fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f71be0c7310b1e5a59e2faa1aa48d7c69c065244

41,236

py

Python

pyscf/fci/selected_ci.py

crisely09/pyscf

cb92f7974bd9c87c0ef5b2b52abf5d3219b3d6b6

[ "Apache-2.0" ]

1

2020-04-07T21:12:08.000Z

pyscf/fci/selected_ci.py

crisely09/pyscf

cb92f7974bd9c87c0ef5b2b52abf5d3219b3d6b6

[ "Apache-2.0" ]

2

2019-09-16T17:58:31.000Z

2019-09-22T17:26:01.000Z

pyscf/fci/selected_ci.py

crisely09/pyscf

cb92f7974bd9c87c0ef5b2b52abf5d3219b3d6b6

[ "Apache-2.0" ]

null

#!/usr/bin/env python # Copyright 2014-2019 The PySCF Developers. 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. # # Author: Qiming Sun <osirpt.sun@gmail.com> # ''' Selected CI Simple usage:: >>> from pyscf import gto, scf, ao2mo, fci >>> mol = gto.M(atom='C 0 0 0; C 0 0 1') >>> mf = scf.RHF(mol).run() >>> h1 = mf.mo_coeff.T.dot(mf.get_hcore()).dot(mf.mo_coeff) >>> h2 = ao2mo.kernel(mol, mf.mo_coeff) >>> e = fci.selected_ci.kernel(h1, h2, mf.mo_coeff.shape[1], mol.nelectron)[0] ''' import ctypes import numpy from pyscf import lib from pyscf.lib import logger from pyscf import ao2mo from pyscf.fci import cistring from pyscf.fci import direct_spin1 from pyscf.fci import rdm from pyscf import __config__ libfci = lib.load_library('libfci') def contract_2e(eri, civec_strs, norb, nelec, link_index=None): ci_coeff, nelec, ci_strs = _unpack(civec_strs, nelec) if link_index is None: link_index = _all_linkstr_index(ci_strs, norb, nelec) cd_indexa, dd_indexa, cd_indexb, dd_indexb = link_index na, nlinka = cd_indexa.shape[:2] nb, nlinkb = cd_indexb.shape[:2] eri = ao2mo.restore(1, eri, norb) eri1 = eri.transpose(0,2,1,3) - eri.transpose(0,2,3,1) idx,idy = numpy.tril_indices(norb, -1) idx = idx * norb + idy eri1 = lib.take_2d(eri1.reshape(norb**2,-1), idx, idx) * 2 fcivec = ci_coeff.reshape(na,nb) # (bb|bb) if nelec[1] > 1: mb, mlinkb = dd_indexb.shape[:2] fcivecT = lib.transpose(fcivec) ci1T = numpy.zeros((nb,na)) libfci.SCIcontract_2e_aaaa(eri1.ctypes.data_as(ctypes.c_void_p), fcivecT.ctypes.data_as(ctypes.c_void_p), ci1T.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nb), ctypes.c_int(na), ctypes.c_int(mb), ctypes.c_int(mlinkb), dd_indexb.ctypes.data_as(ctypes.c_void_p)) ci1 = lib.transpose(ci1T, out=fcivecT) else: ci1 = numpy.zeros_like(fcivec) # (aa|aa) if nelec[0] > 1: ma, mlinka = dd_indexa.shape[:2] libfci.SCIcontract_2e_aaaa(eri1.ctypes.data_as(ctypes.c_void_p), fcivec.ctypes.data_as(ctypes.c_void_p), ci1.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(na), ctypes.c_int(nb), ctypes.c_int(ma), ctypes.c_int(mlinka), dd_indexa.ctypes.data_as(ctypes.c_void_p)) h_ps = numpy.einsum('pqqs->ps', eri) eri1 = eri * 2 for k in range(norb): eri1[:,:,k,k] += h_ps/nelec[0] eri1[k,k,:,:] += h_ps/nelec[1] eri1 = ao2mo.restore(4, eri1, norb) # (bb|aa) libfci.SCIcontract_2e_bbaa(eri1.ctypes.data_as(ctypes.c_void_p), fcivec.ctypes.data_as(ctypes.c_void_p), ci1.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(na), ctypes.c_int(nb), ctypes.c_int(nlinka), ctypes.c_int(nlinkb), cd_indexa.ctypes.data_as(ctypes.c_void_p), cd_indexb.ctypes.data_as(ctypes.c_void_p)) return _as_SCIvector(ci1.reshape(ci_coeff.shape), ci_strs) def select_strs(myci, eri, eri_pq_max, civec_max, strs, norb, nelec): strs = numpy.asarray(strs, dtype=numpy.int64) nstrs = len(strs) nvir = norb - nelec strs_add = numpy.empty((nstrs*(nelec*nvir)**2//4), dtype=numpy.int64) libfci.SCIselect_strs.restype = ctypes.c_int nadd = libfci.SCIselect_strs(strs_add.ctypes.data_as(ctypes.c_void_p), strs.ctypes.data_as(ctypes.c_void_p), eri.ctypes.data_as(ctypes.c_void_p), eri_pq_max.ctypes.data_as(ctypes.c_void_p), civec_max.ctypes.data_as(ctypes.c_void_p), ctypes.c_double(myci.select_cutoff), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs)) strs_add = sorted(set(strs_add[:nadd]) - set(strs)) return numpy.asarray(strs_add, dtype=numpy.int64) def enlarge_space(myci, civec_strs, eri, norb, nelec): if isinstance(civec_strs, (tuple, list)): nelec, (strsa, strsb) = _unpack(civec_strs[0], nelec)[1:] ci_coeff = lib.asarray(civec_strs) else: ci_coeff, nelec, (strsa, strsb) = _unpack(civec_strs, nelec) na = len(strsa) nb = len(strsb) ci0 = ci_coeff.reshape(-1,na,nb) civec_a_max = lib.norm(ci0, axis=2).max(axis=0) civec_b_max = lib.norm(ci0, axis=1).max(axis=0) ci_aidx = numpy.where(civec_a_max > myci.ci_coeff_cutoff)[0] ci_bidx = numpy.where(civec_b_max > myci.ci_coeff_cutoff)[0] civec_a_max = civec_a_max[ci_aidx] civec_b_max = civec_b_max[ci_bidx] strsa = strsa[ci_aidx] strsb = strsb[ci_bidx] eri = ao2mo.restore(1, eri, norb) eri_pq_max = abs(eri.reshape(norb**2,-1)).max(axis=1).reshape(norb,norb) strsa_add = select_strs(myci, eri, eri_pq_max, civec_a_max, strsa, norb, nelec[0]) strsb_add = select_strs(myci, eri, eri_pq_max, civec_b_max, strsb, norb, nelec[1]) strsa = numpy.append(strsa, strsa_add) strsb = numpy.append(strsb, strsb_add) aidx = numpy.argsort(strsa) bidx = numpy.argsort(strsb) ci_strs = (strsa[aidx], strsb[bidx]) aidx = numpy.where(aidx < len(ci_aidx))[0] bidx = numpy.where(bidx < len(ci_bidx))[0] ma = len(strsa) mb = len(strsb) cs = [] for i in range(ci0.shape[0]): ci1 = numpy.zeros((ma,mb)) tmp = lib.take_2d(ci0[i], ci_aidx, ci_bidx) lib.takebak_2d(ci1, tmp, aidx, bidx) cs.append(_as_SCIvector(ci1, ci_strs)) if not isinstance(civec_strs, (tuple, list)) and civec_strs.ndim < 3: cs = cs[0] return cs def cre_des_linkstr(strs, norb, nelec, tril=False): '''Given intermediates, the link table to generate input strs ''' strs = numpy.asarray(strs, dtype=numpy.int64) nvir = norb - nelec nstrs = len(strs) link_index = numpy.zeros((nstrs,nelec+nelec*nvir,4), dtype=numpy.int32) libfci.SCIcre_des_linkstr(link_index.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nstrs), ctypes.c_int(nelec), strs.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(tril)) return link_index def cre_des_linkstr_tril(strs, norb, nelec): '''Given intermediates, the link table to generate input strs ''' return cre_des_linkstr(strs, norb, nelec, True) def des_des_linkstr(strs, norb, nelec, tril=False): '''Given intermediates, the link table to generate input strs ''' if nelec < 2: return None strs = numpy.asarray(strs, dtype=numpy.int64) nvir = norb - nelec nstrs = len(strs) inter1 = numpy.empty((nstrs*nelec), dtype=numpy.int64) libfci.SCIdes_uniq_strs.restype = ctypes.c_int ninter = libfci.SCIdes_uniq_strs(inter1.ctypes.data_as(ctypes.c_void_p), strs.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs)) inter1 = numpy.asarray(sorted(set(inter1[:ninter])), dtype=numpy.int64) ninter = len(inter1) inter = numpy.empty((ninter*nelec), dtype=numpy.int64) ninter = libfci.SCIdes_uniq_strs(inter.ctypes.data_as(ctypes.c_void_p), inter1.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec-1), ctypes.c_int(ninter)) inter = numpy.asarray(sorted(set(inter[:ninter])), dtype=numpy.int64) ninter = len(inter) nvir += 2 link_index = numpy.zeros((ninter,nvir*nvir,4), dtype=numpy.int32) libfci.SCIdes_des_linkstr(link_index.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs), ctypes.c_int(ninter), strs.ctypes.data_as(ctypes.c_void_p), inter.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(tril)) return link_index def des_des_linkstr_tril(strs, norb, nelec): '''Given intermediates, the link table to generate input strs ''' return des_des_linkstr(strs, norb, nelec, True) def gen_des_linkstr(strs, norb, nelec): '''Given intermediates, the link table to generate input strs ''' if nelec < 1: return None strs = numpy.asarray(strs, dtype=numpy.int64) nvir = norb - nelec nstrs = len(strs) inter = numpy.empty((nstrs*nelec), dtype=numpy.int64) libfci.SCIdes_uniq_strs.restype = ctypes.c_int ninter = libfci.SCIdes_uniq_strs(inter.ctypes.data_as(ctypes.c_void_p), strs.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs)) inter = numpy.asarray(sorted(set(inter[:ninter])), dtype=numpy.int64) ninter = len(inter) nvir += 1 link_index = numpy.zeros((ninter,nvir,4), dtype=numpy.int32) libfci.SCIdes_linkstr(link_index.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs), ctypes.c_int(ninter), strs.ctypes.data_as(ctypes.c_void_p), inter.ctypes.data_as(ctypes.c_void_p)) return link_index def gen_cre_linkstr(strs, norb, nelec): '''Given intermediates, the link table to generate input strs ''' if nelec == norb: return None strs = numpy.asarray(strs, dtype=numpy.int64) nvir = norb - nelec nstrs = len(strs) inter = numpy.empty((nstrs*nvir), dtype=numpy.int64) libfci.SCIcre_uniq_strs.restype = ctypes.c_int ninter = libfci.SCIcre_uniq_strs(inter.ctypes.data_as(ctypes.c_void_p), strs.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs)) inter = numpy.asarray(sorted(set(inter[:ninter])), dtype=numpy.int64) ninter = len(inter) link_index = numpy.zeros((ninter,nelec+1,4), dtype=numpy.int32) libfci.SCIcre_linkstr(link_index.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs), ctypes.c_int(ninter), strs.ctypes.data_as(ctypes.c_void_p), inter.ctypes.data_as(ctypes.c_void_p)) return link_index def make_hdiag(h1e, eri, ci_strs, norb, nelec): ci_coeff, nelec, ci_strs = _unpack(None, nelec, ci_strs) na = len(ci_strs[0]) nb = len(ci_strs[1]) hdiag = numpy.empty(na*nb) h1e = numpy.asarray(h1e, order='C') eri = ao2mo.restore(1, eri, norb) jdiag = numpy.asarray(numpy.einsum('iijj->ij',eri), order='C') kdiag = numpy.asarray(numpy.einsum('ijji->ij',eri), order='C') c_h1e = h1e.ctypes.data_as(ctypes.c_void_p) c_jdiag = jdiag.ctypes.data_as(ctypes.c_void_p) c_kdiag = kdiag.ctypes.data_as(ctypes.c_void_p) occslsta = cistring._strs2occslst(ci_strs[0], norb) occslstb = cistring._strs2occslst(ci_strs[1], norb) libfci.FCImake_hdiag_uhf(hdiag.ctypes.data_as(ctypes.c_void_p), c_h1e, c_h1e, c_jdiag, c_jdiag, c_jdiag, c_kdiag, c_kdiag, ctypes.c_int(norb), ctypes.c_int(na), ctypes.c_int(nb), ctypes.c_int(nelec[0]), ctypes.c_int(nelec[1]), occslsta.ctypes.data_as(ctypes.c_void_p), occslstb.ctypes.data_as(ctypes.c_void_p)) return hdiag def kernel_fixed_space(myci, h1e, eri, norb, nelec, ci_strs, ci0=None, tol=None, lindep=None, max_cycle=None, max_space=None, nroots=None, davidson_only=None, max_memory=None, verbose=None, ecore=0, **kwargs): log = logger.new_logger(myci, verbose) if tol is None: tol = myci.conv_tol if lindep is None: lindep = myci.lindep if max_cycle is None: max_cycle = myci.max_cycle if max_space is None: max_space = myci.max_space if max_memory is None: max_memory = myci.max_memory if nroots is None: nroots = myci.nroots if myci.verbose >= logger.WARN: myci.check_sanity() nelec = direct_spin1._unpack_nelec(nelec, myci.spin) ci0, nelec, ci_strs = _unpack(ci0, nelec, ci_strs) na = len(ci_strs[0]) nb = len(ci_strs[1]) h2e = direct_spin1.absorb_h1e(h1e, eri, norb, nelec, .5) h2e = ao2mo.restore(1, h2e, norb) link_index = _all_linkstr_index(ci_strs, norb, nelec) hdiag = myci.make_hdiag(h1e, eri, ci_strs, norb, nelec) if isinstance(ci0, _SCIvector): if ci0.size == na*nb: ci0 = [ci0.ravel()] else: ci0 = [x.ravel() for x in ci0] else: ci0 = myci.get_init_guess(ci_strs, norb, nelec, nroots, hdiag) def hop(c): hc = myci.contract_2e(h2e, _as_SCIvector(c, ci_strs), norb, nelec, link_index) return hc.reshape(-1) precond = lambda x, e, *args: x/(hdiag-e+1e-4) #e, c = lib.davidson(hop, ci0, precond, tol=myci.conv_tol) e, c = myci.eig(hop, ci0, precond, tol=tol, lindep=lindep, max_cycle=max_cycle, max_space=max_space, nroots=nroots, max_memory=max_memory, verbose=log, **kwargs) if nroots > 1: return e+ecore, [_as_SCIvector(ci.reshape(na,nb),ci_strs) for ci in c] else: return e+ecore, _as_SCIvector(c.reshape(na,nb), ci_strs) def kernel_float_space(myci, h1e, eri, norb, nelec, ci0=None, tol=None, lindep=None, max_cycle=None, max_space=None, nroots=None, davidson_only=None, max_memory=None, verbose=None, ecore=0, **kwargs): log = logger.new_logger(myci, verbose) if tol is None: tol = myci.conv_tol if lindep is None: lindep = myci.lindep if max_cycle is None: max_cycle = myci.max_cycle if max_space is None: max_space = myci.max_space if max_memory is None: max_memory = myci.max_memory if nroots is None: nroots = myci.nroots if myci.verbose >= logger.WARN: myci.check_sanity() nelec = direct_spin1._unpack_nelec(nelec, myci.spin) h2e = direct_spin1.absorb_h1e(h1e, eri, norb, nelec, .5) h2e = ao2mo.restore(1, h2e, norb) # TODO: initial guess from CISD if isinstance(ci0, _SCIvector): if ci0.size == len(ci0._strs[0])*len(ci0._strs[1]): ci0 = [ci0.ravel()] else: ci0 = [x.ravel() for x in ci0] else: ci_strs = (numpy.asarray([int('1'*nelec[0], 2)]), numpy.asarray([int('1'*nelec[1], 2)])) ci0 = _as_SCIvector(numpy.ones((1,1)), ci_strs) ci0 = myci.enlarge_space(ci0, h2e, norb, nelec) if ci0.size < nroots: log.warn(''' Selected-CI space generated from HF ground state (by double exciting) is not enough for excited states. HOMO->LUMO excitations are included in the initial guess. NOTE: This may introduce excited states of different symmetry.\n''') corea = '1' * (nelec[0]-1) coreb = '1' * (nelec[1]-1) ci_strs = (numpy.asarray([int('1'+corea, 2), int('10'+corea, 2)]), numpy.asarray([int('1'+coreb, 2), int('10'+coreb, 2)])) ci0 = _as_SCIvector(numpy.ones((2,2)), ci_strs) ci0 = myci.enlarge_space(ci0, h2e, norb, nelec) if ci0.size < nroots: raise RuntimeError('Not enough selected-CI space for %d states' % nroots) ci_strs = ci0._strs hdiag = myci.make_hdiag(h1e, eri, ci_strs, norb, nelec) ci0 = myci.get_init_guess(ci_strs, norb, nelec, nroots, hdiag) def hop(c): hc = myci.contract_2e(h2e, _as_SCIvector(c, ci_strs), norb, nelec, link_index) return hc.ravel() precond = lambda x, e, *args: x/(hdiag-e+myci.level_shift) namax = cistring.num_strings(norb, nelec[0]) nbmax = cistring.num_strings(norb, nelec[1]) e_last = 0 float_tol = myci.start_tol tol_decay_rate = myci.tol_decay_rate conv = False for icycle in range(norb): ci_strs = ci0[0]._strs float_tol = max(float_tol*tol_decay_rate, tol*1e2) log.debug('cycle %d ci.shape %s float_tol %g', icycle, (len(ci_strs[0]), len(ci_strs[1])), float_tol) ci0 = [c.ravel() for c in ci0] link_index = _all_linkstr_index(ci_strs, norb, nelec) hdiag = myci.make_hdiag(h1e, eri, ci_strs, norb, nelec) #e, ci0 = lib.davidson(hop, ci0.reshape(-1), precond, tol=float_tol) e, ci0 = myci.eig(hop, ci0, precond, tol=float_tol, lindep=lindep, max_cycle=max_cycle, max_space=max_space, nroots=nroots, max_memory=max_memory, verbose=log, **kwargs) if nroots > 1: ci0 = [_as_SCIvector(c, ci_strs) for c in ci0] de, e_last = min(e)-e_last, min(e) log.info('cycle %d E = %s dE = %.8g', icycle, e+ecore, de) else: ci0 = [_as_SCIvector(ci0, ci_strs)] de, e_last = e-e_last, e log.info('cycle %d E = %.15g dE = %.8g', icycle, e+ecore, de) if ci0[0].shape == (namax,nbmax) or abs(de) < tol*1e3: conv = True break last_ci0_size = float(len(ci_strs[0])), float(len(ci_strs[1])) ci0 = myci.enlarge_space(ci0, h2e, norb, nelec) na = len(ci0[0]._strs[0]) nb = len(ci0[0]._strs[1]) if ((.99 < na/last_ci0_size[0] < 1.01) and (.99 < nb/last_ci0_size[1] < 1.01)): conv = True break ci_strs = ci0[0]._strs log.debug('Extra CI in selected space %s', (len(ci_strs[0]), len(ci_strs[1]))) ci0 = [c.ravel() for c in ci0] link_index = _all_linkstr_index(ci_strs, norb, nelec) hdiag = myci.make_hdiag(h1e, eri, ci_strs, norb, nelec) e, c = myci.eig(hop, ci0, precond, tol=tol, lindep=lindep, max_cycle=max_cycle, max_space=max_space, nroots=nroots, max_memory=max_memory, verbose=log, **kwargs) na = len(ci_strs[0]) nb = len(ci_strs[1]) if nroots > 1: for i, ei in enumerate(e+ecore): log.info('Selected CI state %d E = %.15g', i, ei) return e+ecore, [_as_SCIvector(ci.reshape(na,nb),ci_strs) for ci in c] else: log.info('Selected CI E = %.15g', e+ecore) return e+ecore, _as_SCIvector(c.reshape(na,nb), ci_strs) def kernel(h1e, eri, norb, nelec, ci0=None, level_shift=1e-3, tol=1e-10, lindep=1e-14, max_cycle=50, max_space=12, nroots=1, davidson_only=False, pspace_size=400, orbsym=None, wfnsym=None, select_cutoff=1e-3, ci_coeff_cutoff=1e-3, ecore=0, **kwargs): return direct_spin1._kfactory(SelectedCI, h1e, eri, norb, nelec, ci0, level_shift, tol, lindep, max_cycle, max_space, nroots, davidson_only, pspace_size, select_cutoff=select_cutoff, ci_coeff_cutoff=ci_coeff_cutoff, ecore=ecore, **kwargs) def make_rdm1s(civec_strs, norb, nelec, link_index=None): '''Spin separated 1-particle density matrices. The return values include two density matrices: (alpha,alpha), (beta,beta) dm1[p,q] = <q^\dagger p> The convention is based on McWeeney's book, Eq (5.4.20). The contraction between 1-particle Hamiltonian and rdm1 is E = einsum('pq,qp', h1, rdm1) ''' ci_coeff, nelec, ci_strs = _unpack(civec_strs, nelec) if link_index is None: cd_indexa = cre_des_linkstr(ci_strs[0], norb, nelec[0]) cd_indexb = cre_des_linkstr(ci_strs[1], norb, nelec[1]) else: cd_indexa, dd_indexa, cd_indexb, dd_indexb = link_index rdm1a = rdm.make_rdm1_spin1('FCImake_rdm1a', ci_coeff, ci_coeff, norb, nelec, (cd_indexa,cd_indexb)) rdm1b = rdm.make_rdm1_spin1('FCImake_rdm1b', ci_coeff, ci_coeff, norb, nelec, (cd_indexa,cd_indexb)) return rdm1a, rdm1b def make_rdm1(civec_strs, norb, nelec, link_index=None): r'''Spin-traced 1-particle density matrix. dm1[p,q] = <q_alpha^\dagger p_alpha> + <q_beta^\dagger p_beta> The convention is based on McWeeney's book, Eq (5.4.20) The contraction between 1-particle Hamiltonian and rdm1 is E = einsum('pq,qp', h1, rdm1) ''' rdm1a, rdm1b = make_rdm1s(civec_strs, norb, nelec, link_index) return rdm1a + rdm1b # dm[p,q,r,s] = <|p^+ q r^+ s|> def make_rdm2s(civec_strs, norb, nelec, link_index=None, **kwargs): r'''Spin separated 2-particle density matrices. The return values include three density matrices: (alpha,alpha,alpha,alpha), (alpha,alpha,beta,beta), (beta,beta,beta,beta) 2pdm[p,q,r,s] = :math:`\langle p^\dagger r^\dagger s q\rangle` ''' ci_coeff, nelec, ci_strs = _unpack(civec_strs, nelec) if link_index is None: cd_indexa = cre_des_linkstr(ci_strs[0], norb, nelec[0]) dd_indexa = des_des_linkstr(ci_strs[0], norb, nelec[0]) cd_indexb = cre_des_linkstr(ci_strs[1], norb, nelec[1]) dd_indexb = des_des_linkstr(ci_strs[1], norb, nelec[1]) else: cd_indexa, dd_indexa, cd_indexb, dd_indexb = link_index na, nlinka = cd_indexa.shape[:2] nb, nlinkb = cd_indexb.shape[:2] fcivec = ci_coeff.reshape(na,nb) # (bb|aa) and (aa|bb) dm2ab = rdm.make_rdm12_spin1('FCItdm12kern_ab', fcivec, fcivec, norb, nelec, (cd_indexa,cd_indexb), 0)[1] # (aa|aa) dm2aa = numpy.zeros([norb]*4) if nelec[0] > 1: ma, mlinka = dd_indexa.shape[:2] libfci.SCIrdm2_aaaa(libfci.SCIrdm2kern_aaaa, dm2aa.ctypes.data_as(ctypes.c_void_p), fcivec.ctypes.data_as(ctypes.c_void_p), fcivec.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(na), ctypes.c_int(nb), ctypes.c_int(ma), ctypes.c_int(mlinka), dd_indexa.ctypes.data_as(ctypes.c_void_p)) # (bb|bb) dm2bb = numpy.zeros([norb]*4) if nelec[1] > 1: mb, mlinkb = dd_indexb.shape[:2] fcivecT = lib.transpose(fcivec) libfci.SCIrdm2_aaaa(libfci.SCIrdm2kern_aaaa, dm2bb.ctypes.data_as(ctypes.c_void_p), fcivecT.ctypes.data_as(ctypes.c_void_p), fcivecT.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nb), ctypes.c_int(na), ctypes.c_int(mb), ctypes.c_int(mlinkb), dd_indexb.ctypes.data_as(ctypes.c_void_p)) return dm2aa, dm2ab, dm2bb def make_rdm2(civec_strs, norb, nelec, link_index=None, **kwargs): r'''Spin-traced two-particle density matrix. 2pdm[p,q,r,s] = :math:`\langle p_\alpha^\dagger r_\alpha^\dagger s_\alpha q_\alpha\rangle + \langle p_\beta^\dagger r_\alpha^\dagger s_\alpha q_\beta\rangle + \langle p_\alpha^\dagger r_\beta^\dagger s_\beta q_\alpha\rangle + \langle p_\beta^\dagger r_\beta^\dagger s_\beta q_\beta\rangle`. ''' dm2aa, dm2ab, dm2bb = make_rdm2s(civec_strs, norb, nelec, link_index) dm2aa += dm2bb dm2aa += dm2ab dm2aa += dm2ab.transpose(2,3,0,1) return dm2aa def trans_rdm1s(cibra_strs, ciket_strs, norb, nelec, link_index=None): r'''Spin separated transition 1-particle density matrices. See also function :func:`make_rdm1s` 1pdm[p,q] = :math:`\langle q^\dagger p \rangle` ''' cibra, nelec, ci_strs = _unpack(cibra_strs, nelec) ciket, nelec1, ci_strs1 = _unpack(ciket_strs, nelec) assert(all(ci_strs[0] == ci_strs1[0]) and all(ci_strs[1] == ci_strs1[1])) if link_index is None: cd_indexa = cre_des_linkstr(ci_strs[0], norb, nelec[0]) cd_indexb = cre_des_linkstr(ci_strs[1], norb, nelec[1]) else: cd_indexa, dd_indexa, cd_indexb, dd_indexb = link_index rdm1a = rdm.make_rdm1_spin1('FCItrans_rdm1a', cibra, ciket, norb, nelec, (cd_indexa,cd_indexb)) rdm1b = rdm.make_rdm1_spin1('FCItrans_rdm1b', cibra, ciket, norb, nelec, (cd_indexa,cd_indexb)) return rdm1a, rdm1b def trans_rdm1(cibra_strs, ciket_strs, norb, nelec, link_index=None): r'''Spin traced transition 1-particle density matrices. See also function :func:`make_rdm1` 1pdm[p,q] = :math:`\langle q_\alpha^\dagger p_\alpha \rangle + \langle q_\beta^\dagger p_\beta \rangle` ''' rdm1a, rdm1b = trans_rdm1s(cibra_strs, ciket_strs, norb, nelec, link_index) return rdm1a + rdm1b def spin_square(civec_strs, norb, nelec): '''Spin square for RHF-FCI CI wfn only (obtained from spin-degenerated Hamiltonian)''' ci1 = contract_ss(civec_strs, norb, nelec) ss = numpy.einsum('ij,ij->', civec_strs.reshape(ci1.shape), ci1) s = numpy.sqrt(ss+.25) - .5 multip = s*2+1 return ss, multip def contract_ss(civec_strs, norb, nelec): r''' S^2 |\Psi\rangle ''' ci_coeff, nelec, ci_strs = _unpack(civec_strs, nelec) strsa, strsb = ci_strs neleca, nelecb = nelec ci_coeff = ci_coeff.reshape(len(strsa),len(strsb)) def gen_map(fstr_index, strs, nelec, des=True): a_index = fstr_index(strs, norb, nelec) amap = numpy.zeros((a_index.shape[0],norb,2), dtype=numpy.int32) if des: for k, tab in enumerate(a_index): sign = tab[:,3] tab = tab[sign!=0] amap[k,tab[:,1]] = tab[:,2:] else: for k, tab in enumerate(a_index): sign = tab[:,3] tab = tab[sign!=0] amap[k,tab[:,0]] = tab[:,2:] return amap if neleca > 0: ades = gen_map(gen_des_linkstr, strsa, neleca) else: ades = None if nelecb > 0: bdes = gen_map(gen_des_linkstr, strsb, nelecb) else: bdes = None if neleca < norb: acre = gen_map(gen_cre_linkstr, strsa, neleca, False) else: acre = None if nelecb < norb: bcre = gen_map(gen_cre_linkstr, strsb, nelecb, False) else: bcre = None def trans(ci1, aindex, bindex): if aindex is None or bindex is None: return None ma = len(aindex) mb = len(bindex) t1 = numpy.zeros((ma,mb)) for i in range(norb): signa = aindex[:,i,1] signb = bindex[:,i,1] maska = numpy.where(signa!=0)[0] maskb = numpy.where(signb!=0)[0] addra = aindex[maska,i,0] addrb = bindex[maskb,i,0] citmp = lib.take_2d(ci_coeff, addra, addrb) citmp *= signa[maska].reshape(-1,1) citmp *= signb[maskb] #: t1[addra.reshape(-1,1),addrb] += citmp lib.takebak_2d(t1, citmp, maska, maskb) for i in range(norb): signa = aindex[:,i,1] signb = bindex[:,i,1] maska = numpy.where(signa!=0)[0] maskb = numpy.where(signb!=0)[0] addra = aindex[maska,i,0] addrb = bindex[maskb,i,0] citmp = lib.take_2d(t1, maska, maskb) citmp *= signa[maska].reshape(-1,1) citmp *= signb[maskb] #: ci1[maska.reshape(-1,1), maskb] += citmp lib.takebak_2d(ci1, citmp, addra, addrb) ci1 = numpy.zeros_like(ci_coeff) trans(ci1, ades, bcre) # S+*S- trans(ci1, acre, bdes) # S-*S+ ci1 *= .5 ci1 += (neleca-nelecb)**2*.25*ci_coeff return _as_SCIvector(ci1, ci_strs) def to_fci(civec_strs, norb, nelec): ci_coeff, nelec, ci_strs = _unpack(civec_strs, nelec) addrsa = [cistring.str2addr(norb, nelec[0], x) for x in ci_strs[0]] addrsb = [cistring.str2addr(norb, nelec[1], x) for x in ci_strs[1]] na = cistring.num_strings(norb, nelec[0]) nb = cistring.num_strings(norb, nelec[1]) ci0 = numpy.zeros((na,nb)) lib.takebak_2d(ci0, ci_coeff, addrsa, addrsb) return ci0 def from_fci(fcivec, ci_strs, norb, nelec): fcivec, nelec, ci_strs = _unpack(fcivec, nelec, ci_strs) addrsa = [cistring.str2addr(norb, nelec[0], x) for x in ci_strs[0]] addrsb = [cistring.str2addr(norb, nelec[1], x) for x in ci_strs[1]] na = cistring.num_strings(norb, nelec[0]) nb = cistring.num_strings(norb, nelec[1]) fcivec = fcivec.reshape(na,nb) civec = lib.take_2d(fcivec, addrsa, addrsb) return _as_SCIvector(civec, ci_strs) class SelectedCI(direct_spin1.FCISolver): ci_coeff_cutoff = getattr(__config__, 'fci_selected_ci_SCI_ci_coeff_cutoff', .5e-3) select_cutoff = getattr(__config__, 'fci_selected_ci_SCI_select_cutoff', .5e-3) conv_tol = getattr(__config__, 'fci_selected_ci_SCI_conv_tol', 1e-9) start_tol = getattr(__config__, 'fci_selected_ci_SCI_start_tol', 3e-4) tol_decay_rate = getattr(__config__, 'fci_selected_ci_SCI_tol_decay_rate', 0.3) def __init__(self, mol=None): direct_spin1.FCISolver.__init__(self, mol) ################################################## # don't modify the following attributes, they are not input options #self.converged = False #self.ci = None self._strs = None keys = set(('ci_coeff_cutoff', 'select_cutoff', 'conv_tol', 'start_tol', 'tol_decay_rate')) self._keys = self._keys.union(keys) def dump_flags(self, verbose=None): direct_spin1.FCISolver.dump_flags(self, verbose) logger.info(self, 'ci_coeff_cutoff %g', self.ci_coeff_cutoff) logger.info(self, 'select_cutoff %g', self.select_cutoff) def contract_2e(self, eri, civec_strs, norb, nelec, link_index=None, **kwargs): # The argument civec_strs is a CI vector in function FCISolver.contract_2e. # Save and patch self._strs to make this contract_2e function compatible to # FCISolver.contract_2e. if getattr(civec_strs, '_strs', None) is not None: self._strs = civec_strs._strs else: assert(civec_strs.size == len(self._strs[0])*len(self._strs[1])) civec_strs = _as_SCIvector(civec_strs, self._strs) return contract_2e(eri, civec_strs, norb, nelec, link_index) def get_init_guess(self, ci_strs, norb, nelec, nroots, hdiag): '''Initial guess is the single Slater determinant ''' na = len(ci_strs[0]) nb = len(ci_strs[1]) ci0 = direct_spin1._get_init_guess(na, nb, nroots, hdiag) return [_as_SCIvector(x, ci_strs) for x in ci0] def make_hdiag(self, h1e, eri, ci_strs, norb, nelec): return make_hdiag(h1e, eri, ci_strs, norb, nelec) enlarge_space = enlarge_space kernel = kernel_float_space kernel_fixed_space = kernel_fixed_space # def approx_kernel(self, h1e, eri, norb, nelec, ci0=None, link_index=None, # tol=None, lindep=None, max_cycle=None, # max_memory=None, verbose=None, **kwargs): # ci_strs = getattr(ci0, '_strs', self._strs) # return self.kernel_fixed_space(h1e, eri, norb, nelec, ci_strs, # ci0, link_index, tol, lindep, 6, # max_memory, verbose, **kwargs) @lib.with_doc(spin_square.__doc__) def spin_square(self, civec_strs, norb, nelec): nelec = direct_spin1._unpack_nelec(nelec, self.spin) return spin_square(_as_SCIvector_if_not(civec_strs, self._strs), norb, nelec) def large_ci(self, civec_strs, norb, nelec, tol=.1, return_strs=True): nelec = direct_spin1._unpack_nelec(nelec, self.spin) ci, _, (strsa, strsb) = _unpack(civec_strs, nelec, self._strs) addra, addrb = numpy.where(abs(ci) > tol) if return_strs: strsa = [bin(x) for x in strsa[addra]] strsb = [bin(x) for x in strsb[addrb]] return list(zip(ci[addra,addrb], strsa, strsb)) else: occslsta = cistring._strs2occslst(strsa[addra], norb) occslstb = cistring._strs2occslst(strsb[addrb], norb) return list(zip(ci[addra,addrb], occslsta, occslstb)) def contract_ss(self, fcivec, norb, nelec): return contract_ss(fcivec, norb, nelec) @lib.with_doc(make_rdm1s.__doc__) def make_rdm1s(self, civec_strs, norb, nelec, link_index=None): nelec = direct_spin1._unpack_nelec(nelec, self.spin) civec_strs = _as_SCIvector_if_not(civec_strs, self._strs) return make_rdm1s(civec_strs, norb, nelec, link_index) @lib.with_doc(make_rdm1.__doc__) def make_rdm1(self, civec_strs, norb, nelec, link_index=None): nelec = direct_spin1._unpack_nelec(nelec, self.spin) rdm1a, rdm1b = self.make_rdm1s(civec_strs, norb, nelec, link_index) return rdm1a + rdm1b @lib.with_doc(make_rdm2s.__doc__) def make_rdm2s(self, civec_strs, norb, nelec, link_index=None, **kwargs): nelec = direct_spin1._unpack_nelec(nelec, self.spin) civec_strs = _as_SCIvector_if_not(civec_strs, self._strs) return make_rdm2s(civec_strs, norb, nelec, link_index) @lib.with_doc(make_rdm2.__doc__) def make_rdm2(self, civec_strs, norb, nelec, link_index=None, **kwargs): nelec = direct_spin1._unpack_nelec(nelec, self.spin) civec_strs = _as_SCIvector_if_not(civec_strs, self._strs) return make_rdm2(civec_strs, norb, nelec, link_index) def make_rdm12s(self, civec_strs, norb, nelec, link_index=None, **kwargs): neleca, nelecb = nelec = direct_spin1._unpack_nelec(nelec, self.spin) civec_strs = _as_SCIvector_if_not(civec_strs, self._strs) dm2aa, dm2ab, dm2bb = make_rdm2s(civec_strs, norb, nelec, link_index) if neleca > 1 and nelecb > 1: dm1a = numpy.einsum('iikl->kl', dm2aa) / (neleca-1) dm1b = numpy.einsum('iikl->kl', dm2bb) / (nelecb-1) else: dm1a, dm1b = make_rdm1s(civec_strs, norb, nelec, link_index) return (dm1a, dm1b), (dm2aa, dm2ab, dm2bb) def make_rdm12(self, civec_strs, norb, nelec, link_index=None, **kwargs): nelec = direct_spin1._unpack_nelec(nelec, self.spin) nelec_tot = sum(nelec) civec_strs = _as_SCIvector_if_not(civec_strs, self._strs) dm2 = make_rdm2(civec_strs, norb, nelec, link_index) if nelec_tot > 1: dm1 = numpy.einsum('iikl->kl', dm2) / (nelec_tot-1) else: dm1 = make_rdm1(civec_strs, norb, nelec, link_index) return dm1, dm2 @lib.with_doc(trans_rdm1s.__doc__) def trans_rdm1s(self, cibra, ciket, norb, nelec, link_index=None): nelec = direct_spin1._unpack_nelec(nelec, self.spin) cibra = _as_SCIvector_if_not(cibra, self._strs) ciket = _as_SCIvector_if_not(ciket, self._strs) return trans_rdm1s(cibra, ciket, norb, nelec, link_index) @lib.with_doc(trans_rdm1.__doc__) def trans_rdm1(self, cibra, ciket, norb, nelec, link_index=None): nelec = direct_spin1._unpack_nelec(nelec, self.spin) cibra = _as_SCIvector_if_not(cibra, self._strs) ciket = _as_SCIvector_if_not(ciket, self._strs) return trans_rdm1(cibra, ciket, norb, nelec, link_index) def gen_linkstr(self, norb, nelec, tril=True, spin=None, ci_strs=None): if spin is None: spin = self.spin if ci_strs is None: ci_strs = self._strs neleca, nelecb = direct_spin1._unpack_nelec(nelec, spin) if tril: cd_indexa = cre_des_linkstr_tril(ci_strs[0], norb, neleca) dd_indexa = des_des_linkstr_tril(ci_strs[0], norb, neleca) cd_indexb = cre_des_linkstr_tril(ci_strs[1], norb, nelecb) dd_indexb = des_des_linkstr_tril(ci_strs[1], norb, nelecb) else: cd_indexa = cre_des_linkstr(ci_strs[0], norb, neleca) dd_indexa = des_des_linkstr(ci_strs[0], norb, neleca) cd_indexb = cre_des_linkstr(ci_strs[1], norb, nelecb) dd_indexb = des_des_linkstr(ci_strs[1], norb, nelecb) return cd_indexa, dd_indexa, cd_indexb, dd_indexb SCI = SelectedCI def _unpack(civec_strs, nelec, ci_strs=None, spin=None): neleca, nelecb = direct_spin1._unpack_nelec(nelec, spin) ci_strs = getattr(civec_strs, '_strs', ci_strs) if ci_strs is not None: strsa, strsb = ci_strs strsa = numpy.asarray(strsa) strsb = numpy.asarray(strsb) ci_strs = (strsa, strsb) return civec_strs, (neleca, nelecb), ci_strs def _all_linkstr_index(ci_strs, norb, nelec): cd_indexa = cre_des_linkstr_tril(ci_strs[0], norb, nelec[0]) dd_indexa = des_des_linkstr_tril(ci_strs[0], norb, nelec[0]) cd_indexb = cre_des_linkstr_tril(ci_strs[1], norb, nelec[1]) dd_indexb = des_des_linkstr_tril(ci_strs[1], norb, nelec[1]) return cd_indexa, dd_indexa, cd_indexb, dd_indexb # numpy.ndarray does not allow to attach attribtues. Overwrite the # numpy.ndarray class to tag the ._strs attribute class _SCIvector(numpy.ndarray): def __array_finalize__(self, obj): self._strs = getattr(obj, '_strs', None) # Whenever the contents of the array was modified (through ufunc), the tag # should be expired. Overwrite the output of ufunc to restore ndarray type. def __array_wrap__(self, out, context=None): return numpy.ndarray.__array_wrap__(self, out, context).view(numpy.ndarray) def _as_SCIvector(civec, ci_strs): civec = civec.view(_SCIvector) civec._strs = ci_strs return civec def _as_SCIvector_if_not(civec, ci_strs): if getattr(civec, '_strs', None) is None: civec = _as_SCIvector(civec, ci_strs) return civec if __name__ == '__main__': from functools import reduce from pyscf import gto from pyscf import scf from pyscf import ao2mo from pyscf.fci import spin_op from pyscf.fci import addons mol = gto.Mole() mol.verbose = 0 mol.output = None mol.atom = [ ['H', ( 1.,-1. , 0. )], ['H', ( 0.,-1. ,-1. )], ['H', ( 1.,-0.5 ,-1. )], ['H', ( 0.,-0. ,-1. )], ['H', ( 1.,-0.5 , 0. )], ['H', ( 0., 1. , 1. )], ['H', ( 1., 2. , 3. )], ['H', ( 1., 2. , 4. )], ] mol.basis = 'sto-3g' mol.build() m = scf.RHF(mol) m.kernel() norb = m.mo_coeff.shape[1] nelec = mol.nelectron h1e = reduce(numpy.dot, (m.mo_coeff.T, m.get_hcore(), m.mo_coeff)) eri = ao2mo.kernel(m._eri, m.mo_coeff, compact=False) eri = eri.reshape(norb,norb,norb,norb) e1, c1 = kernel(h1e, eri, norb, nelec) e2, c2 = direct_spin1.kernel(h1e, eri, norb, nelec) print(e1, e1 - -11.894559902235565, 'diff to FCI', e1-e2) print(c1.shape, c2.shape) dm1_1 = make_rdm1(c1, norb, nelec) dm1_2 = direct_spin1.make_rdm1(c2, norb, nelec) print(abs(dm1_1 - dm1_2).sum()) dm2_1 = make_rdm2(c1, norb, nelec) dm2_2 = direct_spin1.make_rdm12(c2, norb, nelec)[1] print(abs(dm2_1 - dm2_2).sum()) myci = SelectedCI() e, c = kernel_fixed_space(myci, h1e, eri, norb, nelec, c1._strs) print(e - -11.894559902235565) print(myci.large_ci(c1, norb, nelec)) print(myci.spin_square(c1, norb, nelec)[0] - spin_op.spin_square0(to_fci(c1, norb, nelec), norb, nelec)[0]) myci = SelectedCI() myci = addons.fix_spin_(myci) e1, c1 = myci.kernel(h1e, eri, norb, nelec) print(e1, e1 - -11.89467612053687) print(myci.spin_square(c1, norb, nelec))

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import ctypes import numpy from pyscf import lib from pyscf.lib import logger from pyscf import ao2mo from pyscf.fci import cistring from pyscf.fci import direct_spin1 from pyscf.fci import rdm from pyscf import __config__ libfci = lib.load_library('libfci') def contract_2e(eri, civec_strs, norb, nelec, link_index=None): ci_coeff, nelec, ci_strs = _unpack(civec_strs, nelec) if link_index is None: link_index = _all_linkstr_index(ci_strs, norb, nelec) cd_indexa, dd_indexa, cd_indexb, dd_indexb = link_index na, nlinka = cd_indexa.shape[:2] nb, nlinkb = cd_indexb.shape[:2] eri = ao2mo.restore(1, eri, norb) eri1 = eri.transpose(0,2,1,3) - eri.transpose(0,2,3,1) idx,idy = numpy.tril_indices(norb, -1) idx = idx * norb + idy eri1 = lib.take_2d(eri1.reshape(norb**2,-1), idx, idx) * 2 fcivec = ci_coeff.reshape(na,nb) if nelec[1] > 1: mb, mlinkb = dd_indexb.shape[:2] fcivecT = lib.transpose(fcivec) ci1T = numpy.zeros((nb,na)) libfci.SCIcontract_2e_aaaa(eri1.ctypes.data_as(ctypes.c_void_p), fcivecT.ctypes.data_as(ctypes.c_void_p), ci1T.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nb), ctypes.c_int(na), ctypes.c_int(mb), ctypes.c_int(mlinkb), dd_indexb.ctypes.data_as(ctypes.c_void_p)) ci1 = lib.transpose(ci1T, out=fcivecT) else: ci1 = numpy.zeros_like(fcivec) if nelec[0] > 1: ma, mlinka = dd_indexa.shape[:2] libfci.SCIcontract_2e_aaaa(eri1.ctypes.data_as(ctypes.c_void_p), fcivec.ctypes.data_as(ctypes.c_void_p), ci1.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(na), ctypes.c_int(nb), ctypes.c_int(ma), ctypes.c_int(mlinka), dd_indexa.ctypes.data_as(ctypes.c_void_p)) h_ps = numpy.einsum('pqqs->ps', eri) eri1 = eri * 2 for k in range(norb): eri1[:,:,k,k] += h_ps/nelec[0] eri1[k,k,:,:] += h_ps/nelec[1] eri1 = ao2mo.restore(4, eri1, norb) libfci.SCIcontract_2e_bbaa(eri1.ctypes.data_as(ctypes.c_void_p), fcivec.ctypes.data_as(ctypes.c_void_p), ci1.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(na), ctypes.c_int(nb), ctypes.c_int(nlinka), ctypes.c_int(nlinkb), cd_indexa.ctypes.data_as(ctypes.c_void_p), cd_indexb.ctypes.data_as(ctypes.c_void_p)) return _as_SCIvector(ci1.reshape(ci_coeff.shape), ci_strs) def select_strs(myci, eri, eri_pq_max, civec_max, strs, norb, nelec): strs = numpy.asarray(strs, dtype=numpy.int64) nstrs = len(strs) nvir = norb - nelec strs_add = numpy.empty((nstrs*(nelec*nvir)**2//4), dtype=numpy.int64) libfci.SCIselect_strs.restype = ctypes.c_int nadd = libfci.SCIselect_strs(strs_add.ctypes.data_as(ctypes.c_void_p), strs.ctypes.data_as(ctypes.c_void_p), eri.ctypes.data_as(ctypes.c_void_p), eri_pq_max.ctypes.data_as(ctypes.c_void_p), civec_max.ctypes.data_as(ctypes.c_void_p), ctypes.c_double(myci.select_cutoff), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs)) strs_add = sorted(set(strs_add[:nadd]) - set(strs)) return numpy.asarray(strs_add, dtype=numpy.int64) def enlarge_space(myci, civec_strs, eri, norb, nelec): if isinstance(civec_strs, (tuple, list)): nelec, (strsa, strsb) = _unpack(civec_strs[0], nelec)[1:] ci_coeff = lib.asarray(civec_strs) else: ci_coeff, nelec, (strsa, strsb) = _unpack(civec_strs, nelec) na = len(strsa) nb = len(strsb) ci0 = ci_coeff.reshape(-1,na,nb) civec_a_max = lib.norm(ci0, axis=2).max(axis=0) civec_b_max = lib.norm(ci0, axis=1).max(axis=0) ci_aidx = numpy.where(civec_a_max > myci.ci_coeff_cutoff)[0] ci_bidx = numpy.where(civec_b_max > myci.ci_coeff_cutoff)[0] civec_a_max = civec_a_max[ci_aidx] civec_b_max = civec_b_max[ci_bidx] strsa = strsa[ci_aidx] strsb = strsb[ci_bidx] eri = ao2mo.restore(1, eri, norb) eri_pq_max = abs(eri.reshape(norb**2,-1)).max(axis=1).reshape(norb,norb) strsa_add = select_strs(myci, eri, eri_pq_max, civec_a_max, strsa, norb, nelec[0]) strsb_add = select_strs(myci, eri, eri_pq_max, civec_b_max, strsb, norb, nelec[1]) strsa = numpy.append(strsa, strsa_add) strsb = numpy.append(strsb, strsb_add) aidx = numpy.argsort(strsa) bidx = numpy.argsort(strsb) ci_strs = (strsa[aidx], strsb[bidx]) aidx = numpy.where(aidx < len(ci_aidx))[0] bidx = numpy.where(bidx < len(ci_bidx))[0] ma = len(strsa) mb = len(strsb) cs = [] for i in range(ci0.shape[0]): ci1 = numpy.zeros((ma,mb)) tmp = lib.take_2d(ci0[i], ci_aidx, ci_bidx) lib.takebak_2d(ci1, tmp, aidx, bidx) cs.append(_as_SCIvector(ci1, ci_strs)) if not isinstance(civec_strs, (tuple, list)) and civec_strs.ndim < 3: cs = cs[0] return cs def cre_des_linkstr(strs, norb, nelec, tril=False): strs = numpy.asarray(strs, dtype=numpy.int64) nvir = norb - nelec nstrs = len(strs) link_index = numpy.zeros((nstrs,nelec+nelec*nvir,4), dtype=numpy.int32) libfci.SCIcre_des_linkstr(link_index.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nstrs), ctypes.c_int(nelec), strs.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(tril)) return link_index def cre_des_linkstr_tril(strs, norb, nelec): return cre_des_linkstr(strs, norb, nelec, True) def des_des_linkstr(strs, norb, nelec, tril=False): if nelec < 2: return None strs = numpy.asarray(strs, dtype=numpy.int64) nvir = norb - nelec nstrs = len(strs) inter1 = numpy.empty((nstrs*nelec), dtype=numpy.int64) libfci.SCIdes_uniq_strs.restype = ctypes.c_int ninter = libfci.SCIdes_uniq_strs(inter1.ctypes.data_as(ctypes.c_void_p), strs.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs)) inter1 = numpy.asarray(sorted(set(inter1[:ninter])), dtype=numpy.int64) ninter = len(inter1) inter = numpy.empty((ninter*nelec), dtype=numpy.int64) ninter = libfci.SCIdes_uniq_strs(inter.ctypes.data_as(ctypes.c_void_p), inter1.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec-1), ctypes.c_int(ninter)) inter = numpy.asarray(sorted(set(inter[:ninter])), dtype=numpy.int64) ninter = len(inter) nvir += 2 link_index = numpy.zeros((ninter,nvir*nvir,4), dtype=numpy.int32) libfci.SCIdes_des_linkstr(link_index.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs), ctypes.c_int(ninter), strs.ctypes.data_as(ctypes.c_void_p), inter.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(tril)) return link_index def des_des_linkstr_tril(strs, norb, nelec): return des_des_linkstr(strs, norb, nelec, True) def gen_des_linkstr(strs, norb, nelec): if nelec < 1: return None strs = numpy.asarray(strs, dtype=numpy.int64) nvir = norb - nelec nstrs = len(strs) inter = numpy.empty((nstrs*nelec), dtype=numpy.int64) libfci.SCIdes_uniq_strs.restype = ctypes.c_int ninter = libfci.SCIdes_uniq_strs(inter.ctypes.data_as(ctypes.c_void_p), strs.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs)) inter = numpy.asarray(sorted(set(inter[:ninter])), dtype=numpy.int64) ninter = len(inter) nvir += 1 link_index = numpy.zeros((ninter,nvir,4), dtype=numpy.int32) libfci.SCIdes_linkstr(link_index.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs), ctypes.c_int(ninter), strs.ctypes.data_as(ctypes.c_void_p), inter.ctypes.data_as(ctypes.c_void_p)) return link_index def gen_cre_linkstr(strs, norb, nelec): if nelec == norb: return None strs = numpy.asarray(strs, dtype=numpy.int64) nvir = norb - nelec nstrs = len(strs) inter = numpy.empty((nstrs*nvir), dtype=numpy.int64) libfci.SCIcre_uniq_strs.restype = ctypes.c_int ninter = libfci.SCIcre_uniq_strs(inter.ctypes.data_as(ctypes.c_void_p), strs.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs)) inter = numpy.asarray(sorted(set(inter[:ninter])), dtype=numpy.int64) ninter = len(inter) link_index = numpy.zeros((ninter,nelec+1,4), dtype=numpy.int32) libfci.SCIcre_linkstr(link_index.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nelec), ctypes.c_int(nstrs), ctypes.c_int(ninter), strs.ctypes.data_as(ctypes.c_void_p), inter.ctypes.data_as(ctypes.c_void_p)) return link_index def make_hdiag(h1e, eri, ci_strs, norb, nelec): ci_coeff, nelec, ci_strs = _unpack(None, nelec, ci_strs) na = len(ci_strs[0]) nb = len(ci_strs[1]) hdiag = numpy.empty(na*nb) h1e = numpy.asarray(h1e, order='C') eri = ao2mo.restore(1, eri, norb) jdiag = numpy.asarray(numpy.einsum('iijj->ij',eri), order='C') kdiag = numpy.asarray(numpy.einsum('ijji->ij',eri), order='C') c_h1e = h1e.ctypes.data_as(ctypes.c_void_p) c_jdiag = jdiag.ctypes.data_as(ctypes.c_void_p) c_kdiag = kdiag.ctypes.data_as(ctypes.c_void_p) occslsta = cistring._strs2occslst(ci_strs[0], norb) occslstb = cistring._strs2occslst(ci_strs[1], norb) libfci.FCImake_hdiag_uhf(hdiag.ctypes.data_as(ctypes.c_void_p), c_h1e, c_h1e, c_jdiag, c_jdiag, c_jdiag, c_kdiag, c_kdiag, ctypes.c_int(norb), ctypes.c_int(na), ctypes.c_int(nb), ctypes.c_int(nelec[0]), ctypes.c_int(nelec[1]), occslsta.ctypes.data_as(ctypes.c_void_p), occslstb.ctypes.data_as(ctypes.c_void_p)) return hdiag def kernel_fixed_space(myci, h1e, eri, norb, nelec, ci_strs, ci0=None, tol=None, lindep=None, max_cycle=None, max_space=None, nroots=None, davidson_only=None, max_memory=None, verbose=None, ecore=0, **kwargs): log = logger.new_logger(myci, verbose) if tol is None: tol = myci.conv_tol if lindep is None: lindep = myci.lindep if max_cycle is None: max_cycle = myci.max_cycle if max_space is None: max_space = myci.max_space if max_memory is None: max_memory = myci.max_memory if nroots is None: nroots = myci.nroots if myci.verbose >= logger.WARN: myci.check_sanity() nelec = direct_spin1._unpack_nelec(nelec, myci.spin) ci0, nelec, ci_strs = _unpack(ci0, nelec, ci_strs) na = len(ci_strs[0]) nb = len(ci_strs[1]) h2e = direct_spin1.absorb_h1e(h1e, eri, norb, nelec, .5) h2e = ao2mo.restore(1, h2e, norb) link_index = _all_linkstr_index(ci_strs, norb, nelec) hdiag = myci.make_hdiag(h1e, eri, ci_strs, norb, nelec) if isinstance(ci0, _SCIvector): if ci0.size == na*nb: ci0 = [ci0.ravel()] else: ci0 = [x.ravel() for x in ci0] else: ci0 = myci.get_init_guess(ci_strs, norb, nelec, nroots, hdiag) def hop(c): hc = myci.contract_2e(h2e, _as_SCIvector(c, ci_strs), norb, nelec, link_index) return hc.reshape(-1) precond = lambda x, e, *args: x/(hdiag-e+1e-4) e, c = myci.eig(hop, ci0, precond, tol=tol, lindep=lindep, max_cycle=max_cycle, max_space=max_space, nroots=nroots, max_memory=max_memory, verbose=log, **kwargs) if nroots > 1: return e+ecore, [_as_SCIvector(ci.reshape(na,nb),ci_strs) for ci in c] else: return e+ecore, _as_SCIvector(c.reshape(na,nb), ci_strs) def kernel_float_space(myci, h1e, eri, norb, nelec, ci0=None, tol=None, lindep=None, max_cycle=None, max_space=None, nroots=None, davidson_only=None, max_memory=None, verbose=None, ecore=0, **kwargs): log = logger.new_logger(myci, verbose) if tol is None: tol = myci.conv_tol if lindep is None: lindep = myci.lindep if max_cycle is None: max_cycle = myci.max_cycle if max_space is None: max_space = myci.max_space if max_memory is None: max_memory = myci.max_memory if nroots is None: nroots = myci.nroots if myci.verbose >= logger.WARN: myci.check_sanity() nelec = direct_spin1._unpack_nelec(nelec, myci.spin) h2e = direct_spin1.absorb_h1e(h1e, eri, norb, nelec, .5) h2e = ao2mo.restore(1, h2e, norb) if isinstance(ci0, _SCIvector): if ci0.size == len(ci0._strs[0])*len(ci0._strs[1]): ci0 = [ci0.ravel()] else: ci0 = [x.ravel() for x in ci0] else: ci_strs = (numpy.asarray([int('1'*nelec[0], 2)]), numpy.asarray([int('1'*nelec[1], 2)])) ci0 = _as_SCIvector(numpy.ones((1,1)), ci_strs) ci0 = myci.enlarge_space(ci0, h2e, norb, nelec) if ci0.size < nroots: log.warn(''' Selected-CI space generated from HF ground state (by double exciting) is not enough for excited states. HOMO->LUMO excitations are included in the initial guess. NOTE: This may introduce excited states of different symmetry.\n''') corea = '1' * (nelec[0]-1) coreb = '1' * (nelec[1]-1) ci_strs = (numpy.asarray([int('1'+corea, 2), int('10'+corea, 2)]), numpy.asarray([int('1'+coreb, 2), int('10'+coreb, 2)])) ci0 = _as_SCIvector(numpy.ones((2,2)), ci_strs) ci0 = myci.enlarge_space(ci0, h2e, norb, nelec) if ci0.size < nroots: raise RuntimeError('Not enough selected-CI space for %d states' % nroots) ci_strs = ci0._strs hdiag = myci.make_hdiag(h1e, eri, ci_strs, norb, nelec) ci0 = myci.get_init_guess(ci_strs, norb, nelec, nroots, hdiag) def hop(c): hc = myci.contract_2e(h2e, _as_SCIvector(c, ci_strs), norb, nelec, link_index) return hc.ravel() precond = lambda x, e, *args: x/(hdiag-e+myci.level_shift) namax = cistring.num_strings(norb, nelec[0]) nbmax = cistring.num_strings(norb, nelec[1]) e_last = 0 float_tol = myci.start_tol tol_decay_rate = myci.tol_decay_rate conv = False for icycle in range(norb): ci_strs = ci0[0]._strs float_tol = max(float_tol*tol_decay_rate, tol*1e2) log.debug('cycle %d ci.shape %s float_tol %g', icycle, (len(ci_strs[0]), len(ci_strs[1])), float_tol) ci0 = [c.ravel() for c in ci0] link_index = _all_linkstr_index(ci_strs, norb, nelec) hdiag = myci.make_hdiag(h1e, eri, ci_strs, norb, nelec) e, ci0 = myci.eig(hop, ci0, precond, tol=float_tol, lindep=lindep, max_cycle=max_cycle, max_space=max_space, nroots=nroots, max_memory=max_memory, verbose=log, **kwargs) if nroots > 1: ci0 = [_as_SCIvector(c, ci_strs) for c in ci0] de, e_last = min(e)-e_last, min(e) log.info('cycle %d E = %s dE = %.8g', icycle, e+ecore, de) else: ci0 = [_as_SCIvector(ci0, ci_strs)] de, e_last = e-e_last, e log.info('cycle %d E = %.15g dE = %.8g', icycle, e+ecore, de) if ci0[0].shape == (namax,nbmax) or abs(de) < tol*1e3: conv = True break last_ci0_size = float(len(ci_strs[0])), float(len(ci_strs[1])) ci0 = myci.enlarge_space(ci0, h2e, norb, nelec) na = len(ci0[0]._strs[0]) nb = len(ci0[0]._strs[1]) if ((.99 < na/last_ci0_size[0] < 1.01) and (.99 < nb/last_ci0_size[1] < 1.01)): conv = True break ci_strs = ci0[0]._strs log.debug('Extra CI in selected space %s', (len(ci_strs[0]), len(ci_strs[1]))) ci0 = [c.ravel() for c in ci0] link_index = _all_linkstr_index(ci_strs, norb, nelec) hdiag = myci.make_hdiag(h1e, eri, ci_strs, norb, nelec) e, c = myci.eig(hop, ci0, precond, tol=tol, lindep=lindep, max_cycle=max_cycle, max_space=max_space, nroots=nroots, max_memory=max_memory, verbose=log, **kwargs) na = len(ci_strs[0]) nb = len(ci_strs[1]) if nroots > 1: for i, ei in enumerate(e+ecore): log.info('Selected CI state %d E = %.15g', i, ei) return e+ecore, [_as_SCIvector(ci.reshape(na,nb),ci_strs) for ci in c] else: log.info('Selected CI E = %.15g', e+ecore) return e+ecore, _as_SCIvector(c.reshape(na,nb), ci_strs) def kernel(h1e, eri, norb, nelec, ci0=None, level_shift=1e-3, tol=1e-10, lindep=1e-14, max_cycle=50, max_space=12, nroots=1, davidson_only=False, pspace_size=400, orbsym=None, wfnsym=None, select_cutoff=1e-3, ci_coeff_cutoff=1e-3, ecore=0, **kwargs): return direct_spin1._kfactory(SelectedCI, h1e, eri, norb, nelec, ci0, level_shift, tol, lindep, max_cycle, max_space, nroots, davidson_only, pspace_size, select_cutoff=select_cutoff, ci_coeff_cutoff=ci_coeff_cutoff, ecore=ecore, **kwargs) def make_rdm1s(civec_strs, norb, nelec, link_index=None): ci_coeff, nelec, ci_strs = _unpack(civec_strs, nelec) if link_index is None: cd_indexa = cre_des_linkstr(ci_strs[0], norb, nelec[0]) cd_indexb = cre_des_linkstr(ci_strs[1], norb, nelec[1]) else: cd_indexa, dd_indexa, cd_indexb, dd_indexb = link_index rdm1a = rdm.make_rdm1_spin1('FCImake_rdm1a', ci_coeff, ci_coeff, norb, nelec, (cd_indexa,cd_indexb)) rdm1b = rdm.make_rdm1_spin1('FCImake_rdm1b', ci_coeff, ci_coeff, norb, nelec, (cd_indexa,cd_indexb)) return rdm1a, rdm1b def make_rdm1(civec_strs, norb, nelec, link_index=None): rdm1a, rdm1b = make_rdm1s(civec_strs, norb, nelec, link_index) return rdm1a + rdm1b def make_rdm2s(civec_strs, norb, nelec, link_index=None, **kwargs): ci_coeff, nelec, ci_strs = _unpack(civec_strs, nelec) if link_index is None: cd_indexa = cre_des_linkstr(ci_strs[0], norb, nelec[0]) dd_indexa = des_des_linkstr(ci_strs[0], norb, nelec[0]) cd_indexb = cre_des_linkstr(ci_strs[1], norb, nelec[1]) dd_indexb = des_des_linkstr(ci_strs[1], norb, nelec[1]) else: cd_indexa, dd_indexa, cd_indexb, dd_indexb = link_index na, nlinka = cd_indexa.shape[:2] nb, nlinkb = cd_indexb.shape[:2] fcivec = ci_coeff.reshape(na,nb) dm2ab = rdm.make_rdm12_spin1('FCItdm12kern_ab', fcivec, fcivec, norb, nelec, (cd_indexa,cd_indexb), 0)[1] dm2aa = numpy.zeros([norb]*4) if nelec[0] > 1: ma, mlinka = dd_indexa.shape[:2] libfci.SCIrdm2_aaaa(libfci.SCIrdm2kern_aaaa, dm2aa.ctypes.data_as(ctypes.c_void_p), fcivec.ctypes.data_as(ctypes.c_void_p), fcivec.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(na), ctypes.c_int(nb), ctypes.c_int(ma), ctypes.c_int(mlinka), dd_indexa.ctypes.data_as(ctypes.c_void_p)) dm2bb = numpy.zeros([norb]*4) if nelec[1] > 1: mb, mlinkb = dd_indexb.shape[:2] fcivecT = lib.transpose(fcivec) libfci.SCIrdm2_aaaa(libfci.SCIrdm2kern_aaaa, dm2bb.ctypes.data_as(ctypes.c_void_p), fcivecT.ctypes.data_as(ctypes.c_void_p), fcivecT.ctypes.data_as(ctypes.c_void_p), ctypes.c_int(norb), ctypes.c_int(nb), ctypes.c_int(na), ctypes.c_int(mb), ctypes.c_int(mlinkb), dd_indexb.ctypes.data_as(ctypes.c_void_p)) return dm2aa, dm2ab, dm2bb def make_rdm2(civec_strs, norb, nelec, link_index=None, **kwargs): dm2aa, dm2ab, dm2bb = make_rdm2s(civec_strs, norb, nelec, link_index) dm2aa += dm2bb dm2aa += dm2ab dm2aa += dm2ab.transpose(2,3,0,1) return dm2aa def trans_rdm1s(cibra_strs, ciket_strs, norb, nelec, link_index=None): cibra, nelec, ci_strs = _unpack(cibra_strs, nelec) ciket, nelec1, ci_strs1 = _unpack(ciket_strs, nelec) assert(all(ci_strs[0] == ci_strs1[0]) and all(ci_strs[1] == ci_strs1[1])) if link_index is None: cd_indexa = cre_des_linkstr(ci_strs[0], norb, nelec[0]) cd_indexb = cre_des_linkstr(ci_strs[1], norb, nelec[1]) else: cd_indexa, dd_indexa, cd_indexb, dd_indexb = link_index rdm1a = rdm.make_rdm1_spin1('FCItrans_rdm1a', cibra, ciket, norb, nelec, (cd_indexa,cd_indexb)) rdm1b = rdm.make_rdm1_spin1('FCItrans_rdm1b', cibra, ciket, norb, nelec, (cd_indexa,cd_indexb)) return rdm1a, rdm1b def trans_rdm1(cibra_strs, ciket_strs, norb, nelec, link_index=None): rdm1a, rdm1b = trans_rdm1s(cibra_strs, ciket_strs, norb, nelec, link_index) return rdm1a + rdm1b def spin_square(civec_strs, norb, nelec): ci1 = contract_ss(civec_strs, norb, nelec) ss = numpy.einsum('ij,ij->', civec_strs.reshape(ci1.shape), ci1) s = numpy.sqrt(ss+.25) - .5 multip = s*2+1 return ss, multip def contract_ss(civec_strs, norb, nelec): ci_coeff, nelec, ci_strs = _unpack(civec_strs, nelec) strsa, strsb = ci_strs neleca, nelecb = nelec ci_coeff = ci_coeff.reshape(len(strsa),len(strsb)) def gen_map(fstr_index, strs, nelec, des=True): a_index = fstr_index(strs, norb, nelec) amap = numpy.zeros((a_index.shape[0],norb,2), dtype=numpy.int32) if des: for k, tab in enumerate(a_index): sign = tab[:,3] tab = tab[sign!=0] amap[k,tab[:,1]] = tab[:,2:] else: for k, tab in enumerate(a_index): sign = tab[:,3] tab = tab[sign!=0] amap[k,tab[:,0]] = tab[:,2:] return amap if neleca > 0: ades = gen_map(gen_des_linkstr, strsa, neleca) else: ades = None if nelecb > 0: bdes = gen_map(gen_des_linkstr, strsb, nelecb) else: bdes = None if neleca < norb: acre = gen_map(gen_cre_linkstr, strsa, neleca, False) else: acre = None if nelecb < norb: bcre = gen_map(gen_cre_linkstr, strsb, nelecb, False) else: bcre = None def trans(ci1, aindex, bindex): if aindex is None or bindex is None: return None ma = len(aindex) mb = len(bindex) t1 = numpy.zeros((ma,mb)) for i in range(norb): signa = aindex[:,i,1] signb = bindex[:,i,1] maska = numpy.where(signa!=0)[0] maskb = numpy.where(signb!=0)[0] addra = aindex[maska,i,0] addrb = bindex[maskb,i,0] citmp = lib.take_2d(ci_coeff, addra, addrb) citmp *= signa[maska].reshape(-1,1) citmp *= signb[maskb] lib.takebak_2d(t1, citmp, maska, maskb) for i in range(norb): signa = aindex[:,i,1] signb = bindex[:,i,1] maska = numpy.where(signa!=0)[0] maskb = numpy.where(signb!=0)[0] addra = aindex[maska,i,0] addrb = bindex[maskb,i,0] citmp = lib.take_2d(t1, maska, maskb) citmp *= signa[maska].reshape(-1,1) citmp *= signb[maskb] lib.takebak_2d(ci1, citmp, addra, addrb) ci1 = numpy.zeros_like(ci_coeff) trans(ci1, ades, bcre) trans(ci1, acre, bdes) ci1 *= .5 ci1 += (neleca-nelecb)**2*.25*ci_coeff return _as_SCIvector(ci1, ci_strs) def to_fci(civec_strs, norb, nelec): ci_coeff, nelec, ci_strs = _unpack(civec_strs, nelec) addrsa = [cistring.str2addr(norb, nelec[0], x) for x in ci_strs[0]] addrsb = [cistring.str2addr(norb, nelec[1], x) for x in ci_strs[1]] na = cistring.num_strings(norb, nelec[0]) nb = cistring.num_strings(norb, nelec[1]) ci0 = numpy.zeros((na,nb)) lib.takebak_2d(ci0, ci_coeff, addrsa, addrsb) return ci0 def from_fci(fcivec, ci_strs, norb, nelec): fcivec, nelec, ci_strs = _unpack(fcivec, nelec, ci_strs) addrsa = [cistring.str2addr(norb, nelec[0], x) for x in ci_strs[0]] addrsb = [cistring.str2addr(norb, nelec[1], x) for x in ci_strs[1]] na = cistring.num_strings(norb, nelec[0]) nb = cistring.num_strings(norb, nelec[1]) fcivec = fcivec.reshape(na,nb) civec = lib.take_2d(fcivec, addrsa, addrsb) return _as_SCIvector(civec, ci_strs) class SelectedCI(direct_spin1.FCISolver): ci_coeff_cutoff = getattr(__config__, 'fci_selected_ci_SCI_ci_coeff_cutoff', .5e-3) select_cutoff = getattr(__config__, 'fci_selected_ci_SCI_select_cutoff', .5e-3) conv_tol = getattr(__config__, 'fci_selected_ci_SCI_conv_tol', 1e-9) start_tol = getattr(__config__, 'fci_selected_ci_SCI_start_tol', 3e-4) tol_decay_rate = getattr(__config__, 'fci_selected_ci_SCI_tol_decay_rate', 0.3) def __init__(self, mol=None): direct_spin1.FCISolver.__init__(self, mol) et_init_guess(na, nb, nroots, hdiag) return [_as_SCIvector(x, ci_strs) for x in ci0] def make_hdiag(self, h1e, eri, ci_strs, norb, nelec): return make_hdiag(h1e, eri, ci_strs, norb, nelec) enlarge_space = enlarge_space kernel = kernel_float_space kernel_fixed_space = kernel_fixed_space # def approx_kernel(self, h1e, eri, norb, nelec, ci0=None, link_index=None, # tol=None, lindep=None, max_cycle=None, # max_memory=None, verbose=None, **kwargs): # ci_strs = getattr(ci0, '_strs', self._strs) # return self.kernel_fixed_space(h1e, eri, norb, nelec, ci_strs, # ci0, link_index, tol, lindep, 6, # max_memory, verbose, **kwargs) @lib.with_doc(spin_square.__doc__) def spin_square(self, civec_strs, norb, nelec): nelec = direct_spin1._unpack_nelec(nelec, self.spin) return spin_square(_as_SCIvector_if_not(civec_strs, self._strs), norb, nelec) def large_ci(self, civec_strs, norb, nelec, tol=.1, return_strs=True): nelec = direct_spin1._unpack_nelec(nelec, self.spin) ci, _, (strsa, strsb) = _unpack(civec_strs, nelec, self._strs) addra, addrb = numpy.where(abs(ci) > tol) if return_strs: strsa = [bin(x) for x in strsa[addra]] strsb = [bin(x) for x in strsb[addrb]] return list(zip(ci[addra,addrb], strsa, strsb)) else: occslsta = cistring._strs2occslst(strsa[addra], norb) occslstb = cistring._strs2occslst(strsb[addrb], norb) return list(zip(ci[addra,addrb], occslsta, occslstb)) def contract_ss(self, fcivec, norb, nelec): return contract_ss(fcivec, norb, nelec) @lib.with_doc(make_rdm1s.__doc__) def make_rdm1s(self, civec_strs, norb, nelec, link_index=None): nelec = direct_spin1._unpack_nelec(nelec, self.spin) civec_strs = _as_SCIvector_if_not(civec_strs, self._strs) return make_rdm1s(civec_strs, norb, nelec, link_index) @lib.with_doc(make_rdm1.__doc__) def make_rdm1(self, civec_strs, norb, nelec, link_index=None): nelec = direct_spin1._unpack_nelec(nelec, self.spin) rdm1a, rdm1b = self.make_rdm1s(civec_strs, norb, nelec, link_index) return rdm1a + rdm1b @lib.with_doc(make_rdm2s.__doc__) def make_rdm2s(self, civec_strs, norb, nelec, link_index=None, **kwargs): nelec = direct_spin1._unpack_nelec(nelec, self.spin) civec_strs = _as_SCIvector_if_not(civec_strs, self._strs) return make_rdm2s(civec_strs, norb, nelec, link_index) @lib.with_doc(make_rdm2.__doc__) def make_rdm2(self, civec_strs, norb, nelec, link_index=None, **kwargs): nelec = direct_spin1._unpack_nelec(nelec, self.spin) civec_strs = _as_SCIvector_if_not(civec_strs, self._strs) return make_rdm2(civec_strs, norb, nelec, link_index) def make_rdm12s(self, civec_strs, norb, nelec, link_index=None, **kwargs): neleca, nelecb = nelec = direct_spin1._unpack_nelec(nelec, self.spin) civec_strs = _as_SCIvector_if_not(civec_strs, self._strs) dm2aa, dm2ab, dm2bb = make_rdm2s(civec_strs, norb, nelec, link_index) if neleca > 1 and nelecb > 1: dm1a = numpy.einsum('iikl->kl', dm2aa) / (neleca-1) dm1b = numpy.einsum('iikl->kl', dm2bb) / (nelecb-1) else: dm1a, dm1b = make_rdm1s(civec_strs, norb, nelec, link_index) return (dm1a, dm1b), (dm2aa, dm2ab, dm2bb) def make_rdm12(self, civec_strs, norb, nelec, link_index=None, **kwargs): nelec = direct_spin1._unpack_nelec(nelec, self.spin) nelec_tot = sum(nelec) civec_strs = _as_SCIvector_if_not(civec_strs, self._strs) dm2 = make_rdm2(civec_strs, norb, nelec, link_index) if nelec_tot > 1: dm1 = numpy.einsum('iikl->kl', dm2) / (nelec_tot-1) else: dm1 = make_rdm1(civec_strs, norb, nelec, link_index) return dm1, dm2 @lib.with_doc(trans_rdm1s.__doc__) def trans_rdm1s(self, cibra, ciket, norb, nelec, link_index=None): nelec = direct_spin1._unpack_nelec(nelec, self.spin) cibra = _as_SCIvector_if_not(cibra, self._strs) ciket = _as_SCIvector_if_not(ciket, self._strs) return trans_rdm1s(cibra, ciket, norb, nelec, link_index) @lib.with_doc(trans_rdm1.__doc__) def trans_rdm1(self, cibra, ciket, norb, nelec, link_index=None): nelec = direct_spin1._unpack_nelec(nelec, self.spin) cibra = _as_SCIvector_if_not(cibra, self._strs) ciket = _as_SCIvector_if_not(ciket, self._strs) return trans_rdm1(cibra, ciket, norb, nelec, link_index) def gen_linkstr(self, norb, nelec, tril=True, spin=None, ci_strs=None): if spin is None: spin = self.spin if ci_strs is None: ci_strs = self._strs neleca, nelecb = direct_spin1._unpack_nelec(nelec, spin) if tril: cd_indexa = cre_des_linkstr_tril(ci_strs[0], norb, neleca) dd_indexa = des_des_linkstr_tril(ci_strs[0], norb, neleca) cd_indexb = cre_des_linkstr_tril(ci_strs[1], norb, nelecb) dd_indexb = des_des_linkstr_tril(ci_strs[1], norb, nelecb) else: cd_indexa = cre_des_linkstr(ci_strs[0], norb, neleca) dd_indexa = des_des_linkstr(ci_strs[0], norb, neleca) cd_indexb = cre_des_linkstr(ci_strs[1], norb, nelecb) dd_indexb = des_des_linkstr(ci_strs[1], norb, nelecb) return cd_indexa, dd_indexa, cd_indexb, dd_indexb SCI = SelectedCI def _unpack(civec_strs, nelec, ci_strs=None, spin=None): neleca, nelecb = direct_spin1._unpack_nelec(nelec, spin) ci_strs = getattr(civec_strs, '_strs', ci_strs) if ci_strs is not None: strsa, strsb = ci_strs strsa = numpy.asarray(strsa) strsb = numpy.asarray(strsb) ci_strs = (strsa, strsb) return civec_strs, (neleca, nelecb), ci_strs def _all_linkstr_index(ci_strs, norb, nelec): cd_indexa = cre_des_linkstr_tril(ci_strs[0], norb, nelec[0]) dd_indexa = des_des_linkstr_tril(ci_strs[0], norb, nelec[0]) cd_indexb = cre_des_linkstr_tril(ci_strs[1], norb, nelec[1]) dd_indexb = des_des_linkstr_tril(ci_strs[1], norb, nelec[1]) return cd_indexa, dd_indexa, cd_indexb, dd_indexb # numpy.ndarray does not allow to attach attribtues. Overwrite the # numpy.ndarray class to tag the ._strs attribute class _SCIvector(numpy.ndarray): def __array_finalize__(self, obj): self._strs = getattr(obj, '_strs', None) # Whenever the contents of the array was modified (through ufunc), the tag # should be expired. Overwrite the output of ufunc to restore ndarray type. def __array_wrap__(self, out, context=None): return numpy.ndarray.__array_wrap__(self, out, context).view(numpy.ndarray) def _as_SCIvector(civec, ci_strs): civec = civec.view(_SCIvector) civec._strs = ci_strs return civec def _as_SCIvector_if_not(civec, ci_strs): if getattr(civec, '_strs', None) is None: civec = _as_SCIvector(civec, ci_strs) return civec if __name__ == '__main__': from functools import reduce from pyscf import gto from pyscf import scf from pyscf import ao2mo from pyscf.fci import spin_op from pyscf.fci import addons mol = gto.Mole() mol.verbose = 0 mol.output = None mol.atom = [ ['H', ( 1.,-1. , 0. )], ['H', ( 0.,-1. ,-1. )], ['H', ( 1.,-0.5 ,-1. )], ['H', ( 0.,-0. ,-1. )], ['H', ( 1.,-0.5 , 0. )], ['H', ( 0., 1. , 1. )], ['H', ( 1., 2. , 3. )], ['H', ( 1., 2. , 4. )], ] mol.basis = 'sto-3g' mol.build() m = scf.RHF(mol) m.kernel() norb = m.mo_coeff.shape[1] nelec = mol.nelectron h1e = reduce(numpy.dot, (m.mo_coeff.T, m.get_hcore(), m.mo_coeff)) eri = ao2mo.kernel(m._eri, m.mo_coeff, compact=False) eri = eri.reshape(norb,norb,norb,norb) e1, c1 = kernel(h1e, eri, norb, nelec) e2, c2 = direct_spin1.kernel(h1e, eri, norb, nelec) print(e1, e1 - -11.894559902235565, 'diff to FCI', e1-e2) print(c1.shape, c2.shape) dm1_1 = make_rdm1(c1, norb, nelec) dm1_2 = direct_spin1.make_rdm1(c2, norb, nelec) print(abs(dm1_1 - dm1_2).sum()) dm2_1 = make_rdm2(c1, norb, nelec) dm2_2 = direct_spin1.make_rdm12(c2, norb, nelec)[1] print(abs(dm2_1 - dm2_2).sum()) myci = SelectedCI() e, c = kernel_fixed_space(myci, h1e, eri, norb, nelec, c1._strs) print(e - -11.894559902235565) print(myci.large_ci(c1, norb, nelec)) print(myci.spin_square(c1, norb, nelec)[0] - spin_op.spin_square0(to_fci(c1, norb, nelec), norb, nelec)[0]) myci = SelectedCI() myci = addons.fix_spin_(myci) e1, c1 = myci.kernel(h1e, eri, norb, nelec) print(e1, e1 - -11.89467612053687) print(myci.spin_square(c1, norb, nelec))

true

f71be110a2784a6f4b942989944478aaf8facaaa

18,030

py

Python

ultra/tests/test_evaluate.py

MCZhi/SMARTS

3ef5650b04ac6fb7145cf4e23d5534d73e0929fc

[ "MIT" ]

2

2021-12-13T12:41:54.000Z

2021-12-16T03:10:24.000Z

ultra/tests/test_evaluate.py

MCZhi/SMARTS

3ef5650b04ac6fb7145cf4e23d5534d73e0929fc

[ "MIT" ]

null

ultra/tests/test_evaluate.py

MCZhi/SMARTS

3ef5650b04ac6fb7145cf4e23d5534d73e0929fc

[ "MIT" ]

null

# MIT License # # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import glob import os import pickle import re import shutil import time import unittest import dill import gym import ray from ultra.baselines.agent_spec import BaselineAgentSpec from ultra.baselines.sac.sac.policy import SACPolicy from ultra.evaluate import collect_evaluations, evaluate, evaluation_check from ultra.utils.episode import episodes seed = 2 AGENT_ID = "001" class EvaluateTest(unittest.TestCase): # Put generated files and folders in this directory. OUTPUT_DIRECTORY = "tests/evaluate_test/" @classmethod def setUpClass(cls): path = os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "sac_test_models/") multiagent_path = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "multiagent_test_models/" ) generate_command = ( "python ultra/scenarios/interface.py generate " "--task 00 --level eval_test --root-dir tests/scenarios " " --save-dir tests/task/eval_test/" ) multiagent_generate_command = ( "python ultra/scenarios/interface.py generate " "--task 00-multiagent --level eval_test --root-dir tests/scenarios " "--save-dir tests/task/eval_test_multiagent/" ) train_command = ( "python ultra/train.py " "--task 00 --level eval_test --policy sac --headless --episodes 1 " f"--eval-rate 1 --eval-episodes 1 --max-episode-steps 2 --log-dir {path}" ) multiagent_train_command = ( "python ultra/train.py " "--task 00-multiagent --level eval_test --policy sac,dqn,ppo --headless --episodes 1 " f"--eval-rate 1 --eval-episodes 1 --max-episode-steps 2 --log-dir {multiagent_path}" ) # Generate the scenarios. os.system(generate_command) os.system(multiagent_generate_command) # Remove existing models if os.path.exists(path): shutil.rmtree(path) if os.path.exists(multiagent_path): shutil.rmtree(multiagent_path) # Generate models before evaluation tests if not os.path.exists(path): os.system(train_command) if not os.path.exists(multiagent_path): os.system(multiagent_train_command) def test_a_folders(self): path = os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "sac_test_models/") if not os.path.exists(path): self.assertTrue(False) path = glob.glob( os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "sac_test_models/*/models") )[0] if len(os.listdir(path)) == 0: self.assertTrue(False) path = "tests/task/eval_test" if len(os.listdir(path)) <= 2: self.assertTrue(False) multiagent_path = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "multiagent_test_models/" ) if not os.path.exists(path): self.assertTrue(False) multiagent_path = glob.glob( os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "multiagent_test_models/*/models" ) )[0] if len(os.listdir(multiagent_path)) < 2: self.assertTrue(False) multiagent_path = "tests/task/eval_test_multiagent" if len(os.listdir(path)) <= 2: self.assertTrue(False) def test_evaluation_check(self): log_dir = os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "output_eval_check_logs/") # ray.init(ignore_reinit_error=True) ray.shutdown() ray.init() try: run_experiment( scenario_info=("00", "eval_test"), num_agents=1, log_dir=log_dir ) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) if not os.listdir(log_dir): raise "Evaluation failed to generate new experiment folder" self.assertTrue(False) else: shutil.rmtree(log_dir) def test_evaluation_check_multiagent(self): log_dir = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "output_eval_check_multiagent_logs/" ) # ray.init(ignore_reinit_error=True) ray.shutdown() ray.init() try: run_experiment( scenario_info=("00-multiagent", "eval_test"), num_agents=3, log_dir=log_dir, ) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) if not os.listdir(log_dir): raise "Evaluation failed to generate new experiment folder" self.assertTrue(False) else: shutil.rmtree(log_dir) def test_evaluate_cli(self): log_dir = os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "output_eval_cli_logs/") experiment_dir = glob.glob( os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "sac_test_models/*") )[0] evaluate_command = ( f"python ultra/evaluate.py " f"--task 00 --level eval_test --experiment-dir {experiment_dir} " f"--episodes 1 --max-episode-steps 2 --log-dir {log_dir} --headless" ) ray.shutdown() try: os.system(evaluate_command) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) if not os.listdir(log_dir): raise "Evaluation failed to generate new experiment folder" self.assertTrue(False) else: shutil.rmtree(log_dir) def test_evaluate_cli_multiagent(self): log_dir = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "output_eval_cli_multiagent_logs/" ) experiment_dir = glob.glob( os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "multiagent_test_models/*") )[0] evaluate_command = ( f"python ultra/evaluate.py " f"--task 00-multiagent --level eval_test --agents 000 --experiment-dir {experiment_dir} " f"--episodes 1 --max-episode-steps 2 --log-dir {log_dir} --headless" ) ray.shutdown() try: os.system(evaluate_command) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) if not os.listdir(log_dir): raise "Evaluation failed to generate new experiment folder" self.assertTrue(False) else: shutil.rmtree(log_dir) def test_evaluate_agent(self): seed = 2 models_directory = glob.glob( os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "sac_test_models/*/models/") )[0] log_dir = os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "output_eval_agent_logs/") with open( os.path.join(models_directory, "../agent_metadata.pkl"), "rb" ) as metadata_file: agent_metadata = pickle.load(metadata_file) agent_ids = agent_metadata["agent_ids"] policy_classes = agent_metadata["agent_classes"] checkpoint_directories = { agent_id: sorted( glob.glob(os.path.join(models_directory, agent_id, "*")), key=lambda x: int(x.split("/")[-1]), ) for agent_id in agent_ids } ray.shutdown() ray.init(ignore_reinit_error=True) try: evaluate.remote( experiment_dir=None, agent_ids=agent_ids, policy_classes=policy_classes, seed=seed, checkpoint_dirs=checkpoint_directories, scenario_info=("00", "eval_test"), num_episodes=1, max_episode_steps=2, timestep_sec=0.1, headless=True, log_dir=log_dir, ) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) # This test performs evaluation on multiple agents, but the test map # that is created can only support one agent. Skip this for now until # we can specify a map to use that supports multiple agents. @unittest.skip("Test map does not yet support multiple agents.") def test_evaluate_multiagent(self): seed = 2 models_directory = glob.glob( os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "multiagent_test_models/*/models/" ) )[0] log_dir = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "output_eval_multiagent_logs/" ) with open( os.path.join(models_directory, "../agent_metadata.pkl"), "rb" ) as metadata_file: agent_metadata = pickle.load(metadata_file) agent_ids = agent_metadata["agent_ids"] policy_classes = agent_metadata["agent_classes"] checkpoint_directories = { agent_id: sorted( glob.glob(os.path.join(models_directory, agent_id, "*")), key=lambda x: int(x.split("/")[-1]), ) for agent_id in agent_ids } ray.shutdown() ray.init(ignore_reinit_error=True) try: evaluate.remote( experiment_dir=None, agent_ids=agent_ids, policy_classes=policy_classes, seed=seed, checkpoint_dirs=checkpoint_directories, scenario_info=("00-multiagent", "eval_test"), num_episodes=1, max_episode_steps=2, timestep_sec=0.1, headless=True, log_dir=log_dir, ) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) def test_record_evaluation_at_proper_episode_indices(self): """Due to parallelization, there might arise a situation where the episode object at the beginning of an evaluation would not match the episode object when recording to tensorboard. This test ensures that the evaluation data (for both test and train scenarios) is recorded at the proper episode index. """ AGENT_ID = "000" log_dir = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "output_eval_episode_check_log/" ) # Arbitary values for evaluation rate and number of training episodes eval_rate = 4 num_episodes = 20 train_command = ( "python ultra/train.py " f"--task 00 --level eval_test --policy sac --headless --episodes {num_episodes} " f"--eval-rate {eval_rate} --eval-episodes 2 --max-episode-steps 2 --log-dir {log_dir}" ) if not os.path.exists(log_dir): os.system(train_command) with open( os.path.join( log_dir, os.listdir(log_dir)[0], "pkls/Evaluation/results.pkl" ), "rb", ) as handle: evaluation_results = dill.load(handle) # Check if the episode indices are divisible by the evaluation rate. If they # do, then the evaluation data is properly saved under the results.pkl # and also correctly added to the tensorboard for index in evaluation_results[AGENT_ID].keys(): self.assertEqual((index) % eval_rate, 0) with open( os.path.join( log_dir, os.listdir(log_dir)[0], "pkls/Evaluation_Training/results.pkl" ), "rb", ) as handle: evaluation_training_results = dill.load(handle) # Check if the episode indices are divisible by the evaluation rate. If they # do, then the evaluation training data is properly saved under the results.pkl # and also correctly added to the tensorboard for index in evaluation_training_results[AGENT_ID].keys(): self.assertEqual((index) % eval_rate, 0) def test_extract_policy_from_path(self): paths = [ "from.ultra.baselines.sac:sac-v0", "hello.ultra.ppo:ppo-v1", "ultra.custom:custom", "a.sb.ultra.c.d.e.sac:sac-v99", "a.b.c.d.e.ultra.custom_agent.policy:MBPO-v2", ] def extract(path): m = re.search( "ultra(.)*([a-zA-Z0-9_]*.)+([a-zA-Z0-9_])+:[a-zA-Z0-9_]+((-)*[a-zA-Z0-9_]*)*", path, ) try: policy_class = m.group(0) # pytype: disable=attribute-error except AttributeError as e: self.assertTrue(False) for path in paths: extract(path) # @classmethod # def tearDownClass(cls): # os.system("ray stop") @classmethod def tearDownClass(cls): if os.path.exists(EvaluateTest.OUTPUT_DIRECTORY): shutil.rmtree(EvaluateTest.OUTPUT_DIRECTORY) if os.path.exists("tests/task/eval_test/"): shutil.rmtree("tests/task/eval_test/") if os.path.exists("tests/task/eval_test_multiagent/"): shutil.rmtree("tests/task/eval_test_multiagent/") def run_experiment(scenario_info, num_agents, log_dir, headless=True): agent_ids = ["0" * max(0, 3 - len(str(i))) + str(i) for i in range(num_agents)] agent_classes = {agent_id: "ultra.baselines.sac:sac-v0" for agent_id in agent_ids} agent_specs = { agent_id: BaselineAgentSpec(policy_class=SACPolicy, max_episode_steps=2) for agent_id in agent_ids } env = gym.make( "ultra.env:ultra-v0", agent_specs=agent_specs, scenario_info=scenario_info, headless=headless, timestep_sec=0.1, seed=seed, ) agents = { agent_id: agent_spec.build_agent() for agent_id, agent_spec in agent_specs.items() } total_step = 0 etag = ":".join([policy_class.split(":")[-1] for policy_class in agent_classes]) evaluation_task_ids = dict() for episode in episodes(1, etag=etag, log_dir=log_dir): observations = env.reset() dones = {"__all__": False} infos = None episode.reset() experiment_dir = episode.experiment_dir if not os.path.exists(f"{experiment_dir}/agent_metadata.pkl"): if not os.path.exists(experiment_dir): os.makedirs(experiment_dir) with open(f"{experiment_dir}/agent_metadata.pkl", "wb") as metadata_file: dill.dump( { "agent_ids": agent_ids, "agent_classes": agent_classes, "agent_specs": agent_specs, }, metadata_file, pickle.HIGHEST_PROTOCOL, ) while not dones["__all__"]: evaluation_check( agents=agents, agent_ids=agent_ids, episode=episode, eval_rate=10, eval_episodes=1, max_episode_steps=2, policy_classes=agent_classes, scenario_info=scenario_info, evaluation_task_ids=evaluation_task_ids, timestep_sec=0.1, headless=True, log_dir=log_dir, ) collect_evaluations(evaluation_task_ids=evaluation_task_ids) actions = { agent_id: agents[agent_id].act(observation, explore=True) for agent_id, observation in observations.items() } next_observations, rewards, dones, infos = env.step(actions) active_agent_ids = observations.keys() & next_observations.keys() # pytype: disable=attribute-error loss_outputs = { agent_id: agents[agent_id].step( state=observations[agent_id], action=actions[agent_id], reward=rewards[agent_id], next_state=next_observations[agent_id], done=dones[agent_id], info=infos[agent_id], ) for agent_id in active_agent_ids } # pytype: enable=attribute-error episode.record_step( agent_ids_to_record=active_agent_ids, infos=infos, rewards=rewards, total_step=total_step, loss_outputs=loss_outputs, ) total_step += 1 observations = next_observations # Wait on the remaining evaluations to finish. while collect_evaluations(evaluation_task_ids): time.sleep(0.1) env.close()

35.916335

101

0.59279

import glob import os import pickle import re import shutil import time import unittest import dill import gym import ray from ultra.baselines.agent_spec import BaselineAgentSpec from ultra.baselines.sac.sac.policy import SACPolicy from ultra.evaluate import collect_evaluations, evaluate, evaluation_check from ultra.utils.episode import episodes seed = 2 AGENT_ID = "001" class EvaluateTest(unittest.TestCase): OUTPUT_DIRECTORY = "tests/evaluate_test/" @classmethod def setUpClass(cls): path = os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "sac_test_models/") multiagent_path = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "multiagent_test_models/" ) generate_command = ( "python ultra/scenarios/interface.py generate " "--task 00 --level eval_test --root-dir tests/scenarios " " --save-dir tests/task/eval_test/" ) multiagent_generate_command = ( "python ultra/scenarios/interface.py generate " "--task 00-multiagent --level eval_test --root-dir tests/scenarios " "--save-dir tests/task/eval_test_multiagent/" ) train_command = ( "python ultra/train.py " "--task 00 --level eval_test --policy sac --headless --episodes 1 " f"--eval-rate 1 --eval-episodes 1 --max-episode-steps 2 --log-dir {path}" ) multiagent_train_command = ( "python ultra/train.py " "--task 00-multiagent --level eval_test --policy sac,dqn,ppo --headless --episodes 1 " f"--eval-rate 1 --eval-episodes 1 --max-episode-steps 2 --log-dir {multiagent_path}" ) os.system(generate_command) os.system(multiagent_generate_command) if os.path.exists(path): shutil.rmtree(path) if os.path.exists(multiagent_path): shutil.rmtree(multiagent_path) if not os.path.exists(path): os.system(train_command) if not os.path.exists(multiagent_path): os.system(multiagent_train_command) def test_a_folders(self): path = os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "sac_test_models/") if not os.path.exists(path): self.assertTrue(False) path = glob.glob( os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "sac_test_models/*/models") )[0] if len(os.listdir(path)) == 0: self.assertTrue(False) path = "tests/task/eval_test" if len(os.listdir(path)) <= 2: self.assertTrue(False) multiagent_path = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "multiagent_test_models/" ) if not os.path.exists(path): self.assertTrue(False) multiagent_path = glob.glob( os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "multiagent_test_models/*/models" ) )[0] if len(os.listdir(multiagent_path)) < 2: self.assertTrue(False) multiagent_path = "tests/task/eval_test_multiagent" if len(os.listdir(path)) <= 2: self.assertTrue(False) def test_evaluation_check(self): log_dir = os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "output_eval_check_logs/") ray.shutdown() ray.init() try: run_experiment( scenario_info=("00", "eval_test"), num_agents=1, log_dir=log_dir ) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) if not os.listdir(log_dir): raise "Evaluation failed to generate new experiment folder" self.assertTrue(False) else: shutil.rmtree(log_dir) def test_evaluation_check_multiagent(self): log_dir = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "output_eval_check_multiagent_logs/" ) ray.shutdown() ray.init() try: run_experiment( scenario_info=("00-multiagent", "eval_test"), num_agents=3, log_dir=log_dir, ) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) if not os.listdir(log_dir): raise "Evaluation failed to generate new experiment folder" self.assertTrue(False) else: shutil.rmtree(log_dir) def test_evaluate_cli(self): log_dir = os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "output_eval_cli_logs/") experiment_dir = glob.glob( os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "sac_test_models/*") )[0] evaluate_command = ( f"python ultra/evaluate.py " f"--task 00 --level eval_test --experiment-dir {experiment_dir} " f"--episodes 1 --max-episode-steps 2 --log-dir {log_dir} --headless" ) ray.shutdown() try: os.system(evaluate_command) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) if not os.listdir(log_dir): raise "Evaluation failed to generate new experiment folder" self.assertTrue(False) else: shutil.rmtree(log_dir) def test_evaluate_cli_multiagent(self): log_dir = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "output_eval_cli_multiagent_logs/" ) experiment_dir = glob.glob( os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "multiagent_test_models/*") )[0] evaluate_command = ( f"python ultra/evaluate.py " f"--task 00-multiagent --level eval_test --agents 000 --experiment-dir {experiment_dir} " f"--episodes 1 --max-episode-steps 2 --log-dir {log_dir} --headless" ) ray.shutdown() try: os.system(evaluate_command) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) if not os.listdir(log_dir): raise "Evaluation failed to generate new experiment folder" self.assertTrue(False) else: shutil.rmtree(log_dir) def test_evaluate_agent(self): seed = 2 models_directory = glob.glob( os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "sac_test_models/*/models/") )[0] log_dir = os.path.join(EvaluateTest.OUTPUT_DIRECTORY, "output_eval_agent_logs/") with open( os.path.join(models_directory, "../agent_metadata.pkl"), "rb" ) as metadata_file: agent_metadata = pickle.load(metadata_file) agent_ids = agent_metadata["agent_ids"] policy_classes = agent_metadata["agent_classes"] checkpoint_directories = { agent_id: sorted( glob.glob(os.path.join(models_directory, agent_id, "*")), key=lambda x: int(x.split("/")[-1]), ) for agent_id in agent_ids } ray.shutdown() ray.init(ignore_reinit_error=True) try: evaluate.remote( experiment_dir=None, agent_ids=agent_ids, policy_classes=policy_classes, seed=seed, checkpoint_dirs=checkpoint_directories, scenario_info=("00", "eval_test"), num_episodes=1, max_episode_steps=2, timestep_sec=0.1, headless=True, log_dir=log_dir, ) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) @unittest.skip("Test map does not yet support multiple agents.") def test_evaluate_multiagent(self): seed = 2 models_directory = glob.glob( os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "multiagent_test_models/*/models/" ) )[0] log_dir = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "output_eval_multiagent_logs/" ) with open( os.path.join(models_directory, "../agent_metadata.pkl"), "rb" ) as metadata_file: agent_metadata = pickle.load(metadata_file) agent_ids = agent_metadata["agent_ids"] policy_classes = agent_metadata["agent_classes"] checkpoint_directories = { agent_id: sorted( glob.glob(os.path.join(models_directory, agent_id, "*")), key=lambda x: int(x.split("/")[-1]), ) for agent_id in agent_ids } ray.shutdown() ray.init(ignore_reinit_error=True) try: evaluate.remote( experiment_dir=None, agent_ids=agent_ids, policy_classes=policy_classes, seed=seed, checkpoint_dirs=checkpoint_directories, scenario_info=("00-multiagent", "eval_test"), num_episodes=1, max_episode_steps=2, timestep_sec=0.1, headless=True, log_dir=log_dir, ) self.assertTrue(True) except Exception as err: print(err) self.assertTrue(False) def test_record_evaluation_at_proper_episode_indices(self): AGENT_ID = "000" log_dir = os.path.join( EvaluateTest.OUTPUT_DIRECTORY, "output_eval_episode_check_log/" ) eval_rate = 4 num_episodes = 20 train_command = ( "python ultra/train.py " f"--task 00 --level eval_test --policy sac --headless --episodes {num_episodes} " f"--eval-rate {eval_rate} --eval-episodes 2 --max-episode-steps 2 --log-dir {log_dir}" ) if not os.path.exists(log_dir): os.system(train_command) with open( os.path.join( log_dir, os.listdir(log_dir)[0], "pkls/Evaluation/results.pkl" ), "rb", ) as handle: evaluation_results = dill.load(handle) for index in evaluation_results[AGENT_ID].keys(): self.assertEqual((index) % eval_rate, 0) with open( os.path.join( log_dir, os.listdir(log_dir)[0], "pkls/Evaluation_Training/results.pkl" ), "rb", ) as handle: evaluation_training_results = dill.load(handle) for index in evaluation_training_results[AGENT_ID].keys(): self.assertEqual((index) % eval_rate, 0) def test_extract_policy_from_path(self): paths = [ "from.ultra.baselines.sac:sac-v0", "hello.ultra.ppo:ppo-v1", "ultra.custom:custom", "a.sb.ultra.c.d.e.sac:sac-v99", "a.b.c.d.e.ultra.custom_agent.policy:MBPO-v2", ] def extract(path): m = re.search( "ultra(.)*([a-zA-Z0-9_]*.)+([a-zA-Z0-9_])+:[a-zA-Z0-9_]+((-)*[a-zA-Z0-9_]*)*", path, ) try: policy_class = m.group(0) except AttributeError as e: self.assertTrue(False) for path in paths: extract(path) @classmethod def tearDownClass(cls): if os.path.exists(EvaluateTest.OUTPUT_DIRECTORY): shutil.rmtree(EvaluateTest.OUTPUT_DIRECTORY) if os.path.exists("tests/task/eval_test/"): shutil.rmtree("tests/task/eval_test/") if os.path.exists("tests/task/eval_test_multiagent/"): shutil.rmtree("tests/task/eval_test_multiagent/") def run_experiment(scenario_info, num_agents, log_dir, headless=True): agent_ids = ["0" * max(0, 3 - len(str(i))) + str(i) for i in range(num_agents)] agent_classes = {agent_id: "ultra.baselines.sac:sac-v0" for agent_id in agent_ids} agent_specs = { agent_id: BaselineAgentSpec(policy_class=SACPolicy, max_episode_steps=2) for agent_id in agent_ids } env = gym.make( "ultra.env:ultra-v0", agent_specs=agent_specs, scenario_info=scenario_info, headless=headless, timestep_sec=0.1, seed=seed, ) agents = { agent_id: agent_spec.build_agent() for agent_id, agent_spec in agent_specs.items() } total_step = 0 etag = ":".join([policy_class.split(":")[-1] for policy_class in agent_classes]) evaluation_task_ids = dict() for episode in episodes(1, etag=etag, log_dir=log_dir): observations = env.reset() dones = {"__all__": False} infos = None episode.reset() experiment_dir = episode.experiment_dir if not os.path.exists(f"{experiment_dir}/agent_metadata.pkl"): if not os.path.exists(experiment_dir): os.makedirs(experiment_dir) with open(f"{experiment_dir}/agent_metadata.pkl", "wb") as metadata_file: dill.dump( { "agent_ids": agent_ids, "agent_classes": agent_classes, "agent_specs": agent_specs, }, metadata_file, pickle.HIGHEST_PROTOCOL, ) while not dones["__all__"]: evaluation_check( agents=agents, agent_ids=agent_ids, episode=episode, eval_rate=10, eval_episodes=1, max_episode_steps=2, policy_classes=agent_classes, scenario_info=scenario_info, evaluation_task_ids=evaluation_task_ids, timestep_sec=0.1, headless=True, log_dir=log_dir, ) collect_evaluations(evaluation_task_ids=evaluation_task_ids) actions = { agent_id: agents[agent_id].act(observation, explore=True) for agent_id, observation in observations.items() } next_observations, rewards, dones, infos = env.step(actions) active_agent_ids = observations.keys() & next_observations.keys() loss_outputs = { agent_id: agents[agent_id].step( state=observations[agent_id], action=actions[agent_id], reward=rewards[agent_id], next_state=next_observations[agent_id], done=dones[agent_id], info=infos[agent_id], ) for agent_id in active_agent_ids } episode.record_step( agent_ids_to_record=active_agent_ids, infos=infos, rewards=rewards, total_step=total_step, loss_outputs=loss_outputs, ) total_step += 1 observations = next_observations while collect_evaluations(evaluation_task_ids): time.sleep(0.1) env.close()

true

f71be12c5aef84eac371279bc150835aa8551c7d

27

py

Python

tess_py_api/TessPyWrap/__init__.py

orel98/tess_py_api

538cbf64fa795318366ac2ceeec8b15d5cf9ae84

[ "MIT" ]

3

2022-03-16T09:11:33.000Z

2022-03-19T19:43:50.000Z

tess_py_api/TessPyWrap/__init__.py

orel98/tess_py_api

538cbf64fa795318366ac2ceeec8b15d5cf9ae84

[ "MIT" ]

null

tess_py_api/TessPyWrap/__init__.py

orel98/tess_py_api

538cbf64fa795318366ac2ceeec8b15d5cf9ae84

[ "MIT" ]

null

from .CpyAPI import CpyAPI

13.5

26

0.814815

from .CpyAPI import CpyAPI

true

f71be1fb130e1061491a135bc6bfa210726ceb66

650

py

Python

PythonDesafios/d076.py

adaatii/Python-Curso-em-Video-

30b37713b3685469558babb93b557b53210f010c

[ "MIT" ]

null

PythonDesafios/d076.py

adaatii/Python-Curso-em-Video-

30b37713b3685469558babb93b557b53210f010c

[ "MIT" ]

null

PythonDesafios/d076.py

adaatii/Python-Curso-em-Video-

30b37713b3685469558babb93b557b53210f010c

[ "MIT" ]

null

# Crie um programa que tenha uma tupla única com nomes de produtos # e seus respectivos preços, na sequência. No final, mostre uma # listagem de preços, organizando os dados em forma tabular. listagem = ('Lápis', 1.75, 'Borracha', 2, 'Caderno', 15.90, 'Estojo', 10, 'Compasso', 7.95, 'Mochila', 150.45, 'Canetas', 22.30, 'Livro', 34.50) print('-'*40) print(f'{"LISTAGEM DE PREÇOS":^40}') print('-'*40) for i in range(0, len(listagem)): if i % 2 == 0: print(f'{listagem[i]:.<30}', end='') else: print(f'R${listagem[i]:>7.2f}') print('-'*40)

28.26087

66

0.543077

listagem = ('Lápis', 1.75, 'Borracha', 2, 'Caderno', 15.90, 'Estojo', 10, 'Compasso', 7.95, 'Mochila', 150.45, 'Canetas', 22.30, 'Livro', 34.50) print('-'*40) print(f'{"LISTAGEM DE PREÇOS":^40}') print('-'*40) for i in range(0, len(listagem)): if i % 2 == 0: print(f'{listagem[i]:.<30}', end='') else: print(f'R${listagem[i]:>7.2f}') print('-'*40)

true

f71be2a3c566dab4f1526507df28e4b0c96bd528

4,961

py

Python

NMT/dataset.py

MISStingting/NMTmodel

970115d6f9fcd015d7daf3ad0e4844055e2af5d3

[ "Apache-2.0" ]

null

NMT/dataset.py

MISStingting/NMTmodel

970115d6f9fcd015d7daf3ad0e4844055e2af5d3

[ "Apache-2.0" ]

null

NMT/dataset.py

MISStingting/NMTmodel

970115d6f9fcd015d7daf3ad0e4844055e2af5d3

[ "Apache-2.0" ]

null

import tensorflow as tf # tf.enable_eager_execution() class Dataset(object): def get_dataset(self, params, mode): if mode == tf.estimator.ModeKeys.TRAIN: features_path = params["train_features_file"] labels_path = params["train_labels_file"] elif mode == tf.estimator.ModeKeys.EVAL: features_path = params["eval_features_file"] labels_path = params["eval_labels_file"] elif mode == tf.estimator.ModeKeys.PREDICT: features_path = params["test_features_file"] labels_path = params["test_labels_file"] else: raise ValueError("wrong mode!!!") features_dataset, labels_dataset = self._load_dataset(features_path, labels_path, mode) if mode == tf.estimator.ModeKeys.PREDICT: dataset = features_dataset.map(lambda x: tf.string_split([x]).values) dataset = dataset.shuffle(buffer_size=params["buffer_size"], reshuffle_each_iteration=params["reshuffle_each_iteration"]) dataset = dataset.prefetch(buffer_size=params["buffer_size"]) dataset = dataset.map(lambda src: (src, tf.size(src))) dataset = dataset.padded_batch(batch_size=params["batch_size"], padded_shapes=(tf.TensorShape([None]), tf.TensorShape([])), padding_values=(tf.constant("<blank>"), 0)) iterator = dataset.make_one_shot_iterator() src, src_len = iterator.get_next() features = { "input": src, "input_length": src_len } labels = None else: dataset = tf.data.Dataset.zip((features_dataset, labels_dataset)) dataset = dataset.map(lambda x, y: (tf.string_split([x]).values, tf.string_split([y]).values)) dataset = dataset.repeat(params["repeat"]).shuffle(buffer_size=params["buffer_size"], reshuffle_each_iteration=params[ "reshuffle_each_iteration"]) dataset = dataset.prefetch(buffer_size=params["buffer_size"]) if params["src_max_len"] > 0: dataset = dataset.map( lambda src, tgt: (src[:params["src_max_len"]], tgt)) if params["tgt_max_len"] > 0: dataset = dataset.map( lambda src, tgt: (src, tgt[:params["tgt_max_len"]])) dataset = dataset.map( lambda src, tgt: (src, tf.concat((["<s>"], tgt), 0), tf.concat((tgt, ["</s>"]), 0)), num_parallel_calls=params["num_parallel_calls"]) dataset = dataset.map(lambda src, tgt_in, tgt_out: (src, tgt_in, tgt_out, tf.size(src), tf.size(tgt_out))) dataset = dataset.padded_batch(batch_size=params["batch_size"], padded_shapes=( tf.TensorShape([None]), tf.TensorShape([None]), tf.TensorShape([None]), tf.TensorShape([]), tf.TensorShape([])), padding_values=( tf.constant("<blank>", dtype=tf.string), tf.constant("<s>", dtype=tf.string), tf.constant("</s>", dtype=tf.string), 0, 0)) iterator = dataset.make_one_shot_iterator() src, tgt_in, tgt_out, input_length, output_length = iterator.get_next() features = { "input": src, "input_length": input_length } labels = { "output_in": tgt_in, "output_out": tgt_out, "output_length": output_length } return features, labels @staticmethod def _load_dataset(features_path, labels_path, mode): ''' 从文件读取dataset :param mode: :return: ''' if mode == tf.estimator.ModeKeys.TRAIN or mode == tf.estimator.ModeKeys.EVAL: features_dataset = tf.data.TextLineDataset(filenames=features_path) labels_dataset = tf.data.TextLineDataset(filenames=labels_path) return features_dataset, labels_dataset elif mode == tf.estimator.ModeKeys.PREDICT: features_dataset = tf.data.TextLineDataset(filenames=features_path) return features_dataset, None data_util = Dataset()

48.637255

118

0.504939

import tensorflow as tf class Dataset(object): def get_dataset(self, params, mode): if mode == tf.estimator.ModeKeys.TRAIN: features_path = params["train_features_file"] labels_path = params["train_labels_file"] elif mode == tf.estimator.ModeKeys.EVAL: features_path = params["eval_features_file"] labels_path = params["eval_labels_file"] elif mode == tf.estimator.ModeKeys.PREDICT: features_path = params["test_features_file"] labels_path = params["test_labels_file"] else: raise ValueError("wrong mode!!!") features_dataset, labels_dataset = self._load_dataset(features_path, labels_path, mode) if mode == tf.estimator.ModeKeys.PREDICT: dataset = features_dataset.map(lambda x: tf.string_split([x]).values) dataset = dataset.shuffle(buffer_size=params["buffer_size"], reshuffle_each_iteration=params["reshuffle_each_iteration"]) dataset = dataset.prefetch(buffer_size=params["buffer_size"]) dataset = dataset.map(lambda src: (src, tf.size(src))) dataset = dataset.padded_batch(batch_size=params["batch_size"], padded_shapes=(tf.TensorShape([None]), tf.TensorShape([])), padding_values=(tf.constant("<blank>"), 0)) iterator = dataset.make_one_shot_iterator() src, src_len = iterator.get_next() features = { "input": src, "input_length": src_len } labels = None else: dataset = tf.data.Dataset.zip((features_dataset, labels_dataset)) dataset = dataset.map(lambda x, y: (tf.string_split([x]).values, tf.string_split([y]).values)) dataset = dataset.repeat(params["repeat"]).shuffle(buffer_size=params["buffer_size"], reshuffle_each_iteration=params[ "reshuffle_each_iteration"]) dataset = dataset.prefetch(buffer_size=params["buffer_size"]) if params["src_max_len"] > 0: dataset = dataset.map( lambda src, tgt: (src[:params["src_max_len"]], tgt)) if params["tgt_max_len"] > 0: dataset = dataset.map( lambda src, tgt: (src, tgt[:params["tgt_max_len"]])) dataset = dataset.map( lambda src, tgt: (src, tf.concat((["<s>"], tgt), 0), tf.concat((tgt, ["</s>"]), 0)), num_parallel_calls=params["num_parallel_calls"]) dataset = dataset.map(lambda src, tgt_in, tgt_out: (src, tgt_in, tgt_out, tf.size(src), tf.size(tgt_out))) dataset = dataset.padded_batch(batch_size=params["batch_size"], padded_shapes=( tf.TensorShape([None]), tf.TensorShape([None]), tf.TensorShape([None]), tf.TensorShape([]), tf.TensorShape([])), padding_values=( tf.constant("<blank>", dtype=tf.string), tf.constant("<s>", dtype=tf.string), tf.constant("</s>", dtype=tf.string), 0, 0)) iterator = dataset.make_one_shot_iterator() src, tgt_in, tgt_out, input_length, output_length = iterator.get_next() features = { "input": src, "input_length": input_length } labels = { "output_in": tgt_in, "output_out": tgt_out, "output_length": output_length } return features, labels @staticmethod def _load_dataset(features_path, labels_path, mode): if mode == tf.estimator.ModeKeys.TRAIN or mode == tf.estimator.ModeKeys.EVAL: features_dataset = tf.data.TextLineDataset(filenames=features_path) labels_dataset = tf.data.TextLineDataset(filenames=labels_path) return features_dataset, labels_dataset elif mode == tf.estimator.ModeKeys.PREDICT: features_dataset = tf.data.TextLineDataset(filenames=features_path) return features_dataset, None data_util = Dataset()

true

f71be2a65e952c2d97ca924e09c67df982278f0e

66,367

py

Python

tools/run_tests/run_tests.py

jtcho/grpc

99673fcbe341a981c27d2becd572468863bff33b

[ "Apache-2.0" ]

1

2017-09-07T00:48:20.000Z

tools/run_tests/run_tests.py

jtcho/grpc

99673fcbe341a981c27d2becd572468863bff33b

[ "Apache-2.0" ]

null

tools/run_tests/run_tests.py

jtcho/grpc

99673fcbe341a981c27d2becd572468863bff33b

[ "Apache-2.0" ]

null

#!/usr/bin/env python # Copyright 2015 gRPC 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. """Run tests in parallel.""" from __future__ import print_function import argparse import ast import collections import glob import itertools import json import logging import multiprocessing import os import os.path import pipes import platform import random import re import socket import subprocess import sys import tempfile import traceback import time from six.moves import urllib import uuid import six import python_utils.jobset as jobset import python_utils.report_utils as report_utils import python_utils.watch_dirs as watch_dirs import python_utils.start_port_server as start_port_server try: from python_utils.upload_test_results import upload_results_to_bq except (ImportError): pass # It's ok to not import because this is only necessary to upload results to BQ. gcp_utils_dir = os.path.abspath( os.path.join(os.path.dirname(__file__), '../gcp/utils')) sys.path.append(gcp_utils_dir) _ROOT = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), '../..')) os.chdir(_ROOT) _FORCE_ENVIRON_FOR_WRAPPERS = { 'GRPC_VERBOSITY': 'DEBUG', } _POLLING_STRATEGIES = { 'linux': ['epollex', 'epoll1', 'poll', 'poll-cv'], 'mac': ['poll'], } BigQueryTestData = collections.namedtuple('BigQueryTestData', 'name flaky cpu') def get_bqtest_data(limit=None): import big_query_utils bq = big_query_utils.create_big_query() query = """ SELECT filtered_test_name, SUM(result != 'PASSED' AND result != 'SKIPPED') > 0 as flaky, MAX(cpu_measured) + 0.01 as cpu FROM ( SELECT REGEXP_REPLACE(test_name, r'/\d+', '') AS filtered_test_name, result, cpu_measured FROM [grpc-testing:jenkins_test_results.aggregate_results] WHERE timestamp >= DATE_ADD(CURRENT_DATE(), -1, "WEEK") AND platform = '""" + platform_string() + """' AND NOT REGEXP_MATCH(job_name, '.*portability.*') ) GROUP BY filtered_test_name""" if limit: query += " limit {}".format(limit) query_job = big_query_utils.sync_query_job(bq, 'grpc-testing', query) page = bq.jobs().getQueryResults( pageToken=None, **query_job['jobReference']).execute(num_retries=3) test_data = [ BigQueryTestData(row['f'][0]['v'], row['f'][1]['v'] == 'true', float(row['f'][2]['v'])) for row in page['rows'] ] return test_data def platform_string(): return jobset.platform_string() _DEFAULT_TIMEOUT_SECONDS = 5 * 60 def run_shell_command(cmd, env=None, cwd=None): try: subprocess.check_output(cmd, shell=True, env=env, cwd=cwd) except subprocess.CalledProcessError as e: logging.exception( "Error while running command '%s'. Exit status %d. Output:\n%s", e.cmd, e.returncode, e.output) raise def max_parallel_tests_for_current_platform(): # Too much test parallelization has only been seen to be a problem # so far on windows. if jobset.platform_string() == 'windows': return 64 return 1024 # SimpleConfig: just compile with CONFIG=config, and run the binary to test class Config(object): def __init__(self, config, environ=None, timeout_multiplier=1, tool_prefix=[], iomgr_platform='native'): if environ is None: environ = {} self.build_config = config self.environ = environ self.environ['CONFIG'] = config self.tool_prefix = tool_prefix self.timeout_multiplier = timeout_multiplier self.iomgr_platform = iomgr_platform def job_spec(self, cmdline, timeout_seconds=_DEFAULT_TIMEOUT_SECONDS, shortname=None, environ={}, cpu_cost=1.0, flaky=False): """Construct a jobset.JobSpec for a test under this config Args: cmdline: a list of strings specifying the command line the test would like to run """ actual_environ = self.environ.copy() for k, v in environ.items(): actual_environ[k] = v if not flaky and shortname and shortname in flaky_tests: flaky = True if shortname in shortname_to_cpu: cpu_cost = shortname_to_cpu[shortname] return jobset.JobSpec( cmdline=self.tool_prefix + cmdline, shortname=shortname, environ=actual_environ, cpu_cost=cpu_cost, timeout_seconds=(self.timeout_multiplier * timeout_seconds if timeout_seconds else None), flake_retries=4 if flaky or args.allow_flakes else 0, timeout_retries=1 if flaky or args.allow_flakes else 0) def get_c_tests(travis, test_lang): out = [] platforms_str = 'ci_platforms' if travis else 'platforms' with open('tools/run_tests/generated/tests.json') as f: js = json.load(f) return [ tgt for tgt in js if tgt['language'] == test_lang and platform_string() in tgt[platforms_str] and not (travis and tgt['flaky']) ] def _check_compiler(compiler, supported_compilers): if compiler not in supported_compilers: raise Exception( 'Compiler %s not supported (on this platform).' % compiler) def _check_arch(arch, supported_archs): if arch not in supported_archs: raise Exception('Architecture %s not supported.' % arch) def _is_use_docker_child(): """Returns True if running running as a --use_docker child.""" return True if os.getenv('RUN_TESTS_COMMAND') else False _PythonConfigVars = collections.namedtuple('_ConfigVars', [ 'shell', 'builder', 'builder_prefix_arguments', 'venv_relative_python', 'toolchain', 'runner', 'test_name', 'iomgr_platform', ]) def _python_config_generator(name, major, minor, bits, config_vars): name += '_' + config_vars.iomgr_platform return PythonConfig( name, config_vars.shell + config_vars.builder + config_vars.builder_prefix_arguments + [ _python_pattern_function(major=major, minor=minor, bits=bits) ] + [name] + config_vars.venv_relative_python + config_vars.toolchain, config_vars.shell + config_vars.runner + [ os.path.join(name, config_vars.venv_relative_python[0]), config_vars.test_name ]) def _pypy_config_generator(name, major, config_vars): return PythonConfig( name, config_vars.shell + config_vars.builder + config_vars.builder_prefix_arguments + [ _pypy_pattern_function(major=major) ] + [name] + config_vars.venv_relative_python + config_vars.toolchain, config_vars.shell + config_vars.runner + [os.path.join(name, config_vars.venv_relative_python[0])]) def _python_pattern_function(major, minor, bits): # Bit-ness is handled by the test machine's environment if os.name == "nt": if bits == "64": return '/c/Python{major}{minor}/python.exe'.format( major=major, minor=minor, bits=bits) else: return '/c/Python{major}{minor}_{bits}bits/python.exe'.format( major=major, minor=minor, bits=bits) else: return 'python{major}.{minor}'.format(major=major, minor=minor) def _pypy_pattern_function(major): if major == '2': return 'pypy' elif major == '3': return 'pypy3' else: raise ValueError("Unknown PyPy major version") class CLanguage(object): def __init__(self, make_target, test_lang): self.make_target = make_target self.platform = platform_string() self.test_lang = test_lang def configure(self, config, args): self.config = config self.args = args if self.platform == 'windows': _check_compiler( self.args.compiler, ['default', 'cmake', 'cmake_vs2015', 'cmake_vs2017']) _check_arch(self.args.arch, ['default', 'x64', 'x86']) self._cmake_generator_option = 'Visual Studio 15 2017' if self.args.compiler == 'cmake_vs2017' else 'Visual Studio 14 2015' self._cmake_arch_option = 'x64' if self.args.arch == 'x64' else 'Win32' self._use_cmake = True self._make_options = [] elif self.args.compiler == 'cmake': _check_arch(self.args.arch, ['default']) self._use_cmake = True self._docker_distro = 'jessie' self._make_options = [] else: self._use_cmake = False self._docker_distro, self._make_options = self._compiler_options( self.args.use_docker, self.args.compiler) if args.iomgr_platform == "uv": cflags = '-DGRPC_UV -DGRPC_CUSTOM_IOMGR_THREAD_CHECK -DGRPC_CUSTOM_SOCKET ' try: cflags += subprocess.check_output( ['pkg-config', '--cflags', 'libuv']).strip() + ' ' except (subprocess.CalledProcessError, OSError): pass try: ldflags = subprocess.check_output( ['pkg-config', '--libs', 'libuv']).strip() + ' ' except (subprocess.CalledProcessError, OSError): ldflags = '-luv ' self._make_options += [ 'EXTRA_CPPFLAGS={}'.format(cflags), 'EXTRA_LDLIBS={}'.format(ldflags) ] def test_specs(self): out = [] binaries = get_c_tests(self.args.travis, self.test_lang) for target in binaries: if self._use_cmake and target.get('boringssl', False): # cmake doesn't build boringssl tests continue auto_timeout_scaling = target.get('auto_timeout_scaling', True) polling_strategies = (_POLLING_STRATEGIES.get( self.platform, ['all']) if target.get('uses_polling', True) else ['none']) if self.args.iomgr_platform == 'uv': polling_strategies = ['all'] for polling_strategy in polling_strategies: env = { 'GRPC_DEFAULT_SSL_ROOTS_FILE_PATH': _ROOT + '/src/core/tsi/test_creds/ca.pem', 'GRPC_POLL_STRATEGY': polling_strategy, 'GRPC_VERBOSITY': 'DEBUG' } resolver = os.environ.get('GRPC_DNS_RESOLVER', None) if resolver: env['GRPC_DNS_RESOLVER'] = resolver shortname_ext = '' if polling_strategy == 'all' else ' GRPC_POLL_STRATEGY=%s' % polling_strategy if polling_strategy in target.get('excluded_poll_engines', []): continue timeout_scaling = 1 if auto_timeout_scaling: config = self.args.config if ('asan' in config or config == 'msan' or config == 'tsan' or config == 'ubsan' or config == 'helgrind' or config == 'memcheck'): # Scale overall test timeout if running under various sanitizers. # scaling value is based on historical data analysis timeout_scaling *= 3 elif polling_strategy == 'poll-cv': # scale test timeout if running with poll-cv # sanitizer and poll-cv scaling is not cumulative to ensure # reasonable timeout values. # TODO(jtattermusch): based on historical data and 5min default # test timeout poll-cv scaling is currently not useful. # Leaving here so it can be reintroduced if the default test timeout # is decreased in the future. timeout_scaling *= 1 if self.config.build_config in target['exclude_configs']: continue if self.args.iomgr_platform in target.get('exclude_iomgrs', []): continue if self.platform == 'windows': binary = 'cmake/build/%s/%s.exe' % ( _MSBUILD_CONFIG[self.config.build_config], target['name']) else: if self._use_cmake: binary = 'cmake/build/%s' % target['name'] else: binary = 'bins/%s/%s' % (self.config.build_config, target['name']) cpu_cost = target['cpu_cost'] if cpu_cost == 'capacity': cpu_cost = multiprocessing.cpu_count() if os.path.isfile(binary): list_test_command = None filter_test_command = None # these are the flag defined by gtest and benchmark framework to list # and filter test runs. We use them to split each individual test # into its own JobSpec, and thus into its own process. if 'benchmark' in target and target['benchmark']: with open(os.devnull, 'w') as fnull: tests = subprocess.check_output( [binary, '--benchmark_list_tests'], stderr=fnull) for line in tests.split('\n'): test = line.strip() if not test: continue cmdline = [binary, '--benchmark_filter=%s$' % test ] + target['args'] out.append( self.config.job_spec( cmdline, shortname='%s %s' % (' '.join(cmdline), shortname_ext), cpu_cost=cpu_cost, timeout_seconds=target.get( 'timeout_seconds', _DEFAULT_TIMEOUT_SECONDS) * timeout_scaling, environ=env)) elif 'gtest' in target and target['gtest']: # here we parse the output of --gtest_list_tests to build up a complete # list of the tests contained in a binary for each test, we then # add a job to run, filtering for just that test. with open(os.devnull, 'w') as fnull: tests = subprocess.check_output( [binary, '--gtest_list_tests'], stderr=fnull) base = None for line in tests.split('\n'): i = line.find('#') if i >= 0: line = line[:i] if not line: continue if line[0] != ' ': base = line.strip() else: assert base is not None assert line[1] == ' ' test = base + line.strip() cmdline = [binary, '--gtest_filter=%s' % test ] + target['args'] out.append( self.config.job_spec( cmdline, shortname='%s %s' % (' '.join(cmdline), shortname_ext), cpu_cost=cpu_cost, timeout_seconds=target.get( 'timeout_seconds', _DEFAULT_TIMEOUT_SECONDS) * timeout_scaling, environ=env)) else: cmdline = [binary] + target['args'] shortname = target.get('shortname', ' '.join( pipes.quote(arg) for arg in cmdline)) shortname += shortname_ext out.append( self.config.job_spec( cmdline, shortname=shortname, cpu_cost=cpu_cost, flaky=target.get('flaky', False), timeout_seconds=target.get( 'timeout_seconds', _DEFAULT_TIMEOUT_SECONDS) * timeout_scaling, environ=env)) elif self.args.regex == '.*' or self.platform == 'windows': print('\nWARNING: binary not found, skipping', binary) return sorted(out) def make_targets(self): if self.platform == 'windows': # don't build tools on windows just yet return ['buildtests_%s' % self.make_target] return [ 'buildtests_%s' % self.make_target, 'tools_%s' % self.make_target, 'check_epollexclusive' ] def make_options(self): return self._make_options def pre_build_steps(self): if self.platform == 'windows': return [[ 'tools\\run_tests\\helper_scripts\\pre_build_cmake.bat', self._cmake_generator_option, self._cmake_arch_option ]] elif self._use_cmake: return [['tools/run_tests/helper_scripts/pre_build_cmake.sh']] else: return [] def build_steps(self): return [] def post_tests_steps(self): if self.platform == 'windows': return [] else: return [['tools/run_tests/helper_scripts/post_tests_c.sh']] def makefile_name(self): if self._use_cmake: return 'cmake/build/Makefile' else: return 'Makefile' def _clang_make_options(self, version_suffix=''): if self.args.config == 'ubsan': return [ 'CC=clang%s' % version_suffix, 'CXX=clang++%s' % version_suffix, 'LD=clang++%s' % version_suffix, 'LDXX=clang++%s' % version_suffix ] return [ 'CC=clang%s' % version_suffix, 'CXX=clang++%s' % version_suffix, 'LD=clang%s' % version_suffix, 'LDXX=clang++%s' % version_suffix ] def _gcc_make_options(self, version_suffix): return [ 'CC=gcc%s' % version_suffix, 'CXX=g++%s' % version_suffix, 'LD=gcc%s' % version_suffix, 'LDXX=g++%s' % version_suffix ] def _compiler_options(self, use_docker, compiler): """Returns docker distro and make options to use for given compiler.""" if not use_docker and not _is_use_docker_child(): _check_compiler(compiler, ['default']) if compiler == 'gcc4.9' or compiler == 'default': return ('jessie', []) elif compiler == 'gcc4.8': return ('jessie', self._gcc_make_options(version_suffix='-4.8')) elif compiler == 'gcc5.3': return ('ubuntu1604', []) elif compiler == 'gcc7.2': return ('ubuntu1710', []) elif compiler == 'gcc_musl': return ('alpine', []) elif compiler == 'clang3.4': # on ubuntu1404, clang-3.4 alias doesn't exist, just use 'clang' return ('ubuntu1404', self._clang_make_options()) elif compiler == 'clang3.5': return ('jessie', self._clang_make_options(version_suffix='-3.5')) elif compiler == 'clang3.6': return ('ubuntu1604', self._clang_make_options(version_suffix='-3.6')) elif compiler == 'clang3.7': return ('ubuntu1604', self._clang_make_options(version_suffix='-3.7')) elif compiler == 'clang7.0': # clang++-7.0 alias doesn't exist and there are no other clang versions # installed. return ('sanitizers_jessie', self._clang_make_options()) else: raise Exception('Compiler %s not supported.' % compiler) def dockerfile_dir(self): return 'tools/dockerfile/test/cxx_%s_%s' % ( self._docker_distro, _docker_arch_suffix(self.args.arch)) def __str__(self): return self.make_target # This tests Node on grpc/grpc-node and will become the standard for Node testing class RemoteNodeLanguage(object): def __init__(self): self.platform = platform_string() def configure(self, config, args): self.config = config self.args = args # Note: electron ABI only depends on major and minor version, so that's all # we should specify in the compiler argument _check_compiler(self.args.compiler, [ 'default', 'node0.12', 'node4', 'node5', 'node6', 'node7', 'node8', 'electron1.3', 'electron1.6' ]) if self.args.compiler == 'default': self.runtime = 'node' self.node_version = '8' else: if self.args.compiler.startswith('electron'): self.runtime = 'electron' self.node_version = self.args.compiler[8:] else: self.runtime = 'node' # Take off the word "node" self.node_version = self.args.compiler[4:] # TODO: update with Windows/electron scripts when available for grpc/grpc-node def test_specs(self): if self.platform == 'windows': return [ self.config.job_spec( ['tools\\run_tests\\helper_scripts\\run_node.bat']) ] else: return [ self.config.job_spec( ['tools/run_tests/helper_scripts/run_grpc-node.sh'], None, environ=_FORCE_ENVIRON_FOR_WRAPPERS) ] def pre_build_steps(self): return [] def make_targets(self): return [] def make_options(self): return [] def build_steps(self): return [] def post_tests_steps(self): return [] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/node_jessie_%s' % _docker_arch_suffix( self.args.arch) def __str__(self): return 'grpc-node' class PhpLanguage(object): def configure(self, config, args): self.config = config self.args = args _check_compiler(self.args.compiler, ['default']) self._make_options = ['EMBED_OPENSSL=true', 'EMBED_ZLIB=true'] def test_specs(self): return [ self.config.job_spec( ['src/php/bin/run_tests.sh'], environ=_FORCE_ENVIRON_FOR_WRAPPERS) ] def pre_build_steps(self): return [] def make_targets(self): return ['static_c', 'shared_c'] def make_options(self): return self._make_options def build_steps(self): return [['tools/run_tests/helper_scripts/build_php.sh']] def post_tests_steps(self): return [['tools/run_tests/helper_scripts/post_tests_php.sh']] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/php_jessie_%s' % _docker_arch_suffix( self.args.arch) def __str__(self): return 'php' class Php7Language(object): def configure(self, config, args): self.config = config self.args = args _check_compiler(self.args.compiler, ['default']) self._make_options = ['EMBED_OPENSSL=true', 'EMBED_ZLIB=true'] def test_specs(self): return [ self.config.job_spec( ['src/php/bin/run_tests.sh'], environ=_FORCE_ENVIRON_FOR_WRAPPERS) ] def pre_build_steps(self): return [] def make_targets(self): return ['static_c', 'shared_c'] def make_options(self): return self._make_options def build_steps(self): return [['tools/run_tests/helper_scripts/build_php.sh']] def post_tests_steps(self): return [['tools/run_tests/helper_scripts/post_tests_php.sh']] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/php7_jessie_%s' % _docker_arch_suffix( self.args.arch) def __str__(self): return 'php7' class PythonConfig( collections.namedtuple('PythonConfig', ['name', 'build', 'run'])): """Tuple of commands (named s.t. 'what it says on the tin' applies)""" class PythonLanguage(object): def configure(self, config, args): self.config = config self.args = args self.pythons = self._get_pythons(self.args) def test_specs(self): # load list of known test suites with open( 'src/python/grpcio_tests/tests/tests.json') as tests_json_file: tests_json = json.load(tests_json_file) environment = dict(_FORCE_ENVIRON_FOR_WRAPPERS) return [ self.config.job_spec( config.run, timeout_seconds=5 * 60, environ=dict( list(environment.items()) + [( 'GRPC_PYTHON_TESTRUNNER_FILTER', str(suite_name))]), shortname='%s.test.%s' % (config.name, suite_name), ) for suite_name in tests_json for config in self.pythons ] def pre_build_steps(self): return [] def make_targets(self): return [] def make_options(self): return [] def build_steps(self): return [config.build for config in self.pythons] def post_tests_steps(self): if self.config.build_config != 'gcov': return [] else: return [['tools/run_tests/helper_scripts/post_tests_python.sh']] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/python_%s_%s' % ( self._python_manager_name(), _docker_arch_suffix(self.args.arch)) def _python_manager_name(self): """Choose the docker image to use based on python version.""" if self.args.compiler in [ 'python2.7', 'python3.5', 'python3.6', 'python3.7' ]: return 'stretch_' + self.args.compiler[len('python'):] elif self.args.compiler == 'python_alpine': return 'alpine' elif self.args.compiler == 'python3.4': return 'jessie' else: return 'stretch_3.7' def _get_pythons(self, args): """Get python runtimes to test with, based on current platform, architecture, compiler etc.""" if args.arch == 'x86': bits = '32' else: bits = '64' if os.name == 'nt': shell = ['bash'] builder = [ os.path.abspath( 'tools/run_tests/helper_scripts/build_python_msys2.sh') ] builder_prefix_arguments = ['MINGW{}'.format(bits)] venv_relative_python = ['Scripts/python.exe'] toolchain = ['mingw32'] else: shell = [] builder = [ os.path.abspath( 'tools/run_tests/helper_scripts/build_python.sh') ] builder_prefix_arguments = [] venv_relative_python = ['bin/python'] toolchain = ['unix'] test_command = 'test_lite' if args.iomgr_platform == 'gevent': test_command = 'test_gevent' runner = [ os.path.abspath('tools/run_tests/helper_scripts/run_python.sh') ] config_vars = _PythonConfigVars( shell, builder, builder_prefix_arguments, venv_relative_python, toolchain, runner, test_command, args.iomgr_platform) python27_config = _python_config_generator( name='py27', major='2', minor='7', bits=bits, config_vars=config_vars) python34_config = _python_config_generator( name='py34', major='3', minor='4', bits=bits, config_vars=config_vars) python35_config = _python_config_generator( name='py35', major='3', minor='5', bits=bits, config_vars=config_vars) python36_config = _python_config_generator( name='py36', major='3', minor='6', bits=bits, config_vars=config_vars) python37_config = _python_config_generator( name='py37', major='3', minor='7', bits=bits, config_vars=config_vars) pypy27_config = _pypy_config_generator( name='pypy', major='2', config_vars=config_vars) pypy32_config = _pypy_config_generator( name='pypy3', major='3', config_vars=config_vars) if args.compiler == 'default': if os.name == 'nt': return (python35_config,) else: return ( python27_config, python37_config, ) elif args.compiler == 'python2.7': return (python27_config,) elif args.compiler == 'python3.4': return (python34_config,) elif args.compiler == 'python3.5': return (python35_config,) elif args.compiler == 'python3.6': return (python36_config,) elif args.compiler == 'python3.7': return (python37_config,) elif args.compiler == 'pypy': return (pypy27_config,) elif args.compiler == 'pypy3': return (pypy32_config,) elif args.compiler == 'python_alpine': return (python27_config,) elif args.compiler == 'all_the_cpythons': return ( python27_config, python34_config, python35_config, python36_config, python37_config, ) else: raise Exception('Compiler %s not supported.' % args.compiler) def __str__(self): return 'python' class RubyLanguage(object): def configure(self, config, args): self.config = config self.args = args _check_compiler(self.args.compiler, ['default']) def test_specs(self): tests = [ self.config.job_spec( ['tools/run_tests/helper_scripts/run_ruby.sh'], timeout_seconds=10 * 60, environ=_FORCE_ENVIRON_FOR_WRAPPERS) ] tests.append( self.config.job_spec( ['tools/run_tests/helper_scripts/run_ruby_end2end_tests.sh'], timeout_seconds=20 * 60, environ=_FORCE_ENVIRON_FOR_WRAPPERS)) return tests def pre_build_steps(self): return [['tools/run_tests/helper_scripts/pre_build_ruby.sh']] def make_targets(self): return [] def make_options(self): return [] def build_steps(self): return [['tools/run_tests/helper_scripts/build_ruby.sh']] def post_tests_steps(self): return [['tools/run_tests/helper_scripts/post_tests_ruby.sh']] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/ruby_jessie_%s' % _docker_arch_suffix( self.args.arch) def __str__(self): return 'ruby' class CSharpLanguage(object): def __init__(self): self.platform = platform_string() def configure(self, config, args): self.config = config self.args = args if self.platform == 'windows': _check_compiler(self.args.compiler, ['default', 'coreclr']) _check_arch(self.args.arch, ['default']) self._cmake_arch_option = 'x64' else: _check_compiler(self.args.compiler, ['default', 'coreclr']) self._docker_distro = 'jessie' def test_specs(self): with open('src/csharp/tests.json') as f: tests_by_assembly = json.load(f) msbuild_config = _MSBUILD_CONFIG[self.config.build_config] nunit_args = ['--labels=All', '--noresult', '--workers=1'] assembly_subdir = 'bin/%s' % msbuild_config assembly_extension = '.exe' if self.args.compiler == 'coreclr': assembly_subdir += '/netcoreapp1.0' runtime_cmd = ['dotnet', 'exec'] assembly_extension = '.dll' else: assembly_subdir += '/net45' if self.platform == 'windows': runtime_cmd = [] elif self.platform == 'mac': # mono before version 5.2 on MacOS defaults to 32bit runtime runtime_cmd = ['mono', '--arch=64'] else: runtime_cmd = ['mono'] specs = [] for assembly in six.iterkeys(tests_by_assembly): assembly_file = 'src/csharp/%s/%s/%s%s' % (assembly, assembly_subdir, assembly, assembly_extension) if self.config.build_config != 'gcov' or self.platform != 'windows': # normally, run each test as a separate process for test in tests_by_assembly[assembly]: cmdline = runtime_cmd + [assembly_file, '--test=%s' % test] + nunit_args specs.append( self.config.job_spec( cmdline, shortname='csharp.%s' % test, environ=_FORCE_ENVIRON_FOR_WRAPPERS)) else: # For C# test coverage, run all tests from the same assembly at once # using OpenCover.Console (only works on Windows). cmdline = [ 'src\\csharp\\packages\\OpenCover.4.6.519\\tools\\OpenCover.Console.exe', '-target:%s' % assembly_file, '-targetdir:src\\csharp', '-targetargs:%s' % ' '.join(nunit_args), '-filter:+[Grpc.Core]*', '-register:user', '-output:src\\csharp\\coverage_csharp_%s.xml' % assembly ] # set really high cpu_cost to make sure instances of OpenCover.Console run exclusively # to prevent problems with registering the profiler. run_exclusive = 1000000 specs.append( self.config.job_spec( cmdline, shortname='csharp.coverage.%s' % assembly, cpu_cost=run_exclusive, environ=_FORCE_ENVIRON_FOR_WRAPPERS)) return specs def pre_build_steps(self): if self.platform == 'windows': return [[ 'tools\\run_tests\\helper_scripts\\pre_build_csharp.bat', self._cmake_arch_option ]] else: return [['tools/run_tests/helper_scripts/pre_build_csharp.sh']] def make_targets(self): return ['grpc_csharp_ext'] def make_options(self): return [] def build_steps(self): if self.platform == 'windows': return [['tools\\run_tests\\helper_scripts\\build_csharp.bat']] else: return [['tools/run_tests/helper_scripts/build_csharp.sh']] def post_tests_steps(self): if self.platform == 'windows': return [['tools\\run_tests\\helper_scripts\\post_tests_csharp.bat']] else: return [['tools/run_tests/helper_scripts/post_tests_csharp.sh']] def makefile_name(self): if self.platform == 'windows': return 'cmake/build/%s/Makefile' % self._cmake_arch_option else: # no need to set x86 specific flags as run_tests.py # currently forbids x86 C# builds on both Linux and MacOS. return 'cmake/build/Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/csharp_%s_%s' % ( self._docker_distro, _docker_arch_suffix(self.args.arch)) def __str__(self): return 'csharp' class ObjCLanguage(object): def configure(self, config, args): self.config = config self.args = args _check_compiler(self.args.compiler, ['default']) def test_specs(self): return [ self.config.job_spec( ['src/objective-c/tests/run_tests.sh'], timeout_seconds=60 * 60, shortname='objc-tests', cpu_cost=1e6, environ=_FORCE_ENVIRON_FOR_WRAPPERS), self.config.job_spec( ['src/objective-c/tests/run_plugin_tests.sh'], timeout_seconds=60 * 60, shortname='objc-plugin-tests', cpu_cost=1e6, environ=_FORCE_ENVIRON_FOR_WRAPPERS), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-helloworld', cpu_cost=1e6, environ={ 'SCHEME': 'HelloWorld', 'EXAMPLE_PATH': 'examples/objective-c/helloworld' }), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-routeguide', cpu_cost=1e6, environ={ 'SCHEME': 'RouteGuideClient', 'EXAMPLE_PATH': 'examples/objective-c/route_guide' }), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-authsample', cpu_cost=1e6, environ={ 'SCHEME': 'AuthSample', 'EXAMPLE_PATH': 'examples/objective-c/auth_sample' }), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-sample', cpu_cost=1e6, environ={ 'SCHEME': 'Sample', 'EXAMPLE_PATH': 'src/objective-c/examples/Sample' }), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-sample-frameworks', cpu_cost=1e6, environ={ 'SCHEME': 'Sample', 'EXAMPLE_PATH': 'src/objective-c/examples/Sample', 'FRAMEWORKS': 'YES' }), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-switftsample', cpu_cost=1e6, environ={ 'SCHEME': 'SwiftSample', 'EXAMPLE_PATH': 'src/objective-c/examples/SwiftSample' }), self.config.job_spec( ['test/core/iomgr/ios/CFStreamTests/run_tests.sh'], timeout_seconds=10 * 60, shortname='cfstream-tests', cpu_cost=1e6, environ=_FORCE_ENVIRON_FOR_WRAPPERS), ] def pre_build_steps(self): return [] def make_targets(self): return ['interop_server'] def make_options(self): return [] def build_steps(self): return [ ['src/objective-c/tests/build_tests.sh'], ['test/core/iomgr/ios/CFStreamTests/build_tests.sh'], ] def post_tests_steps(self): return [] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return None def __str__(self): return 'objc' class Sanity(object): def configure(self, config, args): self.config = config self.args = args _check_compiler(self.args.compiler, ['default']) def test_specs(self): import yaml with open('tools/run_tests/sanity/sanity_tests.yaml', 'r') as f: environ = {'TEST': 'true'} if _is_use_docker_child(): environ['CLANG_FORMAT_SKIP_DOCKER'] = 'true' environ['CLANG_TIDY_SKIP_DOCKER'] = 'true' return [ self.config.job_spec( cmd['script'].split(), timeout_seconds=30 * 60, environ=environ, cpu_cost=cmd.get('cpu_cost', 1)) for cmd in yaml.load(f) ] def pre_build_steps(self): return [] def make_targets(self): return ['run_dep_checks'] def make_options(self): return [] def build_steps(self): return [] def post_tests_steps(self): return [] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/sanity' def __str__(self): return 'sanity' # different configurations we can run under with open('tools/run_tests/generated/configs.json') as f: _CONFIGS = dict( (cfg['config'], Config(**cfg)) for cfg in ast.literal_eval(f.read())) _LANGUAGES = { 'c++': CLanguage('cxx', 'c++'), 'c': CLanguage('c', 'c'), 'grpc-node': RemoteNodeLanguage(), 'php': PhpLanguage(), 'php7': Php7Language(), 'python': PythonLanguage(), 'ruby': RubyLanguage(), 'csharp': CSharpLanguage(), 'objc': ObjCLanguage(), 'sanity': Sanity() } _MSBUILD_CONFIG = { 'dbg': 'Debug', 'opt': 'Release', 'gcov': 'Debug', } def _windows_arch_option(arch): """Returns msbuild cmdline option for selected architecture.""" if arch == 'default' or arch == 'x86': return '/p:Platform=Win32' elif arch == 'x64': return '/p:Platform=x64' else: print('Architecture %s not supported.' % arch) sys.exit(1) def _check_arch_option(arch): """Checks that architecture option is valid.""" if platform_string() == 'windows': _windows_arch_option(arch) elif platform_string() == 'linux': # On linux, we need to be running under docker with the right architecture. runtime_arch = platform.architecture()[0] if arch == 'default': return elif runtime_arch == '64bit' and arch == 'x64': return elif runtime_arch == '32bit' and arch == 'x86': return else: print('Architecture %s does not match current runtime architecture.' % arch) sys.exit(1) else: if args.arch != 'default': print('Architecture %s not supported on current platform.' % args.arch) sys.exit(1) def _docker_arch_suffix(arch): """Returns suffix to dockerfile dir to use.""" if arch == 'default' or arch == 'x64': return 'x64' elif arch == 'x86': return 'x86' else: print('Architecture %s not supported with current settings.' % arch) sys.exit(1) def runs_per_test_type(arg_str): """Auxilary function to parse the "runs_per_test" flag. Returns: A positive integer or 0, the latter indicating an infinite number of runs. Raises: argparse.ArgumentTypeError: Upon invalid input. """ if arg_str == 'inf': return 0 try: n = int(arg_str) if n <= 0: raise ValueError return n except: msg = '\'{}\' is not a positive integer or \'inf\''.format(arg_str) raise argparse.ArgumentTypeError(msg) def percent_type(arg_str): pct = float(arg_str) if pct > 100 or pct < 0: raise argparse.ArgumentTypeError( "'%f' is not a valid percentage in the [0, 100] range" % pct) return pct # This is math.isclose in python >= 3.5 def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) # parse command line argp = argparse.ArgumentParser(description='Run grpc tests.') argp.add_argument( '-c', '--config', choices=sorted(_CONFIGS.keys()), default='opt') argp.add_argument( '-n', '--runs_per_test', default=1, type=runs_per_test_type, help='A positive integer or "inf". If "inf", all tests will run in an ' 'infinite loop. Especially useful in combination with "-f"') argp.add_argument('-r', '--regex', default='.*', type=str) argp.add_argument('--regex_exclude', default='', type=str) argp.add_argument('-j', '--jobs', default=multiprocessing.cpu_count(), type=int) argp.add_argument('-s', '--slowdown', default=1.0, type=float) argp.add_argument( '-p', '--sample_percent', default=100.0, type=percent_type, help='Run a random sample with that percentage of tests') argp.add_argument( '-f', '--forever', default=False, action='store_const', const=True) argp.add_argument( '-t', '--travis', default=False, action='store_const', const=True) argp.add_argument( '--newline_on_success', default=False, action='store_const', const=True) argp.add_argument( '-l', '--language', choices=sorted(_LANGUAGES.keys()), nargs='+', required=True) argp.add_argument( '-S', '--stop_on_failure', default=False, action='store_const', const=True) argp.add_argument( '--use_docker', default=False, action='store_const', const=True, help='Run all the tests under docker. That provides ' + 'additional isolation and prevents the need to install ' + 'language specific prerequisites. Only available on Linux.') argp.add_argument( '--allow_flakes', default=False, action='store_const', const=True, help= 'Allow flaky tests to show as passing (re-runs failed tests up to five times)' ) argp.add_argument( '--arch', choices=['default', 'x86', 'x64'], default='default', help= 'Selects architecture to target. For some platforms "default" is the only supported choice.' ) argp.add_argument( '--compiler', choices=[ 'default', 'gcc4.4', 'gcc4.6', 'gcc4.8', 'gcc4.9', 'gcc5.3', 'gcc7.2', 'gcc_musl', 'clang3.4', 'clang3.5', 'clang3.6', 'clang3.7', 'clang7.0', 'python2.7', 'python3.4', 'python3.5', 'python3.6', 'python3.7', 'pypy', 'pypy3', 'python_alpine', 'all_the_cpythons', 'electron1.3', 'electron1.6', 'coreclr', 'cmake', 'cmake_vs2015', 'cmake_vs2017' ], default='default', help= 'Selects compiler to use. Allowed values depend on the platform and language.' ) argp.add_argument( '--iomgr_platform', choices=['native', 'uv', 'gevent'], default='native', help='Selects iomgr platform to build on') argp.add_argument( '--build_only', default=False, action='store_const', const=True, help='Perform all the build steps but don\'t run any tests.') argp.add_argument( '--measure_cpu_costs', default=False, action='store_const', const=True, help='Measure the cpu costs of tests') argp.add_argument( '--update_submodules', default=[], nargs='*', help= 'Update some submodules before building. If any are updated, also run generate_projects. ' + 'Submodules are specified as SUBMODULE_NAME:BRANCH; if BRANCH is omitted, master is assumed.' ) argp.add_argument('-a', '--antagonists', default=0, type=int) argp.add_argument( '-x', '--xml_report', default=None, type=str, help='Generates a JUnit-compatible XML report') argp.add_argument( '--report_suite_name', default='tests', type=str, help='Test suite name to use in generated JUnit XML report') argp.add_argument( '--quiet_success', default=False, action='store_const', const=True, help= 'Don\'t print anything when a test passes. Passing tests also will not be reported in XML report. ' + 'Useful when running many iterations of each test (argument -n).') argp.add_argument( '--force_default_poller', default=False, action='store_const', const=True, help='Don\'t try to iterate over many polling strategies when they exist') argp.add_argument( '--force_use_pollers', default=None, type=str, help='Only use the specified comma-delimited list of polling engines. ' 'Example: --force_use_pollers epoll1,poll ' ' (This flag has no effect if --force_default_poller flag is also used)') argp.add_argument( '--max_time', default=-1, type=int, help='Maximum test runtime in seconds') argp.add_argument( '--bq_result_table', default='', type=str, nargs='?', help='Upload test results to a specified BQ table.') argp.add_argument( '--auto_set_flakes', default=False, const=True, action='store_const', help= 'Allow repeated runs for tests that have been failing recently (based on BQ historical data).' ) args = argp.parse_args() flaky_tests = set() shortname_to_cpu = {} if args.auto_set_flakes: try: for test in get_bqtest_data(): if test.flaky: flaky_tests.add(test.name) if test.cpu > 0: shortname_to_cpu[test.name] = test.cpu except: print( "Unexpected error getting flaky tests: %s" % traceback.format_exc()) if args.force_default_poller: _POLLING_STRATEGIES = {} elif args.force_use_pollers: _POLLING_STRATEGIES[platform_string()] = args.force_use_pollers.split(',') jobset.measure_cpu_costs = args.measure_cpu_costs # update submodules if necessary need_to_regenerate_projects = False for spec in args.update_submodules: spec = spec.split(':', 1) if len(spec) == 1: submodule = spec[0] branch = 'master' elif len(spec) == 2: submodule = spec[0] branch = spec[1] cwd = 'third_party/%s' % submodule def git(cmd, cwd=cwd): print('in %s: git %s' % (cwd, cmd)) run_shell_command('git %s' % cmd, cwd=cwd) git('fetch') git('checkout %s' % branch) git('pull origin %s' % branch) if os.path.exists('src/%s/gen_build_yaml.py' % submodule): need_to_regenerate_projects = True if need_to_regenerate_projects: if jobset.platform_string() == 'linux': run_shell_command('tools/buildgen/generate_projects.sh') else: print( 'WARNING: may need to regenerate projects, but since we are not on') print( ' Linux this step is being skipped. Compilation MAY fail.') # grab config run_config = _CONFIGS[args.config] build_config = run_config.build_config if args.travis: _FORCE_ENVIRON_FOR_WRAPPERS = {'GRPC_TRACE': 'api'} languages = set(_LANGUAGES[l] for l in args.language) for l in languages: l.configure(run_config, args) language_make_options = [] if any(language.make_options() for language in languages): if not 'gcov' in args.config and len(languages) != 1: print( 'languages with custom make options cannot be built simultaneously with other languages' ) sys.exit(1) else: # Combining make options is not clean and just happens to work. It allows C & C++ to build # together, and is only used under gcov. All other configs should build languages individually. language_make_options = list( set([ make_option for lang in languages for make_option in lang.make_options() ])) if args.use_docker: if not args.travis: print('Seen --use_docker flag, will run tests under docker.') print('') print( 'IMPORTANT: The changes you are testing need to be locally committed' ) print( 'because only the committed changes in the current branch will be') print('copied to the docker environment.') time.sleep(5) dockerfile_dirs = set([l.dockerfile_dir() for l in languages]) if len(dockerfile_dirs) > 1: if 'gcov' in args.config: dockerfile_dir = 'tools/dockerfile/test/multilang_jessie_x64' print( 'Using multilang_jessie_x64 docker image for code coverage for ' 'all languages.') else: print( 'Languages to be tested require running under different docker ' 'images.') sys.exit(1) else: dockerfile_dir = next(iter(dockerfile_dirs)) child_argv = [arg for arg in sys.argv if not arg == '--use_docker'] run_tests_cmd = 'python tools/run_tests/run_tests.py %s' % ' '.join( child_argv[1:]) env = os.environ.copy() env['RUN_TESTS_COMMAND'] = run_tests_cmd env['DOCKERFILE_DIR'] = dockerfile_dir env['DOCKER_RUN_SCRIPT'] = 'tools/run_tests/dockerize/docker_run_tests.sh' if args.xml_report: env['XML_REPORT'] = args.xml_report if not args.travis: env['TTY_FLAG'] = '-t' # enables Ctrl-C when not on Jenkins. subprocess.check_call( 'tools/run_tests/dockerize/build_docker_and_run_tests.sh', shell=True, env=env) sys.exit(0) _check_arch_option(args.arch) def make_jobspec(cfg, targets, makefile='Makefile'): if platform_string() == 'windows': return [ jobset.JobSpec( [ 'cmake', '--build', '.', '--target', '%s' % target, '--config', _MSBUILD_CONFIG[cfg] ], cwd=os.path.dirname(makefile), timeout_seconds=None) for target in targets ] else: if targets and makefile.startswith('cmake/build/'): # With cmake, we've passed all the build configuration in the pre-build step already return [ jobset.JobSpec( [os.getenv('MAKE', 'make'), '-j', '%d' % args.jobs] + targets, cwd='cmake/build', timeout_seconds=None) ] if targets: return [ jobset.JobSpec( [ os.getenv('MAKE', 'make'), '-f', makefile, '-j', '%d' % args.jobs, 'EXTRA_DEFINES=GRPC_TEST_SLOWDOWN_MACHINE_FACTOR=%f' % args.slowdown, 'CONFIG=%s' % cfg, 'Q=' ] + language_make_options + ([] if not args.travis else ['JENKINS_BUILD=1']) + targets, timeout_seconds=None) ] else: return [] make_targets = {} for l in languages: makefile = l.makefile_name() make_targets[makefile] = make_targets.get(makefile, set()).union( set(l.make_targets())) def build_step_environ(cfg): environ = {'CONFIG': cfg} msbuild_cfg = _MSBUILD_CONFIG.get(cfg) if msbuild_cfg: environ['MSBUILD_CONFIG'] = msbuild_cfg return environ build_steps = list( set( jobset.JobSpec( cmdline, environ=build_step_environ(build_config), flake_retries=2) for l in languages for cmdline in l.pre_build_steps())) if make_targets: make_commands = itertools.chain.from_iterable( make_jobspec(build_config, list(targets), makefile) for (makefile, targets) in make_targets.items()) build_steps.extend(set(make_commands)) build_steps.extend( set( jobset.JobSpec( cmdline, environ=build_step_environ(build_config), timeout_seconds=None) for l in languages for cmdline in l.build_steps())) post_tests_steps = list( set( jobset.JobSpec(cmdline, environ=build_step_environ(build_config)) for l in languages for cmdline in l.post_tests_steps())) runs_per_test = args.runs_per_test forever = args.forever def _shut_down_legacy_server(legacy_server_port): try: version = int( urllib.request.urlopen( 'http://localhost:%d/version_number' % legacy_server_port, timeout=10).read()) except: pass else: urllib.request.urlopen( 'http://localhost:%d/quitquitquit' % legacy_server_port).read() def _calculate_num_runs_failures(list_of_results): """Caculate number of runs and failures for a particular test. Args: list_of_results: (List) of JobResult object. Returns: A tuple of total number of runs and failures. """ num_runs = len(list_of_results) # By default, there is 1 run per JobResult. num_failures = 0 for jobresult in list_of_results: if jobresult.retries > 0: num_runs += jobresult.retries if jobresult.num_failures > 0: num_failures += jobresult.num_failures return num_runs, num_failures # _build_and_run results class BuildAndRunError(object): BUILD = object() TEST = object() POST_TEST = object() def _has_epollexclusive(): binary = 'bins/%s/check_epollexclusive' % args.config if not os.path.exists(binary): return False try: subprocess.check_call(binary) return True except subprocess.CalledProcessError as e: return False except OSError as e: # For languages other than C and Windows the binary won't exist return False # returns a list of things that failed (or an empty list on success) def _build_and_run(check_cancelled, newline_on_success, xml_report=None, build_only=False): """Do one pass of building & running tests.""" # build latest sequentially num_failures, resultset = jobset.run( build_steps, maxjobs=1, stop_on_failure=True, newline_on_success=newline_on_success, travis=args.travis) if num_failures: return [BuildAndRunError.BUILD] if build_only: if xml_report: report_utils.render_junit_xml_report( resultset, xml_report, suite_name=args.report_suite_name) return [] if not args.travis and not _has_epollexclusive() and platform_string( ) in _POLLING_STRATEGIES and 'epollex' in _POLLING_STRATEGIES[platform_string( )]: print('\n\nOmitting EPOLLEXCLUSIVE tests\n\n') _POLLING_STRATEGIES[platform_string()].remove('epollex') # start antagonists antagonists = [ subprocess.Popen(['tools/run_tests/python_utils/antagonist.py']) for _ in range(0, args.antagonists) ] start_port_server.start_port_server() resultset = None num_test_failures = 0 try: infinite_runs = runs_per_test == 0 one_run = set( spec for language in languages for spec in language.test_specs() if (re.search(args.regex, spec.shortname) and (args.regex_exclude == '' or not re.search(args.regex_exclude, spec.shortname)))) # When running on travis, we want out test runs to be as similar as possible # for reproducibility purposes. if args.travis and args.max_time <= 0: massaged_one_run = sorted(one_run, key=lambda x: x.cpu_cost) else: # whereas otherwise, we want to shuffle things up to give all tests a # chance to run. massaged_one_run = list( one_run) # random.sample needs an indexable seq. num_jobs = len(massaged_one_run) # for a random sample, get as many as indicated by the 'sample_percent' # argument. By default this arg is 100, resulting in a shuffle of all # jobs. sample_size = int(num_jobs * args.sample_percent / 100.0) massaged_one_run = random.sample(massaged_one_run, sample_size) if not isclose(args.sample_percent, 100.0): assert args.runs_per_test == 1, "Can't do sampling (-p) over multiple runs (-n)." print("Running %d tests out of %d (~%d%%)" % (sample_size, num_jobs, args.sample_percent)) if infinite_runs: assert len(massaged_one_run ) > 0, 'Must have at least one test for a -n inf run' runs_sequence = (itertools.repeat(massaged_one_run) if infinite_runs else itertools.repeat( massaged_one_run, runs_per_test)) all_runs = itertools.chain.from_iterable(runs_sequence) if args.quiet_success: jobset.message( 'START', 'Running tests quietly, only failing tests will be reported', do_newline=True) num_test_failures, resultset = jobset.run( all_runs, check_cancelled, newline_on_success=newline_on_success, travis=args.travis, maxjobs=args.jobs, maxjobs_cpu_agnostic=max_parallel_tests_for_current_platform(), stop_on_failure=args.stop_on_failure, quiet_success=args.quiet_success, max_time=args.max_time) if resultset: for k, v in sorted(resultset.items()): num_runs, num_failures = _calculate_num_runs_failures(v) if num_failures > 0: if num_failures == num_runs: # what about infinite_runs??? jobset.message('FAILED', k, do_newline=True) else: jobset.message( 'FLAKE', '%s [%d/%d runs flaked]' % (k, num_failures, num_runs), do_newline=True) finally: for antagonist in antagonists: antagonist.kill() if args.bq_result_table and resultset: upload_extra_fields = { 'compiler': args.compiler, 'config': args.config, 'iomgr_platform': args.iomgr_platform, 'language': args.language[ 0], # args.language is a list but will always have one element when uploading to BQ is enabled. 'platform': platform_string() } upload_results_to_bq(resultset, args.bq_result_table, upload_extra_fields) if xml_report and resultset: report_utils.render_junit_xml_report( resultset, xml_report, suite_name=args.report_suite_name) number_failures, _ = jobset.run( post_tests_steps, maxjobs=1, stop_on_failure=False, newline_on_success=newline_on_success, travis=args.travis) out = [] if number_failures: out.append(BuildAndRunError.POST_TEST) if num_test_failures: out.append(BuildAndRunError.TEST) return out if forever: success = True while True: dw = watch_dirs.DirWatcher(['src', 'include', 'test', 'examples']) initial_time = dw.most_recent_change() have_files_changed = lambda: dw.most_recent_change() != initial_time previous_success = success errors = _build_and_run( check_cancelled=have_files_changed, newline_on_success=False, build_only=args.build_only) == 0 if not previous_success and not errors: jobset.message( 'SUCCESS', 'All tests are now passing properly', do_newline=True) jobset.message('IDLE', 'No change detected') while not have_files_changed(): time.sleep(1) else: errors = _build_and_run( check_cancelled=lambda: False, newline_on_success=args.newline_on_success, xml_report=args.xml_report, build_only=args.build_only) if not errors: jobset.message('SUCCESS', 'All tests passed', do_newline=True) else: jobset.message('FAILED', 'Some tests failed', do_newline=True) exit_code = 0 if BuildAndRunError.BUILD in errors: exit_code |= 1 if BuildAndRunError.TEST in errors: exit_code |= 2 if BuildAndRunError.POST_TEST in errors: exit_code |= 4 sys.exit(exit_code)

