1 - 1 c1_b = i - c1 if c1_f >= 0 and (c1_f >= n or s2[c1_f] > s1[c1]): l1 = c1 + 1 elif 0 <= c1_b < n and s2[c1_b] < s1[c1]: r1 = c1 - 1 else: return s1[c1] return get_the_ith_largest(s2, s1, i + 1) if __name__ == '__main__': s1_test = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] s2_test = [2, 3, 4, 6, 10, 20, 100] print(get_the_ith_largest(s2_test, s1_test, 8))

true

790dc2d561a18c3dd73fe88534f970fb94f4432e

2,498

py

Python

UMLRT2Kiltera_MM/Properties/from_thesis/HMM10_then1_IsolatedLHS.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

[ "MIT" ]

3

2017-06-02T19:26:27.000Z

2021-06-14T04:25:45.000Z

UMLRT2Kiltera_MM/Properties/from_thesis/HMM10_then1_IsolatedLHS.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

[ "MIT" ]

8

2016-08-24T07:04:07.000Z

2017-05-26T16:22:47.000Z

UMLRT2Kiltera_MM/Properties/from_thesis/HMM10_then1_IsolatedLHS.py

levilucio/SyVOLT

7526ec794d21565e3efcc925a7b08ae8db27d46a

[ "MIT" ]

1

2019-10-31T06:00:23.000Z

from core.himesis import Himesis, HimesisPreConditionPatternLHS import uuid class HMM10_then1_IsolatedLHS(HimesisPreConditionPatternLHS): def __init__(self): """ Creates the himesis graph representing the AToM3 model HMM10_then1_IsolatedLHS. """ # Flag this instance as compiled now self.is_compiled = True super(HMM10_then1_IsolatedLHS, self).__init__(name='HMM10_then1_IsolatedLHS', num_nodes=0, edges=[]) # Add the edges self.add_edges([]) # Set the graph attributes self["mm__"] = ['MT_pre__FamiliesToPersonsMM', 'MoTifRule'] self["MT_constraint__"] = """#=============================================================================== # This code is executed after the nodes in the LHS have been matched. # You can access a matched node labelled n by: PreNode('n'). # To access attribute x of node n, use: PreNode('n')['x']. # The given constraint must evaluate to a boolean expression: # returning True enables the rule to be applied, # returning False forbids the rule from being applied. #=============================================================================== return True """ self["name"] = """""" self["GUID__"] = uuid.uuid3(uuid.NAMESPACE_DNS,'MM10_then1') # Set the node attributes # Add the attribute equations self["equations"] = [] def constraint(self, PreNode, graph): """ Executable constraint code. @param PreNode: Function taking an integer as parameter and returns the node corresponding to that label. """ #=============================================================================== # This code is executed after the nodes in the LHS have been matched. # You can access a matched node labelled n by: PreNode('n'). # To access attribute x of node n, use: PreNode('n')['x']. # The given constraint must evaluate to a boolean expression: # returning True enables the rule to be applied, # returning False forbids the rule from being applied. #=============================================================================== return True

43.068966

125

0.5004

from core.himesis import Himesis, HimesisPreConditionPatternLHS import uuid class HMM10_then1_IsolatedLHS(HimesisPreConditionPatternLHS): def __init__(self): self.is_compiled = True super(HMM10_then1_IsolatedLHS, self).__init__(name='HMM10_then1_IsolatedLHS', num_nodes=0, edges=[]) self.add_edges([]) self["mm__"] = ['MT_pre__FamiliesToPersonsMM', 'MoTifRule'] self["MT_constraint__"] = """#=============================================================================== # This code is executed after the nodes in the LHS have been matched. # You can access a matched node labelled n by: PreNode('n'). # To access attribute x of node n, use: PreNode('n')['x']. # The given constraint must evaluate to a boolean expression: # returning True enables the rule to be applied, # returning False forbids the rule from being applied. #=============================================================================== return True """ self["name"] = """""" self["GUID__"] = uuid.uuid3(uuid.NAMESPACE_DNS,'MM10_then1') self["equations"] = [] def constraint(self, PreNode, graph): return True

true

790dc41206388cb2bb9037ec3e469e9e586a2a2e

14,501

py

Python

libai/data/datasets/bert_dataset.py

Oneflow-Inc/libai

e473bd3962f07b1e37232d2be39c8257df0ec0f3

[ "Apache-2.0" ]

55

2021-12-10T08:47:06.000Z

2022-03-28T09:02:15.000Z

libai/data/datasets/bert_dataset.py

Oneflow-Inc/libai

e473bd3962f07b1e37232d2be39c8257df0ec0f3

[ "Apache-2.0" ]

106

2021-11-03T05:16:45.000Z

2022-03-31T06:16:23.000Z

libai/data/datasets/bert_dataset.py

Oneflow-Inc/libai

e473bd3962f07b1e37232d2be39c8257df0ec0f3

[ "Apache-2.0" ]

13

2021-12-29T08:12:08.000Z

2022-03-28T06:59:45.000Z

# coding=utf-8 # Copyright 2021 The OneFlow Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """dataset for bert.""" import collections import math import numpy as np import oneflow as flow from libai.data.data_utils import SentenceIndexedDataset from libai.data.structures import DistTensorData, Instance MaskedLmInstance = collections.namedtuple("MaskedLmInstance", ["index", "label"]) def is_start_piece(piece): """Check if the current word piece is the starting piece (BERT).""" # When a word has been split into # WordPieces, the first token does not have any marker and any subsequence # tokens are prefixed with ##. So whenever we see the ## token, we # append it to the previous set of word indexes. return not piece.startswith("##") class BertDataset(flow.utils.data.Dataset): """Dataset containing sentence pairs for BERT training. Each index corresponds to a randomly generated sentence pair. Args: tokenizer: Tokenizer to use. data_prefix: Path to the training dataset. indexed_dataset: Indexed dataset to use. max_seq_length: Maximum length of the sequence. All values are padded to this length. Defaults to 512. mask_lm_prob: Probability to mask tokens. Defaults to 0.15. short_seq_prob: Probability of producing a short sequence. Defaults to 0.0. max_preds_per_seq: Maximum number of mask tokens in each sentence. Defaults to None. seed: Seed for random number generator for reproducibility. Defaults to 1234. binary_head: Specifies whether the underlying dataset generates a pair of blocks along with a sentence_target or not. Setting it to True assumes that the underlying dataset generates a label for the pair of sentences which is surfaced as sentence_target. Defaults to True. """ def __init__( self, tokenizer, data_prefix, indexed_dataset, max_seq_length=512, mask_lm_prob=0.15, short_seq_prob=0.0, max_preds_per_seq=None, seed=1234, binary_head=True, ): self.seed = seed self.mask_lm_prob = mask_lm_prob self.max_seq_length = max_seq_length self.short_seq_prob = short_seq_prob self.binary_head = binary_head if max_preds_per_seq is None: max_preds_per_seq = math.ceil(max_seq_length * mask_lm_prob / 10) * 10 self.max_preds_per_seq = max_preds_per_seq self.dataset = SentenceIndexedDataset( data_prefix, indexed_dataset, max_seq_length=self.max_seq_length - 3, short_seq_prob=self.short_seq_prob, binary_head=self.binary_head, ) self.tokenizer = tokenizer self.vocab_id_list = list(tokenizer.get_vocab().values()) self.cls_id = tokenizer.cls_token_id self.sep_id = tokenizer.sep_token_id self.mask_id = tokenizer.mask_token_id self.pad_id = tokenizer.pad_token_id def __len__(self): return len(self.dataset) def __getitem__(self, idx): # Note that this rng state should be numpy and not python since # python randint is inclusive whereas the numpy one is exclusive. np_rng = np.random.RandomState(seed=(self.seed + idx)) sents = self.dataset[idx] if self.binary_head: tokens_a, tokens_b, is_next_random = self.create_random_sentence_pair(sents, np_rng) else: tokens_a = [] for j in range(len(sents)): tokens_a.extend(sents[j]) tokens_b = [] is_next_random = False tokens_a, tokens_b = self.truncate_seq_pair( tokens_a, tokens_b, self.max_seq_length - 3, np_rng ) tokens, token_types = self.create_tokens_and_token_types(tokens_a, tokens_b) tokens, masked_positions, masked_labels = self.create_masked_lm_predictions(tokens, np_rng) ( tokens, token_types, labels, padding_mask, loss_mask, ) = self.pad_and_convert_to_tensor(tokens, token_types, masked_positions, masked_labels) sample = Instance( input_ids=DistTensorData(tokens), attention_mask=DistTensorData(padding_mask), tokentype_ids=DistTensorData(token_types), ns_labels=DistTensorData( flow.tensor(int(is_next_random), dtype=flow.long), placement_idx=-1 ), lm_labels=DistTensorData(labels, placement_idx=-1), loss_mask=DistTensorData(loss_mask, placement_idx=-1), ) return sample def create_random_sentence_pair(self, sample, np_rng): num_sentences = len(sample) assert num_sentences > 1, "make sure each sample has at least two sentences." a_end = 1 if num_sentences >= 3: a_end = np_rng.randint(1, num_sentences) tokens_a = [] for j in range(a_end): tokens_a.extend(sample[j]) tokens_b = [] for j in range(a_end, num_sentences): tokens_b.extend(sample[j]) is_next_random = False if np_rng.random() < 0.5: is_next_random = True tokens_a, tokens_b = tokens_b, tokens_a return tokens_a, tokens_b, is_next_random def truncate_seq_pair(self, tokens_a, tokens_b, max_num_tokens, np_rng): """Truncate sequence pair to a maximum sequence length.""" len_a, len_b = len(tokens_a), len(tokens_b) while True: total_length = len_a + len_b if total_length <= max_num_tokens: break if len_a > len_b: trunc_tokens = tokens_a len_a -= 1 else: trunc_tokens = tokens_b len_b -= 1 if np_rng.random() < 0.5: trunc_tokens.pop(0) # remove the first element else: trunc_tokens.pop() # remove the last element return tokens_a, tokens_b def create_tokens_and_token_types(self, tokens_a, tokens_b): """Merge segments A and B, add [CLS] and [SEP] and build token types.""" tokens = [self.cls_id] + tokens_a + [self.sep_id] token_types = [0] * (len(tokens_a) + 2) if len(tokens_b) > 0: tokens = tokens + tokens_b + [self.sep_id] token_types = token_types + [1] * (len(tokens_b) + 1) return tokens, token_types def mask_token(self, idx, tokens, np_rng): """ Helper function to mask `idx` token from `tokens` according to section 3.3.1 of https://arxiv.org/pdf/1810.04805.pdf """ label = tokens[idx] if np_rng.random() < 0.8: new_label = self.mask_id else: if np_rng.random() < 0.5: new_label = label else: new_label = np_rng.choice(self.vocab_id_list) tokens[idx] = new_label return label def create_masked_lm_predictions( self, tokens, np_rng, max_ngrams=3, do_whole_word_mask=True, favor_longer_ngram=False, geometric_dist=False, ): """Creates the predictions for the masked LM objective. Note: Tokens here are vocab ids and not text tokens.""" cand_indexes = [] token_boundary = [0] * len(tokens) new_tokens = [] for (i, token) in enumerate(tokens): new_tokens.append(token % len(self.tokenizer)) if token == self.cls_id or token == self.sep_id: token_boundary[i] = 1 continue # Whole Word Masking means that if we mask all of the wordpieces # corresponding to an original word. # # Note that Whole Word Masking does *not* change the training code # at all -- we still predict each WordPiece independently, softmaxed # over the entire vocabulary. if ( do_whole_word_mask and len(cand_indexes) >= 1 and not is_start_piece(self.tokenizer._convert_id_to_token(token)) ): cand_indexes[-1].append(i) else: cand_indexes.append([i]) if is_start_piece(self.tokenizer._convert_id_to_token(token)): token_boundary[i] = 1 tokens = new_tokens masked_positions = [] masked_labels = [] output_tokens = list(tokens) if self.mask_lm_prob == 0: return output_tokens, masked_positions, masked_labels cand_indexes = [] for (i, token) in enumerate(tokens): if token == self.cls_id or token == self.sep_id: continue # Whole Word Masking means that if we mask all of the wordpieces # corresponding to an original word. # # Note that Whole Word Masking does *not* change the training code # at all -- we still predict each WordPiece independently, softmaxed # over the entire vocabulary. if do_whole_word_mask and len(cand_indexes) >= 1 and token_boundary[i] == 0: cand_indexes[-1].append(i) else: cand_indexes.append([i]) num_to_predict = min( self.max_preds_per_seq, max(1, int(round(len(tokens) * self.mask_lm_prob))) ) ngrams = np.arange(1, max_ngrams + 1, dtype=np.int64) if not geometric_dist: # By default, we set the probilities to favor shorter ngram sequences. pvals = 1.0 / np.arange(1, max_ngrams + 1) pvals /= pvals.sum(keepdims=True) if favor_longer_ngram: pvals = pvals[::-1] ngram_indexes = [] for idx in range(len(cand_indexes)): ngram_index = [] for n in ngrams: ngram_index.append(cand_indexes[idx : idx + n]) ngram_indexes.append(ngram_index) np_rng.shuffle(ngram_indexes) masked_lms = [] covered_indexes = set() for cand_index_set in ngram_indexes: if len(masked_lms) >= num_to_predict: break if not cand_index_set: continue # Skip current piece if they are covered in lm masking or previous ngrams. for index_set in cand_index_set[0]: for index in index_set: if index in covered_indexes: continue if not geometric_dist: n = np_rng.choice( ngrams[: len(cand_index_set)], p=pvals[: len(cand_index_set)] / pvals[: len(cand_index_set)].sum(keepdims=True), ) else: # Sampling "n" from the geometric distribution and clipping it to # the max_ngrams. Using p=0.2 default from the SpanBERT paper # https://arxiv.org/pdf/1907.10529.pdf (Sec 3.1) n = min(np_rng.geometric(0.2), max_ngrams) index_set = sum(cand_index_set[n - 1], []) n -= 1 # Repeatedly looking for a candidate that does not exceed the # maximum number of predictions by trying shorter ngrams. while len(masked_lms) + len(index_set) > num_to_predict: if n == 0: break index_set = sum(cand_index_set[n - 1], []) n -= 1 # If adding a whole-word mask would exceed the maximum number of # predictions, then just skip this candidate. if len(masked_lms) + len(index_set) > num_to_predict: continue is_any_index_covered = False for index in index_set: if index in covered_indexes: is_any_index_covered = True break if is_any_index_covered: continue for index in index_set: covered_indexes.add(index) label = self.mask_token(index, output_tokens, np_rng) masked_lms.append(MaskedLmInstance(index=index, label=label)) masked_lms = sorted(masked_lms, key=lambda x: x.index) for p in masked_lms: masked_positions.append(p.index) masked_labels.append(p.label) return output_tokens, masked_positions, masked_labels def pad_and_convert_to_tensor(self, tokens, token_types, masked_positions, masked_labels): """Pad sequences and convert them to tensor.""" # check num_tokens = len(tokens) num_pad = self.max_seq_length - num_tokens assert num_pad >= 0 assert len(token_types) == num_tokens assert len(masked_positions) == len(masked_labels) # tokens and token types filler = [self.pad_id] * num_pad tokens = flow.tensor(tokens + filler, dtype=flow.long) token_types = flow.tensor(token_types + filler, dtype=flow.long) # padding mask padding_mask = flow.tensor([1] * num_tokens + [0] * num_pad, dtype=flow.long) # labels and loss mask labels = [-1] * self.max_seq_length loss_mask = [0] * self.max_seq_length for idx, label in zip(masked_positions, masked_labels): assert idx < num_tokens labels[idx] = label loss_mask[idx] = 1 labels = flow.tensor(labels, dtype=flow.long) loss_mask = flow.tensor(loss_mask, dtype=flow.long) return tokens, token_types, labels, padding_mask, loss_mask @property def supports_prefetch(self): return self.dataset.supports_prefetch def prefetch(self, indices): self.dataset.prefetch(indices)

36.804569

99

0.603614

import collections import math import numpy as np import oneflow as flow from libai.data.data_utils import SentenceIndexedDataset from libai.data.structures import DistTensorData, Instance MaskedLmInstance = collections.namedtuple("MaskedLmInstance", ["index", "label"]) def is_start_piece(piece): _init__( self, tokenizer, data_prefix, indexed_dataset, max_seq_length=512, mask_lm_prob=0.15, short_seq_prob=0.0, max_preds_per_seq=None, seed=1234, binary_head=True, ): self.seed = seed self.mask_lm_prob = mask_lm_prob self.max_seq_length = max_seq_length self.short_seq_prob = short_seq_prob self.binary_head = binary_head if max_preds_per_seq is None: max_preds_per_seq = math.ceil(max_seq_length * mask_lm_prob / 10) * 10 self.max_preds_per_seq = max_preds_per_seq self.dataset = SentenceIndexedDataset( data_prefix, indexed_dataset, max_seq_length=self.max_seq_length - 3, short_seq_prob=self.short_seq_prob, binary_head=self.binary_head, ) self.tokenizer = tokenizer self.vocab_id_list = list(tokenizer.get_vocab().values()) self.cls_id = tokenizer.cls_token_id self.sep_id = tokenizer.sep_token_id self.mask_id = tokenizer.mask_token_id self.pad_id = tokenizer.pad_token_id def __len__(self): return len(self.dataset) def __getitem__(self, idx): np_rng = np.random.RandomState(seed=(self.seed + idx)) sents = self.dataset[idx] if self.binary_head: tokens_a, tokens_b, is_next_random = self.create_random_sentence_pair(sents, np_rng) else: tokens_a = [] for j in range(len(sents)): tokens_a.extend(sents[j]) tokens_b = [] is_next_random = False tokens_a, tokens_b = self.truncate_seq_pair( tokens_a, tokens_b, self.max_seq_length - 3, np_rng ) tokens, token_types = self.create_tokens_and_token_types(tokens_a, tokens_b) tokens, masked_positions, masked_labels = self.create_masked_lm_predictions(tokens, np_rng) ( tokens, token_types, labels, padding_mask, loss_mask, ) = self.pad_and_convert_to_tensor(tokens, token_types, masked_positions, masked_labels) sample = Instance( input_ids=DistTensorData(tokens), attention_mask=DistTensorData(padding_mask), tokentype_ids=DistTensorData(token_types), ns_labels=DistTensorData( flow.tensor(int(is_next_random), dtype=flow.long), placement_idx=-1 ), lm_labels=DistTensorData(labels, placement_idx=-1), loss_mask=DistTensorData(loss_mask, placement_idx=-1), ) return sample def create_random_sentence_pair(self, sample, np_rng): num_sentences = len(sample) assert num_sentences > 1, "make sure each sample has at least two sentences." a_end = 1 if num_sentences >= 3: a_end = np_rng.randint(1, num_sentences) tokens_a = [] for j in range(a_end): tokens_a.extend(sample[j]) tokens_b = [] for j in range(a_end, num_sentences): tokens_b.extend(sample[j]) is_next_random = False if np_rng.random() < 0.5: is_next_random = True tokens_a, tokens_b = tokens_b, tokens_a return tokens_a, tokens_b, is_next_random def truncate_seq_pair(self, tokens_a, tokens_b, max_num_tokens, np_rng): len_a, len_b = len(tokens_a), len(tokens_b) while True: total_length = len_a + len_b if total_length <= max_num_tokens: break if len_a > len_b: trunc_tokens = tokens_a len_a -= 1 else: trunc_tokens = tokens_b len_b -= 1 if np_rng.random() < 0.5: trunc_tokens.pop(0) else: trunc_tokens.pop() return tokens_a, tokens_b def create_tokens_and_token_types(self, tokens_a, tokens_b): tokens = [self.cls_id] + tokens_a + [self.sep_id] token_types = [0] * (len(tokens_a) + 2) if len(tokens_b) > 0: tokens = tokens + tokens_b + [self.sep_id] token_types = token_types + [1] * (len(tokens_b) + 1) return tokens, token_types def mask_token(self, idx, tokens, np_rng): label = tokens[idx] if np_rng.random() < 0.8: new_label = self.mask_id else: if np_rng.random() < 0.5: new_label = label else: new_label = np_rng.choice(self.vocab_id_list) tokens[idx] = new_label return label def create_masked_lm_predictions( self, tokens, np_rng, max_ngrams=3, do_whole_word_mask=True, favor_longer_ngram=False, geometric_dist=False, ): cand_indexes = [] token_boundary = [0] * len(tokens) new_tokens = [] for (i, token) in enumerate(tokens): new_tokens.append(token % len(self.tokenizer)) if token == self.cls_id or token == self.sep_id: token_boundary[i] = 1 continue if ( do_whole_word_mask and len(cand_indexes) >= 1 and not is_start_piece(self.tokenizer._convert_id_to_token(token)) ): cand_indexes[-1].append(i) else: cand_indexes.append([i]) if is_start_piece(self.tokenizer._convert_id_to_token(token)): token_boundary[i] = 1 tokens = new_tokens masked_positions = [] masked_labels = [] output_tokens = list(tokens) if self.mask_lm_prob == 0: return output_tokens, masked_positions, masked_labels cand_indexes = [] for (i, token) in enumerate(tokens): if token == self.cls_id or token == self.sep_id: continue if do_whole_word_mask and len(cand_indexes) >= 1 and token_boundary[i] == 0: cand_indexes[-1].append(i) else: cand_indexes.append([i]) num_to_predict = min( self.max_preds_per_seq, max(1, int(round(len(tokens) * self.mask_lm_prob))) ) ngrams = np.arange(1, max_ngrams + 1, dtype=np.int64) if not geometric_dist: pvals = 1.0 / np.arange(1, max_ngrams + 1) pvals /= pvals.sum(keepdims=True) if favor_longer_ngram: pvals = pvals[::-1] ngram_indexes = [] for idx in range(len(cand_indexes)): ngram_index = [] for n in ngrams: ngram_index.append(cand_indexes[idx : idx + n]) ngram_indexes.append(ngram_index) np_rng.shuffle(ngram_indexes) masked_lms = [] covered_indexes = set() for cand_index_set in ngram_indexes: if len(masked_lms) >= num_to_predict: break if not cand_index_set: continue for index_set in cand_index_set[0]: for index in index_set: if index in covered_indexes: continue if not geometric_dist: n = np_rng.choice( ngrams[: len(cand_index_set)], p=pvals[: len(cand_index_set)] / pvals[: len(cand_index_set)].sum(keepdims=True), ) else: n = min(np_rng.geometric(0.2), max_ngrams) index_set = sum(cand_index_set[n - 1], []) n -= 1 while len(masked_lms) + len(index_set) > num_to_predict: if n == 0: break index_set = sum(cand_index_set[n - 1], []) n -= 1 if len(masked_lms) + len(index_set) > num_to_predict: continue is_any_index_covered = False for index in index_set: if index in covered_indexes: is_any_index_covered = True break if is_any_index_covered: continue for index in index_set: covered_indexes.add(index) label = self.mask_token(index, output_tokens, np_rng) masked_lms.append(MaskedLmInstance(index=index, label=label)) masked_lms = sorted(masked_lms, key=lambda x: x.index) for p in masked_lms: masked_positions.append(p.index) masked_labels.append(p.label) return output_tokens, masked_positions, masked_labels def pad_and_convert_to_tensor(self, tokens, token_types, masked_positions, masked_labels): num_tokens = len(tokens) num_pad = self.max_seq_length - num_tokens assert num_pad >= 0 assert len(token_types) == num_tokens assert len(masked_positions) == len(masked_labels) filler = [self.pad_id] * num_pad tokens = flow.tensor(tokens + filler, dtype=flow.long) token_types = flow.tensor(token_types + filler, dtype=flow.long) padding_mask = flow.tensor([1] * num_tokens + [0] * num_pad, dtype=flow.long) labels = [-1] * self.max_seq_length loss_mask = [0] * self.max_seq_length for idx, label in zip(masked_positions, masked_labels): assert idx < num_tokens labels[idx] = label loss_mask[idx] = 1 labels = flow.tensor(labels, dtype=flow.long) loss_mask = flow.tensor(loss_mask, dtype=flow.long) return tokens, token_types, labels, padding_mask, loss_mask @property def supports_prefetch(self): return self.dataset.supports_prefetch def prefetch(self, indices): self.dataset.prefetch(indices)

true

790dc4c70bdebe3d17d0f764a7a35b9714f96983

43,077

py

Python

pytorch_lightning/trainer/training_loop.py

neggert/pytorch-lightning

8208c330eb1a4e8cca243ee525882854dd366921

[ "Apache-2.0" ]

null

pytorch_lightning/trainer/training_loop.py

neggert/pytorch-lightning

8208c330eb1a4e8cca243ee525882854dd366921

[ "Apache-2.0" ]

null

pytorch_lightning/trainer/training_loop.py

neggert/pytorch-lightning

8208c330eb1a4e8cca243ee525882854dd366921

[ "Apache-2.0" ]

null

# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from contextlib import contextmanager, suppress from copy import copy, deepcopy from typing import Any, Dict, List, Optional, Union import numpy as np import torch from pytorch_lightning.core.optimizer import LightningOptimizer from pytorch_lightning.core.step_result import Result from pytorch_lightning.plugins import ParallelPlugin from pytorch_lightning.trainer.states import TrainerState from pytorch_lightning.trainer.supporters import TensorRunningAccum from pytorch_lightning.utilities import _TPU_AVAILABLE, AMPType, DeviceType from pytorch_lightning.utilities.distributed import rank_zero_info from pytorch_lightning.utilities.exceptions import MisconfigurationException from pytorch_lightning.utilities.finite_checks import detect_nan_parameters from pytorch_lightning.utilities.grads import grad_norm from pytorch_lightning.utilities.model_helpers import is_overridden from pytorch_lightning.utilities.parsing import AttributeDict from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature from pytorch_lightning.utilities.warnings import WarningCache class TrainLoop: def __init__(self, trainer, multiple_trainloader_mode: str): self.trainer = trainer self.accumulated_loss = None self.warning_cache = WarningCache() self._teardown_already_run = False self.running_loss = TensorRunningAccum(window_length=20) self._curr_step_result = None self._cur_grad_norm_dict = None self._multiple_trainloader_mode = multiple_trainloader_mode self._skip_backward = False self.trainer._multiple_trainloader_mode = multiple_trainloader_mode self._optimizer_freq_cumsum = None def on_trainer_init( self, max_epochs: Optional[int], min_epochs: Optional[int], max_steps: Optional[int], min_steps: Optional[int], num_sanity_val_steps: int, ) -> None: self.trainer.global_step = 0 self.trainer.current_epoch = 0 self.trainer.should_stop = False self.trainer.state = TrainerState() self.trainer.total_batch_idx = 0 self.trainer.batch_idx = 0 self.trainer.num_training_batches = 0 self.trainer.train_dataloader = None # If neither max_epochs or max_steps is set, then use existing default of max_epochs = 1000 self.trainer.max_epochs = 1000 if (max_epochs is None and max_steps is None) else max_epochs # If neither min_epochs or min_steps is set, then use existing default of min_epochs = 1 self.trainer.min_epochs = 1 if (min_epochs is None and min_steps is None) else min_epochs self.trainer.max_steps = max_steps self.trainer.min_steps = min_steps if num_sanity_val_steps == -1: self.trainer.num_sanity_val_steps = float("inf") else: self.trainer.num_sanity_val_steps = num_sanity_val_steps @property def num_optimizers(self): num_optimizers = len(self.get_optimizers_iterable()) return num_optimizers @property def optimizer_freq_cumsum(self): if self._optimizer_freq_cumsum is None: self._optimizer_freq_cumsum = np.cumsum(self.trainer.optimizer_frequencies) return self._optimizer_freq_cumsum def should_skip_training(self): should_by_max_steps = self.trainer.max_steps is not None and self.trainer.global_step >= self.trainer.max_steps should_by_epoch = self.trainer.max_epochs is not None and self.trainer.current_epoch >= self.trainer.max_epochs return should_by_max_steps or should_by_epoch or self.trainer.num_training_batches == 0 def on_train_start(self): # hook self.trainer.call_hook("on_train_start") def on_train_end(self): if self._teardown_already_run: return self._teardown_already_run = True # trigger checkpoint check. need to temporarily decrease the global step to avoid saving duplicates # when a checkpoint was saved at the last step self.trainer.global_step -= 1 self.check_checkpoint_callback(should_update=True, is_last=True) self.trainer.global_step += 1 # hook self.trainer.call_hook("on_train_end") # todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers. # It might be related to xla tensors blocked when moving the cpu # kill loggers if self.trainer.logger is not None: self.trainer.logger.finalize("success") # summarize profile results self.trainer.profiler.describe() # give accelerators a chance to finish self.trainer.accelerator.on_train_end() # reset bookkeeping self.trainer.state.stage = None def check_checkpoint_callback(self, should_update, is_last=False): # TODO bake this logic into the ModelCheckpoint callback if should_update and self.trainer.checkpoint_connector.has_trained: callbacks = self.trainer.checkpoint_callbacks if is_last and any(cb.save_last and cb.verbose for cb in callbacks): rank_zero_info("Saving latest checkpoint...") model = self.trainer.lightning_module for cb in callbacks: cb.on_validation_end(self.trainer, model) def on_train_epoch_start(self, epoch): # update training progress in trainer self.trainer.current_epoch = epoch model = self.trainer.lightning_module # reset train dataloader if epoch != 0 and self.trainer.reload_dataloaders_every_epoch: self.trainer.reset_train_dataloader(model) # todo: specify the possible exception with suppress(Exception): # set seed for distributed sampler (enables shuffling for each epoch) self.trainer.train_dataloader.sampler.set_epoch(epoch) # changing gradient according accumulation_scheduler self.trainer.accumulation_scheduler.on_train_epoch_start(self.trainer, self.trainer.lightning_module) # stores accumulated grad fractions per batch self.accumulated_loss = TensorRunningAccum(window_length=self.trainer.accumulate_grad_batches) # hook self.trainer.call_hook("on_epoch_start") self.trainer.call_hook("on_train_epoch_start") def on_train_batch_end(self, epoch_output, batch_end_outputs, batch, batch_idx, dataloader_idx): batch_end_outputs = [opt_idx_out for opt_idx_out in batch_end_outputs if len(opt_idx_out)] processed_batch_end_outputs = TrainLoop._prepare_outputs(batch_end_outputs, batch_mode=True) # hook self.trainer.call_hook('on_train_batch_end', processed_batch_end_outputs, batch, batch_idx, dataloader_idx) self.trainer.call_hook('on_batch_end') # figure out what to track for epoch end self.track_epoch_end_reduce_metrics(epoch_output, batch_end_outputs) # reset batch logger internals self.trainer.logger_connector.on_train_batch_end() def reset_train_val_dataloaders(self, model) -> None: """ Resets train and val dataloaders if none are attached to the trainer. The val dataloader must be initialized before training loop starts, as the training loop inspects the val dataloader to determine whether to run the evaluation loop. """ if self.trainer.train_dataloader is None: self.trainer.reset_train_dataloader(model) if self.trainer.val_dataloaders is None: self.trainer.reset_val_dataloader(model) def track_epoch_end_reduce_metrics(self, epoch_output, batch_end_outputs): hook_overridden = self._should_add_batch_output_to_epoch_output() # track the outputs to reduce at the end of the epoch for opt_idx, opt_outputs in enumerate(batch_end_outputs): sample_output = opt_outputs[-1] # decide if we need to reduce at the end of the epoch automatically auto_reduce_tng_result = isinstance(sample_output, Result) and sample_output.should_reduce_on_epoch_end # only track when a) it needs to be autoreduced OR b) the user wants to manually reduce on epoch end if not (hook_overridden or auto_reduce_tng_result): continue # with 1 step (no tbptt) don't use a sequence at epoch end if isinstance(opt_outputs, list) and len(opt_outputs) == 1 and not isinstance(opt_outputs[0], Result): opt_outputs = opt_outputs[0] epoch_output[opt_idx].append(opt_outputs) def _should_add_batch_output_to_epoch_output(self) -> bool: # We add to the epoch outputs if # 1. The model defines training_epoch_end OR # 2. The model overrides on_train_epoch_end which has `outputs` in the signature # TODO: in v1.5 this only needs to check if training_epoch_end is overridden lightning_module = self.trainer.lightning_module if is_overridden("training_epoch_end", model=lightning_module): return True if is_overridden("on_train_epoch_end", model=lightning_module): model_hook_fx = getattr(lightning_module, "on_train_epoch_end") if is_param_in_hook_signature(model_hook_fx, "outputs"): return True return False def get_optimizers_iterable(self, batch_idx=None): """ Generates an iterable with (idx, optimizer) for each optimizer. """ if not self.trainer.optimizer_frequencies: # call training_step once per optimizer return list(enumerate(self.trainer.optimizers)) if batch_idx is None: batch_idx = self.trainer.total_batch_idx optimizers_loop_length = self.optimizer_freq_cumsum[-1] current_place_in_loop = batch_idx % optimizers_loop_length # find optimzier index by looking for the first {item > current_place} in the cumsum list opt_idx = np.argmax(self.optimizer_freq_cumsum > current_place_in_loop) return [[opt_idx, self.trainer.optimizers[opt_idx]]] def on_after_backward(self, training_step_output, batch_idx, untouched_loss): training_step_output.detach() # insert after step hook self.trainer.call_hook("on_after_backward") # when in dev debugging track the losses self.trainer.dev_debugger.track_train_loss_history(batch_idx, untouched_loss.detach()) def _check_training_step_output(self, training_step_output): if isinstance(training_step_output, torch.Tensor) and not self.trainer.lightning_module.automatic_optimization: if training_step_output.grad_fn is None: # TODO: Find why - RuntimeError: Expected to mark a variable ready only once ... raise MisconfigurationException("In manual optimization, `training_step` should not return a Tensor") def training_step(self, split_batch, batch_idx, opt_idx, hiddens): # give the PL module a result for logging model_ref = self.trainer.lightning_module with self.trainer.profiler.profile("model_forward"): args = self.build_train_args(split_batch, batch_idx, opt_idx, hiddens) # manually capture logged metrics model_ref._current_fx_name = 'training_step' model_ref._results = Result() with self.trainer.profiler.profile("training_step"): training_step_output = self.trainer.accelerator.training_step(args) self.trainer.accelerator.post_training_step() self.trainer.logger_connector.cache_logged_metrics() self._check_training_step_output(training_step_output) training_step_output = self.trainer.call_hook("training_step_end", training_step_output) training_step_output_for_epoch_end, training_step_output = self._process_training_step_output( training_step_output, split_batch ) if training_step_output_for_epoch_end is None: return # enable empty loss when using manual opt closure_loss = None untouched_loss = None if self.trainer.lightning_module.automatic_optimization: # accumulate loss. if accumulate_grad_batches==1, no effect closure_loss = training_step_output.minimize / self.trainer.accumulate_grad_batches # the loss will get scaled for amp. avoid any modifications to it untouched_loss = closure_loss.detach().clone() # result result = AttributeDict( closure_loss=closure_loss, loss=untouched_loss, training_step_output=training_step_output, training_step_output_for_epoch_end=training_step_output_for_epoch_end, ) return result def _process_training_step_output(self, training_step_output, split_batch): training_step_output_for_epoch_end = training_step_output # enable validation_step return None if training_step_output_for_epoch_end is None: return None, None result = self.trainer.lightning_module._results loss = None hiddens = None result["extra"] = {} # handle dict return if isinstance(training_step_output, dict): loss = training_step_output.pop("loss", None) hiddens = training_step_output.pop("hiddens", None) if hiddens is not None: hiddens = hiddens.detach() result["extra"] = training_step_output # handle scalar return elif isinstance(training_step_output, torch.Tensor): loss = training_step_output # map to results under the hood result.minimize = loss self.trainer.hiddens = hiddens # track batch for manual reduction with result result.track_batch_size(len(split_batch)) # track metrics without grads for epoch reduction training_step_output_for_epoch_end = copy(result) training_step_output_for_epoch_end = training_step_output_for_epoch_end.detach() if self.trainer.move_metrics_to_cpu: training_step_output_for_epoch_end = training_step_output_for_epoch_end.cpu() return training_step_output_for_epoch_end, result @staticmethod def _prepare_outputs( outputs: List[List[List[Result]]], batch_mode: bool, ) -> Union[List[List[List[Dict]]], List[List[Dict]], List[Dict], Dict]: """ Extract required information from batch or epoch end results. Args: outputs: A 3-dimensional list of ``Result`` objects with dimensions: [optimizer outs][batch outs][tbptt steps]. batch_mode: If True, ignore the batch output dimension. Returns: The cleaned outputs with ``Result`` objects converted to dictionaries. All list dimensions of size one will be collapsed. """ processed_outputs = [] for opt_outputs in outputs: # handle an edge case where an optimizer output is the empty list if len(opt_outputs) == 0: continue processed_batch_outputs = [] if batch_mode: opt_outputs = [opt_outputs] for batch_outputs in opt_outputs: processed_tbptt_outputs = [] for tbptt_output in batch_outputs: out = tbptt_output.extra out['loss'] = tbptt_output.minimize processed_tbptt_outputs.append(out) # if there was only one tbptt step then we can collapse that dimension if len(processed_tbptt_outputs) == 1: processed_tbptt_outputs = processed_tbptt_outputs[0] processed_batch_outputs.append(processed_tbptt_outputs) # batch_outputs should be just one dict (or a list of dicts if using tbptt) per optimizer if batch_mode: processed_batch_outputs = processed_batch_outputs[0] processed_outputs.append(processed_batch_outputs) # if there is only one optimiser then we collapse that dimension if len(processed_outputs) == 1: processed_outputs = processed_outputs[0] return processed_outputs def optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure): model_ref = self.trainer.lightning_module is_lbfgs = isinstance(optimizer, torch.optim.LBFGS) using_native_amp = self.trainer.amp_backend == AMPType.NATIVE # native amp + lbfgs is a no go right now if using_native_amp and is_lbfgs: raise MisconfigurationException( 'native PyTorch amp and lbfgs are not compatible.' ' To request, please file a Github issue in PyTorch and tag @mcarilli' ) # wraps into LightningOptimizer only for running step optimizer = LightningOptimizer._to_lightning_optimizer(optimizer, self.trainer, opt_idx) # model hook model_ref.optimizer_step( self.trainer.current_epoch, batch_idx, optimizer, opt_idx, train_step_and_backward_closure, on_tpu=self.trainer._device_type == DeviceType.TPU and _TPU_AVAILABLE, using_native_amp=using_native_amp, using_lbfgs=is_lbfgs, ) def on_before_zero_grad(self, optimizer): self.trainer.call_hook('on_before_zero_grad', optimizer) def optimizer_zero_grad(self, batch_idx, optimizer, opt_idx): self.trainer.accelerator.optimizer_zero_grad(self.trainer.current_epoch, batch_idx, optimizer, opt_idx) def track_and_norm_grad(self, optimizer): # track gradient norms grad_norm_dic = self._track_gradient_norm() # clip gradients self.trainer.accelerator.clip_gradients( optimizer, self.trainer.gradient_clip_val, gradient_clip_algorithm=self.trainer.gradient_clip_algorithm ) self._cur_grad_norm_dict = grad_norm_dic def _track_gradient_norm(self): grad_norm_dict = {} if (self.trainer.global_step + 1) % self.trainer.log_every_n_steps == 0: if float(self.trainer.track_grad_norm) > 0: model = self.trainer.lightning_module grad_norm_dict = grad_norm(model, self.trainer.track_grad_norm) return grad_norm_dict def _tbptt_split_batch(self, batch: Any) -> List[Any]: splits = [batch] truncated_bptt_enabled = self._truncated_bptt_enabled() if truncated_bptt_enabled: model_ref = self.trainer.lightning_module with self.trainer.profiler.profile("tbptt_split_batch"): splits = model_ref.tbptt_split_batch(batch, self._truncated_bptt_steps()) return splits def run_training_epoch(self): # modify dataloader if needed (ddp, etc...) train_dataloader = self.trainer.accelerator.process_dataloader(self.trainer.train_dataloader) # track epoch output epoch_output = [[] for _ in range(self.num_optimizers)] train_dataloader = self.trainer.data_connector.get_profiled_train_dataloader(train_dataloader) dataloader_idx = 0 val_loop_called = False batch_idx = None is_last_batch = None for batch_idx, (batch, is_last_batch) in train_dataloader: self.trainer.batch_idx = batch_idx self.trainer.is_last_batch = is_last_batch # ------------------------------------ # TRAINING_STEP + TRAINING_STEP_END # ------------------------------------ with self.trainer.profiler.profile("run_training_batch"): batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx) # when returning -1 from train_step, we end epoch early if batch_output.signal == -1: break # hook # TODO: add outputs to batches self.on_train_batch_end( epoch_output, batch_output.training_step_output_for_epoch_end, batch, batch_idx, dataloader_idx, ) # ----------------------------------------- # SAVE METRICS TO LOGGERS # ----------------------------------------- self.trainer.logger_connector.log_train_step_metrics(batch_output) # ----------------------------------------- # VALIDATE IF NEEDED # ----------------------------------------- should_check_val = self._should_check_val_fx(batch_idx, is_last_batch) if should_check_val: self.trainer.validating = True self.trainer.run_evaluation() self.trainer.training = True val_loop_called = True # ----------------------------------------- # SAVE LOGGERS (ie: Tensorboard, etc...) # ----------------------------------------- self.save_loggers_on_train_batch_end() # update LR schedulers monitor_metrics = deepcopy(self.trainer.logger_connector.callback_metrics) self.update_train_loop_lr_schedulers(monitor_metrics=monitor_metrics) self.trainer.checkpoint_connector.has_trained = True # max steps reached, end training if ( self.trainer.max_steps is not None and self.trainer.max_steps <= self.trainer.global_step + 1 and self._accumulated_batches_reached() ): break # end epoch early # stop when the flag is changed or we've gone past the amount # requested in the batches if self.trainer.should_stop: break self.trainer.total_batch_idx += 1 # stop epoch if we limited the number of training batches if self._num_training_batches_reached(is_last_batch): break # progress global step according to grads progress self.increment_accumulated_grad_global_step() if batch_idx is None: # dataloader/iterator did not produce a batch return # handle epoch_output on epoch end self.on_train_epoch_end(epoch_output) # log epoch metrics self.trainer.logger_connector.log_train_epoch_end_metrics(epoch_output) should_check_val = self._should_check_val_fx(batch_idx, is_last_batch, on_epoch=True) should_skip_eval = self.trainer.evaluation_loop.should_skip_evaluation(self.trainer.num_val_batches) should_train_only = self.trainer.disable_validation or should_skip_eval # update epoch level lr_schedulers if no val loop outside train loop is triggered if (val_loop_called and not should_check_val) or should_train_only: self.trainer.optimizer_connector.update_learning_rates(interval='epoch') if should_train_only: self.check_checkpoint_callback(True) if should_check_val: self.trainer.validating = True self.trainer.run_evaluation(on_epoch=True) self.trainer.training = True # increment the global step once # progress global step according to grads progress self.increment_accumulated_grad_global_step() def on_train_epoch_end(self, epoch_output: List[List[List[Result]]]) -> None: # inform logger the batch loop has finished self.trainer.logger_connector.on_train_epoch_end() # prepare epoch output processed_epoch_output = TrainLoop._prepare_outputs(epoch_output, batch_mode=False) # get the model and call model.training_epoch_end model = self.trainer.lightning_module if is_overridden('training_epoch_end', model=model): # run training_epoch_end # refresh the result for custom logging at the epoch level model._current_fx_name = 'training_epoch_end' # lightningmodule hook training_epoch_end_output = model.training_epoch_end(processed_epoch_output) if training_epoch_end_output is not None: raise MisconfigurationException( 'training_epoch_end expects a return of None. ' 'HINT: remove the return statement in training_epoch_end' ) # capture logging self.trainer.logger_connector.cache_logged_metrics() # call train epoch end hooks self._on_train_epoch_end_hook(processed_epoch_output) self.trainer.call_hook('on_epoch_end') def _on_train_epoch_end_hook(self, processed_epoch_output) -> None: # We cannot rely on Trainer.call_hook because the signatures might be different across # lightning module and callback # As a result, we need to inspect if the module accepts `outputs` in `on_train_epoch_end` # This implementation is copied from Trainer.call_hook hook_name = "on_train_epoch_end" # set hook_name to model + reset Result obj skip = self.trainer._reset_result_and_set_hook_fx_name(hook_name) # always profile hooks with self.trainer.profiler.profile(hook_name): # first call trainer hook if hasattr(self.trainer, hook_name): trainer_hook = getattr(self.trainer, hook_name) trainer_hook(processed_epoch_output) # next call hook in lightningModule model_ref = self.trainer.lightning_module if is_overridden(hook_name, model_ref): hook_fx = getattr(model_ref, hook_name) if is_param_in_hook_signature(hook_fx, "outputs"): self.warning_cache.warn( "The signature of `ModelHooks.on_train_epoch_end` has changed in v1.3." " `outputs` parameter has been deprecated." " Support for the old signature will be removed in v1.5", DeprecationWarning ) model_ref.on_train_epoch_end(processed_epoch_output) else: model_ref.on_train_epoch_end() # if the PL module doesn't have the hook then call the accelerator # used to auto-reduce things for the user with Results obj elif hasattr(self.trainer.accelerator, hook_name): accelerator_hook = getattr(self.trainer.accelerator, hook_name) accelerator_hook() if not skip: self.trainer._cache_logged_metrics() def run_training_batch(self, batch, batch_idx, dataloader_idx): # track grad norms grad_norm_dic = {} # bookkeeping self.trainer.hiddens = None optimizers = self.prepare_optimizers() # track all outputs across time and num of optimizers batch_outputs = [[] for _ in range(len(optimizers))] if batch is None: self.warning_cache.warn("train_dataloader yielded None. If this was on purpose, ignore this warning...") return AttributeDict( signal=0, grad_norm_dic=grad_norm_dic, training_step_output_for_epoch_end=batch_outputs, ) # hook response = self.trainer.call_hook("on_batch_start") if response == -1: return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic) # hook response = self.trainer.call_hook("on_train_batch_start", batch, batch_idx, dataloader_idx) if response == -1: return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic) # lightning module hook splits = self._tbptt_split_batch(batch) for split_idx, split_batch in enumerate(splits): # create an iterable for optimizers and loop over them for opt_idx, optimizer in optimizers: # toggle model params + set info to logger_connector self.run_train_split_start(split_idx, split_batch, opt_idx, optimizer) if self.should_accumulate(): # For gradient accumulation # ------------------- # calculate loss (train step + train step end) # ------------------- # automatic_optimization=True: perform dpp sync only when performing optimizer_step # automatic_optimization=False: don't block synchronization here with self.block_ddp_sync_behaviour(): self.training_step_and_backward( split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens ) batch_outputs = self._process_closure_result( batch_outputs=batch_outputs, opt_idx=opt_idx, ) # ------------------------------ # BACKWARD PASS # ------------------------------ # gradient update with accumulated gradients else: if self.trainer.lightning_module.automatic_optimization: def train_step_and_backward_closure(): result = self.training_step_and_backward( split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens ) return None if result is None else result.loss # optimizer step self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure) else: self._curr_step_result = self.training_step( split_batch, batch_idx, opt_idx, self.trainer.hiddens ) if self._curr_step_result is None: # user decided to skip optimization # make sure to zero grad. continue batch_outputs = self._process_closure_result( batch_outputs=batch_outputs, opt_idx=opt_idx, ) # todo: Properly aggregate grad_norm accros opt_idx and split_idx grad_norm_dic = self._cur_grad_norm_dict self._cur_grad_norm_dict = None # update running loss + reset accumulated loss self.update_running_loss() result = AttributeDict( signal=0, grad_norm_dic=grad_norm_dic, training_step_output_for_epoch_end=batch_outputs, ) return result @contextmanager def block_ddp_sync_behaviour(self, should_block_sync: bool = False): """ automatic_optimization = True Blocks ddp sync gradients behaviour on backwards pass. This is useful for skipping sync when accumulating gradients, reducing communication overhead automatic_optimization = False do not block ddp gradient sync when using manual optimization as gradients are needed within the training step Returns: context manager with sync behaviour off """ if ( isinstance(self.trainer.training_type_plugin, ParallelPlugin) and (self.trainer.lightning_module.automatic_optimization or should_block_sync) ): with self.trainer.training_type_plugin.block_backward_sync(): yield None else: yield None def _process_closure_result(self, batch_outputs: list, opt_idx: int) -> list: opt_closure_result = self._curr_step_result if opt_closure_result is not None: # cache metrics self.trainer.logger_connector.cache_training_step_metrics(opt_closure_result) # check if loss or model weights are nan if self.trainer.terminate_on_nan: self._check_finite(opt_closure_result.loss) # track all the outputs across all steps batch_opt_idx = opt_idx if len(batch_outputs) > 1 else 0 batch_outputs[batch_opt_idx].append(opt_closure_result.training_step_output_for_epoch_end) if self.trainer.lightning_module.automatic_optimization: # track total loss for logging (avoid mem leaks) self.accumulated_loss.append(opt_closure_result.loss) self._curr_step_result = None return batch_outputs def training_step_and_backward(self, split_batch, batch_idx, opt_idx, optimizer, hiddens): """Wrap forward, zero_grad and backward in a closure so second order methods work""" with self.trainer.profiler.profile("training_step_and_backward"): # lightning module hook result = self.training_step(split_batch, batch_idx, opt_idx, hiddens) self._curr_step_result = result if not self._skip_backward and self.trainer.lightning_module.automatic_optimization: is_first_batch_to_accumulate = batch_idx % self.trainer.accumulate_grad_batches == 0 if is_first_batch_to_accumulate: self.on_before_zero_grad(optimizer) self.optimizer_zero_grad(batch_idx, optimizer, opt_idx) # backward pass if result is not None: with self.trainer.profiler.profile("backward"): self.backward(result, optimizer, opt_idx) # hook - call this hook only # when gradients have finished to accumulate if not self.should_accumulate(): self.on_after_backward(result.training_step_output, batch_idx, result.loss) # check if loss or model weights are nan if self.trainer.terminate_on_nan: self._check_finite(result.loss) else: self.warning_cache.warn( "training_step returned None. If this was on purpose, ignore this warning..." ) if len(self.trainer.optimizers) > 1: # revert back to previous state self.trainer.lightning_module.untoggle_optimizer(opt_idx) return result def _check_finite(self, loss: torch.Tensor) -> None: if not torch.isfinite(loss).all(): raise ValueError(f'The loss returned in `training_step` is {loss}.') model = self.trainer.lightning_module detect_nan_parameters(model) def backward(self, result, optimizer, opt_idx, *args, **kwargs): self.trainer.dev_debugger.track_event("backward_call") should_accumulate = self.should_accumulate() # backward can be called manually in the training loop if isinstance(result, torch.Tensor): self.trainer.accelerator.backward(result, optimizer, opt_idx, should_accumulate, *args, **kwargs) else: result.closure_loss = self.trainer.accelerator.backward( result.closure_loss, optimizer, opt_idx, should_accumulate, *args, **kwargs ) if not self.should_accumulate(): # track gradients self.track_and_norm_grad(optimizer=optimizer) def update_train_loop_lr_schedulers(self, monitor_metrics=None): num_accumulated_batches_reached = self._accumulated_batches_reached() num_training_batches_reached = self._num_training_batches_reached() if num_accumulated_batches_reached or num_training_batches_reached: # update lr self.trainer.optimizer_connector.update_learning_rates( interval="step", monitor_metrics=monitor_metrics, opt_indices=[opt_idx for opt_idx, _ in self.get_optimizers_iterable()], ) def increment_accumulated_grad_global_step(self): num_accumulated_batches_reached = self._accumulated_batches_reached() num_training_batches_reached = self._num_training_batches_reached() # progress global step according to grads progress if num_accumulated_batches_reached or num_training_batches_reached: self.trainer.global_step = self.trainer.accelerator.update_global_step( self.trainer.total_batch_idx, self.trainer.global_step ) def _accumulated_batches_reached(self): return (self.trainer.batch_idx + 1) % self.trainer.accumulate_grad_batches == 0 def _num_training_batches_reached(self, is_last_batch=False): return (self.trainer.batch_idx + 1) == self.trainer.num_training_batches or is_last_batch def should_accumulate(self): # checks if backward or backward + optimizer step (via closure) accumulation_done = self._accumulated_batches_reached() is_final_batch = self._num_training_batches_reached() return not (accumulation_done or is_final_batch) def _should_check_val_fx(self, batch_idx: int, is_last_batch: bool, on_epoch: bool = False) -> bool: """ Decide if we should run validation. """ if not self.trainer.enable_validation: return False # check if this epoch is eligible to run validation if (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch != 0: return False # val_check_batch is inf for iterable datasets with no length defined # TODO: let training/eval loop handle logic around limit_*_batches and val_check_batch is_val_check_batch = False if isinstance(self.trainer.limit_train_batches, int) and self.trainer.val_check_batch == float('inf'): is_val_check_batch = (batch_idx + 1) % self.trainer.limit_train_batches == 0 elif self.trainer.val_check_batch != float('inf'): is_val_check_batch = (batch_idx + 1) % self.trainer.val_check_batch == 0 # Note: num_training_batches is also inf for iterable datasets with no length defined epoch_end_val_check = (batch_idx + 1) % self.trainer.num_training_batches == 0 is_last_batch_for_infinite_dataset = is_last_batch and self.trainer.val_check_batch == float("inf") if on_epoch: return ( is_val_check_batch and epoch_end_val_check ) or self.trainer.should_stop or is_last_batch_for_infinite_dataset else: return is_val_check_batch and not epoch_end_val_check def build_train_args(self, batch, batch_idx, opt_idx, hiddens): # enable not needing to add opt_idx to training_step args = [batch, batch_idx] if len(self.trainer.optimizers) > 1: if self.trainer.has_arg("training_step", "optimizer_idx"): if not self.trainer.lightning_module.automatic_optimization: self.warning_cache.warn( "`training_step` hook signature has changed in v1.3." " `optimizer_idx` argument has been removed in case of manual optimization. Support for" " the old signature will be removed in v1.5", DeprecationWarning ) args.append(opt_idx) elif not self.trainer.has_arg( "training_step", "optimizer_idx" ) and self.trainer.lightning_module.automatic_optimization: raise ValueError( f"Your LightningModule defines {len(self.trainer.optimizers)} optimizers but" ' `training_step` is missing the `optimizer_idx` argument.' ) # pass hiddens if using tbptt if self._truncated_bptt_enabled(): args.append(hiddens) return args def _truncated_bptt_enabled(self) -> bool: """ Temporary tbptt utilities until this flag is fully migrated to the lightning module. """ return self._truncated_bptt_steps() > 0 def _truncated_bptt_steps(self) -> int: lightning_module = self.trainer.lightning_module # Give precedence to the LightningModule as the Trainer flag will be removed in v1.5 if lightning_module.truncated_bptt_steps > 0: return lightning_module.truncated_bptt_steps return self.trainer.truncated_bptt_steps or 0 def save_loggers_on_train_batch_end(self): # when loggers should save to disk should_flush_logs = self.trainer.logger_connector.should_flush_logs if should_flush_logs and self.trainer.is_global_zero and self.trainer.logger is not None: self.trainer.logger.save() def prepare_optimizers(self): # in manual optimization we loop over all optimizers at once optimizers = self.get_optimizers_iterable() if not self.trainer.lightning_module.automatic_optimization: optimizers = [optimizers[0]] return optimizers def run_train_split_start(self, split_idx, split_batch, opt_idx, optimizer): # set split_idx to trainer for tracking self.trainer.split_idx = split_idx # make sure only the gradients of the current optimizer's parameters are calculated # in the training step to prevent dangling gradients in multiple-optimizer setup. if self.trainer.lightning_module.automatic_optimization and len(self.trainer.optimizers) > 1: model = self.trainer.lightning_module model.toggle_optimizer(optimizer, opt_idx) # use to track metrics internally self.trainer.logger_connector.on_train_split_start(split_idx, opt_idx, split_batch) def update_running_loss(self): accumulated_loss = self.accumulated_loss.mean() if accumulated_loss is not None: # calculate running loss for display self.running_loss.append(self.accumulated_loss.mean() * self.trainer.accumulate_grad_batches) # reset for next set of accumulated grads self.accumulated_loss.reset()

