PatrickHaller/the-stack-python-100k · Datasets at Hugging Face

31ecb782be25a483f73139a3d3781cbd65bbb995

13,447

py

Python

tests/matrix_extern_irq_reset/matrix_extern_irq_reset.py

RyusukeYamano/nngen

9ed1f7fb83908794aa94d70287d89545d45fe875

[ "Apache-2.0" ]

207

2019-11-12T11:42:25.000Z

2022-03-20T20:32:17.000Z

tests/matrix_extern_irq_reset/matrix_extern_irq_reset.py

RyusukeYamano/nngen

9ed1f7fb83908794aa94d70287d89545d45fe875

[ "Apache-2.0" ]

31

2019-11-25T07:33:30.000Z

2022-03-17T12:34:34.000Z

tests/matrix_extern_irq_reset/matrix_extern_irq_reset.py

RyusukeYamano/nngen

9ed1f7fb83908794aa94d70287d89545d45fe875

[ "Apache-2.0" ]

29

2019-11-07T02:25:48.000Z

2022-03-12T16:22:57.000Z

from __future__ import absolute_import from __future__ import print_function import os import sys import functools import math import numpy as np if sys.version_info.major < 3: from itertools import izip_longest as zip_longest else: from itertools import zip_longest # the next line can be removed after installation sys.path.insert(0, os.path.dirname(os.path.dirname( os.path.dirname(os.path.abspath(__file__))))) import nngen as ng from veriloggen import * import veriloggen.thread as vthread import veriloggen.types.axi as axi def run(a_shape=(15, 15), b_shape=(15, 15), a_dtype=ng.int32, b_dtype=ng.int32, c_dtype=ng.int32, par=1, axi_datawidth=32, interrupt_name='irq', silent=False, filename=None, simtype='iverilog', outputfile=None): # create target hardware a = ng.placeholder(a_dtype, shape=a_shape, name='a') b = ng.placeholder(b_dtype, shape=b_shape, name='b') d = ng.add(a, b, dtype=c_dtype, par=par) e = ng.add(b, a, dtype=c_dtype, par=par) # SW returns ng.add(x, y) f = ng.extern([d, e], shape=a_shape, opcode=0x1, func=lambda x, y: x + y) g = ng.sub(f, a) # SW returns d as-is h = ng.extern([g], shape=a_shape, opcode=0x2, func=lambda x: x) c = ng.sub(h, b) targ = ng.to_veriloggen([c], 'matrix_extern', silent=silent, config={'maxi_datawidth': axi_datawidth, 'interrupt_name': interrupt_name}) # verification data va = np.arange(a.length, dtype=np.int64).reshape(a.shape) % [16] vb = np.arange(b.length, dtype=np.int64).reshape(b.shape) % [32] + [16] eval_outs = ng.eval([c], a=va, b=vb) vc = eval_outs[0] # to memory image size_max = int(math.ceil(max(a.memory_size, b.memory_size, c.memory_size) / 4096)) * 4096 check_addr = max(a.addr, b.addr, c.addr) + size_max size_check = size_max tmp_addr = check_addr + size_check memimg_datawidth = 32 mem = np.zeros([1024 * 1024 * 8 // (memimg_datawidth // 8)], dtype=np.int64) mem = mem + [100] axi.set_memory(mem, va, memimg_datawidth, a_dtype.width, a.addr, max(int(math.ceil(axi_datawidth / a_dtype.width)), par)) axi.set_memory(mem, vb, memimg_datawidth, b_dtype.width, b.addr, max(int(math.ceil(axi_datawidth / b_dtype.width)), par)) axi.set_memory(mem, vc, memimg_datawidth, c_dtype.width, check_addr, max(int(math.ceil(axi_datawidth / c_dtype.width)), par)) # test controller m = Module('test') params = m.copy_params(targ) ports = m.copy_sim_ports(targ) clk = ports['CLK'] resetn = ports['RESETN'] irq = ports[interrupt_name] rst = m.Wire('RST') rst.assign(Not(resetn)) # AXI memory model if outputfile is None: outputfile = os.path.splitext(os.path.basename(__file__))[0] + '.out' memimg_name = 'memimg_' + outputfile memory = axi.AxiMemoryModel(m, 'memory', clk, rst, datawidth=axi_datawidth, memimg_datawidth=memimg_datawidth, memimg=mem, memimg_name=memimg_name) memory.connect(ports, 'maxi') # AXI-Slave controller _saxi = vthread.AXIMLite(m, '_saxi', clk, rst, noio=True) _saxi.connect(ports, 'saxi') # timer time_counter = m.Reg('time_counter', 32, initval=0) seq = Seq(m, 'seq', clk, rst) seq( time_counter.inc() ) num_rep = functools.reduce(lambda x, y: x * y, c.shape[:-1], 1) def irq_join(saxi, irq_bit): while irq == 0: pass araddr = ng.control_reg_interrupt_isr * 4 irq_stat = saxi.read(araddr) if irq_stat != irq_bit: print('# Unexpected irq signal: %d' % irq_stat) print('# verify: FAILED') vthread.finish() print('# irq stat = %d' % irq_stat) awaddr = ng.control_reg_interrupt_iar * 4 saxi.write(awaddr, irq_bit) def ctrl(): for i in range(100): pass ng.sim.set_global_addrs(_saxi, tmp_addr) araddr_ext_snd = ng.control_reg_extern_send * 4 awaddr_ext_rcv = ng.control_reg_extern_recv * 4 awaddr_irq_ier = ng.control_reg_interrupt_ier * 4 araddr_irq_isr = ng.control_reg_interrupt_isr * 4 awaddr_irq_iar = ng.control_reg_interrupt_iar * 4 _saxi.write(awaddr_irq_ier , 3) # irq enable ng.sim.sw_rst(_saxi) print('# 0st software reset (during idle)') for i in range(100): pass irq_stat = _saxi.read(araddr_irq_isr) if irq_stat != 0: print('# Unexpected irq signal: %d' % irq_stat) print('# verify: FAILED') vthread.finish() print('# irq stat = %d' % irq_stat) # no irq busy by software reset when idle start_time = time_counter.value ng.sim.start(_saxi) print('# 1st test start') # from extern-send irq_join(_saxi, 2) v = _saxi.read(araddr_ext_snd) print('# opcode = %d' % v) for i in range(num_rep): for j in range(c.shape[-1]): x_offset = tmp_addr - d.default_global_addr y_offset = tmp_addr - e.default_global_addr z_offset = tmp_addr - f.default_global_addr x = memory.read_word(i * c.aligned_shape[-1] + j, d.addr + x_offset, c_dtype.width) y = memory.read_word(i * c.aligned_shape[-1] + j, e.addr + y_offset, c_dtype.width) z = x + y memory.write_word(i * c.aligned_shape[-1] + j, f.addr + z_offset, z, c_dtype.width) # to extern-recv _saxi.write(awaddr_ext_rcv, 1) # from extern-send irq_join(_saxi, 2) v = _saxi.read(araddr_ext_snd) print('# opcode = %d' % v) # software reset ng.sim.sw_rst(_saxi) print('# 1st software reset (before resume)') # from extern-send irq_join(_saxi, 1) # restart ng.sim.start(_saxi) print('# Restart') # from extern-send irq_join(_saxi, 2) v = _saxi.read(araddr_ext_snd) print('# opcode = %d' % v) for i in range(num_rep): for j in range(c.shape[-1]): x_offset = tmp_addr - d.default_global_addr y_offset = tmp_addr - e.default_global_addr z_offset = tmp_addr - f.default_global_addr x = memory.read_word(i * c.aligned_shape[-1] + j, d.addr + x_offset, c_dtype.width) y = memory.read_word(i * c.aligned_shape[-1] + j, e.addr + y_offset, c_dtype.width) z = x + y memory.write_word(i * c.aligned_shape[-1] + j, f.addr + z_offset, z, c_dtype.width) # to extern-recv _saxi.write(awaddr_ext_rcv, 1) # from extern-send irq_join(_saxi, 2) v = _saxi.read(araddr_ext_snd) print('# opcode = %d' % v) for i in range(num_rep): for j in range(c.shape[-1]): x_offset = tmp_addr - g.default_global_addr z_offset = tmp_addr - h.default_global_addr x = memory.read_word(i * c.aligned_shape[-1] + j, g.addr + x_offset, c_dtype.width) z = x memory.write_word(i * c.aligned_shape[-1] + j, h.addr + z_offset, z, c_dtype.width) # to extern-recv _saxi.write(awaddr_ext_rcv, 1) # from extern-send irq_join(_saxi, 1) #ng.sim.wait(_saxi) end_time = time_counter.value print('# end') print('# execution cycles: %d' % (end_time - start_time)) # verify ok_1st = True for i in range(num_rep): for j in range(c.shape[-1]): orig = memory.read_word(i * c.aligned_shape[-1] + j, c.addr, c_dtype.width) check = memory.read_word(i * c.aligned_shape[-1] + j, check_addr, c_dtype.width) if vthread.verilog.NotEql(orig, check): print('NG', i, j, orig, check) ok_1st = False # else: # print('OK', i, j, orig, check) # 2nd test # start start_time = time_counter.value ng.sim.start(_saxi) print('# 2nd test start') # from extern-send irq_join(_saxi, 2) v = _saxi.read(araddr_ext_snd) print('# opcode = %d' % v) for i in range(num_rep): for j in range(c.shape[-1]): x_offset = tmp_addr - d.default_global_addr y_offset = tmp_addr - e.default_global_addr z_offset = tmp_addr - f.default_global_addr x = memory.read_word(i * c.aligned_shape[-1] + j, d.addr + x_offset, c_dtype.width) y = memory.read_word(i * c.aligned_shape[-1] + j, e.addr + y_offset, c_dtype.width) z = x + y memory.write_word(i * c.aligned_shape[-1] + j, f.addr + z_offset, z, c_dtype.width) # to extern-recv _saxi.write(awaddr_ext_rcv, 1) while (memory.waddr.awvalid) == 0: pass ng.sim.sw_rst(_saxi) print('# 2nd software reset (during Master AXI transaction)') irq_join(_saxi, 1) # irq busy by software reset # restart ng.sim.start(_saxi) print('# Restart') # from extern-send irq_join(_saxi, 2) araddr = ng.control_reg_extern_send * 4 print('# opcode = %d' % v) for i in range(num_rep): for j in range(c.shape[-1]): x_offset = tmp_addr - d.default_global_addr y_offset = tmp_addr - e.default_global_addr z_offset = tmp_addr - f.default_global_addr x = memory.read_word(i * c.aligned_shape[-1] + j, d.addr + x_offset, c_dtype.width) y = memory.read_word(i * c.aligned_shape[-1] + j, e.addr + y_offset, c_dtype.width) z = x + y memory.write_word(i * c.aligned_shape[-1] + j, f.addr + z_offset, z, c_dtype.width) # to extern-recv _saxi.write(awaddr_ext_rcv, 1) # from extern-send irq_join(_saxi, 2) v = _saxi.read(araddr_ext_snd) print('# opcode = %d' % v) for i in range(num_rep): for j in range(c.shape[-1]): x_offset = tmp_addr - g.default_global_addr z_offset = tmp_addr - h.default_global_addr x = memory.read_word(i * c.aligned_shape[-1] + j, g.addr + x_offset, c_dtype.width) z = x memory.write_word(i * c.aligned_shape[-1] + j, h.addr + z_offset, z, c_dtype.width) # to extern-recv _saxi.write(awaddr_ext_rcv, 1) # termination irq_join(_saxi, 1) #ng.sim.wait(_saxi) end_time = time_counter.value print('# end') print('# execution cycles: %d' % (end_time - start_time)) # verify ok_2nd = True for i in range(num_rep): for j in range(c.shape[-1]): orig = memory.read_word(i * c.aligned_shape[-1] + j, c.addr, c_dtype.width) check = memory.read_word(i * c.aligned_shape[-1] + j, check_addr, c_dtype.width) if vthread.verilog.NotEql(orig, check): print('NG', i, j, orig, check) ok_2nd = False # else: # print('OK', i, j, orig, check) if ok_1st and ok_2nd: print('# verify: PASSED') else: print('# verify: FAILED') vthread.finish() th = vthread.Thread(m, 'th_ctrl', clk, rst, ctrl) fsm = th.start() uut = m.Instance(targ, 'uut', params=m.connect_params(targ), ports=m.connect_ports(targ)) # simulation.setup_waveform(m, uut) simulation.setup_clock(m, clk, hperiod=5) init = simulation.setup_reset(m, resetn, m.make_reset(), period=100, polarity='low') init.add( Delay(1000000), Systask('finish'), ) # output source code if filename is not None: m.to_verilog(filename) # run simulation sim = simulation.Simulator(m, sim=simtype) rslt = sim.run(outputfile=outputfile) lines = rslt.splitlines() if simtype == 'verilator' and lines[-1].startswith('-'): rslt = '\n'.join(lines[:-1]) return rslt if __name__ == '__main__': rslt = run(silent=False, filename='tmp.v') print(rslt)

33.039312

93

0.533428

677ca5b0e1c29125ac2883a4cf35b55b7a9ea4d5

2,328

py

Python

roles/models/cta/cta_mixmatch.py

JMFlin/auto-preference-finder

07dd2e6b2b28398ca9bfb6ad328c578eb9987417

[ "MIT" ]

null

roles/models/cta/cta_mixmatch.py

JMFlin/auto-preference-finder

07dd2e6b2b28398ca9bfb6ad328c578eb9987417

[ "MIT" ]

7

2020-11-13T17:50:07.000Z

2022-02-10T02:17:14.000Z

roles/models/cta/cta_mixmatch.py

JMFlin/auto-preference-finder

07dd2e6b2b28398ca9bfb6ad328c578eb9987417

[ "MIT" ]

null

# Copyright 2019 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 # # https://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 os from absl import app from absl import flags from cta.lib.train import CTAClassifySemi from libml import data, utils from mixmatch import MixMatch FLAGS = flags.FLAGS class CTAMixMatch(MixMatch, CTAClassifySemi): pass def main(argv): utils.setup_main() del argv # Unused. dataset = data.PAIR_DATASETS()[FLAGS.dataset]() log_width = utils.ilog2(dataset.width) model = CTAMixMatch( os.path.join(FLAGS.train_dir, dataset.name, CTAMixMatch.cta_name()), dataset, lr=FLAGS.lr, wd=FLAGS.wd, arch=FLAGS.arch, batch=FLAGS.batch, nclass=dataset.nclass, ema=FLAGS.ema, beta=FLAGS.beta, w_match=FLAGS.w_match, scales=FLAGS.scales or (log_width - 2), filters=FLAGS.filters, repeat=FLAGS.repeat) model.train(FLAGS.train_kimg << 10, FLAGS.report_kimg << 10) if __name__ == '__main__': utils.setup_tf() flags.DEFINE_float('wd', 0.02, 'Weight decay.') flags.DEFINE_float('ema', 0.999, 'Exponential moving average of params.') flags.DEFINE_float('beta', 0.75, 'Mixup beta distribution.') flags.DEFINE_float('w_match', 100, 'Weight for distribution matching loss.') flags.DEFINE_integer('scales', 0, 'Number of 2x2 downscalings in the classifier.') flags.DEFINE_integer('filters', 32, 'Filter size of convolutions.') flags.DEFINE_integer('repeat', 4, 'Number of residual layers per stage.') FLAGS.set_default('augment', 'd.d.d') FLAGS.set_default('dataset', 'cifar10.3@250-5000') FLAGS.set_default('batch', 64) FLAGS.set_default('lr', 0.002) FLAGS.set_default('train_kimg', 1 << 16) app.run(main)

34.235294

87

0.675687

2f008983fdb56afb61f5759e739ffba9951f07f4

3,754

py

Python

tests/util/test_merkle.py

MatthiasLohr/bfebench

baca2e18a9c24282ecda99dccfd134fab4c223b3

[ "Apache-2.0" ]

null

tests/util/test_merkle.py

MatthiasLohr/bfebench

baca2e18a9c24282ecda99dccfd134fab4c223b3

[ "Apache-2.0" ]

null

tests/util/test_merkle.py

MatthiasLohr/bfebench

baca2e18a9c24282ecda99dccfd134fab4c223b3

[ "Apache-2.0" ]

null

# This file is part of the Blockchain-based Fair Exchange Benchmark Tool # https://gitlab.com/MatthiasLohr/bfebench # # Copyright 2021-2022 Matthias Lohr <mail@mlohr.com> # # 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 base64 import b64decode, b64encode from math import log2 from unittest import TestCase from bfebench.protocols.fairswap.util import B032, keccak from bfebench.utils.bytes import generate_bytes from bfebench.utils.merkle import ( MerkleTreeLeaf, MerkleTreeNode, from_bytes, mt2obj, obj2mt, ) class MerkleTest(TestCase): EXAMPLE_TREE1 = MerkleTreeNode( keccak, MerkleTreeNode( keccak, MerkleTreeLeaf(keccak, generate_bytes(32)), MerkleTreeLeaf(keccak, generate_bytes(32)), ), MerkleTreeNode( keccak, MerkleTreeLeaf(keccak, generate_bytes(32)), MerkleTreeLeaf(keccak, generate_bytes(32)), ), ) def test_init(self) -> None: self.assertRaises( ValueError, MerkleTreeNode, keccak, MerkleTreeLeaf(keccak, B032), MerkleTreeLeaf(keccak, B032), MerkleTreeLeaf(keccak, B032), ) def test_get_proof_and_validate(self) -> None: for slice_count in [2, 4, 8, 16]: tree = from_bytes(generate_bytes(32 * slice_count), keccak, slice_count) for index, leaf in enumerate(tree.leaves): proof = tree.get_proof(leaf) self.assertEqual(len(proof), int(log2(slice_count))) self.assertTrue(MerkleTreeNode.validate_proof(tree.digest, leaf, index, proof, keccak)) def test_mt2obj2mt_plain(self) -> None: obj = mt2obj(self.EXAMPLE_TREE1) mt2 = obj2mt(obj, keccak) self.assertEqual(self.EXAMPLE_TREE1, mt2) self.assertEqual(self.EXAMPLE_TREE1.digest, mt2.digest) def test_mt2obj2mt_b64(self) -> None: obj = mt2obj(self.EXAMPLE_TREE1, b64encode) mt2 = obj2mt(obj, keccak, b64decode) self.assertEqual(self.EXAMPLE_TREE1, mt2) self.assertEqual(self.EXAMPLE_TREE1.digest, mt2.digest) def test_mt2obj2mt_hex(self) -> None: obj = mt2obj(self.EXAMPLE_TREE1, encode_func=lambda b: bytes(b).hex()) mt2 = obj2mt(obj, digest_func=keccak, decode_func=lambda s: bytes.fromhex(str(s))) self.assertEqual(self.EXAMPLE_TREE1, mt2) self.assertEqual(self.EXAMPLE_TREE1.digest, mt2.digest) def test_obj2mt_error(self) -> None: self.assertRaises(ValueError, obj2mt, 3, keccak) def test_from_bytes_encode_decode(self) -> None: data = generate_bytes(32 * 2 * 16) tree_original = from_bytes(data, digest_func=keccak, slice_count=16) self.assertEqual(b"".join([leaf.data for leaf in tree_original.leaves]), data) tree_encoded = mt2obj(tree_original, encode_func=lambda b: bytes(b).hex()) tree_decoded = obj2mt( tree_encoded, digest_func=keccak, decode_func=lambda s: bytes.fromhex(str(s)), ) self.assertEqual(b"".join([leaf.data for leaf in tree_decoded.leaves]), data) self.assertEqual(tree_original.digest, tree_decoded.digest)

35.752381

103

0.669952

19350fafd622afdc51cd5cfffa5ee18ce5f5f27f

22,151

py

Python

tensorflow_probability/python/internal/backend/numpy/ops.py

PavanKishore21/probability

4bad1b796b0e6ed2959205915d42788817620c4c

[ "Apache-2.0" ]

2

2019-10-30T04:45:07.000Z

2019-10-30T04:45:08.000Z

tensorflow_probability/python/internal/backend/numpy/ops.py

PavanKishore21/probability

4bad1b796b0e6ed2959205915d42788817620c4c

[ "Apache-2.0" ]

null

tensorflow_probability/python/internal/backend/numpy/ops.py

PavanKishore21/probability

4bad1b796b0e6ed2959205915d42788817620c4c

[ "Apache-2.0" ]

null

# Copyright 2018 The TensorFlow Probability 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. # ============================================================================ """Numpy implementations of TensorFlow functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import contextlib import functools # Dependency imports import numpy as np import numpy as onp # Avoid JAX rewrite. # pylint: disable=reimported import six from tensorflow_probability.python.internal.backend.numpy import _utils as utils from tensorflow_probability.python.internal.backend.numpy import nest from tensorflow_probability.python.internal.backend.numpy.gen import tensor_shape try: # May not be available, not a core dep for TFP. import wrapt # pylint: disable=g-import-not-at-top except ImportError: wrapt = None __all__ = [ 'bitcast', 'broadcast_dynamic_shape', 'broadcast_static_shape', 'broadcast_to', 'cast', 'clip_by_value', 'constant', 'control_dependencies', 'convert_to_tensor', 'custom_gradient', 'device', 'enable_v2_behavior', 'ensure_shape', 'executing_eagerly', 'get_static_value', 'identity', 'init_scope', 'is_tensor', 'name_scope', 'newaxis', 'register_tensor_conversion_function', 'stop_gradient', 'GradientTape', 'Module', 'Tensor', 'Variable', # 'gradients', ] JAX_MODE = False if JAX_MODE: import jax # pylint: disable=g-import-not-at-top class _NullContext(object): def __init__(self, *args, **kwargs): pass def __enter__(self): pass def __exit__(self, type_arg, value_arg, traceback_arg): return False # False values do not suppress exceptions. def _broadcast_static_shape(shape_x, shape_y): """Reimplements `tf.broadcast_static_shape` in JAX/NumPy.""" if (tensor_shape.TensorShape(shape_x).ndims is None or tensor_shape.TensorShape(shape_y).ndims is None): return tensor_shape.TensorShape(None) shape_x = tuple(tensor_shape.TensorShape(shape_x).as_list()) shape_y = tuple(tensor_shape.TensorShape(shape_y).as_list()) try: if JAX_MODE: error_message = 'Incompatible shapes for broadcasting' return tensor_shape.TensorShape(lax.broadcast_shapes(shape_x, shape_y)) error_message = ('shape mismatch: objects cannot be broadcast to' ' a single shape') return tensor_shape.TensorShape( np.broadcast(np.zeros(shape_x), np.zeros(shape_y)).shape) except ValueError as e: # Match TF error message if error_message in str(e): raise ValueError( 'Incompatible shapes for broadcasting: {} and {}'.format( shape_x, shape_y)) raise def _broadcast_dynamic_shape(shape_x, shape_y): """Reimplements `tf.broadcast_dynamic_shape` in JAX/NumPy.""" return convert_to_tensor(_broadcast_static_shape(shape_x, shape_y)) broadcast_shape = _broadcast_static_shape def _constant(value, dtype=None, shape=None, name='Const'): # pylint: disable=unused-argument x = convert_to_tensor(value, dtype=dtype) if shape is None: return x if not x.shape: return np.full(shape, x) return np.reshape(x, shape) def _control_dependencies(control_inputs): if control_inputs: for control in control_inputs: if callable(control): control() return _NullContext() tensor_conversion_registry = {} def register_tensor_conversion_function(base_type, conversion_func): # No priority system like TensorFlow yet tensor_conversion_registry[base_type] = conversion_func def _convert_to_tensor(value, dtype=None, dtype_hint=None, name=None): # pylint: disable=unused-argument """Emulates tf.convert_to_tensor.""" dtype = utils.numpy_dtype(dtype) dtype_hint = utils.numpy_dtype(dtype_hint) if is_tensor(value) and not isinstance(value, Variable): # In NumPy mode, we are lenient on the dtype compatibility check because # some codepaths rely on flexible conversion from int/float64 to 32. if dtype is not None and value.dtype != dtype: if JAX_MODE: raise TypeError(('Tensor conversion requested dtype {} for array with ' 'dtype {}: {}').format(dtype, value.dtype, value)) return value.astype(dtype) return value conversion_func = tensor_conversion_registry.get(type(value), _default_convert_to_tensor) ret = None if dtype is None and dtype_hint is not None: try: ret = conversion_func(value, dtype=dtype_hint) except (TypeError, ValueError): pass if ret is None: ret = conversion_func(value, dtype=dtype) return ret def _infer_dtype(value, default_dtype): """Guesses an object's dtype.""" # Need to check for onp type first because onp types are subclasses of Python # types. if hasattr(value, 'dtype'): # Duck-typing onp types return value.dtype elif isinstance(value, bool): return np.bool_ elif isinstance(value, six.integer_types): return np.int32 elif isinstance(value, float): return np.float32 elif isinstance(value, complex): return np.complex128 elif isinstance(value, (tuple, list)): # Try inferring the type from items in the object if possible. for v in nest.flatten(value): if hasattr(v, 'dtype'): return v.dtype try: # Finally fall back to raw types (int, bool). return _infer_dtype(value[0], default_dtype) except (IndexError, TypeError): return default_dtype raise ValueError(('Attempt to convert a value ({})' ' with an unsupported type ({}) to a Tensor.').format( value, type(value))) class _Int64ToInt32Error(TypeError): """Error thrown when trying to convert an int64 to int32.""" def __init__(self, int_value): self.int_value = int_value super(_Int64ToInt32Error, self).__init__('Overflow when casting an int64 to' ' an int32.') class _FloatToIntError(TypeError): """Error thrown when trying to convert a float to an int.""" def _is_int64(value): return value > onp.iinfo(onp.int32).max or value < onp.iinfo(onp.int32).min def _default_convert_to_tensor(value, dtype=None): """Default tensor conversion function for array, bool, int, float, and complex.""" inferred_dtype = _infer_dtype(value, np.float32) # When a dtype is provided, we can go ahead and try converting to the dtype # and force overflow/underflow if an int64 is converted to an int32. if dtype is not None: try: return _default_convert_to_tensor_with_dtype(value, dtype) except _Int64ToInt32Error as e: # Force conversion to int32 if requested return e.int_value # If no dtype is provided, we try the inferred dtype and fallback to int64 or # float32 depending on the type of conversion error we see. try: return _default_convert_to_tensor_with_dtype(value, inferred_dtype) except _Int64ToInt32Error as e: return np.array(value, dtype=np.int64) except _FloatToIntError as e: return np.array(value, dtype=np.float32) class TypeConversionError(TypeError): def __init__(self, value, dtype): super(TypeConversionError, self).__init__( 'Cannot convert {} to array of dtype {}'.format(value, dtype)) class MixedTypesError(ValueError): def __init__(self): super(MixedTypesError, self).__init__('Can\'t convert Python sequence with' ' mixed types to Tensor.') def _default_convert_to_tensor_with_dtype(value, dtype, error_if_mismatch=False): """Converts a value to a tensor with a given dtype. Args: value: An object to be converted to tensor. dtype: A NPTF dtype. error_if_mismatch: Enables a stricter check for use when converting an iterable from a tensor. Returns: A tensor. Raises: TypeConversionError: If type conversion fails. MixedTypesError: If types are mismatched in an iterable context. ValueError: If object isn't convertible to tensor. _Int64ToInt32Error: If trying to convert an int64 to an int32. _FloatToIntError: If trying to convert a float to an int. """ is_arraylike = hasattr(value, 'dtype') if is_arraylike: # Duck-typed for `onp.array`/`onp.generic` arr = np.array(value) if dtype is not None: # arr.astype(None) forces conversion to float64 return arr.astype(dtype) return arr elif isinstance(value, complex): dtype_compatible = np.issubdtype(dtype, np.complexfloating) if not dtype_compatible: if error_if_mismatch: raise MixedTypesError() raise TypeConversionError(value, dtype) elif isinstance(value, bool): # Bool check needs to happen before int check because bools are instances of # int. dtype_compatible = (dtype == np.bool_ or np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.floating)) if not dtype_compatible: if error_if_mismatch: raise MixedTypesError() raise TypeError(value, dtype) elif isinstance(value, six.integer_types): if error_if_mismatch and not (np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.floating)): raise MixedTypesError() if dtype == np.int32 and _is_int64(value): raise _Int64ToInt32Error(np.array(value, dtype=dtype)) if dtype == np.bool_: # Can't downcast an int to a bool raise TypeConversionError(value, dtype) elif isinstance(value, float): if error_if_mismatch and not (np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.floating)): raise MixedTypesError() if np.issubdtype(dtype, np.integer): raise _FloatToIntError( 'Cannot convert {} to array of dtype {}'.format(value, dtype)) if not (np.issubdtype(dtype, np.floating) or np.issubdtype(dtype, np.complexfloating)): raise TypeConversionError(value, dtype) else: # Try to iterate through object and throw ValueError if we can't. if hasattr(value, '__getitem__'): ret = [] error_in_list = False for v in value: ret.append(_default_convert_to_tensor_with_dtype( v, dtype, error_if_mismatch=error_in_list)) error_in_list = True value = ret else: raise ValueError( ('Attempting to convert a value {} with an' ' unsupported type {} to a Tensor.').format(value, type(value))) return np.array(value, dtype=dtype) @contextlib.contextmanager def _init_scope(): yield # --- Begin Public Functions -------------------------------------------------- class GradientTape(object): """tf.GradientTape stub.""" def __init__(self, persistent=False, watch_accessed_variables=True): # pylint: disable=unused-argument raise NotImplementedError('GradientTape not currently supported in JAX and ' 'NumPy backends.') def __enter__(self): return self def __exit__(self, typ, value, traceback): # pylint: disable=unused-argument pass def watch(self, tensor): # pylint: disable=unused-argument pass def gradient(self, target, sources, output_gradients=None, # pylint: disable=unused-argument unconnected_gradients=None): # pylint: disable=unused-argument raise NotImplementedError def batch_jacobian(self, target, source, # pylint: disable=unused-argument unconnected_gradients=None, # pylint: disable=unused-argument parallel_iterations=None, experimental_use_pfor=True): # pylint: disable=unused-argument raise NotImplementedError bitcast = utils.copy_docstring( 'tf.bitcast', lambda input, type, name=None: convert_to_tensor( # pylint: disable=g-long-lambda input, dtype_hint=type).view(type)) broadcast_dynamic_shape = utils.copy_docstring( 'tf.broadcast_dynamic_shape', _broadcast_dynamic_shape) broadcast_static_shape = utils.copy_docstring( 'tf.broadcast_static_shape', _broadcast_static_shape) broadcast_to = utils.copy_docstring( 'tf.broadcast_to', lambda input, shape, name=None: np.broadcast_to(input, shape)) def _cast(x, dtype): x = np.asarray(x) if (np.issubdtype(x.dtype, np.complexfloating) and not np.issubdtype(dtype, np.complexfloating)): x = np.real(x) return x.astype(dtype) cast = utils.copy_docstring( 'tf.cast', lambda x, dtype, name=None: _cast(x, utils.numpy_dtype(dtype))) clip_by_value = utils.copy_docstring( 'tf.clip_by_value', lambda t, clip_value_min, clip_value_max, name=None: # pylint: disable=g-long-lambda np.clip(t, clip_value_min, clip_value_max)) constant = utils.copy_docstring( 'tf.constant', _constant) control_dependencies = utils.copy_docstring( 'tf.control_dependencies', _control_dependencies) convert_to_tensor = utils.copy_docstring( 'tf.convert_to_tensor', _convert_to_tensor) def _custom_gradient(f): """JAX implementation of tf.custom_gradient.""" if not JAX_MODE: # Numpy backend ignores custom gradients, so we do too. return lambda *args, **kwargs: f(*args, **kwargs)[0] @jax.custom_gradient @functools.wraps(f) def wrapped(*args, **kwargs): value, vjp = f(*args, **kwargs) def vjp_(cts_out): cts_in = vjp(cts_out) if isinstance(cts_in, list): cts_in = tuple(cts_in) return cts_in return value, vjp_ return wrapped custom_gradient = utils.copy_docstring( 'tf.custom_gradient', _custom_gradient) device = lambda _: _NullContext() def _ensure_shape(x, shape, name=None): # pylint: disable=unused-argument x_shape = tensor_shape.TensorShape(x.shape) shape = tensor_shape.TensorShape(shape) if not shape.is_compatible_with(x_shape): msg = 'Shape of tensor x {} is not compatible with expected shape {}' raise ValueError(msg.format(x_shape, shape)) return x ensure_shape = utils.copy_docstring( 'tf.ensure_shape', _ensure_shape) executing_eagerly = utils.copy_docstring( 'tf.executing_eagerly', lambda: True) def _get_static_value_jax(tensor, partial=False): """JAX implementation of tf.get_static_value.""" del partial if isinstance(tensor, jax.core.Tracer): return None if isinstance(tensor, NumpyVariable): return None if isinstance(tensor, Module): return None if isinstance(tensor, np.ndarray): return onp.array(tensor) return tensor def _get_static_value_numpy(tensor, partial=False): """NumPy implementation of tf.get_static_value.""" del partial if isinstance(tensor, NumpyVariable): return None if isinstance(tensor, Module): return None return tensor get_static_value = utils.copy_docstring( 'tf.get_static_value', _get_static_value_jax if JAX_MODE else _get_static_value_numpy) identity = utils.copy_docstring( 'tf.identity', lambda input, name=None: np.array(input)) is_tensor = utils.copy_docstring( 'tf.is_tensor', lambda x: isinstance(x, Tensor)) init_scope = utils.copy_docstring('tf.init_scope', _init_scope) class name_scope(object): # pylint: disable=invalid-name """A context manager for use when defining a Python op. This context manager pushes a name scope, which will make the name of all operations added within it have a prefix. For example, to define a new Python op called `my_op`: ```python def my_op(a, b, c, name=None): with tf.name_scope("MyOp") as scope: a = tf.convert_to_tensor(a, name="a") b = tf.convert_to_tensor(b, name="b") c = tf.convert_to_tensor(c, name="c") # Define some computation that uses `a`, `b`, and `c`. return foo_op(..., name=scope) ``` When executed, the Tensors `a`, `b`, `c`, will have names `MyOp/a`, `MyOp/b`, and `MyOp/c`. If the scope name already exists, the name will be made unique by appending `_n`. For example, calling `my_op` the second time will generate `MyOp_1/a`, etc. """ @property def name(self): return self._name def __init__(self, name, *args, **kwargs): del args, kwargs self._name = name def __enter__(self): return self._name def __exit__(self, type_arg, value_arg, traceback_arg): return False # False values do not suppress exceptions. newaxis = np.newaxis if JAX_MODE: from jax import lax # pylint: disable=g-import-not-at-top stop_gradient = utils.copy_docstring( 'tf.stop_gradient', lambda input, name=None: lax.stop_gradient(input)) else: stop_gradient = utils.copy_docstring( 'tf.stop_gradient', lambda input, name=None: np.array(input)) def _convert_tensorshape_to_tensor(value, dtype=None): """Copied from TF's TensorShape conversion.""" if not value.is_fully_defined(): raise ValueError( 'Cannot convert a partially known TensorShape to a Tensor: {}'.format( value)) value_list = value.as_list() int64_value = 0 for dim in value_list: if dim >= 2**31: int64_value = dim break if dtype is not None: if dtype not in (np.int32, np.int64): raise TypeConversionError(value, dtype) if dtype == np.int32 and int64_value: raise ValueError('Cannot convert a TensorShape to dtype int32; ' 'a dimension is too large ({})'.format(int64_value)) else: dtype = np.int64 if int64_value else np.int32 return convert_to_tensor(value_list, dtype=dtype) register_tensor_conversion_function(tensor_shape.TensorShape, _convert_tensorshape_to_tensor) def _convert_dimension_to_tensor(value, dtype=None): dtype = dtype or np.int32 if dtype not in (np.int32, np.int64): raise TypeConversionError(value, dtype) return convert_to_tensor(tensor_shape.dimension_value(value), dtype=dtype) register_tensor_conversion_function(tensor_shape.Dimension, _convert_dimension_to_tensor) class NumpyVariable(getattr(wrapt, 'ObjectProxy', object)): """Stand-in for tf.Variable.""" __slots__ = ('initializer',) # pylint: disable=unused-argument def __init__( self, initial_value=None, trainable=True, validate_shape=True, caching_device=None, name=None, variable_def=None, dtype=None, import_scope=None, constraint=None, shape=None): assert constraint is None v = convert_to_tensor(initial_value) if dtype is not None: v = v.astype(utils.numpy_dtype(dtype)) super(NumpyVariable, self).__init__(v) self._self_name = name self.initializer = None # pylint: enable=unused-argument @property def name(self): return self._self_name if self._self_name is not None else str(id(self)) def __array__(self, dtype=None): if dtype is not None: dtype = utils.numpy_dtype(dtype) return self.__wrapped__.__array__(dtype) # Passing in dtype=None to __array__ has differing behavior in numpy. # When an `np.ndarray` has `.__array__(None)` invoked, the array is casted # to `float64`. Thus we handle this case separately. return self.__wrapped__.__array__() def assign(self, value, **_): super(NumpyVariable, self).__init__(onp.array(value, dtype=self.dtype)) return self def assign_add(self, value, **_): super(NumpyVariable, self).__init__( onp.array(self, dtype=self.dtype) + onp.array(value, dtype=self.dtype)) return self def assign_sub(self, value, **_): super(NumpyVariable, self).__init__( onp.array(self, dtype=self.dtype) - onp.array(value, dtype=self.dtype)) return self if JAX_MODE: jax.interpreters.xla.canonicalize_dtype_handlers[NumpyVariable] = ( jax.interpreters.xla.canonicalize_dtype_handlers[onp.ndarray]) jax.interpreters.xla.pytype_aval_mappings[NumpyVariable] = ( jax.interpreters.xla.pytype_aval_mappings[onp.ndarray]) jax.core.pytype_aval_mappings[NumpyVariable] = ( jax.core.pytype_aval_mappings[onp.ndarray]) def _convert_variable_to_tensor(value, dtype=None): return convert_to_tensor(value.__wrapped__, dtype=dtype) register_tensor_conversion_function(NumpyVariable, _convert_variable_to_tensor) Variable = NumpyVariable class _TensorMeta(type(np.ndarray)): @classmethod def __instancecheck__(cls, instance): if JAX_MODE: return isinstance(instance, (jax.xla.DeviceArray, jax.core.Tracer)) return isinstance(instance, np.ndarray) class Tensor(six.with_metaclass(_TensorMeta)): OVERLOADABLE_OPERATORS = frozenset(( # Binary. '__add__', '__radd__', '__sub__', '__rsub__', '__mul__', '__rmul__', '__truediv__', '__rtruediv__', '__floordiv__', '__rfloordiv__', '__mod__', '__rmod__', '__lt__', '__le__', '__gt__', '__ge__', '__ne__', '__eq__', '__and__', '__rand__', '__or__', '__ror__', '__xor__', '__rxor__', '__getitem__', '__pow__', '__rpow__', # Unary. '__invert__', '__neg__', '__abs__', '__matmul__', '__rmatmul__' )) class Module(object): """tf.Module.""" _TF_MODULE_IGNORED_PROPERTIES = frozenset() def __init__(self, name): self._name = name def _no_dependency(self, x): return x @property def trainable_variables(self): return [] @property def variables(self): return [] enable_v2_behavior = lambda: None

30.937151

110

0.687012

13dce8f8f3e57ff4a6e01d63174a6da7ae30ef3d

76,244

py

Python

lib/galaxy/datatypes/interval.py

igorhollaender/sirv_dashboard

85aec60b80ef6f561d89398e3da5963d3d0f2aa4

[ "CC-BY-3.0" ]

2

2018-10-14T16:42:39.000Z

2018-10-14T16:42:41.000Z

lib/galaxy/datatypes/interval.py

igorhollaender/OBSOLETE_sirv_dashboard

85aec60b80ef6f561d89398e3da5963d3d0f2aa4

[ "CC-BY-3.0" ]

null

lib/galaxy/datatypes/interval.py

igorhollaender/OBSOLETE_sirv_dashboard

85aec60b80ef6f561d89398e3da5963d3d0f2aa4

[ "CC-BY-3.0" ]