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from __future__ import print_function import argparse import ast import collections import glob import itertools import json import logging import multiprocessing import os import os.path import pipes import platform import random import re import socket import subprocess import sys import tempfile import traceback import time from six.moves import urllib import uuid import six import python_utils.jobset as jobset import python_utils.report_utils as report_utils import python_utils.watch_dirs as watch_dirs import python_utils.start_port_server as start_port_server try: from python_utils.upload_test_results import upload_results_to_bq except (ImportError): pass gcp_utils_dir = os.path.abspath( os.path.join(os.path.dirname(__file__), '../gcp/utils')) sys.path.append(gcp_utils_dir) _ROOT = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), '../..')) os.chdir(_ROOT) _FORCE_ENVIRON_FOR_WRAPPERS = { 'GRPC_VERBOSITY': 'DEBUG', } _POLLING_STRATEGIES = { 'linux': ['epollex', 'epoll1', 'poll', 'poll-cv'], 'mac': ['poll'], } BigQueryTestData = collections.namedtuple('BigQueryTestData', 'name flaky cpu') def get_bqtest_data(limit=None): import big_query_utils bq = big_query_utils.create_big_query() query = """ SELECT filtered_test_name, SUM(result != 'PASSED' AND result != 'SKIPPED') > 0 as flaky, MAX(cpu_measured) + 0.01 as cpu FROM ( SELECT REGEXP_REPLACE(test_name, r'/\d+', '') AS filtered_test_name, result, cpu_measured FROM [grpc-testing:jenkins_test_results.aggregate_results] WHERE timestamp >= DATE_ADD(CURRENT_DATE(), -1, "WEEK") AND platform = '""" + platform_string() + """' AND NOT REGEXP_MATCH(job_name, '.*portability.*') ) GROUP BY filtered_test_name""" if limit: query += " limit {}".format(limit) query_job = big_query_utils.sync_query_job(bq, 'grpc-testing', query) page = bq.jobs().getQueryResults( pageToken=None, **query_job['jobReference']).execute(num_retries=3) test_data = [ BigQueryTestData(row['f'][0]['v'], row['f'][1]['v'] == 'true', float(row['f'][2]['v'])) for row in page['rows'] ] return test_data def platform_string(): return jobset.platform_string() _DEFAULT_TIMEOUT_SECONDS = 5 * 60 def run_shell_command(cmd, env=None, cwd=None): try: subprocess.check_output(cmd, shell=True, env=env, cwd=cwd) except subprocess.CalledProcessError as e: logging.exception( "Error while running command '%s'. Exit status %d. Output:\n%s", e.cmd, e.returncode, e.output) raise def max_parallel_tests_for_current_platform(): # Too much test parallelization has only been seen to be a problem # so far on windows. if jobset.platform_string() == 'windows': return 64 return 1024 # SimpleConfig: just compile with CONFIG=config, and run the binary to test class Config(object): def __init__(self, config, environ=None, timeout_multiplier=1, tool_prefix=[], iomgr_platform='native'): if environ is None: environ = {} self.build_config = config self.environ = environ self.environ['CONFIG'] = config self.tool_prefix = tool_prefix self.timeout_multiplier = timeout_multiplier self.iomgr_platform = iomgr_platform def job_spec(self, cmdline, timeout_seconds=_DEFAULT_TIMEOUT_SECONDS, shortname=None, environ={}, cpu_cost=1.0, flaky=False): actual_environ = self.environ.copy() for k, v in environ.items(): actual_environ[k] = v if not flaky and shortname and shortname in flaky_tests: flaky = True if shortname in shortname_to_cpu: cpu_cost = shortname_to_cpu[shortname] return jobset.JobSpec( cmdline=self.tool_prefix + cmdline, shortname=shortname, environ=actual_environ, cpu_cost=cpu_cost, timeout_seconds=(self.timeout_multiplier * timeout_seconds if timeout_seconds else None), flake_retries=4 if flaky or args.allow_flakes else 0, timeout_retries=1 if flaky or args.allow_flakes else 0) def get_c_tests(travis, test_lang): out = [] platforms_str = 'ci_platforms' if travis else 'platforms' with open('tools/run_tests/generated/tests.json') as f: js = json.load(f) return [ tgt for tgt in js if tgt['language'] == test_lang and platform_string() in tgt[platforms_str] and not (travis and tgt['flaky']) ] def _check_compiler(compiler, supported_compilers): if compiler not in supported_compilers: raise Exception( 'Compiler %s not supported (on this platform).' % compiler) def _check_arch(arch, supported_archs): if arch not in supported_archs: raise Exception('Architecture %s not supported.' % arch) def _is_use_docker_child(): return True if os.getenv('RUN_TESTS_COMMAND') else False _PythonConfigVars = collections.namedtuple('_ConfigVars', [ 'shell', 'builder', 'builder_prefix_arguments', 'venv_relative_python', 'toolchain', 'runner', 'test_name', 'iomgr_platform', ]) def _python_config_generator(name, major, minor, bits, config_vars): name += '_' + config_vars.iomgr_platform return PythonConfig( name, config_vars.shell + config_vars.builder + config_vars.builder_prefix_arguments + [ _python_pattern_function(major=major, minor=minor, bits=bits) ] + [name] + config_vars.venv_relative_python + config_vars.toolchain, config_vars.shell + config_vars.runner + [ os.path.join(name, config_vars.venv_relative_python[0]), config_vars.test_name ]) def _pypy_config_generator(name, major, config_vars): return PythonConfig( name, config_vars.shell + config_vars.builder + config_vars.builder_prefix_arguments + [ _pypy_pattern_function(major=major) ] + [name] + config_vars.venv_relative_python + config_vars.toolchain, config_vars.shell + config_vars.runner + [os.path.join(name, config_vars.venv_relative_python[0])]) def _python_pattern_function(major, minor, bits): # Bit-ness is handled by the test machine's environment if os.name == "nt": if bits == "64": return '/c/Python{major}{minor}/python.exe'.format( major=major, minor=minor, bits=bits) else: return '/c/Python{major}{minor}_{bits}bits/python.exe'.format( major=major, minor=minor, bits=bits) else: return 'python{major}.{minor}'.format(major=major, minor=minor) def _pypy_pattern_function(major): if major == '2': return 'pypy' elif major == '3': return 'pypy3' else: raise ValueError("Unknown PyPy major version") class CLanguage(object): def __init__(self, make_target, test_lang): self.make_target = make_target self.platform = platform_string() self.test_lang = test_lang def configure(self, config, args): self.config = config self.args = args if self.platform == 'windows': _check_compiler( self.args.compiler, ['default', 'cmake', 'cmake_vs2015', 'cmake_vs2017']) _check_arch(self.args.arch, ['default', 'x64', 'x86']) self._cmake_generator_option = 'Visual Studio 15 2017' if self.args.compiler == 'cmake_vs2017' else 'Visual Studio 14 2015' self._cmake_arch_option = 'x64' if self.args.arch == 'x64' else 'Win32' self._use_cmake = True self._make_options = [] elif self.args.compiler == 'cmake': _check_arch(self.args.arch, ['default']) self._use_cmake = True self._docker_distro = 'jessie' self._make_options = [] else: self._use_cmake = False self._docker_distro, self._make_options = self._compiler_options( self.args.use_docker, self.args.compiler) if args.iomgr_platform == "uv": cflags = '-DGRPC_UV -DGRPC_CUSTOM_IOMGR_THREAD_CHECK -DGRPC_CUSTOM_SOCKET ' try: cflags += subprocess.check_output( ['pkg-config', '--cflags', 'libuv']).strip() + ' ' except (subprocess.CalledProcessError, OSError): pass try: ldflags = subprocess.check_output( ['pkg-config', '--libs', 'libuv']).strip() + ' ' except (subprocess.CalledProcessError, OSError): ldflags = '-luv ' self._make_options += [ 'EXTRA_CPPFLAGS={}'.format(cflags), 'EXTRA_LDLIBS={}'.format(ldflags) ] def test_specs(self): out = [] binaries = get_c_tests(self.args.travis, self.test_lang) for target in binaries: if self._use_cmake and target.get('boringssl', False): continue auto_timeout_scaling = target.get('auto_timeout_scaling', True) polling_strategies = (_POLLING_STRATEGIES.get( self.platform, ['all']) if target.get('uses_polling', True) else ['none']) if self.args.iomgr_platform == 'uv': polling_strategies = ['all'] for polling_strategy in polling_strategies: env = { 'GRPC_DEFAULT_SSL_ROOTS_FILE_PATH': _ROOT + '/src/core/tsi/test_creds/ca.pem', 'GRPC_POLL_STRATEGY': polling_strategy, 'GRPC_VERBOSITY': 'DEBUG' } resolver = os.environ.get('GRPC_DNS_RESOLVER', None) if resolver: env['GRPC_DNS_RESOLVER'] = resolver shortname_ext = '' if polling_strategy == 'all' else ' GRPC_POLL_STRATEGY=%s' % polling_strategy if polling_strategy in target.get('excluded_poll_engines', []): continue timeout_scaling = 1 if auto_timeout_scaling: config = self.args.config if ('asan' in config or config == 'msan' or config == 'tsan' or config == 'ubsan' or config == 'helgrind' or config == 'memcheck'): # Scale overall test timeout if running under various sanitizers. # scaling value is based on historical data analysis timeout_scaling *= 3 elif polling_strategy == 'poll-cv': # scale test timeout if running with poll-cv # sanitizer and poll-cv scaling is not cumulative to ensure # reasonable timeout values. # TODO(jtattermusch): based on historical data and 5min default # test timeout poll-cv scaling is currently not useful. # Leaving here so it can be reintroduced if the default test timeout # is decreased in the future. timeout_scaling *= 1 if self.config.build_config in target['exclude_configs']: continue if self.args.iomgr_platform in target.get('exclude_iomgrs', []): continue if self.platform == 'windows': binary = 'cmake/build/%s/%s.exe' % ( _MSBUILD_CONFIG[self.config.build_config], target['name']) else: if self._use_cmake: binary = 'cmake/build/%s' % target['name'] else: binary = 'bins/%s/%s' % (self.config.build_config, target['name']) cpu_cost = target['cpu_cost'] if cpu_cost == 'capacity': cpu_cost = multiprocessing.cpu_count() if os.path.isfile(binary): list_test_command = None filter_test_command = None # these are the flag defined by gtest and benchmark framework to list # and filter test runs. We use them to split each individual test # into its own JobSpec, and thus into its own process. if 'benchmark' in target and target['benchmark']: with open(os.devnull, 'w') as fnull: tests = subprocess.check_output( [binary, '--benchmark_list_tests'], stderr=fnull) for line in tests.split('\n'): test = line.strip() if not test: continue cmdline = [binary, '--benchmark_filter=%s$' % test ] + target['args'] out.append( self.config.job_spec( cmdline, shortname='%s %s' % (' '.join(cmdline), shortname_ext), cpu_cost=cpu_cost, timeout_seconds=target.get( 'timeout_seconds', _DEFAULT_TIMEOUT_SECONDS) * timeout_scaling, environ=env)) elif 'gtest' in target and target['gtest']: # here we parse the output of --gtest_list_tests to build up a complete # list of the tests contained in a binary for each test, we then # add a job to run, filtering for just that test. with open(os.devnull, 'w') as fnull: tests = subprocess.check_output( [binary, '--gtest_list_tests'], stderr=fnull) base = None for line in tests.split('\n'): i = line.find(' if i >= 0: line = line[:i] if not line: continue if line[0] != ' ': base = line.strip() else: assert base is not None assert line[1] == ' ' test = base + line.strip() cmdline = [binary, '--gtest_filter=%s' % test ] + target['args'] out.append( self.config.job_spec( cmdline, shortname='%s %s' % (' '.join(cmdline), shortname_ext), cpu_cost=cpu_cost, timeout_seconds=target.get( 'timeout_seconds', _DEFAULT_TIMEOUT_SECONDS) * timeout_scaling, environ=env)) else: cmdline = [binary] + target['args'] shortname = target.get('shortname', ' '.join( pipes.quote(arg) for arg in cmdline)) shortname += shortname_ext out.append( self.config.job_spec( cmdline, shortname=shortname, cpu_cost=cpu_cost, flaky=target.get('flaky', False), timeout_seconds=target.get( 'timeout_seconds', _DEFAULT_TIMEOUT_SECONDS) * timeout_scaling, environ=env)) elif self.args.regex == '.*' or self.platform == 'windows': print('\nWARNING: binary not found, skipping', binary) return sorted(out) def make_targets(self): if self.platform == 'windows': # don't build tools on windows just yet return ['buildtests_%s' % self.make_target] return [ 'buildtests_%s' % self.make_target, 'tools_%s' % self.make_target, 'check_epollexclusive' ] def make_options(self): return self._make_options def pre_build_steps(self): if self.platform == 'windows': return [[ 'tools\\run_tests\\helper_scripts\\pre_build_cmake.bat', self._cmake_generator_option, self._cmake_arch_option ]] elif self._use_cmake: return [['tools/run_tests/helper_scripts/pre_build_cmake.sh']] else: return [] def build_steps(self): return [] def post_tests_steps(self): if self.platform == 'windows': return [] else: return [['tools/run_tests/helper_scripts/post_tests_c.sh']] def makefile_name(self): if self._use_cmake: return 'cmake/build/Makefile' else: return 'Makefile' def _clang_make_options(self, version_suffix=''): if self.args.config == 'ubsan': return [ 'CC=clang%s' % version_suffix, 'CXX=clang++%s' % version_suffix, 'LD=clang++%s' % version_suffix, 'LDXX=clang++%s' % version_suffix ] return [ 'CC=clang%s' % version_suffix, 'CXX=clang++%s' % version_suffix, 'LD=clang%s' % version_suffix, 'LDXX=clang++%s' % version_suffix ] def _gcc_make_options(self, version_suffix): return [ 'CC=gcc%s' % version_suffix, 'CXX=g++%s' % version_suffix, 'LD=gcc%s' % version_suffix, 'LDXX=g++%s' % version_suffix ] def _compiler_options(self, use_docker, compiler): if not use_docker and not _is_use_docker_child(): _check_compiler(compiler, ['default']) if compiler == 'gcc4.9' or compiler == 'default': return ('jessie', []) elif compiler == 'gcc4.8': return ('jessie', self._gcc_make_options(version_suffix='-4.8')) elif compiler == 'gcc5.3': return ('ubuntu1604', []) elif compiler == 'gcc7.2': return ('ubuntu1710', []) elif compiler == 'gcc_musl': return ('alpine', []) elif compiler == 'clang3.4': return ('ubuntu1404', self._clang_make_options()) elif compiler == 'clang3.5': return ('jessie', self._clang_make_options(version_suffix='-3.5')) elif compiler == 'clang3.6': return ('ubuntu1604', self._clang_make_options(version_suffix='-3.6')) elif compiler == 'clang3.7': return ('ubuntu1604', self._clang_make_options(version_suffix='-3.7')) elif compiler == 'clang7.0': # clang++-7.0 alias doesn't exist and there are no other clang versions return ('sanitizers_jessie', self._clang_make_options()) else: raise Exception('Compiler %s not supported.' % compiler) def dockerfile_dir(self): return 'tools/dockerfile/test/cxx_%s_%s' % ( self._docker_distro, _docker_arch_suffix(self.args.arch)) def __str__(self): return self.make_target class RemoteNodeLanguage(object): def __init__(self): self.platform = platform_string() def configure(self, config, args): self.config = config self.args = args # we should specify in the compiler argument _check_compiler(self.args.compiler, [ 'default', 'node0.12', 'node4', 'node5', 'node6', 'node7', 'node8', 'electron1.3', 'electron1.6' ]) if self.args.compiler == 'default': self.runtime = 'node' self.node_version = '8' else: if self.args.compiler.startswith('electron'): self.runtime = 'electron' self.node_version = self.args.compiler[8:] else: self.runtime = 'node' # Take off the word "node" self.node_version = self.args.compiler[4:] # TODO: update with Windows/electron scripts when available for grpc/grpc-node def test_specs(self): if self.platform == 'windows': return [ self.config.job_spec( ['tools\\run_tests\\helper_scripts\\run_node.bat']) ] else: return [ self.config.job_spec( ['tools/run_tests/helper_scripts/run_grpc-node.sh'], None, environ=_FORCE_ENVIRON_FOR_WRAPPERS) ] def pre_build_steps(self): return [] def make_targets(self): return [] def make_options(self): return [] def build_steps(self): return [] def post_tests_steps(self): return [] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/node_jessie_%s' % _docker_arch_suffix( self.args.arch) def __str__(self): return 'grpc-node' class PhpLanguage(object): def configure(self, config, args): self.config = config self.args = args _check_compiler(self.args.compiler, ['default']) self._make_options = ['EMBED_OPENSSL=true', 'EMBED_ZLIB=true'] def test_specs(self): return [ self.config.job_spec( ['src/php/bin/run_tests.sh'], environ=_FORCE_ENVIRON_FOR_WRAPPERS) ] def pre_build_steps(self): return [] def make_targets(self): return ['static_c', 'shared_c'] def make_options(self): return self._make_options def build_steps(self): return [['tools/run_tests/helper_scripts/build_php.sh']] def post_tests_steps(self): return [['tools/run_tests/helper_scripts/post_tests_php.sh']] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/php_jessie_%s' % _docker_arch_suffix( self.args.arch) def __str__(self): return 'php' class Php7Language(object): def configure(self, config, args): self.config = config self.args = args _check_compiler(self.args.compiler, ['default']) self._make_options = ['EMBED_OPENSSL=true', 'EMBED_ZLIB=true'] def test_specs(self): return [ self.config.job_spec( ['src/php/bin/run_tests.sh'], environ=_FORCE_ENVIRON_FOR_WRAPPERS) ] def pre_build_steps(self): return [] def make_targets(self): return ['static_c', 'shared_c'] def make_options(self): return self._make_options def build_steps(self): return [['tools/run_tests/helper_scripts/build_php.sh']] def post_tests_steps(self): return [['tools/run_tests/helper_scripts/post_tests_php.sh']] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/php7_jessie_%s' % _docker_arch_suffix( self.args.arch) def __str__(self): return 'php7' class PythonConfig( collections.namedtuple('PythonConfig', ['name', 'build', 'run'])): class PythonLanguage(object): def configure(self, config, args): self.config = config self.args = args self.pythons = self._get_pythons(self.args) def test_specs(self): # load list of known test suites with open( 'src/python/grpcio_tests/tests/tests.json') as tests_json_file: tests_json = json.load(tests_json_file) environment = dict(_FORCE_ENVIRON_FOR_WRAPPERS) return [ self.config.job_spec( config.run, timeout_seconds=5 * 60, environ=dict( list(environment.items()) + [( 'GRPC_PYTHON_TESTRUNNER_FILTER', str(suite_name))]), shortname='%s.test.%s' % (config.name, suite_name), ) for suite_name in tests_json for config in self.pythons ] def pre_build_steps(self): return [] def make_targets(self): return [] def make_options(self): return [] def build_steps(self): return [config.build for config in self.pythons] def post_tests_steps(self): if self.config.build_config != 'gcov': return [] else: return [['tools/run_tests/helper_scripts/post_tests_python.sh']] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/python_%s_%s' % ( self._python_manager_name(), _docker_arch_suffix(self.args.arch)) def _python_manager_name(self): if self.args.compiler in [ 'python2.7', 'python3.5', 'python3.6', 'python3.7' ]: return 'stretch_' + self.args.compiler[len('python'):] elif self.args.compiler == 'python_alpine': return 'alpine' elif self.args.compiler == 'python3.4': return 'jessie' else: return 'stretch_3.7' def _get_pythons(self, args): if args.arch == 'x86': bits = '32' else: bits = '64' if os.name == 'nt': shell = ['bash'] builder = [ os.path.abspath( 'tools/run_tests/helper_scripts/build_python_msys2.sh') ] builder_prefix_arguments = ['MINGW{}'.format(bits)] venv_relative_python = ['Scripts/python.exe'] toolchain = ['mingw32'] else: shell = [] builder = [ os.path.abspath( 'tools/run_tests/helper_scripts/build_python.sh') ] builder_prefix_arguments = [] venv_relative_python = ['bin/python'] toolchain = ['unix'] test_command = 'test_lite' if args.iomgr_platform == 'gevent': test_command = 'test_gevent' runner = [ os.path.abspath('tools/run_tests/helper_scripts/run_python.sh') ] config_vars = _PythonConfigVars( shell, builder, builder_prefix_arguments, venv_relative_python, toolchain, runner, test_command, args.iomgr_platform) python27_config = _python_config_generator( name='py27', major='2', minor='7', bits=bits, config_vars=config_vars) python34_config = _python_config_generator( name='py34', major='3', minor='4', bits=bits, config_vars=config_vars) python35_config = _python_config_generator( name='py35', major='3', minor='5', bits=bits, config_vars=config_vars) python36_config = _python_config_generator( name='py36', major='3', minor='6', bits=bits, config_vars=config_vars) python37_config = _python_config_generator( name='py37', major='3', minor='7', bits=bits, config_vars=config_vars) pypy27_config = _pypy_config_generator( name='pypy', major='2', config_vars=config_vars) pypy32_config = _pypy_config_generator( name='pypy3', major='3', config_vars=config_vars) if args.compiler == 'default': if os.name == 'nt': return (python35_config,) else: return ( python27_config, python37_config, ) elif args.compiler == 'python2.7': return (python27_config,) elif args.compiler == 'python3.4': return (python34_config,) elif args.compiler == 'python3.5': return (python35_config,) elif args.compiler == 'python3.6': return (python36_config,) elif args.compiler == 'python3.7': return (python37_config,) elif args.compiler == 'pypy': return (pypy27_config,) elif args.compiler == 'pypy3': return (pypy32_config,) elif args.compiler == 'python_alpine': return (python27_config,) elif args.compiler == 'all_the_cpythons': return ( python27_config, python34_config, python35_config, python36_config, python37_config, ) else: raise Exception('Compiler %s not supported.' % args.compiler) def __str__(self): return 'python' class RubyLanguage(object): def configure(self, config, args): self.config = config self.args = args _check_compiler(self.args.compiler, ['default']) def test_specs(self): tests = [ self.config.job_spec( ['tools/run_tests/helper_scripts/run_ruby.sh'], timeout_seconds=10 * 60, environ=_FORCE_ENVIRON_FOR_WRAPPERS) ] tests.append( self.config.job_spec( ['tools/run_tests/helper_scripts/run_ruby_end2end_tests.sh'], timeout_seconds=20 * 60, environ=_FORCE_ENVIRON_FOR_WRAPPERS)) return tests def pre_build_steps(self): return [['tools/run_tests/helper_scripts/pre_build_ruby.sh']] def make_targets(self): return [] def make_options(self): return [] def build_steps(self): return [['tools/run_tests/helper_scripts/build_ruby.sh']] def post_tests_steps(self): return [['tools/run_tests/helper_scripts/post_tests_ruby.sh']] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/ruby_jessie_%s' % _docker_arch_suffix( self.args.arch) def __str__(self): return 'ruby' class CSharpLanguage(object): def __init__(self): self.platform = platform_string() def configure(self, config, args): self.config = config self.args = args if self.platform == 'windows': _check_compiler(self.args.compiler, ['default', 'coreclr']) _check_arch(self.args.arch, ['default']) self._cmake_arch_option = 'x64' else: _check_compiler(self.args.compiler, ['default', 'coreclr']) self._docker_distro = 'jessie' def test_specs(self): with open('src/csharp/tests.json') as f: tests_by_assembly = json.load(f) msbuild_config = _MSBUILD_CONFIG[self.config.build_config] nunit_args = ['--labels=All', '--noresult', '--workers=1'] assembly_subdir = 'bin/%s' % msbuild_config assembly_extension = '.exe' if self.args.compiler == 'coreclr': assembly_subdir += '/netcoreapp1.0' runtime_cmd = ['dotnet', 'exec'] assembly_extension = '.dll' else: assembly_subdir += '/net45' if self.platform == 'windows': runtime_cmd = [] elif self.platform == 'mac': # mono before version 5.2 on MacOS defaults to 32bit runtime runtime_cmd = ['mono', '--arch=64'] else: runtime_cmd = ['mono'] specs = [] for assembly in six.iterkeys(tests_by_assembly): assembly_file = 'src/csharp/%s/%s/%s%s' % (assembly, assembly_subdir, assembly, assembly_extension) if self.config.build_config != 'gcov' or self.platform != 'windows': # normally, run each test as a separate process for test in tests_by_assembly[assembly]: cmdline = runtime_cmd + [assembly_file, '--test=%s' % test] + nunit_args specs.append( self.config.job_spec( cmdline, shortname='csharp.%s' % test, environ=_FORCE_ENVIRON_FOR_WRAPPERS)) else: # For C# test coverage, run all tests from the same assembly at once # using OpenCover.Console (only works on Windows). cmdline = [ 'src\\csharp\\packages\\OpenCover.4.6.519\\tools\\OpenCover.Console.exe', '-target:%s' % assembly_file, '-targetdir:src\\csharp', '-targetargs:%s' % ' '.join(nunit_args), '-filter:+[Grpc.Core]*', '-register:user', '-output:src\\csharp\\coverage_csharp_%s.xml' % assembly ] # set really high cpu_cost to make sure instances of OpenCover.Console run exclusively # to prevent problems with registering the profiler. run_exclusive = 1000000 specs.append( self.config.job_spec( cmdline, shortname='csharp.coverage.%s' % assembly, cpu_cost=run_exclusive, environ=_FORCE_ENVIRON_FOR_WRAPPERS)) return specs def pre_build_steps(self): if self.platform == 'windows': return [[ 'tools\\run_tests\\helper_scripts\\pre_build_csharp.bat', self._cmake_arch_option ]] else: return [['tools/run_tests/helper_scripts/pre_build_csharp.sh']] def make_targets(self): return ['grpc_csharp_ext'] def make_options(self): return [] def build_steps(self): if self.platform == 'windows': return [['tools\\run_tests\\helper_scripts\\build_csharp.bat']] else: return [['tools/run_tests/helper_scripts/build_csharp.sh']] def post_tests_steps(self): if self.platform == 'windows': return [['tools\\run_tests\\helper_scripts\\post_tests_csharp.bat']] else: return [['tools/run_tests/helper_scripts/post_tests_csharp.sh']] def makefile_name(self): if self.platform == 'windows': return 'cmake/build/%s/Makefile' % self._cmake_arch_option else: # no need to set x86 specific flags as run_tests.py # currently forbids x86 C# builds on both Linux and MacOS. return 'cmake/build/Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/csharp_%s_%s' % ( self._docker_distro, _docker_arch_suffix(self.args.arch)) def __str__(self): return 'csharp' class ObjCLanguage(object): def configure(self, config, args): self.config = config self.args = args _check_compiler(self.args.compiler, ['default']) def test_specs(self): return [ self.config.job_spec( ['src/objective-c/tests/run_tests.sh'], timeout_seconds=60 * 60, shortname='objc-tests', cpu_cost=1e6, environ=_FORCE_ENVIRON_FOR_WRAPPERS), self.config.job_spec( ['src/objective-c/tests/run_plugin_tests.sh'], timeout_seconds=60 * 60, shortname='objc-plugin-tests', cpu_cost=1e6, environ=_FORCE_ENVIRON_FOR_WRAPPERS), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-helloworld', cpu_cost=1e6, environ={ 'SCHEME': 'HelloWorld', 'EXAMPLE_PATH': 'examples/objective-c/helloworld' }), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-routeguide', cpu_cost=1e6, environ={ 'SCHEME': 'RouteGuideClient', 'EXAMPLE_PATH': 'examples/objective-c/route_guide' }), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-authsample', cpu_cost=1e6, environ={ 'SCHEME': 'AuthSample', 'EXAMPLE_PATH': 'examples/objective-c/auth_sample' }), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-sample', cpu_cost=1e6, environ={ 'SCHEME': 'Sample', 'EXAMPLE_PATH': 'src/objective-c/examples/Sample' }), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-sample-frameworks', cpu_cost=1e6, environ={ 'SCHEME': 'Sample', 'EXAMPLE_PATH': 'src/objective-c/examples/Sample', 'FRAMEWORKS': 'YES' }), self.config.job_spec( ['src/objective-c/tests/build_one_example.sh'], timeout_seconds=10 * 60, shortname='objc-build-example-switftsample', cpu_cost=1e6, environ={ 'SCHEME': 'SwiftSample', 'EXAMPLE_PATH': 'src/objective-c/examples/SwiftSample' }), self.config.job_spec( ['test/core/iomgr/ios/CFStreamTests/run_tests.sh'], timeout_seconds=10 * 60, shortname='cfstream-tests', cpu_cost=1e6, environ=_FORCE_ENVIRON_FOR_WRAPPERS), ] def pre_build_steps(self): return [] def make_targets(self): return ['interop_server'] def make_options(self): return [] def build_steps(self): return [ ['src/objective-c/tests/build_tests.sh'], ['test/core/iomgr/ios/CFStreamTests/build_tests.sh'], ] def post_tests_steps(self): return [] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return None def __str__(self): return 'objc' class Sanity(object): def configure(self, config, args): self.config = config self.args = args _check_compiler(self.args.compiler, ['default']) def test_specs(self): import yaml with open('tools/run_tests/sanity/sanity_tests.yaml', 'r') as f: environ = {'TEST': 'true'} if _is_use_docker_child(): environ['CLANG_FORMAT_SKIP_DOCKER'] = 'true' environ['CLANG_TIDY_SKIP_DOCKER'] = 'true' return [ self.config.job_spec( cmd['script'].split(), timeout_seconds=30 * 60, environ=environ, cpu_cost=cmd.get('cpu_cost', 1)) for cmd in yaml.load(f) ] def pre_build_steps(self): return [] def make_targets(self): return ['run_dep_checks'] def make_options(self): return [] def build_steps(self): return [] def post_tests_steps(self): return [] def makefile_name(self): return 'Makefile' def dockerfile_dir(self): return 'tools/dockerfile/test/sanity' def __str__(self): return 'sanity' # different configurations we can run under with open('tools/run_tests/generated/configs.json') as f: _CONFIGS = dict( (cfg['config'], Config(**cfg)) for cfg in ast.literal_eval(f.read())) _LANGUAGES = { 'c++': CLanguage('cxx', 'c++'), 'c': CLanguage('c', 'c'), 'grpc-node': RemoteNodeLanguage(), 'php': PhpLanguage(), 'php7': Php7Language(), 'python': PythonLanguage(), 'ruby': RubyLanguage(), 'csharp': CSharpLanguage(), 'objc': ObjCLanguage(), 'sanity': Sanity() } _MSBUILD_CONFIG = { 'dbg': 'Debug', 'opt': 'Release', 'gcov': 'Debug', } def _windows_arch_option(arch): if arch == 'default' or arch == 'x86': return '/p:Platform=Win32' elif arch == 'x64': return '/p:Platform=x64' else: print('Architecture %s not supported.' % arch) sys.exit(1) def _check_arch_option(arch): if platform_string() == 'windows': _windows_arch_option(arch) elif platform_string() == 'linux': # On linux, we need to be running under docker with the right architecture. runtime_arch = platform.architecture()[0] if arch == 'default': return elif runtime_arch == '64bit' and arch == 'x64': return elif runtime_arch == '32bit' and arch == 'x86': return else: print('Architecture %s does not match current runtime architecture.' % arch) sys.exit(1) else: if args.arch != 'default': print('Architecture %s not supported on current platform.' % args.arch) sys.exit(1) def _docker_arch_suffix(arch): if arch == 'default' or arch == 'x64': return 'x64' elif arch == 'x86': return 'x86' else: print('Architecture %s not supported with current settings.' % arch) sys.exit(1) def runs_per_test_type(arg_str): if arg_str == 'inf': return 0 try: n = int(arg_str) if n <= 0: raise ValueError return n except: msg = '\'{}\' is not a positive integer or \'inf\''.format(arg_str) raise argparse.ArgumentTypeError(msg) def percent_type(arg_str): pct = float(arg_str) if pct > 100 or pct < 0: raise argparse.ArgumentTypeError( "'%f' is not a valid percentage in the [0, 100] range" % pct) return pct # This is math.isclose in python >= 3.5 def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) # parse command line argp = argparse.ArgumentParser(description='Run grpc tests.') argp.add_argument( '-c', '--config', choices=sorted(_CONFIGS.keys()), default='opt') argp.add_argument( '-n', '--runs_per_test', default=1, type=runs_per_test_type, help='A positive integer or "inf". If "inf", all tests will run in an ' 'infinite loop. Especially useful in combination with "-f"') argp.add_argument('-r', '--regex', default='.*', type=str) argp.add_argument('--regex_exclude', default='', type=str) argp.add_argument('-j', '--jobs', default=multiprocessing.cpu_count(), type=int) argp.add_argument('-s', '--slowdown', default=1.0, type=float) argp.add_argument( '-p', '--sample_percent', default=100.0, type=percent_type, help='Run a random sample with that percentage of tests') argp.add_argument( '-f', '--forever', default=False, action='store_const', const=True) argp.add_argument( '-t', '--travis', default=False, action='store_const', const=True) argp.add_argument( '--newline_on_success', default=False, action='store_const', const=True) argp.add_argument( '-l', '--language', choices=sorted(_LANGUAGES.keys()), nargs='+', required=True) argp.add_argument( '-S', '--stop_on_failure', default=False, action='store_const', const=True) argp.add_argument( '--use_docker', default=False, action='store_const', const=True, help='Run all the tests under docker. That provides ' + 'additional isolation and prevents the need to install ' + 'language specific prerequisites. Only available on Linux.') argp.add_argument( '--allow_flakes', default=False, action='store_const', const=True, help= 'Allow flaky tests to show as passing (re-runs failed tests up to five times)' ) argp.add_argument( '--arch', choices=['default', 'x86', 'x64'], default='default', help= 'Selects architecture to target. For some platforms "default" is the only supported choice.' ) argp.add_argument( '--compiler', choices=[ 'default', 'gcc4.4', 'gcc4.6', 'gcc4.8', 'gcc4.9', 'gcc5.3', 'gcc7.2', 'gcc_musl', 'clang3.4', 'clang3.5', 'clang3.6', 'clang3.7', 'clang7.0', 'python2.7', 'python3.4', 'python3.5', 'python3.6', 'python3.7', 'pypy', 'pypy3', 'python_alpine', 'all_the_cpythons', 'electron1.3', 'electron1.6', 'coreclr', 'cmake', 'cmake_vs2015', 'cmake_vs2017' ], default='default', help= 'Selects compiler to use. Allowed values depend on the platform and language.' ) argp.add_argument( '--iomgr_platform', choices=['native', 'uv', 'gevent'], default='native', help='Selects iomgr platform to build on') argp.add_argument( '--build_only', default=False, action='store_const', const=True, help='Perform all the build steps but don\'t run any tests.') argp.add_argument( '--measure_cpu_costs', default=False, action='store_const', const=True, help='Measure the cpu costs of tests') argp.add_argument( '--update_submodules', default=[], nargs='*', help= 'Update some submodules before building. If any are updated, also run generate_projects. ' + 'Submodules are specified as SUBMODULE_NAME:BRANCH; if BRANCH is omitted, master is assumed.' ) argp.add_argument('-a', '--antagonists', default=0, type=int) argp.add_argument( '-x', '--xml_report', default=None, type=str, help='Generates a JUnit-compatible XML report') argp.add_argument( '--report_suite_name', default='tests', type=str, help='Test suite name to use in generated JUnit XML report') argp.add_argument( '--quiet_success', default=False, action='store_const', const=True, help= 'Don\'t print anything when a test passes. Passing tests also will not be reported in XML report. ' + 'Useful when running many iterations of each test (argument -n).') argp.add_argument( '--force_default_poller', default=False, action='store_const', const=True, help='Don\'t try to iterate over many polling strategies when they exist') argp.add_argument( '--force_use_pollers', default=None, type=str, help='Only use the specified comma-delimited list of polling engines. ' 'Example: --force_use_pollers epoll1,poll ' ' (This flag has no effect if --force_default_poller flag is also used)') argp.add_argument( '--max_time', default=-1, type=int, help='Maximum test runtime in seconds') argp.add_argument( '--bq_result_table', default='', type=str, nargs='?', help='Upload test results to a specified BQ table.') argp.add_argument( '--auto_set_flakes', default=False, const=True, action='store_const', help= 'Allow repeated runs for tests that have been failing recently (based on BQ historical data).' ) args = argp.parse_args() flaky_tests = set() shortname_to_cpu = {} if args.auto_set_flakes: try: for test in get_bqtest_data(): if test.flaky: flaky_tests.add(test.name) if test.cpu > 0: shortname_to_cpu[test.name] = test.cpu except: print( "Unexpected error getting flaky tests: %s" % traceback.format_exc()) if args.force_default_poller: _POLLING_STRATEGIES = {} elif args.force_use_pollers: _POLLING_STRATEGIES[platform_string()] = args.force_use_pollers.split(',') jobset.measure_cpu_costs = args.measure_cpu_costs need_to_regenerate_projects = False for spec in args.update_submodules: spec = spec.split(':', 1) if len(spec) == 1: submodule = spec[0] branch = 'master' elif len(spec) == 2: submodule = spec[0] branch = spec[1] cwd = 'third_party/%s' % submodule def git(cmd, cwd=cwd): print('in %s: git %s' % (cwd, cmd)) run_shell_command('git %s' % cmd, cwd=cwd) git('fetch') git('checkout %s' % branch) git('pull origin %s' % branch) if os.path.exists('src/%s/gen_build_yaml.py' % submodule): need_to_regenerate_projects = True if need_to_regenerate_projects: if jobset.platform_string() == 'linux': run_shell_command('tools/buildgen/generate_projects.sh') else: print( 'WARNING: may need to regenerate projects, but since we are not on') print( ' Linux this step is being skipped. Compilation MAY fail.') run_config = _CONFIGS[args.config] build_config = run_config.build_config if args.travis: _FORCE_ENVIRON_FOR_WRAPPERS = {'GRPC_TRACE': 'api'} languages = set(_LANGUAGES[l] for l in args.language) for l in languages: l.configure(run_config, args) language_make_options = [] if any(language.make_options() for language in languages): if not 'gcov' in args.config and len(languages) != 1: print( 'languages with custom make options cannot be built simultaneously with other languages' ) sys.exit(1) else: language_make_options = list( set([ make_option for lang in languages for make_option in lang.make_options() ])) if args.use_docker: if not args.travis: print('Seen --use_docker flag, will run tests under docker.') print('') print( 'IMPORTANT: The changes you are testing need to be locally committed' ) print( 'because only the committed changes in the current branch will be') print('copied to the docker environment.') time.sleep(5) dockerfile_dirs = set([l.dockerfile_dir() for l in languages]) if len(dockerfile_dirs) > 1: if 'gcov' in args.config: dockerfile_dir = 'tools/dockerfile/test/multilang_jessie_x64' print( 'Using multilang_jessie_x64 docker image for code coverage for ' 'all languages.') else: print( 'Languages to be tested require running under different docker ' 'images.') sys.exit(1) else: dockerfile_dir = next(iter(dockerfile_dirs)) child_argv = [arg for arg in sys.argv if not arg == '--use_docker'] run_tests_cmd = 'python tools/run_tests/run_tests.py %s' % ' '.join( child_argv[1:]) env = os.environ.copy() env['RUN_TESTS_COMMAND'] = run_tests_cmd env['DOCKERFILE_DIR'] = dockerfile_dir env['DOCKER_RUN_SCRIPT'] = 'tools/run_tests/dockerize/docker_run_tests.sh' if args.xml_report: env['XML_REPORT'] = args.xml_report if not args.travis: env['TTY_FLAG'] = '-t' subprocess.check_call( 'tools/run_tests/dockerize/build_docker_and_run_tests.sh', shell=True, env=env) sys.exit(0) _check_arch_option(args.arch) def make_jobspec(cfg, targets, makefile='Makefile'): if platform_string() == 'windows': return [ jobset.JobSpec( [ 'cmake', '--build', '.', '--target', '%s' % target, '--config', _MSBUILD_CONFIG[cfg] ], cwd=os.path.dirname(makefile), timeout_seconds=None) for target in targets ] else: if targets and makefile.startswith('cmake/build/'): return [ jobset.JobSpec( [os.getenv('MAKE', 'make'), '-j', '%d' % args.jobs] + targets, cwd='cmake/build', timeout_seconds=None) ] if targets: return [ jobset.JobSpec( [ os.getenv('MAKE', 'make'), '-f', makefile, '-j', '%d' % args.jobs, 'EXTRA_DEFINES=GRPC_TEST_SLOWDOWN_MACHINE_FACTOR=%f' % args.slowdown, 'CONFIG=%s' % cfg, 'Q=' ] + language_make_options + ([] if not args.travis else ['JENKINS_BUILD=1']) + targets, timeout_seconds=None) ] else: return [] make_targets = {} for l in languages: makefile = l.makefile_name() make_targets[makefile] = make_targets.get(makefile, set()).union( set(l.make_targets())) def build_step_environ(cfg): environ = {'CONFIG': cfg} msbuild_cfg = _MSBUILD_CONFIG.get(cfg) if msbuild_cfg: environ['MSBUILD_CONFIG'] = msbuild_cfg return environ build_steps = list( set( jobset.JobSpec( cmdline, environ=build_step_environ(build_config), flake_retries=2) for l in languages for cmdline in l.pre_build_steps())) if make_targets: make_commands = itertools.chain.from_iterable( make_jobspec(build_config, list(targets), makefile) for (makefile, targets) in make_targets.items()) build_steps.extend(set(make_commands)) build_steps.extend( set( jobset.JobSpec( cmdline, environ=build_step_environ(build_config), timeout_seconds=None) for l in languages for cmdline in l.build_steps())) post_tests_steps = list( set( jobset.JobSpec(cmdline, environ=build_step_environ(build_config)) for l in languages for cmdline in l.post_tests_steps())) runs_per_test = args.runs_per_test forever = args.forever def _shut_down_legacy_server(legacy_server_port): try: version = int( urllib.request.urlopen( 'http://localhost:%d/version_number' % legacy_server_port, timeout=10).read()) except: pass else: urllib.request.urlopen( 'http://localhost:%d/quitquitquit' % legacy_server_port).read() def _calculate_num_runs_failures(list_of_results): num_runs = len(list_of_results) # By default, there is 1 run per JobResult. num_failures = 0 for jobresult in list_of_results: if jobresult.retries > 0: num_runs += jobresult.retries if jobresult.num_failures > 0: num_failures += jobresult.num_failures return num_runs, num_failures # _build_and_run results class BuildAndRunError(object): BUILD = object() TEST = object() POST_TEST = object() def _has_epollexclusive(): binary = 'bins/%s/check_epollexclusive' % args.config if not os.path.exists(binary): return False try: subprocess.check_call(binary) return True except subprocess.CalledProcessError as e: return False except OSError as e: # For languages other than C and Windows the binary won't exist return False def _build_and_run(check_cancelled, newline_on_success, xml_report=None, build_only=False): num_failures, resultset = jobset.run( build_steps, maxjobs=1, stop_on_failure=True, newline_on_success=newline_on_success, travis=args.travis) if num_failures: return [BuildAndRunError.BUILD] if build_only: if xml_report: report_utils.render_junit_xml_report( resultset, xml_report, suite_name=args.report_suite_name) return [] if not args.travis and not _has_epollexclusive() and platform_string( ) in _POLLING_STRATEGIES and 'epollex' in _POLLING_STRATEGIES[platform_string( )]: print('\n\nOmitting EPOLLEXCLUSIVE tests\n\n') _POLLING_STRATEGIES[platform_string()].remove('epollex') antagonists = [ subprocess.Popen(['tools/run_tests/python_utils/antagonist.py']) for _ in range(0, args.antagonists) ] start_port_server.start_port_server() resultset = None num_test_failures = 0 try: infinite_runs = runs_per_test == 0 one_run = set( spec for language in languages for spec in language.test_specs() if (re.search(args.regex, spec.shortname) and (args.regex_exclude == '' or not re.search(args.regex_exclude, spec.shortname)))) if args.travis and args.max_time <= 0: massaged_one_run = sorted(one_run, key=lambda x: x.cpu_cost) else: massaged_one_run = list( one_run) num_jobs = len(massaged_one_run) sample_size = int(num_jobs * args.sample_percent / 100.0) massaged_one_run = random.sample(massaged_one_run, sample_size) if not isclose(args.sample_percent, 100.0): assert args.runs_per_test == 1, "Can't do sampling (-p) over multiple runs (-n)." print("Running %d tests out of %d (~%d%%)" % (sample_size, num_jobs, args.sample_percent)) if infinite_runs: assert len(massaged_one_run ) > 0, 'Must have at least one test for a -n inf run' runs_sequence = (itertools.repeat(massaged_one_run) if infinite_runs else itertools.repeat( massaged_one_run, runs_per_test)) all_runs = itertools.chain.from_iterable(runs_sequence) if args.quiet_success: jobset.message( 'START', 'Running tests quietly, only failing tests will be reported', do_newline=True) num_test_failures, resultset = jobset.run( all_runs, check_cancelled, newline_on_success=newline_on_success, travis=args.travis, maxjobs=args.jobs, maxjobs_cpu_agnostic=max_parallel_tests_for_current_platform(), stop_on_failure=args.stop_on_failure, quiet_success=args.quiet_success, max_time=args.max_time) if resultset: for k, v in sorted(resultset.items()): num_runs, num_failures = _calculate_num_runs_failures(v) if num_failures > 0: if num_failures == num_runs: # what about infinite_runs??? jobset.message('FAILED', k, do_newline=True) else: jobset.message( 'FLAKE', '%s [%d/%d runs flaked]' % (k, num_failures, num_runs), do_newline=True) finally: for antagonist in antagonists: antagonist.kill() if args.bq_result_table and resultset: upload_extra_fields = { 'compiler': args.compiler, 'config': args.config, 'iomgr_platform': args.iomgr_platform, 'language': args.language[ 0], # args.language is a list but will always have one element when uploading to BQ is enabled. 'platform': platform_string() } upload_results_to_bq(resultset, args.bq_result_table, upload_extra_fields) if xml_report and resultset: report_utils.render_junit_xml_report( resultset, xml_report, suite_name=args.report_suite_name) number_failures, _ = jobset.run( post_tests_steps, maxjobs=1, stop_on_failure=False, newline_on_success=newline_on_success, travis=args.travis) out = [] if number_failures: out.append(BuildAndRunError.POST_TEST) if num_test_failures: out.append(BuildAndRunError.TEST) return out if forever: success = True while True: dw = watch_dirs.DirWatcher(['src', 'include', 'test', 'examples']) initial_time = dw.most_recent_change() have_files_changed = lambda: dw.most_recent_change() != initial_time previous_success = success errors = _build_and_run( check_cancelled=have_files_changed, newline_on_success=False, build_only=args.build_only) == 0 if not previous_success and not errors: jobset.message( 'SUCCESS', 'All tests are now passing properly', do_newline=True) jobset.message('IDLE', 'No change detected') while not have_files_changed(): time.sleep(1) else: errors = _build_and_run( check_cancelled=lambda: False, newline_on_success=args.newline_on_success, xml_report=args.xml_report, build_only=args.build_only) if not errors: jobset.message('SUCCESS', 'All tests passed', do_newline=True) else: jobset.message('FAILED', 'Some tests failed', do_newline=True) exit_code = 0 if BuildAndRunError.BUILD in errors: exit_code |= 1 if BuildAndRunError.TEST in errors: exit_code |= 2 if BuildAndRunError.POST_TEST in errors: exit_code |= 4 sys.exit(exit_code)

true

f71be3b444baa4607d29128c718bb04ae6b2c311

32,709

py

Python

ryu/lib/ofctl_v1_4.py

vinaykothiyal/ryu

32551989c649311854215df29860ccb272c105c0

[ "Apache-2.0" ]

null

ryu/lib/ofctl_v1_4.py

vinaykothiyal/ryu

32551989c649311854215df29860ccb272c105c0

[ "Apache-2.0" ]

null

ryu/lib/ofctl_v1_4.py

vinaykothiyal/ryu

32551989c649311854215df29860ccb272c105c0

[ "Apache-2.0" ]

null

# Copyright (C) 2016 Nippon Telegraph and Telephone Corporation. # # 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 logging from ryu.ofproto import ether from ryu.ofproto import ofproto_v1_4 from ryu.ofproto import ofproto_v1_4_parser from ryu.lib import ofctl_utils LOG = logging.getLogger(__name__) DEFAULT_TIMEOUT = 1.0 UTIL = ofctl_utils.OFCtlUtil(ofproto_v1_4) str_to_int = ofctl_utils.str_to_int def to_action(dp, dic): ofp = dp.ofproto parser = dp.ofproto_parser action_type = dic.get('type') return ofctl_utils.to_action(dic, ofp, parser, action_type, UTIL) def _get_actions(dp, dics): actions = [] for d in dics: action = to_action(dp, d) if action is not None: actions.append(action) else: LOG.error('Unknown action type: %s', d) return actions def to_instructions(dp, insts): instructions = [] ofp = dp.ofproto parser = dp.ofproto_parser for i in insts: inst_type = i.get('type') if inst_type in ['APPLY_ACTIONS', 'WRITE_ACTIONS']: dics = i.get('actions', []) actions = _get_actions(dp, dics) if actions: if inst_type == 'APPLY_ACTIONS': instructions.append( parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS, actions)) else: instructions.append( parser.OFPInstructionActions(ofp.OFPIT_WRITE_ACTIONS, actions)) elif inst_type == 'CLEAR_ACTIONS': instructions.append( parser.OFPInstructionActions(ofp.OFPIT_CLEAR_ACTIONS, [])) elif inst_type == 'GOTO_TABLE': table_id = str_to_int(i.get('table_id')) instructions.append(parser.OFPInstructionGotoTable(table_id)) elif inst_type == 'WRITE_METADATA': metadata = str_to_int(i.get('metadata')) metadata_mask = (str_to_int(i['metadata_mask']) if 'metadata_mask' in i else parser.UINT64_MAX) instructions.append( parser.OFPInstructionWriteMetadata( metadata, metadata_mask)) elif inst_type == 'METER': meter_id = str_to_int(i.get('meter_id')) instructions.append(parser.OFPInstructionMeter(meter_id)) else: LOG.error('Unknown instruction type: %s', inst_type) return instructions def action_to_str(act): s = act.to_jsondict()[act.__class__.__name__] t = UTIL.ofp_action_type_to_user(s['type']) s['type'] = t if t != s['type'] else 'UNKNOWN' if 'field' in s: field = s.pop('field') s['field'] = field['OXMTlv']['field'] s['mask'] = field['OXMTlv']['mask'] s['value'] = field['OXMTlv']['value'] return s def instructions_to_str(instructions): s = [] for i in instructions: v = i.to_jsondict()[i.__class__.__name__] t = UTIL.ofp_instruction_type_to_user(v['type']) inst_type = t if t != v['type'] else 'UNKNOWN' # apply/write/clear-action instruction if isinstance(i, ofproto_v1_4_parser.OFPInstructionActions): acts = [] for a in i.actions: acts.append(action_to_str(a)) v['type'] = inst_type v['actions'] = acts s.append(v) # others else: v['type'] = inst_type s.append(v) return s def to_match(dp, attrs): convert = {'in_port': UTIL.ofp_port_from_user, 'in_phy_port': str_to_int, 'metadata': ofctl_utils.to_match_masked_int, 'eth_dst': ofctl_utils.to_match_eth, 'eth_src': ofctl_utils.to_match_eth, 'eth_type': str_to_int, 'vlan_vid': to_match_vid, 'vlan_pcp': str_to_int, 'ip_dscp': str_to_int, 'ip_ecn': str_to_int, 'ip_proto': str_to_int, 'ipv4_src': ofctl_utils.to_match_ip, 'ipv4_dst': ofctl_utils.to_match_ip, 'tcp_src': str_to_int, 'tcp_dst': str_to_int, 'udp_src': str_to_int, 'udp_dst': str_to_int, 'sctp_src': str_to_int, 'sctp_dst': str_to_int, 'icmpv4_type': str_to_int, 'icmpv4_code': str_to_int, 'arp_op': str_to_int, 'arp_spa': ofctl_utils.to_match_ip, 'arp_tpa': ofctl_utils.to_match_ip, 'arp_sha': ofctl_utils.to_match_eth, 'arp_tha': ofctl_utils.to_match_eth, 'ipv6_src': ofctl_utils.to_match_ip, 'ipv6_dst': ofctl_utils.to_match_ip, 'ipv6_flabel': str_to_int, 'icmpv6_type': str_to_int, 'icmpv6_code': str_to_int, 'ipv6_nd_target': ofctl_utils.to_match_ip, 'ipv6_nd_sll': ofctl_utils.to_match_eth, 'ipv6_nd_tll': ofctl_utils.to_match_eth, 'mpls_label': str_to_int, 'mpls_tc': str_to_int, 'mpls_bos': str_to_int, 'pbb_isid': ofctl_utils.to_match_masked_int, 'tunnel_id': ofctl_utils.to_match_masked_int, 'ipv6_exthdr': ofctl_utils.to_match_masked_int, 'pbb_uca': str_to_int} keys = {'dl_dst': 'eth_dst', 'dl_src': 'eth_src', 'dl_type': 'eth_type', 'dl_vlan': 'vlan_vid', 'nw_src': 'ipv4_src', 'nw_dst': 'ipv4_dst', 'nw_proto': 'ip_proto'} if attrs.get('eth_type') == ether.ETH_TYPE_ARP: if 'ipv4_src' in attrs and 'arp_spa' not in attrs: attrs['arp_spa'] = attrs['ipv4_src'] del attrs['ipv4_src'] if 'ipv4_dst' in attrs and 'arp_tpa' not in attrs: attrs['arp_tpa'] = attrs['ipv4_dst'] del attrs['ipv4_dst'] kwargs = {} for key, value in attrs.items(): if key in keys: # For old field name key = keys[key] if key in convert: value = convert[key](value) kwargs[key] = value else: LOG.error('Unknown match field: %s', key) return dp.ofproto_parser.OFPMatch(**kwargs) def to_match_vid(value): return ofctl_utils.to_match_vid(value, ofproto_v1_4.OFPVID_PRESENT) def match_to_str(ofmatch): match = {} ofmatch = ofmatch.to_jsondict()['OFPMatch'] ofmatch = ofmatch['oxm_fields'] for match_field in ofmatch: key = match_field['OXMTlv']['field'] mask = match_field['OXMTlv']['mask'] value = match_field['OXMTlv']['value'] if key == 'vlan_vid': value = match_vid_to_str(value, mask) elif key == 'in_port': value = UTIL.ofp_port_to_user(value) else: if mask is not None: value = str(value) + '/' + str(mask) match.setdefault(key, value) return match def match_vid_to_str(value, mask): return ofctl_utils.match_vid_to_str( value, mask, ofproto_v1_4.OFPVID_PRESENT) def wrap_dpid_dict(dp, value, to_user=True): if to_user: return {str(dp.id): value} return {dp.id: value} def get_desc_stats(dp, waiters, to_user=True): stats = dp.ofproto_parser.OFPDescStatsRequest(dp, 0) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) s = {} for msg in msgs: stats = msg.body s = stats.to_jsondict()[stats.__class__.__name__] return wrap_dpid_dict(dp, s, to_user) def get_queue_stats(dp, waiters, port_no=None, queue_id=None, to_user=True): if port_no is None: port_no = dp.ofproto.OFPP_ANY else: port_no = UTIL.ofp_port_from_user(port_no) if queue_id is None: queue_id = dp.ofproto.OFPQ_ALL else: queue_id = UTIL.ofp_queue_from_user(queue_id) stats = dp.ofproto_parser.OFPQueueStatsRequest( dp, 0, port_no, queue_id) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) desc = [] for msg in msgs: stats = msg.body for stat in stats: s = stat.to_jsondict()[stat.__class__.__name__] properties = [] for prop in stat.properties: p = prop.to_jsondict()[prop.__class__.__name__] if to_user: t = UTIL.ofp_queue_stats_prop_type_to_user(prop.type) p['type'] = t if t != p['type'] else 'UNKNOWN' properties.append(p) s['properties'] = properties desc.append(s) return wrap_dpid_dict(dp, desc, to_user) def get_queue_desc(dp, waiters, port_no=None, queue_id=None, to_user=True): if port_no is None: port_no = dp.ofproto.OFPP_ANY else: port_no = UTIL.ofp_port_from_user(port_no) if queue_id is None: queue_id = dp.ofproto.OFPQ_ALL else: queue_id = UTIL.ofp_queue_from_user(queue_id) stats = dp.ofproto_parser.OFPQueueDescStatsRequest( dp, 0, port_no, queue_id) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) configs = [] for msg in msgs: for queue in msg.body: q = queue.to_jsondict()[queue.__class__.__name__] prop_list = [] for prop in queue.properties: p = prop.to_jsondict()[prop.__class__.__name__] if to_user: t = UTIL.ofp_queue_desc_prop_type_to_user(prop.type) p['type'] = t if t != prop.type else 'UNKNOWN' prop_list.append(p) q['properties'] = prop_list configs.append(q) return wrap_dpid_dict(dp, configs, to_user) def get_flow_stats(dp, waiters, flow=None, to_user=True): flow = flow if flow else {} table_id = UTIL.ofp_table_from_user( flow.get('table_id', dp.ofproto.OFPTT_ALL)) flags = str_to_int(flow.get('flags', 0)) out_port = UTIL.ofp_port_from_user( flow.get('out_port', dp.ofproto.OFPP_ANY)) out_group = UTIL.ofp_group_from_user( flow.get('out_group', dp.ofproto.OFPG_ANY)) cookie = str_to_int(flow.get('cookie', 0)) cookie_mask = str_to_int(flow.get('cookie_mask', 0)) match = to_match(dp, flow.get('match', {})) # Note: OpenFlow does not allow to filter flow entries by priority, # but for efficiency, ofctl provides the way to do it. priority = str_to_int(flow.get('priority', -1)) stats = dp.ofproto_parser.OFPFlowStatsRequest( dp, flags, table_id, out_port, out_group, cookie, cookie_mask, match) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) flows = [] for msg in msgs: for stats in msg.body: if 0 <= priority != stats.priority: continue s = stats.to_jsondict()[stats.__class__.__name__] s['instructions'] = instructions_to_str(stats.instructions) s['match'] = match_to_str(stats.match) flows.append(s) return wrap_dpid_dict(dp, flows, to_user) def get_aggregate_flow_stats(dp, waiters, flow=None, to_user=True): flow = flow if flow else {} table_id = UTIL.ofp_table_from_user( flow.get('table_id', dp.ofproto.OFPTT_ALL)) flags = str_to_int(flow.get('flags', 0)) out_port = UTIL.ofp_port_from_user( flow.get('out_port', dp.ofproto.OFPP_ANY)) out_group = UTIL.ofp_group_from_user( flow.get('out_group', dp.ofproto.OFPG_ANY)) cookie = str_to_int(flow.get('cookie', 0)) cookie_mask = str_to_int(flow.get('cookie_mask', 0)) match = to_match(dp, flow.get('match', {})) stats = dp.ofproto_parser.OFPAggregateStatsRequest( dp, flags, table_id, out_port, out_group, cookie, cookie_mask, match) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) flows = [] for msg in msgs: stats = msg.body s = stats.to_jsondict()[stats.__class__.__name__] flows.append(s) return wrap_dpid_dict(dp, flows, to_user) def get_table_stats(dp, waiters, to_user=True): stats = dp.ofproto_parser.OFPTableStatsRequest(dp, 0) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) tables = [] for msg in msgs: stats = msg.body for stat in stats: s = stat.to_jsondict()[stat.__class__.__name__] if to_user: s['table_id'] = UTIL.ofp_table_to_user(stat.table_id) tables.append(s) return wrap_dpid_dict(dp, tables, to_user) def get_table_features(dp, waiters, to_user=True): stats = dp.ofproto_parser.OFPTableFeaturesStatsRequest(dp, 0, []) msgs = [] ofproto = dp.ofproto ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) p_type_instructions = [ofproto.OFPTFPT_INSTRUCTIONS, ofproto.OFPTFPT_INSTRUCTIONS_MISS] p_type_next_tables = [ofproto.OFPTFPT_NEXT_TABLES, ofproto.OFPTFPT_NEXT_TABLES_MISS, ofproto.OFPTFPT_TABLE_SYNC_FROM] p_type_actions = [ofproto.OFPTFPT_WRITE_ACTIONS, ofproto.OFPTFPT_WRITE_ACTIONS_MISS, ofproto.OFPTFPT_APPLY_ACTIONS, ofproto.OFPTFPT_APPLY_ACTIONS_MISS] p_type_oxms = [ofproto.OFPTFPT_MATCH, ofproto.OFPTFPT_WILDCARDS, ofproto.OFPTFPT_WRITE_SETFIELD, ofproto.OFPTFPT_WRITE_SETFIELD_MISS, ofproto.OFPTFPT_APPLY_SETFIELD, ofproto.OFPTFPT_APPLY_SETFIELD_MISS] p_type_experimenter = [ofproto.OFPTFPT_EXPERIMENTER, ofproto.OFPTFPT_EXPERIMENTER_MISS] tables = [] for msg in msgs: stats = msg.body for stat in stats: s = stat.to_jsondict()[stat.__class__.__name__] properties = [] for prop in stat.properties: p = {} t = UTIL.ofp_table_feature_prop_type_to_user(prop.type) p['type'] = t if t != prop.type else 'UNKNOWN' if prop.type in p_type_instructions: instruction_ids = [] for i in prop.instruction_ids: inst = {'len': i.len, 'type': i.type} instruction_ids.append(inst) p['instruction_ids'] = instruction_ids elif prop.type in p_type_next_tables: table_ids = [] for i in prop.table_ids: table_ids.append(i) p['table_ids'] = table_ids elif prop.type in p_type_actions: action_ids = [] for i in prop.action_ids: act = i.to_jsondict()[i.__class__.__name__] action_ids.append(act) p['action_ids'] = action_ids elif prop.type in p_type_oxms: oxm_ids = [] for i in prop.oxm_ids: oxm = i.to_jsondict()[i.__class__.__name__] oxm_ids.append(oxm) p['oxm_ids'] = oxm_ids elif prop.type in p_type_experimenter: pass properties.append(p) s['name'] = stat.name.decode('utf-8') s['properties'] = properties if to_user: s['table_id'] = UTIL.ofp_table_to_user(stat.table_id) tables.append(s) return wrap_dpid_dict(dp, tables, to_user) def get_port_stats(dp, waiters, port_no=None, to_user=True): if port_no is None: port_no = dp.ofproto.OFPP_ANY else: port_no = UTIL.ofp_port_from_user(port_no) stats = dp.ofproto_parser.OFPPortStatsRequest(dp, 0, port_no) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) ports = [] for msg in msgs: for stats in msg.body: s = stats.to_jsondict()[stats.__class__.__name__] properties = [] for prop in stats.properties: p = prop.to_jsondict()[prop.__class__.__name__] t = UTIL.ofp_port_stats_prop_type_to_user(prop.type) p['type'] = t if t != prop.type else 'UNKNOWN' properties.append(p) s['properties'] = properties if to_user: s['port_no'] = UTIL.ofp_port_to_user(stats.port_no) ports.append(s) return wrap_dpid_dict(dp, ports, to_user) def get_meter_stats(dp, waiters, meter_id=None, to_user=True): if meter_id is None: meter_id = dp.ofproto.OFPM_ALL else: meter_id = UTIL.ofp_meter_from_user(meter_id) stats = dp.ofproto_parser.OFPMeterStatsRequest( dp, 0, meter_id) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) meters = [] for msg in msgs: for stats in msg.body: s = stats.to_jsondict()[stats.__class__.__name__] bands = [] for band in stats.band_stats: b = band.to_jsondict()[band.__class__.__name__] bands.append(b) s['band_stats'] = bands if to_user: s['meter_id'] = UTIL.ofp_meter_to_user(stats.meter_id) meters.append(s) return wrap_dpid_dict(dp, meters, to_user) def get_meter_features(dp, waiters, to_user=True): ofp = dp.ofproto type_convert = {ofp.OFPMBT_DROP: 'DROP', ofp.OFPMBT_DSCP_REMARK: 'DSCP_REMARK'} capa_convert = {ofp.OFPMF_KBPS: 'KBPS', ofp.OFPMF_PKTPS: 'PKTPS', ofp.OFPMF_BURST: 'BURST', ofp.OFPMF_STATS: 'STATS'} stats = dp.ofproto_parser.OFPMeterFeaturesStatsRequest(dp, 0) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) features = [] for msg in msgs: for feature in msg.body: band_types = [] for k, v in type_convert.items(): if (1 << k) & feature.band_types: if to_user: band_types.append(v) else: band_types.append(k) capabilities = [] for k, v in sorted(capa_convert.items()): if k & feature.capabilities: if to_user: capabilities.append(v) else: capabilities.append(k) f = {'max_meter': feature.max_meter, 'band_types': band_types, 'capabilities': capabilities, 'max_bands': feature.max_bands, 'max_color': feature.max_color} features.append(f) return wrap_dpid_dict(dp, features, to_user) def get_meter_config(dp, waiters, meter_id=None, to_user=True): flags = {dp.ofproto.OFPMF_KBPS: 'KBPS', dp.ofproto.OFPMF_PKTPS: 'PKTPS', dp.ofproto.OFPMF_BURST: 'BURST', dp.ofproto.OFPMF_STATS: 'STATS'} if meter_id is None: meter_id = dp.ofproto.OFPM_ALL else: meter_id = UTIL.ofp_meter_from_user(meter_id) stats = dp.ofproto_parser.OFPMeterConfigStatsRequest( dp, 0, meter_id) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) configs = [] for msg in msgs: for config in msg.body: c = config.to_jsondict()[config.__class__.__name__] bands = [] for band in config.bands: b = band.to_jsondict()[band.__class__.__name__] if to_user: t = UTIL.ofp_meter_band_type_to_user(band.type) b['type'] = t if t != band.type else 'UNKNOWN' bands.append(b) c_flags = [] for k, v in sorted(flags.items()): if k & config.flags: if to_user: c_flags.append(v) else: c_flags.append(k) c['flags'] = c_flags c['bands'] = bands if to_user: c['meter_id'] = UTIL.ofp_meter_to_user(config.meter_id) configs.append(c) return wrap_dpid_dict(dp, configs, to_user) def get_group_stats(dp, waiters, group_id=None, to_user=True): if group_id is None: group_id = dp.ofproto.OFPG_ALL else: group_id = UTIL.ofp_group_from_user(group_id) stats = dp.ofproto_parser.OFPGroupStatsRequest( dp, 0, group_id) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) groups = [] for msg in msgs: for stats in msg.body: g = stats.to_jsondict()[stats.__class__.__name__] bucket_stats = [] for bucket_stat in stats.bucket_stats: c = bucket_stat.to_jsondict()[bucket_stat.__class__.__name__] bucket_stats.append(c) g['bucket_stats'] = bucket_stats if to_user: g['group_id'] = UTIL.ofp_group_to_user(stats.group_id) groups.append(g) return wrap_dpid_dict(dp, groups, to_user) def get_group_features(dp, waiters, to_user=True): ofp = dp.ofproto type_convert = {ofp.OFPGT_ALL: 'ALL', ofp.OFPGT_SELECT: 'SELECT', ofp.OFPGT_INDIRECT: 'INDIRECT', ofp.OFPGT_FF: 'FF'} cap_convert = {ofp.OFPGFC_SELECT_WEIGHT: 'SELECT_WEIGHT', ofp.OFPGFC_SELECT_LIVENESS: 'SELECT_LIVENESS', ofp.OFPGFC_CHAINING: 'CHAINING', ofp.OFPGFC_CHAINING_CHECKS: 'CHAINING_CHECKS'} act_convert = {ofp.OFPAT_OUTPUT: 'OUTPUT', ofp.OFPAT_COPY_TTL_OUT: 'COPY_TTL_OUT', ofp.OFPAT_COPY_TTL_IN: 'COPY_TTL_IN', ofp.OFPAT_SET_MPLS_TTL: 'SET_MPLS_TTL', ofp.OFPAT_DEC_MPLS_TTL: 'DEC_MPLS_TTL', ofp.OFPAT_PUSH_VLAN: 'PUSH_VLAN', ofp.OFPAT_POP_VLAN: 'POP_VLAN', ofp.OFPAT_PUSH_MPLS: 'PUSH_MPLS', ofp.OFPAT_POP_MPLS: 'POP_MPLS', ofp.OFPAT_SET_QUEUE: 'SET_QUEUE', ofp.OFPAT_GROUP: 'GROUP', ofp.OFPAT_SET_NW_TTL: 'SET_NW_TTL', ofp.OFPAT_DEC_NW_TTL: 'DEC_NW_TTL', ofp.OFPAT_SET_FIELD: 'SET_FIELD', ofp.OFPAT_PUSH_PBB: 'PUSH_PBB', ofp.OFPAT_POP_PBB: 'POP_PBB', ofp.OFPAT_EXPERIMENTER: 'EXPERIMENTER'} stats = dp.ofproto_parser.OFPGroupFeaturesStatsRequest(dp, 0) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) features = [] for msg in msgs: feature = msg.body types = [] for k, v in type_convert.items(): if (1 << k) & feature.types: if to_user: types.append(v) else: types.append(k) capabilities = [] for k, v in cap_convert.items(): if k & feature.capabilities: if to_user: capabilities.append(v) else: capabilities.append(k) if to_user: max_groups = [] for k, v in type_convert.items(): max_groups.append({v: feature.max_groups[k]}) else: max_groups = feature.max_groups actions = [] for k1, v1 in type_convert.items(): acts = [] for k2, v2 in act_convert.items(): if (1 << k2) & feature.actions[k1]: if to_user: acts.append(v2) else: acts.append(k2) if to_user: actions.append({v1: acts}) else: actions.append({k1: acts}) f = {'types': types, 'capabilities': capabilities, 'max_groups': max_groups, 'actions': actions} features.append(f) return wrap_dpid_dict(dp, features, to_user) def get_group_desc(dp, waiters, to_user=True): stats = dp.ofproto_parser.OFPGroupDescStatsRequest(dp, 0) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) descs = [] for msg in msgs: for stats in msg.body: d = stats.to_jsondict()[stats.__class__.__name__] buckets = [] for bucket in stats.buckets: b = bucket.to_jsondict()[bucket.__class__.__name__] actions = [] for action in bucket.actions: if to_user: actions.append(action_to_str(action)) else: actions.append(action) b['actions'] = actions buckets.append(b) d['buckets'] = buckets if to_user: d['group_id'] = UTIL.ofp_group_to_user(stats.group_id) t = UTIL.ofp_group_type_to_user(stats.type) d['type'] = t if t != stats.type else 'UNKNOWN' descs.append(d) return wrap_dpid_dict(dp, descs, to_user) def get_port_desc(dp, waiters, port_no=None, to_user=True): if port_no is None: port_no = dp.ofproto.OFPP_ANY else: port_no = UTIL.ofp_port_from_user(port_no) stats = dp.ofproto_parser.OFPPortDescStatsRequest(dp, 0, port_no) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) descs = [] for msg in msgs: stats = msg.body for stat in stats: d = stat.to_jsondict()[stat.__class__.__name__] properties = [] for prop in stat.properties: p = prop.to_jsondict()[prop.__class__.__name__] if to_user: t = UTIL.ofp_port_desc_prop_type_to_user(prop.type) p['type'] = t if t != prop.type else 'UNKNOWN' properties.append(p) d['name'] = stat.name.decode('utf-8') d['properties'] = properties if to_user: d['port_no'] = UTIL.ofp_port_to_user(stat.port_no) descs.append(d) return wrap_dpid_dict(dp, descs, to_user) def mod_flow_entry(dp, flow, cmd): cookie = str_to_int(flow.get('cookie', 0)) cookie_mask = str_to_int(flow.get('cookie_mask', 0)) table_id = UTIL.ofp_table_from_user(flow.get('table_id', 0)) idle_timeout = str_to_int(flow.get('idle_timeout', 0)) hard_timeout = str_to_int(flow.get('hard_timeout', 0)) priority = str_to_int(flow.get('priority', 0)) buffer_id = UTIL.ofp_buffer_from_user( flow.get('buffer_id', dp.ofproto.OFP_NO_BUFFER)) out_port = UTIL.ofp_port_from_user( flow.get('out_port', dp.ofproto.OFPP_ANY)) out_group = UTIL.ofp_group_from_user( flow.get('out_group', dp.ofproto.OFPG_ANY)) importance = str_to_int(flow.get('importance', 0)) flags = str_to_int(flow.get('flags', 0)) match = to_match(dp, flow.get('match', {})) inst = to_instructions(dp, flow.get('instructions', [])) flow_mod = dp.ofproto_parser.OFPFlowMod( dp, cookie, cookie_mask, table_id, cmd, idle_timeout, hard_timeout, priority, buffer_id, out_port, out_group, flags, importance, match, inst) ofctl_utils.send_msg(dp, flow_mod, LOG) def mod_meter_entry(dp, meter, cmd): flags = 0 if 'flags' in meter: meter_flags = meter['flags'] if not isinstance(meter_flags, list): meter_flags = [meter_flags] for flag in meter_flags: t = UTIL.ofp_meter_flags_from_user(flag) f = t if t != flag else None if f is None: LOG.error('Unknown meter flag: %s', flag) continue flags |= f meter_id = UTIL.ofp_meter_from_user(meter.get('meter_id', 0)) bands = [] for band in meter.get('bands', []): band_type = band.get('type') rate = str_to_int(band.get('rate', 0)) burst_size = str_to_int(band.get('burst_size', 0)) if band_type == 'DROP': bands.append( dp.ofproto_parser.OFPMeterBandDrop(rate, burst_size)) elif band_type == 'DSCP_REMARK': prec_level = str_to_int(band.get('prec_level', 0)) bands.append( dp.ofproto_parser.OFPMeterBandDscpRemark( rate, burst_size, prec_level)) elif band_type == 'EXPERIMENTER': experimenter = str_to_int(band.get('experimenter', 0)) bands.append( dp.ofproto_parser.OFPMeterBandExperimenter( rate, burst_size, experimenter)) else: LOG.error('Unknown band type: %s', band_type) meter_mod = dp.ofproto_parser.OFPMeterMod( dp, cmd, flags, meter_id, bands) ofctl_utils.send_msg(dp, meter_mod, LOG) def mod_group_entry(dp, group, cmd): group_type = str(group.get('type', 'ALL')) t = UTIL.ofp_group_type_from_user(group_type) group_type = t if t != group_type else None if group_type is None: LOG.error('Unknown group type: %s', group.get('type')) group_id = UTIL.ofp_group_from_user(group.get('group_id', 0)) buckets = [] for bucket in group.get('buckets', []): weight = str_to_int(bucket.get('weight', 0)) watch_port = str_to_int( bucket.get('watch_port', dp.ofproto.OFPP_ANY)) watch_group = str_to_int( bucket.get('watch_group', dp.ofproto.OFPG_ANY)) actions = [] for dic in bucket.get('actions', []): action = to_action(dp, dic) if action is not None: actions.append(action) buckets.append(dp.ofproto_parser.OFPBucket( weight, watch_port, watch_group, actions)) group_mod = dp.ofproto_parser.OFPGroupMod( dp, cmd, group_type, group_id, buckets) ofctl_utils.send_msg(dp, group_mod, LOG) def mod_port_behavior(dp, port_config): ofp = dp.ofproto parser = dp.ofproto_parser port_no = UTIL.ofp_port_from_user(port_config.get('port_no', 0)) hw_addr = str(port_config.get('hw_addr')) config = str_to_int(port_config.get('config', 0)) mask = str_to_int(port_config.get('mask', 0)) properties = port_config.get('properties') prop = [] for p in properties: type_ = UTIL.ofp_port_mod_prop_type_from_user(p['type']) length = None if type_ == ofp.OFPPDPT_ETHERNET: advertise = UTIL.ofp_port_features_from_user(p['advertise']) prop.append( parser.OFPPortModPropEthernet(type_, length, advertise)) elif type_ == ofp.OFPPDPT_OPTICAL: prop.append( parser.OFPPortModPropOptical( type_, length, p['configure'], p['freq_lmda'], p['fl_offset'], p['grid_span'], p['tx_pwr'])) elif type_ == ofp.OFPPDPT_EXPERIMENTER: prop.append( parser.OFPPortModPropExperimenter( type_, length, p['experimenter'], p['exp_type'], p['data'])) else: LOG.error('Unknown port desc prop type: %s', type_) port_mod = dp.ofproto_parser.OFPPortMod( dp, port_no, hw_addr, config, mask, prop) ofctl_utils.send_msg(dp, port_mod, LOG) def set_role(dp, role): r = UTIL.ofp_role_from_user(role.get('role', dp.ofproto.OFPCR_ROLE_EQUAL)) role_request = dp.ofproto_parser.OFPRoleRequest(dp, r, 0) ofctl_utils.send_msg(dp, role_request, LOG) # NOTE(jkoelker) Alias common funcitons send_experimenter = ofctl_utils.send_experimenter

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import logging from ryu.ofproto import ether from ryu.ofproto import ofproto_v1_4 from ryu.ofproto import ofproto_v1_4_parser from ryu.lib import ofctl_utils LOG = logging.getLogger(__name__) DEFAULT_TIMEOUT = 1.0 UTIL = ofctl_utils.OFCtlUtil(ofproto_v1_4) str_to_int = ofctl_utils.str_to_int def to_action(dp, dic): ofp = dp.ofproto parser = dp.ofproto_parser action_type = dic.get('type') return ofctl_utils.to_action(dic, ofp, parser, action_type, UTIL) def _get_actions(dp, dics): actions = [] for d in dics: action = to_action(dp, d) if action is not None: actions.append(action) else: LOG.error('Unknown action type: %s', d) return actions def to_instructions(dp, insts): instructions = [] ofp = dp.ofproto parser = dp.ofproto_parser for i in insts: inst_type = i.get('type') if inst_type in ['APPLY_ACTIONS', 'WRITE_ACTIONS']: dics = i.get('actions', []) actions = _get_actions(dp, dics) if actions: if inst_type == 'APPLY_ACTIONS': instructions.append( parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS, actions)) else: instructions.append( parser.OFPInstructionActions(ofp.OFPIT_WRITE_ACTIONS, actions)) elif inst_type == 'CLEAR_ACTIONS': instructions.append( parser.OFPInstructionActions(ofp.OFPIT_CLEAR_ACTIONS, [])) elif inst_type == 'GOTO_TABLE': table_id = str_to_int(i.get('table_id')) instructions.append(parser.OFPInstructionGotoTable(table_id)) elif inst_type == 'WRITE_METADATA': metadata = str_to_int(i.get('metadata')) metadata_mask = (str_to_int(i['metadata_mask']) if 'metadata_mask' in i else parser.UINT64_MAX) instructions.append( parser.OFPInstructionWriteMetadata( metadata, metadata_mask)) elif inst_type == 'METER': meter_id = str_to_int(i.get('meter_id')) instructions.append(parser.OFPInstructionMeter(meter_id)) else: LOG.error('Unknown instruction type: %s', inst_type) return instructions def action_to_str(act): s = act.to_jsondict()[act.__class__.__name__] t = UTIL.ofp_action_type_to_user(s['type']) s['type'] = t if t != s['type'] else 'UNKNOWN' if 'field' in s: field = s.pop('field') s['field'] = field['OXMTlv']['field'] s['mask'] = field['OXMTlv']['mask'] s['value'] = field['OXMTlv']['value'] return s def instructions_to_str(instructions): s = [] for i in instructions: v = i.to_jsondict()[i.__class__.__name__] t = UTIL.ofp_instruction_type_to_user(v['type']) inst_type = t if t != v['type'] else 'UNKNOWN' if isinstance(i, ofproto_v1_4_parser.OFPInstructionActions): acts = [] for a in i.actions: acts.append(action_to_str(a)) v['type'] = inst_type v['actions'] = acts s.append(v) else: v['type'] = inst_type s.append(v) return s def to_match(dp, attrs): convert = {'in_port': UTIL.ofp_port_from_user, 'in_phy_port': str_to_int, 'metadata': ofctl_utils.to_match_masked_int, 'eth_dst': ofctl_utils.to_match_eth, 'eth_src': ofctl_utils.to_match_eth, 'eth_type': str_to_int, 'vlan_vid': to_match_vid, 'vlan_pcp': str_to_int, 'ip_dscp': str_to_int, 'ip_ecn': str_to_int, 'ip_proto': str_to_int, 'ipv4_src': ofctl_utils.to_match_ip, 'ipv4_dst': ofctl_utils.to_match_ip, 'tcp_src': str_to_int, 'tcp_dst': str_to_int, 'udp_src': str_to_int, 'udp_dst': str_to_int, 'sctp_src': str_to_int, 'sctp_dst': str_to_int, 'icmpv4_type': str_to_int, 'icmpv4_code': str_to_int, 'arp_op': str_to_int, 'arp_spa': ofctl_utils.to_match_ip, 'arp_tpa': ofctl_utils.to_match_ip, 'arp_sha': ofctl_utils.to_match_eth, 'arp_tha': ofctl_utils.to_match_eth, 'ipv6_src': ofctl_utils.to_match_ip, 'ipv6_dst': ofctl_utils.to_match_ip, 'ipv6_flabel': str_to_int, 'icmpv6_type': str_to_int, 'icmpv6_code': str_to_int, 'ipv6_nd_target': ofctl_utils.to_match_ip, 'ipv6_nd_sll': ofctl_utils.to_match_eth, 'ipv6_nd_tll': ofctl_utils.to_match_eth, 'mpls_label': str_to_int, 'mpls_tc': str_to_int, 'mpls_bos': str_to_int, 'pbb_isid': ofctl_utils.to_match_masked_int, 'tunnel_id': ofctl_utils.to_match_masked_int, 'ipv6_exthdr': ofctl_utils.to_match_masked_int, 'pbb_uca': str_to_int} keys = {'dl_dst': 'eth_dst', 'dl_src': 'eth_src', 'dl_type': 'eth_type', 'dl_vlan': 'vlan_vid', 'nw_src': 'ipv4_src', 'nw_dst': 'ipv4_dst', 'nw_proto': 'ip_proto'} if attrs.get('eth_type') == ether.ETH_TYPE_ARP: if 'ipv4_src' in attrs and 'arp_spa' not in attrs: attrs['arp_spa'] = attrs['ipv4_src'] del attrs['ipv4_src'] if 'ipv4_dst' in attrs and 'arp_tpa' not in attrs: attrs['arp_tpa'] = attrs['ipv4_dst'] del attrs['ipv4_dst'] kwargs = {} for key, value in attrs.items(): if key in keys: key = keys[key] if key in convert: value = convert[key](value) kwargs[key] = value else: LOG.error('Unknown match field: %s', key) return dp.ofproto_parser.OFPMatch(**kwargs) def to_match_vid(value): return ofctl_utils.to_match_vid(value, ofproto_v1_4.OFPVID_PRESENT) def match_to_str(ofmatch): match = {} ofmatch = ofmatch.to_jsondict()['OFPMatch'] ofmatch = ofmatch['oxm_fields'] for match_field in ofmatch: key = match_field['OXMTlv']['field'] mask = match_field['OXMTlv']['mask'] value = match_field['OXMTlv']['value'] if key == 'vlan_vid': value = match_vid_to_str(value, mask) elif key == 'in_port': value = UTIL.ofp_port_to_user(value) else: if mask is not None: value = str(value) + '/' + str(mask) match.setdefault(key, value) return match def match_vid_to_str(value, mask): return ofctl_utils.match_vid_to_str( value, mask, ofproto_v1_4.OFPVID_PRESENT) def wrap_dpid_dict(dp, value, to_user=True): if to_user: return {str(dp.id): value} return {dp.id: value} def get_desc_stats(dp, waiters, to_user=True): stats = dp.ofproto_parser.OFPDescStatsRequest(dp, 0) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) s = {} for msg in msgs: stats = msg.body s = stats.to_jsondict()[stats.__class__.__name__] return wrap_dpid_dict(dp, s, to_user) def get_queue_stats(dp, waiters, port_no=None, queue_id=None, to_user=True): if port_no is None: port_no = dp.ofproto.OFPP_ANY else: port_no = UTIL.ofp_port_from_user(port_no) if queue_id is None: queue_id = dp.ofproto.OFPQ_ALL else: queue_id = UTIL.ofp_queue_from_user(queue_id) stats = dp.ofproto_parser.OFPQueueStatsRequest( dp, 0, port_no, queue_id) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) desc = [] for msg in msgs: stats = msg.body for stat in stats: s = stat.to_jsondict()[stat.__class__.__name__] properties = [] for prop in stat.properties: p = prop.to_jsondict()[prop.__class__.__name__] if to_user: t = UTIL.ofp_queue_stats_prop_type_to_user(prop.type) p['type'] = t if t != p['type'] else 'UNKNOWN' properties.append(p) s['properties'] = properties desc.append(s) return wrap_dpid_dict(dp, desc, to_user) def get_queue_desc(dp, waiters, port_no=None, queue_id=None, to_user=True): if port_no is None: port_no = dp.ofproto.OFPP_ANY else: port_no = UTIL.ofp_port_from_user(port_no) if queue_id is None: queue_id = dp.ofproto.OFPQ_ALL else: queue_id = UTIL.ofp_queue_from_user(queue_id) stats = dp.ofproto_parser.OFPQueueDescStatsRequest( dp, 0, port_no, queue_id) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) configs = [] for msg in msgs: for queue in msg.body: q = queue.to_jsondict()[queue.__class__.__name__] prop_list = [] for prop in queue.properties: p = prop.to_jsondict()[prop.__class__.__name__] if to_user: t = UTIL.ofp_queue_desc_prop_type_to_user(prop.type) p['type'] = t if t != prop.type else 'UNKNOWN' prop_list.append(p) q['properties'] = prop_list configs.append(q) return wrap_dpid_dict(dp, configs, to_user) def get_flow_stats(dp, waiters, flow=None, to_user=True): flow = flow if flow else {} table_id = UTIL.ofp_table_from_user( flow.get('table_id', dp.ofproto.OFPTT_ALL)) flags = str_to_int(flow.get('flags', 0)) out_port = UTIL.ofp_port_from_user( flow.get('out_port', dp.ofproto.OFPP_ANY)) out_group = UTIL.ofp_group_from_user( flow.get('out_group', dp.ofproto.OFPG_ANY)) cookie = str_to_int(flow.get('cookie', 0)) cookie_mask = str_to_int(flow.get('cookie_mask', 0)) match = to_match(dp, flow.get('match', {})) priority = str_to_int(flow.get('priority', -1)) stats = dp.ofproto_parser.OFPFlowStatsRequest( dp, flags, table_id, out_port, out_group, cookie, cookie_mask, match) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) flows = [] for msg in msgs: for stats in msg.body: if 0 <= priority != stats.priority: continue s = stats.to_jsondict()[stats.__class__.__name__] s['instructions'] = instructions_to_str(stats.instructions) s['match'] = match_to_str(stats.match) flows.append(s) return wrap_dpid_dict(dp, flows, to_user) def get_aggregate_flow_stats(dp, waiters, flow=None, to_user=True): flow = flow if flow else {} table_id = UTIL.ofp_table_from_user( flow.get('table_id', dp.ofproto.OFPTT_ALL)) flags = str_to_int(flow.get('flags', 0)) out_port = UTIL.ofp_port_from_user( flow.get('out_port', dp.ofproto.OFPP_ANY)) out_group = UTIL.ofp_group_from_user( flow.get('out_group', dp.ofproto.OFPG_ANY)) cookie = str_to_int(flow.get('cookie', 0)) cookie_mask = str_to_int(flow.get('cookie_mask', 0)) match = to_match(dp, flow.get('match', {})) stats = dp.ofproto_parser.OFPAggregateStatsRequest( dp, flags, table_id, out_port, out_group, cookie, cookie_mask, match) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) flows = [] for msg in msgs: stats = msg.body s = stats.to_jsondict()[stats.__class__.__name__] flows.append(s) return wrap_dpid_dict(dp, flows, to_user) def get_table_stats(dp, waiters, to_user=True): stats = dp.ofproto_parser.OFPTableStatsRequest(dp, 0) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) tables = [] for msg in msgs: stats = msg.body for stat in stats: s = stat.to_jsondict()[stat.__class__.__name__] if to_user: s['table_id'] = UTIL.ofp_table_to_user(stat.table_id) tables.append(s) return wrap_dpid_dict(dp, tables, to_user) def get_table_features(dp, waiters, to_user=True): stats = dp.ofproto_parser.OFPTableFeaturesStatsRequest(dp, 0, []) msgs = [] ofproto = dp.ofproto ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) p_type_instructions = [ofproto.OFPTFPT_INSTRUCTIONS, ofproto.OFPTFPT_INSTRUCTIONS_MISS] p_type_next_tables = [ofproto.OFPTFPT_NEXT_TABLES, ofproto.OFPTFPT_NEXT_TABLES_MISS, ofproto.OFPTFPT_TABLE_SYNC_FROM] p_type_actions = [ofproto.OFPTFPT_WRITE_ACTIONS, ofproto.OFPTFPT_WRITE_ACTIONS_MISS, ofproto.OFPTFPT_APPLY_ACTIONS, ofproto.OFPTFPT_APPLY_ACTIONS_MISS] p_type_oxms = [ofproto.OFPTFPT_MATCH, ofproto.OFPTFPT_WILDCARDS, ofproto.OFPTFPT_WRITE_SETFIELD, ofproto.OFPTFPT_WRITE_SETFIELD_MISS, ofproto.OFPTFPT_APPLY_SETFIELD, ofproto.OFPTFPT_APPLY_SETFIELD_MISS] p_type_experimenter = [ofproto.OFPTFPT_EXPERIMENTER, ofproto.OFPTFPT_EXPERIMENTER_MISS] tables = [] for msg in msgs: stats = msg.body for stat in stats: s = stat.to_jsondict()[stat.__class__.__name__] properties = [] for prop in stat.properties: p = {} t = UTIL.ofp_table_feature_prop_type_to_user(prop.type) p['type'] = t if t != prop.type else 'UNKNOWN' if prop.type in p_type_instructions: instruction_ids = [] for i in prop.instruction_ids: inst = {'len': i.len, 'type': i.type} instruction_ids.append(inst) p['instruction_ids'] = instruction_ids elif prop.type in p_type_next_tables: table_ids = [] for i in prop.table_ids: table_ids.append(i) p['table_ids'] = table_ids elif prop.type in p_type_actions: action_ids = [] for i in prop.action_ids: act = i.to_jsondict()[i.__class__.__name__] action_ids.append(act) p['action_ids'] = action_ids elif prop.type in p_type_oxms: oxm_ids = [] for i in prop.oxm_ids: oxm = i.to_jsondict()[i.__class__.__name__] oxm_ids.append(oxm) p['oxm_ids'] = oxm_ids elif prop.type in p_type_experimenter: pass properties.append(p) s['name'] = stat.name.decode('utf-8') s['properties'] = properties if to_user: s['table_id'] = UTIL.ofp_table_to_user(stat.table_id) tables.append(s) return wrap_dpid_dict(dp, tables, to_user) def get_port_stats(dp, waiters, port_no=None, to_user=True): if port_no is None: port_no = dp.ofproto.OFPP_ANY else: port_no = UTIL.ofp_port_from_user(port_no) stats = dp.ofproto_parser.OFPPortStatsRequest(dp, 0, port_no) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) ports = [] for msg in msgs: for stats in msg.body: s = stats.to_jsondict()[stats.__class__.__name__] properties = [] for prop in stats.properties: p = prop.to_jsondict()[prop.__class__.__name__] t = UTIL.ofp_port_stats_prop_type_to_user(prop.type) p['type'] = t if t != prop.type else 'UNKNOWN' properties.append(p) s['properties'] = properties if to_user: s['port_no'] = UTIL.ofp_port_to_user(stats.port_no) ports.append(s) return wrap_dpid_dict(dp, ports, to_user) def get_meter_stats(dp, waiters, meter_id=None, to_user=True): if meter_id is None: meter_id = dp.ofproto.OFPM_ALL else: meter_id = UTIL.ofp_meter_from_user(meter_id) stats = dp.ofproto_parser.OFPMeterStatsRequest( dp, 0, meter_id) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) meters = [] for msg in msgs: for stats in msg.body: s = stats.to_jsondict()[stats.__class__.__name__] bands = [] for band in stats.band_stats: b = band.to_jsondict()[band.__class__.__name__] bands.append(b) s['band_stats'] = bands if to_user: s['meter_id'] = UTIL.ofp_meter_to_user(stats.meter_id) meters.append(s) return wrap_dpid_dict(dp, meters, to_user) def get_meter_features(dp, waiters, to_user=True): ofp = dp.ofproto type_convert = {ofp.OFPMBT_DROP: 'DROP', ofp.OFPMBT_DSCP_REMARK: 'DSCP_REMARK'} capa_convert = {ofp.OFPMF_KBPS: 'KBPS', ofp.OFPMF_PKTPS: 'PKTPS', ofp.OFPMF_BURST: 'BURST', ofp.OFPMF_STATS: 'STATS'} stats = dp.ofproto_parser.OFPMeterFeaturesStatsRequest(dp, 0) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) features = [] for msg in msgs: for feature in msg.body: band_types = [] for k, v in type_convert.items(): if (1 << k) & feature.band_types: if to_user: band_types.append(v) else: band_types.append(k) capabilities = [] for k, v in sorted(capa_convert.items()): if k & feature.capabilities: if to_user: capabilities.append(v) else: capabilities.append(k) f = {'max_meter': feature.max_meter, 'band_types': band_types, 'capabilities': capabilities, 'max_bands': feature.max_bands, 'max_color': feature.max_color} features.append(f) return wrap_dpid_dict(dp, features, to_user) def get_meter_config(dp, waiters, meter_id=None, to_user=True): flags = {dp.ofproto.OFPMF_KBPS: 'KBPS', dp.ofproto.OFPMF_PKTPS: 'PKTPS', dp.ofproto.OFPMF_BURST: 'BURST', dp.ofproto.OFPMF_STATS: 'STATS'} if meter_id is None: meter_id = dp.ofproto.OFPM_ALL else: meter_id = UTIL.ofp_meter_from_user(meter_id) stats = dp.ofproto_parser.OFPMeterConfigStatsRequest( dp, 0, meter_id) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) configs = [] for msg in msgs: for config in msg.body: c = config.to_jsondict()[config.__class__.__name__] bands = [] for band in config.bands: b = band.to_jsondict()[band.__class__.__name__] if to_user: t = UTIL.ofp_meter_band_type_to_user(band.type) b['type'] = t if t != band.type else 'UNKNOWN' bands.append(b) c_flags = [] for k, v in sorted(flags.items()): if k & config.flags: if to_user: c_flags.append(v) else: c_flags.append(k) c['flags'] = c_flags c['bands'] = bands if to_user: c['meter_id'] = UTIL.ofp_meter_to_user(config.meter_id) configs.append(c) return wrap_dpid_dict(dp, configs, to_user) def get_group_stats(dp, waiters, group_id=None, to_user=True): if group_id is None: group_id = dp.ofproto.OFPG_ALL else: group_id = UTIL.ofp_group_from_user(group_id) stats = dp.ofproto_parser.OFPGroupStatsRequest( dp, 0, group_id) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) groups = [] for msg in msgs: for stats in msg.body: g = stats.to_jsondict()[stats.__class__.__name__] bucket_stats = [] for bucket_stat in stats.bucket_stats: c = bucket_stat.to_jsondict()[bucket_stat.__class__.__name__] bucket_stats.append(c) g['bucket_stats'] = bucket_stats if to_user: g['group_id'] = UTIL.ofp_group_to_user(stats.group_id) groups.append(g) return wrap_dpid_dict(dp, groups, to_user) def get_group_features(dp, waiters, to_user=True): ofp = dp.ofproto type_convert = {ofp.OFPGT_ALL: 'ALL', ofp.OFPGT_SELECT: 'SELECT', ofp.OFPGT_INDIRECT: 'INDIRECT', ofp.OFPGT_FF: 'FF'} cap_convert = {ofp.OFPGFC_SELECT_WEIGHT: 'SELECT_WEIGHT', ofp.OFPGFC_SELECT_LIVENESS: 'SELECT_LIVENESS', ofp.OFPGFC_CHAINING: 'CHAINING', ofp.OFPGFC_CHAINING_CHECKS: 'CHAINING_CHECKS'} act_convert = {ofp.OFPAT_OUTPUT: 'OUTPUT', ofp.OFPAT_COPY_TTL_OUT: 'COPY_TTL_OUT', ofp.OFPAT_COPY_TTL_IN: 'COPY_TTL_IN', ofp.OFPAT_SET_MPLS_TTL: 'SET_MPLS_TTL', ofp.OFPAT_DEC_MPLS_TTL: 'DEC_MPLS_TTL', ofp.OFPAT_PUSH_VLAN: 'PUSH_VLAN', ofp.OFPAT_POP_VLAN: 'POP_VLAN', ofp.OFPAT_PUSH_MPLS: 'PUSH_MPLS', ofp.OFPAT_POP_MPLS: 'POP_MPLS', ofp.OFPAT_SET_QUEUE: 'SET_QUEUE', ofp.OFPAT_GROUP: 'GROUP', ofp.OFPAT_SET_NW_TTL: 'SET_NW_TTL', ofp.OFPAT_DEC_NW_TTL: 'DEC_NW_TTL', ofp.OFPAT_SET_FIELD: 'SET_FIELD', ofp.OFPAT_PUSH_PBB: 'PUSH_PBB', ofp.OFPAT_POP_PBB: 'POP_PBB', ofp.OFPAT_EXPERIMENTER: 'EXPERIMENTER'} stats = dp.ofproto_parser.OFPGroupFeaturesStatsRequest(dp, 0) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) features = [] for msg in msgs: feature = msg.body types = [] for k, v in type_convert.items(): if (1 << k) & feature.types: if to_user: types.append(v) else: types.append(k) capabilities = [] for k, v in cap_convert.items(): if k & feature.capabilities: if to_user: capabilities.append(v) else: capabilities.append(k) if to_user: max_groups = [] for k, v in type_convert.items(): max_groups.append({v: feature.max_groups[k]}) else: max_groups = feature.max_groups actions = [] for k1, v1 in type_convert.items(): acts = [] for k2, v2 in act_convert.items(): if (1 << k2) & feature.actions[k1]: if to_user: acts.append(v2) else: acts.append(k2) if to_user: actions.append({v1: acts}) else: actions.append({k1: acts}) f = {'types': types, 'capabilities': capabilities, 'max_groups': max_groups, 'actions': actions} features.append(f) return wrap_dpid_dict(dp, features, to_user) def get_group_desc(dp, waiters, to_user=True): stats = dp.ofproto_parser.OFPGroupDescStatsRequest(dp, 0) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) descs = [] for msg in msgs: for stats in msg.body: d = stats.to_jsondict()[stats.__class__.__name__] buckets = [] for bucket in stats.buckets: b = bucket.to_jsondict()[bucket.__class__.__name__] actions = [] for action in bucket.actions: if to_user: actions.append(action_to_str(action)) else: actions.append(action) b['actions'] = actions buckets.append(b) d['buckets'] = buckets if to_user: d['group_id'] = UTIL.ofp_group_to_user(stats.group_id) t = UTIL.ofp_group_type_to_user(stats.type) d['type'] = t if t != stats.type else 'UNKNOWN' descs.append(d) return wrap_dpid_dict(dp, descs, to_user) def get_port_desc(dp, waiters, port_no=None, to_user=True): if port_no is None: port_no = dp.ofproto.OFPP_ANY else: port_no = UTIL.ofp_port_from_user(port_no) stats = dp.ofproto_parser.OFPPortDescStatsRequest(dp, 0, port_no) msgs = [] ofctl_utils.send_stats_request(dp, stats, waiters, msgs, LOG) descs = [] for msg in msgs: stats = msg.body for stat in stats: d = stat.to_jsondict()[stat.__class__.__name__] properties = [] for prop in stat.properties: p = prop.to_jsondict()[prop.__class__.__name__] if to_user: t = UTIL.ofp_port_desc_prop_type_to_user(prop.type) p['type'] = t if t != prop.type else 'UNKNOWN' properties.append(p) d['name'] = stat.name.decode('utf-8') d['properties'] = properties if to_user: d['port_no'] = UTIL.ofp_port_to_user(stat.port_no) descs.append(d) return wrap_dpid_dict(dp, descs, to_user) def mod_flow_entry(dp, flow, cmd): cookie = str_to_int(flow.get('cookie', 0)) cookie_mask = str_to_int(flow.get('cookie_mask', 0)) table_id = UTIL.ofp_table_from_user(flow.get('table_id', 0)) idle_timeout = str_to_int(flow.get('idle_timeout', 0)) hard_timeout = str_to_int(flow.get('hard_timeout', 0)) priority = str_to_int(flow.get('priority', 0)) buffer_id = UTIL.ofp_buffer_from_user( flow.get('buffer_id', dp.ofproto.OFP_NO_BUFFER)) out_port = UTIL.ofp_port_from_user( flow.get('out_port', dp.ofproto.OFPP_ANY)) out_group = UTIL.ofp_group_from_user( flow.get('out_group', dp.ofproto.OFPG_ANY)) importance = str_to_int(flow.get('importance', 0)) flags = str_to_int(flow.get('flags', 0)) match = to_match(dp, flow.get('match', {})) inst = to_instructions(dp, flow.get('instructions', [])) flow_mod = dp.ofproto_parser.OFPFlowMod( dp, cookie, cookie_mask, table_id, cmd, idle_timeout, hard_timeout, priority, buffer_id, out_port, out_group, flags, importance, match, inst) ofctl_utils.send_msg(dp, flow_mod, LOG) def mod_meter_entry(dp, meter, cmd): flags = 0 if 'flags' in meter: meter_flags = meter['flags'] if not isinstance(meter_flags, list): meter_flags = [meter_flags] for flag in meter_flags: t = UTIL.ofp_meter_flags_from_user(flag) f = t if t != flag else None if f is None: LOG.error('Unknown meter flag: %s', flag) continue flags |= f meter_id = UTIL.ofp_meter_from_user(meter.get('meter_id', 0)) bands = [] for band in meter.get('bands', []): band_type = band.get('type') rate = str_to_int(band.get('rate', 0)) burst_size = str_to_int(band.get('burst_size', 0)) if band_type == 'DROP': bands.append( dp.ofproto_parser.OFPMeterBandDrop(rate, burst_size)) elif band_type == 'DSCP_REMARK': prec_level = str_to_int(band.get('prec_level', 0)) bands.append( dp.ofproto_parser.OFPMeterBandDscpRemark( rate, burst_size, prec_level)) elif band_type == 'EXPERIMENTER': experimenter = str_to_int(band.get('experimenter', 0)) bands.append( dp.ofproto_parser.OFPMeterBandExperimenter( rate, burst_size, experimenter)) else: LOG.error('Unknown band type: %s', band_type) meter_mod = dp.ofproto_parser.OFPMeterMod( dp, cmd, flags, meter_id, bands) ofctl_utils.send_msg(dp, meter_mod, LOG) def mod_group_entry(dp, group, cmd): group_type = str(group.get('type', 'ALL')) t = UTIL.ofp_group_type_from_user(group_type) group_type = t if t != group_type else None if group_type is None: LOG.error('Unknown group type: %s', group.get('type')) group_id = UTIL.ofp_group_from_user(group.get('group_id', 0)) buckets = [] for bucket in group.get('buckets', []): weight = str_to_int(bucket.get('weight', 0)) watch_port = str_to_int( bucket.get('watch_port', dp.ofproto.OFPP_ANY)) watch_group = str_to_int( bucket.get('watch_group', dp.ofproto.OFPG_ANY)) actions = [] for dic in bucket.get('actions', []): action = to_action(dp, dic) if action is not None: actions.append(action) buckets.append(dp.ofproto_parser.OFPBucket( weight, watch_port, watch_group, actions)) group_mod = dp.ofproto_parser.OFPGroupMod( dp, cmd, group_type, group_id, buckets) ofctl_utils.send_msg(dp, group_mod, LOG) def mod_port_behavior(dp, port_config): ofp = dp.ofproto parser = dp.ofproto_parser port_no = UTIL.ofp_port_from_user(port_config.get('port_no', 0)) hw_addr = str(port_config.get('hw_addr')) config = str_to_int(port_config.get('config', 0)) mask = str_to_int(port_config.get('mask', 0)) properties = port_config.get('properties') prop = [] for p in properties: type_ = UTIL.ofp_port_mod_prop_type_from_user(p['type']) length = None if type_ == ofp.OFPPDPT_ETHERNET: advertise = UTIL.ofp_port_features_from_user(p['advertise']) prop.append( parser.OFPPortModPropEthernet(type_, length, advertise)) elif type_ == ofp.OFPPDPT_OPTICAL: prop.append( parser.OFPPortModPropOptical( type_, length, p['configure'], p['freq_lmda'], p['fl_offset'], p['grid_span'], p['tx_pwr'])) elif type_ == ofp.OFPPDPT_EXPERIMENTER: prop.append( parser.OFPPortModPropExperimenter( type_, length, p['experimenter'], p['exp_type'], p['data'])) else: LOG.error('Unknown port desc prop type: %s', type_) port_mod = dp.ofproto_parser.OFPPortMod( dp, port_no, hw_addr, config, mask, prop) ofctl_utils.send_msg(dp, port_mod, LOG) def set_role(dp, role): r = UTIL.ofp_role_from_user(role.get('role', dp.ofproto.OFPCR_ROLE_EQUAL)) role_request = dp.ofproto_parser.OFPRoleRequest(dp, r, 0) ofctl_utils.send_msg(dp, role_request, LOG) send_experimenter = ofctl_utils.send_experimenter

true

f71be3d981254befffd5928f1197ba90e0eb5617

15,646

py

Python

venv/Lib/site-packages/pooch/downloaders.py

Terrathaw/ba21_loma_2_py

eebf5104dd054cef1ab61f0b257933ff679e75ec

[ "MIT" ]

4

2021-03-29T19:15:29.000Z

2021-06-08T05:34:00.000Z

venv/Lib/site-packages/pooch/downloaders.py

Terrathaw/ba21_loma_2_py

eebf5104dd054cef1ab61f0b257933ff679e75ec

[ "MIT" ]

1

2021-06-08T06:03:51.000Z

venv/Lib/site-packages/pooch/downloaders.py

Terrathaw/ba21_loma_2_py

eebf5104dd054cef1ab61f0b257933ff679e75ec

[ "MIT" ]

1

2021-01-31T18:58:54.000Z

# Copyright (c) 2018 The Pooch Developers. # Distributed under the terms of the BSD 3-Clause License. # SPDX-License-Identifier: BSD-3-Clause # # This code is part of the Fatiando a Terra project (https://www.fatiando.org) # """ The classes that actually handle the downloads. """ import sys import ftplib import requests from .utils import parse_url try: from tqdm import tqdm except ImportError: tqdm = None try: import paramiko except ImportError: paramiko = None def choose_downloader(url): """ Choose the appropriate downloader for the given URL based on the protocol. Parameters ---------- url : str A URL (including protocol). Returns ------- downloader A downloader class (either :class:`pooch.HTTPDownloader`, :class:`pooch.FTPDownloader`, or :class: `pooch.SFTPDownloader`). Examples -------- >>> downloader = choose_downloader("http://something.com") >>> print(downloader.__class__.__name__) HTTPDownloader >>> downloader = choose_downloader("https://something.com") >>> print(downloader.__class__.__name__) HTTPDownloader >>> downloader = choose_downloader("ftp://something.com") >>> print(downloader.__class__.__name__) FTPDownloader """ known_downloaders = { "ftp": FTPDownloader, "https": HTTPDownloader, "http": HTTPDownloader, "sftp": SFTPDownloader, } parsed_url = parse_url(url) if parsed_url["protocol"] not in known_downloaders: raise ValueError( f"Unrecognized URL protocol '{parsed_url['protocol']}' in '{url}'. " f"Must be one of {known_downloaders.keys()}." ) downloader = known_downloaders[parsed_url["protocol"]]() return downloader class HTTPDownloader: # pylint: disable=too-few-public-methods """ Download manager for fetching files over HTTP/HTTPS. When called, downloads the given file URL into the specified local file. Uses the :mod:`requests` library to manage downloads. Use with :meth:`pooch.Pooch.fetch` or :func:`pooch.retrieve` to customize the download of files (for example, to use authentication or print a progress bar). Parameters ---------- progressbar : bool If True, will print a progress bar of the download to standard error (stderr). Requires `tqdm <https://github.com/tqdm/tqdm>`__ to be installed. chunk_size : int Files are streamed *chunk_size* bytes at a time instead of loading everything into memory at one. Usually doesn't need to be changed. **kwargs All keyword arguments given when creating an instance of this class will be passed to :func:`requests.get`. Examples -------- Download one of the data files from the Pooch repository: >>> import os >>> from pooch import version, check_version >>> url = "https://github.com/fatiando/pooch/raw/{}/data/tiny-data.txt" >>> url = url.format(check_version(version.full_version)) >>> downloader = HTTPDownloader() >>> # Not using with Pooch.fetch so no need to pass an instance of Pooch >>> downloader(url=url, output_file="tiny-data.txt", pooch=None) >>> os.path.exists("tiny-data.txt") True >>> with open("tiny-data.txt") as f: ... print(f.read().strip()) # A tiny data file for test purposes only 1 2 3 4 5 6 >>> os.remove("tiny-data.txt") Authentication can be handled by passing a user name and password to :func:`requests.get`. All arguments provided when creating an instance of the class are forwarded to :func:`requests.get`. We'll use ``auth=(username, password)`` to use basic HTTPS authentication. The https://httpbin.org website allows us to make a fake a login request using whatever username and password we provide to it: >>> user = "doggo" >>> password = "goodboy" >>> # httpbin will ask for the user and password we provide in the URL >>> url = f"https://httpbin.org/basic-auth/{user}/{password}" >>> # Trying without the login credentials causes an error >>> downloader = HTTPDownloader() >>> try: ... downloader(url=url, output_file="tiny-data.txt", pooch=None) ... except Exception: ... print("There was an error!") There was an error! >>> # Pass in the credentials to HTTPDownloader >>> downloader = HTTPDownloader(auth=(user, password)) >>> downloader(url=url, output_file="tiny-data.txt", pooch=None) >>> with open("tiny-data.txt") as f: ... for line in f: ... print(line.rstrip()) { "authenticated": true, "user": "doggo" } >>> os.remove("tiny-data.txt") """ def __init__(self, progressbar=False, chunk_size=1024, **kwargs): self.kwargs = kwargs self.progressbar = progressbar self.chunk_size = chunk_size if self.progressbar and tqdm is None: raise ValueError("Missing package 'tqdm' required for progress bars.") def __call__(self, url, output_file, pooch): """ Download the given URL over HTTP to the given output file. Uses :func:`requests.get`. Parameters ---------- url : str The URL to the file you want to download. output_file : str or file-like object Path (and file name) to which the file will be downloaded. pooch : :class:`~pooch.Pooch` The instance of :class:`~pooch.Pooch` that is calling this method. """ kwargs = self.kwargs.copy() kwargs.setdefault("stream", True) ispath = not hasattr(output_file, "write") if ispath: output_file = open(output_file, "w+b") try: response = requests.get(url, **kwargs) response.raise_for_status() content = response.iter_content(chunk_size=self.chunk_size) if self.progressbar: total = int(response.headers.get("content-length", 0)) # Need to use ascii characters on Windows because there isn't # always full unicode support # (see https://github.com/tqdm/tqdm/issues/454) use_ascii = bool(sys.platform == "win32") progress = tqdm( total=total, ncols=79, ascii=use_ascii, unit="B", unit_scale=True, leave=True, ) for chunk in content: if chunk: output_file.write(chunk) output_file.flush() if self.progressbar: # Use the chunk size here because chunk may be much # larger if the data are decompressed by requests after # reading (happens with text files). progress.update(self.chunk_size) # Make sure the progress bar gets filled even if the actual number # is chunks is smaller than expected. This happens when streaming # text files that are compressed by the server when sending (gzip). # Binary files don't experience this. if self.progressbar: progress.reset() progress.update(total) progress.close() finally: if ispath: output_file.close() class FTPDownloader: # pylint: disable=too-few-public-methods """ Download manager for fetching files over FTP. When called, downloads the given file URL into the specified local file. Uses the :mod:`ftplib` module to manage downloads. Use with :meth:`pooch.Pooch.fetch` or :func:`pooch.retrieve` to customize the download of files (for example, to use authentication or print a progress bar). Parameters ---------- port : int Port used for the FTP connection. username : str User name used to login to the server. Only needed if the server requires authentication (i.e., no anonymous FTP). password : str Password used to login to the server. Only needed if the server requires authentication (i.e., no anonymous FTP). Use the empty string to indicate no password is required. account : str Some servers also require an "account" name for authentication. timeout : int Timeout in seconds for ftp socket operations, use None to mean no timeout. progressbar : bool If True, will print a progress bar of the download to standard error (stderr). Requires `tqdm <https://github.com/tqdm/tqdm>`__ to be installed. chunk_size : int Files are streamed *chunk_size* bytes at a time instead of loading everything into memory at one. Usually doesn't need to be changed. """ def __init__( self, port=21, username="anonymous", password="", account="", timeout=None, progressbar=False, chunk_size=1024, ): self.port = port self.username = username self.password = password self.account = account self.timeout = timeout self.progressbar = progressbar self.chunk_size = chunk_size if self.progressbar and tqdm is None: raise ValueError("Missing package 'tqdm' required for progress bars.") def __call__(self, url, output_file, pooch): """ Download the given URL over FTP to the given output file. Parameters ---------- url : str The URL to the file you want to download. output_file : str or file-like object Path (and file name) to which the file will be downloaded. pooch : :class:`~pooch.Pooch` The instance of :class:`~pooch.Pooch` that is calling this method. """ parsed_url = parse_url(url) ftp = ftplib.FTP(timeout=self.timeout) ftp.connect(host=parsed_url["netloc"], port=self.port) ispath = not hasattr(output_file, "write") if ispath: output_file = open(output_file, "w+b") try: ftp.login(user=self.username, passwd=self.password, acct=self.account) command = f"RETR {parsed_url['path']}" if self.progressbar: # Make sure the file is set to binary mode, otherwise we can't # get the file size. See: https://stackoverflow.com/a/22093848 ftp.voidcmd("TYPE I") size = int(ftp.size(parsed_url["path"])) use_ascii = bool(sys.platform == "win32") progress = tqdm( total=size, ncols=79, ascii=use_ascii, unit="B", unit_scale=True, leave=True, ) with progress: def callback(data): "Update the progress bar and write to output" progress.update(len(data)) output_file.write(data) ftp.retrbinary(command, callback, blocksize=self.chunk_size) else: ftp.retrbinary(command, output_file.write, blocksize=self.chunk_size) finally: ftp.quit() if ispath: output_file.close() class SFTPDownloader: # pylint: disable=too-few-public-methods """ Download manager for fetching files over SFTP. When called, downloads the given file URL into the specified local file. Requires `paramiko <https://github.com/paramiko/paramiko>`__ to be installed. Use with :meth:`pooch.Pooch.fetch` or :func:`pooch.retrieve` to customize the download of files (for example, to use authentication or print a progress bar). Parameters ---------- port : int Port used for the SFTP connection. username : str User name used to login to the server. Only needed if the server requires authentication (i.e., no anonymous SFTP). password : str Password used to login to the server. Only needed if the server requires authentication (i.e., no anonymous SFTP). Use the empty string to indicate no password is required. timeout : int Timeout in seconds for sftp socket operations, use None to mean no timeout. progressbar : bool If True, will print a progress bar of the download to standard error (stderr). Requires `tqdm <https://github.com/tqdm/tqdm>`__ to be installed. """ def __init__( self, port=22, username="anonymous", password="", account="", timeout=None, progressbar=False, ): self.port = port self.username = username self.password = password self.account = account self.timeout = timeout self.progressbar = progressbar # Collect errors and raise only once so that both missing packages are # captured. Otherwise, the user is only warned of one of them at a # time (and we can't test properly when they are both missing). errors = [] if self.progressbar and tqdm is None: errors.append("Missing package 'tqdm' required for progress bars.") if paramiko is None: errors.append("Missing package 'paramiko' required for SFTP downloads.") if errors: raise ValueError(" ".join(errors)) def __call__(self, url, output_file, pooch): """ Download the given URL over SFTP to the given output file. The output file must be given as a string (file name/path) and not an open file object! Otherwise, paramiko cannot save to that file. Parameters ---------- url : str The URL to the file you want to download. output_file : str Path (and file name) to which the file will be downloaded. **Cannot be a file object**. pooch : :class:`~pooch.Pooch` The instance of :class:`~pooch.Pooch` that is calling this method. """ parsed_url = parse_url(url) connection = paramiko.Transport(sock=(parsed_url["netloc"], self.port)) sftp = None try: connection.connect(username=self.username, password=self.password) sftp = paramiko.SFTPClient.from_transport(connection) sftp.get_channel().settimeout = self.timeout if self.progressbar: size = int(sftp.stat(parsed_url["path"]).st_size) use_ascii = bool(sys.platform == "win32") progress = tqdm( total=size, ncols=79, ascii=use_ascii, unit="B", unit_scale=True, leave=True, ) with progress: def callback(current, total): "Update the progress bar and write to output" progress.total = int(total) progress.update(int(current - progress.n)) sftp.get(parsed_url["path"], output_file, callback=callback) else: sftp.get(parsed_url["path"], output_file) finally: connection.close() if sftp is not None: sftp.close()

35.803204

85

0.592612

import sys import ftplib import requests from .utils import parse_url try: from tqdm import tqdm except ImportError: tqdm = None try: import paramiko except ImportError: paramiko = None def choose_downloader(url): known_downloaders = { "ftp": FTPDownloader, "https": HTTPDownloader, "http": HTTPDownloader, "sftp": SFTPDownloader, } parsed_url = parse_url(url) if parsed_url["protocol"] not in known_downloaders: raise ValueError( f"Unrecognized URL protocol '{parsed_url['protocol']}' in '{url}'. " f"Must be one of {known_downloaders.keys()}." ) downloader = known_downloaders[parsed_url["protocol"]]() return downloader class HTTPDownloader: def __init__(self, progressbar=False, chunk_size=1024, **kwargs): self.kwargs = kwargs self.progressbar = progressbar self.chunk_size = chunk_size if self.progressbar and tqdm is None: raise ValueError("Missing package 'tqdm' required for progress bars.") def __call__(self, url, output_file, pooch): kwargs = self.kwargs.copy() kwargs.setdefault("stream", True) ispath = not hasattr(output_file, "write") if ispath: output_file = open(output_file, "w+b") try: response = requests.get(url, **kwargs) response.raise_for_status() content = response.iter_content(chunk_size=self.chunk_size) if self.progressbar: total = int(response.headers.get("content-length", 0)) # always full unicode support # (see https://github.com/tqdm/tqdm/issues/454) use_ascii = bool(sys.platform == "win32") progress = tqdm( total=total, ncols=79, ascii=use_ascii, unit="B", unit_scale=True, leave=True, ) for chunk in content: if chunk: output_file.write(chunk) output_file.flush() if self.progressbar: # Use the chunk size here because chunk may be much # larger if the data are decompressed by requests after # reading (happens with text files). progress.update(self.chunk_size) # Make sure the progress bar gets filled even if the actual number # is chunks is smaller than expected. This happens when streaming # text files that are compressed by the server when sending (gzip). # Binary files don't experience this. if self.progressbar: progress.reset() progress.update(total) progress.close() finally: if ispath: output_file.close() class FTPDownloader: def __init__( self, port=21, username="anonymous", password="", account="", timeout=None, progressbar=False, chunk_size=1024, ): self.port = port self.username = username self.password = password self.account = account self.timeout = timeout self.progressbar = progressbar self.chunk_size = chunk_size if self.progressbar and tqdm is None: raise ValueError("Missing package 'tqdm' required for progress bars.") def __call__(self, url, output_file, pooch): parsed_url = parse_url(url) ftp = ftplib.FTP(timeout=self.timeout) ftp.connect(host=parsed_url["netloc"], port=self.port) ispath = not hasattr(output_file, "write") if ispath: output_file = open(output_file, "w+b") try: ftp.login(user=self.username, passwd=self.password, acct=self.account) command = f"RETR {parsed_url['path']}" if self.progressbar: # get the file size. See: https://stackoverflow.com/a/22093848 ftp.voidcmd("TYPE I") size = int(ftp.size(parsed_url["path"])) use_ascii = bool(sys.platform == "win32") progress = tqdm( total=size, ncols=79, ascii=use_ascii, unit="B", unit_scale=True, leave=True, ) with progress: def callback(data): progress.update(len(data)) output_file.write(data) ftp.retrbinary(command, callback, blocksize=self.chunk_size) else: ftp.retrbinary(command, output_file.write, blocksize=self.chunk_size) finally: ftp.quit() if ispath: output_file.close() class SFTPDownloader: # pylint: disable=too-few-public-methods def __init__( self, port=22, username="anonymous", password="", account="", timeout=None, progressbar=False, ): self.port = port self.username = username self.password = password self.account = account self.timeout = timeout self.progressbar = progressbar # Collect errors and raise only once so that both missing packages are # captured. Otherwise, the user is only warned of one of them at a # time (and we can't test properly when they are both missing). errors = [] if self.progressbar and tqdm is None: errors.append("Missing package 'tqdm' required for progress bars.") if paramiko is None: errors.append("Missing package 'paramiko' required for SFTP downloads.") if errors: raise ValueError(" ".join(errors)) def __call__(self, url, output_file, pooch): parsed_url = parse_url(url) connection = paramiko.Transport(sock=(parsed_url["netloc"], self.port)) sftp = None try: connection.connect(username=self.username, password=self.password) sftp = paramiko.SFTPClient.from_transport(connection) sftp.get_channel().settimeout = self.timeout if self.progressbar: size = int(sftp.stat(parsed_url["path"]).st_size) use_ascii = bool(sys.platform == "win32") progress = tqdm( total=size, ncols=79, ascii=use_ascii, unit="B", unit_scale=True, leave=True, ) with progress: def callback(current, total): progress.total = int(total) progress.update(int(current - progress.n)) sftp.get(parsed_url["path"], output_file, callback=callback) else: sftp.get(parsed_url["path"], output_file) finally: connection.close() if sftp is not None: sftp.close()

true

f71be436acf919b617f27f823476c8b4531b4b98

7,043

py

Python

homeassistant/auth/providers/trusted_networks.py

petewill/home-assistant

5859dba4344f05fb8774aa1207e47ac28f627a67

[ "Apache-2.0" ]

3

2020-01-21T18:09:09.000Z

2022-01-17T08:06:03.000Z

homeassistant/auth/providers/trusted_networks.py

petewill/home-assistant

5859dba4344f05fb8774aa1207e47ac28f627a67

[ "Apache-2.0" ]

39

2016-12-16T12:40:34.000Z

2017-02-13T17:53:42.000Z

homeassistant/auth/providers/trusted_networks.py

petewill/home-assistant

5859dba4344f05fb8774aa1207e47ac28f627a67

[ "Apache-2.0" ]

6

2020-04-10T06:21:11.000Z

2021-07-01T08:53:38.000Z

"""Trusted Networks auth provider. It shows list of users if access from trusted network. Abort login flow if not access from trusted network. """ from ipaddress import ip_network, IPv4Address, IPv6Address, IPv4Network, IPv6Network from typing import Any, Dict, List, Optional, Union, cast import voluptuous as vol import homeassistant.helpers.config_validation as cv from homeassistant.core import callback from homeassistant.exceptions import HomeAssistantError from . import AuthProvider, AUTH_PROVIDER_SCHEMA, AUTH_PROVIDERS, LoginFlow from ..models import Credentials, UserMeta IPAddress = Union[IPv4Address, IPv6Address] IPNetwork = Union[IPv4Network, IPv6Network] CONF_TRUSTED_NETWORKS = "trusted_networks" CONF_TRUSTED_USERS = "trusted_users" CONF_GROUP = "group" CONF_ALLOW_BYPASS_LOGIN = "allow_bypass_login" CONFIG_SCHEMA = AUTH_PROVIDER_SCHEMA.extend( { vol.Required(CONF_TRUSTED_NETWORKS): vol.All(cv.ensure_list, [ip_network]), vol.Optional(CONF_TRUSTED_USERS, default={}): vol.Schema( # we only validate the format of user_id or group_id { ip_network: vol.All( cv.ensure_list, [ vol.Or( cv.uuid4_hex, vol.Schema({vol.Required(CONF_GROUP): cv.uuid4_hex}), ) ], ) } ), vol.Optional(CONF_ALLOW_BYPASS_LOGIN, default=False): cv.boolean, }, extra=vol.PREVENT_EXTRA, ) class InvalidAuthError(HomeAssistantError): """Raised when try to access from untrusted networks.""" class InvalidUserError(HomeAssistantError): """Raised when try to login as invalid user.""" @AUTH_PROVIDERS.register("trusted_networks") class TrustedNetworksAuthProvider(AuthProvider): """Trusted Networks auth provider. Allow passwordless access from trusted network. """ DEFAULT_TITLE = "Trusted Networks" @property def trusted_networks(self) -> List[IPNetwork]: """Return trusted networks.""" return cast(List[IPNetwork], self.config[CONF_TRUSTED_NETWORKS]) @property def trusted_users(self) -> Dict[IPNetwork, Any]: """Return trusted users per network.""" return cast(Dict[IPNetwork, Any], self.config[CONF_TRUSTED_USERS]) @property def support_mfa(self) -> bool: """Trusted Networks auth provider does not support MFA.""" return False async def async_login_flow(self, context: Optional[Dict]) -> LoginFlow: """Return a flow to login.""" assert context is not None ip_addr = cast(IPAddress, context.get("ip_address")) users = await self.store.async_get_users() available_users = [ user for user in users if not user.system_generated and user.is_active ] for ip_net, user_or_group_list in self.trusted_users.items(): if ip_addr in ip_net: user_list = [ user_id for user_id in user_or_group_list if isinstance(user_id, str) ] group_list = [ group[CONF_GROUP] for group in user_or_group_list if isinstance(group, dict) ] flattened_group_list = [ group for sublist in group_list for group in sublist ] available_users = [ user for user in available_users if ( user.id in user_list or any( [group.id in flattened_group_list for group in user.groups] ) ) ] break return TrustedNetworksLoginFlow( self, ip_addr, {user.id: user.name for user in available_users}, self.config[CONF_ALLOW_BYPASS_LOGIN], ) async def async_get_or_create_credentials( self, flow_result: Dict[str, str] ) -> Credentials: """Get credentials based on the flow result.""" user_id = flow_result["user"] users = await self.store.async_get_users() for user in users: if not user.system_generated and user.is_active and user.id == user_id: for credential in await self.async_credentials(): if credential.data["user_id"] == user_id: return credential cred = self.async_create_credentials({"user_id": user_id}) await self.store.async_link_user(user, cred) return cred # We only allow login as exist user raise InvalidUserError async def async_user_meta_for_credentials( self, credentials: Credentials ) -> UserMeta: """Return extra user metadata for credentials. Trusted network auth provider should never create new user. """ raise NotImplementedError @callback def async_validate_access(self, ip_addr: IPAddress) -> None: """Make sure the access from trusted networks. Raise InvalidAuthError if not. Raise InvalidAuthError if trusted_networks is not configured. """ if not self.trusted_networks: raise InvalidAuthError("trusted_networks is not configured") if not any( ip_addr in trusted_network for trusted_network in self.trusted_networks ): raise InvalidAuthError("Not in trusted_networks") class TrustedNetworksLoginFlow(LoginFlow): """Handler for the login flow.""" def __init__( self, auth_provider: TrustedNetworksAuthProvider, ip_addr: IPAddress, available_users: Dict[str, Optional[str]], allow_bypass_login: bool, ) -> None: """Initialize the login flow.""" super().__init__(auth_provider) self._available_users = available_users self._ip_address = ip_addr self._allow_bypass_login = allow_bypass_login async def async_step_init( self, user_input: Optional[Dict[str, str]] = None ) -> Dict[str, Any]: """Handle the step of the form.""" try: cast( TrustedNetworksAuthProvider, self._auth_provider ).async_validate_access(self._ip_address) except InvalidAuthError: return self.async_abort(reason="not_whitelisted") if user_input is not None: return await self.async_finish(user_input) if self._allow_bypass_login and len(self._available_users) == 1: return await self.async_finish( {"user": next(iter(self._available_users.keys()))} ) return self.async_show_form( step_id="init", data_schema=vol.Schema({"user": vol.In(self._available_users)}), )

34.18932

87

0.609115

from ipaddress import ip_network, IPv4Address, IPv6Address, IPv4Network, IPv6Network from typing import Any, Dict, List, Optional, Union, cast import voluptuous as vol import homeassistant.helpers.config_validation as cv from homeassistant.core import callback from homeassistant.exceptions import HomeAssistantError from . import AuthProvider, AUTH_PROVIDER_SCHEMA, AUTH_PROVIDERS, LoginFlow from ..models import Credentials, UserMeta IPAddress = Union[IPv4Address, IPv6Address] IPNetwork = Union[IPv4Network, IPv6Network] CONF_TRUSTED_NETWORKS = "trusted_networks" CONF_TRUSTED_USERS = "trusted_users" CONF_GROUP = "group" CONF_ALLOW_BYPASS_LOGIN = "allow_bypass_login" CONFIG_SCHEMA = AUTH_PROVIDER_SCHEMA.extend( { vol.Required(CONF_TRUSTED_NETWORKS): vol.All(cv.ensure_list, [ip_network]), vol.Optional(CONF_TRUSTED_USERS, default={}): vol.Schema( { ip_network: vol.All( cv.ensure_list, [ vol.Or( cv.uuid4_hex, vol.Schema({vol.Required(CONF_GROUP): cv.uuid4_hex}), ) ], ) } ), vol.Optional(CONF_ALLOW_BYPASS_LOGIN, default=False): cv.boolean, }, extra=vol.PREVENT_EXTRA, ) class InvalidAuthError(HomeAssistantError): class InvalidUserError(HomeAssistantError): @AUTH_PROVIDERS.register("trusted_networks") class TrustedNetworksAuthProvider(AuthProvider): DEFAULT_TITLE = "Trusted Networks" @property def trusted_networks(self) -> List[IPNetwork]: return cast(List[IPNetwork], self.config[CONF_TRUSTED_NETWORKS]) @property def trusted_users(self) -> Dict[IPNetwork, Any]: return cast(Dict[IPNetwork, Any], self.config[CONF_TRUSTED_USERS]) @property def support_mfa(self) -> bool: return False async def async_login_flow(self, context: Optional[Dict]) -> LoginFlow: assert context is not None ip_addr = cast(IPAddress, context.get("ip_address")) users = await self.store.async_get_users() available_users = [ user for user in users if not user.system_generated and user.is_active ] for ip_net, user_or_group_list in self.trusted_users.items(): if ip_addr in ip_net: user_list = [ user_id for user_id in user_or_group_list if isinstance(user_id, str) ] group_list = [ group[CONF_GROUP] for group in user_or_group_list if isinstance(group, dict) ] flattened_group_list = [ group for sublist in group_list for group in sublist ] available_users = [ user for user in available_users if ( user.id in user_list or any( [group.id in flattened_group_list for group in user.groups] ) ) ] break return TrustedNetworksLoginFlow( self, ip_addr, {user.id: user.name for user in available_users}, self.config[CONF_ALLOW_BYPASS_LOGIN], ) async def async_get_or_create_credentials( self, flow_result: Dict[str, str] ) -> Credentials: user_id = flow_result["user"] users = await self.store.async_get_users() for user in users: if not user.system_generated and user.is_active and user.id == user_id: for credential in await self.async_credentials(): if credential.data["user_id"] == user_id: return credential cred = self.async_create_credentials({"user_id": user_id}) await self.store.async_link_user(user, cred) return cred raise InvalidUserError async def async_user_meta_for_credentials( self, credentials: Credentials ) -> UserMeta: raise NotImplementedError @callback def async_validate_access(self, ip_addr: IPAddress) -> None: if not self.trusted_networks: raise InvalidAuthError("trusted_networks is not configured") if not any( ip_addr in trusted_network for trusted_network in self.trusted_networks ): raise InvalidAuthError("Not in trusted_networks") class TrustedNetworksLoginFlow(LoginFlow): def __init__( self, auth_provider: TrustedNetworksAuthProvider, ip_addr: IPAddress, available_users: Dict[str, Optional[str]], allow_bypass_login: bool, ) -> None: super().__init__(auth_provider) self._available_users = available_users self._ip_address = ip_addr self._allow_bypass_login = allow_bypass_login async def async_step_init( self, user_input: Optional[Dict[str, str]] = None ) -> Dict[str, Any]: try: cast( TrustedNetworksAuthProvider, self._auth_provider ).async_validate_access(self._ip_address) except InvalidAuthError: return self.async_abort(reason="not_whitelisted") if user_input is not None: return await self.async_finish(user_input) if self._allow_bypass_login and len(self._available_users) == 1: return await self.async_finish( {"user": next(iter(self._available_users.keys()))} ) return self.async_show_form( step_id="init", data_schema=vol.Schema({"user": vol.In(self._available_users)}), )

true

f71be5558aa2a08dbbb15648b144ea54b9b317ff

854

py

Python

graphgallery/nn/layers/tensorflow/dropout/dropout.py

EdisonLeeeee/GraphGallery

4eec9c5136bda14809bd22584b26cc346cdb633b

[ "MIT" ]

300

2020-08-09T04:27:41.000Z

2022-03-30T07:43:41.000Z

graphgallery/nn/layers/tensorflow/dropout/dropout.py

EdisonLeeeee/GraphGallery

4eec9c5136bda14809bd22584b26cc346cdb633b

[ "MIT" ]

5

2020-11-05T06:16:50.000Z

2021-12-11T05:05:22.000Z

graphgallery/nn/layers/tensorflow/dropout/dropout.py

EdisonLeeeee/GraphGallery

4eec9c5136bda14809bd22584b26cc346cdb633b

[ "MIT" ]

51

2020-09-23T15:37:12.000Z

2022-03-05T01:28:56.000Z

import tensorflow as tf import tensorflow.keras.backend as K from tensorflow.keras.layers import Layer, Dropout class SparseDropout(Layer): def __init__(self, p=0.5): super().__init__() self.p = p def call(self, x, training=None): if training is None: training = K.learning_phase() if self.p and training: values = tf.nn.dropout(x.values, self.p) return tf.SparseTensor(x.indices, values, x.dense_shape) return x class MixedDropout(Layer): def __init__(self, p=0.5): super().__init__() self.dense_dropout = Dropout(p) self.sparse_dropout = SparseDropout(p) def call(self, x): if K.is_sparse(x): return self.sparse_dropout(x) else: return self.dense_dropout(x)

26.6875

69

0.591335

import tensorflow as tf import tensorflow.keras.backend as K from tensorflow.keras.layers import Layer, Dropout class SparseDropout(Layer): def __init__(self, p=0.5): super().__init__() self.p = p def call(self, x, training=None): if training is None: training = K.learning_phase() if self.p and training: values = tf.nn.dropout(x.values, self.p) return tf.SparseTensor(x.indices, values, x.dense_shape) return x class MixedDropout(Layer): def __init__(self, p=0.5): super().__init__() self.dense_dropout = Dropout(p) self.sparse_dropout = SparseDropout(p) def call(self, x): if K.is_sparse(x): return self.sparse_dropout(x) else: return self.dense_dropout(x)

true

f71be5cc21797fff7f87730b2e377bc2fe651fa1

14,118

py

Python

clinica/pipelines/deeplearning_prepare_data/deeplearning_prepare_data_pipeline.py

Raelag0112/clinica

d301b1abfdf4d3b62dc4b329622340795ae51ef8

[ "MIT" ]

null

clinica/pipelines/deeplearning_prepare_data/deeplearning_prepare_data_pipeline.py

Raelag0112/clinica

d301b1abfdf4d3b62dc4b329622340795ae51ef8

[ "MIT" ]

null

clinica/pipelines/deeplearning_prepare_data/deeplearning_prepare_data_pipeline.py