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from contextlib import contextmanager, suppress from copy import copy, deepcopy from typing import Any, Dict, List, Optional, Union import numpy as np import torch from pytorch_lightning.core.optimizer import LightningOptimizer from pytorch_lightning.core.step_result import Result from pytorch_lightning.plugins import ParallelPlugin from pytorch_lightning.trainer.states import TrainerState from pytorch_lightning.trainer.supporters import TensorRunningAccum from pytorch_lightning.utilities import _TPU_AVAILABLE, AMPType, DeviceType from pytorch_lightning.utilities.distributed import rank_zero_info from pytorch_lightning.utilities.exceptions import MisconfigurationException from pytorch_lightning.utilities.finite_checks import detect_nan_parameters from pytorch_lightning.utilities.grads import grad_norm from pytorch_lightning.utilities.model_helpers import is_overridden from pytorch_lightning.utilities.parsing import AttributeDict from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature from pytorch_lightning.utilities.warnings import WarningCache class TrainLoop: def __init__(self, trainer, multiple_trainloader_mode: str): self.trainer = trainer self.accumulated_loss = None self.warning_cache = WarningCache() self._teardown_already_run = False self.running_loss = TensorRunningAccum(window_length=20) self._curr_step_result = None self._cur_grad_norm_dict = None self._multiple_trainloader_mode = multiple_trainloader_mode self._skip_backward = False self.trainer._multiple_trainloader_mode = multiple_trainloader_mode self._optimizer_freq_cumsum = None def on_trainer_init( self, max_epochs: Optional[int], min_epochs: Optional[int], max_steps: Optional[int], min_steps: Optional[int], num_sanity_val_steps: int, ) -> None: self.trainer.global_step = 0 self.trainer.current_epoch = 0 self.trainer.should_stop = False self.trainer.state = TrainerState() self.trainer.total_batch_idx = 0 self.trainer.batch_idx = 0 self.trainer.num_training_batches = 0 self.trainer.train_dataloader = None self.trainer.max_epochs = 1000 if (max_epochs is None and max_steps is None) else max_epochs self.trainer.min_epochs = 1 if (min_epochs is None and min_steps is None) else min_epochs self.trainer.max_steps = max_steps self.trainer.min_steps = min_steps if num_sanity_val_steps == -1: self.trainer.num_sanity_val_steps = float("inf") else: self.trainer.num_sanity_val_steps = num_sanity_val_steps @property def num_optimizers(self): num_optimizers = len(self.get_optimizers_iterable()) return num_optimizers @property def optimizer_freq_cumsum(self): if self._optimizer_freq_cumsum is None: self._optimizer_freq_cumsum = np.cumsum(self.trainer.optimizer_frequencies) return self._optimizer_freq_cumsum def should_skip_training(self): should_by_max_steps = self.trainer.max_steps is not None and self.trainer.global_step >= self.trainer.max_steps should_by_epoch = self.trainer.max_epochs is not None and self.trainer.current_epoch >= self.trainer.max_epochs return should_by_max_steps or should_by_epoch or self.trainer.num_training_batches == 0 def on_train_start(self): self.trainer.call_hook("on_train_start") def on_train_end(self): if self._teardown_already_run: return self._teardown_already_run = True self.trainer.global_step -= 1 self.check_checkpoint_callback(should_update=True, is_last=True) self.trainer.global_step += 1 self.trainer.call_hook("on_train_end") if self.trainer.logger is not None: self.trainer.logger.finalize("success") self.trainer.profiler.describe() self.trainer.accelerator.on_train_end() self.trainer.state.stage = None def check_checkpoint_callback(self, should_update, is_last=False): if should_update and self.trainer.checkpoint_connector.has_trained: callbacks = self.trainer.checkpoint_callbacks if is_last and any(cb.save_last and cb.verbose for cb in callbacks): rank_zero_info("Saving latest checkpoint...") model = self.trainer.lightning_module for cb in callbacks: cb.on_validation_end(self.trainer, model) def on_train_epoch_start(self, epoch): self.trainer.current_epoch = epoch model = self.trainer.lightning_module if epoch != 0 and self.trainer.reload_dataloaders_every_epoch: self.trainer.reset_train_dataloader(model) with suppress(Exception): self.trainer.train_dataloader.sampler.set_epoch(epoch) self.trainer.accumulation_scheduler.on_train_epoch_start(self.trainer, self.trainer.lightning_module) self.accumulated_loss = TensorRunningAccum(window_length=self.trainer.accumulate_grad_batches) self.trainer.call_hook("on_epoch_start") self.trainer.call_hook("on_train_epoch_start") def on_train_batch_end(self, epoch_output, batch_end_outputs, batch, batch_idx, dataloader_idx): batch_end_outputs = [opt_idx_out for opt_idx_out in batch_end_outputs if len(opt_idx_out)] processed_batch_end_outputs = TrainLoop._prepare_outputs(batch_end_outputs, batch_mode=True) self.trainer.call_hook('on_train_batch_end', processed_batch_end_outputs, batch, batch_idx, dataloader_idx) self.trainer.call_hook('on_batch_end') self.track_epoch_end_reduce_metrics(epoch_output, batch_end_outputs) self.trainer.logger_connector.on_train_batch_end() def reset_train_val_dataloaders(self, model) -> None: if self.trainer.train_dataloader is None: self.trainer.reset_train_dataloader(model) if self.trainer.val_dataloaders is None: self.trainer.reset_val_dataloader(model) def track_epoch_end_reduce_metrics(self, epoch_output, batch_end_outputs): hook_overridden = self._should_add_batch_output_to_epoch_output() for opt_idx, opt_outputs in enumerate(batch_end_outputs): sample_output = opt_outputs[-1] auto_reduce_tng_result = isinstance(sample_output, Result) and sample_output.should_reduce_on_epoch_end if not (hook_overridden or auto_reduce_tng_result): continue if isinstance(opt_outputs, list) and len(opt_outputs) == 1 and not isinstance(opt_outputs[0], Result): opt_outputs = opt_outputs[0] epoch_output[opt_idx].append(opt_outputs) def _should_add_batch_output_to_epoch_output(self) -> bool: # We add to the epoch outputs if # 1. The model defines training_epoch_end OR # 2. The model overrides on_train_epoch_end which has `outputs` in the signature # TODO: in v1.5 this only needs to check if training_epoch_end is overridden lightning_module = self.trainer.lightning_module if is_overridden("training_epoch_end", model=lightning_module): return True if is_overridden("on_train_epoch_end", model=lightning_module): model_hook_fx = getattr(lightning_module, "on_train_epoch_end") if is_param_in_hook_signature(model_hook_fx, "outputs"): return True return False def get_optimizers_iterable(self, batch_idx=None): if not self.trainer.optimizer_frequencies: # call training_step once per optimizer return list(enumerate(self.trainer.optimizers)) if batch_idx is None: batch_idx = self.trainer.total_batch_idx optimizers_loop_length = self.optimizer_freq_cumsum[-1] current_place_in_loop = batch_idx % optimizers_loop_length # find optimzier index by looking for the first {item > current_place} in the cumsum list opt_idx = np.argmax(self.optimizer_freq_cumsum > current_place_in_loop) return [[opt_idx, self.trainer.optimizers[opt_idx]]] def on_after_backward(self, training_step_output, batch_idx, untouched_loss): training_step_output.detach() # insert after step hook self.trainer.call_hook("on_after_backward") # when in dev debugging track the losses self.trainer.dev_debugger.track_train_loss_history(batch_idx, untouched_loss.detach()) def _check_training_step_output(self, training_step_output): if isinstance(training_step_output, torch.Tensor) and not self.trainer.lightning_module.automatic_optimization: if training_step_output.grad_fn is None: # TODO: Find why - RuntimeError: Expected to mark a variable ready only once ... raise MisconfigurationException("In manual optimization, `training_step` should not return a Tensor") def training_step(self, split_batch, batch_idx, opt_idx, hiddens): # give the PL module a result for logging model_ref = self.trainer.lightning_module with self.trainer.profiler.profile("model_forward"): args = self.build_train_args(split_batch, batch_idx, opt_idx, hiddens) # manually capture logged metrics model_ref._current_fx_name = 'training_step' model_ref._results = Result() with self.trainer.profiler.profile("training_step"): training_step_output = self.trainer.accelerator.training_step(args) self.trainer.accelerator.post_training_step() self.trainer.logger_connector.cache_logged_metrics() self._check_training_step_output(training_step_output) training_step_output = self.trainer.call_hook("training_step_end", training_step_output) training_step_output_for_epoch_end, training_step_output = self._process_training_step_output( training_step_output, split_batch ) if training_step_output_for_epoch_end is None: return # enable empty loss when using manual opt closure_loss = None untouched_loss = None if self.trainer.lightning_module.automatic_optimization: # accumulate loss. if accumulate_grad_batches==1, no effect closure_loss = training_step_output.minimize / self.trainer.accumulate_grad_batches # the loss will get scaled for amp. avoid any modifications to it untouched_loss = closure_loss.detach().clone() # result result = AttributeDict( closure_loss=closure_loss, loss=untouched_loss, training_step_output=training_step_output, training_step_output_for_epoch_end=training_step_output_for_epoch_end, ) return result def _process_training_step_output(self, training_step_output, split_batch): training_step_output_for_epoch_end = training_step_output # enable validation_step return None if training_step_output_for_epoch_end is None: return None, None result = self.trainer.lightning_module._results loss = None hiddens = None result["extra"] = {} # handle dict return if isinstance(training_step_output, dict): loss = training_step_output.pop("loss", None) hiddens = training_step_output.pop("hiddens", None) if hiddens is not None: hiddens = hiddens.detach() result["extra"] = training_step_output # handle scalar return elif isinstance(training_step_output, torch.Tensor): loss = training_step_output # map to results under the hood result.minimize = loss self.trainer.hiddens = hiddens # track batch for manual reduction with result result.track_batch_size(len(split_batch)) # track metrics without grads for epoch reduction training_step_output_for_epoch_end = copy(result) training_step_output_for_epoch_end = training_step_output_for_epoch_end.detach() if self.trainer.move_metrics_to_cpu: training_step_output_for_epoch_end = training_step_output_for_epoch_end.cpu() return training_step_output_for_epoch_end, result @staticmethod def _prepare_outputs( outputs: List[List[List[Result]]], batch_mode: bool, ) -> Union[List[List[List[Dict]]], List[List[Dict]], List[Dict], Dict]: processed_outputs = [] for opt_outputs in outputs: # handle an edge case where an optimizer output is the empty list if len(opt_outputs) == 0: continue processed_batch_outputs = [] if batch_mode: opt_outputs = [opt_outputs] for batch_outputs in opt_outputs: processed_tbptt_outputs = [] for tbptt_output in batch_outputs: out = tbptt_output.extra out['loss'] = tbptt_output.minimize processed_tbptt_outputs.append(out) # if there was only one tbptt step then we can collapse that dimension if len(processed_tbptt_outputs) == 1: processed_tbptt_outputs = processed_tbptt_outputs[0] processed_batch_outputs.append(processed_tbptt_outputs) # batch_outputs should be just one dict (or a list of dicts if using tbptt) per optimizer if batch_mode: processed_batch_outputs = processed_batch_outputs[0] processed_outputs.append(processed_batch_outputs) # if there is only one optimiser then we collapse that dimension if len(processed_outputs) == 1: processed_outputs = processed_outputs[0] return processed_outputs def optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure): model_ref = self.trainer.lightning_module is_lbfgs = isinstance(optimizer, torch.optim.LBFGS) using_native_amp = self.trainer.amp_backend == AMPType.NATIVE # native amp + lbfgs is a no go right now if using_native_amp and is_lbfgs: raise MisconfigurationException( 'native PyTorch amp and lbfgs are not compatible.' ' To request, please file a Github issue in PyTorch and tag @mcarilli' ) # wraps into LightningOptimizer only for running step optimizer = LightningOptimizer._to_lightning_optimizer(optimizer, self.trainer, opt_idx) # model hook model_ref.optimizer_step( self.trainer.current_epoch, batch_idx, optimizer, opt_idx, train_step_and_backward_closure, on_tpu=self.trainer._device_type == DeviceType.TPU and _TPU_AVAILABLE, using_native_amp=using_native_amp, using_lbfgs=is_lbfgs, ) def on_before_zero_grad(self, optimizer): self.trainer.call_hook('on_before_zero_grad', optimizer) def optimizer_zero_grad(self, batch_idx, optimizer, opt_idx): self.trainer.accelerator.optimizer_zero_grad(self.trainer.current_epoch, batch_idx, optimizer, opt_idx) def track_and_norm_grad(self, optimizer): # track gradient norms grad_norm_dic = self._track_gradient_norm() # clip gradients self.trainer.accelerator.clip_gradients( optimizer, self.trainer.gradient_clip_val, gradient_clip_algorithm=self.trainer.gradient_clip_algorithm ) self._cur_grad_norm_dict = grad_norm_dic def _track_gradient_norm(self): grad_norm_dict = {} if (self.trainer.global_step + 1) % self.trainer.log_every_n_steps == 0: if float(self.trainer.track_grad_norm) > 0: model = self.trainer.lightning_module grad_norm_dict = grad_norm(model, self.trainer.track_grad_norm) return grad_norm_dict def _tbptt_split_batch(self, batch: Any) -> List[Any]: splits = [batch] truncated_bptt_enabled = self._truncated_bptt_enabled() if truncated_bptt_enabled: model_ref = self.trainer.lightning_module with self.trainer.profiler.profile("tbptt_split_batch"): splits = model_ref.tbptt_split_batch(batch, self._truncated_bptt_steps()) return splits def run_training_epoch(self): # modify dataloader if needed (ddp, etc...) train_dataloader = self.trainer.accelerator.process_dataloader(self.trainer.train_dataloader) # track epoch output epoch_output = [[] for _ in range(self.num_optimizers)] train_dataloader = self.trainer.data_connector.get_profiled_train_dataloader(train_dataloader) dataloader_idx = 0 val_loop_called = False batch_idx = None is_last_batch = None for batch_idx, (batch, is_last_batch) in train_dataloader: self.trainer.batch_idx = batch_idx self.trainer.is_last_batch = is_last_batch # ------------------------------------ # TRAINING_STEP + TRAINING_STEP_END # ------------------------------------ with self.trainer.profiler.profile("run_training_batch"): batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx) # when returning -1 from train_step, we end epoch early if batch_output.signal == -1: break # hook # TODO: add outputs to batches self.on_train_batch_end( epoch_output, batch_output.training_step_output_for_epoch_end, batch, batch_idx, dataloader_idx, ) # ----------------------------------------- # SAVE METRICS TO LOGGERS # ----------------------------------------- self.trainer.logger_connector.log_train_step_metrics(batch_output) # ----------------------------------------- # VALIDATE IF NEEDED # ----------------------------------------- should_check_val = self._should_check_val_fx(batch_idx, is_last_batch) if should_check_val: self.trainer.validating = True self.trainer.run_evaluation() self.trainer.training = True val_loop_called = True # ----------------------------------------- # SAVE LOGGERS (ie: Tensorboard, etc...) # ----------------------------------------- self.save_loggers_on_train_batch_end() # update LR schedulers monitor_metrics = deepcopy(self.trainer.logger_connector.callback_metrics) self.update_train_loop_lr_schedulers(monitor_metrics=monitor_metrics) self.trainer.checkpoint_connector.has_trained = True # max steps reached, end training if ( self.trainer.max_steps is not None and self.trainer.max_steps <= self.trainer.global_step + 1 and self._accumulated_batches_reached() ): break # end epoch early # stop when the flag is changed or we've gone past the amount if self.trainer.should_stop: break self.trainer.total_batch_idx += 1 if self._num_training_batches_reached(is_last_batch): break self.increment_accumulated_grad_global_step() if batch_idx is None: return self.on_train_epoch_end(epoch_output) self.trainer.logger_connector.log_train_epoch_end_metrics(epoch_output) should_check_val = self._should_check_val_fx(batch_idx, is_last_batch, on_epoch=True) should_skip_eval = self.trainer.evaluation_loop.should_skip_evaluation(self.trainer.num_val_batches) should_train_only = self.trainer.disable_validation or should_skip_eval if (val_loop_called and not should_check_val) or should_train_only: self.trainer.optimizer_connector.update_learning_rates(interval='epoch') if should_train_only: self.check_checkpoint_callback(True) if should_check_val: self.trainer.validating = True self.trainer.run_evaluation(on_epoch=True) self.trainer.training = True self.increment_accumulated_grad_global_step() def on_train_epoch_end(self, epoch_output: List[List[List[Result]]]) -> None: self.trainer.logger_connector.on_train_epoch_end() processed_epoch_output = TrainLoop._prepare_outputs(epoch_output, batch_mode=False) model = self.trainer.lightning_module if is_overridden('training_epoch_end', model=model): model._current_fx_name = 'training_epoch_end' training_epoch_end_output = model.training_epoch_end(processed_epoch_output) if training_epoch_end_output is not None: raise MisconfigurationException( 'training_epoch_end expects a return of None. ' 'HINT: remove the return statement in training_epoch_end' ) self.trainer.logger_connector.cache_logged_metrics() self._on_train_epoch_end_hook(processed_epoch_output) self.trainer.call_hook('on_epoch_end') def _on_train_epoch_end_hook(self, processed_epoch_output) -> None: hook_name = "on_train_epoch_end" skip = self.trainer._reset_result_and_set_hook_fx_name(hook_name) with self.trainer.profiler.profile(hook_name): if hasattr(self.trainer, hook_name): trainer_hook = getattr(self.trainer, hook_name) trainer_hook(processed_epoch_output) model_ref = self.trainer.lightning_module if is_overridden(hook_name, model_ref): hook_fx = getattr(model_ref, hook_name) if is_param_in_hook_signature(hook_fx, "outputs"): self.warning_cache.warn( "The signature of `ModelHooks.on_train_epoch_end` has changed in v1.3." " `outputs` parameter has been deprecated." " Support for the old signature will be removed in v1.5", DeprecationWarning ) model_ref.on_train_epoch_end(processed_epoch_output) else: model_ref.on_train_epoch_end() # used to auto-reduce things for the user with Results obj elif hasattr(self.trainer.accelerator, hook_name): accelerator_hook = getattr(self.trainer.accelerator, hook_name) accelerator_hook() if not skip: self.trainer._cache_logged_metrics() def run_training_batch(self, batch, batch_idx, dataloader_idx): # track grad norms grad_norm_dic = {} # bookkeeping self.trainer.hiddens = None optimizers = self.prepare_optimizers() # track all outputs across time and num of optimizers batch_outputs = [[] for _ in range(len(optimizers))] if batch is None: self.warning_cache.warn("train_dataloader yielded None. If this was on purpose, ignore this warning...") return AttributeDict( signal=0, grad_norm_dic=grad_norm_dic, training_step_output_for_epoch_end=batch_outputs, ) # hook response = self.trainer.call_hook("on_batch_start") if response == -1: return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic) # hook response = self.trainer.call_hook("on_train_batch_start", batch, batch_idx, dataloader_idx) if response == -1: return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic) # lightning module hook splits = self._tbptt_split_batch(batch) for split_idx, split_batch in enumerate(splits): # create an iterable for optimizers and loop over them for opt_idx, optimizer in optimizers: # toggle model params + set info to logger_connector self.run_train_split_start(split_idx, split_batch, opt_idx, optimizer) if self.should_accumulate(): # For gradient accumulation # ------------------- # calculate loss (train step + train step end) # ------------------- # automatic_optimization=True: perform dpp sync only when performing optimizer_step # automatic_optimization=False: don't block synchronization here with self.block_ddp_sync_behaviour(): self.training_step_and_backward( split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens ) batch_outputs = self._process_closure_result( batch_outputs=batch_outputs, opt_idx=opt_idx, ) else: if self.trainer.lightning_module.automatic_optimization: def train_step_and_backward_closure(): result = self.training_step_and_backward( split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens ) return None if result is None else result.loss self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure) else: self._curr_step_result = self.training_step( split_batch, batch_idx, opt_idx, self.trainer.hiddens ) if self._curr_step_result is None: continue batch_outputs = self._process_closure_result( batch_outputs=batch_outputs, opt_idx=opt_idx, ) grad_norm_dic = self._cur_grad_norm_dict self._cur_grad_norm_dict = None self.update_running_loss() result = AttributeDict( signal=0, grad_norm_dic=grad_norm_dic, training_step_output_for_epoch_end=batch_outputs, ) return result @contextmanager def block_ddp_sync_behaviour(self, should_block_sync: bool = False): if ( isinstance(self.trainer.training_type_plugin, ParallelPlugin) and (self.trainer.lightning_module.automatic_optimization or should_block_sync) ): with self.trainer.training_type_plugin.block_backward_sync(): yield None else: yield None def _process_closure_result(self, batch_outputs: list, opt_idx: int) -> list: opt_closure_result = self._curr_step_result if opt_closure_result is not None: self.trainer.logger_connector.cache_training_step_metrics(opt_closure_result) if self.trainer.terminate_on_nan: self._check_finite(opt_closure_result.loss) batch_opt_idx = opt_idx if len(batch_outputs) > 1 else 0 batch_outputs[batch_opt_idx].append(opt_closure_result.training_step_output_for_epoch_end) if self.trainer.lightning_module.automatic_optimization: self.accumulated_loss.append(opt_closure_result.loss) self._curr_step_result = None return batch_outputs def training_step_and_backward(self, split_batch, batch_idx, opt_idx, optimizer, hiddens): with self.trainer.profiler.profile("training_step_and_backward"): result = self.training_step(split_batch, batch_idx, opt_idx, hiddens) self._curr_step_result = result if not self._skip_backward and self.trainer.lightning_module.automatic_optimization: is_first_batch_to_accumulate = batch_idx % self.trainer.accumulate_grad_batches == 0 if is_first_batch_to_accumulate: self.on_before_zero_grad(optimizer) self.optimizer_zero_grad(batch_idx, optimizer, opt_idx) if result is not None: with self.trainer.profiler.profile("backward"): self.backward(result, optimizer, opt_idx) if not self.should_accumulate(): self.on_after_backward(result.training_step_output, batch_idx, result.loss) if self.trainer.terminate_on_nan: self._check_finite(result.loss) else: self.warning_cache.warn( "training_step returned None. If this was on purpose, ignore this warning..." ) if len(self.trainer.optimizers) > 1: self.trainer.lightning_module.untoggle_optimizer(opt_idx) return result def _check_finite(self, loss: torch.Tensor) -> None: if not torch.isfinite(loss).all(): raise ValueError(f'The loss returned in `training_step` is {loss}.') model = self.trainer.lightning_module detect_nan_parameters(model) def backward(self, result, optimizer, opt_idx, *args, **kwargs): self.trainer.dev_debugger.track_event("backward_call") should_accumulate = self.should_accumulate() if isinstance(result, torch.Tensor): self.trainer.accelerator.backward(result, optimizer, opt_idx, should_accumulate, *args, **kwargs) else: result.closure_loss = self.trainer.accelerator.backward( result.closure_loss, optimizer, opt_idx, should_accumulate, *args, **kwargs ) if not self.should_accumulate(): self.track_and_norm_grad(optimizer=optimizer) def update_train_loop_lr_schedulers(self, monitor_metrics=None): num_accumulated_batches_reached = self._accumulated_batches_reached() num_training_batches_reached = self._num_training_batches_reached() if num_accumulated_batches_reached or num_training_batches_reached: self.trainer.optimizer_connector.update_learning_rates( interval="step", monitor_metrics=monitor_metrics, opt_indices=[opt_idx for opt_idx, _ in self.get_optimizers_iterable()], ) def increment_accumulated_grad_global_step(self): num_accumulated_batches_reached = self._accumulated_batches_reached() num_training_batches_reached = self._num_training_batches_reached() if num_accumulated_batches_reached or num_training_batches_reached: self.trainer.global_step = self.trainer.accelerator.update_global_step( self.trainer.total_batch_idx, self.trainer.global_step ) def _accumulated_batches_reached(self): return (self.trainer.batch_idx + 1) % self.trainer.accumulate_grad_batches == 0 def _num_training_batches_reached(self, is_last_batch=False): return (self.trainer.batch_idx + 1) == self.trainer.num_training_batches or is_last_batch def should_accumulate(self): accumulation_done = self._accumulated_batches_reached() is_final_batch = self._num_training_batches_reached() return not (accumulation_done or is_final_batch) def _should_check_val_fx(self, batch_idx: int, is_last_batch: bool, on_epoch: bool = False) -> bool: if not self.trainer.enable_validation: return False if (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch != 0: return False is_val_check_batch = False if isinstance(self.trainer.limit_train_batches, int) and self.trainer.val_check_batch == float('inf'): is_val_check_batch = (batch_idx + 1) % self.trainer.limit_train_batches == 0 elif self.trainer.val_check_batch != float('inf'): is_val_check_batch = (batch_idx + 1) % self.trainer.val_check_batch == 0 epoch_end_val_check = (batch_idx + 1) % self.trainer.num_training_batches == 0 is_last_batch_for_infinite_dataset = is_last_batch and self.trainer.val_check_batch == float("inf") if on_epoch: return ( is_val_check_batch and epoch_end_val_check ) or self.trainer.should_stop or is_last_batch_for_infinite_dataset else: return is_val_check_batch and not epoch_end_val_check def build_train_args(self, batch, batch_idx, opt_idx, hiddens): args = [batch, batch_idx] if len(self.trainer.optimizers) > 1: if self.trainer.has_arg("training_step", "optimizer_idx"): if not self.trainer.lightning_module.automatic_optimization: self.warning_cache.warn( "`training_step` hook signature has changed in v1.3." " `optimizer_idx` argument has been removed in case of manual optimization. Support for" " the old signature will be removed in v1.5", DeprecationWarning ) args.append(opt_idx) elif not self.trainer.has_arg( "training_step", "optimizer_idx" ) and self.trainer.lightning_module.automatic_optimization: raise ValueError( f"Your LightningModule defines {len(self.trainer.optimizers)} optimizers but" ' `training_step` is missing the `optimizer_idx` argument.' ) if self._truncated_bptt_enabled(): args.append(hiddens) return args def _truncated_bptt_enabled(self) -> bool: return self._truncated_bptt_steps() > 0 def _truncated_bptt_steps(self) -> int: lightning_module = self.trainer.lightning_module if lightning_module.truncated_bptt_steps > 0: return lightning_module.truncated_bptt_steps return self.trainer.truncated_bptt_steps or 0 def save_loggers_on_train_batch_end(self): should_flush_logs = self.trainer.logger_connector.should_flush_logs if should_flush_logs and self.trainer.is_global_zero and self.trainer.logger is not None: self.trainer.logger.save() def prepare_optimizers(self): optimizers = self.get_optimizers_iterable() if not self.trainer.lightning_module.automatic_optimization: optimizers = [optimizers[0]] return optimizers def run_train_split_start(self, split_idx, split_batch, opt_idx, optimizer): self.trainer.split_idx = split_idx # in the training step to prevent dangling gradients in multiple-optimizer setup. if self.trainer.lightning_module.automatic_optimization and len(self.trainer.optimizers) > 1: model = self.trainer.lightning_module model.toggle_optimizer(optimizer, opt_idx) # use to track metrics internally self.trainer.logger_connector.on_train_split_start(split_idx, opt_idx, split_batch) def update_running_loss(self): accumulated_loss = self.accumulated_loss.mean() if accumulated_loss is not None: # calculate running loss for display self.running_loss.append(self.accumulated_loss.mean() * self.trainer.accumulate_grad_batches) # reset for next set of accumulated grads self.accumulated_loss.reset()

true

790dc5834f863fef842206787568d1423fd56a03

2,499

py

Python

custom/icds_reports/utils/aggregation_helpers/distributed/thr_forms_child_health.py

kkrampa/commcare-hq

d64d7cad98b240325ad669ccc7effb07721b4d44

[ "BSD-3-Clause" ]

1

2020-05-05T13:10:01.000Z

custom/icds_reports/utils/aggregation_helpers/distributed/thr_forms_child_health.py

kkrampa/commcare-hq

d64d7cad98b240325ad669ccc7effb07721b4d44

[ "BSD-3-Clause" ]

1

2019-12-09T14:00:14.000Z

custom/icds_reports/utils/aggregation_helpers/distributed/thr_forms_child_health.py

MaciejChoromanski/commcare-hq

fd7f65362d56d73b75a2c20d2afeabbc70876867

[ "BSD-3-Clause" ]

5

2015-11-30T13:12:45.000Z

2019-07-01T19:27:07.000Z

from __future__ import absolute_import from __future__ import unicode_literals from dateutil.relativedelta import relativedelta from custom.icds_reports.const import AGG_CHILD_HEALTH_THR_TABLE from custom.icds_reports.utils.aggregation_helpers import month_formatter from custom.icds_reports.utils.aggregation_helpers.distributed.base import BaseICDSAggregationDistributedHelper class THRFormsChildHealthAggregationDistributedHelper(BaseICDSAggregationDistributedHelper): helper_key = 'thr-forms-child-health' ucr_data_source_id = 'static-dashboard_thr_forms' tablename = AGG_CHILD_HEALTH_THR_TABLE def aggregate(self, cursor): drop_query, drop_params = self.drop_table_query() agg_query, agg_params = self.aggregation_query() cursor.execute(drop_query, drop_params) cursor.execute(agg_query, agg_params) def drop_table_query(self): return ( 'DELETE FROM "{}" WHERE month=%(month)s AND state_id = %(state)s'.format(self.tablename), {'month': month_formatter(self.month), 'state': self.state_id} ) def aggregation_query(self): month = self.month.replace(day=1) current_month_start = month_formatter(self.month) next_month_start = month_formatter(self.month + relativedelta(months=1)) query_params = { "month": month_formatter(month), "state_id": self.state_id, "current_month_start": current_month_start, "next_month_start": next_month_start, } return """ INSERT INTO "{tablename}" ( state_id, supervisor_id, month, case_id, latest_time_end_processed, days_ration_given_child ) ( SELECT DISTINCT ON (child_health_case_id) %(state_id)s AS state_id, LAST_VALUE(supervisor_id) over w AS supervisor_id, %(month)s AS month, child_health_case_id AS case_id, MAX(timeend) over w AS latest_time_end_processed, SUM(days_ration_given_child) over w AS days_ration_given_child FROM "{ucr_tablename}" WHERE state_id = %(state_id)s AND timeend >= %(current_month_start)s AND timeend < %(next_month_start)s AND child_health_case_id IS NOT NULL WINDOW w AS (PARTITION BY supervisor_id, child_health_case_id) ) """.format( ucr_tablename=self.ucr_tablename, tablename=self.tablename ), query_params

40.967213

111

0.687875

from __future__ import absolute_import from __future__ import unicode_literals from dateutil.relativedelta import relativedelta from custom.icds_reports.const import AGG_CHILD_HEALTH_THR_TABLE from custom.icds_reports.utils.aggregation_helpers import month_formatter from custom.icds_reports.utils.aggregation_helpers.distributed.base import BaseICDSAggregationDistributedHelper class THRFormsChildHealthAggregationDistributedHelper(BaseICDSAggregationDistributedHelper): helper_key = 'thr-forms-child-health' ucr_data_source_id = 'static-dashboard_thr_forms' tablename = AGG_CHILD_HEALTH_THR_TABLE def aggregate(self, cursor): drop_query, drop_params = self.drop_table_query() agg_query, agg_params = self.aggregation_query() cursor.execute(drop_query, drop_params) cursor.execute(agg_query, agg_params) def drop_table_query(self): return ( 'DELETE FROM "{}" WHERE month=%(month)s AND state_id = %(state)s'.format(self.tablename), {'month': month_formatter(self.month), 'state': self.state_id} ) def aggregation_query(self): month = self.month.replace(day=1) current_month_start = month_formatter(self.month) next_month_start = month_formatter(self.month + relativedelta(months=1)) query_params = { "month": month_formatter(month), "state_id": self.state_id, "current_month_start": current_month_start, "next_month_start": next_month_start, } return """ INSERT INTO "{tablename}" ( state_id, supervisor_id, month, case_id, latest_time_end_processed, days_ration_given_child ) ( SELECT DISTINCT ON (child_health_case_id) %(state_id)s AS state_id, LAST_VALUE(supervisor_id) over w AS supervisor_id, %(month)s AS month, child_health_case_id AS case_id, MAX(timeend) over w AS latest_time_end_processed, SUM(days_ration_given_child) over w AS days_ration_given_child FROM "{ucr_tablename}" WHERE state_id = %(state_id)s AND timeend >= %(current_month_start)s AND timeend < %(next_month_start)s AND child_health_case_id IS NOT NULL WINDOW w AS (PARTITION BY supervisor_id, child_health_case_id) ) """.format( ucr_tablename=self.ucr_tablename, tablename=self.tablename ), query_params

true

790dc58cba6e9460dc0cc024e7ffda0c2b5e7fde

223

py

Python

tests/test_formats/test_seq/asserts.py

NickleDave/conbirt

71db6c6fd68dfef1bdbdcfacd8b2a16b21b86089

[ "BSD-3-Clause" ]

null

tests/test_formats/test_seq/asserts.py

NickleDave/conbirt

71db6c6fd68dfef1bdbdcfacd8b2a16b21b86089

[ "BSD-3-Clause" ]

3

2018-12-16T17:57:22.000Z

2018-12-16T20:12:33.000Z

tests/test_formats/test_seq/asserts.py

NickleDave/conbirt

71db6c6fd68dfef1bdbdcfacd8b2a16b21b86089

[ "BSD-3-Clause" ]

null

def assert_rounded_correct_num_decimals(on_offset_arr, decimals): __tracebackhide__ = True assert all( [len(str(float(boundary_s)).split('.')[-1]) <= decimals for boundary_s in on_offset_arr] )

31.857143

65

0.681614

def assert_rounded_correct_num_decimals(on_offset_arr, decimals): __tracebackhide__ = True assert all( [len(str(float(boundary_s)).split('.')[-1]) <= decimals for boundary_s in on_offset_arr] )

true

790dc5a4df3d2a6abf652c7c38db3d90988645fa

2,617

py

Python

commandWindow.py

sturzl/keyboardControlSocket

8fd862c9e970174a396c13d03631e92197b59ac2

[ "Apache-2.0" ]

null

commandWindow.py

sturzl/keyboardControlSocket

8fd862c9e970174a396c13d03631e92197b59ac2

[ "Apache-2.0" ]

null

commandWindow.py

sturzl/keyboardControlSocket

8fd862c9e970174a396c13d03631e92197b59ac2

[ "Apache-2.0" ]

null

import customSocket import sys, pygame #constants windowSize = width, height = 800, 600 #displayed in the window t ogive directiosn to the driver instructionTextLines = open('commands.txt').readlines() activeColor = (0,175,0) inactiveColor = (255,0,0) textColor = (0,0,0) screen = pygame.display.set_mode(windowSize) ################window initialization################################# #makes hte window, sets color, displays text etc. def initializeWindow(): pygame.init() setBackgorundColor(activeColor) pygame.display.set_caption('CWRU NASA RMC 2015-2016') displayIntructionText() def displayIntructionText(): for lineNumber,lineText in enumerate(instructionTextLines): displayText(lineText, lineNumber) #creating the text object, putting it in the window, updating #takes in a string def displayText(text, lineNumber): font = pygame.font.SysFont("monospace", 20) textSurface, textContainer = getTextObject(text, font) textContainer.center = (width/2,10+25*lineNumber) screen.blit(textSurface, textContainer) pygame.display.update() #getting the font, text rectangle etc. #takes in the string of fonts and a pygame Font def getTextObject(text, font): textSurface = font.render(text, True, textColor) return textSurface, textSurface.get_rect() def setBackgorundColor(colorTuple): screen.fill(colorTuple) pygame.display.update() ################################# Gettting Keyboard state ################################ #gets the currently pressed keys and sends them over the socket def sendKeyPresses(): quit = False keysPressed= [] while(True and (quit == False)): nextEvent = str(pygame.event.wait()) if('KeyDown' in nextEvent): #socket.customSend(lastEvent.split(', ')[1].split(' ')[1]) key = nextEvent.split(', ')[1].split(' ')[1] sendCommand(translateToHex(key)) if(key == '27'): quit = True pygame.quit() def translateToHex(key): return{ '273': 76, '274': 77, '275': 78, '276': 79, '46': 57, '47': 58, '115': 33, '119': 17, '100': 34, '97': 32, '102': 35, '114': 19, '104': 37, '32': 64, '111': 24, '27': 69, }.get(key,0) #Waits for a keyboard event, determines which keys are pressed after each keyboard event, #returns the list of currently pressed keys def getNextKeys(): return getCurrentKeys() #def sendKeys(keys): #socket.send(keys) def getCurrentKeys(): pygameEvent = pygame.event.wait() if pygameEvent.event.event_name() == "KEYDOWN": return pygame.key.getPressed(); ############### Main program #################################### initializeWindow() initializeSocket() sendKeyPresses()

25.910891

92

0.670615

import customSocket import sys, pygame windowSize = width, height = 800, 600 instructionTextLines = open('commands.txt').readlines() activeColor = (0,175,0) inactiveColor = (255,0,0) textColor = (0,0,0) screen = pygame.display.set_mode(windowSize)

true

790dc5d903829dbda860ca4d55f8313cce6fc017

1,209

py

Python

setup.py

NikitaKoshelev/aio-space-track-api

dbf7776b6afbb9ef1917ae1526fe53bb33eb0735

[ "MIT" ]

1

2017-05-19T16:18:55.000Z

setup.py

nkoshell/aio-space-track-api

dbf7776b6afbb9ef1917ae1526fe53bb33eb0735

[ "MIT" ]

null

setup.py

nkoshell/aio-space-track-api

dbf7776b6afbb9ef1917ae1526fe53bb33eb0735

[ "MIT" ]

null

# -*- coding: utf-8 -*- import codecs import re import sys from distutils.core import setup import os if sys.version_info < (3, 5, 0): raise RuntimeError("aio-space-track-api requires Python 3.5.0+") PROJECT_DIR = os.path.abspath(os.path.dirname(__file__)) VERSION_REGEXP = re.compile(r"^__version__ = [\'\"](.+?)[\'\"]$", re.MULTILINE) def read(fn): with codecs.open(os.path.join(PROJECT_DIR, fn), encoding='utf-8') as f: return f.read().strip() def version(): try: return VERSION_REGEXP.findall(read(os.path.join('aio_space_track_api', '__init__.py')))[0] except IndexError: raise RuntimeError('Unable to determine version.') vn = version() url = 'https://github.com/nkoshell/aio-space-track-api' setup( name='aio-space-track-api', description='Small async wrapper for "space-track-api" package.', long_description=read('README.rst'), version=vn, packages=['aio_space_track_api'], url=url, download_url='{url}/archive/{version}.tar.gz'.format(url=url, version=vn), license='MIT', author='NikitaKoshelev', author_email='nikita.koshelev@gmail.com', install_requires=['aiohttp>=2.0.7', 'space-track-api>=1.0.2'], )

26.866667

98

0.671629

import codecs import re import sys from distutils.core import setup import os if sys.version_info < (3, 5, 0): raise RuntimeError("aio-space-track-api requires Python 3.5.0+") PROJECT_DIR = os.path.abspath(os.path.dirname(__file__)) VERSION_REGEXP = re.compile(r"^__version__ = [\'\"](.+?)[\'\"]$", re.MULTILINE) def read(fn): with codecs.open(os.path.join(PROJECT_DIR, fn), encoding='utf-8') as f: return f.read().strip() def version(): try: return VERSION_REGEXP.findall(read(os.path.join('aio_space_track_api', '__init__.py')))[0] except IndexError: raise RuntimeError('Unable to determine version.') vn = version() url = 'https://github.com/nkoshell/aio-space-track-api' setup( name='aio-space-track-api', description='Small async wrapper for "space-track-api" package.', long_description=read('README.rst'), version=vn, packages=['aio_space_track_api'], url=url, download_url='{url}/archive/{version}.tar.gz'.format(url=url, version=vn), license='MIT', author='NikitaKoshelev', author_email='nikita.koshelev@gmail.com', install_requires=['aiohttp>=2.0.7', 'space-track-api>=1.0.2'], )

true

790dc5ee729167a086621216d1bf4f04687ccc62

3,575

py

Python

scrips/search_selfcenter/run_selfcenter_search.py

lonelu/Metalprot

e51bee472c975aa171bdb6ee426a07ca69f110ee

[ "MIT" ]

null

scrips/search_selfcenter/run_selfcenter_search.py

lonelu/Metalprot

e51bee472c975aa171bdb6ee426a07ca69f110ee

[ "MIT" ]

null

scrips/search_selfcenter/run_selfcenter_search.py

lonelu/Metalprot

e51bee472c975aa171bdb6ee426a07ca69f110ee

[ "MIT" ]

null

#You can either add the python package path. #sys.path.append(r'/mnt/e/GitHub_Design/Metalprot') from metalprot.search import search_selfcenter from metalprot.basic import filter import pickle import time import prody as pr ''' python /mnt/e/GitHub_Design/Metalprot/scrips/search_selfcenter/run_selfcenter_search.py ''' start_time = time.time() query_dir = '/mnt/e/DesignData/ligands/ZN_rcsb_datesplit/20211013/20211013_selfcenter/pickle_noCYS/' with open(query_dir + 'all_metal_vdm.pkl', 'rb') as f: query_all_metal = pickle.load(f) with open(query_dir + 'AAMetalPhiPsi.pkl', 'rb') as f: all_querys = pickle.load(f) with open(query_dir + 'cluster_centroid_dict.pkl', 'rb') as f: cluster_centroid_dict = pickle.load(f) print(len(all_querys)) ### run Search_struct workdir = '/mnt/e/DesignData/ligands/LigandBB/MID1sc10/' outdir = workdir + 'output_selfcenter/' target_path = workdir + '5od1_zn.pdb' win_filter = [35, 61, 65] # workdir = '/mnt/e/DesignData/ligands/LigandBB/6dwv/' # outdir = workdir + 'output_selfcenter/' # target_path = workdir + '6dwv_core.pdb' # win_filter = [] # workdir = '/mnt/e/DesignData/ligands/LigandBB/8adh/' # outdir = workdir + 'output_selfcenter/' # target_path = workdir + '1989_8adh_ZN_1.pdb' # win_filter = [] # workdir = '/mnt/e/DesignData/ligands/LigandBB/3f7u_lig/' # outdir = workdir + 'output_selfcenter/' # target_path = workdir + '3f7u1aa.pdb' # win_filter = [94, 96, 119] # workdir = '/mnt/e/DesignData/ligands/LigandBB/2afw_lig/' # outdir = workdir + 'output_selfcenter/' # target_path = workdir + '2afw_aa.pdb' # win_filter = [159, 202, 330] # workdir = '/mnt/e/DesignData/ligands/LigandBB/huong/' # outdir = workdir + 'output_selfcenter/' # target_path = workdir + 'aQ4x_aa.pdb' # win_filter = ['I-3', 'I-6', 'I-10', 'I-13', 'I-17', 'I-20', # 'J-3', 'J-6', 'J-7', 'J-10', 'J-13', 'J-14', 'J-17', 'J-20', 'J-21', # 'K-6', 'K-10', 'K-13', 'K-17', 'K-20', # 'L-3', 'L-6', 'L-7', 'L-10', 'L-13', 'L-14', 'L-17', 'L-20', 'L-21', 'L-24', # 'M-3', 'M-6', 'M-10', 'M-13', 'M-17', 'M-20', # 'N-3', 'N-6', 'N-7', 'N-10', 'N-13', 'N-14', 'N-17', 'N-20', 'N-21' # ] geometry_path = None #geometry_path = workdir + 'tetrahydral_geo.pdb' metal_metal_dist = 0.3 num_contact_vdms = [3] allowed_aa_combinations = [['H', 'H', 'H']] allowed_aa_combinations = [] _filter = filter.Search_filter(filter_abple = False, filter_phipsi = True, max_phipsi_val = 25, filter_vdm_score = False, min_vdm_score = 0, filter_vdm_count = False, min_vdm_clu_num = 20, after_search_filter_geometry = True, filter_based_geometry_structure = False, angle_tol = 15, aa_aa_tol = 0.3, aa_metal_tol = 0.2, pair_angle_range = [85, 130], pair_aa_aa_dist_range = [2.8, 4], pair_metal_aa_dist_range = None, after_search_filter_qt_clash = True, vdm_vdm_clash_dist = 2.7, vdm_bb_clash_dist = 2.2, after_search_open_site_clash = True, open_site_dist = 3.0, write_filtered_result = False, selfcenter_filter_member_phipsi=True) ss = search_selfcenter.Search_selfcenter(target_path, outdir, all_querys, cluster_centroid_dict, query_all_metal, num_contact_vdms, metal_metal_dist, win_filter, validateOriginStruct = True, search_filter= _filter, geometry_path = None, density_radius = 0.6, allowed_aa_combinations = allowed_aa_combinations, output_wincomb_overlap=True) #ss.run_selfcenter_search() search_selfcenter.run_search_selfcenter(ss) end_time = time.time() print(end_time - start_time, "seconds")

30.555556

134

0.687552

from metalprot.search import search_selfcenter from metalprot.basic import filter import pickle import time import prody as pr start_time = time.time() query_dir = '/mnt/e/DesignData/ligands/ZN_rcsb_datesplit/20211013/20211013_selfcenter/pickle_noCYS/' with open(query_dir + 'all_metal_vdm.pkl', 'rb') as f: query_all_metal = pickle.load(f) with open(query_dir + 'AAMetalPhiPsi.pkl', 'rb') as f: all_querys = pickle.load(f) with open(query_dir + 'cluster_centroid_dict.pkl', 'rb') as f: cluster_centroid_dict = pickle.load(f) print(len(all_querys)) LigandBB/MID1sc10/' outdir = workdir + 'output_selfcenter/' target_path = workdir + '5od1_zn.pdb' win_filter = [35, 61, 65] geometry_path = None metal_metal_dist = 0.3 num_contact_vdms = [3] allowed_aa_combinations = [['H', 'H', 'H']] allowed_aa_combinations = [] _filter = filter.Search_filter(filter_abple = False, filter_phipsi = True, max_phipsi_val = 25, filter_vdm_score = False, min_vdm_score = 0, filter_vdm_count = False, min_vdm_clu_num = 20, after_search_filter_geometry = True, filter_based_geometry_structure = False, angle_tol = 15, aa_aa_tol = 0.3, aa_metal_tol = 0.2, pair_angle_range = [85, 130], pair_aa_aa_dist_range = [2.8, 4], pair_metal_aa_dist_range = None, after_search_filter_qt_clash = True, vdm_vdm_clash_dist = 2.7, vdm_bb_clash_dist = 2.2, after_search_open_site_clash = True, open_site_dist = 3.0, write_filtered_result = False, selfcenter_filter_member_phipsi=True) ss = search_selfcenter.Search_selfcenter(target_path, outdir, all_querys, cluster_centroid_dict, query_all_metal, num_contact_vdms, metal_metal_dist, win_filter, validateOriginStruct = True, search_filter= _filter, geometry_path = None, density_radius = 0.6, allowed_aa_combinations = allowed_aa_combinations, output_wincomb_overlap=True) search_selfcenter.run_search_selfcenter(ss) end_time = time.time() print(end_time - start_time, "seconds")

true

790dc638b44f4387a90ba0b5662e7ebdc15d51ee

3,092

py

Python

examples/cluster/plot_digits_linkage.py

emarkou/scikit-learn

d73822f84f2832dcc25f0ff58769f60871a78025

[ "BSD-3-Clause" ]

13

2020-01-04T07:37:38.000Z

2021-08-31T05:19:58.000Z

examples/cluster/plot_digits_linkage.py

emarkou/scikit-learn

d73822f84f2832dcc25f0ff58769f60871a78025

[ "BSD-3-Clause" ]

29

2021-03-04T02:56:48.000Z

2021-04-06T04:06:45.000Z

examples/cluster/plot_digits_linkage.py

emarkou/scikit-learn

d73822f84f2832dcc25f0ff58769f60871a78025

[ "BSD-3-Clause" ]

12

2021-02-05T20:33:04.000Z

2022-02-17T04:11:25.000Z

""" ============================================================================= Various Agglomerative Clustering on a 2D embedding of digits ============================================================================= An illustration of various linkage option for agglomerative clustering on a 2D embedding of the digits dataset. The goal of this example is to show intuitively how the metrics behave, and not to find good clusters for the digits. This is why the example works on a 2D embedding. What this example shows us is the behavior "rich getting richer" of agglomerative clustering that tends to create uneven cluster sizes. This behavior is pronounced for the average linkage strategy, that ends up with a couple of singleton clusters, while in the case of single linkage we get a single central cluster with all other clusters being drawn from noise points around the fringes. """ # Authors: Gael Varoquaux # License: BSD 3 clause (C) INRIA 2014 print(__doc__) from time import time import numpy as np from scipy import ndimage from matplotlib import pyplot as plt from sklearn import manifold, datasets X, y = datasets.load_digits(return_X_y=True) n_samples, n_features = X.shape np.random.seed(0) def nudge_images(X, y): # Having a larger dataset shows more clearly the behavior of the # methods, but we multiply the size of the dataset only by 2, as the # cost of the hierarchical clustering methods are strongly # super-linear in n_samples shift = lambda x: ndimage.shift(x.reshape((8, 8)), .3 * np.random.normal(size=2), mode='constant', ).ravel() X = np.concatenate([X, np.apply_along_axis(shift, 1, X)]) Y = np.concatenate([y, y], axis=0) return X, Y X, y = nudge_images(X, y) #---------------------------------------------------------------------- # Visualize the clustering def plot_clustering(X_red, labels, title=None): x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0) X_red = (X_red - x_min) / (x_max - x_min) plt.figure(figsize=(6, 4)) for i in range(X_red.shape[0]): plt.text(X_red[i, 0], X_red[i, 1], str(y[i]), color=plt.cm.nipy_spectral(labels[i] / 10.), fontdict={'weight': 'bold', 'size': 9}) plt.xticks([]) plt.yticks([]) if title is not None: plt.title(title, size=17) plt.axis('off') plt.tight_layout(rect=[0, 0.03, 1, 0.95]) #---------------------------------------------------------------------- # 2D embedding of the digits dataset print("Computing embedding") X_red = manifold.SpectralEmbedding(n_components=2).fit_transform(X) print("Done.") from sklearn.cluster import AgglomerativeClustering for linkage in ('ward', 'average', 'complete', 'single'): clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10) t0 = time() clustering.fit(X_red) print("%s :\t%.2fs" % (linkage, time() - t0)) plot_clustering(X_red, clustering.labels_, "%s linkage" % linkage) plt.show()

33.608696

77

0.614166

print(__doc__) from time import time import numpy as np from scipy import ndimage from matplotlib import pyplot as plt from sklearn import manifold, datasets X, y = datasets.load_digits(return_X_y=True) n_samples, n_features = X.shape np.random.seed(0) def nudge_images(X, y): shift = lambda x: ndimage.shift(x.reshape((8, 8)), .3 * np.random.normal(size=2), mode='constant', ).ravel() X = np.concatenate([X, np.apply_along_axis(shift, 1, X)]) Y = np.concatenate([y, y], axis=0) return X, Y X, y = nudge_images(X, y) def plot_clustering(X_red, labels, title=None): x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0) X_red = (X_red - x_min) / (x_max - x_min) plt.figure(figsize=(6, 4)) for i in range(X_red.shape[0]): plt.text(X_red[i, 0], X_red[i, 1], str(y[i]), color=plt.cm.nipy_spectral(labels[i] / 10.), fontdict={'weight': 'bold', 'size': 9}) plt.xticks([]) plt.yticks([]) if title is not None: plt.title(title, size=17) plt.axis('off') plt.tight_layout(rect=[0, 0.03, 1, 0.95]) print("Computing embedding") X_red = manifold.SpectralEmbedding(n_components=2).fit_transform(X) print("Done.") from sklearn.cluster import AgglomerativeClustering for linkage in ('ward', 'average', 'complete', 'single'): clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10) t0 = time() clustering.fit(X_red) print("%s :\t%.2fs" % (linkage, time() - t0)) plot_clustering(X_red, clustering.labels_, "%s linkage" % linkage) plt.show()

true

790dc67de74e78dc435d99eafd8bb9751781cbb1

6,253

py

Python

qiushaoyi/programs/qsy_program_codes/python3-webapp/www/coroweb.py

qsyPython/Python_play_now

278b6d5d30082f8f93b26902c854737c4919405a

[ "MIT" ]

2

2018-03-29T08:26:17.000Z

2019-06-17T10:56:19.000Z

qiushaoyi/programs/qsy_program_codes/python3-webapp/www/coroweb.py

qsyPython/Python_play_now

278b6d5d30082f8f93b26902c854737c4919405a

[ "MIT" ]

1

2022-03-22T20:26:08.000Z

qiushaoyi/programs/qsy_program_codes/python3-webapp/www/coroweb.py

qsyPython/Python_play_now

278b6d5d30082f8f93b26902c854737c4919405a

[ "MIT" ]

1

2019-02-18T10:44:20.000Z

# ======🙋🙋🙋实现了 1个函数fn 映射为1个URL处理函数！！！ import asyncio, os, inspect, logging, functools from urllib import parse from aiohttp import web from apis import APIError def get(path): ''' Define decorator @get('/path') ''' def decorator(func): @functools.wraps(func) def wrapper(*args, **kw): return func(*args, **kw) wrapper.__method__ = 'GET' wrapper.__route__ = path return wrapper return decorator def post(path): ''' Define decorator @post('/path') ''' def decorator(func): @functools.wraps(func) def wrapper(*args, **kw): return func(*args, **kw) wrapper.__method__ = 'POST' wrapper.__route__ = path return wrapper return decorator def get_required_kw_args(fn): args = [] params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.KEYWORD_ONLY and param.default == inspect.Parameter.empty: args.append(name) return tuple(args) def get_named_kw_args(fn): args = [] params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.KEYWORD_ONLY: args.append(name) return tuple(args) def has_named_kw_args(fn): params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.KEYWORD_ONLY: return True def has_var_kw_arg(fn): params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.VAR_KEYWORD: return True def has_request_arg(fn): sig = inspect.signature(fn) params = sig.parameters found = False for name, param in params.items(): if name == 'request': found = True continue if found and (param.kind != inspect.Parameter.VAR_POSITIONAL and param.kind != inspect.Parameter.KEYWORD_ONLY and param.kind != inspect.Parameter.VAR_KEYWORD): raise ValueError('request parameter must be the last named parameter in function: %s%s' % (fn.__name__, str(sig))) return found class RequestHandler(object): def __init__(self, app, fn): self._app = app self._func = fn self._has_request_arg = has_request_arg(fn) self._has_var_kw_arg = has_var_kw_arg(fn) self._has_named_kw_args = has_named_kw_args(fn) self._named_kw_args = get_named_kw_args(fn) self._required_kw_args = get_required_kw_args(fn) @asyncio.coroutine def __call__(self, request): kw = None if self._has_var_kw_arg or self._has_named_kw_args or self._required_kw_args: if request.method == 'POST': if not request.content_type: return web.HTTPBadRequest('Missing Content-Type.') ct = request.content_type.lower() if ct.startswith('application/json'): params = yield from request.json() if not isinstance(params, dict): return web.HTTPBadRequest('JSON body must be object.') kw = params elif ct.startswith('application/x-www-form-urlencoded') or ct.startswith('multipart/form-data'): params = yield from request.post() kw = dict(**params) else: return web.HTTPBadRequest('Unsupported Content-Type: %s' % request.content_type) if request.method == 'GET': qs = request.query_string if qs: kw = dict() for k, v in parse.parse_qs(qs, True).items(): kw[k] = v[0] if kw is None: kw = dict(**request.match_info) else: if not self._has_var_kw_arg and self._named_kw_args: # remove all unamed kw: copy = dict() for name in self._named_kw_args: if name in kw: copy[name] = kw[name] kw = copy # check named arg: for k, v in request.match_info.items(): if k in kw: logging.warning('Duplicate arg name in named arg and kw args: %s' % k) kw[k] = v if self._has_request_arg: kw['request'] = request # check required kw: if self._required_kw_args: for name in self._required_kw_args: if not name in kw: return web.HTTPBadRequest('Missing argument: %s' % name) logging.info('call with args: %s' % str(kw)) try: r = yield from self._func(**kw) return r except APIError as e: return dict(error=e.error, data=e.data, message=e.message) def add_static(app): path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'static') app.router.add_static('/static/', path) logging.info('add static %s => %s' % ('/static/', path)) def add_route(app, fn): method = getattr(fn, '__method__', None) path = getattr(fn, '__route__', None) if path is None or method is None: raise ValueError('@get or @post not defined in %s.' % str(fn)) if not asyncio.iscoroutinefunction(fn) and not inspect.isgeneratorfunction(fn): fn = asyncio.coroutine(fn) logging.info('add route %s %s => %s(%s)' % (method, path, fn.__name__, ', '.join(inspect.signature(fn).parameters.keys()))) app.router.add_route(method, path, RequestHandler(app, fn)) def add_routes(app, module_name): n = module_name.rfind('.') if n == (-1): mod = __import__(module_name, globals(), locals()) else: name = module_name[n+1:] mod = getattr(__import__(module_name[:n], globals(), locals(), [name]), name) for attr in dir(mod): if attr.startswith('_'): continue fn = getattr(mod, attr) if callable(fn): method = getattr(fn, '__method__', None) path = getattr(fn, '__route__', None) if method and path: add_route(app, fn)

36.567251

167

0.581481

import asyncio, os, inspect, logging, functools from urllib import parse from aiohttp import web from apis import APIError def get(path): def decorator(func): @functools.wraps(func) def wrapper(*args, **kw): return func(*args, **kw) wrapper.__method__ = 'GET' wrapper.__route__ = path return wrapper return decorator def post(path): def decorator(func): @functools.wraps(func) def wrapper(*args, **kw): return func(*args, **kw) wrapper.__method__ = 'POST' wrapper.__route__ = path return wrapper return decorator def get_required_kw_args(fn): args = [] params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.KEYWORD_ONLY and param.default == inspect.Parameter.empty: args.append(name) return tuple(args) def get_named_kw_args(fn): args = [] params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.KEYWORD_ONLY: args.append(name) return tuple(args) def has_named_kw_args(fn): params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.KEYWORD_ONLY: return True def has_var_kw_arg(fn): params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.VAR_KEYWORD: return True def has_request_arg(fn): sig = inspect.signature(fn) params = sig.parameters found = False for name, param in params.items(): if name == 'request': found = True continue if found and (param.kind != inspect.Parameter.VAR_POSITIONAL and param.kind != inspect.Parameter.KEYWORD_ONLY and param.kind != inspect.Parameter.VAR_KEYWORD): raise ValueError('request parameter must be the last named parameter in function: %s%s' % (fn.__name__, str(sig))) return found class RequestHandler(object): def __init__(self, app, fn): self._app = app self._func = fn self._has_request_arg = has_request_arg(fn) self._has_var_kw_arg = has_var_kw_arg(fn) self._has_named_kw_args = has_named_kw_args(fn) self._named_kw_args = get_named_kw_args(fn) self._required_kw_args = get_required_kw_args(fn) @asyncio.coroutine def __call__(self, request): kw = None if self._has_var_kw_arg or self._has_named_kw_args or self._required_kw_args: if request.method == 'POST': if not request.content_type: return web.HTTPBadRequest('Missing Content-Type.') ct = request.content_type.lower() if ct.startswith('application/json'): params = yield from request.json() if not isinstance(params, dict): return web.HTTPBadRequest('JSON body must be object.') kw = params elif ct.startswith('application/x-www-form-urlencoded') or ct.startswith('multipart/form-data'): params = yield from request.post() kw = dict(**params) else: return web.HTTPBadRequest('Unsupported Content-Type: %s' % request.content_type) if request.method == 'GET': qs = request.query_string if qs: kw = dict() for k, v in parse.parse_qs(qs, True).items(): kw[k] = v[0] if kw is None: kw = dict(**request.match_info) else: if not self._has_var_kw_arg and self._named_kw_args: copy = dict() for name in self._named_kw_args: if name in kw: copy[name] = kw[name] kw = copy for k, v in request.match_info.items(): if k in kw: logging.warning('Duplicate arg name in named arg and kw args: %s' % k) kw[k] = v if self._has_request_arg: kw['request'] = request if self._required_kw_args: for name in self._required_kw_args: if not name in kw: return web.HTTPBadRequest('Missing argument: %s' % name) logging.info('call with args: %s' % str(kw)) try: r = yield from self._func(**kw) return r except APIError as e: return dict(error=e.error, data=e.data, message=e.message) def add_static(app): path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'static') app.router.add_static('/static/', path) logging.info('add static %s => %s' % ('/static/', path)) def add_route(app, fn): method = getattr(fn, '__method__', None) path = getattr(fn, '__route__', None) if path is None or method is None: raise ValueError('@get or @post not defined in %s.' % str(fn)) if not asyncio.iscoroutinefunction(fn) and not inspect.isgeneratorfunction(fn): fn = asyncio.coroutine(fn) logging.info('add route %s %s => %s(%s)' % (method, path, fn.__name__, ', '.join(inspect.signature(fn).parameters.keys()))) app.router.add_route(method, path, RequestHandler(app, fn)) def add_routes(app, module_name): n = module_name.rfind('.') if n == (-1): mod = __import__(module_name, globals(), locals()) else: name = module_name[n+1:] mod = getattr(__import__(module_name[:n], globals(), locals(), [name]), name) for attr in dir(mod): if attr.startswith('_'): continue fn = getattr(mod, attr) if callable(fn): method = getattr(fn, '__method__', None) path = getattr(fn, '__route__', None) if method and path: add_route(app, fn)

true

790dc7a15f3e10652fa91d703d5d33b46dee027c

3,734

py

Python

python/fasta/seqlength.py

lotharwissler/bioinformatics

83a53771222ecb0759e3b4bfa2018d2cd7647643

[ "MIT" ]

10

2016-01-13T00:39:30.000Z

2020-11-30T05:56:19.000Z

python/fasta/seqlength.py

lotharwissler/bioinformatics

83a53771222ecb0759e3b4bfa2018d2cd7647643

[ "MIT" ]

1

2017-02-09T22:46:49.000Z

python/fasta/seqlength.py

lotharwissler/bioinformatics

83a53771222ecb0759e3b4bfa2018d2cd7647643

[ "MIT" ]

10

2015-10-09T00:29:16.000Z

2019-06-09T05:32:15.000Z

#!/usr/bin/python import os, sys # low level handling, such as command line stuff import string # string methods available import re # regular expressions import getopt # comand line argument handling import math # match functions from low import * # custom functions, written by myself # ============================================================================= def show_help( ): """ displays the program parameter list and usage information """ stdout( "usage: " + sys.argv[0] + " -f <path>" ) stdout( " " ) stdout( " option description" ) stdout( " -h help (this text here)" ) stdout( " -f fasta file to import" ) stdout( " -g map file, tab delimited, regex to name (one per line) to group sequences into distinct bins" ) stdout( " " ) sys.exit(1) # ============================================================================= def handle_arguments(): """ verifies the presence of all necessary arguments and returns the data dir """ if len ( sys.argv ) == 1: stderr( "no arguments provided." ) show_help() try: # check for the right arguments keys, values = getopt.getopt( sys.argv[1:], "hf:g:" ) except getopt.GetoptError: stderr( "invalid arguments provided." ) show_help() args = {} for key, value in keys: if key == '-f': args['file'] = value if key == '-g': args['group'] = value if not args.has_key('file'): stderr( "import file argument missing." ) show_help() elif not file_exists( args.get('file') ): stderr( "import file does not exist." ) show_help() return args # ============================================================================= def read_groups( file ): groups = {} fo = open( file ) for line in fo: line = line.rstrip() regex, name = line.split("\t") groups[name] = re.compile(regex) fo.close() return groups # ============================================================================= def read_sequences( file, groups ): def add_entry( hash, groups, id, seq ): group = "*all*" for name, regex in groups.iteritems(): if re.search(regex, id): group = name break if hash[group].has_key(id): sys.stderr.write("WARNING: overwriting entry with the same ID (%s) in group %s...\n" %(id, group)) hash[group][id] = seq return hash hash = {} for name, regex in groups.iteritems(): hash[name] = {} if hash.has_key('*all*'): sys.stderr.write("WARNING: you used \"*all*\" as a group name. This name refers to all non-group-matching entries as well!\n") hash['*all*'] = {} id, seq = "", "" fo = open( file ) for line in fo: line = line.rstrip() if line.startswith(">"): if id != "": add_entry( hash, groups, id, seq ) id = line[1:] seq = "" else: seq += line if id != "": add_entry( hash, groups, id, seq ) fo.close() return hash # ============================================================================= def eval_seq_lengths(hash): for group, seqhash in hash.iteritems(): for id, seq in seqhash.iteritems(): print string.join([group, id, str(len(seq))], "\t") # ============================================================================= # === MAIN ==================================================================== # ============================================================================= def main( args ): groups = {} if args.has_key('group'): groups = read_groups( args.get('group') ) seqhash = read_sequences( args.get('file'), groups ) eval_seq_lengths(seqhash) # ============================================================================= args = handle_arguments() main( args )