null

""" Interval datatypes """ import logging import math import os import sys import tempfile import urllib import numpy from bx.intervals.io import GenomicIntervalReader, ParseError from galaxy import util from galaxy.datatypes import metadata from galaxy.datatypes.metadata import MetadataElement from galaxy.datatypes.sniff import get_headers from galaxy.datatypes.tabular import Tabular from galaxy.datatypes.util.gff_util import parse_gff_attributes from galaxy.web import url_for import data import dataproviders log = logging.getLogger(__name__) # Contains the meta columns and the words that map to it; list aliases on the # right side of the : in decreasing order of priority alias_spec = { 'chromCol' : [ 'chrom' , 'CHROMOSOME' , 'CHROM', 'Chromosome Name' ], 'startCol' : [ 'start' , 'START', 'chromStart', 'txStart', 'Start Position (bp)' ], 'endCol' : [ 'end' , 'END' , 'STOP', 'chromEnd', 'txEnd', 'End Position (bp)' ], 'strandCol' : [ 'strand', 'STRAND', 'Strand' ], 'nameCol' : [ 'name', 'NAME', 'Name', 'name2', 'NAME2', 'Name2', 'Ensembl Gene ID', 'Ensembl Transcript ID', 'Ensembl Peptide ID' ] } # a little faster lookup alias_helper = {} for key, value in alias_spec.items(): for elem in value: alias_helper[elem] = key # Constants for configuring viewport generation: If a line is greater than # VIEWPORT_MAX_READS_PER_LINE * VIEWPORT_READLINE_BUFFER_SIZE bytes in size, # then we will not generate a viewport for that dataset VIEWPORT_READLINE_BUFFER_SIZE = 1048576 # 1MB VIEWPORT_MAX_READS_PER_LINE = 10 @dataproviders.decorators.has_dataproviders class Interval( Tabular ): """Tab delimited data containing interval information""" edam_format = "format_3475" file_ext = "interval" line_class = "region" track_type = "FeatureTrack" data_sources = { "data": "tabix", "index": "bigwig" } """Add metadata elements""" MetadataElement( name="chromCol", default=1, desc="Chrom column", param=metadata.ColumnParameter ) MetadataElement( name="startCol", default=2, desc="Start column", param=metadata.ColumnParameter ) MetadataElement( name="endCol", default=3, desc="End column", param=metadata.ColumnParameter ) MetadataElement( name="strandCol", desc="Strand column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0 ) MetadataElement( name="nameCol", desc="Name/Identifier column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0 ) MetadataElement( name="columns", default=3, desc="Number of columns", readonly=True, visible=False ) def __init__(self, **kwd): """Initialize interval datatype, by adding UCSC display apps""" Tabular.__init__(self, **kwd) self.add_display_app( 'ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links' ) def init_meta( self, dataset, copy_from=None ): Tabular.init_meta( self, dataset, copy_from=copy_from ) def set_meta( self, dataset, overwrite=True, first_line_is_header=False, **kwd ): """Tries to guess from the line the location number of the column for the chromosome, region start-end and strand""" Tabular.set_meta( self, dataset, overwrite=overwrite, skip=0 ) if dataset.has_data(): empty_line_count = 0 num_check_lines = 100 # only check up to this many non empty lines for i, line in enumerate( file( dataset.file_name ) ): line = line.rstrip( '\r\n' ) if line: if ( first_line_is_header or line[0] == '#' ): self.init_meta( dataset ) line = line.strip( '#' ) elems = line.split( '\t' ) for meta_name, header_list in alias_spec.iteritems(): for header_val in header_list: if header_val in elems: # found highest priority header to meta_name setattr( dataset.metadata, meta_name, elems.index( header_val ) + 1 ) break # next meta_name break # Our metadata is set, so break out of the outer loop else: # Header lines in Interval files are optional. For example, BED is Interval but has no header. # We'll make a best guess at the location of the metadata columns. metadata_is_set = False elems = line.split( '\t' ) if len( elems ) > 2: for str in data.col1_startswith: if line.lower().startswith( str ): if overwrite or not dataset.metadata.element_is_set( 'chromCol' ): dataset.metadata.chromCol = 1 try: int( elems[1] ) if overwrite or not dataset.metadata.element_is_set( 'startCol' ): dataset.metadata.startCol = 2 except: pass # Metadata default will be used try: int( elems[2] ) if overwrite or not dataset.metadata.element_is_set( 'endCol' ): dataset.metadata.endCol = 3 except: pass # Metadata default will be used # we no longer want to guess that this column is the 'name', name must now be set manually for interval files # we will still guess at the strand, as we can make a more educated guess # if len( elems ) > 3: # try: # int( elems[3] ) # except: # if overwrite or not dataset.metadata.element_is_set( 'nameCol' ): # dataset.metadata.nameCol = 4 if len( elems ) < 6 or elems[5] not in data.valid_strand: if overwrite or not dataset.metadata.element_is_set( 'strandCol' ): dataset.metadata.strandCol = 0 else: if overwrite or not dataset.metadata.element_is_set( 'strandCol' ): dataset.metadata.strandCol = 6 metadata_is_set = True break if metadata_is_set or ( i - empty_line_count ) > num_check_lines: break # Our metadata is set or we examined 100 non-empty lines, so break out of the outer loop else: empty_line_count += 1 def displayable( self, dataset ): try: return dataset.has_data() \ and dataset.state == dataset.states.OK \ and dataset.metadata.columns > 0 \ and dataset.metadata.data_lines != 0 \ and dataset.metadata.chromCol \ and dataset.metadata.startCol \ and dataset.metadata.endCol except: return False def get_estimated_display_viewport( self, dataset, chrom_col=None, start_col=None, end_col=None ): """Return a chrom, start, stop tuple for viewing a file.""" viewport_feature_count = 100 # viewport should check at least 100 features; excludes comment lines max_line_count = max( viewport_feature_count, 500 ) # maximum number of lines to check; includes comment lines if not self.displayable( dataset ): return ( None, None, None ) try: # If column indexes were not passwed, determine from metadata if chrom_col is None: chrom_col = int( dataset.metadata.chromCol ) - 1 if start_col is None: start_col = int( dataset.metadata.startCol ) - 1 if end_col is None: end_col = int( dataset.metadata.endCol ) - 1 # Scan lines of file to find a reasonable chromosome and range chrom = None start = sys.maxsize end = 0 max_col = max( chrom_col, start_col, end_col ) fh = open( dataset.file_name ) while True: line = fh.readline( VIEWPORT_READLINE_BUFFER_SIZE ) # Stop if at end of file if not line: break # Skip comment lines if not line.startswith( '#' ): try: fields = line.rstrip().split( '\t' ) if len( fields ) > max_col: if chrom is None or chrom == fields[ chrom_col ]: start = min( start, int( fields[ start_col ] ) ) end = max( end, int( fields[ end_col ] ) ) # Set chrom last, in case start and end are not integers chrom = fields[ chrom_col ] viewport_feature_count -= 1 except Exception: # Most likely a non-integer field has been encountered # for start / stop. Just ignore and make sure we finish # reading the line and decrementing the counters. pass # Make sure we are at the next new line readline_count = VIEWPORT_MAX_READS_PER_LINE while line.rstrip( '\n\r' ) == line: assert readline_count > 0, Exception( 'Viewport readline count exceeded for dataset %s.' % dataset.id ) line = fh.readline( VIEWPORT_READLINE_BUFFER_SIZE ) if not line: break # EOF readline_count -= 1 max_line_count -= 1 if not viewport_feature_count or not max_line_count: # exceeded viewport or total line count to check break if chrom is not None: return ( chrom, str( start ), str( end ) ) # Necessary to return strings? except Exception: # Unexpected error, possibly missing metadata log.exception( "Exception caught attempting to generate viewport for dataset '%d'", dataset.id ) return ( None, None, None ) def as_ucsc_display_file( self, dataset, **kwd ): """Returns file contents with only the bed data""" fd, temp_name = tempfile.mkstemp() c, s, e, t, n = dataset.metadata.chromCol, dataset.metadata.startCol, dataset.metadata.endCol, dataset.metadata.strandCol or 0, dataset.metadata.nameCol or 0 c, s, e, t, n = int(c) - 1, int(s) - 1, int(e) - 1, int(t) - 1, int(n) - 1 if t >= 0: # strand column (should) exists for i, elems in enumerate( util.file_iter(dataset.file_name) ): strand = "+" name = "region_%i" % i if n >= 0 and n < len( elems ): name = elems[n] if t < len(elems): strand = elems[t] tmp = [ elems[c], elems[s], elems[e], name, '0', strand ] os.write(fd, '%s\n' % '\t'.join(tmp) ) elif n >= 0: # name column (should) exists for i, elems in enumerate( util.file_iter(dataset.file_name) ): name = "region_%i" % i if n >= 0 and n < len( elems ): name = elems[n] tmp = [ elems[c], elems[s], elems[e], name ] os.write(fd, '%s\n' % '\t'.join(tmp) ) else: for elems in util.file_iter(dataset.file_name): tmp = [ elems[c], elems[s], elems[e] ] os.write(fd, '%s\n' % '\t'.join(tmp) ) os.close(fd) return open(temp_name) def display_peek( self, dataset ): """Returns formated html of peek""" return Tabular.make_html_table( self, dataset, column_parameter_alias={'chromCol': 'Chrom', 'startCol': 'Start', 'endCol': 'End', 'strandCol': 'Strand', 'nameCol': 'Name'} ) def ucsc_links( self, dataset, type, app, base_url ): """ Generate links to UCSC genome browser sites based on the dbkey and content of dataset. """ # Filter UCSC sites to only those that are supported by this build and # enabled. valid_sites = [ ( name, url ) for name, url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey ) if name in app.datatypes_registry.get_display_sites('ucsc') ] if not valid_sites: return [] # If there are any valid sites, we need to generate the estimated # viewport chrom, start, stop = self.get_estimated_display_viewport( dataset ) if chrom is None: return [] # Accumulate links for valid sites ret_val = [] for site_name, site_url in valid_sites: internal_url = url_for( controller='dataset', dataset_id=dataset.id, action='display_at', filename='ucsc_' + site_name ) display_url = urllib.quote_plus( "%s%s/display_as?id=%i&display_app=%s&authz_method=display_at" % (base_url, url_for( controller='root' ), dataset.id, type) ) redirect_url = urllib.quote_plus( "%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" % (site_url, dataset.dbkey, chrom, start, stop ) ) link = '%s?redirect_url=%s&display_url=%s' % ( internal_url, redirect_url, display_url ) ret_val.append( ( site_name, link ) ) return ret_val def validate( self, dataset ): """Validate an interval file using the bx GenomicIntervalReader""" errors = list() c, s, e, t = dataset.metadata.chromCol, dataset.metadata.startCol, dataset.metadata.endCol, dataset.metadata.strandCol c, s, e, t = int(c) - 1, int(s) - 1, int(e) - 1, int(t) - 1 infile = open(dataset.file_name, "r") reader = GenomicIntervalReader( infile, chrom_col=c, start_col=s, end_col=e, strand_col=t) while True: try: reader.next() except ParseError as e: errors.append(e) except StopIteration: infile.close() return errors def repair_methods( self, dataset ): """Return options for removing errors along with a description""" return [("lines", "Remove erroneous lines")] def sniff( self, filename ): """ Checks for 'intervalness' This format is mostly used by galaxy itself. Valid interval files should include a valid header comment, but this seems to be loosely regulated. >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'test_space.txt' ) >>> Interval().sniff( fname ) False >>> fname = get_test_fname( 'interval.interval' ) >>> Interval().sniff( fname ) True """ headers = get_headers( filename, '\t' ) try: """ If we got here, we already know the file is_column_based and is not bed, so we'll just look for some valid data. """ for hdr in headers: if hdr and not hdr[0].startswith( '#' ): if len(hdr) < 3: return False try: # Assume chrom start and end are in column positions 1 and 2 # respectively ( for 0 based columns ) int( hdr[1] ) int( hdr[2] ) except: return False return True except: return False def get_track_window(self, dataset, data, start, end): """ Assumes the incoming track data is sorted already. """ window = list() for record in data: fields = record.rstrip("\n\r").split("\t") record_chrom = fields[dataset.metadata.chromCol - 1] record_start = int(fields[dataset.metadata.startCol - 1]) record_end = int(fields[dataset.metadata.endCol - 1]) if record_start < end and record_end > start: window.append( (record_chrom, record_start, record_end) ) # Yes I did want to use a generator here, but it doesn't work downstream return window def get_track_resolution( self, dataset, start, end): return None # ------------- Dataproviders @dataproviders.decorators.dataprovider_factory( 'genomic-region', dataproviders.dataset.GenomicRegionDataProvider.settings ) def genomic_region_dataprovider( self, dataset, **settings ): return dataproviders.dataset.GenomicRegionDataProvider( dataset, **settings ) @dataproviders.decorators.dataprovider_factory( 'genomic-region-dict', dataproviders.dataset.GenomicRegionDataProvider.settings ) def genomic_region_dict_dataprovider( self, dataset, **settings ): settings[ 'named_columns' ] = True return self.genomic_region_dataprovider( dataset, **settings ) @dataproviders.decorators.dataprovider_factory( 'interval', dataproviders.dataset.IntervalDataProvider.settings ) def interval_dataprovider( self, dataset, **settings ): return dataproviders.dataset.IntervalDataProvider( dataset, **settings ) @dataproviders.decorators.dataprovider_factory( 'interval-dict', dataproviders.dataset.IntervalDataProvider.settings ) def interval_dict_dataprovider( self, dataset, **settings ): settings[ 'named_columns' ] = True return self.interval_dataprovider( dataset, **settings ) class BedGraph( Interval ): """Tab delimited chrom/start/end/datavalue dataset""" file_ext = "bedgraph" track_type = "LineTrack" data_sources = { "data": "bigwig", "index": "bigwig" } def as_ucsc_display_file( self, dataset, **kwd ): """ Returns file contents as is with no modifications. TODO: this is a functional stub and will need to be enhanced moving forward to provide additional support for bedgraph. """ return open( dataset.file_name ) def get_estimated_display_viewport( self, dataset, chrom_col=0, start_col=1, end_col=2 ): """ Set viewport based on dataset's first 100 lines. """ return Interval.get_estimated_display_viewport( self, dataset, chrom_col=chrom_col, start_col=start_col, end_col=end_col ) class Bed( Interval ): """Tab delimited data in BED format""" edam_format = "format_3003" file_ext = "bed" data_sources = { "data": "tabix", "index": "bigwig", "feature_search": "fli" } track_type = Interval.track_type """Add metadata elements""" MetadataElement( name="chromCol", default=1, desc="Chrom column", param=metadata.ColumnParameter ) MetadataElement( name="startCol", default=2, desc="Start column", param=metadata.ColumnParameter ) MetadataElement( name="endCol", default=3, desc="End column", param=metadata.ColumnParameter ) MetadataElement( name="strandCol", desc="Strand column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0 ) MetadataElement( name="columns", default=3, desc="Number of columns", readonly=True, visible=False ) MetadataElement( name="viz_filter_cols", desc="Score column for visualization", default=[4], param=metadata.ColumnParameter, optional=True, multiple=True ) # do we need to repeat these? they are the same as should be inherited from interval type def set_meta( self, dataset, overwrite=True, **kwd ): """Sets the metadata information for datasets previously determined to be in bed format.""" i = 0 if dataset.has_data(): for i, line in enumerate( file(dataset.file_name) ): metadata_set = False line = line.rstrip('\r\n') if line and not line.startswith('#'): elems = line.split('\t') if len(elems) > 2: for startswith in data.col1_startswith: if line.lower().startswith( startswith ): if len( elems ) > 3: if overwrite or not dataset.metadata.element_is_set( 'nameCol' ): dataset.metadata.nameCol = 4 if len(elems) < 6: if overwrite or not dataset.metadata.element_is_set( 'strandCol' ): dataset.metadata.strandCol = 0 else: if overwrite or not dataset.metadata.element_is_set( 'strandCol' ): dataset.metadata.strandCol = 6 metadata_set = True break if metadata_set: break Tabular.set_meta( self, dataset, overwrite=overwrite, skip=i ) def as_ucsc_display_file( self, dataset, **kwd ): """Returns file contents with only the bed data. If bed 6+, treat as interval.""" for line in open(dataset.file_name): line = line.strip() if line == "" or line.startswith("#"): continue fields = line.split('\t') """check to see if this file doesn't conform to strict genome browser accepted bed""" try: if len(fields) > 12: return Interval.as_ucsc_display_file(self, dataset) # too many fields if len(fields) > 6: int(fields[6]) if len(fields) > 7: int(fields[7]) if len(fields) > 8: if int(fields[8]) != 0: return Interval.as_ucsc_display_file(self, dataset) if len(fields) > 9: int(fields[9]) if len(fields) > 10: fields2 = fields[10].rstrip(",").split(",") # remove trailing comma and split on comma for field in fields2: int(field) if len(fields) > 11: fields2 = fields[11].rstrip(",").split(",") # remove trailing comma and split on comma for field in fields2: int(field) except: return Interval.as_ucsc_display_file(self, dataset) # only check first line for proper form break try: return open(dataset.file_name) except: return "This item contains no content" def sniff( self, filename ): """ Checks for 'bedness' BED lines have three required fields and nine additional optional fields. The number of fields per line must be consistent throughout any single set of data in an annotation track. The order of the optional fields is binding: lower-numbered fields must always be populated if higher-numbered fields are used. The data type of all 12 columns is: 1-str, 2-int, 3-int, 4-str, 5-int, 6-str, 7-int, 8-int, 9-int or list, 10-int, 11-list, 12-list For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format1 >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'test_tab.bed' ) >>> Bed().sniff( fname ) True >>> fname = get_test_fname( 'interval1.bed' ) >>> Bed().sniff( fname ) True >>> fname = get_test_fname( 'complete.bed' ) >>> Bed().sniff( fname ) True """ headers = get_headers( filename, '\t' ) try: if not headers: return False for hdr in headers: if (hdr[0] == '' or hdr[0].startswith( '#' )): continue valid_col1 = False if len(hdr) < 3 or len(hdr) > 12: return False for str in data.col1_startswith: if hdr[0].lower().startswith(str): valid_col1 = True break if valid_col1: try: int( hdr[1] ) int( hdr[2] ) except: return False if len( hdr ) > 4: # hdr[3] is a string, 'name', which defines the name of the BED line - difficult to test for this. # hdr[4] is an int, 'score', a score between 0 and 1000. try: if int( hdr[4] ) < 0 or int( hdr[4] ) > 1000: return False except: return False if len( hdr ) > 5: # hdr[5] is strand if hdr[5] not in data.valid_strand: return False if len( hdr ) > 6: # hdr[6] is thickStart, the starting position at which the feature is drawn thickly. try: int( hdr[6] ) except: return False if len( hdr ) > 7: # hdr[7] is thickEnd, the ending position at which the feature is drawn thickly try: int( hdr[7] ) except: return False if len( hdr ) > 8: # hdr[8] is itemRgb, an RGB value of the form R,G,B (e.g. 255,0,0). However, this could also be an int (e.g., 0) try: int( hdr[8] ) except: try: hdr[8].split(',') except: return False if len( hdr ) > 9: # hdr[9] is blockCount, the number of blocks (exons) in the BED line. try: block_count = int( hdr[9] ) except: return False if len( hdr ) > 10: # hdr[10] is blockSizes - A comma-separated list of the block sizes. # Sometimes the blosck_sizes and block_starts lists end in extra commas try: block_sizes = hdr[10].rstrip(',').split(',') except: return False if len( hdr ) > 11: # hdr[11] is blockStarts - A comma-separated list of block starts. try: block_starts = hdr[11].rstrip(',').split(',') except: return False if len(block_sizes) != block_count or len(block_starts) != block_count: return False else: return False return True except: return False class BedStrict( Bed ): """Tab delimited data in strict BED format - no non-standard columns allowed""" file_ext = "bedstrict" # no user change of datatype allowed allow_datatype_change = False # Read only metadata elements MetadataElement( name="chromCol", default=1, desc="Chrom column", readonly=True, param=metadata.MetadataParameter ) MetadataElement( name="startCol", default=2, desc="Start column", readonly=True, param=metadata.MetadataParameter ) # TODO: start and end should be able to be set to these or the proper thick[start/end]? MetadataElement( name="endCol", default=3, desc="End column", readonly=True, param=metadata.MetadataParameter ) MetadataElement( name="strandCol", desc="Strand column (click box & select)", readonly=True, param=metadata.MetadataParameter, no_value=0, optional=True ) MetadataElement( name="nameCol", desc="Name/Identifier column (click box & select)", readonly=True, param=metadata.MetadataParameter, no_value=0, optional=True ) MetadataElement( name="columns", default=3, desc="Number of columns", readonly=True, visible=False ) def __init__( self, **kwd ): Tabular.__init__( self, **kwd ) self.clear_display_apps() # only new style display applications for this datatype def set_meta( self, dataset, overwrite=True, **kwd ): Tabular.set_meta( self, dataset, overwrite=overwrite, **kwd) # need column count first if dataset.metadata.columns >= 4: dataset.metadata.nameCol = 4 if dataset.metadata.columns >= 6: dataset.metadata.strandCol = 6 def sniff( self, filename ): return False # NOTE: This would require aggressively validating the entire file class Bed6( BedStrict ): """Tab delimited data in strict BED format - no non-standard columns allowed; column count forced to 6""" file_ext = "bed6" class Bed12( BedStrict ): """Tab delimited data in strict BED format - no non-standard columns allowed; column count forced to 12""" file_ext = "bed12" class _RemoteCallMixin: def _get_remote_call_url( self, redirect_url, site_name, dataset, type, app, base_url ): """Retrieve the URL to call out to an external site and retrieve data. This routes our external URL through a local galaxy instance which makes the data available, followed by redirecting to the remote site with a link back to the available information. """ internal_url = "%s" % url_for( controller='dataset', dataset_id=dataset.id, action='display_at', filename='%s_%s' % ( type, site_name ) ) base_url = app.config.get( "display_at_callback", base_url ) display_url = urllib.quote_plus( "%s%s/display_as?id=%i&display_app=%s&authz_method=display_at" % ( base_url, url_for( controller='root' ), dataset.id, type ) ) link = '%s?redirect_url=%s&display_url=%s' % ( internal_url, redirect_url, display_url ) return link @dataproviders.decorators.has_dataproviders class Gff( Tabular, _RemoteCallMixin ): """Tab delimited data in Gff format""" edam_format = "format_2305" file_ext = "gff" column_names = [ 'Seqname', 'Source', 'Feature', 'Start', 'End', 'Score', 'Strand', 'Frame', 'Group' ] data_sources = { "data": "interval_index", "index": "bigwig", "feature_search": "fli" } track_type = Interval.track_type """Add metadata elements""" MetadataElement( name="columns", default=9, desc="Number of columns", readonly=True, visible=False ) MetadataElement( name="column_types", default=['str', 'str', 'str', 'int', 'int', 'int', 'str', 'str', 'str'], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False ) MetadataElement( name="attributes", default=0, desc="Number of attributes", readonly=True, visible=False, no_value=0 ) MetadataElement( name="attribute_types", default={}, desc="Attribute types", param=metadata.DictParameter, readonly=True, visible=False, no_value=[] ) def __init__( self, **kwd ): """Initialize datatype, by adding GBrowse display app""" Tabular.__init__(self, **kwd) self.add_display_app( 'ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links' ) self.add_display_app( 'gbrowse', 'display in Gbrowse', 'as_gbrowse_display_file', 'gbrowse_links' ) def set_attribute_metadata( self, dataset ): """ Sets metadata elements for dataset's attributes. """ # Use first N lines to set metadata for dataset attributes. Attributes # not found in the first N lines will not have metadata. num_lines = 200 attribute_types = {} for i, line in enumerate( file( dataset.file_name ) ): if line and not line.startswith( '#' ): elems = line.split( '\t' ) if len( elems ) == 9: try: # Loop through attributes to set types. for name, value in parse_gff_attributes( elems[8] ).items(): # Default type is string. value_type = "str" try: # Try int. int( value ) value_type = "int" except: try: # Try float. float( value ) value_type = "float" except: pass attribute_types[ name ] = value_type except: pass if i + 1 == num_lines: break # Set attribute metadata and then set additional metadata. dataset.metadata.attribute_types = attribute_types dataset.metadata.attributes = len( attribute_types ) def set_meta( self, dataset, overwrite=True, **kwd ): self.set_attribute_metadata( dataset ) i = 0 for i, line in enumerate( file( dataset.file_name ) ): line = line.rstrip('\r\n') if line and not line.startswith( '#' ): elems = line.split( '\t' ) if len(elems) == 9: try: int( elems[3] ) int( elems[4] ) break except: pass Tabular.set_meta( self, dataset, overwrite=overwrite, skip=i ) def display_peek( self, dataset ): """Returns formated html of peek""" return Tabular.make_html_table( self, dataset, column_names=self.column_names ) def get_estimated_display_viewport( self, dataset ): """ Return a chrom, start, stop tuple for viewing a file. There are slight differences between gff 2 and gff 3 formats. This function should correctly handle both... """ viewport_feature_count = 100 # viewport should check at least 100 features; excludes comment lines max_line_count = max( viewport_feature_count, 500 ) # maximum number of lines to check; includes comment lines if self.displayable( dataset ): try: seqid = None start = sys.maxsize stop = 0 fh = open( dataset.file_name ) while True: line = fh.readline( VIEWPORT_READLINE_BUFFER_SIZE ) if not line: break # EOF try: if line.startswith( '##sequence-region' ): # ##sequence-region IV 6000000 6030000 elems = line.rstrip( '\n\r' ).split() if len( elems ) > 3: # line looks like: # sequence-region ctg123 1 1497228 seqid = elems[1] # IV start = int( elems[2] ) # 6000000 stop = int( elems[3] ) # 6030000 break # use location declared in file elif len( elems ) == 2 and elems[1].find( '..' ) > 0: # line looks like this: # sequence-region X:120000..140000 elems = elems[1].split( ':' ) seqid = elems[0] start = int( elems[1].split( '..' )[0] ) stop = int( elems[1].split( '..' )[1] ) break # use location declared in file else: log.exception( "line (%s) uses an unsupported ##sequence-region definition." % str( line ) ) # break #no break, if bad definition, we try another method elif line.startswith("browser position"): # Allow UCSC style browser and track info in the GFF file pos_info = line.split()[-1] seqid, startend = pos_info.split(":") start, stop = map( int, startend.split("-") ) break # use location declared in file elif True not in map( line.startswith, ( '#', 'track', 'browser' ) ): # line.startswith() does not accept iterator in python2.4 viewport_feature_count -= 1 elems = line.rstrip( '\n\r' ).split( '\t' ) if len( elems ) > 3: if not seqid: # We can only set the viewport for a single chromosome seqid = elems[0] if seqid == elems[0]: # Make sure we have not spanned chromosomes start = min( start, int( elems[3] ) ) stop = max( stop, int( elems[4] ) ) except: # most likely start/stop is not an int or not enough fields pass # make sure we are at the next new line readline_count = VIEWPORT_MAX_READS_PER_LINE while line.rstrip( '\n\r' ) == line: assert readline_count > 0, Exception( 'Viewport readline count exceeded for dataset %s.' % dataset.id ) line = fh.readline( VIEWPORT_READLINE_BUFFER_SIZE ) if not line: break # EOF readline_count -= 1 max_line_count -= 1 if not viewport_feature_count or not max_line_count: # exceeded viewport or total line count to check break if seqid is not None: return ( seqid, str( start ), str( stop ) ) # Necessary to return strings? except Exception as e: # unexpected error log.exception( str( e ) ) return ( None, None, None ) # could not determine viewport def ucsc_links( self, dataset, type, app, base_url ): ret_val = [] seqid, start, stop = self.get_estimated_display_viewport( dataset ) if seqid is not None: for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey ): if site_name in app.datatypes_registry.get_display_sites('ucsc'): redirect_url = urllib.quote_plus( "%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" % ( site_url, dataset.dbkey, seqid, start, stop ) ) link = self._get_remote_call_url( redirect_url, site_name, dataset, type, app, base_url ) ret_val.append( ( site_name, link ) ) return ret_val def gbrowse_links( self, dataset, type, app, base_url ): ret_val = [] seqid, start, stop = self.get_estimated_display_viewport( dataset ) if seqid is not None: for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('gbrowse', dataset.dbkey ): if site_name in app.datatypes_registry.get_display_sites('gbrowse'): if seqid.startswith( 'chr' ) and len( seqid ) > 3: seqid = seqid[3:] redirect_url = urllib.quote_plus( "%s/?q=%s:%s..%s&eurl=%%s" % ( site_url, seqid, start, stop ) ) link = self._get_remote_call_url( redirect_url, site_name, dataset, type, app, base_url ) ret_val.append( ( site_name, link ) ) return ret_val def sniff( self, filename ): """ Determines whether the file is in gff format GFF lines have nine required fields that must be tab-separated. For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format3 >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'gff_version_3.gff' ) >>> Gff().sniff( fname ) False >>> fname = get_test_fname( 'test.gff' ) >>> Gff().sniff( fname ) True """ headers = get_headers( filename, '\t' ) try: if len(headers) < 2: return False for hdr in headers: if hdr and hdr[0].startswith( '##gff-version' ) and hdr[0].find( '2' ) < 0: return False if hdr and hdr[0] and not hdr[0].startswith( '#' ): if len(hdr) != 9: return False try: int( hdr[3] ) int( hdr[4] ) except: return False if hdr[5] != '.': try: float( hdr[5] ) except: return False if hdr[6] not in data.valid_strand: return False return True except: return False # ------------- Dataproviders # redefine bc super is Tabular @dataproviders.decorators.dataprovider_factory( 'genomic-region', dataproviders.dataset.GenomicRegionDataProvider.settings ) def genomic_region_dataprovider( self, dataset, **settings ): return dataproviders.dataset.GenomicRegionDataProvider( dataset, 0, 3, 4, **settings ) @dataproviders.decorators.dataprovider_factory( 'genomic-region-dict', dataproviders.dataset.GenomicRegionDataProvider.settings ) def genomic_region_dict_dataprovider( self, dataset, **settings ): settings[ 'named_columns' ] = True return self.genomic_region_dataprovider( dataset, **settings ) @dataproviders.decorators.dataprovider_factory( 'interval', dataproviders.dataset.IntervalDataProvider.settings ) def interval_dataprovider( self, dataset, **settings ): return dataproviders.dataset.IntervalDataProvider( dataset, 0, 3, 4, 6, 2, **settings ) @dataproviders.decorators.dataprovider_factory( 'interval-dict', dataproviders.dataset.IntervalDataProvider.settings ) def interval_dict_dataprovider( self, dataset, **settings ): settings[ 'named_columns' ] = True return self.interval_dataprovider( dataset, **settings ) class Gff3( Gff ): """Tab delimited data in Gff3 format""" edam_format = "format_1975" file_ext = "gff3" valid_gff3_strand = ['+', '-', '.', '?'] valid_gff3_phase = ['.', '0', '1', '2'] column_names = [ 'Seqid', 'Source', 'Type', 'Start', 'End', 'Score', 'Strand', 'Phase', 'Attributes' ] track_type = Interval.track_type """Add metadata elements""" MetadataElement( name="column_types", default=['str', 'str', 'str', 'int', 'int', 'float', 'str', 'int', 'list'], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False ) def __init__(self, **kwd): """Initialize datatype, by adding GBrowse display app""" Gff.__init__(self, **kwd) def set_meta( self, dataset, overwrite=True, **kwd ): self.set_attribute_metadata( dataset ) i = 0 for i, line in enumerate( file( dataset.file_name ) ): line = line.rstrip('\r\n') if line and not line.startswith( '#' ): elems = line.split( '\t' ) valid_start = False valid_end = False if len( elems ) == 9: try: start = int( elems[3] ) valid_start = True except: if elems[3] == '.': valid_start = True try: end = int( elems[4] ) valid_end = True except: if elems[4] == '.': valid_end = True strand = elems[6] phase = elems[7] if valid_start and valid_end and start < end and strand in self.valid_gff3_strand and phase in self.valid_gff3_phase: break Tabular.set_meta( self, dataset, overwrite=overwrite, skip=i ) def sniff( self, filename ): """ Determines whether the file is in gff version 3 format GFF 3 format: 1) adds a mechanism for representing more than one level of hierarchical grouping of features and subfeatures. 2) separates the ideas of group membership and feature name/id 3) constrains the feature type field to be taken from a controlled vocabulary. 4) allows a single feature, such as an exon, to belong to more than one group at a time. 5) provides an explicit convention for pairwise alignments 6) provides an explicit convention for features that occupy disjunct regions The format consists of 9 columns, separated by tabs (NOT spaces). Undefined fields are replaced with the "." character, as described in the original GFF spec. For complete details see http://song.sourceforge.net/gff3.shtml >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'test.gff' ) >>> Gff3().sniff( fname ) False >>> fname = get_test_fname('gff_version_3.gff') >>> Gff3().sniff( fname ) True """ headers = get_headers( filename, '\t' ) try: if len(headers) < 2: return False for hdr in headers: if hdr and hdr[0].startswith( '##gff-version' ) and hdr[0].find( '3' ) >= 0: return True elif hdr and hdr[0].startswith( '##gff-version' ) and hdr[0].find( '3' ) < 0: return False # Header comments may have been stripped, so inspect the data if hdr and hdr[0] and not hdr[0].startswith( '#' ): if len(hdr) != 9: return False try: int( hdr[3] ) except: if hdr[3] != '.': return False try: int( hdr[4] ) except: if hdr[4] != '.': return False if hdr[5] != '.': try: float( hdr[5] ) except: return False if hdr[6] not in self.valid_gff3_strand: return False if hdr[7] not in self.valid_gff3_phase: return False return True except: return False class Gtf( Gff ): """Tab delimited data in Gtf format""" edam_format = "format_2306" file_ext = "gtf" column_names = [ 'Seqname', 'Source', 'Feature', 'Start', 'End', 'Score', 'Strand', 'Frame', 'Attributes' ] track_type = Interval.track_type """Add metadata elements""" MetadataElement( name="columns", default=9, desc="Number of columns", readonly=True, visible=False ) MetadataElement( name="column_types", default=['str', 'str', 'str', 'int', 'int', 'float', 'str', 'int', 'list'], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False ) def sniff( self, filename ): """ Determines whether the file is in gtf format GTF lines have nine required fields that must be tab-separated. The first eight GTF fields are the same as GFF. The group field has been expanded into a list of attributes. Each attribute consists of a type/value pair. Attributes must end in a semi-colon, and be separated from any following attribute by exactly one space. The attribute list must begin with the two mandatory attributes: gene_id value - A globally unique identifier for the genomic source of the sequence. transcript_id value - A globally unique identifier for the predicted transcript. For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format4 >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( '1.bed' ) >>> Gtf().sniff( fname ) False >>> fname = get_test_fname( 'test.gff' ) >>> Gtf().sniff( fname ) False >>> fname = get_test_fname( 'test.gtf' ) >>> Gtf().sniff( fname ) True """ headers = get_headers( filename, '\t' ) try: if len(headers) < 2: return False for hdr in headers: if hdr and hdr[0].startswith( '##gff-version' ) and hdr[0].find( '2' ) < 0: return False if hdr and hdr[0] and not hdr[0].startswith( '#' ): if len(hdr) != 9: return False try: int( hdr[3] ) int( hdr[4] ) except: return False if hdr[5] != '.': try: float( hdr[5] ) except: return False if hdr[6] not in data.valid_strand: return False # Check attributes for gene_id, transcript_id attributes = parse_gff_attributes( hdr[8] ) if len( attributes ) >= 2: if 'gene_id' not in attributes: return False if 'transcript_id' not in attributes: return False else: return False return True except: return False @dataproviders.decorators.has_dataproviders class Wiggle( Tabular, _RemoteCallMixin ): """Tab delimited data in wiggle format""" edam_format = "format_3005" file_ext = "wig" track_type = "LineTrack" data_sources = { "data": "bigwig", "index": "bigwig" } MetadataElement( name="columns", default=3, desc="Number of columns", readonly=True, visible=False ) def __init__( self, **kwd ): Tabular.__init__( self, **kwd ) self.add_display_app( 'ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links' ) self.add_display_app( 'gbrowse', 'display in Gbrowse', 'as_gbrowse_display_file', 'gbrowse_links' ) def get_estimated_display_viewport( self, dataset ): """Return a chrom, start, stop tuple for viewing a file.""" viewport_feature_count = 100 # viewport should check at least 100 features; excludes comment lines max_line_count = max( viewport_feature_count, 500 ) # maximum number of lines to check; includes comment lines if self.displayable( dataset ): try: chrom = None start = sys.maxsize end = 0 span = 1 step = None fh = open( dataset.file_name ) while True: line = fh.readline( VIEWPORT_READLINE_BUFFER_SIZE ) if not line: break # EOF try: if line.startswith( "browser" ): chr_info = line.rstrip( '\n\r' ).split()[-1] chrom, coords = chr_info.split( ":" ) start, end = map( int, coords.split( "-" ) ) break # use the browser line # variableStep chrom=chr20 if line and ( line.lower().startswith( "variablestep" ) or line.lower().startswith( "fixedstep" ) ): if chrom is not None: break # different chrom or different section of the chrom chrom = line.rstrip( '\n\r' ).split("chrom=")[1].split()[0] if 'span=' in line: span = int( line.rstrip( '\n\r' ).split("span=")[1].split()[0] ) if 'step=' in line: step = int( line.rstrip( '\n\r' ).split("step=")[1].split()[0] ) start = int( line.rstrip( '\n\r' ).split("start=")[1].split()[0] ) else: fields = line.rstrip( '\n\r' ).split() if fields: if step is not None: if not end: end = start + span else: end += step else: start = min( int( fields[0] ), start ) end = max( end, int( fields[0] ) + span ) viewport_feature_count -= 1 except: pass # make sure we are at the next new line readline_count = VIEWPORT_MAX_READS_PER_LINE while line.rstrip( '\n\r' ) == line: assert readline_count > 0, Exception( 'Viewport readline count exceeded for dataset %s.' % dataset.id ) line = fh.readline( VIEWPORT_READLINE_BUFFER_SIZE ) if not line: break # EOF readline_count -= 1 max_line_count -= 1 if not viewport_feature_count or not max_line_count: # exceeded viewport or total line count to check break if chrom is not None: return ( chrom, str( start ), str( end ) ) # Necessary to return strings? except Exception as e: # unexpected error log.exception( str( e ) ) return ( None, None, None ) # could not determine viewport def gbrowse_links( self, dataset, type, app, base_url ): ret_val = [] chrom, start, stop = self.get_estimated_display_viewport( dataset ) if chrom is not None: for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('gbrowse', dataset.dbkey ): if site_name in app.datatypes_registry.get_display_sites('gbrowse'): if chrom.startswith( 'chr' ) and len( chrom ) > 3: chrom = chrom[3:] redirect_url = urllib.quote_plus( "%s/?q=%s:%s..%s&eurl=%%s" % ( site_url, chrom, start, stop ) ) link = self._get_remote_call_url( redirect_url, site_name, dataset, type, app, base_url ) ret_val.append( ( site_name, link ) ) return ret_val def ucsc_links( self, dataset, type, app, base_url ): ret_val = [] chrom, start, stop = self.get_estimated_display_viewport( dataset ) if chrom is not None: for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey ): if site_name in app.datatypes_registry.get_display_sites('ucsc'): redirect_url = urllib.quote_plus( "%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" % ( site_url, dataset.dbkey, chrom, start, stop ) ) link = self._get_remote_call_url( redirect_url, site_name, dataset, type, app, base_url ) ret_val.append( ( site_name, link ) ) return ret_val def display_peek( self, dataset ): """Returns formated html of peek""" return Tabular.make_html_table( self, dataset, skipchars=['track', '#'] ) def set_meta( self, dataset, overwrite=True, **kwd ): max_data_lines = None i = 0 for i, line in enumerate( file( dataset.file_name ) ): line = line.rstrip('\r\n') if line and not line.startswith( '#' ): elems = line.split( '\t' ) try: float( elems[0] ) # "Wiggle track data values can be integer or real, positive or negative values" break except: do_break = False for col_startswith in data.col1_startswith: if elems[0].lower().startswith( col_startswith ): do_break = True break if do_break: break if self.max_optional_metadata_filesize >= 0 and dataset.get_size() > self.max_optional_metadata_filesize: # we'll arbitrarily only use the first 100 data lines in this wig file to calculate tabular attributes (column types) # this should be sufficient, except when we have mixed wig track types (bed, variable, fixed), # but those cases are not a single table that would have consistant column definitions # optional metadata values set in Tabular class will be 'None' max_data_lines = 100 Tabular.set_meta( self, dataset, overwrite=overwrite, skip=i, max_data_lines=max_data_lines ) def sniff( self, filename ): """ Determines wether the file is in wiggle format The .wig format is line-oriented. Wiggle data is preceeded by a track definition line, which adds a number of options for controlling the default display of this track. Following the track definition line is the track data, which can be entered in several different formats. The track definition line begins with the word 'track' followed by the track type. The track type with version is REQUIRED, and it currently must be wiggle_0. For example, track type=wiggle_0... For complete details see http://genome.ucsc.edu/goldenPath/help/wiggle.html >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'interval1.bed' ) >>> Wiggle().sniff( fname ) False >>> fname = get_test_fname( 'wiggle.wig' ) >>> Wiggle().sniff( fname ) True """ headers = get_headers( filename, None ) try: for hdr in headers: if len(hdr) > 1 and hdr[0] == 'track' and hdr[1].startswith('type=wiggle'): return True return False except: return False def get_track_window(self, dataset, data, start, end): """ Assumes we have a numpy file. """ range = end - start # Determine appropriate resolution to plot ~1000 points resolution = ( 10 ** math.ceil( math.log10( range / 1000 ) ) ) # Restrict to valid range resolution = min( resolution, 100000 ) resolution = max( resolution, 1 ) # Memory map the array (don't load all the data) data = numpy.load( data ) # Grab just what we need t_start = math.floor( start / resolution ) t_end = math.ceil( end / resolution ) x = numpy.arange( t_start, t_end ) * resolution y = data[ t_start : t_end ] return zip(x.tolist(), y.tolist()) def get_track_resolution( self, dataset, start, end): range = end - start # Determine appropriate resolution to plot ~1000 points resolution = math.ceil( 10 ** math.ceil( math.log10( range / 1000 ) ) ) # Restrict to valid range resolution = min( resolution, 100000 ) resolution = max( resolution, 1 ) return resolution # ------------- Dataproviders @dataproviders.decorators.dataprovider_factory( 'wiggle', dataproviders.dataset.WiggleDataProvider.settings ) def wiggle_dataprovider( self, dataset, **settings ): dataset_source = dataproviders.dataset.DatasetDataProvider( dataset ) return dataproviders.dataset.WiggleDataProvider( dataset_source, **settings ) @dataproviders.decorators.dataprovider_factory( 'wiggle-dict', dataproviders.dataset.WiggleDataProvider.settings ) def wiggle_dict_dataprovider( self, dataset, **settings ): dataset_source = dataproviders.dataset.DatasetDataProvider( dataset ) settings[ 'named_columns' ] = True return dataproviders.dataset.WiggleDataProvider( dataset_source, **settings ) class CustomTrack ( Tabular ): """UCSC CustomTrack""" file_ext = "customtrack" def __init__(self, **kwd): """Initialize interval datatype, by adding UCSC display app""" Tabular.__init__(self, **kwd) self.add_display_app( 'ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links' ) def set_meta( self, dataset, overwrite=True, **kwd ): Tabular.set_meta( self, dataset, overwrite=overwrite, skip=1 ) def display_peek( self, dataset ): """Returns formated html of peek""" return Tabular.make_html_table( self, dataset, skipchars=['track', '#'] ) def get_estimated_display_viewport( self, dataset, chrom_col=None, start_col=None, end_col=None ): """Return a chrom, start, stop tuple for viewing a file.""" # FIXME: only BED and WIG custom tracks are currently supported # As per previously existing behavior, viewport will only be over the first intervals max_line_count = 100 # maximum number of lines to check; includes comment lines variable_step_wig = False chrom = None span = 1 if self.displayable( dataset ): try: fh = open( dataset.file_name ) while True: line = fh.readline( VIEWPORT_READLINE_BUFFER_SIZE ) if not line: break # EOF if not line.startswith( '#' ): try: if variable_step_wig: fields = line.rstrip().split() if len( fields ) == 2: start = int( fields[ 0 ] ) return ( chrom, str( start ), str( start + span ) ) elif line and ( line.lower().startswith( "variablestep" ) or line.lower().startswith( "fixedstep" ) ): chrom = line.rstrip( '\n\r' ).split("chrom=")[1].split()[0] if 'span=' in line: span = int( line.rstrip( '\n\r' ).split("span=")[1].split()[0] ) if 'start=' in line: start = int( line.rstrip( '\n\r' ).split("start=")[1].split()[0] ) return ( chrom, str( start ), str( start + span ) ) else: variable_step_wig = True else: fields = line.rstrip().split( '\t' ) if len( fields ) >= 3: chrom = fields[ 0 ] start = int( fields[ 1 ] ) end = int( fields[ 2 ] ) return ( chrom, str( start ), str( end ) ) except Exception: # most likely a non-integer field has been encountered for start / stop continue # make sure we are at the next new line readline_count = VIEWPORT_MAX_READS_PER_LINE while line.rstrip( '\n\r' ) == line: assert readline_count > 0, Exception( 'Viewport readline count exceeded for dataset %s.' % dataset.id ) line = fh.readline( VIEWPORT_READLINE_BUFFER_SIZE ) if not line: break # EOF readline_count -= 1 max_line_count -= 1 if not max_line_count: # exceeded viewport or total line count to check break except Exception as e: # unexpected error log.exception( str( e ) ) return ( None, None, None ) # could not determine viewport def ucsc_links( self, dataset, type, app, base_url ): ret_val = [] chrom, start, stop = self.get_estimated_display_viewport(dataset) if chrom is not None: for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey): if site_name in app.datatypes_registry.get_display_sites('ucsc'): internal_url = "%s" % url_for( controller='dataset', dataset_id=dataset.id, action='display_at', filename='ucsc_' + site_name ) display_url = urllib.quote_plus( "%s%s/display_as?id=%i&display_app=%s&authz_method=display_at" % (base_url, url_for( controller='root' ), dataset.id, type) ) redirect_url = urllib.quote_plus( "%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" % (site_url, dataset.dbkey, chrom, start, stop ) ) link = '%s?redirect_url=%s&display_url=%s' % ( internal_url, redirect_url, display_url ) ret_val.append( (site_name, link) ) return ret_val def sniff( self, filename ): """ Determines whether the file is in customtrack format. CustomTrack files are built within Galaxy and are basically bed or interval files with the first line looking something like this. track name="User Track" description="User Supplied Track (from Galaxy)" color=0,0,0 visibility=1 >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'complete.bed' ) >>> CustomTrack().sniff( fname ) False >>> fname = get_test_fname( 'ucsc.customtrack' ) >>> CustomTrack().sniff( fname ) True """ headers = get_headers( filename, None ) first_line = True for hdr in headers: if first_line: first_line = False try: if hdr[0].startswith('track'): color_found = False visibility_found = False for elem in hdr[1:]: if elem.startswith('color'): color_found = True if elem.startswith('visibility'): visibility_found = True if color_found and visibility_found: break if not color_found or not visibility_found: return False else: return False except: return False else: try: if hdr[0] and not hdr[0].startswith( '#' ): if len( hdr ) < 3: return False try: int( hdr[1] ) int( hdr[2] ) except: return False except: return False return True class ENCODEPeak( Interval ): ''' Human ENCODE peak format. There are both broad and narrow peak formats. Formats are very similar; narrow peak has an additional column, though. Broad peak ( http://genome.ucsc.edu/FAQ/FAQformat#format13 ): This format is used to provide called regions of signal enrichment based on pooled, normalized (interpreted) data. It is a BED 6+3 format. Narrow peak http://genome.ucsc.edu/FAQ/FAQformat#format12 and : This format is used to provide called peaks of signal enrichment based on pooled, normalized (interpreted) data. It is a BED6+4 format. ''' file_ext = "encodepeak" column_names = [ 'Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'SignalValue', 'pValue', 'qValue', 'Peak' ] data_sources = { "data": "tabix", "index": "bigwig" } """Add metadata elements""" MetadataElement( name="chromCol", default=1, desc="Chrom column", param=metadata.ColumnParameter ) MetadataElement( name="startCol", default=2, desc="Start column", param=metadata.ColumnParameter ) MetadataElement( name="endCol", default=3, desc="End column", param=metadata.ColumnParameter ) MetadataElement( name="strandCol", desc="Strand column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0 ) MetadataElement( name="columns", default=3, desc="Number of columns", readonly=True, visible=False ) def sniff( self, filename ): return False class ChromatinInteractions( Interval ): ''' Chromatin interactions obtained from 3C/5C/Hi-C experiments. ''' file_ext = "chrint" track_type = "DiagonalHeatmapTrack" data_sources = { "data": "tabix", "index": "bigwig" } column_names = [ 'Chrom1', 'Start1', 'End1', 'Chrom2', 'Start2', 'End2', 'Value' ] """Add metadata elements""" MetadataElement( name="chrom1Col", default=1, desc="Chrom1 column", param=metadata.ColumnParameter ) MetadataElement( name="start1Col", default=2, desc="Start1 column", param=metadata.ColumnParameter ) MetadataElement( name="end1Col", default=3, desc="End1 column", param=metadata.ColumnParameter ) MetadataElement( name="chrom2Col", default=4, desc="Chrom2 column", param=metadata.ColumnParameter ) MetadataElement( name="start2Col", default=5, desc="Start2 column", param=metadata.ColumnParameter ) MetadataElement( name="end2Col", default=6, desc="End2 column", param=metadata.ColumnParameter ) MetadataElement( name="valueCol", default=7, desc="Value column", param=metadata.ColumnParameter ) MetadataElement( name="columns", default=7, desc="Number of columns", readonly=True, visible=False ) def sniff( self, filename ): return False class ScIdx(Tabular): """ ScIdx files are 1-based and consist of strand-specific coordinate counts. They always have 5 columns, and the first row is the column labels: 'chrom', 'index', 'forward', 'reverse', 'value'. Each line following the first consists of data: chromosome name (type str), peak index (type int), Forward strand peak count (type int), Reverse strand peak count (type int) and value (type int). The value of the 5th 'value' column is the sum of the forward and reverse peak count values. """ file_ext = "scidx" MetadataElement(name="columns", default=0, desc="Number of columns", readonly=True, visible=False) MetadataElement(name="column_types", default=[], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False, no_value=[]) def __init__(self, **kwd): """ Initialize scidx datatype. """ Tabular.__init__(self, **kwd) # Don't set column names since the first # line of the dataset displays them. self.column_names = ['chrom', 'index', 'forward', 'reverse', 'value'] def sniff(self, filename): """ Checks for 'scidx-ness.' """ try: count = 0 fh = open(filename, "r") while True: line = fh.readline() line = line.strip() # The first line is always a comment like this: # 2015-11-23 20:18:56.51;input.bam;READ1 if count == 0: if line.startswith('#'): count += 1 continue else: return False if not line: # EOF if count > 1: # The second line is always the labels: # chrom index forward reverse value # We need at least the column labels and a data line. return True return False # Skip first line. if count > 1: items = line.split('\t') if len(items) != 5: return False index = items[1] if not index.isdigit(): return False forward = items[2] if not forward.isdigit(): return False reverse = items[3] if not reverse.isdigit(): return False value = items[4] if not value.isdigit(): return False if int(forward) + int(reverse) != int(value): return False if count == 100: return True count += 1 if count < 100 and count > 0: return True except: return False finally: fh.close() return False if __name__ == '__main__': import doctest doctest.testmod(sys.modules[__name__])

48.563057

208

0.52673

be5429303e0356e1730e3ab24c345369c08f6a49

614

py

Python

Lib/site-packages/ghtTests/trivialscript.py

Jriszz/guacamole-python

cf0dfcaaa7d85c3577571954fc5b2b9dcf55ba17

[ "MIT" ]

1

2021-11-02T06:48:13.000Z

Lib/site-packages/ghtTests/trivialscript.py

Jriszz/guacamole-python

cf0dfcaaa7d85c3577571954fc5b2b9dcf55ba17

[ "MIT" ]

null

Lib/site-packages/ghtTests/trivialscript.py

Jriszz/guacamole-python

cf0dfcaaa7d85c3577571954fc5b2b9dcf55ba17

[ "MIT" ]

null

from __future__ import print_function import sys from twisted.application import reactors reactors.installReactor('qt4') from twisted.internet import reactor, task from twisted.python import log, runtime log.startLogging(sys.stdout) def testreactor(): print('tick...') def doit(): log.msg("reactor module: ", reactor.__module__) task.LoopingCall(testreactor).start(1.0) reactor.callLater(5.0, reactor.stop) log.msg("platform runtime: " + repr(runtime.platform.getType())) reactor.callWhenRunning(doit) log.msg('calling reactor.run()') reactor.run() log.msg('fell off the bottom?...')

21.172414

68

0.737785

77b344a2b70254578540c292396381035e9c4739

1,139

py

Python

setup.py

LuizHNLorena/jgraph

fa658299c07a588badcf4fb2cafa1fc68ac5e72d

[ "MIT" ]

91

2015-12-24T09:11:54.000Z

2022-01-22T21:18:50.000Z

setup.py

LuizHNLorena/jgraph

fa658299c07a588badcf4fb2cafa1fc68ac5e72d

[ "MIT" ]

12

2015-12-21T21:57:34.000Z

2019-12-18T17:33:22.000Z

setup.py

LuizHNLorena/jgraph

fa658299c07a588badcf4fb2cafa1fc68ac5e72d

[ "MIT" ]

28

2015-12-25T14:14:56.000Z

2021-12-10T06:24:35.000Z

from setuptools import setup from python import __version__ setup( name="jgraph", version=__version__, description="View graph data structures in the IPython notebook.", url="http://github.com/patrickfuller/jgraph/", license="MIT", author="Patrick Fuller", author_email="patrickfuller@gmail.com", package_dir={'jgraph': 'python', 'jgraph.js': 'js'}, package_data={'jgraph.js': ['js/build/jgraph.min.js']}, include_package_data=True, packages=['jgraph', 'jgraph.js'], install_requires=['ipython'], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Education', 'Natural Language :: English', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Framework :: IPython', 'Topic :: Education :: Computer Aided Instruction (CAI)' ] )

34.515152

70

0.614574

3f6aec647fcbf099ae720454868a43da376da8ce

1,424

py

Python

versions/paper.py

Advik-B/MC-Server-Installer

a52ed35eac828f220044b5a751c5f8ecf4d82f42

[ "MIT" ]

1

2021-08-15T11:23:09.000Z

versions/paper.py

Advik-B/Server-Installer

a52ed35eac828f220044b5a751c5f8ecf4d82f42

[ "MIT" ]

null

versions/paper.py

Advik-B/Server-Installer

a52ed35eac828f220044b5a751c5f8ecf4d82f42

[ "MIT" ]

null

from versions.default import * class Paper(Server): def getlink(version_number:str) -> str: versions = { '1.10.2':'https://www.mediafire.com/file/1ds9hhiuvhazn5e/paper-1.10.2-916.jar/file', '1.11.2':'https://www.mediafire.com/file/eeidpu8dcg0h23v/paper-1.11.2-1104.jar/file', '1.12.2':"https://www.mediafire.com/file/qhop9drado0wwax/paper-1.12.2-1618.jar/file", '1.13.2':'https://www.mediafire.com/file/c0a19hcqag70mcc/paper-1.13.2-655.jar/file', '1.14.4':'https://www.mediafire.com/file/818earslka4fjoy/paper-1.14.4-243.jar/file', '1.15.2':'https://www.mediafire.com/file/zsdkqs8s2ae4z2h/paper-1.15.2-391.jar/file', '1.16.5':'https://www.mediafire.com/file/pfyj4n1lbc2grm7/paper-1.16.5-786.jar/file', '1.17.1':'https://www.mediafire.com/file/6dzmfryu6f7lbwk/paper-1.17.1-186.jar/file', '1.8.9':'https://www.mediafire.com/file/346u26k0t169x7t/paper-1.8.8-443.jar/file', '1.9.4':'https://www.mediafire.com/file/p4qbs1z59emqsy6/paper-1.9.4-773.jar/file' } version = versions.keys() if version_number in version: download_url = versions[version_number] return download_url else: print() print(f'The version "{version_number}" is not found!') print() return "about:blank"

38.486486

97

0.606039

7968cb2897c532040d9f32f888ffdb6e6422ba4f

2,706

py

Python

modules/filter.py

TheHercules/Telegram-UserBot

62f7abf68eaf729779f9e0c4af4c000445d6136b

[ "Unlicense" ]

2

2018-10-23T09:15:18.000Z

2019-02-14T11:47:08.000Z

modules/filter.py

pkp1337x/Telegram-UserBot

f139a1b09f01da795a663fc8ff9d147ecc51e8de

[ "Unlicense" ]

null

modules/filter.py

pkp1337x/Telegram-UserBot

f139a1b09f01da795a663fc8ff9d147ecc51e8de

[ "Unlicense" ]

null

import sqlite3 @bot.on(events.NewMessage(incoming=True)) @bot.on(events.MessageEdited(incoming=True)) async def filter_incoming_handler(e): db=sqlite3.connect("filters.db") cursor=db.cursor() cursor.execute('''SELECT * FROM FILTER''') all_rows = cursor.fetchall() for row in all_rows: if int(row[0]) == int(e.chat_id): if str(row[1]) in str(e.text): await e.reply(row[2]) db.close() @bot.on(events.NewMessage(outgoing=True, pattern='.filter')) @bot.on(events.MessageEdited(outgoing=True, pattern='.filter')) async def add_filter(e): message=e.text kek=message.split() db=sqlite3.connect("filters.db") cursor=db.cursor() string="" for i in range(2,len(kek)): string=string+" "+str(kek[i]) cursor.execute('''INSERT INTO FILTER VALUES(?,?,?)''', (int(e.chat_id),kek[1],string)) db.commit() await e.edit("```Added Filter Successfully```") db.close() @bot.on(events.NewMessage(outgoing=True, pattern='.nofilter')) @bot.on(events.MessageEdited(outgoing=True, pattern='.nofilter')) async def remove_filter(e): message=e.text kek=message.split() db=sqlite3.connect("filters.db") cursor=db.cursor() cursor.execute('''DELETE FROM FILTER WHERE chat_id=? AND filter=?''', (int(e.chat_id),kek[1])) db.commit() await e.edit("```Removed Filter Successfully```") db.close() @bot.on(events.NewMessage(outgoing=True, pattern='.rmfilters')) @bot.on(events.MessageEdited(outgoing=True, pattern='.rmfilters')) async def kick_marie_filter(e): await e.edit("```Will be kicking away all Marie filters.```") time.sleep(3) r = await e.get_reply_message() filters = r.text.split('-')[1:] for filter in filters: await e.reply('/stop %s' % (filter.strip())) await asyncio.sleep(0.3) await e.respond('/filter filters @baalajimaestro kicked them all') await e.respond("```Successfully cleaned Marie filters yaay!```\n Gimme cookies @baalajimaestro") if LOGGER: await bot.send_message(LOGGER_GROUP,"I cleaned all Marie filters at "+str(e.chat_id)) @bot.on(events.NewMessage(outgoing=True, pattern='.get filters')) @bot.on(events.MessageEdited(outgoing=True, pattern='.get filters')) async def filters_active(e): db=sqlite3.connect("filters.db") cursor=db.cursor() transact="Filters active on this chat: \n" cursor.execute('''SELECT * FROM FILTER''') all_rows = cursor.fetchall() for row in all_rows: if int(row[0]) == int(e.chat_id): transact=transact+"-"+str(row[1])+" : "+str(row[2])+"\n" db.close() await e.edit(transact)

41

101

0.642646

abf0666002d1b2b7d4794d89adf2a0ee200d4d85

1,651

py

Python

stonesoup/predictor/base.py

riskaware-ltd/Stone-Soup

ef3a2fb7e121c00dcf458e370794db3785b732e3

[ "MIT" ]

null

stonesoup/predictor/base.py

riskaware-ltd/Stone-Soup

ef3a2fb7e121c00dcf458e370794db3785b732e3

[ "MIT" ]

4

2020-03-10T13:51:00.000Z

2020-03-23T12:38:24.000Z

stonesoup/predictor/base.py

riskaware-ltd/Stone-Soup

ef3a2fb7e121c00dcf458e370794db3785b732e3

[ "MIT" ]

1

2019-12-09T14:33:09.000Z

# -*- coding: utf-8 -*- """Base classes for Stone Soup Predictor interface""" from abc import abstractmethod from ..base import Base, Property from ..models.transition import TransitionModel from ..models.control import ControlModel class Predictor(Base): r"""Predictor base class A predictor is used to predict a new :class:`~.State` given a prior :class:`~.State` and a :class:`~.TransitionModel`. In addition, a :class:`~.ControlModel` may be used to model an external influence on the state. .. math:: \mathbf{x}_{k|k-1} = f_k(\mathbf{x}_{k-1}, \mathbf{\nu}_k) + b_k(\mathbf{u}_k, \mathbf{\eta}_k) where :math:`\mathbf{x}_{k-1}` is the prior state, :math:`f_k(\mathbf{x}_{k-1})` is the transition function, :math:`\mathbf{u}_k` the control vector, :math:`b_k(\mathbf{u}_k)` the control input and :math:`\mathbf{\nu}_k` and :math:`\mathbf{\eta}_k` the transition and control model noise respectively. """ transition_model = Property(TransitionModel, doc="transition model") control_model = Property(ControlModel, default=None, doc="control model") @abstractmethod def predict(self, prior, timestamp=None, *args, **kwargs): """The prediction function itself Parameters ---------- prior : :class:`~.State` The prior state timestamp : :class:`datetime.datetime`, optional Time at which the prediction is made (used by the transition model) Returns ------- : :class:`~.StatePrediction` State prediction """ raise NotImplementedError

32.372549

77

0.632344

1ae23d05c78cd40b3775e9824b2fd1f3576cba5a

404

py

Python

timeseries/computeError.py

Thanduriel/NeuralTurbulence

044729c39b77b0acf73071b15c5ddfa79cb1f9cb

[ "MIT" ]

null

timeseries/computeError.py

Thanduriel/NeuralTurbulence

044729c39b77b0acf73071b15c5ddfa79cb1f9cb

[ "MIT" ]

null

timeseries/computeError.py

Thanduriel/NeuralTurbulence

044729c39b77b0acf73071b15c5ddfa79cb1f9cb

[ "MIT" ]

null

import numpy as np import sys sys.path.append("../utils") import ioext # Computes the L2 error of the given numpy arrays. def error(array1, array2): dif = np.subtract(array1, array2) return np.linalg.norm(dif.flatten(), ord=2) densities = ioext.loadData("data/validation32New/density_*.uni") total = 0 for i in range(0, len(densities)-1): total += error(densities[i], densities[i+1]) print(total)

23.764706

64

0.725248

44a3de06c0518aebb55cfa8f2a1bbfd7551df14d

837

py

Python

aigpyqt/__init__.py

AIGMix/AIGPYQT

a814758d6a1dbdc75cc4ef794470c4686d28685d

[ "MIT" ]

null

aigpyqt/__init__.py

AIGMix/AIGPYQT

a814758d6a1dbdc75cc4ef794470c4686d28685d

[ "MIT" ]

null

aigpyqt/__init__.py

AIGMix/AIGPYQT

a814758d6a1dbdc75cc4ef794470c4686d28685d

[ "MIT" ]

null

#!/usr/bin/env python # -*- encoding: utf-8 -*- ''' @File : __init__.py @Date : 2021/05/08 @Author : Yaronzz @Version : 1.0 @Contact : yaronhuang@foxmail.com @Desc : ''' import sys import aigpy import aigpyqt.theme from PyQt5.Qt import * from aigpyqt.page.loginView import LoginView loginView = None def buttonClicked(): account = loginView.getAccountInfo() username = account['username'] password = account['password'] if aigpy.string.isNull(username) or aigpy.string.isNull(password): loginView.showErrMessage('请检查并重新输入正确的账号密码') if __name__ == '__main__': qss = aigpyqt.theme.getThemeQssContent() app = QApplication(sys.argv) app.setStyleSheet(qss) loginView = LoginView() loginView.setButtonClickFunction(buttonClicked) loginView.show() sys.exit(app.exec_())

22.621622

70

0.691756

fab9e5b224f9e9778b3077b5ce0d556705ac6ea4

788

py

Python

recipes/libnsgif/all/test_package/conanfile.py

Speak2Erase/conan-center-index

36ef6081cd4cd201542bd9790cc654c4eafe7b0f

[ "MIT" ]

null

recipes/libnsgif/all/test_package/conanfile.py

Speak2Erase/conan-center-index

36ef6081cd4cd201542bd9790cc654c4eafe7b0f

[ "MIT" ]

null

recipes/libnsgif/all/test_package/conanfile.py

Speak2Erase/conan-center-index

36ef6081cd4cd201542bd9790cc654c4eafe7b0f

[ "MIT" ]

null

import os from conans import ConanFile, tools from conan.tools.cmake import CMake from conan.tools.layout import cmake_layout class LibnsgifTestConan(ConanFile): settings = "os", "compiler", "build_type", "arch" # VirtualBuildEnv and VirtualRunEnv can be avoided if "tools.env.virtualenv:auto_use" is defined # (it will be defined in Conan 2.0) generators = "CMakeDeps", "CMakeToolchain", "VirtualBuildEnv", "VirtualRunEnv" apply_env = False def build(self): cmake = CMake(self) cmake.configure() cmake.build() def layout(self): cmake_layout(self) def test(self): if not tools.cross_building(self): cmd = os.path.join(self.cpp.build.bindirs[0], "example") self.run(cmd, env="conanrun")

29.185185

100

0.668782

a51e8a2285d0ed669d164028ece47abad9db855a

1,871

py

Python

speaking_portfolio/migrations/0007_add_coverage_model.py

jacobian/jacobian.org

cacff78aa68cb1585a63c04ff5bd7fc484066730

[ "Apache-2.0" ]

8

2018-11-12T21:11:18.000Z

2020-10-20T09:03:54.000Z

speaking_portfolio/migrations/0007_add_coverage_model.py

jacobian/jacobian.org

cacff78aa68cb1585a63c04ff5bd7fc484066730

[ "Apache-2.0" ]

5

2018-11-28T12:56:57.000Z

2020-02-05T21:56:48.000Z

speaking_portfolio/migrations/0007_add_coverage_model.py

jacobian/jacobian.org

cacff78aa68cb1585a63c04ff5bd7fc484066730

[ "Apache-2.0" ]

5

2018-11-19T16:47:15.000Z

2020-02-14T22:34:26.000Z

# Generated by Django 2.1.3 on 2018-11-16 00:21 import django.contrib.postgres.fields.jsonb from django.db import migrations, models import django.db.models.deletion import django_postgres_unlimited_varchar class Migration(migrations.Migration): dependencies = [ ("speaking_portfolio", "0006_drop_conference_fields_from_presentation") ] operations = [ migrations.CreateModel( name="Coverage", fields=[ ( "id", models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name="ID", ), ), ( "type", django_postgres_unlimited_varchar.UnlimitedCharField( choices=[ ("video", "Video"), ("slides", "Slides"), ("link", "Link"), ("notes", "Notes"), ("write-up", "Write-Up"), ] ), ), ("url", models.URLField()), ( "oembed", django.contrib.postgres.fields.jsonb.JSONField( blank=True, null=True ), ), ( "presentation", models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name="coverage", to="speaking_portfolio.Presentation", ), ), ], options={"verbose_name_plural": "coverage"}, ) ]

31.711864

79

0.403528

ebb78a041769cb8b3ecf52fdf5766ac97740e671

9,066

py

Python

classroom/views/teachers.py

jtryan/AssessmentApplication

d7559957fcbce9eac3739dcf2bba366edcd3363b

[ "MIT" ]

null

classroom/views/teachers.py

jtryan/AssessmentApplication

d7559957fcbce9eac3739dcf2bba366edcd3363b

[ "MIT" ]

null

classroom/views/teachers.py

jtryan/AssessmentApplication

d7559957fcbce9eac3739dcf2bba366edcd3363b

[ "MIT" ]

null

from django.contrib import messages from django.contrib.auth import login from django.contrib.auth.decorators import login_required from django.db import transaction from django.db.models import Avg, Count from django.forms import inlineformset_factory from django.shortcuts import get_object_or_404, redirect, render from django.urls import reverse, reverse_lazy from django.utils.decorators import method_decorator from django.views.generic import (CreateView, DeleteView, DetailView, ListView, UpdateView) from ..decorators import teacher_required from ..forms import BaseAnswerInlineFormSet, QuestionForm, TeacherSignUpForm from ..models import Answer, Question, Quiz, User,Subject class TeacherSignUpView(CreateView): model = User form_class = TeacherSignUpForm template_name = 'registration/signup_form.html' def get_context_data(self, **kwargs): kwargs['user_type'] = 'teacher' return super().get_context_data(**kwargs) def form_valid(self, form): user = form.save() login(self.request, user) return redirect('teachers:quiz_change_list') @method_decorator([login_required, teacher_required], name='dispatch') class QuizListView(ListView): model = Quiz ordering = ('name', ) context_object_name = 'quizzes' template_name = 'classroom/teachers/quiz_change_list.html' def get_queryset(self): queryset = self.request.user.quizzes \ .select_related('subject') \ .annotate(questions_count=Count('questions', distinct=True)) \ .annotate(taken_count=Count('taken_quizzes', distinct=True)) return queryset @method_decorator([login_required, teacher_required], name='dispatch') class QuizCreateView(CreateView): model = Quiz fields = ('name', 'subject', 'duration') template_name = 'classroom/teachers/quiz_add_form.html' def form_valid(self, form): quiz = form.save(commit=False) quiz.owner = self.request.user quiz.save() messages.success(self.request, 'The quiz was created with success! Go ahead and add some questions now.') return redirect('teachers:quiz_change', quiz.pk) @method_decorator([login_required, teacher_required], name='dispatch') class QuizUpdateView(UpdateView): model = Quiz fields = ('name', 'subject','duration' ) context_object_name = 'quiz' template_name = 'classroom/teachers/quiz_change_form.html' def get_context_data(self, **kwargs): kwargs['questions'] = self.get_object().questions.annotate(answers_count=Count('answers')) return super().get_context_data(**kwargs) def get_queryset(self): ''' This method is an implicit object-level permission management This view will only match the ids of existing quizzes that belongs to the logged in user. ''' return self.request.user.quizzes.all() def get_success_url(self): return reverse('teachers:quiz_change', kwargs={'pk': self.object.pk}) @method_decorator([login_required, teacher_required], name='dispatch') class QuizDeleteView(DeleteView): model = Quiz context_object_name = 'quiz' template_name = 'classroom/teachers/quiz_delete_confirm.html' success_url = reverse_lazy('teachers:quiz_change_list') def delete(self, request, *args, **kwargs): quiz = self.get_object() messages.success(request, 'The quiz %s was deleted with success!' % quiz.name) return super().delete(request, *args, **kwargs) def get_queryset(self): return self.request.user.quizzes.all() @method_decorator([login_required, teacher_required], name='dispatch') class QuizResultsView(DetailView): model = Quiz context_object_name = 'quiz' template_name = 'classroom/teachers/quiz_results.html' def get_context_data(self, **kwargs): quiz = self.get_object() taken_quizzes = quiz.taken_quizzes.select_related('student__user').order_by('-date') total_taken_quizzes = taken_quizzes.count() quiz_score = quiz.taken_quizzes.aggregate(average_score=Avg('score')) extra_context = { 'taken_quizzes': taken_quizzes, 'total_taken_quizzes': total_taken_quizzes, 'quiz_score': quiz_score } kwargs.update(extra_context) return super().get_context_data(**kwargs) def get_queryset(self): return self.request.user.quizzes.all() @login_required @teacher_required def question_add(request, pk): # By filtering the quiz by the url keyword argument `pk` and # by the owner, which is the logged in user, we are protecting # this view at the object-level. Meaning only the owner of # quiz will be able to add questions to it. quiz = get_object_or_404(Quiz, pk=pk, owner=request.user) if request.method == 'POST': form = QuestionForm(request.POST) if form.is_valid(): question = form.save(commit=False) question.quiz = quiz question.save() messages.success(request, 'You may now add answers/options to the question.') return redirect('teachers:question_change', quiz.pk, question.pk) else: form = QuestionForm() return render(request, 'classroom/teachers/question_add_form.html', {'quiz': quiz, 'form': form}) @login_required @teacher_required def question_change(request, quiz_pk, question_pk): # Simlar to the `question_add` view, this view is also managing # the permissions at object-level. By querying both `quiz` and # `question` we are making sure only the owner of the quiz can # change its details and also only questions that belongs to this # specific quiz can be changed via this url (in cases where the # user might have forged/player with the url params. quiz = get_object_or_404(Quiz, pk=quiz_pk, owner=request.user) question = get_object_or_404(Question, pk=question_pk, quiz=quiz) AnswerFormSet = inlineformset_factory( Question, # parent model Answer, # base model formset=BaseAnswerInlineFormSet, fields=('text', 'is_correct'), min_num=2, validate_min=True, max_num=10, validate_max=True ) if request.method == 'POST': form = QuestionForm(request.POST, instance=question) formset = AnswerFormSet(request.POST, instance=question) if form.is_valid() and formset.is_valid(): with transaction.atomic(): form.save() formset.save() messages.success(request, 'Question and answers saved with success!') return redirect('teachers:quiz_change', quiz.pk) else: form = QuestionForm(instance=question) formset = AnswerFormSet(instance=question) return render(request, 'classroom/teachers/question_change_form.html', { 'quiz': quiz, 'question': question, 'form': form, 'formset': formset }) @method_decorator([login_required, teacher_required], name='dispatch') class QuestionDeleteView(DeleteView): model = Question context_object_name = 'question' template_name = 'classroom/teachers/question_delete_confirm.html' pk_url_kwarg = 'question_pk' def get_context_data(self, **kwargs): question = self.get_object() kwargs['quiz'] = question.quiz return super().get_context_data(**kwargs) def delete(self, request, *args, **kwargs): question = self.get_object() messages.success(request, 'The question %s was deleted with success!' % question.text) return super().delete(request, *args, **kwargs) def get_queryset(self): return Question.objects.filter(quiz__owner=self.request.user) def get_success_url(self): question = self.get_object() return reverse('teachers:quiz_change', kwargs={'pk': question.quiz_id}) # For course option @method_decorator([login_required, teacher_required], name='dispatch') class CourseListView(ListView): model = Subject ordering = ('name', ) context_object_name = 'courses' template_name = 'classroom/teachers/course_change_list.html' def get_queryset(self): queryset = Subject.objects.all() return queryset # queryset = self.request.user.select_related('subject') \ # .annotate(questions_count=Count('questions', distinct=True)) \ # .annotate(taken_count=Count('taken_quizzes', distinct=True)) @method_decorator([login_required, teacher_required], name='dispatch') class CourseCreateView(CreateView): model = Subject fields = ('name', 'color', ) template_name = 'classroom/teachers/course_add_form.html' def form_valid(self, form): course = form.save(commit=False) course.owner = self.request.user course.save() messages.success(self.request, 'The class was created with success! Go ahead and add a quiz now.') # return redirect('teachers:quiz_add', course.pk)

37.155738

113

0.688396

e4237ed0776e04a4dc0f96893d6192a0fbc0bfa2

6,265

py

Python

find_relationships.py

Ernir/icelanders-on-twitter

1cd08e735bc622d44e96d18e7e04a0a7e0cb9665

[ "MIT" ]

null

find_relationships.py

Ernir/icelanders-on-twitter

1cd08e735bc622d44e96d18e7e04a0a7e0cb9665

[ "MIT" ]

null

find_relationships.py

Ernir/icelanders-on-twitter

1cd08e735bc622d44e96d18e7e04a0a7e0cb9665

[ "MIT" ]

null

from pprint import pprint import os import json from time import time import tweepy def get_tweepy_instance(): """ Reads credentials from environment variables and returns a Tweepy instance. """ consumer_key = os.environ.get("ERNIRNET_TWITTER_APP_ID") consumer_secret = os.environ.get("ERNIRNET_TWITTER_APP_SECRET") access_token = os.environ.get("ERNIRNET_TWITTER_APP_ACCESS_TOKEN") access_secret = os.environ.get("ERNIRNET_TWITTER_APP_ACCESS_TOKEN_SECRET") auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.secure = True auth.set_access_token(access_token, access_secret) return tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True) def discover_icelanders(api, known_users): """ Searches for tweets in Iceland, and stores those users whose location is also assocated with Iceland. Updates the given list of known_users. """ iceland_place_id = "c3932d3da7922986" # Predefined by Twitter tweets = api.search(q="place:{0}".format(iceland_place_id), count=100) for tweet in tweets: if looks_icelandic(tweet.user.location) and tweet.user.id not in known_users: known_users.append(tweet.user.id) return known_users def create_new_users(api, relationships, user_ids, verbose=False): """ Adds the given user ids to the relationships dictionary if they are Icelanders. """ known_foreigners = get_foreigners() curated_ids = [id for id in user_ids if id not in relationships and id not in known_foreigners] i = 0 page_size = 100 cache_hits = len(user_ids) - len(curated_ids) users_created = 0 foreigners = 0 while i < len(curated_ids): id_subset = curated_ids[i:i+page_size] if verbose: print("Creating {} users".format(len(id_subset))) try: users_list = api.lookup_users(user_ids=id_subset) i += page_size for user in users_list: user_id = str(user.id) if looks_icelandic(user.location): users_created += 1 associate_id_with_name(user_id, user.screen_name) relationships[user_id] = [] else: store_foreigner(user_id) foreigners += 1 except tweepy.error.TweepError as error: print(str(error) + ", retrying") if verbose: print("{0} cache hits, {1} foreigners found, {2} new users created".format( cache_hits, foreigners, users_created )) return relationships def associate_id_with_name(user_id, user_name): """ Stores an association between a given user id and an user name. """ json_filename = "ids_to_names.json" with open(json_filename) as association_file: associations = json.load(association_file) associations[user_id] = user_name with open(json_filename, "w") as association_file: json.dump(associations, association_file, indent=4) def get_foreigners(): """ Returns a set of all currently recorded non-Icelanders """ json_filename = "foreigners.json" with open(json_filename) as foreigners_file: foreigners = json.load(foreigners_file) return set(foreigners["foreigners"]) def store_foreigner(user_id): """ Stores the given user ID in a file, so it can be skipped later on """ json_filename = "foreigners.json" with open(json_filename) as foreigners_file: foreigners = json.load(foreigners_file) foreigners["foreigners"].append(int(user_id)) with open(json_filename, "w") as foreigners_file: json.dump(foreigners, foreigners_file, indent=4) def discover_followers(api, relationships, user_id, verbose=False): """ Uses the given Tweepy API object instance to find the followers of the user with the given user_id. Updates and returns the given "relationships" dict. """ new_relationships = [] try: for many_users in tweepy.Cursor(api.followers_ids, user_id=user_id).pages(): relationships = create_new_users(api, relationships, many_users, verbose=verbose) new_relationships.extend(many_users) except tweepy.error.TweepError as error: print(str(error) + ", ignored") pass # The API tends to emit the response "buffering", which Tweepy does not understand. Blissfully ignoring! relationships[str(user_id)] = new_relationships return relationships def looks_icelandic(location_string): """ Returns True if the given string is something that could be taken to mean "Iceland", False otherwise. """ return any(loc in location_string for loc in ["Iceland", "Ísland", "Island", "iceland", "ísland", "island"]) def main(): # Some hard-coded variables that probably should be parameters json_filename = "relationships_by_id.json" verbose = True time_limit = 0 #60 * 60 * 16 start_time = time() # Read all the connections we have already discovered: with open(json_filename) as relationship_file: if verbose: print("Reading previously recorded relationships") relationships = json.load(relationship_file) # Look for users we've never seen before, and record them api = get_tweepy_instance() if verbose: print("Searching for tweets in Iceland") icelanders = discover_icelanders(api, list(relationships.keys())) relationships = create_new_users(api, relationships, icelanders, True) # Find users whose follower data we still haven't stored, and remedy the situation without_followers = [user_id for user_id in relationships.keys() if len(relationships[user_id]) == 0] for user_id in without_followers: if verbose: print("Looking for followers for {0}".format(user_id)) relationships = discover_followers(api, relationships, user_id, verbose) print("Time elapsed: {0}".format(time() - start_time)) if time() - start_time > time_limit: break # Store the new data with open(json_filename, "w") as relationship_file: json.dump(relationships, relationship_file, indent=4) if __name__ == '__main__': main()