Raelag0112/clinica

d301b1abfdf4d3b62dc4b329622340795ae51ef8

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Use hash instead of parameters for iterables folder names # Otherwise path will be too long and generate OSError from nipype import config import clinica.pipelines.engine as cpe cfg = dict(execution={"parameterize_dirs": False}) config.update_config(cfg) class DeepLearningPrepareData(cpe.Pipeline): """Deeplearning prepare data - MRI in nifti format are transformed into PyTorch tensors. The transformation is applied to: the whole volume, a selection of 3D patches, or slices extracted from the 3D volume. By default it uses the cropped version of the MRI (see option "--use_uncropper_image") Returns: A clinica pipeline object containing the Deeplearning prepare data pipeline. """ def check_custom_dependencies(self): """Check dependencies that can not be listed in the `info.json` file.""" def get_input_fields(self): """Specify the list of possible inputs of this pipeline. Returns: A list of (string) input fields name. """ return ["input_nifti"] def get_output_fields(self): """Specify the list of possible outputs of this pipeline. Returns: A list of (string) output fields name. """ return ["image_id"] def build_input_node(self): """Build and connect an input node to the pipeline.""" from os import path import nipype.interfaces.utility as nutil import nipype.pipeline.engine as npe from clinica.utils.exceptions import ( ClinicaBIDSError, ClinicaCAPSError, ClinicaException, ) from clinica.utils.input_files import ( T1W_EXTENSIVE, T1W_LINEAR, T1W_LINEAR_CROPPED, pet_linear_nii, ) from clinica.utils.inputs import clinica_file_reader from clinica.utils.stream import cprint from clinica.utils.ux import print_images_to_process from .deeplearning_prepare_data_utils import check_mask_list # Select the correct filetype corresponding to modality if self.parameters.get("modality") == "t1-linear": if self.parameters.get("use_uncropped_image"): FILE_TYPE = T1W_LINEAR else: FILE_TYPE = T1W_LINEAR_CROPPED if self.parameters.get("modality") == "t1-extensive": FILE_TYPE = T1W_EXTENSIVE if self.parameters.get("modality") == "pet-linear": FILE_TYPE = pet_linear_nii( self.parameters.get("acq_label"), self.parameters.get("suvr_reference_region"), self.parameters.get("use_uncropped_image"), ) if self.parameters.get("modality") == "custom": FILE_TYPE = { "pattern": f"*{self.parameters.get('custom_suffix')}", "description": "Custom suffix", } # Input file: try: input_files = clinica_file_reader( self.subjects, self.sessions, self.caps_directory, FILE_TYPE ) except ClinicaException as e: err = ( "Clinica faced error(s) while trying to read files in your CAPS directory.\n" + str(e) ) raise ClinicaBIDSError(err) if len(self.subjects): print_images_to_process(self.subjects, self.sessions) cprint("The pipeline will last approximately 30 seconds per image.") if self.parameters.get("extract_method") == "slice": self.slice_direction = self.parameters.get("slice_direction") self.slice_mode = self.parameters.get("slice_mode") else: self.slice_direction = "axial" self.slice_mode = "rgb" if self.parameters.get("extract_method") == "patch": self.patch_size = self.parameters.get("patch_size") self.stride_size = self.parameters.get("stride_size") else: self.patch_size = 50 self.stride_size = 50 # Load the corresponding masks if self.parameters.get("extract_method") == "roi": self.roi_list = self.parameters.get("roi_list") if self.parameters.get("modality") == "custom": self.mask_pattern = self.parameters.get("custom_mask_pattern") self.template = self.parameters.get("custom_template") if not self.template: raise ValueError( "A custom template must be defined when the modality is set to custom." ) else: self.mask_pattern = None from .deeplearning_prepare_data_utils import TEMPLATE_DICT self.template = TEMPLATE_DICT[self.parameters.get("modality")] self.masks_location = path.join( self.caps_directory, "masks", f"tpl-{self.template}" ) if not self.roi_list: raise ValueError("A list of regions must be given.") else: check_mask_list( self.masks_location, self.roi_list, self.mask_pattern, not self.parameters.get("use_uncropped_image"), ) else: self.masks_location = "" self.mask_pattern = None self.roi_list = [] # The reading node # ------------------------- read_node = npe.Node( name="ReadingFiles", iterables=[ ("input_nifti", input_files), ], synchronize=True, interface=nutil.IdentityInterface(fields=self.get_input_fields()), ) self.connect( [ (read_node, self.input_node, [("input_nifti", "input_nifti")]), ] ) def build_output_node(self): """Build and connect an output node to the pipeline.""" import nipype.interfaces.utility as nutil import nipype.pipeline.engine as npe from nipype.interfaces.io import DataSink from clinica.utils.filemanip import get_subject_id from clinica.utils.nipype import container_from_filename, fix_join # Write node # ---------------------- write_node = npe.Node(name="WriteCaps", interface=DataSink()) write_node.inputs.base_directory = self.caps_directory write_node.inputs.parameterization = False # Get subject ID node # ---------------------- image_id_node = npe.Node( interface=nutil.Function( input_names=["bids_or_caps_file"], output_names=["image_id"], function=get_subject_id, ), name="ImageID", ) # Find container path from input filename # ---------------------- container_path = npe.Node( nutil.Function( input_names=["bids_or_caps_filename"], output_names=["container"], function=container_from_filename, ), name="ContainerPath", ) # fmt: off self.connect( [ (self.input_node, image_id_node, [("input_nifti", "bids_or_caps_file")]), (self.input_node, container_path, [("input_nifti", "bids_or_caps_filename")]), # (image_id_node, write_node, [('image_id', '@image_id')]), (image_id_node, write_node, [("image_id", "@image_id")]), ] ) # fmt: on subfolder = "image_based" if self.parameters.get("extract_method") == "slice": subfolder = "slice_based" # fmt: off self.connect( [ (self.output_node, write_node, [("slices_rgb_output", "@slices_rgb_output")]), (self.output_node, write_node, [("slices_original_output", "@slices_original_output")]), ] ) # fmt: on elif self.parameters.get("extract_method") == "patch": subfolder = "patch_based" # fmt: off self.connect( [ (self.output_node, write_node, [("patches_output", "@patches_output")]) ] ) # fmt: on elif self.parameters.get("extract_method") == "roi": subfolder = "roi_based" # fmt: off self.connect( [ (self.output_node, write_node, [("roi_output", "@roi_output")]) ] ) # fmt: on else: # fmt: off self.connect( [ (self.output_node, write_node, [("output_pt_file", "@output_pt_file")]) ] ) # fmt: on mod_subfolder = "" if self.parameters.get("modality") == "t1-linear": mod_subfolder = "t1_linear" if self.parameters.get("modality") == "t1-extensive": mod_subfolder = "t1_extensive" if self.parameters.get("modality") == "pet-linear": mod_subfolder = "pet_linear" if self.parameters.get("modality") == "custom": mod_subfolder = "custom" # fmt: off self.connect( [ (container_path, write_node, [ (("container", fix_join, "deeplearning_prepare_data", subfolder, mod_subfolder), "container")]), ] ) # fmt: on def build_core_nodes(self): """Build and connect the core nodes of the pipeline.""" import nipype.interfaces.utility as nutil import nipype.pipeline.engine as npe from .deeplearning_prepare_data_utils import ( extract_patches, extract_roi, extract_slices, save_as_pt, ) # The processing nodes # Node to save input in nii.gz format into pytorch .pt format # ---------------------- save_as_pt = npe.MapNode( name="save_as_pt", iterfield=["input_img"], interface=nutil.Function( function=save_as_pt, input_names=["input_img"], output_names=["output_file"], ), ) # Extract slices node (options: 3 directions, mode) # ---------------------- extract_slices_node = npe.MapNode( name="extract_slices", iterfield=["input_tensor"], interface=nutil.Function( function=extract_slices, input_names=["input_tensor", "slice_direction", "slice_mode"], output_names=["output_file_rgb", "output_file_original"], ), ) extract_slices_node.inputs.slice_direction = self.slice_direction extract_slices_node.inputs.slice_mode = self.slice_mode # Extract patches node (options, patch size and stride size) # ---------------------- extract_patches_node = npe.MapNode( name="extract_patches", iterfield=["input_tensor"], interface=nutil.Function( function=extract_patches, input_names=["input_tensor", "patch_size", "stride_size"], output_names=["output_patch"], ), ) extract_patches_node.inputs.patch_size = self.patch_size extract_patches_node.inputs.stride_size = self.stride_size # Extract ROi node extract_roi_node = npe.MapNode( name="extract_ROI", iterfield=["input_tensor"], interface=nutil.Function( function=extract_roi, input_names=[ "input_tensor", "masks_location", "mask_pattern", "cropped_input", "roi_list", "uncrop_output", ], output_names=["output_roi"], ), ) extract_roi_node.inputs.masks_location = self.masks_location extract_roi_node.inputs.mask_pattern = self.mask_pattern extract_roi_node.inputs.cropped_input = not self.parameters.get( "use_uncropped_image" ) extract_roi_node.inputs.roi_list = self.roi_list extract_roi_node.inputs.uncrop_output = self.parameters.get("roi_uncrop_output") # Connections # ---------------------- # fmt: off self.connect( [ (self.input_node, save_as_pt, [("input_nifti", "input_img")]), ] ) if self.parameters.get("extract_method") == "slice": self.connect( [ (save_as_pt, extract_slices_node, [("output_file", "input_tensor")]), (extract_slices_node, self.output_node, [("output_file_rgb", "slices_rgb_output")]), (extract_slices_node, self.output_node, [("output_file_original", "slices_original_output")]), ] ) elif self.parameters.get("extract_method") == "patch": self.connect( [ (save_as_pt, extract_patches_node, [("output_file", "input_tensor")]), (extract_patches_node, self.output_node, [("output_patch", "patches_output")]), ] ) elif self.parameters.get("extract_method") == "roi": self.connect( [ (save_as_pt, extract_roi_node, [("output_file", "input_tensor")]), (extract_roi_node, self.output_node, [("output_roi", "roi_output")]), ] ) else: self.connect( [ (save_as_pt, self.output_node, [("output_file", "output_pt_file")]), ] ) # fmt: on

35.741772

116

0.541507

from nipype import config import clinica.pipelines.engine as cpe cfg = dict(execution={"parameterize_dirs": False}) config.update_config(cfg) class DeepLearningPrepareData(cpe.Pipeline): def check_custom_dependencies(self): def get_input_fields(self): return ["input_nifti"] def get_output_fields(self): return ["image_id"] def build_input_node(self): from os import path import nipype.interfaces.utility as nutil import nipype.pipeline.engine as npe from clinica.utils.exceptions import ( ClinicaBIDSError, ClinicaCAPSError, ClinicaException, ) from clinica.utils.input_files import ( T1W_EXTENSIVE, T1W_LINEAR, T1W_LINEAR_CROPPED, pet_linear_nii, ) from clinica.utils.inputs import clinica_file_reader from clinica.utils.stream import cprint from clinica.utils.ux import print_images_to_process from .deeplearning_prepare_data_utils import check_mask_list if self.parameters.get("modality") == "t1-linear": if self.parameters.get("use_uncropped_image"): FILE_TYPE = T1W_LINEAR else: FILE_TYPE = T1W_LINEAR_CROPPED if self.parameters.get("modality") == "t1-extensive": FILE_TYPE = T1W_EXTENSIVE if self.parameters.get("modality") == "pet-linear": FILE_TYPE = pet_linear_nii( self.parameters.get("acq_label"), self.parameters.get("suvr_reference_region"), self.parameters.get("use_uncropped_image"), ) if self.parameters.get("modality") == "custom": FILE_TYPE = { "pattern": f"*{self.parameters.get('custom_suffix')}", "description": "Custom suffix", } try: input_files = clinica_file_reader( self.subjects, self.sessions, self.caps_directory, FILE_TYPE ) except ClinicaException as e: err = ( "Clinica faced error(s) while trying to read files in your CAPS directory.\n" + str(e) ) raise ClinicaBIDSError(err) if len(self.subjects): print_images_to_process(self.subjects, self.sessions) cprint("The pipeline will last approximately 30 seconds per image.") if self.parameters.get("extract_method") == "slice": self.slice_direction = self.parameters.get("slice_direction") self.slice_mode = self.parameters.get("slice_mode") else: self.slice_direction = "axial" self.slice_mode = "rgb" if self.parameters.get("extract_method") == "patch": self.patch_size = self.parameters.get("patch_size") self.stride_size = self.parameters.get("stride_size") else: self.patch_size = 50 self.stride_size = 50 if self.parameters.get("extract_method") == "roi": self.roi_list = self.parameters.get("roi_list") if self.parameters.get("modality") == "custom": self.mask_pattern = self.parameters.get("custom_mask_pattern") self.template = self.parameters.get("custom_template") if not self.template: raise ValueError( "A custom template must be defined when the modality is set to custom." ) else: self.mask_pattern = None from .deeplearning_prepare_data_utils import TEMPLATE_DICT self.template = TEMPLATE_DICT[self.parameters.get("modality")] self.masks_location = path.join( self.caps_directory, "masks", f"tpl-{self.template}" ) if not self.roi_list: raise ValueError("A list of regions must be given.") else: check_mask_list( self.masks_location, self.roi_list, self.mask_pattern, not self.parameters.get("use_uncropped_image"), ) else: self.masks_location = "" self.mask_pattern = None self.roi_list = [] read_node = npe.Node( name="ReadingFiles", iterables=[ ("input_nifti", input_files), ], synchronize=True, interface=nutil.IdentityInterface(fields=self.get_input_fields()), ) self.connect( [ (read_node, self.input_node, [("input_nifti", "input_nifti")]), ] ) def build_output_node(self): import nipype.interfaces.utility as nutil import nipype.pipeline.engine as npe from nipype.interfaces.io import DataSink from clinica.utils.filemanip import get_subject_id from clinica.utils.nipype import container_from_filename, fix_join write_node = npe.Node(name="WriteCaps", interface=DataSink()) write_node.inputs.base_directory = self.caps_directory write_node.inputs.parameterization = False image_id_node = npe.Node( interface=nutil.Function( input_names=["bids_or_caps_file"], output_names=["image_id"], function=get_subject_id, ), name="ImageID", ) container_path = npe.Node( nutil.Function( input_names=["bids_or_caps_filename"], output_names=["container"], function=container_from_filename, ), name="ContainerPath", ) self.connect( [ (self.input_node, image_id_node, [("input_nifti", "bids_or_caps_file")]), (self.input_node, container_path, [("input_nifti", "bids_or_caps_filename")]), (image_id_node, write_node, [("image_id", "@image_id")]), ] ) subfolder = "image_based" if self.parameters.get("extract_method") == "slice": subfolder = "slice_based" self.connect( [ (self.output_node, write_node, [("slices_rgb_output", "@slices_rgb_output")]), (self.output_node, write_node, [("slices_original_output", "@slices_original_output")]), ] ) elif self.parameters.get("extract_method") == "patch": subfolder = "patch_based" self.connect( [ (self.output_node, write_node, [("patches_output", "@patches_output")]) ] ) elif self.parameters.get("extract_method") == "roi": subfolder = "roi_based" self.connect( [ (self.output_node, write_node, [("roi_output", "@roi_output")]) ] ) else: self.connect( [ (self.output_node, write_node, [("output_pt_file", "@output_pt_file")]) ] ) mod_subfolder = "" if self.parameters.get("modality") == "t1-linear": mod_subfolder = "t1_linear" if self.parameters.get("modality") == "t1-extensive": mod_subfolder = "t1_extensive" if self.parameters.get("modality") == "pet-linear": mod_subfolder = "pet_linear" if self.parameters.get("modality") == "custom": mod_subfolder = "custom" self.connect( [ (container_path, write_node, [ (("container", fix_join, "deeplearning_prepare_data", subfolder, mod_subfolder), "container")]), ] ) def build_core_nodes(self): import nipype.interfaces.utility as nutil import nipype.pipeline.engine as npe from .deeplearning_prepare_data_utils import ( extract_patches, extract_roi, extract_slices, save_as_pt, ) save_as_pt = npe.MapNode( name="save_as_pt", iterfield=["input_img"], interface=nutil.Function( function=save_as_pt, input_names=["input_img"], output_names=["output_file"], ), ) extract_slices_node = npe.MapNode( name="extract_slices", iterfield=["input_tensor"], interface=nutil.Function( function=extract_slices, input_names=["input_tensor", "slice_direction", "slice_mode"], output_names=["output_file_rgb", "output_file_original"], ), ) extract_slices_node.inputs.slice_direction = self.slice_direction extract_slices_node.inputs.slice_mode = self.slice_mode extract_patches_node = npe.MapNode( name="extract_patches", iterfield=["input_tensor"], interface=nutil.Function( function=extract_patches, input_names=["input_tensor", "patch_size", "stride_size"], output_names=["output_patch"], ), ) extract_patches_node.inputs.patch_size = self.patch_size extract_patches_node.inputs.stride_size = self.stride_size extract_roi_node = npe.MapNode( name="extract_ROI", iterfield=["input_tensor"], interface=nutil.Function( function=extract_roi, input_names=[ "input_tensor", "masks_location", "mask_pattern", "cropped_input", "roi_list", "uncrop_output", ], output_names=["output_roi"], ), ) extract_roi_node.inputs.masks_location = self.masks_location extract_roi_node.inputs.mask_pattern = self.mask_pattern extract_roi_node.inputs.cropped_input = not self.parameters.get( "use_uncropped_image" ) extract_roi_node.inputs.roi_list = self.roi_list extract_roi_node.inputs.uncrop_output = self.parameters.get("roi_uncrop_output") self.connect( [ (self.input_node, save_as_pt, [("input_nifti", "input_img")]), ] ) if self.parameters.get("extract_method") == "slice": self.connect( [ (save_as_pt, extract_slices_node, [("output_file", "input_tensor")]), (extract_slices_node, self.output_node, [("output_file_rgb", "slices_rgb_output")]), (extract_slices_node, self.output_node, [("output_file_original", "slices_original_output")]), ] ) elif self.parameters.get("extract_method") == "patch": self.connect( [ (save_as_pt, extract_patches_node, [("output_file", "input_tensor")]), (extract_patches_node, self.output_node, [("output_patch", "patches_output")]), ] ) elif self.parameters.get("extract_method") == "roi": self.connect( [ (save_as_pt, extract_roi_node, [("output_file", "input_tensor")]), (extract_roi_node, self.output_node, [("output_roi", "roi_output")]), ] ) else: self.connect( [ (save_as_pt, self.output_node, [("output_file", "output_pt_file")]), ] )

true

f71be64382d9c7cd419601ae2aa3d28d776baa3a

2,261

py

Python

scripts/xml2txt.py

o8r/pytorch_cpp

70ba1e64270da6d870847c074ce33afb154f1ef8

[ "MIT" ]

181

2020-03-26T12:33:25.000Z

2022-03-28T04:04:25.000Z

scripts/xml2txt.py

o8r/pytorch_cpp

70ba1e64270da6d870847c074ce33afb154f1ef8

[ "MIT" ]

11

2020-07-26T13:18:50.000Z

2022-01-09T10:04:10.000Z

scripts/xml2txt.py

o8r/pytorch_cpp

70ba1e64270da6d870847c074ce33afb154f1ef8

[ "MIT" ]

38

2020-05-04T05:06:55.000Z

2022-03-29T19:10:51.000Z

import os import glob import argparse import xml.etree.ElementTree as ET parser = argparse.ArgumentParser() # Define parameter parser.add_argument('--input_dir', type=str) parser.add_argument('--output_dir', type=str) parser.add_argument('--class_list', type=str) args = parser.parse_args() # Set Class Names def set_class_names(class_list): f = open(class_list, mode='r') class_names = [] while True: line = f.readline().strip() if not line: break class_names += [line] f.close() return class_names # Normalize Bounding Box def normalizeBB(x_min, x_max, y_min, y_max, width, height): x_center = (x_min + x_max) * 0.5 / float(width) y_center = (y_min + y_max) * 0.5 / float(height) x_range = (x_max - x_min) / float(width) y_range = (y_max - y_min) / float(height) return x_center, y_center, x_range, y_range # Convert XML into TXT def convertXML2TXT(class_names, pathI, pathO): fileI = open(pathI, mode='r') fileO = open(pathO, mode='w') tree = ET.parse(fileI) root = tree.getroot() size = root.find('size') width = int(size.find('width').text) height = int(size.find('height').text) for obj in root.iter('object'): class_name = obj.find('name').text class_id = class_names.index(class_name) BB = obj.find('bndbox') x_min = float(BB.find('xmin').text) x_max = float(BB.find('xmax').text) y_min = float(BB.find('ymin').text) y_max = float(BB.find('ymax').text) x_center, y_center, x_range, y_range = normalizeBB(x_min, x_max, y_min, y_max, width, height) fileO.write(f'{class_id} {x_center} {y_center} {x_range} {y_range}\n') fileI.close() fileO.close() if __name__ == '__main__': # Get File Names fnames = [] for f in glob.glob(f'{args.input_dir}/*.xml'): fnames.append(os.path.splitext(os.path.split(f)[1])[0]) # Set Class Names class_names = set_class_names(args.class_list) # Convert XML into TXT os.makedirs(f'{args.output_dir}', exist_ok=False) for f in fnames: pathI = f'{args.input_dir}/{f}.xml' pathO = f'{args.output_dir}/{f}.txt' convertXML2TXT(class_names, pathI, pathO)

27.91358

101

0.632906

import os import glob import argparse import xml.etree.ElementTree as ET parser = argparse.ArgumentParser() parser.add_argument('--input_dir', type=str) parser.add_argument('--output_dir', type=str) parser.add_argument('--class_list', type=str) args = parser.parse_args() def set_class_names(class_list): f = open(class_list, mode='r') class_names = [] while True: line = f.readline().strip() if not line: break class_names += [line] f.close() return class_names def normalizeBB(x_min, x_max, y_min, y_max, width, height): x_center = (x_min + x_max) * 0.5 / float(width) y_center = (y_min + y_max) * 0.5 / float(height) x_range = (x_max - x_min) / float(width) y_range = (y_max - y_min) / float(height) return x_center, y_center, x_range, y_range def convertXML2TXT(class_names, pathI, pathO): fileI = open(pathI, mode='r') fileO = open(pathO, mode='w') tree = ET.parse(fileI) root = tree.getroot() size = root.find('size') width = int(size.find('width').text) height = int(size.find('height').text) for obj in root.iter('object'): class_name = obj.find('name').text class_id = class_names.index(class_name) BB = obj.find('bndbox') x_min = float(BB.find('xmin').text) x_max = float(BB.find('xmax').text) y_min = float(BB.find('ymin').text) y_max = float(BB.find('ymax').text) x_center, y_center, x_range, y_range = normalizeBB(x_min, x_max, y_min, y_max, width, height) fileO.write(f'{class_id} {x_center} {y_center} {x_range} {y_range}\n') fileI.close() fileO.close() if __name__ == '__main__': fnames = [] for f in glob.glob(f'{args.input_dir}/*.xml'): fnames.append(os.path.splitext(os.path.split(f)[1])[0]) class_names = set_class_names(args.class_list) os.makedirs(f'{args.output_dir}', exist_ok=False) for f in fnames: pathI = f'{args.input_dir}/{f}.xml' pathO = f'{args.output_dir}/{f}.txt' convertXML2TXT(class_names, pathI, pathO)

true

f71be6b44b2393e0c5756152ad6854307836bc3b

20,079

py

Python

tests/test_data/test_datasets/test_kitti_dataset.py

ammaryasirnaich/mmdetection3d

5e549546abbb2a7b43aab59e40e87599f61dcc4a

[ "Apache-2.0" ]

null

tests/test_data/test_datasets/test_kitti_dataset.py

ammaryasirnaich/mmdetection3d

5e549546abbb2a7b43aab59e40e87599f61dcc4a

[ "Apache-2.0" ]

null

tests/test_data/test_datasets/test_kitti_dataset.py

ammaryasirnaich/mmdetection3d

5e549546abbb2a7b43aab59e40e87599f61dcc4a

[ "Apache-2.0" ]

null

# Copyright (c) OpenMMLab. All rights reserved. import math import os import tempfile import numpy as np import pytest import torch from mmdet3d.core.bbox import LiDARInstance3DBoxes, limit_period from mmdet3d.datasets import KittiDataset def _generate_kitti_dataset_config(): data_root = 'tests/data/kitti' ann_file = 'tests/data/kitti/kitti_infos_train.pkl' classes = ['Pedestrian', 'Cyclist', 'Car'] pts_prefix = 'velodyne_reduced' pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[1.0, 1.0], translation_std=[0, 0, 0]), dict(type='RandomFlip3D'), dict( type='PointsRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points']) ]) ] modality = dict(use_lidar=True, use_camera=False) split = 'training' return data_root, ann_file, classes, pts_prefix, pipeline, modality, split def _generate_kitti_multi_modality_dataset_config(): data_root = 'tests/data/kitti' ann_file = 'tests/data/kitti/kitti_infos_train.pkl' classes = ['Pedestrian', 'Cyclist', 'Car'] pts_prefix = 'velodyne_reduced' img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='Resize', multiscale_mode='value', keep_ratio=True), dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[1., 1.], translation_std=[0, 0, 0]), dict(type='RandomFlip3D'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict( type='PointsRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points', 'img']) ]) ] modality = dict(use_lidar=True, use_camera=True) split = 'training' return data_root, ann_file, classes, pts_prefix, pipeline, modality, split def test_getitem(): np.random.seed(0) data_root, ann_file, classes, pts_prefix, \ _, modality, split = _generate_kitti_dataset_config() pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, file_client_args=dict(backend='disk')), dict( type='ObjectSample', db_sampler=dict( data_root='tests/data/kitti/', # in coordinate system refactor, this test file is modified info_path='tests/data/kitti/kitti_dbinfos_train.pkl', rate=1.0, prepare=dict( filter_by_difficulty=[-1], filter_by_min_points=dict(Pedestrian=10)), classes=['Pedestrian', 'Cyclist', 'Car'], sample_groups=dict(Pedestrian=6))), dict( type='ObjectNoise', num_try=100, translation_std=[1.0, 1.0, 0.5], global_rot_range=[0.0, 0.0], rot_range=[-0.78539816, 0.78539816]), dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5), dict( type='GlobalRotScaleTrans', rot_range=[-0.78539816, 0.78539816], scale_ratio_range=[0.95, 1.05]), dict( type='PointsRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict( type='ObjectRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict(type='PointShuffle'), dict( type='DefaultFormatBundle3D', class_names=['Pedestrian', 'Cyclist', 'Car']), dict( type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']) ] kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) data = kitti_dataset[0] points = data['points']._data gt_bboxes_3d = data['gt_bboxes_3d']._data gt_labels_3d = data['gt_labels_3d']._data expected_gt_bboxes_3d = torch.tensor( [[9.5081, -5.2269, -1.1370, 1.2288, 0.4915, 1.9353, 1.9988]]) expected_gt_labels_3d = torch.tensor([0]) rot_matrix = data['img_metas']._data['pcd_rotation'] rot_angle = data['img_metas']._data['pcd_rotation_angle'] horizontal_flip = data['img_metas']._data['pcd_horizontal_flip'] vertical_flip = data['img_metas']._data['pcd_vertical_flip'] expected_rot_matrix = torch.tensor([[0.8018, 0.5976, 0.0000], [-0.5976, 0.8018, 0.0000], [0.0000, 0.0000, 1.0000]]) expected_rot_angle = 0.6404654291602163 noise_angle = 0.20247319 assert torch.allclose(expected_rot_matrix, rot_matrix, atol=1e-4) assert math.isclose(expected_rot_angle, rot_angle, abs_tol=1e-4) assert horizontal_flip is True assert vertical_flip is False # after coord system refactor expected_gt_bboxes_3d[:, :3] = \ expected_gt_bboxes_3d[:, :3] @ rot_matrix @ rot_matrix expected_gt_bboxes_3d[:, -1:] = -np.pi - expected_gt_bboxes_3d[:, -1:] \ + 2 * rot_angle - 2 * noise_angle expected_gt_bboxes_3d[:, -1:] = limit_period( expected_gt_bboxes_3d[:, -1:], period=np.pi * 2) assert points.shape == (780, 4) assert torch.allclose( gt_bboxes_3d.tensor, expected_gt_bboxes_3d, atol=1e-4) assert torch.all(gt_labels_3d == expected_gt_labels_3d) # test multi-modality KITTI dataset np.random.seed(0) point_cloud_range = [0, -40, -3, 70.4, 40, 1] img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) multi_modality_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict(type='LoadImageFromFile'), dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), dict( type='Resize', img_scale=[(640, 192), (2560, 768)], multiscale_mode='range', keep_ratio=True), dict( type='GlobalRotScaleTrans', rot_range=[-0.78539816, 0.78539816], scale_ratio_range=[0.95, 1.05], translation_std=[0.2, 0.2, 0.2]), dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5), dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range), dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range), dict(type='PointShuffle'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle3D', class_names=classes), dict( type='Collect3D', keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d']), ] modality = dict(use_lidar=True, use_camera=True) kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, multi_modality_pipeline, classes, modality) data = kitti_dataset[0] img = data['img']._data lidar2img = data['img_metas']._data['lidar2img'] expected_lidar2img = np.array( [[6.02943726e+02, -7.07913330e+02, -1.22748432e+01, -1.70942719e+02], [1.76777252e+02, 8.80879879e+00, -7.07936157e+02, -1.02568634e+02], [9.99984801e-01, -1.52826728e-03, -5.29071223e-03, -3.27567995e-01], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) assert img.shape[:] == (3, 416, 1344) assert np.allclose(lidar2img, expected_lidar2img) def test_evaluate(): if not torch.cuda.is_available(): pytest.skip('test requires GPU and torch+cuda') data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])) labels_3d = torch.tensor([ 0, ]) scores_3d = torch.tensor([0.5]) metric = ['mAP'] result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) ap_dict = kitti_dataset.evaluate([result], metric) assert np.isclose(ap_dict['KITTI/Overall_3D_AP11_easy'], 3.0303030303030307) assert np.isclose(ap_dict['KITTI/Overall_3D_AP11_moderate'], 3.0303030303030307) assert np.isclose(ap_dict['KITTI/Overall_3D_AP11_hard'], 3.0303030303030307) def test_show(): from os import path as osp import mmcv from mmdet3d.core.bbox import LiDARInstance3DBoxes tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset( data_root, ann_file, split=split, modality=modality, pipeline=pipeline) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[46.1218, -4.6496, -0.9275, 0.5316, 1.4442, 1.7450, 1.1749], [33.3189, 0.1981, 0.3136, 0.5656, 1.2301, 1.7985, 1.5723], [46.1366, -4.6404, -0.9510, 0.5162, 1.6501, 1.7540, 1.3778], [33.2646, 0.2297, 0.3446, 0.5746, 1.3365, 1.7947, 1.5430], [58.9079, 16.6272, -1.5829, 1.5656, 3.9313, 1.4899, 1.5505]])) scores_3d = torch.tensor([0.1815, 0.1663, 0.5792, 0.2194, 0.2780]) labels_3d = torch.tensor([0, 0, 1, 1, 2]) result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d) results = [result] kitti_dataset.show(results, temp_dir, show=False) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) tmp_dir.cleanup() # test show with pipeline eval_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points']) ] tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) tmp_dir.cleanup() # test multi-modality show tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name _, _, _, _, multi_modality_pipeline, modality, _ = \ _generate_kitti_multi_modality_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, multi_modality_pipeline, classes, modality) kitti_dataset.show(results, temp_dir, show=False) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') img_file_path = osp.join(temp_dir, '000000', '000000_img.png') img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png') img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) mmcv.check_file_exist(img_file_path) mmcv.check_file_exist(img_pred_path) mmcv.check_file_exist(img_gt_file) tmp_dir.cleanup() # test multi-modality show with pipeline eval_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict(type='LoadImageFromFile'), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points', 'img']) ] tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') img_file_path = osp.join(temp_dir, '000000', '000000_img.png') img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png') img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) mmcv.check_file_exist(img_file_path) mmcv.check_file_exist(img_pred_path) mmcv.check_file_exist(img_gt_file) tmp_dir.cleanup() def test_format_results(): from mmdet3d.core.bbox import LiDARInstance3DBoxes data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) # coord system refactor boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 1.2000, 0.4800, 1.8900, -1.5808]])) labels_3d = torch.tensor([ 0, ]) scores_3d = torch.tensor([0.5]) result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) results = [result] result_files, tmp_dir = kitti_dataset.format_results(results) expected_name = np.array(['Pedestrian']) expected_truncated = np.array([0.]) expected_occluded = np.array([0]) # coord sys refactor expected_alpha = np.array(-3.3410306 + np.pi) expected_bbox = np.array([[710.443, 144.00221, 820.29114, 307.58667]]) expected_dimensions = np.array([[1.2, 1.89, 0.48]]) expected_location = np.array([[1.8399826, 1.4700007, 8.410018]]) expected_rotation_y = np.array([0.0100]) expected_score = np.array([0.5]) expected_sample_idx = np.array([0]) assert np.all(result_files[0]['name'] == expected_name) assert np.allclose(result_files[0]['truncated'], expected_truncated) assert np.all(result_files[0]['occluded'] == expected_occluded) assert np.allclose(result_files[0]['alpha'], expected_alpha, 1e-3) assert np.allclose(result_files[0]['bbox'], expected_bbox) assert np.allclose(result_files[0]['dimensions'], expected_dimensions) assert np.allclose(result_files[0]['location'], expected_location) assert np.allclose(result_files[0]['rotation_y'], expected_rotation_y, 1e-3) assert np.allclose(result_files[0]['score'], expected_score) assert np.allclose(result_files[0]['sample_idx'], expected_sample_idx) tmp_dir.cleanup() def test_bbox2result_kitti(): data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 1.2000, 0.4800, 1.8900, -1.5808]])) labels_3d = torch.tensor([ 0, ]) scores_3d = torch.tensor([0.5]) result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) results = [result] tmp_dir = tempfile.TemporaryDirectory() temp_kitti_result_dir = tmp_dir.name det_annos = kitti_dataset.bbox2result_kitti( results, classes, submission_prefix=temp_kitti_result_dir) expected_file_path = os.path.join(temp_kitti_result_dir, '000000.txt') expected_name = np.array(['Pedestrian']) expected_dimensions = np.array([1.2000, 1.8900, 0.4800]) # coord system refactor (reverse sign) expected_rotation_y = 0.0100 expected_score = np.array([0.5]) assert np.all(det_annos[0]['name'] == expected_name) assert np.allclose(det_annos[0]['rotation_y'], expected_rotation_y, 1e-3) assert np.allclose(det_annos[0]['score'], expected_score) assert np.allclose(det_annos[0]['dimensions'], expected_dimensions) assert os.path.exists(expected_file_path) tmp_dir.cleanup() tmp_dir = tempfile.TemporaryDirectory() temp_kitti_result_dir = tmp_dir.name boxes_3d = LiDARInstance3DBoxes(torch.tensor([])) labels_3d = torch.tensor([]) scores_3d = torch.tensor([]) empty_result = dict( boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) results = [empty_result] det_annos = kitti_dataset.bbox2result_kitti( results, classes, submission_prefix=temp_kitti_result_dir) expected_file_path = os.path.join(temp_kitti_result_dir, '000000.txt') assert os.path.exists(expected_file_path) tmp_dir.cleanup() def test_bbox2result_kitti2d(): data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) bboxes = np.array([[[46.1218, -4.6496, -0.9275, 0.5316, 0.5], [33.3189, 0.1981, 0.3136, 0.5656, 0.5]], [[46.1366, -4.6404, -0.9510, 0.5162, 0.5], [33.2646, 0.2297, 0.3446, 0.5746, 0.5]]]) det_annos = kitti_dataset.bbox2result_kitti2d([bboxes], classes) expected_name = np.array( ['Pedestrian', 'Pedestrian', 'Cyclist', 'Cyclist']) expected_bbox = np.array([[46.1218, -4.6496, -0.9275, 0.5316], [33.3189, 0.1981, 0.3136, 0.5656], [46.1366, -4.6404, -0.951, 0.5162], [33.2646, 0.2297, 0.3446, 0.5746]]) expected_score = np.array([0.5, 0.5, 0.5, 0.5]) assert np.all(det_annos[0]['name'] == expected_name) assert np.allclose(det_annos[0]['bbox'], expected_bbox) assert np.allclose(det_annos[0]['score'], expected_score)

41.91858

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0.619852

import math import os import tempfile import numpy as np import pytest import torch from mmdet3d.core.bbox import LiDARInstance3DBoxes, limit_period from mmdet3d.datasets import KittiDataset def _generate_kitti_dataset_config(): data_root = 'tests/data/kitti' ann_file = 'tests/data/kitti/kitti_infos_train.pkl' classes = ['Pedestrian', 'Cyclist', 'Car'] pts_prefix = 'velodyne_reduced' pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[1.0, 1.0], translation_std=[0, 0, 0]), dict(type='RandomFlip3D'), dict( type='PointsRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points']) ]) ] modality = dict(use_lidar=True, use_camera=False) split = 'training' return data_root, ann_file, classes, pts_prefix, pipeline, modality, split def _generate_kitti_multi_modality_dataset_config(): data_root = 'tests/data/kitti' ann_file = 'tests/data/kitti/kitti_infos_train.pkl' classes = ['Pedestrian', 'Cyclist', 'Car'] pts_prefix = 'velodyne_reduced' img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='Resize', multiscale_mode='value', keep_ratio=True), dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[1., 1.], translation_std=[0, 0, 0]), dict(type='RandomFlip3D'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict( type='PointsRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points', 'img']) ]) ] modality = dict(use_lidar=True, use_camera=True) split = 'training' return data_root, ann_file, classes, pts_prefix, pipeline, modality, split def test_getitem(): np.random.seed(0) data_root, ann_file, classes, pts_prefix, \ _, modality, split = _generate_kitti_dataset_config() pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, file_client_args=dict(backend='disk')), dict( type='ObjectSample', db_sampler=dict( data_root='tests/data/kitti/', info_path='tests/data/kitti/kitti_dbinfos_train.pkl', rate=1.0, prepare=dict( filter_by_difficulty=[-1], filter_by_min_points=dict(Pedestrian=10)), classes=['Pedestrian', 'Cyclist', 'Car'], sample_groups=dict(Pedestrian=6))), dict( type='ObjectNoise', num_try=100, translation_std=[1.0, 1.0, 0.5], global_rot_range=[0.0, 0.0], rot_range=[-0.78539816, 0.78539816]), dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5), dict( type='GlobalRotScaleTrans', rot_range=[-0.78539816, 0.78539816], scale_ratio_range=[0.95, 1.05]), dict( type='PointsRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict( type='ObjectRangeFilter', point_cloud_range=[0, -40, -3, 70.4, 40, 1]), dict(type='PointShuffle'), dict( type='DefaultFormatBundle3D', class_names=['Pedestrian', 'Cyclist', 'Car']), dict( type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']) ] kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) data = kitti_dataset[0] points = data['points']._data gt_bboxes_3d = data['gt_bboxes_3d']._data gt_labels_3d = data['gt_labels_3d']._data expected_gt_bboxes_3d = torch.tensor( [[9.5081, -5.2269, -1.1370, 1.2288, 0.4915, 1.9353, 1.9988]]) expected_gt_labels_3d = torch.tensor([0]) rot_matrix = data['img_metas']._data['pcd_rotation'] rot_angle = data['img_metas']._data['pcd_rotation_angle'] horizontal_flip = data['img_metas']._data['pcd_horizontal_flip'] vertical_flip = data['img_metas']._data['pcd_vertical_flip'] expected_rot_matrix = torch.tensor([[0.8018, 0.5976, 0.0000], [-0.5976, 0.8018, 0.0000], [0.0000, 0.0000, 1.0000]]) expected_rot_angle = 0.6404654291602163 noise_angle = 0.20247319 assert torch.allclose(expected_rot_matrix, rot_matrix, atol=1e-4) assert math.isclose(expected_rot_angle, rot_angle, abs_tol=1e-4) assert horizontal_flip is True assert vertical_flip is False expected_gt_bboxes_3d[:, :3] = \ expected_gt_bboxes_3d[:, :3] @ rot_matrix @ rot_matrix expected_gt_bboxes_3d[:, -1:] = -np.pi - expected_gt_bboxes_3d[:, -1:] \ + 2 * rot_angle - 2 * noise_angle expected_gt_bboxes_3d[:, -1:] = limit_period( expected_gt_bboxes_3d[:, -1:], period=np.pi * 2) assert points.shape == (780, 4) assert torch.allclose( gt_bboxes_3d.tensor, expected_gt_bboxes_3d, atol=1e-4) assert torch.all(gt_labels_3d == expected_gt_labels_3d) np.random.seed(0) point_cloud_range = [0, -40, -3, 70.4, 40, 1] img_norm_cfg = dict( mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) multi_modality_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict(type='LoadImageFromFile'), dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), dict( type='Resize', img_scale=[(640, 192), (2560, 768)], multiscale_mode='range', keep_ratio=True), dict( type='GlobalRotScaleTrans', rot_range=[-0.78539816, 0.78539816], scale_ratio_range=[0.95, 1.05], translation_std=[0.2, 0.2, 0.2]), dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5), dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range), dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range), dict(type='PointShuffle'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle3D', class_names=classes), dict( type='Collect3D', keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d']), ] modality = dict(use_lidar=True, use_camera=True) kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, multi_modality_pipeline, classes, modality) data = kitti_dataset[0] img = data['img']._data lidar2img = data['img_metas']._data['lidar2img'] expected_lidar2img = np.array( [[6.02943726e+02, -7.07913330e+02, -1.22748432e+01, -1.70942719e+02], [1.76777252e+02, 8.80879879e+00, -7.07936157e+02, -1.02568634e+02], [9.99984801e-01, -1.52826728e-03, -5.29071223e-03, -3.27567995e-01], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) assert img.shape[:] == (3, 416, 1344) assert np.allclose(lidar2img, expected_lidar2img) def test_evaluate(): if not torch.cuda.is_available(): pytest.skip('test requires GPU and torch+cuda') data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])) labels_3d = torch.tensor([ 0, ]) scores_3d = torch.tensor([0.5]) metric = ['mAP'] result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) ap_dict = kitti_dataset.evaluate([result], metric) assert np.isclose(ap_dict['KITTI/Overall_3D_AP11_easy'], 3.0303030303030307) assert np.isclose(ap_dict['KITTI/Overall_3D_AP11_moderate'], 3.0303030303030307) assert np.isclose(ap_dict['KITTI/Overall_3D_AP11_hard'], 3.0303030303030307) def test_show(): from os import path as osp import mmcv from mmdet3d.core.bbox import LiDARInstance3DBoxes tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset( data_root, ann_file, split=split, modality=modality, pipeline=pipeline) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[46.1218, -4.6496, -0.9275, 0.5316, 1.4442, 1.7450, 1.1749], [33.3189, 0.1981, 0.3136, 0.5656, 1.2301, 1.7985, 1.5723], [46.1366, -4.6404, -0.9510, 0.5162, 1.6501, 1.7540, 1.3778], [33.2646, 0.2297, 0.3446, 0.5746, 1.3365, 1.7947, 1.5430], [58.9079, 16.6272, -1.5829, 1.5656, 3.9313, 1.4899, 1.5505]])) scores_3d = torch.tensor([0.1815, 0.1663, 0.5792, 0.2194, 0.2780]) labels_3d = torch.tensor([0, 0, 1, 1, 2]) result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d) results = [result] kitti_dataset.show(results, temp_dir, show=False) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) tmp_dir.cleanup() eval_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points']) ] tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) tmp_dir.cleanup() tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name _, _, _, _, multi_modality_pipeline, modality, _ = \ _generate_kitti_multi_modality_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, multi_modality_pipeline, classes, modality) kitti_dataset.show(results, temp_dir, show=False) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') img_file_path = osp.join(temp_dir, '000000', '000000_img.png') img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png') img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) mmcv.check_file_exist(img_file_path) mmcv.check_file_exist(img_pred_path) mmcv.check_file_exist(img_gt_file) tmp_dir.cleanup() eval_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict(type='LoadImageFromFile'), dict( type='DefaultFormatBundle3D', class_names=classes, with_label=False), dict(type='Collect3D', keys=['points', 'img']) ] tmp_dir = tempfile.TemporaryDirectory() temp_dir = tmp_dir.name kitti_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline) pts_file_path = osp.join(temp_dir, '000000', '000000_points.obj') gt_file_path = osp.join(temp_dir, '000000', '000000_gt.obj') pred_file_path = osp.join(temp_dir, '000000', '000000_pred.obj') img_file_path = osp.join(temp_dir, '000000', '000000_img.png') img_pred_path = osp.join(temp_dir, '000000', '000000_pred.png') img_gt_file = osp.join(temp_dir, '000000', '000000_gt.png') mmcv.check_file_exist(pts_file_path) mmcv.check_file_exist(gt_file_path) mmcv.check_file_exist(pred_file_path) mmcv.check_file_exist(img_file_path) mmcv.check_file_exist(img_pred_path) mmcv.check_file_exist(img_gt_file) tmp_dir.cleanup() def test_format_results(): from mmdet3d.core.bbox import LiDARInstance3DBoxes data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 1.2000, 0.4800, 1.8900, -1.5808]])) labels_3d = torch.tensor([ 0, ]) scores_3d = torch.tensor([0.5]) result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) results = [result] result_files, tmp_dir = kitti_dataset.format_results(results) expected_name = np.array(['Pedestrian']) expected_truncated = np.array([0.]) expected_occluded = np.array([0]) expected_alpha = np.array(-3.3410306 + np.pi) expected_bbox = np.array([[710.443, 144.00221, 820.29114, 307.58667]]) expected_dimensions = np.array([[1.2, 1.89, 0.48]]) expected_location = np.array([[1.8399826, 1.4700007, 8.410018]]) expected_rotation_y = np.array([0.0100]) expected_score = np.array([0.5]) expected_sample_idx = np.array([0]) assert np.all(result_files[0]['name'] == expected_name) assert np.allclose(result_files[0]['truncated'], expected_truncated) assert np.all(result_files[0]['occluded'] == expected_occluded) assert np.allclose(result_files[0]['alpha'], expected_alpha, 1e-3) assert np.allclose(result_files[0]['bbox'], expected_bbox) assert np.allclose(result_files[0]['dimensions'], expected_dimensions) assert np.allclose(result_files[0]['location'], expected_location) assert np.allclose(result_files[0]['rotation_y'], expected_rotation_y, 1e-3) assert np.allclose(result_files[0]['score'], expected_score) assert np.allclose(result_files[0]['sample_idx'], expected_sample_idx) tmp_dir.cleanup() def test_bbox2result_kitti(): data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) boxes_3d = LiDARInstance3DBoxes( torch.tensor( [[8.7314, -1.8559, -1.5997, 1.2000, 0.4800, 1.8900, -1.5808]])) labels_3d = torch.tensor([ 0, ]) scores_3d = torch.tensor([0.5]) result = dict(boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) results = [result] tmp_dir = tempfile.TemporaryDirectory() temp_kitti_result_dir = tmp_dir.name det_annos = kitti_dataset.bbox2result_kitti( results, classes, submission_prefix=temp_kitti_result_dir) expected_file_path = os.path.join(temp_kitti_result_dir, '000000.txt') expected_name = np.array(['Pedestrian']) expected_dimensions = np.array([1.2000, 1.8900, 0.4800]) expected_rotation_y = 0.0100 expected_score = np.array([0.5]) assert np.all(det_annos[0]['name'] == expected_name) assert np.allclose(det_annos[0]['rotation_y'], expected_rotation_y, 1e-3) assert np.allclose(det_annos[0]['score'], expected_score) assert np.allclose(det_annos[0]['dimensions'], expected_dimensions) assert os.path.exists(expected_file_path) tmp_dir.cleanup() tmp_dir = tempfile.TemporaryDirectory() temp_kitti_result_dir = tmp_dir.name boxes_3d = LiDARInstance3DBoxes(torch.tensor([])) labels_3d = torch.tensor([]) scores_3d = torch.tensor([]) empty_result = dict( boxes_3d=boxes_3d, labels_3d=labels_3d, scores_3d=scores_3d) results = [empty_result] det_annos = kitti_dataset.bbox2result_kitti( results, classes, submission_prefix=temp_kitti_result_dir) expected_file_path = os.path.join(temp_kitti_result_dir, '000000.txt') assert os.path.exists(expected_file_path) tmp_dir.cleanup() def test_bbox2result_kitti2d(): data_root, ann_file, classes, pts_prefix, \ pipeline, modality, split = _generate_kitti_dataset_config() kitti_dataset = KittiDataset(data_root, ann_file, split, pts_prefix, pipeline, classes, modality) bboxes = np.array([[[46.1218, -4.6496, -0.9275, 0.5316, 0.5], [33.3189, 0.1981, 0.3136, 0.5656, 0.5]], [[46.1366, -4.6404, -0.9510, 0.5162, 0.5], [33.2646, 0.2297, 0.3446, 0.5746, 0.5]]]) det_annos = kitti_dataset.bbox2result_kitti2d([bboxes], classes) expected_name = np.array( ['Pedestrian', 'Pedestrian', 'Cyclist', 'Cyclist']) expected_bbox = np.array([[46.1218, -4.6496, -0.9275, 0.5316], [33.3189, 0.1981, 0.3136, 0.5656], [46.1366, -4.6404, -0.951, 0.5162], [33.2646, 0.2297, 0.3446, 0.5746]]) expected_score = np.array([0.5, 0.5, 0.5, 0.5]) assert np.all(det_annos[0]['name'] == expected_name) assert np.allclose(det_annos[0]['bbox'], expected_bbox) assert np.allclose(det_annos[0]['score'], expected_score)

true

f71be6eab3547a134de4d6606f48330f24f83fbe

8,080

py

Python

scripts/build_load_data.py

millingermarkus/pypsa-eur

2e39a21299036c0cec86fe4707de06a42ec15d62

[ "MIT" ]

null

scripts/build_load_data.py

millingermarkus/pypsa-eur

2e39a21299036c0cec86fe4707de06a42ec15d62

[ "MIT" ]

null

scripts/build_load_data.py

millingermarkus/pypsa-eur

2e39a21299036c0cec86fe4707de06a42ec15d62

[ "MIT" ]

null

# SPDX-FileCopyrightText: : 2020 @JanFrederickUnnewehr, The PyPSA-Eur Authors # # SPDX-License-Identifier: MIT """ This rule downloads the load data from `Open Power System Data Time series <https://data.open-power-system-data.org/time_series/>`_. For all countries in the network, the per country load timeseries with suffix ``_load_actual_entsoe_transparency`` are extracted from the dataset. After filling small gaps linearly and large gaps by copying time-slice of a given period, the load data is exported to a ``.csv`` file. Relevant Settings ----------------- .. code:: yaml snapshots: load: interpolate_limit: time_shift_for_large_gaps: manual_adjustments: .. seealso:: Documentation of the configuration file ``config.yaml`` at :ref:`load_cf` Inputs ------ Outputs ------- - ``resource/time_series_60min_singleindex_filtered.csv``: """ import logging logger = logging.getLogger(__name__) from _helpers import configure_logging import pandas as pd import numpy as np import dateutil from pandas import Timedelta as Delta def load_timeseries(fn, years, countries, powerstatistics=True): """ Read load data from OPSD time-series package version 2020-10-06. Parameters ---------- years : None or slice() Years for which to read load data (defaults to slice("2018","2019")) fn : str File name or url location (file format .csv) countries : listlike Countries for which to read load data. powerstatistics: bool Whether the electricity consumption data of the ENTSOE power statistics (if true) or of the ENTSOE transparency map (if false) should be parsed. Returns ------- load : pd.DataFrame Load time-series with UTC timestamps x ISO-2 countries """ logger.info(f"Retrieving load data from '{fn}'.") pattern = 'power_statistics' if powerstatistics else '_transparency' pattern = f'_load_actual_entsoe_{pattern}' rename = lambda s: s[:-len(pattern)] date_parser = lambda x: dateutil.parser.parse(x, ignoretz=True) return (pd.read_csv(fn, index_col=0, parse_dates=[0], date_parser=date_parser) .filter(like=pattern) .rename(columns=rename) .dropna(how="all", axis=0) .rename(columns={'GB_UKM' : 'GB'}) .filter(items=countries) .loc[years]) def consecutive_nans(ds): return (ds.isnull().astype(int) .groupby(ds.notnull().astype(int).cumsum()[ds.isnull()]) .transform('sum').fillna(0)) def fill_large_gaps(ds, shift): """ Fill up large gaps with load data from the previous week. This function fills gaps ragning from 3 to 168 hours (one week). """ shift = Delta(shift) nhours = shift / np.timedelta64(1, 'h') if (consecutive_nans(ds) > nhours).any(): logger.warning('There exist gaps larger then the time shift used for ' 'copying time slices.') time_shift = pd.Series(ds.values, ds.index + shift) return ds.where(ds.notnull(), time_shift.reindex_like(ds)) def nan_statistics(df): def max_consecutive_nans(ds): return (ds.isnull().astype(int) .groupby(ds.notnull().astype(int).cumsum()) .sum().max()) consecutive = df.apply(max_consecutive_nans) total = df.isnull().sum() max_total_per_month = df.isnull().resample('m').sum().max() return pd.concat([total, consecutive, max_total_per_month], keys=['total', 'consecutive', 'max_total_per_month'], axis=1) def copy_timeslice(load, cntry, start, stop, delta): start = pd.Timestamp(start) stop = pd.Timestamp(stop) if start-delta in load.index and stop in load.index and cntry in load: load.loc[start:stop, cntry] = load.loc[start-delta:stop-delta, cntry].values def manual_adjustment(load, powerstatistics): """ Adjust gaps manual for load data from OPSD time-series package. 1. For the ENTSOE power statistics load data (if powerstatistics is True) Kosovo (KV) and Albania (AL) do not exist in the data set. Kosovo gets the same load curve as Serbia and Albania the same as Macdedonia, both scaled by the corresponding ratio of total energy consumptions reported by IEA Data browser [0] for the year 2013. 2. For the ENTSOE transparency load data (if powerstatistics is False) Albania (AL) and Macedonia (MK) do not exist in the data set. Both get the same load curve as Montenegro, scaled by the corresponding ratio of total energy consumptions reported by IEA Data browser [0] for the year 2016. [0] https://www.iea.org/data-and-statistics?country=WORLD&fuel=Electricity%20and%20heat&indicator=TotElecCons Parameters ---------- load : pd.DataFrame Load time-series with UTC timestamps x ISO-2 countries powerstatistics: bool Whether argument load comprises the electricity consumption data of the ENTSOE power statistics or of the ENTSOE transparency map Returns ------- load : pd.DataFrame Manual adjusted and interpolated load time-series with UTC timestamps x ISO-2 countries """ if powerstatistics: if 'MK' in load.columns: if 'AL' not in load.columns or load.AL.isnull().values.all(): load['AL'] = load['MK'] * (4.1 / 7.4) if 'RS' in load.columns: if 'KV' not in load.columns or load.KV.isnull().values.all(): load['KV'] = load['RS'] * (4.8 / 27.) copy_timeslice(load, 'GR', '2015-08-11 21:00', '2015-08-15 20:00', Delta(weeks=1)) copy_timeslice(load, 'AT', '2018-12-31 22:00', '2019-01-01 22:00', Delta(days=2)) copy_timeslice(load, 'CH', '2010-01-19 07:00', '2010-01-19 22:00', Delta(days=1)) copy_timeslice(load, 'CH', '2010-03-28 00:00', '2010-03-28 21:00', Delta(days=1)) # is a WE, so take WE before copy_timeslice(load, 'CH', '2010-10-08 13:00', '2010-10-10 21:00', Delta(weeks=1)) copy_timeslice(load, 'CH', '2010-11-04 04:00', '2010-11-04 22:00', Delta(days=1)) copy_timeslice(load, 'NO', '2010-12-09 11:00', '2010-12-09 18:00', Delta(days=1)) # whole january missing copy_timeslice(load, 'GB', '2009-12-31 23:00', '2010-01-31 23:00', Delta(days=-364)) else: if 'ME' in load: if 'AL' not in load and 'AL' in countries: load['AL'] = load.ME * (5.7/2.9) if 'MK' not in load and 'MK' in countries: load['MK'] = load.ME * (6.7/2.9) copy_timeslice(load, 'BG', '2018-10-27 21:00', '2018-10-28 22:00', Delta(weeks=1)) return load if __name__ == "__main__": if 'snakemake' not in globals(): from _helpers import mock_snakemake snakemake = mock_snakemake('build_load_data') configure_logging(snakemake) config = snakemake.config powerstatistics = config['load']['power_statistics'] interpolate_limit = config['load']['interpolate_limit'] countries = config['countries'] snapshots = pd.date_range(freq='h', **config['snapshots']) years = slice(snapshots[0], snapshots[-1]) time_shift = config['load']['time_shift_for_large_gaps'] load = load_timeseries(snakemake.input[0], years, countries, powerstatistics) if config['load']['manual_adjustments']: load = manual_adjustment(load, powerstatistics) logger.info(f"Linearly interpolate gaps of size {interpolate_limit} and less.") load = load.interpolate(method='linear', limit=interpolate_limit) logger.info("Filling larger gaps by copying time-slices of period " f"'{time_shift}'.") load = load.apply(fill_large_gaps, shift=time_shift) assert not load.isna().any().any(), ( 'Load data contains nans. Adjust the parameters ' '`time_shift_for_large_gaps` or modify the `manual_adjustment` function ' 'for implementing the needed load data modifications.') load.to_csv(snakemake.output[0])

35.752212

415

0.653713

import logging logger = logging.getLogger(__name__) from _helpers import configure_logging import pandas as pd import numpy as np import dateutil from pandas import Timedelta as Delta def load_timeseries(fn, years, countries, powerstatistics=True): logger.info(f"Retrieving load data from '{fn}'.") pattern = 'power_statistics' if powerstatistics else '_transparency' pattern = f'_load_actual_entsoe_{pattern}' rename = lambda s: s[:-len(pattern)] date_parser = lambda x: dateutil.parser.parse(x, ignoretz=True) return (pd.read_csv(fn, index_col=0, parse_dates=[0], date_parser=date_parser) .filter(like=pattern) .rename(columns=rename) .dropna(how="all", axis=0) .rename(columns={'GB_UKM' : 'GB'}) .filter(items=countries) .loc[years]) def consecutive_nans(ds): return (ds.isnull().astype(int) .groupby(ds.notnull().astype(int).cumsum()[ds.isnull()]) .transform('sum').fillna(0)) def fill_large_gaps(ds, shift): shift = Delta(shift) nhours = shift / np.timedelta64(1, 'h') if (consecutive_nans(ds) > nhours).any(): logger.warning('There exist gaps larger then the time shift used for ' 'copying time slices.') time_shift = pd.Series(ds.values, ds.index + shift) return ds.where(ds.notnull(), time_shift.reindex_like(ds)) def nan_statistics(df): def max_consecutive_nans(ds): return (ds.isnull().astype(int) .groupby(ds.notnull().astype(int).cumsum()) .sum().max()) consecutive = df.apply(max_consecutive_nans) total = df.isnull().sum() max_total_per_month = df.isnull().resample('m').sum().max() return pd.concat([total, consecutive, max_total_per_month], keys=['total', 'consecutive', 'max_total_per_month'], axis=1) def copy_timeslice(load, cntry, start, stop, delta): start = pd.Timestamp(start) stop = pd.Timestamp(stop) if start-delta in load.index and stop in load.index and cntry in load: load.loc[start:stop, cntry] = load.loc[start-delta:stop-delta, cntry].values def manual_adjustment(load, powerstatistics): if powerstatistics: if 'MK' in load.columns: if 'AL' not in load.columns or load.AL.isnull().values.all(): load['AL'] = load['MK'] * (4.1 / 7.4) if 'RS' in load.columns: if 'KV' not in load.columns or load.KV.isnull().values.all(): load['KV'] = load['RS'] * (4.8 / 27.) copy_timeslice(load, 'GR', '2015-08-11 21:00', '2015-08-15 20:00', Delta(weeks=1)) copy_timeslice(load, 'AT', '2018-12-31 22:00', '2019-01-01 22:00', Delta(days=2)) copy_timeslice(load, 'CH', '2010-01-19 07:00', '2010-01-19 22:00', Delta(days=1)) copy_timeslice(load, 'CH', '2010-03-28 00:00', '2010-03-28 21:00', Delta(days=1)) copy_timeslice(load, 'CH', '2010-10-08 13:00', '2010-10-10 21:00', Delta(weeks=1)) copy_timeslice(load, 'CH', '2010-11-04 04:00', '2010-11-04 22:00', Delta(days=1)) copy_timeslice(load, 'NO', '2010-12-09 11:00', '2010-12-09 18:00', Delta(days=1)) copy_timeslice(load, 'GB', '2009-12-31 23:00', '2010-01-31 23:00', Delta(days=-364)) else: if 'ME' in load: if 'AL' not in load and 'AL' in countries: load['AL'] = load.ME * (5.7/2.9) if 'MK' not in load and 'MK' in countries: load['MK'] = load.ME * (6.7/2.9) copy_timeslice(load, 'BG', '2018-10-27 21:00', '2018-10-28 22:00', Delta(weeks=1)) return load if __name__ == "__main__": if 'snakemake' not in globals(): from _helpers import mock_snakemake snakemake = mock_snakemake('build_load_data') configure_logging(snakemake) config = snakemake.config powerstatistics = config['load']['power_statistics'] interpolate_limit = config['load']['interpolate_limit'] countries = config['countries'] snapshots = pd.date_range(freq='h', **config['snapshots']) years = slice(snapshots[0], snapshots[-1]) time_shift = config['load']['time_shift_for_large_gaps'] load = load_timeseries(snakemake.input[0], years, countries, powerstatistics) if config['load']['manual_adjustments']: load = manual_adjustment(load, powerstatistics) logger.info(f"Linearly interpolate gaps of size {interpolate_limit} and less.") load = load.interpolate(method='linear', limit=interpolate_limit) logger.info("Filling larger gaps by copying time-slices of period " f"'{time_shift}'.") load = load.apply(fill_large_gaps, shift=time_shift) assert not load.isna().any().any(), ( 'Load data contains nans. Adjust the parameters ' '`time_shift_for_large_gaps` or modify the `manual_adjustment` function ' 'for implementing the needed load data modifications.') load.to_csv(snakemake.output[0])

true

f71be7330e1eaec34340d30bcdded8315ca8fdf3

5,076

py

Python

tests/unit/driver/generic/test_generic_sync_driver.py

haccht/scrapli

89589ee78c36296ee67813fcbedebee9b41b6bca

[ "MIT" ]

1

2020-02-09T17:43:43.000Z

tests/unit/driver/generic/test_generic_sync_driver.py

haccht/scrapli

89589ee78c36296ee67813fcbedebee9b41b6bca

[ "MIT" ]

null

tests/unit/driver/generic/test_generic_sync_driver.py

haccht/scrapli

89589ee78c36296ee67813fcbedebee9b41b6bca

[ "MIT" ]

null

import pytest from scrapli.driver.generic.base_driver import ReadCallback from scrapli.exceptions import ScrapliValueError def test_get_prompt(monkeypatch, sync_generic_driver): # stupid test w/ the patch, but want coverage and in the future maybe the driver actually # does something to the prompt it gets from the channel monkeypatch.setattr("scrapli.channel.sync_channel.Channel.get_prompt", lambda x: "scrapli>") assert sync_generic_driver.get_prompt() == "scrapli>" def test__send_command(monkeypatch, sync_generic_driver): monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_input", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver._send_command(command="nada") assert actual_response.failed is False assert actual_response.result == "processed" assert actual_response.raw_result == b"raw" def test__send_command_no_base_transport_args(sync_generic_driver): sync_generic_driver._base_transport_args = None with pytest.raises(ScrapliValueError): sync_generic_driver._send_command(command="nada") def test_send_command(monkeypatch, sync_generic_driver): monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_input", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver.send_command(command="nada") assert actual_response.failed is False assert actual_response.result == "processed" assert actual_response.raw_result == b"raw" def test_send_commands(monkeypatch, sync_generic_driver): monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_input", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver.send_commands(commands=["nada", "nada2"]) assert len(actual_response) == 2 assert actual_response.failed is False assert actual_response[0].failed is False assert actual_response[0].result == "processed" assert actual_response[0].raw_result == b"raw" def test_send_commands_from_file(fs, monkeypatch, real_ssh_commands_file_path, sync_generic_driver): fs.add_real_file(source_path=real_ssh_commands_file_path, target_path="/commands") monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_input", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver.send_commands_from_file(file="commands") assert actual_response.failed is False assert actual_response[0].result == "processed" assert actual_response[0].raw_result == b"raw" def test_send_and_read(monkeypatch, sync_generic_driver): monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_input_and_read", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver.send_and_read(channel_input="nada") assert actual_response.failed is False assert actual_response.result == "processed" assert actual_response.raw_result == b"raw" def test_send_and_read_no_base_transport_args(sync_generic_driver): sync_generic_driver._base_transport_args = None with pytest.raises(ScrapliValueError): sync_generic_driver.send_and_read(channel_input="nada") def test_send_interactive(monkeypatch, sync_generic_driver): monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_inputs_interact", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver.send_interactive(interact_events=[("nada", "scrapli>")]) assert actual_response.failed is False assert actual_response.result == "processed" assert actual_response.raw_result == b"raw" def test_send_interact_no_base_transport_args(sync_generic_driver): sync_generic_driver._base_transport_args = None with pytest.raises(ScrapliValueError): sync_generic_driver.send_interactive(interact_events=[]) def test_readcallback_basic(monkeypatch, sync_generic_driver): def _read(cls): return b"rtr1#" def _write(cls, channel_input, redacted=False): return monkeypatch.setattr("scrapli.channel.sync_channel.Channel.read", _read) monkeypatch.setattr("scrapli.channel.sync_channel.Channel.write", _write) callback_one_counter = 0 callback_two_counter = 0 def callback_one(cls, read_output): nonlocal callback_one_counter callback_one_counter += 1 def callback_two(cls, read_output): nonlocal callback_two_counter callback_two_counter += 1 callbacks = [ ReadCallback( contains="rtr1#", callback=callback_one, name="call1", case_insensitive=False, only_once=True, ), ReadCallback( contains_re=r"^rtr1#", callback=callback_two, complete=True, ), ] sync_generic_driver.read_callback(callbacks=callbacks, initial_input="nada") assert callback_one_counter == 1 assert callback_two_counter == 1

35.496503

100

0.731678

import pytest from scrapli.driver.generic.base_driver import ReadCallback from scrapli.exceptions import ScrapliValueError def test_get_prompt(monkeypatch, sync_generic_driver): monkeypatch.setattr("scrapli.channel.sync_channel.Channel.get_prompt", lambda x: "scrapli>") assert sync_generic_driver.get_prompt() == "scrapli>" def test__send_command(monkeypatch, sync_generic_driver): monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_input", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver._send_command(command="nada") assert actual_response.failed is False assert actual_response.result == "processed" assert actual_response.raw_result == b"raw" def test__send_command_no_base_transport_args(sync_generic_driver): sync_generic_driver._base_transport_args = None with pytest.raises(ScrapliValueError): sync_generic_driver._send_command(command="nada") def test_send_command(monkeypatch, sync_generic_driver): monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_input", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver.send_command(command="nada") assert actual_response.failed is False assert actual_response.result == "processed" assert actual_response.raw_result == b"raw" def test_send_commands(monkeypatch, sync_generic_driver): monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_input", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver.send_commands(commands=["nada", "nada2"]) assert len(actual_response) == 2 assert actual_response.failed is False assert actual_response[0].failed is False assert actual_response[0].result == "processed" assert actual_response[0].raw_result == b"raw" def test_send_commands_from_file(fs, monkeypatch, real_ssh_commands_file_path, sync_generic_driver): fs.add_real_file(source_path=real_ssh_commands_file_path, target_path="/commands") monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_input", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver.send_commands_from_file(file="commands") assert actual_response.failed is False assert actual_response[0].result == "processed" assert actual_response[0].raw_result == b"raw" def test_send_and_read(monkeypatch, sync_generic_driver): monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_input_and_read", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver.send_and_read(channel_input="nada") assert actual_response.failed is False assert actual_response.result == "processed" assert actual_response.raw_result == b"raw" def test_send_and_read_no_base_transport_args(sync_generic_driver): sync_generic_driver._base_transport_args = None with pytest.raises(ScrapliValueError): sync_generic_driver.send_and_read(channel_input="nada") def test_send_interactive(monkeypatch, sync_generic_driver): monkeypatch.setattr( "scrapli.channel.sync_channel.Channel.send_inputs_interact", lambda _, **kwargs: (b"raw", b"processed"), ) actual_response = sync_generic_driver.send_interactive(interact_events=[("nada", "scrapli>")]) assert actual_response.failed is False assert actual_response.result == "processed" assert actual_response.raw_result == b"raw" def test_send_interact_no_base_transport_args(sync_generic_driver): sync_generic_driver._base_transport_args = None with pytest.raises(ScrapliValueError): sync_generic_driver.send_interactive(interact_events=[]) def test_readcallback_basic(monkeypatch, sync_generic_driver): def _read(cls): return b"rtr1#" def _write(cls, channel_input, redacted=False): return monkeypatch.setattr("scrapli.channel.sync_channel.Channel.read", _read) monkeypatch.setattr("scrapli.channel.sync_channel.Channel.write", _write) callback_one_counter = 0 callback_two_counter = 0 def callback_one(cls, read_output): nonlocal callback_one_counter callback_one_counter += 1 def callback_two(cls, read_output): nonlocal callback_two_counter callback_two_counter += 1 callbacks = [ ReadCallback( contains="rtr1#", callback=callback_one, name="call1", case_insensitive=False, only_once=True, ), ReadCallback( contains_re=r"^rtr1#", callback=callback_two, complete=True, ), ] sync_generic_driver.read_callback(callbacks=callbacks, initial_input="nada") assert callback_one_counter == 1 assert callback_two_counter == 1

true

f71be7afa0a994c58e2f0076275e35e23af1c2a5

16,119

py

Python

utils_twc/kg.py

daiki-kimura/commonsense-rl

5513926957b6501ce9cfa46f77f8f2c1c4892fa5

[ "Apache-2.0" ]

null

utils_twc/kg.py

daiki-kimura/commonsense-rl

5513926957b6501ce9cfa46f77f8f2c1c4892fa5

[ "Apache-2.0" ]

null

utils_twc/kg.py

daiki-kimura/commonsense-rl

5513926957b6501ce9cfa46f77f8f2c1c4892fa5

[ "Apache-2.0" ]

null

import sys import networkx as nx import logging import json import requests from pathlib import Path import numpy as np import matplotlib.pyplot as plt from collections import defaultdict from utils_twc.generic import escape_entities # Logging formatting FORMAT = '%(asctime)s %(message)s' logging.basicConfig(format=FORMAT, level='INFO', stream=sys.stdout) kg = {} source_paths= defaultdict(dict) def shortest_path_subgraph(kg_graph, prev_graph, nodes, inventory_entities=None, command_entities=None, path_len=2, add_all_path=False): if inventory_entities is None: inventory_entities = [] if command_entities is None: command_entities = [] # Get non-neighbor nodes: nodes without edges between them world_graph = kg_graph.subgraph(list(prev_graph.nodes)+nodes).copy() world_graph = nx.compose(prev_graph,world_graph) world_graph.remove_edges_from(nx.selfloop_edges(world_graph)) if path_len < 2: return world_graph triplets = [] # Add command related relations pruned_entities = list(set(command_entities)-set(inventory_entities)) if pruned_entities: for src_et in inventory_entities: for tgt_et in pruned_entities: if src_et != tgt_et: try: pair_dist = nx.shortest_path_length(kg_graph, source=src_et, target=tgt_et) except nx.NetworkXNoPath: pair_dist = 0 if pair_dist >= 1 and pair_dist <= path_len: triplets.append([src_et, tgt_et, 'relatedTo']) else: # no items in the pruned entities, won't happen for entities in command_entities: for src_et in entities: for tgt_et in entities: if src_et != tgt_et: try: pair_dist = nx.shortest_path_length(kg_graph, source=src_et, target=tgt_et) except nx.NetworkXNoPath: pair_dist=0 if pair_dist >= 1 and pair_dist <= path_len: triplets.append([src_et, tgt_et, 'relatedTo']) world_graph, _= add_triplets_to_graph(world_graph, triplets) return world_graph def construct_graph(triplets): graph = nx.DiGraph() entities = {} for [e1, e2, r] in triplets: e1 = e1.lower().strip() e2 = e2.lower().strip() r = r.lower().strip() if e1 not in entities: graph.add_node(e1) entities[e1] = e1 if e2 not in entities: graph.add_node(e2) entities[e2] = e2 # Add Edge information if graph.has_edge(e1, e2): if r not in graph.edges[e1, e2]['relation']: graph.edges[e1, e2]['relation'] += ' ' + r else: graph.add_edge(e1, e2, relation=r) return graph, entities def add_triplets_to_graph(graph, triplets): entities = dict(graph.nodes.data()) for [e1, e2, r] in triplets: e1 = e1.lower().strip() e2 = e2.lower().strip() r = r.lower().strip() if e1 not in entities: graph.add_node(e1) entities[e1] = e1 if e2 not in entities: graph.add_node(e2) entities[e2] = e2 # Add Edge information if graph.has_edge(e1, e2): if r not in graph.edges[e1, e2]['relation']: graph.edges[e1, e2]['relation'] += ' ' + r else: graph.add_edge(e1, e2, relation=r) return graph, entities def draw_graph(graph, title="cleanup", show_relation=True, weights=None, pos=None): if not pos: pos = nx.spring_layout(graph, k=0.95) if weights: nx.draw(graph, pos, edge_color='black', width=1, linewidths=1, node_size=1000, node_color=weights.tolist(), vmin=np.min(weights), vmax=np.max(weights), node_shape='o', alpha=0.9, font_size=8, with_labels=True, label=title,cmap='Blues') else: nx.draw(graph, pos, edge_color='black', width=1, linewidths=1, node_size=1000, node_color='pink', node_shape='o', alpha=0.9, font_size=8, with_labels=True, label=title) if show_relation: p_edge = nx.draw_networkx_edge_labels(graph, pos, font_size=6, font_color='red', edge_labels=nx.get_edge_attributes(graph, 'relation')) def draw_graph_colormap(graph,node_weights, showbar=False, cmap='YlGnBu'): # node_weights: maps node id/name to attention weights pos = nx.spring_layout(graph, k=0.95) weights = [] for node in graph.nodes: weights.append(node_weights[node]) # cmap = plt.cm.YlGnBu#RdBu cmap = plt.get_cmap(cmap) vmin = np.min(weights) vmax = np.max(weights) nx.draw(graph, pos, edge_color='black', width=1, linewidths=1, node_size=1000, node_color=weights, vmin=vmin, vmax=vmax, cmap=cmap, node_shape='o', alpha=0.9, font_size=8, with_labels=True, label='Attention') sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax)) sm._A = [] if showbar: plt.colorbar(sm) plt.show() def construct_kg(filename: str, print_every=1e6, cache_load=True, logger=logging.getLogger(__name__)) -> (nx.DiGraph, list, set): # access edges with graph.edges.data('relation') if 'graph' in kg and cache_load: return kg['graph'], kg['triplets'], kg['entities'] path = Path(filename) if not path.exists(): filename = './kg/conceptnet/kg.txt' triplets = [] with open(filename, 'r') as fp: for idx, line in enumerate(fp): e1, r, e2 = line.rstrip("\n").rsplit() triplets.append([e1.lower().strip(), e2.lower().strip(), r.lower().strip()]) if idx % print_every == 0: print("*",end='') [graph, entities] = construct_graph(triplets) graph = graph.to_undirected(as_view=True) # Since a->b ==> b->a if cache_load: kg['graph'] = graph kg['triplets'] = triplets kg['entities'] = entities return graph, triplets, entities class RelationExtractor: def __init__(self, tokenizer, openie_url="http://localhost:9000/"): """ :param tokenizer: :param openie_url: server url for Stanford Core NLPOpen IE """ self.tokenizer = tokenizer self.openie_url = openie_url self.kg_vocab = {} self.agent_loc = '' def call_stanford_openie(self,sentence): querystring = { "properties": "%7B%22annotators%22%3A%20%22openie%22%7D", "pipelineLanguage": "en"} response = requests.request("POST", self.openie_url, data=sentence, params=querystring) response = json.JSONDecoder().decode(response.text) return response def fetch_triplets(self,text, current_graph, prev_action=None): triplets = [] remove = [] prev_remove = [] link = [] c_id = len(self.kg_vocab.keys()) obs = self.tokenizer.clean_string(text, preprocess=True) dirs = ['north', 'south', 'east', 'west'] obs = str(obs) doc = self.tokenizer.nlp_eval(obs) sents = {} try: sents = self.call_stanford_openie(doc.text)['sentences'] except: print("Error in connecting to Stanford CoreNLP OpenIE Server") for ov in sents: tokens = ov["tokens"] triple = ov['openie'] for tr in triple: h, r, t = tr['subject'].lower(), tr['relation'].lower(), tr['object'].lower() if h == 'we': h = 'you' if r == 'are in': r = "'ve entered" if h == 'it': break triplets.append((h, r, t)) room = "" room_set = False for rule in triplets: h, r, t = rule if 'entered' in r or 'are in' in r or 'walked' in r: prev_remove.append(r) if not room_set: room = t room_set = True if 'should' in r: prev_remove.append(r) if 'see' in r or 'make out' in r: link.append((r, t)) remove.append(r) # else: # link.append((r, t)) prev_room = self.agent_loc self.agent_loc = room add_rules = [] if prev_action is not None: for d in dirs: if d in prev_action and room != "": add_rules.append((prev_room, d + ' of', room)) prev_room_subgraph = None prev_you_subgraph = None for sent in doc.sents: sent = sent.text if sent == ',' or sent == 'hm .': continue if 'exit' in sent or 'entranceway' in sent: for d in dirs: if d in sent: triplets.append((room, 'has', 'exit to ' + d)) if prev_room != "": graph_copy = current_graph.copy() graph_copy.remove_edge('you', prev_room) con_cs = [graph_copy.subgraph(c) for c in nx.weakly_connected_components(graph_copy)] for con_c in con_cs: if prev_room in con_c.nodes: prev_room_subgraph = nx.induced_subgraph(graph_copy, con_c.nodes) if 'you' in con_c.nodes: prev_you_subgraph = nx.induced_subgraph(graph_copy, con_c.nodes) for l in link: add_rules.append((room, l[0], l[1])) for rule in triplets: h, r, t = rule if r == 'is in': if t == 'room': t = room if r not in remove: add_rules.append((h, r, t)) edges = list(current_graph.edges) for edge in edges: r = 'relatedTo' if 'relation' in current_graph[edge[0]][edge[1]]: r = current_graph[edge[0]][edge[1]]['relation'] if r in prev_remove: current_graph.remove_edge(*edge) if prev_you_subgraph is not None: current_graph.remove_edges_from(prev_you_subgraph.edges) for rule in add_rules: u = '_'.join(str(rule[0]).split()) v = '_'.join(str(rule[2]).split()) if u != 'it' and u not in self.kg_vocab: self.kg_vocab[u] = c_id c_id += 1 if v != 'it' and v not in self.kg_vocab: self.kg_vocab[v] = c_id c_id += 1 skip_flag = False for skip_token in self.tokenizer.ignore_list: if skip_token in u or skip_token in v: skip_flag = True if u != 'it' and v != 'it' and not skip_flag: r = str(rule[1]).lower() if not rule[1] or rule[1] == '': r = 'relatedTo' current_graph.add_edge(str(rule[0]).lower(), str(rule[2]).lower(), relation=r) prev_edges = current_graph.edges if prev_room_subgraph is not None: current_graph.add_edges_from(prev_room_subgraph.edges) current_edges = current_graph.edges return current_graph, add_rules def khop_neighbor_graph(graph, entities, cutoff=1, max_khop_degree=None): all_entities = [] for et in entities: candidates = nx.single_source_shortest_path(graph, et, cutoff=cutoff).keys() if not max_khop_degree or len(candidates)<=max_khop_degree: all_entities.extend(list(candidates)) return graph.subgraph(set(entities)|set(all_entities)) def ego_graph_seed_expansion(graph, seed, radius, undirected=True, max_degree=None): working_graph = graph if undirected: working_graph = graph.to_undirected() marked = set(seed) nodes = set(seed) for _ in range(radius): border = set() for node in marked: neighbors = {n for n in working_graph[node]} if max_degree is None or len(neighbors) <= max_degree: border |= neighbors nodes |= border marked = border return graph.subgraph(nodes) def shortest_path_seed_expansion(graph, seed, cutoff=None, undirected=True, keep_all=True): nodes = set(seed) seed = list(seed) working_graph = graph if undirected: working_graph = graph.to_undirected() for i in range(len(seed)): start = i + 1 if undirected else 0 for j in range(start, len(seed)): try: if not keep_all: path = nx.shortest_path(working_graph, seed[i], seed[j]) if cutoff is None or len(path) <= cutoff: nodes |= set(path) else: paths = nx.all_shortest_paths(working_graph, seed[i], seed[j]) for p in paths: if cutoff is None or len(p) <= cutoff: nodes |= set(p) except nx.NetworkXNoPath: continue return graph.subgraph(nodes) def load_manual_graphs(path): path = Path(path) manual_world_graphs = {} if not path.exists(): print('None Found.') return manual_world_graphs files = path.rglob("conceptnet_manual_subgraph-*.tsv") for file in files: game_id = str(file).split('-')[-1].split('.')[0] graph, triplets, entities = construct_kg(file, cache_load=False) manual_world_graphs[game_id]={} manual_world_graphs[game_id]['graph'] = graph manual_world_graphs[game_id]['triplets'] = triplets manual_world_graphs[game_id]['entities'] = entities print(' DONE') return manual_world_graphs def kg_match(extractor, target_entities, kg_entities): result = set() kg_entities = escape_entities(kg_entities) for e in target_entities: e = e.lower().strip() result |= extractor(e, kg_entities) return result def save_graph_tsv(graph, path): relation_map = nx.get_edge_attributes(graph, 'relation') lines = [] for n1, n2 in graph.edges: relations = relation_map[n1, n2].split() for r in relations: lines.append(f'{n1}\t{r}\t{n2}\n') with open(path, 'w') as f: f.writelines(lines) if __name__ == '__main__': from utils_twc import extractor from utils_twc.nlp import Tokenizer tk_extractor = extractor.get_extractor('max') tokenizer = Tokenizer(extractor=tk_extractor) rel_extract = RelationExtractor(tokenizer,openie_url='http://iqa962.sl.cloud9.ibm.com:9000/') # text = 'On the table, you see an apple, a hat, a key and an umbrella. ' text = "You've just walked into a Living Room. You try to gain information on your " \ "surroundings by using a technique you call looking. You can see a closet. " \ "You idly wonder how they came up with the name TextWorld for this place. " \ "It's pretty fitting. A closed standard looking antique trunk is in the room. " \ "You can see a table. The table is usual. On the table you see an apple, a mug, " \ "a newspaper, a note, a hat and a pencil. You smell a sickening smell, and follow " \ "it to a couch. The couch is standard. But the thing is empty. Hm. Oh well You see a " \ "gleam over in a corner, where you can see a tv stand. The tv stand is ordinary. " \ "On the tv stand you can make out a tv. You don't like doors? Why not try going east, " \ "that entranceway is unguarded. You are carrying nothing." sents = text # clauses = clausie.clausie(text) # propositions = clausie.extract_propositions(clauses) # sents = '' # for prop in propositions: # sent = clausie.proposition_text_str(prop) # sents += sent # print(sent) graph, add_rules = rel_extract.fetch_triplets(sents, nx.DiGraph()) print(add_rules)

37.927059

136

0.578262

import sys import networkx as nx import logging import json import requests from pathlib import Path import numpy as np import matplotlib.pyplot as plt from collections import defaultdict from utils_twc.generic import escape_entities FORMAT = '%(asctime)s %(message)s' logging.basicConfig(format=FORMAT, level='INFO', stream=sys.stdout) kg = {} source_paths= defaultdict(dict) def shortest_path_subgraph(kg_graph, prev_graph, nodes, inventory_entities=None, command_entities=None, path_len=2, add_all_path=False): if inventory_entities is None: inventory_entities = [] if command_entities is None: command_entities = [] world_graph = kg_graph.subgraph(list(prev_graph.nodes)+nodes).copy() world_graph = nx.compose(prev_graph,world_graph) world_graph.remove_edges_from(nx.selfloop_edges(world_graph)) if path_len < 2: return world_graph triplets = [] pruned_entities = list(set(command_entities)-set(inventory_entities)) if pruned_entities: for src_et in inventory_entities: for tgt_et in pruned_entities: if src_et != tgt_et: try: pair_dist = nx.shortest_path_length(kg_graph, source=src_et, target=tgt_et) except nx.NetworkXNoPath: pair_dist = 0 if pair_dist >= 1 and pair_dist <= path_len: triplets.append([src_et, tgt_et, 'relatedTo']) else: for entities in command_entities: for src_et in entities: for tgt_et in entities: if src_et != tgt_et: try: pair_dist = nx.shortest_path_length(kg_graph, source=src_et, target=tgt_et) except nx.NetworkXNoPath: pair_dist=0 if pair_dist >= 1 and pair_dist <= path_len: triplets.append([src_et, tgt_et, 'relatedTo']) world_graph, _= add_triplets_to_graph(world_graph, triplets) return world_graph def construct_graph(triplets): graph = nx.DiGraph() entities = {} for [e1, e2, r] in triplets: e1 = e1.lower().strip() e2 = e2.lower().strip() r = r.lower().strip() if e1 not in entities: graph.add_node(e1) entities[e1] = e1 if e2 not in entities: graph.add_node(e2) entities[e2] = e2 # Add Edge information if graph.has_edge(e1, e2): if r not in graph.edges[e1, e2]['relation']: graph.edges[e1, e2]['relation'] += ' ' + r else: graph.add_edge(e1, e2, relation=r) return graph, entities def add_triplets_to_graph(graph, triplets): entities = dict(graph.nodes.data()) for [e1, e2, r] in triplets: e1 = e1.lower().strip() e2 = e2.lower().strip() r = r.lower().strip() if e1 not in entities: graph.add_node(e1) entities[e1] = e1 if e2 not in entities: graph.add_node(e2) entities[e2] = e2 # Add Edge information if graph.has_edge(e1, e2): if r not in graph.edges[e1, e2]['relation']: graph.edges[e1, e2]['relation'] += ' ' + r else: graph.add_edge(e1, e2, relation=r) return graph, entities def draw_graph(graph, title="cleanup", show_relation=True, weights=None, pos=None): if not pos: pos = nx.spring_layout(graph, k=0.95) if weights: nx.draw(graph, pos, edge_color='black', width=1, linewidths=1, node_size=1000, node_color=weights.tolist(), vmin=np.min(weights), vmax=np.max(weights), node_shape='o', alpha=0.9, font_size=8, with_labels=True, label=title,cmap='Blues') else: nx.draw(graph, pos, edge_color='black', width=1, linewidths=1, node_size=1000, node_color='pink', node_shape='o', alpha=0.9, font_size=8, with_labels=True, label=title) if show_relation: p_edge = nx.draw_networkx_edge_labels(graph, pos, font_size=6, font_color='red', edge_labels=nx.get_edge_attributes(graph, 'relation')) def draw_graph_colormap(graph,node_weights, showbar=False, cmap='YlGnBu'): # node_weights: maps node id/name to attention weights pos = nx.spring_layout(graph, k=0.95) weights = [] for node in graph.nodes: weights.append(node_weights[node]) # cmap = plt.cm.YlGnBu#RdBu cmap = plt.get_cmap(cmap) vmin = np.min(weights) vmax = np.max(weights) nx.draw(graph, pos, edge_color='black', width=1, linewidths=1, node_size=1000, node_color=weights, vmin=vmin, vmax=vmax, cmap=cmap, node_shape='o', alpha=0.9, font_size=8, with_labels=True, label='Attention') sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax)) sm._A = [] if showbar: plt.colorbar(sm) plt.show() def construct_kg(filename: str, print_every=1e6, cache_load=True, logger=logging.getLogger(__name__)) -> (nx.DiGraph, list, set): # access edges with graph.edges.data('relation') if 'graph' in kg and cache_load: return kg['graph'], kg['triplets'], kg['entities'] path = Path(filename) if not path.exists(): filename = './kg/conceptnet/kg.txt' triplets = [] with open(filename, 'r') as fp: for idx, line in enumerate(fp): e1, r, e2 = line.rstrip("\n").rsplit() triplets.append([e1.lower().strip(), e2.lower().strip(), r.lower().strip()]) if idx % print_every == 0: print("*",end='') [graph, entities] = construct_graph(triplets) graph = graph.to_undirected(as_view=True) # Since a->b ==> b->a if cache_load: kg['graph'] = graph kg['triplets'] = triplets kg['entities'] = entities return graph, triplets, entities class RelationExtractor: def __init__(self, tokenizer, openie_url="http://localhost:9000/"): self.tokenizer = tokenizer self.openie_url = openie_url self.kg_vocab = {} self.agent_loc = '' def call_stanford_openie(self,sentence): querystring = { "properties": "%7B%22annotators%22%3A%20%22openie%22%7D", "pipelineLanguage": "en"} response = requests.request("POST", self.openie_url, data=sentence, params=querystring) response = json.JSONDecoder().decode(response.text) return response def fetch_triplets(self,text, current_graph, prev_action=None): triplets = [] remove = [] prev_remove = [] link = [] c_id = len(self.kg_vocab.keys()) obs = self.tokenizer.clean_string(text, preprocess=True) dirs = ['north', 'south', 'east', 'west'] obs = str(obs) doc = self.tokenizer.nlp_eval(obs) sents = {} try: sents = self.call_stanford_openie(doc.text)['sentences'] except: print("Error in connecting to Stanford CoreNLP OpenIE Server") for ov in sents: tokens = ov["tokens"] triple = ov['openie'] for tr in triple: h, r, t = tr['subject'].lower(), tr['relation'].lower(), tr['object'].lower() if h == 'we': h = 'you' if r == 'are in': r = "'ve entered" if h == 'it': break triplets.append((h, r, t)) room = "" room_set = False for rule in triplets: h, r, t = rule if 'entered' in r or 'are in' in r or 'walked' in r: prev_remove.append(r) if not room_set: room = t room_set = True if 'should' in r: prev_remove.append(r) if 'see' in r or 'make out' in r: link.append((r, t)) remove.append(r) prev_room = self.agent_loc self.agent_loc = room add_rules = [] if prev_action is not None: for d in dirs: if d in prev_action and room != "": add_rules.append((prev_room, d + ' of', room)) prev_room_subgraph = None prev_you_subgraph = None for sent in doc.sents: sent = sent.text if sent == ',' or sent == 'hm .': continue if 'exit' in sent or 'entranceway' in sent: for d in dirs: if d in sent: triplets.append((room, 'has', 'exit to ' + d)) if prev_room != "": graph_copy = current_graph.copy() graph_copy.remove_edge('you', prev_room) con_cs = [graph_copy.subgraph(c) for c in nx.weakly_connected_components(graph_copy)] for con_c in con_cs: if prev_room in con_c.nodes: prev_room_subgraph = nx.induced_subgraph(graph_copy, con_c.nodes) if 'you' in con_c.nodes: prev_you_subgraph = nx.induced_subgraph(graph_copy, con_c.nodes) for l in link: add_rules.append((room, l[0], l[1])) for rule in triplets: h, r, t = rule if r == 'is in': if t == 'room': t = room if r not in remove: add_rules.append((h, r, t)) edges = list(current_graph.edges) for edge in edges: r = 'relatedTo' if 'relation' in current_graph[edge[0]][edge[1]]: r = current_graph[edge[0]][edge[1]]['relation'] if r in prev_remove: current_graph.remove_edge(*edge) if prev_you_subgraph is not None: current_graph.remove_edges_from(prev_you_subgraph.edges) for rule in add_rules: u = '_'.join(str(rule[0]).split()) v = '_'.join(str(rule[2]).split()) if u != 'it' and u not in self.kg_vocab: self.kg_vocab[u] = c_id c_id += 1 if v != 'it' and v not in self.kg_vocab: self.kg_vocab[v] = c_id c_id += 1 skip_flag = False for skip_token in self.tokenizer.ignore_list: if skip_token in u or skip_token in v: skip_flag = True if u != 'it' and v != 'it' and not skip_flag: r = str(rule[1]).lower() if not rule[1] or rule[1] == '': r = 'relatedTo' current_graph.add_edge(str(rule[0]).lower(), str(rule[2]).lower(), relation=r) prev_edges = current_graph.edges if prev_room_subgraph is not None: current_graph.add_edges_from(prev_room_subgraph.edges) current_edges = current_graph.edges return current_graph, add_rules def khop_neighbor_graph(graph, entities, cutoff=1, max_khop_degree=None): all_entities = [] for et in entities: candidates = nx.single_source_shortest_path(graph, et, cutoff=cutoff).keys() if not max_khop_degree or len(candidates)<=max_khop_degree: all_entities.extend(list(candidates)) return graph.subgraph(set(entities)|set(all_entities)) def ego_graph_seed_expansion(graph, seed, radius, undirected=True, max_degree=None): working_graph = graph if undirected: working_graph = graph.to_undirected() marked = set(seed) nodes = set(seed) for _ in range(radius): border = set() for node in marked: neighbors = {n for n in working_graph[node]} if max_degree is None or len(neighbors) <= max_degree: border |= neighbors nodes |= border marked = border return graph.subgraph(nodes) def shortest_path_seed_expansion(graph, seed, cutoff=None, undirected=True, keep_all=True): nodes = set(seed) seed = list(seed) working_graph = graph if undirected: working_graph = graph.to_undirected() for i in range(len(seed)): start = i + 1 if undirected else 0 for j in range(start, len(seed)): try: if not keep_all: path = nx.shortest_path(working_graph, seed[i], seed[j]) if cutoff is None or len(path) <= cutoff: nodes |= set(path) else: paths = nx.all_shortest_paths(working_graph, seed[i], seed[j]) for p in paths: if cutoff is None or len(p) <= cutoff: nodes |= set(p) except nx.NetworkXNoPath: continue return graph.subgraph(nodes) def load_manual_graphs(path): path = Path(path) manual_world_graphs = {} if not path.exists(): print('None Found.') return manual_world_graphs files = path.rglob("conceptnet_manual_subgraph-*.tsv") for file in files: game_id = str(file).split('-')[-1].split('.')[0] graph, triplets, entities = construct_kg(file, cache_load=False) manual_world_graphs[game_id]={} manual_world_graphs[game_id]['graph'] = graph manual_world_graphs[game_id]['triplets'] = triplets manual_world_graphs[game_id]['entities'] = entities print(' DONE') return manual_world_graphs def kg_match(extractor, target_entities, kg_entities): result = set() kg_entities = escape_entities(kg_entities) for e in target_entities: e = e.lower().strip() result |= extractor(e, kg_entities) return result def save_graph_tsv(graph, path): relation_map = nx.get_edge_attributes(graph, 'relation') lines = [] for n1, n2 in graph.edges: relations = relation_map[n1, n2].split() for r in relations: lines.append(f'{n1}\t{r}\t{n2}\n') with open(path, 'w') as f: f.writelines(lines) if __name__ == '__main__': from utils_twc import extractor from utils_twc.nlp import Tokenizer tk_extractor = extractor.get_extractor('max') tokenizer = Tokenizer(extractor=tk_extractor) rel_extract = RelationExtractor(tokenizer,openie_url='http://iqa962.sl.cloud9.ibm.com:9000/') text = "You've just walked into a Living Room. You try to gain information on your " \ "surroundings by using a technique you call looking. You can see a closet. " \ "You idly wonder how they came up with the name TextWorld for this place. " \ "It's pretty fitting. A closed standard looking antique trunk is in the room. " \ "You can see a table. The table is usual. On the table you see an apple, a mug, " \ "a newspaper, a note, a hat and a pencil. You smell a sickening smell, and follow " \ "it to a couch. The couch is standard. But the thing is empty. Hm. Oh well You see a " \ "gleam over in a corner, where you can see a tv stand. The tv stand is ordinary. " \ "On the tv stand you can make out a tv. You don't like doors? Why not try going east, " \ "that entranceway is unguarded. You are carrying nothing." sents = text # clauses = clausie.clausie(text) # propositions = clausie.extract_propositions(clauses) # sents = '' # for prop in propositions: # sent = clausie.proposition_text_str(prop) # sents += sent # print(sent) graph, add_rules = rel_extract.fetch_triplets(sents, nx.DiGraph()) print(add_rules)

true

f71be86a3b944cd3f5b50d8b2127cb921b32bfb6

1,048

py

Python

lhc/io/bed/__main__.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

lhc/io/bed/__main__.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

lhc/io/bed/__main__.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

import argparse from .iterator import BedEntryIterator from lhc.io.bed.tools import depth, sort, filter from lhc.io.txt.tools import compress def iter_bed(fname): it = BedEntryIterator(fname) for entry in it: yield entry it.close() def main(): args = get_parser().parse_args() args.func(args) def get_parser(): return define_parser(argparse.ArgumentParser()) def define_parser(parser): subparsers = parser.add_subparsers() # Compress parser compress_parser = subparsers.add_parser('compress') compress.define_parser(compress_parser) compress_parser.set_defaults(block_delimiter='\n') # Depth parser depth_parser = subparsers.add_parser('depth') depth.define_parser(depth_parser) # Filter parser filter_parser = subparsers.add_parser('filter') filter.define_parser(filter_parser) # Sort parser sort_parser = subparsers.add_parser('sort') sort.define_parser(sort_parser) return parser if __name__ == '__main__': import sys sys.exit(main())

23.818182

55

0.719466

import argparse from .iterator import BedEntryIterator from lhc.io.bed.tools import depth, sort, filter from lhc.io.txt.tools import compress def iter_bed(fname): it = BedEntryIterator(fname) for entry in it: yield entry it.close() def main(): args = get_parser().parse_args() args.func(args) def get_parser(): return define_parser(argparse.ArgumentParser()) def define_parser(parser): subparsers = parser.add_subparsers() compress_parser = subparsers.add_parser('compress') compress.define_parser(compress_parser) compress_parser.set_defaults(block_delimiter='\n') depth_parser = subparsers.add_parser('depth') depth.define_parser(depth_parser) filter_parser = subparsers.add_parser('filter') filter.define_parser(filter_parser) sort_parser = subparsers.add_parser('sort') sort.define_parser(sort_parser) return parser if __name__ == '__main__': import sys sys.exit(main())

true

f71be8fe9b1bed16fe43ad8a1ea794cdbcec63d2

7,605

py

Python

tests/test_bootstrap.py

itewk/home-assistant

769cf19052f8c9ef374d8ba8ae7705ccc7bf4cf4

[ "Apache-2.0" ]

23

2017-11-15T21:03:53.000Z

2021-03-29T21:33:48.000Z

tests/test_bootstrap.py

itewk/home-assistant

769cf19052f8c9ef374d8ba8ae7705ccc7bf4cf4

[ "Apache-2.0" ]

6

2021-02-08T20:59:36.000Z

2022-03-12T00:52:11.000Z

tests/test_bootstrap.py

itewk/home-assistant

769cf19052f8c9ef374d8ba8ae7705ccc7bf4cf4

[ "Apache-2.0" ]

10

2018-01-01T00:12:51.000Z

2021-12-21T23:08:05.000Z

"""Test the bootstrapping.""" # pylint: disable=protected-access import asyncio import logging import os from unittest.mock import Mock, patch from homeassistant import bootstrap import homeassistant.config as config_util import homeassistant.util.dt as dt_util from tests.common import ( MockModule, get_test_config_dir, mock_coro, mock_integration, patch_yaml_files, ) ORIG_TIMEZONE = dt_util.DEFAULT_TIME_ZONE VERSION_PATH = os.path.join(get_test_config_dir(), config_util.VERSION_FILE) _LOGGER = logging.getLogger(__name__) # prevent .HA_VERSION file from being written @patch("homeassistant.bootstrap.conf_util.process_ha_config_upgrade", Mock()) @patch( "homeassistant.util.location.async_detect_location_info", Mock(return_value=mock_coro(None)), ) @patch("os.path.isfile", Mock(return_value=True)) @patch("os.access", Mock(return_value=True)) @patch("homeassistant.bootstrap.async_enable_logging", Mock(return_value=True)) def test_from_config_file(hass): """Test with configuration file.""" components = set(["browser", "conversation", "script"]) files = {"config.yaml": "".join("{}:\n".format(comp) for comp in components)} with patch_yaml_files(files, True): yield from bootstrap.async_from_config_file("config.yaml", hass) assert components == hass.config.components @patch("homeassistant.bootstrap.async_enable_logging", Mock()) @asyncio.coroutine def test_home_assistant_core_config_validation(hass): """Test if we pass in wrong information for HA conf.""" # Extensive HA conf validation testing is done result = yield from bootstrap.async_from_config_dict( {"homeassistant": {"latitude": "some string"}}, hass ) assert result is None async def test_async_from_config_file_not_mount_deps_folder(loop): """Test that we not mount the deps folder inside async_from_config_file.""" hass = Mock(async_add_executor_job=Mock(side_effect=lambda *args: mock_coro())) with patch("homeassistant.bootstrap.is_virtual_env", return_value=False), patch( "homeassistant.bootstrap.async_enable_logging", return_value=mock_coro() ), patch( "homeassistant.bootstrap.async_mount_local_lib_path", return_value=mock_coro() ) as mock_mount, patch( "homeassistant.bootstrap.async_from_config_dict", return_value=mock_coro() ): await bootstrap.async_from_config_file("mock-path", hass) assert len(mock_mount.mock_calls) == 1 with patch("homeassistant.bootstrap.is_virtual_env", return_value=True), patch( "homeassistant.bootstrap.async_enable_logging", return_value=mock_coro() ), patch( "homeassistant.bootstrap.async_mount_local_lib_path", return_value=mock_coro() ) as mock_mount, patch( "homeassistant.bootstrap.async_from_config_dict", return_value=mock_coro() ): await bootstrap.async_from_config_file("mock-path", hass) assert len(mock_mount.mock_calls) == 0 async def test_load_hassio(hass): """Test that we load Hass.io component.""" with patch.dict(os.environ, {}, clear=True): assert bootstrap._get_domains(hass, {}) == set() with patch.dict(os.environ, {"HASSIO": "1"}): assert bootstrap._get_domains(hass, {}) == {"hassio"} async def test_empty_setup(hass): """Test an empty set up loads the core.""" await bootstrap._async_set_up_integrations(hass, {}) for domain in bootstrap.CORE_INTEGRATIONS: assert domain in hass.config.components, domain async def test_core_failure_aborts(hass, caplog): """Test failing core setup aborts further setup.""" with patch( "homeassistant.components.homeassistant.async_setup", return_value=mock_coro(False), ): await bootstrap._async_set_up_integrations(hass, {"group": {}}) assert "core failed to initialize" in caplog.text # We aborted early, group not set up assert "group" not in hass.config.components async def test_setting_up_config(hass, caplog): """Test we set up domains in config.""" await bootstrap._async_set_up_integrations( hass, {"group hello": {}, "homeassistant": {}} ) assert "group" in hass.config.components async def test_setup_after_deps_all_present(hass, caplog): """Test after_dependencies when all present.""" caplog.set_level(logging.DEBUG) order = [] def gen_domain_setup(domain): async def async_setup(hass, config): order.append(domain) return True return async_setup mock_integration( hass, MockModule(domain="root", async_setup=gen_domain_setup("root")) ) mock_integration( hass, MockModule( domain="first_dep", async_setup=gen_domain_setup("first_dep"), partial_manifest={"after_dependencies": ["root"]}, ), ) mock_integration( hass, MockModule( domain="second_dep", async_setup=gen_domain_setup("second_dep"), partial_manifest={"after_dependencies": ["first_dep"]}, ), ) await bootstrap._async_set_up_integrations( hass, {"root": {}, "first_dep": {}, "second_dep": {}} ) assert "root" in hass.config.components assert "first_dep" in hass.config.components assert "second_dep" in hass.config.components assert order == ["root", "first_dep", "second_dep"] async def test_setup_after_deps_not_trigger_load(hass, caplog): """Test after_dependencies does not trigger loading it.""" caplog.set_level(logging.DEBUG) order = [] def gen_domain_setup(domain): async def async_setup(hass, config): order.append(domain) return True return async_setup mock_integration( hass, MockModule(domain="root", async_setup=gen_domain_setup("root")) ) mock_integration( hass, MockModule( domain="first_dep", async_setup=gen_domain_setup("first_dep"), partial_manifest={"after_dependencies": ["root"]}, ), ) mock_integration( hass, MockModule( domain="second_dep", async_setup=gen_domain_setup("second_dep"), partial_manifest={"after_dependencies": ["first_dep"]}, ), ) await bootstrap._async_set_up_integrations(hass, {"root": {}, "second_dep": {}}) assert "root" in hass.config.components assert "first_dep" not in hass.config.components assert "second_dep" in hass.config.components assert order == ["root", "second_dep"] async def test_setup_after_deps_not_present(hass, caplog): """Test after_dependencies when referenced integration doesn't exist.""" caplog.set_level(logging.DEBUG) order = [] def gen_domain_setup(domain): async def async_setup(hass, config): order.append(domain) return True return async_setup mock_integration( hass, MockModule(domain="root", async_setup=gen_domain_setup("root")) ) mock_integration( hass, MockModule( domain="second_dep", async_setup=gen_domain_setup("second_dep"), partial_manifest={"after_dependencies": ["first_dep"]}, ), ) await bootstrap._async_set_up_integrations( hass, {"root": {}, "first_dep": {}, "second_dep": {}} ) assert "root" in hass.config.components assert "first_dep" not in hass.config.components assert "second_dep" in hass.config.components assert order == ["root", "second_dep"]

32.224576

86

0.682446

import asyncio import logging import os from unittest.mock import Mock, patch from homeassistant import bootstrap import homeassistant.config as config_util import homeassistant.util.dt as dt_util from tests.common import ( MockModule, get_test_config_dir, mock_coro, mock_integration, patch_yaml_files, ) ORIG_TIMEZONE = dt_util.DEFAULT_TIME_ZONE VERSION_PATH = os.path.join(get_test_config_dir(), config_util.VERSION_FILE) _LOGGER = logging.getLogger(__name__) @patch("homeassistant.bootstrap.conf_util.process_ha_config_upgrade", Mock()) @patch( "homeassistant.util.location.async_detect_location_info", Mock(return_value=mock_coro(None)), ) @patch("os.path.isfile", Mock(return_value=True)) @patch("os.access", Mock(return_value=True)) @patch("homeassistant.bootstrap.async_enable_logging", Mock(return_value=True)) def test_from_config_file(hass): components = set(["browser", "conversation", "script"]) files = {"config.yaml": "".join("{}:\n".format(comp) for comp in components)} with patch_yaml_files(files, True): yield from bootstrap.async_from_config_file("config.yaml", hass) assert components == hass.config.components @patch("homeassistant.bootstrap.async_enable_logging", Mock()) @asyncio.coroutine def test_home_assistant_core_config_validation(hass): result = yield from bootstrap.async_from_config_dict( {"homeassistant": {"latitude": "some string"}}, hass ) assert result is None async def test_async_from_config_file_not_mount_deps_folder(loop): hass = Mock(async_add_executor_job=Mock(side_effect=lambda *args: mock_coro())) with patch("homeassistant.bootstrap.is_virtual_env", return_value=False), patch( "homeassistant.bootstrap.async_enable_logging", return_value=mock_coro() ), patch( "homeassistant.bootstrap.async_mount_local_lib_path", return_value=mock_coro() ) as mock_mount, patch( "homeassistant.bootstrap.async_from_config_dict", return_value=mock_coro() ): await bootstrap.async_from_config_file("mock-path", hass) assert len(mock_mount.mock_calls) == 1 with patch("homeassistant.bootstrap.is_virtual_env", return_value=True), patch( "homeassistant.bootstrap.async_enable_logging", return_value=mock_coro() ), patch( "homeassistant.bootstrap.async_mount_local_lib_path", return_value=mock_coro() ) as mock_mount, patch( "homeassistant.bootstrap.async_from_config_dict", return_value=mock_coro() ): await bootstrap.async_from_config_file("mock-path", hass) assert len(mock_mount.mock_calls) == 0 async def test_load_hassio(hass): with patch.dict(os.environ, {}, clear=True): assert bootstrap._get_domains(hass, {}) == set() with patch.dict(os.environ, {"HASSIO": "1"}): assert bootstrap._get_domains(hass, {}) == {"hassio"} async def test_empty_setup(hass): await bootstrap._async_set_up_integrations(hass, {}) for domain in bootstrap.CORE_INTEGRATIONS: assert domain in hass.config.components, domain async def test_core_failure_aborts(hass, caplog): with patch( "homeassistant.components.homeassistant.async_setup", return_value=mock_coro(False), ): await bootstrap._async_set_up_integrations(hass, {"group": {}}) assert "core failed to initialize" in caplog.text assert "group" not in hass.config.components async def test_setting_up_config(hass, caplog): await bootstrap._async_set_up_integrations( hass, {"group hello": {}, "homeassistant": {}} ) assert "group" in hass.config.components async def test_setup_after_deps_all_present(hass, caplog): caplog.set_level(logging.DEBUG) order = [] def gen_domain_setup(domain): async def async_setup(hass, config): order.append(domain) return True return async_setup mock_integration( hass, MockModule(domain="root", async_setup=gen_domain_setup("root")) ) mock_integration( hass, MockModule( domain="first_dep", async_setup=gen_domain_setup("first_dep"), partial_manifest={"after_dependencies": ["root"]}, ), ) mock_integration( hass, MockModule( domain="second_dep", async_setup=gen_domain_setup("second_dep"), partial_manifest={"after_dependencies": ["first_dep"]}, ), ) await bootstrap._async_set_up_integrations( hass, {"root": {}, "first_dep": {}, "second_dep": {}} ) assert "root" in hass.config.components assert "first_dep" in hass.config.components assert "second_dep" in hass.config.components assert order == ["root", "first_dep", "second_dep"] async def test_setup_after_deps_not_trigger_load(hass, caplog): caplog.set_level(logging.DEBUG) order = [] def gen_domain_setup(domain): async def async_setup(hass, config): order.append(domain) return True return async_setup mock_integration( hass, MockModule(domain="root", async_setup=gen_domain_setup("root")) ) mock_integration( hass, MockModule( domain="first_dep", async_setup=gen_domain_setup("first_dep"), partial_manifest={"after_dependencies": ["root"]}, ), ) mock_integration( hass, MockModule( domain="second_dep", async_setup=gen_domain_setup("second_dep"), partial_manifest={"after_dependencies": ["first_dep"]}, ), ) await bootstrap._async_set_up_integrations(hass, {"root": {}, "second_dep": {}}) assert "root" in hass.config.components assert "first_dep" not in hass.config.components assert "second_dep" in hass.config.components assert order == ["root", "second_dep"] async def test_setup_after_deps_not_present(hass, caplog): caplog.set_level(logging.DEBUG) order = [] def gen_domain_setup(domain): async def async_setup(hass, config): order.append(domain) return True return async_setup mock_integration( hass, MockModule(domain="root", async_setup=gen_domain_setup("root")) ) mock_integration( hass, MockModule( domain="second_dep", async_setup=gen_domain_setup("second_dep"), partial_manifest={"after_dependencies": ["first_dep"]}, ), ) await bootstrap._async_set_up_integrations( hass, {"root": {}, "first_dep": {}, "second_dep": {}} ) assert "root" in hass.config.components assert "first_dep" not in hass.config.components assert "second_dep" in hass.config.components assert order == ["root", "second_dep"]

true

f71beb6536594f288089770f787a036271ea7c72

1,832

py

Python

lite/tests/unittest_py/op/common/test_unique_with_counts_op_base.py

714627034/Paddle-Lite

015ba88a4d639db0b73603e37f83e47be041a4eb

[ "Apache-2.0" ]

808

2018-04-17T17:43:12.000Z

2019-08-18T07:39:13.000Z

lite/tests/unittest_py/op/common/test_unique_with_counts_op_base.py

714627034/Paddle-Lite

015ba88a4d639db0b73603e37f83e47be041a4eb

[ "Apache-2.0" ]

728

2018-04-18T08:15:25.000Z

2019-08-16T07:14:43.000Z

lite/tests/unittest_py/op/common/test_unique_with_counts_op_base.py

714627034/Paddle-Lite

015ba88a4d639db0b73603e37f83e47be041a4eb

[ "Apache-2.0" ]

364

2018-04-18T17:05:02.000Z

2019-08-18T03:25:38.000Z

# Copyright (c) 2021 PaddlePaddle 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 sys sys.path.append('..') from program_config import TensorConfig, ProgramConfig, OpConfig, CxxConfig, TargetType, PrecisionType, DataLayoutType, Place import numpy as np from functools import partial from typing import Optional, List, Callable, Dict, Any, Set import unittest import hypothesis import hypothesis.strategies as st def sample_program_configs(draw): in_shape = draw( st.lists( st.integers( min_value=2, max_value=100), min_size=1, max_size=1)) def generate_IndexTensor(): return np.random.randint(1, 5, size=in_shape).astype(np.int32) unique_with_counts_op = OpConfig( type="unique_with_counts", inputs={"X": ["input_data"]}, outputs={ "Out": ["output_data"], "Index": ["Index_data"], "Count": ["Count_data"] }, attrs={"dtype": 2}) program_config = ProgramConfig( ops=[unique_with_counts_op], weights={ "Index_data": TensorConfig(data_gen=partial(generate_IndexTensor)) }, inputs={"input_data": TensorConfig(shape=in_shape), }, outputs=["output_data", "Index_data", "Count_data"]) return program_config

34.566038

125

0.68559

import sys sys.path.append('..') from program_config import TensorConfig, ProgramConfig, OpConfig, CxxConfig, TargetType, PrecisionType, DataLayoutType, Place import numpy as np from functools import partial from typing import Optional, List, Callable, Dict, Any, Set import unittest import hypothesis import hypothesis.strategies as st def sample_program_configs(draw): in_shape = draw( st.lists( st.integers( min_value=2, max_value=100), min_size=1, max_size=1)) def generate_IndexTensor(): return np.random.randint(1, 5, size=in_shape).astype(np.int32) unique_with_counts_op = OpConfig( type="unique_with_counts", inputs={"X": ["input_data"]}, outputs={ "Out": ["output_data"], "Index": ["Index_data"], "Count": ["Count_data"] }, attrs={"dtype": 2}) program_config = ProgramConfig( ops=[unique_with_counts_op], weights={ "Index_data": TensorConfig(data_gen=partial(generate_IndexTensor)) }, inputs={"input_data": TensorConfig(shape=in_shape), }, outputs=["output_data", "Index_data", "Count_data"]) return program_config

true

f71bebf8420c687bf788c8fe852634cda08565c9

54,956

py

Python

scikit-learn-master/sklearn/linear_model/ridge.py

lqkweb/learnMLflow

13c5decaebba95b1b90f92021be35e343b4764af

[ "Apache-2.0" ]

2

2019-02-21T10:43:16.000Z

2019-07-30T04:56:37.000Z

scikit-learn-master/sklearn/linear_model/ridge.py

lqkweb/learnMLflow

13c5decaebba95b1b90f92021be35e343b4764af

[ "Apache-2.0" ]

null

scikit-learn-master/sklearn/linear_model/ridge.py

lqkweb/learnMLflow

13c5decaebba95b1b90f92021be35e343b4764af

[ "Apache-2.0" ]

null

""" Ridge regression """ # Author: Mathieu Blondel <mathieu@mblondel.org> # Reuben Fletcher-Costin <reuben.fletchercostin@gmail.com> # Fabian Pedregosa <fabian@fseoane.net> # Michael Eickenberg <michael.eickenberg@nsup.org> # License: BSD 3 clause from abc import ABCMeta, abstractmethod import warnings import numpy as np from scipy import linalg from scipy import sparse from scipy.sparse import linalg as sp_linalg from .base import LinearClassifierMixin, LinearModel, _rescale_data from .sag import sag_solver from ..base import RegressorMixin from ..utils.extmath import safe_sparse_dot from ..utils.extmath import row_norms from ..utils import check_X_y from ..utils import check_array from ..utils import check_consistent_length from ..utils import compute_sample_weight from ..utils import column_or_1d from ..preprocessing import LabelBinarizer from ..model_selection import GridSearchCV from ..metrics.scorer import check_scoring from ..exceptions import ConvergenceWarning def _solve_sparse_cg(X, y, alpha, max_iter=None, tol=1e-3, verbose=0): n_samples, n_features = X.shape X1 = sp_linalg.aslinearoperator(X) coefs = np.empty((y.shape[1], n_features), dtype=X.dtype) if n_features > n_samples: def create_mv(curr_alpha): def _mv(x): return X1.matvec(X1.rmatvec(x)) + curr_alpha * x return _mv else: def create_mv(curr_alpha): def _mv(x): return X1.rmatvec(X1.matvec(x)) + curr_alpha * x return _mv for i in range(y.shape[1]): y_column = y[:, i] mv = create_mv(alpha[i]) if n_features > n_samples: # kernel ridge # w = X.T * inv(X X^t + alpha*Id) y C = sp_linalg.LinearOperator( (n_samples, n_samples), matvec=mv, dtype=X.dtype) # FIXME atol try: coef, info = sp_linalg.cg(C, y_column, tol=tol, atol='legacy') except TypeError: # old scipy coef, info = sp_linalg.cg(C, y_column, tol=tol) coefs[i] = X1.rmatvec(coef) else: # linear ridge # w = inv(X^t X + alpha*Id) * X.T y y_column = X1.rmatvec(y_column) C = sp_linalg.LinearOperator( (n_features, n_features), matvec=mv, dtype=X.dtype) # FIXME atol try: coefs[i], info = sp_linalg.cg(C, y_column, maxiter=max_iter, tol=tol, atol='legacy') except TypeError: # old scipy coefs[i], info = sp_linalg.cg(C, y_column, maxiter=max_iter, tol=tol) if info < 0: raise ValueError("Failed with error code %d" % info) if max_iter is None and info > 0 and verbose: warnings.warn("sparse_cg did not converge after %d iterations." % info, ConvergenceWarning) return coefs def _solve_lsqr(X, y, alpha, max_iter=None, tol=1e-3): n_samples, n_features = X.shape coefs = np.empty((y.shape[1], n_features), dtype=X.dtype) n_iter = np.empty(y.shape[1], dtype=np.int32) # According to the lsqr documentation, alpha = damp^2. sqrt_alpha = np.sqrt(alpha) for i in range(y.shape[1]): y_column = y[:, i] info = sp_linalg.lsqr(X, y_column, damp=sqrt_alpha[i], atol=tol, btol=tol, iter_lim=max_iter) coefs[i] = info[0] n_iter[i] = info[2] return coefs, n_iter def _solve_cholesky(X, y, alpha): # w = inv(X^t X + alpha*Id) * X.T y n_samples, n_features = X.shape n_targets = y.shape[1] A = safe_sparse_dot(X.T, X, dense_output=True) Xy = safe_sparse_dot(X.T, y, dense_output=True) one_alpha = np.array_equal(alpha, len(alpha) * [alpha[0]]) if one_alpha: A.flat[::n_features + 1] += alpha[0] return linalg.solve(A, Xy, sym_pos=True, overwrite_a=True).T else: coefs = np.empty([n_targets, n_features], dtype=X.dtype) for coef, target, current_alpha in zip(coefs, Xy.T, alpha): A.flat[::n_features + 1] += current_alpha coef[:] = linalg.solve(A, target, sym_pos=True, overwrite_a=False).ravel() A.flat[::n_features + 1] -= current_alpha return coefs def _solve_cholesky_kernel(K, y, alpha, sample_weight=None, copy=False): # dual_coef = inv(X X^t + alpha*Id) y n_samples = K.shape[0] n_targets = y.shape[1] if copy: K = K.copy() alpha = np.atleast_1d(alpha) one_alpha = (alpha == alpha[0]).all() has_sw = isinstance(sample_weight, np.ndarray) \ or sample_weight not in [1.0, None] if has_sw: # Unlike other solvers, we need to support sample_weight directly # because K might be a pre-computed kernel. sw = np.sqrt(np.atleast_1d(sample_weight)) y = y * sw[:, np.newaxis] K *= np.outer(sw, sw) if one_alpha: # Only one penalty, we can solve multi-target problems in one time. K.flat[::n_samples + 1] += alpha[0] try: # Note: we must use overwrite_a=False in order to be able to # use the fall-back solution below in case a LinAlgError # is raised dual_coef = linalg.solve(K, y, sym_pos=True, overwrite_a=False) except np.linalg.LinAlgError: warnings.warn("Singular matrix in solving dual problem. Using " "least-squares solution instead.") dual_coef = linalg.lstsq(K, y)[0] # K is expensive to compute and store in memory so change it back in # case it was user-given. K.flat[::n_samples + 1] -= alpha[0] if has_sw: dual_coef *= sw[:, np.newaxis] return dual_coef else: # One penalty per target. We need to solve each target separately. dual_coefs = np.empty([n_targets, n_samples], K.dtype) for dual_coef, target, current_alpha in zip(dual_coefs, y.T, alpha): K.flat[::n_samples + 1] += current_alpha dual_coef[:] = linalg.solve(K, target, sym_pos=True, overwrite_a=False).ravel() K.flat[::n_samples + 1] -= current_alpha if has_sw: dual_coefs *= sw[np.newaxis, :] return dual_coefs.T def _solve_svd(X, y, alpha): U, s, Vt = linalg.svd(X, full_matrices=False) idx = s > 1e-15 # same default value as scipy.linalg.pinv s_nnz = s[idx][:, np.newaxis] UTy = np.dot(U.T, y) d = np.zeros((s.size, alpha.size), dtype=X.dtype) d[idx] = s_nnz / (s_nnz ** 2 + alpha) d_UT_y = d * UTy return np.dot(Vt.T, d_UT_y).T def ridge_regression(X, y, alpha, sample_weight=None, solver='auto', max_iter=None, tol=1e-3, verbose=0, random_state=None, return_n_iter=False, return_intercept=False): """Solve the ridge equation by the method of normal equations. Read more in the :ref:`User Guide <ridge_regression>`. Parameters ---------- X : {array-like, sparse matrix, LinearOperator}, shape = [n_samples, n_features] Training data y : array-like, shape = [n_samples] or [n_samples, n_targets] Target values alpha : {float, array-like}, shape = [n_targets] if array-like Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to ``C^-1`` in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number. sample_weight : float or numpy array of shape [n_samples] Individual weights for each sample. If sample_weight is not None and solver='auto', the solver will be set to 'cholesky'. .. versionadded:: 0.17 solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'} Solver to use in the computational routines: - 'auto' chooses the solver automatically based on the type of data. - 'svd' uses a Singular Value Decomposition of X to compute the Ridge coefficients. More stable for singular matrices than 'cholesky'. - 'cholesky' uses the standard scipy.linalg.solve function to obtain a closed-form solution via a Cholesky decomposition of dot(X.T, X) - 'sparse_cg' uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than 'cholesky' for large-scale data (possibility to set `tol` and `max_iter`). - 'lsqr' uses the dedicated regularized least-squares routine scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative procedure. - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses its improved, unbiased version named SAGA. Both methods also use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. Note that 'sag' and 'saga' fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing. All last five solvers support both dense and sparse data. However, only 'sag' and 'saga' supports sparse input when`fit_intercept` is True. .. versionadded:: 0.17 Stochastic Average Gradient descent solver. .. versionadded:: 0.19 SAGA solver. max_iter : int, optional Maximum number of iterations for conjugate gradient solver. For the 'sparse_cg' and 'lsqr' solvers, the default value is determined by scipy.sparse.linalg. For 'sag' and saga solver, the default value is 1000. tol : float Precision of the solution. verbose : int Verbosity level. Setting verbose > 0 will display additional information depending on the solver used. random_state : int, RandomState instance or None, optional, default None The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``solver`` == 'sag'. return_n_iter : boolean, default False If True, the method also returns `n_iter`, the actual number of iteration performed by the solver. .. versionadded:: 0.17 return_intercept : boolean, default False If True and if X is sparse, the method also returns the intercept, and the solver is automatically changed to 'sag'. This is only a temporary fix for fitting the intercept with sparse data. For dense data, use sklearn.linear_model._preprocess_data before your regression. .. versionadded:: 0.17 Returns ------- coef : array, shape = [n_features] or [n_targets, n_features] Weight vector(s). n_iter : int, optional The actual number of iteration performed by the solver. Only returned if `return_n_iter` is True. intercept : float or array, shape = [n_targets] The intercept of the model. Only returned if `return_intercept` is True and if X is a scipy sparse array. Notes ----- This function won't compute the intercept. """ if return_intercept and sparse.issparse(X) and solver != 'sag': if solver != 'auto': warnings.warn("In Ridge, only 'sag' solver can currently fit the " "intercept when X is sparse. Solver has been " "automatically changed into 'sag'.") solver = 'sag' _dtype = [np.float64, np.float32] # SAG needs X and y columns to be C-contiguous and np.float64 if solver in ['sag', 'saga']: X = check_array(X, accept_sparse=['csr'], dtype=np.float64, order='C') y = check_array(y, dtype=np.float64, ensure_2d=False, order='F') else: X = check_array(X, accept_sparse=['csr', 'csc', 'coo'], dtype=_dtype) y = check_array(y, dtype=X.dtype, ensure_2d=False) check_consistent_length(X, y) n_samples, n_features = X.shape if y.ndim > 2: raise ValueError("Target y has the wrong shape %s" % str(y.shape)) ravel = False if y.ndim == 1: y = y.reshape(-1, 1) ravel = True n_samples_, n_targets = y.shape if n_samples != n_samples_: raise ValueError("Number of samples in X and y does not correspond:" " %d != %d" % (n_samples, n_samples_)) has_sw = sample_weight is not None if solver == 'auto': # cholesky if it's a dense array and cg in any other case if not sparse.issparse(X) or has_sw: solver = 'cholesky' else: solver = 'sparse_cg' if has_sw: if np.atleast_1d(sample_weight).ndim > 1: raise ValueError("Sample weights must be 1D array or scalar") if solver not in ['sag', 'saga']: # SAG supports sample_weight directly. For other solvers, # we implement sample_weight via a simple rescaling. X, y = _rescale_data(X, y, sample_weight) # There should be either 1 or n_targets penalties alpha = np.asarray(alpha, dtype=X.dtype).ravel() if alpha.size not in [1, n_targets]: raise ValueError("Number of targets and number of penalties " "do not correspond: %d != %d" % (alpha.size, n_targets)) if alpha.size == 1 and n_targets > 1: alpha = np.repeat(alpha, n_targets) if solver not in ('sparse_cg', 'cholesky', 'svd', 'lsqr', 'sag', 'saga'): raise ValueError('Solver %s not understood' % solver) n_iter = None if solver == 'sparse_cg': coef = _solve_sparse_cg(X, y, alpha, max_iter, tol, verbose) elif solver == 'lsqr': coef, n_iter = _solve_lsqr(X, y, alpha, max_iter, tol) elif solver == 'cholesky': if n_features > n_samples: K = safe_sparse_dot(X, X.T, dense_output=True) try: dual_coef = _solve_cholesky_kernel(K, y, alpha) coef = safe_sparse_dot(X.T, dual_coef, dense_output=True).T except linalg.LinAlgError: # use SVD solver if matrix is singular solver = 'svd' else: try: coef = _solve_cholesky(X, y, alpha) except linalg.LinAlgError: # use SVD solver if matrix is singular solver = 'svd' elif solver in ['sag', 'saga']: # precompute max_squared_sum for all targets max_squared_sum = row_norms(X, squared=True).max() coef = np.empty((y.shape[1], n_features)) n_iter = np.empty(y.shape[1], dtype=np.int32) intercept = np.zeros((y.shape[1], )) for i, (alpha_i, target) in enumerate(zip(alpha, y.T)): init = {'coef': np.zeros((n_features + int(return_intercept), 1))} coef_, n_iter_, _ = sag_solver( X, target.ravel(), sample_weight, 'squared', alpha_i, 0, max_iter, tol, verbose, random_state, False, max_squared_sum, init, is_saga=solver == 'saga') if return_intercept: coef[i] = coef_[:-1] intercept[i] = coef_[-1] else: coef[i] = coef_ n_iter[i] = n_iter_ if intercept.shape[0] == 1: intercept = intercept[0] coef = np.asarray(coef) if solver == 'svd': if sparse.issparse(X): raise TypeError('SVD solver does not support sparse' ' inputs currently') coef = _solve_svd(X, y, alpha) if ravel: # When y was passed as a 1d-array, we flatten the coefficients. coef = coef.ravel() if return_n_iter and return_intercept: return coef, n_iter, intercept elif return_intercept: return coef, intercept elif return_n_iter: return coef, n_iter else: return coef class _BaseRidge(LinearModel, metaclass=ABCMeta): @abstractmethod def __init__(self, alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=1e-3, solver="auto", random_state=None): self.alpha = alpha self.fit_intercept = fit_intercept self.normalize = normalize self.copy_X = copy_X self.max_iter = max_iter self.tol = tol self.solver = solver self.random_state = random_state def fit(self, X, y, sample_weight=None): if self.solver in ('sag', 'saga'): _dtype = np.float64 else: # all other solvers work at both float precision levels _dtype = [np.float64, np.float32] X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=_dtype, multi_output=True, y_numeric=True) if ((sample_weight is not None) and np.atleast_1d(sample_weight).ndim > 1): raise ValueError("Sample weights must be 1D array or scalar") X, y, X_offset, y_offset, X_scale = self._preprocess_data( X, y, self.fit_intercept, self.normalize, self.copy_X, sample_weight=sample_weight) # temporary fix for fitting the intercept with sparse data using 'sag' if sparse.issparse(X) and self.fit_intercept: self.coef_, self.n_iter_, self.intercept_ = ridge_regression( X, y, alpha=self.alpha, sample_weight=sample_weight, max_iter=self.max_iter, tol=self.tol, solver=self.solver, random_state=self.random_state, return_n_iter=True, return_intercept=True) self.intercept_ += y_offset else: self.coef_, self.n_iter_ = ridge_regression( X, y, alpha=self.alpha, sample_weight=sample_weight, max_iter=self.max_iter, tol=self.tol, solver=self.solver, random_state=self.random_state, return_n_iter=True, return_intercept=False) self._set_intercept(X_offset, y_offset, X_scale) return self class Ridge(_BaseRidge, RegressorMixin): """Linear least squares with l2 regularization. Minimizes the objective function:: ||y - Xw||^2_2 + alpha * ||w||^2_2 This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm. Also known as Ridge Regression or Tikhonov regularization. This estimator has built-in support for multi-variate regression (i.e., when y is a 2d-array of shape [n_samples, n_targets]). Read more in the :ref:`User Guide <ridge_regression>`. Parameters ---------- alpha : {float, array-like}, shape (n_targets) Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to ``C^-1`` in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number. fit_intercept : boolean Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional, default False This parameter is ignored when ``fit_intercept`` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``. copy_X : boolean, optional, default True If True, X will be copied; else, it may be overwritten. max_iter : int, optional Maximum number of iterations for conjugate gradient solver. For 'sparse_cg' and 'lsqr' solvers, the default value is determined by scipy.sparse.linalg. For 'sag' solver, the default value is 1000. tol : float Precision of the solution. solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'} Solver to use in the computational routines: - 'auto' chooses the solver automatically based on the type of data. - 'svd' uses a Singular Value Decomposition of X to compute the Ridge coefficients. More stable for singular matrices than 'cholesky'. - 'cholesky' uses the standard scipy.linalg.solve function to obtain a closed-form solution. - 'sparse_cg' uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than 'cholesky' for large-scale data (possibility to set `tol` and `max_iter`). - 'lsqr' uses the dedicated regularized least-squares routine scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative procedure. - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses its improved, unbiased version named SAGA. Both methods also use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. Note that 'sag' and 'saga' fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing. All last five solvers support both dense and sparse data. However, only 'sag' and 'saga' supports sparse input when `fit_intercept` is True. .. versionadded:: 0.17 Stochastic Average Gradient descent solver. .. versionadded:: 0.19 SAGA solver. random_state : int, RandomState instance or None, optional, default None The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``solver`` == 'sag'. .. versionadded:: 0.17 *random_state* to support Stochastic Average Gradient. Attributes ---------- coef_ : array, shape (n_features,) or (n_targets, n_features) Weight vector(s). intercept_ : float | array, shape = (n_targets,) Independent term in decision function. Set to 0.0 if ``fit_intercept = False``. n_iter_ : array or None, shape (n_targets,) Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None. .. versionadded:: 0.17 See also -------- RidgeClassifier : Ridge classifier RidgeCV : Ridge regression with built-in cross validation :class:`sklearn.kernel_ridge.KernelRidge` : Kernel ridge regression combines ridge regression with the kernel trick Examples -------- >>> from sklearn.linear_model import Ridge >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> np.random.seed(0) >>> y = np.random.randn(n_samples) >>> X = np.random.randn(n_samples, n_features) >>> clf = Ridge(alpha=1.0) >>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None, normalize=False, random_state=None, solver='auto', tol=0.001) """ def __init__(self, alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=1e-3, solver="auto", random_state=None): super().__init__( alpha=alpha, fit_intercept=fit_intercept, normalize=normalize, copy_X=copy_X, max_iter=max_iter, tol=tol, solver=solver, random_state=random_state) def fit(self, X, y, sample_weight=None): """Fit Ridge regression model Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training data y : array-like, shape = [n_samples] or [n_samples, n_targets] Target values sample_weight : float or numpy array of shape [n_samples] Individual weights for each sample Returns ------- self : returns an instance of self. """ return super().fit(X, y, sample_weight=sample_weight) class RidgeClassifier(LinearClassifierMixin, _BaseRidge): """Classifier using Ridge regression. Read more in the :ref:`User Guide <ridge_regression>`. Parameters ---------- alpha : float Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to ``C^-1`` in other linear models such as LogisticRegression or LinearSVC. fit_intercept : boolean Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional, default False This parameter is ignored when ``fit_intercept`` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``. copy_X : boolean, optional, default True If True, X will be copied; else, it may be overwritten. max_iter : int, optional Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg. tol : float Precision of the solution. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'} Solver to use in the computational routines: - 'auto' chooses the solver automatically based on the type of data. - 'svd' uses a Singular Value Decomposition of X to compute the Ridge coefficients. More stable for singular matrices than 'cholesky'. - 'cholesky' uses the standard scipy.linalg.solve function to obtain a closed-form solution. - 'sparse_cg' uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than 'cholesky' for large-scale data (possibility to set `tol` and `max_iter`). - 'lsqr' uses the dedicated regularized least-squares routine scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative procedure. - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses its unbiased and more flexible version named SAGA. Both methods use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. Note that 'sag' and 'saga' fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing. .. versionadded:: 0.17 Stochastic Average Gradient descent solver. .. versionadded:: 0.19 SAGA solver. random_state : int, RandomState instance or None, optional, default None The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``solver`` == 'sag'. Attributes ---------- coef_ : array, shape (n_features,) or (n_classes, n_features) Weight vector(s). intercept_ : float | array, shape = (n_targets,) Independent term in decision function. Set to 0.0 if ``fit_intercept = False``. n_iter_ : array or None, shape (n_targets,) Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None. Examples -------- >>> from sklearn.datasets import load_breast_cancer >>> from sklearn.linear_model import RidgeClassifier >>> X, y = load_breast_cancer(return_X_y=True) >>> clf = RidgeClassifier().fit(X, y) >>> clf.score(X, y) # doctest: +ELLIPSIS 0.9595... See also -------- Ridge : Ridge regression RidgeClassifierCV : Ridge classifier with built-in cross validation Notes ----- For multi-class classification, n_class classifiers are trained in a one-versus-all approach. Concretely, this is implemented by taking advantage of the multi-variate response support in Ridge. """ def __init__(self, alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=1e-3, class_weight=None, solver="auto", random_state=None): super().__init__( alpha=alpha, fit_intercept=fit_intercept, normalize=normalize, copy_X=copy_X, max_iter=max_iter, tol=tol, solver=solver, random_state=random_state) self.class_weight = class_weight def fit(self, X, y, sample_weight=None): """Fit Ridge regression model. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples,n_features] Training data y : array-like, shape = [n_samples] Target values sample_weight : float or numpy array of shape (n_samples,) Sample weight. .. versionadded:: 0.17 *sample_weight* support to Classifier. Returns ------- self : returns an instance of self. """ check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], multi_output=True) self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1) Y = self._label_binarizer.fit_transform(y) if not self._label_binarizer.y_type_.startswith('multilabel'): y = column_or_1d(y, warn=True) else: # we don't (yet) support multi-label classification in Ridge raise ValueError( "%s doesn't support multi-label classification" % ( self.__class__.__name__)) if self.class_weight: if sample_weight is None: sample_weight = 1. # modify the sample weights with the corresponding class weight sample_weight = (sample_weight * compute_sample_weight(self.class_weight, y)) super().fit(X, Y, sample_weight=sample_weight) return self @property def classes_(self): return self._label_binarizer.classes_ class _RidgeGCV(LinearModel): """Ridge regression with built-in Generalized Cross-Validation It allows efficient Leave-One-Out cross-validation. This class is not intended to be used directly. Use RidgeCV instead. Notes ----- We want to solve (K + alpha*Id)c = y, where K = X X^T is the kernel matrix. Let G = (K + alpha*Id)^-1. Dual solution: c = Gy Primal solution: w = X^T c Compute eigendecomposition K = Q V Q^T. Then G = Q (V + alpha*Id)^-1 Q^T, where (V + alpha*Id) is diagonal. It is thus inexpensive to inverse for many alphas. Let loov be the vector of prediction values for each example when the model was fitted with all examples but this example. loov = (KGY - diag(KG)Y) / diag(I-KG) Let looe be the vector of prediction errors for each example when the model was fitted with all examples but this example. looe = y - loov = c / diag(G) References ---------- http://cbcl.mit.edu/publications/ps/MIT-CSAIL-TR-2007-025.pdf https://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf """ def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, copy_X=True, gcv_mode=None, store_cv_values=False): self.alphas = np.asarray(alphas) self.fit_intercept = fit_intercept self.normalize = normalize self.scoring = scoring self.copy_X = copy_X self.gcv_mode = gcv_mode self.store_cv_values = store_cv_values def _pre_compute(self, X, y, centered_kernel=True): # even if X is very sparse, K is usually very dense K = safe_sparse_dot(X, X.T, dense_output=True) # the following emulates an additional constant regressor # corresponding to fit_intercept=True # but this is done only when the features have been centered if centered_kernel: K += np.ones_like(K) v, Q = linalg.eigh(K) QT_y = np.dot(Q.T, y) return v, Q, QT_y def _decomp_diag(self, v_prime, Q): # compute diagonal of the matrix: dot(Q, dot(diag(v_prime), Q^T)) return (v_prime * Q ** 2).sum(axis=-1) def _diag_dot(self, D, B): # compute dot(diag(D), B) if len(B.shape) > 1: # handle case where B is > 1-d D = D[(slice(None), ) + (np.newaxis, ) * (len(B.shape) - 1)] return D * B def _errors_and_values_helper(self, alpha, y, v, Q, QT_y): """Helper function to avoid code duplication between self._errors and self._values. Notes ----- We don't construct matrix G, instead compute action on y & diagonal. """ w = 1. / (v + alpha) constant_column = np.var(Q, 0) < 1.e-12 # detect constant columns w[constant_column] = 0 # cancel the regularization for the intercept c = np.dot(Q, self._diag_dot(w, QT_y)) G_diag = self._decomp_diag(w, Q) # handle case where y is 2-d if len(y.shape) != 1: G_diag = G_diag[:, np.newaxis] return G_diag, c def _errors(self, alpha, y, v, Q, QT_y): G_diag, c = self._errors_and_values_helper(alpha, y, v, Q, QT_y) return (c / G_diag) ** 2, c def _values(self, alpha, y, v, Q, QT_y): G_diag, c = self._errors_and_values_helper(alpha, y, v, Q, QT_y) return y - (c / G_diag), c def _pre_compute_svd(self, X, y, centered_kernel=True): if sparse.issparse(X): raise TypeError("SVD not supported for sparse matrices") if centered_kernel: X = np.hstack((X, np.ones((X.shape[0], 1)))) # to emulate fit_intercept=True situation, add a column on ones # Note that by centering, the other columns are orthogonal to that one U, s, _ = linalg.svd(X, full_matrices=0) v = s ** 2 UT_y = np.dot(U.T, y) return v, U, UT_y def _errors_and_values_svd_helper(self, alpha, y, v, U, UT_y): """Helper function to avoid code duplication between self._errors_svd and self._values_svd. """ constant_column = np.var(U, 0) < 1.e-12 # detect columns colinear to ones w = ((v + alpha) ** -1) - (alpha ** -1) w[constant_column] = - (alpha ** -1) # cancel the regularization for the intercept c = np.dot(U, self._diag_dot(w, UT_y)) + (alpha ** -1) * y G_diag = self._decomp_diag(w, U) + (alpha ** -1) if len(y.shape) != 1: # handle case where y is 2-d G_diag = G_diag[:, np.newaxis] return G_diag, c def _errors_svd(self, alpha, y, v, U, UT_y): G_diag, c = self._errors_and_values_svd_helper(alpha, y, v, U, UT_y) return (c / G_diag) ** 2, c def _values_svd(self, alpha, y, v, U, UT_y): G_diag, c = self._errors_and_values_svd_helper(alpha, y, v, U, UT_y) return y - (c / G_diag), c def fit(self, X, y, sample_weight=None): """Fit Ridge regression model Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training data y : array-like, shape = [n_samples] or [n_samples, n_targets] Target values. Will be cast to X's dtype if necessary sample_weight : float or array-like of shape [n_samples] Sample weight Returns ------- self : object """ X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float64, multi_output=True, y_numeric=True) if sample_weight is not None and not isinstance(sample_weight, float): sample_weight = check_array(sample_weight, ensure_2d=False) n_samples, n_features = X.shape X, y, X_offset, y_offset, X_scale = LinearModel._preprocess_data( X, y, self.fit_intercept, self.normalize, self.copy_X, sample_weight=sample_weight) gcv_mode = self.gcv_mode with_sw = len(np.shape(sample_weight)) if gcv_mode is None or gcv_mode == 'auto': if sparse.issparse(X) or n_features > n_samples or with_sw: gcv_mode = 'eigen' else: gcv_mode = 'svd' elif gcv_mode == "svd" and with_sw: # FIXME non-uniform sample weights not yet supported warnings.warn("non-uniform sample weights unsupported for svd, " "forcing usage of eigen") gcv_mode = 'eigen' if gcv_mode == 'eigen': _pre_compute = self._pre_compute _errors = self._errors _values = self._values elif gcv_mode == 'svd': # assert n_samples >= n_features _pre_compute = self._pre_compute_svd _errors = self._errors_svd _values = self._values_svd else: raise ValueError('bad gcv_mode "%s"' % gcv_mode) if sample_weight is not None: X, y = _rescale_data(X, y, sample_weight) centered_kernel = not sparse.issparse(X) and self.fit_intercept v, Q, QT_y = _pre_compute(X, y, centered_kernel) n_y = 1 if len(y.shape) == 1 else y.shape[1] cv_values = np.zeros((n_samples * n_y, len(self.alphas))) C = [] scorer = check_scoring(self, scoring=self.scoring, allow_none=True) error = scorer is None if np.any(self.alphas < 0): raise ValueError("alphas cannot be negative. " "Got {} containing some " "negative value instead.".format(self.alphas)) for i, alpha in enumerate(self.alphas): if error: out, c = _errors(float(alpha), y, v, Q, QT_y) else: out, c = _values(float(alpha), y, v, Q, QT_y) cv_values[:, i] = out.ravel() C.append(c) if error: best = cv_values.mean(axis=0).argmin() else: # The scorer want an object that will make the predictions but # they are already computed efficiently by _RidgeGCV. This # identity_estimator will just return them def identity_estimator(): pass identity_estimator.decision_function = lambda y_predict: y_predict identity_estimator.predict = lambda y_predict: y_predict out = [scorer(identity_estimator, y.ravel(), cv_values[:, i]) for i in range(len(self.alphas))] best = np.argmax(out) self.alpha_ = self.alphas[best] self.dual_coef_ = C[best] self.coef_ = safe_sparse_dot(self.dual_coef_.T, X) self._set_intercept(X_offset, y_offset, X_scale) if self.store_cv_values: if len(y.shape) == 1: cv_values_shape = n_samples, len(self.alphas) else: cv_values_shape = n_samples, n_y, len(self.alphas) self.cv_values_ = cv_values.reshape(cv_values_shape) return self class _BaseRidgeCV(LinearModel): def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, gcv_mode=None, store_cv_values=False): self.alphas = np.asarray(alphas) self.fit_intercept = fit_intercept self.normalize = normalize self.scoring = scoring self.cv = cv self.gcv_mode = gcv_mode self.store_cv_values = store_cv_values def fit(self, X, y, sample_weight=None): """Fit Ridge regression model Parameters ---------- X : array-like, shape = [n_samples, n_features] Training data y : array-like, shape = [n_samples] or [n_samples, n_targets] Target values. Will be cast to X's dtype if necessary sample_weight : float or array-like of shape [n_samples] Sample weight Returns ------- self : object """ if self.cv is None: estimator = _RidgeGCV(self.alphas, fit_intercept=self.fit_intercept, normalize=self.normalize, scoring=self.scoring, gcv_mode=self.gcv_mode, store_cv_values=self.store_cv_values) estimator.fit(X, y, sample_weight=sample_weight) self.alpha_ = estimator.alpha_ if self.store_cv_values: self.cv_values_ = estimator.cv_values_ else: if self.store_cv_values: raise ValueError("cv!=None and store_cv_values=True " " are incompatible") parameters = {'alpha': self.alphas} gs = GridSearchCV(Ridge(fit_intercept=self.fit_intercept, normalize=self.normalize), parameters, cv=self.cv, scoring=self.scoring) gs.fit(X, y, sample_weight=sample_weight) estimator = gs.best_estimator_ self.alpha_ = gs.best_estimator_.alpha self.coef_ = estimator.coef_ self.intercept_ = estimator.intercept_ return self class RidgeCV(_BaseRidgeCV, RegressorMixin): """Ridge regression with built-in cross-validation. See glossary entry for :term:`cross-validation estimator`. By default, it performs Generalized Cross-Validation, which is a form of efficient Leave-One-Out cross-validation. Read more in the :ref:`User Guide <ridge_regression>`. Parameters ---------- alphas : numpy array of shape [n_alphas] Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to ``C^-1`` in other linear models such as LogisticRegression or LinearSVC. fit_intercept : boolean Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional, default False This parameter is ignored when ``fit_intercept`` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``. scoring : string, callable or None, optional, default: None A string (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)``. cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the efficient Leave-One-Out cross-validation - integer, to specify the number of folds. - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if ``y`` is binary or multiclass, :class:`sklearn.model_selection.StratifiedKFold` is used, else, :class:`sklearn.model_selection.KFold` is used. Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here. gcv_mode : {None, 'auto', 'svd', eigen'}, optional Flag indicating which strategy to use when performing Generalized Cross-Validation. Options are:: 'auto' : use svd if n_samples > n_features or when X is a sparse matrix, otherwise use eigen 'svd' : force computation via singular value decomposition of X (does not work for sparse matrices) 'eigen' : force computation via eigendecomposition of X^T X The 'auto' mode is the default and is intended to pick the cheaper option of the two depending upon the shape and format of the training data. store_cv_values : boolean, default=False Flag indicating if the cross-validation values corresponding to each alpha should be stored in the ``cv_values_`` attribute (see below). This flag is only compatible with ``cv=None`` (i.e. using Generalized Cross-Validation). Attributes ---------- cv_values_ : array, shape = [n_samples, n_alphas] or \ shape = [n_samples, n_targets, n_alphas], optional Cross-validation values for each alpha (if ``store_cv_values=True``\ and ``cv=None``). After ``fit()`` has been called, this attribute \ will contain the mean squared errors (by default) or the values \ of the ``{loss,score}_func`` function (if provided in the constructor). coef_ : array, shape = [n_features] or [n_targets, n_features] Weight vector(s). intercept_ : float | array, shape = (n_targets,) Independent term in decision function. Set to 0.0 if ``fit_intercept = False``. alpha_ : float Estimated regularization parameter. Examples -------- >>> from sklearn.datasets import load_diabetes >>> from sklearn.linear_model import RidgeCV >>> X, y = load_diabetes(return_X_y=True) >>> clf = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y) >>> clf.score(X, y) # doctest: +ELLIPSIS 0.5166... See also -------- Ridge : Ridge regression RidgeClassifier : Ridge classifier RidgeClassifierCV : Ridge classifier with built-in cross validation """ pass class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV): """Ridge classifier with built-in cross-validation. See glossary entry for :term:`cross-validation estimator`. By default, it performs Generalized Cross-Validation, which is a form of efficient Leave-One-Out cross-validation. Currently, only the n_features > n_samples case is handled efficiently. Read more in the :ref:`User Guide <ridge_regression>`. Parameters ---------- alphas : numpy array of shape [n_alphas] Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to ``C^-1`` in other linear models such as LogisticRegression or LinearSVC. fit_intercept : boolean Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional, default False This parameter is ignored when ``fit_intercept`` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``. scoring : string, callable or None, optional, default: None A string (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)``. cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the efficient Leave-One-Out cross-validation - integer, to specify the number of folds. - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` store_cv_values : boolean, default=False Flag indicating if the cross-validation values corresponding to each alpha should be stored in the ``cv_values_`` attribute (see below). This flag is only compatible with ``cv=None`` (i.e. using Generalized Cross-Validation). Attributes ---------- cv_values_ : array, shape = [n_samples, n_targets, n_alphas], optional Cross-validation values for each alpha (if ``store_cv_values=True`` and ``cv=None``). After ``fit()`` has been called, this attribute will contain the mean squared errors (by default) or the values of the ``{loss,score}_func`` function (if provided in the constructor). coef_ : array, shape = [n_features] or [n_targets, n_features] Weight vector(s). intercept_ : float | array, shape = (n_targets,) Independent term in decision function. Set to 0.0 if ``fit_intercept = False``. alpha_ : float Estimated regularization parameter Examples -------- >>> from sklearn.datasets import load_breast_cancer >>> from sklearn.linear_model import RidgeClassifierCV >>> X, y = load_breast_cancer(return_X_y=True) >>> clf = RidgeClassifierCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y) >>> clf.score(X, y) # doctest: +ELLIPSIS 0.9630... See also -------- Ridge : Ridge regression RidgeClassifier : Ridge classifier RidgeCV : Ridge regression with built-in cross validation Notes ----- For multi-class classification, n_class classifiers are trained in a one-versus-all approach. Concretely, this is implemented by taking advantage of the multi-variate response support in Ridge. """ def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None, store_cv_values=False): super().__init__( alphas=alphas, fit_intercept=fit_intercept, normalize=normalize, scoring=scoring, cv=cv, store_cv_values=store_cv_values) self.class_weight = class_weight def fit(self, X, y, sample_weight=None): """Fit the ridge classifier. Parameters ---------- X : array-like, shape (n_samples, n_features) Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) Target values. Will be cast to X's dtype if necessary sample_weight : float or numpy array of shape (n_samples,) Sample weight. Returns ------- self : object """ check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], multi_output=True) self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1) Y = self._label_binarizer.fit_transform(y) if not self._label_binarizer.y_type_.startswith('multilabel'): y = column_or_1d(y, warn=True) if self.class_weight: if sample_weight is None: sample_weight = 1. # modify the sample weights with the corresponding class weight sample_weight = (sample_weight * compute_sample_weight(self.class_weight, y)) _BaseRidgeCV.fit(self, X, Y, sample_weight=sample_weight) return self @property def classes_(self): return self._label_binarizer.classes_