33.044248

154

0.497054

import os, sys import string import re import getopt import math from low import * def show_help( ): """ displays the program parameter list and usage information """ stdout( "usage: " + sys.argv[0] + " -f <path>" ) stdout( " " ) stdout( " option description" ) stdout( " -h help (this text here)" ) stdout( " -f fasta file to import" ) stdout( " -g map file, tab delimited, regex to name (one per line) to group sequences into distinct bins" ) stdout( " " ) sys.exit(1) def handle_arguments(): """ verifies the presence of all necessary arguments and returns the data dir """ if len ( sys.argv ) == 1: stderr( "no arguments provided." ) show_help() try: keys, values = getopt.getopt( sys.argv[1:], "hf:g:" ) except getopt.GetoptError: stderr( "invalid arguments provided." ) show_help() args = {} for key, value in keys: if key == '-f': args['file'] = value if key == '-g': args['group'] = value if not args.has_key('file'): stderr( "import file argument missing." ) show_help() elif not file_exists( args.get('file') ): stderr( "import file does not exist." ) show_help() return args def read_groups( file ): groups = {} fo = open( file ) for line in fo: line = line.rstrip() regex, name = line.split("\t") groups[name] = re.compile(regex) fo.close() return groups def read_sequences( file, groups ): def add_entry( hash, groups, id, seq ): group = "*all*" for name, regex in groups.iteritems(): if re.search(regex, id): group = name break if hash[group].has_key(id): sys.stderr.write("WARNING: overwriting entry with the same ID (%s) in group %s...\n" %(id, group)) hash[group][id] = seq return hash hash = {} for name, regex in groups.iteritems(): hash[name] = {} if hash.has_key('*all*'): sys.stderr.write("WARNING: you used \"*all*\" as a group name. This name refers to all non-group-matching entries as well!\n") hash['*all*'] = {} id, seq = "", "" fo = open( file ) for line in fo: line = line.rstrip() if line.startswith(">"): if id != "": add_entry( hash, groups, id, seq ) id = line[1:] seq = "" else: seq += line if id != "": add_entry( hash, groups, id, seq ) fo.close() return hash def eval_seq_lengths(hash): for group, seqhash in hash.iteritems(): for id, seq in seqhash.iteritems(): print string.join([group, id, str(len(seq))], "\t") def main( args ): groups = {} if args.has_key('group'): groups = read_groups( args.get('group') ) seqhash = read_sequences( args.get('file'), groups ) eval_seq_lengths(seqhash) args = handle_arguments() main( args )

false

true

790dc821dd259e60fc21fabc45da87128247863e

21,528

py

Python

train.py

yeong35/MusicTransformer-Pytorch

5cd5e1bab8dfa0ed605089d7f41430e6e0596dc8

[ "MIT" ]

null

train.py

yeong35/MusicTransformer-Pytorch

5cd5e1bab8dfa0ed605089d7f41430e6e0596dc8

[ "MIT" ]

null

train.py

yeong35/MusicTransformer-Pytorch

5cd5e1bab8dfa0ed605089d7f41430e6e0596dc8

[ "MIT" ]

null

import os import csv import shutil from datetime import datetime from numpy import logspace import torch import torch.nn as nn from torch.optim.lr_scheduler import LambdaLR from torch.utils.data import DataLoader from torch.optim import Adam from dataset.e_piano import create_epiano_datasets, create_pop909_datasets from model.music_transformer import MusicTransformer from model.discriminator import MusicDiscriminator from model.classifier import CNNDiscriminator from model.loss import SmoothCrossEntropyLoss from utilities.constants import * from utilities.WGAN_GP import WassersteinLoss from utilities.device import get_device, use_cuda from utilities.lr_scheduling import LrStepTracker, get_lr from utilities.argument_funcs import parse_train_args, print_train_args, write_model_params from utilities.run_model import train_epoch, eval_model CSV_HEADER = ["Epoch", "Learn rate", "Avg Train loss", "Train Accuracy", "Avg Eval loss", "Eval accuracy"] dis_filter_sizes = [2, 3, 4, 5] dis_num_filters = [300, 300, 300, 300] # Baseline is an untrained epoch that we evaluate as a baseline loss and accuracy BASELINE_EPOCH = -1 # main def main(): """ ---------- Author: Damon Gwinn ---------- Entry point. Trains a model specified by command line arguments ---------- """ args = parse_train_args() print_train_args(args) if(args.force_cpu): use_cuda(False) print("WARNING: Forced CPU usage, expect model to perform slower") print("") eventid = f"{datetime.now().strftime('MusicTransformer-%Y.%m.%d')}_gan_{args.gan}_creative_{args.creative}_ce_{args.ce_smoothing}" args.output_dir = args.output_dir + "/" + eventid os.makedirs(args.output_dir, exist_ok=True) ##### Output prep ##### params_file = os.path.join(args.output_dir, "model_params.txt") write_model_params(args, params_file) weights_folder = os.path.join(args.output_dir, "weights") os.makedirs(weights_folder, exist_ok=True) results_folder = os.path.join(args.output_dir, "results") os.makedirs(results_folder, exist_ok=True) results_file = os.path.join(results_folder, "results.csv") best_loss_file = os.path.join(results_folder, "best_loss_weights.pickle") best_acc_file = os.path.join(results_folder, "best_acc_weights.pickle") best_loss_critic_file = os.path.join(results_folder, "best_loss_critic_weights.pickle") best_acc_critic_file = os.path.join(results_folder, "best_acc_critic_weights.pickle") best_loss_classifier_file = os.path.join( results_folder, "best_loss_classifier_weights.pickle") best_acc_classifier_file = os.path.join( results_folder, "best_acc_classifier_weights.pickle") best_text = os.path.join(results_folder, "best_epochs.txt") ##### Tensorboard ##### if(args.no_tensorboard): tensorboard_summary = None else: from torch.utils.tensorboard import SummaryWriter tensorboad_dir = os.path.join(args.output_dir, "tensorboard/" + eventid) tensorboard_summary = SummaryWriter(log_dir=tensorboad_dir) ##### Datasets ##### # 데이터셋이 바뀌기 때문에 아래와같이 해주어야함 if args.interval and args.octave: print("octave interval dataset!!") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/octave_interval_e_piano', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop909_dataset = create_pop909_datasets('./dataset/logscale_pop909', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) elif args.octave and args.fusion_encoding and args.absolute: print("absolute dataset!!") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/octave_fusion_absolute_e_piano', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave, fusion = args.fusion_encoding, absolute = args.absolute) pop909_dataset = create_pop909_datasets('./dataset/pop909_absolute', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave, fusion = args.fusion_encoding, absolute = args.absolute) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) elif args.interval and not args.octave: print("interval dataset!!") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/logscale_e_piano', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop909_dataset = create_pop909_datasets('./dataset/logscale_pop909', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) elif args.octave and args.fusion_encoding: print("Octave_fusion dataset!!") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/octave_fusion_e_piano', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave, fusion = args.fusion_encoding) pop909_dataset = create_pop909_datasets('./dataset/logscale_pop909', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave, fusion = args.fusion_encoding) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) elif not args.interval and args.octave and not args.fusion_encoding: print("Octave dataset!!") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/octave_e_piano', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop909_dataset = create_pop909_datasets('./dataset/pop909_octave', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) elif args.logscale: print("logscvale dataset") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/logscale_epiano0420', args.max_sequence, random_seq=True, condition_token=args.condition_token, interval = args.interval, octave = args.octave, logscale=args.logscale, absolute = args.absolute) pop909_dataset = create_pop909_datasets('./dataset/logscale_pop0420', args.max_sequence, random_seq=True, condition_token=args.condition_token, interval = args.interval, octave = args.octave, logscale=args.logscale, absolute = args.absolute) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) else: classic_train, classic_val, classic_test = create_epiano_datasets(args.classic_input_dir, args.max_sequence, condition_token = args.condition_token, octave = args.octave) pop909_dataset = create_pop909_datasets('dataset/pop_pickle/', args.max_sequence, condition_token = args.condition_token, octave = args.octave) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) if args.data == 'both': print("Dataset: both") train_dataset = torch.utils.data.ConcatDataset([ classic_train, pop_train]) val_dataset = torch.utils.data.ConcatDataset([ classic_val, pop_valid]) elif args.data == 'classic': print("Dataset: classic") train_dataset = torch.utils.data.ConcatDataset([classic_train]) val_dataset = torch.utils.data.ConcatDataset([classic_val]) else: print("Dataset: pop") train_dataset = torch.utils.data.ConcatDataset([pop_train]) val_dataset = torch.utils.data.ConcatDataset([pop_valid]) test_dataset = torch.utils.data.ConcatDataset([classic_test, pop_test]) train_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.n_workers, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=args.batch_size, num_workers=args.n_workers) test_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.n_workers) model = MusicTransformer(n_layers=args.n_layers, num_heads=args.num_heads, d_model=args.d_model, dim_feedforward=args.dim_feedforward, dropout=args.dropout, max_sequence=args.max_sequence, rpr=args.rpr, condition_token = args.condition_token, interval = args.interval, octave = args.octave, fusion = args.fusion_encoding, absolute = args.absolute, logscale=args.logscale).to(get_device()) # EY critic # num_prime = args.num_prime critic = MusicDiscriminator(n_layers=args.n_layers // 2, num_heads=args.num_heads // 2, d_model=args.d_model // 2, dim_feedforward=args.dim_feedforward // 2, dropout=args.dropout, max_sequence=args.max_sequence, rpr=args.rpr).to(get_device()) classifier = MusicDiscriminator(n_layers=args.n_layers // 2, num_heads=args.num_heads // 2, d_model=args.d_model // 2, dim_feedforward=args.dim_feedforward // 2, dropout=args.dropout, max_sequence=args.max_sequence, rpr=args.rpr).to(get_device()) if args.creative: classifier.load_state_dict(torch.load('best_classifier_acc_0.9883.pickle')) ##### Continuing from previous training session ##### start_epoch = BASELINE_EPOCH if(args.continue_weights is not None): if(args.continue_epoch is None): print("ERROR: Need epoch number to continue from (-continue_epoch) when using continue_weights") return else: model.load_state_dict(torch.load(args.continue_weights)) start_epoch = args.continue_epoch elif(args.continue_epoch is not None): print("ERROR: Need continue weights (-continue_weights) when using continue_epoch") return ##### Lr Scheduler vs static lr ##### if(args.lr is None): if(args.continue_epoch is None): init_step = 0 else: init_step = args.continue_epoch * len(train_loader) lr = LR_DEFAULT_START lr_stepper = LrStepTracker(args.d_model, SCHEDULER_WARMUP_STEPS, init_step) else: lr = args.lr ##### Not smoothing evaluation loss ##### if args.interval and args.octave: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_OCTAVE_INTERVAL) elif args.interval and not args.octave: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_INTERVAL) elif args.octave and args.fusion_encoding and args.absolute: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_OCTAVE_FUSION_ABSOLUTE) elif args.octave and args.fusion_encoding: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_OCTAVE_FUSION) elif not args.interval and args.octave and not args.fusion_encoding: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_OCTAVE) elif args.logscale: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_RELATIVE) else: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD) ##### SmoothCrossEntropyLoss or CrossEntropyLoss for training ##### if(args.ce_smoothing is None): train_loss_func = eval_loss_func else: if args.interval and args.octave: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_OCTAVE_INTERVAL, ignore_index=TOKEN_PAD_INTERVAL) elif args.interval and not args.octave: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_INTERVAL, ignore_index=TOKEN_PAD_INTERVAL) elif not args.interval and args.octave and args.fusion_encoding and args.absolute: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_OCTAVE_FUSION_ABSOLUTE, ignore_index=TOKEN_PAD_OCTAVE_FUSION_ABSOLUTE) elif not args.interval and args.octave and args.fusion_encoding: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_OCTAVE_FUSION, ignore_index=TOKEN_PAD_OCTAVE_FUSION) elif not args.interval and args.octave and not args.fusion_encoding: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_OCTAVE, ignore_index=TOKEN_PAD_OCTAVE) elif args.logscale: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_RELATIVE, ignore_index=TOKEN_PAD_RELATIVE) else: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE, ignore_index=TOKEN_PAD) ##### EY - WGAN Loss ##### classifier_loss_func = nn.MSELoss() ##### Optimizer ##### opt = Adam(model.parameters(), lr=lr, betas=(ADAM_BETA_1, ADAM_BETA_2), eps=ADAM_EPSILON) critic_opt = Adam(critic.parameters(), lr=lr, betas=(ADAM_BETA_1, ADAM_BETA_2), eps=ADAM_EPSILON) classifier_opt = Adam(classifier.parameters(), lr=lr, betas=(ADAM_BETA_1, ADAM_BETA_2), eps=ADAM_EPSILON) if(args.lr is None): lr_scheduler = LambdaLR(opt, lr_stepper.step) critic_lr_scheduler = LambdaLR(critic_opt, lr_stepper.step) classifier_lr_scheduler = LambdaLR(classifier_opt, lr_stepper.step) else: lr_scheduler = None ##### Tracking best evaluation accuracy ##### best_eval_acc = 0.0 best_eval_acc_epoch = -1 best_eval_loss = float("inf") best_eval_loss_epoch = -1 ##### Results reporting ##### if(not os.path.isfile(results_file)): with open(results_file, "w", newline="") as o_stream: writer = csv.writer(o_stream) writer.writerow(CSV_HEADER) ##### TRAIN LOOP ##### for epoch in range(start_epoch, args.epochs): # Baseline has no training and acts as a base loss and accuracy (epoch 0 in a sense) if(epoch >= BASELINE_EPOCH): print(SEPERATOR) print("NEW EPOCH:", epoch+1) print(SEPERATOR) print("") # Train # EY 고쳐야 할 부분의 시작 train_loss, train_acc, dis_loss, gen_loss, cre_loss, gan_accuracy, class_accuracy, creativity = train_epoch(epoch+1, model, critic, classifier, train_loader, train_loss_func, classifier_loss_func, opt, critic_opt, classifier_opt, lr_scheduler, critic_lr_scheduler, classifier_lr_scheduler, args) print(SEPERATOR) print("Evaluating:") else: print(SEPERATOR) print("Baseline model evaluation (Epoch 0):") # Eval # train_loss, train_acc = eval_model(model, train_loader, train_loss_func) eval_loss, eval_acc = eval_model(model, val_loader, eval_loss_func, args) # Learn rate lr = get_lr(opt) print("Epoch:", epoch+1) print("Avg train loss:", train_loss) print("Avg train acc:", train_acc) print("Avg eval loss:", eval_loss) print("Avg eval acc:", eval_acc) print(SEPERATOR) print("") new_best = False if(eval_acc > best_eval_acc): best_eval_acc = eval_acc best_eval_acc_epoch = epoch+1 torch.save(model.state_dict(), best_acc_file) torch.save(critic.state_dict(), best_acc_critic_file) torch.save(classifier.state_dict(), best_acc_classifier_file) new_best = True if(eval_loss < best_eval_loss): best_eval_loss = eval_loss best_eval_loss_epoch = epoch+1 torch.save(model.state_dict(), best_loss_file) torch.save(critic.state_dict(), best_loss_critic_file) torch.save(classifier.state_dict(), best_loss_classifier_file) new_best = True # Writing out new bests if(new_best): with open(best_text, "w") as o_stream: print("Best eval acc epoch:", best_eval_acc_epoch, file=o_stream) print("Best eval acc:", best_eval_acc, file=o_stream) print("") print("Best eval loss epoch:", best_eval_loss_epoch, file=o_stream) print("Best eval loss:", best_eval_loss, file=o_stream) if(not args.no_tensorboard): tensorboard_summary.add_scalar("Avg_CE_loss/train", train_loss, global_step=epoch+1) tensorboard_summary.add_scalar("Avg_CE_loss/eval", eval_loss, global_step=epoch+1) tensorboard_summary.add_scalar("Accuracy/train", train_acc, global_step=epoch+1) tensorboard_summary.add_scalar("Accuracy/eval", eval_acc, global_step=epoch+1) tensorboard_summary.add_scalar("Learn_rate/train", lr, global_step=epoch+1) tensorboard_summary.add_scalar("Critic_loss/train", dis_loss, global_step=epoch+1) tensorboard_summary.add_scalar("Gen_loss/train", gen_loss, global_step=epoch+1) tensorboard_summary.add_scalar("Creativity_loss/train", cre_loss, global_step=epoch+1) tensorboard_summary.add_scalar("GAN_accuracy/train", gan_accuracy, global_step=epoch+1) tensorboard_summary.add_scalar("Class_accuracy/train", class_accuracy, global_step=epoch+1) tensorboard_summary.add_scalar("Creativity/train", creativity, global_step=epoch+1) tensorboard_summary.flush() if((epoch+1) % args.weight_modulus == 0): epoch_str = str(epoch+1).zfill(PREPEND_ZEROS_WIDTH) path = os.path.join(weights_folder, "epoch_" + epoch_str + ".pickle") torch.save(model.state_dict(), path) with open(results_file, "a", newline="") as o_stream: writer = csv.writer(o_stream) writer.writerow([epoch+1, lr, train_loss, train_acc, eval_loss, eval_acc]) # Sanity check just to make sure everything is gone if(not args.no_tensorboard): tensorboard_summary.flush() return if __name__ == "__main__": main()

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import os import csv import shutil from datetime import datetime from numpy import logspace import torch import torch.nn as nn from torch.optim.lr_scheduler import LambdaLR from torch.utils.data import DataLoader from torch.optim import Adam from dataset.e_piano import create_epiano_datasets, create_pop909_datasets from model.music_transformer import MusicTransformer from model.discriminator import MusicDiscriminator from model.classifier import CNNDiscriminator from model.loss import SmoothCrossEntropyLoss from utilities.constants import * from utilities.WGAN_GP import WassersteinLoss from utilities.device import get_device, use_cuda from utilities.lr_scheduling import LrStepTracker, get_lr from utilities.argument_funcs import parse_train_args, print_train_args, write_model_params from utilities.run_model import train_epoch, eval_model CSV_HEADER = ["Epoch", "Learn rate", "Avg Train loss", "Train Accuracy", "Avg Eval loss", "Eval accuracy"] dis_filter_sizes = [2, 3, 4, 5] dis_num_filters = [300, 300, 300, 300] BASELINE_EPOCH = -1 def main(): args = parse_train_args() print_train_args(args) if(args.force_cpu): use_cuda(False) print("WARNING: Forced CPU usage, expect model to perform slower") print("") eventid = f"{datetime.now().strftime('MusicTransformer-%Y.%m.%d')}_gan_{args.gan}_creative_{args.creative}_ce_{args.ce_smoothing}" args.output_dir = args.output_dir + "/" + eventid os.makedirs(args.output_dir, exist_ok=True) params_file) weights_folder = os.path.join(args.output_dir, "weights") os.makedirs(weights_folder, exist_ok=True) results_folder = os.path.join(args.output_dir, "results") os.makedirs(results_folder, exist_ok=True) results_file = os.path.join(results_folder, "results.csv") best_loss_file = os.path.join(results_folder, "best_loss_weights.pickle") best_acc_file = os.path.join(results_folder, "best_acc_weights.pickle") best_loss_critic_file = os.path.join(results_folder, "best_loss_critic_weights.pickle") best_acc_critic_file = os.path.join(results_folder, "best_acc_critic_weights.pickle") best_loss_classifier_file = os.path.join( results_folder, "best_loss_classifier_weights.pickle") best_acc_classifier_file = os.path.join( results_folder, "best_acc_classifier_weights.pickle") best_text = os.path.join(results_folder, "best_epochs.txt") ls.tensorboard import SummaryWriter tensorboad_dir = os.path.join(args.output_dir, "tensorboard/" + eventid) tensorboard_summary = SummaryWriter(log_dir=tensorboad_dir) ) classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/octave_interval_e_piano', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop909_dataset = create_pop909_datasets('./dataset/logscale_pop909', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) elif args.octave and args.fusion_encoding and args.absolute: print("absolute dataset!!") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/octave_fusion_absolute_e_piano', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave, fusion = args.fusion_encoding, absolute = args.absolute) pop909_dataset = create_pop909_datasets('./dataset/pop909_absolute', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave, fusion = args.fusion_encoding, absolute = args.absolute) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) elif args.interval and not args.octave: print("interval dataset!!") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/logscale_e_piano', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop909_dataset = create_pop909_datasets('./dataset/logscale_pop909', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) elif args.octave and args.fusion_encoding: print("Octave_fusion dataset!!") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/octave_fusion_e_piano', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave, fusion = args.fusion_encoding) pop909_dataset = create_pop909_datasets('./dataset/logscale_pop909', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave, fusion = args.fusion_encoding) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) elif not args.interval and args.octave and not args.fusion_encoding: print("Octave dataset!!") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/octave_e_piano', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop909_dataset = create_pop909_datasets('./dataset/pop909_octave', args.max_sequence, condition_token=args.condition_token, interval = args.interval, octave = args.octave) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) elif args.logscale: print("logscvale dataset") classic_train, classic_val, classic_test = create_epiano_datasets('./dataset/logscale_epiano0420', args.max_sequence, random_seq=True, condition_token=args.condition_token, interval = args.interval, octave = args.octave, logscale=args.logscale, absolute = args.absolute) pop909_dataset = create_pop909_datasets('./dataset/logscale_pop0420', args.max_sequence, random_seq=True, condition_token=args.condition_token, interval = args.interval, octave = args.octave, logscale=args.logscale, absolute = args.absolute) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) else: classic_train, classic_val, classic_test = create_epiano_datasets(args.classic_input_dir, args.max_sequence, condition_token = args.condition_token, octave = args.octave) pop909_dataset = create_pop909_datasets('dataset/pop_pickle/', args.max_sequence, condition_token = args.condition_token, octave = args.octave) pop_train, pop_valid, pop_test = torch.utils.data.random_split(pop909_dataset, [int(len(pop909_dataset) * 0.8), int(len(pop909_dataset) * 0.1), len(pop909_dataset) - int(len(pop909_dataset) * 0.8) - int(len(pop909_dataset) * 0.1)], generator=torch.Generator().manual_seed(42)) if args.data == 'both': print("Dataset: both") train_dataset = torch.utils.data.ConcatDataset([ classic_train, pop_train]) val_dataset = torch.utils.data.ConcatDataset([ classic_val, pop_valid]) elif args.data == 'classic': print("Dataset: classic") train_dataset = torch.utils.data.ConcatDataset([classic_train]) val_dataset = torch.utils.data.ConcatDataset([classic_val]) else: print("Dataset: pop") train_dataset = torch.utils.data.ConcatDataset([pop_train]) val_dataset = torch.utils.data.ConcatDataset([pop_valid]) test_dataset = torch.utils.data.ConcatDataset([classic_test, pop_test]) train_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.n_workers, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=args.batch_size, num_workers=args.n_workers) test_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.n_workers) model = MusicTransformer(n_layers=args.n_layers, num_heads=args.num_heads, d_model=args.d_model, dim_feedforward=args.dim_feedforward, dropout=args.dropout, max_sequence=args.max_sequence, rpr=args.rpr, condition_token = args.condition_token, interval = args.interval, octave = args.octave, fusion = args.fusion_encoding, absolute = args.absolute, logscale=args.logscale).to(get_device()) critic = MusicDiscriminator(n_layers=args.n_layers // 2, num_heads=args.num_heads // 2, d_model=args.d_model // 2, dim_feedforward=args.dim_feedforward // 2, dropout=args.dropout, max_sequence=args.max_sequence, rpr=args.rpr).to(get_device()) classifier = MusicDiscriminator(n_layers=args.n_layers // 2, num_heads=args.num_heads // 2, d_model=args.d_model // 2, dim_feedforward=args.dim_feedforward // 2, dropout=args.dropout, max_sequence=args.max_sequence, rpr=args.rpr).to(get_device()) if args.creative: classifier.load_state_dict(torch.load('best_classifier_acc_0.9883.pickle')) weights") return else: model.load_state_dict(torch.load(args.continue_weights)) start_epoch = args.continue_epoch elif(args.continue_epoch is not None): print("ERROR: Need continue weights (-continue_weights) when using continue_epoch") return len(train_loader) lr = LR_DEFAULT_START lr_stepper = LrStepTracker(args.d_model, SCHEDULER_WARMUP_STEPS, init_step) else: lr = args.lr eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_INTERVAL) elif args.octave and args.fusion_encoding and args.absolute: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_OCTAVE_FUSION_ABSOLUTE) elif args.octave and args.fusion_encoding: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_OCTAVE_FUSION) elif not args.interval and args.octave and not args.fusion_encoding: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_OCTAVE) elif args.logscale: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD_RELATIVE) else: eval_loss_func = nn.CrossEntropyLoss(ignore_index=TOKEN_PAD) f args.interval and not args.octave: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_INTERVAL, ignore_index=TOKEN_PAD_INTERVAL) elif not args.interval and args.octave and args.fusion_encoding and args.absolute: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_OCTAVE_FUSION_ABSOLUTE, ignore_index=TOKEN_PAD_OCTAVE_FUSION_ABSOLUTE) elif not args.interval and args.octave and args.fusion_encoding: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_OCTAVE_FUSION, ignore_index=TOKEN_PAD_OCTAVE_FUSION) elif not args.interval and args.octave and not args.fusion_encoding: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_OCTAVE, ignore_index=TOKEN_PAD_OCTAVE) elif args.logscale: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE_RELATIVE, ignore_index=TOKEN_PAD_RELATIVE) else: train_loss_func = SmoothCrossEntropyLoss(args.ce_smoothing, VOCAB_SIZE, ignore_index=TOKEN_PAD) ADAM_BETA_1, ADAM_BETA_2), eps=ADAM_EPSILON) classifier_opt = Adam(classifier.parameters(), lr=lr, betas=(ADAM_BETA_1, ADAM_BETA_2), eps=ADAM_EPSILON) if(args.lr is None): lr_scheduler = LambdaLR(opt, lr_stepper.step) critic_lr_scheduler = LambdaLR(critic_opt, lr_stepper.step) classifier_lr_scheduler = LambdaLR(classifier_opt, lr_stepper.step) else: lr_scheduler = None terow(CSV_HEADER) H): print(SEPERATOR) print("NEW EPOCH:", epoch+1) print(SEPERATOR) print("") train_loss, train_acc, dis_loss, gen_loss, cre_loss, gan_accuracy, class_accuracy, creativity = train_epoch(epoch+1, model, critic, classifier, train_loader, train_loss_func, classifier_loss_func, opt, critic_opt, classifier_opt, lr_scheduler, critic_lr_scheduler, classifier_lr_scheduler, args) print(SEPERATOR) print("Evaluating:") else: print(SEPERATOR) print("Baseline model evaluation (Epoch 0):") eval_loss, eval_acc = eval_model(model, val_loader, eval_loss_func, args) lr = get_lr(opt) print("Epoch:", epoch+1) print("Avg train loss:", train_loss) print("Avg train acc:", train_acc) print("Avg eval loss:", eval_loss) print("Avg eval acc:", eval_acc) print(SEPERATOR) print("") new_best = False if(eval_acc > best_eval_acc): best_eval_acc = eval_acc best_eval_acc_epoch = epoch+1 torch.save(model.state_dict(), best_acc_file) torch.save(critic.state_dict(), best_acc_critic_file) torch.save(classifier.state_dict(), best_acc_classifier_file) new_best = True if(eval_loss < best_eval_loss): best_eval_loss = eval_loss best_eval_loss_epoch = epoch+1 torch.save(model.state_dict(), best_loss_file) torch.save(critic.state_dict(), best_loss_critic_file) torch.save(classifier.state_dict(), best_loss_classifier_file) new_best = True if(new_best): with open(best_text, "w") as o_stream: print("Best eval acc epoch:", best_eval_acc_epoch, file=o_stream) print("Best eval acc:", best_eval_acc, file=o_stream) print("") print("Best eval loss epoch:", best_eval_loss_epoch, file=o_stream) print("Best eval loss:", best_eval_loss, file=o_stream) if(not args.no_tensorboard): tensorboard_summary.add_scalar("Avg_CE_loss/train", train_loss, global_step=epoch+1) tensorboard_summary.add_scalar("Avg_CE_loss/eval", eval_loss, global_step=epoch+1) tensorboard_summary.add_scalar("Accuracy/train", train_acc, global_step=epoch+1) tensorboard_summary.add_scalar("Accuracy/eval", eval_acc, global_step=epoch+1) tensorboard_summary.add_scalar("Learn_rate/train", lr, global_step=epoch+1) tensorboard_summary.add_scalar("Critic_loss/train", dis_loss, global_step=epoch+1) tensorboard_summary.add_scalar("Gen_loss/train", gen_loss, global_step=epoch+1) tensorboard_summary.add_scalar("Creativity_loss/train", cre_loss, global_step=epoch+1) tensorboard_summary.add_scalar("GAN_accuracy/train", gan_accuracy, global_step=epoch+1) tensorboard_summary.add_scalar("Class_accuracy/train", class_accuracy, global_step=epoch+1) tensorboard_summary.add_scalar("Creativity/train", creativity, global_step=epoch+1) tensorboard_summary.flush() if((epoch+1) % args.weight_modulus == 0): epoch_str = str(epoch+1).zfill(PREPEND_ZEROS_WIDTH) path = os.path.join(weights_folder, "epoch_" + epoch_str + ".pickle") torch.save(model.state_dict(), path) with open(results_file, "a", newline="") as o_stream: writer = csv.writer(o_stream) writer.writerow([epoch+1, lr, train_loss, train_acc, eval_loss, eval_acc]) if(not args.no_tensorboard): tensorboard_summary.flush() return if __name__ == "__main__": main()

true

790dc82fad44913c8a30acf36c53c51c6aad0661

7,486

py

Python

mayan/apps/web_links/views.py

atitaya1412/Mayan-EDMS

bda9302ba4b743e7d829ad118b8b836221888172

[ "Apache-2.0" ]

343

2015-01-05T14:19:35.000Z

2018-12-10T19:07:48.000Z

mayan/apps/web_links/views.py

atitaya1412/Mayan-EDMS

bda9302ba4b743e7d829ad118b8b836221888172

[ "Apache-2.0" ]

191

2015-01-03T00:48:19.000Z

2018-11-30T09:10:25.000Z

mayan/apps/web_links/views.py

atitaya1412/Mayan-EDMS

bda9302ba4b743e7d829ad118b8b836221888172

[ "Apache-2.0" ]

257

2019-05-14T10:26:37.000Z

2022-03-30T03:37:36.000Z

import logging from django.shortcuts import get_object_or_404 from django.template import RequestContext from django.urls import reverse_lazy from django.utils.translation import ugettext_lazy as _ from django.views.generic import RedirectView from mayan.apps.acls.models import AccessControlList from mayan.apps.documents.models import Document, DocumentType from mayan.apps.documents.permissions import permission_document_type_edit from mayan.apps.views.generics import ( AddRemoveView, SingleObjectCreateView, SingleObjectDeleteView, SingleObjectEditView, SingleObjectListView ) from mayan.apps.views.mixins import ExternalObjectViewMixin from .events import event_web_link_edited from .forms import WebLinkForm from .icons import icon_web_link_setup from .links import link_web_link_create from .models import ResolvedWebLink, WebLink from .permissions import ( permission_web_link_create, permission_web_link_delete, permission_web_link_edit, permission_web_link_instance_view, permission_web_link_view ) logger = logging.getLogger(name=__name__) class DocumentTypeWebLinksView(AddRemoveView): main_object_permission = permission_document_type_edit main_object_model = DocumentType main_object_pk_url_kwarg = 'document_type_id' secondary_object_model = WebLink secondary_object_permission = permission_web_link_edit list_available_title = _('Available web links') list_added_title = _('Web links enabled') related_field = 'web_links' def action_add(self, queryset, _event_actor): for obj in queryset: self.main_object.web_links.add(obj) event_web_link_edited.commit( actor=_event_actor, action_object=self.main_object, target=obj ) def action_remove(self, queryset, _event_actor): for obj in queryset: self.main_object.web_links.remove(obj) event_web_link_edited.commit( actor=_event_actor, action_object=self.main_object, target=obj ) def get_actions_extra_kwargs(self): return {'_event_actor': self.request.user} def get_extra_context(self): return { 'object': self.main_object, 'title': _( 'Web links to enable for document type: %s' ) % self.main_object, } class ResolvedWebLinkView(ExternalObjectViewMixin, RedirectView): external_object_pk_url_kwarg = 'document_id' external_object_permission = permission_web_link_instance_view external_object_queryset = Document.valid.all() def get_redirect_url(self, *args, **kwargs): return self.get_web_link().get_redirect( document=self.external_object, user=self.request.user ).url def get_web_link(self): return get_object_or_404( klass=self.get_web_link_queryset(), pk=self.kwargs['web_link_id'] ) def get_web_link_queryset(self): queryset = ResolvedWebLink.objects.get_for( document=self.external_object, user=self.request.user ) return AccessControlList.objects.restrict_queryset( permission=permission_web_link_instance_view, queryset=queryset, user=self.request.user ) class WebLinkCreateView(SingleObjectCreateView): extra_context = {'title': _('Create new web link')} form_class = WebLinkForm post_action_redirect = reverse_lazy( viewname='web_links:web_link_list' ) view_permission = permission_web_link_create def get_instance_extra_data(self): return {'_event_actor': self.request.user} class WebLinkDeleteView(SingleObjectDeleteView): model = WebLink object_permission = permission_web_link_delete pk_url_kwarg = 'web_link_id' post_action_redirect = reverse_lazy( viewname='web_links:web_link_list' ) def get_extra_context(self): return { 'object': self.object, 'title': _('Delete web link: %s') % self.object } class WebLinkDocumentTypesView(AddRemoveView): main_object_method_add_name = 'document_types_add' main_object_method_remove_name = 'document_types_remove' main_object_permission = permission_web_link_edit main_object_model = WebLink main_object_pk_url_kwarg = 'web_link_id' secondary_object_model = DocumentType secondary_object_permission = permission_document_type_edit list_available_title = _('Available document types') list_added_title = _('Document types enabled') related_field = 'document_types' def get_actions_extra_kwargs(self): return {'_event_actor': self.request.user} def get_extra_context(self): return { 'object': self.main_object, 'title': _( 'Document type for which to enable web link: %s' ) % self.main_object, } class WebLinkEditView(SingleObjectEditView): form_class = WebLinkForm model = WebLink object_permission = permission_web_link_edit pk_url_kwarg = 'web_link_id' post_action_redirect = reverse_lazy( viewname='web_links:web_link_list' ) def get_extra_context(self): return { 'object': self.object, 'title': _('Edit web link: %s') % self.object } def get_instance_extra_data(self): return {'_event_actor': self.request.user} class WebLinkListView(SingleObjectListView): object_permission = permission_web_link_view def get_extra_context(self): return { 'hide_link': True, 'hide_object': True, 'no_results_icon': icon_web_link_setup, 'no_results_main_link': link_web_link_create.resolve( context=RequestContext(request=self.request) ), 'no_results_text': _( 'Web links allow generating HTTP links from documents to ' 'external resources. The link URL\'s can contain document ' 'properties values.' ), 'no_results_title': _( 'There are no web links' ), 'title': _('Web links'), } def get_source_queryset(self): return self.get_web_link_queryset() def get_web_link_queryset(self): return WebLink.objects.all() class DocumentWebLinkListView(ExternalObjectViewMixin, WebLinkListView): external_object_permission = permission_web_link_instance_view external_object_pk_url_kwarg = 'document_id' external_object_queryset = Document.valid.all() object_permission = permission_web_link_instance_view def get_extra_context(self): return { 'document': self.external_object, 'hide_link': True, 'hide_object': True, 'no_results_icon': icon_web_link_setup, 'no_results_text': _( 'Web links allow generating HTTP links from documents to ' 'external resources. The link URL\'s can contain document ' 'properties values.' ), 'no_results_title': _( 'There are no web links for this document' ), 'object': self.external_object, 'title': _('Web links for document: %s') % self.external_object, } def get_web_link_queryset(self): return ResolvedWebLink.objects.get_for( document=self.external_object, user=self.request.user )

34.027273

78

0.687016

import logging from django.shortcuts import get_object_or_404 from django.template import RequestContext from django.urls import reverse_lazy from django.utils.translation import ugettext_lazy as _ from django.views.generic import RedirectView from mayan.apps.acls.models import AccessControlList from mayan.apps.documents.models import Document, DocumentType from mayan.apps.documents.permissions import permission_document_type_edit from mayan.apps.views.generics import ( AddRemoveView, SingleObjectCreateView, SingleObjectDeleteView, SingleObjectEditView, SingleObjectListView ) from mayan.apps.views.mixins import ExternalObjectViewMixin from .events import event_web_link_edited from .forms import WebLinkForm from .icons import icon_web_link_setup from .links import link_web_link_create from .models import ResolvedWebLink, WebLink from .permissions import ( permission_web_link_create, permission_web_link_delete, permission_web_link_edit, permission_web_link_instance_view, permission_web_link_view ) logger = logging.getLogger(name=__name__) class DocumentTypeWebLinksView(AddRemoveView): main_object_permission = permission_document_type_edit main_object_model = DocumentType main_object_pk_url_kwarg = 'document_type_id' secondary_object_model = WebLink secondary_object_permission = permission_web_link_edit list_available_title = _('Available web links') list_added_title = _('Web links enabled') related_field = 'web_links' def action_add(self, queryset, _event_actor): for obj in queryset: self.main_object.web_links.add(obj) event_web_link_edited.commit( actor=_event_actor, action_object=self.main_object, target=obj ) def action_remove(self, queryset, _event_actor): for obj in queryset: self.main_object.web_links.remove(obj) event_web_link_edited.commit( actor=_event_actor, action_object=self.main_object, target=obj ) def get_actions_extra_kwargs(self): return {'_event_actor': self.request.user} def get_extra_context(self): return { 'object': self.main_object, 'title': _( 'Web links to enable for document type: %s' ) % self.main_object, } class ResolvedWebLinkView(ExternalObjectViewMixin, RedirectView): external_object_pk_url_kwarg = 'document_id' external_object_permission = permission_web_link_instance_view external_object_queryset = Document.valid.all() def get_redirect_url(self, *args, **kwargs): return self.get_web_link().get_redirect( document=self.external_object, user=self.request.user ).url def get_web_link(self): return get_object_or_404( klass=self.get_web_link_queryset(), pk=self.kwargs['web_link_id'] ) def get_web_link_queryset(self): queryset = ResolvedWebLink.objects.get_for( document=self.external_object, user=self.request.user ) return AccessControlList.objects.restrict_queryset( permission=permission_web_link_instance_view, queryset=queryset, user=self.request.user ) class WebLinkCreateView(SingleObjectCreateView): extra_context = {'title': _('Create new web link')} form_class = WebLinkForm post_action_redirect = reverse_lazy( viewname='web_links:web_link_list' ) view_permission = permission_web_link_create def get_instance_extra_data(self): return {'_event_actor': self.request.user} class WebLinkDeleteView(SingleObjectDeleteView): model = WebLink object_permission = permission_web_link_delete pk_url_kwarg = 'web_link_id' post_action_redirect = reverse_lazy( viewname='web_links:web_link_list' ) def get_extra_context(self): return { 'object': self.object, 'title': _('Delete web link: %s') % self.object } class WebLinkDocumentTypesView(AddRemoveView): main_object_method_add_name = 'document_types_add' main_object_method_remove_name = 'document_types_remove' main_object_permission = permission_web_link_edit main_object_model = WebLink main_object_pk_url_kwarg = 'web_link_id' secondary_object_model = DocumentType secondary_object_permission = permission_document_type_edit list_available_title = _('Available document types') list_added_title = _('Document types enabled') related_field = 'document_types' def get_actions_extra_kwargs(self): return {'_event_actor': self.request.user} def get_extra_context(self): return { 'object': self.main_object, 'title': _( 'Document type for which to enable web link: %s' ) % self.main_object, } class WebLinkEditView(SingleObjectEditView): form_class = WebLinkForm model = WebLink object_permission = permission_web_link_edit pk_url_kwarg = 'web_link_id' post_action_redirect = reverse_lazy( viewname='web_links:web_link_list' ) def get_extra_context(self): return { 'object': self.object, 'title': _('Edit web link: %s') % self.object } def get_instance_extra_data(self): return {'_event_actor': self.request.user} class WebLinkListView(SingleObjectListView): object_permission = permission_web_link_view def get_extra_context(self): return { 'hide_link': True, 'hide_object': True, 'no_results_icon': icon_web_link_setup, 'no_results_main_link': link_web_link_create.resolve( context=RequestContext(request=self.request) ), 'no_results_text': _( 'Web links allow generating HTTP links from documents to ' 'external resources. The link URL\'s can contain document ' 'properties values.' ), 'no_results_title': _( 'There are no web links' ), 'title': _('Web links'), } def get_source_queryset(self): return self.get_web_link_queryset() def get_web_link_queryset(self): return WebLink.objects.all() class DocumentWebLinkListView(ExternalObjectViewMixin, WebLinkListView): external_object_permission = permission_web_link_instance_view external_object_pk_url_kwarg = 'document_id' external_object_queryset = Document.valid.all() object_permission = permission_web_link_instance_view def get_extra_context(self): return { 'document': self.external_object, 'hide_link': True, 'hide_object': True, 'no_results_icon': icon_web_link_setup, 'no_results_text': _( 'Web links allow generating HTTP links from documents to ' 'external resources. The link URL\'s can contain document ' 'properties values.' ), 'no_results_title': _( 'There are no web links for this document' ), 'object': self.external_object, 'title': _('Web links for document: %s') % self.external_object, } def get_web_link_queryset(self): return ResolvedWebLink.objects.get_for( document=self.external_object, user=self.request.user )

true

790dc9935531c6d0395bf850f4f155b21979dd25

4,273

py

Python

Module 2/B04710_CodeBundle/Chapter 4/B04170_04_Python_Draft_01.py

wagnerhsu/packt-Object-oriented-programming-for-JavaScript-developers

a305fabfa0195e7a6e57a4fe57ff9b4f1d55bdcc

[ "MIT" ]

8

2016-10-16T13:01:30.000Z

2021-11-08T13:10:17.000Z

Module 2/B04710_CodeBundle/Chapter 4/B04170_04_Python_Draft_01.py

wagnerhsu/packt-Object-oriented-programming-for-JavaScript-developers

a305fabfa0195e7a6e57a4fe57ff9b4f1d55bdcc

[ "MIT" ]

null

Module 2/B04710_CodeBundle/Chapter 4/B04170_04_Python_Draft_01.py

wagnerhsu/packt-Object-oriented-programming-for-JavaScript-developers

a305fabfa0195e7a6e57a4fe57ff9b4f1d55bdcc

[ "MIT" ]

5

2016-08-24T09:43:42.000Z

2019-11-20T10:54:29.000Z

class Animal: _number_of_legs = 0 _pairs_of_eyes = 0 def __init__(self, age): self._age = age print("Animal created") @property def age(self): return self._age @age.setter def age(self, age): self._age = age def print_legs_and_eyes(self): print("I have " + str(self._number_of_legs) + " legs and " + str(self._pairs_of_eyes * 2) + " eyes.") def print_age(self): print("I am " + str(self._age) + " years old.") class Mammal(Animal): _pairs_of_eyes = 1 def __init__(self, age, is_pregnant=False): super().__init__(age) self._is_pregnant = is_pregnant print("Mammal created") @property def is_pregnant(self): return self._is_pregnant @is_pregnant.setter def is_pregnant(self, is_pregnant): self._is_pregnant = is_pregnant class DomesticMammal(Mammal): def __init__(self, name, age, favorite_toy, is_pregnant=False): super().__init__(age, is_pregnant) self._name = name self._favorite_toy = favorite_toy print("DomesticMammal created") @property def name(self): return self._name @property def favorite_toy(self): return self._favorite_toy @favorite_toy.setter def favorite_toy(self, favorite_toy): self._favorite_toy = favorite_toy def talk(self): print(self._name + ": talks") class Dog(DomesticMammal): _number_of_legs = 4 _breed = "Just a dog" _breed_family = "Dog" def __init__(self, name, age, favorite_toy, is_pregnant=False): super().__init__(name, age, favorite_toy, is_pregnant) print("Dog created") def bark(self, times=1, other_domestic_mammal=None, is_angry=False): message = self.name if other_domestic_mammal is not None: message += " to " + other_domestic_mammal.name + ": " else: message += ": " if is_angry: message += "Grr " message += "Woof " * times print(message) def talk(self): self.bark() @classmethod def print_breed(cls): print(cls._breed) @classmethod def print_breed_family(cls): print(cls._breed_family) class TerrierDog(Dog): _breed = "Terrier dog" _breed_family = "Terrier" def __init__(self, name, age, favorite_toy, is_pregnant=False): super().__init__(name, age, favorite_toy, is_pregnant) print("TerrierDog created") class SmoothFoxTerrier(TerrierDog): _breed = "Smooth Fox Terrier" def __init__(self, name, age, favorite_toy, is_pregnant=False): super().__init__(name, age, favorite_toy, is_pregnant) print("SmoothFoxTerrier created") class Animal: _number_of_legs = 0 _pairs_of_eyes = 0 def __init__(self, age): self._age = age print("Animal created") @property def age(self): return self._age @age.setter def age(self, age): self._age = age def print_legs_and_eyes(self): print("I have " + str(self._number_of_legs) + " legs and " + str(self._pairs_of_eyes * 2) + " eyes.") def print_age(self): print("I am " + str(self._age) + " years old.") def __lt__(self, other): return self.age < other.age def __le__(self, other): return self.age <= other.age def __gt__(self, other): return self.age > other.age def __ge__(self, other): return self.age >= other.age SmoothFoxTerrier.print_breed() SmoothFoxTerrier.print_breed_family() tom = SmoothFoxTerrier("Tom", 5, "Sneakers") print(isinstance(tom, Animal)) print(isinstance(tom, Mammal)) print(isinstance(tom, DomesticMammal)) print(isinstance(tom, Dog)) print(isinstance(tom, TerrierDog)) print(isinstance(tom, SmoothFoxTerrier)) pluto = SmoothFoxTerrier("Pluto", 6, "Tennis ball") goofy = SmoothFoxTerrier("Goofy", 8, "Soda bottle") print(tom > pluto) print(tom < pluto) print(goofy >= tom) print(tom <= goofy) tom.bark() tom.bark(2) tom.bark(2, pluto) tom.bark(3, pluto, True)

23.097297

110

0.608238

class Animal: _number_of_legs = 0 _pairs_of_eyes = 0 def __init__(self, age): self._age = age print("Animal created") @property def age(self): return self._age @age.setter def age(self, age): self._age = age def print_legs_and_eyes(self): print("I have " + str(self._number_of_legs) + " legs and " + str(self._pairs_of_eyes * 2) + " eyes.") def print_age(self): print("I am " + str(self._age) + " years old.") class Mammal(Animal): _pairs_of_eyes = 1 def __init__(self, age, is_pregnant=False): super().__init__(age) self._is_pregnant = is_pregnant print("Mammal created") @property def is_pregnant(self): return self._is_pregnant @is_pregnant.setter def is_pregnant(self, is_pregnant): self._is_pregnant = is_pregnant class DomesticMammal(Mammal): def __init__(self, name, age, favorite_toy, is_pregnant=False): super().__init__(age, is_pregnant) self._name = name self._favorite_toy = favorite_toy print("DomesticMammal created") @property def name(self): return self._name @property def favorite_toy(self): return self._favorite_toy @favorite_toy.setter def favorite_toy(self, favorite_toy): self._favorite_toy = favorite_toy def talk(self): print(self._name + ": talks") class Dog(DomesticMammal): _number_of_legs = 4 _breed = "Just a dog" _breed_family = "Dog" def __init__(self, name, age, favorite_toy, is_pregnant=False): super().__init__(name, age, favorite_toy, is_pregnant) print("Dog created") def bark(self, times=1, other_domestic_mammal=None, is_angry=False): message = self.name if other_domestic_mammal is not None: message += " to " + other_domestic_mammal.name + ": " else: message += ": " if is_angry: message += "Grr " message += "Woof " * times print(message) def talk(self): self.bark() @classmethod def print_breed(cls): print(cls._breed) @classmethod def print_breed_family(cls): print(cls._breed_family) class TerrierDog(Dog): _breed = "Terrier dog" _breed_family = "Terrier" def __init__(self, name, age, favorite_toy, is_pregnant=False): super().__init__(name, age, favorite_toy, is_pregnant) print("TerrierDog created") class SmoothFoxTerrier(TerrierDog): _breed = "Smooth Fox Terrier" def __init__(self, name, age, favorite_toy, is_pregnant=False): super().__init__(name, age, favorite_toy, is_pregnant) print("SmoothFoxTerrier created") class Animal: _number_of_legs = 0 _pairs_of_eyes = 0 def __init__(self, age): self._age = age print("Animal created") @property def age(self): return self._age @age.setter def age(self, age): self._age = age def print_legs_and_eyes(self): print("I have " + str(self._number_of_legs) + " legs and " + str(self._pairs_of_eyes * 2) + " eyes.") def print_age(self): print("I am " + str(self._age) + " years old.") def __lt__(self, other): return self.age < other.age def __le__(self, other): return self.age <= other.age def __gt__(self, other): return self.age > other.age def __ge__(self, other): return self.age >= other.age SmoothFoxTerrier.print_breed() SmoothFoxTerrier.print_breed_family() tom = SmoothFoxTerrier("Tom", 5, "Sneakers") print(isinstance(tom, Animal)) print(isinstance(tom, Mammal)) print(isinstance(tom, DomesticMammal)) print(isinstance(tom, Dog)) print(isinstance(tom, TerrierDog)) print(isinstance(tom, SmoothFoxTerrier)) pluto = SmoothFoxTerrier("Pluto", 6, "Tennis ball") goofy = SmoothFoxTerrier("Goofy", 8, "Soda bottle") print(tom > pluto) print(tom < pluto) print(goofy >= tom) print(tom <= goofy) tom.bark() tom.bark(2) tom.bark(2, pluto) tom.bark(3, pluto, True)

true

790dc993af14fbf202955f5e6c992d231e8d2f2f

812

py

Python

tests/test_function_definition.py

joseph-hellerstein/symSBML-deprecated

197f1860bb2e8c5648b3d95d51f8b774fadcaa68

[ "MIT" ]

1

2021-01-10T03:39:59.000Z

tests/test_function_definition.py

joseph-hellerstein/symSBML-deprecated

197f1860bb2e8c5648b3d95d51f8b774fadcaa68

[ "MIT" ]

null

tests/test_function_definition.py

joseph-hellerstein/symSBML-deprecated

197f1860bb2e8c5648b3d95d51f8b774fadcaa68

[ "MIT" ]

3

2020-08-06T08:02:31.000Z

2022-01-16T18:08:35.000Z

""" Tests for Reactions """ from src.common import constants as cn from src.common.simple_sbml import SimpleSBML from src.common import simple_sbml from src.common.function_definition import FunctionDefinition from tests.common import helpers import copy import libsbml import numpy as np import unittest IGNORE_TEST = False IS_PLOT = False ############################# # Tests ############################# class TestFunctionDefinition(unittest.TestCase): def setUp(self): self.simple = helpers.getSimple_BIOMD56() self.function_definition = FunctionDefinition( self.simple.model.getFunctionDefinition(0)) def testConstructor(self): if IGNORE_TEST: return self.assertEqual(len(self.function_definition.argument_names), 4) if __name__ == '__main__': unittest.main()

21.368421

69

0.716749

from src.common import constants as cn from src.common.simple_sbml import SimpleSBML from src.common import simple_sbml from src.common.function_definition import FunctionDefinition from tests.common import helpers import copy import libsbml import numpy as np import unittest IGNORE_TEST = False IS_PLOT = False

true

790dc9cd46fdf4e0494e3bb706af8f1b46702da5

2,002

py

Python

ex35.py

Eithandarphyo51/python-test-exercises

85d1cbb82fc878315be46d168e5eb0f949c6ded4

[ "MIT" ]

null

ex35.py

Eithandarphyo51/python-test-exercises

85d1cbb82fc878315be46d168e5eb0f949c6ded4

[ "MIT" ]

null

ex35.py

Eithandarphyo51/python-test-exercises

85d1cbb82fc878315be46d168e5eb0f949c6ded4

[ "MIT" ]

null

from sys import exit def gold_room(): print("This room is full of gold. How much do you t ake?") choice = input("> ") if "0" in choice or "1" in choice: how_much = int(choice) else: dead("Man, learn to type a number.") if how_much < 50: print("Nice, you're not greedy, you win!") exit(0) else: dead("You greedy bastard!") def bear_room(): print("There is a bear here.") print("The bear has a bunch of honey.") print("The fat bear is in front of another door.") print("How are you going to move the bear?") bear_moved = False while True: choice = input("> ") if choice == "take honey": dead("The bear looks at you then slaps your face off.") elif choice == "taunt bear" and not bear_moved: print("The bear has moved from the door.") print("You can go through it now.") bear_moved = True elif choice == "taunt bear" and bear_moved: dead("The bear gets pissed off and chews your leg off.") elif choice == "open door" and bear_moved: gold_room() else: print("I got not idea what that means.") def cthulhu_room(): print("Here you see the great evil Cthulhu.") print("He, it, whatever stares at you and you go insane.") print("Do you flee for your life or eat your head?") choice = input("> ") if "flee" in choice: start() elif "head" in choice: dead("Well that was tasty!") else: cthulhu_room() def dead(why): print(why, "Good job!") exit(0) def start(): print("You are in a dark room.") print("There is a door to your right and left.") print("Which one do you take?") choice = input("> ") if choice == "left": bear_room() elif choice == "right": cthulhu_room() else: dead("You stumble around the room until you starve.") start()

25.025

83

0.564935

from sys import exit def gold_room(): print("This room is full of gold. How much do you t ake?") choice = input("> ") if "0" in choice or "1" in choice: how_much = int(choice) else: dead("Man, learn to type a number.") if how_much < 50: print("Nice, you're not greedy, you win!") exit(0) else: dead("You greedy bastard!") def bear_room(): print("There is a bear here.") print("The bear has a bunch of honey.") print("The fat bear is in front of another door.") print("How are you going to move the bear?") bear_moved = False while True: choice = input("> ") if choice == "take honey": dead("The bear looks at you then slaps your face off.") elif choice == "taunt bear" and not bear_moved: print("The bear has moved from the door.") print("You can go through it now.") bear_moved = True elif choice == "taunt bear" and bear_moved: dead("The bear gets pissed off and chews your leg off.") elif choice == "open door" and bear_moved: gold_room() else: print("I got not idea what that means.") def cthulhu_room(): print("Here you see the great evil Cthulhu.") print("He, it, whatever stares at you and you go insane.") print("Do you flee for your life or eat your head?") choice = input("> ") if "flee" in choice: start() elif "head" in choice: dead("Well that was tasty!") else: cthulhu_room() def dead(why): print(why, "Good job!") exit(0) def start(): print("You are in a dark room.") print("There is a door to your right and left.") print("Which one do you take?") choice = input("> ") if choice == "left": bear_room() elif choice == "right": cthulhu_room() else: dead("You stumble around the room until you starve.") start()

true

790dcb854858e35c21f05a12f5c5e59fd69b88de

17,505

py

Python

tensor2tensor/data_generators/algorithmic.py

shankharaj29/tensor2tensor

b89ba51a6fa9e0c20009cfb57ee8de04f7138392

[ "Apache-2.0" ]

