fumiyau/python_comments_free_500000 · Datasets at Hugging Face

1c4747390ffa6d1824d43557a023d590eb857e75

22,080

py

Python

qiskit/ignis/verification/randomized_benchmarking/circuits.py

hodgestar/qiskit-ignis

0e511df442e864cd0e06efcdd1db7b03c011168b

[ "Apache-2.0" ]

null

qiskit/ignis/verification/randomized_benchmarking/circuits.py

hodgestar/qiskit-ignis

0e511df442e864cd0e06efcdd1db7b03c011168b

[ "Apache-2.0" ]

null

qiskit/ignis/verification/randomized_benchmarking/circuits.py

hodgestar/qiskit-ignis

0e511df442e864cd0e06efcdd1db7b03c011168b

[ "Apache-2.0" ]

1

2021-04-01T17:28:33.000Z

# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM 2019. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. # TODO(mtreinish): Remove these disables when implementation is finished # pylint: disable=unused-argument,unnecessary-pass """ Generates randomized benchmarking sequences """ import copy import numpy as np import qiskit from .Clifford import Clifford from .clifford_utils import CliffordUtils as clutils from .dihedral import CNOTDihedral from .dihedral_utils import DihedralUtils as dutils def handle_length_multiplier(length_multiplier, len_pattern, is_purity=False): """ Check validity of length_multiplier. In addition, transform it into a vector if it is a constant. In case of purity rb the length multiplier should be None. Args: length_multiplier: length of the multiplier len_pattern: length of the RB pattern is_purity: True only for purity rb (default is False) Returns: length_multiplier """ if hasattr(length_multiplier, "__len__"): if is_purity: raise ValueError( "In case of Purity RB the length multiplier should be None") if len(length_multiplier) != len_pattern: raise ValueError( "Length mulitiplier must be the same length as the pattern") length_multiplier = np.array(length_multiplier) if length_multiplier.dtype != 'int' or (length_multiplier < 1).any(): raise ValueError("Invalid length multiplier") else: length_multiplier = np.ones(len_pattern, dtype='int')*length_multiplier return length_multiplier def check_pattern(pattern, is_purity=False): """ Verifies that the input pattern is valid i.e., that each qubit appears at most once In case of purity rb, checks that all simultaneous sequences have the same dimension (e.g. only 1-qubit sequences, or only 2-qubit sequences etc.) Args: pattern: RB pattern n_qubits: number of qubits is_purity: True only for purity rb (default is False) Raises: ValueError: if the pattern is not valid Return: qlist: flat list of all the qubits in the pattern maxqubit: the maximum qubit number maxdim: the maximal dimension (maximal number of qubits in all sequences) """ pattern_flat = [] pattern_dim = [] for pat in pattern: pattern_flat.extend(pat) pattern_dim.append(len(pat)) _, uni_counts = np.unique(np.array(pattern_flat), return_counts=True) if (uni_counts > 1).any(): raise ValueError("Invalid pattern. Duplicate qubit index.") dim_distinct = np.unique(pattern_dim) if is_purity: if len(dim_distinct) > 1: raise ValueError("Invalid pattern for purity RB. \ All simultaneous sequences should have the \ same dimension.") return pattern_flat, np.max(pattern_flat).item(), np.max(pattern_dim) def calc_xdata(length_vector, length_multiplier): """ Calculate the set of sequences lengths Args: length_vector: vector length length_multiplier: length of the multiplier of the vector length Returns: An array of sequences lengths """ xdata = [] for mult in length_multiplier: xdata.append(np.array(length_vector)*mult) return np.array(xdata) def randomized_benchmarking_seq(nseeds=1, length_vector=None, rb_pattern=None, length_multiplier=1, seed_offset=0, align_cliffs=False, interleaved_gates=None, is_purity=False, group_gates=None): """Get a generic randomized benchmarking sequence Args: nseeds: number of seeds length_vector: 'm' length vector of sequence lengths. Must be in ascending order. RB sequences of increasing length grow on top of the previous sequences. rb_pattern: A list of the form [[i,j],[k],...] which will make simultaneous RB sequences where Qi,Qj are a 2Q RB sequence and Qk is a 1Q sequence, etc. E.g. [[0,3],[2],[1]] would create RB sequences that are 2Q for Q0/Q3, 1Q for Q1+Q2 The number of qubits is the sum of the entries. For 'regular' RB the qubit_pattern is just [[0]],[[0,1]]. length_multiplier: if this is an array it scales each rb_sequence by the multiplier seed_offset: What to start the seeds at (e.g. if we want to add more seeds later) align_cliffs: If true adds a barrier across all qubits in rb_pattern after each set of elements, not necessarily Cliffords (note: aligns after each increment of elements including the length multiplier so if the multiplier is [1,3] it will barrier after 1 element for the first pattern and 3 for the second). interleaved_gates: A list of gates of elements that will be interleaved (for interleaved randomized benchmarking) The length of the list would equal the length of the rb_pattern. is_purity: True only for purity rb (default is False) group_gates: On which group (or gate set) we perform RB (default is the Clifford group) '0' or None or 'Clifford': Clifford group '1' or 'CNOT-Dihedral' or 'Non-Clifford': CNOT-Dihedral group Returns: A tuple of different fields depending on inputs. The different fields are: * ``circuits``: list of lists of circuits for the rb sequences (separate list for each seed) * ``xdata``: the sequences lengths (with multiplier if applicable) * ``circuits_interleaved`` `(only if interleaved_gates is not None)`: list of lists of circuits for the interleaved rb sequences (separate list for each seed) * ``circuits_purity`` `(only if is_purity=True)`: list of lists of lists of circuits for purity rb (separate list for each seed and each of the 3^n circuits) * ``npurity`` `(only if is_purity=True)`: the number of purity rb circuits (per seed) which equals to 3^n, where n is the dimension """ # Set modules (default is Clifford) if group_gates is None or group_gates in ('0', 'Clifford', 'clifford'): Gutils = clutils() Ggroup = Clifford rb_circ_type = 'rb' group_gates_type = 0 elif group_gates in ('1', 'Non-Clifford', 'NonClifford' 'CNOTDihedral', 'CNOT-Dihedral'): Gutils = dutils() Ggroup = CNOTDihedral rb_circ_type = 'rb_cnotdihedral' group_gates_type = 1 else: raise ValueError("Unknown group or set of gates.") if rb_pattern is None: rb_pattern = [[0]] if length_vector is None: length_vector = [1, 10, 20] qlist_flat, n_q_max, max_dim = check_pattern(rb_pattern, is_purity) length_multiplier = handle_length_multiplier(length_multiplier, len(rb_pattern), is_purity) # number of purity rb circuits per seed npurity = 3**max_dim xdata = calc_xdata(length_vector, length_multiplier) pattern_sizes = [len(pat) for pat in rb_pattern] max_nrb = np.max(pattern_sizes) # load group tables group_tables = [[] for _ in range(max_nrb)] for rb_num in range(max_nrb): group_tables[rb_num] = Gutils.load_tables(rb_num+1) # initialization: rb sequences circuits = [[] for e in range(nseeds)] # initialization: interleaved rb sequences circuits_interleaved = [[] for e in range(nseeds)] # initialization: non-clifford cnot-dihedral # rb sequences circuits_cnotdihedral = [[] for e in range(nseeds)] # initialization: non-clifford cnot-dihedral # interleaved rb sequences circuits_cnotdihedral_interleaved = [[] for e in range(nseeds)] # initialization: purity rb sequences circuits_purity = [[[] for d in range(npurity)] for e in range(nseeds)] # go through for each seed for seed in range(nseeds): qr = qiskit.QuantumRegister(n_q_max+1, 'qr') cr = qiskit.ClassicalRegister(len(qlist_flat), 'cr') general_circ = qiskit.QuantumCircuit(qr, cr) interleaved_circ = qiskit.QuantumCircuit(qr, cr) # make sequences for each of the separate sequences in # rb_pattern Elmnts = [] for rb_q_num in pattern_sizes: Elmnts.append(Ggroup(rb_q_num)) # Sequences for interleaved rb sequences Elmnts_interleaved = [] for rb_q_num in pattern_sizes: Elmnts_interleaved.append(Ggroup(rb_q_num)) # go through and add elements to RB sequences length_index = 0 for elmnts_index in range(length_vector[-1]): for (rb_pattern_index, rb_q_num) in enumerate(pattern_sizes): for _ in range(length_multiplier[rb_pattern_index]): new_elmnt_gatelist = Gutils.random_gates( rb_q_num) Elmnts[rb_pattern_index] = Gutils.compose_gates( Elmnts[rb_pattern_index], new_elmnt_gatelist) general_circ += replace_q_indices( get_quantum_circuit(Gutils.gatelist(), rb_q_num), rb_pattern[rb_pattern_index], qr) # add a barrier general_circ.barrier( *[qr[x] for x in rb_pattern[rb_pattern_index]]) # interleaved rb sequences if interleaved_gates is not None: Elmnts_interleaved[rb_pattern_index] = \ Gutils.compose_gates( Elmnts_interleaved[rb_pattern_index], new_elmnt_gatelist) interleaved_circ += replace_q_indices( get_quantum_circuit(Gutils.gatelist(), rb_q_num), rb_pattern[rb_pattern_index], qr) Elmnts_interleaved[rb_pattern_index] = \ Gutils.compose_gates( Elmnts_interleaved[rb_pattern_index], interleaved_gates[rb_pattern_index]) # add a barrier - interleaved rb interleaved_circ.barrier( *[qr[x] for x in rb_pattern[rb_pattern_index]]) interleaved_circ += replace_q_indices( get_quantum_circuit(Gutils.gatelist(), rb_q_num), rb_pattern[rb_pattern_index], qr) # add a barrier - interleaved rb interleaved_circ.barrier( *[qr[x] for x in rb_pattern[rb_pattern_index]]) if align_cliffs: # if align at a barrier across all patterns general_circ.barrier( *[qr[x] for x in qlist_flat]) # align for interleaved rb if interleaved_gates is not None: interleaved_circ.barrier( *[qr[x] for x in qlist_flat]) # if the number of elements matches one of the sequence lengths # then calculate the inverse and produce the circuit if (elmnts_index+1) == length_vector[length_index]: # circ for rb: circ = qiskit.QuantumCircuit(qr, cr) circ += general_circ # circ_interleaved for interleaved rb: circ_interleaved = qiskit.QuantumCircuit(qr, cr) circ_interleaved += interleaved_circ for (rb_pattern_index, rb_q_num) in enumerate(pattern_sizes): inv_key = Gutils.find_key(Elmnts[rb_pattern_index], rb_q_num) inv_circuit = Gutils.find_inverse_gates( rb_q_num, group_tables[rb_q_num-1][inv_key]) circ += replace_q_indices( get_quantum_circuit(inv_circuit, rb_q_num), rb_pattern[rb_pattern_index], qr) # calculate the inverse and produce the circuit # for interleaved rb if interleaved_gates is not None: inv_key = Gutils.find_key(Elmnts_interleaved [rb_pattern_index], rb_q_num) inv_circuit = Gutils.find_inverse_gates( rb_q_num, group_tables[rb_q_num - 1][inv_key]) circ_interleaved += replace_q_indices( get_quantum_circuit(inv_circuit, rb_q_num), rb_pattern[rb_pattern_index], qr) # Circuits for purity rb if is_purity: circ_purity = [[] for d in range(npurity)] for d in range(npurity): circ_purity[d] = qiskit.QuantumCircuit(qr, cr) circ_purity[d] += circ circ_purity[d].name = rb_circ_type + '_purity_' ind_d = d purity_qubit_num = 0 while True: # Per each qubit: # do nothing or rx(pi/2) or ry(pi/2) purity_qubit_rot = np.mod(ind_d, 3) ind_d = np.floor_divide(ind_d, 3) if purity_qubit_rot == 0: # do nothing circ_purity[d].name += 'Z' if purity_qubit_rot == 1: # add rx(pi/2) for pat in rb_pattern: circ_purity[d].rx(np.pi / 2, qr[pat[ purity_qubit_num]]) circ_purity[d].name += 'X' if purity_qubit_rot == 2: # add ry(pi/2) for pat in rb_pattern: circ_purity[d].ry(np.pi / 2, qr[pat[ purity_qubit_num]]) circ_purity[d].name += 'Y' purity_qubit_num = purity_qubit_num + 1 if ind_d == 0: break # padding the circuit name with Z's so that # all circuits will have names of the same length for _ in range(max_dim - purity_qubit_num): circ_purity[d].name += 'Z' # add measurement for purity rb for qind, qb in enumerate(qlist_flat): circ_purity[d].measure(qr[qb], cr[qind]) circ_purity[d].name += '_length_%d_seed_%d' \ % (length_index, seed + seed_offset) # add measurement for Non-Clifford cnot-dihedral rb # measure both the ground state |0...0> (circ) # and the |+...+> state (cnot-dihedral_circ) cnotdihedral_circ = qiskit.QuantumCircuit(qr, cr) cnotdihedral_interleaved_circ = qiskit.QuantumCircuit(qr, cr) if group_gates_type == 1: for _, qb in enumerate(qlist_flat): cnotdihedral_circ.h(qr[qb]) cnotdihedral_circ.barrier(qr[qb]) cnotdihedral_interleaved_circ.h(qr[qb]) cnotdihedral_interleaved_circ.barrier(qr[qb]) cnotdihedral_circ += circ cnotdihedral_interleaved_circ += circ_interleaved for _, qb in enumerate(qlist_flat): cnotdihedral_circ.barrier(qr[qb]) cnotdihedral_circ.h(qr[qb]) cnotdihedral_interleaved_circ.barrier(qr[qb]) cnotdihedral_interleaved_circ.h(qr[qb]) for qind, qb in enumerate(qlist_flat): cnotdihedral_circ.measure(qr[qb], cr[qind]) cnotdihedral_interleaved_circ.measure(qr[qb], cr[qind]) # add measurement for standard rb # qubits measure to the c registers as # they appear in the pattern for qind, qb in enumerate(qlist_flat): circ.measure(qr[qb], cr[qind]) # add measurement for interleaved rb circ_interleaved.measure(qr[qb], cr[qind]) circ.name = \ rb_circ_type + '_length_%d_seed_%d' % \ (length_index, seed + seed_offset) circ_interleaved.name = \ rb_circ_type + '_interleaved_length_%d_seed_%d' % \ (length_index, seed + seed_offset) if group_gates_type == 1: circ.name = rb_circ_type + '_Z_length_%d_seed_%d' % \ (length_index, seed + seed_offset) circ_interleaved.name = \ rb_circ_type + '_interleaved_Z_length_%d_seed_%d' % \ (length_index, seed + seed_offset) cnotdihedral_circ.name = \ rb_circ_type + '_X_length_%d_seed_%d' % \ (length_index, seed + seed_offset) cnotdihedral_interleaved_circ.name = \ rb_circ_type + 'interleaved_X_length_%d_seed_%d' % \ (length_index, seed + seed_offset) circuits[seed].append(circ) circuits_interleaved[seed].append(circ_interleaved) circuits_cnotdihedral[seed].append(cnotdihedral_circ) circuits_cnotdihedral_interleaved[seed].append( cnotdihedral_interleaved_circ) if is_purity: for d in range(npurity): circuits_purity[seed][d].append(circ_purity[d]) length_index += 1 # output of purity rb if is_purity: return circuits_purity, xdata, npurity # output of non-clifford cnot-dihedral interleaved rb if interleaved_gates is not None and group_gates_type == 1: return circuits, xdata, circuits_cnotdihedral, circuits_interleaved, \ circuits_cnotdihedral_interleaved # output of interleaved rb if interleaved_gates is not None: return circuits, xdata, circuits_interleaved # output of Non-Clifford cnot-dihedral rb if group_gates_type == 1: return circuits, xdata, circuits_cnotdihedral # output of standard (simultaneous) rb return circuits, xdata def replace_q_indices(circuit, q_nums, qr): """ Take a circuit that is ordered from 0,1,2 qubits and replace 0 with the qubit label in the first index of q_nums, 1 with the second index... Args: circuit: circuit to operate on q_nums: list of qubit indices Returns: updated circuit """ new_circuit = qiskit.QuantumCircuit(qr) for instr, qargs, cargs in circuit.data: new_qargs = [ qr[q_nums[x]] for x in [arg.index for arg in qargs]] new_op = copy.deepcopy((instr, new_qargs, cargs)) new_circuit.data.append(new_op) return new_circuit def get_quantum_circuit(gatelist, num_qubits): """ Returns the circuit in the form of a QuantumCircuit object. Args: num_qubits: the number of qubits (dimension). gatelist: a list of gates. Returns: A QuantumCircuit object. """ qr = qiskit.QuantumRegister(num_qubits) qc = qiskit.QuantumCircuit(qr) for op in gatelist: split = op.split() op_names = [split[0]] # temporary correcting the ops name since QuantumCircuit has no # attributes 'v' or 'w' yet: if op_names == ['v']: op_names = ['sdg', 'h'] elif op_names == ['w']: op_names = ['h', 's'] if op_names == ['u1']: qubits = [qr[int(x)] for x in split[2:]] theta = float(split[1]) else: qubits = [qr[int(x)] for x in split[1:]] for sub_op in op_names: operation = eval('qiskit.QuantumCircuit.' + sub_op) if sub_op == 'u1': operation(qc, theta, *qubits) else: operation(qc, *qubits) return qc

42.217973

79

0.550634

import copy import numpy as np import qiskit from .Clifford import Clifford from .clifford_utils import CliffordUtils as clutils from .dihedral import CNOTDihedral from .dihedral_utils import DihedralUtils as dutils def handle_length_multiplier(length_multiplier, len_pattern, is_purity=False): if hasattr(length_multiplier, "__len__"): if is_purity: raise ValueError( "In case of Purity RB the length multiplier should be None") if len(length_multiplier) != len_pattern: raise ValueError( "Length mulitiplier must be the same length as the pattern") length_multiplier = np.array(length_multiplier) if length_multiplier.dtype != 'int' or (length_multiplier < 1).any(): raise ValueError("Invalid length multiplier") else: length_multiplier = np.ones(len_pattern, dtype='int')*length_multiplier return length_multiplier def check_pattern(pattern, is_purity=False): pattern_flat = [] pattern_dim = [] for pat in pattern: pattern_flat.extend(pat) pattern_dim.append(len(pat)) _, uni_counts = np.unique(np.array(pattern_flat), return_counts=True) if (uni_counts > 1).any(): raise ValueError("Invalid pattern. Duplicate qubit index.") dim_distinct = np.unique(pattern_dim) if is_purity: if len(dim_distinct) > 1: raise ValueError("Invalid pattern for purity RB. \ All simultaneous sequences should have the \ same dimension.") return pattern_flat, np.max(pattern_flat).item(), np.max(pattern_dim) def calc_xdata(length_vector, length_multiplier): xdata = [] for mult in length_multiplier: xdata.append(np.array(length_vector)*mult) return np.array(xdata) def randomized_benchmarking_seq(nseeds=1, length_vector=None, rb_pattern=None, length_multiplier=1, seed_offset=0, align_cliffs=False, interleaved_gates=None, is_purity=False, group_gates=None): if group_gates is None or group_gates in ('0', 'Clifford', 'clifford'): Gutils = clutils() Ggroup = Clifford rb_circ_type = 'rb' group_gates_type = 0 elif group_gates in ('1', 'Non-Clifford', 'NonClifford' 'CNOTDihedral', 'CNOT-Dihedral'): Gutils = dutils() Ggroup = CNOTDihedral rb_circ_type = 'rb_cnotdihedral' group_gates_type = 1 else: raise ValueError("Unknown group or set of gates.") if rb_pattern is None: rb_pattern = [[0]] if length_vector is None: length_vector = [1, 10, 20] qlist_flat, n_q_max, max_dim = check_pattern(rb_pattern, is_purity) length_multiplier = handle_length_multiplier(length_multiplier, len(rb_pattern), is_purity) npurity = 3**max_dim xdata = calc_xdata(length_vector, length_multiplier) pattern_sizes = [len(pat) for pat in rb_pattern] max_nrb = np.max(pattern_sizes) group_tables = [[] for _ in range(max_nrb)] for rb_num in range(max_nrb): group_tables[rb_num] = Gutils.load_tables(rb_num+1) circuits = [[] for e in range(nseeds)] circuits_interleaved = [[] for e in range(nseeds)] circuits_cnotdihedral = [[] for e in range(nseeds)] circuits_cnotdihedral_interleaved = [[] for e in range(nseeds)] circuits_purity = [[[] for d in range(npurity)] for e in range(nseeds)] for seed in range(nseeds): qr = qiskit.QuantumRegister(n_q_max+1, 'qr') cr = qiskit.ClassicalRegister(len(qlist_flat), 'cr') general_circ = qiskit.QuantumCircuit(qr, cr) interleaved_circ = qiskit.QuantumCircuit(qr, cr) Elmnts = [] for rb_q_num in pattern_sizes: Elmnts.append(Ggroup(rb_q_num)) Elmnts_interleaved = [] for rb_q_num in pattern_sizes: Elmnts_interleaved.append(Ggroup(rb_q_num)) length_index = 0 for elmnts_index in range(length_vector[-1]): for (rb_pattern_index, rb_q_num) in enumerate(pattern_sizes): for _ in range(length_multiplier[rb_pattern_index]): new_elmnt_gatelist = Gutils.random_gates( rb_q_num) Elmnts[rb_pattern_index] = Gutils.compose_gates( Elmnts[rb_pattern_index], new_elmnt_gatelist) general_circ += replace_q_indices( get_quantum_circuit(Gutils.gatelist(), rb_q_num), rb_pattern[rb_pattern_index], qr) general_circ.barrier( *[qr[x] for x in rb_pattern[rb_pattern_index]]) if interleaved_gates is not None: Elmnts_interleaved[rb_pattern_index] = \ Gutils.compose_gates( Elmnts_interleaved[rb_pattern_index], new_elmnt_gatelist) interleaved_circ += replace_q_indices( get_quantum_circuit(Gutils.gatelist(), rb_q_num), rb_pattern[rb_pattern_index], qr) Elmnts_interleaved[rb_pattern_index] = \ Gutils.compose_gates( Elmnts_interleaved[rb_pattern_index], interleaved_gates[rb_pattern_index]) interleaved_circ.barrier( *[qr[x] for x in rb_pattern[rb_pattern_index]]) interleaved_circ += replace_q_indices( get_quantum_circuit(Gutils.gatelist(), rb_q_num), rb_pattern[rb_pattern_index], qr) interleaved_circ.barrier( *[qr[x] for x in rb_pattern[rb_pattern_index]]) if align_cliffs: general_circ.barrier( *[qr[x] for x in qlist_flat]) if interleaved_gates is not None: interleaved_circ.barrier( *[qr[x] for x in qlist_flat]) if (elmnts_index+1) == length_vector[length_index]: circ = qiskit.QuantumCircuit(qr, cr) circ += general_circ circ_interleaved = qiskit.QuantumCircuit(qr, cr) circ_interleaved += interleaved_circ for (rb_pattern_index, rb_q_num) in enumerate(pattern_sizes): inv_key = Gutils.find_key(Elmnts[rb_pattern_index], rb_q_num) inv_circuit = Gutils.find_inverse_gates( rb_q_num, group_tables[rb_q_num-1][inv_key]) circ += replace_q_indices( get_quantum_circuit(inv_circuit, rb_q_num), rb_pattern[rb_pattern_index], qr) if interleaved_gates is not None: inv_key = Gutils.find_key(Elmnts_interleaved [rb_pattern_index], rb_q_num) inv_circuit = Gutils.find_inverse_gates( rb_q_num, group_tables[rb_q_num - 1][inv_key]) circ_interleaved += replace_q_indices( get_quantum_circuit(inv_circuit, rb_q_num), rb_pattern[rb_pattern_index], qr) if is_purity: circ_purity = [[] for d in range(npurity)] for d in range(npurity): circ_purity[d] = qiskit.QuantumCircuit(qr, cr) circ_purity[d] += circ circ_purity[d].name = rb_circ_type + '_purity_' ind_d = d purity_qubit_num = 0 while True: purity_qubit_rot = np.mod(ind_d, 3) ind_d = np.floor_divide(ind_d, 3) if purity_qubit_rot == 0: circ_purity[d].name += 'Z' if purity_qubit_rot == 1: for pat in rb_pattern: circ_purity[d].rx(np.pi / 2, qr[pat[ purity_qubit_num]]) circ_purity[d].name += 'X' if purity_qubit_rot == 2: for pat in rb_pattern: circ_purity[d].ry(np.pi / 2, qr[pat[ purity_qubit_num]]) circ_purity[d].name += 'Y' purity_qubit_num = purity_qubit_num + 1 if ind_d == 0: break # all circuits will have names of the same length for _ in range(max_dim - purity_qubit_num): circ_purity[d].name += 'Z' # add measurement for purity rb for qind, qb in enumerate(qlist_flat): circ_purity[d].measure(qr[qb], cr[qind]) circ_purity[d].name += '_length_%d_seed_%d' \ % (length_index, seed + seed_offset) # add measurement for Non-Clifford cnot-dihedral rb # measure both the ground state |0...0> (circ) # and the |+...+> state (cnot-dihedral_circ) cnotdihedral_circ = qiskit.QuantumCircuit(qr, cr) cnotdihedral_interleaved_circ = qiskit.QuantumCircuit(qr, cr) if group_gates_type == 1: for _, qb in enumerate(qlist_flat): cnotdihedral_circ.h(qr[qb]) cnotdihedral_circ.barrier(qr[qb]) cnotdihedral_interleaved_circ.h(qr[qb]) cnotdihedral_interleaved_circ.barrier(qr[qb]) cnotdihedral_circ += circ cnotdihedral_interleaved_circ += circ_interleaved for _, qb in enumerate(qlist_flat): cnotdihedral_circ.barrier(qr[qb]) cnotdihedral_circ.h(qr[qb]) cnotdihedral_interleaved_circ.barrier(qr[qb]) cnotdihedral_interleaved_circ.h(qr[qb]) for qind, qb in enumerate(qlist_flat): cnotdihedral_circ.measure(qr[qb], cr[qind]) cnotdihedral_interleaved_circ.measure(qr[qb], cr[qind]) # add measurement for standard rb # qubits measure to the c registers as # they appear in the pattern for qind, qb in enumerate(qlist_flat): circ.measure(qr[qb], cr[qind]) # add measurement for interleaved rb circ_interleaved.measure(qr[qb], cr[qind]) circ.name = \ rb_circ_type + '_length_%d_seed_%d' % \ (length_index, seed + seed_offset) circ_interleaved.name = \ rb_circ_type + '_interleaved_length_%d_seed_%d' % \ (length_index, seed + seed_offset) if group_gates_type == 1: circ.name = rb_circ_type + '_Z_length_%d_seed_%d' % \ (length_index, seed + seed_offset) circ_interleaved.name = \ rb_circ_type + '_interleaved_Z_length_%d_seed_%d' % \ (length_index, seed + seed_offset) cnotdihedral_circ.name = \ rb_circ_type + '_X_length_%d_seed_%d' % \ (length_index, seed + seed_offset) cnotdihedral_interleaved_circ.name = \ rb_circ_type + 'interleaved_X_length_%d_seed_%d' % \ (length_index, seed + seed_offset) circuits[seed].append(circ) circuits_interleaved[seed].append(circ_interleaved) circuits_cnotdihedral[seed].append(cnotdihedral_circ) circuits_cnotdihedral_interleaved[seed].append( cnotdihedral_interleaved_circ) if is_purity: for d in range(npurity): circuits_purity[seed][d].append(circ_purity[d]) length_index += 1 # output of purity rb if is_purity: return circuits_purity, xdata, npurity # output of non-clifford cnot-dihedral interleaved rb if interleaved_gates is not None and group_gates_type == 1: return circuits, xdata, circuits_cnotdihedral, circuits_interleaved, \ circuits_cnotdihedral_interleaved # output of interleaved rb if interleaved_gates is not None: return circuits, xdata, circuits_interleaved # output of Non-Clifford cnot-dihedral rb if group_gates_type == 1: return circuits, xdata, circuits_cnotdihedral # output of standard (simultaneous) rb return circuits, xdata def replace_q_indices(circuit, q_nums, qr): new_circuit = qiskit.QuantumCircuit(qr) for instr, qargs, cargs in circuit.data: new_qargs = [ qr[q_nums[x]] for x in [arg.index for arg in qargs]] new_op = copy.deepcopy((instr, new_qargs, cargs)) new_circuit.data.append(new_op) return new_circuit def get_quantum_circuit(gatelist, num_qubits): qr = qiskit.QuantumRegister(num_qubits) qc = qiskit.QuantumCircuit(qr) for op in gatelist: split = op.split() op_names = [split[0]] # temporary correcting the ops name since QuantumCircuit has no # attributes 'v' or 'w' yet: if op_names == ['v']: op_names = ['sdg', 'h'] elif op_names == ['w']: op_names = ['h', 's'] if op_names == ['u1']: qubits = [qr[int(x)] for x in split[2:]] theta = float(split[1]) else: qubits = [qr[int(x)] for x in split[1:]] for sub_op in op_names: operation = eval('qiskit.QuantumCircuit.' + sub_op) if sub_op == 'u1': operation(qc, theta, *qubits) else: operation(qc, *qubits) return qc

true

1c4748aa711a339da4d0853a24e1a562118a999c

1,347

py

Python

bokchoy/utils/log.py

ulule/bokchoy

58afaf325ce275edf5c4a955379afb1cc5eb5de3

[ "MIT" ]

null

bokchoy/utils/log.py

ulule/bokchoy

58afaf325ce275edf5c4a955379afb1cc5eb5de3

[ "MIT" ]

null

bokchoy/utils/log.py

ulule/bokchoy

58afaf325ce275edf5c4a955379afb1cc5eb5de3

[ "MIT" ]

null

import six import logging class NullHandler(logging.Handler): def emit(self, record): pass def logger_isa(l, p, max=1000): this, seen = l, set() for _ in range(max): if this == p: return True else: if this in seen: raise RuntimeError( 'Logger {0!r} parents recursive'.format(l), ) seen.add(this) this = this.parent if not this: break else: # pragma: no cover raise RuntimeError('Logger hierarchy exceeds {0}'.format(max)) return False def _get_logger(logger): if isinstance(logger, six.string_types): logger = logging.getLogger(logger) if not logger.handlers: logger.addHandler(NullHandler()) return logger def get_logger(name): l = _get_logger(name) if logging.root not in (l, l.parent) and l is not base_logger: if not logger_isa(l, base_logger): # pragma: no cover l.parent = base_logger return l base_logger = logger = _get_logger('bokchoy') task_logger = get_logger('bokchoy.task') worker_logger = get_logger('bokchoy.worker') def get_task_logger(name): logger = get_logger(name) if not logger_isa(logger, task_logger): logger.parent = task_logger return logger

22.081967

70

0.603563

import six import logging class NullHandler(logging.Handler): def emit(self, record): pass def logger_isa(l, p, max=1000): this, seen = l, set() for _ in range(max): if this == p: return True else: if this in seen: raise RuntimeError( 'Logger {0!r} parents recursive'.format(l), ) seen.add(this) this = this.parent if not this: break else: raise RuntimeError('Logger hierarchy exceeds {0}'.format(max)) return False def _get_logger(logger): if isinstance(logger, six.string_types): logger = logging.getLogger(logger) if not logger.handlers: logger.addHandler(NullHandler()) return logger def get_logger(name): l = _get_logger(name) if logging.root not in (l, l.parent) and l is not base_logger: if not logger_isa(l, base_logger): l.parent = base_logger return l base_logger = logger = _get_logger('bokchoy') task_logger = get_logger('bokchoy.task') worker_logger = get_logger('bokchoy.worker') def get_task_logger(name): logger = get_logger(name) if not logger_isa(logger, task_logger): logger.parent = task_logger return logger

true

1c474bb0722209c98d256697379ddc9a21064447

14,683

py

Python

salt/cloud/clouds/vultrpy.py

yuriks/salt

d2a5bd8adddb98ec1718d79384aa13b4f37e8028

[ "Apache-2.0", "MIT" ]

1

2020-03-31T22:51:16.000Z

salt/cloud/clouds/vultrpy.py

yuriks/salt

d2a5bd8adddb98ec1718d79384aa13b4f37e8028

[ "Apache-2.0", "MIT" ]

null

salt/cloud/clouds/vultrpy.py

yuriks/salt

d2a5bd8adddb98ec1718d79384aa13b4f37e8028

[ "Apache-2.0", "MIT" ]

1

2021-09-30T07:00:01.000Z

# -*- coding: utf-8 -*- ''' Vultr Cloud Module using python-vultr bindings ============================================== .. versionadded:: 2016.3.0 The Vultr cloud module is used to control access to the Vultr VPS system. Use of this module only requires the ``api_key`` parameter. Set up the cloud configuration at ``/etc/salt/cloud.providers`` or ``/etc/salt/cloud.providers.d/vultr.conf``: .. code-block:: yaml my-vultr-config: # Vultr account api key api_key: <supersecretapi_key> driver: vultr Set up the cloud profile at ``/etc/salt/cloud.profiles`` or ``/etc/salt/cloud.profiles.d/vultr.conf``: .. code-block:: yaml nyc-4gb-4cpu-ubuntu-14-04: location: 1 provider: my-vultr-config image: 160 size: 95 enable_private_network: True This driver also supports Vultr's `startup script` feature. You can list startup scripts in your account with .. code-block:: bash salt-cloud -f list_scripts <name of vultr provider> That list will include the IDs of the scripts in your account. Thus, if you have a script called 'setup-networking' with an ID of 493234 you can specify that startup script in a profile like so: .. code-block:: yaml nyc-2gb-1cpu-ubuntu-17-04: location: 1 provider: my-vultr-config image: 223 size: 13 startup_script_id: 493234 ''' # Import python libs from __future__ import absolute_import, print_function, unicode_literals import pprint import logging import time # Import salt libs import salt.config as config from salt.ext import six from salt.ext.six.moves.urllib.parse import urlencode as _urlencode # pylint: disable=E0611 from salt.exceptions import ( SaltCloudConfigError, SaltCloudSystemExit ) # Get logging started log = logging.getLogger(__name__) __virtualname__ = 'vultr' DETAILS = {} def __virtual__(): ''' Set up the Vultr functions and check for configurations ''' if get_configured_provider() is False: return False return __virtualname__ def get_configured_provider(): ''' Return the first configured instance ''' return config.is_provider_configured( __opts__, __active_provider_name__ or 'vultr', ('api_key',) ) def _cache_provider_details(conn=None): ''' Provide a place to hang onto results of --list-[locations|sizes|images] so we don't have to go out to the API and get them every time. ''' DETAILS['avail_locations'] = {} DETAILS['avail_sizes'] = {} DETAILS['avail_images'] = {} locations = avail_locations(conn) images = avail_images(conn) sizes = avail_sizes(conn) for key, location in six.iteritems(locations): DETAILS['avail_locations'][location['name']] = location DETAILS['avail_locations'][key] = location for key, image in six.iteritems(images): DETAILS['avail_images'][image['name']] = image DETAILS['avail_images'][key] = image for key, vm_size in six.iteritems(sizes): DETAILS['avail_sizes'][vm_size['name']] = vm_size DETAILS['avail_sizes'][key] = vm_size def avail_locations(conn=None): ''' return available datacenter locations ''' return _query('regions/list') def avail_scripts(conn=None): ''' return available startup scripts ''' return _query('startupscript/list') def list_scripts(conn=None, call=None): ''' return list of Startup Scripts ''' return avail_scripts() def avail_sizes(conn=None): ''' Return available sizes ("plans" in VultrSpeak) ''' return _query('plans/list') def avail_images(conn=None): ''' Return available images ''' return _query('os/list') def list_nodes(**kwargs): ''' Return basic data on nodes ''' ret = {} nodes = list_nodes_full() for node in nodes: ret[node] = {} for prop in 'id', 'image', 'size', 'state', 'private_ips', 'public_ips': ret[node][prop] = nodes[node][prop] return ret def list_nodes_full(**kwargs): ''' Return all data on nodes ''' nodes = _query('server/list') ret = {} for node in nodes: name = nodes[node]['label'] ret[name] = nodes[node].copy() ret[name]['id'] = node ret[name]['image'] = nodes[node]['os'] ret[name]['size'] = nodes[node]['VPSPLANID'] ret[name]['state'] = nodes[node]['status'] ret[name]['private_ips'] = nodes[node]['internal_ip'] ret[name]['public_ips'] = nodes[node]['main_ip'] return ret def list_nodes_select(conn=None, call=None): ''' Return a list of the VMs that are on the provider, with select fields ''' return __utils__['cloud.list_nodes_select']( list_nodes_full(), __opts__['query.selection'], call, ) def destroy(name): ''' Remove a node from Vultr ''' node = show_instance(name, call='action') params = {'SUBID': node['SUBID']} result = _query('server/destroy', method='POST', decode=False, data=_urlencode(params)) # The return of a destroy call is empty in the case of a success. # Errors are only indicated via HTTP status code. Status code 200 # effetively therefore means "success". if result.get('body') == '' and result.get('text') == '': return True return result def stop(*args, **kwargs): ''' Execute a "stop" action on a VM ''' return _query('server/halt') def start(*args, **kwargs): ''' Execute a "start" action on a VM ''' return _query('server/start') def show_instance(name, call=None): ''' Show the details from the provider concerning an instance ''' if call != 'action': raise SaltCloudSystemExit( 'The show_instance action must be called with -a or --action.' ) nodes = list_nodes_full() # Find under which cloud service the name is listed, if any if name not in nodes: return {} __utils__['cloud.cache_node'](nodes[name], __active_provider_name__, __opts__) return nodes[name] def _lookup_vultrid(which_key, availkey, keyname): ''' Helper function to retrieve a Vultr ID ''' if DETAILS == {}: _cache_provider_details() which_key = six.text_type(which_key) try: return DETAILS[availkey][which_key][keyname] except KeyError: return False def create(vm_): ''' Create a single VM from a data dict ''' if 'driver' not in vm_: vm_['driver'] = vm_['provider'] private_networking = config.get_cloud_config_value( 'enable_private_network', vm_, __opts__, search_global=False, default=False, ) startup_script = config.get_cloud_config_value( 'startup_script_id', vm_, __opts__, search_global=False, default=None, ) if startup_script and str(startup_script) not in avail_scripts(): log.error('Your Vultr account does not have a startup script with ID %s', str(startup_script)) return False if private_networking is not None: if not isinstance(private_networking, bool): raise SaltCloudConfigError("'private_networking' should be a boolean value.") if private_networking is True: enable_private_network = 'yes' else: enable_private_network = 'no' __utils__['cloud.fire_event']( 'event', 'starting create', 'salt/cloud/{0}/creating'.format(vm_['name']), args=__utils__['cloud.filter_event']('creating', vm_, ['name', 'profile', 'provider', 'driver']), sock_dir=__opts__['sock_dir'], transport=__opts__['transport'] ) osid = _lookup_vultrid(vm_['image'], 'avail_images', 'OSID') if not osid: log.error('Vultr does not have an image with id or name %s', vm_['image']) return False vpsplanid = _lookup_vultrid(vm_['size'], 'avail_sizes', 'VPSPLANID') if not vpsplanid: log.error('Vultr does not have a size with id or name %s', vm_['size']) return False dcid = _lookup_vultrid(vm_['location'], 'avail_locations', 'DCID') if not dcid: log.error('Vultr does not have a location with id or name %s', vm_['location']) return False kwargs = { 'label': vm_['name'], 'OSID': osid, 'VPSPLANID': vpsplanid, 'DCID': dcid, 'hostname': vm_['name'], 'enable_private_network': enable_private_network, } if startup_script: kwargs['SCRIPTID'] = startup_script log.info('Creating Cloud VM %s', vm_['name']) __utils__['cloud.fire_event']( 'event', 'requesting instance', 'salt/cloud/{0}/requesting'.format(vm_['name']), args={ 'kwargs': __utils__['cloud.filter_event']('requesting', kwargs, list(kwargs)), }, sock_dir=__opts__['sock_dir'], transport=__opts__['transport'], ) try: data = _query('server/create', method='POST', data=_urlencode(kwargs)) if int(data.get('status', '200')) >= 300: log.error( 'Error creating %s on Vultr\n\n' 'Vultr API returned %s\n', vm_['name'], data ) log.error('Status 412 may mean that you are requesting an\n' 'invalid location, image, or size.') __utils__['cloud.fire_event']( 'event', 'instance request failed', 'salt/cloud/{0}/requesting/failed'.format(vm_['name']), args={'kwargs': kwargs}, sock_dir=__opts__['sock_dir'], transport=__opts__['transport'], ) return False except Exception as exc: # pylint: disable=broad-except log.error( 'Error creating %s on Vultr\n\n' 'The following exception was thrown when trying to ' 'run the initial deployment:\n%s', vm_['name'], exc, # Show the traceback if the debug logging level is enabled exc_info_on_loglevel=logging.DEBUG ) __utils__['cloud.fire_event']( 'event', 'instance request failed', 'salt/cloud/{0}/requesting/failed'.format(vm_['name']), args={'kwargs': kwargs}, sock_dir=__opts__['sock_dir'], transport=__opts__['transport'], ) return False def wait_for_hostname(): ''' Wait for the IP address to become available ''' data = show_instance(vm_['name'], call='action') main_ip = six.text_type(data.get('main_ip', '0')) if main_ip.startswith('0'): time.sleep(3) return False return data['main_ip'] def wait_for_default_password(): ''' Wait for the IP address to become available ''' data = show_instance(vm_['name'], call='action') # print("Waiting for default password") # pprint.pprint(data) if six.text_type(data.get('default_password', '')) == '': time.sleep(1) return False return data['default_password'] def wait_for_status(): ''' Wait for the IP address to become available ''' data = show_instance(vm_['name'], call='action') # print("Waiting for status normal") # pprint.pprint(data) if six.text_type(data.get('status', '')) != 'active': time.sleep(1) return False return data['default_password'] def wait_for_server_state(): ''' Wait for the IP address to become available ''' data = show_instance(vm_['name'], call='action') # print("Waiting for server state ok") # pprint.pprint(data) if six.text_type(data.get('server_state', '')) != 'ok': time.sleep(1) return False return data['default_password'] vm_['ssh_host'] = __utils__['cloud.wait_for_fun']( wait_for_hostname, timeout=config.get_cloud_config_value( 'wait_for_fun_timeout', vm_, __opts__, default=15 * 60), ) vm_['password'] = __utils__['cloud.wait_for_fun']( wait_for_default_password, timeout=config.get_cloud_config_value( 'wait_for_fun_timeout', vm_, __opts__, default=15 * 60), ) __utils__['cloud.wait_for_fun']( wait_for_status, timeout=config.get_cloud_config_value( 'wait_for_fun_timeout', vm_, __opts__, default=15 * 60), ) __utils__['cloud.wait_for_fun']( wait_for_server_state, timeout=config.get_cloud_config_value( 'wait_for_fun_timeout', vm_, __opts__, default=15 * 60), ) __opts__['hard_timeout'] = config.get_cloud_config_value( 'hard_timeout', get_configured_provider(), __opts__, search_global=False, default=None, ) # Bootstrap ret = __utils__['cloud.bootstrap'](vm_, __opts__) ret.update(show_instance(vm_['name'], call='action')) log.info('Created Cloud VM \'%s\'', vm_['name']) log.debug( '\'%s\' VM creation details:\n%s', vm_['name'], pprint.pformat(data) ) __utils__['cloud.fire_event']( 'event', 'created instance', 'salt/cloud/{0}/created'.format(vm_['name']), args=__utils__['cloud.filter_event']('created', vm_, ['name', 'profile', 'provider', 'driver']), sock_dir=__opts__['sock_dir'], transport=__opts__['transport'] ) return ret def _query(path, method='GET', data=None, params=None, header_dict=None, decode=True): ''' Perform a query directly against the Vultr REST API ''' api_key = config.get_cloud_config_value( 'api_key', get_configured_provider(), __opts__, search_global=False, ) management_host = config.get_cloud_config_value( 'management_host', get_configured_provider(), __opts__, search_global=False, default='api.vultr.com' ) url = 'https://{management_host}/v1/{path}?api_key={api_key}'.format( management_host=management_host, path=path, api_key=api_key, ) if header_dict is None: header_dict = {} result = __utils__['http.query']( url, method=method, params=params, data=data, header_dict=header_dict, port=443, text=True, decode=decode, decode_type='json', hide_fields=['api_key'], opts=__opts__, ) if 'dict' in result: return result['dict'] return result

28.236538

105

0.60914

from __future__ import absolute_import, print_function, unicode_literals import pprint import logging import time import salt.config as config from salt.ext import six from salt.ext.six.moves.urllib.parse import urlencode as _urlencode from salt.exceptions import ( SaltCloudConfigError, SaltCloudSystemExit ) log = logging.getLogger(__name__) __virtualname__ = 'vultr' DETAILS = {} def __virtual__(): if get_configured_provider() is False: return False return __virtualname__ def get_configured_provider(): return config.is_provider_configured( __opts__, __active_provider_name__ or 'vultr', ('api_key',) ) def _cache_provider_details(conn=None): DETAILS['avail_locations'] = {} DETAILS['avail_sizes'] = {} DETAILS['avail_images'] = {} locations = avail_locations(conn) images = avail_images(conn) sizes = avail_sizes(conn) for key, location in six.iteritems(locations): DETAILS['avail_locations'][location['name']] = location DETAILS['avail_locations'][key] = location for key, image in six.iteritems(images): DETAILS['avail_images'][image['name']] = image DETAILS['avail_images'][key] = image for key, vm_size in six.iteritems(sizes): DETAILS['avail_sizes'][vm_size['name']] = vm_size DETAILS['avail_sizes'][key] = vm_size def avail_locations(conn=None): return _query('regions/list') def avail_scripts(conn=None): return _query('startupscript/list') def list_scripts(conn=None, call=None): return avail_scripts() def avail_sizes(conn=None): return _query('plans/list') def avail_images(conn=None): return _query('os/list') def list_nodes(**kwargs): ret = {} nodes = list_nodes_full() for node in nodes: ret[node] = {} for prop in 'id', 'image', 'size', 'state', 'private_ips', 'public_ips': ret[node][prop] = nodes[node][prop] return ret def list_nodes_full(**kwargs): nodes = _query('server/list') ret = {} for node in nodes: name = nodes[node]['label'] ret[name] = nodes[node].copy() ret[name]['id'] = node ret[name]['image'] = nodes[node]['os'] ret[name]['size'] = nodes[node]['VPSPLANID'] ret[name]['state'] = nodes[node]['status'] ret[name]['private_ips'] = nodes[node]['internal_ip'] ret[name]['public_ips'] = nodes[node]['main_ip'] return ret def list_nodes_select(conn=None, call=None): return __utils__['cloud.list_nodes_select']( list_nodes_full(), __opts__['query.selection'], call, ) def destroy(name): node = show_instance(name, call='action') params = {'SUBID': node['SUBID']} result = _query('server/destroy', method='POST', decode=False, data=_urlencode(params)) if result.get('body') == '' and result.get('text') == '': return True return result def stop(*args, **kwargs): return _query('server/halt') def start(*args, **kwargs): return _query('server/start') def show_instance(name, call=None): if call != 'action': raise SaltCloudSystemExit( 'The show_instance action must be called with -a or --action.' ) nodes = list_nodes_full() if name not in nodes: return {} __utils__['cloud.cache_node'](nodes[name], __active_provider_name__, __opts__) return nodes[name] def _lookup_vultrid(which_key, availkey, keyname): if DETAILS == {}: _cache_provider_details() which_key = six.text_type(which_key) try: return DETAILS[availkey][which_key][keyname] except KeyError: return False def create(vm_): if 'driver' not in vm_: vm_['driver'] = vm_['provider'] private_networking = config.get_cloud_config_value( 'enable_private_network', vm_, __opts__, search_global=False, default=False, ) startup_script = config.get_cloud_config_value( 'startup_script_id', vm_, __opts__, search_global=False, default=None, ) if startup_script and str(startup_script) not in avail_scripts(): log.error('Your Vultr account does not have a startup script with ID %s', str(startup_script)) return False if private_networking is not None: if not isinstance(private_networking, bool): raise SaltCloudConfigError("'private_networking' should be a boolean value.") if private_networking is True: enable_private_network = 'yes' else: enable_private_network = 'no' __utils__['cloud.fire_event']( 'event', 'starting create', 'salt/cloud/{0}/creating'.format(vm_['name']), args=__utils__['cloud.filter_event']('creating', vm_, ['name', 'profile', 'provider', 'driver']), sock_dir=__opts__['sock_dir'], transport=__opts__['transport'] ) osid = _lookup_vultrid(vm_['image'], 'avail_images', 'OSID') if not osid: log.error('Vultr does not have an image with id or name %s', vm_['image']) return False vpsplanid = _lookup_vultrid(vm_['size'], 'avail_sizes', 'VPSPLANID') if not vpsplanid: log.error('Vultr does not have a size with id or name %s', vm_['size']) return False dcid = _lookup_vultrid(vm_['location'], 'avail_locations', 'DCID') if not dcid: log.error('Vultr does not have a location with id or name %s', vm_['location']) return False kwargs = { 'label': vm_['name'], 'OSID': osid, 'VPSPLANID': vpsplanid, 'DCID': dcid, 'hostname': vm_['name'], 'enable_private_network': enable_private_network, } if startup_script: kwargs['SCRIPTID'] = startup_script log.info('Creating Cloud VM %s', vm_['name']) __utils__['cloud.fire_event']( 'event', 'requesting instance', 'salt/cloud/{0}/requesting'.format(vm_['name']), args={ 'kwargs': __utils__['cloud.filter_event']('requesting', kwargs, list(kwargs)), }, sock_dir=__opts__['sock_dir'], transport=__opts__['transport'], ) try: data = _query('server/create', method='POST', data=_urlencode(kwargs)) if int(data.get('status', '200')) >= 300: log.error( 'Error creating %s on Vultr\n\n' 'Vultr API returned %s\n', vm_['name'], data ) log.error('Status 412 may mean that you are requesting an\n' 'invalid location, image, or size.') __utils__['cloud.fire_event']( 'event', 'instance request failed', 'salt/cloud/{0}/requesting/failed'.format(vm_['name']), args={'kwargs': kwargs}, sock_dir=__opts__['sock_dir'], transport=__opts__['transport'], ) return False except Exception as exc: log.error( 'Error creating %s on Vultr\n\n' 'The following exception was thrown when trying to ' 'run the initial deployment:\n%s', vm_['name'], exc, exc_info_on_loglevel=logging.DEBUG ) __utils__['cloud.fire_event']( 'event', 'instance request failed', 'salt/cloud/{0}/requesting/failed'.format(vm_['name']), args={'kwargs': kwargs}, sock_dir=__opts__['sock_dir'], transport=__opts__['transport'], ) return False def wait_for_hostname(): data = show_instance(vm_['name'], call='action') main_ip = six.text_type(data.get('main_ip', '0')) if main_ip.startswith('0'): time.sleep(3) return False return data['main_ip'] def wait_for_default_password(): data = show_instance(vm_['name'], call='action') if six.text_type(data.get('default_password', '')) == '': time.sleep(1) return False return data['default_password'] def wait_for_status(): data = show_instance(vm_['name'], call='action') if six.text_type(data.get('status', '')) != 'active': time.sleep(1) return False return data['default_password'] def wait_for_server_state(): data = show_instance(vm_['name'], call='action') if six.text_type(data.get('server_state', '')) != 'ok': time.sleep(1) return False return data['default_password'] vm_['ssh_host'] = __utils__['cloud.wait_for_fun']( wait_for_hostname, timeout=config.get_cloud_config_value( 'wait_for_fun_timeout', vm_, __opts__, default=15 * 60), ) vm_['password'] = __utils__['cloud.wait_for_fun']( wait_for_default_password, timeout=config.get_cloud_config_value( 'wait_for_fun_timeout', vm_, __opts__, default=15 * 60), ) __utils__['cloud.wait_for_fun']( wait_for_status, timeout=config.get_cloud_config_value( 'wait_for_fun_timeout', vm_, __opts__, default=15 * 60), ) __utils__['cloud.wait_for_fun']( wait_for_server_state, timeout=config.get_cloud_config_value( 'wait_for_fun_timeout', vm_, __opts__, default=15 * 60), ) __opts__['hard_timeout'] = config.get_cloud_config_value( 'hard_timeout', get_configured_provider(), __opts__, search_global=False, default=None, ) ret = __utils__['cloud.bootstrap'](vm_, __opts__) ret.update(show_instance(vm_['name'], call='action')) log.info('Created Cloud VM \'%s\'', vm_['name']) log.debug( '\'%s\' VM creation details:\n%s', vm_['name'], pprint.pformat(data) ) __utils__['cloud.fire_event']( 'event', 'created instance', 'salt/cloud/{0}/created'.format(vm_['name']), args=__utils__['cloud.filter_event']('created', vm_, ['name', 'profile', 'provider', 'driver']), sock_dir=__opts__['sock_dir'], transport=__opts__['transport'] ) return ret def _query(path, method='GET', data=None, params=None, header_dict=None, decode=True): api_key = config.get_cloud_config_value( 'api_key', get_configured_provider(), __opts__, search_global=False, ) management_host = config.get_cloud_config_value( 'management_host', get_configured_provider(), __opts__, search_global=False, default='api.vultr.com' ) url = 'https://{management_host}/v1/{path}?api_key={api_key}'.format( management_host=management_host, path=path, api_key=api_key, ) if header_dict is None: header_dict = {} result = __utils__['http.query']( url, method=method, params=params, data=data, header_dict=header_dict, port=443, text=True, decode=decode, decode_type='json', hide_fields=['api_key'], opts=__opts__, ) if 'dict' in result: return result['dict'] return result

true

1c474bcbae3af33fdc44d18a2aa1c4f0fe87dcdd

7,974

py

Python

scripts/process_perspective.py

dbckz/crossing-the-line

c5debb20e263e03eab9188ce7229753034939964

[ "MIT" ]

1

2022-02-14T17:11:30.000Z

scripts/process_perspective.py

dbckz/crossing-the-line

c5debb20e263e03eab9188ce7229753034939964

[ "MIT" ]

null

scripts/process_perspective.py

dbckz/crossing-the-line

c5debb20e263e03eab9188ce7229753034939964

[ "MIT" ]

null

""" Script to evaluate tweets against the Perspective API How it's used: * Loads "tweets.csv" files according to 'root_path' and 'day_paths' vars * Sends one tweet at a time to the API * Sleeps for 1 second between requests due to API rate-limit * Appends results to perspective_processed_tweets.csv after every 50 tweets, so that not all progress is lost if the script were to die midway through processing a file """ import os import time import numpy as np import pandas as pd from googleapiclient import discovery def get_perspective_client(api_key): return discovery.build( "commentanalyzer", "v1alpha1", developerKey=api_key, discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1", static_discovery=False, ) def query_perspective(client, text, tweet_id, logfile): analyze_request = { 'comment': { 'text': text }, 'requestedAttributes': { 'TOXICITY': {}, 'SEVERE_TOXICITY': {}, 'IDENTITY_ATTACK': {}, 'INSULT': {}, 'THREAT': {}, 'SEXUALLY_EXPLICIT': {} } } try: response = client.comments().analyze(body=analyze_request).execute() toxicity_score = response['attributeScores']['TOXICITY']['summaryScore']['value'] severe_toxicity_score = response['attributeScores']['SEVERE_TOXICITY']['summaryScore']['value'] identity_attack_score = response['attributeScores']['IDENTITY_ATTACK']['summaryScore']['value'] insult_score = response['attributeScores']['INSULT']['summaryScore']['value'] threat_score = response['attributeScores']['THREAT']['summaryScore']['value'] sexually_explicit_score = response['attributeScores']['SEXUALLY_EXPLICIT']['summaryScore']['value'] return { "toxicity_score": toxicity_score, "severe_toxicity_score": severe_toxicity_score, "identity_attack_score": identity_attack_score, "insult_score": insult_score, "threat_score": threat_score, "sexually_explicit_score": sexually_explicit_score, "error": "" } except Exception as e: with open(logfile, 'a') as f: f.write(f"{time.ctime()}: EXCEPTION. Tweet Id: {tweet_id}: {e}") f.write('\n') print(f"EXCEPTION. Tweet Id: {tweet_id}: {e}") if ('reason' in e.error_details[0] and e.error_details[0]['reason'] == 'RATE_LIMIT_EXCEEDED'): with open(logfile, 'a') as f: sleeptime = 70 f.write(f"{time.ctime()}: Sleeping for {sleeptime} seconds") f.write('\n') print(f"Sleeping for {sleeptime} seconds") time.sleep(70) return query_perspective(client, text, tweet_id, logfile) return { "toxicity_score": -1, "severe_toxicity_score": -1, "identity_attack_score": -1, "insult_score": -1, "threat_score": -1, "sexually_explicit_score": -1, "error": "ERROR" } def process_tweet(tweet, perspective_client, output_dataframe, logfile): data = query_perspective(perspective_client, tweet['tweet_text'], tweet['tweet_id'], logfile) output_dataframe.loc[tweet['tweet_id']] = [ tweet['tweet_id'], data['toxicity_score'], data['severe_toxicity_score'], data['identity_attack_score'], data['insult_score'], data['threat_score'], data['sexually_explicit_score'], data['error'] ] def process_day(directory): logfile = directory + "/perspective_error_log.txt" progress_logfile = directory + "/perspective_progress_log.txt" with open(progress_logfile, 'a') as f: f.write(f"{time.ctime()}: Starting processing for {directory}") f.write('\n') print(f"Starting processing for {directory}") # Load tweet CSV file in_csv = directory + "/tweets.csv" out_csv = directory + "/perspective_processed_tweets.csv" # Delete existing output file if it exists if os.path.exists(out_csv): os.remove(out_csv) number_lines = sum(1 for row in (open(in_csv))) chunk_size = 50 tweets_remaining = number_lines - 1 with open(progress_logfile, 'a') as f: f.write(f"{time.ctime()}: Number of tweets: {tweets_remaining}") f.write('\n') print(f"Number of tweets: {tweets_remaining}") for i in range(0, number_lines, chunk_size): start = time.time() in_tweets = pd.read_csv(in_csv, header=0, nrows=chunk_size, # number of rows to read at each loop skiprows=range(1, i)) # skip rows that have been read if (i == 0): print(f"Loaded first {len(in_tweets.index)} tweets.") out_tweets = pd.DataFrame( columns=["tweet_id", "toxicity_score", "severe_toxicity_score", "identity_attack_score", "insult_score", "threat_score", "sexually_explicit_score", "error"]) # Do processing for tweet for _, row in in_tweets.iterrows(): process_tweet(row, perspective_client, out_tweets, logfile) time.sleep(1) # Sleep due to 1 req/second limit on Perspective API # Ensure tweet_id written as int new_dtypes = { "tweet_id": int, "toxicity_score": np.float64, "severe_toxicity_score": np.float64, "identity_attack_score": np.float64, "insult_score": np.float64, "threat_score": np.float64, "sexually_explicit_score": np.float64, "error": str } out_tweets = out_tweets.astype(new_dtypes) if (i == 0): out_tweets.to_csv(out_csv, index=False, header=True, mode='a', # append data to csv file chunksize=chunk_size) # size of data to append for each loop else: out_tweets.to_csv(out_csv, index=False, header=False, mode='a', # append data to csv file chunksize=chunk_size) # size of data to append for each loop tweets_remaining = tweets_remaining - len(out_tweets.index) msg = f"Processed {len(out_tweets.index)} tweets in {time.time() - start} seconds. {tweets_remaining} tweets remaining." with open(progress_logfile, 'a') as f: f.write(f"{time.ctime()}: {msg}") f.write('\n') print(msg) with open(progress_logfile, 'a') as f: f.write(f"{time.ctime()}: Completed processing for {directory}") f.write('\n') print(f"Completed processing for {directory}") if __name__ == "__main__": root_path = "/Users/davebuckley/Documents/Kings/Dissertation/dissertation/data_collection" day_paths = [ "/01", "/02", "/03", "/04", "/05", "/06", "/07", "/08", "/09", "/10", "/11", "/12", "/13", "/14", "/15", "/16", "/17", "/18", "/19", "/20", "/21", "/22", "/23", "/24", "/25", "/26", "/27", "/28", "/29", "/30", "/31", "/32", "/33", "/34", "/35", "/36" ] # Auth to Perspective API print("Connecting to Perspective API") API_KEY = os.getenv("PERSPECTIVE_API_KEY") perspective_client = get_perspective_client(API_KEY) print("Connected to Perspective API") for day in day_paths: process_day(root_path + day) print("All completed")

34.37069

128

0.568096

import os import time import numpy as np import pandas as pd from googleapiclient import discovery def get_perspective_client(api_key): return discovery.build( "commentanalyzer", "v1alpha1", developerKey=api_key, discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1", static_discovery=False, ) def query_perspective(client, text, tweet_id, logfile): analyze_request = { 'comment': { 'text': text }, 'requestedAttributes': { 'TOXICITY': {}, 'SEVERE_TOXICITY': {}, 'IDENTITY_ATTACK': {}, 'INSULT': {}, 'THREAT': {}, 'SEXUALLY_EXPLICIT': {} } } try: response = client.comments().analyze(body=analyze_request).execute() toxicity_score = response['attributeScores']['TOXICITY']['summaryScore']['value'] severe_toxicity_score = response['attributeScores']['SEVERE_TOXICITY']['summaryScore']['value'] identity_attack_score = response['attributeScores']['IDENTITY_ATTACK']['summaryScore']['value'] insult_score = response['attributeScores']['INSULT']['summaryScore']['value'] threat_score = response['attributeScores']['THREAT']['summaryScore']['value'] sexually_explicit_score = response['attributeScores']['SEXUALLY_EXPLICIT']['summaryScore']['value'] return { "toxicity_score": toxicity_score, "severe_toxicity_score": severe_toxicity_score, "identity_attack_score": identity_attack_score, "insult_score": insult_score, "threat_score": threat_score, "sexually_explicit_score": sexually_explicit_score, "error": "" } except Exception as e: with open(logfile, 'a') as f: f.write(f"{time.ctime()}: EXCEPTION. Tweet Id: {tweet_id}: {e}") f.write('\n') print(f"EXCEPTION. Tweet Id: {tweet_id}: {e}") if ('reason' in e.error_details[0] and e.error_details[0]['reason'] == 'RATE_LIMIT_EXCEEDED'): with open(logfile, 'a') as f: sleeptime = 70 f.write(f"{time.ctime()}: Sleeping for {sleeptime} seconds") f.write('\n') print(f"Sleeping for {sleeptime} seconds") time.sleep(70) return query_perspective(client, text, tweet_id, logfile) return { "toxicity_score": -1, "severe_toxicity_score": -1, "identity_attack_score": -1, "insult_score": -1, "threat_score": -1, "sexually_explicit_score": -1, "error": "ERROR" } def process_tweet(tweet, perspective_client, output_dataframe, logfile): data = query_perspective(perspective_client, tweet['tweet_text'], tweet['tweet_id'], logfile) output_dataframe.loc[tweet['tweet_id']] = [ tweet['tweet_id'], data['toxicity_score'], data['severe_toxicity_score'], data['identity_attack_score'], data['insult_score'], data['threat_score'], data['sexually_explicit_score'], data['error'] ] def process_day(directory): logfile = directory + "/perspective_error_log.txt" progress_logfile = directory + "/perspective_progress_log.txt" with open(progress_logfile, 'a') as f: f.write(f"{time.ctime()}: Starting processing for {directory}") f.write('\n') print(f"Starting processing for {directory}") in_csv = directory + "/tweets.csv" out_csv = directory + "/perspective_processed_tweets.csv" if os.path.exists(out_csv): os.remove(out_csv) number_lines = sum(1 for row in (open(in_csv))) chunk_size = 50 tweets_remaining = number_lines - 1 with open(progress_logfile, 'a') as f: f.write(f"{time.ctime()}: Number of tweets: {tweets_remaining}") f.write('\n') print(f"Number of tweets: {tweets_remaining}") for i in range(0, number_lines, chunk_size): start = time.time() in_tweets = pd.read_csv(in_csv, header=0, nrows=chunk_size, skiprows=range(1, i)) if (i == 0): print(f"Loaded first {len(in_tweets.index)} tweets.") out_tweets = pd.DataFrame( columns=["tweet_id", "toxicity_score", "severe_toxicity_score", "identity_attack_score", "insult_score", "threat_score", "sexually_explicit_score", "error"]) for _, row in in_tweets.iterrows(): process_tweet(row, perspective_client, out_tweets, logfile) time.sleep(1) new_dtypes = { "tweet_id": int, "toxicity_score": np.float64, "severe_toxicity_score": np.float64, "identity_attack_score": np.float64, "insult_score": np.float64, "threat_score": np.float64, "sexually_explicit_score": np.float64, "error": str } out_tweets = out_tweets.astype(new_dtypes) if (i == 0): out_tweets.to_csv(out_csv, index=False, header=True, mode='a', chunksize=chunk_size) else: out_tweets.to_csv(out_csv, index=False, header=False, mode='a', chunksize=chunk_size) tweets_remaining = tweets_remaining - len(out_tweets.index) msg = f"Processed {len(out_tweets.index)} tweets in {time.time() - start} seconds. {tweets_remaining} tweets remaining." with open(progress_logfile, 'a') as f: f.write(f"{time.ctime()}: {msg}") f.write('\n') print(msg) with open(progress_logfile, 'a') as f: f.write(f"{time.ctime()}: Completed processing for {directory}") f.write('\n') print(f"Completed processing for {directory}") if __name__ == "__main__": root_path = "/Users/davebuckley/Documents/Kings/Dissertation/dissertation/data_collection" day_paths = [ "/01", "/02", "/03", "/04", "/05", "/06", "/07", "/08", "/09", "/10", "/11", "/12", "/13", "/14", "/15", "/16", "/17", "/18", "/19", "/20", "/21", "/22", "/23", "/24", "/25", "/26", "/27", "/28", "/29", "/30", "/31", "/32", "/33", "/34", "/35", "/36" ] print("Connecting to Perspective API") API_KEY = os.getenv("PERSPECTIVE_API_KEY") perspective_client = get_perspective_client(API_KEY) print("Connected to Perspective API") for day in day_paths: process_day(root_path + day) print("All completed")

true

1c474c7f2acba2c62fabc8f02e4bf556a023e101

1,066

py

Python

jesse/indicators/pfe.py

leaiannotti/jesse

564c54845774891ff3b5a8d3c02cc7cea890ac54

[ "MIT" ]

5

2021-05-21T07:39:16.000Z

2021-11-17T11:08:41.000Z

jesse/indicators/pfe.py

leaiannotti/jesse

564c54845774891ff3b5a8d3c02cc7cea890ac54

[ "MIT" ]

null

jesse/indicators/pfe.py

leaiannotti/jesse

564c54845774891ff3b5a8d3c02cc7cea890ac54

[ "MIT" ]

2

2021-05-21T10:14:53.000Z

2021-05-27T04:39:51.000Z

from typing import Union import numpy as np import talib from jesse.helpers import get_candle_source, slice_candles, same_length def pfe(candles: np.ndarray, period: int = 10, smoothing: int = 5, source_type: str = "close", sequential: bool = False) -> Union[ float, np.ndarray]: """ Polarized Fractal Efficiency (PFE) :param candles: np.ndarray :param period: int - default: 10 :param smoothing: int - default: 5 :param source_type: str - default: "close" :param sequential: bool - default=False :return: float | np.ndarray """ candles = slice_candles(candles, sequential) source = get_candle_source(candles, source_type=source_type) ln = period - 1 diff = np.diff(source, ln) a = np.sqrt(np.power(diff, 2) + np.power(period, 2)) b = talib.SUM(np.sqrt(1 + np.power(np.diff(source, 1), 2)), ln) pfetmp = 100 * same_length(source, a) / same_length(source, b) res = talib.EMA(np.where(same_length(source, diff) > 0, pfetmp, -pfetmp), smoothing) return res if sequential else res[-1]

31.352941

130

0.67167

from typing import Union import numpy as np import talib from jesse.helpers import get_candle_source, slice_candles, same_length def pfe(candles: np.ndarray, period: int = 10, smoothing: int = 5, source_type: str = "close", sequential: bool = False) -> Union[ float, np.ndarray]: candles = slice_candles(candles, sequential) source = get_candle_source(candles, source_type=source_type) ln = period - 1 diff = np.diff(source, ln) a = np.sqrt(np.power(diff, 2) + np.power(period, 2)) b = talib.SUM(np.sqrt(1 + np.power(np.diff(source, 1), 2)), ln) pfetmp = 100 * same_length(source, a) / same_length(source, b) res = talib.EMA(np.where(same_length(source, diff) > 0, pfetmp, -pfetmp), smoothing) return res if sequential else res[-1]

true

1c474d6b5e003a2cec79900ccf7c78c070a40e62

24,545

py

Python

lib/model_eval/model_eval_ncnet_adap.py

JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching

b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9

[ "MIT" ]

1

2021-07-22T05:18:10.000Z

lib/model_eval/model_eval_ncnet_adap.py

JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching

b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9

[ "MIT" ]

null

lib/model_eval/model_eval_ncnet_adap.py

JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching

b79e0e20fd5a1a9ddc0ffa9d7a92e0ebd21018b9

[ "MIT" ]

null

from __future__ import print_function, division from collections import OrderedDict import torch import torch.nn as nn from torch.autograd import Variable import torchvision.models as models import numpy as np import numpy.matlib import pickle from lib.torch_util import Softmax1D from lib.conv4d import Conv4d from lib.matching_model import CMDTop from lib.matching_model import unNormMap1D_to_NormMap2D, NormMap2D_to_unNormMap2D from lib.showPlot import plot_test_map, plot_test_flow, warpImg_fromMap, warpImg_fromMap2, matplotlib_imshow, return_plot_test_map, get_img_from_fig import torch.nn.functional as F def featureL2Norm(feature): epsilon = 1e-6 norm = torch.pow(torch.sum(torch.pow(feature, 2), 1) + epsilon, 0.5).unsqueeze(1).expand_as(feature) return torch.div(feature, norm) class FeatureExtraction(torch.nn.Module): def __init__(self, train_fe=False, feature_extraction_cnn='resnet101', feature_extraction_model_file='', normalization=False, last_layer='', use_cuda=True): super(FeatureExtraction, self).__init__() self.normalization = normalization self.feature_extraction_cnn = feature_extraction_cnn if feature_extraction_cnn == 'vgg': self.model = models.vgg16(pretrained=True) # keep feature extraction network up to indicated layer vgg_feature_layers = ['conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'pool5'] if last_layer == '': last_layer = 'pool4' last_layer_idx = vgg_feature_layers.index(last_layer) self.model = nn.Sequential(*list(self.model.features.children())[:last_layer_idx + 1]) # for resnet below resnet_feature_layers = ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4'] if feature_extraction_cnn == 'resnet101': self.model = models.resnet101(pretrained=True) if last_layer == '': last_layer = 'layer3' resnet_module_list = [getattr(self.model, l) for l in resnet_feature_layers] last_layer_idx = resnet_feature_layers.index(last_layer) self.model = nn.Sequential(*resnet_module_list[:last_layer_idx + 1]) if feature_extraction_cnn == 'resnet101fpn': if feature_extraction_model_file != '': resnet = models.resnet101(pretrained=True) # swap stride (2,2) and (1,1) in first layers (PyTorch ResNet is slightly different to caffe2 ResNet) # this is required for compatibility with caffe2 models resnet.layer2[0].conv1.stride = (2, 2) resnet.layer2[0].conv2.stride = (1, 1) resnet.layer3[0].conv1.stride = (2, 2) resnet.layer3[0].conv2.stride = (1, 1) resnet.layer4[0].conv1.stride = (2, 2) resnet.layer4[0].conv2.stride = (1, 1) else: resnet = models.resnet101(pretrained=True) resnet_module_list = [getattr(resnet, l) for l in resnet_feature_layers] conv_body = nn.Sequential(*resnet_module_list) self.model = fpn_body(conv_body, resnet_feature_layers, fpn_layers=['layer1', 'layer2', 'layer3'], normalize=normalization, hypercols=True) if feature_extraction_model_file != '': self.model.load_pretrained_weights(feature_extraction_model_file) if feature_extraction_cnn == 'densenet201': self.model = models.densenet201(pretrained=True) # keep feature extraction network up to denseblock3 # self.model = nn.Sequential(*list(self.model.features.children())[:-3]) # keep feature extraction network up to transitionlayer2 self.model = nn.Sequential(*list(self.model.features.children())[:-4]) if train_fe == False: # freeze parameters for param in self.model.parameters(): param.requires_grad = False # move to GPU if use_cuda: self.model = self.model.cuda() def forward(self, image_batch): features = self.model(image_batch) return features class adap_layer_feat3(nn.Module): def __init__(self): super(adap_layer_feat3, self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(1024, 1024, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(1024), nn.ReLU() ) self.conv2 = nn.Sequential( nn.Conv2d(1024, 1024, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(1024), nn.ReLU() ) GPU_NUM = torch.cuda.current_device() device = torch.device(f'cuda:{GPU_NUM}' if torch.cuda.is_available() else 'cpu') print("find_correspondence_gpu:",device) use_cuda = torch.cuda.is_available() if use_cuda: self.conv1.cuda() self.conv2.cuda() def forward(self, feature): feature = feature + self.conv1(feature) feature = feature + self.conv2(feature) return feature class FeatureCorrelation(torch.nn.Module): def __init__(self, shape='3D', normalization=True): super(FeatureCorrelation, self).__init__() self.normalization = normalization self.shape = shape self.ReLU = nn.ReLU() def forward(self, feature_A, feature_B): if self.shape == '3D': b, c, h, w = feature_A.size() # reshape features for matrix multiplication feature_A = feature_A.transpose(2, 3).contiguous().view(b, c, h * w) feature_B = feature_B.view(b, c, h * w).transpose(1, 2) # perform matrix mult. feature_mul = torch.bmm(feature_B, feature_A) # indexed [batch,idx_A=row_A+h*col_A,row_B,col_B] correlation_tensor = feature_mul.view(b, h, w, h * w).transpose(2, 3).transpose(1, 2) elif self.shape == '4D': b, c, hA, wA = feature_A.size() b, c, hB, wB = feature_B.size() # reshape features for matrix multiplication feature_A = feature_A.view(b, c, hA * wA).transpose(1, 2) # size [b,c,h*w] feature_B = feature_B.view(b, c, hB * wB) # size [b,c,h*w] # perform matrix mult. feature_mul = torch.bmm(feature_A, feature_B) # indexed [batch,row_A,col_A,row_B,col_B] correlation_tensor = feature_mul.view(b, hA, wA, hB, wB).unsqueeze(1) if self.normalization: correlation_tensor = featureL2Norm(self.ReLU(correlation_tensor)) return correlation_tensor class NeighConsensus(torch.nn.Module): def __init__(self, use_cuda=True, kernel_sizes=[3, 3, 3], channels=[10, 10, 1], symmetric_mode=False): super(NeighConsensus, self).__init__() self.symmetric_mode = symmetric_mode self.kernel_sizes = kernel_sizes self.channels = channels num_layers = len(kernel_sizes) nn_modules = list() for i in range(num_layers): if i == 0: ch_in = 1 else: ch_in = channels[i - 1] ch_out = channels[i] k_size = kernel_sizes[i] nn_modules.append(Conv4d(in_channels=ch_in, out_channels=ch_out, kernel_size=k_size, bias=True)) nn_modules.append(nn.ReLU(inplace=True)) self.conv = nn.Sequential(*nn_modules) if use_cuda: self.conv.cuda() def forward(self, x): if self.symmetric_mode: # apply network on the input and its "transpose" (swapping A-B to B-A ordering of the correlation tensor), # this second result is "transposed back" to the A-B ordering to match the first result and be able to add together x = self.conv(x) + self.conv(x.permute(0, 1, 4, 5, 2, 3)).permute(0, 1, 4, 5, 2, 3) # because of the ReLU layers in between linear layers, # this operation is different than convolving a single time with the filters+filters^T # and therefore it makes sense to do this. else: x = self.conv(x) return x def MutualMatching(corr4d): # mutual matching batch_size, ch, fs1, fs2, fs3, fs4 = corr4d.size() corr4d_B = corr4d.view(batch_size, fs1 * fs2, fs3, fs4) # [batch_idx,k_A,i_B,j_B] corr4d_A = corr4d.view(batch_size, fs1, fs2, fs3 * fs4) # get max corr4d_B_max, _ = torch.max(corr4d_B, dim=1, keepdim=True) corr4d_A_max, _ = torch.max(corr4d_A, dim=3, keepdim=True) eps = 1e-5 corr4d_B = corr4d_B / (corr4d_B_max + eps) corr4d_A = corr4d_A / (corr4d_A_max + eps) corr4d_B = corr4d_B.view(batch_size, 1, fs1, fs2, fs3, fs4) corr4d_A = corr4d_A.view(batch_size, 1, fs1, fs2, fs3, fs4) corr4d = corr4d * (corr4d_A * corr4d_B) # parenthesis are important for symmetric output return corr4d def maxpool4d(corr4d_hres, k_size=4): slices = [] for i in range(k_size): for j in range(k_size): for k in range(k_size): for l in range(k_size): slices.append(corr4d_hres[:, 0, i::k_size, j::k_size, k::k_size, l::k_size].unsqueeze(0)) slices = torch.cat(tuple(slices), dim=1) corr4d, max_idx = torch.max(slices, dim=1, keepdim=True) max_l = torch.fmod(max_idx, k_size) max_k = torch.fmod(max_idx.sub(max_l).div(k_size), k_size) max_j = torch.fmod(max_idx.sub(max_l).div(k_size).sub(max_k).div(k_size), k_size) max_i = max_idx.sub(max_l).div(k_size).sub(max_k).div(k_size).sub(max_j).div(k_size) # i,j,k,l represent the *relative* coords of the max point in the box of size k_size*k_size*k_size*k_size return (corr4d, max_i, max_j, max_k, max_l) class find_correspondence(nn.Module): def __init__(self, feature_H, feature_W, beta, kernel_sigma): super(find_correspondence, self).__init__() GPU_NUM = torch.cuda.current_device() device = torch.device(f'cuda:{GPU_NUM}' if torch.cuda.is_available() else 'cpu') print("find_correspondence_gpu:",device) self.beta = beta self.kernel_sigma = kernel_sigma # regular grid / [-1,1] normalized self.grid_X, self.grid_Y = np.meshgrid(np.linspace(-1, 1, feature_W), np.linspace(-1, 1, feature_H)) # grid_X & grid_Y : feature_H x feature_W self.grid_X = torch.tensor(self.grid_X, dtype=torch.float, requires_grad=False).to(device) self.grid_Y = torch.tensor(self.grid_Y, dtype=torch.float, requires_grad=False).to(device) # kernels for computing gradients self.dx_kernel = torch.tensor([-1, 0, 1], dtype=torch.float, requires_grad=False).view(1, 1, 1, 3).expand(1, 2, 1, 3).to( device) self.dy_kernel = torch.tensor([-1, 0, 1], dtype=torch.float, requires_grad=False).view(1, 1, 3, 1).expand(1, 2, 3, 1).to( device) # 1-d indices for generating Gaussian kernels self.x = np.linspace(0, feature_W - 1, feature_W) self.x = torch.tensor(self.x, dtype=torch.float, requires_grad=False).to(device) self.y = np.linspace(0, feature_H - 1, feature_H) self.y = torch.tensor(self.y, dtype=torch.float, requires_grad=False).to(device) # 1-d indices for kernel-soft-argmax / [-1,1] normalized self.x_normal = np.linspace(-1, 1, feature_W) self.x_normal = torch.tensor(self.x_normal, dtype=torch.float, requires_grad=False).to(device) self.y_normal = np.linspace(-1, 1, feature_H) self.y_normal = torch.tensor(self.y_normal, dtype=torch.float, requires_grad=False).to(device) def apply_gaussian_kernel(self, corr, sigma=5): b, hw, h, w = corr.size() idx = corr.max(dim=1)[1] # b x h x w get maximum value along channel idx_y = (idx // w).view(b, 1, 1, h, w).float() idx_x = (idx % w).view(b, 1, 1, h, w).float() x = self.x.view(1, 1, w, 1, 1).expand(b, 1, w, h, w) y = self.y.view(1, h, 1, 1, 1).expand(b, h, 1, h, w) gauss_kernel = torch.exp(-((x - idx_x) ** 2 + (y - idx_y) ** 2) / (2 * sigma ** 2)) gauss_kernel = gauss_kernel.view(b, hw, h, w) return gauss_kernel * corr def softmax_with_temperature(self, x, beta, d=1): M, _ = x.max(dim=d, keepdim=True) x = x - M # subtract maximum value for stability exp_x = torch.exp(beta * x) exp_x_sum = exp_x.sum(dim=d, keepdim=True) return exp_x / exp_x_sum def kernel_soft_argmax(self, corr): b, _, h, w = corr.size() # corr = self.apply_gaussian_kernel(corr, sigma=self.kernel_sigma) corr = self.softmax_with_temperature(corr, beta=self.beta, d=1) corr = corr.view(-1, h, w, h, w) # (target hxw) x (source hxw) grid_x = corr.sum(dim=1, keepdim=False) # marginalize to x-coord. x_normal = self.x_normal.expand(b, w) x_normal = x_normal.view(b, w, 1, 1) grid_x = (grid_x * x_normal).sum(dim=1, keepdim=True) # b x 1 x h x w grid_y = corr.sum(dim=2, keepdim=False) # marginalize to y-coord. y_normal = self.y_normal.expand(b, h) y_normal = y_normal.view(b, h, 1, 1) grid_y = (grid_y * y_normal).sum(dim=1, keepdim=True) # b x 1 x h x w return grid_x, grid_y def get_flow_smoothness(self, flow, GT_mask): flow_dx = F.conv2d(F.pad(flow, (1, 1, 0, 0)), self.dx_kernel) / 2 # (padLeft, padRight, padTop, padBottom) flow_dy = F.conv2d(F.pad(flow, (0, 0, 1, 1)), self.dy_kernel) / 2 # (padLeft, padRight, padTop, padBottom) flow_dx = torch.abs(flow_dx) * GT_mask # consider foreground regions only flow_dy = torch.abs(flow_dy) * GT_mask smoothness = torch.cat((flow_dx, flow_dy), 1) return smoothness def forward(self, corr, GT_mask=None): b, _, h, w = corr.size() grid_X = self.grid_X.expand(b, h, w) # x coordinates of a regular grid grid_X = grid_X.unsqueeze(1) # b x 1 x h x w grid_Y = self.grid_Y.expand(b, h, w) # y coordinates of a regular grid grid_Y = grid_Y.unsqueeze(1) if self.beta is not None: grid_x, grid_y = self.kernel_soft_argmax(corr) else: # discrete argmax _, idx = torch.max(corr, dim=1) grid_x = idx % w grid_x = (grid_x.float() / (w - 1) - 0.5) * 2 grid_y = idx // w grid_y = (grid_y.float() / (h - 1) - 0.5) * 2 grid_x = grid_x.unsqueeze(1) # b x 1 x h x w grid_y = grid_y.unsqueeze(1) grid = torch.cat((grid_x.permute(0, 2, 3, 1), grid_y.permute(0, 2, 3, 1)), 3) # 2-channels@3rd-dim, first channel for x / second channel for y flow = torch.cat((grid_x - grid_X, grid_y - grid_Y), 1) # 2-channels@1st-dim, first channel for x / second channel for y if GT_mask is None: # test return grid.permute(0, 3, 1, 2), flow.permute(0, 3, 1, 2) else: # train smoothness = self.get_flow_smoothness(flow, GT_mask) return grid, flow, smoothness class ImMatchNet(nn.Module): def __init__(self, feature_extraction_cnn='resnet101', feature_extraction_last_layer='', feature_extraction_model_file=None, return_correlation=False, ncons_kernel_sizes=[3, 3, 3], ncons_channels=[10, 10, 1], normalize_features=True, train_fe=False, use_cuda=True, relocalization_k_size=0, half_precision=False, checkpoint=None, ): super(ImMatchNet, self).__init__() # Load checkpoint if checkpoint is not None and checkpoint is not '': print('Loading checkpoint...') checkpoint = torch.load(checkpoint, map_location=lambda storage, loc: storage) checkpoint['state_dict'] = OrderedDict( [(k.replace('vgg', 'model'), v) for k, v in checkpoint['state_dict'].items()]) # override relevant parameters print('Using checkpoint parameters: ') ncons_channels = checkpoint['args'].ncons_channels print(' ncons_channels: ' + str(ncons_channels)) ncons_kernel_sizes = checkpoint['args'].ncons_kernel_sizes print(' ncons_kernel_sizes: ' + str(ncons_kernel_sizes)) self.ReLU = nn.ReLU() self.use_cuda = use_cuda self.normalize_features = normalize_features print("self.normalize_features", self.normalize_features) self.return_correlation = return_correlation self.relocalization_k_size = relocalization_k_size self.half_precision = half_precision self.FeatureExtraction = FeatureExtraction(train_fe=train_fe, feature_extraction_cnn=feature_extraction_cnn, feature_extraction_model_file=feature_extraction_model_file, last_layer=feature_extraction_last_layer, normalization=False, use_cuda=self.use_cuda) self.adap_layer_feat3 = adap_layer_feat3() self.FeatureCorrelation = FeatureCorrelation(shape='4D', normalization=False) self.NeighConsensus = NeighConsensus(use_cuda=self.use_cuda, kernel_sizes=ncons_kernel_sizes, channels=ncons_channels) feature_H = 25 feature_W = 25 beta = 50 kernel_sigma = 5 self.find_correspondence = find_correspondence(feature_H, feature_W, beta, kernel_sigma) # nd = 25 * 25 # global correlation # od = nd + 2 # batch_norm = True # self.decoder4 = CMDTop(in_channels=od, bn=batch_norm, use_cuda=self.use_cuda) # Load weights if checkpoint is not None and checkpoint is not '': print('Copying weights...') for name, param in self.FeatureExtraction.state_dict().items(): if 'num_batches_tracked' not in name: self.FeatureExtraction.state_dict()[name].copy_( checkpoint['state_dict']['FeatureExtraction.' + name]) for name, param in self.NeighConsensus.state_dict().items(): self.NeighConsensus.state_dict()[name].copy_(checkpoint['state_dict']['NeighConsensus.' + name]) for name, param in self.adap_layer_feat3.state_dict().items(): self.adap_layer_feat3.state_dict()[name].copy_(checkpoint['state_dict']['adap_layer_feat3.' + name]) print('Done!') self.FeatureExtraction.eval() if self.half_precision: for p in self.NeighConsensus.parameters(): p.data = p.data.half() for l in self.NeighConsensus.conv: if isinstance(l, Conv4d): l.use_half = True # used only for foward pass at eval and for training with strong supervision def forward(self, tnf_batch, writer, writer_position): # feature extraction feature_A = self.FeatureExtraction(tnf_batch['source_image']) feature_B = self.FeatureExtraction(tnf_batch['target_image']) adap_feature_A = self.adap_layer_feat3(feature_A) adap_feature_B = self.adap_layer_feat3(feature_B) adap_feature_A = featureL2Norm(adap_feature_A) adap_feature_B = featureL2Norm(adap_feature_B) if self.half_precision: feature_A = feature_A.half() feature_B = feature_B.half() # feature correlation corr4d = self.FeatureCorrelation(adap_feature_A, adap_feature_B) # corr4d = self.FeatureCorrelation(feature_A, feature_B) # do 4d maxpooling for relocalization if self.relocalization_k_size > 1: corr4d, max_i, max_j, max_k, max_l = maxpool4d(corr4d, k_size=self.relocalization_k_size) # WTA batch_size, ch, fs1, fs2, fs3, fs4 = corr4d.size() nc_B_Avec_WTA = corr4d.view(batch_size, fs1 * fs2, fs3, fs4) # [batch_idx,k_A,i_B,j_B] # nc_B_Avec = featureL2Norm(self.ReLU(nc_B_Avec)) # compute matching scores scores_WTA_B, index_WTA_B = torch.max(nc_B_Avec_WTA, dim=1) # warping Map index1D_WTA_B = index_WTA_B.view(batch_size, -1) Map2D_WTA = unNormMap1D_to_NormMap2D(index1D_WTA_B) # (B,2,S,S) # Map2D_WTA_np = Map2D_WTA.detach().cpu().numpy() # scores_B_np =scores_B.detach().cpu().numpy() # grid_np = grid.detach().cpu().numpy() # corr4d_Net = corr4d.clone() # corr4d_Net = corr4d_Net.detach() # run match processing model corr4d = MutualMatching(corr4d) corr4d_Net = self.NeighConsensus(corr4d.detach()) corr4d_Net = MutualMatching(corr4d_Net) nc_B_Avec_NET = corr4d_Net.view(batch_size, fs1 * fs2, fs3, fs4) # [batch_idx,k_A,i_B,j_B] # nc_B_Avec2 = featureL2Norm(self.ReLU(nc_B_Avec2)) # nc_B_Avec_NET = torch.nn.functional.softmax(nc_B_Avec_NET, 1) Map2D_NET, Flow2D_NET = self.find_correspondence(nc_B_Avec_NET) # scores_B2, index_B2 = torch.max(nc_B_Avec2, dim=1) # index1D_B2 = index_B2.view(batch_size, -1) unNormMap2D_NET = NormMap2D_to_unNormMap2D(Map2D_NET) # (B,2,S,S # img_grid = return_plot_test_map(tnf_batch['source_image'][0].unsqueeze(0), tnf_batch['target_image'][0].unsqueeze(0), Map2D_WTA[0].unsqueeze(0), # Map2D_NET[0].unsqueeze(0), scale_factor=16, plot_name='AtoB_MAP') # writer.add_figure('adap_grid/adap_NET_{}'.format(writer_position), img_grid) # plot_test_map(tnf_batch['source_image'], tnf_batch['target_image'], MAP2D_NET, Map2D_WTA, scale_factor=16,plot_name='AtoB_MAP' ) # Flow2D_WTA = F.interpolate(input=Map2D_WTA, scale_factor=16, mode='bilinear', align_corners= True) # Flow2D_NET = F.interpolate(input=grid, scale_factor=16, mode='bilinear', align_corners= True) # # Flow2D_WTA = unnormalise_and_convert_mapping_to_flow(Flow2D_WTA) # Flow2D_NET = unnormalise_and_convert_mapping_to_flow(Flow2D_NET) # plot_test_flow(tnf_batch['source_image'], tnf_batch['target_image'], Flow2D_NET, Flow2D_WTA, scale_factor=16,plot_name='AtoB_FLOW' ) # Flow2D_WTA = F.interpolate(input = Map2D_WTA, scale_factor = 16, mode = 'bilinear', align_corners= True) # grid = F.interpolate(input=grid, scale_factor=16, mode='bilinear', align_corners=True) # if torch.cuda.is_available(): # init_map = torch.FloatTensor(batch_size, 2, fs3, fs4).zero_().cuda() # else: # init_map = torch.FloatTensor(batch_size, 2, fs3, fs4).zero_() # est_map4 = self.decoder4(x1=nc_B_Avec, x3=init_map) # flow4 = unnormalise_and_convert_mapping_to_flow(est_map4) / self.div # ratio = 16 # flow4[:, 0, :, :] = flow4[:, 0, :, :] / ratio # flow4[:, 1, :, :] = flow4[:, 1, :, :] / ratio if self.relocalization_k_size > 1: delta4d = (max_i, max_j, max_k, max_l) return (corr4d, delta4d) else: return corr4d_Net

48.992016

154

0.5989

from __future__ import print_function, division from collections import OrderedDict import torch import torch.nn as nn from torch.autograd import Variable import torchvision.models as models import numpy as np import numpy.matlib import pickle from lib.torch_util import Softmax1D from lib.conv4d import Conv4d from lib.matching_model import CMDTop from lib.matching_model import unNormMap1D_to_NormMap2D, NormMap2D_to_unNormMap2D from lib.showPlot import plot_test_map, plot_test_flow, warpImg_fromMap, warpImg_fromMap2, matplotlib_imshow, return_plot_test_map, get_img_from_fig import torch.nn.functional as F def featureL2Norm(feature): epsilon = 1e-6 norm = torch.pow(torch.sum(torch.pow(feature, 2), 1) + epsilon, 0.5).unsqueeze(1).expand_as(feature) return torch.div(feature, norm) class FeatureExtraction(torch.nn.Module): def __init__(self, train_fe=False, feature_extraction_cnn='resnet101', feature_extraction_model_file='', normalization=False, last_layer='', use_cuda=True): super(FeatureExtraction, self).__init__() self.normalization = normalization self.feature_extraction_cnn = feature_extraction_cnn if feature_extraction_cnn == 'vgg': self.model = models.vgg16(pretrained=True) vgg_feature_layers = ['conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'pool5'] if last_layer == '': last_layer = 'pool4' last_layer_idx = vgg_feature_layers.index(last_layer) self.model = nn.Sequential(*list(self.model.features.children())[:last_layer_idx + 1]) resnet_feature_layers = ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4'] if feature_extraction_cnn == 'resnet101': self.model = models.resnet101(pretrained=True) if last_layer == '': last_layer = 'layer3' resnet_module_list = [getattr(self.model, l) for l in resnet_feature_layers] last_layer_idx = resnet_feature_layers.index(last_layer) self.model = nn.Sequential(*resnet_module_list[:last_layer_idx + 1]) if feature_extraction_cnn == 'resnet101fpn': if feature_extraction_model_file != '': resnet = models.resnet101(pretrained=True) resnet.layer2[0].conv1.stride = (2, 2) resnet.layer2[0].conv2.stride = (1, 1) resnet.layer3[0].conv1.stride = (2, 2) resnet.layer3[0].conv2.stride = (1, 1) resnet.layer4[0].conv1.stride = (2, 2) resnet.layer4[0].conv2.stride = (1, 1) else: resnet = models.resnet101(pretrained=True) resnet_module_list = [getattr(resnet, l) for l in resnet_feature_layers] conv_body = nn.Sequential(*resnet_module_list) self.model = fpn_body(conv_body, resnet_feature_layers, fpn_layers=['layer1', 'layer2', 'layer3'], normalize=normalization, hypercols=True) if feature_extraction_model_file != '': self.model.load_pretrained_weights(feature_extraction_model_file) if feature_extraction_cnn == 'densenet201': self.model = models.densenet201(pretrained=True) self.model = nn.Sequential(*list(self.model.features.children())[:-4]) if train_fe == False: for param in self.model.parameters(): param.requires_grad = False if use_cuda: self.model = self.model.cuda() def forward(self, image_batch): features = self.model(image_batch) return features class adap_layer_feat3(nn.Module): def __init__(self): super(adap_layer_feat3, self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(1024, 1024, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(1024), nn.ReLU() ) self.conv2 = nn.Sequential( nn.Conv2d(1024, 1024, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(1024), nn.ReLU() ) GPU_NUM = torch.cuda.current_device() device = torch.device(f'cuda:{GPU_NUM}' if torch.cuda.is_available() else 'cpu') print("find_correspondence_gpu:",device) use_cuda = torch.cuda.is_available() if use_cuda: self.conv1.cuda() self.conv2.cuda() def forward(self, feature): feature = feature + self.conv1(feature) feature = feature + self.conv2(feature) return feature class FeatureCorrelation(torch.nn.Module): def __init__(self, shape='3D', normalization=True): super(FeatureCorrelation, self).__init__() self.normalization = normalization self.shape = shape self.ReLU = nn.ReLU() def forward(self, feature_A, feature_B): if self.shape == '3D': b, c, h, w = feature_A.size() feature_A = feature_A.transpose(2, 3).contiguous().view(b, c, h * w) feature_B = feature_B.view(b, c, h * w).transpose(1, 2) feature_mul = torch.bmm(feature_B, feature_A) correlation_tensor = feature_mul.view(b, h, w, h * w).transpose(2, 3).transpose(1, 2) elif self.shape == '4D': b, c, hA, wA = feature_A.size() b, c, hB, wB = feature_B.size() feature_A = feature_A.view(b, c, hA * wA).transpose(1, 2) feature_B = feature_B.view(b, c, hB * wB) feature_mul = torch.bmm(feature_A, feature_B) correlation_tensor = feature_mul.view(b, hA, wA, hB, wB).unsqueeze(1) if self.normalization: correlation_tensor = featureL2Norm(self.ReLU(correlation_tensor)) return correlation_tensor class NeighConsensus(torch.nn.Module): def __init__(self, use_cuda=True, kernel_sizes=[3, 3, 3], channels=[10, 10, 1], symmetric_mode=False): super(NeighConsensus, self).__init__() self.symmetric_mode = symmetric_mode self.kernel_sizes = kernel_sizes self.channels = channels num_layers = len(kernel_sizes) nn_modules = list() for i in range(num_layers): if i == 0: ch_in = 1 else: ch_in = channels[i - 1] ch_out = channels[i] k_size = kernel_sizes[i] nn_modules.append(Conv4d(in_channels=ch_in, out_channels=ch_out, kernel_size=k_size, bias=True)) nn_modules.append(nn.ReLU(inplace=True)) self.conv = nn.Sequential(*nn_modules) if use_cuda: self.conv.cuda() def forward(self, x): if self.symmetric_mode: x = self.conv(x) + self.conv(x.permute(0, 1, 4, 5, 2, 3)).permute(0, 1, 4, 5, 2, 3) else: x = self.conv(x) return x def MutualMatching(corr4d): batch_size, ch, fs1, fs2, fs3, fs4 = corr4d.size() corr4d_B = corr4d.view(batch_size, fs1 * fs2, fs3, fs4) corr4d_A = corr4d.view(batch_size, fs1, fs2, fs3 * fs4) corr4d_B_max, _ = torch.max(corr4d_B, dim=1, keepdim=True) corr4d_A_max, _ = torch.max(corr4d_A, dim=3, keepdim=True) eps = 1e-5 corr4d_B = corr4d_B / (corr4d_B_max + eps) corr4d_A = corr4d_A / (corr4d_A_max + eps) corr4d_B = corr4d_B.view(batch_size, 1, fs1, fs2, fs3, fs4) corr4d_A = corr4d_A.view(batch_size, 1, fs1, fs2, fs3, fs4) corr4d = corr4d * (corr4d_A * corr4d_B) return corr4d def maxpool4d(corr4d_hres, k_size=4): slices = [] for i in range(k_size): for j in range(k_size): for k in range(k_size): for l in range(k_size): slices.append(corr4d_hres[:, 0, i::k_size, j::k_size, k::k_size, l::k_size].unsqueeze(0)) slices = torch.cat(tuple(slices), dim=1) corr4d, max_idx = torch.max(slices, dim=1, keepdim=True) max_l = torch.fmod(max_idx, k_size) max_k = torch.fmod(max_idx.sub(max_l).div(k_size), k_size) max_j = torch.fmod(max_idx.sub(max_l).div(k_size).sub(max_k).div(k_size), k_size) max_i = max_idx.sub(max_l).div(k_size).sub(max_k).div(k_size).sub(max_j).div(k_size) return (corr4d, max_i, max_j, max_k, max_l) class find_correspondence(nn.Module): def __init__(self, feature_H, feature_W, beta, kernel_sigma): super(find_correspondence, self).__init__() GPU_NUM = torch.cuda.current_device() device = torch.device(f'cuda:{GPU_NUM}' if torch.cuda.is_available() else 'cpu') print("find_correspondence_gpu:",device) self.beta = beta self.kernel_sigma = kernel_sigma self.grid_X, self.grid_Y = np.meshgrid(np.linspace(-1, 1, feature_W), np.linspace(-1, 1, feature_H)) self.grid_X = torch.tensor(self.grid_X, dtype=torch.float, requires_grad=False).to(device) self.grid_Y = torch.tensor(self.grid_Y, dtype=torch.float, requires_grad=False).to(device) self.dx_kernel = torch.tensor([-1, 0, 1], dtype=torch.float, requires_grad=False).view(1, 1, 1, 3).expand(1, 2, 1, 3).to( device) self.dy_kernel = torch.tensor([-1, 0, 1], dtype=torch.float, requires_grad=False).view(1, 1, 3, 1).expand(1, 2, 3, 1).to( device) self.x = np.linspace(0, feature_W - 1, feature_W) self.x = torch.tensor(self.x, dtype=torch.float, requires_grad=False).to(device) self.y = np.linspace(0, feature_H - 1, feature_H) self.y = torch.tensor(self.y, dtype=torch.float, requires_grad=False).to(device) self.x_normal = np.linspace(-1, 1, feature_W) self.x_normal = torch.tensor(self.x_normal, dtype=torch.float, requires_grad=False).to(device) self.y_normal = np.linspace(-1, 1, feature_H) self.y_normal = torch.tensor(self.y_normal, dtype=torch.float, requires_grad=False).to(device) def apply_gaussian_kernel(self, corr, sigma=5): b, hw, h, w = corr.size() idx = corr.max(dim=1)[1] idx_y = (idx // w).view(b, 1, 1, h, w).float() idx_x = (idx % w).view(b, 1, 1, h, w).float() x = self.x.view(1, 1, w, 1, 1).expand(b, 1, w, h, w) y = self.y.view(1, h, 1, 1, 1).expand(b, h, 1, h, w) gauss_kernel = torch.exp(-((x - idx_x) ** 2 + (y - idx_y) ** 2) / (2 * sigma ** 2)) gauss_kernel = gauss_kernel.view(b, hw, h, w) return gauss_kernel * corr def softmax_with_temperature(self, x, beta, d=1): M, _ = x.max(dim=d, keepdim=True) x = x - M exp_x = torch.exp(beta * x) exp_x_sum = exp_x.sum(dim=d, keepdim=True) return exp_x / exp_x_sum def kernel_soft_argmax(self, corr): b, _, h, w = corr.size() corr = self.softmax_with_temperature(corr, beta=self.beta, d=1) corr = corr.view(-1, h, w, h, w) grid_x = corr.sum(dim=1, keepdim=False) x_normal = self.x_normal.expand(b, w) x_normal = x_normal.view(b, w, 1, 1) grid_x = (grid_x * x_normal).sum(dim=1, keepdim=True) grid_y = corr.sum(dim=2, keepdim=False) y_normal = self.y_normal.expand(b, h) y_normal = y_normal.view(b, h, 1, 1) grid_y = (grid_y * y_normal).sum(dim=1, keepdim=True) return grid_x, grid_y def get_flow_smoothness(self, flow, GT_mask): flow_dx = F.conv2d(F.pad(flow, (1, 1, 0, 0)), self.dx_kernel) / 2 flow_dy = F.conv2d(F.pad(flow, (0, 0, 1, 1)), self.dy_kernel) / 2 flow_dx = torch.abs(flow_dx) * GT_mask flow_dy = torch.abs(flow_dy) * GT_mask smoothness = torch.cat((flow_dx, flow_dy), 1) return smoothness def forward(self, corr, GT_mask=None): b, _, h, w = corr.size() grid_X = self.grid_X.expand(b, h, w) grid_X = grid_X.unsqueeze(1) grid_Y = self.grid_Y.expand(b, h, w) grid_Y = grid_Y.unsqueeze(1) if self.beta is not None: grid_x, grid_y = self.kernel_soft_argmax(corr) else: _, idx = torch.max(corr, dim=1) grid_x = idx % w grid_x = (grid_x.float() / (w - 1) - 0.5) * 2 grid_y = idx // w grid_y = (grid_y.float() / (h - 1) - 0.5) * 2 grid_x = grid_x.unsqueeze(1) grid_y = grid_y.unsqueeze(1) grid = torch.cat((grid_x.permute(0, 2, 3, 1), grid_y.permute(0, 2, 3, 1)), 3) flow = torch.cat((grid_x - grid_X, grid_y - grid_Y), 1) if GT_mask is None: return grid.permute(0, 3, 1, 2), flow.permute(0, 3, 1, 2) else: smoothness = self.get_flow_smoothness(flow, GT_mask) return grid, flow, smoothness class ImMatchNet(nn.Module): def __init__(self, feature_extraction_cnn='resnet101', feature_extraction_last_layer='', feature_extraction_model_file=None, return_correlation=False, ncons_kernel_sizes=[3, 3, 3], ncons_channels=[10, 10, 1], normalize_features=True, train_fe=False, use_cuda=True, relocalization_k_size=0, half_precision=False, checkpoint=None, ): super(ImMatchNet, self).__init__() if checkpoint is not None and checkpoint is not '': print('Loading checkpoint...') checkpoint = torch.load(checkpoint, map_location=lambda storage, loc: storage) checkpoint['state_dict'] = OrderedDict( [(k.replace('vgg', 'model'), v) for k, v in checkpoint['state_dict'].items()]) print('Using checkpoint parameters: ') ncons_channels = checkpoint['args'].ncons_channels print(' ncons_channels: ' + str(ncons_channels)) ncons_kernel_sizes = checkpoint['args'].ncons_kernel_sizes print(' ncons_kernel_sizes: ' + str(ncons_kernel_sizes)) self.ReLU = nn.ReLU() self.use_cuda = use_cuda self.normalize_features = normalize_features print("self.normalize_features", self.normalize_features) self.return_correlation = return_correlation self.relocalization_k_size = relocalization_k_size self.half_precision = half_precision self.FeatureExtraction = FeatureExtraction(train_fe=train_fe, feature_extraction_cnn=feature_extraction_cnn, feature_extraction_model_file=feature_extraction_model_file, last_layer=feature_extraction_last_layer, normalization=False, use_cuda=self.use_cuda) self.adap_layer_feat3 = adap_layer_feat3() self.FeatureCorrelation = FeatureCorrelation(shape='4D', normalization=False) self.NeighConsensus = NeighConsensus(use_cuda=self.use_cuda, kernel_sizes=ncons_kernel_sizes, channels=ncons_channels) feature_H = 25 feature_W = 25 beta = 50 kernel_sigma = 5 self.find_correspondence = find_correspondence(feature_H, feature_W, beta, kernel_sigma) if checkpoint is not None and checkpoint is not '': print('Copying weights...') for name, param in self.FeatureExtraction.state_dict().items(): if 'num_batches_tracked' not in name: self.FeatureExtraction.state_dict()[name].copy_( checkpoint['state_dict']['FeatureExtraction.' + name]) for name, param in self.NeighConsensus.state_dict().items(): self.NeighConsensus.state_dict()[name].copy_(checkpoint['state_dict']['NeighConsensus.' + name]) for name, param in self.adap_layer_feat3.state_dict().items(): self.adap_layer_feat3.state_dict()[name].copy_(checkpoint['state_dict']['adap_layer_feat3.' + name]) print('Done!') self.FeatureExtraction.eval() if self.half_precision: for p in self.NeighConsensus.parameters(): p.data = p.data.half() for l in self.NeighConsensus.conv: if isinstance(l, Conv4d): l.use_half = True def forward(self, tnf_batch, writer, writer_position): feature_A = self.FeatureExtraction(tnf_batch['source_image']) feature_B = self.FeatureExtraction(tnf_batch['target_image']) adap_feature_A = self.adap_layer_feat3(feature_A) adap_feature_B = self.adap_layer_feat3(feature_B) adap_feature_A = featureL2Norm(adap_feature_A) adap_feature_B = featureL2Norm(adap_feature_B) if self.half_precision: feature_A = feature_A.half() feature_B = feature_B.half() corr4d = self.FeatureCorrelation(adap_feature_A, adap_feature_B) if self.relocalization_k_size > 1: corr4d, max_i, max_j, max_k, max_l = maxpool4d(corr4d, k_size=self.relocalization_k_size) batch_size, ch, fs1, fs2, fs3, fs4 = corr4d.size() nc_B_Avec_WTA = corr4d.view(batch_size, fs1 * fs2, fs3, fs4) scores_WTA_B, index_WTA_B = torch.max(nc_B_Avec_WTA, dim=1) index1D_WTA_B = index_WTA_B.view(batch_size, -1) Map2D_WTA = unNormMap1D_to_NormMap2D(index1D_WTA_B) corr4d = MutualMatching(corr4d) corr4d_Net = self.NeighConsensus(corr4d.detach()) corr4d_Net = MutualMatching(corr4d_Net) nc_B_Avec_NET = corr4d_Net.view(batch_size, fs1 * fs2, fs3, fs4) Map2D_NET, Flow2D_NET = self.find_correspondence(nc_B_Avec_NET) unNormMap2D_NET = NormMap2D_to_unNormMap2D(Map2D_NET) if self.relocalization_k_size > 1: delta4d = (max_i, max_j, max_k, max_l) return (corr4d, delta4d) else: return corr4d_Net

true

1c474eb2a7180c4b80cf9601418dd0b801e92818

1,880

py

Python

pyleecan/Methods/Slot/HoleM53/check.py

Kelos-Zhu/pyleecan

368f8379688e31a6c26d2c1cd426f21dfbceff2a

[ "Apache-2.0" ]

2

2019-06-08T15:04:39.000Z

2020-09-07T13:32:22.000Z

pyleecan/Methods/Slot/HoleM53/check.py

lyhehehe/pyleecan

421e9a843bf30d796415c77dc934546adffd1cd7

[ "Apache-2.0" ]

null

pyleecan/Methods/Slot/HoleM53/check.py

lyhehehe/pyleecan

421e9a843bf30d796415c77dc934546adffd1cd7

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from numpy import pi from ....Methods.Slot.Slot.check import SlotCheckError def check(self): """Check that the HoleM53 object is correct Parameters ---------- self : HoleM53 A HoleM53 object Returns ------- None Raises ------- S53_Rbo0CheckError You must have H0 < Rbo S53_Rbo1CheckError You must have H1 < Rbo S53_W4CheckError You must have W4 < pi/2 S53_W5CheckError You must have W5 >=0 """ # Check that everything is set if self.W1 is None: raise S53_NoneError("You must set W1 !") elif self.W2 is None: raise S53_NoneError("You must set W2 !") elif self.W3 is None: raise S53_NoneError("You must set W3 !") elif self.W4 is None: raise S53_NoneError("You must set W4 !") elif self.H0 is None: raise S53_NoneError("You must set H0 !") elif self.H1 is None: raise S53_NoneError("You must set H1 !") elif self.H2 is None: raise S53_NoneError("You must set H2 !") elif self.H3 is None: raise S53_NoneError("You must set H3 !") Rbo = self.get_Rbo() if Rbo <= self.H0: raise S53_Rbo0CheckError("You must have H0 < Rbo") if Rbo <= self.H1: raise S53_Rbo1CheckError("You must have H1 < Rbo") if pi / 2 <= self.W4: raise S53_W4CheckError("You must have W4 < pi/2") if self.comp_W5() < 0: raise S53_W5CheckError("You must have W5 >=0") class S53_NoneError(SlotCheckError): """Raised when a propery of HoleM53 is None """ pass class S53_Rbo0CheckError(SlotCheckError): """ """ pass class S53_Rbo1CheckError(SlotCheckError): """ """ pass class S53_W4CheckError(SlotCheckError): """ """ pass class S53_W5CheckError(SlotCheckError): """ """ pass

20

58

0.600532

from numpy import pi from ....Methods.Slot.Slot.check import SlotCheckError def check(self): if self.W1 is None: raise S53_NoneError("You must set W1 !") elif self.W2 is None: raise S53_NoneError("You must set W2 !") elif self.W3 is None: raise S53_NoneError("You must set W3 !") elif self.W4 is None: raise S53_NoneError("You must set W4 !") elif self.H0 is None: raise S53_NoneError("You must set H0 !") elif self.H1 is None: raise S53_NoneError("You must set H1 !") elif self.H2 is None: raise S53_NoneError("You must set H2 !") elif self.H3 is None: raise S53_NoneError("You must set H3 !") Rbo = self.get_Rbo() if Rbo <= self.H0: raise S53_Rbo0CheckError("You must have H0 < Rbo") if Rbo <= self.H1: raise S53_Rbo1CheckError("You must have H1 < Rbo") if pi / 2 <= self.W4: raise S53_W4CheckError("You must have W4 < pi/2") if self.comp_W5() < 0: raise S53_W5CheckError("You must have W5 >=0") class S53_NoneError(SlotCheckError): pass class S53_Rbo0CheckError(SlotCheckError): pass class S53_Rbo1CheckError(SlotCheckError): pass class S53_W4CheckError(SlotCheckError): pass class S53_W5CheckError(SlotCheckError): pass

true

1c47503a63b297ae151dad61e17a23efab7bef67

664

py

Python

bot/bot/base.py

TSPS-Team/Project

b1d83cb7957420b8348939f0a1d36f506095519c

[ "MIT" ]

null

bot/bot/base.py

TSPS-Team/Project

b1d83cb7957420b8348939f0a1d36f506095519c

[ "MIT" ]

null

bot/bot/base.py

TSPS-Team/Project

b1d83cb7957420b8348939f0a1d36f506095519c

[ "MIT" ]

null

#!/usr/bin/env python3 from __future__ import annotations from server import Interface from telegram.bot import Bot class State: player: Player app_info: 'AppInfo' bot: Bot def __init__(self, player, app_info) -> None: self.player = player self.bot = app_info.bot self.app_info = app_info def callback(self, update, context): pass def text_callback(self, update, context): pass class Player: lobby: 'Lobby' state: State game: 'Game' def __init__(self, name: str, id: int) -> None: self.name = name self.id = id def __str__(self): return self.name

18.971429

51

0.621988

from __future__ import annotations from server import Interface from telegram.bot import Bot class State: player: Player app_info: 'AppInfo' bot: Bot def __init__(self, player, app_info) -> None: self.player = player self.bot = app_info.bot self.app_info = app_info def callback(self, update, context): pass def text_callback(self, update, context): pass class Player: lobby: 'Lobby' state: State game: 'Game' def __init__(self, name: str, id: int) -> None: self.name = name self.id = id def __str__(self): return self.name

true

1c47504f9eb14b016fc1dc1c1fcbb3dea481e1a2

856

py

Python

aiofcm/client.py

cyberbudy/aiofcm

30e66b872aa2e1fc43ef4884fb84ba23b91879c5

[ "Apache-2.0" ]

30

2017-05-11T08:21:45.000Z

2021-11-20T13:52:13.000Z

aiofcm/client.py

cyberbudy/aiofcm

30e66b872aa2e1fc43ef4884fb84ba23b91879c5

[ "Apache-2.0" ]

12

2017-05-22T16:42:03.000Z

2021-08-09T11:11:47.000Z

aiofcm/client.py

cyberbudy/aiofcm

30e66b872aa2e1fc43ef4884fb84ba23b91879c5

[ "Apache-2.0" ]

16

2017-05-22T11:30:55.000Z

2021-11-11T09:48:04.000Z

import asyncio from typing import Optional, NoReturn from aiofcm.connection import FCMConnectionPool from aiofcm.common import Message, MessageResponse from aiofcm.logging import logger class FCM: def __init__(self, sender_id, api_key, max_connections=10, loop=None): # type: (int, str, int, Optional[asyncio.AbstractEventLoop]) -> NoReturn self.pool = FCMConnectionPool(sender_id, api_key, max_connections, loop) async def send_message(self, message: Message) -> MessageResponse: response = await self.pool.send_message(message) if not response.is_successful: msg = 'Status of message %s is %s' %\ (message.message_id, response.status) if response.description: msg += ' (%s)' % response.description logger.error(msg) return response

37.217391

80

0.679907

import asyncio from typing import Optional, NoReturn from aiofcm.connection import FCMConnectionPool from aiofcm.common import Message, MessageResponse from aiofcm.logging import logger class FCM: def __init__(self, sender_id, api_key, max_connections=10, loop=None): self.pool = FCMConnectionPool(sender_id, api_key, max_connections, loop) async def send_message(self, message: Message) -> MessageResponse: response = await self.pool.send_message(message) if not response.is_successful: msg = 'Status of message %s is %s' %\ (message.message_id, response.status) if response.description: msg += ' (%s)' % response.description logger.error(msg) return response

true

1c4751b7582b662927b44f9a171203401afd2ce3

36,054

py

Python

src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py

LeeCenY/turicreate

fb2f3bf313e831ceb42a2e10aacda6e472ea8d93

[ "BSD-3-Clause" ]

null

src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py

LeeCenY/turicreate

fb2f3bf313e831ceb42a2e10aacda6e472ea8d93

[ "BSD-3-Clause" ]

null

src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py

LeeCenY/turicreate

fb2f3bf313e831ceb42a2e10aacda6e472ea8d93

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Copyright © 2019 Apple Inc. All rights reserved. # # Use of this source code is governed by a BSD-3-clause license that can # be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause import turicreate as _tc import numpy as _np import time as _time from turicreate.toolkits._model import CustomModel as _CustomModel from turicreate.toolkits._model import PythonProxy as _PythonProxy from turicreate.toolkits import evaluation as _evaluation import turicreate.toolkits._internal_utils as _tkutl from turicreate.toolkits._main import ToolkitError as _ToolkitError from .. import _mxnet_utils from turicreate import extensions as _extensions from .. import _pre_trained_models BITMAP_WIDTH = 28 BITMAP_HEIGHT = 28 TRAIN_VALIDATION_SPLIT = .95 def _raise_error_if_not_drawing_classifier_input_sframe( dataset, feature, target): """ Performs some sanity checks on the SFrame provided as input to `turicreate.drawing_classifier.create` and raises a ToolkitError if something in the dataset is missing or wrong. """ from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe _raise_error_if_not_sframe(dataset) if feature not in dataset.column_names(): raise _ToolkitError("Feature column '%s' does not exist" % feature) if target not in dataset.column_names(): raise _ToolkitError("Target column '%s' does not exist" % target) if (dataset[feature].dtype != _tc.Image and dataset[feature].dtype != list): raise _ToolkitError("Feature column must contain images" + " or stroke-based drawings encoded as lists of strokes" + " where each stroke is a list of points and" + " each point is stored as a dictionary") if dataset[target].dtype != int and dataset[target].dtype != str: raise _ToolkitError("Target column contains " + str(dataset[target].dtype) + " but it must contain strings or integers to represent" + " labels for drawings.") if len(dataset) == 0: raise _ToolkitError("Input Dataset is empty!") def create(input_dataset, target, feature=None, validation_set='auto', warm_start='auto', batch_size=256, max_iterations=100, verbose=True): """ Create a :class:`DrawingClassifier` model. Parameters ---------- dataset : SFrame Input data. The columns named by the ``feature`` and ``target`` parameters will be extracted for training the drawing classifier. target : string Name of the column containing the target variable. The values in this column must be of string or integer type. feature : string optional Name of the column containing the input drawings. 'None' (the default) indicates the column in `dataset` named "drawing" should be used as the feature. The feature column can contain both bitmap-based drawings as well as stroke-based drawings. Bitmap-based drawing input can be a grayscale tc.Image of any size. Stroke-based drawing input must be in the following format: Every drawing must be represented by a list of strokes, where each stroke must be a list of points in the order in which they were drawn on the canvas. Each point must be a dictionary with two keys, "x" and "y", and their respective values must be numerical, i.e. either integer or float. validation_set : SFrame optional A dataset for monitoring the model's generalization performance. The format of this SFrame must be the same as the training set. By default this argument is set to 'auto' and a validation set is automatically sampled and used for progress printing. If validation_set is set to None, then no additional metrics are computed. The default value is 'auto'. warm_start : string optional A string to denote which pretrained model to use. Set to "auto" by default which uses a model trained on 245 of the 345 classes in the Quick, Draw! dataset. Here is a list of all the pretrained models that can be passed in as this argument: "auto": Uses quickdraw_245_v0 "quickdraw_245_v0": Uses a model trained on 245 of the 345 classes in the Quick, Draw! dataset. batch_size: int optional The number of drawings per training step. If not set, a default value of 256 will be used. If you are getting memory errors, try decreasing this value. If you have a powerful computer, increasing this value may improve performance. max_iterations : int optional The maximum number of allowed passes through the data. More passes over the data can result in a more accurately trained model. verbose : bool optional If True, print progress updates and model details. Returns ------- out : DrawingClassifier A trained :class:`DrawingClassifier` model. See Also -------- DrawingClassifier Examples -------- .. sourcecode:: python # Train a drawing classifier model >>> model = turicreate.drawing_classifier.create(data) # Make predictions on the training set and as column to the SFrame >>> data['predictions'] = model.predict(data) """ import mxnet as _mx from mxnet import autograd as _autograd from ._model_architecture import Model as _Model from ._sframe_loader import SFrameClassifierIter as _SFrameClassifierIter start_time = _time.time() # @TODO: Should be able to automatically choose number of iterations # based on data size: Tracked in Github Issue #1576 # automatically infer feature column if feature is None: feature = _tkutl._find_only_drawing_column(input_dataset) _raise_error_if_not_drawing_classifier_input_sframe( input_dataset, feature, target) if batch_size is not None and not isinstance(batch_size, int): raise TypeError("'batch_size' must be an integer >= 1") if batch_size is not None and batch_size < 1: raise ValueError("'batch_size' must be >= 1") if max_iterations is not None and not isinstance(max_iterations, int): raise TypeError("'max_iterations' must be an integer >= 1") if max_iterations is not None and max_iterations < 1: raise ValueError("'max_iterations' must be >= 1") is_stroke_input = (input_dataset[feature].dtype != _tc.Image) dataset = _extensions._drawing_classifier_prepare_data( input_dataset, feature) if is_stroke_input else input_dataset iteration = 0 classes = dataset[target].unique() classes = sorted(classes) class_to_index = {name: index for index, name in enumerate(classes)} validation_set_corrective_string = ("'validation_set' parameter must be " + "an SFrame, or None, or must be set to 'auto' for the toolkit to " + "automatically create a validation set.") if isinstance(validation_set, _tc.SFrame): _raise_error_if_not_drawing_classifier_input_sframe( validation_set, feature, target) is_validation_stroke_input = (validation_set[feature].dtype != _tc.Image) validation_dataset = _extensions._drawing_classifier_prepare_data( validation_set, feature) if is_validation_stroke_input else validation_set elif isinstance(validation_set, str): if validation_set == 'auto': if dataset.num_rows() >= 100: if verbose: print ( "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n" " You can set ``validation_set=None`` to disable validation tracking.\n") dataset, validation_dataset = dataset.random_split(TRAIN_VALIDATION_SPLIT, exact=True) else: validation_set = None validation_dataset = _tc.SFrame() else: raise _ToolkitError("Unrecognized value for 'validation_set'. " + validation_set_corrective_string) elif validation_set is None: validation_dataset = _tc.SFrame() else: raise TypeError("Unrecognized type for 'validation_set'." + validation_set_corrective_string) train_loader = _SFrameClassifierIter(dataset, batch_size, feature_column=feature, target_column=target, class_to_index=class_to_index, load_labels=True, shuffle=True, iterations=max_iterations) train_loader_to_compute_accuracy = _SFrameClassifierIter(dataset, batch_size, feature_column=feature, target_column=target, class_to_index=class_to_index, load_labels=True, shuffle=True, iterations=1) validation_loader = _SFrameClassifierIter(validation_dataset, batch_size, feature_column=feature, target_column=target, class_to_index=class_to_index, load_labels=True, shuffle=True, iterations=1) if verbose and iteration == 0: column_names = ['iteration', 'train_loss', 'train_accuracy', 'time'] column_titles = ['Iteration', 'Training Loss', 'Training Accuracy', 'Elapsed Time (seconds)'] if validation_set is not None: column_names.insert(3, 'validation_accuracy') column_titles.insert(3, 'Validation Accuracy') table_printer = _tc.util._ProgressTablePrinter( column_names, column_titles) ctx = _mxnet_utils.get_mxnet_context(max_devices=batch_size) model = _Model(num_classes = len(classes), prefix="drawing_") model_params = model.collect_params() model_params.initialize(_mx.init.Xavier(), ctx=ctx) if warm_start is not None: pretrained_model = _pre_trained_models.DrawingClassifierPreTrainedModel( warm_start) pretrained_model_params_path = pretrained_model.get_model_path() model.load_params(pretrained_model_params_path, ctx=ctx, allow_missing=True) softmax_cross_entropy = _mx.gluon.loss.SoftmaxCrossEntropyLoss() model.hybridize() trainer = _mx.gluon.Trainer(model.collect_params(), 'adam') train_accuracy = _mx.metric.Accuracy() validation_accuracy = _mx.metric.Accuracy() def get_data_and_label_from_batch(batch): if batch.pad is not None: size = batch_size - batch.pad sliced_data = _mx.nd.slice_axis(batch.data[0], axis=0, begin=0, end=size) sliced_label = _mx.nd.slice_axis(batch.label[0], axis=0, begin=0, end=size) num_devices = min(sliced_data.shape[0], len(ctx)) batch_data = _mx.gluon.utils.split_and_load(sliced_data, ctx_list=ctx[:num_devices], even_split=False) batch_label = _mx.gluon.utils.split_and_load(sliced_label, ctx_list=ctx[:num_devices], even_split=False) else: batch_data = _mx.gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0) batch_label = _mx.gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0) return batch_data, batch_label def compute_accuracy(accuracy_metric, batch_loader): batch_loader.reset() accuracy_metric.reset() for batch in batch_loader: batch_data, batch_label = get_data_and_label_from_batch(batch) outputs = [] for x, y in zip(batch_data, batch_label): if x is None or y is None: continue z = model(x) outputs.append(z) accuracy_metric.update(batch_label, outputs) for train_batch in train_loader: train_batch_data, train_batch_label = get_data_and_label_from_batch(train_batch) with _autograd.record(): # Inside training scope for x, y in zip(train_batch_data, train_batch_label): z = model(x) # Computes softmax cross entropy loss. loss = softmax_cross_entropy(z, y) # Backpropagate the error for one iteration. loss.backward() # Make one step of parameter update. Trainer needs to know the # batch size of data to normalize the gradient by 1/batch_size. trainer.step(train_batch.data[0].shape[0]) # calculate training metrics train_loss = loss.mean().asscalar() train_time = _time.time() - start_time if train_batch.iteration > iteration: # Compute training accuracy compute_accuracy(train_accuracy, train_loader_to_compute_accuracy) # Compute validation accuracy if validation_set is not None: compute_accuracy(validation_accuracy, validation_loader) iteration = train_batch.iteration if verbose: kwargs = { "iteration": iteration, "train_loss": float(train_loss), "train_accuracy": train_accuracy.get()[1], "time": train_time} if validation_set is not None: kwargs["validation_accuracy"] = validation_accuracy.get()[1] table_printer.print_row(**kwargs) state = { '_model': model, '_class_to_index': class_to_index, 'num_classes': len(classes), 'classes': classes, 'input_image_shape': (1, BITMAP_WIDTH, BITMAP_HEIGHT), 'batch_size': batch_size, 'training_loss': train_loss, 'training_accuracy': train_accuracy.get()[1], 'training_time': train_time, 'validation_accuracy': validation_accuracy.get()[1], # nan if validation_set=None 'max_iterations': max_iterations, 'target': target, 'feature': feature, 'num_examples': len(input_dataset) } return DrawingClassifier(state) class DrawingClassifier(_CustomModel): """ A trained model that is ready to use for classification, and to be exported to Core ML. This model should not be constructed directly. """ _PYTHON_DRAWING_CLASSIFIER_VERSION = 1 def __init__(self, state): self.__proxy__ = _PythonProxy(state) @classmethod def _native_name(cls): return "drawing_classifier" def _get_native_state(self): state = self.__proxy__.get_state() mxnet_params = state['_model'].collect_params() state['_model'] = _mxnet_utils.get_gluon_net_params_state(mxnet_params) return state def _get_version(self): return self._PYTHON_DRAWING_CLASSIFIER_VERSION @classmethod def _load_version(cls, state, version): _tkutl._model_version_check(version, cls._PYTHON_DRAWING_CLASSIFIER_VERSION) from ._model_architecture import Model as _Model net = _Model(num_classes = len(state['classes']), prefix = 'drawing_') ctx = _mxnet_utils.get_mxnet_context(max_devices=state['batch_size']) net_params = net.collect_params() _mxnet_utils.load_net_params_from_state( net_params, state['_model'], ctx=ctx ) state['_model'] = net # For a model trained on integer classes, when saved and loaded back, # the classes are loaded as floats. The following if statement casts # the loaded "float" classes back to int. if len(state['classes']) > 0 and isinstance(state['classes'][0], float): state['classes'] = list(map(int, state['classes'])) return DrawingClassifier(state) def __str__(self): """ Return a string description of the model to the ``print`` method. Returns ------- out : string A description of the DrawingClassifier. """ return self.__repr__() def __repr__(self): """ Returns a string description of the model when the model name is entered in the terminal. """ width = 40 sections, section_titles = self._get_summary_struct() out = _tkutl._toolkit_repr_print(self, sections, section_titles, width=width) return out def _get_summary_struct(self): """ Returns a structured description of the model, including (where relevant) the schema of the training data, description of the training data, training statistics, and model hyperparameters. Returns ------- sections : list (of list of tuples) A list of summary sections. Each section is a list. Each item in a section list is a tuple of the form: ('<label>','<field>') section_titles: list A list of section titles. The order matches that of the 'sections' object. """ model_fields = [ ('Number of classes', 'num_classes'), ('Feature column', 'feature'), ('Target column', 'target') ] training_fields = [ ('Training Iterations', 'max_iterations'), ('Training Accuracy', 'training_accuracy'), ('Validation Accuracy', 'validation_accuracy'), ('Training Time', 'training_time'), ('Number of Examples', 'num_examples'), ('Batch Size', 'batch_size'), ('Final Loss (specific to model)', 'training_loss') ] section_titles = ['Schema', 'Training summary'] return([model_fields, training_fields], section_titles) def export_coreml(self, filename, verbose=False): """ Save the model in Core ML format. The Core ML model takes a grayscale drawing of fixed size as input and produces two outputs: `classLabel` and `labelProbabilities`. The first one, `classLabel` is an integer or string (depending on the classes the model was trained on) to store the label of the top prediction by the model. The second one, `labelProbabilities`, is a dictionary with all the class labels in the dataset as the keys, and their respective probabilities as the values. See Also -------- save Parameters ---------- filename : string The path of the file where we want to save the Core ML model. verbose : bool optional If True, prints export progress. Examples -------- >>> model.export_coreml('drawing_classifier.mlmodel') """ import mxnet as _mx from .._mxnet_to_coreml import _mxnet_converter import coremltools as _coremltools batch_size = 1 image_shape = (batch_size,) + (1, BITMAP_WIDTH, BITMAP_HEIGHT) s_image = _mx.sym.Variable(self.feature, shape=image_shape, dtype=_np.float32) from copy import copy as _copy net = _copy(self._model) s_ymap = net(s_image) mod = _mx.mod.Module(symbol=s_ymap, label_names=None, data_names=[self.feature]) mod.bind(for_training=False, data_shapes=[(self.feature, image_shape)]) mod.init_params() arg_params, aux_params = mod.get_params() net_params = net.collect_params() new_arg_params = {} for k, param in arg_params.items(): new_arg_params[k] = net_params[k].data(net_params[k].list_ctx()[0]) new_aux_params = {} for k, param in aux_params.items(): new_aux_params[k] = net_params[k].data(net_params[k].list_ctx()[0]) mod.set_params(new_arg_params, new_aux_params) coreml_model = _mxnet_converter.convert(mod, mode='classifier', class_labels=self.classes, input_shape=[(self.feature, image_shape)], builder=None, verbose=verbose, preprocessor_args={ 'image_input_names': [self.feature], 'image_scale': 1.0/255 }) DESIRED_OUTPUT_NAME = self.target + "Probabilities" spec = coreml_model._spec class_label_output_index = 0 if spec.description.output[0].name == "classLabel" else 1 probabilities_output_index = 1-class_label_output_index spec.neuralNetworkClassifier.labelProbabilityLayerName = DESIRED_OUTPUT_NAME spec.neuralNetworkClassifier.layers[-1].name = DESIRED_OUTPUT_NAME spec.neuralNetworkClassifier.layers[-1].output[0] = DESIRED_OUTPUT_NAME spec.description.predictedProbabilitiesName = DESIRED_OUTPUT_NAME spec.description.output[probabilities_output_index].name = DESIRED_OUTPUT_NAME from turicreate.toolkits import _coreml_utils model_type = "drawing classifier" spec.description.metadata.shortDescription = _coreml_utils._mlmodel_short_description(model_type) spec.description.input[0].shortDescription = self.feature spec.description.output[probabilities_output_index].shortDescription = 'Prediction probabilities' spec.description.output[class_label_output_index].shortDescription = 'Class Label of Top Prediction' from coremltools.models.utils import save_spec as _save_spec _save_spec(spec, filename) def _predict_with_probabilities(self, input_dataset, batch_size=None, verbose=True): """ Predict with probabilities. The core prediction part that both `evaluate` and `predict` share. Returns an SFrame with two columns, self.target, and "probabilities". The column with column name, self.target, contains the predictions made by the model for the provided dataset. The "probabilities" column contains the probabilities for each class that the model predicted for the data provided to the function. """ import mxnet as _mx from ._sframe_loader import SFrameClassifierIter as _SFrameClassifierIter is_stroke_input = (input_dataset[self.feature].dtype != _tc.Image) dataset = _extensions._drawing_classifier_prepare_data( input_dataset, self.feature) if is_stroke_input else input_dataset batch_size = self.batch_size if batch_size is None else batch_size loader = _SFrameClassifierIter(dataset, batch_size, class_to_index=self._class_to_index, feature_column=self.feature, target_column=self.target, load_labels=False, shuffle=False, iterations=1) dataset_size = len(dataset) ctx = _mxnet_utils.get_mxnet_context() index = 0 last_time = 0 done = False from turicreate import SArrayBuilder from array import array classes = self.classes all_predicted_builder = SArrayBuilder(dtype=type(classes[0])) all_probabilities_builder = SArrayBuilder(dtype=array) for batch in loader: if batch.pad is not None: size = batch_size - batch.pad batch_data = _mx.nd.slice_axis(batch.data[0], axis=0, begin=0, end=size) else: batch_data = batch.data[0] size = batch_size num_devices = min(batch_data.shape[0], len(ctx)) split_data = _mx.gluon.utils.split_and_load(batch_data, ctx_list=ctx[:num_devices], even_split=False) for data in split_data: z = self._model(data).asnumpy() predicted = list(map(lambda x: classes[x], z.argmax(axis=1))) split_length = z.shape[0] all_predicted_builder.append_multiple(predicted) all_probabilities_builder.append_multiple(z.tolist()) index += split_length if index == dataset_size - 1: done = True cur_time = _time.time() # Do not print progress if only a few samples are predicted if verbose and (dataset_size >= 5 and cur_time > last_time + 10 or done): print('Predicting {cur_n:{width}d}/{max_n:{width}d}'.format( cur_n = index + 1, max_n = dataset_size, width = len(str(dataset_size)))) last_time = cur_time return (_tc.SFrame({self.target: all_predicted_builder.close(), 'probability': all_probabilities_builder.close()})) def evaluate(self, dataset, metric='auto', batch_size=None, verbose=True): """ Evaluate the model by making predictions of target values and comparing these to actual values. Parameters ---------- dataset : SFrame Dataset of new observations. Must include columns with the same names as the feature and target columns used for model training. Additional columns are ignored. metric : str, optional Name of the evaluation metric. Possible values are: - 'auto' : Returns all available metrics. - 'accuracy' : Classification accuracy (micro average). - 'auc' : Area under the ROC curve (macro average) - 'precision' : Precision score (macro average) - 'recall' : Recall score (macro average) - 'f1_score' : F1 score (macro average) - 'confusion_matrix' : An SFrame with counts of possible prediction/true label combinations. - 'roc_curve' : An SFrame containing information needed for an ROC curve verbose : bool, optional If True, prints prediction progress. Returns ------- out : dict Dictionary of evaluation results where the key is the name of the evaluation metric (e.g. `accuracy`) and the value is the evaluation score. See Also ---------- create, predict Examples ---------- .. sourcecode:: python >>> results = model.evaluate(data) >>> print(results['accuracy']) """ if self.target not in dataset.column_names(): raise _ToolkitError("Must provide ground truth column, '" + self.target + "' in the evaluation dataset.") predicted = self._predict_with_probabilities(dataset, batch_size, verbose) avail_metrics = ['accuracy', 'auc', 'precision', 'recall', 'f1_score', 'confusion_matrix', 'roc_curve'] _tkutl._check_categorical_option_type( 'metric', metric, avail_metrics + ['auto']) metrics = avail_metrics if metric == 'auto' else [metric] ret = {} if 'accuracy' in metrics: ret['accuracy'] = _evaluation.accuracy( dataset[self.target], predicted[self.target]) if 'auc' in metrics: ret['auc'] = _evaluation.auc( dataset[self.target], predicted['probability'], index_map=self._class_to_index) if 'precision' in metrics: ret['precision'] = _evaluation.precision( dataset[self.target], predicted[self.target]) if 'recall' in metrics: ret['recall'] = _evaluation.recall( dataset[self.target], predicted[self.target]) if 'f1_score' in metrics: ret['f1_score'] = _evaluation.f1_score( dataset[self.target], predicted[self.target]) if 'confusion_matrix' in metrics: ret['confusion_matrix'] = _evaluation.confusion_matrix( dataset[self.target], predicted[self.target]) if 'roc_curve' in metrics: ret['roc_curve'] = _evaluation.roc_curve( dataset[self.target], predicted['probability'], index_map=self._class_to_index) return ret def predict_topk(self, dataset, output_type="probability", k=3, batch_size=None): """ Return top-k predictions for the ``dataset``, using the trained model. Predictions are returned as an SFrame with three columns: `id`, `class`, and `probability` or `rank`, depending on the ``output_type`` parameter. Parameters ---------- dataset : SFrame | SArray | turicreate.Image | list Drawings to be classified. If dataset is an SFrame, it must include columns with the same names as the features used for model training, but does not require a target column. Additional columns are ignored. output_type : {'probability', 'rank'}, optional Choose the return type of the prediction: - `probability`: Probability associated with each label in the prediction. - `rank` : Rank associated with each label in the prediction. k : int, optional Number of classes to return for each input example. batch_size : int, optional If you are getting memory errors, try decreasing this value. If you have a powerful computer, increasing this value may improve performance. Returns ------- out : SFrame An SFrame with model predictions. See Also -------- predict, evaluate Examples -------- >>> pred = m.predict_topk(validation_data, k=3) >>> pred +----+-------+-------------------+ | id | class | probability | +----+-------+-------------------+ | 0 | 4 | 0.995623886585 | | 0 | 9 | 0.0038311756216 | | 0 | 7 | 0.000301006948575 | | 1 | 1 | 0.928708016872 | | 1 | 3 | 0.0440889261663 | | 1 | 2 | 0.0176190119237 | | 2 | 3 | 0.996967732906 | | 2 | 2 | 0.00151345680933 | | 2 | 7 | 0.000637513934635 | | 3 | 1 | 0.998070061207 | | .. | ... | ... | +----+-------+-------------------+ [35688 rows x 3 columns] """ _tkutl._check_categorical_option_type("output_type", output_type, ["probability", "rank"]) if not isinstance(k, int): raise TypeError("'k' must be an integer >= 1") if k <= 0: raise ValueError("'k' must be >= 1") if batch_size is not None and not isinstance(batch_size, int): raise TypeError("'batch_size' must be an integer >= 1") if batch_size is not None and batch_size < 1: raise ValueError("'batch_size' must be >= 1") prob_vector = self.predict( dataset, output_type='probability_vector', batch_size=batch_size) classes = self.classes if output_type == 'probability': results = prob_vector.apply(lambda p: [ {'class': classes[i], 'probability': p[i]} for i in reversed(_np.argsort(p)[-k:])] ) else: assert(output_type == 'rank') results = prob_vector.apply(lambda p: [ {'class': classes[index], 'rank': rank} for rank, index in enumerate(reversed(_np.argsort(p)[-k:]))] ) results = _tc.SFrame({'X': results}) results = results.add_row_number() results = results.stack('X', new_column_name='X') results = results.unpack('X', column_name_prefix='') return results def predict(self, data, output_type='class', batch_size=None, verbose=True): """ Predict on an SFrame or SArray of drawings, or on a single drawing. Parameters ---------- data : SFrame | SArray | tc.Image | list The drawing(s) on which to perform drawing classification. If dataset is an SFrame, it must have a column with the same name as the feature column during training. Additional columns are ignored. If the data is a single drawing, it can be either of type tc.Image, in which case it is a bitmap-based drawing input, or of type list, in which case it is a stroke-based drawing input. output_type : {'probability', 'class', 'probability_vector'}, optional Form of the predictions which are one of: - 'class': Class prediction. For multi-class classification, this returns the class with maximum probability. - 'probability': Prediction probability associated with the True class (not applicable for multi-class classification) - 'probability_vector': Prediction probability associated with each class as a vector. Label ordering is dictated by the ``classes`` member variable. batch_size : int, optional If you are getting memory errors, try decreasing this value. If you have a powerful computer, increasing this value may improve performance. verbose : bool, optional If True, prints prediction progress. Returns ------- out : SArray An SArray with model predictions. Each element corresponds to a drawing and contains a single value corresponding to the predicted label. Each prediction will have type integer or string depending on the type of the classes the model was trained on. If `data` is a single drawing, the return value will be a single prediction. See Also -------- evaluate Examples -------- .. sourcecode:: python # Make predictions >>> pred = model.predict(data) # Print predictions, for a better overview >>> print(pred) dtype: int Rows: 10 [3, 4, 3, 3, 4, 5, 8, 8, 8, 4] """ _tkutl._check_categorical_option_type("output_type", output_type, ["probability", "class", "probability_vector"]) if isinstance(data, _tc.SArray): predicted = self._predict_with_probabilities( _tc.SFrame({ self.feature: data }), batch_size, verbose ) elif isinstance(data, _tc.SFrame): predicted = self._predict_with_probabilities(data, batch_size, verbose) else: # single input predicted = self._predict_with_probabilities( _tc.SFrame({ self.feature: [data] }), batch_size, verbose ) if output_type == "class": return predicted[self.target] elif output_type == "probability": _class_to_index = self._class_to_index target = self.target return predicted.apply( lambda row: row["probability"][_class_to_index[row[target]]]) else: assert (output_type == "probability_vector") return predicted["probability"]