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from abc import ABCMeta, abstractmethod import warnings import numpy as np from scipy import linalg from scipy import sparse from scipy.sparse import linalg as sp_linalg from .base import LinearClassifierMixin, LinearModel, _rescale_data from .sag import sag_solver from ..base import RegressorMixin from ..utils.extmath import safe_sparse_dot from ..utils.extmath import row_norms from ..utils import check_X_y from ..utils import check_array from ..utils import check_consistent_length from ..utils import compute_sample_weight from ..utils import column_or_1d from ..preprocessing import LabelBinarizer from ..model_selection import GridSearchCV from ..metrics.scorer import check_scoring from ..exceptions import ConvergenceWarning def _solve_sparse_cg(X, y, alpha, max_iter=None, tol=1e-3, verbose=0): n_samples, n_features = X.shape X1 = sp_linalg.aslinearoperator(X) coefs = np.empty((y.shape[1], n_features), dtype=X.dtype) if n_features > n_samples: def create_mv(curr_alpha): def _mv(x): return X1.matvec(X1.rmatvec(x)) + curr_alpha * x return _mv else: def create_mv(curr_alpha): def _mv(x): return X1.rmatvec(X1.matvec(x)) + curr_alpha * x return _mv for i in range(y.shape[1]): y_column = y[:, i] mv = create_mv(alpha[i]) if n_features > n_samples: C = sp_linalg.LinearOperator( (n_samples, n_samples), matvec=mv, dtype=X.dtype) try: coef, info = sp_linalg.cg(C, y_column, tol=tol, atol='legacy') except TypeError: coef, info = sp_linalg.cg(C, y_column, tol=tol) coefs[i] = X1.rmatvec(coef) else: y_column = X1.rmatvec(y_column) C = sp_linalg.LinearOperator( (n_features, n_features), matvec=mv, dtype=X.dtype) try: coefs[i], info = sp_linalg.cg(C, y_column, maxiter=max_iter, tol=tol, atol='legacy') except TypeError: coefs[i], info = sp_linalg.cg(C, y_column, maxiter=max_iter, tol=tol) if info < 0: raise ValueError("Failed with error code %d" % info) if max_iter is None and info > 0 and verbose: warnings.warn("sparse_cg did not converge after %d iterations." % info, ConvergenceWarning) return coefs def _solve_lsqr(X, y, alpha, max_iter=None, tol=1e-3): n_samples, n_features = X.shape coefs = np.empty((y.shape[1], n_features), dtype=X.dtype) n_iter = np.empty(y.shape[1], dtype=np.int32) sqrt_alpha = np.sqrt(alpha) for i in range(y.shape[1]): y_column = y[:, i] info = sp_linalg.lsqr(X, y_column, damp=sqrt_alpha[i], atol=tol, btol=tol, iter_lim=max_iter) coefs[i] = info[0] n_iter[i] = info[2] return coefs, n_iter def _solve_cholesky(X, y, alpha): n_samples, n_features = X.shape n_targets = y.shape[1] A = safe_sparse_dot(X.T, X, dense_output=True) Xy = safe_sparse_dot(X.T, y, dense_output=True) one_alpha = np.array_equal(alpha, len(alpha) * [alpha[0]]) if one_alpha: A.flat[::n_features + 1] += alpha[0] return linalg.solve(A, Xy, sym_pos=True, overwrite_a=True).T else: coefs = np.empty([n_targets, n_features], dtype=X.dtype) for coef, target, current_alpha in zip(coefs, Xy.T, alpha): A.flat[::n_features + 1] += current_alpha coef[:] = linalg.solve(A, target, sym_pos=True, overwrite_a=False).ravel() A.flat[::n_features + 1] -= current_alpha return coefs def _solve_cholesky_kernel(K, y, alpha, sample_weight=None, copy=False): n_samples = K.shape[0] n_targets = y.shape[1] if copy: K = K.copy() alpha = np.atleast_1d(alpha) one_alpha = (alpha == alpha[0]).all() has_sw = isinstance(sample_weight, np.ndarray) \ or sample_weight not in [1.0, None] if has_sw: sw = np.sqrt(np.atleast_1d(sample_weight)) y = y * sw[:, np.newaxis] K *= np.outer(sw, sw) if one_alpha: K.flat[::n_samples + 1] += alpha[0] try: dual_coef = linalg.solve(K, y, sym_pos=True, overwrite_a=False) except np.linalg.LinAlgError: warnings.warn("Singular matrix in solving dual problem. Using " "least-squares solution instead.") dual_coef = linalg.lstsq(K, y)[0] K.flat[::n_samples + 1] -= alpha[0] if has_sw: dual_coef *= sw[:, np.newaxis] return dual_coef else: dual_coefs = np.empty([n_targets, n_samples], K.dtype) for dual_coef, target, current_alpha in zip(dual_coefs, y.T, alpha): K.flat[::n_samples + 1] += current_alpha dual_coef[:] = linalg.solve(K, target, sym_pos=True, overwrite_a=False).ravel() K.flat[::n_samples + 1] -= current_alpha if has_sw: dual_coefs *= sw[np.newaxis, :] return dual_coefs.T def _solve_svd(X, y, alpha): U, s, Vt = linalg.svd(X, full_matrices=False) idx = s > 1e-15 s_nnz = s[idx][:, np.newaxis] UTy = np.dot(U.T, y) d = np.zeros((s.size, alpha.size), dtype=X.dtype) d[idx] = s_nnz / (s_nnz ** 2 + alpha) d_UT_y = d * UTy return np.dot(Vt.T, d_UT_y).T def ridge_regression(X, y, alpha, sample_weight=None, solver='auto', max_iter=None, tol=1e-3, verbose=0, random_state=None, return_n_iter=False, return_intercept=False): if return_intercept and sparse.issparse(X) and solver != 'sag': if solver != 'auto': warnings.warn("In Ridge, only 'sag' solver can currently fit the " "intercept when X is sparse. Solver has been " "automatically changed into 'sag'.") solver = 'sag' _dtype = [np.float64, np.float32] if solver in ['sag', 'saga']: X = check_array(X, accept_sparse=['csr'], dtype=np.float64, order='C') y = check_array(y, dtype=np.float64, ensure_2d=False, order='F') else: X = check_array(X, accept_sparse=['csr', 'csc', 'coo'], dtype=_dtype) y = check_array(y, dtype=X.dtype, ensure_2d=False) check_consistent_length(X, y) n_samples, n_features = X.shape if y.ndim > 2: raise ValueError("Target y has the wrong shape %s" % str(y.shape)) ravel = False if y.ndim == 1: y = y.reshape(-1, 1) ravel = True n_samples_, n_targets = y.shape if n_samples != n_samples_: raise ValueError("Number of samples in X and y does not correspond:" " %d != %d" % (n_samples, n_samples_)) has_sw = sample_weight is not None if solver == 'auto': if not sparse.issparse(X) or has_sw: solver = 'cholesky' else: solver = 'sparse_cg' if has_sw: if np.atleast_1d(sample_weight).ndim > 1: raise ValueError("Sample weights must be 1D array or scalar") if solver not in ['sag', 'saga']: # SAG supports sample_weight directly. For other solvers, # we implement sample_weight via a simple rescaling. X, y = _rescale_data(X, y, sample_weight) # There should be either 1 or n_targets penalties alpha = np.asarray(alpha, dtype=X.dtype).ravel() if alpha.size not in [1, n_targets]: raise ValueError("Number of targets and number of penalties " "do not correspond: %d != %d" % (alpha.size, n_targets)) if alpha.size == 1 and n_targets > 1: alpha = np.repeat(alpha, n_targets) if solver not in ('sparse_cg', 'cholesky', 'svd', 'lsqr', 'sag', 'saga'): raise ValueError('Solver %s not understood' % solver) n_iter = None if solver == 'sparse_cg': coef = _solve_sparse_cg(X, y, alpha, max_iter, tol, verbose) elif solver == 'lsqr': coef, n_iter = _solve_lsqr(X, y, alpha, max_iter, tol) elif solver == 'cholesky': if n_features > n_samples: K = safe_sparse_dot(X, X.T, dense_output=True) try: dual_coef = _solve_cholesky_kernel(K, y, alpha) coef = safe_sparse_dot(X.T, dual_coef, dense_output=True).T except linalg.LinAlgError: # use SVD solver if matrix is singular solver = 'svd' else: try: coef = _solve_cholesky(X, y, alpha) except linalg.LinAlgError: # use SVD solver if matrix is singular solver = 'svd' elif solver in ['sag', 'saga']: # precompute max_squared_sum for all targets max_squared_sum = row_norms(X, squared=True).max() coef = np.empty((y.shape[1], n_features)) n_iter = np.empty(y.shape[1], dtype=np.int32) intercept = np.zeros((y.shape[1], )) for i, (alpha_i, target) in enumerate(zip(alpha, y.T)): init = {'coef': np.zeros((n_features + int(return_intercept), 1))} coef_, n_iter_, _ = sag_solver( X, target.ravel(), sample_weight, 'squared', alpha_i, 0, max_iter, tol, verbose, random_state, False, max_squared_sum, init, is_saga=solver == 'saga') if return_intercept: coef[i] = coef_[:-1] intercept[i] = coef_[-1] else: coef[i] = coef_ n_iter[i] = n_iter_ if intercept.shape[0] == 1: intercept = intercept[0] coef = np.asarray(coef) if solver == 'svd': if sparse.issparse(X): raise TypeError('SVD solver does not support sparse' ' inputs currently') coef = _solve_svd(X, y, alpha) if ravel: # When y was passed as a 1d-array, we flatten the coefficients. coef = coef.ravel() if return_n_iter and return_intercept: return coef, n_iter, intercept elif return_intercept: return coef, intercept elif return_n_iter: return coef, n_iter else: return coef class _BaseRidge(LinearModel, metaclass=ABCMeta): @abstractmethod def __init__(self, alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=1e-3, solver="auto", random_state=None): self.alpha = alpha self.fit_intercept = fit_intercept self.normalize = normalize self.copy_X = copy_X self.max_iter = max_iter self.tol = tol self.solver = solver self.random_state = random_state def fit(self, X, y, sample_weight=None): if self.solver in ('sag', 'saga'): _dtype = np.float64 else: # all other solvers work at both float precision levels _dtype = [np.float64, np.float32] X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=_dtype, multi_output=True, y_numeric=True) if ((sample_weight is not None) and np.atleast_1d(sample_weight).ndim > 1): raise ValueError("Sample weights must be 1D array or scalar") X, y, X_offset, y_offset, X_scale = self._preprocess_data( X, y, self.fit_intercept, self.normalize, self.copy_X, sample_weight=sample_weight) # temporary fix for fitting the intercept with sparse data using 'sag' if sparse.issparse(X) and self.fit_intercept: self.coef_, self.n_iter_, self.intercept_ = ridge_regression( X, y, alpha=self.alpha, sample_weight=sample_weight, max_iter=self.max_iter, tol=self.tol, solver=self.solver, random_state=self.random_state, return_n_iter=True, return_intercept=True) self.intercept_ += y_offset else: self.coef_, self.n_iter_ = ridge_regression( X, y, alpha=self.alpha, sample_weight=sample_weight, max_iter=self.max_iter, tol=self.tol, solver=self.solver, random_state=self.random_state, return_n_iter=True, return_intercept=False) self._set_intercept(X_offset, y_offset, X_scale) return self class Ridge(_BaseRidge, RegressorMixin): def __init__(self, alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=1e-3, solver="auto", random_state=None): super().__init__( alpha=alpha, fit_intercept=fit_intercept, normalize=normalize, copy_X=copy_X, max_iter=max_iter, tol=tol, solver=solver, random_state=random_state) def fit(self, X, y, sample_weight=None): return super().fit(X, y, sample_weight=sample_weight) class RidgeClassifier(LinearClassifierMixin, _BaseRidge): def __init__(self, alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=1e-3, class_weight=None, solver="auto", random_state=None): super().__init__( alpha=alpha, fit_intercept=fit_intercept, normalize=normalize, copy_X=copy_X, max_iter=max_iter, tol=tol, solver=solver, random_state=random_state) self.class_weight = class_weight def fit(self, X, y, sample_weight=None): check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], multi_output=True) self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1) Y = self._label_binarizer.fit_transform(y) if not self._label_binarizer.y_type_.startswith('multilabel'): y = column_or_1d(y, warn=True) else: # we don't (yet) support multi-label classification in Ridge raise ValueError( "%s doesn't support multi-label classification" % ( self.__class__.__name__)) if self.class_weight: if sample_weight is None: sample_weight = 1. # modify the sample weights with the corresponding class weight sample_weight = (sample_weight * compute_sample_weight(self.class_weight, y)) super().fit(X, Y, sample_weight=sample_weight) return self @property def classes_(self): return self._label_binarizer.classes_ class _RidgeGCV(LinearModel): def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, copy_X=True, gcv_mode=None, store_cv_values=False): self.alphas = np.asarray(alphas) self.fit_intercept = fit_intercept self.normalize = normalize self.scoring = scoring self.copy_X = copy_X self.gcv_mode = gcv_mode self.store_cv_values = store_cv_values def _pre_compute(self, X, y, centered_kernel=True): # even if X is very sparse, K is usually very dense K = safe_sparse_dot(X, X.T, dense_output=True) # the following emulates an additional constant regressor # corresponding to fit_intercept=True # but this is done only when the features have been centered if centered_kernel: K += np.ones_like(K) v, Q = linalg.eigh(K) QT_y = np.dot(Q.T, y) return v, Q, QT_y def _decomp_diag(self, v_prime, Q): # compute diagonal of the matrix: dot(Q, dot(diag(v_prime), Q^T)) return (v_prime * Q ** 2).sum(axis=-1) def _diag_dot(self, D, B): # compute dot(diag(D), B) if len(B.shape) > 1: # handle case where B is > 1-d D = D[(slice(None), ) + (np.newaxis, ) * (len(B.shape) - 1)] return D * B def _errors_and_values_helper(self, alpha, y, v, Q, QT_y): w = 1. / (v + alpha) constant_column = np.var(Q, 0) < 1.e-12 # detect constant columns w[constant_column] = 0 # cancel the regularization for the intercept c = np.dot(Q, self._diag_dot(w, QT_y)) G_diag = self._decomp_diag(w, Q) # handle case where y is 2-d if len(y.shape) != 1: G_diag = G_diag[:, np.newaxis] return G_diag, c def _errors(self, alpha, y, v, Q, QT_y): G_diag, c = self._errors_and_values_helper(alpha, y, v, Q, QT_y) return (c / G_diag) ** 2, c def _values(self, alpha, y, v, Q, QT_y): G_diag, c = self._errors_and_values_helper(alpha, y, v, Q, QT_y) return y - (c / G_diag), c def _pre_compute_svd(self, X, y, centered_kernel=True): if sparse.issparse(X): raise TypeError("SVD not supported for sparse matrices") if centered_kernel: X = np.hstack((X, np.ones((X.shape[0], 1)))) # to emulate fit_intercept=True situation, add a column on ones # Note that by centering, the other columns are orthogonal to that one U, s, _ = linalg.svd(X, full_matrices=0) v = s ** 2 UT_y = np.dot(U.T, y) return v, U, UT_y def _errors_and_values_svd_helper(self, alpha, y, v, U, UT_y): constant_column = np.var(U, 0) < 1.e-12 # detect columns colinear to ones w = ((v + alpha) ** -1) - (alpha ** -1) w[constant_column] = - (alpha ** -1) # cancel the regularization for the intercept c = np.dot(U, self._diag_dot(w, UT_y)) + (alpha ** -1) * y G_diag = self._decomp_diag(w, U) + (alpha ** -1) if len(y.shape) != 1: # handle case where y is 2-d G_diag = G_diag[:, np.newaxis] return G_diag, c def _errors_svd(self, alpha, y, v, U, UT_y): G_diag, c = self._errors_and_values_svd_helper(alpha, y, v, U, UT_y) return (c / G_diag) ** 2, c def _values_svd(self, alpha, y, v, U, UT_y): G_diag, c = self._errors_and_values_svd_helper(alpha, y, v, U, UT_y) return y - (c / G_diag), c def fit(self, X, y, sample_weight=None): X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float64, multi_output=True, y_numeric=True) if sample_weight is not None and not isinstance(sample_weight, float): sample_weight = check_array(sample_weight, ensure_2d=False) n_samples, n_features = X.shape X, y, X_offset, y_offset, X_scale = LinearModel._preprocess_data( X, y, self.fit_intercept, self.normalize, self.copy_X, sample_weight=sample_weight) gcv_mode = self.gcv_mode with_sw = len(np.shape(sample_weight)) if gcv_mode is None or gcv_mode == 'auto': if sparse.issparse(X) or n_features > n_samples or with_sw: gcv_mode = 'eigen' else: gcv_mode = 'svd' elif gcv_mode == "svd" and with_sw: # FIXME non-uniform sample weights not yet supported warnings.warn("non-uniform sample weights unsupported for svd, " "forcing usage of eigen") gcv_mode = 'eigen' if gcv_mode == 'eigen': _pre_compute = self._pre_compute _errors = self._errors _values = self._values elif gcv_mode == 'svd': # assert n_samples >= n_features _pre_compute = self._pre_compute_svd _errors = self._errors_svd _values = self._values_svd else: raise ValueError('bad gcv_mode "%s"' % gcv_mode) if sample_weight is not None: X, y = _rescale_data(X, y, sample_weight) centered_kernel = not sparse.issparse(X) and self.fit_intercept v, Q, QT_y = _pre_compute(X, y, centered_kernel) n_y = 1 if len(y.shape) == 1 else y.shape[1] cv_values = np.zeros((n_samples * n_y, len(self.alphas))) C = [] scorer = check_scoring(self, scoring=self.scoring, allow_none=True) error = scorer is None if np.any(self.alphas < 0): raise ValueError("alphas cannot be negative. " "Got {} containing some " "negative value instead.".format(self.alphas)) for i, alpha in enumerate(self.alphas): if error: out, c = _errors(float(alpha), y, v, Q, QT_y) else: out, c = _values(float(alpha), y, v, Q, QT_y) cv_values[:, i] = out.ravel() C.append(c) if error: best = cv_values.mean(axis=0).argmin() else: # The scorer want an object that will make the predictions but # they are already computed efficiently by _RidgeGCV. This # identity_estimator will just return them def identity_estimator(): pass identity_estimator.decision_function = lambda y_predict: y_predict identity_estimator.predict = lambda y_predict: y_predict out = [scorer(identity_estimator, y.ravel(), cv_values[:, i]) for i in range(len(self.alphas))] best = np.argmax(out) self.alpha_ = self.alphas[best] self.dual_coef_ = C[best] self.coef_ = safe_sparse_dot(self.dual_coef_.T, X) self._set_intercept(X_offset, y_offset, X_scale) if self.store_cv_values: if len(y.shape) == 1: cv_values_shape = n_samples, len(self.alphas) else: cv_values_shape = n_samples, n_y, len(self.alphas) self.cv_values_ = cv_values.reshape(cv_values_shape) return self class _BaseRidgeCV(LinearModel): def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, gcv_mode=None, store_cv_values=False): self.alphas = np.asarray(alphas) self.fit_intercept = fit_intercept self.normalize = normalize self.scoring = scoring self.cv = cv self.gcv_mode = gcv_mode self.store_cv_values = store_cv_values def fit(self, X, y, sample_weight=None): if self.cv is None: estimator = _RidgeGCV(self.alphas, fit_intercept=self.fit_intercept, normalize=self.normalize, scoring=self.scoring, gcv_mode=self.gcv_mode, store_cv_values=self.store_cv_values) estimator.fit(X, y, sample_weight=sample_weight) self.alpha_ = estimator.alpha_ if self.store_cv_values: self.cv_values_ = estimator.cv_values_ else: if self.store_cv_values: raise ValueError("cv!=None and store_cv_values=True " " are incompatible") parameters = {'alpha': self.alphas} gs = GridSearchCV(Ridge(fit_intercept=self.fit_intercept, normalize=self.normalize), parameters, cv=self.cv, scoring=self.scoring) gs.fit(X, y, sample_weight=sample_weight) estimator = gs.best_estimator_ self.alpha_ = gs.best_estimator_.alpha self.coef_ = estimator.coef_ self.intercept_ = estimator.intercept_ return self class RidgeCV(_BaseRidgeCV, RegressorMixin): pass class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV): def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None, store_cv_values=False): super().__init__( alphas=alphas, fit_intercept=fit_intercept, normalize=normalize, scoring=scoring, cv=cv, store_cv_values=store_cv_values) self.class_weight = class_weight def fit(self, X, y, sample_weight=None): check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], multi_output=True) self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1) Y = self._label_binarizer.fit_transform(y) if not self._label_binarizer.y_type_.startswith('multilabel'): y = column_or_1d(y, warn=True) if self.class_weight: if sample_weight is None: sample_weight = 1. # modify the sample weights with the corresponding class weight sample_weight = (sample_weight * compute_sample_weight(self.class_weight, y)) _BaseRidgeCV.fit(self, X, Y, sample_weight=sample_weight) return self @property def classes_(self): return self._label_binarizer.classes_

true

f71bec5753bc8e9f3a6c6cc2541b07d58772b075

1,201

py

Python

tests/tests/test_design/test_randomcodons.py

klavinslab/coral

17f59591211562a59a051f474cd6cecba4829df9

[ "MIT" ]

34

2015-12-26T22:13:51.000Z

2021-11-17T11:46:37.000Z

tests/tests/test_design/test_randomcodons.py

klavinslab/coral

17f59591211562a59a051f474cd6cecba4829df9

[ "MIT" ]

13

2015-09-11T23:27:51.000Z

2018-06-25T20:44:28.000Z

tests/tests/test_design/test_randomcodons.py

klavinslab/coral

17f59591211562a59a051f474cd6cecba4829df9

[ "MIT" ]

14

2015-10-08T17:08:48.000Z

2022-02-22T04:25:54.000Z

''' Tests for RandomCodons class of analysis module. ''' from nose.tools import assert_equal, assert_not_equal, assert_raises from coral import design, reaction, RNA def test_randomcodons(): ''' This test is pretty basic right now - not sure how much checking can be done for a random DNA base generator. ''' reference_seq = RNA('AUGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAUAG') reference_peptide = reaction.translate(reference_seq) output = design.random_codons(reference_peptide) output_peptide = reaction.translate(reference_seq) assert_equal(len(output), len(reference_seq) - 3) assert_equal(reference_peptide, output_peptide) assert_not_equal(reference_seq, output) # Setting too high a threshold should raise ValueError assert_raises(ValueError, design.random_codons, reference_peptide, frequency_cutoff=1.5) # Weighted should work w_output = design.random_codons(reference_peptide, weighted=True) w_output_peptide = reaction.translate(reference_seq) assert_equal(len(w_output), len(reference_seq) - 3) assert_equal(reference_peptide, w_output_peptide) assert_not_equal(reference_seq, w_output)

32.459459

70

0.757702

from nose.tools import assert_equal, assert_not_equal, assert_raises from coral import design, reaction, RNA def test_randomcodons(): reference_seq = RNA('AUGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAUAG') reference_peptide = reaction.translate(reference_seq) output = design.random_codons(reference_peptide) output_peptide = reaction.translate(reference_seq) assert_equal(len(output), len(reference_seq) - 3) assert_equal(reference_peptide, output_peptide) assert_not_equal(reference_seq, output) assert_raises(ValueError, design.random_codons, reference_peptide, frequency_cutoff=1.5) w_output = design.random_codons(reference_peptide, weighted=True) w_output_peptide = reaction.translate(reference_seq) assert_equal(len(w_output), len(reference_seq) - 3) assert_equal(reference_peptide, w_output_peptide) assert_not_equal(reference_seq, w_output)

true

f71bec9bd1ce0214c2dc30775d89387898d9b1fb

328

py

Python

truck_app/migrations/0010_remove_truck_likes.py

Svetloni89/truck_project

e365d2a84f32e62f8dbc5c371a3355dbbe93557d

[ "MIT" ]

null

truck_app/migrations/0010_remove_truck_likes.py

Svetloni89/truck_project

e365d2a84f32e62f8dbc5c371a3355dbbe93557d

[ "MIT" ]

null

truck_app/migrations/0010_remove_truck_likes.py

Svetloni89/truck_project

e365d2a84f32e62f8dbc5c371a3355dbbe93557d

[ "MIT" ]

null

# Generated by Django 3.1.3 on 2020-11-14 08:22 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('truck_app', '0009_auto_20201112_1114'), ] operations = [ migrations.RemoveField( model_name='truck', name='likes', ), ]

18.222222

49

0.591463

from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('truck_app', '0009_auto_20201112_1114'), ] operations = [ migrations.RemoveField( model_name='truck', name='likes', ), ]

true

f71bedbe0e1dd8d7b00f8bfa2461591aca993afa

2,299

py

Python

example/exampleapp/models.py

jphilip/django-treewidget

e2eff61f98ea7a520f29a4ed7395ec75d134246b

[ "MIT" ]

null

example/exampleapp/models.py

jphilip/django-treewidget

e2eff61f98ea7a520f29a4ed7395ec75d134246b

[ "MIT" ]

null

example/exampleapp/models.py

jphilip/django-treewidget

e2eff61f98ea7a520f29a4ed7395ec75d134246b

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models from mptt.models import MPTTModel from treebeard.mp_tree import MP_Node from treebeard.al_tree import AL_Node from treebeard.ns_tree import NS_Node from treewidget.fields import TreeForeignKey, TreeManyToManyField from django.utils.encoding import python_2_unicode_compatible # django-mptt @python_2_unicode_compatible class Mptt(MPTTModel): name = models.CharField(max_length=32) parent = TreeForeignKey( 'self', blank=True, null=True, on_delete=models.CASCADE, settings={'filtered': True}) def __str__(self): return self.name # django-treebeard @python_2_unicode_compatible class Treebeardmp(MP_Node): name = models.CharField(max_length=32) def __str__(self): return '%s' % self.name @python_2_unicode_compatible class Treebeardal(AL_Node): name = models.CharField(max_length=32) parent = models.ForeignKey('self', related_name='children_set', null=True, db_index=True, on_delete=models.CASCADE) sib_order = models.PositiveIntegerField() def __str__(self): return '%s' % self.name @python_2_unicode_compatible class Treebeardns(NS_Node): name = models.CharField(max_length=32) def __str__(self): return '%s' % self.name class Example(models.Model): mptt = TreeForeignKey(Mptt, on_delete=models.CASCADE) treebeardmp = TreeForeignKey(Treebeardmp, on_delete=models.CASCADE, settings={'show_buttons': True, 'filtered': True}) treebeardal = TreeForeignKey(Treebeardal, on_delete=models.CASCADE, settings={'search': True, 'dnd': True, 'sort': True}) treebeardns = TreeForeignKey(Treebeardns, on_delete=models.CASCADE, settings={'dnd': True}) mptt_many = TreeManyToManyField(Mptt, related_name='example_many', settings={'show_buttons': True, 'search': True, 'dnd': True}) treebeardmp_many = TreeManyToManyField(Treebeardmp, related_name='example_many') treebeardal_many = TreeManyToManyField(Treebeardal, related_name='example_many') treebeardns_many = TreeManyToManyField(Treebeardns, related_name='example_many')

35.921875

97

0.702044

from __future__ import unicode_literals from django.db import models from mptt.models import MPTTModel from treebeard.mp_tree import MP_Node from treebeard.al_tree import AL_Node from treebeard.ns_tree import NS_Node from treewidget.fields import TreeForeignKey, TreeManyToManyField from django.utils.encoding import python_2_unicode_compatible @python_2_unicode_compatible class Mptt(MPTTModel): name = models.CharField(max_length=32) parent = TreeForeignKey( 'self', blank=True, null=True, on_delete=models.CASCADE, settings={'filtered': True}) def __str__(self): return self.name @python_2_unicode_compatible class Treebeardmp(MP_Node): name = models.CharField(max_length=32) def __str__(self): return '%s' % self.name @python_2_unicode_compatible class Treebeardal(AL_Node): name = models.CharField(max_length=32) parent = models.ForeignKey('self', related_name='children_set', null=True, db_index=True, on_delete=models.CASCADE) sib_order = models.PositiveIntegerField() def __str__(self): return '%s' % self.name @python_2_unicode_compatible class Treebeardns(NS_Node): name = models.CharField(max_length=32) def __str__(self): return '%s' % self.name class Example(models.Model): mptt = TreeForeignKey(Mptt, on_delete=models.CASCADE) treebeardmp = TreeForeignKey(Treebeardmp, on_delete=models.CASCADE, settings={'show_buttons': True, 'filtered': True}) treebeardal = TreeForeignKey(Treebeardal, on_delete=models.CASCADE, settings={'search': True, 'dnd': True, 'sort': True}) treebeardns = TreeForeignKey(Treebeardns, on_delete=models.CASCADE, settings={'dnd': True}) mptt_many = TreeManyToManyField(Mptt, related_name='example_many', settings={'show_buttons': True, 'search': True, 'dnd': True}) treebeardmp_many = TreeManyToManyField(Treebeardmp, related_name='example_many') treebeardal_many = TreeManyToManyField(Treebeardal, related_name='example_many') treebeardns_many = TreeManyToManyField(Treebeardns, related_name='example_many')

true

f71bf0329ac143d3243fa730424a4eebdde5ed63

2,519

py

Python

letsencrypt/constants.py

felixonmars/certbot

324ebf468db402dbe44ba780ed2df682ab99af86

[ "Apache-2.0" ]

null

letsencrypt/constants.py

felixonmars/certbot

324ebf468db402dbe44ba780ed2df682ab99af86

[ "Apache-2.0" ]

null

letsencrypt/constants.py

felixonmars/certbot

324ebf468db402dbe44ba780ed2df682ab99af86

[ "Apache-2.0" ]

null

"""Let's Encrypt constants.""" import os import logging from acme import challenges SETUPTOOLS_PLUGINS_ENTRY_POINT = "letsencrypt.plugins" """Setuptools entry point group name for plugins.""" CLI_DEFAULTS = dict( config_files=[ "/etc/letsencrypt/cli.ini", # http://freedesktop.org/wiki/Software/xdg-user-dirs/ os.path.join(os.environ.get("XDG_CONFIG_HOME", "~/.config"), "letsencrypt", "cli.ini"), ], verbose_count=-(logging.WARNING / 10), server="https://acme-staging.api.letsencrypt.org/directory", rsa_key_size=2048, rollback_checkpoints=1, config_dir="/etc/letsencrypt", work_dir="/var/lib/letsencrypt", logs_dir="/var/log/letsencrypt", no_verify_ssl=False, tls_sni_01_port=challenges.TLSSNI01Response.PORT, auth_cert_path="./cert.pem", auth_chain_path="./chain.pem", strict_permissions=False, ) """Defaults for CLI flags and `.IConfig` attributes.""" RENEWER_DEFAULTS = dict( renewer_enabled="yes", renew_before_expiry="30 days", deploy_before_expiry="20 days", ) """Defaults for renewer script.""" EXCLUSIVE_CHALLENGES = frozenset([frozenset([ challenges.TLSSNI01, challenges.HTTP01])]) """Mutually exclusive challenges.""" ENHANCEMENTS = ["redirect", "http-header", "ocsp-stapling", "spdy"] """List of possible :class:`letsencrypt.interfaces.IInstaller` enhancements. List of expected options parameters: - redirect: None - http-header: TODO - ocsp-stapling: TODO - spdy: TODO """ ARCHIVE_DIR = "archive" """Archive directory, relative to `IConfig.config_dir`.""" CONFIG_DIRS_MODE = 0o755 """Directory mode for ``.IConfig.config_dir`` et al.""" ACCOUNTS_DIR = "accounts" """Directory where all accounts are saved.""" BACKUP_DIR = "backups" """Directory (relative to `IConfig.work_dir`) where backups are kept.""" CSR_DIR = "csr" """See `.IConfig.csr_dir`.""" IN_PROGRESS_DIR = "IN_PROGRESS" """Directory used before a permanent checkpoint is finalized (relative to `IConfig.work_dir`).""" KEY_DIR = "keys" """Directory (relative to `IConfig.config_dir`) where keys are saved.""" LIVE_DIR = "live" """Live directory, relative to `IConfig.config_dir`.""" TEMP_CHECKPOINT_DIR = "temp_checkpoint" """Temporary checkpoint directory (relative to `IConfig.work_dir`).""" RENEWAL_CONFIGS_DIR = "renewal" """Renewal configs directory, relative to `IConfig.config_dir`.""" RENEWER_CONFIG_FILENAME = "renewer.conf" """Renewer config file name (relative to `IConfig.config_dir`)."""

27.086022

73

0.710599

import os import logging from acme import challenges SETUPTOOLS_PLUGINS_ENTRY_POINT = "letsencrypt.plugins" CLI_DEFAULTS = dict( config_files=[ "/etc/letsencrypt/cli.ini", os.path.join(os.environ.get("XDG_CONFIG_HOME", "~/.config"), "letsencrypt", "cli.ini"), ], verbose_count=-(logging.WARNING / 10), server="https://acme-staging.api.letsencrypt.org/directory", rsa_key_size=2048, rollback_checkpoints=1, config_dir="/etc/letsencrypt", work_dir="/var/lib/letsencrypt", logs_dir="/var/log/letsencrypt", no_verify_ssl=False, tls_sni_01_port=challenges.TLSSNI01Response.PORT, auth_cert_path="./cert.pem", auth_chain_path="./chain.pem", strict_permissions=False, ) RENEWER_DEFAULTS = dict( renewer_enabled="yes", renew_before_expiry="30 days", deploy_before_expiry="20 days", ) EXCLUSIVE_CHALLENGES = frozenset([frozenset([ challenges.TLSSNI01, challenges.HTTP01])]) ENHANCEMENTS = ["redirect", "http-header", "ocsp-stapling", "spdy"] ARCHIVE_DIR = "archive" CONFIG_DIRS_MODE = 0o755 ACCOUNTS_DIR = "accounts" BACKUP_DIR = "backups" CSR_DIR = "csr" IN_PROGRESS_DIR = "IN_PROGRESS" KEY_DIR = "keys" LIVE_DIR = "live" TEMP_CHECKPOINT_DIR = "temp_checkpoint" RENEWAL_CONFIGS_DIR = "renewal" RENEWER_CONFIG_FILENAME = "renewer.conf"

true

f71bf03d595623a0ca93dde19a49a3566fbf4da1

2,082

py

Python

scraper.py

Luisf718/web_scraping

df599a1ea77a1475a05d97884f57a4c3761adec5

[ "MIT" ]

null

scraper.py

Luisf718/web_scraping

df599a1ea77a1475a05d97884f57a4c3761adec5

[ "MIT" ]

null

scraper.py

Luisf718/web_scraping

df599a1ea77a1475a05d97884f57a4c3761adec5

[ "MIT" ]

null

import requests import lxml.html as html import os import datetime #El text-fill es un h2 pero la libreria no identifica el h2 como tal sino como text-fill HOME_URL = 'https://www.larepublica.co/' XPATH_LINK_TO_ARTICLE = '//text-fill/a/@href' XPATH_TITLE = '//div[@class="mb-auto"]/text-fill/span//text()' XPATH_SUMMARY = '//div[@class="lead"]/p//text()' XPATH_BODY = '//div[@class="html-content"]/p//text()' def parse_notice(link, today): try: response = requests.get(link) if response.status_code == 200: notice = response.content.decode('utf-8') parsed = html.fromstring(notice) try: title = parsed.xpath(XPATH_TITLE)[0] title = title.replace('\"','') summary = parsed.xpath(XPATH_SUMMARY)[0] body = parsed.xpath(XPATH_BODY) except IndexError: return with open(f'{today}/{title}.txt', 'w', encoding='utf-8') as f: f.write(str(title)) f.write('\n\n') f.write(str(summary)) f.write('\n\n') for p in body: f.write(str(p)) f.write('\n') else: raise ValueError(f'Error: {response.status_code}') except ValueError as ve: print(ve) def parse_home(): try: response = requests.get(HOME_URL) if response.status_code == 200: home = response.content.decode('utf-8') parsed = html.fromstring(home) links_to_notices = parsed.xpath(XPATH_LINK_TO_ARTICLE) # print(links_to_notices) today = datetime.date.today().strftime('%d-%m-%Y') if not os.path.isdir(today): os.mkdir(today) for link in links_to_notices: parse_notice(link, today) else: raise ValueError(f'Error: {response.status_code}') except ValueError as ve: print(ve) def run(): parse_home() if __name__ == '__main__': run()

30.617647

88

0.548511

import requests import lxml.html as html import os import datetime HOME_URL = 'https://www.larepublica.co/' XPATH_LINK_TO_ARTICLE = '//text-fill/a/@href' XPATH_TITLE = '//div[@class="mb-auto"]/text-fill/span//text()' XPATH_SUMMARY = '//div[@class="lead"]/p//text()' XPATH_BODY = '//div[@class="html-content"]/p//text()' def parse_notice(link, today): try: response = requests.get(link) if response.status_code == 200: notice = response.content.decode('utf-8') parsed = html.fromstring(notice) try: title = parsed.xpath(XPATH_TITLE)[0] title = title.replace('\"','') summary = parsed.xpath(XPATH_SUMMARY)[0] body = parsed.xpath(XPATH_BODY) except IndexError: return with open(f'{today}/{title}.txt', 'w', encoding='utf-8') as f: f.write(str(title)) f.write('\n\n') f.write(str(summary)) f.write('\n\n') for p in body: f.write(str(p)) f.write('\n') else: raise ValueError(f'Error: {response.status_code}') except ValueError as ve: print(ve) def parse_home(): try: response = requests.get(HOME_URL) if response.status_code == 200: home = response.content.decode('utf-8') parsed = html.fromstring(home) links_to_notices = parsed.xpath(XPATH_LINK_TO_ARTICLE) # print(links_to_notices) today = datetime.date.today().strftime('%d-%m-%Y') if not os.path.isdir(today): os.mkdir(today) for link in links_to_notices: parse_notice(link, today) else: raise ValueError(f'Error: {response.status_code}') except ValueError as ve: print(ve) def run(): parse_home() if __name__ == '__main__': run()

true

f71bf0555abfc8db7bc79d24972ecc0523d5c881

5,928

py

Python

scaffold_generator/defaults.py

iamswaroopp/django-scaffold-generator

c9aa7269c3a3d9a618dbf41aac8e23649be64d48

[ "MIT" ]

6

2021-05-20T14:46:23.000Z

2022-01-24T07:07:55.000Z

scaffold_generator/defaults.py

iamswaroopp/django-scaffold-generator

c9aa7269c3a3d9a618dbf41aac8e23649be64d48

[ "MIT" ]

null

scaffold_generator/defaults.py

iamswaroopp/django-scaffold-generator

c9aa7269c3a3d9a618dbf41aac8e23649be64d48

[ "MIT" ]

null

DEFAULT_SETTINGS = { 'CREATE_HTML_VIEW_RESOURCES': True, 'CREATE_REST_VIEW_RESOURCES': True, 'DEFAULT_MODEL_IMPORTS': [], 'DEFAULT_FORM_IMPORTS': [ 'django.forms', ], 'FIELDS': {}, 'MODEL_EXTRA_IMPORT_CLASSES': [ 'django.db.models', ], 'MODEL_PARENT_CLASSES': ['django.db.models.Model'], 'FORM_EXTRA_IMPORT_CLASSES': ['django.forms'], 'FORM_PARENT_CLASSES': ['django.forms.ModelForm'], 'VIEW_EXTRA_IMPORT_CLASSES': [], 'VIEW_PERMISSION_CLASSES': [ 'django.contrib.auth.mixins.PermissionRequiredMixin', ], 'VIEW_PERMISSION_CODES': [ 'view', ], 'LIST_VIEW_PARENT_CLASSES': ['django.views.generic.list.ListView'], 'DETAIL_VIEW_PARENT_CLASSES': ['django.views.generic.detail.DetailView'], 'ADD_PERMISSION_CLASSES': [ 'django.contrib.auth.mixins.PermissionRequiredMixin', ], 'ADD_PERMISSION_CODES': ['add', 'create'], 'CREATE_VIEW_PARENT_CLASSES': ['django.views.generic.edit.CreateView'], 'CREATE_URL_PATH': 'create', 'CHANGE_PERMISSION_CLASSES': [ 'django.contrib.auth.mixins.PermissionRequiredMixin', ], 'CHANGE_PERMISSION_CODES': [ 'change', ], 'UPDATE_VIEW_PARENT_CLASSES': ['django.views.generic.edit.UpdateView'], 'UPDATE_URL_PATH': 'update', 'DELETE_PERMISSION_CLASSES': [ 'django.contrib.auth.mixins.PermissionRequiredMixin', ], 'DELETE_PERMISSION_CODES': [ 'delete', ], 'DELETE_VIEW_PARENT_CLASSES': ['django.views.generic.edit.DeleteView'], 'DELETE_URL_PATH': 'delete', 'ADMIN_EXTRA_IMPORT_CLASSES': ['django.contrib.admin'], 'ADMIN_PARENT_CLASSES': ['django.contrib.admin.ModelAdmin'], 'URL_EXTRA_IMPORT_CLASSES': ['django.urls.path'], 'SCAFFOLD_REST_FRAMEWORK': True, 'REST_FRAMEWORK_SERIALIZER_EXTRA_IMPORT_CLASSES': ['rest_framework.serializers'], 'REST_FRAMEWORK_SERIALIZER_PARENT_CLASSES': ['rest_framework.serializers.ModelSerializer'], 'REST_FRAMEWORK_VIEWSET_EXTRA_IMPORT_CLASSES': ['rest_framework.viewsets'], 'REST_FRAMEWORK_VIEWSET_PARENT_CLASSES': ['rest_framework.viewsets.ModelViewSet'], 'REST_FRAMEWORK_VIEWSET_PERMISSION_CLASSES': ['rest_framework.permissions.DjangoModelPermissions'], 'REST_FRAMEWORK_DEFAULT_ROUTER': 'rest_framework.routers.DefaultRouter', 'SCAFFOLD_TEMPLATES': False, 'FORM_EXTRA': '', 'TEMPLATE_VIEW_LIST': 'scaffold_generator/views/model_list.html.template', 'TEMPLATE_VIEW_DETAIL': 'scaffold_generator/views/model_detail.html.template', 'TEMPLATE_VIEW_FORM': 'scaffold_generator/views/model_form.html.template', 'TEMPLATE_VIEW_DELETE': 'scaffold_generator/views/model_delete.html.template', 'ADD_LIST_VIEW_TO_NAVBAR_TEMPLATE': '', 'NAVBAR_ITEM_TEMPLATE': 'scaffold_generator/navbar_item.html.template', } DEFAULT_FIELDS = { 'AutoField': { 'class_name': 'models.AutoField', }, 'BigAutoField': { 'class_name': 'models.BigAutoField', }, 'BigIntegerField': { 'class_name': 'models.BigIntegerField', }, 'BinaryField': { 'class_name': 'models.BinaryField', }, 'BooleanField': { 'class_name': 'models.BooleanField', }, 'CharField': { 'class_name': 'models.CharField', 'default_kwargs': { 'max_length': '128', }, 'nullable': False, }, 'CommaSeparatedIntegerField': { 'class_name': 'models.CommaSeparatedIntegerField', 'nullable': False, }, 'DateField': { 'class_name': 'models.DateField', }, 'DateTimeField': { 'class_name': 'models.DateTimeField', }, 'DecimalField': { 'class_name': 'models.DecimalField', }, 'DurationField': { 'class_name': 'models.DurationField', }, 'EmailField': { 'class_name': 'models.EmailField', 'nullable': False, }, 'FileField': { 'class_name': 'models.FileField', 'nullable': False, }, 'FilePathField': { 'class_name': 'models.FilePathField', }, 'FloatField': { 'class_name': 'models.FloatField', }, 'ForeignKey': { 'class_name': 'models.ForeignKey', 'default_kwargs': { 'on_delete': 'models.CASCADE', }, }, 'GenericIPAddressField': { 'class_name': 'models.GenericIPAddressField', }, 'IPAddressField': { 'class_name': 'models.IPAddressField', }, 'ImageField': { 'class_name': 'models.ImageField', 'nullable': False, }, 'IntegerField': { 'class_name': 'models.IntegerField', }, 'JSONField': { 'class_name': 'models.JSONField', }, 'ManyToManyField': { 'class_name': 'models.ManyToManyField', 'nullable': False, }, 'NullBooleanField': { 'class_name': 'models.NullBooleanField', }, 'OneToOneField': { 'class_name': 'models.OneToOneField', 'default_kwargs': { 'on_delete': 'models.CASCADE', }, }, 'PositiveBigIntegerField': { 'class_name': 'models.PositiveBigIntegerField', }, 'PositiveIntegerField': { 'class_name': 'models.PositiveIntegerField', }, 'PositiveSmallIntegerField': { 'class_name': 'models.PositiveSmallIntegerField', }, 'SlugField': { 'class_name': 'models.SlugField', 'nullable': False, }, 'SmallAutoField': { 'class_name': 'models.SmallAutoField', }, 'SmallIntegerField': { 'class_name': 'models.SmallIntegerField', }, 'TextField': { 'class_name': 'models.TextField', 'nullable': False, }, 'TimeField': { 'class_name': 'models.TimeField', }, 'URLField': { 'class_name': 'models.URLField', 'nullable': False, }, 'UUIDField': { 'class_name': 'models.UUIDField', }, }

31.531915

103

0.624494

DEFAULT_SETTINGS = { 'CREATE_HTML_VIEW_RESOURCES': True, 'CREATE_REST_VIEW_RESOURCES': True, 'DEFAULT_MODEL_IMPORTS': [], 'DEFAULT_FORM_IMPORTS': [ 'django.forms', ], 'FIELDS': {}, 'MODEL_EXTRA_IMPORT_CLASSES': [ 'django.db.models', ], 'MODEL_PARENT_CLASSES': ['django.db.models.Model'], 'FORM_EXTRA_IMPORT_CLASSES': ['django.forms'], 'FORM_PARENT_CLASSES': ['django.forms.ModelForm'], 'VIEW_EXTRA_IMPORT_CLASSES': [], 'VIEW_PERMISSION_CLASSES': [ 'django.contrib.auth.mixins.PermissionRequiredMixin', ], 'VIEW_PERMISSION_CODES': [ 'view', ], 'LIST_VIEW_PARENT_CLASSES': ['django.views.generic.list.ListView'], 'DETAIL_VIEW_PARENT_CLASSES': ['django.views.generic.detail.DetailView'], 'ADD_PERMISSION_CLASSES': [ 'django.contrib.auth.mixins.PermissionRequiredMixin', ], 'ADD_PERMISSION_CODES': ['add', 'create'], 'CREATE_VIEW_PARENT_CLASSES': ['django.views.generic.edit.CreateView'], 'CREATE_URL_PATH': 'create', 'CHANGE_PERMISSION_CLASSES': [ 'django.contrib.auth.mixins.PermissionRequiredMixin', ], 'CHANGE_PERMISSION_CODES': [ 'change', ], 'UPDATE_VIEW_PARENT_CLASSES': ['django.views.generic.edit.UpdateView'], 'UPDATE_URL_PATH': 'update', 'DELETE_PERMISSION_CLASSES': [ 'django.contrib.auth.mixins.PermissionRequiredMixin', ], 'DELETE_PERMISSION_CODES': [ 'delete', ], 'DELETE_VIEW_PARENT_CLASSES': ['django.views.generic.edit.DeleteView'], 'DELETE_URL_PATH': 'delete', 'ADMIN_EXTRA_IMPORT_CLASSES': ['django.contrib.admin'], 'ADMIN_PARENT_CLASSES': ['django.contrib.admin.ModelAdmin'], 'URL_EXTRA_IMPORT_CLASSES': ['django.urls.path'], 'SCAFFOLD_REST_FRAMEWORK': True, 'REST_FRAMEWORK_SERIALIZER_EXTRA_IMPORT_CLASSES': ['rest_framework.serializers'], 'REST_FRAMEWORK_SERIALIZER_PARENT_CLASSES': ['rest_framework.serializers.ModelSerializer'], 'REST_FRAMEWORK_VIEWSET_EXTRA_IMPORT_CLASSES': ['rest_framework.viewsets'], 'REST_FRAMEWORK_VIEWSET_PARENT_CLASSES': ['rest_framework.viewsets.ModelViewSet'], 'REST_FRAMEWORK_VIEWSET_PERMISSION_CLASSES': ['rest_framework.permissions.DjangoModelPermissions'], 'REST_FRAMEWORK_DEFAULT_ROUTER': 'rest_framework.routers.DefaultRouter', 'SCAFFOLD_TEMPLATES': False, 'FORM_EXTRA': '', 'TEMPLATE_VIEW_LIST': 'scaffold_generator/views/model_list.html.template', 'TEMPLATE_VIEW_DETAIL': 'scaffold_generator/views/model_detail.html.template', 'TEMPLATE_VIEW_FORM': 'scaffold_generator/views/model_form.html.template', 'TEMPLATE_VIEW_DELETE': 'scaffold_generator/views/model_delete.html.template', 'ADD_LIST_VIEW_TO_NAVBAR_TEMPLATE': '', 'NAVBAR_ITEM_TEMPLATE': 'scaffold_generator/navbar_item.html.template', } DEFAULT_FIELDS = { 'AutoField': { 'class_name': 'models.AutoField', }, 'BigAutoField': { 'class_name': 'models.BigAutoField', }, 'BigIntegerField': { 'class_name': 'models.BigIntegerField', }, 'BinaryField': { 'class_name': 'models.BinaryField', }, 'BooleanField': { 'class_name': 'models.BooleanField', }, 'CharField': { 'class_name': 'models.CharField', 'default_kwargs': { 'max_length': '128', }, 'nullable': False, }, 'CommaSeparatedIntegerField': { 'class_name': 'models.CommaSeparatedIntegerField', 'nullable': False, }, 'DateField': { 'class_name': 'models.DateField', }, 'DateTimeField': { 'class_name': 'models.DateTimeField', }, 'DecimalField': { 'class_name': 'models.DecimalField', }, 'DurationField': { 'class_name': 'models.DurationField', }, 'EmailField': { 'class_name': 'models.EmailField', 'nullable': False, }, 'FileField': { 'class_name': 'models.FileField', 'nullable': False, }, 'FilePathField': { 'class_name': 'models.FilePathField', }, 'FloatField': { 'class_name': 'models.FloatField', }, 'ForeignKey': { 'class_name': 'models.ForeignKey', 'default_kwargs': { 'on_delete': 'models.CASCADE', }, }, 'GenericIPAddressField': { 'class_name': 'models.GenericIPAddressField', }, 'IPAddressField': { 'class_name': 'models.IPAddressField', }, 'ImageField': { 'class_name': 'models.ImageField', 'nullable': False, }, 'IntegerField': { 'class_name': 'models.IntegerField', }, 'JSONField': { 'class_name': 'models.JSONField', }, 'ManyToManyField': { 'class_name': 'models.ManyToManyField', 'nullable': False, }, 'NullBooleanField': { 'class_name': 'models.NullBooleanField', }, 'OneToOneField': { 'class_name': 'models.OneToOneField', 'default_kwargs': { 'on_delete': 'models.CASCADE', }, }, 'PositiveBigIntegerField': { 'class_name': 'models.PositiveBigIntegerField', }, 'PositiveIntegerField': { 'class_name': 'models.PositiveIntegerField', }, 'PositiveSmallIntegerField': { 'class_name': 'models.PositiveSmallIntegerField', }, 'SlugField': { 'class_name': 'models.SlugField', 'nullable': False, }, 'SmallAutoField': { 'class_name': 'models.SmallAutoField', }, 'SmallIntegerField': { 'class_name': 'models.SmallIntegerField', }, 'TextField': { 'class_name': 'models.TextField', 'nullable': False, }, 'TimeField': { 'class_name': 'models.TimeField', }, 'URLField': { 'class_name': 'models.URLField', 'nullable': False, }, 'UUIDField': { 'class_name': 'models.UUIDField', }, }

true

f71bf0a388d2199024ab2f51328c979f5c521f72

9,018

py

Python

test/functional/wallet_abandonconflict.py

SbercoinCom/sbercoin.com

8fb386e59e4db8a6abb3a2c638a2ecc918f6b9dd

[ "MIT" ]

1

2021-05-17T06:06:57.000Z

test/functional/wallet_abandonconflict.py

SbercoinCom/sbercoin.com

8fb386e59e4db8a6abb3a2c638a2ecc918f6b9dd

[ "MIT" ]

null

test/functional/wallet_abandonconflict.py

SbercoinCom/sbercoin.com

8fb386e59e4db8a6abb3a2c638a2ecc918f6b9dd

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2014-2019 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 the abandontransaction RPC. The abandontransaction RPC marks a transaction and all its in-wallet descendants as abandoned which allows their inputs to be respent. It can be used to replace "stuck" or evicted transactions. It only works on transactions which are not included in a block and are not currently in the mempool. It has no effect on transactions which are already abandoned. """ from decimal import Decimal from test_framework.test_framework import BitcoinTestFramework from test_framework.util import ( assert_equal, assert_raises_rpc_error, connect_nodes, disconnect_nodes, wait_until, ) from test_framework.sbercoinconfig import * class AbandonConflictTest(BitcoinTestFramework): def set_test_params(self): self.num_nodes = 2 self.extra_args = [["-minrelaytxfee=0.00001"], []] def skip_test_if_missing_module(self): self.skip_if_no_wallet() def run_test(self): self.nodes[1].generate(COINBASE_MATURITY) self.sync_blocks() balance = self.nodes[0].getbalance() txA = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), Decimal("10")) txB = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), Decimal("10")) txC = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), Decimal("10")) self.sync_mempools() self.nodes[1].generate(1) # Can not abandon non-wallet transaction assert_raises_rpc_error(-5, 'Invalid or non-wallet transaction id', lambda: self.nodes[0].abandontransaction(txid='ff' * 32)) # Can not abandon confirmed transaction assert_raises_rpc_error(-5, 'Transaction not eligible for abandonment', lambda: self.nodes[0].abandontransaction(txid=txA)) self.sync_blocks() newbalance = self.nodes[0].getbalance() assert balance - newbalance < Decimal("0.01") #no more than fees lost balance = newbalance # Disconnect nodes so node0's transactions don't get into node1's mempool disconnect_nodes(self.nodes[0], 1) # Identify the 10btc outputs nA = next(tx_out["vout"] for tx_out in self.nodes[0].gettransaction(txA)["details"] if tx_out["amount"] == Decimal("10")) nB = next(tx_out["vout"] for tx_out in self.nodes[0].gettransaction(txB)["details"] if tx_out["amount"] == Decimal("10")) nC = next(tx_out["vout"] for tx_out in self.nodes[0].gettransaction(txC)["details"] if tx_out["amount"] == Decimal("10")) inputs = [] # spend 10btc outputs from txA and txB inputs.append({"txid": txA, "vout": nA}) inputs.append({"txid": txB, "vout": nB}) outputs = {} outputs[self.nodes[0].getnewaddress()] = Decimal("14.99998") outputs[self.nodes[1].getnewaddress()] = Decimal("5") signed = self.nodes[0].signrawtransactionwithwallet(self.nodes[0].createrawtransaction(inputs, outputs)) txAB1 = self.nodes[0].sendrawtransaction(signed["hex"]) # Identify the 14.99998btc output nAB = next(tx_out["vout"] for tx_out in self.nodes[0].gettransaction(txAB1)["details"] if tx_out["amount"] == Decimal("14.99998")) #Create a child tx spending AB1 and C inputs = [] inputs.append({"txid": txAB1, "vout": nAB}) inputs.append({"txid": txC, "vout": nC}) outputs = {} outputs[self.nodes[0].getnewaddress()] = Decimal("24.9996") signed2 = self.nodes[0].signrawtransactionwithwallet(self.nodes[0].createrawtransaction(inputs, outputs)) txABC2 = self.nodes[0].sendrawtransaction(signed2["hex"]) # Create a child tx spending ABC2 signed3_change = Decimal("24.999") inputs = [{"txid": txABC2, "vout": 0}] outputs = {self.nodes[0].getnewaddress(): signed3_change} signed3 = self.nodes[0].signrawtransactionwithwallet(self.nodes[0].createrawtransaction(inputs, outputs)) # note tx is never directly referenced, only abandoned as a child of the above self.nodes[0].sendrawtransaction(signed3["hex"]) # In mempool txs from self should increase balance from change newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance - Decimal("30") + signed3_change) balance = newbalance # Restart the node with a higher min relay fee so the parent tx is no longer in mempool # TODO: redo with eviction self.stop_node(0) self.start_node(0, extra_args=["-minrelaytxfee=0.0001"]) wait_until(lambda: self.nodes[0].getmempoolinfo()['loaded']) # Verify txs no longer in either node's mempool assert_equal(len(self.nodes[0].getrawmempool()), 0) assert_equal(len(self.nodes[1].getrawmempool()), 0) # Not in mempool txs from self should only reduce balance # inputs are still spent, but change not received newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance - signed3_change) # Unconfirmed received funds that are not in mempool, also shouldn't show # up in unconfirmed balance unconfbalance = self.nodes[0].getunconfirmedbalance() + self.nodes[0].getbalance() assert_equal(unconfbalance, newbalance) # Also shouldn't show up in listunspent assert not txABC2 in [utxo["txid"] for utxo in self.nodes[0].listunspent(0)] balance = newbalance # Abandon original transaction and verify inputs are available again # including that the child tx was also abandoned self.nodes[0].abandontransaction(txAB1) newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance + Decimal("30")) balance = newbalance # Verify that even with a low min relay fee, the tx is not reaccepted from wallet on startup once abandoned self.stop_node(0) self.start_node(0, extra_args=["-minrelaytxfee=0.00001"]) wait_until(lambda: self.nodes[0].getmempoolinfo()['loaded']) assert_equal(len(self.nodes[0].getrawmempool()), 0) assert_equal(self.nodes[0].getbalance(), balance) # But if it is received again then it is unabandoned # And since now in mempool, the change is available # But its child tx remains abandoned self.nodes[0].sendrawtransaction(signed["hex"]) newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance - Decimal("20") + Decimal("14.99998")) balance = newbalance # Send child tx again so it is unabandoned self.nodes[0].sendrawtransaction(signed2["hex"]) newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance - Decimal("10") - Decimal("14.99998") + Decimal("24.9996")) balance = newbalance # Remove using high relay fee again self.stop_node(0) self.start_node(0, extra_args=["-minrelaytxfee=0.0001"]) wait_until(lambda: self.nodes[0].getmempoolinfo()['loaded']) assert_equal(len(self.nodes[0].getrawmempool()), 0) newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance - Decimal("24.9996")) balance = newbalance # Create a double spend of AB1 by spending again from only A's 10 output # Mine double spend from node 1 inputs = [] inputs.append({"txid": txA, "vout": nA}) outputs = {} outputs[self.nodes[1].getnewaddress()] = Decimal("9.99") tx = self.nodes[0].createrawtransaction(inputs, outputs) signed = self.nodes[0].signrawtransactionwithwallet(tx) self.nodes[1].sendrawtransaction(signed["hex"]) self.nodes[1].generate(1) connect_nodes(self.nodes[0], 1) self.sync_blocks() # Verify that B and C's 10 BTC outputs are available for spending again because AB1 is now conflicted newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance + Decimal("20")) balance = newbalance # There is currently a minor bug around this and so this test doesn't work. See Issue #7315 # Invalidate the block with the double spend and B's 10 BTC output should no longer be available # Don't think C's should either self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash()) newbalance = self.nodes[0].getbalance() #assert_equal(newbalance, balance - Decimal("10")) self.log.info("If balance has not declined after invalidateblock then out of mempool wallet tx which is no longer") self.log.info("conflicted has not resumed causing its inputs to be seen as spent. See Issue #7315") self.log.info(str(balance) + " -> " + str(newbalance) + " ?") if __name__ == '__main__': AbandonConflictTest().main()

47.714286

138

0.66833

from decimal import Decimal from test_framework.test_framework import BitcoinTestFramework from test_framework.util import ( assert_equal, assert_raises_rpc_error, connect_nodes, disconnect_nodes, wait_until, ) from test_framework.sbercoinconfig import * class AbandonConflictTest(BitcoinTestFramework): def set_test_params(self): self.num_nodes = 2 self.extra_args = [["-minrelaytxfee=0.00001"], []] def skip_test_if_missing_module(self): self.skip_if_no_wallet() def run_test(self): self.nodes[1].generate(COINBASE_MATURITY) self.sync_blocks() balance = self.nodes[0].getbalance() txA = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), Decimal("10")) txB = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), Decimal("10")) txC = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), Decimal("10")) self.sync_mempools() self.nodes[1].generate(1) assert_raises_rpc_error(-5, 'Invalid or non-wallet transaction id', lambda: self.nodes[0].abandontransaction(txid='ff' * 32)) assert_raises_rpc_error(-5, 'Transaction not eligible for abandonment', lambda: self.nodes[0].abandontransaction(txid=txA)) self.sync_blocks() newbalance = self.nodes[0].getbalance() assert balance - newbalance < Decimal("0.01") balance = newbalance disconnect_nodes(self.nodes[0], 1) # Identify the 10btc outputs nA = next(tx_out["vout"] for tx_out in self.nodes[0].gettransaction(txA)["details"] if tx_out["amount"] == Decimal("10")) nB = next(tx_out["vout"] for tx_out in self.nodes[0].gettransaction(txB)["details"] if tx_out["amount"] == Decimal("10")) nC = next(tx_out["vout"] for tx_out in self.nodes[0].gettransaction(txC)["details"] if tx_out["amount"] == Decimal("10")) inputs = [] # spend 10btc outputs from txA and txB inputs.append({"txid": txA, "vout": nA}) inputs.append({"txid": txB, "vout": nB}) outputs = {} outputs[self.nodes[0].getnewaddress()] = Decimal("14.99998") outputs[self.nodes[1].getnewaddress()] = Decimal("5") signed = self.nodes[0].signrawtransactionwithwallet(self.nodes[0].createrawtransaction(inputs, outputs)) txAB1 = self.nodes[0].sendrawtransaction(signed["hex"]) # Identify the 14.99998btc output nAB = next(tx_out["vout"] for tx_out in self.nodes[0].gettransaction(txAB1)["details"] if tx_out["amount"] == Decimal("14.99998")) #Create a child tx spending AB1 and C inputs = [] inputs.append({"txid": txAB1, "vout": nAB}) inputs.append({"txid": txC, "vout": nC}) outputs = {} outputs[self.nodes[0].getnewaddress()] = Decimal("24.9996") signed2 = self.nodes[0].signrawtransactionwithwallet(self.nodes[0].createrawtransaction(inputs, outputs)) txABC2 = self.nodes[0].sendrawtransaction(signed2["hex"]) # Create a child tx spending ABC2 signed3_change = Decimal("24.999") inputs = [{"txid": txABC2, "vout": 0}] outputs = {self.nodes[0].getnewaddress(): signed3_change} signed3 = self.nodes[0].signrawtransactionwithwallet(self.nodes[0].createrawtransaction(inputs, outputs)) # note tx is never directly referenced, only abandoned as a child of the above self.nodes[0].sendrawtransaction(signed3["hex"]) # In mempool txs from self should increase balance from change newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance - Decimal("30") + signed3_change) balance = newbalance # Restart the node with a higher min relay fee so the parent tx is no longer in mempool # TODO: redo with eviction self.stop_node(0) self.start_node(0, extra_args=["-minrelaytxfee=0.0001"]) wait_until(lambda: self.nodes[0].getmempoolinfo()['loaded']) # Verify txs no longer in either node's mempool assert_equal(len(self.nodes[0].getrawmempool()), 0) assert_equal(len(self.nodes[1].getrawmempool()), 0) newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance - signed3_change) # up in unconfirmed balance unconfbalance = self.nodes[0].getunconfirmedbalance() + self.nodes[0].getbalance() assert_equal(unconfbalance, newbalance) # Also shouldn't show up in listunspent assert not txABC2 in [utxo["txid"] for utxo in self.nodes[0].listunspent(0)] balance = newbalance self.nodes[0].abandontransaction(txAB1) newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance + Decimal("30")) balance = newbalance self.stop_node(0) self.start_node(0, extra_args=["-minrelaytxfee=0.00001"]) wait_until(lambda: self.nodes[0].getmempoolinfo()['loaded']) assert_equal(len(self.nodes[0].getrawmempool()), 0) assert_equal(self.nodes[0].getbalance(), balance) self.nodes[0].sendrawtransaction(signed["hex"]) newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance - Decimal("20") + Decimal("14.99998")) balance = newbalance self.nodes[0].sendrawtransaction(signed2["hex"]) newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance - Decimal("10") - Decimal("14.99998") + Decimal("24.9996")) balance = newbalance self.stop_node(0) self.start_node(0, extra_args=["-minrelaytxfee=0.0001"]) wait_until(lambda: self.nodes[0].getmempoolinfo()['loaded']) assert_equal(len(self.nodes[0].getrawmempool()), 0) newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance - Decimal("24.9996")) balance = newbalance # Mine double spend from node 1 inputs = [] inputs.append({"txid": txA, "vout": nA}) outputs = {} outputs[self.nodes[1].getnewaddress()] = Decimal("9.99") tx = self.nodes[0].createrawtransaction(inputs, outputs) signed = self.nodes[0].signrawtransactionwithwallet(tx) self.nodes[1].sendrawtransaction(signed["hex"]) self.nodes[1].generate(1) connect_nodes(self.nodes[0], 1) self.sync_blocks() # Verify that B and C's 10 BTC outputs are available for spending again because AB1 is now conflicted newbalance = self.nodes[0].getbalance() assert_equal(newbalance, balance + Decimal("20")) balance = newbalance # Invalidate the block with the double spend and B's 10 BTC output should no longer be available self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash()) newbalance = self.nodes[0].getbalance() self.log.info("If balance has not declined after invalidateblock then out of mempool wallet tx which is no longer") self.log.info("conflicted has not resumed causing its inputs to be seen as spent. See Issue #7315") self.log.info(str(balance) + " -> " + str(newbalance) + " ?") if __name__ == '__main__': AbandonConflictTest().main()

true

f71bf12d2f88d3050797e3faff333cb5b247f6af

4,243

py

Python

BPR-example/tSNE_matlab/tsne_p.py

jsirait/selection-bias-code

5b89b2d9bc3c996e27dde4773be105698fd72db9

[ "BSD-3-Clause" ]

null

BPR-example/tSNE_matlab/tsne_p.py

jsirait/selection-bias-code

5b89b2d9bc3c996e27dde4773be105698fd72db9

[ "BSD-3-Clause" ]

null

BPR-example/tSNE_matlab/tsne_p.py

jsirait/selection-bias-code

5b89b2d9bc3c996e27dde4773be105698fd72db9

[ "BSD-3-Clause" ]

null

# Generated with SMOP 0.41 from libsmop import * # tsne_p.m @function def tsne_p(P=None,labels=None,no_dims=None,*args,**kwargs): varargin = tsne_p.varargin nargin = tsne_p.nargin #TSNE_P Performs symmetric t-SNE on affinity matrix P # mappedX = tsne_p(P, labels, no_dims) # The function performs symmetric t-SNE on pairwise similarity matrix P # to create a low-dimensional map of no_dims dimensions (default = 2). # The matrix P is assumed to be symmetric, sum up to 1, and have zeros # on the diagonal. # The labels of the data are not used by t-SNE itself, however, they # are used to color intermediate plots. Please provide an empty labels # matrix [] if you don't want to plot results during the optimization. # The low-dimensional data representation is returned in mappedX. # (C) Laurens van der Maaten, 2010 # University of California, San Diego if logical_not(exist('labels','var')): labels=[] # tsne_p.m:21 if logical_not(exist('no_dims','var')) or isempty(no_dims): no_dims=2 # tsne_p.m:24 # First check whether we already have an initial solution if numel(no_dims) > 1: initial_solution=copy(true) # tsne_p.m:29 ydata=copy(no_dims) # tsne_p.m:30 no_dims=size(ydata,2) # tsne_p.m:31 else: initial_solution=copy(false) # tsne_p.m:33 # Initialize some variables n=size(P,1) # tsne_p.m:37 momentum=0.5 # tsne_p.m:38 final_momentum=0.8 # tsne_p.m:39 mom_switch_iter=250 # tsne_p.m:40 stop_lying_iter=100 # tsne_p.m:41 max_iter=1000 # tsne_p.m:42 epsilon=500 # tsne_p.m:43 min_gain=0.01 # tsne_p.m:44 # Make sure P-vals are set properly P[arange(1,end(),n + 1)]=0 # tsne_p.m:47 P=dot(0.5,(P + P.T)) # tsne_p.m:48 P=max(P / sum(ravel(P)),realmin) # tsne_p.m:49 const=sum(multiply(ravel(P),log(ravel(P)))) # tsne_p.m:50 if logical_not(initial_solution): P=dot(P,4) # tsne_p.m:52 # Initialize the solution if logical_not(initial_solution): ydata=dot(0.0001,randn(n,no_dims)) # tsne_p.m:57 y_incs=zeros(size(ydata)) # tsne_p.m:59 gains=ones(size(ydata)) # tsne_p.m:60 for iter in arange(1,max_iter).reshape(-1): # Compute joint probability that point i and j are neighbors sum_ydata=sum(ydata ** 2,2) # tsne_p.m:66 num=1 / (1 + bsxfun(plus,sum_ydata,bsxfun(plus,sum_ydata.T,dot(- 2,(dot(ydata,ydata.T)))))) # tsne_p.m:67 num[arange(1,end(),n + 1)]=0 # tsne_p.m:68 Q=max(num / sum(ravel(num)),realmin) # tsne_p.m:69 # Compute the gradients (faster implementation) L=multiply((P - Q),num) # tsne_p.m:72 y_grads=dot(dot(4,(diag(sum(L,1)) - L)),ydata) # tsne_p.m:73 gains=multiply((gains + 0.2),(sign(y_grads) != sign(y_incs))) + multiply((dot(gains,0.8)),(sign(y_grads) == sign(y_incs))) # tsne_p.m:76 gains[gains < min_gain]=min_gain # tsne_p.m:78 y_incs=dot(momentum,y_incs) - dot(epsilon,(multiply(gains,y_grads))) # tsne_p.m:79 ydata=ydata + y_incs # tsne_p.m:80 ydata=bsxfun(minus,ydata,mean(ydata,1)) # tsne_p.m:81 if iter == mom_switch_iter: momentum=copy(final_momentum) # tsne_p.m:85 if iter == stop_lying_iter and logical_not(initial_solution): P=P / 4 # tsne_p.m:88 # Print out progress if logical_not(rem(iter,10)): cost=const - sum(multiply(ravel(P),log(ravel(Q)))) # tsne_p.m:93 disp(concat(['Iteration ',num2str(iter),': error is ',num2str(cost)])) # Display scatter plot (maximally first three dimensions) if logical_not(rem(iter,10)) and logical_not(isempty(labels)): if no_dims == 1: scatter(ydata,ydata,9,labels,'filled') else: if no_dims == 2: scatter(ydata(arange(),1),ydata(arange(),2),9,labels,'filled') else: scatter3(ydata(arange(),1),ydata(arange(),2),ydata(arange(),3),40,labels,'filled') axis('tight') axis('off') drawnow

27.732026

130

0.607825

from libsmop import * @function def tsne_p(P=None,labels=None,no_dims=None,*args,**kwargs): varargin = tsne_p.varargin nargin = tsne_p.nargin # The low-dimensional data representation is returned in mappedX. # (C) Laurens van der Maaten, 2010 # University of California, San Diego if logical_not(exist('labels','var')): labels=[] # tsne_p.m:21 if logical_not(exist('no_dims','var')) or isempty(no_dims): no_dims=2 # tsne_p.m:24 # First check whether we already have an initial solution if numel(no_dims) > 1: initial_solution=copy(true) # tsne_p.m:29 ydata=copy(no_dims) # tsne_p.m:30 no_dims=size(ydata,2) # tsne_p.m:31 else: initial_solution=copy(false) # tsne_p.m:33 # Initialize some variables n=size(P,1) # tsne_p.m:37 momentum=0.5 # tsne_p.m:38 final_momentum=0.8 # tsne_p.m:39 mom_switch_iter=250 # tsne_p.m:40 stop_lying_iter=100 # tsne_p.m:41 max_iter=1000 # tsne_p.m:42 epsilon=500 # tsne_p.m:43 min_gain=0.01 # tsne_p.m:44 # Make sure P-vals are set properly P[arange(1,end(),n + 1)]=0 # tsne_p.m:47 P=dot(0.5,(P + P.T)) # tsne_p.m:48 P=max(P / sum(ravel(P)),realmin) # tsne_p.m:49 const=sum(multiply(ravel(P),log(ravel(P)))) # tsne_p.m:50 if logical_not(initial_solution): P=dot(P,4) # tsne_p.m:52 # Initialize the solution if logical_not(initial_solution): ydata=dot(0.0001,randn(n,no_dims)) # tsne_p.m:57 y_incs=zeros(size(ydata)) # tsne_p.m:59 gains=ones(size(ydata)) # tsne_p.m:60 for iter in arange(1,max_iter).reshape(-1): # Compute joint probability that point i and j are neighbors sum_ydata=sum(ydata ** 2,2) # tsne_p.m:66 num=1 / (1 + bsxfun(plus,sum_ydata,bsxfun(plus,sum_ydata.T,dot(- 2,(dot(ydata,ydata.T)))))) # tsne_p.m:67 num[arange(1,end(),n + 1)]=0 # tsne_p.m:68 Q=max(num / sum(ravel(num)),realmin) # tsne_p.m:69 # Compute the gradients (faster implementation) L=multiply((P - Q),num) # tsne_p.m:72 y_grads=dot(dot(4,(diag(sum(L,1)) - L)),ydata) # tsne_p.m:73 gains=multiply((gains + 0.2),(sign(y_grads) != sign(y_incs))) + multiply((dot(gains,0.8)),(sign(y_grads) == sign(y_incs))) # tsne_p.m:76 gains[gains < min_gain]=min_gain # tsne_p.m:78 y_incs=dot(momentum,y_incs) - dot(epsilon,(multiply(gains,y_grads))) # tsne_p.m:79 ydata=ydata + y_incs # tsne_p.m:80 ydata=bsxfun(minus,ydata,mean(ydata,1)) # tsne_p.m:81 if iter == mom_switch_iter: momentum=copy(final_momentum) # tsne_p.m:85 if iter == stop_lying_iter and logical_not(initial_solution): P=P / 4 # tsne_p.m:88 # Print out progress if logical_not(rem(iter,10)): cost=const - sum(multiply(ravel(P),log(ravel(Q)))) # tsne_p.m:93 disp(concat(['Iteration ',num2str(iter),': error is ',num2str(cost)])) # Display scatter plot (maximally first three dimensions) if logical_not(rem(iter,10)) and logical_not(isempty(labels)): if no_dims == 1: scatter(ydata,ydata,9,labels,'filled') else: if no_dims == 2: scatter(ydata(arange(),1),ydata(arange(),2),9,labels,'filled') else: scatter3(ydata(arange(),1),ydata(arange(),2),ydata(arange(),3),40,labels,'filled') axis('tight') axis('off') drawnow

true

f71bf176600708ef5ff1a1e657e714c9aea3da7b

664

py

Python

dags/externaltasksensor_dag.py

vannguyen3007/Apache-Airflow

1c8a6f3c9c0022807cbae4c4f83de33b8454ae24

[ "Apache-2.0" ]

null

dags/externaltasksensor_dag.py

vannguyen3007/Apache-Airflow

1c8a6f3c9c0022807cbae4c4f83de33b8454ae24

[ "Apache-2.0" ]

null

dags/externaltasksensor_dag.py

vannguyen3007/Apache-Airflow

1c8a6f3c9c0022807cbae4c4f83de33b8454ae24

[ "Apache-2.0" ]

null

import pprint as pp import airflow.utils.dates from airflow import DAG from airflow.sensors.external_task_sensor import ExternalTaskSensor from airflow.operators.dummy_operator import DummyOperator from datetime import datetime, timedelta default_args = { "owner": "airflow", "start_date": airflow.utils.dates.days_ago(1) } with DAG(dag_id="externaltasksensor_dag", default_args=default_args, schedule_interval="@daily") as dag: sensor = ExternalTaskSensor( task_id='sensor', external_dag_id='sleep_dag', external_task_id='t2' ) last_task = DummyOperator(task_id="last_task") sensor >> last_task

30.181818

104

0.733434

import pprint as pp import airflow.utils.dates from airflow import DAG from airflow.sensors.external_task_sensor import ExternalTaskSensor from airflow.operators.dummy_operator import DummyOperator from datetime import datetime, timedelta default_args = { "owner": "airflow", "start_date": airflow.utils.dates.days_ago(1) } with DAG(dag_id="externaltasksensor_dag", default_args=default_args, schedule_interval="@daily") as dag: sensor = ExternalTaskSensor( task_id='sensor', external_dag_id='sleep_dag', external_task_id='t2' ) last_task = DummyOperator(task_id="last_task") sensor >> last_task

true

f71bf1d55740e24cffa947f3eb30e8b50f0271f3

198

py

Python

store_debug/store_debug/main.py

ruber0id/band-services-set

471bb4bf561ca5ec9e0e3a7bda75574b0c718fde

[ "Apache-2.0" ]

4

2018-08-27T05:36:37.000Z

2018-08-29T09:41:50.000Z

store_debug/store_debug/main.py

ruber0id/band-services-set

471bb4bf561ca5ec9e0e3a7bda75574b0c718fde

[ "Apache-2.0" ]

null

store_debug/store_debug/main.py

ruber0id/band-services-set

471bb4bf561ca5ec9e0e3a7bda75574b0c718fde

[ "Apache-2.0" ]

2

2020-05-20T14:50:59.000Z

2020-08-31T14:44:57.000Z

import asyncio import ujson from band import logger, expose """ Listen events and write to output """ @expose.listener() async def broadcast(**params): logger.info('Broadcast', params=params)

16.5

43

0.737374

import asyncio import ujson from band import logger, expose @expose.listener() async def broadcast(**params): logger.info('Broadcast', params=params)

true

f71bf5092e429695b0035e78d005436c626887d2

26,148

py

Python

python/mtap/deployment.py

benknoll-umn/mtap

67d506aa4ffc960acca1988ec12c5391c15ad736

[ "Apache-2.0" ]

3

2020-03-06T21:24:24.000Z

2021-03-21T06:38:00.000Z

python/mtap/deployment.py

benknoll-umn/mtap

67d506aa4ffc960acca1988ec12c5391c15ad736

[ "Apache-2.0" ]

40

2019-10-14T17:02:54.000Z

2022-03-09T13:35:54.000Z

python/mtap/deployment.py

benknoll-umn/mtap

67d506aa4ffc960acca1988ec12c5391c15ad736

[ "Apache-2.0" ]

2

2019-10-14T15:42:46.000Z

2020-03-05T23:29:01.000Z

# Copyright 2020 Regents of the University of Minnesota. # # 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. """Module for deploying a set of processing services and the events server all at once. Examples: An example configuration >>> deploy = Deployment( >>> GlobalSettings(host='0.0.0.0'), >>> EventsDeployment(port=10100, workers=8), >>> SharedProcessorConfig(workers=8, jvm_args=['-Xms32m', '-Xmx8g'], classpath='blah.jar'), >>> ProcessorDeployment(implementation='python', >>> entry_point='mtap.examples.example_processor', >>> instances=4, >>> port=10101, >>> workers=4), >>> ProcessorDeployment(implementation='java', >>> entry_point='edu.umn.nlpie.mtap.WordOccurrencesExampleProcessor', >>> port=10105) >>> ) >>> deploy.run_servers() """ import argparse import logging import os import pathlib import shutil import subprocess import sys import threading import time from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union import grpc from mtap import utilities, _config __all__ = [ 'Deployment', 'GlobalSettings', 'SharedProcessorConfig', 'EventsDeployment', 'ProcessorDeployment', 'main', 'deployment_parser', 'ServiceDeploymentException', ] logger = logging.getLogger(__name__) PYTHON_EXE = sys.executable def _get_java() -> str: try: return str(pathlib.Path(os.environ['JAVA_HOME']) / 'bin' / 'java') except KeyError: return 'java' JAVA_EXE = _get_java() def _listen(process: subprocess.Popen) -> int: for line in process.stdout: print(line.decode(), end='', flush=True) return process.wait() class ServiceDeploymentException(Exception): """Exception raised in the case of a service failing to launch. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) class GlobalSettings: """Settings shared by event service and all processors. Keyword Args: host (Optional[str]): The global host, by default will use "127.0.0.1". mtap_config (Optional[str]): The path to an MTAP config file to load for all services. log_level (Optional[str]): A python logging level to pass to all services. register (Optional[str]): Whether services should register with service discovery. Attributes: host (Optional[str]): The global host, by default will use "127.0.0.1". mtap_config (Optional[str]): The path to an MTAP config file to load for all services. log_level (Optional[str]): A python logging level to pass to all services. register (Optional[str]): Whether services should register with service discovery. """ def __init__(self, *, host: Optional[str] = None, mtap_config: Optional[str] = None, log_level: Optional[str] = None, register: Optional[bool] = None): self.host = host self.mtap_config = mtap_config self.log_level = log_level self.register = register @staticmethod def from_conf(conf: Optional[Dict]) -> 'GlobalSettings': """Creates a global settings object from a configuration dictionary. Keyword Args: conf (Optional[Dict]): The configuration dictionary. Returns: GlobalSettings: The global settings object. """ conf = conf or {} return GlobalSettings(host=conf.get('host'), mtap_config=conf.get('mtap_config'), log_level=conf.get('log_level'), register=conf.get('register')) class SharedProcessorConfig: """Configuration that is shared between multiple processor services. Keyword Args: events_addresses (Optional[str]): An optional GRPC-compatible target for the events service to be used by all processors. workers (Optional[int]): The default number of worker threads which will perform processing. additional_args (Optional[List[str]]): a list of additional arguments that should be appended to every processor. jvm_args (Optional[List[str]]): a list of JVM arguments for all java processors. java_classpath (Optional[str]): A classpath string that will be passed to all java processors. startup_timeout (Optional[int]): The default startup timeout for processors. mp_spawn_method (Optional[str]): A :meth:`multiprocessing.get_context` argument to create the multiprocessing context. Attributes: events_addresses (Optional[List[str]]): An optional GRPC-compatible target for the events service to be used by all processors. workers (Optional[int]): The default number of worker threads which will perform processing. additional_args (Optional[List[str]]): a list of additional arguments that should be appended to every processor. jvm_args (Optional[List[str]]): a list of JVM arguments for all java processors. java_classpath (Optional[str]): A classpath string that will be passed to all java processors. startup_timeout (Optional[int]): The default startup timeout for processors. mp_spawn_method (Optional[str]): A :meth:`multiprocessing.get_context` argument to create the multiprocessing context. """ def __init__(self, events_addresses: Optional[List[str]] = None, workers: Optional[int] = None, additional_args: Optional[List[str]] = None, jvm_args: Optional[List[str]] = None, java_classpath: Optional[str] = None, startup_timeout: Optional[int] = None, mp_spawn_method: Optional[str] = None): self.events_addresses = events_addresses self.workers = workers self.additional_args = additional_args self.jvm_args = jvm_args self.java_classpath = java_classpath self.startup_timeout = startup_timeout or 30 self.mp_spawn_method = mp_spawn_method @staticmethod def from_conf(conf: Optional[Dict]) -> 'SharedProcessorConfig': """Builds a configuration from a dictionary representation. Args: conf (Optional[Dict]): The configuration dictionary. Returns: SharedProcessorConfig object. """ conf = conf or {} return SharedProcessorConfig(**conf) class _ServiceDeployment: def __init__(self, workers: Optional[int], register: Optional[bool], mtap_config: Optional[str], log_level: Optional[str]): self.workers = workers self.register = register self.mtap_config = mtap_config self.log_level = log_level def service_args(self, host: Optional[str] = None, port: Optional[int] = None, register_default: Optional[bool] = None, host_default: Optional[str] = None, workers_default: Optional[int] = None, mtap_config_default: Optional[str] = None, log_level_default: Optional[str] = None): call = [] host = host or host_default if host is not None: call.extend(['--host', str(host)]) if port is not None: call.extend(['--port', str(port)]) if self.register or register_default: call.append('--register') workers = self.workers or workers_default if workers is not None: call.extend(['--workers', str(workers)]) mtap_config = self.mtap_config or mtap_config_default if mtap_config is not None: call.extend(['--mtap-config', mtap_config]) log_level = self.log_level or log_level_default if log_level is not None: call.extend(['--log-level', log_level]) call.append('--write-address') return call class EventsDeployment: """Deployment configuration for the events service. Keyword Args: enabled (bool): Whether an events service should be created. addresses (~typing.Optional[~typing.Sequence[str]]): The host address of the events service. workers (~typing.Optional[int]): The number of worker threads the events service should use. register (~typing.Optional[bool]): Whether to register the events service with discovery. mtap_config (~typing.Optional[str]): Path to an mtap configuration file. log_level (~typing.Optional[str]): The log level for the events service. """ def __init__(self, *, enabled: bool = True, addresses: Optional[Sequence[str]] = None, workers: Optional[int] = None, register: Optional[bool] = None, mtap_config: Optional[str] = None, log_level: Optional[str] = None): self.enabled = enabled self.addresses = addresses self.service_deployment = _ServiceDeployment(workers, register, mtap_config, log_level) def create_calls(self, global_settings: GlobalSettings) -> Iterable[List[str]]: for address in self.addresses: host = None port = None if address: splits = address.split(':') if len(splits) == 2: host, port = splits if host == '': host = None else: host = splits[0] call = [PYTHON_EXE, '-m', 'mtap', 'events'] service_args = self.service_deployment.service_args( host=host, port=port, register_default=global_settings.register, host_default=global_settings.host, mtap_config_default=global_settings.mtap_config, log_level_default=global_settings.log_level ) call.extend(service_args) yield call @staticmethod def from_conf(conf: Optional[Dict]) -> 'EventsDeployment': """Creates the EventsDeployment configuration option from a configuration dictionary. Args: conf (Optional[Dict]): The configuration dictionary Returns: EventsDeployment or None from the configuration dictionary. """ conf = conf or {} enabled = conf.get('enabled') if enabled is None: enabled = False address = conf.get('address', None) or conf.get('addresses', None) if address is None: addresses = [] elif isinstance(address, str): addresses = [address] elif isinstance(address, Iterable): addresses = list(address) else: raise ValueError('Unrecognized type of address: ' + type(address)) return EventsDeployment(enabled=enabled, addresses=addresses, workers=conf.get('workers'), register=conf.get('register'), mtap_config=conf.get('mtap_config')) class ProcessorDeployment: """Deployment configuration for an MTAP processor. Used to construct the command for launching the processor. The processor should be a Java Class with a main method or a Python module with a main block. It should accept the standard MTAP processor deployment arguments and launch an MTAP processor using :func:`mtap.run_processor` or the equivalent Java method. Args: implementation (str): Either "java" or "python". entry_point (str): Either the java main class, or the python main module. enabled (~typing.Optional[bool]): Whether the processor should be launched as part of deployment. Default is `True` if `None`. instances (~typing.Optional[int]): The number of instances of the processor to launch. Default is `1` if `None`. host (~typing.Optional[str]): The listening host for the processor service. port (~typing.Optional[int]): The listening port for the processor service. workers (~typing.Optional[int]): The number of worker threads per instance. register (~typing.Optional[bool]): Whether the processor should register with the discovery service specified in the MTAP configuration mtap_config (~typing.Optional[str]): Path to the MTAP configuration file. log_level (~typing.Optional[str]): The log level for the processor. identifier (~typing.Optional[str]): An optional identifier override to use for registration. pre_args (~typing.Optional[~typing.List[str]]): Arguments that occur prior to the MTAP service arguments (like host, port, etc). additional_args (~typing.Optional[~typing.List[str]]): Arguments that occur after the MTAP service arguments. startup_timeout (~typing.Optional[int]): Optional override startup timeout. mp_spawn_method (~typing.Optional[str]): A :meth:`multiprocessing.get_context` argument to create the multiprocessing context. """ def __init__(self, implementation: str, entry_point: str, *, enabled: Optional[bool] = None, instances: Optional[int] = None, host: Optional[str] = None, port: Optional[int] = None, workers: Optional[int] = None, register: Optional[bool] = None, mtap_config: Optional[str] = None, log_level: Optional[str] = None, identifier: Optional[str] = None, pre_args: Optional[List[str]] = None, additional_args: Optional[List[str]] = None, startup_timeout: Optional[int] = None, mp_spawn_method: Optional[str] = None): self.enabled = enabled if enabled is not None else True self.implementation = implementation self.entry_point = entry_point self.instances = instances or 1 if not isinstance(self.instances, int) or self.instances < 1: raise ValueError("Instances must be strictly positive integer.") self.identifier = identifier self.pre_args = pre_args self.additional_args = additional_args self.host = host self.port = port self.service_deployment = _ServiceDeployment(workers, register, mtap_config, log_level) self.startup_timeout = startup_timeout self.mp_spawn_method = mp_spawn_method @staticmethod def from_conf(conf: Dict) -> 'ProcessorDeployment': """Creates an MTAP processor deployment configuration from a configuration dictionary. Args: conf (Dict): The configuration dictionary. Returns: ProcessorDeployment object that can be used to constuct the call for the processor. """ return ProcessorDeployment(**conf) def create_calls(self, global_settings: GlobalSettings, shared_config: SharedProcessorConfig) -> Iterable[List[str]]: if isinstance(self.port, list): ports = self.port elif self.port is None: ports = [None] * self.instances else: ports = list(range(self.port, self.port + self.instances)) for port in ports: if self.implementation == 'python': call = [PYTHON_EXE, '-m', self.entry_point] mp_spawn_method = shared_config.mp_spawn_method if self.mp_spawn_method is not None: mp_spawn_method = self.mp_spawn_method if mp_spawn_method is not None: call.extend(['--mp-spawn-method', mp_spawn_method]) elif self.implementation == 'java': call = [str(JAVA_EXE)] if shared_config.jvm_args is not None: call.extend(shared_config.jvm_args) if shared_config.java_classpath is not None: call.extend(['-cp', shared_config.java_classpath]) call.append(self.entry_point) else: raise ValueError('Unrecognized implementation: ' + self.implementation) if self.pre_args is not None: call.extend(self.pre_args) service_args = self.service_deployment.service_args( host=self.host, port=port, register_default=global_settings.register, host_default=global_settings.host, mtap_config_default=global_settings.mtap_config, log_level_default=global_settings.log_level, workers_default=shared_config.workers ) call.extend(service_args) if self.identifier is not None: call.extend(['--identifier', self.identifier]) events_addresses = shared_config.events_addresses if events_addresses is not None: call.extend(['--events', ','.join(events_addresses)]) if self.additional_args is not None: call.extend(self.additional_args) if shared_config.additional_args is not None: call.extend(shared_config.additional_args) yield call class Deployment: """A automatic deployment configuration which launches a configurable set of MTAP services. Args: global_settings (~typing.Optional[GlobalSettings]): Settings shared among all services. events_deployment (~typing.Optional[EventsDeployment]): Deployment settings for the events service. shared_processor_config (~typing.Optional[SharedProcessorConfig]): Shared configuration settings for all processors. processors (vararg ProcessorDeployment): Configurations for individual processors. """ def __init__(self, global_settings: Optional[GlobalSettings] = None, events_deployment: Optional[EventsDeployment] = None, shared_processor_config: Optional[SharedProcessorConfig] = None, *processors: ProcessorDeployment): self.global_settings = global_settings self.events_deployment = events_deployment self.shared_processor_config = shared_processor_config self.processors = processors @staticmethod def load_configuration(conf: Dict) -> 'Deployment': """Creates a deployment object from a configuration dictionary. Args: conf (Dict): The configuration dictionary. Returns: Deployment object created. """ global_settings = GlobalSettings.from_conf(conf.get('global')) events = EventsDeployment.from_conf(conf.get('events_service')) shared_processor_config = SharedProcessorConfig.from_conf(conf.get('shared_processor_config')) processors_list = conf.get('processors', []) processors = [ProcessorDeployment.from_conf(c) for c in processors_list] return Deployment(global_settings, events, shared_processor_config, *processors) @staticmethod def from_yaml_file(conf_path: Union[pathlib.Path, str]) -> 'Deployment': """Loads a deployment configuration from a yaml file. Args: conf_path (str or pathlib.Path): The path to the yaml configuration file. Returns: Deployment object created from the configuration. """ conf_path = pathlib.Path(conf_path) from yaml import load try: from yaml import CLoader as Loader except ImportError: from yaml import Loader with conf_path.open('rb') as f: conf = load(f, Loader=Loader) return Deployment.load_configuration(conf) def run_servers(self): """Starts all of the configured services. Raises: ServiceDeploymentException: If one of the services fails to launch. """ with _config.Config() as c: enable_proxy = c.get('grpc.enable_proxy', False) processes_listeners = [] events_addresses = [] def shutdown(kill=False): print("Shutting down all processors") for p, listener in processes_listeners: if kill: p.kill() listener.join(timeout=1) if self.events_deployment.enabled: for call in self.events_deployment.create_calls(self.global_settings): p = subprocess.Popen(call, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) listener, events_address = _listen_test_connectivity(p, "events", 30, enable_proxy) events_addresses.append(events_address) processes_listeners.append((p, listener)) if (not self.global_settings.register and not self.events_deployment.service_deployment.register and self.shared_processor_config.events_addresses is None): self.shared_processor_config.events_addresses = events_addresses for processor_deployment in self.processors: if processor_deployment.enabled: for call in processor_deployment.create_calls(self.global_settings, self.shared_processor_config): logger.debug('Launching processor with call: %s', call) p = subprocess.Popen(call, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) startup_timeout = (processor_deployment.startup_timeout or self.shared_processor_config.startup_timeout) try: listener, _ = _listen_test_connectivity(p, call, startup_timeout, enable_proxy) except ServiceDeploymentException as e: logger.error(str(e)) return shutdown(kill=True) processes_listeners.append((p, listener)) print('Done deploying all servers.', flush=True) try: while True: time.sleep(60 * 60 * 24) except KeyboardInterrupt: return shutdown() def _listen_test_connectivity(p: subprocess.Popen, name: Any, startup_timeout: int, enable_proxy: bool = False) -> Tuple[threading.Thread, str]: listener = threading.Thread(target=_listen, args=(p,)) listener.start() address = None for i in range(startup_timeout): try: address = utilities.read_address(str(p.pid)) break except FileNotFoundError: time.sleep(1) if address is None: raise ServiceDeploymentException('Timed out waiting for {} to launch'.format(name)) with grpc.insecure_channel(address, options=[('grpc.enable_http_proxy', enable_proxy)]) as channel: future = grpc.channel_ready_future(channel) try: future.result(timeout=startup_timeout) except grpc.FutureTimeoutError: raise ServiceDeploymentException('Failed to launch: {}'.format(name)) return listener, address def main(args: Optional[Sequence[str]] = None, conf: Optional[argparse.Namespace] = None): if conf is None: conf = deployment_parser().parse_args(args) if conf.log_level is not None: logging.basicConfig(level=getattr(logging, conf.log_level)) if conf.mode == 'run_servers': deployment = Deployment.from_yaml_file(conf.deploy_config) deployment.run_servers() if conf.mode == 'write_example': example = pathlib.Path(__file__).parent / "examples" / "exampleDeploymentConfiguration.yml" shutil.copyfile(str(example), "exampleDeploymentConfiguration.yml") print('Writing "exampleDeploymentConfiguration.yml" to ' + str(pathlib.Path.cwd())) def deployment_parser() -> argparse.ArgumentParser: """Creates a parser for configuration that can be passed to the deployment main method. Returns: ~argparse.ArgumentParser: The argument parser object that will create a namespace that can be passed to :func:`main`. """ parser = argparse.ArgumentParser(add_help=False) parser.add_argument('--log-level', metavar='LEVEL', help="The log level to use for the deployment script.") subparsers = parser.add_subparsers(title='mode') run_servers = subparsers.add_parser('run_servers') run_servers.add_argument('deploy_config', metavar='CONFIG_FILE', type=pathlib.Path, help="A path to the deployment configuration to deploy.") run_servers.set_defaults(mode='run_servers') write_example = subparsers.add_parser('write_example') write_example.set_defaults(mode='write_example') return parser if __name__ == '__main__': main()

40.728972

105

0.618594

import argparse import logging import os import pathlib import shutil import subprocess import sys import threading import time from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union import grpc from mtap import utilities, _config __all__ = [ 'Deployment', 'GlobalSettings', 'SharedProcessorConfig', 'EventsDeployment', 'ProcessorDeployment', 'main', 'deployment_parser', 'ServiceDeploymentException', ] logger = logging.getLogger(__name__) PYTHON_EXE = sys.executable def _get_java() -> str: try: return str(pathlib.Path(os.environ['JAVA_HOME']) / 'bin' / 'java') except KeyError: return 'java' JAVA_EXE = _get_java() def _listen(process: subprocess.Popen) -> int: for line in process.stdout: print(line.decode(), end='', flush=True) return process.wait() class ServiceDeploymentException(Exception): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) class GlobalSettings: def __init__(self, *, host: Optional[str] = None, mtap_config: Optional[str] = None, log_level: Optional[str] = None, register: Optional[bool] = None): self.host = host self.mtap_config = mtap_config self.log_level = log_level self.register = register @staticmethod def from_conf(conf: Optional[Dict]) -> 'GlobalSettings': conf = conf or {} return GlobalSettings(host=conf.get('host'), mtap_config=conf.get('mtap_config'), log_level=conf.get('log_level'), register=conf.get('register')) class SharedProcessorConfig: def __init__(self, events_addresses: Optional[List[str]] = None, workers: Optional[int] = None, additional_args: Optional[List[str]] = None, jvm_args: Optional[List[str]] = None, java_classpath: Optional[str] = None, startup_timeout: Optional[int] = None, mp_spawn_method: Optional[str] = None): self.events_addresses = events_addresses self.workers = workers self.additional_args = additional_args self.jvm_args = jvm_args self.java_classpath = java_classpath self.startup_timeout = startup_timeout or 30 self.mp_spawn_method = mp_spawn_method @staticmethod def from_conf(conf: Optional[Dict]) -> 'SharedProcessorConfig': conf = conf or {} return SharedProcessorConfig(**conf) class _ServiceDeployment: def __init__(self, workers: Optional[int], register: Optional[bool], mtap_config: Optional[str], log_level: Optional[str]): self.workers = workers self.register = register self.mtap_config = mtap_config self.log_level = log_level def service_args(self, host: Optional[str] = None, port: Optional[int] = None, register_default: Optional[bool] = None, host_default: Optional[str] = None, workers_default: Optional[int] = None, mtap_config_default: Optional[str] = None, log_level_default: Optional[str] = None): call = [] host = host or host_default if host is not None: call.extend(['--host', str(host)]) if port is not None: call.extend(['--port', str(port)]) if self.register or register_default: call.append('--register') workers = self.workers or workers_default if workers is not None: call.extend(['--workers', str(workers)]) mtap_config = self.mtap_config or mtap_config_default if mtap_config is not None: call.extend(['--mtap-config', mtap_config]) log_level = self.log_level or log_level_default if log_level is not None: call.extend(['--log-level', log_level]) call.append('--write-address') return call class EventsDeployment: def __init__(self, *, enabled: bool = True, addresses: Optional[Sequence[str]] = None, workers: Optional[int] = None, register: Optional[bool] = None, mtap_config: Optional[str] = None, log_level: Optional[str] = None): self.enabled = enabled self.addresses = addresses self.service_deployment = _ServiceDeployment(workers, register, mtap_config, log_level) def create_calls(self, global_settings: GlobalSettings) -> Iterable[List[str]]: for address in self.addresses: host = None port = None if address: splits = address.split(':') if len(splits) == 2: host, port = splits if host == '': host = None else: host = splits[0] call = [PYTHON_EXE, '-m', 'mtap', 'events'] service_args = self.service_deployment.service_args( host=host, port=port, register_default=global_settings.register, host_default=global_settings.host, mtap_config_default=global_settings.mtap_config, log_level_default=global_settings.log_level ) call.extend(service_args) yield call @staticmethod def from_conf(conf: Optional[Dict]) -> 'EventsDeployment': conf = conf or {} enabled = conf.get('enabled') if enabled is None: enabled = False address = conf.get('address', None) or conf.get('addresses', None) if address is None: addresses = [] elif isinstance(address, str): addresses = [address] elif isinstance(address, Iterable): addresses = list(address) else: raise ValueError('Unrecognized type of address: ' + type(address)) return EventsDeployment(enabled=enabled, addresses=addresses, workers=conf.get('workers'), register=conf.get('register'), mtap_config=conf.get('mtap_config')) class ProcessorDeployment: def __init__(self, implementation: str, entry_point: str, *, enabled: Optional[bool] = None, instances: Optional[int] = None, host: Optional[str] = None, port: Optional[int] = None, workers: Optional[int] = None, register: Optional[bool] = None, mtap_config: Optional[str] = None, log_level: Optional[str] = None, identifier: Optional[str] = None, pre_args: Optional[List[str]] = None, additional_args: Optional[List[str]] = None, startup_timeout: Optional[int] = None, mp_spawn_method: Optional[str] = None): self.enabled = enabled if enabled is not None else True self.implementation = implementation self.entry_point = entry_point self.instances = instances or 1 if not isinstance(self.instances, int) or self.instances < 1: raise ValueError("Instances must be strictly positive integer.") self.identifier = identifier self.pre_args = pre_args self.additional_args = additional_args self.host = host self.port = port self.service_deployment = _ServiceDeployment(workers, register, mtap_config, log_level) self.startup_timeout = startup_timeout self.mp_spawn_method = mp_spawn_method @staticmethod def from_conf(conf: Dict) -> 'ProcessorDeployment': return ProcessorDeployment(**conf) def create_calls(self, global_settings: GlobalSettings, shared_config: SharedProcessorConfig) -> Iterable[List[str]]: if isinstance(self.port, list): ports = self.port elif self.port is None: ports = [None] * self.instances else: ports = list(range(self.port, self.port + self.instances)) for port in ports: if self.implementation == 'python': call = [PYTHON_EXE, '-m', self.entry_point] mp_spawn_method = shared_config.mp_spawn_method if self.mp_spawn_method is not None: mp_spawn_method = self.mp_spawn_method if mp_spawn_method is not None: call.extend(['--mp-spawn-method', mp_spawn_method]) elif self.implementation == 'java': call = [str(JAVA_EXE)] if shared_config.jvm_args is not None: call.extend(shared_config.jvm_args) if shared_config.java_classpath is not None: call.extend(['-cp', shared_config.java_classpath]) call.append(self.entry_point) else: raise ValueError('Unrecognized implementation: ' + self.implementation) if self.pre_args is not None: call.extend(self.pre_args) service_args = self.service_deployment.service_args( host=self.host, port=port, register_default=global_settings.register, host_default=global_settings.host, mtap_config_default=global_settings.mtap_config, log_level_default=global_settings.log_level, workers_default=shared_config.workers ) call.extend(service_args) if self.identifier is not None: call.extend(['--identifier', self.identifier]) events_addresses = shared_config.events_addresses if events_addresses is not None: call.extend(['--events', ','.join(events_addresses)]) if self.additional_args is not None: call.extend(self.additional_args) if shared_config.additional_args is not None: call.extend(shared_config.additional_args) yield call class Deployment: def __init__(self, global_settings: Optional[GlobalSettings] = None, events_deployment: Optional[EventsDeployment] = None, shared_processor_config: Optional[SharedProcessorConfig] = None, *processors: ProcessorDeployment): self.global_settings = global_settings self.events_deployment = events_deployment self.shared_processor_config = shared_processor_config self.processors = processors @staticmethod def load_configuration(conf: Dict) -> 'Deployment': global_settings = GlobalSettings.from_conf(conf.get('global')) events = EventsDeployment.from_conf(conf.get('events_service')) shared_processor_config = SharedProcessorConfig.from_conf(conf.get('shared_processor_config')) processors_list = conf.get('processors', []) processors = [ProcessorDeployment.from_conf(c) for c in processors_list] return Deployment(global_settings, events, shared_processor_config, *processors) @staticmethod def from_yaml_file(conf_path: Union[pathlib.Path, str]) -> 'Deployment': conf_path = pathlib.Path(conf_path) from yaml import load try: from yaml import CLoader as Loader except ImportError: from yaml import Loader with conf_path.open('rb') as f: conf = load(f, Loader=Loader) return Deployment.load_configuration(conf) def run_servers(self): with _config.Config() as c: enable_proxy = c.get('grpc.enable_proxy', False) processes_listeners = [] events_addresses = [] def shutdown(kill=False): print("Shutting down all processors") for p, listener in processes_listeners: if kill: p.kill() listener.join(timeout=1) if self.events_deployment.enabled: for call in self.events_deployment.create_calls(self.global_settings): p = subprocess.Popen(call, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) listener, events_address = _listen_test_connectivity(p, "events", 30, enable_proxy) events_addresses.append(events_address) processes_listeners.append((p, listener)) if (not self.global_settings.register and not self.events_deployment.service_deployment.register and self.shared_processor_config.events_addresses is None): self.shared_processor_config.events_addresses = events_addresses for processor_deployment in self.processors: if processor_deployment.enabled: for call in processor_deployment.create_calls(self.global_settings, self.shared_processor_config): logger.debug('Launching processor with call: %s', call) p = subprocess.Popen(call, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) startup_timeout = (processor_deployment.startup_timeout or self.shared_processor_config.startup_timeout) try: listener, _ = _listen_test_connectivity(p, call, startup_timeout, enable_proxy) except ServiceDeploymentException as e: logger.error(str(e)) return shutdown(kill=True) processes_listeners.append((p, listener)) print('Done deploying all servers.', flush=True) try: while True: time.sleep(60 * 60 * 24) except KeyboardInterrupt: return shutdown() def _listen_test_connectivity(p: subprocess.Popen, name: Any, startup_timeout: int, enable_proxy: bool = False) -> Tuple[threading.Thread, str]: listener = threading.Thread(target=_listen, args=(p,)) listener.start() address = None for i in range(startup_timeout): try: address = utilities.read_address(str(p.pid)) break except FileNotFoundError: time.sleep(1) if address is None: raise ServiceDeploymentException('Timed out waiting for {} to launch'.format(name)) with grpc.insecure_channel(address, options=[('grpc.enable_http_proxy', enable_proxy)]) as channel: future = grpc.channel_ready_future(channel) try: future.result(timeout=startup_timeout) except grpc.FutureTimeoutError: raise ServiceDeploymentException('Failed to launch: {}'.format(name)) return listener, address def main(args: Optional[Sequence[str]] = None, conf: Optional[argparse.Namespace] = None): if conf is None: conf = deployment_parser().parse_args(args) if conf.log_level is not None: logging.basicConfig(level=getattr(logging, conf.log_level)) if conf.mode == 'run_servers': deployment = Deployment.from_yaml_file(conf.deploy_config) deployment.run_servers() if conf.mode == 'write_example': example = pathlib.Path(__file__).parent / "examples" / "exampleDeploymentConfiguration.yml" shutil.copyfile(str(example), "exampleDeploymentConfiguration.yml") print('Writing "exampleDeploymentConfiguration.yml" to ' + str(pathlib.Path.cwd())) def deployment_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser(add_help=False) parser.add_argument('--log-level', metavar='LEVEL', help="The log level to use for the deployment script.") subparsers = parser.add_subparsers(title='mode') run_servers = subparsers.add_parser('run_servers') run_servers.add_argument('deploy_config', metavar='CONFIG_FILE', type=pathlib.Path, help="A path to the deployment configuration to deploy.") run_servers.set_defaults(mode='run_servers') write_example = subparsers.add_parser('write_example') write_example.set_defaults(mode='write_example') return parser if __name__ == '__main__': main()

true

f71bf509bb30658310cb6206ecdb34d8c4c8f548

6,084

py

Python

sphinx/testing/path.py

Symaxion/sphinx

f4f7936b5c3671153c2646387c8258b1c4e25e3c

[ "BSD-2-Clause" ]

8

2019-04-27T01:19:45.000Z

2020-09-21T03:31:01.000Z

sphinx/testing/path.py

JoeyCluett/sphinx

ff5031c96e90027510ad2d0251972e12da46402c

[ "BSD-2-Clause" ]

12

2021-03-09T03:01:16.000Z

2022-03-11T23:59:36.000Z

sphinx/testing/path.py

JoeyCluett/sphinx

ff5031c96e90027510ad2d0251972e12da46402c

[ "BSD-2-Clause" ]

5

2019-04-27T01:19:47.000Z

2020-09-20T15:15:19.000Z

""" sphinx.testing.path ~~~~~~~~~~~~~~~~~~~ :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ import os import shutil import sys if False: # For type annotation import builtins # NOQA from typing import Any, Callable, IO, List # NOQA FILESYSTEMENCODING = sys.getfilesystemencoding() or sys.getdefaultencoding() class path(str): """ Represents a path which behaves like a string. """ @property def parent(self): # type: () -> path """ The name of the directory the file or directory is in. """ return self.__class__(os.path.dirname(self)) def basename(self): # type: () -> str return os.path.basename(self) def abspath(self): # type: () -> path """ Returns the absolute path. """ return self.__class__(os.path.abspath(self)) def isabs(self): # type: () -> bool """ Returns ``True`` if the path is absolute. """ return os.path.isabs(self) def isdir(self): # type: () -> bool """ Returns ``True`` if the path is a directory. """ return os.path.isdir(self) def isfile(self): # type: () -> bool """ Returns ``True`` if the path is a file. """ return os.path.isfile(self) def islink(self): # type: () -> bool """ Returns ``True`` if the path is a symbolic link. """ return os.path.islink(self) def ismount(self): # type: () -> bool """ Returns ``True`` if the path is a mount point. """ return os.path.ismount(self) def rmtree(self, ignore_errors=False, onerror=None): # type: (bool, Callable) -> None """ Removes the file or directory and any files or directories it may contain. :param ignore_errors: If ``True`` errors are silently ignored, otherwise an exception is raised in case an error occurs. :param onerror: A callback which gets called with the arguments `func`, `path` and `exc_info`. `func` is one of :func:`os.listdir`, :func:`os.remove` or :func:`os.rmdir`. `path` is the argument to the function which caused it to fail and `exc_info` is a tuple as returned by :func:`sys.exc_info`. """ shutil.rmtree(self, ignore_errors=ignore_errors, onerror=onerror) def copytree(self, destination, symlinks=False): # type: (str, bool) -> None """ Recursively copy a directory to the given `destination`. If the given `destination` does not exist it will be created. :param symlinks: If ``True`` symbolic links in the source tree result in symbolic links in the destination tree otherwise the contents of the files pointed to by the symbolic links are copied. """ shutil.copytree(self, destination, symlinks=symlinks) def movetree(self, destination): # type: (str) -> None """ Recursively move the file or directory to the given `destination` similar to the Unix "mv" command. If the `destination` is a file it may be overwritten depending on the :func:`os.rename` semantics. """ shutil.move(self, destination) move = movetree def unlink(self): # type: () -> None """ Removes a file. """ os.unlink(self) def stat(self): # type: () -> Any """ Returns a stat of the file. """ return os.stat(self) def utime(self, arg): # type: (Any) -> None os.utime(self, arg) def open(self, mode='r', **kwargs): # type: (str, Any) -> IO return open(self, mode, **kwargs) def write_text(self, text, encoding='utf-8', **kwargs): # type: (str, str, Any) -> None """ Writes the given `text` to the file. """ with open(self, 'w', encoding=encoding, **kwargs) as f: f.write(text) def text(self, encoding='utf-8', **kwargs): # type: (str, Any) -> str """ Returns the text in the file. """ with open(self, encoding=encoding, **kwargs) as f: return f.read() def bytes(self): # type: () -> builtins.bytes """ Returns the bytes in the file. """ with open(self, mode='rb') as f: return f.read() def write_bytes(self, bytes, append=False): # type: (str, bool) -> None """ Writes the given `bytes` to the file. :param append: If ``True`` given `bytes` are added at the end of the file. """ if append: mode = 'ab' else: mode = 'wb' with open(self, mode=mode) as f: f.write(bytes) def exists(self): # type: () -> bool """ Returns ``True`` if the path exist. """ return os.path.exists(self) def lexists(self): # type: () -> bool """ Returns ``True`` if the path exists unless it is a broken symbolic link. """ return os.path.lexists(self) def makedirs(self, mode=0o777, exist_ok=False): # type: (int, bool) -> None """ Recursively create directories. """ os.makedirs(self, mode, exist_ok=exist_ok) def joinpath(self, *args): # type: (Any) -> path """ Joins the path with the argument given and returns the result. """ return self.__class__(os.path.join(self, *map(self.__class__, args))) def listdir(self): # type: () -> List[str] return os.listdir(self) __div__ = __truediv__ = joinpath def __repr__(self): # type: () -> str return '%s(%s)' % (self.__class__.__name__, super().__repr__())

27.160714

78

0.534845

import os import shutil import sys if False: import builtins from typing import Any, Callable, IO, List FILESYSTEMENCODING = sys.getfilesystemencoding() or sys.getdefaultencoding() class path(str): @property def parent(self): return self.__class__(os.path.dirname(self)) def basename(self): return os.path.basename(self) def abspath(self): return self.__class__(os.path.abspath(self)) def isabs(self): return os.path.isabs(self) def isdir(self): return os.path.isdir(self) def isfile(self): return os.path.isfile(self) def islink(self): return os.path.islink(self) def ismount(self): return os.path.ismount(self) def rmtree(self, ignore_errors=False, onerror=None): shutil.rmtree(self, ignore_errors=ignore_errors, onerror=onerror) def copytree(self, destination, symlinks=False): shutil.copytree(self, destination, symlinks=symlinks) def movetree(self, destination): shutil.move(self, destination) move = movetree def unlink(self): os.unlink(self) def stat(self): return os.stat(self) def utime(self, arg): os.utime(self, arg) def open(self, mode='r', **kwargs): return open(self, mode, **kwargs) def write_text(self, text, encoding='utf-8', **kwargs): with open(self, 'w', encoding=encoding, **kwargs) as f: f.write(text) def text(self, encoding='utf-8', **kwargs): with open(self, encoding=encoding, **kwargs) as f: return f.read() def bytes(self): with open(self, mode='rb') as f: return f.read() def write_bytes(self, bytes, append=False): if append: mode = 'ab' else: mode = 'wb' with open(self, mode=mode) as f: f.write(bytes) def exists(self): return os.path.exists(self) def lexists(self): return os.path.lexists(self) def makedirs(self, mode=0o777, exist_ok=False): os.makedirs(self, mode, exist_ok=exist_ok) def joinpath(self, *args): return self.__class__(os.path.join(self, *map(self.__class__, args))) def listdir(self): return os.listdir(self) __div__ = __truediv__ = joinpath def __repr__(self): return '%s(%s)' % (self.__class__.__name__, super().__repr__())

true

f71bf51348a8a1504b096c6228f4179dd5875c54

1,538

py

Python

consort/tools/ClefSpannerExpression.py

josiah-wolf-oberholtzer/consort

6c7d511835d5ad883ad1ad52ae9cd48c4a7b5571

[ "MIT" ]

9

2015-02-11T09:35:40.000Z

2019-04-29T23:57:49.000Z

consort/tools/ClefSpannerExpression.py

josiah-wolf-oberholtzer/consort

6c7d511835d5ad883ad1ad52ae9cd48c4a7b5571

[ "MIT" ]

2

2016-02-07T18:54:47.000Z

2017-08-10T01:38:01.000Z

consort/tools/ClefSpannerExpression.py

josiah-wolf-oberholtzer/consort

6c7d511835d5ad883ad1ad52ae9cd48c4a7b5571

[ "MIT" ]

1

2019-05-13T12:37:15.000Z

from abjad import attach from abjad import inspect from abjad import iterate from abjad.tools import abctools from abjad.tools import scoretools class ClefSpannerExpression(abctools.AbjadValueObject): r'''A clef spanner expression. ''' ### CLASS VARIABLES ### __slots__ = () ### INITIALIZER ### def __init__(self): pass ### SPECIAL METHODS ### def __call__(self, music, name=None): import consort leaves = list(iterate(music).by_leaf()) weights = [] weighted_pitches = [] for leaf in leaves: weight = float(inspect(leaf).get_duration()) if isinstance(leaf, abjad.Note): pitch = float(leaf.written_pitch) weighted_pitch = pitch * weight weights.append(weight) weighted_pitches.append(weighted_pitch) elif isinstance(leaf, abjad.Chord): for pitch in leaf.written_pitches: pitch = float(pitch) weighted_pitch = pitch * weight weighted_pitches.append(weighted_pitch) weights.append(weight) sum_of_weights = sum(weights) sum_of_weighted_pitches = sum(weighted_pitches) weighted_average = sum_of_weighted_pitches / sum_of_weights if weighted_average < 0: clef_spanner = consort.ClefSpanner('bass') else: clef_spanner = consort.ClefSpanner('treble') attach(clef_spanner, music, name=name)

31.387755

67

0.605982

from abjad import attach from abjad import inspect from abjad import iterate from abjad.tools import abctools from abjad.tools import scoretools class ClefSpannerExpression(abctools.AbjadValueObject): leaves = list(iterate(music).by_leaf()) weights = [] weighted_pitches = [] for leaf in leaves: weight = float(inspect(leaf).get_duration()) if isinstance(leaf, abjad.Note): pitch = float(leaf.written_pitch) weighted_pitch = pitch * weight weights.append(weight) weighted_pitches.append(weighted_pitch) elif isinstance(leaf, abjad.Chord): for pitch in leaf.written_pitches: pitch = float(pitch) weighted_pitch = pitch * weight weighted_pitches.append(weighted_pitch) weights.append(weight) sum_of_weights = sum(weights) sum_of_weighted_pitches = sum(weighted_pitches) weighted_average = sum_of_weighted_pitches / sum_of_weights if weighted_average < 0: clef_spanner = consort.ClefSpanner('bass') else: clef_spanner = consort.ClefSpanner('treble') attach(clef_spanner, music, name=name)

true

f71bf52008d28c422bd88b5ecd34e9e1fab1fa11

2,202

py

Python

models/ModelUtil/util.py

Easonyesheng/StereoCameraToolk

660e43019d0687e96e6da3aca48c1c423ae5abff

[ "MIT" ]

27

2020-10-16T07:21:35.000Z

2022-03-11T02:56:13.000Z

models/ModelUtil/util.py

Easonyesheng/StereoCamera

9319b7f4e5ce36833de722a15e1074e82b8b4f84

[ "MIT" ]

null

models/ModelUtil/util.py

Easonyesheng/StereoCamera

9319b7f4e5ce36833de722a15e1074e82b8b4f84

[ "MIT" ]

6

2021-02-01T09:54:40.000Z

2022-03-11T03:16:39.000Z

"""Utility """ import numpy as np import cv2 import os import logging def check_string_is_empty(string): """name check string empty or not Args: Returns: """ if string == '': return True return False def check_numpy_array(array): """name check array empty or not Args: Returns: True - Exist """ try: array.all() except AttributeError: return False return True def after_cv_imshow(): """name close all the show window if press 'esc' set after cv2.imshow() Args: Returns: """ k = cv2.waitKey(0) if k == 27: cv2.destroyAllWindows() def save_img_with_prefix(img, path, name): """name save as 'path/name.jpg' Args: Returns: """ cv2.imwrite(os.path.join(path,name+'.jpg'), img) def img_show(img, name): """ """ cv2.startWindowThread() img = img / np.max(img) cv2.imshow(name, img) after_cv_imshow() def test_dir_if_not_create(path): """name save as 'path/name.jpg' Args: Returns: """ if os.path.isdir(path): return True else: print('Create New Folder:', path) os.makedirs(path) return True def log_init(logfilename): """name save as 'path/name.jpg' Args: Returns: """ # logging.basicConfig(filename=logfilename, level=logging.INFO) # logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', # filename=logfilename, # level=logging.DEBUG) logger = logging.getLogger() # 不加名称设置root logger logger.setLevel(logging.DEBUG) formatter = logging.Formatter( '%(asctime)s - %(name)s - %(levelname)s: - %(message)s', datefmt='%Y-%m-%d %H:%M:%S') # 使用FileHandler输出到文件 fh = logging.FileHandler(logfilename, 'w') fh.setLevel(logging.DEBUG) fh.setFormatter(formatter) # 使用StreamHandler输出到屏幕 ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) ch.setFormatter(formatter) # 添加两个Handler logger.addHandler(ch) logger.addHandler(fh)

18.504202

108

0.583106

import numpy as np import cv2 import os import logging def check_string_is_empty(string): if string == '': return True return False def check_numpy_array(array): try: array.all() except AttributeError: return False return True def after_cv_imshow(): k = cv2.waitKey(0) if k == 27: cv2.destroyAllWindows() def save_img_with_prefix(img, path, name): cv2.imwrite(os.path.join(path,name+'.jpg'), img) def img_show(img, name): cv2.startWindowThread() img = img / np.max(img) cv2.imshow(name, img) after_cv_imshow() def test_dir_if_not_create(path): if os.path.isdir(path): return True else: print('Create New Folder:', path) os.makedirs(path) return True def log_init(logfilename): logger = logging.getLogger() logger.setLevel(logging.DEBUG) formatter = logging.Formatter( '%(asctime)s - %(name)s - %(levelname)s: - %(message)s', datefmt='%Y-%m-%d %H:%M:%S') fh = logging.FileHandler(logfilename, 'w') fh.setLevel(logging.DEBUG) fh.setFormatter(formatter) ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) ch.setFormatter(formatter) logger.addHandler(ch) logger.addHandler(fh)

true

f71bf5a6846601e2c447085faeecf596137b1330

4,935

py

Python

airflow/api/common/experimental/trigger_dag.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

2

2020-03-24T14:47:18.000Z

2020-03-24T14:48:17.000Z

airflow/api/common/experimental/trigger_dag.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "Apache-2.0" ]

1

2021-09-29T17:37:13.000Z

airflow/api/common/experimental/trigger_dag.py

IGIT-CN/airflow

a6e5bcd59198afe5716813e84ebc4c59eade532c

[ "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. """Triggering DAG runs APIs.""" import json from datetime import datetime from typing import List, Optional, Union from airflow.exceptions import DagNotFound, DagRunAlreadyExists from airflow.models import DagBag, DagModel, DagRun from airflow.utils import timezone from airflow.utils.state import State from airflow.utils.types import DagRunType def _trigger_dag( dag_id: str, dag_bag: DagBag, dag_run: DagModel, run_id: Optional[str], conf: Optional[Union[dict, str]], execution_date: Optional[datetime], replace_microseconds: bool, ) -> List[DagRun]: # pylint: disable=too-many-arguments """Triggers DAG run. :param dag_id: DAG ID :param dag_bag: DAG Bag model :param dag_run: DAG Run model :param run_id: ID of the dag_run :param conf: configuration :param execution_date: date of execution :param replace_microseconds: whether microseconds should be zeroed :return: list of triggered dags """ dag = dag_bag.get_dag(dag_id) # prefetch dag if it is stored serialized if dag_id not in dag_bag.dags: raise DagNotFound("Dag id {} not found".format(dag_id)) execution_date = execution_date if execution_date else timezone.utcnow() if not timezone.is_localized(execution_date): raise ValueError("The execution_date should be localized") if replace_microseconds: execution_date = execution_date.replace(microsecond=0) if dag.default_args and 'start_date' in dag.default_args: min_dag_start_date = dag.default_args["start_date"] if min_dag_start_date and execution_date < min_dag_start_date: raise ValueError( "The execution_date [{0}] should be >= start_date [{1}] from DAG's default_args".format( execution_date.isoformat(), min_dag_start_date.isoformat())) if not run_id: run_id = "{}{}".format(DagRunType.MANUAL.value, execution_date.isoformat()) dag_run_id = dag_run.find(dag_id=dag_id, run_id=run_id) if dag_run_id: raise DagRunAlreadyExists("Run id {} already exists for dag id {}".format( run_id, dag_id )) run_conf = None if conf: if isinstance(conf, dict): run_conf = conf else: run_conf = json.loads(conf) triggers = [] dags_to_trigger = [dag] while dags_to_trigger: dag = dags_to_trigger.pop() trigger = dag.create_dagrun( run_id=run_id, execution_date=execution_date, state=State.RUNNING, conf=run_conf, external_trigger=True, ) triggers.append(trigger) if dag.subdags: dags_to_trigger.extend(dag.subdags) return triggers def trigger_dag( dag_id: str, run_id: Optional[str] = None, conf: Optional[Union[dict, str]] = None, execution_date: Optional[datetime] = None, replace_microseconds: bool = True, ) -> Optional[DagRun]: """Triggers execution of DAG specified by dag_id :param dag_id: DAG ID :param run_id: ID of the dag_run :param conf: configuration :param execution_date: date of execution :param replace_microseconds: whether microseconds should be zeroed :return: first dag run triggered - even if more than one Dag Runs were triggered or None """ dag_model = DagModel.get_current(dag_id) if dag_model is None: raise DagNotFound("Dag id {} not found in DagModel".format(dag_id)) def read_store_serialized_dags(): from airflow.configuration import conf return conf.getboolean('core', 'store_serialized_dags') dagbag = DagBag( dag_folder=dag_model.fileloc, store_serialized_dags=read_store_serialized_dags() ) dag_run = DagRun() triggers = _trigger_dag( dag_id=dag_id, dag_run=dag_run, dag_bag=dagbag, run_id=run_id, conf=conf, execution_date=execution_date, replace_microseconds=replace_microseconds, ) return triggers[0] if triggers else None

34.270833

104

0.680446

import json from datetime import datetime from typing import List, Optional, Union from airflow.exceptions import DagNotFound, DagRunAlreadyExists from airflow.models import DagBag, DagModel, DagRun from airflow.utils import timezone from airflow.utils.state import State from airflow.utils.types import DagRunType def _trigger_dag( dag_id: str, dag_bag: DagBag, dag_run: DagModel, run_id: Optional[str], conf: Optional[Union[dict, str]], execution_date: Optional[datetime], replace_microseconds: bool, ) -> List[DagRun]: dag = dag_bag.get_dag(dag_id) if dag_id not in dag_bag.dags: raise DagNotFound("Dag id {} not found".format(dag_id)) execution_date = execution_date if execution_date else timezone.utcnow() if not timezone.is_localized(execution_date): raise ValueError("The execution_date should be localized") if replace_microseconds: execution_date = execution_date.replace(microsecond=0) if dag.default_args and 'start_date' in dag.default_args: min_dag_start_date = dag.default_args["start_date"] if min_dag_start_date and execution_date < min_dag_start_date: raise ValueError( "The execution_date [{0}] should be >= start_date [{1}] from DAG's default_args".format( execution_date.isoformat(), min_dag_start_date.isoformat())) if not run_id: run_id = "{}{}".format(DagRunType.MANUAL.value, execution_date.isoformat()) dag_run_id = dag_run.find(dag_id=dag_id, run_id=run_id) if dag_run_id: raise DagRunAlreadyExists("Run id {} already exists for dag id {}".format( run_id, dag_id )) run_conf = None if conf: if isinstance(conf, dict): run_conf = conf else: run_conf = json.loads(conf) triggers = [] dags_to_trigger = [dag] while dags_to_trigger: dag = dags_to_trigger.pop() trigger = dag.create_dagrun( run_id=run_id, execution_date=execution_date, state=State.RUNNING, conf=run_conf, external_trigger=True, ) triggers.append(trigger) if dag.subdags: dags_to_trigger.extend(dag.subdags) return triggers def trigger_dag( dag_id: str, run_id: Optional[str] = None, conf: Optional[Union[dict, str]] = None, execution_date: Optional[datetime] = None, replace_microseconds: bool = True, ) -> Optional[DagRun]: dag_model = DagModel.get_current(dag_id) if dag_model is None: raise DagNotFound("Dag id {} not found in DagModel".format(dag_id)) def read_store_serialized_dags(): from airflow.configuration import conf return conf.getboolean('core', 'store_serialized_dags') dagbag = DagBag( dag_folder=dag_model.fileloc, store_serialized_dags=read_store_serialized_dags() ) dag_run = DagRun() triggers = _trigger_dag( dag_id=dag_id, dag_run=dag_run, dag_bag=dagbag, run_id=run_id, conf=conf, execution_date=execution_date, replace_microseconds=replace_microseconds, ) return triggers[0] if triggers else None

true

f71bf6d7e4ee3f8699dd771f06d36619668ab2db

8,225

py

Python

authors/apps/notify/tests/test_mail_list_model.py

andela/Ah-backend-valkyrie

f0eb64c27e1fe37d5c81e4b9a8762dcf3c336a79

[ "BSD-3-Clause" ]

null

authors/apps/notify/tests/test_mail_list_model.py

andela/Ah-backend-valkyrie

f0eb64c27e1fe37d5c81e4b9a8762dcf3c336a79

[ "BSD-3-Clause" ]

46

2019-01-08T13:16:41.000Z

2021-04-30T20:47:08.000Z

authors/apps/notify/tests/test_mail_list_model.py

andela/Ah-backend-valkyrie

f0eb64c27e1fe37d5c81e4b9a8762dcf3c336a79

[ "BSD-3-Clause" ]

3

2019-01-07T08:21:59.000Z

2019-09-20T06:43:18.000Z

import json from authors.apps.authentication.tests.base import BaseTestMethods from authors.apps.authentication.models import User from rest_framework.reverse import reverse from rest_framework import status class NotificationTests(BaseTestMethods): def test_create_mail_list(self): user = self.register_and_loginUser() token = user.data['token'] auth = f'Bearer {token}' url = reverse('mail-list-status') response = self.client.get(url, HTTP_AUTHORIZATION=auth) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertIsInstance(response.data, dict) self.assertEqual( response.data['user']['email'], 'testuser@andela.com') def test_create_update_notification_status(self): user = self.register_and_loginUser() token = user.data['token'] auth = f'Bearer {token}' url = reverse('mail-list-status') data = {'recieve_email_notifications': 'false'} response = self.client.put(url, data=data, HTTP_AUTHORIZATION=auth) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual( response.data['recieve_email_notifications'], False) def test_fetch_all_user_notifications(self): user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] this_user_2 = User.objects.get(email=user_2.data['email']) response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) # fetch notification object url = reverse('all-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual(notifications.status_code, status.HTTP_200_OK) self.assertEqual( notifications.data['count'], 1) def test_cant_fetch_notifications_for_different_user(self): # register and login user user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() this_user_2 = User.objects.get(email=user_2.data['email']) # follow a registered user response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) # fetch user notification objects url = reverse('all-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual( notifications.data['message'], 'You currently dont have any notifications') def test_fetch_all_user_unread_notifications(self): # register and login user user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] this_user_2 = User.objects.get(email=user_2.data['email']) # follow a registered user response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) # fetch notification object url = reverse('unread-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual(notifications.status_code, status.HTTP_200_OK) self.assertEqual( notifications.data['count'], 1) def test_failed_fetch_all_user_unread_notifications(self): # register and login user self.register_and_loginUser() user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] # fetch notification object url = reverse('unread-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual( notifications.data['message'], 'You currently dont have any unread notifications') def test_fetch_all_user_read_notofications(self): # register and login user user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] this_user_2 = User.objects.get(email=user_2.data['email']) # follow a registered user response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) # mark notifications as read url = reverse('mark-all-as-read') self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') # fetch notification object url = reverse('read-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual( notifications.data['count'], 1) def test_failed_fetch_all_user_read_notifications(self): # register and login user user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] this_user_2 = User.objects.get(email=user_2.data['email']) # follow a registered user response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) # fetch notification object url = reverse('read-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual( notifications.data['message'], 'You currently dont have any read notifications') def test_mark_all_notofications_as_read(self): # register and login user user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] this_user_2 = User.objects.get(email=user_2.data['email']) # follow a registered user response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) # mark notifications as read url = reverse('mark-all-as-read') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual( notifications.data['message'], 'All notifications marked as read')

39.927184

92

0.646565

import json from authors.apps.authentication.tests.base import BaseTestMethods from authors.apps.authentication.models import User from rest_framework.reverse import reverse from rest_framework import status class NotificationTests(BaseTestMethods): def test_create_mail_list(self): user = self.register_and_loginUser() token = user.data['token'] auth = f'Bearer {token}' url = reverse('mail-list-status') response = self.client.get(url, HTTP_AUTHORIZATION=auth) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertIsInstance(response.data, dict) self.assertEqual( response.data['user']['email'], 'testuser@andela.com') def test_create_update_notification_status(self): user = self.register_and_loginUser() token = user.data['token'] auth = f'Bearer {token}' url = reverse('mail-list-status') data = {'recieve_email_notifications': 'false'} response = self.client.put(url, data=data, HTTP_AUTHORIZATION=auth) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual( response.data['recieve_email_notifications'], False) def test_fetch_all_user_notifications(self): user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] this_user_2 = User.objects.get(email=user_2.data['email']) response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) url = reverse('all-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual(notifications.status_code, status.HTTP_200_OK) self.assertEqual( notifications.data['count'], 1) def test_cant_fetch_notifications_for_different_user(self): user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() this_user_2 = User.objects.get(email=user_2.data['email']) response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) url = reverse('all-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual( notifications.data['message'], 'You currently dont have any notifications') def test_fetch_all_user_unread_notifications(self): user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] this_user_2 = User.objects.get(email=user_2.data['email']) response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) url = reverse('unread-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual(notifications.status_code, status.HTTP_200_OK) self.assertEqual( notifications.data['count'], 1) def test_failed_fetch_all_user_unread_notifications(self): self.register_and_loginUser() user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] url = reverse('unread-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual( notifications.data['message'], 'You currently dont have any unread notifications') def test_fetch_all_user_read_notofications(self): user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] this_user_2 = User.objects.get(email=user_2.data['email']) response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) url = reverse('mark-all-as-read') self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') url = reverse('read-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual( notifications.data['count'], 1) def test_failed_fetch_all_user_read_notifications(self): user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] this_user_2 = User.objects.get(email=user_2.data['email']) response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) url = reverse('read-notifications') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual( notifications.data['message'], 'You currently dont have any read notifications') def test_mark_all_notofications_as_read(self): user_1 = self.register_and_loginUser() user_1_token = user_1.data['token'] user_2 = self.register_and_login_user2() user_2_token = user_2.data['token'] this_user_2 = User.objects.get(email=user_2.data['email']) response = self.client.post( '/api/v1/users/{}/profile/follow'.format(this_user_2.username), HTTP_AUTHORIZATION=f'Bearer {user_1_token}') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual( json.loads(response.content)[ 'profile']['following'], True ) url = reverse('mark-all-as-read') notifications = self.client.get( url, format='json', HTTP_AUTHORIZATION=f'Bearer {user_2_token}') self.assertEqual( notifications.data['message'], 'All notifications marked as read')

true

f71bf861822822818626a562f2930b021abda1d2

596

py

Python

x2py/__init__.py

jaykang920/x2py

b8bd473f94ff4b9576e984cc384f4159ab71278d

[ "MIT" ]

null

x2py/__init__.py

jaykang920/x2py

b8bd473f94ff4b9576e984cc384f4159ab71278d

[ "MIT" ]

1

2019-06-05T09:35:09.000Z

2020-07-02T09:46:46.000Z

x2py/__init__.py

jaykang920/x2py

b8bd473f94ff4b9576e984cc384f4159ab71278d

[ "MIT" ]

null

# Copyright (c) 2017, 2018 Jae-jun Kang # See the file LICENSE for details. """Import core names of x2py.""" __version__ = '0.4.3' from x2py.buffer_transform import BufferTransform from x2py.builtin_events import * from x2py.case import Case from x2py.config import Config from x2py.coroutine import Coroutine, CoroutineHandler from x2py.event import Event from x2py.event_factory import EventFactory from x2py.event_sink import EventSink from x2py.flow import Flow from x2py.hub import Hub from x2py.link import Link from x2py.flows import * from x2py.util import * from x2py.yields import *

27.090909

54

0.798658

__version__ = '0.4.3' from x2py.buffer_transform import BufferTransform from x2py.builtin_events import * from x2py.case import Case from x2py.config import Config from x2py.coroutine import Coroutine, CoroutineHandler from x2py.event import Event from x2py.event_factory import EventFactory from x2py.event_sink import EventSink from x2py.flow import Flow from x2py.hub import Hub from x2py.link import Link from x2py.flows import * from x2py.util import * from x2py.yields import *

true

f71bf97329b821d2dc2e07b818ab784fb1ed22a7

2,815

py

Python

test/lint/check-doc.py

BakedInside/beanscore

daa9b2ddbfd3305881749bda7f32146738154260

[ "MIT" ]

null

test/lint/check-doc.py

BakedInside/beanscore

daa9b2ddbfd3305881749bda7f32146738154260

[ "MIT" ]

null

test/lint/check-doc.py

BakedInside/beanscore

daa9b2ddbfd3305881749bda7f32146738154260

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2015-2020 The Beans Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. ''' This checks if all command line args are documented. Return value is 0 to indicate no error. Author: @MarcoFalke ''' from subprocess import check_output import re FOLDER_GREP = 'src' FOLDER_TEST = 'src/test/' REGEX_ARG = r'(?:ForceSet|SoftSet|Get|Is)(?:Bool)?Args?(?:Set)?\("(-[^"]+)"' REGEX_DOC = r'AddArg\("(-[^"=]+?)(?:=|")' CMD_ROOT_DIR = '$(git rev-parse --show-toplevel)/{}'.format(FOLDER_GREP) CMD_GREP_ARGS = r"git grep --perl-regexp '{}' -- {} ':(exclude){}'".format(REGEX_ARG, CMD_ROOT_DIR, FOLDER_TEST) CMD_GREP_WALLET_ARGS = r"git grep --function-context 'void WalletInit::AddWalletOptions' -- {} | grep AddArg".format(CMD_ROOT_DIR) CMD_GREP_WALLET_HIDDEN_ARGS = r"git grep --function-context 'void DummyWalletInit::AddWalletOptions' -- {}".format(CMD_ROOT_DIR) CMD_GREP_DOCS = r"git grep --perl-regexp '{}' {}".format(REGEX_DOC, CMD_ROOT_DIR) # list unsupported, deprecated and duplicate args as they need no documentation SET_DOC_OPTIONAL = set(['-h', '-help', '-dbcrashratio', '-forcecompactdb', '-zapwallettxes']) def lint_missing_argument_documentation(): used = check_output(CMD_GREP_ARGS, shell=True).decode('utf8').strip() docd = check_output(CMD_GREP_DOCS, shell=True).decode('utf8').strip() args_used = set(re.findall(re.compile(REGEX_ARG), used)) args_docd = set(re.findall(re.compile(REGEX_DOC), docd)).union(SET_DOC_OPTIONAL) args_need_doc = args_used.difference(args_docd) args_unknown = args_docd.difference(args_used) print("Args used : {}".format(len(args_used))) print("Args documented : {}".format(len(args_docd))) print("Args undocumented: {}".format(len(args_need_doc))) print(args_need_doc) print("Args unknown : {}".format(len(args_unknown))) print(args_unknown) assert 0 == len(args_need_doc), "Please document the following arguments: {}".format(args_need_doc) def lint_missing_hidden_wallet_args(): wallet_args = check_output(CMD_GREP_WALLET_ARGS, shell=True).decode('utf8').strip() wallet_hidden_args = check_output(CMD_GREP_WALLET_HIDDEN_ARGS, shell=True).decode('utf8').strip() wallet_args = set(re.findall(re.compile(REGEX_DOC), wallet_args)) wallet_hidden_args = set(re.findall(re.compile(r' "([^"=]+)'), wallet_hidden_args)) hidden_missing = wallet_args.difference(wallet_hidden_args) if hidden_missing: assert 0, "Please add {} to the hidden args in DummyWalletInit::AddWalletOptions".format(hidden_missing) def main(): lint_missing_argument_documentation() lint_missing_hidden_wallet_args() if __name__ == "__main__": main()

42.014925

130

0.722913

from subprocess import check_output import re FOLDER_GREP = 'src' FOLDER_TEST = 'src/test/' REGEX_ARG = r'(?:ForceSet|SoftSet|Get|Is)(?:Bool)?Args?(?:Set)?\("(-[^"]+)"' REGEX_DOC = r'AddArg\("(-[^"=]+?)(?:=|")' CMD_ROOT_DIR = '$(git rev-parse --show-toplevel)/{}'.format(FOLDER_GREP) CMD_GREP_ARGS = r"git grep --perl-regexp '{}' -- {} ':(exclude){}'".format(REGEX_ARG, CMD_ROOT_DIR, FOLDER_TEST) CMD_GREP_WALLET_ARGS = r"git grep --function-context 'void WalletInit::AddWalletOptions' -- {} | grep AddArg".format(CMD_ROOT_DIR) CMD_GREP_WALLET_HIDDEN_ARGS = r"git grep --function-context 'void DummyWalletInit::AddWalletOptions' -- {}".format(CMD_ROOT_DIR) CMD_GREP_DOCS = r"git grep --perl-regexp '{}' {}".format(REGEX_DOC, CMD_ROOT_DIR) SET_DOC_OPTIONAL = set(['-h', '-help', '-dbcrashratio', '-forcecompactdb', '-zapwallettxes']) def lint_missing_argument_documentation(): used = check_output(CMD_GREP_ARGS, shell=True).decode('utf8').strip() docd = check_output(CMD_GREP_DOCS, shell=True).decode('utf8').strip() args_used = set(re.findall(re.compile(REGEX_ARG), used)) args_docd = set(re.findall(re.compile(REGEX_DOC), docd)).union(SET_DOC_OPTIONAL) args_need_doc = args_used.difference(args_docd) args_unknown = args_docd.difference(args_used) print("Args used : {}".format(len(args_used))) print("Args documented : {}".format(len(args_docd))) print("Args undocumented: {}".format(len(args_need_doc))) print(args_need_doc) print("Args unknown : {}".format(len(args_unknown))) print(args_unknown) assert 0 == len(args_need_doc), "Please document the following arguments: {}".format(args_need_doc) def lint_missing_hidden_wallet_args(): wallet_args = check_output(CMD_GREP_WALLET_ARGS, shell=True).decode('utf8').strip() wallet_hidden_args = check_output(CMD_GREP_WALLET_HIDDEN_ARGS, shell=True).decode('utf8').strip() wallet_args = set(re.findall(re.compile(REGEX_DOC), wallet_args)) wallet_hidden_args = set(re.findall(re.compile(r' "([^"=]+)'), wallet_hidden_args)) hidden_missing = wallet_args.difference(wallet_hidden_args) if hidden_missing: assert 0, "Please add {} to the hidden args in DummyWalletInit::AddWalletOptions".format(hidden_missing) def main(): lint_missing_argument_documentation() lint_missing_hidden_wallet_args() if __name__ == "__main__": main()

true

f71bf97c41d24bf4383061ccf726c43fc54c6be9

4,880

py

Python

py3status/modules/file_status.py

JackDoan/py3status

e7f56fc0bec8c1a625328c3549b88f66ee8b41ab

[ "BSD-3-Clause" ]

null

py3status/modules/file_status.py

JackDoan/py3status

e7f56fc0bec8c1a625328c3549b88f66ee8b41ab

[ "BSD-3-Clause" ]

null

py3status/modules/file_status.py

JackDoan/py3status

e7f56fc0bec8c1a625328c3549b88f66ee8b41ab

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Display if files or directories exists. Configuration parameters: cache_timeout: refresh interval for this module (default 10) format: display format for this module (default '\?color=path [\?if=path ●|■]') format_path: format for paths (default '{basename}') format_path_separator: show separator if more than one (default ' ') paths: specify a string or a list of paths to check (default None) thresholds: specify color thresholds to use (default [(0, 'bad'), (1, 'good')]) Format placeholders: {format_path} format for paths {path} number of paths, eg 1, 2, 3 format_path placeholders: {basename} basename of pathname {pathname} pathname Color options: color_bad: files or directories does not exist color_good: files or directories exists Color thresholds: format: path: print a color based on the number of paths Examples: # add multiple paths with wildcard or with pathnames ``` file_status { paths = ['/tmp/test*', '~user/test1', '~/Videos/*.mp4'] } # colorize basenames file_status { paths = ['~/.config/i3/modules/*.py'] format = '{format_path}' format_path = '\?color=good {basename}' format_path_separator = ', ' } ``` @author obb, Moritz Lüdecke, Cyril Levis (@cyrinux) SAMPLE OUTPUT {'color': '#00FF00', 'full_text': u'\u25cf'} missing {'color': '#FF0000', 'full_text': u'\u25a0'} """ from glob import glob from os.path import basename, expanduser STRING_NO_PATHS = "missing paths" class Py3status: """ """ # available configuration parameters cache_timeout = 10 format = u"\?color=path [\?if=path \u25cf|\u25a0]" format_path = u"{basename}" format_path_separator = u" " paths = None thresholds = [(0, "bad"), (1, "good")] class Meta: deprecated = { "rename": [ { "param": "format_available", "new": "icon_available", "msg": "obsolete parameter use `icon_available`", }, { "param": "format_unavailable", "new": "icon_unavailable", "msg": "obsolete parameter use `icon_unavailable`", }, { "param": "path", "new": "paths", "msg": "obsolete parameter use `paths`", }, ], "rename_placeholder": [ {"placeholder": "paths", "new": "path", "format_strings": ["format"]} ], } def post_config_hook(self): if not self.paths: raise Exception(STRING_NO_PATHS) # icon deprecation on = getattr(self, "icon_available", u"\u25cf") off = getattr(self, "icon_unavailable", u"\u25a0") new_icon = u"\?color=path [\?if=path {}|{}]".format(on, off) self.format = self.format.replace("{icon}", new_icon) # convert str to list + expand path if not isinstance(self.paths, list): self.paths = [self.paths] self.paths = list(map(expanduser, self.paths)) self.init = {"format_path": []} if self.py3.format_contains(self.format, "format_path"): self.init["format_path"] = self.py3.get_placeholders_list(self.format_path) def file_status(self): # init datas paths = sorted([files for path in self.paths for files in glob(path)]) count_path = len(paths) format_path = None # format paths if self.init["format_path"]: new_data = [] format_path_separator = self.py3.safe_format(self.format_path_separator) for pathname in paths: path = {} for key in self.init["format_path"]: if key == "basename": value = basename(pathname) elif key == "pathname": value = pathname else: continue path[key] = self.py3.safe_format(value) new_data.append(self.py3.safe_format(self.format_path, path)) format_path = self.py3.composite_join(format_path_separator, new_data) if self.thresholds: self.py3.threshold_get_color(count_path, "path") self.py3.threshold_get_color(count_path, "paths") return { "cached_until": self.py3.time_in(self.cache_timeout), "full_text": self.py3.safe_format( self.format, {"path": count_path, "paths": count_path, "format_path": format_path}, ), } if __name__ == "__main__": """ Run module in test mode. """ from py3status.module_test import module_test module_test(Py3status)

30.310559

87

0.565369

from glob import glob from os.path import basename, expanduser STRING_NO_PATHS = "missing paths" class Py3status: cache_timeout = 10 format = u"\?color=path [\?if=path \u25cf|\u25a0]" format_path = u"{basename}" format_path_separator = u" " paths = None thresholds = [(0, "bad"), (1, "good")] class Meta: deprecated = { "rename": [ { "param": "format_available", "new": "icon_available", "msg": "obsolete parameter use `icon_available`", }, { "param": "format_unavailable", "new": "icon_unavailable", "msg": "obsolete parameter use `icon_unavailable`", }, { "param": "path", "new": "paths", "msg": "obsolete parameter use `paths`", }, ], "rename_placeholder": [ {"placeholder": "paths", "new": "path", "format_strings": ["format"]} ], } def post_config_hook(self): if not self.paths: raise Exception(STRING_NO_PATHS) on = getattr(self, "icon_available", u"\u25cf") off = getattr(self, "icon_unavailable", u"\u25a0") new_icon = u"\?color=path [\?if=path {}|{}]".format(on, off) self.format = self.format.replace("{icon}", new_icon) if not isinstance(self.paths, list): self.paths = [self.paths] self.paths = list(map(expanduser, self.paths)) self.init = {"format_path": []} if self.py3.format_contains(self.format, "format_path"): self.init["format_path"] = self.py3.get_placeholders_list(self.format_path) def file_status(self): paths = sorted([files for path in self.paths for files in glob(path)]) count_path = len(paths) format_path = None if self.init["format_path"]: new_data = [] format_path_separator = self.py3.safe_format(self.format_path_separator) for pathname in paths: path = {} for key in self.init["format_path"]: if key == "basename": value = basename(pathname) elif key == "pathname": value = pathname else: continue path[key] = self.py3.safe_format(value) new_data.append(self.py3.safe_format(self.format_path, path)) format_path = self.py3.composite_join(format_path_separator, new_data) if self.thresholds: self.py3.threshold_get_color(count_path, "path") self.py3.threshold_get_color(count_path, "paths") return { "cached_until": self.py3.time_in(self.cache_timeout), "full_text": self.py3.safe_format( self.format, {"path": count_path, "paths": count_path, "format_path": format_path}, ), } if __name__ == "__main__": from py3status.module_test import module_test module_test(Py3status)

true

f71bfb7e6138d20826c27d50c937e36b4fb9c103

454

py

Python

lesson_4_transformation/lesson_4_affine_transformation.py

DewMaple/opencv-learning

51991a5b9badf24cda740c1377f6be30dea91e1d

[ "MIT" ]

null

lesson_4_transformation/lesson_4_affine_transformation.py

DewMaple/opencv-learning

51991a5b9badf24cda740c1377f6be30dea91e1d

[ "MIT" ]

null

lesson_4_transformation/lesson_4_affine_transformation.py

DewMaple/opencv-learning

51991a5b9badf24cda740c1377f6be30dea91e1d

[ "MIT" ]