1

2019-02-16T10:39:45.000Z

tensor2tensor/data_generators/algorithmic.py

PedroLelis/tensor2tensor

5a867d031bd493eeb7d2776e1118d1594ff0a623

[ "Apache-2.0" ]

null

tensor2tensor/data_generators/algorithmic.py

PedroLelis/tensor2tensor

5a867d031bd493eeb7d2776e1118d1594ff0a623

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2018 The Tensor2Tensor Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Algorithmic data generators.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import shutil import numpy as np from six.moves import range # pylint: disable=redefined-builtin from tensor2tensor.data_generators import generator_utils as utils from tensor2tensor.data_generators import problem from tensor2tensor.data_generators import text_encoder from tensor2tensor.layers import modalities from tensor2tensor.utils import metrics from tensor2tensor.utils import registry import tensorflow as tf class AlgorithmicProblem(problem.Problem): """Base class for algorithmic problems.""" @property def num_symbols(self): raise NotImplementedError() def generator(self, nbr_symbols, max_length, nbr_cases): """Generates the data.""" raise NotImplementedError() @property def train_length(self): return 40 @property def dev_length(self): return 400 @property def train_size(self): return 100000 @property def dev_size(self): return 10000 @property def num_shards(self): return 10 def generate_data(self, data_dir, _, task_id=-1): def generator_eos(nbr_symbols, max_length, nbr_cases): """Shift by NUM_RESERVED_IDS and append EOS token.""" for case in self.generator(nbr_symbols, max_length, nbr_cases): new_case = {} for feature in case: new_case[feature] = [ i + text_encoder.NUM_RESERVED_TOKENS for i in case[feature] ] + [text_encoder.EOS_ID] yield new_case utils.generate_dataset_and_shuffle( generator_eos(self.num_symbols, self.train_length, self.train_size), self.training_filepaths(data_dir, self.num_shards, shuffled=True), generator_eos(self.num_symbols, self.dev_length, self.dev_size), self.dev_filepaths(data_dir, 1, shuffled=True), shuffle=False) def hparams(self, defaults, unused_model_hparams): p = defaults vocab_size = self.num_symbols + text_encoder.NUM_RESERVED_TOKENS p.modality = {"inputs": modalities.ModalityType.SYMBOL, "targets": modalities.ModalityType.SYMBOL} p.vocab_size = {"inputs": vocab_size, "targets": vocab_size} p.input_space_id = problem.SpaceID.DIGIT_0 p.target_space_id = problem.SpaceID.DIGIT_1 @registry.register_problem class AlgorithmicIdentityBinary40(AlgorithmicProblem): """Problem spec for algorithmic binary identity task.""" @property def num_symbols(self): return 2 def generator(self, nbr_symbols, max_length, nbr_cases): """Generator for the identity (copy) task on sequences of symbols. The length of the sequence is drawn uniformly at random from [1, max_length] and then symbols are drawn uniformly at random from [0, nbr_symbols) until nbr_cases sequences have been produced. Args: nbr_symbols: number of symbols to use in each sequence. max_length: integer, maximum length of sequences to generate. nbr_cases: the number of cases to generate. Yields: A dictionary {"inputs": input-list, "targets": target-list} where input-list and target-list are the same. """ for _ in range(nbr_cases): l = np.random.randint(max_length) + 1 inputs = [np.random.randint(nbr_symbols) for _ in range(l)] yield {"inputs": inputs, "targets": inputs} @registry.register_problem class AlgorithmicIdentityDecimal40(AlgorithmicIdentityBinary40): """Problem spec for algorithmic decimal identity task.""" @property def num_symbols(self): return 10 @registry.register_problem class AlgorithmicShiftDecimal40(AlgorithmicProblem): """Problem spec for algorithmic decimal shift task.""" @property def num_symbols(self): return 20 def generator(self, nbr_symbols, max_length, nbr_cases): """Generator for the shift task on sequences of symbols. The length of the sequence is drawn uniformly at random from [1, max_length] and then symbols are drawn uniformly at random from [0, nbr_symbols - shift] until nbr_cases sequences have been produced (output[i] = input[i] + shift). Args: nbr_symbols: number of symbols to use in each sequence (input + output). max_length: integer, maximum length of sequences to generate. nbr_cases: the number of cases to generate. Yields: A dictionary {"inputs": input-list, "targets": target-list} where target-list[i] = input-list[i] + shift. """ shift = 10 for _ in range(nbr_cases): l = np.random.randint(max_length) + 1 inputs = [np.random.randint(nbr_symbols - shift) for _ in range(l)] yield {"inputs": inputs, "targets": [i + shift for i in inputs]} @property def dev_length(self): return 80 @registry.register_problem class AlgorithmicReverseBinary40(AlgorithmicProblem): """Problem spec for algorithmic binary reversing task.""" @property def num_symbols(self): return 2 def generator(self, nbr_symbols, max_length, nbr_cases): """Generator for the reversing task on sequences of symbols. The length of the sequence is drawn uniformly at random from [1, max_length] and then symbols are drawn uniformly at random from [0, nbr_symbols) until nbr_cases sequences have been produced. Args: nbr_symbols: number of symbols to use in each sequence. max_length: integer, maximum length of sequences to generate. nbr_cases: the number of cases to generate. Yields: A dictionary {"inputs": input-list, "targets": target-list} where target-list is input-list reversed. """ for _ in range(nbr_cases): l = np.random.randint(max_length) + 1 inputs = [np.random.randint(nbr_symbols) for _ in range(l)] yield {"inputs": inputs, "targets": list(reversed(inputs))} @registry.register_problem class AlgorithmicReverseDecimal40(AlgorithmicReverseBinary40): """Problem spec for algorithmic decimal reversing task.""" @property def num_symbols(self): return 10 def zipf_distribution(nbr_symbols, alpha): """Helper function: Create a Zipf distribution. Args: nbr_symbols: number of symbols to use in the distribution. alpha: float, Zipf's Law Distribution parameter. Default = 1.5. Usually for modelling natural text distribution is in the range [1.1-1.6]. Returns: distr_map: list of float, Zipf's distribution over nbr_symbols. """ tmp = np.power(np.arange(1, nbr_symbols + 1), -alpha) zeta = np.r_[0.0, np.cumsum(tmp)] return [x / zeta[-1] for x in zeta] def zipf_random_sample(distr_map, sample_len): """Helper function: Generate a random Zipf sample of given length. Args: distr_map: list of float, Zipf's distribution over nbr_symbols. sample_len: integer, length of sequence to generate. Returns: sample: list of integer, Zipf's random sample over nbr_symbols. """ u = np.random.random(sample_len) # Random produces values in range [0.0,1.0); even if it is almost # improbable(but possible) that it can generate a clear 0.000..0. return list(np.searchsorted(distr_map, u)) def reverse_generator_nlplike(nbr_symbols, max_length, nbr_cases, scale_std_dev=100, alpha=1.5): """Generator for the reversing nlp-like task on sequences of symbols. The length of the sequence is drawn from a Gaussian(Normal) distribution at random from [1, max_length] and with std deviation of 1%, then symbols are drawn from Zipf's law at random from [0, nbr_symbols) until nbr_cases sequences have been produced. Args: nbr_symbols: integer, number of symbols. max_length: integer, maximum length of sequences to generate. nbr_cases: the number of cases to generate. scale_std_dev: float, Normal distribution's standard deviation scale factor used to draw the length of sequence. Default = 1% of the max_length. alpha: float, Zipf's Law Distribution parameter. Default = 1.5. Usually for modelling natural text distribution is in the range [1.1-1.6]. Yields: A dictionary {"inputs": input-list, "targets": target-list} where target-list is input-list reversed. """ std_dev = max_length / scale_std_dev distr_map = zipf_distribution(nbr_symbols, alpha) for _ in range(nbr_cases): l = int(abs(np.random.normal(loc=max_length / 2, scale=std_dev)) + 1) inputs = zipf_random_sample(distr_map, l) yield {"inputs": inputs, "targets": list(reversed(inputs))} @registry.register_problem class AlgorithmicReverseNlplike8k(AlgorithmicProblem): """Problem spec for algorithmic nlp-like reversing task.""" @property def num_symbols(self): return 8000 def generator(self, nbr_symbols, max_length, nbr_cases): return reverse_generator_nlplike(nbr_symbols, max_length, nbr_cases, 10, 1.300) @property def train_length(self): return 70 @property def dev_length(self): return 70 @registry.register_problem class AlgorithmicReverseNlplike32k(AlgorithmicReverseNlplike8k): """Problem spec for algorithmic nlp-like reversing task, 32k vocab.""" @property def num_symbols(self): return 32000 def generator(self, nbr_symbols, max_length, nbr_cases): return reverse_generator_nlplike(nbr_symbols, max_length, nbr_cases, 10, 1.050) def lower_endian_to_number(l, base): """Helper function: convert a list of digits in the given base to a number.""" return sum([d * (base**i) for i, d in enumerate(l)]) def number_to_lower_endian(n, base): """Helper function: convert a number to a list of digits in the given base.""" if n < base: return [n] return [n % base] + number_to_lower_endian(n // base, base) def random_number_lower_endian(length, base): """Helper function: generate a random number as a lower-endian digits list.""" if length == 1: # Last digit can be 0 only if length is 1. return [np.random.randint(base)] prefix = [np.random.randint(base) for _ in range(length - 1)] return prefix + [np.random.randint(base - 1) + 1] # Last digit is not 0. @registry.register_problem class AlgorithmicAdditionBinary40(AlgorithmicProblem): """Problem spec for algorithmic binary addition task.""" @property def num_symbols(self): return 2 def generator(self, base, max_length, nbr_cases): # pylint: disable=arguments-differ """Generator for the addition task. The length of each number is drawn uniformly at random in [1, max_length/2] and then digits are drawn uniformly at random. The numbers are added and separated by [base] in the input. Stops at nbr_cases. Args: base: in which base are the numbers. max_length: integer, maximum length of sequences to generate. nbr_cases: the number of cases to generate. Yields: A dictionary {"inputs": input-list, "targets": target-list} where input-list are the 2 numbers and target-list is the result of adding them. Raises: ValueError: if max_length is lower than 3. """ if max_length < 3: raise ValueError("Maximum length must be at least 3.") for _ in range(nbr_cases): l1 = np.random.randint(max_length // 2) + 1 l2 = np.random.randint(max_length - l1 - 1) + 1 n1 = random_number_lower_endian(l1, base) n2 = random_number_lower_endian(l2, base) result = lower_endian_to_number(n1, base) + lower_endian_to_number( n2, base) inputs = n1 + [base] + n2 targets = number_to_lower_endian(result, base) yield {"inputs": inputs, "targets": targets} @registry.register_problem class AlgorithmicAdditionDecimal40(AlgorithmicAdditionBinary40): """Problem spec for algorithmic decimal addition task.""" @property def num_symbols(self): return 10 @registry.register_problem class AlgorithmicMultiplicationBinary40(AlgorithmicProblem): """Problem spec for algorithmic binary multiplication task.""" @property def num_symbols(self): return 2 def generator(self, base, max_length, nbr_cases): # pylint: disable=arguments-differ """Generator for the multiplication task. The length of each number is drawn uniformly at random in [1, max_length/2] and then digits are drawn uniformly at random. The numbers are multiplied and separated by [base] in the input. Stops at nbr_cases. Args: base: in which base are the numbers. max_length: integer, maximum length of sequences to generate. nbr_cases: the number of cases to generate. Yields: A dictionary {"inputs": input-list, "targets": target-list} where input-list are the 2 numbers and target-list is the result of multiplying them. Raises: ValueError: if max_length is lower than 3. """ if max_length < 3: raise ValueError("Maximum length must be at least 3.") for _ in range(nbr_cases): l1 = np.random.randint(max_length // 2) + 1 l2 = np.random.randint(max_length - l1 - 1) + 1 n1 = random_number_lower_endian(l1, base) n2 = random_number_lower_endian(l2, base) result = lower_endian_to_number(n1, base) * lower_endian_to_number( n2, base) inputs = n1 + [base] + n2 targets = number_to_lower_endian(result, base) yield {"inputs": inputs, "targets": targets} @registry.register_problem class AlgorithmicMultiplicationDecimal40(AlgorithmicMultiplicationBinary40): """Problem spec for algorithmic decimal multiplication task.""" @property def num_symbols(self): return 10 @registry.register_problem class AlgorithmicReverseBinary40Test(AlgorithmicReverseBinary40): """Test Problem with tiny dataset.""" @property def train_length(self): return 10 @property def dev_length(self): return 10 @property def train_size(self): return 1000 @property def dev_size(self): return 100 @property def num_shards(self): return 1 @registry.register_problem class AlgorithmicSortProblem(AlgorithmicProblem): """Problem spec for sorting numbers.""" @property def num_symbols(self): return max(self.train_length, self.dev_length) @property def train_length(self): return 10 @property def dev_length(self): return self.train_length * 2 @property def unique(self): """Unique numbers wo/ replacement or w/ replacement in sorting task.""" return False def generator(self, nbr_symbols, max_length, nbr_cases): """Generating for sorting task on sequence of symbols. The length of the sequence is drawn uniformly at random from [1, max_length] and then symbols are drawn (uniquely w/ or w/o replacement) uniformly at random from [0, nbr_symbols) until nbr_cases sequences have been produced. Args: nbr_symbols: number of symbols to use in each sequence. max_length: integer, maximum length of sequences to generate. nbr_cases: the number of cases to generate. Yields: A dictionary {"inputs": input-list, "targets": target-list} where target-list is input-list sorted. """ for _ in range(nbr_cases): # Sample the sequence length. length = np.random.randint(max_length) + 1 if self.unique: # Sample our inputs w/o replacement. inputs = np.arange(nbr_symbols) np.random.shuffle(inputs) # Truncate to the desired length. inputs = inputs[:length] inputs = list(inputs) else: inputs = list(np.random.randint(nbr_symbols, size=length)) # Targets are simply the sorted inputs. targets = list(sorted(inputs)) yield {"inputs": inputs, "targets": targets} def eval_metrics(self): defaults = super(AlgorithmicSortProblem, self).eval_metrics() return defaults + [metrics.Metrics.EDIT_DISTANCE] @registry.register_problem class TinyAlgo(AlgorithmicIdentityBinary40): """A small algorthmic problem for testing.""" def generate_data(self, data_dir, tmp_dir, task_id=-1): """Ganerate data for this problem.""" del tmp_dir, task_id identity_problem = AlgorithmicIdentityBinary40() utils.generate_files( identity_problem.generator(self.num_symbols, 40, 100000), self.training_filepaths(data_dir, 1, shuffled=True), 100) utils.generate_files( identity_problem.generator(self.num_symbols, 400, 10000), self.dev_filepaths(data_dir, 1, shuffled=True), 100) @classmethod def setup_for_test(cls): """Setup directories and files required to run the problem.""" tmp_dir = tf.test.get_temp_dir() shutil.rmtree(tmp_dir) os.mkdir(tmp_dir) cls.data_dir = tmp_dir # Generate a small test dataset cls().generate_data(TinyAlgo.data_dir, None)

32.06044

87

0.70517

from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import shutil import numpy as np from six.moves import range from tensor2tensor.data_generators import generator_utils as utils from tensor2tensor.data_generators import problem from tensor2tensor.data_generators import text_encoder from tensor2tensor.layers import modalities from tensor2tensor.utils import metrics from tensor2tensor.utils import registry import tensorflow as tf class AlgorithmicProblem(problem.Problem): @property def num_symbols(self): raise NotImplementedError() def generator(self, nbr_symbols, max_length, nbr_cases): raise NotImplementedError() @property def train_length(self): return 40 @property def dev_length(self): return 400 @property def train_size(self): return 100000 @property def dev_size(self): return 10000 @property def num_shards(self): return 10 def generate_data(self, data_dir, _, task_id=-1): def generator_eos(nbr_symbols, max_length, nbr_cases): for case in self.generator(nbr_symbols, max_length, nbr_cases): new_case = {} for feature in case: new_case[feature] = [ i + text_encoder.NUM_RESERVED_TOKENS for i in case[feature] ] + [text_encoder.EOS_ID] yield new_case utils.generate_dataset_and_shuffle( generator_eos(self.num_symbols, self.train_length, self.train_size), self.training_filepaths(data_dir, self.num_shards, shuffled=True), generator_eos(self.num_symbols, self.dev_length, self.dev_size), self.dev_filepaths(data_dir, 1, shuffled=True), shuffle=False) def hparams(self, defaults, unused_model_hparams): p = defaults vocab_size = self.num_symbols + text_encoder.NUM_RESERVED_TOKENS p.modality = {"inputs": modalities.ModalityType.SYMBOL, "targets": modalities.ModalityType.SYMBOL} p.vocab_size = {"inputs": vocab_size, "targets": vocab_size} p.input_space_id = problem.SpaceID.DIGIT_0 p.target_space_id = problem.SpaceID.DIGIT_1 @registry.register_problem class AlgorithmicIdentityBinary40(AlgorithmicProblem): @property def num_symbols(self): return 2 def generator(self, nbr_symbols, max_length, nbr_cases): for _ in range(nbr_cases): l = np.random.randint(max_length) + 1 inputs = [np.random.randint(nbr_symbols) for _ in range(l)] yield {"inputs": inputs, "targets": inputs} @registry.register_problem class AlgorithmicIdentityDecimal40(AlgorithmicIdentityBinary40): @property def num_symbols(self): return 10 @registry.register_problem class AlgorithmicShiftDecimal40(AlgorithmicProblem): @property def num_symbols(self): return 20 def generator(self, nbr_symbols, max_length, nbr_cases): shift = 10 for _ in range(nbr_cases): l = np.random.randint(max_length) + 1 inputs = [np.random.randint(nbr_symbols - shift) for _ in range(l)] yield {"inputs": inputs, "targets": [i + shift for i in inputs]} @property def dev_length(self): return 80 @registry.register_problem class AlgorithmicReverseBinary40(AlgorithmicProblem): @property def num_symbols(self): return 2 def generator(self, nbr_symbols, max_length, nbr_cases): for _ in range(nbr_cases): l = np.random.randint(max_length) + 1 inputs = [np.random.randint(nbr_symbols) for _ in range(l)] yield {"inputs": inputs, "targets": list(reversed(inputs))} @registry.register_problem class AlgorithmicReverseDecimal40(AlgorithmicReverseBinary40): @property def num_symbols(self): return 10 def zipf_distribution(nbr_symbols, alpha): tmp = np.power(np.arange(1, nbr_symbols + 1), -alpha) zeta = np.r_[0.0, np.cumsum(tmp)] return [x / zeta[-1] for x in zeta] def zipf_random_sample(distr_map, sample_len): u = np.random.random(sample_len) return list(np.searchsorted(distr_map, u)) def reverse_generator_nlplike(nbr_symbols, max_length, nbr_cases, scale_std_dev=100, alpha=1.5): std_dev = max_length / scale_std_dev distr_map = zipf_distribution(nbr_symbols, alpha) for _ in range(nbr_cases): l = int(abs(np.random.normal(loc=max_length / 2, scale=std_dev)) + 1) inputs = zipf_random_sample(distr_map, l) yield {"inputs": inputs, "targets": list(reversed(inputs))} @registry.register_problem class AlgorithmicReverseNlplike8k(AlgorithmicProblem): @property def num_symbols(self): return 8000 def generator(self, nbr_symbols, max_length, nbr_cases): return reverse_generator_nlplike(nbr_symbols, max_length, nbr_cases, 10, 1.300) @property def train_length(self): return 70 @property def dev_length(self): return 70 @registry.register_problem class AlgorithmicReverseNlplike32k(AlgorithmicReverseNlplike8k): @property def num_symbols(self): return 32000 def generator(self, nbr_symbols, max_length, nbr_cases): return reverse_generator_nlplike(nbr_symbols, max_length, nbr_cases, 10, 1.050) def lower_endian_to_number(l, base): return sum([d * (base**i) for i, d in enumerate(l)]) def number_to_lower_endian(n, base): if n < base: return [n] return [n % base] + number_to_lower_endian(n // base, base) def random_number_lower_endian(length, base): if length == 1: return [np.random.randint(base)] prefix = [np.random.randint(base) for _ in range(length - 1)] return prefix + [np.random.randint(base - 1) + 1] @registry.register_problem class AlgorithmicAdditionBinary40(AlgorithmicProblem): @property def num_symbols(self): return 2 def generator(self, base, max_length, nbr_cases): if max_length < 3: raise ValueError("Maximum length must be at least 3.") for _ in range(nbr_cases): l1 = np.random.randint(max_length // 2) + 1 l2 = np.random.randint(max_length - l1 - 1) + 1 n1 = random_number_lower_endian(l1, base) n2 = random_number_lower_endian(l2, base) result = lower_endian_to_number(n1, base) + lower_endian_to_number( n2, base) inputs = n1 + [base] + n2 targets = number_to_lower_endian(result, base) yield {"inputs": inputs, "targets": targets} @registry.register_problem class AlgorithmicAdditionDecimal40(AlgorithmicAdditionBinary40): @property def num_symbols(self): return 10 @registry.register_problem class AlgorithmicMultiplicationBinary40(AlgorithmicProblem): @property def num_symbols(self): return 2 def generator(self, base, max_length, nbr_cases): if max_length < 3: raise ValueError("Maximum length must be at least 3.") for _ in range(nbr_cases): l1 = np.random.randint(max_length // 2) + 1 l2 = np.random.randint(max_length - l1 - 1) + 1 n1 = random_number_lower_endian(l1, base) n2 = random_number_lower_endian(l2, base) result = lower_endian_to_number(n1, base) * lower_endian_to_number( n2, base) inputs = n1 + [base] + n2 targets = number_to_lower_endian(result, base) yield {"inputs": inputs, "targets": targets} @registry.register_problem class AlgorithmicMultiplicationDecimal40(AlgorithmicMultiplicationBinary40): @property def num_symbols(self): return 10 @registry.register_problem class AlgorithmicReverseBinary40Test(AlgorithmicReverseBinary40): @property def train_length(self): return 10 @property def dev_length(self): return 10 @property def train_size(self): return 1000 @property def dev_size(self): return 100 @property def num_shards(self): return 1 @registry.register_problem class AlgorithmicSortProblem(AlgorithmicProblem): @property def num_symbols(self): return max(self.train_length, self.dev_length) @property def train_length(self): return 10 @property def dev_length(self): return self.train_length * 2 @property def unique(self): return False def generator(self, nbr_symbols, max_length, nbr_cases): for _ in range(nbr_cases): length = np.random.randint(max_length) + 1 if self.unique: inputs = np.arange(nbr_symbols) np.random.shuffle(inputs) inputs = inputs[:length] inputs = list(inputs) else: inputs = list(np.random.randint(nbr_symbols, size=length)) targets = list(sorted(inputs)) yield {"inputs": inputs, "targets": targets} def eval_metrics(self): defaults = super(AlgorithmicSortProblem, self).eval_metrics() return defaults + [metrics.Metrics.EDIT_DISTANCE] @registry.register_problem class TinyAlgo(AlgorithmicIdentityBinary40): def generate_data(self, data_dir, tmp_dir, task_id=-1): del tmp_dir, task_id identity_problem = AlgorithmicIdentityBinary40() utils.generate_files( identity_problem.generator(self.num_symbols, 40, 100000), self.training_filepaths(data_dir, 1, shuffled=True), 100) utils.generate_files( identity_problem.generator(self.num_symbols, 400, 10000), self.dev_filepaths(data_dir, 1, shuffled=True), 100) @classmethod def setup_for_test(cls): tmp_dir = tf.test.get_temp_dir() shutil.rmtree(tmp_dir) os.mkdir(tmp_dir) cls.data_dir = tmp_dir cls().generate_data(TinyAlgo.data_dir, None)

true

790dcc8a39bc21aaba4a154ea6758f2c3a81d1da

4,509

py

Python

tests/test_mysql_build.py

littlewatkins/nepc

3e16e3a9622ca0ebb4484c9e4af253046367773a

[ "CC0-1.0" ]

10

2020-06-17T14:48:09.000Z

2022-01-12T14:15:56.000Z

tests/test_mysql_build.py

littlewatkins/nepc

3e16e3a9622ca0ebb4484c9e4af253046367773a

[ "CC0-1.0" ]

52

2020-06-24T20:09:43.000Z

2022-01-16T18:24:01.000Z

tests/test_mysql_build.py

littlewatkins/nepc

3e16e3a9622ca0ebb4484c9e4af253046367773a

[ "CC0-1.0" ]

10

2020-06-18T14:24:53.000Z

2021-10-15T19:39:42.000Z

from nepc import nepc from nepc.util import util import pandas as pd import os import pytest import platform # TODO: remove dependence on csv; put function in scraper that uses built-in # readlines function import csv # TODO: test that all values in [nepc]/tests/data are in the nepc database @pytest.mark.usefixtures("data_config", "nepc_connect") def test_states_table_has_species_metadata(data_config, nepc_connect): """ check that the states table has a species_id column """ NEPC_DATA = data_config[0] number_of_states = util.wc_fxn(NEPC_DATA + 'states.tsv') - 1 df_states = nepc.table_as_df(nepc_connect[1], 'states') assert len(df_states) == number_of_states assert 'species_id' in list(df_states.columns) @pytest.mark.usefixtures("data_config", "nepc_connect") def test_csdata_lines(data_config, nepc_connect): DIR_NAMES = data_config[1] cs_lines = 0 for directoryname in DIR_NAMES: directory = os.fsencode(directoryname) for file in os.listdir(directory): filename = os.fsdecode(file) if filename.endswith(".met") or filename.endswith(".mod"): continue else: # subtract 1 to account for header cs_lines += util.wc_fxn(directoryname + filename) - 1 assert cs_lines == nepc.count_table_rows(nepc_connect[1], "csdata") @pytest.mark.usefixtures("data_config", "nepc_connect") def test_data_entered(data_config, nepc_connect, local): NEPC_DATA = data_config[0] if local is False or platform.node() == 'ppdadamsonlinux': cs_dat_files = pd.read_csv(NEPC_DATA + 'cs_datfile_prod.tsv', delimiter='\t') else: cs_dat_files = pd.read_csv(NEPC_DATA + 'cs_datfile_local.tsv', delimiter='\t') for index, row in cs_dat_files.iterrows(): cs_id = row['cs_id'] dat_file = row['filename'] df = pd.read_csv(NEPC_DATA + dat_file + '.dat', delimiter='\t', usecols=['e_energy', 'sigma']) e_energy, sigma = nepc.cs_e_sigma(nepc_connect[1], cs_id) # assert e_energy == pytest.approx(df['e_energy'].tolist()) assert sigma == pytest.approx(df['sigma'].tolist()) @pytest.mark.usefixtures("data_config", "nepc_connect") def test_meta_entered(data_config, nepc_connect, local, dbug): NEPC_DATA = data_config[0] if local is False or platform.node() == 'ppdadamsonlinux': cs_dat_files = pd.read_csv(NEPC_DATA + 'cs_datfile_prod.tsv', delimiter='\t') else: cs_dat_files = pd.read_csv(NEPC_DATA + 'cs_datfile_local.tsv', delimiter='\t') for index, row in cs_dat_files.iterrows(): cs_id = row['cs_id'] met_file = row['filename'] if dbug: print(cs_id, met_file) e, sigma = nepc.cs_e_sigma(nepc_connect[1], cs_id) meta_cols = ['cs_id', 'process', 'units_e', 'units_sigma', 'ref', 'lhsA', 'lhsB', 'rhsA', 'rhsB', 'threshold', 'wavelength', 'lhs_v', 'rhs_v', 'lhs_j', 'rhs_j', 'background', 'lpu', 'upu'] with open(NEPC_DATA + met_file + ".met", 'r', newline='') as f: reader = csv.reader(f, delimiter='\t') next(reader) meta_disk = list(reader)[0] meta_disk = [meta_disk[i] for i in list(range(len(meta_cols)))] for i in [0, 11, 12, 13, 14]: meta_disk[i] = (int(meta_disk[i]) if meta_disk[i] != '\\N' else meta_disk[i]) for i in [2, 3, 9, 10, 16, 17]: meta_disk[i] = (float(meta_disk[i]) if meta_disk[i] != '\\N' else meta_disk[i]) meta_db = [nepc.cs_metadata(nepc_connect[1], cs_id)[i] for i in list(range(0, len(meta_cols)))] if dbug: print('meta_db: {}\t from {}'.format(meta_db, met_file)) for i in range(len(meta_cols)): if dbug: print('meta_db[{}]: {}\t from {}'.format(str(i), str(meta_db[i]), met_file)) if (type(meta_db[i]) is float): assert (pytest.approx(meta_disk[i]) == pytest.approx(meta_db[i])) elif meta_db[i] is None: assert meta_disk[i] == '\\N' else: assert meta_disk[i] == meta_db[i]

39.552632

92

0.578399

from nepc import nepc from nepc.util import util import pandas as pd import os import pytest import platform import csv @pytest.mark.usefixtures("data_config", "nepc_connect") def test_states_table_has_species_metadata(data_config, nepc_connect): NEPC_DATA = data_config[0] number_of_states = util.wc_fxn(NEPC_DATA + 'states.tsv') - 1 df_states = nepc.table_as_df(nepc_connect[1], 'states') assert len(df_states) == number_of_states assert 'species_id' in list(df_states.columns) @pytest.mark.usefixtures("data_config", "nepc_connect") def test_csdata_lines(data_config, nepc_connect): DIR_NAMES = data_config[1] cs_lines = 0 for directoryname in DIR_NAMES: directory = os.fsencode(directoryname) for file in os.listdir(directory): filename = os.fsdecode(file) if filename.endswith(".met") or filename.endswith(".mod"): continue else: cs_lines += util.wc_fxn(directoryname + filename) - 1 assert cs_lines == nepc.count_table_rows(nepc_connect[1], "csdata") @pytest.mark.usefixtures("data_config", "nepc_connect") def test_data_entered(data_config, nepc_connect, local): NEPC_DATA = data_config[0] if local is False or platform.node() == 'ppdadamsonlinux': cs_dat_files = pd.read_csv(NEPC_DATA + 'cs_datfile_prod.tsv', delimiter='\t') else: cs_dat_files = pd.read_csv(NEPC_DATA + 'cs_datfile_local.tsv', delimiter='\t') for index, row in cs_dat_files.iterrows(): cs_id = row['cs_id'] dat_file = row['filename'] df = pd.read_csv(NEPC_DATA + dat_file + '.dat', delimiter='\t', usecols=['e_energy', 'sigma']) e_energy, sigma = nepc.cs_e_sigma(nepc_connect[1], cs_id) assert sigma == pytest.approx(df['sigma'].tolist()) @pytest.mark.usefixtures("data_config", "nepc_connect") def test_meta_entered(data_config, nepc_connect, local, dbug): NEPC_DATA = data_config[0] if local is False or platform.node() == 'ppdadamsonlinux': cs_dat_files = pd.read_csv(NEPC_DATA + 'cs_datfile_prod.tsv', delimiter='\t') else: cs_dat_files = pd.read_csv(NEPC_DATA + 'cs_datfile_local.tsv', delimiter='\t') for index, row in cs_dat_files.iterrows(): cs_id = row['cs_id'] met_file = row['filename'] if dbug: print(cs_id, met_file) e, sigma = nepc.cs_e_sigma(nepc_connect[1], cs_id) meta_cols = ['cs_id', 'process', 'units_e', 'units_sigma', 'ref', 'lhsA', 'lhsB', 'rhsA', 'rhsB', 'threshold', 'wavelength', 'lhs_v', 'rhs_v', 'lhs_j', 'rhs_j', 'background', 'lpu', 'upu'] with open(NEPC_DATA + met_file + ".met", 'r', newline='') as f: reader = csv.reader(f, delimiter='\t') next(reader) meta_disk = list(reader)[0] meta_disk = [meta_disk[i] for i in list(range(len(meta_cols)))] for i in [0, 11, 12, 13, 14]: meta_disk[i] = (int(meta_disk[i]) if meta_disk[i] != '\\N' else meta_disk[i]) for i in [2, 3, 9, 10, 16, 17]: meta_disk[i] = (float(meta_disk[i]) if meta_disk[i] != '\\N' else meta_disk[i]) meta_db = [nepc.cs_metadata(nepc_connect[1], cs_id)[i] for i in list(range(0, len(meta_cols)))] if dbug: print('meta_db: {}\t from {}'.format(meta_db, met_file)) for i in range(len(meta_cols)): if dbug: print('meta_db[{}]: {}\t from {}'.format(str(i), str(meta_db[i]), met_file)) if (type(meta_db[i]) is float): assert (pytest.approx(meta_disk[i]) == pytest.approx(meta_db[i])) elif meta_db[i] is None: assert meta_disk[i] == '\\N' else: assert meta_disk[i] == meta_db[i]

true

790dccae985884711795bd64d05aa6e0beaa90d5

2,068

py

Python

util/chplenv/chpl_unwind.py

ShreyasKhandekar/chapel

811ad7f6cfa35c6d88f344a90743fe5f9d3c980b

[ "ECL-2.0", "Apache-2.0" ]

null

util/chplenv/chpl_unwind.py

ShreyasKhandekar/chapel

811ad7f6cfa35c6d88f344a90743fe5f9d3c980b

[ "ECL-2.0", "Apache-2.0" ]

null

util/chplenv/chpl_unwind.py

ShreyasKhandekar/chapel

811ad7f6cfa35c6d88f344a90743fe5f9d3c980b

[ "ECL-2.0", "Apache-2.0" ]

null

#!/usr/bin/env python3 import sys import chpl_platform, overrides, third_party_utils from utils import error, memoize, warning @memoize def get(): platform_val = chpl_platform.get('target') linux = platform_val.startswith('linux64') osx = platform_val.startswith('darwin') val = overrides.get('CHPL_UNWIND') if val == 'libunwind': warning("CHPL_UNWIND=libunwind is deprecated. Use CHPL_UNWIND=bundled.") val = 'bundled' if linux: if val == 'bundled': return 'bundled' elif val == 'system': return 'system' if osx: if val == 'bundled': error("Using CHPL_UNWIND=bundled is not supported on Mac OS X." "\nUse CHPL_UNWIND=system instead.", ValueError) elif val == 'system': return 'system' return 'none' @memoize def get_uniq_cfg_path(): return third_party_utils.default_uniq_cfg_path() @memoize def get_link_args(unwind): platform_val = chpl_platform.get('target') osx = platform_val.startswith('darwin') # Mac OS X supports libunwind in the C library # it's not actually a special library. if osx: return [] libs = [] # Get the link arguments (e.g. -lunwind) if unwind == 'system': # Try using pkg-config to get the libraries to link # libunwind with. libs = third_party_utils.pkgconfig_get_link_args( 'libunwind', system=True, static=True) elif unwind == 'bundled': # the pkg-config file for libunwind is nice, but as of 1.1 # it doesn't include -lzma when it probably should. # So try to get the libraries out of libunwind.la. libs = third_party_utils.default_get_link_args( 'libunwind', libs=['libunwind.la', 'libunwind-x86_64.la']) # add -ldl so that we can call dladdr if "-ldl" not in libs: libs.append("-ldl") return libs def _main(): unwind_val = get() sys.stdout.write("{0}\n".format(unwind_val)) if __name__ == '__main__': _main()

27.210526

81

0.626692

import sys import chpl_platform, overrides, third_party_utils from utils import error, memoize, warning @memoize def get(): platform_val = chpl_platform.get('target') linux = platform_val.startswith('linux64') osx = platform_val.startswith('darwin') val = overrides.get('CHPL_UNWIND') if val == 'libunwind': warning("CHPL_UNWIND=libunwind is deprecated. Use CHPL_UNWIND=bundled.") val = 'bundled' if linux: if val == 'bundled': return 'bundled' elif val == 'system': return 'system' if osx: if val == 'bundled': error("Using CHPL_UNWIND=bundled is not supported on Mac OS X." "\nUse CHPL_UNWIND=system instead.", ValueError) elif val == 'system': return 'system' return 'none' @memoize def get_uniq_cfg_path(): return third_party_utils.default_uniq_cfg_path() @memoize def get_link_args(unwind): platform_val = chpl_platform.get('target') osx = platform_val.startswith('darwin') if osx: return [] libs = [] # Get the link arguments (e.g. -lunwind) if unwind == 'system': # Try using pkg-config to get the libraries to link # libunwind with. libs = third_party_utils.pkgconfig_get_link_args( 'libunwind', system=True, static=True) elif unwind == 'bundled': # the pkg-config file for libunwind is nice, but as of 1.1 # it doesn't include -lzma when it probably should. libs = third_party_utils.default_get_link_args( 'libunwind', libs=['libunwind.la', 'libunwind-x86_64.la']) if "-ldl" not in libs: libs.append("-ldl") return libs def _main(): unwind_val = get() sys.stdout.write("{0}\n".format(unwind_val)) if __name__ == '__main__': _main()

true

790dccb5c8ce8196f72ffe4f4be41dd4a837a2c2

1,019

py

Python

refinery/units/pattern/xtw.py

bronxc/refinery

9448facf48a0008f27861dd1a5ee8f5218e6bb86

[ "BSD-3-Clause" ]

null

refinery/units/pattern/xtw.py

bronxc/refinery

9448facf48a0008f27861dd1a5ee8f5218e6bb86

[ "BSD-3-Clause" ]

null

refinery/units/pattern/xtw.py

bronxc/refinery

9448facf48a0008f27861dd1a5ee8f5218e6bb86

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from refinery.units.pattern import PatternExtractor from refinery.units import RefineryCriticalException from refinery.lib.patterns import wallets class xtw(PatternExtractor): """ Extract Wallets: Extracts anything that looks like a cryptocurrency wallet address. This works similar to the `refinery.xtp` unit. """ def __init__(self, stripspace=False, duplicates=False, longest=False, take=None): self.superinit(super(), **vars(), ascii=True, utf16=True) def process(self, data): pattern = '|'.join(F'(?P<{p.name}>{p.value})' for p in wallets).encode('latin1') def check(match): for name, value in match.groupdict().items(): if value is not None: break else: raise RefineryCriticalException('Received empty match.') return self.labelled(value, kind=name) yield from self.matches_filtered(memoryview(data), pattern, check)

35.137931

88

0.651619

from refinery.units.pattern import PatternExtractor from refinery.units import RefineryCriticalException from refinery.lib.patterns import wallets class xtw(PatternExtractor): def __init__(self, stripspace=False, duplicates=False, longest=False, take=None): self.superinit(super(), **vars(), ascii=True, utf16=True) def process(self, data): pattern = '|'.join(F'(?P<{p.name}>{p.value})' for p in wallets).encode('latin1') def check(match): for name, value in match.groupdict().items(): if value is not None: break else: raise RefineryCriticalException('Received empty match.') return self.labelled(value, kind=name) yield from self.matches_filtered(memoryview(data), pattern, check)

true

790dcdff0a0df3dd5d7ac7d87dc2aa691f1779d3

3,132

py

Python

finite_element_networks/lightning/callbacks.py

martenlienen/finite-element-networks

5e8f6ecc473d1e93ccf366fcc45a47b08492ffde

[ "MIT" ]

5

2022-03-21T12:39:01.000Z

2022-03-31T06:02:01.000Z

finite_element_networks/lightning/callbacks.py

martenlienen/finite-element-networks

5e8f6ecc473d1e93ccf366fcc45a47b08492ffde

[ "MIT" ]

null

finite_element_networks/lightning/callbacks.py

martenlienen/finite-element-networks

5e8f6ecc473d1e93ccf366fcc45a47b08492ffde

[ "MIT" ]

1

2022-03-26T02:58:58.000Z

import pytorch_lightning as pl from pytorch_lightning.utilities.parsing import lightning_getattr, lightning_setattr class MultipleShootingCallback(pl.Callback): """This callback increases the length of the training sequences each epoch. This technique is well known in the SciML community and documented in their tutorials [1] as a way to avoid falling into local minima when training ODE based models. We can also see this as an instance of multiple shooting [2, 3] in the data space, where the penalty function enforcing the equality constraints at the splitting points is equal to the loss function. Note that the number of target steps will never increase over the initial number of target steps configured in the data module. [1] https://diffeqflux.sciml.ai/dev/examples/local_minima/ [2] https://diffeqflux.sciml.ai/dev/examples/multiple_shooting/ [3] Evren Mert Turan, Johannes Jäschke, "Multiple shooting for training neural differential equations on time series", https://arxiv.org/abs/2109.06786 Attributes ---------- initial_steps Number of target steps in the first epoch increase The target steps increase by this much in each following epoch target_steps_attr Name of the data module attribute that should be modified """ def __init__( self, *, initial_steps: int = 3, increase: int = 1, target_steps_attr: str = "train_target_steps", ): super().__init__() self.initial_steps = initial_steps self.increase = increase self.target_steps_attr = target_steps_attr self.initial_target_steps = None def on_train_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule): self.initial_target_steps = lightning_getattr(pl_module, self.target_steps_attr) # Set the initial steps in this hook because the trainer selects the train # dataloader internally before train_epoch_start is called. lightning_setattr(pl_module, self.target_steps_attr, self.initial_steps) trainer.reset_train_dataloader(pl_module) def on_train_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule): pl_module.log( self.target_steps_attr, float(lightning_getattr(pl_module, self.target_steps_attr)), on_step=False, on_epoch=True, ) def on_train_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule): # Trainer loads the data loader before the train_epoch_start hook is called, so we # set the target steps already at the end of the previous epoch prev_target_steps = lightning_getattr(pl_module, self.target_steps_attr) target_steps = prev_target_steps + self.increase if self.initial_target_steps is not None: target_steps = min(target_steps, self.initial_target_steps) if target_steps != prev_target_steps: lightning_setattr(pl_module, self.target_steps_attr, target_steps) trainer.reset_train_dataloader(pl_module)

43.5

90

0.715837

import pytorch_lightning as pl from pytorch_lightning.utilities.parsing import lightning_getattr, lightning_setattr class MultipleShootingCallback(pl.Callback): def __init__( self, *, initial_steps: int = 3, increase: int = 1, target_steps_attr: str = "train_target_steps", ): super().__init__() self.initial_steps = initial_steps self.increase = increase self.target_steps_attr = target_steps_attr self.initial_target_steps = None def on_train_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule): self.initial_target_steps = lightning_getattr(pl_module, self.target_steps_attr) lightning_setattr(pl_module, self.target_steps_attr, self.initial_steps) trainer.reset_train_dataloader(pl_module) def on_train_epoch_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule): pl_module.log( self.target_steps_attr, float(lightning_getattr(pl_module, self.target_steps_attr)), on_step=False, on_epoch=True, ) def on_train_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule): prev_target_steps = lightning_getattr(pl_module, self.target_steps_attr) target_steps = prev_target_steps + self.increase if self.initial_target_steps is not None: target_steps = min(target_steps, self.initial_target_steps) if target_steps != prev_target_steps: lightning_setattr(pl_module, self.target_steps_attr, target_steps) trainer.reset_train_dataloader(pl_module)

true

790dceeee3e32779d9ead14d2c0c4b5ea90fb07e

1,946

py

Python

zuul.d/octavia/tests/unit/common/test_config.py

yi-cloud/octavia

b7f5cfa4c3c454925a90c24984049539228806d7

[ "Apache-2.0" ]

null

zuul.d/octavia/tests/unit/common/test_config.py

yi-cloud/octavia

b7f5cfa4c3c454925a90c24984049539228806d7

[ "Apache-2.0" ]

null

zuul.d/octavia/tests/unit/common/test_config.py

yi-cloud/octavia

b7f5cfa4c3c454925a90c24984049539228806d7

[ "Apache-2.0" ]

null

# Copyright 2014, Doug Wiegley, A10 Networks. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from oslo_config import cfg from oslo_config import fixture as oslo_fixture import octavia.common.config as config import octavia.tests.unit.base as base class TestConfig(base.TestCase): def test_sanity(self): config.init([]) config.setup_logging(cfg.CONF) # Resetting because this will cause inconsistent errors when run with # other tests self.addCleanup(cfg.CONF.reset) def test_validate_server_certs_key_passphrase(self): conf = self.useFixture(oslo_fixture.Config(config.cfg.CONF)) conf.config( group="certificates", server_certs_key_passphrase="insecure-key-do-not-use-this-key" ) # Test too short self.assertRaises(ValueError, conf.config, group="certificates", server_certs_key_passphrase="short_passphrase") # Test too long self.assertRaises( ValueError, conf.config, group="certificates", server_certs_key_passphrase="long-insecure-key-do-not-use-this") # Test invalid characters self.assertRaises( ValueError, conf.config, group="certificates", server_certs_key_passphrase="insecure-key-do-not-u$e-this-key")

37.423077

79

0.660843

from oslo_config import cfg from oslo_config import fixture as oslo_fixture import octavia.common.config as config import octavia.tests.unit.base as base class TestConfig(base.TestCase): def test_sanity(self): config.init([]) config.setup_logging(cfg.CONF) self.addCleanup(cfg.CONF.reset) def test_validate_server_certs_key_passphrase(self): conf = self.useFixture(oslo_fixture.Config(config.cfg.CONF)) conf.config( group="certificates", server_certs_key_passphrase="insecure-key-do-not-use-this-key" ) self.assertRaises(ValueError, conf.config, group="certificates", server_certs_key_passphrase="short_passphrase") self.assertRaises( ValueError, conf.config, group="certificates", server_certs_key_passphrase="long-insecure-key-do-not-use-this") self.assertRaises( ValueError, conf.config, group="certificates", server_certs_key_passphrase="insecure-key-do-not-u$e-this-key")

true

790dd0e09ae1d8a6244aea4ae0d4ef34ef667fe1

771

py

Python

server/models/bitcoin_price_API.py

johnjdailey/JS-Realtime-Dashboard

aa62cab32096fbd4bdb8be657dd99d3d162e7097

[ "MIT" ]

null

server/models/bitcoin_price_API.py

johnjdailey/JS-Realtime-Dashboard

aa62cab32096fbd4bdb8be657dd99d3d162e7097

[ "MIT" ]

null

server/models/bitcoin_price_API.py

johnjdailey/JS-Realtime-Dashboard

aa62cab32096fbd4bdb8be657dd99d3d162e7097

[ "MIT" ]

null

import requests from datetime import datetime import psycopg2 import time def setup(): # Create database connection conn = psycopg2.connect(database="postgres", user="postgres", password="password", host="127.0.0.1", port="5432") return conn def call_api(): URL = "https://api.coindesk.com/v1/bpi/currentprice.json" conn = setup() while 1: r = requests.get(url=URL) current_time = datetime.now() data = r.json() price = data["bpi"]["USD"]["rate_float"] cur = conn.cursor() cur.execute( f"INSERT INTO BT_Price (Created_at,Price) VALUES ('{str(current_time)}', {price})") conn.commit() time.sleep(15) if __name__ == "__main__": call_api()

24.870968

95

0.597925

import requests from datetime import datetime import psycopg2 import time def setup(): conn = psycopg2.connect(database="postgres", user="postgres", password="password", host="127.0.0.1", port="5432") return conn def call_api(): URL = "https://api.coindesk.com/v1/bpi/currentprice.json" conn = setup() while 1: r = requests.get(url=URL) current_time = datetime.now() data = r.json() price = data["bpi"]["USD"]["rate_float"] cur = conn.cursor() cur.execute( f"INSERT INTO BT_Price (Created_at,Price) VALUES ('{str(current_time)}', {price})") conn.commit() time.sleep(15) if __name__ == "__main__": call_api()

true

790dd1c98aa84804fd6deae9806710c465315553

528

py

Python

ex009a.py

emerfelippini/Curso_em_video-Aulas_Python

5b1d78b259732bb9bbad27cd30ce91bba77c5ef0

[ "MIT" ]

null

ex009a.py

emerfelippini/Curso_em_video-Aulas_Python

5b1d78b259732bb9bbad27cd30ce91bba77c5ef0

[ "MIT" ]

null

ex009a.py

emerfelippini/Curso_em_video-Aulas_Python

5b1d78b259732bb9bbad27cd30ce91bba77c5ef0

[ "MIT" ]

null

a = int(input('Digite um número para saber sua tabuada :')) n1 = a*1 n2 = a*2 n3 = a*3 n4 = a*4 n5 = a*5 n6 = a*6 n7 = a*7 n8 = a*8 n9 = a*9 n10 = a*10 print('A sua tabuada é') print('{} x 1 = {}'.format(a, n1)) print('{} x 2 = {}'.format(a, n2)) print('{} x 3 = {}'.format(a, n3)) print('{} x 4 = {}'.format(a, n4)) print('{} x 5 = {}'.format(a, n5)) print('{} x 6 = {}'.format(a, n6)) print('{} x 7 = {}'.format(a, n7)) print('{} x 8 = {}'.format(a, n8)) print('{} x 9 = {}'.format(a, n9)) print('{} x 10 = {}'.format(a, n10))

24

59

0.498106

a = int(input('Digite um número para saber sua tabuada :')) n1 = a*1 n2 = a*2 n3 = a*3 n4 = a*4 n5 = a*5 n6 = a*6 n7 = a*7 n8 = a*8 n9 = a*9 n10 = a*10 print('A sua tabuada é') print('{} x 1 = {}'.format(a, n1)) print('{} x 2 = {}'.format(a, n2)) print('{} x 3 = {}'.format(a, n3)) print('{} x 4 = {}'.format(a, n4)) print('{} x 5 = {}'.format(a, n5)) print('{} x 6 = {}'.format(a, n6)) print('{} x 7 = {}'.format(a, n7)) print('{} x 8 = {}'.format(a, n8)) print('{} x 9 = {}'.format(a, n9)) print('{} x 10 = {}'.format(a, n10))

true

790dd28cf200f8e4925057ba449528fba67df010

158

py

Python

projects/constants.py

IdmFoundInHim/streamsort

d55bdebd0c84d035affe087892712cf3e26974e5

[ "MIT" ]

null

projects/constants.py

IdmFoundInHim/streamsort

d55bdebd0c84d035affe087892712cf3e26974e5

[ "MIT" ]

13

2020-04-30T20:55:17.000Z

2021-08-23T04:02:51.000Z

projects/constants.py

IdmFoundInHim/streamsort

d55bdebd0c84d035affe087892712cf3e26974e5

[ "MIT" ]

null

22.571429

51

0.740506

SINGLE_MAX_MS = 15 * 60 * 1000 SINGLE_MAX_TRACKS = 4

true

790dd2ae5bdef111d96c5fc9702a39c1ef79d422

611

py

Python

bonus2/collateral/modules/library/test_module.py

kinther/ansible_course

5ff96b857d7b1ddb359526fed128feefba8ebb90

[ "Apache-2.0" ]

14

2020-01-24T21:52:51.000Z

2021-05-24T01:58:08.000Z

bonus2/collateral/modules/library/test_module.py

kinther/ansible_course

5ff96b857d7b1ddb359526fed128feefba8ebb90

[ "Apache-2.0" ]

null

bonus2/collateral/modules/library/test_module.py

kinther/ansible_course

5ff96b857d7b1ddb359526fed128feefba8ebb90

[ "Apache-2.0" ]

26

2020-03-29T20:17:29.000Z

2022-03-28T19:13:40.000Z

#!/usr/bin/python from ansible.module_utils.basic import AnsibleModule def main(): # Define your modules arguments module_args = dict( name=dict(type="str", required=True), new=dict(type="bool", required=False, default=False), ) # Create an instance of the AnsibleModule class module = AnsibleModule(argument_spec=module_args, supports_check_mode=True) # Define standard results result = dict(changed=False, original_message="Something", message="It worked!!!") # Return items as JSON module.exit_json(**result) if __name__ == "__main__": main()

23.5

86

0.690671

from ansible.module_utils.basic import AnsibleModule def main(): module_args = dict( name=dict(type="str", required=True), new=dict(type="bool", required=False, default=False), ) module = AnsibleModule(argument_spec=module_args, supports_check_mode=True) result = dict(changed=False, original_message="Something", message="It worked!!!") module.exit_json(**result) if __name__ == "__main__": main()

true

790dd3a146c61c8a59d268a9ef30aafa08803c2b

3,543

py

Python

dict_interdiff.py

Zafara1/MITx-6.00.1x

7ab0e5e188fae86685033954e774dfe07e03a639

[ "MIT" ]

null

dict_interdiff.py

Zafara1/MITx-6.00.1x

7ab0e5e188fae86685033954e774dfe07e03a639

[ "MIT" ]

null

dict_interdiff.py

Zafara1/MITx-6.00.1x

7ab0e5e188fae86685033954e774dfe07e03a639

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Feb 14 08:23:08 2017 Author: Zachary W. Mikus """ #These are testing variables d1 = {1:30, 2:20, 3:30, 5:80} d2 = {1:40, 2:50, 3:60, 4:70} def f(x, y): k = x + y return k def commonKeys(longerList, shorterList): commonKeyList = [] #Variables #intersectDictionary = The final returned intersect dictionary #commonKeyList = The list of keys that appear in both dictionaries for i in range(len(longerList)): if longerList[i] in shorterList: commonKeyList.append(longerList[i]) return commonKeyList def differentKeys(longerList, shorterList): #This function uses similar logic to the commonKeys function #Except it will see if the index is NOT in the other list and remove it #This runs the loop twice once through each loop to find the missing numbers #in each list differentKeyList = [] for i in range(len(longerList)): if longerList[i] not in shorterList: differentKeyList.append(longerList[i]) for i in range(len(shorterList)): if shorterList[i] not in longerList: differentKeyList.append(shorterList[i]) return differentKeyList def intersect(commonList, d1, d2): intersectDict = {} #This function takes the common list of keys, grabs the common values in #both dictionaries and performs the f(x, y) function on them for i in range(len(commonList)): #currentIndex is the index in the dictionary, it will move currentIndex = commonList[i] x = d1[currentIndex] y = d2[currentIndex] functionValue = f(x, y) intersectDict[currentIndex] = functionValue return intersectDict def difference(differentKeyList, d1, d2): differenceDict = {} #This function takes the different list of keys, grabs the relevant values and #creates a dictionary #searches d for i in range(len(differentKeyList)): currentIndex = differentKeyList[i] if currentIndex in d1: differenceDict[currentIndex] = d1[currentIndex] if currentIndex in d2: differenceDict[currentIndex] = d2[currentIndex] return differenceDict def diff_dictionary(d1, d2): differentKeyList = [] #Turns key values in lists and finds the longest #keyListD1 = list of keys in d1 #keyListD2 = list of keys in d2 keyListD1 = list(d1.keys()) keyListD2 = list(d2.keys()) #determines which of the two lists is the longest and assigned it values #for the common list function if len(keyListD1) > len(keyListD2): longerList = keyListD1 shorterList = keyListD2 else: longerList = keyListD2 shorterList = keyListD1 #Finds the common keys commonList = commonKeys(longerList, shorterList) #Makes the intersect dictionary intersectDict = intersect(commonList, d1, d2) #Finds the different keys differentKeyList = differentKeys(longerList, shorterList) #Makes the different key dictionary differenceDict = difference(differentKeyList, d1, d2) #This now creates a list of the dictionaries put together return (intersectDict, differenceDict) ''' #This is for calculating the difference dictionary. #The difference dictionary consists of every #KEY VALUE# in the dictionaries that does not exist #in the other dictionary. ''' #Variables #differenceDictionary = The final returned difference dictionary print(diff_dictionary(d1, d2))

31.633929

82

0.686988

d1 = {1:30, 2:20, 3:30, 5:80} d2 = {1:40, 2:50, 3:60, 4:70} def f(x, y): k = x + y return k def commonKeys(longerList, shorterList): commonKeyList = [] for i in range(len(longerList)): if longerList[i] in shorterList: commonKeyList.append(longerList[i]) return commonKeyList def differentKeys(longerList, shorterList): differentKeyList = [] for i in range(len(longerList)): if longerList[i] not in shorterList: differentKeyList.append(longerList[i]) for i in range(len(shorterList)): if shorterList[i] not in longerList: differentKeyList.append(shorterList[i]) return differentKeyList def intersect(commonList, d1, d2): intersectDict = {} for i in range(len(commonList)): currentIndex = commonList[i] x = d1[currentIndex] y = d2[currentIndex] functionValue = f(x, y) intersectDict[currentIndex] = functionValue return intersectDict def difference(differentKeyList, d1, d2): differenceDict = {} for i in range(len(differentKeyList)): currentIndex = differentKeyList[i] if currentIndex in d1: differenceDict[currentIndex] = d1[currentIndex] if currentIndex in d2: differenceDict[currentIndex] = d2[currentIndex] return differenceDict def diff_dictionary(d1, d2): differentKeyList = [] keyListD1 = list(d1.keys()) keyListD2 = list(d2.keys()) if len(keyListD1) > len(keyListD2): longerList = keyListD1 shorterList = keyListD2 else: longerList = keyListD2 shorterList = keyListD1 commonList = commonKeys(longerList, shorterList) intersectDict = intersect(commonList, d1, d2) differentKeyList = differentKeys(longerList, shorterList) differenceDict = difference(differentKeyList, d1, d2) return (intersectDict, differenceDict) print(diff_dictionary(d1, d2))

true

790dd3e88fd0cb334c6be5fcb08e85a2cb6784e1

7,245

py

Python

docker-images/taigav2/taiga-back/tests/integration/test_stats.py

mattcongy/itshop

6be025a9eaa7fe7f495b5777d1f0e5a3184121c9

[ "MIT" ]

1

2017-05-29T19:01:06.000Z

docker-images/taigav2/taiga-back/tests/integration/test_stats.py

mattcongy/itshop

6be025a9eaa7fe7f495b5777d1f0e5a3184121c9

[ "MIT" ]

null

docker-images/taigav2/taiga-back/tests/integration/test_stats.py

mattcongy/itshop

6be025a9eaa7fe7f495b5777d1f0e5a3184121c9

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (C) 2014-2016 Andrey Antukh <niwi@niwi.nz> # Copyright (C) 2014-2016 Jesús Espino <jespinog@gmail.com> # Copyright (C) 2014-2016 David Barragán <bameda@dbarragan.com> # Copyright (C) 2014-2016 Alejandro Alonso <alejandro.alonso@kaleidos.net> # Copyright (C) 2014-2016 Anler Hernández <hello@anler.me> # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. import pytest from .. import factories as f from tests.utils import disconnect_signals, reconnect_signals from taiga.projects.services.stats import get_stats_for_project pytestmark = pytest.mark.django_db def setup_module(module): disconnect_signals() def teardown_module(module): reconnect_signals() @pytest.fixture def data(): m = type("Models", (object,), {}) m.user = f.UserFactory.create() m.project = f.ProjectFactory(is_private=False, owner=m.user) m.role1 = f.RoleFactory(project=m.project) m.role2 = f.RoleFactory(project=m.project) m.null_points = f.PointsFactory(project=m.project, value=None) m.default_points = f.PointsFactory(project=m.project, value=0) m.points1 = f.PointsFactory(project=m.project, value=1) m.points2 = f.PointsFactory(project=m.project, value=2) m.points3 = f.PointsFactory(project=m.project, value=4) m.points4 = f.PointsFactory(project=m.project, value=8) m.points5 = f.PointsFactory(project=m.project, value=16) m.points6 = f.PointsFactory(project=m.project, value=32) m.open_status = f.UserStoryStatusFactory(is_closed=False) m.closed_status = f.UserStoryStatusFactory(is_closed=True) m.project.default_points = m.default_points m.project.save() m.user_story1 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone=None) m.user_story1.role_points.filter(role=m.role1).update(points=m.points1) m.user_story2 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone=None) m.user_story2.role_points.filter(role=m.role1).update(points=m.points2) m.user_story3 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone=None) m.user_story3.role_points.filter(role=m.role1).update(points=m.points3) m.user_story4 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone=None) m.user_story4.role_points.filter(role=m.role1).update(points=m.points4) # 5 and 6 are inclosed milestones m.user_story5 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone__closed=True, milestone__project=m.project) m.user_story5.role_points.filter(role=m.role1).update(points=m.points5) m.user_story6 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone__closed=True, milestone__project=m.project) m.user_story6.role_points.filter(role=m.role1).update(points=m.points6) return m def test_project_defined_points(client, data): project_stats = get_stats_for_project(data.project) assert project_stats["defined_points_per_role"] == {data.role1.pk: 63, data.role2.pk: 0} data.user_story1.role_points.filter(role=data.role1).update(points=data.default_points) data.user_story1.role_points.filter(role=data.role2).update(points=data.points1) project_stats = get_stats_for_project(data.project) assert project_stats["defined_points_per_role"] == {data.role1.pk: 62, data.role2.pk: 1} def test_project_closed_points(client, data): project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {} data.user_story1.is_closed = True data.user_story1.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 1, data.role2.pk: 0} data.user_story2.is_closed = True data.user_story2.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 3, data.role2.pk: 0} data.user_story3.is_closed = True data.user_story3.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 7, data.role2.pk: 0} data.user_story4.is_closed = True data.user_story4.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 15, data.role2.pk: 0} data.user_story5.is_closed = True data.user_story5.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 31, data.role2.pk: 0} data.user_story6.is_closed = True data.user_story6.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 63, data.role2.pk: 0} project_stats = get_stats_for_project(data.project) assert project_stats["closed_points"] == 63 assert project_stats["speed"] == 24 def test_project_assigned_points(client, data): project_stats = get_stats_for_project(data.project) assert project_stats["assigned_points_per_role"] == {data.role1.pk: 48, data.role2.pk: 0} data.user_story1.milestone = data.user_story6.milestone data.user_story1.save() project_stats = get_stats_for_project(data.project) assert project_stats["assigned_points_per_role"] == {data.role1.pk: 49, data.role2.pk: 0} data.user_story2.milestone = data.user_story6.milestone data.user_story2.save() project_stats = get_stats_for_project(data.project) assert project_stats["assigned_points_per_role"] == {data.role1.pk: 51, data.role2.pk: 0} data.user_story3.milestone = data.user_story6.milestone data.user_story3.save() project_stats = get_stats_for_project(data.project) assert project_stats["assigned_points_per_role"] == {data.role1.pk: 55, data.role2.pk: 0} data.user_story4.milestone = data.user_story6.milestone data.user_story4.save() project_stats = get_stats_for_project(data.project) assert project_stats["assigned_points_per_role"] == {data.role1.pk: 63, data.role2.pk: 0}

44.447853

93

0.696894

import pytest from .. import factories as f from tests.utils import disconnect_signals, reconnect_signals from taiga.projects.services.stats import get_stats_for_project pytestmark = pytest.mark.django_db def setup_module(module): disconnect_signals() def teardown_module(module): reconnect_signals() @pytest.fixture def data(): m = type("Models", (object,), {}) m.user = f.UserFactory.create() m.project = f.ProjectFactory(is_private=False, owner=m.user) m.role1 = f.RoleFactory(project=m.project) m.role2 = f.RoleFactory(project=m.project) m.null_points = f.PointsFactory(project=m.project, value=None) m.default_points = f.PointsFactory(project=m.project, value=0) m.points1 = f.PointsFactory(project=m.project, value=1) m.points2 = f.PointsFactory(project=m.project, value=2) m.points3 = f.PointsFactory(project=m.project, value=4) m.points4 = f.PointsFactory(project=m.project, value=8) m.points5 = f.PointsFactory(project=m.project, value=16) m.points6 = f.PointsFactory(project=m.project, value=32) m.open_status = f.UserStoryStatusFactory(is_closed=False) m.closed_status = f.UserStoryStatusFactory(is_closed=True) m.project.default_points = m.default_points m.project.save() m.user_story1 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone=None) m.user_story1.role_points.filter(role=m.role1).update(points=m.points1) m.user_story2 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone=None) m.user_story2.role_points.filter(role=m.role1).update(points=m.points2) m.user_story3 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone=None) m.user_story3.role_points.filter(role=m.role1).update(points=m.points3) m.user_story4 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone=None) m.user_story4.role_points.filter(role=m.role1).update(points=m.points4) m.user_story5 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone__closed=True, milestone__project=m.project) m.user_story5.role_points.filter(role=m.role1).update(points=m.points5) m.user_story6 = f.UserStoryFactory(project=m.project, status=m.open_status, milestone__closed=True, milestone__project=m.project) m.user_story6.role_points.filter(role=m.role1).update(points=m.points6) return m def test_project_defined_points(client, data): project_stats = get_stats_for_project(data.project) assert project_stats["defined_points_per_role"] == {data.role1.pk: 63, data.role2.pk: 0} data.user_story1.role_points.filter(role=data.role1).update(points=data.default_points) data.user_story1.role_points.filter(role=data.role2).update(points=data.points1) project_stats = get_stats_for_project(data.project) assert project_stats["defined_points_per_role"] == {data.role1.pk: 62, data.role2.pk: 1} def test_project_closed_points(client, data): project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {} data.user_story1.is_closed = True data.user_story1.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 1, data.role2.pk: 0} data.user_story2.is_closed = True data.user_story2.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 3, data.role2.pk: 0} data.user_story3.is_closed = True data.user_story3.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 7, data.role2.pk: 0} data.user_story4.is_closed = True data.user_story4.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 15, data.role2.pk: 0} data.user_story5.is_closed = True data.user_story5.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 31, data.role2.pk: 0} data.user_story6.is_closed = True data.user_story6.save() project_stats = get_stats_for_project(data.project) assert project_stats["closed_points_per_role"] == {data.role1.pk: 63, data.role2.pk: 0} project_stats = get_stats_for_project(data.project) assert project_stats["closed_points"] == 63 assert project_stats["speed"] == 24 def test_project_assigned_points(client, data): project_stats = get_stats_for_project(data.project) assert project_stats["assigned_points_per_role"] == {data.role1.pk: 48, data.role2.pk: 0} data.user_story1.milestone = data.user_story6.milestone data.user_story1.save() project_stats = get_stats_for_project(data.project) assert project_stats["assigned_points_per_role"] == {data.role1.pk: 49, data.role2.pk: 0} data.user_story2.milestone = data.user_story6.milestone data.user_story2.save() project_stats = get_stats_for_project(data.project) assert project_stats["assigned_points_per_role"] == {data.role1.pk: 51, data.role2.pk: 0} data.user_story3.milestone = data.user_story6.milestone data.user_story3.save() project_stats = get_stats_for_project(data.project) assert project_stats["assigned_points_per_role"] == {data.role1.pk: 55, data.role2.pk: 0} data.user_story4.milestone = data.user_story6.milestone data.user_story4.save() project_stats = get_stats_for_project(data.project) assert project_stats["assigned_points_per_role"] == {data.role1.pk: 63, data.role2.pk: 0}

true

790dd55b6ff7676f8e99726f40e97383ef46a967

35,128

py

Python

Plugins/UnrealEnginePython/Binaries/Win64/Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py