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import turicreate as _tc import numpy as _np import time as _time from turicreate.toolkits._model import CustomModel as _CustomModel from turicreate.toolkits._model import PythonProxy as _PythonProxy from turicreate.toolkits import evaluation as _evaluation import turicreate.toolkits._internal_utils as _tkutl from turicreate.toolkits._main import ToolkitError as _ToolkitError from .. import _mxnet_utils from turicreate import extensions as _extensions from .. import _pre_trained_models BITMAP_WIDTH = 28 BITMAP_HEIGHT = 28 TRAIN_VALIDATION_SPLIT = .95 def _raise_error_if_not_drawing_classifier_input_sframe( dataset, feature, target): from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe _raise_error_if_not_sframe(dataset) if feature not in dataset.column_names(): raise _ToolkitError("Feature column '%s' does not exist" % feature) if target not in dataset.column_names(): raise _ToolkitError("Target column '%s' does not exist" % target) if (dataset[feature].dtype != _tc.Image and dataset[feature].dtype != list): raise _ToolkitError("Feature column must contain images" + " or stroke-based drawings encoded as lists of strokes" + " where each stroke is a list of points and" + " each point is stored as a dictionary") if dataset[target].dtype != int and dataset[target].dtype != str: raise _ToolkitError("Target column contains " + str(dataset[target].dtype) + " but it must contain strings or integers to represent" + " labels for drawings.") if len(dataset) == 0: raise _ToolkitError("Input Dataset is empty!") def create(input_dataset, target, feature=None, validation_set='auto', warm_start='auto', batch_size=256, max_iterations=100, verbose=True): import mxnet as _mx from mxnet import autograd as _autograd from ._model_architecture import Model as _Model from ._sframe_loader import SFrameClassifierIter as _SFrameClassifierIter start_time = _time.time() if feature is None: feature = _tkutl._find_only_drawing_column(input_dataset) _raise_error_if_not_drawing_classifier_input_sframe( input_dataset, feature, target) if batch_size is not None and not isinstance(batch_size, int): raise TypeError("'batch_size' must be an integer >= 1") if batch_size is not None and batch_size < 1: raise ValueError("'batch_size' must be >= 1") if max_iterations is not None and not isinstance(max_iterations, int): raise TypeError("'max_iterations' must be an integer >= 1") if max_iterations is not None and max_iterations < 1: raise ValueError("'max_iterations' must be >= 1") is_stroke_input = (input_dataset[feature].dtype != _tc.Image) dataset = _extensions._drawing_classifier_prepare_data( input_dataset, feature) if is_stroke_input else input_dataset iteration = 0 classes = dataset[target].unique() classes = sorted(classes) class_to_index = {name: index for index, name in enumerate(classes)} validation_set_corrective_string = ("'validation_set' parameter must be " + "an SFrame, or None, or must be set to 'auto' for the toolkit to " + "automatically create a validation set.") if isinstance(validation_set, _tc.SFrame): _raise_error_if_not_drawing_classifier_input_sframe( validation_set, feature, target) is_validation_stroke_input = (validation_set[feature].dtype != _tc.Image) validation_dataset = _extensions._drawing_classifier_prepare_data( validation_set, feature) if is_validation_stroke_input else validation_set elif isinstance(validation_set, str): if validation_set == 'auto': if dataset.num_rows() >= 100: if verbose: print ( "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n" " You can set ``validation_set=None`` to disable validation tracking.\n") dataset, validation_dataset = dataset.random_split(TRAIN_VALIDATION_SPLIT, exact=True) else: validation_set = None validation_dataset = _tc.SFrame() else: raise _ToolkitError("Unrecognized value for 'validation_set'. " + validation_set_corrective_string) elif validation_set is None: validation_dataset = _tc.SFrame() else: raise TypeError("Unrecognized type for 'validation_set'." + validation_set_corrective_string) train_loader = _SFrameClassifierIter(dataset, batch_size, feature_column=feature, target_column=target, class_to_index=class_to_index, load_labels=True, shuffle=True, iterations=max_iterations) train_loader_to_compute_accuracy = _SFrameClassifierIter(dataset, batch_size, feature_column=feature, target_column=target, class_to_index=class_to_index, load_labels=True, shuffle=True, iterations=1) validation_loader = _SFrameClassifierIter(validation_dataset, batch_size, feature_column=feature, target_column=target, class_to_index=class_to_index, load_labels=True, shuffle=True, iterations=1) if verbose and iteration == 0: column_names = ['iteration', 'train_loss', 'train_accuracy', 'time'] column_titles = ['Iteration', 'Training Loss', 'Training Accuracy', 'Elapsed Time (seconds)'] if validation_set is not None: column_names.insert(3, 'validation_accuracy') column_titles.insert(3, 'Validation Accuracy') table_printer = _tc.util._ProgressTablePrinter( column_names, column_titles) ctx = _mxnet_utils.get_mxnet_context(max_devices=batch_size) model = _Model(num_classes = len(classes), prefix="drawing_") model_params = model.collect_params() model_params.initialize(_mx.init.Xavier(), ctx=ctx) if warm_start is not None: pretrained_model = _pre_trained_models.DrawingClassifierPreTrainedModel( warm_start) pretrained_model_params_path = pretrained_model.get_model_path() model.load_params(pretrained_model_params_path, ctx=ctx, allow_missing=True) softmax_cross_entropy = _mx.gluon.loss.SoftmaxCrossEntropyLoss() model.hybridize() trainer = _mx.gluon.Trainer(model.collect_params(), 'adam') train_accuracy = _mx.metric.Accuracy() validation_accuracy = _mx.metric.Accuracy() def get_data_and_label_from_batch(batch): if batch.pad is not None: size = batch_size - batch.pad sliced_data = _mx.nd.slice_axis(batch.data[0], axis=0, begin=0, end=size) sliced_label = _mx.nd.slice_axis(batch.label[0], axis=0, begin=0, end=size) num_devices = min(sliced_data.shape[0], len(ctx)) batch_data = _mx.gluon.utils.split_and_load(sliced_data, ctx_list=ctx[:num_devices], even_split=False) batch_label = _mx.gluon.utils.split_and_load(sliced_label, ctx_list=ctx[:num_devices], even_split=False) else: batch_data = _mx.gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0) batch_label = _mx.gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0) return batch_data, batch_label def compute_accuracy(accuracy_metric, batch_loader): batch_loader.reset() accuracy_metric.reset() for batch in batch_loader: batch_data, batch_label = get_data_and_label_from_batch(batch) outputs = [] for x, y in zip(batch_data, batch_label): if x is None or y is None: continue z = model(x) outputs.append(z) accuracy_metric.update(batch_label, outputs) for train_batch in train_loader: train_batch_data, train_batch_label = get_data_and_label_from_batch(train_batch) with _autograd.record(): for x, y in zip(train_batch_data, train_batch_label): z = model(x) loss = softmax_cross_entropy(z, y) loss.backward() trainer.step(train_batch.data[0].shape[0]) train_loss = loss.mean().asscalar() train_time = _time.time() - start_time if train_batch.iteration > iteration: compute_accuracy(train_accuracy, train_loader_to_compute_accuracy) if validation_set is not None: compute_accuracy(validation_accuracy, validation_loader) iteration = train_batch.iteration if verbose: kwargs = { "iteration": iteration, "train_loss": float(train_loss), "train_accuracy": train_accuracy.get()[1], "time": train_time} if validation_set is not None: kwargs["validation_accuracy"] = validation_accuracy.get()[1] table_printer.print_row(**kwargs) state = { '_model': model, '_class_to_index': class_to_index, 'num_classes': len(classes), 'classes': classes, 'input_image_shape': (1, BITMAP_WIDTH, BITMAP_HEIGHT), 'batch_size': batch_size, 'training_loss': train_loss, 'training_accuracy': train_accuracy.get()[1], 'training_time': train_time, 'validation_accuracy': validation_accuracy.get()[1], 'max_iterations': max_iterations, 'target': target, 'feature': feature, 'num_examples': len(input_dataset) } return DrawingClassifier(state) class DrawingClassifier(_CustomModel): _PYTHON_DRAWING_CLASSIFIER_VERSION = 1 def __init__(self, state): self.__proxy__ = _PythonProxy(state) @classmethod def _native_name(cls): return "drawing_classifier" def _get_native_state(self): state = self.__proxy__.get_state() mxnet_params = state['_model'].collect_params() state['_model'] = _mxnet_utils.get_gluon_net_params_state(mxnet_params) return state def _get_version(self): return self._PYTHON_DRAWING_CLASSIFIER_VERSION @classmethod def _load_version(cls, state, version): _tkutl._model_version_check(version, cls._PYTHON_DRAWING_CLASSIFIER_VERSION) from ._model_architecture import Model as _Model net = _Model(num_classes = len(state['classes']), prefix = 'drawing_') ctx = _mxnet_utils.get_mxnet_context(max_devices=state['batch_size']) net_params = net.collect_params() _mxnet_utils.load_net_params_from_state( net_params, state['_model'], ctx=ctx ) state['_model'] = net if len(state['classes']) > 0 and isinstance(state['classes'][0], float): state['classes'] = list(map(int, state['classes'])) return DrawingClassifier(state) def __str__(self): return self.__repr__() def __repr__(self): width = 40 sections, section_titles = self._get_summary_struct() out = _tkutl._toolkit_repr_print(self, sections, section_titles, width=width) return out def _get_summary_struct(self): model_fields = [ ('Number of classes', 'num_classes'), ('Feature column', 'feature'), ('Target column', 'target') ] training_fields = [ ('Training Iterations', 'max_iterations'), ('Training Accuracy', 'training_accuracy'), ('Validation Accuracy', 'validation_accuracy'), ('Training Time', 'training_time'), ('Number of Examples', 'num_examples'), ('Batch Size', 'batch_size'), ('Final Loss (specific to model)', 'training_loss') ] section_titles = ['Schema', 'Training summary'] return([model_fields, training_fields], section_titles) def export_coreml(self, filename, verbose=False): import mxnet as _mx from .._mxnet_to_coreml import _mxnet_converter import coremltools as _coremltools batch_size = 1 image_shape = (batch_size,) + (1, BITMAP_WIDTH, BITMAP_HEIGHT) s_image = _mx.sym.Variable(self.feature, shape=image_shape, dtype=_np.float32) from copy import copy as _copy net = _copy(self._model) s_ymap = net(s_image) mod = _mx.mod.Module(symbol=s_ymap, label_names=None, data_names=[self.feature]) mod.bind(for_training=False, data_shapes=[(self.feature, image_shape)]) mod.init_params() arg_params, aux_params = mod.get_params() net_params = net.collect_params() new_arg_params = {} for k, param in arg_params.items(): new_arg_params[k] = net_params[k].data(net_params[k].list_ctx()[0]) new_aux_params = {} for k, param in aux_params.items(): new_aux_params[k] = net_params[k].data(net_params[k].list_ctx()[0]) mod.set_params(new_arg_params, new_aux_params) coreml_model = _mxnet_converter.convert(mod, mode='classifier', class_labels=self.classes, input_shape=[(self.feature, image_shape)], builder=None, verbose=verbose, preprocessor_args={ 'image_input_names': [self.feature], 'image_scale': 1.0/255 }) DESIRED_OUTPUT_NAME = self.target + "Probabilities" spec = coreml_model._spec class_label_output_index = 0 if spec.description.output[0].name == "classLabel" else 1 probabilities_output_index = 1-class_label_output_index spec.neuralNetworkClassifier.labelProbabilityLayerName = DESIRED_OUTPUT_NAME spec.neuralNetworkClassifier.layers[-1].name = DESIRED_OUTPUT_NAME spec.neuralNetworkClassifier.layers[-1].output[0] = DESIRED_OUTPUT_NAME spec.description.predictedProbabilitiesName = DESIRED_OUTPUT_NAME spec.description.output[probabilities_output_index].name = DESIRED_OUTPUT_NAME from turicreate.toolkits import _coreml_utils model_type = "drawing classifier" spec.description.metadata.shortDescription = _coreml_utils._mlmodel_short_description(model_type) spec.description.input[0].shortDescription = self.feature spec.description.output[probabilities_output_index].shortDescription = 'Prediction probabilities' spec.description.output[class_label_output_index].shortDescription = 'Class Label of Top Prediction' from coremltools.models.utils import save_spec as _save_spec _save_spec(spec, filename) def _predict_with_probabilities(self, input_dataset, batch_size=None, verbose=True): import mxnet as _mx from ._sframe_loader import SFrameClassifierIter as _SFrameClassifierIter is_stroke_input = (input_dataset[self.feature].dtype != _tc.Image) dataset = _extensions._drawing_classifier_prepare_data( input_dataset, self.feature) if is_stroke_input else input_dataset batch_size = self.batch_size if batch_size is None else batch_size loader = _SFrameClassifierIter(dataset, batch_size, class_to_index=self._class_to_index, feature_column=self.feature, target_column=self.target, load_labels=False, shuffle=False, iterations=1) dataset_size = len(dataset) ctx = _mxnet_utils.get_mxnet_context() index = 0 last_time = 0 done = False from turicreate import SArrayBuilder from array import array classes = self.classes all_predicted_builder = SArrayBuilder(dtype=type(classes[0])) all_probabilities_builder = SArrayBuilder(dtype=array) for batch in loader: if batch.pad is not None: size = batch_size - batch.pad batch_data = _mx.nd.slice_axis(batch.data[0], axis=0, begin=0, end=size) else: batch_data = batch.data[0] size = batch_size num_devices = min(batch_data.shape[0], len(ctx)) split_data = _mx.gluon.utils.split_and_load(batch_data, ctx_list=ctx[:num_devices], even_split=False) for data in split_data: z = self._model(data).asnumpy() predicted = list(map(lambda x: classes[x], z.argmax(axis=1))) split_length = z.shape[0] all_predicted_builder.append_multiple(predicted) all_probabilities_builder.append_multiple(z.tolist()) index += split_length if index == dataset_size - 1: done = True cur_time = _time.time() if verbose and (dataset_size >= 5 and cur_time > last_time + 10 or done): print('Predicting {cur_n:{width}d}/{max_n:{width}d}'.format( cur_n = index + 1, max_n = dataset_size, width = len(str(dataset_size)))) last_time = cur_time return (_tc.SFrame({self.target: all_predicted_builder.close(), 'probability': all_probabilities_builder.close()})) def evaluate(self, dataset, metric='auto', batch_size=None, verbose=True): if self.target not in dataset.column_names(): raise _ToolkitError("Must provide ground truth column, '" + self.target + "' in the evaluation dataset.") predicted = self._predict_with_probabilities(dataset, batch_size, verbose) avail_metrics = ['accuracy', 'auc', 'precision', 'recall', 'f1_score', 'confusion_matrix', 'roc_curve'] _tkutl._check_categorical_option_type( 'metric', metric, avail_metrics + ['auto']) metrics = avail_metrics if metric == 'auto' else [metric] ret = {} if 'accuracy' in metrics: ret['accuracy'] = _evaluation.accuracy( dataset[self.target], predicted[self.target]) if 'auc' in metrics: ret['auc'] = _evaluation.auc( dataset[self.target], predicted['probability'], index_map=self._class_to_index) if 'precision' in metrics: ret['precision'] = _evaluation.precision( dataset[self.target], predicted[self.target]) if 'recall' in metrics: ret['recall'] = _evaluation.recall( dataset[self.target], predicted[self.target]) if 'f1_score' in metrics: ret['f1_score'] = _evaluation.f1_score( dataset[self.target], predicted[self.target]) if 'confusion_matrix' in metrics: ret['confusion_matrix'] = _evaluation.confusion_matrix( dataset[self.target], predicted[self.target]) if 'roc_curve' in metrics: ret['roc_curve'] = _evaluation.roc_curve( dataset[self.target], predicted['probability'], index_map=self._class_to_index) return ret def predict_topk(self, dataset, output_type="probability", k=3, batch_size=None): _tkutl._check_categorical_option_type("output_type", output_type, ["probability", "rank"]) if not isinstance(k, int): raise TypeError("'k' must be an integer >= 1") if k <= 0: raise ValueError("'k' must be >= 1") if batch_size is not None and not isinstance(batch_size, int): raise TypeError("'batch_size' must be an integer >= 1") if batch_size is not None and batch_size < 1: raise ValueError("'batch_size' must be >= 1") prob_vector = self.predict( dataset, output_type='probability_vector', batch_size=batch_size) classes = self.classes if output_type == 'probability': results = prob_vector.apply(lambda p: [ {'class': classes[i], 'probability': p[i]} for i in reversed(_np.argsort(p)[-k:])] ) else: assert(output_type == 'rank') results = prob_vector.apply(lambda p: [ {'class': classes[index], 'rank': rank} for rank, index in enumerate(reversed(_np.argsort(p)[-k:]))] ) results = _tc.SFrame({'X': results}) results = results.add_row_number() results = results.stack('X', new_column_name='X') results = results.unpack('X', column_name_prefix='') return results def predict(self, data, output_type='class', batch_size=None, verbose=True): _tkutl._check_categorical_option_type("output_type", output_type, ["probability", "class", "probability_vector"]) if isinstance(data, _tc.SArray): predicted = self._predict_with_probabilities( _tc.SFrame({ self.feature: data }), batch_size, verbose ) elif isinstance(data, _tc.SFrame): predicted = self._predict_with_probabilities(data, batch_size, verbose) else: predicted = self._predict_with_probabilities( _tc.SFrame({ self.feature: [data] }), batch_size, verbose ) if output_type == "class": return predicted[self.target] elif output_type == "probability": _class_to_index = self._class_to_index target = self.target return predicted.apply( lambda row: row["probability"][_class_to_index[row[target]]]) else: assert (output_type == "probability_vector") return predicted["probability"]

true

1c47529775227539b203847b8de750e8bd66423a

407

py

Python

cont/contapp/models.py

Chuox/Contador_Palabras

2be98392351536416baa38c90fc62950138d84f1

[ "MIT" ]

null

cont/contapp/models.py

Chuox/Contador_Palabras

2be98392351536416baa38c90fc62950138d84f1

[ "MIT" ]

null

cont/contapp/models.py

Chuox/Contador_Palabras

2be98392351536416baa38c90fc62950138d84f1

[ "MIT" ]

null

from django.db import models from django.urls import reverse # Create your models here. class Palabras(models.Model): url = models.CharField(max_length=99999,default="https://es.wikipedia.org/") texto = models.CharField(max_length=9999999,default="") def __str__(self): return self.url def get_absolute_url(self): return reverse('count-detail', kwargs={'pk': self.pk})

31.307692

80

0.702703

from django.db import models from django.urls import reverse class Palabras(models.Model): url = models.CharField(max_length=99999,default="https://es.wikipedia.org/") texto = models.CharField(max_length=9999999,default="") def __str__(self): return self.url def get_absolute_url(self): return reverse('count-detail', kwargs={'pk': self.pk})

true

1c4752b75cce49cce05e2ea439f39e239799fab9

2,740

py

Python

mvmv/mvmv.py

movermeyer/mvmv

23c1c4202b6fb0ef08d6c07975107dcec87d7208

[ "MIT" ]

1

2019-01-26T16:35:31.000Z

mvmv/mvmv.py

movermeyer/mvmv

23c1c4202b6fb0ef08d6c07975107dcec87d7208

[ "MIT" ]

5

2015-01-22T23:24:05.000Z

2015-01-25T04:49:03.000Z

mvmv/mvmv.py

movermeyer/mvmv

23c1c4202b6fb0ef08d6c07975107dcec87d7208

[ "MIT" ]

3

2015-02-25T17:51:41.000Z

2018-03-04T20:29:59.000Z

import codecs import mimetypes import os import re import sqlite3 from fuzzywuzzy import fuzz # common words in movies that we don't want to search the database for common_words = [ "The", "Them", "A", "An", "In", ] # blacklist of common garbage that fills up movie names blacklist = [ "BluRay", "\d{3,4}p", "(HD|DVD|BR)Rip", "x\d{3}", "XViD(-.*)?", "AC3-EVO", ] # compile the blacklist into a regex bl_re = re.compile("(" + "|".join(blacklist) + ")(\s|$)", re.IGNORECASE) # Setup the sqlite database def search(query, cursor): # remove all instancer of 'WORD ' for WORD in blacklist query = query.replace(".", " ") query = bl_re.sub("", query) year = re.search("(19|20)\d{2}", query) if year: year = year.group(0) # Find the first relevant word word = "" for item in query.split(" "): if item not in common_words and len(item) > 3: word = item.replace("-", " ") break cursor.execute("SELECT * FROM movies WHERE movies MATCH ?", ["%s %s" % (word, year)]) ratio = 0 best = query if year: best = best.replace(year, "") best = best.strip() for item in cursor: current = fuzz.ratio(item[0], query) for word in item[0].split(): if word not in query: current -= 10 if item[0] in query and len(item[0].split()) > 1: ratio = 100 best = item[0] elif current > ratio: ratio = current best = item[0] return best def is_valid_file(filename, excludes): return str(mimetypes.guess_type(filename)[0]).find('video/') == 0 and \ not any(map(lambda x: bool(x.match(filename)), excludes)) def get_movies_list(dirname, excludes=None): if excludes is None: excludes = [] movies = [] for root, _, files in os.walk(dirname): if any(map(lambda x: x.match(root), excludes)): continue movies += [(root, mov) for mov in files if is_valid_file(mov, excludes)] return movies def movemovie(src, dst, cursor): filename, extension = os.path.splitext(src[1]) os.rename(os.path.join(src[0], src[1]), "%s/%s%s" % (dst, search(filename, cursor), extension)) def movemovies(dirname, dst, cursor, excludes=None): for movie in get_movies_list(dirname, excludes): movemovie(movie, dst, cursor) if __name__ == "__main__": conn = sqlite3.connect("movies.db") cursor = conn.cursor() import sys print(search(sys.argv[1], cursor)) conn.close()

26.346154

80

0.55365

import codecs import mimetypes import os import re import sqlite3 from fuzzywuzzy import fuzz common_words = [ "The", "Them", "A", "An", "In", ] # blacklist of common garbage that fills up movie names blacklist = [ "BluRay", "\d{3,4}p", "(HD|DVD|BR)Rip", "x\d{3}", "XViD(-.*)?", "AC3-EVO", ] # compile the blacklist into a regex bl_re = re.compile("(" + "|".join(blacklist) + ")(\s|$)", re.IGNORECASE) # Setup the sqlite database def search(query, cursor): # remove all instancer of 'WORD ' for WORD in blacklist query = query.replace(".", " ") query = bl_re.sub("", query) year = re.search("(19|20)\d{2}", query) if year: year = year.group(0) # Find the first relevant word word = "" for item in query.split(" "): if item not in common_words and len(item) > 3: word = item.replace("-", " ") break cursor.execute("SELECT * FROM movies WHERE movies MATCH ?", ["%s %s" % (word, year)]) ratio = 0 best = query if year: best = best.replace(year, "") best = best.strip() for item in cursor: current = fuzz.ratio(item[0], query) for word in item[0].split(): if word not in query: current -= 10 if item[0] in query and len(item[0].split()) > 1: ratio = 100 best = item[0] elif current > ratio: ratio = current best = item[0] return best def is_valid_file(filename, excludes): return str(mimetypes.guess_type(filename)[0]).find('video/') == 0 and \ not any(map(lambda x: bool(x.match(filename)), excludes)) def get_movies_list(dirname, excludes=None): if excludes is None: excludes = [] movies = [] for root, _, files in os.walk(dirname): if any(map(lambda x: x.match(root), excludes)): continue movies += [(root, mov) for mov in files if is_valid_file(mov, excludes)] return movies def movemovie(src, dst, cursor): filename, extension = os.path.splitext(src[1]) os.rename(os.path.join(src[0], src[1]), "%s/%s%s" % (dst, search(filename, cursor), extension)) def movemovies(dirname, dst, cursor, excludes=None): for movie in get_movies_list(dirname, excludes): movemovie(movie, dst, cursor) if __name__ == "__main__": conn = sqlite3.connect("movies.db") cursor = conn.cursor() import sys print(search(sys.argv[1], cursor)) conn.close()

true

1c4752ee09bf70092f224bcea3d2adc5f3dcac59

708

py

Python

Switches.py

ProgrammingNerdGit/GBLS

6fcc3acc4b2797ef7c97f6d88c42cef66f8e7b50

[ "MIT" ]

1

2020-11-04T18:50:54.000Z

Switches.py

ProgrammingNerdGit/GBLS

6fcc3acc4b2797ef7c97f6d88c42cef66f8e7b50

[ "MIT" ]

null

Switches.py

ProgrammingNerdGit/GBLS

6fcc3acc4b2797ef7c97f6d88c42cef66f8e7b50

[ "MIT" ]

null

class switch: def __init__(self): self.cases = [] self.triggered = False def anyCase(self,func,*args): if(len(args) <= 1): args += tuple([False]) for i in args: if(args[i] and not self.triggered): self.triggered = True func() def exclusiveCase(self,func,*args): if(len(args) <= 1): args += tuple([False]) numOfExepts = 0 for i in args: if(args[i] and not self.triggered): numOfExepts += 1 if(numOfExepts == len(args)): self.triggered = True func() def default(self,func): if(not self.triggered): func()

29.5

50

0.492938

class switch: def __init__(self): self.cases = [] self.triggered = False def anyCase(self,func,*args): if(len(args) <= 1): args += tuple([False]) for i in args: if(args[i] and not self.triggered): self.triggered = True func() def exclusiveCase(self,func,*args): if(len(args) <= 1): args += tuple([False]) numOfExepts = 0 for i in args: if(args[i] and not self.triggered): numOfExepts += 1 if(numOfExepts == len(args)): self.triggered = True func() def default(self,func): if(not self.triggered): func()

true

1c4753ab0132900bf58f1a4ebd6b8e9c3f876049

924

bzl

Python

tools/repositories.bzl

guibou/rules_haskell

ea0e70ace2432a490d4ab4c4e54617612466e584

[ "Apache-2.0" ]

222

2017-11-06T09:01:12.000Z

2022-03-28T08:24:22.000Z

tools/repositories.bzl

guibou/rules_haskell

ea0e70ace2432a490d4ab4c4e54617612466e584

[ "Apache-2.0" ]

1,168

2017-11-19T07:43:13.000Z

2022-03-31T12:40:39.000Z

tools/repositories.bzl

guibou/rules_haskell

ea0e70ace2432a490d4ab4c4e54617612466e584

[ "Apache-2.0" ]

94

2017-11-17T22:46:37.000Z

2022-03-15T00:16:56.000Z

"""Workspace rules (tools/repositories)""" load("@rules_haskell//haskell:cabal.bzl", "stack_snapshot") def rules_haskell_worker_dependencies(**stack_kwargs): """Provide all repositories that are necessary for `rules_haskell`'s tools to function. """ excludes = native.existing_rules().keys() if "rules_haskell_worker_dependencies" not in excludes: stack_snapshot( name = "rules_haskell_worker_dependencies", packages = [ "base", "bytestring", "filepath", "ghc", "ghc-paths", "microlens", "process", "profunctors-5.5.2", "proto-lens-0.7.0.0", "proto-lens-runtime-0.7.0.0", "text", "vector", ], snapshot = "lts-18.0", **stack_kwargs )

29.806452

81

0.504329

load("@rules_haskell//haskell:cabal.bzl", "stack_snapshot") def rules_haskell_worker_dependencies(**stack_kwargs): excludes = native.existing_rules().keys() if "rules_haskell_worker_dependencies" not in excludes: stack_snapshot( name = "rules_haskell_worker_dependencies", packages = [ "base", "bytestring", "filepath", "ghc", "ghc-paths", "microlens", "process", "profunctors-5.5.2", "proto-lens-0.7.0.0", "proto-lens-runtime-0.7.0.0", "text", "vector", ], snapshot = "lts-18.0", **stack_kwargs )

true

1c4753ba6758fb3028d113543431f667163dd0f4

3,120

py

Python

newproject_1/newproject_1/settings.py

Chinmoy-Prasad-Dutta/scrapy_scraper

09f6abfc3bcf10ee28f486d83b450c89a07e066e

[ "MIT" ]

null

newproject_1/newproject_1/settings.py

Chinmoy-Prasad-Dutta/scrapy_scraper

09f6abfc3bcf10ee28f486d83b450c89a07e066e

[ "MIT" ]

null

newproject_1/newproject_1/settings.py

Chinmoy-Prasad-Dutta/scrapy_scraper

09f6abfc3bcf10ee28f486d83b450c89a07e066e

[ "MIT" ]

null

# Scrapy settings for newproject_1 project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # https://docs.scrapy.org/en/latest/topics/settings.html # https://docs.scrapy.org/en/latest/topics/downloader-middleware.html # https://docs.scrapy.org/en/latest/topics/spider-middleware.html BOT_NAME = 'newproject_1' SPIDER_MODULES = ['newproject_1.spiders'] NEWSPIDER_MODULE = 'newproject_1.spiders' # Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = 'newproject_1 (+http://www.yourdomain.com)' # Obey robots.txt rules ROBOTSTXT_OBEY = True # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs #DOWNLOAD_DELAY = 3 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) #COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: #DEFAULT_REQUEST_HEADERS = { # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', # 'Accept-Language': 'en', #} # Enable or disable spider middlewares # See https://docs.scrapy.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { # 'newproject_1.middlewares.Newproject1SpiderMiddleware': 543, #} # Enable or disable downloader middlewares # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { # 'newproject_1.middlewares.Newproject1DownloaderMiddleware': 543, #} # Enable or disable extensions # See https://docs.scrapy.org/en/latest/topics/extensions.html #EXTENSIONS = { # 'scrapy.extensions.telnet.TelnetConsole': None, #} # Configure item pipelines # See https://docs.scrapy.org/en/latest/topics/item-pipeline.html #ITEM_PIPELINES = { # 'newproject_1.pipelines.Newproject1Pipeline': 300, #} # Enable and configure the AutoThrottle extension (disabled by default) # See https://docs.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 5 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 60 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings #HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

35.05618

103

0.780769

BOT_NAME = 'newproject_1' SPIDER_MODULES = ['newproject_1.spiders'] NEWSPIDER_MODULE = 'newproject_1.spiders' ROBOTSTXT_OBEY = True

true

1c4753eff116b910c9c93958d56825d7720f1568

1,444

py

Python

samples/generated_samples/dialogflow_v2_generated_versions_get_version_async.py

rkdfc93/python-dialogflow

a59cff0298ef18674c0b4133ef0a6ab82e288920

[ "Apache-2.0" ]

171

2018-09-19T21:16:18.000Z

2020-12-07T17:41:10.000Z

samples/generated_samples/dialogflow_v2_generated_versions_get_version_async.py

rkdfc93/python-dialogflow

a59cff0298ef18674c0b4133ef0a6ab82e288920

[ "Apache-2.0" ]

150

2018-09-25T14:04:28.000Z

2020-12-09T21:45:43.000Z

samples/generated_samples/dialogflow_v2_generated_versions_get_version_async.py

rkdfc93/python-dialogflow

a59cff0298ef18674c0b4133ef0a6ab82e288920

[ "Apache-2.0" ]

75

2018-09-22T14:12:18.000Z

2020-12-08T07:12:12.000Z

# -*- coding: utf-8 -*- # Copyright 2022 Google LLC # # 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. # # Generated code. DO NOT EDIT! # # Snippet for GetVersion # NOTE: This snippet has been automatically generated for illustrative purposes only. # It may require modifications to work in your environment. # To install the latest published package dependency, execute the following: # python3 -m pip install google-cloud-dialogflow # [START dialogflow_v2_generated_Versions_GetVersion_async] from google.cloud import dialogflow_v2 async def sample_get_version(): # Create a client client = dialogflow_v2.VersionsAsyncClient() # Initialize request argument(s) request = dialogflow_v2.GetVersionRequest( name="name_value", ) # Make the request response = await client.get_version(request=request) # Handle the response print(response) # [END dialogflow_v2_generated_Versions_GetVersion_async]

31.391304

85

0.756925

from google.cloud import dialogflow_v2 async def sample_get_version(): client = dialogflow_v2.VersionsAsyncClient() request = dialogflow_v2.GetVersionRequest( name="name_value", ) response = await client.get_version(request=request) print(response)

true

1c4755892a095d9eed7918634a6edef5688ce027

1,624

py

Python

sdks/python/http_client/v1/test/test_v1_list_searches_response.py

TariqAHassan/polyaxon

6fc7f6a6ec49ef02d525887b6d18a893203e5b29

[ "Apache-2.0" ]

null

sdks/python/http_client/v1/test/test_v1_list_searches_response.py

TariqAHassan/polyaxon

6fc7f6a6ec49ef02d525887b6d18a893203e5b29

[ "Apache-2.0" ]

null

sdks/python/http_client/v1/test/test_v1_list_searches_response.py

TariqAHassan/polyaxon

6fc7f6a6ec49ef02d525887b6d18a893203e5b29

[ "Apache-2.0" ]

null

#!/usr/bin/python # # Copyright 2019 Polyaxon, Inc. # # 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. # coding: utf-8 """ Polyaxon sdk No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 1.14.4 Contact: contact@polyaxon.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import polyaxon_sdk from polyaxon_sdk.models.v1_list_searches_response import V1ListSearchesResponse # noqa: E501 from polyaxon_sdk.rest import ApiException class TestV1ListSearchesResponse(unittest.TestCase): """V1ListSearchesResponse unit test stubs""" def setUp(self): pass def tearDown(self): pass def testV1ListSearchesResponse(self): """Test V1ListSearchesResponse""" # FIXME: construct object with mandatory attributes with example values # model = polyaxon_sdk.models.v1_list_searches_response.V1ListSearchesResponse() # noqa: E501 pass if __name__ == '__main__': unittest.main()

28.491228

119

0.738916

from __future__ import absolute_import import unittest import polyaxon_sdk from polyaxon_sdk.models.v1_list_searches_response import V1ListSearchesResponse from polyaxon_sdk.rest import ApiException class TestV1ListSearchesResponse(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testV1ListSearchesResponse(self): s if __name__ == '__main__': unittest.main()

true

1c475637e60225ae646c1b529f1fa216fb2c6c1a

10,082

py

Python

doc/source/conf.py

genomicsengland/gel-coverage

61a671a53ac52a0b62c8aea983ced65fd0bed6cc

[ "Apache-2.0" ]

2

2019-07-15T08:13:22.000Z

2020-09-30T18:47:59.000Z

doc/source/conf.py

genomicsengland/gel-coverage

61a671a53ac52a0b62c8aea983ced65fd0bed6cc

[ "Apache-2.0" ]

null

doc/source/conf.py

genomicsengland/gel-coverage

61a671a53ac52a0b62c8aea983ced65fd0bed6cc

[ "Apache-2.0" ]

null

import sphinx_rtd_theme # -*- coding: utf-8 -*- # # GelCoverage documentation build configuration file, created by # sphinx-quickstart on Tue Dec 13 14:37:07 2016. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # # import os # import sys # sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The encoding of source files. # # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'GelCoverage' copyright = u'2016, Pablo Riesgo, Pedro Furio, Matthew Parker, Antonio Rueda, Alona Sosinsky' author = u'Pablo Riesgo, Pedro Furio, Matthew Parker, Antonio Rueda, Alona Sosinsky' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = u'1.0.0' # The full version, including alpha/beta/rc tags. release = u'1.0.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # # today = '' # # Else, today_fmt is used as the format for a strftime call. # # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all # documents. # # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # # html_theme = 'alabaster' html_theme = "sphinx_rtd_theme" html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. # "<project> v<release> documentation" by default. # # html_title = u'GelCoverage v1.0.0' # A shorter title for the navigation bar. Default is the same as html_title. # # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # # html_logo = None # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # # html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. # # html_extra_path = [] # If not None, a 'Last updated on:' timestamp is inserted at every page # bottom, using the given strftime format. # The empty string is equivalent to '%b %d, %Y'. # # html_last_updated_fmt = None # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # # html_additional_pages = {} # If false, no module index is generated. # # html_domain_indices = True # If false, no index is generated. # # html_use_index = True # If true, the index is split into individual pages for each letter. # # html_split_index = False # If true, links to the reST sources are added to the pages. # # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr', 'zh' # # html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # 'ja' uses this config value. # 'zh' user can custom change `jieba` dictionary path. # # html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. # # html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'GelCoveragedoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'GelCoverage.tex', u'GelCoverage Documentation', u'Pablo Riesgo, Pedro Furio, Matthew Parker, Antonio Rueda, Alona Sosinsky', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # # latex_use_parts = False # If true, show page references after internal links. # # latex_show_pagerefs = False # If true, show URL addresses after external links. # # latex_show_urls = False # Documents to append as an appendix to all manuals. # # latex_appendices = [] # It false, will not define \strong, \code, itleref, \crossref ... but only # \sphinxstrong, ..., \sphinxtitleref, ... To help avoid clash with user added # packages. # # latex_keep_old_macro_names = True # If false, no module index is generated. # # latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'gelcoverage', u'GelCoverage Documentation', [author], 1) ] # If true, show URL addresses after external links. # # man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'GelCoverage', u'GelCoverage Documentation', author, 'GelCoverage', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. # # texinfo_appendices = [] # If false, no module index is generated. # # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # # texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. # # texinfo_no_detailmenu = False

29.223188

93

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import sphinx_rtd_theme extensions = [] templates_path = ['_templates'] source_suffix = '.rst' master_doc = 'index' project = u'GelCoverage' copyright = u'2016, Pablo Riesgo, Pedro Furio, Matthew Parker, Antonio Rueda, Alona Sosinsky' author = u'Pablo Riesgo, Pedro Furio, Matthew Parker, Antonio Rueda, Alona Sosinsky' # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = u'1.0.0' # The full version, including alpha/beta/rc tags. release = u'1.0.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # # today = '' # # Else, today_fmt is used as the format for a strftime call. # # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all # documents. # # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # # html_theme = 'alabaster' html_theme = "sphinx_rtd_theme" html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. # "<project> v<release> documentation" by default. # # html_title = u'GelCoverage v1.0.0' # A shorter title for the navigation bar. Default is the same as html_title. # # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # # html_logo = None # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # # html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. # # html_extra_path = [] # If not None, a 'Last updated on:' timestamp is inserted at every page # bottom, using the given strftime format. # The empty string is equivalent to '%b %d, %Y'. # # html_last_updated_fmt = None # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # # html_additional_pages = {} # If false, no module index is generated. # # html_domain_indices = True # If false, no index is generated. # # html_use_index = True # If true, the index is split into individual pages for each letter. # # html_split_index = False # If true, links to the reST sources are added to the pages. # # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr', 'zh' # # html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # 'ja' uses this config value. # 'zh' user can custom change `jieba` dictionary path. # # html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. # # html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'GelCoveragedoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'GelCoverage.tex', u'GelCoverage Documentation', u'Pablo Riesgo, Pedro Furio, Matthew Parker, Antonio Rueda, Alona Sosinsky', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # # latex_use_parts = False # If true, show page references after internal links. # # latex_show_pagerefs = False # If true, show URL addresses after external links. # # latex_show_urls = False # Documents to append as an appendix to all manuals. # # latex_appendices = [] # It false, will not define \strong, \code, itleref, \crossref ... but only # \sphinxstrong, ..., \sphinxtitleref, ... To help avoid clash with user added # packages. # # latex_keep_old_macro_names = True # If false, no module index is generated. # # latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'gelcoverage', u'GelCoverage Documentation', [author], 1) ] # If true, show URL addresses after external links. # # man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'GelCoverage', u'GelCoverage Documentation', author, 'GelCoverage', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. # # texinfo_appendices = [] # If false, no module index is generated. # # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # # texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu.

true

1c475707181d966447b38a87fe651934c279aaa0

1,151

py

Python

aiida/tools/importexport/__init__.py

aiace9/aiida-core

09ac91654648adb684a58d5d2d7b1c11a503dae8

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

1

2020-10-01T17:11:58.000Z

aiida/tools/importexport/__init__.py

blokhin/aiida-core

29331b558b45ba74acf1ca633a2d8bfabc1bdd05

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

2

2019-03-06T11:23:42.000Z

2020-03-09T09:34:07.000Z

aiida/tools/importexport/__init__.py

blokhin/aiida-core

29331b558b45ba74acf1ca633a2d8bfabc1bdd05

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

null

# -*- coding: utf-8 -*- ########################################################################### # Copyright (c), The AiiDA team. All rights reserved. # # This file is part of the AiiDA code. # # # # The code is hosted on GitHub at https://github.com/aiidateam/aiida-core # # For further information on the license, see the LICENSE.txt file # # For further information please visit http://www.aiida.net # ########################################################################### # pylint: disable=wildcard-import,undefined-variable """Provides import/export functionalities. To see history/git blame prior to the move to aiida.tools.importexport, explore tree: https://github.com/aiidateam/aiida-core/tree/eebef392c81e8b130834a92e1d7abf5e2e30b3ce Functionality: <tree>/aiida/orm/importexport.py Tests: <tree>/aiida/backends/tests/test_export_and_import.py """ from .dbexport import * from .dbimport import * from .common import * __all__ = (dbexport.__all__ + dbimport.__all__ + common.__all__)

47.958333

99

0.564726

true

1c47573535fc8458d412b298db9ec2766ec449c9

645

py

Python

modules/sample/src/sample/CSV/pf.py

AsmaBRZ/rcrs-server

d67a84a17b73dd95c5553bed68b8c4c08cd5651a

[ "BSD-3-Clause" ]

null

modules/sample/src/sample/CSV/pf.py

AsmaBRZ/rcrs-server

d67a84a17b73dd95c5553bed68b8c4c08cd5651a

[ "BSD-3-Clause" ]

null

modules/sample/src/sample/CSV/pf.py

AsmaBRZ/rcrs-server

d67a84a17b73dd95c5553bed68b8c4c08cd5651a

[ "BSD-3-Clause" ]

null

import matplotlib.pyplot as plt import numpy as np import os time=np.arange(1,301) array=np.zeros(250) a=[] fichiers=os.listdir("d") for f in fichiers: print(f) i=0 with open("d/"+f, "r") as ins: for line in ins: if i<300: print(line) l=line.split(" ") print(int(l[1])) print(i) print('jjjjjjjj') print(array[i]) array[i]=array[i]+int(l[1]) i=i+1 print (array) plt.plot(array) plt.ylabel("Nombre d'obstacles nettoyés") plt.xlabel('Temps') plt.suptitle('Agent random') plt.show()

18.970588

43

0.516279

import matplotlib.pyplot as plt import numpy as np import os time=np.arange(1,301) array=np.zeros(250) a=[] fichiers=os.listdir("d") for f in fichiers: print(f) i=0 with open("d/"+f, "r") as ins: for line in ins: if i<300: print(line) l=line.split(" ") print(int(l[1])) print(i) print('jjjjjjjj') print(array[i]) array[i]=array[i]+int(l[1]) i=i+1 print (array) plt.plot(array) plt.ylabel("Nombre d'obstacles nettoyés") plt.xlabel('Temps') plt.suptitle('Agent random') plt.show()

true

1c47577594847e925fd3f69b3081b42da3d8500b

49,232

py

Python

tests/test_data_tokenizers.py

sxjscience/gluon-nlp

e6c39a80f4155cdb9c5fe8145287ddd322b4952b

[ "Apache-2.0" ]

1

2020-03-20T08:01:34.000Z

tests/test_data_tokenizers.py

sxjscience/gluon-nlp

e6c39a80f4155cdb9c5fe8145287ddd322b4952b

[ "Apache-2.0" ]

null

tests/test_data_tokenizers.py

sxjscience/gluon-nlp

e6c39a80f4155cdb9c5fe8145287ddd322b4952b

[ "Apache-2.0" ]

null

import pytest import random import collections import pickle from uuid import uuid4 import os import unicodedata import tempfile from pkg_resources import parse_version import gluonnlp from gluonnlp.data.tokenizers import WhitespaceTokenizer, MosesTokenizer, JiebaTokenizer,\ SpacyTokenizer, SubwordNMTTokenizer, YTTMTokenizer, SentencepieceTokenizer, \ HuggingFaceBPETokenizer, HuggingFaceByteBPETokenizer, HuggingFaceWordPieceTokenizer, \ HuggingFaceTokenizer from gluonnlp.base import get_repo_url from gluonnlp.data import Vocab from gluonnlp.utils.misc import download EN_SAMPLES = ['Four score and seven years ago our fathers brought forth on this continent, ' 'a new nation, conceived in Liberty, and dedicated to the proposition ' 'that all men are created equal.', 'In spite of the debate going on for months about the photos of Özil with the ' 'Turkish President Recep Tayyip Erdogan, he regrets the return of ' 'the 92-match national player Özil.'] DE_SAMPLES = ['Goethe stammte aus einer angesehenen bürgerlichen Familie; sein Großvater' ' mütterlicherseits war als Stadtschultheiß höchster Justizbeamter der' ' Stadt Frankfurt, sein Vater Doktor der Rechte und kaiserlicher Rat.', '"Das ist eine Frage, die natürlich davon abhängt, dass man einmal ins ' 'Gespräch kommt, dass man mit ihm auch darüber spricht, warum er das eine ' 'oder andere offenbar so empfunden hat, wie das in seinem Statement niedergelegt' ' ist", sagte Grindel im Fußball-Podcast "Phrasenmäher" der "Bild-Zeitung.'] ZH_SAMPLES = ['苟活者在淡红的血色中，会依稀看见微茫的希望；真的猛士，将更奋然而前行。', '参加工作，哈尔滨工业大学无线电工程系电子仪器及测量技术专业毕业。'] SUBWORD_TEST_SAMPLES = ["Hello, y'all! How are you Ⅷ 😁 😁 😁 ?", 'GluonNLP is great！！！!!!', "GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# 'abc'"] def random_inject_space(sentence): words = sentence.split() ret = '' for i, word in enumerate(words): ret += word if i < len(words) - 1: n_space_tokens = random.randint(1, 10) for j in range(n_space_tokens): ret += random.choice([' ', '\t', '\r', '\n']) return ret def verify_encode_token_with_offsets(tokenizer, all_sentences, gt_offsets=None): if gt_offsets is None: for sentences in [all_sentences[0], all_sentences]: enc_tokens = tokenizer.encode(sentences, str) tokens, offsets = tokenizer.encode_with_offsets(sentences, str) if isinstance(sentences, list): for ele_tokens, ele_enc_tokens, ele_offsets, ele_sentence in\ zip(tokens, enc_tokens, offsets, sentences): for tok, offset, enc_tok in zip(ele_tokens, ele_offsets, ele_enc_tokens): assert ele_sentence[offset[0]:offset[1]] == tok assert tok == enc_tok else: for tok, offset, enc_tok in zip(tokens, offsets, enc_tokens): assert sentences[offset[0]:offset[1]] == tok assert tok == enc_tok else: for sentences, ele_gt_offsets in [(all_sentences[0], gt_offsets[0]), (all_sentences, gt_offsets)]: enc_tokens = tokenizer.encode(sentences, str) tokens, offsets = tokenizer.encode_with_offsets(sentences, str) assert ele_gt_offsets == offsets assert enc_tokens == tokens def verify_sentencepiece_tokenizer_with_offsets(tokenizer, all_sentences): for sentences in [all_sentences[0], all_sentences]: enc_tokens = tokenizer.encode(sentences, str) tokens, offsets = tokenizer.encode_with_offsets(sentences, str) if isinstance(sentences, list): for ele_tokens, ele_enc_tokens, ele_offsets, ele_sentence\ in zip(tokens, enc_tokens, offsets, sentences): for i, (tok, offset, enc_tok) in enumerate(zip(ele_tokens, ele_offsets, ele_enc_tokens)): assert tok == enc_tok ele_sel_tok = unicodedata.normalize('NFKC', ele_sentence[offset[0]:offset[1]]).strip() if tokenizer.is_first_subword(tok): real_tok = tok[1:] else: real_tok = tok assert ele_sel_tok == real_tok,\ 'ele_sel_tok={}, real_tok={}'.format(ele_sel_tok, real_tok) def verify_encode_with_offsets_consistency(tokenizer, all_sentences): for sentences in [all_sentences[0], all_sentences]: enc_tokens = tokenizer.encode(sentences, int) tokens, offsets = tokenizer.encode_with_offsets(sentences, int) str_tokens, str_offsets = tokenizer.encode_with_offsets(sentences, str) assert offsets == str_offsets assert tokens == enc_tokens def verify_encode_token(tokenizer, all_sentences, all_gt_tokens): for sentences, gt_tokens in [(all_sentences[0], all_gt_tokens[0]), (all_sentences, all_gt_tokens)]: tokenizer_encode_ret = tokenizer.encode(sentences) assert tokenizer_encode_ret == gt_tokens,\ 'Whole Encoded: {}, \nWhole GT: {}'.format(tokenizer_encode_ret, gt_tokens) def verify_decode(tokenizer, all_sentences, out_type=str): for sentences in [all_sentences[0], all_sentences]: assert tokenizer.decode(tokenizer.encode(sentences, out_type)) == sentences def verify_decode_spm(tokenizer, all_sentences, gt_int_decode_sentences): for sentences, case_gt_int_decode in [(all_sentences[0], gt_int_decode_sentences[0]), (all_sentences, gt_int_decode_sentences)]: if isinstance(sentences, str): gt_str_decode_sentences = sentences if tokenizer.lowercase: gt_str_decode_sentences = gt_str_decode_sentences.lower() gt_str_decode_sentences = unicodedata.normalize('NFKC', gt_str_decode_sentences) elif isinstance(sentences, list): gt_str_decode_sentences = [] for ele in sentences: ele_gt_decode = ele if tokenizer.lowercase: ele_gt_decode = ele_gt_decode.lower() ele_gt_decode = unicodedata.normalize('NFKC', ele_gt_decode) gt_str_decode_sentences.append(ele_gt_decode) else: raise NotImplementedError assert tokenizer.decode(tokenizer.encode(sentences, str)) == gt_str_decode_sentences assert tokenizer.decode(tokenizer.encode(sentences, int)) == case_gt_int_decode def verify_decode_subword_nmt(tokenizer, all_sentences, gt_int_decode, gt_str_decode): for sentences, case_gt_int_decode, case_gt_str_decode in [(all_sentences[0], gt_int_decode[0], gt_str_decode[0]), (all_sentences, gt_int_decode, gt_str_decode)]: assert tokenizer.decode(tokenizer.encode(sentences, str)) == case_gt_str_decode assert tokenizer.decode(tokenizer.encode(sentences, int)) == case_gt_int_decode def verify_decode_hf(tokenizer, all_sentences, gt_decode_sentences): for sentences, case_gt_decode in [(all_sentences[0], gt_decode_sentences[0]), (all_sentences, gt_decode_sentences)]: assert tokenizer.decode(tokenizer.encode(sentences, str)) == case_gt_decode assert tokenizer.decode(tokenizer.encode(sentences, int)) == case_gt_decode if isinstance(sentences, list): for sentence in sentences: assert tokenizer.vocab.to_tokens(tokenizer.encode(sentence, int))\ == tokenizer.encode(sentence, str) assert tokenizer.vocab[tokenizer.encode(sentence, str)]\ == tokenizer.encode(sentence, int) else: assert tokenizer.vocab.to_tokens(tokenizer.encode(sentences, int)) \ == tokenizer.encode(sentences, str) assert tokenizer.vocab[tokenizer.encode(sentences, str)] \ == tokenizer.encode(sentences, int) def verify_decode_no_vocab_raise(tokenizer): # When the vocab is not attached, should raise ValueError for sentences in [EN_SAMPLES[0], EN_SAMPLES]: with pytest.raises(ValueError): tokenizer.encode(sentences, int) with pytest.raises(ValueError): tokenizer.decode([0]) with pytest.raises(ValueError): tokenizer.decode([[0], [1]]) def verify_pickleble(tokenizer, cls): print(tokenizer) # Verify if the tokenizer is pickleable and has the same behavior after dumping/loading tokenizer_p = pickle.loads(pickle.dumps(tokenizer)) assert isinstance(tokenizer_p, cls) assert tokenizer.encode(SUBWORD_TEST_SAMPLES, str) == tokenizer_p.encode(SUBWORD_TEST_SAMPLES, str) def test_whitespace_tokenizer(): tokenizer = WhitespaceTokenizer() gt_en_tokenized = [['Four', 'score', 'and', 'seven', 'years', 'ago', 'our', 'fathers', 'brought', 'forth', 'on', 'this', 'continent,', 'a', 'new', 'nation,', 'conceived', 'in', 'Liberty,', 'and', 'dedicated', 'to', 'the', 'proposition', 'that', 'all', 'men', 'are', 'created', 'equal.'], ['In', 'spite', 'of', 'the', 'debate', 'going', 'on', 'for', 'months', 'about', 'the', 'photos', 'of', 'Özil', 'with', 'the', 'Turkish', 'President', 'Recep', 'Tayyip', 'Erdogan,', 'he', 'regrets', 'the', 'return', 'of', 'the', '92-match', 'national', 'player', 'Özil.']] gt_de_tokenized = [['Goethe', 'stammte', 'aus', 'einer', 'angesehenen', 'bürgerlichen', 'Familie;', 'sein', 'Großvater', 'mütterlicherseits', 'war', 'als', 'Stadtschultheiß', 'höchster', 'Justizbeamter', 'der', 'Stadt', 'Frankfurt,', 'sein', 'Vater', 'Doktor', 'der', 'Rechte', 'und', 'kaiserlicher', 'Rat.'], ['"Das', 'ist', 'eine', 'Frage,', 'die', 'natürlich', 'davon', 'abhängt,', 'dass', 'man', 'einmal', 'ins', 'Gespräch', 'kommt,', 'dass', 'man', 'mit', 'ihm', 'auch', 'darüber', 'spricht,', 'warum', 'er', 'das', 'eine', 'oder', 'andere', 'offenbar', 'so', 'empfunden', 'hat,', 'wie', 'das', 'in', 'seinem', 'Statement', 'niedergelegt', 'ist",', 'sagte', 'Grindel', 'im', 'Fußball-Podcast', '"Phrasenmäher"', 'der', '"Bild-Zeitung.']] for _ in range(2): # Inject noise and test for encode noisy_en_samples = [random_inject_space(ele) for ele in EN_SAMPLES] noisy_de_samples = [random_inject_space(ele) for ele in DE_SAMPLES] verify_encode_token(tokenizer, noisy_en_samples + noisy_de_samples, gt_en_tokenized + gt_de_tokenized) # Test for decode verify_decode(tokenizer, EN_SAMPLES + DE_SAMPLES, str) # Test for encode_with_offsets verify_encode_token_with_offsets(tokenizer, noisy_en_samples + noisy_de_samples) verify_decode_no_vocab_raise(tokenizer) # Test for output_type = int vocab = Vocab(collections.Counter(sum(gt_en_tokenized + gt_de_tokenized, []))) tokenizer.set_vocab(vocab) verify_decode(tokenizer, EN_SAMPLES + DE_SAMPLES, int) verify_pickleble(tokenizer, WhitespaceTokenizer) verify_encode_token_with_offsets(tokenizer, EN_SAMPLES + DE_SAMPLES) def test_moses_tokenizer(): en_tokenizer = MosesTokenizer('en') de_tokenizer = MosesTokenizer('de') gt_en_tokenized = [['Four', 'score', 'and', 'seven', 'years', 'ago', 'our', 'fathers', 'brought', 'forth', 'on', 'this', 'continent', ',', 'a', 'new', 'nation', ',', 'conceived', 'in', 'Liberty', ',', 'and', 'dedicated', 'to', 'the', 'proposition', 'that', 'all', 'men', 'are', 'created', 'equal', '.'], ['In', 'spite', 'of', 'the', 'debate', 'going', 'on', 'for', 'months', 'about', 'the', 'photos', 'of', 'Özil', 'with', 'the', 'Turkish', 'President', 'Recep', 'Tayyip', 'Erdogan', ',', 'he', 'regrets', 'the', 'return', 'of', 'the', '92-match', 'national', 'player', 'Özil', '.']] gt_de_tokenized = [['Goethe', 'stammte', 'aus', 'einer', 'angesehenen', 'bürgerlichen', 'Familie', ';', 'sein', 'Großvater', 'mütterlicherseits', 'war', 'als', 'Stadtschultheiß', 'höchster', 'Justizbeamter', 'der', 'Stadt', 'Frankfurt', ',', 'sein', 'Vater', 'Doktor', 'der', 'Rechte', 'und', 'kaiserlicher', 'Rat', '.'], ['"', 'Das', 'ist', 'eine', 'Frage', ',', 'die', 'natürlich', 'davon', 'abhängt', ',', 'dass', 'man', 'einmal', 'ins', 'Gespräch', 'kommt', ',', 'dass', 'man', 'mit', 'ihm', 'auch', 'darüber', 'spricht', ',', 'warum', 'er', 'das', 'eine', 'oder', 'andere', 'offenbar', 'so', 'empfunden', 'hat', ',', 'wie', 'das', 'in', 'seinem', 'Statement', 'niedergelegt', 'ist', '"', ',', 'sagte', 'Grindel', 'im', 'Fußball-Podcast', '"', 'Phrasenmäher', '"', 'der', '"', 'Bild-Zeitung', '.']] verify_encode_token(en_tokenizer, EN_SAMPLES, gt_en_tokenized) verify_encode_token(de_tokenizer, DE_SAMPLES, gt_de_tokenized) verify_decode(en_tokenizer, EN_SAMPLES, str) verify_decode(de_tokenizer, DE_SAMPLES, str) vocab = Vocab(collections.Counter(sum(gt_en_tokenized + gt_de_tokenized, []))) verify_decode_no_vocab_raise(en_tokenizer) verify_decode_no_vocab_raise(de_tokenizer) en_tokenizer.set_vocab(vocab) de_tokenizer.set_vocab(vocab) verify_decode(en_tokenizer, EN_SAMPLES, int) verify_decode(de_tokenizer, DE_SAMPLES, int) verify_pickleble(en_tokenizer, MosesTokenizer) verify_pickleble(de_tokenizer, MosesTokenizer) def test_jieba_tokenizer(): tokenizer = JiebaTokenizer() gt_zh_tokenized = [['苟活', '者', '在', '淡红', '的', '血色', '中', '，', '会', '依稀', '看见', '微茫', '的', '希望', '；', '真的', '猛士', '，', '将', '更奋', '然而', '前行', '。'], ['参加', '工作', '，', '哈尔滨工业大学', '无线电', '工程系', '电子仪器', '及', '测量', '技术', '专业', '毕业', '。']] verify_encode_token(tokenizer, ZH_SAMPLES, gt_zh_tokenized) verify_decode(tokenizer, ZH_SAMPLES, str) vocab = Vocab(collections.Counter(sum(gt_zh_tokenized, []))) verify_decode_no_vocab_raise(tokenizer) tokenizer.set_vocab(vocab) verify_decode(tokenizer, ZH_SAMPLES, int) verify_pickleble(tokenizer, JiebaTokenizer) def test_spacy_tokenizer(): en_tokenizer = SpacyTokenizer('en') de_tokenizer = SpacyTokenizer('de') gt_en_tokenized = [['Four', 'score', 'and', 'seven', 'years', 'ago', 'our', 'fathers', 'brought', 'forth', 'on', 'this', 'continent', ',', 'a', 'new', 'nation', ',', 'conceived', 'in', 'Liberty', ',', 'and', 'dedicated', 'to', 'the', 'proposition', 'that', 'all', 'men', 'are', 'created', 'equal', '.'], ['In', 'spite', 'of', 'the', 'debate', 'going', 'on', 'for', 'months', 'about', 'the', 'photos', 'of', 'Özil', 'with', 'the', 'Turkish', 'President', 'Recep', 'Tayyip', 'Erdogan', ',', 'he', 'regrets', 'the', 'return', 'of', 'the', '92-match', 'national', 'player', 'Özil', '.']] gt_de_tokenized = [['Goethe', 'stammte', 'aus', 'einer', 'angesehenen', 'bürgerlichen', 'Familie', ';', 'sein', 'Großvater', 'mütterlicherseits', 'war', 'als', 'Stadtschultheiß', 'höchster', 'Justizbeamter', 'der', 'Stadt', 'Frankfurt', ',', 'sein', 'Vater', 'Doktor', 'der', 'Rechte', 'und', 'kaiserlicher', 'Rat', '.'], ['"', 'Das', 'ist', 'eine', 'Frage', ',', 'die', 'natürlich', 'davon', 'abhängt', ',', 'dass', 'man', 'einmal', 'ins', 'Gespräch', 'kommt', ',', 'dass', 'man', 'mit', 'ihm', 'auch', 'darüber', 'spricht', ',', 'warum', 'er', 'das', 'eine', 'oder', 'andere', 'offenbar', 'so', 'empfunden', 'hat', ',', 'wie', 'das', 'in', 'seinem', 'Statement', 'niedergelegt', 'ist', '"', ',', 'sagte', 'Grindel', 'im', 'Fußball-Podcast', '"', 'Phrasenmäher', '"', 'der', '"', 'Bild-Zeitung', '.']] verify_encode_token(en_tokenizer, EN_SAMPLES, gt_en_tokenized) verify_encode_token(de_tokenizer, DE_SAMPLES, gt_de_tokenized) vocab = Vocab(collections.Counter(sum(gt_en_tokenized + gt_de_tokenized, []))) en_tokenizer.set_vocab(vocab) de_tokenizer.set_vocab(vocab) verify_pickleble(en_tokenizer, SpacyTokenizer) verify_pickleble(de_tokenizer, SpacyTokenizer) verify_encode_token_with_offsets(en_tokenizer, EN_SAMPLES) verify_encode_token_with_offsets(de_tokenizer, DE_SAMPLES) # Test for loading spacy tokenizer from specifying the "model" flag en_tokenizer = SpacyTokenizer(model='en_core_web_lg') out = en_tokenizer.encode(EN_SAMPLES) def test_yttm_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'yttm.model') download(url=get_repo_url() + 'tokenizer_test_models/yttm/test_ende_yttm-6f2c39.model', path=model_path) tokenizer = YTTMTokenizer(model_path=model_path) gt_tokenized = [['▁He', 'll', 'o', ',', '▁y', "'", 'all', '!', '▁How', '▁are', '▁you', '▁', 'Ⅷ', '▁', '😁', '▁', '😁', '▁', '😁', '▁?'], ['▁Gl', 'u', 'on', 'N', 'L', 'P', '▁is', '▁great', '！', '！', '！', '!', '!', '!'], ['▁Gl', 'u', 'on', 'N', 'L', 'P', '-A', 'm', 'az', 'on', '-H', 'a', 'ib', 'in', '-L', 'e', 'on', 'ard', '-S', 'hen', 'g', '-S', 'h', 'u', 'ai', '-', 'X', 'ing', 'j', 'ian', '.', '.', '.', '.', '.', '/', ':', '!', '@', '#', '▁', "'", 'ab', 'c', "'"]] gt_offsets = [[(0, 2), (2, 4), (4, 5), (5, 6), (6, 8), (8, 9), (9, 12), (12, 13), (13, 17), (17, 21), (21, 25), (25, 26), (26, 27), (27, 28), (28, 29), (29, 30), (30, 31), (31, 32), (32, 33), (33, 35)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 11), (11, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 10), (10, 11), (11, 13), (13, 15), (15, 17), (17, 18), (18, 20), (20, 22), (22, 24), (24, 25), (25, 27), (27, 30), (30, 32), (32, 35), (35, 36), (36, 38), (38, 39), (39, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (61, 62), (62, 63), (63, 65), (65, 66), (66, 67)]] gt_int_decode = ['Hello, y<UNK>all! How are you <UNK> <UNK> <UNK> <UNK> ?', 'GluonNLP is great！！！!!!', 'GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# <UNK>abc<UNK>'] gt_str_decode = ["Hello, y'all! How are you Ⅷ 😁 😁 😁 ?", 'GluonNLP is great！！！!!!', "GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# 'abc'"] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, YTTMTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) # Begin to verify decode for sample_sentences, ele_gt_int_decode, ele_gt_str_decode in [(SUBWORD_TEST_SAMPLES[0], gt_int_decode[0], gt_str_decode[0]), (SUBWORD_TEST_SAMPLES, gt_int_decode, gt_str_decode)]: int_decode = tokenizer.decode(tokenizer.encode(sample_sentences, int)) str_decode = tokenizer.decode(tokenizer.encode(sample_sentences, str)) assert int_decode == ele_gt_int_decode assert str_decode == ele_gt_str_decode os.remove(model_path) assert tokenizer.decode([]) == '' assert tokenizer.decode([[]]) == [''] @pytest.mark.seed(123) def test_sentencepiece_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'spm.model') download(url=get_repo_url() + 'tokenizer_test_models/sentencepiece/case1/test_ende-a9bee4.model', path=model_path) # Case1 tokenizer = SentencepieceTokenizer(model_path) gt_tokenized = [['▁Hel', 'lo', ',', '▁y', "'", 'all', '!', '▁How', '▁are', '▁you', '▁', 'VI', 'II', '▁', '😁', '▁', '😁', '▁', '😁', '▁?'], ['▁G', 'lu', 'on', 'N', 'L', 'P', '▁is', '▁great', '!', '!', '!', '!', '!', '!'], ['▁G', 'lu', 'on', 'N', 'L', 'P', '-', 'A', 'ma', 'zo', 'n', '-', 'H', 'ai', 'bin', '-', 'L', 'e', 'on', 'ard', '-', 'S', 'hen', 'g', '-', 'S', 'hu', 'ai', '-', 'X', 'ing', 'j', 'ian', '.', '.', '.', '.', '.', '/', ':', '!', '@', '#', '▁', "'", 'ab', 'c', "'"]] gt_offsets = [[(0, 3), (3, 5), (5, 6), (6, 8), (8, 9), (9, 12), (12, 13), (13, 17), (17, 21), (21, 25), (25, 26), (26, 26), (26, 27), (27, 28), (28, 29), (29, 30), (30, 31), (31, 32), (32, 33), (33, 35)], [(0, 1), (1, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 11), (11, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 1), (1, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 10), (10, 12), (12, 14), (14, 15), (15, 16), (16, 17), (17, 19), (19, 22), (22, 23), (23, 24), (24, 25), (25, 27), (27, 30), (30, 31), (31, 32), (32, 35), (35, 36), (36, 37), (37, 38), (38, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (61, 62), (62, 63), (63, 65), (65, 66), (66, 67)]] gt_int_decode = ['Hello, y ⁇ all! How are you VIII ⁇ ⁇ ⁇ ?', 'GluonNLP is great!!!!!!', 'GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:! ⁇ # ⁇ abc ⁇ '] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, SentencepieceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_spm(tokenizer, SUBWORD_TEST_SAMPLES, gt_int_decode) # Case2, lower_case gt_lower_case_int_decode = ['hello, y ⁇ all! how are you viii ⁇ ⁇ ⁇ ?', 'gluonnlp is great!!!!!!', 'gluonnlp-amazon-haibin-leonard-sheng-shuai-xingjian...../:! ⁇ # ⁇ abc ⁇ '] tokenizer = SentencepieceTokenizer(model_path, lowercase=True) verify_decode_spm(tokenizer, SUBWORD_TEST_SAMPLES, gt_lower_case_int_decode) # Case3, Use the sentencepiece regularization commands, we test whether we can obtain different encoding results tokenizer = SentencepieceTokenizer(model_path, lowercase=True, nbest=-1, alpha=1.0) has_different_encode_out = False encode_out = None for _ in range(10): if encode_out is None: encode_out = tokenizer.encode(SUBWORD_TEST_SAMPLES[0]) else: ele_out = tokenizer.encode(SUBWORD_TEST_SAMPLES[0]) if ele_out != encode_out: has_different_encode_out = True break assert has_different_encode_out os.remove(model_path) def test_subword_nmt_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'subword_nmt.model') download(url=get_repo_url() + 'tokenizer_test_models/subword-nmt/test_ende-d189ff.model', path=model_path) vocab_path = os.path.join(dir_path, 'subword_nmt.vocab') download(url=get_repo_url() + 'tokenizer_test_models/subword-nmt/test_ende_vocab-900f81.json', path=vocab_path) # Case 1 tokenizer = SubwordNMTTokenizer(model_path, vocab_path) gt_tokenized = [["Hel", "lo", ",</w>", "y", "\'", "all", "!</w>", "How</w>", "are</w>", "you</w>", "Ⅷ</w>", "😁</w>", "😁</w>", "😁</w>", "?</w>"], ["Gl", "u", "on", "N", "L", "P</w>", "is</w>", "great", "！", "！", "！", "!!", "!</w>"], ["Gl", "u", "on", "N", "L", "P", "-", "Amaz", "on-", "H", "ai", "b", "in-", "Le", "on", "ard", "-", "Sh", "eng", "-", "Sh", "u", "ai", "-", "X", "ing", "ji", "an", "..", "...", "/", ":", "!", "@", "#</w>", "\'", "ab", "c", "\'</w>"]] gt_offsets = [[(0, 3), (3, 5), (5, 6), (7, 8), (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27), (28, 29), (30, 31), (32, 33), (34, 35)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (9, 11), (12, 17), (17, 18), (18, 19), (19, 20), (20, 22), (22, 23)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 13), (13, 16), (16, 17), (17, 19), (19, 20), (20, 23), (23, 25), (25, 27), (27, 30), (30, 31), (31, 33), (33, 36), (36, 37), (37, 39), (39, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 49), (49, 51), (51, 53), (53, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (62, 63), (63, 65), (65, 66), (66, 67)]] gt_int_decode = ["Hello, y\'all! How are you Ⅷ 😁 😁 😁 ?", "GluonNLP is great！！！!!!", "GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# \'abc\'"] gt_str_decode = SUBWORD_TEST_SAMPLES verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, SubwordNMTTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_subword_nmt(tokenizer, SUBWORD_TEST_SAMPLES, gt_int_decode, gt_str_decode) # Case 2, bpe_dropout # We use str decode here because we may not perfectly recover the original sentence with int decode. tokenizer = SubwordNMTTokenizer(model_path, vocab_path, bpe_dropout=0.5) verify_decode(tokenizer, SUBWORD_TEST_SAMPLES, out_type=str) os.remove(model_path) os.remove(vocab_path) def test_huggingface_bpe_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'test_hf_bpe.model') download(url=get_repo_url() + 'tokenizer_test_models/hf_bpe/test_hf_bpe.model', path=model_path) vocab_path = os.path.join(dir_path, 'test_hf_bpe.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bpe/test_hf_bpe.vocab', path=vocab_path) hf_vocab_path = os.path.join(dir_path, 'test_hf_bpe.hf_vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bpe/test_hf_bpe.hf_vocab', path=hf_vocab_path) # Case 1, default lowercase=False tokenizer = HuggingFaceBPETokenizer(model_path, vocab_path) gt_tokenized = [['Hello</w>', ',</w>', 'y</w>', "'</w>", 'all</w>', '!</w>', 'How</w>', 'are</w>', 'you</w>', '<unk>', '<unk>', '<unk>', '<unk>', '?</w>'], ['Gl', 'u', 'on', 'N', 'LP</w>', 'is</w>', 'great</w>', '！</w>', '！</w>', '！</w>', '!</w>', '!</w>', '!</w>'], ['Gl', 'u', 'on', 'N', 'LP</w>', '-</w>', 'Amazon</w>', '-</w>', 'H', 'ai', 'bin</w>', '-</w>', 'Leonard</w>', '-</w>', 'Sh', 'en', 'g</w>', '-</w>', 'Sh', 'u', 'ai</w>', '-</w>', 'X', 'ing', 'j', 'ian</w>', '.</w>', '.</w>', '.</w>', '.</w>', '.</w>', '/</w>', ':</w>', '!</w>', '@</w>', '#</w>', "'</w>", 'ab', 'c</w>', "'</w>"]] gt_offsets = [[(0, 5), (5, 6), (7, 8), (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27), (28, 29), (30, 31), (32, 33), (34, 35)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 8), (9, 11), (12, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 8), (8, 9), (9, 15), (15, 16), (16, 17), (17, 19), (19, 22), (22, 23), (23, 30), (30, 31), (31, 33), (33, 35), (35, 36), (36, 37), (37, 39), (39, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (62, 63), (63, 65), (65, 66), (66, 67)]] # gt_int_decode = gt_str_decode for hf # hf removed the unk tokens in decode result gt_decode = ["Hello , y ' all ! How are you ?", 'GluonNLP is great ！！！ ! ! !', "GluonNLP - Amazon - Haibin - Leonard - Sheng - Shuai - Xingjian . . . . . / : ! @ # ' abc '"] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceBPETokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) # Case 2, lowercase=True gt_lowercase_decode = ["hello , y ' all ! how are you ?", 'gluonnlp is great ！！！ ! ! !', "gluonnlp - amazon - haibin - leonard - sheng - shuai - xingjian . . . . . / : ! @ # ' abc '"] tokenizer = HuggingFaceBPETokenizer(model_path, vocab_path, lowercase=True) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_lowercase_decode) # Case 3, using original hf vocab tokenizer = HuggingFaceBPETokenizer(model_path, hf_vocab_path) verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceBPETokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) os.remove(model_path) os.remove(vocab_path) os.remove(hf_vocab_path) def test_huggingface_bytebpe_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'hf_bytebpe.model') download(url=get_repo_url() + 'tokenizer_test_models/hf_bytebpe/test_hf_bytebpe.model', path=model_path) vocab_path = os.path.join(dir_path, 'hf_bytebpe.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bytebpe/test_hf_bytebpe.vocab', path=vocab_path) hf_vocab_path = os.path.join(dir_path, 'hf_bytebpe.hf_vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bytebpe/test_hf_bytebpe.hf_vocab', path=hf_vocab_path) # Case 1, default lowercase=False tokenizer = HuggingFaceByteBPETokenizer(model_path, vocab_path) gt_tokenized = [['Hello', ',', 'Ġy', "'", 'all', '!', 'ĠHow', 'Ġare', 'Ġyou', 'Ġâ', 'ħ', '§', 'ĠðŁĺ', 'ģ', 'ĠðŁĺ', 'ģ', 'ĠðŁĺ', 'ģ', 'Ġ?'], ['Gl', 'u', 'on', 'N', 'LP', 'Ġis', 'Ġgreat', 'ï¼', 'ģ', 'ï¼', 'ģ', 'ï¼', 'ģ', '!!!'], ['Gl', 'u', 'on', 'N', 'LP', '-', 'Amazon', '-', 'Ha', 'ib', 'in', '-', 'Le', 'on', 'ard', '-', 'She', 'ng', '-', 'Sh', 'u', 'ai', '-', 'X', 'ing', 'j', 'ian', '.....', '/', ':', '!', '@', '#', "Ġ'", 'ab', 'c', "'"]] # the defination of the offsets of bytelevel seems not clear gt_offsets = [[(0, 5), (5, 6), (6, 8), (8, 9), (9, 12), (12, 13), (13, 17), (17, 21), (21, 25), (25, 27), (26, 27), (26, 27), (27, 29), (28, 29), (29, 31), (30, 31), (31, 33), (32, 33), (33, 35)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 8), (8, 11), (11, 17), (17, 18), (17, 18), (18, 19), (18, 19), (19, 20), (19, 20), (20, 23)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 8), (8, 9), (9, 15), (15, 16), (16, 18), (18, 20), (20, 22), (22, 23), (23, 25), (25, 27), (27, 30), (30, 31), (31, 34), (34, 36), (36, 37), (37, 39), (39, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (61, 63), (63, 65), (65, 66), (66, 67)]] gt_decode = ["Hello, y'all! How are you Ⅷ 😁 😁 😁 ?", 'GluonNLP is great！！！!!!', "GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# 'abc'"] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceByteBPETokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) # Case 2, lowercase=True gt_lowercase_int_decode = ["hello, y'all! how are you ⅷ 😁 😁 😁 ?", 'gluonnlp is great！！！!!!', "gluonnlp-amazon-haibin-leonard-sheng-shuai-xingjian...../:!@# 'abc'"] tokenizer = HuggingFaceByteBPETokenizer(model_path, vocab_path, lowercase=True) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_lowercase_int_decode) # Case 3, using original hf vocab tokenizer = HuggingFaceByteBPETokenizer(model_path, hf_vocab_path) verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceByteBPETokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) os.remove(model_path) os.remove(vocab_path) os.remove(hf_vocab_path) def test_huggingface_wordpiece_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: vocab_path = os.path.join(dir_path, 'hf_wordpiece.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_wordpiece/test_hf_wordpiece.vocab', path=vocab_path) hf_vocab_path = os.path.join(dir_path, 'hf_wordpiece.hf_vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_wordpiece/test_hf_wordpiece.hf_vocab', path=hf_vocab_path) # Case 1, lowercase=True tokenizer = HuggingFaceWordPieceTokenizer(vocab_path, lowercase=True) gt_tokenized = [["hello", ",", "y", "'", "all", "!", "how", "are", "you", "<unk>", "<unk>", "<unk>", "<unk>", "?"], ["gl", "##uo", "##nn", "##l", "##p", "is", "great", "\uff01", "\uff01", "\uff01", "!", "!", "!"], ["gl", "##uo", "##nn", "##l", "##p", "-", "amazon", "-", "hai", "##bin", "-", "leonard", "-", "shen", "##g", "-", "shu", "##ai", "-", "xin", "##g", "##ji", "##an", ".", ".", ".", ".", ".", "/", ":", "!", "@", "#", "'", "abc", "'"]] gt_offsets = [[(0, 5), (5, 6), (7, 8), (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27), (28, 29), (30, 31), (32, 33), (34, 35)], [(0, 2), (2, 4), (4, 6), (6, 7), (7, 8), (9, 11), (12, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 2), (2, 4), (4, 6), (6, 7), (7, 8), (8, 9), (9, 15), (15, 16), (16, 19), (19, 22), (22, 23), (23, 30), (30, 31), (31, 35), (35, 36), (36, 37), (37, 40), (40, 42), (42, 43), (43, 46), (46, 47), (47, 49), (49, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (62, 63), (63, 66), (66, 67)]] gt_decode = ["hello, y'all! how are you?", "gluonnlp is great ！！！!!!", "gluonnlp - amazon - haibin - leonard - sheng - shuai - xingjian..... / :! @ #'abc '"] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceWordPieceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) # Case 2, lowercase=False gt_lowercase_decode = [", y'all! are you?", "is great ！！！!!!", "- - - - - -..... / :! @ #'abc '"] tokenizer = HuggingFaceWordPieceTokenizer(vocab_path, lowercase=False) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_lowercase_decode) # Case 3, using original hf vocab tokenizer = HuggingFaceWordPieceTokenizer(hf_vocab_path, lowercase=True) verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceWordPieceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) os.remove(vocab_path) os.remove(hf_vocab_path) @pytest.mark.skipif(parse_version(gluonnlp.utils.lazy_imports.try_import_huggingface_tokenizers().__version__) >= parse_version('0.9.0.dev0'), reason="Test is only valid for tokenizers 0.8.x") def test_huggingface_wordpiece_tokenizer_v08(): """Test for huggingface tokenizer >=0.8""" with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'hf_wordpiece_new_0.8.model') download(url=get_repo_url() + 'tokenizer_test_models/hf_wordpiece_new_0.8/hf_wordpiece.model', path=model_path, sha1_hash='66ccadf6e5e354ff9604e4a82f107a2ac873abd5') vocab_path = os.path.join(dir_path, 'hf_wordpiece_new_0.8.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_wordpiece_new_0.8/hf_wordpiece.vocab', path=vocab_path, sha1_hash='dd6fdf4bbc74eaa8806d12cb3d38a4d9a306aea8') tokenizer = HuggingFaceTokenizer(model_path, vocab_path) gt_tokenized = [['Hel', '##lo', ',', 'y', '[UNK]', 'all', '!', 'How', 'are', 'you', '[UNK]', '[UNK]', '[UNK]', '[UNK]', '?'], ['Gl', '##u', '##on', '##N', '##L', '##P', 'is', 'great', '[UNK]', '[UNK]', '[UNK]', '!', '!', '!'], ['Gl', '##u', '##on', '##N', '##L', '##P', '-', 'Am', '##az', '##on', '-', 'Ha', '##ibi', '##n', '-', 'Leon', '##ard', '-', 'She', '##n', '##g', '-', 'Sh', '##ua', '##i', '-', 'X', '##ing', '##j', '##ian', '.', '.', '.', '.', '.', '/', ':', '!', '@', '#', '[UNK]', 'ab', '##c', '[UNK]']] gt_offsets = [[(0, 3), (3, 5), (5, 6), (7, 8), (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27), (28, 29), (30, 31), (32, 33), (34, 35)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (9, 11), (12, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 11), (11, 13), (13, 15), (15, 16), (16, 18), (18, 21), (21, 22), (22, 23), (23, 27), (27, 30), (30, 31), (31, 34), (34, 35), (35, 36), (36, 37), (37, 39), (39, 41), (41, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (62, 63), (63, 65), (65, 66), (66, 67)]] gt_decode = ['Hello, y all! How are you?', 'GluonNLP is great!!!', 'GluonNLP - Amazon - Haibin - Leonard - Sheng - Shuai - Xingjian..... / ' ':! @ # abc'] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) @pytest.mark.skipif(parse_version(gluonnlp.utils.lazy_imports.try_import_huggingface_tokenizers().__version__) >= parse_version('0.9.0.dev0'), reason="Test is only valid for tokenizers 0.8.x") def test_huggingface_bpe_tokenizer_v08(): """Test for huggingface BPE tokenizer >=0.8""" with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'hf_bpe_new_0.8.model') download(url=get_repo_url() + 'tokenizer_test_models/hf_bpe_new_0.8/hf_bpe.model', path=model_path, sha1_hash='ecda90979561ca4c5a8d769b5e3c9fa2270d5317') vocab_path = os.path.join(dir_path, 'hf_bpe_new_0.8.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bpe_new_0.8/hf_bpe.vocab', path=vocab_path, sha1_hash='b92dde0b094f405208f3ec94b5eae88430bf4262') tokenizer = HuggingFaceTokenizer(model_path, vocab_path) gt_tokenized = [['H', 'ello</w>', ',</w>', 'y</w>', 'all</w>', '!</w>', 'How</w>', 'are</w>', 'you</w>', '?</w>'], ['G', 'lu', 'on', 'N', 'L', 'P</w>', 'is</w>', 'great</w>', '!</w>', '!</w>', '!</w>'], ['G', 'lu', 'on', 'N', 'L', 'P</w>', '-</w>', 'Amaz', 'on</w>', '-</w>', 'Ha', 'i', 'bin</w>', '-</w>', 'Leon', 'ard</w>', '-</w>', 'Sh', 'eng</w>', '-</w>', 'S', 'hu', 'ai</w>', '-</w>', 'X', 'ing', 'j', 'ian</w>', '.</w>', '.</w>', '.</w>', '.</w>', '.</w>', '/</w>', ':</w>', '!</w>', '@</w>', '#</w>', 'ab', 'c</w>']] gt_offsets = [[(0, 1), (1, 5), (5, 6), (7, 8), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (34, 35)], [(0, 1), (1, 3), (3, 5), (5, 6), (6, 7), (7, 8), (9, 11), (12, 17), (20, 21), (21, 22), (22, 23)], [(0, 1), (1, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 13), (13, 15), (15, 16), (16, 18), (18, 19), (19, 22), (22, 23), (23, 27), (27, 30), (30, 31), (31, 33), (33, 36), (36, 37), (37, 38), (38, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (63, 65), (65, 66)]] gt_decode = ['Hello , y all ! How are you ?', 'GluonNLP is great ! ! !', 'GluonNLP - Amazon - Haibin - Leonard - Sheng - Shuai - Xingjian' ' . . . . . / : ! @ # abc'] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) @pytest.mark.skipif(parse_version(gluonnlp.utils.lazy_imports.try_import_huggingface_tokenizers().__version__) >= parse_version('0.9.0.dev0'), reason="Test is only valid for tokenizers 0.8.x") def test_huggingface_bytebpe_tokenizer_v08(): """Test for huggingface bytebpe tokenizer >=0.8""" with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'hf_bytebpe_new_0.8.model') download(url=get_repo_url() + 'tokenizer_test_models/hf_bytebpe_new_0.8/hf_bytebpe.model', path=model_path, sha1_hash='a1c4da1f6c21df923e150f56dbb5b7a53c61808b') vocab_path = os.path.join(dir_path, 'hf_bytebpe_new_0.8.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bytebpe_new_0.8/hf_bytebpe.vocab', path=vocab_path, sha1_hash='7831b19078a3222f450e65b2188dc0770473123b') tokenizer = HuggingFaceTokenizer(model_path, vocab_path) gt_tokenized = [['He', 'llo', ',', 'Ġy', "'", 'all', '!', 'ĠHow', 'Ġare', 'Ġyou', 'Ġâ', 'ħ', '§', 'Ġ', 'ð', 'Ł', 'ĺ', 'ģ', 'Ġ', 'ð', 'Ł', 'ĺ', 'ģ', 'Ġ', 'ð', 'Ł', 'ĺ', 'ģ', 'Ġ?'], ['G', 'l', 'u', 'on', 'N', 'L', 'P', 'Ġis', 'Ġgreat', 'ï', '¼', 'ģ', 'ï', '¼', 'ģ', 'ï', '¼', 'ģ', '!', '!', '!'], ['G', 'l', 'u', 'on', 'N', 'L', 'P', '-', 'Am', 'az', 'on', '-', 'Ha', 'ib', 'in', '-', 'Le', 'on', 'ard', '-', 'S', 'hen', 'g', '-', 'Sh', 'u', 'ai', '-', 'X', 'ing', 'j', 'ian', '..', '...', '/', ':', '!', '@', '#', 'Ġ', "'", 'ab', 'c', "'"]] gt_offsets = [[(0, 2), (2, 5), (5, 6), (6, 8), (8, 9), (9, 12), (12, 13), (13, 17), (17, 21), (21, 25), (25, 27), (26, 27), (26, 27), (27, 28), (28, 29), (28, 29), (28, 29), (28, 29), (29, 30), (30, 31), (30, 31), (30, 31), (30, 31), (31, 32), (32, 33), (32, 33), (32, 33), (32, 33), (33, 35)], [(0, 1), (1, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 11), (11, 17), (17, 18), (17, 18), (17, 18), (18, 19), (18, 19), (18, 19), (19, 20), (19, 20), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 1), (1, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 11), (11, 13), (13, 15), (15, 16), (16, 18), (18, 20), (20, 22), (22, 23), (23, 25), (25, 27), (27, 30), (30, 31), (31, 32), (32, 35), (35, 36), (36, 37), (37, 39), (39, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 53), (53, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (61, 62), (62, 63), (63, 65), (65, 66), (66, 67)]] gt_decode = ["Hello, y'all! How are you Ⅷ 😁 😁 😁 ?", 'GluonNLP is great！！！!!!', "GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# 'abc'"] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) def test_tokenizers_create(): tokenizer = gluonnlp.data.tokenizers.create('moses', 'en') tokenizer.encode('hello world!')

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import pytest import random import collections import pickle from uuid import uuid4 import os import unicodedata import tempfile from pkg_resources import parse_version import gluonnlp from gluonnlp.data.tokenizers import WhitespaceTokenizer, MosesTokenizer, JiebaTokenizer,\ SpacyTokenizer, SubwordNMTTokenizer, YTTMTokenizer, SentencepieceTokenizer, \ HuggingFaceBPETokenizer, HuggingFaceByteBPETokenizer, HuggingFaceWordPieceTokenizer, \ HuggingFaceTokenizer from gluonnlp.base import get_repo_url from gluonnlp.data import Vocab from gluonnlp.utils.misc import download EN_SAMPLES = ['Four score and seven years ago our fathers brought forth on this continent, ' 'a new nation, conceived in Liberty, and dedicated to the proposition ' 'that all men are created equal.', 'In spite of the debate going on for months about the photos of Özil with the ' 'Turkish President Recep Tayyip Erdogan, he regrets the return of ' 'the 92-match national player Özil.'] DE_SAMPLES = ['Goethe stammte aus einer angesehenen bürgerlichen Familie; sein Großvater' ' mütterlicherseits war als Stadtschultheiß höchster Justizbeamter der' ' Stadt Frankfurt, sein Vater Doktor der Rechte und kaiserlicher Rat.', '"Das ist eine Frage, die natürlich davon abhängt, dass man einmal ins ' 'Gespräch kommt, dass man mit ihm auch darüber spricht, warum er das eine ' 'oder andere offenbar so empfunden hat, wie das in seinem Statement niedergelegt' ' ist", sagte Grindel im Fußball-Podcast "Phrasenmäher" der "Bild-Zeitung.'] ZH_SAMPLES = ['苟活者在淡红的血色中，会依稀看见微茫的希望；真的猛士，将更奋然而前行。', '参加工作，哈尔滨工业大学无线电工程系电子仪器及测量技术专业毕业。'] SUBWORD_TEST_SAMPLES = ["Hello, y'all! How are you Ⅷ 😁 😁 😁 ?", 'GluonNLP is great！！！!!!', "GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# 'abc'"] def random_inject_space(sentence): words = sentence.split() ret = '' for i, word in enumerate(words): ret += word if i < len(words) - 1: n_space_tokens = random.randint(1, 10) for j in range(n_space_tokens): ret += random.choice([' ', '\t', '\r', '\n']) return ret def verify_encode_token_with_offsets(tokenizer, all_sentences, gt_offsets=None): if gt_offsets is None: for sentences in [all_sentences[0], all_sentences]: enc_tokens = tokenizer.encode(sentences, str) tokens, offsets = tokenizer.encode_with_offsets(sentences, str) if isinstance(sentences, list): for ele_tokens, ele_enc_tokens, ele_offsets, ele_sentence in\ zip(tokens, enc_tokens, offsets, sentences): for tok, offset, enc_tok in zip(ele_tokens, ele_offsets, ele_enc_tokens): assert ele_sentence[offset[0]:offset[1]] == tok assert tok == enc_tok else: for tok, offset, enc_tok in zip(tokens, offsets, enc_tokens): assert sentences[offset[0]:offset[1]] == tok assert tok == enc_tok else: for sentences, ele_gt_offsets in [(all_sentences[0], gt_offsets[0]), (all_sentences, gt_offsets)]: enc_tokens = tokenizer.encode(sentences, str) tokens, offsets = tokenizer.encode_with_offsets(sentences, str) assert ele_gt_offsets == offsets assert enc_tokens == tokens def verify_sentencepiece_tokenizer_with_offsets(tokenizer, all_sentences): for sentences in [all_sentences[0], all_sentences]: enc_tokens = tokenizer.encode(sentences, str) tokens, offsets = tokenizer.encode_with_offsets(sentences, str) if isinstance(sentences, list): for ele_tokens, ele_enc_tokens, ele_offsets, ele_sentence\ in zip(tokens, enc_tokens, offsets, sentences): for i, (tok, offset, enc_tok) in enumerate(zip(ele_tokens, ele_offsets, ele_enc_tokens)): assert tok == enc_tok ele_sel_tok = unicodedata.normalize('NFKC', ele_sentence[offset[0]:offset[1]]).strip() if tokenizer.is_first_subword(tok): real_tok = tok[1:] else: real_tok = tok assert ele_sel_tok == real_tok,\ 'ele_sel_tok={}, real_tok={}'.format(ele_sel_tok, real_tok) def verify_encode_with_offsets_consistency(tokenizer, all_sentences): for sentences in [all_sentences[0], all_sentences]: enc_tokens = tokenizer.encode(sentences, int) tokens, offsets = tokenizer.encode_with_offsets(sentences, int) str_tokens, str_offsets = tokenizer.encode_with_offsets(sentences, str) assert offsets == str_offsets assert tokens == enc_tokens def verify_encode_token(tokenizer, all_sentences, all_gt_tokens): for sentences, gt_tokens in [(all_sentences[0], all_gt_tokens[0]), (all_sentences, all_gt_tokens)]: tokenizer_encode_ret = tokenizer.encode(sentences) assert tokenizer_encode_ret == gt_tokens,\ 'Whole Encoded: {}, \nWhole GT: {}'.format(tokenizer_encode_ret, gt_tokens) def verify_decode(tokenizer, all_sentences, out_type=str): for sentences in [all_sentences[0], all_sentences]: assert tokenizer.decode(tokenizer.encode(sentences, out_type)) == sentences def verify_decode_spm(tokenizer, all_sentences, gt_int_decode_sentences): for sentences, case_gt_int_decode in [(all_sentences[0], gt_int_decode_sentences[0]), (all_sentences, gt_int_decode_sentences)]: if isinstance(sentences, str): gt_str_decode_sentences = sentences if tokenizer.lowercase: gt_str_decode_sentences = gt_str_decode_sentences.lower() gt_str_decode_sentences = unicodedata.normalize('NFKC', gt_str_decode_sentences) elif isinstance(sentences, list): gt_str_decode_sentences = [] for ele in sentences: ele_gt_decode = ele if tokenizer.lowercase: ele_gt_decode = ele_gt_decode.lower() ele_gt_decode = unicodedata.normalize('NFKC', ele_gt_decode) gt_str_decode_sentences.append(ele_gt_decode) else: raise NotImplementedError assert tokenizer.decode(tokenizer.encode(sentences, str)) == gt_str_decode_sentences assert tokenizer.decode(tokenizer.encode(sentences, int)) == case_gt_int_decode def verify_decode_subword_nmt(tokenizer, all_sentences, gt_int_decode, gt_str_decode): for sentences, case_gt_int_decode, case_gt_str_decode in [(all_sentences[0], gt_int_decode[0], gt_str_decode[0]), (all_sentences, gt_int_decode, gt_str_decode)]: assert tokenizer.decode(tokenizer.encode(sentences, str)) == case_gt_str_decode assert tokenizer.decode(tokenizer.encode(sentences, int)) == case_gt_int_decode def verify_decode_hf(tokenizer, all_sentences, gt_decode_sentences): for sentences, case_gt_decode in [(all_sentences[0], gt_decode_sentences[0]), (all_sentences, gt_decode_sentences)]: assert tokenizer.decode(tokenizer.encode(sentences, str)) == case_gt_decode assert tokenizer.decode(tokenizer.encode(sentences, int)) == case_gt_decode if isinstance(sentences, list): for sentence in sentences: assert tokenizer.vocab.to_tokens(tokenizer.encode(sentence, int))\ == tokenizer.encode(sentence, str) assert tokenizer.vocab[tokenizer.encode(sentence, str)]\ == tokenizer.encode(sentence, int) else: assert tokenizer.vocab.to_tokens(tokenizer.encode(sentences, int)) \ == tokenizer.encode(sentences, str) assert tokenizer.vocab[tokenizer.encode(sentences, str)] \ == tokenizer.encode(sentences, int) def verify_decode_no_vocab_raise(tokenizer): # When the vocab is not attached, should raise ValueError for sentences in [EN_SAMPLES[0], EN_SAMPLES]: with pytest.raises(ValueError): tokenizer.encode(sentences, int) with pytest.raises(ValueError): tokenizer.decode([0]) with pytest.raises(ValueError): tokenizer.decode([[0], [1]]) def verify_pickleble(tokenizer, cls): print(tokenizer) # Verify if the tokenizer is pickleable and has the same behavior after dumping/loading tokenizer_p = pickle.loads(pickle.dumps(tokenizer)) assert isinstance(tokenizer_p, cls) assert tokenizer.encode(SUBWORD_TEST_SAMPLES, str) == tokenizer_p.encode(SUBWORD_TEST_SAMPLES, str) def test_whitespace_tokenizer(): tokenizer = WhitespaceTokenizer() gt_en_tokenized = [['Four', 'score', 'and', 'seven', 'years', 'ago', 'our', 'fathers', 'brought', 'forth', 'on', 'this', 'continent,', 'a', 'new', 'nation,', 'conceived', 'in', 'Liberty,', 'and', 'dedicated', 'to', 'the', 'proposition', 'that', 'all', 'men', 'are', 'created', 'equal.'], ['In', 'spite', 'of', 'the', 'debate', 'going', 'on', 'for', 'months', 'about', 'the', 'photos', 'of', 'Özil', 'with', 'the', 'Turkish', 'President', 'Recep', 'Tayyip', 'Erdogan,', 'he', 'regrets', 'the', 'return', 'of', 'the', '92-match', 'national', 'player', 'Özil.']] gt_de_tokenized = [['Goethe', 'stammte', 'aus', 'einer', 'angesehenen', 'bürgerlichen', 'Familie;', 'sein', 'Großvater', 'mütterlicherseits', 'war', 'als', 'Stadtschultheiß', 'höchster', 'Justizbeamter', 'der', 'Stadt', 'Frankfurt,', 'sein', 'Vater', 'Doktor', 'der', 'Rechte', 'und', 'kaiserlicher', 'Rat.'], ['"Das', 'ist', 'eine', 'Frage,', 'die', 'natürlich', 'davon', 'abhängt,', 'dass', 'man', 'einmal', 'ins', 'Gespräch', 'kommt,', 'dass', 'man', 'mit', 'ihm', 'auch', 'darüber', 'spricht,', 'warum', 'er', 'das', 'eine', 'oder', 'andere', 'offenbar', 'so', 'empfunden', 'hat,', 'wie', 'das', 'in', 'seinem', 'Statement', 'niedergelegt', 'ist",', 'sagte', 'Grindel', 'im', 'Fußball-Podcast', '"Phrasenmäher"', 'der', '"Bild-Zeitung.']] for _ in range(2): # Inject noise and test for encode noisy_en_samples = [random_inject_space(ele) for ele in EN_SAMPLES] noisy_de_samples = [random_inject_space(ele) for ele in DE_SAMPLES] verify_encode_token(tokenizer, noisy_en_samples + noisy_de_samples, gt_en_tokenized + gt_de_tokenized) # Test for decode verify_decode(tokenizer, EN_SAMPLES + DE_SAMPLES, str) # Test for encode_with_offsets verify_encode_token_with_offsets(tokenizer, noisy_en_samples + noisy_de_samples) verify_decode_no_vocab_raise(tokenizer) # Test for output_type = int vocab = Vocab(collections.Counter(sum(gt_en_tokenized + gt_de_tokenized, []))) tokenizer.set_vocab(vocab) verify_decode(tokenizer, EN_SAMPLES + DE_SAMPLES, int) verify_pickleble(tokenizer, WhitespaceTokenizer) verify_encode_token_with_offsets(tokenizer, EN_SAMPLES + DE_SAMPLES) def test_moses_tokenizer(): en_tokenizer = MosesTokenizer('en') de_tokenizer = MosesTokenizer('de') gt_en_tokenized = [['Four', 'score', 'and', 'seven', 'years', 'ago', 'our', 'fathers', 'brought', 'forth', 'on', 'this', 'continent', ',', 'a', 'new', 'nation', ',', 'conceived', 'in', 'Liberty', ',', 'and', 'dedicated', 'to', 'the', 'proposition', 'that', 'all', 'men', 'are', 'created', 'equal', '.'], ['In', 'spite', 'of', 'the', 'debate', 'going', 'on', 'for', 'months', 'about', 'the', 'photos', 'of', 'Özil', 'with', 'the', 'Turkish', 'President', 'Recep', 'Tayyip', 'Erdogan', ',', 'he', 'regrets', 'the', 'return', 'of', 'the', '92-match', 'national', 'player', 'Özil', '.']] gt_de_tokenized = [['Goethe', 'stammte', 'aus', 'einer', 'angesehenen', 'bürgerlichen', 'Familie', ';', 'sein', 'Großvater', 'mütterlicherseits', 'war', 'als', 'Stadtschultheiß', 'höchster', 'Justizbeamter', 'der', 'Stadt', 'Frankfurt', ',', 'sein', 'Vater', 'Doktor', 'der', 'Rechte', 'und', 'kaiserlicher', 'Rat', '.'], ['"', 'Das', 'ist', 'eine', 'Frage', ',', 'die', 'natürlich', 'davon', 'abhängt', ',', 'dass', 'man', 'einmal', 'ins', 'Gespräch', 'kommt', ',', 'dass', 'man', 'mit', 'ihm', 'auch', 'darüber', 'spricht', ',', 'warum', 'er', 'das', 'eine', 'oder', 'andere', 'offenbar', 'so', 'empfunden', 'hat', ',', 'wie', 'das', 'in', 'seinem', 'Statement', 'niedergelegt', 'ist', '"', ',', 'sagte', 'Grindel', 'im', 'Fußball-Podcast', '"', 'Phrasenmäher', '"', 'der', '"', 'Bild-Zeitung', '.']] verify_encode_token(en_tokenizer, EN_SAMPLES, gt_en_tokenized) verify_encode_token(de_tokenizer, DE_SAMPLES, gt_de_tokenized) verify_decode(en_tokenizer, EN_SAMPLES, str) verify_decode(de_tokenizer, DE_SAMPLES, str) vocab = Vocab(collections.Counter(sum(gt_en_tokenized + gt_de_tokenized, []))) verify_decode_no_vocab_raise(en_tokenizer) verify_decode_no_vocab_raise(de_tokenizer) en_tokenizer.set_vocab(vocab) de_tokenizer.set_vocab(vocab) verify_decode(en_tokenizer, EN_SAMPLES, int) verify_decode(de_tokenizer, DE_SAMPLES, int) verify_pickleble(en_tokenizer, MosesTokenizer) verify_pickleble(de_tokenizer, MosesTokenizer) def test_jieba_tokenizer(): tokenizer = JiebaTokenizer() gt_zh_tokenized = [['苟活', '者', '在', '淡红', '的', '血色', '中', '，', '会', '依稀', '看见', '微茫', '的', '希望', '；', '真的', '猛士', '，', '将', '更奋', '然而', '前行', '。'], ['参加', '工作', '，', '哈尔滨工业大学', '无线电', '工程系', '电子仪器', '及', '测量', '技术', '专业', '毕业', '。']] verify_encode_token(tokenizer, ZH_SAMPLES, gt_zh_tokenized) verify_decode(tokenizer, ZH_SAMPLES, str) vocab = Vocab(collections.Counter(sum(gt_zh_tokenized, []))) verify_decode_no_vocab_raise(tokenizer) tokenizer.set_vocab(vocab) verify_decode(tokenizer, ZH_SAMPLES, int) verify_pickleble(tokenizer, JiebaTokenizer) def test_spacy_tokenizer(): en_tokenizer = SpacyTokenizer('en') de_tokenizer = SpacyTokenizer('de') gt_en_tokenized = [['Four', 'score', 'and', 'seven', 'years', 'ago', 'our', 'fathers', 'brought', 'forth', 'on', 'this', 'continent', ',', 'a', 'new', 'nation', ',', 'conceived', 'in', 'Liberty', ',', 'and', 'dedicated', 'to', 'the', 'proposition', 'that', 'all', 'men', 'are', 'created', 'equal', '.'], ['In', 'spite', 'of', 'the', 'debate', 'going', 'on', 'for', 'months', 'about', 'the', 'photos', 'of', 'Özil', 'with', 'the', 'Turkish', 'President', 'Recep', 'Tayyip', 'Erdogan', ',', 'he', 'regrets', 'the', 'return', 'of', 'the', '92-match', 'national', 'player', 'Özil', '.']] gt_de_tokenized = [['Goethe', 'stammte', 'aus', 'einer', 'angesehenen', 'bürgerlichen', 'Familie', ';', 'sein', 'Großvater', 'mütterlicherseits', 'war', 'als', 'Stadtschultheiß', 'höchster', 'Justizbeamter', 'der', 'Stadt', 'Frankfurt', ',', 'sein', 'Vater', 'Doktor', 'der', 'Rechte', 'und', 'kaiserlicher', 'Rat', '.'], ['"', 'Das', 'ist', 'eine', 'Frage', ',', 'die', 'natürlich', 'davon', 'abhängt', ',', 'dass', 'man', 'einmal', 'ins', 'Gespräch', 'kommt', ',', 'dass', 'man', 'mit', 'ihm', 'auch', 'darüber', 'spricht', ',', 'warum', 'er', 'das', 'eine', 'oder', 'andere', 'offenbar', 'so', 'empfunden', 'hat', ',', 'wie', 'das', 'in', 'seinem', 'Statement', 'niedergelegt', 'ist', '"', ',', 'sagte', 'Grindel', 'im', 'Fußball-Podcast', '"', 'Phrasenmäher', '"', 'der', '"', 'Bild-Zeitung', '.']] verify_encode_token(en_tokenizer, EN_SAMPLES, gt_en_tokenized) verify_encode_token(de_tokenizer, DE_SAMPLES, gt_de_tokenized) vocab = Vocab(collections.Counter(sum(gt_en_tokenized + gt_de_tokenized, []))) en_tokenizer.set_vocab(vocab) de_tokenizer.set_vocab(vocab) verify_pickleble(en_tokenizer, SpacyTokenizer) verify_pickleble(de_tokenizer, SpacyTokenizer) verify_encode_token_with_offsets(en_tokenizer, EN_SAMPLES) verify_encode_token_with_offsets(de_tokenizer, DE_SAMPLES) # Test for loading spacy tokenizer from specifying the "model" flag en_tokenizer = SpacyTokenizer(model='en_core_web_lg') out = en_tokenizer.encode(EN_SAMPLES) def test_yttm_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'yttm.model') download(url=get_repo_url() + 'tokenizer_test_models/yttm/test_ende_yttm-6f2c39.model', path=model_path) tokenizer = YTTMTokenizer(model_path=model_path) gt_tokenized = [['▁He', 'll', 'o', ',', '▁y', "'", 'all', '!', '▁How', '▁are', '▁you', '▁', 'Ⅷ', '▁', '😁', '▁', '😁', '▁', '😁', '▁?'], ['▁Gl', 'u', 'on', 'N', 'L', 'P', '▁is', '▁great', '！', '！', '！', '!', '!', '!'], ['▁Gl', 'u', 'on', 'N', 'L', 'P', '-A', 'm', 'az', 'on', '-H', 'a', 'ib', 'in', '-L', 'e', 'on', 'ard', '-S', 'hen', 'g', '-S', 'h', 'u', 'ai', '-', 'X', 'ing', 'j', 'ian', '.', '.', '.', '.', '.', '/', ':', '!', '@', '#', '▁', "'", 'ab', 'c', "'"]] gt_offsets = [[(0, 2), (2, 4), (4, 5), (5, 6), (6, 8), (8, 9), (9, 12), (12, 13), (13, 17), (17, 21), (21, 25), (25, 26), (26, 27), (27, 28), (28, 29), (29, 30), (30, 31), (31, 32), (32, 33), (33, 35)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 11), (11, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 10), (10, 11), (11, 13), (13, 15), (15, 17), (17, 18), (18, 20), (20, 22), (22, 24), (24, 25), (25, 27), (27, 30), (30, 32), (32, 35), (35, 36), (36, 38), (38, 39), (39, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (61, 62), (62, 63), (63, 65), (65, 66), (66, 67)]] gt_int_decode = ['Hello, y<UNK>all! How are you <UNK> <UNK> <UNK> <UNK> ?', 'GluonNLP is great！！！!!!', 'GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# <UNK>abc<UNK>'] gt_str_decode = ["Hello, y'all! How are you Ⅷ 😁 😁 😁 ?", 'GluonNLP is great！！！!!!', "GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# 'abc'"] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, YTTMTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) # Begin to verify decode for sample_sentences, ele_gt_int_decode, ele_gt_str_decode in [(SUBWORD_TEST_SAMPLES[0], gt_int_decode[0], gt_str_decode[0]), (SUBWORD_TEST_SAMPLES, gt_int_decode, gt_str_decode)]: int_decode = tokenizer.decode(tokenizer.encode(sample_sentences, int)) str_decode = tokenizer.decode(tokenizer.encode(sample_sentences, str)) assert int_decode == ele_gt_int_decode assert str_decode == ele_gt_str_decode os.remove(model_path) assert tokenizer.decode([]) == '' assert tokenizer.decode([[]]) == [''] @pytest.mark.seed(123) def test_sentencepiece_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'spm.model') download(url=get_repo_url() + 'tokenizer_test_models/sentencepiece/case1/test_ende-a9bee4.model', path=model_path) # Case1 tokenizer = SentencepieceTokenizer(model_path) gt_tokenized = [['▁Hel', 'lo', ',', '▁y', "'", 'all', '!', '▁How', '▁are', '▁you', '▁', 'VI', 'II', '▁', '😁', '▁', '😁', '▁', '😁', '▁?'], ['▁G', 'lu', 'on', 'N', 'L', 'P', '▁is', '▁great', '!', '!', '!', '!', '!', '!'], ['▁G', 'lu', 'on', 'N', 'L', 'P', '-', 'A', 'ma', 'zo', 'n', '-', 'H', 'ai', 'bin', '-', 'L', 'e', 'on', 'ard', '-', 'S', 'hen', 'g', '-', 'S', 'hu', 'ai', '-', 'X', 'ing', 'j', 'ian', '.', '.', '.', '.', '.', '/', ':', '!', '@', '#', '▁', "'", 'ab', 'c', "'"]] gt_offsets = [[(0, 3), (3, 5), (5, 6), (6, 8), (8, 9), (9, 12), (12, 13), (13, 17), (17, 21), (21, 25), (25, 26), (26, 26), (26, 27), (27, 28), (28, 29), (29, 30), (30, 31), (31, 32), (32, 33), (33, 35)], [(0, 1), (1, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 11), (11, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 1), (1, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 10), (10, 12), (12, 14), (14, 15), (15, 16), (16, 17), (17, 19), (19, 22), (22, 23), (23, 24), (24, 25), (25, 27), (27, 30), (30, 31), (31, 32), (32, 35), (35, 36), (36, 37), (37, 38), (38, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (61, 62), (62, 63), (63, 65), (65, 66), (66, 67)]] gt_int_decode = ['Hello, y ⁇ all! How are you VIII ⁇ ⁇ ⁇ ?', 'GluonNLP is great!!!!!!', 'GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:! ⁇ # ⁇ abc ⁇ '] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, SentencepieceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_spm(tokenizer, SUBWORD_TEST_SAMPLES, gt_int_decode) # Case2, lower_case gt_lower_case_int_decode = ['hello, y ⁇ all! how are you viii ⁇ ⁇ ⁇ ?', 'gluonnlp is great!!!!!!', 'gluonnlp-amazon-haibin-leonard-sheng-shuai-xingjian...../:! ⁇ # ⁇ abc ⁇ '] tokenizer = SentencepieceTokenizer(model_path, lowercase=True) verify_decode_spm(tokenizer, SUBWORD_TEST_SAMPLES, gt_lower_case_int_decode) # Case3, Use the sentencepiece regularization commands, we test whether we can obtain different encoding results tokenizer = SentencepieceTokenizer(model_path, lowercase=True, nbest=-1, alpha=1.0) has_different_encode_out = False encode_out = None for _ in range(10): if encode_out is None: encode_out = tokenizer.encode(SUBWORD_TEST_SAMPLES[0]) else: ele_out = tokenizer.encode(SUBWORD_TEST_SAMPLES[0]) if ele_out != encode_out: has_different_encode_out = True break assert has_different_encode_out os.remove(model_path) def test_subword_nmt_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'subword_nmt.model') download(url=get_repo_url() + 'tokenizer_test_models/subword-nmt/test_ende-d189ff.model', path=model_path) vocab_path = os.path.join(dir_path, 'subword_nmt.vocab') download(url=get_repo_url() + 'tokenizer_test_models/subword-nmt/test_ende_vocab-900f81.json', path=vocab_path) # Case 1 tokenizer = SubwordNMTTokenizer(model_path, vocab_path) gt_tokenized = [["Hel", "lo", ",</w>", "y", "\'", "all", "!</w>", "How</w>", "are</w>", "you</w>", "Ⅷ</w>", "😁</w>", "😁</w>", "😁</w>", "?</w>"], ["Gl", "u", "on", "N", "L", "P</w>", "is</w>", "great", "！", "！", "！", "!!", "!</w>"], ["Gl", "u", "on", "N", "L", "P", "-", "Amaz", "on-", "H", "ai", "b", "in-", "Le", "on", "ard", "-", "Sh", "eng", "-", "Sh", "u", "ai", "-", "X", "ing", "ji", "an", "..", "...", "/", ":", "!", "@", "#</w>", "\'", "ab", "c", "\'</w>"]] gt_offsets = [[(0, 3), (3, 5), (5, 6), (7, 8), (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27), (28, 29), (30, 31), (32, 33), (34, 35)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (9, 11), (12, 17), (17, 18), (18, 19), (19, 20), (20, 22), (22, 23)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 13), (13, 16), (16, 17), (17, 19), (19, 20), (20, 23), (23, 25), (25, 27), (27, 30), (30, 31), (31, 33), (33, 36), (36, 37), (37, 39), (39, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 49), (49, 51), (51, 53), (53, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (62, 63), (63, 65), (65, 66), (66, 67)]] gt_int_decode = ["Hello, y\'all! How are you Ⅷ 😁 😁 😁 ?", "GluonNLP is great！！！!!!", "GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@ gt_str_decode = SUBWORD_TEST_SAMPLES verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, SubwordNMTTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_subword_nmt(tokenizer, SUBWORD_TEST_SAMPLES, gt_int_decode, gt_str_decode) # Case 2, bpe_dropout # We use str decode here because we may not perfectly recover the original sentence with int decode. tokenizer = SubwordNMTTokenizer(model_path, vocab_path, bpe_dropout=0.5) verify_decode(tokenizer, SUBWORD_TEST_SAMPLES, out_type=str) os.remove(model_path) os.remove(vocab_path) def test_huggingface_bpe_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'test_hf_bpe.model') download(url=get_repo_url() + 'tokenizer_test_models/hf_bpe/test_hf_bpe.model', path=model_path) vocab_path = os.path.join(dir_path, 'test_hf_bpe.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bpe/test_hf_bpe.vocab', path=vocab_path) hf_vocab_path = os.path.join(dir_path, 'test_hf_bpe.hf_vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bpe/test_hf_bpe.hf_vocab', path=hf_vocab_path) # Case 1, default lowercase=False tokenizer = HuggingFaceBPETokenizer(model_path, vocab_path) gt_tokenized = [['Hello</w>', ',</w>', 'y</w>', "'</w>", 'all</w>', '!</w>', 'How</w>', 'are</w>', 'you</w>', '<unk>', '<unk>', '<unk>', '<unk>', '?</w>'], ['Gl', 'u', 'on', 'N', 'LP</w>', 'is</w>', 'great</w>', '！</w>', '！</w>', '！</w>', '!</w>', '!</w>', '!</w>'], ['Gl', 'u', 'on', 'N', 'LP</w>', '-</w>', 'Amazon</w>', '-</w>', 'H', 'ai', 'bin</w>', '-</w>', 'Leonard</w>', '-</w>', 'Sh', 'en', 'g</w>', '-</w>', 'Sh', 'u', 'ai</w>', '-</w>', 'X', 'ing', 'j', 'ian</w>', '.</w>', '.</w>', '.</w>', '.</w>', '.</w>', '/</w>', ':</w>', '!</w>', '@</w>', '#</w>', "'</w>", 'ab', 'c</w>', "'</w>"]] gt_offsets = [[(0, 5), (5, 6), (7, 8), (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27), (28, 29), (30, 31), (32, 33), (34, 35)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 8), (9, 11), (12, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 8), (8, 9), (9, 15), (15, 16), (16, 17), (17, 19), (19, 22), (22, 23), (23, 30), (30, 31), (31, 33), (33, 35), (35, 36), (36, 37), (37, 39), (39, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (62, 63), (63, 65), (65, 66), (66, 67)]] # gt_int_decode = gt_str_decode for hf # hf removed the unk tokens in decode result gt_decode = ["Hello , y ' all ! How are you ?", 'GluonNLP is great ！！！ ! ! !', "GluonNLP - Amazon - Haibin - Leonard - Sheng - Shuai - Xingjian . . . . . / : ! @ verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceBPETokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) # Case 2, lowercase=True gt_lowercase_decode = ["hello , y ' all ! how are you ?", 'gluonnlp is great ！！！ ! ! !', "gluonnlp - amazon - haibin - leonard - sheng - shuai - xingjian . . . . . / : ! @ # ' abc '"] tokenizer = HuggingFaceBPETokenizer(model_path, vocab_path, lowercase=True) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_lowercase_decode) # Case 3, using original hf vocab tokenizer = HuggingFaceBPETokenizer(model_path, hf_vocab_path) verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceBPETokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) os.remove(model_path) os.remove(vocab_path) os.remove(hf_vocab_path) def test_huggingface_bytebpe_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'hf_bytebpe.model') download(url=get_repo_url() + 'tokenizer_test_models/hf_bytebpe/test_hf_bytebpe.model', path=model_path) vocab_path = os.path.join(dir_path, 'hf_bytebpe.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bytebpe/test_hf_bytebpe.vocab', path=vocab_path) hf_vocab_path = os.path.join(dir_path, 'hf_bytebpe.hf_vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bytebpe/test_hf_bytebpe.hf_vocab', path=hf_vocab_path) # Case 1, default lowercase=False tokenizer = HuggingFaceByteBPETokenizer(model_path, vocab_path) gt_tokenized = [['Hello', ',', 'Ġy', "'", 'all', '!', 'ĠHow', 'Ġare', 'Ġyou', 'Ġâ', 'ħ', '§', 'ĠðŁĺ', 'ģ', 'ĠðŁĺ', 'ģ', 'ĠðŁĺ', 'ģ', 'Ġ?'], ['Gl', 'u', 'on', 'N', 'LP', 'Ġis', 'Ġgreat', 'ï¼', 'ģ', 'ï¼', 'ģ', 'ï¼', 'ģ', '!!!'], ['Gl', 'u', 'on', 'N', 'LP', '-', 'Amazon', '-', 'Ha', 'ib', 'in', '-', 'Le', 'on', 'ard', '-', 'She', 'ng', '-', 'Sh', 'u', 'ai', '-', 'X', 'ing', 'j', 'ian', '.....', '/', ':', '!', '@', '#', "Ġ'", 'ab', 'c', "'"]] # the defination of the offsets of bytelevel seems not clear gt_offsets = [[(0, 5), (5, 6), (6, 8), (8, 9), (9, 12), (12, 13), (13, 17), (17, 21), (21, 25), (25, 27), (26, 27), (26, 27), (27, 29), (28, 29), (29, 31), (30, 31), (31, 33), (32, 33), (33, 35)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 8), (8, 11), (11, 17), (17, 18), (17, 18), (18, 19), (18, 19), (19, 20), (19, 20), (20, 23)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 8), (8, 9), (9, 15), (15, 16), (16, 18), (18, 20), (20, 22), (22, 23), (23, 25), (25, 27), (27, 30), (30, 31), (31, 34), (34, 36), (36, 37), (37, 39), (39, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (61, 63), (63, 65), (65, 66), (66, 67)]] gt_decode = ["Hello, y'all! How are you Ⅷ 😁 😁 😁 ?", 'GluonNLP is great！！！!!!', "GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# 'abc'"] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceByteBPETokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) # Case 2, lowercase=True gt_lowercase_int_decode = ["hello, y'all! how are you ⅷ 😁 😁 😁 ?", 'gluonnlp is great！！！!!!', "gluonnlp-amazon-haibin-leonard-sheng-shuai-xingjian...../:!@ tokenizer = HuggingFaceByteBPETokenizer(model_path, vocab_path, lowercase=True) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_lowercase_int_decode) # Case 3, using original hf vocab tokenizer = HuggingFaceByteBPETokenizer(model_path, hf_vocab_path) verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceByteBPETokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) os.remove(model_path) os.remove(vocab_path) os.remove(hf_vocab_path) def test_huggingface_wordpiece_tokenizer(): with tempfile.TemporaryDirectory() as dir_path: vocab_path = os.path.join(dir_path, 'hf_wordpiece.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_wordpiece/test_hf_wordpiece.vocab', path=vocab_path) hf_vocab_path = os.path.join(dir_path, 'hf_wordpiece.hf_vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_wordpiece/test_hf_wordpiece.hf_vocab', path=hf_vocab_path) # Case 1, lowercase=True tokenizer = HuggingFaceWordPieceTokenizer(vocab_path, lowercase=True) gt_tokenized = [["hello", ",", "y", "'", "all", "!", "how", "are", "you", "<unk>", "<unk>", "<unk>", "<unk>", "?"], ["gl", "##uo", "##nn", "##l", "##p", "is", "great", "\uff01", "\uff01", "\uff01", "!", "!", "!"], ["gl", "##uo", "##nn", "##l", "##p", "-", "amazon", "-", "hai", "##bin", "-", "leonard", "-", "shen", "##g", "-", "shu", "##ai", "-", "xin", "##g", "##ji", "##an", ".", ".", ".", ".", ".", "/", ":", "!", "@", "#", "'", "abc", "'"]] gt_offsets = [[(0, 5), (5, 6), (7, 8), (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27), (28, 29), (30, 31), (32, 33), (34, 35)], [(0, 2), (2, 4), (4, 6), (6, 7), (7, 8), (9, 11), (12, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 2), (2, 4), (4, 6), (6, 7), (7, 8), (8, 9), (9, 15), (15, 16), (16, 19), (19, 22), (22, 23), (23, 30), (30, 31), (31, 35), (35, 36), (36, 37), (37, 40), (40, 42), (42, 43), (43, 46), (46, 47), (47, 49), (49, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (62, 63), (63, 66), (66, 67)]] gt_decode = ["hello, y'all! how are you?", "gluonnlp is great ！！！!!!", "gluonnlp - amazon - haibin - leonard - sheng - shuai - xingjian..... / :! @ verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceWordPieceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) # Case 2, lowercase=False gt_lowercase_decode = [", y'all! are you?", "is great ！！！!!!", "- - - - - -..... / :! @ #'abc '"] tokenizer = HuggingFaceWordPieceTokenizer(vocab_path, lowercase=False) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_lowercase_decode) # Case 3, using original hf vocab tokenizer = HuggingFaceWordPieceTokenizer(hf_vocab_path, lowercase=True) verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceWordPieceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) os.remove(vocab_path) os.remove(hf_vocab_path) @pytest.mark.skipif(parse_version(gluonnlp.utils.lazy_imports.try_import_huggingface_tokenizers().__version__) >= parse_version('0.9.0.dev0'), reason="Test is only valid for tokenizers 0.8.x") def test_huggingface_wordpiece_tokenizer_v08(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'hf_wordpiece_new_0.8.model') download(url=get_repo_url() + 'tokenizer_test_models/hf_wordpiece_new_0.8/hf_wordpiece.model', path=model_path, sha1_hash='66ccadf6e5e354ff9604e4a82f107a2ac873abd5') vocab_path = os.path.join(dir_path, 'hf_wordpiece_new_0.8.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_wordpiece_new_0.8/hf_wordpiece.vocab', path=vocab_path, sha1_hash='dd6fdf4bbc74eaa8806d12cb3d38a4d9a306aea8') tokenizer = HuggingFaceTokenizer(model_path, vocab_path) gt_tokenized = [['Hel', '##lo', ',', 'y', '[UNK]', 'all', '!', 'How', 'are', 'you', '[UNK]', '[UNK]', '[UNK]', '[UNK]', '?'], ['Gl', '##u', '##on', '##N', '##L', '##P', 'is', 'great', '[UNK]', '[UNK]', '[UNK]', '!', '!', '!'], ['Gl', '##u', '##on', '##N', '##L', '##P', '-', 'Am', '##az', '##on', '-', 'Ha', '##ibi', '##n', '-', 'Leon', '##ard', '-', 'She', '##n', '##g', '-', 'Sh', '##ua', '##i', '-', 'X', '##ing', '##j', '##ian', '.', '.', '.', '.', '.', '/', ':', '!', '@', '#', '[UNK]', 'ab', '##c', '[UNK]']] gt_offsets = [[(0, 3), (3, 5), (5, 6), (7, 8), (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27), (28, 29), (30, 31), (32, 33), (34, 35)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (9, 11), (12, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 11), (11, 13), (13, 15), (15, 16), (16, 18), (18, 21), (21, 22), (22, 23), (23, 27), (27, 30), (30, 31), (31, 34), (34, 35), (35, 36), (36, 37), (37, 39), (39, 41), (41, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (62, 63), (63, 65), (65, 66), (66, 67)]] gt_decode = ['Hello, y all! How are you?', 'GluonNLP is great!!!', 'GluonNLP - Amazon - Haibin - Leonard - Sheng - Shuai - Xingjian..... / ' ':! @ # abc'] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) @pytest.mark.skipif(parse_version(gluonnlp.utils.lazy_imports.try_import_huggingface_tokenizers().__version__) >= parse_version('0.9.0.dev0'), reason="Test is only valid for tokenizers 0.8.x") def test_huggingface_bpe_tokenizer_v08(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'hf_bpe_new_0.8.model') download(url=get_repo_url() + 'tokenizer_test_models/hf_bpe_new_0.8/hf_bpe.model', path=model_path, sha1_hash='ecda90979561ca4c5a8d769b5e3c9fa2270d5317') vocab_path = os.path.join(dir_path, 'hf_bpe_new_0.8.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bpe_new_0.8/hf_bpe.vocab', path=vocab_path, sha1_hash='b92dde0b094f405208f3ec94b5eae88430bf4262') tokenizer = HuggingFaceTokenizer(model_path, vocab_path) gt_tokenized = [['H', 'ello</w>', ',</w>', 'y</w>', 'all</w>', '!</w>', 'How</w>', 'are</w>', 'you</w>', '?</w>'], ['G', 'lu', 'on', 'N', 'L', 'P</w>', 'is</w>', 'great</w>', '!</w>', '!</w>', '!</w>'], ['G', 'lu', 'on', 'N', 'L', 'P</w>', '-</w>', 'Amaz', 'on</w>', '-</w>', 'Ha', 'i', 'bin</w>', '-</w>', 'Leon', 'ard</w>', '-</w>', 'Sh', 'eng</w>', '-</w>', 'S', 'hu', 'ai</w>', '-</w>', 'X', 'ing', 'j', 'ian</w>', '.</w>', '.</w>', '.</w>', '.</w>', '.</w>', '/</w>', ':</w>', '!</w>', '@</w>', '#</w>', 'ab', 'c</w>']] gt_offsets = [[(0, 1), (1, 5), (5, 6), (7, 8), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (34, 35)], [(0, 1), (1, 3), (3, 5), (5, 6), (6, 7), (7, 8), (9, 11), (12, 17), (20, 21), (21, 22), (22, 23)], [(0, 1), (1, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 13), (13, 15), (15, 16), (16, 18), (18, 19), (19, 22), (22, 23), (23, 27), (27, 30), (30, 31), (31, 33), (33, 36), (36, 37), (37, 38), (38, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (63, 65), (65, 66)]] gt_decode = ['Hello , y all ! How are you ?', 'GluonNLP is great ! ! !', 'GluonNLP - Amazon - Haibin - Leonard - Sheng - Shuai - Xingjian' ' . . . . . / : ! @ # abc'] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) @pytest.mark.skipif(parse_version(gluonnlp.utils.lazy_imports.try_import_huggingface_tokenizers().__version__) >= parse_version('0.9.0.dev0'), reason="Test is only valid for tokenizers 0.8.x") def test_huggingface_bytebpe_tokenizer_v08(): with tempfile.TemporaryDirectory() as dir_path: model_path = os.path.join(dir_path, 'hf_bytebpe_new_0.8.model') download(url=get_repo_url() + 'tokenizer_test_models/hf_bytebpe_new_0.8/hf_bytebpe.model', path=model_path, sha1_hash='a1c4da1f6c21df923e150f56dbb5b7a53c61808b') vocab_path = os.path.join(dir_path, 'hf_bytebpe_new_0.8.vocab') download(url=get_repo_url() + 'tokenizer_test_models/hf_bytebpe_new_0.8/hf_bytebpe.vocab', path=vocab_path, sha1_hash='7831b19078a3222f450e65b2188dc0770473123b') tokenizer = HuggingFaceTokenizer(model_path, vocab_path) gt_tokenized = [['He', 'llo', ',', 'Ġy', "'", 'all', '!', 'ĠHow', 'Ġare', 'Ġyou', 'Ġâ', 'ħ', '§', 'Ġ', 'ð', 'Ł', 'ĺ', 'ģ', 'Ġ', 'ð', 'Ł', 'ĺ', 'ģ', 'Ġ', 'ð', 'Ł', 'ĺ', 'ģ', 'Ġ?'], ['G', 'l', 'u', 'on', 'N', 'L', 'P', 'Ġis', 'Ġgreat', 'ï', '¼', 'ģ', 'ï', '¼', 'ģ', 'ï', '¼', 'ģ', '!', '!', '!'], ['G', 'l', 'u', 'on', 'N', 'L', 'P', '-', 'Am', 'az', 'on', '-', 'Ha', 'ib', 'in', '-', 'Le', 'on', 'ard', '-', 'S', 'hen', 'g', '-', 'Sh', 'u', 'ai', '-', 'X', 'ing', 'j', 'ian', '..', '...', '/', ':', '!', '@', '#', 'Ġ', "'", 'ab', 'c', "'"]] gt_offsets = [[(0, 2), (2, 5), (5, 6), (6, 8), (8, 9), (9, 12), (12, 13), (13, 17), (17, 21), (21, 25), (25, 27), (26, 27), (26, 27), (27, 28), (28, 29), (28, 29), (28, 29), (28, 29), (29, 30), (30, 31), (30, 31), (30, 31), (30, 31), (31, 32), (32, 33), (32, 33), (32, 33), (32, 33), (33, 35)], [(0, 1), (1, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 11), (11, 17), (17, 18), (17, 18), (17, 18), (18, 19), (18, 19), (18, 19), (19, 20), (19, 20), (19, 20), (20, 21), (21, 22), (22, 23)], [(0, 1), (1, 2), (2, 3), (3, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 11), (11, 13), (13, 15), (15, 16), (16, 18), (18, 20), (20, 22), (22, 23), (23, 25), (25, 27), (27, 30), (30, 31), (31, 32), (32, 35), (35, 36), (36, 37), (37, 39), (39, 40), (40, 42), (42, 43), (43, 44), (44, 47), (47, 48), (48, 51), (51, 53), (53, 56), (56, 57), (57, 58), (58, 59), (59, 60), (60, 61), (61, 62), (62, 63), (63, 65), (65, 66), (66, 67)]] gt_decode = ["Hello, y'all! How are you Ⅷ 😁 😁 😁 ?", 'GluonNLP is great！！！!!!', "GluonNLP-Amazon-Haibin-Leonard-Sheng-Shuai-Xingjian...../:!@# 'abc'"] verify_encode_token(tokenizer, SUBWORD_TEST_SAMPLES, gt_tokenized) verify_pickleble(tokenizer, HuggingFaceTokenizer) verify_encode_token_with_offsets(tokenizer, SUBWORD_TEST_SAMPLES, gt_offsets) verify_decode_hf(tokenizer, SUBWORD_TEST_SAMPLES, gt_decode) def test_tokenizers_create(): tokenizer = gluonnlp.data.tokenizers.create('moses', 'en') tokenizer.encode('hello world!')

true

1c475796efa58d436a4aeaa031170fd8364ddc7a

256

py

Python

09/01/01/5.py

pylangstudy/201707

c1cc72667f1e0b6e8eef4ee85067d7fa4ca500b6

[ "CC0-1.0" ]

null

09/01/01/5.py

pylangstudy/201707

c1cc72667f1e0b6e8eef4ee85067d7fa4ca500b6

[ "CC0-1.0" ]

46

2017-06-30T22:19:07.000Z

2017-07-31T22:51:31.000Z

10/01/01/5.py

pylangstudy/201707

c1cc72667f1e0b6e8eef4ee85067d7fa4ca500b6

[ "CC0-1.0" ]

null

class Base1: def __init__(self): print('Base1.__init__'); class Base2: def __init__(self): print('Base2.__init__'); class Super(Base1, Base2): def __init__(self): print('Super.__init__'); Base1.__init__(self); Base2.__init__(self) c = Super()

28.444444

91

0.699219

class Base1: def __init__(self): print('Base1.__init__'); class Base2: def __init__(self): print('Base2.__init__'); class Super(Base1, Base2): def __init__(self): print('Super.__init__'); Base1.__init__(self); Base2.__init__(self) c = Super()

true

1c4757eb287bb3f279ee1609d1ef569abd806f07

156

py

Python

tests/models.py

rtidatascience/django-postgres-power

cf3f714ab9d8919187dc478f1d0679945017ae17

[ "BSD-3-Clause" ]

16

2015-12-10T06:37:49.000Z

2021-07-16T00:02:41.000Z

tests/models.py

rtidatascience/django-postgres-power

cf3f714ab9d8919187dc478f1d0679945017ae17

[ "BSD-3-Clause" ]

4

2016-08-23T13:31:33.000Z

2019-04-08T15:47:38.000Z

tests/models.py

rtidatascience/django-postgres-power

cf3f714ab9d8919187dc478f1d0679945017ae17

[ "BSD-3-Clause" ]

7

2016-08-23T12:57:55.000Z

2020-11-14T21:08:53.000Z

from django.db import models class Checkin(models.Model): logged_at = models.DateTimeField() class Number(models.Model): n = models.IntegerField()

22.285714

38

0.74359

from django.db import models class Checkin(models.Model): logged_at = models.DateTimeField() class Number(models.Model): n = models.IntegerField()

true

1c475960ea7c505c741557bae2f651bd3511c226

2,710

py

Python

cimcb_lite/utils/table_check.py

RuibingS/cimcb

382f7d8fff30d3d276f18ac8c7dc686e0e643fa9

[ "MIT" ]

3

2019-05-19T10:36:50.000Z

2020-10-12T08:13:04.000Z

cimcb_lite/utils/table_check.py

RuibingS/cimcb

382f7d8fff30d3d276f18ac8c7dc686e0e643fa9

[ "MIT" ]

1

2019-03-24T11:04:39.000Z

2019-03-26T03:54:51.000Z

cimcb_lite/utils/table_check.py

RuibingS/cimcb

382f7d8fff30d3d276f18ac8c7dc686e0e643fa9

[ "MIT" ]

3

2019-05-19T10:37:03.000Z

2020-10-12T08:13:05.000Z

import numpy as np def table_check(DataTable, PeakTable, print_statement=True): """Error checking for DataTable and PeakTable (used in load_dataXL). Parameters ---------- DataTable: DataFrame Data sheet with the required columns. PeakTable: DataFrame Peak sheet with the required columns. print_statement: boolean (default True) If the error checks are successful and print_statement is True, the following is printed: "Data Table & Peak Table is suitable." """ # Check DataTable for Idx, Class and SampleID data_columns = DataTable.columns.values if "Idx" not in data_columns: raise ValueError("Data Table does not contain the required 'Idx' column") if DataTable.Idx.isnull().values.any() == True: raise ValueError("Data Table Idx column cannot contain missing values") if len(np.unique(DataTable.Idx)) != len(DataTable.Idx): raise ValueError("Data Table Idx numbers are not unique. Please change") if "Class" not in data_columns: raise ValueError("Data Table does not contain the required 'Class' column") if "SampleID" not in data_columns: raise ValueError("Data Table does not contain the required 'SampleID' column") # Check PeakTable for Idx, Name, Label peak_columns = PeakTable.columns.values if "Idx" not in peak_columns: raise ValueError("Peak Table does not contain the required 'Idx' column") if PeakTable.Idx.isnull().values.any() == True: raise ValueError("Peak Table Idx column cannot contain missing values") if len(np.unique(PeakTable.Idx)) != len(PeakTable.Idx): raise ValueError("Peak Table Idx numbers are not unique. Please change") if "Name" not in peak_columns: raise ValueError("Peak Table does not contain the required 'Name' column") if PeakTable.Idx.isnull().values.any() == True: raise ValueError("Peak Table Name column cannot contain missing values") if len(np.unique(PeakTable.Idx)) != len(PeakTable.Idx): raise ValueError("Peak Table Name numbers are not unique. Please change") if "Label" not in peak_columns: raise ValueError("Data Table does not contain the required 'Label' column") # Check that Peak Names in PeakTable & DataTable match peak_list = PeakTable.Name data_columns = DataTable.columns.values temp = np.intersect1d(data_columns, peak_list) if len(temp) != len(peak_list): raise ValueError("The Peak Names in Data Table should exactly match the Peak Names in Peak Table. Remember that all Peak Names should be unique.") if print_statement is True: print("Data Table & Peak Table is suitable.")