1

2018-09-10T15:51:23.000Z

import cv2 import numpy as np from utils import find_image image_path = find_image('girls_01.jpg') img = cv2.imread(image_path) rows, cols, channel = img.shape pts_src = np.float32([[50, 50], [200, 50], [50, 200]]) pts_dst = np.float32([[10, 100], [200, 80], [100, 650]]) M = cv2.getAffineTransform(pts_src, pts_dst) res = cv2.warpAffine(img, M, (cols, rows)) cv2.imshow('transformation by three points', res) cv2.waitKey(0) cv2.destroyAllWindows()

23.894737

56

0.707048

import cv2 import numpy as np from utils import find_image image_path = find_image('girls_01.jpg') img = cv2.imread(image_path) rows, cols, channel = img.shape pts_src = np.float32([[50, 50], [200, 50], [50, 200]]) pts_dst = np.float32([[10, 100], [200, 80], [100, 650]]) M = cv2.getAffineTransform(pts_src, pts_dst) res = cv2.warpAffine(img, M, (cols, rows)) cv2.imshow('transformation by three points', res) cv2.waitKey(0) cv2.destroyAllWindows()

true

f71bfbbb90ec970a284d4548547ab11e37b35557

21,393

py

Python

androidtv/constants.py

deviant-aut/python-androidtv

4bd5421e107949784b292a5f4a0397875a18e908

[ "MIT" ]

10

2022-01-17T14:46:04.000Z

2022-03-19T16:19:06.000Z

androidtv/constants.py

deviant-aut/python-androidtv

4bd5421e107949784b292a5f4a0397875a18e908

[ "MIT" ]

5

2022-01-18T20:33:18.000Z

2022-03-30T15:57:24.000Z

androidtv/constants.py

deviant-aut/python-androidtv

4bd5421e107949784b292a5f4a0397875a18e908

[ "MIT" ]

7

2022-01-25T01:26:47.000Z

2022-03-13T05:54:53.000Z

"""Constants used throughout the code. **Links** * `ADB key event codes <https://developer.android.com/reference/android/view/KeyEvent>`_ * `MediaSession PlaybackState property <https://developer.android.com/reference/android/media/session/PlaybackState.html>`_ """ import re import sys if sys.version_info[0] == 3 and sys.version_info[1] >= 5: from enum import IntEnum, unique else: # pragma: no cover IntEnum = object def unique(cls): """A class decorator that does nothing.""" return cls @unique class DeviceEnum(IntEnum): """An enum for the various device types.""" BASETV = 0 ANDROIDTV = 1 FIRETV = 2 # Intents INTENT_LAUNCH = "android.intent.category.LEANBACK_LAUNCHER" INTENT_LAUNCH_FIRETV = "android.intent.category.LAUNCHER" INTENT_HOME = "android.intent.category.HOME" # Customizable commands CUSTOM_AUDIO_STATE = "audio_state" CUSTOM_CURRENT_APP = "current_app" CUSTOM_CURRENT_APP_MEDIA_SESSION_STATE = "current_app_media_session_state" CUSTOM_HDMI_INPUT = "hdmi_input" CUSTOM_LAUNCH_APP = "launch_app" CUSTOM_RUNNING_APPS = "running_apps" CUSTOM_TURN_OFF = "turn_off" CUSTOM_TURN_ON = "turn_on" CUSTOMIZABLE_COMMANDS = { CUSTOM_AUDIO_STATE, CUSTOM_CURRENT_APP, CUSTOM_CURRENT_APP_MEDIA_SESSION_STATE, CUSTOM_HDMI_INPUT, CUSTOM_LAUNCH_APP, CUSTOM_RUNNING_APPS, CUSTOM_TURN_OFF, CUSTOM_TURN_ON, } #: The subset of `CUSTOMIZABLE_COMMANDS` that is potentially used in the ``update()`` method HA_CUSTOMIZABLE_COMMANDS = ( CUSTOM_AUDIO_STATE, CUSTOM_CURRENT_APP_MEDIA_SESSION_STATE, CUSTOM_HDMI_INPUT, CUSTOM_LAUNCH_APP, CUSTOM_RUNNING_APPS, CUSTOM_TURN_OFF, CUSTOM_TURN_ON, ) # echo '1' if the previous shell command was successful CMD_SUCCESS1 = r" && echo -e '1\c'" # echo '1' if the previous shell command was successful, echo '0' if it was not CMD_SUCCESS1_FAILURE0 = r" && echo -e '1\c' || echo -e '0\c'" #: Get the audio state CMD_AUDIO_STATE = r"dumpsys audio | grep paused | grep -qv 'Buffer Queue' && echo -e '1\c' || (dumpsys audio | grep started | grep -qv 'Buffer Queue' && echo '2\c' || echo '0\c')" #: Get the audio state for an Android 11 device CMD_AUDIO_STATE11 = ( "CURRENT_AUDIO_STATE=$(dumpsys audio | sed -r -n '/[0-9]{2}-[0-9]{2}.*player piid:.*state:.*$/h; ${x;p;}') && " + r"echo $CURRENT_AUDIO_STATE | grep -q paused && echo -e '1\c' || { echo $CURRENT_AUDIO_STATE | grep -q started && echo '2\c' || echo '0\c' ; }" ) #: Determine whether the device is awake CMD_AWAKE = "dumpsys power | grep mWakefulness | grep -q Awake" #: Parse current application identifier from dumpsys output and assign it to ``CURRENT_APP`` variable (assumes dumpsys output is momentarily set to ``CURRENT_APP`` variable) CMD_PARSE_CURRENT_APP = "CURRENT_APP=${CURRENT_APP#*ActivityRecord{* * } && CURRENT_APP=${CURRENT_APP#*{* * } && CURRENT_APP=${CURRENT_APP%%/*} && CURRENT_APP=${CURRENT_APP%\\}*}" #: Parse current application for an Android 11 device CMD_PARSE_CURRENT_APP11 = "CURRENT_APP=${CURRENT_APP%%/*} && CURRENT_APP=${CURRENT_APP##* }" #: Assign focused application identifier to ``CURRENT_APP`` variable CMD_DEFINE_CURRENT_APP_VARIABLE = ( "CURRENT_APP=$(dumpsys window windows | grep -E 'mCurrentFocus|mFocusedApp') && " + CMD_PARSE_CURRENT_APP ) #: Assign focused application identifier to ``CURRENT_APP`` variable for an Android 11 device CMD_DEFINE_CURRENT_APP_VARIABLE11 = ( "CURRENT_APP=$(dumpsys window windows | grep 'Window #1') && " + CMD_PARSE_CURRENT_APP11 ) #: Output identifier for current/focused application CMD_CURRENT_APP = CMD_DEFINE_CURRENT_APP_VARIABLE + " && echo $CURRENT_APP" #: Output identifier for current/focused application for an Android 11 device CMD_CURRENT_APP11 = CMD_DEFINE_CURRENT_APP_VARIABLE11 + " && echo $CURRENT_APP" #: Assign focused application identifier to ``CURRENT_APP`` variable (for a Google TV device) CMD_DEFINE_CURRENT_APP_VARIABLE_GOOGLE_TV = ( "CURRENT_APP=$(dumpsys activity a . | grep mResumedActivity) && " + CMD_PARSE_CURRENT_APP ) #: Output identifier for current/focused application (for a Google TV device) CMD_CURRENT_APP_GOOGLE_TV = CMD_DEFINE_CURRENT_APP_VARIABLE_GOOGLE_TV + " && echo $CURRENT_APP" #: Get the HDMI input CMD_HDMI_INPUT = ( "dumpsys activity starter | grep -E -o '(ExternalTv|HDMI)InputService/HW[0-9]' -m 1 | grep -o 'HW[0-9]'" ) #: Get the HDMI input for an Android 11 device CMD_HDMI_INPUT11 = ( "(HDMI=$(dumpsys tv_input | grep 'ResourceClientProfile {.*}' | grep -o -E '(hdmi_port=[0-9]|TV)') && { echo ${HDMI/hdmi_port=/HW} | cut -d' ' -f1 ; }) || " + CMD_HDMI_INPUT ) #: Launch an app if it is not already the current app (assumes the variable ``CURRENT_APP`` has already been set) CMD_LAUNCH_APP_CONDITION = ( "if [ $CURRENT_APP != '{0}' ]; then monkey -p {0} -c " + INTENT_LAUNCH + " --pct-syskeys 0 1; fi" ) #: Launch an app if it is not already the current app (assumes the variable ``CURRENT_APP`` has already been set) on a Fire TV CMD_LAUNCH_APP_CONDITION_FIRETV = ( "if [ $CURRENT_APP != '{0}' ]; then monkey -p {0} -c " + INTENT_LAUNCH_FIRETV + " --pct-syskeys 0 1; fi" ) #: Launch an app if it is not already the current app CMD_LAUNCH_APP = ( CMD_DEFINE_CURRENT_APP_VARIABLE.replace("{", "{{").replace("}", "}}") + " && " + CMD_LAUNCH_APP_CONDITION ) #: Launch an app if it is not already the current app on an Android 11 device CMD_LAUNCH_APP11 = ( CMD_DEFINE_CURRENT_APP_VARIABLE11.replace("{", "{{").replace("}", "}}") + " && " + CMD_LAUNCH_APP_CONDITION ) #: Launch an app on a Fire TV device CMD_LAUNCH_APP_FIRETV = ( CMD_DEFINE_CURRENT_APP_VARIABLE.replace("{", "{{").replace("}", "}}") + " && " + CMD_LAUNCH_APP_CONDITION_FIRETV ) #: Launch an app on a Google TV device CMD_LAUNCH_APP_GOOGLE_TV = ( CMD_DEFINE_CURRENT_APP_VARIABLE_GOOGLE_TV.replace("{", "{{").replace("}", "}}") + " && " + CMD_LAUNCH_APP_CONDITION ) #: Get the state from ``dumpsys media_session``; this assumes that the variable ``CURRENT_APP`` has been defined CMD_MEDIA_SESSION_STATE = "dumpsys media_session | grep -A 100 'Sessions Stack' | grep -A 100 $CURRENT_APP | grep -m 1 'state=PlaybackState {'" #: Determine the current app and get the state from ``dumpsys media_session`` CMD_CURRENT_APP_MEDIA_SESSION_STATE = CMD_CURRENT_APP + " && " + CMD_MEDIA_SESSION_STATE #: Determine the current app and get the state from ``dumpsys media_session`` for an Android 11 device CMD_CURRENT_APP_MEDIA_SESSION_STATE11 = CMD_CURRENT_APP11 + " && " + CMD_MEDIA_SESSION_STATE #: Determine the current app and get the state from ``dumpsys media_session`` for a Google TV device CMD_CURRENT_APP_MEDIA_SESSION_STATE_GOOGLE_TV = CMD_CURRENT_APP_GOOGLE_TV + " && " + CMD_MEDIA_SESSION_STATE #: Get the running apps for an Android TV device CMD_RUNNING_APPS_ANDROIDTV = "ps -A | grep u0_a" #: Get the running apps for a Fire TV device CMD_RUNNING_APPS_FIRETV = "ps | grep u0_a" #: Get installed apps CMD_INSTALLED_APPS = "pm list packages" #: Determine if the device is on CMD_SCREEN_ON = ( "(dumpsys power | grep 'Display Power' | grep -q 'state=ON' || dumpsys power | grep -q 'mScreenOn=true')" ) #: Get the "STREAM_MUSIC" block from ``dumpsys audio`` CMD_STREAM_MUSIC = r"dumpsys audio | grep '\- STREAM_MUSIC:' -A 11" #: Turn off an Android TV device (note: `KEY_POWER = 26` is defined below) CMD_TURN_OFF_ANDROIDTV = CMD_SCREEN_ON + " && input keyevent 26" #: Turn off a Fire TV device (note: `KEY_SLEEP = 223` is defined below) CMD_TURN_OFF_FIRETV = CMD_SCREEN_ON + " && input keyevent 223" #: Turn on an Android TV device (note: `KEY_POWER = 26` is defined below) CMD_TURN_ON_ANDROIDTV = CMD_SCREEN_ON + " || input keyevent 26" #: Turn on a Fire TV device (note: `KEY_POWER = 26` and `KEY_HOME = 3` are defined below) CMD_TURN_ON_FIRETV = CMD_SCREEN_ON + " || (input keyevent 26 && input keyevent 3)" #: Get the wake lock size CMD_WAKE_LOCK_SIZE = "dumpsys power | grep Locks | grep 'size='" #: Determine if the device is on, the screen is on, and get the wake lock size CMD_SCREEN_ON_AWAKE_WAKE_LOCK_SIZE = ( CMD_SCREEN_ON + CMD_SUCCESS1_FAILURE0 + " && " + CMD_AWAKE + CMD_SUCCESS1_FAILURE0 + " && " + CMD_WAKE_LOCK_SIZE ) # `getprop` commands CMD_MANUFACTURER = "getprop ro.product.manufacturer" CMD_MODEL = "getprop ro.product.model" CMD_SERIALNO = "getprop ro.serialno" CMD_VERSION = "getprop ro.build.version.release" # Commands for getting the MAC address CMD_MAC_WLAN0 = "ip addr show wlan0 | grep -m 1 ether" CMD_MAC_ETH0 = "ip addr show eth0 | grep -m 1 ether" #: The command used for getting the device properties CMD_DEVICE_PROPERTIES = CMD_MANUFACTURER + " && " + CMD_MODEL + " && " + CMD_SERIALNO + " && " + CMD_VERSION # ADB key event codes # https://developer.android.com/reference/android/view/KeyEvent KEY_BACK = 4 KEY_BLUE = 186 KEY_CENTER = 23 KEY_COMPONENT1 = 249 KEY_COMPONENT2 = 250 KEY_COMPOSITE1 = 247 KEY_COMPOSITE2 = 248 KEY_DOWN = 20 KEY_END = 123 KEY_ENTER = 66 KEY_ESCAPE = 111 KEY_FAST_FORWARD = 90 KEY_GREEN = 184 KEY_HDMI1 = 243 KEY_HDMI2 = 244 KEY_HDMI3 = 245 KEY_HDMI4 = 246 KEY_HOME = 3 KEY_INPUT = 178 KEY_LEFT = 21 KEY_MENU = 82 KEY_MOVE_HOME = 122 KEY_MUTE = 164 KEY_NEXT = 87 KEY_PAIRING = 225 KEY_PAUSE = 127 KEY_PLAY = 126 KEY_PLAY_PAUSE = 85 KEY_POWER = 26 KEY_PREVIOUS = 88 KEY_RED = 183 KEY_RESUME = 224 KEY_REWIND = 89 KEY_RIGHT = 22 KEY_SAT = 237 KEY_SEARCH = 84 KEY_SETTINGS = 176 KEY_SLEEP = 223 KEY_SPACE = 62 KEY_STOP = 86 KEY_SUSPEND = 276 KEY_SYSDOWN = 281 KEY_SYSLEFT = 282 KEY_SYSRIGHT = 283 KEY_SYSUP = 280 KEY_TEXT = 233 KEY_TOP = 122 KEY_UP = 19 KEY_VGA = 251 KEY_VOLUME_DOWN = 25 KEY_VOLUME_UP = 24 KEY_WAKEUP = 224 KEY_YELLOW = 185 # Alphanumeric key event codes KEY_0 = 7 KEY_1 = 8 KEY_2 = 9 KEY_3 = 10 KEY_4 = 11 KEY_5 = 12 KEY_6 = 13 KEY_7 = 14 KEY_8 = 15 KEY_9 = 16 KEY_A = 29 KEY_B = 30 KEY_C = 31 KEY_D = 32 KEY_E = 33 KEY_F = 34 KEY_G = 35 KEY_H = 36 KEY_I = 37 KEY_J = 38 KEY_K = 39 KEY_L = 40 KEY_M = 41 KEY_N = 42 KEY_O = 43 KEY_P = 44 KEY_Q = 45 KEY_R = 46 KEY_S = 47 KEY_T = 48 KEY_U = 49 KEY_V = 50 KEY_W = 51 KEY_X = 52 KEY_Y = 53 KEY_Z = 54 # Android TV keys KEYS = { "BACK": KEY_BACK, "BLUE": KEY_BLUE, "CENTER": KEY_CENTER, "COMPONENT1": KEY_COMPONENT1, "COMPONENT2": KEY_COMPONENT2, "COMPOSITE1": KEY_COMPOSITE1, "COMPOSITE2": KEY_COMPOSITE2, "DOWN": KEY_DOWN, "END": KEY_END, "ENTER": KEY_ENTER, "ESCAPE": KEY_ESCAPE, "FAST_FORWARD": KEY_FAST_FORWARD, "GREEN": KEY_GREEN, "HDMI1": KEY_HDMI1, "HDMI2": KEY_HDMI2, "HDMI3": KEY_HDMI3, "HDMI4": KEY_HDMI4, "HOME": KEY_HOME, "INPUT": KEY_INPUT, "LEFT": KEY_LEFT, "MENU": KEY_MENU, "MOVE_HOME": KEY_MOVE_HOME, "MUTE": KEY_MUTE, "PAIRING": KEY_PAIRING, "POWER": KEY_POWER, "RED": KEY_RED, "RESUME": KEY_RESUME, "REWIND": KEY_REWIND, "RIGHT": KEY_RIGHT, "SAT": KEY_SAT, "SEARCH": KEY_SEARCH, "SETTINGS": KEY_SETTINGS, "SLEEP": KEY_SLEEP, "SUSPEND": KEY_SUSPEND, "SYSDOWN": KEY_SYSDOWN, "SYSLEFT": KEY_SYSLEFT, "SYSRIGHT": KEY_SYSRIGHT, "SYSUP": KEY_SYSUP, "TEXT": KEY_TEXT, "TOP": KEY_TOP, "UP": KEY_UP, "VGA": KEY_VGA, "VOLUME_DOWN": KEY_VOLUME_DOWN, "VOLUME_UP": KEY_VOLUME_UP, "WAKEUP": KEY_WAKEUP, "YELLOW": KEY_YELLOW, } # Android TV / Fire TV states STATE_ON = "on" STATE_IDLE = "idle" STATE_OFF = "off" STATE_PLAYING = "playing" STATE_PAUSED = "paused" STATE_STANDBY = "standby" STATE_STOPPED = "stopped" STATE_UNKNOWN = "unknown" #: States that are valid (used by :func:`~androidtv.basetv.state_detection_rules_validator`) VALID_STATES = (STATE_IDLE, STATE_OFF, STATE_PLAYING, STATE_PAUSED, STATE_STANDBY) #: Properties that can be used to determine the current state (used by :func:`~androidtv.basetv.state_detection_rules_validator`) VALID_STATE_PROPERTIES = ("audio_state", "media_session_state") #: Properties that can be checked for custom state detection (used by :func:`~androidtv.basetv.state_detection_rules_validator`) VALID_PROPERTIES = VALID_STATE_PROPERTIES + ("wake_lock_size",) #: The required type for each entry in :py:const:`VALID_PROPERTIES` (used by :func:`~androidtv.basetv.state_detection_rules_validator`) VALID_PROPERTIES_TYPES = {"audio_state": str, "media_session_state": int, "wake_lock_size": int} # https://developer.android.com/reference/android/media/session/PlaybackState.html #: States for the :attr:`~androidtv.basetv.basetv.BaseTV.media_session_state` property MEDIA_SESSION_STATES = {0: None, 1: STATE_STOPPED, 2: STATE_PAUSED, 3: STATE_PLAYING} # Apps APP_AE_TV = "com.aetn.aetv.watch" APP_AMAZON_PRIME_VIDEO = "com.amazon.avod.thirdpartyclient" APP_AMAZON_VIDEO = "com.amazon.avod" APP_APPLE_TV_PLUS = "com.apple.atve.android.appletv" APP_APPLE_TV_PLUS_FIRETV = "com.apple.atve.amazon.appletv" APP_APPLE_TV_PLUS_SONY = "com.apple.atve.sony.appletv" APP_ATV_LAUNCHER = "com.google.android.tvlauncher" APP_BELL_FIBE = "com.quickplay.android.bellmediaplayer" APP_CBC_GEM = "ca.cbc.android.cbctv" APP_COMEDY_CENTRAL = "com.vmn.android.comedycentral" APP_CRAVE = "ca.bellmedia.cravetv" APP_DAILYMOTION = "com.dailymotion.dailymotion" APP_DEEZER = "deezer.android.tv" APP_DISNEY_PLUS = "com.disney.disneyplus" APP_DISNEY_PLUS_HOTSTAR = "in.startv.hotstar" APP_DS_PHOTO = "com.synology.dsphoto" APP_DS_VIDEO = "com.synology.dsvideo" APP_ES_FILE_EXPLORER = "com.estrongs.android.pop" APP_FACEBOOK = "com.facebook.katana" APP_FAWESOME = "com.future.moviesByFawesomeAndroidTV" APP_FIREFOX = "org.mozilla.tv.firefox" APP_FIRETV_PACKAGE_LAUNCHER = "com.amazon.tv.launcher" APP_FIRETV_PACKAGE_SETTINGS = "com.amazon.tv.settings" APP_FIRETV_STORE = "com.amazon.venezia" APP_FOOD_NETWORK_GO = "tv.accedo.foodnetwork" APP_FRANCE_TV = "fr.francetv.pluzz" APP_GLOBAL_TV = "com.shawmedia.smglobal" APP_GOOGLE_CAST = "com.google.android.apps.mediashell" APP_GOOGLE_TV_LAUNCHER = "com.google.android.apps.tv.launcherx" APP_HAYSTACK_NEWS = "com.haystack.android" APP_HBO_GO = "eu.hbogo.androidtv.production" APP_HBO_GO_2 = "com.HBO" APP_HOICHOI = "com.viewlift.hoichoi" APP_HULU = "com.hulu.plus" APP_HUNGAMA_PLAY = "com.hungama.movies.tv" APP_IMDB_TV = "com.amazon.imdb.tv.android.app" APP_IPTV = "ru.iptvremote.android.iptv" APP_IPTV_SMARTERS_PRO = "com.nst.iptvsmarterstvbox" APP_JELLYFIN_TV = "org.jellyfin.androidtv" APP_JIO_CINEMA = "com.jio.media.stb.ondemand" APP_KODI = "org.xbmc.kodi" APP_LIVE_CHANNELS = "com.google.android.tv" APP_MIJN_RADIO = "org.samsonsen.nederlandse.radio.holland.nl" APP_MOLOTOV = "tv.molotov.app" APP_MRMC = "tv.mrmc.mrmc" APP_MRMC_LITE = "tv.mrmc.mrmc.lite" APP_MX_PLAYER = "com.mxtech.videoplayer.ad" APP_NETFLIX = "com.netflix.ninja" APP_NLZIET = "nl.nlziet" APP_NOS = "nl.nos.app" APP_NPO = "nl.uitzendinggemist" APP_OCS = "com.orange.ocsgo" APP_PLAY_GAMES = "com.google.android.play.games" APP_PLAY_MUSIC = "com.google.android.music" APP_PLAY_STORE = "com.android.vending" APP_PLAY_VIDEOS = "com.google.android.videos" APP_PLEX = "com.plexapp.android" APP_PRIME_VIDEO = "com.amazon.amazonvideo.livingroom" APP_PRIME_VIDEO_FIRETV = "com.amazon.firebat" APP_SETTINGS = "com.android.tv.settings" APP_SMART_YOUTUBE_TV = "com.liskovsoft.videomanager" APP_SONY_ACTION_MENU = "com.sony.dtv.scrums.action" APP_SONY_ALBUM = "com.sony.dtv.osat.album" APP_SONY_BRAVIA_SYNC_MENU = "com.sony.dtv.braviasyncmenu" APP_SONY_BRAVIA_TUTORIALS = "com.sony.dtv.bravialifehack" APP_SONY_DISCOVER = "com.sony.dtv.discovery" APP_SONY_HELP = "com.sony.dtv.smarthelp" APP_SONY_INTERNET_BROWSER = "com.vewd.core.integration.dia" APP_SONY_LIV = "com.sonyliv" APP_SONY_MUSIC = "com.sony.dtv.osat.music" APP_SONY_SCREEN_MIRRORING = "com.sony.dtv.networkapp.wifidirect" APP_SONY_SELECT = "com.sony.dtv.sonyselect" APP_SONY_TIMERS = "com.sony.dtv.timers" APP_SONY_TV = "com.sony.dtv.tvx" APP_SONY_VIDEO = "com.sony.dtv.osat.video" APP_SPORT1 = "de.sport1.firetv.video" APP_SPOTIFY = "com.spotify.tv.android" APP_STEAM_LINK = "com.valvesoftware.steamlink" APP_SYFY = "com.amazon.webapps.nbc.syfy" APP_T2 = "tv.perception.clients.tv.android" APP_TED = "com.ted.android.tv" APP_TUNEIN = "tunein.player" APP_TVHEADEND = "de.cyberdream.dreamepg.tvh.tv.player" APP_TWITCH = "tv.twitch.android.app" APP_TWITCH_FIRETV = "tv.twitch.android.viewer" APP_VEVO = "com.vevo.tv" APP_VH1 = "com.mtvn.vh1android" APP_VIMEO = "com.vimeo.android.videoapp" APP_VLC = "org.videolan.vlc" APP_VOYO = "com.phonegap.voyo" APP_VRV = "com.ellation.vrv" APP_WAIPU_TV = "de.exaring.waipu.firetv.live" APP_WATCH_TNT = "com.turner.tnt.android.networkapp" APP_YOUTUBE = "com.google.android.youtube.tv" APP_YOUTUBE_FIRETV = "com.amazon.firetv.youtube" APP_YOUTUBE_KIDS = "com.google.android.youtube.tvkids" APP_YOUTUBE_KIDS_FIRETV = "com.amazon.firetv.youtube.kids" APP_YOUTUBE_MUSIC = "com.google.android.youtube.tvmusic" APP_YOUTUBE_TV = "com.google.android.youtube.tvunplugged" APP_ZEE5 = "com.graymatrix.did" APP_ZIGGO_GO_TV = "com.ziggo.tv" APPS = { APP_AE_TV: "A&E", APP_AMAZON_PRIME_VIDEO: "Amazon Prime Video", APP_AMAZON_VIDEO: "Amazon Video", APP_APPLE_TV_PLUS: "Apple TV+", APP_APPLE_TV_PLUS_FIRETV: "Apple TV+ (Fire TV)", APP_APPLE_TV_PLUS_SONY: "Apple TV+ (Sony)", APP_ATV_LAUNCHER: "Android TV Launcher", APP_BELL_FIBE: "Bell Fibe", APP_CBC_GEM: "CBC Gem", APP_COMEDY_CENTRAL: "Comedy Central", APP_CRAVE: "Crave", APP_DAILYMOTION: "Dailymotion", APP_DEEZER: "Deezer", APP_DISNEY_PLUS: "Disney+", APP_DISNEY_PLUS_HOTSTAR: "Disney+ Hotstar", APP_DS_PHOTO: "DS photo", APP_DS_VIDEO: "DS video", APP_ES_FILE_EXPLORER: "ES File Explorer", APP_FACEBOOK: "Facebook Watch", APP_FAWESOME: "Fawsome", APP_FIREFOX: "Firefox", APP_FIRETV_STORE: "FireTV Store", APP_FOOD_NETWORK_GO: "Food Network GO", APP_FRANCE_TV: "France TV", APP_GLOBAL_TV: "Global TV", APP_GOOGLE_CAST: "Google Cast", APP_GOOGLE_TV_LAUNCHER: "Google TV Launcher", APP_HAYSTACK_NEWS: "Haystack News", APP_HBO_GO: "HBO GO", APP_HBO_GO_2: "HBO GO (2)", APP_HOICHOI: "Hoichoi", APP_HULU: "Hulu", APP_HUNGAMA_PLAY: "Hungama Play", APP_IMDB_TV: "IMDb TV", APP_IPTV: "IPTV", APP_IPTV_SMARTERS_PRO: "IPTV Smarters Pro", APP_JELLYFIN_TV: "Jellyfin", APP_JIO_CINEMA: "Jio Cinema", APP_KODI: "Kodi", APP_LIVE_CHANNELS: "Live Channels", APP_MIJN_RADIO: "Mijn Radio", APP_MOLOTOV: "Molotov", APP_MRMC: "MrMC", APP_MRMC_LITE: "MrMC Lite", APP_MX_PLAYER: "MX Player", APP_NETFLIX: "Netflix", APP_NLZIET: "NLZIET", APP_NOS: "NOS", APP_NPO: "NPO", APP_OCS: "OCS", APP_PLAY_GAMES: "Play Games", APP_PLAY_MUSIC: "Play Music", APP_PLAY_STORE: "Play Store", APP_PLAY_VIDEOS: "Play Movies & TV", APP_PLEX: "Plex", APP_PRIME_VIDEO: "Prime Video", APP_PRIME_VIDEO_FIRETV: "Prime Video (FireTV)", APP_SETTINGS: "Settings", APP_SMART_YOUTUBE_TV: "Smart YouTube TV", APP_SONY_ACTION_MENU: "Action Menu", APP_SONY_ALBUM: "Album", APP_SONY_BRAVIA_SYNC_MENU: "Sync Menu", APP_SONY_BRAVIA_TUTORIALS: "BRAVIA Tutorials", APP_SONY_DISCOVER: "Discover", APP_SONY_HELP: "Help", APP_SONY_INTERNET_BROWSER: "Internet Browser", APP_SONY_LIV: "SonyLIV", APP_SONY_MUSIC: "Music", APP_SONY_SCREEN_MIRRORING: "Screen mirroring", APP_SONY_SELECT: "Sony Select", APP_SONY_TIMERS: "Timers", APP_SONY_TV: "TV", APP_SONY_VIDEO: "Video", APP_SPORT1: "Sport 1", APP_SPOTIFY: "Spotify", APP_STEAM_LINK: "Steam Link", APP_SYFY: "Syfy", APP_T2: "T-2 TV", APP_TED: "TED", APP_TUNEIN: "TuneIn Radio", APP_TVHEADEND: "DreamPlayer TVHeadend", APP_TWITCH: "Twitch", APP_TWITCH_FIRETV: "Twitch (FireTV)", APP_VEVO: "Vevo", APP_VH1: "VH1", APP_VIMEO: "Vimeo", APP_VLC: "VLC", APP_VOYO: "VOYO", APP_VRV: "VRV", APP_WAIPU_TV: "Waipu TV", APP_WATCH_TNT: "Watch TNT", APP_YOUTUBE: "YouTube", APP_YOUTUBE_FIRETV: "YouTube (FireTV)", APP_YOUTUBE_KIDS: "YouTube Kids", APP_YOUTUBE_KIDS_FIRETV: "YouTube Kids (FireTV)", APP_YOUTUBE_MUSIC: "YouTube Music", APP_YOUTUBE_TV: "YouTube TV", APP_ZEE5: "ZEE5", APP_ZIGGO_GO_TV: "Ziggo GO TV", } # Regular expressions REGEX_MEDIA_SESSION_STATE = re.compile(r"state=(?P<state>[0-9]+)", re.MULTILINE) REGEX_WAKE_LOCK_SIZE = re.compile(r"size=(?P<size>[0-9]+)") # Regular expression patterns DEVICE_REGEX_PATTERN = r"Devices: (.*?)\W" MAC_REGEX_PATTERN = "ether (.*?) brd" MAX_VOLUME_REGEX_PATTERN = r"Max: (\d{1,})" MUTED_REGEX_PATTERN = r"Muted: (.*?)\W" STREAM_MUSIC_REGEX_PATTERN = "STREAM_MUSIC(.*?)- STREAM" VOLUME_REGEX_PATTERN = r"\): (\d{1,})" #: Default authentication timeout (in s) for :meth:`adb_shell.handle.tcp_handle.TcpHandle.connect` and :meth:`adb_shell.handle.tcp_handle_async.TcpHandleAsync.connect` DEFAULT_AUTH_TIMEOUT_S = 10.0 #: Default transport timeout (in s) for :meth:`adb_shell.handle.tcp_handle.TcpHandle.connect` and :meth:`adb_shell.handle.tcp_handle_async.TcpHandleAsync.connect` DEFAULT_TRANSPORT_TIMEOUT_S = 1.0 #: Default timeout (in s) for :class:`adb_shell.handle.tcp_handle.TcpHandle` and :class:`adb_shell.handle.tcp_handle_async.TcpHandleAsync` DEFAULT_ADB_TIMEOUT_S = 9.0 #: Default timeout for acquiring the lock that protects ADB commands DEFAULT_LOCK_TIMEOUT_S = 3.0

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import re import sys if sys.version_info[0] == 3 and sys.version_info[1] >= 5: from enum import IntEnum, unique else: IntEnum = object def unique(cls): """A class decorator that does nothing.""" return cls @unique class DeviceEnum(IntEnum): BASETV = 0 ANDROIDTV = 1 FIRETV = 2 INTENT_LAUNCH = "android.intent.category.LEANBACK_LAUNCHER" INTENT_LAUNCH_FIRETV = "android.intent.category.LAUNCHER" INTENT_HOME = "android.intent.category.HOME" CUSTOM_AUDIO_STATE = "audio_state" CUSTOM_CURRENT_APP = "current_app" CUSTOM_CURRENT_APP_MEDIA_SESSION_STATE = "current_app_media_session_state" CUSTOM_HDMI_INPUT = "hdmi_input" CUSTOM_LAUNCH_APP = "launch_app" CUSTOM_RUNNING_APPS = "running_apps" CUSTOM_TURN_OFF = "turn_off" CUSTOM_TURN_ON = "turn_on" CUSTOMIZABLE_COMMANDS = { CUSTOM_AUDIO_STATE, CUSTOM_CURRENT_APP, CUSTOM_CURRENT_APP_MEDIA_SESSION_STATE, CUSTOM_HDMI_INPUT, CUSTOM_LAUNCH_APP, CUSTOM_RUNNING_APPS, CUSTOM_TURN_OFF, CUSTOM_TURN_ON, } HA_CUSTOMIZABLE_COMMANDS = ( CUSTOM_AUDIO_STATE, CUSTOM_CURRENT_APP_MEDIA_SESSION_STATE, CUSTOM_HDMI_INPUT, CUSTOM_LAUNCH_APP, CUSTOM_RUNNING_APPS, CUSTOM_TURN_OFF, CUSTOM_TURN_ON, ) CMD_SUCCESS1 = r" && echo -e '1\c'" CMD_SUCCESS1_FAILURE0 = r" && echo -e '1\c' || echo -e '0\c'" CMD_AUDIO_STATE = r"dumpsys audio | grep paused | grep -qv 'Buffer Queue' && echo -e '1\c' || (dumpsys audio | grep started | grep -qv 'Buffer Queue' && echo '2\c' || echo '0\c')" CMD_AUDIO_STATE11 = ( "CURRENT_AUDIO_STATE=$(dumpsys audio | sed -r -n '/[0-9]{2}-[0-9]{2}.*player piid:.*state:.*$/h; ${x;p;}') && " + r"echo $CURRENT_AUDIO_STATE | grep -q paused && echo -e '1\c' || { echo $CURRENT_AUDIO_STATE | grep -q started && echo '2\c' || echo '0\c' ; }" ) CMD_AWAKE = "dumpsys power | grep mWakefulness | grep -q Awake" CMD_PARSE_CURRENT_APP = "CURRENT_APP=${CURRENT_APP#*ActivityRecord{* * } && CURRENT_APP=${CURRENT_APP#*{* * } && CURRENT_APP=${CURRENT_APP%%/*} && CURRENT_APP=${CURRENT_APP%\\}*}" CMD_PARSE_CURRENT_APP11 = "CURRENT_APP=${CURRENT_APP%%/*} && CURRENT_APP=${CURRENT_APP##* }" CMD_DEFINE_CURRENT_APP_VARIABLE = ( "CURRENT_APP=$(dumpsys window windows | grep -E 'mCurrentFocus|mFocusedApp') && " + CMD_PARSE_CURRENT_APP ) CMD_DEFINE_CURRENT_APP_VARIABLE11 = ( "CURRENT_APP=$(dumpsys window windows | grep 'Window #1') && " + CMD_PARSE_CURRENT_APP11 ) CMD_CURRENT_APP = CMD_DEFINE_CURRENT_APP_VARIABLE + " && echo $CURRENT_APP" CMD_CURRENT_APP11 = CMD_DEFINE_CURRENT_APP_VARIABLE11 + " && echo $CURRENT_APP" CMD_DEFINE_CURRENT_APP_VARIABLE_GOOGLE_TV = ( "CURRENT_APP=$(dumpsys activity a . | grep mResumedActivity) && " + CMD_PARSE_CURRENT_APP ) CMD_CURRENT_APP_GOOGLE_TV = CMD_DEFINE_CURRENT_APP_VARIABLE_GOOGLE_TV + " && echo $CURRENT_APP" CMD_HDMI_INPUT = ( "dumpsys activity starter | grep -E -o '(ExternalTv|HDMI)InputService/HW[0-9]' -m 1 | grep -o 'HW[0-9]'" ) CMD_HDMI_INPUT11 = ( "(HDMI=$(dumpsys tv_input | grep 'ResourceClientProfile {.*}' | grep -o -E '(hdmi_port=[0-9]|TV)') && { echo ${HDMI/hdmi_port=/HW} | cut -d' ' -f1 ; }) || " + CMD_HDMI_INPUT ) CMD_LAUNCH_APP_CONDITION = ( "if [ $CURRENT_APP != '{0}' ]; then monkey -p {0} -c " + INTENT_LAUNCH + " --pct-syskeys 0 1; fi" ) CMD_LAUNCH_APP_CONDITION_FIRETV = ( "if [ $CURRENT_APP != '{0}' ]; then monkey -p {0} -c " + INTENT_LAUNCH_FIRETV + " --pct-syskeys 0 1; fi" ) CMD_LAUNCH_APP = ( CMD_DEFINE_CURRENT_APP_VARIABLE.replace("{", "{{").replace("}", "}}") + " && " + CMD_LAUNCH_APP_CONDITION ) CMD_LAUNCH_APP11 = ( CMD_DEFINE_CURRENT_APP_VARIABLE11.replace("{", "{{").replace("}", "}}") + " && " + CMD_LAUNCH_APP_CONDITION ) CMD_LAUNCH_APP_FIRETV = ( CMD_DEFINE_CURRENT_APP_VARIABLE.replace("{", "{{").replace("}", "}}") + " && " + CMD_LAUNCH_APP_CONDITION_FIRETV ) CMD_LAUNCH_APP_GOOGLE_TV = ( CMD_DEFINE_CURRENT_APP_VARIABLE_GOOGLE_TV.replace("{", "{{").replace("}", "}}") + " && " + CMD_LAUNCH_APP_CONDITION ) CMD_MEDIA_SESSION_STATE = "dumpsys media_session | grep -A 100 'Sessions Stack' | grep -A 100 $CURRENT_APP | grep -m 1 'state=PlaybackState {'" CMD_CURRENT_APP_MEDIA_SESSION_STATE = CMD_CURRENT_APP + " && " + CMD_MEDIA_SESSION_STATE CMD_CURRENT_APP_MEDIA_SESSION_STATE11 = CMD_CURRENT_APP11 + " && " + CMD_MEDIA_SESSION_STATE CMD_CURRENT_APP_MEDIA_SESSION_STATE_GOOGLE_TV = CMD_CURRENT_APP_GOOGLE_TV + " && " + CMD_MEDIA_SESSION_STATE CMD_RUNNING_APPS_ANDROIDTV = "ps -A | grep u0_a" CMD_RUNNING_APPS_FIRETV = "ps | grep u0_a" CMD_INSTALLED_APPS = "pm list packages" CMD_SCREEN_ON = ( "(dumpsys power | grep 'Display Power' | grep -q 'state=ON' || dumpsys power | grep -q 'mScreenOn=true')" ) CMD_STREAM_MUSIC = r"dumpsys audio | grep '\- STREAM_MUSIC:' -A 11" CMD_TURN_OFF_ANDROIDTV = CMD_SCREEN_ON + " && input keyevent 26" CMD_TURN_OFF_FIRETV = CMD_SCREEN_ON + " && input keyevent 223" CMD_TURN_ON_ANDROIDTV = CMD_SCREEN_ON + " || input keyevent 26" CMD_TURN_ON_FIRETV = CMD_SCREEN_ON + " || (input keyevent 26 && input keyevent 3)" CMD_WAKE_LOCK_SIZE = "dumpsys power | grep Locks | grep 'size='" CMD_SCREEN_ON_AWAKE_WAKE_LOCK_SIZE = ( CMD_SCREEN_ON + CMD_SUCCESS1_FAILURE0 + " && " + CMD_AWAKE + CMD_SUCCESS1_FAILURE0 + " && " + CMD_WAKE_LOCK_SIZE ) CMD_MANUFACTURER = "getprop ro.product.manufacturer" CMD_MODEL = "getprop ro.product.model" CMD_SERIALNO = "getprop ro.serialno" CMD_VERSION = "getprop ro.build.version.release" CMD_MAC_WLAN0 = "ip addr show wlan0 | grep -m 1 ether" CMD_MAC_ETH0 = "ip addr show eth0 | grep -m 1 ether" CMD_DEVICE_PROPERTIES = CMD_MANUFACTURER + " && " + CMD_MODEL + " && " + CMD_SERIALNO + " && " + CMD_VERSION KEY_BACK = 4 KEY_BLUE = 186 KEY_CENTER = 23 KEY_COMPONENT1 = 249 KEY_COMPONENT2 = 250 KEY_COMPOSITE1 = 247 KEY_COMPOSITE2 = 248 KEY_DOWN = 20 KEY_END = 123 KEY_ENTER = 66 KEY_ESCAPE = 111 KEY_FAST_FORWARD = 90 KEY_GREEN = 184 KEY_HDMI1 = 243 KEY_HDMI2 = 244 KEY_HDMI3 = 245 KEY_HDMI4 = 246 KEY_HOME = 3 KEY_INPUT = 178 KEY_LEFT = 21 KEY_MENU = 82 KEY_MOVE_HOME = 122 KEY_MUTE = 164 KEY_NEXT = 87 KEY_PAIRING = 225 KEY_PAUSE = 127 KEY_PLAY = 126 KEY_PLAY_PAUSE = 85 KEY_POWER = 26 KEY_PREVIOUS = 88 KEY_RED = 183 KEY_RESUME = 224 KEY_REWIND = 89 KEY_RIGHT = 22 KEY_SAT = 237 KEY_SEARCH = 84 KEY_SETTINGS = 176 KEY_SLEEP = 223 KEY_SPACE = 62 KEY_STOP = 86 KEY_SUSPEND = 276 KEY_SYSDOWN = 281 KEY_SYSLEFT = 282 KEY_SYSRIGHT = 283 KEY_SYSUP = 280 KEY_TEXT = 233 KEY_TOP = 122 KEY_UP = 19 KEY_VGA = 251 KEY_VOLUME_DOWN = 25 KEY_VOLUME_UP = 24 KEY_WAKEUP = 224 KEY_YELLOW = 185 KEY_0 = 7 KEY_1 = 8 KEY_2 = 9 KEY_3 = 10 KEY_4 = 11 KEY_5 = 12 KEY_6 = 13 KEY_7 = 14 KEY_8 = 15 KEY_9 = 16 KEY_A = 29 KEY_B = 30 KEY_C = 31 KEY_D = 32 KEY_E = 33 KEY_F = 34 KEY_G = 35 KEY_H = 36 KEY_I = 37 KEY_J = 38 KEY_K = 39 KEY_L = 40 KEY_M = 41 KEY_N = 42 KEY_O = 43 KEY_P = 44 KEY_Q = 45 KEY_R = 46 KEY_S = 47 KEY_T = 48 KEY_U = 49 KEY_V = 50 KEY_W = 51 KEY_X = 52 KEY_Y = 53 KEY_Z = 54 KEYS = { "BACK": KEY_BACK, "BLUE": KEY_BLUE, "CENTER": KEY_CENTER, "COMPONENT1": KEY_COMPONENT1, "COMPONENT2": KEY_COMPONENT2, "COMPOSITE1": KEY_COMPOSITE1, "COMPOSITE2": KEY_COMPOSITE2, "DOWN": KEY_DOWN, "END": KEY_END, "ENTER": KEY_ENTER, "ESCAPE": KEY_ESCAPE, "FAST_FORWARD": KEY_FAST_FORWARD, "GREEN": KEY_GREEN, "HDMI1": KEY_HDMI1, "HDMI2": KEY_HDMI2, "HDMI3": KEY_HDMI3, "HDMI4": KEY_HDMI4, "HOME": KEY_HOME, "INPUT": KEY_INPUT, "LEFT": KEY_LEFT, "MENU": KEY_MENU, "MOVE_HOME": KEY_MOVE_HOME, "MUTE": KEY_MUTE, "PAIRING": KEY_PAIRING, "POWER": KEY_POWER, "RED": KEY_RED, "RESUME": KEY_RESUME, "REWIND": KEY_REWIND, "RIGHT": KEY_RIGHT, "SAT": KEY_SAT, "SEARCH": KEY_SEARCH, "SETTINGS": KEY_SETTINGS, "SLEEP": KEY_SLEEP, "SUSPEND": KEY_SUSPEND, "SYSDOWN": KEY_SYSDOWN, "SYSLEFT": KEY_SYSLEFT, "SYSRIGHT": KEY_SYSRIGHT, "SYSUP": KEY_SYSUP, "TEXT": KEY_TEXT, "TOP": KEY_TOP, "UP": KEY_UP, "VGA": KEY_VGA, "VOLUME_DOWN": KEY_VOLUME_DOWN, "VOLUME_UP": KEY_VOLUME_UP, "WAKEUP": KEY_WAKEUP, "YELLOW": KEY_YELLOW, } STATE_ON = "on" STATE_IDLE = "idle" STATE_OFF = "off" STATE_PLAYING = "playing" STATE_PAUSED = "paused" STATE_STANDBY = "standby" STATE_STOPPED = "stopped" STATE_UNKNOWN = "unknown" VALID_STATES = (STATE_IDLE, STATE_OFF, STATE_PLAYING, STATE_PAUSED, STATE_STANDBY) VALID_STATE_PROPERTIES = ("audio_state", "media_session_state") VALID_PROPERTIES = VALID_STATE_PROPERTIES + ("wake_lock_size",) VALID_PROPERTIES_TYPES = {"audio_state": str, "media_session_state": int, "wake_lock_size": int} MEDIA_SESSION_STATES = {0: None, 1: STATE_STOPPED, 2: STATE_PAUSED, 3: STATE_PLAYING} APP_AE_TV = "com.aetn.aetv.watch" APP_AMAZON_PRIME_VIDEO = "com.amazon.avod.thirdpartyclient" APP_AMAZON_VIDEO = "com.amazon.avod" APP_APPLE_TV_PLUS = "com.apple.atve.android.appletv" APP_APPLE_TV_PLUS_FIRETV = "com.apple.atve.amazon.appletv" APP_APPLE_TV_PLUS_SONY = "com.apple.atve.sony.appletv" APP_ATV_LAUNCHER = "com.google.android.tvlauncher" APP_BELL_FIBE = "com.quickplay.android.bellmediaplayer" APP_CBC_GEM = "ca.cbc.android.cbctv" APP_COMEDY_CENTRAL = "com.vmn.android.comedycentral" APP_CRAVE = "ca.bellmedia.cravetv" APP_DAILYMOTION = "com.dailymotion.dailymotion" APP_DEEZER = "deezer.android.tv" APP_DISNEY_PLUS = "com.disney.disneyplus" APP_DISNEY_PLUS_HOTSTAR = "in.startv.hotstar" APP_DS_PHOTO = "com.synology.dsphoto" APP_DS_VIDEO = "com.synology.dsvideo" APP_ES_FILE_EXPLORER = "com.estrongs.android.pop" APP_FACEBOOK = "com.facebook.katana" APP_FAWESOME = "com.future.moviesByFawesomeAndroidTV" APP_FIREFOX = "org.mozilla.tv.firefox" APP_FIRETV_PACKAGE_LAUNCHER = "com.amazon.tv.launcher" APP_FIRETV_PACKAGE_SETTINGS = "com.amazon.tv.settings" APP_FIRETV_STORE = "com.amazon.venezia" APP_FOOD_NETWORK_GO = "tv.accedo.foodnetwork" APP_FRANCE_TV = "fr.francetv.pluzz" APP_GLOBAL_TV = "com.shawmedia.smglobal" APP_GOOGLE_CAST = "com.google.android.apps.mediashell" APP_GOOGLE_TV_LAUNCHER = "com.google.android.apps.tv.launcherx" APP_HAYSTACK_NEWS = "com.haystack.android" APP_HBO_GO = "eu.hbogo.androidtv.production" APP_HBO_GO_2 = "com.HBO" APP_HOICHOI = "com.viewlift.hoichoi" APP_HULU = "com.hulu.plus" APP_HUNGAMA_PLAY = "com.hungama.movies.tv" APP_IMDB_TV = "com.amazon.imdb.tv.android.app" APP_IPTV = "ru.iptvremote.android.iptv" APP_IPTV_SMARTERS_PRO = "com.nst.iptvsmarterstvbox" APP_JELLYFIN_TV = "org.jellyfin.androidtv" APP_JIO_CINEMA = "com.jio.media.stb.ondemand" APP_KODI = "org.xbmc.kodi" APP_LIVE_CHANNELS = "com.google.android.tv" APP_MIJN_RADIO = "org.samsonsen.nederlandse.radio.holland.nl" APP_MOLOTOV = "tv.molotov.app" APP_MRMC = "tv.mrmc.mrmc" APP_MRMC_LITE = "tv.mrmc.mrmc.lite" APP_MX_PLAYER = "com.mxtech.videoplayer.ad" APP_NETFLIX = "com.netflix.ninja" APP_NLZIET = "nl.nlziet" APP_NOS = "nl.nos.app" APP_NPO = "nl.uitzendinggemist" APP_OCS = "com.orange.ocsgo" APP_PLAY_GAMES = "com.google.android.play.games" APP_PLAY_MUSIC = "com.google.android.music" APP_PLAY_STORE = "com.android.vending" APP_PLAY_VIDEOS = "com.google.android.videos" APP_PLEX = "com.plexapp.android" APP_PRIME_VIDEO = "com.amazon.amazonvideo.livingroom" APP_PRIME_VIDEO_FIRETV = "com.amazon.firebat" APP_SETTINGS = "com.android.tv.settings" APP_SMART_YOUTUBE_TV = "com.liskovsoft.videomanager" APP_SONY_ACTION_MENU = "com.sony.dtv.scrums.action" APP_SONY_ALBUM = "com.sony.dtv.osat.album" APP_SONY_BRAVIA_SYNC_MENU = "com.sony.dtv.braviasyncmenu" APP_SONY_BRAVIA_TUTORIALS = "com.sony.dtv.bravialifehack" APP_SONY_DISCOVER = "com.sony.dtv.discovery" APP_SONY_HELP = "com.sony.dtv.smarthelp" APP_SONY_INTERNET_BROWSER = "com.vewd.core.integration.dia" APP_SONY_LIV = "com.sonyliv" APP_SONY_MUSIC = "com.sony.dtv.osat.music" APP_SONY_SCREEN_MIRRORING = "com.sony.dtv.networkapp.wifidirect" APP_SONY_SELECT = "com.sony.dtv.sonyselect" APP_SONY_TIMERS = "com.sony.dtv.timers" APP_SONY_TV = "com.sony.dtv.tvx" APP_SONY_VIDEO = "com.sony.dtv.osat.video" APP_SPORT1 = "de.sport1.firetv.video" APP_SPOTIFY = "com.spotify.tv.android" APP_STEAM_LINK = "com.valvesoftware.steamlink" APP_SYFY = "com.amazon.webapps.nbc.syfy" APP_T2 = "tv.perception.clients.tv.android" APP_TED = "com.ted.android.tv" APP_TUNEIN = "tunein.player" APP_TVHEADEND = "de.cyberdream.dreamepg.tvh.tv.player" APP_TWITCH = "tv.twitch.android.app" APP_TWITCH_FIRETV = "tv.twitch.android.viewer" APP_VEVO = "com.vevo.tv" APP_VH1 = "com.mtvn.vh1android" APP_VIMEO = "com.vimeo.android.videoapp" APP_VLC = "org.videolan.vlc" APP_VOYO = "com.phonegap.voyo" APP_VRV = "com.ellation.vrv" APP_WAIPU_TV = "de.exaring.waipu.firetv.live" APP_WATCH_TNT = "com.turner.tnt.android.networkapp" APP_YOUTUBE = "com.google.android.youtube.tv" APP_YOUTUBE_FIRETV = "com.amazon.firetv.youtube" APP_YOUTUBE_KIDS = "com.google.android.youtube.tvkids" APP_YOUTUBE_KIDS_FIRETV = "com.amazon.firetv.youtube.kids" APP_YOUTUBE_MUSIC = "com.google.android.youtube.tvmusic" APP_YOUTUBE_TV = "com.google.android.youtube.tvunplugged" APP_ZEE5 = "com.graymatrix.did" APP_ZIGGO_GO_TV = "com.ziggo.tv" APPS = { APP_AE_TV: "A&E", APP_AMAZON_PRIME_VIDEO: "Amazon Prime Video", APP_AMAZON_VIDEO: "Amazon Video", APP_APPLE_TV_PLUS: "Apple TV+", APP_APPLE_TV_PLUS_FIRETV: "Apple TV+ (Fire TV)", APP_APPLE_TV_PLUS_SONY: "Apple TV+ (Sony)", APP_ATV_LAUNCHER: "Android TV Launcher", APP_BELL_FIBE: "Bell Fibe", APP_CBC_GEM: "CBC Gem", APP_COMEDY_CENTRAL: "Comedy Central", APP_CRAVE: "Crave", APP_DAILYMOTION: "Dailymotion", APP_DEEZER: "Deezer", APP_DISNEY_PLUS: "Disney+", APP_DISNEY_PLUS_HOTSTAR: "Disney+ Hotstar", APP_DS_PHOTO: "DS photo", APP_DS_VIDEO: "DS video", APP_ES_FILE_EXPLORER: "ES File Explorer", APP_FACEBOOK: "Facebook Watch", APP_FAWESOME: "Fawsome", APP_FIREFOX: "Firefox", APP_FIRETV_STORE: "FireTV Store", APP_FOOD_NETWORK_GO: "Food Network GO", APP_FRANCE_TV: "France TV", APP_GLOBAL_TV: "Global TV", APP_GOOGLE_CAST: "Google Cast", APP_GOOGLE_TV_LAUNCHER: "Google TV Launcher", APP_HAYSTACK_NEWS: "Haystack News", APP_HBO_GO: "HBO GO", APP_HBO_GO_2: "HBO GO (2)", APP_HOICHOI: "Hoichoi", APP_HULU: "Hulu", APP_HUNGAMA_PLAY: "Hungama Play", APP_IMDB_TV: "IMDb TV", APP_IPTV: "IPTV", APP_IPTV_SMARTERS_PRO: "IPTV Smarters Pro", APP_JELLYFIN_TV: "Jellyfin", APP_JIO_CINEMA: "Jio Cinema", APP_KODI: "Kodi", APP_LIVE_CHANNELS: "Live Channels", APP_MIJN_RADIO: "Mijn Radio", APP_MOLOTOV: "Molotov", APP_MRMC: "MrMC", APP_MRMC_LITE: "MrMC Lite", APP_MX_PLAYER: "MX Player", APP_NETFLIX: "Netflix", APP_NLZIET: "NLZIET", APP_NOS: "NOS", APP_NPO: "NPO", APP_OCS: "OCS", APP_PLAY_GAMES: "Play Games", APP_PLAY_MUSIC: "Play Music", APP_PLAY_STORE: "Play Store", APP_PLAY_VIDEOS: "Play Movies & TV", APP_PLEX: "Plex", APP_PRIME_VIDEO: "Prime Video", APP_PRIME_VIDEO_FIRETV: "Prime Video (FireTV)", APP_SETTINGS: "Settings", APP_SMART_YOUTUBE_TV: "Smart YouTube TV", APP_SONY_ACTION_MENU: "Action Menu", APP_SONY_ALBUM: "Album", APP_SONY_BRAVIA_SYNC_MENU: "Sync Menu", APP_SONY_BRAVIA_TUTORIALS: "BRAVIA Tutorials", APP_SONY_DISCOVER: "Discover", APP_SONY_HELP: "Help", APP_SONY_INTERNET_BROWSER: "Internet Browser", APP_SONY_LIV: "SonyLIV", APP_SONY_MUSIC: "Music", APP_SONY_SCREEN_MIRRORING: "Screen mirroring", APP_SONY_SELECT: "Sony Select", APP_SONY_TIMERS: "Timers", APP_SONY_TV: "TV", APP_SONY_VIDEO: "Video", APP_SPORT1: "Sport 1", APP_SPOTIFY: "Spotify", APP_STEAM_LINK: "Steam Link", APP_SYFY: "Syfy", APP_T2: "T-2 TV", APP_TED: "TED", APP_TUNEIN: "TuneIn Radio", APP_TVHEADEND: "DreamPlayer TVHeadend", APP_TWITCH: "Twitch", APP_TWITCH_FIRETV: "Twitch (FireTV)", APP_VEVO: "Vevo", APP_VH1: "VH1", APP_VIMEO: "Vimeo", APP_VLC: "VLC", APP_VOYO: "VOYO", APP_VRV: "VRV", APP_WAIPU_TV: "Waipu TV", APP_WATCH_TNT: "Watch TNT", APP_YOUTUBE: "YouTube", APP_YOUTUBE_FIRETV: "YouTube (FireTV)", APP_YOUTUBE_KIDS: "YouTube Kids", APP_YOUTUBE_KIDS_FIRETV: "YouTube Kids (FireTV)", APP_YOUTUBE_MUSIC: "YouTube Music", APP_YOUTUBE_TV: "YouTube TV", APP_ZEE5: "ZEE5", APP_ZIGGO_GO_TV: "Ziggo GO TV", } REGEX_MEDIA_SESSION_STATE = re.compile(r"state=(?P<state>[0-9]+)", re.MULTILINE) REGEX_WAKE_LOCK_SIZE = re.compile(r"size=(?P<size>[0-9]+)") DEVICE_REGEX_PATTERN = r"Devices: (.*?)\W" MAC_REGEX_PATTERN = "ether (.*?) brd" MAX_VOLUME_REGEX_PATTERN = r"Max: (\d{1,})" MUTED_REGEX_PATTERN = r"Muted: (.*?)\W" STREAM_MUSIC_REGEX_PATTERN = "STREAM_MUSIC(.*?)- STREAM" VOLUME_REGEX_PATTERN = r"\): (\d{1,})" DEFAULT_AUTH_TIMEOUT_S = 10.0 DEFAULT_TRANSPORT_TIMEOUT_S = 1.0 DEFAULT_ADB_TIMEOUT_S = 9.0 DEFAULT_LOCK_TIMEOUT_S = 3.0

true

f71bfc3874575acdd3b3cfdfd9209ae815ab7e10

7,497

py

Python

tf/experiment6.py

wichtounet/frameworks

e0cac9d4ffbbf0b1e9d2491eb70bf2c6154f313b

[ "MIT" ]

20

2016-12-01T17:39:04.000Z

2019-08-25T12:50:05.000Z

tf/experiment6.py

wichtounet/frameworks

e0cac9d4ffbbf0b1e9d2491eb70bf2c6154f313b

[ "MIT" ]

4

2017-10-08T13:56:21.000Z

2019-01-21T12:49:09.000Z

tf/experiment6.py

wichtounet/frameworks

e0cac9d4ffbbf0b1e9d2491eb70bf2c6154f313b

[ "MIT" ]

7

2018-05-09T01:29:20.000Z

2019-02-09T10:49:39.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function import keras from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img import argparse import gzip import os import sys import time import os import math import numpy from PIL import Image import numpy from six.moves import urllib from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf batch_size = 128 batches = 10009 num_epochs = 5 num_classes = 1000 FLAGS = None from urllib.request import urlretrieve from os.path import isfile, isdir import tarfile import pickle def data_type(): return tf.float32 def get_batch(): index = 0 global current_index global training_images global training_labels B = numpy.zeros(shape=(batch_size, 256, 256, 3)) L = numpy.zeros(shape=(batch_size)) while index < batch_size: try: img = load_img(training_images[current_index]) B[index] = img_to_array(img) B[index] /= 255 L[index] = training_labels[current_index] index = index + 1 current_index = current_index + 1 except: print("Ignore image {}".format(training_images[current_index])) current_index = current_index + 1 return B, keras.utils.to_categorical(L, num_classes) def main(_): global current_index global training_images global training_labels label_counter = 0 training_images = [] training_labels = [] for subdir, dirs, files in os.walk('/data/datasets/imagenet_resized/train/'): for folder in dirs: for folder_subdir, folder_dirs, folder_files in os.walk(os.path.join(subdir, folder)): for file in folder_files: training_images.append(os.path.join(folder_subdir, file)) training_labels.append(label_counter) label_counter = label_counter + 1 nice_n = math.floor(len(training_images) / batch_size) * batch_size print(nice_n) print(len(training_images)) print(len(training_labels)) import random perm = list(range(len(training_images))) random.shuffle(perm) training_images = [training_images[index] for index in perm] training_labels = [training_labels[index] for index in perm] print("Data is ready...") train_data_node = tf.placeholder(data_type(), shape=(batch_size, 256, 256, 3)) train_labels_node = tf.placeholder(tf.int64, shape=(batch_size,1000)) # Convolutional weights conv1_weights = tf.Variable(tf.truncated_normal([3, 3, 3, 16], stddev=0.1, dtype=data_type())) conv1_biases = tf.Variable(tf.zeros([16], dtype=data_type())) conv2_weights = tf.Variable(tf.truncated_normal([3, 3, 16, 16], stddev=0.1, dtype=data_type())) conv2_biases = tf.Variable(tf.zeros([16], dtype=data_type())) conv3_weights = tf.Variable(tf.truncated_normal([3, 3, 16, 32], stddev=0.1, dtype=data_type())) conv3_biases = tf.Variable(tf.zeros([32], dtype=data_type())) conv4_weights = tf.Variable(tf.truncated_normal([3, 3, 32, 32], stddev=0.1, dtype=data_type())) conv4_biases = tf.Variable(tf.zeros([32], dtype=data_type())) conv5_weights = tf.Variable(tf.truncated_normal([3, 3, 32, 32], stddev=0.1, dtype=data_type())) conv5_biases = tf.Variable(tf.zeros([32], dtype=data_type())) # Fully connected weights fc1_weights = tf.Variable(tf.truncated_normal([2048, 2048], stddev=0.1, dtype=data_type())) fc1_biases = tf.Variable(tf.constant(0.1, shape=[2048], dtype=data_type())) fc2_weights = tf.Variable(tf.truncated_normal([2048, 1000], stddev=0.1, dtype=data_type())) fc2_biases = tf.Variable(tf.constant(0.1, shape=[1000], dtype=data_type())) def model(data): # Conv 1 conv = tf.nn.conv2d(data, conv1_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Conv 2 conv = tf.nn.conv2d(pool, conv2_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Conv 3 conv = tf.nn.conv2d(pool, conv3_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv3_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Conv 4 conv = tf.nn.conv2d(pool, conv4_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv4_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Conv 5 conv = tf.nn.conv2d(pool, conv5_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv5_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Fully Connected reshape = tf.reshape(pool, [batch_size, 2048]) hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases) return tf.matmul(hidden, fc2_weights) + fc2_biases # Training computation: logits + cross-entropy loss. logits = model(train_data_node) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = train_labels_node)) # Use simple momentum for the optimization. optimizer = tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.9).minimize(loss) acc_pred = tf.equal(tf.argmax(logits,1), tf.argmax(train_labels_node,1)) accuracy = tf.reduce_mean(tf.cast(acc_pred, tf.float32)) # Predictions for the current training minibatch. # train_prediction = tf.nn.softmax(logits) # Create a local session to run the training. with tf.Session() as sess: # Run all the initializers to prepare the trainable parameters. tf.global_variables_initializer().run(session = sess) print('Initialized!') for epoch in range(0, num_epochs): current_index = 0 while current_index + batch_size < len(training_images): start_time = time.time() b, l = get_batch() feed_dict = {train_data_node: b, train_labels_node: l} # Run the optimizer to update weights. _, batch_loss, batch_accuracy = sess.run([optimizer, loss, accuracy], feed_dict=feed_dict) end_time = time.time() print('batch {}/{} loss: {} accuracy: {} duration: {}ms'.format(int(current_index / batch_size), int(nice_n / batch_size), batch_loss, batch_accuracy, 1000 * (end_time - start_time)), flush = True) print('epoch {}/{}'.format(epoch, num_epochs)) # Finally print the result! current_index = 0 acc = 0.0 while current_index + batch_size < len(training_images): b, l = get_batch() feed_dict = {train_data_node: b, train_labels_node: l} [batch_accuracy] = sess.run([accuracy], feed_dict=feed_dict) print('Test batch accuracy:', batch_accuracy, flush = True) acc += batch_accuracy acc /= batches print('Test accuracy: %.1f%%' % acc) tf.app.run(main=main, argv=[sys.argv[0]])

35.870813

213

0.652528

from __future__ import absolute_import from __future__ import division from __future__ import print_function import keras from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img import argparse import gzip import os import sys import time import os import math import numpy from PIL import Image import numpy from six.moves import urllib from six.moves import xrange import tensorflow as tf batch_size = 128 batches = 10009 num_epochs = 5 num_classes = 1000 FLAGS = None from urllib.request import urlretrieve from os.path import isfile, isdir import tarfile import pickle def data_type(): return tf.float32 def get_batch(): index = 0 global current_index global training_images global training_labels B = numpy.zeros(shape=(batch_size, 256, 256, 3)) L = numpy.zeros(shape=(batch_size)) while index < batch_size: try: img = load_img(training_images[current_index]) B[index] = img_to_array(img) B[index] /= 255 L[index] = training_labels[current_index] index = index + 1 current_index = current_index + 1 except: print("Ignore image {}".format(training_images[current_index])) current_index = current_index + 1 return B, keras.utils.to_categorical(L, num_classes) def main(_): global current_index global training_images global training_labels label_counter = 0 training_images = [] training_labels = [] for subdir, dirs, files in os.walk('/data/datasets/imagenet_resized/train/'): for folder in dirs: for folder_subdir, folder_dirs, folder_files in os.walk(os.path.join(subdir, folder)): for file in folder_files: training_images.append(os.path.join(folder_subdir, file)) training_labels.append(label_counter) label_counter = label_counter + 1 nice_n = math.floor(len(training_images) / batch_size) * batch_size print(nice_n) print(len(training_images)) print(len(training_labels)) import random perm = list(range(len(training_images))) random.shuffle(perm) training_images = [training_images[index] for index in perm] training_labels = [training_labels[index] for index in perm] print("Data is ready...") train_data_node = tf.placeholder(data_type(), shape=(batch_size, 256, 256, 3)) train_labels_node = tf.placeholder(tf.int64, shape=(batch_size,1000)) conv1_weights = tf.Variable(tf.truncated_normal([3, 3, 3, 16], stddev=0.1, dtype=data_type())) conv1_biases = tf.Variable(tf.zeros([16], dtype=data_type())) conv2_weights = tf.Variable(tf.truncated_normal([3, 3, 16, 16], stddev=0.1, dtype=data_type())) conv2_biases = tf.Variable(tf.zeros([16], dtype=data_type())) conv3_weights = tf.Variable(tf.truncated_normal([3, 3, 16, 32], stddev=0.1, dtype=data_type())) conv3_biases = tf.Variable(tf.zeros([32], dtype=data_type())) conv4_weights = tf.Variable(tf.truncated_normal([3, 3, 32, 32], stddev=0.1, dtype=data_type())) conv4_biases = tf.Variable(tf.zeros([32], dtype=data_type())) conv5_weights = tf.Variable(tf.truncated_normal([3, 3, 32, 32], stddev=0.1, dtype=data_type())) conv5_biases = tf.Variable(tf.zeros([32], dtype=data_type())) fc1_weights = tf.Variable(tf.truncated_normal([2048, 2048], stddev=0.1, dtype=data_type())) fc1_biases = tf.Variable(tf.constant(0.1, shape=[2048], dtype=data_type())) fc2_weights = tf.Variable(tf.truncated_normal([2048, 1000], stddev=0.1, dtype=data_type())) fc2_biases = tf.Variable(tf.constant(0.1, shape=[1000], dtype=data_type())) def model(data): conv = tf.nn.conv2d(data, conv1_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') conv = tf.nn.conv2d(pool, conv2_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') conv = tf.nn.conv2d(pool, conv3_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv3_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') conv = tf.nn.conv2d(pool, conv4_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv4_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') conv = tf.nn.conv2d(pool, conv5_weights, strides=[1, 1, 1, 1], padding='SAME') relu = tf.nn.relu(tf.nn.bias_add(conv, conv5_biases)) pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') reshape = tf.reshape(pool, [batch_size, 2048]) hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases) return tf.matmul(hidden, fc2_weights) + fc2_biases logits = model(train_data_node) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = train_labels_node)) optimizer = tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.9).minimize(loss) acc_pred = tf.equal(tf.argmax(logits,1), tf.argmax(train_labels_node,1)) accuracy = tf.reduce_mean(tf.cast(acc_pred, tf.float32)) with tf.Session() as sess: tf.global_variables_initializer().run(session = sess) print('Initialized!') for epoch in range(0, num_epochs): current_index = 0 while current_index + batch_size < len(training_images): start_time = time.time() b, l = get_batch() feed_dict = {train_data_node: b, train_labels_node: l} _, batch_loss, batch_accuracy = sess.run([optimizer, loss, accuracy], feed_dict=feed_dict) end_time = time.time() print('batch {}/{} loss: {} accuracy: {} duration: {}ms'.format(int(current_index / batch_size), int(nice_n / batch_size), batch_loss, batch_accuracy, 1000 * (end_time - start_time)), flush = True) print('epoch {}/{}'.format(epoch, num_epochs)) current_index = 0 acc = 0.0 while current_index + batch_size < len(training_images): b, l = get_batch() feed_dict = {train_data_node: b, train_labels_node: l} [batch_accuracy] = sess.run([accuracy], feed_dict=feed_dict) print('Test batch accuracy:', batch_accuracy, flush = True) acc += batch_accuracy acc /= batches print('Test accuracy: %.1f%%' % acc) tf.app.run(main=main, argv=[sys.argv[0]])

true

f71bfd19f426d8f03872939dfd8bb1e2d6d2dcdf

332

py

Python

week1/the_real_deal/is_prime.py

sevgo/Programming101

ac25c4d9695563b449a629c60ec77a739c9f5be3

[ "BSD-3-Clause" ]

null

week1/the_real_deal/is_prime.py

sevgo/Programming101

ac25c4d9695563b449a629c60ec77a739c9f5be3

[ "BSD-3-Clause" ]

1

2021-09-16T05:44:31.000Z

week1/the_real_deal/is_prime.py

sevgo/Programming101

ac25c4d9695563b449a629c60ec77a739c9f5be3

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 from sum_of_divisors import sum_of_divisors def is_prime(n): # If N is prime it could only be devided # to 1 and N, so sum of divisors has to be # equal to N + 1 return n + 1 == sum_of_divisors(n) if __name__ == "__main__": number = int(input("Number: ")) print (is_prime(number))

22.133333

46

0.653614

from sum_of_divisors import sum_of_divisors def is_prime(n): return n + 1 == sum_of_divisors(n) if __name__ == "__main__": number = int(input("Number: ")) print (is_prime(number))

true

f71bfd37000184d8a9e59d68561043e6514fa6d5

4,211

py

Python

src/data_imputation_paper/imputation/dl.py

se-jaeger/data-imputation-paper

498d2d871302d917f58ecf6a9576e3a3451c5faa

[ "Apache-2.0" ]

2

2022-01-18T09:59:01.000Z

2022-02-02T10:01:45.000Z

src/data_imputation_paper/imputation/dl.py

se-jaeger/data-imputation-paper

498d2d871302d917f58ecf6a9576e3a3451c5faa

[ "Apache-2.0" ]

null

src/data_imputation_paper/imputation/dl.py

se-jaeger/data-imputation-paper

498d2d871302d917f58ecf6a9576e3a3451c5faa

[ "Apache-2.0" ]

null

import logging from typing import Dict, List, Optional, Tuple import pandas as pd from autokeras import StructuredDataClassifier, StructuredDataRegressor from tensorflow.keras import Model from ._base import BaseImputer logger = logging.getLogger() class AutoKerasImputer(BaseImputer): def __init__( self, max_trials: Optional[int] = 10, tuner: Optional[str] = None, validation_split: Optional[float] = 0.2, epochs: Optional[int] = 10, seed: Optional[int] = None ): """ Deep Learning-learning based imputation mehtod. It uses AutoKeras to find good architecture/hyperparameters. Args: max_trials (Optional[int], optional): maximum number of trials for model selection. Defaults to 10. tuner (Optional[str], optional): AutoKeras hyperparameter tuning strategy. Defaults to None. validation_split (Optional[float], optional): validation split for AutoKeras fit. Defaults to 0.2. epochs (Optional[int], optional): number of epochs for AutoKeras fit. Defaults to 10. seed (Optional[int], optional): Seed to make behavior deterministic. Defaults to None. """ super().__init__( seed=seed ) self.max_trials = max_trials self.epochs = epochs self.validation_split = validation_split self.tuner = tuner self._predictors: Dict[str, Model] = {} def get_best_hyperparameters(self): super().get_best_hyperparameters() return { column: self._predictors[column].tuner.get_best_hyperparameters()[0].values for column in self._predictors.keys() } def fit(self, data: pd.DataFrame, target_columns: List[str]) -> BaseImputer: super().fit(data=data, target_columns=target_columns) # cast categorical columns to strings fixes problems where categories are integer values and treated as regression task data = self._categorical_columns_to_string(data.copy()) # We don't want to change the input dataframe -> copy it for target_column in self._target_columns: missing_mask = data[target_column].isna() feature_cols = [c for c in self._categorical_columns + self._numerical_columns if c != target_column] if target_column in self._numerical_columns: StructuredDataModelSearch = StructuredDataRegressor elif target_column in self._categorical_columns: StructuredDataModelSearch = StructuredDataClassifier self._predictors[target_column] = StructuredDataModelSearch( column_names=feature_cols, overwrite=True, max_trials=self.max_trials, tuner=self.tuner, directory="../models" ) self._predictors[target_column].fit( x=data.loc[~missing_mask, feature_cols], y=data.loc[~missing_mask, target_column], epochs=self.epochs ) self._fitted = True return self def transform(self, data: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]: super().transform(data=data) imputed_mask = data[self._target_columns].isna() # save the original dtypes because .. dtypes = data.dtypes # ... dtypes of data need to be same as for fitting data = self._categorical_columns_to_string(data.copy()) # We don't want to change the input dataframe -> copy it for target_column in self._target_columns: feature_cols = [c for c in self._categorical_columns + self._numerical_columns if c != target_column] missing_mask = data[target_column].isna() amount_missing_in_columns = missing_mask.sum() if amount_missing_in_columns > 0: data.loc[missing_mask, target_column] = self._predictors[target_column].predict(data.loc[missing_mask, feature_cols])[:, 0] logger.debug(f'Imputed {amount_missing_in_columns} values in column {target_column}') self._restore_dtype(data, dtypes) return data, imputed_mask

37.265487

139

0.654001

import logging from typing import Dict, List, Optional, Tuple import pandas as pd from autokeras import StructuredDataClassifier, StructuredDataRegressor from tensorflow.keras import Model from ._base import BaseImputer logger = logging.getLogger() class AutoKerasImputer(BaseImputer): def __init__( self, max_trials: Optional[int] = 10, tuner: Optional[str] = None, validation_split: Optional[float] = 0.2, epochs: Optional[int] = 10, seed: Optional[int] = None ): super().__init__( seed=seed ) self.max_trials = max_trials self.epochs = epochs self.validation_split = validation_split self.tuner = tuner self._predictors: Dict[str, Model] = {} def get_best_hyperparameters(self): super().get_best_hyperparameters() return { column: self._predictors[column].tuner.get_best_hyperparameters()[0].values for column in self._predictors.keys() } def fit(self, data: pd.DataFrame, target_columns: List[str]) -> BaseImputer: super().fit(data=data, target_columns=target_columns) data = self._categorical_columns_to_string(data.copy()) for target_column in self._target_columns: missing_mask = data[target_column].isna() feature_cols = [c for c in self._categorical_columns + self._numerical_columns if c != target_column] if target_column in self._numerical_columns: StructuredDataModelSearch = StructuredDataRegressor elif target_column in self._categorical_columns: StructuredDataModelSearch = StructuredDataClassifier self._predictors[target_column] = StructuredDataModelSearch( column_names=feature_cols, overwrite=True, max_trials=self.max_trials, tuner=self.tuner, directory="../models" ) self._predictors[target_column].fit( x=data.loc[~missing_mask, feature_cols], y=data.loc[~missing_mask, target_column], epochs=self.epochs ) self._fitted = True return self def transform(self, data: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]: super().transform(data=data) imputed_mask = data[self._target_columns].isna() # save the original dtypes because .. dtypes = data.dtypes # ... dtypes of data need to be same as for fitting data = self._categorical_columns_to_string(data.copy()) # We don't want to change the input dataframe -> copy it for target_column in self._target_columns: feature_cols = [c for c in self._categorical_columns + self._numerical_columns if c != target_column] missing_mask = data[target_column].isna() amount_missing_in_columns = missing_mask.sum() if amount_missing_in_columns > 0: data.loc[missing_mask, target_column] = self._predictors[target_column].predict(data.loc[missing_mask, feature_cols])[:, 0] logger.debug(f'Imputed {amount_missing_in_columns} values in column {target_column}') self._restore_dtype(data, dtypes) return data, imputed_mask

true

f71bfd5b0f2615891de5ac70368d3a37c96767b7

17,386

py

Python

HER_mod/rl_modules/get_path_costs.py

schrammlb2/policy-guided-sst

8dce6619b9c771c39915c60fe9c54270ea1e621e

[ "Apache-2.0" ]

null

HER_mod/rl_modules/get_path_costs.py

schrammlb2/policy-guided-sst

8dce6619b9c771c39915c60fe9c54270ea1e621e

[ "Apache-2.0" ]

null

HER_mod/rl_modules/get_path_costs.py

schrammlb2/policy-guided-sst

8dce6619b9c771c39915c60fe9c54270ea1e621e

[ "Apache-2.0" ]

null

import numpy as np import matplotlib.pyplot as plt import pickle import os from scipy import stats from HER_mod.rl_modules.tsp import generate_path from HER_mod.rl_modules.hyperparams import NUM_GOALS, NUM_AGENTS gd_step_list = [0,2,5, 10, 20, 40] # NUM_AGENTS = 3 N=200 def get_path_costs(train_pos_agent, train_vel_agent, perm_search=True): pos_run_time_list = [] vel_run_time_list = [] # gd_step_list = [0,5,10] num_agents = NUM_AGENTS num_goals=NUM_GOALS n=N # gd_step_list = [0,1] # num_agents = 2 # num_goals=2 # n=2 pos_time_list = [] vel_time_list = [] for _ in range(num_agents): pos_agent = train_pos_agent() vel_agent = train_vel_agent() pos_agent_time_list = [] vel_agent_time_list = [] for i in range(n): # goals = [np.random.rand(2)*2-1 for i in range(num_goals)] # pos = np.random.rand(2)*2-1 goals = generate_path(num_goals + 1) pos = goals[0] goals = goals[1:-1] # pos_agent_time_list = [] min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = pos_agent.find_shortest_path(pos, goals, gd_steps=0, perm_search=perm_search) pos_test_time_list = [len(min_trajectory)]*len(gd_step_list) pos_agent_time_list.append(pos_test_time_list) vel_test_time_list = [] for gd_steps in gd_step_list: min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = vel_agent.find_shortest_path(pos, goals, gd_steps=gd_steps, perm_search=perm_search) vel_test_time_list.append(len(min_trajectory)) vel_agent_time_list.append(vel_test_time_list) pos_time_list.append(pos_agent_time_list) vel_time_list.append(vel_agent_time_list) vel_time_list = np.array(vel_time_list).squeeze() pos_time_list = np.array(pos_time_list).squeeze() relative_time_change = (vel_time_list-pos_time_list)/pos_time_list relative_time_change = np.mean(relative_time_change, axis=1) try: pickle.dump(vel_time_list, open("velocity_target.pkl", 'wb')) pickle.dump(pos_time_list, open("no_velocity_target.pkl", 'wb')) pickle.dump(relative_time_change, open("relative_time_change.pkl", 'wb')) except: print("pickle failure") import pdb pdb.set_trace() mean = relative_time_change.mean(axis=0) t_score = stats.t.ppf(.975, num_agents) ci = t_score*relative_time_change.std(axis=0)/(num_agents**.5) steps = np.array(gd_step_list) plt.plot(steps, mean) plt.fill_between(steps, mean+ci, mean-ci, alpha=.4) plt.xlabel("Gradient steps") plt.ylabel("Relative Improvement vs standard HER") plt.title("Relative Improvement") plt.savefig(os.path.join('results', "Relative Improvement" + '.png')) plt.close() # import pdb # pdb.set_trace() # def method_comparison(train_pos_agent, train_vel_agent): # # method_list = ['random search', "gradient descent", "gradient descent (40 steps)", "random", "0 velocity target"] # method_list = ['random search', "gradient descent", "random", "0 velocity target"] # method_runtime_dict = {'greedy': []} # for method in method_list: # method_runtime_dict[method] = [] # num_agents = NUM_AGENTS # num_goals=NUM_GOALS # n=N # pos_time_list = [] # vel_time_list = [] # for _ in range(num_agents): # pos_agent = train_pos_agent() # vel_agent = train_vel_agent() # for method in method_runtime_dict.keys(): # method_runtime_dict[method].append([]) # for i in range(n): # # goals = [np.random.rand(2)*2-1 for i in range(num_goals)] # # pos = np.random.rand(2)*2-1 # goals = generate_path(num_goals + 1) # pos = goals[0] # goals = goals[1:-1] # # pos_agent_time_list = [] # min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = pos_agent.select_path(pos, goals, method="0 velocity target") # # pos_test_time_list = [len(min_trajectory)]*len(gd_step_list) # method_runtime_dict['greedy'][-1].append(len(min_trajectory)) # # vel_test_time_list = [] # for method in method_list: # min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = vel_agent.select_path(pos, goals, method=method) # method_runtime_dict[method][-1].append(len(min_trajectory)) # # vel_agent_time_list.append(vel_test_time_list) # greedy = method_runtime_dict['greedy'] # method_runtime_dict = {method: np.array(method_runtime_dict[method]) for method in method_runtime_dict.keys()} # performance_dict = {method: (method_runtime_dict[method].mean(), 2*(method_runtime_dict[method].mean(axis=-1)).std()/(num_agents**.5)) for method in method_runtime_dict.keys()} # relative_time_dict = {method: (method_runtime_dict[method] - greedy)/greedy for method in method_list} # improvement_dict = {method: (relative_time_dict[method].mean(), 2*(relative_time_dict[method].mean(axis=-1)).std()/(num_agents**.5)) for method in method_list} # performance_list = [performance_dict[m][0] for m in method_runtime_dict.keys()] # performance_ci_list = [performance_dict[m][1] for m in method_runtime_dict.keys()] # relative_time_list = [improvement_dict[m][0] for m in method_list] # relative_time_ci_list = [improvement_dict[m][1] for m in method_list] # plt.xticks(range(len(method_runtime_dict.keys())), list(method_runtime_dict.keys())) # plt.xlabel("Method") # plt.ylabel('Time to complete') # plt.title('Comparison of velocity target-setting methods') # plt.bar(range(len(performance_list)), performance_list, yerr=performance_ci_list) # plt.savefig(os.path.join('results', "Method comparison -- Performance" + '.png')) # plt.close() # plt.xticks(range(len(method_list)), method_list) # plt.xlabel("Method") # plt.ylabel('Cost reduction over greedy baseline') # plt.title('Comparison of velocity target-setting methods') # plt.bar(range(len(relative_time_list)), relative_time_list, yerr=relative_time_ci_list) # plt.savefig(os.path.join('results', "Method comparison -- Relative Improvement" + '.png')) # plt.close() def method_comparison(train_pos_agent, train_vel_agent): method_list = ['random search', "gradient descent", "gradient descent (40 steps)", "random", "0 velocity target"] # method_list = ['random search', "gradient descent", "random", "0 velocity target"] method_runtime_dict = {'greedy': []} for method in method_list: method_runtime_dict[method] = [] num_agents = NUM_AGENTS num_goals=NUM_GOALS n=N pos_time_list = [] vel_time_list = [] failed_counter_dict = {'greedy': 0} for method in method_list: failed_counter_dict[method] = 0 for _ in range(num_agents): pos_agent = train_pos_agent() vel_agent = train_vel_agent() for method in method_runtime_dict.keys(): method_runtime_dict[method].append([]) for i in range(n): # goals = [np.random.rand(2)*2-1 for i in range(num_goals)] # pos = np.random.rand(2)*2-1 goals = generate_path(num_goals + 1) pos = goals[0] goals = goals[1:-1] # pos_agent_time_list = [] min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = pos_agent.select_path(pos, goals, method="0 velocity target") # pos_test_time_list = [len(min_trajectory)]*len(gd_step_list) if successful: method_runtime_dict['greedy'][-1].append(len(min_trajectory)) else: method_runtime_dict['greedy'][-1].append("NULL") failed_counter_dict['greedy'] += 1 # vel_test_time_list = [] for method in method_list: min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = vel_agent.select_path(pos, goals, method=method) if successful: method_runtime_dict[method][-1].append(len(min_trajectory)) else: method_runtime_dict[method][-1].append("NULL") failed_counter_dict[method] += 1 # vel_agent_time_list.append(vel_test_time_list) success_rates = {method: 1-failed_counter_dict[method]/(num_agents*n) for method in failed_counter_dict.keys()} greedy = method_runtime_dict['greedy'] agent_performance_dict = {} mean_performance_dict = {} ci_performance_dict = {} improvement_dict = {} mean_improvement_dict = {} ci_improvement_dict = {} t_score = stats.t.ppf(.975, num_agents) for method in method_runtime_dict.keys(): agent_performance_dict[method] = [[time for time in agent_list if time != "NULL"] for agent_list in method_runtime_dict[method]] agent_performance_dict[method] = [sum(agent_list)/len(agent_list) for agent_list in agent_performance_dict[method]] mean = sum(agent_performance_dict[method])/len(agent_performance_dict[method]) mean_performance_dict[method] = mean ci_performance_dict[method] = t_score*sum([(v-mean)**2 for v in agent_performance_dict[method]])**.5/len(agent_performance_dict[method]) improvement_list = [] mean_list = [] for agent_ind in range(num_agents): agent_list = method_runtime_dict[method][agent_ind] greedy_list = greedy[agent_ind] improvement_list.append([(agent_list[i] - greedy_list[i])/greedy_list[i] for i in range(n) if (agent_list[i] != "NULL" and greedy_list[i]!= "NULL")]) mean_list.append(sum(improvement_list[agent_ind])/len(improvement_list[agent_ind])) mean = sum(mean_list)/len(mean_list) mean_improvement_dict[method] = mean ci_improvement_dict[method] = t_score*sum([(v-mean)**2 for v in mean_list])**.5/len(mean_list) # agent_improvement_dict[method] = [[(time - greedy_time)/greedy_time for time in agent_list if time != "NULL"] for agent_list in method_runtime_dict[method]] # agent_performance_dict[method] = [sum(agent_list)/len(agent_list) for agent_list in agent_performance_dict[method]] # mean_performance_dict[method] = sum(agent_performance_dict[method])/len(agent_performance_dict[method]) # ci_performance_dict[method] = 2*sum([(v-mean)**2 for v in agent_performance_dict[method]])**.5/len(agent_performance_dict[method]) # method_runtime_dict = {method: np.array(method_runtime_dict[method]) for method in method_runtime_dict.keys()} # mean_performance_dict = {method: method_runtime_dict[method] for method in method_runtime_dict.keys()} # relative_time_dict = {method: (method_runtime_dict[method] - greedy)/greedy for method in method_list} # improvement_dict = {method: (relative_time_dict[method].mean(), 2*(relative_time_dict[method].mean(axis=-1)).std()/(num_agents**.5)) for method in method_list} # greedy = method_runtime_dict['greedy'] # method_runtime_dict = {method: np.array(method_runtime_dict[method]) for method in method_runtime_dict.keys()} # performance_dict = {method: (method_runtime_dict[method].mean(), 2*(method_runtime_dict[method].mean(axis=-1)).std()/(num_agents**.5)) for method in method_runtime_dict.keys()} # relative_time_dict = {method: (method_runtime_dict[method] - greedy)/greedy for method in method_list} # improvement_dict = {method: (relative_time_dict[method].mean(), 2*(relative_time_dict[method].mean(axis=-1)).std()/(num_agents**.5)) for method in method_list} performance_list = [mean_performance_dict[m] for m in method_runtime_dict.keys()] performance_ci_list = [ci_performance_dict[m] for m in method_runtime_dict.keys()] relative_time_list = [mean_improvement_dict[m] for m in method_list] relative_time_ci_list = [ci_improvement_dict[m] for m in method_list] sr_list = [success_rates[m] for m in method_runtime_dict.keys()]#method_list] # plt.xticks(range(len(method_list)), method_list) plt.xticks(range(len(method_runtime_dict.keys())), list(method_runtime_dict.keys())) plt.xlabel("Method") plt.ylabel('Success rate') plt.title('Comparison of velocity target-setting methods') plt.bar(range(len(sr_list)), sr_list) plt.savefig(os.path.join('results', "Method comparison -- Success Rate" + '.png')) plt.close() plt.xticks(range(len(method_runtime_dict.keys())), list(method_runtime_dict.keys())) plt.xlabel("Method") plt.ylabel('Time to complete') plt.title('Comparison of velocity target-setting methods') plt.bar(range(len(performance_list)), performance_list, yerr=performance_ci_list) plt.savefig(os.path.join('results', "Method comparison -- Performance" + '.png')) plt.close() plt.xticks(range(len(method_list)), method_list) plt.xlabel("Method") plt.ylabel('Cost reduction over greedy baseline') plt.title('Comparison of velocity target-setting methods') plt.bar(range(len(relative_time_list)), relative_time_list, yerr=relative_time_ci_list) plt.savefig(os.path.join('results', "Method comparison -- Relative Improvement" + '.png')) plt.close() def get_random_search_costs(train_vel_agent, perm_search=True): pos_run_time_list = [] vel_run_time_list = [] # gd_step_list = [0,5,10] num_agents = NUM_AGENTS num_goals=NUM_GOALS n=N # gd_step_list = [0,1] # num_agents = 2 # num_goals=2 # n=2 rand_time_list = [] gd_time_list = [] for _ in range(num_agents): vel_agent = train_vel_agent() rand_search_time_list = [] gd_search_time_list = [] for i in range(n): # goals = [np.random.rand(2)*2-1 for i in range(num_goals)] # pos = np.random.rand(2)*2-1 goals = generate_path(num_goals + 1) pos = goals[0] goals = goals[1:-1] rand_test_time_list = [] gd_test_time_list = [] for gd_steps in gd_step_list: # min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = vel_agent.find_shortest_path(pos, goals, gd_steps=gd_steps, random_start=True, perm_search=perm_search) min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = vel_agent.find_shortest_path(pos, goals, gd_steps=gd_steps, random_start=False, perm_search=perm_search) print("GD: " + str(min_time)) gd_test_time_list.append(len(min_trajectory)) min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = vel_agent.find_shortest_path(pos, goals, gd_steps=gd_steps, random_search=True, perm_search=perm_search) print("random_search: " + str(min_time)) rand_test_time_list.append(len(min_trajectory)) rand_search_time_list.append(rand_test_time_list) gd_search_time_list.append(gd_test_time_list) rand_time_list.append(rand_search_time_list) gd_time_list.append(gd_search_time_list) rand_time_list = np.array(rand_time_list).squeeze() gd_time_list = np.array(gd_time_list).squeeze() # best = np.minimum(rand_time_list.min(axis=2),gd_time_list.min(axis=2)) relative_time_change = (gd_time_list-rand_time_list)/rand_time_list relative_time_change = np.mean(relative_time_change, axis=1) # try: # pickle.dump(vel_time_list, open("velocity_target.pkl", 'wb')) # pickle.dump(pos_time_list, open("no_velocity_target.pkl", 'wb')) # pickle.dump(relative_time_change, open("relative_time_change.pkl", 'wb')) # except: # print("pickle failure") # import pdb # pdb.set_trace() mean = relative_time_change.mean(axis=0) ci = 2*relative_time_change.std(axis=0)/(num_agents**.5) steps = np.array(gd_step_list) plt.plot(steps, mean) plt.fill_between(steps, mean+ci, mean-ci, alpha=.4) plt.xlabel("Gradient steps") plt.ylabel("Relative Improvement vs random search") plt.title("Relative Improvement vs random search") plt.savefig(os.path.join('results', "Improvement vs random search" + '.png')) plt.close() t_score = stats.t.ppf(.975, num_agents) rands = rand_time_list.mean(axis=1) rand_mean = rands.mean(axis=0) rand_ci = t_score*rands.std(axis=0)/(num_agents**.5) gds = gd_time_list.mean(axis=1) gd_mean = gds.mean(axis=0) gd_ci = t_score*gds.std(axis=0)/(num_agents**.5) plt.plot(steps, rand_mean, color='red', label='Random Search') plt.fill_between(steps, rand_mean+rand_ci, rand_mean-rand_ci, alpha=.4, color='red') plt.plot(steps, gd_mean, color='blue', label='Gradient Descent') plt.fill_between(steps, gd_mean+gd_ci, gd_mean-gd_ci, alpha=.4, color='blue') plt.legend() plt.xlabel("Gradient steps") plt.ylabel("Relative Improvement vs random search") plt.title("Relative Improvement vs random search") plt.savefig(os.path.join('results', "Gradient Descent vs random search" + '.png')) plt.close()

44.352041

195

0.673588

import numpy as np import matplotlib.pyplot as plt import pickle import os from scipy import stats from HER_mod.rl_modules.tsp import generate_path from HER_mod.rl_modules.hyperparams import NUM_GOALS, NUM_AGENTS gd_step_list = [0,2,5, 10, 20, 40] N=200 def get_path_costs(train_pos_agent, train_vel_agent, perm_search=True): pos_run_time_list = [] vel_run_time_list = [] num_agents = NUM_AGENTS num_goals=NUM_GOALS n=N pos_time_list = [] vel_time_list = [] for _ in range(num_agents): pos_agent = train_pos_agent() vel_agent = train_vel_agent() pos_agent_time_list = [] vel_agent_time_list = [] for i in range(n): goals = generate_path(num_goals + 1) pos = goals[0] goals = goals[1:-1] min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = pos_agent.find_shortest_path(pos, goals, gd_steps=0, perm_search=perm_search) pos_test_time_list = [len(min_trajectory)]*len(gd_step_list) pos_agent_time_list.append(pos_test_time_list) vel_test_time_list = [] for gd_steps in gd_step_list: min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = vel_agent.find_shortest_path(pos, goals, gd_steps=gd_steps, perm_search=perm_search) vel_test_time_list.append(len(min_trajectory)) vel_agent_time_list.append(vel_test_time_list) pos_time_list.append(pos_agent_time_list) vel_time_list.append(vel_agent_time_list) vel_time_list = np.array(vel_time_list).squeeze() pos_time_list = np.array(pos_time_list).squeeze() relative_time_change = (vel_time_list-pos_time_list)/pos_time_list relative_time_change = np.mean(relative_time_change, axis=1) try: pickle.dump(vel_time_list, open("velocity_target.pkl", 'wb')) pickle.dump(pos_time_list, open("no_velocity_target.pkl", 'wb')) pickle.dump(relative_time_change, open("relative_time_change.pkl", 'wb')) except: print("pickle failure") import pdb pdb.set_trace() mean = relative_time_change.mean(axis=0) t_score = stats.t.ppf(.975, num_agents) ci = t_score*relative_time_change.std(axis=0)/(num_agents**.5) steps = np.array(gd_step_list) plt.plot(steps, mean) plt.fill_between(steps, mean+ci, mean-ci, alpha=.4) plt.xlabel("Gradient steps") plt.ylabel("Relative Improvement vs standard HER") plt.title("Relative Improvement") plt.savefig(os.path.join('results', "Relative Improvement" + '.png')) plt.close() [] num_agents = NUM_AGENTS num_goals=NUM_GOALS n=N pos_time_list = [] vel_time_list = [] failed_counter_dict = {'greedy': 0} for method in method_list: failed_counter_dict[method] = 0 for _ in range(num_agents): pos_agent = train_pos_agent() vel_agent = train_vel_agent() for method in method_runtime_dict.keys(): method_runtime_dict[method].append([]) for i in range(n): goals = generate_path(num_goals + 1) pos = goals[0] goals = goals[1:-1] min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = pos_agent.select_path(pos, goals, method="0 velocity target") if successful: method_runtime_dict['greedy'][-1].append(len(min_trajectory)) else: method_runtime_dict['greedy'][-1].append("NULL") failed_counter_dict['greedy'] += 1 for method in method_list: min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = vel_agent.select_path(pos, goals, method=method) if successful: method_runtime_dict[method][-1].append(len(min_trajectory)) else: method_runtime_dict[method][-1].append("NULL") failed_counter_dict[method] += 1 success_rates = {method: 1-failed_counter_dict[method]/(num_agents*n) for method in failed_counter_dict.keys()} greedy = method_runtime_dict['greedy'] agent_performance_dict = {} mean_performance_dict = {} ci_performance_dict = {} improvement_dict = {} mean_improvement_dict = {} ci_improvement_dict = {} t_score = stats.t.ppf(.975, num_agents) for method in method_runtime_dict.keys(): agent_performance_dict[method] = [[time for time in agent_list if time != "NULL"] for agent_list in method_runtime_dict[method]] agent_performance_dict[method] = [sum(agent_list)/len(agent_list) for agent_list in agent_performance_dict[method]] mean = sum(agent_performance_dict[method])/len(agent_performance_dict[method]) mean_performance_dict[method] = mean ci_performance_dict[method] = t_score*sum([(v-mean)**2 for v in agent_performance_dict[method]])**.5/len(agent_performance_dict[method]) improvement_list = [] mean_list = [] for agent_ind in range(num_agents): agent_list = method_runtime_dict[method][agent_ind] greedy_list = greedy[agent_ind] improvement_list.append([(agent_list[i] - greedy_list[i])/greedy_list[i] for i in range(n) if (agent_list[i] != "NULL" and greedy_list[i]!= "NULL")]) mean_list.append(sum(improvement_list[agent_ind])/len(improvement_list[agent_ind])) mean = sum(mean_list)/len(mean_list) mean_improvement_dict[method] = mean ci_improvement_dict[method] = t_score*sum([(v-mean)**2 for v in mean_list])**.5/len(mean_list) performance_list = [mean_performance_dict[m] for m in method_runtime_dict.keys()] performance_ci_list = [ci_performance_dict[m] for m in method_runtime_dict.keys()] relative_time_list = [mean_improvement_dict[m] for m in method_list] relative_time_ci_list = [ci_improvement_dict[m] for m in method_list] sr_list = [success_rates[m] for m in method_runtime_dict.keys()] plt.xticks(range(len(method_runtime_dict.keys())), list(method_runtime_dict.keys())) plt.xlabel("Method") plt.ylabel('Success rate') plt.title('Comparison of velocity target-setting methods') plt.bar(range(len(sr_list)), sr_list) plt.savefig(os.path.join('results', "Method comparison -- Success Rate" + '.png')) plt.close() plt.xticks(range(len(method_runtime_dict.keys())), list(method_runtime_dict.keys())) plt.xlabel("Method") plt.ylabel('Time to complete') plt.title('Comparison of velocity target-setting methods') plt.bar(range(len(performance_list)), performance_list, yerr=performance_ci_list) plt.savefig(os.path.join('results', "Method comparison -- Performance" + '.png')) plt.close() plt.xticks(range(len(method_list)), method_list) plt.xlabel("Method") plt.ylabel('Cost reduction over greedy baseline') plt.title('Comparison of velocity target-setting methods') plt.bar(range(len(relative_time_list)), relative_time_list, yerr=relative_time_ci_list) plt.savefig(os.path.join('results', "Method comparison -- Relative Improvement" + '.png')) plt.close() def get_random_search_costs(train_vel_agent, perm_search=True): pos_run_time_list = [] vel_run_time_list = [] num_agents = NUM_AGENTS num_goals=NUM_GOALS n=N rand_time_list = [] gd_time_list = [] for _ in range(num_agents): vel_agent = train_vel_agent() rand_search_time_list = [] gd_search_time_list = [] for i in range(n): goals = generate_path(num_goals + 1) pos = goals[0] goals = goals[1:-1] rand_test_time_list = [] gd_test_time_list = [] for gd_steps in gd_step_list: min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = vel_agent.find_shortest_path(pos, goals, gd_steps=gd_steps, random_start=False, perm_search=perm_search) print("GD: " + str(min_time)) gd_test_time_list.append(len(min_trajectory)) min_time, min_trajectory, min_path, min_vel_path, successful, pass_vals = vel_agent.find_shortest_path(pos, goals, gd_steps=gd_steps, random_search=True, perm_search=perm_search) print("random_search: " + str(min_time)) rand_test_time_list.append(len(min_trajectory)) rand_search_time_list.append(rand_test_time_list) gd_search_time_list.append(gd_test_time_list) rand_time_list.append(rand_search_time_list) gd_time_list.append(gd_search_time_list) rand_time_list = np.array(rand_time_list).squeeze() gd_time_list = np.array(gd_time_list).squeeze() relative_time_change = (gd_time_list-rand_time_list)/rand_time_list relative_time_change = np.mean(relative_time_change, axis=1) mean = relative_time_change.mean(axis=0) ci = 2*relative_time_change.std(axis=0)/(num_agents**.5) steps = np.array(gd_step_list) plt.plot(steps, mean) plt.fill_between(steps, mean+ci, mean-ci, alpha=.4) plt.xlabel("Gradient steps") plt.ylabel("Relative Improvement vs random search") plt.title("Relative Improvement vs random search") plt.savefig(os.path.join('results', "Improvement vs random search" + '.png')) plt.close() t_score = stats.t.ppf(.975, num_agents) rands = rand_time_list.mean(axis=1) rand_mean = rands.mean(axis=0) rand_ci = t_score*rands.std(axis=0)/(num_agents**.5) gds = gd_time_list.mean(axis=1) gd_mean = gds.mean(axis=0) gd_ci = t_score*gds.std(axis=0)/(num_agents**.5) plt.plot(steps, rand_mean, color='red', label='Random Search') plt.fill_between(steps, rand_mean+rand_ci, rand_mean-rand_ci, alpha=.4, color='red') plt.plot(steps, gd_mean, color='blue', label='Gradient Descent') plt.fill_between(steps, gd_mean+gd_ci, gd_mean-gd_ci, alpha=.4, color='blue') plt.legend() plt.xlabel("Gradient steps") plt.ylabel("Relative Improvement vs random search") plt.title("Relative Improvement vs random search") plt.savefig(os.path.join('results', "Gradient Descent vs random search" + '.png')) plt.close()

true

f71bfd9867454fe2f4dc8062137be298a7bcf44a

40,230

py

Python

python/ccxt/async_support/coinbasepro.py

dgdiginex/ccxt

cccd590576cbf48d26cf9e3f65cc54fdd466a139

[ "MIT" ]

1

2021-02-08T21:56:13.000Z

python/ccxt/async_support/coinbasepro.py

yucelalbar/ccxt

672510401fba809172fac8272e1af463c778358a

[ "MIT" ]

null

python/ccxt/async_support/coinbasepro.py

yucelalbar/ccxt

672510401fba809172fac8272e1af463c778358a

[ "MIT" ]

2

2020-10-13T03:24:08.000Z

2020-10-15T06:25:07.000Z

# -*- coding: utf-8 -*- # PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN: # https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code from ccxt.async_support.base.exchange import Exchange # ----------------------------------------------------------------------------- try: basestring # Python 3 except NameError: basestring = str # Python 2 import hashlib from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import ArgumentsRequired from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidAddress from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import NotSupported from ccxt.base.errors import RateLimitExceeded from ccxt.base.errors import OnMaintenance from ccxt.base.decimal_to_precision import TICK_SIZE class coinbasepro(Exchange): def describe(self): return self.deep_extend(super(coinbasepro, self).describe(), { 'id': 'coinbasepro', 'name': 'Coinbase Pro', 'countries': ['US'], 'rateLimit': 1000, 'userAgent': self.userAgents['chrome'], 'pro': True, 'has': { 'cancelAllOrders': True, 'cancelOrder': True, 'CORS': True, 'createDepositAddress': True, 'createOrder': True, 'deposit': True, 'fetchAccounts': True, 'fetchBalance': True, 'fetchCurrencies': True, 'fetchClosedOrders': True, 'fetchDepositAddress': True, 'fetchMarkets': True, 'fetchMyTrades': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchOrder': True, 'fetchOrderBook': True, 'fetchOrders': True, 'fetchOrderTrades': True, 'fetchTime': True, 'fetchTicker': True, 'fetchTrades': True, 'fetchTransactions': True, 'withdraw': True, }, 'timeframes': { '1m': 60, '5m': 300, '15m': 900, '1h': 3600, '6h': 21600, '1d': 86400, }, 'urls': { 'test': { 'public': 'https://api-public.sandbox.pro.coinbase.com', 'private': 'https://api-public.sandbox.pro.coinbase.com', }, 'logo': 'https://user-images.githubusercontent.com/1294454/41764625-63b7ffde-760a-11e8-996d-a6328fa9347a.jpg', 'api': { 'public': 'https://api.pro.coinbase.com', 'private': 'https://api.pro.coinbase.com', }, 'www': 'https://pro.coinbase.com/', 'doc': 'https://docs.pro.coinbase.com', 'fees': [ 'https://docs.pro.coinbase.com/#fees', 'https://support.pro.coinbase.com/customer/en/portal/articles/2945310-fees', ], }, 'requiredCredentials': { 'apiKey': True, 'secret': True, 'password': True, }, 'api': { 'public': { 'get': [ 'currencies', 'products', 'products/{id}', 'products/{id}/book', 'products/{id}/candles', 'products/{id}/stats', 'products/{id}/ticker', 'products/{id}/trades', 'time', ], }, 'private': { 'get': [ 'accounts', 'accounts/{id}', 'accounts/{id}/holds', 'accounts/{id}/ledger', 'accounts/{id}/transfers', 'coinbase-accounts', 'coinbase-accounts/{id}/addresses', 'fills', 'funding', 'fees', 'margin/profile_information', 'margin/buying_power', 'margin/withdrawal_power', 'margin/withdrawal_power_all', 'margin/exit_plan', 'margin/liquidation_history', 'margin/position_refresh_amounts', 'margin/status', 'oracle', 'orders', 'orders/{id}', 'orders/client:{client_oid}', 'otc/orders', 'payment-methods', 'position', 'profiles', 'profiles/{id}', 'reports/{report_id}', 'transfers', 'transfers/{transfer_id}', 'users/self/trailing-volume', 'users/self/exchange-limits', 'withdrawals/fee-estimate', ], 'post': [ 'conversions', 'deposits/coinbase-account', 'deposits/payment-method', 'coinbase-accounts/{id}/addresses', 'funding/repay', 'orders', 'position/close', 'profiles/margin-transfer', 'profiles/transfer', 'reports', 'withdrawals/coinbase', 'withdrawals/coinbase-account', 'withdrawals/crypto', 'withdrawals/payment-method', ], 'delete': [ 'orders', 'orders/client:{client_oid}', 'orders/{id}', ], }, }, 'precisionMode': TICK_SIZE, 'fees': { 'trading': { 'tierBased': True, # complicated tier system per coin 'percentage': True, 'maker': 0.5 / 100, # highest fee of all tiers 'taker': 0.5 / 100, # highest fee of all tiers }, 'funding': { 'tierBased': False, 'percentage': False, 'withdraw': { 'BCH': 0, 'BTC': 0, 'LTC': 0, 'ETH': 0, 'EUR': 0.15, 'USD': 25, }, 'deposit': { 'BCH': 0, 'BTC': 0, 'LTC': 0, 'ETH': 0, 'EUR': 0.15, 'USD': 10, }, }, }, 'exceptions': { 'exact': { 'Insufficient funds': InsufficientFunds, 'NotFound': OrderNotFound, 'Invalid API Key': AuthenticationError, 'invalid signature': AuthenticationError, 'Invalid Passphrase': AuthenticationError, 'Invalid order id': InvalidOrder, 'Private rate limit exceeded': RateLimitExceeded, 'Trading pair not available': PermissionDenied, 'Product not found': InvalidOrder, }, 'broad': { 'Order already done': OrderNotFound, 'order not found': OrderNotFound, 'price too small': InvalidOrder, 'price too precise': InvalidOrder, 'under maintenance': OnMaintenance, 'size is too small': InvalidOrder, 'Cancel only mode': OnMaintenance, # https://github.com/ccxt/ccxt/issues/7690 }, }, }) async def fetch_currencies(self, params={}): response = await self.publicGetCurrencies(params) # # [ # { # id: 'XTZ', # name: 'Tezos', # min_size: '0.000001', # status: 'online', # message: '', # max_precision: '0.000001', # convertible_to: [], # details: { # type: 'crypto', # symbol: 'Τ', # network_confirmations: 60, # sort_order: 53, # crypto_address_link: 'https://tzstats.com/{{address}}', # crypto_transaction_link: 'https://tzstats.com/{{txId}}', # push_payment_methods: ['crypto'], # group_types: [], # display_name: '', # processing_time_seconds: 0, # min_withdrawal_amount: 1 # } # } # ] # result = {} for i in range(0, len(response)): currency = response[i] id = self.safe_string(currency, 'id') name = self.safe_string(currency, 'name') code = self.safe_currency_code(id) details = self.safe_value(currency, 'details', {}) precision = self.safe_float(currency, 'max_precision') status = self.safe_string(currency, 'status') active = (status == 'online') result[code] = { 'id': id, 'code': code, 'info': currency, 'type': self.safe_string(details, 'type'), 'name': name, 'active': active, 'fee': None, 'precision': precision, 'limits': { 'amount': { 'min': self.safe_float(details, 'min_size'), 'max': None, }, 'price': { 'min': None, 'max': None, }, 'cost': { 'min': None, 'max': None, }, 'withdraw': { 'min': self.safe_float(details, 'min_withdrawal_amount'), 'max': None, }, }, } return result async def fetch_markets(self, params={}): response = await self.publicGetProducts(params) # # [ # { # "id":"ZEC-BTC", # "base_currency":"ZEC", # "quote_currency":"BTC", # "base_min_size":"0.01000000", # "base_max_size":"1500.00000000", # "quote_increment":"0.00000100", # "base_increment":"0.00010000", # "display_name":"ZEC/BTC", # "min_market_funds":"0.001", # "max_market_funds":"30", # "margin_enabled":false, # "post_only":false, # "limit_only":false, # "cancel_only":false, # "trading_disabled":false, # "status":"online", # "status_message":"" # } # ] # result = [] for i in range(0, len(response)): market = response[i] id = self.safe_string(market, 'id') baseId = self.safe_string(market, 'base_currency') quoteId = self.safe_string(market, 'quote_currency') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote priceLimits = { 'min': self.safe_float(market, 'quote_increment'), 'max': None, } precision = { 'amount': self.safe_float(market, 'base_increment'), 'price': self.safe_float(market, 'quote_increment'), } status = self.safe_string(market, 'status') active = (status == 'online') result.append(self.extend(self.fees['trading'], { 'id': id, 'symbol': symbol, 'baseId': baseId, 'quoteId': quoteId, 'base': base, 'quote': quote, 'precision': precision, 'limits': { 'amount': { 'min': self.safe_float(market, 'base_min_size'), 'max': self.safe_float(market, 'base_max_size'), }, 'price': priceLimits, 'cost': { 'min': self.safe_float(market, 'min_market_funds'), 'max': self.safe_float(market, 'max_market_funds'), }, }, 'active': active, 'info': market, })) return result async def fetch_accounts(self, params={}): response = await self.privateGetAccounts(params) # # [ # { # id: '4aac9c60-cbda-4396-9da4-4aa71e95fba0', # currency: 'BTC', # balance: '0.0000000000000000', # available: '0', # hold: '0.0000000000000000', # profile_id: 'b709263e-f42a-4c7d-949a-a95c83d065da' # }, # { # id: 'f75fa69a-1ad1-4a80-bd61-ee7faa6135a3', # currency: 'USDC', # balance: '0.0000000000000000', # available: '0', # hold: '0.0000000000000000', # profile_id: 'b709263e-f42a-4c7d-949a-a95c83d065da' # }, # ] # result = [] for i in range(0, len(response)): account = response[i] accountId = self.safe_string(account, 'id') currencyId = self.safe_string(account, 'currency') code = self.safe_currency_code(currencyId) result.append({ 'id': accountId, 'type': None, 'currency': code, 'info': account, }) return result async def fetch_balance(self, params={}): await self.load_markets() response = await self.privateGetAccounts(params) result = {'info': response} for i in range(0, len(response)): balance = response[i] currencyId = self.safe_string(balance, 'currency') code = self.safe_currency_code(currencyId) account = { 'free': self.safe_float(balance, 'available'), 'used': self.safe_float(balance, 'hold'), 'total': self.safe_float(balance, 'balance'), } result[code] = account return self.parse_balance(result) async def fetch_order_book(self, symbol, limit=None, params={}): await self.load_markets() # level 1 - only the best bid and ask # level 2 - top 50 bids and asks(aggregated) # level 3 - full order book(non aggregated) request = { 'id': self.market_id(symbol), 'level': 2, # 1 best bidask, 2 aggregated, 3 full } response = await self.publicGetProductsIdBook(self.extend(request, params)) # # { # "sequence":1924393896, # "bids":[ # ["0.01825","24.34811287",2], # ["0.01824","72.5463",3], # ["0.01823","424.54298049",6], # ], # "asks":[ # ["0.01826","171.10414904",4], # ["0.01827","22.60427028",1], # ["0.01828","397.46018784",7], # ] # } # orderbook = self.parse_order_book(response) orderbook['nonce'] = self.safe_integer(response, 'sequence') return orderbook def parse_ticker(self, ticker, market=None): # # publicGetProductsIdTicker # # { # "trade_id":843439, # "price":"0.997999", # "size":"80.29769", # "time":"2020-01-28T02:13:33.012523Z", # "bid":"0.997094", # "ask":"0.998", # "volume":"1903188.03750000" # } # # publicGetProductsIdStats # # { # "open": "34.19000000", # "high": "95.70000000", # "low": "7.06000000", # "volume": "2.41000000" # } # timestamp = self.parse8601(self.safe_value(ticker, 'time')) bid = self.safe_float(ticker, 'bid') ask = self.safe_float(ticker, 'ask') last = self.safe_float(ticker, 'price') symbol = None if (market is None) else market['symbol'] return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'high'), 'low': self.safe_float(ticker, 'low'), 'bid': bid, 'bidVolume': None, 'ask': ask, 'askVolume': None, 'vwap': None, 'open': self.safe_float(ticker, 'open'), 'close': last, 'last': last, 'previousClose': None, 'change': None, 'percentage': None, 'average': None, 'baseVolume': self.safe_float(ticker, 'volume'), 'quoteVolume': None, 'info': ticker, } async def fetch_ticker(self, symbol, params={}): await self.load_markets() market = self.market(symbol) request = { 'id': market['id'], } # publicGetProductsIdTicker or publicGetProductsIdStats method = self.safe_string(self.options, 'fetchTickerMethod', 'publicGetProductsIdTicker') response = await getattr(self, method)(self.extend(request, params)) # # publicGetProductsIdTicker # # { # "trade_id":843439, # "price":"0.997999", # "size":"80.29769", # "time":"2020-01-28T02:13:33.012523Z", # "bid":"0.997094", # "ask":"0.998", # "volume":"1903188.03750000" # } # # publicGetProductsIdStats # # { # "open": "34.19000000", # "high": "95.70000000", # "low": "7.06000000", # "volume": "2.41000000" # } # return self.parse_ticker(response, market) def parse_trade(self, trade, market=None): # # { # type: 'match', # trade_id: 82047307, # maker_order_id: '0f358725-2134-435e-be11-753912a326e0', # taker_order_id: '252b7002-87a3-425c-ac73-f5b9e23f3caf', # side: 'sell', # size: '0.00513192', # price: '9314.78', # product_id: 'BTC-USD', # sequence: 12038915443, # time: '2020-01-31T20:03:41.158814Z' # } # timestamp = self.parse8601(self.safe_string_2(trade, 'time', 'created_at')) marketId = self.safe_string(trade, 'product_id') symbol = self.safe_symbol(marketId, market, '-') feeRate = None feeCurrency = None takerOrMaker = None if market is not None: feeCurrency = market['quote'] if 'liquidity' in trade: takerOrMaker = 'taker' if (trade['liquidity'] == 'T') else 'maker' feeRate = market[takerOrMaker] feeCost = self.safe_float_2(trade, 'fill_fees', 'fee') fee = { 'cost': feeCost, 'currency': feeCurrency, 'rate': feeRate, } type = None id = self.safe_string(trade, 'trade_id') side = 'sell' if (trade['side'] == 'buy') else 'buy' orderId = self.safe_string(trade, 'order_id') # Coinbase Pro returns inverted side to fetchMyTrades vs fetchTrades if orderId is not None: side = 'buy' if (trade['side'] == 'buy') else 'sell' price = self.safe_float(trade, 'price') amount = self.safe_float(trade, 'size') return { 'id': id, 'order': orderId, 'info': trade, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'type': type, 'takerOrMaker': takerOrMaker, 'side': side, 'price': price, 'amount': amount, 'fee': fee, 'cost': price * amount, } async def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}): # as of 2018-08-23 if symbol is None: raise ArgumentsRequired(self.id + ' fetchMyTrades requires a symbol argument') await self.load_markets() market = self.market(symbol) request = { 'product_id': market['id'], } if limit is not None: request['limit'] = limit response = await self.privateGetFills(self.extend(request, params)) return self.parse_trades(response, market, since, limit) async def fetch_trades(self, symbol, since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) request = { 'id': market['id'], # fixes issue #2 } response = await self.publicGetProductsIdTrades(self.extend(request, params)) return self.parse_trades(response, market, since, limit) def parse_ohlcv(self, ohlcv, market=None): # # [ # 1591514160, # 0.02507, # 0.02507, # 0.02507, # 0.02507, # 0.02816506 # ] # return [ self.safe_timestamp(ohlcv, 0), self.safe_float(ohlcv, 3), self.safe_float(ohlcv, 2), self.safe_float(ohlcv, 1), self.safe_float(ohlcv, 4), self.safe_float(ohlcv, 5), ] async def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) granularity = self.timeframes[timeframe] request = { 'id': market['id'], 'granularity': granularity, } if since is not None: request['start'] = self.iso8601(since) if limit is None: # https://docs.pro.coinbase.com/#get-historic-rates limit = 300 # max = 300 request['end'] = self.iso8601(self.sum((limit - 1) * granularity * 1000, since)) response = await self.publicGetProductsIdCandles(self.extend(request, params)) # # [ # [1591514160,0.02507,0.02507,0.02507,0.02507,0.02816506], # [1591514100,0.02507,0.02507,0.02507,0.02507,1.63830323], # [1591514040,0.02505,0.02507,0.02505,0.02507,0.19918178] # ] # return self.parse_ohlcvs(response, market, timeframe, since, limit) async def fetch_time(self, params={}): response = await self.publicGetTime(params) # # { # "iso":"2020-05-12T08:00:51.504Z", # "epoch":1589270451.504 # } # return self.safe_timestamp(response, 'epoch') def parse_order_status(self, status): statuses = { 'pending': 'open', 'active': 'open', 'open': 'open', 'done': 'closed', 'canceled': 'canceled', 'canceling': 'open', } return self.safe_string(statuses, status, status) def parse_order(self, order, market=None): timestamp = self.parse8601(self.safe_string(order, 'created_at')) marketId = self.safe_string(order, 'product_id') market = self.safe_market(marketId, market, '-') status = self.parse_order_status(self.safe_string(order, 'status')) price = self.safe_float(order, 'price') filled = self.safe_float(order, 'filled_size') amount = self.safe_float(order, 'size', filled) remaining = None if amount is not None: if filled is not None: remaining = amount - filled cost = self.safe_float(order, 'executed_value') feeCost = self.safe_float(order, 'fill_fees') fee = None if feeCost is not None: feeCurrencyCode = None if market is not None: feeCurrencyCode = market['quote'] fee = { 'cost': feeCost, 'currency': feeCurrencyCode, 'rate': None, } id = self.safe_string(order, 'id') type = self.safe_string(order, 'type') side = self.safe_string(order, 'side') return { 'id': id, 'clientOrderId': None, 'info': order, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': None, 'status': status, 'symbol': market['symbol'], 'type': type, 'side': side, 'price': price, 'cost': cost, 'amount': amount, 'filled': filled, 'remaining': remaining, 'fee': fee, 'average': None, 'trades': None, } async def fetch_order(self, id, symbol=None, params={}): await self.load_markets() request = { 'id': id, } response = await self.privateGetOrdersId(self.extend(request, params)) return self.parse_order(response) async def fetch_order_trades(self, id, symbol=None, since=None, limit=None, params={}): await self.load_markets() market = None if symbol is not None: market = self.market(symbol) request = { 'order_id': id, } response = await self.privateGetFills(self.extend(request, params)) return self.parse_trades(response, market, since, limit) async def fetch_orders(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() request = { 'status': 'all', } market = None if symbol is not None: market = self.market(symbol) request['product_id'] = market['id'] response = await self.privateGetOrders(self.extend(request, params)) return self.parse_orders(response, market, since, limit) async def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() request = {} market = None if symbol is not None: market = self.market(symbol) request['product_id'] = market['id'] response = await self.privateGetOrders(self.extend(request, params)) return self.parse_orders(response, market, since, limit) async def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() request = { 'status': 'done', } market = None if symbol is not None: market = self.market(symbol) request['product_id'] = market['id'] response = await self.privateGetOrders(self.extend(request, params)) return self.parse_orders(response, market, since, limit) async def create_order(self, symbol, type, side, amount, price=None, params={}): await self.load_markets() # oid = str(self.nonce()) request = { 'product_id': self.market_id(symbol), 'side': side, 'size': self.amount_to_precision(symbol, amount), 'type': type, } if type == 'limit': request['price'] = self.price_to_precision(symbol, price) response = await self.privatePostOrders(self.extend(request, params)) return self.parse_order(response) async def cancel_order(self, id, symbol=None, params={}): await self.load_markets() return await self.privateDeleteOrdersId({'id': id}) async def cancel_all_orders(self, symbol=None, params={}): return await self.privateDeleteOrders(params) def calculate_fee(self, symbol, type, side, amount, price, takerOrMaker='taker', params={}): market = self.markets[symbol] rate = market[takerOrMaker] cost = amount * price currency = market['quote'] return { 'type': takerOrMaker, 'currency': currency, 'rate': rate, 'cost': float(self.currency_to_precision(currency, rate * cost)), } async def fetch_payment_methods(self, params={}): return await self.privateGetPaymentMethods(params) async def deposit(self, code, amount, address, params={}): await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], 'amount': amount, } method = 'privatePostDeposits' if 'payment_method_id' in params: # deposit from a payment_method, like a bank account method += 'PaymentMethod' elif 'coinbase_account_id' in params: # deposit into Coinbase Pro account from a Coinbase account method += 'CoinbaseAccount' else: # deposit methodotherwise we did not receive a supported deposit location # relevant docs link for the Googlers # https://docs.pro.coinbase.com/#deposits raise NotSupported(self.id + ' deposit() requires one of `coinbase_account_id` or `payment_method_id` extra params') response = await getattr(self, method)(self.extend(request, params)) if not response: raise ExchangeError(self.id + ' deposit() error: ' + self.json(response)) return { 'info': response, 'id': response['id'], } async def withdraw(self, code, amount, address, tag=None, params={}): self.check_address(address) await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], 'amount': amount, } method = 'privatePostWithdrawals' if 'payment_method_id' in params: method += 'PaymentMethod' elif 'coinbase_account_id' in params: method += 'CoinbaseAccount' else: method += 'Crypto' request['crypto_address'] = address response = await getattr(self, method)(self.extend(request, params)) if not response: raise ExchangeError(self.id + ' withdraw() error: ' + self.json(response)) return { 'info': response, 'id': response['id'], } async def fetch_transactions(self, code=None, since=None, limit=None, params={}): await self.load_markets() await self.load_accounts() currency = None id = self.safe_string(params, 'id') # account id if id is None: if code is None: raise ArgumentsRequired(self.id + ' fetchTransactions() requires a currency code argument if no account id specified in params') currency = self.currency(code) accountsByCurrencyCode = self.index_by(self.accounts, 'currency') account = self.safe_value(accountsByCurrencyCode, code) if account is None: raise ExchangeError(self.id + ' fetchTransactions() could not find account id for ' + code) id = account['id'] request = { 'id': id, } if limit is not None: request['limit'] = limit response = await self.privateGetAccountsIdTransfers(self.extend(request, params)) for i in range(0, len(response)): response[i]['currency'] = code return self.parse_transactions(response, currency, since, limit) def parse_transaction_status(self, transaction): canceled = self.safe_value(transaction, 'canceled_at') if canceled: return 'canceled' processed = self.safe_value(transaction, 'processed_at') completed = self.safe_value(transaction, 'completed_at') if completed: return 'ok' elif processed and not completed: return 'failed' else: return 'pending' def parse_transaction(self, transaction, currency=None): details = self.safe_value(transaction, 'details', {}) id = self.safe_string(transaction, 'id') txid = self.safe_string(details, 'crypto_transaction_hash') timestamp = self.parse8601(self.safe_string(transaction, 'created_at')) updated = self.parse8601(self.safe_string(transaction, 'processed_at')) currencyId = self.safe_string(transaction, 'currency') code = self.safe_currency_code(currencyId, currency) fee = None status = self.parse_transaction_status(transaction) amount = self.safe_float(transaction, 'amount') type = self.safe_string(transaction, 'type') address = self.safe_string(details, 'crypto_address') tag = self.safe_string(details, 'destination_tag') address = self.safe_string(transaction, 'crypto_address', address) if type == 'withdraw': type = 'withdrawal' address = self.safe_string(details, 'sent_to_address', address) return { 'info': transaction, 'id': id, 'txid': txid, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'address': address, 'tag': tag, 'type': type, 'amount': amount, 'currency': code, 'status': status, 'updated': updated, 'fee': fee, } def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): request = '/' + self.implode_params(path, params) query = self.omit(params, self.extract_params(path)) if method == 'GET': if query: request += '?' + self.urlencode(query) url = self.urls['api'][api] + request if api == 'private': self.check_required_credentials() nonce = str(self.nonce()) payload = '' if method != 'GET': if query: body = self.json(query) payload = body what = nonce + method + request + payload secret = self.base64_to_binary(self.secret) signature = self.hmac(self.encode(what), secret, hashlib.sha256, 'base64') headers = { 'CB-ACCESS-KEY': self.apiKey, 'CB-ACCESS-SIGN': signature, 'CB-ACCESS-TIMESTAMP': nonce, 'CB-ACCESS-PASSPHRASE': self.password, 'Content-Type': 'application/json', } return {'url': url, 'method': method, 'body': body, 'headers': headers} async def fetch_deposit_address(self, code, params={}): await self.load_markets() currency = self.currency(code) accounts = self.safe_value(self.options, 'coinbaseAccounts') if accounts is None: accounts = await self.privateGetCoinbaseAccounts() self.options['coinbaseAccounts'] = accounts # cache it self.options['coinbaseAccountsByCurrencyId'] = self.index_by(accounts, 'currency') currencyId = currency['id'] account = self.safe_value(self.options['coinbaseAccountsByCurrencyId'], currencyId) if account is None: # eslint-disable-next-line quotes raise InvalidAddress(self.id + " fetchDepositAddress() could not find currency code " + code + " with id = " + currencyId + " in self.options['coinbaseAccountsByCurrencyId']") request = { 'id': account['id'], } response = await self.privateGetCoinbaseAccountsIdAddresses(self.extend(request, params)) address = self.safe_string(response, 'address') tag = self.safe_string(response, 'destination_tag') return { 'currency': code, 'address': self.check_address(address), 'tag': tag, 'info': response, } async def create_deposit_address(self, code, params={}): await self.load_markets() currency = self.currency(code) accounts = self.safe_value(self.options, 'coinbaseAccounts') if accounts is None: accounts = await self.privateGetCoinbaseAccounts() self.options['coinbaseAccounts'] = accounts # cache it self.options['coinbaseAccountsByCurrencyId'] = self.index_by(accounts, 'currency') currencyId = currency['id'] account = self.safe_value(self.options['coinbaseAccountsByCurrencyId'], currencyId) if account is None: # eslint-disable-next-line quotes raise InvalidAddress(self.id + " fetchDepositAddress() could not find currency code " + code + " with id = " + currencyId + " in self.options['coinbaseAccountsByCurrencyId']") request = { 'id': account['id'], } response = await self.privatePostCoinbaseAccountsIdAddresses(self.extend(request, params)) address = self.safe_string(response, 'address') tag = self.safe_string(response, 'destination_tag') return { 'currency': code, 'address': self.check_address(address), 'tag': tag, 'info': response, } def handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody): if (code == 400) or (code == 404): if body[0] == '{': message = self.safe_string(response, 'message') feedback = self.id + ' ' + message self.throw_exactly_matched_exception(self.exceptions['exact'], message, feedback) self.throw_broadly_matched_exception(self.exceptions['broad'], message, feedback) raise ExchangeError(feedback) # unknown message raise ExchangeError(self.id + ' ' + body) async def request(self, path, api='public', method='GET', params={}, headers=None, body=None): response = await self.fetch2(path, api, method, params, headers, body) if not isinstance(response, basestring): if 'message' in response: raise ExchangeError(self.id + ' ' + self.json(response)) return response

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rt.base.exchange import Exchange try: basestring except NameError: basestring = str import hashlib from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import ArgumentsRequired from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidAddress from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import NotSupported from ccxt.base.errors import RateLimitExceeded from ccxt.base.errors import OnMaintenance from ccxt.base.decimal_to_precision import TICK_SIZE class coinbasepro(Exchange): def describe(self): return self.deep_extend(super(coinbasepro, self).describe(), { 'id': 'coinbasepro', 'name': 'Coinbase Pro', 'countries': ['US'], 'rateLimit': 1000, 'userAgent': self.userAgents['chrome'], 'pro': True, 'has': { 'cancelAllOrders': True, 'cancelOrder': True, 'CORS': True, 'createDepositAddress': True, 'createOrder': True, 'deposit': True, 'fetchAccounts': True, 'fetchBalance': True, 'fetchCurrencies': True, 'fetchClosedOrders': True, 'fetchDepositAddress': True, 'fetchMarkets': True, 'fetchMyTrades': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchOrder': True, 'fetchOrderBook': True, 'fetchOrders': True, 'fetchOrderTrades': True, 'fetchTime': True, 'fetchTicker': True, 'fetchTrades': True, 'fetchTransactions': True, 'withdraw': True, }, 'timeframes': { '1m': 60, '5m': 300, '15m': 900, '1h': 3600, '6h': 21600, '1d': 86400, }, 'urls': { 'test': { 'public': 'https://api-public.sandbox.pro.coinbase.com', 'private': 'https://api-public.sandbox.pro.coinbase.com', }, 'logo': 'https://user-images.githubusercontent.com/1294454/41764625-63b7ffde-760a-11e8-996d-a6328fa9347a.jpg', 'api': { 'public': 'https://api.pro.coinbase.com', 'private': 'https://api.pro.coinbase.com', }, 'www': 'https://pro.coinbase.com/', 'doc': 'https://docs.pro.coinbase.com', 'fees': [ 'https://docs.pro.coinbase.com/#fees', 'https://support.pro.coinbase.com/customer/en/portal/articles/2945310-fees', ], }, 'requiredCredentials': { 'apiKey': True, 'secret': True, 'password': True, }, 'api': { 'public': { 'get': [ 'currencies', 'products', 'products/{id}', 'products/{id}/book', 'products/{id}/candles', 'products/{id}/stats', 'products/{id}/ticker', 'products/{id}/trades', 'time', ], }, 'private': { 'get': [ 'accounts', 'accounts/{id}', 'accounts/{id}/holds', 'accounts/{id}/ledger', 'accounts/{id}/transfers', 'coinbase-accounts', 'coinbase-accounts/{id}/addresses', 'fills', 'funding', 'fees', 'margin/profile_information', 'margin/buying_power', 'margin/withdrawal_power', 'margin/withdrawal_power_all', 'margin/exit_plan', 'margin/liquidation_history', 'margin/position_refresh_amounts', 'margin/status', 'oracle', 'orders', 'orders/{id}', 'orders/client:{client_oid}', 'otc/orders', 'payment-methods', 'position', 'profiles', 'profiles/{id}', 'reports/{report_id}', 'transfers', 'transfers/{transfer_id}', 'users/self/trailing-volume', 'users/self/exchange-limits', 'withdrawals/fee-estimate', ], 'post': [ 'conversions', 'deposits/coinbase-account', 'deposits/payment-method', 'coinbase-accounts/{id}/addresses', 'funding/repay', 'orders', 'position/close', 'profiles/margin-transfer', 'profiles/transfer', 'reports', 'withdrawals/coinbase', 'withdrawals/coinbase-account', 'withdrawals/crypto', 'withdrawals/payment-method', ], 'delete': [ 'orders', 'orders/client:{client_oid}', 'orders/{id}', ], }, }, 'precisionMode': TICK_SIZE, 'fees': { 'trading': { 'tierBased': True, 'percentage': True, 'maker': 0.5 / 100, 'taker': 0.5 / 100, }, 'funding': { 'tierBased': False, 'percentage': False, 'withdraw': { 'BCH': 0, 'BTC': 0, 'LTC': 0, 'ETH': 0, 'EUR': 0.15, 'USD': 25, }, 'deposit': { 'BCH': 0, 'BTC': 0, 'LTC': 0, 'ETH': 0, 'EUR': 0.15, 'USD': 10, }, }, }, 'exceptions': { 'exact': { 'Insufficient funds': InsufficientFunds, 'NotFound': OrderNotFound, 'Invalid API Key': AuthenticationError, 'invalid signature': AuthenticationError, 'Invalid Passphrase': AuthenticationError, 'Invalid order id': InvalidOrder, 'Private rate limit exceeded': RateLimitExceeded, 'Trading pair not available': PermissionDenied, 'Product not found': InvalidOrder, }, 'broad': { 'Order already done': OrderNotFound, 'order not found': OrderNotFound, 'price too small': InvalidOrder, 'price too precise': InvalidOrder, 'under maintenance': OnMaintenance, 'size is too small': InvalidOrder, 'Cancel only mode': OnMaintenance, }, }, }) async def fetch_currencies(self, params={}): response = await self.publicGetCurrencies(params) result = {} for i in range(0, len(response)): currency = response[i] id = self.safe_string(currency, 'id') name = self.safe_string(currency, 'name') code = self.safe_currency_code(id) details = self.safe_value(currency, 'details', {}) precision = self.safe_float(currency, 'max_precision') status = self.safe_string(currency, 'status') active = (status == 'online') result[code] = { 'id': id, 'code': code, 'info': currency, 'type': self.safe_string(details, 'type'), 'name': name, 'active': active, 'fee': None, 'precision': precision, 'limits': { 'amount': { 'min': self.safe_float(details, 'min_size'), 'max': None, }, 'price': { 'min': None, 'max': None, }, 'cost': { 'min': None, 'max': None, }, 'withdraw': { 'min': self.safe_float(details, 'min_withdrawal_amount'), 'max': None, }, }, } return result async def fetch_markets(self, params={}): response = await self.publicGetProducts(params) result = [] for i in range(0, len(response)): market = response[i] id = self.safe_string(market, 'id') baseId = self.safe_string(market, 'base_currency') quoteId = self.safe_string(market, 'quote_currency') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote priceLimits = { 'min': self.safe_float(market, 'quote_increment'), 'max': None, } precision = { 'amount': self.safe_float(market, 'base_increment'), 'price': self.safe_float(market, 'quote_increment'), } status = self.safe_string(market, 'status') active = (status == 'online') result.append(self.extend(self.fees['trading'], { 'id': id, 'symbol': symbol, 'baseId': baseId, 'quoteId': quoteId, 'base': base, 'quote': quote, 'precision': precision, 'limits': { 'amount': { 'min': self.safe_float(market, 'base_min_size'), 'max': self.safe_float(market, 'base_max_size'), }, 'price': priceLimits, 'cost': { 'min': self.safe_float(market, 'min_market_funds'), 'max': self.safe_float(market, 'max_market_funds'), }, }, 'active': active, 'info': market, })) return result async def fetch_accounts(self, params={}): response = await self.privateGetAccounts(params) result = [] for i in range(0, len(response)): account = response[i] accountId = self.safe_string(account, 'id') currencyId = self.safe_string(account, 'currency') code = self.safe_currency_code(currencyId) result.append({ 'id': accountId, 'type': None, 'currency': code, 'info': account, }) return result async def fetch_balance(self, params={}): await self.load_markets() response = await self.privateGetAccounts(params) result = {'info': response} for i in range(0, len(response)): balance = response[i] currencyId = self.safe_string(balance, 'currency') code = self.safe_currency_code(currencyId) account = { 'free': self.safe_float(balance, 'available'), 'used': self.safe_float(balance, 'hold'), 'total': self.safe_float(balance, 'balance'), } result[code] = account return self.parse_balance(result) async def fetch_order_book(self, symbol, limit=None, params={}): await self.load_markets() request = { 'id': self.market_id(symbol), 'level': 2, } response = await self.publicGetProductsIdBook(self.extend(request, params)) orderbook = self.parse_order_book(response) orderbook['nonce'] = self.safe_integer(response, 'sequence') return orderbook def parse_ticker(self, ticker, market=None): timestamp = self.parse8601(self.safe_value(ticker, 'time')) bid = self.safe_float(ticker, 'bid') ask = self.safe_float(ticker, 'ask') last = self.safe_float(ticker, 'price') symbol = None if (market is None) else market['symbol'] return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'high'), 'low': self.safe_float(ticker, 'low'), 'bid': bid, 'bidVolume': None, 'ask': ask, 'askVolume': None, 'vwap': None, 'open': self.safe_float(ticker, 'open'), 'close': last, 'last': last, 'previousClose': None, 'change': None, 'percentage': None, 'average': None, 'baseVolume': self.safe_float(ticker, 'volume'), 'quoteVolume': None, 'info': ticker, } async def fetch_ticker(self, symbol, params={}): await self.load_markets() market = self.market(symbol) request = { 'id': market['id'], } method = self.safe_string(self.options, 'fetchTickerMethod', 'publicGetProductsIdTicker') response = await getattr(self, method)(self.extend(request, params)) return self.parse_ticker(response, market) def parse_trade(self, trade, market=None): timestamp = self.parse8601(self.safe_string_2(trade, 'time', 'created_at')) marketId = self.safe_string(trade, 'product_id') symbol = self.safe_symbol(marketId, market, '-') feeRate = None feeCurrency = None takerOrMaker = None if market is not None: feeCurrency = market['quote'] if 'liquidity' in trade: takerOrMaker = 'taker' if (trade['liquidity'] == 'T') else 'maker' feeRate = market[takerOrMaker] feeCost = self.safe_float_2(trade, 'fill_fees', 'fee') fee = { 'cost': feeCost, 'currency': feeCurrency, 'rate': feeRate, } type = None id = self.safe_string(trade, 'trade_id') side = 'sell' if (trade['side'] == 'buy') else 'buy' orderId = self.safe_string(trade, 'order_id') if orderId is not None: side = 'buy' if (trade['side'] == 'buy') else 'sell' price = self.safe_float(trade, 'price') amount = self.safe_float(trade, 'size') return { 'id': id, 'order': orderId, 'info': trade, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'type': type, 'takerOrMaker': takerOrMaker, 'side': side, 'price': price, 'amount': amount, 'fee': fee, 'cost': price * amount, } async def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}): if symbol is None: raise ArgumentsRequired(self.id + ' fetchMyTrades requires a symbol argument') await self.load_markets() market = self.market(symbol) request = { 'product_id': market['id'], } if limit is not None: request['limit'] = limit response = await self.privateGetFills(self.extend(request, params)) return self.parse_trades(response, market, since, limit) async def fetch_trades(self, symbol, since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) request = { 'id': market['id'], } response = await self.publicGetProductsIdTrades(self.extend(request, params)) return self.parse_trades(response, market, since, limit) def parse_ohlcv(self, ohlcv, market=None): return [ self.safe_timestamp(ohlcv, 0), self.safe_float(ohlcv, 3), self.safe_float(ohlcv, 2), self.safe_float(ohlcv, 1), self.safe_float(ohlcv, 4), self.safe_float(ohlcv, 5), ] async def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) granularity = self.timeframes[timeframe] request = { 'id': market['id'], 'granularity': granularity, } if since is not None: request['start'] = self.iso8601(since) if limit is None: mit = 300 request['end'] = self.iso8601(self.sum((limit - 1) * granularity * 1000, since)) response = await self.publicGetProductsIdCandles(self.extend(request, params)) return self.parse_ohlcvs(response, market, timeframe, since, limit) async def fetch_time(self, params={}): response = await self.publicGetTime(params) return self.safe_timestamp(response, 'epoch') def parse_order_status(self, status): statuses = { 'pending': 'open', 'active': 'open', 'open': 'open', 'done': 'closed', 'canceled': 'canceled', 'canceling': 'open', } return self.safe_string(statuses, status, status) def parse_order(self, order, market=None): timestamp = self.parse8601(self.safe_string(order, 'created_at')) marketId = self.safe_string(order, 'product_id') market = self.safe_market(marketId, market, '-') status = self.parse_order_status(self.safe_string(order, 'status')) price = self.safe_float(order, 'price') filled = self.safe_float(order, 'filled_size') amount = self.safe_float(order, 'size', filled) remaining = None if amount is not None: if filled is not None: remaining = amount - filled cost = self.safe_float(order, 'executed_value') feeCost = self.safe_float(order, 'fill_fees') fee = None if feeCost is not None: feeCurrencyCode = None if market is not None: feeCurrencyCode = market['quote'] fee = { 'cost': feeCost, 'currency': feeCurrencyCode, 'rate': None, } id = self.safe_string(order, 'id') type = self.safe_string(order, 'type') side = self.safe_string(order, 'side') return { 'id': id, 'clientOrderId': None, 'info': order, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': None, 'status': status, 'symbol': market['symbol'], 'type': type, 'side': side, 'price': price, 'cost': cost, 'amount': amount, 'filled': filled, 'remaining': remaining, 'fee': fee, 'average': None, 'trades': None, } async def fetch_order(self, id, symbol=None, params={}): await self.load_markets() request = { 'id': id, } response = await self.privateGetOrdersId(self.extend(request, params)) return self.parse_order(response) async def fetch_order_trades(self, id, symbol=None, since=None, limit=None, params={}): await self.load_markets() market = None if symbol is not None: market = self.market(symbol) request = { 'order_id': id, } response = await self.privateGetFills(self.extend(request, params)) return self.parse_trades(response, market, since, limit) async def fetch_orders(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() request = { 'status': 'all', } market = None if symbol is not None: market = self.market(symbol) request['product_id'] = market['id'] response = await self.privateGetOrders(self.extend(request, params)) return self.parse_orders(response, market, since, limit) async def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() request = {} market = None if symbol is not None: market = self.market(symbol) request['product_id'] = market['id'] response = await self.privateGetOrders(self.extend(request, params)) return self.parse_orders(response, market, since, limit) async def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): await self.load_markets() request = { 'status': 'done', } market = None if symbol is not None: market = self.market(symbol) request['product_id'] = market['id'] response = await self.privateGetOrders(self.extend(request, params)) return self.parse_orders(response, market, since, limit) async def create_order(self, symbol, type, side, amount, price=None, params={}): await self.load_markets() request = { 'product_id': self.market_id(symbol), 'side': side, 'size': self.amount_to_precision(symbol, amount), 'type': type, } if type == 'limit': request['price'] = self.price_to_precision(symbol, price) response = await self.privatePostOrders(self.extend(request, params)) return self.parse_order(response) async def cancel_order(self, id, symbol=None, params={}): await self.load_markets() return await self.privateDeleteOrdersId({'id': id}) async def cancel_all_orders(self, symbol=None, params={}): return await self.privateDeleteOrders(params) def calculate_fee(self, symbol, type, side, amount, price, takerOrMaker='taker', params={}): market = self.markets[symbol] rate = market[takerOrMaker] cost = amount * price currency = market['quote'] return { 'type': takerOrMaker, 'currency': currency, 'rate': rate, 'cost': float(self.currency_to_precision(currency, rate * cost)), } async def fetch_payment_methods(self, params={}): return await self.privateGetPaymentMethods(params) async def deposit(self, code, amount, address, params={}): await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], 'amount': amount, } method = 'privatePostDeposits' if 'payment_method_id' in params: method += 'PaymentMethod' elif 'coinbase_account_id' in params: method += 'CoinbaseAccount' else: raise NotSupported(self.id + ' deposit() requires one of `coinbase_account_id` or `payment_method_id` extra params') response = await getattr(self, method)(self.extend(request, params)) if not response: raise ExchangeError(self.id + ' deposit() error: ' + self.json(response)) return { 'info': response, 'id': response['id'], } async def withdraw(self, code, amount, address, tag=None, params={}): self.check_address(address) await self.load_markets() currency = self.currency(code) request = { 'currency': currency['id'], 'amount': amount, } method = 'privatePostWithdrawals' if 'payment_method_id' in params: method += 'PaymentMethod' elif 'coinbase_account_id' in params: method += 'CoinbaseAccount' else: method += 'Crypto' request['crypto_address'] = address response = await getattr(self, method)(self.extend(request, params)) if not response: raise ExchangeError(self.id + ' withdraw() error: ' + self.json(response)) return { 'info': response, 'id': response['id'], } async def fetch_transactions(self, code=None, since=None, limit=None, params={}): await self.load_markets() await self.load_accounts() currency = None id = self.safe_string(params, 'id') if id is None: if code is None: raise ArgumentsRequired(self.id + ' fetchTransactions() requires a currency code argument if no account id specified in params') currency = self.currency(code) accountsByCurrencyCode = self.index_by(self.accounts, 'currency') account = self.safe_value(accountsByCurrencyCode, code) if account is None: raise ExchangeError(self.id + ' fetchTransactions() could not find account id for ' + code) id = account['id'] request = { 'id': id, } if limit is not None: request['limit'] = limit response = await self.privateGetAccountsIdTransfers(self.extend(request, params)) for i in range(0, len(response)): response[i]['currency'] = code return self.parse_transactions(response, currency, since, limit) def parse_transaction_status(self, transaction): canceled = self.safe_value(transaction, 'canceled_at') if canceled: return 'canceled' processed = self.safe_value(transaction, 'processed_at') completed = self.safe_value(transaction, 'completed_at') if completed: return 'ok' elif processed and not completed: return 'failed' else: return 'pending' def parse_transaction(self, transaction, currency=None): details = self.safe_value(transaction, 'details', {}) id = self.safe_string(transaction, 'id') txid = self.safe_string(details, 'crypto_transaction_hash') timestamp = self.parse8601(self.safe_string(transaction, 'created_at')) updated = self.parse8601(self.safe_string(transaction, 'processed_at')) currencyId = self.safe_string(transaction, 'currency') code = self.safe_currency_code(currencyId, currency) fee = None status = self.parse_transaction_status(transaction) amount = self.safe_float(transaction, 'amount') type = self.safe_string(transaction, 'type') address = self.safe_string(details, 'crypto_address') tag = self.safe_string(details, 'destination_tag') address = self.safe_string(transaction, 'crypto_address', address) if type == 'withdraw': type = 'withdrawal' address = self.safe_string(details, 'sent_to_address', address) return { 'info': transaction, 'id': id, 'txid': txid, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'address': address, 'tag': tag, 'type': type, 'amount': amount, 'currency': code, 'status': status, 'updated': updated, 'fee': fee, } def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): request = '/' + self.implode_params(path, params) query = self.omit(params, self.extract_params(path)) if method == 'GET': if query: request += '?' + self.urlencode(query) url = self.urls['api'][api] + request if api == 'private': self.check_required_credentials() nonce = str(self.nonce()) payload = '' if method != 'GET': if query: body = self.json(query) payload = body what = nonce + method + request + payload secret = self.base64_to_binary(self.secret) signature = self.hmac(self.encode(what), secret, hashlib.sha256, 'base64') headers = { 'CB-ACCESS-KEY': self.apiKey, 'CB-ACCESS-SIGN': signature, 'CB-ACCESS-TIMESTAMP': nonce, 'CB-ACCESS-PASSPHRASE': self.password, 'Content-Type': 'application/json', } return {'url': url, 'method': method, 'body': body, 'headers': headers} async def fetch_deposit_address(self, code, params={}): await self.load_markets() currency = self.currency(code) accounts = self.safe_value(self.options, 'coinbaseAccounts') if accounts is None: accounts = await self.privateGetCoinbaseAccounts() self.options['coinbaseAccounts'] = accounts self.options['coinbaseAccountsByCurrencyId'] = self.index_by(accounts, 'currency') currencyId = currency['id'] account = self.safe_value(self.options['coinbaseAccountsByCurrencyId'], currencyId) if account is None: raise InvalidAddress(self.id + " fetchDepositAddress() could not find currency code " + code + " with id = " + currencyId + " in self.options['coinbaseAccountsByCurrencyId']") request = { 'id': account['id'], } response = await self.privateGetCoinbaseAccountsIdAddresses(self.extend(request, params)) address = self.safe_string(response, 'address') tag = self.safe_string(response, 'destination_tag') return { 'currency': code, 'address': self.check_address(address), 'tag': tag, 'info': response, } async def create_deposit_address(self, code, params={}): await self.load_markets() currency = self.currency(code) accounts = self.safe_value(self.options, 'coinbaseAccounts') if accounts is None: accounts = await self.privateGetCoinbaseAccounts() self.options['coinbaseAccounts'] = accounts self.options['coinbaseAccountsByCurrencyId'] = self.index_by(accounts, 'currency') currencyId = currency['id'] account = self.safe_value(self.options['coinbaseAccountsByCurrencyId'], currencyId) if account is None: raise InvalidAddress(self.id + " fetchDepositAddress() could not find currency code " + code + " with id = " + currencyId + " in self.options['coinbaseAccountsByCurrencyId']") request = { 'id': account['id'], } response = await self.privatePostCoinbaseAccountsIdAddresses(self.extend(request, params)) address = self.safe_string(response, 'address') tag = self.safe_string(response, 'destination_tag') return { 'currency': code, 'address': self.check_address(address), 'tag': tag, 'info': response, } def handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody): if (code == 400) or (code == 404): if body[0] == '{': message = self.safe_string(response, 'message') feedback = self.id + ' ' + message self.throw_exactly_matched_exception(self.exceptions['exact'], message, feedback) self.throw_broadly_matched_exception(self.exceptions['broad'], message, feedback) raise ExchangeError(feedback) raise ExchangeError(self.id + ' ' + body) async def request(self, path, api='public', method='GET', params={}, headers=None, body=None): response = await self.fetch2(path, api, method, params, headers, body) if not isinstance(response, basestring): if 'message' in response: raise ExchangeError(self.id + ' ' + self.json(response)) return response

true

f71bff11a394180fe27dbab6c598aaf8b04768c6

10,059

py

Python

mlflow/spacy.py

Roffild/mlflow

8351d82e6ad4103bc58159175b29b406abb1e052

[ "Apache-2.0" ]

null

mlflow/spacy.py

Roffild/mlflow

8351d82e6ad4103bc58159175b29b406abb1e052

[ "Apache-2.0" ]

null

mlflow/spacy.py

Roffild/mlflow

8351d82e6ad4103bc58159175b29b406abb1e052

[ "Apache-2.0" ]

null

""" The ``mlflow.spacy`` module provides an API for logging and loading spaCy models. This module exports spacy models with the following flavors: spaCy (native) format This is the main flavor that can be loaded back into spaCy. :py:mod:`mlflow.pyfunc` Produced for use by generic pyfunc-based deployment tools and batch inference. """ from __future__ import absolute_import import logging import os import pandas as pd import yaml import mlflow from mlflow import pyfunc from mlflow.exceptions import MlflowException from mlflow.models import Model, ModelSignature from mlflow.models.utils import ModelInputExample from mlflow.tracking.artifact_utils import _download_artifact_from_uri from mlflow.utils.environment import _mlflow_conda_env from mlflow.utils.model_utils import _get_flavor_configuration FLAVOR_NAME = "spacy" _logger = logging.getLogger(__name__) def get_default_conda_env(): """ :return: The default Conda environment for MLflow Models produced by calls to :func:`save_model()` and :func:`log_model()`. """ import spacy return _mlflow_conda_env( additional_conda_deps=None, additional_pip_deps=[ "spacy=={}".format(spacy.__version__), ], additional_conda_channels=None) def save_model(spacy_model, path, conda_env=None, mlflow_model=Model()): """ Save a spaCy model to a path on the local file system. :param spacy_model: spaCy model to be saved. :param path: Local path where the model is to be saved. :param conda_env: Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this describes the environment this model should be run in. At minimum, it should specify the dependencies contained in :func:`get_default_conda_env()`. If ``None``, the default :func:`get_default_conda_env()` environment is added to the model. The following is an *example* dictionary representation of a Conda environment:: { 'name': 'mlflow-env', 'channels': ['defaults'], 'dependencies': [ 'python=3.7.0', 'pip': [ 'spacy==2.2.3' ] ] } :param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. """ import spacy path = os.path.abspath(path) if os.path.exists(path): raise MlflowException("Unable to save MLflow model to {path} - path '{path}' " "already exists".format(path=path)) model_data_subpath = "model.spacy" model_data_path = os.path.join(path, model_data_subpath) os.makedirs(model_data_path) # Save spacy-model spacy_model.to_disk(path=model_data_path) conda_env_subpath = "conda.yaml" if conda_env is None: conda_env = get_default_conda_env() elif not isinstance(conda_env, dict): with open(conda_env, "r") as f: conda_env = yaml.safe_load(f) with open(os.path.join(path, conda_env_subpath), "w") as f: yaml.safe_dump(conda_env, stream=f, default_flow_style=False) # Save the pyfunc flavor if at least one text categorizer in spaCy pipeline if any([isinstance(pipe_component[1], spacy.pipeline.TextCategorizer) for pipe_component in spacy_model.pipeline]): pyfunc.add_to_model(mlflow_model, loader_module="mlflow.spacy", data=model_data_subpath, env=conda_env_subpath) else: _logger.warning( "Generating only the spacy flavor for the provided spacy model. This means the model " "can be loaded back via `mlflow.spacy.load_model`, but cannot be loaded back using " "pyfunc APIs like `mlflow.pyfunc.load_model` or via the `mlflow models` CLI commands. " "MLflow will only generate the pyfunc flavor for spacy models containing a pipeline " "component that is an instance of spacy.pipeline.TextCategorizer.") mlflow_model.add_flavor(FLAVOR_NAME, spacy_version=spacy.__version__, data=model_data_subpath) mlflow_model.save(os.path.join(path, "MLmodel")) def log_model(spacy_model, artifact_path, conda_env=None, registered_model_name=None, signature: ModelSignature = None, input_example: ModelInputExample = None, **kwargs): """ Log a spaCy model as an MLflow artifact for the current run. :param spacy_model: spaCy model to be saved. :param artifact_path: Run-relative artifact path. :param conda_env: Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this decsribes the environment this model should be run in. At minimum, it should specify the dependencies contained in :func:`get_default_conda_env()`. If ``None``, the default :func:`get_default_conda_env()` environment is added to the model. The following is an *example* dictionary representation of a Conda environment:: { 'name': 'mlflow-env', 'channels': ['defaults'], 'dependencies': [ 'python=3.7.0', 'pip': [ 'spacy==2.2.3' ] ] } :param registered_model_name: (Experimental) If given, create a model version under ``registered_model_name``, also creating a registered model if one with the given name does not exist. :param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>` describes model input and output :py:class:`Schema <mlflow.types.Schema>`. The model signature can be :py:func:`inferred <mlflow.models.infer_signature>` from datasets with valid model input (e.g. the training dataset) and valid model output (e.g. model predictions generated on the training dataset), for example: .. code-block:: python from mlflow.models.signature import infer_signature train = df.drop_column("target_label") signature = infer_signature(train, model.predict(train)) :param input_example: (Experimental) Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded. :param kwargs: kwargs to pass to ``spacy.save_model`` method. """ Model.log(artifact_path=artifact_path, flavor=mlflow.spacy, registered_model_name=registered_model_name, spacy_model=spacy_model, conda_env=conda_env, signature=signature, input_example=input_example, **kwargs) def _load_model(path): import spacy path = os.path.abspath(path) return spacy.load(path) class _SpacyModelWrapper: def __init__(self, spacy_model): self.spacy_model = spacy_model def predict(self, dataframe): """ Only works for predicting using text categorizer. Not suitable for other pipeline components (e.g: parser) :param dataframe: pandas dataframe containing texts to be categorized expected shape is (n_rows,1 column) :return: dataframe with predictions """ if len(dataframe.columns) != 1: raise MlflowException('Shape of input dataframe must be (n_rows, 1column)') return pd.DataFrame({ 'predictions': dataframe.ix[:, 0].apply(lambda text: self.spacy_model(text).cats) }) def _load_pyfunc(path): """ Load PyFunc implementation. Called by ``pyfunc.load_pyfunc``. :param path: Local filesystem path to the MLflow Model with the ``spacy`` flavor. """ return _SpacyModelWrapper(_load_model(path)) def load_model(model_uri): """ Load a spaCy model from a local file (if ``run_id`` is ``None``) or a run. :param model_uri: The location, in URI format, of the MLflow model. For example: - ``/Users/me/path/to/local/model`` - ``relative/path/to/local/model`` - ``s3://my_bucket/path/to/model`` - ``runs:/<mlflow_run_id>/run-relative/path/to/model`` - ``models:/<model_name>/<model_version>`` - ``models:/<model_name>/<stage>`` For more information about supported URI schemes, see `Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html# artifact-locations>`_. :return: A spaCy loaded model """ local_model_path = _download_artifact_from_uri(artifact_uri=model_uri) flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) # Flavor configurations for models saved in MLflow version <= 0.8.0 may not contain a # `data` key; in this case, we assume the model artifact path to be `model.spacy` spacy_model_file_path = os.path.join(local_model_path, flavor_conf.get("data", "model.spacy")) return _load_model(path=spacy_model_file_path)