36.005747

118

0.68332

2bb240936067da76ac5445b23514763a9d05014d

7,773

py

Python

tests/test_tversky_loss.py

benduffy1/MONAI

046e625b09262261373d7b8039fb652547201368

[ "Apache-2.0" ]

3

2020-06-22T20:59:14.000Z

2021-04-09T21:24:45.000Z

tests/test_tversky_loss.py

Borda/MONAI

e0db5a564225a7cb62e7a23df97267019006302f

[ "Apache-2.0" ]

null

tests/test_tversky_loss.py

Borda/MONAI

e0db5a564225a7cb62e7a23df97267019006302f

[ "Apache-2.0" ]

1

2020-05-27T12:53:58.000Z

# Copyright (c) MONAI Consortium # 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 unittest import numpy as np import torch from parameterized import parameterized from monai.losses import TverskyLoss from tests.utils import SkipIfBeforePyTorchVersion, test_script_save TEST_CASES = [ [ # shape: (1, 1, 2, 2), (1, 1, 2, 2) {"include_background": True, "sigmoid": True, "smooth_nr": 1e-6, "smooth_dr": 1e-6}, {"input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]]), "target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])}, 0.307576, ], [ # shape: (2, 1, 2, 2), (2, 1, 2, 2) {"include_background": True, "sigmoid": True, "smooth_nr": 1e-4, "smooth_dr": 1e-4}, { "input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]], [[[1.0, -1.0], [-1.0, 1.0]]]]), "target": torch.tensor([[[[1.0, 1.0], [1.0, 1.0]]], [[[1.0, 0.0], [1.0, 0.0]]]]), }, 0.416657, ], [ # shape: (2, 2, 3), (2, 1, 3) {"include_background": False, "to_onehot_y": True, "smooth_nr": 0, "smooth_dr": 0}, { "input": torch.tensor([[[1.0, 1.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]]), "target": torch.tensor([[[0.0, 0.0, 1.0]], [[0.0, 1.0, 0.0]]]), }, 0.0, ], [ # shape: (2, 2, 3), (2, 1, 3) {"include_background": False, "to_onehot_y": True, "smooth_nr": 0, "smooth_dr": 1e-3}, { "input": torch.tensor([[[1.0, 1.0, 0.0], [0.0, 0.0, 1.0]], [[1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]]), "target": torch.tensor([[[0.0, 0.0, 1.0]], [[0.0, 1.0, 0.0]]]), }, 0.000999, ], [ # shape: (2, 2, 3), (2, 1, 3) {"include_background": True, "to_onehot_y": True, "sigmoid": True, "smooth_nr": 1e-4, "smooth_dr": 1e-4}, { "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]), "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]), }, 0.435050, ], [ # shape: (2, 2, 3), (2, 1, 3) {"include_background": True, "to_onehot_y": True, "sigmoid": True, "batch": True}, { "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]), "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]), }, 0.422979, ], [ # shape: (2, 2, 3), (2, 1, 3) { "include_background": True, "to_onehot_y": True, "sigmoid": True, "reduction": "sum", "smooth_nr": 1e-4, "smooth_dr": 1e-4, }, { "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]), "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]), }, 1.74013, ], [ # shape: (2, 2, 3), (2, 1, 3) {"include_background": True, "to_onehot_y": True, "softmax": True, "smooth_nr": 1e-4, "smooth_dr": 1e-4}, { "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]), "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]), }, 0.383713, ], [ # shape: (2, 2, 3), (2, 1, 3) { "include_background": True, "to_onehot_y": True, "softmax": True, "reduction": "none", "smooth_nr": 1e-4, "smooth_dr": 1e-4, }, { "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]), "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]), }, [[0.210961, 0.295339], [0.599952, 0.428547]], ], [ # shape: (1, 1, 2, 2), (1, 1, 2, 2) {"include_background": True, "sigmoid": True, "alpha": 0.3, "beta": 0.7, "smooth_nr": 1e-6, "smooth_dr": 1e-6}, {"input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]]), "target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])}, 0.3589, ], [ # shape: (1, 1, 2, 2), (1, 1, 2, 2) {"include_background": True, "sigmoid": True, "alpha": 0.7, "beta": 0.3, "smooth_nr": 1e-6, "smooth_dr": 1e-6}, {"input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]]]), "target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])}, 0.247366, ], [ # shape: (2, 1, 2, 2), (2, 1, 2, 2) {"include_background": True, "other_act": torch.tanh, "smooth_nr": 1e-4, "smooth_dr": 1e-4}, { "input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]], [[[1.0, -1.0], [-1.0, 1.0]]]]), "target": torch.tensor([[[[1.0, 1.0], [1.0, 1.0]]], [[[1.0, 0.0], [1.0, 0.0]]]]), }, 0.999963, ], [ # shape: (2, 1, 2, 2), (2, 1, 2, 2) {"include_background": True, "other_act": torch.tanh, "smooth_nr": 0, "smooth_dr": 1e-3, "batch": True}, { "input": torch.tensor([[[[1.0, -1.0], [-1.0, 1.0]]], [[[1.0, -1.0], [-1.0, 1.0]]]]), "target": torch.tensor([[[[1.0, 1.0], [1.0, 1.0]]], [[[1.0, 0.0], [1.0, 0.0]]]]), }, 0.999963, ], [ # shape: (2, 2, 3), (2, 1, 3) { "include_background": True, "to_onehot_y": True, "other_act": lambda x: torch.log_softmax(x, dim=1), "smooth_nr": 1e-4, "smooth_dr": 1e-4, }, { "input": torch.tensor([[[-1.0, 0.0, 1.0], [1.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]]), "target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]), }, -8.533317, ], ] class TestTverskyLoss(unittest.TestCase): @parameterized.expand(TEST_CASES) def test_shape(self, input_param, input_data, expected_val): result = TverskyLoss(**input_param).forward(**input_data) np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-4) def test_ill_shape(self): loss = TverskyLoss() with self.assertRaisesRegex(AssertionError, ""): loss.forward(torch.ones((2, 2, 3)), torch.ones((4, 5, 6))) chn_input = torch.ones((1, 1, 3)) chn_target = torch.ones((1, 1, 3)) with self.assertRaisesRegex(ValueError, ""): TverskyLoss(reduction="unknown")(chn_input, chn_target) with self.assertRaisesRegex(ValueError, ""): TverskyLoss(reduction=None)(chn_input, chn_target) def test_input_warnings(self): chn_input = torch.ones((1, 1, 3)) chn_target = torch.ones((1, 1, 3)) with self.assertWarns(Warning): loss = TverskyLoss(include_background=False) loss.forward(chn_input, chn_target) with self.assertWarns(Warning): loss = TverskyLoss(softmax=True) loss.forward(chn_input, chn_target) with self.assertWarns(Warning): loss = TverskyLoss(to_onehot_y=True) loss.forward(chn_input, chn_target) @SkipIfBeforePyTorchVersion((1, 7, 0)) def test_script(self): loss = TverskyLoss() test_input = torch.ones(2, 1, 8, 8) test_script_save(loss, test_input, test_input) if __name__ == "__main__": unittest.main()

41.566845

119

0.487328

831a8fbfc0a2088437ce47ecdd01a6351032a489

3,630

py

Python

libetrv/data_struct.py

AdamStrojek/libetrv

28a48676f15180d71feadca163859d4b2d8832c3

[ "Apache-2.0" ]

36

2019-06-20T09:00:19.000Z

2022-01-26T23:36:46.000Z

libetrv/data_struct.py

AdamStrojek/libetrv

28a48676f15180d71feadca163859d4b2d8832c3

[ "Apache-2.0" ]

23

2019-05-10T09:47:05.000Z

2021-09-28T22:19:31.000Z

libetrv/data_struct.py

AdamStrojek/libetrv

28a48676f15180d71feadca163859d4b2d8832c3

[ "Apache-2.0" ]

14

2019-10-28T01:29:29.000Z

2021-02-10T19:54:35.000Z

import enum from .properties import eTRVData, eTRVSingleData from .fields import eTRVField, TemperatureField, UTCDateTimeField, LocalDateTimeField, EnumField, \ HexField, TextField, BitField class BatteryData(eTRVSingleData): battery = eTRVField(read_only=True) class Meta: structure = { 0x10: """ unsigned char battery; """, } use_encoding = False read_only = True direct_field = 'battery' class ScheduleMode(enum.Enum): MANUAL = 0 SCHEDULED = 1 VACATION = 3 HOLD = 5 class ConfigBits(enum.IntEnum): ADAPTABLE_REGULATION = 0 VERTICAL_INSTALATION = 2 DISPLAY_FLIP = 3 SLOW_REGULATION = 4 VALVE_INSTALLED = 6 LOCK_CONTROL = 7 class SettingsData(eTRVData): adaptable_regulation = BitField(name='config_bits', bit_position=ConfigBits.ADAPTABLE_REGULATION) vertical_instalation = BitField(name='config_bits', bit_position=ConfigBits.VERTICAL_INSTALATION) display_flip = BitField(name='config_bits', bit_position=ConfigBits.DISPLAY_FLIP) slow_regulation = BitField(name='config_bits', bit_position=ConfigBits.SLOW_REGULATION) valve_installed = BitField(name='config_bits', bit_position=ConfigBits.VALVE_INSTALLED) lock_control = BitField(name='config_bits', bit_position=ConfigBits.LOCK_CONTROL) temperature_min = TemperatureField() temperature_max = TemperatureField() frost_protection_temperature = TemperatureField() schedule_mode = EnumField(enum_class=ScheduleMode) vacation_temperature = TemperatureField() vacation_from = UTCDateTimeField() vacation_to = UTCDateTimeField() class Meta: structure = { 0x2a: """ unsigned char config_bits; unsigned char temperature_min; unsigned char temperature_max; unsigned char frost_protection_temperature; unsigned char schedule_mode; unsigned char vacation_temperature; int vacation_from; int vacation_to; unsigned char padding[2]; """ } class TemperatureData(eTRVData): room_temperature = TemperatureField(read_only=True) set_point_temperature = TemperatureField(auto_save=True) class Meta: structure = { 0x2d: """ unsigned char set_point_temperature; unsigned char room_temperature; unsigned char padding[6]; """ } class NameData(eTRVSingleData): name = TextField(max_length=16, auto_save=True) class Meta: structure = { 0x30: """ char name[16]; """ } direct_field = 'name' class CurrentTimeData(eTRVSingleData): current_time = LocalDateTimeField('time_local', tz_field='time_offset') class Meta: structure = { 0x36: """ int time_local; int time_offset; """ } direct_field = 'current_time' class SecretKeyData(eTRVSingleData): key = HexField(read_only=True) class Meta: structure = { 0x3f: """ char key[16]; """ } use_encoding = False direct_field = 'key' # class DaySchedule(eTRVData): # __struct__ = """ # unsigned char _data[6]; # """ # class ScheduleStruct(eTRVData): # __struct__ = """ # unsigned char _home_temperature; # unsigned char _away_temperature; # struct DaySchedule _schedule[7]; # """

27.709924

101

0.622865

f2d93478688e5faa20126822fc567b09c05fc7fc

569

py

Python

test/rules/test_bite_child.py

rileyhazard/SmartVA-Analyze-1

0573eeff27d03f54e7506db4f1631c0cd9f54bbb

[ "MIT" ]

4

2019-01-23T12:57:47.000Z

2020-04-18T17:13:08.000Z

test/rules/test_bite_child.py

rileyhazard/SmartVA-Analyze-1

0573eeff27d03f54e7506db4f1631c0cd9f54bbb

[ "MIT" ]

4

2019-01-09T22:10:07.000Z

2022-02-16T04:57:06.000Z

test/rules/test_bite_child.py

rileyhazard/SmartVA-Analyze-1

0573eeff27d03f54e7506db4f1631c0cd9f54bbb

[ "MIT" ]

11

2018-12-11T22:01:13.000Z

2022-01-07T11:38:02.000Z

from smartva.rules import bite_child as bite from smartva.data.constants import * VA = Child def test_pass(): row = { VA.BITE: YES, VA.INJURY_DAYS: 0, } assert bite.logic_rule(row) is True def test_fail_bite(): row = { VA.BITE: NO, VA.INJURY_DAYS: 0, } assert bite.logic_rule(row) is False def test_fail_days(): row = { VA.BITE: YES, VA.INJURY_DAYS: 31, } assert bite.logic_rule(row) is False def test_fail_no_data(): row = {} assert bite.logic_rule(row) is False

14.973684

44

0.599297

e8adedc05b8e2d3d6f2457179753a4f571f787f9

12,446

py

Python

parlai/tasks/multidogo/build.py

twstewart42/ParlAI

db8dc7b684a089427ab5338a67fe50220d2fa622

[ "MIT" ]

1

2022-03-13T21:02:22.000Z

parlai/tasks/multidogo/build.py

twstewart42/ParlAI

db8dc7b684a089427ab5338a67fe50220d2fa622

[ "MIT" ]

1

2022-01-18T09:14:27.000Z

parlai/tasks/multidogo/build.py

twstewart42/ParlAI

db8dc7b684a089427ab5338a67fe50220d2fa622

[ "MIT" ]

1

2022-01-24T13:22:18.000Z

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import parlai.core.build_data as build_data from parlai.core.build_data import DownloadableFile import csv from itertools import islice from pathlib import Path import os import json import re import tqdm DEBUG_MISSING_RAW_CONVERSATIONS = False # Unnecessary once Amazon fixes multidogo RESOURCE = DownloadableFile( "https://github.com/awslabs/multi-domain-goal-oriented-dialogues-dataset/archive/master.zip", "raw_data.zip", "fb59c7261da2d30d9d24b9af309ebb4bf0e5b39f97d718201a7160e591e76a3c", zipped=True, ) RAW_DATA_PREFIX = "multi-domain-goal-oriented-dialogues-dataset-master/data/" RAW_DATA_ANNOTATED_DATA_PATH = "paper_splits" RAW_DATA_UNANNOTATED_DATA_PATH = "unannotated" TURN_INTENT = "turn" SENTENCE_INTENT = "sentence" TURN_AND_SENTENCE_INTENT = "both" RAW_DATA_SENTENCE_INTENT_PATH = "splits_annotated_at_sentence_level" RAW_DATA_TURN_INTENT_PATH = "splits_annotated_at_turn_level" RAW_DATA_INTENT_BY_TYPE_PATH = { TURN_INTENT: RAW_DATA_TURN_INTENT_PATH, SENTENCE_INTENT: RAW_DATA_SENTENCE_INTENT_PATH, } DOMAINS = ["airline", "fastfood", "finance", "insurance", "media", "software"] DATATYPE_TO_RAW_DATA_FILE_NAME = { "test": "test.tsv", "train": "train.tsv", "valid": "dev.tsv", } PROCESSED = "processed/" def _preprocess(opt, datapath, datatype, version): """ MultiDoGo conversations take place between an "agent" and a customer". Labeled customer data is stored in one set of files while the agent data is in another. There is a common conversation ID between the two, but the conversations are not listed in a consistent way between the documents. Since we'll have to do work to associate the data between the files anyway, we might as well process the data into a new file that'll be easier to deal with. Stores the data as <multidogo_data_path>/processed/<domain>/<datatype>.txt. Will skip preprocessing if this file already exists. """ domains = opt.get("domains", DOMAINS) intent_type = opt.get("intent_type", TURN_INTENT) for domain in domains: out_dir = get_processed_multidogo_folder( datapath, domain, datatype, intent_type ) if build_data.built(out_dir, version): continue print( f" Preprocessing '{domain}' data for '{datatype}' with '{intent_type}' intent labels." ) Path(out_dir).mkdir(parents=True, exist_ok=True) # The agent responses for *all* datatypes are in one file. # We need to iterate through the datatype file to know which lines # we'll actually need... so build a quick lookup table to know which # lines in the tsv file we'll need to care about so we're not scanning # through the whole thing a bunch unannotated_id_map = _build_conversation_span_map( _get_unannotated_tsv_data(datapath, domain) ) # Actually do the work of collating all of the conversations + annotations # For turn + sentence intent labels, we do two passes, one for sentence # then one for turn so that we do not add two sets of labels for the # same conversation ID. We can use this forced structure to do the # separate categories of turn intent and sentence intent labels. We # also do a bit of chuking file_idx = 0 seen_conversations_set = set() if intent_type == TURN_AND_SENTENCE_INTENT or intent_type == SENTENCE_INTENT: file_idx, seen_conversations_set = _aggregate_and_write_conversations( intent_type, SENTENCE_INTENT, datapath, domain, datatype, unannotated_id_map, start_file_idx=file_idx, skip_ids=set(), ) if intent_type == TURN_AND_SENTENCE_INTENT or intent_type == TURN_INTENT: _, _ = _aggregate_and_write_conversations( intent_type, TURN_INTENT, datapath, domain, datatype, unannotated_id_map, start_file_idx=file_idx, skip_ids=seen_conversations_set, ) # mark that we've built this combinations build_data.mark_done(out_dir, version_string=version) def get_processed_multidogo_folder(datapath, domain, datatype, intent_type): return os.path.join(datapath, PROCESSED, domain, intent_type, datatype) # unannotated data is UNANNOTATED_DATA_PROFIX + <domain> + '.tsv' # annotated data is ANNOTATED_DATA_PATH + <annotations type> + <domain> + '/' + <datatype> + '.tsv' def _get_unannotated_tsv_data(datapath, domain): file_name = os.path.join( datapath, RAW_DATA_PREFIX, RAW_DATA_UNANNOTATED_DATA_PATH, domain + ".tsv" ) return csv.reader(open(file_name, "r"), delimiter=",") # comma-separated tsv, lol def _get_annotated_tsv_data(datapath, domain, datatype, annotation_type): file_name = os.path.join( datapath, RAW_DATA_PREFIX, RAW_DATA_ANNOTATED_DATA_PATH, RAW_DATA_INTENT_BY_TYPE_PATH[annotation_type], domain, DATATYPE_TO_RAW_DATA_FILE_NAME[datatype], ) return csv.reader(open(file_name, "r"), delimiter="\t") def _get_annotated_tsv_data_size(datapath, domain, datatype, annotation_type): file_name = os.path.join( datapath, RAW_DATA_PREFIX, RAW_DATA_ANNOTATED_DATA_PATH, RAW_DATA_INTENT_BY_TYPE_PATH[annotation_type], domain, DATATYPE_TO_RAW_DATA_FILE_NAME[datatype], ) return sum(1 for line in open(file_name, 'r')) def _build_conversation_span_map(unannotated_tsv_object): result = {} # conversationId to (start line, length) map start = 0 prev_conversation_id = "" length = 0 for i, row in enumerate(unannotated_tsv_object): conversation_id = row[0][ 4:-2 ] # do substring cause conversationId has extra filler in unannotated if conversation_id != prev_conversation_id: result[prev_conversation_id] = (start, length) start = i prev_conversation_id = conversation_id length = 0 length += 1 result[conversation_id] = (start, length) return result def _get_slots_map(utterance, slot_string): values = slot_string.split(" ") cleaned = re.sub(r"[^\w\s]", "", utterance) words = cleaned.split(" ") result = {} for i in range(len(words)): if values[i] != "O": result[values[i]] = words[i] return result def _aggregate_and_write_conversations( raw_intent_type, fetch_intent_type, datapath, domain, datatype, unannotated_id_map, skip_ids, start_file_idx=0, ): conversations_to_write = {} # conversationId -> list of turns seen_conversations = set() out_dir = get_processed_multidogo_folder( datapath, domain, datatype, raw_intent_type ) file_idx = start_file_idx intent_tsv = _get_annotated_tsv_data(datapath, domain, datatype, fetch_intent_type) next(intent_tsv) # don't need the header in the first line print(f"Processing for {domain}, {fetch_intent_type}, {datatype}") for labeled_line in tqdm.tqdm( intent_tsv, total=_get_annotated_tsv_data_size( datapath, domain, datatype, fetch_intent_type ) - 1, ): conversation_id = labeled_line[0] if conversation_id in skip_ids: continue if conversation_id not in seen_conversations: # new conversation, add text of conversation to conversations_to_write conversations_to_write[conversation_id] = {} found_raw_conversation = _add_utterances( unannotated_id_map, conversation_id, conversations_to_write, datapath, domain, ) seen_conversations.add(conversation_id) if not found_raw_conversation: if DEBUG_MISSING_RAW_CONVERSATIONS: print(f"Could not find raw conversations for {conversation_id}") skip_ids.add(conversation_id) conversations_to_write.pop(conversation_id, None) continue if fetch_intent_type == SENTENCE_INTENT: _get_sentence_labels_and_slots_map(labeled_line, conversations_to_write) elif fetch_intent_type == TURN_INTENT: _get_turn_labels_and_slots_map(labeled_line, conversations_to_write) else: raise KeyError( "Invalid `fetch_intent_type`. This case should never be hit. Something is broken in the `build.py` file." ) # Don't forget to dump out last file with open(f"{out_dir}/{file_idx}.json", "w+") as out_file: json.dump(conversations_to_write, out_file, indent=4) file_idx += 1 # Return necessary outputs for next pass return file_idx, seen_conversations def _add_utterances( unannotated_id_map, conversation_id, conversations_to_write, datapath, domain ): try: start, length = unannotated_id_map[conversation_id] except KeyError: return False conversation_text = islice( _get_unannotated_tsv_data(datapath, domain), start, start + length ) for line in conversation_text: # Format of unannotated: conversationId,turnNumber,utteranceId,utterance,authorRole conversations_to_write[conversation_id] = { **conversations_to_write[conversation_id], int(line[1]): {"text": line[3], "role": line[4]}, } return True def _get_sentence_labels_and_slots_map(labeled_line, output): # Sentence tsv format: conversationId turnNumber sentenceNumber utteranceId utterance slot-labels intent conversation_id = labeled_line[0] turn_number = int(float(labeled_line[1])) # cause a few got saved as float. if conversation_id not in output: raise RuntimeError("Should never happen; raw conversation text should be here") if turn_number not in output[conversation_id]: output[conversation_id][turn_number] = {} output[conversation_id][turn_number] = { **output[conversation_id][turn_number], "slots": _get_slots_map(labeled_line[4], labeled_line[5]), } if "intents" not in output[conversation_id][turn_number]: output[conversation_id][turn_number]["intents"] = [] output[conversation_id][turn_number]["intents"].append(labeled_line[6]) def _get_turn_labels_and_slots_map(labeled_line, output): # Turn tsv format: conversationId turnNumber utteranceId utterance slot-labels intent conversation_id = labeled_line[0] turn_number = int(float(labeled_line[1])) # cause a few got saved as float if conversation_id not in output: raise RuntimeError("Should never happen; raw conversation text should be here") if turn_number not in output[conversation_id]: output[conversation_id][turn_number] = {} output[conversation_id][turn_number] = { **output[conversation_id][turn_number], "slots": _get_slots_map(labeled_line[3], labeled_line[4]), "intents": [labeled_line[5]], } def build(opt): # get path to data directory datapath = os.path.join(opt["datapath"], "multidogo") # define version if any version = "v1.1" # check if data had been previously downloaded if not build_data.built(datapath, version_string=version): print("[building data: " + datapath + "]") # make a clean directory if needed if build_data.built(datapath): # an older version exists, so remove these outdated files. build_data.remove_dir(datapath) build_data.make_dir(datapath) # Download the data. RESOURCE.download_file(datapath) # mark the data as built build_data.mark_done(datapath, version_string=version) # do preprocessing on the data to put it into FBDialogueData format. There's a lot so check to make sure it's okay for fold in ["train", "valid", "test"]: _preprocess(opt, datapath, fold, version)

37.375375

121

0.676764

1fbfef5b5f45c87385bf1b1860ed8a395724d17a

8,022

py

Python

pex/vendor/__init__.py

jneuff/pex

f7eb554ecdbfdb9cd24a793b4dda358035e26855

[ "Apache-2.0" ]

null

pex/vendor/__init__.py

jneuff/pex

f7eb554ecdbfdb9cd24a793b4dda358035e26855

[ "Apache-2.0" ]

null

pex/vendor/__init__.py

jneuff/pex

f7eb554ecdbfdb9cd24a793b4dda358035e26855

[ "Apache-2.0" ]

null

# Copyright 2018 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from __future__ import absolute_import import collections import os from pex.common import filter_pyc_dirs, filter_pyc_files, touch from pex.compatibility import urlparse from pex.tracer import TRACER _PACKAGE_COMPONENTS = __name__.split(".") def _root(): path = os.path.dirname(os.path.abspath(__file__)) for _ in _PACKAGE_COMPONENTS: path = os.path.dirname(path) return path class VendorSpec( collections.namedtuple("VendorSpec", ["key", "requirement", "rewrite", "constrain"]) ): """Represents a vendored distribution. :field str key: The distribution requirement key; e.g.: for a requirement of requests[security]==2.22.0 the key is 'requests'. :field str requirement: The distribution requirement string; e.g.: requests[security]==2.22.0. :field bool rewrite: Whether to re-write the distribution's imports for use with the `pex.third_party` importer. :field bool constrain: Whether to attempt to constrain the requirement via pip's --constraint mechanism. NB: Vendored distributions should comply with the host distribution platform constraints. In the case of pex, which is a py2.py3 platform agnostic wheel, vendored libraries should be as well. """ ROOT = _root() _VENDOR_DIR = "_vendored" @classmethod def vendor_root(cls): return os.path.join(cls.ROOT, *(_PACKAGE_COMPONENTS + [cls._VENDOR_DIR])) @classmethod def pinned(cls, key, version, rewrite=True): return cls( key=key, requirement="{}=={}".format(key, version), rewrite=rewrite, constrain=True ) @classmethod def vcs(cls, url, rewrite=True): result = urlparse.urlparse(url) fragment_params = urlparse.parse_qs(result.fragment) values = fragment_params.get("egg") if not values or len(values) != 1: raise ValueError( "Expected the vcs requirement url to have an #egg=<name> fragment. " "Got: {}".format(url) ) # N.B.: Constraints do not work for vcs urls. return cls(key=values[0], requirement=url, rewrite=rewrite, constrain=False) @property def _subpath_components(self): return [self._VENDOR_DIR, self.key] @property def relpath(self): return os.path.join(*(_PACKAGE_COMPONENTS + self._subpath_components)) @property def target_dir(self): return os.path.join(self.ROOT, self.relpath) def create_packages(self): """Create missing packages joining the vendor root to the base of the vendored distribution. For example, given a root at ``/home/jake/dev/pantsbuild/pex`` and a vendored distribution at ``pex/vendor/_vendored/requests`` this method would create the following package files:: pex/vendor/_vendored/__init__.py pex/vendor/_vendored/requests/__init__.py These package files allow for standard python importers to find vendored code via re-directs from a `PEP-302 <https://www.python.org/dev/peps/pep-0302/>`_ importer like :class:`pex.third_party.VendorImporter`. """ if not self.rewrite: # The extra package structure is only required for vendored code used via import rewrites. return for index, _ in enumerate(self._subpath_components): relpath = _PACKAGE_COMPONENTS + self._subpath_components[: index + 1] + ["__init__.py"] touch(os.path.join(self.ROOT, *relpath)) def iter_vendor_specs(): """Iterate specifications for code vendored by pex. :return: An iterator over specs of all vendored code. :rtype: :class:`collection.Iterator` of :class:`VendorSpec` """ # We use this via pex.third_party at runtime to check for compatible wheel tags and at build # time to implement resolving distributions from a PEX repository. yield VendorSpec.pinned("packaging", "20.4") # We shell out to pip at buildtime to resolve and install dependencies. # N.B.: This is pip 20.0.dev0 with a patch to support foreign download targets more fully. yield VendorSpec.vcs( "git+https://github.com/pantsbuild/pip@f9dde7cb6bab#egg=pip", rewrite=False ) # We expose this to pip at buildtime for legacy builds, but we also use pkg_resources via # pex.third_party at runtime in various ways. # N.B.: 44.0.0 is the last setuptools version compatible with Python 2. yield VendorSpec.pinned("setuptools", "44.0.0") # We expose this to pip at buildtime for legacy builds. yield VendorSpec.pinned("wheel", "0.35.1", rewrite=False) def vendor_runtime(chroot, dest_basedir, label, root_module_names): """Includes portions of vendored distributions in a chroot. The portion to include is selected by root module name. If the module is a file, just it is included. If the module represents a package, the package and all its sub-packages are added recursively. :param chroot: The chroot to add vendored code to. :type chroot: :class:`pex.common.Chroot` :param str dest_basedir: The prefix to store the vendored code under in the ``chroot``. :param str label: The chroot label for the vendored code fileset. :param root_module_names: The names of the root vendored modules to include in the chroot. :type root_module_names: :class:`collections.Iterable` of str :raise: :class:`ValueError` if any of the given ``root_module_names`` could not be found amongst the vendored code and added to the chroot. """ vendor_module_names = {root_module_name: False for root_module_name in root_module_names} for spec in iter_vendor_specs(): for root, dirs, files in os.walk(spec.target_dir): if root == spec.target_dir: dirs[:] = [pkg_name for pkg_name in dirs if pkg_name in vendor_module_names] files[:] = [mod_name for mod_name in files if mod_name[:-3] in vendor_module_names] vendored_names = dirs + [filename[:-3] for filename in files] if vendored_names: pkg_path = "" for pkg in spec.relpath.split(os.sep): pkg_path = os.path.join(pkg_path, pkg) pkg_file = os.path.join(pkg_path, "__init__.py") src = os.path.join(VendorSpec.ROOT, pkg_file) dest = os.path.join(dest_basedir, pkg_file) if os.path.exists(src): chroot.copy(src, dest, label) else: # We delete `pex/vendor/_vendored/<dist>/__init__.py` when isolating third_party. chroot.touch(dest, label) for name in vendored_names: vendor_module_names[name] = True TRACER.log( "Vendoring {} from {} @ {}".format(name, spec, spec.target_dir), V=3 ) # We copy over sources and data only; no pyc files. dirs[:] = filter_pyc_dirs(dirs) for filename in filter_pyc_files(files): src = os.path.join(root, filename) dest = os.path.join( dest_basedir, spec.relpath, os.path.relpath(src, spec.target_dir) ) chroot.copy(src, dest, label) if not all(vendor_module_names.values()): raise ValueError( "Failed to extract {module_names} from:\n\t{specs}".format( module_names=", ".join( module for module, written in vendor_module_names.items() if not written ), specs="\n\t".join( "{} @ {}".format(spec, spec.target_dir) for spec in iter_vendor_specs() ), ) )

42.670213

109

0.639865

05bd693ed23d6ac36953df0e3331778102f52fde

17,416

py

Python

google/cloud/gaming_v1/services/realms_service/transports/grpc.py

renovate-bot/python-game-servers

d60305505e0afbed940a2a7f0c1a6fc0e6eca56d

[ "Apache-2.0" ]

null

google/cloud/gaming_v1/services/realms_service/transports/grpc.py

renovate-bot/python-game-servers

d60305505e0afbed940a2a7f0c1a6fc0e6eca56d

[ "Apache-2.0" ]

null

google/cloud/gaming_v1/services/realms_service/transports/grpc.py

renovate-bot/python-game-servers

d60305505e0afbed940a2a7f0c1a6fc0e6eca56d

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2020 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. # import warnings from typing import Callable, Dict, Optional, Sequence, Tuple, Union from google.api_core import grpc_helpers # type: ignore from google.api_core import operations_v1 # type: ignore from google.api_core import gapic_v1 # type: ignore import google.auth # type: ignore from google.auth import credentials as ga_credentials # type: ignore from google.auth.transport.grpc import SslCredentials # type: ignore import grpc # type: ignore from google.cloud.gaming_v1.types import realms from google.longrunning import operations_pb2 # type: ignore from .base import RealmsServiceTransport, DEFAULT_CLIENT_INFO class RealmsServiceGrpcTransport(RealmsServiceTransport): """gRPC backend transport for RealmsService. A realm is a grouping of game server clusters that are considered interchangeable. This class defines the same methods as the primary client, so the primary client can load the underlying transport implementation and call it. It sends protocol buffers over the wire using gRPC (which is built on top of HTTP/2); the ``grpcio`` package must be installed. """ _stubs: Dict[str, Callable] def __init__( self, *, host: str = "gameservices.googleapis.com", credentials: ga_credentials.Credentials = None, credentials_file: str = None, scopes: Sequence[str] = None, channel: grpc.Channel = None, api_mtls_endpoint: str = None, client_cert_source: Callable[[], Tuple[bytes, bytes]] = None, ssl_channel_credentials: grpc.ChannelCredentials = None, client_cert_source_for_mtls: Callable[[], Tuple[bytes, bytes]] = None, quota_project_id: Optional[str] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, always_use_jwt_access: Optional[bool] = False, ) -> None: """Instantiate the transport. Args: host (Optional[str]): The hostname to connect to. credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. This argument is ignored if ``channel`` is provided. credentials_file (Optional[str]): A file with credentials that can be loaded with :func:`google.auth.load_credentials_from_file`. This argument is ignored if ``channel`` is provided. scopes (Optional(Sequence[str])): A list of scopes. This argument is ignored if ``channel`` is provided. channel (Optional[grpc.Channel]): A ``Channel`` instance through which to make calls. api_mtls_endpoint (Optional[str]): Deprecated. The mutual TLS endpoint. If provided, it overrides the ``host`` argument and tries to create a mutual TLS channel with client SSL credentials from ``client_cert_source`` or application default SSL credentials. client_cert_source (Optional[Callable[[], Tuple[bytes, bytes]]]): Deprecated. A callback to provide client SSL certificate bytes and private key bytes, both in PEM format. It is ignored if ``api_mtls_endpoint`` is None. ssl_channel_credentials (grpc.ChannelCredentials): SSL credentials for the grpc channel. It is ignored if ``channel`` is provided. client_cert_source_for_mtls (Optional[Callable[[], Tuple[bytes, bytes]]]): A callback to provide client certificate bytes and private key bytes, both in PEM format. It is used to configure a mutual TLS channel. It is ignored if ``channel`` or ``ssl_channel_credentials`` is provided. quota_project_id (Optional[str]): An optional project to use for billing and quota. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you're developing your own client library. always_use_jwt_access (Optional[bool]): Whether self signed JWT should be used for service account credentials. Raises: google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport creation failed for any reason. google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials`` and ``credentials_file`` are passed. """ self._grpc_channel = None self._ssl_channel_credentials = ssl_channel_credentials self._stubs: Dict[str, Callable] = {} self._operations_client = None if api_mtls_endpoint: warnings.warn("api_mtls_endpoint is deprecated", DeprecationWarning) if client_cert_source: warnings.warn("client_cert_source is deprecated", DeprecationWarning) if channel: # Ignore credentials if a channel was passed. credentials = False # If a channel was explicitly provided, set it. self._grpc_channel = channel self._ssl_channel_credentials = None else: if api_mtls_endpoint: host = api_mtls_endpoint # Create SSL credentials with client_cert_source or application # default SSL credentials. if client_cert_source: cert, key = client_cert_source() self._ssl_channel_credentials = grpc.ssl_channel_credentials( certificate_chain=cert, private_key=key ) else: self._ssl_channel_credentials = SslCredentials().ssl_credentials else: if client_cert_source_for_mtls and not ssl_channel_credentials: cert, key = client_cert_source_for_mtls() self._ssl_channel_credentials = grpc.ssl_channel_credentials( certificate_chain=cert, private_key=key ) # The base transport sets the host, credentials and scopes super().__init__( host=host, credentials=credentials, credentials_file=credentials_file, scopes=scopes, quota_project_id=quota_project_id, client_info=client_info, always_use_jwt_access=always_use_jwt_access, ) if not self._grpc_channel: self._grpc_channel = type(self).create_channel( self._host, credentials=self._credentials, credentials_file=credentials_file, scopes=self._scopes, ssl_credentials=self._ssl_channel_credentials, quota_project_id=quota_project_id, options=[ ("grpc.max_send_message_length", -1), ("grpc.max_receive_message_length", -1), ], ) # Wrap messages. This must be done after self._grpc_channel exists self._prep_wrapped_messages(client_info) @classmethod def create_channel( cls, host: str = "gameservices.googleapis.com", credentials: ga_credentials.Credentials = None, credentials_file: str = None, scopes: Optional[Sequence[str]] = None, quota_project_id: Optional[str] = None, **kwargs, ) -> grpc.Channel: """Create and return a gRPC channel object. Args: host (Optional[str]): The host for the channel to use. credentials (Optional[~.Credentials]): The authorization credentials to attach to requests. These credentials identify this application to the service. If none are specified, the client will attempt to ascertain the credentials from the environment. credentials_file (Optional[str]): A file with credentials that can be loaded with :func:`google.auth.load_credentials_from_file`. This argument is mutually exclusive with credentials. scopes (Optional[Sequence[str]]): A optional list of scopes needed for this service. These are only used when credentials are not specified and are passed to :func:`google.auth.default`. quota_project_id (Optional[str]): An optional project to use for billing and quota. kwargs (Optional[dict]): Keyword arguments, which are passed to the channel creation. Returns: grpc.Channel: A gRPC channel object. Raises: google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials`` and ``credentials_file`` are passed. """ return grpc_helpers.create_channel( host, credentials=credentials, credentials_file=credentials_file, quota_project_id=quota_project_id, default_scopes=cls.AUTH_SCOPES, scopes=scopes, default_host=cls.DEFAULT_HOST, **kwargs, ) @property def grpc_channel(self) -> grpc.Channel: """Return the channel designed to connect to this service. """ return self._grpc_channel @property def operations_client(self) -> operations_v1.OperationsClient: """Create the client designed to process long-running operations. This property caches on the instance; repeated calls return the same client. """ # Sanity check: Only create a new client if we do not already have one. if self._operations_client is None: self._operations_client = operations_v1.OperationsClient(self.grpc_channel) # Return the client from cache. return self._operations_client @property def list_realms( self, ) -> Callable[[realms.ListRealmsRequest], realms.ListRealmsResponse]: r"""Return a callable for the list realms method over gRPC. Lists realms in a given project and location. Returns: Callable[[~.ListRealmsRequest], ~.ListRealmsResponse]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if "list_realms" not in self._stubs: self._stubs["list_realms"] = self.grpc_channel.unary_unary( "/google.cloud.gaming.v1.RealmsService/ListRealms", request_serializer=realms.ListRealmsRequest.serialize, response_deserializer=realms.ListRealmsResponse.deserialize, ) return self._stubs["list_realms"] @property def get_realm(self) -> Callable[[realms.GetRealmRequest], realms.Realm]: r"""Return a callable for the get realm method over gRPC. Gets details of a single realm. Returns: Callable[[~.GetRealmRequest], ~.Realm]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if "get_realm" not in self._stubs: self._stubs["get_realm"] = self.grpc_channel.unary_unary( "/google.cloud.gaming.v1.RealmsService/GetRealm", request_serializer=realms.GetRealmRequest.serialize, response_deserializer=realms.Realm.deserialize, ) return self._stubs["get_realm"] @property def create_realm( self, ) -> Callable[[realms.CreateRealmRequest], operations_pb2.Operation]: r"""Return a callable for the create realm method over gRPC. Creates a new realm in a given project and location. Returns: Callable[[~.CreateRealmRequest], ~.Operation]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if "create_realm" not in self._stubs: self._stubs["create_realm"] = self.grpc_channel.unary_unary( "/google.cloud.gaming.v1.RealmsService/CreateRealm", request_serializer=realms.CreateRealmRequest.serialize, response_deserializer=operations_pb2.Operation.FromString, ) return self._stubs["create_realm"] @property def delete_realm( self, ) -> Callable[[realms.DeleteRealmRequest], operations_pb2.Operation]: r"""Return a callable for the delete realm method over gRPC. Deletes a single realm. Returns: Callable[[~.DeleteRealmRequest], ~.Operation]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if "delete_realm" not in self._stubs: self._stubs["delete_realm"] = self.grpc_channel.unary_unary( "/google.cloud.gaming.v1.RealmsService/DeleteRealm", request_serializer=realms.DeleteRealmRequest.serialize, response_deserializer=operations_pb2.Operation.FromString, ) return self._stubs["delete_realm"] @property def update_realm( self, ) -> Callable[[realms.UpdateRealmRequest], operations_pb2.Operation]: r"""Return a callable for the update realm method over gRPC. Patches a single realm. Returns: Callable[[~.UpdateRealmRequest], ~.Operation]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if "update_realm" not in self._stubs: self._stubs["update_realm"] = self.grpc_channel.unary_unary( "/google.cloud.gaming.v1.RealmsService/UpdateRealm", request_serializer=realms.UpdateRealmRequest.serialize, response_deserializer=operations_pb2.Operation.FromString, ) return self._stubs["update_realm"] @property def preview_realm_update( self, ) -> Callable[ [realms.PreviewRealmUpdateRequest], realms.PreviewRealmUpdateResponse ]: r"""Return a callable for the preview realm update method over gRPC. Previews patches to a single realm. Returns: Callable[[~.PreviewRealmUpdateRequest], ~.PreviewRealmUpdateResponse]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if "preview_realm_update" not in self._stubs: self._stubs["preview_realm_update"] = self.grpc_channel.unary_unary( "/google.cloud.gaming.v1.RealmsService/PreviewRealmUpdate", request_serializer=realms.PreviewRealmUpdateRequest.serialize, response_deserializer=realms.PreviewRealmUpdateResponse.deserialize, ) return self._stubs["preview_realm_update"] def close(self): self.grpc_channel.close() __all__ = ("RealmsServiceGrpcTransport",)

42.791155

87

0.631833

e26490a446b4843739b32641e1a95211864cb58f

8,781

py

Python

test/units/modules/network/f5/test_bigip_command.py

Container-Projects/ansible-provider-docs

100b695b0b0c4d8d08af362069557ffc735d0d7e

[ "PSF-2.0", "BSD-2-Clause", "MIT" ]

37

2017-08-15T15:02:43.000Z

2021-07-23T03:44:31.000Z

test/units/modules/network/f5/test_bigip_command.py

Container-Projects/ansible-provider-docs

100b695b0b0c4d8d08af362069557ffc735d0d7e

[ "PSF-2.0", "BSD-2-Clause", "MIT" ]

12

2018-01-10T05:25:25.000Z

2021-11-28T06:55:48.000Z

test/units/modules/network/f5/test_bigip_command.py

Container-Projects/ansible-provider-docs

100b695b0b0c4d8d08af362069557ffc735d0d7e

[ "PSF-2.0", "BSD-2-Clause", "MIT" ]

49

2017-08-15T09:52:13.000Z

2022-03-21T17:11:54.000Z

# -*- coding: utf-8 -*- # # Copyright (c) 2017 F5 Networks Inc. # GNU General Public License v3.0 (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import (absolute_import, division, print_function) __metaclass__ = type import os import json import sys from nose.plugins.skip import SkipTest if sys.version_info < (2, 7): raise SkipTest("F5 Ansible modules require Python >= 2.7") from ansible.compat.tests import unittest from ansible.compat.tests.mock import patch from ansible.compat.tests.mock import Mock from ansible.module_utils.basic import AnsibleModule try: from library.modules.bigip_command import Parameters from library.modules.bigip_command import ModuleManager from library.modules.bigip_command import V1Manager from library.modules.bigip_command import V2Manager from library.modules.bigip_command import ArgumentSpec from library.module_utils.network.f5.common import F5ModuleError from library.module_utils.network.f5.common import iControlUnexpectedHTTPError from test.unit.modules.utils import set_module_args except ImportError: try: from ansible.modules.network.f5.bigip_command import Parameters from ansible.modules.network.f5.bigip_command import ModuleManager from ansible.modules.network.f5.bigip_command import V1Manager from ansible.modules.network.f5.bigip_command import V2Manager from ansible.modules.network.f5.bigip_command import ArgumentSpec from ansible.module_utils.network.f5.common import F5ModuleError from ansible.module_utils.network.f5.common import iControlUnexpectedHTTPError from units.modules.utils import set_module_args except ImportError: raise SkipTest("F5 Ansible modules require the f5-sdk Python library") fixture_path = os.path.join(os.path.dirname(__file__), 'fixtures') fixture_data = {} def load_fixture(name): path = os.path.join(fixture_path, name) with open(path) as f: data = f.read() try: data = json.loads(data) except Exception: pass return data class TestParameters(unittest.TestCase): def test_module_parameters(self): args = dict( commands=[ "tmsh show sys version" ], server='localhost', user='admin', password='password' ) p = Parameters(params=args) assert len(p.commands) == 1 class TestManager(unittest.TestCase): def setUp(self): self.spec = ArgumentSpec() self.patcher1 = patch('time.sleep') self.patcher1.start() def tearDown(self): self.patcher1.stop() def test_run_single_command(self, *args): set_module_args(dict( commands=[ "tmsh show sys version" ], server='localhost', user='admin', password='password' )) module = AnsibleModule( argument_spec=self.spec.argument_spec, supports_check_mode=self.spec.supports_check_mode ) m1 = V2Manager(module=module) m1.execute_on_device = Mock(return_value=['resp1', 'resp2']) mm = ModuleManager(module=module) mm._run_commands = Mock(return_value=[]) mm.get_manager = Mock(return_value=m1) results = mm.exec_module() assert results['changed'] is False assert mm._run_commands.call_count == 0 assert m1.execute_on_device.call_count == 2 def test_run_single_modification_command(self, *args): set_module_args(dict( commands=[ "tmsh create ltm virtual foo" ], server='localhost', user='admin', password='password' )) module = AnsibleModule( argument_spec=self.spec.argument_spec, supports_check_mode=self.spec.supports_check_mode ) m1 = V2Manager(module=module) m1.execute_on_device = Mock(return_value=['resp1', 'resp2']) mm = ModuleManager(module=module) mm._run_commands = Mock(return_value=[]) mm.get_manager = Mock(return_value=m1) results = mm.exec_module() assert results['changed'] is True assert mm._run_commands.call_count == 0 assert m1.execute_on_device.call_count == 2 def test_cli_command(self, *args): set_module_args(dict( commands=[ "show sys version" ], server='localhost', user='admin', password='password', transport='cli' )) module = AnsibleModule( argument_spec=self.spec.argument_spec, supports_check_mode=self.spec.supports_check_mode ) m1 = V1Manager(module=module) m1.execute_on_device = Mock(return_value=['resp1', 'resp2', 'resp3']) mm = ModuleManager(module=module) mm._run_commands = Mock(return_value=[]) mm.get_manager = Mock(return_value=m1) results = mm.exec_module() assert results['changed'] is False # call count is two on CLI transport because we must first # determine if the remote CLI is in tmsh mode or advanced shell # (bash) mode. # # 1 call for the shell check # 1 call for the command in the "commands" list above # # Can we change this in the future by making the terminal plugin # find this out ahead of time? assert m1.execute_on_device.call_count == 3 def test_command_with_commas(self, *args): set_module_args(dict( commands=""" tmsh create /auth ldap system-auth {bind-dn uid=binduser, cn=users,dc=domain,dc=com bind-pw $ENCRYPTEDPW check-roles-group enabled search-base-dn cn=users,dc=domain,dc=com servers add { ldap.server.com } } """, server='localhost', user='admin', password='password' )) module = AnsibleModule( argument_spec=self.spec.argument_spec, supports_check_mode=self.spec.supports_check_mode ) m1 = V2Manager(module=module) m1.execute_on_device = Mock(return_value=['resp1', 'resp2']) mm = ModuleManager(module=module) mm.get_manager = Mock(return_value=m1) results = mm.exec_module() assert results['changed'] is True assert m1.execute_on_device.call_count == 2 def test_normalizing_command_show(self, *args): args = dict( commands=[ "show sys version" ], ) result = V2Manager.normalize_commands(args['commands']) assert result[0] == 'show sys version' def test_normalizing_command_delete(self, *args): args = dict( commands=[ "delete sys version" ], ) result = V2Manager.normalize_commands(args['commands']) assert result[0] == 'delete sys version' def test_normalizing_command_modify(self, *args): args = dict( commands=[ "modify sys version" ], ) result = V2Manager.normalize_commands(args['commands']) assert result[0] == 'modify sys version' def test_normalizing_command_list(self, *args): args = dict( commands=[ "list sys version" ], ) result = V2Manager.normalize_commands(args['commands']) assert result[0] == 'list sys version' def test_normalizing_command_tmsh_show(self, *args): args = dict( commands=[ "tmsh show sys version" ], ) result = V2Manager.normalize_commands(args['commands']) assert result[0] == 'show sys version' def test_normalizing_command_tmsh_delete(self, *args): args = dict( commands=[ "tmsh delete sys version" ], ) result = V2Manager.normalize_commands(args['commands']) assert result[0] == 'delete sys version' def test_normalizing_command_tmsh_modify(self, *args): args = dict( commands=[ "tmsh modify sys version" ], ) result = V2Manager.normalize_commands(args['commands']) assert result[0] == 'modify sys version' def test_normalizing_command_tmsh_list(self, *args): args = dict( commands=[ "tmsh list sys version" ], ) result = V2Manager.normalize_commands(args['commands']) assert result[0] == 'list sys version'

30.071918

91

0.613028

64a60980f86d283938f5b5efaa137005cf7c282d

3,103

py

Python

setup.py

xiaomengy/moolib

d46826b4dead28e7a9be6545f46bcf159c77ca17

[ "MIT" ]

2

2022-02-08T02:42:22.000Z

2022-02-10T03:41:54.000Z

setup.py

xiaomengy/moolib

d46826b4dead28e7a9be6545f46bcf159c77ca17

[ "MIT" ]

null

setup.py

xiaomengy/moolib

d46826b4dead28e7a9be6545f46bcf159c77ca17

[ "MIT" ]

null

#!/usr/bin/env python # # Copyright (c) Facebook, Inc. and its affiliates. # # To install: pip install . # # For debug builds: python setup.py build --debug install # # The environment variable USE_CUDA can be set to "OFF" (or 0). # import os import pathlib import subprocess import sys import setuptools from setuptools.command import build_ext from distutils import spawn class CMakeBuild(build_ext.build_ext): def run(self): # Necessary for pip install -e. for ext in self.extensions: self.build_extension(ext) def build_extension(self, ext): source_path = pathlib.Path(__file__).parent.resolve() output_path = pathlib.Path(self.get_ext_fullpath(ext.name)).parent.absolute() os.makedirs(self.build_temp, exist_ok=True) build_type = "Debug" if self.debug else "RelWithDebInfo" generator = "Ninja" if spawn.find_executable("ninja") else "Unix Makefiles" cmake_cmd = [ "cmake", str(source_path), "-G%s" % generator, "-DCMAKE_BUILD_TYPE=%s" % build_type, "-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=%s" % output_path, ] use_cuda = os.environ.get("USE_CUDA", True) if use_cuda == "OFF": use_cuda = False if not int(use_cuda): cmake_cmd.append("-DUSE_CUDA=OFF") build_cmd = ["cmake", "--build", ".", "--parallel"] # pip install (but not python setup.py install) runs with a modified PYTHONPATH. # This can prevent cmake from finding the torch libraries. env = os.environ.copy() if "PYTHONPATH" in env: del env["PYTHONPATH"] try: subprocess.check_call(cmake_cmd, cwd=self.build_temp, env=env) subprocess.check_call(build_cmd, cwd=self.build_temp, env=env) except subprocess.CalledProcessError: # Don't obscure the error with a setuptools backtrace. sys.exit(1) def main(): with open("README.md") as f: long_description = f.read() setuptools.setup( name="moolib", version="0.0.9", description=("A library for distributed ML training with PyTorch"), long_description=long_description, long_description_content_type="text/markdown", author="tscmoo & the moolib dev team", url="https://github.com/facebookresearch/moolib", classifiers=[ "Programming Language :: C++", "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: POSIX :: Linux", "Operating System :: MacOS :: MacOS X", "Environment :: GPU :: NVIDIA CUDA", ], packages=["moolib", "moolib.examples.common", "moolib.examples.vtrace"], package_dir={"": "py", "moolib.examples": "examples"}, ext_modules=[setuptools.Extension("moolib._C", sources=[])], install_requires=["torch>=1.6.0"], cmdclass={"build_ext": CMakeBuild}, zip_safe=False, ) if __name__ == "__main__": main()

31.989691

88

0.614567

b0fc72a05a6d7210e5155a3f940d34eb99842c4d

8,645

py

Python

tests/api/benchmark/test_permission_group.py

baitsanape/saleor

9594b40fb23bb6e665ba207c8ef436043e372bba

[ "CC-BY-4.0" ]

2

2020-05-28T19:29:33.000Z

2020-05-28T19:29:39.000Z

tests/api/benchmark/test_permission_group.py

baitsanape/saleor

9594b40fb23bb6e665ba207c8ef436043e372bba

[ "CC-BY-4.0" ]

13

2021-03-19T02:54:47.000Z

2022-03-12T00:36:26.000Z

tests/api/benchmark/test_permission_group.py

baitsanape/saleor

9594b40fb23bb6e665ba207c8ef436043e372bba

[ "CC-BY-4.0" ]

1

2020-05-15T18:18:52.000Z

import graphene import pytest from django.contrib.auth.models import Group from saleor.core.permissions import AccountPermissions from tests.api.utils import get_graphql_content @pytest.mark.django_db @pytest.mark.count_queries(autouse=False) def test_permission_group_create( staff_user, permission_manage_staff, staff_api_client, permission_manage_users, permission_manage_apps, count_queries, ): staff_user.user_permissions.add(permission_manage_users, permission_manage_apps) query = """ mutation PermissionGroupCreate( $input: PermissionGroupCreateInput!) { permissionGroupCreate( input: $input) { group{ id name permissions { name code } users { email } } permissionGroupErrors{ field code permissions users message } } } """ group_count = Group.objects.count() variables = { "input": { "name": "New permission group", "addPermissions": [ AccountPermissions.MANAGE_USERS.name, AccountPermissions.MANAGE_SERVICE_ACCOUNTS.name, ], "addUsers": [graphene.Node.to_global_id("User", staff_user.id)], } } response = staff_api_client.post_graphql( query, variables, permissions=(permission_manage_staff,) ) content = get_graphql_content(response) data = content["data"]["permissionGroupCreate"] groups = Group.objects.all() assert data["permissionGroupErrors"] == [] assert len(groups) == group_count + 1 @pytest.mark.django_db @pytest.mark.count_queries(autouse=False) def test_permission_group_update( permission_group_manage_users, staff_users, permission_manage_staff, staff_api_client, permission_manage_apps, permission_manage_users, count_queries, ): query = """ mutation PermissionGroupUpdate( $id: ID!, $input: PermissionGroupUpdateInput!) { permissionGroupUpdate( id: $id, input: $input) { group{ id name permissions { name code } } permissionGroupErrors{ field code permissions users message } } } """ staff_user = staff_users[0] staff_user.user_permissions.add(permission_manage_apps, permission_manage_users) group1, group2 = Group.objects.bulk_create( [Group(name="manage users"), Group(name="manage staff and users")] ) group1.permissions.add(permission_manage_users) group2.permissions.add(permission_manage_users, permission_manage_staff) group1_user = staff_users[1] group1.user_set.add(group1_user) group2.user_set.add(staff_user) group_count = Group.objects.count() variables = { "id": graphene.Node.to_global_id("Group", group1.id), "input": { "name": "New permission group", "addPermissions": [AccountPermissions.MANAGE_SERVICE_ACCOUNTS.name], "removePermissions": [AccountPermissions.MANAGE_USERS.name], "addUsers": [graphene.Node.to_global_id("User", staff_user.pk)], "removeUsers": [ graphene.Node.to_global_id("User", group1.user_set.first().pk) ], }, } response = staff_api_client.post_graphql(query, variables) content = get_graphql_content(response) data = content["data"]["permissionGroupUpdate"] groups = Group.objects.all() assert data["permissionGroupErrors"] == [] assert len(groups) == group_count @pytest.mark.django_db @pytest.mark.count_queries(autouse=False) def test_permission_group_update_remove_users_with_manage_staff( permission_group_manage_users, staff_users, permission_manage_staff, staff_api_client, permission_manage_apps, permission_manage_users, permission_manage_orders, count_queries, ): query = """ mutation PermissionGroupUpdate( $id: ID!, $input: PermissionGroupUpdateInput!) { permissionGroupUpdate( id: $id, input: $input) { group{ id name permissions { name code } users { email } } permissionGroupErrors{ field code permissions users message } } } """ staff_user, staff_user1, staff_user2 = staff_users groups = Group.objects.bulk_create( [Group(name="manage users"), Group(name="manage staff, order and users")] ) group1, group2 = groups group1.permissions.add(permission_manage_staff, permission_manage_users) group2.permissions.add( permission_manage_staff, permission_manage_orders, permission_manage_users ) group1.user_set.add(staff_user1, staff_user2) group2.user_set.add(staff_user2) staff_user.user_permissions.add(permission_manage_users, permission_manage_orders) variables = { "id": graphene.Node.to_global_id("Group", group1.id), "input": { "removeUsers": [ graphene.Node.to_global_id("User", user.id) for user in [staff_user1, staff_user2] ], }, } response = staff_api_client.post_graphql( query, variables, permissions=(permission_manage_staff,) ) content = get_graphql_content(response) data = content["data"]["permissionGroupUpdate"] assert len(data["group"]["users"]) == 0 @pytest.mark.django_db @pytest.mark.count_queries(autouse=False) def test_permission_group_delete( staff_users, permission_manage_staff, permission_manage_orders, permission_manage_products, staff_api_client, count_queries, ): query = """ mutation PermissionGroupDelete($id: ID!) { permissionGroupDelete( id: $id) { group{ id name permissions { name code } } permissionGroupErrors{ field code users permissions message } } } """ staff_user1, staff_user2, _ = staff_users staff_user1.user_permissions.add( permission_manage_orders, permission_manage_products ) groups = Group.objects.bulk_create( [Group(name="manage orders"), Group(name="manage orders and products")] ) group1, group2 = groups group1.permissions.add(permission_manage_orders, permission_manage_staff) group2.permissions.add( permission_manage_orders, permission_manage_products, permission_manage_staff ) staff_user2.groups.add(group1, group2) group_count = Group.objects.count() variables = {"id": graphene.Node.to_global_id("Group", group1.id)} response = staff_api_client.post_graphql( query, variables, permissions=(permission_manage_staff,) ) content = get_graphql_content(response) data = content["data"]["permissionGroupDelete"] assert data assert Group.objects.count() == group_count - 1 @pytest.mark.django_db @pytest.mark.count_queries(autouse=False) def test_permission_group_query( permission_group_manage_users, staff_user, permission_manage_staff, permission_manage_users, staff_api_client, count_queries, ): staff_user.user_permissions.add(permission_manage_staff, permission_manage_users) group = permission_group_manage_users query = """ query ($id: ID!){ permissionGroup(id: $id){ id name permissions { name code } users{ email } userCanManage } } """ variables = {"id": graphene.Node.to_global_id("Group", group.id)} response = staff_api_client.post_graphql(query, variables) content = get_graphql_content(response) data = content["data"]["permissionGroup"] assert data