JustinACoder/H22-GR3-UnrealAI

361eb9ef1147f8a2991e5f98c4118cd823184adf

[ "MIT" ]

6

2022-02-04T18:12:24.000Z

2022-03-21T23:57:12.000Z

Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py

shfkdroal/Robot-Learning-in-Mixed-Adversarial-and-Collaborative-Settings

1fa4cd6a566c8745f455fc3d2273208f21f88ced

[ "bzip2-1.0.6" ]

null

Lib/site-packages/tensorflow/contrib/rnn/ops/gen_lstm_ops.py

shfkdroal/Robot-Learning-in-Mixed-Adversarial-and-Collaborative-Settings

1fa4cd6a566c8745f455fc3d2273208f21f88ced

[ "bzip2-1.0.6" ]

1

2022-02-08T03:53:23.000Z

"""Python wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. """ import collections as _collections import six as _six from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow from tensorflow.python.eager import context as _context from tensorflow.python.eager import core as _core from tensorflow.python.eager import execute as _execute from tensorflow.python.framework import dtypes as _dtypes from tensorflow.python.framework import errors as _errors from tensorflow.python.framework import tensor_shape as _tensor_shape from tensorflow.core.framework import op_def_pb2 as _op_def_pb2 # Needed to trigger the call to _set_call_cpp_shape_fn. from tensorflow.python.framework import common_shapes as _common_shapes from tensorflow.python.framework import op_def_registry as _op_def_registry from tensorflow.python.framework import ops as _ops from tensorflow.python.framework import op_def_library as _op_def_library from tensorflow.python.util.deprecation import deprecated_endpoints from tensorflow.python.util.tf_export import tf_export _block_lstm_outputs = ["i", "cs", "f", "o", "ci", "co", "h"] _BlockLSTMOutput = _collections.namedtuple( "BlockLSTM", _block_lstm_outputs) @tf_export('block_lstm') def block_lstm(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None): r"""Computes the LSTM cell forward propagation for all the time steps. This is equivalent to applying LSTMBlockCell in a loop, like so: ```python for x1 in unpack(x): i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock( x1, cs_prev, h_prev, w, wci, wcf, wco, b) cs_prev = cs1 h_prev = h1 i.append(i1) cs.append(cs1) f.append(f1) o.append(o1) ci.append(ci1) co.append(co1) h.append(h1) return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h) ``` Args: seq_len_max: A `Tensor` of type `int64`. Maximum time length actually used by this input. Outputs are padded with zeros beyond this length. x: A `Tensor`. Must be one of the following types: `half`, `float32`. The sequence input to the LSTM, shape (timelen, batch_size, num_inputs). cs_prev: A `Tensor`. Must have the same type as `x`. Value of the initial cell state. h_prev: A `Tensor`. Must have the same type as `x`. Initial output of cell (to be used for peephole). w: A `Tensor`. Must have the same type as `x`. The weight matrix. wci: A `Tensor`. Must have the same type as `x`. The weight matrix for input gate peephole connection. wcf: A `Tensor`. Must have the same type as `x`. The weight matrix for forget gate peephole connection. wco: A `Tensor`. Must have the same type as `x`. The weight matrix for output gate peephole connection. b: A `Tensor`. Must have the same type as `x`. The bias vector. forget_bias: An optional `float`. Defaults to `1`. The forget gate bias. cell_clip: An optional `float`. Defaults to `3`. Value to clip the 'cs' value to. use_peephole: An optional `bool`. Defaults to `False`. Whether to use peephole weights. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (i, cs, f, o, ci, co, h). i: A `Tensor`. Has the same type as `x`. The input gate over the whole time sequence. cs: A `Tensor`. Has the same type as `x`. The cell state before the tanh over the whole time sequence. f: A `Tensor`. Has the same type as `x`. The forget gate over the whole time sequence. o: A `Tensor`. Has the same type as `x`. The output gate over the whole time sequence. ci: A `Tensor`. Has the same type as `x`. The cell input over the whole time sequence. co: A `Tensor`. Has the same type as `x`. The cell after the tanh over the whole time sequence. h: A `Tensor`. Has the same type as `x`. The output h vector over the whole time sequence. """ _ctx = _context._context if _ctx is None or not _ctx._eager_context.is_eager: if forget_bias is None: forget_bias = 1 forget_bias = _execute.make_float(forget_bias, "forget_bias") if cell_clip is None: cell_clip = 3 cell_clip = _execute.make_float(cell_clip, "cell_clip") if use_peephole is None: use_peephole = False use_peephole = _execute.make_bool(use_peephole, "use_peephole") _, _, _op = _op_def_lib._apply_op_helper( "BlockLSTM", seq_len_max=seq_len_max, x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, forget_bias=forget_bias, cell_clip=cell_clip, use_peephole=use_peephole, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("forget_bias", _op.get_attr("forget_bias"), "cell_clip", _op.get_attr("cell_clip"), "use_peephole", _op.get_attr("use_peephole"), "T", _op.get_attr("T")) _execute.record_gradient( "BlockLSTM", _inputs_flat, _attrs, _result, name) _result = _BlockLSTMOutput._make(_result) return _result else: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "BlockLSTM", name, _ctx._post_execution_callbacks, seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, "forget_bias", forget_bias, "cell_clip", cell_clip, "use_peephole", use_peephole) _result = _BlockLSTMOutput._make(_result) return _result except _core._FallbackException: return block_lstm_eager_fallback( seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=forget_bias, cell_clip=cell_clip, use_peephole=use_peephole, name=name, ctx=_ctx) except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) def block_lstm_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function block_lstm """ _ctx = ctx if ctx else _context.context() if forget_bias is None: forget_bias = 1 forget_bias = _execute.make_float(forget_bias, "forget_bias") if cell_clip is None: cell_clip = 3 cell_clip = _execute.make_float(cell_clip, "cell_clip") if use_peephole is None: use_peephole = False use_peephole = _execute.make_bool(use_peephole, "use_peephole") _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b], _ctx) (x, cs_prev, h_prev, w, wci, wcf, wco, b) = _inputs_T seq_len_max = _ops.convert_to_tensor(seq_len_max, _dtypes.int64) _inputs_flat = [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b] _attrs = ("forget_bias", forget_bias, "cell_clip", cell_clip, "use_peephole", use_peephole, "T", _attr_T) _result = _execute.execute(b"BlockLSTM", 7, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "BlockLSTM", _inputs_flat, _attrs, _result, name) _result = _BlockLSTMOutput._make(_result) return _result _ops.RegisterShape("BlockLSTM")(None) _block_lstm_grad_outputs = ["x_grad", "cs_prev_grad", "h_prev_grad", "w_grad", "wci_grad", "wcf_grad", "wco_grad", "b_grad"] _BlockLSTMGradOutput = _collections.namedtuple( "BlockLSTMGrad", _block_lstm_grad_outputs) @tf_export('block_lstm_grad') def block_lstm_grad(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None): r"""Computes the LSTM cell backward propagation for the entire time sequence. This implementation is to be used in conjunction of LSTMBlock. Args: seq_len_max: A `Tensor` of type `int64`. Maximum time length actually used by this input. Outputs are padded with zeros beyond this length. x: A `Tensor`. Must be one of the following types: `half`, `float32`. The sequence input to the LSTM, shape (timelen, batch_size, num_inputs). cs_prev: A `Tensor`. Must have the same type as `x`. Value of the initial cell state. h_prev: A `Tensor`. Must have the same type as `x`. Initial output of cell (to be used for peephole). w: A `Tensor`. Must have the same type as `x`. The weight matrix. wci: A `Tensor`. Must have the same type as `x`. The weight matrix for input gate peephole connection. wcf: A `Tensor`. Must have the same type as `x`. The weight matrix for forget gate peephole connection. wco: A `Tensor`. Must have the same type as `x`. The weight matrix for output gate peephole connection. b: A `Tensor`. Must have the same type as `x`. The bias vector. i: A `Tensor`. Must have the same type as `x`. The input gate over the whole time sequence. cs: A `Tensor`. Must have the same type as `x`. The cell state before the tanh over the whole time sequence. f: A `Tensor`. Must have the same type as `x`. The forget gate over the whole time sequence. o: A `Tensor`. Must have the same type as `x`. The output gate over the whole time sequence. ci: A `Tensor`. Must have the same type as `x`. The cell input over the whole time sequence. co: A `Tensor`. Must have the same type as `x`. The cell after the tanh over the whole time sequence. h: A `Tensor`. Must have the same type as `x`. The output h vector over the whole time sequence. cs_grad: A `Tensor`. Must have the same type as `x`. The current gradient of cs. h_grad: A `Tensor`. Must have the same type as `x`. The gradient of h vector. use_peephole: A `bool`. Whether to use peephole weights. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad). x_grad: A `Tensor`. Has the same type as `x`. The gradient of x to be back-propped. cs_prev_grad: A `Tensor`. Has the same type as `x`. The gradient of cs_prev to be back-propped. h_prev_grad: A `Tensor`. Has the same type as `x`. The gradient of h_prev to be back-propped. w_grad: A `Tensor`. Has the same type as `x`. The gradient for w to be back-propped. wci_grad: A `Tensor`. Has the same type as `x`. The gradient for wci to be back-propped. wcf_grad: A `Tensor`. Has the same type as `x`. The gradient for wcf to be back-propped. wco_grad: A `Tensor`. Has the same type as `x`. The gradient for wco to be back-propped. b_grad: A `Tensor`. Has the same type as `x`. The gradient for w to be back-propped. """ _ctx = _context._context if _ctx is None or not _ctx._eager_context.is_eager: use_peephole = _execute.make_bool(use_peephole, "use_peephole") _, _, _op = _op_def_lib._apply_op_helper( "BlockLSTMGrad", seq_len_max=seq_len_max, x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, i=i, cs=cs, f=f, o=o, ci=ci, co=co, h=h, cs_grad=cs_grad, h_grad=h_grad, use_peephole=use_peephole, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("use_peephole", _op.get_attr("use_peephole"), "T", _op.get_attr("T")) _execute.record_gradient( "BlockLSTMGrad", _inputs_flat, _attrs, _result, name) _result = _BlockLSTMGradOutput._make(_result) return _result else: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "BlockLSTMGrad", name, _ctx._post_execution_callbacks, seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, "use_peephole", use_peephole) _result = _BlockLSTMGradOutput._make(_result) return _result except _core._FallbackException: return block_lstm_grad_eager_fallback( seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole=use_peephole, name=name, ctx=_ctx) except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) def block_lstm_grad_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function block_lstm_grad """ _ctx = ctx if ctx else _context.context() use_peephole = _execute.make_bool(use_peephole, "use_peephole") _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad], _ctx) (x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad) = _inputs_T seq_len_max = _ops.convert_to_tensor(seq_len_max, _dtypes.int64) _inputs_flat = [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad] _attrs = ("use_peephole", use_peephole, "T", _attr_T) _result = _execute.execute(b"BlockLSTMGrad", 8, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "BlockLSTMGrad", _inputs_flat, _attrs, _result, name) _result = _BlockLSTMGradOutput._make(_result) return _result _ops.RegisterShape("BlockLSTMGrad")(None) _lstm_block_cell_outputs = ["i", "cs", "f", "o", "ci", "co", "h"] _LSTMBlockCellOutput = _collections.namedtuple( "LSTMBlockCell", _lstm_block_cell_outputs) @tf_export('lstm_block_cell') def lstm_block_cell(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None): r"""Computes the LSTM cell forward propagation for 1 time step. This implementation uses 1 weight matrix and 1 bias vector, and there's an optional peephole connection. This kernel op implements the following mathematical equations: ```python xh = [x, h_prev] [i, f, ci, o] = xh * w + b f = f + forget_bias if not use_peephole: wci = wcf = wco = 0 i = sigmoid(cs_prev * wci + i) f = sigmoid(cs_prev * wcf + f) ci = tanh(ci) cs = ci .* i + cs_prev .* f cs = clip(cs, cell_clip) o = sigmoid(cs * wco + o) co = tanh(cs) h = co .* o ``` Args: x: A `Tensor`. Must be one of the following types: `half`, `float32`. The input to the LSTM cell, shape (batch_size, num_inputs). cs_prev: A `Tensor`. Must have the same type as `x`. Value of the cell state at previous time step. h_prev: A `Tensor`. Must have the same type as `x`. Output of the previous cell at previous time step. w: A `Tensor`. Must have the same type as `x`. The weight matrix. wci: A `Tensor`. Must have the same type as `x`. The weight matrix for input gate peephole connection. wcf: A `Tensor`. Must have the same type as `x`. The weight matrix for forget gate peephole connection. wco: A `Tensor`. Must have the same type as `x`. The weight matrix for output gate peephole connection. b: A `Tensor`. Must have the same type as `x`. The bias vector. forget_bias: An optional `float`. Defaults to `1`. The forget gate bias. cell_clip: An optional `float`. Defaults to `3`. Value to clip the 'cs' value to. use_peephole: An optional `bool`. Defaults to `False`. Whether to use peephole weights. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (i, cs, f, o, ci, co, h). i: A `Tensor`. Has the same type as `x`. The input gate. cs: A `Tensor`. Has the same type as `x`. The cell state before the tanh. f: A `Tensor`. Has the same type as `x`. The forget gate. o: A `Tensor`. Has the same type as `x`. The output gate. ci: A `Tensor`. Has the same type as `x`. The cell input. co: A `Tensor`. Has the same type as `x`. The cell after the tanh. h: A `Tensor`. Has the same type as `x`. The output h vector. """ _ctx = _context._context if _ctx is None or not _ctx._eager_context.is_eager: if forget_bias is None: forget_bias = 1 forget_bias = _execute.make_float(forget_bias, "forget_bias") if cell_clip is None: cell_clip = 3 cell_clip = _execute.make_float(cell_clip, "cell_clip") if use_peephole is None: use_peephole = False use_peephole = _execute.make_bool(use_peephole, "use_peephole") _, _, _op = _op_def_lib._apply_op_helper( "LSTMBlockCell", x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, forget_bias=forget_bias, cell_clip=cell_clip, use_peephole=use_peephole, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("forget_bias", _op.get_attr("forget_bias"), "cell_clip", _op.get_attr("cell_clip"), "use_peephole", _op.get_attr("use_peephole"), "T", _op.get_attr("T")) _execute.record_gradient( "LSTMBlockCell", _inputs_flat, _attrs, _result, name) _result = _LSTMBlockCellOutput._make(_result) return _result else: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "LSTMBlockCell", name, _ctx._post_execution_callbacks, x, cs_prev, h_prev, w, wci, wcf, wco, b, "forget_bias", forget_bias, "cell_clip", cell_clip, "use_peephole", use_peephole) _result = _LSTMBlockCellOutput._make(_result) return _result except _core._FallbackException: return lstm_block_cell_eager_fallback( x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=forget_bias, cell_clip=cell_clip, use_peephole=use_peephole, name=name, ctx=_ctx) except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) def lstm_block_cell_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function lstm_block_cell """ _ctx = ctx if ctx else _context.context() if forget_bias is None: forget_bias = 1 forget_bias = _execute.make_float(forget_bias, "forget_bias") if cell_clip is None: cell_clip = 3 cell_clip = _execute.make_float(cell_clip, "cell_clip") if use_peephole is None: use_peephole = False use_peephole = _execute.make_bool(use_peephole, "use_peephole") _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b], _ctx) (x, cs_prev, h_prev, w, wci, wcf, wco, b) = _inputs_T _inputs_flat = [x, cs_prev, h_prev, w, wci, wcf, wco, b] _attrs = ("forget_bias", forget_bias, "cell_clip", cell_clip, "use_peephole", use_peephole, "T", _attr_T) _result = _execute.execute(b"LSTMBlockCell", 7, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "LSTMBlockCell", _inputs_flat, _attrs, _result, name) _result = _LSTMBlockCellOutput._make(_result) return _result _ops.RegisterShape("LSTMBlockCell")(None) _lstm_block_cell_grad_outputs = ["cs_prev_grad", "dicfo", "wci_grad", "wcf_grad", "wco_grad"] _LSTMBlockCellGradOutput = _collections.namedtuple( "LSTMBlockCellGrad", _lstm_block_cell_grad_outputs) @tf_export('lstm_block_cell_grad') def lstm_block_cell_grad(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None): r"""Computes the LSTM cell backward propagation for 1 timestep. This implementation is to be used in conjunction of LSTMBlockCell. Args: x: A `Tensor`. Must be one of the following types: `half`, `float32`. The input to the LSTM cell, shape (batch_size, num_inputs). cs_prev: A `Tensor`. Must have the same type as `x`. The previous cell state. h_prev: A `Tensor`. Must have the same type as `x`. The previous h state. w: A `Tensor`. Must have the same type as `x`. The weight matrix. wci: A `Tensor`. Must have the same type as `x`. The weight matrix for input gate peephole connection. wcf: A `Tensor`. Must have the same type as `x`. The weight matrix for forget gate peephole connection. wco: A `Tensor`. Must have the same type as `x`. The weight matrix for output gate peephole connection. b: A `Tensor`. Must have the same type as `x`. The bias vector. i: A `Tensor`. Must have the same type as `x`. The input gate. cs: A `Tensor`. Must have the same type as `x`. The cell state before the tanh. f: A `Tensor`. Must have the same type as `x`. The forget gate. o: A `Tensor`. Must have the same type as `x`. The output gate. ci: A `Tensor`. Must have the same type as `x`. The cell input. co: A `Tensor`. Must have the same type as `x`. The cell after the tanh. cs_grad: A `Tensor`. Must have the same type as `x`. The current gradient of cs. h_grad: A `Tensor`. Must have the same type as `x`. The gradient of h vector. use_peephole: A `bool`. Whether the cell uses peephole connections. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (cs_prev_grad, dicfo, wci_grad, wcf_grad, wco_grad). cs_prev_grad: A `Tensor`. Has the same type as `x`. The gradient of cs to be back-propped. dicfo: A `Tensor`. Has the same type as `x`. The derivative wrt to [i, cs, f, o]. wci_grad: A `Tensor`. Has the same type as `x`. The gradient for wci to be back-propped. wcf_grad: A `Tensor`. Has the same type as `x`. The gradient for wcf to be back-propped. wco_grad: A `Tensor`. Has the same type as `x`. The gradient for wco to be back-propped. """ _ctx = _context._context if _ctx is None or not _ctx._eager_context.is_eager: use_peephole = _execute.make_bool(use_peephole, "use_peephole") _, _, _op = _op_def_lib._apply_op_helper( "LSTMBlockCellGrad", x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, i=i, cs=cs, f=f, o=o, ci=ci, co=co, cs_grad=cs_grad, h_grad=h_grad, use_peephole=use_peephole, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("use_peephole", _op.get_attr("use_peephole"), "T", _op.get_attr("T")) _execute.record_gradient( "LSTMBlockCellGrad", _inputs_flat, _attrs, _result, name) _result = _LSTMBlockCellGradOutput._make(_result) return _result else: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "LSTMBlockCellGrad", name, _ctx._post_execution_callbacks, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, "use_peephole", use_peephole) _result = _LSTMBlockCellGradOutput._make(_result) return _result except _core._FallbackException: return lstm_block_cell_grad_eager_fallback( x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole=use_peephole, name=name, ctx=_ctx) except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) def lstm_block_cell_grad_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None, ctx=None): r"""This is the slowpath function for Eager mode. This is for function lstm_block_cell_grad """ _ctx = ctx if ctx else _context.context() use_peephole = _execute.make_bool(use_peephole, "use_peephole") _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad], _ctx) (x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad) = _inputs_T _inputs_flat = [x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad] _attrs = ("use_peephole", use_peephole, "T", _attr_T) _result = _execute.execute(b"LSTMBlockCellGrad", 5, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "LSTMBlockCellGrad", _inputs_flat, _attrs, _result, name) _result = _LSTMBlockCellGradOutput._make(_result) return _result _ops.RegisterShape("LSTMBlockCellGrad")(None) def _InitOpDefLibrary(op_list_proto_bytes): op_list = _op_def_pb2.OpList() op_list.ParseFromString(op_list_proto_bytes) _op_def_registry.register_op_list(op_list) op_def_lib = _op_def_library.OpDefLibrary() op_def_lib.add_op_list(op_list) return op_def_lib # op { # name: "BlockLSTM" # input_arg { # name: "seq_len_max" # type: DT_INT64 # } # input_arg { # name: "x" # type_attr: "T" # } # input_arg { # name: "cs_prev" # type_attr: "T" # } # input_arg { # name: "h_prev" # type_attr: "T" # } # input_arg { # name: "w" # type_attr: "T" # } # input_arg { # name: "wci" # type_attr: "T" # } # input_arg { # name: "wcf" # type_attr: "T" # } # input_arg { # name: "wco" # type_attr: "T" # } # input_arg { # name: "b" # type_attr: "T" # } # output_arg { # name: "i" # type_attr: "T" # } # output_arg { # name: "cs" # type_attr: "T" # } # output_arg { # name: "f" # type_attr: "T" # } # output_arg { # name: "o" # type_attr: "T" # } # output_arg { # name: "ci" # type_attr: "T" # } # output_arg { # name: "co" # type_attr: "T" # } # output_arg { # name: "h" # type_attr: "T" # } # attr { # name: "forget_bias" # type: "float" # default_value { # f: 1 # } # } # attr { # name: "cell_clip" # type: "float" # default_value { # f: 3 # } # } # attr { # name: "use_peephole" # type: "bool" # default_value { # b: false # } # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_HALF # type: DT_FLOAT # } # } # } # } # op { # name: "BlockLSTMGrad" # input_arg { # name: "seq_len_max" # type: DT_INT64 # } # input_arg { # name: "x" # type_attr: "T" # } # input_arg { # name: "cs_prev" # type_attr: "T" # } # input_arg { # name: "h_prev" # type_attr: "T" # } # input_arg { # name: "w" # type_attr: "T" # } # input_arg { # name: "wci" # type_attr: "T" # } # input_arg { # name: "wcf" # type_attr: "T" # } # input_arg { # name: "wco" # type_attr: "T" # } # input_arg { # name: "b" # type_attr: "T" # } # input_arg { # name: "i" # type_attr: "T" # } # input_arg { # name: "cs" # type_attr: "T" # } # input_arg { # name: "f" # type_attr: "T" # } # input_arg { # name: "o" # type_attr: "T" # } # input_arg { # name: "ci" # type_attr: "T" # } # input_arg { # name: "co" # type_attr: "T" # } # input_arg { # name: "h" # type_attr: "T" # } # input_arg { # name: "cs_grad" # type_attr: "T" # } # input_arg { # name: "h_grad" # type_attr: "T" # } # output_arg { # name: "x_grad" # type_attr: "T" # } # output_arg { # name: "cs_prev_grad" # type_attr: "T" # } # output_arg { # name: "h_prev_grad" # type_attr: "T" # } # output_arg { # name: "w_grad" # type_attr: "T" # } # output_arg { # name: "wci_grad" # type_attr: "T" # } # output_arg { # name: "wcf_grad" # type_attr: "T" # } # output_arg { # name: "wco_grad" # type_attr: "T" # } # output_arg { # name: "b_grad" # type_attr: "T" # } # attr { # name: "use_peephole" # type: "bool" # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_HALF # type: DT_FLOAT # } # } # } # } # op { # name: "LSTMBlockCell" # input_arg { # name: "x" # type_attr: "T" # } # input_arg { # name: "cs_prev" # type_attr: "T" # } # input_arg { # name: "h_prev" # type_attr: "T" # } # input_arg { # name: "w" # type_attr: "T" # } # input_arg { # name: "wci" # type_attr: "T" # } # input_arg { # name: "wcf" # type_attr: "T" # } # input_arg { # name: "wco" # type_attr: "T" # } # input_arg { # name: "b" # type_attr: "T" # } # output_arg { # name: "i" # type_attr: "T" # } # output_arg { # name: "cs" # type_attr: "T" # } # output_arg { # name: "f" # type_attr: "T" # } # output_arg { # name: "o" # type_attr: "T" # } # output_arg { # name: "ci" # type_attr: "T" # } # output_arg { # name: "co" # type_attr: "T" # } # output_arg { # name: "h" # type_attr: "T" # } # attr { # name: "forget_bias" # type: "float" # default_value { # f: 1 # } # } # attr { # name: "cell_clip" # type: "float" # default_value { # f: 3 # } # } # attr { # name: "use_peephole" # type: "bool" # default_value { # b: false # } # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_HALF # type: DT_FLOAT # } # } # } # } # op { # name: "LSTMBlockCellGrad" # input_arg { # name: "x" # type_attr: "T" # } # input_arg { # name: "cs_prev" # type_attr: "T" # } # input_arg { # name: "h_prev" # type_attr: "T" # } # input_arg { # name: "w" # type_attr: "T" # } # input_arg { # name: "wci" # type_attr: "T" # } # input_arg { # name: "wcf" # type_attr: "T" # } # input_arg { # name: "wco" # type_attr: "T" # } # input_arg { # name: "b" # type_attr: "T" # } # input_arg { # name: "i" # type_attr: "T" # } # input_arg { # name: "cs" # type_attr: "T" # } # input_arg { # name: "f" # type_attr: "T" # } # input_arg { # name: "o" # type_attr: "T" # } # input_arg { # name: "ci" # type_attr: "T" # } # input_arg { # name: "co" # type_attr: "T" # } # input_arg { # name: "cs_grad" # type_attr: "T" # } # input_arg { # name: "h_grad" # type_attr: "T" # } # output_arg { # name: "cs_prev_grad" # type_attr: "T" # } # output_arg { # name: "dicfo" # type_attr: "T" # } # output_arg { # name: "wci_grad" # type_attr: "T" # } # output_arg { # name: "wcf_grad" # type_attr: "T" # } # output_arg { # name: "wco_grad" # type_attr: "T" # } # attr { # name: "use_peephole" # type: "bool" # } # attr { # name: "T" # type: "type" # allowed_values { # list { # type: DT_HALF # type: DT_FLOAT # } # } # } # } _op_def_lib = _InitOpDefLibrary(b"\n\215\002\n\tBlockLSTM\022\017\n\013seq_len_max\030\t\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\032\006\n\001i\"\001T\032\007\n\002cs\"\001T\032\006\n\001f\"\001T\032\006\n\001o\"\001T\032\007\n\002ci\"\001T\032\007\n\002co\"\001T\032\006\n\001h\"\001T\"\033\n\013forget_bias\022\005float\032\005%\000\000\200?\"\031\n\tcell_clip\022\005float\032\005%\000\000@@\"\030\n\014use_peephole\022\004bool\032\002(\000\"\021\n\001T\022\004type:\006\n\0042\002\023\001\n\351\002\n\rBlockLSTMGrad\022\017\n\013seq_len_max\030\t\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\022\006\n\001i\"\001T\022\007\n\002cs\"\001T\022\006\n\001f\"\001T\022\006\n\001o\"\001T\022\007\n\002ci\"\001T\022\007\n\002co\"\001T\022\006\n\001h\"\001T\022\014\n\007cs_grad\"\001T\022\013\n\006h_grad\"\001T\032\013\n\006x_grad\"\001T\032\021\n\014cs_prev_grad\"\001T\032\020\n\013h_prev_grad\"\001T\032\013\n\006w_grad\"\001T\032\r\n\010wci_grad\"\001T\032\r\n\010wcf_grad\"\001T\032\r\n\010wco_grad\"\001T\032\013\n\006b_grad\"\001T\"\024\n\014use_peephole\022\004bool\"\021\n\001T\022\004type:\006\n\0042\002\023\001\n\200\002\n\rLSTMBlockCell\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\032\006\n\001i\"\001T\032\007\n\002cs\"\001T\032\006\n\001f\"\001T\032\006\n\001o\"\001T\032\007\n\002ci\"\001T\032\007\n\002co\"\001T\032\006\n\001h\"\001T\"\033\n\013forget_bias\022\005float\032\005%\000\000\200?\"\031\n\tcell_clip\022\005float\032\005%\000\000@@\"\030\n\014use_peephole\022\004bool\032\002(\000\"\021\n\001T\022\004type:\006\n\0042\002\023\001\n\247\002\n\021LSTMBlockCellGrad\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\022\006\n\001i\"\001T\022\007\n\002cs\"\001T\022\006\n\001f\"\001T\022\006\n\001o\"\001T\022\007\n\002ci\"\001T\022\007\n\002co\"\001T\022\014\n\007cs_grad\"\001T\022\013\n\006h_grad\"\001T\032\021\n\014cs_prev_grad\"\001T\032\n\n\005dicfo\"\001T\032\r\n\010wci_grad\"\001T\032\r\n\010wcf_grad\"\001T\032\r\n\010wco_grad\"\001T\"\024\n\014use_peephole\022\004bool\"\021\n\001T\022\004type:\006\n\0042\002\023\001")

36.591667

2,646

0.624488

import collections as _collections import six as _six from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow from tensorflow.python.eager import context as _context from tensorflow.python.eager import core as _core from tensorflow.python.eager import execute as _execute from tensorflow.python.framework import dtypes as _dtypes from tensorflow.python.framework import errors as _errors from tensorflow.python.framework import tensor_shape as _tensor_shape from tensorflow.core.framework import op_def_pb2 as _op_def_pb2 from tensorflow.python.framework import common_shapes as _common_shapes from tensorflow.python.framework import op_def_registry as _op_def_registry from tensorflow.python.framework import ops as _ops from tensorflow.python.framework import op_def_library as _op_def_library from tensorflow.python.util.deprecation import deprecated_endpoints from tensorflow.python.util.tf_export import tf_export _block_lstm_outputs = ["i", "cs", "f", "o", "ci", "co", "h"] _BlockLSTMOutput = _collections.namedtuple( "BlockLSTM", _block_lstm_outputs) @tf_export('block_lstm') def block_lstm(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None): _ctx = _context._context if _ctx is None or not _ctx._eager_context.is_eager: if forget_bias is None: forget_bias = 1 forget_bias = _execute.make_float(forget_bias, "forget_bias") if cell_clip is None: cell_clip = 3 cell_clip = _execute.make_float(cell_clip, "cell_clip") if use_peephole is None: use_peephole = False use_peephole = _execute.make_bool(use_peephole, "use_peephole") _, _, _op = _op_def_lib._apply_op_helper( "BlockLSTM", seq_len_max=seq_len_max, x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, forget_bias=forget_bias, cell_clip=cell_clip, use_peephole=use_peephole, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("forget_bias", _op.get_attr("forget_bias"), "cell_clip", _op.get_attr("cell_clip"), "use_peephole", _op.get_attr("use_peephole"), "T", _op.get_attr("T")) _execute.record_gradient( "BlockLSTM", _inputs_flat, _attrs, _result, name) _result = _BlockLSTMOutput._make(_result) return _result else: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "BlockLSTM", name, _ctx._post_execution_callbacks, seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, "forget_bias", forget_bias, "cell_clip", cell_clip, "use_peephole", use_peephole) _result = _BlockLSTMOutput._make(_result) return _result except _core._FallbackException: return block_lstm_eager_fallback( seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=forget_bias, cell_clip=cell_clip, use_peephole=use_peephole, name=name, ctx=_ctx) except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) def block_lstm_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None, ctx=None): _ctx = ctx if ctx else _context.context() if forget_bias is None: forget_bias = 1 forget_bias = _execute.make_float(forget_bias, "forget_bias") if cell_clip is None: cell_clip = 3 cell_clip = _execute.make_float(cell_clip, "cell_clip") if use_peephole is None: use_peephole = False use_peephole = _execute.make_bool(use_peephole, "use_peephole") _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b], _ctx) (x, cs_prev, h_prev, w, wci, wcf, wco, b) = _inputs_T seq_len_max = _ops.convert_to_tensor(seq_len_max, _dtypes.int64) _inputs_flat = [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b] _attrs = ("forget_bias", forget_bias, "cell_clip", cell_clip, "use_peephole", use_peephole, "T", _attr_T) _result = _execute.execute(b"BlockLSTM", 7, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "BlockLSTM", _inputs_flat, _attrs, _result, name) _result = _BlockLSTMOutput._make(_result) return _result _ops.RegisterShape("BlockLSTM")(None) _block_lstm_grad_outputs = ["x_grad", "cs_prev_grad", "h_prev_grad", "w_grad", "wci_grad", "wcf_grad", "wco_grad", "b_grad"] _BlockLSTMGradOutput = _collections.namedtuple( "BlockLSTMGrad", _block_lstm_grad_outputs) @tf_export('block_lstm_grad') def block_lstm_grad(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None): _ctx = _context._context if _ctx is None or not _ctx._eager_context.is_eager: use_peephole = _execute.make_bool(use_peephole, "use_peephole") _, _, _op = _op_def_lib._apply_op_helper( "BlockLSTMGrad", seq_len_max=seq_len_max, x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, i=i, cs=cs, f=f, o=o, ci=ci, co=co, h=h, cs_grad=cs_grad, h_grad=h_grad, use_peephole=use_peephole, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("use_peephole", _op.get_attr("use_peephole"), "T", _op.get_attr("T")) _execute.record_gradient( "BlockLSTMGrad", _inputs_flat, _attrs, _result, name) _result = _BlockLSTMGradOutput._make(_result) return _result else: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "BlockLSTMGrad", name, _ctx._post_execution_callbacks, seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, "use_peephole", use_peephole) _result = _BlockLSTMGradOutput._make(_result) return _result except _core._FallbackException: return block_lstm_grad_eager_fallback( seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole=use_peephole, name=name, ctx=_ctx) except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) def block_lstm_grad_eager_fallback(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole, name=None, ctx=None): _ctx = ctx if ctx else _context.context() use_peephole = _execute.make_bool(use_peephole, "use_peephole") _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad], _ctx) (x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad) = _inputs_T seq_len_max = _ops.convert_to_tensor(seq_len_max, _dtypes.int64) _inputs_flat = [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad] _attrs = ("use_peephole", use_peephole, "T", _attr_T) _result = _execute.execute(b"BlockLSTMGrad", 8, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "BlockLSTMGrad", _inputs_flat, _attrs, _result, name) _result = _BlockLSTMGradOutput._make(_result) return _result _ops.RegisterShape("BlockLSTMGrad")(None) _lstm_block_cell_outputs = ["i", "cs", "f", "o", "ci", "co", "h"] _LSTMBlockCellOutput = _collections.namedtuple( "LSTMBlockCell", _lstm_block_cell_outputs) @tf_export('lstm_block_cell') def lstm_block_cell(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None): _ctx = _context._context if _ctx is None or not _ctx._eager_context.is_eager: if forget_bias is None: forget_bias = 1 forget_bias = _execute.make_float(forget_bias, "forget_bias") if cell_clip is None: cell_clip = 3 cell_clip = _execute.make_float(cell_clip, "cell_clip") if use_peephole is None: use_peephole = False use_peephole = _execute.make_bool(use_peephole, "use_peephole") _, _, _op = _op_def_lib._apply_op_helper( "LSTMBlockCell", x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, forget_bias=forget_bias, cell_clip=cell_clip, use_peephole=use_peephole, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("forget_bias", _op.get_attr("forget_bias"), "cell_clip", _op.get_attr("cell_clip"), "use_peephole", _op.get_attr("use_peephole"), "T", _op.get_attr("T")) _execute.record_gradient( "LSTMBlockCell", _inputs_flat, _attrs, _result, name) _result = _LSTMBlockCellOutput._make(_result) return _result else: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "LSTMBlockCell", name, _ctx._post_execution_callbacks, x, cs_prev, h_prev, w, wci, wcf, wco, b, "forget_bias", forget_bias, "cell_clip", cell_clip, "use_peephole", use_peephole) _result = _LSTMBlockCellOutput._make(_result) return _result except _core._FallbackException: return lstm_block_cell_eager_fallback( x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=forget_bias, cell_clip=cell_clip, use_peephole=use_peephole, name=name, ctx=_ctx) except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) def lstm_block_cell_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias=1, cell_clip=3, use_peephole=False, name=None, ctx=None): _ctx = ctx if ctx else _context.context() if forget_bias is None: forget_bias = 1 forget_bias = _execute.make_float(forget_bias, "forget_bias") if cell_clip is None: cell_clip = 3 cell_clip = _execute.make_float(cell_clip, "cell_clip") if use_peephole is None: use_peephole = False use_peephole = _execute.make_bool(use_peephole, "use_peephole") _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b], _ctx) (x, cs_prev, h_prev, w, wci, wcf, wco, b) = _inputs_T _inputs_flat = [x, cs_prev, h_prev, w, wci, wcf, wco, b] _attrs = ("forget_bias", forget_bias, "cell_clip", cell_clip, "use_peephole", use_peephole, "T", _attr_T) _result = _execute.execute(b"LSTMBlockCell", 7, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "LSTMBlockCell", _inputs_flat, _attrs, _result, name) _result = _LSTMBlockCellOutput._make(_result) return _result _ops.RegisterShape("LSTMBlockCell")(None) _lstm_block_cell_grad_outputs = ["cs_prev_grad", "dicfo", "wci_grad", "wcf_grad", "wco_grad"] _LSTMBlockCellGradOutput = _collections.namedtuple( "LSTMBlockCellGrad", _lstm_block_cell_grad_outputs) @tf_export('lstm_block_cell_grad') def lstm_block_cell_grad(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None): _ctx = _context._context if _ctx is None or not _ctx._eager_context.is_eager: use_peephole = _execute.make_bool(use_peephole, "use_peephole") _, _, _op = _op_def_lib._apply_op_helper( "LSTMBlockCellGrad", x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, i=i, cs=cs, f=f, o=o, ci=ci, co=co, cs_grad=cs_grad, h_grad=h_grad, use_peephole=use_peephole, name=name) _result = _op.outputs[:] _inputs_flat = _op.inputs _attrs = ("use_peephole", _op.get_attr("use_peephole"), "T", _op.get_attr("T")) _execute.record_gradient( "LSTMBlockCellGrad", _inputs_flat, _attrs, _result, name) _result = _LSTMBlockCellGradOutput._make(_result) return _result else: try: _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( _ctx._context_handle, _ctx._eager_context.device_name, "LSTMBlockCellGrad", name, _ctx._post_execution_callbacks, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, "use_peephole", use_peephole) _result = _LSTMBlockCellGradOutput._make(_result) return _result except _core._FallbackException: return lstm_block_cell_grad_eager_fallback( x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole=use_peephole, name=name, ctx=_ctx) except _core._NotOkStatusException as e: if name is not None: message = e.message + " name: " + name else: message = e.message _six.raise_from(_core._status_to_exception(e.code, message), None) def lstm_block_cell_grad_eager_fallback(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole, name=None, ctx=None): _ctx = ctx if ctx else _context.context() use_peephole = _execute.make_bool(use_peephole, "use_peephole") _attr_T, _inputs_T = _execute.args_to_matching_eager([x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad], _ctx) (x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad) = _inputs_T _inputs_flat = [x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad] _attrs = ("use_peephole", use_peephole, "T", _attr_T) _result = _execute.execute(b"LSTMBlockCellGrad", 5, inputs=_inputs_flat, attrs=_attrs, ctx=_ctx, name=name) _execute.record_gradient( "LSTMBlockCellGrad", _inputs_flat, _attrs, _result, name) _result = _LSTMBlockCellGradOutput._make(_result) return _result _ops.RegisterShape("LSTMBlockCellGrad")(None) def _InitOpDefLibrary(op_list_proto_bytes): op_list = _op_def_pb2.OpList() op_list.ParseFromString(op_list_proto_bytes) _op_def_registry.register_op_list(op_list) op_def_lib = _op_def_library.OpDefLibrary() op_def_lib.add_op_list(op_list) return op_def_lib _op_def_lib = _InitOpDefLibrary(b"\n\215\002\n\tBlockLSTM\022\017\n\013seq_len_max\030\t\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\032\006\n\001i\"\001T\032\007\n\002cs\"\001T\032\006\n\001f\"\001T\032\006\n\001o\"\001T\032\007\n\002ci\"\001T\032\007\n\002co\"\001T\032\006\n\001h\"\001T\"\033\n\013forget_bias\022\005float\032\005%\000\000\200?\"\031\n\tcell_clip\022\005float\032\005%\000\000@@\"\030\n\014use_peephole\022\004bool\032\002(\000\"\021\n\001T\022\004type:\006\n\0042\002\023\001\n\351\002\n\rBlockLSTMGrad\022\017\n\013seq_len_max\030\t\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\022\006\n\001i\"\001T\022\007\n\002cs\"\001T\022\006\n\001f\"\001T\022\006\n\001o\"\001T\022\007\n\002ci\"\001T\022\007\n\002co\"\001T\022\006\n\001h\"\001T\022\014\n\007cs_grad\"\001T\022\013\n\006h_grad\"\001T\032\013\n\006x_grad\"\001T\032\021\n\014cs_prev_grad\"\001T\032\020\n\013h_prev_grad\"\001T\032\013\n\006w_grad\"\001T\032\r\n\010wci_grad\"\001T\032\r\n\010wcf_grad\"\001T\032\r\n\010wco_grad\"\001T\032\013\n\006b_grad\"\001T\"\024\n\014use_peephole\022\004bool\"\021\n\001T\022\004type:\006\n\0042\002\023\001\n\200\002\n\rLSTMBlockCell\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\032\006\n\001i\"\001T\032\007\n\002cs\"\001T\032\006\n\001f\"\001T\032\006\n\001o\"\001T\032\007\n\002ci\"\001T\032\007\n\002co\"\001T\032\006\n\001h\"\001T\"\033\n\013forget_bias\022\005float\032\005%\000\000\200?\"\031\n\tcell_clip\022\005float\032\005%\000\000@@\"\030\n\014use_peephole\022\004bool\032\002(\000\"\021\n\001T\022\004type:\006\n\0042\002\023\001\n\247\002\n\021LSTMBlockCellGrad\022\006\n\001x\"\001T\022\014\n\007cs_prev\"\001T\022\013\n\006h_prev\"\001T\022\006\n\001w\"\001T\022\010\n\003wci\"\001T\022\010\n\003wcf\"\001T\022\010\n\003wco\"\001T\022\006\n\001b\"\001T\022\006\n\001i\"\001T\022\007\n\002cs\"\001T\022\006\n\001f\"\001T\022\006\n\001o\"\001T\022\007\n\002ci\"\001T\022\007\n\002co\"\001T\022\014\n\007cs_grad\"\001T\022\013\n\006h_grad\"\001T\032\021\n\014cs_prev_grad\"\001T\032\n\n\005dicfo\"\001T\032\r\n\010wci_grad\"\001T\032\r\n\010wcf_grad\"\001T\032\r\n\010wco_grad\"\001T\"\024\n\014use_peephole\022\004bool\"\021\n\001T\022\004type:\006\n\0042\002\023\001")

true

790dd5c09d0dc1c89b8d4a0593284a76bc85748f

3,042

py

Python

aliyun-python-sdk-iot/aliyunsdkiot/request/v20180120/CreateRuleRequest.py

liuzheng/aliyun-openapi-python-sdk

1ba6743f3d6f2cef57ec9e3be1754b04293c3150

[ "Apache-2.0" ]

null

aliyun-python-sdk-iot/aliyunsdkiot/request/v20180120/CreateRuleRequest.py

liuzheng/aliyun-openapi-python-sdk

1ba6743f3d6f2cef57ec9e3be1754b04293c3150

[ "Apache-2.0" ]

null

aliyun-python-sdk-iot/aliyunsdkiot/request/v20180120/CreateRuleRequest.py

liuzheng/aliyun-openapi-python-sdk

1ba6743f3d6f2cef57ec9e3be1754b04293c3150

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # # http://www.apache.org/licenses/LICENSE-2.0 # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest from aliyunsdkiot.endpoint import endpoint_data class CreateRuleRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'Iot', '2018-01-20', 'CreateRule') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_Select(self): return self.get_query_params().get('Select') def set_Select(self,Select): self.add_query_param('Select',Select) def get_RuleDesc(self): return self.get_query_params().get('RuleDesc') def set_RuleDesc(self,RuleDesc): self.add_query_param('RuleDesc',RuleDesc) def get_ShortTopic(self): return self.get_query_params().get('ShortTopic') def set_ShortTopic(self,ShortTopic): self.add_query_param('ShortTopic',ShortTopic) def get_ResourceGroupId(self): return self.get_query_params().get('ResourceGroupId') def set_ResourceGroupId(self,ResourceGroupId): self.add_query_param('ResourceGroupId',ResourceGroupId) def get_DataType(self): return self.get_query_params().get('DataType') def set_DataType(self,DataType): self.add_query_param('DataType',DataType) def get_IotInstanceId(self): return self.get_query_params().get('IotInstanceId') def set_IotInstanceId(self,IotInstanceId): self.add_query_param('IotInstanceId',IotInstanceId) def get_Where(self): return self.get_query_params().get('Where') def set_Where(self,Where): self.add_query_param('Where',Where) def get_TopicType(self): return self.get_query_params().get('TopicType') def set_TopicType(self,TopicType): self.add_query_param('TopicType',TopicType) def get_ProductKey(self): return self.get_query_params().get('ProductKey') def set_ProductKey(self,ProductKey): self.add_query_param('ProductKey',ProductKey) def get_Name(self): return self.get_query_params().get('Name') def set_Name(self,Name): self.add_query_param('Name',Name) def get_Topic(self): return self.get_query_params().get('Topic') def set_Topic(self,Topic): self.add_query_param('Topic',Topic)

31.040816

74

0.753123

from aliyunsdkcore.request import RpcRequest from aliyunsdkiot.endpoint import endpoint_data class CreateRuleRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'Iot', '2018-01-20', 'CreateRule') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_Select(self): return self.get_query_params().get('Select') def set_Select(self,Select): self.add_query_param('Select',Select) def get_RuleDesc(self): return self.get_query_params().get('RuleDesc') def set_RuleDesc(self,RuleDesc): self.add_query_param('RuleDesc',RuleDesc) def get_ShortTopic(self): return self.get_query_params().get('ShortTopic') def set_ShortTopic(self,ShortTopic): self.add_query_param('ShortTopic',ShortTopic) def get_ResourceGroupId(self): return self.get_query_params().get('ResourceGroupId') def set_ResourceGroupId(self,ResourceGroupId): self.add_query_param('ResourceGroupId',ResourceGroupId) def get_DataType(self): return self.get_query_params().get('DataType') def set_DataType(self,DataType): self.add_query_param('DataType',DataType) def get_IotInstanceId(self): return self.get_query_params().get('IotInstanceId') def set_IotInstanceId(self,IotInstanceId): self.add_query_param('IotInstanceId',IotInstanceId) def get_Where(self): return self.get_query_params().get('Where') def set_Where(self,Where): self.add_query_param('Where',Where) def get_TopicType(self): return self.get_query_params().get('TopicType') def set_TopicType(self,TopicType): self.add_query_param('TopicType',TopicType) def get_ProductKey(self): return self.get_query_params().get('ProductKey') def set_ProductKey(self,ProductKey): self.add_query_param('ProductKey',ProductKey) def get_Name(self): return self.get_query_params().get('Name') def set_Name(self,Name): self.add_query_param('Name',Name) def get_Topic(self): return self.get_query_params().get('Topic') def set_Topic(self,Topic): self.add_query_param('Topic',Topic)

true

790dd60430b6b1e8b7c79bdda4a8bfebf564e295

3,307

py

Python

src/compas_rhino/artists/lineartist.py

archimarkGit/compas

a953df2fca778e27bdf02437fcf8ff2b7d924c73

[ "MIT" ]

null

src/compas_rhino/artists/lineartist.py

archimarkGit/compas

a953df2fca778e27bdf02437fcf8ff2b7d924c73

[ "MIT" ]

null

src/compas_rhino/artists/lineartist.py

archimarkGit/compas

a953df2fca778e27bdf02437fcf8ff2b7d924c73

[ "MIT" ]

null

from __future__ import print_function from __future__ import absolute_import from __future__ import division try: basestring except NameError: basestring = str import compas_rhino from compas.utilities import iterable_like from compas_rhino.artists._primitiveartist import PrimitiveArtist __all__ = ['LineArtist'] class LineArtist(PrimitiveArtist): """Artist for drawing lines. Parameters ---------- primitive : :class:`compas.geometry.Line` A COMPAS line. Notes ----- See :class:`compas_rhino.artists.PrimitiveArtist` for all other parameters. """ def draw(self): """Draw the line. Returns ------- list The GUIDs of the created Rhino objects. """ start = list(self.primitive.start) end = list(self.primitive.end) lines = [{'start': start, 'end': end, 'color': self.color, 'name': self.name}] guids = compas_rhino.draw_lines(lines, layer=self.layer, clear=False, redraw=False) self._guids = guids return guids @staticmethod def draw_collection(collection, names=None, colors=None, layer=None, clear=False, add_to_group=False, group_name=None): """Draw a collection of lines. Parameters ---------- collection: list of compas.geometry.Line A collection of ``Line`` objects. names : list of str, optional Individual names for the lines. colors : color or list of color, optional A color specification for the lines as a single color or a list of individual colors. layer : str, optional A layer path. clear : bool, optional Clear the layer before drawing. add_to_group : bool, optional Add the frames to a group. group_name : str, optional Name of the group. Returns ------- guids: list A list of GUIDs if the collection is not grouped. groupname: str The name of the group if the collection objects are grouped. """ lines = [{'start': list(line[0]), 'end': list(line[1])} for line in collection] if colors: if isinstance(colors[0], (int, float)): colors = iterable_like(collection, [colors], colors) else: colors = iterable_like(collection, colors, colors[0]) for line, rgb in zip(lines, colors): line['color'] = rgb if names: if isinstance(names, basestring): names = iterable_like(collection, [names], names) else: names = iterable_like(collection, names, names[0]) for line, name in zip(lines, names): line['name'] = name guids = compas_rhino.draw_lines(lines, layer=layer, clear=clear) if not add_to_group: return guids group = compas_rhino.rs.AddGroup(group_name) if group: compas_rhino.rs.AddObjectsToGroup(guids, group) return group # ============================================================================== # Main # ============================================================================== if __name__ == "__main__": pass

30.62037

123

0.566979

from __future__ import print_function from __future__ import absolute_import from __future__ import division try: basestring except NameError: basestring = str import compas_rhino from compas.utilities import iterable_like from compas_rhino.artists._primitiveartist import PrimitiveArtist __all__ = ['LineArtist'] class LineArtist(PrimitiveArtist): def draw(self): start = list(self.primitive.start) end = list(self.primitive.end) lines = [{'start': start, 'end': end, 'color': self.color, 'name': self.name}] guids = compas_rhino.draw_lines(lines, layer=self.layer, clear=False, redraw=False) self._guids = guids return guids @staticmethod def draw_collection(collection, names=None, colors=None, layer=None, clear=False, add_to_group=False, group_name=None): lines = [{'start': list(line[0]), 'end': list(line[1])} for line in collection] if colors: if isinstance(colors[0], (int, float)): colors = iterable_like(collection, [colors], colors) else: colors = iterable_like(collection, colors, colors[0]) for line, rgb in zip(lines, colors): line['color'] = rgb if names: if isinstance(names, basestring): names = iterable_like(collection, [names], names) else: names = iterable_like(collection, names, names[0]) for line, name in zip(lines, names): line['name'] = name guids = compas_rhino.draw_lines(lines, layer=layer, clear=clear) if not add_to_group: return guids group = compas_rhino.rs.AddGroup(group_name) if group: compas_rhino.rs.AddObjectsToGroup(guids, group) return group if __name__ == "__main__": pass

true

790dd6370c24d19ef81de7752b882b6cd395e555

1,901

py

Python

Version2/ivan/rlcard/rlcard/agents/mcmphelp.py

guy477/Poker

d10e5af396509cd425aedc27198bb30c0709f43b

[ "MIT" ]

2

2021-05-03T01:57:06.000Z

2022-03-30T02:56:11.000Z

ignitionBot/ivan/rlcard/rlcard/agents/mcmphelp.py

guy477/Poker

d10e5af396509cd425aedc27198bb30c0709f43b

[ "MIT" ]

null

ignitionBot/ivan/rlcard/rlcard/agents/mcmphelp.py

guy477/Poker

d10e5af396509cd425aedc27198bb30c0709f43b

[ "MIT" ]

1

2021-02-17T06:17:37.000Z

def par_UCT(rootstate, rootnode, itermax): print('hi') for i in range(0): node = rootnode state = rootstate.clone() # Select while node.untriedMoves == [] and node.childNodes != []: # node is fully expanded and non-terminal node = node.UCTSelectChild() state.do_move(node.move) # Expand if node.untriedMoves != []: # if we can expand (i.e. state/node is non-terminal) m = random.choice(node.untriedMoves) state.do_move(m) node = node.AddChild(m,state) # add child and descend tree # Rollout - this can often be made orders of magnitude quicker using a state.GetRandomMove() function while state.get_moves() != []: # while state is non-terminal # print('---------') # print(state.credits) # print(state._get_player_turn()) # print(state.get_moves()) # print(state.moves_taken) # probs = [1 for x in state.get_moves()] # if(5 in state.get_moves()): # probs[-1] -= .5 state.do_move(random.choice(state.get_moves())) # Backpropagate while node != None: # backpropagate from the expanded node and work back to the root node node.Update(state.get_result(node.playerJustMoved)) # state is terminal. Update node with result from POV of node.playerJustMoved node = node.parentNode # Output some information about the tree - can be omitted if (verbose): print(rootnode.TreeToString(0)) else: # print(rootnode.ChildrenToString()) pass # determine general performance of hand return sorted(rootnode.childNodes, key = lambda c: c.visits)[-1].move

44.209302

145

0.557601

def par_UCT(rootstate, rootnode, itermax): print('hi') for i in range(0): node = rootnode state = rootstate.clone() while node.untriedMoves == [] and node.childNodes != []: node = node.UCTSelectChild() state.do_move(node.move) if node.untriedMoves != []: m = random.choice(node.untriedMoves) state.do_move(m) node = node.AddChild(m,state) while state.get_moves() != []: state.do_move(random.choice(state.get_moves())) while node != None: node.Update(state.get_result(node.playerJustMoved)) node = node.parentNode if (verbose): print(rootnode.TreeToString(0)) else: pass return sorted(rootnode.childNodes, key = lambda c: c.visits)[-1].move

false

true

790dd65a065516ff8e7ef93065f71014e83c9436

1,905

py

Python

python/eggroll/core/aspects.py

liszekei/eggroll

6a8cc5e1c9106d2633dc415092151f921f003743

[ "Apache-2.0" ]

209

2019-08-08T18:38:26.000Z

2022-03-23T06:20:40.000Z

python/eggroll/core/aspects.py

liszekei/eggroll

6a8cc5e1c9106d2633dc415092151f921f003743

[ "Apache-2.0" ]

110

2019-08-09T02:50:47.000Z

2022-03-07T10:30:21.000Z

python/eggroll/core/aspects.py

liszekei/eggroll

6a8cc5e1c9106d2633dc415092151f921f003743

[ "Apache-2.0" ]

77

2019-08-15T08:11:52.000Z

2022-03-23T06:19:44.000Z

# Copyright (c) 2019 - now, Eggroll Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # import inspect from time import time, perf_counter from eggroll.utils.log_utils import get_logger L = get_logger(filename='profile') def _method_profile_logger(func): def wrapper(*args, **kwargs): start_wall_time = time() start_cpu_time = perf_counter() result = func(*args, **kwargs) end_wall_time = time() end_cpu_time = perf_counter() code = func.__code__ try: outerframes = inspect.getouterframes(inspect.currentframe(), 2) real_caller = outerframes[1] L.trace(f'{{"metric_type": "func_profile", ' f'"qualname": "{func.__qualname__}", ' f'"caller": "{real_caller.filename.rsplit("/", 1)[-1]}:{real_caller.lineno}", ' f'"cpu_time": {end_cpu_time - start_cpu_time}, ' f'"wall_time": {end_wall_time - start_wall_time}}}') return result except Exception as e: L.trace(f'{{"metric_type": "func_profile", ' f'"qualname": "{func.__qualname__}", ' f'"caller": "unknown", ' f'"cpu_time": {end_cpu_time - start_cpu_time}, ' f'"wall_time": {end_wall_time - start_wall_time}}}') return wrapper

35.943396

98

0.623097

import inspect from time import time, perf_counter from eggroll.utils.log_utils import get_logger L = get_logger(filename='profile') def _method_profile_logger(func): def wrapper(*args, **kwargs): start_wall_time = time() start_cpu_time = perf_counter() result = func(*args, **kwargs) end_wall_time = time() end_cpu_time = perf_counter() code = func.__code__ try: outerframes = inspect.getouterframes(inspect.currentframe(), 2) real_caller = outerframes[1] L.trace(f'{{"metric_type": "func_profile", ' f'"qualname": "{func.__qualname__}", ' f'"caller": "{real_caller.filename.rsplit("/", 1)[-1]}:{real_caller.lineno}", ' f'"cpu_time": {end_cpu_time - start_cpu_time}, ' f'"wall_time": {end_wall_time - start_wall_time}}}') return result except Exception as e: L.trace(f'{{"metric_type": "func_profile", ' f'"qualname": "{func.__qualname__}", ' f'"caller": "unknown", ' f'"cpu_time": {end_cpu_time - start_cpu_time}, ' f'"wall_time": {end_wall_time - start_wall_time}}}') return wrapper

true

790dd66ed1fd5503977ff280cc651f855ea31907

501

py

Python

examples/tutorials/parallel_distributed/mth_exception.py

lethaiq/machin

7873cada457328952310394afeedcad4bb6a7c4a

[ "MIT" ]

1

2021-04-01T21:21:23.000Z

examples/tutorials/parallel_distributed/mth_exception.py

lethaiq/machin

7873cada457328952310394afeedcad4bb6a7c4a

[ "MIT" ]

null

examples/tutorials/parallel_distributed/mth_exception.py

lethaiq/machin

7873cada457328952310394afeedcad4bb6a7c4a

[ "MIT" ]

null

from machin.parallel.thread import Thread, ThreadException import time def test1(): time.sleep(1) print("Exception occurred at {}".format(time.time())) raise RuntimeError("Error") if __name__ == "__main__": t1 = Thread(target=test1) t1.start() while True: try: t1.watch() except ThreadException as e: print("Exception caught at {}".format(time.time())) print("Exception is: {}".format(e)) break t1.join()

22.772727

63

0.590818

from machin.parallel.thread import Thread, ThreadException import time def test1(): time.sleep(1) print("Exception occurred at {}".format(time.time())) raise RuntimeError("Error") if __name__ == "__main__": t1 = Thread(target=test1) t1.start() while True: try: t1.watch() except ThreadException as e: print("Exception caught at {}".format(time.time())) print("Exception is: {}".format(e)) break t1.join()

true

790dd67805d984f9cd1a0d14782011a31fbc0274

14,858

py

Python

jina/orchestrate/deployments/config/k8s.py

gauribhutani/jina

e1e23643f8260e6917d9704b63edc54bcebbc7e9

[ "Apache-2.0" ]

null

jina/orchestrate/deployments/config/k8s.py

gauribhutani/jina

e1e23643f8260e6917d9704b63edc54bcebbc7e9

[ "Apache-2.0" ]