41.692308

154

0.700738

import numpy as np def table_check(DataTable, PeakTable, print_statement=True): data_columns = DataTable.columns.values if "Idx" not in data_columns: raise ValueError("Data Table does not contain the required 'Idx' column") if DataTable.Idx.isnull().values.any() == True: raise ValueError("Data Table Idx column cannot contain missing values") if len(np.unique(DataTable.Idx)) != len(DataTable.Idx): raise ValueError("Data Table Idx numbers are not unique. Please change") if "Class" not in data_columns: raise ValueError("Data Table does not contain the required 'Class' column") if "SampleID" not in data_columns: raise ValueError("Data Table does not contain the required 'SampleID' column") peak_columns = PeakTable.columns.values if "Idx" not in peak_columns: raise ValueError("Peak Table does not contain the required 'Idx' column") if PeakTable.Idx.isnull().values.any() == True: raise ValueError("Peak Table Idx column cannot contain missing values") if len(np.unique(PeakTable.Idx)) != len(PeakTable.Idx): raise ValueError("Peak Table Idx numbers are not unique. Please change") if "Name" not in peak_columns: raise ValueError("Peak Table does not contain the required 'Name' column") if PeakTable.Idx.isnull().values.any() == True: raise ValueError("Peak Table Name column cannot contain missing values") if len(np.unique(PeakTable.Idx)) != len(PeakTable.Idx): raise ValueError("Peak Table Name numbers are not unique. Please change") if "Label" not in peak_columns: raise ValueError("Data Table does not contain the required 'Label' column") peak_list = PeakTable.Name data_columns = DataTable.columns.values temp = np.intersect1d(data_columns, peak_list) if len(temp) != len(peak_list): raise ValueError("The Peak Names in Data Table should exactly match the Peak Names in Peak Table. Remember that all Peak Names should be unique.") if print_statement is True: print("Data Table & Peak Table is suitable.")

true

1c475968ebbd39e752c755cb7b4598bf947a6220

556

py

Python

src/log.py

ENDERZOMBI102/endc-lang

554c540111adae52c3ec23c75474d2121d339df4

[ "MIT" ]

null

src/log.py

ENDERZOMBI102/endc-lang

554c540111adae52c3ec23c75474d2121d339df4

[ "MIT" ]

null

src/log.py

ENDERZOMBI102/endc-lang

554c540111adae52c3ec23c75474d2121d339df4

[ "MIT" ]

null

import sys from typing import TextIO from cli import args def _log(level: int, msg: str, file: TextIO) -> None: if args.verboseLevel <= level: print(msg, file=file) def debug(msg: str, file: TextIO = sys.stdout) -> None: if args.debug: _log( 0, f'[DEBUG] {msg}', file ) def info(msg: str, file: TextIO = sys.stdout) -> None: _log( 1, f'[INFO] {msg}', file ) def warn(msg: str, file: TextIO = sys.stderr) -> None: _log( 2, f'[WARN] {msg}', file ) def error(msg: str, file: TextIO = sys.stderr) -> None: _log( 3, f'[ERROR] {msg}', file )

20.592593

55

0.627698

import sys from typing import TextIO from cli import args def _log(level: int, msg: str, file: TextIO) -> None: if args.verboseLevel <= level: print(msg, file=file) def debug(msg: str, file: TextIO = sys.stdout) -> None: if args.debug: _log( 0, f'[DEBUG] {msg}', file ) def info(msg: str, file: TextIO = sys.stdout) -> None: _log( 1, f'[INFO] {msg}', file ) def warn(msg: str, file: TextIO = sys.stderr) -> None: _log( 2, f'[WARN] {msg}', file ) def error(msg: str, file: TextIO = sys.stderr) -> None: _log( 3, f'[ERROR] {msg}', file )

true

1c47596b8a5035d0ebdff520ba15dc9448d843dc

7,887

py

Python

sphinx/builders/singlehtml.py

choldgraf/sphinx

97d2f9fbf8eab478908af981c1a36aed1d75a4ce

[ "BSD-2-Clause" ]

null

sphinx/builders/singlehtml.py

choldgraf/sphinx

97d2f9fbf8eab478908af981c1a36aed1d75a4ce

[ "BSD-2-Clause" ]

null

sphinx/builders/singlehtml.py

choldgraf/sphinx

97d2f9fbf8eab478908af981c1a36aed1d75a4ce

[ "BSD-2-Clause" ]

null

""" sphinx.builders.singlehtml ~~~~~~~~~~~~~~~~~~~~~~~~~~ Single HTML builders. :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ from os import path from typing import Any, Dict, List, Tuple, Union from docutils import nodes from docutils.nodes import Node from sphinx.application import Sphinx from sphinx.builders.html import StandaloneHTMLBuilder from sphinx.deprecation import RemovedInSphinx40Warning, deprecated_alias from sphinx.environment.adapters.toctree import TocTree from sphinx.locale import __ from sphinx.util import logging from sphinx.util import progress_message from sphinx.util.console import darkgreen # type: ignore from sphinx.util.nodes import inline_all_toctrees logger = logging.getLogger(__name__) class SingleFileHTMLBuilder(StandaloneHTMLBuilder): """ A StandaloneHTMLBuilder subclass that puts the whole document tree on one HTML page. """ name = 'singlehtml' epilog = __('The HTML page is in %(outdir)s.') copysource = False def get_outdated_docs(self) -> Union[str, List[str]]: # type: ignore return 'all documents' def get_target_uri(self, docname: str, typ: str = None) -> str: if docname in self.env.all_docs: # all references are on the same page... return self.config.master_doc + self.out_suffix + \ '#document-' + docname else: # chances are this is a html_additional_page return docname + self.out_suffix def get_relative_uri(self, from_: str, to: str, typ: str = None) -> str: # ignore source return self.get_target_uri(to, typ) def fix_refuris(self, tree: Node) -> None: # fix refuris with double anchor fname = self.config.master_doc + self.out_suffix for refnode in tree.traverse(nodes.reference): if 'refuri' not in refnode: continue refuri = refnode['refuri'] hashindex = refuri.find('#') if hashindex < 0: continue hashindex = refuri.find('#', hashindex + 1) if hashindex >= 0: refnode['refuri'] = fname + refuri[hashindex:] def _get_local_toctree(self, docname: str, collapse: bool = True, **kwds: Any) -> str: if 'includehidden' not in kwds: kwds['includehidden'] = False toctree = TocTree(self.env).get_toctree_for(docname, self, collapse, **kwds) if toctree is not None: self.fix_refuris(toctree) return self.render_partial(toctree)['fragment'] def assemble_doctree(self) -> nodes.document: master = self.config.master_doc tree = self.env.get_doctree(master) tree = inline_all_toctrees(self, set(), master, tree, darkgreen, [master]) tree['docname'] = master self.env.resolve_references(tree, master, self) self.fix_refuris(tree) return tree def assemble_toc_secnumbers(self) -> Dict[str, Dict[str, Tuple[int, ...]]]: # Assemble toc_secnumbers to resolve section numbers on SingleHTML. # Merge all secnumbers to single secnumber. # # Note: current Sphinx has refid confliction in singlehtml mode. # To avoid the problem, it replaces key of secnumbers to # tuple of docname and refid. # # There are related codes in inline_all_toctres() and # HTMLTranslter#add_secnumber(). new_secnumbers = {} # type: Dict[str, Tuple[int, ...]] for docname, secnums in self.env.toc_secnumbers.items(): for id, secnum in secnums.items(): alias = "%s/%s" % (docname, id) new_secnumbers[alias] = secnum return {self.config.master_doc: new_secnumbers} def assemble_toc_fignumbers(self) -> Dict[str, Dict[str, Dict[str, Tuple[int, ...]]]]: # Assemble toc_fignumbers to resolve figure numbers on SingleHTML. # Merge all fignumbers to single fignumber. # # Note: current Sphinx has refid confliction in singlehtml mode. # To avoid the problem, it replaces key of secnumbers to # tuple of docname and refid. # # There are related codes in inline_all_toctres() and # HTMLTranslter#add_fignumber(). new_fignumbers = {} # type: Dict[str, Dict[str, Tuple[int, ...]]] # {'foo': {'figure': {'id2': (2,), 'id1': (1,)}}, 'bar': {'figure': {'id1': (3,)}}} for docname, fignumlist in self.env.toc_fignumbers.items(): for figtype, fignums in fignumlist.items(): alias = "%s/%s" % (docname, figtype) new_fignumbers.setdefault(alias, {}) for id, fignum in fignums.items(): new_fignumbers[alias][id] = fignum return {self.config.master_doc: new_fignumbers} def get_doc_context(self, docname: str, body: str, metatags: str) -> Dict: # no relation links... toctree = TocTree(self.env).get_toctree_for(self.config.master_doc, self, False) # if there is no toctree, toc is None if toctree: self.fix_refuris(toctree) toc = self.render_partial(toctree)['fragment'] display_toc = True else: toc = '' display_toc = False return { 'parents': [], 'prev': None, 'next': None, 'docstitle': None, 'title': self.config.html_title, 'meta': None, 'body': body, 'metatags': metatags, 'rellinks': [], 'sourcename': '', 'toc': toc, 'display_toc': display_toc, } def write(self, *ignored: Any) -> None: docnames = self.env.all_docs with progress_message(__('preparing documents')): self.prepare_writing(docnames) # type: ignore with progress_message(__('assembling single document')): doctree = self.assemble_doctree() self.env.toc_secnumbers = self.assemble_toc_secnumbers() self.env.toc_fignumbers = self.assemble_toc_fignumbers() with progress_message(__('writing')): self.write_doc_serialized(self.config.master_doc, doctree) self.write_doc(self.config.master_doc, doctree) def finish(self) -> None: self.write_additional_files() self.copy_image_files() self.copy_download_files() self.copy_static_files() self.copy_extra_files() self.write_buildinfo() self.dump_inventory() @progress_message(__('writing additional files')) def write_additional_files(self) -> None: # no indices or search pages are supported # additional pages from conf.py for pagename, template in self.config.html_additional_pages.items(): logger.info(' ' + pagename, nonl=True) self.handle_page(pagename, {}, template) if self.config.html_use_opensearch: logger.info(' opensearch', nonl=True) fn = path.join(self.outdir, '_static', 'opensearch.xml') self.handle_page('opensearch', {}, 'opensearch.xml', outfilename=fn) # for compatibility deprecated_alias('sphinx.builders.html', { 'SingleFileHTMLBuilder': SingleFileHTMLBuilder, }, RemovedInSphinx40Warning) def setup(app: Sphinx) -> Dict[str, Any]: app.setup_extension('sphinx.builders.html') app.add_builder(SingleFileHTMLBuilder) app.add_config_value('singlehtml_sidebars', lambda self: self.html_sidebars, 'html') return { 'version': 'builtin', 'parallel_read_safe': True, 'parallel_write_safe': True, }

37.557143

91

0.613034

from os import path from typing import Any, Dict, List, Tuple, Union from docutils import nodes from docutils.nodes import Node from sphinx.application import Sphinx from sphinx.builders.html import StandaloneHTMLBuilder from sphinx.deprecation import RemovedInSphinx40Warning, deprecated_alias from sphinx.environment.adapters.toctree import TocTree from sphinx.locale import __ from sphinx.util import logging from sphinx.util import progress_message from sphinx.util.console import darkgreen from sphinx.util.nodes import inline_all_toctrees logger = logging.getLogger(__name__) class SingleFileHTMLBuilder(StandaloneHTMLBuilder): name = 'singlehtml' epilog = __('The HTML page is in %(outdir)s.') copysource = False def get_outdated_docs(self) -> Union[str, List[str]]: return 'all documents' def get_target_uri(self, docname: str, typ: str = None) -> str: if docname in self.env.all_docs: return self.config.master_doc + self.out_suffix + \ '#document-' + docname else: return docname + self.out_suffix def get_relative_uri(self, from_: str, to: str, typ: str = None) -> str: return self.get_target_uri(to, typ) def fix_refuris(self, tree: Node) -> None: fname = self.config.master_doc + self.out_suffix for refnode in tree.traverse(nodes.reference): if 'refuri' not in refnode: continue refuri = refnode['refuri'] hashindex = refuri.find('#') if hashindex < 0: continue hashindex = refuri.find('#', hashindex + 1) if hashindex >= 0: refnode['refuri'] = fname + refuri[hashindex:] def _get_local_toctree(self, docname: str, collapse: bool = True, **kwds: Any) -> str: if 'includehidden' not in kwds: kwds['includehidden'] = False toctree = TocTree(self.env).get_toctree_for(docname, self, collapse, **kwds) if toctree is not None: self.fix_refuris(toctree) return self.render_partial(toctree)['fragment'] def assemble_doctree(self) -> nodes.document: master = self.config.master_doc tree = self.env.get_doctree(master) tree = inline_all_toctrees(self, set(), master, tree, darkgreen, [master]) tree['docname'] = master self.env.resolve_references(tree, master, self) self.fix_refuris(tree) return tree def assemble_toc_secnumbers(self) -> Dict[str, Dict[str, Tuple[int, ...]]]: umbers = {} for docname, secnums in self.env.toc_secnumbers.items(): for id, secnum in secnums.items(): alias = "%s/%s" % (docname, id) new_secnumbers[alias] = secnum return {self.config.master_doc: new_secnumbers} def assemble_toc_fignumbers(self) -> Dict[str, Dict[str, Dict[str, Tuple[int, ...]]]]: umbers = {} for docname, fignumlist in self.env.toc_fignumbers.items(): for figtype, fignums in fignumlist.items(): alias = "%s/%s" % (docname, figtype) new_fignumbers.setdefault(alias, {}) for id, fignum in fignums.items(): new_fignumbers[alias][id] = fignum return {self.config.master_doc: new_fignumbers} def get_doc_context(self, docname: str, body: str, metatags: str) -> Dict: toctree = TocTree(self.env).get_toctree_for(self.config.master_doc, self, False) if toctree: self.fix_refuris(toctree) toc = self.render_partial(toctree)['fragment'] display_toc = True else: toc = '' display_toc = False return { 'parents': [], 'prev': None, 'next': None, 'docstitle': None, 'title': self.config.html_title, 'meta': None, 'body': body, 'metatags': metatags, 'rellinks': [], 'sourcename': '', 'toc': toc, 'display_toc': display_toc, } def write(self, *ignored: Any) -> None: docnames = self.env.all_docs with progress_message(__('preparing documents')): self.prepare_writing(docnames) with progress_message(__('assembling single document')): doctree = self.assemble_doctree() self.env.toc_secnumbers = self.assemble_toc_secnumbers() self.env.toc_fignumbers = self.assemble_toc_fignumbers() with progress_message(__('writing')): self.write_doc_serialized(self.config.master_doc, doctree) self.write_doc(self.config.master_doc, doctree) def finish(self) -> None: self.write_additional_files() self.copy_image_files() self.copy_download_files() self.copy_static_files() self.copy_extra_files() self.write_buildinfo() self.dump_inventory() @progress_message(__('writing additional files')) def write_additional_files(self) -> None: for pagename, template in self.config.html_additional_pages.items(): logger.info(' ' + pagename, nonl=True) self.handle_page(pagename, {}, template) if self.config.html_use_opensearch: logger.info(' opensearch', nonl=True) fn = path.join(self.outdir, '_static', 'opensearch.xml') self.handle_page('opensearch', {}, 'opensearch.xml', outfilename=fn) deprecated_alias('sphinx.builders.html', { 'SingleFileHTMLBuilder': SingleFileHTMLBuilder, }, RemovedInSphinx40Warning) def setup(app: Sphinx) -> Dict[str, Any]: app.setup_extension('sphinx.builders.html') app.add_builder(SingleFileHTMLBuilder) app.add_config_value('singlehtml_sidebars', lambda self: self.html_sidebars, 'html') return { 'version': 'builtin', 'parallel_read_safe': True, 'parallel_write_safe': True, }

true

1c4759c0cc109175a0ac69b07dc02aafad9b54f6

26,867

py

Python

src/test/isolation2/sql_isolation_testcase.py

kalensk/gpdb

52d17ad2057c0b74360e4693f683cc537178d86a

[ "PostgreSQL", "Apache-2.0" ]

null

src/test/isolation2/sql_isolation_testcase.py

kalensk/gpdb

52d17ad2057c0b74360e4693f683cc537178d86a

[ "PostgreSQL", "Apache-2.0" ]

null

src/test/isolation2/sql_isolation_testcase.py

kalensk/gpdb

52d17ad2057c0b74360e4693f683cc537178d86a

[ "PostgreSQL", "Apache-2.0" ]

null

""" Copyright (c) 2004-Present Pivotal Software, Inc. This program and the accompanying materials are made available under the terms of the 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 pygresql.pg import os import subprocess import re import multiprocessing import tempfile import time import sys import socket from optparse import OptionParser import traceback def is_digit(n): try: int(n) return True except ValueError: return False def load_helper_file(helper_file): with open(helper_file) as file: return "".join(file.readlines()).strip() def parse_include_statement(sql): include_statement, command = sql.split(None, 1) stripped_command = command.strip() if stripped_command.endswith(";"): return stripped_command.replace(";", "") else: raise SyntaxError("expected 'include: %s' to end with a semicolon." % stripped_command) class SQLIsolationExecutor(object): def __init__(self, dbname=''): self.processes = {} # The re.S flag makes the "." in the regex match newlines. # When matched against a command in process_command(), all # lines in the command are matched and sent as SQL query. self.command_pattern = re.compile(r"^(-?\d+|[*])([&\\<\\>USIq]*?)\:(.*)", re.S) if dbname: self.dbname = dbname else: self.dbname = os.environ.get('PGDATABASE') class SQLConnection(object): def __init__(self, out_file, name, mode, dbname): self.name = name self.mode = mode self.out_file = out_file self.dbname = dbname parent_conn, child_conn = multiprocessing.Pipe(True) self.p = multiprocessing.Process(target=self.session_process, args=(child_conn,)) self.pipe = parent_conn self.has_open = False self.p.start() # Close "our" copy of the child's handle, so that if the child dies, # recv() on the pipe will fail. child_conn.close(); self.out_file = out_file def session_process(self, pipe): sp = SQLIsolationExecutor.SQLSessionProcess(self.name, self.mode, pipe, self.dbname) sp.do() def query(self, command): print >>self.out_file self.out_file.flush() if len(command.strip()) == 0: return if self.has_open: raise Exception("Cannot query command while waiting for results") self.pipe.send((command, False)) r = self.pipe.recv() if r is None: raise Exception("Execution failed") print >>self.out_file, r.rstrip() def fork(self, command, blocking): print >>self.out_file, " <waiting ...>" self.pipe.send((command, True)) if blocking: time.sleep(0.5) if self.pipe.poll(0): p = self.pipe.recv() raise Exception("Forked command is not blocking; got output: %s" % p.strip()) self.has_open = True def join(self): r = None print >>self.out_file, " <... completed>" if self.has_open: r = self.pipe.recv() if r is None: raise Exception("Execution failed") print >>self.out_file, r.rstrip() self.has_open = False def stop(self): self.pipe.send(("", False)) self.p.join() if self.has_open: raise Exception("Should not finish test case while waiting for results") def quit(self): print >>self.out_file, "... <quitting>" self.stop() def terminate(self): self.pipe.close() self.p.terminate() class SQLSessionProcess(object): def __init__(self, name, mode, pipe, dbname): """ Constructor """ self.name = name self.mode = mode self.pipe = pipe self.dbname = dbname if self.mode == "utility": (hostname, port) = self.get_hostname_port(name, 'p') self.con = self.connectdb(given_dbname=self.dbname, given_host=hostname, given_port=port, given_opt="-c gp_session_role=utility") elif self.mode == "standby": # Connect to standby even when it's role is recorded # as mirror. This is useful for scenarios where a # test needs to promote a standby without using # gpactivatestandby. (hostname, port) = self.get_hostname_port(name, 'm') self.con = self.connectdb(given_dbname=self.dbname, given_host=hostname, given_port=port) else: self.con = self.connectdb(self.dbname) def connectdb(self, given_dbname, given_host = None, given_port = None, given_opt = None): con = None retry = 1000 while retry: try: if (given_port is None): con = pygresql.pg.connect(host= given_host, opt= given_opt, dbname= given_dbname) else: con = pygresql.pg.connect(host= given_host, port= given_port, opt= given_opt, dbname= given_dbname) break except Exception as e: if (("the database system is starting up" in str(e) or "the database system is in recovery mode" in str(e)) and retry > 1): retry -= 1 time.sleep(0.1) else: raise return con def get_hostname_port(self, contentid, role): """ Gets the port number/hostname combination of the contentid and role """ query = ("SELECT hostname, port FROM gp_segment_configuration WHERE" " content = %s AND role = '%s'") % (contentid, role) con = self.connectdb(self.dbname) r = con.query(query).getresult() if len(r) == 0: raise Exception("Invalid content %s" % contentid) if r[0][0] == socket.gethostname(): return (None, int(r[0][1])) return (r[0][0], int(r[0][1])) # Print out a pygresql result set (a Query object, after the query # has been executed), in a format that imitates the default # formatting of psql. This isn't a perfect imitation: we left-justify # all the fields and headers, whereas psql centers the header, and # right-justifies numeric fields. But this is close enough, to make # gpdiff.pl recognize the result sets as such. (We used to just call # str(r), and let PyGreSQL do the formatting. But even though # PyGreSQL's default formatting is close to psql's, it's not close # enough.) def printout_result(self, r): widths = [] # Figure out the widths of each column. fields = r.listfields() for f in fields: widths.append(len(str(f))) rset = r.getresult() for row in rset: colno = 0 for col in row: if col is None: col = "" widths[colno] = max(widths[colno], len(str(col))) colno = colno + 1 # Start printing. Header first. result = "" colno = 0 for f in fields: if colno > 0: result += "|" result += " " + f.ljust(widths[colno]) + " " colno = colno + 1 result += "\n" # Then the bar ("----+----") colno = 0 for f in fields: if colno > 0: result += "+" result += "".ljust(widths[colno] + 2, "-") colno = colno + 1 result += "\n" # Then the result set itself for row in rset: colno = 0 for col in row: if colno > 0: result += "|" if col is None: col = "" result += " " + str(col).ljust(widths[colno]) + " " colno = colno + 1 result += "\n" # Finally, the row count if len(rset) == 1: result += "(1 row)\n" else: result += "(" + str(len(rset)) +" rows)\n" return result def execute_command(self, command): """ Executes a given command """ try: r = self.con.query(command) if r and type(r) == str: echo_content = command[:-1].partition(" ")[0].upper() return "%s %s" % (echo_content, r) elif r: return self.printout_result(r) else: echo_content = command[:-1].partition(" ")[0].upper() return echo_content except Exception as e: return str(e) def do(self): """ Process loop. Ends when the command None is received """ (c, wait) = self.pipe.recv() while c: if wait: time.sleep(0.1) r = self.execute_command(c) self.pipe.send(r) r = None (c, wait) = self.pipe.recv() def get_process(self, out_file, name, mode="", dbname=""): """ Gets or creates the process by the given name """ if len(name) > 0 and not is_digit(name): raise Exception("Name should be a number") if len(name) > 0 and mode != "utility" and int(name) >= 1024: raise Exception("Session name should be smaller than 1024 unless it is utility mode number") if not (name, mode) in self.processes: if not dbname: dbname = self.dbname self.processes[(name, mode)] = SQLIsolationExecutor.SQLConnection(out_file, name, mode, dbname) return self.processes[(name, mode)] def quit_process(self, out_file, name, mode="", dbname=""): """ Quits a process with the given name """ if len(name) > 0 and not is_digit(name): raise Exception("Name should be a number") if len(name) > 0 and mode != "utility" and int(name) >= 1024: raise Exception("Session name should be smaller than 1024 unless it is utility mode number") if not (name, mode) in self.processes: raise Exception("Sessions not started cannot be quit") self.processes[(name, mode)].quit() del self.processes[(name, mode)] def get_all_primary_contentids(self, dbname): """ Retrieves all primary content IDs (including the master). Intended for use by *U queries. """ if not dbname: dbname = self.dbname con = pygresql.pg.connect(dbname=dbname) result = con.query("SELECT content FROM gp_segment_configuration WHERE role = 'p'").getresult() if len(result) == 0: raise Exception("Invalid gp_segment_configuration contents") return [int(content[0]) for content in result] def process_command(self, command, output_file): """ Processes the given command. The command at this point still includes the isolation behavior flags, e.g. which session to use. """ process_name = "" sql = command flag = "" con_mode = "" dbname = "" m = self.command_pattern.match(command) if m: process_name = m.groups()[0] flag = m.groups()[1] if flag and flag[0] == "U": con_mode = "utility" elif flag and flag[0] == "S": if len(flag) > 1: flag = flag[1:] con_mode = "standby" sql = m.groups()[2] sql = sql.lstrip() # If db_name is specifed , it should be of the following syntax: # 1:@db_name <db_name>: <sql> if sql.startswith('@db_name'): sql_parts = sql.split(':', 2) if not len(sql_parts) == 2: raise Exception("Invalid syntax with dbname, should be of the form 1:@db_name <db_name>: <sql>") if not sql_parts[0].startswith('@db_name'): raise Exception("Invalid syntax with dbname, should be of the form 1:@db_name <db_name>: <sql>") if not len(sql_parts[0].split()) == 2: raise Exception("Invalid syntax with dbname, should be of the form 1:@db_name <db_name>: <sql>") dbname = sql_parts[0].split()[1].strip() if not dbname: raise Exception("Invalid syntax with dbname, should be of the form 1:@db_name <db_name>: <sql>") sql = sql_parts[1] if not flag: if sql.startswith('!'): sql = sql[1:] # Check for execution mode. E.g. # !\retcode path/to/executable --option1 --option2 ... # # At the moment, we only recognize the \retcode mode, which # ignores all program output in the diff (it's still printed) # and adds the return code. mode = None if sql.startswith('\\'): mode, sql = sql.split(None, 1) if mode != '\\retcode': raise Exception('Invalid execution mode: {}'.format(mode)) cmd_output = subprocess.Popen(sql.strip(), stderr=subprocess.STDOUT, stdout=subprocess.PIPE, shell=True) stdout, _ = cmd_output.communicate() print >> output_file if mode == '\\retcode': print >> output_file, '-- start_ignore' print >> output_file, stdout if mode == '\\retcode': print >> output_file, '-- end_ignore' print >> output_file, '(exited with code {})'.format(cmd_output.returncode) elif sql.startswith('include:'): helper_file = parse_include_statement(sql) self.get_process( output_file, process_name, dbname=dbname ).query( load_helper_file(helper_file) ) else: self.get_process(output_file, process_name, con_mode, dbname=dbname).query(sql.strip()) elif flag == "&": self.get_process(output_file, process_name, con_mode, dbname=dbname).fork(sql.strip(), True) elif flag == ">": self.get_process(output_file, process_name, con_mode, dbname=dbname).fork(sql.strip(), False) elif flag == "<": if len(sql) > 0: raise Exception("No query should be given on join") self.get_process(output_file, process_name, con_mode, dbname=dbname).join() elif flag == "q": if len(sql) > 0: raise Exception("No query should be given on quit") self.quit_process(output_file, process_name, con_mode, dbname=dbname) elif flag == "U": if process_name == '*': process_names = [str(content) for content in self.get_all_primary_contentids(dbname)] else: process_names = [process_name] for name in process_names: self.get_process(output_file, name, con_mode, dbname=dbname).query(sql.strip()) elif flag == "U&": self.get_process(output_file, process_name, con_mode, dbname=dbname).fork(sql.strip(), True) elif flag == "U<": if len(sql) > 0: raise Exception("No query should be given on join") self.get_process(output_file, process_name, con_mode, dbname=dbname).join() elif flag == "Uq": if len(sql) > 0: raise Exception("No query should be given on quit") self.quit_process(output_file, process_name, con_mode, dbname=dbname) elif flag == "S": self.get_process(output_file, process_name, con_mode, dbname=dbname).query(sql.strip()) else: raise Exception("Invalid isolation flag") def process_isolation_file(self, sql_file, output_file): """ Processes the given sql file and writes the output to output file """ try: command = "" for line in sql_file: #tinctest.logger.info("re.match: %s" %re.match(r"^\d+[q\\<]:$", line)) print >>output_file, line.strip(), if line[0] == "!": command_part = line # shell commands can use -- for multichar options like --include else: command_part = line.partition("--")[0] # remove comment from line if command_part == "" or command_part == "\n": print >>output_file elif command_part.endswith(";\n") or re.match(r"^\d+[q\\<]:$", line) or re.match(r"^-?\d+[SU][q\\<]:$", line): command += command_part try: self.process_command(command, output_file) except Exception as e: print >>output_file, "FAILED: ", e command = "" else: command += command_part for process in self.processes.values(): process.stop() except: for process in self.processes.values(): process.terminate() raise finally: for process in self.processes.values(): process.terminate() class SQLIsolationTestCase: """ The isolation test case allows a fine grained control of interleaved executing transactions. This is mainly used to test isolation behavior. [<#>[flag]:] <sql> | ! <shell scripts or command> #: either an integer indicating a unique session, or a content-id if followed by U (for utility-mode connections). In 'U' mode, the content-id can alternatively be an asterisk '*' to perform a utility-mode query on the master and all primaries. flag: &: expect blocking behavior >: running in background without blocking <: join an existing session q: quit the given session U: connect in utility mode to primary contentid from gp_segment_configuration U&: expect blocking behavior in utility mode (does not currently support an asterisk target) U<: join an existing utility mode session (does not currently support an asterisk target) I: include a file of sql statements (useful for loading reusable functions) An example is: Execute BEGIN in transaction 1 Execute BEGIN in transaction 2 Execute INSERT in transaction 2 Execute SELECT in transaction 1 Execute COMMIT in transaction 2 Execute SELECT in transaction 1 The isolation tests are specified identical to sql-scripts in normal SQLTestCases. However, it is possible to prefix a SQL line with an tranaction identifier followed by a colon (":"). The above example would be defined by 1: BEGIN; 2: BEGIN; 2: INSERT INTO a VALUES (1); 1: SELECT * FROM a; 2: COMMIT; 1: SELECT * FROM a; Blocking behavior can be tested by forking and joining. 1: BEGIN; 2: BEGIN; 1: DELETE FROM foo WHERE a = 4; 2&: DELETE FROM foo WHERE a = 4; 1: COMMIT; 2<: 2: COMMIT; 2& forks the command. It is executed in the background. If the command is NOT blocking at this point, it is considered an error. 2< joins the background command and outputs the result of the command execution. Session ids should be smaller than 1024. 2U: Executes a utility command connected to port 40000. One difference to SQLTestCase is the output of INSERT. SQLTestCase would output "INSERT 0 1" if one tuple is inserted. SQLIsolationTestCase would output "INSERT 1". As the SQLIsolationTestCase needs to have a more fine-grained control over the execution order than possible with PSQL, it uses the pygresql python library instead. Connecting to a specific database: 1. If you specify a db_name metadata in the sql file, connect to that database in all open sessions. 2. If you want a specific session to be connected to a specific database , specify the sql as follows: 1:@db_name testdb: <sql> 2:@db_name test2db: <sql> 1: <sql> 2: <sql> etc Here session 1 will be connected to testdb and session 2 will be connected to test2db. You can specify @db_name only at the beginning of the session. For eg:, following would error out: 1:@db_name testdb: <sql> 2:@db_name test2db: <sql> 1: @db_name testdb: <sql> 2: <sql> etc Quitting sessions: By default, all opened sessions will be stopped only at the end of the sql file execution. If you want to explicitly quit a session in the middle of the test execution, you can specify a flag 'q' with the session identifier. For eg: 1:@db_name testdb: <sql> 2:@db_name test2db: <sql> 1: <sql> 2: <sql> 1q: 2: <sql> 3: <sql> 2q: 3: <sql> 2: @db_name test: <sql> 1q: ---> Will quit the session established with testdb. 2q: ---> Will quit the session established with test2db. The subsequent 2: @db_name test: <sql> will open a new session with the database test and execute the sql against that session. Catalog Modification: Some tests are easier to write if it's possible to modify a system catalog across the *entire* cluster. To perform a utility-mode query on all segments and the master, you can use *U commands: *U: SET allow_system_table_mods = true; *U: UPDATE pg_catalog.<table> SET <column> = <value> WHERE <cond>; Since the number of query results returned by a *U command depends on the developer's cluster configuration, it can be useful to wrap them in a start_/end_ignore block. (Unfortunately, this also hides legitimate failures; a better long-term solution is needed.) Block/join flags are not currently supported with *U. Including files: -- example contents for file.sql: create function some_test_function() returning void ... include: path/to/some/file.sql; select some_helper_function(); """ def run_sql_file(self, sql_file, out_file = None, out_dir = None, optimizer = None): """ Given a sql file and an ans file, this adds the specified gucs (self.gucs) to the sql file , runs the sql against the test case database (self.db_name) and verifies the output with the ans file. If an 'init_file' exists in the same location as the sql_file, this will be used while doing gpdiff. """ # Add gucs to the test sql and form the actual sql file to be run if not out_dir: out_dir = self.get_out_dir() if not os.path.exists(out_dir): TINCSystem.make_dirs(out_dir, ignore_exists_error = True) if optimizer is None: gucs_sql_file = os.path.join(out_dir, os.path.basename(sql_file)) else: # sql file will be <basename>_opt.sql or <basename>_planner.sql based on optimizer gucs_sql_file = os.path.join(out_dir, os.path.basename(sql_file).replace('.sql', '_%s.sql' %self._optimizer_suffix(optimizer))) self._add_gucs_to_sql_file(sql_file, gucs_sql_file, optimizer) self.test_artifacts.append(gucs_sql_file) if not out_file: if optimizer is None: out_file = os.path.join(self.get_out_dir(), os.path.basename(sql_file).replace('.sql', '.out')) else: # out file will be *_opt.out or *_planner.out based on optimizer out_file = os.path.join(self.get_out_dir(), os.path.basename(sql_file).replace('.sql', '_%s.out' %self._optimizer_suffix(optimizer))) self.test_artifacts.append(out_file) executor = SQLIsolationExecutor(dbname=self.db_name) with open(out_file, "w") as f: executor.process_isolation_file(open(sql_file), f) f.flush() if out_file[-2:] == '.t': out_file = out_file[:-2] return out_file if __name__ == "__main__": parser = OptionParser() parser.add_option("--dbname", dest="dbname", help="connect to database DBNAME", metavar="DBNAME") (options, args) = parser.parse_args() executor = SQLIsolationExecutor(dbname=options.dbname) executor.process_isolation_file(sys.stdin, sys.stdout)

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import pygresql.pg import os import subprocess import re import multiprocessing import tempfile import time import sys import socket from optparse import OptionParser import traceback def is_digit(n): try: int(n) return True except ValueError: return False def load_helper_file(helper_file): with open(helper_file) as file: return "".join(file.readlines()).strip() def parse_include_statement(sql): include_statement, command = sql.split(None, 1) stripped_command = command.strip() if stripped_command.endswith(";"): return stripped_command.replace(";", "") else: raise SyntaxError("expected 'include: %s' to end with a semicolon." % stripped_command) class SQLIsolationExecutor(object): def __init__(self, dbname=''): self.processes = {} self.command_pattern = re.compile(r"^(-?\d+|[*])([&\\<\\>USIq]*?)\:(.*)", re.S) if dbname: self.dbname = dbname else: self.dbname = os.environ.get('PGDATABASE') class SQLConnection(object): def __init__(self, out_file, name, mode, dbname): self.name = name self.mode = mode self.out_file = out_file self.dbname = dbname parent_conn, child_conn = multiprocessing.Pipe(True) self.p = multiprocessing.Process(target=self.session_process, args=(child_conn,)) self.pipe = parent_conn self.has_open = False self.p.start() # recv() on the pipe will fail. child_conn.close(); self.out_file = out_file def session_process(self, pipe): sp = SQLIsolationExecutor.SQLSessionProcess(self.name, self.mode, pipe, self.dbname) sp.do() def query(self, command): print >>self.out_file self.out_file.flush() if len(command.strip()) == 0: return if self.has_open: raise Exception("Cannot query command while waiting for results") self.pipe.send((command, False)) r = self.pipe.recv() if r is None: raise Exception("Execution failed") print >>self.out_file, r.rstrip() def fork(self, command, blocking): print >>self.out_file, " <waiting ...>" self.pipe.send((command, True)) if blocking: time.sleep(0.5) if self.pipe.poll(0): p = self.pipe.recv() raise Exception("Forked command is not blocking; got output: %s" % p.strip()) self.has_open = True def join(self): r = None print >>self.out_file, " <... completed>" if self.has_open: r = self.pipe.recv() if r is None: raise Exception("Execution failed") print >>self.out_file, r.rstrip() self.has_open = False def stop(self): self.pipe.send(("", False)) self.p.join() if self.has_open: raise Exception("Should not finish test case while waiting for results") def quit(self): print >>self.out_file, "... <quitting>" self.stop() def terminate(self): self.pipe.close() self.p.terminate() class SQLSessionProcess(object): def __init__(self, name, mode, pipe, dbname): self.name = name self.mode = mode self.pipe = pipe self.dbname = dbname if self.mode == "utility": (hostname, port) = self.get_hostname_port(name, 'p') self.con = self.connectdb(given_dbname=self.dbname, given_host=hostname, given_port=port, given_opt="-c gp_session_role=utility") elif self.mode == "standby": # Connect to standby even when it's role is recorded (hostname, port) = self.get_hostname_port(name, 'm') self.con = self.connectdb(given_dbname=self.dbname, given_host=hostname, given_port=port) else: self.con = self.connectdb(self.dbname) def connectdb(self, given_dbname, given_host = None, given_port = None, given_opt = None): con = None retry = 1000 while retry: try: if (given_port is None): con = pygresql.pg.connect(host= given_host, opt= given_opt, dbname= given_dbname) else: con = pygresql.pg.connect(host= given_host, port= given_port, opt= given_opt, dbname= given_dbname) break except Exception as e: if (("the database system is starting up" in str(e) or "the database system is in recovery mode" in str(e)) and retry > 1): retry -= 1 time.sleep(0.1) else: raise return con def get_hostname_port(self, contentid, role): query = ("SELECT hostname, port FROM gp_segment_configuration WHERE" " content = %s AND role = '%s'") % (contentid, role) con = self.connectdb(self.dbname) r = con.query(query).getresult() if len(r) == 0: raise Exception("Invalid content %s" % contentid) if r[0][0] == socket.gethostname(): return (None, int(r[0][1])) return (r[0][0], int(r[0][1])) # all the fields and headers, whereas psql centers the header, and # right-justifies numeric fields. But this is close enough, to make # gpdiff.pl recognize the result sets as such. (We used to just call # str(r), and let PyGreSQL do the formatting. But even though # PyGreSQL's default formatting is close to psql's, it's not close def printout_result(self, r): widths = [] fields = r.listfields() for f in fields: widths.append(len(str(f))) rset = r.getresult() for row in rset: colno = 0 for col in row: if col is None: col = "" widths[colno] = max(widths[colno], len(str(col))) colno = colno + 1 result = "" colno = 0 for f in fields: if colno > 0: result += "|" result += " " + f.ljust(widths[colno]) + " " colno = colno + 1 result += "\n" colno = 0 for f in fields: if colno > 0: result += "+" result += "".ljust(widths[colno] + 2, "-") colno = colno + 1 result += "\n" for row in rset: colno = 0 for col in row: if colno > 0: result += "|" if col is None: col = "" result += " " + str(col).ljust(widths[colno]) + " " colno = colno + 1 result += "\n" if len(rset) == 1: result += "(1 row)\n" else: result += "(" + str(len(rset)) +" rows)\n" return result def execute_command(self, command): try: r = self.con.query(command) if r and type(r) == str: echo_content = command[:-1].partition(" ")[0].upper() return "%s %s" % (echo_content, r) elif r: return self.printout_result(r) else: echo_content = command[:-1].partition(" ")[0].upper() return echo_content except Exception as e: return str(e) def do(self): (c, wait) = self.pipe.recv() while c: if wait: time.sleep(0.1) r = self.execute_command(c) self.pipe.send(r) r = None (c, wait) = self.pipe.recv() def get_process(self, out_file, name, mode="", dbname=""): if len(name) > 0 and not is_digit(name): raise Exception("Name should be a number") if len(name) > 0 and mode != "utility" and int(name) >= 1024: raise Exception("Session name should be smaller than 1024 unless it is utility mode number") if not (name, mode) in self.processes: if not dbname: dbname = self.dbname self.processes[(name, mode)] = SQLIsolationExecutor.SQLConnection(out_file, name, mode, dbname) return self.processes[(name, mode)] def quit_process(self, out_file, name, mode="", dbname=""): if len(name) > 0 and not is_digit(name): raise Exception("Name should be a number") if len(name) > 0 and mode != "utility" and int(name) >= 1024: raise Exception("Session name should be smaller than 1024 unless it is utility mode number") if not (name, mode) in self.processes: raise Exception("Sessions not started cannot be quit") self.processes[(name, mode)].quit() del self.processes[(name, mode)] def get_all_primary_contentids(self, dbname): if not dbname: dbname = self.dbname con = pygresql.pg.connect(dbname=dbname) result = con.query("SELECT content FROM gp_segment_configuration WHERE role = 'p'").getresult() if len(result) == 0: raise Exception("Invalid gp_segment_configuration contents") return [int(content[0]) for content in result] def process_command(self, command, output_file): process_name = "" sql = command flag = "" con_mode = "" dbname = "" m = self.command_pattern.match(command) if m: process_name = m.groups()[0] flag = m.groups()[1] if flag and flag[0] == "U": con_mode = "utility" elif flag and flag[0] == "S": if len(flag) > 1: flag = flag[1:] con_mode = "standby" sql = m.groups()[2] sql = sql.lstrip() if sql.startswith('@db_name'): sql_parts = sql.split(':', 2) if not len(sql_parts) == 2: raise Exception("Invalid syntax with dbname, should be of the form 1:@db_name <db_name>: <sql>") if not sql_parts[0].startswith('@db_name'): raise Exception("Invalid syntax with dbname, should be of the form 1:@db_name <db_name>: <sql>") if not len(sql_parts[0].split()) == 2: raise Exception("Invalid syntax with dbname, should be of the form 1:@db_name <db_name>: <sql>") dbname = sql_parts[0].split()[1].strip() if not dbname: raise Exception("Invalid syntax with dbname, should be of the form 1:@db_name <db_name>: <sql>") sql = sql_parts[1] if not flag: if sql.startswith('!'): sql = sql[1:] # and adds the return code. mode = None if sql.startswith('\\'): mode, sql = sql.split(None, 1) if mode != '\\retcode': raise Exception('Invalid execution mode: {}'.format(mode)) cmd_output = subprocess.Popen(sql.strip(), stderr=subprocess.STDOUT, stdout=subprocess.PIPE, shell=True) stdout, _ = cmd_output.communicate() print >> output_file if mode == '\\retcode': print >> output_file, '-- start_ignore' print >> output_file, stdout if mode == '\\retcode': print >> output_file, '-- end_ignore' print >> output_file, '(exited with code {})'.format(cmd_output.returncode) elif sql.startswith('include:'): helper_file = parse_include_statement(sql) self.get_process( output_file, process_name, dbname=dbname ).query( load_helper_file(helper_file) ) else: self.get_process(output_file, process_name, con_mode, dbname=dbname).query(sql.strip()) elif flag == "&": self.get_process(output_file, process_name, con_mode, dbname=dbname).fork(sql.strip(), True) elif flag == ">": self.get_process(output_file, process_name, con_mode, dbname=dbname).fork(sql.strip(), False) elif flag == "<": if len(sql) > 0: raise Exception("No query should be given on join") self.get_process(output_file, process_name, con_mode, dbname=dbname).join() elif flag == "q": if len(sql) > 0: raise Exception("No query should be given on quit") self.quit_process(output_file, process_name, con_mode, dbname=dbname) elif flag == "U": if process_name == '*': process_names = [str(content) for content in self.get_all_primary_contentids(dbname)] else: process_names = [process_name] for name in process_names: self.get_process(output_file, name, con_mode, dbname=dbname).query(sql.strip()) elif flag == "U&": self.get_process(output_file, process_name, con_mode, dbname=dbname).fork(sql.strip(), True) elif flag == "U<": if len(sql) > 0: raise Exception("No query should be given on join") self.get_process(output_file, process_name, con_mode, dbname=dbname).join() elif flag == "Uq": if len(sql) > 0: raise Exception("No query should be given on quit") self.quit_process(output_file, process_name, con_mode, dbname=dbname) elif flag == "S": self.get_process(output_file, process_name, con_mode, dbname=dbname).query(sql.strip()) else: raise Exception("Invalid isolation flag") def process_isolation_file(self, sql_file, output_file): try: command = "" for line in sql_file: #tinctest.logger.info("re.match: %s" %re.match(r"^\d+[q\\<]:$", line)) print >>output_file, line.strip(), if line[0] == "!": command_part = line # shell commands can use -- for multichar options like --include else: command_part = line.partition("--")[0] # remove comment from line if command_part == "" or command_part == "\n": print >>output_file elif command_part.endswith(";\n") or re.match(r"^\d+[q\\<]:$", line) or re.match(r"^-?\d+[SU][q\\<]:$", line): command += command_part try: self.process_command(command, output_file) except Exception as e: print >>output_file, "FAILED: ", e command = "" else: command += command_part for process in self.processes.values(): process.stop() except: for process in self.processes.values(): process.terminate() raise finally: for process in self.processes.values(): process.terminate() class SQLIsolationTestCase: def run_sql_file(self, sql_file, out_file = None, out_dir = None, optimizer = None): # Add gucs to the test sql and form the actual sql file to be run if not out_dir: out_dir = self.get_out_dir() if not os.path.exists(out_dir): TINCSystem.make_dirs(out_dir, ignore_exists_error = True) if optimizer is None: gucs_sql_file = os.path.join(out_dir, os.path.basename(sql_file)) else: # sql file will be <basename>_opt.sql or <basename>_planner.sql based on optimizer gucs_sql_file = os.path.join(out_dir, os.path.basename(sql_file).replace('.sql', '_%s.sql' %self._optimizer_suffix(optimizer))) self._add_gucs_to_sql_file(sql_file, gucs_sql_file, optimizer) self.test_artifacts.append(gucs_sql_file) if not out_file: if optimizer is None: out_file = os.path.join(self.get_out_dir(), os.path.basename(sql_file).replace('.sql', '.out')) else: # out file will be *_opt.out or *_planner.out based on optimizer out_file = os.path.join(self.get_out_dir(), os.path.basename(sql_file).replace('.sql', '_%s.out' %self._optimizer_suffix(optimizer))) self.test_artifacts.append(out_file) executor = SQLIsolationExecutor(dbname=self.db_name) with open(out_file, "w") as f: executor.process_isolation_file(open(sql_file), f) f.flush() if out_file[-2:] == '.t': out_file = out_file[:-2] return out_file if __name__ == "__main__": parser = OptionParser() parser.add_option("--dbname", dest="dbname", help="connect to database DBNAME", metavar="DBNAME") (options, args) = parser.parse_args() executor = SQLIsolationExecutor(dbname=options.dbname) executor.process_isolation_file(sys.stdin, sys.stdout)

true

1c475a28b1d83edba4b3c614df0405e3f55f79f0

53,813

py

Python

lib/sqlalchemy/sql/sqltypes.py

mjpieters/sqlalchemy

a8efeb6c052330b7b8d44960132d638b08d42d18

[ "MIT" ]

null

lib/sqlalchemy/sql/sqltypes.py

mjpieters/sqlalchemy

a8efeb6c052330b7b8d44960132d638b08d42d18

[ "MIT" ]

null

lib/sqlalchemy/sql/sqltypes.py

mjpieters/sqlalchemy

a8efeb6c052330b7b8d44960132d638b08d42d18

[ "MIT" ]

null

# sql/sqltypes.py # Copyright (C) 2005-2013 the SQLAlchemy authors and contributors <see AUTHORS file> # # This module is part of SQLAlchemy and is released under # the MIT License: http://www.opensource.org/licenses/mit-license.php """SQL specific types. """ import datetime as dt import codecs from .type_api import TypeEngine, TypeDecorator, to_instance from .elements import quoted_name, type_coerce from .default_comparator import _DefaultColumnComparator from .. import exc, util, processors from .base import _bind_or_error, SchemaEventTarget from . import operators from .. import event from ..util import pickle import decimal if util.jython: import array class _DateAffinity(object): """Mixin date/time specific expression adaptations. Rules are implemented within Date,Time,Interval,DateTime, Numeric, Integer. Based on http://www.postgresql.org/docs/current/static /functions-datetime.html. """ @property def _expression_adaptations(self): raise NotImplementedError() class Comparator(TypeEngine.Comparator): _blank_dict = util.immutabledict() def _adapt_expression(self, op, other_comparator): othertype = other_comparator.type._type_affinity return op, \ to_instance(self.type._expression_adaptations.get(op, self._blank_dict).\ get(othertype, NULLTYPE)) comparator_factory = Comparator class Concatenable(object): """A mixin that marks a type as supporting 'concatenation', typically strings.""" class Comparator(TypeEngine.Comparator): def _adapt_expression(self, op, other_comparator): if op is operators.add and isinstance(other_comparator, (Concatenable.Comparator, NullType.Comparator)): return operators.concat_op, self.expr.type else: return op, self.expr.type comparator_factory = Comparator class String(Concatenable, TypeEngine): """The base for all string and character types. In SQL, corresponds to VARCHAR. Can also take Python unicode objects and encode to the database's encoding in bind params (and the reverse for result sets.) The `length` field is usually required when the `String` type is used within a CREATE TABLE statement, as VARCHAR requires a length on most databases. """ __visit_name__ = 'string' def __init__(self, length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False ): """ Create a string-holding type. :param length: optional, a length for the column for use in DDL and CAST expressions. May be safely omitted if no ``CREATE TABLE`` will be issued. Certain databases may require a ``length`` for use in DDL, and will raise an exception when the ``CREATE TABLE`` DDL is issued if a ``VARCHAR`` with no length is included. Whether the value is interpreted as bytes or characters is database specific. :param collation: Optional, a column-level collation for use in DDL and CAST expressions. Renders using the COLLATE keyword supported by SQLite, MySQL, and Postgresql. E.g.:: >>> from sqlalchemy import cast, select, String >>> print select([cast('some string', String(collation='utf8'))]) SELECT CAST(:param_1 AS VARCHAR COLLATE utf8) AS anon_1 .. versionadded:: 0.8 Added support for COLLATE to all string types. :param convert_unicode: When set to ``True``, the :class:`.String` type will assume that input is to be passed as Python ``unicode`` objects, and results returned as Python ``unicode`` objects. If the DBAPI in use does not support Python unicode (which is fewer and fewer these days), SQLAlchemy will encode/decode the value, using the value of the ``encoding`` parameter passed to :func:`.create_engine` as the encoding. When using a DBAPI that natively supports Python unicode objects, this flag generally does not need to be set. For columns that are explicitly intended to store non-ASCII data, the :class:`.Unicode` or :class:`.UnicodeText` types should be used regardless, which feature the same behavior of ``convert_unicode`` but also indicate an underlying column type that directly supports unicode, such as ``NVARCHAR``. For the extremely rare case that Python ``unicode`` is to be encoded/decoded by SQLAlchemy on a backend that does natively support Python ``unicode``, the value ``force`` can be passed here which will cause SQLAlchemy's encode/decode services to be used unconditionally. :param unicode_error: Optional, a method to use to handle Unicode conversion errors. Behaves like the ``errors`` keyword argument to the standard library's ``string.decode()`` functions. This flag requires that ``convert_unicode`` is set to ``force`` - otherwise, SQLAlchemy is not guaranteed to handle the task of unicode conversion. Note that this flag adds significant performance overhead to row-fetching operations for backends that already return unicode objects natively (which most DBAPIs do). This flag should only be used as a last resort for reading strings from a column with varied or corrupted encodings. """ if unicode_error is not None and convert_unicode != 'force': raise exc.ArgumentError("convert_unicode must be 'force' " "when unicode_error is set.") self.length = length self.collation = collation self.convert_unicode = convert_unicode self.unicode_error = unicode_error self._warn_on_bytestring = _warn_on_bytestring def literal_processor(self, dialect): def process(value): value = value.replace("'", "''") return "'%s'" % value return process def bind_processor(self, dialect): if self.convert_unicode or dialect.convert_unicode: if dialect.supports_unicode_binds and \ self.convert_unicode != 'force': if self._warn_on_bytestring: def process(value): if isinstance(value, util.binary_type): util.warn("Unicode type received non-unicode bind " "param value.") return value return process else: return None else: encoder = codecs.getencoder(dialect.encoding) warn_on_bytestring = self._warn_on_bytestring def process(value): if isinstance(value, util.text_type): return encoder(value, self.unicode_error)[0] elif warn_on_bytestring and value is not None: util.warn("Unicode type received non-unicode bind " "param value") return value return process else: return None def result_processor(self, dialect, coltype): wants_unicode = self.convert_unicode or dialect.convert_unicode needs_convert = wants_unicode and \ (dialect.returns_unicode_strings is not True or self.convert_unicode == 'force') if needs_convert: to_unicode = processors.to_unicode_processor_factory( dialect.encoding, self.unicode_error) if dialect.returns_unicode_strings: # we wouldn't be here unless convert_unicode='force' # was specified, or the driver has erratic unicode-returning # habits. since we will be getting back unicode # in most cases, we check for it (decode will fail). def process(value): if isinstance(value, util.text_type): return value else: return to_unicode(value) return process else: # here, we assume that the object is not unicode, # avoiding expensive isinstance() check. return to_unicode else: return None @property def python_type(self): if self.convert_unicode: return util.text_type else: return str def get_dbapi_type(self, dbapi): return dbapi.STRING class Text(String): """A variably sized string type. In SQL, usually corresponds to CLOB or TEXT. Can also take Python unicode objects and encode to the database's encoding in bind params (and the reverse for result sets.) In general, TEXT objects do not have a length; while some databases will accept a length argument here, it will be rejected by others. """ __visit_name__ = 'text' class Unicode(String): """A variable length Unicode string type. The :class:`.Unicode` type is a :class:`.String` subclass that assumes input and output as Python ``unicode`` data, and in that regard is equivalent to the usage of the ``convert_unicode`` flag with the :class:`.String` type. However, unlike plain :class:`.String`, it also implies an underlying column type that is explicitly supporting of non-ASCII data, such as ``NVARCHAR`` on Oracle and SQL Server. This can impact the output of ``CREATE TABLE`` statements and ``CAST`` functions at the dialect level, and can also affect the handling of bound parameters in some specific DBAPI scenarios. The encoding used by the :class:`.Unicode` type is usually determined by the DBAPI itself; most modern DBAPIs feature support for Python ``unicode`` objects as bound values and result set values, and the encoding should be configured as detailed in the notes for the target DBAPI in the :ref:`dialect_toplevel` section. For those DBAPIs which do not support, or are not configured to accommodate Python ``unicode`` objects directly, SQLAlchemy does the encoding and decoding outside of the DBAPI. The encoding in this scenario is determined by the ``encoding`` flag passed to :func:`.create_engine`. When using the :class:`.Unicode` type, it is only appropriate to pass Python ``unicode`` objects, and not plain ``str``. If a plain ``str`` is passed under Python 2, a warning is emitted. If you notice your application emitting these warnings but you're not sure of the source of them, the Python ``warnings`` filter, documented at http://docs.python.org/library/warnings.html, can be used to turn these warnings into exceptions which will illustrate a stack trace:: import warnings warnings.simplefilter('error') For an application that wishes to pass plain bytestrings and Python ``unicode`` objects to the ``Unicode`` type equally, the bytestrings must first be decoded into unicode. The recipe at :ref:`coerce_to_unicode` illustrates how this is done. See also: :class:`.UnicodeText` - unlengthed textual counterpart to :class:`.Unicode`. """ __visit_name__ = 'unicode' def __init__(self, length=None, **kwargs): """ Create a :class:`.Unicode` object. Parameters are the same as that of :class:`.String`, with the exception that ``convert_unicode`` defaults to ``True``. """ kwargs.setdefault('convert_unicode', True) kwargs.setdefault('_warn_on_bytestring', True) super(Unicode, self).__init__(length=length, **kwargs) class UnicodeText(Text): """An unbounded-length Unicode string type. See :class:`.Unicode` for details on the unicode behavior of this object. Like :class:`.Unicode`, usage the :class:`.UnicodeText` type implies a unicode-capable type being used on the backend, such as ``NCLOB``, ``NTEXT``. """ __visit_name__ = 'unicode_text' def __init__(self, length=None, **kwargs): """ Create a Unicode-converting Text type. Parameters are the same as that of :class:`.Text`, with the exception that ``convert_unicode`` defaults to ``True``. """ kwargs.setdefault('convert_unicode', True) kwargs.setdefault('_warn_on_bytestring', True) super(UnicodeText, self).__init__(length=length, **kwargs) class Integer(_DateAffinity, TypeEngine): """A type for ``int`` integers.""" __visit_name__ = 'integer' def get_dbapi_type(self, dbapi): return dbapi.NUMBER @property def python_type(self): return int def literal_processor(self, dialect): def process(value): return str(value) return process @util.memoized_property def _expression_adaptations(self): # TODO: need a dictionary object that will # handle operators generically here, this is incomplete return { operators.add: { Date: Date, Integer: self.__class__, Numeric: Numeric, }, operators.mul: { Interval: Interval, Integer: self.__class__, Numeric: Numeric, }, operators.div: { Integer: self.__class__, Numeric: Numeric, }, operators.truediv: { Integer: self.__class__, Numeric: Numeric, }, operators.sub: { Integer: self.__class__, Numeric: Numeric, }, } class SmallInteger(Integer): """A type for smaller ``int`` integers. Typically generates a ``SMALLINT`` in DDL, and otherwise acts like a normal :class:`.Integer` on the Python side. """ __visit_name__ = 'small_integer' class BigInteger(Integer): """A type for bigger ``int`` integers. Typically generates a ``BIGINT`` in DDL, and otherwise acts like a normal :class:`.Integer` on the Python side. """ __visit_name__ = 'big_integer' class Numeric(_DateAffinity, TypeEngine): """A type for fixed precision numbers. Typically generates DECIMAL or NUMERIC. Returns ``decimal.Decimal`` objects by default, applying conversion as needed. .. note:: The `cdecimal <http://pypi.python.org/pypi/cdecimal/>`_ library is a high performing alternative to Python's built-in ``decimal.Decimal`` type, which performs very poorly in high volume situations. SQLAlchemy 0.7 is tested against ``cdecimal`` and supports it fully. The type is not necessarily supported by DBAPI implementations however, most of which contain an import for plain ``decimal`` in their source code, even though some such as psycopg2 provide hooks for alternate adapters. SQLAlchemy imports ``decimal`` globally as well. The most straightforward and foolproof way to use "cdecimal" given current DBAPI and Python support is to patch it directly into sys.modules before anything else is imported:: import sys import cdecimal sys.modules["decimal"] = cdecimal While the global patch is a little ugly, it's particularly important to use just one decimal library at a time since Python Decimal and cdecimal Decimal objects are not currently compatible *with each other*:: >>> import cdecimal >>> import decimal >>> decimal.Decimal("10") == cdecimal.Decimal("10") False SQLAlchemy will provide more natural support of cdecimal if and when it becomes a standard part of Python installations and is supported by all DBAPIs. """ __visit_name__ = 'numeric' _default_decimal_return_scale = 10 def __init__(self, precision=None, scale=None, decimal_return_scale=None, asdecimal=True): """ Construct a Numeric. :param precision: the numeric precision for use in DDL ``CREATE TABLE``. :param scale: the numeric scale for use in DDL ``CREATE TABLE``. :param asdecimal: default True. Return whether or not values should be sent as Python Decimal objects, or as floats. Different DBAPIs send one or the other based on datatypes - the Numeric type will ensure that return values are one or the other across DBAPIs consistently. :param decimal_return_scale: Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don't have a notion of "scale", so by default the float type looks for the first ten decimal places when converting. Specfiying this value will override that length. Types which do include an explicit ".scale" value, such as the base :class:`.Numeric` as well as the MySQL float types, will use the value of ".scale" as the default for decimal_return_scale, if not otherwise specified. .. versionadded:: 0.9.0 When using the ``Numeric`` type, care should be taken to ensure that the asdecimal setting is apppropriate for the DBAPI in use - when Numeric applies a conversion from Decimal->float or float-> Decimal, this conversion incurs an additional performance overhead for all result columns received. DBAPIs that return Decimal natively (e.g. psycopg2) will have better accuracy and higher performance with a setting of ``True``, as the native translation to Decimal reduces the amount of floating- point issues at play, and the Numeric type itself doesn't need to apply any further conversions. However, another DBAPI which returns floats natively *will* incur an additional conversion overhead, and is still subject to floating point data loss - in which case ``asdecimal=False`` will at least remove the extra conversion overhead. """ self.precision = precision self.scale = scale self.decimal_return_scale = decimal_return_scale self.asdecimal = asdecimal @property def _effective_decimal_return_scale(self): if self.decimal_return_scale is not None: return self.decimal_return_scale elif getattr(self, "scale", None) is not None: return self.scale else: return self._default_decimal_return_scale def get_dbapi_type(self, dbapi): return dbapi.NUMBER def literal_processor(self, dialect): def process(value): return str(value) return process @property def python_type(self): if self.asdecimal: return decimal.Decimal else: return float def bind_processor(self, dialect): if dialect.supports_native_decimal: return None else: return processors.to_float def result_processor(self, dialect, coltype): if self.asdecimal: if dialect.supports_native_decimal: # we're a "numeric", DBAPI will give us Decimal directly return None else: util.warn('Dialect %s+%s does *not* support Decimal ' 'objects natively, and SQLAlchemy must ' 'convert from floating point - rounding ' 'errors and other issues may occur. Please ' 'consider storing Decimal numbers as strings ' 'or integers on this platform for lossless ' 'storage.' % (dialect.name, dialect.driver)) # we're a "numeric", DBAPI returns floats, convert. return processors.to_decimal_processor_factory( decimal.Decimal, self.scale if self.scale is not None else self._default_decimal_return_scale) else: if dialect.supports_native_decimal: return processors.to_float else: return None @util.memoized_property def _expression_adaptations(self): return { operators.mul: { Interval: Interval, Numeric: self.__class__, Integer: self.__class__, }, operators.div: { Numeric: self.__class__, Integer: self.__class__, }, operators.truediv: { Numeric: self.__class__, Integer: self.__class__, }, operators.add: { Numeric: self.__class__, Integer: self.__class__, }, operators.sub: { Numeric: self.__class__, Integer: self.__class__, } } class Float(Numeric): """A type for ``float`` numbers. Returns Python ``float`` objects by default, applying conversion as needed. """ __visit_name__ = 'float' scale = None def __init__(self, precision=None, asdecimal=False, decimal_return_scale=None, **kwargs): """ Construct a Float. :param precision: the numeric precision for use in DDL ``CREATE TABLE``. :param asdecimal: the same flag as that of :class:`.Numeric`, but defaults to ``False``. Note that setting this flag to ``True`` results in floating point conversion. :param decimal_return_scale: Default scale to use when converting from floats to Python decimals. Floating point values will typically be much longer due to decimal inaccuracy, and most floating point database types don't have a notion of "scale", so by default the float type looks for the first ten decimal places when converting. Specfiying this value will override that length. Note that the MySQL float types, which do include "scale", will use "scale" as the default for decimal_return_scale, if not otherwise specified. .. versionadded:: 0.9.0 :param \**kwargs: deprecated. Additional arguments here are ignored by the default :class:`.Float` type. For database specific floats that support additional arguments, see that dialect's documentation for details, such as :class:`sqlalchemy.dialects.mysql.FLOAT`. """ self.precision = precision self.asdecimal = asdecimal self.decimal_return_scale = decimal_return_scale if kwargs: util.warn_deprecated("Additional keyword arguments " "passed to Float ignored.") def result_processor(self, dialect, coltype): if self.asdecimal: return processors.to_decimal_processor_factory( decimal.Decimal, self._effective_decimal_return_scale) else: return None @util.memoized_property def _expression_adaptations(self): return { operators.mul: { Interval: Interval, Numeric: self.__class__, }, operators.div: { Numeric: self.__class__, }, operators.truediv: { Numeric: self.__class__, }, operators.add: { Numeric: self.__class__, }, operators.sub: { Numeric: self.__class__, } } class DateTime(_DateAffinity, TypeEngine): """A type for ``datetime.datetime()`` objects. Date and time types return objects from the Python ``datetime`` module. Most DBAPIs have built in support for the datetime module, with the noted exception of SQLite. In the case of SQLite, date and time types are stored as strings which are then converted back to datetime objects when rows are returned. """ __visit_name__ = 'datetime' def __init__(self, timezone=False): """Construct a new :class:`.DateTime`. :param timezone: boolean. If True, and supported by the backend, will produce 'TIMESTAMP WITH TIMEZONE'. For backends that don't support timezone aware timestamps, has no effect. """ self.timezone = timezone def get_dbapi_type(self, dbapi): return dbapi.DATETIME @property def python_type(self): return dt.datetime @util.memoized_property def _expression_adaptations(self): return { operators.add: { Interval: self.__class__, }, operators.sub: { Interval: self.__class__, DateTime: Interval, }, } class Date(_DateAffinity, TypeEngine): """A type for ``datetime.date()`` objects.""" __visit_name__ = 'date' def get_dbapi_type(self, dbapi): return dbapi.DATETIME @property def python_type(self): return dt.date @util.memoized_property def _expression_adaptations(self): return { operators.add: { Integer: self.__class__, Interval: DateTime, Time: DateTime, }, operators.sub: { # date - integer = date Integer: self.__class__, # date - date = integer. Date: Integer, Interval: DateTime, # date - datetime = interval, # this one is not in the PG docs # but works DateTime: Interval, }, } class Time(_DateAffinity, TypeEngine): """A type for ``datetime.time()`` objects.""" __visit_name__ = 'time' def __init__(self, timezone=False): self.timezone = timezone def get_dbapi_type(self, dbapi): return dbapi.DATETIME @property def python_type(self): return dt.time @util.memoized_property def _expression_adaptations(self): return { operators.add: { Date: DateTime, Interval: self.__class__ }, operators.sub: { Time: Interval, Interval: self.__class__, }, } class _Binary(TypeEngine): """Define base behavior for binary types.""" def __init__(self, length=None): self.length = length def literal_processor(self, dialect): def process(value): value = value.decode(self.dialect.encoding).replace("'", "''") return "'%s'" % value return process @property def python_type(self): return util.binary_type # Python 3 - sqlite3 doesn't need the `Binary` conversion # here, though pg8000 does to indicate "bytea" def bind_processor(self, dialect): DBAPIBinary = dialect.dbapi.Binary def process(value): if value is not None: return DBAPIBinary(value) else: return None return process # Python 3 has native bytes() type # both sqlite3 and pg8000 seem to return it, # psycopg2 as of 2.5 returns 'memoryview' if util.py2k: def result_processor(self, dialect, coltype): if util.jython: def process(value): if value is not None: if isinstance(value, array.array): return value.tostring() return str(value) else: return None else: process = processors.to_str return process else: def result_processor(self, dialect, coltype): def process(value): if value is not None: value = bytes(value) return value return process def coerce_compared_value(self, op, value): """See :meth:`.TypeEngine.coerce_compared_value` for a description.""" if isinstance(value, util.string_types): return self else: return super(_Binary, self).coerce_compared_value(op, value) def get_dbapi_type(self, dbapi): return dbapi.BINARY class LargeBinary(_Binary): """A type for large binary byte data. The Binary type generates BLOB or BYTEA when tables are created, and also converts incoming values using the ``Binary`` callable provided by each DB-API. """ __visit_name__ = 'large_binary' def __init__(self, length=None): """ Construct a LargeBinary type. :param length: optional, a length for the column for use in DDL statements, for those BLOB types that accept a length (i.e. MySQL). It does *not* produce a small BINARY/VARBINARY type - use the BINARY/VARBINARY types specifically for those. May be safely omitted if no ``CREATE TABLE`` will be issued. Certain databases may require a *length* for use in DDL, and will raise an exception when the ``CREATE TABLE`` DDL is issued. """ _Binary.__init__(self, length=length) class Binary(LargeBinary): """Deprecated. Renamed to LargeBinary.""" def __init__(self, *arg, **kw): util.warn_deprecated('The Binary type has been renamed to ' 'LargeBinary.') LargeBinary.__init__(self, *arg, **kw) class SchemaType(SchemaEventTarget): """Mark a type as possibly requiring schema-level DDL for usage. Supports types that must be explicitly created/dropped (i.e. PG ENUM type) as well as types that are complimented by table or schema level constraints, triggers, and other rules. :class:`.SchemaType` classes can also be targets for the :meth:`.DDLEvents.before_parent_attach` and :meth:`.DDLEvents.after_parent_attach` events, where the events fire off surrounding the association of the type object with a parent :class:`.Column`. .. seealso:: :class:`.Enum` :class:`.Boolean` """ def __init__(self, **kw): name = kw.pop('name', None) if name is not None: self.name = quoted_name(name, kw.pop('quote', None)) else: self.name = None self.schema = kw.pop('schema', None) self.metadata = kw.pop('metadata', None) self.inherit_schema = kw.pop('inherit_schema', False) if self.metadata: event.listen( self.metadata, "before_create", util.portable_instancemethod(self._on_metadata_create) ) event.listen( self.metadata, "after_drop", util.portable_instancemethod(self._on_metadata_drop) ) def _set_parent(self, column): column._on_table_attach(util.portable_instancemethod(self._set_table)) def _set_table(self, column, table): if self.inherit_schema: self.schema = table.schema event.listen( table, "before_create", util.portable_instancemethod( self._on_table_create) ) event.listen( table, "after_drop", util.portable_instancemethod(self._on_table_drop) ) if self.metadata is None: # TODO: what's the difference between self.metadata # and table.metadata here ? event.listen( table.metadata, "before_create", util.portable_instancemethod(self._on_metadata_create) ) event.listen( table.metadata, "after_drop", util.portable_instancemethod(self._on_metadata_drop) ) def copy(self, **kw): return self.adapt(self.__class__) def adapt(self, impltype, **kw): schema = kw.pop('schema', self.schema) metadata = kw.pop('metadata', self.metadata) return impltype(name=self.name, schema=schema, metadata=metadata, inherit_schema=self.inherit_schema, **kw ) @property def bind(self): return self.metadata and self.metadata.bind or None def create(self, bind=None, checkfirst=False): """Issue CREATE ddl for this type, if applicable.""" if bind is None: bind = _bind_or_error(self) t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t.create(bind=bind, checkfirst=checkfirst) def drop(self, bind=None, checkfirst=False): """Issue DROP ddl for this type, if applicable.""" if bind is None: bind = _bind_or_error(self) t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t.drop(bind=bind, checkfirst=checkfirst) def _on_table_create(self, target, bind, **kw): t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_table_create(target, bind, **kw) def _on_table_drop(self, target, bind, **kw): t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_table_drop(target, bind, **kw) def _on_metadata_create(self, target, bind, **kw): t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_metadata_create(target, bind, **kw) def _on_metadata_drop(self, target, bind, **kw): t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_metadata_drop(target, bind, **kw) class Enum(String, SchemaType): """Generic Enum Type. The Enum type provides a set of possible string values which the column is constrained towards. By default, uses the backend's native ENUM type if available, else uses VARCHAR + a CHECK constraint. .. seealso:: :class:`~.postgresql.ENUM` - PostgreSQL-specific type, which has additional functionality. """ __visit_name__ = 'enum' def __init__(self, *enums, **kw): """Construct an enum. Keyword arguments which don't apply to a specific backend are ignored by that backend. :param \*enums: string or unicode enumeration labels. If unicode labels are present, the `convert_unicode` flag is auto-enabled. :param convert_unicode: Enable unicode-aware bind parameter and result-set processing for this Enum's data. This is set automatically based on the presence of unicode label strings. :param metadata: Associate this type directly with a ``MetaData`` object. For types that exist on the target database as an independent schema construct (Postgresql), this type will be created and dropped within ``create_all()`` and ``drop_all()`` operations. If the type is not associated with any ``MetaData`` object, it will associate itself with each ``Table`` in which it is used, and will be created when any of those individual tables are created, after a check is performed for it's existence. The type is only dropped when ``drop_all()`` is called for that ``Table`` object's metadata, however. :param name: The name of this type. This is required for Postgresql and any future supported database which requires an explicitly named type, or an explicitly named constraint in order to generate the type and/or a table that uses it. :param native_enum: Use the database's native ENUM type when available. Defaults to True. When False, uses VARCHAR + check constraint for all backends. :param schema: Schema name of this type. For types that exist on the target database as an independent schema construct (Postgresql), this parameter specifies the named schema in which the type is present. .. note:: The ``schema`` of the :class:`.Enum` type does not by default make use of the ``schema`` established on the owning :class:`.Table`. If this behavior is desired, set the ``inherit_schema`` flag to ``True``. :param quote: Set explicit quoting preferences for the type's name. :param inherit_schema: When ``True``, the "schema" from the owning :class:`.Table` will be copied to the "schema" attribute of this :class:`.Enum`, replacing whatever value was passed for the ``schema`` attribute. This also takes effect when using the :meth:`.Table.tometadata` operation. .. versionadded:: 0.8 """ self.enums = enums self.native_enum = kw.pop('native_enum', True) convert_unicode = kw.pop('convert_unicode', None) if convert_unicode is None: for e in enums: if isinstance(e, util.text_type): convert_unicode = True break else: convert_unicode = False if self.enums: length = max(len(x) for x in self.enums) else: length = 0 String.__init__(self, length=length, convert_unicode=convert_unicode, ) SchemaType.__init__(self, **kw) def __repr__(self): return util.generic_repr(self, [ ("native_enum", True), ("name", None) ]) def _should_create_constraint(self, compiler): return not self.native_enum or \ not compiler.dialect.supports_native_enum @util.dependencies("sqlalchemy.sql.schema") def _set_table(self, schema, column, table): if self.native_enum: SchemaType._set_table(self, column, table) e = schema.CheckConstraint( type_coerce(column, self).in_(self.enums), name=self.name, _create_rule=util.portable_instancemethod( self._should_create_constraint) ) table.append_constraint(e) def adapt(self, impltype, **kw): schema = kw.pop('schema', self.schema) metadata = kw.pop('metadata', self.metadata) if issubclass(impltype, Enum): return impltype(name=self.name, schema=schema, metadata=metadata, convert_unicode=self.convert_unicode, native_enum=self.native_enum, inherit_schema=self.inherit_schema, *self.enums, **kw ) else: return super(Enum, self).adapt(impltype, **kw) class PickleType(TypeDecorator): """Holds Python objects, which are serialized using pickle. PickleType builds upon the Binary type to apply Python's ``pickle.dumps()`` to incoming objects, and ``pickle.loads()`` on the way out, allowing any pickleable Python object to be stored as a serialized binary field. To allow ORM change events to propagate for elements associated with :class:`.PickleType`, see :ref:`mutable_toplevel`. """ impl = LargeBinary def __init__(self, protocol=pickle.HIGHEST_PROTOCOL, pickler=None, comparator=None): """ Construct a PickleType. :param protocol: defaults to ``pickle.HIGHEST_PROTOCOL``. :param pickler: defaults to cPickle.pickle or pickle.pickle if cPickle is not available. May be any object with pickle-compatible ``dumps` and ``loads`` methods. :param comparator: a 2-arg callable predicate used to compare values of this type. If left as ``None``, the Python "equals" operator is used to compare values. """ self.protocol = protocol self.pickler = pickler or pickle self.comparator = comparator super(PickleType, self).__init__() def __reduce__(self): return PickleType, (self.protocol, None, self.comparator) def bind_processor(self, dialect): impl_processor = self.impl.bind_processor(dialect) dumps = self.pickler.dumps protocol = self.protocol if impl_processor: def process(value): if value is not None: value = dumps(value, protocol) return impl_processor(value) else: def process(value): if value is not None: value = dumps(value, protocol) return value return process def result_processor(self, dialect, coltype): impl_processor = self.impl.result_processor(dialect, coltype) loads = self.pickler.loads if impl_processor: def process(value): value = impl_processor(value) if value is None: return None return loads(value) else: def process(value): if value is None: return None return loads(value) return process def compare_values(self, x, y): if self.comparator: return self.comparator(x, y) else: return x == y class Boolean(TypeEngine, SchemaType): """A bool datatype. Boolean typically uses BOOLEAN or SMALLINT on the DDL side, and on the Python side deals in ``True`` or ``False``. """ __visit_name__ = 'boolean' def __init__(self, create_constraint=True, name=None): """Construct a Boolean. :param create_constraint: defaults to True. If the boolean is generated as an int/smallint, also create a CHECK constraint on the table that ensures 1 or 0 as a value. :param name: if a CHECK constraint is generated, specify the name of the constraint. """ self.create_constraint = create_constraint self.name = name def _should_create_constraint(self, compiler): return not compiler.dialect.supports_native_boolean @util.dependencies("sqlalchemy.sql.schema") def _set_table(self, schema, column, table): if not self.create_constraint: return e = schema.CheckConstraint( type_coerce(column, self).in_([0, 1]), name=self.name, _create_rule=util.portable_instancemethod( self._should_create_constraint) ) table.append_constraint(e) @property def python_type(self): return bool def bind_processor(self, dialect): if dialect.supports_native_boolean: return None else: return processors.boolean_to_int def result_processor(self, dialect, coltype): if dialect.supports_native_boolean: return None else: return processors.int_to_boolean class Interval(_DateAffinity, TypeDecorator): """A type for ``datetime.timedelta()`` objects. The Interval type deals with ``datetime.timedelta`` objects. In PostgreSQL, the native ``INTERVAL`` type is used; for others, the value is stored as a date which is relative to the "epoch" (Jan. 1, 1970). Note that the ``Interval`` type does not currently provide date arithmetic operations on platforms which do not support interval types natively. Such operations usually require transformation of both sides of the expression (such as, conversion of both sides into integer epoch values first) which currently is a manual procedure (such as via :attr:`~sqlalchemy.sql.expression.func`). """ impl = DateTime epoch = dt.datetime.utcfromtimestamp(0) def __init__(self, native=True, second_precision=None, day_precision=None): """Construct an Interval object. :param native: when True, use the actual INTERVAL type provided by the database, if supported (currently Postgresql, Oracle). Otherwise, represent the interval data as an epoch value regardless. :param second_precision: For native interval types which support a "fractional seconds precision" parameter, i.e. Oracle and Postgresql :param day_precision: for native interval types which support a "day precision" parameter, i.e. Oracle. """ super(Interval, self).__init__() self.native = native self.second_precision = second_precision self.day_precision = day_precision def adapt(self, cls, **kw): if self.native and hasattr(cls, '_adapt_from_generic_interval'): return cls._adapt_from_generic_interval(self, **kw) else: return self.__class__( native=self.native, second_precision=self.second_precision, day_precision=self.day_precision, **kw) @property def python_type(self): return dt.timedelta def bind_processor(self, dialect): impl_processor = self.impl.bind_processor(dialect) epoch = self.epoch if impl_processor: def process(value): if value is not None: value = epoch + value return impl_processor(value) else: def process(value): if value is not None: value = epoch + value return value return process def result_processor(self, dialect, coltype): impl_processor = self.impl.result_processor(dialect, coltype) epoch = self.epoch if impl_processor: def process(value): value = impl_processor(value) if value is None: return None return value - epoch else: def process(value): if value is None: return None return value - epoch return process @util.memoized_property def _expression_adaptations(self): return { operators.add: { Date: DateTime, Interval: self.__class__, DateTime: DateTime, Time: Time, }, operators.sub: { Interval: self.__class__ }, operators.mul: { Numeric: self.__class__ }, operators.truediv: { Numeric: self.__class__ }, operators.div: { Numeric: self.__class__ } } @property def _type_affinity(self): return Interval def coerce_compared_value(self, op, value): """See :meth:`.TypeEngine.coerce_compared_value` for a description.""" return self.impl.coerce_compared_value(op, value) class REAL(Float): """The SQL REAL type.""" __visit_name__ = 'REAL' class FLOAT(Float): """The SQL FLOAT type.""" __visit_name__ = 'FLOAT' class NUMERIC(Numeric): """The SQL NUMERIC type.""" __visit_name__ = 'NUMERIC' class DECIMAL(Numeric): """The SQL DECIMAL type.""" __visit_name__ = 'DECIMAL' class INTEGER(Integer): """The SQL INT or INTEGER type.""" __visit_name__ = 'INTEGER' INT = INTEGER class SMALLINT(SmallInteger): """The SQL SMALLINT type.""" __visit_name__ = 'SMALLINT' class BIGINT(BigInteger): """The SQL BIGINT type.""" __visit_name__ = 'BIGINT' class TIMESTAMP(DateTime): """The SQL TIMESTAMP type.""" __visit_name__ = 'TIMESTAMP' def get_dbapi_type(self, dbapi): return dbapi.TIMESTAMP class DATETIME(DateTime): """The SQL DATETIME type.""" __visit_name__ = 'DATETIME' class DATE(Date): """The SQL DATE type.""" __visit_name__ = 'DATE' class TIME(Time): """The SQL TIME type.""" __visit_name__ = 'TIME' class TEXT(Text): """The SQL TEXT type.""" __visit_name__ = 'TEXT' class CLOB(Text): """The CLOB type. This type is found in Oracle and Informix. """ __visit_name__ = 'CLOB' class VARCHAR(String): """The SQL VARCHAR type.""" __visit_name__ = 'VARCHAR' class NVARCHAR(Unicode): """The SQL NVARCHAR type.""" __visit_name__ = 'NVARCHAR' class CHAR(String): """The SQL CHAR type.""" __visit_name__ = 'CHAR' class NCHAR(Unicode): """The SQL NCHAR type.""" __visit_name__ = 'NCHAR' class BLOB(LargeBinary): """The SQL BLOB type.""" __visit_name__ = 'BLOB' class BINARY(_Binary): """The SQL BINARY type.""" __visit_name__ = 'BINARY' class VARBINARY(_Binary): """The SQL VARBINARY type.""" __visit_name__ = 'VARBINARY' class BOOLEAN(Boolean): """The SQL BOOLEAN type.""" __visit_name__ = 'BOOLEAN' class NullType(TypeEngine): """An unknown type. :class:`.NullType` is used as a default type for those cases where a type cannot be determined, including: * During table reflection, when the type of a column is not recognized by the :class:`.Dialect` * When constructing SQL expressions using plain Python objects of unknown types (e.g. ``somecolumn == my_special_object``) * When a new :class:`.Column` is created, and the given type is passed as ``None`` or is not passed at all. The :class:`.NullType` can be used within SQL expression invocation without issue, it just has no behavior either at the expression construction level or at the bind-parameter/result processing level. :class:`.NullType` will result in a :exc:`.CompileError` if the compiler is asked to render the type itself, such as if it is used in a :func:`.cast` operation or within a schema creation operation such as that invoked by :meth:`.MetaData.create_all` or the :class:`.CreateTable` construct. """ __visit_name__ = 'null' _isnull = True def literal_processor(self, dialect): def process(value): return "NULL" return process class Comparator(TypeEngine.Comparator): def _adapt_expression(self, op, other_comparator): if isinstance(other_comparator, NullType.Comparator) or \ not operators.is_commutative(op): return op, self.expr.type else: return other_comparator._adapt_expression(op, self) comparator_factory = Comparator NULLTYPE = NullType() BOOLEANTYPE = Boolean() STRINGTYPE = String() INTEGERTYPE = Integer() _type_map = { int: Integer(), float: Numeric(), bool: BOOLEANTYPE, decimal.Decimal: Numeric(), dt.date: Date(), dt.datetime: DateTime(), dt.time: Time(), dt.timedelta: Interval(), util.NoneType: NULLTYPE } if util.py3k: _type_map[bytes] = LargeBinary() _type_map[str] = Unicode() else: _type_map[unicode] = Unicode() _type_map[str] = String() # back-assign to type_api from . import type_api type_api.BOOLEANTYPE = BOOLEANTYPE type_api.STRINGTYPE = STRINGTYPE type_api.INTEGERTYPE = INTEGERTYPE type_api.NULLTYPE = NULLTYPE type_api._type_map = _type_map # this one, there's all kinds of ways to play it, but at the EOD # there's just a giant dependency cycle between the typing system and # the expression element system, as you might expect. We can use # importlaters or whatnot, but the typing system just necessarily has # to have some kind of connection like this. right now we're injecting the # _DefaultColumnComparator implementation into the TypeEngine.Comparator interface. # Alternatively TypeEngine.Comparator could have an "impl" injected, though # just injecting the base is simpler, error free, and more performant. class Comparator(_DefaultColumnComparator): BOOLEANTYPE = BOOLEANTYPE TypeEngine.Comparator.__bases__ = (Comparator, ) + TypeEngine.Comparator.__bases__

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import datetime as dt import codecs from .type_api import TypeEngine, TypeDecorator, to_instance from .elements import quoted_name, type_coerce from .default_comparator import _DefaultColumnComparator from .. import exc, util, processors from .base import _bind_or_error, SchemaEventTarget from . import operators from .. import event from ..util import pickle import decimal if util.jython: import array class _DateAffinity(object): @property def _expression_adaptations(self): raise NotImplementedError() class Comparator(TypeEngine.Comparator): _blank_dict = util.immutabledict() def _adapt_expression(self, op, other_comparator): othertype = other_comparator.type._type_affinity return op, \ to_instance(self.type._expression_adaptations.get(op, self._blank_dict).\ get(othertype, NULLTYPE)) comparator_factory = Comparator class Concatenable(object): class Comparator(TypeEngine.Comparator): def _adapt_expression(self, op, other_comparator): if op is operators.add and isinstance(other_comparator, (Concatenable.Comparator, NullType.Comparator)): return operators.concat_op, self.expr.type else: return op, self.expr.type comparator_factory = Comparator class String(Concatenable, TypeEngine): __visit_name__ = 'string' def __init__(self, length=None, collation=None, convert_unicode=False, unicode_error=None, _warn_on_bytestring=False ): if unicode_error is not None and convert_unicode != 'force': raise exc.ArgumentError("convert_unicode must be 'force' " "when unicode_error is set.") self.length = length self.collation = collation self.convert_unicode = convert_unicode self.unicode_error = unicode_error self._warn_on_bytestring = _warn_on_bytestring def literal_processor(self, dialect): def process(value): value = value.replace("'", "''") return "'%s'" % value return process def bind_processor(self, dialect): if self.convert_unicode or dialect.convert_unicode: if dialect.supports_unicode_binds and \ self.convert_unicode != 'force': if self._warn_on_bytestring: def process(value): if isinstance(value, util.binary_type): util.warn("Unicode type received non-unicode bind " "param value.") return value return process else: return None else: encoder = codecs.getencoder(dialect.encoding) warn_on_bytestring = self._warn_on_bytestring def process(value): if isinstance(value, util.text_type): return encoder(value, self.unicode_error)[0] elif warn_on_bytestring and value is not None: util.warn("Unicode type received non-unicode bind " "param value") return value return process else: return None def result_processor(self, dialect, coltype): wants_unicode = self.convert_unicode or dialect.convert_unicode needs_convert = wants_unicode and \ (dialect.returns_unicode_strings is not True or self.convert_unicode == 'force') if needs_convert: to_unicode = processors.to_unicode_processor_factory( dialect.encoding, self.unicode_error) if dialect.returns_unicode_strings: # we wouldn't be here unless convert_unicode='force' def process(value): if isinstance(value, util.text_type): return value else: return to_unicode(value) return process else: return to_unicode else: return None @property def python_type(self): if self.convert_unicode: return util.text_type else: return str def get_dbapi_type(self, dbapi): return dbapi.STRING class Text(String): __visit_name__ = 'text' class Unicode(String): __visit_name__ = 'unicode' def __init__(self, length=None, **kwargs): kwargs.setdefault('convert_unicode', True) kwargs.setdefault('_warn_on_bytestring', True) super(Unicode, self).__init__(length=length, **kwargs) class UnicodeText(Text): __visit_name__ = 'unicode_text' def __init__(self, length=None, **kwargs): kwargs.setdefault('convert_unicode', True) kwargs.setdefault('_warn_on_bytestring', True) super(UnicodeText, self).__init__(length=length, **kwargs) class Integer(_DateAffinity, TypeEngine): __visit_name__ = 'integer' def get_dbapi_type(self, dbapi): return dbapi.NUMBER @property def python_type(self): return int def literal_processor(self, dialect): def process(value): return str(value) return process @util.memoized_property def _expression_adaptations(self): return { operators.add: { Date: Date, Integer: self.__class__, Numeric: Numeric, }, operators.mul: { Interval: Interval, Integer: self.__class__, Numeric: Numeric, }, operators.div: { Integer: self.__class__, Numeric: Numeric, }, operators.truediv: { Integer: self.__class__, Numeric: Numeric, }, operators.sub: { Integer: self.__class__, Numeric: Numeric, }, } class SmallInteger(Integer): __visit_name__ = 'small_integer' class BigInteger(Integer): __visit_name__ = 'big_integer' class Numeric(_DateAffinity, TypeEngine): __visit_name__ = 'numeric' _default_decimal_return_scale = 10 def __init__(self, precision=None, scale=None, decimal_return_scale=None, asdecimal=True): self.precision = precision self.scale = scale self.decimal_return_scale = decimal_return_scale self.asdecimal = asdecimal @property def _effective_decimal_return_scale(self): if self.decimal_return_scale is not None: return self.decimal_return_scale elif getattr(self, "scale", None) is not None: return self.scale else: return self._default_decimal_return_scale def get_dbapi_type(self, dbapi): return dbapi.NUMBER def literal_processor(self, dialect): def process(value): return str(value) return process @property def python_type(self): if self.asdecimal: return decimal.Decimal else: return float def bind_processor(self, dialect): if dialect.supports_native_decimal: return None else: return processors.to_float def result_processor(self, dialect, coltype): if self.asdecimal: if dialect.supports_native_decimal: return None else: util.warn('Dialect %s+%s does *not* support Decimal ' 'objects natively, and SQLAlchemy must ' 'convert from floating point - rounding ' 'errors and other issues may occur. Please ' 'consider storing Decimal numbers as strings ' 'or integers on this platform for lossless ' 'storage.' % (dialect.name, dialect.driver)) # we're a "numeric", DBAPI returns floats, convert. return processors.to_decimal_processor_factory( decimal.Decimal, self.scale if self.scale is not None else self._default_decimal_return_scale) else: if dialect.supports_native_decimal: return processors.to_float else: return None @util.memoized_property def _expression_adaptations(self): return { operators.mul: { Interval: Interval, Numeric: self.__class__, Integer: self.__class__, }, operators.div: { Numeric: self.__class__, Integer: self.__class__, }, operators.truediv: { Numeric: self.__class__, Integer: self.__class__, }, operators.add: { Numeric: self.__class__, Integer: self.__class__, }, operators.sub: { Numeric: self.__class__, Integer: self.__class__, } } class Float(Numeric): __visit_name__ = 'float' scale = None def __init__(self, precision=None, asdecimal=False, decimal_return_scale=None, **kwargs): self.precision = precision self.asdecimal = asdecimal self.decimal_return_scale = decimal_return_scale if kwargs: util.warn_deprecated("Additional keyword arguments " "passed to Float ignored.") def result_processor(self, dialect, coltype): if self.asdecimal: return processors.to_decimal_processor_factory( decimal.Decimal, self._effective_decimal_return_scale) else: return None @util.memoized_property def _expression_adaptations(self): return { operators.mul: { Interval: Interval, Numeric: self.__class__, }, operators.div: { Numeric: self.__class__, }, operators.truediv: { Numeric: self.__class__, }, operators.add: { Numeric: self.__class__, }, operators.sub: { Numeric: self.__class__, } } class DateTime(_DateAffinity, TypeEngine): __visit_name__ = 'datetime' def __init__(self, timezone=False): self.timezone = timezone def get_dbapi_type(self, dbapi): return dbapi.DATETIME @property def python_type(self): return dt.datetime @util.memoized_property def _expression_adaptations(self): return { operators.add: { Interval: self.__class__, }, operators.sub: { Interval: self.__class__, DateTime: Interval, }, } class Date(_DateAffinity, TypeEngine): __visit_name__ = 'date' def get_dbapi_type(self, dbapi): return dbapi.DATETIME @property def python_type(self): return dt.date @util.memoized_property def _expression_adaptations(self): return { operators.add: { Integer: self.__class__, Interval: DateTime, Time: DateTime, }, operators.sub: { Integer: self.__class__, Date: Integer, Interval: DateTime, DateTime: Interval, }, } class Time(_DateAffinity, TypeEngine): __visit_name__ = 'time' def __init__(self, timezone=False): self.timezone = timezone def get_dbapi_type(self, dbapi): return dbapi.DATETIME @property def python_type(self): return dt.time @util.memoized_property def _expression_adaptations(self): return { operators.add: { Date: DateTime, Interval: self.__class__ }, operators.sub: { Time: Interval, Interval: self.__class__, }, } class _Binary(TypeEngine): def __init__(self, length=None): self.length = length def literal_processor(self, dialect): def process(value): value = value.decode(self.dialect.encoding).replace("'", "''") return "'%s'" % value return process @property def python_type(self): return util.binary_type # Python 3 - sqlite3 doesn't need the `Binary` conversion def bind_processor(self, dialect): DBAPIBinary = dialect.dbapi.Binary def process(value): if value is not None: return DBAPIBinary(value) else: return None return process if util.py2k: def result_processor(self, dialect, coltype): if util.jython: def process(value): if value is not None: if isinstance(value, array.array): return value.tostring() return str(value) else: return None else: process = processors.to_str return process else: def result_processor(self, dialect, coltype): def process(value): if value is not None: value = bytes(value) return value return process def coerce_compared_value(self, op, value): if isinstance(value, util.string_types): return self else: return super(_Binary, self).coerce_compared_value(op, value) def get_dbapi_type(self, dbapi): return dbapi.BINARY class LargeBinary(_Binary): __visit_name__ = 'large_binary' def __init__(self, length=None): _Binary.__init__(self, length=length) class Binary(LargeBinary): def __init__(self, *arg, **kw): util.warn_deprecated('The Binary type has been renamed to ' 'LargeBinary.') LargeBinary.__init__(self, *arg, **kw) class SchemaType(SchemaEventTarget): def __init__(self, **kw): name = kw.pop('name', None) if name is not None: self.name = quoted_name(name, kw.pop('quote', None)) else: self.name = None self.schema = kw.pop('schema', None) self.metadata = kw.pop('metadata', None) self.inherit_schema = kw.pop('inherit_schema', False) if self.metadata: event.listen( self.metadata, "before_create", util.portable_instancemethod(self._on_metadata_create) ) event.listen( self.metadata, "after_drop", util.portable_instancemethod(self._on_metadata_drop) ) def _set_parent(self, column): column._on_table_attach(util.portable_instancemethod(self._set_table)) def _set_table(self, column, table): if self.inherit_schema: self.schema = table.schema event.listen( table, "before_create", util.portable_instancemethod( self._on_table_create) ) event.listen( table, "after_drop", util.portable_instancemethod(self._on_table_drop) ) if self.metadata is None: # and table.metadata here ? event.listen( table.metadata, "before_create", util.portable_instancemethod(self._on_metadata_create) ) event.listen( table.metadata, "after_drop", util.portable_instancemethod(self._on_metadata_drop) ) def copy(self, **kw): return self.adapt(self.__class__) def adapt(self, impltype, **kw): schema = kw.pop('schema', self.schema) metadata = kw.pop('metadata', self.metadata) return impltype(name=self.name, schema=schema, metadata=metadata, inherit_schema=self.inherit_schema, **kw ) @property def bind(self): return self.metadata and self.metadata.bind or None def create(self, bind=None, checkfirst=False): if bind is None: bind = _bind_or_error(self) t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t.create(bind=bind, checkfirst=checkfirst) def drop(self, bind=None, checkfirst=False): if bind is None: bind = _bind_or_error(self) t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t.drop(bind=bind, checkfirst=checkfirst) def _on_table_create(self, target, bind, **kw): t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_table_create(target, bind, **kw) def _on_table_drop(self, target, bind, **kw): t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_table_drop(target, bind, **kw) def _on_metadata_create(self, target, bind, **kw): t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_metadata_create(target, bind, **kw) def _on_metadata_drop(self, target, bind, **kw): t = self.dialect_impl(bind.dialect) if t.__class__ is not self.__class__ and isinstance(t, SchemaType): t._on_metadata_drop(target, bind, **kw) class Enum(String, SchemaType): __visit_name__ = 'enum' def __init__(self, *enums, **kw): self.enums = enums self.native_enum = kw.pop('native_enum', True) convert_unicode = kw.pop('convert_unicode', None) if convert_unicode is None: for e in enums: if isinstance(e, util.text_type): convert_unicode = True break else: convert_unicode = False if self.enums: length = max(len(x) for x in self.enums) else: length = 0 String.__init__(self, length=length, convert_unicode=convert_unicode, ) SchemaType.__init__(self, **kw) def __repr__(self): return util.generic_repr(self, [ ("native_enum", True), ("name", None) ]) def _should_create_constraint(self, compiler): return not self.native_enum or \ not compiler.dialect.supports_native_enum @util.dependencies("sqlalchemy.sql.schema") def _set_table(self, schema, column, table): if self.native_enum: SchemaType._set_table(self, column, table) e = schema.CheckConstraint( type_coerce(column, self).in_(self.enums), name=self.name, _create_rule=util.portable_instancemethod( self._should_create_constraint) ) table.append_constraint(e) def adapt(self, impltype, **kw): schema = kw.pop('schema', self.schema) metadata = kw.pop('metadata', self.metadata) if issubclass(impltype, Enum): return impltype(name=self.name, schema=schema, metadata=metadata, convert_unicode=self.convert_unicode, native_enum=self.native_enum, inherit_schema=self.inherit_schema, *self.enums, **kw ) else: return super(Enum, self).adapt(impltype, **kw) class PickleType(TypeDecorator): impl = LargeBinary def __init__(self, protocol=pickle.HIGHEST_PROTOCOL, pickler=None, comparator=None): self.protocol = protocol self.pickler = pickler or pickle self.comparator = comparator super(PickleType, self).__init__() def __reduce__(self): return PickleType, (self.protocol, None, self.comparator) def bind_processor(self, dialect): impl_processor = self.impl.bind_processor(dialect) dumps = self.pickler.dumps protocol = self.protocol if impl_processor: def process(value): if value is not None: value = dumps(value, protocol) return impl_processor(value) else: def process(value): if value is not None: value = dumps(value, protocol) return value return process def result_processor(self, dialect, coltype): impl_processor = self.impl.result_processor(dialect, coltype) loads = self.pickler.loads if impl_processor: def process(value): value = impl_processor(value) if value is None: return None return loads(value) else: def process(value): if value is None: return None return loads(value) return process def compare_values(self, x, y): if self.comparator: return self.comparator(x, y) else: return x == y class Boolean(TypeEngine, SchemaType): __visit_name__ = 'boolean' def __init__(self, create_constraint=True, name=None): self.create_constraint = create_constraint self.name = name def _should_create_constraint(self, compiler): return not compiler.dialect.supports_native_boolean @util.dependencies("sqlalchemy.sql.schema") def _set_table(self, schema, column, table): if not self.create_constraint: return e = schema.CheckConstraint( type_coerce(column, self).in_([0, 1]), name=self.name, _create_rule=util.portable_instancemethod( self._should_create_constraint) ) table.append_constraint(e) @property def python_type(self): return bool def bind_processor(self, dialect): if dialect.supports_native_boolean: return None else: return processors.boolean_to_int def result_processor(self, dialect, coltype): if dialect.supports_native_boolean: return None else: return processors.int_to_boolean class Interval(_DateAffinity, TypeDecorator): impl = DateTime epoch = dt.datetime.utcfromtimestamp(0) def __init__(self, native=True, second_precision=None, day_precision=None): super(Interval, self).__init__() self.native = native self.second_precision = second_precision self.day_precision = day_precision def adapt(self, cls, **kw): if self.native and hasattr(cls, '_adapt_from_generic_interval'): return cls._adapt_from_generic_interval(self, **kw) else: return self.__class__( native=self.native, second_precision=self.second_precision, day_precision=self.day_precision, **kw) @property def python_type(self): return dt.timedelta def bind_processor(self, dialect): impl_processor = self.impl.bind_processor(dialect) epoch = self.epoch if impl_processor: def process(value): if value is not None: value = epoch + value return impl_processor(value) else: def process(value): if value is not None: value = epoch + value return value return process def result_processor(self, dialect, coltype): impl_processor = self.impl.result_processor(dialect, coltype) epoch = self.epoch if impl_processor: def process(value): value = impl_processor(value) if value is None: return None return value - epoch else: def process(value): if value is None: return None return value - epoch return process @util.memoized_property def _expression_adaptations(self): return { operators.add: { Date: DateTime, Interval: self.__class__, DateTime: DateTime, Time: Time, }, operators.sub: { Interval: self.__class__ }, operators.mul: { Numeric: self.__class__ }, operators.truediv: { Numeric: self.__class__ }, operators.div: { Numeric: self.__class__ } } @property def _type_affinity(self): return Interval def coerce_compared_value(self, op, value): return self.impl.coerce_compared_value(op, value) class REAL(Float): __visit_name__ = 'REAL' class FLOAT(Float): __visit_name__ = 'FLOAT' class NUMERIC(Numeric): __visit_name__ = 'NUMERIC' class DECIMAL(Numeric): __visit_name__ = 'DECIMAL' class INTEGER(Integer): __visit_name__ = 'INTEGER' INT = INTEGER class SMALLINT(SmallInteger): __visit_name__ = 'SMALLINT' class BIGINT(BigInteger): __visit_name__ = 'BIGINT' class TIMESTAMP(DateTime): __visit_name__ = 'TIMESTAMP' def get_dbapi_type(self, dbapi): return dbapi.TIMESTAMP class DATETIME(DateTime): __visit_name__ = 'DATETIME' class DATE(Date): __visit_name__ = 'DATE' class TIME(Time): __visit_name__ = 'TIME' class TEXT(Text): __visit_name__ = 'TEXT' class CLOB(Text): __visit_name__ = 'CLOB' class VARCHAR(String): __visit_name__ = 'VARCHAR' class NVARCHAR(Unicode): __visit_name__ = 'NVARCHAR' class CHAR(String): __visit_name__ = 'CHAR' class NCHAR(Unicode): __visit_name__ = 'NCHAR' class BLOB(LargeBinary): __visit_name__ = 'BLOB' class BINARY(_Binary): __visit_name__ = 'BINARY' class VARBINARY(_Binary): __visit_name__ = 'VARBINARY' class BOOLEAN(Boolean): __visit_name__ = 'BOOLEAN' class NullType(TypeEngine): __visit_name__ = 'null' _isnull = True def literal_processor(self, dialect): def process(value): return "NULL" return process class Comparator(TypeEngine.Comparator): def _adapt_expression(self, op, other_comparator): if isinstance(other_comparator, NullType.Comparator) or \ not operators.is_commutative(op): return op, self.expr.type else: return other_comparator._adapt_expression(op, self) comparator_factory = Comparator NULLTYPE = NullType() BOOLEANTYPE = Boolean() STRINGTYPE = String() INTEGERTYPE = Integer() _type_map = { int: Integer(), float: Numeric(), bool: BOOLEANTYPE, decimal.Decimal: Numeric(), dt.date: Date(), dt.datetime: DateTime(), dt.time: Time(), dt.timedelta: Interval(), util.NoneType: NULLTYPE } if util.py3k: _type_map[bytes] = LargeBinary() _type_map[str] = Unicode() else: _type_map[unicode] = Unicode() _type_map[str] = String() # back-assign to type_api from . import type_api type_api.BOOLEANTYPE = BOOLEANTYPE type_api.STRINGTYPE = STRINGTYPE type_api.INTEGERTYPE = INTEGERTYPE type_api.NULLTYPE = NULLTYPE type_api._type_map = _type_map # this one, there's all kinds of ways to play it, but at the EOD # the expression element system, as you might expect. We can use # importlaters or whatnot, but the typing system just necessarily has # to have some kind of connection like this. right now we're injecting the class Comparator(_DefaultColumnComparator): BOOLEANTYPE = BOOLEANTYPE TypeEngine.Comparator.__bases__ = (Comparator, ) + TypeEngine.Comparator.__bases__

true

1c475b01d3f2a15d38e7166284a6e4891d718fa6

4,466

py

Python

tinkt/cmap_utils.py

claydodo/tinkt

dfd07fe7cad34c0d5a1ec0e03a6437a502410918

[ "Unlicense" ]

null

tinkt/cmap_utils.py

claydodo/tinkt

dfd07fe7cad34c0d5a1ec0e03a6437a502410918

[ "Unlicense" ]

null

tinkt/cmap_utils.py

claydodo/tinkt

dfd07fe7cad34c0d5a1ec0e03a6437a502410918

[ "Unlicense" ]

null

# -*- coding:utf-8 -*- # cmap utils import six import numpy as np from matplotlib import cm as mpl_cm from matplotlib import colors as mpl_colors from . import cm as tinkt_cm CM_FAMILIES = { 'mpl': mpl_cm, 'tinkt': tinkt_cm } def set_under_over_bad_colors(cmap, under=None, over=None, bad=None): if under is not None: cmap.set_under(under) if over is not None: cmap.set_over(over) if bad is not None: cmap.set_bad(bad) return cmap def get_cmap(base_cmap, clip_min=None, clip_max=None, N=None, sample_points=None, bad=None, over=None, under=None, *args, **kwargs): """ Get cmap object by name, and optionally tweak it into a new one. Currently only supports tweaking of continuous cmaps. :param base_cmap: either a name or a cmap object. :param clip_min: lower clip point, valid range: 0.0~1.0, default: None. :param clip_max: upper clip point, valid range: 0.0~1.0, default: None. :param N: new cmap's color number, default: None (inherits from base_cmap). :param sample_points: a series of sampling points (0.0~1.0) on the base_cmap. When using this arg, clip_min, clip_max and N are ignored. :param bad: bad color, default None (inherits from base_cmap) :param over: over color, default None (inherits from base_cmap) :param under: under color, default None (inherits from base_cmap) :return: a cmap object (matplotlib.colors.Colormap) """ if isinstance(base_cmap, tuple): # The tuple-form is for compatibility of old codes using metlib.color.cmap_utils.get_cmap , which read opts from json file. # Please neglect the complex logics and use named args whenever possible. return _parse_tuple_form_args_for_get_cmap(base_cmap) if isinstance(base_cmap, six.string_types): for cm_family in CM_FAMILIES.values(): try: base_cmap = getattr(cm_family, base_cmap) break except AttributeError: pass if not isinstance(base_cmap, mpl_colors.Colormap): raise RuntimeError(u'Cannot find base_cmap: {}'.format(base_cmap)) if sample_points is not None: new_name = u'Resampled from {}'.format(base_cmap.name) new_cmap = mpl_colors.LinearSegmentedColormap.from_list(new_name, base_cmap(sample_points)) elif clip_min is not None or clip_max is not None: clip_min = 0.0 if clip_min is None else float(clip_min) clip_max = 0.0 if clip_max is None else float(clip_max) N = base_cmap.N if N is None else int(N) sample_points = np.linspace(clip_min, clip_max, N) new_name = u'Clipped from {}'.format(base_cmap.name) new_cmap = mpl_colors.LinearSegmentedColormap.from_list(new_name, base_cmap(sample_points)) else: N = int(N) if N is not None else base_cmap.N new_cmap = base_cmap._resample(N) if bad is not None: new_cmap.set_bad(bad) elif base_cmap._rgba_bad: new_cmap.set_bad(base_cmap._rgba_bad) if over is not None: new_cmap.set_over(over) elif base_cmap._rgba_over: new_cmap.set_over(base_cmap._rgba_over) if under is not None: new_cmap.set_under(under) elif base_cmap._rgba_under: new_cmap.set_under(base_cmap._rgba_under) return new_cmap def _parse_tuple_form_args_for_get_cmap(opts): # The tuple-form is for compatibility of old codes using metlib.color.cmap_utils.get_cmap, which read opts from json file. if len(opts) == 1: return get_cmap(opts[0]) elif len(opts) == 2: if isinstance(opts[1], (tuple, list, np.ndarray)): if len(opts[1]) == 0: return get_cmap(opts[0]) elif len(opts[1]) == 1: if isinstance(opts[1][0], (tuple, list, np.ndarray)): return get_cmap(opts[0], sample_points=opts[1][0]) else: raise ValueError("") elif len(opts[1]) == 2: clip_min, clip_max = opts[1] N = None elif len(opts[1]) == 3: clip_min, clip_max, N = opts[1] else: return get_cmap(opts[0], sample_points=opts[1]) return get_cmap(opts[0], clip_min=clip_min, clip_max=clip_max, N=N) else: raise ValueError("") else: raise ValueError("")