27.797428

86

0.595604

facb1ab8b0e67ee945169f2cf225a64e4303e93b

4,784

py

Python

pypureclient/flasharray/FA_2_6/models/session_get_response.py

Flav-STOR-WL/py-pure-client

03b889c997d90380ac5d6380ca5d5432792d3e89

[ "BSD-2-Clause" ]

14

2018-12-07T18:30:27.000Z

2022-02-22T09:12:33.000Z

pypureclient/flasharray/FA_2_6/models/session_get_response.py

Flav-STOR-WL/py-pure-client

03b889c997d90380ac5d6380ca5d5432792d3e89

[ "BSD-2-Clause" ]

28

2019-09-17T21:03:52.000Z

2022-03-29T22:07:35.000Z

pypureclient/flasharray/FA_2_6/models/session_get_response.py

Flav-STOR-WL/py-pure-client

03b889c997d90380ac5d6380ca5d5432792d3e89

[ "BSD-2-Clause" ]

15

2020-06-11T15:50:08.000Z

2022-03-21T09:27:25.000Z

# coding: utf-8 """ FlashArray REST API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: 2.6 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re import six import typing from ....properties import Property if typing.TYPE_CHECKING: from pypureclient.flasharray.FA_2_6 import models class SessionGetResponse(object): """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'more_items_remaining': 'bool', 'total_item_count': 'int', 'continuation_token': 'str', 'items': 'list[Session]' } attribute_map = { 'more_items_remaining': 'more_items_remaining', 'total_item_count': 'total_item_count', 'continuation_token': 'continuation_token', 'items': 'items' } required_args = { } def __init__( self, more_items_remaining=None, # type: bool total_item_count=None, # type: int continuation_token=None, # type: str items=None, # type: List[models.Session] ): """ Keyword args: more_items_remaining (bool): Returns a value of `true` if subsequent items can be retrieved. total_item_count (int): The total number of records after applying all filter query parameters. The `total_item_count` will be calculated if and only if the corresponding query parameter `total_item_count` is set to `true`. If this query parameter is not set or set to `false`, a value of `null` will be returned. continuation_token (str): Continuation token that can be provided in the `continuation_token` query param to get the next page of data. If you use the continuation token to page through data you are guaranteed to get all items exactly once regardless of how items are modified. If an item is added or deleted during the pagination then it may or may not be returned. The continuation token is generated if the limit is less than the remaining number of items, and the default sort is used (no sort is specified). items (list[Session]) """ if more_items_remaining is not None: self.more_items_remaining = more_items_remaining if total_item_count is not None: self.total_item_count = total_item_count if continuation_token is not None: self.continuation_token = continuation_token if items is not None: self.items = items def __setattr__(self, key, value): if key not in self.attribute_map: raise KeyError("Invalid key `{}` for `SessionGetResponse`".format(key)) self.__dict__[key] = value def __getattribute__(self, item): value = object.__getattribute__(self, item) if isinstance(value, Property): raise AttributeError else: return value def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): if hasattr(self, attr): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(SessionGetResponse, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, SessionGetResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

36.8

524

0.60556

ce4b29e97a1dc1db0ab9476748a50f68188b3598

25,644

py

Python

code_summary/onmt/translate/translator.py

Nrgeup/review_assistant

bf03d62773501b84069afcc8b3da66d6d7829218

[ "Apache-2.0" ]

1

2020-01-17T00:41:51.000Z

code_summary/onmt/translate/translator.py

Nrgeup/review_assistant

bf03d62773501b84069afcc8b3da66d6d7829218

[ "Apache-2.0" ]

null

code_summary/onmt/translate/translator.py

Nrgeup/review_assistant

bf03d62773501b84069afcc8b3da66d6d7829218

[ "Apache-2.0" ]

null

#!/usr/bin/env python """ Translator Class and builder """ from __future__ import print_function import codecs import os import math import time from itertools import count import torch import onmt.model_builder import onmt.inputters as inputters import onmt.decoders.ensemble from onmt.translate.beam_search import BeamSearch from onmt.translate.greedy_search import GreedySearch from onmt.utils.misc import set_random_seed from onmt.modules.copy_generator import collapse_copy_scores def build_translator(opt, report_score=True, logger=None, out_file=None): if out_file is None: out_file = codecs.open(opt.output, 'w+', 'utf-8') load_test_model = onmt.decoders.ensemble.load_test_model \ if len(opt.models) > 1 else onmt.model_builder.load_test_model fields, model, model_opt = load_test_model(opt) scorer = onmt.translate.GNMTGlobalScorer.from_opt(opt) translator = Translator.from_opt( model, fields, opt, model_opt, global_scorer=scorer, out_file=out_file, report_score=report_score, logger=logger ) return translator def max_tok_len(new, count, sofar): """ In token batching scheme, the number of sequences is limited such that the total number of src/tgt tokens (including padding) in a batch <= batch_size """ # Maintains the longest src and tgt length in the current batch global max_src_in_batch # this is a hack # Reset current longest length at a new batch (count=1) if count == 1: max_src_in_batch = 0 # max_tgt_in_batch = 0 # Src: [<bos> w1 ... wN <eos>] max_src_in_batch = max(max_src_in_batch, len(new.src[0]) + 2) # Tgt: [w1 ... wM <eos>] src_elements = count * max_src_in_batch return src_elements class Translator(object): """Translate a batch of sentences with a saved model. Args: model (onmt.modules.NMTModel): NMT model to use for translation fields (dict[str, torchtext.data.Field]): A dict mapping each side to its list of name-Field pairs. src_reader (onmt.inputters.DataReaderBase): Source reader. tgt_reader (onmt.inputters.TextDataReader): Target reader. gpu (int): GPU device. Set to negative for no GPU. n_best (int): How many beams to wait for. min_length (int): See :class:`onmt.translate.decode_strategy.DecodeStrategy`. max_length (int): See :class:`onmt.translate.decode_strategy.DecodeStrategy`. beam_size (int): Number of beams. random_sampling_topk (int): See :class:`onmt.translate.greedy_search.GreedySearch`. random_sampling_temp (int): See :class:`onmt.translate.greedy_search.GreedySearch`. stepwise_penalty (bool): Whether coverage penalty is applied every step or not. dump_beam (bool): Debugging option. block_ngram_repeat (int): See :class:`onmt.translate.decode_strategy.DecodeStrategy`. ignore_when_blocking (set or frozenset): See :class:`onmt.translate.decode_strategy.DecodeStrategy`. replace_unk (bool): Replace unknown token. data_type (str): Source data type. verbose (bool): Print/log every translation. report_bleu (bool): Print/log Bleu metric. report_rouge (bool): Print/log Rouge metric. report_time (bool): Print/log total time/frequency. copy_attn (bool): Use copy attention. global_scorer (onmt.translate.GNMTGlobalScorer): Translation scoring/reranking object. out_file (TextIO or codecs.StreamReaderWriter): Output file. report_score (bool) : Whether to report scores logger (logging.Logger or NoneType): Logger. """ def __init__( self, model, fields, src_reader, tgt_reader, gpu=-1, n_best=1, min_length=0, max_length=100, ratio=0., beam_size=30, random_sampling_topk=1, random_sampling_temp=1, stepwise_penalty=None, dump_beam=False, block_ngram_repeat=0, ignore_when_blocking=frozenset(), replace_unk=False, phrase_table="", data_type="text", verbose=False, report_bleu=False, report_rouge=False, report_time=False, copy_attn=False, global_scorer=None, out_file=None, report_score=True, logger=None, seed=-1): self.model = model self.fields = fields tgt_field = dict(self.fields)["tgt"].base_field self._tgt_vocab = tgt_field.vocab self._tgt_eos_idx = self._tgt_vocab.stoi[tgt_field.eos_token] self._tgt_pad_idx = self._tgt_vocab.stoi[tgt_field.pad_token] self._tgt_bos_idx = self._tgt_vocab.stoi[tgt_field.init_token] self._tgt_unk_idx = self._tgt_vocab.stoi[tgt_field.unk_token] self._tgt_vocab_len = len(self._tgt_vocab) self._gpu = gpu self._use_cuda = gpu > -1 self._dev = torch.device("cuda", self._gpu) \ if self._use_cuda else torch.device("cpu") self.n_best = n_best self.max_length = max_length self.beam_size = beam_size self.random_sampling_temp = random_sampling_temp self.sample_from_topk = random_sampling_topk self.min_length = min_length self.ratio = ratio self.stepwise_penalty = stepwise_penalty self.dump_beam = dump_beam self.block_ngram_repeat = block_ngram_repeat self.ignore_when_blocking = ignore_when_blocking self._exclusion_idxs = { self._tgt_vocab.stoi[t] for t in self.ignore_when_blocking} self.src_reader = src_reader self.tgt_reader = tgt_reader self.replace_unk = replace_unk if self.replace_unk and not self.model.decoder.attentional: raise ValueError( "replace_unk requires an attentional decoder.") self.phrase_table = phrase_table self.data_type = data_type self.verbose = verbose self.report_bleu = report_bleu self.report_rouge = report_rouge self.report_time = report_time self.copy_attn = copy_attn self.global_scorer = global_scorer if self.global_scorer.has_cov_pen and \ not self.model.decoder.attentional: raise ValueError( "Coverage penalty requires an attentional decoder.") self.out_file = out_file self.report_score = report_score self.logger = logger self.use_filter_pred = False self._filter_pred = None # for debugging self.beam_trace = self.dump_beam != "" self.beam_accum = None if self.beam_trace: self.beam_accum = { "predicted_ids": [], "beam_parent_ids": [], "scores": [], "log_probs": []} set_random_seed(seed, self._use_cuda) @classmethod def from_opt( cls, model, fields, opt, model_opt, global_scorer=None, out_file=None, report_score=True, logger=None): """Alternate constructor. Args: model (onmt.modules.NMTModel): See :func:`__init__()`. fields (dict[str, torchtext.data.Field]): See :func:`__init__()`. opt (argparse.Namespace): Command line options model_opt (argparse.Namespace): Command line options saved with the model checkpoint. global_scorer (onmt.translate.GNMTGlobalScorer): See :func:`__init__()`.. out_file (TextIO or codecs.StreamReaderWriter): See :func:`__init__()`. report_score (bool) : See :func:`__init__()`. logger (logging.Logger or NoneType): See :func:`__init__()`. """ src_reader = inputters.str2reader[opt.data_type].from_opt(opt) tgt_reader = inputters.str2reader["text"].from_opt(opt) return cls( model, fields, src_reader, tgt_reader, gpu=opt.gpu, n_best=opt.n_best, min_length=opt.min_length, max_length=opt.max_length, ratio=opt.ratio, beam_size=opt.beam_size, random_sampling_topk=opt.random_sampling_topk, random_sampling_temp=opt.random_sampling_temp, stepwise_penalty=opt.stepwise_penalty, dump_beam=opt.dump_beam, block_ngram_repeat=opt.block_ngram_repeat, ignore_when_blocking=set(opt.ignore_when_blocking), replace_unk=opt.replace_unk, phrase_table=opt.phrase_table, data_type=opt.data_type, verbose=opt.verbose, report_bleu=opt.report_bleu, report_rouge=opt.report_rouge, report_time=opt.report_time, copy_attn=model_opt.copy_attn, global_scorer=global_scorer, out_file=out_file, report_score=report_score, logger=logger, seed=opt.seed) def _log(self, msg): if self.logger: self.logger.info(msg) else: # print(msg) pass def _gold_score(self, batch, memory_bank, src_lengths, src_vocabs, use_src_map, enc_states, batch_size, src): if "tgt" in batch.__dict__: gs = self._score_target( batch, memory_bank, src_lengths, src_vocabs, batch.src_map if use_src_map else None) self.model.decoder.init_state(src, memory_bank, enc_states) else: gs = [0] * batch_size return gs def translate( self, src, tgt=None, src_dir=None, batch_size=None, batch_type="sents", attn_debug=False, phrase_table=""): """Translate content of ``src`` and get gold scores from ``tgt``. Args: src: See :func:`self.src_reader.read()`. tgt: See :func:`self.tgt_reader.read()`. src_dir: See :func:`self.src_reader.read()` (only relevant for certain types of data). batch_size (int): size of examples per mini-batch attn_debug (bool): enables the attention logging Returns: (`list`, `list`) * all_scores is a list of `batch_size` lists of `n_best` scores * all_predictions is a list of `batch_size` lists of `n_best` predictions """ if batch_size is None: raise ValueError("batch_size must be set") data = inputters.Dataset( self.fields, readers=([self.src_reader, self.tgt_reader] if tgt else [self.src_reader]), data=[("src", src), ("tgt", tgt)] if tgt else [("src", src)], dirs=[src_dir, None] if tgt else [src_dir], sort_key=inputters.str2sortkey[self.data_type], filter_pred=self._filter_pred ) data_iter = inputters.OrderedIterator( dataset=data, device=self._dev, batch_size=batch_size, batch_size_fn=max_tok_len if batch_type == "tokens" else None, train=False, sort=False, sort_within_batch=True, shuffle=False ) xlation_builder = onmt.translate.TranslationBuilder( data, self.fields, self.n_best, self.replace_unk, tgt, self.phrase_table ) # Statistics counter = count(1) pred_score_total, pred_words_total = 0, 0 gold_score_total, gold_words_total = 0, 0 all_scores = [] all_predictions = [] start_time = time.time() for batch in data_iter: batch_data = self.translate_batch( batch, data.src_vocabs, attn_debug ) translations = xlation_builder.from_batch(batch_data) for trans in translations: all_scores += [trans.pred_scores[:self.n_best]] pred_score_total += trans.pred_scores[0] pred_words_total += len(trans.pred_sents[0]) if tgt is not None: gold_score_total += trans.gold_score gold_words_total += len(trans.gold_sent) + 1 n_best_preds = [" ".join(pred) for pred in trans.pred_sents[:self.n_best]] all_predictions += [n_best_preds] self.out_file.write('\n'.join(n_best_preds) + '\n') self.out_file.flush() if self.verbose: sent_number = next(counter) output = trans.log(sent_number) if self.logger: self.logger.info(output) else: os.write(1, output.encode('utf-8')) if attn_debug: preds = trans.pred_sents[0] preds.append('</s>') attns = trans.attns[0].tolist() if self.data_type == 'text': srcs = trans.src_raw else: srcs = [str(item) for item in range(len(attns[0]))] header_format = "{:>10.10} " + "{:>10.7} " * len(srcs) row_format = "{:>10.10} " + "{:>10.7f} " * len(srcs) output = header_format.format("", *srcs) + '\n' for word, row in zip(preds, attns): max_index = row.index(max(row)) row_format = row_format.replace( "{:>10.7f} ", "{:*>10.7f} ", max_index + 1) row_format = row_format.replace( "{:*>10.7f} ", "{:>10.7f} ", max_index) output += row_format.format(word, *row) + '\n' row_format = "{:>10.10} " + "{:>10.7f} " * len(srcs) if self.logger: self.logger.info(output) else: os.write(1, output.encode('utf-8')) end_time = time.time() if self.report_score: msg = self._report_score('PRED', pred_score_total, pred_words_total) self._log(msg) if tgt is not None: msg = self._report_score('GOLD', gold_score_total, gold_words_total) self._log(msg) if self.report_bleu: msg = self._report_bleu(tgt) self._log(msg) if self.report_rouge: msg = self._report_rouge(tgt) self._log(msg) if self.report_time: total_time = end_time - start_time self._log("Total translation time (s): %f" % total_time) self._log("Average translation time (s): %f" % ( total_time / len(all_predictions))) self._log("Tokens per second: %f" % ( pred_words_total / total_time)) if self.dump_beam: import json json.dump(self.translator.beam_accum, codecs.open(self.dump_beam, 'w', 'utf-8')) return all_scores, all_predictions def translate_batch(self, batch, src_vocabs, attn_debug): """Translate a batch of sentences.""" with torch.no_grad(): if self.beam_size == 1: decode_strategy = GreedySearch( pad=self._tgt_pad_idx, bos=self._tgt_bos_idx, eos=self._tgt_eos_idx, batch_size=batch.batch_size, min_length=self.min_length, max_length=self.max_length, block_ngram_repeat=self.block_ngram_repeat, exclusion_tokens=self._exclusion_idxs, return_attention=attn_debug or self.replace_unk, sampling_temp=self.random_sampling_temp, keep_topk=self.sample_from_topk) else: # TODO: support these blacklisted features assert not self.dump_beam decode_strategy = BeamSearch( self.beam_size, batch_size=batch.batch_size, pad=self._tgt_pad_idx, bos=self._tgt_bos_idx, eos=self._tgt_eos_idx, n_best=self.n_best, global_scorer=self.global_scorer, min_length=self.min_length, max_length=self.max_length, return_attention=attn_debug or self.replace_unk, block_ngram_repeat=self.block_ngram_repeat, exclusion_tokens=self._exclusion_idxs, stepwise_penalty=self.stepwise_penalty, ratio=self.ratio) return self._translate_batch_with_strategy(batch, src_vocabs, decode_strategy) def _run_encoder(self, batch): src, src_lengths = batch.src if isinstance(batch.src, tuple) \ else (batch.src, None) enc_states, memory_bank, src_lengths = self.model.encoder( src, src_lengths) if src_lengths is None: assert not isinstance(memory_bank, tuple), \ 'Ensemble decoding only supported for text data' src_lengths = torch.Tensor(batch.batch_size) \ .type_as(memory_bank) \ .long() \ .fill_(memory_bank.size(0)) return src, enc_states, memory_bank, src_lengths def _decode_and_generate( self, decoder_in, memory_bank, batch, src_vocabs, memory_lengths, src_map=None, step=None, batch_offset=None): if self.copy_attn: # Turn any copied words into UNKs. decoder_in = decoder_in.masked_fill( decoder_in.gt(self._tgt_vocab_len - 1), self._tgt_unk_idx ) # Decoder forward, takes [tgt_len, batch, nfeats] as input # and [src_len, batch, hidden] as memory_bank # in case of inference tgt_len = 1, batch = beam times batch_size # in case of Gold Scoring tgt_len = actual length, batch = 1 batch dec_out, dec_attn = self.model.decoder( decoder_in, memory_bank, memory_lengths=memory_lengths, step=step ) # Generator forward. if not self.copy_attn: if "std" in dec_attn: attn = dec_attn["std"] else: attn = None log_probs = self.model.generator(dec_out.squeeze(0)) # returns [(batch_size x beam_size) , vocab ] when 1 step # or [ tgt_len, batch_size, vocab ] when full sentence else: attn = dec_attn["copy"] scores = self.model.generator(dec_out.view(-1, dec_out.size(2)), attn.view(-1, attn.size(2)), src_map) # here we have scores [tgt_lenxbatch, vocab] or [beamxbatch, vocab] if batch_offset is None: scores = scores.view(-1, batch.batch_size, scores.size(-1)) scores = scores.transpose(0, 1).contiguous() else: scores = scores.view(-1, self.beam_size, scores.size(-1)) scores = collapse_copy_scores( scores, batch, self._tgt_vocab, src_vocabs, batch_dim=0, batch_offset=batch_offset ) scores = scores.view(decoder_in.size(0), -1, scores.size(-1)) log_probs = scores.squeeze(0).log() # returns [(batch_size x beam_size) , vocab ] when 1 step # or [ tgt_len, batch_size, vocab ] when full sentence return log_probs, attn def _translate_batch_with_strategy( self, batch, src_vocabs, decode_strategy): """Translate a batch of sentences step by step using cache. Args: batch: a batch of sentences, yield by data iterator. src_vocabs (list): list of torchtext.data.Vocab if can_copy. decode_strategy (DecodeStrategy): A decode strategy to use for generate translation step by step. Returns: results (dict): The translation results. """ # (0) Prep the components of the search. use_src_map = self.copy_attn parallel_paths = decode_strategy.parallel_paths # beam_size batch_size = batch.batch_size # (1) Run the encoder on the src. src, enc_states, memory_bank, src_lengths = self._run_encoder(batch) self.model.decoder.init_state(src, memory_bank, enc_states) results = { "predictions": None, "scores": None, "attention": None, "batch": batch, "gold_score": self._gold_score( batch, memory_bank, src_lengths, src_vocabs, use_src_map, enc_states, batch_size, src)} # (2) prep decode_strategy. Possibly repeat src objects. src_map = batch.src_map if use_src_map else None fn_map_state, memory_bank, memory_lengths, src_map = \ decode_strategy.initialize(memory_bank, src_lengths, src_map) if fn_map_state is not None: self.model.decoder.map_state(fn_map_state) # (3) Begin decoding step by step: for step in range(decode_strategy.max_length): decoder_input = decode_strategy.current_predictions.view(1, -1, 1) log_probs, attn = self._decode_and_generate( decoder_input, memory_bank, batch, src_vocabs, memory_lengths=memory_lengths, src_map=src_map, step=step, batch_offset=decode_strategy.batch_offset) decode_strategy.advance(log_probs, attn) any_finished = decode_strategy.is_finished.any() if any_finished: decode_strategy.update_finished() if decode_strategy.done: break select_indices = decode_strategy.select_indices if any_finished: # Reorder states. if isinstance(memory_bank, tuple): memory_bank = tuple(x.index_select(1, select_indices) for x in memory_bank) else: memory_bank = memory_bank.index_select(1, select_indices) memory_lengths = memory_lengths.index_select(0, select_indices) if src_map is not None: src_map = src_map.index_select(1, select_indices) if parallel_paths > 1 or any_finished: self.model.decoder.map_state( lambda state, dim: state.index_select(dim, select_indices)) results["scores"] = decode_strategy.scores results["predictions"] = decode_strategy.predictions results["attention"] = decode_strategy.attention return results def _score_target(self, batch, memory_bank, src_lengths, src_vocabs, src_map): tgt = batch.tgt tgt_in = tgt[:-1] log_probs, attn = self._decode_and_generate( tgt_in, memory_bank, batch, src_vocabs, memory_lengths=src_lengths, src_map=src_map) log_probs[:, :, self._tgt_pad_idx] = 0 gold = tgt[1:] gold_scores = log_probs.gather(2, gold) gold_scores = gold_scores.sum(dim=0).view(-1) return gold_scores def _report_score(self, name, score_total, words_total): if words_total == 0: msg = "%s No words predicted" % (name,) else: msg = ("%s AVG SCORE: %.4f, %s PPL: %.4f" % ( name, score_total / words_total, name, math.exp(-score_total / words_total))) return msg def _report_bleu(self, tgt_path): import subprocess base_dir = os.path.abspath(__file__ + "/../../..") # Rollback pointer to the beginning. self.out_file.seek(0) print() res = subprocess.check_output( "perl %s/tools/multi-bleu.perl %s" % (base_dir, tgt_path), stdin=self.out_file, shell=True ).decode("utf-8") msg = ">> " + res.strip() return msg def _report_rouge(self, tgt_path): import subprocess path = os.path.split(os.path.realpath(__file__))[0] msg = subprocess.check_output( "python %s/tools/test_rouge.py -r %s -c STDIN" % (path, tgt_path), shell=True, stdin=self.out_file ).decode("utf-8").strip() return msg

38.047478

79

0.563368

9b3deadc900c6a8d3c71ad795785a21ab7b044da

2,747

py

Python

src/garage/tf/distributions/bernoulli.py

bainro/garage

c5afbb19524792d9bbad9b9741f45e1d48ddca3d

[ "MIT" ]

null

src/garage/tf/distributions/bernoulli.py

bainro/garage

c5afbb19524792d9bbad9b9741f45e1d48ddca3d

[ "MIT" ]

null

src/garage/tf/distributions/bernoulli.py

bainro/garage

c5afbb19524792d9bbad9b9741f45e1d48ddca3d

[ "MIT" ]

null

import numpy as np import tensorflow as tf from garage.tf.distributions.base import Distribution TINY = 1e-8 class Bernoulli(Distribution): def __init__(self, dim, name='Bernoulli'): self._name = name self._dim = dim @property def dim(self): return self._dim def kl_sym(self, old_dist_info_vars, new_dist_info_vars, name='kl_sym'): with tf.name_scope(name): old_p = old_dist_info_vars['p'] new_p = new_dist_info_vars['p'] kl = (old_p * (tf.math.log(old_p + TINY) - tf.math.log(new_p + TINY)) + (1 - old_p) * (tf.math.log(1 - old_p + TINY) - tf.math.log(1 - new_p + TINY))) ndims = kl.get_shape().ndims return tf.reduce_sum(kl, axis=ndims - 1) def kl(self, old_dist_info, new_dist_info): old_p = old_dist_info['p'] new_p = new_dist_info['p'] kl = old_p * (np.log(old_p + TINY) - np.log(new_p + TINY)) \ + (1 - old_p) \ * (np.log(1 - old_p + TINY) - np.log(1 - new_p + TINY)) return np.sum(kl, axis=-1) def sample(self, dist_info): p = np.asarray(dist_info['p']) return np.cast['int']( np.random.uniform(low=0., high=1., size=p.shape) < p) def likelihood_ratio_sym(self, x_var, old_dist_info_vars, new_dist_info_vars, name='likelihood_ratio_sym'): with tf.name_scope(name): old_p = old_dist_info_vars['p'] new_p = new_dist_info_vars['p'] ndims = old_p.get_shape().ndims return tf.reduce_prod(x_var * new_p / (old_p + TINY) + (1 - x_var) * (1 - new_p) / (1 - old_p + TINY), axis=ndims - 1) def log_likelihood_sym(self, x_var, dist_info_vars, name='log_likelihood_sym'): with tf.name_scope(name): p = dist_info_vars['p'] ndims = p.get_shape().ndims return tf.reduce_sum(x_var * tf.math.log(p + TINY) + (1 - x_var) * tf.math.log(1 - p + TINY), axis=ndims - 1) def log_likelihood(self, xs, dist_info): p = dist_info['p'] return np.sum(xs * np.log(p + TINY) + (1 - xs) * np.log(1 - p + TINY), axis=-1) def entropy(self, dist_info): p = dist_info['p'] return np.sum(-p * np.log(p + TINY) - (1 - p) * np.log(1 - p + TINY), axis=-1) @property def dist_info_keys(self): return ['p']

35.217949

83

0.495086

92fb8180b972de53bb253c06e1b38359376ef11e

1,180

py

Python

clay/markdown_ext/md_superscript.py

TuxCoder/Clay

04f15b4d742b14d09df9049dd91cfa4386cba66e

[ "MIT" ]

null

clay/markdown_ext/md_superscript.py

TuxCoder/Clay

04f15b4d742b14d09df9049dd91cfa4386cba66e

[ "MIT" ]

null

clay/markdown_ext/md_superscript.py

TuxCoder/Clay

04f15b4d742b14d09df9049dd91cfa4386cba66e

[ "MIT" ]

null

# coding=utf-8 """ Superscipt extension for Markdown ================================== Examples: >>> import markdown >>> md = markdown.Markdown(extensions=[SuperscriptExtension()]) >>> md.convert('lorem ipsum^1 sit.') u'lorem ipsum1 sit.' >>> md.convert('6.02 x 10^23') u'6.02 x 1023' >>> md.convert('10^(2x + 3).') u'102x + 3.' """ import markdown from markdown.inlinepatterns import Pattern from markdown.util import etree, AtomicString SUPER_RE = r'\^(?:([^$\s]+)|\(([^\n$]+)\))' class SuperscriptPattern(Pattern): """ Return a superscript Element (`word^2^`). """ def handleMatch(self, m): supr = m.group(2) or m.group(3) text = supr el = etree.Element("sup") el.text = AtomicString(text) return el class SuperscriptExtension(markdown.Extension): """ Superscript Extension for Python-Markdown. """ def extendMarkdown(self, md, md_globals): """ Replace superscript with SuperscriptPattern """ md.inlinePatterns['superscript'] = SuperscriptPattern(SUPER_RE, md) if __name__ == "__main__": import doctest doctest.testmod()

23.137255

75

0.616102

c0c91e4430ccbb59fc5f0b146c18d4971a58198b

1,282

py

Python

dict_read.py

liaocm/LLHTranslate

c3e62f91b9f8b3594ce07601e123f0757efe63f3

[ "Apache-2.0" ]

null

dict_read.py

liaocm/LLHTranslate

c3e62f91b9f8b3594ce07601e123f0757efe63f3

[ "Apache-2.0" ]

null

dict_read.py

liaocm/LLHTranslate

c3e62f91b9f8b3594ce07601e123f0757efe63f3

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import io, sys def verify_line(line): if not line: return -1 if line == '\n': return 1 return 0 def read_dict(fname, show_warning=True): content = dict() try: fd = io.open(fname, 'r') except BaseException: sys.exit("Unable to open the dictionary.") line_num = 1 key_line = "" val_line = "" while True: key_line = fd.readline() check = verify_line(key_line) if check == -1: sys.exit("ERROR: Unexpected EOF at line {0}.".format(line_num)) elif check == 1: break line_num += 1 key_line = key_line[:-1] val_line = fd.readline() check = verify_line(val_line) if check == -1: sys.exit("ERROR: Unexpected EOF at line {0}.".format(line_num)) elif check == 1 and show_warning: print("Warning: No translation found for {0} at line {1}.".format(key_line, line_num)) line_num += 1 val_line = val_line[:-1] if key_line in content and show_warning: print("Warning: Collision for {0} at line {1}. Overriding.".format(key_line, line_num - 1)) content[key_line] = val_line key_line = fd.readline() if key_line and show_warning: print("Warning: EOF not found after ending literal at line {0}.".format(line_num)) fd.close() return content

26.708333

97

0.641186

02465a2f656014272a2fa4896a41ebcd98e6c5d8

1,463

py

Python

ooobuild/lo/configuration/backend/merge_importer.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/lo/configuration/backend/merge_importer.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/lo/configuration/backend/merge_importer.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2022 :Barry-Thomas-Paul: Moss # # 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. # # Service Class # this is a auto generated file generated by Cheetah # Libre Office Version: 7.3 # Namespace: com.sun.star.configuration.backend from .importer import Importer as Importer_6e7810c8 class MergeImporter(Importer_6e7810c8): """ Service Class imports data into a configuration layer by merging with existing data. No named arguments to com.sun.star.lang.XInitialization.initialize() are supported. **since** OOo 1.1.2 See Also: `API MergeImporter <https://api.libreoffice.org/docs/idl/ref/servicecom_1_1sun_1_1star_1_1configuration_1_1backend_1_1MergeImporter.html>`_ """ __ooo_ns__: str = 'com.sun.star.configuration.backend' __ooo_full_ns__: str = 'com.sun.star.configuration.backend.MergeImporter' __ooo_type_name__: str = 'service' __all__ = ['MergeImporter']

31.804348

147

0.74026

d201c51658bb23fef026f46026d60002b900ac20

1,730

py

Python

plotly/validators/candlestick/hoverlabel/_font.py

fcollonval/plotly.py

5c7f100db1af8c82bb740a38ef684955a8ed6d0e

[ "MIT" ]

2

2020-03-24T11:41:14.000Z

2021-01-14T07:59:43.000Z

plotly/validators/candlestick/hoverlabel/_font.py

fcollonval/plotly.py

5c7f100db1af8c82bb740a38ef684955a8ed6d0e

[ "MIT" ]

null

plotly/validators/candlestick/hoverlabel/_font.py

fcollonval/plotly.py

5c7f100db1af8c82bb740a38ef684955a8ed6d0e

[ "MIT" ]

4

2019-06-03T14:49:12.000Z

2022-01-06T01:05:12.000Z

import _plotly_utils.basevalidators class FontValidator(_plotly_utils.basevalidators.CompoundValidator): def __init__( self, plotly_name='font', parent_name='candlestick.hoverlabel', **kwargs ): super(FontValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, data_class_str='Font', data_docs=""" color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . """, **kwargs )

35.306122

68

0.537572

6fe02521649d88d87fe829b3712574c90316d3f1

5,486

py

Python

detection/dota15_demo.py

chandlerbing65nm/PVT

e171519b2a1a44e36ebdf0732f274a190b50ce29

[ "Apache-2.0" ]

null

detection/dota15_demo.py

chandlerbing65nm/PVT

e171519b2a1a44e36ebdf0732f274a190b50ce29

[ "Apache-2.0" ]

null

detection/dota15_demo.py

chandlerbing65nm/PVT

e171519b2a1a44e36ebdf0732f274a190b50ce29

[ "Apache-2.0" ]

null

import cv2 import math import mmcv import numpy as np import os import pdb from mmcv import Config from tqdm import tqdm from argparse import ArgumentParser import DOTA_devkit.polyiou as polyiou from mmdet.apis import init_detector, inference_detector, draw_poly_detections from mmdet.datasets import get_dataset dota15_colormap = [ (54, 67, 244), (99, 30, 233), (176, 39, 156), (183, 58, 103), (181, 81, 63), (243, 150, 33), (212, 188, 0), (136, 150, 0), (80, 175, 76), (74, 195, 139), (57, 220, 205), (59, 235, 255), (0, 152, 255), (34, 87, 255), (72, 85, 121), (139, 125, 96)] def parse_args(): parser = ArgumentParser() parser.add_argument('img', help='Image file') parser.add_argument('config', help='Config file') parser.add_argument('checkpoint', help='Checkpoint file') parser.add_argument('--out', help='output image') args = parser.parse_args() return args def py_cpu_nms_poly_fast_np(dets, thresh): obbs = dets[:, 0:-1] x1 = np.min(obbs[:, 0::2], axis=1) y1 = np.min(obbs[:, 1::2], axis=1) x2 = np.max(obbs[:, 0::2], axis=1) y2 = np.max(obbs[:, 1::2], axis=1) scores = dets[:, 8] areas = (x2 - x1 + 1) * (y2 - y1 + 1) polys = [] for i in range(len(dets)): tm_polygon = polyiou.VectorDouble([dets[i][0], dets[i][1], dets[i][2], dets[i][3], dets[i][4], dets[i][5], dets[i][6], dets[i][7]]) polys.append(tm_polygon) order = scores.argsort()[::-1] keep = [] while order.size > 0: ovr = [] i = order[0] keep.append(i) xx1 = np.maximum(x1[i], x1[order[1:]]) yy1 = np.maximum(y1[i], y1[order[1:]]) xx2 = np.minimum(x2[i], x2[order[1:]]) yy2 = np.minimum(y2[i], y2[order[1:]]) w = np.maximum(0.0, xx2 - xx1) h = np.maximum(0.0, yy2 - yy1) hbb_inter = w * h hbb_ovr = hbb_inter / (areas[i] + areas[order[1:]] - hbb_inter) h_inds = np.where(hbb_ovr > 0)[0] tmp_order = order[h_inds + 1] for j in range(tmp_order.size): iou = polyiou.iou_poly(polys[i], polys[tmp_order[j]]) hbb_ovr[h_inds[j]] = iou try: if math.isnan(ovr[0]): pdb.set_trace() except: pass inds = np.where(hbb_ovr <= thresh)[0] order = order[inds + 1] return keep class DetectorModel(): def __init__(self, config_file, checkpoint_file): # init RoITransformer self.config_file = config_file self.checkpoint_file = checkpoint_file self.cfg = Config.fromfile(self.config_file) self.data_test = self.cfg.data['test'] self.dataset = get_dataset(self.data_test) self.classnames = self.dataset.CLASSES self.model = init_detector(config_file, checkpoint_file, device='cuda:0') def inference_single(self, imagname, slide_size, chip_size): img = mmcv.imread(imagname) height, width, channel = img.shape slide_h, slide_w = slide_size hn, wn = chip_size # TODO: check the corner case # import pdb; pdb.set_trace() total_detections = [np.zeros((0, 9)) for _ in range(len(self.classnames))] for i in tqdm(range(int(width / slide_w + 1))): for j in range(int(height / slide_h) + 1): subimg = np.zeros((hn, wn, channel)) # print('i: ', i, 'j: ', j) chip = img[j * slide_h:j * slide_h + hn, i * slide_w:i * slide_w + wn, :3] subimg[:chip.shape[0], :chip.shape[1], :] = chip chip_detections = inference_detector(self.model, subimg) # print('result: ', result) for cls_id, name in enumerate(self.classnames): chip_detections[cls_id][:, :8][:, ::2] = chip_detections[cls_id][:, :8][:, ::2] + i * slide_w chip_detections[cls_id][:, :8][:, 1::2] = chip_detections[cls_id][:, :8][:, 1::2] + j * slide_h # import pdb;pdb.set_trace() try: total_detections[cls_id] = np.concatenate((total_detections[cls_id], chip_detections[cls_id])) except: import pdb; pdb.set_trace() # nms for i in range(len(self.classnames)): keep = py_cpu_nms_poly_fast_np(total_detections[i], 0.1) total_detections[i] = total_detections[i][keep] return total_detections def inference_single_vis(self, srcpath, dstpath, slide_size, chip_size): detections = self.inference_single(srcpath, slide_size, chip_size) img = draw_poly_detections(srcpath, detections, self.classnames, scale=1, threshold=0.2, colormap=dota15_colormap) cv2.imwrite(dstpath, img) if __name__ == '__main__': args = parse_args() model = DetectorModel(args.config, args.checkpoint) img_dir = args.img out_dir = args.out img_names = os.listdir(img_dir) for img_name in img_names: print(img_name) img_path = os.path.join(img_dir, img_name) out_path = os.path.join(out_dir, img_name) model.inference_single_vis(img_path, out_path, (512, 512), (1024, 1024))

35.166667

118

0.556325

53c67ddc4d522fcc2aa592daeb46c15301b5636a

7,276

py

Python

mealpy/dummy/PIO.py

JokerHB/mealpy

4bd00f47ed575d01f246d5fd0ef306d7c1fa5a5f

[ "MIT" ]

162

2020-08-31T10:13:06.000Z

2022-03-31T09:38:19.000Z

mealpy/dummy/PIO.py

JokerHB/mealpy

4bd00f47ed575d01f246d5fd0ef306d7c1fa5a5f

[ "MIT" ]

51

2020-09-13T10:46:31.000Z

2022-03-30T06:12:08.000Z

mealpy/dummy/PIO.py

JokerHB/mealpy

4bd00f47ed575d01f246d5fd0ef306d7c1fa5a5f

[ "MIT" ]

58

2020-09-12T13:29:18.000Z

2022-03-31T09:38:21.000Z

#!/usr/bin/env python # ------------------------------------------------------------------------------------------------------% # Created by "Thieu Nguyen" at 11:41, 08/04/2020 % # % # Email: nguyenthieu2102@gmail.com % # Homepage: https://www.researchgate.net/profile/Thieu_Nguyen6 % # Github: https://github.com/thieu1995 % #-------------------------------------------------------------------------------------------------------% from numpy.random import uniform from numpy import exp, sum from mealpy.optimizer import Root class BasePIO(Root): """ My improved version of: Pigeon-Inspired Optimization (PIO) (Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning) Link: + DOI: 10.1108/IJICC-02-2014-0005 Noted: + The paper is very unclear most the parameters and the flow of algorithm (some points even wrong) + This is my version, I changed almost everything, even parameters and flow of algorithm + Also the personal best no need in this version (So it is now much different than PSO) """ def __init__(self, obj_func=None, lb=None, ub=None, verbose=True, epoch=750, pop_size=100, R=0.2, n_switch=0.75, **kwargs): super().__init__(obj_func, lb, ub, verbose, kwargs) self.epoch = epoch # Nc1 + Nc2 self.pop_size = pop_size # Np self.R = R if n_switch < 1: self.n_switch = int(self.epoch * n_switch) else: self.n_switch = int(n_switch) # Represent Nc1 and Nc2 in the paper def train(self): pop = [self.create_solution() for _ in range(self.pop_size)] g_best = self.get_global_best_solution(pop=pop, id_fit=self.ID_FIT, id_best=self.ID_MIN_PROB) list_velocity = uniform(self.lb, self.ub, (self.pop_size, self.problem_size)) n_p = int(self.pop_size / 2) for epoch in range(0, self.epoch): if epoch < self.n_switch: # Map and compass operations for i in range(0, self.pop_size): v_new = list_velocity[i] * exp(-self.R * (epoch + 1)) + uniform() * (g_best[self.ID_POS] - pop[i][self.ID_POS]) x_new = pop[i][self.ID_POS] + v_new x_new = self.amend_position_random(x_new) fit = self.get_fitness_position(x_new) if fit < pop[i][self.ID_FIT]: pop[i] = [x_new, fit] list_velocity[i] = v_new else: # Landmark operations pop = sorted(pop, key=lambda item: item[self.ID_FIT]) list_fit = [pop[i][self.ID_FIT] for i in range(0, n_p)] list_pos = [pop[i][self.ID_FIT] for i in range(0, n_p)] frac_up = sum([list_fit[i] * list_pos[i] for i in range(0, n_p)], axis=0) frac_down = n_p * sum(list_fit) x_c = frac_up / frac_down ## Move all pigeon based on target x_c for i in range(0, self.pop_size): x_new = pop[i][self.ID_POS] + uniform() * (x_c - pop[i][self.ID_POS]) fit_new = self.get_fitness_position(x_new) if fit_new < pop[i][self.ID_FIT]: pop[i] = [x_new, fit_new] # Update the global best g_best = self.update_global_best_solution(pop, self.ID_MIN_PROB, g_best) self.loss_train.append(g_best[self.ID_FIT]) if self.verbose: print(">Epoch: {}, Best fit: {}".format(epoch + 1, g_best[self.ID_FIT])) self.solution = g_best return g_best[self.ID_POS], g_best[self.ID_FIT], self.loss_train class LevyPIO(BasePIO): """ My levy version of: Pigeon-Inspired Optimization (PIO) (Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning) Noted: + The paper is very unclear most the parameters and the flow of algorithm (some points even wrong) + This is my version, I changed almost everything, even parameters and flow of algorithm + Also the personal best no need in this version (So it is now much different than PSO) + I applied the levy-flight here for more robust """ def __init__(self, obj_func=None, lb=None, ub=None, verbose=True, epoch=750, pop_size=100, R=0.2, n_switch=0.75, **kwargs): BasePIO.__init__(self, obj_func, lb, ub, verbose, epoch, pop_size, R, n_switch, kwargs = kwargs) def train(self): pop = [self.create_solution() for _ in range(self.pop_size)] g_best = self.get_global_best_solution(pop=pop, id_fit=self.ID_FIT, id_best=self.ID_MIN_PROB) list_velocity = uniform(self.lb, self.ub, (self.pop_size, self.problem_size)) n_p = int(self.pop_size / 2) for epoch in range(0, self.epoch): if epoch < self.n_switch: # Map and compass operations for i in range(0, self.pop_size): v_new = list_velocity[i] * exp(-self.R * (epoch + 1)) + uniform() * (g_best[self.ID_POS] - pop[i][self.ID_POS]) x_new = pop[i][self.ID_POS] + v_new x_new = self.amend_position_random(x_new) fit_new = self.get_fitness_position(x_new) if fit_new < pop[i][self.ID_FIT]: pop[i] = [x_new, fit_new] list_velocity[i] = v_new else: # Landmark operations pop = sorted(pop, key=lambda item: item[self.ID_FIT]) list_fit = [pop[i][self.ID_FIT] for i in range(0, n_p)] list_pos = [pop[i][self.ID_FIT] for i in range(0, n_p)] frac_up = sum([list_fit[i] * list_pos[i] for i in range(0, n_p)], axis=0) frac_down = n_p * sum(list_fit) x_c = frac_up / frac_down ## Move all pigeon based on target x_c for i in range(0, self.pop_size): if uniform() < 0.5: x_new = pop[i][self.ID_POS] + uniform() * (x_c - pop[i][self.ID_POS]) else: x_new = self.levy_flight(epoch, pop[i][self.ID_POS], g_best[self.ID_POS]) fit_new = self.get_fitness_position(x_new) if fit_new < pop[i][self.ID_FIT]: pop[i] = [x_new, fit_new] # Update the global best g_best = self.update_global_best_solution(pop, self.ID_MIN_PROB, g_best) self.loss_train.append(g_best[self.ID_FIT]) if self.verbose: print(">Epoch: {}, Best fit: {}".format(epoch + 1, g_best[self.ID_FIT])) self.solution = g_best return g_best[self.ID_POS], g_best[self.ID_FIT], self.loss_train

53.5

131

0.529962

4ce39ad8be08a0d7a389868b75324c47d637fec9

39,651

py

Python

datalad/metadata/aggregate.py

jelmer/datalad

fedc04867d87e0191bd500991d0df97e97113457

[ "MIT" ]

null

datalad/metadata/aggregate.py

jelmer/datalad

fedc04867d87e0191bd500991d0df97e97113457

[ "MIT" ]

null

datalad/metadata/aggregate.py

jelmer/datalad

fedc04867d87e0191bd500991d0df97e97113457

[ "MIT" ]

null

# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*- # ex: set sts=4 ts=4 sw=4 noet: # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the datalad package for the # copyright and license terms. # # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Interface for aggregating metadata """ __docformat__ = 'restructuredtext' import logging import os from os import makedirs from os import listdir import os.path as op from os.path import join as opj from os.path import dirname from os.path import relpath from os.path import isabs from os.path import exists from os.path import lexists from os.path import curdir from hashlib import md5 import shutil # API commands we need from datalad.distribution.get import Get from datalad.distribution.remove import Remove from datalad.distribution.subdatasets import Subdatasets from datalad.interface.unlock import Unlock import datalad from datalad.interface.annotate_paths import AnnotatePaths from datalad.interface.base import Interface from datalad.interface.utils import eval_results from datalad.interface.utils import discover_dataset_trace_to_targets from datalad.interface.save import Save from datalad.interface.base import build_doc from datalad.interface.common_opts import recursion_limit, recursion_flag from datalad.interface.common_opts import nosave_opt from datalad.interface.results import get_status_dict from datalad.distribution.dataset import Dataset from datalad.metadata.metadata import agginfo_relpath from datalad.metadata.metadata import exclude_from_metadata from datalad.metadata.metadata import get_metadata_type from datalad.metadata.metadata import _load_json_object from datalad.metadata.metadata import _get_metadata from datalad.metadata.metadata import _get_metadatarelevant_paths from datalad.metadata.metadata import _get_containingds_from_agginfo from datalad.distribution.dataset import datasetmethod, EnsureDataset, require_dataset from datalad.support.param import Parameter from datalad.support.constraints import EnsureStr from datalad.support.constraints import EnsureNone from datalad.support.constraints import EnsureBool from datalad.support.constraints import EnsureChoice from datalad.support.gitrepo import GitRepo from datalad.support.annexrepo import AnnexRepo from datalad.support import json_py from datalad.utils import path_is_subpath from datalad.utils import assure_list lgr = logging.getLogger('datalad.metadata.aggregate') # TODO filepath_info is obsolete location_keys = ('dataset_info', 'content_info', 'filepath_info') def _get_dsinfo_from_aggmetadata(ds_path, path, recursive, db): """Grab info on aggregated metadata for a path from a given dataset. The actual info is stored in a `db` dict under the absolute path of the dataset that contains the query path, plus any subdataset in case of recursion (with their own DB entries). Parameters ---------- ds : Dataset source dataset path : str absolute path for which to obtain metadata recursive : bool Returns ------- str or list A string is an error message, a list contains all absolute paths for all datasets on which info was put into the DB. """ info_fpath = opj(ds_path, agginfo_relpath) info_basepath = dirname(info_fpath) # TODO cache these agginfos = _load_json_object(info_fpath) def _ensure_abs_obj_location(rec): # object location in the DB must be absolute so we can copy easily # to all relevant datasets for key in location_keys: if key in rec and not isabs(rec[key]): rec[key] = opj(info_basepath, rec[key]) return rec rpath = relpath(path, start=ds_path) seed_ds = _get_containingds_from_agginfo(agginfos, rpath) if seed_ds is None: # nothing found # this will be the message in the result for the query path # and could be a tuple return ("No matching aggregated metadata for path '%s' in Dataset at %s", rpath, ds_path) # easy peasy seed_abs = opj(ds_path, seed_ds) db[seed_abs] = _ensure_abs_obj_location(agginfos[seed_ds]) hits = [seed_abs] if not recursive: return hits # a little more complicated: we need to loop over all subdataset # records and pick the ones that are underneath the seed for agginfo_path in agginfos: if path_is_subpath(agginfo_path, seed_ds): absp = opj(ds_path, agginfo_path) db[absp] = _ensure_abs_obj_location(agginfos[agginfo_path]) hits.append(absp) # TODO we must keep the info on these recursively discovered datasets # somewhere, because we cannot rediscover them on the filesystem # when updating the datasets later on return hits def _dump_extracted_metadata(agginto_ds, aggfrom_ds, db, to_save, force_extraction): """Dump metadata from a dataset into object in the metadata store of another Info on the metadata objects is placed into a DB dict under the absolute path of the dataset whose metadata was aggregated. Parameters ---------- agginto_ds : Dataset aggfrom_ds : Dataset db : dict """ subds_relpaths = aggfrom_ds.subdatasets(result_xfm='relpaths', return_type='list') # figure out a "state" of the dataset wrt its metadata that we are describing # 1. the latest commit that changed any file for which we could have native metadata refcommit = _get_latest_refcommit(aggfrom_ds, subds_relpaths) objid = refcommit if refcommit else '' # 2, our own dataset-global metadata and the dataset config for tfile in ( opj(aggfrom_ds.path, '.datalad', 'metadata', 'dataset.json'), opj(aggfrom_ds.path, '.datalad', 'config')): if exists(tfile): objid += md5(open(tfile, 'r').read().encode()).hexdigest() # 3. potential annex-based metadata # XXX TODO shouldn't this be the annex extractor? if isinstance(aggfrom_ds, AnnexRepo) and \ aggfrom_ds.config.obtain( 'datalad.metadata.aggregate-content-datalad-core', default=True, valtype=EnsureBool()): # if there is no annex metadata, this will come out empty, # hence hash would be same as for a plain GitRepo # and no, we cannot use the shasum of the annex branch, # because this will change even when no metadata has changed timestamps, _ = aggfrom_ds.repo._run_annex_command( 'metadata', '.', '-g', 'lastchanged') objid += timestamps.strip() if not objid: lgr.debug('%s has no metadata-relevant content', aggfrom_ds) else: lgr.debug( 'Dump metadata of %s into %s', aggfrom_ds, agginto_ds) # check if we already have in store what we are about to create old_agginfo = db.get(aggfrom_ds.path, {}) agginfo = {} # dataset global if aggfrom_ds.id: agginfo['id'] = aggfrom_ds.id agginfo['refcommit'] = refcommit # put in DB db[aggfrom_ds.path] = agginfo if not objid: # this is no error, there is simply no metadata whatsoever return False # shorten to MD5sum objid = md5(objid.encode()).hexdigest() # assemble info on the metadata extraction and storage # label type targetds storage method metasources = {'ds': {'type': 'dataset', 'targetds': agginto_ds, 'dumper': json_py.dump}} # do not store content metadata if either the source or the target dataset # do not want it # TODO this AND was an OR before (wrong), misses a test if aggfrom_ds.config.obtain( 'datalad.metadata.store-aggregate-content', default=True, valtype=EnsureBool()) and \ agginto_ds.config.obtain( 'datalad.metadata.store-aggregate-content', default=True, valtype=EnsureBool()): metasources['cn'] = { 'type': 'content', 'targetds': agginto_ds, 'dumper': json_py.dump2xzstream} # check if we have the extracted metadata for this state already # either in the source or in the destination dataset metafound = { s: d # look in targetds last to not have to move things # unnecessarily for d in (aggfrom_ds, agginto_ds) for s in metasources # important to test for lexists() as we do not need to # or want to `get()` metadata files for this test # info on identity is sufficient if op.lexists( op.join( d.path, dirname(agginfo_relpath), _get_obj_location(objid, s))) } if not force_extraction else False if not metafound: lgr.debug('Performing metadata extraction from %s', aggfrom_ds) # no metadata found -> extract # this places metadata dump files into the configured # target dataset and lists them in `to_save`, as well # as updates the `db` record for `aggfrom_ds` return _extract_metadata( agginto_ds, aggfrom_ds, db, to_save, objid, metasources, refcommit, subds_relpaths) # we did not actually run an extraction, so we need to # assemble an aggregation record from the existing pieces # that we found # simple case: the target dataset has all the records already: if all(d is agginto_ds for s, d in metafound.items()): lgr.debug('Sticking with up-to-date metadata for %s', aggfrom_ds) # no change, use old record from the target dataset db[aggfrom_ds.path] = old_agginfo # no error return False else: lgr.debug('Reusing previously extracted metadata for %s', aggfrom_ds) # we need to move the metadata dump(s) into the target dataset objrelpaths = { label: op.join( dirname(agginfo_relpath), _get_obj_location(objid, label)) for label in metafound } # make sure all the to-be-moved metadata records are present # locally aggfrom_ds.get( # prep annotated path records to speed up the call path=[dict(path=op.join(aggfrom_ds.path, p), parentds=aggfrom_ds.path, type='file') for p in objrelpaths.values()], result_renderer='disabled') # actually copy dump files for objrelpath in objrelpaths.values(): objpath = op.join(agginto_ds.path, objrelpath) objdir = dirname(objpath) if not exists(objdir): makedirs(objdir) # XXX TODO once we have a command that can copy/move files # from one dataset to another including file availability # info, this should be used here shutil.copyfile( op.join(aggfrom_ds.path, objrelpath), objpath) # mark for saving to_save.append(dict( path=objpath, parentds=agginto_ds.path, type='file')) # lastly get 'self' aggregation record from source dataset and # use in target dataset db[aggfrom_ds.path] = _load_agginfo_db(aggfrom_ds.path)[aggfrom_ds.path] return False def _extract_metadata(agginto_ds, aggfrom_ds, db, to_save, objid, metasources, refcommit, subds_relpaths): # we will replace any conflicting info on this dataset with fresh stuff agginfo = db.get(aggfrom_ds.path, {}) # paths to extract from relevant_paths = sorted(_get_metadatarelevant_paths(aggfrom_ds, subds_relpaths)) # get extractors to engage from source dataset nativetypes = ['datalad_core', 'annex'] + assure_list(get_metadata_type(aggfrom_ds)) # store esssential extraction config in dataset record agginfo['extractors'] = nativetypes agginfo['datalad_version'] = datalad.__version__ # perform the actual extraction dsmeta, contentmeta, errored = _get_metadata( aggfrom_ds, nativetypes, # None indicates to honor a datasets per-extractor configuration and to be # on by default global_meta=None, content_meta=None, paths=relevant_paths) meta = { 'ds': dsmeta, 'cn': (dict(contentmeta[k], path=k) for k in sorted(contentmeta)) } # inject the info which commmit we are describing into the core metadata # this is done here in order to avoid feeding it all the way down coremeta = dsmeta.get('datalad_core', {}) version = aggfrom_ds.repo.describe(commitish=refcommit) if version: coremeta['version'] = version coremeta['refcommit'] = refcommit dsmeta['datalad_core'] = coremeta # for both types of metadata for label, props in metasources.items(): dest = props['targetds'] if not meta[label]: continue # only write to disk if there is something objrelpath = _get_obj_location(objid, label) if props['dumper'] is json_py.dump2xzstream: objrelpath += '.xz' # place metadata object into the source dataset objpath = opj(dest.path, dirname(agginfo_relpath), objrelpath) # write obj files if exists(objpath): dest.unlock(objpath) elif lexists(objpath): # if it gets here, we have a symlink that is pointing nowhere # kill it, to be replaced with the newly aggregated content dest.repo.remove(objpath) # TODO actually dump a compressed file when annexing is possible # to speed up on-demand access props['dumper'](meta[label], objpath) # stage for dataset.save() to_save.append(dict(path=objpath, type='file')) # important to use abspath here, needs to be rewritten relative to # all receiving datasets agginfo['{}_info'.format(props['type'])] = objpath # overwrite existing info with stuff from just finished extraction db[aggfrom_ds.path] = agginfo return errored def _adj2subtrees(base, adj, subs): # given a set of parent-child mapping, compute a mapping of each parent # to all its (grand)children of any depth level subtrees = dict(adj) subs = set(subs) # from bottom up for ds in sorted(adj, reverse=True): subtree = [] for sub in subtrees[ds]: subtree.append(sub) subtree.extend(subtrees.get(sub, [])) subtrees[ds] = subtree # give each leaf dataset an entry too for sub in subs: if sub not in subtrees and GitRepo.is_valid_repo(sub): subtrees[sub] = [] return subtrees def _get_latest_refcommit(ds, subds_relpaths): """Find the latest commit that changed any real content This will ignore anything at or underneath: - .git - .datalad - .gitmodules - .gitattributes - any submodule Returns ------- str or None None is return if no commit can be found, or no relevant content files were found at all. Otherwise the full commit hash if the last commit that touch any relevant content is returned. """ def _filterpaths(basepath, paths, exclude): final_paths = [] for rp in [opj(basepath, p) if basepath else p for p in paths]: if rp in exclude: continue elif any(path_is_subpath(ep, rp) for ep in exclude): final_paths.extend( _filterpaths(rp, listdir(opj(ds.path, rp)), exclude)) pass else: final_paths.append(rp) return final_paths relevant_paths = _filterpaths( '', listdir(ds.path), # NOTE: this will also ignore datalad's native dataset-global metadata # rationale: the metadata still describes the dataset content, so # even if it changes, the description changes, but not the content # it is describing -> ref commit should be unaffected list(exclude_from_metadata) + subds_relpaths) if not relevant_paths: return None return ds.repo.get_last_commit_hash(relevant_paths) def _get_obj_location(hash_str, ref_type): return opj( 'objects', hash_str[:2], '{}-{}'.format( ref_type, hash_str[2:])) def _update_ds_agginfo(refds_path, ds_path, subds_paths, incremental, agginfo_db, to_save): """Perform metadata aggregation for ds and a given list of subdataset paths Parameters ---------- refds_path : str Absolute path to the reference dataset that aggregate_metadata() was called on. ds_path : str Absolute path to the dataset to have its aggregate info updates subds_paths : list(str) Sequence of absolute paths of subdatasets of the to-be-updated dataset, whose agginfo shall be updated within the to-be-updated dataset. Any subdataset that is not listed here is assumed to be gone (i.e. no longer a subdataset at all, not just not locally installed) incremental : bool If set, the update will not remove any information on datasets not listed in subds_paths agginfo_db : dict Dictionary with all information on aggregate metadata on all datasets. Keys are absolute paths of datasets. to_save : list List of paths to save eventually. This function will add new paths as necessary. """ ds = Dataset(ds_path) # location info of aggregate metadata # aggregate.json agginfo_fpath = opj(ds_path, agginfo_relpath) # base path in which aggregate.json and objects is located agg_base_path = dirname(agginfo_fpath) # load existing aggregate info dict # makes sure all file/dataset paths become absolute # TODO take from cache, once used in _get_dsinfo_from_aggmetadata() ds_agginfos = _load_agginfo_db(ds_path) # object locations referenced initially objlocs_was = set(ai[k] for ai in ds_agginfos.values() for k in location_keys if k in ai) # track which objects need to be copied (each item is a from/to tuple objs2copy = [] # for each subdataset (any depth level) procds_paths = [ds.path] + subds_paths for dpath in procds_paths: ds_dbinfo = agginfo_db.get(dpath, {}).copy() # relative path of the currect dataset within the dataset we are updating drelpath = relpath(dpath, start=ds.path) for loclabel in location_keys: # TODO filepath_info is obsolete if loclabel == 'filepath_info' and drelpath == curdir: # do not write a file list into the dataset it is from if 'filepath_info' in ds_dbinfo: del ds_dbinfo['filepath_info'] continue # abspath to object objloc = ds_dbinfo.get(loclabel, None) if objloc is None: continue # XXX needs to change when layout of object store is changed # current is ./datalad/metadata/objects/{hash}/{hash} target_objpath = op.join(agg_base_path, *objloc.split(os.sep)[-3:]) # make sure we copy the file from its current location to where it is # needed in this dataset objs2copy.append(( # this needs to turn into an absolute path # `dpath` will be relative to the reference dataset #op.normpath(op.join(ds.path, dpath, dirname(agginfo_relpath), objloc)), objloc, target_objpath)) # now build needed local relpath ds_dbinfo[loclabel] = target_objpath # (re)assign in case record is new ds_agginfos[dpath] = ds_dbinfo # remove all entries for which we did not (no longer) have a corresponding # subdataset to take care of if not incremental: ds_agginfos = {k: v for k, v in ds_agginfos.items() if k in procds_paths} # set of metadata objects now referenced objlocs_is = set( ai[k] for sdsrpath, ai in ds_agginfos.items() for k in location_keys if k in ai) objs2add = objlocs_is # yoh: we appanretly do need to filter the ones to remove - I did # "git reset --hard HEAD^" and # aggregate-metadata failed upon next run trying to remove # an unknown to git file. I am yet to figure out why that # mattered (hopefully not that reflog is used somehow) objs2remove = [] for obj in objlocs_was.difference(objlocs_is): if lexists(obj): objs2remove.append(obj) else: # not really a warning, we don't need it anymore, it is already gone lgr.debug( "To-be-deleted metadata object not found, skip deletion (%s)", obj ) # secretly remove obsolete object files, not really a result from a # user's perspective if not incremental and objs2remove: ds.remove( objs2remove, # Don't use the misleading default commit message of `remove`: message='[DATALAD] Remove obsolete metadata object files', # we do not want to drop these files by default, because we would # loose them for other branches, and earlier tags # TODO evaluate whether this should be exposed as a switch # to run an explicit force-drop prior to calling remove() check=False, result_renderer=None, return_type=list) if not objs2add and not refds_path == ds_path: # this is not the base dataset, make sure to save removal in the # parentds -- not needed when objects get added, as removal itself # is already committed to_save.append(dict(path=ds_path, type='dataset', staged=True)) objs2copy = [(f, t) for f, t in objs2copy if f != t] # must copy object files to local target destination # make sure those objects are present # use the reference dataset to resolve paths, as they might point to # any location in the dataset tree Dataset(refds_path).get( [f for f, t in objs2copy], result_renderer='disabled') for copy_from, copy_to in objs2copy: copy_from = op.join(agg_base_path, copy_from) copy_to = op.join(agg_base_path, copy_to) target_dir = dirname(copy_to) if not exists(target_dir): makedirs(target_dir) # TODO we could be more clever (later) and maybe `addurl` (or similar) # the file from another dataset if lexists(copy_to): # no need to unlock, just wipe out and replace os.remove(copy_to) shutil.copy(copy_from, copy_to) to_save.append( dict(path=agginfo_fpath, type='file', staged=True)) if objs2add: # they are added standard way, depending on the repo type ds.add( [opj(agg_base_path, p) for p in objs2add], save=False, result_renderer=None, return_type=list) # queue for save, and mark as staged to_save.extend( [dict(path=opj(agg_base_path, p), type='file', staged=True) for p in objs2add]) # write aggregate info file if not ds_agginfos: return _store_agginfo_db(ds_path, ds_agginfos) ds.add(agginfo_fpath, save=False, to_git=True, result_renderer=None, return_type=list) # queue for save, and mark as staged to_save.append( dict(path=agginfo_fpath, type='file', staged=True)) # FIXME look for empty object dirs and remove them def _load_agginfo_db(ds_path): return { # paths in DB on disk are always relative # make absolute to ease processing during aggregation op.normpath(op.join(ds_path, p)): {k: op.normpath(op.join(ds_path, op.dirname(agginfo_relpath), v)) if k in location_keys else v for k, v in props.items()} for p, props in _load_json_object(opj(ds_path, agginfo_relpath)).items() } def _store_agginfo_db(ds_path, db): # base path in which aggregate.json and objects is located agg_base_path = dirname(op.join(ds_path, agginfo_relpath)) # make DB paths on disk always relative json_py.dump( { op.relpath(p, start=ds_path): {k: op.relpath(v, start=agg_base_path) if k in location_keys else v for k, v in props.items()} for p, props in db.items() }, op.join(ds_path, agginfo_relpath) ) @build_doc class AggregateMetaData(Interface): """Aggregate metadata of one or more datasets for later query. Metadata aggregation refers to a procedure that extracts metadata present in a dataset into a portable representation that is stored a single standardized format. Moreover, metadata aggregation can also extract metadata in this format from one dataset and store it in another (super)dataset. Based on such collections of aggregated metadata it is possible to discover particular datasets and specific parts of their content, without having to obtain the target datasets first (see the DataLad 'search' command). To enable aggregation of metadata that are contained in files of a dataset, one has to enable one or more metadata extractor for a dataset. DataLad supports a number of common metadata standards, such as the Exchangeable Image File Format (EXIF), Adobe's Extensible Metadata Platform (XMP), and various audio file metadata systems like ID3. DataLad extension packages can provide metadata data extractors for additional metadata sources. For example, the neuroimaging extension provides extractors for scientific (meta)data standards like BIDS, DICOM, and NIfTI1. Some metadata extractors depend on particular 3rd-party software. The list of metadata extractors available to a particular DataLad installation is reported by the 'wtf' command ('datalad wtf'). Enabling a metadata extractor for a dataset is done by adding its name to the 'datalad.metadata.nativetype' configuration variable -- typically in the dataset's configuration file (.datalad/config), e.g.:: [datalad "metadata"] nativetype = exif nativetype = xmp If an enabled metadata extractor is not available in a particular DataLad installation, metadata extraction will not succeed in order to avoid inconsistent aggregation results. Enabling multiple extractors is supported. In this case, metadata are extracted by each extractor individually, and stored alongside each other. Metadata aggregation will also extract DataLad's own metadata (extractors 'datalad_core', and 'annex'). Metadata aggregation can be performed recursively, in order to aggregate all metadata across all subdatasets, for example, to be able to search across any content in any dataset of a collection. Aggregation can also be performed for subdatasets that are not available locally. In this case, pre-aggregated metadata from the closest available superdataset will be considered instead. Depending on the versatility of the present metadata and the number of dataset or files, aggregated metadata can grow prohibitively large. A number of configuration switches are provided to mitigate such issues. datalad.metadata.aggregate-content-<extractor-name> If set to false, content metadata aggregation will not be performed for the named metadata extractor (a potential underscore '_' in the extractor name must be replaced by a dash '-'). This can substantially reduce the runtime for metadata extraction, and also reduce the size of the generated metadata aggregate. Note, however, that some extractors may not produce any metadata when this is disabled, because their metadata might come from individual file headers only. 'datalad.metadata.store-aggregate-content' might be a more appropriate setting in such cases. datalad.metadata.aggregate-ignore-fields Any metadata key matching any regular expression in this configuration setting is removed prior to generating the dataset-level metadata summary (keys and their unique values across all dataset content), and from the dataset metadata itself. This switch can also be used to filter out sensitive information prior aggregation. datalad.metadata.generate-unique-<extractor-name> If set to false, DataLad will not auto-generate a summary of unique content metadata values for a particular extractor as part of the dataset-global metadata (a potential underscore '_' in the extractor name must be replaced by a dash '-'). This can be useful if such a summary is bloated due to minor uninformative (e.g. numerical) differences, or when a particular extractor already provides a carefully designed content metadata summary. datalad.metadata.maxfieldsize Any metadata value that exceeds the size threshold given by this configuration setting (in bytes/characters) is removed. datalad.metadata.store-aggregate-content If set, extracted content metadata are still used to generate a dataset-level summary of present metadata (all keys and their unique values across all files in a dataset are determined and stored as part of the dataset-level metadata aggregate, see datalad.metadata.generate-unique-<extractor-name>), but metadata on individual files are not stored. This switch can be used to avoid prohibitively large metadata files. Discovery of datasets containing content matching particular metadata properties will still be possible, but such datasets would have to be obtained first in order to discover which particular files in them match these properties. """ _params_ = dict( dataset=Parameter( args=("-d", "--dataset"), doc="""topmost dataset metadata will be aggregated into. All dataset between this dataset and any given path will receive updated aggregated metadata from all given paths.""", constraints=EnsureDataset() | EnsureNone()), path=Parameter( args=("path",), metavar="PATH", doc="""path to datasets that shall be aggregated. When a given path is pointing into a dataset, the metadata of the containing dataset will be aggregated.""", nargs="*", constraints=EnsureStr() | EnsureNone()), recursive=recursion_flag, recursion_limit=recursion_limit, update_mode=Parameter( args=('--update-mode',), constraints=EnsureChoice('all', 'target'), doc="""which datasets to update with newly aggregated metadata: all datasets from any leaf dataset to the top-level target dataset including all intermediate datasets (all), or just the top-level target dataset (target)."""), incremental=Parameter( args=('--incremental',), action='store_true', doc="""If set, all information on metadata records of subdatasets that have not been (re-)aggregated in this run will be kept unchanged. This is useful when (re-)aggregation only a subset of a dataset hierarchy, for example, because not all subdatasets are locally available."""), force_extraction=Parameter( args=('--force-extraction',), action='store_true', doc="""If set, all enabled extractors will be engaged regardless of whether change detection indicates that metadata has already been extracted for a given dataset state."""), save=nosave_opt, ) @staticmethod @datasetmethod(name='aggregate_metadata') @eval_results def __call__( path=None, dataset=None, recursive=False, recursion_limit=None, update_mode='target', incremental=False, force_extraction=False, save=True): refds_path = Interface.get_refds_path(dataset) # it really doesn't work without a dataset ds = require_dataset( dataset, check_installed=True, purpose='metadata aggregation') # always include the reference dataset path = assure_list(path) path.append(ds.path) agginfo_db = _load_agginfo_db(ds.path) to_save = [] to_aggregate = set() for ap in AnnotatePaths.__call__( dataset=refds_path, path=path, recursive=recursive, recursion_limit=recursion_limit, action='aggregate_metadata', # uninstalled subdatasets could be queried via aggregated metadata # -> no 'error' unavailable_path_status='', nondataset_path_status='error', return_type='generator', on_failure='ignore'): if ap.get('status', None): # this is done yield ap continue ap_type = ap.get('type', None) ap_state = ap.get('state', None) assert('parentds' in ap or ap_type == 'dataset') if ap_type == 'dataset' and ap_state != 'absent': # a present dataset, we can take directly from it aggsrc = ap['path'] lgr.info('Aggregate metadata for dataset %s', aggsrc) else: # everything else needs to come from the parent aggsrc = ap['parentds'] if ap_state == 'absent': lgr.info( 'Attempt to use pre-aggregate metadata for absent %s from dataset at %s', ap['path'], aggsrc) else: lgr.info( 'Aggregate metadata for %s from dataset at %s', ap['path'], aggsrc) to_aggregate.add(aggsrc) if ap_state == 'absent': # key thought: recursive is done by path annotation, hence # once we hit an absent dataset, we are 100% certain that # there is nothing to recurse into on the file system # hence we only have to look into the aggregated metadata # of the last available dataset in the dataset tree edge # # if there is nothing at this path, we need to look into the # parentds and check if we know anything about this path # if we do, we need to grab all the info and objects # if not, we need to error res = _get_dsinfo_from_aggmetadata( aggsrc, ap['path'], recursive, agginfo_db) if not isinstance(res, list): yield get_status_dict( status='impossible', message=res, action='aggregate_metadata', path=ap['path'], logger=lgr) continue # cue for aggregation to_aggregate.update(res) else: # actually aggregate metadata for this dataset, immediately place # generated objects into the aggregated or reference dataset, # and put info into DB to get the distributed to all datasets # that need to be updated errored = _dump_extracted_metadata( ds, Dataset(aggsrc), agginfo_db, to_save, force_extraction) if errored: yield get_status_dict( status='error', message='Metadata extraction failed (see previous error message, set datalad.runtime.raiseonerror=yes to fail immediately)', action='aggregate_metadata', path=aggsrc, logger=lgr) # at this point we have dumped all aggregated metadata into object files # somewhere, we know what needs saving, but having saved anything, and # we know about the states of all aggregated dataset in the DB # what remains to do is to update all dataset, so they have there own copy # of aggregated metadata and update their respective aggregate.json with # info on what states we just aggregated from # first, let's figure out what dataset need updating at all # get adjencency info of the dataset tree spanning the base to all leaf dataset # associated with the path arguments if update_mode == 'all': ds_adj = {} discover_dataset_trace_to_targets( ds.path, to_aggregate, [], ds_adj, # we know that to_aggregate only lists datasets, existing and # absent ones -- we want to aggregate all of them, either from # just extracted metadata, or from previously aggregated metadata # of the closest superdataset includeds=to_aggregate) # TODO we need to work in the info about dataset that we only got from # aggregated metadata, that had no trace on the file system in here!! subtrees = _adj2subtrees(ds.path, ds_adj, to_aggregate) elif update_mode == 'target': subtrees = {ds.path: list(agginfo_db.keys())} else: raise ValueError( "unknown `update_mode` '%s' for metadata aggregation", update_mode) # go over datasets in bottom-up fashion for parentds_path in sorted(subtrees, reverse=True): lgr.info('Update aggregate metadata in dataset at: %s', parentds_path) _update_ds_agginfo( ds.path, parentds_path, subtrees[parentds_path], incremental, agginfo_db, to_save) # update complete res = get_status_dict( status='ok', action='aggregate_metadata', path=parentds_path, type='dataset', logger=lgr) res.update(agginfo_db.get(parentds_path, {})) yield res # # save potential modifications to dataset global metadata # if not to_save: return lgr.info('Attempting to save %i files/datasets', len(to_save)) for res in Save.__call__( path=to_save, dataset=refds_path, message='[DATALAD] Dataset aggregate metadata update', return_type='generator', result_xfm=None, result_filter=None, on_failure='ignore'): yield res