1

2022-03-08T18:46:28.000Z

2022-03-08T18:47:24.000Z

jina/orchestrate/deployments/config/k8s.py

kuraakhilesh8230/jina

7cc23944fcdfd9944dc805ce8a116818d45317ee

[ "Apache-2.0" ]

1

2022-03-17T04:50:07.000Z

import copy from argparse import Namespace from typing import Dict, Union, List, Optional, Tuple from jina import __default_executor__ from jina.enums import PodRoleType from jina.excepts import NoContainerizedError from jina.orchestrate.deployments.config.k8slib import kubernetes_deployment from jina.orchestrate.deployments.config.helper import ( get_image_name, to_compatible_name, get_base_executor_version, construct_runtime_container_args, validate_uses, ) from jina.serve.networking import K8sGrpcConnectionPool from jina.orchestrate.deployments import BaseDeployment class K8sDeploymentConfig: """ Class that implements the output of configuration files for Kubernetes for a given Deployment. """ class _K8sDeployment: def __init__( self, name: str, version: str, pod_type: PodRoleType, jina_deployment_name: str, shard_id: Optional[int], common_args: Union['Namespace', Dict], deployment_args: Union['Namespace', Dict], k8s_namespace: str, k8s_connection_pool: bool = True, k8s_deployments_addresses: Optional[Dict[str, List[str]]] = None, ): self.name = name self.dns_name = to_compatible_name(name) self.version = version self.pod_type = pod_type self.jina_deployment_name = jina_deployment_name self.shard_id = shard_id self.common_args = common_args self.deployment_args = deployment_args self.num_replicas = getattr(self.deployment_args, 'replicas', 1) self.k8s_namespace = k8s_namespace self.k8s_connection_pool = k8s_connection_pool self.k8s_deployments_addresses = k8s_deployments_addresses def get_gateway_yamls( self, ) -> List[Dict]: import os test_pip = os.getenv('JINA_K8S_USE_TEST_PIP') is not None image_name = ( 'jinaai/jina:test-pip' if test_pip else f'jinaai/jina:{self.version}-py38-standard' ) cargs = copy.copy(self.deployment_args) cargs.env = None cargs.deployments_addresses = self.k8s_deployments_addresses from jina.helper import ArgNamespace from jina.parsers import set_gateway_parser taboo = { 'uses_with', 'uses_metas', 'volumes', 'uses_before', 'uses_after', 'workspace', 'workspace_id', 'upload_files', 'noblock_on_start', } non_defaults = ArgNamespace.get_non_defaults_args( cargs, set_gateway_parser(), taboo=taboo ) _args = ArgNamespace.kwargs2list(non_defaults) container_args = ['gateway'] + _args if not cargs.k8s_connection_pool: container_args.append('--k8s-disable-connection-pool') return kubernetes_deployment.get_deployment_yamls( self.dns_name, namespace=self.k8s_namespace, image_name=image_name, container_cmd='["jina"]', container_args=f'{container_args}', replicas=1, pull_policy='IfNotPresent', jina_deployment_name='gateway', pod_type=self.pod_type, port=self.common_args.port, env=cargs.env, ) def _get_image_name(self, uses: Optional[str]): import os test_pip = os.getenv('JINA_K8S_USE_TEST_PIP') is not None image_name = ( 'jinaai/jina:test-pip' if test_pip else f'jinaai/jina:{self.version}-py38-perf' ) if uses is not None and uses != __default_executor__: image_name = get_image_name(uses) return image_name def _get_container_args(self, cargs, pod_type): uses_metas = cargs.uses_metas or {} uses_with = self.deployment_args.uses_with if cargs.uses != __default_executor__: cargs.uses = 'config.yml' return construct_runtime_container_args( cargs, uses_metas, uses_with, pod_type ) def get_runtime_yamls( self, ) -> List[Dict]: cargs = copy.copy(self.deployment_args) image_name = self._get_image_name(cargs.uses) image_name_uses_before = ( self._get_image_name(cargs.uses_before) if hasattr(cargs, 'uses_before') and cargs.uses_before else None ) image_name_uses_after = ( self._get_image_name(cargs.uses_after) if hasattr(cargs, 'uses_after') and cargs.uses_after else None ) container_args = self._get_container_args(cargs, pod_type=self.pod_type) container_args_uses_before = None if getattr(cargs, 'uses_before', False): uses_before_cargs = copy.copy(cargs) uses_before_cargs.uses = cargs.uses_before uses_before_cargs.name = f'{self.common_args.name}/uses-before' uses_before_cargs.port = K8sGrpcConnectionPool.K8S_PORT_USES_BEFORE uses_before_cargs.uses_before_address = None uses_before_cargs.uses_after_address = None uses_before_cargs.uses_before = None uses_before_cargs.uses_after = None uses_before_cargs.uses_with = None uses_before_cargs.uses_metas = None uses_before_cargs.env = None uses_before_cargs.connection_list = None uses_before_cargs.runtime_cls = 'WorkerRuntime' uses_before_cargs.pod_role = PodRoleType.WORKER uses_before_cargs.polling = None container_args_uses_before = self._get_container_args( uses_before_cargs, PodRoleType.WORKER ) container_args_uses_after = None if getattr(cargs, 'uses_after', False): uses_after_cargs = copy.copy(cargs) uses_after_cargs.uses = cargs.uses_after uses_after_cargs.name = f'{self.common_args.name}/uses-after' uses_after_cargs.port = K8sGrpcConnectionPool.K8S_PORT_USES_AFTER uses_after_cargs.uses_before_address = None uses_after_cargs.uses_after_address = None uses_after_cargs.uses_before = None uses_after_cargs.uses_after = None uses_after_cargs.uses_with = None uses_after_cargs.uses_metas = None uses_after_cargs.env = None uses_after_cargs.connection_list = None uses_after_cargs.runtime_cls = 'WorkerRuntime' uses_after_cargs.pod_role = PodRoleType.WORKER uses_after_cargs.polling = None container_args_uses_after = self._get_container_args( uses_after_cargs, PodRoleType.WORKER ) return kubernetes_deployment.get_deployment_yamls( self.dns_name, namespace=self.k8s_namespace, image_name=image_name, image_name_uses_after=image_name_uses_after, image_name_uses_before=image_name_uses_before, container_cmd='["jina"]', container_cmd_uses_before='["jina"]', container_cmd_uses_after='["jina"]', container_args=f'{container_args}', container_args_uses_before=container_args_uses_before, container_args_uses_after=container_args_uses_after, replicas=self.num_replicas, pull_policy='IfNotPresent', jina_deployment_name=self.jina_deployment_name, pod_type=self.pod_type, shard_id=self.shard_id, env=cargs.env, gpus=cargs.gpus if hasattr(cargs, 'gpus') else None, ) def __init__( self, args: Union['Namespace', Dict], k8s_namespace: Optional[str] = None, k8s_connection_pool: bool = True, k8s_deployments_addresses: Optional[Dict[str, List[str]]] = None, ): # External Deployments should be ignored in a K8s based Flow assert not (hasattr(args, 'external') and args.external) if not validate_uses(args.uses): raise NoContainerizedError( f'Executor "{args.uses}" is not valid to be used in K8s. ' 'You need to use a containerized Executor. You may check `jina hub --help` to see how Jina Hub can help you building containerized Executors.' ) self.k8s_namespace = k8s_namespace self.k8s_connection_pool = k8s_connection_pool self.k8s_deployments_addresses = k8s_deployments_addresses self.head_deployment = None self.args = copy.copy(args) if k8s_namespace is not None: # otherwise it will remain with the one from the original Deployment self.args.k8s_namespace = k8s_namespace self.args.k8s_connection_pool = k8s_connection_pool self.name = self.args.name self.deployment_args = self._get_deployment_args(self.args) if self.deployment_args['head_deployment'] is not None: self.head_deployment = self._K8sDeployment( name=self.deployment_args['head_deployment'].name, version=get_base_executor_version(), shard_id=None, jina_deployment_name=self.name, common_args=self.args, deployment_args=self.deployment_args['head_deployment'], pod_type=PodRoleType.HEAD, k8s_namespace=self.k8s_namespace, k8s_connection_pool=self.k8s_connection_pool, k8s_deployments_addresses=self.k8s_deployments_addresses, ) self.worker_deployments = [] deployment_args = self.deployment_args['deployments'] for i, args in enumerate(deployment_args): name = f'{self.name}-{i}' if len(deployment_args) > 1 else f'{self.name}' self.worker_deployments.append( self._K8sDeployment( name=name, version=get_base_executor_version(), shard_id=i, common_args=self.args, deployment_args=args, pod_type=PodRoleType.WORKER if name != 'gateway' else PodRoleType.GATEWAY, jina_deployment_name=self.name, k8s_namespace=self.k8s_namespace, k8s_connection_pool=self.k8s_connection_pool, k8s_deployments_addresses=self.k8s_deployments_addresses if name == 'gateway' else None, ) ) def _get_deployment_args(self, args): parsed_args = { 'head_deployment': None, 'deployments': [], } shards = getattr(args, 'shards', 1) uses_before = getattr(args, 'uses_before', None) uses_after = getattr(args, 'uses_after', None) if args.name != 'gateway': parsed_args['head_deployment'] = BaseDeployment._copy_to_head_args( self.args ) parsed_args['head_deployment'].gpus = None parsed_args['head_deployment'].port = K8sGrpcConnectionPool.K8S_PORT parsed_args['head_deployment'].uses = None parsed_args['head_deployment'].uses_metas = None parsed_args['head_deployment'].uses_with = None parsed_args['head_deployment'].env = None # if the k8s connection pool is disabled, the connection pool is managed manually if not self.k8s_connection_pool: import json connection_list = {} for i in range(shards): name = ( f'{to_compatible_name(self.name)}-{i}' if shards > 1 else f'{to_compatible_name(self.name)}' ) connection_list[ str(i) ] = f'{name}.{self.k8s_namespace}.svc:{K8sGrpcConnectionPool.K8S_PORT}' parsed_args['head_deployment'].connection_list = json.dumps( connection_list ) if uses_before: parsed_args[ 'head_deployment' ].uses_before_address = ( f'127.0.0.1:{K8sGrpcConnectionPool.K8S_PORT_USES_BEFORE}' ) if uses_after: parsed_args[ 'head_deployment' ].uses_after_address = ( f'127.0.0.1:{K8sGrpcConnectionPool.K8S_PORT_USES_AFTER}' ) for i in range(shards): cargs = copy.deepcopy(args) cargs.shard_id = i cargs.uses_before = None cargs.uses_after = None if args.name != 'gateway': cargs.port = K8sGrpcConnectionPool.K8S_PORT cargs.uses_before_address = None cargs.uses_after_address = None if shards > 1: cargs.name = f'{cargs.name}-{i}' if args.name == 'gateway': cargs.pod_role = PodRoleType.GATEWAY # the worker runtimes do not care else: cargs.k8s_connection_pool = False parsed_args['deployments'].append(cargs) return parsed_args def to_k8s_yaml( self, ) -> List[Tuple[str, List[Dict]]]: """ Return a list of dictionary configurations. One for each deployment in this Deployment .. # noqa: DAR201 .. # noqa: DAR101 """ if self.name == 'gateway': return [ ( 'gateway', self.worker_deployments[0].get_gateway_yamls(), ) ] else: deployments = [self.head_deployment] deployments.extend(self.worker_deployments) return [ ( deployment.dns_name, deployment.get_runtime_yamls(), ) for deployment in deployments ]

40.485014

158

0.573428

import copy from argparse import Namespace from typing import Dict, Union, List, Optional, Tuple from jina import __default_executor__ from jina.enums import PodRoleType from jina.excepts import NoContainerizedError from jina.orchestrate.deployments.config.k8slib import kubernetes_deployment from jina.orchestrate.deployments.config.helper import ( get_image_name, to_compatible_name, get_base_executor_version, construct_runtime_container_args, validate_uses, ) from jina.serve.networking import K8sGrpcConnectionPool from jina.orchestrate.deployments import BaseDeployment class K8sDeploymentConfig: class _K8sDeployment: def __init__( self, name: str, version: str, pod_type: PodRoleType, jina_deployment_name: str, shard_id: Optional[int], common_args: Union['Namespace', Dict], deployment_args: Union['Namespace', Dict], k8s_namespace: str, k8s_connection_pool: bool = True, k8s_deployments_addresses: Optional[Dict[str, List[str]]] = None, ): self.name = name self.dns_name = to_compatible_name(name) self.version = version self.pod_type = pod_type self.jina_deployment_name = jina_deployment_name self.shard_id = shard_id self.common_args = common_args self.deployment_args = deployment_args self.num_replicas = getattr(self.deployment_args, 'replicas', 1) self.k8s_namespace = k8s_namespace self.k8s_connection_pool = k8s_connection_pool self.k8s_deployments_addresses = k8s_deployments_addresses def get_gateway_yamls( self, ) -> List[Dict]: import os test_pip = os.getenv('JINA_K8S_USE_TEST_PIP') is not None image_name = ( 'jinaai/jina:test-pip' if test_pip else f'jinaai/jina:{self.version}-py38-standard' ) cargs = copy.copy(self.deployment_args) cargs.env = None cargs.deployments_addresses = self.k8s_deployments_addresses from jina.helper import ArgNamespace from jina.parsers import set_gateway_parser taboo = { 'uses_with', 'uses_metas', 'volumes', 'uses_before', 'uses_after', 'workspace', 'workspace_id', 'upload_files', 'noblock_on_start', } non_defaults = ArgNamespace.get_non_defaults_args( cargs, set_gateway_parser(), taboo=taboo ) _args = ArgNamespace.kwargs2list(non_defaults) container_args = ['gateway'] + _args if not cargs.k8s_connection_pool: container_args.append('--k8s-disable-connection-pool') return kubernetes_deployment.get_deployment_yamls( self.dns_name, namespace=self.k8s_namespace, image_name=image_name, container_cmd='["jina"]', container_args=f'{container_args}', replicas=1, pull_policy='IfNotPresent', jina_deployment_name='gateway', pod_type=self.pod_type, port=self.common_args.port, env=cargs.env, ) def _get_image_name(self, uses: Optional[str]): import os test_pip = os.getenv('JINA_K8S_USE_TEST_PIP') is not None image_name = ( 'jinaai/jina:test-pip' if test_pip else f'jinaai/jina:{self.version}-py38-perf' ) if uses is not None and uses != __default_executor__: image_name = get_image_name(uses) return image_name def _get_container_args(self, cargs, pod_type): uses_metas = cargs.uses_metas or {} uses_with = self.deployment_args.uses_with if cargs.uses != __default_executor__: cargs.uses = 'config.yml' return construct_runtime_container_args( cargs, uses_metas, uses_with, pod_type ) def get_runtime_yamls( self, ) -> List[Dict]: cargs = copy.copy(self.deployment_args) image_name = self._get_image_name(cargs.uses) image_name_uses_before = ( self._get_image_name(cargs.uses_before) if hasattr(cargs, 'uses_before') and cargs.uses_before else None ) image_name_uses_after = ( self._get_image_name(cargs.uses_after) if hasattr(cargs, 'uses_after') and cargs.uses_after else None ) container_args = self._get_container_args(cargs, pod_type=self.pod_type) container_args_uses_before = None if getattr(cargs, 'uses_before', False): uses_before_cargs = copy.copy(cargs) uses_before_cargs.uses = cargs.uses_before uses_before_cargs.name = f'{self.common_args.name}/uses-before' uses_before_cargs.port = K8sGrpcConnectionPool.K8S_PORT_USES_BEFORE uses_before_cargs.uses_before_address = None uses_before_cargs.uses_after_address = None uses_before_cargs.uses_before = None uses_before_cargs.uses_after = None uses_before_cargs.uses_with = None uses_before_cargs.uses_metas = None uses_before_cargs.env = None uses_before_cargs.connection_list = None uses_before_cargs.runtime_cls = 'WorkerRuntime' uses_before_cargs.pod_role = PodRoleType.WORKER uses_before_cargs.polling = None container_args_uses_before = self._get_container_args( uses_before_cargs, PodRoleType.WORKER ) container_args_uses_after = None if getattr(cargs, 'uses_after', False): uses_after_cargs = copy.copy(cargs) uses_after_cargs.uses = cargs.uses_after uses_after_cargs.name = f'{self.common_args.name}/uses-after' uses_after_cargs.port = K8sGrpcConnectionPool.K8S_PORT_USES_AFTER uses_after_cargs.uses_before_address = None uses_after_cargs.uses_after_address = None uses_after_cargs.uses_before = None uses_after_cargs.uses_after = None uses_after_cargs.uses_with = None uses_after_cargs.uses_metas = None uses_after_cargs.env = None uses_after_cargs.connection_list = None uses_after_cargs.runtime_cls = 'WorkerRuntime' uses_after_cargs.pod_role = PodRoleType.WORKER uses_after_cargs.polling = None container_args_uses_after = self._get_container_args( uses_after_cargs, PodRoleType.WORKER ) return kubernetes_deployment.get_deployment_yamls( self.dns_name, namespace=self.k8s_namespace, image_name=image_name, image_name_uses_after=image_name_uses_after, image_name_uses_before=image_name_uses_before, container_cmd='["jina"]', container_cmd_uses_before='["jina"]', container_cmd_uses_after='["jina"]', container_args=f'{container_args}', container_args_uses_before=container_args_uses_before, container_args_uses_after=container_args_uses_after, replicas=self.num_replicas, pull_policy='IfNotPresent', jina_deployment_name=self.jina_deployment_name, pod_type=self.pod_type, shard_id=self.shard_id, env=cargs.env, gpus=cargs.gpus if hasattr(cargs, 'gpus') else None, ) def __init__( self, args: Union['Namespace', Dict], k8s_namespace: Optional[str] = None, k8s_connection_pool: bool = True, k8s_deployments_addresses: Optional[Dict[str, List[str]]] = None, ): assert not (hasattr(args, 'external') and args.external) if not validate_uses(args.uses): raise NoContainerizedError( f'Executor "{args.uses}" is not valid to be used in K8s. ' 'You need to use a containerized Executor. You may check `jina hub --help` to see how Jina Hub can help you building containerized Executors.' ) self.k8s_namespace = k8s_namespace self.k8s_connection_pool = k8s_connection_pool self.k8s_deployments_addresses = k8s_deployments_addresses self.head_deployment = None self.args = copy.copy(args) if k8s_namespace is not None: self.args.k8s_namespace = k8s_namespace self.args.k8s_connection_pool = k8s_connection_pool self.name = self.args.name self.deployment_args = self._get_deployment_args(self.args) if self.deployment_args['head_deployment'] is not None: self.head_deployment = self._K8sDeployment( name=self.deployment_args['head_deployment'].name, version=get_base_executor_version(), shard_id=None, jina_deployment_name=self.name, common_args=self.args, deployment_args=self.deployment_args['head_deployment'], pod_type=PodRoleType.HEAD, k8s_namespace=self.k8s_namespace, k8s_connection_pool=self.k8s_connection_pool, k8s_deployments_addresses=self.k8s_deployments_addresses, ) self.worker_deployments = [] deployment_args = self.deployment_args['deployments'] for i, args in enumerate(deployment_args): name = f'{self.name}-{i}' if len(deployment_args) > 1 else f'{self.name}' self.worker_deployments.append( self._K8sDeployment( name=name, version=get_base_executor_version(), shard_id=i, common_args=self.args, deployment_args=args, pod_type=PodRoleType.WORKER if name != 'gateway' else PodRoleType.GATEWAY, jina_deployment_name=self.name, k8s_namespace=self.k8s_namespace, k8s_connection_pool=self.k8s_connection_pool, k8s_deployments_addresses=self.k8s_deployments_addresses if name == 'gateway' else None, ) ) def _get_deployment_args(self, args): parsed_args = { 'head_deployment': None, 'deployments': [], } shards = getattr(args, 'shards', 1) uses_before = getattr(args, 'uses_before', None) uses_after = getattr(args, 'uses_after', None) if args.name != 'gateway': parsed_args['head_deployment'] = BaseDeployment._copy_to_head_args( self.args ) parsed_args['head_deployment'].gpus = None parsed_args['head_deployment'].port = K8sGrpcConnectionPool.K8S_PORT parsed_args['head_deployment'].uses = None parsed_args['head_deployment'].uses_metas = None parsed_args['head_deployment'].uses_with = None parsed_args['head_deployment'].env = None if not self.k8s_connection_pool: import json connection_list = {} for i in range(shards): name = ( f'{to_compatible_name(self.name)}-{i}' if shards > 1 else f'{to_compatible_name(self.name)}' ) connection_list[ str(i) ] = f'{name}.{self.k8s_namespace}.svc:{K8sGrpcConnectionPool.K8S_PORT}' parsed_args['head_deployment'].connection_list = json.dumps( connection_list ) if uses_before: parsed_args[ 'head_deployment' ].uses_before_address = ( f'127.0.0.1:{K8sGrpcConnectionPool.K8S_PORT_USES_BEFORE}' ) if uses_after: parsed_args[ 'head_deployment' ].uses_after_address = ( f'127.0.0.1:{K8sGrpcConnectionPool.K8S_PORT_USES_AFTER}' ) for i in range(shards): cargs = copy.deepcopy(args) cargs.shard_id = i cargs.uses_before = None cargs.uses_after = None if args.name != 'gateway': cargs.port = K8sGrpcConnectionPool.K8S_PORT cargs.uses_before_address = None cargs.uses_after_address = None if shards > 1: cargs.name = f'{cargs.name}-{i}' if args.name == 'gateway': cargs.pod_role = PodRoleType.GATEWAY else: cargs.k8s_connection_pool = False parsed_args['deployments'].append(cargs) return parsed_args def to_k8s_yaml( self, ) -> List[Tuple[str, List[Dict]]]: if self.name == 'gateway': return [ ( 'gateway', self.worker_deployments[0].get_gateway_yamls(), ) ] else: deployments = [self.head_deployment] deployments.extend(self.worker_deployments) return [ ( deployment.dns_name, deployment.get_runtime_yamls(), ) for deployment in deployments ]

true

790dd77e78274904638c9891f524368b9cccc01a

6,422

py

Python

pal/generator/rust_generator.py

mars-research/pal

5977394cda8750ff5dcb89c2bf193ec1ef4cd137

[ "MIT" ]

null

pal/generator/rust_generator.py

mars-research/pal

5977394cda8750ff5dcb89c2bf193ec1ef4cd137

[ "MIT" ]

1

2021-08-23T15:54:10.000Z

2021-09-28T12:44:36.000Z

pal/generator/rust_generator.py

mars-research/pal

5977394cda8750ff5dcb89c2bf193ec1ef4cd137

[ "MIT" ]

null

import os import pathlib from pal.generator.abstract_generator import AbstractGenerator from pal.logger import logger from pal.exception import PalGeneratorException from pal.filter import filters from pal.transform import transforms class RustGenerator(AbstractGenerator): def generate_registers(self, regs, outpath): try: regs = transforms["remove_reserved_0"].transform(regs) regs = transforms["remove_reserved_1"].transform(regs) regs = transforms["remove_reserved_sign_extended"].transform(regs) regs = transforms["remove_implementation_defined"].transform(regs) regs = transforms["special_to_underscore"].transform(regs) regs = transforms["insert_valid_first_character"].transform(regs) regs = transforms["remove_redundant_am"].transform(regs) regs = transforms["remove_redundant_fields"].transform(regs) regs = transforms["unique_fieldset_names"].transform(regs) regs = filters["no_access_mechanism"].filter_exclusive(regs) regs = filters["irregular_size"].filter_exclusive(regs) logger.info("Generating Rust register accessors to: " + str(outpath)) for reg in regs: outfile_path = os.path.join(outpath, reg.name.lower() + ".rs") outfile_path = os.path.abspath(outfile_path) with open(outfile_path, "w") as outfile: self._generate_register(outfile, reg) self.__update_module_files(outpath) self.__update_lib_file(outpath) except Exception as e: msg = "{g} failed to generate output {out}: {exception}".format( g=str(type(self).__name__), out=outpath, exception=e) raise PalGeneratorException(msg) def generate_instructions(self, instructions, outpath): try: logger.info("Generating Rust instruction accessors to: " + str(outpath)) for inst in instructions: outfile_path = os.path.join(outpath, inst.name.lower() + ".rs") outfile_path = os.path.abspath(outfile_path) with open(outfile_path, "w") as outfile: self._generate_instruction(outfile, inst) self.__update_module_files(outpath) self.__update_lib_file(outpath) except Exception as e: msg = "{g} failed to generate output {out}: {exception}".format( g=str(type(self).__name__), out=outpath, exception=e) raise PalGeneratorException(msg) def _generate_register(self, outfile, reg): self.writer.declare_register_dependencies(outfile, reg, self.config) if self.config.enable_printers == True: self.writer.declare_print_mechanism_dependencies(outfile, reg) for am_key, am_list in reg.access_mechanisms.items(): for am in am_list: self.writer.declare_access_mechanism_dependencies(outfile, reg, am) self.writer.write_newline(outfile) self._generate_register_comment(outfile, reg) self.writer.declare_register_accessors(outfile, reg) for idx, fieldset in enumerate(reg.fieldsets): if fieldset.condition: self.writer.declare_comment(outfile, fieldset.condition, 79) for field in fieldset.fields: self.writer.declare_field_accessors(outfile, reg, field) if self.config.enable_printers == True: self.writer.declare_field_printers(outfile, reg, field) if reg.is_readable() and self.config.enable_printers == True: self.writer.declare_fieldset_printers(outfile, reg, fieldset) def _generate_instruction(self, outfile, inst): self.writer.declare_instruction_dependencies(outfile, inst, self.config) self.writer.declare_instruction_accessor(outfile, inst) self.writer.write_newline(outfile) def _generate_register_comment(self, outfile, reg): comment = "{name} ({long_name}){separator}{purpose}".format( name=str(reg.name), long_name=str(reg.long_name), separator=" - " if reg.purpose else "", purpose=str(reg.purpose) ) self.writer.declare_comment(outfile, comment, 75) def __update_module_files(self, outpath): modfile_path = os.path.join(outpath, "mod.rs") modfile_path = os.path.abspath(modfile_path) for root, dirs, files in os.walk(outpath): logger.info("Updating modfile: " + os.path.join(root, "mod.rs")) with open(os.path.join(root, "mod.rs"), "w") as modfile: for name in sorted(files): if name != "mod.rs" and name.endswith(".rs"): modname = os.path.splitext(name)[0] modfile.write("pub mod " + modname + ";") self.writer.write_newline(modfile) modfile.write("pub use " + modname + "::*;") self.writer.write_newline(modfile) for name in sorted(dirs): modname = os.path.splitext(name)[0] modfile.write("pub mod " + modname + ";") self.writer.write_newline(modfile) modfile.write("pub use " + modname + "::*;") self.writer.write_newline(modfile) def __update_lib_file(self, outpath): libfile_path = os.path.abspath(os.path.join(outpath, "lib.rs")) libfile_dir = os.path.abspath(outpath) if not os.path.exists(libfile_path): libfile_path = os.path.abspath(os.path.join(outpath, "../lib.rs")) libfile_dir = os.path.abspath(os.path.join(outpath, "../")) if not os.path.exists(libfile_path): return logger.info("Updating lib.rs: " + str(libfile_path)) with open(libfile_path, "w") as libfile: for child in [f.path for f in os.scandir(libfile_dir)]: logger.info("child: " + str(child)) modname = os.path.splitext(os.path.basename(child))[0] if not modname == "lib": libfile.write("pub mod " + modname + ";") self.writer.write_newline(libfile)

42.25

84

0.608066

import os import pathlib from pal.generator.abstract_generator import AbstractGenerator from pal.logger import logger from pal.exception import PalGeneratorException from pal.filter import filters from pal.transform import transforms class RustGenerator(AbstractGenerator): def generate_registers(self, regs, outpath): try: regs = transforms["remove_reserved_0"].transform(regs) regs = transforms["remove_reserved_1"].transform(regs) regs = transforms["remove_reserved_sign_extended"].transform(regs) regs = transforms["remove_implementation_defined"].transform(regs) regs = transforms["special_to_underscore"].transform(regs) regs = transforms["insert_valid_first_character"].transform(regs) regs = transforms["remove_redundant_am"].transform(regs) regs = transforms["remove_redundant_fields"].transform(regs) regs = transforms["unique_fieldset_names"].transform(regs) regs = filters["no_access_mechanism"].filter_exclusive(regs) regs = filters["irregular_size"].filter_exclusive(regs) logger.info("Generating Rust register accessors to: " + str(outpath)) for reg in regs: outfile_path = os.path.join(outpath, reg.name.lower() + ".rs") outfile_path = os.path.abspath(outfile_path) with open(outfile_path, "w") as outfile: self._generate_register(outfile, reg) self.__update_module_files(outpath) self.__update_lib_file(outpath) except Exception as e: msg = "{g} failed to generate output {out}: {exception}".format( g=str(type(self).__name__), out=outpath, exception=e) raise PalGeneratorException(msg) def generate_instructions(self, instructions, outpath): try: logger.info("Generating Rust instruction accessors to: " + str(outpath)) for inst in instructions: outfile_path = os.path.join(outpath, inst.name.lower() + ".rs") outfile_path = os.path.abspath(outfile_path) with open(outfile_path, "w") as outfile: self._generate_instruction(outfile, inst) self.__update_module_files(outpath) self.__update_lib_file(outpath) except Exception as e: msg = "{g} failed to generate output {out}: {exception}".format( g=str(type(self).__name__), out=outpath, exception=e) raise PalGeneratorException(msg) def _generate_register(self, outfile, reg): self.writer.declare_register_dependencies(outfile, reg, self.config) if self.config.enable_printers == True: self.writer.declare_print_mechanism_dependencies(outfile, reg) for am_key, am_list in reg.access_mechanisms.items(): for am in am_list: self.writer.declare_access_mechanism_dependencies(outfile, reg, am) self.writer.write_newline(outfile) self._generate_register_comment(outfile, reg) self.writer.declare_register_accessors(outfile, reg) for idx, fieldset in enumerate(reg.fieldsets): if fieldset.condition: self.writer.declare_comment(outfile, fieldset.condition, 79) for field in fieldset.fields: self.writer.declare_field_accessors(outfile, reg, field) if self.config.enable_printers == True: self.writer.declare_field_printers(outfile, reg, field) if reg.is_readable() and self.config.enable_printers == True: self.writer.declare_fieldset_printers(outfile, reg, fieldset) def _generate_instruction(self, outfile, inst): self.writer.declare_instruction_dependencies(outfile, inst, self.config) self.writer.declare_instruction_accessor(outfile, inst) self.writer.write_newline(outfile) def _generate_register_comment(self, outfile, reg): comment = "{name} ({long_name}){separator}{purpose}".format( name=str(reg.name), long_name=str(reg.long_name), separator=" - " if reg.purpose else "", purpose=str(reg.purpose) ) self.writer.declare_comment(outfile, comment, 75) def __update_module_files(self, outpath): modfile_path = os.path.join(outpath, "mod.rs") modfile_path = os.path.abspath(modfile_path) for root, dirs, files in os.walk(outpath): logger.info("Updating modfile: " + os.path.join(root, "mod.rs")) with open(os.path.join(root, "mod.rs"), "w") as modfile: for name in sorted(files): if name != "mod.rs" and name.endswith(".rs"): modname = os.path.splitext(name)[0] modfile.write("pub mod " + modname + ";") self.writer.write_newline(modfile) modfile.write("pub use " + modname + "::*;") self.writer.write_newline(modfile) for name in sorted(dirs): modname = os.path.splitext(name)[0] modfile.write("pub mod " + modname + ";") self.writer.write_newline(modfile) modfile.write("pub use " + modname + "::*;") self.writer.write_newline(modfile) def __update_lib_file(self, outpath): libfile_path = os.path.abspath(os.path.join(outpath, "lib.rs")) libfile_dir = os.path.abspath(outpath) if not os.path.exists(libfile_path): libfile_path = os.path.abspath(os.path.join(outpath, "../lib.rs")) libfile_dir = os.path.abspath(os.path.join(outpath, "../")) if not os.path.exists(libfile_path): return logger.info("Updating lib.rs: " + str(libfile_path)) with open(libfile_path, "w") as libfile: for child in [f.path for f in os.scandir(libfile_dir)]: logger.info("child: " + str(child)) modname = os.path.splitext(os.path.basename(child))[0] if not modname == "lib": libfile.write("pub mod " + modname + ";") self.writer.write_newline(libfile)

true

790dd884584a8f5b70472b478f3fbd6d4b1e067e

1,498

py

Python

test/browser/window/controller/remove_handle_by_id_test.py

jakob-bagterp/browserist

76bd916dd217b7da3759fd6ec3374191002dc091

[ "Apache-2.0" ]

2

2022-02-20T10:03:19.000Z

2022-03-22T11:17:10.000Z

test/browser/window/controller/remove_handle_by_id_test.py

jakob-bagterp/browserist

76bd916dd217b7da3759fd6ec3374191002dc091

[ "Apache-2.0" ]

null

test/browser/window/controller/remove_handle_by_id_test.py

jakob-bagterp/browserist

76bd916dd217b7da3759fd6ec3374191002dc091

[ "Apache-2.0" ]

null

from contextlib import nullcontext as does_not_raise from typing import Any import pytest from _mock_data.window_handles import WINDOW_HANDLE_1_ID, WINDOW_HANDLE_4_ID from browserist.exception.window_handle import WindowHandleIdNotFoundError, WindowHandleIdNotValidError from browserist.model.window.controller import WindowHandleController @pytest.mark.parametrize("id", [ (WINDOW_HANDLE_1_ID), ]) def test_window_handle_controller_remove_handle_by_id(id: str, window_handle_controller: WindowHandleController) -> None: assert window_handle_controller.count() == 3 window_handle_controller.remove_handle_by_id(id) assert window_handle_controller.count() == 2 @pytest.mark.parametrize("id, expectation", [ (WINDOW_HANDLE_1_ID, does_not_raise()), ("Not valid ID", pytest.raises(WindowHandleIdNotValidError)), ]) def test_window_handle_controller_remove_handle_by_id_invalid_error(id: str, expectation: Any, window_handle_controller: WindowHandleController) -> None: with expectation: window_handle_controller.remove_handle_by_id(id) is not None @pytest.mark.parametrize("id, expectation", [ (WINDOW_HANDLE_1_ID, does_not_raise()), (WINDOW_HANDLE_4_ID, pytest.raises(WindowHandleIdNotFoundError)), ]) def test_window_handle_controller_remove_handle_by_id_not_found_error(id: str, expectation: Any, window_handle_controller: WindowHandleController) -> None: with expectation: window_handle_controller.remove_handle_by_id(id) is not None

41.611111

155

0.814419

from contextlib import nullcontext as does_not_raise from typing import Any import pytest from _mock_data.window_handles import WINDOW_HANDLE_1_ID, WINDOW_HANDLE_4_ID from browserist.exception.window_handle import WindowHandleIdNotFoundError, WindowHandleIdNotValidError from browserist.model.window.controller import WindowHandleController @pytest.mark.parametrize("id", [ (WINDOW_HANDLE_1_ID), ]) def test_window_handle_controller_remove_handle_by_id(id: str, window_handle_controller: WindowHandleController) -> None: assert window_handle_controller.count() == 3 window_handle_controller.remove_handle_by_id(id) assert window_handle_controller.count() == 2 @pytest.mark.parametrize("id, expectation", [ (WINDOW_HANDLE_1_ID, does_not_raise()), ("Not valid ID", pytest.raises(WindowHandleIdNotValidError)), ]) def test_window_handle_controller_remove_handle_by_id_invalid_error(id: str, expectation: Any, window_handle_controller: WindowHandleController) -> None: with expectation: window_handle_controller.remove_handle_by_id(id) is not None @pytest.mark.parametrize("id, expectation", [ (WINDOW_HANDLE_1_ID, does_not_raise()), (WINDOW_HANDLE_4_ID, pytest.raises(WindowHandleIdNotFoundError)), ]) def test_window_handle_controller_remove_handle_by_id_not_found_error(id: str, expectation: Any, window_handle_controller: WindowHandleController) -> None: with expectation: window_handle_controller.remove_handle_by_id(id) is not None

true

790dd8b5cd7edaafbdebd0957f76e3486a2f9a9e

6,153

py

Python

applications/javelin/models/menu.py

jjacobson93/javelin-web2py

d4de493156c6893acca74d4be7f4597c90c418f3

[ "BSD-3-Clause" ]

null

applications/javelin/models/menu.py

jjacobson93/javelin-web2py

d4de493156c6893acca74d4be7f4597c90c418f3

[ "BSD-3-Clause" ]

null

applications/javelin/models/menu.py

jjacobson93/javelin-web2py

d4de493156c6893acca74d4be7f4597c90c418f3

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # this file is released under public domain and you can use without limitations ######################################################################### ## Customize your APP title, subtitle and menus here ######################################################################### response.logo = A('Javelin',_class="brand",_href="/") response.title = request.application.replace('_',' ').title() ## read more at http://dev.w3.org/html5/markup/meta.name.html response.meta.author = 'Your Name <you@example.com>' response.meta.description = 'a cool new app' response.meta.keywords = 'web2py, python, framework' response.meta.generator = 'Web2py Web Framework' ## your http://google.com/analytics id response.google_analytics_id = None ######################################################################### ## this is the main application menu add/remove items as required ######################################################################### response.menu = [ (T('Home'), False, URL('default', 'index'), []) ] DEVELOPMENT_MENU = True ######################################################################### ## provide shortcuts for development. remove in production ######################################################################### def _(): # shortcuts app = request.application ctr = request.controller # useful links to internal and external resources response.menu += [ (SPAN('web2py', _class='highlighted'), False, 'http://web2py.com', [ (T('My Sites'), False, URL('admin', 'default', 'site')), (T('This App'), False, URL('admin', 'default', 'design/%s' % app), [ (T('Controller'), False, URL( 'admin', 'default', 'edit/%s/controllers/%s.py' % (app, ctr))), (T('View'), False, URL( 'admin', 'default', 'edit/%s/views/%s' % (app, response.view))), (T('Layout'), False, URL( 'admin', 'default', 'edit/%s/views/layout.html' % app)), (T('Stylesheet'), False, URL( 'admin', 'default', 'edit/%s/static/css/web2py.css' % app)), (T('DB Model'), False, URL( 'admin', 'default', 'edit/%s/models/db.py' % app)), (T('Menu Model'), False, URL( 'admin', 'default', 'edit/%s/models/menu.py' % app)), (T('Database'), False, URL(app, 'appadmin', 'index')), (T('Errors'), False, URL( 'admin', 'default', 'errors/' + app)), (T('About'), False, URL( 'admin', 'default', 'about/' + app)), ]), ('web2py.com', False, 'http://www.web2py.com', [ (T('Download'), False, 'http://www.web2py.com/examples/default/download'), (T('Support'), False, 'http://www.web2py.com/examples/default/support'), (T('Demo'), False, 'http://web2py.com/demo_admin'), (T('Quick Examples'), False, 'http://web2py.com/examples/default/examples'), (T('FAQ'), False, 'http://web2py.com/AlterEgo'), (T('Videos'), False, 'http://www.web2py.com/examples/default/videos/'), (T('Free Applications'), False, 'http://web2py.com/appliances'), (T('Plugins'), False, 'http://web2py.com/plugins'), (T('Layouts'), False, 'http://web2py.com/layouts'), (T('Recipes'), False, 'http://web2pyslices.com/'), (T('Semantic'), False, 'http://web2py.com/semantic'), ]), (T('Documentation'), False, 'http://www.web2py.com/book', [ (T('Preface'), False, 'http://www.web2py.com/book/default/chapter/00'), (T('Introduction'), False, 'http://www.web2py.com/book/default/chapter/01'), (T('Python'), False, 'http://www.web2py.com/book/default/chapter/02'), (T('Overview'), False, 'http://www.web2py.com/book/default/chapter/03'), (T('The Core'), False, 'http://www.web2py.com/book/default/chapter/04'), (T('The Views'), False, 'http://www.web2py.com/book/default/chapter/05'), (T('Database'), False, 'http://www.web2py.com/book/default/chapter/06'), (T('Forms and Validators'), False, 'http://www.web2py.com/book/default/chapter/07'), (T('Email and SMS'), False, 'http://www.web2py.com/book/default/chapter/08'), (T('Access Control'), False, 'http://www.web2py.com/book/default/chapter/09'), (T('Services'), False, 'http://www.web2py.com/book/default/chapter/10'), (T('Ajax Recipes'), False, 'http://www.web2py.com/book/default/chapter/11'), (T('Components and Plugins'), False, 'http://www.web2py.com/book/default/chapter/12'), (T('Deployment Recipes'), False, 'http://www.web2py.com/book/default/chapter/13'), (T('Other Recipes'), False, 'http://www.web2py.com/book/default/chapter/14'), (T('Buy this book'), False, 'http://stores.lulu.com/web2py'), ]), (T('Community'), False, None, [ (T('Groups'), False, 'http://www.web2py.com/examples/default/usergroups'), (T('Twitter'), False, 'http://twitter.com/web2py'), (T('Live Chat'), False, 'http://webchat.freenode.net/?channels=web2py'), ]), (T('Plugins'), False, None, [ ('plugin_wiki', False, 'http://web2py.com/examples/default/download'), (T('Other Plugins'), False, 'http://web2py.com/plugins'), (T('Layout Plugins'), False, 'http://web2py.com/layouts'), ]) ] )] if DEVELOPMENT_MENU: _() if "auth" in locals(): auth.wikimenu()

44.266187

79

0.478628

true

790dd98e40c32e5ef94d00d466dc9334c2cc8fca

376

py

Python

noncookingjob.py

vmlane/jobMatcher

f5929134e6e14786ca9f71cc0329f0fed59b35da

[ "MIT" ]

null

noncookingjob.py

vmlane/jobMatcher

f5929134e6e14786ca9f71cc0329f0fed59b35da

[ "MIT" ]

null

noncookingjob.py

vmlane/jobMatcher

f5929134e6e14786ca9f71cc0329f0fed59b35da

[ "MIT" ]

null

from job import * class NoncookingJob(Job): def __init__(self, name, prefs, maxMatches): Job.__init__(self, name, prefs, maxMatches) # remove pairs & underclassmen self.prefs = filter(lambda x: x.numPeople != 1 and x.semsCooked < 4, self.prefs) # sort all the people by number of semesters cooked, high to low prefs.sort(key=lambda x: x.semsCooked, reverse=True)

37.6

83

0.728723

from job import * class NoncookingJob(Job): def __init__(self, name, prefs, maxMatches): Job.__init__(self, name, prefs, maxMatches) self.prefs = filter(lambda x: x.numPeople != 1 and x.semsCooked < 4, self.prefs) prefs.sort(key=lambda x: x.semsCooked, reverse=True)

true

790ddbcd7888dfff9258436b8a98ccebabef1cd9

4,941

py

Python

torch/distributed/algorithms/model_averaging/averagers.py

vuanvin/pytorch

9267fd8d7395074001ad7cf2a8f28082dbff6b0b

[ "Intel" ]

1

2022-01-20T03:49:23.000Z

torch/distributed/algorithms/model_averaging/averagers.py

vuanvin/pytorch

9267fd8d7395074001ad7cf2a8f28082dbff6b0b

[ "Intel" ]

14

2021-10-14T06:58:50.000Z

2021-12-17T11:51:07.000Z

torch/distributed/algorithms/model_averaging/averagers.py

vuanvin/pytorch

9267fd8d7395074001ad7cf2a8f28082dbff6b0b

[ "Intel" ]

null

import warnings from abc import ABC, abstractmethod import torch.distributed as dist import torch.distributed.algorithms.model_averaging.utils as utils class ModelAverager(ABC): r"""Base class for all model averagers. Args: process_group: The process group to be used for all-reduce. If ``None``, the default process group, which is created by :func:`torch.distributed.init_process_group`, will be used. (default: ``None``) """ def __init__(self, process_group=None): self.process_group = ( process_group if process_group is not None else dist.group.WORLD ) self.step = 0 @abstractmethod def average_parameters(self, params): raise NotImplementedError class PeriodicModelAverager(ModelAverager): r""" Averages parameters periodically after the warm-up stage. This can be used for running `post-local SGD <https://arxiv.org/abs/1808.07217>`_, by running :class:`~torch.nn.DistributedDataParallel` (DDP) using the subgroups created by :meth:`~torch.distributed.new_subgroups`. Args: period (int): The number of steps per model averaging. Usually the period should be greater than ``1`` to reduce the communication cost. Otherwise, only DDP needs to be used. warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. process_group: The process group to be used for all-reduce. If ``None``, the default process group, which is created by :func:`torch.distributed.init_process_group`, will be used. (default: ``None``) Example:: >>> import torch >>> import torch.distributed as dist >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD >>> import torch.distributed.algorithms.model_averaging.averagers as averagers >>> import torch.nn as nn >>> >>> dist.init_process_group("nccl", rank=rank, world_size=16) >>> torch.cuda.set_device(rank) >>> module = nn.Linear(1, 1, bias=False).to(rank) >>> model = nn.parallel.DistributedDataParallel( >>> module, device_ids=[rank], output_device=rank >>> ) >>> # Register a post-localSGD communication hook. >>> subgroup, subgroups = dist.new_subgroups() >>> state = PostLocalSGDState(subgroup=subgroup, start_localSGD_iter=100) >>> model.register_comm_hook(state, post_localSGD_hook) >>> >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. >>> # After 100 steps, run model averaging every 4 steps. >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) >>> for step in range(0, 200): >>> optimizer.zero_grad() >>> loss = loss_fn(output, labels) >>> loss.backward() >>> optimizer.step() >>> # Average parameters globally after ``optimizer.step()``. >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. >>> averager.average_parameters(model.parameters()) .. warning :: `PeriodicModelAverager` is experimental and subject to change. """ def __init__( self, period, warmup_steps=0, process_group=None, ): super().__init__(process_group) if warmup_steps < 0: raise ValueError("Arg ``warmup_steps`` must be a non-negative number.") self.warmup_steps = warmup_steps if period < 1: raise ValueError("Arg ``period`` must be a positive value.") elif period == 1: warnings.warn( "When period is 1, no need to use model averaging because the communication cost " "of all-reducing parameters will be no less than the cost of all-reducing gradients " "by DistributedDataParall in the backward pass. Therefore, only " "DistributedDataParallel should be used for this case." ) self.period = period def average_parameters(self, params): r""" Averages parameters if ``step`` is no less than ``warmup_steps`` and it can be divided by ``period``, where ``step`` is increased by 1 at each iteration in the training loop. """ if ( self.step >= self.warmup_steps and (self.step - self.warmup_steps) % self.period == 0 ): utils.average_parameters(iter(params), self.process_group) self.step += 1

42.230769

116

0.612224

import warnings from abc import ABC, abstractmethod import torch.distributed as dist import torch.distributed.algorithms.model_averaging.utils as utils class ModelAverager(ABC): def __init__(self, process_group=None): self.process_group = ( process_group if process_group is not None else dist.group.WORLD ) self.step = 0 @abstractmethod def average_parameters(self, params): raise NotImplementedError class PeriodicModelAverager(ModelAverager): def __init__( self, period, warmup_steps=0, process_group=None, ): super().__init__(process_group) if warmup_steps < 0: raise ValueError("Arg ``warmup_steps`` must be a non-negative number.") self.warmup_steps = warmup_steps if period < 1: raise ValueError("Arg ``period`` must be a positive value.") elif period == 1: warnings.warn( "When period is 1, no need to use model averaging because the communication cost " "of all-reducing parameters will be no less than the cost of all-reducing gradients " "by DistributedDataParall in the backward pass. Therefore, only " "DistributedDataParallel should be used for this case." ) self.period = period def average_parameters(self, params): if ( self.step >= self.warmup_steps and (self.step - self.warmup_steps) % self.period == 0 ): utils.average_parameters(iter(params), self.process_group) self.step += 1

true

790ddc222efaf49cc540383db964dfb094041101

1,097

py

Python

Day 18/Queue and stacks.py

SayanBan/HackerRank-30-Days-of-code

c2fea8304d7c9af13748fcce57c07a7ca180eda4

[ "MIT" ]

2

2019-11-20T04:45:27.000Z

2019-12-07T04:31:47.000Z

Day 18/Queue and stacks.py

SayanBan/HackerRank-30-Days-of-code

c2fea8304d7c9af13748fcce57c07a7ca180eda4

[ "MIT" ]

null

Day 18/Queue and stacks.py

SayanBan/HackerRank-30-Days-of-code

c2fea8304d7c9af13748fcce57c07a7ca180eda4

[ "MIT" ]

1

2019-12-07T04:31:59.000Z

import sys class Solution: # Write your code here def __init__(self): self.stack = [] self.queue = [] def popCharacter(self): return self.stack.pop() def pushCharacter(self, char): self.stack.append(char) def dequeueCharacter(self): char = self.queue[0] self.queue = self.queue[1:] return char def enqueueCharacter(self, char): self.queue.append(char) # read the string s s=input() #Create the Solution class object obj=Solution() l=len(s) # push/enqueue all the characters of string s to stack for i in range(l): obj.pushCharacter(s[i]) obj.enqueueCharacter(s[i]) isPalindrome=True ''' pop the top character from stack dequeue the first character from queue compare both the characters ''' for i in range(l // 2): if obj.popCharacter()!=obj.dequeueCharacter(): isPalindrome=False break #finally print whether string s is palindrome or not. if isPalindrome: print("The word, "+s+", is a palindrome.") else: print("The word, "+s+", is not a palindrome.")

22.854167

54

0.646308

import sys class Solution: def __init__(self): self.stack = [] self.queue = [] def popCharacter(self): return self.stack.pop() def pushCharacter(self, char): self.stack.append(char) def dequeueCharacter(self): char = self.queue[0] self.queue = self.queue[1:] return char def enqueueCharacter(self, char): self.queue.append(char) s=input() obj=Solution() l=len(s) for i in range(l): obj.pushCharacter(s[i]) obj.enqueueCharacter(s[i]) isPalindrome=True for i in range(l // 2): if obj.popCharacter()!=obj.dequeueCharacter(): isPalindrome=False break if isPalindrome: print("The word, "+s+", is a palindrome.") else: print("The word, "+s+", is not a palindrome.")

true

790ddc9a7829e5f42dca0f59b4903dfd9d2f3621

4,564

py

Python

locallibrary/settings.py

TheRedemp7ion/DjangoLocalLibrary

e3c49da272d863185681b2b934c45a4693054a7a

[ "Unlicense" ]

null

locallibrary/settings.py

TheRedemp7ion/DjangoLocalLibrary

e3c49da272d863185681b2b934c45a4693054a7a

[ "Unlicense" ]

null

locallibrary/settings.py

TheRedemp7ion/DjangoLocalLibrary

e3c49da272d863185681b2b934c45a4693054a7a

[ "Unlicense" ]

null

""" Django settings for locallibrary project. Generated by 'django-admin startproject' using Django 3.2.2. For more information on this file, see https://docs.djangoproject.com/en/3.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.2/ref/settings/ """ from pathlib import Path import pytz import os # needed by code below # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'django-insecure-@*cqyd6l)4*=yg7r19zmp#y32mpus(a2d-)ny&hstt^kq!13jk' import os #SECRET_KEY = os.environ.get('DJANGO_SECRET_KEY', 'cg#p$g+j9tax!#a3cup@1$8obt2_+&k3q+pmu)5%asj6yjpkag') #with open('/etc/secret_key.txt') as f: # SECRET_KEY = f.read().strip() # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['obscure-plateau-04602.herokuapp.com'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'catalog.apps.CatalogConfig', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'whitenoise.middleware.WhiteNoiseMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'locallibrary.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'locallibrary.wsgi.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Asia/Kolkata' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' # Redirect to home URL after login (Default redirects to /accounts/profile/) LOGIN_REDIRECT_URL = '/' EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend' # to view it console, never add in actual site # Heroku: Update database configuration from $DATABASE_URL. import dj_database_url db_from_env = dj_database_url.config(conn_max_age=500) DATABASES['default'].update(db_from_env) # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ # The absolute path to the directory where collectstatic will collect static files for deployment. STATIC_ROOT = BASE_DIR / 'staticfiles' # The URL to use when referring to static files (where they will be served from) STATIC_URL = '/static/' # Simplified static file serving. # https://warehouse.python.org/project/whitenoise/ STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage'

29.070064

103

0.724803

from pathlib import Path import pytz import os BASE_DIR = Path(__file__).resolve().parent.parent SECRET_KEY = 'django-insecure-@*cqyd6l)4*=yg7r19zmp#y32mpus(a2d-)ny&hstt^kq!13jk' import os DEBUG = True ALLOWED_HOSTS = ['obscure-plateau-04602.herokuapp.com'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'catalog.apps.CatalogConfig', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'whitenoise.middleware.WhiteNoiseMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'locallibrary.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'locallibrary.wsgi.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Asia/Kolkata' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField' # Redirect to home URL after login (Default redirects to /accounts/profile/) LOGIN_REDIRECT_URL = '/' EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend' # to view it console, never add in actual site # Heroku: Update database configuration from $DATABASE_URL. import dj_database_url db_from_env = dj_database_url.config(conn_max_age=500) DATABASES['default'].update(db_from_env) # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ # The absolute path to the directory where collectstatic will collect static files for deployment. STATIC_ROOT = BASE_DIR / 'staticfiles' # The URL to use when referring to static files (where they will be served from) STATIC_URL = '/static/' # Simplified static file serving. # https://warehouse.python.org/project/whitenoise/ STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage'

true

790ddcb0947ec5c383af11aaf8bdbcf8ab880246

60,361

bzl

Python

third_party/toolchains/bazel_0.23.2_rbe_windows/cc_toolchain_config.bzl

suever/grpc

0bee8c41f9c52f81a06fbd8444b2d53249c484a9

[ "Apache-2.0" ]

2

2019-05-26T05:00:55.000Z

2019-06-15T10:18:57.000Z

third_party/toolchains/bazel_0.23.2_rbe_windows/cc_toolchain_config.bzl

suever/grpc

0bee8c41f9c52f81a06fbd8444b2d53249c484a9

[ "Apache-2.0" ]

2

2017-03-07T22:54:36.000Z

2017-04-14T15:17:36.000Z

third_party/toolchains/bazel_0.23.2_rbe_windows/cc_toolchain_config.bzl

suever/grpc

0bee8c41f9c52f81a06fbd8444b2d53249c484a9

[ "Apache-2.0" ]