36.606557

140

0.638155

import six import numpy as np from matplotlib import cm as mpl_cm from matplotlib import colors as mpl_colors from . import cm as tinkt_cm CM_FAMILIES = { 'mpl': mpl_cm, 'tinkt': tinkt_cm } def set_under_over_bad_colors(cmap, under=None, over=None, bad=None): if under is not None: cmap.set_under(under) if over is not None: cmap.set_over(over) if bad is not None: cmap.set_bad(bad) return cmap def get_cmap(base_cmap, clip_min=None, clip_max=None, N=None, sample_points=None, bad=None, over=None, under=None, *args, **kwargs): if isinstance(base_cmap, tuple): return _parse_tuple_form_args_for_get_cmap(base_cmap) if isinstance(base_cmap, six.string_types): for cm_family in CM_FAMILIES.values(): try: base_cmap = getattr(cm_family, base_cmap) break except AttributeError: pass if not isinstance(base_cmap, mpl_colors.Colormap): raise RuntimeError(u'Cannot find base_cmap: {}'.format(base_cmap)) if sample_points is not None: new_name = u'Resampled from {}'.format(base_cmap.name) new_cmap = mpl_colors.LinearSegmentedColormap.from_list(new_name, base_cmap(sample_points)) elif clip_min is not None or clip_max is not None: clip_min = 0.0 if clip_min is None else float(clip_min) clip_max = 0.0 if clip_max is None else float(clip_max) N = base_cmap.N if N is None else int(N) sample_points = np.linspace(clip_min, clip_max, N) new_name = u'Clipped from {}'.format(base_cmap.name) new_cmap = mpl_colors.LinearSegmentedColormap.from_list(new_name, base_cmap(sample_points)) else: N = int(N) if N is not None else base_cmap.N new_cmap = base_cmap._resample(N) if bad is not None: new_cmap.set_bad(bad) elif base_cmap._rgba_bad: new_cmap.set_bad(base_cmap._rgba_bad) if over is not None: new_cmap.set_over(over) elif base_cmap._rgba_over: new_cmap.set_over(base_cmap._rgba_over) if under is not None: new_cmap.set_under(under) elif base_cmap._rgba_under: new_cmap.set_under(base_cmap._rgba_under) return new_cmap def _parse_tuple_form_args_for_get_cmap(opts): if len(opts) == 1: return get_cmap(opts[0]) elif len(opts) == 2: if isinstance(opts[1], (tuple, list, np.ndarray)): if len(opts[1]) == 0: return get_cmap(opts[0]) elif len(opts[1]) == 1: if isinstance(opts[1][0], (tuple, list, np.ndarray)): return get_cmap(opts[0], sample_points=opts[1][0]) else: raise ValueError("") elif len(opts[1]) == 2: clip_min, clip_max = opts[1] N = None elif len(opts[1]) == 3: clip_min, clip_max, N = opts[1] else: return get_cmap(opts[0], sample_points=opts[1]) return get_cmap(opts[0], clip_min=clip_min, clip_max=clip_max, N=N) else: raise ValueError("") else: raise ValueError("")

true

1c475e064511372aa11c413ea6aad9da5ab26d2e

10,185

py

Python

test_nfc.py

tnoumar/ST-M24SR64-NFC

6f5b2ec574fb51d3ffc458b562eb0f6df657a6a4

[ "MIT" ]

null

test_nfc.py

tnoumar/ST-M24SR64-NFC

6f5b2ec574fb51d3ffc458b562eb0f6df657a6a4

[ "MIT" ]

null

test_nfc.py

tnoumar/ST-M24SR64-NFC

6f5b2ec574fb51d3ffc458b562eb0f6df657a6a4

[ "MIT" ]

null

# Author: Taha NOUMAR tnoumar@enseirb-matmeca.fr # DATA SHEETS # https://www.st.com/resource/en/datasheet/m24sr64-y.pdf # CONFIGURATION # tag type: M24SR64Y # eeprom size: 64KBit # I2C address: 0x56 import machine import binascii import utime def byte0(b): return b & 0x00FF def byte1(b): return (b & 0xFF00) >> 8 class NFCTag(): I2C_ADDRESS_7BIT = 0x56 SYSTEM = 0xE101 CC = 0xE103 NDEF = 0x0001 NDEF_HEADER=[0xd1, 0x01, 0x00, 0x54, 0x02, 0x65, 0x6e] verbose = True # not to supercharge the user's console def __init__(self, i2c): self.i2c = i2c self.addr = self.I2C_ADDRESS_7BIT def wait(self, msg): ''' Wait a certain amount of time between operations''' utime.sleep_ms(500) if self.verbose: print("\n" + str(msg)) def write(self, data, crc=False): """Write a string of data bytes, with optional CRC""" if crc: crc0, crc1 = CRC.compute(data) data.append(crc0) data.append(crc1) data_hex = "" for i in range(len(data)): data_hex += hex(data[i]) + " " print("i2c write: [AC] " + data_hex) result = self.i2c.writeto(self.addr, bytes(data)) print("write:" + str(result)) if result == 0: raise RuntimeError("write result:" + str(result)) def read(self, len, checkCrc=False): """read a string of data bytes, with optional CRC checking""" data = bytearray(len) result = self.i2c.readfrom_into(0x56, data) if checkCrc: raise RuntimeError("CRC checking not yet written") #print("read:" + str(data)) # print('type of data is'+type(data)) # if len(data) == 0: # raise RuntimeError("read result:" + len(str(data))) return data def killRFSelectI2C(self): """Kill off any RF session and open an I2C session""" # tx: [0xAC] 0x52 # rx: TODO self.wait("Selecting I2C, deselecting RF ...") self.write([0x52]) def selectNFCT4Application(self, pcb=0x02): """Select the NFC app""" # tx: [0xAC] 0x02 0x00 0xA4 0x04 0x00 0x07 0xD2 0x76 0x00 0x00 0x85 0x01 0x01 0x00 [0x35 0xC0] # rx: [0xAD] 0x02 0x90 0x00 [0xF1 0x09] self.write([pcb, 0x00, 0xA4, 0x04, 0x00, 0x07, 0xD2, 0x76, 0x00, 0x00, 0x85, 0x01, 0x01, 0x00], crc=True) self.wait('Selecting NFC APP ...') result = self.read(5) return result def selectFile(self, fileId, pcb=0x02): """Select a nominated file""" # tx: [0xAC] 0x03 0x00 0xA4 0x00 0x0c 0x02 (0xE101) 0xCCCC # rx: TODO self.write([pcb, 0x00, 0xA4, 0x00, 0x0C, 0x02, byte1(fileId), byte0(fileId)], crc=True) self.wait('Selecting file ...') result = self.read(5) return result def readBinary(self, offset, length, pcb=0x02): """Read binary from the currently selected file""" # read length # tx: [0xAD] 0x03 0x00 0xB0 (0x00 0x00) (0x02) 0xCCCC # rx: TODO self.write([pcb, 0x00, 0xB0, byte1(offset), byte0(offset), byte0(length)], crc=True) self.wait('Reading binary ...') result = self.read(length+5) print("readBinary:" + str(result)) return result def updateBinaryLength(self, data, pcb=0x03): """ Update binary length in the currently selected file""" # tx: ERASE BINARY [AC] 03 00 D6 00 00 02 00 00 6B 37 # rx: self.write([pcb, 0x00, 0xD6, 0x00, 0x00, 0x02, byte1(data), byte0(data)], crc=True) utime.sleep(1) result = self.read(5) print("updateBinaryLength:"+str(result)) return result def updateBinary(self, offset, length, data, pcb=0x02): """ Update binary data in the currently selected file""" # UPDATE BINARY with HELLO WORLD e.g. # tx: 0xAC 0x02 0x00 0xD6 0x00 0x02 0x0B 0x68 0x65 0x6C 0x6C 0x6F 0x20 0x77 0x6F 0x72 0x6C 0x64 0x2F 0xFC # rx: payload = self.NDEF_HEADER + data payload[2] = length - 4 self.write([pcb, 0x00, 0xD6, byte1(offset), byte0( offset), byte0(length)]+payload, crc=True) self.wait('Updating Binary ...') result = self.read(5) print("updateBinary: "+str(result)) return result def deselect(self): """Deselect the I2C (allow RF to come in again)""" # deselect # tx: [0xAC] 0xC2 0xE0 B4 # rx: 0xC2 0xE0 0xB4 self.write([0xC2], crc=True) self.wait('Deselecting I2C, selecting RF ') result = self.read(3) return result def readNDEFFile(self): ''' select I2C select NFC application select CC read CC file and length select NDEF file read NDEF length read NDEF file ''' self.killRFSelectI2C() self.selectNFCT4Application() self.selectFile(self.CC, pcb=0x03) data = self.readBinary(0x0000, 0x02, pcb=0x02) data = self.readBinary(0x0000, 0x0F, pcb=0x03) self.selectFile(self.NDEF, pcb=0x02) data = self.readBinary(0x0000, 0x02, pcb=0x03) ndef_len = (data[1]*256) + data[2] print("NDEF len:" + str(ndef_len)) data = self.readBinary(0x0002, ndef_len, pcb=0x02) ndef = data[8:-4] s = "" for i in range(len(ndef)): s += chr(ndef[i]) print("ndef message:" + s) return s def eraseNDEFFile(self): ''' select I2C select NFC application select CC read CC file and length select NDEF file set NDEF length to 0 ''' self.killRFSelectI2C() self.selectNFCT4Application() self.selectFile(self.CC, pcb=0x03) data = self.readBinary(0x0000, 0x02, pcb=0x02) data = self.readBinary(0x0000, 0x0F, pcb=0x03) self.selectFile(self.NDEF, pcb=0x02) try: data = self.updateBinaryLength(0) print("File erased successfully") except: print("error while erasing file") def writeNDEFFile(self, text): ''' erase NDEF length update NDEF message set new NDEF length deselect I2C ''' self.eraseNDEFFile() # Write hello world in the tag print("Storing " + text + " in NDEF message") hex_text = binascii.hexlify(text.encode('utf8')) hex_list = [0x00 for i in range(0, int((len(hex_text)/2)))] for i in range(0, int((len(hex_text)/2))): hex_list[i] = int("0x"+str(hex_text[2*i:2*(i+1)] ).replace("b'", "").replace("'", "")) data = self.updateBinary(0x0002, len(text), hex_list) utime.sleep(1) try: data = self.updateBinaryLength(len(text)) print("File written successfully") except: print("error while writing file") print("deselecting I2C") self.deselect() utime.sleep(2) # PCB means "protocol control byte", # Takes 0x02 or 0x03 # CLA is class byte (always 0x00 for these apps) # INS is the instruction to send # P1 P2 are parameter 1 and 2, # Lc is length of command # Data is the payload of the command # Le is the length of expected response # CRC2 is the cyclic redundancy check bytes #Structure of NDEF message (NFC Data Exchange Format) ######################################################## # Byte 0 Byte 1 Byte 2 Byte 3 # 0x0000 NDEF message length User data User data # 0x0004 User data User data User data User data # ... ... ... ... ... ############################################################################################################## # COMMANDS # SEL PCB CLA INS P1 P2 Lc Data Le CRC2 # kill RF session, open I2C 0xAC 0x52 # select system file 0xAC 0x02 0x00 0xA4 0x00 0x0c 0x02 0xE101 0xCCCC # read length 0xAD 0x03 0x00 0xB0 0x00 0x00 0x02 0xCCCC # read memsize 0xAD 0x03 0x00 0xB0 0x00 0x0F 0x02 0xCCCC # deselect (Kill I2C, open RF) 0xAC 0xC2 0xE0 0xB4 # erase NDEF len 0xAC 0x03 0x00 0xD6 0x00 0x00 0x02 0x00 0x00 0x6B 0x37 # write HELLO WORLD in tag 0xAC 0x02 0x00 0xD6 0x00 0x02 0x0B 0x68 0x65 0x6C 0x6C 0x6F 0x20 0x77 0x6F 0x72 0x6C 0x64 0x2F 0xFC ##################################################################################################################################################### class CRC(): def __init__(self, initial=0x6363): # initialize CRC OBJ self.initial = initial def start(self): self.crc = self.initial def update(self, data): # update hex entries for CRC computation datain = data data = data ^ ((self.crc) & 0x00FF) data = data ^ ((data << 4) & 0x00FF) self.crc = (self.crc >> 8) \ ^ (data << 8) \ ^ (data << 3) \ ^ (data >> 4) self.crc = self.crc & 0xFFFF return self.crc def getCRC(self): return (self.crc & 0xFF), ((self.crc & 0xFF00) >> 8) def compute(block): c = CRC() c.start() for i in range(len(block)): c.update(block[i]) crc0, crc1 = c.getCRC() return crc0, crc1 tag = NFCTag(machine.I2C(1)) print('(before) text in the tag is '+tag.readNDEFFile()) tag.eraseNDEFFile() print('text in the tag is '+tag.readNDEFFile()) while True: pass

34.880137

149

0.525282

import machine import binascii import utime def byte0(b): return b & 0x00FF def byte1(b): return (b & 0xFF00) >> 8 class NFCTag(): I2C_ADDRESS_7BIT = 0x56 SYSTEM = 0xE101 CC = 0xE103 NDEF = 0x0001 NDEF_HEADER=[0xd1, 0x01, 0x00, 0x54, 0x02, 0x65, 0x6e] verbose = True def __init__(self, i2c): self.i2c = i2c self.addr = self.I2C_ADDRESS_7BIT def wait(self, msg): utime.sleep_ms(500) if self.verbose: print("\n" + str(msg)) def write(self, data, crc=False): if crc: crc0, crc1 = CRC.compute(data) data.append(crc0) data.append(crc1) data_hex = "" for i in range(len(data)): data_hex += hex(data[i]) + " " print("i2c write: [AC] " + data_hex) result = self.i2c.writeto(self.addr, bytes(data)) print("write:" + str(result)) if result == 0: raise RuntimeError("write result:" + str(result)) def read(self, len, checkCrc=False): data = bytearray(len) result = self.i2c.readfrom_into(0x56, data) if checkCrc: raise RuntimeError("CRC checking not yet written") #print("read:" + str(data)) # print('type of data is'+type(data)) # if len(data) == 0: # raise RuntimeError("read result:" + len(str(data))) return data def killRFSelectI2C(self): # tx: [0xAC] 0x52 # rx: TODO self.wait("Selecting I2C, deselecting RF ...") self.write([0x52]) def selectNFCT4Application(self, pcb=0x02): # tx: [0xAC] 0x02 0x00 0xA4 0x04 0x00 0x07 0xD2 0x76 0x00 0x00 0x85 0x01 0x01 0x00 [0x35 0xC0] # rx: [0xAD] 0x02 0x90 0x00 [0xF1 0x09] self.write([pcb, 0x00, 0xA4, 0x04, 0x00, 0x07, 0xD2, 0x76, 0x00, 0x00, 0x85, 0x01, 0x01, 0x00], crc=True) self.wait('Selecting NFC APP ...') result = self.read(5) return result def selectFile(self, fileId, pcb=0x02): # tx: [0xAC] 0x03 0x00 0xA4 0x00 0x0c 0x02 (0xE101) 0xCCCC # rx: TODO self.write([pcb, 0x00, 0xA4, 0x00, 0x0C, 0x02, byte1(fileId), byte0(fileId)], crc=True) self.wait('Selecting file ...') result = self.read(5) return result def readBinary(self, offset, length, pcb=0x02): # read length # tx: [0xAD] 0x03 0x00 0xB0 (0x00 0x00) (0x02) 0xCCCC # rx: TODO self.write([pcb, 0x00, 0xB0, byte1(offset), byte0(offset), byte0(length)], crc=True) self.wait('Reading binary ...') result = self.read(length+5) print("readBinary:" + str(result)) return result def updateBinaryLength(self, data, pcb=0x03): # tx: ERASE BINARY [AC] 03 00 D6 00 00 02 00 00 6B 37 # rx: self.write([pcb, 0x00, 0xD6, 0x00, 0x00, 0x02, byte1(data), byte0(data)], crc=True) utime.sleep(1) result = self.read(5) print("updateBinaryLength:"+str(result)) return result def updateBinary(self, offset, length, data, pcb=0x02): # UPDATE BINARY with HELLO WORLD e.g. # tx: 0xAC 0x02 0x00 0xD6 0x00 0x02 0x0B 0x68 0x65 0x6C 0x6C 0x6F 0x20 0x77 0x6F 0x72 0x6C 0x64 0x2F 0xFC # rx: payload = self.NDEF_HEADER + data payload[2] = length - 4 self.write([pcb, 0x00, 0xD6, byte1(offset), byte0( offset), byte0(length)]+payload, crc=True) self.wait('Updating Binary ...') result = self.read(5) print("updateBinary: "+str(result)) return result def deselect(self): # deselect # tx: [0xAC] 0xC2 0xE0 B4 # rx: 0xC2 0xE0 0xB4 self.write([0xC2], crc=True) self.wait('Deselecting I2C, selecting RF ') result = self.read(3) return result def readNDEFFile(self): self.killRFSelectI2C() self.selectNFCT4Application() self.selectFile(self.CC, pcb=0x03) data = self.readBinary(0x0000, 0x02, pcb=0x02) data = self.readBinary(0x0000, 0x0F, pcb=0x03) self.selectFile(self.NDEF, pcb=0x02) data = self.readBinary(0x0000, 0x02, pcb=0x03) ndef_len = (data[1]*256) + data[2] print("NDEF len:" + str(ndef_len)) data = self.readBinary(0x0002, ndef_len, pcb=0x02) ndef = data[8:-4] s = "" for i in range(len(ndef)): s += chr(ndef[i]) print("ndef message:" + s) return s def eraseNDEFFile(self): self.killRFSelectI2C() self.selectNFCT4Application() self.selectFile(self.CC, pcb=0x03) data = self.readBinary(0x0000, 0x02, pcb=0x02) data = self.readBinary(0x0000, 0x0F, pcb=0x03) self.selectFile(self.NDEF, pcb=0x02) try: data = self.updateBinaryLength(0) print("File erased successfully") except: print("error while erasing file") def writeNDEFFile(self, text): self.eraseNDEFFile() # Write hello world in the tag print("Storing " + text + " in NDEF message") hex_text = binascii.hexlify(text.encode('utf8')) hex_list = [0x00 for i in range(0, int((len(hex_text)/2)))] for i in range(0, int((len(hex_text)/2))): hex_list[i] = int("0x"+str(hex_text[2*i:2*(i+1)] ).replace("b'", "").replace("'", "")) data = self.updateBinary(0x0002, len(text), hex_list) utime.sleep(1) try: data = self.updateBinaryLength(len(text)) print("File written successfully") except: print("error while writing file") print("deselecting I2C") self.deselect() utime.sleep(2) # PCB means "protocol control byte", # Takes 0x02 or 0x03 # CLA is class byte (always 0x00 for these apps) # INS is the instruction to send # P1 P2 are parameter 1 and 2, # Lc is length of command # Data is the payload of the command # Le is the length of expected response # CRC2 is the cyclic redundancy check bytes #Structure of NDEF message (NFC Data Exchange Format) ######################################################## # Byte 0 Byte 1 Byte 2 Byte 3 # 0x0000 NDEF message length User data User data # 0x0004 User data User data User data User data # ... ... ... ... ... ############################################################################################################## # COMMANDS # SEL PCB CLA INS P1 P2 Lc Data Le CRC2 # kill RF session, open I2C 0xAC 0x52 # select system file 0xAC 0x02 0x00 0xA4 0x00 0x0c 0x02 0xE101 0xCCCC # read length 0xAD 0x03 0x00 0xB0 0x00 0x00 0x02 0xCCCC # read memsize 0xAD 0x03 0x00 0xB0 0x00 0x0F 0x02 0xCCCC # deselect (Kill I2C, open RF) 0xAC 0xC2 0xE0 0xB4 # erase NDEF len 0xAC 0x03 0x00 0xD6 0x00 0x00 0x02 0x00 0x00 0x6B 0x37 # write HELLO WORLD in tag 0xAC 0x02 0x00 0xD6 0x00 0x02 0x0B 0x68 0x65 0x6C 0x6C 0x6F 0x20 0x77 0x6F 0x72 0x6C 0x64 0x2F 0xFC ##################################################################################################################################################### class CRC(): def __init__(self, initial=0x6363): # initialize CRC OBJ self.initial = initial def start(self): self.crc = self.initial def update(self, data): # update hex entries for CRC computation datain = data data = data ^ ((self.crc) & 0x00FF) data = data ^ ((data << 4) & 0x00FF) self.crc = (self.crc >> 8) \ ^ (data << 8) \ ^ (data << 3) \ ^ (data >> 4) self.crc = self.crc & 0xFFFF return self.crc def getCRC(self): return (self.crc & 0xFF), ((self.crc & 0xFF00) >> 8) def compute(block): c = CRC() c.start() for i in range(len(block)): c.update(block[i]) crc0, crc1 = c.getCRC() return crc0, crc1 tag = NFCTag(machine.I2C(1)) print('(before) text in the tag is '+tag.readNDEFFile()) tag.eraseNDEFFile() print('text in the tag is '+tag.readNDEFFile()) while True: pass

true

1c475e204df91f662e807804eaf4a475b120362c

18,766

py

Python

OgreVertexBuffer.py

lamogui/ogre_blender_importer

4742e27909f57598889bdfa8a956001c6776d056

[ "MIT" ]

13

2016-01-23T08:00:34.000Z

2022-02-16T10:27:08.000Z

OgreVertexBuffer.py

lamogui/ogre_blender_importer

4742e27909f57598889bdfa8a956001c6776d056

[ "MIT" ]

3

2016-09-20T15:22:28.000Z

2021-05-31T01:25:05.000Z

OgreVertexBuffer.py

lamogui/ogre_blender_importer

4742e27909f57598889bdfa8a956001c6776d056

[ "MIT" ]

9

2016-07-13T23:23:55.000Z

2022-03-24T21:22:53.000Z

from enum import IntEnum; from struct import unpack_from; try: from OgreHardwareBuffer import OgreFakeHardwareBuffer except ImportError as e: directory = os.path.dirname(os.path.realpath(__file__)); print("Import error: " + str(e) + " manual compilation" ); srcfile="OgreHardwareBuffer.py"; exec(compile(open(os.path.join(directory,srcfile)).read(), srcfile, 'exec')) class OgreVertexBuffer(OgreFakeHardwareBuffer): """ Just a class to simulate a graphic card memory buffer """ def __init__(self, vertexSize, numVertices): OgreFakeHardwareBuffer.__init__(self); self._vertexSize = vertexSize; self._numVertices = numVertices; @property def vertexSize(self): return self._vertexSize; @property def numVertices(self): return self._numVertices; @property def sizeInBytes(self): return self.vertexSize * self.numVertices; class OgreVertexElementSemantic(IntEnum): """ Vertex element semantics, used to identify the meaning of vertex buffer contents """ VES_UNKNOWN = 0; # Position, 3 reals per vertex VES_POSITION = 1; # Blending weights VES_BLEND_WEIGHTS = 2; # Blending indices VES_BLEND_INDICES = 3; # Normal, 3 reals per vertex VES_NORMAL = 4; # Diffuse colours VES_DIFFUSE = 5; # Specular colours VES_SPECULAR = 6; # Texture coordinates VES_TEXTURE_COORDINATES = 7; # Binormal (Y axis if normal is Z) VES_BINORMAL = 8; # Tangent (X axis if normal is Z) VES_TANGENT = 9; # The number of VertexElementSemantic elements (note - the first value VES_POSITION is 1) VES_COUNT = 9; def toStr(ves): if (ves==OgreVertexElementSemantic.VES_UNKNOWN): return "VES_UNKNOWN"; elif (ves==OgreVertexElementSemantic.VES_POSITION): return "VES_POSITION"; elif (ves==OgreVertexElementSemantic.VES_BLEND_WEIGHTS): return "VES_BLEND_WEIGHTS"; elif (ves==OgreVertexElementSemantic.VES_BLEND_INDICES): return "VES_BLEND_INDICES"; elif (ves==OgreVertexElementSemantic.VES_NORMAL): return "VES_NORMAL"; elif (ves==OgreVertexElementSemantic.VES_DIFFUSE): return "VES_DIFFUSE"; elif (ves==OgreVertexElementSemantic.VES_SPECULAR): return "VES_SPECULAR"; elif (ves==OgreVertexElementSemantic.VES_TEXTURE_COORDINATES): return "VES_TEXTURE_COORDINATES"; elif (ves==OgreVertexElementSemantic.VES_BINORMAL): return "VES_BINORMAL"; elif (ves==OgreVertexElementSemantic.VES_TANGENT): return "VES_TANGENT"; elif (ves==OgreVertexElementSemantic.VES_COUNT): return "VES_COUNT"; class OgreVertexElementType(IntEnum): """ Vertex element type, used to identify the base types of the vertex contents """ VET_FLOAT1 = 0; VET_FLOAT2 = 1; VET_FLOAT3 = 2; VET_FLOAT4 = 3; # alias to more specific colour type - use the current rendersystem's colour packing VET_COLOUR = 4; VET_SHORT1 = 5; VET_SHORT2 = 6; VET_SHORT3 = 7; VET_SHORT4 = 8; VET_UBYTE4 = 9; # D3D style compact colour VET_COLOUR_ARGB = 10; # GL style compact colour VET_COLOUR_ABGR = 11; VET_DOUBLE1 = 12; VET_DOUBLE2 = 13; VET_DOUBLE3 = 14; VET_DOUBLE4 = 15; VET_USHORT1 = 16; VET_USHORT2 = 17; VET_USHORT3 = 18; VET_USHORT4 = 19; VET_INT1 = 20; VET_INT2 = 21; VET_INT3 = 22; VET_INT4 = 23; VET_UINT1 = 24; VET_UINT2 = 25; VET_UINT3 = 26; VET_UINT4 = 27; def toStr(vet): if (vet==OgreVertexElementType.VET_FLOAT1): return "VET_FLOAT1"; elif (vet==OgreVertexElementType.VET_FLOAT2): return "VET_FLOAT2"; elif (vet==OgreVertexElementType.VET_FLOAT3): return "VET_FLOAT3"; elif (vet==OgreVertexElementType.VET_FLOAT4): return "VET_FLOAT4"; elif (vet==OgreVertexElementType.VET_COLOUR): return "VET_COLOUR"; elif (vet==OgreVertexElementType.VET_SHORT1): return "VET_SHORT1"; elif (vet==OgreVertexElementType.VET_SHORT2): return "VET_SHORT2"; elif (vet==OgreVertexElementType.VET_SHORT3): return "VET_SHORT3"; elif (vet==OgreVertexElementType.VET_SHORT4): return "VET_SHORT4"; elif (vet==OgreVertexElementType.VET_USHORT1): return "VET_USHORT1"; elif (vet==OgreVertexElementType.VET_USHORT2): return "VET_USHORT2"; elif (vet==OgreVertexElementType.VET_USHORT3): return "VET_USHORT3"; elif (vet==OgreVertexElementType.VET_USHORT4): return "VET_USHORT4"; elif (vet==OgreVertexElementType.VET_UBYTE4): return "VET_UBYTE4"; elif (vet==OgreVertexElementType.VET_COLOUR_ABGR): return "VET_COLOUR_ABGR"; elif (vet==OgreVertexElementType.VET_COLOUR_ARGB): return "VET_COLOUR_ARGB"; elif (vet==OgreVertexElementType.VET_DOUBLE1): return "VET_COLOUR_DOUBLE1"; elif (vet==OgreVertexElementType.VET_DOUBLE2): return "VET_COLOUR_DOUBLE2"; elif (vet==OgreVertexElementType.VET_DOUBLE3): return "VET_COLOUR_DOUBLE3"; elif (vet==OgreVertexElementType.VET_DOUBLE4): return "VET_COLOUR_DOUBLE4"; elif (vet==OgreVertexElementType.VET_INT1): return "VET_COLOUR_INT1"; elif (vet==OgreVertexElementType.VET_INT2): return "VET_COLOUR_INT2"; elif (vet==OgreVertexElementType.VET_INT3): return "VET_COLOUR_INT3"; elif (vet==OgreVertexElementType.VET_INT4): return "VET_COLOUR_INT4"; elif (vet==OgreVertexElementType.VET_UINT1): return "VET_COLOUR_UINT1"; elif (vet==OgreVertexElementType.VET_UINT2): return "VET_COLOUR_UINT2"; elif (vet==OgreVertexElementType.VET_UINT3): return "VET_COLOUR_UINT3"; elif (vet==OgreVertexElementType.VET_UINT4): return "VET_COLOUR_UINT4"; class OgreVertexElement: """ This class declares the usage of a single vertex buffer as a component of a complete VertexDeclaration. @remarks Several vertex buffers can be used to supply the input geometry for a rendering operation, and in each case a vertex buffer can be used in different ways for different operations; the buffer itself does not define the semantics (position, normal etc), the VertexElement class does. """ def __init__(self, source, offset, theType, semantic, index): assert(type(source) is int and type(source) is int and type(index) is int); self._source = source; self._offset = offset; self._type = theType; self._semantic = semantic; self._index = index; def getType(self): return self._type; @property def semantic(self): return self._semantic; @property def index(self): return self._index; @property def offset(self): return self._offset; @property def source(self): return self._source; def getTypeSize(t): if (t==OgreVertexElementType.VET_COLOUR or \ t==OgreVertexElementType.VET_COLOUR_ABGR or \ t==OgreVertexElementType.VET_COLOUR_ARGB): return 4; elif (t==OgreVertexElementType.VET_FLOAT1): return 4*1; elif (t==OgreVertexElementType.VET_FLOAT2): return 4*2; elif (t==OgreVertexElementType.VET_FLOAT3): return 4*3; elif (t==OgreVertexElementType.VET_FLOAT4): return 4*4; elif (t==OgreVertexElementType.VET_DOUBLE1): return 8*1; elif (t==OgreVertexElementType.VET_DOUBLE2): return 8*2; elif (t==OgreVertexElementType.VET_DOUBLE3): return 8*3; elif (t==OgreVertexElementType.VET_DOUBLE4): return 8*4; elif (t==OgreVertexElementType.VET_SHORT1): return 2*1; elif (t==OgreVertexElementType.VET_SHORT2): return 2*2; elif (t==OgreVertexElementType.VET_SHORT3): return 2*3; elif (t==OgreVertexElementType.VET_SHORT4): return 2*4; elif (t==OgreVertexElementType.VET_USHORT1): return 2*1; elif (t==OgreVertexElementType.VET_USHORT2): return 2*2; elif (t==OgreVertexElementType.VET_USHORT3): return 2*3; elif (t==OgreVertexElementType.VET_USHORT4): return 2*4; elif (t==OgreVertexElementType.VET_INT1): return 4*1; elif (t==OgreVertexElementType.VET_INT2): return 4*2; elif (t==OgreVertexElementType.VET_INT3): return 4*3; elif (t==OgreVertexElementType.VET_INT4): return 4*4; elif (t==OgreVertexElementType.VET_UINT1): return 4*1; elif (t==OgreVertexElementType.VET_UINT2): return 4*2; elif (t==OgreVertexElementType.VET_UINT3): return 4*3; elif (t==OgreVertexElementType.VET_UINT4): return 4*4; elif (t==OgreVertexElementType.VET_UBYTE4): return 4; return 0; def getTypeCount(t): if (t==OgreVertexElementType.VET_COLOUR or \ t==OgreVertexElementType.VET_COLOUR_ABGR or \ t==OgreVertexElementType.VET_COLOUR_ARGB or \ t==OgreVertexElementType.VET_FLOAT1 or \ t==OgreVertexElementType.VET_DOUBLE1 or \ t==OgreVertexElementType.VET_SHORT1 or \ t==OgreVertexElementType.VET_USHORT1 or \ t==OgreVertexElementType.VET_INT1 or \ t==OgreVertexElementType.VET_UINT1): return 1; elif (t==OgreVertexElementType.VET_FLOAT2 or \ t==OgreVertexElementType.VET_DOUBLE2 or \ t==OgreVertexElementType.VET_SHORT2 or \ t==OgreVertexElementType.VET_USHORT2 or \ t==OgreVertexElementType.VET_INT2 or \ t==OgreVertexElementType.VET_UINT2): return 2; elif (t==OgreVertexElementType.VET_FLOAT3 or \ t==OgreVertexElementType.VET_DOUBLE3 or \ t==OgreVertexElementType.VET_SHORT3 or \ t==OgreVertexElementType.VET_USHORT3 or \ t==OgreVertexElementType.VET_INT3 or \ t==OgreVertexElementType.VET_UINT3): return 3; elif (t==OgreVertexElementType.VET_FLOAT4 or \ t==OgreVertexElementType.VET_DOUBLE4 or \ t==OgreVertexElementType.VET_SHORT4 or \ t==OgreVertexElementType.VET_USHORT4 or \ t==OgreVertexElementType.VET_INT4 or \ t==OgreVertexElementType.VET_UINT4): return 4; raise ValueError("OgreVertexElement.getTypeCount(type): Invalid type"); def getTypePythonUnpackStr(t): if (t==OgreVertexElementType.VET_COLOUR or \ t==OgreVertexElementType.VET_COLOUR_ABGR or \ t==OgreVertexElementType.VET_COLOUR_ARGB): raise ValueError("OgreVertexElement.getTypePythonUnpackStr(type): Color unsupported yet"); elif (t==OgreVertexElementType.VET_FLOAT1 or \ t==OgreVertexElementType.VET_FLOAT2 or \ t==OgreVertexElementType.VET_FLOAT3 or \ t==OgreVertexElementType.VET_FLOAT4): return 'f' * OgreVertexElement.getTypeCount(t); elif (t==OgreVertexElementType.VET_DOUBLE1 or \ t==OgreVertexElementType.VET_DOUBLE2 or \ t==OgreVertexElementType.VET_DOUBLE3 or \ t==OgreVertexElementType.VET_DOUBLE4): return 'd' * OgreVertexElement.getTypeCount(t); elif (t==OgreVertexElementType.VET_SHORT1 or \ t==OgreVertexElementType.VET_SHORT2 or \ t==OgreVertexElementType.VET_SHORT3 or \ t==OgreVertexElementType.VET_SHORT4): return 'h' * OgreVertexElement.getTypeCount(t); elif (t==OgreVertexElementType.VET_USHORT1 or \ t==OgreVertexElementType.VET_USHORT2 or \ t==OgreVertexElementType.VET_USHORT3 or \ t==OgreVertexElementType.VET_USHORT4): return 'H' * OgreVertexElement.getTypeCount(t); elif (t==OgreVertexElementType.VET_INT1 or \ t==OgreVertexElementType.VET_INT2 or \ t==OgreVertexElementType.VET_INT3 or \ t==OgreVertexElementType.VET_INT4): return 'i' * OgreVertexElement.getTypeCount(t); elif (t==OgreVertexElementType.VET_UINT1 or \ t==OgreVertexElementType.VET_UINT2 or \ t==OgreVertexElementType.VET_UINT3 or \ t==OgreVertexElementType.VET_UINT4): return 'I' * OgreVertexElement.getTypeCount(t); raise ValueError("OgreVertexElement.getTypePythonUnpackStr(type): Invalid type"); def getBestCoulourVertexElementType(): #Blender use opengl return OgreVertexElementType.VET_COLOUR_ABGR; def __eq__(self, other): if (self._source == other._source and \ self._index == other._index and \ self._offet == other._offset and \ self._semantic == other._semantic and \ self._type == other._type): return True; else: return False; def getSize(self): return OgreVertexElement.getTypeSize(self._type); def extractFromBuffer(self, vertexBufferBinding, dest, endianess): buf = vertexBufferBinding.getBuffer(self.source); cmd = ""; #FIXME: endianess not working... #if (endianess.value == 'big'): # cmd = '<'; #elif (endianess.value == 'little'): # cmd = '>'; #else : # cmd = endianess; #assert(cmd == '<' or cmd == '>'); cmd = "=" cmd = cmd + OgreVertexElement.getTypePythonUnpackStr(self.getType()); print(cmd); data = buf.data[self.offset:] for i in range(buf.numVertices): v = unpack_from(cmd, data, i * buf.vertexSize); dest.append(v); class OgreVertexDeclaration: """ This class declares the format of a set of vertex inputs, which can be issued to the rendering API through a RenderOperation. @remarks You should be aware that the ordering and structure of the VertexDeclaration can be very important on DirectX with older cards,so if you want to maintain maximum compatibility with all render systems and all cards you should be careful to follow these rules:<ol> <li>VertexElements should be added in the following order, and the order of the elements within a shared buffer should be as follows: position, blending weights, normals, diffuse colours, specular colours, texture coordinates (in order, with no gaps)</li> <li>You must not have unused gaps in your buffers which are not referenced by any VertexElement</li> <li>You must not cause the buffer & offset settings of 2 VertexElements to overlap</li> </ol> Whilst GL and more modern graphics cards in D3D will allow you to defy these rules, sticking to them will ensure that your buffers have the maximum compatibility. @par Like the other classes in this functional area, these declarations should be created and destroyed using the HardwareBufferManager. """ def __init__(self): self._elementList = []; def getElements(self): return self._elementList; def addElement(self, source, offset, theType, semantic, index): if (theType == OgreVertexElementType.VET_COLOUR): theType = OgreVertexElement.getBestCoulourVertexElementType(); self._elementList.append(OgreVertexElement(source,offset,theType,semantic,index)); return self._elementList[-1]; def insertElement(self, atPosition, source, offset, theType, semantic, index): if (atPosition >= len(_elementList)): return self.addElement(source,offset,theType,semantic,index); _elementList.insert(atPosition,OgreVertexElement(source,offset,theType,semantic,index)); return _elementList[-1]; def getElement(self, index): return self._elementList[index]; def removeElement(self, index): del self._elementList[index]; def removeElementWithSemantic(self, semantic, index): for i in range(self._elementList): if (self._elementList[i].semantic == semantic and self._elementList[i].index == index): del self._elementList[i]; break; def removeAllElements(self): self._elementList = []; def findElementBySemantic(self, sem, index): for e in self._elementList: if (e.semantic == sem and e.index == index): return e; return None; def findElementsBySemantic(self,sem): elements = [] for e in self._elementList: if (e.semantic == sem): elements.append(e); return elements; def findElementBySource(self,source): return [e for e in self._elementList if e.source == source]; def getVertexSize(self, source): sz = 0; for e in self._elementList: if (e.source == source): sz += e.getSize(); return sz; def vertexElementLess(e1, e2): if (e1.source < e2.source): return True; elif (e1.source == e2.source): if (e1.semantic < e2.semantic): return True; elif (e1.semantic == e2.semantic): if (e1.index < e2.index): return True; return False; def sort(self): self._elementList.sort(cmp=OgreVertexDeclaration.vertexElementLess); def closeGapInSource(self): if (not self._elementList): return; self.sort(); raise NotImplementedError; class OgreVertexBufferBinding: """ This is the legacy of Ogre code. Because ogre separate vertex declarations from vertex buffer in his file. So this class allow us to associate the correct declaration with the correct buffer. """ def __init__(self): self._bindingMap = {}; def setBinding(self, index, vbuffer): self._bindingMap[str(index)]=vbuffer; def getBuffer(self, source): return self._bindingMap[str(source)]; def unsetAllBindings(self): self._bindingMap = {};

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from enum import IntEnum; from struct import unpack_from; try: from OgreHardwareBuffer import OgreFakeHardwareBuffer except ImportError as e: directory = os.path.dirname(os.path.realpath(__file__)); print("Import error: " + str(e) + " manual compilation" ); srcfile="OgreHardwareBuffer.py"; exec(compile(open(os.path.join(directory,srcfile)).read(), srcfile, 'exec')) class OgreVertexBuffer(OgreFakeHardwareBuffer): def __init__(self, vertexSize, numVertices): OgreFakeHardwareBuffer.__init__(self); self._vertexSize = vertexSize; self._numVertices = numVertices; @property def vertexSize(self): return self._vertexSize; @property def numVertices(self): return self._numVertices; @property def sizeInBytes(self): return self.vertexSize * self.numVertices; class OgreVertexElementSemantic(IntEnum): VES_UNKNOWN = 0; VES_POSITION = 1; VES_BLEND_WEIGHTS = 2; VES_BLEND_INDICES = 3; VES_NORMAL = 4; VES_DIFFUSE = 5; VES_SPECULAR = 6; VES_TEXTURE_COORDINATES = 7; VES_BINORMAL = 8; VES_TANGENT = 9; VES_COUNT = 9; def toStr(ves): if (ves==OgreVertexElementSemantic.VES_UNKNOWN): return "VES_UNKNOWN"; elif (ves==OgreVertexElementSemantic.VES_POSITION): return "VES_POSITION"; elif (ves==OgreVertexElementSemantic.VES_BLEND_WEIGHTS): return "VES_BLEND_WEIGHTS"; elif (ves==OgreVertexElementSemantic.VES_BLEND_INDICES): return "VES_BLEND_INDICES"; elif (ves==OgreVertexElementSemantic.VES_NORMAL): return "VES_NORMAL"; elif (ves==OgreVertexElementSemantic.VES_DIFFUSE): return "VES_DIFFUSE"; elif (ves==OgreVertexElementSemantic.VES_SPECULAR): return "VES_SPECULAR"; elif (ves==OgreVertexElementSemantic.VES_TEXTURE_COORDINATES): return "VES_TEXTURE_COORDINATES"; elif (ves==OgreVertexElementSemantic.VES_BINORMAL): return "VES_BINORMAL"; elif (ves==OgreVertexElementSemantic.VES_TANGENT): return "VES_TANGENT"; elif (ves==OgreVertexElementSemantic.VES_COUNT): return "VES_COUNT"; class OgreVertexElementType(IntEnum): VET_FLOAT1 = 0; VET_FLOAT2 = 1; VET_FLOAT3 = 2; VET_FLOAT4 = 3; VET_COLOUR = 4; VET_SHORT1 = 5; VET_SHORT2 = 6; VET_SHORT3 = 7; VET_SHORT4 = 8; VET_UBYTE4 = 9; # D3D style compact colour VET_COLOUR_ARGB = 10; # GL style compact colour VET_COLOUR_ABGR = 11; VET_DOUBLE1 = 12; VET_DOUBLE2 = 13; VET_DOUBLE3 = 14; VET_DOUBLE4 = 15; VET_USHORT1 = 16; VET_USHORT2 = 17; VET_USHORT3 = 18; VET_USHORT4 = 19; VET_INT1 = 20; VET_INT2 = 21; VET_INT3 = 22; VET_INT4 = 23; VET_UINT1 = 24; VET_UINT2 = 25; VET_UINT3 = 26; VET_UINT4 = 27; def toStr(vet): if (vet==OgreVertexElementType.VET_FLOAT1): return "VET_FLOAT1"; elif (vet==OgreVertexElementType.VET_FLOAT2): return "VET_FLOAT2"; elif (vet==OgreVertexElementType.VET_FLOAT3): return "VET_FLOAT3"; elif (vet==OgreVertexElementType.VET_FLOAT4): return "VET_FLOAT4"; elif (vet==OgreVertexElementType.VET_COLOUR): return "VET_COLOUR"; elif (vet==OgreVertexElementType.VET_SHORT1): return "VET_SHORT1"; elif (vet==OgreVertexElementType.VET_SHORT2): return "VET_SHORT2"; elif (vet==OgreVertexElementType.VET_SHORT3): return "VET_SHORT3"; elif (vet==OgreVertexElementType.VET_SHORT4): return "VET_SHORT4"; elif (vet==OgreVertexElementType.VET_USHORT1): return "VET_USHORT1"; elif (vet==OgreVertexElementType.VET_USHORT2): return "VET_USHORT2"; elif (vet==OgreVertexElementType.VET_USHORT3): return "VET_USHORT3"; elif (vet==OgreVertexElementType.VET_USHORT4): return "VET_USHORT4"; elif (vet==OgreVertexElementType.VET_UBYTE4): return "VET_UBYTE4"; elif (vet==OgreVertexElementType.VET_COLOUR_ABGR): return "VET_COLOUR_ABGR"; elif (vet==OgreVertexElementType.VET_COLOUR_ARGB): return "VET_COLOUR_ARGB"; elif (vet==OgreVertexElementType.VET_DOUBLE1): return "VET_COLOUR_DOUBLE1"; elif (vet==OgreVertexElementType.VET_DOUBLE2): return "VET_COLOUR_DOUBLE2"; elif (vet==OgreVertexElementType.VET_DOUBLE3): return "VET_COLOUR_DOUBLE3"; elif (vet==OgreVertexElementType.VET_DOUBLE4): return "VET_COLOUR_DOUBLE4"; elif (vet==OgreVertexElementType.VET_INT1): return "VET_COLOUR_INT1"; elif (vet==OgreVertexElementType.VET_INT2): return "VET_COLOUR_INT2"; elif (vet==OgreVertexElementType.VET_INT3): return "VET_COLOUR_INT3"; elif (vet==OgreVertexElementType.VET_INT4): return "VET_COLOUR_INT4"; elif (vet==OgreVertexElementType.VET_UINT1): return "VET_COLOUR_UINT1"; elif (vet==OgreVertexElementType.VET_UINT2): return "VET_COLOUR_UINT2"; elif (vet==OgreVertexElementType.VET_UINT3): return "VET_COLOUR_UINT3"; elif (vet==OgreVertexElementType.VET_UINT4): return "VET_COLOUR_UINT4"; class OgreVertexElement: def __init__(self, source, offset, theType, semantic, index): assert(type(source) is int and type(source) is int and type(index) is int); self._source = source; self._offset = offset; self._type = theType; self._semantic = semantic; self._index = index; def getType(self): return self._type; @property def semantic(self): return self._semantic; @property def index(self): return self._index; @property def offset(self): return self._offset; @property def source(self): return self._source; def getTypeSize(t): if (t==OgreVertexElementType.VET_COLOUR or \ t==OgreVertexElementType.VET_COLOUR_ABGR or \ t==OgreVertexElementType.VET_COLOUR_ARGB): return 4; elif (t==OgreVertexElementType.VET_FLOAT1): return 4*1; elif (t==OgreVertexElementType.VET_FLOAT2): return 4*2; elif (t==OgreVertexElementType.VET_FLOAT3): return 4*3; elif (t==OgreVertexElementType.VET_FLOAT4): return 4*4; elif (t==OgreVertexElementType.VET_DOUBLE1): return 8*1; elif (t==OgreVertexElementType.VET_DOUBLE2): return 8*2; elif (t==OgreVertexElementType.VET_DOUBLE3): return 8*3; elif (t==OgreVertexElementType.VET_DOUBLE4): return 8*4; elif (t==OgreVertexElementType.VET_SHORT1): return 2*1; elif (t==OgreVertexElementType.VET_SHORT2): return 2*2; elif (t==OgreVertexElementType.VET_SHORT3): return 2*3; elif (t==OgreVertexElementType.VET_SHORT4): return 2*4; elif (t==OgreVertexElementType.VET_USHORT1): return 2*1; elif (t==OgreVertexElementType.VET_USHORT2): return 2*2; elif (t==OgreVertexElementType.VET_USHORT3): return 2*3; elif (t==OgreVertexElementType.VET_USHORT4): return 2*4; elif (t==OgreVertexElementType.VET_INT1): return 4*1; elif (t==OgreVertexElementType.VET_INT2): return 4*2; elif (t==OgreVertexElementType.VET_INT3): return 4*3; elif (t==OgreVertexElementType.VET_INT4): return 4*4; elif (t==OgreVertexElementType.VET_UINT1): return 4*1; elif (t==OgreVertexElementType.VET_UINT2): return 4*2; elif (t==OgreVertexElementType.VET_UINT3): return 4*3; elif (t==OgreVertexElementType.VET_UINT4): return 4*4; elif (t==OgreVertexElementType.VET_UBYTE4): return 4; return 0; def getTypeCount(t): if (t==OgreVertexElementType.VET_COLOUR or \ t==OgreVertexElementType.VET_COLOUR_ABGR or \ t==OgreVertexElementType.VET_COLOUR_ARGB or \ t==OgreVertexElementType.VET_FLOAT1 or \ t==OgreVertexElementType.VET_DOUBLE1 or \ t==OgreVertexElementType.VET_SHORT1 or \ t==OgreVertexElementType.VET_USHORT1 or \ t==OgreVertexElementType.VET_INT1 or \ t==OgreVertexElementType.VET_UINT1): return 1; elif (t==OgreVertexElementType.VET_FLOAT2 or \ t==OgreVertexElementType.VET_DOUBLE2 or \ t==OgreVertexElementType.VET_SHORT2 or \ t==OgreVertexElementType.VET_USHORT2 or \ t==OgreVertexElementType.VET_INT2 or \ t==OgreVertexElementType.VET_UINT2): return 2; elif (t==OgreVertexElementType.VET_FLOAT3 or \ t==OgreVertexElementType.VET_DOUBLE3 or \ t==OgreVertexElementType.VET_SHORT3 or \ t==OgreVertexElementType.VET_USHORT3 or \ t==OgreVertexElementType.VET_INT3 or \ t==OgreVertexElementType.VET_UINT3): return 3; elif (t==OgreVertexElementType.VET_FLOAT4 or \ t==OgreVertexElementType.VET_DOUBLE4 or \ t==OgreVertexElementType.VET_SHORT4 or \ t==OgreVertexElementType.VET_USHORT4 or \ t==OgreVertexElementType.VET_INT4 or \ t==OgreVertexElementType.VET_UINT4): return 4; raise ValueError("OgreVertexElement.getTypeCount(type): Invalid type"); def getTypePythonUnpackStr(t): if (t==OgreVertexElementType.VET_COLOUR or \ t==OgreVertexElementType.VET_COLOUR_ABGR or \ t==OgreVertexElementType.VET_COLOUR_ARGB): raise ValueError("OgreVertexElement.getTypePythonUnpackStr(type): Color unsupported yet"); elif (t==OgreVertexElementType.VET_FLOAT1 or \ t==OgreVertexElementType.VET_FLOAT2 or \ t==OgreVertexElementType.VET_FLOAT3 or \ t==OgreVertexElementType.VET_FLOAT4): return 'f' * OgreVertexElement.getTypeCount(t); elif (t==OgreVertexElementType.VET_DOUBLE1 or \ t==OgreVertexElementType.VET_DOUBLE2 or \ t==OgreVertexElementType.VET_DOUBLE3 or \ t==OgreVertexElementType.VET_DOUBLE4): return 'd' * OgreVertexElement.getTypeCount(t); elif (t==OgreVertexElementType.VET_SHORT1 or \ t==OgreVertexElementType.VET_SHORT2 or \ t==OgreVertexElementType.VET_SHORT3 or \ t==OgreVertexElementType.VET_SHORT4): return 'h' * OgreVertexElement.getTypeCount(t); elif (t==OgreVertexElementType.VET_USHORT1 or \ t==OgreVertexElementType.VET_USHORT2 or \ t==OgreVertexElementType.VET_USHORT3 or \ t==OgreVertexElementType.VET_USHORT4): return 'H' * OgreVertexElement.getTypeCount(t); elif (t==OgreVertexElementType.VET_INT1 or \ t==OgreVertexElementType.VET_INT2 or \ t==OgreVertexElementType.VET_INT3 or \ t==OgreVertexElementType.VET_INT4): return 'i' * OgreVertexElement.getTypeCount(t); elif (t==OgreVertexElementType.VET_UINT1 or \ t==OgreVertexElementType.VET_UINT2 or \ t==OgreVertexElementType.VET_UINT3 or \ t==OgreVertexElementType.VET_UINT4): return 'I' * OgreVertexElement.getTypeCount(t); raise ValueError("OgreVertexElement.getTypePythonUnpackStr(type): Invalid type"); def getBestCoulourVertexElementType(): #Blender use opengl return OgreVertexElementType.VET_COLOUR_ABGR; def __eq__(self, other): if (self._source == other._source and \ self._index == other._index and \ self._offet == other._offset and \ self._semantic == other._semantic and \ self._type == other._type): return True; else: return False; def getSize(self): return OgreVertexElement.getTypeSize(self._type); def extractFromBuffer(self, vertexBufferBinding, dest, endianess): buf = vertexBufferBinding.getBuffer(self.source); cmd = ""; #FIXME: endianess not working... #if (endianess.value == 'big'): # cmd = '<'; #elif (endianess.value == 'little'): # cmd = '>'; #else : # cmd = endianess; #assert(cmd == '<' or cmd == '>'); cmd = "=" cmd = cmd + OgreVertexElement.getTypePythonUnpackStr(self.getType()); print(cmd); data = buf.data[self.offset:] for i in range(buf.numVertices): v = unpack_from(cmd, data, i * buf.vertexSize); dest.append(v); class OgreVertexDeclaration: def __init__(self): self._elementList = []; def getElements(self): return self._elementList; def addElement(self, source, offset, theType, semantic, index): if (theType == OgreVertexElementType.VET_COLOUR): theType = OgreVertexElement.getBestCoulourVertexElementType(); self._elementList.append(OgreVertexElement(source,offset,theType,semantic,index)); return self._elementList[-1]; def insertElement(self, atPosition, source, offset, theType, semantic, index): if (atPosition >= len(_elementList)): return self.addElement(source,offset,theType,semantic,index); _elementList.insert(atPosition,OgreVertexElement(source,offset,theType,semantic,index)); return _elementList[-1]; def getElement(self, index): return self._elementList[index]; def removeElement(self, index): del self._elementList[index]; def removeElementWithSemantic(self, semantic, index): for i in range(self._elementList): if (self._elementList[i].semantic == semantic and self._elementList[i].index == index): del self._elementList[i]; break; def removeAllElements(self): self._elementList = []; def findElementBySemantic(self, sem, index): for e in self._elementList: if (e.semantic == sem and e.index == index): return e; return None; def findElementsBySemantic(self,sem): elements = [] for e in self._elementList: if (e.semantic == sem): elements.append(e); return elements; def findElementBySource(self,source): return [e for e in self._elementList if e.source == source]; def getVertexSize(self, source): sz = 0; for e in self._elementList: if (e.source == source): sz += e.getSize(); return sz; def vertexElementLess(e1, e2): if (e1.source < e2.source): return True; elif (e1.source == e2.source): if (e1.semantic < e2.semantic): return True; elif (e1.semantic == e2.semantic): if (e1.index < e2.index): return True; return False; def sort(self): self._elementList.sort(cmp=OgreVertexDeclaration.vertexElementLess); def closeGapInSource(self): if (not self._elementList): return; self.sort(); raise NotImplementedError; class OgreVertexBufferBinding: def __init__(self): self._bindingMap = {}; def setBinding(self, index, vbuffer): self._bindingMap[str(index)]=vbuffer; def getBuffer(self, source): return self._bindingMap[str(source)]; def unsetAllBindings(self): self._bindingMap = {};

true

1c475e3625b49e36e394562fd00fe1877c86b2a5

4,692

py

Python

env/Lib/site-packages/sqlalchemy/dialects/sqlite/pysqlcipher.py

aammjian/cotton

f72b814f795f79a4054688e465c8b0ae5560f3b7

[ "Apache-2.0" ]

5,079

2015-01-01T03:39:46.000Z

2022-03-31T07:38:22.000Z

env/Lib/site-packages/sqlalchemy/dialects/sqlite/pysqlcipher.py

aammjian/cotton

f72b814f795f79a4054688e465c8b0ae5560f3b7

[ "Apache-2.0" ]

1,623

2015-01-01T08:06:24.000Z

2022-03-30T19:48:52.000Z

env/Lib/site-packages/sqlalchemy/dialects/sqlite/pysqlcipher.py

aammjian/cotton

f72b814f795f79a4054688e465c8b0ae5560f3b7

[ "Apache-2.0" ]

2,033

2015-01-04T07:18:02.000Z

2022-03-28T19:55:47.000Z

# sqlite/pysqlcipher.py # Copyright (C) 2005-2020 the SQLAlchemy authors and contributors # <see AUTHORS file> # # This module is part of SQLAlchemy and is released under # the MIT License: http://www.opensource.org/licenses/mit-license.php """ .. dialect:: sqlite+pysqlcipher :name: pysqlcipher :dbapi: pysqlcipher :connectstring: sqlite+pysqlcipher://:passphrase/file_path[?kdf_iter=<iter>] :url: https://pypi.python.org/pypi/pysqlcipher ``pysqlcipher`` is a fork of the standard ``pysqlite`` driver to make use of the `SQLCipher <https://www.zetetic.net/sqlcipher>`_ backend. ``pysqlcipher3`` is a fork of ``pysqlcipher`` for Python 3. This dialect will attempt to import it if ``pysqlcipher`` is non-present. .. versionadded:: 1.1.4 - added fallback import for pysqlcipher3 .. versionadded:: 0.9.9 - added pysqlcipher dialect Driver ------ The driver here is the `pysqlcipher <https://pypi.python.org/pypi/pysqlcipher>`_ driver, which makes use of the SQLCipher engine. This system essentially introduces new PRAGMA commands to SQLite which allows the setting of a passphrase and other encryption parameters, allowing the database file to be encrypted. `pysqlcipher3` is a fork of `pysqlcipher` with support for Python 3, the driver is the same. Connect Strings --------------- The format of the connect string is in every way the same as that of the :mod:`~sqlalchemy.dialects.sqlite.pysqlite` driver, except that the "password" field is now accepted, which should contain a passphrase:: e = create_engine('sqlite+pysqlcipher://:testing@/foo.db') For an absolute file path, two leading slashes should be used for the database name:: e = create_engine('sqlite+pysqlcipher://:testing@//path/to/foo.db') A selection of additional encryption-related pragmas supported by SQLCipher as documented at https://www.zetetic.net/sqlcipher/sqlcipher-api/ can be passed in the query string, and will result in that PRAGMA being called for each new connection. Currently, ``cipher``, ``kdf_iter`` ``cipher_page_size`` and ``cipher_use_hmac`` are supported:: e = create_engine('sqlite+pysqlcipher://:testing@/foo.db?cipher=aes-256-cfb&kdf_iter=64000') Pooling Behavior ---------------- The driver makes a change to the default pool behavior of pysqlite as described in :ref:`pysqlite_threading_pooling`. The pysqlcipher driver has been observed to be significantly slower on connection than the pysqlite driver, most likely due to the encryption overhead, so the dialect here defaults to using the :class:`.SingletonThreadPool` implementation, instead of the :class:`.NullPool` pool used by pysqlite. As always, the pool implementation is entirely configurable using the :paramref:`_sa.create_engine.poolclass` parameter; the :class:`.StaticPool` may be more feasible for single-threaded use, or :class:`.NullPool` may be used to prevent unencrypted connections from being held open for long periods of time, at the expense of slower startup time for new connections. """ # noqa from __future__ import absolute_import from .pysqlite import SQLiteDialect_pysqlite from ... import pool from ...engine import url as _url class SQLiteDialect_pysqlcipher(SQLiteDialect_pysqlite): driver = "pysqlcipher" pragmas = ("kdf_iter", "cipher", "cipher_page_size", "cipher_use_hmac") @classmethod def dbapi(cls): try: from pysqlcipher import dbapi2 as sqlcipher except ImportError as e: try: from pysqlcipher3 import dbapi2 as sqlcipher except ImportError: raise e return sqlcipher @classmethod def get_pool_class(cls, url): return pool.SingletonThreadPool def connect(self, *cargs, **cparams): passphrase = cparams.pop("passphrase", "") pragmas = dict((key, cparams.pop(key, None)) for key in self.pragmas) conn = super(SQLiteDialect_pysqlcipher, self).connect( *cargs, **cparams ) conn.execute('pragma key="%s"' % passphrase) for prag, value in pragmas.items(): if value is not None: conn.execute('pragma %s="%s"' % (prag, value)) return conn def create_connect_args(self, url): super_url = _url.URL( url.drivername, username=url.username, host=url.host, database=url.database, query=url.query, ) c_args, opts = super( SQLiteDialect_pysqlcipher, self ).create_connect_args(super_url) opts["passphrase"] = url.password return c_args, opts dialect = SQLiteDialect_pysqlcipher

33.755396

96

0.702472

from __future__ import absolute_import from .pysqlite import SQLiteDialect_pysqlite from ... import pool from ...engine import url as _url class SQLiteDialect_pysqlcipher(SQLiteDialect_pysqlite): driver = "pysqlcipher" pragmas = ("kdf_iter", "cipher", "cipher_page_size", "cipher_use_hmac") @classmethod def dbapi(cls): try: from pysqlcipher import dbapi2 as sqlcipher except ImportError as e: try: from pysqlcipher3 import dbapi2 as sqlcipher except ImportError: raise e return sqlcipher @classmethod def get_pool_class(cls, url): return pool.SingletonThreadPool def connect(self, *cargs, **cparams): passphrase = cparams.pop("passphrase", "") pragmas = dict((key, cparams.pop(key, None)) for key in self.pragmas) conn = super(SQLiteDialect_pysqlcipher, self).connect( *cargs, **cparams ) conn.execute('pragma key="%s"' % passphrase) for prag, value in pragmas.items(): if value is not None: conn.execute('pragma %s="%s"' % (prag, value)) return conn def create_connect_args(self, url): super_url = _url.URL( url.drivername, username=url.username, host=url.host, database=url.database, query=url.query, ) c_args, opts = super( SQLiteDialect_pysqlcipher, self ).create_connect_args(super_url) opts["passphrase"] = url.password return c_args, opts dialect = SQLiteDialect_pysqlcipher

true

1c475e7b96a4c7661d55f944dc305ea0b892c612

2,727

py

Python

facerec_py/facerec/svm.py

idf/FaceReader

d649bf7ca7f9cf66ac99e81a5187cfcc2b54f49d

[ "MIT" ]

7

2015-04-17T02:12:32.000Z

2018-08-08T01:29:24.000Z

facerec_py/facerec/svm.py

idf/FaceReader

d649bf7ca7f9cf66ac99e81a5187cfcc2b54f49d

[ "MIT" ]

null

facerec_py/facerec/svm.py

idf/FaceReader

d649bf7ca7f9cf66ac99e81a5187cfcc2b54f49d

[ "MIT" ]

4

2017-08-26T11:44:20.000Z

2021-06-13T11:50:11.000Z

from facerec_py.facerec.classifier import SVM from facerec_py.facerec.validation import KFoldCrossValidation from facerec_py.facerec.model import PredictableModel from svmutil import * from itertools import product import numpy as np import logging def range_f(begin, end, step): seq = [] while True: if step == 0: break if step > 0 and begin > end: break if step < 0 and begin < end: break seq.append(begin) begin = begin + step return seq def grid(grid_parameters): grid = [] for parameter in grid_parameters: begin, end, step = parameter grid.append(range_f(begin, end, step)) return product(*grid) def grid_search(model, X, y, C_range=(-5, 15, 2), gamma_range=(3, -15, -2), k=5, num_cores=1): if not isinstance(model, PredictableModel): raise TypeError("GridSearch expects a PredictableModel. If you want to perform optimization on raw data use facerec.feature.Identity to pass unpreprocessed data!") if not isinstance(model.classifier, SVM): raise TypeError("GridSearch expects a SVM as classifier. Please use a facerec.classifier.SVM!") logger = logging.getLogger("facerec.svm.gridsearch") logger.info("Performing a Grid Search.") # best parameter combination to return best_parameter = svm_parameter("-q") best_parameter.kernel_type = model.classifier.param.kernel_type best_parameter.nu = model.classifier.param.nu best_parameter.coef0 = model.classifier.param.coef0 # either no gamma given or kernel is linear (only C to optimize) if (gamma_range is None) or (model.classifier.param.kernel_type == LINEAR): gamma_range = (0, 0, 1) # best validation error so far best_accuracy = np.finfo('float').min # create grid (cartesian product of ranges) g = grid([C_range, gamma_range]) results = [] for p in g: C, gamma = p C, gamma = 2**C, 2**gamma model.classifier.param.C, model.classifier.param.gamma = C, gamma # perform a k-fold cross validation cv = KFoldCrossValidation(model=model,k=k) cv.validate(X,y) # append parameter into list with accuracies for all parameter combinations results.append([C, gamma, cv.accuracy]) # store best parameter combination if cv.accuracy > best_accuracy: logger.info("best_accuracy=%s" % (cv.accuracy)) best_accuracy = cv.accuracy best_parameter.C, best_parameter.gamma = C, gamma logger.info("%d-CV Result = %.2f." % (k, cv.accuracy)) # set best parameter combination to best found return best_parameter, results

35.881579

171

0.6641

from facerec_py.facerec.classifier import SVM from facerec_py.facerec.validation import KFoldCrossValidation from facerec_py.facerec.model import PredictableModel from svmutil import * from itertools import product import numpy as np import logging def range_f(begin, end, step): seq = [] while True: if step == 0: break if step > 0 and begin > end: break if step < 0 and begin < end: break seq.append(begin) begin = begin + step return seq def grid(grid_parameters): grid = [] for parameter in grid_parameters: begin, end, step = parameter grid.append(range_f(begin, end, step)) return product(*grid) def grid_search(model, X, y, C_range=(-5, 15, 2), gamma_range=(3, -15, -2), k=5, num_cores=1): if not isinstance(model, PredictableModel): raise TypeError("GridSearch expects a PredictableModel. If you want to perform optimization on raw data use facerec.feature.Identity to pass unpreprocessed data!") if not isinstance(model.classifier, SVM): raise TypeError("GridSearch expects a SVM as classifier. Please use a facerec.classifier.SVM!") logger = logging.getLogger("facerec.svm.gridsearch") logger.info("Performing a Grid Search.") best_parameter = svm_parameter("-q") best_parameter.kernel_type = model.classifier.param.kernel_type best_parameter.nu = model.classifier.param.nu best_parameter.coef0 = model.classifier.param.coef0 if (gamma_range is None) or (model.classifier.param.kernel_type == LINEAR): gamma_range = (0, 0, 1) best_accuracy = np.finfo('float').min g = grid([C_range, gamma_range]) results = [] for p in g: C, gamma = p C, gamma = 2**C, 2**gamma model.classifier.param.C, model.classifier.param.gamma = C, gamma cv = KFoldCrossValidation(model=model,k=k) cv.validate(X,y) results.append([C, gamma, cv.accuracy]) if cv.accuracy > best_accuracy: logger.info("best_accuracy=%s" % (cv.accuracy)) best_accuracy = cv.accuracy best_parameter.C, best_parameter.gamma = C, gamma logger.info("%d-CV Result = %.2f." % (k, cv.accuracy)) return best_parameter, results

true

1c475ea363209a3a683098d4d7dce556761ceb57

7,113

py

Python

app/main.py

ri10073/tracardi-api

828bc0939b3915af4c32906c65769c5b5fd992c3

[ "MIT" ]

null

app/main.py

ri10073/tracardi-api

828bc0939b3915af4c32906c65769c5b5fd992c3

[ "MIT" ]

null

app/main.py

ri10073/tracardi-api

828bc0939b3915af4c32906c65769c5b5fd992c3

[ "MIT" ]

null

import logging import os import asyncio from time import time import elasticsearch from fastapi.middleware.cors import CORSMiddleware from fastapi import FastAPI, Request, Depends from starlette.staticfiles import StaticFiles from app.api import token_endpoint, rule_endpoint, resource_endpoint, event_endpoint, \ profile_endpoint, flow_endpoint, generic_endpoint, project_endpoint, \ credentials_endpoint, segments_endpoint, \ tql_endpoint, health_endpoint, session_endpoint, instance_endpoint, plugins_endpoint, test_endpoint, \ settings_endpoint, \ purchases_endpoint, event_tag_endpoint, consent_type_endpoint from app.api.auth.authentication import get_current_user from app.api.graphql.profile import graphql_profiles from app.api.scheduler import tasks_endpoint from app.api.track import event_server_endpoint from app.config import server from app.setup.on_start import add_plugins, update_api_instance from tracardi.config import tracardi from tracardi.service.storage.elastic_client import ElasticClient from app.setup.indices_setup import create_indices from tracardi.service.storage.index import resources logging.basicConfig(level=logging.ERROR) logger = logging.getLogger('app.main') logger.setLevel(tracardi.logging_level) _local_dir = os.path.dirname(__file__) tags_metadata = [ { "name": "profile", "description": "Manage profiles. Read more about core concepts of TRACARDI in documentation.", "externalDocs": { "description": "Profile external docs", "url": "https://github/atompie/docs/en/docs", }, }, { "name": "resource", "description": "Manage data resources. Read more about core concepts of TRACARDI in documentation.", "externalDocs": { "description": "Resource external docs", "url": "https://github/atompie/docs/en/docs", }, }, { "name": "rule", "description": "Manage flow rule triggers. Read more about core concepts of TRACARDI in documentation.", "externalDocs": { "description": "Rule external docs", "url": "https://github/atompie/docs/en/docs", }, }, { "name": "flow", "description": "Manage flows. Read more about core concepts of TRACARDI in documentation.", "externalDocs": { "description": "Flows external docs", "url": "https://github/atompie/docs/en/docs", }, }, { "name": "event", "description": "Manage events. Read more about core concepts of TRACARDI in documentation.", "externalDocs": { "description": "Events external docs", "url": "https://github/atompie/docs/en/docs", }, }, { "name": "authorization", "description": "OAuth authorization.", }, { "name": "tracker", "description": "Read more about TRACARDI event server in documentation. http://localhost:8686/manual/en/site", "externalDocs": { "description": "External docs", "url": "https://github/atompie/docs/en/docs", }, } ] application = FastAPI( title="Tracardi Customer Data Platform Project", description="TRACARDI open-source customer data platform offers you excellent control over your customer data with its broad set of features", version="0.6.0", openapi_tags=tags_metadata if server.expose_gui_api else None, contact={ "name": "Risto Kowaczewski", "url": "http://github.com/atompie/tracardi", "email": "office@tracardi.com", }, ) application.add_middleware( CORSMiddleware, allow_origins=['*'], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) application.mount("/tracker", StaticFiles( html=True, directory=os.path.join(_local_dir, "tracker")), name="tracker") application.mount("/manual", StaticFiles( html=True, directory=os.path.join(_local_dir, "../manual")), name="manual") application.include_router(event_server_endpoint.router) application.include_router(tql_endpoint.router) application.include_router(segments_endpoint.router) application.include_router(credentials_endpoint.router) application.include_router(project_endpoint.router) application.include_router(resource_endpoint.router) application.include_router(rule_endpoint.router) application.include_router(flow_endpoint.router) application.include_router(event_endpoint.router) application.include_router(profile_endpoint.router) application.include_router(token_endpoint.router) application.include_router(generic_endpoint.router) application.include_router(health_endpoint.router) application.include_router(session_endpoint.router) application.include_router(tasks_endpoint.router) application.include_router(instance_endpoint.router) application.include_router(plugins_endpoint.router) application.include_router(test_endpoint.router) application.include_router(settings_endpoint.router) application.include_router(purchases_endpoint.router) application.include_router(event_tag_endpoint.router) application.include_router(consent_type_endpoint.router) # GraphQL application.include_router(graphql_profiles, prefix="/graphql/profile", # dependencies=[Depends(get_current_user)], tags=["graphql"]) @application.on_event("startup") async def app_starts(): while True: try: if server.reset_plugins is True: es = ElasticClient.instance() index = resources.resources['action'] if await es.exists_index(index.get_write_index()): await es.remove_index(index.get_read_index()) await create_indices() await update_api_instance() if server.update_plugins_on_start_up is not False: await add_plugins() break except elasticsearch.exceptions.ConnectionError: await asyncio.sleep(5) report_i_am_alive() logger.info("START UP exits.") @application.middleware("http") async def add_process_time_header(request: Request, call_next): start_time = time() if server.make_slower_responses > 0: await asyncio.sleep(server.make_slower_responses) response = await call_next(request) process_time = time() - start_time response.headers["X-Process-Time"] = str(process_time) return response @application.on_event("shutdown") async def app_shutdown(): elastic = ElasticClient.instance() await elastic.close() def report_i_am_alive(): async def heartbeat(): while True: await asyncio.sleep(server.heartbeat_every) await update_api_instance() asyncio.create_task(heartbeat()) if __name__ == "__main__": import uvicorn uvicorn.run("app.main:application", host="0.0.0.0", port=8686, log_level="info")

34.529126

146

0.685505

import logging import os import asyncio from time import time import elasticsearch from fastapi.middleware.cors import CORSMiddleware from fastapi import FastAPI, Request, Depends from starlette.staticfiles import StaticFiles from app.api import token_endpoint, rule_endpoint, resource_endpoint, event_endpoint, \ profile_endpoint, flow_endpoint, generic_endpoint, project_endpoint, \ credentials_endpoint, segments_endpoint, \ tql_endpoint, health_endpoint, session_endpoint, instance_endpoint, plugins_endpoint, test_endpoint, \ settings_endpoint, \ purchases_endpoint, event_tag_endpoint, consent_type_endpoint from app.api.auth.authentication import get_current_user from app.api.graphql.profile import graphql_profiles from app.api.scheduler import tasks_endpoint from app.api.track import event_server_endpoint from app.config import server from app.setup.on_start import add_plugins, update_api_instance from tracardi.config import tracardi from tracardi.service.storage.elastic_client import ElasticClient from app.setup.indices_setup import create_indices from tracardi.service.storage.index import resources logging.basicConfig(level=logging.ERROR) logger = logging.getLogger('app.main') logger.setLevel(tracardi.logging_level) _local_dir = os.path.dirname(__file__) tags_metadata = [ { "name": "profile", "description": "Manage profiles. Read more about core concepts of TRACARDI in documentation.", "externalDocs": { "description": "Profile external docs", "url": "https://github/atompie/docs/en/docs", }, }, { "name": "resource", "description": "Manage data resources. Read more about core concepts of TRACARDI in documentation.", "externalDocs": { "description": "Resource external docs", "url": "https://github/atompie/docs/en/docs", }, }, { "name": "rule", "description": "Manage flow rule triggers. Read more about core concepts of TRACARDI in documentation.", "externalDocs": { "description": "Rule external docs", "url": "https://github/atompie/docs/en/docs", }, }, { "name": "flow", "description": "Manage flows. Read more about core concepts of TRACARDI in documentation.", "externalDocs": { "description": "Flows external docs", "url": "https://github/atompie/docs/en/docs", }, }, { "name": "event", "description": "Manage events. Read more about core concepts of TRACARDI in documentation.", "externalDocs": { "description": "Events external docs", "url": "https://github/atompie/docs/en/docs", }, }, { "name": "authorization", "description": "OAuth authorization.", }, { "name": "tracker", "description": "Read more about TRACARDI event server in documentation. http://localhost:8686/manual/en/site", "externalDocs": { "description": "External docs", "url": "https://github/atompie/docs/en/docs", }, } ] application = FastAPI( title="Tracardi Customer Data Platform Project", description="TRACARDI open-source customer data platform offers you excellent control over your customer data with its broad set of features", version="0.6.0", openapi_tags=tags_metadata if server.expose_gui_api else None, contact={ "name": "Risto Kowaczewski", "url": "http://github.com/atompie/tracardi", "email": "office@tracardi.com", }, ) application.add_middleware( CORSMiddleware, allow_origins=['*'], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) application.mount("/tracker", StaticFiles( html=True, directory=os.path.join(_local_dir, "tracker")), name="tracker") application.mount("/manual", StaticFiles( html=True, directory=os.path.join(_local_dir, "../manual")), name="manual") application.include_router(event_server_endpoint.router) application.include_router(tql_endpoint.router) application.include_router(segments_endpoint.router) application.include_router(credentials_endpoint.router) application.include_router(project_endpoint.router) application.include_router(resource_endpoint.router) application.include_router(rule_endpoint.router) application.include_router(flow_endpoint.router) application.include_router(event_endpoint.router) application.include_router(profile_endpoint.router) application.include_router(token_endpoint.router) application.include_router(generic_endpoint.router) application.include_router(health_endpoint.router) application.include_router(session_endpoint.router) application.include_router(tasks_endpoint.router) application.include_router(instance_endpoint.router) application.include_router(plugins_endpoint.router) application.include_router(test_endpoint.router) application.include_router(settings_endpoint.router) application.include_router(purchases_endpoint.router) application.include_router(event_tag_endpoint.router) application.include_router(consent_type_endpoint.router) application.include_router(graphql_profiles, prefix="/graphql/profile", tags=["graphql"]) @application.on_event("startup") async def app_starts(): while True: try: if server.reset_plugins is True: es = ElasticClient.instance() index = resources.resources['action'] if await es.exists_index(index.get_write_index()): await es.remove_index(index.get_read_index()) await create_indices() await update_api_instance() if server.update_plugins_on_start_up is not False: await add_plugins() break except elasticsearch.exceptions.ConnectionError: await asyncio.sleep(5) report_i_am_alive() logger.info("START UP exits.") @application.middleware("http") async def add_process_time_header(request: Request, call_next): start_time = time() if server.make_slower_responses > 0: await asyncio.sleep(server.make_slower_responses) response = await call_next(request) process_time = time() - start_time response.headers["X-Process-Time"] = str(process_time) return response @application.on_event("shutdown") async def app_shutdown(): elastic = ElasticClient.instance() await elastic.close() def report_i_am_alive(): async def heartbeat(): while True: await asyncio.sleep(server.heartbeat_every) await update_api_instance() asyncio.create_task(heartbeat()) if __name__ == "__main__": import uvicorn uvicorn.run("app.main:application", host="0.0.0.0", port=8686, log_level="info")

true

1c475ed89de55cb2f813d13f5130ed38d968d27a

3,572

py

Python

bindings/python/ensmallen/datasets/string/sulfurospirillumhalorespiransdsm13726.py

AnacletoLAB/ensmallen_graph

b2c1b18fb1e5801712852bcc239f239e03076f09

[ "MIT" ]

5

2021-02-17T00:44:45.000Z

2021-08-09T16:41:47.000Z

bindings/python/ensmallen/datasets/string/sulfurospirillumhalorespiransdsm13726.py

AnacletoLAB/ensmallen_graph

b2c1b18fb1e5801712852bcc239f239e03076f09

[ "MIT" ]

18

2021-01-07T16:47:39.000Z

2021-08-12T21:51:32.000Z

bindings/python/ensmallen/datasets/string/sulfurospirillumhalorespiransdsm13726.py

AnacletoLAB/ensmallen

b2c1b18fb1e5801712852bcc239f239e03076f09

[ "MIT" ]

3

2021-01-14T02:20:59.000Z

2021-08-04T19:09:52.000Z

""" This file offers the methods to automatically retrieve the graph Sulfurospirillum halorespirans DSM 13726. The graph is automatically retrieved from the STRING repository. References --------------------- Please cite the following if you use the data: ```bib @article{szklarczyk2019string, title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets}, author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others}, journal={Nucleic acids research}, volume={47}, number={D1}, pages={D607--D613}, year={2019}, publisher={Oxford University Press} } ``` """ from typing import Dict from ..automatic_graph_retrieval import AutomaticallyRetrievedGraph from ...ensmallen import Graph # pylint: disable=import-error def SulfurospirillumHalorespiransDsm13726( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = "graphs/string", version: str = "links.v11.5", **additional_graph_kwargs: Dict ) -> Graph: """Return new instance of the Sulfurospirillum halorespirans DSM 13726 graph. The graph is automatically retrieved from the STRING repository. Parameters ------------------- directed: bool = False Wether to load the graph as directed or undirected. By default false. preprocess: bool = True Whether to preprocess the graph to be loaded in optimal time and memory. load_nodes: bool = True, Whether to load the nodes vocabulary or treat the nodes simply as a numeric range. verbose: int = 2, Wether to show loading bars during the retrieval and building of the graph. cache: bool = True Whether to use cache, i.e. download files only once and preprocess them only once. cache_path: str = "graphs" Where to store the downloaded graphs. version: str = "links.v11.5" The version of the graph to retrieve. The available versions are: - homology.v11.5 - physical.links.v11.5 - links.v11.5 additional_graph_kwargs: Dict Additional graph kwargs. Returns ----------------------- Instace of Sulfurospirillum halorespirans DSM 13726 graph. References --------------------- Please cite the following if you use the data: ```bib @article{szklarczyk2019string, title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets}, author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others}, journal={Nucleic acids research}, volume={47}, number={D1}, pages={D607--D613}, year={2019}, publisher={Oxford University Press} } ``` """ return AutomaticallyRetrievedGraph( graph_name="SulfurospirillumHalorespiransDsm13726", repository="string", version=version, directed=directed, preprocess=preprocess, load_nodes=load_nodes, verbose=verbose, cache=cache, cache_path=cache_path, additional_graph_kwargs=additional_graph_kwargs )()

34.019048

223

0.68505

from typing import Dict from ..automatic_graph_retrieval import AutomaticallyRetrievedGraph from ...ensmallen import Graph def SulfurospirillumHalorespiransDsm13726( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = "graphs/string", version: str = "links.v11.5", **additional_graph_kwargs: Dict ) -> Graph: return AutomaticallyRetrievedGraph( graph_name="SulfurospirillumHalorespiransDsm13726", repository="string", version=version, directed=directed, preprocess=preprocess, load_nodes=load_nodes, verbose=verbose, cache=cache, cache_path=cache_path, additional_graph_kwargs=additional_graph_kwargs )()

true

1c475eea3e539ba4d1a9a72d6264384d25b277e3

204

py

Python

book/recursion/base_conversion.py

Web-Dev-Collaborative/algos

d280581d74ded382094283d931a202eb55fd8369

[ "CC0-1.0" ]

153

2015-12-24T00:32:23.000Z

2022-02-24T06:00:29.000Z

book/recursion/base_conversion.py

Web-Dev-Collaborative/algos

d280581d74ded382094283d931a202eb55fd8369

[ "CC0-1.0" ]

78

2015-11-17T11:46:15.000Z

2021-06-28T18:37:58.000Z

book/recursion/base_conversion.py

rhivent/algo-books-python

c4fa29616ca9a8a15ba40fa12d21fd8f35096d40

[ "CC0-1.0" ]

66

2015-11-02T03:38:02.000Z

2022-03-05T17:36:26.000Z

CHAR_FOR_INT = '0123456789abcdef' def to_string(n, base): if n < base: return CHAR_FOR_INT[n] return to_string(n // base, base) + CHAR_FOR_INT[n % base] to_string(1453, 16) # => 5Ad

17

62

0.637255

CHAR_FOR_INT = '0123456789abcdef' def to_string(n, base): if n < base: return CHAR_FOR_INT[n] return to_string(n // base, base) + CHAR_FOR_INT[n % base] to_string(1453, 16)

true

1c475efe695ee9d1a051a1330fe3636e05ac3b4c

579

py

Python

setup.py

tijko/shadow

8ba9a8c2de2be51fa4eb387a179dbc0ac4641575

[ "MIT" ]

null

setup.py

tijko/shadow

8ba9a8c2de2be51fa4eb387a179dbc0ac4641575

[ "MIT" ]

null

setup.py

tijko/shadow

8ba9a8c2de2be51fa4eb387a179dbc0ac4641575

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- try: from setuptools import setup, Extension, find_packages except ImportError: from distutils.core import setup, Extension setup( name = 'shadow', version = '0.0.1', author='Tim Konick', author_email='konick781@gmail.com', url='', description='Provides auxillary data on processes', long_description=open('README.md').read(), license=open('LICENSE').read(), packages=['shadow', 'shadow.taskstats'], ext_modules=[Extension('libshadow', sources=['shadow/libshadow/libshadow.c'])] )

26.318182

82

0.670121

try: from setuptools import setup, Extension, find_packages except ImportError: from distutils.core import setup, Extension setup( name = 'shadow', version = '0.0.1', author='Tim Konick', author_email='konick781@gmail.com', url='', description='Provides auxillary data on processes', long_description=open('README.md').read(), license=open('LICENSE').read(), packages=['shadow', 'shadow.taskstats'], ext_modules=[Extension('libshadow', sources=['shadow/libshadow/libshadow.c'])] )

true

1c475f7fce5478d597fc5b92d7692cf01e58b4c5

1,559

py

Python

thenewboston_node/business_logic/models/signed_change_request/base.py

nishp77/thenewboston-node

158b1f1739b2c6c9c21c80e9da854ca141f1cf8f

[ "MIT" ]

null

thenewboston_node/business_logic/models/signed_change_request/base.py

nishp77/thenewboston-node

158b1f1739b2c6c9c21c80e9da854ca141f1cf8f

[ "MIT" ]

null

thenewboston_node/business_logic/models/signed_change_request/base.py

nishp77/thenewboston-node

158b1f1739b2c6c9c21c80e9da854ca141f1cf8f

[ "MIT" ]

null

import copy import logging from dataclasses import dataclass from typing import ClassVar, Type, TypeVar from thenewboston_node.business_logic.models.base import BaseDataclass from thenewboston_node.core.logging import validates from thenewboston_node.core.utils.cryptography import derive_public_key from thenewboston_node.core.utils.dataclass import cover_docstring, revert_docstring from thenewboston_node.core.utils.types import hexstr from ..mixins.signable import SignableMixin from ..signed_change_request_message import SignedChangeRequestMessage T = TypeVar('T', bound='SignedChangeRequest') logger = logging.getLogger(__name__) @revert_docstring @dataclass @cover_docstring class SignedChangeRequest(SignableMixin, BaseDataclass): block_type: ClassVar[str] message: SignedChangeRequestMessage @classmethod def create_from_signed_change_request_message( cls: Type[T], message: SignedChangeRequestMessage, signing_key: hexstr ) -> T: request = cls(signer=derive_public_key(signing_key), message=copy.deepcopy(message)) request.sign(signing_key) return request @validates('signed request') def validate(self, blockchain, block_number: int): self.validate_message() with validates('block signature'): self.validate_signature() @validates('signed request message') def validate_message(self): self.message.validate() def get_updated_account_states(self, blockchain): raise NotImplementedError('Must be implemented in subclass')

32.479167

92

0.77678

import copy import logging from dataclasses import dataclass from typing import ClassVar, Type, TypeVar from thenewboston_node.business_logic.models.base import BaseDataclass from thenewboston_node.core.logging import validates from thenewboston_node.core.utils.cryptography import derive_public_key from thenewboston_node.core.utils.dataclass import cover_docstring, revert_docstring from thenewboston_node.core.utils.types import hexstr from ..mixins.signable import SignableMixin from ..signed_change_request_message import SignedChangeRequestMessage T = TypeVar('T', bound='SignedChangeRequest') logger = logging.getLogger(__name__) @revert_docstring @dataclass @cover_docstring class SignedChangeRequest(SignableMixin, BaseDataclass): block_type: ClassVar[str] message: SignedChangeRequestMessage @classmethod def create_from_signed_change_request_message( cls: Type[T], message: SignedChangeRequestMessage, signing_key: hexstr ) -> T: request = cls(signer=derive_public_key(signing_key), message=copy.deepcopy(message)) request.sign(signing_key) return request @validates('signed request') def validate(self, blockchain, block_number: int): self.validate_message() with validates('block signature'): self.validate_signature() @validates('signed request message') def validate_message(self): self.message.validate() def get_updated_account_states(self, blockchain): raise NotImplementedError('Must be implemented in subclass')

true

1c475f9553b3a997c5e9fa81cedd6cc86997d3a6

4,621

py

Python

ProjectFiles/UMKCEntrepreneurialLegalServicesClinicDocuments/IntakeForm.py

KCLegalHackers/2016-Coding-For-Lawyers

0e7aeaf3b446defcfa60c862dfac5627cedd1560

[ "MIT" ]

1

2021-01-15T00:34:54.000Z

ProjectFiles/UMKCEntrepreneurialLegalServicesClinicDocuments/IntakeForm.py

KCLegalHackers/2016-Coding-For-Lawyers

0e7aeaf3b446defcfa60c862dfac5627cedd1560

[ "MIT" ]

null

ProjectFiles/UMKCEntrepreneurialLegalServicesClinicDocuments/IntakeForm.py

KCLegalHackers/2016-Coding-For-Lawyers

0e7aeaf3b446defcfa60c862dfac5627cedd1560

[ "MIT" ]

null

print('Application for Services: To be considered for acceptance as a client, you must complete this form and return it to the Entrepreneurial Legal Services Clinic. Acceptance as a client of the UMKC Entrepreneurial Legal Services Clinic is not guaranteed, and is ultimately based upon available of resources and time to provide services, absence of conflicts of interest, financial need of the client, and educational value for our students. What is your full name?') clientName = input() print('What is the date? (dd/mm/yyyy)') date = input() print('What is the name of the entity?') companyName = input() print('What is your mailing address') clientAddress = input() print('What city do you live in?') clientCity = input() print('What state do you live in?') clientState = input() print('What zip code do you live in?') clientZip = input() print('What is your telephone number?') clientPhone = input() print('What is your email address? By providing your email address you are giving the Entrepreneurial Legal Services Clinic express permission to contact you via email with matters regarding your business and to contact you regarding other information that may be of interest to you. If you do not want the Entrepreneurial Legal Services Clinic type N/A') clientEmail = input() print('Applicants for services are hereby notified that the University of Missouri-Kansas City and the Entrepreneurial Legal Services Clinic do not discriminate on the basis of race, color, creed, sex, sexual orientation, age, national origin, disability or Vietnam era veterans status in admission or access to, or treatment or employment in, its programs and activities. Financial Information (required for means testing): What is your total expected income for this year?') expectedAnnualIncome = input() print('What was your total expected income for last year?') pastAnnualIncome = input() print('How much available capital do you have to spend for your entity?') availableCapital = input() print('Are you currently employed?') clientEmployment = input() if str(clientEmployment) == 'yes': #I'm not sure if these next few lines are properly formatted print('If so, where?') employmentLocation = input() else: print('Demographic Information. This information is collected for demographic purposes only; it is anonymous and does not affect your acceptance as a client. What is your race?') print('Demographic Information. This information is collected for demographic purposes only; it is anonymous and does not affect your acceptance as a client. What is your race?') clientRace = input() print('What is your gender?') clientGender = input() print('What is your marital status?') clientMaritalStatus = input() print('What is your highest level of education?') clientEducation = input() print('Required for conflicts check List any person or company, if any, who may have a claim against you or your business. If none, type n/a') clientClaimants = input() print('Are you currently a student at the University of Missouri at Kansas City or any other U- System campus?') clientUMKCStudent = input() print('Do you currently have or expect to have any contracts, employment, or other business relationship with the University of Missouri-Kansas City or any other campus, office or operation of the University of Missouri System?') clientUMKCContracts = input() print('Briefly state your legal question or problem/type of legal advice sought. If unsure, type n/a') legalAdivceSought = input() print('Please list any deadlines under which you are operating (court dates, etc. if any). If none, type n/a') clientDeadlines = input() print('I hereby state the above information is true to the best of my knowledge, and give permission to the Entrepreneurial Legal Services Clinic to check for potential conflicts of interests between myself and affiliates, and with current and former clients of the clinic, clients of firms at which students may be working, UMKC ,and the University of Missouri. I further confirm that I understand that work in the Entrepreneurial Legal Services Clinic is performed by law students under the supervision of licensed attorneys and therefore I may experience a delay due to the work being completed by said students. Type your signature in the following box to confirm that you are comfortable with the preceding obligations.') clientSignature = input() print('Type the date in the following box to confirm that you are comfortable with the preceding obligations') clientDate = input() # [Client Intake Form](http://www1.law.umkc.edu/clinics/els/application.pdf)

78.322034

726

0.781649

print('Application for Services: To be considered for acceptance as a client, you must complete this form and return it to the Entrepreneurial Legal Services Clinic. Acceptance as a client of the UMKC Entrepreneurial Legal Services Clinic is not guaranteed, and is ultimately based upon available of resources and time to provide services, absence of conflicts of interest, financial need of the client, and educational value for our students. What is your full name?') clientName = input() print('What is the date? (dd/mm/yyyy)') date = input() print('What is the name of the entity?') companyName = input() print('What is your mailing address') clientAddress = input() print('What city do you live in?') clientCity = input() print('What state do you live in?') clientState = input() print('What zip code do you live in?') clientZip = input() print('What is your telephone number?') clientPhone = input() print('What is your email address? By providing your email address you are giving the Entrepreneurial Legal Services Clinic express permission to contact you via email with matters regarding your business and to contact you regarding other information that may be of interest to you. If you do not want the Entrepreneurial Legal Services Clinic type N/A') clientEmail = input() print('Applicants for services are hereby notified that the University of Missouri-Kansas City and the Entrepreneurial Legal Services Clinic do not discriminate on the basis of race, color, creed, sex, sexual orientation, age, national origin, disability or Vietnam era veterans status in admission or access to, or treatment or employment in, its programs and activities. Financial Information (required for means testing): What is your total expected income for this year?') expectedAnnualIncome = input() print('What was your total expected income for last year?') pastAnnualIncome = input() print('How much available capital do you have to spend for your entity?') availableCapital = input() print('Are you currently employed?') clientEmployment = input() if str(clientEmployment) == 'yes': print('If so, where?') employmentLocation = input() else: print('Demographic Information. This information is collected for demographic purposes only; it is anonymous and does not affect your acceptance as a client. What is your race?') print('Demographic Information. This information is collected for demographic purposes only; it is anonymous and does not affect your acceptance as a client. What is your race?') clientRace = input() print('What is your gender?') clientGender = input() print('What is your marital status?') clientMaritalStatus = input() print('What is your highest level of education?') clientEducation = input() print('Required for conflicts check List any person or company, if any, who may have a claim against you or your business. If none, type n/a') clientClaimants = input() print('Are you currently a student at the University of Missouri at Kansas City or any other U- System campus?') clientUMKCStudent = input() print('Do you currently have or expect to have any contracts, employment, or other business relationship with the University of Missouri-Kansas City or any other campus, office or operation of the University of Missouri System?') clientUMKCContracts = input() print('Briefly state your legal question or problem/type of legal advice sought. If unsure, type n/a') legalAdivceSought = input() print('Please list any deadlines under which you are operating (court dates, etc. if any). If none, type n/a') clientDeadlines = input() print('I hereby state the above information is true to the best of my knowledge, and give permission to the Entrepreneurial Legal Services Clinic to check for potential conflicts of interests between myself and affiliates, and with current and former clients of the clinic, clients of firms at which students may be working, UMKC ,and the University of Missouri. I further confirm that I understand that work in the Entrepreneurial Legal Services Clinic is performed by law students under the supervision of licensed attorneys and therefore I may experience a delay due to the work being completed by said students. Type your signature in the following box to confirm that you are comfortable with the preceding obligations.') clientSignature = input() print('Type the date in the following box to confirm that you are comfortable with the preceding obligations') clientDate = input() # [Client Intake Form](http://www1.law.umkc.edu/clinics/els/application.pdf)

true

1c475fd0731889687d14b2130b367eb0ec6cbbcf

1,749

py

Python

setup.py

gaussian/django-sql-explorer

844c8f59f8a3de31ef445e18356e97afded50dfc

[ "MIT" ]

null

setup.py

gaussian/django-sql-explorer

844c8f59f8a3de31ef445e18356e97afded50dfc

[ "MIT" ]

null

setup.py

gaussian/django-sql-explorer

844c8f59f8a3de31ef445e18356e97afded50dfc

[ "MIT" ]

null

import os from setuptools import setup from explorer import __version__ # Utility function to read the README file. # Used for the long_description. It's nice, because now 1) we have a top level # README file and 2) it's easier to type in the README file than to put a raw # string in below ... def read(fname): return open(os.path.join(os.path.dirname(__file__), fname)).read() setup( name="django-sql-explorer", version=__version__, author="Chris Clark", author_email="chris@untrod.com", description=("A pluggable app that allows users (admins) to execute SQL," " view, and export the results."), license="MIT", keywords="django sql explorer reports reporting csv database query", url="https://github.com/groveco/django-sql-explorer", packages=['explorer'], long_description=read('README.rst'), classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Topic :: Utilities', 'Framework :: Django :: 1.10', 'Framework :: Django :: 1.11', 'Framework :: Django :: 2.0', 'Framework :: Django :: 2.1', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', ], install_requires=[ 'Django>=2.2.14', 'sqlparse>=0.1.18', 'unicodecsv>=0.14.1', 'six>=1.10.0', ], include_package_data=True, zip_safe=False, )

33.634615

79

0.612922

import os from setuptools import setup from explorer import __version__ # README file and 2) it's easier to type in the README file than to put a raw def read(fname): return open(os.path.join(os.path.dirname(__file__), fname)).read() setup( name="django-sql-explorer", version=__version__, author="Chris Clark", author_email="chris@untrod.com", description=("A pluggable app that allows users (admins) to execute SQL," " view, and export the results."), license="MIT", keywords="django sql explorer reports reporting csv database query", url="https://github.com/groveco/django-sql-explorer", packages=['explorer'], long_description=read('README.rst'), classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Topic :: Utilities', 'Framework :: Django :: 1.10', 'Framework :: Django :: 1.11', 'Framework :: Django :: 2.0', 'Framework :: Django :: 2.1', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', ], install_requires=[ 'Django>=2.2.14', 'sqlparse>=0.1.18', 'unicodecsv>=0.14.1', 'six>=1.10.0', ], include_package_data=True, zip_safe=False, )

true

1c4760d27cf1f4616f2f9ae082e15fd487249b5e

3,074

py

Python

tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/keras_evaluation_test.py

andrewyguo/privacy

a33afde0c105ece6c48b17a80f13899cf3e7c1b3

[ "Apache-2.0" ]

null

tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/keras_evaluation_test.py

andrewyguo/privacy

a33afde0c105ece6c48b17a80f13899cf3e7c1b3

[ "Apache-2.0" ]

null

tensorflow_privacy/privacy/privacy_tests/membership_inference_attack/keras_evaluation_test.py

andrewyguo/privacy

a33afde0c105ece6c48b17a80f13899cf3e7c1b3

[ "Apache-2.0" ]

null

# Copyright 2020, The TensorFlow 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. from absl.testing import absltest import numpy as np import tensorflow as tf from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import keras_evaluation from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResults from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import get_flattened_attack_metrics class UtilsTest(absltest.TestCase): def __init__(self, methodname): """Initialize the test class.""" super().__init__(methodname) self.ntrain, self.ntest = 50, 100 self.nclass = 5 self.ndim = 10 # Generate random training and test data self.train_data = np.random.rand(self.ntrain, self.ndim) self.test_data = np.random.rand(self.ntest, self.ndim) self.train_labels = np.random.randint(self.nclass, size=self.ntrain) self.test_labels = np.random.randint(self.nclass, size=self.ntest) self.model = tf.keras.Sequential([tf.keras.layers.Dense(self.nclass)]) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) self.model.compile(optimizer='Adam', loss=loss, metrics=['accuracy']) def test_calculate_losses(self): """Test calculating the loss.""" pred, loss = keras_evaluation.calculate_losses(self.model, self.train_data, self.train_labels) self.assertEqual(pred.shape, (self.ntrain, self.nclass)) self.assertEqual(loss.shape, (self.ntrain,)) pred, loss = keras_evaluation.calculate_losses(self.model, self.test_data, self.test_labels) self.assertEqual(pred.shape, (self.ntest, self.nclass)) self.assertEqual(loss.shape, (self.ntest,)) def test_run_attack_on_keras_model(self): """Test the attack.""" results = keras_evaluation.run_attack_on_keras_model( self.model, (self.train_data, self.train_labels), (self.test_data, self.test_labels), attack_types=[AttackType.THRESHOLD_ATTACK]) self.assertIsInstance(results, AttackResults) att_types, att_slices, att_metrics, att_values = get_flattened_attack_metrics( results) self.assertLen(att_types, 2) self.assertLen(att_slices, 2) self.assertLen(att_metrics, 2) self.assertLen(att_values, 2) if __name__ == '__main__': absltest.main()

41.540541

125

0.737801

from absl.testing import absltest import numpy as np import tensorflow as tf from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import keras_evaluation from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResults from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import get_flattened_attack_metrics class UtilsTest(absltest.TestCase): def __init__(self, methodname): super().__init__(methodname) self.ntrain, self.ntest = 50, 100 self.nclass = 5 self.ndim = 10 self.train_data = np.random.rand(self.ntrain, self.ndim) self.test_data = np.random.rand(self.ntest, self.ndim) self.train_labels = np.random.randint(self.nclass, size=self.ntrain) self.test_labels = np.random.randint(self.nclass, size=self.ntest) self.model = tf.keras.Sequential([tf.keras.layers.Dense(self.nclass)]) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) self.model.compile(optimizer='Adam', loss=loss, metrics=['accuracy']) def test_calculate_losses(self): pred, loss = keras_evaluation.calculate_losses(self.model, self.train_data, self.train_labels) self.assertEqual(pred.shape, (self.ntrain, self.nclass)) self.assertEqual(loss.shape, (self.ntrain,)) pred, loss = keras_evaluation.calculate_losses(self.model, self.test_data, self.test_labels) self.assertEqual(pred.shape, (self.ntest, self.nclass)) self.assertEqual(loss.shape, (self.ntest,)) def test_run_attack_on_keras_model(self): results = keras_evaluation.run_attack_on_keras_model( self.model, (self.train_data, self.train_labels), (self.test_data, self.test_labels), attack_types=[AttackType.THRESHOLD_ATTACK]) self.assertIsInstance(results, AttackResults) att_types, att_slices, att_metrics, att_values = get_flattened_attack_metrics( results) self.assertLen(att_types, 2) self.assertLen(att_slices, 2) self.assertLen(att_metrics, 2) self.assertLen(att_values, 2) if __name__ == '__main__': absltest.main()

true

1c47629a3fff6341d9f92bd348f85e77bc92bff9

282

py

Python

html_downloader.py

etworker/TinySpider

b3e3c67451d361d064d915875582341b84f0d49d

[ "MIT" ]

null

html_downloader.py

etworker/TinySpider

b3e3c67451d361d064d915875582341b84f0d49d

[ "MIT" ]

null

html_downloader.py

etworker/TinySpider

b3e3c67451d361d064d915875582341b84f0d49d

[ "MIT" ]

null

__author__ = 'worker' import urllib2 class HtmlDownloader(object): def download(self, url): if url is None: return None response = urllib2.urlopen(url) if response.getcode() != 200: return None return response.read()

20.142857

39

0.588652

__author__ = 'worker' import urllib2 class HtmlDownloader(object): def download(self, url): if url is None: return None response = urllib2.urlopen(url) if response.getcode() != 200: return None return response.read()

true

1c4762e3f34e2ed7a22ada6411f795fe540463d8

18,315

py

Python

pfp/native/compat_io.py

krx/pfp-construct

248c43781e15ba6eb0a9a6c0982a40c0e380d9b6

[ "MIT" ]

null

pfp/native/compat_io.py

krx/pfp-construct

248c43781e15ba6eb0a9a6c0982a40c0e380d9b6

[ "MIT" ]

null

pfp/native/compat_io.py

krx/pfp-construct

248c43781e15ba6eb0a9a6c0982a40c0e380d9b6

[ "MIT" ]

null

#!/usr/bin/env python # encoding: utf-8 """ This module of native functions is implemented for compatability with 010 editor functions. Some of these functions are nops, some are fully implemented. """ from pytest import skip import six import sys from pfp.native import native import pfp.interp import pfp.errors as errors import pfp.bitwrap as bitwrap from .. import utils import construct as C # http://www.sweetscape.com/010editor/manual/FuncIO.htm # void BigEndian() @native(name="BigEndian", ret=None) def BigEndian(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) pfp.interp.Endian.current = pfp.interp.Endian.BIG # void BitfieldDisablePadding() @native(name="BitfieldDisablePadding", ret=None, send_interp=True) def BitfieldDisablePadding(params, ctxt, scope, stream, coord, interp): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) interp.set_bitfield_padded(False) # void BitfieldEnablePadding() @native(name="BitfieldEnablePadding", ret=None, send_interp=True) def BitfieldEnablePadding(params, ctxt, scope, stream, coord, interp): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) interp.set_bitfield_padded(True) # void BitfieldLeftToRight() @native(name="BitfieldLeftToRight", ret=None, send_interp=True) def BitfieldLeftToRight(params, ctxt, scope, stream, coord, interp): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) interp.set_bitfield_direction(interp.BITFIELD_DIR_LEFT_RIGHT) # void BitfieldRightToLeft() @native(name="BitfieldRightToLeft", ret=None, send_interp=True) def BitfieldRightToLeft(params, ctxt, scope, stream, coord, interp): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) interp.set_bitfield_direction(interp.BITFIELD_DIR_RIGHT_LEFT) # double ConvertBytesToDouble( uchar byteArray[] ) @native(name="ConvertBytesToDouble", ret=C.Double) def ConvertBytesToDouble(params, ctxt, scope, stream, coord): raise NotImplementedError() # float ConvertBytesToFloat( uchar byteArray[] ) @native(name="ConvertBytesToFloat", ret=C.Single) def ConvertBytesToFloat(params, ctxt, scope, stream, coord): raise NotImplementedError() # hfloat ConvertBytesToHFloat( uchar byteArray[] ) @native(name="ConvertBytesToHFloat", ret=C.Single) def ConvertBytesToHFloat(params, ctxt, scope, stream, coord): raise NotImplementedError() # int ConvertDataToBytes( data_type value, uchar byteArray[] ) @native(name="ConvertDataToBytes", ret=C.Int) def ConvertDataToBytes(params, ctxt, scope, stream, coord): raise NotImplementedError() # void DeleteBytes( int64 start, int64 size ) @native(name="DeleteBytes", ret=None) def DeleteBytes(params, ctxt, scope, stream, coord): raise NotImplementedError() # int DirectoryExists( string dir ) @native(name="DirectoryExists", ret=C.Int) def DirectoryExists(params, ctxt, scope, stream, coord): raise NotImplementedError() # int FEof() @native(name="FEof", ret=bool) def FEof(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) # now that streams are _ALL_ BitwrappedStreams, we can use BitwrappedStream-specific # functions return C.stream_iseof(ctxt._io) # int64 FileSize() @native(name="FileSize", ret=int) def FileSize(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) return ctxt._io.size() # TFileList FindFiles( string dir, string filter ) @native(name="FindFiles", ret=None) def FindFiles(params, ctxt, scope, stream, coord): raise NotImplementedError() # int FPrintf( int fileNum, char format[], ... ) @native(name="FPrintf", ret=C.Int) def FPrintf(params, ctxt, scope, stream, coord): raise NotImplementedError() # int FSeek( int64 pos ) @native(name="FSeek", ret=int) def FSeek(params, ctxt, scope, stream, coord): """Returns 0 if successful or -1 if the address is out of range """ if len(params) != 1: raise errors.InvalidArguments( coord, "{} args".format(len(params)), "FSeek accepts only one argument", ) pos = utils.evaluate(params[0], ctxt) if pos > ctxt._io.size(): return -1 C.stream_seek(ctxt._io, pos, 0, "") return 0 # curr_pos = stream.tell() # fsize = stream.size() # if pos > fsize: # stream.seek(fsize) # return -1 # elif pos < 0: # stream.seek(0) # return -1 # diff = pos - curr_pos # if diff < 0: # stream.seek(pos) # return 0 # data = stream.read(diff) # # let the ctxt automatically append numbers, as needed, unless the previous # # child was also a skipped field # skipped_name = "_skipped" # if len(ctxt._pfp__children) > 0 and ctxt._pfp__children[ # -1 # ]._pfp__name.startswith("_skipped"): # old_name = ctxt._pfp__children[-1]._pfp__name # data = ctxt._pfp__children[-1].raw_data + data # skipped_name = old_name # ctxt._pfp__children = ctxt._pfp__children[:-1] # del ctxt._pfp__children_map[old_name] # tmp_stream = bitwrap.BitwrappedStream(six.BytesIO(data)) # new_field = pfp.fields.Array(len(data), C.Byte, tmp_stream) # ctxt._pfp__add_child(skipped_name, new_field, stream) # scope.add_var(skipped_name, new_field) # return 0 # int FSkip( int64 offset ) @native(name="FSkip", ret=int) def FSkip(params, ctxt, scope, stream, coord): """Returns 0 if successful or -1 if the address is out of range """ if len(params) != 1: raise errors.InvalidArguments( coord, "{} args".format(len(params)), "FSkip accepts only one argument", ) skip_amt = params[0] while callable(skip_amt): skip_amt = skip_amt(ctxt) return C.stream_seek(ctxt._io, skip_amt, whence=1, path="") # pos = skip_amt + stream.tell() # return FSeek([pos], ctxt, scope, stream, coord) # int64 FTell() @native(name="FTell", ret=int) def FTell(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) # print() return C.stream_tell(ctxt._io, None) # void InsertBytes( int64 start, int64 size, uchar value=0 ) @native(name="InsertBytes", ret=None) def InsertBytes(params, ctxt, scope, stream, coord): raise NotImplementedError() # int IsBigEndian() @native(name="IsBigEndian", ret=bool) def IsBigEndian(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) return pfp.interp.Endian.current == pfp.interp.Endian.BIG # int IsLittleEndian() @native(name="IsLittleEndian", ret=bool) def IsLittleEndian(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) return pfp.interp.Endian.current == pfp.interp.Endian.LITTLE # void LittleEndian() @native(name="LittleEndian", ret=None) def LittleEndian(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) pfp.interp.Endian.current = pfp.interp.Endian.LITTLE # int MakeDir( string dir ) @native(name="MakeDir", ret=C.Int) def MakeDir(params, ctxt, scope, stream, coord): raise NotImplementedError() # void OverwriteBytes( int64 start, int64 size, uchar value=0 ) @native(name="OverwriteBytes", ret=None) def OverwriteBytes(params, ctxt, scope, stream, coord): raise NotImplementedError() def _read_data(params, ctxt, cls, coord): stream = ctxt._io bits = stream._bits curr_pos = stream.tell() if len(params) == 1: pos = utils.evaluate(params[0], ctxt) stream.seek(pos, 0) elif len(params) > 1: raise errors.InvalidArguments( coord, "at most 1 arguments", "{} args".format(len(params)) ) # Make sure to use the right endianness cls.fmtstr = pfp.interp.Endian.current + cls.fmtstr[1:] res = cls.parse_stream(stream) # reset the stream stream.seek(curr_pos, 0) stream._bits = bits return res # char ReadByte( int64 pos=FTell() ) @native(name="ReadByte", ret=int) def ReadByte(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int8sb, coord) # double ReadDouble( int64 pos=FTell() ) @native(name="ReadDouble", ret=float) def ReadDouble(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Double, coord) # float ReadFloat( int64 pos=FTell() ) @native(name="ReadFloat", ret=float) def ReadFloat(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Single, coord) # hfloat ReadHFloat( int64 pos=FTell() ) @native(name="ReadHFloat", ret=float) def ReadHFloat(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Single, coord) # int ReadInt( int64 pos=FTell() ) @native(name="ReadInt", ret=int) def ReadInt(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int32sb, coord) # int64 ReadInt64( int64 pos=FTell() ) @native(name="ReadInt64", ret=int) def ReadInt64(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int64sb, coord) # int64 ReadQuad( int64 pos=FTell() ) @native(name="ReadQuad", ret=int) def ReadQuad(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int64sb, coord) # short ReadShort( int64 pos=FTell() ) @native(name="ReadShort", ret=int) def ReadShort(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int16sb, coord) # uchar ReadUByte( int64 pos=FTell() ) @native(name="ReadUByte", ret=int) def ReadUByte(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Byte, coord) # uint ReadUInt( int64 pos=FTell() ) @native(name="ReadUInt", ret=int) def ReadUInt(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int32ub, coord) # uint64 ReadUInt64( int64 pos=FTell() ) @native(name="ReadUInt64", ret=int) def ReadUInt64(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int64ub, coord) # uint64 ReadUQuad( int64 pos=FTell() ) @native(name="ReadUQuad", ret=int) def ReadUQuad(params, ctxt, scope, stream, coord): return _read_data(params, ctxt,C.Int64ub, coord) # ushort ReadUShort( int64 pos=FTell() ) @native(name="ReadUShort", ret=int) def ReadUShort(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int16ub, coord) # char[] ReadLine( int64 pos, int maxLen=-1, int includeLinefeeds=true ) @native(name="ReadLine", ret=C.CString) def ReadLine(params, ctxt, scope, stream, coord): raise NotImplementedError() # void ReadBytes( uchar buffer[], int64 pos, int n ) @native(name="ReadBytes", ret=None) def ReadBytes(params, ctxt, scope, stream, coord): if len(params) != 3: raise errors.InvalidArguments( coord, "3 arguments (buffer, pos, n)", "{} args".format(len(params)), ) if not isinstance(params[0], C.Bytes): raise errors.InvalidArguments( coord, "buffer must be Bytes", params[0].__class__.__name__ ) if params[0].field_cls not in [pfp.fields.UChar, C.Byte]: raise errors.InvalidArguments( coord, "buffer must be an array of uchar or char", params[0].field_cls.__name__, ) if not isinstance(params[1], C.IntBase): raise errors.InvalidArguments( coord, "pos must be an integer", params[1].__class__.__name__ ) if not isinstance(params[2], C.IntBase): raise errors.InvalidArguments( coord, "n must be an integer", params[2].__class__.__name__ ) bits = stream._bits curr_pos = stream.tell() vals = [ params[0].field_cls(stream) for x in six.moves.range(utils.evaluate(params[2], ctxt)) ] stream.seek(curr_pos, 0) stream._bits = bits params[0]._pfp__set_value(vals) # char[] ReadString( int64 pos, int maxLen=-1 ) @native(name="ReadString", ret=C.CString) def ReadString(params, ctxt, scope, stream, coord): raise NotImplementedError() # int ReadStringLength( int64 pos, int maxLen=-1 ) @native(name="ReadStringLength", ret=C.Int) def ReadStringLength(params, ctxt, scope, stream, coord): raise NotImplementedError() # wstring ReadWLine( int64 pos, int maxLen=-1 ) @native(name="ReadWLine", ret=C.CString) def ReadWLine(params, ctxt, scope, stream, coord): raise NotImplementedError() # wstring ReadWString( int64 pos, int maxLen=-1 ) @native(name="ReadWString", ret=C.CString) def ReadWString(params, ctxt, scope, stream, coord): raise NotImplementedError() # int ReadWStringLength( int64 pos, int maxLen=-1 ) @native(name="ReadWStringLength", ret=C.Int) def ReadWStringLength(params, ctxt, scope, stream, coord): raise NotImplementedError() # int64 TextAddressToLine( int64 address ) @native(name="TextAddressToLine", ret=C.Long) def TextAddressToLine(params, ctxt, scope, stream, coord): raise NotImplementedError() # int TextAddressToColumn( int64 address ) @native(name="TextAddressToColumn", ret=C.Int) def TextAddressToColumn(params, ctxt, scope, stream, coord): raise NotImplementedError() # int64 TextColumnToAddress( int64 line, int column ) @native(name="TextColumnToAddress", ret=C.Long) def TextColumnToAddress(params, ctxt, scope, stream, coord): raise NotImplementedError() # int64 TextGetNumLines() @native(name="TextGetNumLines", ret=C.Long) def TextGetNumLines(params, ctxt, scope, stream, coord): raise NotImplementedError() # int TextGetLineSize( int64 line, int includeLinefeeds=true ) @native(name="TextGetLineSize", ret=C.Int) def TextGetLineSize(params, ctxt, scope, stream, coord): raise NotImplementedError() # int64 TextLineToAddress( int64 line ) @native(name="TextLineToAddress", ret=C.Long) def TextLineToAddress(params, ctxt, scope, stream, coord): raise NotImplementedError() # int TextReadLine( char buffer[], int64 line, int maxsize, int includeLinefeeds=true ) @native(name="TextReadLine", ret=C.Int) def TextReadLine(params, ctxt, scope, stream, coord): raise NotImplementedError() # int TextReadLineW( wchar_t buffer[], int64 line, int maxsize, int includeLinefeeds=true ) @native(name="TextReadLineW", ret=C.Int) def TextReadLineW(params, ctxt, scope, stream, coord): raise NotImplementedError() # void TextWriteLine( const char buffer[], int64 line, int includeLinefeeds=true ) @native(name="TextWriteLine", ret=None) def TextWriteLine(params, ctxt, scope, stream, coord): raise NotImplementedError() # void TextWriteLineW( const wchar_t buffer[], int64 line, int includeLinefeeds=true ) @native(name="TextWriteLineW", ret=None) def TextWriteLineW(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteByte( int64 pos, char value ) @native(name="WriteByte", ret=None) def WriteByte(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteDouble( int64 pos, double value ) @native(name="WriteDouble", ret=None) def WriteDouble(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteFloat( int64 pos, float value ) @native(name="WriteFloat", ret=None) def WriteFloat(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteHFloat( int64 pos, float value ) @native(name="WriteHFloat", ret=None) def WriteHFloat(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteInt( int64 pos, int value ) @native(name="WriteInt", ret=None) def WriteInt(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteInt64( int64 pos, int64 value ) @native(name="WriteInt64", ret=None) def WriteInt64(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteQuad( int64 pos, int64 value ) @native(name="WriteQuad", ret=None) def WriteQuad(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteShort( int64 pos, short value ) @native(name="WriteShort", ret=None) def WriteShort(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteUByte( int64 pos, uchar value ) @native(name="WriteUByte", ret=None) def WriteUByte(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteUInt( int64 pos, uint value ) @native(name="WriteUInt", ret=None) def WriteUInt(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteUInt64( int64 pos, uint64 value ) @native(name="WriteUInt64", ret=None) def WriteUInt64(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteUQuad( int64 pos, uint64 value ) @native(name="WriteUQuad", ret=None) def WriteUQuad(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteUShort( int64 pos, ushort value ) @native(name="WriteUShort", ret=None) def WriteUShort(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteBytes( const uchar buffer[], int64 pos, int n ) @native(name="WriteBytes", ret=None) def WriteBytes(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteString( int64 pos, const char value[] ) @native(name="WriteString", ret=None) def WriteString(params, ctxt, scope, stream, coord): raise NotImplementedError() # void WriteWString( int64 pos, const wstring value ) @native(name="WriteWString", ret=None) def WriteWString(params, ctxt, scope, stream, coord): raise NotImplementedError()

29.82899

93

0.690527

from pytest import skip import six import sys from pfp.native import native import pfp.interp import pfp.errors as errors import pfp.bitwrap as bitwrap from .. import utils import construct as C @native(name="BigEndian", ret=None) def BigEndian(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) pfp.interp.Endian.current = pfp.interp.Endian.BIG @native(name="BitfieldDisablePadding", ret=None, send_interp=True) def BitfieldDisablePadding(params, ctxt, scope, stream, coord, interp): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) interp.set_bitfield_padded(False) @native(name="BitfieldEnablePadding", ret=None, send_interp=True) def BitfieldEnablePadding(params, ctxt, scope, stream, coord, interp): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) interp.set_bitfield_padded(True) @native(name="BitfieldLeftToRight", ret=None, send_interp=True) def BitfieldLeftToRight(params, ctxt, scope, stream, coord, interp): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) interp.set_bitfield_direction(interp.BITFIELD_DIR_LEFT_RIGHT) @native(name="BitfieldRightToLeft", ret=None, send_interp=True) def BitfieldRightToLeft(params, ctxt, scope, stream, coord, interp): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) interp.set_bitfield_direction(interp.BITFIELD_DIR_RIGHT_LEFT) @native(name="ConvertBytesToDouble", ret=C.Double) def ConvertBytesToDouble(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="ConvertBytesToFloat", ret=C.Single) def ConvertBytesToFloat(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="ConvertBytesToHFloat", ret=C.Single) def ConvertBytesToHFloat(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="ConvertDataToBytes", ret=C.Int) def ConvertDataToBytes(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="DeleteBytes", ret=None) def DeleteBytes(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="DirectoryExists", ret=C.Int) def DirectoryExists(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="FEof", ret=bool) def FEof(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) return C.stream_iseof(ctxt._io) @native(name="FileSize", ret=int) def FileSize(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) return ctxt._io.size() @native(name="FindFiles", ret=None) def FindFiles(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="FPrintf", ret=C.Int) def FPrintf(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="FSeek", ret=int) def FSeek(params, ctxt, scope, stream, coord): if len(params) != 1: raise errors.InvalidArguments( coord, "{} args".format(len(params)), "FSeek accepts only one argument", ) pos = utils.evaluate(params[0], ctxt) if pos > ctxt._io.size(): return -1 C.stream_seek(ctxt._io, pos, 0, "") return 0 =int) def FSkip(params, ctxt, scope, stream, coord): if len(params) != 1: raise errors.InvalidArguments( coord, "{} args".format(len(params)), "FSkip accepts only one argument", ) skip_amt = params[0] while callable(skip_amt): skip_amt = skip_amt(ctxt) return C.stream_seek(ctxt._io, skip_amt, whence=1, path="") @native(name="FTell", ret=int) def FTell(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) return C.stream_tell(ctxt._io, None) @native(name="InsertBytes", ret=None) def InsertBytes(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="IsBigEndian", ret=bool) def IsBigEndian(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) return pfp.interp.Endian.current == pfp.interp.Endian.BIG @native(name="IsLittleEndian", ret=bool) def IsLittleEndian(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) return pfp.interp.Endian.current == pfp.interp.Endian.LITTLE @native(name="LittleEndian", ret=None) def LittleEndian(params, ctxt, scope, stream, coord): if len(params) > 0: raise errors.InvalidArguments( coord, "0 arguments", "{} args".format(len(params)) ) pfp.interp.Endian.current = pfp.interp.Endian.LITTLE @native(name="MakeDir", ret=C.Int) def MakeDir(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="OverwriteBytes", ret=None) def OverwriteBytes(params, ctxt, scope, stream, coord): raise NotImplementedError() def _read_data(params, ctxt, cls, coord): stream = ctxt._io bits = stream._bits curr_pos = stream.tell() if len(params) == 1: pos = utils.evaluate(params[0], ctxt) stream.seek(pos, 0) elif len(params) > 1: raise errors.InvalidArguments( coord, "at most 1 arguments", "{} args".format(len(params)) ) cls.fmtstr = pfp.interp.Endian.current + cls.fmtstr[1:] res = cls.parse_stream(stream) stream.seek(curr_pos, 0) stream._bits = bits return res @native(name="ReadByte", ret=int) def ReadByte(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int8sb, coord) @native(name="ReadDouble", ret=float) def ReadDouble(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Double, coord) @native(name="ReadFloat", ret=float) def ReadFloat(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Single, coord) @native(name="ReadHFloat", ret=float) def ReadHFloat(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Single, coord) @native(name="ReadInt", ret=int) def ReadInt(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int32sb, coord) @native(name="ReadInt64", ret=int) def ReadInt64(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int64sb, coord) @native(name="ReadQuad", ret=int) def ReadQuad(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int64sb, coord) @native(name="ReadShort", ret=int) def ReadShort(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int16sb, coord) @native(name="ReadUByte", ret=int) def ReadUByte(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Byte, coord) @native(name="ReadUInt", ret=int) def ReadUInt(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int32ub, coord) @native(name="ReadUInt64", ret=int) def ReadUInt64(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int64ub, coord) @native(name="ReadUQuad", ret=int) def ReadUQuad(params, ctxt, scope, stream, coord): return _read_data(params, ctxt,C.Int64ub, coord) @native(name="ReadUShort", ret=int) def ReadUShort(params, ctxt, scope, stream, coord): return _read_data(params, ctxt, C.Int16ub, coord) @native(name="ReadLine", ret=C.CString) def ReadLine(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="ReadBytes", ret=None) def ReadBytes(params, ctxt, scope, stream, coord): if len(params) != 3: raise errors.InvalidArguments( coord, "3 arguments (buffer, pos, n)", "{} args".format(len(params)), ) if not isinstance(params[0], C.Bytes): raise errors.InvalidArguments( coord, "buffer must be Bytes", params[0].__class__.__name__ ) if params[0].field_cls not in [pfp.fields.UChar, C.Byte]: raise errors.InvalidArguments( coord, "buffer must be an array of uchar or char", params[0].field_cls.__name__, ) if not isinstance(params[1], C.IntBase): raise errors.InvalidArguments( coord, "pos must be an integer", params[1].__class__.__name__ ) if not isinstance(params[2], C.IntBase): raise errors.InvalidArguments( coord, "n must be an integer", params[2].__class__.__name__ ) bits = stream._bits curr_pos = stream.tell() vals = [ params[0].field_cls(stream) for x in six.moves.range(utils.evaluate(params[2], ctxt)) ] stream.seek(curr_pos, 0) stream._bits = bits params[0]._pfp__set_value(vals) @native(name="ReadString", ret=C.CString) def ReadString(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="ReadStringLength", ret=C.Int) def ReadStringLength(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="ReadWLine", ret=C.CString) def ReadWLine(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="ReadWString", ret=C.CString) def ReadWString(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="ReadWStringLength", ret=C.Int) def ReadWStringLength(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="TextAddressToLine", ret=C.Long) def TextAddressToLine(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="TextAddressToColumn", ret=C.Int) def TextAddressToColumn(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="TextColumnToAddress", ret=C.Long) def TextColumnToAddress(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="TextGetNumLines", ret=C.Long) def TextGetNumLines(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="TextGetLineSize", ret=C.Int) def TextGetLineSize(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="TextLineToAddress", ret=C.Long) def TextLineToAddress(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="TextReadLine", ret=C.Int) def TextReadLine(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="TextReadLineW", ret=C.Int) def TextReadLineW(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="TextWriteLine", ret=None) def TextWriteLine(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="TextWriteLineW", ret=None) def TextWriteLineW(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteByte", ret=None) def WriteByte(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteDouble", ret=None) def WriteDouble(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteFloat", ret=None) def WriteFloat(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteHFloat", ret=None) def WriteHFloat(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteInt", ret=None) def WriteInt(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteInt64", ret=None) def WriteInt64(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteQuad", ret=None) def WriteQuad(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteShort", ret=None) def WriteShort(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteUByte", ret=None) def WriteUByte(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteUInt", ret=None) def WriteUInt(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteUInt64", ret=None) def WriteUInt64(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteUQuad", ret=None) def WriteUQuad(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteUShort", ret=None) def WriteUShort(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteBytes", ret=None) def WriteBytes(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteString", ret=None) def WriteString(params, ctxt, scope, stream, coord): raise NotImplementedError() @native(name="WriteWString", ret=None) def WriteWString(params, ctxt, scope, stream, coord): raise NotImplementedError()

true

1c4763580d072403c8ca37e045aa564412f3085f

3,801

py

Python

train_utils.py

Jack407/TFCNs_source_code

f41466ad18457dd6335287112191e5daacf6d80d

[ "MIT" ]

null

train_utils.py

Jack407/TFCNs_source_code

f41466ad18457dd6335287112191e5daacf6d80d

[ "MIT" ]

null

train_utils.py

Jack407/TFCNs_source_code

f41466ad18457dd6335287112191e5daacf6d80d

[ "MIT" ]

null

import argparse import logging import random import sys import time import numpy as np import torch import torch.nn as nn import torch.optim as optim from tensorboardX import SummaryWriter from torch.nn.modules.loss import CrossEntropyLoss from torch.utils.data import DataLoader from tqdm import tqdm from utils import one_hot_encoder from loss import mixed_focal_loss from loss import dice_loss as dl from torchvision import transforms import os def train_starter(args, model, snapshot_path): from preprocess import TFCNs_dataset, RandomGenerator logging.basicConfig(filename=snapshot_path + "/log.txt", level=logging.INFO, format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S') logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) logging.info(str(args)) base_lr = args.base_lr num_classes = args.num_classes batch_size = args.batch_size * args.n_gpu db_train = TFCNs_dataset(base_dir=args.root_path, list_dir=args.list_dir, split="train", transform=transforms.Compose( [RandomGenerator(output_size=[args.img_size, args.img_size])])) print("The length of train set is: {}".format(len(db_train))) def worker_init_fn(worker_id): random.seed(args.seed + worker_id) trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True, worker_init_fn=worker_init_fn) if args.n_gpu > 1: model = nn.DataParallel(model) model.train() optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001) writer = SummaryWriter(snapshot_path + '/log') iter_num = 0 max_epoch = args.max_epochs max_iterations = args.max_epochs * len(trainloader) # max_epoch = max_iterations // len(trainloader) + 1 logging.info("{} iterations per epoch. {} max iterations ".format(len(trainloader), max_iterations)) best_performance = 0.0 iterator = tqdm(range(max_epoch), ncols=70) for epoch_num in iterator: for i_batch, sampled_batch in enumerate(trainloader): image_batch, label_batch = sampled_batch['image'], sampled_batch['label'] image_batch, label_batch = image_batch.cuda(), label_batch.cuda() outputs = model(image_batch) label_batch = one_hot_encoder(label_batch,args.dataset,args.num_classes) outputs = torch.softmax(outputs,dim=1) loss = mixed_focal_loss(label_batch,outputs) loss = torch.mean(loss,axis=0) optimizer.zero_grad() loss.backward() optimizer.step() lr_ = base_lr * (1.0 - iter_num / max_iterations) ** 0.9 for param_group in optimizer.param_groups: param_group['lr'] = lr_ iter_num = iter_num + 1 writer.add_scalar('info/lr', lr_, iter_num) writer.add_scalar('info/total_loss', loss, iter_num) logging.info('iteration %d : loss : %f' % (iter_num, loss.item())) save_interval = 50 # int(max_epoch/6) if epoch_num > int(max_epoch / 2) and (epoch_num + 1) % save_interval == 0: save_mode_path = os.path.join(snapshot_path, 'epoch_' + str(epoch_num) + '.pth') torch.save(model.state_dict(), save_mode_path) logging.info("save model to {}".format(save_mode_path)) if epoch_num >= max_epoch - 1: save_mode_path = os.path.join(snapshot_path, 'epoch_' + str(epoch_num) + '.pth') torch.save(model.state_dict(), save_mode_path) logging.info("save model to {}".format(save_mode_path)) iterator.close() break writer.close() return "Training Finished!"