41.737895

148

0.638496

bb73395bd605264ccf66f386c60be56bb28c7a54

201

py

Python

tests/test_project/api/urls.py

ShreeshaRelysys/openwisp-utils

7c0b5f249b0e8e1f3af7bf1942b6543c9375dd75

[ "BSD-3-Clause" ]

80

2017-06-28T08:17:41.000Z

2022-03-29T19:38:31.000Z

tests/test_project/api/urls.py

ShreeshaRelysys/openwisp-utils

7c0b5f249b0e8e1f3af7bf1942b6543c9375dd75

[ "BSD-3-Clause" ]

275

2017-06-22T09:57:40.000Z

2022-03-29T19:20:12.000Z

tests/test_project/api/urls.py

ShreeshaRelysys/openwisp-utils

7c0b5f249b0e8e1f3af7bf1942b6543c9375dd75

[ "BSD-3-Clause" ]

91

2017-06-22T10:12:16.000Z

2022-03-19T12:53:55.000Z

from django.urls import re_path from . import views urlpatterns = [ re_path( r'^receive_project/(?P<pk>[^/\?]+)/$', views.receive_project, name='receive_project', ) ]

16.75

46

0.587065

9c79db4eb1cafba18fee252bb250583513fc7c12

2,294

py

Python

endpage.py

MarcelloEdocia/battleship_remake

ab26edbfc42dae5d96d11540f7f14fc7f1bbbf68

[ "Apache-2.0" ]

null

endpage.py

MarcelloEdocia/battleship_remake

ab26edbfc42dae5d96d11540f7f14fc7f1bbbf68

[ "Apache-2.0" ]

null

endpage.py

MarcelloEdocia/battleship_remake

ab26edbfc42dae5d96d11540f7f14fc7f1bbbf68

[ "Apache-2.0" ]

null

import tkinter class EndPage(tkinter.Frame): def __init__(self, parent, App): self.application = App self.config = App.config super().__init__(parent) self.configure(bg="grey") self.grid(row=0, column=0, sticky="nsew") parent.grid_rowconfigure(0, weight=1) parent.grid_columnconfigure(0, weight=1) self.pixelVirtual = tkinter.PhotoImage(width=5, height=2) self.button_width = self.config.side//4 self.button_height = self.config.side//20 self.pixelVirtual = tkinter.PhotoImage(width=5, height=2) self.button_width = self.config.side//2.5 self.button_height = self.config.side//20 self.mainFrame = tkinter.Frame(self, height=self.config.side, width=self.config.side, bg="grey") self.mainFrame.pack(expand=True) self.create_label() self.create_playbutton() self.see_highscore() self.create_exitbutton() def create_label(self): self.firsttext = tkinter.Label(self.mainFrame, text="You Win!", font=("Arial", 22), bg="grey") self.firsttext.pack() self.secondtext = tkinter.Label(self.mainFrame, text="", bg="grey") self.secondtext.pack() self.thirdtext = tkinter.Label(self.mainFrame, text="", bg="grey") self.thirdtext.pack() def create_playbutton(self): self.playbutton = tkinter.Button(self.mainFrame, text="Play again", font=("Arial", 18), image=self.pixelVirtual, width=self.button_width, height=self.button_height, compound="c", command=lambda:self.application.play_again()) self.playbutton.pack(pady=10) def create_exitbutton(self): self.exitbutton = tkinter.Button(self.mainFrame, text="Exit", font=("Arial", 18), command=lambda:self.application.create_popup(), image=self.pixelVirtual, width=self.button_width,height=self.button_height, compound="c") self.exitbutton.pack(pady=10) def see_highscore(self): self.returnbutton = tkinter.Button(self.mainFrame, text="Return", font=("Arial", 18), command=lambda:self.application.gotomenu(), image=self.pixelVirtual, width=self.button_width,height=self.button_height, compound="c") self.returnbutton.pack(pady=50)

44.980392

233

0.664342

bf5ce8942686127a7a2d145cf0175f61e09e54c7

4,882

py

Python

tests/e2e/tests/test_kiali_workload_endpoint.py

jsloyer/kiali

bd7e97b46c936fe9ce0d9fffa3b89d06f676c24e

[ "Apache-2.0" ]

null

tests/e2e/tests/test_kiali_workload_endpoint.py

jsloyer/kiali

bd7e97b46c936fe9ce0d9fffa3b89d06f676c24e

[ "Apache-2.0" ]

null

tests/e2e/tests/test_kiali_workload_endpoint.py

jsloyer/kiali

bd7e97b46c936fe9ce0d9fffa3b89d06f676c24e

[ "Apache-2.0" ]

null

import pytest import tests.conftest as conftest import time from utils.command_exec import command_exec from utils.timeout import timeout WORKLOAD_TO_VALIDATE = 'details-v1' WORKLOAD_TYPE = 'Deployment' BOOKINFO_WORKLOADS_COUNT = 6 EXTRA_WORKLOAD_COUNT = 4 EXTRA_WORKLOADS = set(['details-v2', 'reviews-v4', 'reviews-v5','reviews-v6']) def test_workload_list_endpoint(kiali_client): bookinfo_namespace = conftest.get_bookinfo_namespace() workload_list = kiali_client.request(method_name='workloadList', path={'namespace': bookinfo_namespace}).json() assert workload_list != None for workload in workload_list.get('workloads'): assert workload != None assert workload.get('name') != None and workload.get('name') != '' if ('traffic-generator' not in workload.get('name')): assert workload.get('istioSidecar') == True assert workload.get('versionLabel') == True assert workload.get('appLabel') == True def test_diversity_in_workload_list_endpoint(kiali_client): bookinfo_namespace = conftest.get_bookinfo_namespace() try: # Add extra workloads that will be tested assert command_exec.oc_apply(conftest.WORKLOADS_FILE, bookinfo_namespace) == True with timeout(seconds=90, error_message='Timed out waiting for extra workloads creation'): while True: workload_list = kiali_client.request(method_name='workloadList', path={'namespace': bookinfo_namespace}).json() if workload_list != None and workload_list.get('workloads') != None: workload_names = set(list(map(lambda workload: workload.get('name'), workload_list.get('workloads')))) if EXTRA_WORKLOADS.issubset(workload_names): break time.sleep(1) # Dictionary that maps Workloads with its own types dicWorkloadType = { 'details-v2': 'Pod', 'reviews-v4': 'ReplicaSet', 'reviews-v5': 'ReplicationController', 'reviews-v6': 'StatefulSet' } for workload in workload_list.get('workloads'): if workload.get('name') in EXTRA_WORKLOADS: workloadType = dicWorkloadType[workload.get('name')] assert workload.get('type') == workloadType finally: assert command_exec.oc_delete(conftest.WORKLOADS_FILE, bookinfo_namespace) == True with timeout(seconds=90, error_message='Timed out waiting for extra workloads deletion'): print('Extra workloads added for this test:', EXTRA_WORKLOADS) while True: workload_list = kiali_client.request(method_name='workloadList', path={'namespace': bookinfo_namespace}).json() if workload_list != None and workload_list.get('workloads') != None: workload_names = set(list(map(lambda workload: workload.get('name'), workload_list.get('workloads')))) print('Still existing workloads:', workload_names) if EXTRA_WORKLOADS.intersection(workload_names) == set(): break time.sleep(1) def test_workload_details(kiali_client): bookinfo_namespace = conftest.get_bookinfo_namespace() workload = kiali_client.request(method_name='workloadDetails', path={'namespace': bookinfo_namespace, 'workload': WORKLOAD_TO_VALIDATE}).json() assert workload != None assert WORKLOAD_TO_VALIDATE in workload.get('name') assert WORKLOAD_TYPE in workload.get('type') assert 'labels' in workload assert 'pods' in workload assert 'services' in workload def test_workload_metrics(kiali_client): bookinfo_namespace = conftest.get_bookinfo_namespace() workload = kiali_client.request(method_name='workloadMetrics', path={'namespace': bookinfo_namespace, 'workload': WORKLOAD_TO_VALIDATE}).json() for direction in ['dest', 'source']: assert workload != None metrics = workload.get(direction).get('metrics') assert 'request_count_in' in metrics assert 'request_count_out' in metrics assert 'request_error_count_in' in metrics assert 'request_error_count_out' in metrics assert 'tcp_received_in' in metrics assert 'tcp_received_out' in metrics assert 'tcp_sent_in' in metrics assert 'tcp_sent_out' in metrics histograms = workload.get(direction).get('histograms') assert 'request_duration_in' in histograms assert 'request_duration_out' in histograms assert 'request_size_in' in histograms assert 'request_size_out' in histograms assert 'response_size_in' in histograms assert 'response_size_out' in histograms def test_workload_health(kiali_client): bookinfo_namespace = conftest.get_bookinfo_namespace() workload = kiali_client.request(method_name='workloadHealth', path={'namespace': bookinfo_namespace, 'workload': WORKLOAD_TO_VALIDATE}).json() assert workload != None assert WORKLOAD_TO_VALIDATE in workload.get('workloadStatus').get('name') assert 'requests' in workload

42.452174

147

0.723884

d40b0e6d3c923233ed749f2749e1c5a5a27bc1ce

4,348

py

Python

docs/conf.py

wesleykendall/django-kmatch

0ca5d8465461210aa98fd3fb9afd2ec3838a4f9b

[ "MIT" ]

null

docs/conf.py

wesleykendall/django-kmatch

0ca5d8465461210aa98fd3fb9afd2ec3838a4f9b

[ "MIT" ]

2

2015-03-27T18:10:34.000Z

2015-03-30T17:39:44.000Z

docs/conf.py

wesleykendall/django-kmatch

0ca5d8465461210aa98fd3fb9afd2ec3838a4f9b

[ "MIT" ]

5

2015-03-27T17:49:20.000Z

2016-11-28T22:29:54.000Z

# -*- coding: utf-8 -*- # # django-kmatch documentation build configuration file import os import re def get_version(): """ Extracts the version number from the version.py file. """ VERSION_FILE = '../django_kmatch/version.py' mo = re.search(r'^__version__ = [\'"]([^\'"]*)[\'"]', open(VERSION_FILE, 'rt').read(), re.M) if mo: return mo.group(1) else: raise RuntimeError('Unable to find version string in {0}.'.format(VERSION_FILE)) # 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. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.intersphinx', #'sphinx.ext.viewcode', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The master toctree document. master_doc = 'toc' # General information about the project. project = u'django_kmatch' copyright = u'2014, Ambition Inc.' # The short X.Y version. version = get_version() # The full version, including alpha/beta/rc tags. release = version exclude_patterns = ['_build'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' intersphinx_mapping = { 'python': ('http://python.readthedocs.org/en/v2.7.2/', None), 'django': ('http://django.readthedocs.org/en/latest/', None), #'celery': ('http://celery.readthedocs.org/en/latest/', None), } # -- Options for HTML output ---------------------------------------------- html_theme = 'default' #html_theme_path = [] on_rtd = os.environ.get('READTHEDOCS', None) == 'True' if not on_rtd: # only import and set the theme if we're building docs locally import sphinx_rtd_theme html_theme = 'sphinx_rtd_theme' html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # 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'] # 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 true, "Created using Sphinx" is shown in the HTML footer. Default is True. html_show_sphinx = False # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. html_show_copyright = True # Output file base name for HTML help builder. htmlhelp_basename = 'django-kmatchdoc' # -- 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': '', } # 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 = [ ('index', 'django-kmatch.tex', u'django-kmatch Documentation', u'Wes Kendall', 'manual'), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'django-kmatch', u'django-kmatch Documentation', [u'Wes Kendall'], 1) ] # -- 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 = [ ('index', 'django-kmatch', u'django-kmatch Documentation', u'Wes Kendall', 'django-kmatch', 'A short description', 'Miscellaneous'), ] # -- Django configuration ------------------------------------------------- import sys sys.path.insert(0, os.path.abspath('..')) from settings import configure_settings configure_settings()

30.619718

96

0.663293

b28296d62c8c5fdb41c330ad745a325f3e511dc9

936

py

Python

get_data.py

himoto/3d-surface-plot

733c5195b1b35a218fdce51d6aa49a67f4159e95

[ "MIT" ]

2

2020-07-01T18:49:16.000Z

2021-12-22T12:14:51.000Z

get_data.py

himoto/3d-surface-plot

733c5195b1b35a218fdce51d6aa49a67f4159e95

[ "MIT" ]

null

get_data.py

himoto/3d-surface-plot

733c5195b1b35a218fdce51d6aa49a67f4159e95

[ "MIT" ]

null

import os import numpy as np from biomass.dynamics.solver import * from tqdm import tqdm from model import * def compute_matrix(): os.makedirs("data", exist_ok=True) sim_n = 101 sim_t = range(5401) sim_ligand = ["EGF", "HRG"] z_cFosmRNA = np.empty((len(sim_ligand), sim_n, len(sim_t))) norm_max = np.empty_like(sim_ligand, dtype=float) x = param_values() y0 = initial_values() y_ss = get_steady_state(diffeq, y0, tuple(x)) for i in tqdm(range(sim_n)): x = param_values() x[C.p11] *= (1 - 0.01 * i) for j, ligand in enumerate(sim_ligand): x[C.Ligand] = x[C.NAMES.index(ligand)] sol = solve_ode(diffeq, y_ss, sim_t, tuple(x)) if i == 0: norm_max[j] = np.max(sol.y[V.cfosmRNAc, :]) z_cFosmRNA[j, i, :] = sol.y[V.cfosmRNAc, :] / norm_max[j] np.save(os.path.join("data", "z_cFosmRNA.npy"), z_cFosmRNA)

28.363636

69

0.595085

83c0aa5a04c40b72fb40975c94f5be4c44f968a1

12,636

py

Python

tfx/extensions/google_cloud_ai_platform/runner.py

seyedrezamirkhani/tfx

b2dc2338b27d6ccf1cae690e65c4e358e17ec626

[ "Apache-2.0" ]

1

2021-07-21T15:54:20.000Z

tfx/extensions/google_cloud_ai_platform/runner.py

seyedrezamirkhani/tfx

b2dc2338b27d6ccf1cae690e65c4e358e17ec626

[ "Apache-2.0" ]

1

2020-08-28T09:59:13.000Z

tfx/extensions/google_cloud_ai_platform/runner.py

seyedrezamirkhani/tfx

b2dc2338b27d6ccf1cae690e65c4e358e17ec626

[ "Apache-2.0" ]

1

2020-11-06T11:44:33.000Z

# Lint as: python3 # Copyright 2019 Google LLC. 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. """Helper class to start TFX training jobs on AI Platform.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import datetime import json import sys import time from typing import Any, Dict, List, Optional, Text from absl import logging from googleapiclient import discovery from googleapiclient import errors import tensorflow as tf from tfx import types from tfx import version from tfx.types import artifact_utils from tfx.utils import telemetry_utils _POLLING_INTERVAL_IN_SECONDS = 30 _CONNECTION_ERROR_RETRY_LIMIT = 5 # TODO(b/139934802) Ensure mirroring of released TFX containers in Docker Hub # and gcr.io/tfx-oss-public/ registries. _TFX_IMAGE = 'gcr.io/tfx-oss-public/tfx:{}'.format(version.__version__) _TF_COMPATIBILITY_OVERRIDE = { # Generally, runtimeVersion should be same as <major>.<minor> of currently # installed tensorflow version, with certain compatibility hacks since # some TensorFlow runtime versions are not explicitly supported by # CAIP pusher. See: # https://cloud.google.com/ai-platform/prediction/docs/runtime-version-list '2.0': '1.15', # TODO(b/157039850) Remove this once CAIP model support TF 2.2 runtime. '2.2': '2.1', '2.3': '2.1', '2.4': '2.1' } def _get_tf_runtime_version(tf_version: Text) -> Text: """Returns the tensorflow runtime version used in Cloud AI Platform. This is only used for prediction service. Args: tf_version: version string returned from `tf.__version__`. Returns: same major.minor version of installed tensorflow, except when overriden by _TF_COMPATIBILITY_OVERRIDE. """ tf_version = '.'.join(tf_version.split('.')[0:2]) return _TF_COMPATIBILITY_OVERRIDE.get(tf_version) or tf_version def _get_caip_python_version(caip_tf_runtime_version: Text) -> Text: """Returns supported python version on Cloud AI Platform. See https://cloud.google.com/ml-engine/docs/tensorflow/versioning#set-python-version-training Args: caip_tf_runtime_version: version string returned from _get_tf_runtime_version(). Returns: '2.7' for PY2. '3.5' or '3.7' for PY3 depending on caip_tf_runtime_version. """ if sys.version_info.major == 2: return '2.7' (major, minor) = caip_tf_runtime_version.split('.')[0:2] if (int(major), int(minor)) >= (1, 15): return '3.7' return '3.5' def start_aip_training(input_dict: Dict[Text, List[types.Artifact]], output_dict: Dict[Text, List[types.Artifact]], exec_properties: Dict[Text, Any], executor_class_path: Text, training_inputs: Dict[Text, Any], job_id: Optional[Text]): """Start a trainer job on AI Platform (AIP). This is done by forwarding the inputs/outputs/exec_properties to the tfx.scripts.run_executor module on a AI Platform training job interpreter. Args: input_dict: Passthrough input dict for tfx.components.Trainer.executor. output_dict: Passthrough input dict for tfx.components.Trainer.executor. exec_properties: Passthrough input dict for tfx.components.Trainer.executor. executor_class_path: class path for TFX core default trainer. training_inputs: Training input argument for AI Platform training job. 'pythonModule', 'pythonVersion' and 'runtimeVersion' will be inferred. For the full set of parameters, refer to https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#TrainingInput job_id: Job ID for AI Platform Training job. If not supplied, system-determined unique ID is given. Refer to https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#resource-job Returns: None Raises: RuntimeError: if the Google Cloud AI Platform training job failed/cancelled. """ training_inputs = training_inputs.copy() json_inputs = artifact_utils.jsonify_artifact_dict(input_dict) logging.info('json_inputs=\'%s\'.', json_inputs) json_outputs = artifact_utils.jsonify_artifact_dict(output_dict) logging.info('json_outputs=\'%s\'.', json_outputs) json_exec_properties = json.dumps(exec_properties, sort_keys=True) logging.info('json_exec_properties=\'%s\'.', json_exec_properties) # Configure AI Platform training job api_client = discovery.build('ml', 'v1') # We use custom containers to launch training on AI Platform, which invokes # the specified image using the container's entrypoint. The default # entrypoint for TFX containers is to call scripts/run_executor.py. The # arguments below are passed to this run_executor entry to run the executor # specified in `executor_class_path`. job_args = [ '--executor_class_path', executor_class_path, '--inputs', json_inputs, '--outputs', json_outputs, '--exec-properties', json_exec_properties ] if not training_inputs.get('masterConfig'): training_inputs['masterConfig'] = { 'imageUri': _TFX_IMAGE, } training_inputs['args'] = job_args # Pop project_id so AIP doesn't complain about an unexpected parameter. # It's been a stowaway in aip_args and has finally reached its destination. project = training_inputs.pop('project') project_id = 'projects/{}'.format(project) with telemetry_utils.scoped_labels( {telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}): job_labels = telemetry_utils.get_labels_dict() # 'tfx_YYYYmmddHHMMSS' is the default job ID if not explicitly specified. job_id = job_id or 'tfx_{}'.format( datetime.datetime.now().strftime('%Y%m%d%H%M%S')) job_spec = { 'jobId': job_id, 'trainingInput': training_inputs, 'labels': job_labels, } # Submit job to AIP Training logging.info('Submitting job=\'%s\', project=\'%s\' to AI Platform.', job_id, project) request = api_client.projects().jobs().create( body=job_spec, parent=project_id) request.execute() # Wait for AIP Training job to finish job_name = '{}/jobs/{}'.format(project_id, job_id) request = api_client.projects().jobs().get(name=job_name) response = request.execute() retry_count = 0 # Monitors the long-running operation by polling the job state periodically, # and retries the polling when a transient connectivity issue is encountered. # # Long-running operation monitoring: # The possible states of "get job" response can be found at # https://cloud.google.com/ai-platform/training/docs/reference/rest/v1/projects.jobs#State # where SUCCEEDED/FAILED/CANCELLED are considered to be final states. # The following logic will keep polling the state of the job until the job # enters a final state. # # During the polling, if a connection error was encountered, the GET request # will be retried by recreating the Python API client to refresh the lifecycle # of the connection being used. See # https://github.com/googleapis/google-api-python-client/issues/218 # for a detailed description of the problem. If the error persists for # _CONNECTION_ERROR_RETRY_LIMIT consecutive attempts, the function will exit # with code 1. while response['state'] not in ('SUCCEEDED', 'FAILED', 'CANCELLED'): time.sleep(_POLLING_INTERVAL_IN_SECONDS) try: response = request.execute() retry_count = 0 # Handle transient connection error. except ConnectionError as err: if retry_count < _CONNECTION_ERROR_RETRY_LIMIT: retry_count += 1 logging.warning( 'ConnectionError (%s) encountered when polling job: %s. Trying to ' 'recreate the API client.', err, job_id) # Recreate the Python API client. api_client = discovery.build('ml', 'v1') request = api_client.projects().jobs().get(name=job_name) else: # TODO(b/158433873): Consider raising the error instead of exit with # code 1 after CMLE supports configurable retry policy. # Currently CMLE will automatically retry the job unless return code # 1-128 is returned. logging.error('Request failed after %s retries.', _CONNECTION_ERROR_RETRY_LIMIT) sys.exit(1) if response['state'] in ('FAILED', 'CANCELLED'): err_msg = 'Job \'{}\' did not succeed. Detailed response {}.'.format( job_name, response) logging.error(err_msg) raise RuntimeError(err_msg) # AIP training complete logging.info('Job \'%s\' successful.', job_name) def deploy_model_for_aip_prediction( serving_path: Text, model_version: Text, ai_platform_serving_args: Dict[Text, Any], executor_class_path: Text, ): """Deploys a model for serving with AI Platform. Args: serving_path: The path to the model. Must be a GCS URI. model_version: Version of the model being deployed. Must be different from what is currently being served. ai_platform_serving_args: Dictionary containing arguments for pushing to AI Platform. For the full set of parameters supported, refer to https://cloud.google.com/ml-engine/reference/rest/v1/projects.models.versions#Version executor_class_path: class path for TFX core default trainer. Raises: RuntimeError: if an error is encountered when trying to push. """ logging.info( 'Deploying to model with version %s to AI Platform for serving: %s', model_version, ai_platform_serving_args) model_name = ai_platform_serving_args['model_name'] project_id = ai_platform_serving_args['project_id'] regions = ai_platform_serving_args.get('regions', []) default_runtime_version = _get_tf_runtime_version(tf.__version__) runtime_version = ai_platform_serving_args.get('runtime_version', default_runtime_version) python_version = _get_caip_python_version(runtime_version) api = discovery.build('ml', 'v1') body = {'name': model_name, 'regions': regions} parent = 'projects/{}'.format(project_id) try: api.projects().models().create(body=body, parent=parent).execute() except errors.HttpError as e: # If the error is to create an already existing model, it's ok to ignore. # TODO(b/135211463): Remove the disable once the pytype bug is fixed. if e.resp.status == 409: # pytype: disable=attribute-error logging.warn('Model %s already exists', model_name) else: raise RuntimeError('AI Platform Push failed: {}'.format(e)) with telemetry_utils.scoped_labels( {telemetry_utils.LABEL_TFX_EXECUTOR: executor_class_path}): job_labels = telemetry_utils.get_labels_dict() body = { 'name': model_version, 'deployment_uri': serving_path, 'runtime_version': runtime_version, 'python_version': python_version, 'labels': job_labels, } # Push to AIP, and record the operation name so we can poll for its state. model_name = 'projects/{}/models/{}'.format(project_id, model_name) response = api.projects().models().versions().create( body=body, parent=model_name).execute() op_name = response['name'] deploy_status_resc = api.projects().operations().get(name=op_name) while not deploy_status_resc.execute().get('done'): time.sleep(_POLLING_INTERVAL_IN_SECONDS) logging.info('Model still being deployed...') deploy_status = deploy_status_resc.execute() if deploy_status.get('error'): # The operation completed with an error. raise RuntimeError( 'Failed to deploy model to AI Platform for serving: {}'.format( deploy_status['error'])) # Set the new version as default. # By API specification, if Long-Running-Operation is done and there is # no error, 'response' is guaranteed to exist. api.projects().models().versions().setDefault(name='{}/versions/{}'.format( model_name, deploy_status['response']['name'])).execute() logging.info( 'Successfully deployed model %s with version %s, serving from %s', model_name, model_version, serving_path)

40.114286

94

0.714941

17fd2f4f724fa323f264ea8111f1726eb7895c5e

850

py

Python

apoli/apoli/urls.py

masaki1002/backend

24d186f3a224a0ee0e8f410b9c0d4832881c21e3

[ "MIT" ]

null

apoli/apoli/urls.py

masaki1002/backend

24d186f3a224a0ee0e8f410b9c0d4832881c21e3

[ "MIT" ]

null

apoli/apoli/urls.py

masaki1002/backend

24d186f3a224a0ee0e8f410b9c0d4832881c21e3

[ "MIT" ]

null

"""apoli URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include urlpatterns = [ path('admin/', admin.site.urls), path('api/user/', include('user.urls')), path('api/recipe/', include('recipe.urls')), ]

35.416667

77

0.696471

7a30ecc970c7975dfeda75d8772592219d20fa7a

1,535

py

Python

tests/events/test_mq.py

chuckwondo/aws-lambda-typing

8417ab67f2492be1508fe38b2c34bc106619a56d

[ "MIT" ]

29

2021-01-07T13:35:16.000Z

2022-03-25T07:20:54.000Z

tests/events/test_mq.py

chuckwondo/aws-lambda-typing

8417ab67f2492be1508fe38b2c34bc106619a56d

[ "MIT" ]

13

2021-02-28T00:31:00.000Z

2022-03-29T15:24:01.000Z

tests/events/test_mq.py

chuckwondo/aws-lambda-typing

8417ab67f2492be1508fe38b2c34bc106619a56d

[ "MIT" ]

5

2021-02-27T13:50:42.000Z

2022-01-13T15:05:44.000Z

from aws_lambda_typing.events import MQEvent def test_mq_event() -> None: event: MQEvent = { "eventSource": "aws:amq", "eventSourceArn": "arn:aws:mq:us-west-2:112556298976:broker:test:b-9bcfa592-423a-4942-879d-eb284b418fc8", # noqa: E501 "messages": { [ { "messageID": "ID:b-9bcfa592-423a-4942-879d-eb284b418fc8-1.mq.us-west-2.amazonaws.com-37557-1234520418293-4:1:1:1:1", # noqa: E501 "messageType": "jms/text-message", "data": "QUJDOkFBQUE=", "connectionId": "myJMSCoID", "redelivered": False, "destination": {"physicalname": "testQueue"}, "timestamp": 1598827811958, "brokerInTime": 1598827811958, "brokerOutTime": 1598827811959, }, { "messageID": "ID:b-9bcfa592-423a-4942-879d-eb284b418fc8-1.mq.us-west-2.amazonaws.com-37557-1234520418293-4:1:1:1:1", # noqa: E501 "messageType": "jms/bytes-message", "data": "3DTOOW7crj51prgVLQaGQ82S48k=", "connectionId": "myJMSCoID1", "persistent": False, "destination": {"physicalname": "testQueue"}, "timestamp": 1598827811958, "brokerInTime": 1598827811958, "brokerOutTime": 1598827811959, }, ] }, }

43.857143

150

0.49772

81f132148495342e6188c1aca3e6d01510a0be15

1,462

py

Python

custom/nic_compliance/tests/test_utils.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

471

2015-01-10T02:55:01.000Z

2022-03-29T18:07:18.000Z

custom/nic_compliance/tests/test_utils.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

14,354

2015-01-01T07:38:23.000Z

2022-03-31T20:55:14.000Z

custom/nic_compliance/tests/test_utils.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

175

2015-01-06T07:16:47.000Z

2022-03-29T13:27:01.000Z

from django.test import TestCase from custom.nic_compliance.utils import extract_password OBFUSCATED_PASSWORD_MAPPING = { "sha256$1e2d5bc2hhMjU2JDFlMmQ1Yk1USXpORFUyZjc5MTI3PQ==f79127=": "123456", "sha256$8f5008c2hhMjU2JDhmNTAwOFlXSmpNVEl6TFE9PTRhNjBhOT0=4a60a9=": "abc123-", "sha256$4bf7cdc2hhMjU2JDRiZjdjZE1USXpRQ01rSlRFeTEzMGM4ZD0=130c8d=": "123@#$%12", "sha256$29df66c2hhMjU2JDI5ZGY2NklDRkFJeVFsWGlZcUtDbGZLeTFjYTQwN2VkPQ==a407ed=": " !@#$%^&*()_+-\\", "sha256$ad5e3ac2hhMjU2JGFkNWUzYTRLU0o0S1NxNEtTVjRLU3c0S1NqTVRJejQyNDgyOT0=424829=": "उपकरण123", "sha256$nhiyhsc2hhMjU2JG5oaXloc2FsWmlWVUEvVmxsWk5RPT16eWwzeHU9zyl3xu=": "jVbU@?VYY5" } class TestExtractPassword(TestCase): def test_password_decoding(self): for obfuscated_password, password in OBFUSCATED_PASSWORD_MAPPING.items(): self.assertEqual(extract_password(obfuscated_password), password) def test_invalid_regex_format(self): obfuscated_password = "sha255$1e2d5bc2hhMjU2JDFlMmQ1Yk1USXpORFUyZjc5MTI3PQ==f79127=" self.assertEqual(extract_password(obfuscated_password), None) obfuscated_password = "sha255$1e2d5bc2hhMjU2JDFlMmQ1Yk1USXpORFUyZjc5MTI3PQ==" self.assertEqual(extract_password(obfuscated_password), None) def test_invalid_padding(self): obfuscated_password = "sha256$1e456bc2hhMjU2JDFlMmQ1Yk1USXpORFUyZjc5MTI3PQ==f79127=" self.assertEqual(extract_password(obfuscated_password), '')

48.733333

103

0.787962

2584aaa1d2f37ee517c9ac1bce084409d8b80da8

1,094

py

Python

week7-8/week8/hw32/file_stats.py

fac33/py_intro_exercise

e2c3f3044537b5cc8980bf7aa7651fd16c5fd34b

[ "MIT" ]

null

week7-8/week8/hw32/file_stats.py

fac33/py_intro_exercise

e2c3f3044537b5cc8980bf7aa7651fd16c5fd34b

[ "MIT" ]

null

week7-8/week8/hw32/file_stats.py

fac33/py_intro_exercise

e2c3f3044537b5cc8980bf7aa7651fd16c5fd34b

[ "MIT" ]

null

################################################################################ # Author:Fanyang Cheng # Date:27/03/2021 # Description: This file read one sepcific text file and track the number of words #, number of lines and the average words per line. ################################################################################ def main(): with open('rumpelstiltskin.txt','r') as fo: cont = fo.read() cont_l = cont.split("\n") # split by enter. l_n = len(cont_l) while not (cont_l[-1]): #at the end of file, if there are several blank lines, then delete them. l_n -= 1 cont_l.pop() cont_wm = " ".join(cont_l) cont_wt = cont_wm.split(" ") # split by blank cont_w = [i for i in cont_wt if i] #delete the '' item in the list created by split method. w_n = len(cont_w) avg = w_n/l_n print("Total number of words:",w_n) print("Total number of lines:",l_n) print("Average number of words per line:",format(avg,'.1f')) if __name__ == '__main__': main()

40.518519

106

0.508227

7bd8f8d78b2c4f5dccb96fea010dd3900e9e48d8

812

py

Python

log_offload/__init__.py

our-city-app/log-offload

1842dbd6d10af4435582f1d1a9994c9dc15c6d0c

[ "Apache-2.0" ]

null

log_offload/__init__.py

our-city-app/log-offload

1842dbd6d10af4435582f1d1a9994c9dc15c6d0c

[ "Apache-2.0" ]

1

2018-09-13T11:00:59.000Z

2022-03-23T08:31:35.000Z

log_offload/__init__.py

our-city-app/log-offload

1842dbd6d10af4435582f1d1a9994c9dc15c6d0c

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2019 Green Valley Belgium NV # NOTICE: THIS FILE HAS BEEN MODIFIED BY GREEN VALLEY BELGIUM NV IN ACCORDANCE WITH THE APACHE LICENSE VERSION 2.0 # Copyright 2018 GIG Technology NV # # 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. # # @@license_version:1.6@@ __version__ = '0.2.0'

38.666667

114

0.751232

ce57b1f3c2f669f588fcecc073631f78263acb2c

4,707

py

Python

amazon_paapi/sdk/models/browse_node_info.py

frenners/python-amazon-paapi

9cb744bef17f5127231367430191df12126e9c24

[ "MIT" ]

121

2020-03-07T06:56:56.000Z

2022-03-30T21:03:43.000Z

amazon_paapi/sdk/models/browse_node_info.py

frenners/python-amazon-paapi

9cb744bef17f5127231367430191df12126e9c24

[ "MIT" ]

61

2020-03-03T11:03:58.000Z

2022-01-20T19:27:49.000Z

amazon_paapi/sdk/models/browse_node_info.py

frenners/python-amazon-paapi

9cb744bef17f5127231367430191df12126e9c24

[ "MIT" ]

29

2020-03-04T18:08:22.000Z

2022-03-25T18:49:46.000Z

# coding: utf-8 """ Copyright 2020 Amazon.com, Inc. or its affiliates. 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. A copy of the License is located at http://www.apache.org/licenses/LICENSE-2.0 or in the "license" file accompanying this file. This file 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. """ """ ProductAdvertisingAPI https://webservices.amazon.com/paapi5/documentation/index.html # noqa: E501 """ import pprint import re # noqa: F401 import six from .browse_node import BrowseNode # noqa: F401,E501 from .website_sales_rank import WebsiteSalesRank # noqa: F401,E501 class BrowseNodeInfo(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'browse_nodes': 'list[BrowseNode]', 'website_sales_rank': 'WebsiteSalesRank' } attribute_map = { 'browse_nodes': 'BrowseNodes', 'website_sales_rank': 'WebsiteSalesRank' } def __init__(self, browse_nodes=None, website_sales_rank=None): # noqa: E501 """BrowseNodeInfo - a model defined in Swagger""" # noqa: E501 self._browse_nodes = None self._website_sales_rank = None self.discriminator = None if browse_nodes is not None: self.browse_nodes = browse_nodes if website_sales_rank is not None: self.website_sales_rank = website_sales_rank @property def browse_nodes(self): """Gets the browse_nodes of this BrowseNodeInfo. # noqa: E501 :return: The browse_nodes of this BrowseNodeInfo. # noqa: E501 :rtype: list[BrowseNode] """ return self._browse_nodes @browse_nodes.setter def browse_nodes(self, browse_nodes): """Sets the browse_nodes of this BrowseNodeInfo. :param browse_nodes: The browse_nodes of this BrowseNodeInfo. # noqa: E501 :type: list[BrowseNode] """ self._browse_nodes = browse_nodes @property def website_sales_rank(self): """Gets the website_sales_rank of this BrowseNodeInfo. # noqa: E501 :return: The website_sales_rank of this BrowseNodeInfo. # noqa: E501 :rtype: WebsiteSalesRank """ return self._website_sales_rank @website_sales_rank.setter def website_sales_rank(self, website_sales_rank): """Sets the website_sales_rank of this BrowseNodeInfo. :param website_sales_rank: The website_sales_rank of this BrowseNodeInfo. # noqa: E501 :type: WebsiteSalesRank """ self._website_sales_rank = website_sales_rank def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(BrowseNodeInfo, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, BrowseNodeInfo): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

29.980892

95

0.619503

e15a63067e4ac44d9fcc3899f66fff79b14de19e

6,685

py

Python

chemdataextractor/parse/nmr.py

edbeard/chemdataextractor-uvvis2018

a5750d5313a250468e29d244cd4aeafdfc3250da

[ "MIT" ]

6

2019-12-05T17:10:19.000Z

2021-08-10T15:15:10.000Z

chemdataextractor/parse/nmr.py

edbeard/chemdataextractor-uvvis2018

a5750d5313a250468e29d244cd4aeafdfc3250da

[ "MIT" ]

null

chemdataextractor/parse/nmr.py

edbeard/chemdataextractor-uvvis2018

a5750d5313a250468e29d244cd4aeafdfc3250da

[ "MIT" ]

2

2020-06-29T06:58:53.000Z

2021-03-21T08:39:36.000Z

# -*- coding: utf-8 -*- """ chemdataextractor.parse.nmr ~~~~~~~~~~~~~~~~~~~~~~~~~~~ NMR text parser. :copyright: Copyright 2016 by Matt Swain. :license: MIT, see LICENSE file for more details. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import copy import logging import re from ..model import Compound, NmrSpectrum, NmrPeak from ..utils import first from .actions import join, merge, strip_stop, fix_whitespace from .base import BaseParser from .common import cc, equals from .cem import chemical_name, nmr_solvent from .elements import W, I, T, R, Optional, ZeroOrMore, SkipTo, OneOrMore, Not, Group log = logging.getLogger(__name__) number = R('^\d+(\.\d+)?$') nucleus = ( W('1H') | W('13C') | W('15N') | W('31P') | W('19F') | W('11B') | W('29Si') | W('17O') | W('73Ge') | W('195Pt') | W('33S') | W('13C{1H}') | W('13C{1H') + W('}') | W('H1') | W('C13') | W('N15') | W('P31') | W('F19') | W('B11') | W('Si29') | W('Ge73') | W('Pt195') | W('S33') )('nucleus').add_action(merge) nmr_name = R('^N\.?M\.?R\.?$?$', re.I).hide() nmr_name_with_nucleus = R('^(1H|13C)N\.?M\.?R\.?\(?$', re.I, group=1)('nucleus') frequency = (number('value') + R('^M?Hz$')('units'))('frequency') delim = R('^[;:,\./]$').hide() solvent = ((nmr_solvent | chemical_name) + Optional((R('^(\+|&|and)$') | cc) + (nmr_solvent | chemical_name)) + Optional(SkipTo(R('^([;:,\.$]|at)$'))) + Optional(Optional(delim) + I('solvent').hide()))('solvent').add_action(join).add_action(fix_whitespace) temp_value = (Optional(R('^[~∼\<\>]$')) + Optional(R('^[\-–−]$')) + R('^[\+\-–−]?\d+(\.\d+)?$'))('value').add_action(merge) temp_word = (I('room') + R('^temp(erature)?$') | R('^r\.?t\.?$', re.I))('value').add_action(join) temp_units = (W('°') + R('[CFK]') | W('K'))('units').add_action(merge) temperature = Optional(I('at').hide()) + Group((temp_value + temp_units) | temp_word)('temperature') def fix_nmr_peak_whitespace_error(tokens, start, result): """""" new_result = [] for e in result: shift = e.find('shift') if ',' in shift.text: for peak_text in shift.text.split(','): new_e = copy.deepcopy(e) new_e.find('shift').text = peak_text new_result.append(new_e) else: new_result.append(e) return new_result def strip_delta(tokens, start, result): """""" for e in result: for child in e.iter(): if child.text.startswith('δ'): child.text = child.text[1:] return result shift_range = (Optional(R('^[\-–−‒]$')) + (R('^δ?[\+\-–−‒]?\d+(\.+\d+)?[\-–−‒]\d+(\.+\d+)?\.?$') | (R('^[\+\-–−‒]?\d+(\.+\d+)?$') + R('^[\-–−‒]$') + R('^[\+\-–−‒]?\d+(\.+\d+)?\.?$'))))('shift').add_action(merge) shift_value = (Optional(R('^[\-–−‒]$')) + R('^δ?[\+\-–−‒]?\d+(\.+\d+)?\.?$'))('shift').add_action(merge) shift_error = (Optional(R('^[\-–−‒]$')) + R('^δ?[\+\-–−‒]?\d+(\.+\d+)?,\d+(\.+\d+)?\.?$'))('shift').add_action(merge) shift = (shift_range | shift_value | shift_error).add_action(strip_stop).add_action(strip_delta) split = R('^(br?)?(s|S|d|D|t|T|q|Q|quint|sept|m|M|dd|ddd|dt|td|tt|br|bs|sb|h|ABq|broad|singlet|doublet|triplet|qua(rtet)?|quintet|septet|multiplet|multiple|peaks)$') multiplicity = (OneOrMore(split) + Optional(W('of') + split))('multiplicity').add_action(join) coupling_value = (number + ZeroOrMore(R('^[,;&]$') + number + Not(W('H'))))('value').add_action(join) coupling = ((R('^\d?J([HCNPFD\d,]*|cis|trans)$') + Optional(R('^[\-–−‒]$') + R('^[HCNPF\d]$')) + Optional('=')).hide() + coupling_value + Optional(W('Hz')('units')) + ZeroOrMore(R('^[,;&]$').hide() + coupling_value + W('Hz')('units')))('coupling') number = (R('^\d+(\.\d+)?[HCNPF]\.?$') | (R('^\d+(\.\d+)?$') + R('^[HCNPF]\.?$')))('number').add_action(merge) assignment_options = (OneOrMore(R('([CNHOPS\-–−‒=]+\d*[A-Za-z]?′*)+') | chemical_name | R('^(C?quat\.?|Ac|Ar|Ph|linker|bridge)$')) + Optional(W('×') + R('^\d+$')))('assignment').add_action(join) assignment = Optional(R('^\d{1,2}$')('number') + Optional(W('×')).hide()) + (assignment_options + ZeroOrMore(T('CC').hide() + assignment_options)) note = (W('overlapped') | (W('×') + R('^\d+$')))('note').add_action(join) peak_meta_options = multiplicity | coupling | number | assignment | note peak_meta = W('(').hide() + peak_meta_options + ZeroOrMore(ZeroOrMore(delim) + peak_meta_options) + Optional(delim) + W(')').hide() delta = (R('^[δd][HCNPF]?$') + Optional(equals)).hide() ppm = Optional(R('^[(\[]$')) + Optional(I('in')) + I('ppm') + Optional(R('^[)\]]$')) spectrum_meta = Optional(W('(').hide()) + (frequency | solvent | delta | temperature) + ZeroOrMore(Optional(delim) + (frequency | solvent | I('ppm') | delta | temperature)) + Optional(temperature) + Optional(W(')').hide()) prelude_options = spectrum_meta | delta | delim | ppm.hide() | equals.hide() prelude = ((nucleus + Optional(R('^[\-–−‒]$')).hide() + nmr_name | nmr_name_with_nucleus) + ZeroOrMore(prelude_options)) | (R('^δ[HC]?$')('nucleus') + spectrum_meta + ZeroOrMore(prelude_options)) peak = Optional(delta) + (shift + Not(R('^M?Hz$')) + Optional(ppm).hide() + Optional(peak_meta))('peak').add_action(fix_nmr_peak_whitespace_error) peaks = (peak + ZeroOrMore(ZeroOrMore(delim | W('and')).hide() + peak))('peaks') nmr = (prelude + peaks)('nmr') class NmrParser(BaseParser): """""" root = nmr def __init__(self): pass def interpret(self, result, start, end): c = Compound() n = NmrSpectrum( nucleus=first(result.xpath('./nucleus/text()')), solvent=first(result.xpath('./solvent/text()')), frequency=first(result.xpath('./frequency/value/text()')), frequency_units=first(result.xpath('./frequency/units/text()')), temperature=first(result.xpath('./temperature/value/text()')), temperature_units=first(result.xpath('./temperature/units/text()')) ) for peak_result in result.xpath('./peaks/peak'): nmr_peak = NmrPeak( shift=first(peak_result.xpath('./shift/text()')), multiplicity=first(peak_result.xpath('./multiplicity/text()')), coupling=first(peak_result.xpath('./coupling/value/text()')), coupling_units=first(peak_result.xpath('./coupling/units/text()')), number=first(peak_result.xpath('./number/text()')), assignment=first(peak_result.xpath('./assignment/text()')) ) n.peaks.append(nmr_peak) c.nmr_spectra.append(n) yield c

44.865772

257

0.577711

ac2da2f89add792a14eadb69d2d8b44ae798cd56

590

py

Python

nengo/utils/neurons.py

pedrombmachado/nengo

abc85e1a75ce2f980e19eef195d98081f95efd28

[ "BSD-2-Clause" ]

null

nengo/utils/neurons.py

pedrombmachado/nengo

abc85e1a75ce2f980e19eef195d98081f95efd28

[ "BSD-2-Clause" ]

null

nengo/utils/neurons.py

pedrombmachado/nengo

abc85e1a75ce2f980e19eef195d98081f95efd28

[ "BSD-2-Clause" ]

null

from nengo.exceptions import MovedError def spikes2events(*args, **kwargs): raise MovedError(location="nengo_extras.neurons") def _rates_isi_events(*args, **kwargs): raise MovedError(location="nengo_extras.neurons") def rates_isi(*args, **kwargs): raise MovedError(location="nengo_extras.neurons") def lowpass_filter(*args, **kwargs): raise MovedError(location="nengo_extras.neurons") def rates_kernel(*args, **kwargs): raise MovedError(location="nengo_extras.neurons") def settled_firingrate(*args, **kwargs): raise MovedError(location="nengo.neurons")