4

2020-08-10T06:05:01.000Z

2021-12-12T09:26:50.000Z

# Copyright 2019 The Bazel Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """A Starlark cc_toolchain configuration rule""" load( "@bazel_tools//tools/cpp:cc_toolchain_config_lib.bzl", "action_config", "artifact_name_pattern", "env_entry", "env_set", "feature", "feature_set", "flag_group", "flag_set", "make_variable", "tool", "tool_path", "variable_with_value", "with_feature_set", ) load("@bazel_tools//tools/build_defs/cc:action_names.bzl", "ACTION_NAMES") all_compile_actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.clif_match, ACTION_NAMES.lto_backend, ] all_cpp_compile_actions = [ ACTION_NAMES.cpp_compile, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.clif_match, ] preprocessor_compile_actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.clif_match, ] codegen_compile_actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ] all_link_actions = [ ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ] def _windows_msvc_impl(ctx): toolchain_identifier = "msvc_x64" host_system_name = "local" target_system_name = "local" target_cpu = "x64_windows" target_libc = "msvcrt" compiler = "msvc-cl" abi_version = "local" abi_libc_version = "local" cc_target_os = None builtin_sysroot = None cxx_builtin_include_directories = [ # This is a workaround for https://github.com/bazelbuild/bazel/issues/5087. "C:\\botcode\\w", "c:/tools/msys64/usr/", "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\INCLUDE", "C:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.10240.0\\ucrt", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\shared", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\um", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\winrt", ] cpp_link_nodeps_dynamic_library_action = action_config( action_name = ACTION_NAMES.cpp_link_nodeps_dynamic_library, implies = [ "nologo", "shared_flag", "linkstamps", "output_execpath_flags", "input_param_flags", "user_link_flags", "default_link_flags", "linker_subsystem_flag", "linker_param_file", "msvc_env", "no_stripping", "has_configured_linker_path", "def_file", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/link.exe")], ) cpp_link_static_library_action = action_config( action_name = ACTION_NAMES.cpp_link_static_library, implies = [ "nologo", "archiver_flags", "input_param_flags", "linker_param_file", "msvc_env", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/lib.exe")], ) assemble_action = action_config( action_name = ACTION_NAMES.assemble, implies = [ "compiler_input_flags", "compiler_output_flags", "nologo", "msvc_env", "sysroot", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/ml64.exe")], ) preprocess_assemble_action = action_config( action_name = ACTION_NAMES.preprocess_assemble, implies = [ "compiler_input_flags", "compiler_output_flags", "nologo", "msvc_env", "sysroot", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/ml64.exe")], ) c_compile_action = action_config( action_name = ACTION_NAMES.c_compile, implies = [ "compiler_input_flags", "compiler_output_flags", "default_compile_flags", "nologo", "msvc_env", "parse_showincludes", "user_compile_flags", "sysroot", "unfiltered_compile_flags", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/cl.exe")], ) cpp_compile_action = action_config( action_name = ACTION_NAMES.cpp_compile, implies = [ "compiler_input_flags", "compiler_output_flags", "default_compile_flags", "nologo", "msvc_env", "parse_showincludes", "user_compile_flags", "sysroot", "unfiltered_compile_flags", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/cl.exe")], ) cpp_link_executable_action = action_config( action_name = ACTION_NAMES.cpp_link_executable, implies = [ "nologo", "linkstamps", "output_execpath_flags", "input_param_flags", "user_link_flags", "default_link_flags", "linker_subsystem_flag", "linker_param_file", "msvc_env", "no_stripping", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/link.exe")], ) cpp_link_dynamic_library_action = action_config( action_name = ACTION_NAMES.cpp_link_dynamic_library, implies = [ "nologo", "shared_flag", "linkstamps", "output_execpath_flags", "input_param_flags", "user_link_flags", "default_link_flags", "linker_subsystem_flag", "linker_param_file", "msvc_env", "no_stripping", "has_configured_linker_path", "def_file", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/link.exe")], ) action_configs = [ assemble_action, preprocess_assemble_action, c_compile_action, cpp_compile_action, cpp_link_executable_action, cpp_link_dynamic_library_action, cpp_link_nodeps_dynamic_library_action, cpp_link_static_library_action, ] msvc_link_env_feature = feature( name = "msvc_link_env", env_sets = [ env_set( actions = all_link_actions + [ACTION_NAMES.cpp_link_static_library], env_entries = [env_entry(key = "LIB", value = "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\LIB\\amd64;C:\\Program Files (x86)\\Windows Kits\\10\\lib\\10.0.10240.0\\ucrt\\x64;C:\\Program Files (x86)\\Windows Kits\\8.1\\lib\\winv6.3\\um\\x64;")], ), ], ) shared_flag_feature = feature( name = "shared_flag", flag_sets = [ flag_set( actions = [ ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ], flag_groups = [flag_group(flags = ["/DLL"])], ), ], ) determinism_feature = feature( name = "determinism", enabled = True, flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [ flag_group( flags = [ "/wd4117", "-D__DATE__=\"redacted\"", "-D__TIMESTAMP__=\"redacted\"", "-D__TIME__=\"redacted\"", ], ), ], ), ], ) sysroot_feature = feature( name = "sysroot", flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ], flag_groups = [ flag_group( flags = ["--sysroot=%{sysroot}"], iterate_over = "sysroot", expand_if_available = "sysroot", ), ], ), ], ) unfiltered_compile_flags_feature = feature( name = "unfiltered_compile_flags", flag_sets = [ flag_set( actions = [ ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ], flag_groups = [ flag_group( flags = ["%{unfiltered_compile_flags}"], iterate_over = "unfiltered_compile_flags", expand_if_available = "unfiltered_compile_flags", ), ], ), ], ) copy_dynamic_libraries_to_binary_feature = feature(name = "copy_dynamic_libraries_to_binary") input_param_flags_feature = feature( name = "input_param_flags", flag_sets = [ flag_set( actions = [ ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ], flag_groups = [ flag_group( flags = ["/IMPLIB:%{interface_library_output_path}"], expand_if_available = "interface_library_output_path", ), ], ), flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["%{libopts}"], iterate_over = "libopts", expand_if_available = "libopts", ), ], ), flag_set( actions = all_link_actions + [ACTION_NAMES.cpp_link_static_library], flag_groups = [ flag_group( iterate_over = "libraries_to_link", flag_groups = [ flag_group( iterate_over = "libraries_to_link.object_files", flag_groups = [flag_group(flags = ["%{libraries_to_link.object_files}"])], expand_if_equal = variable_with_value( name = "libraries_to_link.type", value = "object_file_group", ), ), flag_group( flag_groups = [flag_group(flags = ["%{libraries_to_link.name}"])], expand_if_equal = variable_with_value( name = "libraries_to_link.type", value = "object_file", ), ), flag_group( flag_groups = [flag_group(flags = ["%{libraries_to_link.name}"])], expand_if_equal = variable_with_value( name = "libraries_to_link.type", value = "interface_library", ), ), flag_group( flag_groups = [ flag_group( flags = ["%{libraries_to_link.name}"], expand_if_false = "libraries_to_link.is_whole_archive", ), flag_group( flags = ["/WHOLEARCHIVE:%{libraries_to_link.name}"], expand_if_true = "libraries_to_link.is_whole_archive", ), ], expand_if_equal = variable_with_value( name = "libraries_to_link.type", value = "static_library", ), ), ], expand_if_available = "libraries_to_link", ), ], ), ], ) fastbuild_feature = feature( name = "fastbuild", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/Od", "/Z7"])], ), flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["/DEBUG:FASTLINK", "/INCREMENTAL:NO"], ), ], ), ], implies = ["generate_pdb_file"], ) user_compile_flags_feature = feature( name = "user_compile_flags", flag_sets = [ flag_set( actions = [ ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ], flag_groups = [ flag_group( flags = ["%{user_compile_flags}"], iterate_over = "user_compile_flags", expand_if_available = "user_compile_flags", ), ], ), ], ) archiver_flags_feature = feature( name = "archiver_flags", flag_sets = [ flag_set( actions = [ACTION_NAMES.cpp_link_static_library], flag_groups = [ flag_group( flags = ["/OUT:%{output_execpath}"], expand_if_available = "output_execpath", ), ], ), ], ) default_link_flags_feature = feature( name = "default_link_flags", enabled = True, flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/MACHINE:X64"])], ), ], ) static_link_msvcrt_feature = feature(name = "static_link_msvcrt") dynamic_link_msvcrt_debug_feature = feature( name = "dynamic_link_msvcrt_debug", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/MDd"])], ), flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/DEFAULTLIB:msvcrtd.lib"])], ), ], requires = [feature_set(features = ["dbg"])], ) dbg_feature = feature( name = "dbg", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/Od", "/Z7"])], ), flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["/DEBUG:FULL", "/INCREMENTAL:NO"], ), ], ), ], implies = ["generate_pdb_file"], ) opt_feature = feature( name = "opt", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/O2"])], ), ], implies = ["frame_pointer"], ) supports_interface_shared_libraries_feature = feature( name = "supports_interface_shared_libraries", enabled = True, ) user_link_flags_feature = feature( name = "user_link_flags", flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["%{user_link_flags}"], iterate_over = "user_link_flags", expand_if_available = "user_link_flags", ), ], ), ], ) default_compile_flags_feature = feature( name = "default_compile_flags", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = [ flag_group( flags = [ "/DCOMPILER_MSVC", "/DNOMINMAX", "/D_WIN32_WINNT=0x0601", "/D_CRT_SECURE_NO_DEPRECATE", "/D_CRT_SECURE_NO_WARNINGS", "/bigobj", "/Zm500", "/EHsc", "/wd4351", "/wd4291", "/wd4250", "/wd4996", ], ), ], ), ], ) msvc_compile_env_feature = feature( name = "msvc_compile_env", env_sets = [ env_set( actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ], env_entries = [env_entry(key = "INCLUDE", value = "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\INCLUDE;C:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.10240.0\\ucrt;C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\shared;C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\um;C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\winrt;")], ), ], ) preprocessor_defines_feature = feature( name = "preprocessor_defines", flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ], flag_groups = [ flag_group( flags = ["/D%{preprocessor_defines}"], iterate_over = "preprocessor_defines", ), ], ), ], ) generate_pdb_file_feature = feature( name = "generate_pdb_file", requires = [ feature_set(features = ["dbg"]), feature_set(features = ["fastbuild"]), ], ) output_execpath_flags_feature = feature( name = "output_execpath_flags", flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["/OUT:%{output_execpath}"], expand_if_available = "output_execpath", ), ], ), ], ) dynamic_link_msvcrt_no_debug_feature = feature( name = "dynamic_link_msvcrt_no_debug", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/MD"])], ), flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/DEFAULTLIB:msvcrt.lib"])], ), ], requires = [ feature_set(features = ["fastbuild"]), feature_set(features = ["opt"]), ], ) disable_assertions_feature = feature( name = "disable_assertions", enabled = True, flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/DNDEBUG"])], with_features = [with_feature_set(features = ["opt"])], ), ], ) has_configured_linker_path_feature = feature(name = "has_configured_linker_path") supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) no_stripping_feature = feature(name = "no_stripping") linker_param_file_feature = feature( name = "linker_param_file", flag_sets = [ flag_set( actions = all_link_actions + [ACTION_NAMES.cpp_link_static_library], flag_groups = [ flag_group( flags = ["@%{linker_param_file}"], expand_if_available = "linker_param_file", ), ], ), ], ) ignore_noisy_warnings_feature = feature( name = "ignore_noisy_warnings", enabled = True, flag_sets = [ flag_set( actions = [ACTION_NAMES.cpp_link_static_library], flag_groups = [flag_group(flags = ["/ignore:4221"])], ), ], ) no_legacy_features_feature = feature(name = "no_legacy_features") parse_showincludes_feature = feature( name = "parse_showincludes", flag_sets = [ flag_set( actions = [ ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_header_parsing, ], flag_groups = [flag_group(flags = ["/showIncludes"])], ), ], ) static_link_msvcrt_no_debug_feature = feature( name = "static_link_msvcrt_no_debug", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/MT"])], ), flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/DEFAULTLIB:libcmt.lib"])], ), ], requires = [ feature_set(features = ["fastbuild"]), feature_set(features = ["opt"]), ], ) treat_warnings_as_errors_feature = feature( name = "treat_warnings_as_errors", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/WX"])], ), ], ) windows_export_all_symbols_feature = feature(name = "windows_export_all_symbols") no_windows_export_all_symbols_feature = feature(name = "no_windows_export_all_symbols") include_paths_feature = feature( name = "include_paths", flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ], flag_groups = [ flag_group( flags = ["/I%{quote_include_paths}"], iterate_over = "quote_include_paths", ), flag_group( flags = ["/I%{include_paths}"], iterate_over = "include_paths", ), flag_group( flags = ["/I%{system_include_paths}"], iterate_over = "system_include_paths", ), ], ), ], ) linkstamps_feature = feature( name = "linkstamps", flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["%{linkstamp_paths}"], iterate_over = "linkstamp_paths", expand_if_available = "linkstamp_paths", ), ], ), ], ) targets_windows_feature = feature( name = "targets_windows", enabled = True, implies = ["copy_dynamic_libraries_to_binary"], ) linker_subsystem_flag_feature = feature( name = "linker_subsystem_flag", flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/SUBSYSTEM:CONSOLE"])], ), ], ) static_link_msvcrt_debug_feature = feature( name = "static_link_msvcrt_debug", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/MTd"])], ), flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/DEFAULTLIB:libcmtd.lib"])], ), ], requires = [feature_set(features = ["dbg"])], ) frame_pointer_feature = feature( name = "frame_pointer", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/Oy-"])], ), ], ) compiler_output_flags_feature = feature( name = "compiler_output_flags", flag_sets = [ flag_set( actions = [ACTION_NAMES.assemble], flag_groups = [ flag_group( flag_groups = [ flag_group( flags = ["/Fo%{output_file}", "/Zi"], expand_if_available = "output_file", expand_if_not_available = "output_assembly_file", ), ], expand_if_not_available = "output_preprocess_file", ), ], ), flag_set( actions = [ ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ], flag_groups = [ flag_group( flag_groups = [ flag_group( flags = ["/Fo%{output_file}"], expand_if_not_available = "output_preprocess_file", ), ], expand_if_available = "output_file", expand_if_not_available = "output_assembly_file", ), flag_group( flag_groups = [ flag_group( flags = ["/Fa%{output_file}"], expand_if_available = "output_assembly_file", ), ], expand_if_available = "output_file", ), flag_group( flag_groups = [ flag_group( flags = ["/P", "/Fi%{output_file}"], expand_if_available = "output_preprocess_file", ), ], expand_if_available = "output_file", ), ], ), ], ) nologo_feature = feature( name = "nologo", flag_sets = [ flag_set( actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ACTION_NAMES.cpp_link_static_library, ], flag_groups = [flag_group(flags = ["/nologo"])], ), ], ) smaller_binary_feature = feature( name = "smaller_binary", enabled = True, flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/Gy", "/Gw"])], with_features = [with_feature_set(features = ["opt"])], ), flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/OPT:ICF", "/OPT:REF"])], with_features = [with_feature_set(features = ["opt"])], ), ], ) compiler_input_flags_feature = feature( name = "compiler_input_flags", flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ], flag_groups = [ flag_group( flags = ["/c", "%{source_file}"], expand_if_available = "source_file", ), ], ), ], ) def_file_feature = feature( name = "def_file", flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["/DEF:%{def_file_path}", "/ignore:4070"], expand_if_available = "def_file_path", ), ], ), ], ) msvc_env_feature = feature( name = "msvc_env", env_sets = [ env_set( actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ACTION_NAMES.cpp_link_static_library, ], env_entries = [ env_entry(key = "PATH", value = "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\amd64;C:\\Windows\\Microsoft.NET\\Framework64\\v4.0.30319;C:\\Windows\\Microsoft.NET\\Framework64\\;C:\\Program Files (x86)\\Windows Kits\\8.1\\bin\\x64;C:\\Program Files (x86)\\Windows Kits\\8.1\\bin\\x86;;C:\\Windows\\system32"), env_entry(key = "TMP", value = "C:\\Users\\ContainerAdministrator\\AppData\\Local\\Temp"), env_entry(key = "TEMP", value = "C:\\Users\\ContainerAdministrator\\AppData\\Local\\Temp"), ], ), ], implies = ["msvc_compile_env", "msvc_link_env"], ) features = [ no_legacy_features_feature, nologo_feature, has_configured_linker_path_feature, no_stripping_feature, targets_windows_feature, copy_dynamic_libraries_to_binary_feature, default_compile_flags_feature, msvc_env_feature, msvc_compile_env_feature, msvc_link_env_feature, include_paths_feature, preprocessor_defines_feature, parse_showincludes_feature, generate_pdb_file_feature, shared_flag_feature, linkstamps_feature, output_execpath_flags_feature, archiver_flags_feature, input_param_flags_feature, linker_subsystem_flag_feature, user_link_flags_feature, default_link_flags_feature, linker_param_file_feature, static_link_msvcrt_feature, static_link_msvcrt_no_debug_feature, dynamic_link_msvcrt_no_debug_feature, static_link_msvcrt_debug_feature, dynamic_link_msvcrt_debug_feature, dbg_feature, fastbuild_feature, opt_feature, frame_pointer_feature, disable_assertions_feature, determinism_feature, treat_warnings_as_errors_feature, smaller_binary_feature, ignore_noisy_warnings_feature, user_compile_flags_feature, sysroot_feature, unfiltered_compile_flags_feature, compiler_output_flags_feature, compiler_input_flags_feature, def_file_feature, windows_export_all_symbols_feature, no_windows_export_all_symbols_feature, supports_dynamic_linker_feature, supports_interface_shared_libraries_feature, ] artifact_name_patterns = [ artifact_name_pattern( category_name = "object_file", prefix = "", extension = ".obj", ), artifact_name_pattern( category_name = "static_library", prefix = "", extension = ".lib", ), artifact_name_pattern( category_name = "alwayslink_static_library", prefix = "", extension = ".lo.lib", ), artifact_name_pattern( category_name = "executable", prefix = "", extension = ".exe", ), artifact_name_pattern( category_name = "dynamic_library", prefix = "", extension = ".dll", ), artifact_name_pattern( category_name = "interface_library", prefix = "", extension = ".if.lib", ), ] make_variables = [] tool_paths = [ tool_path(name = "ar", path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/lib.exe"), tool_path(name = "ml", path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/ml64.exe"), tool_path(name = "cpp", path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/cl.exe"), tool_path(name = "gcc", path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/cl.exe"), tool_path(name = "gcov", path = "wrapper/bin/msvc_nop.bat"), tool_path(name = "ld", path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/link.exe"), tool_path(name = "nm", path = "wrapper/bin/msvc_nop.bat"), tool_path( name = "objcopy", path = "wrapper/bin/msvc_nop.bat", ), tool_path( name = "objdump", path = "wrapper/bin/msvc_nop.bat", ), tool_path( name = "strip", path = "wrapper/bin/msvc_nop.bat", ), ] return cc_common.create_cc_toolchain_config_info( ctx = ctx, features = features, action_configs = action_configs, artifact_name_patterns = artifact_name_patterns, cxx_builtin_include_directories = cxx_builtin_include_directories, toolchain_identifier = toolchain_identifier, host_system_name = host_system_name, target_system_name = target_system_name, target_cpu = target_cpu, target_libc = target_libc, compiler = compiler, abi_version = abi_version, abi_libc_version = abi_libc_version, tool_paths = tool_paths, make_variables = make_variables, builtin_sysroot = builtin_sysroot, cc_target_os = None, ) def _windows_msys_mingw_impl(ctx): toolchain_identifier = "msys_x64_mingw" host_system_name = "local" target_system_name = "local" target_cpu = "x64_windows" target_libc = "mingw" compiler = "mingw-gcc" abi_version = "local" abi_libc_version = "local" cc_target_os = None builtin_sysroot = None action_configs = [] targets_windows_feature = feature( name = "targets_windows", implies = ["copy_dynamic_libraries_to_binary"], enabled = True, ) copy_dynamic_libraries_to_binary_feature = feature(name = "copy_dynamic_libraries_to_binary") gcc_env_feature = feature( name = "gcc_env", enabled = True, env_sets = [ env_set( actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ACTION_NAMES.cpp_link_static_library, ], env_entries = [ env_entry(key = "PATH", value = "c:/tools/msys64/mingw64/bin"), ], ), ], ) msys_mingw_flags = [ "-std=gnu++0x", ] msys_mingw_link_flags = [ "-lstdc++", ] default_compile_flags_feature = feature( name = "default_compile_flags", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], ), flag_set( actions = [ ACTION_NAMES.linkstamp_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = msys_mingw_flags)] if msys_mingw_flags else []), ), ], ) default_link_flags_feature = feature( name = "default_link_flags", enabled = True, flag_sets = [ flag_set( actions = all_link_actions, flag_groups = ([flag_group(flags = msys_mingw_link_flags)] if msys_mingw_link_flags else []), ), ], ) supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) features = [ targets_windows_feature, copy_dynamic_libraries_to_binary_feature, gcc_env_feature, default_compile_flags_feature, default_link_flags_feature, supports_dynamic_linker_feature, ] cxx_builtin_include_directories = [ # This is a workaround for https://github.com/bazelbuild/bazel/issues/5087. "C:\\botcode\\w", "c:/tools/msys64/mingw64/", "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\INCLUDE", "C:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.10240.0\\ucrt", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\shared", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\um", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\winrt", ] artifact_name_patterns = [ artifact_name_pattern( category_name = "executable", prefix = "", extension = ".exe", ), ] make_variables = [] tool_paths = [ tool_path(name = "ar", path = "c:/tools/msys64/mingw64/bin/ar"), tool_path(name = "compat-ld", path = "c:/tools/msys64/mingw64/bin/ld"), tool_path(name = "cpp", path = "c:/tools/msys64/mingw64/bin/cpp"), tool_path(name = "dwp", path = "c:/tools/msys64/mingw64/bin/dwp"), tool_path(name = "gcc", path = "c:/tools/msys64/mingw64/bin/gcc"), tool_path(name = "gcov", path = "c:/tools/msys64/mingw64/bin/gcov"), tool_path(name = "ld", path = "c:/tools/msys64/mingw64/bin/ld"), tool_path(name = "nm", path = "c:/tools/msys64/mingw64/bin/nm"), tool_path(name = "objcopy", path = "c:/tools/msys64/mingw64/bin/objcopy"), tool_path(name = "objdump", path = "c:/tools/msys64/mingw64/bin/objdump"), tool_path(name = "strip", path = "c:/tools/msys64/mingw64/bin/strip"), ] return cc_common.create_cc_toolchain_config_info( ctx = ctx, features = features, action_configs = action_configs, artifact_name_patterns = artifact_name_patterns, cxx_builtin_include_directories = cxx_builtin_include_directories, toolchain_identifier = toolchain_identifier, host_system_name = host_system_name, target_system_name = target_system_name, target_cpu = target_cpu, target_libc = target_libc, compiler = compiler, abi_version = abi_version, abi_libc_version = abi_libc_version, tool_paths = tool_paths, make_variables = make_variables, builtin_sysroot = builtin_sysroot, cc_target_os = cc_target_os, ) def _armeabi_impl(ctx): toolchain_identifier = "stub_armeabi-v7a" host_system_name = "armeabi-v7a" target_system_name = "armeabi-v7a" target_cpu = "armeabi-v7a" target_libc = "armeabi-v7a" compiler = "compiler" abi_version = "armeabi-v7a" abi_libc_version = "armeabi-v7a" cc_target_os = None builtin_sysroot = None action_configs = [] supports_pic_feature = feature(name = "supports_pic", enabled = True) supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) features = [supports_dynamic_linker_feature, supports_pic_feature] cxx_builtin_include_directories = [ # This is a workaround for https://github.com/bazelbuild/bazel/issues/5087. "C:\\botcode\\w", ] artifact_name_patterns = [] make_variables = [] tool_paths = [ tool_path(name = "ar", path = "/bin/false"), tool_path(name = "compat-ld", path = "/bin/false"), tool_path(name = "cpp", path = "/bin/false"), tool_path(name = "dwp", path = "/bin/false"), tool_path(name = "gcc", path = "/bin/false"), tool_path(name = "gcov", path = "/bin/false"), tool_path(name = "ld", path = "/bin/false"), tool_path(name = "nm", path = "/bin/false"), tool_path(name = "objcopy", path = "/bin/false"), tool_path(name = "objdump", path = "/bin/false"), tool_path(name = "strip", path = "/bin/false"), ] return cc_common.create_cc_toolchain_config_info( ctx = ctx, features = features, action_configs = action_configs, artifact_name_patterns = artifact_name_patterns, cxx_builtin_include_directories = cxx_builtin_include_directories, toolchain_identifier = toolchain_identifier, host_system_name = host_system_name, target_system_name = target_system_name, target_cpu = target_cpu, target_libc = target_libc, compiler = compiler, abi_version = abi_version, abi_libc_version = abi_libc_version, tool_paths = tool_paths, make_variables = make_variables, builtin_sysroot = builtin_sysroot, cc_target_os = cc_target_os, ) def _impl(ctx): if ctx.attr.cpu == "armeabi-v7a": return _armeabi_impl(ctx) elif ctx.attr.cpu == "x64_windows" and ctx.attr.compiler == "msvc-cl": return _windows_msvc_impl(ctx) elif ctx.attr.cpu == "x64_windows" and ctx.attr.compiler == "mingw-gcc": return _windows_msys_mingw_impl(ctx) tool_paths = [ tool_path(name = "ar", path = "c:/tools/msys64/usr/bin/ar"), tool_path(name = "compat-ld", path = "c:/tools/msys64/usr/bin/ld"), tool_path(name = "cpp", path = "c:/tools/msys64/usr/bin/cpp"), tool_path(name = "dwp", path = "c:/tools/msys64/usr/bin/dwp"), tool_path(name = "gcc", path = "c:/tools/msys64/usr/bin/gcc"), tool_path(name = "gcov", path = "c:/tools/msys64/usr/bin/gcov"), tool_path(name = "ld", path = "c:/tools/msys64/usr/bin/ld"), tool_path(name = "nm", path = "c:/tools/msys64/usr/bin/nm"), tool_path(name = "objcopy", path = "c:/tools/msys64/usr/bin/objcopy"), tool_path(name = "objdump", path = "c:/tools/msys64/usr/bin/objdump"), tool_path(name = "strip", path = "c:/tools/msys64/usr/bin/strip"), ] cxx_builtin_include_directories = [ # This is a workaround for https://github.com/bazelbuild/bazel/issues/5087. "C:\\botcode\\w", "c:/tools/msys64/usr/", "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\INCLUDE", "C:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.10240.0\\ucrt", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\shared", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\um", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\winrt", ] action_configs = [] compile_flags = [ ] dbg_compile_flags = [ ] opt_compile_flags = [ ] cxx_flags = [ "-std=gnu++0x", ] link_flags = [ "-lstdc++", ] opt_link_flags = [ ] unfiltered_compile_flags = [ ] targets_windows_feature = feature( name = "targets_windows", implies = ["copy_dynamic_libraries_to_binary"], enabled = True, ) copy_dynamic_libraries_to_binary_feature = feature(name = "copy_dynamic_libraries_to_binary") gcc_env_feature = feature( name = "gcc_env", enabled = True, env_sets = [ env_set( actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ACTION_NAMES.cpp_link_static_library, ], env_entries = [ env_entry(key = "PATH", value = "c:/tools/msys64/usr/bin"), ], ), ], ) windows_features = [ targets_windows_feature, copy_dynamic_libraries_to_binary_feature, gcc_env_feature, ] supports_pic_feature = feature( name = "supports_pic", enabled = True, ) supports_start_end_lib_feature = feature( name = "supports_start_end_lib", enabled = True, ) default_compile_flags_feature = feature( name = "default_compile_flags", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = compile_flags)] if compile_flags else []), ), flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = dbg_compile_flags)] if dbg_compile_flags else []), with_features = [with_feature_set(features = ["dbg"])], ), flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = opt_compile_flags)] if opt_compile_flags else []), with_features = [with_feature_set(features = ["opt"])], ), flag_set( actions = [ ACTION_NAMES.linkstamp_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = cxx_flags)] if cxx_flags else []), ), ], ) default_link_flags_feature = feature( name = "default_link_flags", enabled = True, flag_sets = [ flag_set( actions = all_link_actions, flag_groups = ([flag_group(flags = link_flags)] if link_flags else []), ), flag_set( actions = all_link_actions, flag_groups = ([flag_group(flags = opt_link_flags)] if opt_link_flags else []), with_features = [with_feature_set(features = ["opt"])], ), ], ) dbg_feature = feature(name = "dbg") opt_feature = feature(name = "opt") sysroot_feature = feature( name = "sysroot", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ], flag_groups = [ flag_group( flags = ["--sysroot=%{sysroot}"], expand_if_available = "sysroot", ), ], ), ], ) fdo_optimize_feature = feature( name = "fdo_optimize", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [ flag_group( flags = [ "-fprofile-use=%{fdo_profile_path}", "-fprofile-correction", ], expand_if_available = "fdo_profile_path", ), ], ), ], provides = ["profile"], ) supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) user_compile_flags_feature = feature( name = "user_compile_flags", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = [ flag_group( flags = ["%{user_compile_flags}"], iterate_over = "user_compile_flags", expand_if_available = "user_compile_flags", ), ], ), ], ) unfiltered_compile_flags_feature = feature( name = "unfiltered_compile_flags", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = unfiltered_compile_flags)] if unfiltered_compile_flags else []), ), ], ) features = windows_features + [ supports_pic_feature, default_compile_flags_feature, default_link_flags_feature, fdo_optimize_feature, supports_dynamic_linker_feature, dbg_feature, opt_feature, user_compile_flags_feature, sysroot_feature, unfiltered_compile_flags_feature, ] artifact_name_patterns = [ artifact_name_pattern(category_name = "executable", prefix = "", extension = ".exe"), ] make_variables = [] return cc_common.create_cc_toolchain_config_info( ctx = ctx, features = features, action_configs = action_configs, artifact_name_patterns = artifact_name_patterns, cxx_builtin_include_directories = cxx_builtin_include_directories, toolchain_identifier = "msys_x64", host_system_name = "local", target_system_name = "local", target_cpu = "x64_windows", target_libc = "msys", compiler = "msys-gcc", abi_version = "local", abi_libc_version = "local", tool_paths = tool_paths, make_variables = make_variables, builtin_sysroot = "", cc_target_os = None, ) cc_toolchain_config = rule( implementation = _impl, attrs = { "cpu": attr.string(mandatory = True), "compiler": attr.string(), }, provides = [CcToolchainConfigInfo], )

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load( "@bazel_tools//tools/cpp:cc_toolchain_config_lib.bzl", "action_config", "artifact_name_pattern", "env_entry", "env_set", "feature", "feature_set", "flag_group", "flag_set", "make_variable", "tool", "tool_path", "variable_with_value", "with_feature_set", ) load("@bazel_tools//tools/build_defs/cc:action_names.bzl", "ACTION_NAMES") all_compile_actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.clif_match, ACTION_NAMES.lto_backend, ] all_cpp_compile_actions = [ ACTION_NAMES.cpp_compile, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.clif_match, ] preprocessor_compile_actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.clif_match, ] codegen_compile_actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ] all_link_actions = [ ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ] def _windows_msvc_impl(ctx): toolchain_identifier = "msvc_x64" host_system_name = "local" target_system_name = "local" target_cpu = "x64_windows" target_libc = "msvcrt" compiler = "msvc-cl" abi_version = "local" abi_libc_version = "local" cc_target_os = None builtin_sysroot = None cxx_builtin_include_directories = [ "C:\\botcode\\w", "c:/tools/msys64/usr/", "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\INCLUDE", "C:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.10240.0\\ucrt", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\shared", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\um", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\winrt", ] cpp_link_nodeps_dynamic_library_action = action_config( action_name = ACTION_NAMES.cpp_link_nodeps_dynamic_library, implies = [ "nologo", "shared_flag", "linkstamps", "output_execpath_flags", "input_param_flags", "user_link_flags", "default_link_flags", "linker_subsystem_flag", "linker_param_file", "msvc_env", "no_stripping", "has_configured_linker_path", "def_file", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/link.exe")], ) cpp_link_static_library_action = action_config( action_name = ACTION_NAMES.cpp_link_static_library, implies = [ "nologo", "archiver_flags", "input_param_flags", "linker_param_file", "msvc_env", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/lib.exe")], ) assemble_action = action_config( action_name = ACTION_NAMES.assemble, implies = [ "compiler_input_flags", "compiler_output_flags", "nologo", "msvc_env", "sysroot", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/ml64.exe")], ) preprocess_assemble_action = action_config( action_name = ACTION_NAMES.preprocess_assemble, implies = [ "compiler_input_flags", "compiler_output_flags", "nologo", "msvc_env", "sysroot", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/ml64.exe")], ) c_compile_action = action_config( action_name = ACTION_NAMES.c_compile, implies = [ "compiler_input_flags", "compiler_output_flags", "default_compile_flags", "nologo", "msvc_env", "parse_showincludes", "user_compile_flags", "sysroot", "unfiltered_compile_flags", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/cl.exe")], ) cpp_compile_action = action_config( action_name = ACTION_NAMES.cpp_compile, implies = [ "compiler_input_flags", "compiler_output_flags", "default_compile_flags", "nologo", "msvc_env", "parse_showincludes", "user_compile_flags", "sysroot", "unfiltered_compile_flags", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/cl.exe")], ) cpp_link_executable_action = action_config( action_name = ACTION_NAMES.cpp_link_executable, implies = [ "nologo", "linkstamps", "output_execpath_flags", "input_param_flags", "user_link_flags", "default_link_flags", "linker_subsystem_flag", "linker_param_file", "msvc_env", "no_stripping", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/link.exe")], ) cpp_link_dynamic_library_action = action_config( action_name = ACTION_NAMES.cpp_link_dynamic_library, implies = [ "nologo", "shared_flag", "linkstamps", "output_execpath_flags", "input_param_flags", "user_link_flags", "default_link_flags", "linker_subsystem_flag", "linker_param_file", "msvc_env", "no_stripping", "has_configured_linker_path", "def_file", ], tools = [tool(path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/link.exe")], ) action_configs = [ assemble_action, preprocess_assemble_action, c_compile_action, cpp_compile_action, cpp_link_executable_action, cpp_link_dynamic_library_action, cpp_link_nodeps_dynamic_library_action, cpp_link_static_library_action, ] msvc_link_env_feature = feature( name = "msvc_link_env", env_sets = [ env_set( actions = all_link_actions + [ACTION_NAMES.cpp_link_static_library], env_entries = [env_entry(key = "LIB", value = "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\LIB\\amd64;C:\\Program Files (x86)\\Windows Kits\\10\\lib\\10.0.10240.0\\ucrt\\x64;C:\\Program Files (x86)\\Windows Kits\\8.1\\lib\\winv6.3\\um\\x64;")], ), ], ) shared_flag_feature = feature( name = "shared_flag", flag_sets = [ flag_set( actions = [ ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ], flag_groups = [flag_group(flags = ["/DLL"])], ), ], ) determinism_feature = feature( name = "determinism", enabled = True, flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [ flag_group( flags = [ "/wd4117", "-D__DATE__=\"redacted\"", "-D__TIMESTAMP__=\"redacted\"", "-D__TIME__=\"redacted\"", ], ), ], ), ], ) sysroot_feature = feature( name = "sysroot", flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ], flag_groups = [ flag_group( flags = ["--sysroot=%{sysroot}"], iterate_over = "sysroot", expand_if_available = "sysroot", ), ], ), ], ) unfiltered_compile_flags_feature = feature( name = "unfiltered_compile_flags", flag_sets = [ flag_set( actions = [ ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ], flag_groups = [ flag_group( flags = ["%{unfiltered_compile_flags}"], iterate_over = "unfiltered_compile_flags", expand_if_available = "unfiltered_compile_flags", ), ], ), ], ) copy_dynamic_libraries_to_binary_feature = feature(name = "copy_dynamic_libraries_to_binary") input_param_flags_feature = feature( name = "input_param_flags", flag_sets = [ flag_set( actions = [ ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ], flag_groups = [ flag_group( flags = ["/IMPLIB:%{interface_library_output_path}"], expand_if_available = "interface_library_output_path", ), ], ), flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["%{libopts}"], iterate_over = "libopts", expand_if_available = "libopts", ), ], ), flag_set( actions = all_link_actions + [ACTION_NAMES.cpp_link_static_library], flag_groups = [ flag_group( iterate_over = "libraries_to_link", flag_groups = [ flag_group( iterate_over = "libraries_to_link.object_files", flag_groups = [flag_group(flags = ["%{libraries_to_link.object_files}"])], expand_if_equal = variable_with_value( name = "libraries_to_link.type", value = "object_file_group", ), ), flag_group( flag_groups = [flag_group(flags = ["%{libraries_to_link.name}"])], expand_if_equal = variable_with_value( name = "libraries_to_link.type", value = "object_file", ), ), flag_group( flag_groups = [flag_group(flags = ["%{libraries_to_link.name}"])], expand_if_equal = variable_with_value( name = "libraries_to_link.type", value = "interface_library", ), ), flag_group( flag_groups = [ flag_group( flags = ["%{libraries_to_link.name}"], expand_if_false = "libraries_to_link.is_whole_archive", ), flag_group( flags = ["/WHOLEARCHIVE:%{libraries_to_link.name}"], expand_if_true = "libraries_to_link.is_whole_archive", ), ], expand_if_equal = variable_with_value( name = "libraries_to_link.type", value = "static_library", ), ), ], expand_if_available = "libraries_to_link", ), ], ), ], ) fastbuild_feature = feature( name = "fastbuild", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/Od", "/Z7"])], ), flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["/DEBUG:FASTLINK", "/INCREMENTAL:NO"], ), ], ), ], implies = ["generate_pdb_file"], ) user_compile_flags_feature = feature( name = "user_compile_flags", flag_sets = [ flag_set( actions = [ ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ], flag_groups = [ flag_group( flags = ["%{user_compile_flags}"], iterate_over = "user_compile_flags", expand_if_available = "user_compile_flags", ), ], ), ], ) archiver_flags_feature = feature( name = "archiver_flags", flag_sets = [ flag_set( actions = [ACTION_NAMES.cpp_link_static_library], flag_groups = [ flag_group( flags = ["/OUT:%{output_execpath}"], expand_if_available = "output_execpath", ), ], ), ], ) default_link_flags_feature = feature( name = "default_link_flags", enabled = True, flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/MACHINE:X64"])], ), ], ) static_link_msvcrt_feature = feature(name = "static_link_msvcrt") dynamic_link_msvcrt_debug_feature = feature( name = "dynamic_link_msvcrt_debug", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/MDd"])], ), flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/DEFAULTLIB:msvcrtd.lib"])], ), ], requires = [feature_set(features = ["dbg"])], ) dbg_feature = feature( name = "dbg", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/Od", "/Z7"])], ), flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["/DEBUG:FULL", "/INCREMENTAL:NO"], ), ], ), ], implies = ["generate_pdb_file"], ) opt_feature = feature( name = "opt", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/O2"])], ), ], implies = ["frame_pointer"], ) supports_interface_shared_libraries_feature = feature( name = "supports_interface_shared_libraries", enabled = True, ) user_link_flags_feature = feature( name = "user_link_flags", flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["%{user_link_flags}"], iterate_over = "user_link_flags", expand_if_available = "user_link_flags", ), ], ), ], ) default_compile_flags_feature = feature( name = "default_compile_flags", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = [ flag_group( flags = [ "/DCOMPILER_MSVC", "/DNOMINMAX", "/D_WIN32_WINNT=0x0601", "/D_CRT_SECURE_NO_DEPRECATE", "/D_CRT_SECURE_NO_WARNINGS", "/bigobj", "/Zm500", "/EHsc", "/wd4351", "/wd4291", "/wd4250", "/wd4996", ], ), ], ), ], ) msvc_compile_env_feature = feature( name = "msvc_compile_env", env_sets = [ env_set( actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ], env_entries = [env_entry(key = "INCLUDE", value = "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\INCLUDE;C:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.10240.0\\ucrt;C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\shared;C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\um;C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\winrt;")], ), ], ) preprocessor_defines_feature = feature( name = "preprocessor_defines", flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ], flag_groups = [ flag_group( flags = ["/D%{preprocessor_defines}"], iterate_over = "preprocessor_defines", ), ], ), ], ) generate_pdb_file_feature = feature( name = "generate_pdb_file", requires = [ feature_set(features = ["dbg"]), feature_set(features = ["fastbuild"]), ], ) output_execpath_flags_feature = feature( name = "output_execpath_flags", flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["/OUT:%{output_execpath}"], expand_if_available = "output_execpath", ), ], ), ], ) dynamic_link_msvcrt_no_debug_feature = feature( name = "dynamic_link_msvcrt_no_debug", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/MD"])], ), flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/DEFAULTLIB:msvcrt.lib"])], ), ], requires = [ feature_set(features = ["fastbuild"]), feature_set(features = ["opt"]), ], ) disable_assertions_feature = feature( name = "disable_assertions", enabled = True, flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/DNDEBUG"])], with_features = [with_feature_set(features = ["opt"])], ), ], ) has_configured_linker_path_feature = feature(name = "has_configured_linker_path") supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) no_stripping_feature = feature(name = "no_stripping") linker_param_file_feature = feature( name = "linker_param_file", flag_sets = [ flag_set( actions = all_link_actions + [ACTION_NAMES.cpp_link_static_library], flag_groups = [ flag_group( flags = ["@%{linker_param_file}"], expand_if_available = "linker_param_file", ), ], ), ], ) ignore_noisy_warnings_feature = feature( name = "ignore_noisy_warnings", enabled = True, flag_sets = [ flag_set( actions = [ACTION_NAMES.cpp_link_static_library], flag_groups = [flag_group(flags = ["/ignore:4221"])], ), ], ) no_legacy_features_feature = feature(name = "no_legacy_features") parse_showincludes_feature = feature( name = "parse_showincludes", flag_sets = [ flag_set( actions = [ ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_header_parsing, ], flag_groups = [flag_group(flags = ["/showIncludes"])], ), ], ) static_link_msvcrt_no_debug_feature = feature( name = "static_link_msvcrt_no_debug", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/MT"])], ), flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/DEFAULTLIB:libcmt.lib"])], ), ], requires = [ feature_set(features = ["fastbuild"]), feature_set(features = ["opt"]), ], ) treat_warnings_as_errors_feature = feature( name = "treat_warnings_as_errors", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/WX"])], ), ], ) windows_export_all_symbols_feature = feature(name = "windows_export_all_symbols") no_windows_export_all_symbols_feature = feature(name = "no_windows_export_all_symbols") include_paths_feature = feature( name = "include_paths", flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ], flag_groups = [ flag_group( flags = ["/I%{quote_include_paths}"], iterate_over = "quote_include_paths", ), flag_group( flags = ["/I%{include_paths}"], iterate_over = "include_paths", ), flag_group( flags = ["/I%{system_include_paths}"], iterate_over = "system_include_paths", ), ], ), ], ) linkstamps_feature = feature( name = "linkstamps", flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["%{linkstamp_paths}"], iterate_over = "linkstamp_paths", expand_if_available = "linkstamp_paths", ), ], ), ], ) targets_windows_feature = feature( name = "targets_windows", enabled = True, implies = ["copy_dynamic_libraries_to_binary"], ) linker_subsystem_flag_feature = feature( name = "linker_subsystem_flag", flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/SUBSYSTEM:CONSOLE"])], ), ], ) static_link_msvcrt_debug_feature = feature( name = "static_link_msvcrt_debug", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/MTd"])], ), flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/DEFAULTLIB:libcmtd.lib"])], ), ], requires = [feature_set(features = ["dbg"])], ) frame_pointer_feature = feature( name = "frame_pointer", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/Oy-"])], ), ], ) compiler_output_flags_feature = feature( name = "compiler_output_flags", flag_sets = [ flag_set( actions = [ACTION_NAMES.assemble], flag_groups = [ flag_group( flag_groups = [ flag_group( flags = ["/Fo%{output_file}", "/Zi"], expand_if_available = "output_file", expand_if_not_available = "output_assembly_file", ), ], expand_if_not_available = "output_preprocess_file", ), ], ), flag_set( actions = [ ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ], flag_groups = [ flag_group( flag_groups = [ flag_group( flags = ["/Fo%{output_file}"], expand_if_not_available = "output_preprocess_file", ), ], expand_if_available = "output_file", expand_if_not_available = "output_assembly_file", ), flag_group( flag_groups = [ flag_group( flags = ["/Fa%{output_file}"], expand_if_available = "output_assembly_file", ), ], expand_if_available = "output_file", ), flag_group( flag_groups = [ flag_group( flags = ["/P", "/Fi%{output_file}"], expand_if_available = "output_preprocess_file", ), ], expand_if_available = "output_file", ), ], ), ], ) nologo_feature = feature( name = "nologo", flag_sets = [ flag_set( actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ACTION_NAMES.cpp_link_static_library, ], flag_groups = [flag_group(flags = ["/nologo"])], ), ], ) smaller_binary_feature = feature( name = "smaller_binary", enabled = True, flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [flag_group(flags = ["/Gy", "/Gw"])], with_features = [with_feature_set(features = ["opt"])], ), flag_set( actions = all_link_actions, flag_groups = [flag_group(flags = ["/OPT:ICF", "/OPT:REF"])], with_features = [with_feature_set(features = ["opt"])], ), ], ) compiler_input_flags_feature = feature( name = "compiler_input_flags", flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ], flag_groups = [ flag_group( flags = ["/c", "%{source_file}"], expand_if_available = "source_file", ), ], ), ], ) def_file_feature = feature( name = "def_file", flag_sets = [ flag_set( actions = all_link_actions, flag_groups = [ flag_group( flags = ["/DEF:%{def_file_path}", "/ignore:4070"], expand_if_available = "def_file_path", ), ], ), ], ) msvc_env_feature = feature( name = "msvc_env", env_sets = [ env_set( actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ACTION_NAMES.cpp_link_static_library, ], env_entries = [ env_entry(key = "PATH", value = "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\amd64;C:\\Windows\\Microsoft.NET\\Framework64\\v4.0.30319;C:\\Windows\\Microsoft.NET\\Framework64\\;C:\\Program Files (x86)\\Windows Kits\\8.1\\bin\\x64;C:\\Program Files (x86)\\Windows Kits\\8.1\\bin\\x86;;C:\\Windows\\system32"), env_entry(key = "TMP", value = "C:\\Users\\ContainerAdministrator\\AppData\\Local\\Temp"), env_entry(key = "TEMP", value = "C:\\Users\\ContainerAdministrator\\AppData\\Local\\Temp"), ], ), ], implies = ["msvc_compile_env", "msvc_link_env"], ) features = [ no_legacy_features_feature, nologo_feature, has_configured_linker_path_feature, no_stripping_feature, targets_windows_feature, copy_dynamic_libraries_to_binary_feature, default_compile_flags_feature, msvc_env_feature, msvc_compile_env_feature, msvc_link_env_feature, include_paths_feature, preprocessor_defines_feature, parse_showincludes_feature, generate_pdb_file_feature, shared_flag_feature, linkstamps_feature, output_execpath_flags_feature, archiver_flags_feature, input_param_flags_feature, linker_subsystem_flag_feature, user_link_flags_feature, default_link_flags_feature, linker_param_file_feature, static_link_msvcrt_feature, static_link_msvcrt_no_debug_feature, dynamic_link_msvcrt_no_debug_feature, static_link_msvcrt_debug_feature, dynamic_link_msvcrt_debug_feature, dbg_feature, fastbuild_feature, opt_feature, frame_pointer_feature, disable_assertions_feature, determinism_feature, treat_warnings_as_errors_feature, smaller_binary_feature, ignore_noisy_warnings_feature, user_compile_flags_feature, sysroot_feature, unfiltered_compile_flags_feature, compiler_output_flags_feature, compiler_input_flags_feature, def_file_feature, windows_export_all_symbols_feature, no_windows_export_all_symbols_feature, supports_dynamic_linker_feature, supports_interface_shared_libraries_feature, ] artifact_name_patterns = [ artifact_name_pattern( category_name = "object_file", prefix = "", extension = ".obj", ), artifact_name_pattern( category_name = "static_library", prefix = "", extension = ".lib", ), artifact_name_pattern( category_name = "alwayslink_static_library", prefix = "", extension = ".lo.lib", ), artifact_name_pattern( category_name = "executable", prefix = "", extension = ".exe", ), artifact_name_pattern( category_name = "dynamic_library", prefix = "", extension = ".dll", ), artifact_name_pattern( category_name = "interface_library", prefix = "", extension = ".if.lib", ), ] make_variables = [] tool_paths = [ tool_path(name = "ar", path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/lib.exe"), tool_path(name = "ml", path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/ml64.exe"), tool_path(name = "cpp", path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/cl.exe"), tool_path(name = "gcc", path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/cl.exe"), tool_path(name = "gcov", path = "wrapper/bin/msvc_nop.bat"), tool_path(name = "ld", path = "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/amd64/link.exe"), tool_path(name = "nm", path = "wrapper/bin/msvc_nop.bat"), tool_path( name = "objcopy", path = "wrapper/bin/msvc_nop.bat", ), tool_path( name = "objdump", path = "wrapper/bin/msvc_nop.bat", ), tool_path( name = "strip", path = "wrapper/bin/msvc_nop.bat", ), ] return cc_common.create_cc_toolchain_config_info( ctx = ctx, features = features, action_configs = action_configs, artifact_name_patterns = artifact_name_patterns, cxx_builtin_include_directories = cxx_builtin_include_directories, toolchain_identifier = toolchain_identifier, host_system_name = host_system_name, target_system_name = target_system_name, target_cpu = target_cpu, target_libc = target_libc, compiler = compiler, abi_version = abi_version, abi_libc_version = abi_libc_version, tool_paths = tool_paths, make_variables = make_variables, builtin_sysroot = builtin_sysroot, cc_target_os = None, ) def _windows_msys_mingw_impl(ctx): toolchain_identifier = "msys_x64_mingw" host_system_name = "local" target_system_name = "local" target_cpu = "x64_windows" target_libc = "mingw" compiler = "mingw-gcc" abi_version = "local" abi_libc_version = "local" cc_target_os = None builtin_sysroot = None action_configs = [] targets_windows_feature = feature( name = "targets_windows", implies = ["copy_dynamic_libraries_to_binary"], enabled = True, ) copy_dynamic_libraries_to_binary_feature = feature(name = "copy_dynamic_libraries_to_binary") gcc_env_feature = feature( name = "gcc_env", enabled = True, env_sets = [ env_set( actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ACTION_NAMES.cpp_link_static_library, ], env_entries = [ env_entry(key = "PATH", value = "c:/tools/msys64/mingw64/bin"), ], ), ], ) msys_mingw_flags = [ "-std=gnu++0x", ] msys_mingw_link_flags = [ "-lstdc++", ] default_compile_flags_feature = feature( name = "default_compile_flags", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], ), flag_set( actions = [ ACTION_NAMES.linkstamp_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = msys_mingw_flags)] if msys_mingw_flags else []), ), ], ) default_link_flags_feature = feature( name = "default_link_flags", enabled = True, flag_sets = [ flag_set( actions = all_link_actions, flag_groups = ([flag_group(flags = msys_mingw_link_flags)] if msys_mingw_link_flags else []), ), ], ) supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) features = [ targets_windows_feature, copy_dynamic_libraries_to_binary_feature, gcc_env_feature, default_compile_flags_feature, default_link_flags_feature, supports_dynamic_linker_feature, ] cxx_builtin_include_directories = [ "C:\\botcode\\w", "c:/tools/msys64/mingw64/", "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\INCLUDE", "C:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.10240.0\\ucrt", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\shared", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\um", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\winrt", ] artifact_name_patterns = [ artifact_name_pattern( category_name = "executable", prefix = "", extension = ".exe", ), ] make_variables = [] tool_paths = [ tool_path(name = "ar", path = "c:/tools/msys64/mingw64/bin/ar"), tool_path(name = "compat-ld", path = "c:/tools/msys64/mingw64/bin/ld"), tool_path(name = "cpp", path = "c:/tools/msys64/mingw64/bin/cpp"), tool_path(name = "dwp", path = "c:/tools/msys64/mingw64/bin/dwp"), tool_path(name = "gcc", path = "c:/tools/msys64/mingw64/bin/gcc"), tool_path(name = "gcov", path = "c:/tools/msys64/mingw64/bin/gcov"), tool_path(name = "ld", path = "c:/tools/msys64/mingw64/bin/ld"), tool_path(name = "nm", path = "c:/tools/msys64/mingw64/bin/nm"), tool_path(name = "objcopy", path = "c:/tools/msys64/mingw64/bin/objcopy"), tool_path(name = "objdump", path = "c:/tools/msys64/mingw64/bin/objdump"), tool_path(name = "strip", path = "c:/tools/msys64/mingw64/bin/strip"), ] return cc_common.create_cc_toolchain_config_info( ctx = ctx, features = features, action_configs = action_configs, artifact_name_patterns = artifact_name_patterns, cxx_builtin_include_directories = cxx_builtin_include_directories, toolchain_identifier = toolchain_identifier, host_system_name = host_system_name, target_system_name = target_system_name, target_cpu = target_cpu, target_libc = target_libc, compiler = compiler, abi_version = abi_version, abi_libc_version = abi_libc_version, tool_paths = tool_paths, make_variables = make_variables, builtin_sysroot = builtin_sysroot, cc_target_os = cc_target_os, ) def _armeabi_impl(ctx): toolchain_identifier = "stub_armeabi-v7a" host_system_name = "armeabi-v7a" target_system_name = "armeabi-v7a" target_cpu = "armeabi-v7a" target_libc = "armeabi-v7a" compiler = "compiler" abi_version = "armeabi-v7a" abi_libc_version = "armeabi-v7a" cc_target_os = None builtin_sysroot = None action_configs = [] supports_pic_feature = feature(name = "supports_pic", enabled = True) supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) features = [supports_dynamic_linker_feature, supports_pic_feature] cxx_builtin_include_directories = [ "C:\\botcode\\w", ] artifact_name_patterns = [] make_variables = [] tool_paths = [ tool_path(name = "ar", path = "/bin/false"), tool_path(name = "compat-ld", path = "/bin/false"), tool_path(name = "cpp", path = "/bin/false"), tool_path(name = "dwp", path = "/bin/false"), tool_path(name = "gcc", path = "/bin/false"), tool_path(name = "gcov", path = "/bin/false"), tool_path(name = "ld", path = "/bin/false"), tool_path(name = "nm", path = "/bin/false"), tool_path(name = "objcopy", path = "/bin/false"), tool_path(name = "objdump", path = "/bin/false"), tool_path(name = "strip", path = "/bin/false"), ] return cc_common.create_cc_toolchain_config_info( ctx = ctx, features = features, action_configs = action_configs, artifact_name_patterns = artifact_name_patterns, cxx_builtin_include_directories = cxx_builtin_include_directories, toolchain_identifier = toolchain_identifier, host_system_name = host_system_name, target_system_name = target_system_name, target_cpu = target_cpu, target_libc = target_libc, compiler = compiler, abi_version = abi_version, abi_libc_version = abi_libc_version, tool_paths = tool_paths, make_variables = make_variables, builtin_sysroot = builtin_sysroot, cc_target_os = cc_target_os, ) def _impl(ctx): if ctx.attr.cpu == "armeabi-v7a": return _armeabi_impl(ctx) elif ctx.attr.cpu == "x64_windows" and ctx.attr.compiler == "msvc-cl": return _windows_msvc_impl(ctx) elif ctx.attr.cpu == "x64_windows" and ctx.attr.compiler == "mingw-gcc": return _windows_msys_mingw_impl(ctx) tool_paths = [ tool_path(name = "ar", path = "c:/tools/msys64/usr/bin/ar"), tool_path(name = "compat-ld", path = "c:/tools/msys64/usr/bin/ld"), tool_path(name = "cpp", path = "c:/tools/msys64/usr/bin/cpp"), tool_path(name = "dwp", path = "c:/tools/msys64/usr/bin/dwp"), tool_path(name = "gcc", path = "c:/tools/msys64/usr/bin/gcc"), tool_path(name = "gcov", path = "c:/tools/msys64/usr/bin/gcov"), tool_path(name = "ld", path = "c:/tools/msys64/usr/bin/ld"), tool_path(name = "nm", path = "c:/tools/msys64/usr/bin/nm"), tool_path(name = "objcopy", path = "c:/tools/msys64/usr/bin/objcopy"), tool_path(name = "objdump", path = "c:/tools/msys64/usr/bin/objdump"), tool_path(name = "strip", path = "c:/tools/msys64/usr/bin/strip"), ] cxx_builtin_include_directories = [ "C:\\botcode\\w", "c:/tools/msys64/usr/", "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\INCLUDE", "C:\\Program Files (x86)\\Windows Kits\\10\\include\\10.0.10240.0\\ucrt", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\shared", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\um", "C:\\Program Files (x86)\\Windows Kits\\8.1\\include\\winrt", ] action_configs = [] compile_flags = [ ] dbg_compile_flags = [ ] opt_compile_flags = [ ] cxx_flags = [ "-std=gnu++0x", ] link_flags = [ "-lstdc++", ] opt_link_flags = [ ] unfiltered_compile_flags = [ ] targets_windows_feature = feature( name = "targets_windows", implies = ["copy_dynamic_libraries_to_binary"], enabled = True, ) copy_dynamic_libraries_to_binary_feature = feature(name = "copy_dynamic_libraries_to_binary") gcc_env_feature = feature( name = "gcc_env", enabled = True, env_sets = [ env_set( actions = [ ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ACTION_NAMES.cpp_link_static_library, ], env_entries = [ env_entry(key = "PATH", value = "c:/tools/msys64/usr/bin"), ], ), ], ) windows_features = [ targets_windows_feature, copy_dynamic_libraries_to_binary_feature, gcc_env_feature, ] supports_pic_feature = feature( name = "supports_pic", enabled = True, ) supports_start_end_lib_feature = feature( name = "supports_start_end_lib", enabled = True, ) default_compile_flags_feature = feature( name = "default_compile_flags", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = compile_flags)] if compile_flags else []), ), flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = dbg_compile_flags)] if dbg_compile_flags else []), with_features = [with_feature_set(features = ["dbg"])], ), flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = opt_compile_flags)] if opt_compile_flags else []), with_features = [with_feature_set(features = ["opt"])], ), flag_set( actions = [ ACTION_NAMES.linkstamp_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = cxx_flags)] if cxx_flags else []), ), ], ) default_link_flags_feature = feature( name = "default_link_flags", enabled = True, flag_sets = [ flag_set( actions = all_link_actions, flag_groups = ([flag_group(flags = link_flags)] if link_flags else []), ), flag_set( actions = all_link_actions, flag_groups = ([flag_group(flags = opt_link_flags)] if opt_link_flags else []), with_features = [with_feature_set(features = ["opt"])], ), ], ) dbg_feature = feature(name = "dbg") opt_feature = feature(name = "opt") sysroot_feature = feature( name = "sysroot", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ACTION_NAMES.cpp_link_executable, ACTION_NAMES.cpp_link_dynamic_library, ACTION_NAMES.cpp_link_nodeps_dynamic_library, ], flag_groups = [ flag_group( flags = ["--sysroot=%{sysroot}"], expand_if_available = "sysroot", ), ], ), ], ) fdo_optimize_feature = feature( name = "fdo_optimize", flag_sets = [ flag_set( actions = [ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile], flag_groups = [ flag_group( flags = [ "-fprofile-use=%{fdo_profile_path}", "-fprofile-correction", ], expand_if_available = "fdo_profile_path", ), ], ), ], provides = ["profile"], ) supports_dynamic_linker_feature = feature(name = "supports_dynamic_linker", enabled = True) user_compile_flags_feature = feature( name = "user_compile_flags", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = [ flag_group( flags = ["%{user_compile_flags}"], iterate_over = "user_compile_flags", expand_if_available = "user_compile_flags", ), ], ), ], ) unfiltered_compile_flags_feature = feature( name = "unfiltered_compile_flags", enabled = True, flag_sets = [ flag_set( actions = [ ACTION_NAMES.assemble, ACTION_NAMES.preprocess_assemble, ACTION_NAMES.linkstamp_compile, ACTION_NAMES.c_compile, ACTION_NAMES.cpp_compile, ACTION_NAMES.cpp_header_parsing, ACTION_NAMES.cpp_module_compile, ACTION_NAMES.cpp_module_codegen, ACTION_NAMES.lto_backend, ACTION_NAMES.clif_match, ], flag_groups = ([flag_group(flags = unfiltered_compile_flags)] if unfiltered_compile_flags else []), ), ], ) features = windows_features + [ supports_pic_feature, default_compile_flags_feature, default_link_flags_feature, fdo_optimize_feature, supports_dynamic_linker_feature, dbg_feature, opt_feature, user_compile_flags_feature, sysroot_feature, unfiltered_compile_flags_feature, ] artifact_name_patterns = [ artifact_name_pattern(category_name = "executable", prefix = "", extension = ".exe"), ] make_variables = [] return cc_common.create_cc_toolchain_config_info( ctx = ctx, features = features, action_configs = action_configs, artifact_name_patterns = artifact_name_patterns, cxx_builtin_include_directories = cxx_builtin_include_directories, toolchain_identifier = "msys_x64", host_system_name = "local", target_system_name = "local", target_cpu = "x64_windows", target_libc = "msys", compiler = "msys-gcc", abi_version = "local", abi_libc_version = "local", tool_paths = tool_paths, make_variables = make_variables, builtin_sysroot = "", cc_target_os = None, ) cc_toolchain_config = rule( implementation = _impl, attrs = { "cpu": attr.string(mandatory = True), "compiler": attr.string(), }, provides = [CcToolchainConfigInfo], )

true

790ddea0a7befd113cbee96ab0c5dd2da399d1ad

8,729

py

Python

mri_convert_ppc64/mri_convert_ppc64.py

quinnyyy/pl-mri_convert_ppc64

8a3e1bd5778c350432467ad19f2262809ee2833c

[ "MIT" ]

1

2021-04-22T10:48:36.000Z

mri_convert_ppc64/mri_convert_ppc64.py

quinnyyy/pl-mri_convert_ppc64

8a3e1bd5778c350432467ad19f2262809ee2833c

[ "MIT" ]

null

mri_convert_ppc64/mri_convert_ppc64.py

quinnyyy/pl-mri_convert_ppc64

8a3e1bd5778c350432467ad19f2262809ee2833c

[ "MIT" ]

null

#!/usr/bin/env python # # mri_convert_ppc64 ds ChRIS plugin app # # (c) 2016-2019 Fetal-Neonatal Neuroimaging & Developmental Science Center # Boston Children's Hospital # # http://childrenshospital.org/FNNDSC/ # dev@babyMRI.org # import os import sys sys.path.append(os.path.dirname(__file__)) # import the Chris app superclass from chrisapp.base import ChrisApp Gstr_title = """ _ _ ____ ___ (_) | | / ___| / | _ __ ___ _ __ _ ___ ___ _ ____ _____ _ __| |_ _ __ _ __ ___/ /___ / /| | | '_ ` _ \| '__| | / __/ _ \| '_ \ \ / / _ \ '__| __| | '_ \| '_ \ / __| ___ \/ /_| | | | | | | | | | || (_| (_) | | | \ V / __/ | | |_ | |_) | |_) | (__| \_/ |\___ | |_| |_| |_|_| |_| \___\___/|_| |_|\_/ \___|_| \__| | .__/| .__/ \___\_____/ |_/ ______ ______| | | | |______| |______|_| |_| """ Gstr_synopsis = """ NAME mri_convert_ppc64.py SYNOPSIS python mri_convert_ppc64.py \\ [-h] [--help] \\ [--json] \\ [--man] \\ [--meta] \\ [--savejson <DIR>] \\ [-v <level>] [--verbosity <level>] \\ [--version] \\ [--inputFile <inputFile>] \\ [--outputFile <outputFile>] \\ [--executable <executable>] \\ [--execArgs <execArgs>] \\ <inputDir> \\ <outputDir> BRIEF EXAMPLE * Bare bones execution mkdir in out && chmod 777 out python mri_convert_ppc64.py \\ in out DESCRIPTION `mri_convert_ppc64.py` calls an underlying executable (typically 'mri_convert') and passes it an input and output spec. ARGS [--inputFile <inputFile>] The input file, relative to <inputDir>. [--outputFile <outputFile>] The output file, relative to <outpufDir>. [--executable <executable>] The actual executable to run. [--execArgs <execArgs>] Additional executable-specific command line args. [-h] [--help] If specified, show help message and exit. [--json] If specified, show json representation of app and exit. [--man] If specified, print (this) man page and exit. [--meta] If specified, print plugin meta data and exit. [--savejson <DIR>] If specified, save json representation file to DIR and exit. [-v <level>] [--verbosity <level>] Verbosity level for app. Not used currently. [--version] If specified, print version number and exit. """ class Mri_convert_ppc64(ChrisApp): """ This calls a pre-built PPC64 'mri_convert' that is housed in a base container.. """ AUTHORS = 'BU-2019-Power9 (dev@babyMRI.org)' SELFPATH = os.path.dirname(os.path.abspath(__file__)) SELFEXEC = os.path.basename(__file__) EXECSHELL = 'python3' TITLE = 'A PowerPPC plugin to run the FreeSurfer mri_convert' CATEGORY = '' TYPE = 'ds' DESCRIPTION = 'This calls a pre-built PPC64 mri_convert that is housed in a base container.' DOCUMENTATION = 'http://wiki' VERSION = '0.1' ICON = '' # url of an icon image LICENSE = 'Opensource (MIT)' MAX_NUMBER_OF_WORKERS = 1 # Override with integer value MIN_NUMBER_OF_WORKERS = 1 # Override with integer value MAX_CPU_LIMIT = '' # Override with millicore value as string, e.g. '2000m' MIN_CPU_LIMIT = '' # Override with millicore value as string, e.g. '2000m' MAX_MEMORY_LIMIT = '' # Override with string, e.g. '1Gi', '2000Mi' MIN_MEMORY_LIMIT = '' # Override with string, e.g. '1Gi', '2000Mi' MIN_GPU_LIMIT = 0 # Override with the minimum number of GPUs, as an integer, for your plugin MAX_GPU_LIMIT = 0 # Override with the maximum number of GPUs, as an integer, for your plugin # Use this dictionary structure to provide key-value output descriptive information # that may be useful for the next downstream plugin. For example: # # { # "finalOutputFile": "final/file.out", # "viewer": "genericTextViewer", # } # # The above dictionary is saved when plugin is called with a ``--saveoutputmeta`` # flag. Note also that all file paths are relative to the system specified # output directory. OUTPUT_META_DICT = {} def define_parameters(self): """ Define the CLI arguments accepted by this plugin app. Use self.add_argument to specify a new app argument. """ self.add_argument('--executable', dest = 'executable', type = str, optional = True, help = 'the conversion program to use', default = '/usr/bin/mri_convert') self.add_argument('--inputFile', dest = 'inputFile', type = str, optional = True, help = 'the input file', default = '') self.add_argument('--outputFile', dest = 'outputFile', type = str, optional = True, help = 'the output file', default = '') self.add_argument('--execArgs', dest = 'execArgs', type = str, optional = True, help = 'additonal arguments for the chosen executable', default = '') def run(self, options): """ Define the code to be run by this plugin app. """ if not len(options.inputFile): print("ERROR: No input file has been specified!") print("You must specify an input file relative to the input directory.") sys.exit(1) if not len(options.outputFile): print("ERROR: No output file has been specified!") print("You must specicy an output file relative to the output directory.") sys.exit(1) str_cmd = '%s %s %s/%s %s/%s' % ( options.executable, options.execArgs, options.inputdir, options.inputFile, options.outputdir, options.outputFile) os.system(str_cmd) def show_man_page(self): """ Print the app's man page. """ print(Gstr_title) print(Gstr_synopsis) # ENTRYPOINT if __name__ == "__main__": chris_app = Mri_convert_ppc64() chris_app.launch()