43.357759

100

0.617457

from __future__ import absolute_import import logging import os import pandas as pd import yaml import mlflow from mlflow import pyfunc from mlflow.exceptions import MlflowException from mlflow.models import Model, ModelSignature from mlflow.models.utils import ModelInputExample from mlflow.tracking.artifact_utils import _download_artifact_from_uri from mlflow.utils.environment import _mlflow_conda_env from mlflow.utils.model_utils import _get_flavor_configuration FLAVOR_NAME = "spacy" _logger = logging.getLogger(__name__) def get_default_conda_env(): import spacy return _mlflow_conda_env( additional_conda_deps=None, additional_pip_deps=[ "spacy=={}".format(spacy.__version__), ], additional_conda_channels=None) def save_model(spacy_model, path, conda_env=None, mlflow_model=Model()): import spacy path = os.path.abspath(path) if os.path.exists(path): raise MlflowException("Unable to save MLflow model to {path} - path '{path}' " "already exists".format(path=path)) model_data_subpath = "model.spacy" model_data_path = os.path.join(path, model_data_subpath) os.makedirs(model_data_path) spacy_model.to_disk(path=model_data_path) conda_env_subpath = "conda.yaml" if conda_env is None: conda_env = get_default_conda_env() elif not isinstance(conda_env, dict): with open(conda_env, "r") as f: conda_env = yaml.safe_load(f) with open(os.path.join(path, conda_env_subpath), "w") as f: yaml.safe_dump(conda_env, stream=f, default_flow_style=False) if any([isinstance(pipe_component[1], spacy.pipeline.TextCategorizer) for pipe_component in spacy_model.pipeline]): pyfunc.add_to_model(mlflow_model, loader_module="mlflow.spacy", data=model_data_subpath, env=conda_env_subpath) else: _logger.warning( "Generating only the spacy flavor for the provided spacy model. This means the model " "can be loaded back via `mlflow.spacy.load_model`, but cannot be loaded back using " "pyfunc APIs like `mlflow.pyfunc.load_model` or via the `mlflow models` CLI commands. " "MLflow will only generate the pyfunc flavor for spacy models containing a pipeline " "component that is an instance of spacy.pipeline.TextCategorizer.") mlflow_model.add_flavor(FLAVOR_NAME, spacy_version=spacy.__version__, data=model_data_subpath) mlflow_model.save(os.path.join(path, "MLmodel")) def log_model(spacy_model, artifact_path, conda_env=None, registered_model_name=None, signature: ModelSignature = None, input_example: ModelInputExample = None, **kwargs): Model.log(artifact_path=artifact_path, flavor=mlflow.spacy, registered_model_name=registered_model_name, spacy_model=spacy_model, conda_env=conda_env, signature=signature, input_example=input_example, **kwargs) def _load_model(path): import spacy path = os.path.abspath(path) return spacy.load(path) class _SpacyModelWrapper: def __init__(self, spacy_model): self.spacy_model = spacy_model def predict(self, dataframe): if len(dataframe.columns) != 1: raise MlflowException('Shape of input dataframe must be (n_rows, 1column)') return pd.DataFrame({ 'predictions': dataframe.ix[:, 0].apply(lambda text: self.spacy_model(text).cats) }) def _load_pyfunc(path): return _SpacyModelWrapper(_load_model(path)) def load_model(model_uri): local_model_path = _download_artifact_from_uri(artifact_uri=model_uri) flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) spacy_model_file_path = os.path.join(local_model_path, flavor_conf.get("data", "model.spacy")) return _load_model(path=spacy_model_file_path)

true

f71bff714d7bf1d24454e59616e23e72f9782452

629

py

Python

December Month Challenge/2LinkedListRandomNodeReservoirSampling.py

adesh-gadge/LeetCodePractice

4b142c102e64ec93465af7f4193762e8fd2866ec

[ "MIT" ]

null

December Month Challenge/2LinkedListRandomNodeReservoirSampling.py

adesh-gadge/LeetCodePractice

4b142c102e64ec93465af7f4193762e8fd2866ec

[ "MIT" ]

null

December Month Challenge/2LinkedListRandomNodeReservoirSampling.py

adesh-gadge/LeetCodePractice

4b142c102e64ec93465af7f4193762e8fd2866ec

[ "MIT" ]

null

import random class Solution: def __init__(self, head: ListNode): """ @param head The linked list's head. Note that the head is guaranteed to be not null, so it contains at least one node. """ self.head = head def getRandom(self) -> int: """ Returns a random node's value. """ scope = 1 chosen_value = 0 curr = self.head while curr: if random.random() < 1/scope: chosen_value = curr.val curr = curr.next scope +=1 return chosen_value

23.296296

90

0.497615

import random class Solution: def __init__(self, head: ListNode): self.head = head def getRandom(self) -> int: scope = 1 chosen_value = 0 curr = self.head while curr: if random.random() < 1/scope: chosen_value = curr.val curr = curr.next scope +=1 return chosen_value

true

f71bff92a942ff8d052d7208bd78c572f2d01c55

132

py

Python

artistapp/artist/admin.py

fallprojects/ArtistApp

5564a1f7f4fc95261beb462abfa4ca53f3e5c17f

[ "MIT" ]

null

artistapp/artist/admin.py

fallprojects/ArtistApp

5564a1f7f4fc95261beb462abfa4ca53f3e5c17f

[ "MIT" ]

null

artistapp/artist/admin.py

fallprojects/ArtistApp

5564a1f7f4fc95261beb462abfa4ca53f3e5c17f

[ "MIT" ]

null

from django.contrib import admin from .models import * # Register your models here. admin.site.register([Content,Profile,Comment])

22

46

0.787879

from django.contrib import admin from .models import * admin.site.register([Content,Profile,Comment])

true

f71c00b21518c6757cb661f74b98f03394d9f8a6

749

py

Python

parqueo/urls.py

gersonjuarez/Laboratorio

3ed5dc57136ec593e3edb8ef8ca3b29abeb7dabc

[ "bzip2-1.0.6" ]

null

parqueo/urls.py

gersonjuarez/Laboratorio

3ed5dc57136ec593e3edb8ef8ca3b29abeb7dabc

[ "bzip2-1.0.6" ]

null

parqueo/urls.py

gersonjuarez/Laboratorio

3ed5dc57136ec593e3edb8ef8ca3b29abeb7dabc

[ "bzip2-1.0.6" ]

null

"""parqueo URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path urlpatterns = [ path('admin/', admin.site.urls), ]

34.045455

77

0.708945

from django.contrib import admin from django.urls import path urlpatterns = [ path('admin/', admin.site.urls), ]

true

f71c0110baa8d07d6722fcdc95decbbb7f63ec63

2,576

py

Python

arviz/plots/backends/matplotlib/distplot.py

StanczakDominik/arviz

ec33b4cc7d4a6d5ba95a87a43ef226a49c2cb287

[ "Apache-2.0" ]

null

arviz/plots/backends/matplotlib/distplot.py

StanczakDominik/arviz

ec33b4cc7d4a6d5ba95a87a43ef226a49c2cb287

[ "Apache-2.0" ]

null

arviz/plots/backends/matplotlib/distplot.py

StanczakDominik/arviz

ec33b4cc7d4a6d5ba95a87a43ef226a49c2cb287

[ "Apache-2.0" ]

null

"""Matplotlib distplot.""" import warnings import matplotlib.pyplot as plt import numpy as np from . import backend_show from ...kdeplot import plot_kde from ...plot_utils import matplotlib_kwarg_dealiaser from ....numeric_utils import get_bins def plot_dist( values, values2, color, kind, cumulative, label, rotated, rug, bw, quantiles, contour, fill_last, textsize, plot_kwargs, fill_kwargs, rug_kwargs, contour_kwargs, contourf_kwargs, pcolormesh_kwargs, hist_kwargs, ax, backend_kwargs, show, ): """Matplotlib distplot.""" if backend_kwargs is not None: warnings.warn( ( "Argument backend_kwargs has not effect in matplotlib.plot_dist" "Supplied value won't be used" ) ) backend_kwargs = None if ax is None: ax = plt.gca() if kind == "hist": ax = _histplot_mpl_op( values=values, values2=values2, rotated=rotated, ax=ax, hist_kwargs=hist_kwargs ) elif kind == "kde": plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") plot_kwargs.setdefault("color", color) legend = label is not None ax = plot_kde( values, values2, cumulative=cumulative, rug=rug, label=label, bw=bw, quantiles=quantiles, rotated=rotated, contour=contour, legend=legend, fill_last=fill_last, textsize=textsize, plot_kwargs=plot_kwargs, fill_kwargs=fill_kwargs, rug_kwargs=rug_kwargs, contour_kwargs=contour_kwargs, contourf_kwargs=contourf_kwargs, pcolormesh_kwargs=pcolormesh_kwargs, ax=ax, backend="matplotlib", backend_kwargs=backend_kwargs, show=show, ) if backend_show(show): plt.show() return ax def _histplot_mpl_op(values, values2, rotated, ax, hist_kwargs): """Add a histogram for the data to the axes.""" if values2 is not None: raise NotImplementedError("Insert hexbin plot here") bins = hist_kwargs.pop("bins") if bins is None: bins = get_bins(values) ax.hist(np.asarray(values).flatten(), bins=bins, **hist_kwargs) if rotated: ax.set_yticks(bins[:-1]) else: ax.set_xticks(bins[:-1]) if hist_kwargs.get("label") is not None: ax.legend() return ax

24.301887

91

0.588898

import warnings import matplotlib.pyplot as plt import numpy as np from . import backend_show from ...kdeplot import plot_kde from ...plot_utils import matplotlib_kwarg_dealiaser from ....numeric_utils import get_bins def plot_dist( values, values2, color, kind, cumulative, label, rotated, rug, bw, quantiles, contour, fill_last, textsize, plot_kwargs, fill_kwargs, rug_kwargs, contour_kwargs, contourf_kwargs, pcolormesh_kwargs, hist_kwargs, ax, backend_kwargs, show, ): if backend_kwargs is not None: warnings.warn( ( "Argument backend_kwargs has not effect in matplotlib.plot_dist" "Supplied value won't be used" ) ) backend_kwargs = None if ax is None: ax = plt.gca() if kind == "hist": ax = _histplot_mpl_op( values=values, values2=values2, rotated=rotated, ax=ax, hist_kwargs=hist_kwargs ) elif kind == "kde": plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") plot_kwargs.setdefault("color", color) legend = label is not None ax = plot_kde( values, values2, cumulative=cumulative, rug=rug, label=label, bw=bw, quantiles=quantiles, rotated=rotated, contour=contour, legend=legend, fill_last=fill_last, textsize=textsize, plot_kwargs=plot_kwargs, fill_kwargs=fill_kwargs, rug_kwargs=rug_kwargs, contour_kwargs=contour_kwargs, contourf_kwargs=contourf_kwargs, pcolormesh_kwargs=pcolormesh_kwargs, ax=ax, backend="matplotlib", backend_kwargs=backend_kwargs, show=show, ) if backend_show(show): plt.show() return ax def _histplot_mpl_op(values, values2, rotated, ax, hist_kwargs): if values2 is not None: raise NotImplementedError("Insert hexbin plot here") bins = hist_kwargs.pop("bins") if bins is None: bins = get_bins(values) ax.hist(np.asarray(values).flatten(), bins=bins, **hist_kwargs) if rotated: ax.set_yticks(bins[:-1]) else: ax.set_xticks(bins[:-1]) if hist_kwargs.get("label") is not None: ax.legend() return ax

true

f71c01c109a11f63936b4d9b8326f9b1b1a2d93f

452

py

Python

cookies/ex2/main.py

acandreani/ads_web_exercicios

a97ee7ebd0dba9e308b8e2d2318e577903f83f72

[ "MIT" ]

1

2019-03-13T14:33:28.000Z

cookies/ex2/main.py

acandreani/ads_web_exercicios

a97ee7ebd0dba9e308b8e2d2318e577903f83f72

[ "MIT" ]

1

2021-06-23T20:56:49.000Z

cookies/ex2/main.py

acandreani/ads_web_exercicios

a97ee7ebd0dba9e308b8e2d2318e577903f83f72

[ "MIT" ]

1

2019-04-24T13:10:58.000Z

from flask import Flask, render_template, request, session,redirect, url_for, escape, request, make_response app = Flask(__name__) # configure a chave secreta app.secret_key = "segredo" @app.route('/') def index(): return render_template("index.html") # use cookies.get(key) instead of cookies[key] to not get a # KeyError if the cookie is missing. if __name__== "__main__": app.run(host="0.0.0.0",debug= True)

22.6

110

0.679204

from flask import Flask, render_template, request, session,redirect, url_for, escape, request, make_response app = Flask(__name__) app.secret_key = "segredo" @app.route('/') def index(): return render_template("index.html") if __name__== "__main__": app.run(host="0.0.0.0",debug= True)

true

f71c0215c62089a43b38c7a560870eff98f3266c

540

py

Python

Exercicios-Python/exercicios-curso-em-video/d004.py

PedroGoes16/Estudos

142a697a1d375590bb76847a74ed2b8f9fa44a9d

[ "MIT" ]

null

Exercicios-Python/exercicios-curso-em-video/d004.py

PedroGoes16/Estudos

142a697a1d375590bb76847a74ed2b8f9fa44a9d

[ "MIT" ]

null

Exercicios-Python/exercicios-curso-em-video/d004.py

PedroGoes16/Estudos

142a697a1d375590bb76847a74ed2b8f9fa44a9d

[ "MIT" ]

null

n = input('Digite algo: ') print('É composto por número e letras? ',n.isalnum()) print('É composto somente por letras maiúsculas? ',n.isupper()) print('É composto somente por letras? ',n.isalpha()) print('É composto somente por números? ',n.isnumeric()) print('É um número decimal? ',n.isdecimal()) print('É composto somente por dígitos? ',n.isdigit()) print('É composto somente por letras minúsculas? ',n.islower()) print('É imprimível? ',n.isprintable()) print('É somente um espaço vazio? ',n.isspace()) print('É um título? ',n.istitle())

49.090909

63

0.709259

n = input('Digite algo: ') print('É composto por número e letras? ',n.isalnum()) print('É composto somente por letras maiúsculas? ',n.isupper()) print('É composto somente por letras? ',n.isalpha()) print('É composto somente por números? ',n.isnumeric()) print('É um número decimal? ',n.isdecimal()) print('É composto somente por dígitos? ',n.isdigit()) print('É composto somente por letras minúsculas? ',n.islower()) print('É imprimível? ',n.isprintable()) print('É somente um espaço vazio? ',n.isspace()) print('É um título? ',n.istitle())

true

f71c027cc59d99ded5c1d7d4bb2ac9fc391c9882

9,319

py

Python

squares.py

IAmUnStTV/Tetris-Python

4a676b6f72ceabce796592611f2541665e4010be

[ "Apache-2.0" ]

16

2019-03-20T12:33:53.000Z

2021-10-16T12:13:39.000Z

squares.py

IAmUnStTV/Tetris-Python

4a676b6f72ceabce796592611f2541665e4010be

[ "Apache-2.0" ]

2

2019-04-01T14:07:07.000Z

2021-07-15T14:08:58.000Z

squares.py

IAmUnStTV/Tetris-Python

4a676b6f72ceabce796592611f2541665e4010be

[ "Apache-2.0" ]

12

2019-03-30T11:32:30.000Z

2021-09-15T02:49:00.000Z

from random import randrange from pygame import Rect, draw from clock import Clock class Squares: """method for malipulating squares in the game""" def __init__(self, st, status, screen): self.st = st self.status = status self.screen = screen self.empty_line = ['none' for i in range(st.square_num_x)] self.squares = [self.empty_line.copy() for i in range(st.square_num_y)] self.new_sq(self) self.clock = Clock(st) # draw all squares def draw_squares(self): self.screen.fill(self.st.space_color) self.draw_tip(self) self.draw_exist_sq(self) self.draw_curr_sq(self) # update squares' information def update(self): updated = False # for update screen # vertical drop, straight drop if self.status.straight_drop and self.clock.is_time_to_straight_drop(): updated = True self.drop_straight(self) self.clock.update_straight_drop() # vertical drop, force drop elif self.clock.is_time_to_drop(): updated = True self.drop(self) self.clock.update_drop() # vertical drop, quick drop elif self.status.down and self.clock.is_time_to_quick_drop(): updated = True self.drop(self) self.clock.update_quick_drop() # rotation if self.status.rotate and self.clock.is_time_to_rotate(): updated = True self.rotate(self) self.clock.update_rotate() # horizontal move if self.status.right: updated = True if self.clock.is_time_to_move() or self.clock.is_time_to_quick_right(): self.right(self) self.clock.update_move() if self.status.left: updated = True if self.clock.is_time_to_move() or self.clock.is_time_to_quick_left(): self.left(self) self.clock.update_move() # crash detection if self.should_stop(self): updated = True self.stop(self) return updated # renew current square @staticmethod def new_sq(self): self.curr_sq = self.st.new.copy() shape = self.get_shape(self) self.origin_shape = shape['pos'] self.curr_shape = shape['pos'] self.curr_color = shape['color'] self.rotate_limit = shape['rotate'] self.rotate_curr = 1 # if new squares are crashed, game over. if not self.valid(self, self.curr_sq, self.curr_shape): self.status.game_status = self.status.GAMEOVER # return a random shape dictionary @staticmethod def get_shape(self): shape_index = randrange(0, self.st.shape_num) return self.st.shapes[shape_index].copy() @staticmethod def drop_straight(self): while not self.should_stop(self): self.curr_sq[0] += 1 @staticmethod def drop(self): new_sq = self.curr_sq.copy() new_sq[0] += 1 if self.valid(self, new_sq, self.curr_shape): self.curr_sq = new_sq @staticmethod def rotate(self): new_shape = self.get_rotated_shape(self) # regular check if self.valid(self, self.curr_sq, new_shape): self.curr_shape = new_shape # move horizontally if not valid else: tolerance = 2 for i in range(tolerance): # left new_sq_left = self.curr_sq.copy() new_sq_left[1] -= 1 if self.valid(self, new_sq_left, new_shape): self.curr_sq = new_sq_left self.curr_shape = new_shape return # right new_sq_right = self.curr_sq.copy() new_sq_right[1] += 1 if self.valid(self, new_sq_right, new_shape): self.curr_sq = new_sq_right self.curr_shape = new_shape return @staticmethod def get_rotated_shape(self): # rotation limit must not exceed, if exceed, reset it if self.rotate_curr >= self.rotate_limit: self.rotate_curr = 1 new_shape = self.origin_shape else: self.rotate_curr += 1 new_shape = [] for sq in self.curr_shape: new_shape.append([sq[1], -sq[0]]) return new_shape @staticmethod def right(self): new_sq = self.curr_sq.copy() new_sq[1] += 1 if self.valid(self, new_sq, self.curr_shape): self.curr_sq = new_sq @staticmethod def left(self): new_sq = self.curr_sq.copy() new_sq[1] -= 1 if self.valid(self, new_sq, self.curr_shape): self.curr_sq = new_sq @staticmethod def stop(self): # wait for a moment before stop, give player time to adjust if not self.clock.is_time_to_stop(): self.clock.update_should_stop(True) return else: self.clock.update_should_stop(None) self.clock.update_stop() # copy squares to map for sq in self.curr_shape: x = sq[1] + self.curr_sq[1] y = sq[0] + self.curr_sq[0] if y >= 0: self.squares[y][x] = self.curr_color x = self.curr_sq[1] y = self.curr_sq[0] if y >= 0: self.squares[y][x] = self.curr_color full_lines = self.clean_full_lines(self) self.status.score += full_lines # add score self.new_sq(self) # delete full lines and insert empty lines at the front @staticmethod def clean_full_lines(self): full_lines = 0 for index, line in enumerate(self.squares): if line.count('none') == 0: full_lines += 1 self.st.time_drop *= self.st.time_drop_adjust # adjust time self.squares.pop(index) self.squares.insert(0, self.empty_line.copy()) return full_lines # validate current squares of shapes relative to center with with one drop vertically @staticmethod def should_stop(self): # check shape squares for sq in self.curr_shape: x = sq[1] + self.curr_sq[1] y = sq[0] + self.curr_sq[0] + 1 if y - 1 >= 0 and not self.valid_sq(self, [y, x]): return True # check center square x = self.curr_sq[1] y = self.curr_sq[0] + 1 return not (self.valid_sq(self, [y, x])) # validate the given center square and shape squires relative to center square @staticmethod def valid(self, square, shape): # check shape squares for sq in shape: x = sq[1] + square[1] y = sq[0] + square[0] if y >= 0 and not (self.valid_sq(self, [y, x])): return False # check center square return self.valid_sq(self, square) @staticmethod def valid_sq(self, sq): # check border if sq[0] >= self.st.square_num_y or \ sq[1] >= self.st.square_num_x or \ sq[1] < 0: return False # check crash return self.squares[sq[0]][sq[1]] == 'none' @staticmethod def draw_exist_sq(self): for y, row in enumerate(self.squares): for x, square in enumerate(row): color = self.st.colors[self.squares[y][x]] self.draw_square(self, y, x, color) @staticmethod def draw_tip(self): # find the lowrest position curr_sq = self.curr_sq.copy() while not self.should_stop(self): self.curr_sq[0] += 1 curr_sq, self.curr_sq = self.curr_sq, curr_sq # draw their tips color = self.st.colors['tip'] self.draw_square(self, curr_sq[0], curr_sq[1], color, True) self.draw_square(self, curr_sq[0], curr_sq[1], self.st.colors['none']) for y, x in self.curr_shape: curr_y, curr_x = curr_sq[0], curr_sq[1] self.draw_square(self, y + curr_y, x + curr_x, color, True) self.draw_square(self, y + curr_y, x + curr_x, self.st.colors['none']) @staticmethod def draw_curr_sq(self): # draw center color = self.st.colors[self.curr_color] self.draw_square(self, self.curr_sq[0], self.curr_sq[1], color) # draw shapes curr_y, curr_x = self.curr_sq[0], self.curr_sq[1] for y, x in self.curr_shape: self.draw_square(self, y + curr_y, x + curr_x, color) # draw one single square with given information @staticmethod def draw_square(self, y, x, color, border=False): x_pos = x * (self.st.square_space + self.st.square_length) y_pos = y * (self.st.square_space + self.st.square_length) length = self.st.square_length # adding borders borders if border: y_pos -= self.st.square_space x_pos -= self.st.square_space length += 2 * self.st.square_space rect = Rect(x_pos + self.st.square_space, y_pos + self.st.square_space, length, length) draw.rect(self.screen, color, rect)

35.166038

95

0.570877

from random import randrange from pygame import Rect, draw from clock import Clock class Squares: def __init__(self, st, status, screen): self.st = st self.status = status self.screen = screen self.empty_line = ['none' for i in range(st.square_num_x)] self.squares = [self.empty_line.copy() for i in range(st.square_num_y)] self.new_sq(self) self.clock = Clock(st) def draw_squares(self): self.screen.fill(self.st.space_color) self.draw_tip(self) self.draw_exist_sq(self) self.draw_curr_sq(self) def update(self): updated = False # for update screen # vertical drop, straight drop if self.status.straight_drop and self.clock.is_time_to_straight_drop(): updated = True self.drop_straight(self) self.clock.update_straight_drop() # vertical drop, force drop elif self.clock.is_time_to_drop(): updated = True self.drop(self) self.clock.update_drop() # vertical drop, quick drop elif self.status.down and self.clock.is_time_to_quick_drop(): updated = True self.drop(self) self.clock.update_quick_drop() # rotation if self.status.rotate and self.clock.is_time_to_rotate(): updated = True self.rotate(self) self.clock.update_rotate() # horizontal move if self.status.right: updated = True if self.clock.is_time_to_move() or self.clock.is_time_to_quick_right(): self.right(self) self.clock.update_move() if self.status.left: updated = True if self.clock.is_time_to_move() or self.clock.is_time_to_quick_left(): self.left(self) self.clock.update_move() # crash detection if self.should_stop(self): updated = True self.stop(self) return updated # renew current square @staticmethod def new_sq(self): self.curr_sq = self.st.new.copy() shape = self.get_shape(self) self.origin_shape = shape['pos'] self.curr_shape = shape['pos'] self.curr_color = shape['color'] self.rotate_limit = shape['rotate'] self.rotate_curr = 1 # if new squares are crashed, game over. if not self.valid(self, self.curr_sq, self.curr_shape): self.status.game_status = self.status.GAMEOVER # return a random shape dictionary @staticmethod def get_shape(self): shape_index = randrange(0, self.st.shape_num) return self.st.shapes[shape_index].copy() @staticmethod def drop_straight(self): while not self.should_stop(self): self.curr_sq[0] += 1 @staticmethod def drop(self): new_sq = self.curr_sq.copy() new_sq[0] += 1 if self.valid(self, new_sq, self.curr_shape): self.curr_sq = new_sq @staticmethod def rotate(self): new_shape = self.get_rotated_shape(self) # regular check if self.valid(self, self.curr_sq, new_shape): self.curr_shape = new_shape # move horizontally if not valid else: tolerance = 2 for i in range(tolerance): # left new_sq_left = self.curr_sq.copy() new_sq_left[1] -= 1 if self.valid(self, new_sq_left, new_shape): self.curr_sq = new_sq_left self.curr_shape = new_shape return # right new_sq_right = self.curr_sq.copy() new_sq_right[1] += 1 if self.valid(self, new_sq_right, new_shape): self.curr_sq = new_sq_right self.curr_shape = new_shape return @staticmethod def get_rotated_shape(self): # rotation limit must not exceed, if exceed, reset it if self.rotate_curr >= self.rotate_limit: self.rotate_curr = 1 new_shape = self.origin_shape else: self.rotate_curr += 1 new_shape = [] for sq in self.curr_shape: new_shape.append([sq[1], -sq[0]]) return new_shape @staticmethod def right(self): new_sq = self.curr_sq.copy() new_sq[1] += 1 if self.valid(self, new_sq, self.curr_shape): self.curr_sq = new_sq @staticmethod def left(self): new_sq = self.curr_sq.copy() new_sq[1] -= 1 if self.valid(self, new_sq, self.curr_shape): self.curr_sq = new_sq @staticmethod def stop(self): # wait for a moment before stop, give player time to adjust if not self.clock.is_time_to_stop(): self.clock.update_should_stop(True) return else: self.clock.update_should_stop(None) self.clock.update_stop() # copy squares to map for sq in self.curr_shape: x = sq[1] + self.curr_sq[1] y = sq[0] + self.curr_sq[0] if y >= 0: self.squares[y][x] = self.curr_color x = self.curr_sq[1] y = self.curr_sq[0] if y >= 0: self.squares[y][x] = self.curr_color full_lines = self.clean_full_lines(self) self.status.score += full_lines # add score self.new_sq(self) # delete full lines and insert empty lines at the front @staticmethod def clean_full_lines(self): full_lines = 0 for index, line in enumerate(self.squares): if line.count('none') == 0: full_lines += 1 self.st.time_drop *= self.st.time_drop_adjust # adjust time self.squares.pop(index) self.squares.insert(0, self.empty_line.copy()) return full_lines # validate current squares of shapes relative to center with with one drop vertically @staticmethod def should_stop(self): # check shape squares for sq in self.curr_shape: x = sq[1] + self.curr_sq[1] y = sq[0] + self.curr_sq[0] + 1 if y - 1 >= 0 and not self.valid_sq(self, [y, x]): return True # check center square x = self.curr_sq[1] y = self.curr_sq[0] + 1 return not (self.valid_sq(self, [y, x])) # validate the given center square and shape squires relative to center square @staticmethod def valid(self, square, shape): # check shape squares for sq in shape: x = sq[1] + square[1] y = sq[0] + square[0] if y >= 0 and not (self.valid_sq(self, [y, x])): return False # check center square return self.valid_sq(self, square) @staticmethod def valid_sq(self, sq): # check border if sq[0] >= self.st.square_num_y or \ sq[1] >= self.st.square_num_x or \ sq[1] < 0: return False # check crash return self.squares[sq[0]][sq[1]] == 'none' @staticmethod def draw_exist_sq(self): for y, row in enumerate(self.squares): for x, square in enumerate(row): color = self.st.colors[self.squares[y][x]] self.draw_square(self, y, x, color) @staticmethod def draw_tip(self): # find the lowrest position curr_sq = self.curr_sq.copy() while not self.should_stop(self): self.curr_sq[0] += 1 curr_sq, self.curr_sq = self.curr_sq, curr_sq # draw their tips color = self.st.colors['tip'] self.draw_square(self, curr_sq[0], curr_sq[1], color, True) self.draw_square(self, curr_sq[0], curr_sq[1], self.st.colors['none']) for y, x in self.curr_shape: curr_y, curr_x = curr_sq[0], curr_sq[1] self.draw_square(self, y + curr_y, x + curr_x, color, True) self.draw_square(self, y + curr_y, x + curr_x, self.st.colors['none']) @staticmethod def draw_curr_sq(self): # draw center color = self.st.colors[self.curr_color] self.draw_square(self, self.curr_sq[0], self.curr_sq[1], color) # draw shapes curr_y, curr_x = self.curr_sq[0], self.curr_sq[1] for y, x in self.curr_shape: self.draw_square(self, y + curr_y, x + curr_x, color) # draw one single square with given information @staticmethod def draw_square(self, y, x, color, border=False): x_pos = x * (self.st.square_space + self.st.square_length) y_pos = y * (self.st.square_space + self.st.square_length) length = self.st.square_length # adding borders borders if border: y_pos -= self.st.square_space x_pos -= self.st.square_space length += 2 * self.st.square_space rect = Rect(x_pos + self.st.square_space, y_pos + self.st.square_space, length, length) draw.rect(self.screen, color, rect)

true

f71c030abc2d29b0f256a337f0e78e71b90e4000

3,839

py

Python

spirit/topic/views.py

BinaryTree0/fer3

85c3bbf2f328e69ad4d7c01b6e2c8d4ef1d9e0a3

[ "MIT" ]

null

spirit/topic/views.py

BinaryTree0/fer3

85c3bbf2f328e69ad4d7c01b6e2c8d4ef1d9e0a3

[ "MIT" ]

5

2021-06-08T21:03:58.000Z

2022-03-12T00:18:43.000Z

spirit/topic/views.py

BinaryTree0/fer3

85c3bbf2f328e69ad4d7c01b6e2c8d4ef1d9e0a3

[ "MIT" ]

null

# -*- coding: utf-8 -*- from django.contrib.auth.decorators import login_required from django.shortcuts import render, redirect, get_object_or_404 from django.http import HttpResponsePermanentRedirect from djconfig import config from ..core.utils.views import is_post, post_data from ..core.utils.paginator import paginate, yt_paginate from ..core.utils.ratelimit.decorators import ratelimit from ..category.models import Category from ..comment.models import MOVED from ..comment.forms import CommentForm from ..comment.utils import comment_posted from ..comment.models import Comment from .models import Topic from .forms import TopicForm from . import utils @login_required @ratelimit(rate='1/10s') def publish(request, category_id=None): if category_id: get_object_or_404( Category.objects.visible(), pk=category_id) user = request.user form = TopicForm( user=user, data=post_data(request), initial={'category': category_id}) cform = CommentForm( user=user, data=post_data(request)) if (is_post(request) and all([form.is_valid(), cform.is_valid()]) and not request.is_limited()): if not user.st.update_post_hash(form.get_topic_hash()): return redirect( request.POST.get('next', None) or form.get_category().get_absolute_url()) # wrap in transaction.atomic? topic = form.save() cform.topic = topic comment = cform.save() comment_posted(comment=comment, mentions=cform.mentions) return redirect(topic.get_absolute_url()) return render( request=request, template_name='spirit/topic/publish.html', context={'form': form, 'cform': cform}) @login_required def update(request, pk): topic = Topic.objects.for_update_or_404(pk, request.user) category_id = topic.category_id form = TopicForm( user=request.user, data=post_data(request), instance=topic) if is_post(request) and form.is_valid(): topic = form.save() if topic.category_id != category_id: Comment.create_moderation_action( user=request.user, topic=topic, action=MOVED) return redirect(request.POST.get('next', topic.get_absolute_url())) return render( request=request, template_name='spirit/topic/update.html', context={'form': form}) def detail(request, pk, slug): topic = Topic.objects.get_public_or_404(pk, request.user) if topic.slug != slug: return HttpResponsePermanentRedirect(topic.get_absolute_url()) utils.topic_viewed(request=request, topic=topic) comments = ( Comment.objects .for_topic(topic=topic) .with_likes(user=request.user) .with_polls(user=request.user) .order_by('date')) comments = paginate( comments, per_page=config.comments_per_page, page_number=request.GET.get('page', 1)) return render( request=request, template_name='spirit/topic/detail.html', context={ 'topic': topic, 'comments': comments}) def index_active(request): categories = ( Category.objects .visible() .parents()) topics = ( Topic.objects .visible() .global_() .with_bookmarks(user=request.user) .order_by('-is_globally_pinned', '-last_active') .select_related('category')) topics = yt_paginate( topics, per_page=config.topics_per_page, page_number=request.GET.get('page', 1)) return render( request=request, template_name='spirit/topic/active.html', context={ 'categories': categories, 'topics': topics})

29.75969

75

0.64496

from django.contrib.auth.decorators import login_required from django.shortcuts import render, redirect, get_object_or_404 from django.http import HttpResponsePermanentRedirect from djconfig import config from ..core.utils.views import is_post, post_data from ..core.utils.paginator import paginate, yt_paginate from ..core.utils.ratelimit.decorators import ratelimit from ..category.models import Category from ..comment.models import MOVED from ..comment.forms import CommentForm from ..comment.utils import comment_posted from ..comment.models import Comment from .models import Topic from .forms import TopicForm from . import utils @login_required @ratelimit(rate='1/10s') def publish(request, category_id=None): if category_id: get_object_or_404( Category.objects.visible(), pk=category_id) user = request.user form = TopicForm( user=user, data=post_data(request), initial={'category': category_id}) cform = CommentForm( user=user, data=post_data(request)) if (is_post(request) and all([form.is_valid(), cform.is_valid()]) and not request.is_limited()): if not user.st.update_post_hash(form.get_topic_hash()): return redirect( request.POST.get('next', None) or form.get_category().get_absolute_url()) topic = form.save() cform.topic = topic comment = cform.save() comment_posted(comment=comment, mentions=cform.mentions) return redirect(topic.get_absolute_url()) return render( request=request, template_name='spirit/topic/publish.html', context={'form': form, 'cform': cform}) @login_required def update(request, pk): topic = Topic.objects.for_update_or_404(pk, request.user) category_id = topic.category_id form = TopicForm( user=request.user, data=post_data(request), instance=topic) if is_post(request) and form.is_valid(): topic = form.save() if topic.category_id != category_id: Comment.create_moderation_action( user=request.user, topic=topic, action=MOVED) return redirect(request.POST.get('next', topic.get_absolute_url())) return render( request=request, template_name='spirit/topic/update.html', context={'form': form}) def detail(request, pk, slug): topic = Topic.objects.get_public_or_404(pk, request.user) if topic.slug != slug: return HttpResponsePermanentRedirect(topic.get_absolute_url()) utils.topic_viewed(request=request, topic=topic) comments = ( Comment.objects .for_topic(topic=topic) .with_likes(user=request.user) .with_polls(user=request.user) .order_by('date')) comments = paginate( comments, per_page=config.comments_per_page, page_number=request.GET.get('page', 1)) return render( request=request, template_name='spirit/topic/detail.html', context={ 'topic': topic, 'comments': comments}) def index_active(request): categories = ( Category.objects .visible() .parents()) topics = ( Topic.objects .visible() .global_() .with_bookmarks(user=request.user) .order_by('-is_globally_pinned', '-last_active') .select_related('category')) topics = yt_paginate( topics, per_page=config.topics_per_page, page_number=request.GET.get('page', 1)) return render( request=request, template_name='spirit/topic/active.html', context={ 'categories': categories, 'topics': topics})

true

f71c03b58cbe884de8ef7d23450e58f05962f59e

1,242

py

Python

src/alocacao/camada_servicos/unit_of_work.py

ralphribeiro/APWP-T2

1ed5552a32ae9320eadbbd0489c2082a6f8750a8

[ "MIT" ]

null

src/alocacao/camada_servicos/unit_of_work.py

ralphribeiro/APWP-T2

1ed5552a32ae9320eadbbd0489c2082a6f8750a8

[ "MIT" ]

null

src/alocacao/camada_servicos/unit_of_work.py

ralphribeiro/APWP-T2

1ed5552a32ae9320eadbbd0489c2082a6f8750a8

[ "MIT" ]

null

from __future__ import annotations import abc from alocacao.adapters import repository from alocacao.config import DEFAULT_SESSION_FACTORY class AbstractUOW(abc.ABC): produtos: repository.AbstractRepository def __enter__(self) -> AbstractUOW: return self def __exit__(self, *args): self.rollback() def commit(self): self._commit() def collect_new_messages(self): for produto in self.produtos.seen: while produto.eventos: yield produto.eventos.pop(0) @abc.abstractmethod def _commit(self): pass @abc.abstractmethod def rollback(self): pass class SQLAlchemyUOW(AbstractUOW): def __init__(self, session_factory=DEFAULT_SESSION_FACTORY): self.session_factory = session_factory def __enter__(self): self.session = self.session_factory() self.produtos = repository.TrackingRepository( repository.SQLAlchemyRepository(self.session) ) return super().__enter__() def __exit__(self, *args): super().__exit__(*args) self.session.close() def _commit(self): self.session.commit() def rollback(self): self.session.rollback()

23

64

0.661031

from __future__ import annotations import abc from alocacao.adapters import repository from alocacao.config import DEFAULT_SESSION_FACTORY class AbstractUOW(abc.ABC): produtos: repository.AbstractRepository def __enter__(self) -> AbstractUOW: return self def __exit__(self, *args): self.rollback() def commit(self): self._commit() def collect_new_messages(self): for produto in self.produtos.seen: while produto.eventos: yield produto.eventos.pop(0) @abc.abstractmethod def _commit(self): pass @abc.abstractmethod def rollback(self): pass class SQLAlchemyUOW(AbstractUOW): def __init__(self, session_factory=DEFAULT_SESSION_FACTORY): self.session_factory = session_factory def __enter__(self): self.session = self.session_factory() self.produtos = repository.TrackingRepository( repository.SQLAlchemyRepository(self.session) ) return super().__enter__() def __exit__(self, *args): super().__exit__(*args) self.session.close() def _commit(self): self.session.commit() def rollback(self): self.session.rollback()

true

f71c045146eab77490951be322d1c2d7585e636d

1,143

py

Python

ISMLnextGen/ipTest.py

Ravenclaw-OIer/ISML_auto_voter

9c53bd87530697d374163f571186542c3fc9734b

[ "MIT" ]

128

2020-11-16T09:28:17.000Z

2022-03-14T10:38:52.000Z

ISMLnextGen/ipTest.py

Ravenclaw-OIer/ISML_auto_voter

9c53bd87530697d374163f571186542c3fc9734b

[ "MIT" ]

7

2020-11-27T14:45:19.000Z

2022-02-15T09:47:12.000Z

ISMLnextGen/ipTest.py

Ravenclaw-OIer/ISML_auto_voter

9c53bd87530697d374163f571186542c3fc9734b

[ "MIT" ]

11

2020-12-11T12:24:38.000Z

2022-02-20T12:42:31.000Z

#coding:utf-8 #访问这个服务器会获得一些'ip:端口'字符串。仅用于测试 import tornado.ioloop import tornado.web class MainHandler(tornado.web.RequestHandler): @tornado.gen.coroutine def get(self, *args, **kwargs): self.write(''' <!DOCTYPE html><html> <head><meta charset="utf-8" /> <title>html 标签换行符详细介绍</title></head> <body bgcolor="burlywood"> 我是一个段落。 我是一个段落。 我是一个段落。 192.168.1.1:99999\n 192.168.1.1:91241\n 192.168.1.1:91343\n 192.168.1.1:94223\n 192.168.1.1:97546\n 192.168.1.1:92342\n </body></html> ''') app=tornado.web.Application([ (r'/',MainHandler), ]) if __name__ == '__main__': print('访问这个服务器:55556会获得一些“ip:端口”字符串。仅用于测试') app.listen(55556) tornado.ioloop.IOLoop.instance().start()

30.891892

59

0.451444

import tornado.ioloop import tornado.web class MainHandler(tornado.web.RequestHandler): @tornado.gen.coroutine def get(self, *args, **kwargs): self.write(''' <!DOCTYPE html><html> <head><meta charset="utf-8" /> <title>html 标签换行符详细介绍</title></head> <body bgcolor="burlywood"> 我是一个段落。 我是一个段落。 我是一个段落。 192.168.1.1:99999\n 192.168.1.1:91241\n 192.168.1.1:91343\n 192.168.1.1:94223\n 192.168.1.1:97546\n 192.168.1.1:92342\n </body></html> ''') app=tornado.web.Application([ (r'/',MainHandler), ]) if __name__ == '__main__': print('访问这个服务器:55556会获得一些“ip:端口”字符串。仅用于测试') app.listen(55556) tornado.ioloop.IOLoop.instance().start()

true

f71c05e483d33dc4be29042dc38a37ccadce4386

6,756

py

Python

Sensors/softskin.py

zhaocy14/SmartWalker

b025a7b4a2b305838a22fe4e6116ddb951c4d7bf

[ "MIT" ]

2

2021-11-13T14:16:06.000Z

2022-01-12T06:07:32.000Z

Sensors/softskin.py

zhaocy14/SmartWalker

b025a7b4a2b305838a22fe4e6116ddb951c4d7bf

[ "MIT" ]

null

Sensors/softskin.py

zhaocy14/SmartWalker

b025a7b4a2b305838a22fe4e6116ddb951c4d7bf

[ "MIT" ]

3

2021-08-30T04:40:39.000Z

2022-01-09T11:34:04.000Z

import serial import serial.tools.list_ports import numpy as np import math import threading import re import os import sys import time import matplotlib.pyplot as plt pwd = os.path.abspath(os.path.abspath(__file__)) father_path = os.path.abspath(os.path.dirname(pwd) + os.path.sep + "..") sys.path.append(father_path) data_path = os.path.abspath( os.path.dirname(os.path.abspath(__file__)) + os.path.sep + ".." + os.path.sep + "data") def print_serial(port): print("---------------[ %s ]---------------" % port.name) print("Path: %s" % port.device) print("Descript: %s" % port.description) print("HWID: %s" % port.hwid) if not None == port.manufacturer: print("Manufacture: %s" % port.manufacturer) if not None == port.product: print("Product: %s" % port.product) if not None == port.interface: print("Interface: %s" % port.interface) print() def detect_serials(location="1-1.1:1.0", vid=0x10c4, pid=0xea60): ports = serial.tools.list_ports.comports() for port in ports: print_serial(port) if port.location.__contains__(location): port_path = port.device return port_path else: print("Cannot find the target device: %s" % location) return None class SoftSkin(object): def __init__(self, is_STM32: bool = True): port_name = detect_serials("1-1.3:1.0") # Arduino Mega 2560 ttyACM0 baud_rate = 115200 print(port_name, baud_rate) self.serial = serial.Serial(port_name, baud_rate, timeout=None) self.pwd = os.path.abspath(os.path.abspath(__file__)) self.father_path = os.path.abspath(os.path.dirname(pwd) + os.path.sep + "..") self.serial = serial.Serial(port_name, baud_rate, timeout=None) self.raw_data = [] # 保存一帧数据 self.base_data = [] # 建立一组基准值用于初始化 self.temp_data = [] self.port_num = 32 self.average_length = 10 self.average_buffer = np.zeros((self.average_length, self.port_num)) # detect abnormal signal self.max_pressure = 0 self.safe_change_rate = 10 self.emergency_change_rate = 50 self.detect_length = 10 self.detect_buffer = np.zeros((self.detect_length, self.port_num)) self.skin_unlock_event = threading.Event() self.skin_unlock_event.clear() self.build_base_line_data() pass def read_data(self, is_shown=1): try: one_line_data = self.serial.readline().decode("utf-8") # print(one_line_data) one_line_data = one_line_data.strip('SS') one_line_data = one_line_data.strip('\n') one_line_data = one_line_data.strip('\r') one_line_data = one_line_data.split('|') # print(one_line_data) if is_shown == 1: print(one_line_data) if len(one_line_data) == self.port_num: one_line_data = list(map(float, one_line_data)) one_line_data = list(map(int, one_line_data)) self.raw_data = one_line_data # print(self.raw_data, type(self.raw_data), type(self.raw_data[0])) except BaseException as be: print("Data Error:", be) def build_base_line_data(self, initial_size=10): """ expired, no use 1.建立一组基准数值检测异常值取平均值 :return: not in use because the original signals are stable enough """ base_list = [] for i in range(initial_size): self.read_data(0) if len(self.raw_data) == self.port_num: temp_raw_data = self.raw_data base_list += temp_raw_data mean_base_list = np.array(base_list).reshape([-1, self.port_num]) add_col = np.ones(mean_base_list.shape[0]).reshape([1, -1]) mean_base_list = add_col.dot(mean_base_list) / mean_base_list.shape[0] self.base_data = mean_base_list.tolist()[0] self.base_data = list(map(lambda x: int(x) - 1, self.base_data)) print("base line data: ", self.base_data) pass def read_and_record(self, record=False, show=False, plot=False, plot_num=30): file_path = data_path + os.path.sep + "Softskin.txt" plot_array = np.zeros((plot_num, self.port_num)) if record: file = open(file_path, 'w') while True: try: # self.serial.flushInput() self.read_data(0) if len(self.raw_data) == len(self.base_data): temp_data = np.array(self.raw_data) - np.array(self.base_data) if show: print(temp_data) print(self.max_pressure) if record: time_index = time.time() write_data = temp_data.tolist() write_data.insert(0, time_index) file.write(str(write_data) + '\n') file.flush() self.temp_data = temp_data self.max_pressure = self.temp_data.max() self.detect_buffer[0:-1, :] = self.detect_buffer[1:self.detect_length, :] self.detect_buffer[-1, :] = np.array(self.temp_data) if plot: # plt.ion() plot_array[0:plot_num - 1, :] = plot_array[1:plot_num, :] plot_array[plot_num - 1, :] = np.array(temp_data) plt.clf() plt.xlabel('Time') plt.ylabel('pressure') plt.ylim((-10, 270)) plt.plot(range(0, plot_num), plot_array) # plt.ioff() # plt.show() # plt.draw() plt.pause(0.0000000001) except BaseException as be: print("Data Error:", be) def update_from_STM32(self, STM32_data: np.ndarray): try: self.raw_data = STM32_data except: pass def unlock(self): while True: change_rate = self.detect_buffer[-1, :] - self.detect_buffer[0, :] change_rate = change_rate.max() if self.safe_change_rate <= change_rate < self.emergency_change_rate: print("unlock!") break time.sleep(0.1) if __name__ == '__main__': skin = SoftSkin() # skin.build_base_line_data() thread_reading = threading.Thread(target=skin.read_and_record, args=()) time.sleep(1) thread_reading.start() skin.unlock()

36.518919

93

0.558467

import serial import serial.tools.list_ports import numpy as np import math import threading import re import os import sys import time import matplotlib.pyplot as plt pwd = os.path.abspath(os.path.abspath(__file__)) father_path = os.path.abspath(os.path.dirname(pwd) + os.path.sep + "..") sys.path.append(father_path) data_path = os.path.abspath( os.path.dirname(os.path.abspath(__file__)) + os.path.sep + ".." + os.path.sep + "data") def print_serial(port): print("---------------[ %s ]---------------" % port.name) print("Path: %s" % port.device) print("Descript: %s" % port.description) print("HWID: %s" % port.hwid) if not None == port.manufacturer: print("Manufacture: %s" % port.manufacturer) if not None == port.product: print("Product: %s" % port.product) if not None == port.interface: print("Interface: %s" % port.interface) print() def detect_serials(location="1-1.1:1.0", vid=0x10c4, pid=0xea60): ports = serial.tools.list_ports.comports() for port in ports: print_serial(port) if port.location.__contains__(location): port_path = port.device return port_path else: print("Cannot find the target device: %s" % location) return None class SoftSkin(object): def __init__(self, is_STM32: bool = True): port_name = detect_serials("1-1.3:1.0") baud_rate = 115200 print(port_name, baud_rate) self.serial = serial.Serial(port_name, baud_rate, timeout=None) self.pwd = os.path.abspath(os.path.abspath(__file__)) self.father_path = os.path.abspath(os.path.dirname(pwd) + os.path.sep + "..") self.serial = serial.Serial(port_name, baud_rate, timeout=None) self.raw_data = [] self.base_data = [] self.temp_data = [] self.port_num = 32 self.average_length = 10 self.average_buffer = np.zeros((self.average_length, self.port_num)) self.max_pressure = 0 self.safe_change_rate = 10 self.emergency_change_rate = 50 self.detect_length = 10 self.detect_buffer = np.zeros((self.detect_length, self.port_num)) self.skin_unlock_event = threading.Event() self.skin_unlock_event.clear() self.build_base_line_data() pass def read_data(self, is_shown=1): try: one_line_data = self.serial.readline().decode("utf-8") one_line_data = one_line_data.strip('SS') one_line_data = one_line_data.strip('\n') one_line_data = one_line_data.strip('\r') one_line_data = one_line_data.split('|') if is_shown == 1: print(one_line_data) if len(one_line_data) == self.port_num: one_line_data = list(map(float, one_line_data)) one_line_data = list(map(int, one_line_data)) self.raw_data = one_line_data except BaseException as be: print("Data Error:", be) def build_base_line_data(self, initial_size=10): base_list = [] for i in range(initial_size): self.read_data(0) if len(self.raw_data) == self.port_num: temp_raw_data = self.raw_data base_list += temp_raw_data mean_base_list = np.array(base_list).reshape([-1, self.port_num]) add_col = np.ones(mean_base_list.shape[0]).reshape([1, -1]) mean_base_list = add_col.dot(mean_base_list) / mean_base_list.shape[0] self.base_data = mean_base_list.tolist()[0] self.base_data = list(map(lambda x: int(x) - 1, self.base_data)) print("base line data: ", self.base_data) pass def read_and_record(self, record=False, show=False, plot=False, plot_num=30): file_path = data_path + os.path.sep + "Softskin.txt" plot_array = np.zeros((plot_num, self.port_num)) if record: file = open(file_path, 'w') while True: try: self.read_data(0) if len(self.raw_data) == len(self.base_data): temp_data = np.array(self.raw_data) - np.array(self.base_data) if show: print(temp_data) print(self.max_pressure) if record: time_index = time.time() write_data = temp_data.tolist() write_data.insert(0, time_index) file.write(str(write_data) + '\n') file.flush() self.temp_data = temp_data self.max_pressure = self.temp_data.max() self.detect_buffer[0:-1, :] = self.detect_buffer[1:self.detect_length, :] self.detect_buffer[-1, :] = np.array(self.temp_data) if plot: plot_array[0:plot_num - 1, :] = plot_array[1:plot_num, :] plot_array[plot_num - 1, :] = np.array(temp_data) plt.clf() plt.xlabel('Time') plt.ylabel('pressure') plt.ylim((-10, 270)) plt.plot(range(0, plot_num), plot_array) plt.pause(0.0000000001) except BaseException as be: print("Data Error:", be) def update_from_STM32(self, STM32_data: np.ndarray): try: self.raw_data = STM32_data except: pass def unlock(self): while True: change_rate = self.detect_buffer[-1, :] - self.detect_buffer[0, :] change_rate = change_rate.max() if self.safe_change_rate <= change_rate < self.emergency_change_rate: print("unlock!") break time.sleep(0.1) if __name__ == '__main__': skin = SoftSkin() thread_reading = threading.Thread(target=skin.read_and_record, args=()) time.sleep(1) thread_reading.start() skin.unlock()

true

f71c0620c464a57b3ebe69ac3d9aba39ae33da92

1,518

py

Python

olass/models/base_query.py

ufbmi/olass-client

2fc949d4d59959e7e3ba5ec737b20d8db856b54b

[ "MIT" ]

null

olass/models/base_query.py

ufbmi/olass-client

2fc949d4d59959e7e3ba5ec737b20d8db856b54b

[ "MIT" ]

1

2016-08-12T20:52:09.000Z

2016-08-12T20:59:35.000Z

olass/models/base_query.py

ufbmi/olass-client

2fc949d4d59959e7e3ba5ec737b20d8db856b54b

[ "MIT" ]

1

2016-06-28T16:43:09.000Z

""" Goal: Provides paginate() function """ from sqlalchemy import orm from olass.models.pagination import Pagination class BaseQuery(orm.Query): """ @see: flask-sqlalchemy/flask_sqlalchemy/__init__.py """ def paginate(self, page=None, per_page=None, error_out=True): """Returns ``per_page`` items from page ``page``. If no items are found and ``page`` is greater than 1, or if page is less than 1, it aborts with 404. This behavior can be disabled by passing ``error_out=False``. If ``page`` or ``per_page`` are ``None``, they will default to 1 and 20 respectively. If the values are not ints and ``error_out`` is ``True``, this function will rais exceptions. Returns a :class:`Pagination` object. """ if page is None: page = 1 if per_page is None: per_page = 20 if error_out and page < 1: raise Exception("Pagination error: page < 1") items = self.limit(per_page).offset( (page - 1) * per_page).all() if not items and page != 1 and error_out: raise Exception("Pagination error: no items and page != 1") # No need to count if we're on the first page and there are fewer # items than we expected. if page == 1 and len(items) < per_page: total = len(items) else: total = self.order_by(None).count() return Pagination(self, page, per_page, total, items)

30.36

79

0.597497

from sqlalchemy import orm from olass.models.pagination import Pagination class BaseQuery(orm.Query): def paginate(self, page=None, per_page=None, error_out=True): if page is None: page = 1 if per_page is None: per_page = 20 if error_out and page < 1: raise Exception("Pagination error: page < 1") items = self.limit(per_page).offset( (page - 1) * per_page).all() if not items and page != 1 and error_out: raise Exception("Pagination error: no items and page != 1") # items than we expected. if page == 1 and len(items) < per_page: total = len(items) else: total = self.order_by(None).count() return Pagination(self, page, per_page, total, items)

true

f71c06f54e6701786b0f29d7880b93fa9b637655

649

py

Python

gui.py

eduardokimmel/ofx_to_xlsx

f36dc430ca2424055feba8f04c7f48cd4741d82c

[ "MIT" ]

null

gui.py

eduardokimmel/ofx_to_xlsx

f36dc430ca2424055feba8f04c7f48cd4741d82c

[ "MIT" ]

null

gui.py

eduardokimmel/ofx_to_xlsx

f36dc430ca2424055feba8f04c7f48cd4741d82c

[ "MIT" ]

null

from tkinter.filedialog import askopenfilename from tkinter import * import cli import gettext window = Tk() window.title("ofx_to_xlsx") def close_window(): window.destroy() def callback(): ofx = askopenfilename() cli.run(ofx) gettext.install('ofx_to_xlsx') t = gettext.translation('gui_i18n', 'locale', fallback=True) _ = t.gettext frame = Frame(window) frame.pack() w1 = Label (frame,text = _("Select a OFX file to convert it to Excel")) w1.pack() arq = Button (frame, text = _("Select File"), command = callback) arq.pack() sair = Button (frame, text = _("Quit"), command = close_window) sair.pack() window.mainloop() exit()

19.088235

71

0.702619

from tkinter.filedialog import askopenfilename from tkinter import * import cli import gettext window = Tk() window.title("ofx_to_xlsx") def close_window(): window.destroy() def callback(): ofx = askopenfilename() cli.run(ofx) gettext.install('ofx_to_xlsx') t = gettext.translation('gui_i18n', 'locale', fallback=True) _ = t.gettext frame = Frame(window) frame.pack() w1 = Label (frame,text = _("Select a OFX file to convert it to Excel")) w1.pack() arq = Button (frame, text = _("Select File"), command = callback) arq.pack() sair = Button (frame, text = _("Quit"), command = close_window) sair.pack() window.mainloop() exit()

true

f71c071affad74a0e7aea1a05a898c897c918ab8

691

py

Python

emtract/model_inference.py

dvamossy/EmTract

68a00e3d63fbc2c401b0d2b297bf96ffb75940e8

[ "MIT" ]

16

2021-12-02T18:59:56.000Z

2022-03-31T11:42:12.000Z

emtract/model_inference.py

dvamossy/EmTract

68a00e3d63fbc2c401b0d2b297bf96ffb75940e8

[ "MIT" ]

null

emtract/model_inference.py

dvamossy/EmTract

68a00e3d63fbc2c401b0d2b297bf96ffb75940e8

[ "MIT" ]

1

2021-12-09T06:05:22.000Z

import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' from emtract.model import Model, ModelType import pandas as pd class ModelInference: MODEL_BASE_PATH = 'build/models/' DATA_BASE_PATH = './emtract/data/' def __init__(self, model_type): if model_type == 'twitter': self.model = Model(ModelType.TWITTER) else: self.model = Model(ModelType.STOCK_TWITS) def inference(self, text): return self.model.predict([text]) def file_inference(self, file_name, output): df = pd.read_csv(file_name, header=None) predictions = self.model.predict(df.iloc[:, 0].values) predictions.to_csv(output, index=False)

27.64

62

0.662808

import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' from emtract.model import Model, ModelType import pandas as pd class ModelInference: MODEL_BASE_PATH = 'build/models/' DATA_BASE_PATH = './emtract/data/' def __init__(self, model_type): if model_type == 'twitter': self.model = Model(ModelType.TWITTER) else: self.model = Model(ModelType.STOCK_TWITS) def inference(self, text): return self.model.predict([text]) def file_inference(self, file_name, output): df = pd.read_csv(file_name, header=None) predictions = self.model.predict(df.iloc[:, 0].values) predictions.to_csv(output, index=False)

true

f71c07a140ce757dc105e33553450a64b401d600

147

py

Python

students/k33422/Alexandrin_Anton/Lr2/homework/main/admin.py

aytakr/ITMO_ICT_WebDevelopment_2021-2022

57c0eef5e1f413c7f031ee001d59e5122f990f26

[ "MIT" ]

7

2021-09-02T08:20:58.000Z

2022-01-12T11:48:07.000Z

students/k33422/Alexandrin_Anton/Lr2/homework/main/admin.py

aytakr/ITMO_ICT_WebDevelopment_2021-2022

57c0eef5e1f413c7f031ee001d59e5122f990f26

[ "MIT" ]

76

2021-09-17T23:01:50.000Z

2022-03-18T16:42:03.000Z

students/k33422/Alexandrin_Anton/Lr2/homework/main/admin.py

aytakr/ITMO_ICT_WebDevelopment_2021-2022

57c0eef5e1f413c7f031ee001d59e5122f990f26

[ "MIT" ]

60

2021-09-04T16:47:39.000Z

2022-03-21T04:41:27.000Z

from django.contrib import admin from .models import * admin.site.register(Student) admin.site.register(Homework) admin.site.register(Assignment)

21

32

0.816327

from django.contrib import admin from .models import * admin.site.register(Student) admin.site.register(Homework) admin.site.register(Assignment)

true

f71c0829da8f53bf5fb8a4964502e266c6e624a6

1,918

py

Python

tests/test_rabbitmq_consumer_command.py

LaEmma/sparrow_cloud

fb9f76ea70b3ba5782c33f3b3379e2ffe4bab08c

[ "MIT" ]

null

tests/test_rabbitmq_consumer_command.py

LaEmma/sparrow_cloud

fb9f76ea70b3ba5782c33f3b3379e2ffe4bab08c

[ "MIT" ]

null

tests/test_rabbitmq_consumer_command.py

LaEmma/sparrow_cloud

fb9f76ea70b3ba5782c33f3b3379e2ffe4bab08c

[ "MIT" ]

null

import os import unittest from django.conf.urls import url from django.http import HttpResponse def task(*args, **kwargs): print('*'*10) def detail(request, question_id): return HttpResponse("You're looking at question %s." % question_id) urlpatterns = [ url(r'^/ssss/xxx/$', detail), url(r'^/ssuuu/xxddx/$', detail), ] class RestClientTestCase(unittest.TestCase): def setUp(self): os.environ["SPARROW_BROKER_HOST"] = "127.0.0.1:8001" os.environ["SPARROW_BACKEND_HOST"] = "127.0.0.1:8002" os.environ["DJANGO_SETTINGS_MODULE"] = "tests.mock_settings" # @mock.patch('rabbitmq_consumer.RabbitMQConsumer.target_func_map', return_value='') # @mock.patch('rabbitmq_consumer.RabbitMQConsumer.consume', return_value='接收任务成功') # def test_consumer_command(self, mock_target_func_map, mock_consume): # from django.conf import settings # self.setup_settings(settings) # django.setup() # out = StringIO() # call_command('rabbitmq_consumer', '--queue', 'QUEUE_CONF', stdout=out) # self.assertEqual(out.read(), '') def setup_settings(self, settings): settings.XX = "1" settings.SECRET_KEY = "ss" settings.SPARROW_RABBITMQ_CONSUMER_CONF = { "MESSAGE_BROKER_CONF": { "USER_NAME": "test_name", "PASSWORD": "test_password", "VIRTUAL_HOST": "test_virtual", "BROKER_SERVICE_CONF": "sparrow-test:8001", }, "MESSAGE_BACKEND_CONF": { "BACKEND_SERVICE_CONF": "sparrow-test:8001", "API_PATH": "/api/sparrow_test/task/test_update/" } } settings.QUEUE_CONF={ "QUEUE": "TEST_QUEUE", "TARGET_FUNC_MAP": { "ORDER_PAY_SUC_ONLINE": "./task", } } settings.ROOT_URLCONF = __name__

31.442623

88

0.606361

import os import unittest from django.conf.urls import url from django.http import HttpResponse def task(*args, **kwargs): print('*'*10) def detail(request, question_id): return HttpResponse("You're looking at question %s." % question_id) urlpatterns = [ url(r'^/ssss/xxx/$', detail), url(r'^/ssuuu/xxddx/$', detail), ] class RestClientTestCase(unittest.TestCase): def setUp(self): os.environ["SPARROW_BROKER_HOST"] = "127.0.0.1:8001" os.environ["SPARROW_BACKEND_HOST"] = "127.0.0.1:8002" os.environ["DJANGO_SETTINGS_MODULE"] = "tests.mock_settings" # @mock.patch('rabbitmq_consumer.RabbitMQConsumer.target_func_map', return_value='') # @mock.patch('rabbitmq_consumer.RabbitMQConsumer.consume', return_value='接收任务成功') # def test_consumer_command(self, mock_target_func_map, mock_consume): # from django.conf import settings # self.setup_settings(settings) # django.setup() # out = StringIO() # call_command('rabbitmq_consumer', '--queue', 'QUEUE_CONF', stdout=out) # self.assertEqual(out.read(), '') def setup_settings(self, settings): settings.XX = "1" settings.SECRET_KEY = "ss" settings.SPARROW_RABBITMQ_CONSUMER_CONF = { "MESSAGE_BROKER_CONF": { "USER_NAME": "test_name", "PASSWORD": "test_password", "VIRTUAL_HOST": "test_virtual", "BROKER_SERVICE_CONF": "sparrow-test:8001", }, "MESSAGE_BACKEND_CONF": { "BACKEND_SERVICE_CONF": "sparrow-test:8001", "API_PATH": "/api/sparrow_test/task/test_update/" } } settings.QUEUE_CONF={ "QUEUE": "TEST_QUEUE", "TARGET_FUNC_MAP": { "ORDER_PAY_SUC_ONLINE": "./task", } } settings.ROOT_URLCONF = __name__

true

f71c08e2095d6c92591d4e24b87dfa59366adf76

1,108

py

Python

manage.py

cjmabry/PoliChart

787d987669de4891b1b1ac5f8ebc0ecd38ac2785

[ "BSD-3-Clause" ]

null

manage.py

cjmabry/PoliChart

787d987669de4891b1b1ac5f8ebc0ecd38ac2785

[ "BSD-3-Clause" ]

null

manage.py

cjmabry/PoliChart

787d987669de4891b1b1ac5f8ebc0ecd38ac2785

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- import os from flask.ext.script import Manager from polichart import create_app, polling from polichart.extensions import db from polichart.utils import MALE from flask import url_for app = create_app() manager = Manager(app) @app.context_processor def override_url_for(): return dict(url_for=dated_url_for) def dated_url_for(endpoint, **values): if endpoint == 'static': filename = values.get('filename', None) if filename: file_path = os.path.join(app.root_path, endpoint, filename) values['q'] = int(os.stat(file_path).st_mtime) return url_for(endpoint, **values) @manager.command def run(): """Run in local machine.""" app.run() @manager.command def initdb(): """Init/reset database.""" db.drop_all() db.create_all() db.session.commit() polling.populate_db() manager.add_option('-c', '--config', dest="config", required=False, help="config file") if __name__ == "__main__": manager.run()

21.307692

58

0.617329

import os from flask.ext.script import Manager from polichart import create_app, polling from polichart.extensions import db from polichart.utils import MALE from flask import url_for app = create_app() manager = Manager(app) @app.context_processor def override_url_for(): return dict(url_for=dated_url_for) def dated_url_for(endpoint, **values): if endpoint == 'static': filename = values.get('filename', None) if filename: file_path = os.path.join(app.root_path, endpoint, filename) values['q'] = int(os.stat(file_path).st_mtime) return url_for(endpoint, **values) @manager.command def run(): app.run() @manager.command def initdb(): db.drop_all() db.create_all() db.session.commit() polling.populate_db() manager.add_option('-c', '--config', dest="config", required=False, help="config file") if __name__ == "__main__": manager.run()

true

f71c093c62e3d49d16d3c9cbf3b0a2a8b7fd68d6

2,128

py

Python

integrationtest/vm/installation/upgrade/test_zs_upgd_1.3_latest_on_cos7.py

bgerxx/woodpecker

fdc51245945cc9be4d1f028988079213eb99b2ad

[ "Apache-2.0" ]

null

integrationtest/vm/installation/upgrade/test_zs_upgd_1.3_latest_on_cos7.py

bgerxx/woodpecker

fdc51245945cc9be4d1f028988079213eb99b2ad

[ "Apache-2.0" ]

null

integrationtest/vm/installation/upgrade/test_zs_upgd_1.3_latest_on_cos7.py

bgerxx/woodpecker

fdc51245945cc9be4d1f028988079213eb99b2ad

[ "Apache-2.0" ]

null

''' @author: MengLai ''' import os import tempfile import uuid import time import zstackwoodpecker.test_util as test_util import zstackwoodpecker.test_lib as test_lib import zstackwoodpecker.test_state as test_state import zstacklib.utils.ssh as ssh import zstackwoodpecker.operations.scenario_operations as scen_ops test_stub = test_lib.lib_get_test_stub() test_obj_dict = test_state.TestStateDict() tmp_file = '/tmp/%s' % uuid.uuid1().get_hex() vm_inv = None def test(): global vm_inv test_util.test_dsc('Create test vm to test zstack upgrade by -u.') image_name = os.environ.get('imageName_i_c7_z_1.3') iso_path = os.environ.get('iso_path') zstack_latest_version = os.environ.get('zstackLatestVersion') zstack_latest_path = os.environ.get('zstackLatestInstaller') vm_name = os.environ.get('vmName') upgrade_script_path = os.environ.get('upgradeScript') vm_inv = test_stub.create_vm_scenario(image_name, vm_name) vm_ip = vm_inv.vmNics[0].ip test_lib.lib_wait_target_up(vm_ip, 22) test_stub.make_ssh_no_password(vm_ip, tmp_file) test_util.test_logger('Update MN IP') test_stub.update_mn_ip(vm_ip, vm_ip, tmp_file) test_stub.reset_rabbitmq_for_13(vm_ip, tmp_file) test_stub.start_mn(vm_ip, tmp_file) test_stub.check_installation(vm_ip, tmp_file) test_util.test_logger('Upgrade zstack to latest') test_stub.update_iso(vm_ip, tmp_file, iso_path, upgrade_script_path) test_stub.upgrade_zstack(vm_ip, zstack_latest_path, tmp_file) test_stub.check_zstack_version(vm_ip, tmp_file, zstack_latest_version) test_stub.start_mn(vm_ip, tmp_file) test_stub.check_installation(vm_ip, tmp_file) os.system('rm -f %s' % tmp_file) test_stub.destroy_vm_scenario(vm_inv.uuid) test_util.test_pass('ZStack upgrade Test Success') #Will be called only if exception happens in test(). def error_cleanup(): global vm_inv os.system('rm -f %s' % tmp_file) if vm_inv: test_stub.destroy_vm_scenario(vm_inv.uuid) test_lib.lib_error_cleanup(test_obj_dict)

33.777778

75

0.740132

import os import tempfile import uuid import time import zstackwoodpecker.test_util as test_util import zstackwoodpecker.test_lib as test_lib import zstackwoodpecker.test_state as test_state import zstacklib.utils.ssh as ssh import zstackwoodpecker.operations.scenario_operations as scen_ops test_stub = test_lib.lib_get_test_stub() test_obj_dict = test_state.TestStateDict() tmp_file = '/tmp/%s' % uuid.uuid1().get_hex() vm_inv = None def test(): global vm_inv test_util.test_dsc('Create test vm to test zstack upgrade by -u.') image_name = os.environ.get('imageName_i_c7_z_1.3') iso_path = os.environ.get('iso_path') zstack_latest_version = os.environ.get('zstackLatestVersion') zstack_latest_path = os.environ.get('zstackLatestInstaller') vm_name = os.environ.get('vmName') upgrade_script_path = os.environ.get('upgradeScript') vm_inv = test_stub.create_vm_scenario(image_name, vm_name) vm_ip = vm_inv.vmNics[0].ip test_lib.lib_wait_target_up(vm_ip, 22) test_stub.make_ssh_no_password(vm_ip, tmp_file) test_util.test_logger('Update MN IP') test_stub.update_mn_ip(vm_ip, vm_ip, tmp_file) test_stub.reset_rabbitmq_for_13(vm_ip, tmp_file) test_stub.start_mn(vm_ip, tmp_file) test_stub.check_installation(vm_ip, tmp_file) test_util.test_logger('Upgrade zstack to latest') test_stub.update_iso(vm_ip, tmp_file, iso_path, upgrade_script_path) test_stub.upgrade_zstack(vm_ip, zstack_latest_path, tmp_file) test_stub.check_zstack_version(vm_ip, tmp_file, zstack_latest_version) test_stub.start_mn(vm_ip, tmp_file) test_stub.check_installation(vm_ip, tmp_file) os.system('rm -f %s' % tmp_file) test_stub.destroy_vm_scenario(vm_inv.uuid) test_util.test_pass('ZStack upgrade Test Success') def error_cleanup(): global vm_inv os.system('rm -f %s' % tmp_file) if vm_inv: test_stub.destroy_vm_scenario(vm_inv.uuid) test_lib.lib_error_cleanup(test_obj_dict)

true

f71c0a5782bf191b5540c2eb75fdf8f2a7d65eb4

4,189

py

Python

tools/ISM_ticket_creation/ISM_ticket_creation.py

thahasinab/risksense_tools

55399f21c680735e3c557484ec8788a33c1525e7

[ "Apache-2.0" ]

null

tools/ISM_ticket_creation/ISM_ticket_creation.py

thahasinab/risksense_tools

55399f21c680735e3c557484ec8788a33c1525e7

[ "Apache-2.0" ]

null

tools/ISM_ticket_creation/ISM_ticket_creation.py

thahasinab/risksense_tools

55399f21c680735e3c557484ec8788a33c1525e7

[ "Apache-2.0" ]

null

import requests import json import time import os import toml import zipfile import csv import pandas as pd from datetime import date from datetime import datetime import logging def incident_create(ism_url,ism_key,ism_attachment_url,final_directory,tag_name_list,flag_AH,assignee,assignee_desc,profile_link): Patches_list=[] Solution_list=[] VRR_group_list=[] VRR_list=[] Plugin_id_list=[] Asset_info_list=[] Scanner_name_list=[] Scanner_title_list=[] df_App = pd.read_csv(final_directory+"/Assets_"+str(tag_name_list)+".csv", low_memory=False) df_Host = pd.read_csv(final_directory+"/Findings_"+str(tag_name_list)+".csv", low_memory=False) df_multiple = pd.read_csv(final_directory+"/Ticket_Findings_"+str(tag_name_list)+".csv", low_memory=False) #print(assignee) for j in range(len(df_multiple.axes[0])): time.sleep(1) flag = False create = False #print(flag_AH) Scanner_name_list.append(df_multiple.iloc[j]['Scanner Name']) if(flag_AH == "A"): Plugin_id_list.append(df_multiple.iloc[j]["Scanner Plugin"]) Asset_info_list.append(df_App.iloc[0]["Address"]) elif(flag_AH == "H"): Plugin_id_list.append(df_multiple.iloc[j]["Scanner Plugin ID"]) Asset_info_list.append(df_App.iloc[0]["IP Address"]) Scanner_title_list.append(df_multiple.iloc[j]["Vulnerability"]) VRR_list.append(df_multiple.iloc[j]["Vulnerability Risk Rating"]) VRR_group_list.append(df_multiple.iloc[j]["VRR Group"]) Solution_list.append(df_multiple.iloc[j]['Possible Solution']) Patches_list.append(df_multiple.iloc[j]['Possible Patches']) payload = json.dumps({"Category": "Account Lockout","Impact": "Medium","Priority": "3","ProfileLink": profile_link,"Service": "Email Service","Source": "Phone","Status": "Active","Subject": "Scanner Name : " + ' , '.join(map(str, Scanner_name_list)) + "|" + " Scanner Plugin ID : " + ' , '.join(map(str, Plugin_id_list)) + "|" + " Scanner Title : " + ' , '.join(map(str, Scanner_title_list)) ,"Symptom": 'Plugin information : \n----------------------------\nPlugin ID : ' + ' , '.join(map(str, Plugin_id_list)) + "\n\nVRR : " + ' , '.join(map(str, VRR_list)) + "|" + ' , '.join(map(str, VRR_group_list)) + "\n\n----------------------------------------------------------------------------------------------------\nAsset Information : \n----------------------------\n" + "Hostname : " + ' , '.join(map(str, Asset_info_list)) + "\n\nSolution : \n\n*) " + '\n*) '.join(map(str, Solution_list)) + "\n\nPatches : \n\n*) " + '\n*) '.join(map(str, Patches_list)),"Urgency": "Medium","Owner": assignee,"OwnerTeam": "Service Desk"}) headers = { 'Authorization': ism_key, 'Content-Type': 'application/json', 'Cookie': 'SID=' } try: response = requests.request("POST", ism_url, headers=headers, data=payload) except Exception as e: print(e,response.text) logging.error(e,response.text) Rec_id_json = response.json() Rec_id = Rec_id_json["RecId"] Incident_num = Rec_id_json["IncidentNumber"] #print(Rec_id,Incident_num) ####### Attachment ####### files = [('file', open(final_directory+"/Assets_"+str(tag_name_list)+".csv",'rb') ), ('file',open(final_directory+"/Ticket_Findings_"+str(tag_name_list)+".csv",'rb') )] payload={"ObjectID":Rec_id,"ObjectType":"incident#"} headers = { 'Authorization': ism_key, 'Cookie': 'SID=' } response = requests.request("POST", ism_attachment_url, headers=headers, data=payload,files=files) if(response.status_code == 200): print("Incident is created and attachment is included...") logging.info("Incident is created and attachment is included...\n") print(assignee_desc) logging.info(assignee_desc) else: print("There is a problem in attaching the files to the ticket") logging.error("There is a problem in attaching the files to the ticket\n") print(assignee_desc) logging.info(assignee_desc) return Incident_num

46.544444

1,031

0.627596

import requests import json import time import os import toml import zipfile import csv import pandas as pd from datetime import date from datetime import datetime import logging def incident_create(ism_url,ism_key,ism_attachment_url,final_directory,tag_name_list,flag_AH,assignee,assignee_desc,profile_link): Patches_list=[] Solution_list=[] VRR_group_list=[] VRR_list=[] Plugin_id_list=[] Asset_info_list=[] Scanner_name_list=[] Scanner_title_list=[] df_App = pd.read_csv(final_directory+"/Assets_"+str(tag_name_list)+".csv", low_memory=False) df_Host = pd.read_csv(final_directory+"/Findings_"+str(tag_name_list)+".csv", low_memory=False) df_multiple = pd.read_csv(final_directory+"/Ticket_Findings_"+str(tag_name_list)+".csv", low_memory=False) for j in range(len(df_multiple.axes[0])): time.sleep(1) flag = False create = False Scanner_name_list.append(df_multiple.iloc[j]['Scanner Name']) if(flag_AH == "A"): Plugin_id_list.append(df_multiple.iloc[j]["Scanner Plugin"]) Asset_info_list.append(df_App.iloc[0]["Address"]) elif(flag_AH == "H"): Plugin_id_list.append(df_multiple.iloc[j]["Scanner Plugin ID"]) Asset_info_list.append(df_App.iloc[0]["IP Address"]) Scanner_title_list.append(df_multiple.iloc[j]["Vulnerability"]) VRR_list.append(df_multiple.iloc[j]["Vulnerability Risk Rating"]) VRR_group_list.append(df_multiple.iloc[j]["VRR Group"]) Solution_list.append(df_multiple.iloc[j]['Possible Solution']) Patches_list.append(df_multiple.iloc[j]['Possible Patches']) payload = json.dumps({"Category": "Account Lockout","Impact": "Medium","Priority": "3","ProfileLink": profile_link,"Service": "Email Service","Source": "Phone","Status": "Active","Subject": "Scanner Name : " + ' , '.join(map(str, Scanner_name_list)) + "|" + " Scanner Plugin ID : " + ' , '.join(map(str, Plugin_id_list)) + "|" + " Scanner Title : " + ' , '.join(map(str, Scanner_title_list)) ,"Symptom": 'Plugin information : \n----------------------------\nPlugin ID : ' + ' , '.join(map(str, Plugin_id_list)) + "\n\nVRR : " + ' , '.join(map(str, VRR_list)) + "|" + ' , '.join(map(str, VRR_group_list)) + "\n\n----------------------------------------------------------------------------------------------------\nAsset Information : \n----------------------------\n" + "Hostname : " + ' , '.join(map(str, Asset_info_list)) + "\n\nSolution : \n\n*) " + '\n*) '.join(map(str, Solution_list)) + "\n\nPatches : \n\n*) " + '\n*) '.join(map(str, Patches_list)),"Urgency": "Medium","Owner": assignee,"OwnerTeam": "Service Desk"}) headers = { 'Authorization': ism_key, 'Content-Type': 'application/json', 'Cookie': 'SID=' } try: response = requests.request("POST", ism_url, headers=headers, data=payload) except Exception as e: print(e,response.text) logging.error(e,response.text) Rec_id_json = response.json() Rec_id = Rec_id_json["RecId"] Incident_num = Rec_id_json["IncidentNumber"] sv",'rb') )] payload={"ObjectID":Rec_id,"ObjectType":"incident#"} headers = { 'Authorization': ism_key, 'Cookie': 'SID=' } response = requests.request("POST", ism_attachment_url, headers=headers, data=payload,files=files) if(response.status_code == 200): print("Incident is created and attachment is included...") logging.info("Incident is created and attachment is included...\n") print(assignee_desc) logging.info(assignee_desc) else: print("There is a problem in attaching the files to the ticket") logging.error("There is a problem in attaching the files to the ticket\n") print(assignee_desc) logging.info(assignee_desc) return Incident_num

true

f71c0b020116ae97bd345db15ac8c2ee8e6c6d43

28,999

py

Python

venv/Lib/site-packages/pip/_internal/cli/cmdoptions.py

EkremBayar/bayar

aad1a32044da671d0b4f11908416044753360b39

[ "MIT" ]

38,667

2015-01-01T00:15:34.000Z

2022-03-31T22:57:03.000Z

venv/Lib/site-packages/pip/_internal/cli/cmdoptions.py

EkremBayar/bayar

aad1a32044da671d0b4f11908416044753360b39

[ "MIT" ]

1,192

2015-01-03T07:59:34.000Z

2022-03-31T13:22:26.000Z

venv/Lib/site-packages/pip/_internal/cli/cmdoptions.py

EkremBayar/bayar

aad1a32044da671d0b4f11908416044753360b39

[ "MIT" ]

11,269

2015-01-01T08:41:17.000Z

2022-03-31T16:12:52.000Z

""" shared options and groups The principle here is to define options once, but *not* instantiate them globally. One reason being that options with action='append' can carry state between parses. pip parses general options twice internally, and shouldn't pass on state. To be consistent, all options will follow this design. """ # The following comment should be removed at some point in the future. # mypy: strict-optional=False import os import textwrap import warnings from functools import partial from optparse import SUPPRESS_HELP, Option, OptionGroup, OptionParser, Values from textwrap import dedent from typing import Any, Callable, Dict, Optional, Tuple from pip._vendor.packaging.utils import canonicalize_name from pip._internal.cli.parser import ConfigOptionParser from pip._internal.cli.progress_bars import BAR_TYPES from pip._internal.exceptions import CommandError from pip._internal.locations import USER_CACHE_DIR, get_src_prefix from pip._internal.models.format_control import FormatControl from pip._internal.models.index import PyPI from pip._internal.models.target_python import TargetPython from pip._internal.utils.hashes import STRONG_HASHES from pip._internal.utils.misc import strtobool def raise_option_error(parser, option, msg): # type: (OptionParser, Option, str) -> None """ Raise an option parsing error using parser.error(). Args: parser: an OptionParser instance. option: an Option instance. msg: the error text. """ msg = f"{option} error: {msg}" msg = textwrap.fill(" ".join(msg.split())) parser.error(msg) def make_option_group(group, parser): # type: (Dict[str, Any], ConfigOptionParser) -> OptionGroup """ Return an OptionGroup object group -- assumed to be dict with 'name' and 'options' keys parser -- an optparse Parser """ option_group = OptionGroup(parser, group["name"]) for option in group["options"]: option_group.add_option(option()) return option_group def check_install_build_global(options, check_options=None): # type: (Values, Optional[Values]) -> None """Disable wheels if per-setup.py call options are set. :param options: The OptionParser options to update. :param check_options: The options to check, if not supplied defaults to options. """ if check_options is None: check_options = options def getname(n): # type: (str) -> Optional[Any] return getattr(check_options, n, None) names = ["build_options", "global_options", "install_options"] if any(map(getname, names)): control = options.format_control control.disallow_binaries() warnings.warn( "Disabling all use of wheels due to the use of --build-option " "/ --global-option / --install-option.", stacklevel=2, ) def check_dist_restriction(options, check_target=False): # type: (Values, bool) -> None """Function for determining if custom platform options are allowed. :param options: The OptionParser options. :param check_target: Whether or not to check if --target is being used. """ dist_restriction_set = any( [ options.python_version, options.platforms, options.abis, options.implementation, ] ) binary_only = FormatControl(set(), {":all:"}) sdist_dependencies_allowed = ( options.format_control != binary_only and not options.ignore_dependencies ) # Installations or downloads using dist restrictions must not combine # source distributions and dist-specific wheels, as they are not # guaranteed to be locally compatible. if dist_restriction_set and sdist_dependencies_allowed: raise CommandError( "When restricting platform and interpreter constraints using " "--python-version, --platform, --abi, or --implementation, " "either --no-deps must be set, or --only-binary=:all: must be " "set and --no-binary must not be set (or must be set to " ":none:)." ) if check_target: if dist_restriction_set and not options.target_dir: raise CommandError( "Can not use any platform or abi specific options unless " "installing via '--target'" ) def _path_option_check(option, opt, value): # type: (Option, str, str) -> str return os.path.expanduser(value) def _package_name_option_check(option, opt, value): # type: (Option, str, str) -> str return canonicalize_name(value) class PipOption(Option): TYPES = Option.TYPES + ("path", "package_name") TYPE_CHECKER = Option.TYPE_CHECKER.copy() TYPE_CHECKER["package_name"] = _package_name_option_check TYPE_CHECKER["path"] = _path_option_check ########### # options # ########### help_ = partial( Option, "-h", "--help", dest="help", action="help", help="Show help.", ) # type: Callable[..., Option] isolated_mode = partial( Option, "--isolated", dest="isolated_mode", action="store_true", default=False, help=( "Run pip in an isolated mode, ignoring environment variables and user " "configuration." ), ) # type: Callable[..., Option] require_virtualenv = partial( Option, # Run only if inside a virtualenv, bail if not. "--require-virtualenv", "--require-venv", dest="require_venv", action="store_true", default=False, help=SUPPRESS_HELP, ) # type: Callable[..., Option] verbose = partial( Option, "-v", "--verbose", dest="verbose", action="count", default=0, help="Give more output. Option is additive, and can be used up to 3 times.", ) # type: Callable[..., Option] no_color = partial( Option, "--no-color", dest="no_color", action="store_true", default=False, help="Suppress colored output.", ) # type: Callable[..., Option] version = partial( Option, "-V", "--version", dest="version", action="store_true", help="Show version and exit.", ) # type: Callable[..., Option] quiet = partial( Option, "-q", "--quiet", dest="quiet", action="count", default=0, help=( "Give less output. Option is additive, and can be used up to 3" " times (corresponding to WARNING, ERROR, and CRITICAL logging" " levels)." ), ) # type: Callable[..., Option] progress_bar = partial( Option, "--progress-bar", dest="progress_bar", type="choice", choices=list(BAR_TYPES.keys()), default="on", help=( "Specify type of progress to be displayed [" + "|".join(BAR_TYPES.keys()) + "] (default: %default)" ), ) # type: Callable[..., Option] log = partial( PipOption, "--log", "--log-file", "--local-log", dest="log", metavar="path", type="path", help="Path to a verbose appending log.", ) # type: Callable[..., Option] no_input = partial( Option, # Don't ask for input "--no-input", dest="no_input", action="store_true", default=False, help="Disable prompting for input.", ) # type: Callable[..., Option] proxy = partial( Option, "--proxy", dest="proxy", type="str", default="", help="Specify a proxy in the form [user:passwd@]proxy.server:port.", ) # type: Callable[..., Option] retries = partial( Option, "--retries", dest="retries", type="int", default=5, help="Maximum number of retries each connection should attempt " "(default %default times).", ) # type: Callable[..., Option] timeout = partial( Option, "--timeout", "--default-timeout", metavar="sec", dest="timeout", type="float", default=15, help="Set the socket timeout (default %default seconds).", ) # type: Callable[..., Option] def exists_action(): # type: () -> Option return Option( # Option when path already exist "--exists-action", dest="exists_action", type="choice", choices=["s", "i", "w", "b", "a"], default=[], action="append", metavar="action", help="Default action when a path already exists: " "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.", ) cert = partial( PipOption, "--cert", dest="cert", type="path", metavar="path", help=( "Path to PEM-encoded CA certificate bundle. " "If provided, overrides the default. " "See 'SSL Certificate Verification' in pip documentation " "for more information." ), ) # type: Callable[..., Option] client_cert = partial( PipOption, "--client-cert", dest="client_cert", type="path", default=None, metavar="path", help="Path to SSL client certificate, a single file containing the " "private key and the certificate in PEM format.", ) # type: Callable[..., Option] index_url = partial( Option, "-i", "--index-url", "--pypi-url", dest="index_url", metavar="URL", default=PyPI.simple_url, help="Base URL of the Python Package Index (default %default). " "This should point to a repository compliant with PEP 503 " "(the simple repository API) or a local directory laid out " "in the same format.", ) # type: Callable[..., Option] def extra_index_url(): # type: () -> Option return Option( "--extra-index-url", dest="extra_index_urls", metavar="URL", action="append", default=[], help="Extra URLs of package indexes to use in addition to " "--index-url. Should follow the same rules as " "--index-url.", ) no_index = partial( Option, "--no-index", dest="no_index", action="store_true", default=False, help="Ignore package index (only looking at --find-links URLs instead).", ) # type: Callable[..., Option] def find_links(): # type: () -> Option return Option( "-f", "--find-links", dest="find_links", action="append", default=[], metavar="url", help="If a URL or path to an html file, then parse for links to " "archives such as sdist (.tar.gz) or wheel (.whl) files. " "If a local path or file:// URL that's a directory, " "then look for archives in the directory listing. " "Links to VCS project URLs are not supported.", ) def trusted_host(): # type: () -> Option return Option( "--trusted-host", dest="trusted_hosts", action="append", metavar="HOSTNAME", default=[], help="Mark this host or host:port pair as trusted, even though it " "does not have valid or any HTTPS.", ) def constraints(): # type: () -> Option return Option( "-c", "--constraint", dest="constraints", action="append", default=[], metavar="file", help="Constrain versions using the given constraints file. " "This option can be used multiple times.", ) def requirements(): # type: () -> Option return Option( "-r", "--requirement", dest="requirements", action="append", default=[], metavar="file", help="Install from the given requirements file. " "This option can be used multiple times.", ) def editable(): # type: () -> Option return Option( "-e", "--editable", dest="editables", action="append", default=[], metavar="path/url", help=( "Install a project in editable mode (i.e. setuptools " '"develop mode") from a local project path or a VCS url.' ), ) def _handle_src(option, opt_str, value, parser): # type: (Option, str, str, OptionParser) -> None value = os.path.abspath(value) setattr(parser.values, option.dest, value) src = partial( PipOption, "--src", "--source", "--source-dir", "--source-directory", dest="src_dir", type="path", metavar="dir", default=get_src_prefix(), action="callback", callback=_handle_src, help="Directory to check out editable projects into. " 'The default in a virtualenv is "<venv path>/src". ' 'The default for global installs is "<current dir>/src".', ) # type: Callable[..., Option] def _get_format_control(values, option): # type: (Values, Option) -> Any """Get a format_control object.""" return getattr(values, option.dest) def _handle_no_binary(option, opt_str, value, parser): # type: (Option, str, str, OptionParser) -> None existing = _get_format_control(parser.values, option) FormatControl.handle_mutual_excludes( value, existing.no_binary, existing.only_binary, ) def _handle_only_binary(option, opt_str, value, parser): # type: (Option, str, str, OptionParser) -> None existing = _get_format_control(parser.values, option) FormatControl.handle_mutual_excludes( value, existing.only_binary, existing.no_binary, ) def no_binary(): # type: () -> Option format_control = FormatControl(set(), set()) return Option( "--no-binary", dest="format_control", action="callback", callback=_handle_no_binary, type="str", default=format_control, help="Do not use binary packages. Can be supplied multiple times, and " 'each time adds to the existing value. Accepts either ":all:" to ' 'disable all binary packages, ":none:" to empty the set (notice ' "the colons), or one or more package names with commas between " "them (no colons). Note that some packages are tricky to compile " "and may fail to install when this option is used on them.", ) def only_binary(): # type: () -> Option format_control = FormatControl(set(), set()) return Option( "--only-binary", dest="format_control", action="callback", callback=_handle_only_binary, type="str", default=format_control, help="Do not use source packages. Can be supplied multiple times, and " 'each time adds to the existing value. Accepts either ":all:" to ' 'disable all source packages, ":none:" to empty the set, or one ' "or more package names with commas between them. Packages " "without binary distributions will fail to install when this " "option is used on them.", ) platforms = partial( Option, "--platform", dest="platforms", metavar="platform", action="append", default=None, help=( "Only use wheels compatible with <platform>. Defaults to the " "platform of the running system. Use this option multiple times to " "specify multiple platforms supported by the target interpreter." ), ) # type: Callable[..., Option] # This was made a separate function for unit-testing purposes. def _convert_python_version(value): # type: (str) -> Tuple[Tuple[int, ...], Optional[str]] """ Convert a version string like "3", "37", or "3.7.3" into a tuple of ints. :return: A 2-tuple (version_info, error_msg), where `error_msg` is non-None if and only if there was a parsing error. """ if not value: # The empty string is the same as not providing a value. return (None, None) parts = value.split(".") if len(parts) > 3: return ((), "at most three version parts are allowed") if len(parts) == 1: # Then we are in the case of "3" or "37". value = parts[0] if len(value) > 1: parts = [value[0], value[1:]] try: version_info = tuple(int(part) for part in parts) except ValueError: return ((), "each version part must be an integer") return (version_info, None) def _handle_python_version(option, opt_str, value, parser): # type: (Option, str, str, OptionParser) -> None """ Handle a provided --python-version value. """ version_info, error_msg = _convert_python_version(value) if error_msg is not None: msg = "invalid --python-version value: {!r}: {}".format( value, error_msg, ) raise_option_error(parser, option=option, msg=msg) parser.values.python_version = version_info python_version = partial( Option, "--python-version", dest="python_version", metavar="python_version", action="callback", callback=_handle_python_version, type="str", default=None, help=dedent( """\ The Python interpreter version to use for wheel and "Requires-Python" compatibility checks. Defaults to a version derived from the running interpreter. The version can be specified using up to three dot-separated integers (e.g. "3" for 3.0.0, "3.7" for 3.7.0, or "3.7.3"). A major-minor version can also be given as a string without dots (e.g. "37" for 3.7.0). """ ), ) # type: Callable[..., Option] implementation = partial( Option, "--implementation", dest="implementation", metavar="implementation", default=None, help=( "Only use wheels compatible with Python " "implementation <implementation>, e.g. 'pp', 'jy', 'cp', " " or 'ip'. If not specified, then the current " "interpreter implementation is used. Use 'py' to force " "implementation-agnostic wheels." ), ) # type: Callable[..., Option] abis = partial( Option, "--abi", dest="abis", metavar="abi", action="append", default=None, help=( "Only use wheels compatible with Python abi <abi>, e.g. 'pypy_41'. " "If not specified, then the current interpreter abi tag is used. " "Use this option multiple times to specify multiple abis supported " "by the target interpreter. Generally you will need to specify " "--implementation, --platform, and --python-version when using this " "option." ), ) # type: Callable[..., Option] def add_target_python_options(cmd_opts): # type: (OptionGroup) -> None cmd_opts.add_option(platforms()) cmd_opts.add_option(python_version()) cmd_opts.add_option(implementation()) cmd_opts.add_option(abis()) def make_target_python(options): # type: (Values) -> TargetPython target_python = TargetPython( platforms=options.platforms, py_version_info=options.python_version, abis=options.abis, implementation=options.implementation, ) return target_python def prefer_binary(): # type: () -> Option return Option( "--prefer-binary", dest="prefer_binary", action="store_true", default=False, help="Prefer older binary packages over newer source packages.", ) cache_dir = partial( PipOption, "--cache-dir", dest="cache_dir", default=USER_CACHE_DIR, metavar="dir", type="path", help="Store the cache data in <dir>.", ) # type: Callable[..., Option] def _handle_no_cache_dir(option, opt, value, parser): # type: (Option, str, str, OptionParser) -> None """ Process a value provided for the --no-cache-dir option. This is an optparse.Option callback for the --no-cache-dir option. """ # The value argument will be None if --no-cache-dir is passed via the # command-line, since the option doesn't accept arguments. However, # the value can be non-None if the option is triggered e.g. by an # environment variable, like PIP_NO_CACHE_DIR=true. if value is not None: # Then parse the string value to get argument error-checking. try: strtobool(value) except ValueError as exc: raise_option_error(parser, option=option, msg=str(exc)) # Originally, setting PIP_NO_CACHE_DIR to a value that strtobool() # converted to 0 (like "false" or "no") caused cache_dir to be disabled # rather than enabled (logic would say the latter). Thus, we disable # the cache directory not just on values that parse to True, but (for # backwards compatibility reasons) also on values that parse to False. # In other words, always set it to False if the option is provided in # some (valid) form. parser.values.cache_dir = False no_cache = partial( Option, "--no-cache-dir", dest="cache_dir", action="callback", callback=_handle_no_cache_dir, help="Disable the cache.", ) # type: Callable[..., Option] no_deps = partial( Option, "--no-deps", "--no-dependencies", dest="ignore_dependencies", action="store_true", default=False, help="Don't install package dependencies.", ) # type: Callable[..., Option] build_dir = partial( PipOption, "-b", "--build", "--build-dir", "--build-directory", dest="build_dir", type="path", metavar="dir", help=SUPPRESS_HELP, ) # type: Callable[..., Option] ignore_requires_python = partial( Option, "--ignore-requires-python", dest="ignore_requires_python", action="store_true", help="Ignore the Requires-Python information.", ) # type: Callable[..., Option] no_build_isolation = partial( Option, "--no-build-isolation", dest="build_isolation", action="store_false", default=True, help="Disable isolation when building a modern source distribution. " "Build dependencies specified by PEP 518 must be already installed " "if this option is used.", ) # type: Callable[..., Option] def _handle_no_use_pep517(option, opt, value, parser): # type: (Option, str, str, OptionParser) -> None """ Process a value provided for the --no-use-pep517 option. This is an optparse.Option callback for the no_use_pep517 option. """ # Since --no-use-pep517 doesn't accept arguments, the value argument # will be None if --no-use-pep517 is passed via the command-line. # However, the value can be non-None if the option is triggered e.g. # by an environment variable, for example "PIP_NO_USE_PEP517=true". if value is not None: msg = """A value was passed for --no-use-pep517, probably using either the PIP_NO_USE_PEP517 environment variable or the "no-use-pep517" config file option. Use an appropriate value of the PIP_USE_PEP517 environment variable or the "use-pep517" config file option instead. """ raise_option_error(parser, option=option, msg=msg) # Otherwise, --no-use-pep517 was passed via the command-line. parser.values.use_pep517 = False use_pep517 = partial( Option, "--use-pep517", dest="use_pep517", action="store_true", default=None, help="Use PEP 517 for building source distributions " "(use --no-use-pep517 to force legacy behaviour).", ) # type: Any no_use_pep517 = partial( Option, "--no-use-pep517", dest="use_pep517", action="callback", callback=_handle_no_use_pep517, default=None, help=SUPPRESS_HELP, ) # type: Any install_options = partial( Option, "--install-option", dest="install_options", action="append", metavar="options", help="Extra arguments to be supplied to the setup.py install " 'command (use like --install-option="--install-scripts=/usr/local/' 'bin"). Use multiple --install-option options to pass multiple ' "options to setup.py install. If you are using an option with a " "directory path, be sure to use absolute path.", ) # type: Callable[..., Option] build_options = partial( Option, "--build-option", dest="build_options", metavar="options", action="append", help="Extra arguments to be supplied to 'setup.py bdist_wheel'.", ) # type: Callable[..., Option] global_options = partial( Option, "--global-option", dest="global_options", action="append", metavar="options", help="Extra global options to be supplied to the setup.py " "call before the install or bdist_wheel command.", ) # type: Callable[..., Option] no_clean = partial( Option, "--no-clean", action="store_true", default=False, help="Don't clean up build directories.", ) # type: Callable[..., Option] pre = partial( Option, "--pre", action="store_true", default=False, help="Include pre-release and development versions. By default, " "pip only finds stable versions.", ) # type: Callable[..., Option] disable_pip_version_check = partial( Option, "--disable-pip-version-check", dest="disable_pip_version_check", action="store_true", default=False, help="Don't periodically check PyPI to determine whether a new version " "of pip is available for download. Implied with --no-index.", ) # type: Callable[..., Option] def _handle_merge_hash(option, opt_str, value, parser): # type: (Option, str, str, OptionParser) -> None """Given a value spelled "algo:digest", append the digest to a list pointed to in a dict by the algo name.""" if not parser.values.hashes: parser.values.hashes = {} try: algo, digest = value.split(":", 1) except ValueError: parser.error( "Arguments to {} must be a hash name " # noqa "followed by a value, like --hash=sha256:" "abcde...".format(opt_str) ) if algo not in STRONG_HASHES: parser.error( "Allowed hash algorithms for {} are {}.".format( # noqa opt_str, ", ".join(STRONG_HASHES) ) ) parser.values.hashes.setdefault(algo, []).append(digest) hash = partial( Option, "--hash", # Hash values eventually end up in InstallRequirement.hashes due to # __dict__ copying in process_line(). dest="hashes", action="callback", callback=_handle_merge_hash, type="string", help="Verify that the package's archive matches this " "hash before installing. Example: --hash=sha256:abcdef...", ) # type: Callable[..., Option] require_hashes = partial( Option, "--require-hashes", dest="require_hashes", action="store_true", default=False, help="Require a hash to check each requirement against, for " "repeatable installs. This option is implied when any package in a " "requirements file has a --hash option.", ) # type: Callable[..., Option] list_path = partial( PipOption, "--path", dest="path", type="path", action="append", help="Restrict to the specified installation path for listing " "packages (can be used multiple times).", ) # type: Callable[..., Option] def check_list_path_option(options): # type: (Values) -> None if options.path and (options.user or options.local): raise CommandError("Cannot combine '--path' with '--user' or '--local'") list_exclude = partial( PipOption, "--exclude", dest="excludes", action="append", metavar="package", type="package_name", help="Exclude specified package from the output", ) # type: Callable[..., Option] no_python_version_warning = partial( Option, "--no-python-version-warning", dest="no_python_version_warning", action="store_true", default=False, help="Silence deprecation warnings for upcoming unsupported Pythons.", ) # type: Callable[..., Option] use_new_feature = partial( Option, "--use-feature", dest="features_enabled", metavar="feature", action="append", default=[], choices=["2020-resolver", "fast-deps", "in-tree-build"], help="Enable new functionality, that may be backward incompatible.", ) # type: Callable[..., Option] use_deprecated_feature = partial( Option, "--use-deprecated", dest="deprecated_features_enabled", metavar="feature", action="append", default=[], choices=["legacy-resolver"], help=("Enable deprecated functionality, that will be removed in the future."), ) # type: Callable[..., Option] ########## # groups # ########## general_group = { "name": "General Options", "options": [ help_, isolated_mode, require_virtualenv, verbose, version, quiet, log, no_input, proxy, retries, timeout, exists_action, trusted_host, cert, client_cert, cache_dir, no_cache, disable_pip_version_check, no_color, no_python_version_warning, use_new_feature, use_deprecated_feature, ], } # type: Dict[str, Any] index_group = { "name": "Package Index Options", "options": [ index_url, extra_index_url, no_index, find_links, ], } # type: Dict[str, Any]

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import os import textwrap import warnings from functools import partial from optparse import SUPPRESS_HELP, Option, OptionGroup, OptionParser, Values from textwrap import dedent from typing import Any, Callable, Dict, Optional, Tuple from pip._vendor.packaging.utils import canonicalize_name from pip._internal.cli.parser import ConfigOptionParser from pip._internal.cli.progress_bars import BAR_TYPES from pip._internal.exceptions import CommandError from pip._internal.locations import USER_CACHE_DIR, get_src_prefix from pip._internal.models.format_control import FormatControl from pip._internal.models.index import PyPI from pip._internal.models.target_python import TargetPython from pip._internal.utils.hashes import STRONG_HASHES from pip._internal.utils.misc import strtobool def raise_option_error(parser, option, msg): msg = f"{option} error: {msg}" msg = textwrap.fill(" ".join(msg.split())) parser.error(msg) def make_option_group(group, parser): option_group = OptionGroup(parser, group["name"]) for option in group["options"]: option_group.add_option(option()) return option_group def check_install_build_global(options, check_options=None): if check_options is None: check_options = options def getname(n): return getattr(check_options, n, None) names = ["build_options", "global_options", "install_options"] if any(map(getname, names)): control = options.format_control control.disallow_binaries() warnings.warn( "Disabling all use of wheels due to the use of --build-option " "/ --global-option / --install-option.", stacklevel=2, ) def check_dist_restriction(options, check_target=False): dist_restriction_set = any( [ options.python_version, options.platforms, options.abis, options.implementation, ] ) binary_only = FormatControl(set(), {":all:"}) sdist_dependencies_allowed = ( options.format_control != binary_only and not options.ignore_dependencies ) if dist_restriction_set and sdist_dependencies_allowed: raise CommandError( "When restricting platform and interpreter constraints using " "--python-version, --platform, --abi, or --implementation, " "either --no-deps must be set, or --only-binary=:all: must be " "set and --no-binary must not be set (or must be set to " ":none:)." ) if check_target: if dist_restriction_set and not options.target_dir: raise CommandError( "Can not use any platform or abi specific options unless " "installing via '--target'" ) def _path_option_check(option, opt, value): return os.path.expanduser(value) def _package_name_option_check(option, opt, value): return canonicalize_name(value) class PipOption(Option): TYPES = Option.TYPES + ("path", "package_name") TYPE_CHECKER = Option.TYPE_CHECKER.copy() TYPE_CHECKER["package_name"] = _package_name_option_check TYPE_CHECKER["path"] = _path_option_check p.", ) isolated_mode = partial( Option, "--isolated", dest="isolated_mode", action="store_true", default=False, help=( "Run pip in an isolated mode, ignoring environment variables and user " "configuration." ), ) require_virtualenv = partial( Option, "--require-virtualenv", "--require-venv", dest="require_venv", action="store_true", default=False, help=SUPPRESS_HELP, ) verbose = partial( Option, "-v", "--verbose", dest="verbose", action="count", default=0, help="Give more output. Option is additive, and can be used up to 3 times.", ) no_color = partial( Option, "--no-color", dest="no_color", action="store_true", default=False, help="Suppress colored output.", ) version = partial( Option, "-V", "--version", dest="version", action="store_true", help="Show version and exit.", ) quiet = partial( Option, "-q", "--quiet", dest="quiet", action="count", default=0, help=( "Give less output. Option is additive, and can be used up to 3" " times (corresponding to WARNING, ERROR, and CRITICAL logging" " levels)." ), ) progress_bar = partial( Option, "--progress-bar", dest="progress_bar", type="choice", choices=list(BAR_TYPES.keys()), default="on", help=( "Specify type of progress to be displayed [" + "|".join(BAR_TYPES.keys()) + "] (default: %default)" ), ) log = partial( PipOption, "--log", "--log-file", "--local-log", dest="log", metavar="path", type="path", help="Path to a verbose appending log.", ) no_input = partial( Option, "--no-input", dest="no_input", action="store_true", default=False, help="Disable prompting for input.", ) # type: Callable[..., Option] proxy = partial( Option, "--proxy", dest="proxy", type="str", default="", help="Specify a proxy in the form [user:passwd@]proxy.server:port.", ) # type: Callable[..., Option] retries = partial( Option, "--retries", dest="retries", type="int", default=5, help="Maximum number of retries each connection should attempt " "(default %default times).", ) # type: Callable[..., Option] timeout = partial( Option, "--timeout", "--default-timeout", metavar="sec", dest="timeout", type="float", default=15, help="Set the socket timeout (default %default seconds).", ) # type: Callable[..., Option] def exists_action(): # type: () -> Option return Option( # Option when path already exist "--exists-action", dest="exists_action", type="choice", choices=["s", "i", "w", "b", "a"], default=[], action="append", metavar="action", help="Default action when a path already exists: " "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.", ) cert = partial( PipOption, "--cert", dest="cert", type="path", metavar="path", help=( "Path to PEM-encoded CA certificate bundle. " "If provided, overrides the default. " "See 'SSL Certificate Verification' in pip documentation " "for more information." ), ) # type: Callable[..., Option] client_cert = partial( PipOption, "--client-cert", dest="client_cert", type="path", default=None, metavar="path", help="Path to SSL client certificate, a single file containing the " "private key and the certificate in PEM format.", ) # type: Callable[..., Option] index_url = partial( Option, "-i", "--index-url", "--pypi-url", dest="index_url", metavar="URL", default=PyPI.simple_url, help="Base URL of the Python Package Index (default %default). " "This should point to a repository compliant with PEP 503 " "(the simple repository API) or a local directory laid out " "in the same format.", ) # type: Callable[..., Option] def extra_index_url(): # type: () -> Option return Option( "--extra-index-url", dest="extra_index_urls", metavar="URL", action="append", default=[], help="Extra URLs of package indexes to use in addition to " "--index-url. Should follow the same rules as " "--index-url.", ) no_index = partial( Option, "--no-index", dest="no_index", action="store_true", default=False, help="Ignore package index (only looking at --find-links URLs instead).", ) # type: Callable[..., Option] def find_links(): # type: () -> Option return Option( "-f", "--find-links", dest="find_links", action="append", default=[], metavar="url", help="If a URL or path to an html file, then parse for links to " "archives such as sdist (.tar.gz) or wheel (.whl) files. " "If a local path or file:// URL that's a directory, " "then look for archives in the directory listing. " "Links to VCS project URLs are not supported.", ) def trusted_host(): return Option( "--trusted-host", dest="trusted_hosts", action="append", metavar="HOSTNAME", default=[], help="Mark this host or host:port pair as trusted, even though it " "does not have valid or any HTTPS.", ) def constraints(): return Option( "-c", "--constraint", dest="constraints", action="append", default=[], metavar="file", help="Constrain versions using the given constraints file. " "This option can be used multiple times.", ) def requirements(): return Option( "-r", "--requirement", dest="requirements", action="append", default=[], metavar="file", help="Install from the given requirements file. " "This option can be used multiple times.", ) def editable(): return Option( "-e", "--editable", dest="editables", action="append", default=[], metavar="path/url", help=( "Install a project in editable mode (i.e. setuptools " '"develop mode") from a local project path or a VCS url.' ), ) def _handle_src(option, opt_str, value, parser): value = os.path.abspath(value) setattr(parser.values, option.dest, value) src = partial( PipOption, "--src", "--source", "--source-dir", "--source-directory", dest="src_dir", type="path", metavar="dir", default=get_src_prefix(), action="callback", callback=_handle_src, help="Directory to check out editable projects into. " 'The default in a virtualenv is "<venv path>/src". ' 'The default for global installs is "<current dir>/src".', ) def _get_format_control(values, option): return getattr(values, option.dest) def _handle_no_binary(option, opt_str, value, parser): existing = _get_format_control(parser.values, option) FormatControl.handle_mutual_excludes( value, existing.no_binary, existing.only_binary, ) def _handle_only_binary(option, opt_str, value, parser): existing = _get_format_control(parser.values, option) FormatControl.handle_mutual_excludes( value, existing.only_binary, existing.no_binary, ) def no_binary(): format_control = FormatControl(set(), set()) return Option( "--no-binary", dest="format_control", action="callback", callback=_handle_no_binary, type="str", default=format_control, help="Do not use binary packages. Can be supplied multiple times, and " 'each time adds to the existing value. Accepts either ":all:" to ' 'disable all binary packages, ":none:" to empty the set (notice ' "the colons), or one or more package names with commas between " "them (no colons). Note that some packages are tricky to compile " "and may fail to install when this option is used on them.", ) def only_binary(): format_control = FormatControl(set(), set()) return Option( "--only-binary", dest="format_control", action="callback", callback=_handle_only_binary, type="str", default=format_control, help="Do not use source packages. Can be supplied multiple times, and " 'each time adds to the existing value. Accepts either ":all:" to ' 'disable all source packages, ":none:" to empty the set, or one ' "or more package names with commas between them. Packages " "without binary distributions will fail to install when this " "option is used on them.", ) platforms = partial( Option, "--platform", dest="platforms", metavar="platform", action="append", default=None, help=( "Only use wheels compatible with <platform>. Defaults to the " "platform of the running system. Use this option multiple times to " "specify multiple platforms supported by the target interpreter." ), ) def _convert_python_version(value): if not value: return (None, None) parts = value.split(".") if len(parts) > 3: return ((), "at most three version parts are allowed") if len(parts) == 1: value = parts[0] if len(value) > 1: parts = [value[0], value[1:]] try: version_info = tuple(int(part) for part in parts) except ValueError: return ((), "each version part must be an integer") return (version_info, None) def _handle_python_version(option, opt_str, value, parser): version_info, error_msg = _convert_python_version(value) if error_msg is not None: msg = "invalid --python-version value: {!r}: {}".format( value, error_msg, ) raise_option_error(parser, option=option, msg=msg) parser.values.python_version = version_info python_version = partial( Option, "--python-version", dest="python_version", metavar="python_version", action="callback", callback=_handle_python_version, type="str", default=None, help=dedent( """\ The Python interpreter version to use for wheel and "Requires-Python" compatibility checks. Defaults to a version derived from the running interpreter. The version can be specified using up to three dot-separated integers (e.g. "3" for 3.0.0, "3.7" for 3.7.0, or "3.7.3"). A major-minor version can also be given as a string without dots (e.g. "37" for 3.7.0). """ ), ) implementation = partial( Option, "--implementation", dest="implementation", metavar="implementation", default=None, help=( "Only use wheels compatible with Python " "implementation <implementation>, e.g. 'pp', 'jy', 'cp', " " or 'ip'. If not specified, then the current " "interpreter implementation is used. Use 'py' to force " "implementation-agnostic wheels." ), ) abis = partial( Option, "--abi", dest="abis", metavar="abi", action="append", default=None, help=( "Only use wheels compatible with Python abi <abi>, e.g. 'pypy_41'. " "If not specified, then the current interpreter abi tag is used. " "Use this option multiple times to specify multiple abis supported " "by the target interpreter. Generally you will need to specify " "--implementation, --platform, and --python-version when using this " "option." ), ) def add_target_python_options(cmd_opts): cmd_opts.add_option(platforms()) cmd_opts.add_option(python_version()) cmd_opts.add_option(implementation()) cmd_opts.add_option(abis()) def make_target_python(options): target_python = TargetPython( platforms=options.platforms, py_version_info=options.python_version, abis=options.abis, implementation=options.implementation, ) return target_python def prefer_binary(): return Option( "--prefer-binary", dest="prefer_binary", action="store_true", default=False, help="Prefer older binary packages over newer source packages.", ) cache_dir = partial( PipOption, "--cache-dir", dest="cache_dir", default=USER_CACHE_DIR, metavar="dir", type="path", help="Store the cache data in <dir>.", ) def _handle_no_cache_dir(option, opt, value, parser): # the value can be non-None if the option is triggered e.g. by an # environment variable, like PIP_NO_CACHE_DIR=true. if value is not None: # Then parse the string value to get argument error-checking. try: strtobool(value) except ValueError as exc: raise_option_error(parser, option=option, msg=str(exc)) # Originally, setting PIP_NO_CACHE_DIR to a value that strtobool() # converted to 0 (like "false" or "no") caused cache_dir to be disabled # rather than enabled (logic would say the latter). Thus, we disable # the cache directory not just on values that parse to True, but (for # backwards compatibility reasons) also on values that parse to False. # In other words, always set it to False if the option is provided in # some (valid) form. parser.values.cache_dir = False no_cache = partial( Option, "--no-cache-dir", dest="cache_dir", action="callback", callback=_handle_no_cache_dir, help="Disable the cache.", ) # type: Callable[..., Option] no_deps = partial( Option, "--no-deps", "--no-dependencies", dest="ignore_dependencies", action="store_true", default=False, help="Don't install package dependencies.", ) build_dir = partial( PipOption, "-b", "--build", "--build-dir", "--build-directory", dest="build_dir", type="path", metavar="dir", help=SUPPRESS_HELP, ) ignore_requires_python = partial( Option, "--ignore-requires-python", dest="ignore_requires_python", action="store_true", help="Ignore the Requires-Python information.", ) no_build_isolation = partial( Option, "--no-build-isolation", dest="build_isolation", action="store_false", default=True, help="Disable isolation when building a modern source distribution. " "Build dependencies specified by PEP 518 must be already installed " "if this option is used.", ) def _handle_no_use_pep517(option, opt, value, parser): # will be None if --no-use-pep517 is passed via the command-line. # However, the value can be non-None if the option is triggered e.g. # by an environment variable, for example "PIP_NO_USE_PEP517=true". if value is not None: msg = """A value was passed for --no-use-pep517, probably using either the PIP_NO_USE_PEP517 environment variable or the "no-use-pep517" config file option. Use an appropriate value of the PIP_USE_PEP517 environment variable or the "use-pep517" config file option instead. """ raise_option_error(parser, option=option, msg=msg) # Otherwise, --no-use-pep517 was passed via the command-line. parser.values.use_pep517 = False use_pep517 = partial( Option, "--use-pep517", dest="use_pep517", action="store_true", default=None, help="Use PEP 517 for building source distributions " "(use --no-use-pep517 to force legacy behaviour).", ) # type: Any no_use_pep517 = partial( Option, "--no-use-pep517", dest="use_pep517", action="callback", callback=_handle_no_use_pep517, default=None, help=SUPPRESS_HELP, ) # type: Any install_options = partial( Option, "--install-option", dest="install_options", action="append", metavar="options", help="Extra arguments to be supplied to the setup.py install " 'command (use like --install-option="--install-scripts=/usr/local/' 'bin"). Use multiple --install-option options to pass multiple ' "options to setup.py install. If you are using an option with a " "directory path, be sure to use absolute path.", ) # type: Callable[..., Option] build_options = partial( Option, "--build-option", dest="build_options", metavar="options", action="append", help="Extra arguments to be supplied to 'setup.py bdist_wheel'.", ) # type: Callable[..., Option] global_options = partial( Option, "--global-option", dest="global_options", action="append", metavar="options", help="Extra global options to be supplied to the setup.py " "call before the install or bdist_wheel command.", ) # type: Callable[..., Option] no_clean = partial( Option, "--no-clean", action="store_true", default=False, help="Don't clean up build directories.", ) pre = partial( Option, "--pre", action="store_true", default=False, help="Include pre-release and development versions. By default, " "pip only finds stable versions.", ) disable_pip_version_check = partial( Option, "--disable-pip-version-check", dest="disable_pip_version_check", action="store_true", default=False, help="Don't periodically check PyPI to determine whether a new version " "of pip is available for download. Implied with --no-index.", ) # type: Callable[..., Option] def _handle_merge_hash(option, opt_str, value, parser): # type: (Option, str, str, OptionParser) -> None if not parser.values.hashes: parser.values.hashes = {} try: algo, digest = value.split(":", 1) except ValueError: parser.error( "Arguments to {} must be a hash name " # noqa "followed by a value, like --hash=sha256:" "abcde...".format(opt_str) ) if algo not in STRONG_HASHES: parser.error( "Allowed hash algorithms for {} are {}.".format( # noqa opt_str, ", ".join(STRONG_HASHES) ) ) parser.values.hashes.setdefault(algo, []).append(digest) hash = partial( Option, "--hash", # Hash values eventually end up in InstallRequirement.hashes due to # __dict__ copying in process_line(). dest="hashes", action="callback", callback=_handle_merge_hash, type="string", help="Verify that the package's archive matches this " "hash before installing. Example: --hash=sha256:abcdef...", ) require_hashes = partial( Option, "--require-hashes", dest="require_hashes", action="store_true", default=False, help="Require a hash to check each requirement against, for " "repeatable installs. This option is implied when any package in a " "requirements file has a --hash option.", ) list_path = partial( PipOption, "--path", dest="path", type="path", action="append", help="Restrict to the specified installation path for listing " "packages (can be used multiple times).", ) def check_list_path_option(options): if options.path and (options.user or options.local): raise CommandError("Cannot combine '--path' with '--user' or '--local'") list_exclude = partial( PipOption, "--exclude", dest="excludes", action="append", metavar="package", type="package_name", help="Exclude specified package from the output", ) no_python_version_warning = partial( Option, "--no-python-version-warning", dest="no_python_version_warning", action="store_true", default=False, help="Silence deprecation warnings for upcoming unsupported Pythons.", ) use_new_feature = partial( Option, "--use-feature", dest="features_enabled", metavar="feature", action="append", default=[], choices=["2020-resolver", "fast-deps", "in-tree-build"], help="Enable new functionality, that may be backward incompatible.", ) use_deprecated_feature = partial( Option, "--use-deprecated", dest="deprecated_features_enabled", metavar="feature", action="append", default=[], choices=["legacy-resolver"], help=("Enable deprecated functionality, that will be removed in the future."), ) isolated_mode, require_virtualenv, verbose, version, quiet, log, no_input, proxy, retries, timeout, exists_action, trusted_host, cert, client_cert, cache_dir, no_cache, disable_pip_version_check, no_color, no_python_version_warning, use_new_feature, use_deprecated_feature, ], } index_group = { "name": "Package Index Options", "options": [ index_url, extra_index_url, no_index, find_links, ], }

true

f71c0e613c8658e9c5d1b5f8b68e473cd366b6c5

8,773

py

Python

src/sst/elements/memHierarchy/tests/testsuite_default_memHierarchy_sdl.py

sudhanshu2/sst-elements

d658e5e4b26e5725488f9e93528506ddb22072ee

[ "BSD-3-Clause" ]

null

src/sst/elements/memHierarchy/tests/testsuite_default_memHierarchy_sdl.py

sudhanshu2/sst-elements

d658e5e4b26e5725488f9e93528506ddb22072ee

[ "BSD-3-Clause" ]

null

src/sst/elements/memHierarchy/tests/testsuite_default_memHierarchy_sdl.py

sudhanshu2/sst-elements

d658e5e4b26e5725488f9e93528506ddb22072ee

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from sst_unittest import * from sst_unittest_support import * import os.path ################################################################################ # Code to support a single instance module initialize, must be called setUp method module_init = 0 module_sema = threading.Semaphore() def initializeTestModule_SingleInstance(class_inst): global module_init global module_sema module_sema.acquire() if module_init != 1: try: # Put your single instance Init Code Here pass except: pass module_init = 1 module_sema.release() ################################################################################ ################################################################################ ################################################################################ class testcase_memHierarchy_sdl(SSTTestCase): def initializeClass(self, testName): super(type(self), self).initializeClass(testName) # Put test based setup code here. it is called before testing starts # NOTE: This method is called once for every test def setUp(self): super(type(self), self).setUp() initializeTestModule_SingleInstance(self) # Put test based setup code here. it is called once before every test def tearDown(self): # Put test based teardown code here. it is called once after every test super(type(self), self).tearDown() ##### def test_memHierarchy_sdl_1(self): # sdl-1 Simple CPU + 1 level cache + Memory self.memHierarchy_Template("sdl-1") def test_memHierarchy_sdl_2(self): # sdl-2 Simple CPU + 1 level cache + DRAMSim Memory self.memHierarchy_Template("sdl-2") def test_memHierarchy_sdl_3(self): # sdl-3 Simple CPU + 1 level cache + DRAMSim Memory (alternate block size) self.memHierarchy_Template("sdl-3") def test_memHierarchy_sdl2_1(self): # sdl2-1 Simple CPU + 2 levels cache + Memory self.memHierarchy_Template("sdl2-1") def test_memHierarchy_sdl3_1(self): # sdl3-1 2 Simple CPUs + 2 levels cache + Memory self.memHierarchy_Template("sdl3-1") def test_memHierarchy_sdl3_2(self): # sdl3-2 2 Simple CPUs + 2 levels cache + DRAMSim Memory self.memHierarchy_Template("sdl3-2") def test_memHierarchy_sdl3_3(self): self.memHierarchy_Template("sdl3-3") def test_memHierarchy_sdl4_1(self): self.memHierarchy_Template("sdl4-1") @skip_on_sstsimulator_conf_empty_str("DRAMSIM", "LIBDIR", "DRAMSIM is not included as part of this build") def test_memHierarchy_sdl4_2_dramsim(self): self.memHierarchy_Template("sdl4-2", ignore_err_file=True) @skip_on_sstsimulator_conf_empty_str("RAMULATOR", "LIBDIR", "RAMULATOR is not included as part of this build") def test_memHierarchy_sdl4_2_ramulator(self): self.memHierarchy_Template("sdl4-2-ramulator") @skip_on_sstsimulator_conf_empty_str("DRAMSIM", "LIBDIR", "DRAMSIM is not included as part of this build") def test_memHierarchy_sdl5_1_dramsim(self): self.memHierarchy_Template("sdl5-1", ignore_err_file=True) @skip_on_sstsimulator_conf_empty_str("RAMULATOR", "LIBDIR", "RAMULATOR is not included as part of this build") def test_memHierarchy_sdl5_1_ramulator(self): if testing_check_get_num_ranks() > 1 or testing_check_get_num_threads() > 1: self.memHierarchy_Template("sdl5-1-ramulator_MC") else: self.memHierarchy_Template("sdl5-1-ramulator") def test_memHierarchy_sdl8_1(self): self.memHierarchy_Template("sdl8-1") def test_memHierarchy_sdl8_3(self): self.memHierarchy_Template("sdl8-3") def test_memHierarchy_sdl8_4(self): self.memHierarchy_Template("sdl8-4") def test_memHierarchy_sdl9_1(self): self.memHierarchy_Template("sdl9-1") def test_memHierarchy_sdl9_2(self): self.memHierarchy_Template("sdl9-2") ##### def memHierarchy_Template(self, testcase, ignore_err_file=False): # Get the path to the test files test_path = self.get_testsuite_dir() outdir = self.get_test_output_run_dir() tmpdir = self.get_test_output_tmp_dir() # Some tweeking of file names are due to inconsistencys with testcase name testcasename_sdl = testcase.replace("_MC", "") testcasename_out = testcase.replace("-", "_") # Set the various file paths testDataFileName=("test_memHierarchy_{0}".format(testcasename_out)) sdlfile = "{0}/{1}.py".format(test_path, testcasename_sdl) reffile = "{0}/refFiles/{1}.out".format(test_path, testDataFileName) outfile = "{0}/{1}.out".format(outdir, testDataFileName) errfile = "{0}/{1}.err".format(outdir, testDataFileName) mpioutfiles = "{0}/{1}.testfile".format(outdir, testDataFileName) log_debug("testcase = {0}".format(testcase)) log_debug("sdl file = {0}".format(sdlfile)) log_debug("ref file = {0}".format(reffile)) # Run SST in the tests directory self.run_sst(sdlfile, outfile, errfile, set_cwd=test_path, mpi_out_files=mpioutfiles) # Lines to ignore # These are generated by DRAMSim ignore_lines = ["===== MemorySystem"] ignore_lines.append("TOTAL_STORAGE : 2048MB | 1 Ranks | 16 Devices per rank") ignore_lines.append("== Loading") ignore_lines.append("DRAMSim2 Clock Frequency =1Hz, CPU Clock Frequency=1Hz") ignore_lines.append("WARNING: UNKNOWN KEY 'DEBUG_TRANS_FLOW' IN INI FILE") # This is generated by SST when the number of ranks/threads > # of components ignore_lines.append("WARNING: No components are assigned to") #These are warnings/info generated by SST/memH in debug mode ignore_lines.append("Notice: memory controller's region is larger than the backend's mem_size") ignore_lines.append("Region: start=") # This may be present if ranks < 2 ignore_lines.append("not aligned to the request size") # Statistics that count occupancy on each cycle sometimes diff in parallel execution # due to the synchronization interval sometimes allowing the clock to run ahead a cycle or so tol_stats = { "outstanding_requests" : [0, 0, 20, 0, 0], # Only diffs in number of cycles "total_cycles" : [20, 'X', 20, 20, 20], # This stat is set once at the end of sim. May vary in all fields "MSHR_occupancy" : [0, 0, 20, 0, 0] } # Only diffs in number of cycles filesAreTheSame, statDiffs, othDiffs = testing_stat_output_diff(outfile, reffile, ignore_lines, tol_stats, True) # Perform the tests if ignore_err_file is False: if os_test_file(errfile, "-s"): log_testing_note("memHierarchy SDL test {0} has a Non-Empty Error File {1}".format(testDataFileName, errfile)) if filesAreTheSame: log_debug(" -- Output file {0} passed check against the Reference File {1}".format(outfile, reffile)) else: diffdata = self._prettyPrintDiffs(statDiffs, othDiffs) log_failure(diffdata) self.assertTrue(filesAreTheSame, "Output file {0} does not pass check against the Reference File {1} ".format(outfile, reffile)) ### # Remove lines containing any string found in 'remove_strs' from in_file # If out_file != None, output is out_file # Otherwise, in_file is overwritten def _remove_lines_cleanup_file(self, remove_strs, in_file, out_file = None, append = False): with open(in_file, 'r') as fp: lines = fp.readlines() if out_file == None: out_file = in_file if append == True: mode = 'a' else: mode = 'w' with open(out_file, mode) as fp: if not append: fp.truncate(0) for line in lines: skip = False for search in remove_strs: if search in line: skip = True continue if not skip: fp.write(line) def _prettyPrintDiffs(self, stat_diff, oth_diff): out = "" if len(stat_diff) != 0: out = "Statistic diffs:\n" for x in stat_diff: out += (x[0] + " " + ",".join(str(y) for y in x[1:]) + "\n") if len(oth_diff) != 0: out += "Non-statistic diffs:\n" for x in oth_diff: out += x[0] + " " + x[1] + "\n" return out

40.428571

140

0.619514

from sst_unittest import * from sst_unittest_support import * import os.path

true

f71c0f38bd33c235f501fe3157c2816cd7eb598d

2,430

py

Python

data/p4VQE/R4/benchmark/startQiskit_noisy62.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/p4VQE/R4/benchmark/startQiskit_noisy62.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/p4VQE/R4/benchmark/startQiskit_noisy62.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

# qubit number=3 # total number=10 import numpy as np from qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ import networkx as nx from qiskit.visualization import plot_histogram from typing import * from pprint import pprint from math import log2 from collections import Counter from qiskit.test.mock import FakeVigo, FakeYorktown kernel = 'circuit/bernstein' def make_circuit(n:int) -> QuantumCircuit: # circuit begin input_qubit = QuantumRegister(n,"qc") prog = QuantumCircuit(input_qubit) prog.h(input_qubit[0]) # number=1 prog.h(input_qubit[1]) # number=2 prog.h(input_qubit[2]) # number=3 prog.h(input_qubit[3]) # number=4 prog.y(input_qubit[3]) # number=5 for edge in E: k = edge[0] l = edge[1] prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1]) prog.p(gamma, k) prog.p(gamma, l) prog.rx(2 * beta, range(len(V))) prog.swap(input_qubit[1],input_qubit[0]) # number=6 prog.swap(input_qubit[1],input_qubit[0]) # number=7 prog.y(input_qubit[3]) # number=8 prog.y(input_qubit[3]) # number=9 # circuit end return prog if __name__ == '__main__': n = 4 V = np.arange(0, n, 1) E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)] G = nx.Graph() G.add_nodes_from(V) G.add_weighted_edges_from(E) step_size = 0.1 a_gamma = np.arange(0, np.pi, step_size) a_beta = np.arange(0, np.pi, step_size) a_gamma, a_beta = np.meshgrid(a_gamma, a_beta) F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * ( 1 + np.cos(4 * a_gamma) ** 2) result = np.where(F1 == np.amax(F1)) a = list(zip(result[0], result[1]))[0] gamma = a[0] * step_size beta = a[1] * step_size prog = make_circuit(4) sample_shot =5600 writefile = open("../data/startQiskit_noisy62.csv", "w") # prog.draw('mpl', filename=(kernel + '.png')) backend = FakeYorktown() circuit1 = transpile(prog, FakeYorktown()) circuit1.measure_all() prog = circuit1 info = execute(prog,backend=backend, shots=sample_shot).result().get_counts() print(info, file=writefile) print("results end", file=writefile) print(circuit1.depth(), file=writefile) print(circuit1, file=writefile) writefile.close()

27

118

0.631687

import numpy as np from qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ import networkx as nx from qiskit.visualization import plot_histogram from typing import * from pprint import pprint from math import log2 from collections import Counter from qiskit.test.mock import FakeVigo, FakeYorktown kernel = 'circuit/bernstein' def make_circuit(n:int) -> QuantumCircuit: input_qubit = QuantumRegister(n,"qc") prog = QuantumCircuit(input_qubit) prog.h(input_qubit[0]) prog.h(input_qubit[1]) prog.h(input_qubit[2]) prog.h(input_qubit[3]) prog.y(input_qubit[3]) for edge in E: k = edge[0] l = edge[1] prog.cp(-2 * gamma, input_qubit[k-1], input_qubit[l-1]) prog.p(gamma, k) prog.p(gamma, l) prog.rx(2 * beta, range(len(V))) prog.swap(input_qubit[1],input_qubit[0]) prog.swap(input_qubit[1],input_qubit[0]) prog.y(input_qubit[3]) prog.y(input_qubit[3]) return prog if __name__ == '__main__': n = 4 V = np.arange(0, n, 1) E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)] G = nx.Graph() G.add_nodes_from(V) G.add_weighted_edges_from(E) step_size = 0.1 a_gamma = np.arange(0, np.pi, step_size) a_beta = np.arange(0, np.pi, step_size) a_gamma, a_beta = np.meshgrid(a_gamma, a_beta) F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * ( 1 + np.cos(4 * a_gamma) ** 2) result = np.where(F1 == np.amax(F1)) a = list(zip(result[0], result[1]))[0] gamma = a[0] * step_size beta = a[1] * step_size prog = make_circuit(4) sample_shot =5600 writefile = open("../data/startQiskit_noisy62.csv", "w") backend = FakeYorktown() circuit1 = transpile(prog, FakeYorktown()) circuit1.measure_all() prog = circuit1 info = execute(prog,backend=backend, shots=sample_shot).result().get_counts() print(info, file=writefile) print("results end", file=writefile) print(circuit1.depth(), file=writefile) print(circuit1, file=writefile) writefile.close()

true

f71c0f815fea461fc753f56d6e32829a423105fd

431

py

Python

project/tasks/sample_tasks.py

idjemaoune/django-celery

411e854fc63a4d42be2a6e8861c4dc6b89969161

[ "MIT" ]

null

project/tasks/sample_tasks.py

idjemaoune/django-celery

411e854fc63a4d42be2a6e8861c4dc6b89969161

[ "MIT" ]

null

project/tasks/sample_tasks.py

idjemaoune/django-celery

411e854fc63a4d42be2a6e8861c4dc6b89969161

[ "MIT" ]

null

# project/tasks/sample_tasks.py import time from celery import shared_task @shared_task def send_email(email_id, message): time.sleep(10) print(f"Email is sent to {email_id}. Message sent was - {message}") @shared_task def get_micro_app_status(app): print(f"La micro app {app}. est UP") @shared_task def create_task(task_type): time.sleep(int(task_type) * 10) print("je suis execueter") return True

17.958333

71

0.716937

import time from celery import shared_task @shared_task def send_email(email_id, message): time.sleep(10) print(f"Email is sent to {email_id}. Message sent was - {message}") @shared_task def get_micro_app_status(app): print(f"La micro app {app}. est UP") @shared_task def create_task(task_type): time.sleep(int(task_type) * 10) print("je suis execueter") return True

true

f71c0fb67deffb5ad5e92f615b5c852fabdd95ff

9,938

py

Python

docs/_downloads/d923ca53b1bfbeb3c222ae46d65d485e/transfer_learning_tutorial.py

pleiades-s/PyTorch-tutorials-kr

3d749ea2fe67363b5d46340b742308b744fa0419

[ "BSD-3-Clause" ]

2

2021-01-18T04:59:05.000Z

2021-03-20T00:56:24.000Z

docs/_downloads/d923ca53b1bfbeb3c222ae46d65d485e/transfer_learning_tutorial.py

pleiades-s/PyTorch-tutorials-kr

3d749ea2fe67363b5d46340b742308b744fa0419

[ "BSD-3-Clause" ]

null

docs/_downloads/d923ca53b1bfbeb3c222ae46d65d485e/transfer_learning_tutorial.py

pleiades-s/PyTorch-tutorials-kr

3d749ea2fe67363b5d46340b742308b744fa0419

[ "BSD-3-Clause" ]

1

2022-02-27T10:47:39.000Z

# -*- coding: utf-8 -*- """ 컴퓨터 비전(Vision)을 위한 전이학습(Transfer Learning) ======================================================= **Author**: `Sasank Chilamkurthy <https://chsasank.github.io>`_ **번역**: `박정환 <http://github.com/9bow>`_ 이 튜토리얼에서는 전이학습(Transfer Learning)을 이용하여 이미지 분류를 위한 합성곱 신경망을 어떻게 학습시키는지 배워보겠습니다. 전이학습에 대해서는 `CS231n 노트 <http://cs231n.github.io/transfer-learning/>`__ 에서 더 많은 내용을 읽어보실 수 있습니다. 위 노트를 인용해보면, 실제로 충분한 크기의 데이터셋을 갖추기는 상대적으로 드물기 때문에, (무작위 초기화를 통해) 맨 처음부터 합성곱 신경망(Convolutional Network) 전체를 학습하는 사람은 매우 적습니다. 대신, 매우 큰 데이터셋(예. 100가지 분류에 대해 120만개의 이미지가 포함된 ImageNet)에서 합성곱 신경망(ConvNet)을 미리 학습한 후, 이 합성곱 신경망을 관심있는 작업 을 위한 초기 설정 또는 고정된 특징 추출기(fixed feature extractor)로 사용합니다. 이러한 전이학습 시나리오의 주요한 2가지는 다음과 같습니다: - **합성곱 신경망의 미세조정(finetuning)**: 무작위 초기화 대신, 신경망을 ImageNet 1000 데이터셋 등으로 미리 학습한 신경망으로 초기화합니다. 학습의 나머지 과정들은 평상시와 같습니다. - **고정된 특징 추출기로써의 합성곱 신경망**: 여기서는 마지막에 완전히 연결 된 계층을 제외한 모든 신경망의 가중치를 고정합니다. 이 마지막의 완전히 연결된 계층은 새로운 무작위의 가중치를 갖는 계층으로 대체되어 이 계층만 학습합니다. """ # License: BSD # Author: Sasank Chilamkurthy from __future__ import print_function, division import torch import torch.nn as nn import torch.optim as optim from torch.optim import lr_scheduler import numpy as np import torchvision from torchvision import datasets, models, transforms import matplotlib.pyplot as plt import time import os import copy plt.ion() # 대화형 모드 ###################################################################### # 데이터 불러오기 # --------------- # # 데이터를 불러오기 위해 torchvision과 torch.utils.data 패키지를 사용하겠습니다. # # 여기서 풀고자 하는 문제는 **개미** 와 **벌** 을 분류하는 모델을 학습하는 것입니다. # 개미와 벌 각각의 학습용 이미지는 대략 120장 정도 있고, 75개의 검증용 이미지가 # 있습니다. 일반적으로 맨 처음부터 학습을 한다면 이는 일반화하기에는 아주 작은 # 데이터셋입니다. 하지만 우리는 전이학습을 할 것이므로, 일반화를 제법 잘 할 수 있을 # 것입니다. # # 이 데이터셋은 ImageNet의 아주 작은 일부입니다. # # .. Note :: # 데이터를 `여기 <https://download.pytorch.org/tutorial/hymenoptera_data.zip>`_ # 에서 다운로드 받아 현재 디렉토리에 압축을 푸십시오. # 학습을 위해 데이터 증가(augmentation) 및 일반화(normalization) # 검증을 위한 일반화 data_transforms = { 'train': transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), 'val': transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), } data_dir = 'data/hymenoptera_data' image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']} dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=4) for x in ['train', 'val']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} class_names = image_datasets['train'].classes device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") ###################################################################### # 일부 이미지 시각화하기 # ^^^^^^^^^^^^^^^^^^^^^^^^^ # 데이터 증가를 이해하기 위해 일부 학습용 이미지를 시각화해보겠습니다. def imshow(inp, title=None): """Imshow for Tensor.""" inp = inp.numpy().transpose((1, 2, 0)) mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) inp = std * inp + mean inp = np.clip(inp, 0, 1) plt.imshow(inp) if title is not None: plt.title(title) plt.pause(0.001) # 갱신이 될 때까지 잠시 기다립니다. # 학습 데이터의 배치를 얻습니다. inputs, classes = next(iter(dataloaders['train'])) # 배치로부터 격자 형태의 이미지를 만듭니다. out = torchvision.utils.make_grid(inputs) imshow(out, title=[class_names[x] for x in classes]) ###################################################################### # 모델 학습하기 # -------------- # # 이제 모델을 학습하기 위한 일반 함수를 작성해보겠습니다. 여기서는 다음 내용들을 # 설명합니다: # # - 학습율(learning rate) 관리(scheduling) # - 최적의 모델 구하기 # # 아래에서 ``scheduler`` 매개변수는 ``torch.optim.lr_scheduler`` 의 LR 스케쥴러 # 객체(Object)입니다. def train_model(model, criterion, optimizer, scheduler, num_epochs=25): since = time.time() best_model_wts = copy.deepcopy(model.state_dict()) best_acc = 0.0 for epoch in range(num_epochs): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10) # 각 에폭(epoch)은 학습 단계와 검증 단계를 갖습니다. for phase in ['train', 'val']: if phase == 'train': model.train() # 모델을 학습 모드로 설정 else: model.eval() # 모델을 평가 모드로 설정 running_loss = 0.0 running_corrects = 0 # 데이터를 반복 for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) # 매개변수 경사도를 0으로 설정 optimizer.zero_grad() # 순전파 # 학습 시에만 연산 기록을 추적 with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) # 학습 단계인 경우 역전파 + 최적화 if phase == 'train': loss.backward() optimizer.step() # 통계 running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) if phase == 'train': scheduler.step() epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] print('{} Loss: {:.4f} Acc: {:.4f}'.format( phase, epoch_loss, epoch_acc)) # 모델을 깊은 복사(deep copy)함 if phase == 'val' and epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = copy.deepcopy(model.state_dict()) print() time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60)) print('Best val Acc: {:4f}'.format(best_acc)) # 가장 나은 모델 가중치를 불러옴 model.load_state_dict(best_model_wts) return model ###################################################################### # 모델 예측값 시각화하기 # ^^^^^^^^^^^^^^^^^^^^^^^ # # 일부 이미지에 대한 예측값을 보여주는 일반화된 함수입니다. # def visualize_model(model, num_images=6): was_training = model.training model.eval() images_so_far = 0 fig = plt.figure() with torch.no_grad(): for i, (inputs, labels) in enumerate(dataloaders['val']): inputs = inputs.to(device) labels = labels.to(device) outputs = model(inputs) _, preds = torch.max(outputs, 1) for j in range(inputs.size()[0]): images_so_far += 1 ax = plt.subplot(num_images//2, 2, images_so_far) ax.axis('off') ax.set_title('predicted: {}'.format(class_names[preds[j]])) imshow(inputs.cpu().data[j]) if images_so_far == num_images: model.train(mode=was_training) return model.train(mode=was_training) ###################################################################### # 합성곱 신경망 미세조정(finetuning) # ---------------------------------- # # 미리 학습한 모델을 불러온 후 마지막의 완전히 연결된 계층을 초기화합니다. # model_ft = models.resnet18(pretrained=True) num_ftrs = model_ft.fc.in_features # 여기서 각 출력 샘플의 크기는 2로 설정합니다. # 또는, nn.Linear(num_ftrs, len (class_names))로 일반화할 수 있습니다. model_ft.fc = nn.Linear(num_ftrs, 2) model_ft = model_ft.to(device) criterion = nn.CrossEntropyLoss() # 모든 매개변수들이 최적화되었는지 관찰 optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9) # 7 에폭마다 0.1씩 학습율 감소 exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1) ###################################################################### # 학습 및 평가하기 # ^^^^^^^^^^^^^^^^^^ # # CPU에서는 15-25분 가량, GPU에서는 1분도 이내의 시간이 걸립니다. # model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25) ###################################################################### # visualize_model(model_ft) ###################################################################### # 고정된 특징 추출기로써의 합성곱 신경망 # --------------------------------------- # # 이제, 마지막 계층을 제외한 신경망의 모든 부분을 고정해야 합니다. # ``requires_grad == False`` 로 설정하여 매개변수를 고정하여 ``backward()`` 중에 # 경사도가 계산되지 않도록 해야합니다. # # 이에 대한 문서는 # `여기 <http://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward>`__ # 에서 확인할 수 있습니다. # model_conv = torchvision.models.resnet18(pretrained=True) for param in model_conv.parameters(): param.requires_grad = False # 새로 생성된 모듈의 매개변수는 기본값이 requires_grad=True 임 num_ftrs = model_conv.fc.in_features model_conv.fc = nn.Linear(num_ftrs, 2) model_conv = model_conv.to(device) criterion = nn.CrossEntropyLoss() # 이전과는 다르게 마지막 계층의 매개변수들만 최적화되는지 관찰 optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9) # 7 에폭마다 0.1씩 학습율 감소 exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1) ###################################################################### # 학습 및 평가하기 # ^^^^^^^^^^^^^^^^^ # # CPU에서 실행하는 경우 이전과 비교했을 때 약 절반 가량의 시간만이 소요될 것입니다. # 이는 대부분의 신경망에서 경사도를 계산할 필요가 없기 때문입니다. 하지만, # 순전파는 계산이 필요합니다. # model_conv = train_model(model_conv, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=25) ###################################################################### # visualize_model(model_conv) plt.ioff() plt.show() ###################################################################### # 더 배워볼 내용 # ----------------- # # 전이학습의 응용 사례(application)들을 더 알아보려면, # :doc:`/intermediate/quantized_transfer_learning_tutorial` 을 참조해보세요. # #