22.692308

53

0.747458

92c416223b4fd21f3533a253df13ff23e2b0ef77

14,251

py

Python

wyze_sdk/service/base.py

mrdranias/wyze-sdk

ab329092eca1f4b00264e18a42da8d0e7e4bf71c

[ "Unlicense" ]

null

wyze_sdk/service/base.py

mrdranias/wyze-sdk

ab329092eca1f4b00264e18a42da8d0e7e4bf71c

[ "Unlicense" ]

null

wyze_sdk/service/base.py

mrdranias/wyze-sdk

ab329092eca1f4b00264e18a42da8d0e7e4bf71c

[ "Unlicense" ]

null

from __future__ import annotations import logging import platform import sys import time import uuid from abc import ABCMeta from contextlib import suppress from json import dumps from typing import Dict, Optional, Union from urllib.parse import urljoin import requests from wyze_sdk import version from wyze_sdk.errors import WyzeRequestError from wyze_sdk.signature import RequestVerifier from .wyze_response import WyzeResponse class BaseServiceClient(metaclass=ABCMeta): WYZE_APP_ID = "9319141212m2ik" WYZE_APP_NAME = "wyze" WYZE_APP_VERSION = "2.19.14" WYZE_PHONE_TYPE = 2 def __init__( self, token: Optional[str] = None, base_url: Optional[str] = None, timeout: int = 30, headers: Optional[dict] = None, app_id: Optional[str] = WYZE_APP_ID, app_name: Optional[str] = WYZE_APP_NAME, app_version: Optional[str] = WYZE_APP_VERSION, user_agent_prefix: Optional[str] = None, user_agent_suffix: Optional[str] = None, phone_id: Optional[str] = None, phone_type: Optional[int] = WYZE_PHONE_TYPE, request_verifier: Optional[RequestVerifier] = None, logger: Optional[logging.Logger] = None, ): self.token = None if token is None else token.strip() self.base_url = base_url self.timeout = timeout self.app_id = app_id self.app_name = app_name self.app_version = app_version self.headers = headers or {} # self.headers["User-Agent"] = self._get_user_agent( # user_agent_prefix, user_agent_suffix # ) self.phone_id = phone_id if phone_id else str(uuid.uuid4()) self.phone_type = phone_type self.default_params = {} self.request_verifier = request_verifier self._logger = logger if logger is not None else logging.getLogger(__name__) def _get_user_agent(self, prefix: Optional[str] = None, suffix: Optional[str] = None): """Construct the user-agent header with the package info, Python version and OS version. Returns: The user agent string. e.g. 'Python/3.6.7 wyzeclient/2.0.0 Darwin/17.7.0' """ # __name__ returns all classes, we only want the client client = "{0}/{1}".format("wyzeclient", version.__version__) python_version = "Python/{v.major}.{v.minor}.{v.micro}".format(v=sys.version_info) system_info = "{0}/{1}".format(platform.system(), platform.release()) user_agent_string = " ".join([python_version, client, system_info]) prefix = f"{prefix} " if prefix else "" suffix = f" {suffix}" if suffix else "" return prefix + user_agent_string + suffix def _do_request( self, session: requests.Session, request: requests.Request) -> WyzeResponse: with suppress(requests.exceptions.HTTPError, requests.exceptions.RequestException, ValueError): self._logger.info(f"requesting {request.method} to {request.url}") self._logger.debug(f"headers: {request.headers}") self._logger.debug(f"body: {request.body}") settings = session.merge_environment_settings(request.url, {}, None, None, None) self._logger.debug(f"settings: {settings}") response = session.send(request, **settings) return WyzeResponse( client=self, http_verb=request.method, api_url=request.url, req_args=request.body, data=response.json(), headers=response.headers, status_code=response.status_code, ).validate() def do_post(self, url: str, headers: dict, payload: dict, params: Optional[dict] = None) -> WyzeResponse: with requests.Session() as client: if headers is not None: # add the request-specific headers self._logger.debug('merging request-specific headers into session headers') client.headers.update(headers) # we have to use a prepared request because the requests module # doesn't allow us to specify the separators in our json dumping # and the server expects no extra whitespace req = client.prepare_request(requests.Request('POST', url, json=payload, params=params)) self._logger.debug('unmodified prepared request') self._logger.debug(req) if isinstance(payload, dict): payload = dumps(payload, separators=(',', ':')) if isinstance(payload, str): req.body = payload.encode('utf-8') req.prepare_content_length(req.body) return self._do_request(client, req) def do_get(self, url: str, headers: dict, payload: dict) -> WyzeResponse: # params = req_args["params"] if "params" in req_args else None # data = req_args["data"] if "data" in req_args else None # headers = req_args["headers"] if "headers" in req_args else None # token = params.get("token") if params and "token" in params else None # auth = ( # req_args["auth"] if "auth" in req_args else None # ) # Basic Auth for oauth.v2.access / oauth.access with requests.Session() as client: if headers is not None: # add the request-specific headers self._logger.debug('merging request-specific headers into session headers') client.headers.update(headers) req = client.prepare_request(requests.Request('GET', url, params=payload)) return self._do_request(client, req) def _nonce(self): return str(round(time.time() * 1000)) def api_call( self, api_endpoint: str, *, http_verb: str = "POST", data: Union[dict] = None, params: dict = None, json: dict = None, headers: dict = None, auth: dict = None, ) -> WyzeResponse: """Create a request and execute the API call to Wyze. Args: api_endpoint (str): The target Wyze API endpoint. e.g. '/app/v2/home_page/get_object_list' http_verb (str): HTTP Verb. e.g. 'POST' data: The body to attach to the request. If a dictionary is provided, form-encoding will take place. e.g. {'key1': 'value1', 'key2': 'value2'} params (dict): The URL parameters to append to the URL. e.g. {'key1': 'value1', 'key2': 'value2'} json (dict): JSON for the body to attach to the request (if data is not specified). e.g. {'key1': 'value1', 'key2': 'value2'} headers (dict): Additional request headers auth (dict): A dictionary that consists of access_token and refresh_token Returns: (WyzeResponse) The server's response to an HTTP request. Data from the response can be accessed like a dict. Raises: WyzeApiError: The following Wyze API call failed: '/app/v2/home_page/get_object_list'. WyzeRequestError: JSON data can only be submitted as POST requests. """ has_json = json is not None if has_json and http_verb != "POST": msg = "JSON data can only be submitted as POST requests. GET requests should use the 'params' argument." raise WyzeRequestError(msg) api_url = self._get_url(self.base_url, api_endpoint) headers = headers or {} headers.update(self.headers) if http_verb == "POST": return self.do_post(url=api_url, headers=headers, payload=json, params=params) elif http_verb == "GET": return self.do_get(url=api_url, headers=headers, payload=params) msg = "Unknown request type." raise WyzeRequestError(msg) def _get_url(self, base_url: str, api_endpoint: str) -> str: """Joins the base URL and an API endpoint path to form an absolute URL. Args: base_url (str): The base URL. e.g. 'https://api.wyzecam.com' api_endpoint (str): The API path. e.g. '/app/v2/home_page/get_object_list' Returns: The absolute endpoint URL. e.g. 'https://api.wyzecam.com/app/v2/home_page/get_object_list' """ return urljoin(base_url, api_endpoint) def _get_headers( self, *, headers: dict, signature: Optional[str] = None, signature2: Optional[str] = None, has_json: bool, request_specific_headers: Optional[dict], ) -> Dict[str, str]: """Constructs the headers needed for a request. Args: has_json (bool): Whether or not the request has json. has_files (bool): Whether or not the request has files. request_specific_headers (dict): Additional headers specified by the user for a specific request. Returns: The headers dictionary. e.g. { 'Content-Type': 'application/json;charset=utf-8', 'Signature': 'erewf3254rgt453f34f..==', 'User-Agent': 'Python/3.6.8 wyzeclient/2.1.0 Darwin/17.7.0' } """ final_headers = { # "Content-Type": "application/json;charset=utf-8", 'Accept-Encoding': 'gzip', } if headers is None or "User-Agent" not in headers: final_headers["User-Agent"] = "okhttp/4.7.2" # self._get_user_agent() if signature: final_headers.update({"Signature": "{}".format(signature)}) if signature2: final_headers.update({"Signature2": "{}".format(signature2)}) if headers is None: headers = {} # Merge headers specified at client initialization. final_headers.update(headers) # Merge headers specified for a specific request. e.g. oauth.access if request_specific_headers: final_headers.update(request_specific_headers) if has_json: final_headers.update({"Content-Type": "application/json;charset=utf-8"}) return final_headers def get_sorted_params(self, params: dict = {}) -> str: return '&'.join(map(lambda x: x[0] + '=' + str(x[1]), params)) class WpkNetServiceClient(BaseServiceClient, metaclass=ABCMeta): """ wpk net service client is the wrapper to newer Wyze services like WpkWyzeSignatureService and WpkWyzeExService. """ WYZE_APP_NAME = "com.hualai" WYZE_SALTS = { "9319141212m2ik": "wyze_app_secret_key_132", "venp_4c30f812828de875": "CVCSNoa0ALsNEpgKls6ybVTVOmGzFoiq", } def __init__( self, token: Optional[str] = None, base_url: Optional[str] = "https://api.wyzecam.com/", app_name: str = WYZE_APP_NAME, app_id: str = BaseServiceClient.WYZE_APP_ID, request_verifier: RequestVerifier = None ): super().__init__( token=token, base_url=base_url, app_name=app_name, app_id=app_id, request_verifier=request_verifier if request_verifier is not None else RequestVerifier(signing_secret=WpkNetServiceClient.WYZE_SALTS[app_id], access_token=token) ) def _get_headers( self, *, request_specific_headers: Optional[dict] = None, nonce: int = None, ) -> Dict[str, str]: if request_specific_headers is None: request_specific_headers = {} request_specific_headers.update({ 'access_token': self.token, 'requestid': self.request_verifier.request_id(nonce), }) return super()._get_headers(headers=None, has_json=False, request_specific_headers=request_specific_headers) def api_call( self, api_method: str, *, http_verb: str = "POST", params: dict = None, json: dict = None, headers: Optional[dict] = None, nonce: int = None, ) -> WyzeResponse: if headers is None: headers = {} if http_verb == "POST": # this must be done here so that it will be included in the signing if json is None: json = {} json['nonce'] = str(nonce) request_data = dumps(json, separators=(',', ':')) headers.update({ 'signature2': self.request_verifier.generate_dynamic_signature(timestamp=nonce, body=request_data) }) elif http_verb == "GET": if params is None: params = {} # this must be done here so that it will be included in the signing params['nonce'] = nonce headers.update({ 'signature2': self.request_verifier.generate_dynamic_signature(timestamp=nonce, body=self.get_sorted_params(sorted(params.items()))) }) return super().api_call( api_method, http_verb=http_verb, data=None, params=params, json=json, headers=self._get_headers(request_specific_headers=headers, nonce=nonce), auth=None, ) class ExServiceClient(WpkNetServiceClient, metaclass=ABCMeta): """ ex service client is the wrapper for WpkWyzeExService. """ def _get_headers( self, *, request_specific_headers: Optional[dict] = None, nonce: Optional[int] = None, ) -> Dict[str, str]: if request_specific_headers is None: request_specific_headers = {} request_specific_headers.update({ 'appid': self.app_id, 'appinfo': f"wyze_android_{self.app_version}", 'phoneid': self.phone_id, }) return super()._get_headers(request_specific_headers=request_specific_headers) class SignatureServiceClient(WpkNetServiceClient, metaclass=ABCMeta): """ signature service client is the wrapper for WpkWyzeSignatureService """

37.901596

173

0.604308

a7e7408c9152580b48d14a73a3cd7970174eb732

949

py

Python

azure-cognitiveservices-vision-customvision/azure/cognitiveservices/vision/customvision/training/models/image_tag_create_batch_py3.py

NMijat1024/azure-sdk-for-python

c49e1d6d797dceaca81813cafb1a486d67185182

[ "MIT" ]

1

2018-07-23T08:59:24.000Z

azure-cognitiveservices-vision-customvision/azure/cognitiveservices/vision/customvision/training/models/image_tag_create_batch_py3.py

NMijat1024/azure-sdk-for-python

c49e1d6d797dceaca81813cafb1a486d67185182

[ "MIT" ]

null

azure-cognitiveservices-vision-customvision/azure/cognitiveservices/vision/customvision/training/models/image_tag_create_batch_py3.py

NMijat1024/azure-sdk-for-python

c49e1d6d797dceaca81813cafb1a486d67185182

[ "MIT" ]

1

2018-08-28T14:36:47.000Z

# 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 ImageTagCreateBatch(Model): """ImageTagCreateBatch. :param tags: :type tags: list[~azure.cognitiveservices.vision.customvision.training.models.ImageTagCreateEntry] """ _attribute_map = { 'tags': {'key': 'tags', 'type': '[ImageTagCreateEntry]'}, } def __init__(self, *, tags=None, **kwargs) -> None: super(ImageTagCreateBatch, self).__init__(**kwargs) self.tags = tags

31.633333

91

0.586934

fbf02a8244b372c9e2ed168da237cb2c18234951

3,772

py

Python

lemur_cms/settings.py

lemur-cms/lemur-cms

6dfa087f9c16f1452c0af935855abfedd73fa239

[ "BSD-2-Clause" ]

null

lemur_cms/settings.py

lemur-cms/lemur-cms

6dfa087f9c16f1452c0af935855abfedd73fa239

[ "BSD-2-Clause" ]

2

2022-02-12T23:03:18.000Z

2022-02-26T19:08:27.000Z

lemur_cms/settings.py

lemur-cms/lemur-cms

6dfa087f9c16f1452c0af935855abfedd73fa239

[ "BSD-2-Clause" ]

1

2019-10-19T22:21:33.000Z

from __future__ import absolute_import import os from django.apps import apps from lemur_cms.base import lemur_cms, default from lemur_cms.utils.settings import merge import os import six import logging import warnings from django.apps import apps from lemur_cms.conf.spec import DJANGO_CONF from lemur_cms.base import lemur_cms, default from lemur_cms.utils.settings import (get_conf_from_module, merge, get_loaded_modules) from importlib import import_module # noqa from django.utils.module_loading import module_has_submodule # noqa from lemur_cms.conf.default import * """ Lemur CMS settings """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '0vks5u62=+q71pwlgg4np(e8-(r21nck7(a_(c%yjr5gnr==r=' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True # Application definition APPS = merge(APPS, default.core) if not apps.ready: # load directly specified apps lemur_cms.get_app_modules(APPS) # propagate settings to lemur_cms lemur_cms.MODULES_AUTOLOAD = LEMURCMS_MODULE_AUTO_INCLUDE # load all modules lemur_cms.load_modules() # just propagate all loaded modules to settings LEMURCMS_MODULES = lemur_cms.get_modules() # iterate over sorted modules for mod, mod_cfg in LEMURCMS_MODULES: try: # go through django keys and merge it to main settings for key in DJANGO_CONF.keys(): updated_value = mod_cfg.get_value(key, globals()[key]) globals()[key] = updated_value locals()[key] = updated_value # map value to lemur_cms but under our internal name setattr(lemur_cms, DJANGO_CONF[key], updated_value) if mod_cfg.urls_conf: MODULE_URLS[mod_cfg.urls_conf] = {'is_public': mod_cfg.public} except Exception as e: warnings.warn( 'Exception "{}" raised during loading ' 'module {}'.format(str(e), mod)) else: warnings.warn("LemurCMS modules are already loaded. Skiped now.") setattr(lemur_cms, 'widgets', WIDGETS) # and again merge core with others APPS = merge(APPS, default.core) WSGI_APPLICATION = 'lemur_cms.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'lemur_db'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' LANGUAGES = [ ('en', 'EN') ] TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'static') MEDIA_URL = '/media/' MEDIA_ROOT = os.path.join(BASE_DIR, 'media') # cors headers CORS_ORIGIN_WHITELIST = [ 'http://localhost:3000', 'http://localhost:5000', 'http://localhost:8000', 'http://localhost:8080', ]

26.013793

91

0.688759

403bdbad1bd39062aa5916f154e0f36dd271e115

735

py

Python

docs/examples/container/kubernetes/instantiate_driver.py

rgharris/libcloud

90971e17bfd7b6bb97b2489986472c531cc8e140

[ "Apache-2.0" ]

null

docs/examples/container/kubernetes/instantiate_driver.py

rgharris/libcloud

90971e17bfd7b6bb97b2489986472c531cc8e140

[ "Apache-2.0" ]

1

2021-12-06T12:29:13.000Z

docs/examples/container/kubernetes/instantiate_driver.py

rgharris/libcloud

90971e17bfd7b6bb97b2489986472c531cc8e140

[ "Apache-2.0" ]

1

2019-08-05T10:12:02.000Z

from libcloud.container.types import Provider from libcloud.container.providers import get_driver cls = get_driver(Provider.KUBERNETES) # 1. Client side cert auth conn = cls( host="192.168.99.103", port=8443, secure=True, key_file="/home/user/.minikube/client.key", cert_file="/home/user/.minikube/client.crt", ca_cert="/home/user/.minikube/ca.crt", ) # 2. Bearer bootstrap token auth conn = cls(key="my_token", host="126.32.21.4", ex_token_bearer_auth=True) # 3. Basic auth conn = cls( key="my_username", secret="THIS_IS)+_MY_SECRET_KEY+I6TVkv68o4H", host="126.32.21.4" ) for container in conn.list_containers(): print(container.name) for cluster in conn.list_clusters(): print(cluster.name)

25.344828

87

0.717007

258c730049a5832c9489afb89c6c88f5b191e95c

12,757

py

Python

tests/requirements.py

DanCardin/alembic

2719c0ef1917af0b74b9d642a75c9b6dd5cf537a

[ "MIT" ]

1,324

2018-11-27T05:44:41.000Z

2022-03-30T19:49:20.000Z

tests/requirements.py

DanCardin/alembic

2719c0ef1917af0b74b9d642a75c9b6dd5cf537a

[ "MIT" ]

452

2018-11-27T22:43:38.000Z

2022-03-28T04:33:43.000Z

tests/requirements.py

DanCardin/alembic

2719c0ef1917af0b74b9d642a75c9b6dd5cf537a

[ "MIT" ]

159

2018-11-29T18:46:15.000Z

2022-03-28T16:34:19.000Z

from sqlalchemy import exc as sqla_exc from sqlalchemy import text from alembic.testing import exclusions from alembic.testing.requirements import SuiteRequirements from alembic.util import compat from alembic.util import sqla_compat class DefaultRequirements(SuiteRequirements): @property def unicode_string(self): return exclusions.skip_if(["oracle"]) @property def alter_column(self): return exclusions.skip_if(["sqlite"], "no ALTER COLUMN support") @property def schemas(self): """Target database must support external schemas, and have one named 'test_schema'.""" return exclusions.skip_if(["sqlite", "firebird"], "no schema support") @property def no_referential_integrity(self): """test will fail if referential integrity is enforced""" return exclusions.fails_on_everything_except("sqlite") @property def non_native_boolean(self): """test will fail if native boolean is provided""" return exclusions.fails_if( exclusions.LambdaPredicate( lambda config: config.db.dialect.supports_native_boolean ) ) @property def non_native_boolean_check_constraint(self): """backend creates a check constraint for booleans if enabled""" return exclusions.only_on( exclusions.LambdaPredicate( lambda config: not config.db.dialect.supports_native_boolean and config.db.dialect.non_native_boolean_check_constraint ) ) @property def check_constraints_w_enforcement(self): return exclusions.fails_on(["mysql", "mariadb"]) @property def unnamed_constraints(self): """constraints without names are supported.""" return exclusions.only_on(["sqlite"]) @property def fk_names(self): """foreign key constraints always have names in the DB""" return exclusions.fails_on("sqlite") @property def reflects_fk_options(self): return exclusions.open() @property def fk_initially(self): """backend supports INITIALLY option in foreign keys""" return exclusions.only_on(["postgresql"]) @property def fk_deferrable(self): """backend supports DEFERRABLE option in foreign keys""" return exclusions.only_on(["postgresql", "oracle"]) @property def fk_deferrable_is_reflected(self): return self.fk_deferrable + exclusions.fails_on("oracle") @property def fk_ondelete_restrict(self): return exclusions.only_on(["postgresql", "sqlite", "mysql"]) @property def fk_onupdate_restrict(self): return self.fk_onupdate + exclusions.fails_on(["mssql"]) @property def fk_ondelete_noaction(self): return exclusions.only_on( ["postgresql", "mysql", "mariadb", "sqlite", "mssql"] ) @property def fk_ondelete_is_reflected(self): def go(config): if exclusions.against(config, "mssql"): return not sqla_compat.sqla_14_26 else: return False return exclusions.fails_if(go) @property def fk_onupdate_is_reflected(self): def go(config): if exclusions.against(config, "mssql"): return not sqla_compat.sqla_14_26 else: return False return self.fk_onupdate + exclusions.fails_if(go) @property def fk_onupdate(self): return exclusions.only_on( ["postgresql", "mysql", "mariadb", "sqlite", "mssql"] ) @property def reflects_unique_constraints_unambiguously(self): return exclusions.fails_on(["mysql", "mariadb", "oracle"]) @property def reflects_indexes_w_sorting(self): # TODO: figure out what's happening on the SQLAlchemy side # when we reflect an index that has asc() / desc() on the column return exclusions.fails_on(["oracle"]) @property def long_names(self): if sqla_compat.sqla_14: return exclusions.skip_if("oracle<18") else: return exclusions.skip_if("oracle") @property def reflects_pk_names(self): """Target driver reflects the name of primary key constraints.""" return exclusions.fails_on_everything_except( "postgresql", "oracle", "mssql", "sybase", "sqlite" ) @property def datetime_timezone(self): """target dialect supports timezone with datetime types.""" return exclusions.only_on(["postgresql"]) @property def postgresql(self): return exclusions.only_on(["postgresql"]) @property def mysql(self): return exclusions.only_on(["mysql", "mariadb"]) @property def oracle(self): return exclusions.only_on(["oracle"]) @property def mssql(self): return exclusions.only_on(["mssql"]) @property def postgresql_uuid_ossp(self): def check_uuid_ossp(config): if not exclusions.against(config, "postgresql"): return False try: config.db.execute("SELECT uuid_generate_v4()") return True except: return False return exclusions.only_if(check_uuid_ossp) def _has_pg_extension(self, name): def check(config): if not exclusions.against(config, "postgresql"): return False with config.db.connect() as conn: count = conn.scalar( text( "SELECT count(*) FROM pg_extension " "WHERE extname='%s'" % name ) ) return bool(count) return exclusions.only_if(check, "needs %s extension" % name) @property def hstore(self): return self._has_pg_extension("hstore") @property def btree_gist(self): return self._has_pg_extension("btree_gist") @property def autoincrement_on_composite_pk(self): return exclusions.skip_if(["sqlite"], "not supported by database") @property def integer_subtype_comparisons(self): """if a compare of Integer and BigInteger is supported yet.""" return exclusions.skip_if(["oracle"], "not supported by alembic impl") @property def autocommit_isolation(self): """target database should support 'AUTOCOMMIT' isolation level""" return exclusions.only_on(["postgresql", "mysql", "mariadb"]) @property def computed_columns(self): # TODO: in theory if these could come from SQLAlchemy dialects # that would be helpful return self.computed_columns_api + exclusions.skip_if( ["postgresql < 12", "sqlite < 3.31", "mysql < 5.7"] ) @property def computed_reflects_as_server_default(self): # note that this rule will go away when SQLAlchemy correctly # supports reflection of the "computed" construct; the element # will consistently be present as both column.computed and # column.server_default for all supported backends. return ( self.computed_columns + exclusions.only_if( ["postgresql", "oracle"], "backend reflects computed construct as a server default", ) + exclusions.skip_if(self.computed_reflects_normally) ) @property def computed_doesnt_reflect_as_server_default(self): # note that this rule will go away when SQLAlchemy correctly # supports reflection of the "computed" construct; the element # will consistently be present as both column.computed and # column.server_default for all supported backends. return ( self.computed_columns + exclusions.skip_if( ["postgresql", "oracle"], "backend reflects computed construct as a server default", ) + exclusions.skip_if(self.computed_reflects_normally) ) @property def check_constraint_reflection(self): return exclusions.fails_on_everything_except( "postgresql", "sqlite", "oracle", self._mysql_and_check_constraints_exist, ) def mysql_check_col_name_change(self, config): # MySQL has check constraints that enforce an reflect, however # they prevent a column's name from being changed due to a bug in # MariaDB 10.2 as well as MySQL 8.0.16 if exclusions.against(config, ["mysql", "mariadb"]): if sqla_compat._is_mariadb(config.db.dialect): mnvi = sqla_compat._mariadb_normalized_version_info norm_version_info = mnvi(config.db.dialect) return norm_version_info >= (10, 2) and norm_version_info < ( 10, 2, 22, ) else: norm_version_info = config.db.dialect.server_version_info return norm_version_info >= (8, 0, 16) else: return True def _mysql_and_check_constraints_exist(self, config): # 1. we have mysql / mariadb and # 2. it enforces check constraints if exclusions.against(config, ["mysql", "mariadb"]): if sqla_compat._is_mariadb(config.db.dialect): mnvi = sqla_compat._mariadb_normalized_version_info norm_version_info = mnvi(config.db.dialect) return norm_version_info >= (10, 2) else: norm_version_info = config.db.dialect.server_version_info return norm_version_info >= (8, 0, 16) else: return False @property def json_type(self): return exclusions.only_on( [ lambda config: exclusions.against(config, "mysql") and ( ( not config.db.dialect._is_mariadb and exclusions.against(config, "mysql >= 5.7") ) or ( config.db.dialect._mariadb_normalized_version_info >= (10, 2, 7) ) ), "mariadb>=10.2.7", "postgresql >= 9.3", self._sqlite_json, self._mssql_json, ] ) def _mssql_json(self, config): if not sqla_compat.sqla_14: return False else: return exclusions.against(config, "mssql") def _sqlite_json(self, config): if not sqla_compat.sqla_14: return False elif not exclusions.against(config, "sqlite >= 3.9"): return False else: with config.db.connect() as conn: try: return ( conn.execute( text( """select json_extract('{"foo": "bar"}', """ """'$."foo"')""" ) ).scalar() == "bar" ) except sqla_exc.DBAPIError: return False @property def identity_columns(self): # TODO: in theory if these could come from SQLAlchemy dialects # that would be helpful return self.identity_columns_api + exclusions.only_on( ["postgresql >= 10", "oracle >= 12", "mssql"] ) @property def identity_columns_alter(self): # TODO: in theory if these could come from SQLAlchemy dialects # that would be helpful return self.identity_columns_api + exclusions.only_on( ["postgresql >= 10", "oracle >= 12"] ) @property def supports_identity_on_null(self): return self.identity_columns + exclusions.only_on(["oracle"]) @property def legacy_engine(self): return exclusions.only_if( lambda config: not getattr(config.db, "_is_future", False) ) @property def stubs_test(self): def requirements(): try: import black # noqa import zimports # noqa return False except Exception: return True imports = exclusions.skip_if( requirements, "black and zimports are required for this test" ) version = exclusions.only_if( lambda _: compat.py39, "python 3.9 is required" ) return imports + version

32.214646

78

0.586502

9485a808d7b665f3fe3f2188435263697d4a4d59

2,376

py

Python

src/Algopylib/algo/sort.py

siddhartha18101/Algopylib

5890fbb20ae9998a4ade9f7e3df602b24e49ea17

[ "MIT" ]

null

src/Algopylib/algo/sort.py

siddhartha18101/Algopylib

5890fbb20ae9998a4ade9f7e3df602b24e49ea17

[ "MIT" ]

null

src/Algopylib/algo/sort.py

siddhartha18101/Algopylib

5890fbb20ae9998a4ade9f7e3df602b24e49ea17

[ "MIT" ]

null

from typing import List, Union def bubble_sort(arr : List , simulation : bool = False) -> List: """Sorts A List using bubble sort algorithm https://en.wikipedia.org/wiki/Bubble_sort Worst-case performance: O(N^2) Parameters: arr(List) : Unsorted List simulation(bool) : to enable simulation (default argument is False) Returns: arr(List) : Returns sorted List """ def swap(i : int, j : int) -> None: """Swaps two element of List Parameters: i(int) : index of first element j(int) : index of second element Returns: None : Function returns nothing """ arr[i], arr[j] = arr[j], arr[i] n : int = len(arr) swapped : bool = True iteration : int = 0 if simulation: print("iteration",iteration,":",*arr) x : int = -1 while swapped: swapped = False x = x + 1 for i in range(1, n-x): if arr[i - 1] > arr[i]: swap(i - 1, i) swapped = True if simulation: iteration = iteration + 1 print("iteration",iteration,":",*arr) return arr def insertion_sort(arr : List , simulation : bool = False) -> List: """ Insertion Sort Complexity: O(n^2) 1: Iterate from arr[1] to arr[n] over the array. 2: Compare the current element (key) to its predecessor. 3: If the key element is smaller than its predecessor, compare it to the elements before. Move the greater elements one position up to make space for the swapped element. """ iteration : int = 0 if simulation: print("iteration", iteration, ":", *arr) for i in range(len(arr)): cursor : Union[int, float, complex, str] = arr[i] pos : int = i """ Move elements of arr[0..i-1], that are greater than key, to one position ahead of their current position""" while pos > 0 and arr[pos - 1] > cursor: """ Swap the number down the list""" arr[pos] = arr[pos - 1] pos = pos - 1 """ Break and do the final swap""" arr[pos] = cursor if simulation: iteration = iteration + 1 print("iteration",iteration,":",*arr) return arr

30.461538

174

0.539983

9e4cb5022fa7a44ee0abf8b70020c173a1196146

4,758

py

Python

nova/api/openstack/compute/snapshot.py

xuweiliang/Codelibrary

54e45b2daa205132c05b0ff5a2c3db7fca2853a7

[ "Apache-2.0" ]

null

nova/api/openstack/compute/snapshot.py

xuweiliang/Codelibrary

54e45b2daa205132c05b0ff5a2c3db7fca2853a7

[ "Apache-2.0" ]

null

nova/api/openstack/compute/snapshot.py

xuweiliang/Codelibrary

54e45b2daa205132c05b0ff5a2c3db7fca2853a7

[ "Apache-2.0" ]

null

from oslo_utils import strutils import webob from nova.api.openstack import common from nova import compute from nova.api.openstack import extensions from nova.api.openstack import wsgi from oslo_log import log as logging from nova import exception from nova.i18n import _ from nova import utils ALIAS = "os-snapshot" LOG = logging.getLogger(__name__) class SnapshotController(wsgi.Controller): """the Documents API Controller declearation""" def __init__(self): super(SnapshotController, self).__init__() self.compute_api = compute.API() def _get_params(self, req, key): if key: return req.params.get(key, None) return @extensions.expected_errors((400, 404, 501)) def revert_dev_snapshot(self, req, id): context = req.environ['nova.context'] name = self._get_params(req, "name") try: instance = common.get_instance(self.compute_api, context, id) self.compute_api.dev_snapshot_revert(context, instance, name) except exception.NotFound as e: raise exc.HTTPNotFound(explanation=e.format_message()) except NotImplementedError: msg = _("set recovery function error.") raise webob.exc.HTTPNotImplemented(explanation=msg) return {"snapshot":{"execute":True}} @extensions.expected_errors((400, 404, 501)) def set_dev_snapshot(self, req, id): context = req.environ['nova.context'] name = self._get_params(req, "name") try: instance = common.get_instance(self.compute_api, context, id) if name: instance.dev_snapshot = name instance.save() except exception.NotFound as e: raise exc.HTTPNotFound(explanation=e.format_message()) except NotImplementedError: msg = _("set recovery function error.") raise webob.exc.HTTPNotImplemented(explanation=msg) return {"snapshot":{"execute":True}} @extensions.expected_errors(404) def index(self, req): context = req.environ['nova.context'] instance_id = self._get_params(req, "instance_id") instance = common.get_instance(self.compute_api, context, instance_id) try: snapshot = self.compute_api.dev_snapshot_list(context, instance) except exception.NotFound as e: raise exc.HTTPNotFound(explanation=e.format_message()) except NotImplementedError: msg = _("does not support this function.") raise webob.exc.HTTPNotImplemented(explanation=msg) return {'snapshot':snapshot} @extensions.expected_errors(400) def create(self, req, body): context = req.environ['nova.context'] if 'instance_id' not in body['snapshot'] \ and 'name' not in body['snapshot']: return instance_id = body['snapshot']['instance_id'] snapshot_name = body['snapshot']['name'] try: instance = common.get_instance(self.compute_api, context, instance_id) self.compute_api.dev_snapshot_create(context, instance, snapshot_name) except exception.NotFound as e: raise exc.HTTPNotFound(explanation=e.format_message()) except NotImplementedError: msg = _("create dev snapshot function error.") raise webob.exc.HTTPNotImplemented(explanation=msg) return {"snapshot":{"execute":True}} @extensions.expected_errors(400) def delete(self, req, id): context = req.environ['nova.context'] name = self._get_params(req, 'name') try: instance = common.get_instance(self.compute_api, context, id) self.compute_api.dev_snapshot_delete(context, instance, name) except exception.NotFound as e: raise exc.HTTPNotFound(explanation=e.format_message()) except NotImplementedError: msg = _("delete dev snapshot function error.") raise webob.exc.HTTPNotImplemented(explanation=msg) class DevSnapshot(extensions.V21APIExtensionBase): """Documents ExtensionDescriptor implementation""" name = "DevSnapshot" alias = ALIAS version = 1 def get_resources(self): resource = extensions.ResourceExtension(ALIAS, SnapshotController(), member_actions={'set_dev_snapshot':'GET', 'revert_dev_snapshot':'GET'}) return [resource] def get_controller_extensions(self): return []

37.171875

82

0.6219

b4a5887c593bd7a9995f589acd3bd2b7aaa0fab9

10,959

py

Python

python/paddle/fluid/tests/unittests/rnn/test_rnn_cells_static.py

L-Net-1992/Paddle

4d0ca02ba56760b456f3d4b42a538555b9b6c307

[ "Apache-2.0" ]

11

2016-08-29T07:43:26.000Z

2016-08-29T07:51:24.000Z

python/paddle/fluid/tests/unittests/rnn/test_rnn_cells_static.py

L-Net-1992/Paddle

4d0ca02ba56760b456f3d4b42a538555b9b6c307

[ "Apache-2.0" ]

null

python/paddle/fluid/tests/unittests/rnn/test_rnn_cells_static.py

L-Net-1992/Paddle

4d0ca02ba56760b456f3d4b42a538555b9b6c307

[ "Apache-2.0" ]

1

2021-09-24T11:23:36.000Z

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import paddle paddle.framework.set_default_dtype("float64") paddle.enable_static() import numpy as np import unittest from convert import convert_params_for_cell_static from rnn_numpy import SimpleRNNCell, LSTMCell, GRUCell class TestSimpleRNNCell(unittest.TestCase): def __init__(self, bias=True, place="cpu"): super(TestSimpleRNNCell, self).__init__(methodName="runTest") self.bias = bias self.place = paddle.CPUPlace() if place == "cpu" \ else paddle.CUDAPlace(0) def setUp(self): rnn1 = SimpleRNNCell(16, 32, bias=self.bias) mp = paddle.static.Program() sp = paddle.static.Program() with paddle.fluid.unique_name.guard(): with paddle.static.program_guard(mp, sp): rnn2 = paddle.nn.SimpleRNNCell(16, 32, bias_ih_attr=self.bias, bias_hh_attr=self.bias) place = self.place exe = paddle.static.Executor(place) scope = paddle.fluid.Scope() with paddle.static.scope_guard(scope): exe.run(sp) convert_params_for_cell_static(rnn1, rnn2, place) self.mp = mp self.sp = sp self.rnn1 = rnn1 self.rnn2 = rnn2 self.executor = exe self.scope = scope def test_with_initial_state(self): mp = self.mp.clone() sp = self.sp rnn1 = self.rnn1 rnn2 = self.rnn2 exe = self.executor scope = self.scope x = np.random.randn(4, 16) prev_h = np.random.randn(4, 32) y1, h1 = rnn1(x, prev_h) with paddle.fluid.unique_name.guard(): with paddle.static.program_guard(mp, sp): x_data = paddle.fluid.data( "input", [-1, 16], dtype=paddle.framework.get_default_dtype()) init_h = paddle.fluid.data( "init_h", [-1, 32], dtype=paddle.framework.get_default_dtype()) y, h = rnn2(x_data, init_h) feed_dict = {x_data.name: x, init_h.name: prev_h} with paddle.static.scope_guard(scope): y2, h2 = exe.run(mp, feed=feed_dict, fetch_list=[y, h]) np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5) def test_with_zero_state(self): mp = self.mp.clone() sp = self.sp rnn1 = self.rnn1 rnn2 = self.rnn2 exe = self.executor scope = self.scope x = np.random.randn(4, 16) y1, h1 = rnn1(x) with paddle.fluid.unique_name.guard(): with paddle.static.program_guard(mp, sp): x_data = paddle.fluid.data( "input", [-1, 16], dtype=paddle.framework.get_default_dtype()) y, h = rnn2(x_data) feed_dict = {x_data.name: x} with paddle.static.scope_guard(scope): y2, h2 = exe.run(mp, feed=feed_dict, fetch_list=[y, h], use_prune=True) np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5) def runTest(self): self.test_with_initial_state() self.test_with_zero_state() class TestGRUCell(unittest.TestCase): def __init__(self, bias=True, place="cpu"): super(TestGRUCell, self).__init__(methodName="runTest") self.bias = bias self.place = paddle.CPUPlace() if place == "cpu" \ else paddle.CUDAPlace(0) def setUp(self): rnn1 = GRUCell(16, 32, bias=self.bias) mp = paddle.static.Program() sp = paddle.static.Program() with paddle.fluid.unique_name.guard(): with paddle.static.program_guard(mp, sp): rnn2 = paddle.nn.GRUCell(16, 32, bias_ih_attr=self.bias, bias_hh_attr=self.bias) place = self.place exe = paddle.static.Executor(place) scope = paddle.fluid.Scope() with paddle.static.scope_guard(scope): exe.run(sp) convert_params_for_cell_static(rnn1, rnn2, place) self.mp = mp self.sp = sp self.rnn1 = rnn1 self.rnn2 = rnn2 self.place = place self.executor = exe self.scope = scope def test_with_initial_state(self): mp = self.mp.clone() sp = self.sp rnn1 = self.rnn1 rnn2 = self.rnn2 exe = self.executor scope = self.scope x = np.random.randn(4, 16) prev_h = np.random.randn(4, 32) y1, h1 = rnn1(x, prev_h) with paddle.fluid.unique_name.guard(): with paddle.static.program_guard(mp, sp): x_data = paddle.fluid.data( "input", [-1, 16], dtype=paddle.framework.get_default_dtype()) init_h = paddle.fluid.data( "init_h", [-1, 32], dtype=paddle.framework.get_default_dtype()) y, h = rnn2(x_data, init_h) feed_dict = {x_data.name: x, init_h.name: prev_h} with paddle.static.scope_guard(scope): y2, h2 = exe.run(mp, feed=feed_dict, fetch_list=[y, h]) np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5) def test_with_zero_state(self): mp = self.mp.clone() sp = self.sp rnn1 = self.rnn1 rnn2 = self.rnn2 exe = self.executor scope = self.scope x = np.random.randn(4, 16) y1, h1 = rnn1(x) with paddle.fluid.unique_name.guard(): with paddle.static.program_guard(mp, sp): x_data = paddle.fluid.data( "input", [-1, 16], dtype=paddle.framework.get_default_dtype()) y, h = rnn2(x_data) feed_dict = {x_data.name: x} with paddle.static.scope_guard(scope): y2, h2 = exe.run(mp, feed=feed_dict, fetch_list=[y, h], use_prune=True) np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5) def runTest(self): self.test_with_initial_state() self.test_with_zero_state() class TestLSTMCell(unittest.TestCase): def __init__(self, bias=True, place="cpu"): super(TestLSTMCell, self).__init__(methodName="runTest") self.bias = bias self.place = paddle.CPUPlace() if place == "cpu" \ else paddle.CUDAPlace(0) def setUp(self): rnn1 = LSTMCell(16, 32, bias=self.bias) mp = paddle.static.Program() sp = paddle.static.Program() with paddle.fluid.unique_name.guard(): with paddle.static.program_guard(mp, sp): rnn2 = paddle.nn.LSTMCell(16, 32, bias_ih_attr=self.bias, bias_hh_attr=self.bias) place = self.place exe = paddle.static.Executor(place) scope = paddle.fluid.Scope() with paddle.static.scope_guard(scope): exe.run(sp) convert_params_for_cell_static(rnn1, rnn2, place) self.mp = mp self.sp = sp self.rnn1 = rnn1 self.rnn2 = rnn2 self.place = place self.executor = exe self.scope = scope def test_with_initial_state(self): mp = self.mp.clone() sp = self.sp rnn1 = self.rnn1 rnn2 = self.rnn2 exe = self.executor scope = self.scope x = np.random.randn(4, 16) prev_h = np.random.randn(4, 32) prev_c = np.random.randn(4, 32) y1, (h1, c1) = rnn1(x, (prev_h, prev_c)) with paddle.fluid.unique_name.guard(): with paddle.static.program_guard(mp, sp): x_data = paddle.fluid.data( "input", [-1, 16], dtype=paddle.framework.get_default_dtype()) init_h = paddle.fluid.data( "init_h", [-1, 32], dtype=paddle.framework.get_default_dtype()) init_c = paddle.fluid.data( "init_c", [-1, 32], dtype=paddle.framework.get_default_dtype()) y, (h, c) = rnn2(x_data, (init_h, init_c)) feed_dict = {x_data.name: x, init_h.name: prev_h, init_c.name: prev_c} with paddle.static.scope_guard(scope): y2, h2, c2 = exe.run(mp, feed=feed_dict, fetch_list=[y, h, c]) np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5) np.testing.assert_allclose(c1, c2, atol=1e-8, rtol=1e-5) def test_with_zero_state(self): mp = self.mp.clone() sp = self.sp rnn1 = self.rnn1 rnn2 = self.rnn2 exe = self.executor scope = self.scope x = np.random.randn(4, 16) y1, (h1, c1) = rnn1(x) with paddle.fluid.unique_name.guard(): with paddle.static.program_guard(mp, sp): x_data = paddle.fluid.data( "input", [-1, 16], dtype=paddle.framework.get_default_dtype()) y, (h, c) = rnn2(x_data) feed_dict = {x_data.name: x} with paddle.static.scope_guard(scope): y2, h2, c2 = exe.run(mp, feed=feed_dict, fetch_list=[y, h, c], use_prune=True) np.testing.assert_allclose(h1, h2, atol=1e-8, rtol=1e-5) np.testing.assert_allclose(c1, c2, atol=1e-8, rtol=1e-5) def runTest(self): self.test_with_initial_state() self.test_with_zero_state() def load_tests(loader, tests, pattern): suite = unittest.TestSuite() devices = ["cpu", "gpu"] if paddle.fluid.is_compiled_with_cuda() \ else ["cpu"] for bias in [True, False]: for device in devices: for test_class in [TestSimpleRNNCell, TestGRUCell, TestLSTMCell]: suite.addTest(test_class(bias, device)) return suite

32.423077

78

0.543024

766d42b96a08d22c77f3f38d1898747f5efa2c2a

2,531

py

Python

utils/poincare.py

thanhhoangvan/Authentication-system-using-fingerprint

2f7819d959f8d8b737dc0c3e8c4d7eda8e0edd94

[ "Apache-2.0" ]

1

2021-07-06T00:20:56.000Z

utils/poincare.py

thanhhoangvan/Authentication-system-using-fingerprint

2f7819d959f8d8b737dc0c3e8c4d7eda8e0edd94

[ "Apache-2.0" ]

null

utils/poincare.py

thanhhoangvan/Authentication-system-using-fingerprint

2f7819d959f8d8b737dc0c3e8c4d7eda8e0edd94

[ "Apache-2.0" ]

null

from utils import orientation import math import cv2 as cv import numpy as np def poincare_index_at(i, j, angles, tolerance): """ compute the summation difference between the adjacent orientations such that the orientations is less then 90 degrees https://books.google.pl/books?id=1Wpx25D8qOwC&lpg=PA120&ots=9wRY0Rosb7&dq=poincare%20index%20fingerprint&hl=pl&pg=PA120#v=onepage&q=poincare%20index%20fingerprint&f=false :param i: :param j: :param angles: :param tolerance: :return: """ cells = [(-1, -1), (-1, 0), (-1, 1), # p1 p2 p3 (0, 1), (1, 1), (1, 0), # p8 p4 (1, -1), (0, -1), (-1, -1)] # p7 p6 p5 angles_around_index = [math.degrees(angles[i - k][j - l]) for k, l in cells] index = 0 for k in range(0, 8): # calculate the difference difference = angles_around_index[k] - angles_around_index[k + 1] if difference > 90: difference -= 180 elif difference < -90: difference += 180 index += difference if 180 - tolerance <= index <= 180 + tolerance: return "loop" if -180 - tolerance <= index <= -180 + tolerance: return "delta" if 360 - tolerance <= index <= 360 + tolerance: return "whorl" return "none" def calculate_singularities(im, angles, tolerance, W, mask): result = cv.cvtColor(im, cv.COLOR_GRAY2RGB) # DELTA: RED, LOOP:ORAGNE, whorl:INK colors = {"loop" : (0, 0, 255), "delta" : (0, 128, 255), "whorl": (255, 153, 255)} FingerType = [] for i in range(3, len(angles) - 2): # Y for j in range(3, len(angles[i]) - 2): # x # mask any singularity outside of the mask mask_slice = mask[(i-2)*W:(i+3)*W, (j-2)*W:(j+3)*W] mask_flag = np.sum(mask_slice) if mask_flag == (W*5)**2: singularity = poincare_index_at(i, j, angles, tolerance) # if singularity != "none": if singularity == "delta": FingerType.append([singularity, j*W, i*W]) cv.rectangle(result, ((j+0)*W, (i+0)*W), ((j+1)*W, (i+1)*W), colors[singularity], 3) return result, FingerType if __name__ == '__main__': img = cv.imread('../test_img.png', 0) cv.imshow('original', img) angles = orientation.calculate_angles(img, 16, smoth=True) result = calculate_singularities(img, angles, 1, 16)

35.152778

174

0.556302

964bfe94e67253b12bff9fcfb796256d0a568d26

3,612

py

Python

tests/test_pysms.py

argandas/pysms

f0eb21435e65ddee151705cfd8d9a0449c03eea2

[ "MIT" ]

null

tests/test_pysms.py

argandas/pysms

f0eb21435e65ddee151705cfd8d9a0449c03eea2

[ "MIT" ]

null

tests/test_pysms.py

argandas/pysms

f0eb21435e65ddee151705cfd8d9a0449c03eea2

[ "MIT" ]

null

import pytest import simxxx.simxxx as simxxx from unittest.mock import Mock import serial @pytest.fixture def sim(): sim = simxxx.SERIAL_GSM_MODEM() sim.open("COM4") yield sim sim.close() def test_simxxx_send(): stub_serial = Mock(serial.Serial) stub_serial.write = lambda x: len(x) stub_serial.is_open = True sim = simxxx.SERIAL_GSM_MODEM(stub_serial) res = sim.send("Joe") assert 3 == res def test_simxxx_write(): stub_serial = Mock(serial.Serial) stub_serial.write = lambda x: len(x) stub_serial.is_open = True sim = simxxx.SERIAL_GSM_MODEM(stub_serial) res = sim.write([bytes(x, 'utf-8') for x in "1234567890"]) assert 10 == res def test_simxxx_write_port_not_open(): stub_serial = Mock(serial.Serial) stub_serial.write = lambda x: len(x) stub_serial.is_open = False sim = simxxx.SERIAL_GSM_MODEM(stub_serial) with pytest.raises(Exception) as excinfo: sim.write([bytes(x, 'utf-8') for x in "1234567890"]) assert "ERROR" in str(excinfo.value) def test_simxxx_ping(): stub_serial = Mock(serial.Serial) data_to_read = "OK\r\n" stub_serial.write = lambda x: len(x) stub_serial.is_open = True stub_serial.read.side_effect = [bytes(x, 'utf-8') for x in data_to_read] sim = simxxx.SIMXXX(stub_serial) assert sim.ping() is True def test_simxxx_read_error(): stub_serial = Mock(serial.Serial) data_to_read = "ERROR:\r\n" stub_serial.write = lambda x: len(x) stub_serial.is_open = True stub_serial.read.side_effect = [bytes(x, 'utf-8') for x in data_to_read] sim = simxxx.SIMXXX(stub_serial) with pytest.raises(Exception) as excinfo: sim.wait_for_ok() assert "ERROR" in str(excinfo.value) def test_simxxx_read_error_cme(): stub_serial = Mock(serial.Serial) data_to_read = "+CME ERROR:\r\n" stub_serial.write = lambda x: len(x) stub_serial.is_open = True stub_serial.read.side_effect = [bytes(x, 'utf-8') for x in data_to_read] sim = simxxx.SIMXXX(stub_serial) with pytest.raises(Exception) as excinfo: sim.wait_for_ok() assert "ERROR" in str(excinfo.value) def test_simxxx_read_error_cms(): stub_serial = Mock(serial.Serial) data_to_read = "+CME ERROR:\r\n" stub_serial.write = lambda x: len(x) stub_serial.is_open = True stub_serial.read.side_effect = [bytes(x, 'utf-8') for x in data_to_read] sim = simxxx.SIMXXX(stub_serial) with pytest.raises(Exception) as excinfo: sim.wait_for_ok() assert "ERROR" in str(excinfo.value) """ def test_cme_error_exception(): with pytest.raises(Exception) as excinfo: stub_serial = Mock(serial.Serial) stub_serial.write.return_value = 4 stub_serial.readline.return_value = b"+CME ERROR: Unexpected error\r\n" sim = pysms.SIMXXX(stub_serial) sim.ping() assert "+CME ERROR" in str(excinfo.value) def test_cms_error_exception(): with pytest.raises(Exception) as excinfo: stub_serial = Mock(serial.Serial) stub_serial.write.return_value = 4 stub_serial.readline.return_value = b"+CMS ERROR: Unexpected error\r\n" sim = pysms.SIMXXX(stub_serial) sim.ping() assert "+CMS ERROR" in str(excinfo.value) def test_generic_error_exception(): with pytest.raises(Exception) as excinfo: stub_serial = Mock(serial.Serial) stub_serial.write.return_value = 4 stub_serial.readline.return_value = b"ERROR\r\n" sim = pysms.SIMXXX(stub_serial) sim.ping() assert "ERROR" in str(excinfo.value) """

30.610169

79

0.683832

a8a1cf5a689f766bf805554c9637f0b7913d053b

20,452

py

Python

finrl_meta/factors/factors.py

AI4Finance-Foundation/FinRL-Metaverse

7ada7fe917d9fdc55300409cb0a3cc719129e03a

[ "MIT" ]

5

2021-11-04T07:13:08.000Z

2021-11-07T01:41:49.000Z

finrl_meta/factors/factors.py

AI4Finance-Foundation/FinRL-Metaverse

7ada7fe917d9fdc55300409cb0a3cc719129e03a

[ "MIT" ]

1

2021-11-06T18:23:21.000Z

2021-11-07T07:57:33.000Z

finrl_meta/factors/factors.py

AI4Finance-Foundation/FinRL-Metaverse

7ada7fe917d9fdc55300409cb0a3cc719129e03a

[ "MIT" ]