43.689655

109

0.660353

import argparse import logging import random import sys import time import numpy as np import torch import torch.nn as nn import torch.optim as optim from tensorboardX import SummaryWriter from torch.nn.modules.loss import CrossEntropyLoss from torch.utils.data import DataLoader from tqdm import tqdm from utils import one_hot_encoder from loss import mixed_focal_loss from loss import dice_loss as dl from torchvision import transforms import os def train_starter(args, model, snapshot_path): from preprocess import TFCNs_dataset, RandomGenerator logging.basicConfig(filename=snapshot_path + "/log.txt", level=logging.INFO, format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S') logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) logging.info(str(args)) base_lr = args.base_lr num_classes = args.num_classes batch_size = args.batch_size * args.n_gpu db_train = TFCNs_dataset(base_dir=args.root_path, list_dir=args.list_dir, split="train", transform=transforms.Compose( [RandomGenerator(output_size=[args.img_size, args.img_size])])) print("The length of train set is: {}".format(len(db_train))) def worker_init_fn(worker_id): random.seed(args.seed + worker_id) trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True, worker_init_fn=worker_init_fn) if args.n_gpu > 1: model = nn.DataParallel(model) model.train() optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0001) writer = SummaryWriter(snapshot_path + '/log') iter_num = 0 max_epoch = args.max_epochs max_iterations = args.max_epochs * len(trainloader) logging.info("{} iterations per epoch. {} max iterations ".format(len(trainloader), max_iterations)) best_performance = 0.0 iterator = tqdm(range(max_epoch), ncols=70) for epoch_num in iterator: for i_batch, sampled_batch in enumerate(trainloader): image_batch, label_batch = sampled_batch['image'], sampled_batch['label'] image_batch, label_batch = image_batch.cuda(), label_batch.cuda() outputs = model(image_batch) label_batch = one_hot_encoder(label_batch,args.dataset,args.num_classes) outputs = torch.softmax(outputs,dim=1) loss = mixed_focal_loss(label_batch,outputs) loss = torch.mean(loss,axis=0) optimizer.zero_grad() loss.backward() optimizer.step() lr_ = base_lr * (1.0 - iter_num / max_iterations) ** 0.9 for param_group in optimizer.param_groups: param_group['lr'] = lr_ iter_num = iter_num + 1 writer.add_scalar('info/lr', lr_, iter_num) writer.add_scalar('info/total_loss', loss, iter_num) logging.info('iteration %d : loss : %f' % (iter_num, loss.item())) save_interval = 50 if epoch_num > int(max_epoch / 2) and (epoch_num + 1) % save_interval == 0: save_mode_path = os.path.join(snapshot_path, 'epoch_' + str(epoch_num) + '.pth') torch.save(model.state_dict(), save_mode_path) logging.info("save model to {}".format(save_mode_path)) if epoch_num >= max_epoch - 1: save_mode_path = os.path.join(snapshot_path, 'epoch_' + str(epoch_num) + '.pth') torch.save(model.state_dict(), save_mode_path) logging.info("save model to {}".format(save_mode_path)) iterator.close() break writer.close() return "Training Finished!"

true

1c4763d96158d165cbae23a7f534f6cbe67be1a2

78,654

py

Python

source/codegen/metadata/nifgen/functions.py

zhindes/grpc-device

616aa913963098b12d276693895b7eb946f82df4

[ "MIT" ]

null

source/codegen/metadata/nifgen/functions.py

zhindes/grpc-device

616aa913963098b12d276693895b7eb946f82df4

[ "MIT" ]

23

2021-04-16T06:22:40.000Z

2021-06-11T05:51:45.000Z

source/codegen/metadata/nifgen/functions.py

zhindes/grpc-device

616aa913963098b12d276693895b7eb946f82df4

[ "MIT" ]

1

2021-10-30T09:23:49.000Z

functions = { 'AbortGeneration':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'AdjustSampleClockRelativeDelay':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'adjustmentTime', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'AllocateNamedWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformSize', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'AllocateWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CheckAttributeViBoolean':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'CheckAttributeViInt32':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CheckAttributeViInt64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViInt64' } ], 'returns':'ViStatus' }, 'CheckAttributeViReal64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'CheckAttributeViSession':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'CheckAttributeViString':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ClearArbMemory':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ClearArbSequence':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sequenceHandle', 'direction':'in', 'type':'ViInt32', 'enum':'SequenceHandle' } ], 'returns':'ViStatus' }, 'ClearArbWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32', 'enum':'WaveformHandle' } ], 'returns':'ViStatus' }, 'ClearError':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ClearFreqList':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'frequencyListHandle', 'direction':'in', 'type':'ViInt32', 'enum':'FrequencyListOptions' } ], 'returns':'ViStatus' }, 'ClearInterchangeWarnings':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ClearUserStandardWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'Close':{ 'cname' : 'niFgen_close', 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'Commit':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ConfigureAmplitude':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'amplitude', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureArbSequence':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'sequenceHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'gain', 'direction':'in', 'type':'ViReal64' }, { 'name':'offset', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureArbWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'gain', 'direction':'in', 'type':'ViReal64' }, { 'name':'offset', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureChannels':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channels', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ConfigureClockMode':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'clockMode', 'direction':'in', 'type':'ViInt32', 'enum':'ClockMode' } ], 'returns':'ViStatus' }, 'ConfigureCustomFIRFilterCoefficients':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'numberOfCoefficients', 'direction':'in', 'type':'ViInt32' }, { 'name':'coefficientsArray', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'numberOfCoefficients' } } ], 'returns':'ViStatus' }, 'ConfigureDigitalEdgeScriptTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'triggerId', 'direction':'in', 'type':'ViConstString' }, { 'name':'source', 'direction':'in', 'type':'ViConstString' }, { 'name':'edge', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureDigitalEdgeStartTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'source', 'direction':'in', 'type':'ViConstString' }, { 'name':'edge', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureDigitalLevelScriptTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'triggerId', 'direction':'in', 'type':'ViConstString' }, { 'name':'source', 'direction':'in', 'type':'ViConstString' }, { 'name':'triggerWhen', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureFreqList':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'frequencyListHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'amplitude', 'direction':'in', 'type':'ViReal64' }, { 'name':'dcOffset', 'direction':'in', 'type':'ViReal64' }, { 'name':'startPhase', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureFrequency':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'frequency', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureOperationMode':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'operationMode', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureOutputEnabled':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'enabled', 'direction':'in', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'ConfigureOutputImpedance':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'impedance', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureOutputMode':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'outputMode', 'direction':'in', 'type':'ViInt32', 'enum':'OutputMode' } ], 'returns':'ViStatus' }, 'ConfigureP2PEndpointFullnessStartTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'p2pEndpointFullnessLevel', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureReferenceClock':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'referenceClockSource', 'direction':'in', 'type':'ViConstString' }, { 'name':'referenceClockFrequency', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureSampleClockSource':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sampleClockSource', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ConfigureSampleRate':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sampleRate', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureSoftwareEdgeScriptTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'triggerId', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ConfigureSoftwareEdgeStartTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ConfigureStandardWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveform', 'direction':'in', 'type':'ViInt32', 'enum':'Waveform' }, { 'name':'amplitude', 'direction':'in', 'type':'ViReal64' }, { 'name':'dcOffset', 'direction':'in', 'type':'ViReal64' }, { 'name':'frequency', 'direction':'in', 'type':'ViReal64' }, { 'name':'startPhase', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureSynchronization':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'synchronizationSource', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureTriggerMode':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'triggerMode', 'direction':'in', 'type':'ViInt32', 'enum':'TriggerMode' } ], 'returns':'ViStatus' }, 'CreateAdvancedArbSequence':{ 'codegen_method': 'CustomCode', 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sequenceLength', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformHandlesArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'loopCountsArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'sampleCountsArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'markerLocationArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'coercedMarkersArray', 'direction':'out', 'type':'ViInt32[]', 'size':{ 'mechanism':'custom-code', 'value':'sequenceLength' } }, { 'name':'sequenceHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateArbSequence':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sequenceLength', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformHandlesArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'loopCountsArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'sequenceHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateFreqList':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'waveform', 'direction':'in', 'type':'ViInt32', 'enum':'Waveform' }, { 'name':'frequencyListLength', 'direction':'in', 'type':'ViInt32' }, { 'name':'frequencyArray', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'frequencyListLength' } }, { 'name':'durationArray', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'frequencyListLength' } }, { 'name':'frequencyListHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateWaveformComplexF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'numberOfSamples', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformDataArray', 'direction':'in', 'type': 'struct NIComplexNumber_struct[]', 'grpc_type': 'repeated NIComplexNumber', 'size': { 'mechanism': 'len', 'value': 'numberOfSamples' } }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateWaveformF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformDataArray', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'waveformSize' } }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateWaveformFromFileF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'fileName', 'direction':'in', 'type':'ViConstString' }, { 'name':'byteOrder', 'direction':'in', 'type':'ViInt32', 'enum':'ByteOrder' }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateWaveformFromFileHWS':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'fileName', 'direction':'in', 'type':'ViConstString' }, { 'name':'useRateFromWaveform', 'direction':'in', 'type':'ViBoolean' }, { 'name':'useGainAndOffsetFromWaveform', 'direction':'in', 'type':'ViBoolean' }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateWaveformI16': { 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'in', 'name': 'channelName', 'type': 'ViConstString' }, { 'direction': 'in', 'name': 'waveformSize', 'type': 'ViInt32' }, { 'direction': 'in', 'name': 'waveformDataArray', 'size': { 'mechanism': 'len', 'value': 'waveformSize' }, 'type': 'ViInt16[]', 'use_array': True }, { 'direction': 'out', 'name': 'waveformHandle', 'type': 'ViInt32' } ], 'returns': 'ViStatus' }, 'CreateWaveformFromFileI16':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'fileName', 'direction':'in', 'type':'ViConstString' }, { 'name':'byteOrder', 'direction':'in', 'type':'ViInt32', 'enum':'ByteOrder' }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'DefineUserStandardWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformDataArray', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'waveformSize' } } ], 'returns':'ViStatus' }, 'DeleteNamedWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'DeleteScript':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'scriptName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'Disable':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'DisableAnalogFilter':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'DisableDigitalFilter':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'DisableDigitalPatterning':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'DisableScriptTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'triggerId', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'DisableStartTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'EnableAnalogFilter':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'filterCorrectionFrequency', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'EnableDigitalFilter':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'EnableDigitalPatterning':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ErrorHandler':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'errorCode', 'direction':'in', 'type':'ViStatus' }, { 'name':'errorMessage', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'fixed', 'value':256 } } ], 'returns':'ViStatus' }, 'ErrorMessage':{ 'cname' : 'niFgen_error_message', 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'errorCode', 'direction':'in', 'type':'ViStatus' }, { 'name':'errorMessage', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'fixed', 'value':256 } } ], 'returns':'ViStatus' }, 'ErrorQuery': { 'cname' : 'niFgen_error_query', 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'out', 'name': 'errorCode', 'type': 'ViInt32' }, { 'direction': 'out', 'name': 'errorMessage', 'size': { 'mechanism': 'fixed', 'value': 256 }, 'type': 'ViChar[]' } ], 'returns': 'ViStatus' }, 'ExportAttributeConfigurationBuffer':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sizeInBytes', 'direction':'in', 'type':'ViInt32' }, { 'name':'configuration', 'direction':'out', 'type':'ViAddr[]', 'size':{ 'mechanism':'ivi-dance', 'value':'sizeInBytes' } } ], 'returns':'ViStatus' }, 'ExportAttributeConfigurationFile':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'filePath', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ExportSignal':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'signal', 'direction':'in', 'enum':'Signal', 'type':'ViInt32' }, { 'name':'signalIdentifier', 'direction':'in', 'type':'ViConstString' }, { 'name':'outputTerminal', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'GetAttributeViBoolean':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'GetAttributeViInt32':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'GetAttributeViInt64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViInt64' } ], 'returns':'ViStatus' }, 'GetAttributeViReal64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'GetAttributeViSession':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'GetAttributeViString':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'arraySize', 'direction':'in', 'type':'ViInt32' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'ivi-dance', 'value':'arraySize' } } ], 'returns':'ViStatus' }, 'GetChannelName':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'index', 'direction':'in', 'type':'ViInt32' }, { 'name':'bufferSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'channelString', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'ivi-dance', 'value':'bufferSize' } } ], 'returns':'ViStatus' }, 'GetError':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'errorCode', 'direction':'out', 'type':'ViStatus' }, { 'name':'errorDescriptionBufferSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'errorDescription', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'ivi-dance', 'value':'errorDescriptionBufferSize' } } ], 'returns':'ViStatus' }, 'GetExtCalLastDateAndTime':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'year', 'direction':'out', 'type':'ViInt32' }, { 'name':'month', 'direction':'out', 'type':'ViInt32' }, { 'name':'day', 'direction':'out', 'type':'ViInt32' }, { 'name':'hour', 'direction':'out', 'type':'ViInt32' }, { 'name':'minute', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'GetExtCalLastTemp':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'temperature', 'direction':'out', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'GetExtCalRecommendedInterval':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'months', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'GetFIRFilterCoefficients':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'arraySize', 'direction':'in', 'type':'ViInt32' }, { 'name':'coefficientsArray', 'direction':'out', 'type':'ViReal64[]', 'size':{ 'mechanism':'ivi-dance-with-a-twist', 'value':'arraySize', 'value_twist':'numberOfCoefficientsRead', } }, { 'name':'numberOfCoefficientsRead', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'GetHardwareState':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'state', 'direction':'out', 'type':'ViInt32', 'enum':'HardwareState' } ], 'returns':'ViStatus' }, 'GetNextCoercionRecord':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'bufferSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'coercionRecord', 'direction':'out', 'type':'ViChar[]', 'size': { 'mechanism': 'ivi-dance', 'value': 'bufferSize' } } ], 'returns':'ViStatus' }, 'GetNextInterchangeWarning':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'bufferSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'interchangeWarning', 'direction':'out', 'type':'ViChar[]', 'size': { 'mechanism': 'ivi-dance', 'value': 'bufferSize' } } ], 'returns':'ViStatus' }, 'GetSelfCalLastDateAndTime':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'year', 'direction':'out', 'type':'ViInt32' }, { 'name':'month', 'direction':'out', 'type':'ViInt32' }, { 'name':'day', 'direction':'out', 'type':'ViInt32' }, { 'name':'hour', 'direction':'out', 'type':'ViInt32' }, { 'name':'minute', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'GetSelfCalLastTemp':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'temperature', 'direction':'out', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'GetSelfCalSupported':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'selfCalSupported', 'direction':'out', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'GetStreamEndpointHandle':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'streamEndpoint', 'direction':'in', 'type':'ViConstString' }, { 'name':'readerHandle', 'direction':'out', 'type':'ViUInt32' } ], 'returns':'ViStatus' }, 'ImportAttributeConfigurationBuffer':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sizeInBytes', 'direction':'in', 'type':'ViInt32' }, { 'name':'configuration', 'direction':'in', 'type':'ViAddr[]', 'size':{ 'mechanism':'len', 'value':'sizeInBytes' } } ], 'returns':'ViStatus' }, 'ImportAttributeConfigurationFile':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'filePath', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'Init': { 'init_method': True, 'cname': 'niFgen_init ', 'parameters': [ { 'direction': 'in', 'name': 'resourceName', 'type': 'ViRsrc' }, { 'direction': 'in', 'name': 'idQuery', 'type': 'ViBoolean' }, { 'direction': 'in', 'name': 'resetDevice', 'type': 'ViBoolean' }, { 'direction': 'out', 'name': 'vi', 'type': 'ViSession' } ], 'returns': 'ViStatus', }, 'InitWithOptions':{ 'init_method' : True, 'parameters':[ { 'name':'resourceName', 'direction':'in', 'type':'ViRsrc' }, { 'name':'idQuery', 'direction':'in', 'type':'ViBoolean' }, { 'name':'resetDevice', 'direction':'in', 'type':'ViBoolean' }, { 'name':'optionString', 'direction':'in', 'type':'ViConstString' }, { 'name':'vi', 'direction':'out', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'InitializeWithChannels':{ 'init_method' : True, 'parameters':[ { 'name':'resourceName', 'direction':'in', 'type':'ViRsrc' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'resetDevice', 'direction':'in', 'type':'ViBoolean' }, { 'name':'optionString', 'direction':'in', 'type':'ViConstString' }, { 'name':'vi', 'direction':'out', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'InitiateGeneration':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'InvalidateAllAttributes':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'IsDone':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'done', 'direction':'out', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'LockSession':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'callerHasLock', 'direction':'out', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'ManualEnableP2PStream':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'endpointName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'QueryArbSeqCapabilities':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'maximumNumberOfSequences', 'direction':'out', 'type':'ViInt32' }, { 'name':'minimumSequenceLength', 'direction':'out', 'type':'ViInt32' }, { 'name':'maximumSequenceLength', 'direction':'out', 'type':'ViInt32' }, { 'name':'maximumLoopCount', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'QueryArbWfmCapabilities':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'maximumNumberOfWaveforms', 'direction':'out', 'type':'ViInt32' }, { 'name':'waveformQuantum', 'direction':'out', 'type':'ViInt32' }, { 'name':'minimumWaveformSize', 'direction':'out', 'type':'ViInt32' }, { 'name':'maximumWaveformSize', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'QueryFreqListCapabilities':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'maximumNumberOfFreqLists', 'direction':'out', 'type':'ViInt32' }, { 'name':'minimumFrequencyListLength', 'direction':'out', 'type':'ViInt32' }, { 'name':'maximumFrequencyListLength', 'direction':'out', 'type':'ViInt32' }, { 'name':'minimumFrequencyListDuration', 'direction':'out', 'type':'ViReal64' }, { 'name':'maximumFrequencyListDuration', 'direction':'out', 'type':'ViReal64' }, { 'name':'frequencyListDurationQuantum', 'direction':'out', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ReadCurrentTemperature':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'temperature', 'direction':'out', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'Reset':{ 'cname' : 'niFgen_reset', 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ResetAttribute':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' } ], 'returns':'ViStatus' }, 'ResetDevice':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ResetInterchangeCheck':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ResetWithDefaults':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'RevisionQuery': { 'cname' : 'niFgen_revision_query', 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'out', 'name': 'instrumentDriverRevision', 'size': { 'mechanism': 'fixed', 'value': 256 }, 'type': 'ViChar[]' }, { 'direction': 'out', 'name': 'firmwareRevision', 'size': { 'mechanism': 'fixed', 'value': 256 }, 'type': 'ViChar[]' } ], 'returns': 'ViStatus' }, 'RouteSignalOut':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'routeSignalFrom', 'direction':'in', 'type':'ViInt32', 'enum':'RouteSignalFrom' }, { 'name':'routeSignalTo', 'direction':'in', 'type':'ViInt32', 'enum':'RouteSignalTo' } ], 'returns':'ViStatus' }, 'SelfCal':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'SelfTest':{ 'cname' : 'niFgen_self_test', 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'selfTestResult', 'direction':'out', 'type':'ViInt16' }, { 'name':'selfTestMessage', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'fixed', 'value':256 } } ], 'returns':'ViStatus' }, 'SendSoftwareEdgeTrigger': { 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'in', 'enum': 'Trigger', 'name': 'trigger', 'type': 'ViInt32', }, { 'direction': 'in', 'name': 'triggerId', 'type': 'ViString' } ], 'returns': 'ViStatus' }, 'SetAttributeViBoolean':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'SetAttributeViInt32':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'SetAttributeViInt64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViInt64' } ], 'returns':'ViStatus' }, 'SetAttributeViReal64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'SetAttributeViSession':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'SetAttributeViString':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'SetNamedWaveformNextWritePosition':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' }, { 'name':'relativeTo', 'direction':'in', 'type':'ViInt32', 'enum':'RelativeTo' }, { 'name':'offset', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'SetWaveformNextWritePosition':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'relativeTo', 'direction':'in', 'type':'ViInt32', 'enum':'RelativeTo' }, { 'name':'offset', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'UnlockSession':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'callerHasLock', 'direction':'out', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'WaitUntilDone':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'maxTime', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'WriteBinary16Waveform': { 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'in', 'name': 'channelName', 'type': 'ViConstString' }, { 'direction': 'in', 'name': 'waveformHandle', 'type': 'ViInt32' }, { 'direction': 'in', 'name': 'size', 'type': 'ViInt32' }, { 'direction': 'in', 'name': 'data', 'size': { 'mechanism': 'len', 'value': 'size' }, 'type': 'ViInt16[]', 'use_array': True } ], 'returns': 'ViStatus' }, 'WriteComplexBinary16Waveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'size', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'struct NIComplexI16_struct[]', 'grpc_type':'repeated NIComplexInt32', 'size':{ 'mechanism':'len', 'value':'size' } } ], 'returns':'ViStatus' }, 'WriteNamedWaveformF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' }, { 'name':'size', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'size' } } ], 'returns':'ViStatus' }, 'WriteNamedWaveformI16': { 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'in', 'name': 'channelName', 'type': 'ViConstString' }, { 'direction': 'in', 'name': 'waveformName', 'type': 'ViConstString' }, { 'direction': 'in', 'name': 'size', 'type': 'ViInt32' }, { 'direction': 'in', 'name': 'data', 'size': { 'mechanism': 'len', 'value': 'size' }, 'type': 'ViInt16[]', 'use_array': True } ], 'returns': 'ViStatus' }, 'WriteP2PEndpointI16':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'endpointName', 'direction':'in', 'type':'ViConstString' }, { 'name':'numberOfSamples', 'direction':'in', 'type':'ViInt32' }, { 'name':'endpointData', 'direction':'in', 'type':'ViInt16[]', 'size': { 'mechanism': 'len', 'value': 'numberOfSamples' } } ], 'returns':'ViStatus' }, 'WriteScript':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'script', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'WriteWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'size', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'size' } } ], 'returns':'ViStatus' }, 'WriteWaveformComplexF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'numberOfSamples', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'struct NIComplexNumber_struct[]', 'grpc_type':'repeated NIComplexNumber', 'size':{ 'mechanism':'len', 'value':'numberOfSamples' } }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'WriteNamedWaveformComplexF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' }, { 'name':'size', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'struct NIComplexNumber_struct[]', 'grpc_type':'repeated NIComplexNumber', 'size':{ 'mechanism':'len', 'value':'size' } } ], 'returns':'ViStatus' }, 'WriteNamedWaveformComplexI16':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' }, { 'name':'size', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'struct NIComplexI16_struct[]', 'grpc_type':'repeated NIComplexInt32', 'size':{ 'mechanism':'len', 'value':'size' } } ], 'returns':'ViStatus' } }

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functions = { 'AbortGeneration':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'AdjustSampleClockRelativeDelay':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'adjustmentTime', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'AllocateNamedWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformSize', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'AllocateWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CheckAttributeViBoolean':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'CheckAttributeViInt32':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CheckAttributeViInt64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViInt64' } ], 'returns':'ViStatus' }, 'CheckAttributeViReal64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'CheckAttributeViSession':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'CheckAttributeViString':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ClearArbMemory':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ClearArbSequence':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sequenceHandle', 'direction':'in', 'type':'ViInt32', 'enum':'SequenceHandle' } ], 'returns':'ViStatus' }, 'ClearArbWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32', 'enum':'WaveformHandle' } ], 'returns':'ViStatus' }, 'ClearError':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ClearFreqList':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'frequencyListHandle', 'direction':'in', 'type':'ViInt32', 'enum':'FrequencyListOptions' } ], 'returns':'ViStatus' }, 'ClearInterchangeWarnings':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ClearUserStandardWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'Close':{ 'cname' : 'niFgen_close', 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'Commit':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ConfigureAmplitude':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'amplitude', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureArbSequence':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'sequenceHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'gain', 'direction':'in', 'type':'ViReal64' }, { 'name':'offset', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureArbWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'gain', 'direction':'in', 'type':'ViReal64' }, { 'name':'offset', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureChannels':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channels', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ConfigureClockMode':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'clockMode', 'direction':'in', 'type':'ViInt32', 'enum':'ClockMode' } ], 'returns':'ViStatus' }, 'ConfigureCustomFIRFilterCoefficients':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'numberOfCoefficients', 'direction':'in', 'type':'ViInt32' }, { 'name':'coefficientsArray', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'numberOfCoefficients' } } ], 'returns':'ViStatus' }, 'ConfigureDigitalEdgeScriptTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'triggerId', 'direction':'in', 'type':'ViConstString' }, { 'name':'source', 'direction':'in', 'type':'ViConstString' }, { 'name':'edge', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureDigitalEdgeStartTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'source', 'direction':'in', 'type':'ViConstString' }, { 'name':'edge', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureDigitalLevelScriptTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'triggerId', 'direction':'in', 'type':'ViConstString' }, { 'name':'source', 'direction':'in', 'type':'ViConstString' }, { 'name':'triggerWhen', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureFreqList':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'frequencyListHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'amplitude', 'direction':'in', 'type':'ViReal64' }, { 'name':'dcOffset', 'direction':'in', 'type':'ViReal64' }, { 'name':'startPhase', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureFrequency':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'frequency', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureOperationMode':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'operationMode', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureOutputEnabled':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'enabled', 'direction':'in', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'ConfigureOutputImpedance':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'impedance', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureOutputMode':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'outputMode', 'direction':'in', 'type':'ViInt32', 'enum':'OutputMode' } ], 'returns':'ViStatus' }, 'ConfigureP2PEndpointFullnessStartTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'p2pEndpointFullnessLevel', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureReferenceClock':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'referenceClockSource', 'direction':'in', 'type':'ViConstString' }, { 'name':'referenceClockFrequency', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureSampleClockSource':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sampleClockSource', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ConfigureSampleRate':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sampleRate', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureSoftwareEdgeScriptTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'triggerId', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ConfigureSoftwareEdgeStartTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ConfigureStandardWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveform', 'direction':'in', 'type':'ViInt32', 'enum':'Waveform' }, { 'name':'amplitude', 'direction':'in', 'type':'ViReal64' }, { 'name':'dcOffset', 'direction':'in', 'type':'ViReal64' }, { 'name':'frequency', 'direction':'in', 'type':'ViReal64' }, { 'name':'startPhase', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ConfigureSynchronization':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'synchronizationSource', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'ConfigureTriggerMode':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'triggerMode', 'direction':'in', 'type':'ViInt32', 'enum':'TriggerMode' } ], 'returns':'ViStatus' }, 'CreateAdvancedArbSequence':{ 'codegen_method': 'CustomCode', 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sequenceLength', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformHandlesArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'loopCountsArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'sampleCountsArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'markerLocationArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'coercedMarkersArray', 'direction':'out', 'type':'ViInt32[]', 'size':{ 'mechanism':'custom-code', 'value':'sequenceLength' } }, { 'name':'sequenceHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateArbSequence':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sequenceLength', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformHandlesArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'loopCountsArray', 'direction':'in', 'type':'ViInt32[]', 'size':{ 'mechanism':'len', 'value':'sequenceLength' } }, { 'name':'sequenceHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateFreqList':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'waveform', 'direction':'in', 'type':'ViInt32', 'enum':'Waveform' }, { 'name':'frequencyListLength', 'direction':'in', 'type':'ViInt32' }, { 'name':'frequencyArray', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'frequencyListLength' } }, { 'name':'durationArray', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'frequencyListLength' } }, { 'name':'frequencyListHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateWaveformComplexF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'numberOfSamples', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformDataArray', 'direction':'in', 'type': 'struct NIComplexNumber_struct[]', 'grpc_type': 'repeated NIComplexNumber', 'size': { 'mechanism': 'len', 'value': 'numberOfSamples' } }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateWaveformF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformDataArray', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'waveformSize' } }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateWaveformFromFileF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'fileName', 'direction':'in', 'type':'ViConstString' }, { 'name':'byteOrder', 'direction':'in', 'type':'ViInt32', 'enum':'ByteOrder' }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateWaveformFromFileHWS':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'fileName', 'direction':'in', 'type':'ViConstString' }, { 'name':'useRateFromWaveform', 'direction':'in', 'type':'ViBoolean' }, { 'name':'useGainAndOffsetFromWaveform', 'direction':'in', 'type':'ViBoolean' }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'CreateWaveformI16': { 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'in', 'name': 'channelName', 'type': 'ViConstString' }, { 'direction': 'in', 'name': 'waveformSize', 'type': 'ViInt32' }, { 'direction': 'in', 'name': 'waveformDataArray', 'size': { 'mechanism': 'len', 'value': 'waveformSize' }, 'type': 'ViInt16[]', 'use_array': True }, { 'direction': 'out', 'name': 'waveformHandle', 'type': 'ViInt32' } ], 'returns': 'ViStatus' }, 'CreateWaveformFromFileI16':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'fileName', 'direction':'in', 'type':'ViConstString' }, { 'name':'byteOrder', 'direction':'in', 'type':'ViInt32', 'enum':'ByteOrder' }, { 'name':'waveformHandle', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'DefineUserStandardWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'waveformDataArray', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'waveformSize' } } ], 'returns':'ViStatus' }, 'DeleteNamedWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'DeleteScript':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'scriptName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'Disable':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'DisableAnalogFilter':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'DisableDigitalFilter':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'DisableDigitalPatterning':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'DisableScriptTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'triggerId', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'DisableStartTrigger':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'EnableAnalogFilter':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'filterCorrectionFrequency', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'EnableDigitalFilter':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'EnableDigitalPatterning':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ErrorHandler':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'errorCode', 'direction':'in', 'type':'ViStatus' }, { 'name':'errorMessage', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'fixed', 'value':256 } } ], 'returns':'ViStatus' }, 'ErrorMessage':{ 'cname' : 'niFgen_error_message', 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'errorCode', 'direction':'in', 'type':'ViStatus' }, { 'name':'errorMessage', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'fixed', 'value':256 } } ], 'returns':'ViStatus' }, 'ErrorQuery': { 'cname' : 'niFgen_error_query', 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'out', 'name': 'errorCode', 'type': 'ViInt32' }, { 'direction': 'out', 'name': 'errorMessage', 'size': { 'mechanism': 'fixed', 'value': 256 }, 'type': 'ViChar[]' } ], 'returns': 'ViStatus' }, 'ExportAttributeConfigurationBuffer':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sizeInBytes', 'direction':'in', 'type':'ViInt32' }, { 'name':'configuration', 'direction':'out', 'type':'ViAddr[]', 'size':{ 'mechanism':'ivi-dance', 'value':'sizeInBytes' } } ], 'returns':'ViStatus' }, 'ExportAttributeConfigurationFile':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'filePath', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'ExportSignal':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'signal', 'direction':'in', 'enum':'Signal', 'type':'ViInt32' }, { 'name':'signalIdentifier', 'direction':'in', 'type':'ViConstString' }, { 'name':'outputTerminal', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'GetAttributeViBoolean':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'GetAttributeViInt32':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'GetAttributeViInt64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViInt64' } ], 'returns':'ViStatus' }, 'GetAttributeViReal64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'GetAttributeViSession':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'GetAttributeViString':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'arraySize', 'direction':'in', 'type':'ViInt32' }, { 'name':'attributeValue', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'ivi-dance', 'value':'arraySize' } } ], 'returns':'ViStatus' }, 'GetChannelName':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'index', 'direction':'in', 'type':'ViInt32' }, { 'name':'bufferSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'channelString', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'ivi-dance', 'value':'bufferSize' } } ], 'returns':'ViStatus' }, 'GetError':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'errorCode', 'direction':'out', 'type':'ViStatus' }, { 'name':'errorDescriptionBufferSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'errorDescription', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'ivi-dance', 'value':'errorDescriptionBufferSize' } } ], 'returns':'ViStatus' }, 'GetExtCalLastDateAndTime':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'year', 'direction':'out', 'type':'ViInt32' }, { 'name':'month', 'direction':'out', 'type':'ViInt32' }, { 'name':'day', 'direction':'out', 'type':'ViInt32' }, { 'name':'hour', 'direction':'out', 'type':'ViInt32' }, { 'name':'minute', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'GetExtCalLastTemp':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'temperature', 'direction':'out', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'GetExtCalRecommendedInterval':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'months', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'GetFIRFilterCoefficients':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'arraySize', 'direction':'in', 'type':'ViInt32' }, { 'name':'coefficientsArray', 'direction':'out', 'type':'ViReal64[]', 'size':{ 'mechanism':'ivi-dance-with-a-twist', 'value':'arraySize', 'value_twist':'numberOfCoefficientsRead', } }, { 'name':'numberOfCoefficientsRead', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'GetHardwareState':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'state', 'direction':'out', 'type':'ViInt32', 'enum':'HardwareState' } ], 'returns':'ViStatus' }, 'GetNextCoercionRecord':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'bufferSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'coercionRecord', 'direction':'out', 'type':'ViChar[]', 'size': { 'mechanism': 'ivi-dance', 'value': 'bufferSize' } } ], 'returns':'ViStatus' }, 'GetNextInterchangeWarning':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'bufferSize', 'direction':'in', 'type':'ViInt32' }, { 'name':'interchangeWarning', 'direction':'out', 'type':'ViChar[]', 'size': { 'mechanism': 'ivi-dance', 'value': 'bufferSize' } } ], 'returns':'ViStatus' }, 'GetSelfCalLastDateAndTime':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'year', 'direction':'out', 'type':'ViInt32' }, { 'name':'month', 'direction':'out', 'type':'ViInt32' }, { 'name':'day', 'direction':'out', 'type':'ViInt32' }, { 'name':'hour', 'direction':'out', 'type':'ViInt32' }, { 'name':'minute', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'GetSelfCalLastTemp':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'temperature', 'direction':'out', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'GetSelfCalSupported':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'selfCalSupported', 'direction':'out', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'GetStreamEndpointHandle':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'streamEndpoint', 'direction':'in', 'type':'ViConstString' }, { 'name':'readerHandle', 'direction':'out', 'type':'ViUInt32' } ], 'returns':'ViStatus' }, 'ImportAttributeConfigurationBuffer':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'sizeInBytes', 'direction':'in', 'type':'ViInt32' }, { 'name':'configuration', 'direction':'in', 'type':'ViAddr[]', 'size':{ 'mechanism':'len', 'value':'sizeInBytes' } } ], 'returns':'ViStatus' }, 'ImportAttributeConfigurationFile':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'filePath', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'Init': { 'init_method': True, 'cname': 'niFgen_init ', 'parameters': [ { 'direction': 'in', 'name': 'resourceName', 'type': 'ViRsrc' }, { 'direction': 'in', 'name': 'idQuery', 'type': 'ViBoolean' }, { 'direction': 'in', 'name': 'resetDevice', 'type': 'ViBoolean' }, { 'direction': 'out', 'name': 'vi', 'type': 'ViSession' } ], 'returns': 'ViStatus', }, 'InitWithOptions':{ 'init_method' : True, 'parameters':[ { 'name':'resourceName', 'direction':'in', 'type':'ViRsrc' }, { 'name':'idQuery', 'direction':'in', 'type':'ViBoolean' }, { 'name':'resetDevice', 'direction':'in', 'type':'ViBoolean' }, { 'name':'optionString', 'direction':'in', 'type':'ViConstString' }, { 'name':'vi', 'direction':'out', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'InitializeWithChannels':{ 'init_method' : True, 'parameters':[ { 'name':'resourceName', 'direction':'in', 'type':'ViRsrc' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'resetDevice', 'direction':'in', 'type':'ViBoolean' }, { 'name':'optionString', 'direction':'in', 'type':'ViConstString' }, { 'name':'vi', 'direction':'out', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'InitiateGeneration':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'InvalidateAllAttributes':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'IsDone':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'done', 'direction':'out', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'LockSession':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'callerHasLock', 'direction':'out', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'ManualEnableP2PStream':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'endpointName', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'QueryArbSeqCapabilities':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'maximumNumberOfSequences', 'direction':'out', 'type':'ViInt32' }, { 'name':'minimumSequenceLength', 'direction':'out', 'type':'ViInt32' }, { 'name':'maximumSequenceLength', 'direction':'out', 'type':'ViInt32' }, { 'name':'maximumLoopCount', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'QueryArbWfmCapabilities':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'maximumNumberOfWaveforms', 'direction':'out', 'type':'ViInt32' }, { 'name':'waveformQuantum', 'direction':'out', 'type':'ViInt32' }, { 'name':'minimumWaveformSize', 'direction':'out', 'type':'ViInt32' }, { 'name':'maximumWaveformSize', 'direction':'out', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'QueryFreqListCapabilities':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'maximumNumberOfFreqLists', 'direction':'out', 'type':'ViInt32' }, { 'name':'minimumFrequencyListLength', 'direction':'out', 'type':'ViInt32' }, { 'name':'maximumFrequencyListLength', 'direction':'out', 'type':'ViInt32' }, { 'name':'minimumFrequencyListDuration', 'direction':'out', 'type':'ViReal64' }, { 'name':'maximumFrequencyListDuration', 'direction':'out', 'type':'ViReal64' }, { 'name':'frequencyListDurationQuantum', 'direction':'out', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'ReadCurrentTemperature':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'temperature', 'direction':'out', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'Reset':{ 'cname' : 'niFgen_reset', 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ResetAttribute':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' } ], 'returns':'ViStatus' }, 'ResetDevice':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ResetInterchangeCheck':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'ResetWithDefaults':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'RevisionQuery': { 'cname' : 'niFgen_revision_query', 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'out', 'name': 'instrumentDriverRevision', 'size': { 'mechanism': 'fixed', 'value': 256 }, 'type': 'ViChar[]' }, { 'direction': 'out', 'name': 'firmwareRevision', 'size': { 'mechanism': 'fixed', 'value': 256 }, 'type': 'ViChar[]' } ], 'returns': 'ViStatus' }, 'RouteSignalOut':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'routeSignalFrom', 'direction':'in', 'type':'ViInt32', 'enum':'RouteSignalFrom' }, { 'name':'routeSignalTo', 'direction':'in', 'type':'ViInt32', 'enum':'RouteSignalTo' } ], 'returns':'ViStatus' }, 'SelfCal':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'SelfTest':{ 'cname' : 'niFgen_self_test', 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'selfTestResult', 'direction':'out', 'type':'ViInt16' }, { 'name':'selfTestMessage', 'direction':'out', 'type':'ViChar[]', 'size':{ 'mechanism':'fixed', 'value':256 } } ], 'returns':'ViStatus' }, 'SendSoftwareEdgeTrigger': { 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'in', 'enum': 'Trigger', 'name': 'trigger', 'type': 'ViInt32', }, { 'direction': 'in', 'name': 'triggerId', 'type': 'ViString' } ], 'returns': 'ViStatus' }, 'SetAttributeViBoolean':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'SetAttributeViInt32':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'SetAttributeViInt64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViInt64' } ], 'returns':'ViStatus' }, 'SetAttributeViReal64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViReal64' } ], 'returns':'ViStatus' }, 'SetAttributeViSession':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViSession' } ], 'returns':'ViStatus' }, 'SetAttributeViString':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'attributeId', 'direction':'in', 'type':'ViAttr' }, { 'name':'attributeValue', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'SetNamedWaveformNextWritePosition':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' }, { 'name':'relativeTo', 'direction':'in', 'type':'ViInt32', 'enum':'RelativeTo' }, { 'name':'offset', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'SetWaveformNextWritePosition':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'relativeTo', 'direction':'in', 'type':'ViInt32', 'enum':'RelativeTo' }, { 'name':'offset', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'UnlockSession':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'callerHasLock', 'direction':'out', 'type':'ViBoolean' } ], 'returns':'ViStatus' }, 'WaitUntilDone':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'maxTime', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'WriteBinary16Waveform': { 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'in', 'name': 'channelName', 'type': 'ViConstString' }, { 'direction': 'in', 'name': 'waveformHandle', 'type': 'ViInt32' }, { 'direction': 'in', 'name': 'size', 'type': 'ViInt32' }, { 'direction': 'in', 'name': 'data', 'size': { 'mechanism': 'len', 'value': 'size' }, 'type': 'ViInt16[]', 'use_array': True } ], 'returns': 'ViStatus' }, 'WriteComplexBinary16Waveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'size', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'struct NIComplexI16_struct[]', 'grpc_type':'repeated NIComplexInt32', 'size':{ 'mechanism':'len', 'value':'size' } } ], 'returns':'ViStatus' }, 'WriteNamedWaveformF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' }, { 'name':'size', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'size' } } ], 'returns':'ViStatus' }, 'WriteNamedWaveformI16': { 'parameters': [ { 'direction': 'in', 'name': 'vi', 'type': 'ViSession' }, { 'direction': 'in', 'name': 'channelName', 'type': 'ViConstString' }, { 'direction': 'in', 'name': 'waveformName', 'type': 'ViConstString' }, { 'direction': 'in', 'name': 'size', 'type': 'ViInt32' }, { 'direction': 'in', 'name': 'data', 'size': { 'mechanism': 'len', 'value': 'size' }, 'type': 'ViInt16[]', 'use_array': True } ], 'returns': 'ViStatus' }, 'WriteP2PEndpointI16':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'endpointName', 'direction':'in', 'type':'ViConstString' }, { 'name':'numberOfSamples', 'direction':'in', 'type':'ViInt32' }, { 'name':'endpointData', 'direction':'in', 'type':'ViInt16[]', 'size': { 'mechanism': 'len', 'value': 'numberOfSamples' } } ], 'returns':'ViStatus' }, 'WriteScript':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'script', 'direction':'in', 'type':'ViConstString' } ], 'returns':'ViStatus' }, 'WriteWaveform':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32' }, { 'name':'size', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'ViReal64[]', 'size':{ 'mechanism':'len', 'value':'size' } } ], 'returns':'ViStatus' }, 'WriteWaveformComplexF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'numberOfSamples', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'struct NIComplexNumber_struct[]', 'grpc_type':'repeated NIComplexNumber', 'size':{ 'mechanism':'len', 'value':'numberOfSamples' } }, { 'name':'waveformHandle', 'direction':'in', 'type':'ViInt32' } ], 'returns':'ViStatus' }, 'WriteNamedWaveformComplexF64':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' }, { 'name':'size', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'struct NIComplexNumber_struct[]', 'grpc_type':'repeated NIComplexNumber', 'size':{ 'mechanism':'len', 'value':'size' } } ], 'returns':'ViStatus' }, 'WriteNamedWaveformComplexI16':{ 'parameters':[ { 'name':'vi', 'direction':'in', 'type':'ViSession' }, { 'name':'channelName', 'direction':'in', 'type':'ViConstString' }, { 'name':'waveformName', 'direction':'in', 'type':'ViConstString' }, { 'name':'size', 'direction':'in', 'type':'ViInt32' }, { 'name':'data', 'direction':'in', 'type':'struct NIComplexI16_struct[]', 'grpc_type':'repeated NIComplexInt32', 'size':{ 'mechanism':'len', 'value':'size' } } ], 'returns':'ViStatus' } }

true

1c476528ea9e0ab39dc368d76e84eab32c00fa45

724

py

Python

mldp/tests/transformers/test_seq_len_computer.py

prashantlv/mltoolkit

acc192bafc66b7661d541ef4f604b5e5ab7df5ca

[ "MIT" ]

1

2020-10-03T05:23:31.000Z

mldp/tests/transformers/test_seq_len_computer.py

prashantlv/mltoolkit

acc192bafc66b7661d541ef4f604b5e5ab7df5ca

[ "MIT" ]

null

mldp/tests/transformers/test_seq_len_computer.py

prashantlv/mltoolkit

acc192bafc66b7661d541ef4f604b5e5ab7df5ca

[ "MIT" ]

null

import unittest from mldp.steps.transformers.nlp import SeqLenComputer from mldp.utils.tools import DataChunk from copy import deepcopy import numpy as np class TestSeqLenComputer(unittest.TestCase): def test_output(self): fn = "dummy" new_fn = "dummy_len" data = [[1, 2, 3], [12], ["a", "b", "d", "e"]] actual_dc = DataChunk(**{fn: np.array(deepcopy(data))}) expected_dc = DataChunk(**{fn: np.array(deepcopy(data)), new_fn: np.array([3, 1, 4])}) slc = SeqLenComputer(fname=fn, new_len_fname=new_fn) actual_dc = slc(actual_dc) self.assertTrue(actual_dc == expected_dc) if __name__ == '__main__': unittest.main()

27.846154

64

0.618785

import unittest from mldp.steps.transformers.nlp import SeqLenComputer from mldp.utils.tools import DataChunk from copy import deepcopy import numpy as np class TestSeqLenComputer(unittest.TestCase): def test_output(self): fn = "dummy" new_fn = "dummy_len" data = [[1, 2, 3], [12], ["a", "b", "d", "e"]] actual_dc = DataChunk(**{fn: np.array(deepcopy(data))}) expected_dc = DataChunk(**{fn: np.array(deepcopy(data)), new_fn: np.array([3, 1, 4])}) slc = SeqLenComputer(fname=fn, new_len_fname=new_fn) actual_dc = slc(actual_dc) self.assertTrue(actual_dc == expected_dc) if __name__ == '__main__': unittest.main()

true

1c4765731326549e159d462a7abaa90cb1582cbf

181

py

Python

apps/profile/apps.py

OpenAdaptronik/Rattler

c3bdde0ca56b6d77f49bc830fa2b8bb41a26bae4

[ "MIT" ]

2

2018-05-18T08:38:29.000Z

2018-05-22T08:26:09.000Z

apps/profile/apps.py

IT-PM-OpenAdaptronik/Webapp

c3bdde0ca56b6d77f49bc830fa2b8bb41a26bae4

[ "MIT" ]

118

2017-10-31T13:45:09.000Z

2018-02-24T20:51:42.000Z

apps/profile/apps.py

OpenAdaptronik/Rattler

c3bdde0ca56b6d77f49bc830fa2b8bb41a26bae4

[ "MIT" ]

null

from django.apps import AppConfig from django.utils.translation import gettext_lazy as _ class ProfileConfig(AppConfig): name = 'apps.profile' verbose_name = _('profile')

22.625

54

0.762431

from django.apps import AppConfig from django.utils.translation import gettext_lazy as _ class ProfileConfig(AppConfig): name = 'apps.profile' verbose_name = _('profile')

true

1c47670eaf2832f39b529a294728b4e11a136702

629

py

Python

src/create_experiment.py

G-Simeone/Learning_Accident_Occurence_on_Dutch_Highways

1f3992a529fed70fd488811d68128a1e255fac5f

[ "MIT" ]

4

2018-11-09T16:18:28.000Z

2019-04-09T11:19:23.000Z

src/create_experiment.py

G-Simeone/Learning_Accident_Occurence_on_Dutch_Highways

1f3992a529fed70fd488811d68128a1e255fac5f

[ "MIT" ]

null

src/create_experiment.py

G-Simeone/Learning_Accident_Occurence_on_Dutch_Highways

1f3992a529fed70fd488811d68128a1e255fac5f

[ "MIT" ]

1

2020-05-28T18:48:17.000Z

import sys from utils import write_exp_utils import pandas as pd from utils import misc_utils import psycopg2 from psycopg2.extras import Json, DictCursor def main(argv): print(argv[1]) w = write_exp_utils.ExperimentConfig(argv[1], argv[2]) print("writing {} to database".format(argv[1]) ) w.write_to_db()# write experiment on database # check if the experiment is written correctly q = 'select experiment_id from rws_experiment.experiment_table order by experiment_id desc limit 1;' conn = misc_utils.connect_rds() print(pd.read_sql(q, conn)) if __name__== '__main__': main(sys.argv)

29.952381

105

0.732909

import sys from utils import write_exp_utils import pandas as pd from utils import misc_utils import psycopg2 from psycopg2.extras import Json, DictCursor def main(argv): print(argv[1]) w = write_exp_utils.ExperimentConfig(argv[1], argv[2]) print("writing {} to database".format(argv[1]) ) w.write_to_db() q = 'select experiment_id from rws_experiment.experiment_table order by experiment_id desc limit 1;' conn = misc_utils.connect_rds() print(pd.read_sql(q, conn)) if __name__== '__main__': main(sys.argv)

true

1c4767c28a173b87d61645270342bcabb9c6929c

7,674

py

Python

setup.py

WildbookOrg/wbia-deprecate-tpl-brambox

9aa6a69f706d0653a65520c696a7cd66715b6a37

[ "MIT" ]

2

2019-03-23T03:14:11.000Z

2019-11-21T07:16:13.000Z

setup.py

WildbookOrg/wbia-deprecate-tpl-brambox

9aa6a69f706d0653a65520c696a7cd66715b6a37

[ "MIT" ]

null

setup.py

WildbookOrg/wbia-deprecate-tpl-brambox

9aa6a69f706d0653a65520c696a7cd66715b6a37

[ "MIT" ]

1

2021-12-01T03:04:53.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function import sys from os.path import exists from collections import OrderedDict from setuptools import find_packages from skbuild import setup def native_mb_python_tag(plat_impl=None, version_info=None): """ Example: >>> print(native_mb_python_tag()) >>> print(native_mb_python_tag('PyPy', (2, 7))) >>> print(native_mb_python_tag('CPython', (3, 8))) """ if plat_impl is None: import platform plat_impl = platform.python_implementation() if version_info is None: import sys version_info = sys.version_info major, minor = version_info[0:2] ver = '{}{}'.format(major, minor) if plat_impl == 'CPython': # TODO: get if cp27m or cp27mu impl = 'cp' if ver == '27': IS_27_BUILT_WITH_UNICODE = True # how to determine this? if IS_27_BUILT_WITH_UNICODE: abi = 'mu' else: abi = 'm' else: if ver == '38': # no abi in 38? abi = '' else: abi = 'm' mb_tag = '{impl}{ver}-{impl}{ver}{abi}'.format(**locals()) elif plat_impl == 'PyPy': abi = '' impl = 'pypy' ver = '{}{}'.format(major, minor) mb_tag = '{impl}-{ver}'.format(**locals()) else: raise NotImplementedError(plat_impl) return mb_tag def parse_version(fpath='brambox/__init__.py'): """ Statically parse the version number from a python file """ import ast if not exists(fpath): raise ValueError('fpath={!r} does not exist'.format(fpath)) with open(fpath, 'r') as file_: sourcecode = file_.read() pt = ast.parse(sourcecode) class VersionVisitor(ast.NodeVisitor): def visit_Assign(self, node): for target in node.targets: if getattr(target, 'id', None) == '__version__': self.version = node.value.s visitor = VersionVisitor() visitor.visit(pt) return visitor.version def parse_long_description(fpath='README.rst'): """ Reads README text, but doesn't break if README does not exist. """ if exists(fpath): with open(fpath, 'r') as file: return file.read() return '' def parse_requirements(fname='requirements.txt', with_version=False): """ Parse the package dependencies listed in a requirements file but strips specific versioning information. Args: fname (str): path to requirements file with_version (bool, default=False): if true include version specs Returns: List[str]: list of requirements items CommandLine: python -c "import setup; print(setup.parse_requirements())" python -c "import setup; print(chr(10).join(setup.parse_requirements(with_version=True)))" """ from os.path import exists import re require_fpath = fname def parse_line(line): """ Parse information from a line in a requirements text file """ if line.startswith('-r '): # Allow specifying requirements in other files target = line.split(' ')[1] for info in parse_require_file(target): yield info else: info = {'line': line} if line.startswith('-e '): info['package'] = line.split('#egg=')[1] else: # Remove versioning from the package pat = '(' + '|'.join(['>=', '==', '>']) + ')' parts = re.split(pat, line, maxsplit=1) parts = [p.strip() for p in parts] info['package'] = parts[0] if len(parts) > 1: op, rest = parts[1:] if ';' in rest: # Handle platform specific dependencies # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies version, platform_deps = map(str.strip, rest.split(';')) info['platform_deps'] = platform_deps else: version = rest # NOQA info['version'] = (op, version) yield info def parse_require_file(fpath): with open(fpath, 'r') as f: for line in f.readlines(): line = line.strip() if line and not line.startswith('#'): for info in parse_line(line): yield info def gen_packages_items(): if exists(require_fpath): for info in parse_require_file(require_fpath): parts = [info['package']] if with_version and 'version' in info: parts.extend(info['version']) if not sys.version.startswith('3.4'): # apparently package_deps are broken in 3.4 platform_deps = info.get('platform_deps') if platform_deps is not None: parts.append(';' + platform_deps) item = ''.join(parts) yield item packages = list(gen_packages_items()) return packages NAME = 'wbia-brambox' MB_PYTHON_TAG = native_mb_python_tag() # NOQA AUTHORS = [ 'EAVISE', 'Jason Parham', 'WildMe Developers', ] AUTHOR_EMAIL = 'dev@wildme.org' URL = 'https://github.com/WildbookOrg/wbia-tpl-brambox' LICENSE = 'BSD' DESCRIPTION = 'brambox - Basic Recipes for Annotations and Modeling' KWARGS = OrderedDict( name=NAME, author=', '.join(AUTHORS), author_email=AUTHOR_EMAIL, description=DESCRIPTION, long_description=parse_long_description('README.rst'), long_description_content_type='text/x-rst', url=URL, license=LICENSE, install_requires=parse_requirements('requirements/runtime.txt'), extras_require={ 'all': parse_requirements('requirements.txt'), 'tests': parse_requirements('requirements/tests.txt'), 'build': parse_requirements('requirements/build.txt'), 'runtime': parse_requirements('requirements/runtime.txt'), }, # --- VERSION --- # The following settings retreive the version from git. # See https://github.com/pypa/setuptools_scm/ for more information setup_requires=['setuptools_scm'], use_scm_version={ 'write_to': 'brambox/_version.py', 'write_to_template': '__version__ = "{version}"', 'tag_regex': '^(?P<prefix>v)?(?P<version>[^\\+]+)(?P<suffix>.*)?$', 'local_scheme': 'dirty-tag', }, packages=find_packages(), include_package_data=False, # List of classifiers available at: # https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ 'Development Status :: 6 - Mature', 'License :: OSI Approved :: BSD License', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'Operating System :: MacOS :: MacOS X', 'Operating System :: Unix', 'Topic :: Software Development :: Libraries :: Python Modules', 'Topic :: Utilities', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', ], ) if __name__ == '__main__': """ python -c "import brambox; print(brambox.__file__)" """ setup(**KWARGS)

31.975

125

0.572974

from __future__ import absolute_import, division, print_function import sys from os.path import exists from collections import OrderedDict from setuptools import find_packages from skbuild import setup def native_mb_python_tag(plat_impl=None, version_info=None): if plat_impl is None: import platform plat_impl = platform.python_implementation() if version_info is None: import sys version_info = sys.version_info major, minor = version_info[0:2] ver = '{}{}'.format(major, minor) if plat_impl == 'CPython': impl = 'cp' if ver == '27': IS_27_BUILT_WITH_UNICODE = True if IS_27_BUILT_WITH_UNICODE: abi = 'mu' else: abi = 'm' else: if ver == '38': abi = '' else: abi = 'm' mb_tag = '{impl}{ver}-{impl}{ver}{abi}'.format(**locals()) elif plat_impl == 'PyPy': abi = '' impl = 'pypy' ver = '{}{}'.format(major, minor) mb_tag = '{impl}-{ver}'.format(**locals()) else: raise NotImplementedError(plat_impl) return mb_tag def parse_version(fpath='brambox/__init__.py'): import ast if not exists(fpath): raise ValueError('fpath={!r} does not exist'.format(fpath)) with open(fpath, 'r') as file_: sourcecode = file_.read() pt = ast.parse(sourcecode) class VersionVisitor(ast.NodeVisitor): def visit_Assign(self, node): for target in node.targets: if getattr(target, 'id', None) == '__version__': self.version = node.value.s visitor = VersionVisitor() visitor.visit(pt) return visitor.version def parse_long_description(fpath='README.rst'): if exists(fpath): with open(fpath, 'r') as file: return file.read() return '' def parse_requirements(fname='requirements.txt', with_version=False): from os.path import exists import re require_fpath = fname def parse_line(line): if line.startswith('-r '): target = line.split(' ')[1] for info in parse_require_file(target): yield info else: info = {'line': line} if line.startswith('-e '): info['package'] = line.split('#egg=')[1] else: pat = '(' + '|'.join(['>=', '==', '>']) + ')' parts = re.split(pat, line, maxsplit=1) parts = [p.strip() for p in parts] info['package'] = parts[0] if len(parts) > 1: op, rest = parts[1:] if ';' in rest: m_deps = map(str.strip, rest.split(';')) info['platform_deps'] = platform_deps else: version = rest info['version'] = (op, version) yield info def parse_require_file(fpath): with open(fpath, 'r') as f: for line in f.readlines(): line = line.strip() if line and not line.startswith('#'): for info in parse_line(line): yield info def gen_packages_items(): if exists(require_fpath): for info in parse_require_file(require_fpath): parts = [info['package']] if with_version and 'version' in info: parts.extend(info['version']) if not sys.version.startswith('3.4'): platform_deps = info.get('platform_deps') if platform_deps is not None: parts.append(';' + platform_deps) item = ''.join(parts) yield item packages = list(gen_packages_items()) return packages NAME = 'wbia-brambox' MB_PYTHON_TAG = native_mb_python_tag() AUTHORS = [ 'EAVISE', 'Jason Parham', 'WildMe Developers', ] AUTHOR_EMAIL = 'dev@wildme.org' URL = 'https://github.com/WildbookOrg/wbia-tpl-brambox' LICENSE = 'BSD' DESCRIPTION = 'brambox - Basic Recipes for Annotations and Modeling' KWARGS = OrderedDict( name=NAME, author=', '.join(AUTHORS), author_email=AUTHOR_EMAIL, description=DESCRIPTION, long_description=parse_long_description('README.rst'), long_description_content_type='text/x-rst', url=URL, license=LICENSE, install_requires=parse_requirements('requirements/runtime.txt'), extras_require={ 'all': parse_requirements('requirements.txt'), 'tests': parse_requirements('requirements/tests.txt'), 'build': parse_requirements('requirements/build.txt'), 'runtime': parse_requirements('requirements/runtime.txt'), }, setup_requires=['setuptools_scm'], use_scm_version={ 'write_to': 'brambox/_version.py', 'write_to_template': '__version__ = "{version}"', 'tag_regex': '^(?P<prefix>v)?(?P<version>[^\\+]+)(?P<suffix>.*)?$', 'local_scheme': 'dirty-tag', }, packages=find_packages(), include_package_data=False, classifiers=[ 'Development Status :: 6 - Mature', 'License :: OSI Approved :: BSD License', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'Operating System :: MacOS :: MacOS X', 'Operating System :: Unix', 'Topic :: Software Development :: Libraries :: Python Modules', 'Topic :: Utilities', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', ], ) if __name__ == '__main__': setup(**KWARGS)

true

1c476801c70edbae6a98a7915c2d93aa454b9a2d

5,022

py

Python

Analysis/SampleVisualization_AE.py

melodist/MELTNET

47548e4a027ea4e23cdcb5ba1f1d9aa1aa7bbf29

[ "MIT" ]

9

2020-03-16T04:17:05.000Z

2022-02-08T12:51:45.000Z

Analysis/SampleVisualization_AE.py

melodist/MELTNET

47548e4a027ea4e23cdcb5ba1f1d9aa1aa7bbf29

[ "MIT" ]

1

2019-11-26T08:18:16.000Z

2020-09-10T15:21:40.000Z

Analysis/SampleVisualization_AE.py

melodist/MELTNET

47548e4a027ea4e23cdcb5ba1f1d9aa1aa7bbf29

[ "MIT" ]

3

2020-03-16T04:17:30.000Z

2021-12-02T07:10:22.000Z

""" Sample Visualization Make 2-D image of sample distribution 1-1. Extract Features using initial network 1-2. Extract Features using trained network 2. Using K-means to classify the patches 3. Dimension reduction using PCA 4. Visualize results """ import tensorflow as tf import numpy as np from Network import NetworkKeras import os import time from Extraction import PatchExtraction from sklearn.cluster import KMeans from sklearn.decomposition import PCA import matplotlib.pyplot as plt from datetime import datetime def SampleVisualization_AE(path_model, path_image): """ Visualize sample distribution using PCA. The result image will be saved on 'Results_%Y%m%d_%H%M%S' Input ______ path_model: path of trained model path_image: path of test image Output ______ """ tf.enable_eager_execution() time_start = time.time() # Extract Features using trained network # Load model input_shape = (17 * 17) initial_model_CT = NetworkKeras.create_autoencoder(input_shape) initial_model_PT = NetworkKeras.create_autoencoder(input_shape) trained_model_CT = NetworkKeras.create_autoencoder(input_shape) trained_model_CT.load_weights(path_model + 'CT') trained_model_PT = NetworkKeras.create_autoencoder(input_shape) trained_model_PT.load_weights(path_model + 'PT') # Make feature extraction model initial_extractor_CT = tf.keras.models.Model(inputs=initial_model_CT.input, outputs=initial_model_CT.get_layer('tf_op_layer_l2_normalize').output) initial_extractor_PT = tf.keras.models.Model(inputs=initial_model_PT.input, outputs=initial_model_PT.get_layer('tf_op_layer_l2_normalize_2').output) feature_extractor_CT = tf.keras.models.Model(inputs=trained_model_CT.input, outputs=trained_model_CT.get_layer('tf_op_layer_l2_normalize_4').output) feature_extractor_PT = tf.keras.models.Model(inputs=trained_model_PT.input, outputs=trained_model_PT.get_layer('tf_op_layer_l2_normalize_6').output) # Load Images ind_CT = [[230, 380], [150, 370]] ind_PT = [[230, 380], [150, 370]] # Make Results Folder now = datetime.now() path_result = f"./Results_{now.strftime('%Y%m%d_%H%M%S')}/" os.makedirs(path_result) # Print Patients Number patient_dir = os.listdir(path_image) print(f'Patients Number: {len(patient_dir)}') for path_patient in patient_dir: addr_patient = f'{path_image}/{path_patient}/'\ img_CT, img_PT = PatchExtraction.stackImages(addr_patient, ind_CT, ind_PT) patches_CT, patches_PT = PatchExtraction.patch_extraction_thres(img_CT, img_PT, 0) # Extract Features using initial network print(f"Extract Features using initial network...") features_init_CT = initial_extractor_CT.predict(patches_CT, steps=1) features_init_PT = initial_extractor_PT.predict(patches_PT, steps=1) features_init = np.hstack((features_init_CT, features_init_PT)) # Extract Features print(f"Extract Features...") features_CT = feature_extractor_CT.predict(patches_CT, steps=1) features_PT = feature_extractor_PT.predict(patches_PT, steps=1) features = np.hstack((features_CT, features_PT)) # Using K-means print(f"K-means Clustering...") num_labels = 5 model_k_means = KMeans(n_clusters=num_labels, random_state=0) model_k_means.fit(features) # Merging Patches num_x = 44 num_y = 30 stride = 5 label_predict = model_k_means.fit_predict(features) label_predict_batch = label_predict.reshape((-1, num_y * num_x)) # Dimension reduction using PCA pca = PCA(n_components=2) features_low = pca.fit_transform(features) features_init_low = pca.transform(features_init) colors = ['salmon', 'orange', 'steelblue', 'violet', 'khaki'] fig, ax = plt.subplots(2, figsize=(5, 5), constrained_layout=True) for i in range(5): data_init = features_init_low[label_predict == i] X_init = data_init[:, 0] Y_init = data_init[:, 1] ax[0].scatter(X_init, Y_init, color=colors[i], label=i, s=1) data = features_low[label_predict == i] X = data[:, 0] Y = data[:, 1] ax[1].scatter(X, Y, color=colors[i], label=i, s=1) ax[0].legend(loc='best') ax[0].set_xticks([]) ax[0].set_yticks([]) ax[1].legend(loc='best') ax[1].set_xticks([]) ax[1].set_yticks([]) fig.suptitle('Distribution of patches') plt.savefig(f"{path_result}Plot_{path_patient}.png", format='png', dpi=300) time_end = time.time() print(f"Evaluation Finished! Elapsed time: {time_end - time_start}")

35.871429

121

0.660892

import tensorflow as tf import numpy as np from Network import NetworkKeras import os import time from Extraction import PatchExtraction from sklearn.cluster import KMeans from sklearn.decomposition import PCA import matplotlib.pyplot as plt from datetime import datetime def SampleVisualization_AE(path_model, path_image): tf.enable_eager_execution() time_start = time.time() input_shape = (17 * 17) initial_model_CT = NetworkKeras.create_autoencoder(input_shape) initial_model_PT = NetworkKeras.create_autoencoder(input_shape) trained_model_CT = NetworkKeras.create_autoencoder(input_shape) trained_model_CT.load_weights(path_model + 'CT') trained_model_PT = NetworkKeras.create_autoencoder(input_shape) trained_model_PT.load_weights(path_model + 'PT') initial_extractor_CT = tf.keras.models.Model(inputs=initial_model_CT.input, outputs=initial_model_CT.get_layer('tf_op_layer_l2_normalize').output) initial_extractor_PT = tf.keras.models.Model(inputs=initial_model_PT.input, outputs=initial_model_PT.get_layer('tf_op_layer_l2_normalize_2').output) feature_extractor_CT = tf.keras.models.Model(inputs=trained_model_CT.input, outputs=trained_model_CT.get_layer('tf_op_layer_l2_normalize_4').output) feature_extractor_PT = tf.keras.models.Model(inputs=trained_model_PT.input, outputs=trained_model_PT.get_layer('tf_op_layer_l2_normalize_6').output) ind_CT = [[230, 380], [150, 370]] ind_PT = [[230, 380], [150, 370]] now = datetime.now() path_result = f"./Results_{now.strftime('%Y%m%d_%H%M%S')}/" os.makedirs(path_result) patient_dir = os.listdir(path_image) print(f'Patients Number: {len(patient_dir)}') for path_patient in patient_dir: addr_patient = f'{path_image}/{path_patient}/'\ img_CT, img_PT = PatchExtraction.stackImages(addr_patient, ind_CT, ind_PT) patches_CT, patches_PT = PatchExtraction.patch_extraction_thres(img_CT, img_PT, 0) print(f"Extract Features using initial network...") features_init_CT = initial_extractor_CT.predict(patches_CT, steps=1) features_init_PT = initial_extractor_PT.predict(patches_PT, steps=1) features_init = np.hstack((features_init_CT, features_init_PT)) print(f"Extract Features...") features_CT = feature_extractor_CT.predict(patches_CT, steps=1) features_PT = feature_extractor_PT.predict(patches_PT, steps=1) features = np.hstack((features_CT, features_PT)) print(f"K-means Clustering...") num_labels = 5 model_k_means = KMeans(n_clusters=num_labels, random_state=0) model_k_means.fit(features) num_x = 44 num_y = 30 stride = 5 label_predict = model_k_means.fit_predict(features) label_predict_batch = label_predict.reshape((-1, num_y * num_x)) pca = PCA(n_components=2) features_low = pca.fit_transform(features) features_init_low = pca.transform(features_init) colors = ['salmon', 'orange', 'steelblue', 'violet', 'khaki'] fig, ax = plt.subplots(2, figsize=(5, 5), constrained_layout=True) for i in range(5): data_init = features_init_low[label_predict == i] X_init = data_init[:, 0] Y_init = data_init[:, 1] ax[0].scatter(X_init, Y_init, color=colors[i], label=i, s=1) data = features_low[label_predict == i] X = data[:, 0] Y = data[:, 1] ax[1].scatter(X, Y, color=colors[i], label=i, s=1) ax[0].legend(loc='best') ax[0].set_xticks([]) ax[0].set_yticks([]) ax[1].legend(loc='best') ax[1].set_xticks([]) ax[1].set_yticks([]) fig.suptitle('Distribution of patches') plt.savefig(f"{path_result}Plot_{path_patient}.png", format='png', dpi=300) time_end = time.time() print(f"Evaluation Finished! Elapsed time: {time_end - time_start}")

true

1c4768746d5b6ffc5563045f2c062c9a11652afe

7,689

py

Python

tests/components/hue/test_init.py

sgrzys/AIS-home-assistant

7bfc4d6d90de75eea06702c36474d91bf38df3bf

[ "Apache-2.0" ]

1

2019-04-22T06:05:09.000Z

tests/components/hue/test_init.py

sgrzys/AIS-home-assistant

7bfc4d6d90de75eea06702c36474d91bf38df3bf

[ "Apache-2.0" ]

2

2022-01-13T04:26:00.000Z

2022-03-12T01:05:37.000Z

tests/components/hue/test_init.py

sgrzys/AIS-home-assistant

7bfc4d6d90de75eea06702c36474d91bf38df3bf

[ "Apache-2.0" ]

1

2021-09-20T01:52:31.000Z

"""Test Hue setup process.""" from unittest.mock import Mock, patch from homeassistant.setup import async_setup_component from homeassistant.components import hue from tests.common import mock_coro, MockConfigEntry async def test_setup_with_no_config(hass): """Test that we do not discover anything or try to set up a bridge.""" with patch.object(hass, 'config_entries') as mock_config_entries, \ patch.object(hue, 'configured_hosts', return_value=[]): assert await async_setup_component(hass, hue.DOMAIN, {}) is True # No flows started assert len(mock_config_entries.flow.mock_calls) == 0 # No configs stored assert hass.data[hue.DOMAIN] == {} async def test_setup_with_discovery_no_known_auth(hass, aioclient_mock): """Test discovering a bridge and not having known auth.""" aioclient_mock.get(hue.API_NUPNP, json=[ { 'internalipaddress': '0.0.0.0', 'id': 'abcd1234' } ]) with patch.object(hass, 'config_entries') as mock_config_entries, \ patch.object(hue, 'configured_hosts', return_value=[]): mock_config_entries.flow.async_init.return_value = mock_coro() assert await async_setup_component(hass, hue.DOMAIN, { hue.DOMAIN: {} }) is True # Flow started for discovered bridge assert len(mock_config_entries.flow.mock_calls) == 1 assert mock_config_entries.flow.mock_calls[0][2]['data'] == { 'host': '0.0.0.0', 'path': '.hue_abcd1234.conf', } # Config stored for domain. assert hass.data[hue.DOMAIN] == { '0.0.0.0': { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: '.hue_abcd1234.conf', hue.CONF_ALLOW_HUE_GROUPS: hue.DEFAULT_ALLOW_HUE_GROUPS, hue.CONF_ALLOW_UNREACHABLE: hue.DEFAULT_ALLOW_UNREACHABLE, } } async def test_setup_with_discovery_known_auth(hass, aioclient_mock): """Test we don't do anything if we discover already configured hub.""" aioclient_mock.get(hue.API_NUPNP, json=[ { 'internalipaddress': '0.0.0.0', 'id': 'abcd1234' } ]) with patch.object(hass, 'config_entries') as mock_config_entries, \ patch.object(hue, 'configured_hosts', return_value=['0.0.0.0']): assert await async_setup_component(hass, hue.DOMAIN, { hue.DOMAIN: {} }) is True # Flow started for discovered bridge assert len(mock_config_entries.flow.mock_calls) == 0 # Config stored for domain. assert hass.data[hue.DOMAIN] == {} async def test_setup_defined_hosts_known_auth(hass): """Test we don't initiate a config entry if config bridge is known.""" with patch.object(hass, 'config_entries') as mock_config_entries, \ patch.object(hue, 'configured_hosts', return_value=['0.0.0.0']): assert await async_setup_component(hass, hue.DOMAIN, { hue.DOMAIN: { hue.CONF_BRIDGES: { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: 'bla.conf', hue.CONF_ALLOW_HUE_GROUPS: False, hue.CONF_ALLOW_UNREACHABLE: True } } }) is True # Flow started for discovered bridge assert len(mock_config_entries.flow.mock_calls) == 0 # Config stored for domain. assert hass.data[hue.DOMAIN] == { '0.0.0.0': { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: 'bla.conf', hue.CONF_ALLOW_HUE_GROUPS: False, hue.CONF_ALLOW_UNREACHABLE: True } } async def test_setup_defined_hosts_no_known_auth(hass): """Test we initiate config entry if config bridge is not known.""" with patch.object(hass, 'config_entries') as mock_config_entries, \ patch.object(hue, 'configured_hosts', return_value=[]): mock_config_entries.flow.async_init.return_value = mock_coro() assert await async_setup_component(hass, hue.DOMAIN, { hue.DOMAIN: { hue.CONF_BRIDGES: { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: 'bla.conf', hue.CONF_ALLOW_HUE_GROUPS: False, hue.CONF_ALLOW_UNREACHABLE: True } } }) is True # Flow started for discovered bridge assert len(mock_config_entries.flow.mock_calls) == 1 assert mock_config_entries.flow.mock_calls[0][2]['data'] == { 'host': '0.0.0.0', 'path': 'bla.conf', } # Config stored for domain. assert hass.data[hue.DOMAIN] == { '0.0.0.0': { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: 'bla.conf', hue.CONF_ALLOW_HUE_GROUPS: False, hue.CONF_ALLOW_UNREACHABLE: True } } async def test_config_passed_to_config_entry(hass): """Test that configured options for a host are loaded via config entry.""" entry = MockConfigEntry(domain=hue.DOMAIN, data={ 'host': '0.0.0.0', }) entry.add_to_hass(hass) mock_registry = Mock() with patch.object(hue, 'HueBridge') as mock_bridge, \ patch('homeassistant.helpers.device_registry.async_get_registry', return_value=mock_coro(mock_registry)): mock_bridge.return_value.async_setup.return_value = mock_coro(True) mock_bridge.return_value.api.config = Mock( mac='mock-mac', bridgeid='mock-bridgeid', raw={ 'modelid': 'mock-modelid', 'swversion': 'mock-swversion', } ) # Can't set name via kwargs mock_bridge.return_value.api.config.name = 'mock-name' assert await async_setup_component(hass, hue.DOMAIN, { hue.DOMAIN: { hue.CONF_BRIDGES: { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: 'bla.conf', hue.CONF_ALLOW_HUE_GROUPS: False, hue.CONF_ALLOW_UNREACHABLE: True } } }) is True assert len(mock_bridge.mock_calls) == 2 p_hass, p_entry, p_allow_unreachable, p_allow_groups = \ mock_bridge.mock_calls[0][1] assert p_hass is hass assert p_entry is entry assert p_allow_unreachable is True assert p_allow_groups is False assert len(mock_registry.mock_calls) == 1 assert mock_registry.mock_calls[0][2] == { 'config_entry': entry.entry_id, 'connections': { ('mac', 'mock-mac') }, 'identifiers': { ('hue', 'mock-bridgeid') }, 'manufacturer': 'Signify', 'name': 'mock-name', 'model': 'mock-modelid', 'sw_version': 'mock-swversion' } async def test_unload_entry(hass): """Test being able to unload an entry.""" entry = MockConfigEntry(domain=hue.DOMAIN, data={ 'host': '0.0.0.0', }) entry.add_to_hass(hass) with patch.object(hue, 'HueBridge') as mock_bridge, \ patch('homeassistant.helpers.device_registry.async_get_registry', return_value=mock_coro(Mock())): mock_bridge.return_value.async_setup.return_value = mock_coro(True) mock_bridge.return_value.api.config = Mock() assert await async_setup_component(hass, hue.DOMAIN, {}) is True assert len(mock_bridge.return_value.mock_calls) == 1 mock_bridge.return_value.async_reset.return_value = mock_coro(True) assert await hue.async_unload_entry(hass, entry) assert len(mock_bridge.return_value.async_reset.mock_calls) == 1 assert hass.data[hue.DOMAIN] == {}

35.109589

78

0.613864

from unittest.mock import Mock, patch from homeassistant.setup import async_setup_component from homeassistant.components import hue from tests.common import mock_coro, MockConfigEntry async def test_setup_with_no_config(hass): with patch.object(hass, 'config_entries') as mock_config_entries, \ patch.object(hue, 'configured_hosts', return_value=[]): assert await async_setup_component(hass, hue.DOMAIN, {}) is True assert len(mock_config_entries.flow.mock_calls) == 0 assert hass.data[hue.DOMAIN] == {} async def test_setup_with_discovery_no_known_auth(hass, aioclient_mock): aioclient_mock.get(hue.API_NUPNP, json=[ { 'internalipaddress': '0.0.0.0', 'id': 'abcd1234' } ]) with patch.object(hass, 'config_entries') as mock_config_entries, \ patch.object(hue, 'configured_hosts', return_value=[]): mock_config_entries.flow.async_init.return_value = mock_coro() assert await async_setup_component(hass, hue.DOMAIN, { hue.DOMAIN: {} }) is True assert len(mock_config_entries.flow.mock_calls) == 1 assert mock_config_entries.flow.mock_calls[0][2]['data'] == { 'host': '0.0.0.0', 'path': '.hue_abcd1234.conf', } assert hass.data[hue.DOMAIN] == { '0.0.0.0': { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: '.hue_abcd1234.conf', hue.CONF_ALLOW_HUE_GROUPS: hue.DEFAULT_ALLOW_HUE_GROUPS, hue.CONF_ALLOW_UNREACHABLE: hue.DEFAULT_ALLOW_UNREACHABLE, } } async def test_setup_with_discovery_known_auth(hass, aioclient_mock): aioclient_mock.get(hue.API_NUPNP, json=[ { 'internalipaddress': '0.0.0.0', 'id': 'abcd1234' } ]) with patch.object(hass, 'config_entries') as mock_config_entries, \ patch.object(hue, 'configured_hosts', return_value=['0.0.0.0']): assert await async_setup_component(hass, hue.DOMAIN, { hue.DOMAIN: {} }) is True assert len(mock_config_entries.flow.mock_calls) == 0 assert hass.data[hue.DOMAIN] == {} async def test_setup_defined_hosts_known_auth(hass): with patch.object(hass, 'config_entries') as mock_config_entries, \ patch.object(hue, 'configured_hosts', return_value=['0.0.0.0']): assert await async_setup_component(hass, hue.DOMAIN, { hue.DOMAIN: { hue.CONF_BRIDGES: { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: 'bla.conf', hue.CONF_ALLOW_HUE_GROUPS: False, hue.CONF_ALLOW_UNREACHABLE: True } } }) is True assert len(mock_config_entries.flow.mock_calls) == 0 assert hass.data[hue.DOMAIN] == { '0.0.0.0': { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: 'bla.conf', hue.CONF_ALLOW_HUE_GROUPS: False, hue.CONF_ALLOW_UNREACHABLE: True } } async def test_setup_defined_hosts_no_known_auth(hass): with patch.object(hass, 'config_entries') as mock_config_entries, \ patch.object(hue, 'configured_hosts', return_value=[]): mock_config_entries.flow.async_init.return_value = mock_coro() assert await async_setup_component(hass, hue.DOMAIN, { hue.DOMAIN: { hue.CONF_BRIDGES: { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: 'bla.conf', hue.CONF_ALLOW_HUE_GROUPS: False, hue.CONF_ALLOW_UNREACHABLE: True } } }) is True assert len(mock_config_entries.flow.mock_calls) == 1 assert mock_config_entries.flow.mock_calls[0][2]['data'] == { 'host': '0.0.0.0', 'path': 'bla.conf', } assert hass.data[hue.DOMAIN] == { '0.0.0.0': { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: 'bla.conf', hue.CONF_ALLOW_HUE_GROUPS: False, hue.CONF_ALLOW_UNREACHABLE: True } } async def test_config_passed_to_config_entry(hass): entry = MockConfigEntry(domain=hue.DOMAIN, data={ 'host': '0.0.0.0', }) entry.add_to_hass(hass) mock_registry = Mock() with patch.object(hue, 'HueBridge') as mock_bridge, \ patch('homeassistant.helpers.device_registry.async_get_registry', return_value=mock_coro(mock_registry)): mock_bridge.return_value.async_setup.return_value = mock_coro(True) mock_bridge.return_value.api.config = Mock( mac='mock-mac', bridgeid='mock-bridgeid', raw={ 'modelid': 'mock-modelid', 'swversion': 'mock-swversion', } ) mock_bridge.return_value.api.config.name = 'mock-name' assert await async_setup_component(hass, hue.DOMAIN, { hue.DOMAIN: { hue.CONF_BRIDGES: { hue.CONF_HOST: '0.0.0.0', hue.CONF_FILENAME: 'bla.conf', hue.CONF_ALLOW_HUE_GROUPS: False, hue.CONF_ALLOW_UNREACHABLE: True } } }) is True assert len(mock_bridge.mock_calls) == 2 p_hass, p_entry, p_allow_unreachable, p_allow_groups = \ mock_bridge.mock_calls[0][1] assert p_hass is hass assert p_entry is entry assert p_allow_unreachable is True assert p_allow_groups is False assert len(mock_registry.mock_calls) == 1 assert mock_registry.mock_calls[0][2] == { 'config_entry': entry.entry_id, 'connections': { ('mac', 'mock-mac') }, 'identifiers': { ('hue', 'mock-bridgeid') }, 'manufacturer': 'Signify', 'name': 'mock-name', 'model': 'mock-modelid', 'sw_version': 'mock-swversion' } async def test_unload_entry(hass): entry = MockConfigEntry(domain=hue.DOMAIN, data={ 'host': '0.0.0.0', }) entry.add_to_hass(hass) with patch.object(hue, 'HueBridge') as mock_bridge, \ patch('homeassistant.helpers.device_registry.async_get_registry', return_value=mock_coro(Mock())): mock_bridge.return_value.async_setup.return_value = mock_coro(True) mock_bridge.return_value.api.config = Mock() assert await async_setup_component(hass, hue.DOMAIN, {}) is True assert len(mock_bridge.return_value.mock_calls) == 1 mock_bridge.return_value.async_reset.return_value = mock_coro(True) assert await hue.async_unload_entry(hass, entry) assert len(mock_bridge.return_value.async_reset.mock_calls) == 1 assert hass.data[hue.DOMAIN] == {}

true

1c476b5d686fb5d71b925dc5ae700b71ab106d76

3,587

py

Python

auto_xml.py

tdwitham/AutohammerPy

1621400fd148f012bc59176ad51aa05c5c879c4f

[ "BSD-2-Clause" ]

null

auto_xml.py

tdwitham/AutohammerPy

1621400fd148f012bc59176ad51aa05c5c879c4f

[ "BSD-2-Clause" ]

null

auto_xml.py

tdwitham/AutohammerPy

1621400fd148f012bc59176ad51aa05c5c879c4f

[ "BSD-2-Clause" ]

null

# (c) 2016,2017 - Timothy D. Witham tim.wookie.witham@gmail.com # Licensed under BSD 2-Clause __author__ = 'wookie' import pprint from components.FileOps import writeLog, initialLogDir, makeLogDir from components.infrastructure import getSysInfo from components.MySQL import MySQLOps global DBOP runConfig = dict() secParms = dict() def ckheader(cEvent, cTag, cText): if (cEvent == 'start'): setTo = cTag else: setTo = None if (cTag == 'autohammer'): return True elif (cTag == 'config'): return True elif (cTag == 'connect'): return True elif cTag == 'import_code': return True elif cTag == 'run_code': return True elif (cTag == 'run_sql'): return True elif (cTag == 'sys_info'): return True return False def finishSection(thisSection): if (thisSection == 'config'): initialLogDir(runConfig) elif (thisSection == 'connect'): writeLog(1, '<connect>') connectDB(runConfig) writeLog(-1,'</connect>') elif (thisSection =='sys_info'): writeLog(1, '<sys_info>') getSysInfo() writeLog(-1, '</sys_info>') elif (thisSection =='run_sql'): runSQL(runConfig, secParms) elif (thisSection =='run_code'): runCode(runConfig, secParms ) elif (thisSection =='load_code'): loadCode() elif (thisSection =='autohammer'): finishIt() def doSection(thisSection, cEvent, cTag, cText): if thisSection == None: return elif thisSection == 'config': runConfig[cTag] = cText elif thisSection == 'run_sql': secParms[cTag] = cText elif thisSection == 'run_code': secParms[cTag] = cText elif thisSection == 'load_code': runConfig[cTag] = cText def validateConfig(): global runConfig global dbConfig print("runConfig") pprint.pprint(runConfig, width=1) print("dbConfig") pprint.pprint(dbConfig, width=1) print('Validate config') if dbConfig['test'].upper() == 'TPCC': runConfig['logDir'] = makeLogDir(dbConfig['rdbms'], dbConfig['test'], dbConfig['warehouses']) copyFiles(runConfig['logDir']) elif dbConfig['test'].upper() == 'TPCH': runConfig['logDir'] = makeLogDir(dbConfig['rdbms'], dbConfig['test'], dbConfig['db_scale']) copyFiles(runConfig['logDir']) def validateTest(): print('Validate test') def setupCode(): #validateCode() print('setting up code section - I think that this is a do not care') def runCode(): # validateCode() print('Running a code section ') def validateSQL(): print('Validate SQL config') def validateCode(): print('Validate Code config') def loadCode(): print("Inside of load code") def finishIt(): print("Done with Autohammer") def runSQL(runConfig, secParms): global DBOP if (secParms['use_db'] == 'system'): DBOP.connectAdmin(runConfig) DBOP.nowDoSQL(runConfig, secParms) DBOP.disconnectAdmin() else: DBOP.connectUser(runConfig) DBOP.nowDoSQL(runConfig, secParms) DBOP.disconnectUser() def connectDB(runCOnfig): global DBOP if runConfig['rdbms'].lower() == 'oracle': adminOracle(runConfig) elif runConfig['rdbms'].lower() == 'mysql': DBOP = MySQLOps() elif runConfig['rdbms'].lower() == 'mssql': DBOP = MSSQLDB() elif runConfig['rdbms'].lower() == 'pgsql': adminMSSQL(runConfig) else: writeLog("ERROR: Unknown RDBMS {}i\n".format(runConfig['rdbms']))

26.182482

101

0.622805

__author__ = 'wookie' import pprint from components.FileOps import writeLog, initialLogDir, makeLogDir from components.infrastructure import getSysInfo from components.MySQL import MySQLOps global DBOP runConfig = dict() secParms = dict() def ckheader(cEvent, cTag, cText): if (cEvent == 'start'): setTo = cTag else: setTo = None if (cTag == 'autohammer'): return True elif (cTag == 'config'): return True elif (cTag == 'connect'): return True elif cTag == 'import_code': return True elif cTag == 'run_code': return True elif (cTag == 'run_sql'): return True elif (cTag == 'sys_info'): return True return False def finishSection(thisSection): if (thisSection == 'config'): initialLogDir(runConfig) elif (thisSection == 'connect'): writeLog(1, '<connect>') connectDB(runConfig) writeLog(-1,'</connect>') elif (thisSection =='sys_info'): writeLog(1, '<sys_info>') getSysInfo() writeLog(-1, '</sys_info>') elif (thisSection =='run_sql'): runSQL(runConfig, secParms) elif (thisSection =='run_code'): runCode(runConfig, secParms ) elif (thisSection =='load_code'): loadCode() elif (thisSection =='autohammer'): finishIt() def doSection(thisSection, cEvent, cTag, cText): if thisSection == None: return elif thisSection == 'config': runConfig[cTag] = cText elif thisSection == 'run_sql': secParms[cTag] = cText elif thisSection == 'run_code': secParms[cTag] = cText elif thisSection == 'load_code': runConfig[cTag] = cText def validateConfig(): global runConfig global dbConfig print("runConfig") pprint.pprint(runConfig, width=1) print("dbConfig") pprint.pprint(dbConfig, width=1) print('Validate config') if dbConfig['test'].upper() == 'TPCC': runConfig['logDir'] = makeLogDir(dbConfig['rdbms'], dbConfig['test'], dbConfig['warehouses']) copyFiles(runConfig['logDir']) elif dbConfig['test'].upper() == 'TPCH': runConfig['logDir'] = makeLogDir(dbConfig['rdbms'], dbConfig['test'], dbConfig['db_scale']) copyFiles(runConfig['logDir']) def validateTest(): print('Validate test') def setupCode(): print('setting up code section - I think that this is a do not care') def runCode(): print('Running a code section ') def validateSQL(): print('Validate SQL config') def validateCode(): print('Validate Code config') def loadCode(): print("Inside of load code") def finishIt(): print("Done with Autohammer") def runSQL(runConfig, secParms): global DBOP if (secParms['use_db'] == 'system'): DBOP.connectAdmin(runConfig) DBOP.nowDoSQL(runConfig, secParms) DBOP.disconnectAdmin() else: DBOP.connectUser(runConfig) DBOP.nowDoSQL(runConfig, secParms) DBOP.disconnectUser() def connectDB(runCOnfig): global DBOP if runConfig['rdbms'].lower() == 'oracle': adminOracle(runConfig) elif runConfig['rdbms'].lower() == 'mysql': DBOP = MySQLOps() elif runConfig['rdbms'].lower() == 'mssql': DBOP = MSSQLDB() elif runConfig['rdbms'].lower() == 'pgsql': adminMSSQL(runConfig) else: writeLog("ERROR: Unknown RDBMS {}i\n".format(runConfig['rdbms']))

true

1c476bd27893d69e83ccb306a1d2ce80722a4ad1

9,534

py

Python

piqa/fsim.py

francois-rozet/spiq

a2e68c38da9129c85867e77641ed29d88e84c9d7

[ "MIT" ]

19

2020-10-12T13:57:21.000Z

2020-12-05T12:23:41.000Z

piqa/fsim.py

francois-rozet/spiq

a2e68c38da9129c85867e77641ed29d88e84c9d7

[ "MIT" ]

null

piqa/fsim.py

francois-rozet/spiq

a2e68c38da9129c85867e77641ed29d88e84c9d7

[ "MIT" ]

null

r"""Feature Similarity (FSIM) This module implements the FSIM in PyTorch. Original: https://www4.comp.polyu.edu.hk/~cslzhang/IQA/FSIM/FSIM.htm References: .. [Zhang2011] FSIM: A Feature Similarity Index for Image Quality Assessment (Zhang et al., 2011) .. [Kovesi1999] Image Features From Phase Congruency (Kovesi, 1999) """ import math import torch import torch.fft as fft import torch.nn as nn import torch.nn.functional as F from torch import Tensor from .utils import _jit, assert_type, reduce_tensor from .utils import complex as cx from .utils.color import ColorConv from .utils.functional import ( scharr_kernel, gradient_kernel, filter_grid, log_gabor, channel_conv, l2_norm, ) @_jit def fsim( x: Tensor, y: Tensor, pc_x: Tensor, pc_y: Tensor, kernel: Tensor, value_range: float = 1., t1: float = 0.85, t2: float = 160. / (255. ** 2), t3: float = 200. / (255. ** 2), t4: float = 200. / (255. ** 2), lmbda: float = 0.03, ) -> Tensor: r"""Returns the FSIM between :math:`x` and :math:`y`, without color space conversion and downsampling. Args: x: An input tensor, :math:`(N, 3 \text{ or } 1, H, W)`. y: A target tensor, :math:`(N, 3 \text{ or } 1, H, W)`. pc_x: The input phase congruency, :math:`(N, H, W)`. pc_y: The target phase congruency, :math:`(N, H, W)`. kernel: A gradient kernel, :math:`(2, 1, K, K)`. value_range: The value range :math:`L` of the inputs (usually `1.` or `255`). Note: For the remaining arguments, refer to [Zhang2011]_. Returns: The FSIM vector, :math:`(N,)`. Example: >>> x = torch.rand(5, 3, 256, 256) >>> y = torch.rand(5, 3, 256, 256) >>> filters = pc_filters(x) >>> pc_x = phase_congruency(x[:, :1], filters) >>> pc_y = phase_congruency(y[:, :1], filters) >>> kernel = gradient_kernel(scharr_kernel()) >>> l = fsim(x, y, pc_x, pc_y, kernel) >>> l.size() torch.Size([5]) """ t2 *= value_range ** 2 t3 *= value_range ** 2 t4 *= value_range ** 2 y_x, y_y = x[:, :1], y[:, :1] # Phase congruency similarity pc_m = torch.max(pc_x, pc_y) s_pc = (2 * pc_x * pc_y + t1) / (pc_x ** 2 + pc_y ** 2 + t1) # Gradient magnitude similarity pad = kernel.size(-1) // 2 g_x = l2_norm(channel_conv(y_x, kernel, padding=pad), dims=[1]) g_y = l2_norm(channel_conv(y_y, kernel, padding=pad), dims=[1]) s_g = (2 * g_x * g_y + t2) / (g_x ** 2 + g_y ** 2 + t2) # Chrominance similarity s_l = s_pc * s_g if x.size(1) == 3: i_x, i_y = x[:, 1], y[:, 1] q_x, q_y = x[:, 2], y[:, 2] s_i = (2 * i_x * i_y + t3) / (i_x ** 2 + i_y ** 2 + t3) s_q = (2 * q_x * q_y + t4) / (q_x ** 2 + q_y ** 2 + t4) s_iq = s_i * s_q s_iq = cx.complx(s_iq, torch.zeros_like(s_iq)) s_iq_lambda = cx.real(cx.pow(s_iq, lmbda)) s_l = s_l * s_iq_lambda # Feature similarity fs = (s_l * pc_m).sum(dim=(-1, -2)) / pc_m.sum(dim=(-1, -2)) return fs @_jit def pc_filters( x: Tensor, scales: int = 4, orientations: int = 4, wavelength: float = 6., factor: float = 2., sigma_f: float = 0.5978, # -log(0.55) sigma_theta: float = 0.6545, # pi / (4 * 1.2) ) -> Tensor: r"""Returns the log-Gabor filters for :func:`phase_congruency`. Args: x: An input tensor, :math:`(*, H, W)`. scales: The number of scales, :math:`S_1`. orientations: The number of orientations, :math:`S_2`. Note: For the remaining arguments, refer to [Kovesi1999]_. Returns: The filters tensor, :math:`(S_1, S_2, H, W)`. """ r, theta = filter_grid(x) # Low-pass filter lowpass = 1 / (1 + (r / 0.45) ** (2 * 15)) # Radial radial = [] for i in range(scales): f_0 = 1 / (wavelength * factor ** i) lg = log_gabor(r, f_0, sigma_f) radial.append(lg) radial = torch.stack(radial) # Angular cos_theta = torch.cos(theta) sin_theta = torch.sin(theta) theta_j = math.pi * torch.arange(orientations).to(x) / orientations theta_j = theta_j.reshape(orientations, 1, 1) ## Measure (theta - theta_j) in the sine/cosine domains ## to prevent wrap-around errors delta_sin = sin_theta * theta_j.cos() - cos_theta * theta_j.sin() delta_cos = cos_theta * theta_j.cos() + sin_theta * theta_j.sin() delta_theta = torch.atan2(delta_sin, delta_cos) angular = torch.exp(-delta_theta ** 2 / (2 * sigma_theta ** 2)) # Combination filters = lowpass * radial[:, None] * angular[None, :] return filters @_jit def phase_congruency( x: Tensor, filters: Tensor, value_range: float = 1., k: float = 2., rescale: float = 1.7, eps: float = 1e-8, ) -> Tensor: r"""Returns the Phase Congruency (PC) of :math:`x`. Args: x: An input tensor, :math:`(N, 1, H, W)`. filters: The frequency domain filters, :math:`(S_1, S_2, H, W)`. value_range: The value range :math:`L` of the input (usually `1.` or `255`). Note: For the remaining arguments, refer to [Kovesi1999]_. Returns: The PC tensor, :math:`(N, H, W)`. Example: >>> x = torch.rand(5, 1, 256, 256) >>> filters = pc_filters(x) >>> pc = phase_congruency(x, filters) >>> pc.size() torch.Size([5, 256, 256]) """ x = x * (255. / value_range) # Filters M_hat = filters M = fft.ifft2(M_hat) M = cx.real(torch.view_as_real(M)) # Even & odd (real and imaginary) responses eo = fft.ifft2(fft.fft2(x[:, None]) * M_hat) eo = torch.view_as_real(eo) # Amplitude A = cx.mod(eo) # Expected E^2 A2 = A[:, 0] ** 2 median_A2, _ = A2.flatten(-2).median(dim=-1) expect_A2 = median_A2 / math.log(2) expect_M2_hat = (M_hat[0] ** 2).mean(dim=(-1, -2)) expect_MiMj = (M[:, None] * M[None, :]).sum(dim=(0, 1, 3, 4)) expect_E2 = expect_A2 * expect_MiMj / expect_M2_hat # Threshold sigma_G = expect_E2.sqrt() mu_R = sigma_G * (math.pi / 2) ** 0.5 sigma_R = sigma_G * (2 - math.pi / 2) ** 0.5 T = mu_R + k * sigma_R T = T / rescale # emprirical rescaling T = T[..., None, None] # Phase deviation FH = eo.sum(dim=1, keepdim=True) phi_eo = FH / (cx.mod(FH)[..., None] + eps) E = cx.dot(eo, phi_eo) - cx.dot(eo, cx.turn(phi_eo)).abs() E = E.sum(dim=1) # Phase congruency pc = (E - T).relu().sum(dim=1) / (A.sum(dim=(1, 2)) + eps) return pc class FSIM(nn.Module): r"""Creates a criterion that measures the FSIM between an input and a target. Before applying :func:`fsim`, the input and target are converted from RBG to Y(IQ) and downsampled by a factor :math:`\frac{\min(H, W)}{256}`. Args: chromatic: Whether to use the chromatic channels (IQ) or not. downsample: Whether downsampling is enabled or not. kernel: A gradient kernel, :math:`(2, 1, K, K)`. If `None`, use the Scharr kernel instead. reduction: Specifies the reduction to apply to the output: `'none'` | `'mean'` | `'sum'`. Note: `**kwargs` are passed to :func:`fsim`. Shapes: input: :math:`(N, 3, H, W)` target: :math:`(N, 3, H, W)` output: :math:`(N,)` or :math:`()` depending on `reduction` Example: >>> criterion = FSIM().cuda() >>> x = torch.rand(5, 3, 256, 256, requires_grad=True).cuda() >>> y = torch.rand(5, 3, 256, 256).cuda() >>> l = 1 - criterion(x, y) >>> l.size() torch.Size([]) >>> l.backward() """ def __init__( self, chromatic: bool = True, downsample: bool = True, kernel: Tensor = None, reduction: str = 'mean', **kwargs, ): super().__init__() if kernel is None: kernel = gradient_kernel(scharr_kernel()) self.register_buffer('kernel', kernel) self.register_buffer('filters', torch.zeros((0, 0, 0, 0))) self.convert = ColorConv('RGB', 'YIQ' if chromatic else 'Y') self.downsample = downsample self.reduction = reduction self.value_range = kwargs.get('value_range', 1.) self.kwargs = kwargs def forward(self, input: Tensor, target: Tensor) -> Tensor: assert_type( input, target, device=self.kernel.device, dim_range=(4, 4), n_channels=3, value_range=(0., self.value_range), ) # Downsample if self.downsample: _, _, h, w = input.size() M = round(min(h, w) / 256) if M > 1: input = F.avg_pool2d(input, kernel_size=M, ceil_mode=True) target = F.avg_pool2d(target, kernel_size=M, ceil_mode=True) # RGB to Y(IQ) input = self.convert(input) target = self.convert(target) # Phase congruency if self.filters.shape[-2:] != input.shape[-2:]: self.filters = pc_filters(input) pc_input = phase_congruency(input[:, :1], self.filters, self.value_range) pc_target = phase_congruency(target[:, :1], self.filters, self.value_range) # FSIM l = fsim(input, target, pc_input, pc_target, kernel=self.kernel, **self.kwargs) return reduce_tensor(l, self.reduction)

27.877193

101

0.560835

import math import torch import torch.fft as fft import torch.nn as nn import torch.nn.functional as F from torch import Tensor from .utils import _jit, assert_type, reduce_tensor from .utils import complex as cx from .utils.color import ColorConv from .utils.functional import ( scharr_kernel, gradient_kernel, filter_grid, log_gabor, channel_conv, l2_norm, ) @_jit def fsim( x: Tensor, y: Tensor, pc_x: Tensor, pc_y: Tensor, kernel: Tensor, value_range: float = 1., t1: float = 0.85, t2: float = 160. / (255. ** 2), t3: float = 200. / (255. ** 2), t4: float = 200. / (255. ** 2), lmbda: float = 0.03, ) -> Tensor: t2 *= value_range ** 2 t3 *= value_range ** 2 t4 *= value_range ** 2 y_x, y_y = x[:, :1], y[:, :1] pc_m = torch.max(pc_x, pc_y) s_pc = (2 * pc_x * pc_y + t1) / (pc_x ** 2 + pc_y ** 2 + t1) pad = kernel.size(-1) // 2 g_x = l2_norm(channel_conv(y_x, kernel, padding=pad), dims=[1]) g_y = l2_norm(channel_conv(y_y, kernel, padding=pad), dims=[1]) s_g = (2 * g_x * g_y + t2) / (g_x ** 2 + g_y ** 2 + t2) s_l = s_pc * s_g if x.size(1) == 3: i_x, i_y = x[:, 1], y[:, 1] q_x, q_y = x[:, 2], y[:, 2] s_i = (2 * i_x * i_y + t3) / (i_x ** 2 + i_y ** 2 + t3) s_q = (2 * q_x * q_y + t4) / (q_x ** 2 + q_y ** 2 + t4) s_iq = s_i * s_q s_iq = cx.complx(s_iq, torch.zeros_like(s_iq)) s_iq_lambda = cx.real(cx.pow(s_iq, lmbda)) s_l = s_l * s_iq_lambda fs = (s_l * pc_m).sum(dim=(-1, -2)) / pc_m.sum(dim=(-1, -2)) return fs @_jit def pc_filters( x: Tensor, scales: int = 4, orientations: int = 4, wavelength: float = 6., factor: float = 2., sigma_f: float = 0.5978, sigma_theta: float = 0.6545, ) -> Tensor: r, theta = filter_grid(x) lowpass = 1 / (1 + (r / 0.45) ** (2 * 15)) radial = [] for i in range(scales): f_0 = 1 / (wavelength * factor ** i) lg = log_gabor(r, f_0, sigma_f) radial.append(lg) radial = torch.stack(radial) cos_theta = torch.cos(theta) sin_theta = torch.sin(theta) theta_j = math.pi * torch.arange(orientations).to(x) / orientations theta_j = theta_j.reshape(orientations, 1, 1) _cos = cos_theta * theta_j.cos() + sin_theta * theta_j.sin() delta_theta = torch.atan2(delta_sin, delta_cos) angular = torch.exp(-delta_theta ** 2 / (2 * sigma_theta ** 2)) filters = lowpass * radial[:, None] * angular[None, :] return filters @_jit def phase_congruency( x: Tensor, filters: Tensor, value_range: float = 1., k: float = 2., rescale: float = 1.7, eps: float = 1e-8, ) -> Tensor: x = x * (255. / value_range) M_hat = filters M = fft.ifft2(M_hat) M = cx.real(torch.view_as_real(M)) eo = fft.ifft2(fft.fft2(x[:, None]) * M_hat) eo = torch.view_as_real(eo) A = cx.mod(eo) A2 = A[:, 0] ** 2 median_A2, _ = A2.flatten(-2).median(dim=-1) expect_A2 = median_A2 / math.log(2) expect_M2_hat = (M_hat[0] ** 2).mean(dim=(-1, -2)) expect_MiMj = (M[:, None] * M[None, :]).sum(dim=(0, 1, 3, 4)) expect_E2 = expect_A2 * expect_MiMj / expect_M2_hat sigma_G = expect_E2.sqrt() mu_R = sigma_G * (math.pi / 2) ** 0.5 sigma_R = sigma_G * (2 - math.pi / 2) ** 0.5 T = mu_R + k * sigma_R T = T / rescale T = T[..., None, None] FH = eo.sum(dim=1, keepdim=True) phi_eo = FH / (cx.mod(FH)[..., None] + eps) E = cx.dot(eo, phi_eo) - cx.dot(eo, cx.turn(phi_eo)).abs() E = E.sum(dim=1) pc = (E - T).relu().sum(dim=1) / (A.sum(dim=(1, 2)) + eps) return pc class FSIM(nn.Module): def __init__( self, chromatic: bool = True, downsample: bool = True, kernel: Tensor = None, reduction: str = 'mean', **kwargs, ): super().__init__() if kernel is None: kernel = gradient_kernel(scharr_kernel()) self.register_buffer('kernel', kernel) self.register_buffer('filters', torch.zeros((0, 0, 0, 0))) self.convert = ColorConv('RGB', 'YIQ' if chromatic else 'Y') self.downsample = downsample self.reduction = reduction self.value_range = kwargs.get('value_range', 1.) self.kwargs = kwargs def forward(self, input: Tensor, target: Tensor) -> Tensor: assert_type( input, target, device=self.kernel.device, dim_range=(4, 4), n_channels=3, value_range=(0., self.value_range), ) if self.downsample: _, _, h, w = input.size() M = round(min(h, w) / 256) if M > 1: input = F.avg_pool2d(input, kernel_size=M, ceil_mode=True) target = F.avg_pool2d(target, kernel_size=M, ceil_mode=True) input = self.convert(input) target = self.convert(target) if self.filters.shape[-2:] != input.shape[-2:]: self.filters = pc_filters(input) pc_input = phase_congruency(input[:, :1], self.filters, self.value_range) pc_target = phase_congruency(target[:, :1], self.filters, self.value_range) l = fsim(input, target, pc_input, pc_target, kernel=self.kernel, **self.kwargs) return reduce_tensor(l, self.reduction)

true

1c476c016e38e87c7a75eeb62acb50db4e2d2883

1,623

py

Python

tests/test_exceptions.py

dobisel/yhttp

4396c03905d71b801a92dead3504cc3ef7d98d79

[ "MIT" ]

10

2020-01-30T16:23:28.000Z

2021-12-12T23:24:37.000Z

tests/test_exceptions.py

dobisel/yhttp

4396c03905d71b801a92dead3504cc3ef7d98d79

[ "MIT" ]

1

2021-07-12T21:07:06.000Z

2021-08-08T10:42:27.000Z

tests/test_exceptions.py

dobisel/yhttp

4396c03905d71b801a92dead3504cc3ef7d98d79

[ "MIT" ]

1

2020-01-26T13:28:35.000Z

import pytest from bddrest import status, response, when from yhttp import statuses def test_httpstatus(app, Given): @app.route() def get(req): raise statuses.badrequest() @app.route('/foo') def get(req): return statuses.badrequest() with Given(): assert status == '400 Bad Request' assert response.text.startswith('400 Bad Request\r\n') assert response.headers['content-type'] == 'text/plain; charset=utf-8' app.settings.debug = False when() assert status == '400 Bad Request' assert response.text == '400 Bad Request' assert response.headers['content-type'] == 'text/plain; charset=utf-8' when('/foo') assert status == 400 def test_unhandledexception(app, Given): class MyException(Exception): pass @app.route() def get(req): raise MyException() with pytest.raises(MyException), Given(): pass def test_redirect(app, Given): @app.route() def get(req): raise statuses.found('http://example.com') with Given(): assert status == 302 assert response.headers['location'] == 'http://example.com' assert response.text == '' def test_modified(app, Given): @app.route() def get(req): raise statuses.notmodified() with Given(): assert status == 304 assert response.text == '' def test_nocontent(app, Given): @app.route() def remove(req): raise statuses.nocontent() with Given(verb='REMOVE'): assert status == 204 assert response == ''

21.077922

78

0.601356

import pytest from bddrest import status, response, when from yhttp import statuses def test_httpstatus(app, Given): @app.route() def get(req): raise statuses.badrequest() @app.route('/foo') def get(req): return statuses.badrequest() with Given(): assert status == '400 Bad Request' assert response.text.startswith('400 Bad Request\r\n') assert response.headers['content-type'] == 'text/plain; charset=utf-8' app.settings.debug = False when() assert status == '400 Bad Request' assert response.text == '400 Bad Request' assert response.headers['content-type'] == 'text/plain; charset=utf-8' when('/foo') assert status == 400 def test_unhandledexception(app, Given): class MyException(Exception): pass @app.route() def get(req): raise MyException() with pytest.raises(MyException), Given(): pass def test_redirect(app, Given): @app.route() def get(req): raise statuses.found('http://example.com') with Given(): assert status == 302 assert response.headers['location'] == 'http://example.com' assert response.text == '' def test_modified(app, Given): @app.route() def get(req): raise statuses.notmodified() with Given(): assert status == 304 assert response.text == '' def test_nocontent(app, Given): @app.route() def remove(req): raise statuses.nocontent() with Given(verb='REMOVE'): assert status == 204 assert response == ''

true

1c476cdd7fb60214bfeb7c01ad0034abc05bd191

3,585

py

Python

plots/thresholds/vit.py

drunkcoding/model-inference

02d2240bc7052fa32223a80fa63625fe681db102

[ "MIT" ]

1

2021-11-15T19:07:13.000Z

plots/thresholds/vit.py

drunkcoding/model-inference

02d2240bc7052fa32223a80fa63625fe681db102

[ "MIT" ]

null

plots/thresholds/vit.py

drunkcoding/model-inference

02d2240bc7052fa32223a80fa63625fe681db102

[ "MIT" ]

null

from dataclasses import dataclass, field from functools import partial import itertools import json import logging import os import time import seaborn as sns import matplotlib.pyplot as plt from sklearn.metrics import accuracy_score import torch from transformers import AutoModelForImageClassification, ViTForImageClassification from torchvision.datasets import ImageNet import datasets from hfutils.preprocess import ( split_train_test, vit_collate_fn, ViTFeatureExtractorTransforms, ) import pandas as pd from torch.utils.data import DataLoader from tqdm import tqdm import numpy as np from hfutils.logger import Logger from hfutils.pipe.vit import ViTPyTorchPipeForImageClassification from hfutils.calibration import temperature_scale import sys sys.path.append(".") from plots.thresholds.utils import * home_dir = "/mnt/raid0nvme1" dataset_path = os.path.join(home_dir, "ImageNet") model_keys = [ "XS", "S", "M", "L", ] model_names = [ "vit-tiny-patch16-224", "vit-small-patch16-224", "vit-base-patch16-224", "vit-large-patch16-224", ] device_map = [ "cuda:4", "cuda:4", "cuda:4", "cuda:4", ] model_paths = [ f"{home_dir}/HuggingFace/WinKawaks/vit-tiny-patch16-224", f"{home_dir}/HuggingFace/WinKawaks/vit-small-patch16-224", f"{home_dir}/HuggingFace/google/vit-base-patch16-224", f"{home_dir}/HuggingFace/google/vit-large-patch16-224", ] model_paths = dict(zip(model_keys, model_paths)) model_names = dict(zip(model_keys, model_names)) model_device = dict(zip(model_keys, device_map)) def model_inference(model, batch, temperature=None, device="cuda:0"): pixel_values = batch["pixel_values"].to(device) logits = model((pixel_values,)) if temperature is not None: logits = temperature_scale(logits, temperature) return logits with open("tests/kernel_duration/latency.json", "r") as fp: model_latency = json.load(fp) with open("repository/repo_vit/meta.json", "r") as fp: model_meta = json.load(fp) dataset = ImageNet( dataset_path, split="train", transform=ViTFeatureExtractorTransforms(model_paths[model_keys[0]], split="val"), ) dataset, _ = split_train_test(dataset, 0.98) num_labels = len(dataset) dataloader = DataLoader( dataset, shuffle=True, collate_fn=vit_collate_fn, batch_size=32, drop_last=True, ) models = load_models( model_keys, model_paths, model_device, ViTForImageClassification, ViTPyTorchPipeForImageClassification, ) n_models = len(model_keys) model_outputs = dict(zip(model_keys, [list() for _ in range(n_models)])) m = torch.nn.Softmax(dim=-1) labels = [] for batch in tqdm(dataloader, desc="Collect Train Data"): label = batch["labels"] for i, key in enumerate(model_keys): logits = model_inference( models[key], batch, device=model_device[key], temperature=model_meta[model_names[key]]["temperature"], ) model_outputs[key].append(logits) labels.append(label) model_probs, model_ans, model_outputs, labels = postprocessing_inference( model_keys, model_outputs, labels, m ) all_thresholds = list( itertools.product(np.linspace(0, 1, endpoint=True, num=100), repeat=n_models - 1) ) max_size = 100000 if len(all_thresholds) > max_size: rnd_idx = np.random.randint(0, len(all_thresholds), max_size) all_thresholds = [all_thresholds[i] for i in rnd_idx] profile_thresholds( model_keys, model_probs, model_ans, model_latency, model_names, all_thresholds, "vit", )

24.724138

85

0.72106

from dataclasses import dataclass, field from functools import partial import itertools import json import logging import os import time import seaborn as sns import matplotlib.pyplot as plt from sklearn.metrics import accuracy_score import torch from transformers import AutoModelForImageClassification, ViTForImageClassification from torchvision.datasets import ImageNet import datasets from hfutils.preprocess import ( split_train_test, vit_collate_fn, ViTFeatureExtractorTransforms, ) import pandas as pd from torch.utils.data import DataLoader from tqdm import tqdm import numpy as np from hfutils.logger import Logger from hfutils.pipe.vit import ViTPyTorchPipeForImageClassification from hfutils.calibration import temperature_scale import sys sys.path.append(".") from plots.thresholds.utils import * home_dir = "/mnt/raid0nvme1" dataset_path = os.path.join(home_dir, "ImageNet") model_keys = [ "XS", "S", "M", "L", ] model_names = [ "vit-tiny-patch16-224", "vit-small-patch16-224", "vit-base-patch16-224", "vit-large-patch16-224", ] device_map = [ "cuda:4", "cuda:4", "cuda:4", "cuda:4", ] model_paths = [ f"{home_dir}/HuggingFace/WinKawaks/vit-tiny-patch16-224", f"{home_dir}/HuggingFace/WinKawaks/vit-small-patch16-224", f"{home_dir}/HuggingFace/google/vit-base-patch16-224", f"{home_dir}/HuggingFace/google/vit-large-patch16-224", ] model_paths = dict(zip(model_keys, model_paths)) model_names = dict(zip(model_keys, model_names)) model_device = dict(zip(model_keys, device_map)) def model_inference(model, batch, temperature=None, device="cuda:0"): pixel_values = batch["pixel_values"].to(device) logits = model((pixel_values,)) if temperature is not None: logits = temperature_scale(logits, temperature) return logits with open("tests/kernel_duration/latency.json", "r") as fp: model_latency = json.load(fp) with open("repository/repo_vit/meta.json", "r") as fp: model_meta = json.load(fp) dataset = ImageNet( dataset_path, split="train", transform=ViTFeatureExtractorTransforms(model_paths[model_keys[0]], split="val"), ) dataset, _ = split_train_test(dataset, 0.98) num_labels = len(dataset) dataloader = DataLoader( dataset, shuffle=True, collate_fn=vit_collate_fn, batch_size=32, drop_last=True, ) models = load_models( model_keys, model_paths, model_device, ViTForImageClassification, ViTPyTorchPipeForImageClassification, ) n_models = len(model_keys) model_outputs = dict(zip(model_keys, [list() for _ in range(n_models)])) m = torch.nn.Softmax(dim=-1) labels = [] for batch in tqdm(dataloader, desc="Collect Train Data"): label = batch["labels"] for i, key in enumerate(model_keys): logits = model_inference( models[key], batch, device=model_device[key], temperature=model_meta[model_names[key]]["temperature"], ) model_outputs[key].append(logits) labels.append(label) model_probs, model_ans, model_outputs, labels = postprocessing_inference( model_keys, model_outputs, labels, m ) all_thresholds = list( itertools.product(np.linspace(0, 1, endpoint=True, num=100), repeat=n_models - 1) ) max_size = 100000 if len(all_thresholds) > max_size: rnd_idx = np.random.randint(0, len(all_thresholds), max_size) all_thresholds = [all_thresholds[i] for i in rnd_idx] profile_thresholds( model_keys, model_probs, model_ans, model_latency, model_names, all_thresholds, "vit", )

true

1c476e3ec222661def123f38fb26ec5839432659

1,087

py

Python

src/utils/etc.py

slowwavesleep/NeuralMorphemeSegmenter

b32f47ecc380262755bf436cf793f35901919f0f

[ "MIT" ]

null

src/utils/etc.py

slowwavesleep/NeuralMorphemeSegmenter

b32f47ecc380262755bf436cf793f35901919f0f

[ "MIT" ]

null

src/utils/etc.py

slowwavesleep/NeuralMorphemeSegmenter

b32f47ecc380262755bf436cf793f35901919f0f

[ "MIT" ]

null

import itertools import json from typing import Iterable, List, Tuple def remove_pads(sequences: Iterable[Iterable[int]], true_lengths: Iterable[int], *, pre_pad: bool = False) -> List[List[int]]: assert len(sequences) == len(true_lengths) output = [] for element, true_length in zip(sequences, true_lengths): if pre_pad: element = element[max(0, len(element) - true_length):] else: element = element[:true_length] output.append(list(element)) return output def flatten_list(list_to_flatten: List[list]) -> list: return list(itertools.chain(*list_to_flatten)) def read_experiment_data(path: str) -> Tuple[List[int], List[str], List[str]]: indices = [] original = [] segmented = [] with open(path) as file: for line in file: data = json.loads(line) indices.append(data["index"]) original.append(data["original"]) segmented.append(data["segmented"]) return indices, original, segmented

26.512195

78

0.609936

import itertools import json from typing import Iterable, List, Tuple def remove_pads(sequences: Iterable[Iterable[int]], true_lengths: Iterable[int], *, pre_pad: bool = False) -> List[List[int]]: assert len(sequences) == len(true_lengths) output = [] for element, true_length in zip(sequences, true_lengths): if pre_pad: element = element[max(0, len(element) - true_length):] else: element = element[:true_length] output.append(list(element)) return output def flatten_list(list_to_flatten: List[list]) -> list: return list(itertools.chain(*list_to_flatten)) def read_experiment_data(path: str) -> Tuple[List[int], List[str], List[str]]: indices = [] original = [] segmented = [] with open(path) as file: for line in file: data = json.loads(line) indices.append(data["index"]) original.append(data["original"]) segmented.append(data["segmented"]) return indices, original, segmented

true

1c476f371ca7d1b74fa727dff3dcc27f059ba338

4,943

py

Python

tiddlyweb/serializations/json.py

angeluseve/tiddlyweb

d24a45d48faa2b014e1c1598ec176c4c1c98fb07

[ "BSD-3-Clause" ]

1

2016-05-09T15:26:17.000Z

tiddlyweb/serializations/json.py

angeluseve/tiddlyweb

d24a45d48faa2b014e1c1598ec176c4c1c98fb07

[ "BSD-3-Clause" ]

null

tiddlyweb/serializations/json.py

angeluseve/tiddlyweb

d24a45d48faa2b014e1c1598ec176c4c1c98fb07

[ "BSD-3-Clause" ]

null

""" JSON based serializer. """ import simplejson from base64 import b64encode, b64decode from tiddlyweb.serializations import SerializationInterface from tiddlyweb.model.bag import Bag from tiddlyweb.model.policy import Policy class Serialization(SerializationInterface): """ Turn various entities to and from JSON. """ def list_recipes(self, recipes): """ Create a JSON list of recipe names from the provided recipes. """ return simplejson.dumps([recipe.name for recipe in recipes]) def list_bags(self, bags): """ Create a JSON list of bag names from the provided bags. """ return simplejson.dumps([bag.name for bag in bags]) def list_tiddlers(self, bag): """ List the tiddlers in a bag as JSON. The format is a list of dicts in the form described by self._tiddler_dict. """ return simplejson.dumps([self._tiddler_dict(tiddler) for tiddler in bag.list_tiddlers()]) def recipe_as(self, recipe): """ A recipe as a JSON dictionary. """ policy = recipe.policy policy_dict = {} for key in ['owner', 'read', 'write', 'create', 'delete', 'manage']: policy_dict[key] = getattr(policy, key) return simplejson.dumps(dict(desc=recipe.desc, policy=policy_dict, recipe=recipe.get_recipe())) def as_recipe(self, recipe, input_string): """ Turn a JSON dictionary into a Recipe if it is in the proper form. Include the policy. """ info = simplejson.loads(input_string) try: recipe.set_recipe(info['recipe']) recipe.desc = info['desc'] if info['policy']: recipe.policy = Policy() for key, value in info['policy'].items(): recipe.policy.__setattr__(key, value) except KeyError: pass return recipe def bag_as(self, bag): """ Create a JSON dictionary representing a Bag and Policy. """ policy = bag.policy policy_dict = {} for key in ['owner', 'read', 'write', 'create', 'delete', 'manage']: policy_dict[key] = getattr(policy, key) info = dict(policy=policy_dict, desc=bag.desc) return simplejson.dumps(info) def as_bag(self, bag, input_string): """ Turn a JSON string into a bag. """ info = simplejson.loads(input_string) if info['policy']: bag.policy = Policy() for key, value in info['policy'].items(): bag.policy.__setattr__(key, value) bag.desc = info.get('desc', '') return bag def tiddler_as(self, tiddler): """ Create a JSON dictionary representing a tiddler, as described by _tiddler_dict plus the text of the tiddler. """ tiddler_dict = self._tiddler_dict(tiddler) if tiddler.type and tiddler.type != 'None': tiddler_dict['text'] = b64encode(tiddler.text) else: tiddler_dict['text'] = tiddler.text return simplejson.dumps(tiddler_dict) def as_tiddler(self, tiddler, input_string): """ Turn a JSON dictionary into a Tiddler. """ dict_from_input = simplejson.loads(input_string) accepted_keys = ['created', 'modified', 'modifier', 'tags', 'fields', 'text', 'type'] for key, value in dict_from_input.iteritems(): if value and key in accepted_keys: setattr(tiddler, key, value) if tiddler.type and tiddler.type != 'None': tiddler.text = b64decode(tiddler.text) return tiddler def _tiddler_dict(self, tiddler): """ Select fields from a tiddler to create a dictonary. """ unwanted_keys = ['text', 'store'] wanted_keys = [attribute for attribute in tiddler.slots if attribute not in unwanted_keys] wanted_info = {} for attribute in wanted_keys: wanted_info[attribute] = getattr(tiddler, attribute, None) wanted_info['permissions'] = self._tiddler_permissions(tiddler) try: fat = self.environ['tiddlyweb.query'].get('fat', [None])[0] if fat: wanted_info['text'] = tiddler.text except KeyError: pass # tiddlyweb.query is not there return dict(wanted_info) def _tiddler_permissions(self, tiddler): """ Make a list of the permissions the current user has on this tiddler. """ perms = [] bag = Bag(tiddler.bag) if tiddler.store: bag = tiddler.store.get(bag) if 'tiddlyweb.usersign' in self.environ: perms = bag.policy.user_perms(self.environ['tiddlyweb.usersign']) return perms

32.519737

103

0.584665

import simplejson from base64 import b64encode, b64decode from tiddlyweb.serializations import SerializationInterface from tiddlyweb.model.bag import Bag from tiddlyweb.model.policy import Policy class Serialization(SerializationInterface): def list_recipes(self, recipes): return simplejson.dumps([recipe.name for recipe in recipes]) def list_bags(self, bags): return simplejson.dumps([bag.name for bag in bags]) def list_tiddlers(self, bag): return simplejson.dumps([self._tiddler_dict(tiddler) for tiddler in bag.list_tiddlers()]) def recipe_as(self, recipe): policy = recipe.policy policy_dict = {} for key in ['owner', 'read', 'write', 'create', 'delete', 'manage']: policy_dict[key] = getattr(policy, key) return simplejson.dumps(dict(desc=recipe.desc, policy=policy_dict, recipe=recipe.get_recipe())) def as_recipe(self, recipe, input_string): info = simplejson.loads(input_string) try: recipe.set_recipe(info['recipe']) recipe.desc = info['desc'] if info['policy']: recipe.policy = Policy() for key, value in info['policy'].items(): recipe.policy.__setattr__(key, value) except KeyError: pass return recipe def bag_as(self, bag): policy = bag.policy policy_dict = {} for key in ['owner', 'read', 'write', 'create', 'delete', 'manage']: policy_dict[key] = getattr(policy, key) info = dict(policy=policy_dict, desc=bag.desc) return simplejson.dumps(info) def as_bag(self, bag, input_string): info = simplejson.loads(input_string) if info['policy']: bag.policy = Policy() for key, value in info['policy'].items(): bag.policy.__setattr__(key, value) bag.desc = info.get('desc', '') return bag def tiddler_as(self, tiddler): tiddler_dict = self._tiddler_dict(tiddler) if tiddler.type and tiddler.type != 'None': tiddler_dict['text'] = b64encode(tiddler.text) else: tiddler_dict['text'] = tiddler.text return simplejson.dumps(tiddler_dict) def as_tiddler(self, tiddler, input_string): dict_from_input = simplejson.loads(input_string) accepted_keys = ['created', 'modified', 'modifier', 'tags', 'fields', 'text', 'type'] for key, value in dict_from_input.iteritems(): if value and key in accepted_keys: setattr(tiddler, key, value) if tiddler.type and tiddler.type != 'None': tiddler.text = b64decode(tiddler.text) return tiddler def _tiddler_dict(self, tiddler): unwanted_keys = ['text', 'store'] wanted_keys = [attribute for attribute in tiddler.slots if attribute not in unwanted_keys] wanted_info = {} for attribute in wanted_keys: wanted_info[attribute] = getattr(tiddler, attribute, None) wanted_info['permissions'] = self._tiddler_permissions(tiddler) try: fat = self.environ['tiddlyweb.query'].get('fat', [None])[0] if fat: wanted_info['text'] = tiddler.text except KeyError: pass return dict(wanted_info) def _tiddler_permissions(self, tiddler): perms = [] bag = Bag(tiddler.bag) if tiddler.store: bag = tiddler.store.get(bag) if 'tiddlyweb.usersign' in self.environ: perms = bag.policy.user_perms(self.environ['tiddlyweb.usersign']) return perms

true

1c4771447baf8ca0aea72d01cd74569e19c6a862

7,917

py

Python

solo/methods/nnsiam.py

ludysama/crp

08027b67f174426ddac5eef8186349e8337481fc

[ "MIT" ]

2

2021-11-02T07:38:33.000Z

2021-11-21T12:55:28.000Z

solo/methods/nnsiam.py

ludysama/crp

08027b67f174426ddac5eef8186349e8337481fc

[ "MIT" ]

null

solo/methods/nnsiam.py

ludysama/crp

08027b67f174426ddac5eef8186349e8337481fc

[ "MIT" ]

null

# Copyright 2021 solo-learn development team. # 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 argparse from typing import Any, Dict, List, Sequence, Tuple import torch import torch.nn as nn import torch.nn.functional as F from solo.losses.simsiam import simsiam_loss_func from solo.methods.base import BaseMethod from solo.utils.misc import gather class NNSiam(BaseMethod): def __init__( self, proj_output_dim: int, proj_hidden_dim: int, pred_hidden_dim: int, queue_size: int, **kwargs, ): """Implements NNSiam (https://arxiv.org/abs/2104.14548). Args: proj_output_dim (int): number of dimensions of projected features. proj_hidden_dim (int): number of neurons of the hidden layers of the projector. pred_hidden_dim (int): number of neurons of the hidden layers of the predictor. queue_size (int): number of samples to keep in the queue. """ super().__init__(**kwargs) self.queue_size = queue_size # projector self.projector = nn.Sequential( nn.Linear(self.features_dim, proj_hidden_dim, bias=False), nn.BatchNorm1d(proj_hidden_dim), nn.ReLU(), nn.Linear(proj_hidden_dim, proj_hidden_dim, bias=False), nn.BatchNorm1d(proj_hidden_dim), nn.ReLU(), nn.Linear(proj_hidden_dim, proj_output_dim), nn.BatchNorm1d(proj_output_dim, affine=False), ) self.projector[6].bias.requires_grad = False # hack: not use bias as it is followed by BN # predictor self.predictor = nn.Sequential( nn.Linear(proj_output_dim, pred_hidden_dim, bias=False), nn.BatchNorm1d(pred_hidden_dim), nn.ReLU(), nn.Linear(pred_hidden_dim, proj_output_dim), ) # queue self.register_buffer("queue", torch.randn(self.queue_size, proj_output_dim)) self.register_buffer("queue_y", -torch.ones(self.queue_size, dtype=torch.long)) self.queue = F.normalize(self.queue, dim=1) self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long)) @staticmethod def add_model_specific_args(parent_parser: argparse.ArgumentParser) -> argparse.ArgumentParser: parent_parser = super(NNSiam, NNSiam).add_model_specific_args(parent_parser) parser = parent_parser.add_argument_group("nnsiam") # projector parser.add_argument("--proj_output_dim", type=int, default=128) parser.add_argument("--proj_hidden_dim", type=int, default=2048) # predictor parser.add_argument("--pred_hidden_dim", type=int, default=512) # queue settings parser.add_argument("--queue_size", default=65536, type=int) return parent_parser @property def learnable_params(self) -> List[dict]: """Adds projector and predictor parameters to the parent's learnable parameters. Returns: List[dict]: list of learnable parameters. """ extra_learnable_params: List[dict] = [ {"params": self.projector.parameters()}, {"params": self.predictor.parameters(), "static_lr": True}, ] return super().learnable_params + extra_learnable_params @torch.no_grad() def dequeue_and_enqueue(self, z: torch.Tensor, y: torch.Tensor): """Adds new samples and removes old samples from the queue in a fifo manner. Also stores the labels of the samples. Args: z (torch.Tensor): batch of projected features. y (torch.Tensor): labels of the samples in the batch. """ z = gather(z) y = gather(y) batch_size = z.shape[0] ptr = int(self.queue_ptr) # type: ignore assert self.queue_size % batch_size == 0 self.queue[ptr : ptr + batch_size, :] = z self.queue_y[ptr : ptr + batch_size] = y # type: ignore ptr = (ptr + batch_size) % self.queue_size self.queue_ptr[0] = ptr # type: ignore @torch.no_grad() def find_nn(self, z: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """Finds the nearest neighbor of a sample. Args: z (torch.Tensor): a batch of projected features. Returns: Tuple[torch.Tensor, torch.Tensor]: indices and projected features of the nearest neighbors. """ idx = (z @ self.queue.T).max(dim=1)[1] nn = self.queue[idx] return idx, nn def forward(self, X: torch.Tensor, *args, **kwargs) -> Dict[str, Any]: """Performs the forward pass of the encoder, the projector and the predictor. Args: X (torch.Tensor): a batch of images in the tensor format. Returns: Dict[str, Any]: a dict containing the outputs of the parent and the projected and predicted features. """ out = super().forward(X, *args, **kwargs) z = self.projector(out["feats"]) p = self.predictor(z) return {**out, "z": z, "p": p} def training_step(self, batch: Sequence[Any], batch_idx: int) -> torch.Tensor: """Training step for NNSiam reusing BaseMethod training step. Args: batch (Sequence[Any]): a batch of data in the format of [img_indexes, [X], Y], where [X] is a list of size self.num_crops containing batches of images batch_idx (int): index of the batch Returns: torch.Tensor: total loss composed of SimSiam loss and classification loss """ targets = batch[-1] out = super().training_step(batch, batch_idx) class_loss = out["loss"] feats1, feats2 = out["feats"] z1 = self.projector(feats1) z2 = self.projector(feats2) p1 = self.predictor(z1) p2 = self.predictor(z2) z1 = F.normalize(z1, dim=-1) z2 = F.normalize(z2, dim=-1) # find nn idx1, nn1 = self.find_nn(z1) _, nn2 = self.find_nn(z2) # ------- negative cosine similarity loss ------- neg_cos_sim = simsiam_loss_func(p1, nn2) / 2 + simsiam_loss_func(p2, nn1) / 2 # compute nn accuracy b = targets.size(0) nn_acc = (targets == self.queue_y[idx1]).sum() / b # dequeue and enqueue self.dequeue_and_enqueue(z1, targets) # calculate std of features z1_std = F.normalize(z1, dim=-1).std(dim=0).mean() z2_std = F.normalize(z2, dim=-1).std(dim=0).mean() z_std = (z1_std + z2_std) / 2 metrics = { "train_neg_cos_sim": neg_cos_sim, "train_z_std": z_std, "train_nn_acc": nn_acc, } self.log_dict(metrics, on_epoch=True, sync_dist=True) return neg_cos_sim + class_loss