39.677273

108

0.414251

# # http://childrenshospital.org/FNNDSC/ # dev@babyMRI.org # import os import sys sys.path.append(os.path.dirname(__file__)) # import the Chris app superclass from chrisapp.base import ChrisApp Gstr_title = """ _ _ ____ ___ (_) | | / ___| / | _ __ ___ _ __ _ ___ ___ _ ____ _____ _ __| |_ _ __ _ __ ___/ /___ / /| | | '_ ` _ \| '__| | / __/ _ \| '_ \ \ / / _ \ '__| __| | '_ \| '_ \ / __| ___ \/ /_| | | | | | | | | | || (_| (_) | | | \ V / __/ | | |_ | |_) | |_) | (__| \_/ |\___ | |_| |_| |_|_| |_| \___\___/|_| |_|\_/ \___|_| \__| | .__/| .__/ \___\_____/ |_/ ______ ______| | | | |______| |______|_| |_| """ Gstr_synopsis = """ NAME mri_convert_ppc64.py SYNOPSIS python mri_convert_ppc64.py \\ [-h] [--help] \\ [--json] \\ [--man] \\ [--meta] \\ [--savejson <DIR>] \\ [-v <level>] [--verbosity <level>] \\ [--version] \\ [--inputFile <inputFile>] \\ [--outputFile <outputFile>] \\ [--executable <executable>] \\ [--execArgs <execArgs>] \\ <inputDir> \\ <outputDir> BRIEF EXAMPLE * Bare bones execution mkdir in out && chmod 777 out python mri_convert_ppc64.py \\ in out DESCRIPTION `mri_convert_ppc64.py` calls an underlying executable (typically 'mri_convert') and passes it an input and output spec. ARGS [--inputFile <inputFile>] The input file, relative to <inputDir>. [--outputFile <outputFile>] The output file, relative to <outpufDir>. [--executable <executable>] The actual executable to run. [--execArgs <execArgs>] Additional executable-specific command line args. [-h] [--help] If specified, show help message and exit. [--json] If specified, show json representation of app and exit. [--man] If specified, print (this) man page and exit. [--meta] If specified, print plugin meta data and exit. [--savejson <DIR>] If specified, save json representation file to DIR and exit. [-v <level>] [--verbosity <level>] Verbosity level for app. Not used currently. [--version] If specified, print version number and exit. """ class Mri_convert_ppc64(ChrisApp): AUTHORS = 'BU-2019-Power9 (dev@babyMRI.org)' SELFPATH = os.path.dirname(os.path.abspath(__file__)) SELFEXEC = os.path.basename(__file__) EXECSHELL = 'python3' TITLE = 'A PowerPPC plugin to run the FreeSurfer mri_convert' CATEGORY = '' TYPE = 'ds' DESCRIPTION = 'This calls a pre-built PPC64 mri_convert that is housed in a base container.' DOCUMENTATION = 'http://wiki' VERSION = '0.1' ICON = '' # url of an icon image LICENSE = 'Opensource (MIT)' MAX_NUMBER_OF_WORKERS = 1 # Override with integer value MIN_NUMBER_OF_WORKERS = 1 # Override with integer value MAX_CPU_LIMIT = '' # Override with millicore value as string, e.g. '2000m' MIN_CPU_LIMIT = '' # Override with millicore value as string, e.g. '2000m' MAX_MEMORY_LIMIT = '' # Override with string, e.g. '1Gi', '2000Mi' MIN_MEMORY_LIMIT = '' # Override with string, e.g. '1Gi', '2000Mi' MIN_GPU_LIMIT = 0 # Override with the minimum number of GPUs, as an integer, for your plugin MAX_GPU_LIMIT = 0 # Override with the maximum number of GPUs, as an integer, for your plugin # Use this dictionary structure to provide key-value output descriptive information # that may be useful for the next downstream plugin. For example: # # { # "finalOutputFile": "final/file.out", # "viewer": "genericTextViewer", # } # # The above dictionary is saved when plugin is called with a ``--saveoutputmeta`` # flag. Note also that all file paths are relative to the system specified # output directory. OUTPUT_META_DICT = {} def define_parameters(self): self.add_argument('--executable', dest = 'executable', type = str, optional = True, help = 'the conversion program to use', default = '/usr/bin/mri_convert') self.add_argument('--inputFile', dest = 'inputFile', type = str, optional = True, help = 'the input file', default = '') self.add_argument('--outputFile', dest = 'outputFile', type = str, optional = True, help = 'the output file', default = '') self.add_argument('--execArgs', dest = 'execArgs', type = str, optional = True, help = 'additonal arguments for the chosen executable', default = '') def run(self, options): if not len(options.inputFile): print("ERROR: No input file has been specified!") print("You must specify an input file relative to the input directory.") sys.exit(1) if not len(options.outputFile): print("ERROR: No output file has been specified!") print("You must specicy an output file relative to the output directory.") sys.exit(1) str_cmd = '%s %s %s/%s %s/%s' % ( options.executable, options.execArgs, options.inputdir, options.inputFile, options.outputdir, options.outputFile) os.system(str_cmd) def show_man_page(self): print(Gstr_title) print(Gstr_synopsis) # ENTRYPOINT if __name__ == "__main__": chris_app = Mri_convert_ppc64() chris_app.launch()

true

790ddf0a84ca9a769e99f0de79a3d0d263813d02

1,100

py

Python

python/ecs/cluster/app.py

marclyo/aws-cdk-examples

f041f07ebd4c94897e16d37ff813a38eb32645a1

[ "Apache-2.0" ]

2,941

2019-02-08T15:29:36.000Z

2022-03-31T23:57:42.000Z

python/ecs/cluster/app.py

marclyo/aws-cdk-examples

f041f07ebd4c94897e16d37ff813a38eb32645a1

[ "Apache-2.0" ]

558

2019-02-14T23:32:02.000Z

2022-03-30T00:35:11.000Z

python/ecs/cluster/app.py

marclyo/aws-cdk-examples

f041f07ebd4c94897e16d37ff813a38eb32645a1

[ "Apache-2.0" ]

1,409

2019-02-12T19:13:04.000Z

2022-03-31T18:46:21.000Z

from aws_cdk import ( aws_autoscaling as autoscaling, aws_ec2 as ec2, aws_ecs as ecs, core, ) class ECSCluster(core.Stack): def __init__(self, scope: core.Construct, id: str, **kwargs) -> None: super().__init__(scope, id, *kwargs) vpc = ec2.Vpc( self, "MyVpc", max_azs=2 ) asg = autoscaling.AutoScalingGroup( self, "MyFleet", instance_type=ec2.InstanceType("t2.xlarge"), machine_image=ecs.EcsOptimizedAmi(), associate_public_ip_address=True, update_type=autoscaling.UpdateType.REPLACING_UPDATE, desired_capacity=3, vpc=vpc, vpc_subnets={ 'subnet_type': ec2.SubnetType.PUBLIC }, ) cluster = ecs.Cluster( self, 'EcsCluster', vpc=vpc ) cluster.add_auto_scaling_group(asg) cluster.add_capacity("DefaultAutoScalingGroup", instance_type=ec2.InstanceType("t2.micro")) app = core.App() ECSCluster(app, "MyFirstEcsCluster") app.synth()

26.190476

73

0.583636

from aws_cdk import ( aws_autoscaling as autoscaling, aws_ec2 as ec2, aws_ecs as ecs, core, ) class ECSCluster(core.Stack): def __init__(self, scope: core.Construct, id: str, **kwargs) -> None: super().__init__(scope, id, *kwargs) vpc = ec2.Vpc( self, "MyVpc", max_azs=2 ) asg = autoscaling.AutoScalingGroup( self, "MyFleet", instance_type=ec2.InstanceType("t2.xlarge"), machine_image=ecs.EcsOptimizedAmi(), associate_public_ip_address=True, update_type=autoscaling.UpdateType.REPLACING_UPDATE, desired_capacity=3, vpc=vpc, vpc_subnets={ 'subnet_type': ec2.SubnetType.PUBLIC }, ) cluster = ecs.Cluster( self, 'EcsCluster', vpc=vpc ) cluster.add_auto_scaling_group(asg) cluster.add_capacity("DefaultAutoScalingGroup", instance_type=ec2.InstanceType("t2.micro")) app = core.App() ECSCluster(app, "MyFirstEcsCluster") app.synth()

true

790ddf55f9a0fae73beca060e792297299998965

1,165

py

Python

XD/mysite/polls/migrations/0001_initial.py

ChyiLin/HAHA

d0492b7dee2881d35c000659c44099dad8b41083

[ "MIT" ]

null

XD/mysite/polls/migrations/0001_initial.py

ChyiLin/HAHA

d0492b7dee2881d35c000659c44099dad8b41083

[ "MIT" ]

null

XD/mysite/polls/migrations/0001_initial.py

ChyiLin/HAHA

d0492b7dee2881d35c000659c44099dad8b41083

[ "MIT" ]

null

# Generated by Django 2.1.4 on 2018-12-22 04:01 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Choice', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('choice_text', models.CharField(max_length=200)), ('votes', models.IntegerField(default=0)), ], ), migrations.CreateModel( name='Question', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('question_text', models.CharField(max_length=200)), ('pub_date', models.DateTimeField(verbose_name='date published')), ], ), migrations.AddField( model_name='choice', name='question', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='polls.Question'), ), ]

31.486486

114

0.577682

from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Choice', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('choice_text', models.CharField(max_length=200)), ('votes', models.IntegerField(default=0)), ], ), migrations.CreateModel( name='Question', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('question_text', models.CharField(max_length=200)), ('pub_date', models.DateTimeField(verbose_name='date published')), ], ), migrations.AddField( model_name='choice', name='question', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='polls.Question'), ), ]

true

790ddf7082426bbcac3a19c111985878009197dc

3,413

py

Python

Tools/Scripts/webkitpy/tool/mocktool.py

jacadcaps/webkitty

9aebd2081349f9a7b5d168673c6f676a1450a66d

[ "BSD-2-Clause" ]

6

2021-07-05T16:09:39.000Z

2022-03-06T22:44:42.000Z

Tools/Scripts/webkitpy/tool/mocktool.py

jacadcaps/webkitty

9aebd2081349f9a7b5d168673c6f676a1450a66d

[ "BSD-2-Clause" ]

7

2022-03-15T13:25:39.000Z

2022-03-15T13:25:44.000Z

Tools/Scripts/webkitpy/tool/mocktool.py

jacadcaps/webkitty

9aebd2081349f9a7b5d168673c6f676a1450a66d

[ "BSD-2-Clause" ]

null

# Copyright (C) 2011 Google Inc. All rights reserved. # Copyright (C) 2019 Apple Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following disclaimer # in the documentation and/or other materials provided with the # distribution. # * Neither the name of Google Inc. nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import threading from webkitpy.common.host_mock import MockHost from webkitpy.common.net.buildbot.buildbot_mock import MockBuildBot from webkitpy.common.net.ewsserver_mock import MockEWSServer from webkitpy.common.net.irc.irc_mock import MockIRC # FIXME: Old-style "Ports" need to die and be replaced by modern layout_tests.port which needs to move to common. from webkitpy.common.config.ports_mock import MockPort # FIXME: We should just replace this with optparse.Values(default=kwargs) class MockOptions(object): """Mock implementation of optparse.Values.""" def __init__(self, **kwargs): # The caller can set option values using keyword arguments. We don't # set any values by default because we don't know how this # object will be used. Generally speaking unit tests should # subclass this or provider wrapper functions that set a common # set of options. self.update(**kwargs) def update(self, **kwargs): self.__dict__.update(**kwargs) return self def ensure_value(self, key, value): if getattr(self, key, None) == None: self.__dict__[key] = value return self.__dict__[key] # FIXME: This should be renamed MockWebKitPatch. class MockTool(MockHost): def __init__(self, *args, **kwargs): MockHost.__init__(self, *args, **kwargs) self._deprecated_port = MockPort() self.ews_server = MockEWSServer() self._irc = None self.irc_password = "MOCK irc password" self.wakeup_event = threading.Event() def deprecated_port(self): return self._deprecated_port def path(self): return "echo" def ensure_irc_connected(self, delegate): if not self._irc: self._irc = MockIRC() def irc(self): return self._irc

39.229885

113

0.730735

import threading from webkitpy.common.host_mock import MockHost from webkitpy.common.net.buildbot.buildbot_mock import MockBuildBot from webkitpy.common.net.ewsserver_mock import MockEWSServer from webkitpy.common.net.irc.irc_mock import MockIRC from webkitpy.common.config.ports_mock import MockPort class MockOptions(object): def __init__(self, **kwargs): # set any values by default because we don't know how this self.update(**kwargs) def update(self, **kwargs): self.__dict__.update(**kwargs) return self def ensure_value(self, key, value): if getattr(self, key, None) == None: self.__dict__[key] = value return self.__dict__[key] class MockTool(MockHost): def __init__(self, *args, **kwargs): MockHost.__init__(self, *args, **kwargs) self._deprecated_port = MockPort() self.ews_server = MockEWSServer() self._irc = None self.irc_password = "MOCK irc password" self.wakeup_event = threading.Event() def deprecated_port(self): return self._deprecated_port def path(self): return "echo" def ensure_irc_connected(self, delegate): if not self._irc: self._irc = MockIRC() def irc(self): return self._irc

true

790ddf754117b4c22fe3e2bc8015b1a7f07d3419

1,107

py

Python

pypy/rlib/rsdl/test/test_basic.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

1

2019-05-27T00:58:46.000Z

pypy/rlib/rsdl/test/test_basic.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

null

pypy/rlib/rsdl/test/test_basic.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

null

import py from pypy.rlib.rsdl import RSDL from pypy.rlib.rarithmetic import r_uint from pypy.rpython.lltypesystem import rffi def test_sdl_init(): assert RSDL.Init(RSDL.INIT_VIDEO) >= 0 RSDL.Quit() def test_surface_basic(): assert RSDL.Init(RSDL.INIT_VIDEO) >= 0 surface = RSDL.CreateRGBSurface(0, 150, 50, 32, r_uint(0x000000FF), r_uint(0x0000FF00), r_uint(0x00FF0000), r_uint(0xFF000000)) assert surface assert rffi.getintfield(surface, 'c_w') == 150 assert rffi.getintfield(surface, 'c_h') == 50 RSDL.FreeSurface(surface) RSDL.Quit() def test_get_keyname(): assert RSDL.Init(RSDL.INIT_VIDEO) >= 0 assert RSDL.GetKeyName(RSDL.K_PLUS)[0] == '+' assert RSDL.GetKeyName(RSDL.K_RIGHTPAREN)[0] == ')' assert RSDL.GetKeyName(RSDL.K_z)[0] == 'z' def test_delay_getticks(): assert RSDL.Init(RSDL.INIT_VIDEO) >= 0 RSDL.Delay(10) i = RSDL.GetTicks() assert i >= 10 RSDL.Quit()

29.918919

55

0.591689

import py from pypy.rlib.rsdl import RSDL from pypy.rlib.rarithmetic import r_uint from pypy.rpython.lltypesystem import rffi def test_sdl_init(): assert RSDL.Init(RSDL.INIT_VIDEO) >= 0 RSDL.Quit() def test_surface_basic(): assert RSDL.Init(RSDL.INIT_VIDEO) >= 0 surface = RSDL.CreateRGBSurface(0, 150, 50, 32, r_uint(0x000000FF), r_uint(0x0000FF00), r_uint(0x00FF0000), r_uint(0xFF000000)) assert surface assert rffi.getintfield(surface, 'c_w') == 150 assert rffi.getintfield(surface, 'c_h') == 50 RSDL.FreeSurface(surface) RSDL.Quit() def test_get_keyname(): assert RSDL.Init(RSDL.INIT_VIDEO) >= 0 assert RSDL.GetKeyName(RSDL.K_PLUS)[0] == '+' assert RSDL.GetKeyName(RSDL.K_RIGHTPAREN)[0] == ')' assert RSDL.GetKeyName(RSDL.K_z)[0] == 'z' def test_delay_getticks(): assert RSDL.Init(RSDL.INIT_VIDEO) >= 0 RSDL.Delay(10) i = RSDL.GetTicks() assert i >= 10 RSDL.Quit()

true

790ddfee99fe92fd2db69b46c73d0d1385a07ab0

3,219

py

Python

build/lib/crowdkit/aggregation/base/__init__.py

artinmajdi/crowd-kit

174e15f256a4929ed71699ffc1797ea87e0e8a99

[ "Apache-2.0" ]

null

build/lib/crowdkit/aggregation/base/__init__.py

artinmajdi/crowd-kit

174e15f256a4929ed71699ffc1797ea87e0e8a99

[ "Apache-2.0" ]

null

build/lib/crowdkit/aggregation/base/__init__.py

artinmajdi/crowd-kit

174e15f256a4929ed71699ffc1797ea87e0e8a99

[ "Apache-2.0" ]

1

2021-12-24T02:26:57.000Z

__all__ = [ 'BaseClassificationAggregator', 'BaseImageSegmentationAggregator', 'BaseEmbeddingsAggregator', 'BaseTextsAggregator', 'BasePairwiseAggregator', ] import attr from .. import annotations @attr.s @annotations.manage_docstring class BaseClassificationAggregator: """ This is a base class for all classification aggregators""" labels_: annotations.OPTIONAL_LABELS = attr.ib(init=False) @annotations.manage_docstring def fit(self, data: annotations.LABELED_DATA) -> annotations.Annotation(type='BaseClassificationAggregator', title='self'): raise NotImplementedError() @annotations.manage_docstring def fit_predict(self, data: annotations.LABELED_DATA) -> annotations.TASKS_LABELS: raise NotImplementedError() @attr.s @annotations.manage_docstring class BaseImageSegmentationAggregator: """This is a base class for all image segmentation aggregators""" segmentations_: annotations.TASKS_SEGMENTATIONS = attr.ib(init=False) @annotations.manage_docstring def fit(self, data: annotations.SEGMENTATION_DATA) -> annotations.Annotation(type='BaseImageSegmentationAggregator', title='self'): raise NotImplementedError() @annotations.manage_docstring def fit_predict(self, data: annotations.SEGMENTATION_DATA) -> annotations.TASKS_SEGMENTATIONS: raise NotImplementedError() @attr.s @annotations.manage_docstring class BaseEmbeddingsAggregator: """This is a base class for all embeddings aggregators""" embeddings_and_outputs_: annotations.TASKS_EMBEDDINGS_AND_OUTPUTS = attr.ib(init=False) @annotations.manage_docstring def fit(self, data: annotations.EMBEDDED_DATA) -> annotations.Annotation(type='BaseEmbeddingsAggregator', title='self'): raise NotImplementedError() @annotations.manage_docstring def fit_predict(self, data: annotations.EMBEDDED_DATA) -> annotations.TASKS_EMBEDDINGS_AND_OUTPUTS: raise NotImplementedError() @attr.s @annotations.manage_docstring class BaseTextsAggregator: """ This is a base class for all texts aggregators""" texts_: annotations.TASKS_TEXTS = attr.ib(init=False) @annotations.manage_docstring def fit(self, data: annotations.TEXT_DATA) -> annotations.Annotation(type='BaseTextsAggregator', title='self'): raise NotImplementedError() @annotations.manage_docstring def fit_predict(self, data: annotations.TEXT_DATA) -> annotations.TASKS_TEXTS: raise NotImplementedError() @attr.s @annotations.manage_docstring class BasePairwiseAggregator: """ This is a base class for all pairwise comparison aggregators""" scores_: annotations.LABEL_SCORES = attr.ib(init=False) @annotations.manage_docstring def fit(self, data: annotations.PAIRWISE_DATA) -> annotations.Annotation(type='BasePairwiseAggregator', title='self'): raise NotImplementedError() @annotations.manage_docstring def fit_predict(self, data: annotations.PAIRWISE_DATA) -> annotations.LABEL_SCORES: raise NotImplementedError()

34.612903

124

0.724138

__all__ = [ 'BaseClassificationAggregator', 'BaseImageSegmentationAggregator', 'BaseEmbeddingsAggregator', 'BaseTextsAggregator', 'BasePairwiseAggregator', ] import attr from .. import annotations @attr.s @annotations.manage_docstring class BaseClassificationAggregator: labels_: annotations.OPTIONAL_LABELS = attr.ib(init=False) @annotations.manage_docstring def fit(self, data: annotations.LABELED_DATA) -> annotations.Annotation(type='BaseClassificationAggregator', title='self'): raise NotImplementedError() @annotations.manage_docstring def fit_predict(self, data: annotations.LABELED_DATA) -> annotations.TASKS_LABELS: raise NotImplementedError() @attr.s @annotations.manage_docstring class BaseImageSegmentationAggregator: segmentations_: annotations.TASKS_SEGMENTATIONS = attr.ib(init=False) @annotations.manage_docstring def fit(self, data: annotations.SEGMENTATION_DATA) -> annotations.Annotation(type='BaseImageSegmentationAggregator', title='self'): raise NotImplementedError() @annotations.manage_docstring def fit_predict(self, data: annotations.SEGMENTATION_DATA) -> annotations.TASKS_SEGMENTATIONS: raise NotImplementedError() @attr.s @annotations.manage_docstring class BaseEmbeddingsAggregator: embeddings_and_outputs_: annotations.TASKS_EMBEDDINGS_AND_OUTPUTS = attr.ib(init=False) @annotations.manage_docstring def fit(self, data: annotations.EMBEDDED_DATA) -> annotations.Annotation(type='BaseEmbeddingsAggregator', title='self'): raise NotImplementedError() @annotations.manage_docstring def fit_predict(self, data: annotations.EMBEDDED_DATA) -> annotations.TASKS_EMBEDDINGS_AND_OUTPUTS: raise NotImplementedError() @attr.s @annotations.manage_docstring class BaseTextsAggregator: texts_: annotations.TASKS_TEXTS = attr.ib(init=False) @annotations.manage_docstring def fit(self, data: annotations.TEXT_DATA) -> annotations.Annotation(type='BaseTextsAggregator', title='self'): raise NotImplementedError() @annotations.manage_docstring def fit_predict(self, data: annotations.TEXT_DATA) -> annotations.TASKS_TEXTS: raise NotImplementedError() @attr.s @annotations.manage_docstring class BasePairwiseAggregator: scores_: annotations.LABEL_SCORES = attr.ib(init=False) @annotations.manage_docstring def fit(self, data: annotations.PAIRWISE_DATA) -> annotations.Annotation(type='BasePairwiseAggregator', title='self'): raise NotImplementedError() @annotations.manage_docstring def fit_predict(self, data: annotations.PAIRWISE_DATA) -> annotations.LABEL_SCORES: raise NotImplementedError()

true

790de0b9bf41aabb534f397b8543e51a0f9d7132

7,299

py

Python

python/pyspark/taskcontext.py

zhouyuan/sparkV

688e2a5850c66084d592855b4ca345baeaeabee3

[ "Apache-2.0" ]

6

2020-06-28T08:23:22.000Z

2021-12-25T07:25:32.000Z

python/pyspark/taskcontext.py

zhouyuan/sparkV

688e2a5850c66084d592855b4ca345baeaeabee3

[ "Apache-2.0" ]

4

2019-11-14T13:25:17.000Z

2021-01-21T00:08:25.000Z

python/pyspark/taskcontext.py

zhouyuan/sparkV

688e2a5850c66084d592855b4ca345baeaeabee3

[ "Apache-2.0" ]

4

2020-06-28T08:23:33.000Z

2021-08-04T07:24:45.000Z

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import print_function from pyspark.java_gateway import local_connect_and_auth from pyspark.serializers import write_int, UTF8Deserializer class TaskContext(object): """ .. note:: Experimental Contextual information about a task which can be read or mutated during execution. To access the TaskContext for a running task, use: :meth:`TaskContext.get`. """ _taskContext = None _attemptNumber = None _partitionId = None _stageId = None _taskAttemptId = None _localProperties = None _resources = None def __new__(cls): """Even if users construct TaskContext instead of using get, give them the singleton.""" taskContext = cls._taskContext if taskContext is not None: return taskContext cls._taskContext = taskContext = object.__new__(cls) return taskContext @classmethod def _getOrCreate(cls): """Internal function to get or create global TaskContext.""" if cls._taskContext is None: cls._taskContext = TaskContext() return cls._taskContext @classmethod def get(cls): """ Return the currently active TaskContext. This can be called inside of user functions to access contextual information about running tasks. .. note:: Must be called on the worker, not the driver. Returns None if not initialized. """ return cls._taskContext def stageId(self): """The ID of the stage that this task belong to.""" return self._stageId def partitionId(self): """ The ID of the RDD partition that is computed by this task. """ return self._partitionId def attemptNumber(self): """" How many times this task has been attempted. The first task attempt will be assigned attemptNumber = 0, and subsequent attempts will have increasing attempt numbers. """ return self._attemptNumber def taskAttemptId(self): """ An ID that is unique to this task attempt (within the same SparkContext, no two task attempts will share the same attempt ID). This is roughly equivalent to Hadoop's TaskAttemptID. """ return self._taskAttemptId def getLocalProperty(self, key): """ Get a local property set upstream in the driver, or None if it is missing. """ return self._localProperties.get(key, None) def resources(self): """ Resources allocated to the task. The key is the resource name and the value is information about the resource. """ return self._resources BARRIER_FUNCTION = 1 def _load_from_socket(port, auth_secret): """ Load data from a given socket, this is a blocking method thus only return when the socket connection has been closed. """ (sockfile, sock) = local_connect_and_auth(port, auth_secret) # The barrier() call may block forever, so no timeout sock.settimeout(None) # Make a barrier() function call. write_int(BARRIER_FUNCTION, sockfile) sockfile.flush() # Collect result. res = UTF8Deserializer().loads(sockfile) # Release resources. sockfile.close() sock.close() return res class BarrierTaskContext(TaskContext): """ .. note:: Experimental A :class:`TaskContext` with extra contextual info and tooling for tasks in a barrier stage. Use :func:`BarrierTaskContext.get` to obtain the barrier context for a running barrier task. .. versionadded:: 2.4.0 """ _port = None _secret = None @classmethod def _getOrCreate(cls): """ Internal function to get or create global BarrierTaskContext. We need to make sure BarrierTaskContext is returned from here because it is needed in python worker reuse scenario, see SPARK-25921 for more details. """ if not isinstance(cls._taskContext, BarrierTaskContext): cls._taskContext = object.__new__(cls) return cls._taskContext @classmethod def get(cls): """ .. note:: Experimental Return the currently active :class:`BarrierTaskContext`. This can be called inside of user functions to access contextual information about running tasks. .. note:: Must be called on the worker, not the driver. Returns None if not initialized. """ return cls._taskContext @classmethod def _initialize(cls, port, secret): """ Initialize BarrierTaskContext, other methods within BarrierTaskContext can only be called after BarrierTaskContext is initialized. """ cls._port = port cls._secret = secret def barrier(self): """ .. note:: Experimental Sets a global barrier and waits until all tasks in this stage hit this barrier. Similar to `MPI_Barrier` function in MPI, this function blocks until all tasks in the same stage have reached this routine. .. warning:: In a barrier stage, each task much have the same number of `barrier()` calls, in all possible code branches. Otherwise, you may get the job hanging or a SparkException after timeout. .. versionadded:: 2.4.0 """ if self._port is None or self._secret is None: raise Exception("Not supported to call barrier() before initialize " + "BarrierTaskContext.") else: _load_from_socket(self._port, self._secret) def getTaskInfos(self): """ .. note:: Experimental Returns :class:`BarrierTaskInfo` for all tasks in this barrier stage, ordered by partition ID. .. versionadded:: 2.4.0 """ if self._port is None or self._secret is None: raise Exception("Not supported to call getTaskInfos() before initialize " + "BarrierTaskContext.") else: addresses = self._localProperties.get("addresses", "") return [BarrierTaskInfo(h.strip()) for h in addresses.split(",")] class BarrierTaskInfo(object): """ .. note:: Experimental Carries all task infos of a barrier task. :var address: The IPv4 address (host:port) of the executor that the barrier task is running on .. versionadded:: 2.4.0 """ def __init__(self, address): self.address = address

31.873362

98

0.65735

from __future__ import print_function from pyspark.java_gateway import local_connect_and_auth from pyspark.serializers import write_int, UTF8Deserializer class TaskContext(object): _taskContext = None _attemptNumber = None _partitionId = None _stageId = None _taskAttemptId = None _localProperties = None _resources = None def __new__(cls): taskContext = cls._taskContext if taskContext is not None: return taskContext cls._taskContext = taskContext = object.__new__(cls) return taskContext @classmethod def _getOrCreate(cls): if cls._taskContext is None: cls._taskContext = TaskContext() return cls._taskContext @classmethod def get(cls): return cls._taskContext def stageId(self): return self._stageId def partitionId(self): return self._partitionId def attemptNumber(self): return self._attemptNumber def taskAttemptId(self): return self._taskAttemptId def getLocalProperty(self, key): return self._localProperties.get(key, None) def resources(self): return self._resources BARRIER_FUNCTION = 1 def _load_from_socket(port, auth_secret): (sockfile, sock) = local_connect_and_auth(port, auth_secret) sock.settimeout(None) write_int(BARRIER_FUNCTION, sockfile) sockfile.flush() res = UTF8Deserializer().loads(sockfile) sockfile.close() sock.close() return res class BarrierTaskContext(TaskContext): _port = None _secret = None @classmethod def _getOrCreate(cls): if not isinstance(cls._taskContext, BarrierTaskContext): cls._taskContext = object.__new__(cls) return cls._taskContext @classmethod def get(cls): return cls._taskContext @classmethod def _initialize(cls, port, secret): cls._port = port cls._secret = secret def barrier(self): if self._port is None or self._secret is None: raise Exception("Not supported to call barrier() before initialize " + "BarrierTaskContext.") else: _load_from_socket(self._port, self._secret) def getTaskInfos(self): if self._port is None or self._secret is None: raise Exception("Not supported to call getTaskInfos() before initialize " + "BarrierTaskContext.") else: addresses = self._localProperties.get("addresses", "") return [BarrierTaskInfo(h.strip()) for h in addresses.split(",")] class BarrierTaskInfo(object): def __init__(self, address): self.address = address

true

790de0d7084706fafc0cf001bb4f424782073242

4,045

py

Python

userbot/plugins/fconvert.py

anandhu-dev/catuserbot

0ae10db978c1a9bf3f4f0da991a86d85fc29c0f1

[ "MIT" ]

null

userbot/plugins/fconvert.py

anandhu-dev/catuserbot

0ae10db978c1a9bf3f4f0da991a86d85fc29c0f1

[ "MIT" ]

null

userbot/plugins/fconvert.py

anandhu-dev/catuserbot

0ae10db978c1a9bf3f4f0da991a86d85fc29c0f1

[ "MIT" ]

null

"""File Converter .nfc """ import asyncio import os import time from datetime import datetime from userbot.utils import admin_cmd, progress @borg.on(admin_cmd(pattern="nfc (.*)")) # pylint:disable=E0602 async def _(event): if event.fwd_from: return input_str = event.pattern_match.group(1) reply_message = await event.get_reply_message() if reply_message is None: await event.edit("reply to a media to use the `nfc` operation.\nInspired by @FileConverterBot") return await event.edit("trying to download media file, to my local") try: start = datetime.now() c_time = time.time() downloaded_file_name = await borg.download_media( reply_message, Config.TMP_DOWNLOAD_DIRECTORY, progress_callback=lambda d, t: asyncio.get_event_loop().create_task( progress(d, t, event, c_time, "trying to download") ) ) except Exception as e: # pylint:disable=C0103,W0703 await event.edit(str(e)) else: end = datetime.now() ms = (end - start).seconds await event.edit("Downloaded to `{}` in {} seconds.".format(downloaded_file_name, ms)) new_required_file_name = "" new_required_file_caption = "" command_to_run = [] force_document = False voice_note = False supports_streaming = False if input_str == "voice": new_required_file_caption = "NLFC_" + str(round(time.time())) + ".opus" new_required_file_name = Config.TMP_DOWNLOAD_DIRECTORY + "/" + new_required_file_caption command_to_run = [ "ffmpeg", "-i", downloaded_file_name, "-map", "0:a", "-codec:a", "libopus", "-b:a", "100k", "-vbr", "on", new_required_file_name ] voice_note = True supports_streaming = True elif input_str == "mp3": new_required_file_caption = "NLFC_" + str(round(time.time())) + ".mp3" new_required_file_name = Config.TMP_DOWNLOAD_DIRECTORY + "/" + new_required_file_caption command_to_run = [ "ffmpeg", "-i", downloaded_file_name, "-vn", new_required_file_name ] voice_note = False supports_streaming = True else: await event.edit("not supported") os.remove(downloaded_file_name) return logger.info(command_to_run) # TODO: re-write create_subprocess_exec 😉 process = await asyncio.create_subprocess_exec( *command_to_run, # stdout must a pipe to be accessible as process.stdout stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE, ) # Wait for the subprocess to finish stdout, stderr = await process.communicate() e_response = stderr.decode().strip() t_response = stdout.decode().strip() os.remove(downloaded_file_name) if os.path.exists(new_required_file_name): end_two = datetime.now() await borg.send_file( entity=event.chat_id, file=new_required_file_name, caption="`File Successfully converted by` @kannappan04", allow_cache=False, silent=True, force_document=force_document, voice_note=voice_note, supports_streaming=supports_streaming, progress_callback=lambda d, t: asyncio.get_event_loop().create_task( progress(d, t, event, c_time, "trying to upload") ) ) ms_two = (end_two - end).seconds os.remove(new_required_file_name) await event.edit(f"converted in {ms_two} seconds")

37.110092

103

0.5644

import asyncio import os import time from datetime import datetime from userbot.utils import admin_cmd, progress @borg.on(admin_cmd(pattern="nfc (.*)")) async def _(event): if event.fwd_from: return input_str = event.pattern_match.group(1) reply_message = await event.get_reply_message() if reply_message is None: await event.edit("reply to a media to use the `nfc` operation.\nInspired by @FileConverterBot") return await event.edit("trying to download media file, to my local") try: start = datetime.now() c_time = time.time() downloaded_file_name = await borg.download_media( reply_message, Config.TMP_DOWNLOAD_DIRECTORY, progress_callback=lambda d, t: asyncio.get_event_loop().create_task( progress(d, t, event, c_time, "trying to download") ) ) except Exception as e: await event.edit(str(e)) else: end = datetime.now() ms = (end - start).seconds await event.edit("Downloaded to `{}` in {} seconds.".format(downloaded_file_name, ms)) new_required_file_name = "" new_required_file_caption = "" command_to_run = [] force_document = False voice_note = False supports_streaming = False if input_str == "voice": new_required_file_caption = "NLFC_" + str(round(time.time())) + ".opus" new_required_file_name = Config.TMP_DOWNLOAD_DIRECTORY + "/" + new_required_file_caption command_to_run = [ "ffmpeg", "-i", downloaded_file_name, "-map", "0:a", "-codec:a", "libopus", "-b:a", "100k", "-vbr", "on", new_required_file_name ] voice_note = True supports_streaming = True elif input_str == "mp3": new_required_file_caption = "NLFC_" + str(round(time.time())) + ".mp3" new_required_file_name = Config.TMP_DOWNLOAD_DIRECTORY + "/" + new_required_file_caption command_to_run = [ "ffmpeg", "-i", downloaded_file_name, "-vn", new_required_file_name ] voice_note = False supports_streaming = True else: await event.edit("not supported") os.remove(downloaded_file_name) return logger.info(command_to_run) process = await asyncio.create_subprocess_exec( *command_to_run, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE, ) stdout, stderr = await process.communicate() e_response = stderr.decode().strip() t_response = stdout.decode().strip() os.remove(downloaded_file_name) if os.path.exists(new_required_file_name): end_two = datetime.now() await borg.send_file( entity=event.chat_id, file=new_required_file_name, caption="`File Successfully converted by` @kannappan04", allow_cache=False, silent=True, force_document=force_document, voice_note=voice_note, supports_streaming=supports_streaming, progress_callback=lambda d, t: asyncio.get_event_loop().create_task( progress(d, t, event, c_time, "trying to upload") ) ) ms_two = (end_two - end).seconds os.remove(new_required_file_name) await event.edit(f"converted in {ms_two} seconds")

true

790de0eafb238d094bd686b29f075e37c87c9f95

3,638

py

Python

tests/test_buffer.py

xcgx/streamlink

b635e0d9d0fe9363817a96ec7d31faefed95cb57

[ "BSD-2-Clause" ]

10

2017-04-10T18:25:41.000Z

2021-09-15T20:14:58.000Z

tests/test_buffer.py

xcgx/streamlink

b635e0d9d0fe9363817a96ec7d31faefed95cb57

[ "BSD-2-Clause" ]

9

2020-04-04T09:49:52.000Z

2020-04-21T01:52:02.000Z

tests/test_buffer.py

xcgx/streamlink

b635e0d9d0fe9363817a96ec7d31faefed95cb57

[ "BSD-2-Clause" ]

12

2022-01-30T23:34:18.000Z

2022-03-26T17:09:43.000Z

import unittest from streamlink.buffers import Buffer, RingBuffer class TestBuffer(unittest.TestCase): def setUp(self): self.buffer = Buffer() def test_write(self): self.buffer.write(b"1" * 8192) self.buffer.write(b"2" * 4096) self.assertEqual(self.buffer.length, 8192 + 4096) def test_read(self): self.buffer.write(b"1" * 8192) self.buffer.write(b"2" * 4096) self.assertEqual(self.buffer.length, 8192 + 4096) self.assertEqual(self.buffer.read(4096), b"1" * 4096) self.assertEqual(self.buffer.read(4096), b"1" * 4096) self.assertEqual(self.buffer.read(), b"2" * 4096) self.assertEqual(self.buffer.read(4096), b"") self.assertEqual(self.buffer.read(), b"") self.assertEqual(self.buffer.length, 0) def test_readwrite(self): self.buffer.write(b"1" * 8192) self.assertEqual(self.buffer.length, 8192) self.assertEqual(self.buffer.read(4096), b"1" * 4096) self.assertEqual(self.buffer.length, 4096) self.buffer.write(b"2" * 4096) self.assertEqual(self.buffer.length, 8192) self.assertEqual(self.buffer.read(1), b"1") self.assertEqual(self.buffer.read(4095), b"1" * 4095) self.assertEqual(self.buffer.read(8192), b"2" * 4096) self.assertEqual(self.buffer.read(8192), b"") self.assertEqual(self.buffer.read(), b"") self.assertEqual(self.buffer.length, 0) def test_close(self): self.buffer.write(b"1" * 8192) self.assertEqual(self.buffer.length, 8192) self.buffer.close() self.buffer.write(b"2" * 8192) self.assertEqual(self.buffer.length, 8192) def test_reuse_input(self): """Objects should be reusable after write()""" original = b"original" tests = [bytearray(original), memoryview(bytearray(original))] for data in tests: self.buffer.write(data) data[:] = b"reused!!" self.assertEqual(self.buffer.read(), original) def test_read_empty(self): self.assertRaises( StopIteration, lambda: next(self.buffer._iterate_chunks(10))) class TestRingBuffer(unittest.TestCase): BUFFER_SIZE = 8192 * 4 def setUp(self): self.buffer = RingBuffer(size=self.BUFFER_SIZE) def test_write(self): self.buffer.write(b"1" * 8192) self.buffer.write(b"2" * 4096) self.assertEqual(self.buffer.length, 8192 + 4096) def test_read(self): self.buffer.write(b"1" * 8192) self.buffer.write(b"2" * 4096) self.assertEqual(self.buffer.length, 8192 + 4096) self.assertEqual(self.buffer.read(4096), b"1" * 4096) self.assertEqual(self.buffer.read(4096), b"1" * 4096) self.assertEqual(self.buffer.read(), b"2" * 4096) self.assertEqual(self.buffer.length, 0) def test_read_timeout(self): self.assertRaises( IOError, self.buffer.read, timeout=0.1) def test_write_after_close(self): self.buffer.close() self.buffer.write(b"1" * 8192) self.assertEqual(self.buffer.length, 0) self.assertTrue(self.buffer.closed) def test_resize(self): self.assertEqual(self.buffer.buffer_size, self.BUFFER_SIZE) self.buffer.resize(self.BUFFER_SIZE * 2) self.assertEqual(self.buffer.buffer_size, self.BUFFER_SIZE * 2) def test_free(self): self.assertEqual(self.buffer.free, self.BUFFER_SIZE) self.buffer.write(b'1' * 100) self.assertEqual(self.buffer.free, self.BUFFER_SIZE - 100)

33.072727

71

0.628642

import unittest from streamlink.buffers import Buffer, RingBuffer class TestBuffer(unittest.TestCase): def setUp(self): self.buffer = Buffer() def test_write(self): self.buffer.write(b"1" * 8192) self.buffer.write(b"2" * 4096) self.assertEqual(self.buffer.length, 8192 + 4096) def test_read(self): self.buffer.write(b"1" * 8192) self.buffer.write(b"2" * 4096) self.assertEqual(self.buffer.length, 8192 + 4096) self.assertEqual(self.buffer.read(4096), b"1" * 4096) self.assertEqual(self.buffer.read(4096), b"1" * 4096) self.assertEqual(self.buffer.read(), b"2" * 4096) self.assertEqual(self.buffer.read(4096), b"") self.assertEqual(self.buffer.read(), b"") self.assertEqual(self.buffer.length, 0) def test_readwrite(self): self.buffer.write(b"1" * 8192) self.assertEqual(self.buffer.length, 8192) self.assertEqual(self.buffer.read(4096), b"1" * 4096) self.assertEqual(self.buffer.length, 4096) self.buffer.write(b"2" * 4096) self.assertEqual(self.buffer.length, 8192) self.assertEqual(self.buffer.read(1), b"1") self.assertEqual(self.buffer.read(4095), b"1" * 4095) self.assertEqual(self.buffer.read(8192), b"2" * 4096) self.assertEqual(self.buffer.read(8192), b"") self.assertEqual(self.buffer.read(), b"") self.assertEqual(self.buffer.length, 0) def test_close(self): self.buffer.write(b"1" * 8192) self.assertEqual(self.buffer.length, 8192) self.buffer.close() self.buffer.write(b"2" * 8192) self.assertEqual(self.buffer.length, 8192) def test_reuse_input(self): original = b"original" tests = [bytearray(original), memoryview(bytearray(original))] for data in tests: self.buffer.write(data) data[:] = b"reused!!" self.assertEqual(self.buffer.read(), original) def test_read_empty(self): self.assertRaises( StopIteration, lambda: next(self.buffer._iterate_chunks(10))) class TestRingBuffer(unittest.TestCase): BUFFER_SIZE = 8192 * 4 def setUp(self): self.buffer = RingBuffer(size=self.BUFFER_SIZE) def test_write(self): self.buffer.write(b"1" * 8192) self.buffer.write(b"2" * 4096) self.assertEqual(self.buffer.length, 8192 + 4096) def test_read(self): self.buffer.write(b"1" * 8192) self.buffer.write(b"2" * 4096) self.assertEqual(self.buffer.length, 8192 + 4096) self.assertEqual(self.buffer.read(4096), b"1" * 4096) self.assertEqual(self.buffer.read(4096), b"1" * 4096) self.assertEqual(self.buffer.read(), b"2" * 4096) self.assertEqual(self.buffer.length, 0) def test_read_timeout(self): self.assertRaises( IOError, self.buffer.read, timeout=0.1) def test_write_after_close(self): self.buffer.close() self.buffer.write(b"1" * 8192) self.assertEqual(self.buffer.length, 0) self.assertTrue(self.buffer.closed) def test_resize(self): self.assertEqual(self.buffer.buffer_size, self.BUFFER_SIZE) self.buffer.resize(self.BUFFER_SIZE * 2) self.assertEqual(self.buffer.buffer_size, self.BUFFER_SIZE * 2) def test_free(self): self.assertEqual(self.buffer.free, self.BUFFER_SIZE) self.buffer.write(b'1' * 100) self.assertEqual(self.buffer.free, self.BUFFER_SIZE - 100)

true

790de139870f747ae341b9866271e007ce38a944

3,093

py

Python

dakara_server/users/tests/test_backends.py

DakaraProject/dakara-server

b28fc1a8561e431d562102932f3d6ff3607e545b

[ "MIT" ]

4

2018-07-24T18:22:16.000Z

2020-01-24T16:30:54.000Z

dakara_server/users/tests/test_backends.py

DakaraProject/dakara-server

b28fc1a8561e431d562102932f3d6ff3607e545b

[ "MIT" ]

88

2017-11-04T08:58:02.000Z

2022-03-30T11:39:08.000Z

dakara_server/users/tests/test_backends.py

DakaraProject/dakara-server

b28fc1a8561e431d562102932f3d6ff3607e545b

[ "MIT" ]

1

2018-05-05T15:37:20.000Z

from unittest.mock import MagicMock from django.core.exceptions import ValidationError from users.backends import DakaraModelBackend from users.tests.base_test import UsersAPITestCase, config_email_disabled class DakaraModelBackendTestCase(UsersAPITestCase): """Test the authentication backend.""" def setUp(self): # create a user without any rights self.user = self.create_user("TestUser", email="test@user.com", password="pass") def test_authenticate_username_superuser(self): """Test to authenticate as superuser.""" self.user.is_superuser = True self.user.validated_by_email = False self.user.validated_by_manager = False self.user.save() backend = DakaraModelBackend() self.assertEqual( backend.authenticate(MagicMock(), username="TestUser", password="pass"), self.user, ) def test_authenticate_username_not_active(self): """Test to authenticate an inactive user.""" self.user.is_active = False self.user.save() backend = DakaraModelBackend() self.assertIsNone( backend.authenticate(MagicMock(), username="TestUser", password="pass"), ) def test_authenticate_username_not_validated_by_email(self): """Test to authenticate when not validated by email.""" self.user.validated_by_email = False self.user.validated_by_manager = True self.user.save() backend = DakaraModelBackend() with self.assertRaisesRegex( ValidationError, "This user email has not been validated" ): backend.authenticate(MagicMock(), username="TestUser", password="pass") @config_email_disabled def test_authenticate_username_not_validated_by_email_no_email(self): """Test to authenticate when not validated by email and emails disabled.""" self.user.validated_by_email = False self.user.validated_by_manager = True self.user.save() backend = DakaraModelBackend() self.assertEqual( backend.authenticate(MagicMock(), username="TestUser", password="pass"), self.user, ) def test_authenticate_username_not_validated_by_manager(self): """Test to authenticate when not validated by manager.""" self.user.validated_by_email = True self.user.validated_by_manager = False self.user.save() backend = DakaraModelBackend() with self.assertRaisesRegex( ValidationError, "This user account has not been validated by a manager" ): backend.authenticate(MagicMock(), username="TestUser", password="pass") def test_authenticate_username_ok(self): """Test to authenticate.""" self.user.validated_by_email = True self.user.validated_by_manager = True self.user.save() backend = DakaraModelBackend() self.assertEqual( backend.authenticate(MagicMock(), username="TestUser", password="pass"), self.user, )

35.551724

88

0.66602

from unittest.mock import MagicMock from django.core.exceptions import ValidationError from users.backends import DakaraModelBackend from users.tests.base_test import UsersAPITestCase, config_email_disabled class DakaraModelBackendTestCase(UsersAPITestCase): def setUp(self): self.user = self.create_user("TestUser", email="test@user.com", password="pass") def test_authenticate_username_superuser(self): self.user.is_superuser = True self.user.validated_by_email = False self.user.validated_by_manager = False self.user.save() backend = DakaraModelBackend() self.assertEqual( backend.authenticate(MagicMock(), username="TestUser", password="pass"), self.user, ) def test_authenticate_username_not_active(self): self.user.is_active = False self.user.save() backend = DakaraModelBackend() self.assertIsNone( backend.authenticate(MagicMock(), username="TestUser", password="pass"), ) def test_authenticate_username_not_validated_by_email(self): self.user.validated_by_email = False self.user.validated_by_manager = True self.user.save() backend = DakaraModelBackend() with self.assertRaisesRegex( ValidationError, "This user email has not been validated" ): backend.authenticate(MagicMock(), username="TestUser", password="pass") @config_email_disabled def test_authenticate_username_not_validated_by_email_no_email(self): self.user.validated_by_email = False self.user.validated_by_manager = True self.user.save() backend = DakaraModelBackend() self.assertEqual( backend.authenticate(MagicMock(), username="TestUser", password="pass"), self.user, ) def test_authenticate_username_not_validated_by_manager(self): self.user.validated_by_email = True self.user.validated_by_manager = False self.user.save() backend = DakaraModelBackend() with self.assertRaisesRegex( ValidationError, "This user account has not been validated by a manager" ): backend.authenticate(MagicMock(), username="TestUser", password="pass") def test_authenticate_username_ok(self): self.user.validated_by_email = True self.user.validated_by_manager = True self.user.save() backend = DakaraModelBackend() self.assertEqual( backend.authenticate(MagicMock(), username="TestUser", password="pass"), self.user, )

true

790de37a120d52978b54761134121afb95f0f831

7,659

py

Python

yassd/testing_utils/videotest.py

hanhejia/SSD

0c5684ad786768b46b119fb503f4f7174e2c78ed

[ "MIT" ]

null

yassd/testing_utils/videotest.py

hanhejia/SSD

0c5684ad786768b46b119fb503f4f7174e2c78ed

[ "MIT" ]

null

yassd/testing_utils/videotest.py

hanhejia/SSD

0c5684ad786768b46b119fb503f4f7174e2c78ed

[ "MIT" ]

null

""" A class for testing a SSD model on a video file or webcam """ import cv2 import keras from keras.applications.imagenet_utils import preprocess_input from keras.backend.tensorflow_backend import set_session from keras.models import Model from keras.preprocessing import image import pickle import numpy as np from random import shuffle from scipy.misc import imread, imresize from timeit import default_timer as timer import sys sys.path.append("..") from ssd_utils import BBoxUtility class VideoTest(object): """ Class for testing a trained SSD model on a video file and show the result in a window. Class is designed so that one VideoTest object can be created for a model, and the same object can then be used on multiple videos and webcams. Arguments: class_names: A list of strings, each containing the name of a class. The first name should be that of the background class which is not used. model: An SSD model. It should already be trained for images similar to the video to test on. input_shape: The shape that the model expects for its input, as a tuple, for example (300, 300, 3) bbox_util: An instance of the BBoxUtility class in ssd_utils.py The BBoxUtility needs to be instantiated with the same number of classes as the length of class_names. """ def __init__(self, class_names, model, input_shape): self.class_names = class_names self.num_classes = len(class_names) self.model = model self.input_shape = input_shape self.bbox_util = BBoxUtility(self.num_classes) # Create unique and somewhat visually distinguishable bright # colors for the different classes. self.class_colors = [] for i in range(0, self.num_classes): # This can probably be written in a more elegant manner hue = 255*i/self.num_classes col = np.zeros((1,1,3)).astype("uint8") col[0][0][0] = hue col[0][0][1] = 128 # Saturation col[0][0][2] = 255 # Value cvcol = cv2.cvtColor(col, cv2.COLOR_HSV2BGR) col = (int(cvcol[0][0][0]), int(cvcol[0][0][1]), int(cvcol[0][0][2])) self.class_colors.append(col) def run(self, video_path = 0, start_frame = 0, conf_thresh = 0.6): """ Runs the test on a video (or webcam) # Arguments video_path: A file path to a video to be tested on. Can also be a number, in which case the webcam with the same number (i.e. 0) is used instead start_frame: The number of the first frame of the video to be processed by the network. conf_thresh: Threshold of confidence. Any boxes with lower confidence are not visualized. """ vid = cv2.VideoCapture(video_path) if not vid.isOpened(): raise IOError(("Couldn't open video file or webcam. If you're " "trying to open a webcam, make sure you video_path is an integer!")) # Compute aspect ratio of video vidw = vid.get(cv2.CAP_PROP_FRAME_WIDTH) vidh = vid.get(cv2.CAP_PROP_FRAME_HEIGHT) vidar = vidw/vidh # Skip frames until reaching start_frame if start_frame > 0: vid.set(cv2.CAP_PROP_POS_MSEC, start_frame) accum_time = 0 curr_fps = 0 fps = "FPS: ??" prev_time = timer() while True: retval, orig_image = vid.read() if not retval: print("Done!") return im_size = (self.input_shape[0], self.input_shape[1]) resized = cv2.resize(orig_image, im_size) rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB) # Reshape to original aspect ratio for later visualization # The resized version is used, to visualize what kind of resolution # the network has to work with. to_draw = cv2.resize(resized, (int(self.input_shape[0]*vidar), self.input_shape[1])) # Use model to predict inputs = [image.img_to_array(rgb)] tmp_inp = np.array(inputs) x = preprocess_input(tmp_inp) y = self.model.predict(x) # This line creates a new TensorFlow device every time. Is there a # way to avoid that? results = self.bbox_util.detection_out(y) if len(results) > 0 and len(results[0]) > 0: # Interpret output, only one frame is used det_label = results[0][:, 0] det_conf = results[0][:, 1] det_xmin = results[0][:, 2] det_ymin = results[0][:, 3] det_xmax = results[0][:, 4] det_ymax = results[0][:, 5] top_indices = [i for i, conf in enumerate(det_conf) if conf >= conf_thresh] top_conf = det_conf[top_indices] top_label_indices = det_label[top_indices].tolist() top_xmin = det_xmin[top_indices] top_ymin = det_ymin[top_indices] top_xmax = det_xmax[top_indices] top_ymax = det_ymax[top_indices] for i in range(top_conf.shape[0]): xmin = int(round(top_xmin[i] * to_draw.shape[1])) ymin = int(round(top_ymin[i] * to_draw.shape[0])) xmax = int(round(top_xmax[i] * to_draw.shape[1])) ymax = int(round(top_ymax[i] * to_draw.shape[0])) # Draw the box on top of the to_draw image class_num = int(top_label_indices[i]) cv2.rectangle(to_draw, (xmin, ymin), (xmax, ymax), self.class_colors[class_num], 2) text = self.class_names[class_num] + " " + ('%.2f' % top_conf[i]) text_top = (xmin, ymin-10) text_bot = (xmin + 80, ymin + 5) text_pos = (xmin + 5, ymin) cv2.rectangle(to_draw, text_top, text_bot, self.class_colors[class_num], -1) cv2.putText(to_draw, text, text_pos, cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0,0,0), 1) # Calculate FPS # This computes FPS for everything, not just the model's execution # which may or may not be what you want curr_time = timer() exec_time = curr_time - prev_time prev_time = curr_time accum_time = accum_time + exec_time curr_fps = curr_fps + 1 if accum_time > 1: accum_time = accum_time - 1 fps = "FPS: " + str(curr_fps) curr_fps = 0 # Draw FPS in top left corner cv2.rectangle(to_draw, (0,0), (50, 17), (255,255,255), -1) cv2.putText(to_draw, fps, (3,10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0,0,0), 1) cv2.imshow("SSD result", to_draw) cv2.waitKey(10)

41.625

100

0.536362

import cv2 import keras from keras.applications.imagenet_utils import preprocess_input from keras.backend.tensorflow_backend import set_session from keras.models import Model from keras.preprocessing import image import pickle import numpy as np from random import shuffle from scipy.misc import imread, imresize from timeit import default_timer as timer import sys sys.path.append("..") from ssd_utils import BBoxUtility class VideoTest(object): def __init__(self, class_names, model, input_shape): self.class_names = class_names self.num_classes = len(class_names) self.model = model self.input_shape = input_shape self.bbox_util = BBoxUtility(self.num_classes) self.class_colors = [] for i in range(0, self.num_classes): hue = 255*i/self.num_classes col = np.zeros((1,1,3)).astype("uint8") col[0][0][0] = hue col[0][0][1] = 128 col[0][0][2] = 255 cvcol = cv2.cvtColor(col, cv2.COLOR_HSV2BGR) col = (int(cvcol[0][0][0]), int(cvcol[0][0][1]), int(cvcol[0][0][2])) self.class_colors.append(col) def run(self, video_path = 0, start_frame = 0, conf_thresh = 0.6): vid = cv2.VideoCapture(video_path) if not vid.isOpened(): raise IOError(("Couldn't open video file or webcam. If you're " "trying to open a webcam, make sure you video_path is an integer!")) vidw = vid.get(cv2.CAP_PROP_FRAME_WIDTH) vidh = vid.get(cv2.CAP_PROP_FRAME_HEIGHT) vidar = vidw/vidh if start_frame > 0: vid.set(cv2.CAP_PROP_POS_MSEC, start_frame) accum_time = 0 curr_fps = 0 fps = "FPS: ??" prev_time = timer() while True: retval, orig_image = vid.read() if not retval: print("Done!") return im_size = (self.input_shape[0], self.input_shape[1]) resized = cv2.resize(orig_image, im_size) rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB) to_draw = cv2.resize(resized, (int(self.input_shape[0]*vidar), self.input_shape[1])) inputs = [image.img_to_array(rgb)] tmp_inp = np.array(inputs) x = preprocess_input(tmp_inp) y = self.model.predict(x) results = self.bbox_util.detection_out(y) if len(results) > 0 and len(results[0]) > 0: det_label = results[0][:, 0] det_conf = results[0][:, 1] det_xmin = results[0][:, 2] det_ymin = results[0][:, 3] det_xmax = results[0][:, 4] det_ymax = results[0][:, 5] top_indices = [i for i, conf in enumerate(det_conf) if conf >= conf_thresh] top_conf = det_conf[top_indices] top_label_indices = det_label[top_indices].tolist() top_xmin = det_xmin[top_indices] top_ymin = det_ymin[top_indices] top_xmax = det_xmax[top_indices] top_ymax = det_ymax[top_indices] for i in range(top_conf.shape[0]): xmin = int(round(top_xmin[i] * to_draw.shape[1])) ymin = int(round(top_ymin[i] * to_draw.shape[0])) xmax = int(round(top_xmax[i] * to_draw.shape[1])) ymax = int(round(top_ymax[i] * to_draw.shape[0])) class_num = int(top_label_indices[i]) cv2.rectangle(to_draw, (xmin, ymin), (xmax, ymax), self.class_colors[class_num], 2) text = self.class_names[class_num] + " " + ('%.2f' % top_conf[i]) text_top = (xmin, ymin-10) text_bot = (xmin + 80, ymin + 5) text_pos = (xmin + 5, ymin) cv2.rectangle(to_draw, text_top, text_bot, self.class_colors[class_num], -1) cv2.putText(to_draw, text, text_pos, cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0,0,0), 1) # which may or may not be what you want curr_time = timer() exec_time = curr_time - prev_time prev_time = curr_time accum_time = accum_time + exec_time curr_fps = curr_fps + 1 if accum_time > 1: accum_time = accum_time - 1 fps = "FPS: " + str(curr_fps) curr_fps = 0 # Draw FPS in top left corner cv2.rectangle(to_draw, (0,0), (50, 17), (255,255,255), -1) cv2.putText(to_draw, fps, (3,10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0,0,0), 1) cv2.imshow("SSD result", to_draw) cv2.waitKey(10)

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