null

import numpy as np import scipy import statsmodels.api as sm import pandas as pd def filter_Nan(df): ''' 各特征NaN的个数 ''' naCount_dict = {} for col in df.columns.values: if df[col].dtypes == float: naCount_dict[col] = len(np.where(np.isnan(df[col]).values)[0]) for i in naCount_dict: if (naCount_dict[i] > df.shape[0] / 10): print(i, naCount_dict[i]) return naCount_dict def del_Nan(data, columns): ''' 保留指定因子, 并从中去除含有NaN的项 ''' df = data[columns] df.dropna(axis=1, how='all', inplace=True) df.dropna(axis=0, how='any', inplace=True) return df def pearson_corr(df_, target): ''' 计算因子与目标值的皮尔逊系数相关性 ''' Pearson_dict = {} df_.replace([np.inf, -np.inf], np.nan, inplace=True) df = df_.dropna(axis=1, how='all') df = df_.dropna(axis=0, how='any') for i in df.columns.values: if (type(df[i].values[-1]) == float or type( df[i].values[-1]) == np.float64) and i != 'alpha084' and i != 'alpha191-017': Pearson_dict[i] = scipy.stats.pearsonr(df[target].values, df[i].values)[0] df_Pearson = pd.DataFrame(data=Pearson_dict, index=[0]).T return abs(df_Pearson).sort_values(by=[0], ascending=False) def spearmanr_corr(df_, target): ''' 计算因子与目标值的斯皮尔曼系数相关性 ''' Spearmanr_dict = {} df_.replace([np.inf, -np.inf], np.nan, inplace=True) df = df_.dropna(axis=1, how='all') df = df_.dropna(axis=0, how='any') for i in df.columns.values: if (type(df[i].values[-1]) == float or type( df[i].values[-1]) == np.float64) and i != 'alpha084' and i != 'alpha191-017': Spearmanr_dict[i] = scipy.stats.spearmanr(df[target].values, df[i].values)[0] df_Spearmanr = pd.DataFrame(data=Spearmanr_dict, index=[0]).T return abs(df_Spearmanr).sort_values(by=[0], ascending=False) def series_sum(S, N): #对序列求N天累计和，返回序列 N=0对序列所有依次求和 return pd.Series(S).rolling(N).sum().values if N>0 else pd.Series(S).cumsum().values def ref(S, N=1): #对序列整体下移动N,返回序列(shift后会产生NAN) return pd.Series(S).shift(N).values def ma(S,N): #求序列的N日简单移动平均值，返回序列 return pd.Series(S).rolling(N).mean().values def ema(S,N): #指数移动平均,为了精度 S>4*N EMA至少需要120周期 alpha=2/(span+1) return pd.Series(S).ewm(span=N, adjust=False).mean().values def avedev(S, N): #平均绝对偏差 (序列与其平均值的绝对差的平均值) return pd.Series(S).rolling(N).apply(lambda x: (np.abs(x - x.mean())).mean()).values def std(S,N): #求序列的N日标准差，返回序列 return pd.Series(S).rolling(N).std(ddof=0).values def llv(S,N): #llv(C, 5) 最近5天收盘最低价 return pd.Series(S).rolling(N).min().values def hhv(S,N): #hhv(C, 5) 最近5天收盘最高价 return pd.Series(S).rolling(N).max().values def sma(S, N, M=1): #中国式的SMA,至少需要120周期才精确 (雪球180周期) alpha=1/(1+com) return pd.Series(S).ewm(alpha=M/N,adjust=False).mean().values #com=N-M/M def atr(CLOSE,HIGH,LOW, N=20): #真实波动N日平均值 TR = np.maximum(np.maximum((HIGH - LOW), np.abs(ref(CLOSE, 1) - HIGH)), np.abs(ref(CLOSE, 1) - LOW)) return ma(TR, N) def dma(S, A): #求S的动态移动平均，A作平滑因子,必须 0<A<1 (此为核心函数，非指标） if isinstance(A,(int,float)): return pd.Series(S).ewm(alpha=A,adjust=False).mean().values A=np.array(A) A[np.isnan(A)]=1.0 Y= np.zeros(len(S)) Y[0]=S[0] for i in range(1,len(S)): Y[i]=A[i]*S[i]+(1-A[i])*Y[i-1] #A支持序列 by jqz1226 return Y class MomentumFactors: ''' 动量类因子 ''' # 5日乖离率 'ic_mean': '-0.045657' def bias_5_days(close, N=5): #（收盘价-收盘价的N日简单平均）/ 收盘价的N日简单平均*100，在此n取5 mac = ma(close, N) return (close - mac) / (mac * 100) # 10日乖离率 'ic_mean': '-0.043967' def bias_10_days(close, N=10): # （收盘价-收盘价的N日简单平均）/ 收盘价的N日简单平均*100，在此n取10 mac = ma(close, N) return (close - mac) / (mac * 100) # 60日乖离率 'ic_mean': '-0.039533' def bias_60_days(close, N=60): # （收盘价-收盘价的N日简单平均）/ 收盘价的N日简单平均*100，在此n取60 mac = ma(close, N) return (close - mac) / (mac * 100) # 当前股价除以过去一个月股价均值再减1 'ic_mean': '-0.039303' def price_1_month(close, N=21): # 当日收盘价 / mean(过去一个月(21天)的收盘价) -1 return close / close.rolling(N).mean() - 1 # 当前股价除以过去三个月股价均值再减1 'ic_mean': '-0.034927' def price_3_monthes(close, N=61): # 当日收盘价 / mean(过去三个月(61天)的收盘价) -1 return close / close.rolling(N).mean() - 1 # 6日变动速率（Price Rate of Change） 'ic_mean': '-0.030587' def roc_6_days(close, N=6): # ①AX=今天的收盘价—6天前的收盘价 # ②BX=6天前的收盘价 # ③ROC=AX/BX*100 BX = close.shift(N) AX = close - BX return AX / (BX * 100) # 12日变动速率（Price Rate of Change） 'ic_mean': '-0.034748' def roc_12_days(close, N=12): # ①AX=今天的收盘价—12天前的收盘价 ②BX=12天前的收盘价 ③ROC=AX/BX*100 BX = close.shift(N) AX = close - BX return AX / (BX * 100) # 20日变动速率（Price Rate of Change） 'ic_mean': '-0.031276' def roc_20_days(close, N=20): # ①AX=今天的收盘价—20天前的收盘价 ②BX=20天前的收盘价 ③ROC=AX/BX*100 BX = close.shift(N) AX = close - BX return AX / (BX * 100) # 单日价量趋势 'ic_mean': '-0.051037' def single_day_vpt(df): # （今日收盘价 - 昨日收盘价）/ 昨日收盘价 * 当日成交量 # (复权方法为基于当日前复权) sft = df['close_price'].shift(1) return (df['close_price'] - sft) / sft * df['volume'] # 单日价量趋势6日均值 'ic_mean': '-0.032458' def single_day_vpt_6(df): # ma(single_day_VPT, 6) sft = df['close_price'].shift(1) return pd.Series(ma((df['close_price'] - sft) / sft * df['volume'], 6)) # 单日价量趋势12均值 'ic_mean': '-0.031016' def single_day_vpt_12(df): # ma(single_day_VPT, 12) sft = df['close_price'].shift(1) return pd.Series(ma((df['close_price'] - sft) / sft * df['volume'], 12)) # 10日顺势指标 'ic_mean': '-0.038179' def cci_10_days(df, N=10): # CCI:=(TYP-ma(TYP,N))/(0.015*avedev(TYP,N)) TYP:=(HIGH+LOW+CLOSE)/3 N:=10 TYP = (df['high_price'] + df['low_price'] + df['close_price']) / 3 return (TYP - ma(TYP,N)) / (0.015 * avedev(TYP,N)) # 15日顺势指标 'ic_mean': '-0.035973' def cci_15_days(df, N=15): # CCI:=(TYP-ma(TYP,N))/(0.015*avedev(TYP,N)) TYP:=(HIGH+LOW+CLOSE)/3 N:=15 TYP = (df['high_price'] + df['low_price'] + df['close_price']) / 3 return (TYP - ma(TYP, N)) / (0.015 * avedev(TYP, N)) # 20日顺势指标 'ic_mean': '-0.033437' def cci_20_days(df, N=20): # CCI:=(TYP-ma(TYP,N))/(0.015*avedev(TYP,N)) TYP:=(HIGH+LOW+CLOSE)/3 N:=20 TYP = (df['high_price'] + df['low_price'] + df['close_price']) / 3 return (TYP - ma(TYP, N)) / (0.015 * avedev(TYP, N)) # 当前交易量相比过去1个月日均交易量与过去过去20日日均收益率乘积 'ic_mean': '-0.032789' # def Volume1M(volume, profit): # # 当日交易量 / 过去20日交易量MEAN * 过去20日收益率MEAN def volume_1_month(df, N=21): # 当日交易量 / 过去20日交易量MEAN * 过去20日收益率MEAN return df['volume'] / df['volume'].rolling(N).mean() * df['target'].rolling(N).mean() # 多头力道 'ic_mean': '-0.039968' def bull_power(df, timeperiod=13): return (df['high_price'] - ema(df['close_price'], timeperiod)) / df['close_price'] class EmotionFactors: ''' 情绪类因子 ''' # 换手率：某一段时期内的成交量/发行总股数×100% # 5日平均换手率 'ic_mean': '-0.044' def vol_5_days(S, total_volume, N=5): # 5日换手率均值 S = S / total_volume return pd.Series(S).rolling(N).mean() # 10日平均换手率 'ic_mean': '-0.040' def vol_10_days(S, total_volume, N=10): # 10日换手率的均值 S = S / total_volume return pd.Series(S).rolling(N).mean() # 20日平均换手率 'ic_mean': '-0.035' def vol_20_days(S, total_volume, N=20): # 20日换手率的均值,单位为% S = S / total_volume return pd.Series(S).rolling(N).mean() # 5日平均换手率与120日平均换手率 'ic_mean': '-0.039' def davol_5_days(S): # 5日平均换手率 / 120日平均换手率 return EmotionFactors.vol_5_days(S) / EmotionFactors.vol_5_days(S, N=120) # 10日平均换手率与120日平均换手率之比 'ic_mean': '-0.033' def davol_10_days(S): # 10日平均换手率 / 120日平均换手率 return EmotionFactors.vol_10_days(S) / EmotionFactors.vol_5_days(S, N=120) # 10日成交量标准差 'ic_mean': '-0.037' def vstd_10_days(volume, N=10): # 10日成交量去标准差 return pd.Series(std(volume, N)) # 20日成交量标准差 'ic_mean': '-0.033' def vstd_20_days(volume, N=20): # 20日成交量去标准差 return pd.Series(std(volume, N)) # 6日成交金额的标准差 'ic_mean': '-0.044' def tvstd_6_days(df, N=6): # 6日成交额的标准差 trades = df['close_price'] * df['volume'] return pd.Series(std(trades, N)) # 20日成交金额的标准差 'ic_mean': '-0.038' def tvstd_20_days(df, N=20): # 20日成交额的标准差 trades = df['close_price'] * df['volume'] return pd.Series(std(trades, N)) # 成交量的5日指数移动平均 'ic_mean': '-0.035' def vema_5_days(volume, N=5): # return pd.Series(ema(volume, N)) # 成交量的10日指数移动平均 'ic_mean': '-0.032' def vema_10_days(volume, N=10): # return pd.Series(ema(volume, N)) # 12日成交量的移动平均值 'ic_mean': '-0.031' def vema_12_days(volume, N=12): # return pd.Series(ema(volume, N)) # 成交量震荡 'ic_mean': '-0.039' def vosc(volume): # 'VEMA12'和'VEMA26'两者的差值，再求差值与'VEMA12'的比，最后将比值放大100倍，得到VOSC值 ema12 = ema(volume, 12) return pd.Series((ema(volume, 26) - ema12 / (ema12 * 100))) # 6日量变动速率指标 'ic_mean': '-0.032' def vroc_6_days(volume, N=6): # 成交量减N日前的成交量，再除以N日前的成交量，放大100倍，得到VROC值，n=6 sft = volume.shift(N) return pd.Series((volume - sft) / (sft * 100)) # 12日量变动速率指标 'ic_mean': '-0.040' def vroc_12_days(volume, N=12): # 成交量减N日前的成交量，再除以N日前的成交量，放大100倍，得到VROC值，n=12 sft = volume.shift(N) return pd.Series((volume - sft) / (sft * 100)) # 6日成交金额的移动平均值 'ic_mean': '-0.038' def tvma_6_days(df, N=6): # 6日成交金额的移动平均值 trades = df['close_price'] * df['volume'] return pd.Series(ma(trades, N)) # 威廉变异离散量 'ic_mean': '-0.031' def wvad(df, N=6): # (收盘价－开盘价)/(最高价－最低价)×成交量，再做加和，使用过去6个交易日的数据 WVA = (df['close_price'] - df['open_price']) / (df['high_price'] - df['low_price']) * df['volume'] return WVA.rolling(N).sum() # 换手率相对波动率 'ic_mean': '-0.042' def turnover_volatility(volume, total_volume, N=20): # 取20个交易日个股换手率的标准差 turnover = volume / total_volume return pd.Series(std(turnover, N)) # 人气指标 'ic_mean': '-0.031' def ar(df, N=26): # AR=N日内（当日最高价—当日开市价）之和 / N日内（当日开市价—当日最低价）之和 * 100，n设定为26 ho = (df['high_price'] - df['open_price']).rolling(N).sum() ol = (df['open_price'] - df['low_price']).rolling(N).sum() return ho / (ol * 100) class extraFacters: ''' 特殊因子 ''' def rsrs(df, N): #用于记录回归后的beta值，即斜率 ans = [] #用于计算被决定系数加权修正后的贝塔值 ans_rightdev= [] # 一：RSRS指标的构建过程： # 1，取前N日的最高价序列与最低价序列。 # 2，将两列数据按前述OLS线性回归模型拟合出当日的斜率值（Beta）。 # 3，取前M日的斜率时间序列，计算当日斜率的标准分z。 # 4，将z与拟合方程的决定系数相乘，作为当日RSRS指标值。 X = sm.add_constant(df['low_price']) model = sm.OLS(df['high_price'], X) beta = model.fit().params r2=model.fit().rsquared ans.append(beta) # 计算标准化的RSRS指标 # 计算均值序列 section = ans[-N:] # 计算均值序列 mu = np.mean(section) # 计算标准化RSRS指标序列 sigma = np.std(section) zscore = (section[-1]-mu)/sigma #计算右偏RSRS标准分 return pd.Series(zscore*beta*r2) def vix(): pass class generalFactors: ''' 常见因子 ''' def macd(CLOSE, SHORT=12, LONG=26, M=9): # EMA的关系，S取120日，和雪球小数点2位相同 DIF = ema(CLOSE,SHORT)-ema(CLOSE,LONG) DEA = ema(DIF,M) MACD=(DIF-DEA)*2 return np.round(MACD,3) def kdj(df, KDJ_type, N=9, M1=3, M2=3): # KDJ指标 RSV = (df['close_price'] - llv(df['low_price'], N)) / (hhv(df['high_price'], N) - llv(df['low_price'], N)) * 100 K = ema(RSV, (M1*2-1)) if KDJ_type == 'KDJ_K': return K elif KDJ_type == 'KDJ_D': return ema(K, (M2*2-1)) elif KDJ_type == 'KDJ_J': D = ema(K, (M2*2-1)) return K*3-D*2 def rsi(CLOSE, N=24): # RSI指标,和通达信小数点2位相同 DIF = CLOSE-ref(CLOSE, 1) return np.round(sma(np.maximum(DIF, 0), N) / sma(np.abs(DIF), N) * 100,3) def wr(df, N=10): #W&R 威廉指标 WR = (hhv(df['high_price'], N) - df['close_price']) / (hhv(df['high_price'], N) - llv(df['low_price'], N)) * 100 return np.round(WR,3) def roll(CLOSE, BOLL_type, N=20, P=2): #BOLL指标，布林带 MID = ma(CLOSE, N) if BOLL_type == 'BOLL_mid': return MID elif BOLL_type == 'BOLL_upper': return MID + std(CLOSE, N) * P elif BOLL_type == 'BOLL_lower': return MID - std(CLOSE, N) * P # return RD(UPPER), RD(MID), RD(LOWER) def psy(CLOSE, PSY_type, N=12, M=6): PSY=series_sum(CLOSE>ref(CLOSE,1),N)/N*100 if PSY_type == 'PSY': return PSY elif PSY_type == 'PSYMA': return ma(PSY,M) # return RD(PSY), RD(PSYMA) def atr(df, N=20): #真实波动N日平均值 TR = np.maximum(np.maximum((df['high_price'] - df['low_price']), np.abs(ref(df['close_price'], 1) - df['high_price'])), np.abs(ref(df['close_price'], 1) - df['low_price'])) return ma(TR, N) def bbi(CLOSE,M1=3,M2=6,M3=12,M4=20): #BBI多空指标 return (ma(CLOSE,M1)+ma(CLOSE,M2)+ma(CLOSE,M3)+ma(CLOSE,M4))/4 def dmi(df, DMI_type, M1=14,M2=6): #动向指标：结果和同花顺，通达信完全一致 TR = series_sum(np.maximum(np.maximum(df['high_price'] - df['low_price'], np.abs(df['high_price'] - ref(df['close_price'], 1))), np.abs(df['low_price'] - ref(df['close_price'], 1))), M1) HD = df['high_price'] - ref(df['high_price'], 1) LD = ref(df['low_price'], 1) - df['low_price'] DMP = series_sum(np.where((HD > 0) & (HD > LD), HD, 0), M1) DMM = series_sum(np.where((LD > 0) & (LD > HD), LD, 0), M1) PDI = DMP * 100 / TR MDI = DMM * 100 / TR if DMI_type == 'DMI_PDI': return PDI elif DMI_type == 'DMI_MDI': return MDI elif DMI_type == 'DMI_ADX': return ma(np.abs(MDI - PDI) / (PDI + MDI) * 100, M2) elif DMI_type == 'DMI_ADXR': ADX = ma(np.abs(MDI - PDI) / (PDI + MDI) * 100, M2) return (ADX + ref(ADX, M2)) / 2 # return PDI, MDI, ADX, ADXR def taq(df, TAQ_type, N=6): #唐安奇通道(海龟)交易指标，大道至简，能穿越牛熊 UP=hhv(df['high_price'], N) DOWN=llv(df['low_price'], N) #MID=(UP+DOWN)/2 if TAQ_type == 'TAQ_UP': return UP elif TAQ_type == 'TAQ_DOWN': return DOWN elif TAQ_type == 'TAQ_MID': return (UP+DOWN)/2 # return UP,MID,DOWN def ktn(df, KTN_type, N=20, M=10): #肯特纳交易通道, N选20日，ATR选10日 MID=ema((df['high_price'] + df['low_price'] + df['close_price'])/3, N) if KTN_type == 'KTN_mid': return MID elif KTN_type == 'KTN_upper': ATRN=atr(df['close_price'], df['high_price'], df['low_price'], M) return MID+2*ATRN elif KTN_type == 'KTN_lower': ATRN=atr(df['close_price'], df['high_price'], df['low_price'], M) return MID-2*ATRN # return UPPER,MID,LOWER def trix(CLOSE, TRIX_type, M1=12, M2=20): #三重指数平滑平均线 TR = ema(ema(ema(CLOSE, M1), M1), M1) TRIX = (TR - ref(TR, 1)) / ref(TR, 1) * 100 if TRIX_type == 'TRIX': return TRIX elif TRIX_type == 'TRMA': return ma(TRIX, M2) # return TRIX, TRMA def vr(df,M1=26): #VR容量比率 LC = ref(df['close_price'], 1) return series_sum(np.where(df['close_price'] > LC, df['volume'], 0), M1) / series_sum(np.where(df['close_price'] <= LC, df['volume'], 0), M1) * 100 def emv(df, EMV_type, N=14, M=9): #简易波动指标 VOLUME=ma(df['volume'], N)/df['volume'] MID=100*(df['high_price'] + df['low_price'] - ref(df['high_price'] + df['low_price'], 1))/(df['high_price'] + df['low_price']) EMV=ma(MID*VOLUME*(df['high_price'] - df['low_price'])/ma(df['high_price'] - df['low_price'], N), N) if EMV_type == 'EMV': return EMV elif EMV_type == 'MAEMV': return ma(EMV,M) # return EMV,MAEMV def dpo(CLOSE, DPO_type, M1=20, M2=10, M3=6): #区间震荡线 DPO = CLOSE - ref(ma(CLOSE, M1), M2) if DPO_type == 'DPO': return DPO elif DPO_type == 'MADPO': return ma(DPO, M3) # return DPO, MADPO def brar(df, M1=26): #BRAR-ARBR 情绪指标 # AR = series_sum(HIGH - OPEN, M1) / series_sum(OPEN - LOW, M1) * 100 return series_sum(np.maximum(0, df['high_price'] - ref(df['close_price'], 1)), M1) / series_sum(np.maximum(0, ref(df['close_price'], 1) - df['low_price']), M1) * 100 # return AR, BR def dfma(CLOSE, N1=10, N2=50, M=10): #平行线差指标 DIF=ma(CLOSE,N1)-ma(CLOSE,N2) DIFMA=ma(DIF,M) #通达信指标叫DMA 同花顺叫新DMA return DIFMA def mtm(CLOSE, MTM_type, N=12,M=6): #动量指标 MTM=CLOSE-ref(CLOSE, N) if MTM_type == 'MTM': return MTM elif MTM_type == 'MTMMA': return ma(MTM,M) # return MTM,MTMMA def mass(df, MASS_type, N1=9, N2=25, M=6): #梅斯线 MASS=series_sum(ma(df['high_price'] - df['low_price'], N1)/ma(ma(df['high_price'] - df['low_price'], N1), N1), N2) if MASS_type == 'MASS': return MASS elif MASS_type == 'MA_MASS': return ma(MASS,M) # MA_MASS=ma(MASS,M) # return MASS,MA_MASS def obv(df): #能量潮指标 return series_sum(np.where(df['close_price']>ref(df['close_price'], 1), df['volume'], np.where(df['close_price']<ref(df['close_price'], 1), - df['volume'], 0)), 0)/10000 def mfi(df, N=14): #MFI指标是成交量的RSI指标 TYP = (df['high_price'] + df['low_price'] + df['close_price'])/3 V1 = series_sum(np.where(TYP>ref(TYP,1), TYP*df['volume'], 0), N)/series_sum(np.where(TYP<ref(TYP, 1), TYP*df['volume'], 0), N) return 100-(100/(1+V1)) def asi(df, ASI_type, M1=26, M2=10): #振动升降指标 LC = ref(df['close_price'], 1) AA = np.abs(df['high_price'] - LC) BB = np.abs(df['low_price'] - LC) CC = np.abs(df['high_price'] - ref(df['low_price'],1)) DD = np.abs(LC-ref(df['open_price'], 1)) R = np.where( (AA>BB) & (AA>CC), AA+BB/2+DD/4, np.where( (BB>CC) & (BB>AA),BB+AA/2+DD/4,CC+DD/4)) X = (df['close_price'] - LC + (df['close_price'] - df['open_price'])/2 + LC - ref(df['open_price'], 1)) SI = 16*X/R*np.maximum(AA, BB) ASI = series_sum(SI, M1) if ASI_type == 'ASI': return ASI elif ASI_type == 'ASIT': return ma(ASI, M2) # return ASI,ASIT def xsii(df, XSII_type, N=102, M=7): #薛斯通道II AA = ma((2*df['close_price'] + df['high_price'] + df['low_price'])/4, 5) #最新版DMA才支持 2021-12-4 # TD1 = AA*N/100 TD2 = AA*(200-N) / 100 if XSII_type == 'XSII_TD1': return AA*N/100 elif XSII_type == 'XSII_TD2': return AA*(200-N) / 100 elif XSII_type == 'XSII_TD3': CC = np.abs((2*df['close_price'] + df['high_price'] + df['low_price'])/4 - ma(df['close_price'], 20))/ma(df['close_price'], 20) BB = df['close_price'].reset_index()['close_price'] DD = dma(BB, CC) return (1+M/100)*DD elif XSII_type == 'XSII_TD4': CC = np.abs((2*df['close_price'] + df['high_price'] + df['low_price'])/4 - ma(df['close_price'], 20))/ma(df['close_price'], 20) BB = df['close_price'].reset_index()['close_price'] DD = dma(BB, CC) return (1-M/100)*DD

37.185455

194

0.542832

f042658a648342bb2b7e35e72e9f826105b2d1ba

587

py

Python

myawwards/migrations/0004_auto_20210721_1838.py

VGichuki/awwards-app

67ea12812ef93f0fc4735a65c87af64c86fd0a31

[ "Unlicense", "MIT" ]

null

myawwards/migrations/0004_auto_20210721_1838.py

VGichuki/awwards-app

67ea12812ef93f0fc4735a65c87af64c86fd0a31

[ "Unlicense", "MIT" ]

null

myawwards/migrations/0004_auto_20210721_1838.py

VGichuki/awwards-app

67ea12812ef93f0fc4735a65c87af64c86fd0a31

[ "Unlicense", "MIT" ]

null

# Generated by Django 3.2.5 on 2021-07-21 15:38 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('myawwards', '0003_auto_20210721_1627'), ] operations = [ migrations.AlterField( model_name='profile', name='profile_pic', field=models.ImageField(blank=True, default='', upload_to='images/'), ), migrations.AlterField( model_name='project', name='image', field=models.ImageField(upload_to='projects/'), ), ]

24.458333

81

0.584327

f8af979a421c6b37426f54e77c952e741896f170

2,685

py

Python

src/language_model/MaskedLM/IsRoBERTa.py

NeuroCode-io/icelandic-language-model

3ac81bf0470926ca96abacef2c63118b07609afc

[ "MIT" ]

null

src/language_model/MaskedLM/IsRoBERTa.py

NeuroCode-io/icelandic-language-model

3ac81bf0470926ca96abacef2c63118b07609afc

[ "MIT" ]

null

src/language_model/MaskedLM/IsRoBERTa.py

NeuroCode-io/icelandic-language-model

3ac81bf0470926ca96abacef2c63118b07609afc

[ "MIT" ]

null

from pathlib import Path from transformers import RobertaConfig, DataCollatorForLanguageModeling, RobertaForMaskedLM, Trainer, TrainingArguments from torch.utils.data.dataset import Dataset from transformers.modeling_auto import AutoModelForMaskedLM from transformers.tokenization_utils import PreTrainedTokenizer from language_model.lib import log, azure_storage logger = log.get_logger(__file__) class IsRoBERTa: def __init__(self, data_dir: Path, tokenizer: PreTrainedTokenizer, dataset: Dataset, local_rank=-1): assert data_dir, "data_dir input needed" self.model_dir = f"{data_dir}/results" self.dataset = dataset self.config = RobertaConfig( vocab_size=52_000, max_position_embeddings=514, num_attention_heads=12, num_hidden_layers=6, type_vocab_size=1, ) self.training_args = TrainingArguments( run_name=data_dir.name, local_rank=local_rank, learning_rate=0.00005, # default 0.00005 output_dir=f"{self.model_dir}", overwrite_output_dir=False, num_train_epochs=1, per_device_train_batch_size=48, # Nvidia K80 99% seed=42, save_steps=10_000, save_total_limit=1, ) self.data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15) def upload(self): paths = [str(x) for x in Path(self.model_dir).glob("**/*")] for file in paths: azure_storage.upload(file) def has_started(self): paths = [str(x) for x in Path(self.model_dir).glob("*")] for path in paths: if "checkpoint" in path: return True return False def get_latest_checkpoint(self): paths = [str(x) for x in Path(self.model_dir).glob("*")] checkpoints = [path for path in paths if "checkpoint" in path] return sorted(checkpoints)[-1] def train(self): if self.has_started(): last_checkpoint = self.get_latest_checkpoint() logger.info(f"Resuming training from: {last_checkpoint}") model = AutoModelForMaskedLM.from_pretrained(last_checkpoint, config=self.config) else: model = RobertaForMaskedLM(config=self.config) trainer = Trainer( model=model, args=self.training_args, data_collator=self.data_collator, train_dataset=self.dataset, prediction_loss_only=True, ) trainer.train() trainer.save_model(f"{self.model_dir}") self.upload()

31.588235

119

0.640596

578516c2fae5b00511c223f8a357a7fa3a229e3d

2,475

py

Python

.github/validators/validate-syntax.py

DSOTraining/kics

1b5aff071b3221f03b008eb7d09ae5ce616ce96c

[ "Apache-2.0" ]

null

.github/validators/validate-syntax.py

DSOTraining/kics

1b5aff071b3221f03b008eb7d09ae5ce616ce96c

[ "Apache-2.0" ]

null

.github/validators/validate-syntax.py

DSOTraining/kics

1b5aff071b3221f03b008eb7d09ae5ce616ce96c

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import argparse import glob import os import subprocess from subprocess import CalledProcessError LINTER_PATH = os.getenv('LINTER_PATH') SKIP_LIST = os.getenv('SKIP_LIST_PATH') EXTRA_ARGS = os.getenv('EXTRA_ARGS') NO_PROGRESS = os.getenv('NO_PROGRESS', False) parser = argparse.ArgumentParser(description='Execute linter against files') parser.add_argument('filesglob', metavar='FILES', type=str, nargs='+', help='List of file globs to check') args = parser.parse_args() ############################## # show summary and exit # # code 1 if errors > 0 # ############################## def summary(files, error_files): print('\n\n>>------------------------------------------------') print(f'found {len(error_files)} issues in {len(files)} file checked') print('>>------------------------------------------------') if len(error_files) > 0: exit(1) else: exit(0) print('starting validator') ############################## # get files and skip list # ############################## all_files = [] ignore_list = [] for my_glob in args.filesglob: all_files.extend(glob.glob(my_glob)) with open(SKIP_LIST, 'r') as reader: ignore_list = [line.rstrip() for line in reader] print(f"Ignore list is:{os.linesep} {os.linesep.join(ignore_list)}") files = [file for file in all_files if file not in ignore_list and 'positive_expected_result.json' not in file] error_files = [] print(f'found {len(files)} files to check') ############################## # run linter # ############################## for file in files: result = '' try: cmds = [LINTER_PATH, file] if EXTRA_ARGS: if len(EXTRA_ARGS.split(' ')): cmds = cmds + EXTRA_ARGS.split(' ') else: cmds.append(EXTRA_ARGS) if not NO_PROGRESS: print(f'Validating {file}') result = subprocess.check_output(cmds, shell=False).decode('utf-8').rstrip() except CalledProcessError as e: error_files.append(e) if result: for line in result.split('\n'): if line: print(f"{line}") ################################ # list errors # ################################ if len(error_files) > 0: print("\n--- errors ---") for error in error_files: print(error) error_result = error.output.decode('utf-8').rstrip() for line in error_result.split('\n'): print(line) summary(files, error_files) else: summary(files, error_files)

27.5

80

0.569293

91cbd945870ef109116d07e02b1c233f2f5d0870

2,814

py

Python

Testes com Pygame/basic_tutorial.py

fotavio16/PycharmProjects

f5be49db941de69159ec543e8a6dde61f9f94d86

[ "MIT" ]

null

Testes com Pygame/basic_tutorial.py

fotavio16/PycharmProjects

f5be49db941de69159ec543e8a6dde61f9f94d86

[ "MIT" ]

null

Testes com Pygame/basic_tutorial.py

fotavio16/PycharmProjects

f5be49db941de69159ec543e8a6dde61f9f94d86

[ "MIT" ]

null

import pygame from pygame.locals import * # Inicializa todos os módulos pygame.init() # Ou inicializa um módulo específico pygame.font.init() screen = [1, 1, 2, 2, 2, 1] print(screen) # [1, 1, 2, 2, 2, 1] screen[3] = 8 print(screen) # Creating a MAP background = [1, 1, 2, 2, 2, 1] screen = [0]*6 #a new blank screen for i in range(6): screen[i] = background[i] print(screen) #[1, 1, 2, 2, 2, 1] playerpos = 3 screen[playerpos] = 8 print(screen) #[1, 1, 2, 8, 2, 1] screen[playerpos] = background[playerpos] playerpos = playerpos - 1 screen[playerpos] = 8 print(screen) #[1, 1, 8, 2, 2, 1] # Smooth Movement screen = pygame.display.set_mode((640, 480)) player = pygame.image.load('player.bmp').convert() background = pygame.image.load('liquid.bmp').convert() screen.blit(background, (0, 0)) #draw the background position = player.get_rect() screen.blit(player, position) #draw the player pygame.display.update() #and show it all for x in range(100): #animate 100 frames screen.blit(background, position, position) #erase position = position.move(2, 0) #move player screen.blit(player, position) #draw new player pygame.display.update() #and show it all pygame.time.delay(100) #stop the program for 1/10 second ''' # First, The Mystery Functions >>> player = pygame.image.load('player.bmp').convert() >>> background = pygame.image.load('liquid.bmp').convert() >>> screen = pygame.display.set_mode((640, 480)) # Handling Some Input >>> while 1: ... for event in pygame.event.get(): ... if event.type in (QUIT, KEYDOWN): ... sys.exit() ... move_and_draw_all_game_objects() # Moving Multiple Images >>> class GameObject: ... def __init__(self, image, height, speed): ... self.speed = speed ... self.image = image ... self.pos = image.get_rect().move(0, height) ... def move(self): ... self.pos = self.pos.move(0, self.speed) ... if self.pos.right > 600: ... self.pos.left = 0 # Putting It All Together >>> screen = pygame.display.set_mode((640, 480)) >>> player = pygame.image.load('player.bmp').convert() >>> background = pygame.image.load('background.bmp').convert() >>> screen.blit(background, (0, 0)) >>> objects = [] >>> for x in range(10): #create 10 objects ... o = GameObject(player, x*40, x) ... objects.append(o) >>> while 1: ... for event in pygame.event.get(): ... if event.type in (QUIT, KEYDOWN): ... sys.exit() ... for o in objects: ... screen.blit(background, o.pos, o.pos) ... for o in objects: ... o.move() ... screen.blit(o.image, o.pos) ... pygame.display.update() ... pygame.time.delay(100) '''

25.816514

72

0.603056

008ee961246b37fface2b7624885c21ec27161c5

234

py

Python

setup.py

eswk22/titanicdisaster-challenge-koggle

8384a91a6e221bfb5a9b90d1317fa985eaf34abe

[ "MIT" ]

null

setup.py

eswk22/titanicdisaster-challenge-koggle

8384a91a6e221bfb5a9b90d1317fa985eaf34abe

[ "MIT" ]

null

setup.py

eswk22/titanicdisaster-challenge-koggle

8384a91a6e221bfb5a9b90d1317fa985eaf34abe

[ "MIT" ]

null

from setuptools import find_packages, setup setup( name='src', packages=find_packages(), version='0.0.1', description='Koggle chanllege for titanic disaster', author='Eswaran Krishnamoorthy', license='MIT', )

21.272727

56

0.692308

07bf2f664f0ab65a9fb47404770737f078e879ae

2,842

py

Python

ScienceCruiseDataManagement/ship_data/utilities.py

Swiss-Polar-Institute/science-cruise-data-management

67721a0f4a1255b8ac43e530ed95a8c324239c7c

[ "MIT" ]

6

2017-10-06T09:18:04.000Z

2022-02-10T08:54:56.000Z

ScienceCruiseDataManagement/ship_data/utilities.py

Swiss-Polar-Institute/science-cruise-data-management

67721a0f4a1255b8ac43e530ed95a8c324239c7c

[ "MIT" ]

12

2020-02-27T09:24:50.000Z

2021-09-22T17:39:55.000Z

ScienceCruiseDataManagement/ship_data/utilities.py

Swiss-Polar-Institute/science-cruise-data-management

67721a0f4a1255b8ac43e530ed95a8c324239c7c

[ "MIT" ]

1

2017-10-16T13:49:33.000Z

import unittest import datetime # This file is part of https://github.com/cpina/science-cruise-data-management # # This project was programmed in a hurry without any prior Django experience, # while circumnavigating the Antarctic on the ACE expedition, without proper # Internet access, with 150 scientists using the system and doing at the same # cruise other data management and system administration tasks. # # Sadly there aren't unit tests and we didn't have time to refactor the code # during the cruise, which is really needed. # # Carles Pina (carles@pina.cat) and Jen Thomas (jenny_t152@yahoo.co.uk), 2016-2017. def nmea_degrees_to_decimal_degrees(nmea_degrees): decimal_position = nmea_degrees.find(".") minutes = nmea_degrees[decimal_position-2:] degrees = nmea_degrees[0:decimal_position-2] if degrees == "": degrees = "0" return float(degrees) + float(minutes)/60 def nmea_lat_long_to_normal(latitude, north_south, longitude, east_west): """ Converts: From: "3550.28461074,S,01801.84299457,E" # a string To: (-35.838000,18.030666) # a tuple Note that the parameters in the previous string would be: latitude=3550.28461074 north_south=S longitude=01801.84299457 east_west=E """ latitude_degrees = nmea_degrees_to_decimal_degrees(latitude) longitude_degrees = nmea_degrees_to_decimal_degrees(longitude) if north_south == "S": latitude_degrees *= -1 elif north_south == "N": pass else: assert False if east_west == "W": longitude_degrees *= -1 elif east_west == "E": pass else: assert False return (latitude_degrees, longitude_degrees) if __name__ == "__main__": assert nmea_lat_long_to_normal("3550.28461074","S","01801.84299457","E") \ == (-35.83807684566667, 18.030716576166668) def check_lat_lon_direction(north_south, east_west): '''Check that the latitude and longitude direction eg. N, S, E, W is valid.''' if (north_south == 'N' or north_south =='S') and (east_west =='E' or east_west == 'W'): return True else: return False def string_date_time_to_tuple(date_time_string): '''Convert string formatted date and time to integers which can then be handled by the python module, datetime.''' [date, time] = date_time_string.split(' ') year = int(date.split('-')[0]) month = int(date.split('-')[1]) day = int(date.split('-')[2]) hour = int(time.split(':')[0]) minute = int(time.split(':')[1]) second = int(time.split(':')[2]) utc = datetime.timezone(datetime.timedelta(0)) millions_of_sec = 0 return (year, month, day, hour, minute, second, millions_of_sec, utc)

32.666667

118

0.657635

4f39d27579162cde41c642afaba007cc92bdd33d

12,119

py

Python

epgrefresh/src/EPGRefreshResource.py

Haehnchen/enigma2-plugins

23007eb0b78665cd3a2faf98d1d6145b4f0ada3f

[ "OLDAP-2.3" ]

1

2020-01-27T22:53:56.000Z

epgrefresh/src/EPGRefreshResource.py

Haehnchen/enigma2-plugins

23007eb0b78665cd3a2faf98d1d6145b4f0ada3f

[ "OLDAP-2.3" ]

null

epgrefresh/src/EPGRefreshResource.py

Haehnchen/enigma2-plugins

23007eb0b78665cd3a2faf98d1d6145b4f0ada3f

[ "OLDAP-2.3" ]

null

from twisted.web import http, resource from EPGRefresh import epgrefresh from EPGRefreshService import EPGRefreshService from enigma import eServiceReference from Components.config import config from Components.SystemInfo import SystemInfo from time import localtime from OrderedSet import OrderedSet from ServiceReference import ServiceReference from Tools.XMLTools import stringToXML try: from urllib import unquote iteritems = lambda d: d.iteritems() except ImportError as ie: from urllib.parse import unquote iteritems = lambda d: d.items() API_VERSION = "1.4" class EPGRefreshStartRefreshResource(resource.Resource): def render(self, req): state = False if epgrefresh.forceRefresh(): output = "initiated refresh" state = True else: output = "could not initiate refresh" req.setResponseCode(http.OK) req.setHeader('Content-type', 'application/xhtml+xml') req.setHeader('charset', 'UTF-8') return """<?xml version=\"1.0\" encoding=\"UTF-8\" ?> <e2simplexmlresult> <e2state>%s</e2state> <e2statetext>%s</e2statetext> </e2simplexmlresult>""" % ('true' if state else 'false', output) class EPGRefreshAddRemoveServiceResource(resource.Resource): TYPE_ADD = 0 TYPE_DEL = 1 def __init__(self, type): assert(type in (self.TYPE_ADD, self.TYPE_DEL)) self.type = type def render(self, req): do_add = self.type == self.TYPE_ADD state = False if 'multi' in req.args: if epgrefresh.services[0]: epgrefresh.services[0].clear() state = True if epgrefresh.services[1]: epgrefresh.services[1].clear() state = True if 'sref' in req.args: duration = req.args.get("duration", None) try: duration = duration and int(duration) except ValueError as ve: output = 'invalid value for "duration": ' + str(duration) else: for sref in req.args.get('sref'): sref = unquote(sref) ref = eServiceReference(sref) if not ref.valid(): output = 'invalid value for "sref": ' + str(sref) elif (ref.flags & 7) == 7: epgservice = EPGRefreshService(sref, duration) # bouquet if epgservice in epgrefresh.services[1]: if do_add: output = "bouquet already in list" else: epgrefresh.services[1].remove(epgservice) output = "bouquet removed from list" state = True else: if do_add: epgrefresh.services[1].add(epgservice) output = "bouquet added to list" state = True else: output = "bouquet not in list" else: if not (ref.flags & eServiceReference.isGroup): # strip all after last : pos = sref.rfind(':') if pos != -1: if sref[pos-1] == ':': pos -= 1 sref = sref[:pos+1] epgservice = EPGRefreshService(sref, duration) # assume service if epgservice in epgrefresh.services[0]: if do_add: output = "service already in list" else: epgrefresh.services[0].remove(epgservice) output = "service removed from list" state = True else: if do_add: epgrefresh.services[0].add(epgservice) output = "service added to list" state = True else: output = "service not in list" # save if list changed if state: epgrefresh.saveConfiguration() else: output = 'missing argument "sref"' if 'multi' in req.args: output = 'service restriction changed' req.setResponseCode(http.OK) req.setHeader('Content-type', 'application/xhtml+xml') req.setHeader('charset', 'UTF-8') return """<?xml version=\"1.0\" encoding=\"UTF-8\" ?> <e2simplexmlresult> <e2state>%s</e2state> <e2statetext>%s</e2statetext> </e2simplexmlresult> """ % ('True' if state else 'False', output) class EPGRefreshListServicesResource(resource.Resource): def render(self, req): # show xml req.setResponseCode(http.OK) req.setHeader('Content-type', 'application/xhtml+xml') req.setHeader('charset', 'UTF-8') return ''.join(epgrefresh.buildConfiguration(webif = True)) class EPGRefreshPreviewServicesResource(resource.Resource): def render(self, req): req.setResponseCode(http.OK) req.setHeader('Content-type', 'application/xhtml+xml') req.setHeader('charset', 'UTF-8') if 'sref' in req.args: services = OrderedSet() bouquets = OrderedSet() for sref in req.args.get('sref'): sref = unquote(sref) ref = eServiceReference(sref) if not ref.valid(): services = bouquets = None break elif (ref.flags & 7) == 7: epgservice = EPGRefreshService(sref, None) if epgservice not in bouquets: bouquets.add(epgservice) else: if not (ref.flags & eServiceReference.isGroup): # strip all after last : pos = sref.rfind(':') if pos != -1: if sref[pos-1] == ':': pos -= 1 sref = sref[:pos+1] epgservice = EPGRefreshService(sref, None) if epgservice not in services: services.add(epgservice) if services is not None and bouquets is not None: scanServices = epgrefresh.generateServicelist(services, bouquets) else: scanServices = [] else: scanServices = epgrefresh.generateServicelist(epgrefresh.services[0], epgrefresh.services[1]) returnlist = ["<?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n<e2servicelist>"] extend = returnlist.extend for serviceref in scanServices: ref = ServiceReference(str(serviceref)) returnlist.extend(( ' <e2service>\n', ' <e2servicereference>', stringToXML(str(serviceref)), '</e2servicereference>\n', ' <e2servicename>', stringToXML(ref.getServiceName().replace('\xc2\x86', '').replace('\xc2\x87', '')), '</e2servicename>\n', ' </e2service>\n', )) returnlist.append('\n</e2servicelist>') return ''.join(returnlist) class EPGRefreshChangeSettingsResource(resource.Resource): def render(self, req): statetext = "config changed." for key, value in iteritems(req.args): value = value[0] if key == "enabled": config.plugins.epgrefresh.enabled.value = True if value == "true" else False elif key == "enablemessage": config.plugins.epgrefresh.enablemessage.value = True if value == "true" else False elif key == "begin": value = int(value) if value: t = localtime(value) config.plugins.epgrefresh.begin.value = [t.tm_hour, t.tm_min] elif key == "end": value = int(value) if value: t = localtime(int(value)) config.plugins.epgrefresh.end.value = [t.tm_hour, t.tm_min] elif key == "interval_seconds": value = int(value) if value: config.plugins.epgrefresh.interval_seconds.value = value elif key == "delay_standby": value = int(value) if value: config.plugins.epgrefresh.delay_standby.value = value elif key == "inherit_autotimer": config.plugins.epgrefresh.inherit_autotimer.value = True if value == "true" else False elif key == "afterevent": config.plugins.epgrefresh.afterevent.value = True if value == "true" else False elif key == "force": config.plugins.epgrefresh.force.value = True if value == "true" else False elif key == "wakeup": config.plugins.epgrefresh.wakeup.value = True if value == "true" else False elif key == "parse_autotimer": if value in config.plugins.epgrefresh.parse_autotimer.choices: config.plugins.epgrefresh.parse_autotimer.value = value elif value == "true": config.plugins.epgrefresh.parse_autotimer.value = "always" else: config.plugins.epgrefresh.parse_autotimer.value = "never" elif key == "adapter": if value in config.plugins.epgrefresh.adapter.choices: config.plugins.epgrefresh.adapter.value = value elif key == "skipProtectedServices": if value in config.plugins.epgrefresh.skipProtectedServices.choices: config.plugins.epgrefresh.skipProtectedServices.value = value config.plugins.epgrefresh.save() if config.plugins.epgrefresh.enabled.value: epgrefresh.start() else: epgrefresh.stop() req.setResponseCode(http.OK) req.setHeader('Content-type', 'application/xhtml+xml') req.setHeader('charset', 'UTF-8') return """<?xml version=\"1.0\" encoding=\"UTF-8\" ?> <e2simplexmlresult> <e2state>true</e2state> <e2statetext>%s</e2statetext> </e2simplexmlresult>""" % (statetext,) class EPGRefreshSettingsResource(resource.Resource): def render(self, req): req.setResponseCode(http.OK) req.setHeader('Content-type', 'application/xhtml+xml') req.setHeader('charset', 'UTF-8') from time import time, localtime, mktime now = localtime() begin_h = config.plugins.epgrefresh.begin.value begin = mktime(( now.tm_year, now.tm_mon, now.tm_mday, begin_h[0], begin_h[1], 0, now.tm_wday, now.tm_yday, now.tm_isdst) ) end_h = config.plugins.epgrefresh.end.value end = mktime(( now.tm_year, now.tm_mon, now.tm_mday, end_h[0], end_h[1], 0, now.tm_wday, now.tm_yday, now.tm_isdst) ) canDoBackgroundRefresh = SystemInfo.get("NumVideoDecoders", 1) > 1 hasAutoTimer = False try: from Plugins.Extensions.AutoTimer.AutoTimer import AutoTimer hasAutoTimer = True except ImportError as ie: pass return """<?xml version=\"1.0\" encoding=\"UTF-8\" ?> <e2settings> <e2setting> <e2settingname>config.plugins.epgrefresh.enabled</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.enablemessage</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.begin</e2settingname> <e2settingvalue>%d</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.end</e2settingname> <e2settingvalue>%d</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.interval_seconds</e2settingname> <e2settingvalue>%d</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.delay_standby</e2settingname> <e2settingvalue>%d</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.inherit_autotimer</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.afterevent</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.force</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.wakeup</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.parse_autotimer</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.lastscan</e2settingname> <e2settingvalue>%d</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.adapter</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>config.plugins.epgrefresh.skipProtectedServices</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>canDoBackgroundRefresh</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>hasAutoTimer</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> <e2setting> <e2settingname>api_version</e2settingname> <e2settingvalue>%s</e2settingvalue> </e2setting> </e2settings>""" % ( config.plugins.epgrefresh.enabled.value, config.plugins.epgrefresh.enablemessage.value, begin, end, config.plugins.epgrefresh.interval_seconds.value, config.plugins.epgrefresh.delay_standby.value, config.plugins.epgrefresh.inherit_autotimer.value, config.plugins.epgrefresh.afterevent.value, config.plugins.epgrefresh.force.value, config.plugins.epgrefresh.wakeup.value, config.plugins.epgrefresh.parse_autotimer.value, config.plugins.epgrefresh.lastscan.value, config.plugins.epgrefresh.adapter.value, config.plugins.epgrefresh.skipProtectedServices.value, canDoBackgroundRefresh, hasAutoTimer, API_VERSION, )

32.317333

129

0.702286

38d7b6c57d4acd4b162b007912161c2f75df5e4b

14,928

py

Python

modules/eden/doc.py

unimauro/eden

b739d334e6828d0db14b3790f2f5e2666fc83576

[ "MIT" ]

null

modules/eden/doc.py

unimauro/eden

b739d334e6828d0db14b3790f2f5e2666fc83576

[ "MIT" ]

null

modules/eden/doc.py

unimauro/eden

b739d334e6828d0db14b3790f2f5e2666fc83576

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Sahana Eden Document Library @copyright: 2011-2012 (c) Sahana Software Foundation @license: MIT 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. """ __all__ = ["S3DocumentLibrary", "doc_image_represent"] import os from gluon import * from gluon.storage import Storage from ..s3 import * # ============================================================================= class S3DocumentLibrary(S3Model): names = ["doc_entity", "doc_document", "doc_image"] def model(self): T = current.T db = current.db request = current.request s3 = current.response.s3 person_comment = self.pr_person_comment person_id = self.pr_person_id location_id = self.gis_location_id organisation_id = self.org_organisation_id messages = current.messages NONE = messages.NONE UNKNOWN_OPT = messages.UNKNOWN_OPT s3_date_format = current.deployment_settings.get_L10n_date_format() s3_date_represent = lambda dt: S3DateTime.date_represent(dt, utc=True) # Shortcuts add_component = self.add_component comments = s3.comments configure = self.configure define_table = self.define_table meta_fields = s3.meta_fields super_link = self.super_link # --------------------------------------------------------------------- # Document-referencing entities # entity_types = Storage(asset_asset=T("Asset"), irs_ireport=T("Incident Report"), project_project=T("Project"), project_activity=T("Project Activity"), project_task=T("Task"), hms_hospital=T("Hospital")) tablename = "doc_entity" doc_entity = self.super_entity(tablename, "doc_id", entity_types) # Components add_component("doc_document", doc_entity=self.super_key(doc_entity)) add_component("doc_image", doc_entity=self.super_key(doc_entity)) # --------------------------------------------------------------------- # Documents # tablename = "doc_document" table = define_table(tablename, super_link("site_id", "org_site"), super_link("doc_id", doc_entity), Field("name", length=128, notnull=True, label=T("Name")), Field("file", "upload", autodelete=True), Field("url", label=T("URL"), requires = [IS_NULL_OR(IS_URL()), IS_NULL_OR(IS_NOT_ONE_OF(db, "%s.url" % tablename))], represent = lambda url: \ url and A(url,_href=url) or NONE), person_id(label=T("Author"), comment=person_comment(T("Author"), T("The Author of this Document (optional)"))), organisation_id(widget = S3OrganisationAutocompleteWidget(default_from_profile = True)), Field("date", "date", label = T("Date Published"), represent = s3_date_represent, requires = IS_NULL_OR(IS_DATE(format = s3_date_format)), widget = S3DateWidget() ), location_id(), comments(), #Field("entered", "boolean", label=T("Entered")), Field("checksum", readable=False, writable=False), *meta_fields()) # Field configuration table.file.represent = lambda file, table=table: \ self.doc_file_represent(file, table) #table.location_id.readable = False #table.location_id.writable = False #table.entered.comment = DIV(_class="tooltip", # _title="%s|%s" % (T("Entered"), # T("Has data from this Reference Document been entered into Sahana?"))) # CRUD Strings DOCUMENT = T("Reference Document") ADD_DOCUMENT = T("Add Reference Document") LIST_DOCUMENTS = T("List Documents") s3.crud_strings[tablename] = Storage( title_create = ADD_DOCUMENT, title_display = T("Document Details"), title_list = LIST_DOCUMENTS, title_update = T("Edit Document"), title_search = T("Search Documents"), subtitle_create = T("Add New Document"), subtitle_list = DOCUMENT, label_list_button = LIST_DOCUMENTS, label_create_button = ADD_DOCUMENT, label_delete_button = T("Delete Document"), msg_record_created = T("Document added"), msg_record_modified = T("Document updated"), msg_record_deleted = T("Document deleted"), msg_list_empty = T("No Documents found") ) # Search Method? # Resource Configuration configure(tablename, #mark_required=["file"], onvalidation=self.document_onvalidation) # --------------------------------------------------------------------- # Images # doc_image_type_opts = { 1:T("Photograph"), 2:T("Map"), 3:T("Document Scan"), 99:T("other") } tablename = "doc_image" table = define_table(tablename, super_link("site_id", "org_site"), super_link("pe_id", "pr_pentity"), super_link("doc_id", doc_entity), Field("name", length=128, notnull=True, label=T("Name")), Field("file", "upload", autodelete=True, requires = IS_NULL_OR(IS_IMAGE(extensions=(s3.IMAGE_EXTENSIONS))), # upload folder needs to be visible to the download() function as well as the upload uploadfolder = os.path.join(request.folder, "uploads", "images")), Field("url", label=T("URL"), requires = IS_NULL_OR(IS_URL())), Field("type", "integer", requires = IS_IN_SET(doc_image_type_opts, zero=None), default = 1, label = T("Image Type"), represent = lambda opt: doc_image_type_opts.get(opt, UNKNOWN_OPT)), person_id(label=T("Author")), organisation_id(widget = S3OrganisationAutocompleteWidget(default_from_profile = True)), location_id(), Field("date", "date", label = T("Date Taken"), represent = s3_date_represent, requires = IS_NULL_OR(IS_DATE(format = s3_date_format)), widget = S3DateWidget() ), comments(), Field("checksum", readable=False, writable=False), *meta_fields()) # Field configuration table.file.represent = doc_image_represent # CRUD Strings ADD_IMAGE = T("Add Photo") LIST_IMAGES = T("List Photos") s3.crud_strings[tablename] = Storage( title_create = ADD_IMAGE, title_display = T("Photo Details"), title_list = LIST_IMAGES, title_update = T("Edit Photo"), title_search = T("Search Photos"), subtitle_create = T("Add New Photo"), subtitle_list = T("Photo"), label_list_button = LIST_IMAGES, label_create_button = ADD_IMAGE, label_delete_button = T("Delete Photo"), msg_record_created = T("Photo added"), msg_record_modified = T("Photo updated"), msg_record_deleted = T("Photo deleted"), msg_list_empty = T("No Photos found")) # Search Method # Resource Configuration configure(tablename, onvalidation=lambda form: \ self.document_onvalidation(form, document=False)) # --------------------------------------------------------------------- # Pass model-global names to response.s3 # return Storage() # ------------------------------------------------------------------------- def defaults(self): """ Safe defaults if the module is disabled """ return Storage() # ------------------------------------------------------------------------- @staticmethod def doc_file_represent(file, table): """ File representation """ if file: return A(table.file.retrieve(file)[0], _href=URL(c="default", f="download", args=[file])) else: return current.messages.NONE # ------------------------------------------------------------------------- @staticmethod def document_represent(id): """ Foreign key representation """ if not id: return current.messages.NONE represent = s3_get_db_field_value(tablename = "doc_document", fieldname = "name", look_up_value = id) return A(represent, _href = URL(c="doc", f="document", args=[id], extension=""), _target = "blank") # ------------------------------------------------------------------------- @staticmethod def document_onvalidation(form, document=True): """ Form validation for both, documents and images """ import cgi T = current.T db = current.db s3db = current.s3db request = current.request if document: tablename = "doc_document" msg = T("Either file upload or document URL required.") else: tablename = "doc_image" msg = T("Either file upload or image URL required.") table = s3db[tablename] doc = form.vars.file url = form.vars.url if not hasattr(doc, "file"): id = request.post_vars.id if id: record = db(table.id == id).select(table.file, limitby=(0, 1)).first() if record: doc = record.file if not hasattr(doc, "file") and not doc and not url: form.errors.file = msg form.errors.url = msg # Do a checksum on the file to see if it's a duplicate if isinstance(doc, cgi.FieldStorage) and doc.filename: f = doc.file form.vars.checksum = doc_checksum(f.read()) f.seek(0) if form.vars.checksum is not None: # Duplicate allowed if original version is deleted query = ((table.checksum == form.vars.checksum) & \ (table.deleted == False)) result = db(query).select(table.name, limitby=(0, 1)).first() if result: doc_name = result.name form.errors["file"] = "%s %s" % \ (T("This file already exists on the server as"), doc_name) return # ============================================================================= def doc_image_represent(filename): """ Represent an image as a clickable thumbnail @param filename: name of the image file """ return DIV(A(IMG(_src=URL(c="default", f="download", args=filename), _height=40), _class="zoom", _href=URL(c="default", f="download", args=filename))) # @todo: implement/activate the JavaScript for this: #import uuid #anchor = "zoom-media-image-%s" % uuid.uuid4() #return DIV(A(IMG(_src=URL(c="default", f="download", #args=filename), #_height=40), #_class="zoom", #_href="#%s" % anchor), #DIV(IMG(_src=URL(c="default", f="download", #args=filename), #_width=600), #_id="%s" % anchor, #_class="hide")) # ============================================================================= def doc_checksum(docstr): """ Calculate a checksum for a file """ import hashlib converted = hashlib.sha1(docstr).hexdigest() return converted # END =========================================================================

41.237569

125

0.470056

59edea0cd015d7849aa96dac0684d95e5a2184fb

3,071

py

Python

build-tools/code_generator/function_generator/generate_include_nbla_function_hpp.py

enomotom/nnabla

1947fe16a0a41d19d76cd916f151aa1991ea1b44

[ "Apache-2.0" ]

1

2021-04-08T00:33:23.000Z

build-tools/code_generator/function_generator/generate_include_nbla_function_hpp.py

enomotom/nnabla

1947fe16a0a41d19d76cd916f151aa1991ea1b44

[ "Apache-2.0" ]

null

build-tools/code_generator/function_generator/generate_include_nbla_function_hpp.py

enomotom/nnabla

1947fe16a0a41d19d76cd916f151aa1991ea1b44

[ "Apache-2.0" ]

null

# Copyright (c) 2017 Sony Corporation. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import generator_common.common as common import utils.type_conv def generate(info, func_name, func_name_snakecase, template): arg_info = common.function_arguments(info) func_arg_variable_defines = '\n'.join(['protected:'] + ['{} {}_;'.format(utils.type_conv.type_from_proto[ t]['cpp_var'], n) for t, n in zip(arg_info['types'], arg_info['names'])]) func_arg_variable_types = ', '.join( [func_name] + [utils.type_conv.type_from_proto[t]['cpp'] for t in arg_info['types']]) func_arg_initializers = ', ' + ', '.join(['{0}_({0})'.format(n) for n in arg_info['names']]) func_arg_variables = ', '.join( ['ctx_'] + ['{}_'.format(n) for n in arg_info['names']]) func_args = ', '.join(['const Context &ctx'] + ['{} {}'.format(utils.type_conv.type_from_proto[ t]['cpp'], n) for t, n in zip(arg_info['types'], arg_info['names'])]) io_info = common.function_io(info) template_defines = ', '.join(['typename {}'.format(t) for t in io_info['templates']]) in_types = ', '.join(['get_dtype<{}>()'.format(t) for t in io_info['input']['types']]) out_types = ', '.join(['get_dtype<{}>()'.format(t) for t in io_info['output']['types']]) min_inputs = io_info['input']['min'] min_outputs = io_info['output']['min'] base_function_types = ', '.join([utils.type_conv.type_from_proto[t]['cpp'] for t in arg_info['types']]) base_function_args = ', '.join(['ctx'] + ['{}'.format(n) for n in arg_info['names']]) return template.format(func_name=func_name, func_name_upcase=func_name.upper(), template_defines=template_defines, func_args=func_args, func_arg_variable_defines=func_arg_variable_defines, func_arg_variable_types=func_arg_variable_types, func_arg_variables=func_arg_variables, func_arg_initializers=func_arg_initializers, in_types=in_types, out_types=out_types, min_inputs=min_inputs, min_outputs=min_outputs, base_function_types=base_function_types, base_function_args=base_function_args)

52.050847

115

0.596874

6b45eae37df05e31fcd4f361da0d0c026799ea8f

493

py

Python

numba/cuda/simulator/cudadrv/nvvm.py

blair1306/numba

3b9647d17d653abac15363da604eeb804dbdd15a

[ "BSD-2-Clause" ]

76

2020-07-06T14:44:05.000Z

2022-02-14T15:30:21.000Z

numba/cuda/simulator/cudadrv/nvvm.py

blair1306/numba

3b9647d17d653abac15363da604eeb804dbdd15a

[ "BSD-2-Clause" ]

11

2020-08-09T02:30:14.000Z

2022-03-12T00:50:14.000Z

numba/cuda/simulator/cudadrv/nvvm.py

blair1306/numba

3b9647d17d653abac15363da604eeb804dbdd15a

[ "BSD-2-Clause" ]

11

2020-07-12T16:18:07.000Z

2022-02-05T16:48:35.000Z

''' NVVM is not supported in the simulator, but stubs are provided to allow tests to import correctly. ''' class NvvmSupportError(ImportError): pass class NVVM(object): def __init__(self): raise NvvmSupportError('NVVM not supported in the simulator') CompilationUnit = None llvm_to_ptx = None set_cuda_kernel = None fix_data_layout = None get_arch_option = None LibDevice = None NvvmError = None def is_available(): return False def get_supported_ccs(): return ()

18.961538

77

0.742394

7d3508373aaa604580dc5ad546f4655943e26c3b

3,373

py

Python

Scrapy/WebData/WebData/spiders/Qidian.py

LieonShelly/PythonFun

811760d368885109f9359c2663d8ce74886f6ad6

[ "MIT" ]

null

Scrapy/WebData/WebData/spiders/Qidian.py

LieonShelly/PythonFun

811760d368885109f9359c2663d8ce74886f6ad6

[ "MIT" ]

null

Scrapy/WebData/WebData/spiders/Qidian.py

LieonShelly/PythonFun

811760d368885109f9359c2663d8ce74886f6ad6

[ "MIT" ]

null

# -*- coding: utf-8 -*- from scrapy import Spider from scrapy import Request from urllib.parse import urlencode from WebData.items import BookItem from WebData.items import ChapterItem from WebData.items import ContentItem class QidianSpider(Spider): name = 'Qidian' allowed_domains = ['www.qidian.com', 'book.qidian.com', 'read.qidian.com'] def start_requests(self): # yield Request("https://read.qidian.com/chapter/8s7RmxQkntY3LbtcZNMchg2/fn_NgkMI2962uJcMpdsVgA2",callback=self.parseChapterContent) data = {"page": 1} maxPage = 1 baseURL = "https://www.qidian.com/free/all?" for page in range(1, maxPage + 1): data["page"] = page url = baseURL + urlencode(data) yield Request(url=url) def parse(self, response): list = response.css("div.all-book-list") for item in list: uls = item.css('.book-img-text').xpath('./ul') for ul in uls: lis = ul.xpath('./li') for li in lis: img = li.css('.book-img-box').xpath('./a/@href').extract_first() detailURI = li.css('.book-mid-info').xpath('./h4//a/@href').extract_first() bookTitle = li.css('.book-mid-info').xpath('./h4/a/text()').extract_first() intro = li.css('.book-mid-info').css("p.intro::text").extract_first() lastUpdateTitle = li.css('.book-mid-info').css('p.update::text').extract_first() lastUpdateURI = li.css('.book-mid-info').css('p.update').xpath('./a/@href').extract_first() catlog = "https:" + detailURI + "#Catalog" bookItem = BookItem() bookItem['detailURI'] = "https:" + detailURI bookItem['bookTitle'] = bookTitle bookItem['intro'] = intro yield bookItem yield Request(catlog,callback=self.parseChapterList) def parseChapterList(self, response): divWrap = response.css("div.volume-wrap") for volume in divWrap: uls = volume.css("ul.cf") for ul in uls: chapterContentURIs = ul.xpath('./li/a/@href') chapterTitles = ul.xpath('./li/a/text()') for (chapterContentURI, chapterTitle) in zip(chapterContentURIs, chapterTitles): content = "https:" + chapterContentURI.extract() chapater = ChapterItem() chapater['chapterContentURI'] = content chapater['chapterTitle'] = chapterTitle.extract() chapater['detailURI'] = response.url yield chapater yield Request(content,callback=self.parseChapterContent) def parseChapterContent(self, response): chapterURI = response.url contents = response.css('div.read-content').xpath('./p/text()') contentStr = "".join(contents.extract()).replace('\u3000', "") content = ContentItem() content['content'] = contentStr content['chapterContentURI'] = chapterURI yield content # 章节内容 https://read.qidian.com/chapter/j1uUFQtIDcYuTkiRw_sFYA2/j3uJh4HjyHn4p8iEw--PPw2 # 目录列表 https://book.qidian.com/info/1005269238#Catalog # 书籍列表

43.805195

140

0.572191

0fa1ae524dcb8eb637c890f5a7f61ff426e3505b

1,230

py

Python

tests/test_singleton.py

tipofthesowrd/SoCo

56bdc655b323d9178b591cab96b58dd28e5155f2

[ "MIT" ]

1,149

2015-01-02T02:08:34.000Z

2022-03-30T13:58:04.000Z

tests/test_singleton.py

tipofthesowrd/SoCo

56bdc655b323d9178b591cab96b58dd28e5155f2

[ "MIT" ]

630

2015-01-01T10:44:22.000Z

2022-03-17T00:25:55.000Z

tests/test_singleton.py

tipofthesowrd/SoCo

56bdc655b323d9178b591cab96b58dd28e5155f2

[ "MIT" ]

249

2015-01-07T20:11:10.000Z

2022-03-14T05:54:20.000Z

"""Tests for the SoCoSingletonBase and _ArgsSingleton classes in core.""" import pytest from soco.core import _SocoSingletonBase as Base class ASingleton(Base): def __init__(self, arg): pass class AnotherSingleton(ASingleton): pass class ThirdSingleton(Base): _class_group = "somegroup" def __init__(self, arg): pass class FourthSingleton(ASingleton): _class_group = "somegroup" pass def test_singleton(): """Check basic functionality. For a given arg, there is only one instance """ assert ASingleton("aa") is ASingleton("aa") assert ASingleton("aa") is not ASingleton("bb") def test_singleton_inherit(): """Check that subclasses behave properly.""" assert ASingleton("aa") is not AnotherSingleton("aa") assert AnotherSingleton("aa") is AnotherSingleton("aa") def test_class_group_singleton(): """Check _class_group functionality. For a given arg, instances of FourthGroup are Instances of ThirdGroup because they share a `_class_group` valur """ assert ThirdSingleton("aa") is FourthSingleton("aa") assert ThirdSingleton("aa") is not FourthSingleton("bb") assert ThirdSingleton("aa") is not ASingleton("aa")

22.777778

73

0.705691

b82c1daaef4ddcbdd7d2965c6bbb297dfbac3592

665

py

Python

testCurveFit.py

CaptainEven/PyScripts

5852501634e1ff86d2ff6820b33bfffcea7112c9

[ "MIT" ]

5

2020-10-27T06:39:00.000Z

2020-11-01T17:19:03.000Z

testCurveFit.py

CaptainEven/PyScripts

5852501634e1ff86d2ff6820b33bfffcea7112c9

[ "MIT" ]

null

testCurveFit.py

CaptainEven/PyScripts

5852501634e1ff86d2ff6820b33bfffcea7112c9

[ "MIT" ]

2

2020-10-27T15:30:10.000Z

2020-11-01T17:38:05.000Z

from scipy.optimize import curve_fit import matplotlib.pyplot as plt import numpy as np def func_1(x, a, b, c): return a * np.exp(b * x) + c def func_2(x, a, b): return x**a + b x_data = np.linspace(0, 4, 50) # y = func_1(x_data, 2.5, 1.5, 0.5) y = func_2(x_data, 2.5, 1.3) print('origin a: 2.50, b: 1.30') y_data = y + 0.5 * np.random.normal(size=len(x_data)) plt.plot(x_data, y_data, 'b-') popt, pcov = curve_fit(func_1, x_data, y_data) # y2 = [func_1(i, popt[0], popt[1], popt[2]) for i in x_data] y2 = [func_2(x_val, popt[0], popt[1]) for x_val in x_data] plt.plot(x_data, y2, 'r--') print('fitted a: %.2f, b: %.2f' %(popt[0], popt[1])) plt.show()