35.662162

99

0.630794

import argparse from typing import Any, Dict, List, Sequence, Tuple import torch import torch.nn as nn import torch.nn.functional as F from solo.losses.simsiam import simsiam_loss_func from solo.methods.base import BaseMethod from solo.utils.misc import gather class NNSiam(BaseMethod): def __init__( self, proj_output_dim: int, proj_hidden_dim: int, pred_hidden_dim: int, queue_size: int, **kwargs, ): super().__init__(**kwargs) self.queue_size = queue_size self.projector = nn.Sequential( nn.Linear(self.features_dim, proj_hidden_dim, bias=False), nn.BatchNorm1d(proj_hidden_dim), nn.ReLU(), nn.Linear(proj_hidden_dim, proj_hidden_dim, bias=False), nn.BatchNorm1d(proj_hidden_dim), nn.ReLU(), nn.Linear(proj_hidden_dim, proj_output_dim), nn.BatchNorm1d(proj_output_dim, affine=False), ) self.projector[6].bias.requires_grad = False self.predictor = nn.Sequential( nn.Linear(proj_output_dim, pred_hidden_dim, bias=False), nn.BatchNorm1d(pred_hidden_dim), nn.ReLU(), nn.Linear(pred_hidden_dim, proj_output_dim), ) self.register_buffer("queue", torch.randn(self.queue_size, proj_output_dim)) self.register_buffer("queue_y", -torch.ones(self.queue_size, dtype=torch.long)) self.queue = F.normalize(self.queue, dim=1) self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long)) @staticmethod def add_model_specific_args(parent_parser: argparse.ArgumentParser) -> argparse.ArgumentParser: parent_parser = super(NNSiam, NNSiam).add_model_specific_args(parent_parser) parser = parent_parser.add_argument_group("nnsiam") parser.add_argument("--proj_output_dim", type=int, default=128) parser.add_argument("--proj_hidden_dim", type=int, default=2048) parser.add_argument("--pred_hidden_dim", type=int, default=512) parser.add_argument("--queue_size", default=65536, type=int) return parent_parser @property def learnable_params(self) -> List[dict]: extra_learnable_params: List[dict] = [ {"params": self.projector.parameters()}, {"params": self.predictor.parameters(), "static_lr": True}, ] return super().learnable_params + extra_learnable_params @torch.no_grad() def dequeue_and_enqueue(self, z: torch.Tensor, y: torch.Tensor): z = gather(z) y = gather(y) batch_size = z.shape[0] ptr = int(self.queue_ptr) assert self.queue_size % batch_size == 0 self.queue[ptr : ptr + batch_size, :] = z self.queue_y[ptr : ptr + batch_size] = y ptr = (ptr + batch_size) % self.queue_size self.queue_ptr[0] = ptr @torch.no_grad() def find_nn(self, z: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: idx = (z @ self.queue.T).max(dim=1)[1] nn = self.queue[idx] return idx, nn def forward(self, X: torch.Tensor, *args, **kwargs) -> Dict[str, Any]: out = super().forward(X, *args, **kwargs) z = self.projector(out["feats"]) p = self.predictor(z) return {**out, "z": z, "p": p} def training_step(self, batch: Sequence[Any], batch_idx: int) -> torch.Tensor: targets = batch[-1] out = super().training_step(batch, batch_idx) class_loss = out["loss"] feats1, feats2 = out["feats"] z1 = self.projector(feats1) z2 = self.projector(feats2) p1 = self.predictor(z1) p2 = self.predictor(z2) z1 = F.normalize(z1, dim=-1) z2 = F.normalize(z2, dim=-1) idx1, nn1 = self.find_nn(z1) _, nn2 = self.find_nn(z2) neg_cos_sim = simsiam_loss_func(p1, nn2) / 2 + simsiam_loss_func(p2, nn1) / 2 b = targets.size(0) nn_acc = (targets == self.queue_y[idx1]).sum() / b self.dequeue_and_enqueue(z1, targets) z1_std = F.normalize(z1, dim=-1).std(dim=0).mean() z2_std = F.normalize(z2, dim=-1).std(dim=0).mean() z_std = (z1_std + z2_std) / 2 metrics = { "train_neg_cos_sim": neg_cos_sim, "train_z_std": z_std, "train_nn_acc": nn_acc, } self.log_dict(metrics, on_epoch=True, sync_dist=True) return neg_cos_sim + class_loss

true

1c47724e4746e520c60664378824afb818843692

6,421

py

Python

src/create_embedded_tools.py

erenon/bazel

9bf885afeb01c766d84acf86ca847a7b5e7bd0d8

[ "Apache-2.0" ]

null

src/create_embedded_tools.py

erenon/bazel

9bf885afeb01c766d84acf86ca847a7b5e7bd0d8

[ "Apache-2.0" ]

null

src/create_embedded_tools.py

erenon/bazel

9bf885afeb01c766d84acf86ca847a7b5e7bd0d8

[ "Apache-2.0" ]

null

# pylint: disable=g-direct-third-party-import # pylint: disable=g-bad-file-header # Copyright 2017 The Bazel Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http:#www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Creates the embedded_tools.zip that is part of the Bazel binary.""" import contextlib import fnmatch import os import os.path import re import sys import zipfile from src.create_embedded_tools_lib import copy_tar_to_zip from src.create_embedded_tools_lib import copy_zip_to_zip from src.create_embedded_tools_lib import is_executable output_paths = [ ('*tools/jdk/BUILD*', lambda x: 'tools/jdk/BUILD'), ('*tools/platforms/platforms.BUILD', lambda x: 'platforms/BUILD'), ('*tools/platforms/*', lambda x: 'platforms/' + os.path.basename(x)), ('*tools/cpp/runfiles/generated_*', lambda x: 'tools/cpp/runfiles/' + os.path.basename(x)[len('generated_'):]), ('*JavaBuilder*_deploy.jar', lambda x: 'tools/jdk/' + os.path.basename(x)), ('*JacocoCoverage*_deploy.jar', lambda x: 'tools/jdk/JacocoCoverage_deploy.jar'), ('*turbine_deploy.jar', lambda x: 'tools/jdk/turbine_deploy.jar'), ('*turbine_direct*', lambda x: 'tools/jdk/' + os.path.basename(x)), ('*javac-9+181-r4173-1.jar', lambda x: 'third_party/java/jdk/langtools/javac-9+181-r4173-1.jar'), ('*bazel-singlejar_deploy.jar', lambda x: 'tools/jdk/singlejar/bazel-singlejar_deploy.jar'), ('*GenClass_deploy.jar', lambda x: 'tools/jdk/GenClass_deploy.jar'), ('*ExperimentalRunner_deploy.jar', lambda x: 'tools/jdk/ExperimentalTestRunner_deploy.jar'), ('*Runner_deploy.jar', lambda x: 'tools/jdk/TestRunner_deploy.jar'), ('*singlejar_local.exe', lambda x: 'tools/jdk/singlejar/singlejar.exe'), ('*singlejar_local', lambda x: 'tools/jdk/singlejar/singlejar'), ('*launcher.exe', lambda x: 'tools/launcher/launcher.exe'), ('*def_parser.exe', lambda x: 'tools/def_parser/def_parser.exe'), ('*ijar.exe', lambda x: 'tools/jdk/ijar/ijar.exe'), ('*ijar', lambda x: 'tools/jdk/ijar/ijar'), ('*zipper.exe', lambda x: 'tools/zip/zipper/zipper.exe'), ('*zipper', lambda x: 'tools/zip/zipper/zipper'), ('*src/objc_tools/*', lambda x: 'tools/objc/precomp_' + os.path.basename(x)), ('*xcode*StdRedirect.dylib', lambda x: 'tools/objc/StdRedirect.dylib'), ('*xcode*make_hashed_objlist.py', lambda x: 'tools/objc/make_hashed_objlist.py'), ('*xcode*realpath', lambda x: 'tools/objc/realpath'), ('*xcode*xcode-locator', lambda x: 'tools/objc/xcode-locator'), ('*src/tools/xcode/*.sh', lambda x: 'tools/objc/' + os.path.basename(x)), ('*src/tools/xcode/*', lambda x: 'tools/objc/' + os.path.basename(x) + '.sh'), ('*external/openjdk_*/file/*.tar.gz', lambda x: 'jdk.tar.gz'), ('*external/openjdk_*/file/*.zip', lambda x: 'jdk.zip'), ('*src/minimal_jdk.tar.gz', lambda x: 'jdk.tar.gz'), ('*src/minimal_jdk.zip', lambda x: 'jdk.zip'), ('*', lambda x: re.sub(r'^.*bazel-out/[^/]*/bin/', '', x, count=1)), ] def get_output_path(path): for pattern, transformer in output_paths: if fnmatch.fnmatch(path.replace('\\', '/'), pattern): # BUILD.tools are stored as BUILD files. return transformer(path).replace('/BUILD.tools', '/BUILD') def get_input_files(argsfile): """Returns a sorted list of tuples (archive_file, input_file). This describes the files that should be put into the generated archive. Args: argsfile: The file containing the list of input files. """ with open(argsfile, 'r') as f: input_files = set(x.strip() for x in f.readlines()) result = {} for input_file in input_files: # If we have both a BUILD and a BUILD.tools file, take the latter only. if (os.path.basename(input_file) == 'BUILD' and input_file + '.tools' in input_files): continue # This gives us the same behavior as the older bash version of this # tool: If two input files map to the same output files, the one that # comes last in the list of input files overrides all earlier ones. result[get_output_path(input_file)] = input_file # By sorting the file list, the resulting ZIP file will not be reproducible # and deterministic. return sorted(result.items()) def copy_jdk_into_archive(output_zip, archive_file, input_file): """Extract the JDK and adds it to the archive under jdk/*.""" def _replace_dirname(filename): # Rename the first folder to 'jdk', because Bazel looks for a # bundled JDK in the embedded tools using that folder name. return 'jdk/' + '/'.join(filename.split('/')[1:]) # The JDK is special - it's extracted instead of copied. if archive_file.endswith('.tar.gz'): copy_tar_to_zip(output_zip, input_file, _replace_dirname) elif archive_file.endswith('.zip'): copy_zip_to_zip(output_zip, input_file, _replace_dirname) def main(): output_zip = os.path.join(os.getcwd(), sys.argv[1]) input_files = get_input_files(sys.argv[2]) # Copy all the input_files into output_zip. # Adding contextlib.closing to be python 2.6 (for centos 6.7) compatible with contextlib.closing( zipfile.ZipFile(output_zip, 'w', zipfile.ZIP_DEFLATED)) as output_zip: zipinfo = zipfile.ZipInfo('WORKSPACE', (1980, 1, 1, 0, 0, 0)) zipinfo.external_attr = 0o644 << 16 output_zip.writestr(zipinfo, 'workspace(name = "bazel_tools")\n') for archive_file, input_file in input_files: if os.path.basename(archive_file) in ('jdk.tar.gz', 'jdk.zip'): copy_jdk_into_archive(output_zip, archive_file, input_file) else: zipinfo = zipfile.ZipInfo(archive_file, (1980, 1, 1, 0, 0, 0)) zipinfo.external_attr = 0o755 << 16 if is_executable( input_file) else 0o644 << 16 zipinfo.compress_type = zipfile.ZIP_DEFLATED with open(input_file, 'rb') as f: output_zip.writestr(zipinfo, f.read()) if __name__ == '__main__': main()

42.523179

80

0.686653

atch import os import os.path import re import sys import zipfile from src.create_embedded_tools_lib import copy_tar_to_zip from src.create_embedded_tools_lib import copy_zip_to_zip from src.create_embedded_tools_lib import is_executable output_paths = [ ('*tools/jdk/BUILD*', lambda x: 'tools/jdk/BUILD'), ('*tools/platforms/platforms.BUILD', lambda x: 'platforms/BUILD'), ('*tools/platforms/*', lambda x: 'platforms/' + os.path.basename(x)), ('*tools/cpp/runfiles/generated_*', lambda x: 'tools/cpp/runfiles/' + os.path.basename(x)[len('generated_'):]), ('*JavaBuilder*_deploy.jar', lambda x: 'tools/jdk/' + os.path.basename(x)), ('*JacocoCoverage*_deploy.jar', lambda x: 'tools/jdk/JacocoCoverage_deploy.jar'), ('*turbine_deploy.jar', lambda x: 'tools/jdk/turbine_deploy.jar'), ('*turbine_direct*', lambda x: 'tools/jdk/' + os.path.basename(x)), ('*javac-9+181-r4173-1.jar', lambda x: 'third_party/java/jdk/langtools/javac-9+181-r4173-1.jar'), ('*bazel-singlejar_deploy.jar', lambda x: 'tools/jdk/singlejar/bazel-singlejar_deploy.jar'), ('*GenClass_deploy.jar', lambda x: 'tools/jdk/GenClass_deploy.jar'), ('*ExperimentalRunner_deploy.jar', lambda x: 'tools/jdk/ExperimentalTestRunner_deploy.jar'), ('*Runner_deploy.jar', lambda x: 'tools/jdk/TestRunner_deploy.jar'), ('*singlejar_local.exe', lambda x: 'tools/jdk/singlejar/singlejar.exe'), ('*singlejar_local', lambda x: 'tools/jdk/singlejar/singlejar'), ('*launcher.exe', lambda x: 'tools/launcher/launcher.exe'), ('*def_parser.exe', lambda x: 'tools/def_parser/def_parser.exe'), ('*ijar.exe', lambda x: 'tools/jdk/ijar/ijar.exe'), ('*ijar', lambda x: 'tools/jdk/ijar/ijar'), ('*zipper.exe', lambda x: 'tools/zip/zipper/zipper.exe'), ('*zipper', lambda x: 'tools/zip/zipper/zipper'), ('*src/objc_tools/*', lambda x: 'tools/objc/precomp_' + os.path.basename(x)), ('*xcode*StdRedirect.dylib', lambda x: 'tools/objc/StdRedirect.dylib'), ('*xcode*make_hashed_objlist.py', lambda x: 'tools/objc/make_hashed_objlist.py'), ('*xcode*realpath', lambda x: 'tools/objc/realpath'), ('*xcode*xcode-locator', lambda x: 'tools/objc/xcode-locator'), ('*src/tools/xcode/*.sh', lambda x: 'tools/objc/' + os.path.basename(x)), ('*src/tools/xcode/*', lambda x: 'tools/objc/' + os.path.basename(x) + '.sh'), ('*external/openjdk_*/file/*.tar.gz', lambda x: 'jdk.tar.gz'), ('*external/openjdk_*/file/*.zip', lambda x: 'jdk.zip'), ('*src/minimal_jdk.tar.gz', lambda x: 'jdk.tar.gz'), ('*src/minimal_jdk.zip', lambda x: 'jdk.zip'), ('*', lambda x: re.sub(r'^.*bazel-out/[^/]*/bin/', '', x, count=1)), ] def get_output_path(path): for pattern, transformer in output_paths: if fnmatch.fnmatch(path.replace('\\', '/'), pattern): return transformer(path).replace('/BUILD.tools', '/BUILD') def get_input_files(argsfile): with open(argsfile, 'r') as f: input_files = set(x.strip() for x in f.readlines()) result = {} for input_file in input_files: if (os.path.basename(input_file) == 'BUILD' and input_file + '.tools' in input_files): continue result[get_output_path(input_file)] = input_file return sorted(result.items()) def copy_jdk_into_archive(output_zip, archive_file, input_file): def _replace_dirname(filename): return 'jdk/' + '/'.join(filename.split('/')[1:]) if archive_file.endswith('.tar.gz'): copy_tar_to_zip(output_zip, input_file, _replace_dirname) elif archive_file.endswith('.zip'): copy_zip_to_zip(output_zip, input_file, _replace_dirname) def main(): output_zip = os.path.join(os.getcwd(), sys.argv[1]) input_files = get_input_files(sys.argv[2]) # Copy all the input_files into output_zip. # Adding contextlib.closing to be python 2.6 (for centos 6.7) compatible with contextlib.closing( zipfile.ZipFile(output_zip, 'w', zipfile.ZIP_DEFLATED)) as output_zip: zipinfo = zipfile.ZipInfo('WORKSPACE', (1980, 1, 1, 0, 0, 0)) zipinfo.external_attr = 0o644 << 16 output_zip.writestr(zipinfo, 'workspace(name = "bazel_tools")\n') for archive_file, input_file in input_files: if os.path.basename(archive_file) in ('jdk.tar.gz', 'jdk.zip'): copy_jdk_into_archive(output_zip, archive_file, input_file) else: zipinfo = zipfile.ZipInfo(archive_file, (1980, 1, 1, 0, 0, 0)) zipinfo.external_attr = 0o755 << 16 if is_executable( input_file) else 0o644 << 16 zipinfo.compress_type = zipfile.ZIP_DEFLATED with open(input_file, 'rb') as f: output_zip.writestr(zipinfo, f.read()) if __name__ == '__main__': main()

true

1c47729e783feede84d393f9c877b04a40b6c1cf

5,680

py

Python

src/morphforgeexamples/exset6_poster_ocns2012/poster1.py

mikehulluk/morphforge

2a95096f144ed4ea487decb735ce66706357d3c7

[ "BSD-2-Clause" ]

1

2021-01-21T11:31:59.000Z

src/morphforgeexamples/exset6_poster_ocns2012/poster1.py

mikehulluk/morphforge

2a95096f144ed4ea487decb735ce66706357d3c7

[ "BSD-2-Clause" ]

null

src/morphforgeexamples/exset6_poster_ocns2012/poster1.py

mikehulluk/morphforge

2a95096f144ed4ea487decb735ce66706357d3c7

[ "BSD-2-Clause" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- # --------------------------------------------------------------------- # Copyright (c) 2012 Michael Hull. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # - Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # - Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in # the documentation and/or other materials provided with the # distribution. # # 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. # ---------------------------------------------------------------------- """ Simulation of a HodgkinHuxley-type neuron specified through NeuroUnits. """ import matplotlib as mpl mpl.rcParams['font.size'] = 14 from morphforge.stdimports import * from morphforgecontrib.stdimports import * eqnset_txt_na = """ define_component hh_na { i = g * (v-erev) * m**3*h m_inf = m_alpha_rate / (m_alpha_rate + m_beta_rate) m_tau = 1.0 / (m_alpha_rate + m_beta_rate) m' = (m_inf-m) / m_tau h_inf = h_alpha_rate / (h_alpha_rate + h_beta_rate) h_tau = 1.0 / (h_alpha_rate + h_beta_rate) h' = (h_inf-h) / h_tau StdFormAB(V, a1, a2, a3, a4, a5) = (a1+a2*V)/(a3+std.math.exp((V+a4)/a5)) m_alpha_rate = StdFormAB(V=v, a1=m_a1, a2=m_a2, a3=m_a3, a4=m_a4, a5=m_a5) m_beta_rate = StdFormAB(V=v, a1=m_b1, a2=m_b2, a3=m_b3, a4=m_b4, a5=m_b5) h_alpha_rate = StdFormAB(V=v, a1=h_a1, a2=h_a2, a3=h_a3, a4=h_a4, a5=h_a5) h_beta_rate = StdFormAB(V=v, a1=h_b1, a2=h_b2, a3=h_b3, a4=h_b4, a5=h_b5) m_a1={-4.00 ms-1}; m_a2={-0.10 mV-1 ms-1}; m_a3={-1.00}; m_a4={40.00 mV}; m_a5={-10.00 mV}; m_b1={ 4.00 ms-1}; m_b2={ 0.00 mV-1 ms-1}; m_b3={ 0.00}; m_b4={65.00 mV}; m_b5={ 18.00 mV}; h_a1={ 0.07 ms-1}; h_a2={ 0.00 mV-1 ms-1}; h_a3={ 0.00}; h_a4={65.00 mV}; h_a5={ 20.00 mV}; h_b1={ 1.00 ms-1}; h_b2={ 0.00 mV-1 ms-1}; h_b3={ 1.00}; h_b4={35.00 mV}; h_b5={-10.00 mV}; erev = 50.0mV; <=> PARAMETER g:(S/m2) <=> OUTPUT i:(A/m2) METADATA {"mf":{"role":"TRANSMEMBRANECURRENT"} } <=> INPUT v: V METADATA {"mf":{"role":"MEMBRANEVOLTAGE"} } } """ eqnset_txt_k = """ define_component hh_k { i = g * (v-erev) * n*n*n*n n_inf = n_alpha_rate / (n_alpha_rate + n_beta_rate) n_tau = 1.0 / (n_alpha_rate + n_beta_rate) n' = (n_inf-n) / n_tau StdFormAB(V, a1, a2, a3, a4, a5) = (a1 + a2*V)/(a3+std.math.exp((V+a4)/a5)) n_alpha_rate = StdFormAB(V=v, a1=n_a1, a2=n_a2, a3=n_a3, a4=n_a4, a5=n_a5) n_beta_rate = StdFormAB(V=v, a1=n_b1, a2=n_b2, a3=n_b3, a4=n_b4, a5=n_b5) n_a1={-0.55 ms-1}; n_a2={-0.01 mV-1 ms-1}; n_a3={-1.00}; n_a4={55.00 mV}; n_a5={-10.00 mV} n_b1={0.125 ms-1}; n_b2={ 0.00 mV-1 ms-1}; n_b3={ 0.00}; n_b4={65.00 mV}; n_b5={ 80.00 mV} g = {36.0mS/cm2} erev = {-77.0mV} <=> OUTPUT i:(A/m2) METADATA {"mf":{"role":"TRANSMEMBRANECURRENT"} } <=> INPUT v: V METADATA {"mf":{"role":"MEMBRANEVOLTAGE"} } } """ eqnset_txt_lk = """ define_component hh_lk { i = {0.3mS/cm2} * (v- {-54.3mV}) <=> OUTPUT i:(A/m2) METADATA {"mf":{"role":"TRANSMEMBRANECURRENT"} } <=> INPUT v: V METADATA {"mf":{"role":"MEMBRANEVOLTAGE"} } } """ env = NEURONEnvironment() sim = env.Simulation() # Create a cell: morph_dict = {'root': {'length': 18.8, 'diam': 18.8, 'id':'soma'} } my_morph = MorphologyTree.fromDictionary(morph_dict) cell = sim.create_cell(name="Cell1", morphology=my_morph) #soma = cell.get_location("soma") # Setup passive channels: cell.set_passive( PassiveProperty.SpecificCapacitance, qty('1.0:uF/cm2')) # Setup active channels: na_chl = env.Channel(NeuroUnitEqnsetMechanism, name="NaChl", eqnset=eqnset_txt_na, default_parameters={"g":qty("120:mS/cm2")}, ) k_chl = env.Channel(NeuroUnitEqnsetMechanism, name="KChl", eqnset=eqnset_txt_k, ) lk_chl = env.Channel(NeuroUnitEqnsetMechanism, name="LKChl", eqnset=eqnset_txt_lk, ) cell.apply_channel( na_chl) cell.apply_channel( lk_chl) cell.apply_channel( k_chl) # Define what to record: sim.record(cell, what=StandardTags.Voltage, name="SomaVoltage", cell_location = cell.soma) sim.record(na_chl, what='m', cell_location=cell.soma, user_tags=[StandardTags.StateVariable]) sim.record(na_chl, what='h', cell_location=cell.soma, user_tags=[StandardTags.StateVariable]) sim.record(k_chl, what='n', cell_location=cell.soma, user_tags=[StandardTags.StateVariable]) # Create the stimulus and record the injected current: cc = sim.create_currentclamp(name="CC1", amp=qty("100:pA"), dur=qty("100:ms"), delay=qty("100:ms"), cell_location=cell.soma) sim.record(cc, what=StandardTags.Current) # run the simulation results = sim.run() TagViewer(results, timerange=(50, 250)*units.ms, show=True)

40.283688

124

0.660915

import matplotlib as mpl mpl.rcParams['font.size'] = 14 from morphforge.stdimports import * from morphforgecontrib.stdimports import * eqnset_txt_na = """ define_component hh_na { i = g * (v-erev) * m**3*h m_inf = m_alpha_rate / (m_alpha_rate + m_beta_rate) m_tau = 1.0 / (m_alpha_rate + m_beta_rate) m' = (m_inf-m) / m_tau h_inf = h_alpha_rate / (h_alpha_rate + h_beta_rate) h_tau = 1.0 / (h_alpha_rate + h_beta_rate) h' = (h_inf-h) / h_tau StdFormAB(V, a1, a2, a3, a4, a5) = (a1+a2*V)/(a3+std.math.exp((V+a4)/a5)) m_alpha_rate = StdFormAB(V=v, a1=m_a1, a2=m_a2, a3=m_a3, a4=m_a4, a5=m_a5) m_beta_rate = StdFormAB(V=v, a1=m_b1, a2=m_b2, a3=m_b3, a4=m_b4, a5=m_b5) h_alpha_rate = StdFormAB(V=v, a1=h_a1, a2=h_a2, a3=h_a3, a4=h_a4, a5=h_a5) h_beta_rate = StdFormAB(V=v, a1=h_b1, a2=h_b2, a3=h_b3, a4=h_b4, a5=h_b5) m_a1={-4.00 ms-1}; m_a2={-0.10 mV-1 ms-1}; m_a3={-1.00}; m_a4={40.00 mV}; m_a5={-10.00 mV}; m_b1={ 4.00 ms-1}; m_b2={ 0.00 mV-1 ms-1}; m_b3={ 0.00}; m_b4={65.00 mV}; m_b5={ 18.00 mV}; h_a1={ 0.07 ms-1}; h_a2={ 0.00 mV-1 ms-1}; h_a3={ 0.00}; h_a4={65.00 mV}; h_a5={ 20.00 mV}; h_b1={ 1.00 ms-1}; h_b2={ 0.00 mV-1 ms-1}; h_b3={ 1.00}; h_b4={35.00 mV}; h_b5={-10.00 mV}; erev = 50.0mV; <=> PARAMETER g:(S/m2) <=> OUTPUT i:(A/m2) METADATA {"mf":{"role":"TRANSMEMBRANECURRENT"} } <=> INPUT v: V METADATA {"mf":{"role":"MEMBRANEVOLTAGE"} } } """ eqnset_txt_k = """ define_component hh_k { i = g * (v-erev) * n*n*n*n n_inf = n_alpha_rate / (n_alpha_rate + n_beta_rate) n_tau = 1.0 / (n_alpha_rate + n_beta_rate) n' = (n_inf-n) / n_tau StdFormAB(V, a1, a2, a3, a4, a5) = (a1 + a2*V)/(a3+std.math.exp((V+a4)/a5)) n_alpha_rate = StdFormAB(V=v, a1=n_a1, a2=n_a2, a3=n_a3, a4=n_a4, a5=n_a5) n_beta_rate = StdFormAB(V=v, a1=n_b1, a2=n_b2, a3=n_b3, a4=n_b4, a5=n_b5) n_a1={-0.55 ms-1}; n_a2={-0.01 mV-1 ms-1}; n_a3={-1.00}; n_a4={55.00 mV}; n_a5={-10.00 mV} n_b1={0.125 ms-1}; n_b2={ 0.00 mV-1 ms-1}; n_b3={ 0.00}; n_b4={65.00 mV}; n_b5={ 80.00 mV} g = {36.0mS/cm2} erev = {-77.0mV} <=> OUTPUT i:(A/m2) METADATA {"mf":{"role":"TRANSMEMBRANECURRENT"} } <=> INPUT v: V METADATA {"mf":{"role":"MEMBRANEVOLTAGE"} } } """ eqnset_txt_lk = """ define_component hh_lk { i = {0.3mS/cm2} * (v- {-54.3mV}) <=> OUTPUT i:(A/m2) METADATA {"mf":{"role":"TRANSMEMBRANECURRENT"} } <=> INPUT v: V METADATA {"mf":{"role":"MEMBRANEVOLTAGE"} } } """ env = NEURONEnvironment() sim = env.Simulation() # Create a cell: morph_dict = {'root': {'length': 18.8, 'diam': 18.8, 'id':'soma'} } my_morph = MorphologyTree.fromDictionary(morph_dict) cell = sim.create_cell(name="Cell1", morphology=my_morph) #soma = cell.get_location("soma") # Setup passive channels: cell.set_passive( PassiveProperty.SpecificCapacitance, qty('1.0:uF/cm2')) # Setup active channels: na_chl = env.Channel(NeuroUnitEqnsetMechanism, name="NaChl", eqnset=eqnset_txt_na, default_parameters={"g":qty("120:mS/cm2")}, ) k_chl = env.Channel(NeuroUnitEqnsetMechanism, name="KChl", eqnset=eqnset_txt_k, ) lk_chl = env.Channel(NeuroUnitEqnsetMechanism, name="LKChl", eqnset=eqnset_txt_lk, ) cell.apply_channel( na_chl) cell.apply_channel( lk_chl) cell.apply_channel( k_chl) # Define what to record: sim.record(cell, what=StandardTags.Voltage, name="SomaVoltage", cell_location = cell.soma) sim.record(na_chl, what='m', cell_location=cell.soma, user_tags=[StandardTags.StateVariable]) sim.record(na_chl, what='h', cell_location=cell.soma, user_tags=[StandardTags.StateVariable]) sim.record(k_chl, what='n', cell_location=cell.soma, user_tags=[StandardTags.StateVariable]) # Create the stimulus and record the injected current: cc = sim.create_currentclamp(name="CC1", amp=qty("100:pA"), dur=qty("100:ms"), delay=qty("100:ms"), cell_location=cell.soma) sim.record(cc, what=StandardTags.Current) # run the simulation results = sim.run() TagViewer(results, timerange=(50, 250)*units.ms, show=True)

true

1c4772bee94a9049c31da5ef09d5c7071e017e16

2,599

py

Python

tasrif/processing_pipeline/pandas/convert_to_datetime.py

qcri/tasrif

327bc1eccb8f8e11d8869ba65a7c72ad038aa094

[ "BSD-3-Clause" ]

20

2021-12-06T10:41:54.000Z

2022-03-13T16:25:43.000Z

tasrif/processing_pipeline/pandas/convert_to_datetime.py

qcri/tasrif

327bc1eccb8f8e11d8869ba65a7c72ad038aa094

[ "BSD-3-Clause" ]

33

2021-12-06T08:27:18.000Z

2022-03-14T05:07:53.000Z

tasrif/processing_pipeline/pandas/convert_to_datetime.py

qcri/tasrif

327bc1eccb8f8e11d8869ba65a7c72ad038aa094

[ "BSD-3-Clause" ]

2

2022-02-07T08:06:48.000Z

2022-02-14T07:13:42.000Z

""" Operator to convert a column feature from string to datetime """ import pandas as pd from tasrif.processing_pipeline import PandasOperator from tasrif.processing_pipeline.validators import InputsAreDataFramesValidatorMixin class ConvertToDatetimeOperator(InputsAreDataFramesValidatorMixin, PandasOperator): """ Converts a set of (string) features to datetime using Pandas ``to_datetime`` Examples -------- >>> import pandas as pd >>> from tasrif.processing_pipeline.pandas import ConvertToDatetimeOperator >>> >>> df0 = pd.DataFrame([[1, "2020-05-01 00:00:00", 1], [1, "2020-05-01 01:00:00", 1], >>> [1, "2020-05-01 03:00:00", 2], [2, "2020-05-02 00:00:00", 1], >>> [2, "2020-05-02 01:00:00", 1]], >>> columns=['logId', 'timestamp', 'sleep_level']) >>> >>> operator = ConvertToDatetime(feature_names=["timestamp"], utc=True) >>> df0 = operator.process(df0) >>> >>> print(df0) . logId timestamp sleep_level 0 1 2020-05-01 00:00:00+00:00 1 1 1 2020-05-01 01:00:00+00:00 1 2 1 2020-05-01 03:00:00+00:00 2 3 2 2020-05-02 00:00:00+00:00 1 4 2 2020-05-02 01:00:00+00:00 1 """ def __init__(self, feature_names, **kwargs): """Convert a set of columns features from string to datetime Args: feature_names (str): Name of the string columns that represent datetime objects **kwargs: key word arguments passed to pandas ``to_datetime`` method """ self.feature_names = feature_names super().__init__(kwargs) self.kwargs = kwargs def _process(self, *data_frames): """Processes the passed data frame as per the configuration define in the constructor. Args: *data_frames (list of pd.DataFrame): Variable number of pandas dataframes to be processed Returns: pd.DataFrame -or- list[pd.DataFrame] Processed dataframe(s) resulting from applying the operator """ columns = ( self.feature_names.copy() if isinstance(self.feature_names, list) else [self.feature_names] ) processed = [] for data_frame in data_frames: for col in columns: data_frame[col] = pd.to_datetime( data_frame[col], errors="coerce", **self.kwargs ) processed.append(data_frame) return processed

33.320513

94

0.58561

import pandas as pd from tasrif.processing_pipeline import PandasOperator from tasrif.processing_pipeline.validators import InputsAreDataFramesValidatorMixin class ConvertToDatetimeOperator(InputsAreDataFramesValidatorMixin, PandasOperator): def __init__(self, feature_names, **kwargs): self.feature_names = feature_names super().__init__(kwargs) self.kwargs = kwargs def _process(self, *data_frames): columns = ( self.feature_names.copy() if isinstance(self.feature_names, list) else [self.feature_names] ) processed = [] for data_frame in data_frames: for col in columns: data_frame[col] = pd.to_datetime( data_frame[col], errors="coerce", **self.kwargs ) processed.append(data_frame) return processed

true

1c4772d8628f28ac08f50f8f4e940c76e95bac8c

2,757

py

Python

deploy/env/local/lib/python2.7/site-packages/mercurial-3.1-py2.7-linux-x86_64.egg/mercurial/filelog.py

wangvictor2012/liuwei

0a06f8fd56d78162f81f1e7e7def7bfdeb4472e1

[ "BSD-3-Clause" ]

3

2015-11-05T07:42:43.000Z

2017-05-29T22:59:47.000Z

vendor/lib/python2.7/site-packages/mercurial/filelog.py

ddollar/gobuild

c1b0e52ab6849a13a95a3fdae4913b925f658272

[ "MIT" ]

null

vendor/lib/python2.7/site-packages/mercurial/filelog.py

ddollar/gobuild

c1b0e52ab6849a13a95a3fdae4913b925f658272

[ "MIT" ]

null

# filelog.py - file history class for mercurial # # Copyright 2005-2007 Matt Mackall <mpm@selenic.com> # # This software may be used and distributed according to the terms of the # GNU General Public License version 2 or any later version. import revlog import re _mdre = re.compile('\1\n') def _parsemeta(text): """return (metadatadict, keylist, metadatasize)""" # text can be buffer, so we can't use .startswith or .index if text[:2] != '\1\n': return None, None, None s = _mdre.search(text, 2).start() mtext = text[2:s] meta = {} keys = [] for l in mtext.splitlines(): k, v = l.split(": ", 1) meta[k] = v keys.append(k) return meta, keys, (s + 2) def _packmeta(meta, keys=None): if not keys: keys = sorted(meta.iterkeys()) return "".join("%s: %s\n" % (k, meta[k]) for k in keys) class filelog(revlog.revlog): def __init__(self, opener, path): super(filelog, self).__init__(opener, "/".join(("data", path + ".i"))) def read(self, node): t = self.revision(node) if not t.startswith('\1\n'): return t s = t.index('\1\n', 2) return t[s + 2:] def add(self, text, meta, transaction, link, p1=None, p2=None): if meta or text.startswith('\1\n'): text = "\1\n%s\1\n%s" % (_packmeta(meta), text) return self.addrevision(text, transaction, link, p1, p2) def renamed(self, node): if self.parents(node)[0] != revlog.nullid: return False t = self.revision(node) m = _parsemeta(t)[0] if m and "copy" in m: return (m["copy"], revlog.bin(m["copyrev"])) return False def size(self, rev): """return the size of a given revision""" # for revisions with renames, we have to go the slow way node = self.node(rev) if self.renamed(node): return len(self.read(node)) # XXX if self.read(node).startswith("\1\n"), this returns (size+4) return super(filelog, self).size(rev) def cmp(self, node, text): """compare text with a given file revision returns True if text is different than what is stored. """ t = text if text.startswith('\1\n'): t = '\1\n\1\n' + text samehashes = not super(filelog, self).cmp(node, t) if samehashes: return False # renaming a file produces a different hash, even if the data # remains unchanged. Check if it's the case (slow): if self.renamed(node): t2 = self.read(node) return t2 != text return True def _file(self, f): return filelog(self.opener, f)

29.645161

74

0.564744

import revlog import re _mdre = re.compile('\1\n') def _parsemeta(text): if text[:2] != '\1\n': return None, None, None s = _mdre.search(text, 2).start() mtext = text[2:s] meta = {} keys = [] for l in mtext.splitlines(): k, v = l.split(": ", 1) meta[k] = v keys.append(k) return meta, keys, (s + 2) def _packmeta(meta, keys=None): if not keys: keys = sorted(meta.iterkeys()) return "".join("%s: %s\n" % (k, meta[k]) for k in keys) class filelog(revlog.revlog): def __init__(self, opener, path): super(filelog, self).__init__(opener, "/".join(("data", path + ".i"))) def read(self, node): t = self.revision(node) if not t.startswith('\1\n'): return t s = t.index('\1\n', 2) return t[s + 2:] def add(self, text, meta, transaction, link, p1=None, p2=None): if meta or text.startswith('\1\n'): text = "\1\n%s\1\n%s" % (_packmeta(meta), text) return self.addrevision(text, transaction, link, p1, p2) def renamed(self, node): if self.parents(node)[0] != revlog.nullid: return False t = self.revision(node) m = _parsemeta(t)[0] if m and "copy" in m: return (m["copy"], revlog.bin(m["copyrev"])) return False def size(self, rev): # for revisions with renames, we have to go the slow way node = self.node(rev) if self.renamed(node): return len(self.read(node)) # XXX if self.read(node).startswith("\1\n"), this returns (size+4) return super(filelog, self).size(rev) def cmp(self, node, text): t = text if text.startswith('\1\n'): t = '\1\n\1\n' + text samehashes = not super(filelog, self).cmp(node, t) if samehashes: return False # renaming a file produces a different hash, even if the data # remains unchanged. Check if it's the case (slow): if self.renamed(node): t2 = self.read(node) return t2 != text return True def _file(self, f): return filelog(self.opener, f)

true

1c47737253ed550c0b8f08ac8b7f413886c1457e

14,684

py

Python

train.py

solmn/parallel_wavenet

45e9eceb7a2d1982b3d45823332575eb26f333c0

[ "MIT" ]

3

2018-10-30T13:45:14.000Z

2020-03-29T06:56:10.000Z

train.py

solmn/parallel_wavenet

45e9eceb7a2d1982b3d45823332575eb26f333c0

[ "MIT" ]

null

train.py

solmn/parallel_wavenet

45e9eceb7a2d1982b3d45823332575eb26f333c0

[ "MIT" ]

null

"""Training script for the WaveNet network on the VCTK corpus. This script trains a network with the WaveNet using data from the VCTK corpus, which can be freely downloaded at the following site (~10 GB): http://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html """ from __future__ import print_function import argparse from datetime import datetime import json import os import sys import time import tensorflow as tf from tensorflow.python.client import timeline from wavenet import WaveNetModel, AudioReader, optimizer_factory BATCH_SIZE = 1 DATA_DIRECTORY = './dataset/LJSpeech/wavs/' LOGDIR_ROOT = './logdir' CHECKPOINT_EVERY = 100 NUM_STEPS = int(1e6) LEARNING_RATE = 2 *1e-5 WAVENET_PARAMS = './wavenet_params.json' STARTED_DATESTRING = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now()) SAMPLE_SIZE = 8000 L2_REGULARIZATION_STRENGTH = 0 SILENCE_THRESHOLD = 0.1 EPSILON = 1e-8 MOMENTUM = 0.9 MAX_TO_KEEP = 5 METADATA = False def get_arguments(): def _str_to_bool(s): """Convert string to bool (in argparse context).""" if s.lower() not in ['true', 'false']: raise ValueError('Argument needs to be a ' 'boolean, got {}'.format(s)) return {'true': True, 'false': False}[s.lower()] parser = argparse.ArgumentParser(description='WaveNet example network') parser.add_argument('--batch_size', type=int, default=BATCH_SIZE, help='How many wav files to process at once. Default: ' + str(BATCH_SIZE) + '.') parser.add_argument('--data_dir', type=str, default=DATA_DIRECTORY, help='The directory containing the VCTK corpus.') parser.add_argument('--store_metadata', type=bool, default=METADATA, help='Whether to store advanced debugging information ' '(execution time, memory consumption) for use with ' 'TensorBoard. Default: ' + str(METADATA) + '.') parser.add_argument('--logdir', type=str, default=None, help='Directory in which to store the logging ' 'information for TensorBoard. ' 'If the model already exists, it will restore ' 'the state and will continue training. ' 'Cannot use with --logdir_root and --restore_from.') parser.add_argument('--logdir_root', type=str, default=None, help='Root directory to place the logging ' 'output and generated model. These are stored ' 'under the dated subdirectory of --logdir_root. ' 'Cannot use with --logdir.') parser.add_argument('--restore_from', type=str, default=None, help='Directory in which to restore the model from. ' 'This creates the new model under the dated directory ' 'in --logdir_root. ' 'Cannot use with --logdir.') parser.add_argument('--checkpoint_every', type=int, default=CHECKPOINT_EVERY, help='How many steps to save each checkpoint after. Default: ' + str(CHECKPOINT_EVERY) + '.') parser.add_argument('--num_steps', type=int, default=NUM_STEPS, help='Number of training steps. Default: ' + str(NUM_STEPS) + '.') parser.add_argument('--learning_rate', type=float, default=LEARNING_RATE, help='Learning rate for training. Default: ' + str(LEARNING_RATE) + '.') parser.add_argument('--wavenet_params', type=str, default=WAVENET_PARAMS, help='JSON file with the network parameters. Default: ' + WAVENET_PARAMS + '.') parser.add_argument('--sample_size', type=int, default=SAMPLE_SIZE, help='Concatenate and cut audio samples to this many ' 'samples. Default: ' + str(SAMPLE_SIZE) + '.') parser.add_argument('--l2_regularization_strength', type=float, default=L2_REGULARIZATION_STRENGTH, help='Coefficient in the L2 regularization. ' 'Default: False') parser.add_argument('--silence_threshold', type=float, default=SILENCE_THRESHOLD, help='Volume threshold below which to trim the start ' 'and the end from the training set samples. Default: ' + str(SILENCE_THRESHOLD) + '.') parser.add_argument('--optimizer', type=str, default='adam', choices=optimizer_factory.keys(), help='Select the optimizer specified by this option. Default: adam.') parser.add_argument('--momentum', type=float, default=MOMENTUM, help='Specify the momentum to be ' 'used by sgd or rmsprop optimizer. Ignored by the ' 'adam optimizer. Default: ' + str(MOMENTUM) + '.') parser.add_argument('--histograms', type=_str_to_bool, default=False, help='Whether to store histogram summaries. Default: False') parser.add_argument('--gc_channels', type=int, default=None, help='Number of global condition channels. Default: None. Expecting: Int') parser.add_argument('--lc_channels', type=int, default=None, help='Number of local condition channels. Default: None. Expecting: Int') parser.add_argument('--max_checkpoints', type=int, default=MAX_TO_KEEP, help='Maximum amount of checkpoints that will be kept alive. Default: ' + str(MAX_TO_KEEP) + '.') return parser.parse_args() def save(saver, sess, logdir, step): model_name = 'model.ckpt' checkpoint_path = os.path.join(logdir, model_name) print('Storing checkpoint to {} ...'.format(logdir), end="") sys.stdout.flush() if not os.path.exists(logdir): os.makedirs(logdir) saver.save(sess, checkpoint_path, global_step=step) print(' Done.') def load(saver, sess, logdir): # logdir = "logdir/train/2018-09-07T19-20-47/" print("Trying to restore saved checkpoints from {} ...".format(logdir), end="") ckpt = tf.train.get_checkpoint_state(logdir) if ckpt: print(" Checkpoint found: {}".format(ckpt.model_checkpoint_path)) global_step = int(ckpt.model_checkpoint_path .split('/')[-1] .split('-')[-1]) print(" Global step was: {}".format(global_step)) print(" Restoring...", end="") saver.restore(sess, ckpt.model_checkpoint_path) print(" Done.") return global_step else: print(" No checkpoint found.") return None def get_default_logdir(logdir_root): logdir = os.path.join(logdir_root, 'train', STARTED_DATESTRING) return logdir def validate_directories(args): """Validate and arrange directory related arguments.""" # Validation if args.logdir and args.logdir_root: raise ValueError("--logdir and --logdir_root cannot be " "specified at the same time.") if args.logdir and args.restore_from: raise ValueError( "--logdir and --restore_from cannot be specified at the same " "time. This is to keep your previous model from unexpected " "overwrites.\n" "Use --logdir_root to specify the root of the directory which " "will be automatically created with current date and time, or use " "only --logdir to just continue the training from the last " "checkpoint.") # Arrangement logdir_root = args.logdir_root if logdir_root is None: logdir_root = LOGDIR_ROOT logdir = args.logdir if logdir is None: logdir = get_default_logdir(logdir_root) print('Using default logdir: {}'.format(logdir)) restore_from = args.restore_from if restore_from is None: # args.logdir and args.restore_from are exclusive, # so it is guaranteed the logdir here is newly created. restore_from = logdir return { 'logdir': logdir, 'logdir_root': args.logdir_root, 'restore_from': restore_from } def main(): args = get_arguments() try: directories = validate_directories(args) except ValueError as e: print("Some arguments are wrong:") print(str(e)) return logdir = directories['logdir'] restore_from = directories['restore_from'] # Even if we restored the model, we will treat it as new training # if the trained model is written into an arbitrary location. is_overwritten_training = logdir != restore_from with open(args.wavenet_params, 'r') as f: wavenet_params = json.load(f) # Create coordinator. coord = tf.train.Coordinator() # Load raw waveform from VCTK corpus. with tf.name_scope('create_inputs'): # Allow silence trimming to be skipped by specifying a threshold near # zero. silence_threshold = args.silence_threshold if args.silence_threshold > \ EPSILON else None gc_enabled = args.gc_channels is not None reader = AudioReader( args.data_dir, coord, sample_rate=wavenet_params['sample_rate'], gc_enabled=gc_enabled, receptive_field=WaveNetModel.calculate_receptive_field(wavenet_params["filter_width"], wavenet_params["dilations"], wavenet_params["scalar_input"], wavenet_params["initial_filter_width"]), sample_size=args.sample_size, silence_threshold=silence_threshold) audio_batch = reader.dequeue(args.batch_size) if gc_enabled: gc_id_batch = reader.dequeue_gc(args.batch_size) else: gc_id_batch = None # Create network. net = WaveNetModel( batch_size=args.batch_size, dilations=wavenet_params["dilations"], filter_width=wavenet_params["filter_width"], residual_channels=wavenet_params["residual_channels"], dilation_channels=wavenet_params["dilation_channels"], skip_channels=wavenet_params["skip_channels"], quantization_channels=wavenet_params["quantization_channels"], output_channels = wavenet_params["output_channels"], log_scale_min = wavenet_params["log_scale_min"], use_biases=wavenet_params["use_biases"], scalar_input=wavenet_params["scalar_input"], initial_filter_width=wavenet_params["initial_filter_width"], histograms=args.histograms, local_condition_channels = args.lc_channels, global_condition_channels=args.gc_channels, global_condition_cardinality=reader.gc_category_cardinality) if args.l2_regularization_strength == 0: args.l2_regularization_strength = None loss = net.loss(input_batch=audio_batch, global_condition_batch=gc_id_batch, l2_regularization_strength=args.l2_regularization_strength) optimizer = optimizer_factory[args.optimizer]( learning_rate=args.learning_rate, momentum=args.momentum) trainable = tf.trainable_variables() optim = optimizer.minimize(loss, var_list=trainable) # Set up logging for TensorBoard. writer = tf.summary.FileWriter(logdir) writer.add_graph(tf.get_default_graph()) run_metadata = tf.RunMetadata() summaries = tf.summary.merge_all() # Set up session sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)) init = tf.global_variables_initializer() sess.run(init) # Saver for storing checkpoints of the model. saver = tf.train.Saver(var_list=tf.trainable_variables(), max_to_keep=args.max_checkpoints) try: saved_global_step = load(saver, sess, restore_from) if is_overwritten_training or saved_global_step is None: # The first training step will be saved_global_step + 1, # therefore we put -1 here for new or overwritten trainings. saved_global_step = -1 except: print("Something went wrong while restoring checkpoint. " "We will terminate training to avoid accidentally overwriting " "the previous model.") raise threads = tf.train.start_queue_runners(sess=sess, coord=coord) reader.start_threads(sess) step = None last_saved_step = saved_global_step try: for step in range(saved_global_step + 1, args.num_steps): start_time = time.time() if args.store_metadata and step % 50 == 0: # Slow run that stores extra information for debugging. print('Storing metadata') run_options = tf.RunOptions( trace_level=tf.RunOptions.FULL_TRACE) summary, loss_value, _ = sess.run( [summaries, loss, optim], options=run_options, run_metadata=run_metadata) writer.add_summary(summary, step) writer.add_run_metadata(run_metadata, 'step_{:04d}'.format(step)) tl = timeline.Timeline(run_metadata.step_stats) timeline_path = os.path.join(logdir, 'timeline.trace') with open(timeline_path, 'w') as f: f.write(tl.generate_chrome_trace_format(show_memory=True)) else: summary, loss_value, _ = sess.run([summaries, loss, optim]) writer.add_summary(summary, step) duration = time.time() - start_time print('step {:d} - loss = {:.3f}, ({:.3f} sec/step)' .format(step, loss_value, duration)) if step % args.checkpoint_every == 0: save(saver, sess, logdir, step) last_saved_step = step except KeyboardInterrupt: # Introduce a line break after ^C is displayed so save message # is on its own line. print() finally: if step > last_saved_step: save(saver, sess, logdir, step) coord.request_stop() coord.join(threads) if __name__ == '__main__': main()

42.686047

117

0.611959

from __future__ import print_function import argparse from datetime import datetime import json import os import sys import time import tensorflow as tf from tensorflow.python.client import timeline from wavenet import WaveNetModel, AudioReader, optimizer_factory BATCH_SIZE = 1 DATA_DIRECTORY = './dataset/LJSpeech/wavs/' LOGDIR_ROOT = './logdir' CHECKPOINT_EVERY = 100 NUM_STEPS = int(1e6) LEARNING_RATE = 2 *1e-5 WAVENET_PARAMS = './wavenet_params.json' STARTED_DATESTRING = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now()) SAMPLE_SIZE = 8000 L2_REGULARIZATION_STRENGTH = 0 SILENCE_THRESHOLD = 0.1 EPSILON = 1e-8 MOMENTUM = 0.9 MAX_TO_KEEP = 5 METADATA = False def get_arguments(): def _str_to_bool(s): if s.lower() not in ['true', 'false']: raise ValueError('Argument needs to be a ' 'boolean, got {}'.format(s)) return {'true': True, 'false': False}[s.lower()] parser = argparse.ArgumentParser(description='WaveNet example network') parser.add_argument('--batch_size', type=int, default=BATCH_SIZE, help='How many wav files to process at once. Default: ' + str(BATCH_SIZE) + '.') parser.add_argument('--data_dir', type=str, default=DATA_DIRECTORY, help='The directory containing the VCTK corpus.') parser.add_argument('--store_metadata', type=bool, default=METADATA, help='Whether to store advanced debugging information ' '(execution time, memory consumption) for use with ' 'TensorBoard. Default: ' + str(METADATA) + '.') parser.add_argument('--logdir', type=str, default=None, help='Directory in which to store the logging ' 'information for TensorBoard. ' 'If the model already exists, it will restore ' 'the state and will continue training. ' 'Cannot use with --logdir_root and --restore_from.') parser.add_argument('--logdir_root', type=str, default=None, help='Root directory to place the logging ' 'output and generated model. These are stored ' 'under the dated subdirectory of --logdir_root. ' 'Cannot use with --logdir.') parser.add_argument('--restore_from', type=str, default=None, help='Directory in which to restore the model from. ' 'This creates the new model under the dated directory ' 'in --logdir_root. ' 'Cannot use with --logdir.') parser.add_argument('--checkpoint_every', type=int, default=CHECKPOINT_EVERY, help='How many steps to save each checkpoint after. Default: ' + str(CHECKPOINT_EVERY) + '.') parser.add_argument('--num_steps', type=int, default=NUM_STEPS, help='Number of training steps. Default: ' + str(NUM_STEPS) + '.') parser.add_argument('--learning_rate', type=float, default=LEARNING_RATE, help='Learning rate for training. Default: ' + str(LEARNING_RATE) + '.') parser.add_argument('--wavenet_params', type=str, default=WAVENET_PARAMS, help='JSON file with the network parameters. Default: ' + WAVENET_PARAMS + '.') parser.add_argument('--sample_size', type=int, default=SAMPLE_SIZE, help='Concatenate and cut audio samples to this many ' 'samples. Default: ' + str(SAMPLE_SIZE) + '.') parser.add_argument('--l2_regularization_strength', type=float, default=L2_REGULARIZATION_STRENGTH, help='Coefficient in the L2 regularization. ' 'Default: False') parser.add_argument('--silence_threshold', type=float, default=SILENCE_THRESHOLD, help='Volume threshold below which to trim the start ' 'and the end from the training set samples. Default: ' + str(SILENCE_THRESHOLD) + '.') parser.add_argument('--optimizer', type=str, default='adam', choices=optimizer_factory.keys(), help='Select the optimizer specified by this option. Default: adam.') parser.add_argument('--momentum', type=float, default=MOMENTUM, help='Specify the momentum to be ' 'used by sgd or rmsprop optimizer. Ignored by the ' 'adam optimizer. Default: ' + str(MOMENTUM) + '.') parser.add_argument('--histograms', type=_str_to_bool, default=False, help='Whether to store histogram summaries. Default: False') parser.add_argument('--gc_channels', type=int, default=None, help='Number of global condition channels. Default: None. Expecting: Int') parser.add_argument('--lc_channels', type=int, default=None, help='Number of local condition channels. Default: None. Expecting: Int') parser.add_argument('--max_checkpoints', type=int, default=MAX_TO_KEEP, help='Maximum amount of checkpoints that will be kept alive. Default: ' + str(MAX_TO_KEEP) + '.') return parser.parse_args() def save(saver, sess, logdir, step): model_name = 'model.ckpt' checkpoint_path = os.path.join(logdir, model_name) print('Storing checkpoint to {} ...'.format(logdir), end="") sys.stdout.flush() if not os.path.exists(logdir): os.makedirs(logdir) saver.save(sess, checkpoint_path, global_step=step) print(' Done.') def load(saver, sess, logdir): print("Trying to restore saved checkpoints from {} ...".format(logdir), end="") ckpt = tf.train.get_checkpoint_state(logdir) if ckpt: print(" Checkpoint found: {}".format(ckpt.model_checkpoint_path)) global_step = int(ckpt.model_checkpoint_path .split('/')[-1] .split('-')[-1]) print(" Global step was: {}".format(global_step)) print(" Restoring...", end="") saver.restore(sess, ckpt.model_checkpoint_path) print(" Done.") return global_step else: print(" No checkpoint found.") return None def get_default_logdir(logdir_root): logdir = os.path.join(logdir_root, 'train', STARTED_DATESTRING) return logdir def validate_directories(args): if args.logdir and args.logdir_root: raise ValueError("--logdir and --logdir_root cannot be " "specified at the same time.") if args.logdir and args.restore_from: raise ValueError( "--logdir and --restore_from cannot be specified at the same " "time. This is to keep your previous model from unexpected " "overwrites.\n" "Use --logdir_root to specify the root of the directory which " "will be automatically created with current date and time, or use " "only --logdir to just continue the training from the last " "checkpoint.") logdir_root = args.logdir_root if logdir_root is None: logdir_root = LOGDIR_ROOT logdir = args.logdir if logdir is None: logdir = get_default_logdir(logdir_root) print('Using default logdir: {}'.format(logdir)) restore_from = args.restore_from if restore_from is None: restore_from = logdir return { 'logdir': logdir, 'logdir_root': args.logdir_root, 'restore_from': restore_from } def main(): args = get_arguments() try: directories = validate_directories(args) except ValueError as e: print("Some arguments are wrong:") print(str(e)) return logdir = directories['logdir'] restore_from = directories['restore_from'] is_overwritten_training = logdir != restore_from with open(args.wavenet_params, 'r') as f: wavenet_params = json.load(f) coord = tf.train.Coordinator() with tf.name_scope('create_inputs'): silence_threshold = args.silence_threshold if args.silence_threshold > \ EPSILON else None gc_enabled = args.gc_channels is not None reader = AudioReader( args.data_dir, coord, sample_rate=wavenet_params['sample_rate'], gc_enabled=gc_enabled, receptive_field=WaveNetModel.calculate_receptive_field(wavenet_params["filter_width"], wavenet_params["dilations"], wavenet_params["scalar_input"], wavenet_params["initial_filter_width"]), sample_size=args.sample_size, silence_threshold=silence_threshold) audio_batch = reader.dequeue(args.batch_size) if gc_enabled: gc_id_batch = reader.dequeue_gc(args.batch_size) else: gc_id_batch = None net = WaveNetModel( batch_size=args.batch_size, dilations=wavenet_params["dilations"], filter_width=wavenet_params["filter_width"], residual_channels=wavenet_params["residual_channels"], dilation_channels=wavenet_params["dilation_channels"], skip_channels=wavenet_params["skip_channels"], quantization_channels=wavenet_params["quantization_channels"], output_channels = wavenet_params["output_channels"], log_scale_min = wavenet_params["log_scale_min"], use_biases=wavenet_params["use_biases"], scalar_input=wavenet_params["scalar_input"], initial_filter_width=wavenet_params["initial_filter_width"], histograms=args.histograms, local_condition_channels = args.lc_channels, global_condition_channels=args.gc_channels, global_condition_cardinality=reader.gc_category_cardinality) if args.l2_regularization_strength == 0: args.l2_regularization_strength = None loss = net.loss(input_batch=audio_batch, global_condition_batch=gc_id_batch, l2_regularization_strength=args.l2_regularization_strength) optimizer = optimizer_factory[args.optimizer]( learning_rate=args.learning_rate, momentum=args.momentum) trainable = tf.trainable_variables() optim = optimizer.minimize(loss, var_list=trainable) writer = tf.summary.FileWriter(logdir) writer.add_graph(tf.get_default_graph()) run_metadata = tf.RunMetadata() summaries = tf.summary.merge_all() sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)) init = tf.global_variables_initializer() sess.run(init) saver = tf.train.Saver(var_list=tf.trainable_variables(), max_to_keep=args.max_checkpoints) try: saved_global_step = load(saver, sess, restore_from) if is_overwritten_training or saved_global_step is None: saved_global_step = -1 except: print("Something went wrong while restoring checkpoint. " "We will terminate training to avoid accidentally overwriting " "the previous model.") raise threads = tf.train.start_queue_runners(sess=sess, coord=coord) reader.start_threads(sess) step = None last_saved_step = saved_global_step try: for step in range(saved_global_step + 1, args.num_steps): start_time = time.time() if args.store_metadata and step % 50 == 0: print('Storing metadata') run_options = tf.RunOptions( trace_level=tf.RunOptions.FULL_TRACE) summary, loss_value, _ = sess.run( [summaries, loss, optim], options=run_options, run_metadata=run_metadata) writer.add_summary(summary, step) writer.add_run_metadata(run_metadata, 'step_{:04d}'.format(step)) tl = timeline.Timeline(run_metadata.step_stats) timeline_path = os.path.join(logdir, 'timeline.trace') with open(timeline_path, 'w') as f: f.write(tl.generate_chrome_trace_format(show_memory=True)) else: summary, loss_value, _ = sess.run([summaries, loss, optim]) writer.add_summary(summary, step) duration = time.time() - start_time print('step {:d} - loss = {:.3f}, ({:.3f} sec/step)' .format(step, loss_value, duration)) if step % args.checkpoint_every == 0: save(saver, sess, logdir, step) last_saved_step = step except KeyboardInterrupt: print() finally: if step > last_saved_step: save(saver, sess, logdir, step) coord.request_stop() coord.join(threads) if __name__ == '__main__': main()

true

1c4773afb9dfe031efe91c301916c555e9dcc6a3

9,570

py

Python

src/HYPERPLUME/hyperplume.py

Pabsm94/Easyplume

ee54194c1c0930b2a0ef442c47f80bd4570913d2

[ "MIT" ]

null

src/HYPERPLUME/hyperplume.py

Pabsm94/Easyplume

ee54194c1c0930b2a0ef442c47f80bd4570913d2

[ "MIT" ]

null

src/HYPERPLUME/hyperplume.py

Pabsm94/Easyplume

ee54194c1c0930b2a0ef442c47f80bd4570913d2

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Apr 22 14:07:39 2016 @author: pablo """ import numpy as np import abc import matplotlib.pyplot as plt class Hyperplume(): """ Parent class Hyperplume loads target plasma and defines common attributes as well as shared methods in the AEM and SSM plume classes""" __metaclass__= abc.ABCMeta # Python decorator used to define abstract methods at any location in the class @abc.abstractclassmethod # Defining abstract method def solver(self): """Solver Abstract Method to be particularised by each Plume code. It is only defined for structure purposes in parent class Hyperplume""" return @abc.abstractclassmethod def query(self,z,r): """Query abstract method returns plasma profile data at specified grid points. query method is to be particularised by each plume code.It is only defined forstructure purposes in parent class Hyperplume""" return def __init__(self,plasma={'Electrons': {'Gamma': 1,'T_0_electron': 2.1801714e-19,'q_electron': -1.6e-19},'Ions': {'mass_ion': 2.1801714e-25, 'q_ion': 1.6e-19}},z_span=np.linspace(0,100,500),r_span=np.linspace(0,40,500),n_init=0.0472*np.linspace(1,0,500)**2): """ plume_constructor loads common class properties for AEM and SSM plume classes Args: plasma (dict): simple_plasma object dictionary containing basic plasma parameters. z_span (numpy.ndarray): axial region where the problem will be integrated. r_span (numpy.ndarray): initial far-field plasma radial profile. n_init (numpy.ndarray): initial dimensional density front. Usage: >>> Plasma = {'Electrons': {'Gamma': 1,'T_0_electron': 2.1801714e-19,'q_electron': -1.6e-19},'Ions': {'mass_ion': 2.1801714e-25, 'q_ion': 1.6e-19}} >>> z_span = np.linspace(0,100,100) >>> r0 = np.linspace(0,3,100) >>> n0 = np.exp(-6.15/2*r_span**2) >>> Plume = Hyperplume(Plasma,z_span,r0,n0) """ self.plasma = plasma self.Gamma = plasma['Electrons']['Gamma'] self.T_0 = plasma['Electrons']['T_0_electron'] self.m_ion = plasma['Ions']['mass_ion'] self.q_ion = plasma['Ions']['q_ion'] self.z_span = z_span self.eta = r_span self.n0 = n_init def simple_plasma(self,charge=1.6e-19,ion_mass=2.1801714e-25,init_plasma_temp=2.1801714e-19,Gamma=1): """ Method simple_plasma allows the user to quickly create a Plasma dictionary with two particle species (ions and electrons), and well defined attributes. Args: charge (float): Electron charge given dimensional in units [C] ion_mass(float): Ion mass given in dimensional units [Kg] init_plasma_temp(float): Initial plasma temperature given in dimensional units [J] Gamma(int or float): Dimensionless thermal expansion constant. Must be inside isothermal and polytropic boundaries [1,5/3] Returns: plasma (dict): Dictionary containing two simple plasma species (ions and electrons) with the before mentioned properties stored in favorable form Usage: >>> Plasma = Hyperplume().simple_plasma(charge=1.6e-19,ion_mass=2.1801714e-25,init_plasma_temp=2.1801714e-19,Gamma=1) """ if Gamma < 1 or Gamma > 2: #checking thermal expansion model print ('Gamma is outside isothermal or polytropic boundaries') else: plasma={'Ions':{'mass_ion': ion_mass,'q_ion':charge}, 'Electrons':{'q_electron': -charge,'T_0_electron':init_plasma_temp,'Gamma':Gamma} } return plasma def temp(self,n,n_0,T_0,Gamma): """ Method temp calculates plasma temperature (T) as function of plasma density (n) Args: n(int or np.ndarray): plasma density at specific (z,r) location in the plume grid n_0 (int):Iinitial density of plasma T_0 (float): Initial temperature of plasma Gamma (int): Dimensionless thermal expansion constant Returns: T (float or np.ndarray): Temperature of plasma at targeted (z,r) grid points in plume Usage: >>> T = Hyperplume().temp(n=0.65,n_0=1,T_0=2.1801714e-19,Gamma=1) """ if Gamma == 1: #Checking expansion model T = T_0*(n*0 + 1) else: T = T_0*((n/n_0)**(Gamma-1)) return T def phi (self,n,n_0,T_0,Gamma,e_charge): """Method phi calculates electric potential (\phi) as function of plasma density (n) Args: n(int or np.ndarray): plasma density at specific (z,r) location in the plume grid n_0 (int):Iinitial density of plasma T_0 (float): Initial temperature of plasma Gamma (int): Dimensionless thermal expansion constant e_charge (float):Electron charge Returns: phi(float or np.ndarray): Electric potential of plasma at (z,r) targeted grid point Usage: >>> phi = Hyperplume().phi(n=0.65,n_0=1,T_0=2.1801714e-19,Gamma=1,e_charge=-1.6e-19) """ if Gamma == 1: #Checking expansion model phi = (T_0/e_charge)*np.log(n/n_0) else : phi = (T_0/e_charge)*(Gamma / ((Gamma - 1)) * ((n/n_0)**(Gamma-1)-1)) return phi def n(self,n_0,T_0,phi,Gamma,e_charge): """Method n calculates plasma density (n) as function of plasma potential (\phi) Args: n_0 (int):Iinitial density of plasma T_0 (float): Initial temperature of plasma Gamma (int): Dimensionless thermal expansion constant e_charge (float):Electron charge Returns: n (float or numpy.ndarray): Pasma density at (z,r) targeted grid point in the plume. Usage: n = Hyperplume.n(n_0=1,T_0=2.1801714e-19,phi=-5.7,Gamma=1,e_charge=-1.6e-19) """ if Gamma == 1: #Checking expansion model n = n_0*np.exp(phi*e_charge/T_0) else: n = n_0*(((Gamma-1)/Gamma*phi*e_charge/T_0 + 1 )**1/(Gamma-1)) return n def eta_deriver(self,x,y): """Method eta_derivar calculates the numerical derivatives of the variables along eta, with a Args: x (np.ndarray): represents the derivative step (dx,dy) y (np.ndarray): vector to derive with respect to x Returns: y_prime(np.ndarray): derivaive of y over x stored in array format Usage: >>> x = np.array([0,0.5,1,1.2,2,2.3,2.6]) >>> y = np.array([10,17,23,27,36,40,45]) >>> dydx = Hyperplume.eta_deriver(x,y) """ dx = np.gradient(x) y_prime = np.gradient(y,dx) return y_prime def plot(self,z=np.array([15,20,25,30]),r=np.array([20,25,30,35]),var_name='n',contour_levels=[0,1,2,3,4,5,6,7,8]): """ Hyperplume Class method to plot the contours of important plasma variables along the specified (z,r) plume grid points Args: z (int,float, or np.ndarray): new interpolation axial region where plasma variabes are to be calculated and plotted. Must be inside z_grid limits r (int,float, or np.ndarray): new interpolation axial region where plasma variabes are to be calculated and plotted. Must be inside z_grid limits var_name (str): string containing the name of the variable to be visualized. Options are: 'lnn': logarithm of plasma density 'u_z': axial plume velocity 'u_r':radial plume velocity 'T': plasmaTemperature 'phi': ambipolar electric field 'eta': ion stream lines contour_levels (array or of list): contour lables of plasma varialbled at the targets z,r points. Returns: None Usage: >>> Plasma = Hyperplume().SIMPLE_plasma() >>> Plume = AEM() """ lnn,u_z,u_r,T,phi,error,eta = self.query(z,r) #Retrievibg plasma variables at z,r gid points fig = plt.figure() CE = plt.contour(z,r,eval(var_name),contour_levels) plt.title(var_name) plt.xlabel(r'$\ z/R_0 $') plt.ylabel(r'$\ r/R_0 $') plt.ylim(0,10) plt.clabel(CE,CE.levels,fontsize=6) plt.savefig(var_name + '.pdf',bbox_inches='tight') fig.show()

34.301075

262

0.546604

import numpy as np import abc import matplotlib.pyplot as plt class Hyperplume(): __metaclass__= abc.ABCMeta @abc.abstractclassmethod def solver(self): return @abc.abstractclassmethod def query(self,z,r): return def __init__(self,plasma={'Electrons': {'Gamma': 1,'T_0_electron': 2.1801714e-19,'q_electron': -1.6e-19},'Ions': {'mass_ion': 2.1801714e-25, 'q_ion': 1.6e-19}},z_span=np.linspace(0,100,500),r_span=np.linspace(0,40,500),n_init=0.0472*np.linspace(1,0,500)**2): self.plasma = plasma self.Gamma = plasma['Electrons']['Gamma'] self.T_0 = plasma['Electrons']['T_0_electron'] self.m_ion = plasma['Ions']['mass_ion'] self.q_ion = plasma['Ions']['q_ion'] self.z_span = z_span self.eta = r_span self.n0 = n_init def simple_plasma(self,charge=1.6e-19,ion_mass=2.1801714e-25,init_plasma_temp=2.1801714e-19,Gamma=1): if Gamma < 1 or Gamma > 2: print ('Gamma is outside isothermal or polytropic boundaries') else: plasma={'Ions':{'mass_ion': ion_mass,'q_ion':charge}, 'Electrons':{'q_electron': -charge,'T_0_electron':init_plasma_temp,'Gamma':Gamma} } return plasma def temp(self,n,n_0,T_0,Gamma): if Gamma == 1: T = T_0*(n*0 + 1) else: T = T_0*((n/n_0)**(Gamma-1)) return T def phi (self,n,n_0,T_0,Gamma,e_charge): if Gamma == 1: phi = (T_0/e_charge)*np.log(n/n_0) else : phi = (T_0/e_charge)*(Gamma / ((Gamma - 1)) * ((n/n_0)**(Gamma-1)-1)) return phi def n(self,n_0,T_0,phi,Gamma,e_charge): if Gamma == 1: n = n_0*np.exp(phi*e_charge/T_0) else: n = n_0*(((Gamma-1)/Gamma*phi*e_charge/T_0 + 1 )**1/(Gamma-1)) return n def eta_deriver(self,x,y): dx = np.gradient(x) y_prime = np.gradient(y,dx) return y_prime def plot(self,z=np.array([15,20,25,30]),r=np.array([20,25,30,35]),var_name='n',contour_levels=[0,1,2,3,4,5,6,7,8]): lnn,u_z,u_r,T,phi,error,eta = self.query(z,r) fig = plt.figure() CE = plt.contour(z,r,eval(var_name),contour_levels) plt.title(var_name) plt.xlabel(r'$\ z/R_0 $') plt.ylabel(r'$\ r/R_0 $') plt.ylim(0,10) plt.clabel(CE,CE.levels,fontsize=6) plt.savefig(var_name + '.pdf',bbox_inches='tight') fig.show()

true

1c47745f1c0e2c39646a97885253608082c44006

46

py

Python

__init__.py

lucaskjaero/WiktionaryParser

c60a7cb7e50ca929e02c8e6e258c23f4d4114c21

[ "MIT" ]

1

2021-08-24T17:51:41.000Z

__init__.py

lucaskjaero/WiktionaryParser

c60a7cb7e50ca929e02c8e6e258c23f4d4114c21

[ "MIT" ]

null

__init__.py

lucaskjaero/WiktionaryParser

c60a7cb7e50ca929e02c8e6e258c23f4d4114c21

[ "MIT" ]

1

2020-12-14T16:22:31.000Z

from .wiktionaryparser import WiktionaryParser

46

0.913043

from .wiktionaryparser import WiktionaryParser

true

1c477468c75e4642c2f29e87bfdbf22ef08e11fd

4,043

py

Python

models/definitions/flownet/inference.py

HaydenFaulkner/VidDet

2dbc104a41bf1192a00ffde07695180eab18cea8

[ "MIT" ]

19

2019-08-05T12:20:17.000Z

2020-10-29T11:33:50.000Z

models/definitions/flownet/inference.py

HaydenFaulkner/VideoYOLO

2dbc104a41bf1192a00ffde07695180eab18cea8

[ "MIT" ]

2

2021-08-25T14:47:55.000Z

2022-02-09T23:30:49.000Z

models/definitions/flownet/inference.py

HaydenFaulkner/VideoYOLO

2dbc104a41bf1192a00ffde07695180eab18cea8

[ "MIT" ]

3

2020-03-02T14:52:18.000Z

2020-06-05T07:51:18.000Z

import cv2 import mxnet as mx import numpy as np from scipy.misc import imresize from tqdm import tqdm from flownet import get_flownet from utils import flow_to_image, crop, normalise def process_two_images(model, imgs, ctx=None): """ Process two images into one flow image Args: model: The model to use imgs: a list of 2 images ctx: the model ctx Returns: """ if len(imgs) != 2: return None if isinstance(imgs[0], str): if os.path.exists(imgs[0]): imgs[0] = cv2.cvtColor(cv2.imread(files[i]), cv2.COLOR_BGR2RGB) else: return None if isinstance(imgs[1], str): if os.path.exists(imgs[1]): imgs[1] = cv2.cvtColor(cv2.imread(files[i]), cv2.COLOR_BGR2RGB) else: return None imgs = crop(imgs) imgs = np.array(imgs) imgs = np.moveaxis(imgs, -1, 1) imgs = normalise(imgs) imgs = mx.nd.array(imgs, ctx=ctx) imgs = mx.nd.expand_dims(imgs, 0) # add batch axis flow = model(imgs) # run the model flow = flow.asnumpy() flow = flow.squeeze() flow = flow.transpose(1, 2, 0) img = flow_to_image(flow) img = imresize(img, 4.0) # doing the bilinear interpolation on the img, NOT flow cause was too hard :'( return img, flow def process_imagedir(model, input_dir, output_dir=None, ctx=None): """ Process a directory of images Args: model: input_dir: output_dir: ctx: Returns: """ files = [] for ext in [".jpg", ".png", ".jpeg", ".JPG", ".PNG", ".JPEG"]: files = glob.glob(input_dir + "/**/*" + ext, recursive=True) if len(files) > 0: break if not len(files) > 0: print("Couldn't find any files in {}".format(input_dir)) return None files.sort() for i in tqdm(range(len(files) - 1), desc='Calculating Flow'): img, flow = process_two_images(model, files[i:i+2], ctx) dir, file = os.path.split(files[i]) if output_dir is None: output_dir = os.path.join(dir, 'flow') os.makedirs(output_dir, exists_ok=True) cv2.imwrite(os.path.join(output_dir, file), cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) return output_dir def process_video(model, input_path, output_path=None, ctx=None): """ Process a video into a flow video Args: model: input_path: output_path: ctx: Returns: """ capture = cv2.VideoCapture(input_path) frames = [] while_safety = 0 while len(frames) < 200:# int(capture.get(cv2.CAP_PROP_FRAME_COUNT))-1: _, image = capture.read() # read an image from the capture if while_safety > 500: # break the while if our safety maxs out at 500 break if image is None: while_safety += 1 continue while_safety = 0 # reset the safety count frames.append(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) capture.release() if len(frames) < 2: return None if output_path is None: output_path = input_path[:-4] + '_flow.mp4' cropped_frames = crop(frames) h, w, _= cropped_frames[0].shape video = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), 25, (w, h)) for i in tqdm(range(len(frames)-1), desc='Calculating Flow'): mx.nd.waitall() img, flow = process_two_images(model, frames[i:i+2], ctx) video.write(img) video.release() # release the video return output_path if __name__ == '__main__': # just for debugging # save_path = "models/definitions/flownet/weights/FlowNet2-S_checkpoint.params" save_path = "models/definitions/flownet/weights/FlowNet2-C_checkpoint.params" ctx = mx.gpu(0) # net = get_flownet('S', pretrained=True, ctx=ctx) net = get_flownet('C', pretrained=True, ctx=ctx) net.hybridize() input_path = "/path/to/test.mp4" process_video(net, input_path, ctx=ctx) print("DONE")

26.083871

108

0.606728

import cv2 import mxnet as mx import numpy as np from scipy.misc import imresize from tqdm import tqdm from flownet import get_flownet from utils import flow_to_image, crop, normalise def process_two_images(model, imgs, ctx=None): if len(imgs) != 2: return None if isinstance(imgs[0], str): if os.path.exists(imgs[0]): imgs[0] = cv2.cvtColor(cv2.imread(files[i]), cv2.COLOR_BGR2RGB) else: return None if isinstance(imgs[1], str): if os.path.exists(imgs[1]): imgs[1] = cv2.cvtColor(cv2.imread(files[i]), cv2.COLOR_BGR2RGB) else: return None imgs = crop(imgs) imgs = np.array(imgs) imgs = np.moveaxis(imgs, -1, 1) imgs = normalise(imgs) imgs = mx.nd.array(imgs, ctx=ctx) imgs = mx.nd.expand_dims(imgs, 0) flow = model(imgs) flow = flow.asnumpy() flow = flow.squeeze() flow = flow.transpose(1, 2, 0) img = flow_to_image(flow) img = imresize(img, 4.0) return img, flow def process_imagedir(model, input_dir, output_dir=None, ctx=None): files = [] for ext in [".jpg", ".png", ".jpeg", ".JPG", ".PNG", ".JPEG"]: files = glob.glob(input_dir + "/**/*" + ext, recursive=True) if len(files) > 0: break if not len(files) > 0: print("Couldn't find any files in {}".format(input_dir)) return None files.sort() for i in tqdm(range(len(files) - 1), desc='Calculating Flow'): img, flow = process_two_images(model, files[i:i+2], ctx) dir, file = os.path.split(files[i]) if output_dir is None: output_dir = os.path.join(dir, 'flow') os.makedirs(output_dir, exists_ok=True) cv2.imwrite(os.path.join(output_dir, file), cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) return output_dir def process_video(model, input_path, output_path=None, ctx=None): capture = cv2.VideoCapture(input_path) frames = [] while_safety = 0 while len(frames) < 200: _, image = capture.read() if while_safety > 500: break if image is None: while_safety += 1 continue while_safety = 0 frames.append(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) capture.release() if len(frames) < 2: return None if output_path is None: output_path = input_path[:-4] + '_flow.mp4' cropped_frames = crop(frames) h, w, _= cropped_frames[0].shape video = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), 25, (w, h)) for i in tqdm(range(len(frames)-1), desc='Calculating Flow'): mx.nd.waitall() img, flow = process_two_images(model, frames[i:i+2], ctx) video.write(img) video.release() return output_path if __name__ == '__main__': save_path = "models/definitions/flownet/weights/FlowNet2-C_checkpoint.params" ctx = mx.gpu(0) net = get_flownet('C', pretrained=True, ctx=ctx) net.hybridize() input_path = "/path/to/test.mp4" process_video(net, input_path, ctx=ctx) print("DONE")

true

1c47763f1386690bf0efd66398f708660e2f5d45

5,537

py

Python

scripts/automation/trex_control_plane/astf/trex_astf_lib/trex_astf_global_info.py

alialnu/trex-core

ae4ab05a6215fd0a859adde40dac6afa8bf0f950

[ "Apache-2.0" ]

null

scripts/automation/trex_control_plane/astf/trex_astf_lib/trex_astf_global_info.py

alialnu/trex-core

ae4ab05a6215fd0a859adde40dac6afa8bf0f950

[ "Apache-2.0" ]

null

scripts/automation/trex_control_plane/astf/trex_astf_lib/trex_astf_global_info.py

alialnu/trex-core

ae4ab05a6215fd0a859adde40dac6afa8bf0f950

[ "Apache-2.0" ]

null

import socket class ASTFGlobalInfoBase(object): _g_params = {} class inner(object): def __init__(self, params, name): self._fields = {} self._params = params self._name = name def __setattr__(self, name, val): if name.startswith("_"): return super(ASTFGlobalInfoBase.inner, self).__setattr__(name, val) for p in self._params: if name == p["name"]: if "sub_type" in p: if p["sub_type"]=="ipv6_addr": if (type(val)!=str): raise AttributeError("{0} in {1} should have one of the following types: {2}" .format(name, self._name, str)) b=socket.inet_pton(socket.AF_INET6, val) l = list(b); # in case of Python 2 if not(type(l[0]) is int): l=[ord(i) for i in l] self._fields[name] = l; return; if "type" in p and type(val) not in p["type"]: raise AttributeError("{0} in {1} should have one of the following types: {2}" .format(name, self._name, p["type"])) self._fields[name] = val return raise AttributeError("%r has no attribute %s" % (self._name, name)) def __getattr__(self, name): if name.startswith("_"): return super(ASTFGlobalInfoBase.inner, self).__getattr__(name) for p in self._params: if name == p["name"]: return self._fields[name] raise AttributeError("%r has no attribute %s" % (self._name, name)) def to_json(self): return self._fields def __init__(self, params=_g_params, name="globalp"): self._fields = {} self._params = params self._name = name def __setattr__(self, name, val): if name.startswith("_"): return super(ASTFGlobalInfoBase, self).__setattr__(name, val) if name in self._params: if type(self._params[name]) is dict: next_level_params = self._params[name].keys() else: next_level_params = [] for n in self._params[name]: next_level_params.append(n["name"]) raise AttributeError("{0} in {1} should be followed by one of {2}".format(name, self._name, next_level_params)) else: raise AttributeError("{0} is not part of valid params".format(name)) def __getattr__(self, name): if name.startswith("_"): return super(ASTFGlobalInfoBase.in_tcp, self).__getattr__(name) if name in self._params: long_name = self._name + "." + name if type(self._params[name]) is dict: return self._fields.setdefault(name, ASTFGlobalInfoBase(params=self._params[name], name=long_name)) elif type(self._params[name]) is list: return self._fields.setdefault(name, ASTFGlobalInfoBase.inner(params=self._params[name], name=long_name)) raise AttributeError("{0} has no attribute {1} it has {2}".format(self._name, name, self._params.keys())) def to_json(self): ret = {} for field in self._fields.keys(): ret[field] = self._fields[field].to_json() return ret class ASTFGlobalInfo(ASTFGlobalInfoBase): _g_params = { "scheduler" : [ {"name": "rampup_sec", "type": [int]}, {"name": "accurate", "type": [int]} ], "ipv6": [ {"name": "src_msb", "sub_type" : "ipv6_addr" }, {"name": "dst_msb", "sub_type" : "ipv6_addr" }, {"name": "enable", "type": [int]} ], "tcp": [ {"name": "mss", "type": [int]}, {"name": "initwnd", "type": [int]}, {"name": "rxbufsize", "type": [int]}, {"name": "txbufsize", "type": [int]}, {"name": "rexmtthresh", "type": [int]}, {"name": "do_rfc1323", "type": [int]}, {"name": "keepinit", "type": [int]}, {"name": "keepidle", "type": [int]}, {"name": "keepintvl", "type": [int]}, {"name": "delay_ack_msec", "type": [int]}, {"name": "no_delay", "type": [int]}, ], "ip": [ {"name": "tos", "type": [int]}, {"name": "ttl", "type": [int]} ], } def __init__(self, params=_g_params, name="GlobalInfo"): return super(ASTFGlobalInfo, self).__init__(params, name) class ASTFGlobalInfoPerTemplate(ASTFGlobalInfoBase): _g_params = { "tcp": [ {"name": "initwnd", "type": [int]}, {"name": "mss", "type": [int]}, {"name": "no_delay", "type": [int]}, {"name": "rxbufsize", "type": [int]}, {"name": "txbufsize", "type": [int]}, ], "ip": [ {"name": "tos", "type": [int]}, {"name": "ttl", "type": [int]} ], } def __init__(self, params=_g_params, name="GlobalInfoPerTemplate"): return super(ASTFGlobalInfoPerTemplate, self).__init__(params, name)

37.161074

123

0.483475

import socket class ASTFGlobalInfoBase(object): _g_params = {} class inner(object): def __init__(self, params, name): self._fields = {} self._params = params self._name = name def __setattr__(self, name, val): if name.startswith("_"): return super(ASTFGlobalInfoBase.inner, self).__setattr__(name, val) for p in self._params: if name == p["name"]: if "sub_type" in p: if p["sub_type"]=="ipv6_addr": if (type(val)!=str): raise AttributeError("{0} in {1} should have one of the following types: {2}" .format(name, self._name, str)) b=socket.inet_pton(socket.AF_INET6, val) l = list(b); if not(type(l[0]) is int): l=[ord(i) for i in l] self._fields[name] = l; return; if "type" in p and type(val) not in p["type"]: raise AttributeError("{0} in {1} should have one of the following types: {2}" .format(name, self._name, p["type"])) self._fields[name] = val return raise AttributeError("%r has no attribute %s" % (self._name, name)) def __getattr__(self, name): if name.startswith("_"): return super(ASTFGlobalInfoBase.inner, self).__getattr__(name) for p in self._params: if name == p["name"]: return self._fields[name] raise AttributeError("%r has no attribute %s" % (self._name, name)) def to_json(self): return self._fields def __init__(self, params=_g_params, name="globalp"): self._fields = {} self._params = params self._name = name def __setattr__(self, name, val): if name.startswith("_"): return super(ASTFGlobalInfoBase, self).__setattr__(name, val) if name in self._params: if type(self._params[name]) is dict: next_level_params = self._params[name].keys() else: next_level_params = [] for n in self._params[name]: next_level_params.append(n["name"]) raise AttributeError("{0} in {1} should be followed by one of {2}".format(name, self._name, next_level_params)) else: raise AttributeError("{0} is not part of valid params".format(name)) def __getattr__(self, name): if name.startswith("_"): return super(ASTFGlobalInfoBase.in_tcp, self).__getattr__(name) if name in self._params: long_name = self._name + "." + name if type(self._params[name]) is dict: return self._fields.setdefault(name, ASTFGlobalInfoBase(params=self._params[name], name=long_name)) elif type(self._params[name]) is list: return self._fields.setdefault(name, ASTFGlobalInfoBase.inner(params=self._params[name], name=long_name)) raise AttributeError("{0} has no attribute {1} it has {2}".format(self._name, name, self._params.keys())) def to_json(self): ret = {} for field in self._fields.keys(): ret[field] = self._fields[field].to_json() return ret class ASTFGlobalInfo(ASTFGlobalInfoBase): _g_params = { "scheduler" : [ {"name": "rampup_sec", "type": [int]}, {"name": "accurate", "type": [int]} ], "ipv6": [ {"name": "src_msb", "sub_type" : "ipv6_addr" }, {"name": "dst_msb", "sub_type" : "ipv6_addr" }, {"name": "enable", "type": [int]} ], "tcp": [ {"name": "mss", "type": [int]}, {"name": "initwnd", "type": [int]}, {"name": "rxbufsize", "type": [int]}, {"name": "txbufsize", "type": [int]}, {"name": "rexmtthresh", "type": [int]}, {"name": "do_rfc1323", "type": [int]}, {"name": "keepinit", "type": [int]}, {"name": "keepidle", "type": [int]}, {"name": "keepintvl", "type": [int]}, {"name": "delay_ack_msec", "type": [int]}, {"name": "no_delay", "type": [int]}, ], "ip": [ {"name": "tos", "type": [int]}, {"name": "ttl", "type": [int]} ], } def __init__(self, params=_g_params, name="GlobalInfo"): return super(ASTFGlobalInfo, self).__init__(params, name) class ASTFGlobalInfoPerTemplate(ASTFGlobalInfoBase): _g_params = { "tcp": [ {"name": "initwnd", "type": [int]}, {"name": "mss", "type": [int]}, {"name": "no_delay", "type": [int]}, {"name": "rxbufsize", "type": [int]}, {"name": "txbufsize", "type": [int]}, ], "ip": [ {"name": "tos", "type": [int]}, {"name": "ttl", "type": [int]} ], } def __init__(self, params=_g_params, name="GlobalInfoPerTemplate"): return super(ASTFGlobalInfoPerTemplate, self).__init__(params, name)

true

1c4777590dcdd7cd0868594deb226eb09b523f7d

16,358

py

Python

senlin/objects/fields.py

openstack/senlin

390779ca1e08f819683e79993696f945f1c0393e

[ "Apache-2.0" ]

45

2015-10-18T02:56:50.000Z

2022-03-01T15:28:02.000Z

senlin/objects/fields.py

openstack/senlin

390779ca1e08f819683e79993696f945f1c0393e

[ "Apache-2.0" ]

2

2019-04-26T10:44:47.000Z

2020-12-16T19:45:34.000Z

senlin/objects/fields.py

openstack/senlin

390779ca1e08f819683e79993696f945f1c0393e

[ "Apache-2.0" ]

45

2015-10-19T02:35:57.000Z

2021-09-28T09:01:42.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from oslo_config import cfg from oslo_serialization import jsonutils from oslo_utils import strutils from oslo_utils import uuidutils from oslo_versionedobjects import fields import re from senlin.common import consts from senlin.common.i18n import _ CONF = cfg.CONF # Field alias for code readability # BooleanField = fields.BooleanField FlexibleBooleanField = fields.FlexibleBooleanField StringField = fields.StringField IntegerField = fields.IntegerField FloatField = fields.FloatField UUIDField = fields.UUIDField DateTimeField = fields.DateTimeField DictOfStringsField = fields.DictOfStringsField ListOfStringsField = fields.ListOfStringsField ListOfEnumField = fields.ListOfEnumField class Boolean(fields.FieldType): # NOTE: The following definition is much more stricter than the oslo # version. Also note that the treatment of default values here: # we are using the user specified default value when invoking # the 'bool_from_string' until function. def __init__(self, default=False): super(Boolean, self).__init__() self._default = default def coerce(self, obj, attr, value): return strutils.bool_from_string(value, strict=True, default=self._default) def get_schema(self): return {'type': ['boolean']} class NonNegativeInteger(fields.FieldType): # NOTE: This definition is kept because we want the error message from # 'int' conversion to be user friendly. @staticmethod def coerce(obj, attr, value): try: v = int(value) except (TypeError, ValueError): raise ValueError(_("The value for %(attr)s must be an integer: " "'%(value)s'.") % {'attr': attr, 'value': value}) if v < 0: err = _("Value must be >= 0 for field '%s'.") % attr raise ValueError(err) return v def get_schema(self): return { 'type': ['integer', 'string'], 'minimum': 0 } # Senlin has a stricter field checking for object fields. class Object(fields.Object): def get_schema(self): schema = super(Object, self).get_schema() # we are not checking whether self._obj_name is registered, an # exception will be raised anyway if it is not registered. data_key = 'senlin_object.data' schema['properties'][data_key]['additionalProperties'] = False return schema class UUID(fields.FieldType): _PATTERN = (r'^[a-fA-F0-9]{8}-?[a-fA-F0-9]{4}-?[a-fA-F0-9]{4}-?[a-fA-F0-9]' r'{4}-?[a-fA-F0-9]{12}$') @staticmethod def coerce(obj, attr, value): if not uuidutils.is_uuid_like(value): msg = _("The value for %(attr)s is not a valid UUID: '%(value)s'." ) % {'attr': attr, 'value': value} raise ValueError(msg) return str(value) def get_schema(self): return {'type': ['string'], 'pattern': self._PATTERN} class Json(fields.FieldType): def coerce(self, obj, attr, value): if isinstance(value, str): try: return jsonutils.loads(value) except ValueError: msg = _("The value (%s) is not a valid JSON.") % value raise ValueError(msg) return value def from_primitive(self, obj, attr, value): return self.coerce(obj, attr, value) def to_primitive(self, obj, attr, value): return jsonutils.dumps(value) def stringify(self, value): if isinstance(value, str): try: return jsonutils.loads(value) except ValueError: raise return str(value) def get_schema(self): return {'type': ['object']} class NotificationPriority(fields.Enum): # The priorities here are derived from oslo_messaging.notify.notifier ALL = consts.NOTIFICATION_PRIORITIES def __init__(self): super(NotificationPriority, self).__init__(self.ALL) class NotificationPhase(fields.Enum): ALL = consts.NOTIFICATION_PHASES def __init__(self): super(NotificationPhase, self).__init__(self.ALL) class Name(fields.String): def __init__(self, min_len=1, max_len=255): super(Name, self).__init__() self.min_len = min_len self.max_len = max_len def coerce(self, obj, attr, value): err = None if len(value) < self.min_len: err = _("The value for the %(attr)s field must be at least " "%(count)d characters long." ) % {'attr': attr, 'count': self.min_len} elif len(value) > self.max_len: err = _("The value for the %(attr)s field must be less than " "%(count)d characters long." ) % {'attr': attr, 'count': self.max_len} else: # NOTE: This is pretty restrictive. We can relax it later when # there are requests to do so regex = re.compile(u'^[a-zA-Z\u4e00-\u9fa5\d\.\_\~-]*$', re.IGNORECASE) if not regex.search(value): err = _("The value for the '%(attr)s' (%(value)s) contains " "illegal characters. It must contain only " "alphanumeric or \"_-.~\" characters and must start " "with letter." ) % {'attr': attr, 'value': value} if err: raise ValueError(err) return super(Name, self).coerce(obj, attr, value) def get_schema(self): return { 'type': ['string'], 'minLength': self.min_len, 'maxLength': self.max_len } class Capacity(fields.Integer): def __init__(self, minimum=0, maximum=None): super(Capacity, self).__init__() CONF.import_opt("max_nodes_per_cluster", "senlin.conf") if minimum > CONF.max_nodes_per_cluster: err = _("The value of 'minimum' cannot be greater than the global " "constraint (%(m)d).") % {'m': CONF.max_nodes_per_cluster} raise ValueError(err) self.minimum = minimum if maximum is not None: if maximum < minimum: err = _("The value of 'maximum' must be greater than or equal " "to that of the 'minimum' specified.") raise ValueError(err) if maximum > CONF.max_nodes_per_cluster: err = _("The value of 'maximum' cannot be greater than the " "global constraint (%(m)d)." ) % {'m': CONF.max_nodes_per_cluster} raise ValueError(err) self.maximum = maximum else: self.maximum = CONF.max_nodes_per_cluster def coerce(self, obj, attr, value): try: v = int(value) except Exception: raise ValueError(_("The value for %(attr)s must be an integer: " "'%(value)s'.") % {'attr': attr, 'value': value}) if v < self.minimum: raise ValueError(_("The value for the %(a)s field must be greater " "than or equal to %(n)d.") % {'a': attr, 'n': self.minimum}) elif v > self.maximum: raise ValueError(_("The value for the %(a)s field must be less " "than or equal to %(n)d.") % {'a': attr, 'n': self.maximum}) return super(Capacity, self).coerce(obj, attr, v) def get_schema(self): return { 'type': ['integer', 'string'], 'minimum': self.minimum, 'maximum': self.maximum, 'pattern': '^[0-9]*$', } class Sort(fields.String): def __init__(self, valid_keys): super(Sort, self).__init__() self.valid_keys = valid_keys def coerce(self, obj, attr, value): for s in value.split(','): s_key, _sep, s_dir = s.partition(':') err = None if not s_key: err = _("Missing sort key for '%s'.") % attr raise ValueError(err) if s_key not in self.valid_keys: err = _("Unsupported sort key '%(value)s' for '%(attr)s'." ) % {'attr': attr, 'value': s_key} if s_dir and s_dir not in ('asc', 'desc'): err = _("Unsupported sort dir '%(value)s' for '%(attr)s'." ) % {'attr': attr, 'value': s_dir} if err: raise ValueError(err) return super(Sort, self).coerce(obj, attr, value) def get_schema(self): return { 'type': ['string'], } class IdentityList(fields.List): def __init__(self, element_type, min_items=0, unique=True, nullable=False, **kwargs): super(IdentityList, self).__init__(element_type, **kwargs) self.min_items = min_items self.unique_items = unique self.nullable = nullable def coerce(self, obj, attr, value): res = super(IdentityList, self).coerce(obj, attr, value) if len(res) < self.min_items: raise ValueError(_("Value for '%(attr)s' must have at least " "%(num)s item(s).") % {'attr': attr, 'num': self.min_items}) if len(set(res)) != len(res) and self.unique_items: raise ValueError(_("Items for '%(attr)s' must be unique") % {'attr': attr}) return res def get_schema(self): schema = super(IdentityList, self).get_schema() if self.nullable: schema['type'].append('null') schema['minItems'] = self.min_items schema['uniqueItems'] = self.unique_items return schema class BaseEnum(fields.FieldType): # NOTE: We are not basing Enum on String because String is not working # correctly when handling None value. def __init__(self, nullable=False): valid_values = list(self.__class__.ALL) if not valid_values: raise ValueError(_("No list of valid values provided for enum.")) for value in valid_values: if not isinstance(value, str): raise ValueError(_("Enum field only support string values.")) self._valid_values = list(valid_values) self._nullable = nullable super(BaseEnum, self).__init__() def coerce(self, obj, attr, value): value = str(value) if value not in self._valid_values: raise ValueError(_("Value '%(value)s' is not acceptable for " "field '%(attr)s'.") % {'value': value, 'attr': attr}) return value def stringify(self, value): if value is None: return None return '\'%s\'' % value class AdjustmentType(BaseEnum): ALL = consts.ADJUSTMENT_TYPES def get_schema(self): return {'type': ['string'], 'enum': self._valid_values} class ClusterActionName(BaseEnum): ALL = consts.CLUSTER_ACTION_NAMES def get_schema(self): return {'type': ['string'], 'enum': self._valid_values} class ClusterStatus(BaseEnum): ALL = consts.CLUSTER_STATUSES class NodeStatus(BaseEnum): ALL = consts.NODE_STATUSES class ActionStatus(BaseEnum): ALL = consts.ACTION_STATUSES class ReceiverType(BaseEnum): ALL = consts.RECEIVER_TYPES def get_schema(self): return {'type': ['string'], 'enum': self._valid_values} class UniqueDict(fields.Dict): def coerce(self, obj, attr, value): res = super(UniqueDict, self).coerce(obj, attr, value) new_nodes = res.values() if len(new_nodes) != len(set(new_nodes)): raise ValueError(_("Map contains duplicated values")) return res # TODO(Qiming): remove this when oslo patch is released # https://review.openstack.org/#/c/360095 class NonNegativeIntegerField(fields.AutoTypedField): AUTO_TYPE = NonNegativeInteger() class BooleanField(fields.AutoTypedField): AUTO_TYPE = Boolean() # An override to the oslo.versionedobjects version so that we are using # our own Object definition. class ObjectField(fields.AutoTypedField): def __init__(self, objtype, subclasses=False, **kwargs): self.AUTO_TYPE = Object(objtype, subclasses) self.objname = objtype super(ObjectField, self).__init__(**kwargs) class JsonField(fields.AutoTypedField): AUTO_TYPE = Json() class ListField(fields.AutoTypedField): AUTO_TYPE = fields.List(fields.FieldType()) class NotificationPriorityField(fields.BaseEnumField): AUTO_TYPE = NotificationPriority() class NotificationPhaseField(fields.BaseEnumField): AUTO_TYPE = NotificationPhase() class NameField(fields.AutoTypedField): AUTO_TYPE = Name() class UUIDField(fields.AutoTypedField): AUTO_TYPE = UUID() class CapacityField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, nullable=False, default=None, minimum=0, maximum=None): self.AUTO_TYPE = Capacity(minimum=minimum, maximum=maximum) super(CapacityField, self).__init__(nullable=nullable, default=default) class SortField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, valid_keys, nullable=False, default=None): self.AUTO_TYPE = Sort(valid_keys) super(SortField, self).__init__(nullable=nullable, default=default) class IdentityListField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, min_items=0, unique=True, nullable=False, default=None): if default is None: default = [] self.AUTO_TYPE = IdentityList(fields.String(), min_items=min_items, unique=unique) super(IdentityListField, self).__init__(nullable=nullable, default=default) class AdjustmentTypeField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, **kwargs): nullable = kwargs.get('nullable', False) self.AUTO_TYPE = AdjustmentType(nullable=nullable) super(AdjustmentTypeField, self).__init__(**kwargs) class ClusterActionNameField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, **kwargs): nullable = kwargs.get('nullable', False) self.AUTO_TYPE = ClusterActionName(nullable=nullable) super(ClusterActionNameField, self).__init__(**kwargs) class ClusterStatusField(fields.AutoTypedField): AUTO_TYPE = ClusterStatus class NodeStatusField(fields.AutoTypedField): AUTO_TYPE = NodeStatus class ActionStatusField(fields.AutoTypedField): AUTO_TYPE = ActionStatus class ReceiverTypeField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, **kwargs): nullable = kwargs.get('nullable', False) self.AUTO_TYPE = ReceiverType(nullable=nullable) super(ReceiverTypeField, self).__init__(**kwargs) class NodeReplaceMapField(fields.AutoTypedField): AUTO_TYPE = UniqueDict(fields.String()) class CustomListField(ListField): def __init__(self, attr_name, **kwargs): self.attr_name = attr_name super(CustomListField, self).__init__(**kwargs) def coerce(self, obj, attr, value): objs = super(CustomListField, self).coerce(obj, attr, value) custom_list = [] for i in objs: custom_list.append(getattr(i, self.attr_name)) return custom_list

30.575701

79

0.602152

from oslo_config import cfg from oslo_serialization import jsonutils from oslo_utils import strutils from oslo_utils import uuidutils from oslo_versionedobjects import fields import re from senlin.common import consts from senlin.common.i18n import _ CONF = cfg.CONF FlexibleBooleanField = fields.FlexibleBooleanField StringField = fields.StringField IntegerField = fields.IntegerField FloatField = fields.FloatField UUIDField = fields.UUIDField DateTimeField = fields.DateTimeField DictOfStringsField = fields.DictOfStringsField ListOfStringsField = fields.ListOfStringsField ListOfEnumField = fields.ListOfEnumField class Boolean(fields.FieldType): def __init__(self, default=False): super(Boolean, self).__init__() self._default = default def coerce(self, obj, attr, value): return strutils.bool_from_string(value, strict=True, default=self._default) def get_schema(self): return {'type': ['boolean']} class NonNegativeInteger(fields.FieldType): @staticmethod def coerce(obj, attr, value): try: v = int(value) except (TypeError, ValueError): raise ValueError(_("The value for %(attr)s must be an integer: " "'%(value)s'.") % {'attr': attr, 'value': value}) if v < 0: err = _("Value must be >= 0 for field '%s'.") % attr raise ValueError(err) return v def get_schema(self): return { 'type': ['integer', 'string'], 'minimum': 0 } class Object(fields.Object): def get_schema(self): schema = super(Object, self).get_schema() data_key = 'senlin_object.data' schema['properties'][data_key]['additionalProperties'] = False return schema class UUID(fields.FieldType): _PATTERN = (r'^[a-fA-F0-9]{8}-?[a-fA-F0-9]{4}-?[a-fA-F0-9]{4}-?[a-fA-F0-9]' r'{4}-?[a-fA-F0-9]{12}$') @staticmethod def coerce(obj, attr, value): if not uuidutils.is_uuid_like(value): msg = _("The value for %(attr)s is not a valid UUID: '%(value)s'." ) % {'attr': attr, 'value': value} raise ValueError(msg) return str(value) def get_schema(self): return {'type': ['string'], 'pattern': self._PATTERN} class Json(fields.FieldType): def coerce(self, obj, attr, value): if isinstance(value, str): try: return jsonutils.loads(value) except ValueError: msg = _("The value (%s) is not a valid JSON.") % value raise ValueError(msg) return value def from_primitive(self, obj, attr, value): return self.coerce(obj, attr, value) def to_primitive(self, obj, attr, value): return jsonutils.dumps(value) def stringify(self, value): if isinstance(value, str): try: return jsonutils.loads(value) except ValueError: raise return str(value) def get_schema(self): return {'type': ['object']} class NotificationPriority(fields.Enum): ALL = consts.NOTIFICATION_PRIORITIES def __init__(self): super(NotificationPriority, self).__init__(self.ALL) class NotificationPhase(fields.Enum): ALL = consts.NOTIFICATION_PHASES def __init__(self): super(NotificationPhase, self).__init__(self.ALL) class Name(fields.String): def __init__(self, min_len=1, max_len=255): super(Name, self).__init__() self.min_len = min_len self.max_len = max_len def coerce(self, obj, attr, value): err = None if len(value) < self.min_len: err = _("The value for the %(attr)s field must be at least " "%(count)d characters long." ) % {'attr': attr, 'count': self.min_len} elif len(value) > self.max_len: err = _("The value for the %(attr)s field must be less than " "%(count)d characters long." ) % {'attr': attr, 'count': self.max_len} else: regex = re.compile(u'^[a-zA-Z\u4e00-\u9fa5\d\.\_\~-]*$', re.IGNORECASE) if not regex.search(value): err = _("The value for the '%(attr)s' (%(value)s) contains " "illegal characters. It must contain only " "alphanumeric or \"_-.~\" characters and must start " "with letter." ) % {'attr': attr, 'value': value} if err: raise ValueError(err) return super(Name, self).coerce(obj, attr, value) def get_schema(self): return { 'type': ['string'], 'minLength': self.min_len, 'maxLength': self.max_len } class Capacity(fields.Integer): def __init__(self, minimum=0, maximum=None): super(Capacity, self).__init__() CONF.import_opt("max_nodes_per_cluster", "senlin.conf") if minimum > CONF.max_nodes_per_cluster: err = _("The value of 'minimum' cannot be greater than the global " "constraint (%(m)d).") % {'m': CONF.max_nodes_per_cluster} raise ValueError(err) self.minimum = minimum if maximum is not None: if maximum < minimum: err = _("The value of 'maximum' must be greater than or equal " "to that of the 'minimum' specified.") raise ValueError(err) if maximum > CONF.max_nodes_per_cluster: err = _("The value of 'maximum' cannot be greater than the " "global constraint (%(m)d)." ) % {'m': CONF.max_nodes_per_cluster} raise ValueError(err) self.maximum = maximum else: self.maximum = CONF.max_nodes_per_cluster def coerce(self, obj, attr, value): try: v = int(value) except Exception: raise ValueError(_("The value for %(attr)s must be an integer: " "'%(value)s'.") % {'attr': attr, 'value': value}) if v < self.minimum: raise ValueError(_("The value for the %(a)s field must be greater " "than or equal to %(n)d.") % {'a': attr, 'n': self.minimum}) elif v > self.maximum: raise ValueError(_("The value for the %(a)s field must be less " "than or equal to %(n)d.") % {'a': attr, 'n': self.maximum}) return super(Capacity, self).coerce(obj, attr, v) def get_schema(self): return { 'type': ['integer', 'string'], 'minimum': self.minimum, 'maximum': self.maximum, 'pattern': '^[0-9]*$', } class Sort(fields.String): def __init__(self, valid_keys): super(Sort, self).__init__() self.valid_keys = valid_keys def coerce(self, obj, attr, value): for s in value.split(','): s_key, _sep, s_dir = s.partition(':') err = None if not s_key: err = _("Missing sort key for '%s'.") % attr raise ValueError(err) if s_key not in self.valid_keys: err = _("Unsupported sort key '%(value)s' for '%(attr)s'." ) % {'attr': attr, 'value': s_key} if s_dir and s_dir not in ('asc', 'desc'): err = _("Unsupported sort dir '%(value)s' for '%(attr)s'." ) % {'attr': attr, 'value': s_dir} if err: raise ValueError(err) return super(Sort, self).coerce(obj, attr, value) def get_schema(self): return { 'type': ['string'], } class IdentityList(fields.List): def __init__(self, element_type, min_items=0, unique=True, nullable=False, **kwargs): super(IdentityList, self).__init__(element_type, **kwargs) self.min_items = min_items self.unique_items = unique self.nullable = nullable def coerce(self, obj, attr, value): res = super(IdentityList, self).coerce(obj, attr, value) if len(res) < self.min_items: raise ValueError(_("Value for '%(attr)s' must have at least " "%(num)s item(s).") % {'attr': attr, 'num': self.min_items}) if len(set(res)) != len(res) and self.unique_items: raise ValueError(_("Items for '%(attr)s' must be unique") % {'attr': attr}) return res def get_schema(self): schema = super(IdentityList, self).get_schema() if self.nullable: schema['type'].append('null') schema['minItems'] = self.min_items schema['uniqueItems'] = self.unique_items return schema class BaseEnum(fields.FieldType): def __init__(self, nullable=False): valid_values = list(self.__class__.ALL) if not valid_values: raise ValueError(_("No list of valid values provided for enum.")) for value in valid_values: if not isinstance(value, str): raise ValueError(_("Enum field only support string values.")) self._valid_values = list(valid_values) self._nullable = nullable super(BaseEnum, self).__init__() def coerce(self, obj, attr, value): value = str(value) if value not in self._valid_values: raise ValueError(_("Value '%(value)s' is not acceptable for " "field '%(attr)s'.") % {'value': value, 'attr': attr}) return value def stringify(self, value): if value is None: return None return '\'%s\'' % value class AdjustmentType(BaseEnum): ALL = consts.ADJUSTMENT_TYPES def get_schema(self): return {'type': ['string'], 'enum': self._valid_values} class ClusterActionName(BaseEnum): ALL = consts.CLUSTER_ACTION_NAMES def get_schema(self): return {'type': ['string'], 'enum': self._valid_values} class ClusterStatus(BaseEnum): ALL = consts.CLUSTER_STATUSES class NodeStatus(BaseEnum): ALL = consts.NODE_STATUSES class ActionStatus(BaseEnum): ALL = consts.ACTION_STATUSES class ReceiverType(BaseEnum): ALL = consts.RECEIVER_TYPES def get_schema(self): return {'type': ['string'], 'enum': self._valid_values} class UniqueDict(fields.Dict): def coerce(self, obj, attr, value): res = super(UniqueDict, self).coerce(obj, attr, value) new_nodes = res.values() if len(new_nodes) != len(set(new_nodes)): raise ValueError(_("Map contains duplicated values")) return res NegativeIntegerField(fields.AutoTypedField): AUTO_TYPE = NonNegativeInteger() class BooleanField(fields.AutoTypedField): AUTO_TYPE = Boolean() class ObjectField(fields.AutoTypedField): def __init__(self, objtype, subclasses=False, **kwargs): self.AUTO_TYPE = Object(objtype, subclasses) self.objname = objtype super(ObjectField, self).__init__(**kwargs) class JsonField(fields.AutoTypedField): AUTO_TYPE = Json() class ListField(fields.AutoTypedField): AUTO_TYPE = fields.List(fields.FieldType()) class NotificationPriorityField(fields.BaseEnumField): AUTO_TYPE = NotificationPriority() class NotificationPhaseField(fields.BaseEnumField): AUTO_TYPE = NotificationPhase() class NameField(fields.AutoTypedField): AUTO_TYPE = Name() class UUIDField(fields.AutoTypedField): AUTO_TYPE = UUID() class CapacityField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, nullable=False, default=None, minimum=0, maximum=None): self.AUTO_TYPE = Capacity(minimum=minimum, maximum=maximum) super(CapacityField, self).__init__(nullable=nullable, default=default) class SortField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, valid_keys, nullable=False, default=None): self.AUTO_TYPE = Sort(valid_keys) super(SortField, self).__init__(nullable=nullable, default=default) class IdentityListField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, min_items=0, unique=True, nullable=False, default=None): if default is None: default = [] self.AUTO_TYPE = IdentityList(fields.String(), min_items=min_items, unique=unique) super(IdentityListField, self).__init__(nullable=nullable, default=default) class AdjustmentTypeField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, **kwargs): nullable = kwargs.get('nullable', False) self.AUTO_TYPE = AdjustmentType(nullable=nullable) super(AdjustmentTypeField, self).__init__(**kwargs) class ClusterActionNameField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, **kwargs): nullable = kwargs.get('nullable', False) self.AUTO_TYPE = ClusterActionName(nullable=nullable) super(ClusterActionNameField, self).__init__(**kwargs) class ClusterStatusField(fields.AutoTypedField): AUTO_TYPE = ClusterStatus class NodeStatusField(fields.AutoTypedField): AUTO_TYPE = NodeStatus class ActionStatusField(fields.AutoTypedField): AUTO_TYPE = ActionStatus class ReceiverTypeField(fields.AutoTypedField): AUTO_TYPE = None def __init__(self, **kwargs): nullable = kwargs.get('nullable', False) self.AUTO_TYPE = ReceiverType(nullable=nullable) super(ReceiverTypeField, self).__init__(**kwargs) class NodeReplaceMapField(fields.AutoTypedField): AUTO_TYPE = UniqueDict(fields.String()) class CustomListField(ListField): def __init__(self, attr_name, **kwargs): self.attr_name = attr_name super(CustomListField, self).__init__(**kwargs) def coerce(self, obj, attr, value): objs = super(CustomListField, self).coerce(obj, attr, value) custom_list = [] for i in objs: custom_list.append(getattr(i, self.attr_name)) return custom_list

true

1c477804be4c4bf6d36610dc17cf96819da6d6fc

45,319

py

Python

nessai/nestedsampler.py

Rodrigo-Tenorio/nessai

2b4175da61b3a7250d1154a126ad93481836df0d

[ "MIT" ]

null

nessai/nestedsampler.py

Rodrigo-Tenorio/nessai

2b4175da61b3a7250d1154a126ad93481836df0d

[ "MIT" ]

null

nessai/nestedsampler.py

Rodrigo-Tenorio/nessai

2b4175da61b3a7250d1154a126ad93481836df0d

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Functions and objects related to the main nested sampling algorithm. """ from collections import deque import datetime import logging import os import pickle import matplotlib.pyplot as plt from matplotlib.lines import Line2D import numpy as np import seaborn as sns import torch from tqdm import tqdm from .livepoint import get_dtype, DEFAULT_FLOAT_DTYPE from .plot import plot_indices, plot_trace from .evidence import _NSIntegralState from .proposal import FlowProposal from .utils import ( safe_file_dump, compute_indices_ks_test, rolling_mean, ) sns.set() sns.set_style('ticks') logger = logging.getLogger(__name__) class NestedSampler: """ Nested Sampler class. Initialisation arguments: Parameters ---------- model: :obj:`nessai.model.Model` User defined model nlive : int, optional Number of live points. output : str Path for output stopping : float, optional Stop when remaining samples wouldn't change logZ estimate by this much. max_iteration : int, optional Maximum number of iterations to run before force sampler to stop. If stopping criteria is met before max. is reached sampler will stop. checkpointing : bool, optional Boolean to toggle checkpointing, must be enabled to resume the sampler. If false the sampler is still saved at the end of sampling. resume_file : str, optional If specified sampler will be resumed from this file. Still requires correct model. seed : int, optional seed for the initialisation of the pseudorandom chain n_pool : int, optional Number of threads to when for creating the multiprocessing pool. pool : object User defined multiprocessing pool that will be used when evaluating the likelihood. close_pool : bool Boolean to indicated if the pool should be closed at the end of the nested sampling loop. If False, the user must manually close the pool. plot : bool (True) Boolean to toggle plotting proposal_plots : bool (True) Boolean to enable additional plots for the population stage of the sampler. Overwritten by plot. prior_sampling : bool (False) produce nlive samples from the prior. analytic_priors : bool (False) Boolean that indicates that the `new_point` method in the model draws directly from the priors meaning rejection sampling is not needed. maximum_uninformed : int (1000) Maximum number of iterations before forcing the sampler to switch to using the proposal method with the flow. uninformed_proposal : :obj:`nessai.proposal.Proposal`: (None) Class to use for initial sampling before training the flow. If None RejectionProposal or AnalyticProposal are used depending if `analytic_priors` is False or True. uninformed_acceptance_threshold : float (None) Acceptance threshold for initialising sampling, if acceptance falls below this value sampler switches to flow-based proposal. If None then value is set to 10 times `acceptance_threshold` uninformed_proposal_kwargs : dict, ({}) Dictionary of keyword argument to pass to the class use for the initial sampling when it is initialised. flow_class : :obj:`nessai.proposal.FlowProposal` Class to use for flow-based proposal method flow_config : dict ({}) Dictionary used to configure instance of `nessai.flowmodel.FlowModel`, this includes configuring the normalising flow and the training. training_frequency : int (None) Number of iterations between re-training the flow. If None flow is only re-trained based on other criteria. train_on_empty : bool (True) If true the flow is retrained every time the proposal pool is empty. If false it is only training according to the other criteria. cooldown : int (100) Minimum number of iterations between training. Can be overridden if `train_on_empty=True` and the pool is empty. memory : int, False (False) Number of old live points to use in training. If False only the current live points are used. reset_weights : bool, int, (False) Boolean to toggle resetting the flow weights whenever re-training. If an integer is specified the flow is reset every nth time it is trained. reset_permutations: bool, int, (False) Boolean to toggle resetting the permutation layers in the flow whenever re-training. If an integer is specified the flow is reset every nth time it is trained. reset_acceptance : bool, (True) If true use mean acceptance of samples produced with current flow as a criteria for retraining retrain_acceptance : bool (False) Force the flow to be reset if the acceptance falls below the acceptance threshold. Requires `reset_acceptance=True` acceptance_threshold : float (0.01) Threshold to determine if the flow should be retrained, will not retrain if cooldown is not satisfied. kwargs : Keyword arguments passed to the flow proposal class """ def __init__( self, model, nlive=2000, output=None, stopping=0.1, max_iteration=None, checkpointing=True, checkpoint_on_training=False, resume_file=None, seed=None, pool=None, close_pool=True, n_pool=None, plot=True, proposal_plots=False, prior_sampling=False, analytic_priors=False, maximum_uninformed=None, uninformed_proposal=None, uninformed_acceptance_threshold=None, uninformed_proposal_kwargs=None, flow_class=None, flow_config=None, training_frequency=None, train_on_empty=True, cooldown=200, memory=False, reset_weights=False, reset_permutations=False, retrain_acceptance=True, reset_acceptance=False, acceptance_threshold=0.01, **kwargs ): logger.info('Initialising nested sampler') self.info_enabled = logger.isEnabledFor(logging.INFO) model.verify_model() self.model = model self.model.configure_pool(pool=pool, n_pool=n_pool) self.close_pool = close_pool self.nlive = nlive self.live_points = None self.prior_sampling = prior_sampling self.setup_random_seed(seed) self.accepted = 0 self.rejected = 1 self.initialised = False self.checkpointing = checkpointing self.checkpoint_on_training = checkpoint_on_training self.iteration = 0 self.acceptance_history = deque(maxlen=(nlive // 10)) self.mean_acceptance_history = [] self.block_acceptance = 1. self.mean_block_acceptance = 1. self.block_iteration = 0 self.retrain_acceptance = retrain_acceptance self.reset_acceptance = reset_acceptance self.insertion_indices = [] self.rolling_p = [] self.resumed = False self.tolerance = stopping self.condition = np.inf self.logLmin = -np.inf self.logLmax = -np.inf self.nested_samples = [] self.logZ = None self.state = _NSIntegralState(self.nlive, track_gradients=plot) self.plot = plot self.resume_file = self.setup_output(output, resume_file) self.output = output # Timing self.training_time = datetime.timedelta() self.sampling_time = datetime.timedelta() self.sampling_start_time = datetime.datetime.now() # Resume flags self.completed_training = True self.finalised = False # History self.likelihood_evaluations = [] self.training_iterations = [] self.min_likelihood = [] self.max_likelihood = [] self.logZ_history = [] self.dZ_history = [] self.population_acceptance = [] self.population_radii = [] self.population_iterations = [] self.checkpoint_iterations = [] self.acceptance_threshold = acceptance_threshold self.train_on_empty = train_on_empty self.cooldown = cooldown self.memory = memory self.configure_max_iteration(max_iteration) self.configure_flow_reset(reset_weights, reset_permutations) self.configure_training_frequency(training_frequency) if uninformed_proposal_kwargs is None: uninformed_proposal_kwargs = {} self.configure_uninformed_proposal(uninformed_proposal, analytic_priors, maximum_uninformed, uninformed_acceptance_threshold, **uninformed_proposal_kwargs) self.configure_flow_proposal(flow_class, flow_config, proposal_plots, **kwargs) # Uninformed proposal is used for prior sampling # If maximum uninformed is greater than 0, the it will be used for # another n iterations or until it becomes inefficient self.store_live_points = False if self.store_live_points: self.live_points_dir = f'{self.output}/live_points/' os.makedirs(self.live_points_dir, exist_ok=True) self.replacement_points = [] @property def log_evidence(self): return self.state.logZ @property def information(self): return self.state.info[-1] @property def likelihood_calls(self): return self.model.likelihood_evaluations @property def likelihood_evaluation_time(self): return self.model.likelihood_evaluation_time @property def proposal_population_time(self): t = self._uninformed_proposal.population_time t += self._flow_proposal.population_time return t @property def acceptance(self): return self.iteration / self.likelihood_calls @property def current_sampling_time(self): if self.finalised: return self.sampling_time else: return self.sampling_time \ + (datetime.datetime.now() - self.sampling_start_time) @property def last_updated(self): """Last time the normalising flow was retrained""" if self.training_iterations: return self.training_iterations[-1] else: return 0 @property def mean_acceptance(self): """ Mean acceptance of the last nlive // 10 points """ if self.acceptance_history: return np.mean(self.acceptance_history) else: return np.nan def configure_max_iteration(self, max_iteration): """Configure the maximum iteration. If None then no maximum is set. Parameter --------- max_iteration : int, None Maximum iteration. """ if max_iteration is None: self.max_iteration = np.inf else: self.max_iteration = max_iteration def configure_training_frequency(self, training_frequency): """Configure the training frequency. If None, 'inf' or 'None' flow will only train when empty. """ if training_frequency in [None, 'inf', 'None']: logger.warning('Proposal will only train when empty') self.training_frequency = np.inf else: self.training_frequency = training_frequency def configure_uninformed_proposal(self, uninformed_proposal, analytic_priors, maximum_uninformed, uninformed_acceptance_threshold, **kwargs): """ Setup the uninformed proposal method (is NOT trained) Parameters ---------- uninformed_proposal : None or obj Class to use for uninformed proposal analytic_priors : bool If True `AnalyticProposal` is used to directly sample from the priors rather than using rejection sampling. maximum_uninformed : {False, None, int, float} Maximum number of iterations before switching to FlowProposal. If None, two times nlive is used. If False uninformed sampling is not used. uninformed_acceptance_threshold : float or None: Threshold to use for uninformed proposal, once reached proposal method will switch. If None acceptance_threshold is used if greater than 0.1 else 10 x acceptance_threshold is used. kwargs Kwargs are passed to init method for uninformed proposal class """ if maximum_uninformed is None: self.uninformed_sampling = True self.maximum_uninformed = 2 * self.nlive elif not maximum_uninformed: self.uninformed_sampling = False self.maximum_uninformed = 0 else: self.uninformed_sampling = True self.maximum_uninformed = float(maximum_uninformed) if uninformed_acceptance_threshold is None: if self.acceptance_threshold < 0.1: self.uninformed_acceptance_threshold = \ 10 * self.acceptance_threshold else: self.uninformed_acceptance_threshold = \ self.acceptance_threshold else: self.uninformed_acceptance_threshold = \ uninformed_acceptance_threshold if uninformed_proposal is None: if analytic_priors: from .proposal import AnalyticProposal as uninformed_proposal else: from .proposal import RejectionProposal as uninformed_proposal kwargs['poolsize'] = self.nlive logger.debug(f'Using uninformed proposal: {uninformed_proposal}') logger.debug(f'Parsing kwargs to uninformed proposal: {kwargs}') self._uninformed_proposal = uninformed_proposal( self.model, **kwargs ) def configure_flow_proposal(self, flow_class, flow_config, proposal_plots, **kwargs): """ Set up the flow-based proposal method Parameters ---------- flow_class : None or obj or str Class to use for proposal. If None FlowProposal is used. flow_config : dict Configuration dictionary passed to the class. proposal_plots : bool or str Configuration of plotting in proposal class. **kwargs : Kwargs passed to init function. """ proposal_output = self.output + '/proposal/' if not self.plot: proposal_plots = False if flow_class is not None: if isinstance(flow_class, str): flow_class = flow_class.lower() if flow_class == 'gwflowproposal': from .gw.proposal import GWFlowProposal as flow_class elif flow_class == 'augmentedgwflowproposal': from .gw.proposal import ( AugmentedGWFlowProposal as flow_class) elif flow_class == 'legacygwflowproposal': from .gw.legacy import LegacyGWFlowProposal as flow_class elif flow_class == 'flowproposal': flow_class = FlowProposal elif flow_class == 'augmentedflowproposal': from .proposal import AugmentedFlowProposal flow_class = AugmentedFlowProposal else: raise ValueError(f'Unknown flow class: {flow_class}') elif not issubclass(flow_class, FlowProposal): raise RuntimeError('Flow class must be string or class that ' 'inherits from FlowProposal') else: flow_class = FlowProposal if kwargs.get('poolsize', None) is None: kwargs['poolsize'] = self.nlive logger.debug(f'Using flow class: {flow_class}') logger.info(f'Parsing kwargs to FlowProposal: {kwargs}') self._flow_proposal = flow_class( self.model, flow_config=flow_config, output=proposal_output, plot=proposal_plots, **kwargs ) def setup_output(self, output, resume_file=None): """ Set up the output folder Parameters ---------- output : str Directory where the results will be stored resume_file : optional Specific file to use for checkpointing. If not specified the default is used (nested_sampler_resume.pkl) Returns ------- resume_file : str File used for checkpointing """ if not os.path.exists(output): os.makedirs(output, exist_ok=True) if resume_file is None: resume_file = os.path.join(output, "nested_sampler_resume.pkl") else: resume_file = os.path.join(output, resume_file) if self.plot: os.makedirs(output + '/diagnostics/', exist_ok=True) return resume_file def setup_random_seed(self, seed): """ initialise the random seed """ self.seed = seed if self.seed is not None: logger.debug(f'Setting random seed to {seed}') np.random.seed(seed=self.seed) torch.manual_seed(self.seed) def configure_flow_reset(self, reset_weights, reset_permutations): """Configure how often the flow parameters are reset. Values are converted to floats. Parameters ---------- reset_weights : int, float or bool Frequency with which the weights will be reset. reset_permutations : int, float or bool Frequency with which the permutations will be reset. """ if isinstance(reset_weights, (int, float)): self.reset_weights = float(reset_weights) else: raise TypeError( '`reset_weights` must be a bool, int or float') if isinstance(reset_permutations, (int, float)): self.reset_permutations = float(reset_permutations) else: raise TypeError( '`reset_permutations` must be a bool, int or float') def check_insertion_indices(self, rolling=True, filename=None): """ Checking the distribution of the insertion indices either during the nested sampling run (rolling=True) or for the whole run (rolling=False). """ if rolling: indices = self.insertion_indices[-self.nlive:] else: indices = self.insertion_indices D, p = compute_indices_ks_test(indices, self.nlive) if p is not None: if rolling: logger.warning(f'Rolling KS test: D={D:.4}, p-value={p:.4}') self.rolling_p.append(p) else: logger.warning(f'Final KS test: D={D:.4}, p-value={p:.4}') if filename is not None: np.savetxt(os.path.join(self.output, filename), self.insertion_indices, newline='\n', delimiter=' ') def log_likelihood(self, x): """ Wrapper for the model likelihood so evaluations are counted """ return self.model.log_likelihood(x) def yield_sample(self, oldparam): """ Draw points and applying rejection sampling """ while True: counter = 0 while True: counter += 1 newparam = self.proposal.draw(oldparam.copy()) # Prior is computed in the proposal if newparam['logP'] != -np.inf: if not newparam['logL']: newparam['logL'] = \ self.model.evaluate_log_likelihood(newparam) if newparam['logL'] > self.logLmin: self.logLmax = max(self.logLmax, newparam['logL']) oldparam = newparam.copy() break # Only here if proposed and then empty # This returns the old point and allows for a training check if not self.proposal.populated: break yield counter, oldparam def insert_live_point(self, live_point): """ Insert a live point """ # This is the index including the current worst point, so final index # is one less, otherwise index=0 would never be possible index = np.searchsorted(self.live_points['logL'], live_point['logL']) self.live_points[:index - 1] = self.live_points[1:index] self.live_points[index - 1] = live_point return index - 1 def consume_sample(self): """ Replace a sample for single thread """ worst = self.live_points[0].copy() self.logLmin = worst['logL'] self.state.increment(worst['logL']) self.nested_samples.append(worst) self.condition = np.logaddexp(self.state.logZ, self.logLmax - self.iteration / float(self.nlive)) \ - self.state.logZ # Replace the points we just consumed with the next acceptable ones # Make sure we are mixing the chains self.iteration += 1 self.block_iteration += 1 count = 0 while(True): c, proposed = next(self.yield_sample(worst)) count += c if proposed['logL'] > self.logLmin: # Assuming point was proposed # replace worst point with new one index = self.insert_live_point(proposed) self.insertion_indices.append(index) self.accepted += 1 self.block_acceptance += 1 / count self.acceptance_history.append(1 / count) break else: # Only get here if the yield sample returns worse point # which can only happen if the pool is empty self.rejected += 1 self.check_state() # if retrained whilst proposing a sample then update the # iteration count since will be zero otherwise if not self.block_iteration: self.block_iteration += 1 self.mean_block_acceptance = self.block_acceptance \ / self.block_iteration if self.info_enabled: logger.info(f"{self.iteration:5d}: n: {count:3d} " f"b_acc: {self.mean_block_acceptance:.3f} " f"H: {self.state.info[-1]:.2f} " f"logL: {self.logLmin:.5f} --> {proposed['logL']:.5f} " f"dZ: {self.condition:.3f} " f"logZ: {self.state.logZ:.3f} " f"+/- {np.sqrt(self.state.info[-1] / self.nlive):.3f} " f"logLmax: {self.logLmax:.2f}") def populate_live_points(self): """ Initialise the pool of live points. """ i = 0 live_points = np.empty(self.nlive, dtype=get_dtype(self.model.names, DEFAULT_FLOAT_DTYPE)) with tqdm(total=self.nlive, desc='Drawing live points') as pbar: while i < self.nlive: while i < self.nlive: count, live_point = next( self.yield_sample(self.model.new_point())) if np.isnan(live_point['logL']): logger.warning( 'Likelihood function returned NaN for ' f'live_point {live_point}' ) logger.warning( 'You may want to check your likelihood function' ) break if ( np.isfinite(live_point['logP']) and np.isfinite(live_point['logL']) ): live_points[i] = live_point i += 1 pbar.update() break self.live_points = np.sort(live_points, order='logL') if self.store_live_points: np.savetxt(self.live_points_dir + '/initial_live_points.dat', self.live_points, header='\t'.join(self.live_points.dtype.names)) def initialise(self, live_points=True): """ Initialise the nested sampler Parameters ---------- live_points : bool, optional (True) If true and there are no live points, new live points are drawn using `populate_live_points` else all other initialisation steps are complete but live points remain empty. """ flags = [False] * 3 if not self._flow_proposal.initialised: self._flow_proposal.initialise() flags[0] = True if not self._uninformed_proposal.initialised: self._uninformed_proposal.initialise() flags[1] = True if ( self.iteration < self.maximum_uninformed and self.uninformed_sampling ): self.proposal = self._uninformed_proposal else: self.proposal = self._flow_proposal if live_points and self.live_points is None: self.populate_live_points() flags[2] = True if self.condition > self.tolerance: self.finalised = False if all(flags): self.initialised = True def check_proposal_switch(self, force=False): """ Check if the proposal should be switch from uninformed to flowproposal given the current state. If the flow proposal is already in use, no changes are made. Parameters ---------- force : bool, optional If True proposal is forced to switch. Returns ------- bool Flag to indicated if proposal was switched """ if ( (self.mean_acceptance < self.uninformed_acceptance_threshold) or (self.iteration >= self.maximum_uninformed) or force ): if self.proposal is self._flow_proposal: logger.warning('Already using flowproposal') return True logger.warning('Switching to FlowProposal') self.proposal = self._flow_proposal self.proposal.ns_acceptance = self.mean_block_acceptance self.uninformed_sampling = False return True # If using uninformed sampling, don't check training else: return False def check_training(self): """ Check if the normalising flow should be trained Checks that can force training: - Training was previously stopped before completion - The pool is empty and the proposal was not in the process of populating when stopped. Checks that cannot force training is still on cooldown: - Acceptance falls below threshold and `retrain_acceptance` is true - The number of iterations since last training is equal to the training frequency Returns ------- train : bool Try to train if true force : bool Force the training irrespective of cooldown """ if not self.completed_training: logger.debug('Training flow (resume)') return True, True elif (not self.proposal.populated and self.train_on_empty and not self.proposal.populating): logger.debug('Training flow (proposal empty)') return True, True elif (self.mean_block_acceptance < self.acceptance_threshold and self.retrain_acceptance): logger.debug('Training flow (acceptance)') return True, False elif (self.iteration - self.last_updated) == self.training_frequency: logger.debug('Training flow (iteration)') return True, False else: return False, False def check_flow_model_reset(self): """ Check if the normalising flow model should be reset. Checks acceptance if `reset_acceptance` is True and always checks how many times the flow has been trained. Flow will not be reset if it has not been trained. To force a reset manually call `proposal.reset_model_weights`. """ if not self.proposal.training_count: return if (self.reset_acceptance and self.mean_block_acceptance < self.acceptance_threshold): self.proposal.reset_model_weights(weights=True, permutations=True) return self.proposal.reset_model_weights( weights=( self.reset_weights and not (self.proposal.training_count % self.reset_weights) ), permutations=( self.reset_permutations and not (self.proposal.training_count % self.reset_permutations) ), ) def train_proposal(self, force=False): """ Try to train the proposal. Proposal will not train if cooldown is not exceeded unless force is True. Parameters ---------- force : bool Override training checks """ if (self.iteration - self.last_updated < self.cooldown and not force): logger.debug('Not training, still cooling down!') else: self.completed_training = False self.check_flow_model_reset() training_data = self.live_points.copy() if self.memory and (len(self.nested_samples) >= self.memory): training_data = np.concatenate([ training_data, self.nested_samples[-self.memory:].copy()]) st = datetime.datetime.now() self.proposal.train(training_data) self.training_time += (datetime.datetime.now() - st) self.training_iterations.append(self.iteration) self.block_iteration = 0 self.block_acceptance = 0. self.completed_training = True if self.checkpoint_on_training: self.checkpoint(periodic=True) def check_state(self, force=False): """ Check if state should be updated prior to drawing a new sample Force will override the cooldown mechanism. """ if self.uninformed_sampling: if self.check_proposal_switch(): force = True else: return # General override train = False if force: train = True logger.debug('Training flow (force)') elif not train: train, force = self.check_training() if train or force: self.train_proposal(force=force) def plot_state(self, filename=None): """ Produce plots with the current state of the nested sampling run. Plots are saved to the output directory specified at initialisation. Parameters ---------- filename : str, optional If specified the figure will be saved, otherwise the figure is returned. """ fig, ax = plt.subplots(6, 1, sharex=True, figsize=(12, 12)) ax = ax.ravel() it = (np.arange(len(self.min_likelihood))) * (self.nlive // 10) it[-1] = self.iteration colours = ['#4575b4', '#d73027', '#fad117'] ls = ['-', '--', ':'] for t in self.training_iterations: for a in ax: a.axvline(t, ls='-', color='lightgrey') if not self.train_on_empty: for p in self.population_iterations: for a in ax: a.axvline(p, ls='-', color='tab:orange') for i in self.checkpoint_iterations: for a in ax: a.axvline(i, ls=':', color='#66ccff') for a in ax: a.axvline(self.iteration, c='#ff9900', ls='-.') ax[0].plot(it, self.min_likelihood, label='Min logL', c=colours[0], ls=ls[0]) ax[0].plot(it, self.max_likelihood, label='Max logL', c=colours[1], ls=ls[1]) ax[0].set_ylabel('logL') ax[0].legend(frameon=False) logX_its = np.arange(len(self.state.log_vols)) ax[1].plot( logX_its, self.state.log_vols, ls=ls[0], c=colours[0], label='log X' ) ax[1].set_ylabel('Log X') ax[1].legend(frameon=False) if self.state.track_gradients: ax_logX_grad = plt.twinx(ax[1]) # Use dotted linestyle (ls[2]) because dashed isn't clear ax_logX_grad.plot( logX_its, rolling_mean(np.abs(self.state.gradients), self.nlive // 10), c=colours[1], ls=ls[2], label='Gradient' ) ax_logX_grad.set_ylabel(r'$|d\log L/d \log X|$') ax_logX_grad.set_yscale('log') handles, labels = ax[1].get_legend_handles_labels() handles_tw, labels_tw = ax_logX_grad.get_legend_handles_labels() ax[1].legend( handles + handles_tw, labels + labels_tw, frameon=False ) ax[2].plot(it, self.likelihood_evaluations, c=colours[0], ls=ls[0], label='Evaluations') ax[2].set_ylabel('logL evaluations') ax[3].plot(it, self.logZ_history, label='logZ', c=colours[0], ls=ls[0]) ax[3].set_ylabel('logZ') ax[3].legend(frameon=False) ax_dz = plt.twinx(ax[3]) ax_dz.plot(it, self.dZ_history, label='dZ', c=colours[1], ls=ls[1]) ax_dz.set_ylabel('dZ') handles, labels = ax[3].get_legend_handles_labels() handles_dz, labels_dz = ax_dz.get_legend_handles_labels() ax[3].legend(handles + handles_dz, labels + labels_dz, frameon=False) ax[4].plot(it, self.mean_acceptance_history, c=colours[0], label='Proposal') ax[4].plot(self.population_iterations, self.population_acceptance, c=colours[1], ls=ls[1], label='Population') ax[4].set_ylabel('Acceptance') ax[4].set_ylim((-0.1, 1.1)) handles, labels = ax[4].get_legend_handles_labels() ax_r = plt.twinx(ax[4]) ax_r.plot(self.population_iterations, self.population_radii, label='Radius', color=colours[2], ls=ls[2]) ax_r.set_ylabel('Population radius') handles_r, labels_r = ax_r.get_legend_handles_labels() ax[4].legend(handles + handles_r, labels + labels_r, frameon=False) if len(self.rolling_p): it = (np.arange(len(self.rolling_p)) + 1) * self.nlive ax[5].plot(it, self.rolling_p, 'o', c=colours[0], label='p-value') ax[5].set_ylabel('p-value') ax[5].set_ylim([-0.1, 1.1]) ax[-1].set_xlabel('Iteration') fig.suptitle(f'Sampling time: {self.current_sampling_time}', fontsize=16) handles = [ Line2D([0], [0], color='#ff9900', linestyle='-.', label='Current iteration'), Line2D([0], [0], color='lightgrey', linestyle='-', markersize=10, markeredgewidth=1.5, label='Training'), Line2D([0], [0], color='#66ccff', linestyle=':', label='Checkpoint'), ] fig.legend( handles=handles, frameon=False, ncol=3, loc=(0.6, 0.0) ) fig.tight_layout() fig.subplots_adjust(top=0.95) if filename is not None: fig.savefig(filename) plt.close(fig) else: return fig def plot_trace(self, filename=None): """ Make trace plots for the nested samples. Parameters ---------- filename : str, optional If filename is None, the figure is returned. Else the figure is saved with that file name. """ if self.nested_samples: fig = plot_trace(self.state.log_vols[1:], self.nested_samples, filename=filename) return fig else: logger.warning('Could not produce trace plot. No nested samples!') def plot_insertion_indices(self, filename=None, **kwargs): """ Make a plot of all the insertion indices. Parameters ---------- filename : str, optional If filename is None, the figure is returned. Else the figure is saved with that file name. kwargs : Keyword arguments passed to `nessai.plot.plot_indices`. """ return plot_indices( self.insertion_indices, self.nlive, filename=filename, **kwargs ) def update_state(self, force=False): """ Update state after replacing a live point """ # Check if acceptance is not None, this indicates the proposal # was populated if not self.proposal._checked_population: self.population_acceptance.append( self.proposal.population_acceptance) self.population_radii.append(self.proposal.r) self.population_iterations.append(self.iteration) self.proposal._checked_population = True if not (self.iteration % (self.nlive // 10)) or force: self.likelihood_evaluations.append( self.model.likelihood_evaluations) self.min_likelihood.append(self.logLmin) self.max_likelihood.append(self.logLmax) self.logZ_history.append(self.state.logZ) self.dZ_history.append(self.condition) self.mean_acceptance_history.append(self.mean_acceptance) if not (self.iteration % self.nlive) or force: logger.warning( f"it: {self.iteration:5d}: " f"n eval: {self.likelihood_calls} " f"H: {self.state.info[-1]:.2f} " f"dZ: {self.condition:.3f} logZ: {self.state.logZ:.3f} " f"+/- {np.sqrt(self.state.info[-1] / self.nlive):.3f} " f"logLmax: {self.logLmax:.2f}") if self.checkpointing: self.checkpoint(periodic=True) if not force: self.check_insertion_indices() if self.plot: plot_indices(self.insertion_indices[-self.nlive:], self.nlive, plot_breakdown=False, filename=(f'{self.output}/diagnostics/' 'insertion_indices_' f'{self.iteration}.png')) if self.plot: self.plot_state(filename=f'{self.output}/state.png') self.plot_trace(filename=f'{self.output}/trace.png') if self.uninformed_sampling: self.block_acceptance = 0. self.block_iteration = 0 self.proposal.ns_acceptance = self.mean_block_acceptance def checkpoint(self, periodic=False): """ Checkpoint the classes internal state Parameters ---------- periodic : bool Indicates if the checkpoint is regular periodic checkpointing or forced by a signal. If forced by a signal, it will show up on the state plot. """ if not periodic: self.checkpoint_iterations += [self.iteration] self.sampling_time += \ (datetime.datetime.now() - self.sampling_start_time) logger.critical('Checkpointing nested sampling') safe_file_dump(self, self.resume_file, pickle, save_existing=True) self.sampling_start_time = datetime.datetime.now() def check_resume(self): """ Check the normalising flow is correctly configured is the sampler was resumed. """ if self.resumed: if self.uninformed_sampling is False: self.check_proposal_switch(force=True) # If pool is populated reset the flag since it is set to # false during initialisation if hasattr(self._flow_proposal, 'resume_populated'): if (self._flow_proposal.resume_populated and self._flow_proposal.indices): self._flow_proposal.populated = True logger.info('Resumed with populated pool') self.resumed = False def finalise(self): """ Finalise things after sampling """ logger.info('Finalising') for i, p in enumerate(self.live_points): self.state.increment(p['logL'], nlive=self.nlive-i) self.nested_samples.append(p) # Refine evidence estimate self.update_state(force=True) self.state.finalise() # output the chain and evidence self.finalised = True def nested_sampling_loop(self): """ Main nested sampling loop """ self.sampling_start_time = datetime.datetime.now() if not self.initialised: self.initialise(live_points=True) if self.prior_sampling: self.nested_samples = self.live_points.copy() if self.close_pool: self.model.close_pool() return self.nested_samples self.check_resume() if self.iteration: self.update_state() logger.critical('Starting nested sampling loop') while self.condition > self.tolerance: self.check_state() self.consume_sample() self.update_state() if self.iteration >= self.max_iteration: break # final adjustments # avoid repeating final adjustments if resuming a completed run. if not self.finalised and (self.condition <= self.tolerance): self.finalise() logger.critical(f'Final evidence: {self.state.logZ:.3f} +/- ' f'{np.sqrt(self.state.info[-1] / self.nlive):.3f}') logger.critical('Information: {0:.2f}'.format(self.state.info[-1])) self.check_insertion_indices(rolling=False) # This includes updating the total sampling time self.checkpoint(periodic=True) if self.close_pool: self.model.close_pool() logger.info(f'Total sampling time: {self.sampling_time}') logger.info(f'Total training time: {self.training_time}') logger.info(f'Total population time: {self.proposal_population_time}') logger.info( f'Total likelihood evaluations: {self.likelihood_calls:3d}') logger.info( 'Time spent evaluating likelihood: ' f'{self.likelihood_evaluation_time}' ) return self.state.logZ, np.array(self.nested_samples) @classmethod def resume(cls, filename, model, flow_config={}, weights_file=None): """ Resumes the interrupted state from a checkpoint pickle file. Parameters ---------- filename : str Pickle pickle to resume from model : :obj:`nessai.model.Model` User-defined model flow_config : dict, optional Dictionary for configuring the flow weights_file : str, optional Weights files to use in place of the weights file stored in the pickle file. Returns ------- obj Instance of NestedSampler """ logger.critical('Resuming NestedSampler from ' + filename) with open(filename, 'rb') as f: obj = pickle.load(f) model.likelihood_evaluations += obj.likelihood_evaluations[-1] obj.model = model obj._uninformed_proposal.resume(model) obj._flow_proposal.resume(model, flow_config, weights_file) obj.resumed = True return obj def __getstate__(self): state = self.__dict__.copy() del state['model'] return state def __setstate__(self, state): self.__dict__ = state