29.229412

88

0.553934

from __future__ import print_function, division import torch import torch.nn as nn import torch.optim as optim from torch.optim import lr_scheduler import numpy as np import torchvision from torchvision import datasets, models, transforms import matplotlib.pyplot as plt import time import os import copy plt.ion()

true

f71c100fdfb127051c66f6dd3ec6cfe317c4ad61

3,783

py

Python

my3b1b/old/MicoaelPrimo.py

Micoael/3b1b-styled-video-code

036b339573e48f807e215bc7c7be9c6fe32b601d

[ "Apache-2.0" ]

7

2020-03-02T23:56:39.000Z

2020-06-08T15:05:46.000Z

my3b1b/old/MicoaelPrimo.py

Micoael/3b1b-styled-video-code

036b339573e48f807e215bc7c7be9c6fe32b601d

[ "Apache-2.0" ]

null

my3b1b/old/MicoaelPrimo.py

Micoael/3b1b-styled-video-code

036b339573e48f807e215bc7c7be9c6fe32b601d

[ "Apache-2.0" ]

null

from manimlib.imports import * class StartingScene(Scene): def construct(_): e = Text("Manim homework by mp",font="Consolas",color=BLUE) _.play(Write(e),run_time=3) _.wait() _.play(Uncreate(e)) A = Dot().move_to(np.array([0-2,0,0])) B = Dot().move_to(np.array([9/10-2,12/10,0])) C = Dot().move_to(np.array([5/2-2,0,0])) D = B.copy().shift(9/10*UP+6/5*LEFT) E = A.copy().shift(9/10*UP+6/5*LEFT) F = B.copy().shift(8/5*UP+6/5*RIGHT) G = C.copy().shift(8/5*UP+6/5*RIGHT) H = A.copy().shift(5/2*DOWN) I = C.copy().shift(5/2*DOWN) lab = VGroup() labtxt = [TextMobject("A").next_to(A).scale(0.5), TextMobject("B").next_to(B).scale(0.5), TextMobject("C").next_to(C).scale(0.5), TextMobject("D").next_to(D).scale(0.5), TextMobject("E").next_to(E).scale(0.5), TextMobject("F").next_to(F).scale(0.5), TextMobject("G").next_to(G).scale(0.5), TextMobject("H").next_to(H).scale(0.5), TextMobject("I").next_to(I).scale(0.5), ] for i in range(len(labtxt)): lab.add(labtxt[i]) original_trangle = Polygon(A.get_center(),B.get_center(),C.get_center(),color=ORANGE,fill_color = ORANGE,fill_opacity=0.5) rect1 = Polygon(A.get_center(),B.get_center(),D.get_center(),E.get_center(),color=GREEN,fill_color = GREEN,fill_opacity=0.5) rect2 = Polygon(B.get_center(),F.get_center(),G.get_center(),C.get_center(),color=GREEN,fill_color = GREEN,fill_opacity=0.5) rect3 = Polygon(A.get_center(),C.get_center(),I.get_center(),H.get_center(),color=GREEN,fill_color = GREEN,fill_opacity=0.5) tran1 = Polygon(D.get_center(),F.get_center(),B.get_center(),color=YELLOW,fill_color = YELLOW,fill_opacity=0.5) tran2 = Polygon(E.get_center(),A.get_center(),H.get_center(),color=YELLOW,fill_color = YELLOW,fill_opacity=0.5) tran3 = Polygon(C.get_center(),G.get_center(),I.get_center(),color=YELLOW,fill_color = YELLOW,fill_opacity=0.5) def getc1(obj): obj.move_to(tran1.get_center()) def getc2(obj): obj.move_to(tran2.get_center()) def getc3(obj): obj.move_to(tran3.get_center()) S1 = TexMobject("S1").add_updater(getc1) S2 = TexMobject("S2").add_updater(getc2) S3 = TexMobject("S3").add_updater(getc3) trans = VGroup(tran1,tran2,tran3,S1,S2,S3) # _.add(A,B,C,D,E,F,G,H,I,lab,original_trangle,rect1,rect2,rect3,tran1,tran2,tran3,S1,S2,S3) _.play(ShowCreation(original_trangle)) _.wait() _.play(ShowCreation(rect1),ShowCreation(rect2),ShowCreation(rect3)) _.wait() _.play(ShowCreation(tran1),ShowCreation(tran2),ShowCreation(tran3) ,Write(S1),Write(S2),Write(S3) ,) _.wait() _.play(FadeOut(rect1),FadeOut(rect2),FadeOut(rect3)) _.wait() _.play(Rotate(tran1,PI/2,about_point = B.get_center()), Rotate(tran2,PI/2,about_point = A.get_center()), Rotate(tran3,PI/2,about_point = C.get_center()) ) _.play(Transform(tran1,original_trangle)) _.play(Transform(tran2,original_trangle)) _.play(Transform(tran3,original_trangle)) S1.clear_updaters() S2.clear_updaters() S3.clear_updaters() _.play(S1.shift,2*UP+1.5*LEFT) _.play(S2.shift,2*UP) _.play(S3.shift,2*UP+1.5*RIGHT) eq = TextMobject("=").next_to(S1) eq2 = TextMobject("=").next_to(S2) _.play(Write(eq),Write(eq2))

49.12987

133

0.576791

from manimlib.imports import * class StartingScene(Scene): def construct(_): e = Text("Manim homework by mp",font="Consolas",color=BLUE) _.play(Write(e),run_time=3) _.wait() _.play(Uncreate(e)) A = Dot().move_to(np.array([0-2,0,0])) B = Dot().move_to(np.array([9/10-2,12/10,0])) C = Dot().move_to(np.array([5/2-2,0,0])) D = B.copy().shift(9/10*UP+6/5*LEFT) E = A.copy().shift(9/10*UP+6/5*LEFT) F = B.copy().shift(8/5*UP+6/5*RIGHT) G = C.copy().shift(8/5*UP+6/5*RIGHT) H = A.copy().shift(5/2*DOWN) I = C.copy().shift(5/2*DOWN) lab = VGroup() labtxt = [TextMobject("A").next_to(A).scale(0.5), TextMobject("B").next_to(B).scale(0.5), TextMobject("C").next_to(C).scale(0.5), TextMobject("D").next_to(D).scale(0.5), TextMobject("E").next_to(E).scale(0.5), TextMobject("F").next_to(F).scale(0.5), TextMobject("G").next_to(G).scale(0.5), TextMobject("H").next_to(H).scale(0.5), TextMobject("I").next_to(I).scale(0.5), ] for i in range(len(labtxt)): lab.add(labtxt[i]) original_trangle = Polygon(A.get_center(),B.get_center(),C.get_center(),color=ORANGE,fill_color = ORANGE,fill_opacity=0.5) rect1 = Polygon(A.get_center(),B.get_center(),D.get_center(),E.get_center(),color=GREEN,fill_color = GREEN,fill_opacity=0.5) rect2 = Polygon(B.get_center(),F.get_center(),G.get_center(),C.get_center(),color=GREEN,fill_color = GREEN,fill_opacity=0.5) rect3 = Polygon(A.get_center(),C.get_center(),I.get_center(),H.get_center(),color=GREEN,fill_color = GREEN,fill_opacity=0.5) tran1 = Polygon(D.get_center(),F.get_center(),B.get_center(),color=YELLOW,fill_color = YELLOW,fill_opacity=0.5) tran2 = Polygon(E.get_center(),A.get_center(),H.get_center(),color=YELLOW,fill_color = YELLOW,fill_opacity=0.5) tran3 = Polygon(C.get_center(),G.get_center(),I.get_center(),color=YELLOW,fill_color = YELLOW,fill_opacity=0.5) def getc1(obj): obj.move_to(tran1.get_center()) def getc2(obj): obj.move_to(tran2.get_center()) def getc3(obj): obj.move_to(tran3.get_center()) S1 = TexMobject("S1").add_updater(getc1) S2 = TexMobject("S2").add_updater(getc2) S3 = TexMobject("S3").add_updater(getc3) trans = VGroup(tran1,tran2,tran3,S1,S2,S3) _.play(ShowCreation(original_trangle)) _.wait() _.play(ShowCreation(rect1),ShowCreation(rect2),ShowCreation(rect3)) _.wait() _.play(ShowCreation(tran1),ShowCreation(tran2),ShowCreation(tran3) ,Write(S1),Write(S2),Write(S3) ,) _.wait() _.play(FadeOut(rect1),FadeOut(rect2),FadeOut(rect3)) _.wait() _.play(Rotate(tran1,PI/2,about_point = B.get_center()), Rotate(tran2,PI/2,about_point = A.get_center()), Rotate(tran3,PI/2,about_point = C.get_center()) ) _.play(Transform(tran1,original_trangle)) _.play(Transform(tran2,original_trangle)) _.play(Transform(tran3,original_trangle)) S1.clear_updaters() S2.clear_updaters() S3.clear_updaters() _.play(S1.shift,2*UP+1.5*LEFT) _.play(S2.shift,2*UP) _.play(S3.shift,2*UP+1.5*RIGHT) eq = TextMobject("=").next_to(S1) eq2 = TextMobject("=").next_to(S2) _.play(Write(eq),Write(eq2))

true

f71c111b67dac5359468b1d2de3970e43bfa4ea3

5,551

py

Python

leetcode_python/Array/longest-arithmetic-subsequence.py

yennanliu/Python_basics

6a597442d39468295946cefbfb11d08f61424dc3

[ "Unlicense" ]

null

leetcode_python/Array/longest-arithmetic-subsequence.py

yennanliu/Python_basics

6a597442d39468295946cefbfb11d08f61424dc3

[ "Unlicense" ]

null

leetcode_python/Array/longest-arithmetic-subsequence.py

yennanliu/Python_basics

6a597442d39468295946cefbfb11d08f61424dc3

[ "Unlicense" ]

null

""" 1027. Longest Arithmetic Subsequence Medium Given an array nums of integers, return the length of the longest arithmetic subsequence in nums. Recall that a subsequence of an array nums is a list nums[i1], nums[i2], ..., nums[ik] with 0 <= i1 < i2 < ... < ik <= nums.length - 1, and that a sequence seq is arithmetic if seq[i+1] - seq[i] are all the same value (for 0 <= i < seq.length - 1). Example 1: Input: nums = [3,6,9,12] Output: 4 Explanation: The whole array is an arithmetic sequence with steps of length = 3. Example 2: Input: nums = [9,4,7,2,10] Output: 3 Explanation: The longest arithmetic subsequence is [4,7,10]. Example 3: Input: nums = [20,1,15,3,10,5,8] Output: 4 Explanation: The longest arithmetic subsequence is [20,15,10,5]. Constraints: 2 <= nums.length <= 1000 0 <= nums[i] <= 500 """ # V0 # IDEA : DP class Solution: def longestArithSeqLength(self, A): dp = {} for i in range(len(A)): for j in range(i + 1, len(A)): dp[j, A[j] - A[i]] = dp.get((i, A[j] - A[i]), 1) + 1 return max(dp.values()) # V0' # IDEA : HASH TABLE # https://leetcode.com/problems/longest-arithmetic-subsequence/discuss/274657/Short-Python-solution class Solution: def longestArithSeqLength(self, A): aux, cnt, prefix = {a : {} for a in A}, {}, set() for a in A: cnt[a] = cnt[a] + 1 if a in cnt else 1 for b in prefix: if a != b: aux[a][a - b] = 1 + aux[b][a - b] if a - b in aux[b] else 2 prefix.add(a) max_const = max(cnt.values()) max_aux = max(max(d.values()) for a, d in aux.items() if d) return max(max_const, max_aux, 2) # V1 # https://www.796t.com/article.php?id=154559 # http://www.noteanddata.com/leetcode-1027-Longest-Arithmetic-Sequence-Google-Interview-Problem-java-solution-note.html # https://blog.csdn.net/w5688414/article/details/109696664 # V1 # IDEA : HASH # https://leetcode.com/problems/longest-arithmetic-subsequence/discuss/274657/Short-Python-solution class Solution: def longestArithSeqLength(self, A): aux, cnt, prefix = {a : {} for a in A}, {}, set() for a in A: cnt[a] = cnt[a] + 1 if a in cnt else 1 for b in prefix: if a != b: aux[a][a - b] = 1 + aux[b][a - b] if a - b in aux[b] else 2 prefix.add(a) max_const = max(cnt.values()) max_aux = max(max(d.values()) for a, d in aux.items() if d) return max(max_const, max_aux, 2) # V1' # https://leetcode.com/problems/longest-arithmetic-subsequence/discuss/275395/python-O(n**2)-solution class Solution: def longestArithSeqLength(self, A): # Constant seq: '0000', O(len(A) ) ct = collections.Counter(A) ans = max(2, max(ct[i] for i in ct)) # Increasing seq:'1234', O(len(A)**2 ) ansdic = {} for i in range(len(A)): for j in range(i): a0, a1, a2 = A[j]*2-A[i], A[j], A[i] if a0 == a1:continue if (a0, a1) in ansdic: ansdic[a1, a2] = ansdic[a0, a1] + 1 ans = max(ansdic[a1, a2], ans) else: ansdic[a1, a2] = 2 return ans # V1'' # IDEA : HASH SET # https://leetcode.com/problems/longest-arithmetic-subsequence/discuss/274625/simple-hash-Set-Python class Solution(object): def longestArithSeqLength(self, A): res = 2 if len(A) <= 2: return len(A) cnt = {} node = {} mx = {} curr = A[1] - A[0] cnt[(curr,1)] = 2 node[curr] = set() node[curr].add(1) mx[curr] = 2 res = 2 for i in range(2,len(A)): for j in range(i): dis = A[i] - A[j] if dis in node: if j in node[dis]: cnt[(dis,i)] = cnt[(dis,j)] + 1 #node[dis].remove(j) node[dis].add(i) mx[dis] = max(mx[dis], cnt[(dis,i)]) res = max(mx[dis],res) else: cnt[(dis,i)] = 2 node[dis].add(i) else: cnt[(dis,i)] = 2 node[dis] = set() node[dis].add(i) mx[dis] = 2 return res # V1''' # IDEA : DP # https://leetcode.com/problems/longest-arithmetic-subsequence/discuss/274611/JavaC%2B%2BPython-DP class Solution: def longestArithSeqLength(self, A): dp = {} for i in range(len(A)): for j in range(i + 1, len(A)): dp[j, A[j] - A[i]] = dp.get((i, A[j] - A[i]), 1) + 1 return max(dp.values()) # V1'''' # IDEA : DP # https://leetcode.com/problems/longest-arithmetic-subsequence/discuss/514742/Python-DP class Solution: def longestArithSeqLength(self, A): DP = {} A_len = len(A) for right in range(1, A_len): for left in range(right): diff = A[right] - A[left] #if (diff, left) in DP: # DP[(diff, right)] = DP[(diff, left)] + 1 #else: # DP[(diff, right)] = 2 DP[(diff, right)] = DP.get((diff,left), 1) + 1 return max(DP.values()) # V2

31.361582

248

0.503513

class Solution: def longestArithSeqLength(self, A): dp = {} for i in range(len(A)): for j in range(i + 1, len(A)): dp[j, A[j] - A[i]] = dp.get((i, A[j] - A[i]), 1) + 1 return max(dp.values()) # IDEA : HASH TABLE # https://leetcode.com/problems/longest-arithmetic-subsequence/discuss/274657/Short-Python-solution class Solution: def longestArithSeqLength(self, A): aux, cnt, prefix = {a : {} for a in A}, {}, set() for a in A: cnt[a] = cnt[a] + 1 if a in cnt else 1 for b in prefix: if a != b: aux[a][a - b] = 1 + aux[b][a - b] if a - b in aux[b] else 2 prefix.add(a) max_const = max(cnt.values()) max_aux = max(max(d.values()) for a, d in aux.items() if d) return max(max_const, max_aux, 2) # V1 # https://www.796t.com/article.php?id=154559 # http://www.noteanddata.com/leetcode-1027-Longest-Arithmetic-Sequence-Google-Interview-Problem-java-solution-note.html # https://blog.csdn.net/w5688414/article/details/109696664 # V1 # IDEA : HASH # https://leetcode.com/problems/longest-arithmetic-subsequence/discuss/274657/Short-Python-solution class Solution: def longestArithSeqLength(self, A): aux, cnt, prefix = {a : {} for a in A}, {}, set() for a in A: cnt[a] = cnt[a] + 1 if a in cnt else 1 for b in prefix: if a != b: aux[a][a - b] = 1 + aux[b][a - b] if a - b in aux[b] else 2 prefix.add(a) max_const = max(cnt.values()) max_aux = max(max(d.values()) for a, d in aux.items() if d) return max(max_const, max_aux, 2) # V1' class Solution: def longestArithSeqLength(self, A): ct = collections.Counter(A) ans = max(2, max(ct[i] for i in ct)) ansdic = {} for i in range(len(A)): for j in range(i): a0, a1, a2 = A[j]*2-A[i], A[j], A[i] if a0 == a1:continue if (a0, a1) in ansdic: ansdic[a1, a2] = ansdic[a0, a1] + 1 ans = max(ansdic[a1, a2], ans) else: ansdic[a1, a2] = 2 return ans class Solution(object): def longestArithSeqLength(self, A): res = 2 if len(A) <= 2: return len(A) cnt = {} node = {} mx = {} curr = A[1] - A[0] cnt[(curr,1)] = 2 node[curr] = set() node[curr].add(1) mx[curr] = 2 res = 2 for i in range(2,len(A)): for j in range(i): dis = A[i] - A[j] if dis in node: if j in node[dis]: cnt[(dis,i)] = cnt[(dis,j)] + 1 node[dis].add(i) mx[dis] = max(mx[dis], cnt[(dis,i)]) res = max(mx[dis],res) else: cnt[(dis,i)] = 2 node[dis].add(i) else: cnt[(dis,i)] = 2 node[dis] = set() node[dis].add(i) mx[dis] = 2 return res # IDEA : DP # https://leetcode.com/problems/longest-arithmetic-subsequence/discuss/274611/JavaC%2B%2BPython-DP class Solution: def longestArithSeqLength(self, A): dp = {} for i in range(len(A)): for j in range(i + 1, len(A)): dp[j, A[j] - A[i]] = dp.get((i, A[j] - A[i]), 1) + 1 return max(dp.values()) # V1'''' # IDEA : DP # https://leetcode.com/problems/longest-arithmetic-subsequence/discuss/514742/Python-DP class Solution: def longestArithSeqLength(self, A): DP = {} A_len = len(A) for right in range(1, A_len): for left in range(right): diff = A[right] - A[left] #if (diff, left) in DP: # DP[(diff, right)] = DP[(diff, left)] + 1 #else: # DP[(diff, right)] = 2 DP[(diff, right)] = DP.get((diff,left), 1) + 1 return max(DP.values()) # V2

true

f71c1149bd0ccd4c108d6852f0e7e33eb102e6e2

1,820

py

Python

tests/util/test_i18n.py

zsluedem/MonkTrader

760942a59919b34c876467bc0eb4afb30689cbc1

[ "MIT" ]

2

2018-11-17T06:39:36.000Z

2019-01-18T13:14:15.000Z

tests/util/test_i18n.py

zsluedem/MonkTrader

760942a59919b34c876467bc0eb4afb30689cbc1

[ "MIT" ]

37

2018-11-04T15:05:04.000Z

2019-03-09T09:26:30.000Z

tests/util/test_i18n.py

zsluedem/MonkTrader

760942a59919b34c876467bc0eb4afb30689cbc1

[ "MIT" ]

null

# # MIT License # # Copyright (c) 2018 WillQ # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # import os from unittest.mock import MagicMock, patch from monkq.utils.i18n import LazyTranslation def test_lazytranslation_not_setting() -> None: with patch("monkq.utils.i18n.gettext", MagicMock()) as mockg: mockg.find.return_value = None trans = LazyTranslation() trans.setup("CN") trans.gettext("hello") mockg.NullTranslations().gettext.assert_called() def test_lazytranslation() -> None: with patch("monkq.utils.i18n.gettext", MagicMock()) as mockg: mockg.find.return_value = os.path.abspath(__file__) trans = LazyTranslation() trans.setup("CN") trans.gettext("hello") mockg.GNUTranslations().gettext.assert_called()

37.142857

79

0.737363

import os from unittest.mock import MagicMock, patch from monkq.utils.i18n import LazyTranslation def test_lazytranslation_not_setting() -> None: with patch("monkq.utils.i18n.gettext", MagicMock()) as mockg: mockg.find.return_value = None trans = LazyTranslation() trans.setup("CN") trans.gettext("hello") mockg.NullTranslations().gettext.assert_called() def test_lazytranslation() -> None: with patch("monkq.utils.i18n.gettext", MagicMock()) as mockg: mockg.find.return_value = os.path.abspath(__file__) trans = LazyTranslation() trans.setup("CN") trans.gettext("hello") mockg.GNUTranslations().gettext.assert_called()

true

f71c138dfb3853f24c17b2b530c0d786d88a9cf0

5,959

py

Python

modelEpochs.py

JDMusc/Online-Bullying-Image-Classifcation

9196c60c554cf160d68cb9e9c41fda124abebf63

[ "MIT" ]

null

modelEpochs.py

JDMusc/Online-Bullying-Image-Classifcation

9196c60c554cf160d68cb9e9c41fda124abebf63

[ "MIT" ]

null

modelEpochs.py

JDMusc/Online-Bullying-Image-Classifcation

9196c60c554cf160d68cb9e9c41fda124abebf63

[ "MIT" ]

null

import copy import numpy as np from numpy import log10 import os from toolz import pipe as p from tensorboardX import SummaryWriter import torch import torch.nn as nn import numpy as np import preprocessing as pp def findParam(model, name_filter): if callable(name_filter): fn = name_filter else: name_filter = [name_filter] if type(name_filter) is str else name_filter fn = lambda param_name: all( component in param_name for component in name_filter) return [(pn, pv) for (pn, pv) in model.named_parameters() if fn(pn)] def setParameterRequiresGrad(model, requires_grad = False, params = None): params = model.parameters() if params is None else params for param in params: param.requires_grad = requires_grad def runEpochs( model, criterion, dataloaders, dataset_sizes, device, log_params_verbose, num_epochs, optimizer, scheduler, writer): best_model_wts = copy.deepcopy(model.state_dict()) best_acc = 0.0 prev_model_wts = best_model_wts for epoch in range(num_epochs): epoch_acc, model_wts = _run_epoch( model, criterion, dataloaders, dataset_sizes, device, epoch, log_params_verbose, num_epochs, optimizer, scheduler, writer) _log_coef_diffs(writer, epoch, prev_model_wts, model_wts) prev_model_wts = model_wts if epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = model_wts # load best model weights model.load_state_dict(best_model_wts) return (model, best_acc) def viewParamsToBeUpdated(model): return [n for (n,p) in model.named_parameters() if p.requires_grad == True] def add_graph_model(writer, model, dataloaders, device): inputs, classes = p(dataloaders['train'], iter, next) inputs = inputs.to(device) classes = classes.to(device) writer.add_graph(model, inputs) def _run_epoch(model, criterion, dataloaders, dataset_sizes, device, epoch, log_params_verbose, num_epochs, optimizer, scheduler, writer): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10) n_samples = {'train': 0, 'val': 0} # Each epoch has a training and validation phase for phase in ['train', 'val']: is_train = phase == 'train' if is_train: scheduler.step() model.train() else: model.eval() running_loss = 0.0 running_corrects = 0 for inputs, labels in dataloaders[phase]: n_samples[phase] = n_samples[phase] + len(labels) inputs = inputs.to(device) labels = labels.to(device) preds, loss = _take_step( model, criterion, optimizer, inputs, labels, is_train) # statistics running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] _log_epoch_phase_stats(writer, epoch, phase, epoch_loss, epoch_acc) if log_params_verbose: _log_model_params_verbose(writer, model, epoch, phase) # deep copy the model model_wts = copy.deepcopy(model.state_dict()) _log_lr(writer, epoch, scheduler) print('# training samples') print(n_samples['train']) print('# val samples') print(n_samples['val']) return epoch_acc, model_wts def _take_step(model, criterion, optimizer, inputs, labels, is_train): # zero the parameter gradients optimizer.zero_grad() # forward # track history if only in train with torch.set_grad_enabled(is_train): outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) # backward + optimize only if in training phase if is_train: loss.backward() optimizer.step() return preds, loss def _add_scope(scope, k): return scope + '/' + k def _add_scope_gen(scope): return lambda k: _add_scope(scope, k) def _log_model_params_verbose(writer, model, run_num, scope, use_hist = False): def write(tag, param): fn = writer.add_histogram if use_hist else writer.add_scalar param = param if use_hist else param.abs().mean() return fn(tag, param, run_num) with torch.no_grad(): for (name, param) in model.named_parameters(): p(name, _add_scope_gen(scope), lambda tag: write(tag, param) ) def _log_lr(writer, epoch, scheduler): lr = p(scheduler.get_lr(), np.array)[0] p('lr', _add_scope_gen('lr'), lambda _: writer.add_scalar(_, lr, epoch) ) p('log10_lr', _add_scope_gen('lr'), lambda _: writer.add_scalar(_, log10(lr), epoch) ) def _log_epoch_phase_stats(writer, epoch, scope, epoch_loss, epoch_acc): log_measure = lambda k, v: p(k, _add_scope_gen(scope), lambda _ : writer.add_scalar(_, v, epoch) ) log_measure('loss', epoch_loss) log_measure('accuracy', epoch_acc) print('{} Loss: {:.4f} Acc: {:.4f}'.format( scope, epoch_loss, epoch_acc)) def _log_coef_diffs(writer, epoch, prev_model_state, curr_model_state): def write(name, curr): diff = curr - prev_model_state[name] p(name, _add_scope_gen('params'), lambda _: writer.add_scalar( _ + '.diff', diff.abs().mean(), epoch) ) with torch.no_grad(): for name in curr_model_state: if ('weight' in name or 'bias' in name): write(name, curr_model_state[name])

27.587963

80

0.614365

import copy import numpy as np from numpy import log10 import os from toolz import pipe as p from tensorboardX import SummaryWriter import torch import torch.nn as nn import numpy as np import preprocessing as pp def findParam(model, name_filter): if callable(name_filter): fn = name_filter else: name_filter = [name_filter] if type(name_filter) is str else name_filter fn = lambda param_name: all( component in param_name for component in name_filter) return [(pn, pv) for (pn, pv) in model.named_parameters() if fn(pn)] def setParameterRequiresGrad(model, requires_grad = False, params = None): params = model.parameters() if params is None else params for param in params: param.requires_grad = requires_grad def runEpochs( model, criterion, dataloaders, dataset_sizes, device, log_params_verbose, num_epochs, optimizer, scheduler, writer): best_model_wts = copy.deepcopy(model.state_dict()) best_acc = 0.0 prev_model_wts = best_model_wts for epoch in range(num_epochs): epoch_acc, model_wts = _run_epoch( model, criterion, dataloaders, dataset_sizes, device, epoch, log_params_verbose, num_epochs, optimizer, scheduler, writer) _log_coef_diffs(writer, epoch, prev_model_wts, model_wts) prev_model_wts = model_wts if epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = model_wts model.load_state_dict(best_model_wts) return (model, best_acc) def viewParamsToBeUpdated(model): return [n for (n,p) in model.named_parameters() if p.requires_grad == True] def add_graph_model(writer, model, dataloaders, device): inputs, classes = p(dataloaders['train'], iter, next) inputs = inputs.to(device) classes = classes.to(device) writer.add_graph(model, inputs) def _run_epoch(model, criterion, dataloaders, dataset_sizes, device, epoch, log_params_verbose, num_epochs, optimizer, scheduler, writer): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10) n_samples = {'train': 0, 'val': 0} for phase in ['train', 'val']: is_train = phase == 'train' if is_train: scheduler.step() model.train() else: model.eval() running_loss = 0.0 running_corrects = 0 for inputs, labels in dataloaders[phase]: n_samples[phase] = n_samples[phase] + len(labels) inputs = inputs.to(device) labels = labels.to(device) preds, loss = _take_step( model, criterion, optimizer, inputs, labels, is_train) running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] _log_epoch_phase_stats(writer, epoch, phase, epoch_loss, epoch_acc) if log_params_verbose: _log_model_params_verbose(writer, model, epoch, phase) model_wts = copy.deepcopy(model.state_dict()) _log_lr(writer, epoch, scheduler) print('# training samples') print(n_samples['train']) print('# val samples') print(n_samples['val']) return epoch_acc, model_wts def _take_step(model, criterion, optimizer, inputs, labels, is_train): optimizer.zero_grad() with torch.set_grad_enabled(is_train): outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) if is_train: loss.backward() optimizer.step() return preds, loss def _add_scope(scope, k): return scope + '/' + k def _add_scope_gen(scope): return lambda k: _add_scope(scope, k) def _log_model_params_verbose(writer, model, run_num, scope, use_hist = False): def write(tag, param): fn = writer.add_histogram if use_hist else writer.add_scalar param = param if use_hist else param.abs().mean() return fn(tag, param, run_num) with torch.no_grad(): for (name, param) in model.named_parameters(): p(name, _add_scope_gen(scope), lambda tag: write(tag, param) ) def _log_lr(writer, epoch, scheduler): lr = p(scheduler.get_lr(), np.array)[0] p('lr', _add_scope_gen('lr'), lambda _: writer.add_scalar(_, lr, epoch) ) p('log10_lr', _add_scope_gen('lr'), lambda _: writer.add_scalar(_, log10(lr), epoch) ) def _log_epoch_phase_stats(writer, epoch, scope, epoch_loss, epoch_acc): log_measure = lambda k, v: p(k, _add_scope_gen(scope), lambda _ : writer.add_scalar(_, v, epoch) ) log_measure('loss', epoch_loss) log_measure('accuracy', epoch_acc) print('{} Loss: {:.4f} Acc: {:.4f}'.format( scope, epoch_loss, epoch_acc)) def _log_coef_diffs(writer, epoch, prev_model_state, curr_model_state): def write(name, curr): diff = curr - prev_model_state[name] p(name, _add_scope_gen('params'), lambda _: writer.add_scalar( _ + '.diff', diff.abs().mean(), epoch) ) with torch.no_grad(): for name in curr_model_state: if ('weight' in name or 'bias' in name): write(name, curr_model_state[name])

true

f71c143502daacb5d0cac62a6b711503065c58e7

505

py

Python

functions/l3Consume.py

yuklia/serverless-lambda-chaining

dd24129933489c2f1a522b37d8f4c3e16eb47285

[ "MIT" ]

null

functions/l3Consume.py

yuklia/serverless-lambda-chaining

dd24129933489c2f1a522b37d8f4c3e16eb47285

[ "MIT" ]

null

functions/l3Consume.py

yuklia/serverless-lambda-chaining

dd24129933489c2f1a522b37d8f4c3e16eb47285

[ "MIT" ]

null

import json def handler(event, context): message_from_publisher = json.loads(event['Records'][0]['Sns']['Message']) my_param = message_from_publisher['myParamFromConsumerPublisher'] print("👷 Received paramater from ConsumerPublisher: '{0}'".format(my_param)) body = { "message": "Go Serverless v1.0! Your function executed successfully!", "input": event } response = { "statusCode": 200, "body": json.dumps(body) } return response

24.047619

80

0.637624

import json def handler(event, context): message_from_publisher = json.loads(event['Records'][0]['Sns']['Message']) my_param = message_from_publisher['myParamFromConsumerPublisher'] print("👷 Received paramater from ConsumerPublisher: '{0}'".format(my_param)) body = { "message": "Go Serverless v1.0! Your function executed successfully!", "input": event } response = { "statusCode": 200, "body": json.dumps(body) } return response

true

f71c145bb6baf46c8e813d23d04f55fadd9b6a4e

112

py

Python

test/nohtml.py

eaybek/nohtml

9df8fc032891591516d8a719ebc15440d8cc7a0c

[ "MIT" ]

null

test/nohtml.py

eaybek/nohtml

9df8fc032891591516d8a719ebc15440d8cc7a0c

[ "MIT" ]

null

test/nohtml.py

eaybek/nohtml

9df8fc032891591516d8a719ebc15440d8cc7a0c

[ "MIT" ]

null

import unittest class NohtmlTest(unittest.TestCase): pass if __name__ == "__main__": unittest.main()

12.444444

36

0.705357

import unittest class NohtmlTest(unittest.TestCase): pass if __name__ == "__main__": unittest.main()

true

f71c154fa91e7b9687d0c58c927b63ccf6253ccb

3,682

py

Python

ReUseModel/TestFAEModel.py

Eggiverse/FAE

1b953ba6dfcced83e5929eeaa8f525ec4acde5ed

[ "MIT" ]

null

ReUseModel/TestFAEModel.py

Eggiverse/FAE

1b953ba6dfcced83e5929eeaa8f525ec4acde5ed

[ "MIT" ]

null

ReUseModel/TestFAEModel.py

Eggiverse/FAE

1b953ba6dfcced83e5929eeaa8f525ec4acde5ed

[ "MIT" ]

null

import os import csv import numpy as np from FAE.FeatureAnalysis.Normalizer import Normalizer from FAE.DataContainer.DataContainer import DataContainer from FAE.FeatureAnalysis.Classifier import Classifier from FAE.Func.Metric import EstimateMetirc from FAE.FeatureAnalysis.FeatureSelector import FeatureSelector from FAE.FeatureAnalysis.CrossValidation import CrossValidation def LoadTrainInfo(model_folder): train_info = {} ##Load normalizaiton normalizer = Normalizer() normalization_path = '' for sub_file in os.listdir(model_folder): if sub_file.rfind('_normalization_training.csv') != -1: normalization_path = os.path.join(model_folder, sub_file) if not os.path.exists(normalization_path): print('Check the normalization name : zero_center_normalization') else: normalizer.Load(normalization_path) train_info['normalizer'] = normalizer ## Load selected features selected_feature_path = os.path.join(model_folder, 'feature_select_info.csv') selected_feature_list = [] with open(selected_feature_path, 'r', newline='') as f: f_reader = csv.reader(f) for index in f_reader: if index[0] == 'selected_feature': selected_feature_list = index[1:] if selected_feature_list == []: print('No selected features') train_info['selected_features'] = selected_feature_list ## Load FAE model classifier = Classifier() classifier.Load(model_folder) train_info['classifier'] = classifier return train_info def TestNewData(NewDataCsv, model_folder, result_save_path): ''' :param NewDataCsv: New radiomics feature matrix csv file path :param model_folder:The trained model path :return:classification result ''' train_info = LoadTrainInfo(model_folder) new_data_container = DataContainer() #Normlization new_data_container.Load(NewDataCsv) feature_selector = FeatureSelector() feature_selector.SelectFeatureByName(new_data_container, train_info['selected_features'], is_replace=True) new_data_container = train_info['normalizer'].Transform(new_data_container) # data_frame = new_data_container.GetFrame() # data_frame = data_frame[train_info['selected_features']] # new_data_container.SetFrame(data_frame) # new_data_container.UpdateDataByFrame() ##Model train_info['classifier'].SetDataContainer(new_data_container) model = train_info['classifier'].GetModel() predict = model.predict_proba(new_data_container.GetArray())[:, 1] label = new_data_container.GetLabel() case_name = new_data_container.GetCaseName() np.save(os.path.join(result_save_path, 'test_predict.npy'), predict) np.save(os.path.join(result_save_path, 'test_label.npy'), label) test_result_info = [['CaseName', 'Pred', 'Label']] for index in range(len(label)): test_result_info.append([case_name[index], predict[index], label[index]]) with open(os.path.join(result_save_path, 'test_info.csv'), 'w', newline='') as csvfile: writer = csv.writer(csvfile) writer.writerows(test_result_info) metric = EstimateMetirc(predict, label) info = {} info.update(metric) cv = CrossValidation() cv.SaveResult(info, result_save_path) # print(metric) return metric if __name__ == '__main__': TestNewData(r'D:\hospital\Huangli\smote\test_numeric_feature.csv', r'D:\hospital\Huangli\smote\process-result\Norm0Center_PCC_ANOVA_5_LR', r'D:\MyScript\demo')

32.875

111

0.69962

import os import csv import numpy as np from FAE.FeatureAnalysis.Normalizer import Normalizer from FAE.DataContainer.DataContainer import DataContainer from FAE.FeatureAnalysis.Classifier import Classifier from FAE.Func.Metric import EstimateMetirc from FAE.FeatureAnalysis.FeatureSelector import FeatureSelector from FAE.FeatureAnalysis.CrossValidation import CrossValidation def LoadTrainInfo(model_folder): train_info = {} Normalizer() normalization_path = '' for sub_file in os.listdir(model_folder): if sub_file.rfind('_normalization_training.csv') != -1: normalization_path = os.path.join(model_folder, sub_file) if not os.path.exists(normalization_path): print('Check the normalization name : zero_center_normalization') else: normalizer.Load(normalization_path) train_info['normalizer'] = normalizer ath = os.path.join(model_folder, 'feature_select_info.csv') selected_feature_list = [] with open(selected_feature_path, 'r', newline='') as f: f_reader = csv.reader(f) for index in f_reader: if index[0] == 'selected_feature': selected_feature_list = index[1:] if selected_feature_list == []: print('No selected features') train_info['selected_features'] = selected_feature_list = Classifier() classifier.Load(model_folder) train_info['classifier'] = classifier return train_info def TestNewData(NewDataCsv, model_folder, result_save_path): train_info = LoadTrainInfo(model_folder) new_data_container = DataContainer() new_data_container.Load(NewDataCsv) feature_selector = FeatureSelector() feature_selector.SelectFeatureByName(new_data_container, train_info['selected_features'], is_replace=True) new_data_container = train_info['normalizer'].Transform(new_data_container) ain_info['classifier'].SetDataContainer(new_data_container) model = train_info['classifier'].GetModel() predict = model.predict_proba(new_data_container.GetArray())[:, 1] label = new_data_container.GetLabel() case_name = new_data_container.GetCaseName() np.save(os.path.join(result_save_path, 'test_predict.npy'), predict) np.save(os.path.join(result_save_path, 'test_label.npy'), label) test_result_info = [['CaseName', 'Pred', 'Label']] for index in range(len(label)): test_result_info.append([case_name[index], predict[index], label[index]]) with open(os.path.join(result_save_path, 'test_info.csv'), 'w', newline='') as csvfile: writer = csv.writer(csvfile) writer.writerows(test_result_info) metric = EstimateMetirc(predict, label) info = {} info.update(metric) cv = CrossValidation() cv.SaveResult(info, result_save_path) return metric if __name__ == '__main__': TestNewData(r'D:\hospital\Huangli\smote\test_numeric_feature.csv', r'D:\hospital\Huangli\smote\process-result\Norm0Center_PCC_ANOVA_5_LR', r'D:\MyScript\demo')

true

f71c159e39dcb104058740ebee5fb752312a3553

160

py

Python

cranes/__init__.py

annehulsey/high-resolution_post-earthquake_recovery_simulation_of_safety_cordons

8b8bedceee0343d22143f48992136fc2fc34e191

[ "MIT" ]

null

cranes/__init__.py

annehulsey/high-resolution_post-earthquake_recovery_simulation_of_safety_cordons

8b8bedceee0343d22143f48992136fc2fc34e191

[ "MIT" ]

null

cranes/__init__.py

annehulsey/high-resolution_post-earthquake_recovery_simulation_of_safety_cordons

8b8bedceee0343d22143f48992136fc2fc34e191

[ "MIT" ]

null

from .base import * from .mapping import * from .community_damage_sampling import * from .downtime_logistics import * from .analysis_and_visualization import *

26.666667

41

0.8125

from .base import * from .mapping import * from .community_damage_sampling import * from .downtime_logistics import * from .analysis_and_visualization import *

true

f71c1615d02213bb8e244cd957fa6b17a89b9787

2,153

py

Python

plugins/zscaler/icon_zscaler/actions/lookup_url/schema.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

46

2019-06-05T20:47:58.000Z

2022-03-29T10:18:01.000Z

plugins/zscaler/icon_zscaler/actions/lookup_url/schema.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

386

2019-06-07T20:20:39.000Z

2022-03-30T17:35:01.000Z

plugins/zscaler/icon_zscaler/actions/lookup_url/schema.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

43

2019-07-09T14:13:58.000Z

2022-03-28T12:04:46.000Z

# GENERATED BY KOMAND SDK - DO NOT EDIT import insightconnect_plugin_runtime import json class Component: DESCRIPTION = "Look up the categorization of a given set of URLs" class Input: URLS = "urls" class Output: URL_CATEGORIZATION = "url_categorization" class LookupUrlInput(insightconnect_plugin_runtime.Input): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "urls": { "type": "array", "title": "URLs", "description": "The given set of URLs or domains to be looked up", "items": { "type": "string" }, "order": 1 } }, "required": [ "urls" ] } """) def __init__(self): super(self.__class__, self).__init__(self.schema) class LookupUrlOutput(insightconnect_plugin_runtime.Output): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "url_categorization": { "type": "array", "title": "URL Categorization", "description": "Information about given URLs", "items": { "$ref": "#/definitions/url_categorization" }, "order": 1 } }, "required": [ "url_categorization" ], "definitions": { "url_categorization": { "type": "object", "title": "url_categorization", "properties": { "url": { "type": "string", "title": "URL", "description": "Checked URL", "order": 1 }, "urlClassifications": { "type": "array", "title": "URL Classifications", "description": "URL classifications", "items": { "type": "string" }, "order": 2 }, "urlClassificationsWithSecurityAlert": { "type": "array", "title": "URL classifications with security alert", "description": "URL classifications with security alert", "items": { "type": "string" }, "order": 3 } } } } } """) def __init__(self): super(self.__class__, self).__init__(self.schema)

21.53

72

0.533674

import insightconnect_plugin_runtime import json class Component: DESCRIPTION = "Look up the categorization of a given set of URLs" class Input: URLS = "urls" class Output: URL_CATEGORIZATION = "url_categorization" class LookupUrlInput(insightconnect_plugin_runtime.Input): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "urls": { "type": "array", "title": "URLs", "description": "The given set of URLs or domains to be looked up", "items": { "type": "string" }, "order": 1 } }, "required": [ "urls" ] } """) def __init__(self): super(self.__class__, self).__init__(self.schema) class LookupUrlOutput(insightconnect_plugin_runtime.Output): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "url_categorization": { "type": "array", "title": "URL Categorization", "description": "Information about given URLs", "items": { "$ref": "#/definitions/url_categorization" }, "order": 1 } }, "required": [ "url_categorization" ], "definitions": { "url_categorization": { "type": "object", "title": "url_categorization", "properties": { "url": { "type": "string", "title": "URL", "description": "Checked URL", "order": 1 }, "urlClassifications": { "type": "array", "title": "URL Classifications", "description": "URL classifications", "items": { "type": "string" }, "order": 2 }, "urlClassificationsWithSecurityAlert": { "type": "array", "title": "URL classifications with security alert", "description": "URL classifications with security alert", "items": { "type": "string" }, "order": 3 } } } } } """) def __init__(self): super(self.__class__, self).__init__(self.schema)

true

f71c1852ffabfd6520c29aa0e5896783707f3f65

9,759

py

Python

pyhtmlcv.py

lietu/pyhtmlcv

68e75d5b761f4cbf4315d9c573fdc39872abbc20

[ "BSD-3-Clause" ]

3

2017-02-15T14:02:57.000Z

2019-04-30T23:33:55.000Z

pyhtmlcv.py

lietu/pyhtmlcv

68e75d5b761f4cbf4315d9c573fdc39872abbc20

[ "BSD-3-Clause" ]

null

pyhtmlcv.py

lietu/pyhtmlcv

68e75d5b761f4cbf4315d9c573fdc39872abbc20

[ "BSD-3-Clause" ]

1

2017-06-01T15:58:09.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ PyHtmlCv is a tool to that can be used to generate HTML CVs from a simple JSON configuration. :copyright: (c) 2012-2019 Janne Enberg :license: BSD """ from argparse import ArgumentParser, ArgumentTypeError import codecs from datetime import datetime from jinja2 import Environment, FileSystemLoader import json try: from pathlib import Path except ImportError: from pathlib2 import Path import re import sass import six import shutil import sys from time import sleep TEMPLATE_PATH = Path("templates") def str2bool(value): """ Convert CLI args to boolean :param str value: :return bool: """ if value.lower() in ("yes", "true", "t", "y", "1"): return True elif value.lower() in ("no", "false", "f", "n", "0"): return False else: raise ArgumentTypeError("Boolean value expected.") def get_last_change(path): """ Figure out when the given path has last changed, recursively if a directory """ last_changed = path.stat().st_mtime if path.is_dir(): for entry in path.glob("**/*"): entry_changed = entry.stat().st_mtime if entry_changed > last_changed: last_changed = entry_changed return last_changed def run(options): """ Generate the CV page from the source + template """ try: with codecs.open(options.source, encoding="utf-8") as f: config = json.load(f) except ValueError as e: print("Error parsing config {}.".format(options.source)) print("") raise except IOError as e: print("Configuration file not found: {}".format(options.source)) print("") raise validate_config(config) process_config(config) generate_cv(options.target, options.template, config) def generate_cv(destination, template, config): # Get the template template_path = str(TEMPLATE_PATH / template) env = Environment(loader=FileSystemLoader(template_path)) template = env.get_template("index.html") # Generate a few variables for the template now = datetime.now() current_time = now.strftime("%Y-%m-%d %H:%M:%S %z") year = now.strftime("%Y") navigation = generate_navigation(config) # Render the template into HTML html = template.render( name=config["name"], contact=config["contact"], sections=config["sections"], navigation=navigation, now=current_time, year=year, ) # Make sure that the destination path is deleted first dst_path = Path(destination) if dst_path.exists(): shutil.rmtree(destination) shutil.copytree(template_path, destination) # Compile Sass/SCSS scss_files = [] for entry in dst_path.glob("**/*.scss"): scss_files.append(entry) entry_name = entry.name if entry_name.endswith(".scss") and not entry_name.startswith("_"): entry_str = str(entry) compiled = sass.compile(filename=entry_str) entry_css = Path(entry_str[:-5] + ".css") with entry_css.open("w", encoding="utf-8") as f: f.write(compiled) print("Compiled {} to {}".format(entry, entry_css)) # Delete unnecessary files after compilation for entry in scss_files: entry.unlink() # And any left over empty directories for entry in reversed(list(dst_path.rglob("*"))): if entry.exists() and entry.is_dir(): empty = True for _ in entry.iterdir(): empty = False break if empty: entry.rmdir() # Write the result HTML full_path = Path(destination) / "index.html" with full_path.open("w", encoding="utf-8") as f: f.write(html) print("Generated CV HTML to {}".format(full_path)) def validate_config(config): error = False if "name" not in config: print('Missing name definition, e.g. { "name": "Janne Enberg", ' "... }") error = True if "contact" not in config: print( "Missing contact definition, e.g. { ..., " '"contact": "+1 (2) 345 678 | contact@example.com", ... }' ) error = True if "sections" not in config: print("Missing sections definition, e.g. { ..., " '"sections": [ ... ] }') error = True else: for section in config["sections"]: # String sections need no other validation if isinstance(section, six.string_types): continue if "title" not in section: print( "Missing title from section definition, , " 'e.g. { ..., "sections": [ {"title": "Section ' 'title", ...} ] }' ) print("Found: {}".format(section)) error = True if ( "fields" not in section and "large" not in section and "largeList" not in section ): print( "No fields, largeList or large definition for " "section, , " 'e.g. { ..., "sections": [ {..., ' '"large": "Yadi yadi yada", ...} ] }' ) error = True if "fields" in section: for field in section["fields"]: if not isinstance(field, list) or len(field) != 2: print( "Invalid field definition, " "it should have two items, e.g. { ..., " '"sections": [ {..., "fields": [ ["Label",' ' "Value"], ... }, ... ] }' ) error = True if error: print("") print("Please fix errors in configuration file.") sys.exit(1) def process_config(config): """ Process the configuration from the readable format to a more useful format """ # Process sections for index, section in enumerate(config["sections"]): # String sections will be converted to type = heading if isinstance(section, six.string_types): if section == "-": config["sections"][index] = {"type": "page-break"} else: config["sections"][index] = {"type": "heading", "title": section} continue # The rest are just normal sections section["type"] = "normal" # Convert ["Label", "Value"] to {"label": "Label", # "value": "Value"} if "fields" in section: fields = [] for fieldColumns in section["fields"]: fields.append({"label": fieldColumns[0], "value": fieldColumns[1]}) section["fields"] = fields # Convert arrays in "largeList" field to <ul> -lists in "large" if "largeList" in section: section["large"] = ( "<ul><li>" + "</li><li>".join(section["largeList"]) + "</li></ul>" ) del section["largeList"] heading = config["mainHeading"] main_heading = {"type": "heading", "title": heading} config["sections"] = [main_heading] + config["sections"] def generate_navigation(config): i = 1 nav = {"headings": []} for _, section in enumerate(config["sections"]): # Page breaks don't need navigation if section["type"] == "page-break": continue name = section["title"] section["id"] = make_id(name, i) if section["type"] == "heading": nav[name] = [section] nav["headings"].append(name) heading = name else: nav[heading].append(section) i += 1 return nav def make_id(text, index): # Replace characters not valid in IDs text = re.sub(r"[^0-9a-zA-Z\-_.:]", "-", text) # Text must not begin with a number if re.match(r"^[0-9]", text): text = "id-{}-{}".format(text, index) return text def main(): ap = ArgumentParser() ap.add_argument("--source", default="cv.json", type=str, help="CV JSON source") ap.add_argument( "--target", type=str, help="Target directory, defaults to generated/<source>/" ) ap.add_argument( "--template", type=str, default="default", help="One of the subfolders of templates/", ) ap.add_argument( "--watch", type=str2bool, nargs="?", const=True, default=False, help="Keep watching for changes", ) options = ap.parse_args() if not options.target: options.target = str(Path("generated") / options.source) if options.watch: print("Press CTRL+C to stop monitoring for changes") last_change = 0 source_path = Path(options.source) template_path = TEMPLATE_PATH / options.template while True: changes = False source_change = get_last_change(source_path) if source_change > last_change: changes = True template_change = get_last_change(template_path) if template_change > last_change: changes = True if changes: last_change = max(template_change, source_change) try: run(options) except Exception as e: print(e) except SystemExit: pass sleep(0.25) else: run(options) if __name__ == "__main__": main()

28.043103

86

0.544216

from argparse import ArgumentParser, ArgumentTypeError import codecs from datetime import datetime from jinja2 import Environment, FileSystemLoader import json try: from pathlib import Path except ImportError: from pathlib2 import Path import re import sass import six import shutil import sys from time import sleep TEMPLATE_PATH = Path("templates") def str2bool(value): if value.lower() in ("yes", "true", "t", "y", "1"): return True elif value.lower() in ("no", "false", "f", "n", "0"): return False else: raise ArgumentTypeError("Boolean value expected.") def get_last_change(path): last_changed = path.stat().st_mtime if path.is_dir(): for entry in path.glob("**/*"): entry_changed = entry.stat().st_mtime if entry_changed > last_changed: last_changed = entry_changed return last_changed def run(options): try: with codecs.open(options.source, encoding="utf-8") as f: config = json.load(f) except ValueError as e: print("Error parsing config {}.".format(options.source)) print("") raise except IOError as e: print("Configuration file not found: {}".format(options.source)) print("") raise validate_config(config) process_config(config) generate_cv(options.target, options.template, config) def generate_cv(destination, template, config): template_path = str(TEMPLATE_PATH / template) env = Environment(loader=FileSystemLoader(template_path)) template = env.get_template("index.html") now = datetime.now() current_time = now.strftime("%Y-%m-%d %H:%M:%S %z") year = now.strftime("%Y") navigation = generate_navigation(config) html = template.render( name=config["name"], contact=config["contact"], sections=config["sections"], navigation=navigation, now=current_time, year=year, ) dst_path = Path(destination) if dst_path.exists(): shutil.rmtree(destination) shutil.copytree(template_path, destination) scss_files = [] for entry in dst_path.glob("**/*.scss"): scss_files.append(entry) entry_name = entry.name if entry_name.endswith(".scss") and not entry_name.startswith("_"): entry_str = str(entry) compiled = sass.compile(filename=entry_str) entry_css = Path(entry_str[:-5] + ".css") with entry_css.open("w", encoding="utf-8") as f: f.write(compiled) print("Compiled {} to {}".format(entry, entry_css)) for entry in scss_files: entry.unlink() for entry in reversed(list(dst_path.rglob("*"))): if entry.exists() and entry.is_dir(): empty = True for _ in entry.iterdir(): empty = False break if empty: entry.rmdir() full_path = Path(destination) / "index.html" with full_path.open("w", encoding="utf-8") as f: f.write(html) print("Generated CV HTML to {}".format(full_path)) def validate_config(config): error = False if "name" not in config: print('Missing name definition, e.g. { "name": "Janne Enberg", ' "... }") error = True if "contact" not in config: print( "Missing contact definition, e.g. { ..., " '"contact": "+1 (2) 345 678 | contact@example.com", ... }' ) error = True if "sections" not in config: print("Missing sections definition, e.g. { ..., " '"sections": [ ... ] }') error = True else: for section in config["sections"]: if isinstance(section, six.string_types): continue if "title" not in section: print( "Missing title from section definition, , " 'e.g. { ..., "sections": [ {"title": "Section ' 'title", ...} ] }' ) print("Found: {}".format(section)) error = True if ( "fields" not in section and "large" not in section and "largeList" not in section ): print( "No fields, largeList or large definition for " "section, , " 'e.g. { ..., "sections": [ {..., ' '"large": "Yadi yadi yada", ...} ] }' ) error = True if "fields" in section: for field in section["fields"]: if not isinstance(field, list) or len(field) != 2: print( "Invalid field definition, " "it should have two items, e.g. { ..., " '"sections": [ {..., "fields": [ ["Label",' ' "Value"], ... }, ... ] }' ) error = True if error: print("") print("Please fix errors in configuration file.") sys.exit(1) def process_config(config): for index, section in enumerate(config["sections"]): if isinstance(section, six.string_types): if section == "-": config["sections"][index] = {"type": "page-break"} else: config["sections"][index] = {"type": "heading", "title": section} continue section["type"] = "normal" if "fields" in section: fields = [] for fieldColumns in section["fields"]: fields.append({"label": fieldColumns[0], "value": fieldColumns[1]}) section["fields"] = fields if "largeList" in section: section["large"] = ( "<ul><li>" + "</li><li>".join(section["largeList"]) + "</li></ul>" ) del section["largeList"] heading = config["mainHeading"] main_heading = {"type": "heading", "title": heading} config["sections"] = [main_heading] + config["sections"] def generate_navigation(config): i = 1 nav = {"headings": []} for _, section in enumerate(config["sections"]): if section["type"] == "page-break": continue name = section["title"] section["id"] = make_id(name, i) if section["type"] == "heading": nav[name] = [section] nav["headings"].append(name) heading = name else: nav[heading].append(section) i += 1 return nav def make_id(text, index): # Replace characters not valid in IDs text = re.sub(r"[^0-9a-zA-Z\-_.:]", "-", text) # Text must not begin with a number if re.match(r"^[0-9]", text): text = "id-{}-{}".format(text, index) return text def main(): ap = ArgumentParser() ap.add_argument("--source", default="cv.json", type=str, help="CV JSON source") ap.add_argument( "--target", type=str, help="Target directory, defaults to generated/<source>/" ) ap.add_argument( "--template", type=str, default="default", help="One of the subfolders of templates/", ) ap.add_argument( "--watch", type=str2bool, nargs="?", const=True, default=False, help="Keep watching for changes", ) options = ap.parse_args() if not options.target: options.target = str(Path("generated") / options.source) if options.watch: print("Press CTRL+C to stop monitoring for changes") last_change = 0 source_path = Path(options.source) template_path = TEMPLATE_PATH / options.template while True: changes = False source_change = get_last_change(source_path) if source_change > last_change: changes = True template_change = get_last_change(template_path) if template_change > last_change: changes = True if changes: last_change = max(template_change, source_change) try: run(options) except Exception as e: print(e) except SystemExit: pass sleep(0.25) else: run(options) if __name__ == "__main__": main()

true

f71c190a14841ef42f527cfad6bd4742ebd5bc55

929

py

Python

nlpaug/util/audio/loader.py

lucidworks/nlpaug

8e47fa39200db17f4dc1d61567af1419bc389071

[ "MIT" ]

1

2021-06-09T20:07:30.000Z

nlpaug/util/audio/loader.py

lucidworks/nlpaug

8e47fa39200db17f4dc1d61567af1419bc389071

[ "MIT" ]

null

nlpaug/util/audio/loader.py

lucidworks/nlpaug

8e47fa39200db17f4dc1d61567af1419bc389071

[ "MIT" ]

null

try: import librosa except ImportError: # No installation required if not using this function pass class AudioLoader: @staticmethod def load_audio(file_path): try: import librosa except ModuleNotFoundError: raise ModuleNotFoundError( "Missed librosa library. Install import librosa by `pip install librosa`" ) return librosa.load(file_path) @staticmethod def load_mel_spectrogram(file_path, n_mels=128, fmax=8000): try: import librosa except ModuleNotFoundError: raise ModuleNotFoundError( "Missed librosa library. Install import librosa by `pip install librosa`" ) audio, sampling_rate = AudioLoader.load_audio(file_path) return librosa.feature.melspectrogram( y=audio, sr=sampling_rate, n_mels=n_mels, fmax=fmax )

28.151515

89

0.634015

try: import librosa except ImportError: pass class AudioLoader: @staticmethod def load_audio(file_path): try: import librosa except ModuleNotFoundError: raise ModuleNotFoundError( "Missed librosa library. Install import librosa by `pip install librosa`" ) return librosa.load(file_path) @staticmethod def load_mel_spectrogram(file_path, n_mels=128, fmax=8000): try: import librosa except ModuleNotFoundError: raise ModuleNotFoundError( "Missed librosa library. Install import librosa by `pip install librosa`" ) audio, sampling_rate = AudioLoader.load_audio(file_path) return librosa.feature.melspectrogram( y=audio, sr=sampling_rate, n_mels=n_mels, fmax=fmax )

true

f71c191c5eb6a1641c149fff6ae72d57a8d19cda

4,286

py

Python

dataset/DeepFakes/faceswap-master/lib/training_data.py

MrThiago/FaceForensics

1806e70d0dd2294a12a8afd1c3f59d6ecac639bf

[ "MIT" ]

1,930

2018-04-20T14:52:01.000Z

2022-03-30T13:53:31.000Z

dataset/DeepFakes/faceswap-master/lib/training_data.py

chrisgorgo/FaceForensics

a815daa9ebb7c12240a4b7162c431af0e1b959fa

[ "MIT" ]

68

2019-02-14T09:09:02.000Z

2022-03-23T08:55:23.000Z

dataset/DeepFakes/faceswap-master/lib/training_data.py

chrisgorgo/FaceForensics

a815daa9ebb7c12240a4b7162c431af0e1b959fa

[ "MIT" ]

499

2018-04-20T11:27:11.000Z

2022-03-29T16:29:50.000Z

import cv2 import numpy from random import shuffle from .utils import BackgroundGenerator from .umeyama import umeyama class TrainingDataGenerator(): def __init__(self, random_transform_args, coverage, scale=5, zoom=1): #TODO thos default should stay in the warp function self.random_transform_args = random_transform_args self.coverage = coverage self.scale = scale self.zoom = zoom def minibatchAB(self, images, batchsize): batch = BackgroundGenerator(self.minibatch(images, batchsize), 1) for ep1, warped_img, target_img in batch.iterator(): yield ep1, warped_img, target_img # A generator function that yields epoch, batchsize of warped_img and batchsize of target_img def minibatch(self, data, batchsize): length = len(data) assert length >= batchsize, "Number of images is lower than batch-size (Note that too few images may lead to bad training). # images: {}, batch-size: {}".format(length, batchsize) epoch = i = 0 shuffle(data) while True: size = batchsize if i+size > length: shuffle(data) i = 0 epoch+=1 rtn = numpy.float32([self.read_image(img) for img in data[i:i+size]]) i+=size yield epoch, rtn[:,0,:,:,:], rtn[:,1,:,:,:] def color_adjust(self, img): return img / 255.0 def read_image(self, fn): try: image = self.color_adjust(cv2.imread(fn)) except TypeError: raise Exception("Error while reading image", fn) image = cv2.resize(image, (256,256)) image = self.random_transform( image, **self.random_transform_args ) warped_img, target_img = self.random_warp( image, self.coverage, self.scale, self.zoom ) return warped_img, target_img def random_transform(self, image, rotation_range, zoom_range, shift_range, random_flip): h, w = image.shape[0:2] rotation = numpy.random.uniform(-rotation_range, rotation_range) scale = numpy.random.uniform(1 - zoom_range, 1 + zoom_range) tx = numpy.random.uniform(-shift_range, shift_range) * w ty = numpy.random.uniform(-shift_range, shift_range) * h mat = cv2.getRotationMatrix2D((w // 2, h // 2), rotation, scale) mat[:, 2] += (tx, ty) result = cv2.warpAffine( image, mat, (w, h), borderMode=cv2.BORDER_REPLICATE) if numpy.random.random() < random_flip: result = result[:, ::-1] return result # get pair of random warped images from aligned face image def random_warp(self, image, coverage, scale = 5, zoom = 1): assert image.shape == (256, 256, 3) range_ = numpy.linspace(128 - coverage//2, 128 + coverage//2, 5) mapx = numpy.broadcast_to(range_, (5, 5)) mapy = mapx.T mapx = mapx + numpy.random.normal(size=(5,5), scale=scale) mapy = mapy + numpy.random.normal(size=(5,5), scale=scale) interp_mapx = cv2.resize(mapx, (80*zoom,80*zoom))[8*zoom:72*zoom,8*zoom:72*zoom].astype('float32') interp_mapy = cv2.resize(mapy, (80*zoom,80*zoom))[8*zoom:72*zoom,8*zoom:72*zoom].astype('float32') warped_image = cv2.remap(image, interp_mapx, interp_mapy, cv2.INTER_LINEAR) src_points = numpy.stack([mapx.ravel(), mapy.ravel() ], axis=-1) dst_points = numpy.mgrid[0:65*zoom:16*zoom,0:65*zoom:16*zoom].T.reshape(-1,2) mat = umeyama(src_points, dst_points, True)[0:2] target_image = cv2.warpAffine(image, mat, (64*zoom,64*zoom)) return warped_image, target_image def stack_images(images): def get_transpose_axes(n): if n % 2 == 0: y_axes = list(range(1, n - 1, 2)) x_axes = list(range(0, n - 1, 2)) else: y_axes = list(range(0, n - 1, 2)) x_axes = list(range(1, n - 1, 2)) return y_axes, x_axes, [n - 1] images_shape = numpy.array(images.shape) new_axes = get_transpose_axes(len(images_shape)) new_shape = [numpy.prod(images_shape[x]) for x in new_axes] return numpy.transpose( images, axes=numpy.concatenate(new_axes) ).reshape(new_shape)

40.819048

187

0.617592

import cv2 import numpy from random import shuffle from .utils import BackgroundGenerator from .umeyama import umeyama class TrainingDataGenerator(): def __init__(self, random_transform_args, coverage, scale=5, zoom=1): self.random_transform_args = random_transform_args self.coverage = coverage self.scale = scale self.zoom = zoom def minibatchAB(self, images, batchsize): batch = BackgroundGenerator(self.minibatch(images, batchsize), 1) for ep1, warped_img, target_img in batch.iterator(): yield ep1, warped_img, target_img def minibatch(self, data, batchsize): length = len(data) assert length >= batchsize, "Number of images is lower than batch-size (Note that too few images may lead to bad training). # images: {}, batch-size: {}".format(length, batchsize) epoch = i = 0 shuffle(data) while True: size = batchsize if i+size > length: shuffle(data) i = 0 epoch+=1 rtn = numpy.float32([self.read_image(img) for img in data[i:i+size]]) i+=size yield epoch, rtn[:,0,:,:,:], rtn[:,1,:,:,:] def color_adjust(self, img): return img / 255.0 def read_image(self, fn): try: image = self.color_adjust(cv2.imread(fn)) except TypeError: raise Exception("Error while reading image", fn) image = cv2.resize(image, (256,256)) image = self.random_transform( image, **self.random_transform_args ) warped_img, target_img = self.random_warp( image, self.coverage, self.scale, self.zoom ) return warped_img, target_img def random_transform(self, image, rotation_range, zoom_range, shift_range, random_flip): h, w = image.shape[0:2] rotation = numpy.random.uniform(-rotation_range, rotation_range) scale = numpy.random.uniform(1 - zoom_range, 1 + zoom_range) tx = numpy.random.uniform(-shift_range, shift_range) * w ty = numpy.random.uniform(-shift_range, shift_range) * h mat = cv2.getRotationMatrix2D((w // 2, h // 2), rotation, scale) mat[:, 2] += (tx, ty) result = cv2.warpAffine( image, mat, (w, h), borderMode=cv2.BORDER_REPLICATE) if numpy.random.random() < random_flip: result = result[:, ::-1] return result def random_warp(self, image, coverage, scale = 5, zoom = 1): assert image.shape == (256, 256, 3) range_ = numpy.linspace(128 - coverage//2, 128 + coverage//2, 5) mapx = numpy.broadcast_to(range_, (5, 5)) mapy = mapx.T mapx = mapx + numpy.random.normal(size=(5,5), scale=scale) mapy = mapy + numpy.random.normal(size=(5,5), scale=scale) interp_mapx = cv2.resize(mapx, (80*zoom,80*zoom))[8*zoom:72*zoom,8*zoom:72*zoom].astype('float32') interp_mapy = cv2.resize(mapy, (80*zoom,80*zoom))[8*zoom:72*zoom,8*zoom:72*zoom].astype('float32') warped_image = cv2.remap(image, interp_mapx, interp_mapy, cv2.INTER_LINEAR) src_points = numpy.stack([mapx.ravel(), mapy.ravel() ], axis=-1) dst_points = numpy.mgrid[0:65*zoom:16*zoom,0:65*zoom:16*zoom].T.reshape(-1,2) mat = umeyama(src_points, dst_points, True)[0:2] target_image = cv2.warpAffine(image, mat, (64*zoom,64*zoom)) return warped_image, target_image def stack_images(images): def get_transpose_axes(n): if n % 2 == 0: y_axes = list(range(1, n - 1, 2)) x_axes = list(range(0, n - 1, 2)) else: y_axes = list(range(0, n - 1, 2)) x_axes = list(range(1, n - 1, 2)) return y_axes, x_axes, [n - 1] images_shape = numpy.array(images.shape) new_axes = get_transpose_axes(len(images_shape)) new_shape = [numpy.prod(images_shape[x]) for x in new_axes] return numpy.transpose( images, axes=numpy.concatenate(new_axes) ).reshape(new_shape)

true

f71c1b0d0a31e515a655f2a67f62120cc4232d70

1,106

py

Python

setup.py

albertosottile/SmartGadget-gatt

b5b4002f3635afcb97de5106676cc7142b1e9ca5

[ "MIT" ]

2

2021-06-14T18:08:16.000Z

2021-08-29T06:48:10.000Z

setup.py

albertosottile/SmartGadget-gatt

b5b4002f3635afcb97de5106676cc7142b1e9ca5

[ "MIT" ]

null

setup.py

albertosottile/SmartGadget-gatt

b5b4002f3635afcb97de5106676cc7142b1e9ca5

[ "MIT" ]

1

2021-08-29T06:48:11.000Z

#!/usr/bin/env python3 import setuptools def read(fname): with open(fname, 'r') as f: return f.read() setuptools.setup( name="smartgadget", version="0.1", author="Alberto Sottile", author_email="alby128@gmail.com", description=' '.join([ 'Interact with a Sensirion SHT31 Smart Gadget', 'Development Kit using the Bluetooth GATT SDK for Python' ]), url="https://github.com/albertosottile/SmartGadget-gatt", packages=['smartgadget'], install_requires=read('requirements.txt').splitlines(), python_requires=">=3.5", include_package_data=True, classifiers=[ "Development Status :: 4 - Beta", "Intended Audience :: Education", "Intended Audience :: Manufacturing", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering" ], )

30.722222

65

0.621157

import setuptools def read(fname): with open(fname, 'r') as f: return f.read() setuptools.setup( name="smartgadget", version="0.1", author="Alberto Sottile", author_email="alby128@gmail.com", description=' '.join([ 'Interact with a Sensirion SHT31 Smart Gadget', 'Development Kit using the Bluetooth GATT SDK for Python' ]), url="https://github.com/albertosottile/SmartGadget-gatt", packages=['smartgadget'], install_requires=read('requirements.txt').splitlines(), python_requires=">=3.5", include_package_data=True, classifiers=[ "Development Status :: 4 - Beta", "Intended Audience :: Education", "Intended Audience :: Manufacturing", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering" ], )

true

f71c1b1f9c2288ed03520cd964f1b666fcfab9a4

1,302

py

Python

CV_A4_/A4_compute_descriptors.py

pseudowasabi/computer-vision-exercises

34b7c8402c32dbb00e484f90780ebb6546a3f8dc

[ "MIT" ]

null

CV_A4_/A4_compute_descriptors.py

pseudowasabi/computer-vision-exercises

34b7c8402c32dbb00e484f90780ebb6546a3f8dc

[ "MIT" ]

null

CV_A4_/A4_compute_descriptors.py

pseudowasabi/computer-vision-exercises

34b7c8402c32dbb00e484f90780ebb6546a3f8dc

[ "MIT" ]

null

''' Computer vision assignment 4 by Yoseob Kim A4_compute_descriptors.py Compute similarity-reflected image descriptors with L1, L2 norm distances by using SIFT descriptors. * Status: (working on it) * GitHub Link: https://github.com/pseudowasabi/computer-vision-exercises/tree/master/CV_A4_ ''' import cv2 import numpy as np import math import time import operator import random img = cv2.imread('ukbench00000.jpg', cv2.IMREAD_GRAYSCALE) ''' my_min = np.inf my_max = 0''' for i in range(1000): offset = '00' if i < 10 else '0' if i < 100 else '' offset += str(i) #print(offset) f = open('./sift/sift100'+offset, 'rb') # reference - https://numpy.org/doc/stable/reference/generated/numpy.frombuffer.html sift_des = np.frombuffer(f.read(), dtype=np.uint8) #print(sift_des.shape) #print(sift_des) ''' if sift_des.shape[0] % 128 != 0: print('divide error') ''' sift_des_reshaped = np.reshape(sift_des, (sift_des.shape[0] // 128, 128)) #print(sift_des_reshaped.shape) ''' if sift_des_reshaped.shape[0] < my_min: my_min = sift_des_reshaped.shape[0] if sift_des_reshaped.shape[0] > my_max: my_max = sift_des_reshaped.shape[0]''' f.close() #print(my_min, my_max) # N size # min = 73, max = 2388

22.842105

100

0.669739

import cv2 import numpy as np import math import time import operator import random img = cv2.imread('ukbench00000.jpg', cv2.IMREAD_GRAYSCALE) for i in range(1000): offset = '00' if i < 10 else '0' if i < 100 else '' offset += str(i) f = open('./sift/sift100'+offset, 'rb') sift_des = np.frombuffer(f.read(), dtype=np.uint8) sift_des_reshaped = np.reshape(sift_des, (sift_des.shape[0] // 128, 128)) f.close()

true

f71c1c5ae6f65d71e66399bf4776b2b7437ab5a8

713

py

Python

audiophiler/util.py

Mstrodl/audiophiler

2e3a8299b58ab92a851cae726cc9184a2dad05f8

[ "MIT" ]

5

2017-05-08T05:07:22.000Z

2021-06-01T18:48:30.000Z

audiophiler/util.py

Mstrodl/audiophiler

2e3a8299b58ab92a851cae726cc9184a2dad05f8

[ "MIT" ]

34

2017-09-11T19:18:40.000Z

2021-08-28T21:38:15.000Z

audiophiler/util.py

Mstrodl/audiophiler

2e3a8299b58ab92a851cae726cc9184a2dad05f8

[ "MIT" ]

20

2017-09-09T22:02:11.000Z

2021-08-28T17:45:59.000Z

# File: util.py # Audiophiler utility functions # Credit to Liam Middlebrook and Ram Zallan # https://github.com/liam-middlebrook/gallery from functools import wraps from flask import session from audiophiler.models import Tour def audiophiler_auth(func): @wraps(func) def wrapped_function(*args, **kwargs): uuid = str(session["userinfo"].get("sub", "")) uid = str(session["userinfo"].get("preferred_username", "")) auth_dict = { "uuid": uuid, "uid": uid } kwargs["auth_dict"] = auth_dict return func(*args, **kwargs) return wrapped_function def get_tour_lock_status(): lock = Tour.query.first() return lock.tour_lock

27.423077

68

0.652174

from functools import wraps from flask import session from audiophiler.models import Tour def audiophiler_auth(func): @wraps(func) def wrapped_function(*args, **kwargs): uuid = str(session["userinfo"].get("sub", "")) uid = str(session["userinfo"].get("preferred_username", "")) auth_dict = { "uuid": uuid, "uid": uid } kwargs["auth_dict"] = auth_dict return func(*args, **kwargs) return wrapped_function def get_tour_lock_status(): lock = Tour.query.first() return lock.tour_lock

true

f71c1c8decca6e84cea60dec4d962fc15cc6ae66

4,251

py

Python

qutip/hardware_info.py

kiuthed/qutip

b6fb8e5bbd9ffeae117b54e56313e8617038deab

[ "BSD-3-Clause" ]

null

qutip/hardware_info.py

kiuthed/qutip

b6fb8e5bbd9ffeae117b54e56313e8617038deab

[ "BSD-3-Clause" ]

null

qutip/hardware_info.py

kiuthed/qutip

b6fb8e5bbd9ffeae117b54e56313e8617038deab

[ "BSD-3-Clause" ]

null

# This file is part of QuTiP: Quantum Toolbox in Python. # # Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. 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. # # 3. Neither the name of the QuTiP: Quantum Toolbox in Python 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 # HOLDER 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. ############################################################################### __all__ = ['hardware_info'] import os import sys def _mac_hardware_info(): info = dict() results = dict() for l in [l.split(':') for l in os.popen('sysctl hw').readlines()[1:20]]: info[l[0].strip(' "').replace(' ', '_').lower().strip('hw.')] = \ l[1].strip('.\n ') results.update({'cpus': int(info['physicalcpu'])}) results.update({'cpu_freq': int(info['cpufrequency']) / (1000. ** 3)}) results.update({'memsize': int(info['memsize']) / (1024 ** 2)}) # add OS information results.update({'os': 'Mac OSX'}) return results def _linux_hardware_info(): results = {} # get cpu number cpu_info = dict() for l in [l.split(':') for l in os.popen('lscpu').readlines()]: cpu_info[l[0]] = l[1].strip('.\n ').strip('kB') sockets = int(cpu_info['Socket(s)']) cores_per_socket = int(cpu_info['Core(s) per socket']) results.update({'cpus': sockets * cores_per_socket}) # get cpu frequency directly (bypasses freq scaling) try: file = "/sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_max_freq" cpu_freq = open(file).readlines()[0] cpu_freq = float(cpu_freq.strip('\n')) results.update({'cpu_freq': cpu_freq / (1000. ** 2)}) except: cpu_freq = float(cpu_info['CPU MHz']) / 1000. results.update({'cpu_freq': cpu_freq}) # get total amount of memory mem_info = dict() for l in [l.split(':') for l in open("/proc/meminfo").readlines()]: mem_info[l[0]] = l[1].strip('.\n ').strip('kB') results.update({'memsize': int(mem_info['MemTotal']) / 1024}) # add OS information results.update({'os': 'Linux'}) return results def _win_hardware_info(): return {'os': 'Windows'} def hardware_info(): """ Returns basic hardware information about the computer. Gives actual number of CPU's in the machine, even when hyperthreading is turned on. Returns ------- info : dict Dictionary containing cpu and memory information. """ try: if sys.platform == 'darwin': out = _mac_hardware_info() elif sys.platform == 'win32': out = _win_hardware_info() elif sys.platform in ['linux', 'linux2']: out = _linux_hardware_info() else: out = {} except: return {} else: return out if __name__ == '__main__': print(hardware_info())

36.646552

79

0.638908

true

f71c1cb01d78b077e343818c4290f9abafc4925e

1,900

py

Python

cron_descriptor/GetText.py

nathmo/cron-descriptor

2475065be9e203ed5cea49ec6ca365384f433cb6

[ "MIT" ]

3

2018-12-11T18:51:36.000Z

2019-10-16T19:10:19.000Z

cron_descriptor/GetText.py

nathmo/cron-descriptor

2475065be9e203ed5cea49ec6ca365384f433cb6

[ "MIT" ]

2

2019-04-14T04:14:31.000Z

2019-10-15T03:23:54.000Z

cron_descriptor/cron_descriptor/GetText.py

michaelblyons/SublimeSyntax-Crontab

54f1fa7ff0c9d18aea3790555dba6e533ce3749b

[ "MIT" ]

2

2019-04-11T06:13:54.000Z

2019-10-04T02:49:58.000Z

# The MIT License (MIT) # # Copyright (c) 2016 Adam Schubert # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import gettext import os import logging logger = logging.getLogger(__name__) class GetText(object): """ Handles language translations and Initializes global _() function """ def __init__(self, locale_code): """ Initialize GetText :param locale_code selected locale """ try: filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'locale', '{}.mo'.format(locale_code)) trans = gettext.GNUTranslations(open(filename, "rb")) logger.debug('{} Loaded'.format(filename)) except IOError: logger.debug('Failed to find locale {}'.format(locale_code)) trans = gettext.NullTranslations() trans.install()

36.538462

80

0.7

import gettext import os import logging logger = logging.getLogger(__name__) class GetText(object): def __init__(self, locale_code): try: filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'locale', '{}.mo'.format(locale_code)) trans = gettext.GNUTranslations(open(filename, "rb")) logger.debug('{} Loaded'.format(filename)) except IOError: logger.debug('Failed to find locale {}'.format(locale_code)) trans = gettext.NullTranslations() trans.install()

true

f71c1cd7ddd442ab1b3e8652cdd667ec554c98f4

23,533

py

Python

adnmtf/nmtf_core.py

Advestis/adnmtf

7b36da64669894506071a75d8bd341edb0e75b9f

[ "MIT" ]

null

adnmtf/nmtf_core.py

Advestis/adnmtf

7b36da64669894506071a75d8bd341edb0e75b9f

[ "MIT" ]

null

adnmtf/nmtf_core.py

Advestis/adnmtf

7b36da64669894506071a75d8bd341edb0e75b9f

[ "MIT" ]

null

"""Non-negative matrix and tensor factorization core functions """ # Author: Paul Fogel # License: MIT # Jan 4, '20 from typing import Tuple import numpy as np from .nmtf_utils import EPSILON, sparse_opt import logging logger = logging.getLogger(__name__) # TODO (pcotte): typing # TODO (pcotte): docstrings (with parameters and returns) def ntf_stack(m, mmis, n_blocks): """Unfold tensor M for future use with NMF """ n, p = m.shape mmis = mmis.astype(np.int) n_mmis = mmis.shape[0] n_blocks = int(n_blocks) mstacked = np.zeros((int(n * p / n_blocks), n_blocks)) if n_mmis > 0: mmis_stacked = np.zeros((int(n * p / n_blocks), n_blocks)) else: mmis_stacked = np.array([]) for i_block in range(0, n_blocks): for j in range(0, int(p / n_blocks)): i1 = j * n i2 = i1 + n mstacked[i1:i2, i_block] = m[:, int(i_block * p / n_blocks + j)] if n_mmis > 0: mmis_stacked[i1:i2, i_block] = mmis[:, int(i_block * p / n_blocks + j)] return mstacked, mmis_stacked def ntf_solve( m, mmis, mt0, mw0, mb0, nc, tolerance, log_iter, status0, max_iterations, nmf_fix_user_lhe, nmf_fix_user_rhe, nmf_fix_user_bhe, nmf_sparse_level, ntf_unimodal, ntf_smooth, ntf_left_components, ntf_right_components, ntf_block_components, n_blocks, nmf_priors, my_status_box, ): """Interface to: - NTFSolve_simple """ if len(nmf_priors) > 0: n_nmf_priors, nc = nmf_priors.shape else: n_nmf_priors = 0 if n_nmf_priors > 0: nmf_priors[nmf_priors > 0] = 1 return ntf_solve_simple( m=m, mmis=mmis, mt0=mt0, mw0=mw0, mb0=mb0, nc=nc, tolerance=tolerance, log_iter=log_iter, status0=status0, max_iterations=max_iterations, nmf_fix_user_lhe=nmf_fix_user_lhe, nmf_fix_user_rhe=nmf_fix_user_rhe, nmf_fix_user_bhe=nmf_fix_user_bhe, nmf_sparse_level=nmf_sparse_level, ntf_unimodal=ntf_unimodal, ntf_smooth=ntf_smooth, ntf_left_components=ntf_left_components, ntf_right_components=ntf_right_components, ntf_block_components=ntf_block_components, n_blocks=n_blocks, nmf_priors=nmf_priors, my_status_box=my_status_box, ) def ntf_solve_simple( m, mmis, mt0, mw0, mb0, nc, tolerance, log_iter, status0, max_iterations, nmf_fix_user_lhe, nmf_fix_user_rhe, nmf_fix_user_bhe, nmf_sparse_level, ntf_unimodal, ntf_smooth, ntf_left_components, ntf_right_components, ntf_block_components, n_blocks, nmf_priors, my_status_box, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, float, int]: """ Estimate NTF matrices (HALS) Parameters ---------- m: Input matrix mmis: Define missing values (0 = missing cell, 1 = real cell) mt0: Initial left hand matrix mw0: Initial right hand matrix mb0: Initial block hand matrix nc: NTF rank tolerance: Convergence threshold log_iter: Log results through iterations status0: Initial displayed status to be updated during iterations max_iterations: Max iterations nmf_fix_user_lhe: = 1 => fixed left hand matrix columns nmf_fix_user_rhe: = 1 => fixed right hand matrix columns nmf_fix_user_bhe: = 1 => fixed block hand matrix columns nmf_sparse_level: sparsity level (as defined by Hoyer); +/- = make RHE/LHe sparse ntf_unimodal: Apply Unimodal constraint on factoring vectors ntf_smooth: Apply Smooth constraint on factoring vectors ntf_left_components: Apply Unimodal/Smooth constraint on left hand matrix ntf_right_components: Apply Unimodal/Smooth constraint on right hand matrix ntf_block_components: Apply Unimodal/Smooth constraint on block hand matrix n_blocks: Number of NTF blocks nmf_priors: Elements in mw that should be updated (others remain 0) my_status_box Returns ------- Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, float, int]\n * mt: Left hand matrix\n * mw: Right hand matrix\n * mb: Block hand matrix\n * diff: objective cost\n * cancel_pressed\n Reference --------- a. Cichocki, P.H.a.N. Anh-Huym, Fast local algorithms for large scale nonnegative matrix and tensor factorizations, IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 92 (3) (2009) 708–721. """ cancel_pressed = 0 n, p0 = m.shape n_mmis = mmis.shape[0] nc = int(nc) n_blocks = int(n_blocks) p = int(p0 / n_blocks) nxp = int(n * p) nxp0 = int(n * p0) mt = np.copy(mt0) mw = np.copy(mw0) mb = np.copy(mb0) # step_iter = math.ceil(MaxIterations/10) step_iter = 1 pbar_step = 100 * step_iter / max_iterations id_blockp = np.arange(0, (n_blocks - 1) * p + 1, p) a = np.zeros(n) b = np.zeros(p) c = np.zeros(n_blocks) alpha = np.zeros(nc) # Compute Residual tensor mfit = np.zeros((n, p0)) for k in range(0, nc): if n_blocks > 1: for i_block in range(0, n_blocks): mfit[:, id_blockp[i_block]: id_blockp[i_block] + p] += ( mb[i_block, k] * np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) ) else: mfit[:, id_blockp[0]: id_blockp[0] + p] += np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) denomt = np.zeros(n) denomw = np.zeros(p) denom_block = np.zeros((n_blocks, nc)) mt2 = np.zeros(n) mw2 = np.zeros(p) mt_mw = np.zeros(nxp) denom_cutoff = 0.1 if n_mmis > 0: mres = (m - mfit) * mmis else: mres = m - mfit my_status_box.init_bar() # Loop cont = 1 i_iter = 0 diff0 = 1.0e99 mpart = np.zeros((n, p0)) if abs(nmf_sparse_level) < 1: alpha[0] = nmf_sparse_level * 0.8 else: alpha[0] = nmf_sparse_level percent_zeros = 0 iter_sparse = 0 while (cont > 0) & (i_iter < max_iterations): for k in range(0, nc): ( n_blocks, mpart, id_blockp, p, mb, k, mt, n, mw, n_mmis, mmis, mres, nmf_fix_user_lhe, denomt, mw2, denom_cutoff, alpha, ntf_unimodal, ntf_left_components, ntf_smooth, a, nmf_fix_user_rhe, denomw, mt2, ntf_right_components, b, nmf_fix_user_bhe, mt_mw, nxp, denom_block, ntf_block_components, c, mfit, nmf_priors, ) = ntf_update( n_blocks=n_blocks, mpart=mpart, id_blockp=id_blockp, p=p, mb=mb, k=k, mt=mt, n=n, mw=mw, n_mmis=n_mmis, mmis=mmis, mres=mres, nmf_fix_user_lhe=nmf_fix_user_lhe, denomt=denomt, mw2=mw2, denom_cutoff=denom_cutoff, alpha=alpha, ntf_unimodal=ntf_unimodal, ntf_left_components=ntf_left_components, ntf_smooth=ntf_smooth, a=a, nmf_fix_user_rhe=nmf_fix_user_rhe, denomw=denomw, mt2=mt2, ntf_right_components=ntf_right_components, b=b, nmf_fix_user_bhe=nmf_fix_user_bhe, mt_mw=mt_mw, nxp=nxp, denom_block=denom_block, ntf_block_components=ntf_block_components, c=c, mfit=mfit, nmf_priors=nmf_priors, ) if i_iter % step_iter == 0: # Check convergence diff = np.linalg.norm(mres) ** 2 / nxp0 if (diff0 - diff) / diff0 < tolerance: cont = 0 else: if diff > diff0: my_status_box.my_print(f"{status0} Iter: {i_iter} MSR does not improve") diff0 = diff Status = f"{status0} Iteration: {i_iter}" if nmf_sparse_level != 0: Status = f"{Status} ; Achieved sparsity: {round(percent_zeros, 2)}; alpha: {round(alpha[0], 2)}" if log_iter == 1: my_status_box.my_print(Status) my_status_box.update_status(status=Status) my_status_box.update_bar(step=pbar_step) if my_status_box.cancel_pressed: cancel_pressed = 1 return np.array([]), mt, mw, mb, mres, cancel_pressed if log_iter == 1: my_status_box.my_print(status0 + " Iter: " + str(i_iter) + " MSR: " + str(diff)) i_iter += 1 if cont == 0 or i_iter == max_iterations or (cont == 0 and abs(nmf_sparse_level) == 1): if 0 < nmf_sparse_level < 1: sparse_test = np.zeros((nc, 1)) percent_zeros0 = percent_zeros for k in range(0, nc): sparse_test[k] = np.where(mw[:, k] == 0)[0].size percent_zeros = np.mean(sparse_test) / p if percent_zeros < percent_zeros0: iter_sparse += 1 else: iter_sparse = 0 if (percent_zeros < 0.99 * nmf_sparse_level) & (iter_sparse < 50): alpha[0] *= min(1.05 * nmf_sparse_level / percent_zeros, 1.1) if alpha[0] < 1: i_iter = 0 cont = 1 elif 0 > nmf_sparse_level > -1: sparse_test = np.zeros((nc, 1)) percent_zeros0 = percent_zeros for k in range(0, nc): sparse_test[k] = np.where(mt[:, k] == 0)[0].size percent_zeros = np.mean(sparse_test) / n if percent_zeros < percent_zeros0: iter_sparse += 1 else: iter_sparse = 0 if (percent_zeros < 0.99 * abs(nmf_sparse_level)) & (iter_sparse < 50): alpha[0] *= min(1.05 * abs(nmf_sparse_level) / percent_zeros, 1.1) if abs(alpha[0]) < 1: i_iter = 0 cont = 1 elif abs(alpha[0]) == 1: if alpha[0] == -1: for k in range(0, nc): if np.max(mt[:, k]) > 0: hhi = int( np.round( (np.linalg.norm(mt[:, k], ord=1) / (np.linalg.norm(mt[:, k], ord=2) + EPSILON)) ** 2, decimals=0, ) ) alpha[k] = -1 - (n - hhi) / (n - 1) else: alpha[k] = 0 else: for k in range(0, nc): if np.max(mw[:, k]) > 0: hhi = int( np.round( (np.linalg.norm(mw[:, k], ord=1) / (np.linalg.norm(mw[:, k], ord=2) + EPSILON)) ** 2, decimals=0, ) ) alpha[k] = 1 + (p - hhi) / (p - 1) else: alpha[k] = 0 if alpha[0] <= -1: alpha_real = -(alpha + 1) # noinspection PyTypeChecker alpha_min = min(alpha_real) for k in range(0, nc): # noinspection PyUnresolvedReferences alpha[k] = min(alpha_real[k], 2 * alpha_min) alpha[k] = -alpha[k] - 1 else: alpha_real = alpha - 1 alpha_min = min(alpha_real) for k in range(0, nc): alpha[k] = min(alpha_real[k], 2 * alpha_min) alpha[k] = alpha[k] + 1 i_iter = 0 cont = 1 diff0 = 1.0e99 for k in range(0, nc): hhi = np.round((np.linalg.norm(mt[:, k], ord=1) / np.linalg.norm(mt[:, k], ord=2)) ** 2, decimals=0) logger.info(f"component: {k}, left hhi: {hhi}") hhi = np.round((np.linalg.norm(mw[:, k], ord=1) / np.linalg.norm(mw[:, k], ord=2)) ** 2, decimals=0) logger.info(f"component: {k} right hhi: {hhi}") if (n_mmis > 0) & (nmf_fix_user_bhe == 0): mb *= denom_block # TODO (pcotte): mt and mw can be not yet referenced: fix that return np.array([]), mt, mw, mb, diff, cancel_pressed def ntf_update( n_blocks, mpart, id_blockp, p, mb, k, mt, n, mw, n_mmis, mmis, mres, nmf_fix_user_lhe, denomt, mw2, denom_cutoff, alpha, ntf_unimodal, ntf_left_components, ntf_smooth, a, nmf_fix_user_rhe, denomw, mt2, ntf_right_components, b, nmf_fix_user_bhe, mt_mw, nxp, denom_block, ntf_block_components, c, mfit, nmf_priors, ): """Core updating code called by NTFSolve_simple & NTF Solve_conv Input: All variables in the calling function used in the function Output: Same as Input """ if len(nmf_priors) > 0: n_nmf_priors, nc = nmf_priors.shape else: n_nmf_priors = 0 # Compute kth-part if n_blocks > 1: for i_block in range(0, n_blocks): mpart[:, id_blockp[i_block]: id_blockp[i_block] + p] = ( mb[i_block, k] * np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) ) else: mpart[:, id_blockp[0]: id_blockp[0] + p] = np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) if n_mmis > 0: mpart *= mmis mpart += mres if nmf_fix_user_bhe > 0: norm_bhe = True if nmf_fix_user_rhe == 0: norm_lhe = True norm_rhe = False else: norm_lhe = False norm_rhe = True else: norm_bhe = False norm_lhe = True norm_rhe = True if (nmf_fix_user_lhe > 0) & norm_lhe: norm = np.linalg.norm(mt[:, k]) if norm > 0: mt[:, k] /= norm if (nmf_fix_user_rhe > 0) & norm_rhe: norm = np.linalg.norm(mw[:, k]) if norm > 0: mw[:, k] /= norm if (nmf_fix_user_bhe > 0) & norm_bhe & (n_blocks > 1): norm = np.linalg.norm(mb[:, k]) if norm > 0: mb[:, k] /= norm if nmf_fix_user_lhe == 0: # Update Mt mt[:, k] = 0 if n_blocks > 1: for i_block in range(0, n_blocks): mt[:, k] += mb[i_block, k] * mpart[:, id_blockp[i_block]: id_blockp[i_block] + p] @ mw[:, k] else: mt[:, k] += mpart[:, id_blockp[0]: id_blockp[0] + p] @ mw[:, k] if n_mmis > 0: denomt[:] = 0 mw2[:] = mw[:, k] ** 2 if n_blocks > 1: for i_block in range(0, n_blocks): # Broadcast missing cells into Mw to calculate Mw.T * Mw denomt += mb[i_block, k] ** 2 * mmis[:, id_blockp[i_block]: id_blockp[i_block] + p] @ mw2 else: denomt += mmis[:, id_blockp[0]: id_blockp[0] + p] @ mw2 denomt /= np.max(denomt) denomt[denomt < denom_cutoff] = denom_cutoff mt[:, k] /= denomt mt[mt[:, k] < 0, k] = 0 if alpha[0] < 0: if alpha[0] <= -1: if (alpha[0] == -1) & (np.max(mt[:, k]) > 0): t_threshold = mt[:, k] hhi = int( np.round( (np.linalg.norm(t_threshold, ord=1) / (np.linalg.norm(t_threshold, ord=2) + EPSILON)) ** 2, decimals=0, ) ) t_rank = np.argsort(t_threshold) t_threshold[t_rank[0: n - hhi]] = 0 else: mt[:, k] = sparse_opt(mt[:, k], -alpha[k] - 1, False) else: mt[:, k] = sparse_opt(mt[:, k], -alpha[0], False) if (ntf_unimodal > 0) & (ntf_left_components > 0): # Enforce unimodal distribution tmax = np.argmax(mt[:, k]) for i in range(tmax + 1, n): mt[i, k] = min(mt[i - 1, k], mt[i, k]) for i in range(tmax - 1, -1, -1): mt[i, k] = min(mt[i + 1, k], mt[i, k]) if (ntf_smooth > 0) & (ntf_left_components > 0): # Smooth distribution a[0] = 0.75 * mt[0, k] + 0.25 * mt[1, k] a[n - 1] = 0.25 * mt[n - 2, k] + 0.75 * mt[n - 1, k] for i in range(1, n - 1): a[i] = 0.25 * mt[i - 1, k] + 0.5 * mt[i, k] + 0.25 * mt[i + 1, k] mt[:, k] = a if norm_lhe: norm = np.linalg.norm(mt[:, k]) if norm > 0: mt[:, k] /= norm if nmf_fix_user_rhe == 0: # Update Mw mw[:, k] = 0 if n_blocks > 1: for i_block in range(0, n_blocks): mw[:, k] += mpart[:, id_blockp[i_block]: id_blockp[i_block] + p].T @ mt[:, k] * mb[i_block, k] else: mw[:, k] += mpart[:, id_blockp[0]: id_blockp[0] + p].T @ mt[:, k] if n_mmis > 0: denomw[:] = 0 mt2[:] = mt[:, k] ** 2 if n_blocks > 1: for i_block in range(0, n_blocks): # Broadcast missing cells into Mw to calculate Mt.T * Mt denomw += mb[i_block, k] ** 2 * mmis[:, id_blockp[i_block]: id_blockp[i_block] + p].T @ mt2 else: denomw += mmis[:, id_blockp[0]: id_blockp[0] + p].T @ mt2 denomw /= np.max(denomw) denomw[denomw < denom_cutoff] = denom_cutoff mw[:, k] /= denomw mw[mw[:, k] < 0, k] = 0 if alpha[0] > 0: if alpha[0] >= 1: if (alpha[0] == 1) & (np.max(mw[:, k]) > 0): w_threshold = mw[:, k] hhi = int( np.round( (np.linalg.norm(w_threshold, ord=1) / (np.linalg.norm(w_threshold, ord=2) + EPSILON)) ** 2, decimals=0, ) ) w_rank = np.argsort(w_threshold) w_threshold[w_rank[0: p - hhi]] = 0 else: mw[:, k] = sparse_opt(mw[:, k], alpha[k] - 1, False) else: mw[:, k] = sparse_opt(mw[:, k], alpha[0], False) if (ntf_unimodal > 0) & (ntf_right_components > 0): # Enforce unimodal distribution wmax = np.argmax(mw[:, k]) for j in range(wmax + 1, p): mw[j, k] = min(mw[j - 1, k], mw[j, k]) for j in range(wmax - 1, -1, -1): mw[j, k] = min(mw[j + 1, k], mw[j, k]) if (ntf_smooth > 0) & (ntf_right_components > 0): # Smooth distribution b[0] = 0.75 * mw[0, k] + 0.25 * mw[1, k] b[p - 1] = 0.25 * mw[p - 2, k] + 0.75 * mw[p - 1, k] for j in range(1, p - 1): b[j] = 0.25 * mw[j - 1, k] + 0.5 * mw[j, k] + 0.25 * mw[j + 1, k] mw[:, k] = b if n_nmf_priors > 0: mw[:, k] = mw[:, k] * nmf_priors[:, k] if norm_rhe: norm = np.linalg.norm(mw[:, k]) if norm > 0: mw[:, k] /= norm if nmf_fix_user_bhe == 0: # Update Mb mb[:, k] = 0 mt_mw[:] = np.reshape((np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p))), nxp) for i_block in range(0, n_blocks): mb[i_block, k] = np.reshape(mpart[:, id_blockp[i_block]: id_blockp[i_block] + p], nxp).T @ mt_mw if n_mmis > 0: mt_mw[:] = mt_mw[:] ** 2 for i_block in range(0, n_blocks): # Broadcast missing cells into Mb to calculate Mb.T * Mb denom_block[i_block, k] = ( np.reshape(mmis[:, id_blockp[i_block]: id_blockp[i_block] + p], (1, nxp)) @ mt_mw ) maxdenom_block = np.max(denom_block[:, k]) denom_block[denom_block[:, k] < denom_cutoff * maxdenom_block] = denom_cutoff * maxdenom_block mb[:, k] /= denom_block[:, k] mb[mb[:, k] < 0, k] = 0 if (ntf_unimodal > 0) & (ntf_block_components > 0): # Enforce unimodal distribution bmax = np.argmax(mb[:, k]) for i_block in range(bmax + 1, n_blocks): mb[i_block, k] = min(mb[i_block - 1, k], mb[i_block, k]) for i_block in range(bmax - 1, -1, -1): mb[i_block, k] = min(mb[i_block + 1, k], mb[i_block, k]) if (ntf_smooth > 0) & (ntf_block_components > 0): # Smooth distribution c[0] = 0.75 * mb[0, k] + 0.25 * mb[1, k] c[n_blocks - 1] = 0.25 * mb[n_blocks - 2, k] + 0.75 * mb[n_blocks - 1, k] for i_block in range(1, n_blocks - 1): c[i_block] = 0.25 * mb[i_block - 1, k] + 0.5 * mb[i_block, k] + 0.25 * mb[i_block + 1, k] mb[:, k] = c if norm_bhe: norm = np.linalg.norm(mb[:, k]) if norm > 0: mb[:, k] /= norm # Update residual tensor mfit[:, :] = 0 if n_blocks > 1: for i_block in range(0, n_blocks): mfit[:, id_blockp[i_block]: id_blockp[i_block] + p] += ( mb[i_block, k] * np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) ) else: mfit[:, id_blockp[0]: id_blockp[0] + p] += np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) if n_mmis > 0: mres[:, :] = (mpart - mfit) * mmis else: mres[:, :] = mpart - mfit return ( n_blocks, mpart, id_blockp, p, mb, k, mt, n, mw, n_mmis, mmis, mres, nmf_fix_user_lhe, denomt, mw2, denom_cutoff, alpha, ntf_unimodal, ntf_left_components, ntf_smooth, a, nmf_fix_user_rhe, denomw, mt2, ntf_right_components, b, nmf_fix_user_bhe, mt_mw, nxp, denom_block, ntf_block_components, c, mfit, nmf_priors, )

31.169536

119

0.470658

from typing import Tuple import numpy as np from .nmtf_utils import EPSILON, sparse_opt import logging logger = logging.getLogger(__name__) # TODO (pcotte): typing # TODO (pcotte): docstrings (with parameters and returns) def ntf_stack(m, mmis, n_blocks): n, p = m.shape mmis = mmis.astype(np.int) n_mmis = mmis.shape[0] n_blocks = int(n_blocks) mstacked = np.zeros((int(n * p / n_blocks), n_blocks)) if n_mmis > 0: mmis_stacked = np.zeros((int(n * p / n_blocks), n_blocks)) else: mmis_stacked = np.array([]) for i_block in range(0, n_blocks): for j in range(0, int(p / n_blocks)): i1 = j * n i2 = i1 + n mstacked[i1:i2, i_block] = m[:, int(i_block * p / n_blocks + j)] if n_mmis > 0: mmis_stacked[i1:i2, i_block] = mmis[:, int(i_block * p / n_blocks + j)] return mstacked, mmis_stacked def ntf_solve( m, mmis, mt0, mw0, mb0, nc, tolerance, log_iter, status0, max_iterations, nmf_fix_user_lhe, nmf_fix_user_rhe, nmf_fix_user_bhe, nmf_sparse_level, ntf_unimodal, ntf_smooth, ntf_left_components, ntf_right_components, ntf_block_components, n_blocks, nmf_priors, my_status_box, ): if len(nmf_priors) > 0: n_nmf_priors, nc = nmf_priors.shape else: n_nmf_priors = 0 if n_nmf_priors > 0: nmf_priors[nmf_priors > 0] = 1 return ntf_solve_simple( m=m, mmis=mmis, mt0=mt0, mw0=mw0, mb0=mb0, nc=nc, tolerance=tolerance, log_iter=log_iter, status0=status0, max_iterations=max_iterations, nmf_fix_user_lhe=nmf_fix_user_lhe, nmf_fix_user_rhe=nmf_fix_user_rhe, nmf_fix_user_bhe=nmf_fix_user_bhe, nmf_sparse_level=nmf_sparse_level, ntf_unimodal=ntf_unimodal, ntf_smooth=ntf_smooth, ntf_left_components=ntf_left_components, ntf_right_components=ntf_right_components, ntf_block_components=ntf_block_components, n_blocks=n_blocks, nmf_priors=nmf_priors, my_status_box=my_status_box, ) def ntf_solve_simple( m, mmis, mt0, mw0, mb0, nc, tolerance, log_iter, status0, max_iterations, nmf_fix_user_lhe, nmf_fix_user_rhe, nmf_fix_user_bhe, nmf_sparse_level, ntf_unimodal, ntf_smooth, ntf_left_components, ntf_right_components, ntf_block_components, n_blocks, nmf_priors, my_status_box, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, float, int]: cancel_pressed = 0 n, p0 = m.shape n_mmis = mmis.shape[0] nc = int(nc) n_blocks = int(n_blocks) p = int(p0 / n_blocks) nxp = int(n * p) nxp0 = int(n * p0) mt = np.copy(mt0) mw = np.copy(mw0) mb = np.copy(mb0) # step_iter = math.ceil(MaxIterations/10) step_iter = 1 pbar_step = 100 * step_iter / max_iterations id_blockp = np.arange(0, (n_blocks - 1) * p + 1, p) a = np.zeros(n) b = np.zeros(p) c = np.zeros(n_blocks) alpha = np.zeros(nc) # Compute Residual tensor mfit = np.zeros((n, p0)) for k in range(0, nc): if n_blocks > 1: for i_block in range(0, n_blocks): mfit[:, id_blockp[i_block]: id_blockp[i_block] + p] += ( mb[i_block, k] * np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) ) else: mfit[:, id_blockp[0]: id_blockp[0] + p] += np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) denomt = np.zeros(n) denomw = np.zeros(p) denom_block = np.zeros((n_blocks, nc)) mt2 = np.zeros(n) mw2 = np.zeros(p) mt_mw = np.zeros(nxp) denom_cutoff = 0.1 if n_mmis > 0: mres = (m - mfit) * mmis else: mres = m - mfit my_status_box.init_bar() # Loop cont = 1 i_iter = 0 diff0 = 1.0e99 mpart = np.zeros((n, p0)) if abs(nmf_sparse_level) < 1: alpha[0] = nmf_sparse_level * 0.8 else: alpha[0] = nmf_sparse_level percent_zeros = 0 iter_sparse = 0 while (cont > 0) & (i_iter < max_iterations): for k in range(0, nc): ( n_blocks, mpart, id_blockp, p, mb, k, mt, n, mw, n_mmis, mmis, mres, nmf_fix_user_lhe, denomt, mw2, denom_cutoff, alpha, ntf_unimodal, ntf_left_components, ntf_smooth, a, nmf_fix_user_rhe, denomw, mt2, ntf_right_components, b, nmf_fix_user_bhe, mt_mw, nxp, denom_block, ntf_block_components, c, mfit, nmf_priors, ) = ntf_update( n_blocks=n_blocks, mpart=mpart, id_blockp=id_blockp, p=p, mb=mb, k=k, mt=mt, n=n, mw=mw, n_mmis=n_mmis, mmis=mmis, mres=mres, nmf_fix_user_lhe=nmf_fix_user_lhe, denomt=denomt, mw2=mw2, denom_cutoff=denom_cutoff, alpha=alpha, ntf_unimodal=ntf_unimodal, ntf_left_components=ntf_left_components, ntf_smooth=ntf_smooth, a=a, nmf_fix_user_rhe=nmf_fix_user_rhe, denomw=denomw, mt2=mt2, ntf_right_components=ntf_right_components, b=b, nmf_fix_user_bhe=nmf_fix_user_bhe, mt_mw=mt_mw, nxp=nxp, denom_block=denom_block, ntf_block_components=ntf_block_components, c=c, mfit=mfit, nmf_priors=nmf_priors, ) if i_iter % step_iter == 0: # Check convergence diff = np.linalg.norm(mres) ** 2 / nxp0 if (diff0 - diff) / diff0 < tolerance: cont = 0 else: if diff > diff0: my_status_box.my_print(f"{status0} Iter: {i_iter} MSR does not improve") diff0 = diff Status = f"{status0} Iteration: {i_iter}" if nmf_sparse_level != 0: Status = f"{Status} ; Achieved sparsity: {round(percent_zeros, 2)}; alpha: {round(alpha[0], 2)}" if log_iter == 1: my_status_box.my_print(Status) my_status_box.update_status(status=Status) my_status_box.update_bar(step=pbar_step) if my_status_box.cancel_pressed: cancel_pressed = 1 return np.array([]), mt, mw, mb, mres, cancel_pressed if log_iter == 1: my_status_box.my_print(status0 + " Iter: " + str(i_iter) + " MSR: " + str(diff)) i_iter += 1 if cont == 0 or i_iter == max_iterations or (cont == 0 and abs(nmf_sparse_level) == 1): if 0 < nmf_sparse_level < 1: sparse_test = np.zeros((nc, 1)) percent_zeros0 = percent_zeros for k in range(0, nc): sparse_test[k] = np.where(mw[:, k] == 0)[0].size percent_zeros = np.mean(sparse_test) / p if percent_zeros < percent_zeros0: iter_sparse += 1 else: iter_sparse = 0 if (percent_zeros < 0.99 * nmf_sparse_level) & (iter_sparse < 50): alpha[0] *= min(1.05 * nmf_sparse_level / percent_zeros, 1.1) if alpha[0] < 1: i_iter = 0 cont = 1 elif 0 > nmf_sparse_level > -1: sparse_test = np.zeros((nc, 1)) percent_zeros0 = percent_zeros for k in range(0, nc): sparse_test[k] = np.where(mt[:, k] == 0)[0].size percent_zeros = np.mean(sparse_test) / n if percent_zeros < percent_zeros0: iter_sparse += 1 else: iter_sparse = 0 if (percent_zeros < 0.99 * abs(nmf_sparse_level)) & (iter_sparse < 50): alpha[0] *= min(1.05 * abs(nmf_sparse_level) / percent_zeros, 1.1) if abs(alpha[0]) < 1: i_iter = 0 cont = 1 elif abs(alpha[0]) == 1: if alpha[0] == -1: for k in range(0, nc): if np.max(mt[:, k]) > 0: hhi = int( np.round( (np.linalg.norm(mt[:, k], ord=1) / (np.linalg.norm(mt[:, k], ord=2) + EPSILON)) ** 2, decimals=0, ) ) alpha[k] = -1 - (n - hhi) / (n - 1) else: alpha[k] = 0 else: for k in range(0, nc): if np.max(mw[:, k]) > 0: hhi = int( np.round( (np.linalg.norm(mw[:, k], ord=1) / (np.linalg.norm(mw[:, k], ord=2) + EPSILON)) ** 2, decimals=0, ) ) alpha[k] = 1 + (p - hhi) / (p - 1) else: alpha[k] = 0 if alpha[0] <= -1: alpha_real = -(alpha + 1) # noinspection PyTypeChecker alpha_min = min(alpha_real) for k in range(0, nc): # noinspection PyUnresolvedReferences alpha[k] = min(alpha_real[k], 2 * alpha_min) alpha[k] = -alpha[k] - 1 else: alpha_real = alpha - 1 alpha_min = min(alpha_real) for k in range(0, nc): alpha[k] = min(alpha_real[k], 2 * alpha_min) alpha[k] = alpha[k] + 1 i_iter = 0 cont = 1 diff0 = 1.0e99 for k in range(0, nc): hhi = np.round((np.linalg.norm(mt[:, k], ord=1) / np.linalg.norm(mt[:, k], ord=2)) ** 2, decimals=0) logger.info(f"component: {k}, left hhi: {hhi}") hhi = np.round((np.linalg.norm(mw[:, k], ord=1) / np.linalg.norm(mw[:, k], ord=2)) ** 2, decimals=0) logger.info(f"component: {k} right hhi: {hhi}") if (n_mmis > 0) & (nmf_fix_user_bhe == 0): mb *= denom_block # TODO (pcotte): mt and mw can be not yet referenced: fix that return np.array([]), mt, mw, mb, diff, cancel_pressed def ntf_update( n_blocks, mpart, id_blockp, p, mb, k, mt, n, mw, n_mmis, mmis, mres, nmf_fix_user_lhe, denomt, mw2, denom_cutoff, alpha, ntf_unimodal, ntf_left_components, ntf_smooth, a, nmf_fix_user_rhe, denomw, mt2, ntf_right_components, b, nmf_fix_user_bhe, mt_mw, nxp, denom_block, ntf_block_components, c, mfit, nmf_priors, ): if len(nmf_priors) > 0: n_nmf_priors, nc = nmf_priors.shape else: n_nmf_priors = 0 # Compute kth-part if n_blocks > 1: for i_block in range(0, n_blocks): mpart[:, id_blockp[i_block]: id_blockp[i_block] + p] = ( mb[i_block, k] * np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) ) else: mpart[:, id_blockp[0]: id_blockp[0] + p] = np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) if n_mmis > 0: mpart *= mmis mpart += mres if nmf_fix_user_bhe > 0: norm_bhe = True if nmf_fix_user_rhe == 0: norm_lhe = True norm_rhe = False else: norm_lhe = False norm_rhe = True else: norm_bhe = False norm_lhe = True norm_rhe = True if (nmf_fix_user_lhe > 0) & norm_lhe: norm = np.linalg.norm(mt[:, k]) if norm > 0: mt[:, k] /= norm if (nmf_fix_user_rhe > 0) & norm_rhe: norm = np.linalg.norm(mw[:, k]) if norm > 0: mw[:, k] /= norm if (nmf_fix_user_bhe > 0) & norm_bhe & (n_blocks > 1): norm = np.linalg.norm(mb[:, k]) if norm > 0: mb[:, k] /= norm if nmf_fix_user_lhe == 0: # Update Mt mt[:, k] = 0 if n_blocks > 1: for i_block in range(0, n_blocks): mt[:, k] += mb[i_block, k] * mpart[:, id_blockp[i_block]: id_blockp[i_block] + p] @ mw[:, k] else: mt[:, k] += mpart[:, id_blockp[0]: id_blockp[0] + p] @ mw[:, k] if n_mmis > 0: denomt[:] = 0 mw2[:] = mw[:, k] ** 2 if n_blocks > 1: for i_block in range(0, n_blocks): # Broadcast missing cells into Mw to calculate Mw.T * Mw denomt += mb[i_block, k] ** 2 * mmis[:, id_blockp[i_block]: id_blockp[i_block] + p] @ mw2 else: denomt += mmis[:, id_blockp[0]: id_blockp[0] + p] @ mw2 denomt /= np.max(denomt) denomt[denomt < denom_cutoff] = denom_cutoff mt[:, k] /= denomt mt[mt[:, k] < 0, k] = 0 if alpha[0] < 0: if alpha[0] <= -1: if (alpha[0] == -1) & (np.max(mt[:, k]) > 0): t_threshold = mt[:, k] hhi = int( np.round( (np.linalg.norm(t_threshold, ord=1) / (np.linalg.norm(t_threshold, ord=2) + EPSILON)) ** 2, decimals=0, ) ) t_rank = np.argsort(t_threshold) t_threshold[t_rank[0: n - hhi]] = 0 else: mt[:, k] = sparse_opt(mt[:, k], -alpha[k] - 1, False) else: mt[:, k] = sparse_opt(mt[:, k], -alpha[0], False) if (ntf_unimodal > 0) & (ntf_left_components > 0): # Enforce unimodal distribution tmax = np.argmax(mt[:, k]) for i in range(tmax + 1, n): mt[i, k] = min(mt[i - 1, k], mt[i, k]) for i in range(tmax - 1, -1, -1): mt[i, k] = min(mt[i + 1, k], mt[i, k]) if (ntf_smooth > 0) & (ntf_left_components > 0): # Smooth distribution a[0] = 0.75 * mt[0, k] + 0.25 * mt[1, k] a[n - 1] = 0.25 * mt[n - 2, k] + 0.75 * mt[n - 1, k] for i in range(1, n - 1): a[i] = 0.25 * mt[i - 1, k] + 0.5 * mt[i, k] + 0.25 * mt[i + 1, k] mt[:, k] = a if norm_lhe: norm = np.linalg.norm(mt[:, k]) if norm > 0: mt[:, k] /= norm if nmf_fix_user_rhe == 0: # Update Mw mw[:, k] = 0 if n_blocks > 1: for i_block in range(0, n_blocks): mw[:, k] += mpart[:, id_blockp[i_block]: id_blockp[i_block] + p].T @ mt[:, k] * mb[i_block, k] else: mw[:, k] += mpart[:, id_blockp[0]: id_blockp[0] + p].T @ mt[:, k] if n_mmis > 0: denomw[:] = 0 mt2[:] = mt[:, k] ** 2 if n_blocks > 1: for i_block in range(0, n_blocks): # Broadcast missing cells into Mw to calculate Mt.T * Mt denomw += mb[i_block, k] ** 2 * mmis[:, id_blockp[i_block]: id_blockp[i_block] + p].T @ mt2 else: denomw += mmis[:, id_blockp[0]: id_blockp[0] + p].T @ mt2 denomw /= np.max(denomw) denomw[denomw < denom_cutoff] = denom_cutoff mw[:, k] /= denomw mw[mw[:, k] < 0, k] = 0 if alpha[0] > 0: if alpha[0] >= 1: if (alpha[0] == 1) & (np.max(mw[:, k]) > 0): w_threshold = mw[:, k] hhi = int( np.round( (np.linalg.norm(w_threshold, ord=1) / (np.linalg.norm(w_threshold, ord=2) + EPSILON)) ** 2, decimals=0, ) ) w_rank = np.argsort(w_threshold) w_threshold[w_rank[0: p - hhi]] = 0 else: mw[:, k] = sparse_opt(mw[:, k], alpha[k] - 1, False) else: mw[:, k] = sparse_opt(mw[:, k], alpha[0], False) if (ntf_unimodal > 0) & (ntf_right_components > 0): # Enforce unimodal distribution wmax = np.argmax(mw[:, k]) for j in range(wmax + 1, p): mw[j, k] = min(mw[j - 1, k], mw[j, k]) for j in range(wmax - 1, -1, -1): mw[j, k] = min(mw[j + 1, k], mw[j, k]) if (ntf_smooth > 0) & (ntf_right_components > 0): # Smooth distribution b[0] = 0.75 * mw[0, k] + 0.25 * mw[1, k] b[p - 1] = 0.25 * mw[p - 2, k] + 0.75 * mw[p - 1, k] for j in range(1, p - 1): b[j] = 0.25 * mw[j - 1, k] + 0.5 * mw[j, k] + 0.25 * mw[j + 1, k] mw[:, k] = b if n_nmf_priors > 0: mw[:, k] = mw[:, k] * nmf_priors[:, k] if norm_rhe: norm = np.linalg.norm(mw[:, k]) if norm > 0: mw[:, k] /= norm if nmf_fix_user_bhe == 0: # Update Mb mb[:, k] = 0 mt_mw[:] = np.reshape((np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p))), nxp) for i_block in range(0, n_blocks): mb[i_block, k] = np.reshape(mpart[:, id_blockp[i_block]: id_blockp[i_block] + p], nxp).T @ mt_mw if n_mmis > 0: mt_mw[:] = mt_mw[:] ** 2 for i_block in range(0, n_blocks): # Broadcast missing cells into Mb to calculate Mb.T * Mb denom_block[i_block, k] = ( np.reshape(mmis[:, id_blockp[i_block]: id_blockp[i_block] + p], (1, nxp)) @ mt_mw ) maxdenom_block = np.max(denom_block[:, k]) denom_block[denom_block[:, k] < denom_cutoff * maxdenom_block] = denom_cutoff * maxdenom_block mb[:, k] /= denom_block[:, k] mb[mb[:, k] < 0, k] = 0 if (ntf_unimodal > 0) & (ntf_block_components > 0): # Enforce unimodal distribution bmax = np.argmax(mb[:, k]) for i_block in range(bmax + 1, n_blocks): mb[i_block, k] = min(mb[i_block - 1, k], mb[i_block, k]) for i_block in range(bmax - 1, -1, -1): mb[i_block, k] = min(mb[i_block + 1, k], mb[i_block, k]) if (ntf_smooth > 0) & (ntf_block_components > 0): # Smooth distribution c[0] = 0.75 * mb[0, k] + 0.25 * mb[1, k] c[n_blocks - 1] = 0.25 * mb[n_blocks - 2, k] + 0.75 * mb[n_blocks - 1, k] for i_block in range(1, n_blocks - 1): c[i_block] = 0.25 * mb[i_block - 1, k] + 0.5 * mb[i_block, k] + 0.25 * mb[i_block + 1, k] mb[:, k] = c if norm_bhe: norm = np.linalg.norm(mb[:, k]) if norm > 0: mb[:, k] /= norm # Update residual tensor mfit[:, :] = 0 if n_blocks > 1: for i_block in range(0, n_blocks): mfit[:, id_blockp[i_block]: id_blockp[i_block] + p] += ( mb[i_block, k] * np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) ) else: mfit[:, id_blockp[0]: id_blockp[0] + p] += np.reshape(mt[:, k], (n, 1)) @ np.reshape(mw[:, k], (1, p)) if n_mmis > 0: mres[:, :] = (mpart - mfit) * mmis else: mres[:, :] = mpart - mfit return ( n_blocks, mpart, id_blockp, p, mb, k, mt, n, mw, n_mmis, mmis, mres, nmf_fix_user_lhe, denomt, mw2, denom_cutoff, alpha, ntf_unimodal, ntf_left_components, ntf_smooth, a, nmf_fix_user_rhe, denomw, mt2, ntf_right_components, b, nmf_fix_user_bhe, mt_mw, nxp, denom_block, ntf_block_components, c, mfit, nmf_priors, )

true

f71c1d13e2b4281f5e462dfbd3127b8ee3404a3a

6,928

py

Python

actions.py

prateekralhan/Chatbot-Development-using-RASA-Framework-and-Zomato-API---PGD-IIITB-Assignment

5e33586df2bbe146d950f33e018e9e990031e347

[ "Apache-2.0" ]

1

2020-05-21T10:20:03.000Z

actions.py

prateekralhan/Chatbot-Development-using-RASA-Framework-and-Zomato-API---PGD-IIITB-Assignment

5e33586df2bbe146d950f33e018e9e990031e347

[ "Apache-2.0" ]

null

actions.py

prateekralhan/Chatbot-Development-using-RASA-Framework-and-Zomato-API---PGD-IIITB-Assignment

5e33586df2bbe146d950f33e018e9e990031e347

[ "Apache-2.0" ]

1

2021-12-06T03:10:37.000Z

from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals # importing the necessary libraries # from rasa_core.actions.action import Action from rasa_sdk import Action #from rasa_sdk.forms import ( BooleanFormField, EntityFormField, FormAction, FreeTextFormField ) from rasa_sdk.events import SlotSet # from rasa_core.events import SlotSet import zomatopy import json # providng the API key zomato_config={ "user_key":"<Enter your Zomato API key here >"} result_of_last_query = "" class ActionSearchRestaurants(Action): def name(self): return 'action_restaurant' def filterRestaurantBasedOnBudget(self, userbudget, allRestaurants): rangeMin = 0 rangeMax = 100000 # defining the characteristics of the budget. if userbudget.isdigit(): price = int(userbudget) if price == 1: rangeMax = 299 elif price == 2: rangeMin = 300 rangeMax = 699 elif price == 3: rangeMin = 700 elif price < 300: rangeMax = 299 elif price < 700 and price >= 300: rangeMin = 300 rangeMax = 699 else: rangeMin = 700 else: # default budget rangeMin = 300 rangeMax = 699 index = 0 count = 0 response = "" global result_of_last_query result_of_last_query = "" for restaurant in allRestaurants: ++count res = "[" + restaurant['restaurant']['user_rating']['aggregate_rating'] + "/5] " + restaurant['restaurant']['name'] + " in " + restaurant['restaurant']['location']['address'] # price_range = str(restaurant['restaurant']['price_range']) avg_c_2 = restaurant['restaurant']['average_cost_for_two'] # if price_range == "1": if avg_c_2 <= rangeMax and avg_c_2 >= rangeMin: # mapbybudget["1"].append(restaurant) # if userbudget == price_range: res = restaurant['restaurant']['currency'] + str(restaurant['restaurant']['average_cost_for_two']) + " " + res + "\n" if(index < 5): response = response + res if(index < 10): result_of_last_query = result_of_last_query + res index = index + 1 # getting the restaur details using the API # modifying the search results # if the no. of result fall short, appending the results of other price range if index == 0: response = "Oops! no restaurant found for this query. " + " search results = " + str(count) elif index < 5: # we can add restaurants from the higher range but for now i am appending an extra message response = response + "\n \nFor more results please search in higher budget range...\n \n" elif index < 10: result_of_last_query = result_of_last_query + "\n \nFor more results please search in higher budget range...\n \n" return response def run(self, dispatcher, tracker, domain): loc = tracker.get_slot('location') cuisine = tracker.get_slot('cuisine') budget = tracker.get_slot('budget') zomato = zomatopy.initialize_app(zomato_config) location_detail=zomato.get_location(loc, 1) d1 = json.loads(location_detail) lat=d1["location_suggestions"][0]["latitude"] lon=d1["location_suggestions"][0]["longitude"] #cuisines and their respective cuisine IDs cuisines_dict={ 'american':1, 'mexican':73, 'italian':55, 'chinese':25, 'north indian':50, 'south indian':85 } results=zomato.restaurant_search("", lat, lon, str(cuisines_dict.get(cuisine)), 50) d = json.loads(results) response="" if d['results_found'] == 0: response= "Sorry, we didn't find any results for this query." else: # dispatcher.utter_message(str(d)) response = self.filterRestaurantBasedOnBudget(budget, d['restaurants']) dispatcher.utter_message(str(response)) return [SlotSet('location',loc)] # list of TIER1 and TIER2 cities t1_t2_cities = ["Ahmedabad","Bangalore","Chennai","Delhi","Hyderabad","Kolkata","Mumbai","Pune", "Agra","Ajmer","Aligarh","Amravati","Amritsar","Asansol","Aurangabad", "Bareilly","Belgaum","Bhavnagar","Bhiwandi","Bhopal","Bhubaneswar", "Bikaner","Bokaro Steel City","Chandigarh","Coimbatore","Cuttack","Dehradun", "Dhanbad","Bhilai","Durgapur","Erode","Faridabad","Firozabad","Ghaziabad", "Gorakhpur","Gulbarga","Guntur","Gurgaon","Guwahati","Hamirpur", "Gwalior","Hubli-Dharwad","Indore","Jabalpur","Jaipur","Jalandhar","Jammu","Jamnagar","Jamshedpur","Jhansi","Jodhpur", "Kannur","Kanpur","Kakinada","Kochi","Kottayam","Kolhapur","Kollam","Kota","Kozhikode","Kurnool","Lucknow","Ludhiana", "Madurai","Malappuram","Mathura","Goa","Mangalore","Meerut", "Moradabad","Mysore","Nagpur","Nanded","Nashik","Nellore","Noida","Patna","Pondicherry","Purulia Prayagraj","Raipur","Rajkot", "Rajahmundry","Ranchi","Rourkela","Salem","Sangli","Siliguri","Shimla" "Solapur","Srinagar","Surat","Thiruvananthapuram","Thrissur","Tiruchirappalli","Tiruppur", "Ujjain","Bijapur","Vadodara","Varanasi", "Vasai-Virar City","Vijayawada","Visakhapatnam","Vellore","Warangal"] t1_t2_cities_list = [x.lower() for x in t1_t2_cities] # Check if the location exists. using zomato api.if found then save it, else utter not found. class ActionValidateLocation(Action): def name(self): return 'action_check_location' def run(self, dispatcher, tracker, domain): loc = tracker.get_slot('location') city = str(loc) # dispatcher.utter_message(city) if city.lower() in t1_t2_cities_list: return [SlotSet('location_match',"one")] else: zomato = zomatopy.initialize_app(zomato_config) try: results = zomato.get_city_ID(city) return [SlotSet('location_match',"one")] except: # results = "Sorry, didn’t find any such location. Can you please tell again?" + "-----" + city # dispatcher.utter_message(city) return [SlotSet('location_match',"zero")] # Send email the list of 10 restaurants class ActionSendEmail(Action): def name(self): return 'action_send_email' def run(self, dispatcher, tracker, domain): email = tracker.get_slot('email') # for slack handling if len(email.split("|")) == 2: email = email.split("|")[1] import smtplib s = smtplib.SMTP('smtp.gmail.com', 587) s.starttls() s.login("<Enter login E-mail ID here>", "<Enter the password here>") SUBJECT = "Foodbot - Be Foodoholic in Nature !!" TEXT = "Hi Foodoholic :) !!\nThe details of all the restaurants you inquried \n \n" message = 'Subject: {}\n\n{}'.format(SUBJECT,TEXT) global result_of_last_query message = message + result_of_last_query try: s.sendmail("<Enter login E-mail ID here>", str(email), message) s.quit() except: dispatcher.utter_message(email) result_of_last_query = "" return [AllSlotsReset()] from rasa_sdk.events import AllSlotsReset from rasa_sdk.events import Restarted class ActionRestarted(Action): def name(self): return 'action_restart' def run(self, dispatcher, tracker, domain): return[Restarted()] class ActionSlotReset(Action): def name(self): return 'action_slot_reset' def run(self, dispatcher, tracker, domain): return[AllSlotsReset()]

32.679245

177

0.705831

from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from rasa_sdk import Action from rasa_sdk.events import SlotSet import zomatopy import json zomato_config={ "user_key":"<Enter your Zomato API key here >"} result_of_last_query = "" class ActionSearchRestaurants(Action): def name(self): return 'action_restaurant' def filterRestaurantBasedOnBudget(self, userbudget, allRestaurants): rangeMin = 0 rangeMax = 100000 if userbudget.isdigit(): price = int(userbudget) if price == 1: rangeMax = 299 elif price == 2: rangeMin = 300 rangeMax = 699 elif price == 3: rangeMin = 700 elif price < 300: rangeMax = 299 elif price < 700 and price >= 300: rangeMin = 300 rangeMax = 699 else: rangeMin = 700 else: rangeMin = 300 rangeMax = 699 index = 0 count = 0 response = "" global result_of_last_query result_of_last_query = "" for restaurant in allRestaurants: ++count res = "[" + restaurant['restaurant']['user_rating']['aggregate_rating'] + "/5] " + restaurant['restaurant']['name'] + " in " + restaurant['restaurant']['location']['address'] avg_c_2 = restaurant['restaurant']['average_cost_for_two'] if avg_c_2 <= rangeMax and avg_c_2 >= rangeMin: res = restaurant['restaurant']['currency'] + str(restaurant['restaurant']['average_cost_for_two']) + " " + res + "\n" if(index < 5): response = response + res if(index < 10): result_of_last_query = result_of_last_query + res index = index + 1 if index == 0: response = "Oops! no restaurant found for this query. " + " search results = " + str(count) elif index < 5: response = response + "\n \nFor more results please search in higher budget range...\n \n" elif index < 10: result_of_last_query = result_of_last_query + "\n \nFor more results please search in higher budget range...\n \n" return response def run(self, dispatcher, tracker, domain): loc = tracker.get_slot('location') cuisine = tracker.get_slot('cuisine') budget = tracker.get_slot('budget') zomato = zomatopy.initialize_app(zomato_config) location_detail=zomato.get_location(loc, 1) d1 = json.loads(location_detail) lat=d1["location_suggestions"][0]["latitude"] lon=d1["location_suggestions"][0]["longitude"] cuisines_dict={ 'american':1, 'mexican':73, 'italian':55, 'chinese':25, 'north indian':50, 'south indian':85 } results=zomato.restaurant_search("", lat, lon, str(cuisines_dict.get(cuisine)), 50) d = json.loads(results) response="" if d['results_found'] == 0: response= "Sorry, we didn't find any results for this query." else: # dispatcher.utter_message(str(d)) response = self.filterRestaurantBasedOnBudget(budget, d['restaurants']) dispatcher.utter_message(str(response)) return [SlotSet('location',loc)] # list of TIER1 and TIER2 cities t1_t2_cities = ["Ahmedabad","Bangalore","Chennai","Delhi","Hyderabad","Kolkata","Mumbai","Pune", "Agra","Ajmer","Aligarh","Amravati","Amritsar","Asansol","Aurangabad", "Bareilly","Belgaum","Bhavnagar","Bhiwandi","Bhopal","Bhubaneswar", "Bikaner","Bokaro Steel City","Chandigarh","Coimbatore","Cuttack","Dehradun", "Dhanbad","Bhilai","Durgapur","Erode","Faridabad","Firozabad","Ghaziabad", "Gorakhpur","Gulbarga","Guntur","Gurgaon","Guwahati","Hamirpur", "Gwalior","Hubli-Dharwad","Indore","Jabalpur","Jaipur","Jalandhar","Jammu","Jamnagar","Jamshedpur","Jhansi","Jodhpur", "Kannur","Kanpur","Kakinada","Kochi","Kottayam","Kolhapur","Kollam","Kota","Kozhikode","Kurnool","Lucknow","Ludhiana", "Madurai","Malappuram","Mathura","Goa","Mangalore","Meerut", "Moradabad","Mysore","Nagpur","Nanded","Nashik","Nellore","Noida","Patna","Pondicherry","Purulia Prayagraj","Raipur","Rajkot", "Rajahmundry","Ranchi","Rourkela","Salem","Sangli","Siliguri","Shimla" "Solapur","Srinagar","Surat","Thiruvananthapuram","Thrissur","Tiruchirappalli","Tiruppur", "Ujjain","Bijapur","Vadodara","Varanasi", "Vasai-Virar City","Vijayawada","Visakhapatnam","Vellore","Warangal"] t1_t2_cities_list = [x.lower() for x in t1_t2_cities] # Check if the location exists. using zomato api.if found then save it, else utter not found. class ActionValidateLocation(Action): def name(self): return 'action_check_location' def run(self, dispatcher, tracker, domain): loc = tracker.get_slot('location') city = str(loc) # dispatcher.utter_message(city) if city.lower() in t1_t2_cities_list: return [SlotSet('location_match',"one")] else: zomato = zomatopy.initialize_app(zomato_config) try: results = zomato.get_city_ID(city) return [SlotSet('location_match',"one")] except: # results = "Sorry, didn’t find any such location. Can you please tell again?" + "-----" + city # dispatcher.utter_message(city) return [SlotSet('location_match',"zero")] # Send email the list of 10 restaurants class ActionSendEmail(Action): def name(self): return 'action_send_email' def run(self, dispatcher, tracker, domain): email = tracker.get_slot('email') # for slack handling if len(email.split("|")) == 2: email = email.split("|")[1] import smtplib s = smtplib.SMTP('smtp.gmail.com', 587) s.starttls() s.login("<Enter login E-mail ID here>", "<Enter the password here>") SUBJECT = "Foodbot - Be Foodoholic in Nature !!" TEXT = "Hi Foodoholic :) !!\nThe details of all the restaurants you inquried \n \n" message = 'Subject: {}\n\n{}'.format(SUBJECT,TEXT) global result_of_last_query message = message + result_of_last_query try: s.sendmail("<Enter login E-mail ID here>", str(email), message) s.quit() except: dispatcher.utter_message(email) result_of_last_query = "" return [AllSlotsReset()] from rasa_sdk.events import AllSlotsReset from rasa_sdk.events import Restarted class ActionRestarted(Action): def name(self): return 'action_restart' def run(self, dispatcher, tracker, domain): return[Restarted()] class ActionSlotReset(Action): def name(self): return 'action_slot_reset' def run(self, dispatcher, tracker, domain): return[AllSlotsReset()]

true

f71c1d88fc7cda064575b3b49230553d31ba1b56

9,193

py

Python

services/ui/paths/label.py

sbworth/getnoc

a9a5647df31822062db3db7afe7ae1c005d166f7

[ "BSD-3-Clause" ]

84

2017-10-22T11:01:39.000Z

2022-02-27T03:43:48.000Z

services/ui/paths/label.py

sbworth/getnoc

a9a5647df31822062db3db7afe7ae1c005d166f7

[ "BSD-3-Clause" ]

22

2017-12-11T07:21:56.000Z

2021-09-23T02:53:50.000Z

services/ui/paths/label.py

sbworth/getnoc

a9a5647df31822062db3db7afe7ae1c005d166f7

[ "BSD-3-Clause" ]

23

2017-12-06T06:59:52.000Z

2022-02-24T00:02:25.000Z

# ---------------------------------------------------------------------- # Label REST API # ---------------------------------------------------------------------- # Copyright (C) 2007-2021 The NOC Project # See LICENSE for details # ---------------------------------------------------------------------- # Third-party modules from fastapi import APIRouter # NOC modules from noc.main.models.label import Label from ..models.label import DefaultLabelItem, FormLabelItem, LabelLabelItem from ..utils.ref import get_reference from ..utils.rest.document import DocumentResourceAPI from ..utils.rest.op import FilterExact, FuncFilter, FilterBool router = APIRouter() class LabelAPI(DocumentResourceAPI[Label]): prefix = "/api/ui/label" model = Label list_ops = [ FuncFilter("query", function=lambda qs, values: qs.filter(name__icontains=values[0])), FuncFilter("id", function=lambda qs, values: qs.filter(name__in=values)), FilterExact("name"), FilterBool("enable_agent"), FilterBool("enable_service"), FilterBool("enable_serviceprofile"), FilterBool("enable_managedobject"), FilterBool("enable_managedobjectprofile"), FilterBool("enable_administrativedomain"), FilterBool("enable_authprofile"), FilterBool("enable_commandsnippet"), FilterBool("enable_allocationgroup"), FilterBool("enable_networksegment"), FilterBool("enable_object"), FilterBool("enable_objectmodel"), FilterBool("enable_platform"), FilterBool("enable_resourcegroup"), FilterBool("enable_sensor"), FilterBool("enable_sensorprofile"), FilterBool("enable_subscriber"), FilterBool("enable_subscriberprofile"), FilterBool("enable_supplier"), FilterBool("enable_supplierprofile"), FilterBool("enable_dnszone"), FilterBool("enable_dnszonerecord"), FilterBool("enable_division"), FilterBool("enable_kbentry"), FilterBool("enable_ipaddress"), FilterBool("enable_addressprofile"), FilterBool("enable_ipaddressrange"), FilterBool("enable_ipprefix"), FilterBool("enable_prefixprofile"), FilterBool("enable_vrf"), FilterBool("enable_vrfgroup"), FilterBool("enable_asn"), FilterBool("enable_peer"), FilterBool("enable_vc"), FilterBool("enable_vlan"), FilterBool("enable_vlanprofile"), FilterBool("enable_vpn"), FilterBool("enable_slaprobe"), FilterBool("enable_slaprofile"), FilterBool("enable_alarm"), FilterBool("enable_vpnprofile"), ] @classmethod def item_to_label(cls, item: Label) -> LabelLabelItem: return LabelLabelItem( id=str(item.name), label=str(item.name), is_protected=item.is_protected, scope=item.name.rsplit("::", 1)[0] if item.is_scoped else "", value=item.name.split("::")[-1], bg_color1=f"#{item.bg_color1:06x}", fg_color1=f"#{item.fg_color1:06x}", bg_color2=f"#{item.bg_color2:06x}", fg_color2=f"#{item.fg_color2:06x}", ) @classmethod def item_to_default(cls, item: Label) -> DefaultLabelItem: return DefaultLabelItem( id=str(item.id), name=str(item.name), description=item.description, bg_color1=item.bg_color1, fg_color1=item.fg_color1, bg_color2=item.bg_color2, fg_color2=item.fg_color2, is_protected=item.is_protected, is_autogenerated=item.is_autogenerated, # Label scope enable_agent=bool(item.enable_agent), enable_service=bool(item.enable_service), enable_serviceprofile=bool(item.enable_serviceprofile), enable_managedobject=bool(item.enable_managedobject), enable_managedobjectprofile=bool(item.enable_managedobjectprofile), enable_administrativedomain=bool(item.enable_administrativedomain), enable_authprofile=bool(item.enable_authprofile), enable_commandsnippet=bool(item.enable_commandsnippet), enable_allocationgroup=bool(item.enable_allocationgroup), enable_networksegment=bool(item.enable_networksegment), enable_object=bool(item.enable_object), enable_objectmodel=bool(item.enable_objectmodel), enable_platform=bool(item.enable_platform), enable_resourcegroup=bool(item.enable_resourcegroup), enable_sensor=bool(item.enable_sensor), enable_sensorprofile=bool(item.enable_sensorprofile), enable_subscriber=bool(item.enable_subscriber), enable_subscriberprofile=bool(item.enable_subscriberprofile), enable_supplier=bool(item.enable_supplier), enable_supplierprofile=bool(item.enable_supplierprofile), enable_dnszone=bool(item.enable_dnszone), enable_dnszonerecord=bool(item.enable_dnszonerecord), enable_division=bool(item.enable_division), enable_kbentry=bool(item.enable_kbentry), enable_ipaddress=bool(item.enable_ipaddress), enable_addressprofile=bool(item.enable_addressprofile), enable_ipaddressrange=bool(item.enable_ipaddressrange), enable_ipprefix=bool(item.enable_ipprefix), enable_prefixprofile=bool(item.enable_prefixprofile), enable_vrf=bool(item.enable_vrf), enable_vrfgroup=bool(item.enable_vrfgroup), enable_asn=bool(item.enable_asn), enable_assetpeer=bool(item.enable_assetpeer), enable_peer=bool(item.enable_peer), enable_vc=bool(item.enable_vc), enable_vlan=bool(item.enable_vlan), enable_vlanprofile=bool(item.enable_vlanprofile), enable_vpn=bool(item.enable_vpn), enable_vpnprofile=bool(item.enable_vpnprofile), enable_slaprobe=bool(item.enable_slaprobe), enable_slaprofile=bool(item.enable_slaprofile), enable_alarm=bool(item.enable_alarm), expose_metric=bool(item.expose_metric), expose_datastream=bool(item.expose_datastream), remote_system=get_reference(item.remote_system), remote_id=item.remote_id, ) @classmethod def item_to_form(cls, item: Label) -> FormLabelItem: return FormLabelItem( name=item.name, description=item.description, bg_color1=item.bg_color1, fg_color1=item.fg_color1, bg_color2=item.bg_color2, fg_color2=item.fg_color2, is_protected=item.is_protected, enable_agent=item.enable_agent, enable_service=item.enable_service, enable_serviceprofile=item.enable_serviceprofile, enable_managedobject=item.enable_managedobject, enable_managedobjectprofile=item.enable_managedobjectprofile, enable_administrativedomain=item.enable_administrativedomain, enable_authprofile=item.enable_authprofile, enable_commandsnippet=item.enable_commandsnippet, enable_allocationgroup=item.enable_allocationgroup, enable_networksegment=item.enable_networksegment, enable_object=item.enable_object, enable_objectmodel=item.enable_objectmodel, enable_platform=item.enable_platform, enable_resourcegroup=item.enable_resourcegroup, enable_sensor=item.enable_sensor, enable_sensorprofile=item.enable_sensorprofile, enable_subscriber=item.enable_subscriber, enable_subscriberprofile=item.enable_subscriberprofile, enable_supplier=item.enable_supplier, enable_supplierprofile=item.enable_supplierprofile, enable_dnszone=item.enable_dnszone, enable_dnszonerecord=item.enable_dnszonerecord, enable_division=item.enable_division, enable_kbentry=item.enable_kbentry, enable_ipaddress=item.enable_ipaddress, enable_addressprofile=item.enable_addressprofile, enable_ipaddressrange=item.enable_ipaddressrange, enable_ipprefix=item.enable_ipprefix, enable_prefixprofile=item.enable_prefixprofile, enable_vrf=item.enable_vrf, enable_vrfgroup=item.enable_vrfgroup, enable_asn=item.enable_asn, enable_assetpeer=item.enable_assetpeer, enable_peer=item.enable_peer, enable_vc=item.enable_vc, enable_vlan=item.enable_vlan, enable_vlanprofile=item.enable_vlanprofile, enable_vpn=item.enable_vpn, enable_vpnprofile=item.enable_vpnprofile, enable_slaprobe=item.enable_slaprobe, enable_slaprofile=item.enable_slaprofile, enable_alarm=item.enable_alarm, expose_metric=item.expose_metric, expose_datastream=item.expose_datastream, ) # Install endpoints LabelAPI(router)