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from collections import deque import datetime import logging import os import pickle import matplotlib.pyplot as plt from matplotlib.lines import Line2D import numpy as np import seaborn as sns import torch from tqdm import tqdm from .livepoint import get_dtype, DEFAULT_FLOAT_DTYPE from .plot import plot_indices, plot_trace from .evidence import _NSIntegralState from .proposal import FlowProposal from .utils import ( safe_file_dump, compute_indices_ks_test, rolling_mean, ) sns.set() sns.set_style('ticks') logger = logging.getLogger(__name__) class NestedSampler: def __init__( self, model, nlive=2000, output=None, stopping=0.1, max_iteration=None, checkpointing=True, checkpoint_on_training=False, resume_file=None, seed=None, pool=None, close_pool=True, n_pool=None, plot=True, proposal_plots=False, prior_sampling=False, analytic_priors=False, maximum_uninformed=None, uninformed_proposal=None, uninformed_acceptance_threshold=None, uninformed_proposal_kwargs=None, flow_class=None, flow_config=None, training_frequency=None, train_on_empty=True, cooldown=200, memory=False, reset_weights=False, reset_permutations=False, retrain_acceptance=True, reset_acceptance=False, acceptance_threshold=0.01, **kwargs ): logger.info('Initialising nested sampler') self.info_enabled = logger.isEnabledFor(logging.INFO) model.verify_model() self.model = model self.model.configure_pool(pool=pool, n_pool=n_pool) self.close_pool = close_pool self.nlive = nlive self.live_points = None self.prior_sampling = prior_sampling self.setup_random_seed(seed) self.accepted = 0 self.rejected = 1 self.initialised = False self.checkpointing = checkpointing self.checkpoint_on_training = checkpoint_on_training self.iteration = 0 self.acceptance_history = deque(maxlen=(nlive // 10)) self.mean_acceptance_history = [] self.block_acceptance = 1. self.mean_block_acceptance = 1. self.block_iteration = 0 self.retrain_acceptance = retrain_acceptance self.reset_acceptance = reset_acceptance self.insertion_indices = [] self.rolling_p = [] self.resumed = False self.tolerance = stopping self.condition = np.inf self.logLmin = -np.inf self.logLmax = -np.inf self.nested_samples = [] self.logZ = None self.state = _NSIntegralState(self.nlive, track_gradients=plot) self.plot = plot self.resume_file = self.setup_output(output, resume_file) self.output = output self.training_time = datetime.timedelta() self.sampling_time = datetime.timedelta() self.sampling_start_time = datetime.datetime.now() self.completed_training = True self.finalised = False self.likelihood_evaluations = [] self.training_iterations = [] self.min_likelihood = [] self.max_likelihood = [] self.logZ_history = [] self.dZ_history = [] self.population_acceptance = [] self.population_radii = [] self.population_iterations = [] self.checkpoint_iterations = [] self.acceptance_threshold = acceptance_threshold self.train_on_empty = train_on_empty self.cooldown = cooldown self.memory = memory self.configure_max_iteration(max_iteration) self.configure_flow_reset(reset_weights, reset_permutations) self.configure_training_frequency(training_frequency) if uninformed_proposal_kwargs is None: uninformed_proposal_kwargs = {} self.configure_uninformed_proposal(uninformed_proposal, analytic_priors, maximum_uninformed, uninformed_acceptance_threshold, **uninformed_proposal_kwargs) self.configure_flow_proposal(flow_class, flow_config, proposal_plots, **kwargs) self.store_live_points = False if self.store_live_points: self.live_points_dir = f'{self.output}/live_points/' os.makedirs(self.live_points_dir, exist_ok=True) self.replacement_points = [] @property def log_evidence(self): return self.state.logZ @property def information(self): return self.state.info[-1] @property def likelihood_calls(self): return self.model.likelihood_evaluations @property def likelihood_evaluation_time(self): return self.model.likelihood_evaluation_time @property def proposal_population_time(self): t = self._uninformed_proposal.population_time t += self._flow_proposal.population_time return t @property def acceptance(self): return self.iteration / self.likelihood_calls @property def current_sampling_time(self): if self.finalised: return self.sampling_time else: return self.sampling_time \ + (datetime.datetime.now() - self.sampling_start_time) @property def last_updated(self): if self.training_iterations: return self.training_iterations[-1] else: return 0 @property def mean_acceptance(self): if self.acceptance_history: return np.mean(self.acceptance_history) else: return np.nan def configure_max_iteration(self, max_iteration): if max_iteration is None: self.max_iteration = np.inf else: self.max_iteration = max_iteration def configure_training_frequency(self, training_frequency): if training_frequency in [None, 'inf', 'None']: logger.warning('Proposal will only train when empty') self.training_frequency = np.inf else: self.training_frequency = training_frequency def configure_uninformed_proposal(self, uninformed_proposal, analytic_priors, maximum_uninformed, uninformed_acceptance_threshold, **kwargs): if maximum_uninformed is None: self.uninformed_sampling = True self.maximum_uninformed = 2 * self.nlive elif not maximum_uninformed: self.uninformed_sampling = False self.maximum_uninformed = 0 else: self.uninformed_sampling = True self.maximum_uninformed = float(maximum_uninformed) if uninformed_acceptance_threshold is None: if self.acceptance_threshold < 0.1: self.uninformed_acceptance_threshold = \ 10 * self.acceptance_threshold else: self.uninformed_acceptance_threshold = \ self.acceptance_threshold else: self.uninformed_acceptance_threshold = \ uninformed_acceptance_threshold if uninformed_proposal is None: if analytic_priors: from .proposal import AnalyticProposal as uninformed_proposal else: from .proposal import RejectionProposal as uninformed_proposal kwargs['poolsize'] = self.nlive logger.debug(f'Using uninformed proposal: {uninformed_proposal}') logger.debug(f'Parsing kwargs to uninformed proposal: {kwargs}') self._uninformed_proposal = uninformed_proposal( self.model, **kwargs ) def configure_flow_proposal(self, flow_class, flow_config, proposal_plots, **kwargs): proposal_output = self.output + '/proposal/' if not self.plot: proposal_plots = False if flow_class is not None: if isinstance(flow_class, str): flow_class = flow_class.lower() if flow_class == 'gwflowproposal': from .gw.proposal import GWFlowProposal as flow_class elif flow_class == 'augmentedgwflowproposal': from .gw.proposal import ( AugmentedGWFlowProposal as flow_class) elif flow_class == 'legacygwflowproposal': from .gw.legacy import LegacyGWFlowProposal as flow_class elif flow_class == 'flowproposal': flow_class = FlowProposal elif flow_class == 'augmentedflowproposal': from .proposal import AugmentedFlowProposal flow_class = AugmentedFlowProposal else: raise ValueError(f'Unknown flow class: {flow_class}') elif not issubclass(flow_class, FlowProposal): raise RuntimeError('Flow class must be string or class that ' 'inherits from FlowProposal') else: flow_class = FlowProposal if kwargs.get('poolsize', None) is None: kwargs['poolsize'] = self.nlive logger.debug(f'Using flow class: {flow_class}') logger.info(f'Parsing kwargs to FlowProposal: {kwargs}') self._flow_proposal = flow_class( self.model, flow_config=flow_config, output=proposal_output, plot=proposal_plots, **kwargs ) def setup_output(self, output, resume_file=None): if not os.path.exists(output): os.makedirs(output, exist_ok=True) if resume_file is None: resume_file = os.path.join(output, "nested_sampler_resume.pkl") else: resume_file = os.path.join(output, resume_file) if self.plot: os.makedirs(output + '/diagnostics/', exist_ok=True) return resume_file def setup_random_seed(self, seed): self.seed = seed if self.seed is not None: logger.debug(f'Setting random seed to {seed}') np.random.seed(seed=self.seed) torch.manual_seed(self.seed) def configure_flow_reset(self, reset_weights, reset_permutations): if isinstance(reset_weights, (int, float)): self.reset_weights = float(reset_weights) else: raise TypeError( '`reset_weights` must be a bool, int or float') if isinstance(reset_permutations, (int, float)): self.reset_permutations = float(reset_permutations) else: raise TypeError( '`reset_permutations` must be a bool, int or float') def check_insertion_indices(self, rolling=True, filename=None): if rolling: indices = self.insertion_indices[-self.nlive:] else: indices = self.insertion_indices D, p = compute_indices_ks_test(indices, self.nlive) if p is not None: if rolling: logger.warning(f'Rolling KS test: D={D:.4}, p-value={p:.4}') self.rolling_p.append(p) else: logger.warning(f'Final KS test: D={D:.4}, p-value={p:.4}') if filename is not None: np.savetxt(os.path.join(self.output, filename), self.insertion_indices, newline='\n', delimiter=' ') def log_likelihood(self, x): return self.model.log_likelihood(x) def yield_sample(self, oldparam): while True: counter = 0 while True: counter += 1 newparam = self.proposal.draw(oldparam.copy()) if newparam['logP'] != -np.inf: if not newparam['logL']: newparam['logL'] = \ self.model.evaluate_log_likelihood(newparam) if newparam['logL'] > self.logLmin: self.logLmax = max(self.logLmax, newparam['logL']) oldparam = newparam.copy() break if not self.proposal.populated: break yield counter, oldparam def insert_live_point(self, live_point): index = np.searchsorted(self.live_points['logL'], live_point['logL']) self.live_points[:index - 1] = self.live_points[1:index] self.live_points[index - 1] = live_point return index - 1 def consume_sample(self): worst = self.live_points[0].copy() self.logLmin = worst['logL'] self.state.increment(worst['logL']) self.nested_samples.append(worst) self.condition = np.logaddexp(self.state.logZ, self.logLmax - self.iteration / float(self.nlive)) \ - self.state.logZ self.iteration += 1 self.block_iteration += 1 count = 0 while(True): c, proposed = next(self.yield_sample(worst)) count += c if proposed['logL'] > self.logLmin: index = self.insert_live_point(proposed) self.insertion_indices.append(index) self.accepted += 1 self.block_acceptance += 1 / count self.acceptance_history.append(1 / count) break else: self.rejected += 1 self.check_state() if not self.block_iteration: self.block_iteration += 1 self.mean_block_acceptance = self.block_acceptance \ / self.block_iteration if self.info_enabled: logger.info(f"{self.iteration:5d}: n: {count:3d} " f"b_acc: {self.mean_block_acceptance:.3f} " f"H: {self.state.info[-1]:.2f} " f"logL: {self.logLmin:.5f} --> {proposed['logL']:.5f} " f"dZ: {self.condition:.3f} " f"logZ: {self.state.logZ:.3f} " f"+/- {np.sqrt(self.state.info[-1] / self.nlive):.3f} " f"logLmax: {self.logLmax:.2f}") def populate_live_points(self): i = 0 live_points = np.empty(self.nlive, dtype=get_dtype(self.model.names, DEFAULT_FLOAT_DTYPE)) with tqdm(total=self.nlive, desc='Drawing live points') as pbar: while i < self.nlive: while i < self.nlive: count, live_point = next( self.yield_sample(self.model.new_point())) if np.isnan(live_point['logL']): logger.warning( 'Likelihood function returned NaN for ' f'live_point {live_point}' ) logger.warning( 'You may want to check your likelihood function' ) break if ( np.isfinite(live_point['logP']) and np.isfinite(live_point['logL']) ): live_points[i] = live_point i += 1 pbar.update() break self.live_points = np.sort(live_points, order='logL') if self.store_live_points: np.savetxt(self.live_points_dir + '/initial_live_points.dat', self.live_points, header='\t'.join(self.live_points.dtype.names)) def initialise(self, live_points=True): flags = [False] * 3 if not self._flow_proposal.initialised: self._flow_proposal.initialise() flags[0] = True if not self._uninformed_proposal.initialised: self._uninformed_proposal.initialise() flags[1] = True if ( self.iteration < self.maximum_uninformed and self.uninformed_sampling ): self.proposal = self._uninformed_proposal else: self.proposal = self._flow_proposal if live_points and self.live_points is None: self.populate_live_points() flags[2] = True if self.condition > self.tolerance: self.finalised = False if all(flags): self.initialised = True def check_proposal_switch(self, force=False): if ( (self.mean_acceptance < self.uninformed_acceptance_threshold) or (self.iteration >= self.maximum_uninformed) or force ): if self.proposal is self._flow_proposal: logger.warning('Already using flowproposal') return True logger.warning('Switching to FlowProposal') self.proposal = self._flow_proposal self.proposal.ns_acceptance = self.mean_block_acceptance self.uninformed_sampling = False return True else: return False def check_training(self): if not self.completed_training: logger.debug('Training flow (resume)') return True, True elif (not self.proposal.populated and self.train_on_empty and not self.proposal.populating): logger.debug('Training flow (proposal empty)') return True, True elif (self.mean_block_acceptance < self.acceptance_threshold and self.retrain_acceptance): logger.debug('Training flow (acceptance)') return True, False elif (self.iteration - self.last_updated) == self.training_frequency: logger.debug('Training flow (iteration)') return True, False else: return False, False def check_flow_model_reset(self): if not self.proposal.training_count: return if (self.reset_acceptance and self.mean_block_acceptance < self.acceptance_threshold): self.proposal.reset_model_weights(weights=True, permutations=True) return self.proposal.reset_model_weights( weights=( self.reset_weights and not (self.proposal.training_count % self.reset_weights) ), permutations=( self.reset_permutations and not (self.proposal.training_count % self.reset_permutations) ), ) def train_proposal(self, force=False): if (self.iteration - self.last_updated < self.cooldown and not force): logger.debug('Not training, still cooling down!') else: self.completed_training = False self.check_flow_model_reset() training_data = self.live_points.copy() if self.memory and (len(self.nested_samples) >= self.memory): training_data = np.concatenate([ training_data, self.nested_samples[-self.memory:].copy()]) st = datetime.datetime.now() self.proposal.train(training_data) self.training_time += (datetime.datetime.now() - st) self.training_iterations.append(self.iteration) self.block_iteration = 0 self.block_acceptance = 0. self.completed_training = True if self.checkpoint_on_training: self.checkpoint(periodic=True) def check_state(self, force=False): if self.uninformed_sampling: if self.check_proposal_switch(): force = True else: return # General override train = False if force: train = True logger.debug('Training flow (force)') elif not train: train, force = self.check_training() if train or force: self.train_proposal(force=force) def plot_state(self, filename=None): fig, ax = plt.subplots(6, 1, sharex=True, figsize=(12, 12)) ax = ax.ravel() it = (np.arange(len(self.min_likelihood))) * (self.nlive // 10) it[-1] = self.iteration colours = [''] for t in self.training_iterations: for a in ax: a.axvline(t, ls='-', color='lightgrey') if not self.train_on_empty: for p in self.population_iterations: for a in ax: a.axvline(p, ls='-', color='tab:orange') for i in self.checkpoint_iterations: for a in ax: a.axvline(i, ls=':', color=' for a in ax: a.axvline(self.iteration, c=' ax[0].plot(it, self.min_likelihood, label='Min logL', c=colours[0], ls=ls[0]) ax[0].plot(it, self.max_likelihood, label='Max logL', c=colours[1], ls=ls[1]) ax[0].set_ylabel('logL') ax[0].legend(frameon=False) logX_its = np.arange(len(self.state.log_vols)) ax[1].plot( logX_its, self.state.log_vols, ls=ls[0], c=colours[0], label='log X' ) ax[1].set_ylabel('Log X') ax[1].legend(frameon=False) if self.state.track_gradients: ax_logX_grad = plt.twinx(ax[1]) # Use dotted linestyle (ls[2]) because dashed isn't clear ax_logX_grad.plot( logX_its, rolling_mean(np.abs(self.state.gradients), self.nlive // 10), c=colours[1], ls=ls[2], label='Gradient' ) ax_logX_grad.set_ylabel(r'$|d\log L/d \log X|$') ax_logX_grad.set_yscale('log') handles, labels = ax[1].get_legend_handles_labels() handles_tw, labels_tw = ax_logX_grad.get_legend_handles_labels() ax[1].legend( handles + handles_tw, labels + labels_tw, frameon=False ) ax[2].plot(it, self.likelihood_evaluations, c=colours[0], ls=ls[0], label='Evaluations') ax[2].set_ylabel('logL evaluations') ax[3].plot(it, self.logZ_history, label='logZ', c=colours[0], ls=ls[0]) ax[3].set_ylabel('logZ') ax[3].legend(frameon=False) ax_dz = plt.twinx(ax[3]) ax_dz.plot(it, self.dZ_history, label='dZ', c=colours[1], ls=ls[1]) ax_dz.set_ylabel('dZ') handles, labels = ax[3].get_legend_handles_labels() handles_dz, labels_dz = ax_dz.get_legend_handles_labels() ax[3].legend(handles + handles_dz, labels + labels_dz, frameon=False) ax[4].plot(it, self.mean_acceptance_history, c=colours[0], label='Proposal') ax[4].plot(self.population_iterations, self.population_acceptance, c=colours[1], ls=ls[1], label='Population') ax[4].set_ylabel('Acceptance') ax[4].set_ylim((-0.1, 1.1)) handles, labels = ax[4].get_legend_handles_labels() ax_r = plt.twinx(ax[4]) ax_r.plot(self.population_iterations, self.population_radii, label='Radius', color=colours[2], ls=ls[2]) ax_r.set_ylabel('Population radius') handles_r, labels_r = ax_r.get_legend_handles_labels() ax[4].legend(handles + handles_r, labels + labels_r, frameon=False) if len(self.rolling_p): it = (np.arange(len(self.rolling_p)) + 1) * self.nlive ax[5].plot(it, self.rolling_p, 'o', c=colours[0], label='p-value') ax[5].set_ylabel('p-value') ax[5].set_ylim([-0.1, 1.1]) ax[-1].set_xlabel('Iteration') fig.suptitle(f'Sampling time: {self.current_sampling_time}', fontsize=16) handles = [ Line2D([0], [0], color='#ff9900', linestyle='-.', label='Current iteration'), Line2D([0], [0], color='lightgrey', linestyle='-', markersize=10, markeredgewidth=1.5, label='Training'), Line2D([0], [0], color='#66ccff', linestyle=':', label='Checkpoint'), ] fig.legend( handles=handles, frameon=False, ncol=3, loc=(0.6, 0.0) ) fig.tight_layout() fig.subplots_adjust(top=0.95) if filename is not None: fig.savefig(filename) plt.close(fig) else: return fig def plot_trace(self, filename=None): if self.nested_samples: fig = plot_trace(self.state.log_vols[1:], self.nested_samples, filename=filename) return fig else: logger.warning('Could not produce trace plot. No nested samples!') def plot_insertion_indices(self, filename=None, **kwargs): return plot_indices( self.insertion_indices, self.nlive, filename=filename, **kwargs ) def update_state(self, force=False): if not self.proposal._checked_population: self.population_acceptance.append( self.proposal.population_acceptance) self.population_radii.append(self.proposal.r) self.population_iterations.append(self.iteration) self.proposal._checked_population = True if not (self.iteration % (self.nlive // 10)) or force: self.likelihood_evaluations.append( self.model.likelihood_evaluations) self.min_likelihood.append(self.logLmin) self.max_likelihood.append(self.logLmax) self.logZ_history.append(self.state.logZ) self.dZ_history.append(self.condition) self.mean_acceptance_history.append(self.mean_acceptance) if not (self.iteration % self.nlive) or force: logger.warning( f"it: {self.iteration:5d}: " f"n eval: {self.likelihood_calls} " f"H: {self.state.info[-1]:.2f} " f"dZ: {self.condition:.3f} logZ: {self.state.logZ:.3f} " f"+/- {np.sqrt(self.state.info[-1] / self.nlive):.3f} " f"logLmax: {self.logLmax:.2f}") if self.checkpointing: self.checkpoint(periodic=True) if not force: self.check_insertion_indices() if self.plot: plot_indices(self.insertion_indices[-self.nlive:], self.nlive, plot_breakdown=False, filename=(f'{self.output}/diagnostics/' 'insertion_indices_' f'{self.iteration}.png')) if self.plot: self.plot_state(filename=f'{self.output}/state.png') self.plot_trace(filename=f'{self.output}/trace.png') if self.uninformed_sampling: self.block_acceptance = 0. self.block_iteration = 0 self.proposal.ns_acceptance = self.mean_block_acceptance def checkpoint(self, periodic=False): if not periodic: self.checkpoint_iterations += [self.iteration] self.sampling_time += \ (datetime.datetime.now() - self.sampling_start_time) logger.critical('Checkpointing nested sampling') safe_file_dump(self, self.resume_file, pickle, save_existing=True) self.sampling_start_time = datetime.datetime.now() def check_resume(self): if self.resumed: if self.uninformed_sampling is False: self.check_proposal_switch(force=True) if hasattr(self._flow_proposal, 'resume_populated'): if (self._flow_proposal.resume_populated and self._flow_proposal.indices): self._flow_proposal.populated = True logger.info('Resumed with populated pool') self.resumed = False def finalise(self): logger.info('Finalising') for i, p in enumerate(self.live_points): self.state.increment(p['logL'], nlive=self.nlive-i) self.nested_samples.append(p) self.update_state(force=True) self.state.finalise() self.finalised = True def nested_sampling_loop(self): self.sampling_start_time = datetime.datetime.now() if not self.initialised: self.initialise(live_points=True) if self.prior_sampling: self.nested_samples = self.live_points.copy() if self.close_pool: self.model.close_pool() return self.nested_samples self.check_resume() if self.iteration: self.update_state() logger.critical('Starting nested sampling loop') while self.condition > self.tolerance: self.check_state() self.consume_sample() self.update_state() if self.iteration >= self.max_iteration: break if not self.finalised and (self.condition <= self.tolerance): self.finalise() logger.critical(f'Final evidence: {self.state.logZ:.3f} +/- ' f'{np.sqrt(self.state.info[-1] / self.nlive):.3f}') logger.critical('Information: {0:.2f}'.format(self.state.info[-1])) self.check_insertion_indices(rolling=False) self.checkpoint(periodic=True) if self.close_pool: self.model.close_pool() logger.info(f'Total sampling time: {self.sampling_time}') logger.info(f'Total training time: {self.training_time}') logger.info(f'Total population time: {self.proposal_population_time}') logger.info( f'Total likelihood evaluations: {self.likelihood_calls:3d}') logger.info( 'Time spent evaluating likelihood: ' f'{self.likelihood_evaluation_time}' ) return self.state.logZ, np.array(self.nested_samples) @classmethod def resume(cls, filename, model, flow_config={}, weights_file=None): logger.critical('Resuming NestedSampler from ' + filename) with open(filename, 'rb') as f: obj = pickle.load(f) model.likelihood_evaluations += obj.likelihood_evaluations[-1] obj.model = model obj._uninformed_proposal.resume(model) obj._flow_proposal.resume(model, flow_config, weights_file) obj.resumed = True return obj def __getstate__(self): state = self.__dict__.copy() del state['model'] return state def __setstate__(self, state): self.__dict__ = state

true

1c47785da9d34f0b1c8a9845b5a3002f171b51df

8,815

py

Python

src/sensor_placement.py

tolgadur/Sensor-Placement

ad33477d1fb14052e1a9e58d149d0b8e767ea318

[ "MIT" ]

3

2020-05-10T20:37:50.000Z

2022-03-31T08:25:23.000Z

src/sensor_placement.py

tolgadur/Sensor-Placement

ad33477d1fb14052e1a9e58d149d0b8e767ea318

[ "MIT" ]

null

src/sensor_placement.py

tolgadur/Sensor-Placement

ad33477d1fb14052e1a9e58d149d0b8e767ea318

[ "MIT" ]

2

2021-02-26T10:15:24.000Z

2021-06-07T11:11:08.000Z

#!/usr/bin/python import numpy as np import heapq import pandas as pd """ FILE NAME: 'sensor_placement.py' DESCRIPTION: This file is implementing the class that will be used for sensor positioning according to solution proposed by Krause, Singh and Guestrin (2008). """ class SensorPlacement: @staticmethod def isMonotonic(cov, k, V, S, U): """ This method checks if values in the dataset are monotonic or not. For datasets > 2000 observations, non-monotonicity might lead to suboptimal results. Input: - cov: covariance matrix - k: number of Sensors to be placed - V: indices of all position - S: indices of all possible sensor positions - U: indices of all impossible sensor positions """ A = np.array([]) for j in range(k): S_A = np.setdiff1d(S, A).astype(int) for y in S_A: AHat = np.setdiff1d(V, np.append(A, [y])) condition = SensorPlacement.__conditionalEntropy(cov, y, A) - SensorPlacement.__conditionalEntropy(cov, y, AHat) if condition < 0: print(condition) return False return True @staticmethod def __conditionalVariance(cov, y, A): """ This method calculates the conditional variance of y given A. """ var = cov[y, y] - (cov[np.ix_([y], A)] @ np.linalg.inv(cov[np.ix_(A, A)]) @ cov[np.ix_(A, [y])]) # var = np.absolute(cov[y, y] - (cov[np.ix_([y], A)] @ np.linalg.inv(cov[np.ix_(A, A)]) @ cov[np.ix_(A, [y])])) return var[0][0] @staticmethod def __conditionalEntropy(cov, y, A): """ This method calculates the conditional entropy of y given A. """ conditionalVariance = SensorPlacement.__conditionalVariance(cov, y, A) return 0.5 * np.log(2*np.pi*conditionalVariance) @staticmethod def __localConditionalEntropy(cov, y, A, epsilon): """ This method calculates the conditional entropy of y given A for all values where cov[y, A] > epsilon. """ A_ = SensorPlacement.__localSet(cov, y, A, epsilon) return SensorPlacement.__conditionalEntropy(cov, y, A_) @staticmethod def __localConditionalVariance(cov, y, A, epsilon): """ This method calculates the conditional variance of y given A for all values where cov[y, A] > epsilon. """ A_ = SensorPlacement.__localSet(cov, y, A, epsilon) return SensorPlacement.__conditionalVariance(cov, y, A_) @staticmethod def __localSet(cov, y, A, epsilon): """ This method returns the set of points X in S for which K(y*, x) > epsilon. Input: - cov: covariance matrix - S_i: array with all indices of i - epsilon: hyperparameter """ return [x for x in A if cov[y, x] > epsilon] @staticmethod def naiveSensorPlacement(cov, k, V, S, U, A, subdomain=None, output=None): """ This is an implementation of the first approximation method suggested in the 'Near-Optimal Sensor Placement' paper. Input: - cov: covariance matrix - k: number of Sensors to be placed - V: indices of all position - S: indices of all possible sensor positions - U: indices of all impossible sensor positions """ print('Algorithm is starting for subdomain', subdomain, flush=True) A = A for j in range(k): S_A = np.setdiff1d(S, A).astype(int) delta = np.array([]) for y in S_A: AHat = np.setdiff1d(V, np.append(A, [y])) delta = np.append(delta, SensorPlacement.__conditionalVariance(cov, y, A) / \ SensorPlacement.__conditionalVariance(cov, y, AHat)) y_star = S_A[np.argmax(delta)] A = np.append(A, y_star).astype(int) print('subdomain ', subdomain, ': ', A, flush=True) if subdomain != None: output.put((subdomain, 2*A)) return 2*A @staticmethod def lazySensorPlacement(cov, k, V, S, U, A, subdomain=None, output=None): """ This is an implementation of the second approximation method suggested in the 'Near-Optimal Sensor Placement' paper. It uses a priority queue in order to reduce the time complexity from O(k*n^4) to O(k*n^3). Input: - cov: covariance matrix - k: number of Sensors to be placed - V: indices of all position - S: indices of all possible sensor positions - U: indices of all impossible sensor positions """ print('Algorithm is starting for subdomain', subdomain, flush=True) A = A delta = -1 * np.inf * np.ones((len(S), 1)) heap = [(delta[i], S[i], -1) for i in range(len(delta))] heapq.heapify(heap) for j in range(k): while True: delta_star, y_star, current = heapq.heappop(heap) if current == j: break AHat = np.setdiff1d(V, np.append(A, [y_star])) criterion = SensorPlacement.__conditionalVariance(cov, y_star, A) / \ SensorPlacement.__conditionalVariance(cov, y_star, AHat) heapq.heappush(heap, (-1 * criterion, y_star, j)) A = np.append(A, y_star).astype(int) print('subdomain ', subdomain, ': ', 2*A, flush=True) if subdomain != None: output.put((subdomain, 2*A)) return 2*A @staticmethod def localKernelPlacement(cov, k, V, S, U, A, subdomain=None, output=None): """ This is an implementation of the third approximation method suggested in the 'Near-Optimal Sensor Placement' paper. It only considers local kernels in order to reduce the time complexity O(k*n). Input: - cov: covariance matrix - k: number of Sensors to be placed - V: indices of all position - S: indices of all possible sensor positions - U: indices of all impossible sensor positions """ print('Algorithm is starting for subdomain', subdomain, flush=True) A = A epsilon = 1e-10 delta = np.array([]); N = S for y in S: V_y = np.setdiff1d(V, y).astype(int) delta = np.append(delta, cov[y, y] / SensorPlacement.__localConditionalVariance(cov, y, V_y, epsilon)) for j in range(k): y_star = N[np.argmax(delta)] A = np.append(A, y_star).astype(int) print('subdomain ', subdomain, ': ', A, flush=True) N = SensorPlacement.__localSet(cov, y_star, S, epsilon) N = np.setdiff1d(S, A).astype(int) delta = np.array([]) for y in N: AHat = np.setdiff1d(V, np.append(A, [y])) delta = np.append(delta, SensorPlacement.__localConditionalVariance(cov, y, A, epsilon) / \ SensorPlacement.__localConditionalVariance(cov, y, AHat, epsilon)) if subdomain != None: output.put((subdomain, 2*A)) return 2*A @staticmethod def lazyLocalKernelPlacement(cov, k, V, S, U, A, subdomain=None, output=None): """ This is a mix between the lazySensorPlacement method and the localKernelPlacement method. Input: - cov: covariance matrix - k: number of Sensors to be placed - V: indices of all position - S: indices of all possible sensor positions - U: indices of all impossible sensor positions """ print('Algorithm is starting for subdomain', subdomain, flush=True) A = A epsilon = 1e-10 delta = -1 * np.inf * np.ones((len(S), 1)) heap = [(delta[i], S[i], -1) for i in range(len(delta))] heapq.heapify(heap) for j in range(k): while True: delta_star, y_star, current = heapq.heappop(heap) if current == j: break AHat = np.setdiff1d(V, np.append(A, [y_star])) criterion = SensorPlacement.__localConditionalVariance(cov, y_star, A, epsilon) / \ SensorPlacement.__localConditionalVariance(cov, y_star, AHat, epsilon) heapq.heappush(heap, (-1 * criterion, y_star, j)) A = np.append(A, y_star).astype(int) print('subdomain ', subdomain, ': ', A, flush=True) if subdomain != None: output.put((subdomain, 2*A)) return 2*A

42.584541

128

0.569484

import numpy as np import heapq import pandas as pd class SensorPlacement: @staticmethod def isMonotonic(cov, k, V, S, U): A = np.array([]) for j in range(k): S_A = np.setdiff1d(S, A).astype(int) for y in S_A: AHat = np.setdiff1d(V, np.append(A, [y])) condition = SensorPlacement.__conditionalEntropy(cov, y, A) - SensorPlacement.__conditionalEntropy(cov, y, AHat) if condition < 0: print(condition) return False return True @staticmethod def __conditionalVariance(cov, y, A): var = cov[y, y] - (cov[np.ix_([y], A)] @ np.linalg.inv(cov[np.ix_(A, A)]) @ cov[np.ix_(A, [y])]) return var[0][0] @staticmethod def __conditionalEntropy(cov, y, A): conditionalVariance = SensorPlacement.__conditionalVariance(cov, y, A) return 0.5 * np.log(2*np.pi*conditionalVariance) @staticmethod def __localConditionalEntropy(cov, y, A, epsilon): A_ = SensorPlacement.__localSet(cov, y, A, epsilon) return SensorPlacement.__conditionalEntropy(cov, y, A_) @staticmethod def __localConditionalVariance(cov, y, A, epsilon): A_ = SensorPlacement.__localSet(cov, y, A, epsilon) return SensorPlacement.__conditionalVariance(cov, y, A_) @staticmethod def __localSet(cov, y, A, epsilon): return [x for x in A if cov[y, x] > epsilon] @staticmethod def naiveSensorPlacement(cov, k, V, S, U, A, subdomain=None, output=None): print('Algorithm is starting for subdomain', subdomain, flush=True) A = A for j in range(k): S_A = np.setdiff1d(S, A).astype(int) delta = np.array([]) for y in S_A: AHat = np.setdiff1d(V, np.append(A, [y])) delta = np.append(delta, SensorPlacement.__conditionalVariance(cov, y, A) / \ SensorPlacement.__conditionalVariance(cov, y, AHat)) y_star = S_A[np.argmax(delta)] A = np.append(A, y_star).astype(int) print('subdomain ', subdomain, ': ', A, flush=True) if subdomain != None: output.put((subdomain, 2*A)) return 2*A @staticmethod def lazySensorPlacement(cov, k, V, S, U, A, subdomain=None, output=None): print('Algorithm is starting for subdomain', subdomain, flush=True) A = A delta = -1 * np.inf * np.ones((len(S), 1)) heap = [(delta[i], S[i], -1) for i in range(len(delta))] heapq.heapify(heap) for j in range(k): while True: delta_star, y_star, current = heapq.heappop(heap) if current == j: break AHat = np.setdiff1d(V, np.append(A, [y_star])) criterion = SensorPlacement.__conditionalVariance(cov, y_star, A) / \ SensorPlacement.__conditionalVariance(cov, y_star, AHat) heapq.heappush(heap, (-1 * criterion, y_star, j)) A = np.append(A, y_star).astype(int) print('subdomain ', subdomain, ': ', 2*A, flush=True) if subdomain != None: output.put((subdomain, 2*A)) return 2*A @staticmethod def localKernelPlacement(cov, k, V, S, U, A, subdomain=None, output=None): print('Algorithm is starting for subdomain', subdomain, flush=True) A = A epsilon = 1e-10 delta = np.array([]); N = S for y in S: V_y = np.setdiff1d(V, y).astype(int) delta = np.append(delta, cov[y, y] / SensorPlacement.__localConditionalVariance(cov, y, V_y, epsilon)) for j in range(k): y_star = N[np.argmax(delta)] A = np.append(A, y_star).astype(int) print('subdomain ', subdomain, ': ', A, flush=True) N = SensorPlacement.__localSet(cov, y_star, S, epsilon) N = np.setdiff1d(S, A).astype(int) delta = np.array([]) for y in N: AHat = np.setdiff1d(V, np.append(A, [y])) delta = np.append(delta, SensorPlacement.__localConditionalVariance(cov, y, A, epsilon) / \ SensorPlacement.__localConditionalVariance(cov, y, AHat, epsilon)) if subdomain != None: output.put((subdomain, 2*A)) return 2*A @staticmethod def lazyLocalKernelPlacement(cov, k, V, S, U, A, subdomain=None, output=None): print('Algorithm is starting for subdomain', subdomain, flush=True) A = A epsilon = 1e-10 delta = -1 * np.inf * np.ones((len(S), 1)) heap = [(delta[i], S[i], -1) for i in range(len(delta))] heapq.heapify(heap) for j in range(k): while True: delta_star, y_star, current = heapq.heappop(heap) if current == j: break AHat = np.setdiff1d(V, np.append(A, [y_star])) criterion = SensorPlacement.__localConditionalVariance(cov, y_star, A, epsilon) / \ SensorPlacement.__localConditionalVariance(cov, y_star, AHat, epsilon) heapq.heappush(heap, (-1 * criterion, y_star, j)) A = np.append(A, y_star).astype(int) print('subdomain ', subdomain, ': ', A, flush=True) if subdomain != None: output.put((subdomain, 2*A)) return 2*A

true

1c4778cd6ee4e3e7a884ff4789b58f8fe5d8053a

1,178

py

Python

prime_numbers_test.py

mkiterian/prime-numbers

be8b3b1250ec8351964c2ef93f8d5e6463efcc7b

[ "MIT" ]

null

prime_numbers_test.py

mkiterian/prime-numbers

be8b3b1250ec8351964c2ef93f8d5e6463efcc7b

[ "MIT" ]

null

prime_numbers_test.py

mkiterian/prime-numbers

be8b3b1250ec8351964c2ef93f8d5e6463efcc7b

[ "MIT" ]

null

import unittest from prime_numbers import generate_prime_numbers class PrimeNumberTest(unittest.TestCase): def test_n_is_an_integer(self): #tests if n is an integer with self.assertRaises(TypeError, msg='n is not an integer'): generate_prime_numbers('number') def test_if_number_is_a_positive_integer(self): #test if number is a positive integer self.assertEqual(generate_prime_numbers(-10), 'N should be a positive integer', msg='Number Should be a positive integer') def test_if_returned_value_is_a_list(self): #check if number return is a list self.assertIsInstance(generate_prime_numbers(10), list) def test_if_number_of_returned_numbers_is_correct(self): #test if list returned has correct number actual = len(generate_prime_numbers(11)) expected = 5 self.assertEqual(actual, expected, msg='Number of returned items is not as expected') def test_generates_correct_prime_numbers(self): #tests if function returns correct values given n is an integer actual = generate_prime_numbers(10) expected = [2,3,5,7] self.assertEqual(actual, expected, msg='Expected [2,3,5,7] when n is 10') if __name__ == '__main__': unittest.main()

33.657143

124

0.77674

import unittest from prime_numbers import generate_prime_numbers class PrimeNumberTest(unittest.TestCase): def test_n_is_an_integer(self): with self.assertRaises(TypeError, msg='n is not an integer'): generate_prime_numbers('number') def test_if_number_is_a_positive_integer(self): self.assertEqual(generate_prime_numbers(-10), 'N should be a positive integer', msg='Number Should be a positive integer') def test_if_returned_value_is_a_list(self): self.assertIsInstance(generate_prime_numbers(10), list) def test_if_number_of_returned_numbers_is_correct(self): actual = len(generate_prime_numbers(11)) expected = 5 self.assertEqual(actual, expected, msg='Number of returned items is not as expected') def test_generates_correct_prime_numbers(self): actual = generate_prime_numbers(10) expected = [2,3,5,7] self.assertEqual(actual, expected, msg='Expected [2,3,5,7] when n is 10') if __name__ == '__main__': unittest.main()

true

1c4779a4e3f7663805d73bbf5c2232d96cc76f28

1,621

py

Python

axley/cogs/misc.py

1olipop/Axley

9ace6706be58c2a8e066a0dbcdcc337b34cc5da7

[ "Apache-2.0" ]

18

2021-05-08T10:28:34.000Z

2021-12-30T16:44:19.000Z

axley/cogs/misc.py

vedrecide/Axley

9ace6706be58c2a8e066a0dbcdcc337b34cc5da7

[ "Apache-2.0" ]

1

2021-07-05T13:07:20.000Z

axley/cogs/misc.py

1olipop/Axley

9ace6706be58c2a8e066a0dbcdcc337b34cc5da7

[ "Apache-2.0" ]

6

2021-06-01T15:31:10.000Z

2021-07-21T17:17:36.000Z

import discord import psutil import os from discord.ext import commands class Misc(commands.Cog): def __init__(self, bot): self.bot = bot self.process = psutil.Process(os.getpid()) @commands.command(name="Ping", description="Ping of the bot") @commands.guild_only() async def ping(self, ctx: commands.Context): await ctx.message.reply( "**Pong!** `{}ms`".format(round(self.bot.latency * 1000)), mention_author=False, ) @commands.command(name="Source", description="Source code of Axley <3") @commands.guild_only() async def source(self, ctx: commands.Context): embed = discord.Embed( color=0xD9E6D1, description=f"[Click Me!]({self.bot.github_repo})" ) embed.set_footer(text="Kindly go through the LICENSE file in the repository before blindy checking and copying the codes") await ctx.message.reply(embed=embed, mention_author=False) @commands.command( name="Stats", aliases=["Botstats", "Botinfo"], description="You can check bot statistics using this command", ) @commands.guild_only() async def stats(self, ctx: commands.Context): ram_usage = self.process.memory_full_info().rss / 1024 ** 2 embed = discord.Embed( color=0xD9E6D1, description="> **RAM:** {:.2f} MB\n> **Commands:** {}\n".format( ram_usage, len([a.name for a in self.bot.commands]) ), ) await ctx.message.reply(embed=embed, mention_author=False) def setup(bot): bot.add_cog(Misc(bot))

31.173077

130

0.623689

import discord import psutil import os from discord.ext import commands class Misc(commands.Cog): def __init__(self, bot): self.bot = bot self.process = psutil.Process(os.getpid()) @commands.command(name="Ping", description="Ping of the bot") @commands.guild_only() async def ping(self, ctx: commands.Context): await ctx.message.reply( "**Pong!** `{}ms`".format(round(self.bot.latency * 1000)), mention_author=False, ) @commands.command(name="Source", description="Source code of Axley <3") @commands.guild_only() async def source(self, ctx: commands.Context): embed = discord.Embed( color=0xD9E6D1, description=f"[Click Me!]({self.bot.github_repo})" ) embed.set_footer(text="Kindly go through the LICENSE file in the repository before blindy checking and copying the codes") await ctx.message.reply(embed=embed, mention_author=False) @commands.command( name="Stats", aliases=["Botstats", "Botinfo"], description="You can check bot statistics using this command", ) @commands.guild_only() async def stats(self, ctx: commands.Context): ram_usage = self.process.memory_full_info().rss / 1024 ** 2 embed = discord.Embed( color=0xD9E6D1, description="> **RAM:** {:.2f} MB\n> **Commands:** {}\n".format( ram_usage, len([a.name for a in self.bot.commands]) ), ) await ctx.message.reply(embed=embed, mention_author=False) def setup(bot): bot.add_cog(Misc(bot))

true

1c477bb7d2693680a90d4f6220d45872d11fc4b0

1,663

py

Python

vm.py

Ccode-lang/CHex

f8138da241a8b96fae5691de7a9d789a9dbcbeb2

[ "MIT" ]

1

2022-01-31T18:36:36.000Z

vm.py

Ccode-lang/CHex

f8138da241a8b96fae5691de7a9d789a9dbcbeb2

[ "MIT" ]

null

vm.py

Ccode-lang/CHex

f8138da241a8b96fae5691de7a9d789a9dbcbeb2

[ "MIT" ]

null

import os import sys import codecs try: file = open(sys.argv[1], "rb") except: print("File does not exist or is not given.") sys.exit() bytecode = file.read() file.close() bytecode = list(bytecode) hexcode = [] for dec in bytecode: hexcode += [hex(dec)] # print(hexcode) # magic number check if hexcode[0] == "0x68" and hexcode[1] == "0x69": pass else: print("Not a CHex bianary file.") # set offset to 2 because of magic number offset = 2 # init mem memory = {} while True: try: hex = hexcode[offset] except: sys.exit() # blank hex if hex == "0x0": offset += 1 # print ascii from memory elif hex == "0x1": # print(memory[int(hexcode[offset + 1][2:], 16)][2:]) hexval = memory[int(hexcode[offset + 1][2:], 16)][2:] if not len(hexval) == 2: hexval = "0" + hexval print(str(codecs.decode(hexval, "hex"), "utf-8"), end="") offset += 2 # same as asm jmp elif hex == "0x2": offset = int(hexcode[offset + 1], 16) # store value in mem elif hex == "0x3": memory[int(hexcode[offset + 1], 16)] = hexcode[offset + 2] offset += 3 # jump to hex stored in memory elif hex == "0x4": offset = int(memory[int(hexcode[offset + 1], 16)], 16) # check if values in memory are equal and jump if so elif hex == "0x5": if int(memory[int(hexcode[offset + 1], 16)], 16) == int(memory[int(hexcode[offset + 2], 16)], 16): offset = int(hexcode[offset + 3], 16) else: offset += 4 else: print("Unknown hex at offset: " + str(offset)) sys.exit()

27.262295

106

0.556825

import os import sys import codecs try: file = open(sys.argv[1], "rb") except: print("File does not exist or is not given.") sys.exit() bytecode = file.read() file.close() bytecode = list(bytecode) hexcode = [] for dec in bytecode: hexcode += [hex(dec)] if hexcode[0] == "0x68" and hexcode[1] == "0x69": pass else: print("Not a CHex bianary file.") offset = 2 memory = {} while True: try: hex = hexcode[offset] except: sys.exit() if hex == "0x0": offset += 1 elif hex == "0x1": hexval = memory[int(hexcode[offset + 1][2:], 16)][2:] if not len(hexval) == 2: hexval = "0" + hexval print(str(codecs.decode(hexval, "hex"), "utf-8"), end="") offset += 2 elif hex == "0x2": offset = int(hexcode[offset + 1], 16) elif hex == "0x3": memory[int(hexcode[offset + 1], 16)] = hexcode[offset + 2] offset += 3 elif hex == "0x4": offset = int(memory[int(hexcode[offset + 1], 16)], 16) elif hex == "0x5": if int(memory[int(hexcode[offset + 1], 16)], 16) == int(memory[int(hexcode[offset + 2], 16)], 16): offset = int(hexcode[offset + 3], 16) else: offset += 4 else: print("Unknown hex at offset: " + str(offset)) sys.exit()

true

1c477bc4296ae17f76dbbd9dad1779671e3a34ae

9,426

py

Python

_backend_api/migrations/0022_initial.py

Amechi101/indieapp

606c1346f65c343eb2cc8f7fba9d555b8c30a7fa

[ "MIT" ]

null

_backend_api/migrations/0022_initial.py

Amechi101/indieapp

606c1346f65c343eb2cc8f7fba9d555b8c30a7fa

[ "MIT" ]

null

_backend_api/migrations/0022_initial.py

Amechi101/indieapp

606c1346f65c343eb2cc8f7fba9d555b8c30a7fa

[ "MIT" ]

null

# -*- coding: utf-8 -*- from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'Brand' db.create_table(u'_backend_api_brand', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('brand_name', self.gf('django.db.models.fields.CharField')(max_length=255, unique=True, null=True, blank=True)), ('brand_founded', self.gf('django.db.models.fields.IntegerField')(max_length=4, null=True)), ('brand_origin_city', self.gf('django.db.models.fields.CharField')(max_length=255, null=True, blank=True)), ('brand_origin_state', self.gf('django.db.models.fields.CharField')(max_length=2, null=True, blank=True)), ('brand_about_description', self.gf('django.db.models.fields.TextField')(null=True, blank=True)), ('brand_collection_description', self.gf('django.db.models.fields.TextField')(null=True, blank=True)), ('slug', self.gf('django.db.models.fields.SlugField')(max_length=255, unique=True, null=True, blank=True)), ('brand_logo', self.gf('cloudinary.models.CloudinaryField')(max_length=100, null=True, blank=True)), ('brand_feature_image', self.gf('cloudinary.models.CloudinaryField')(max_length=100, null=True, blank=True)), ('brand_about_image', self.gf('cloudinary.models.CloudinaryField')(max_length=100, null=True, blank=True)), ('brand_collection_image', self.gf('cloudinary.models.CloudinaryField')(max_length=100, null=True, blank=True)), ('brand_connect_image', self.gf('cloudinary.models.CloudinaryField')(max_length=100, null=True, blank=True)), ('brand_website_url', self.gf('django.db.models.fields.URLField')(max_length=200, null=True, blank=True)), ('brand_email', self.gf('django.db.models.fields.EmailField')(max_length=75, null=True, blank=True)), ('brand_state', self.gf('django.db.models.fields.BooleanField')(default=False)), ('brand_location_state', self.gf('django.db.models.fields.BooleanField')(default=False)), ('brand_email_state', self.gf('django.db.models.fields.BooleanField')(default=False)), ('brand_website_state', self.gf('django.db.models.fields.BooleanField')(default=False)), ('menswear', self.gf('django.db.models.fields.BooleanField')(default=False)), ('womenswear', self.gf('django.db.models.fields.BooleanField')(default=False)), ('date_added', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, null=True, blank=True)), ('last_modified', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, null=True, blank=True)), )) db.send_create_signal(u'_backend_api', ['Brand']) # Adding model 'Product' db.create_table(u'_backend_api_product', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('product_name', self.gf('django.db.models.fields.CharField')(max_length=255, null=True, blank=True)), ('product_price', self.gf('django.db.models.fields.DecimalField')(default='0.0', max_digits=30, decimal_places=2)), ('product_image', self.gf('cloudinary.models.CloudinaryField')(max_length=255, null=True, blank=True)), ('date_added', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, null=True, blank=True)), ('last_modified', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, null=True, blank=True)), ('brand', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['_backend_api.Brand'], null=True)), )) db.send_create_signal(u'_backend_api', ['Product']) # Adding model 'Location' db.create_table(u'_backend_api_location', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('brand_address', self.gf('django.db.models.fields.CharField')(max_length=255, null=True, blank=True)), ('brand_city', self.gf('django.db.models.fields.CharField')(max_length=50, null=True, blank=True)), ('brand_state', self.gf('django.db.models.fields.CharField')(max_length=2, null=True, blank=True)), ('brand', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['_backend_api.Brand'], null=True)), )) db.send_create_signal(u'_backend_api', ['Location']) def backwards(self, orm): # Deleting model 'Brand' db.delete_table(u'_backend_api_brand') # Deleting model 'Product' db.delete_table(u'_backend_api_product') # Deleting model 'Location' db.delete_table(u'_backend_api_location') models = { u'_backend_api.brand': { 'Meta': {'object_name': 'Brand'}, 'brand_about_description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'brand_about_image': ('cloudinary.models.CloudinaryField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'brand_collection_description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'brand_collection_image': ('cloudinary.models.CloudinaryField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'brand_connect_image': ('cloudinary.models.CloudinaryField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'brand_email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'null': 'True', 'blank': 'True'}), 'brand_email_state': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'brand_feature_image': ('cloudinary.models.CloudinaryField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'brand_founded': ('django.db.models.fields.IntegerField', [], {'max_length': '4', 'null': 'True'}), 'brand_location_state': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'brand_logo': ('cloudinary.models.CloudinaryField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'brand_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'unique': 'True', 'null': 'True', 'blank': 'True'}), 'brand_origin_city': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'brand_origin_state': ('django.db.models.fields.CharField', [], {'max_length': '2', 'null': 'True', 'blank': 'True'}), 'brand_state': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'brand_website_state': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'brand_website_url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'date_added': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'null': 'True', 'blank': 'True'}), 'menswear': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '255', 'unique': 'True', 'null': 'True', 'blank': 'True'}), 'womenswear': ('django.db.models.fields.BooleanField', [], {'default': 'False'}) }, u'_backend_api.location': { 'Meta': {'object_name': 'Location'}, 'brand': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['_backend_api.Brand']", 'null': 'True'}), 'brand_address': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'brand_city': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True', 'blank': 'True'}), 'brand_state': ('django.db.models.fields.CharField', [], {'max_length': '2', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, u'_backend_api.product': { 'Meta': {'object_name': 'Product'}, 'brand': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['_backend_api.Brand']", 'null': 'True'}), 'date_added': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'null': 'True', 'blank': 'True'}), 'product_image': ('cloudinary.models.CloudinaryField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'product_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'product_price': ('django.db.models.fields.DecimalField', [], {'default': "'0.0'", 'max_digits': '30', 'decimal_places': '2'}) } } complete_apps = ['_backend_api']

78.55

142

0.619032

from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): db.create_table(u'_backend_api_brand', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('brand_name', self.gf('django.db.models.fields.CharField')(max_length=255, unique=True, null=True, blank=True)), ('brand_founded', self.gf('django.db.models.fields.IntegerField')(max_length=4, null=True)), ('brand_origin_city', self.gf('django.db.models.fields.CharField')(max_length=255, null=True, blank=True)), ('brand_origin_state', self.gf('django.db.models.fields.CharField')(max_length=2, null=True, blank=True)), ('brand_about_description', self.gf('django.db.models.fields.TextField')(null=True, blank=True)), ('brand_collection_description', self.gf('django.db.models.fields.TextField')(null=True, blank=True)), ('slug', self.gf('django.db.models.fields.SlugField')(max_length=255, unique=True, null=True, blank=True)), ('brand_logo', self.gf('cloudinary.models.CloudinaryField')(max_length=100, null=True, blank=True)), ('brand_feature_image', self.gf('cloudinary.models.CloudinaryField')(max_length=100, null=True, blank=True)), ('brand_about_image', self.gf('cloudinary.models.CloudinaryField')(max_length=100, null=True, blank=True)), ('brand_collection_image', self.gf('cloudinary.models.CloudinaryField')(max_length=100, null=True, blank=True)), ('brand_connect_image', self.gf('cloudinary.models.CloudinaryField')(max_length=100, null=True, blank=True)), ('brand_website_url', self.gf('django.db.models.fields.URLField')(max_length=200, null=True, blank=True)), ('brand_email', self.gf('django.db.models.fields.EmailField')(max_length=75, null=True, blank=True)), ('brand_state', self.gf('django.db.models.fields.BooleanField')(default=False)), ('brand_location_state', self.gf('django.db.models.fields.BooleanField')(default=False)), ('brand_email_state', self.gf('django.db.models.fields.BooleanField')(default=False)), ('brand_website_state', self.gf('django.db.models.fields.BooleanField')(default=False)), ('menswear', self.gf('django.db.models.fields.BooleanField')(default=False)), ('womenswear', self.gf('django.db.models.fields.BooleanField')(default=False)), ('date_added', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, null=True, blank=True)), ('last_modified', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, null=True, blank=True)), )) db.send_create_signal(u'_backend_api', ['Brand']) db.create_table(u'_backend_api_product', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('product_name', self.gf('django.db.models.fields.CharField')(max_length=255, null=True, blank=True)), ('product_price', self.gf('django.db.models.fields.DecimalField')(default='0.0', max_digits=30, decimal_places=2)), ('product_image', self.gf('cloudinary.models.CloudinaryField')(max_length=255, null=True, blank=True)), ('date_added', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, null=True, blank=True)), ('last_modified', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, null=True, blank=True)), ('brand', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['_backend_api.Brand'], null=True)), )) db.send_create_signal(u'_backend_api', ['Product']) db.create_table(u'_backend_api_location', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('brand_address', self.gf('django.db.models.fields.CharField')(max_length=255, null=True, blank=True)), ('brand_city', self.gf('django.db.models.fields.CharField')(max_length=50, null=True, blank=True)), ('brand_state', self.gf('django.db.models.fields.CharField')(max_length=2, null=True, blank=True)), ('brand', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['_backend_api.Brand'], null=True)), )) db.send_create_signal(u'_backend_api', ['Location']) def backwards(self, orm): db.delete_table(u'_backend_api_brand') db.delete_table(u'_backend_api_product') db.delete_table(u'_backend_api_location') models = { u'_backend_api.brand': { 'Meta': {'object_name': 'Brand'}, 'brand_about_description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'brand_about_image': ('cloudinary.models.CloudinaryField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'brand_collection_description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'brand_collection_image': ('cloudinary.models.CloudinaryField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'brand_connect_image': ('cloudinary.models.CloudinaryField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'brand_email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'null': 'True', 'blank': 'True'}), 'brand_email_state': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'brand_feature_image': ('cloudinary.models.CloudinaryField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'brand_founded': ('django.db.models.fields.IntegerField', [], {'max_length': '4', 'null': 'True'}), 'brand_location_state': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'brand_logo': ('cloudinary.models.CloudinaryField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'brand_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'unique': 'True', 'null': 'True', 'blank': 'True'}), 'brand_origin_city': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'brand_origin_state': ('django.db.models.fields.CharField', [], {'max_length': '2', 'null': 'True', 'blank': 'True'}), 'brand_state': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'brand_website_state': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'brand_website_url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'date_added': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'null': 'True', 'blank': 'True'}), 'menswear': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '255', 'unique': 'True', 'null': 'True', 'blank': 'True'}), 'womenswear': ('django.db.models.fields.BooleanField', [], {'default': 'False'}) }, u'_backend_api.location': { 'Meta': {'object_name': 'Location'}, 'brand': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['_backend_api.Brand']", 'null': 'True'}), 'brand_address': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'brand_city': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True', 'blank': 'True'}), 'brand_state': ('django.db.models.fields.CharField', [], {'max_length': '2', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, u'_backend_api.product': { 'Meta': {'object_name': 'Product'}, 'brand': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['_backend_api.Brand']", 'null': 'True'}), 'date_added': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'null': 'True', 'blank': 'True'}), 'product_image': ('cloudinary.models.CloudinaryField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'product_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'product_price': ('django.db.models.fields.DecimalField', [], {'default': "'0.0'", 'max_digits': '30', 'decimal_places': '2'}) } } complete_apps = ['_backend_api']

true

1c477c54727b29435a21a6019d3960076fc447e1

4,706

py

Python

nibabel/minc2.py

tobon/nibabel

ff2b5457207bb5fd6097b08f7f11123dc660fda7

[ "BSD-3-Clause" ]

null

nibabel/minc2.py

tobon/nibabel

ff2b5457207bb5fd6097b08f7f11123dc660fda7

[ "BSD-3-Clause" ]

null

nibabel/minc2.py

tobon/nibabel

ff2b5457207bb5fd6097b08f7f11123dc660fda7

[ "BSD-3-Clause" ]

null

# emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the NiBabel package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Preliminary MINC2 support Use with care; I haven't tested this against a wide range of MINC files. If you have a file that isn't read correctly, please send an example. Test reading with something like:: import nibabel as nib img = nib.load('my_funny.mnc') data = img.get_data() print(data.mean()) print(data.max()) print(data.min()) and compare against command line output of:: mincstats my_funny.mnc """ import numpy as np from .optpkg import optional_package h5py, have_h5py, setup_module = optional_package('h5py') from .minc1 import Minc1File, Minc1Image, MincError class Hdf5Bunch(object): """ Make object for accessing attributes of variable """ def __init__(self, var): for name, value in var.attrs.items(): setattr(self, name, value) class Minc2File(Minc1File): ''' Class to wrap MINC2 format file Although it has some of the same methods as a ``Header``, we use this only when reading a MINC2 file, to pull out useful header information, and for the method of reading the data out ''' def __init__(self, mincfile): self._mincfile = mincfile minc_part = mincfile['minc-2.0'] # The whole image is the first of the entries in 'image' image = minc_part['image']['0'] self._image = image['image'] self._dim_names = self._get_dimensions(self._image) dimensions = minc_part['dimensions'] self._dims = [Hdf5Bunch(dimensions[s]) for s in self._dim_names] # We don't currently support irregular spacing # http://en.wikibooks.org/wiki/MINC/Reference/MINC2.0_File_Format_Reference#Dimension_variable_attributes for dim in self._dims: if dim.spacing != b'regular__': raise ValueError('Irregular spacing not supported') self._spatial_dims = [name for name in self._dim_names if name.endswith('space')] self._image_max = image['image-max'] self._image_min = image['image-min'] def _get_dimensions(self, var): # Dimensions for a particular variable # Differs for MINC1 and MINC2 - see: # http://en.wikibooks.org/wiki/MINC/Reference/MINC2.0_File_Format_Reference#Associating_HDF5_dataspaces_with_MINC_dimensions return var.attrs['dimorder'].split(',') def get_data_dtype(self): return self._image.dtype def get_data_shape(self): return self._image.shape def _get_valid_range(self): ''' Return valid range for image data The valid range can come from the image 'valid_range' or failing that, from the data type range ''' ddt = self.get_data_dtype() info = np.iinfo(ddt.type) try: valid_range = self._image.attrs['valid_range'] except AttributeError: valid_range = [info.min, info.max] else: if valid_range[0] < info.min or valid_range[1] > info.max: raise ValueError('Valid range outside input ' 'data type range') return np.asarray(valid_range, dtype=np.float) def get_scaled_data(self): data = np.asarray(self._image) return self._normalize(data) class Minc2Image(Minc1Image): ''' Class for MINC2 images The MINC2 image class uses the default header type, rather than a specific MINC header type - and reads the relevant information from the MINC file on load. ''' # MINC2 does not do compressed whole files _compressed_exts = () @classmethod def from_file_map(klass, file_map): holder = file_map['image'] if holder.filename is None: raise MincError('MINC2 needs filename for load') minc_file = Minc2File(h5py.File(holder.filename, 'r')) affine = minc_file.get_affine() if affine.shape != (4, 4): raise MincError('Image does not have 3 spatial dimensions') data_dtype = minc_file.get_data_dtype() shape = minc_file.get_data_shape() zooms = minc_file.get_zooms() header = klass.header_class(data_dtype, shape, zooms) data = klass.ImageArrayProxy(minc_file) return klass(data, affine, header, extra=None, file_map=file_map) load = Minc2Image.load

35.383459

132

0.632172

true

1c477c95b5afb69f25ef37ab384ae3c2d5026cb5

4,980

py

Python

tests/integrationtest/api/test_guards.py

RasmusGodske/eo-platform-utils

4d7c5bdc102d1eb7a5edff096f2783dbdbaa283d

[ "Apache-2.0" ]

null

tests/integrationtest/api/test_guards.py

RasmusGodske/eo-platform-utils

4d7c5bdc102d1eb7a5edff096f2783dbdbaa283d

[ "Apache-2.0" ]

null

tests/integrationtest/api/test_guards.py

RasmusGodske/eo-platform-utils

4d7c5bdc102d1eb7a5edff096f2783dbdbaa283d

[ "Apache-2.0" ]

null

from typing import List from uuid import uuid4 import pytest from flask.testing import FlaskClient from origin.api import ( Application, EndpointGuard, TokenGuard, ScopedGuard, ) from .endpoints import EmptyEndpoint class TestGuards: """ TODO """ @pytest.mark.parametrize('guard', [ TokenGuard(), ScopedGuard('scope1'), ]) def test__provide_no_token__should_return_status_401( self, guard: EndpointGuard, app: Application, client: FlaskClient, ): """ TODO """ # -- Arrange --------------------------------------------------------- app.add_endpoint( method='POST', path='/something', endpoint=EmptyEndpoint(), guards=[guard], ) # -- Act ------------------------------------------------------------- r = client.post('/something') # -- Assert ---------------------------------------------------------- assert r.status_code == 401 @pytest.mark.parametrize('guard', [ TokenGuard(), ScopedGuard('scope1'), ]) def test__provide_invalid_token__should_return_status_401( self, guard: EndpointGuard, app: Application, client: FlaskClient, ): """ TODO """ # -- Arrange --------------------------------------------------------- app.add_endpoint( method='POST', path='/something', endpoint=EmptyEndpoint(), guards=[guard], ) # -- Act ------------------------------------------------------------- r = client.post( path='/something', headers={'Authorization': 'Bearer: NOT-A-VALID-TOKEN'}, ) # -- Assert ---------------------------------------------------------- assert r.status_code == 401 @pytest.mark.parametrize('guard', [ TokenGuard(), ScopedGuard('scope1'), ]) def test__provide_valid_token__should_return_status_200( self, guard: EndpointGuard, app: Application, client: FlaskClient, valid_token_encoded: str, ): """ TODO """ # -- Arrange --------------------------------------------------------- app.add_endpoint( method='POST', path='/something', endpoint=EmptyEndpoint(), guards=[guard], ) # -- Act ------------------------------------------------------------- r = client.post( path='/something', headers={'Authorization': f'Bearer: {valid_token_encoded}'}, ) # -- Assert ---------------------------------------------------------- assert r.status_code == 200 def test__token_missing_required_scope__should_return_status_401( self, app: Application, client: FlaskClient, valid_token_encoded: str, ): """ TODO """ # -- Arrange --------------------------------------------------------- required_scope = str(uuid4()) # Something random app.add_endpoint( method='POST', path='/something', endpoint=EmptyEndpoint(), guards=[ScopedGuard(required_scope)], ) # -- Act ------------------------------------------------------------- r = client.post( path='/something', headers={'Authorization': f'Bearer: {valid_token_encoded}'}, ) # -- Assert ---------------------------------------------------------- assert r.status_code == 401 @pytest.mark.parametrize('guards', [ [ScopedGuard('scope1')], [ScopedGuard('scope2')], [ScopedGuard('scope1', 'scope2')], [TokenGuard(), ScopedGuard('scope1')], [TokenGuard(), ScopedGuard('scope1', 'scope2')], [TokenGuard(), ScopedGuard('scope1'), ScopedGuard('scope2')], ]) def test__token_has_required_scope__should_return_status_200( self, guards: List[EndpointGuard], app: Application, client: FlaskClient, valid_token_encoded: str, ): """ TODO """ # -- Arrange --------------------------------------------------------- app.add_endpoint( method='POST', path='/something', endpoint=EmptyEndpoint(), guards=guards, ) # -- Act ------------------------------------------------------------- r = client.post( path='/something', headers={'Authorization': f'Bearer: {valid_token_encoded}'}, ) # -- Assert ---------------------------------------------------------- assert r.status_code == 200

25.9375

78

0.41245

from typing import List from uuid import uuid4 import pytest from flask.testing import FlaskClient from origin.api import ( Application, EndpointGuard, TokenGuard, ScopedGuard, ) from .endpoints import EmptyEndpoint class TestGuards: @pytest.mark.parametrize('guard', [ TokenGuard(), ScopedGuard('scope1'), ]) def test__provide_no_token__should_return_status_401( self, guard: EndpointGuard, app: Application, client: FlaskClient, ): app.add_endpoint( method='POST', path='/something', endpoint=EmptyEndpoint(), guards=[guard], ) r = client.post('/something') assert r.status_code == 401 @pytest.mark.parametrize('guard', [ TokenGuard(), ScopedGuard('scope1'), ]) def test__provide_invalid_token__should_return_status_401( self, guard: EndpointGuard, app: Application, client: FlaskClient, ): app.add_endpoint( method='POST', path='/something', endpoint=EmptyEndpoint(), guards=[guard], ) r = client.post( path='/something', headers={'Authorization': 'Bearer: NOT-A-VALID-TOKEN'}, ) assert r.status_code == 401 @pytest.mark.parametrize('guard', [ TokenGuard(), ScopedGuard('scope1'), ]) def test__provide_valid_token__should_return_status_200( self, guard: EndpointGuard, app: Application, client: FlaskClient, valid_token_encoded: str, ): app.add_endpoint( method='POST', path='/something', endpoint=EmptyEndpoint(), guards=[guard], ) r = client.post( path='/something', headers={'Authorization': f'Bearer: {valid_token_encoded}'}, ) assert r.status_code == 200 def test__token_missing_required_scope__should_return_status_401( self, app: Application, client: FlaskClient, valid_token_encoded: str, ): required_scope = str(uuid4()) app.add_endpoint( method='POST', path='/something', endpoint=EmptyEndpoint(), guards=[ScopedGuard(required_scope)], ) r = client.post( path='/something', headers={'Authorization': f'Bearer: {valid_token_encoded}'}, ) assert r.status_code == 401 @pytest.mark.parametrize('guards', [ [ScopedGuard('scope1')], [ScopedGuard('scope2')], [ScopedGuard('scope1', 'scope2')], [TokenGuard(), ScopedGuard('scope1')], [TokenGuard(), ScopedGuard('scope1', 'scope2')], [TokenGuard(), ScopedGuard('scope1'), ScopedGuard('scope2')], ]) def test__token_has_required_scope__should_return_status_200( self, guards: List[EndpointGuard], app: Application, client: FlaskClient, valid_token_encoded: str, ): app.add_endpoint( method='POST', path='/something', endpoint=EmptyEndpoint(), guards=guards, ) r = client.post( path='/something', headers={'Authorization': f'Bearer: {valid_token_encoded}'}, ) assert r.status_code == 200

true

1c477d2e6f7e2a1431cc5681d3d4bbd7036d06ed

191

py

Python

alphapept/__init__.py

enryH/alphapept

a4a1155b820f3567e21a872e0883e653661efe2b

[ "Apache-2.0" ]

null

alphapept/__init__.py

enryH/alphapept

a4a1155b820f3567e21a872e0883e653661efe2b

[ "Apache-2.0" ]

null

alphapept/__init__.py

enryH/alphapept

a4a1155b820f3567e21a872e0883e653661efe2b

[ "Apache-2.0" ]

null

__version__ = "0.4.0" __requirements__ = { "": "requirements/requirements.txt", "develop": "requirements/requirements_develop.txt", "gui": "requirements/requirements_gui.txt", }

23.875

55

0.696335

__version__ = "0.4.0" __requirements__ = { "": "requirements/requirements.txt", "develop": "requirements/requirements_develop.txt", "gui": "requirements/requirements_gui.txt", }

true

1c477dc103178022d9d4cec538afb84e72df6950

167

py

Python

django_chatserver/chat/routing.py

zhiqiyu/Random-Web

10b89776fbcdaa012e1f42a49a050d1b397b73a2

[ "MIT" ]

null

django_chatserver/chat/routing.py

zhiqiyu/Random-Web

10b89776fbcdaa012e1f42a49a050d1b397b73a2

[ "MIT" ]

null

django_chatserver/chat/routing.py

zhiqiyu/Random-Web

10b89776fbcdaa012e1f42a49a050d1b397b73a2

[ "MIT" ]

null

from django.urls import re_path from . import consumers websocket_urlpatterns = [ re_path(r'ws/chat/(?P<room_name>\w+)/$', consumers.ChatConsumer.as_asgi()), ]

18.555556

79

0.718563

from django.urls import re_path from . import consumers websocket_urlpatterns = [ re_path(r'ws/chat/(?P<room_name>\w+)/$', consumers.ChatConsumer.as_asgi()), ]

true

1c477e610890926de828f933fc42e26ec8d369e3

83

py

Python

MachineLearningToolkitCore/Loss/__init__.py

showintime/MachineLearningToolkit

cb265f8b0d3ca5aa16ad92cdbe74e138b5f56023

[ "Apache-2.0" ]

null

MachineLearningToolkitCore/Loss/__init__.py

showintime/MachineLearningToolkit

cb265f8b0d3ca5aa16ad92cdbe74e138b5f56023

[ "Apache-2.0" ]

null

MachineLearningToolkitCore/Loss/__init__.py

showintime/MachineLearningToolkit

cb265f8b0d3ca5aa16ad92cdbe74e138b5f56023

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Dec 2 22:43:31 2019 @author: ZWH """

10.375

35

0.542169

true

1c477f84e4323ce0c780a036d579746b5abba31d

522

py

Python

dynabuffers-python/tests/usecase/Schema03Test.py

leftshiftone/dynabuffers

c3e94c56989be3df87b50b8d9e17d1ea86199ede

[ "Apache-2.0" ]

2

2019-10-28T12:28:01.000Z

2020-07-07T12:25:40.000Z

dynabuffers-python/tests/usecase/Schema03Test.py

leftshiftone/dynabuffers

c3e94c56989be3df87b50b8d9e17d1ea86199ede

[ "Apache-2.0" ]

1

2021-12-21T07:35:22.000Z

dynabuffers-python/tests/usecase/Schema03Test.py

leftshiftone/dynabuffers

c3e94c56989be3df87b50b8d9e17d1ea86199ede

[ "Apache-2.0" ]

1

2020-03-19T09:19:43.000Z

import os import unittest from antlr4 import FileStream from dynabuffers.Dynabuffers import Dynabuffers class Schema03Test(unittest.TestCase): root_dir = os.path.dirname(os.path.realpath(__file__)) def test_parse(self): engine = Dynabuffers.parse(FileStream(self.root_dir + "/schema03.dbs")) map = engine.deserialize(engine.serialize({"results": [{"text":"hello world"}]})) self.assertEqual(map, {"results": [{"text":"hello world"}]}) if __name__ == "__main__": unittest.main()

24.857143

89

0.697318

import os import unittest from antlr4 import FileStream from dynabuffers.Dynabuffers import Dynabuffers class Schema03Test(unittest.TestCase): root_dir = os.path.dirname(os.path.realpath(__file__)) def test_parse(self): engine = Dynabuffers.parse(FileStream(self.root_dir + "/schema03.dbs")) map = engine.deserialize(engine.serialize({"results": [{"text":"hello world"}]})) self.assertEqual(map, {"results": [{"text":"hello world"}]}) if __name__ == "__main__": unittest.main()

true

1c47802de2045227fcff56755ff71d4d4d7c6eba

150

py

Python

hubspot/cms/performance/api/__init__.py

fakepop/hubspot-api-python

f04103a09f93f5c26c99991b25fa76801074f3d3

[ "Apache-2.0" ]

1

2020-11-12T08:46:32.000Z

hubspot/cms/performance/api/__init__.py

fakepop/hubspot-api-python

f04103a09f93f5c26c99991b25fa76801074f3d3

[ "Apache-2.0" ]

null

hubspot/cms/performance/api/__init__.py

fakepop/hubspot-api-python

f04103a09f93f5c26c99991b25fa76801074f3d3

[ "Apache-2.0" ]

null

from __future__ import absolute_import # flake8: noqa # import apis into api package from hubspot.cms.performance.api.default_api import DefaultApi

21.428571

62

0.826667

from __future__ import absolute_import from hubspot.cms.performance.api.default_api import DefaultApi

true

1c47816a5c703047ace0c887f2f265050774570e

1,174

py

Python

dir_test/test_me.py

splbio/pytestdoc

08a8ee1a4014bb78169ee4fc41cc6b722032826e

[ "BSD-2-Clause" ]

9

2015-07-08T16:25:32.000Z

2021-04-15T10:50:12.000Z

dir_test/test_me.py

splbio/pytestdoc

08a8ee1a4014bb78169ee4fc41cc6b722032826e

[ "BSD-2-Clause" ]

1

2015-08-18T06:53:50.000Z

2015-10-11T04:55:41.000Z

dir_test/test_me.py

splbio/pytestdoc

08a8ee1a4014bb78169ee4fc41cc6b722032826e

[ "BSD-2-Clause" ]

2

2019-04-04T08:44:13.000Z

2021-02-22T08:12:03.000Z

import json import pytestdoc WHAT_IS_THIS = True def times(x, y): return x * y TEST_CATEGORY="derp" @pytestdoc.tattr_redmine_feature(7474) @pytestdoc.tattr_redmine_bug(7475, 1776) @pytestdoc.tattr_incomplete @pytestdoc.tattr_category("herp") @pytestdoc.tattr_doc(""" This is the *documentation* for my function It tests the following things: - if derps are herps - all fives are half of 10 """) def test_positive(): assert times(5,5) == 25 @pytestdoc.tattr_doc("""Test that this works when first item is negative""") @pytestdoc.tattr_category("herp:negatives") def test_firstnegative(): assert times(-2,5) == -10 @pytestdoc.tattr_doc("""Test that this works when second item is negative""") @pytestdoc.tattr_category("herp:negatives") def test_secondnegative(): assert times(3,-12) == -36 @pytestdoc.tattr_doc("""Test that this works when both items are negative""") @pytestdoc.tattr_category("herp:negatives") def test_bothnegative(): assert times(-12,-12) == 144 @pytestdoc.tattr_doc("""Test that this works when first item is negative""") @pytestdoc.tattr_category("herp:negatives") def test_firstnegative(): assert times(-2,5) == -10

24.978723

77

0.736797

import json import pytestdoc WHAT_IS_THIS = True def times(x, y): return x * y TEST_CATEGORY="derp" @pytestdoc.tattr_redmine_feature(7474) @pytestdoc.tattr_redmine_bug(7475, 1776) @pytestdoc.tattr_incomplete @pytestdoc.tattr_category("herp") @pytestdoc.tattr_doc(""" This is the *documentation* for my function It tests the following things: - if derps are herps - all fives are half of 10 """) def test_positive(): assert times(5,5) == 25 @pytestdoc.tattr_doc("""Test that this works when first item is negative""") @pytestdoc.tattr_category("herp:negatives") def test_firstnegative(): assert times(-2,5) == -10 @pytestdoc.tattr_doc("""Test that this works when second item is negative""") @pytestdoc.tattr_category("herp:negatives") def test_secondnegative(): assert times(3,-12) == -36 @pytestdoc.tattr_doc("""Test that this works when both items are negative""") @pytestdoc.tattr_category("herp:negatives") def test_bothnegative(): assert times(-12,-12) == 144 @pytestdoc.tattr_doc("""Test that this works when first item is negative""") @pytestdoc.tattr_category("herp:negatives") def test_firstnegative(): assert times(-2,5) == -10

true

1c4781b885c055266febe549972d98ad995a452c

2,740

py

Python

aiida_defect/calculations.py

unkcpz/aiida-defect

592c1d8dd8130b06d06b543d5e5d35286afa63a3

[ "MIT" ]

1

2021-02-18T07:20:02.000Z

aiida_defect/calculations.py

unkcpz/aiida-defect

592c1d8dd8130b06d06b543d5e5d35286afa63a3

[ "MIT" ]

null

aiida_defect/calculations.py

unkcpz/aiida-defect

592c1d8dd8130b06d06b543d5e5d35286afa63a3

[ "MIT" ]

null

""" Calculations provided by aiida_defect. Register calculations via the "aiida.calculations" entry point in setup.json. """ from __future__ import absolute_import import six from aiida.common import datastructures from aiida.engine import CalcJob from aiida.orm import SinglefileData from aiida.plugins import DataFactory DiffParameters = DataFactory('defect') class DiffCalculation(CalcJob): """ AiiDA calculation plugin wrapping the diff executable. Simple AiiDA plugin wrapper for 'diffing' two files. """ @classmethod def define(cls, spec): """Define inputs and outputs of the calculation.""" # yapf: disable super(DiffCalculation, cls).define(spec) spec.input('metadata.options.resources', valid_type=dict, default={'num_machines': 1, 'num_mpiprocs_per_machine': 1}) spec.input('metadata.options.parser_name', valid_type=six.string_types, default='defect') spec.input('metadata.options.output_filename', valid_type=six.string_types, default='patch.diff') spec.input('parameters', valid_type=DiffParameters, help='Command line parameters for diff') spec.input('file1', valid_type=SinglefileData, help='First file to be compared.') spec.input('file2', valid_type=SinglefileData, help='Second file to be compared.') spec.output('defect', valid_type=SinglefileData, help='diff between file1 and file2.') spec.exit_code(100, 'ERROR_MISSING_OUTPUT_FILES', message='Calculation did not produce all expected output files.') def prepare_for_submission(self, folder): """ Create input files. :param folder: an `aiida.common.folders.Folder` where the plugin should temporarily place all files needed by the calculation. :return: `aiida.common.datastructures.CalcInfo` instance """ codeinfo = datastructures.CodeInfo() codeinfo.cmdline_params = self.inputs.parameters.cmdline_params( file1_name=self.inputs.file1.filename, file2_name=self.inputs.file2.filename) codeinfo.code_uuid = self.inputs.code.uuid codeinfo.stdout_name = self.metadata.options.output_filename codeinfo.withmpi = self.inputs.metadata.options.withmpi # Prepare a `CalcInfo` to be returned to the engine calcinfo = datastructures.CalcInfo() calcinfo.codes_info = [codeinfo] calcinfo.local_copy_list = [ (self.inputs.file1.uuid, self.inputs.file1.filename, self.inputs.file1.filename), (self.inputs.file2.uuid, self.inputs.file2.filename, self.inputs.file2.filename), ] calcinfo.retrieve_list = [self.metadata.options.output_filename] return calcinfo

40.895522

125

0.708394

from __future__ import absolute_import import six from aiida.common import datastructures from aiida.engine import CalcJob from aiida.orm import SinglefileData from aiida.plugins import DataFactory DiffParameters = DataFactory('defect') class DiffCalculation(CalcJob): @classmethod def define(cls, spec): super(DiffCalculation, cls).define(spec) spec.input('metadata.options.resources', valid_type=dict, default={'num_machines': 1, 'num_mpiprocs_per_machine': 1}) spec.input('metadata.options.parser_name', valid_type=six.string_types, default='defect') spec.input('metadata.options.output_filename', valid_type=six.string_types, default='patch.diff') spec.input('parameters', valid_type=DiffParameters, help='Command line parameters for diff') spec.input('file1', valid_type=SinglefileData, help='First file to be compared.') spec.input('file2', valid_type=SinglefileData, help='Second file to be compared.') spec.output('defect', valid_type=SinglefileData, help='diff between file1 and file2.') spec.exit_code(100, 'ERROR_MISSING_OUTPUT_FILES', message='Calculation did not produce all expected output files.') def prepare_for_submission(self, folder): codeinfo = datastructures.CodeInfo() codeinfo.cmdline_params = self.inputs.parameters.cmdline_params( file1_name=self.inputs.file1.filename, file2_name=self.inputs.file2.filename) codeinfo.code_uuid = self.inputs.code.uuid codeinfo.stdout_name = self.metadata.options.output_filename codeinfo.withmpi = self.inputs.metadata.options.withmpi calcinfo = datastructures.CalcInfo() calcinfo.codes_info = [codeinfo] calcinfo.local_copy_list = [ (self.inputs.file1.uuid, self.inputs.file1.filename, self.inputs.file1.filename), (self.inputs.file2.uuid, self.inputs.file2.filename, self.inputs.file2.filename), ] calcinfo.retrieve_list = [self.metadata.options.output_filename] return calcinfo

true

1c4782033a601ea0f3de81c2b2d2f03f95b1884b

2,006

py

Python

examples/gui/__main__.py

vcokltfre/aionasa

8cd1d496d7373c806e38eb75e0103e4377da0875

[ "MIT" ]

4

2020-11-26T10:49:53.000Z

2021-05-18T17:56:08.000Z

examples/gui/__main__.py

vcokltfre/aionasa

8cd1d496d7373c806e38eb75e0103e4377da0875

[ "MIT" ]

1

2021-01-07T01:41:27.000Z

examples/gui/__main__.py

vcokltfre/aionasa

8cd1d496d7373c806e38eb75e0103e4377da0875

[ "MIT" ]

1

2021-08-19T18:49:53.000Z

import argparse import asyncio import os from aionasa.epic.api import EPIC from aionasa.utils import date_strptime from gui import open_gui __doc__ = "Download some images from NASA's EPIC archive and open them in a gui browser." usage = "python -m aionasa.epic [-h] [--date DATE] [--collection COLLECTION] img_folder" def argument_parser(): """Generates the parser used by the aionasa.epic.__main__ script.""" parser = argparse.ArgumentParser(description=__doc__, usage=usage) parser.add_argument( '--date', '-d', type=date_strptime, default=None, help="Format: YYYY-MM-DD" ) parser.add_argument( '--collection', '-c', default='natural', help="Collection to get images from. Should be 'natural', 'enhanced', or 'natural,enhanced'" ) parser.add_argument( 'img_folder', help='Directory to download the images to.' ) return parser async def _task(coro, arg): """Safely execute an async function""" try: await coro(arg) except: pass async def setup(date, path, collection): """Downloads all EPIC images in a collection to a directory given by the 'path' parameter.""" # make image directory if necessary if not os.path.exists(path): os.mkdir(path) async with EPIC() as epic: # API request, gets images (urls etc) images = [] if 'natural' in collection: images += await epic.natural_images(date) if 'enhanced' in collection: images += await epic.enhanced_images(date) # download the images asynchronously print('downloading', len(images), 'images.') tasks = [_task(image.save, path + '/' + image.filename) for image in images] await asyncio.gather(*tasks) async def main(): await setup(args.date, args.img_folder, args.collection.split(',')) open_gui(args.img_folder) if __name__ == '__main__': args = argument_parser().parse_args() asyncio.run(main())

28.657143

100

0.653539

import argparse import asyncio import os from aionasa.epic.api import EPIC from aionasa.utils import date_strptime from gui import open_gui __doc__ = "Download some images from NASA's EPIC archive and open them in a gui browser." usage = "python -m aionasa.epic [-h] [--date DATE] [--collection COLLECTION] img_folder" def argument_parser(): parser = argparse.ArgumentParser(description=__doc__, usage=usage) parser.add_argument( '--date', '-d', type=date_strptime, default=None, help="Format: YYYY-MM-DD" ) parser.add_argument( '--collection', '-c', default='natural', help="Collection to get images from. Should be 'natural', 'enhanced', or 'natural,enhanced'" ) parser.add_argument( 'img_folder', help='Directory to download the images to.' ) return parser async def _task(coro, arg): try: await coro(arg) except: pass async def setup(date, path, collection): # make image directory if necessary if not os.path.exists(path): os.mkdir(path) async with EPIC() as epic: # API request, gets images (urls etc) images = [] if 'natural' in collection: images += await epic.natural_images(date) if 'enhanced' in collection: images += await epic.enhanced_images(date) # download the images asynchronously print('downloading', len(images), 'images.') tasks = [_task(image.save, path + '/' + image.filename) for image in images] await asyncio.gather(*tasks) async def main(): await setup(args.date, args.img_folder, args.collection.split(',')) open_gui(args.img_folder) if __name__ == '__main__': args = argument_parser().parse_args() asyncio.run(main())

true

1c4782238324e2454e74dfd129755995c5656e98

11,993

py

Python

aesara/graph/utils.py

danhphan/aesara

5a0fb0e731358d54648823170acd911cc1534d6a

[ "BSD-3-Clause" ]

null

aesara/graph/utils.py

danhphan/aesara

5a0fb0e731358d54648823170acd911cc1534d6a

[ "BSD-3-Clause" ]

null

aesara/graph/utils.py

danhphan/aesara

5a0fb0e731358d54648823170acd911cc1534d6a

[ "BSD-3-Clause" ]

null

import linecache import sys import traceback from abc import ABCMeta from io import StringIO from typing import TYPE_CHECKING, List, Optional, Sequence, Tuple, TypeVar, Union if TYPE_CHECKING: from aesara.graph.basic import Apply, Variable T = TypeVar("T", bound=Union["Apply", "Variable"]) def simple_extract_stack( f=None, limit: Optional[int] = None, skips: Optional[Sequence[str]] = None ) -> List[Tuple[Optional[str], int, str, Optional[str]]]: """This is traceback.extract_stack from python 2.7 with this change: - Comment the update of the cache. - Skip internal stack trace level. The update of the cache call os.stat to verify is the cache is up to date. This take too much time on cluster. limit - The number of stack level we want to return. If None, mean all what we can. skips - partial path of stack level we don't want to keep and count. When we find one level that isn't skipped, we stop skipping. """ if skips is None: skips = [] if f is None: f = sys._getframe().f_back if limit is None: if hasattr(sys, "tracebacklimit"): limit = sys.tracebacklimit trace: List[Tuple[Optional[str], int, str, Optional[str]]] = [] n = 0 while f is not None and (limit is None or n < limit): lineno = f.f_lineno co = f.f_code filename = co.co_filename name = co.co_name # linecache.checkcache(filename) line: Optional[str] = linecache.getline(filename, lineno, f.f_globals) if line: line = line.strip() else: line = None f = f.f_back # Just skip inner level if len(trace) == 0: rm = False for p in skips: # Julian: I added the 'tests' exception together with # Arnaud. Otherwise, we'd lose the stack trace during # in our test cases (e.g. in test_opt.py). We're not # sure this is the right way to do it though. if p in filename and "tests" not in filename: rm = True break if rm: continue trace.append((filename, lineno, name, line)) n = n + 1 trace.reverse() return trace def add_tag_trace(thing: T, user_line: Optional[int] = None) -> T: """Add tag.trace to a node or variable. The argument is returned after being affected (inplace). Parameters ---------- thing The object where we add .tag.trace. user_line The max number of user line to keep. Notes ----- We also use config.traceback__limit for the maximum number of stack level we look. """ from aesara.configdefaults import config if user_line is None: user_line = config.traceback__limit if user_line == -1: user_line = None skips = [ "aesara/tensor/", "aesara\\tensor\\", "aesara/compile/", "aesara\\compile\\", "aesara/graph/", "aesara\\graph\\", "aesara/scalar/basic.py", "aesara\\scalar\\basic.py", "aesara/sandbox/", "aesara\\sandbox\\", "aesara/scan/", "aesara\\scan\\", "aesara/sparse/", "aesara\\sparse\\", "aesara/typed_list/", "aesara\\typed_list\\", ] if config.traceback__compile_limit > 0: skips = [] tr = simple_extract_stack(limit=user_line, skips=skips) # Different python version use different sementic for # limit. python 2.7 include the call to extrack_stack. The -1 get # rid of it. if tr: thing.tag.trace = [tr] else: thing.tag.trace = tr return thing def get_variable_trace_string(v): sio = StringIO() # For backward compatibility with old trace tr = getattr(v.tag, "trace", []) if isinstance(tr, list) and len(tr) > 0: print(" \nBacktrace when that variable is created:\n", file=sio) # The isinstance is needed to handle old pickled trace if isinstance(tr[0], tuple): traceback.print_list(v.tag.trace, sio) else: # Print separate message for each element in the list of # backtraces for idx, subtr in enumerate(tr): if len(tr) > 1: print(f"trace {int(idx)}", file=sio) traceback.print_list(subtr, sio) return sio.getvalue() class InconsistencyError(Exception): """ This exception should be thrown by listeners to FunctionGraph when the graph's state is invalid. """ class MissingInputError(Exception): """ A symbolic input needed to compute the outputs is missing. """ def __init__(self, *args, **kwargs): if kwargs: # The call to list is needed for Python 3 assert list(kwargs.keys()) == ["variable"] error_msg = get_variable_trace_string(kwargs["variable"]) if error_msg: args = args + (error_msg,) s = "\n".join(args) # Needed to have the new line print correctly super().__init__(s) class TestValueError(Exception): """Base exception class for all test value errors.""" class MethodNotDefined(Exception): """ To be raised by functions defined as part of an interface. When the user sees such an error, it is because an important interface function has been left out of an implementation class. """ class MetaType(ABCMeta): def __new__(cls, name, bases, dct): props = dct.get("__props__", None) if props is not None: if not isinstance(props, tuple): raise TypeError("__props__ has to be a tuple") if not all(isinstance(p, str) for p in props): raise TypeError("elements of __props__ have to be strings") def _props(self): """ Tuple of properties of all attributes """ return tuple(getattr(self, a) for a in props) dct["_props"] = _props def _props_dict(self): """This return a dict of all ``__props__`` key-> value. This is useful in optimization to swap op that should have the same props. This help detect error that the new op have at least all the original props. """ return {a: getattr(self, a) for a in props} dct["_props_dict"] = _props_dict if "__hash__" not in dct: def __hash__(self): return hash((type(self), tuple(getattr(self, a) for a in props))) dct["__hash__"] = __hash__ if "__eq__" not in dct: def __eq__(self, other): return type(self) == type(other) and tuple( getattr(self, a) for a in props ) == tuple(getattr(other, a) for a in props) dct["__eq__"] = __eq__ if "__str__" not in dct: if len(props) == 0: def __str__(self): return f"{self.__class__.__name__}" else: def __str__(self): return "{}{{{}}}".format( self.__class__.__name__, ", ".join( "{}={!r}".format(p, getattr(self, p)) for p in props ), ) dct["__str__"] = __str__ return super().__new__(cls, name, bases, dct) class MetaObject(metaclass=MetaType): __slots__: List = [] def __ne__(self, other): return not self == other class Scratchpad: def clear(self): self.__dict__.clear() def __update__(self, other): self.__dict__.update(other.__dict__) return self def __str__(self): return "scratchpad" + str(self.__dict__) def __repr__(self): return "scratchpad" + str(self.__dict__) def info(self): print(f"<aesara.graph.utils.scratchpad instance at {id(self)}>") for k, v in self.__dict__.items(): print(f" {k}: {v}") class ValidatingScratchpad(Scratchpad): """This `Scratchpad` validates attribute values.""" def __init__(self, attr, attr_filter): super().__init__() object.__setattr__(self, "attr", attr) object.__setattr__(self, "attr_filter", attr_filter) def __setattr__(self, attr, obj): if getattr(self, "attr", None) == attr: obj = self.attr_filter(obj) return object.__setattr__(self, attr, obj) class D: def __init__(self, **d): self.__dict__.update(d) class AssocList: """An associative list. This class is like a `dict` that accepts unhashable keys by using an assoc list for internal use only """ def __init__(self): self._dict = {} self._list = [] def __getitem__(self, item): return self.get(item, None) def __setitem__(self, item, value): try: self._dict[item] = value except Exception: for i, (key, val) in enumerate(self._list): if key == item: self._list[i] = (item, value) return self._list.append((item, value)) def __delitem__(self, item): try: if item in self._dict: del self._dict[item] return except TypeError as e: assert "unhashable type" in str(e) for i, (key, val) in enumerate(self._list): if key == item: del self._list[i] return raise KeyError(item) def discard(self, item): try: if item in self._dict: del self._dict[item] return except TypeError as e: assert "unhashable type" in str(e) for i, (key, val) in enumerate(self._list): if key == item: del self._list[i] return def get(self, item, default): try: return self._dict[item] except Exception: for item2, value in self._list: try: if item == item2: return value if item.equals(item2): return value except Exception: if item is item2: return value return default def clear(self): self._dict = {} self._list = [] def __repr__(self): return f"AssocList({self._dict}, {self._list})" def toposort(prereqs_d): """ Sorts prereqs_d.keys() topologically. prereqs_d[x] contains all the elements that must come before x in the ordering. """ # all1 = set(prereqs_d.keys()) # all2 = set() # for x, y in prereqs_d.items(): # all2.update(y) # print all1.difference(all2) seq = [] done = set() postreqs_d = {} for x, prereqs in prereqs_d.items(): for prereq in prereqs: postreqs_d.setdefault(prereq, set()).add(x) next = {k for k in prereqs_d if not prereqs_d[k]} while next: bases = next next = set() for x in bases: done.add(x) seq.append(x) for x in bases: for postreq in postreqs_d.get(x, []): if not prereqs_d[postreq].difference(done): next.add(postreq) if len(prereqs_d) != len(seq): raise Exception( "Cannot sort topologically: there might be cycles, " "prereqs_d does not have a key for each element or " "some orderings contain invalid elements." ) return seq

28.622912

85

0.547736

import linecache import sys import traceback from abc import ABCMeta from io import StringIO from typing import TYPE_CHECKING, List, Optional, Sequence, Tuple, TypeVar, Union if TYPE_CHECKING: from aesara.graph.basic import Apply, Variable T = TypeVar("T", bound=Union["Apply", "Variable"]) def simple_extract_stack( f=None, limit: Optional[int] = None, skips: Optional[Sequence[str]] = None ) -> List[Tuple[Optional[str], int, str, Optional[str]]]: if skips is None: skips = [] if f is None: f = sys._getframe().f_back if limit is None: if hasattr(sys, "tracebacklimit"): limit = sys.tracebacklimit trace: List[Tuple[Optional[str], int, str, Optional[str]]] = [] n = 0 while f is not None and (limit is None or n < limit): lineno = f.f_lineno co = f.f_code filename = co.co_filename name = co.co_name line: Optional[str] = linecache.getline(filename, lineno, f.f_globals) if line: line = line.strip() else: line = None f = f.f_back if len(trace) == 0: rm = False for p in skips: # in our test cases (e.g. in test_opt.py). We're not if p in filename and "tests" not in filename: rm = True break if rm: continue trace.append((filename, lineno, name, line)) n = n + 1 trace.reverse() return trace def add_tag_trace(thing: T, user_line: Optional[int] = None) -> T: from aesara.configdefaults import config if user_line is None: user_line = config.traceback__limit if user_line == -1: user_line = None skips = [ "aesara/tensor/", "aesara\\tensor\\", "aesara/compile/", "aesara\\compile\\", "aesara/graph/", "aesara\\graph\\", "aesara/scalar/basic.py", "aesara\\scalar\\basic.py", "aesara/sandbox/", "aesara\\sandbox\\", "aesara/scan/", "aesara\\scan\\", "aesara/sparse/", "aesara\\sparse\\", "aesara/typed_list/", "aesara\\typed_list\\", ] if config.traceback__compile_limit > 0: skips = [] tr = simple_extract_stack(limit=user_line, skips=skips) if tr: thing.tag.trace = [tr] else: thing.tag.trace = tr return thing def get_variable_trace_string(v): sio = StringIO() tr = getattr(v.tag, "trace", []) if isinstance(tr, list) and len(tr) > 0: print(" \nBacktrace when that variable is created:\n", file=sio) if isinstance(tr[0], tuple): traceback.print_list(v.tag.trace, sio) else: for idx, subtr in enumerate(tr): if len(tr) > 1: print(f"trace {int(idx)}", file=sio) traceback.print_list(subtr, sio) return sio.getvalue() class InconsistencyError(Exception): class MissingInputError(Exception): def __init__(self, *args, **kwargs): if kwargs: assert list(kwargs.keys()) == ["variable"] error_msg = get_variable_trace_string(kwargs["variable"]) if error_msg: args = args + (error_msg,) s = "\n".join(args) super().__init__(s) class TestValueError(Exception): class MethodNotDefined(Exception): class MetaType(ABCMeta): def __new__(cls, name, bases, dct): props = dct.get("__props__", None) if props is not None: if not isinstance(props, tuple): raise TypeError("__props__ has to be a tuple") if not all(isinstance(p, str) for p in props): raise TypeError("elements of __props__ have to be strings") def _props(self): return tuple(getattr(self, a) for a in props) dct["_props"] = _props def _props_dict(self): return {a: getattr(self, a) for a in props} dct["_props_dict"] = _props_dict if "__hash__" not in dct: def __hash__(self): return hash((type(self), tuple(getattr(self, a) for a in props))) dct["__hash__"] = __hash__ if "__eq__" not in dct: def __eq__(self, other): return type(self) == type(other) and tuple( getattr(self, a) for a in props ) == tuple(getattr(other, a) for a in props) dct["__eq__"] = __eq__ if "__str__" not in dct: if len(props) == 0: def __str__(self): return f"{self.__class__.__name__}" else: def __str__(self): return "{}{{{}}}".format( self.__class__.__name__, ", ".join( "{}={!r}".format(p, getattr(self, p)) for p in props ), ) dct["__str__"] = __str__ return super().__new__(cls, name, bases, dct) class MetaObject(metaclass=MetaType): __slots__: List = [] def __ne__(self, other): return not self == other class Scratchpad: def clear(self): self.__dict__.clear() def __update__(self, other): self.__dict__.update(other.__dict__) return self def __str__(self): return "scratchpad" + str(self.__dict__) def __repr__(self): return "scratchpad" + str(self.__dict__) def info(self): print(f"<aesara.graph.utils.scratchpad instance at {id(self)}>") for k, v in self.__dict__.items(): print(f" {k}: {v}") class ValidatingScratchpad(Scratchpad): def __init__(self, attr, attr_filter): super().__init__() object.__setattr__(self, "attr", attr) object.__setattr__(self, "attr_filter", attr_filter) def __setattr__(self, attr, obj): if getattr(self, "attr", None) == attr: obj = self.attr_filter(obj) return object.__setattr__(self, attr, obj) class D: def __init__(self, **d): self.__dict__.update(d) class AssocList: def __init__(self): self._dict = {} self._list = [] def __getitem__(self, item): return self.get(item, None) def __setitem__(self, item, value): try: self._dict[item] = value except Exception: for i, (key, val) in enumerate(self._list): if key == item: self._list[i] = (item, value) return self._list.append((item, value)) def __delitem__(self, item): try: if item in self._dict: del self._dict[item] return except TypeError as e: assert "unhashable type" in str(e) for i, (key, val) in enumerate(self._list): if key == item: del self._list[i] return raise KeyError(item) def discard(self, item): try: if item in self._dict: del self._dict[item] return except TypeError as e: assert "unhashable type" in str(e) for i, (key, val) in enumerate(self._list): if key == item: del self._list[i] return def get(self, item, default): try: return self._dict[item] except Exception: for item2, value in self._list: try: if item == item2: return value if item.equals(item2): return value except Exception: if item is item2: return value return default def clear(self): self._dict = {} self._list = [] def __repr__(self): return f"AssocList({self._dict}, {self._list})" def toposort(prereqs_d): seq = [] done = set() postreqs_d = {} for x, prereqs in prereqs_d.items(): for prereq in prereqs: postreqs_d.setdefault(prereq, set()).add(x) next = {k for k in prereqs_d if not prereqs_d[k]} while next: bases = next next = set() for x in bases: done.add(x) seq.append(x) for x in bases: for postreq in postreqs_d.get(x, []): if not prereqs_d[postreq].difference(done): next.add(postreq) if len(prereqs_d) != len(seq): raise Exception( "Cannot sort topologically: there might be cycles, " "prereqs_d does not have a key for each element or " "some orderings contain invalid elements." ) return seq

true

1c47827600948d2b87bf218f91b1371ea3cbb3eb

3,488

py

Python

fhirclient/models/coding.py

mdx-dev/client-py

f6c16c9bd386c5b05d69753b89c6519d568814ac

[ "Apache-2.0" ]

null

fhirclient/models/coding.py

mdx-dev/client-py

f6c16c9bd386c5b05d69753b89c6519d568814ac

[ "Apache-2.0" ]

null

fhirclient/models/coding.py

mdx-dev/client-py

f6c16c9bd386c5b05d69753b89c6519d568814ac

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Generated from FHIR 4.0.0-a53ec6ee1b (http://hl7.org/fhir/StructureDefinition/Coding) on 2019-01-22. # 2019, SMART Health IT. from . import element class Coding(element.Element): """ A r e f e r e n c e t o a c o d e d e f i n e d b y a t e r m i n o l o g y s y s t e m . """ resource_type = "Coding" def __init__(self, jsondict=None, strict=True): """ Initialize all valid properties. :raises: FHIRValidationError on validation errors, unless strict is False :param dict jsondict: A JSON dictionary to use for initialization :param bool strict: If True (the default), invalid variables will raise a TypeError """ self.code = None """ S y m b o l i n s y n t a x d e f i n e d b y t h e s y s t e m . Type `str`. """ self.display = None """ R e p r e s e n t a t i o n d e f i n e d b y t h e s y s t e m . Type `str`. """ self.system = None """ I d e n t i t y o f t h e t e r m i n o l o g y s y s t e m . Type `str`. """ self.userSelected = None """ I f t h i s c o d i n g w a s c h o s e n d i r e c t l y b y t h e u s e r . Type `bool`. """ self.version = None """ V e r s i o n o f t h e s y s t e m - i f r e l e v a n t . Type `str`. """ super(Coding, self).__init__(jsondict=jsondict, strict=strict) def elementProperties(self): js = super(Coding, self).elementProperties() js.extend([ ("code", "code", str, False, None, False), ("display", "display", str, False, None, False), ("system", "system", str, False, None, False), ("userSelected", "userSelected", bool, False, None, False), ("version", "version", str, False, None, False), ]) return js

12.966543

103

0.294725

from . import element class Coding(element.Element): resource_type = "Coding" def __init__(self, jsondict=None, strict=True): self.code = None self.display = None self.system = None self.userSelected = None self.version = None super(Coding, self).__init__(jsondict=jsondict, strict=strict) def elementProperties(self): js = super(Coding, self).elementProperties() js.extend([ ("code", "code", str, False, None, False), ("display", "display", str, False, None, False), ("system", "system", str, False, None, False), ("userSelected", "userSelected", bool, False, None, False), ("version", "version", str, False, None, False), ]) return js

true

1c4782c8740a735f5aa4dfddb82ffcdda14f7ceb

689

py

Python

packages/cuda/cuSolverDn.py

lijun99/pyre

004dfd4c06489b4ba5b32877338ca6440f2d523b

[ "BSD-3-Clause" ]

3

2019-08-02T21:02:47.000Z

2021-09-08T13:59:43.000Z

packages/cuda/cuSolverDn.py

lijun99/pyre

004dfd4c06489b4ba5b32877338ca6440f2d523b

[ "BSD-3-Clause" ]

null

packages/cuda/cuSolverDn.py

lijun99/pyre

004dfd4c06489b4ba5b32877338ca6440f2d523b

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # # Lijun Zhu # california institute of technology # (c) 2016-2019 all rights reserved # # externals from . import cuda as libcuda # the extension from .Matrix import Matrix class cuSolverDn: """ Wrapper for cusolverDn lib utitilies """ def create_handle(): """ create a cusolverDn handle """ handle = libcuda.cusolverDnCreate() return handle def get_current_handle(): # default device handle from . import manager if manager.current_device is None: manager.device(0) handle = manager.current_device.cusolverdn_handle return handle # end of file

20.264706

57

0.628447

from . import cuda as libcuda from .Matrix import Matrix class cuSolverDn: def create_handle(): handle = libcuda.cusolverDnCreate() return handle def get_current_handle(): from . import manager if manager.current_device is None: manager.device(0) handle = manager.current_device.cusolverdn_handle return handle

true

1c478487162412bd45e541a0e720bee7c90272d6

42,379

py

Python

tensorflow/python/framework/func_graph.py

fraudies/tensorflow

a42423e302b71893bbd24aa896869941013c07fb

[ "Apache-2.0" ]

3

2016-08-20T04:02:24.000Z

2019-04-21T06:18:41.000Z

tensorflow/python/framework/func_graph.py

fraudies/tensorflow

a42423e302b71893bbd24aa896869941013c07fb

[ "Apache-2.0" ]

59

2019-06-17T09:37:49.000Z

2022-01-19T01:21:34.000Z

tensorflow/python/framework/func_graph.py

fraudies/tensorflow

a42423e302b71893bbd24aa896869941013c07fb

[ "Apache-2.0" ]

1

2019-10-31T09:22:30.000Z

# Copyright 2018 The TensorFlow 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. # ============================================================================== """FuncGraph and related functionality.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections as py_collections import itertools import weakref from tensorflow.core.framework import attr_value_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import execute from tensorflow.python.eager import tape from tensorflow.python.eager.graph_only_ops import graph_placeholder from tensorflow.python.framework import composite_tensor from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_spec from tensorflow.python.framework.auto_control_deps import AutomaticControlDependencies from tensorflow.python.ops import array_ops from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope from tensorflow.python.util import compat from tensorflow.python.util import memory from tensorflow.python.util import nest from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util.lazy_loader import LazyLoader # This is to avoid a circular dependency: # function -> func_graph function = LazyLoader("function", globals(), "tensorflow.python.eager.function") def_function = LazyLoader( "def_function", globals(), "tensorflow.python.eager.def_function") WHITELIST_COLLECTIONS = [ ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.TRAINABLE_VARIABLES, variable_scope._VARSTORE_KEY, # pylint: disable=protected-access variable_scope._VARSCOPESTORE_KEY # pylint: disable=protected-access ] class UnknownArgument(object): """Signifies an argument which is not currently handled.""" pass def convert_structure_to_signature(structure, arg_names=None): """Convert a potentially nested structure to a signature. Args: structure: Structure to convert, where top level collection is a list or a tuple. arg_names: Optional list of arguments that has equal number of elements as `structure` and is used for naming corresponding TensorSpecs. Returns: Identical structure that has TensorSpec objects instead of Tensors and UknownArgument instead of any unsupported types. """ structure = composite_tensor.replace_composites_with_components(structure) def encode_arg(arg, path): """A representation for this argument, for converting into signatures.""" if isinstance(arg, ops.Tensor): user_specified_name = None try: user_specified_name = compat.as_str( arg.op.get_attr("_user_specified_name")) except ValueError: pass if path and user_specified_name and user_specified_name != path[0]: # The user has explicitly named the argument differently than the name # of the function argument. name = user_specified_name else: name = "/".join([str(p) for p in path]) return tensor_spec.TensorSpec(arg.shape, arg.dtype, name) if isinstance(arg, ( int, float, bool, type(None), dtypes.DType, tensor_spec.TensorSpec, )): return arg return UnknownArgument() # We are using the flattened paths to name the TensorSpecs. We need an # explicit name for them downstream. flattened = nest.flatten_with_tuple_paths(structure, expand_composites=True) if arg_names: if len(arg_names) != len(structure): raise ValueError( "Passed in arg_names don't match actual signature (%s)." % arg_names) # Replace all top-level names with their actual arg_names. If a path before # was "(2,'a',1)", it will become "(arg_names[2],'a',1)". flattened = [ ((arg_names[path[0]],) + path[1:], arg) for path, arg in flattened ] mapped = [encode_arg(arg, path) for path, arg in flattened] return nest.pack_sequence_as(structure, mapped, expand_composites=True) class FuncGraph(ops.Graph): """Graph representing a function body. Attributes: name: The name of the function. inputs: Placeholder tensors representing the inputs to this function. The tensors are in this FuncGraph. This represents "regular" inputs as well as captured inputs (i.e. the values of self.captures), with the regular inputs coming first. outputs: Tensors that will be returned by this function. The tensors are in this FuncGraph. control_outputs: Operations that must be executed before the function represented by this graph can be said to have been executed. structured_input_signature: A tuple of (args, kwargs), which are both possibly-nested python objects that were received by this function. Note that these structures might contain Python `None`s. structured_outputs: A possibly-nested python object which will be returned by this function. The Tensors in this structure are the same as those of self.outputs. Note that this structure might contain Python `None`s. variables: Variables that should be watched during function execution. outer_graph: The graph this function is defined in. May be another FuncGraph or the global default Graph. captures: Maps external tensor -> internal tensor (i.e. input placeholder). The entries are in the order they were captured. control_captures: Set of external ops on which this graph has a control dependency. seed: The graph-level random seed. capture_by_value: If True, the func graph will capture Variables by value instead of reference. """ def __init__(self, name, collections=None, capture_by_value=None): """Construct a new FuncGraph. The graph will inherit its graph key, collections, seed, and distribution strategy stack from the current context or graph. Args: name: the name of the function. collections: a dictionary of collections this FuncGraph should start with. If not specified (None), the FuncGraph will read (but not write to) the outer graph's collections that are not whitelisted, and both read and write to the outer graph's collections that are whitelisted. The current whitelisted collections are the global variables, the local variables, and the trainable variables. Defaults to None. capture_by_value: An optional boolean. If True, the func graph will capture Variables by value instead of reference. By default inherit from outer graphs, and failing that will default to False. """ super(FuncGraph, self).__init__() self.name = name self.inputs = [] self.outputs = [] self.control_outputs = [] self.control_captures = set() self.structured_input_signature = None self.structured_outputs = None self._weak_variables = [] self._watched_variables = weakref.WeakSet() self.outer_graph = ops.get_default_graph() self.captures = py_collections.OrderedDict() # Inherit capture-by-value from outer graph. if capture_by_value is not None: self.capture_by_value = capture_by_value elif self.outer_graph is not None and isinstance( self.outer_graph, FuncGraph): self.capture_by_value = self.outer_graph.capture_by_value else: self.capture_by_value = False self._building_function = True # Map from resource tensor name to last op (in program order) which uses # this tensor. Used to enforce that execution order matches program order # for resource tensors. self._last_op_using_resource_tensor = {} graph = self.outer_graph if context.executing_eagerly(): self.seed = context.global_seed() # [for tf-data user migration from TF1.0 to 2.0] seed_used keep track of # any None op_seed for random_op in the function, in which case we end up # using function seed, which could be unintended behavior for the op. self._seed_used = False else: self.seed = graph.seed self._seed_used = False # TODO(allenl): Figure out if we can remove colocation stack # specialization (currently used in cond_v2), here and in the cache key. self._colocation_stack = graph._colocation_stack.copy() # pylint: disable=protected-access if collections is None: for collection_name in graph.get_all_collection_keys(): if collection_name not in WHITELIST_COLLECTIONS: self._collections[collection_name] = graph.get_collection( collection_name) for collection_name in WHITELIST_COLLECTIONS: self._collections[collection_name] = graph.get_collection_ref( collection_name) else: self._collections = collections def __str__(self): return "FuncGraph(name=%s, id=%s)" % (self.name, id(self)) def watch_variable(self, v): """Marks the variable v as accessed while building this graph.""" while self is not None and isinstance(self, FuncGraph): self._watched_variables.add(v) self = self.outer_graph def control_dependencies(self, control_inputs): """Handles control dependencies. FuncGraph wraps Graph's control_dependencies logic by first filtering out any external tensors / operations and storing them in the graph's control_captures member. Any consumers of this function graph must then decide how to handle the control captures. Args: control_inputs: A list of `Operation` or `Tensor` objects which must be executed or computed before running the operations defined in the context. Can also be `None` to clear the control dependencies. Returns: A context manager that specifies control dependencies for all operations constructed within the context. Raises: TypeError: If `control_inputs` is not a list of `Operation` or `Tensor` objects. """ if control_inputs is None: return super(FuncGraph, self).control_dependencies(control_inputs) filtered_control_inputs = [] for c in control_inputs: # Check for _UnreadVariable if (isinstance(c, ops.IndexedSlices) or (hasattr(c, "_handle") and hasattr(c, "op"))): c = c.op graph_element = ops._as_graph_element(c) # pylint: disable=protected-access if graph_element is None: graph_element = c if graph_element is not None and getattr( graph_element, "graph", None) is not self: self.control_captures.add(graph_element) else: filtered_control_inputs.append(graph_element) return super(FuncGraph, self).control_dependencies(filtered_control_inputs) def as_default(self): outer_cm = super(FuncGraph, self).as_default() @tf_contextlib.contextmanager def inner_cm(): """Context manager for copying distribute.Strategy scope information.""" graph = ops.get_default_graph() # pylint: disable=protected-access # TODO(b/112906995, nareshmodi): distribution strategy depends on # inheriting this stack from the default graph even in eager mode. Maybe # it should be part of the eager context? This would also allow us to # remove a get_default_graph() call from the function cache lookup. old_strategy_stack = self._distribution_strategy_stack self._distribution_strategy_stack = list( graph._distribution_strategy_stack) # We ignore device placements from any outer scopes while tracing the # function when possible, to avoid hard-coding them in the function # graph. "Default" placements come from the PartitionedCallOp's placement, # so that the same trace of the Python function may be placed on several # different devices and saved functions may be placed on new devices when # restored. old_device_stack = self._device_function_stack if context.executing_eagerly(): if self._distribution_strategy_stack: self._add_device_to_stack(context.context().device_name) else: if (self._distribution_strategy_stack or device_stack_has_callable(graph._device_function_stack)): # Hard-code devices from device functions in the function body self._device_function_stack = graph._device_function_stack.copy() old_creator_stack = self._variable_creator_stack self._variable_creator_stack = graph._variable_creator_stack # Inherit the graph key, since this is used for matching variables in # optimizers. old_graph_key = self._graph_key self._graph_key = graph._graph_key # pylint: enable=protected-access with outer_cm as g: try: yield g finally: self._distribution_strategy_stack = old_strategy_stack self._device_function_stack = old_device_stack self._variable_creator_stack = old_creator_stack self._graph_key = old_graph_key return inner_cm() @property def output_types(self): return [t.dtype for t in self.outputs] @property def output_shapes(self): return [t.shape for t in self.outputs] @property def variables(self): """A list of variables accessed by this FuncGraph. Note that functions keep only weak references to variables. Calling the function after a variable it accesses has been deleted is an error. Yields: Strong references to variables accessed by this FuncGraph. """ for weak_v in self._weak_variables: v = weak_v() if v is None: raise AssertionError( "Called a function referencing variables which have been deleted. " "This likely means that function-local variables were created and " "not referenced elsewhere in the program. This is generally a " "mistake; consider storing variables in an object attribute on " "first call.") yield v @variables.setter def variables(self, var_list): self._weak_variables = [weakref.ref(v) for v in var_list] def _capture_by_value( self, op_type, inputs, dtypes, # pylint: disable=redefined-outer-name input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True): # When capturing by value, do the read outside reverse_captures = dict((v, k) for k, v in self.captures.items()) uncaptured_inputs = [reverse_captures.get(t, t) for t in inputs] with ops.init_scope(): if context.executing_eagerly(): attr_list = ("dtype", int(attrs["dtype"].type)) value, = execute.execute( compat.as_bytes(op_type), 1, uncaptured_inputs, attr_list, context.context()) else: op = ops.get_default_graph().create_op( op_type, uncaptured_inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device) value = op.outputs[0] captured_value = self.capture(value) return captured_value.op def create_op( self, op_type, inputs, dtypes=None, # pylint: disable=redefined-outer-name input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True): """Like Graph.create_op, except handles external input tensors. This overload adds functionality to create_op to "capture" any external input tensors, i.e. tensors from the eager context or outer function graphs if this is a nested function. See `capture` for more information. Args: op_type: The `Operation` type to create. This corresponds to the `OpDef.name` field for the proto that defines the operation. inputs: A list of `Tensor` objects that will be inputs to the `Operation`. dtypes: (Optional) A list of `DType` objects that will be the types of the tensors that the operation produces. input_types: (Optional.) A list of `DType`s that will be the types of the tensors that the operation consumes. By default, uses the base `DType` of each input in `inputs`. Operations that expect reference-typed inputs must specify `input_types` explicitly. name: (Optional.) A string name for the operation. If not specified, a name is generated based on `op_type`. attrs: (Optional.) A dictionary where the key is the attribute name (a string) and the value is the respective `attr` attribute of the `NodeDef` proto that will represent the operation (an `AttrValue` proto). op_def: (Optional.) The `OpDef` proto that describes the `op_type` that the operation will have. compute_shapes: (Optional.) Deprecated. Has no effect (shapes are always computed). compute_device: (Optional.) If True, device functions will be executed to compute the device property of the Operation. Returns: An `Operation` object. """ if self.capture_by_value and op_type in ["ReadVariableOp", "ResourceGather"]: return self._capture_by_value( op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device) # This capturing logic interacts poorly with control flow contexts which # want to replace inputs of ops far too late in the process. This can lead # the context to get confused and try to create an Enter for an Enter. We # can detect this here and skip the additional Enter which can confuse loop # validation logic. if op_type == "Enter" and inputs[0].op.type == "Enter": if inputs[0].op.get_attr("frame_name") == attrs["frame_name"].s: return inputs[0].op # Calling AddValue on the control flow contexts to force creation of the # backward accumulators in the original graph before we create placeholders # to capture the inputs. ctxt = ops.get_default_graph()._control_flow_context # pylint: disable=protected-access for i, inp in enumerate(inputs): # TPU Estimator defines a control flow context with no AddValue method. if ctxt is not None and hasattr(ctxt, "AddValue"): inp = ctxt.AddValue(inp) inp = self.capture(inp) inputs[i] = inp return super(FuncGraph, self).create_op( op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device=compute_device) def capture(self, tensor, name=None): """Captures `tensor` if it's external to this graph. If `tensor` is from a different graph, returns a placeholder for it. `tensor` and the placeholder will appear in self.captures, and the placeholder will appear in self.inputs. Multiple calls to this method with the same `tensor` argument will return the same placeholder. If `tensor` is from this graph, returns `tensor`. Args: tensor: Tensor. May be from this FuncGraph or a different graph. name: Optional name if a placeholder is created. Returns: Tensor from this FuncGraph. """ # Note: _forward_func_graph is currently only set when building the gradient # graph graph of a defun call. If the backwards graph tries to capture # tensors those will be captured first in the forward graph. This # makes sure that any tensor needed by a custom_gradient is correctly # captured. if (getattr(tensor, "graph", None) is not self and hasattr(self, "_forward_func_graph") and isinstance(self._forward_func_graph, FuncGraph)): tensor = self._forward_func_graph.capture(tensor) if isinstance(tensor, ops.EagerTensor): if name is None: name = str(ops.uid()) return self._capture_helper(tensor, name) if tensor.graph is not self: if name is None: name = tensor.op.name inner_graph = tensor.graph while inner_graph is not None and isinstance(inner_graph, FuncGraph): if inner_graph is self: raise ValueError( "Trying to capture a tensor from an inner function. This can be " "caused by accessing a tensor defined inside a loop or " "conditional body, or a subfunction, from a calling function, " "without going through the proper return value mechanism. " "Consider using TensorFlow mechanisms such as TensorArrays " "to return tensors from inner functions or loop / conditional " "bodies. Tensor: %s; tensor graph: %s; this graph: %s" % (tensor, tensor.graph, self)) inner_graph = inner_graph.outer_graph return self._capture_helper(tensor, name) return tensor def _capture_helper(self, tensor, name): captured_tensor = self.captures.get(tensor, None) if captured_tensor is None: captured_tensor = _create_substitute_placeholder(tensor, name=name, dtype=tensor.dtype) self.captures[tensor] = captured_tensor self.inputs.append(captured_tensor) tape.record_operation("captured_value", [captured_tensor], [tensor], lambda x: [x]) return captured_tensor @property def external_captures(self): """External tensors captured by this function.""" return list(self.captures.keys()) @property def internal_captures(self): """Placeholders in this function corresponding captured tensors.""" return list(self.captures.values()) def func_graph_from_py_func(name, python_func, args, kwargs, signature=None, func_graph=None, autograph=False, autograph_options=None, add_control_dependencies=True, arg_names=None, op_return_value=None, collections=None, capture_by_value=None, override_flat_arg_shapes=None): """Returns a `FuncGraph` generated from `python_func`. Args: name: an identifier for the function. python_func: the Python function to trace. args: the positional args with which the Python function should be called; ignored if a signature is provided. kwargs: the keyword args with which the Python function should be called; ignored if a signature is provided. signature: a possibly nested sequence of `TensorSpecs` specifying the shapes and dtypes of the arguments. When a signature is provided, `args` and `kwargs` are ignored, and `python_func` is traced with Tensors conforming to `signature`. If `None`, the shapes and dtypes are inferred from the inputs. func_graph: Optional. An instance of FuncGraph. If provided, we will use this graph else a new one is built and returned. autograph: whether to use autograph to compile `python_func`. See https://www.tensorflow.org/guide/autograph for more information. autograph_options: additional knobs to control when `autograph=True`. See https://www.tensorflow.org/guide/autograph for more information. add_control_dependencies: If True, automatically adds control dependencies to ensure program order matches execution order and stateful ops always execute. arg_names: Optional list of argument names, used to give input placeholders recognizable names. op_return_value: Optional. A Tensor. If set and `python_func` returns Operations, those return values will be replaced with this value. If not set, returning an Operation triggers an error. collections: a dictionary of collections this FuncGraph should start with. If not specified (None), the FuncGraph will read (but not write to) the outer graph's collections that are not whitelisted, and both read and write to the outer graph's collections that are whitelisted. The current whitelisted collections are the global variables, the local variables, and the trainable variables. Defaults to None. capture_by_value: An optional boolean. If True, the func graph will capture Variables by value instead of reference. By default inherit from outer graphs, and failing that will default to False. override_flat_arg_shapes: An optional list of instances that are either `None` or `TensorShape`. The length must match that of `nest.flatten((args, kwargs), expand_composites=True)`. The entries containing value `None` must match entries in flattened arguments containing non-tensors, while entries containing a `TensorShape` must match entries in the flattened arguments containing tensors. Returns: A FuncGraph. Raises: TypeError: If any of `python_func`'s return values is neither `None` nor a `Tensor`. ValueError: If both `signature` and `override_flat_arg_shapes` are passed in. """ if op_return_value is not None: assert isinstance(op_return_value, ops.Tensor), op_return_value if func_graph is None: func_graph = FuncGraph(name, collections=collections, capture_by_value=capture_by_value) assert isinstance(func_graph, FuncGraph) if add_control_dependencies: control_manager = AutomaticControlDependencies() else: control_manager = ops.NullContextmanager() with func_graph.as_default(), control_manager as a: current_scope = variable_scope.get_variable_scope() default_use_recource = current_scope.use_resource current_scope.set_use_resource(True) if signature is not None and override_flat_arg_shapes is not None: raise ValueError( "Passed both signature and override_flat_arg_shapes: %s and %s." % (signature, override_flat_arg_shapes)) if signature is not None: args = signature kwargs = {} # Creates and names placeholders for all arguments. if override_flat_arg_shapes is not None: flat_args = nest.flatten(args, expand_composites=True) arg_shapes = override_flat_arg_shapes[:len(flat_args)] kwarg_shapes = override_flat_arg_shapes[len(flat_args):] else: arg_shapes = None kwarg_shapes = None func_args = _get_defun_inputs_from_args( args, arg_names, flat_shapes=arg_shapes) func_kwargs = _get_defun_inputs_from_kwargs( kwargs, flat_shapes=kwarg_shapes) # Convert all Tensors into TensorSpecs before saving the structured inputs. # If storing pure concrete functions that are not called through polymorphic # functions, we don't have access to FunctionSpec, so we need to call the # TensorSpecs by their `arg_names` for later binding. func_graph.structured_input_signature = ( convert_structure_to_signature(func_args, arg_names), convert_structure_to_signature(func_kwargs)) flat_func_args = nest.flatten(func_args, expand_composites=True) flat_func_kwargs = nest.flatten(func_kwargs, expand_composites=True) # Temporarily set inputs to allow graph building code to inspect # them. Reassigned below. func_graph.inputs = [arg for arg in flat_func_args + flat_func_kwargs if isinstance(arg, ops.Tensor)] # Note: `nest.flatten` sorts by keys, as does `_deterministic_dict_values`. # Variables to help check whether mutation happens in calling the function # Copy the recursive list, tuple and map structure, but not base objects func_args_before = nest.pack_sequence_as(func_args, flat_func_args, expand_composites=True) func_kwargs_before = nest.pack_sequence_as( func_kwargs, flat_func_kwargs, expand_composites=True) def convert(x): """Converts a function output to a Tensor.""" if x is None: return None if op_return_value is not None and isinstance(x, ops.Operation): # TODO(b/79881896): we currently can't capture external control deps, so # this won't work if x needs to be captured (i.e. if python_func returns # captured Operations). with ops.control_dependencies([x]): x = array_ops.identity(op_return_value) elif not isinstance(x, tensor_array_ops.TensorArray): try: x = ops.convert_to_tensor_or_composite(x) except (ValueError, TypeError): raise TypeError( "To be compatible with tf.contrib.eager.defun, Python functions " "must return zero or more Tensors; in compilation of %s, found " "return value of type %s, which is not a Tensor." % (str(python_func), type(x))) if add_control_dependencies: x = a.mark_as_return(x) return x try: if autograph: from tensorflow.python import autograph # pylint: disable=g-import-not-at-top _, original_func = tf_decorator.unwrap(python_func) def wrapper(*args, **kwargs): # Note: functions annotated with @tf.function should always be # converted even though they would meet autograph's whitelisting # criteria. # If this assumption is ever broken, converted_call will need to # handle the possibility of original_func still being a shim, e.g. # bound to WeakrefSelf. return autograph.converted_call( original_func, None, autograph.ConversionOptions( recursive=True, optional_features=autograph_options, force_conversion=True, ), args, kwargs) # Wrapping around a decorator allows checks like tf_inspect.getargspec # to be accurate. converted_func = tf_decorator.make_decorator(original_func, wrapper) python_func = tf_decorator.rewrap(python_func, original_func, converted_func) func_outputs = python_func(*func_args, **func_kwargs) # invariant: `func_outputs` contains only Tensors, CompositeTensors, # TensorArrays and `None`s. func_outputs = nest.map_structure(convert, func_outputs, expand_composites=True) check_mutation(func_args_before, func_args) check_mutation(func_kwargs_before, func_kwargs) finally: current_scope.set_use_resource(default_use_recource) # Variables in `func_args`, `func_kwargs` should be explicit inputs # to the function, not captured inputs. graph_variables = list(func_graph._watched_variables) # pylint: disable=protected-access arg_variables = set() inputs = [] for arg in (nest.flatten(func_args, expand_composites=True) + nest.flatten(func_kwargs, expand_composites=True)): if isinstance(arg, resource_variable_ops.ResourceVariable): # Even if an argument variable was not used in the function, we've # already manually captured the resource Tensor when creating argument # placeholders. resource_placeholder = func_graph.captures.pop(arg.handle, None) if resource_placeholder is None: continue arg_variables.add(arg) inputs.append(resource_placeholder) elif isinstance(arg, ops.Tensor): inputs.append(arg) variables = [v for v in graph_variables if v not in arg_variables] func_graph.inputs = inputs + list(func_graph.captures.values()) func_graph.structured_outputs = func_outputs # Returning a closed-over tensor does not trigger convert_to_tensor. func_graph.outputs.extend( func_graph.capture(x) for x in flatten(func_graph.structured_outputs) if x is not None) func_graph.variables = variables if add_control_dependencies: func_graph.control_outputs.extend(control_manager.ops_which_must_run) # Register any other functions defined in the graph. with ops.init_scope(): if context.executing_eagerly(): for f in func_graph._functions.values(): # pylint: disable=protected-access # TODO(ashankar): What about the gradient registry? context.add_function(f._c_func.func) # pylint: disable=protected-access return func_graph def maybe_captured(tensor): """If t is a captured value placeholder, returns the original captured value. Args: tensor: Tensor. Returns: A tensor, potentially from a different Graph/FuncGraph. """ if (not isinstance(tensor, ops.EagerTensor) and tensor.op.graph.building_function and tensor.op.type == "Placeholder"): for input_t, placeholder_t in tensor.op.graph.captures.items(): if tensor == placeholder_t: return maybe_captured(input_t) # pylint: enable=protected-access return tensor def device_stack_has_callable(device_stack): """Checks whether a device stack contains a callable.""" return any(callable(spec._device_name_or_function) # pylint: disable=protected-access for spec in device_stack.peek_objs()) def check_mutation(n1, n2): """Check if two list of arguments are exactly the same.""" errmsg = ("Function to be traced should not modify structure of input " "arguments. Check if your function has list and dictionary " "operations that alter input arguments, " "such as `list.pop`, `list.append`") try: nest.assert_same_structure(n1, n2, expand_composites=True) except ValueError: raise ValueError(errmsg) for arg1, arg2 in zip(nest.flatten(n1, expand_composites=True), nest.flatten(n2, expand_composites=True)): if arg1 is not arg2: raise ValueError(errmsg) # TODO(edloper): If TensorArray becomes a CompositeTensor, then delete this. def flatten(sequence): """Like nest.flatten w/ expand_composites, but returns flow for TensorArrays. Args: sequence: A nested structure of Tensors, CompositeTensors, and TensorArrays. Returns: A list of tensors. """ flat_sequence = nest.flatten(sequence, expand_composites=True) return [ item.flow if isinstance(item, tensor_array_ops.TensorArray) else item for item in flat_sequence] # TODO(edloper): If TensorArray becomes a CompositeTensor, then delete this. def pack_sequence_as(structure, flat_sequence): """Like `nest.pack_sequence_as` but also builds TensorArrays from flows. Args: structure: The structure to pack into. May contain Tensors, CompositeTensors, or TensorArrays. flat_sequence: An iterable containing tensors. Returns: A nested structure. Raises: AssertionError if `structure` and `flat_sequence` are not compatible. """ flat_sequence = list(flat_sequence) flattened_structure = nest.flatten(structure, expand_composites=True) if len(flattened_structure) != len(flat_sequence): raise ValueError("Mismatch in element count") for i in range(len(flat_sequence)): if isinstance(flattened_structure[i], tensor_array_ops.TensorArray): flat_sequence[i] = tensor_array_ops.build_ta_with_new_flow( old_ta=flattened_structure[i], flow=flat_sequence[i]) return nest.pack_sequence_as(structure, flat_sequence, expand_composites=True) def _create_substitute_placeholder(value, name=None, dtype=None): """Creates a placeholder for `value` and propagates shape info to it.""" # Note: setting ops.control_dependencies(None) ensures we always put # capturing placeholders outside of any control flow context. with ops.control_dependencies(None): placeholder = graph_placeholder( dtype=dtype or value.dtype, shape=value.shape, name=name) custom_gradient.copy_handle_data(value, placeholder) return placeholder def _get_defun_inputs_from_args(args, names, flat_shapes=None): """Maps Python function positional args to graph-construction inputs.""" return _get_defun_inputs( args, names, structure=args, flat_shapes=flat_shapes) def _get_defun_inputs(args, names, structure, flat_shapes=None): """Maps python function args to graph-construction inputs. Args: args: A flat list of user-specified arguments. names: A list of strings with user-specified argument names, same length as `args`. May be `None`, in which case a generic name is used. structure: The original argument list or dictionary. flat_shapes: A flat list of values that are either `None` or instances of `TensorShape`. If provided, then length must match that of `nest.flatten(args, expand_composites=True)`; and locations where `args` are instances of `Tensor` must have a corresponding `TensorShape` in `flat_shapes`. May be `None`, in which case exact shapes are read directly from the args. Returns: Placeholders with the same structure as `structure`. Raises: RuntimeError: if `flat_shapes` is provided, but `len(flat_shapes) != len(nest.flatten(args, expand_composites=True))`. RuntimeError: if a shape from `flat_shapes` is not None for an argument that is not a `Tensor`, `TensorSpec`, or `ResourceVariable`. """ func_graph = ops.get_default_graph() function_inputs = [] if names is None: names = [None] * len(args) if flat_shapes is None: shapes_iter = itertools.repeat(None) else: len_flat_args = len(nest.flatten(args, expand_composites=True)) if len_flat_args != len(flat_shapes): raise RuntimeError( "Length of fully flat shapes (%d) must match that of " "flatten(args) (%d). args: %s, flat_shapes: %s" % (len(flat_shapes), len_flat_args, args, flat_shapes)) shapes_iter = iter(flat_shapes) for arg_value, name in zip(args, names): flattened = nest.flatten(arg_value, expand_composites=True) tensor_specs = [ arg for arg in flattened if isinstance(arg, tensor_spec.TensorSpec) ] specified_names = [arg.name for arg in tensor_specs if arg.name] if specified_names and len(specified_names) < len(tensor_specs): raise ValueError("If specifying TensorSpec names for nested structures, " "either zero or all names have to be specified.") for arg in flattened: # We have a shape entry for each arg, regadless of whether it's a real # Tensor or not. For non-tensor entries it should be None. shape = next(shapes_iter) if isinstance(arg, (ops.Tensor, tensor_spec.TensorSpec)): if isinstance(arg, tensor_spec.TensorSpec) and arg.name: requested_name = arg.name else: requested_name = name placeholder_shape = shape if shape is not None else arg.shape try: placeholder = graph_placeholder( arg.dtype, placeholder_shape, name=requested_name) except ValueError: # Sometimes parameter names are not valid op names, so fall back to # unnamed placeholders. placeholder = graph_placeholder(arg.dtype, placeholder_shape) if name is not None: # Record the requested/user-specified name in case it's different than # the uniquified name, for validation when exporting signatures. placeholder.op._set_attr( # pylint: disable=protected-access "_user_specified_name", attr_value_pb2.AttrValue(s=compat.as_bytes(requested_name))) function_inputs.append(placeholder) elif isinstance(arg, resource_variable_ops.ResourceVariable): # Capture arg variables to create placeholders for them. These will be # removed as captures after the function is traced (since otherwise we'd # just add it back with a new placeholder when the variable was # referenced). placeholder = func_graph.capture(arg.handle, name=name) placeholder.op._set_attr( # pylint: disable=protected-access "_user_specified_name", attr_value_pb2.AttrValue(s=compat.as_bytes(name))) function_inputs.append(arg) else: if shape is not None: raise RuntimeError( "Expected provided shape override to be None for arg that isn't " "a Tensor, but saw arg: '%s', shape: '%s'. args: %s" % (arg, shape, args)) function_inputs.append(arg) return nest.pack_sequence_as(structure, function_inputs, expand_composites=True) def _get_defun_inputs_from_kwargs(kwargs, flat_shapes): """Maps Python function keyword args to graph-construction inputs.""" if kwargs: names, args = zip(*sorted(kwargs.items())) else: names = [] args = [] return _get_defun_inputs( args, names, structure=kwargs, flat_shapes=flat_shapes) def dismantle_func_graph(func_graph): """Removes reference cycles in `func_graph` FuncGraph. Helpful for making sure the garbage collector doesn't need to run when the FuncGraph goes out of scope, e.g. in tests using defun with @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True). Args: func_graph: A `FuncGraph` object to destroy. `func_graph` is unusable after this function. """ # TODO(b/115366440): Delete this method when a custom OrderedDict is added. # Clearing captures using clear() leaves some cycles around. while func_graph.captures: func_graph.captures.popitem() memory.dismantle_ordered_dict(func_graph.captures) ops.dismantle_graph(func_graph)

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections as py_collections import itertools import weakref from tensorflow.core.framework import attr_value_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import execute from tensorflow.python.eager import tape from tensorflow.python.eager.graph_only_ops import graph_placeholder from tensorflow.python.framework import composite_tensor from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_spec from tensorflow.python.framework.auto_control_deps import AutomaticControlDependencies from tensorflow.python.ops import array_ops from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope from tensorflow.python.util import compat from tensorflow.python.util import memory from tensorflow.python.util import nest from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util.lazy_loader import LazyLoader function = LazyLoader("function", globals(), "tensorflow.python.eager.function") def_function = LazyLoader( "def_function", globals(), "tensorflow.python.eager.def_function") WHITELIST_COLLECTIONS = [ ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.TRAINABLE_VARIABLES, variable_scope._VARSTORE_KEY, variable_scope._VARSCOPESTORE_KEY ] class UnknownArgument(object): pass def convert_structure_to_signature(structure, arg_names=None): structure = composite_tensor.replace_composites_with_components(structure) def encode_arg(arg, path): if isinstance(arg, ops.Tensor): user_specified_name = None try: user_specified_name = compat.as_str( arg.op.get_attr("_user_specified_name")) except ValueError: pass if path and user_specified_name and user_specified_name != path[0]: name = user_specified_name else: name = "/".join([str(p) for p in path]) return tensor_spec.TensorSpec(arg.shape, arg.dtype, name) if isinstance(arg, ( int, float, bool, type(None), dtypes.DType, tensor_spec.TensorSpec, )): return arg return UnknownArgument() flattened = nest.flatten_with_tuple_paths(structure, expand_composites=True) if arg_names: if len(arg_names) != len(structure): raise ValueError( "Passed in arg_names don't match actual signature (%s)." % arg_names) # Replace all top-level names with their actual arg_names. If a path before # was "(2,'a',1)", it will become "(arg_names[2],'a',1)". flattened = [ ((arg_names[path[0]],) + path[1:], arg) for path, arg in flattened ] mapped = [encode_arg(arg, path) for path, arg in flattened] return nest.pack_sequence_as(structure, mapped, expand_composites=True) class FuncGraph(ops.Graph): def __init__(self, name, collections=None, capture_by_value=None): super(FuncGraph, self).__init__() self.name = name self.inputs = [] self.outputs = [] self.control_outputs = [] self.control_captures = set() self.structured_input_signature = None self.structured_outputs = None self._weak_variables = [] self._watched_variables = weakref.WeakSet() self.outer_graph = ops.get_default_graph() self.captures = py_collections.OrderedDict() # Inherit capture-by-value from outer graph. if capture_by_value is not None: self.capture_by_value = capture_by_value elif self.outer_graph is not None and isinstance( self.outer_graph, FuncGraph): self.capture_by_value = self.outer_graph.capture_by_value else: self.capture_by_value = False self._building_function = True # Map from resource tensor name to last op (in program order) which uses # this tensor. Used to enforce that execution order matches program order # for resource tensors. self._last_op_using_resource_tensor = {} graph = self.outer_graph if context.executing_eagerly(): self.seed = context.global_seed() # [for tf-data user migration from TF1.0 to 2.0] seed_used keep track of # any None op_seed for random_op in the function, in which case we end up # using function seed, which could be unintended behavior for the op. self._seed_used = False else: self.seed = graph.seed self._seed_used = False # TODO(allenl): Figure out if we can remove colocation stack # specialization (currently used in cond_v2), here and in the cache key. self._colocation_stack = graph._colocation_stack.copy() # pylint: disable=protected-access if collections is None: for collection_name in graph.get_all_collection_keys(): if collection_name not in WHITELIST_COLLECTIONS: self._collections[collection_name] = graph.get_collection( collection_name) for collection_name in WHITELIST_COLLECTIONS: self._collections[collection_name] = graph.get_collection_ref( collection_name) else: self._collections = collections def __str__(self): return "FuncGraph(name=%s, id=%s)" % (self.name, id(self)) def watch_variable(self, v): while self is not None and isinstance(self, FuncGraph): self._watched_variables.add(v) self = self.outer_graph def control_dependencies(self, control_inputs): if control_inputs is None: return super(FuncGraph, self).control_dependencies(control_inputs) filtered_control_inputs = [] for c in control_inputs: # Check for _UnreadVariable if (isinstance(c, ops.IndexedSlices) or (hasattr(c, "_handle") and hasattr(c, "op"))): c = c.op graph_element = ops._as_graph_element(c) # pylint: disable=protected-access if graph_element is None: graph_element = c if graph_element is not None and getattr( graph_element, "graph", None) is not self: self.control_captures.add(graph_element) else: filtered_control_inputs.append(graph_element) return super(FuncGraph, self).control_dependencies(filtered_control_inputs) def as_default(self): outer_cm = super(FuncGraph, self).as_default() @tf_contextlib.contextmanager def inner_cm(): graph = ops.get_default_graph() # pylint: disable=protected-access # TODO(b/112906995, nareshmodi): distribution strategy depends on # inheriting this stack from the default graph even in eager mode. Maybe # it should be part of the eager context? This would also allow us to # remove a get_default_graph() call from the function cache lookup. old_strategy_stack = self._distribution_strategy_stack self._distribution_strategy_stack = list( graph._distribution_strategy_stack) # We ignore device placements from any outer scopes while tracing the # function when possible, to avoid hard-coding them in the function # graph. "Default" placements come from the PartitionedCallOp's placement, old_device_stack = self._device_function_stack if context.executing_eagerly(): if self._distribution_strategy_stack: self._add_device_to_stack(context.context().device_name) else: if (self._distribution_strategy_stack or device_stack_has_callable(graph._device_function_stack)): self._device_function_stack = graph._device_function_stack.copy() old_creator_stack = self._variable_creator_stack self._variable_creator_stack = graph._variable_creator_stack old_graph_key = self._graph_key self._graph_key = graph._graph_key with outer_cm as g: try: yield g finally: self._distribution_strategy_stack = old_strategy_stack self._device_function_stack = old_device_stack self._variable_creator_stack = old_creator_stack self._graph_key = old_graph_key return inner_cm() @property def output_types(self): return [t.dtype for t in self.outputs] @property def output_shapes(self): return [t.shape for t in self.outputs] @property def variables(self): for weak_v in self._weak_variables: v = weak_v() if v is None: raise AssertionError( "Called a function referencing variables which have been deleted. " "This likely means that function-local variables were created and " "not referenced elsewhere in the program. This is generally a " "mistake; consider storing variables in an object attribute on " "first call.") yield v @variables.setter def variables(self, var_list): self._weak_variables = [weakref.ref(v) for v in var_list] def _capture_by_value( self, op_type, inputs, dtypes, input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True): reverse_captures = dict((v, k) for k, v in self.captures.items()) uncaptured_inputs = [reverse_captures.get(t, t) for t in inputs] with ops.init_scope(): if context.executing_eagerly(): attr_list = ("dtype", int(attrs["dtype"].type)) value, = execute.execute( compat.as_bytes(op_type), 1, uncaptured_inputs, attr_list, context.context()) else: op = ops.get_default_graph().create_op( op_type, uncaptured_inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device) value = op.outputs[0] captured_value = self.capture(value) return captured_value.op def create_op( self, op_type, inputs, dtypes=None, input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True): if self.capture_by_value and op_type in ["ReadVariableOp", "ResourceGather"]: return self._capture_by_value( op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device) if op_type == "Enter" and inputs[0].op.type == "Enter": if inputs[0].op.get_attr("frame_name") == attrs["frame_name"].s: return inputs[0].op ctxt = ops.get_default_graph()._control_flow_context for i, inp in enumerate(inputs): if ctxt is not None and hasattr(ctxt, "AddValue"): inp = ctxt.AddValue(inp) inp = self.capture(inp) inputs[i] = inp return super(FuncGraph, self).create_op( op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device=compute_device) def capture(self, tensor, name=None): if (getattr(tensor, "graph", None) is not self and hasattr(self, "_forward_func_graph") and isinstance(self._forward_func_graph, FuncGraph)): tensor = self._forward_func_graph.capture(tensor) if isinstance(tensor, ops.EagerTensor): if name is None: name = str(ops.uid()) return self._capture_helper(tensor, name) if tensor.graph is not self: if name is None: name = tensor.op.name inner_graph = tensor.graph while inner_graph is not None and isinstance(inner_graph, FuncGraph): if inner_graph is self: raise ValueError( "Trying to capture a tensor from an inner function. This can be " "caused by accessing a tensor defined inside a loop or " "conditional body, or a subfunction, from a calling function, " "without going through the proper return value mechanism. " "Consider using TensorFlow mechanisms such as TensorArrays " "to return tensors from inner functions or loop / conditional " "bodies. Tensor: %s; tensor graph: %s; this graph: %s" % (tensor, tensor.graph, self)) inner_graph = inner_graph.outer_graph return self._capture_helper(tensor, name) return tensor def _capture_helper(self, tensor, name): captured_tensor = self.captures.get(tensor, None) if captured_tensor is None: captured_tensor = _create_substitute_placeholder(tensor, name=name, dtype=tensor.dtype) self.captures[tensor] = captured_tensor self.inputs.append(captured_tensor) tape.record_operation("captured_value", [captured_tensor], [tensor], lambda x: [x]) return captured_tensor @property def external_captures(self): return list(self.captures.keys()) @property def internal_captures(self): return list(self.captures.values()) def func_graph_from_py_func(name, python_func, args, kwargs, signature=None, func_graph=None, autograph=False, autograph_options=None, add_control_dependencies=True, arg_names=None, op_return_value=None, collections=None, capture_by_value=None, override_flat_arg_shapes=None): if op_return_value is not None: assert isinstance(op_return_value, ops.Tensor), op_return_value if func_graph is None: func_graph = FuncGraph(name, collections=collections, capture_by_value=capture_by_value) assert isinstance(func_graph, FuncGraph) if add_control_dependencies: control_manager = AutomaticControlDependencies() else: control_manager = ops.NullContextmanager() with func_graph.as_default(), control_manager as a: current_scope = variable_scope.get_variable_scope() default_use_recource = current_scope.use_resource current_scope.set_use_resource(True) if signature is not None and override_flat_arg_shapes is not None: raise ValueError( "Passed both signature and override_flat_arg_shapes: %s and %s." % (signature, override_flat_arg_shapes)) if signature is not None: args = signature kwargs = {} if override_flat_arg_shapes is not None: flat_args = nest.flatten(args, expand_composites=True) arg_shapes = override_flat_arg_shapes[:len(flat_args)] kwarg_shapes = override_flat_arg_shapes[len(flat_args):] else: arg_shapes = None kwarg_shapes = None func_args = _get_defun_inputs_from_args( args, arg_names, flat_shapes=arg_shapes) func_kwargs = _get_defun_inputs_from_kwargs( kwargs, flat_shapes=kwarg_shapes) # TensorSpecs by their `arg_names` for later binding. func_graph.structured_input_signature = ( convert_structure_to_signature(func_args, arg_names), convert_structure_to_signature(func_kwargs)) flat_func_args = nest.flatten(func_args, expand_composites=True) flat_func_kwargs = nest.flatten(func_kwargs, expand_composites=True) # Temporarily set inputs to allow graph building code to inspect # them. Reassigned below. func_graph.inputs = [arg for arg in flat_func_args + flat_func_kwargs if isinstance(arg, ops.Tensor)] # Note: `nest.flatten` sorts by keys, as does `_deterministic_dict_values`. # Variables to help check whether mutation happens in calling the function # Copy the recursive list, tuple and map structure, but not base objects func_args_before = nest.pack_sequence_as(func_args, flat_func_args, expand_composites=True) func_kwargs_before = nest.pack_sequence_as( func_kwargs, flat_func_kwargs, expand_composites=True) def convert(x): if x is None: return None if op_return_value is not None and isinstance(x, ops.Operation): # TODO(b/79881896): we currently can't capture external control deps, so # captured Operations). with ops.control_dependencies([x]): x = array_ops.identity(op_return_value) elif not isinstance(x, tensor_array_ops.TensorArray): try: x = ops.convert_to_tensor_or_composite(x) except (ValueError, TypeError): raise TypeError( "To be compatible with tf.contrib.eager.defun, Python functions " "must return zero or more Tensors; in compilation of %s, found " "return value of type %s, which is not a Tensor." % (str(python_func), type(x))) if add_control_dependencies: x = a.mark_as_return(x) return x try: if autograph: from tensorflow.python import autograph # pylint: disable=g-import-not-at-top _, original_func = tf_decorator.unwrap(python_func) def wrapper(*args, **kwargs): # Note: functions annotated with @tf.function should always be # converted even though they would meet autograph's whitelisting return autograph.converted_call( original_func, None, autograph.ConversionOptions( recursive=True, optional_features=autograph_options, force_conversion=True, ), args, kwargs) converted_func = tf_decorator.make_decorator(original_func, wrapper) python_func = tf_decorator.rewrap(python_func, original_func, converted_func) func_outputs = python_func(*func_args, **func_kwargs) func_outputs = nest.map_structure(convert, func_outputs, expand_composites=True) check_mutation(func_args_before, func_args) check_mutation(func_kwargs_before, func_kwargs) finally: current_scope.set_use_resource(default_use_recource) graph_variables = list(func_graph._watched_variables) arg_variables = set() inputs = [] for arg in (nest.flatten(func_args, expand_composites=True) + nest.flatten(func_kwargs, expand_composites=True)): if isinstance(arg, resource_variable_ops.ResourceVariable): # already manually captured the resource Tensor when creating argument # placeholders. resource_placeholder = func_graph.captures.pop(arg.handle, None) if resource_placeholder is None: continue arg_variables.add(arg) inputs.append(resource_placeholder) elif isinstance(arg, ops.Tensor): inputs.append(arg) variables = [v for v in graph_variables if v not in arg_variables] func_graph.inputs = inputs + list(func_graph.captures.values()) func_graph.structured_outputs = func_outputs # Returning a closed-over tensor does not trigger convert_to_tensor. func_graph.outputs.extend( func_graph.capture(x) for x in flatten(func_graph.structured_outputs) if x is not None) func_graph.variables = variables if add_control_dependencies: func_graph.control_outputs.extend(control_manager.ops_which_must_run) # Register any other functions defined in the graph. with ops.init_scope(): if context.executing_eagerly(): for f in func_graph._functions.values(): # pylint: disable=protected-access # TODO(ashankar): What about the gradient registry? context.add_function(f._c_func.func) # pylint: disable=protected-access return func_graph def maybe_captured(tensor): if (not isinstance(tensor, ops.EagerTensor) and tensor.op.graph.building_function and tensor.op.type == "Placeholder"): for input_t, placeholder_t in tensor.op.graph.captures.items(): if tensor == placeholder_t: return maybe_captured(input_t) # pylint: enable=protected-access return tensor def device_stack_has_callable(device_stack): return any(callable(spec._device_name_or_function) # pylint: disable=protected-access for spec in device_stack.peek_objs()) def check_mutation(n1, n2): errmsg = ("Function to be traced should not modify structure of input " "arguments. Check if your function has list and dictionary " "operations that alter input arguments, " "such as `list.pop`, `list.append`") try: nest.assert_same_structure(n1, n2, expand_composites=True) except ValueError: raise ValueError(errmsg) for arg1, arg2 in zip(nest.flatten(n1, expand_composites=True), nest.flatten(n2, expand_composites=True)): if arg1 is not arg2: raise ValueError(errmsg) # TODO(edloper): If TensorArray becomes a CompositeTensor, then delete this. def flatten(sequence): flat_sequence = nest.flatten(sequence, expand_composites=True) return [ item.flow if isinstance(item, tensor_array_ops.TensorArray) else item for item in flat_sequence] # TODO(edloper): If TensorArray becomes a CompositeTensor, then delete this. def pack_sequence_as(structure, flat_sequence): flat_sequence = list(flat_sequence) flattened_structure = nest.flatten(structure, expand_composites=True) if len(flattened_structure) != len(flat_sequence): raise ValueError("Mismatch in element count") for i in range(len(flat_sequence)): if isinstance(flattened_structure[i], tensor_array_ops.TensorArray): flat_sequence[i] = tensor_array_ops.build_ta_with_new_flow( old_ta=flattened_structure[i], flow=flat_sequence[i]) return nest.pack_sequence_as(structure, flat_sequence, expand_composites=True) def _create_substitute_placeholder(value, name=None, dtype=None): # Note: setting ops.control_dependencies(None) ensures we always put # capturing placeholders outside of any control flow context. with ops.control_dependencies(None): placeholder = graph_placeholder( dtype=dtype or value.dtype, shape=value.shape, name=name) custom_gradient.copy_handle_data(value, placeholder) return placeholder def _get_defun_inputs_from_args(args, names, flat_shapes=None): return _get_defun_inputs( args, names, structure=args, flat_shapes=flat_shapes) def _get_defun_inputs(args, names, structure, flat_shapes=None): func_graph = ops.get_default_graph() function_inputs = [] if names is None: names = [None] * len(args) if flat_shapes is None: shapes_iter = itertools.repeat(None) else: len_flat_args = len(nest.flatten(args, expand_composites=True)) if len_flat_args != len(flat_shapes): raise RuntimeError( "Length of fully flat shapes (%d) must match that of " "flatten(args) (%d). args: %s, flat_shapes: %s" % (len(flat_shapes), len_flat_args, args, flat_shapes)) shapes_iter = iter(flat_shapes) for arg_value, name in zip(args, names): flattened = nest.flatten(arg_value, expand_composites=True) tensor_specs = [ arg for arg in flattened if isinstance(arg, tensor_spec.TensorSpec) ] specified_names = [arg.name for arg in tensor_specs if arg.name] if specified_names and len(specified_names) < len(tensor_specs): raise ValueError("If specifying TensorSpec names for nested structures, " "either zero or all names have to be specified.") for arg in flattened: # We have a shape entry for each arg, regadless of whether it's a real shape = next(shapes_iter) if isinstance(arg, (ops.Tensor, tensor_spec.TensorSpec)): if isinstance(arg, tensor_spec.TensorSpec) and arg.name: requested_name = arg.name else: requested_name = name placeholder_shape = shape if shape is not None else arg.shape try: placeholder = graph_placeholder( arg.dtype, placeholder_shape, name=requested_name) except ValueError: placeholder = graph_placeholder(arg.dtype, placeholder_shape) if name is not None: # the uniquified name, for validation when exporting signatures. placeholder.op._set_attr( # pylint: disable=protected-access "_user_specified_name", attr_value_pb2.AttrValue(s=compat.as_bytes(requested_name))) function_inputs.append(placeholder) elif isinstance(arg, resource_variable_ops.ResourceVariable): # Capture arg variables to create placeholders for them. These will be # removed as captures after the function is traced (since otherwise we'd placeholder = func_graph.capture(arg.handle, name=name) placeholder.op._set_attr( "_user_specified_name", attr_value_pb2.AttrValue(s=compat.as_bytes(name))) function_inputs.append(arg) else: if shape is not None: raise RuntimeError( "Expected provided shape override to be None for arg that isn't " "a Tensor, but saw arg: '%s', shape: '%s'. args: %s" % (arg, shape, args)) function_inputs.append(arg) return nest.pack_sequence_as(structure, function_inputs, expand_composites=True) def _get_defun_inputs_from_kwargs(kwargs, flat_shapes): if kwargs: names, args = zip(*sorted(kwargs.items())) else: names = [] args = [] return _get_defun_inputs( args, names, structure=kwargs, flat_shapes=flat_shapes) def dismantle_func_graph(func_graph): # TODO(b/115366440): Delete this method when a custom OrderedDict is added. # Clearing captures using clear() leaves some cycles around. while func_graph.captures: func_graph.captures.popitem() memory.dismantle_ordered_dict(func_graph.captures) ops.dismantle_graph(func_graph)

true

1c478522810cfe82e7a178b902b41a16a8504685

14,006

py

Python

sdk/python/pulumi_azure_native/azurestackhci/v20210101preview/get_cluster.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/azurestackhci/v20210101preview/get_cluster.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/azurestackhci/v20210101preview/get_cluster.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs __all__ = [ 'GetClusterResult', 'AwaitableGetClusterResult', 'get_cluster', ] @pulumi.output_type class GetClusterResult: """ Cluster details. """ def __init__(__self__, aad_client_id=None, aad_tenant_id=None, billing_model=None, cloud_id=None, cloud_management_endpoint=None, created_at=None, created_by=None, created_by_type=None, id=None, last_billing_timestamp=None, last_modified_at=None, last_modified_by=None, last_modified_by_type=None, last_sync_timestamp=None, location=None, name=None, provisioning_state=None, registration_timestamp=None, reported_properties=None, status=None, tags=None, trial_days_remaining=None, type=None): if aad_client_id and not isinstance(aad_client_id, str): raise TypeError("Expected argument 'aad_client_id' to be a str") pulumi.set(__self__, "aad_client_id", aad_client_id) if aad_tenant_id and not isinstance(aad_tenant_id, str): raise TypeError("Expected argument 'aad_tenant_id' to be a str") pulumi.set(__self__, "aad_tenant_id", aad_tenant_id) if billing_model and not isinstance(billing_model, str): raise TypeError("Expected argument 'billing_model' to be a str") pulumi.set(__self__, "billing_model", billing_model) if cloud_id and not isinstance(cloud_id, str): raise TypeError("Expected argument 'cloud_id' to be a str") pulumi.set(__self__, "cloud_id", cloud_id) if cloud_management_endpoint and not isinstance(cloud_management_endpoint, str): raise TypeError("Expected argument 'cloud_management_endpoint' to be a str") pulumi.set(__self__, "cloud_management_endpoint", cloud_management_endpoint) if created_at and not isinstance(created_at, str): raise TypeError("Expected argument 'created_at' to be a str") pulumi.set(__self__, "created_at", created_at) if created_by and not isinstance(created_by, str): raise TypeError("Expected argument 'created_by' to be a str") pulumi.set(__self__, "created_by", created_by) if created_by_type and not isinstance(created_by_type, str): raise TypeError("Expected argument 'created_by_type' to be a str") pulumi.set(__self__, "created_by_type", created_by_type) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if last_billing_timestamp and not isinstance(last_billing_timestamp, str): raise TypeError("Expected argument 'last_billing_timestamp' to be a str") pulumi.set(__self__, "last_billing_timestamp", last_billing_timestamp) if last_modified_at and not isinstance(last_modified_at, str): raise TypeError("Expected argument 'last_modified_at' to be a str") pulumi.set(__self__, "last_modified_at", last_modified_at) if last_modified_by and not isinstance(last_modified_by, str): raise TypeError("Expected argument 'last_modified_by' to be a str") pulumi.set(__self__, "last_modified_by", last_modified_by) if last_modified_by_type and not isinstance(last_modified_by_type, str): raise TypeError("Expected argument 'last_modified_by_type' to be a str") pulumi.set(__self__, "last_modified_by_type", last_modified_by_type) if last_sync_timestamp and not isinstance(last_sync_timestamp, str): raise TypeError("Expected argument 'last_sync_timestamp' to be a str") pulumi.set(__self__, "last_sync_timestamp", last_sync_timestamp) if location and not isinstance(location, str): raise TypeError("Expected argument 'location' to be a str") pulumi.set(__self__, "location", location) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if registration_timestamp and not isinstance(registration_timestamp, str): raise TypeError("Expected argument 'registration_timestamp' to be a str") pulumi.set(__self__, "registration_timestamp", registration_timestamp) if reported_properties and not isinstance(reported_properties, dict): raise TypeError("Expected argument 'reported_properties' to be a dict") pulumi.set(__self__, "reported_properties", reported_properties) if status and not isinstance(status, str): raise TypeError("Expected argument 'status' to be a str") pulumi.set(__self__, "status", status) if tags and not isinstance(tags, dict): raise TypeError("Expected argument 'tags' to be a dict") pulumi.set(__self__, "tags", tags) if trial_days_remaining and not isinstance(trial_days_remaining, float): raise TypeError("Expected argument 'trial_days_remaining' to be a float") pulumi.set(__self__, "trial_days_remaining", trial_days_remaining) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) @property @pulumi.getter(name="aadClientId") def aad_client_id(self) -> str: """ App id of cluster AAD identity. """ return pulumi.get(self, "aad_client_id") @property @pulumi.getter(name="aadTenantId") def aad_tenant_id(self) -> str: """ Tenant id of cluster AAD identity. """ return pulumi.get(self, "aad_tenant_id") @property @pulumi.getter(name="billingModel") def billing_model(self) -> str: """ Type of billing applied to the resource. """ return pulumi.get(self, "billing_model") @property @pulumi.getter(name="cloudId") def cloud_id(self) -> str: """ Unique, immutable resource id. """ return pulumi.get(self, "cloud_id") @property @pulumi.getter(name="cloudManagementEndpoint") def cloud_management_endpoint(self) -> Optional[str]: """ Endpoint configured for management from the Azure portal """ return pulumi.get(self, "cloud_management_endpoint") @property @pulumi.getter(name="createdAt") def created_at(self) -> Optional[str]: """ The timestamp of resource creation (UTC). """ return pulumi.get(self, "created_at") @property @pulumi.getter(name="createdBy") def created_by(self) -> Optional[str]: """ The identity that created the resource. """ return pulumi.get(self, "created_by") @property @pulumi.getter(name="createdByType") def created_by_type(self) -> Optional[str]: """ The type of identity that created the resource. """ return pulumi.get(self, "created_by_type") @property @pulumi.getter def id(self) -> str: """ Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName} """ return pulumi.get(self, "id") @property @pulumi.getter(name="lastBillingTimestamp") def last_billing_timestamp(self) -> str: """ Most recent billing meter timestamp. """ return pulumi.get(self, "last_billing_timestamp") @property @pulumi.getter(name="lastModifiedAt") def last_modified_at(self) -> Optional[str]: """ The timestamp of resource last modification (UTC) """ return pulumi.get(self, "last_modified_at") @property @pulumi.getter(name="lastModifiedBy") def last_modified_by(self) -> Optional[str]: """ The identity that last modified the resource. """ return pulumi.get(self, "last_modified_by") @property @pulumi.getter(name="lastModifiedByType") def last_modified_by_type(self) -> Optional[str]: """ The type of identity that last modified the resource. """ return pulumi.get(self, "last_modified_by_type") @property @pulumi.getter(name="lastSyncTimestamp") def last_sync_timestamp(self) -> str: """ Most recent cluster sync timestamp. """ return pulumi.get(self, "last_sync_timestamp") @property @pulumi.getter def location(self) -> str: """ The geo-location where the resource lives """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> str: """ The name of the resource """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> str: """ Provisioning state. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="registrationTimestamp") def registration_timestamp(self) -> str: """ First cluster sync timestamp. """ return pulumi.get(self, "registration_timestamp") @property @pulumi.getter(name="reportedProperties") def reported_properties(self) -> 'outputs.ClusterReportedPropertiesResponse': """ Properties reported by cluster agent. """ return pulumi.get(self, "reported_properties") @property @pulumi.getter def status(self) -> str: """ Status of the cluster agent. """ return pulumi.get(self, "status") @property @pulumi.getter def tags(self) -> Optional[Mapping[str, str]]: """ Resource tags. """ return pulumi.get(self, "tags") @property @pulumi.getter(name="trialDaysRemaining") def trial_days_remaining(self) -> float: """ Number of days remaining in the trial period. """ return pulumi.get(self, "trial_days_remaining") @property @pulumi.getter def type(self) -> str: """ The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts" """ return pulumi.get(self, "type") class AwaitableGetClusterResult(GetClusterResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetClusterResult( aad_client_id=self.aad_client_id, aad_tenant_id=self.aad_tenant_id, billing_model=self.billing_model, cloud_id=self.cloud_id, cloud_management_endpoint=self.cloud_management_endpoint, created_at=self.created_at, created_by=self.created_by, created_by_type=self.created_by_type, id=self.id, last_billing_timestamp=self.last_billing_timestamp, last_modified_at=self.last_modified_at, last_modified_by=self.last_modified_by, last_modified_by_type=self.last_modified_by_type, last_sync_timestamp=self.last_sync_timestamp, location=self.location, name=self.name, provisioning_state=self.provisioning_state, registration_timestamp=self.registration_timestamp, reported_properties=self.reported_properties, status=self.status, tags=self.tags, trial_days_remaining=self.trial_days_remaining, type=self.type) def get_cluster(cluster_name: Optional[str] = None, resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetClusterResult: """ Cluster details. :param str cluster_name: The name of the cluster. :param str resource_group_name: The name of the resource group. The name is case insensitive. """ __args__ = dict() __args__['clusterName'] = cluster_name __args__['resourceGroupName'] = resource_group_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:azurestackhci/v20210101preview:getCluster', __args__, opts=opts, typ=GetClusterResult).value return AwaitableGetClusterResult( aad_client_id=__ret__.aad_client_id, aad_tenant_id=__ret__.aad_tenant_id, billing_model=__ret__.billing_model, cloud_id=__ret__.cloud_id, cloud_management_endpoint=__ret__.cloud_management_endpoint, created_at=__ret__.created_at, created_by=__ret__.created_by, created_by_type=__ret__.created_by_type, id=__ret__.id, last_billing_timestamp=__ret__.last_billing_timestamp, last_modified_at=__ret__.last_modified_at, last_modified_by=__ret__.last_modified_by, last_modified_by_type=__ret__.last_modified_by_type, last_sync_timestamp=__ret__.last_sync_timestamp, location=__ret__.location, name=__ret__.name, provisioning_state=__ret__.provisioning_state, registration_timestamp=__ret__.registration_timestamp, reported_properties=__ret__.reported_properties, status=__ret__.status, tags=__ret__.tags, trial_days_remaining=__ret__.trial_days_remaining, type=__ret__.type)

39.677054

496

0.667214

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs __all__ = [ 'GetClusterResult', 'AwaitableGetClusterResult', 'get_cluster', ] @pulumi.output_type class GetClusterResult: def __init__(__self__, aad_client_id=None, aad_tenant_id=None, billing_model=None, cloud_id=None, cloud_management_endpoint=None, created_at=None, created_by=None, created_by_type=None, id=None, last_billing_timestamp=None, last_modified_at=None, last_modified_by=None, last_modified_by_type=None, last_sync_timestamp=None, location=None, name=None, provisioning_state=None, registration_timestamp=None, reported_properties=None, status=None, tags=None, trial_days_remaining=None, type=None): if aad_client_id and not isinstance(aad_client_id, str): raise TypeError("Expected argument 'aad_client_id' to be a str") pulumi.set(__self__, "aad_client_id", aad_client_id) if aad_tenant_id and not isinstance(aad_tenant_id, str): raise TypeError("Expected argument 'aad_tenant_id' to be a str") pulumi.set(__self__, "aad_tenant_id", aad_tenant_id) if billing_model and not isinstance(billing_model, str): raise TypeError("Expected argument 'billing_model' to be a str") pulumi.set(__self__, "billing_model", billing_model) if cloud_id and not isinstance(cloud_id, str): raise TypeError("Expected argument 'cloud_id' to be a str") pulumi.set(__self__, "cloud_id", cloud_id) if cloud_management_endpoint and not isinstance(cloud_management_endpoint, str): raise TypeError("Expected argument 'cloud_management_endpoint' to be a str") pulumi.set(__self__, "cloud_management_endpoint", cloud_management_endpoint) if created_at and not isinstance(created_at, str): raise TypeError("Expected argument 'created_at' to be a str") pulumi.set(__self__, "created_at", created_at) if created_by and not isinstance(created_by, str): raise TypeError("Expected argument 'created_by' to be a str") pulumi.set(__self__, "created_by", created_by) if created_by_type and not isinstance(created_by_type, str): raise TypeError("Expected argument 'created_by_type' to be a str") pulumi.set(__self__, "created_by_type", created_by_type) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if last_billing_timestamp and not isinstance(last_billing_timestamp, str): raise TypeError("Expected argument 'last_billing_timestamp' to be a str") pulumi.set(__self__, "last_billing_timestamp", last_billing_timestamp) if last_modified_at and not isinstance(last_modified_at, str): raise TypeError("Expected argument 'last_modified_at' to be a str") pulumi.set(__self__, "last_modified_at", last_modified_at) if last_modified_by and not isinstance(last_modified_by, str): raise TypeError("Expected argument 'last_modified_by' to be a str") pulumi.set(__self__, "last_modified_by", last_modified_by) if last_modified_by_type and not isinstance(last_modified_by_type, str): raise TypeError("Expected argument 'last_modified_by_type' to be a str") pulumi.set(__self__, "last_modified_by_type", last_modified_by_type) if last_sync_timestamp and not isinstance(last_sync_timestamp, str): raise TypeError("Expected argument 'last_sync_timestamp' to be a str") pulumi.set(__self__, "last_sync_timestamp", last_sync_timestamp) if location and not isinstance(location, str): raise TypeError("Expected argument 'location' to be a str") pulumi.set(__self__, "location", location) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if registration_timestamp and not isinstance(registration_timestamp, str): raise TypeError("Expected argument 'registration_timestamp' to be a str") pulumi.set(__self__, "registration_timestamp", registration_timestamp) if reported_properties and not isinstance(reported_properties, dict): raise TypeError("Expected argument 'reported_properties' to be a dict") pulumi.set(__self__, "reported_properties", reported_properties) if status and not isinstance(status, str): raise TypeError("Expected argument 'status' to be a str") pulumi.set(__self__, "status", status) if tags and not isinstance(tags, dict): raise TypeError("Expected argument 'tags' to be a dict") pulumi.set(__self__, "tags", tags) if trial_days_remaining and not isinstance(trial_days_remaining, float): raise TypeError("Expected argument 'trial_days_remaining' to be a float") pulumi.set(__self__, "trial_days_remaining", trial_days_remaining) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) @property @pulumi.getter(name="aadClientId") def aad_client_id(self) -> str: return pulumi.get(self, "aad_client_id") @property @pulumi.getter(name="aadTenantId") def aad_tenant_id(self) -> str: return pulumi.get(self, "aad_tenant_id") @property @pulumi.getter(name="billingModel") def billing_model(self) -> str: return pulumi.get(self, "billing_model") @property @pulumi.getter(name="cloudId") def cloud_id(self) -> str: return pulumi.get(self, "cloud_id") @property @pulumi.getter(name="cloudManagementEndpoint") def cloud_management_endpoint(self) -> Optional[str]: return pulumi.get(self, "cloud_management_endpoint") @property @pulumi.getter(name="createdAt") def created_at(self) -> Optional[str]: return pulumi.get(self, "created_at") @property @pulumi.getter(name="createdBy") def created_by(self) -> Optional[str]: return pulumi.get(self, "created_by") @property @pulumi.getter(name="createdByType") def created_by_type(self) -> Optional[str]: return pulumi.get(self, "created_by_type") @property @pulumi.getter def id(self) -> str: return pulumi.get(self, "id") @property @pulumi.getter(name="lastBillingTimestamp") def last_billing_timestamp(self) -> str: return pulumi.get(self, "last_billing_timestamp") @property @pulumi.getter(name="lastModifiedAt") def last_modified_at(self) -> Optional[str]: return pulumi.get(self, "last_modified_at") @property @pulumi.getter(name="lastModifiedBy") def last_modified_by(self) -> Optional[str]: return pulumi.get(self, "last_modified_by") @property @pulumi.getter(name="lastModifiedByType") def last_modified_by_type(self) -> Optional[str]: return pulumi.get(self, "last_modified_by_type") @property @pulumi.getter(name="lastSyncTimestamp") def last_sync_timestamp(self) -> str: return pulumi.get(self, "last_sync_timestamp") @property @pulumi.getter def location(self) -> str: return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> str: return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> str: return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="registrationTimestamp") def registration_timestamp(self) -> str: return pulumi.get(self, "registration_timestamp") @property @pulumi.getter(name="reportedProperties") def reported_properties(self) -> 'outputs.ClusterReportedPropertiesResponse': return pulumi.get(self, "reported_properties") @property @pulumi.getter def status(self) -> str: return pulumi.get(self, "status") @property @pulumi.getter def tags(self) -> Optional[Mapping[str, str]]: return pulumi.get(self, "tags") @property @pulumi.getter(name="trialDaysRemaining") def trial_days_remaining(self) -> float: return pulumi.get(self, "trial_days_remaining") @property @pulumi.getter def type(self) -> str: return pulumi.get(self, "type") class AwaitableGetClusterResult(GetClusterResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetClusterResult( aad_client_id=self.aad_client_id, aad_tenant_id=self.aad_tenant_id, billing_model=self.billing_model, cloud_id=self.cloud_id, cloud_management_endpoint=self.cloud_management_endpoint, created_at=self.created_at, created_by=self.created_by, created_by_type=self.created_by_type, id=self.id, last_billing_timestamp=self.last_billing_timestamp, last_modified_at=self.last_modified_at, last_modified_by=self.last_modified_by, last_modified_by_type=self.last_modified_by_type, last_sync_timestamp=self.last_sync_timestamp, location=self.location, name=self.name, provisioning_state=self.provisioning_state, registration_timestamp=self.registration_timestamp, reported_properties=self.reported_properties, status=self.status, tags=self.tags, trial_days_remaining=self.trial_days_remaining, type=self.type) def get_cluster(cluster_name: Optional[str] = None, resource_group_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetClusterResult: __args__ = dict() __args__['clusterName'] = cluster_name __args__['resourceGroupName'] = resource_group_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:azurestackhci/v20210101preview:getCluster', __args__, opts=opts, typ=GetClusterResult).value return AwaitableGetClusterResult( aad_client_id=__ret__.aad_client_id, aad_tenant_id=__ret__.aad_tenant_id, billing_model=__ret__.billing_model, cloud_id=__ret__.cloud_id, cloud_management_endpoint=__ret__.cloud_management_endpoint, created_at=__ret__.created_at, created_by=__ret__.created_by, created_by_type=__ret__.created_by_type, id=__ret__.id, last_billing_timestamp=__ret__.last_billing_timestamp, last_modified_at=__ret__.last_modified_at, last_modified_by=__ret__.last_modified_by, last_modified_by_type=__ret__.last_modified_by_type, last_sync_timestamp=__ret__.last_sync_timestamp, location=__ret__.location, name=__ret__.name, provisioning_state=__ret__.provisioning_state, registration_timestamp=__ret__.registration_timestamp, reported_properties=__ret__.reported_properties, status=__ret__.status, tags=__ret__.tags, trial_days_remaining=__ret__.trial_days_remaining, type=__ret__.type)

true

1c478544308d1c24ccd1470dc7b2c5e5197b8d45

1,181

py

Python

src/main.py

ITAnalyst-JU/process-logger

a51d4604b2dc3047dec9adfec96334ff20a3782f

[ "MIT" ]

null

src/main.py

ITAnalyst-JU/process-logger

a51d4604b2dc3047dec9adfec96334ff20a3782f

[ "MIT" ]

null

src/main.py

ITAnalyst-JU/process-logger

a51d4604b2dc3047dec9adfec96334ff20a3782f

[ "MIT" ]

null

#!/bin/python import time import argparse from Invoker import Invoker def parse_cmd_name(parts): # TODO maybe for eg. 'make testall' this shoule return 'make testall' and not 'make'? assert len(parts) >= 1 assert len(parts[0]) >= 1 x = parts[0].split('/')[-1] assert ' ' not in x return x def get_time_str(): t = time.localtime() return f'{t.tm_year}.{t.tm_mon}.{t.tm_mday} {t.tm_hour}:{t.tm_min}:{t.tm_sec}' # TODO better process for deciding filename and title def main(): parser = argparse.ArgumentParser(description="Monitor command's output in real time.") parser.add_argument('cmd', type=str, nargs='+', help='command invocation to be monitored') parser.add_argument('-o', '--output', metavar='filename', dest='log_file_location', default=None, type=str, help='write the output to a given filename') args = parser.parse_args() title = parse_cmd_name(args.cmd) + ' ' + get_time_str() if args.log_file_location is None: args.log_file_location = title + '.html' Invoker(args.cmd, args.log_file_location, title) if __name__ == '__main__': main()

28.804878

111

0.647756

import time import argparse from Invoker import Invoker def parse_cmd_name(parts): assert len(parts) >= 1 assert len(parts[0]) >= 1 x = parts[0].split('/')[-1] assert ' ' not in x return x def get_time_str(): t = time.localtime() return f'{t.tm_year}.{t.tm_mon}.{t.tm_mday} {t.tm_hour}:{t.tm_min}:{t.tm_sec}' def main(): parser = argparse.ArgumentParser(description="Monitor command's output in real time.") parser.add_argument('cmd', type=str, nargs='+', help='command invocation to be monitored') parser.add_argument('-o', '--output', metavar='filename', dest='log_file_location', default=None, type=str, help='write the output to a given filename') args = parser.parse_args() title = parse_cmd_name(args.cmd) + ' ' + get_time_str() if args.log_file_location is None: args.log_file_location = title + '.html' Invoker(args.cmd, args.log_file_location, title) if __name__ == '__main__': main()

true

1c478548a8539ffc957d7a9e7b5a3ba080deb1de

1,052

py

Python

manabe/public/management/commands/fake_server.py

luoyedao/manabe

90c158bd23e956308263b542634adc97f6526276

[ "Apache-2.0" ]

16

2018-08-12T08:28:00.000Z

2022-03-15T02:13:42.000Z

manabe/public/management/commands/fake_server.py

luoyedao/manabe

90c158bd23e956308263b542634adc97f6526276

[ "Apache-2.0" ]

14

2020-02-11T23:27:29.000Z

2022-02-11T03:43:26.000Z

manabe/public/management/commands/fake_server.py

luoyedao/manabe

90c158bd23e956308263b542634adc97f6526276

[ "Apache-2.0" ]

25

2018-08-26T07:38:46.000Z

2022-03-15T02:13:45.000Z

from random import choice from django.contrib.auth.models import User from appinput.models import App from envx.models import Env from serverinput.models import Server def fake_server_data(): Server.objects.all().delete() print('delete all server data') user_set = User.objects.all() app_set = App.objects.all() env_set = Env.objects.all() for i in range(100): ip_address = salt_name = "192.168.0.{}".format(i) for j in [80, 443, 8080, 8888]: port = j name = "192.168.0.{}_{}".format(i, port) app_user = choice(['root', 'tomcat', 'javauser']) op_user = choice(user_set) app_item = choice(app_set) env_item = choice(env_set) Server.objects.create(name=name, ip_address=ip_address, port=port, salt_name=salt_name, env_name=env_item, app_name=app_item, op_user=op_user, app_user=app_user) print('create all server data')

35.066667

78

0.586502

from random import choice from django.contrib.auth.models import User from appinput.models import App from envx.models import Env from serverinput.models import Server def fake_server_data(): Server.objects.all().delete() print('delete all server data') user_set = User.objects.all() app_set = App.objects.all() env_set = Env.objects.all() for i in range(100): ip_address = salt_name = "192.168.0.{}".format(i) for j in [80, 443, 8080, 8888]: port = j name = "192.168.0.{}_{}".format(i, port) app_user = choice(['root', 'tomcat', 'javauser']) op_user = choice(user_set) app_item = choice(app_set) env_item = choice(env_set) Server.objects.create(name=name, ip_address=ip_address, port=port, salt_name=salt_name, env_name=env_item, app_name=app_item, op_user=op_user, app_user=app_user) print('create all server data')

true

1c47869bfa0f88eba2e94f57df3c36bcb2331ede

404

py

Python

server/src/prefect_server/utilities/__init__.py

louisditzel/prefect

b1a02fee623b965e756a38aa09059db780ab67eb

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

1

2020-05-10T14:32:32.000Z

server/src/prefect_server/utilities/__init__.py

louisditzel/prefect

b1a02fee623b965e756a38aa09059db780ab67eb

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

3

2022-02-14T11:25:57.000Z

2022-02-27T16:25:14.000Z

server/src/prefect_server/utilities/__init__.py

louisditzel/prefect

b1a02fee623b965e756a38aa09059db780ab67eb

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

1

2020-05-31T04:42:56.000Z

# Licensed under the Prefect Community License, available at # https://www.prefect.io/legal/prefect-community-license import prefect_server.utilities.context import prefect_server.utilities.exceptions import prefect_server.utilities.graphql import prefect_server.utilities.logging import prefect_server.utilities.names import prefect_server.utilities.tests import prefect_server.utilities.asynchronous

33.666667

60

0.868812

import prefect_server.utilities.context import prefect_server.utilities.exceptions import prefect_server.utilities.graphql import prefect_server.utilities.logging import prefect_server.utilities.names import prefect_server.utilities.tests import prefect_server.utilities.asynchronous

true

1c47883aeba99de2cb069da42b1663aff45d1bfb

11,011

py

Python

data_kits/nf_kits.py

Jarvis73/DINs

fe967115182a47b9ad1018658cd1be745831e7aa

[ "MIT" ]

null

data_kits/nf_kits.py

Jarvis73/DINs

fe967115182a47b9ad1018658cd1be745831e7aa

[ "MIT" ]

null

data_kits/nf_kits.py

Jarvis73/DINs

fe967115182a47b9ad1018658cd1be745831e7aa

[ "MIT" ]

null

# Copyright 2019-2020 Jianwei Zhang All Right 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 pickle import zlib from pathlib import Path import nibabel as nib import numpy as np import pandas as pd import scipy.ndimage as ndi import tqdm ROOT = Path(__file__).parent.parent.parent DATA_ROOT = ROOT / "data/NF" def read_nii(file_name, out_dtype=np.int16, special=False, only_header=False): nib_vol = nib.load(str(file_name)) vh = nib_vol.header if only_header: return vh affine = vh.get_best_affine() # assert len(np.where(affine[:3, :3].reshape(-1) != 0)[0]) == 3, affine trans = np.argmax(np.abs(affine[:3, :3]), axis=1) data = nib_vol.get_fdata().astype(out_dtype).transpose(*trans[::-1]) if special: data = np.flip(data, axis=2) if affine[0, trans[0]] > 0: # Increase x from Right to Left data = np.flip(data, axis=2) if affine[1, trans[1]] > 0: # Increase y from Anterior to Posterior data = np.flip(data, axis=1) if affine[2, trans[2]] < 0: # Increase z from Interior to Superior data = np.flip(data, axis=0) return vh, data def write_nii(data, header, out_path, out_dtype=np.int16, special=False, affine=None): if header is not None: affine = header.get_best_affine() # assert len(np.where(affine[:3, :3].reshape(-1) != 0)[0]) == 3, affine trans = np.argmax(np.abs(affine[:3, :3]), axis=1) trans_bk = [np.argwhere(np.array(trans[::-1]) == i)[0][0] for i in range(3)] if special: data = np.flip(data, axis=2) if affine[0, trans[0]] > 0: # Increase x from Right to Left data = np.flip(data, axis=2) if affine[1, trans[1]] > 0: # Increase y from Anterior to Posterior data = np.flip(data, axis=1) if affine[2, trans[2]] < 0: # Increase z from Interior to Superior data = np.flip(data, axis=0) out_image = np.transpose(data, trans_bk).astype(out_dtype) if header is None and affine is not None: out = nib.Nifti1Image(out_image, affine=affine) else: out = nib.Nifti1Image(out_image, affine=None, header=header) nib.save(out, str(out_path)) def load_data(logger): data_dir = DATA_ROOT / "nii_NF" path_list = list(data_dir.glob("volume*")) logger.info(f"Loading data ({len(path_list)} examples) ...") cache_path = DATA_ROOT / "cache.pkl.gz" if cache_path.exists(): logger.info(f"Loading data cache from {cache_path}") with cache_path.open("rb") as f: data = zlib.decompress(f.read()) _data_cache = pickle.loads(data) logger.info("Finished!") return _data_cache _data_cache = {} for path in tqdm.tqdm(path_list): pid = path.name.split(".")[0].split("-")[-1] header, volume = read_nii(path) la_path = path.parent / path.name.replace("volume", "segmentation") _, label = read_nii(la_path) assert volume.shape == label.shape, f"{volume.shape} vs {label.shape}" _data_cache[int(pid)] = {"im_path": path.absolute(), "la_path": la_path.absolute(), "img": volume, "lab": label.astype(np.uint8), "pos": np.stack(np.where(label > 0), axis=1), "meta": header, "lab_rng": np.unique(label)} with cache_path.open("wb") as f: logger.info(f"Saving data cache to {cache_path}") cache_s = pickle.dumps(_data_cache, pickle.HIGHEST_PROTOCOL) f.write(zlib.compress(cache_s)) logger.info("Finished!") return _data_cache def pre_filter_data(data, filter_thresh, connectivity=3, down_sampling=False): """ For object-based segmentation tasks. Pre-compute connected components and remove small objects """ _pre_filter_cache = None cache_path = DATA_ROOT / ("pre-filter.pkl.gz" if not down_sampling else "pre-filter_ds.pkl.gz") if cache_path.exists(): logger.info(f"Loading pre-filter cache from {cache_path}") with cache_path.open("rb") as f: data = zlib.decompress(f.read()) _pre_filter_cache = pickle.loads(data) logger.info("Finished!") return _pre_filter_cache _pre_filter_cache = {} for pid in data: mask = data[pid]["lab"] struct = ndi.generate_binary_structure(3, connectivity) labeled, n_obj = ndi.label(mask, struct) slices = ndi.find_objects(labeled) obj_list = [] for i, sli in enumerate(slices): patch = labeled[sli] z, y, x = np.where(patch == i + 1) if z.shape[0] < filter_thresh: patch[z, y, x] = 0 else: obj_list.append(np.stack((z, y, x), axis=1)) better_label = np.clip(labeled, 0, 1) _pre_filter_cache[pid] = {"lab": better_label, "obj_list": obj_list} with cache_path.open("wb") as f: logger.info(f"Saving pre-filter cache to {cache_path}") cache_s = pickle.dumps(_pre_filter_cache, pickle.HIGHEST_PROTOCOL) f.write(zlib.compress(cache_s)) logger.info("Finished!") return _pre_filter_cache def load_split(set_key, test_fold): if set_key in ["train", "val", "eval"]: fold_path = DATA_ROOT / "split.csv" folds = pd.read_csv(str(fold_path)).fillna(0).astype(int) val_split = folds.loc[folds.split == test_fold] if set_key != "train": return val_split train_folds = list(range(5)) train_folds.remove(test_fold) train_split = folds.loc[folds.split.isin(train_folds)] return train_split elif set_key == "test": fold_path = DATA_ROOT / "split_test.csv" folds = pd.read_csv(str(fold_path)).fillna(0).astype(int) test_split = folds.loc[folds.split == 0] return test_split elif set_key == "extra": # The dataset with 45 cases of 15 patients fold_path = DATA_ROOT / "split_extra.csv" folds = pd.read_csv(str(fold_path)).fillna(0).astype(int) test_split = folds.loc[folds.split == 0] return test_split else: raise ValueError(f"`set_key` supports [train|val|test|extra], got {set_key}") def filter_tiny_nf(mask): struct2 = ndi.generate_binary_structure(2, 1) for i in range(mask.shape[0]): res, n_obj = ndi.label(mask[i], struct2) size = np.bincount(res.flat) for j in np.where(size <= 2)[0]: mask[i][res == j] = 0 struct3 = ndi.generate_binary_structure(3, 2) res, n_obj = ndi.label(mask, struct3) size = np.bincount(res.flat) for i in np.where(size <= 5)[0]: mask[res == i] = 0 return mask def slim_labels(data, logger): slim_labels_path = DATA_ROOT / "slim_labels.pkl.gz" if slim_labels_path.exists(): logger.info(f"Loading slimmed label cache from {slim_labels_path}") with slim_labels_path.open("rb") as f: new_labels = pickle.loads(zlib.decompress(f.read())) for i in data: data[i]['slim'] = new_labels[i] logger.info("Finished!") else: new_labels = {} logger.info(f"Saving slimmed label cache to {slim_labels_path}") for i, item in data.items(): new_labels[i] = filter_tiny_nf(np.clip(item['lab'], 0, 1).copy()) data[i]['slim'] = new_labels[i] with slim_labels_path.open("wb") as f: f.write(zlib.compress(pickle.dumps(new_labels, pickle.HIGHEST_PROTOCOL))) logger.info("Finished!") return data def load_test_data_paths(): data_dir = DATA_ROOT / "test_NF" path_list = list(data_dir.glob("*img.nii.gz")) dataset = {} for path in path_list: pid = int(path.name.split("-")[0]) dataset[pid] = {"img_path": path, "lab_path": path.parent / path.name.replace("img", "mask")} return dataset extra_name_mapping = { "---Abdomen1__20080620-img.nii.gz": 0, "---Abdomen1__20101129-img.nii.gz": 1, "---Abdomen1__20130625-img.nii.gz": 2, "---Airway1__20031216-img.nii.gz": 3, "---Airway1__20041020-img.nii.gz": 4, "---Airway1__20060907-img.nii.gz": 5, "---Airway2__20080707-img.nii.gz": 6, "---Airway2__20110124-img.nii.gz": 7, "---Airway2__20130204-img.nii.gz": 8, "---Back1__20070330-img.nii.gz": 9, "---Back1__20081117-img.nii.gz": 10, "---Back1__20100323-img.nii.gz": 11, "---Brachial-plexus1__20130205-img.nii.gz": 12, "---Br-plexus1__20120223-img.nii.gz": 13, "---Br-plexus1__20120625-img.nii.gz": 14, "---Chest2__20011227-img.nii.gz": 15, "---Chest2__20050914-img.nii.gz": 16, "---Chest2__20080918-img.nii.gz": 17, "---Chest3__20081222-img.nii.gz": 18, "---Chest3__20110602-img.nii.gz": 19, "---Chest3__20131122-img.nii.gz": 20, "---Face1__20100719-img.nii.gz": 21, "---Face1__20110418-img.nii.gz": 22, "---Face1__20120924-img.nii.gz": 23, "---Leg1__20080714-img.nii.gz": 24, "---Leg1__20100726-img.nii.gz": 25, "---Leg1__20110228-img.nii.gz": 26, "---Neck1__20020726-img.nii.gz": 27, "---Neck1__20040315-img.nii.gz": 28, "---Neck1__20050527-img.nii.gz": 29, "---Orbit1__20030225-img.nii.gz": 30, "---Orbit1__20050217-img.nii.gz": 31, "---Orbit1__20061016-img.nii.gz": 32, "---Orbit2__20090403-img.nii.gz": 33, "---Orbit2__20121018-img.nii.gz": 34, "---Orbit2__20140520-img.nii.gz": 35, "---Pelvis1__20030916-img.nii.gz": 36, "---Pelvis1__20060109-img.nii.gz": 37, "---Pelvis1__20100726-img.nii.gz": 38, "---Pelvis2__20090114-img.nii.gz": 39, "---Pelvis2__20100112-img.nii.gz": 40, "---Pelvis2__20120423-img.nii.gz": 41, "---Thigh1__20071019-img.nii.gz": 42, "---Thigh1__20100712-img.nii.gz": 43, "---Thigh1__20120106-img.nii.gz": 44, } def load_extra_data_paths(): data_dir = DATA_ROOT / "NCI_NF1_InaLabeled" path_list = list(data_dir.glob("*img.nii.gz")) dataset = {} for path in path_list: pid = extra_name_mapping[path.name] dataset[pid] = {"img_path": path, "lab_path": path.parent / path.name.replace("img", "mask")} return dataset def load_box_csv(): box_file = DATA_ROOT / "nf_box.csv" box_df = pd.read_csv(box_file) return box_df

38.365854

101

0.612297

import pickle import zlib from pathlib import Path import nibabel as nib import numpy as np import pandas as pd import scipy.ndimage as ndi import tqdm ROOT = Path(__file__).parent.parent.parent DATA_ROOT = ROOT / "data/NF" def read_nii(file_name, out_dtype=np.int16, special=False, only_header=False): nib_vol = nib.load(str(file_name)) vh = nib_vol.header if only_header: return vh affine = vh.get_best_affine() trans = np.argmax(np.abs(affine[:3, :3]), axis=1) data = nib_vol.get_fdata().astype(out_dtype).transpose(*trans[::-1]) if special: data = np.flip(data, axis=2) if affine[0, trans[0]] > 0: data = np.flip(data, axis=2) if affine[1, trans[1]] > 0: data = np.flip(data, axis=1) if affine[2, trans[2]] < 0: data = np.flip(data, axis=0) return vh, data def write_nii(data, header, out_path, out_dtype=np.int16, special=False, affine=None): if header is not None: affine = header.get_best_affine() trans = np.argmax(np.abs(affine[:3, :3]), axis=1) trans_bk = [np.argwhere(np.array(trans[::-1]) == i)[0][0] for i in range(3)] if special: data = np.flip(data, axis=2) if affine[0, trans[0]] > 0: data = np.flip(data, axis=2) if affine[1, trans[1]] > 0: data = np.flip(data, axis=1) if affine[2, trans[2]] < 0: data = np.flip(data, axis=0) out_image = np.transpose(data, trans_bk).astype(out_dtype) if header is None and affine is not None: out = nib.Nifti1Image(out_image, affine=affine) else: out = nib.Nifti1Image(out_image, affine=None, header=header) nib.save(out, str(out_path)) def load_data(logger): data_dir = DATA_ROOT / "nii_NF" path_list = list(data_dir.glob("volume*")) logger.info(f"Loading data ({len(path_list)} examples) ...") cache_path = DATA_ROOT / "cache.pkl.gz" if cache_path.exists(): logger.info(f"Loading data cache from {cache_path}") with cache_path.open("rb") as f: data = zlib.decompress(f.read()) _data_cache = pickle.loads(data) logger.info("Finished!") return _data_cache _data_cache = {} for path in tqdm.tqdm(path_list): pid = path.name.split(".")[0].split("-")[-1] header, volume = read_nii(path) la_path = path.parent / path.name.replace("volume", "segmentation") _, label = read_nii(la_path) assert volume.shape == label.shape, f"{volume.shape} vs {label.shape}" _data_cache[int(pid)] = {"im_path": path.absolute(), "la_path": la_path.absolute(), "img": volume, "lab": label.astype(np.uint8), "pos": np.stack(np.where(label > 0), axis=1), "meta": header, "lab_rng": np.unique(label)} with cache_path.open("wb") as f: logger.info(f"Saving data cache to {cache_path}") cache_s = pickle.dumps(_data_cache, pickle.HIGHEST_PROTOCOL) f.write(zlib.compress(cache_s)) logger.info("Finished!") return _data_cache def pre_filter_data(data, filter_thresh, connectivity=3, down_sampling=False): _pre_filter_cache = None cache_path = DATA_ROOT / ("pre-filter.pkl.gz" if not down_sampling else "pre-filter_ds.pkl.gz") if cache_path.exists(): logger.info(f"Loading pre-filter cache from {cache_path}") with cache_path.open("rb") as f: data = zlib.decompress(f.read()) _pre_filter_cache = pickle.loads(data) logger.info("Finished!") return _pre_filter_cache _pre_filter_cache = {} for pid in data: mask = data[pid]["lab"] struct = ndi.generate_binary_structure(3, connectivity) labeled, n_obj = ndi.label(mask, struct) slices = ndi.find_objects(labeled) obj_list = [] for i, sli in enumerate(slices): patch = labeled[sli] z, y, x = np.where(patch == i + 1) if z.shape[0] < filter_thresh: patch[z, y, x] = 0 else: obj_list.append(np.stack((z, y, x), axis=1)) better_label = np.clip(labeled, 0, 1) _pre_filter_cache[pid] = {"lab": better_label, "obj_list": obj_list} with cache_path.open("wb") as f: logger.info(f"Saving pre-filter cache to {cache_path}") cache_s = pickle.dumps(_pre_filter_cache, pickle.HIGHEST_PROTOCOL) f.write(zlib.compress(cache_s)) logger.info("Finished!") return _pre_filter_cache def load_split(set_key, test_fold): if set_key in ["train", "val", "eval"]: fold_path = DATA_ROOT / "split.csv" folds = pd.read_csv(str(fold_path)).fillna(0).astype(int) val_split = folds.loc[folds.split == test_fold] if set_key != "train": return val_split train_folds = list(range(5)) train_folds.remove(test_fold) train_split = folds.loc[folds.split.isin(train_folds)] return train_split elif set_key == "test": fold_path = DATA_ROOT / "split_test.csv" folds = pd.read_csv(str(fold_path)).fillna(0).astype(int) test_split = folds.loc[folds.split == 0] return test_split elif set_key == "extra": fold_path = DATA_ROOT / "split_extra.csv" folds = pd.read_csv(str(fold_path)).fillna(0).astype(int) test_split = folds.loc[folds.split == 0] return test_split else: raise ValueError(f"`set_key` supports [train|val|test|extra], got {set_key}") def filter_tiny_nf(mask): struct2 = ndi.generate_binary_structure(2, 1) for i in range(mask.shape[0]): res, n_obj = ndi.label(mask[i], struct2) size = np.bincount(res.flat) for j in np.where(size <= 2)[0]: mask[i][res == j] = 0 struct3 = ndi.generate_binary_structure(3, 2) res, n_obj = ndi.label(mask, struct3) size = np.bincount(res.flat) for i in np.where(size <= 5)[0]: mask[res == i] = 0 return mask def slim_labels(data, logger): slim_labels_path = DATA_ROOT / "slim_labels.pkl.gz" if slim_labels_path.exists(): logger.info(f"Loading slimmed label cache from {slim_labels_path}") with slim_labels_path.open("rb") as f: new_labels = pickle.loads(zlib.decompress(f.read())) for i in data: data[i]['slim'] = new_labels[i] logger.info("Finished!") else: new_labels = {} logger.info(f"Saving slimmed label cache to {slim_labels_path}") for i, item in data.items(): new_labels[i] = filter_tiny_nf(np.clip(item['lab'], 0, 1).copy()) data[i]['slim'] = new_labels[i] with slim_labels_path.open("wb") as f: f.write(zlib.compress(pickle.dumps(new_labels, pickle.HIGHEST_PROTOCOL))) logger.info("Finished!") return data def load_test_data_paths(): data_dir = DATA_ROOT / "test_NF" path_list = list(data_dir.glob("*img.nii.gz")) dataset = {} for path in path_list: pid = int(path.name.split("-")[0]) dataset[pid] = {"img_path": path, "lab_path": path.parent / path.name.replace("img", "mask")} return dataset extra_name_mapping = { "---Abdomen1__20080620-img.nii.gz": 0, "---Abdomen1__20101129-img.nii.gz": 1, "---Abdomen1__20130625-img.nii.gz": 2, "---Airway1__20031216-img.nii.gz": 3, "---Airway1__20041020-img.nii.gz": 4, "---Airway1__20060907-img.nii.gz": 5, "---Airway2__20080707-img.nii.gz": 6, "---Airway2__20110124-img.nii.gz": 7, "---Airway2__20130204-img.nii.gz": 8, "---Back1__20070330-img.nii.gz": 9, "---Back1__20081117-img.nii.gz": 10, "---Back1__20100323-img.nii.gz": 11, "---Brachial-plexus1__20130205-img.nii.gz": 12, "---Br-plexus1__20120223-img.nii.gz": 13, "---Br-plexus1__20120625-img.nii.gz": 14, "---Chest2__20011227-img.nii.gz": 15, "---Chest2__20050914-img.nii.gz": 16, "---Chest2__20080918-img.nii.gz": 17, "---Chest3__20081222-img.nii.gz": 18, "---Chest3__20110602-img.nii.gz": 19, "---Chest3__20131122-img.nii.gz": 20, "---Face1__20100719-img.nii.gz": 21, "---Face1__20110418-img.nii.gz": 22, "---Face1__20120924-img.nii.gz": 23, "---Leg1__20080714-img.nii.gz": 24, "---Leg1__20100726-img.nii.gz": 25, "---Leg1__20110228-img.nii.gz": 26, "---Neck1__20020726-img.nii.gz": 27, "---Neck1__20040315-img.nii.gz": 28, "---Neck1__20050527-img.nii.gz": 29, "---Orbit1__20030225-img.nii.gz": 30, "---Orbit1__20050217-img.nii.gz": 31, "---Orbit1__20061016-img.nii.gz": 32, "---Orbit2__20090403-img.nii.gz": 33, "---Orbit2__20121018-img.nii.gz": 34, "---Orbit2__20140520-img.nii.gz": 35, "---Pelvis1__20030916-img.nii.gz": 36, "---Pelvis1__20060109-img.nii.gz": 37, "---Pelvis1__20100726-img.nii.gz": 38, "---Pelvis2__20090114-img.nii.gz": 39, "---Pelvis2__20100112-img.nii.gz": 40, "---Pelvis2__20120423-img.nii.gz": 41, "---Thigh1__20071019-img.nii.gz": 42, "---Thigh1__20100712-img.nii.gz": 43, "---Thigh1__20120106-img.nii.gz": 44, } def load_extra_data_paths(): data_dir = DATA_ROOT / "NCI_NF1_InaLabeled" path_list = list(data_dir.glob("*img.nii.gz")) dataset = {} for path in path_list: pid = extra_name_mapping[path.name] dataset[pid] = {"img_path": path, "lab_path": path.parent / path.name.replace("img", "mask")} return dataset def load_box_csv(): box_file = DATA_ROOT / "nf_box.csv" box_df = pd.read_csv(box_file) return box_df

true

1c4788a7fec1e92cf4988f8cd63897bc0a883269

1,288

py

Python

setup.py

AbhiProjects/TagLib

214139259157a7b3ec3f2fb7b342411a33b85839

[ "BSD-3-Clause" ]

null

setup.py

AbhiProjects/TagLib

214139259157a7b3ec3f2fb7b342411a33b85839

[ "BSD-3-Clause" ]

null

setup.py

AbhiProjects/TagLib

214139259157a7b3ec3f2fb7b342411a33b85839

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python """Setup script for taglib""" import sys if sys.hexversion < 0x02060000: print >> sys.stderr, 'Sorry, Python 2.6 is required.' sys.exit(1) from distutils.core import setup sys.dont_write_bytecode = True # don't leave turds from taglib import __version__ def main(): setup(name='taglib', author='Chris Jones', author_email='cjones@gruntle.org', url='http://code.google.com/p/python-taglib/', description='Library to manipulate audio file metadata', license='BSD', version=__version__, py_modules=['taglib'], scripts=['scripts/tagdump'], # http://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Console', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 2.6', 'Topic :: Multimedia :: Sound/Audio', 'Topic :: Software Development :: Libraries :: Python Modules']) return 0 if __name__ == '__main__': sys.exit(main())

29.953488

78

0.585404

import sys if sys.hexversion < 0x02060000: print >> sys.stderr, 'Sorry, Python 2.6 is required.' sys.exit(1) from distutils.core import setup sys.dont_write_bytecode = True from taglib import __version__ def main(): setup(name='taglib', author='Chris Jones', author_email='cjones@gruntle.org', url='http://code.google.com/p/python-taglib/', description='Library to manipulate audio file metadata', license='BSD', version=__version__, py_modules=['taglib'], scripts=['scripts/tagdump'], # http://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Console', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 2.6', 'Topic :: Multimedia :: Sound/Audio', 'Topic :: Software Development :: Libraries :: Python Modules']) return 0 if __name__ == '__main__': sys.exit(main())

true

1c478921c64292aa5b2d3adeb81064377fca26e0

1,101

py

Python

azure-mgmt-recoveryservicesbackup/azure/mgmt/recoveryservicesbackup/models/schedule_policy.py

v-Ajnava/azure-sdk-for-python

a1f6f80eb5869c5b710e8bfb66146546697e2a6f

[ "MIT" ]

4

2016-06-17T23:25:29.000Z

2022-03-30T22:37:45.000Z

azure-mgmt-recoveryservicesbackup/azure/mgmt/recoveryservicesbackup/models/schedule_policy.py

v-Ajnava/azure-sdk-for-python

a1f6f80eb5869c5b710e8bfb66146546697e2a6f

[ "MIT" ]

54

2016-03-25T17:25:01.000Z

2018-10-22T17:27:54.000Z

azure-mgmt-recoveryservicesbackup/azure/mgmt/recoveryservicesbackup/models/schedule_policy.py

v-Ajnava/azure-sdk-for-python

a1f6f80eb5869c5b710e8bfb66146546697e2a6f

[ "MIT" ]

3

2016-05-03T20:49:46.000Z

2017-10-05T21:05:27.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class SchedulePolicy(Model): """Base class for backup schedule. :param schedule_policy_type: Polymorphic Discriminator :type schedule_policy_type: str """ _validation = { 'schedule_policy_type': {'required': True}, } _attribute_map = { 'schedule_policy_type': {'key': 'schedulePolicyType', 'type': 'str'}, } _subtype_map = { 'schedule_policy_type': {'LongTermSchedulePolicy': 'LongTermSchedulePolicy', 'SimpleSchedulePolicy': 'SimpleSchedulePolicy'} } def __init__(self): self.schedule_policy_type = None

30.583333

132

0.608538

from msrest.serialization import Model class SchedulePolicy(Model): _validation = { 'schedule_policy_type': {'required': True}, } _attribute_map = { 'schedule_policy_type': {'key': 'schedulePolicyType', 'type': 'str'}, } _subtype_map = { 'schedule_policy_type': {'LongTermSchedulePolicy': 'LongTermSchedulePolicy', 'SimpleSchedulePolicy': 'SimpleSchedulePolicy'} } def __init__(self): self.schedule_policy_type = None

true

1c478b7837a4774911d31634003c88b12e9c37bc

3,439

py

Python

assets/winc_firmware_upgrade/firmware/handler_search.py

rashedtalukder/cryptoauth_trustplatform_designsuite

6b42c64071a9fb5dc9894bfedbbfabbcfb7961c1

[ "MIT" ]

11

2019-12-03T14:18:38.000Z

2021-08-25T16:41:27.000Z

assets/winc_firmware_upgrade/firmware/handler_search.py

rashedtalukder/cryptoauth_trustplatform_designsuite

6b42c64071a9fb5dc9894bfedbbfabbcfb7961c1

[ "MIT" ]

9

2020-02-13T09:07:42.000Z

2022-03-18T18:29:24.000Z

assets/winc_firmware_upgrade/firmware/handler_search.py

rashedtalukder/cryptoauth_trustplatform_designsuite

6b42c64071a9fb5dc9894bfedbbfabbcfb7961c1

[ "MIT" ]

10

2020-04-28T10:35:48.000Z

2021-11-03T23:03:30.000Z

''' Simple program to get a hint where simple programs might be installed by chasing thru registry, does not deal with things like word which are beyonf complicated. Pass in extention to check and a hint at what program you want. Returns 0 if found. 2 for parm error 1 for not found Eg C:\work_repos\>python handler_search.py cpP studio ""C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\IDE\devenv.exe" C:\work_repos\>python handler_search.py cpP atmelstudio "C:\Program Files (x86)\Atmel\Studio\7.0\atmelstudio.exe" ''' import sys import os import winreg roots_hives = { "HKEY_CLASSES_ROOT": winreg.HKEY_CLASSES_ROOT, "HKEY_CURRENT_USER": winreg.HKEY_CURRENT_USER, "HKEY_LOCAL_MACHINE": winreg.HKEY_LOCAL_MACHINE, "HKEY_USERS": winreg.HKEY_USERS, "HKEY_PERFORMANCE_DATA": winreg.HKEY_PERFORMANCE_DATA, "HKEY_CURRENT_CONFIG": winreg.HKEY_CURRENT_CONFIG, "HKEY_DYN_DATA": winreg.HKEY_DYN_DATA } def join(path, *paths): path = path.strip('/\\') paths = map(lambda x: x.strip('/\\'), paths) paths = list(paths) result = os.path.join(path, *paths) result = result.replace('/', '\\') return result def parse_key(key): key = key.upper() aparts = key.split('\\') parts = list(filter(None, aparts)) root_hive_name = parts[0] root_hive = roots_hives.get(root_hive_name) partial_key = '\\'.join(parts[1:]) if not root_hive: raise Exception('root hive "{}" was not found'.format(root_hive_name)) return partial_key, root_hive def get_all_values(key): data = {} data[0] = [[''],['']] try: partial_key, root_hive = parse_key(key) with winreg.ConnectRegistry(None, root_hive) as reg: with winreg.OpenKey(reg, partial_key) as key_object: i = 0 while True: try: ret = winreg.EnumValue(key_object, i) if ret[2] == winreg.REG_EXPAND_SZ: if ret[0] == '': data[i] = ["(Default)", expandvars(ret[1])] else: data[i] = [ret[0], expandvars(ret[1])] else: if ret[0] == '': data[i] = ["(Default)", ret[1]] else: data[i] = [ret[0], ret[1]] except WindowsError: break i += 1 key_object.Close() except: pass return data def main(argv=None): argv = sys.argv args = argv[1:] key = r'HKEY_CLASSES_ROOT\.' + args[0] + '\\OpenWithProgids' pkey = r'' data = get_all_values(key) for x in range(0, len(data)): strdatax = str(data[x][0]) if args[1].upper() in strdatax.upper(): pkey = r'HKEY_CLASSES_ROOT\\' + strdatax + '\\shell\\open\\command' break if str(data[0][1]) == '[\'\']': print ("Assoc not found") sys.exit(1) data = get_all_values(pkey) for x in range(0, len(data)): if ".EXE" in str(data[x][1]).upper(): exeind = str(data[x][1]).upper().find('.EXE') print ('"' + str(data[x][1])[:exeind+4] + '"') sys.exit(0) print ("Handler not found") sys.exit(1) if __name__ == "__main__": main()

27.95935

95

0.549578

import sys import os import winreg roots_hives = { "HKEY_CLASSES_ROOT": winreg.HKEY_CLASSES_ROOT, "HKEY_CURRENT_USER": winreg.HKEY_CURRENT_USER, "HKEY_LOCAL_MACHINE": winreg.HKEY_LOCAL_MACHINE, "HKEY_USERS": winreg.HKEY_USERS, "HKEY_PERFORMANCE_DATA": winreg.HKEY_PERFORMANCE_DATA, "HKEY_CURRENT_CONFIG": winreg.HKEY_CURRENT_CONFIG, "HKEY_DYN_DATA": winreg.HKEY_DYN_DATA } def join(path, *paths): path = path.strip('/\\') paths = map(lambda x: x.strip('/\\'), paths) paths = list(paths) result = os.path.join(path, *paths) result = result.replace('/', '\\') return result def parse_key(key): key = key.upper() aparts = key.split('\\') parts = list(filter(None, aparts)) root_hive_name = parts[0] root_hive = roots_hives.get(root_hive_name) partial_key = '\\'.join(parts[1:]) if not root_hive: raise Exception('root hive "{}" was not found'.format(root_hive_name)) return partial_key, root_hive def get_all_values(key): data = {} data[0] = [[''],['']] try: partial_key, root_hive = parse_key(key) with winreg.ConnectRegistry(None, root_hive) as reg: with winreg.OpenKey(reg, partial_key) as key_object: i = 0 while True: try: ret = winreg.EnumValue(key_object, i) if ret[2] == winreg.REG_EXPAND_SZ: if ret[0] == '': data[i] = ["(Default)", expandvars(ret[1])] else: data[i] = [ret[0], expandvars(ret[1])] else: if ret[0] == '': data[i] = ["(Default)", ret[1]] else: data[i] = [ret[0], ret[1]] except WindowsError: break i += 1 key_object.Close() except: pass return data def main(argv=None): argv = sys.argv args = argv[1:] key = r'HKEY_CLASSES_ROOT\.' + args[0] + '\\OpenWithProgids' pkey = r'' data = get_all_values(key) for x in range(0, len(data)): strdatax = str(data[x][0]) if args[1].upper() in strdatax.upper(): pkey = r'HKEY_CLASSES_ROOT\\' + strdatax + '\\shell\\open\\command' break if str(data[0][1]) == '[\'\']': print ("Assoc not found") sys.exit(1) data = get_all_values(pkey) for x in range(0, len(data)): if ".EXE" in str(data[x][1]).upper(): exeind = str(data[x][1]).upper().find('.EXE') print ('"' + str(data[x][1])[:exeind+4] + '"') sys.exit(0) print ("Handler not found") sys.exit(1) if __name__ == "__main__": main()

true

1c478b7de55a29c23c21c47bbecf9e11a14c3e20

7,684

py

Python

test/test_grapher.py

leehyoeun96/rosprofiler

c7bee4e98d8417cd3e2a8ef246b7930c97c74dc5

[ "Apache-2.0" ]

6

2017-11-18T05:59:22.000Z

2022-01-01T11:56:00.000Z

test/test_grapher.py

leehyoeun96/rosprofiler

c7bee4e98d8417cd3e2a8ef246b7930c97c74dc5

[ "Apache-2.0" ]

3

2015-04-11T20:04:24.000Z

2018-06-19T21:55:39.000Z

test/test_grapher.py

leehyoeun96/rosprofiler

c7bee4e98d8417cd3e2a8ef246b7930c97c74dc5

[ "Apache-2.0" ]

15

2017-11-19T05:03:29.000Z

2021-03-15T15:26:37.000Z

#!/usr/bin/env python # Copyright 2014 Open Source Robotics Foundation, Inc. # # 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 import unittest import time import rospy import rostest from ros_topology_msgs.msg import * PKG = 'rosprofiler' NAME = 'test_grapher' # TODO: Check services EXPECTED_NODES = dict() talker1 = Node(name="/talker1") talker1.publishes.append("/chatter") talker1.publishes.append("/rosout") talker1.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) talker1.connections.append(Connection(destination='/listener1',topic='/chatter',direction=2,transport="TCPROS")) talker1.connections.append(Connection(destination='/listener2',topic='/chatter',direction=2,transport="TCPROS")) talker2 = Node(name="/talker2") talker2.publishes.append("/chatter") talker2.publishes.append("/rosout") talker2.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) talker2.connections.append(Connection(destination='/listener1',topic='/chatter',direction=2,transport="TCPROS")) talker2.connections.append(Connection(destination='/listener2',topic='/chatter',direction=2,transport="TCPROS")) listener1 = Node(name="/listener1") listener1.publishes.append("/rosout") listener1.subscribes.append("/chatter") listener1.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) listener1.connections.append(Connection(destination='/talker1',topic='/chatter',direction=1,transport="TCPROS")) listener1.connections.append(Connection(destination='/talker2',topic='/chatter',direction=1,transport="TCPROS")) listener2 = Node(name="/listener2") listener2.publishes.append("/rosout") listener2.subscribes.append("/chatter") listener2.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) listener2.connections.append(Connection(destination='/talker1',topic='/chatter',direction=1,transport="TCPROS")) listener2.connections.append(Connection(destination='/talker2',topic='/chatter',direction=1,transport="TCPROS")) rosout = Node(name="/rosout") rosout.publishes.append("/rosout_agg") rosout.subscribes.append("/rosout") rosout.connections.append(Connection(destination='/talker1',topic='/rosout',direction=1,transport="TCPROS")) rosout.connections.append(Connection(destination='/talker2',topic='/rosout',direction=1,transport="TCPROS")) rosout.connections.append(Connection(destination='/listener1',topic='/rosout',direction=1,transport="TCPROS")) rosout.connections.append(Connection(destination='/listener2',topic='/rosout',direction=1,transport="TCPROS")) rosout.connections.append(Connection(destination='/test_grapher',topic='/rosout',direction=1,transport="TCPROS")) rosout.connections.append(Connection(destination='/rosgrapher',topic='/rosout',direction=1,transport="TCPROS")) grapher = Node(name="/rosgrapher") grapher.publishes.append("/rosout") grapher.publishes.append("/topology") grapher.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) grapher.connections.append(Connection(destination='/'+NAME,topic='/topology',direction=2,transport="TCPROS")) tester = Node(name="/test_grapher") tester.publishes.append("/rosout") tester.subscribes.append("/topology") tester.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) tester.connections.append(Connection(destination='/rosgrapher',topic='/topology',direction=1,transport="TCPROS")) EXPECTED_NODES['/talker1'] = talker1 EXPECTED_NODES['/talker2'] = talker2 EXPECTED_NODES['/listener1'] = listener1 EXPECTED_NODES['/listener2'] = listener2 EXPECTED_NODES['/rosout'] = rosout EXPECTED_NODES['/rosgrapher'] = grapher EXPECTED_NODES['/'+NAME] = tester t_chatter = Topic(name="/chatter", type="std_msgs/String") t_rosout = Topic(name="/rosout", type="rosgraph_msgs/Log") t_rosout_agg = Topic(name="/rosout_agg", type="rosgraph_msgs/Log") t_topology = Topic(name="/topology", type="ros_topology_msgs/Graph") EXPECTED_TOPICS = [t_chatter, t_rosout, t_rosout_agg, t_topology] class TestGrapher(unittest.TestCase): def __init__(self, *args): super(TestGrapher, self).__init__(*args) # Start time - for calculating timeout self.start_time = None self.graph = Graph() def setUp(self): rospy.init_node(NAME) rospy.Subscriber('/topology', Graph, self.callback) self.wait_for_data(10.0) def callback(self, data): self.graph = data def wait_for_data(self, duration): """ Waits to receive statistics data """ start_time = rospy.get_rostime() while not rospy.is_shutdown() and not (rospy.get_rostime() > (start_time + rospy.Duration(duration))): if len(self.graph.nodes) >= len(EXPECTED_NODES) and len(self.graph.topics) >= len(EXPECTED_TOPICS): return rospy.sleep(1.0) def test_nodes_publishers(self): for node in self.graph.nodes: assert node.name in EXPECTED_NODES, "%s not found!"%node.name testnode = EXPECTED_NODES[node.name] assert set(node.publishes) == set(testnode.publishes), "%s.publishes=%s, but should be %s"%(node.name,node.publishes,testnode.publishes) def test_nodes_subscribers(self): for node in self.graph.nodes: assert node.name in EXPECTED_NODES, "%s not found!"%node.name testnode = EXPECTED_NODES[node.name] assert set(node.subscribes) == set(testnode.subscribes), "%s.subscribes=%s, but should be %s"%(node.name,node.subscribes,testnode.subscribes) def test_nodes_connections_present(self): for node in self.graph.nodes: assert node.name in EXPECTED_NODES, "%s not found!"%node.name testnode = EXPECTED_NODES[node.name] for connection in node.connections: assert connection in testnode.connections, "Node %s has extra connection %s"%(node.name, connection) def test_nodes_connections_missing(self): for node in self.graph.nodes: assert node.name in EXPECTED_NODES, "%s not found!"%node.name testnode = EXPECTED_NODES[node.name] for connection in testnode.connections: assert connection in node.connections, "Node %s expected to find missing connection %s"%(node.name, connection) def test_nodes_present(self): for node in self.graph.nodes: assert node.name in EXPECTED_NODES.keys(), "Found extra node '%s'"%node.name def test_nodes_missing(self): for node_name in EXPECTED_NODES.keys(): assert node_name in [n.name for n in self.graph.nodes], "Expected to find missing node '%s'"%node_name def test_topics_present(self): for topic in self.graph.topics: assert topic in EXPECTED_TOPICS, "Found extra topic '%s'"%topic def test_topics_missing(self): for topic in EXPECTED_TOPICS: assert topic in self.graph.topics, "Expected to find missing topic '%s'"%topic if __name__ == '__main__': rostest.rosrun(PKG, NAME, TestGrapher, sys.argv)

49.574194

153

0.730739

import sys import unittest import time import rospy import rostest from ros_topology_msgs.msg import * PKG = 'rosprofiler' NAME = 'test_grapher' EXPECTED_NODES = dict() talker1 = Node(name="/talker1") talker1.publishes.append("/chatter") talker1.publishes.append("/rosout") talker1.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) talker1.connections.append(Connection(destination='/listener1',topic='/chatter',direction=2,transport="TCPROS")) talker1.connections.append(Connection(destination='/listener2',topic='/chatter',direction=2,transport="TCPROS")) talker2 = Node(name="/talker2") talker2.publishes.append("/chatter") talker2.publishes.append("/rosout") talker2.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) talker2.connections.append(Connection(destination='/listener1',topic='/chatter',direction=2,transport="TCPROS")) talker2.connections.append(Connection(destination='/listener2',topic='/chatter',direction=2,transport="TCPROS")) listener1 = Node(name="/listener1") listener1.publishes.append("/rosout") listener1.subscribes.append("/chatter") listener1.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) listener1.connections.append(Connection(destination='/talker1',topic='/chatter',direction=1,transport="TCPROS")) listener1.connections.append(Connection(destination='/talker2',topic='/chatter',direction=1,transport="TCPROS")) listener2 = Node(name="/listener2") listener2.publishes.append("/rosout") listener2.subscribes.append("/chatter") listener2.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) listener2.connections.append(Connection(destination='/talker1',topic='/chatter',direction=1,transport="TCPROS")) listener2.connections.append(Connection(destination='/talker2',topic='/chatter',direction=1,transport="TCPROS")) rosout = Node(name="/rosout") rosout.publishes.append("/rosout_agg") rosout.subscribes.append("/rosout") rosout.connections.append(Connection(destination='/talker1',topic='/rosout',direction=1,transport="TCPROS")) rosout.connections.append(Connection(destination='/talker2',topic='/rosout',direction=1,transport="TCPROS")) rosout.connections.append(Connection(destination='/listener1',topic='/rosout',direction=1,transport="TCPROS")) rosout.connections.append(Connection(destination='/listener2',topic='/rosout',direction=1,transport="TCPROS")) rosout.connections.append(Connection(destination='/test_grapher',topic='/rosout',direction=1,transport="TCPROS")) rosout.connections.append(Connection(destination='/rosgrapher',topic='/rosout',direction=1,transport="TCPROS")) grapher = Node(name="/rosgrapher") grapher.publishes.append("/rosout") grapher.publishes.append("/topology") grapher.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) grapher.connections.append(Connection(destination='/'+NAME,topic='/topology',direction=2,transport="TCPROS")) tester = Node(name="/test_grapher") tester.publishes.append("/rosout") tester.subscribes.append("/topology") tester.connections.append(Connection(destination='/rosout',topic='/rosout',direction=2,transport="TCPROS")) tester.connections.append(Connection(destination='/rosgrapher',topic='/topology',direction=1,transport="TCPROS")) EXPECTED_NODES['/talker1'] = talker1 EXPECTED_NODES['/talker2'] = talker2 EXPECTED_NODES['/listener1'] = listener1 EXPECTED_NODES['/listener2'] = listener2 EXPECTED_NODES['/rosout'] = rosout EXPECTED_NODES['/rosgrapher'] = grapher EXPECTED_NODES['/'+NAME] = tester t_chatter = Topic(name="/chatter", type="std_msgs/String") t_rosout = Topic(name="/rosout", type="rosgraph_msgs/Log") t_rosout_agg = Topic(name="/rosout_agg", type="rosgraph_msgs/Log") t_topology = Topic(name="/topology", type="ros_topology_msgs/Graph") EXPECTED_TOPICS = [t_chatter, t_rosout, t_rosout_agg, t_topology] class TestGrapher(unittest.TestCase): def __init__(self, *args): super(TestGrapher, self).__init__(*args) self.start_time = None self.graph = Graph() def setUp(self): rospy.init_node(NAME) rospy.Subscriber('/topology', Graph, self.callback) self.wait_for_data(10.0) def callback(self, data): self.graph = data def wait_for_data(self, duration): start_time = rospy.get_rostime() while not rospy.is_shutdown() and not (rospy.get_rostime() > (start_time + rospy.Duration(duration))): if len(self.graph.nodes) >= len(EXPECTED_NODES) and len(self.graph.topics) >= len(EXPECTED_TOPICS): return rospy.sleep(1.0) def test_nodes_publishers(self): for node in self.graph.nodes: assert node.name in EXPECTED_NODES, "%s not found!"%node.name testnode = EXPECTED_NODES[node.name] assert set(node.publishes) == set(testnode.publishes), "%s.publishes=%s, but should be %s"%(node.name,node.publishes,testnode.publishes) def test_nodes_subscribers(self): for node in self.graph.nodes: assert node.name in EXPECTED_NODES, "%s not found!"%node.name testnode = EXPECTED_NODES[node.name] assert set(node.subscribes) == set(testnode.subscribes), "%s.subscribes=%s, but should be %s"%(node.name,node.subscribes,testnode.subscribes) def test_nodes_connections_present(self): for node in self.graph.nodes: assert node.name in EXPECTED_NODES, "%s not found!"%node.name testnode = EXPECTED_NODES[node.name] for connection in node.connections: assert connection in testnode.connections, "Node %s has extra connection %s"%(node.name, connection) def test_nodes_connections_missing(self): for node in self.graph.nodes: assert node.name in EXPECTED_NODES, "%s not found!"%node.name testnode = EXPECTED_NODES[node.name] for connection in testnode.connections: assert connection in node.connections, "Node %s expected to find missing connection %s"%(node.name, connection) def test_nodes_present(self): for node in self.graph.nodes: assert node.name in EXPECTED_NODES.keys(), "Found extra node '%s'"%node.name def test_nodes_missing(self): for node_name in EXPECTED_NODES.keys(): assert node_name in [n.name for n in self.graph.nodes], "Expected to find missing node '%s'"%node_name def test_topics_present(self): for topic in self.graph.topics: assert topic in EXPECTED_TOPICS, "Found extra topic '%s'"%topic def test_topics_missing(self): for topic in EXPECTED_TOPICS: assert topic in self.graph.topics, "Expected to find missing topic '%s'"%topic if __name__ == '__main__': rostest.rosrun(PKG, NAME, TestGrapher, sys.argv)

true

1c478bdc9499e3ce8182dc63c0c4b8edbd2abeb0

12,032

py

Python

stellapy/stellapy_old/stella_read.py

SStroteich/stella-1

104556a07b9736e7c28e6f1bf2f799384732f38b

[ "MIT" ]

4

2021-12-15T08:23:45.000Z

2022-02-18T15:14:42.000Z

stellapy/stellapy_old/stella_read.py

SStroteich/stella-1

104556a07b9736e7c28e6f1bf2f799384732f38b

[ "MIT" ]

37

2021-07-05T16:41:33.000Z

2022-03-21T15:58:05.000Z

stellapy/stellapy_old/stella_read.py

SStroteich/stella-1

104556a07b9736e7c28e6f1bf2f799384732f38b

[ "MIT" ]

7

2021-07-05T15:35:55.000Z

2022-03-09T09:23:42.000Z

import numpy as np from stella_dirs import * from scipy.io import netcdf #plt.rcParams.update({'font.size': 28}) #plt.rcParams['lines.linewidth'] = 2 import tabCompleter from tabCompleter import * from plotbox import * from aux_functions import * from os import listdir from netCDF4 import * import glob import os.path # ============================================================== # Some utils def format1(value): return "%.3e" % value def format2(value): return "%14.6e" % value def format3(value): return "%4.2f" % value def format4(value): return "%6.2f" % value def format6(value): return "%7.3f" % value def format5(value): return "%.5e" % value def format7(value): return "%22.3f" % value def format8(value): return "%04d" % value def format9(value): return "%7.5f" % value # Some utils ended #=============================================================== def casestr(case=None): # Function that returns the string of the input, which # determines the name of the rest of output files. if case.endswith(".in"): buff = case.split("/") return buff[size(buff)-1].split(".in")[0] else: if size(inputlist(case)) > 1: print("\nSpecify the input in the case field, more than one input file found:\n") print(inputlist(case)) exit elif size(inputlist(case) == 1): return inputlist(case)[0].split(".in")[0] def inputlist_r(case): inputs_level_0 = glob.glob(outdir(case)+'/*.in', recursive = True) inputs_level_1 = glob.glob(outdir(case)+'/*/*.in', recursive = True) return (inputs_level_0+inputs_level_1) def inputlist(case, recursive=False): # Function that returns all the input file names # with extention ".in" inlist = [] if recursive: inlist = inputlist_r(case=case) else: for f in listdir(outdir(case)): if f.endswith('.in'): if not f.startswith('.'): inputname=f inlist.append(f) return inlist def outdir(case=None): if case.endswith(".in"): vcase=case.split("/") return runsdir()+'/'+ case.replace("/"+vcase[size(vcase)-1], '') else: return runsdir()+'/'+ case def geotxtfile(case=None): # It returns the full path of an output file, endind with # the string value of "quant". if os.path.isfile(case): return case.split('.in')[0] + '.geometry' else: return outdir(case) + '/' + casestr(case) + '.geometry' def outfile(case=None, quant=None): # It returns the full path of an output file, endind with # the string value of "quant". if os.path.isfile(case): return case.split('.in')[0] + '.' + quant else: return outdir(case) + '/' + casestr(case) + '.' + quant def infile(case=None): # infile = input("Path to netcdf file: ") return outfile(case, quant='out.nc') def fluxes_txt(case=None): # infile = input("Path to netcdf file: ") return outfile(case, quant='fluxes') # ================================================================== # Reading variables in the input *.in file def torflux(case): # get torflux from input file. myfile = open(outfile(case, quant='in')) content = float(myfile.read().split('torflux')[1].split('\n')[0].split('=')[1]) return content # ================================================================== # Translation of quantities in stella_data module by Michael into # functions with the run directory ("case") as single argument. def read_stella_float(case, var): import numpy as np ncfile = netcdf.netcdf_file(infile(case),'r') try: arr = np.copy(ncfile.variables[var][:]) flag = True except KeyError: print('INFO: '+var+' not found in netcdf file') arr = np.arange(1,dtype=float) flag = False return arr def read_stella_value(case, var): woutfile = infile(case) d = Dataset(woutfile, mode='r') return d.variables[var][:] def kx(case): # get kx grid # this is the index of the first negative value of kx # note stella orders kx as (0, dkx, ..., kx_max, -kx_max, -kx_max+dkx, ..., -dkx) ncfile = netcdf.netcdf_file(infile(case),'r') kx_stella = np.copy(ncfile.variables['kx'][:]) nakx = ncfile.dimensions['kx'] nakx_mid = nakx//2+1 kx = np.concatenate((kx_stella[nakx_mid:],kx_stella[:nakx_mid])) return kx, nakx, nakx_mid def kx_stella(case): ncfile = netcdf.netcdf_file(infile(case),'r') kx_stella = np.copy(ncfile.variables['kx'][:]) return kx_stella def ky(case): # get ky grid ncfile = netcdf.netcdf_file(infile(case),'r') ky = np.copy(ncfile.variables['ky'][:]) naky = ncfile.dimensions['ky'] return ky, naky def zed(case): # get zed grid ncfile = netcdf.netcdf_file(infile(case),'r') zed = np.copy(ncfile.variables['zed'][:]) nzed = zed.size iz0 = nzed//2+1 return zed, nzed, iz0 def time(case): # get time grid ncfile = netcdf.netcdf_file(infile(case),'r') time = np.copy(ncfile.variables['t'][:]) ntime = time.size return time, ntime def nspec(case): # number of kinetic species ncfile = netcdf.netcdf_file(infile(case),'r') nspec = ncfile.dimensions['species'] return nspec def geo(case): # get geometric quantities d = Dataset(infile(case), mode='r') ncfile = netcdf.netcdf_file(infile(case),'r') bmag = np.copy(ncfile.variables['bmag'][:]) gradpar = np.copy(ncfile.variables['gradpar'][:]) gbdrift = np.copy(ncfile.variables['gbdrift'][:]) gbdrift0 = np.copy(ncfile.variables['gbdrift0'][:]) cvdrift = np.copy(ncfile.variables['cvdrift'][:]) cvdrift0 = np.copy(ncfile.variables['cvdrift0'][:]) gds2 = np.copy(ncfile.variables['gds2'][:]) gds21 = np.copy(ncfile.variables['gds21'][:]) gds22 = np.copy(ncfile.variables['gds22'][:]) shat = float(d.variables['shat'][:]) return bmag, gradpar, gbdrift, gbdrift0, cvdrift, cvdrift0, gds2, gds21, gds22, shat def phi2_vs_kxky(case): # electrostatic potential averaged over z as function of (ky,kx,t) phi2_vs_kxky_stella = read_stella_float(case, 'phi2_vs_kxky') # phi2_vs_kxky_stella[:,0,0] = 0.0 # phi2_vs_kxky = np.concatenate((phi2_vs_kxky_stella[:, kx(case)[2]:,:],\ # phi2_vs_kxky_stella[:,:kx(case)[2] ,:]),axis=1) return phi2_vs_kxky_stella def pflux_vs_kxky(case): pflux_vs_kxky_stella = read_stella_float(case, 'pflx_kxky') return pflux_vs_kxky_stella def vflux_vs_kxky(case): vflux_vs_kxky_stella = read_stella_float(case, 'vflx_kxky') return vflux_vs_kxky_stella def qflux_vs_kxky(case): qflux_vs_kxky_stella = read_stella_float(case, 'qflx_kxky') return qflux_vs_kxky_stella def density_vs_kxky(case): density_vs_kxky_stella = read_stella_float(case, 'density') return density_vs_kxky_stella def upar_vs_kxky(case): upar_vs_kxky_stella = read_stella_float(case, 'upar') return upar_vs_kxky_stella def temperature_vs_kxky(case): temperature_vs_kxky_stella = read_stella_float(case, 'temperature') return temperature_vs_kxky_stella def phi_vs_t(case): # electrostatic potential as a function of (z,kx,ky,t) phi_vs_t_stella = read_stella_float(case, 'phi_vs_t') return phi_vs_t_stella def gvmus(case): # |g|^2 averaged over kx, ky, and z return read_stella_float(case, 'gvmus') def gzvs(case): # |g|^2 averaged over kx, ky, and mu return read_stella_float(case, 'gzvs') def jacob(case): # jacobian for transformation to (rho,alpha,z) coordinates return read_stella_float(case, 'jacob') def jtwist(case): # jtwist factor for twist-and-shift BC return read_stella_value(case, 'jtwist') def grho(case): # gradient of normalized radial coordinate rho return read_stella_float(case, 'grho') def phi2_stella(case): # modulus squared of electrostatic potential (averaged over space) return read_stella_float(case, 'phi2') def es_part_flux(case): # time-dependent electrostatic particle flux for each species return read_stella_float(case, 'es_part_flux') def es_heat_flux(case): # electrostatic heat flux return read_stella_float(case, 'es_heat_flux') def es_mom_flux(case): # electrostatic momentum flux return read_stella_float(case, 'es_mom_flux') def es_energy_exchange(case): return read_stella_float(case, 'es_energy_exchange') def es_part_by_k(case): # time-dependent particle flux for each species as a function of (kx,ky) es_part_by_k_stella, es_part_by_k_present = \ read_stella_float(case, 'es_part_by_k') if es_part_by_k_present is not True: es_part_by_k_stella, es_part_by_k_present = \ read_stella_float(case, 'es_part_flux_by_mode') return es_part_by_k_stella, es_part_by_k_present def es_mom_by_k(case): # time-dependent momentum flux for each species as a function of (kx,ky) es_mom_by_k_stella, es_mom_by_k_present = \ read_stella_float(case, 'es_mom_by_k') if es_mom_by_k_present is not True: es_mom_by_k_stella, es_mom_by_k_present = \ read_stella_float(case, 'es_mom_flux_by_mode') return es_mom_by_k_stella, es_mom_by_k_present def es_energy_exchange_by_k(case): es_energy_exchange_by_k_stella, es_energy_exchange_by_k_present = \ read_stella_float(case, 'es_energy_exchange_by_k') if es_energy_exchange_by_k_present is not True: es_energy_exchange_by_k_stella, es_energy_exchange_by_k_present = \ read_stella_float(case, 'es_energy_exchange_by_mode') return es_energy_exchange_by_k_stella, es_energy_exchange_by_k_present def es_energy_exchange_by_ky(case): return read_stella_float(case, 'es_energy_exchange_by_ky') def vpa(case): # parallel velocity grid return read_stella_float(case, 'vpa') def mu(case): # mu grid return read_stella_float(case, 'mu') def es_part_sym(case): # electrostatic particle flux as function of (vpa,z) return read_stella_float(case, 'es_part_sym') def es_heat_sym(case): # electrostatic heat flux as function of (vpa,z) return read_stella_float(case, 'es_heat_sym') def es_mom_sym(case): # electrostatic momentum flux as function of (vpa,z) es_mom_sym_stella, es_mom_sym_present = read_stella_float(case, 'es_mom_sym') if vpa(case)[1] == False: es_mom_sym_present = False return es_mom_sym_stella, es_mom_sym_present def xgrid(case): xgrid_stella, xgrid_present = \ read_stella_float(case, 'xgrid') xgrid = np.concatenate((xgrid_stella[kx_stella(case).shape[0]//2+1:],\ xgrid_stella[:kx_stella(case).shape[0]//2+1])) return xgrid, xgrid_present def dens(case): dens=read_stella_float(case, 'dens') dens_exp=factormult(dens,1e19) return dens_exp, size(dens) def upar(case): # parallel flow fluctuation (kx,ky,z,t) return read_stella_float(case,'upar') def temp(case): # temperature fluctuation (kx,ky,z,t) temp=read_stella_float(case,'temp') temp_exp=factormult(temp,1000) return temp_exp, size(temp) def species(case): species=read_stella_float(case,'type_of_species') return species, size(species) def nprim(case): return read_stella_float(case,'fprim') def tprim(case): return read_stella_float(case,'tprim') def charge(case): charge=read_stella_float(case,'charge') return charge, size(charge) def mass(case): charge=read_stella_float(case,'mass') return charge, size(mass) # ==================================================================

31.915119

93

0.640376

import numpy as np from stella_dirs import * from scipy.io import netcdf import tabCompleter from tabCompleter import * from plotbox import * from aux_functions import * from os import listdir from netCDF4 import * import glob import os.path def format1(value): return "%.3e" % value def format2(value): return "%14.6e" % value def format3(value): return "%4.2f" % value def format4(value): return "%6.2f" % value def format6(value): return "%7.3f" % value def format5(value): return "%.5e" % value def format7(value): return "%22.3f" % value def format8(value): return "%04d" % value def format9(value): return "%7.5f" % value def casestr(case=None): if case.endswith(".in"): buff = case.split("/") return buff[size(buff)-1].split(".in")[0] else: if size(inputlist(case)) > 1: print("\nSpecify the input in the case field, more than one input file found:\n") print(inputlist(case)) exit elif size(inputlist(case) == 1): return inputlist(case)[0].split(".in")[0] def inputlist_r(case): inputs_level_0 = glob.glob(outdir(case)+'/*.in', recursive = True) inputs_level_1 = glob.glob(outdir(case)+'/*/*.in', recursive = True) return (inputs_level_0+inputs_level_1) def inputlist(case, recursive=False): inlist = [] if recursive: inlist = inputlist_r(case=case) else: for f in listdir(outdir(case)): if f.endswith('.in'): if not f.startswith('.'): inputname=f inlist.append(f) return inlist def outdir(case=None): if case.endswith(".in"): vcase=case.split("/") return runsdir()+'/'+ case.replace("/"+vcase[size(vcase)-1], '') else: return runsdir()+'/'+ case def geotxtfile(case=None): if os.path.isfile(case): return case.split('.in')[0] + '.geometry' else: return outdir(case) + '/' + casestr(case) + '.geometry' def outfile(case=None, quant=None): if os.path.isfile(case): return case.split('.in')[0] + '.' + quant else: return outdir(case) + '/' + casestr(case) + '.' + quant def infile(case=None): return outfile(case, quant='out.nc') def fluxes_txt(case=None): return outfile(case, quant='fluxes') def torflux(case): myfile = open(outfile(case, quant='in')) content = float(myfile.read().split('torflux')[1].split('\n')[0].split('=')[1]) return content def read_stella_float(case, var): import numpy as np ncfile = netcdf.netcdf_file(infile(case),'r') try: arr = np.copy(ncfile.variables[var][:]) flag = True except KeyError: print('INFO: '+var+' not found in netcdf file') arr = np.arange(1,dtype=float) flag = False return arr def read_stella_value(case, var): woutfile = infile(case) d = Dataset(woutfile, mode='r') return d.variables[var][:] def kx(case): ncfile = netcdf.netcdf_file(infile(case),'r') kx_stella = np.copy(ncfile.variables['kx'][:]) nakx = ncfile.dimensions['kx'] nakx_mid = nakx//2+1 kx = np.concatenate((kx_stella[nakx_mid:],kx_stella[:nakx_mid])) return kx, nakx, nakx_mid def kx_stella(case): ncfile = netcdf.netcdf_file(infile(case),'r') kx_stella = np.copy(ncfile.variables['kx'][:]) return kx_stella def ky(case): ncfile = netcdf.netcdf_file(infile(case),'r') ky = np.copy(ncfile.variables['ky'][:]) naky = ncfile.dimensions['ky'] return ky, naky def zed(case): ncfile = netcdf.netcdf_file(infile(case),'r') zed = np.copy(ncfile.variables['zed'][:]) nzed = zed.size iz0 = nzed//2+1 return zed, nzed, iz0 def time(case): ncfile = netcdf.netcdf_file(infile(case),'r') time = np.copy(ncfile.variables['t'][:]) ntime = time.size return time, ntime def nspec(case): ncfile = netcdf.netcdf_file(infile(case),'r') nspec = ncfile.dimensions['species'] return nspec def geo(case): d = Dataset(infile(case), mode='r') ncfile = netcdf.netcdf_file(infile(case),'r') bmag = np.copy(ncfile.variables['bmag'][:]) gradpar = np.copy(ncfile.variables['gradpar'][:]) gbdrift = np.copy(ncfile.variables['gbdrift'][:]) gbdrift0 = np.copy(ncfile.variables['gbdrift0'][:]) cvdrift = np.copy(ncfile.variables['cvdrift'][:]) cvdrift0 = np.copy(ncfile.variables['cvdrift0'][:]) gds2 = np.copy(ncfile.variables['gds2'][:]) gds21 = np.copy(ncfile.variables['gds21'][:]) gds22 = np.copy(ncfile.variables['gds22'][:]) shat = float(d.variables['shat'][:]) return bmag, gradpar, gbdrift, gbdrift0, cvdrift, cvdrift0, gds2, gds21, gds22, shat def phi2_vs_kxky(case): phi2_vs_kxky_stella = read_stella_float(case, 'phi2_vs_kxky') return phi2_vs_kxky_stella def pflux_vs_kxky(case): pflux_vs_kxky_stella = read_stella_float(case, 'pflx_kxky') return pflux_vs_kxky_stella def vflux_vs_kxky(case): vflux_vs_kxky_stella = read_stella_float(case, 'vflx_kxky') return vflux_vs_kxky_stella def qflux_vs_kxky(case): qflux_vs_kxky_stella = read_stella_float(case, 'qflx_kxky') return qflux_vs_kxky_stella def density_vs_kxky(case): density_vs_kxky_stella = read_stella_float(case, 'density') return density_vs_kxky_stella def upar_vs_kxky(case): upar_vs_kxky_stella = read_stella_float(case, 'upar') return upar_vs_kxky_stella def temperature_vs_kxky(case): temperature_vs_kxky_stella = read_stella_float(case, 'temperature') return temperature_vs_kxky_stella def phi_vs_t(case): phi_vs_t_stella = read_stella_float(case, 'phi_vs_t') return phi_vs_t_stella def gvmus(case): return read_stella_float(case, 'gvmus') def gzvs(case): return read_stella_float(case, 'gzvs') def jacob(case): return read_stella_float(case, 'jacob') def jtwist(case): return read_stella_value(case, 'jtwist') def grho(case): return read_stella_float(case, 'grho') def phi2_stella(case): return read_stella_float(case, 'phi2') def es_part_flux(case): return read_stella_float(case, 'es_part_flux') def es_heat_flux(case): return read_stella_float(case, 'es_heat_flux') def es_mom_flux(case): return read_stella_float(case, 'es_mom_flux') def es_energy_exchange(case): return read_stella_float(case, 'es_energy_exchange') def es_part_by_k(case): es_part_by_k_stella, es_part_by_k_present = \ read_stella_float(case, 'es_part_by_k') if es_part_by_k_present is not True: es_part_by_k_stella, es_part_by_k_present = \ read_stella_float(case, 'es_part_flux_by_mode') return es_part_by_k_stella, es_part_by_k_present def es_mom_by_k(case): es_mom_by_k_stella, es_mom_by_k_present = \ read_stella_float(case, 'es_mom_by_k') if es_mom_by_k_present is not True: es_mom_by_k_stella, es_mom_by_k_present = \ read_stella_float(case, 'es_mom_flux_by_mode') return es_mom_by_k_stella, es_mom_by_k_present def es_energy_exchange_by_k(case): es_energy_exchange_by_k_stella, es_energy_exchange_by_k_present = \ read_stella_float(case, 'es_energy_exchange_by_k') if es_energy_exchange_by_k_present is not True: es_energy_exchange_by_k_stella, es_energy_exchange_by_k_present = \ read_stella_float(case, 'es_energy_exchange_by_mode') return es_energy_exchange_by_k_stella, es_energy_exchange_by_k_present def es_energy_exchange_by_ky(case): return read_stella_float(case, 'es_energy_exchange_by_ky') def vpa(case): return read_stella_float(case, 'vpa') def mu(case): return read_stella_float(case, 'mu') def es_part_sym(case): return read_stella_float(case, 'es_part_sym') def es_heat_sym(case): return read_stella_float(case, 'es_heat_sym') def es_mom_sym(case): es_mom_sym_stella, es_mom_sym_present = read_stella_float(case, 'es_mom_sym') if vpa(case)[1] == False: es_mom_sym_present = False return es_mom_sym_stella, es_mom_sym_present def xgrid(case): xgrid_stella, xgrid_present = \ read_stella_float(case, 'xgrid') xgrid = np.concatenate((xgrid_stella[kx_stella(case).shape[0]//2+1:],\ xgrid_stella[:kx_stella(case).shape[0]//2+1])) return xgrid, xgrid_present def dens(case): dens=read_stella_float(case, 'dens') dens_exp=factormult(dens,1e19) return dens_exp, size(dens) def upar(case): return read_stella_float(case,'upar') def temp(case): temp=read_stella_float(case,'temp') temp_exp=factormult(temp,1000) return temp_exp, size(temp) def species(case): species=read_stella_float(case,'type_of_species') return species, size(species) def nprim(case): return read_stella_float(case,'fprim') def tprim(case): return read_stella_float(case,'tprim') def charge(case): charge=read_stella_float(case,'charge') return charge, size(charge) def mass(case): charge=read_stella_float(case,'mass') return charge, size(mass)

true

1c478c2f72be04820d92305cfffce27aa98c7fa4

907

py

Python

electrum/tests/__init__.py

checho1989/electrum-civx

4853bf42f0aa96bb894992c1abf7b8bdda587543

[ "MIT" ]

null

electrum/tests/__init__.py

checho1989/electrum-civx

4853bf42f0aa96bb894992c1abf7b8bdda587543

[ "MIT" ]

null

electrum/tests/__init__.py

checho1989/electrum-civx

4853bf42f0aa96bb894992c1abf7b8bdda587543

[ "MIT" ]

null

import unittest import threading from electrum_civx import constants # Set this locally to make the test suite run faster. # If set, unit tests that would normally test functions with multiple implementations, # will only be run once, using the fastest implementation. # e.g. libsecp256k1 vs python-ecdsa. pycryptodomex vs pyaes. FAST_TESTS = False # some unit tests are modifying globals; sorry. class SequentialTestCase(unittest.TestCase): test_lock = threading.Lock() def setUp(self): super().setUp() self.test_lock.acquire() def tearDown(self): super().tearDown() self.test_lock.release() class TestCaseForTestnet(SequentialTestCase): @classmethod def setUpClass(cls): super().setUpClass() constants.set_testnet() @classmethod def tearDownClass(cls): super().tearDownClass() constants.set_mainnet()

23.25641

86

0.705623

import unittest import threading from electrum_civx import constants FAST_TESTS = False class SequentialTestCase(unittest.TestCase): test_lock = threading.Lock() def setUp(self): super().setUp() self.test_lock.acquire() def tearDown(self): super().tearDown() self.test_lock.release() class TestCaseForTestnet(SequentialTestCase): @classmethod def setUpClass(cls): super().setUpClass() constants.set_testnet() @classmethod def tearDownClass(cls): super().tearDownClass() constants.set_mainnet()

true

1c478c32bd4fd3adda92f37777aa80cd495fcafb

926

py

Python

common/models/notice/UserNews.py

apanly/python_learn_master

93a214241812f77a006cc8350a7bad6c4eec6c89

[ "BSD-3-Clause" ]

5

2020-11-29T14:21:18.000Z

2021-10-07T04:11:29.000Z

common/models/notice/UserNews.py

linkgeek/python_flask_cms

ff5e794b5b11075670e5d11a8cbda0a137319876

[ "BSD-3-Clause" ]

null

common/models/notice/UserNews.py

linkgeek/python_flask_cms

ff5e794b5b11075670e5d11a8cbda0a137319876

[ "BSD-3-Clause" ]

2

2020-11-30T09:55:53.000Z

2022-03-19T12:49:40.000Z

# coding: utf-8 from application import db class UserNews(db.Model): __tablename__ = 'user_news' id = db.Column(db.Integer, primary_key=True, info='消息id') uid = db.Column(db.Integer, nullable=False, server_default=db.FetchedValue(), info='用户id') title = db.Column(db.String(255), nullable=False, server_default=db.FetchedValue(), info='标题') content = db.Column(db.String(1500), nullable=False, server_default=db.FetchedValue(), info='内容') status = db.Column(db.Integer, nullable=False, server_default=db.FetchedValue(), info='状态 0：未读 1：已读') updated_time = db.Column(db.DateTime, nullable=False, server_default=db.FetchedValue(), info='更新时间') created_time = db.Column(db.DateTime, nullable=False, server_default=db.FetchedValue(), info='创建时间') def __init__(self, **items): for key in items: if hasattr(self, key): setattr(self, key, items[key])

51.444444

105

0.686825

from application import db class UserNews(db.Model): __tablename__ = 'user_news' id = db.Column(db.Integer, primary_key=True, info='消息id') uid = db.Column(db.Integer, nullable=False, server_default=db.FetchedValue(), info='用户id') title = db.Column(db.String(255), nullable=False, server_default=db.FetchedValue(), info='标题') content = db.Column(db.String(1500), nullable=False, server_default=db.FetchedValue(), info='内容') status = db.Column(db.Integer, nullable=False, server_default=db.FetchedValue(), info='状态 0：未读 1：已读') updated_time = db.Column(db.DateTime, nullable=False, server_default=db.FetchedValue(), info='更新时间') created_time = db.Column(db.DateTime, nullable=False, server_default=db.FetchedValue(), info='创建时间') def __init__(self, **items): for key in items: if hasattr(self, key): setattr(self, key, items[key])

true