44.843902

94

0.657892

from fastapi import APIRouter from noc.main.models.label import Label from ..models.label import DefaultLabelItem, FormLabelItem, LabelLabelItem from ..utils.ref import get_reference from ..utils.rest.document import DocumentResourceAPI from ..utils.rest.op import FilterExact, FuncFilter, FilterBool router = APIRouter() class LabelAPI(DocumentResourceAPI[Label]): prefix = "/api/ui/label" model = Label list_ops = [ FuncFilter("query", function=lambda qs, values: qs.filter(name__icontains=values[0])), FuncFilter("id", function=lambda qs, values: qs.filter(name__in=values)), FilterExact("name"), FilterBool("enable_agent"), FilterBool("enable_service"), FilterBool("enable_serviceprofile"), FilterBool("enable_managedobject"), FilterBool("enable_managedobjectprofile"), FilterBool("enable_administrativedomain"), FilterBool("enable_authprofile"), FilterBool("enable_commandsnippet"), FilterBool("enable_allocationgroup"), FilterBool("enable_networksegment"), FilterBool("enable_object"), FilterBool("enable_objectmodel"), FilterBool("enable_platform"), FilterBool("enable_resourcegroup"), FilterBool("enable_sensor"), FilterBool("enable_sensorprofile"), FilterBool("enable_subscriber"), FilterBool("enable_subscriberprofile"), FilterBool("enable_supplier"), FilterBool("enable_supplierprofile"), FilterBool("enable_dnszone"), FilterBool("enable_dnszonerecord"), FilterBool("enable_division"), FilterBool("enable_kbentry"), FilterBool("enable_ipaddress"), FilterBool("enable_addressprofile"), FilterBool("enable_ipaddressrange"), FilterBool("enable_ipprefix"), FilterBool("enable_prefixprofile"), FilterBool("enable_vrf"), FilterBool("enable_vrfgroup"), FilterBool("enable_asn"), FilterBool("enable_peer"), FilterBool("enable_vc"), FilterBool("enable_vlan"), FilterBool("enable_vlanprofile"), FilterBool("enable_vpn"), FilterBool("enable_slaprobe"), FilterBool("enable_slaprofile"), FilterBool("enable_alarm"), FilterBool("enable_vpnprofile"), ] @classmethod def item_to_label(cls, item: Label) -> LabelLabelItem: return LabelLabelItem( id=str(item.name), label=str(item.name), is_protected=item.is_protected, scope=item.name.rsplit("::", 1)[0] if item.is_scoped else "", value=item.name.split("::")[-1], bg_color1=f"#{item.bg_color1:06x}", fg_color1=f"#{item.fg_color1:06x}", bg_color2=f"#{item.bg_color2:06x}", fg_color2=f"#{item.fg_color2:06x}", ) @classmethod def item_to_default(cls, item: Label) -> DefaultLabelItem: return DefaultLabelItem( id=str(item.id), name=str(item.name), description=item.description, bg_color1=item.bg_color1, fg_color1=item.fg_color1, bg_color2=item.bg_color2, fg_color2=item.fg_color2, is_protected=item.is_protected, is_autogenerated=item.is_autogenerated, enable_agent=bool(item.enable_agent), enable_service=bool(item.enable_service), enable_serviceprofile=bool(item.enable_serviceprofile), enable_managedobject=bool(item.enable_managedobject), enable_managedobjectprofile=bool(item.enable_managedobjectprofile), enable_administrativedomain=bool(item.enable_administrativedomain), enable_authprofile=bool(item.enable_authprofile), enable_commandsnippet=bool(item.enable_commandsnippet), enable_allocationgroup=bool(item.enable_allocationgroup), enable_networksegment=bool(item.enable_networksegment), enable_object=bool(item.enable_object), enable_objectmodel=bool(item.enable_objectmodel), enable_platform=bool(item.enable_platform), enable_resourcegroup=bool(item.enable_resourcegroup), enable_sensor=bool(item.enable_sensor), enable_sensorprofile=bool(item.enable_sensorprofile), enable_subscriber=bool(item.enable_subscriber), enable_subscriberprofile=bool(item.enable_subscriberprofile), enable_supplier=bool(item.enable_supplier), enable_supplierprofile=bool(item.enable_supplierprofile), enable_dnszone=bool(item.enable_dnszone), enable_dnszonerecord=bool(item.enable_dnszonerecord), enable_division=bool(item.enable_division), enable_kbentry=bool(item.enable_kbentry), enable_ipaddress=bool(item.enable_ipaddress), enable_addressprofile=bool(item.enable_addressprofile), enable_ipaddressrange=bool(item.enable_ipaddressrange), enable_ipprefix=bool(item.enable_ipprefix), enable_prefixprofile=bool(item.enable_prefixprofile), enable_vrf=bool(item.enable_vrf), enable_vrfgroup=bool(item.enable_vrfgroup), enable_asn=bool(item.enable_asn), enable_assetpeer=bool(item.enable_assetpeer), enable_peer=bool(item.enable_peer), enable_vc=bool(item.enable_vc), enable_vlan=bool(item.enable_vlan), enable_vlanprofile=bool(item.enable_vlanprofile), enable_vpn=bool(item.enable_vpn), enable_vpnprofile=bool(item.enable_vpnprofile), enable_slaprobe=bool(item.enable_slaprobe), enable_slaprofile=bool(item.enable_slaprofile), enable_alarm=bool(item.enable_alarm), expose_metric=bool(item.expose_metric), expose_datastream=bool(item.expose_datastream), remote_system=get_reference(item.remote_system), remote_id=item.remote_id, ) @classmethod def item_to_form(cls, item: Label) -> FormLabelItem: return FormLabelItem( name=item.name, description=item.description, bg_color1=item.bg_color1, fg_color1=item.fg_color1, bg_color2=item.bg_color2, fg_color2=item.fg_color2, is_protected=item.is_protected, enable_agent=item.enable_agent, enable_service=item.enable_service, enable_serviceprofile=item.enable_serviceprofile, enable_managedobject=item.enable_managedobject, enable_managedobjectprofile=item.enable_managedobjectprofile, enable_administrativedomain=item.enable_administrativedomain, enable_authprofile=item.enable_authprofile, enable_commandsnippet=item.enable_commandsnippet, enable_allocationgroup=item.enable_allocationgroup, enable_networksegment=item.enable_networksegment, enable_object=item.enable_object, enable_objectmodel=item.enable_objectmodel, enable_platform=item.enable_platform, enable_resourcegroup=item.enable_resourcegroup, enable_sensor=item.enable_sensor, enable_sensorprofile=item.enable_sensorprofile, enable_subscriber=item.enable_subscriber, enable_subscriberprofile=item.enable_subscriberprofile, enable_supplier=item.enable_supplier, enable_supplierprofile=item.enable_supplierprofile, enable_dnszone=item.enable_dnszone, enable_dnszonerecord=item.enable_dnszonerecord, enable_division=item.enable_division, enable_kbentry=item.enable_kbentry, enable_ipaddress=item.enable_ipaddress, enable_addressprofile=item.enable_addressprofile, enable_ipaddressrange=item.enable_ipaddressrange, enable_ipprefix=item.enable_ipprefix, enable_prefixprofile=item.enable_prefixprofile, enable_vrf=item.enable_vrf, enable_vrfgroup=item.enable_vrfgroup, enable_asn=item.enable_asn, enable_assetpeer=item.enable_assetpeer, enable_peer=item.enable_peer, enable_vc=item.enable_vc, enable_vlan=item.enable_vlan, enable_vlanprofile=item.enable_vlanprofile, enable_vpn=item.enable_vpn, enable_vpnprofile=item.enable_vpnprofile, enable_slaprobe=item.enable_slaprobe, enable_slaprofile=item.enable_slaprofile, enable_alarm=item.enable_alarm, expose_metric=item.expose_metric, expose_datastream=item.expose_datastream, ) LabelAPI(router)

true

f71c1e7d575a6e860d1fb2e06dd08a3844470ff2

51,171

py

Python

dojo/engagement/views.py

pabloalexsandroalmeida83/django-DefectDojo

094fbbf0d994c2f9b5c7ff071a28d4d5ed1f3a1c

[ "BSD-3-Clause" ]

null

dojo/engagement/views.py

pabloalexsandroalmeida83/django-DefectDojo

094fbbf0d994c2f9b5c7ff071a28d4d5ed1f3a1c

[ "BSD-3-Clause" ]

null

dojo/engagement/views.py

pabloalexsandroalmeida83/django-DefectDojo

094fbbf0d994c2f9b5c7ff071a28d4d5ed1f3a1c

[ "BSD-3-Clause" ]

null

import logging import csv import re from openpyxl import Workbook from openpyxl.styles import Font from tempfile import NamedTemporaryFile from datetime import datetime import operator from django.contrib.auth.models import User from django.conf import settings from django.contrib import messages from django.core.exceptions import ValidationError, PermissionDenied from django.urls import reverse from django.db.models import Q, Count from django.http import HttpResponseRedirect, StreamingHttpResponse, HttpResponse, FileResponse, QueryDict from django.shortcuts import render, get_object_or_404 from django.views.decorators.cache import cache_page from django.utils import timezone from time import strftime from django.contrib.admin.utils import NestedObjects from django.db import DEFAULT_DB_ALIAS from dojo.engagement.services import close_engagement, reopen_engagement from dojo.filters import EngagementFilter, EngagementDirectFilter, EngagementTestFilter from dojo.forms import CheckForm, \ UploadThreatForm, RiskAcceptanceForm, NoteForm, DoneForm, \ EngForm, TestForm, ReplaceRiskAcceptanceProofForm, AddFindingsRiskAcceptanceForm, DeleteEngagementForm, ImportScanForm, \ CredMappingForm, JIRAEngagementForm, JIRAImportScanForm, TypedNoteForm, JIRAProjectForm, \ EditRiskAcceptanceForm from dojo.models import Finding, Product, Engagement, Test, \ Check_List, Test_Import, Notes, \ Risk_Acceptance, Development_Environment, Endpoint, \ Cred_Mapping, Dojo_User, System_Settings, Note_Type, Product_API_Scan_Configuration from dojo.tools.factory import get_scan_types_sorted from dojo.utils import add_error_message_to_response, add_success_message_to_response, get_page_items, add_breadcrumb, handle_uploaded_threat, \ FileIterWrapper, get_cal_event, Product_Tab, is_scan_file_too_large, \ get_system_setting, redirect_to_return_url_or_else, get_return_url from dojo.notifications.helper import create_notification from dojo.finding.views import find_available_notetypes from functools import reduce from django.db.models.query import Prefetch, QuerySet import dojo.jira_link.helper as jira_helper import dojo.risk_acceptance.helper as ra_helper from dojo.risk_acceptance.helper import prefetch_for_expiration from dojo.finding.helper import NOT_ACCEPTED_FINDINGS_QUERY from django.views.decorators.vary import vary_on_cookie from dojo.authorization.authorization import user_has_permission_or_403 from dojo.authorization.roles_permissions import Permissions from dojo.product.queries import get_authorized_products from dojo.engagement.queries import get_authorized_engagements from dojo.authorization.authorization_decorators import user_is_authorized from dojo.importers.importer.importer import DojoDefaultImporter as Importer import dojo.notifications.helper as notifications_helper from dojo.endpoint.utils import save_endpoints_to_add logger = logging.getLogger(__name__) @cache_page(60 * 5) # cache for 5 minutes @vary_on_cookie def engagement_calendar(request): if 'lead' not in request.GET or '0' in request.GET.getlist('lead'): engagements = get_authorized_engagements(Permissions.Engagement_View) else: filters = [] leads = request.GET.getlist('lead', '') if '-1' in request.GET.getlist('lead'): leads.remove('-1') filters.append(Q(lead__isnull=True)) filters.append(Q(lead__in=leads)) engagements = get_authorized_engagements(Permissions.Engagement_View).filter(reduce(operator.or_, filters)) engagements = engagements.select_related('lead') engagements = engagements.prefetch_related('product') add_breadcrumb( title="Engagement Calendar", top_level=True, request=request) return render( request, 'dojo/calendar.html', { 'caltype': 'engagements', 'leads': request.GET.getlist('lead', ''), 'engagements': engagements, 'users': Dojo_User.objects.all() }) def get_filtered_engagements(request, view): if view not in ['all', 'active']: raise ValidationError(f'View {view} is not allowed') engagements = get_authorized_engagements(Permissions.Engagement_View).order_by('-target_start') if view == 'active': engagements = engagements.filter(active=True) engagements = engagements.select_related('product', 'product__prod_type') \ .prefetch_related('lead', 'tags', 'product__tags') if System_Settings.objects.get().enable_jira: engagements = engagements.prefetch_related( 'jira_project__jira_instance', 'product__jira_project_set__jira_instance' ) engagements = EngagementDirectFilter(request.GET, queryset=engagements) return engagements def get_test_counts(engagements): # Get the test counts per engagement. As a separate query, this is much # faster than annotating the above `engagements` query. engagement_test_counts = { test['engagement']: test['test_count'] for test in Test.objects.filter( engagement__in=engagements ).values( 'engagement' ).annotate( test_count=Count('engagement') ) } return engagement_test_counts def engagements(request, view): if not view: view = 'active' filtered_engagements = get_filtered_engagements(request, view) engs = get_page_items(request, filtered_engagements.qs, 25) product_name_words = sorted(get_authorized_products(Permissions.Product_View).values_list('name', flat=True)) engagement_name_words = sorted(get_authorized_engagements(Permissions.Engagement_View).values_list('name', flat=True).distinct()) add_breadcrumb( title=f"{view.capitalize()} Engagements", top_level=not len(request.GET), request=request) return render( request, 'dojo/engagement.html', { 'engagements': engs, 'engagement_test_counts': get_test_counts(filtered_engagements.qs), 'filter_form': filtered_engagements.form, 'product_name_words': product_name_words, 'engagement_name_words': engagement_name_words, 'view': view.capitalize(), }) def engagements_all(request): products_with_engagements = get_authorized_products(Permissions.Engagement_View) products_with_engagements = products_with_engagements.filter(~Q(engagement=None)).distinct() # count using prefetch instead of just using 'engagement__set_test_test` to avoid loading all test in memory just to count them filter_qs = products_with_engagements.prefetch_related( Prefetch('engagement_set', queryset=Engagement.objects.all().annotate(test_count=Count('test__id'))) ) filter_qs = filter_qs.prefetch_related( 'engagement_set__tags', 'prod_type', 'engagement_set__lead', 'tags', ) if System_Settings.objects.get().enable_jira: filter_qs = filter_qs.prefetch_related( 'engagement_set__jira_project__jira_instance', 'jira_project_set__jira_instance' ) filtered = EngagementFilter( request.GET, queryset=filter_qs ) prods = get_page_items(request, filtered.qs, 25) name_words = products_with_engagements.values_list('name', flat=True) eng_words = get_authorized_engagements(Permissions.Engagement_View).values_list('name', flat=True).distinct() add_breadcrumb( title="All Engagements", top_level=not len(request.GET), request=request) return render( request, 'dojo/engagements_all.html', { 'products': prods, 'filter_form': filtered.form, 'name_words': sorted(set(name_words)), 'eng_words': sorted(set(eng_words)), }) @user_is_authorized(Engagement, Permissions.Engagement_Edit, 'eid') def edit_engagement(request, eid): engagement = Engagement.objects.get(pk=eid) is_ci_cd = engagement.engagement_type == "CI/CD" jira_project_form = None jira_epic_form = None jira_project = None jira_error = False if request.method == 'POST': form = EngForm(request.POST, instance=engagement, cicd=is_ci_cd, product=engagement.product, user=request.user) jira_project = jira_helper.get_jira_project(engagement, use_inheritance=False) if form.is_valid(): # first save engagement details new_status = form.cleaned_data.get('status') engagement = form.save(commit=False) if (new_status == "Cancelled" or new_status == "Completed"): engagement.active = False create_notification(event='close_engagement', title='Closure of %s' % engagement.name, description='The engagement "%s" was closed' % (engagement.name), engagement=engagement, url=reverse('engagement_all_findings', args=(engagement.id, ))), else: engagement.active = True engagement.save() form.save_m2m() messages.add_message( request, messages.SUCCESS, 'Engagement updated successfully.', extra_tags='alert-success') success, jira_project_form = jira_helper.process_jira_project_form(request, instance=jira_project, target='engagement', engagement=engagement, product=engagement.product) error = not success success, jira_epic_form = jira_helper.process_jira_epic_form(request, engagement=engagement) error = error or not success if not error: if '_Add Tests' in request.POST: return HttpResponseRedirect( reverse('add_tests', args=(engagement.id, ))) else: return HttpResponseRedirect( reverse('view_engagement', args=(engagement.id, ))) else: logger.debug(form.errors) else: form = EngForm(initial={'product': engagement.product}, instance=engagement, cicd=is_ci_cd, product=engagement.product, user=request.user) jira_epic_form = None if get_system_setting('enable_jira'): jira_project = jira_helper.get_jira_project(engagement, use_inheritance=False) jira_project_form = JIRAProjectForm(instance=jira_project, target='engagement', product=engagement.product) logger.debug('showing jira-epic-form') jira_epic_form = JIRAEngagementForm(instance=engagement) if is_ci_cd: title = 'Edit CI/CD Engagement' else: title = 'Edit Interactive Engagement' product_tab = Product_Tab(engagement.product.id, title=title, tab="engagements") product_tab.setEngagement(engagement) return render(request, 'dojo/new_eng.html', { 'product_tab': product_tab, 'title': title, 'form': form, 'edit': True, 'jira_epic_form': jira_epic_form, 'jira_project_form': jira_project_form, 'engagement': engagement, }) @user_is_authorized(Engagement, Permissions.Engagement_Delete, 'eid') def delete_engagement(request, eid): engagement = get_object_or_404(Engagement, pk=eid) product = engagement.product form = DeleteEngagementForm(instance=engagement) if request.method == 'POST': if 'id' in request.POST and str(engagement.id) == request.POST['id']: form = DeleteEngagementForm(request.POST, instance=engagement) if form.is_valid(): product = engagement.product engagement.delete() messages.add_message( request, messages.SUCCESS, 'Engagement and relationships removed.', extra_tags='alert-success') create_notification(event='other', title='Deletion of %s' % engagement.name, product=product, description='The engagement "%s" was deleted by %s' % (engagement.name, request.user), url=request.build_absolute_uri(reverse('view_engagements', args=(product.id, ))), recipients=[engagement.lead], icon="exclamation-triangle") return HttpResponseRedirect(reverse("view_engagements", args=(product.id, ))) collector = NestedObjects(using=DEFAULT_DB_ALIAS) collector.collect([engagement]) rels = collector.nested() product_tab = Product_Tab(product.id, title="Delete Engagement", tab="engagements") product_tab.setEngagement(engagement) return render(request, 'dojo/delete_engagement.html', { 'product_tab': product_tab, 'engagement': engagement, 'form': form, 'rels': rels, }) @user_is_authorized(Engagement, Permissions.Engagement_View, 'eid') def view_engagement(request, eid): eng = get_object_or_404(Engagement, id=eid) tests = eng.test_set.all().order_by('test_type__name', '-updated') default_page_num = 10 tests_filter = EngagementTestFilter(request.GET, queryset=tests, engagement=eng) paged_tests = get_page_items(request, tests_filter.qs, default_page_num) # prefetch only after creating the filters to avoid https://code.djangoproject.com/ticket/23771 and https://code.djangoproject.com/ticket/25375 paged_tests.object_list = prefetch_for_view_tests(paged_tests.object_list) prod = eng.product risks_accepted = eng.risk_acceptance.all().select_related('owner').annotate(accepted_findings_count=Count('accepted_findings__id')) preset_test_type = None network = None if eng.preset: preset_test_type = eng.preset.test_type.all() network = eng.preset.network_locations.all() system_settings = System_Settings.objects.get() jissue = jira_helper.get_jira_issue(eng) jira_project = jira_helper.get_jira_project(eng) try: check = Check_List.objects.get(engagement=eng) except: check = None pass notes = eng.notes.all() note_type_activation = Note_Type.objects.filter(is_active=True).count() if note_type_activation: available_note_types = find_available_notetypes(notes) form = DoneForm() files = eng.files.all() if request.method == 'POST': user_has_permission_or_403(request.user, eng, Permissions.Note_Add) eng.progress = 'check_list' eng.save() if note_type_activation: form = TypedNoteForm(request.POST, available_note_types=available_note_types) else: form = NoteForm(request.POST) if form.is_valid(): new_note = form.save(commit=False) new_note.author = request.user new_note.date = timezone.now() new_note.save() eng.notes.add(new_note) if note_type_activation: form = TypedNoteForm(available_note_types=available_note_types) else: form = NoteForm() url = request.build_absolute_uri(reverse("view_engagement", args=(eng.id,))) title = "Engagement: %s on %s" % (eng.name, eng.product.name) messages.add_message(request, messages.SUCCESS, 'Note added successfully.', extra_tags='alert-success') else: if note_type_activation: form = TypedNoteForm(available_note_types=available_note_types) else: form = NoteForm() creds = Cred_Mapping.objects.filter( product=eng.product).select_related('cred_id').order_by('cred_id') cred_eng = Cred_Mapping.objects.filter( engagement=eng.id).select_related('cred_id').order_by('cred_id') add_breadcrumb(parent=eng, top_level=False, request=request) title = "" if eng.engagement_type == "CI/CD": title = " CI/CD" product_tab = Product_Tab(prod.id, title="View" + title + " Engagement", tab="engagements") product_tab.setEngagement(eng) return render( request, 'dojo/view_eng.html', { 'eng': eng, 'product_tab': product_tab, 'system_settings': system_settings, 'tests': paged_tests, 'filter': tests_filter, 'check': check, 'threat': eng.tmodel_path, 'form': form, 'notes': notes, 'files': files, 'risks_accepted': risks_accepted, 'jissue': jissue, 'jira_project': jira_project, 'creds': creds, 'cred_eng': cred_eng, 'network': network, 'preset_test_type': preset_test_type }) def prefetch_for_view_tests(tests): prefetched = tests if isinstance(tests, QuerySet): # old code can arrive here with prods being a list because the query was already executed prefetched = prefetched.select_related('lead') prefetched = prefetched.prefetch_related('tags', 'test_type', 'notes') prefetched = prefetched.annotate(count_findings_test_all=Count('finding__id', distinct=True)) prefetched = prefetched.annotate(count_findings_test_active=Count('finding__id', filter=Q(finding__active=True), distinct=True)) prefetched = prefetched.annotate(count_findings_test_active_verified=Count('finding__id', filter=Q(finding__active=True) & Q(finding__verified=True), distinct=True)) prefetched = prefetched.annotate(count_findings_test_mitigated=Count('finding__id', filter=Q(finding__is_mitigated=True), distinct=True)) prefetched = prefetched.annotate(count_findings_test_dups=Count('finding__id', filter=Q(finding__duplicate=True), distinct=True)) prefetched = prefetched.annotate(total_reimport_count=Count('test_import__id', filter=Q(test_import__type=Test_Import.REIMPORT_TYPE), distinct=True)) else: logger.warn('unable to prefetch because query was already executed') return prefetched @user_is_authorized(Engagement, Permissions.Test_Add, 'eid') def add_tests(request, eid): eng = Engagement.objects.get(id=eid) cred_form = CredMappingForm() cred_form.fields["cred_user"].queryset = Cred_Mapping.objects.filter( engagement=eng).order_by('cred_id') if request.method == 'POST': form = TestForm(request.POST, engagement=eng) cred_form = CredMappingForm(request.POST) cred_form.fields["cred_user"].queryset = Cred_Mapping.objects.filter( engagement=eng).order_by('cred_id') if form.is_valid(): new_test = form.save(commit=False) # set default scan_type as it's used in reimport new_test.scan_type = new_test.test_type.name new_test.engagement = eng try: new_test.lead = User.objects.get(id=form['lead'].value()) except: new_test.lead = None pass # Set status to in progress if a test is added if eng.status != "In Progress" and eng.active is True: eng.status = "In Progress" eng.save() new_test.save() # Save the credential to the test if cred_form.is_valid(): if cred_form.cleaned_data['cred_user']: # Select the credential mapping object from the selected list and only allow if the credential is associated with the product cred_user = Cred_Mapping.objects.filter( pk=cred_form.cleaned_data['cred_user'].id, engagement=eid).first() new_f = cred_form.save(commit=False) new_f.test = new_test new_f.cred_id = cred_user.cred_id new_f.save() messages.add_message( request, messages.SUCCESS, 'Test added successfully.', extra_tags='alert-success') notifications_helper.notify_test_created(new_test) if '_Add Another Test' in request.POST: return HttpResponseRedirect( reverse('add_tests', args=(eng.id, ))) elif '_Add Findings' in request.POST: return HttpResponseRedirect( reverse('add_findings', args=(new_test.id, ))) elif '_Finished' in request.POST: return HttpResponseRedirect( reverse('view_engagement', args=(eng.id, ))) else: form = TestForm(engagement=eng) form.initial['target_start'] = eng.target_start form.initial['target_end'] = eng.target_end form.initial['lead'] = request.user add_breadcrumb( parent=eng, title="Add Tests", top_level=False, request=request) product_tab = Product_Tab(eng.product.id, title="Add Tests", tab="engagements") product_tab.setEngagement(eng) return render(request, 'dojo/add_tests.html', { 'product_tab': product_tab, 'form': form, 'cred_form': cred_form, 'eid': eid, 'eng': eng }) # Cant use the easy decorator because of the potential for either eid/pid being used def import_scan_results(request, eid=None, pid=None): engagement = None form = ImportScanForm() cred_form = CredMappingForm() finding_count = 0 jform = None user = request.user if eid: engagement = get_object_or_404(Engagement, id=eid) engagement_or_product = engagement cred_form.fields["cred_user"].queryset = Cred_Mapping.objects.filter(engagement=engagement).order_by('cred_id') elif pid: product = get_object_or_404(Product, id=pid) engagement_or_product = product elif not user.is_staff: raise PermissionDenied user_has_permission_or_403(user, engagement_or_product, Permissions.Import_Scan_Result) push_all_jira_issues = jira_helper.is_push_all_issues(engagement_or_product) if request.method == "POST": form = ImportScanForm(request.POST, request.FILES) cred_form = CredMappingForm(request.POST) cred_form.fields["cred_user"].queryset = Cred_Mapping.objects.filter( engagement=engagement).order_by('cred_id') if jira_helper.get_jira_project(engagement_or_product): jform = JIRAImportScanForm(request.POST, push_all=push_all_jira_issues, prefix='jiraform') logger.debug('jform valid: %s', jform.is_valid()) logger.debug('jform errors: %s', jform.errors) if form.is_valid() and (jform is None or jform.is_valid()): scan = request.FILES.get('file', None) scan_date = form.cleaned_data['scan_date'] minimum_severity = form.cleaned_data['minimum_severity'] active = form.cleaned_data['active'] verified = form.cleaned_data['verified'] scan_type = request.POST['scan_type'] tags = form.cleaned_data['tags'] version = form.cleaned_data['version'] branch_tag = form.cleaned_data.get('branch_tag', None) build_id = form.cleaned_data.get('build_id', None) commit_hash = form.cleaned_data.get('commit_hash', None) api_scan_configuration = form.cleaned_data.get('api_scan_configuration', None) service = form.cleaned_data.get('service', None) close_old_findings = form.cleaned_data.get('close_old_findings', None) # Will save in the provided environment or in the `Development` one if absent environment_id = request.POST.get('environment', 'Development') environment = Development_Environment.objects.get(id=environment_id) group_by = form.cleaned_data.get('group_by', None) # TODO move to form validation? if scan and is_scan_file_too_large(scan): messages.add_message(request, messages.ERROR, "Report file is too large. Maximum supported size is {} MB".format(settings.SCAN_FILE_MAX_SIZE), extra_tags='alert-danger') return HttpResponseRedirect(reverse('import_scan_results', args=(engagement,))) # Allows for a test to be imported with an engagement created on the fly if engagement is None: engagement = Engagement() engagement.name = "AdHoc Import - " + strftime("%a, %d %b %Y %X", timezone.now().timetuple()) engagement.threat_model = False engagement.api_test = False engagement.pen_test = False engagement.check_list = False engagement.target_start = timezone.now().date() engagement.target_end = timezone.now().date() engagement.product = product engagement.active = True engagement.status = 'In Progress' engagement.version = version engagement.branch_tag = branch_tag engagement.build_id = build_id engagement.commit_hash = commit_hash engagement.save() # can't use helper as when push_all_jira_issues is True, the checkbox gets disabled and is always false # push_to_jira = jira_helper.is_push_to_jira(new_finding, jform.cleaned_data.get('push_to_jira')) push_to_jira = push_all_jira_issues or (jform and jform.cleaned_data.get('push_to_jira')) error = False # Save newly added endpoints added_endpoints = save_endpoints_to_add(form.endpoints_to_add_list, engagement.product) try: importer = Importer() test, finding_count, closed_finding_count = importer.import_scan(scan, scan_type, engagement, user, environment, active=active, verified=verified, tags=tags, minimum_severity=minimum_severity, endpoints_to_add=list(form.cleaned_data['endpoints']) + added_endpoints, scan_date=scan_date, version=version, branch_tag=branch_tag, build_id=build_id, commit_hash=commit_hash, push_to_jira=push_to_jira, close_old_findings=close_old_findings, group_by=group_by, api_scan_configuration=api_scan_configuration, service=service) message = f'{scan_type} processed a total of {finding_count} findings' if close_old_findings: message = message + ' and closed %d findings' % (closed_finding_count) message = message + "." add_success_message_to_response(message) except Exception as e: logger.exception(e) add_error_message_to_response('An exception error occurred during the report import:%s' % str(e)) error = True # Save the credential to the test if cred_form.is_valid(): if cred_form.cleaned_data['cred_user']: # Select the credential mapping object from the selected list and only allow if the credential is associated with the product cred_user = Cred_Mapping.objects.filter( pk=cred_form.cleaned_data['cred_user'].id, engagement=eid).first() new_f = cred_form.save(commit=False) new_f.test = test new_f.cred_id = cred_user.cred_id new_f.save() if not error: return HttpResponseRedirect( reverse('product_open_findings', args=(pid, ))) prod_id = None custom_breadcrumb = None title = "Import Scan Results" if engagement: prod_id = engagement.product.id product_tab = Product_Tab(prod_id, title=title, tab="engagements") product_tab.setEngagement(engagement) else: prod_id = pid custom_breadcrumb = {"", ""} product_tab = Product_Tab(prod_id, title=title, tab="findings") if jira_helper.get_jira_project(engagement_or_product): jform = JIRAImportScanForm(push_all=push_all_jira_issues, prefix='jiraform') form.fields['endpoints'].queryset = Endpoint.objects.filter(product__id=product_tab.product.id) form.fields['api_scan_configuration'].queryset = Product_API_Scan_Configuration.objects.filter(product__id=product_tab.product.id) return render(request, 'dojo/import_scan_results.html', {'form': form, 'product_tab': product_tab, 'engagement_or_product': engagement_or_product, 'custom_breadcrumb': custom_breadcrumb, 'title': title, 'cred_form': cred_form, 'jform': jform, 'scan_types': get_scan_types_sorted(), }) @user_is_authorized(Engagement, Permissions.Engagement_Edit, 'eid') def close_eng(request, eid): eng = Engagement.objects.get(id=eid) close_engagement(eng) messages.add_message( request, messages.SUCCESS, 'Engagement closed successfully.', extra_tags='alert-success') create_notification(event='close_engagement', title='Closure of %s' % eng.name, description='The engagement "%s" was closed' % (eng.name), engagement=eng, url=reverse('engagement_all_findings', args=(eng.id, ))), return HttpResponseRedirect(reverse("view_engagements", args=(eng.product.id, ))) @user_is_authorized(Engagement, Permissions.Engagement_Edit, 'eid') def reopen_eng(request, eid): eng = Engagement.objects.get(id=eid) reopen_engagement(eng) messages.add_message( request, messages.SUCCESS, 'Engagement reopened successfully.', extra_tags='alert-success') create_notification(event='other', title='Reopening of %s' % eng.name, engagement=eng, description='The engagement "%s" was reopened' % (eng.name), url=reverse('view_engagement', args=(eng.id, ))), return HttpResponseRedirect(reverse("view_engagements", args=(eng.product.id, ))) """ Greg: status: in production method to complete checklists from the engagement view """ @user_is_authorized(Engagement, Permissions.Engagement_Edit, 'eid') def complete_checklist(request, eid): eng = get_object_or_404(Engagement, id=eid) try: checklist = Check_List.objects.get(engagement=eng) except: checklist = None pass add_breadcrumb( parent=eng, title="Complete checklist", top_level=False, request=request) if request.method == 'POST': tests = Test.objects.filter(engagement=eng) findings = Finding.objects.filter(test__in=tests).all() form = CheckForm(request.POST, instance=checklist, findings=findings) if form.is_valid(): cl = form.save(commit=False) try: check_l = Check_List.objects.get(engagement=eng) cl.id = check_l.id cl.save() form.save_m2m() except: cl.engagement = eng cl.save() form.save_m2m() pass messages.add_message( request, messages.SUCCESS, 'Checklist saved.', extra_tags='alert-success') return HttpResponseRedirect( reverse('view_engagement', args=(eid, ))) else: tests = Test.objects.filter(engagement=eng) findings = Finding.objects.filter(test__in=tests).all() form = CheckForm(instance=checklist, findings=findings) product_tab = Product_Tab(eng.product.id, title="Checklist", tab="engagements") product_tab.setEngagement(eng) return render(request, 'dojo/checklist.html', { 'form': form, 'product_tab': product_tab, 'eid': eng.id, 'findings': findings, }) @user_is_authorized(Engagement, Permissions.Risk_Acceptance, 'eid') def add_risk_acceptance(request, eid, fid=None): eng = get_object_or_404(Engagement, id=eid) finding = None if fid: finding = get_object_or_404(Finding, id=fid) if not eng.product.enable_full_risk_acceptance: raise PermissionDenied() if request.method == 'POST': form = RiskAcceptanceForm(request.POST, request.FILES) if form.is_valid(): # first capture notes param as it cannot be saved directly as m2m notes = None if form.cleaned_data['notes']: notes = Notes( entry=form.cleaned_data['notes'], author=request.user, date=timezone.now()) notes.save() del form.cleaned_data['notes'] try: # we sometimes see a weird exception here, but are unable to reproduce. # we add some logging in case it happens risk_acceptance = form.save() except Exception as e: logger.debug(vars(request.POST)) logger.error(vars(form)) logger.exception(e) raise # attach note to risk acceptance object now in database if notes: risk_acceptance.notes.add(notes) eng.risk_acceptance.add(risk_acceptance) findings = form.cleaned_data['accepted_findings'] risk_acceptance = ra_helper.add_findings_to_risk_acceptance(risk_acceptance, findings) messages.add_message( request, messages.SUCCESS, 'Risk acceptance saved.', extra_tags='alert-success') return redirect_to_return_url_or_else(request, reverse('view_engagement', args=(eid, ))) else: risk_acceptance_title_suggestion = 'Accept: %s' % finding form = RiskAcceptanceForm(initial={'owner': request.user, 'name': risk_acceptance_title_suggestion}) finding_choices = Finding.objects.filter(duplicate=False, test__engagement=eng).filter(NOT_ACCEPTED_FINDINGS_QUERY).order_by('title') form.fields['accepted_findings'].queryset = finding_choices if fid: form.fields['accepted_findings'].initial = {fid} product_tab = Product_Tab(eng.product.id, title="Risk Acceptance", tab="engagements") product_tab.setEngagement(eng) return render(request, 'dojo/add_risk_acceptance.html', { 'eng': eng, 'product_tab': product_tab, 'form': form }) @user_is_authorized(Engagement, Permissions.Engagement_View, 'eid') def view_risk_acceptance(request, eid, raid): return view_edit_risk_acceptance(request, eid=eid, raid=raid, edit_mode=False) @user_is_authorized(Engagement, Permissions.Risk_Acceptance, 'eid') def edit_risk_acceptance(request, eid, raid): return view_edit_risk_acceptance(request, eid=eid, raid=raid, edit_mode=True) # will only be called by view_risk_acceptance and edit_risk_acceptance def view_edit_risk_acceptance(request, eid, raid, edit_mode=False): risk_acceptance = get_object_or_404(Risk_Acceptance, pk=raid) eng = get_object_or_404(Engagement, pk=eid) if edit_mode and not eng.product.enable_full_risk_acceptance: raise PermissionDenied() risk_acceptance_form = None errors = False if request.method == 'POST': # deleting before instantiating the form otherwise django messes up and we end up with an empty path value if len(request.FILES) > 0: logger.debug('new proof uploaded') risk_acceptance.path.delete() if 'decision' in request.POST: old_expiration_date = risk_acceptance.expiration_date risk_acceptance_form = EditRiskAcceptanceForm(request.POST, request.FILES, instance=risk_acceptance) errors = errors or not risk_acceptance_form.is_valid() if not errors: logger.debug('path: %s', risk_acceptance_form.cleaned_data['path']) risk_acceptance_form.save() if risk_acceptance.expiration_date != old_expiration_date: # risk acceptance was changed, check if risk acceptance needs to be reinstated and findings made accepted again ra_helper.reinstate(risk_acceptance, old_expiration_date) messages.add_message( request, messages.SUCCESS, 'Risk Acceptance saved successfully.', extra_tags='alert-success') if 'entry' in request.POST: note_form = NoteForm(request.POST) errors = errors or not note_form.is_valid() if not errors: new_note = note_form.save(commit=False) new_note.author = request.user new_note.date = timezone.now() new_note.save() risk_acceptance.notes.add(new_note) messages.add_message( request, messages.SUCCESS, 'Note added successfully.', extra_tags='alert-success') if 'delete_note' in request.POST: note = get_object_or_404(Notes, pk=request.POST['delete_note_id']) if note.author.username == request.user.username: risk_acceptance.notes.remove(note) note.delete() messages.add_message( request, messages.SUCCESS, 'Note deleted successfully.', extra_tags='alert-success') else: messages.add_message( request, messages.ERROR, "Since you are not the note's author, it was not deleted.", extra_tags='alert-danger') if 'remove_finding' in request.POST: finding = get_object_or_404( Finding, pk=request.POST['remove_finding_id']) ra_helper.remove_finding_from_risk_acceptance(risk_acceptance, finding) messages.add_message( request, messages.SUCCESS, 'Finding removed successfully from risk acceptance.', extra_tags='alert-success') if 'replace_file' in request.POST: replace_form = ReplaceRiskAcceptanceProofForm( request.POST, request.FILES, instance=risk_acceptance) errors = errors or not replace_form.is_valid() if not errors: replace_form.save() messages.add_message( request, messages.SUCCESS, 'New Proof uploaded successfully.', extra_tags='alert-success') else: logger.error(replace_form.errors) if 'add_findings' in request.POST: add_findings_form = AddFindingsRiskAcceptanceForm( request.POST, request.FILES, instance=risk_acceptance) errors = errors or not add_findings_form.is_valid() if not errors: findings = add_findings_form.cleaned_data['accepted_findings'] ra_helper.add_findings_to_risk_acceptance(risk_acceptance, findings) messages.add_message( request, messages.SUCCESS, 'Finding%s added successfully.' % ('s' if len(findings) > 1 else ''), extra_tags='alert-success') if not errors: logger.debug('redirecting to return_url') return redirect_to_return_url_or_else(request, reverse("view_risk_acceptance", args=(eid, raid))) else: logger.error('errors found') else: if edit_mode: risk_acceptance_form = EditRiskAcceptanceForm(instance=risk_acceptance) note_form = NoteForm() replace_form = ReplaceRiskAcceptanceProofForm(instance=risk_acceptance) add_findings_form = AddFindingsRiskAcceptanceForm(instance=risk_acceptance) accepted_findings = risk_acceptance.accepted_findings.order_by('numerical_severity') fpage = get_page_items(request, accepted_findings, 15) unaccepted_findings = Finding.objects.filter(test__in=eng.test_set.all()) \ .exclude(id__in=accepted_findings).order_by("title") add_fpage = get_page_items(request, unaccepted_findings, 10, 'apage') # on this page we need to add unaccepted findings as possible findings to add as accepted add_findings_form.fields[ "accepted_findings"].queryset = add_fpage.object_list product_tab = Product_Tab(eng.product.id, title="Risk Acceptance", tab="engagements") product_tab.setEngagement(eng) return render( request, 'dojo/view_risk_acceptance.html', { 'risk_acceptance': risk_acceptance, 'engagement': eng, 'product_tab': product_tab, 'accepted_findings': fpage, 'notes': risk_acceptance.notes.all(), 'eng': eng, 'edit_mode': edit_mode, 'risk_acceptance_form': risk_acceptance_form, 'note_form': note_form, 'replace_form': replace_form, 'add_findings_form': add_findings_form, # 'show_add_findings_form': len(unaccepted_findings), 'request': request, 'add_findings': add_fpage, 'return_url': get_return_url(request), }) @user_is_authorized(Engagement, Permissions.Risk_Acceptance, 'eid') def expire_risk_acceptance(request, eid, raid): risk_acceptance = get_object_or_404(prefetch_for_expiration(Risk_Acceptance.objects.all()), pk=raid) eng = get_object_or_404(Engagement, pk=eid) ra_helper.expire_now(risk_acceptance) return redirect_to_return_url_or_else(request, reverse("view_risk_acceptance", args=(eid, raid))) @user_is_authorized(Engagement, Permissions.Risk_Acceptance, 'eid') def reinstate_risk_acceptance(request, eid, raid): risk_acceptance = get_object_or_404(prefetch_for_expiration(Risk_Acceptance.objects.all()), pk=raid) eng = get_object_or_404(Engagement, pk=eid) if not eng.product.enable_full_risk_acceptance: raise PermissionDenied() ra_helper.reinstate(risk_acceptance, risk_acceptance.expiration_date) return redirect_to_return_url_or_else(request, reverse("view_risk_acceptance", args=(eid, raid))) @user_is_authorized(Engagement, Permissions.Risk_Acceptance, 'eid') def delete_risk_acceptance(request, eid, raid): risk_acceptance = get_object_or_404(Risk_Acceptance, pk=raid) eng = get_object_or_404(Engagement, pk=eid) ra_helper.delete(eng, risk_acceptance) messages.add_message( request, messages.SUCCESS, 'Risk acceptance deleted successfully.', extra_tags='alert-success') return HttpResponseRedirect(reverse("view_engagement", args=(eng.id, ))) @user_is_authorized(Engagement, Permissions.Engagement_View, 'eid') def download_risk_acceptance(request, eid, raid): import mimetypes mimetypes.init() risk_acceptance = get_object_or_404(Risk_Acceptance, pk=raid) response = StreamingHttpResponse( FileIterWrapper( open(settings.MEDIA_ROOT + "/" + risk_acceptance.path.name, mode='rb'))) response['Content-Disposition'] = 'attachment; filename="%s"' \ % risk_acceptance.filename() mimetype, encoding = mimetypes.guess_type(risk_acceptance.path.name) response['Content-Type'] = mimetype return response """ Greg status: in production Upload a threat model at the engagement level. Threat models are stored under media folder """ @user_is_authorized(Engagement, Permissions.Engagement_Edit, 'eid') def upload_threatmodel(request, eid): eng = Engagement.objects.get(id=eid) add_breadcrumb( parent=eng, title="Upload a threat model", top_level=False, request=request) if request.method == 'POST': form = UploadThreatForm(request.POST, request.FILES) if form.is_valid(): handle_uploaded_threat(request.FILES['file'], eng) eng.progress = 'other' eng.threat_model = True eng.save() messages.add_message( request, messages.SUCCESS, 'Threat model saved.', extra_tags='alert-success') return HttpResponseRedirect( reverse('view_engagement', args=(eid, ))) else: form = UploadThreatForm() product_tab = Product_Tab(eng.product.id, title="Upload Threat Model", tab="engagements") return render(request, 'dojo/up_threat.html', { 'form': form, 'product_tab': product_tab, 'eng': eng, }) @user_is_authorized(Engagement, Permissions.Engagement_View, 'eid') def view_threatmodel(request, eid): eng = get_object_or_404(Engagement, pk=eid) response = FileResponse(open(eng.tmodel_path, 'rb')) return response @user_is_authorized(Engagement, Permissions.Engagement_View, 'eid') def engagement_ics(request, eid): eng = get_object_or_404(Engagement, id=eid) start_date = datetime.combine(eng.target_start, datetime.min.time()) end_date = datetime.combine(eng.target_end, datetime.max.time()) uid = "dojo_eng_%d_%d" % (eng.id, eng.product.id) cal = get_cal_event( start_date, end_date, "Engagement: %s (%s)" % (eng.name, eng.product.name), "Set aside for engagement %s, on product %s. Additional detail can be found at %s" % (eng.name, eng.product.name, request.build_absolute_uri( (reverse("view_engagement", args=(eng.id, ))))), uid) output = cal.serialize() response = HttpResponse(content=output) response['Content-Type'] = 'text/calendar' response['Content-Disposition'] = 'attachment; filename=%s.ics' % eng.name return response def get_list_index(list, index): try: element = list[index] except Exception as e: element = None return element def get_engagements(request): url = request.META.get('QUERY_STRING') if not url: raise ValidationError('Please use the export button when exporting engagements') else: if url.startswith('url='): url = url[4:] path_items = list(filter(None, re.split('/|\?', url))) # noqa W605 if not path_items or path_items[0] != 'engagement': raise ValidationError('URL is not an engagement view') view = query = None if get_list_index(path_items, 1) in ['active', 'all']: view = get_list_index(path_items, 1) query = get_list_index(path_items, 2) else: view = 'active' query = get_list_index(path_items, 1) request.GET = QueryDict(query) engagements = get_filtered_engagements(request, view).qs test_counts = get_test_counts(engagements) return engagements, test_counts def get_excludes(): return ['is_ci_cd', 'jira_issue', 'jira_project', 'objects', 'unaccepted_open_findings'] def get_foreign_keys(): return ['build_server', 'lead', 'orchestration_engine', 'preset', 'product', 'report_type', 'requester', 'source_code_management_server'] def csv_export(request): engagements, test_counts = get_engagements(request) response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename=engagements.csv' writer = csv.writer(response) first_row = True for engagement in engagements: if first_row: fields = [] for key in dir(engagement): if key not in get_excludes() and not callable(getattr(engagement, key)) and not key.startswith('_'): fields.append(key) fields.append('tests') writer.writerow(fields) first_row = False if not first_row: fields = [] for key in dir(engagement): if key not in get_excludes() and not callable(getattr(engagement, key)) and not key.startswith('_'): value = engagement.__dict__.get(key) if key in get_foreign_keys() and getattr(engagement, key): value = str(getattr(engagement, key)) if value and isinstance(value, str): value = value.replace('\n', ' NEWLINE ').replace('\r', '') fields.append(value) fields.append(test_counts.get(engagement.id, 0)) writer.writerow(fields) return response def excel_export(request): engagements, test_counts = get_engagements(request) workbook = Workbook() workbook.iso_dates = True worksheet = workbook.active worksheet.title = 'Engagements' font_bold = Font(bold=True) row_num = 1 for engagement in engagements: if row_num == 1: col_num = 1 for key in dir(engagement): if key not in get_excludes() and not callable(getattr(engagement, key)) and not key.startswith('_'): cell = worksheet.cell(row=row_num, column=col_num, value=key) cell.font = font_bold col_num += 1 cell = worksheet.cell(row=row_num, column=col_num, value='tests') cell.font = font_bold row_num = 2 if row_num > 1: col_num = 1 for key in dir(engagement): if key not in get_excludes() and not callable(getattr(engagement, key)) and not key.startswith('_'): value = engagement.__dict__.get(key) if key in get_foreign_keys() and getattr(engagement, key): value = str(getattr(engagement, key)) if value and isinstance(value, datetime): value = value.replace(tzinfo=None) worksheet.cell(row=row_num, column=col_num, value=value) col_num += 1 worksheet.cell(row=row_num, column=col_num, value=test_counts.get(engagement.id, 0)) row_num += 1 with NamedTemporaryFile() as tmp: workbook.save(tmp.name) tmp.seek(0) stream = tmp.read() response = HttpResponse( content=stream, content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' ) response['Content-Disposition'] = 'attachment; filename=engagements.xlsx' return response

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import logging import csv import re from openpyxl import Workbook from openpyxl.styles import Font from tempfile import NamedTemporaryFile from datetime import datetime import operator from django.contrib.auth.models import User from django.conf import settings from django.contrib import messages from django.core.exceptions import ValidationError, PermissionDenied from django.urls import reverse from django.db.models import Q, Count from django.http import HttpResponseRedirect, StreamingHttpResponse, HttpResponse, FileResponse, QueryDict from django.shortcuts import render, get_object_or_404 from django.views.decorators.cache import cache_page from django.utils import timezone from time import strftime from django.contrib.admin.utils import NestedObjects from django.db import DEFAULT_DB_ALIAS from dojo.engagement.services import close_engagement, reopen_engagement from dojo.filters import EngagementFilter, EngagementDirectFilter, EngagementTestFilter from dojo.forms import CheckForm, \ UploadThreatForm, RiskAcceptanceForm, NoteForm, DoneForm, \ EngForm, TestForm, ReplaceRiskAcceptanceProofForm, AddFindingsRiskAcceptanceForm, DeleteEngagementForm, ImportScanForm, \ CredMappingForm, JIRAEngagementForm, JIRAImportScanForm, TypedNoteForm, JIRAProjectForm, \ EditRiskAcceptanceForm from dojo.models import Finding, Product, Engagement, Test, \ Check_List, Test_Import, Notes, \ Risk_Acceptance, Development_Environment, Endpoint, \ Cred_Mapping, Dojo_User, System_Settings, Note_Type, Product_API_Scan_Configuration from dojo.tools.factory import get_scan_types_sorted from dojo.utils import add_error_message_to_response, add_success_message_to_response, get_page_items, add_breadcrumb, handle_uploaded_threat, \ FileIterWrapper, get_cal_event, Product_Tab, is_scan_file_too_large, \ get_system_setting, redirect_to_return_url_or_else, get_return_url from dojo.notifications.helper import create_notification from dojo.finding.views import find_available_notetypes from functools import reduce from django.db.models.query import Prefetch, QuerySet import dojo.jira_link.helper as jira_helper import dojo.risk_acceptance.helper as ra_helper from dojo.risk_acceptance.helper import prefetch_for_expiration from dojo.finding.helper import NOT_ACCEPTED_FINDINGS_QUERY from django.views.decorators.vary import vary_on_cookie from dojo.authorization.authorization import user_has_permission_or_403 from dojo.authorization.roles_permissions import Permissions from dojo.product.queries import get_authorized_products from dojo.engagement.queries import get_authorized_engagements from dojo.authorization.authorization_decorators import user_is_authorized from dojo.importers.importer.importer import DojoDefaultImporter as Importer import dojo.notifications.helper as notifications_helper from dojo.endpoint.utils import save_endpoints_to_add logger = logging.getLogger(__name__) @cache_page(60 * 5) @vary_on_cookie def engagement_calendar(request): if 'lead' not in request.GET or '0' in request.GET.getlist('lead'): engagements = get_authorized_engagements(Permissions.Engagement_View) else: filters = [] leads = request.GET.getlist('lead', '') if '-1' in request.GET.getlist('lead'): leads.remove('-1') filters.append(Q(lead__isnull=True)) filters.append(Q(lead__in=leads)) engagements = get_authorized_engagements(Permissions.Engagement_View).filter(reduce(operator.or_, filters)) engagements = engagements.select_related('lead') engagements = engagements.prefetch_related('product') add_breadcrumb( title="Engagement Calendar", top_level=True, request=request) return render( request, 'dojo/calendar.html', { 'caltype': 'engagements', 'leads': request.GET.getlist('lead', ''), 'engagements': engagements, 'users': Dojo_User.objects.all() }) def get_filtered_engagements(request, view): if view not in ['all', 'active']: raise ValidationError(f'View {view} is not allowed') engagements = get_authorized_engagements(Permissions.Engagement_View).order_by('-target_start') if view == 'active': engagements = engagements.filter(active=True) engagements = engagements.select_related('product', 'product__prod_type') \ .prefetch_related('lead', 'tags', 'product__tags') if System_Settings.objects.get().enable_jira: engagements = engagements.prefetch_related( 'jira_project__jira_instance', 'product__jira_project_set__jira_instance' ) engagements = EngagementDirectFilter(request.GET, queryset=engagements) return engagements def get_test_counts(engagements): engagement_test_counts = { test['engagement']: test['test_count'] for test in Test.objects.filter( engagement__in=engagements ).values( 'engagement' ).annotate( test_count=Count('engagement') ) } return engagement_test_counts def engagements(request, view): if not view: view = 'active' filtered_engagements = get_filtered_engagements(request, view) engs = get_page_items(request, filtered_engagements.qs, 25) product_name_words = sorted(get_authorized_products(Permissions.Product_View).values_list('name', flat=True)) engagement_name_words = sorted(get_authorized_engagements(Permissions.Engagement_View).values_list('name', flat=True).distinct()) add_breadcrumb( title=f"{view.capitalize()} Engagements", top_level=not len(request.GET), request=request) return render( request, 'dojo/engagement.html', { 'engagements': engs, 'engagement_test_counts': get_test_counts(filtered_engagements.qs), 'filter_form': filtered_engagements.form, 'product_name_words': product_name_words, 'engagement_name_words': engagement_name_words, 'view': view.capitalize(), }) def engagements_all(request): products_with_engagements = get_authorized_products(Permissions.Engagement_View) products_with_engagements = products_with_engagements.filter(~Q(engagement=None)).distinct() filter_qs = products_with_engagements.prefetch_related( Prefetch('engagement_set', queryset=Engagement.objects.all().annotate(test_count=Count('test__id'))) ) filter_qs = filter_qs.prefetch_related( 'engagement_set__tags', 'prod_type', 'engagement_set__lead', 'tags', ) if System_Settings.objects.get().enable_jira: filter_qs = filter_qs.prefetch_related( 'engagement_set__jira_project__jira_instance', 'jira_project_set__jira_instance' ) filtered = EngagementFilter( request.GET, queryset=filter_qs ) prods = get_page_items(request, filtered.qs, 25) name_words = products_with_engagements.values_list('name', flat=True) eng_words = get_authorized_engagements(Permissions.Engagement_View).values_list('name', flat=True).distinct() add_breadcrumb( title="All Engagements", top_level=not len(request.GET), request=request) return render( request, 'dojo/engagements_all.html', { 'products': prods, 'filter_form': filtered.form, 'name_words': sorted(set(name_words)), 'eng_words': sorted(set(eng_words)), }) @user_is_authorized(Engagement, Permissions.Engagement_Edit, 'eid') def edit_engagement(request, eid): engagement = Engagement.objects.get(pk=eid) is_ci_cd = engagement.engagement_type == "CI/CD" jira_project_form = None jira_epic_form = None jira_project = None jira_error = False if request.method == 'POST': form = EngForm(request.POST, instance=engagement, cicd=is_ci_cd, product=engagement.product, user=request.user) jira_project = jira_helper.get_jira_project(engagement, use_inheritance=False) if form.is_valid(): # first save engagement details new_status = form.cleaned_data.get('status') engagement = form.save(commit=False) if (new_status == "Cancelled" or new_status == "Completed"): engagement.active = False create_notification(event='close_engagement', title='Closure of %s' % engagement.name, description='The engagement "%s" was closed' % (engagement.name), engagement=engagement, url=reverse('engagement_all_findings', args=(engagement.id, ))), else: engagement.active = True engagement.save() form.save_m2m() messages.add_message( request, messages.SUCCESS, 'Engagement updated successfully.', extra_tags='alert-success') success, jira_project_form = jira_helper.process_jira_project_form(request, instance=jira_project, target='engagement', engagement=engagement, product=engagement.product) error = not success success, jira_epic_form = jira_helper.process_jira_epic_form(request, engagement=engagement) error = error or not success if not error: if '_Add Tests' in request.POST: return HttpResponseRedirect( reverse('add_tests', args=(engagement.id, ))) else: return HttpResponseRedirect( reverse('view_engagement', args=(engagement.id, ))) else: logger.debug(form.errors) else: form = EngForm(initial={'product': engagement.product}, instance=engagement, cicd=is_ci_cd, product=engagement.product, user=request.user) jira_epic_form = None if get_system_setting('enable_jira'): jira_project = jira_helper.get_jira_project(engagement, use_inheritance=False) jira_project_form = JIRAProjectForm(instance=jira_project, target='engagement', product=engagement.product) logger.debug('showing jira-epic-form') jira_epic_form = JIRAEngagementForm(instance=engagement) if is_ci_cd: title = 'Edit CI/CD Engagement' else: title = 'Edit Interactive Engagement' product_tab = Product_Tab(engagement.product.id, title=title, tab="engagements") product_tab.setEngagement(engagement) return render(request, 'dojo/new_eng.html', { 'product_tab': product_tab, 'title': title, 'form': form, 'edit': True, 'jira_epic_form': jira_epic_form, 'jira_project_form': jira_project_form, 'engagement': engagement, }) @user_is_authorized(Engagement, Permissions.Engagement_Delete, 'eid') def delete_engagement(request, eid): engagement = get_object_or_404(Engagement, pk=eid) product = engagement.product form = DeleteEngagementForm(instance=engagement) if request.method == 'POST': if 'id' in request.POST and str(engagement.id) == request.POST['id']: form = DeleteEngagementForm(request.POST, instance=engagement) if form.is_valid(): product = engagement.product engagement.delete() messages.add_message( request, messages.SUCCESS, 'Engagement and relationships removed.', extra_tags='alert-success') create_notification(event='other', title='Deletion of %s' % engagement.name, product=product, description='The engagement "%s" was deleted by %s' % (engagement.name, request.user), url=request.build_absolute_uri(reverse('view_engagements', args=(product.id, ))), recipients=[engagement.lead], icon="exclamation-triangle") return HttpResponseRedirect(reverse("view_engagements", args=(product.id, ))) collector = NestedObjects(using=DEFAULT_DB_ALIAS) collector.collect([engagement]) rels = collector.nested() product_tab = Product_Tab(product.id, title="Delete Engagement", tab="engagements") product_tab.setEngagement(engagement) return render(request, 'dojo/delete_engagement.html', { 'product_tab': product_tab, 'engagement': engagement, 'form': form, 'rels': rels, }) @user_is_authorized(Engagement, Permissions.Engagement_View, 'eid') def view_engagement(request, eid): eng = get_object_or_404(Engagement, id=eid) tests = eng.test_set.all().order_by('test_type__name', '-updated') default_page_num = 10 tests_filter = EngagementTestFilter(request.GET, queryset=tests, engagement=eng) paged_tests = get_page_items(request, tests_filter.qs, default_page_num) # prefetch only after creating the filters to avoid https://code.djangoproject.com/ticket/23771 and https://code.djangoproject.com/ticket/25375 paged_tests.object_list = prefetch_for_view_tests(paged_tests.object_list) prod = eng.product risks_accepted = eng.risk_acceptance.all().select_related('owner').annotate(accepted_findings_count=Count('accepted_findings__id')) preset_test_type = None network = None if eng.preset: preset_test_type = eng.preset.test_type.all() network = eng.preset.network_locations.all() system_settings = System_Settings.objects.get() jissue = jira_helper.get_jira_issue(eng) jira_project = jira_helper.get_jira_project(eng) try: check = Check_List.objects.get(engagement=eng) except: check = None pass notes = eng.notes.all() note_type_activation = Note_Type.objects.filter(is_active=True).count() if note_type_activation: available_note_types = find_available_notetypes(notes) form = DoneForm() files = eng.files.all() if request.method == 'POST': user_has_permission_or_403(request.user, eng, Permissions.Note_Add) eng.progress = 'check_list' eng.save() if note_type_activation: form = TypedNoteForm(request.POST, available_note_types=available_note_types) else: form = NoteForm(request.POST) if form.is_valid(): new_note = form.save(commit=False) new_note.author = request.user new_note.date = timezone.now() new_note.save() eng.notes.add(new_note) if note_type_activation: form = TypedNoteForm(available_note_types=available_note_types) else: form = NoteForm() url = request.build_absolute_uri(reverse("view_engagement", args=(eng.id,))) title = "Engagement: %s on %s" % (eng.name, eng.product.name) messages.add_message(request, messages.SUCCESS, 'Note added successfully.', extra_tags='alert-success') else: if note_type_activation: form = TypedNoteForm(available_note_types=available_note_types) else: form = NoteForm() creds = Cred_Mapping.objects.filter( product=eng.product).select_related('cred_id').order_by('cred_id') cred_eng = Cred_Mapping.objects.filter( engagement=eng.id).select_related('cred_id').order_by('cred_id') add_breadcrumb(parent=eng, top_level=False, request=request) title = "" if eng.engagement_type == "CI/CD": title = " CI/CD" product_tab = Product_Tab(prod.id, title="View" + title + " Engagement", tab="engagements") product_tab.setEngagement(eng) return render( request, 'dojo/view_eng.html', { 'eng': eng, 'product_tab': product_tab, 'system_settings': system_settings, 'tests': paged_tests, 'filter': tests_filter, 'check': check, 'threat': eng.tmodel_path, 'form': form, 'notes': notes, 'files': files, 'risks_accepted': risks_accepted, 'jissue': jissue, 'jira_project': jira_project, 'creds': creds, 'cred_eng': cred_eng, 'network': network, 'preset_test_type': preset_test_type }) def prefetch_for_view_tests(tests): prefetched = tests if isinstance(tests, QuerySet): # old code can arrive here with prods being a list because the query was already executed prefetched = prefetched.select_related('lead') prefetched = prefetched.prefetch_related('tags', 'test_type', 'notes') prefetched = prefetched.annotate(count_findings_test_all=Count('finding__id', distinct=True)) prefetched = prefetched.annotate(count_findings_test_active=Count('finding__id', filter=Q(finding__active=True), distinct=True)) prefetched = prefetched.annotate(count_findings_test_active_verified=Count('finding__id', filter=Q(finding__active=True) & Q(finding__verified=True), distinct=True)) prefetched = prefetched.annotate(count_findings_test_mitigated=Count('finding__id', filter=Q(finding__is_mitigated=True), distinct=True)) prefetched = prefetched.annotate(count_findings_test_dups=Count('finding__id', filter=Q(finding__duplicate=True), distinct=True)) prefetched = prefetched.annotate(total_reimport_count=Count('test_import__id', filter=Q(test_import__type=Test_Import.REIMPORT_TYPE), distinct=True)) else: logger.warn('unable to prefetch because query was already executed') return prefetched @user_is_authorized(Engagement, Permissions.Test_Add, 'eid') def add_tests(request, eid): eng = Engagement.objects.get(id=eid) cred_form = CredMappingForm() cred_form.fields["cred_user"].queryset = Cred_Mapping.objects.filter( engagement=eng).order_by('cred_id') if request.method == 'POST': form = TestForm(request.POST, engagement=eng) cred_form = CredMappingForm(request.POST) cred_form.fields["cred_user"].queryset = Cred_Mapping.objects.filter( engagement=eng).order_by('cred_id') if form.is_valid(): new_test = form.save(commit=False) # set default scan_type as it's used in reimport new_test.scan_type = new_test.test_type.name new_test.engagement = eng try: new_test.lead = User.objects.get(id=form['lead'].value()) except: new_test.lead = None pass if eng.status != "In Progress" and eng.active is True: eng.status = "In Progress" eng.save() new_test.save() if cred_form.is_valid(): if cred_form.cleaned_data['cred_user']: cred_user = Cred_Mapping.objects.filter( pk=cred_form.cleaned_data['cred_user'].id, engagement=eid).first() new_f = cred_form.save(commit=False) new_f.test = new_test new_f.cred_id = cred_user.cred_id new_f.save() messages.add_message( request, messages.SUCCESS, 'Test added successfully.', extra_tags='alert-success') notifications_helper.notify_test_created(new_test) if '_Add Another Test' in request.POST: return HttpResponseRedirect( reverse('add_tests', args=(eng.id, ))) elif '_Add Findings' in request.POST: return HttpResponseRedirect( reverse('add_findings', args=(new_test.id, ))) elif '_Finished' in request.POST: return HttpResponseRedirect( reverse('view_engagement', args=(eng.id, ))) else: form = TestForm(engagement=eng) form.initial['target_start'] = eng.target_start form.initial['target_end'] = eng.target_end form.initial['lead'] = request.user add_breadcrumb( parent=eng, title="Add Tests", top_level=False, request=request) product_tab = Product_Tab(eng.product.id, title="Add Tests", tab="engagements") product_tab.setEngagement(eng) return render(request, 'dojo/add_tests.html', { 'product_tab': product_tab, 'form': form, 'cred_form': cred_form, 'eid': eid, 'eng': eng }) def import_scan_results(request, eid=None, pid=None): engagement = None form = ImportScanForm() cred_form = CredMappingForm() finding_count = 0 jform = None user = request.user if eid: engagement = get_object_or_404(Engagement, id=eid) engagement_or_product = engagement cred_form.fields["cred_user"].queryset = Cred_Mapping.objects.filter(engagement=engagement).order_by('cred_id') elif pid: product = get_object_or_404(Product, id=pid) engagement_or_product = product elif not user.is_staff: raise PermissionDenied user_has_permission_or_403(user, engagement_or_product, Permissions.Import_Scan_Result) push_all_jira_issues = jira_helper.is_push_all_issues(engagement_or_product) if request.method == "POST": form = ImportScanForm(request.POST, request.FILES) cred_form = CredMappingForm(request.POST) cred_form.fields["cred_user"].queryset = Cred_Mapping.objects.filter( engagement=engagement).order_by('cred_id') if jira_helper.get_jira_project(engagement_or_product): jform = JIRAImportScanForm(request.POST, push_all=push_all_jira_issues, prefix='jiraform') logger.debug('jform valid: %s', jform.is_valid()) logger.debug('jform errors: %s', jform.errors) if form.is_valid() and (jform is None or jform.is_valid()): scan = request.FILES.get('file', None) scan_date = form.cleaned_data['scan_date'] minimum_severity = form.cleaned_data['minimum_severity'] active = form.cleaned_data['active'] verified = form.cleaned_data['verified'] scan_type = request.POST['scan_type'] tags = form.cleaned_data['tags'] version = form.cleaned_data['version'] branch_tag = form.cleaned_data.get('branch_tag', None) build_id = form.cleaned_data.get('build_id', None) commit_hash = form.cleaned_data.get('commit_hash', None) api_scan_configuration = form.cleaned_data.get('api_scan_configuration', None) service = form.cleaned_data.get('service', None) close_old_findings = form.cleaned_data.get('close_old_findings', None) environment_id = request.POST.get('environment', 'Development') environment = Development_Environment.objects.get(id=environment_id) group_by = form.cleaned_data.get('group_by', None) if scan and is_scan_file_too_large(scan): messages.add_message(request, messages.ERROR, "Report file is too large. Maximum supported size is {} MB".format(settings.SCAN_FILE_MAX_SIZE), extra_tags='alert-danger') return HttpResponseRedirect(reverse('import_scan_results', args=(engagement,))) if engagement is None: engagement = Engagement() engagement.name = "AdHoc Import - " + strftime("%a, %d %b %Y %X", timezone.now().timetuple()) engagement.threat_model = False engagement.api_test = False engagement.pen_test = False engagement.check_list = False engagement.target_start = timezone.now().date() engagement.target_end = timezone.now().date() engagement.product = product engagement.active = True engagement.status = 'In Progress' engagement.version = version engagement.branch_tag = branch_tag engagement.build_id = build_id engagement.commit_hash = commit_hash engagement.save() # push_to_jira = jira_helper.is_push_to_jira(new_finding, jform.cleaned_data.get('push_to_jira')) push_to_jira = push_all_jira_issues or (jform and jform.cleaned_data.get('push_to_jira')) error = False # Save newly added endpoints added_endpoints = save_endpoints_to_add(form.endpoints_to_add_list, engagement.product) try: importer = Importer() test, finding_count, closed_finding_count = importer.import_scan(scan, scan_type, engagement, user, environment, active=active, verified=verified, tags=tags, minimum_severity=minimum_severity, endpoints_to_add=list(form.cleaned_data['endpoints']) + added_endpoints, scan_date=scan_date, version=version, branch_tag=branch_tag, build_id=build_id, commit_hash=commit_hash, push_to_jira=push_to_jira, close_old_findings=close_old_findings, group_by=group_by, api_scan_configuration=api_scan_configuration, service=service) message = f'{scan_type} processed a total of {finding_count} findings' if close_old_findings: message = message + ' and closed %d findings' % (closed_finding_count) message = message + "." add_success_message_to_response(message) except Exception as e: logger.exception(e) add_error_message_to_response('An exception error occurred during the report import:%s' % str(e)) error = True # Save the credential to the test if cred_form.is_valid(): if cred_form.cleaned_data['cred_user']: # Select the credential mapping object from the selected list and only allow if the credential is associated with the product cred_user = Cred_Mapping.objects.filter( pk=cred_form.cleaned_data['cred_user'].id, engagement=eid).first() new_f = cred_form.save(commit=False) new_f.test = test new_f.cred_id = cred_user.cred_id new_f.save() if not error: return HttpResponseRedirect( reverse('product_open_findings', args=(pid, ))) prod_id = None custom_breadcrumb = None title = "Import Scan Results" if engagement: prod_id = engagement.product.id product_tab = Product_Tab(prod_id, title=title, tab="engagements") product_tab.setEngagement(engagement) else: prod_id = pid custom_breadcrumb = {"", ""} product_tab = Product_Tab(prod_id, title=title, tab="findings") if jira_helper.get_jira_project(engagement_or_product): jform = JIRAImportScanForm(push_all=push_all_jira_issues, prefix='jiraform') form.fields['endpoints'].queryset = Endpoint.objects.filter(product__id=product_tab.product.id) form.fields['api_scan_configuration'].queryset = Product_API_Scan_Configuration.objects.filter(product__id=product_tab.product.id) return render(request, 'dojo/import_scan_results.html', {'form': form, 'product_tab': product_tab, 'engagement_or_product': engagement_or_product, 'custom_breadcrumb': custom_breadcrumb, 'title': title, 'cred_form': cred_form, 'jform': jform, 'scan_types': get_scan_types_sorted(), }) @user_is_authorized(Engagement, Permissions.Engagement_Edit, 'eid') def close_eng(request, eid): eng = Engagement.objects.get(id=eid) close_engagement(eng) messages.add_message( request, messages.SUCCESS, 'Engagement closed successfully.', extra_tags='alert-success') create_notification(event='close_engagement', title='Closure of %s' % eng.name, description='The engagement "%s" was closed' % (eng.name), engagement=eng, url=reverse('engagement_all_findings', args=(eng.id, ))), return HttpResponseRedirect(reverse("view_engagements", args=(eng.product.id, ))) @user_is_authorized(Engagement, Permissions.Engagement_Edit, 'eid') def reopen_eng(request, eid): eng = Engagement.objects.get(id=eid) reopen_engagement(eng) messages.add_message( request, messages.SUCCESS, 'Engagement reopened successfully.', extra_tags='alert-success') create_notification(event='other', title='Reopening of %s' % eng.name, engagement=eng, description='The engagement "%s" was reopened' % (eng.name), url=reverse('view_engagement', args=(eng.id, ))), return HttpResponseRedirect(reverse("view_engagements", args=(eng.product.id, ))) @user_is_authorized(Engagement, Permissions.Engagement_Edit, 'eid') def complete_checklist(request, eid): eng = get_object_or_404(Engagement, id=eid) try: checklist = Check_List.objects.get(engagement=eng) except: checklist = None pass add_breadcrumb( parent=eng, title="Complete checklist", top_level=False, request=request) if request.method == 'POST': tests = Test.objects.filter(engagement=eng) findings = Finding.objects.filter(test__in=tests).all() form = CheckForm(request.POST, instance=checklist, findings=findings) if form.is_valid(): cl = form.save(commit=False) try: check_l = Check_List.objects.get(engagement=eng) cl.id = check_l.id cl.save() form.save_m2m() except: cl.engagement = eng cl.save() form.save_m2m() pass messages.add_message( request, messages.SUCCESS, 'Checklist saved.', extra_tags='alert-success') return HttpResponseRedirect( reverse('view_engagement', args=(eid, ))) else: tests = Test.objects.filter(engagement=eng) findings = Finding.objects.filter(test__in=tests).all() form = CheckForm(instance=checklist, findings=findings) product_tab = Product_Tab(eng.product.id, title="Checklist", tab="engagements") product_tab.setEngagement(eng) return render(request, 'dojo/checklist.html', { 'form': form, 'product_tab': product_tab, 'eid': eng.id, 'findings': findings, }) @user_is_authorized(Engagement, Permissions.Risk_Acceptance, 'eid') def add_risk_acceptance(request, eid, fid=None): eng = get_object_or_404(Engagement, id=eid) finding = None if fid: finding = get_object_or_404(Finding, id=fid) if not eng.product.enable_full_risk_acceptance: raise PermissionDenied() if request.method == 'POST': form = RiskAcceptanceForm(request.POST, request.FILES) if form.is_valid(): # first capture notes param as it cannot be saved directly as m2m notes = None if form.cleaned_data['notes']: notes = Notes( entry=form.cleaned_data['notes'], author=request.user, date=timezone.now()) notes.save() del form.cleaned_data['notes'] try: # we sometimes see a weird exception here, but are unable to reproduce. # we add some logging in case it happens risk_acceptance = form.save() except Exception as e: logger.debug(vars(request.POST)) logger.error(vars(form)) logger.exception(e) raise # attach note to risk acceptance object now in database if notes: risk_acceptance.notes.add(notes) eng.risk_acceptance.add(risk_acceptance) findings = form.cleaned_data['accepted_findings'] risk_acceptance = ra_helper.add_findings_to_risk_acceptance(risk_acceptance, findings) messages.add_message( request, messages.SUCCESS, 'Risk acceptance saved.', extra_tags='alert-success') return redirect_to_return_url_or_else(request, reverse('view_engagement', args=(eid, ))) else: risk_acceptance_title_suggestion = 'Accept: %s' % finding form = RiskAcceptanceForm(initial={'owner': request.user, 'name': risk_acceptance_title_suggestion}) finding_choices = Finding.objects.filter(duplicate=False, test__engagement=eng).filter(NOT_ACCEPTED_FINDINGS_QUERY).order_by('title') form.fields['accepted_findings'].queryset = finding_choices if fid: form.fields['accepted_findings'].initial = {fid} product_tab = Product_Tab(eng.product.id, title="Risk Acceptance", tab="engagements") product_tab.setEngagement(eng) return render(request, 'dojo/add_risk_acceptance.html', { 'eng': eng, 'product_tab': product_tab, 'form': form }) @user_is_authorized(Engagement, Permissions.Engagement_View, 'eid') def view_risk_acceptance(request, eid, raid): return view_edit_risk_acceptance(request, eid=eid, raid=raid, edit_mode=False) @user_is_authorized(Engagement, Permissions.Risk_Acceptance, 'eid') def edit_risk_acceptance(request, eid, raid): return view_edit_risk_acceptance(request, eid=eid, raid=raid, edit_mode=True) # will only be called by view_risk_acceptance and edit_risk_acceptance def view_edit_risk_acceptance(request, eid, raid, edit_mode=False): risk_acceptance = get_object_or_404(Risk_Acceptance, pk=raid) eng = get_object_or_404(Engagement, pk=eid) if edit_mode and not eng.product.enable_full_risk_acceptance: raise PermissionDenied() risk_acceptance_form = None errors = False if request.method == 'POST': # deleting before instantiating the form otherwise django messes up and we end up with an empty path value if len(request.FILES) > 0: logger.debug('new proof uploaded') risk_acceptance.path.delete() if 'decision' in request.POST: old_expiration_date = risk_acceptance.expiration_date risk_acceptance_form = EditRiskAcceptanceForm(request.POST, request.FILES, instance=risk_acceptance) errors = errors or not risk_acceptance_form.is_valid() if not errors: logger.debug('path: %s', risk_acceptance_form.cleaned_data['path']) risk_acceptance_form.save() if risk_acceptance.expiration_date != old_expiration_date: # risk acceptance was changed, check if risk acceptance needs to be reinstated and findings made accepted again ra_helper.reinstate(risk_acceptance, old_expiration_date) messages.add_message( request, messages.SUCCESS, 'Risk Acceptance saved successfully.', extra_tags='alert-success') if 'entry' in request.POST: note_form = NoteForm(request.POST) errors = errors or not note_form.is_valid() if not errors: new_note = note_form.save(commit=False) new_note.author = request.user new_note.date = timezone.now() new_note.save() risk_acceptance.notes.add(new_note) messages.add_message( request, messages.SUCCESS, 'Note added successfully.', extra_tags='alert-success') if 'delete_note' in request.POST: note = get_object_or_404(Notes, pk=request.POST['delete_note_id']) if note.author.username == request.user.username: risk_acceptance.notes.remove(note) note.delete() messages.add_message( request, messages.SUCCESS, 'Note deleted successfully.', extra_tags='alert-success') else: messages.add_message( request, messages.ERROR, "Since you are not the note's author, it was not deleted.", extra_tags='alert-danger') if 'remove_finding' in request.POST: finding = get_object_or_404( Finding, pk=request.POST['remove_finding_id']) ra_helper.remove_finding_from_risk_acceptance(risk_acceptance, finding) messages.add_message( request, messages.SUCCESS, 'Finding removed successfully from risk acceptance.', extra_tags='alert-success') if 'replace_file' in request.POST: replace_form = ReplaceRiskAcceptanceProofForm( request.POST, request.FILES, instance=risk_acceptance) errors = errors or not replace_form.is_valid() if not errors: replace_form.save() messages.add_message( request, messages.SUCCESS, 'New Proof uploaded successfully.', extra_tags='alert-success') else: logger.error(replace_form.errors) if 'add_findings' in request.POST: add_findings_form = AddFindingsRiskAcceptanceForm( request.POST, request.FILES, instance=risk_acceptance) errors = errors or not add_findings_form.is_valid() if not errors: findings = add_findings_form.cleaned_data['accepted_findings'] ra_helper.add_findings_to_risk_acceptance(risk_acceptance, findings) messages.add_message( request, messages.SUCCESS, 'Finding%s added successfully.' % ('s' if len(findings) > 1 else ''), extra_tags='alert-success') if not errors: logger.debug('redirecting to return_url') return redirect_to_return_url_or_else(request, reverse("view_risk_acceptance", args=(eid, raid))) else: logger.error('errors found') else: if edit_mode: risk_acceptance_form = EditRiskAcceptanceForm(instance=risk_acceptance) note_form = NoteForm() replace_form = ReplaceRiskAcceptanceProofForm(instance=risk_acceptance) add_findings_form = AddFindingsRiskAcceptanceForm(instance=risk_acceptance) accepted_findings = risk_acceptance.accepted_findings.order_by('numerical_severity') fpage = get_page_items(request, accepted_findings, 15) unaccepted_findings = Finding.objects.filter(test__in=eng.test_set.all()) \ .exclude(id__in=accepted_findings).order_by("title") add_fpage = get_page_items(request, unaccepted_findings, 10, 'apage') add_findings_form.fields[ "accepted_findings"].queryset = add_fpage.object_list product_tab = Product_Tab(eng.product.id, title="Risk Acceptance", tab="engagements") product_tab.setEngagement(eng) return render( request, 'dojo/view_risk_acceptance.html', { 'risk_acceptance': risk_acceptance, 'engagement': eng, 'product_tab': product_tab, 'accepted_findings': fpage, 'notes': risk_acceptance.notes.all(), 'eng': eng, 'edit_mode': edit_mode, 'risk_acceptance_form': risk_acceptance_form, 'note_form': note_form, 'replace_form': replace_form, 'add_findings_form': add_findings_form, 'request': request, 'add_findings': add_fpage, 'return_url': get_return_url(request), }) @user_is_authorized(Engagement, Permissions.Risk_Acceptance, 'eid') def expire_risk_acceptance(request, eid, raid): risk_acceptance = get_object_or_404(prefetch_for_expiration(Risk_Acceptance.objects.all()), pk=raid) eng = get_object_or_404(Engagement, pk=eid) ra_helper.expire_now(risk_acceptance) return redirect_to_return_url_or_else(request, reverse("view_risk_acceptance", args=(eid, raid))) @user_is_authorized(Engagement, Permissions.Risk_Acceptance, 'eid') def reinstate_risk_acceptance(request, eid, raid): risk_acceptance = get_object_or_404(prefetch_for_expiration(Risk_Acceptance.objects.all()), pk=raid) eng = get_object_or_404(Engagement, pk=eid) if not eng.product.enable_full_risk_acceptance: raise PermissionDenied() ra_helper.reinstate(risk_acceptance, risk_acceptance.expiration_date) return redirect_to_return_url_or_else(request, reverse("view_risk_acceptance", args=(eid, raid))) @user_is_authorized(Engagement, Permissions.Risk_Acceptance, 'eid') def delete_risk_acceptance(request, eid, raid): risk_acceptance = get_object_or_404(Risk_Acceptance, pk=raid) eng = get_object_or_404(Engagement, pk=eid) ra_helper.delete(eng, risk_acceptance) messages.add_message( request, messages.SUCCESS, 'Risk acceptance deleted successfully.', extra_tags='alert-success') return HttpResponseRedirect(reverse("view_engagement", args=(eng.id, ))) @user_is_authorized(Engagement, Permissions.Engagement_View, 'eid') def download_risk_acceptance(request, eid, raid): import mimetypes mimetypes.init() risk_acceptance = get_object_or_404(Risk_Acceptance, pk=raid) response = StreamingHttpResponse( FileIterWrapper( open(settings.MEDIA_ROOT + "/" + risk_acceptance.path.name, mode='rb'))) response['Content-Disposition'] = 'attachment; filename="%s"' \ % risk_acceptance.filename() mimetype, encoding = mimetypes.guess_type(risk_acceptance.path.name) response['Content-Type'] = mimetype return response @user_is_authorized(Engagement, Permissions.Engagement_Edit, 'eid') def upload_threatmodel(request, eid): eng = Engagement.objects.get(id=eid) add_breadcrumb( parent=eng, title="Upload a threat model", top_level=False, request=request) if request.method == 'POST': form = UploadThreatForm(request.POST, request.FILES) if form.is_valid(): handle_uploaded_threat(request.FILES['file'], eng) eng.progress = 'other' eng.threat_model = True eng.save() messages.add_message( request, messages.SUCCESS, 'Threat model saved.', extra_tags='alert-success') return HttpResponseRedirect( reverse('view_engagement', args=(eid, ))) else: form = UploadThreatForm() product_tab = Product_Tab(eng.product.id, title="Upload Threat Model", tab="engagements") return render(request, 'dojo/up_threat.html', { 'form': form, 'product_tab': product_tab, 'eng': eng, }) @user_is_authorized(Engagement, Permissions.Engagement_View, 'eid') def view_threatmodel(request, eid): eng = get_object_or_404(Engagement, pk=eid) response = FileResponse(open(eng.tmodel_path, 'rb')) return response @user_is_authorized(Engagement, Permissions.Engagement_View, 'eid') def engagement_ics(request, eid): eng = get_object_or_404(Engagement, id=eid) start_date = datetime.combine(eng.target_start, datetime.min.time()) end_date = datetime.combine(eng.target_end, datetime.max.time()) uid = "dojo_eng_%d_%d" % (eng.id, eng.product.id) cal = get_cal_event( start_date, end_date, "Engagement: %s (%s)" % (eng.name, eng.product.name), "Set aside for engagement %s, on product %s. Additional detail can be found at %s" % (eng.name, eng.product.name, request.build_absolute_uri( (reverse("view_engagement", args=(eng.id, ))))), uid) output = cal.serialize() response = HttpResponse(content=output) response['Content-Type'] = 'text/calendar' response['Content-Disposition'] = 'attachment; filename=%s.ics' % eng.name return response def get_list_index(list, index): try: element = list[index] except Exception as e: element = None return element def get_engagements(request): url = request.META.get('QUERY_STRING') if not url: raise ValidationError('Please use the export button when exporting engagements') else: if url.startswith('url='): url = url[4:] path_items = list(filter(None, re.split('/|\?', url))) if not path_items or path_items[0] != 'engagement': raise ValidationError('URL is not an engagement view') view = query = None if get_list_index(path_items, 1) in ['active', 'all']: view = get_list_index(path_items, 1) query = get_list_index(path_items, 2) else: view = 'active' query = get_list_index(path_items, 1) request.GET = QueryDict(query) engagements = get_filtered_engagements(request, view).qs test_counts = get_test_counts(engagements) return engagements, test_counts def get_excludes(): return ['is_ci_cd', 'jira_issue', 'jira_project', 'objects', 'unaccepted_open_findings'] def get_foreign_keys(): return ['build_server', 'lead', 'orchestration_engine', 'preset', 'product', 'report_type', 'requester', 'source_code_management_server'] def csv_export(request): engagements, test_counts = get_engagements(request) response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename=engagements.csv' writer = csv.writer(response) first_row = True for engagement in engagements: if first_row: fields = [] for key in dir(engagement): if key not in get_excludes() and not callable(getattr(engagement, key)) and not key.startswith('_'): fields.append(key) fields.append('tests') writer.writerow(fields) first_row = False if not first_row: fields = [] for key in dir(engagement): if key not in get_excludes() and not callable(getattr(engagement, key)) and not key.startswith('_'): value = engagement.__dict__.get(key) if key in get_foreign_keys() and getattr(engagement, key): value = str(getattr(engagement, key)) if value and isinstance(value, str): value = value.replace('\n', ' NEWLINE ').replace('\r', '') fields.append(value) fields.append(test_counts.get(engagement.id, 0)) writer.writerow(fields) return response def excel_export(request): engagements, test_counts = get_engagements(request) workbook = Workbook() workbook.iso_dates = True worksheet = workbook.active worksheet.title = 'Engagements' font_bold = Font(bold=True) row_num = 1 for engagement in engagements: if row_num == 1: col_num = 1 for key in dir(engagement): if key not in get_excludes() and not callable(getattr(engagement, key)) and not key.startswith('_'): cell = worksheet.cell(row=row_num, column=col_num, value=key) cell.font = font_bold col_num += 1 cell = worksheet.cell(row=row_num, column=col_num, value='tests') cell.font = font_bold row_num = 2 if row_num > 1: col_num = 1 for key in dir(engagement): if key not in get_excludes() and not callable(getattr(engagement, key)) and not key.startswith('_'): value = engagement.__dict__.get(key) if key in get_foreign_keys() and getattr(engagement, key): value = str(getattr(engagement, key)) if value and isinstance(value, datetime): value = value.replace(tzinfo=None) worksheet.cell(row=row_num, column=col_num, value=value) col_num += 1 worksheet.cell(row=row_num, column=col_num, value=test_counts.get(engagement.id, 0)) row_num += 1 with NamedTemporaryFile() as tmp: workbook.save(tmp.name) tmp.seek(0) stream = tmp.read() response = HttpResponse( content=stream, content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' ) response['Content-Disposition'] = 'attachment; filename=engagements.xlsx' return response

true

f71c1eaf10e717ab23c28074e182c01dfdc1b475

5,366

py

Python

distributed/distributed/db.py

Yuanmessi/Bold-Falcon

00fcaba0b3d9c462b9d20ecb256ff85db5d119e2

[ "BSD-3-Clause" ]

71

2016-11-13T03:26:45.000Z

2022-02-22T08:13:04.000Z

distributed/distributed/db.py

Yuanmessi/Bold-Falcon

00fcaba0b3d9c462b9d20ecb256ff85db5d119e2

[ "BSD-3-Clause" ]

3

2021-07-01T08:09:05.000Z

2022-01-28T03:38:36.000Z

distributed/distributed/db.py

Yuanmessi/Bold-Falcon

00fcaba0b3d9c462b9d20ecb256ff85db5d119e2

[ "BSD-3-Clause" ]

36

2016-12-13T11:37:56.000Z

2021-11-11T12:20:10.000Z

# Copyright (C) 2014-2016 Cuckoo Foundation. # This file is part of Cuckoo Sandbox - http://www.cuckoosandbox.org # See the file 'docs/LICENSE' for copying permission. import json from datetime import datetime from flask.ext.sqlalchemy import SQLAlchemy from sqlalchemy.inspection import inspect db = SQLAlchemy(session_options=dict(autoflush=True)) ALEMBIC_VERSION = "4b86bc0d40aa" class Serializer(object): """Serialize a query result object.""" def to_dict(self): ret = {} for key in inspect(self).attrs.keys(): ret[key] = getattr(self, key) return ret class StringList(db.TypeDecorator): """List of comma-separated strings as field.""" impl = db.Text def process_bind_param(self, value, dialect): return ", ".join(value) def process_result_value(self, value, dialect): return value.split(", ") class JsonType(db.TypeDecorator): """List of comma-separated strings as field.""" impl = db.Text def process_bind_param(self, value, dialect): return json.dumps(value) def process_result_value(self, value, dialect): return json.loads(value) class Node(db.Model): """Cuckoo node database model.""" id = db.Column(db.Integer, primary_key=True) name = db.Column(db.Text, nullable=False, unique=True) url = db.Column(db.Text, nullable=False) mode = db.Column(db.Text, nullable=False) enabled = db.Column(db.Boolean, nullable=False) machines = db.relationship("Machine", backref="node", lazy="dynamic") def __init__(self, name, url, mode, enabled=True): self.name = name self.url = url self.mode = mode self.enabled = enabled class Machine(db.Model): """Machine database model related to a Cuckoo node.""" id = db.Column(db.Integer, primary_key=True) name = db.Column(db.Text, nullable=False) platform = db.Column(db.Text, nullable=False) tags = db.Column(StringList) node_id = db.Column(db.Integer, db.ForeignKey("node.id")) def __init__(self, name, platform, tags): self.name = name self.platform = platform self.tags = tags class Task(db.Model, Serializer): """Analysis task database model.""" PENDING = "pending" ASSIGNED = "assigned" PROCESSING = "processing" FINISHED = "finished" DELETED = "deleted" task_status = db.Enum(PENDING, ASSIGNED, PROCESSING, FINISHED, DELETED, name="task_status_type") id = db.Column(db.Integer, primary_key=True) path = db.Column(db.Text) filename = db.Column(db.Text) package = db.Column(db.Text) timeout = db.Column(db.Integer) priority = db.Column(db.Integer) options = db.Column(db.Text) machine = db.Column(db.Text) platform = db.Column(db.Text) tags = db.Column(db.Text) custom = db.Column(db.Text) owner = db.Column(db.Text) memory = db.Column(db.Text) clock = db.Column(db.Integer) enforce_timeout = db.Column(db.Text) # Cuckoo node and Task ID this has been submitted to. node_id = db.Column(db.Integer, db.ForeignKey("node.id")) task_id = db.Column(db.Integer) status = db.Column(task_status, nullable=False) # Timestamps for this task. When it was submitted, when it was delegated # to a Cuckoo node, when the analysis started, and when we retrieved # the report. submitted = db.Column(db.DateTime(timezone=False), default=datetime.now) delegated = db.Column(db.DateTime(timezone=False), nullable=True) started = db.Column(db.DateTime(timezone=False), nullable=True) completed = db.Column(db.DateTime(timezone=False), nullable=True) __table_args__ = db.Index("ix_node_task", node_id, task_id), def __init__(self, path=None, filename=None, package=None, timeout=None, priority=None, options=None, machine=None, platform=None, tags=None, custom=None, owner=None, memory=None, clock=None, enforce_timeout=None, node_id=None, task_id=None, status=PENDING): self.path = path self.filename = filename self.package = package self.timeout = timeout self.priority = priority self.options = options self.machine = machine self.platform = platform self.tags = tags self.custom = custom self.owner = owner self.memory = memory self.clock = clock self.enforce_timeout = enforce_timeout self.node_id = node_id self.task_id = task_id self.status = status class NodeStatus(db.Model, Serializer): """Node status monitoring database model.""" id = db.Column(db.Integer, primary_key=True) name = db.Column(db.Text, nullable=False) timestamp = db.Column(db.DateTime(timezone=False), nullable=False, index=True) status = db.Column(JsonType, nullable=False) def __init__(self, name, timestamp, status): self.name = name self.timestamp = timestamp self.status = status class AlembicVersion(db.Model): """Support model for keeping track of the alembic revision identifier.""" VERSION = ALEMBIC_VERSION version_num = db.Column(db.Text, nullable=False, primary_key=True) def __init__(self, version_num): self.version_num = version_num

34.619355

77

0.660268

import json from datetime import datetime from flask.ext.sqlalchemy import SQLAlchemy from sqlalchemy.inspection import inspect db = SQLAlchemy(session_options=dict(autoflush=True)) ALEMBIC_VERSION = "4b86bc0d40aa" class Serializer(object): def to_dict(self): ret = {} for key in inspect(self).attrs.keys(): ret[key] = getattr(self, key) return ret class StringList(db.TypeDecorator): impl = db.Text def process_bind_param(self, value, dialect): return ", ".join(value) def process_result_value(self, value, dialect): return value.split(", ") class JsonType(db.TypeDecorator): impl = db.Text def process_bind_param(self, value, dialect): return json.dumps(value) def process_result_value(self, value, dialect): return json.loads(value) class Node(db.Model): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.Text, nullable=False, unique=True) url = db.Column(db.Text, nullable=False) mode = db.Column(db.Text, nullable=False) enabled = db.Column(db.Boolean, nullable=False) machines = db.relationship("Machine", backref="node", lazy="dynamic") def __init__(self, name, url, mode, enabled=True): self.name = name self.url = url self.mode = mode self.enabled = enabled class Machine(db.Model): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.Text, nullable=False) platform = db.Column(db.Text, nullable=False) tags = db.Column(StringList) node_id = db.Column(db.Integer, db.ForeignKey("node.id")) def __init__(self, name, platform, tags): self.name = name self.platform = platform self.tags = tags class Task(db.Model, Serializer): PENDING = "pending" ASSIGNED = "assigned" PROCESSING = "processing" FINISHED = "finished" DELETED = "deleted" task_status = db.Enum(PENDING, ASSIGNED, PROCESSING, FINISHED, DELETED, name="task_status_type") id = db.Column(db.Integer, primary_key=True) path = db.Column(db.Text) filename = db.Column(db.Text) package = db.Column(db.Text) timeout = db.Column(db.Integer) priority = db.Column(db.Integer) options = db.Column(db.Text) machine = db.Column(db.Text) platform = db.Column(db.Text) tags = db.Column(db.Text) custom = db.Column(db.Text) owner = db.Column(db.Text) memory = db.Column(db.Text) clock = db.Column(db.Integer) enforce_timeout = db.Column(db.Text) node_id = db.Column(db.Integer, db.ForeignKey("node.id")) task_id = db.Column(db.Integer) status = db.Column(task_status, nullable=False) submitted = db.Column(db.DateTime(timezone=False), default=datetime.now) delegated = db.Column(db.DateTime(timezone=False), nullable=True) started = db.Column(db.DateTime(timezone=False), nullable=True) completed = db.Column(db.DateTime(timezone=False), nullable=True) __table_args__ = db.Index("ix_node_task", node_id, task_id), def __init__(self, path=None, filename=None, package=None, timeout=None, priority=None, options=None, machine=None, platform=None, tags=None, custom=None, owner=None, memory=None, clock=None, enforce_timeout=None, node_id=None, task_id=None, status=PENDING): self.path = path self.filename = filename self.package = package self.timeout = timeout self.priority = priority self.options = options self.machine = machine self.platform = platform self.tags = tags self.custom = custom self.owner = owner self.memory = memory self.clock = clock self.enforce_timeout = enforce_timeout self.node_id = node_id self.task_id = task_id self.status = status class NodeStatus(db.Model, Serializer): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.Text, nullable=False) timestamp = db.Column(db.DateTime(timezone=False), nullable=False, index=True) status = db.Column(JsonType, nullable=False) def __init__(self, name, timestamp, status): self.name = name self.timestamp = timestamp self.status = status class AlembicVersion(db.Model): VERSION = ALEMBIC_VERSION version_num = db.Column(db.Text, nullable=False, primary_key=True) def __init__(self, version_num): self.version_num = version_num

true

f71c1f3f5d963f1a9f0cad8f49f08a8a8952fb0f

7,941

py

Python

CTFd/config.py

mayoneko/CTFd

825190ba3aef10f9cdc736f4d6f8ea2a5c8590ac

[ "Apache-2.0" ]

2

2019-06-19T07:11:28.000Z

2019-06-21T05:30:07.000Z

CTFd/config.py

mayoneko/CTFd

825190ba3aef10f9cdc736f4d6f8ea2a5c8590ac

[ "Apache-2.0" ]

null

CTFd/config.py

mayoneko/CTFd

825190ba3aef10f9cdc736f4d6f8ea2a5c8590ac

[ "Apache-2.0" ]

null

import os ''' GENERATE SECRET KEY ''' if not os.getenv('SECRET_KEY'): # Attempt to read the secret from the secret file # This will fail if the secret has not been written try: with open('.ctfd_secret_key', 'rb') as secret: key = secret.read() except (OSError, IOError): key = None if not key: key = os.urandom(64) # Attempt to write the secret file # This will fail if the filesystem is read-only try: with open('.ctfd_secret_key', 'wb') as secret: secret.write(key) secret.flush() except (OSError, IOError): pass ''' SERVER SETTINGS ''' class Config(object): """ CTFd Configuration Object """ ''' === REQUIRED SETTINGS === SECRET_KEY: The secret value used to creation sessions and sign strings. This should be set to a random string. In the interest of ease, CTFd will automatically create a secret key file for you. If you wish to add this secret key to your instance you should hard code this value to a random static value. You can also remove .ctfd_secret_key from the .gitignore file and commit this file into whatever repository you are using. http://flask.pocoo.org/docs/latest/quickstart/#sessions SQLALCHEMY_DATABASE_URI: The URI that specifies the username, password, hostname, port, and database of the server used to hold the CTFd database. e.g. mysql+pymysql://root:<YOUR_PASSWORD_HERE>@localhost/ctfd CACHE_TYPE: Specifies how CTFd should cache configuration values. If CACHE_TYPE is set to 'redis', CTFd will make use of the REDIS_URL specified in environment variables. You can also choose to hardcode the REDIS_URL here. It is important that you specify some sort of cache as CTFd uses it to store values received from the database. If no cache is specified, CTFd will default to a simple per-worker cache. The simple cache cannot be effectively used with multiple workers. REDIS_URL is the URL to connect to a Redis server. e.g. redis://user:password@localhost:6379 http://pythonhosted.org/Flask-Caching/#configuring-flask-caching ''' SECRET_KEY = os.getenv('SECRET_KEY') or key DATABASE_URL = os.getenv('DATABASE_URL') or 'sqlite:///{}/ctfd.db'.format(os.path.dirname(os.path.abspath(__file__))) REDIS_URL = os.getenv('REDIS_URL') SQLALCHEMY_DATABASE_URI = DATABASE_URL CACHE_REDIS_URL = REDIS_URL if CACHE_REDIS_URL: CACHE_TYPE = 'redis' else: CACHE_TYPE = 'filesystem' CACHE_DIR = os.path.join(os.path.dirname(__file__), os.pardir, '.data', 'filesystem_cache') ''' === SECURITY === SESSION_COOKIE_HTTPONLY: Controls if cookies should be set with the HttpOnly flag. PERMANENT_SESSION_LIFETIME: The lifetime of a session. The default is 604800 seconds. TRUSTED_PROXIES: Defines a set of regular expressions used for finding a user's IP address if the CTFd instance is behind a proxy. If you are running a CTF and users are on the same network as you, you may choose to remove some proxies from the list. CTFd only uses IP addresses for cursory tracking purposes. It is ill-advised to do anything complicated based solely on IP addresses unless you know what you are doing. ''' SESSION_COOKIE_HTTPONLY = (not os.getenv("SESSION_COOKIE_HTTPONLY")) # Defaults True PERMANENT_SESSION_LIFETIME = int(os.getenv("PERMANENT_SESSION_LIFETIME") or 604800) # 7 days in seconds TRUSTED_PROXIES = [ r'^127\.0\.0\.1$', # Remove the following proxies if you do not trust the local network # For example if you are running a CTF on your laptop and the teams are # all on the same network r'^::1$', r'^fc00:', r'^10\.', r'^172\.(1[6-9]|2[0-9]|3[0-1])\.', r'^192\.168\.' ] ''' === EMAIL === MAILFROM_ADDR: The email address that emails are sent from if not overridden in the configuration panel. MAIL_SERVER: The mail server that emails are sent from if not overriden in the configuration panel. MAIL_PORT: The mail port that emails are sent from if not overriden in the configuration panel. ''' MAILFROM_ADDR = os.getenv("MAILFROM_ADDR") or "noreply@ctfd.io" MAIL_SERVER = os.getenv("MAIL_SERVER") or None MAIL_PORT = os.getenv("MAIL_PORT") MAIL_USERNAME = os.getenv("MAIL_USERNAME") MAIL_PASSWORD = os.getenv("MAIL_PASSWORD") MAIL_TLS = os.getenv("MAIL_TLS") or False MAIL_SSL = os.getenv("MAIL_SSL") or False MAILGUN_API_KEY = os.getenv("MAILGUN_API_KEY") MAILGUN_BASE_URL = os.getenv("MAILGUN_BASE_URL") ''' === LOGS === LOG_FOLDER: The location where logs are written. These are the logs for CTFd key submissions, registrations, and logins. The default location is the CTFd/logs folder. ''' LOG_FOLDER = os.getenv('LOG_FOLDER') or os.path.join(os.path.dirname(os.path.abspath(__file__)), 'logs') ''' === UPLOADS === UPLOAD_PROVIDER: Specifies the service that CTFd should use to store files. UPLOAD_FOLDER: The location where files are uploaded. The default destination is the CTFd/uploads folder. AWS_ACCESS_KEY_ID: AWS access token used to authenticate to the S3 bucket. AWS_SECRET_ACCESS_KEY: AWS secret token used to authenticate to the S3 bucket. AWS_S3_BUCKET: The unique identifier for your S3 bucket. AWS_S3_ENDPOINT_URL: A URL pointing to a custom S3 implementation. ''' UPLOAD_PROVIDER = os.getenv('UPLOAD_PROVIDER') or 'filesystem' UPLOAD_FOLDER = os.getenv('UPLOAD_FOLDER') or os.path.join(os.path.dirname(os.path.abspath(__file__)), 'uploads') if UPLOAD_PROVIDER == 's3': AWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID') AWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY') AWS_S3_BUCKET = os.getenv('AWS_S3_BUCKET') AWS_S3_ENDPOINT_URL = os.getenv('AWS_S3_ENDPOINT_URL') ''' === OPTIONAL === REVERSE_PROXY: Specifies whether CTFd is behind a reverse proxy or not. Set to True if using a reverse proxy like nginx. TEMPLATES_AUTO_RELOAD: Specifies whether Flask should check for modifications to templates and reload them automatically. SQLALCHEMY_TRACK_MODIFICATIONS: Automatically disabled to suppress warnings and save memory. You should only enable this if you need it. UPDATE_CHECK: Specifies whether or not CTFd will check whether or not there is a new version of CTFd APPLICATION_ROOT: Specifies what path CTFd is mounted under. It can be used to run CTFd in a subdirectory. Example: /ctfd ''' REVERSE_PROXY = os.getenv("REVERSE_PROXY") or False TEMPLATES_AUTO_RELOAD = (not os.getenv("TEMPLATES_AUTO_RELOAD")) # Defaults True SQLALCHEMY_TRACK_MODIFICATIONS = (not os.getenv("SQLALCHEMY_TRACK_MODIFICATIONS")) # Defaults True UPDATE_CHECK = (not os.getenv("UPDATE_CHECK")) # Defaults True APPLICATION_ROOT = os.getenv('APPLICATION_ROOT') or '/' ''' === OAUTH === MajorLeagueCyber Integration Register an event at https://majorleaguecyber.org/ and use the Client ID and Client Secret here ''' OAUTH_CLIENT_ID = os.getenv("OAUTH_CLIENT_ID") OAUTH_CLIENT_SECRET = os.getenv("OAUTH_CLIENT_SECRET") class TestingConfig(Config): SECRET_KEY = 'AAAAAAAAAAAAAAAAAAAA' PRESERVE_CONTEXT_ON_EXCEPTION = False TESTING = True DEBUG = True SQLALCHEMY_DATABASE_URI = os.getenv('TESTING_DATABASE_URL') or 'sqlite://' SERVER_NAME = 'localhost' UPDATE_CHECK = False REDIS_URL = None CACHE_TYPE = 'simple' SAFE_MODE = True

37.107477

122

0.679008

import os if not os.getenv('SECRET_KEY'): try: with open('.ctfd_secret_key', 'rb') as secret: key = secret.read() except (OSError, IOError): key = None if not key: key = os.urandom(64) try: with open('.ctfd_secret_key', 'wb') as secret: secret.write(key) secret.flush() except (OSError, IOError): pass class Config(object): SECRET_KEY = os.getenv('SECRET_KEY') or key DATABASE_URL = os.getenv('DATABASE_URL') or 'sqlite:///{}/ctfd.db'.format(os.path.dirname(os.path.abspath(__file__))) REDIS_URL = os.getenv('REDIS_URL') SQLALCHEMY_DATABASE_URI = DATABASE_URL CACHE_REDIS_URL = REDIS_URL if CACHE_REDIS_URL: CACHE_TYPE = 'redis' else: CACHE_TYPE = 'filesystem' CACHE_DIR = os.path.join(os.path.dirname(__file__), os.pardir, '.data', 'filesystem_cache') SESSION_COOKIE_HTTPONLY = (not os.getenv("SESSION_COOKIE_HTTPONLY")) PERMANENT_SESSION_LIFETIME = int(os.getenv("PERMANENT_SESSION_LIFETIME") or 604800) TRUSTED_PROXIES = [ r'^127\.0\.0\.1$', r'^::1$', r'^fc00:', r'^10\.', r'^172\.(1[6-9]|2[0-9]|3[0-1])\.', r'^192\.168\.' ] MAILFROM_ADDR = os.getenv("MAILFROM_ADDR") or "noreply@ctfd.io" MAIL_SERVER = os.getenv("MAIL_SERVER") or None MAIL_PORT = os.getenv("MAIL_PORT") MAIL_USERNAME = os.getenv("MAIL_USERNAME") MAIL_PASSWORD = os.getenv("MAIL_PASSWORD") MAIL_TLS = os.getenv("MAIL_TLS") or False MAIL_SSL = os.getenv("MAIL_SSL") or False MAILGUN_API_KEY = os.getenv("MAILGUN_API_KEY") MAILGUN_BASE_URL = os.getenv("MAILGUN_BASE_URL") LOG_FOLDER = os.getenv('LOG_FOLDER') or os.path.join(os.path.dirname(os.path.abspath(__file__)), 'logs') UPLOAD_PROVIDER = os.getenv('UPLOAD_PROVIDER') or 'filesystem' UPLOAD_FOLDER = os.getenv('UPLOAD_FOLDER') or os.path.join(os.path.dirname(os.path.abspath(__file__)), 'uploads') if UPLOAD_PROVIDER == 's3': AWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID') AWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY') AWS_S3_BUCKET = os.getenv('AWS_S3_BUCKET') AWS_S3_ENDPOINT_URL = os.getenv('AWS_S3_ENDPOINT_URL') REVERSE_PROXY = os.getenv("REVERSE_PROXY") or False TEMPLATES_AUTO_RELOAD = (not os.getenv("TEMPLATES_AUTO_RELOAD")) SQLALCHEMY_TRACK_MODIFICATIONS = (not os.getenv("SQLALCHEMY_TRACK_MODIFICATIONS")) UPDATE_CHECK = (not os.getenv("UPDATE_CHECK")) APPLICATION_ROOT = os.getenv('APPLICATION_ROOT') or '/' OAUTH_CLIENT_ID = os.getenv("OAUTH_CLIENT_ID") OAUTH_CLIENT_SECRET = os.getenv("OAUTH_CLIENT_SECRET") class TestingConfig(Config): SECRET_KEY = 'AAAAAAAAAAAAAAAAAAAA' PRESERVE_CONTEXT_ON_EXCEPTION = False TESTING = True DEBUG = True SQLALCHEMY_DATABASE_URI = os.getenv('TESTING_DATABASE_URL') or 'sqlite://' SERVER_NAME = 'localhost' UPDATE_CHECK = False REDIS_URL = None CACHE_TYPE = 'simple' SAFE_MODE = True

true

f71c20b62ca77df8f80354e21bf08002ca39890b

984

py

Python

src/jsm/api/mixins/infos.py

charbonnierg/jetstream.py

4d8dc56fc6953d0a28d207b9b162c6f8d0080d37

[ "Apache-2.0" ]

8

2021-07-26T10:54:10.000Z

2021-12-06T08:41:02.000Z

src/jsm/api/mixins/infos.py

charbonnierg/jetstream.py

4d8dc56fc6953d0a28d207b9b162c6f8d0080d37

[ "Apache-2.0" ]

3

2021-08-09T10:25:39.000Z

2021-12-06T08:40:41.000Z

src/jsm/api/mixins/infos.py

charbonnierg/jetstream.py

4d8dc56fc6953d0a28d207b9b162c6f8d0080d37

[ "Apache-2.0" ]

3

2021-08-22T01:55:11.000Z

2021-09-13T13:51:42.000Z

# Copyright 2021 - Guillaume Charbonnier # Licensed under the Apache License, Version 2.0 (the "License"); # http://www.apache.org/licenses/LICENSE-2.0 from __future__ import annotations from typing import Optional, Union from jsm.models.account_info import IoNatsJetstreamApiV1AccountInfoResponse from jsm.models.errors import IoNatsJetstreamApiV1ErrorResponse from .request_reply import BaseJetStreamRequestReplyMixin, JetStreamResponse class AccountInfosMixin(BaseJetStreamRequestReplyMixin): async def account_info( self, timeout: Optional[float] = None, raise_on_error: Optional[bool] = None, ) -> Union[ IoNatsJetstreamApiV1AccountInfoResponse, IoNatsJetstreamApiV1ErrorResponse, ]: return await self._jetstream_request( "INFO", None, JetStreamResponse[IoNatsJetstreamApiV1AccountInfoResponse], raise_on_error=raise_on_error, timeout=timeout, )

32.8

76

0.729675

from __future__ import annotations from typing import Optional, Union from jsm.models.account_info import IoNatsJetstreamApiV1AccountInfoResponse from jsm.models.errors import IoNatsJetstreamApiV1ErrorResponse from .request_reply import BaseJetStreamRequestReplyMixin, JetStreamResponse class AccountInfosMixin(BaseJetStreamRequestReplyMixin): async def account_info( self, timeout: Optional[float] = None, raise_on_error: Optional[bool] = None, ) -> Union[ IoNatsJetstreamApiV1AccountInfoResponse, IoNatsJetstreamApiV1ErrorResponse, ]: return await self._jetstream_request( "INFO", None, JetStreamResponse[IoNatsJetstreamApiV1AccountInfoResponse], raise_on_error=raise_on_error, timeout=timeout, )

true

f71c21777d66133ec1da30715a6556ddee5fd447

467

py

Python

integration/keeper_secrets_manager_cli/keeper_secrets_manager_cli/exception.py

Keeper-Security/secrets-manager

0044dec7f323ae2e531f52ef2435bd7205949fe9

[ "MIT" ]

9

2022-01-10T18:39:45.000Z

2022-03-06T03:51:41.000Z

integration/keeper_secrets_manager_cli/keeper_secrets_manager_cli/exception.py

Keeper-Security/secrets-manager

0044dec7f323ae2e531f52ef2435bd7205949fe9

[ "MIT" ]

10

2022-01-27T00:51:05.000Z

2022-03-30T08:42:01.000Z

integration/keeper_secrets_manager_cli/keeper_secrets_manager_cli/exception.py

Keeper-Security/secrets-manager

0044dec7f323ae2e531f52ef2435bd7205949fe9

[ "MIT" ]

6

2021-12-17T18:59:26.000Z

2022-03-28T16:47:28.000Z

import click from colorama import Fore, Style class KsmCliException(click.ClickException): in_a_shell = False def colorize(self): if KsmCliException.in_a_shell is False: return str(self.message) else: return Fore.RED + str(self.message) + Style.RESET_ALL def format_message(self): return self.colorize() def __str__(self): return self.colorize() class KsmRecordSyntaxException: pass

19.458333

65

0.663812

import click from colorama import Fore, Style class KsmCliException(click.ClickException): in_a_shell = False def colorize(self): if KsmCliException.in_a_shell is False: return str(self.message) else: return Fore.RED + str(self.message) + Style.RESET_ALL def format_message(self): return self.colorize() def __str__(self): return self.colorize() class KsmRecordSyntaxException: pass

true

f71c235a81782aee7af143ed9a0e3681bf47b496

5,934

py

Python

simple_distillation_mgr/VLE_data_ethanol_water.py

ykholod/kettlebell-simple-distillation-mgr

172900253be790f9fe7712ba5f20bcbb12a2a635

[ "MIT" ]

null

simple_distillation_mgr/VLE_data_ethanol_water.py

ykholod/kettlebell-simple-distillation-mgr

172900253be790f9fe7712ba5f20bcbb12a2a635

[ "MIT" ]

null

simple_distillation_mgr/VLE_data_ethanol_water.py

ykholod/kettlebell-simple-distillation-mgr

172900253be790f9fe7712ba5f20bcbb12a2a635

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- """ VLE data for ethanol-water mixture, isobaric, 1.01 bar """ __author__ = "Yaroslav Kholod" __copyright__ = "Copyright 2019, The Kettlebell project" __credits__ = "Yaroslav Kholod" __license__ = "MIT" __version__ = "0.1.0" __maintainer__ = "Yaroslav Kholod" __email__ = "pretorian.yaroslav@gmail.com" __status__ = "Development" # VLE data map mol fract to temperature vle_data_dew = {} # Ethanol dew point data # Ethanol mol fract # Temperature, C vle_data_dew[100] = 78.21 vle_data_dew[99] = 78.18 vle_data_dew[98] = 78.16 vle_data_dew[97] = 78.14 vle_data_dew[96] = 78.12 vle_data_dew[95] = 78.23 vle_data_dew[94] = 78.22 vle_data_dew[93] = 78.21 vle_data_dew[92] = 78.21 vle_data_dew[91] = 78.06 vle_data_dew[90] = 78.05 vle_data_dew[89] = 78.06 vle_data_dew[88] = 78.07 vle_data_dew[87] = 78.08 vle_data_dew[86] = 78.09 vle_data_dew[85] = 78.11 vle_data_dew[84] = 78.13 vle_data_dew[83] = 78.15 vle_data_dew[82] = 78.17 vle_data_dew[81] = 78.22 vle_data_dew[80] = 78.27 vle_data_dew[79] = 78.32 vle_data_dew[78] = 78.44 vle_data_dew[77] = 78.49 vle_data_dew[76] = 78.52 vle_data_dew[75] = 78.60 vle_data_dew[74] = 78.68 vle_data_dew[73] = 78.77 vle_data_dew[72] = 78.87 vle_data_dew[71] = 78.95 vle_data_dew[70] = 79.05 vle_data_dew[69] = 79.20 vle_data_dew[68] = 79.37 vle_data_dew[67] = 79.50 vle_data_dew[66] = 79.72 vle_data_dew[65] = 79.92 vle_data_dew[64] = 80.10 vle_data_dew[63] = 80.31 vle_data_dew[62] = 80.52 vle_data_dew[61] = 80.76 vle_data_dew[60] = 81.00 vle_data_dew[59] = 81.26 vle_data_dew[58] = 81.53 vle_data_dew[57] = 81.80 vle_data_dew[56] = 82.18 vle_data_dew[55] = 82.55 vle_data_dew[54] = 82.80 vle_data_dew[53] = 83.04 vle_data_dew[52] = 83.44 vle_data_dew[51] = 83.88 vle_data_dew[50] = 84.27 vle_data_dew[49] = 84.73 vle_data_dew[48] = 85.02 vle_data_dew[47] = 85.34 vle_data_dew[46] = 85.69 vle_data_dew[45] = 86.08 vle_data_dew[44] = 86.31 vle_data_dew[43] = 86.99 vle_data_dew[42] = 87.30 vle_data_dew[41] = 87.55 vle_data_dew[40] = 87.85 vle_data_dew[39] = 88.15 vle_data_dew[38] = 88.50 vle_data_dew[37] = 88.87 vle_data_dew[36] = 89.20 vle_data_dew[35] = 89.70 vle_data_dew[34] = 90.02 vle_data_dew[33] = 90.34 vle_data_dew[32] = 90.67 vle_data_dew[31] = 91.00 vle_data_dew[30] = 91.50 vle_data_dew[29] = 91.67 vle_data_dew[28] = 91.84 vle_data_dew[27] = 92.25 vle_data_dew[26] = 92.60 vle_data_dew[25] = 93.01 vle_data_dew[24] = 93.25 vle_data_dew[23] = 93.78 vle_data_dew[22] = 94.01 vle_data_dew[21] = 94.34 vle_data_dew[20] = 94.50 vle_data_dew[19] = 94.89 vle_data_dew[18] = 94.99 vle_data_dew[17] = 95.27 vle_data_dew[16] = 95.55 vle_data_dew[15] = 95.82 vle_data_dew[14] = 96.10 vle_data_dew[13] = 96.38 vle_data_dew[12] = 96.66 vle_data_dew[11] = 97.25 vle_data_dew[10] = 97.50 vle_data_dew[9] = 97.80 vle_data_dew[8] = 98.00 vle_data_dew[7] = 98.39 vle_data_dew[6] = 98.79 vle_data_dew[5] = 99.00 vle_data_dew[4] = 99.33 vle_data_dew[3] = 99.66 vle_data_dew[2] = 99.80 vle_data_dew[1] = 100.00 # VLE data map mol fract to temperature vle_data_bubble = {} # Ethanol bubble point data # Ethanol mol fract # Temperature, C vle_data_bubble[100] = 78.21 vle_data_bubble[99] = 78.19 vle_data_bubble[98] = 78.16 vle_data_bubble[97] = 78.14 vle_data_bubble[96] = 78.12 vle_data_bubble[95] = 78.14 vle_data_bubble[94] = 78.16 vle_data_bubble[93] = 78.17 vle_data_bubble[92] = 78.18 vle_data_bubble[91] = 78.19 vle_data_bubble[90] = 78.20 vle_data_bubble[89] = 78.21 vle_data_bubble[88] = 78.22 vle_data_bubble[87] = 78.23 vle_data_bubble[86] = 78.24 vle_data_bubble[85] = 78.26 vle_data_bubble[84] = 78.28 vle_data_bubble[83] = 78.30 vle_data_bubble[82] = 78.32 vle_data_bubble[81] = 78.32 vle_data_bubble[80] = 78.37 vle_data_bubble[79] = 78.40 vle_data_bubble[78] = 78.44 vle_data_bubble[77] = 78.47 vle_data_bubble[76] = 78.51 vle_data_bubble[75] = 78.54 vle_data_bubble[74] = 78.58 vle_data_bubble[73] = 78.63 vle_data_bubble[72] = 78.67 vle_data_bubble[71] = 78.72 vle_data_bubble[70] = 78.76 vle_data_bubble[69] = 78.81 vle_data_bubble[68] = 78.86 vle_data_bubble[67] = 78.92 vle_data_bubble[66] = 78.97 vle_data_bubble[65] = 79.03 vle_data_bubble[64] = 79.09 vle_data_bubble[63] = 79.15 vle_data_bubble[62] = 79.21 vle_data_bubble[61] = 79.27 vle_data_bubble[60] = 79.34 vle_data_bubble[59] = 79.40 vle_data_bubble[58] = 79.47 vle_data_bubble[57] = 79.54 vle_data_bubble[56] = 79.61 vle_data_bubble[55] = 79.68 vle_data_bubble[54] = 79.76 vle_data_bubble[53] = 79.83 vle_data_bubble[52] = 79.91 vle_data_bubble[51] = 79.99 vle_data_bubble[50] = 80.07 vle_data_bubble[49] = 80.15 vle_data_bubble[48] = 80.24 vle_data_bubble[47] = 80.32 vle_data_bubble[46] = 80.41 vle_data_bubble[45] = 80.50 vle_data_bubble[44] = 80.59 vle_data_bubble[43] = 80.68 vle_data_bubble[42] = 80.78 vle_data_bubble[41] = 80.88 vle_data_bubble[40] = 80.98 vle_data_bubble[39] = 81.08 vle_data_bubble[38] = 81.18 vle_data_bubble[37] = 81.29 vle_data_bubble[36] = 81.40 vle_data_bubble[35] = 81.51 vle_data_bubble[34] = 81.62 vle_data_bubble[33] = 81.74 vle_data_bubble[32] = 81.86 vle_data_bubble[31] = 81.99 vle_data_bubble[30] = 82.12 vle_data_bubble[29] = 82.26 vle_data_bubble[28] = 82.39 vle_data_bubble[27] = 82.54 vle_data_bubble[26] = 82.69 vle_data_bubble[25] = 82.18 vle_data_bubble[24] = 83.01 vle_data_bubble[23] = 83.19 vle_data_bubble[22] = 83.37 vle_data_bubble[21] = 83.56 vle_data_bubble[20] = 83.04 vle_data_bubble[19] = 83.98 vle_data_bubble[18] = 84.21 vle_data_bubble[17] = 84.46 vle_data_bubble[16] = 84.73 vle_data_bubble[15] = 84.27 vle_data_bubble[14] = 85.34 vle_data_bubble[13] = 85.69 vle_data_bubble[12] = 86.08 vle_data_bubble[11] = 86.51 vle_data_bubble[10] = 86.30 vle_data_bubble[9] = 87.53 vle_data_bubble[8] = 88.15 vle_data_bubble[7] = 88.87 vle_data_bubble[6] = 89.20 vle_data_bubble[5] = 90.67 vle_data_bubble[4] = 91.50 vle_data_bubble[3] = 93.01 vle_data_bubble[2] = 94.89 vle_data_bubble[1] = 97.25

26.373333

62

0.731378

__author__ = "Yaroslav Kholod" __copyright__ = "Copyright 2019, The Kettlebell project" __credits__ = "Yaroslav Kholod" __license__ = "MIT" __version__ = "0.1.0" __maintainer__ = "Yaroslav Kholod" __email__ = "pretorian.yaroslav@gmail.com" __status__ = "Development" vle_data_dew = {} 0] = 78.21 vle_data_dew[99] = 78.18 vle_data_dew[98] = 78.16 vle_data_dew[97] = 78.14 vle_data_dew[96] = 78.12 vle_data_dew[95] = 78.23 vle_data_dew[94] = 78.22 vle_data_dew[93] = 78.21 vle_data_dew[92] = 78.21 vle_data_dew[91] = 78.06 vle_data_dew[90] = 78.05 vle_data_dew[89] = 78.06 vle_data_dew[88] = 78.07 vle_data_dew[87] = 78.08 vle_data_dew[86] = 78.09 vle_data_dew[85] = 78.11 vle_data_dew[84] = 78.13 vle_data_dew[83] = 78.15 vle_data_dew[82] = 78.17 vle_data_dew[81] = 78.22 vle_data_dew[80] = 78.27 vle_data_dew[79] = 78.32 vle_data_dew[78] = 78.44 vle_data_dew[77] = 78.49 vle_data_dew[76] = 78.52 vle_data_dew[75] = 78.60 vle_data_dew[74] = 78.68 vle_data_dew[73] = 78.77 vle_data_dew[72] = 78.87 vle_data_dew[71] = 78.95 vle_data_dew[70] = 79.05 vle_data_dew[69] = 79.20 vle_data_dew[68] = 79.37 vle_data_dew[67] = 79.50 vle_data_dew[66] = 79.72 vle_data_dew[65] = 79.92 vle_data_dew[64] = 80.10 vle_data_dew[63] = 80.31 vle_data_dew[62] = 80.52 vle_data_dew[61] = 80.76 vle_data_dew[60] = 81.00 vle_data_dew[59] = 81.26 vle_data_dew[58] = 81.53 vle_data_dew[57] = 81.80 vle_data_dew[56] = 82.18 vle_data_dew[55] = 82.55 vle_data_dew[54] = 82.80 vle_data_dew[53] = 83.04 vle_data_dew[52] = 83.44 vle_data_dew[51] = 83.88 vle_data_dew[50] = 84.27 vle_data_dew[49] = 84.73 vle_data_dew[48] = 85.02 vle_data_dew[47] = 85.34 vle_data_dew[46] = 85.69 vle_data_dew[45] = 86.08 vle_data_dew[44] = 86.31 vle_data_dew[43] = 86.99 vle_data_dew[42] = 87.30 vle_data_dew[41] = 87.55 vle_data_dew[40] = 87.85 vle_data_dew[39] = 88.15 vle_data_dew[38] = 88.50 vle_data_dew[37] = 88.87 vle_data_dew[36] = 89.20 vle_data_dew[35] = 89.70 vle_data_dew[34] = 90.02 vle_data_dew[33] = 90.34 vle_data_dew[32] = 90.67 vle_data_dew[31] = 91.00 vle_data_dew[30] = 91.50 vle_data_dew[29] = 91.67 vle_data_dew[28] = 91.84 vle_data_dew[27] = 92.25 vle_data_dew[26] = 92.60 vle_data_dew[25] = 93.01 vle_data_dew[24] = 93.25 vle_data_dew[23] = 93.78 vle_data_dew[22] = 94.01 vle_data_dew[21] = 94.34 vle_data_dew[20] = 94.50 vle_data_dew[19] = 94.89 vle_data_dew[18] = 94.99 vle_data_dew[17] = 95.27 vle_data_dew[16] = 95.55 vle_data_dew[15] = 95.82 vle_data_dew[14] = 96.10 vle_data_dew[13] = 96.38 vle_data_dew[12] = 96.66 vle_data_dew[11] = 97.25 vle_data_dew[10] = 97.50 vle_data_dew[9] = 97.80 vle_data_dew[8] = 98.00 vle_data_dew[7] = 98.39 vle_data_dew[6] = 98.79 vle_data_dew[5] = 99.00 vle_data_dew[4] = 99.33 vle_data_dew[3] = 99.66 vle_data_dew[2] = 99.80 vle_data_dew[1] = 100.00 vle_data_bubble = {} [100] = 78.21 vle_data_bubble[99] = 78.19 vle_data_bubble[98] = 78.16 vle_data_bubble[97] = 78.14 vle_data_bubble[96] = 78.12 vle_data_bubble[95] = 78.14 vle_data_bubble[94] = 78.16 vle_data_bubble[93] = 78.17 vle_data_bubble[92] = 78.18 vle_data_bubble[91] = 78.19 vle_data_bubble[90] = 78.20 vle_data_bubble[89] = 78.21 vle_data_bubble[88] = 78.22 vle_data_bubble[87] = 78.23 vle_data_bubble[86] = 78.24 vle_data_bubble[85] = 78.26 vle_data_bubble[84] = 78.28 vle_data_bubble[83] = 78.30 vle_data_bubble[82] = 78.32 vle_data_bubble[81] = 78.32 vle_data_bubble[80] = 78.37 vle_data_bubble[79] = 78.40 vle_data_bubble[78] = 78.44 vle_data_bubble[77] = 78.47 vle_data_bubble[76] = 78.51 vle_data_bubble[75] = 78.54 vle_data_bubble[74] = 78.58 vle_data_bubble[73] = 78.63 vle_data_bubble[72] = 78.67 vle_data_bubble[71] = 78.72 vle_data_bubble[70] = 78.76 vle_data_bubble[69] = 78.81 vle_data_bubble[68] = 78.86 vle_data_bubble[67] = 78.92 vle_data_bubble[66] = 78.97 vle_data_bubble[65] = 79.03 vle_data_bubble[64] = 79.09 vle_data_bubble[63] = 79.15 vle_data_bubble[62] = 79.21 vle_data_bubble[61] = 79.27 vle_data_bubble[60] = 79.34 vle_data_bubble[59] = 79.40 vle_data_bubble[58] = 79.47 vle_data_bubble[57] = 79.54 vle_data_bubble[56] = 79.61 vle_data_bubble[55] = 79.68 vle_data_bubble[54] = 79.76 vle_data_bubble[53] = 79.83 vle_data_bubble[52] = 79.91 vle_data_bubble[51] = 79.99 vle_data_bubble[50] = 80.07 vle_data_bubble[49] = 80.15 vle_data_bubble[48] = 80.24 vle_data_bubble[47] = 80.32 vle_data_bubble[46] = 80.41 vle_data_bubble[45] = 80.50 vle_data_bubble[44] = 80.59 vle_data_bubble[43] = 80.68 vle_data_bubble[42] = 80.78 vle_data_bubble[41] = 80.88 vle_data_bubble[40] = 80.98 vle_data_bubble[39] = 81.08 vle_data_bubble[38] = 81.18 vle_data_bubble[37] = 81.29 vle_data_bubble[36] = 81.40 vle_data_bubble[35] = 81.51 vle_data_bubble[34] = 81.62 vle_data_bubble[33] = 81.74 vle_data_bubble[32] = 81.86 vle_data_bubble[31] = 81.99 vle_data_bubble[30] = 82.12 vle_data_bubble[29] = 82.26 vle_data_bubble[28] = 82.39 vle_data_bubble[27] = 82.54 vle_data_bubble[26] = 82.69 vle_data_bubble[25] = 82.18 vle_data_bubble[24] = 83.01 vle_data_bubble[23] = 83.19 vle_data_bubble[22] = 83.37 vle_data_bubble[21] = 83.56 vle_data_bubble[20] = 83.04 vle_data_bubble[19] = 83.98 vle_data_bubble[18] = 84.21 vle_data_bubble[17] = 84.46 vle_data_bubble[16] = 84.73 vle_data_bubble[15] = 84.27 vle_data_bubble[14] = 85.34 vle_data_bubble[13] = 85.69 vle_data_bubble[12] = 86.08 vle_data_bubble[11] = 86.51 vle_data_bubble[10] = 86.30 vle_data_bubble[9] = 87.53 vle_data_bubble[8] = 88.15 vle_data_bubble[7] = 88.87 vle_data_bubble[6] = 89.20 vle_data_bubble[5] = 90.67 vle_data_bubble[4] = 91.50 vle_data_bubble[3] = 93.01 vle_data_bubble[2] = 94.89 vle_data_bubble[1] = 97.25

true

f71c249886014075a96d3e57f520b3963e70bc00

675

py

Python

pytest/testSigning.py

RomanValov/ArmoryDB

625eff9712161676ad83deb03616e6edb48283ca

[ "MIT" ]

505

2016-02-04T15:54:46.000Z

2022-03-27T18:43:01.000Z

pytest/testSigning.py

jimmysong/BitcoinArmory

1c7190176897a2e0f3e4e198ab2f199059bb2402

[ "MIT" ]

528

2016-02-06T19:50:12.000Z

2022-01-15T10:21:16.000Z

pytest/testSigning.py

jimmysong/BitcoinArmory

1c7190176897a2e0f3e4e198ab2f199059bb2402

[ "MIT" ]

208

2015-01-02T10:31:40.000Z

2021-12-14T07:37:36.000Z

import sys sys.path.append('..') import unittest import random from armoryengine.ALL import * class SigningTester(unittest.TestCase): def testLowSig(self): sbdPrivKey = SecureBinaryData(b'\x01'*32) pub = CryptoECDSA().ComputePublicKey(sbdPrivKey).toBinStr() for i in range(100): msg = "some random msg %s" % random.random() sbdSig = CryptoECDSA().SignData(SecureBinaryData(msg), sbdPrivKey, False) binSig = sbdSig.toBinStr() derSig = createDERSigFromRS(binSig[:32], binSig[32:]) r, s = getRSFromDERSig(derSig) j = binary_to_int(s, BIGENDIAN) self.assertTrue( j <= SECP256K1_ORDER / 2)

28.125

82

0.657778

import sys sys.path.append('..') import unittest import random from armoryengine.ALL import * class SigningTester(unittest.TestCase): def testLowSig(self): sbdPrivKey = SecureBinaryData(b'\x01'*32) pub = CryptoECDSA().ComputePublicKey(sbdPrivKey).toBinStr() for i in range(100): msg = "some random msg %s" % random.random() sbdSig = CryptoECDSA().SignData(SecureBinaryData(msg), sbdPrivKey, False) binSig = sbdSig.toBinStr() derSig = createDERSigFromRS(binSig[:32], binSig[32:]) r, s = getRSFromDERSig(derSig) j = binary_to_int(s, BIGENDIAN) self.assertTrue( j <= SECP256K1_ORDER / 2)

true

f71c26869793672d4719326ffb01b5d7f0f78eb2

12,414

py

Python

texar/torch/modules/decoders/rnn_decoders_test.py

wwt17/texar-pytorch

9fb3ae8f7b541da5c808357033a93fba1817bfbd

[ "Apache-2.0" ]

19

2020-07-29T15:25:45.000Z

2022-01-19T17:49:42.000Z

texar/torch/modules/decoders/rnn_decoders_test.py

wwt17/texar-pytorch

9fb3ae8f7b541da5c808357033a93fba1817bfbd

[ "Apache-2.0" ]

3

2021-02-16T10:26:23.000Z

2021-06-08T16:50:40.000Z

texar/torch/modules/decoders/rnn_decoders_test.py

wwt17/texar-pytorch

9fb3ae8f7b541da5c808357033a93fba1817bfbd

[ "Apache-2.0" ]

1

2019-10-06T07:54:30.000Z

""" Unit tests for RNN decoders. """ import unittest import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from texar.torch.hyperparams import HParams from texar.torch.modules.decoders.decoder_helpers import get_helper from texar.torch.modules.decoders.rnn_decoders import ( AttentionRNNDecoder, AttentionRNNDecoderOutput, BasicRNNDecoder, BasicRNNDecoderOutput) from texar.torch.modules.embedders.embedders import WordEmbedder from texar.torch.utils.utils import map_structure class BasicRNNDecoderTest(unittest.TestCase): r"""Tests :class:`~texar.torch.modules.decoders.rnn_decoders.BasicRNNDecoder`. """ def setUp(self): self._vocab_size = 4 self._max_time = 8 self._batch_size = 16 self._emb_dim = 20 self._inputs = torch.randint( self._vocab_size, size=(self._batch_size, self._max_time)) embedding = torch.rand( self._vocab_size, self._emb_dim, dtype=torch.float) self._embedder = WordEmbedder(init_value=embedding) self._hparams = HParams(None, BasicRNNDecoder.default_hparams()) def _test_outputs(self, decoder, outputs, final_state, sequence_lengths, test_mode=False): hidden_size = decoder.hparams.rnn_cell.kwargs.num_units self.assertIsInstance(outputs, BasicRNNDecoderOutput) max_time = (self._max_time if not test_mode else max(sequence_lengths).item()) self.assertEqual( outputs.logits.shape, (self._batch_size, max_time, self._vocab_size)) if not test_mode: np.testing.assert_array_equal( sequence_lengths, [max_time] * self._batch_size) self.assertEqual(final_state[0].shape, (self._batch_size, hidden_size)) def test_decode_train(self): r"""Tests decoding in training mode. """ decoder = BasicRNNDecoder( token_embedder=self._embedder, input_size=self._emb_dim, vocab_size=self._vocab_size, hparams=self._hparams) sequence_length = torch.tensor([self._max_time] * self._batch_size) # Helper by default HParams helper_train = decoder.create_helper() outputs, final_state, sequence_lengths = decoder( helper=helper_train, inputs=self._inputs, sequence_length=sequence_length) self._test_outputs(decoder, outputs, final_state, sequence_lengths) # Helper by decoding strategy helper_train = decoder.create_helper(decoding_strategy='train_greedy') outputs, final_state, sequence_lengths = decoder( helper=helper_train, inputs=self._inputs, sequence_length=sequence_length) self._test_outputs(decoder, outputs, final_state, sequence_lengths) # Implicit helper outputs, final_state, sequence_lengths = decoder( inputs=self._inputs, sequence_length=sequence_length) self._test_outputs(decoder, outputs, final_state, sequence_lengths) # Eval helper through forward args outputs, final_state, sequence_lengths = decoder( embedding=self._embedder, start_tokens=torch.tensor([1] * self._batch_size), end_token=2, infer_mode=True) self._test_outputs( decoder, outputs, final_state, sequence_lengths, test_mode=True) @staticmethod def _assert_tensor_equal(a: torch.Tensor, b: torch.Tensor) -> bool: if torch.is_tensor(a): a = a.detach().numpy() if torch.is_tensor(b): b = b.detach().numpy() if any(np.issubdtype(array.dtype, np.floating) for array in [a, b]): return np.testing.assert_allclose(a, b, rtol=1e-5, atol=1e-8) return np.testing.assert_array_equal(a, b) def test_decode_train_with_torch(self): r"""Compares decoding results with PyTorch built-in decoder. """ decoder = BasicRNNDecoder( token_embedder=self._embedder, input_size=self._emb_dim, vocab_size=self._vocab_size, hparams=self._hparams) input_size = self._emb_dim hidden_size = decoder.hparams.rnn_cell.kwargs.num_units num_layers = decoder.hparams.rnn_cell.num_layers torch_lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) # match parameters for name in ['weight_ih', 'weight_hh', 'bias_ih', 'bias_hh']: setattr(torch_lstm, f'{name}_l0', getattr(decoder._cell._cell, name)) torch_lstm.flatten_parameters() output_layer = decoder._output_layer input_lengths = torch.tensor([self._max_time] * self._batch_size) inputs = torch.randint( self._vocab_size, size=(self._batch_size, self._max_time)) # decoder outputs helper_train = decoder.create_helper() outputs, final_state, sequence_lengths = decoder( inputs=inputs, sequence_length=input_lengths, helper=helper_train) # torch LSTM outputs lstm_inputs = F.embedding(inputs, self._embedder.embedding) torch_outputs, torch_states = torch_lstm(lstm_inputs) torch_outputs = output_layer(torch_outputs) torch_sample_id = torch.argmax(torch_outputs, dim=-1) self.assertEqual(final_state[0].shape, (self._batch_size, hidden_size)) self._assert_tensor_equal(outputs.logits, torch_outputs) self._assert_tensor_equal(outputs.sample_id, torch_sample_id) self._assert_tensor_equal(final_state[0], torch_states[0].squeeze(0)) self._assert_tensor_equal(final_state[1], torch_states[1].squeeze(0)) self._assert_tensor_equal(sequence_lengths, input_lengths) def test_decode_infer(self): r"""Tests decoding in inference mode.""" decoder = BasicRNNDecoder( token_embedder=self._embedder, input_size=self._emb_dim, vocab_size=self._vocab_size, hparams=self._hparams) decoder.eval() start_tokens = torch.tensor([self._vocab_size - 2] * self._batch_size) helpers = [] for strategy in ['infer_greedy', 'infer_sample']: helper = decoder.create_helper( decoding_strategy=strategy, start_tokens=start_tokens, end_token=self._vocab_size - 1) helpers.append(helper) for klass in ['TopKSampleEmbeddingHelper', 'SoftmaxEmbeddingHelper', 'GumbelSoftmaxEmbeddingHelper']: helper = get_helper( klass, start_tokens=start_tokens, end_token=self._vocab_size - 1, top_k=self._vocab_size // 2, tau=2.0, straight_through=True) helpers.append(helper) for helper in helpers: max_length = 100 outputs, final_state, sequence_lengths = decoder( helper=helper, max_decoding_length=max_length) self.assertLessEqual(max(sequence_lengths), max_length) self._test_outputs(decoder, outputs, final_state, sequence_lengths, test_mode=True) class AttentionRNNDecoderTest(unittest.TestCase): r"""Tests :class:`~texar.torch.modules.decoders.rnn_decoders.AttentionRNNDecoder`. """ def setUp(self): self._vocab_size = 10 self._max_time = 16 self._batch_size = 8 self._emb_dim = 20 self._attention_dim = 256 self._inputs = torch.randint( self._vocab_size, size=(self._batch_size, self._max_time)) embedding = torch.rand( self._vocab_size, self._emb_dim, dtype=torch.float) self._embedder = WordEmbedder(init_value=embedding) self._encoder_output = torch.rand( self._batch_size, self._max_time, 64) self._test_hparams = {} # (cell_type, is_multi) -> hparams for cell_type in ["RNNCell", "LSTMCell", "GRUCell"]: hparams = { "rnn_cell": { 'type': cell_type, 'kwargs': { 'num_units': 256, }, }, "attention": { "kwargs": { "num_units": self._attention_dim }, } } self._test_hparams[(cell_type, False)] = HParams( hparams, AttentionRNNDecoder.default_hparams()) hparams = { "rnn_cell": { 'type': 'LSTMCell', 'kwargs': { 'num_units': 256, }, 'num_layers': 3, }, "attention": { "kwargs": { "num_units": self._attention_dim }, } } self._test_hparams[("LSTMCell", True)] = HParams( hparams, AttentionRNNDecoder.default_hparams()) def _test_outputs(self, decoder, outputs, final_state, sequence_lengths, test_mode=False): hidden_size = decoder.hparams.rnn_cell.kwargs.num_units cell_type = decoder.hparams.rnn_cell.type is_multi = decoder.hparams.rnn_cell.num_layers > 1 self.assertIsInstance(outputs, AttentionRNNDecoderOutput) max_time = (self._max_time if not test_mode else max(sequence_lengths).item()) self.assertEqual( outputs.logits.shape, (self._batch_size, max_time, self._vocab_size)) if not test_mode: np.testing.assert_array_equal( sequence_lengths, [max_time] * self._batch_size) map_structure( lambda t: self.assertEqual( t.size(), (self._batch_size, hidden_size)), final_state.cell_state) state = final_state.cell_state if is_multi: self.assertIsInstance(state, list) state = state[0] if cell_type == "LSTMCell": self.assertIsInstance(state, tuple) state = state[0] self.assertIsInstance(state, torch.Tensor) def test_decode_infer(self): r"""Tests decoding in inference mode. """ seq_length = np.random.randint( self._max_time, size=[self._batch_size]) + 1 encoder_values_length = torch.tensor(seq_length) for (cell_type, is_multi), hparams in self._test_hparams.items(): decoder = AttentionRNNDecoder( encoder_output_size=64, token_embedder=self._embedder, vocab_size=self._vocab_size, input_size=self._emb_dim, hparams=hparams) decoder.eval() helper_infer = decoder.create_helper( start_tokens=torch.tensor([1] * self._batch_size), end_token=2) outputs, final_state, sequence_lengths = decoder( memory=self._encoder_output, memory_sequence_length=encoder_values_length, helper=helper_infer) self._test_outputs(decoder, outputs, final_state, sequence_lengths, test_mode=True) def test_decode_train(self): r"""Tests decoding in training mode. """ seq_length = np.random.randint( self._max_time, size=[self._batch_size]) + 1 encoder_values_length = torch.tensor(seq_length) for (cell_type, is_multi), hparams in self._test_hparams.items(): decoder = AttentionRNNDecoder( encoder_output_size=64, token_embedder=self._embedder, vocab_size=self._vocab_size, input_size=self._emb_dim, hparams=hparams) sequence_length = torch.tensor([self._max_time] * self._batch_size) helper_train = decoder.create_helper() outputs, final_state, sequence_lengths = decoder( memory=self._encoder_output, memory_sequence_length=encoder_values_length, helper=helper_train, inputs=self._inputs, sequence_length=sequence_length) self._test_outputs(decoder, outputs, final_state, sequence_lengths) if __name__ == "__main__": unittest.main()

39.160883

86

0.617931

import unittest import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from texar.torch.hyperparams import HParams from texar.torch.modules.decoders.decoder_helpers import get_helper from texar.torch.modules.decoders.rnn_decoders import ( AttentionRNNDecoder, AttentionRNNDecoderOutput, BasicRNNDecoder, BasicRNNDecoderOutput) from texar.torch.modules.embedders.embedders import WordEmbedder from texar.torch.utils.utils import map_structure class BasicRNNDecoderTest(unittest.TestCase): def setUp(self): self._vocab_size = 4 self._max_time = 8 self._batch_size = 16 self._emb_dim = 20 self._inputs = torch.randint( self._vocab_size, size=(self._batch_size, self._max_time)) embedding = torch.rand( self._vocab_size, self._emb_dim, dtype=torch.float) self._embedder = WordEmbedder(init_value=embedding) self._hparams = HParams(None, BasicRNNDecoder.default_hparams()) def _test_outputs(self, decoder, outputs, final_state, sequence_lengths, test_mode=False): hidden_size = decoder.hparams.rnn_cell.kwargs.num_units self.assertIsInstance(outputs, BasicRNNDecoderOutput) max_time = (self._max_time if not test_mode else max(sequence_lengths).item()) self.assertEqual( outputs.logits.shape, (self._batch_size, max_time, self._vocab_size)) if not test_mode: np.testing.assert_array_equal( sequence_lengths, [max_time] * self._batch_size) self.assertEqual(final_state[0].shape, (self._batch_size, hidden_size)) def test_decode_train(self): decoder = BasicRNNDecoder( token_embedder=self._embedder, input_size=self._emb_dim, vocab_size=self._vocab_size, hparams=self._hparams) sequence_length = torch.tensor([self._max_time] * self._batch_size) helper_train = decoder.create_helper() outputs, final_state, sequence_lengths = decoder( helper=helper_train, inputs=self._inputs, sequence_length=sequence_length) self._test_outputs(decoder, outputs, final_state, sequence_lengths) helper_train = decoder.create_helper(decoding_strategy='train_greedy') outputs, final_state, sequence_lengths = decoder( helper=helper_train, inputs=self._inputs, sequence_length=sequence_length) self._test_outputs(decoder, outputs, final_state, sequence_lengths) outputs, final_state, sequence_lengths = decoder( inputs=self._inputs, sequence_length=sequence_length) self._test_outputs(decoder, outputs, final_state, sequence_lengths) outputs, final_state, sequence_lengths = decoder( embedding=self._embedder, start_tokens=torch.tensor([1] * self._batch_size), end_token=2, infer_mode=True) self._test_outputs( decoder, outputs, final_state, sequence_lengths, test_mode=True) @staticmethod def _assert_tensor_equal(a: torch.Tensor, b: torch.Tensor) -> bool: if torch.is_tensor(a): a = a.detach().numpy() if torch.is_tensor(b): b = b.detach().numpy() if any(np.issubdtype(array.dtype, np.floating) for array in [a, b]): return np.testing.assert_allclose(a, b, rtol=1e-5, atol=1e-8) return np.testing.assert_array_equal(a, b) def test_decode_train_with_torch(self): decoder = BasicRNNDecoder( token_embedder=self._embedder, input_size=self._emb_dim, vocab_size=self._vocab_size, hparams=self._hparams) input_size = self._emb_dim hidden_size = decoder.hparams.rnn_cell.kwargs.num_units num_layers = decoder.hparams.rnn_cell.num_layers torch_lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) for name in ['weight_ih', 'weight_hh', 'bias_ih', 'bias_hh']: setattr(torch_lstm, f'{name}_l0', getattr(decoder._cell._cell, name)) torch_lstm.flatten_parameters() output_layer = decoder._output_layer input_lengths = torch.tensor([self._max_time] * self._batch_size) inputs = torch.randint( self._vocab_size, size=(self._batch_size, self._max_time)) helper_train = decoder.create_helper() outputs, final_state, sequence_lengths = decoder( inputs=inputs, sequence_length=input_lengths, helper=helper_train) lstm_inputs = F.embedding(inputs, self._embedder.embedding) torch_outputs, torch_states = torch_lstm(lstm_inputs) torch_outputs = output_layer(torch_outputs) torch_sample_id = torch.argmax(torch_outputs, dim=-1) self.assertEqual(final_state[0].shape, (self._batch_size, hidden_size)) self._assert_tensor_equal(outputs.logits, torch_outputs) self._assert_tensor_equal(outputs.sample_id, torch_sample_id) self._assert_tensor_equal(final_state[0], torch_states[0].squeeze(0)) self._assert_tensor_equal(final_state[1], torch_states[1].squeeze(0)) self._assert_tensor_equal(sequence_lengths, input_lengths) def test_decode_infer(self): decoder = BasicRNNDecoder( token_embedder=self._embedder, input_size=self._emb_dim, vocab_size=self._vocab_size, hparams=self._hparams) decoder.eval() start_tokens = torch.tensor([self._vocab_size - 2] * self._batch_size) helpers = [] for strategy in ['infer_greedy', 'infer_sample']: helper = decoder.create_helper( decoding_strategy=strategy, start_tokens=start_tokens, end_token=self._vocab_size - 1) helpers.append(helper) for klass in ['TopKSampleEmbeddingHelper', 'SoftmaxEmbeddingHelper', 'GumbelSoftmaxEmbeddingHelper']: helper = get_helper( klass, start_tokens=start_tokens, end_token=self._vocab_size - 1, top_k=self._vocab_size // 2, tau=2.0, straight_through=True) helpers.append(helper) for helper in helpers: max_length = 100 outputs, final_state, sequence_lengths = decoder( helper=helper, max_decoding_length=max_length) self.assertLessEqual(max(sequence_lengths), max_length) self._test_outputs(decoder, outputs, final_state, sequence_lengths, test_mode=True) class AttentionRNNDecoderTest(unittest.TestCase): def setUp(self): self._vocab_size = 10 self._max_time = 16 self._batch_size = 8 self._emb_dim = 20 self._attention_dim = 256 self._inputs = torch.randint( self._vocab_size, size=(self._batch_size, self._max_time)) embedding = torch.rand( self._vocab_size, self._emb_dim, dtype=torch.float) self._embedder = WordEmbedder(init_value=embedding) self._encoder_output = torch.rand( self._batch_size, self._max_time, 64) self._test_hparams = {} for cell_type in ["RNNCell", "LSTMCell", "GRUCell"]: hparams = { "rnn_cell": { 'type': cell_type, 'kwargs': { 'num_units': 256, }, }, "attention": { "kwargs": { "num_units": self._attention_dim }, } } self._test_hparams[(cell_type, False)] = HParams( hparams, AttentionRNNDecoder.default_hparams()) hparams = { "rnn_cell": { 'type': 'LSTMCell', 'kwargs': { 'num_units': 256, }, 'num_layers': 3, }, "attention": { "kwargs": { "num_units": self._attention_dim }, } } self._test_hparams[("LSTMCell", True)] = HParams( hparams, AttentionRNNDecoder.default_hparams()) def _test_outputs(self, decoder, outputs, final_state, sequence_lengths, test_mode=False): hidden_size = decoder.hparams.rnn_cell.kwargs.num_units cell_type = decoder.hparams.rnn_cell.type is_multi = decoder.hparams.rnn_cell.num_layers > 1 self.assertIsInstance(outputs, AttentionRNNDecoderOutput) max_time = (self._max_time if not test_mode else max(sequence_lengths).item()) self.assertEqual( outputs.logits.shape, (self._batch_size, max_time, self._vocab_size)) if not test_mode: np.testing.assert_array_equal( sequence_lengths, [max_time] * self._batch_size) map_structure( lambda t: self.assertEqual( t.size(), (self._batch_size, hidden_size)), final_state.cell_state) state = final_state.cell_state if is_multi: self.assertIsInstance(state, list) state = state[0] if cell_type == "LSTMCell": self.assertIsInstance(state, tuple) state = state[0] self.assertIsInstance(state, torch.Tensor) def test_decode_infer(self): seq_length = np.random.randint( self._max_time, size=[self._batch_size]) + 1 encoder_values_length = torch.tensor(seq_length) for (cell_type, is_multi), hparams in self._test_hparams.items(): decoder = AttentionRNNDecoder( encoder_output_size=64, token_embedder=self._embedder, vocab_size=self._vocab_size, input_size=self._emb_dim, hparams=hparams) decoder.eval() helper_infer = decoder.create_helper( start_tokens=torch.tensor([1] * self._batch_size), end_token=2) outputs, final_state, sequence_lengths = decoder( memory=self._encoder_output, memory_sequence_length=encoder_values_length, helper=helper_infer) self._test_outputs(decoder, outputs, final_state, sequence_lengths, test_mode=True) def test_decode_train(self): seq_length = np.random.randint( self._max_time, size=[self._batch_size]) + 1 encoder_values_length = torch.tensor(seq_length) for (cell_type, is_multi), hparams in self._test_hparams.items(): decoder = AttentionRNNDecoder( encoder_output_size=64, token_embedder=self._embedder, vocab_size=self._vocab_size, input_size=self._emb_dim, hparams=hparams) sequence_length = torch.tensor([self._max_time] * self._batch_size) helper_train = decoder.create_helper() outputs, final_state, sequence_lengths = decoder( memory=self._encoder_output, memory_sequence_length=encoder_values_length, helper=helper_train, inputs=self._inputs, sequence_length=sequence_length) self._test_outputs(decoder, outputs, final_state, sequence_lengths) if __name__ == "__main__": unittest.main()

true

f71c2776956637e7ed8b7da0a4acf3481ed4e4c7

3,001

py

Python

huaweicloud-sdk-vss/huaweicloudsdkvss/v3/model/delete_domains_request.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

64

2020-06-12T07:05:07.000Z

2022-03-30T03:32:50.000Z

huaweicloud-sdk-vss/huaweicloudsdkvss/v3/model/delete_domains_request.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

11

2020-07-06T07:56:54.000Z

2022-01-11T11:14:40.000Z

huaweicloud-sdk-vss/huaweicloudsdkvss/v3/model/delete_domains_request.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

24

2020-06-08T11:42:13.000Z

2022-03-04T06:44:08.000Z

# coding: utf-8 import re import six from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization class DeleteDomainsRequest: """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ sensitive_list = [] openapi_types = { 'domain_name': 'str' } attribute_map = { 'domain_name': 'domain_name' } def __init__(self, domain_name=None): """DeleteDomainsRequest - a model defined in huaweicloud sdk""" self._domain_name = None self.discriminator = None self.domain_name = domain_name @property def domain_name(self): """Gets the domain_name of this DeleteDomainsRequest. 域名 :return: The domain_name of this DeleteDomainsRequest. :rtype: str """ return self._domain_name @domain_name.setter def domain_name(self, domain_name): """Sets the domain_name of this DeleteDomainsRequest. 域名 :param domain_name: The domain_name of this DeleteDomainsRequest. :type: str """ self._domain_name = domain_name def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: if attr in self.sensitive_list: result[attr] = "****" else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" import simplejson as json if six.PY2: import sys reload(sys) sys.setdefaultencoding("utf-8") return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) def __repr__(self): """For `print`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, DeleteDomainsRequest): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

26.324561

79

0.553149

import re import six from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization class DeleteDomainsRequest: sensitive_list = [] openapi_types = { 'domain_name': 'str' } attribute_map = { 'domain_name': 'domain_name' } def __init__(self, domain_name=None): self._domain_name = None self.discriminator = None self.domain_name = domain_name @property def domain_name(self): return self._domain_name @domain_name.setter def domain_name(self, domain_name): self._domain_name = domain_name def to_dict(self): result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: if attr in self.sensitive_list: result[attr] = "****" else: result[attr] = value return result def to_str(self): import simplejson as json if six.PY2: import sys reload(sys) sys.setdefaultencoding("utf-8") return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, DeleteDomainsRequest): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true