26.6

61

0.622556

de940a7c2991796eee9fdeb88cfe60e809c8aff9

119

py

Python

src/tuberlin/inventory/emails.py

CircularBerlin/gmit

fbc995f631fa42815deab5cf07e8115a1b964d90

[ "MIT" ]

null

src/tuberlin/inventory/emails.py

CircularBerlin/gmit

fbc995f631fa42815deab5cf07e8115a1b964d90

[ "MIT" ]

null

src/tuberlin/inventory/emails.py

CircularBerlin/gmit

fbc995f631fa42815deab5cf07e8115a1b964d90

[ "MIT" ]

null

from django.conf import settings from django.template.loader import get_template from django.core.mail import send_mail

39.666667

47

0.865546

3491403825aa685bf29e40ea61c30f312c6f0b46

2,489

py

Python

pygameweb/static/views.py

leereilly/pygameweb

c4df39770716bf737f3379da987538fa32f9f3d8

[ "BSD-2-Clause" ]

2

2021-05-11T19:29:03.000Z

2021-05-12T06:08:00.000Z

pygameweb/static/views.py

leereilly/pygameweb

c4df39770716bf737f3379da987538fa32f9f3d8

[ "BSD-2-Clause" ]

null

pygameweb/static/views.py

leereilly/pygameweb

c4df39770716bf737f3379da987538fa32f9f3d8

[ "BSD-2-Clause" ]

null

"""Adding static folders. """ from flask import Blueprint, send_from_directory from pathlib import Path static_blueprint = Blueprint('static', __name__, template_folder='../templates/') files = ['lofi.html', 'server.json', 'hifi.html', 'favicon.ico', 'robots.txt'] folders = ['content', 'contests', 'css', 'ctypes', 'docs', 'docs-old', 'ftp', 'galleries', 'gamelets', 'games', 'html5media', 'images', 'interview', 'iscroll', 'js', 'ludumcontest1', 'ludumcontest2', 'mediaelement', 'mediagit', 'music', 'neu', 'new', 'old', 'old_bug_attachments', 'oldhtml', 'pcr', 'pcr_old', 'pygame_wincvs', 'search', 'shots', 'shredwheat', 'siteswing', 'skins', 'swfs', 'test', 'thumb', 'tiny_mce', 'tinymce_3_2_3_1', 'tmp', 'webalizer'] def is_there(app, full_path, file_folder): full_path = Path('..', app.config['WWW']) / file_folder return full_path.exists() def add_folder(app, static_blueprint, folder): """Add a static folder. """ full_path = Path(app.config['WWW']) / folder full_path_index = Path(app.config['WWW']) / folder / 'index.html' full_path_str = str(full_path.absolute()) if not full_path.exists(): return def download_file(path): return send_from_directory(full_path_str, path) url = f'/{folder}/<path:path>' path = f'static_{folder}' app.add_url_rule(url, path, download_file) if full_path_index.exists(): def download_index_file(): return send_from_directory(full_path_str, 'index.html') url = f'/{folder}/' path = f'static_{folder}_index' app.add_url_rule(url, path, download_index_file) def add_file(app, static_blueprint, file): """Add a file to serve. """ full_path = Path(app.config['WWW']) full_path_str = str(full_path.absolute()) if not (full_path / file).exists(): return def download_file(): return send_from_directory(full_path_str, file) url = f'/{file}' path = f'static_{file}' app.add_url_rule(url, path, download_file) def add_static_blueprint(app): """ to the app. """ for folder in folders: add_folder(app, static_blueprint, folder) for file in files: add_file(app, static_blueprint, file) from pygameweb.cache import limiter limiter.limit("1000/hour")(static_blueprint) app.register_blueprint(static_blueprint)

30.728395

81

0.626758

6ee1c27f2c95ce066a2c361ed768aef48cf51d19

566

py

Python

autojail/utils/__init__.py

ekut-es/autojail

bc16e40e6df55c0a28a3059715851ffa59b14ba8

[ "MIT" ]

6

2020-08-12T08:16:15.000Z

2022-03-05T02:25:53.000Z

autojail/utils/__init__.py

ekut-es/autojail

bc16e40e6df55c0a28a3059715851ffa59b14ba8

[ "MIT" ]

1

2021-03-30T10:34:51.000Z

2021-06-09T11:24:00.000Z

autojail/utils/__init__.py

ekut-es/autojail

bc16e40e6df55c0a28a3059715851ffa59b14ba8

[ "MIT" ]

1

2021-11-21T09:30:58.000Z

from .board import start_board, stop_board from .collections import SortedCollection from .connection import connect # noqa from .debug import debug from .deploy import deploy_target from .fs import which from .intervall_arithmetic import get_overlap from .logging import ClikitLoggingHandler from .string import pprint_tree, remove_prefix __all__ = [ "connect", "remove_prefix", "ClikitLoggingHandler", "SortedCollection", "debug", "pprint_tree", "get_overlap", "which", "start_board", "stop_board", "deploy_target", ]

23.583333

46

0.736749

b0773ef73a68f1895d06caf86f2630117313dee4

3,145

py

Python

bing_img_collector.py

kita83/bing_image_collector

0f3163c7e7bf52b41400946434f022dc1bc1698d

[ "Apache-2.0" ]

null

bing_img_collector.py

kita83/bing_image_collector

0f3163c7e7bf52b41400946434f022dc1bc1698d

[ "Apache-2.0" ]

null

bing_img_collector.py

kita83/bing_image_collector

0f3163c7e7bf52b41400946434f022dc1bc1698d

[ "Apache-2.0" ]

null

# coding: utf-8 import requests import os import math import configparser # for Python3 import urllib import re import datetime import bing_util def get_headers(api_key): return {"Ocp-Apim-Subscription-Key": api_key} def get_params(search_term, num_imgs_per_transaction, offset): return urllib.parse.urlencode({ "q": search_term, "license": "All", "imageType": "photo", "count": num_imgs_per_transaction, "offset": offset * num_imgs_per_transaction, "mkt": "ja-JP"}) def get_search_results(search_url, headers, params): response = requests.get(search_url, headers=headers, params=params) response.raise_for_status() search_results = response.json() return search_results def save_urls(results, filepath): format_ = ['jpg', 'jpeg'] with open(filepath, mode='a') as f: for values in results: if values['encodingFormat'] in format_: print(values['contentUrl'], file=f) def get_filename(path, fn, ext): return os.path.join(path, '%s.%s' % (fn, ext)) def gen_url_save_file(search_term, url_dir_path, total_count): ext = 'txt' fn = bing_util.search_term2file_name(search_term) filename = get_filename(url_dir_path, fn, ext) dt = datetime.datetime.now().strftime("%Y%m%d%H%M%S") if os.path.isfile(filename): fn = '%s_%s' % (fn, dt) filename = get_filename(url_dir_path, fn, ext) with open(filename, mode='w') as f: print("date=%s, search_term=%s, totalEstimatedMatches=%d" % (dt, search_term, total_count), file=f) return filename if __name__ == '__main__': config = configparser.ConfigParser() config.read('authentication.ini') bing_api_key = config['auth']['bing_api_key'] save_dir_path = './bing_img' bing_util.make_dir(save_dir_path) url_dir_path = os.path.join(save_dir_path, 'url') bing_util.make_dir(url_dir_path) num_imgs_required = 3 # Number of images you want. num_imgs_per_transaction = 50 # default 30, Max 150 images search_term = "dog" search_url = "https://api.cognitive.microsoft.com/bing/v7.0/images/search" headers = get_headers(bing_api_key) params = get_params(search_term, num_imgs_per_transaction, 0) first_search_results = get_search_results(search_url, headers, params) total_count = first_search_results["totalEstimatedMatches"] print("totalEstimatedMatches=%d" % total_count) filepath = gen_url_save_file(search_term, url_dir_path, total_count) print("len=%d" % (len(first_search_results["value"]))) save_urls(first_search_results["value"], filepath) if num_imgs_required > total_count: num_imgs_required = total_count offset_count = math.ceil(num_imgs_required / num_imgs_per_transaction) print('offset_count=%d' % offset_count) for offset in range(1, offset_count): params = get_params(search_term, num_imgs_per_transaction, offset) search_results = get_search_results(search_url, headers, params) print("len=%d" % len(search_results["value"])) save_urls(search_results["value"], filepath)

31.767677

107

0.696343

4b6ba6399b3273df6f1270c20f23d2b7e8512a92

284

py

Python

ca_qc_westmount/__init__.py

dcycle/scrapers-ca

4c7a6cd01d603221b5b3b7a400d2e5ca0c6e916f

[ "MIT" ]

19

2015-05-26T03:18:50.000Z

2022-01-31T03:27:41.000Z

ca_qc_westmount/__init__.py

dcycle/scrapers-ca

4c7a6cd01d603221b5b3b7a400d2e5ca0c6e916f

[ "MIT" ]

119

2015-01-09T06:09:35.000Z

2022-01-20T23:05:05.000Z

ca_qc_westmount/__init__.py

dcycle/scrapers-ca

4c7a6cd01d603221b5b3b7a400d2e5ca0c6e916f

[ "MIT" ]

17

2015-11-23T05:00:10.000Z

2021-09-15T16:03:33.000Z

from utils import CanadianJurisdiction class Westmount(CanadianJurisdiction): classification = 'legislature' division_id = 'ocd-division/country:ca/csd:2466032' division_name = 'Westmount' name = 'Conseil municipal de Westmount' url = 'http://www.westmount.org'

28.4

55

0.739437

bed747a7170aaf89c9ff53d9c3da2698b564eaa4

9,470

py

Python

tools/webdriver/webdriver/transport.py

qanat/wpt

7c61a4594a95682531367b6956d1c37f8b8fd486

[ "BSD-3-Clause" ]

1

2021-12-12T18:13:24.000Z

tools/webdriver/webdriver/transport.py

qanat/wpt

7c61a4594a95682531367b6956d1c37f8b8fd486

[ "BSD-3-Clause" ]

112

2021-09-27T14:39:02.000Z

2022-03-30T14:26:35.000Z

tools/webdriver/webdriver/transport.py

qanat/wpt

7c61a4594a95682531367b6956d1c37f8b8fd486

[ "BSD-3-Clause" ]

null

import json import select from http.client import HTTPConnection from typing import Dict, List, Mapping, Sequence, Tuple from urllib import parse as urlparse from . import error """Implements HTTP transport for the WebDriver wire protocol.""" missing = object() class ResponseHeaders(Mapping[str, str]): """Read-only dictionary-like API for accessing response headers. This class: * Normalizes the header keys it is built with to lowercase (such that iterating the items will return lowercase header keys). * Has case-insensitive header lookup. * Always returns all header values that have the same name, separated by commas. """ def __init__(self, items: Sequence[Tuple[str, str]]): self.headers_dict: Dict[str, List[str]] = {} for key, value in items: key = key.lower() if key not in self.headers_dict: self.headers_dict[key] = [] self.headers_dict[key].append(value) def __getitem__(self, key): """Get all headers of a certain (case-insensitive) name. If there is more than one, the values are returned comma separated""" values = self.headers_dict[key.lower()] if len(values) == 1: return values[0] else: return ", ".join(values) def get_list(self, key, default=missing): """Get all the header values for a particular field name as a list""" try: return self.headers_dict[key.lower()] except KeyError: if default is not missing: return default else: raise def __iter__(self): yield from self.headers_dict def __len__(self): return len(self.headers_dict) class Response: """ Describes an HTTP response received from a remote end whose body has been read and parsed as appropriate. """ def __init__(self, status, body, headers): self.status = status self.body = body self.headers = headers def __repr__(self): cls_name = self.__class__.__name__ if self.error: return "<%s status=%s error=%s>" % (cls_name, self.status, repr(self.error)) return "<% status=%s body=%s>" % (cls_name, self.status, json.dumps(self.body)) def __str__(self): return json.dumps(self.body, indent=2) @property def error(self): if self.status != 200: return error.from_response(self) return None @classmethod def from_http(cls, http_response, decoder=json.JSONDecoder, **kwargs): try: body = json.load(http_response, cls=decoder, **kwargs) headers = ResponseHeaders(http_response.getheaders()) except ValueError: raise ValueError("Failed to decode response body as JSON:\n" + http_response.read()) return cls(http_response.status, body, headers) class HTTPWireProtocol: """ Transports messages (commands and responses) over the WebDriver wire protocol. Complex objects, such as ``webdriver.Element``, ``webdriver.Frame``, and ``webdriver.Window`` are by default not marshaled to enable use of `session.transport.send` in WPT tests:: session = webdriver.Session("127.0.0.1", 4444) response = transport.send("GET", "element/active", None) print response.body["value"] # => {u'element-6066-11e4-a52e-4f735466cecf': u'<uuid>'} Automatic marshaling is provided by ``webdriver.protocol.Encoder`` and ``webdriver.protocol.Decoder``, which can be passed in to ``HTTPWireProtocol.send`` along with a reference to the current ``webdriver.Session``:: session = webdriver.Session("127.0.0.1", 4444) response = transport.send("GET", "element/active", None, encoder=protocol.Encoder, decoder=protocol.Decoder, session=session) print response.body["value"] # => webdriver.Element """ def __init__(self, host, port, url_prefix="/"): """ Construct interface for communicating with the remote server. :param url: URL of remote WebDriver server. :param wait: Duration to wait for remote to appear. """ self.host = host self.port = port self.url_prefix = url_prefix self._conn = None self._last_request_is_blocked = False def __del__(self): self.close() def close(self): """Closes the current HTTP connection, if there is one.""" if self._conn: try: self._conn.close() except OSError: # The remote closed the connection pass self._conn = None @property def connection(self): """Gets the current HTTP connection, or lazily creates one.""" if not self._conn: conn_kwargs = {} # We are not setting an HTTP timeout other than the default when the # connection its created. The send method has a timeout value if needed. self._conn = HTTPConnection(self.host, self.port, **conn_kwargs) return self._conn def url(self, suffix): """ From the relative path to a command end-point, craft a full URL suitable to be used in a request to the HTTPD. """ return urlparse.urljoin(self.url_prefix, suffix) def send(self, method, uri, body=None, headers=None, encoder=json.JSONEncoder, decoder=json.JSONDecoder, timeout=None, **codec_kwargs): """ Send a command to the remote. The request `body` must be JSON serialisable unless a custom `encoder` has been provided. This means complex objects such as ``webdriver.Element``, ``webdriver.Frame``, and `webdriver.Window`` are not automatically made into JSON. This behaviour is, however, provided by ``webdriver.protocol.Encoder``, should you want it. Similarly, the response body is returned au natural as plain JSON unless a `decoder` that converts web element references to ``webdriver.Element`` is provided. Use ``webdriver.protocol.Decoder`` to achieve this behaviour. The client will attempt to use persistent HTTP connections. :param method: `GET`, `POST`, or `DELETE`. :param uri: Relative endpoint of the requests URL path. :param body: Body of the request. Defaults to an empty dictionary if ``method`` is `POST`. :param headers: Additional dictionary of headers to include in the request. :param encoder: JSON encoder class, which defaults to ``json.JSONEncoder`` unless specified. :param decoder: JSON decoder class, which defaults to ``json.JSONDecoder`` unless specified. :param codec_kwargs: Surplus arguments passed on to `encoder` and `decoder` on construction. :return: Instance of ``webdriver.transport.Response`` describing the HTTP response received from the remote end. :raises ValueError: If `body` or the response body are not JSON serialisable. """ if body is None and method == "POST": body = {} payload = None if body is not None: try: payload = json.dumps(body, cls=encoder, **codec_kwargs) except ValueError: raise ValueError("Failed to encode request body as JSON:\n" "%s" % json.dumps(body, indent=2)) # When the timeout triggers, the TestRunnerManager thread will reuse # this connection to check if the WebDriver its alive and we may end # raising an httplib.CannotSendRequest exception if the WebDriver is # not responding and this httplib.request() call is blocked on the # runner thread. We use the boolean below to check for that and restart # the connection in that case. self._last_request_is_blocked = True response = self._request(method, uri, payload, headers, timeout=None) self._last_request_is_blocked = False return Response.from_http(response, decoder=decoder, **codec_kwargs) def _request(self, method, uri, payload, headers=None, timeout=None): if isinstance(payload, str): payload = payload.encode("utf-8") if headers is None: headers = {} headers.update({"Connection": "keep-alive"}) url = self.url(uri) if self._last_request_is_blocked or self._has_unread_data(): self.close() self.connection.request(method, url, payload, headers) # timeout for request has to be set just before calling httplib.getresponse() # and the previous value restored just after that, even on exception raised try: if timeout: previous_timeout = self._conn.gettimeout() self._conn.settimeout(timeout) response = self.connection.getresponse() finally: if timeout: self._conn.settimeout(previous_timeout) return response def _has_unread_data(self): return self._conn and self._conn.sock and select.select([self._conn.sock], [], [], 0)[0]

35.601504

96

0.618374

bcf72b5f13a0d174d200e1783f32c66110bd478a

1,926

py

Python

setup.py

sommersoft/Adafruit_CircuitPython_GC_IOT_Core

7e1ab12200d41f13c3561fc13f7eaaca2da86a40

[ "Apache-2.0" ]

null

setup.py

sommersoft/Adafruit_CircuitPython_GC_IOT_Core

7e1ab12200d41f13c3561fc13f7eaaca2da86a40

[ "Apache-2.0" ]

null

setup.py

sommersoft/Adafruit_CircuitPython_GC_IOT_Core

7e1ab12200d41f13c3561fc13f7eaaca2da86a40

[ "Apache-2.0" ]

null

"""A setuptools based setup module. See: https://packaging.python.org/en/latest/distributing.html https://github.com/pypa/sampleproject """ from setuptools import setup, find_packages # To use a consistent encoding from codecs import open from os import path here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, "README.rst"), encoding="utf-8") as f: long_description = f.read() setup( name="adafruit-circuitpython-gc-iot-core", use_scm_version=True, setup_requires=["setuptools_scm"], description="Google Cloud IoT Core Client for CircuitPython", long_description=long_description, long_description_content_type="text/x-rst", # The project's main homepage. url="https://github.com/adafruit/Adafruit_CircuitPython_GC_IOT_CORE", # Author details author="Adafruit Industries", author_email="circuitpython@adafruit.com", install_requires=["Adafruit-Blinka"], # Choose your license license="MIT", # See https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Topic :: Software Development :: Libraries", "Topic :: System :: Hardware", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", ], # What does your project relate to? keywords="adafruit blinka circuitpython micropython gc_iot_core gcs, google cloud " "platform, cloud, iot, core", # You can just specify the packages manually here if your project is # simple. Or you can use find_packages(). # TODO: IF LIBRARY FILES ARE A PACKAGE FOLDER, # CHANGE `py_modules=['...']` TO `packages=['...']` py_modules=["adafruit_gc_iot_core"], )

35.018182

87

0.687435

74a99fe25d427a5880e4355c435f83e5b2503dfe

234

py

Python

src/sql/master/stock_trans.py

springmaple/yotimes_sql_bridge

0d1d17c63d867bb2c2ad286851343c1884fdddf8

[ "Apache-2.0" ]

null

src/sql/master/stock_trans.py

springmaple/yotimes_sql_bridge

0d1d17c63d867bb2c2ad286851343c1884fdddf8

[ "Apache-2.0" ]

4

2020-06-02T14:29:17.000Z

2021-05-16T09:13:52.000Z

src/sql/master/stock_trans.py

springmaple/yotimes_sql_bridge

0d1d17c63d867bb2c2ad286851343c1884fdddf8

[ "Apache-2.0" ]

null

from sql.entity import Entity class StockTrans(Entity): def __init__(self, data): super().__init__(data) self.trans_no = self._get_int('TransNo') # Primary key self.item_code = self._get_str('ItemCode')

26

63

0.666667

5967cde6a6ba67ca9be59e67309d47239668fbd1

3,668

py

Python

tests/test_scenes.py

Karagusto/Game_ppg

f5f7eccaf46495e3872b94aba8df56abab83b622

[ "Artistic-2.0" ]

1

2018-07-30T15:05:45.000Z

tests/test_scenes.py

jamesBrosnahan/pursuedpybear

8e5498b66de71c1fbe62f869d50803e22860b6f8

[ "Artistic-2.0" ]

null

tests/test_scenes.py

jamesBrosnahan/pursuedpybear

8e5498b66de71c1fbe62f869d50803e22860b6f8

[ "Artistic-2.0" ]

null

from unittest.mock import Mock from pytest import fixture from pytest import mark from pytest import raises from ppb.scenes import BaseScene from ppb.camera import Camera from ppb.scenes import GameObjectCollection class TestEnemy: pass class TestPlayer: pass class TestSubclassPlayer(TestPlayer): pass class TestSprite: pass def containers(): yield GameObjectCollection() yield BaseScene() def players(): yield TestPlayer() yield TestSubclassPlayer() def players_and_containers(): for player in players(): for container in containers(): yield player, container @fixture() def enemies(): return TestEnemy(), TestEnemy() @fixture() def scene(): return BaseScene() @mark.parametrize("player, container", players_and_containers()) def test_add_methods(container, player, enemies): container.add(player) for group, sprite in zip(("red", "blue"), enemies): container.add(sprite, [group]) assert player in container for enemy in enemies: assert enemy in container @mark.parametrize("player, container", players_and_containers()) def test_get_methods(container, player, enemies): sprite = TestSprite() container.add(player, ["red"]) container.add(enemies[0]) container.add(enemies[1], ["red"]) container.add(sprite) assert set(container.get(kind=TestEnemy)) == set(enemies) assert set(container.get(kind=TestPlayer)) == {player} assert set(container.get(kind=TestSprite)) == {sprite} assert set(container.get(tag="red")) == {player, enemies[1]} assert set(container.get(tag="this doesn't exist")) == set() with raises(TypeError): container.get() @mark.parametrize("player, container", players_and_containers()) def test_get_with_string_tags(container, player): """Test that addings a string instead of an array-like throws.""" with raises(TypeError): container.add(player, "player") @mark.parametrize("player, container", players_and_containers()) def test_remove_methods(container, player, enemies): container.add(player, ["test"]) container.add(enemies[0], ["test"]) container.add(enemies[1], ["blue"]) assert player in container assert enemies[0] in container assert enemies[1] in container container.remove(player) assert player not in container for kind in container.kinds: assert player not in container.get(kind=kind) for tag in container.tags: assert player not in container.get(tag=tag) assert enemies[0] in container assert enemies[0] in container.get(tag="test") assert enemies[1] in container @mark.parametrize("player", players()) def test_collection_methods(player, enemies): container = GameObjectCollection() container.add(player) container.add(enemies[0]) # Test __len__ assert len(container) == 2 # Test __contains__ assert player in container assert enemies[1] not in container # Test __iter__ for game_object in container: assert game_object is player or game_object is enemies[0] def test_main_camera(scene): assert isinstance(scene.main_camera, Camera) old_cam = scene.main_camera new_cam = Camera() scene.main_camera = new_cam assert scene.main_camera == new_cam assert old_cam not in scene assert new_cam in scene def test_class_attrs(): class BackgroundScene(BaseScene): background_color = (0, 4, 2) scene = BackgroundScene() assert scene.background_color == (0, 4, 2) scene = BackgroundScene(background_color=(2, 4, 0)) assert scene.background_color == (2, 4, 0)

23.818182

69

0.698473

0106cff2042a26b0a9a8691849dc5f17d184736b

19,924

py

Python

built-in/TensorFlow/Official/nlp/Bert-qa_ID0369_for_TensorFlow/run_pretraining.py

Ascend/modelzoo

f018cfed33dbb1cc2110b9ea2e233333f71cc509

[ "Apache-2.0" ]

12

2020-12-13T08:34:24.000Z

2022-03-20T15:17:17.000Z

built-in/TensorFlow/Official/nlp/Bert-qa_ID0369_for_TensorFlow/run_pretraining.py

Ascend/modelzoo

f018cfed33dbb1cc2110b9ea2e233333f71cc509

[ "Apache-2.0" ]

1

2022-01-20T03:11:05.000Z

2022-01-20T06:53:39.000Z

built-in/TensorFlow/Official/nlp/Bert-qa_ID0369_for_TensorFlow/run_pretraining.py

Ascend/modelzoo

f018cfed33dbb1cc2110b9ea2e233333f71cc509

[ "Apache-2.0" ]

2

2021-07-10T12:40:46.000Z

2021-12-17T07:55:15.000Z

# Apache License # Version 2.0, January 2004 # http://www.apache.org/licenses/ # coding=utf-8 # Copyright 2018 The Google AI Language Team 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. # ============================================================================ # Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Run masked LM/next sentence masked_lm pre-training for BERT.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from npu_bridge.npu_init import * import os import modeling import optimization import tensorflow as tf flags = tf.flags FLAGS = flags.FLAGS ## Required parameters flags.DEFINE_string( "bert_config_file", None, "The config json file corresponding to the pre-trained BERT model. " "This specifies the model architecture.") flags.DEFINE_string( "input_file", None, "Input TF example files (can be a glob or comma separated).") flags.DEFINE_string( "output_dir", None, "The output directory where the model checkpoints will be written.") ## Other parameters flags.DEFINE_string( "init_checkpoint", None, "Initial checkpoint (usually from a pre-trained BERT model).") flags.DEFINE_integer( "max_seq_length", 128, "The maximum total input sequence length after WordPiece tokenization. " "Sequences longer than this will be truncated, and sequences shorter " "than this will be padded. Must match data generation.") flags.DEFINE_integer( "max_predictions_per_seq", 20, "Maximum number of masked LM predictions per sequence. " "Must match data generation.") flags.DEFINE_bool("do_train", False, "Whether to run training.") flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.") flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.") flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.") flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.") flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.") flags.DEFINE_integer("save_checkpoints_steps", 1000, "How often to save the model checkpoint.") flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.") flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.") flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.") tf.flags.DEFINE_string( "tpu_name", None, "The Cloud TPU to use for training. This should be either the name " "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " "url.") tf.flags.DEFINE_string( "tpu_zone", None, "[Optional] GCE zone where the Cloud TPU is located in. If not " "specified, we will attempt to automatically detect the GCE project from " "metadata.") tf.flags.DEFINE_string( "gcp_project", None, "[Optional] Project name for the Cloud TPU-enabled project. If not " "specified, we will attempt to automatically detect the GCE project from " "metadata.") tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.") flags.DEFINE_integer( "num_tpu_cores", 8, "Only used if `use_tpu` is True. Total number of TPU cores to use.") def model_fn_builder(bert_config, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, use_tpu, use_one_hot_embeddings): """Returns `model_fn` closure for TPUEstimator.""" def model_fn(features, labels, mode, params): # pylint: disable=unused-argument """The `model_fn` for TPUEstimator.""" tf.logging.info("*** Features ***") for name in sorted(features.keys()): tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) input_ids = features["input_ids"] input_mask = features["input_mask"] segment_ids = features["segment_ids"] masked_lm_positions = features["masked_lm_positions"] masked_lm_ids = features["masked_lm_ids"] masked_lm_weights = features["masked_lm_weights"] next_sentence_labels = features["next_sentence_labels"] is_training = (mode == tf.estimator.ModeKeys.TRAIN) model = modeling.BertModel( config=bert_config, is_training=is_training, input_ids=input_ids, input_mask=input_mask, token_type_ids=segment_ids, use_one_hot_embeddings=use_one_hot_embeddings) (masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output( bert_config, model.get_sequence_output(), model.get_embedding_table(), masked_lm_positions, masked_lm_ids, masked_lm_weights) (next_sentence_loss, next_sentence_example_loss, next_sentence_log_probs) = get_next_sentence_output( bert_config, model.get_pooled_output(), next_sentence_labels) total_loss = masked_lm_loss + next_sentence_loss tvars = tf.trainable_variables() initialized_variable_names = {} scaffold_fn = None if init_checkpoint: (assignment_map, initialized_variable_names ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) if use_tpu: def tpu_scaffold(): tf.train.init_from_checkpoint(init_checkpoint, assignment_map) return tf.train.Scaffold() scaffold_fn = tpu_scaffold else: tf.train.init_from_checkpoint(init_checkpoint, assignment_map) tf.logging.info("**** Trainable Variables ****") for var in tvars: init_string = "" if var.name in initialized_variable_names: init_string = ", *INIT_FROM_CKPT*" tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string) output_spec = None if mode == tf.estimator.ModeKeys.TRAIN: train_op = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu) output_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, train_op=train_op, scaffold_fn=scaffold_fn) elif mode == tf.estimator.ModeKeys.EVAL: def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights, next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels): """Computes the loss and accuracy of the model.""" masked_lm_log_probs = tf.reshape(masked_lm_log_probs, [-1, masked_lm_log_probs.shape[-1]]) masked_lm_predictions = tf.argmax( masked_lm_log_probs, axis=-1, output_type=tf.int32) masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1]) masked_lm_ids = tf.reshape(masked_lm_ids, [-1]) masked_lm_weights = tf.reshape(masked_lm_weights, [-1]) masked_lm_accuracy = tf.metrics.accuracy( labels=masked_lm_ids, predictions=masked_lm_predictions, weights=masked_lm_weights) masked_lm_mean_loss = tf.metrics.mean( values=masked_lm_example_loss, weights=masked_lm_weights) next_sentence_log_probs = tf.reshape( next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]]) next_sentence_predictions = tf.argmax( next_sentence_log_probs, axis=-1, output_type=tf.int32) next_sentence_labels = tf.reshape(next_sentence_labels, [-1]) next_sentence_accuracy = tf.metrics.accuracy( labels=next_sentence_labels, predictions=next_sentence_predictions) next_sentence_mean_loss = tf.metrics.mean( values=next_sentence_example_loss) return { "masked_lm_accuracy": masked_lm_accuracy, "masked_lm_loss": masked_lm_mean_loss, "next_sentence_accuracy": next_sentence_accuracy, "next_sentence_loss": next_sentence_mean_loss, } eval_metrics = (metric_fn, [ masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights, next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels ]) output_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, eval_metrics=eval_metrics, scaffold_fn=scaffold_fn) else: raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode)) return output_spec return model_fn def get_masked_lm_output(bert_config, input_tensor, output_weights, positions, label_ids, label_weights): """Get loss and log probs for the masked LM.""" input_tensor = gather_indexes(input_tensor, positions) with tf.variable_scope("cls/predictions"): # We apply one more non-linear transformation before the output layer. # This matrix is not used after pre-training. with tf.variable_scope("transform"): input_tensor = tf.layers.dense( input_tensor, units=bert_config.hidden_size, activation=modeling.get_activation(bert_config.hidden_act), kernel_initializer=modeling.create_initializer( bert_config.initializer_range)) input_tensor = modeling.layer_norm(input_tensor) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. output_bias = tf.get_variable( "output_bias", shape=[bert_config.vocab_size], initializer=tf.zeros_initializer()) logits = tf.matmul(input_tensor, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) log_probs = tf.nn.log_softmax(logits, axis=-1) label_ids = tf.reshape(label_ids, [-1]) label_weights = tf.reshape(label_weights, [-1]) one_hot_labels = tf.one_hot( label_ids, depth=bert_config.vocab_size, dtype=tf.float32) # The `positions` tensor might be zero-padded (if the sequence is too # short to have the maximum number of predictions). The `label_weights` # tensor has a value of 1.0 for every real prediction and 0.0 for the # padding predictions. per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1]) numerator = tf.reduce_sum(label_weights * per_example_loss) denominator = tf.reduce_sum(label_weights) + 1e-5 loss = numerator / denominator return (loss, per_example_loss, log_probs) def get_next_sentence_output(bert_config, input_tensor, labels): """Get loss and log probs for the next sentence prediction.""" # Simple binary classification. Note that 0 is "next sentence" and 1 is # "random sentence". This weight matrix is not used after pre-training. with tf.variable_scope("cls/seq_relationship"): output_weights = tf.get_variable( "output_weights", shape=[2, bert_config.hidden_size], initializer=modeling.create_initializer(bert_config.initializer_range)) output_bias = tf.get_variable( "output_bias", shape=[2], initializer=tf.zeros_initializer()) logits = tf.matmul(input_tensor, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) log_probs = tf.nn.log_softmax(logits, axis=-1) labels = tf.reshape(labels, [-1]) one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32) per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) loss = tf.reduce_mean(per_example_loss) return (loss, per_example_loss, log_probs) def gather_indexes(sequence_tensor, positions): """Gathers the vectors at the specific positions over a minibatch.""" sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3) batch_size = sequence_shape[0] seq_length = sequence_shape[1] width = sequence_shape[2] flat_offsets = tf.reshape( tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1]) flat_positions = tf.reshape(positions + flat_offsets, [-1]) flat_sequence_tensor = tf.reshape(sequence_tensor, [batch_size * seq_length, width]) output_tensor = tf.gather(flat_sequence_tensor, flat_positions) return output_tensor def input_fn_builder(input_files, max_seq_length, max_predictions_per_seq, is_training, num_cpu_threads=4): """Creates an `input_fn` closure to be passed to TPUEstimator.""" def input_fn(params): """The actual input function.""" batch_size = params["batch_size"] name_to_features = { "input_ids": tf.FixedLenFeature([max_seq_length], tf.int64), "input_mask": tf.FixedLenFeature([max_seq_length], tf.int64), "segment_ids": tf.FixedLenFeature([max_seq_length], tf.int64), "masked_lm_positions": tf.FixedLenFeature([max_predictions_per_seq], tf.int64), "masked_lm_ids": tf.FixedLenFeature([max_predictions_per_seq], tf.int64), "masked_lm_weights": tf.FixedLenFeature([max_predictions_per_seq], tf.float32), "next_sentence_labels": tf.FixedLenFeature([1], tf.int64), } # For training, we want a lot of parallel reading and shuffling. # For eval, we want no shuffling and parallel reading doesn't matter. if is_training: d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files)) d = d.repeat() d = d.shuffle(buffer_size=len(input_files)) # `cycle_length` is the number of parallel files that get read. cycle_length = min(num_cpu_threads, len(input_files)) # `sloppy` mode means that the interleaving is not exact. This adds # even more randomness to the training pipeline. d = d.apply( tf.contrib.data.parallel_interleave( tf.data.TFRecordDataset, sloppy=is_training, cycle_length=cycle_length)) d = d.shuffle(buffer_size=100) else: d = tf.data.TFRecordDataset(input_files) # Since we evaluate for a fixed number of steps we don't want to encounter # out-of-range exceptions. d = d.repeat() # We must `drop_remainder` on training because the TPU requires fixed # size dimensions. For eval, we assume we are evaluating on the CPU or GPU # and we *don't* want to drop the remainder, otherwise we wont cover # every sample. d = d.apply( tf.contrib.data.map_and_batch( lambda record: _decode_record(record, name_to_features), batch_size=batch_size, num_parallel_batches=num_cpu_threads, drop_remainder=True)) return d return input_fn def _decode_record(record, name_to_features): """Decodes a record to a TensorFlow example.""" example = tf.parse_single_example(record, name_to_features) # tf.Example only supports tf.int64, but the TPU only supports tf.int32. # So cast all int64 to int32. for name in list(example.keys()): t = example[name] if t.dtype == tf.int64: t = tf.to_int32(t) example[name] = t return example def main(_): tf.logging.set_verbosity(tf.logging.INFO) if not FLAGS.do_train and not FLAGS.do_eval: raise ValueError("At least one of `do_train` or `do_eval` must be True.") bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) tf.gfile.MakeDirs(FLAGS.output_dir) input_files = [] for input_pattern in FLAGS.input_file.split(","): input_files.extend(tf.gfile.Glob(input_pattern)) tf.logging.info("*** Input Files ***") for input_file in input_files: tf.logging.info(" %s" % input_file) tpu_cluster_resolver = None if FLAGS.use_tpu and FLAGS.tpu_name: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 #for NPU session_config = tf.ConfigProto() custom_op = session_config.graph_options.rewrite_options.custom_optimizers.add() custom_op.name = "NpuOptimizer" custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") #for NPU run_config = tf.contrib.tpu.RunConfig( session_config = session_config, cluster=tpu_cluster_resolver, master=FLAGS.master, model_dir=FLAGS.output_dir, save_checkpoints_steps=FLAGS.save_checkpoints_steps, tpu_config=tf.contrib.tpu.TPUConfig( iterations_per_loop=FLAGS.iterations_per_loop, num_shards=FLAGS.num_tpu_cores, per_host_input_for_training=is_per_host)) model_fn = model_fn_builder( bert_config=bert_config, init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=FLAGS.num_train_steps, num_warmup_steps=FLAGS.num_warmup_steps, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu) # If TPU is not available, this will fall back to normal Estimator on CPU # or GPU. estimator = tf.contrib.tpu.TPUEstimator( use_tpu=False, model_fn=model_fn, config=npu_run_config_init(run_config=run_config), train_batch_size=FLAGS.train_batch_size, eval_batch_size=FLAGS.eval_batch_size, eval_on_tpu=False, export_to_tpu=False) if FLAGS.do_train: tf.logging.info("***** Running training *****") tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) train_input_fn = input_fn_builder( input_files=input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=True) estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps, hooks=npu_hooks_append()) if FLAGS.do_eval: tf.logging.info("***** Running evaluation *****") tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) eval_input_fn = input_fn_builder( input_files=input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=False) result = estimator.evaluate( input_fn=eval_input_fn, steps=FLAGS.max_eval_steps) output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") with tf.gfile.GFile(output_eval_file, "w") as writer: tf.logging.info("***** Eval results *****") for key in sorted(result.keys()): tf.logging.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) if __name__ == "__main__": flags.mark_flag_as_required("input_file") flags.mark_flag_as_required("bert_config_file") flags.mark_flag_as_required("output_dir") tf.app.run()

38.38921

103

0.696748

7f95d96d263b208d9663c4a9037679523e9642ad

3,582

py

Python

reportportal_client/core/rp_response.py

Scanters/client-Python

ece2a12ac390d8d66d507c5d61104b695851ff5d

[ "Apache-2.0" ]

null

reportportal_client/core/rp_response.py

Scanters/client-Python

ece2a12ac390d8d66d507c5d61104b695851ff5d

[ "Apache-2.0" ]

null

reportportal_client/core/rp_response.py

Scanters/client-Python

ece2a12ac390d8d66d507c5d61104b695851ff5d

[ "Apache-2.0" ]

1

2020-04-10T07:23:15.000Z

# -* encoding: utf-8 *- from requests import Response from reportportal_client.static.defines import NOT_FOUND from reportportal_client.static.exception import EntryCreatedError, OperationCompletionError, ResponseError class RPMessage(object): __slots__ = ["message", "error_code"] def __init__(self, data): assert isinstance(data, dict) self.message = data.get("message", NOT_FOUND) self.error_code = data.get("error_code", NOT_FOUND) def __str__(self): if self.error_code is NOT_FOUND: return self.message return "{error_code}: {message}".format(error_code=self.error_code, message=self.message) @property def is_empty(self): return self.message is NOT_FOUND class RPResponse(object): __slots__ = ["_data"] def __init__(self, data): # type: (Response) -> None self._data = self._to_json(data) # type: dict @staticmethod def _get_json(data): # type: (Response) -> dict if not data.text: return {} try: return data.json() except ValueError as error: raise ResponseError("Invalid response: {}: {}".format(error, data.text)) @property def json(self): return self._data @property def is_success(self): return True @property def id(self): return self.json.get("id", NOT_FOUND) @property def message(self): return self.json.get("msg", NOT_FOUND) @property def messages(self): return tuple(self.iter_messages()) def iter_messages(self): data = self.json.get("responses", [self.json]) for chunk in data: message = RPMessage(chunk) if not message.is_empty: yield message # ---- OLD --- def _get_id(self, response): try: return self._get_data(response)["id"] except KeyError: raise EntryCreatedError( "No 'id' in response: {0}".format(response.text)) def _get_msg(self, response): try: return self._get_data(response)["msg"] except KeyError: raise OperationCompletionError( "No 'msg' in response: {0}".format(response.text)) def _get_data(self, response): data = self._to_json(response) error_messages = self._get_messages(data) error_count = len(error_messages) if error_count == 1: raise ResponseError(error_messages[0]) elif error_count > 1: raise ResponseError( "\n - ".join(["Multiple errors:"] + error_messages)) elif not response.ok: response.raise_for_status() elif not data: raise ResponseError("Empty response") else: return data def _to_json(self, response): try: if response.text: return response.json() else: return {} except ValueError as value_error: raise ResponseError( "Invalid response: {0}: {1}".format(value_error, response.text)) def _get_messages(self, data): error_messages = [] for ret in data.get("responses", [data]): if "message" in ret: if "error_code" in ret: error_messages.append( "{0}: {1}".format(ret["error_code"], ret["message"])) else: error_messages.append(ret["message"]) return error_messages

27.984375

107

0.577052

fbbd8e3d5f7ba1c8b1f2cae7c06a71daeba960ea

6,718

py

Python

accounts/views.py

rabiyulfahimhasim786/classbasedview

b09bfcb20e9cd5584a3fef9797251864ff0233a1

[ "MIT" ]

null

accounts/views.py

rabiyulfahimhasim786/classbasedview

b09bfcb20e9cd5584a3fef9797251864ff0233a1

[ "MIT" ]

null

accounts/views.py

rabiyulfahimhasim786/classbasedview

b09bfcb20e9cd5584a3fef9797251864ff0233a1

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpResponse, JsonResponse from rest_framework.response import Response from .serializers import CurrenciesSerializer, StandardsSerializer, CountriesSerializer, TagSerializer from .models import Currencies, Standards, Countries, Tag from django.http import Http404 from rest_framework.views import APIView from rest_framework import status def new(request): return HttpResponse("Hello, world. You're at the accounts index.") def index(request): return render(request, './index.html') class CurrencyList(APIView): """ List all snippets, or create a new snippet. """ def get(self, request, format=None): currencies = Currencies.objects.all() serializer = CurrenciesSerializer(currencies, many=True) return Response(serializer.data) def post(self, request, format=None): serializer = CurrenciesSerializer(data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data, status=status.HTTP_201_CREATED) return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) class CurrencyDetail(APIView): """ Retrieve, update or delete a snippet instance. """ def get_object(self, pk): try: return Currencies.objects.get(pk=pk) except Currencies.DoesNotExist: raise Http404 def get(self, request, pk, format=None): currencies = self.get_object(pk) serializer = CurrenciesSerializer(currencies) return Response(serializer.data) def put(self, request, pk, format=None): currencies = self.get_object(pk) serializer = CurrenciesSerializer(currencies, data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data) return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) def delete(self, request, pk, format=None): snippet = self.get_object(pk) snippet.delete() return Response(status=status.HTTP_204_NO_CONTENT) class CountryList(APIView): """ List all snippets, or create a new snippet. """ def get(self, request, format=None): countries = Countries.objects.all() serializer = CountriesSerializer(countries, many=True) return Response(serializer.data) def post(self, request, format=None): serializer = CountriesSerializer(data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data, status=status.HTTP_201_CREATED) return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) class CountryDetail(APIView): """ Retrieve, update or delete a snippet instance. """ def get_object(self, pk): try: return Countries.objects.get(pk=pk) except Countries.DoesNotExist: raise Http404 def get(self, request, pk, format=None): countries = self.get_object(pk) serializer = CountriesSerializer(countries) return Response(serializer.data) def put(self, request, pk, format=None): countries = self.get_object(pk) serializer = CountriesSerializer(countries, data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data) return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) def delete(self, request, pk, format=None): snippet = self.get_object(pk) snippet.delete() return Response(status=status.HTTP_204_NO_CONTENT) class StandardList(APIView): """ List all snippets, or create a new snippet. """ def get(self, request, format=None): standards = Standards.objects.all() serializer = StandardsSerializer(standards, many=True) return Response(serializer.data) def post(self, request, format=None): serializer = StandardsSerializer(data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data, status=status.HTTP_201_CREATED) return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) class StandardDetail(APIView): """ Retrieve, update or delete a snippet instance. """ def get_object(self, pk): try: return Standards.objects.get(pk=pk) except Standards.DoesNotExist: raise Http404 def get(self, request, pk, format=None): standards = self.get_object(pk) serializer = StandardsSerializer(standards) return Response(serializer.data) def put(self, request, pk, format=None): standards = self.get_object(pk) serializer = StandardsSerializer(standards, data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data) return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) def delete(self, request, pk, format=None): snippet = self.get_object(pk) snippet.delete() return Response(status=status.HTTP_204_NO_CONTENT) class TagList(APIView): """ List all snippets, or create a new snippet. """ def get(self, request, format=None): tag = Tag.objects.all() serializer = TagSerializer(tag, many=True) return Response(serializer.data) def post(self, request, format=None): serializer = TagSerializer(data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data, status=status.HTTP_201_CREATED) return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) class TagDetail(APIView): """ Retrieve, update or delete a snippet instance. """ def get_object(self, pk): try: return Tag.objects.get(pk=pk) except Tag.DoesNotExist: raise Http404 def get(self, request, pk, format=None): standards = self.get_object(pk) serializer = TagSerializer(standards) return Response(serializer.data) def put(self, request, pk, format=None): Tag = self.get_object(pk) serializer = TagSerializer(Tag, data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data) return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) def delete(self, request, pk, format=None): snippet = self.get_object(pk) snippet.delete() return Response(status=status.HTTP_204_NO_CONTENT)

34.27551

102

0.670586

b68fe2e6aae1a445fb0badd6bc2c5d265f57a98b

5,184

py

Python

examples/models/glyphs.py

pyjsdev/googlemap_flask

9d0dd899a9cbf756b3d83c33e3d8a47e7db40cc5

[ "BSD-3-Clause" ]

6

2018-02-13T11:06:40.000Z

2020-10-23T09:30:46.000Z

examples/models/glyphs.py

pyjsdev/googlemap_flask

9d0dd899a9cbf756b3d83c33e3d8a47e7db40cc5

[ "BSD-3-Clause" ]

6

2018-11-29T15:40:20.000Z

2021-05-07T14:59:30.000Z

examples/models/glyphs.py

pyjsdev/googlemap_flask

9d0dd899a9cbf756b3d83c33e3d8a47e7db40cc5

[ "BSD-3-Clause" ]

3

2018-06-20T11:43:40.000Z

2021-12-21T06:51:56.000Z

import numpy as np from bokeh.models import ColumnDataSource, DataRange1d, Plot, LinearAxis, Grid, HoverTool from bokeh.models.widgets import Tabs, Panel, Paragraph from bokeh.models.layouts import Column from bokeh.models.glyphs import ( AnnularWedge, Annulus, Arc, Bezier, Circle, ImageURL, Line, MultiLine, Oval, Patch, Patches, Quad, Quadratic, Ray, Rect, Segment, Square, Text, Wedge, CircleX, Triangle, Cross, Diamond, InvertedTriangle, SquareX, Asterisk, SquareCross, DiamondCross, CircleCross, X ) from bokeh.document import Document from bokeh.embed import file_html from bokeh.resources import INLINE from bokeh.util.browser import view N = 9 x = np.linspace(-2, 2, N) y = x**2 sizes = np.linspace(10, 20, N) xpts = np.array([-.09, -.12, .0, .12, .09]) ypts = np.array([-.1, .02, .1, .02, -.1]) source = ColumnDataSource(dict( x = x, y = y, sizes = sizes, xs = [ xpts + xx for xx in x ], ys = [ ypts + yy for yy in y ], xp02 = x + 0.2, xp01 = x + 0.1, xm01 = x - 0.1, yp01 = y + 0.1, ym01 = y - 0.1, )) xdr = DataRange1d() ydr = DataRange1d() def screen(value): return dict(value=value, units="screen") glyphs = [ ("annular_wedge", AnnularWedge(x="x", y="y", inner_radius=screen(10), outer_radius=screen(20), start_angle=0.6, end_angle=4.1, fill_color="#8888ee")), ("annulus", Annulus(x="x", y="y", inner_radius=screen(10), outer_radius=screen(20), fill_color="#7FC97F")), ("arc", Arc(x="x", y="y", radius=screen(20), start_angle=0.6, end_angle=4.1, line_color="#BEAED4", line_width=3)), ("bezier", Bezier(x0="x", y0="y", x1="xp02", y1="y", cx0="xp01", cy0="yp01", cx1="xm01", cy1="ym01", line_color="#D95F02", line_width=2)), ("image_url", ImageURL(x="x", y="y", w=0.4, h=0.4, url=dict(value="http://bokeh.pydata.org/en/latest/_static/images/logo.png"), anchor="center")), ("line", Line(x="x", y="y", line_color="#F46D43")), ("multi_line", MultiLine(xs="xs", ys="ys", line_color="#8073AC", line_width=2)), ("oval", Oval(x="x", y="y", width=screen(15), height=screen(25), angle=-0.7, fill_color="#1D91C0")), ("patch", Patch(x="x", y="y", fill_color="#A6CEE3")), ("patches", Patches(xs="xs", ys="ys", fill_color="#FB9A99")), ("quad", Quad(left="x", right="xp01", top="y", bottom="ym01", fill_color="#B3DE69")), ("quadratic", Quadratic(x0="x", y0="y", x1="xp02", y1="y", cx="xp01", cy="yp01", line_color="#4DAF4A", line_width=3)), ("ray", Ray(x="x", y="y", length=45, angle=-0.7, line_color="#FB8072", line_width=2)), ("rect", Rect(x="x", y="y", width=screen(10), height=screen(20), angle=-0.7, fill_color="#CAB2D6")), ("segment", Segment(x0="x", y0="y", x1="xm01", y1="ym01", line_color="#F4A582", line_width=3)), ("text", Text(x="x", y="y", text=["hello"])), ("wedge", Wedge(x="x", y="y", radius=screen(15), start_angle=0.6, end_angle=4.1, fill_color="#B3DE69")), ] markers = [ ("circle", Circle(x="x", y="y", radius=0.1, fill_color="#3288BD")), ("circle_x", CircleX(x="x", y="y", size="sizes", line_color="#DD1C77", fill_color=None)), ("circle_cross", CircleCross(x="x", y="y", size="sizes", line_color="#FB8072", fill_color=None, line_width=2)), ("square", Square(x="x", y="y", size="sizes", fill_color="#74ADD1")), ("square_x", SquareX(x="x", y="y", size="sizes", line_color="#FDAE6B", fill_color=None, line_width=2)), ("square_cross", SquareCross(x="x", y="y", size="sizes", line_color="#7FC97F", fill_color=None, line_width=2)), ("diamond", Diamond(x="x", y="y", size="sizes", line_color="#1C9099", line_width=2)), ("diamond_cross", DiamondCross(x="x", y="y", size="sizes", line_color="#386CB0", fill_color=None, line_width=2)), ("triangle", Triangle(x="x", y="y", size="sizes", line_color="#99D594", line_width=2)), ("inverted_triangle", InvertedTriangle(x="x", y="y", size="sizes", line_color="#DE2D26", line_width=2)), ("cross", Cross(x="x", y="y", size="sizes", line_color="#E6550D", fill_color=None, line_width=2)), ("asterisk", Asterisk(x="x", y="y", size="sizes", line_color="#F0027F", fill_color=None, line_width=2)), ("x", X(x="x", y="y", size="sizes", line_color="thistle", fill_color=None, line_width=2)), ] def make_tab(title, glyph): plot = Plot(x_range=xdr, y_range=ydr) plot.title.text = title plot.add_glyph(source, glyph) xaxis = LinearAxis() plot.add_layout(xaxis, 'below') yaxis = LinearAxis() plot.add_layout(yaxis, 'left') plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker)) plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker)) plot.add_tools(HoverTool()) tab = Panel(child=plot, title=title) return tab def make_tabs(objs): return Tabs(tabs=[ make_tab(title, obj) for title, obj in objs ], width=600) layout = Column(children=[Paragraph(text="Only Image and ImageRGBA glyphs are not demonstrated."), make_tabs(glyphs), make_tabs(markers)]) doc = Document() doc.add_root(layout) if __name__ == "__main__": doc.validate() filename = "glyphs.html" with open(filename, "w") as f: f.write(file_html(doc, INLINE, "Glyphs")) print("Wrote %s" % filename) view(filename)

44.689655

154

0.63561

656ba7dcf0a02a597ec93c664469f8d58f0d198f

7,189

py

Python

src/decoder.py

aarora8/VistaOCR

dd8a41e45d4efe49816054c097d06be41937fae1

[ "Apache-2.0" ]

24

2018-06-05T06:38:24.000Z

2021-12-08T12:20:06.000Z

src/decoder.py

aarora8/VistaOCR

dd8a41e45d4efe49816054c097d06be41937fae1

[ "Apache-2.0" ]

3

2018-08-16T23:47:30.000Z

2020-02-13T09:48:53.000Z

src/decoder.py

aarora8/VistaOCR

dd8a41e45d4efe49816054c097d06be41937fae1

[ "Apache-2.0" ]

10

2018-05-07T17:30:45.000Z

2020-01-29T10:34:02.000Z

import sys import os import numpy as np import torch from datetime import datetime from concurrent.futures import ThreadPoolExecutor from textutils import uxxxx_to_utf8 class LmDecoder: # max active 3k - 7k # beam: 11 - 17 # lattice_beam 6-9 def __init__(self, alphabet, lm_file, word_sym_file, lm_units, acoustic_weight=0.8, max_active=5000, beam=16.0, lattice_beam=10.0): self.alphabet = alphabet # Only pull in if needed script_path = os.path.dirname(os.path.realpath(__file__)) #sys.path.append(script_path + "/../eesen") sys.path.insert(0,'/home/hltcoe/srawls/pyeesen') import eesen print("Loading eesen from: %s" % eesen.__file__) self.acoustic_weight = acoustic_weight self.lattice_decoder = eesen.LatticeDecoder(lm_file, word_sym_file, self.acoustic_weight, max_active, beam, lattice_beam) #self.lattice_decoder.EnableLattices("/home/hltcoe/srawls/tmp.lat.gz") # Need to keep track of model-alphabet to LM-alphabet conversion units = ['<ctc-blank>'] with open(lm_units, 'r') as fh: for line in fh: units.append(line.strip().split(' ')[0]) self.lmidx_to_char = units self.lmchar_to_idx = dict(zip(units, range(len(units)))) # Let's precompute some stuff to make lm faster print("Prep work...") self.lm_swap_idxs = [] self.lm_swap_idxs_modelidx = [] self.lm_swap_idxs_lmidx = [] self.add_to_blank_char = [] self.add_to_blank_idx = [] for model_idx in range(len(self.alphabet.idx_to_char)): char = self.alphabet.idx_to_char[model_idx] if not char in self.lmchar_to_idx: self.add_to_blank_char.append(char) self.add_to_blank_idx.append(model_idx) continue lm_idx = self.lmchar_to_idx[char] self.lm_swap_idxs.append( (model_idx,lm_idx) ) self.lm_swap_idxs_modelidx.append(model_idx) self.lm_swap_idxs_lmidx.append(lm_idx) print("Done prep work") if len(self.add_to_blank_char) > 0: print("\tFYI: these chars were in model but not in LM: %s" % str(self.add_to_blank_char)) def decode(self, tp_executor, model_output, batch_actual_timesteps, uttids, uxxxx=False): T = model_output.size()[0] B = model_output.size()[1] # Actual model output is not set to probability vector yet, need to run softmax probs = torch.nn.functional.log_softmax(model_output.view(-1, model_output.size(2)), dim=1).view(model_output.size(0), model_output.size(1), -1) # Need to take care of issue where prob goes to a char in model-alphabet but not in lm-alphabet # Just assign high prob to ctc-blank? #print("Sum of missing chars' prob = %s" % str(model_output[:,:,self.add_to_blank_idx].sum(dim=2))) #probs[:,:,0] += probs[:,:,self.add_to_blank_idx].sum(dim=2) #probs[:,:,self.add_to_blank_idx] = 0 # Make sure we're on CPU probs = probs.data.cpu() # We process decoder parallely in worker threads; store those async futures here decoder_futures = [None]*B def decode_helper(probs, uttid, uxxxx): res = self.lattice_decoder.Decode(probs, uttid) res_utf8 = '' if uxxxx == False: for uxxxx_word in res.split(' '): res_utf8 += ''.join([uxxxx_to_utf8(r) for r in uxxxx_word.split('_')]) res = res_utf8 else: res_flatten = '' for uxxxx_word in res.split(' '): for uxxxx_char in uxxxx_word.split('_'): res_flatten += uxxxx_char res_flatten += ' ' res = res_flatten.strip() return res for b in range(B): probs_remapped = np.full( (batch_actual_timesteps[b], len(self.lmidx_to_char)), np.log(1e-10)) probs_remapped[:,self.lm_swap_idxs_lmidx] = probs[:batch_actual_timesteps[b], b, self.lm_swap_idxs_modelidx] # Just for right-to-left languages! #probs_remapped = probs_remapped[::-1] decoder_futures[b] = tp_executor.submit(decode_helper, probs_remapped, uttids[b], uxxxx) # At this point all decoder tasks are done (we are outside scope of with ThreadPoolExecutor, so it has finished) return decoder_futures class ArgmaxDecoder: def __init__(self, alphabet): self.alphabet = alphabet def decode(self, model_output, batch_actual_timesteps, uxxxx=False, lang=None): start_decode = datetime.now() if lang is None: min_prob_thresh = 3 * 1 / len(self.alphabet) else: min_prob_thresh = 3 * 1 / len(self.alphabet[lang]) T = model_output.size()[0] B = model_output.size()[1] prev_char = ['' for _ in range(B)] result = ['' for _ in range(B)] for t in range(T): # #gpu argmax (bug!!!!!) # gpu_argmax = True # argmaxs, argmax_idxs = model_output.data[t].max(dim=1) # argmaxs.squeeze_() # argmax_idxs.squeeze_() # cpu argmax gpu_argmax = False model_output_at_t_cpu = model_output.data[t].cpu().numpy() argmaxs = model_output_at_t_cpu.max(1).flatten() argmax_idxs = model_output_at_t_cpu.argmax(1).flatten() for b in range(B): # Only look at valid model output for this batch entry if t >= batch_actual_timesteps[b]: continue if argmax_idxs[b] == 0: # CTC Blank prev_char[b] = '' continue # Heuristic # If model is predicting very low probability for all letters in alphabet, treat that the # samed as a CTC blank if argmaxs[b] < min_prob_thresh: prev_char[b] = '' continue if lang is None: char = self.alphabet.idx_to_char[argmax_idxs[b]] else: char = self.alphabet[lang].idx_to_char[argmax_idxs[b]] if prev_char[b] == char: continue result[b] += char prev_char[b] = char # Add a space to all but last iteration if t != T - 1: result[b] += ' ' # Strip off final token-stream space if needed for b in range(B): if len(result[b]) > 0 and result[b][-1] == ' ': result[b] = result[b][:-1] # Check if we should return utf8 output if uxxxx == False: result = [uxxxx_to_utf8(r) for r in result] return result

38.650538

135

0.559327

30ef54f0af0c0a8d43905af968d5f90406306d35

19,109

py

Python

gamestonk_terminal/main_helper.py

crsdgzmn/GamestonkTerminal

8cfa224cbb2833496ee4a1199356096a8b824a13

[ "MIT" ]

1

2021-05-04T04:57:13.000Z

gamestonk_terminal/main_helper.py

lolrenx/GamestonkTerminal

eb2b0d766bf1b6bb8656d6733083962efb152fe2

[ "MIT" ]

null

gamestonk_terminal/main_helper.py

lolrenx/GamestonkTerminal

eb2b0d766bf1b6bb8656d6733083962efb152fe2

[ "MIT" ]

null

import argparse from sys import stdout import random from datetime import datetime, timedelta import matplotlib.pyplot as plt import pandas as pd from alpha_vantage.timeseries import TimeSeries import mplfinance as mpf import yfinance as yf import pytz from gamestonk_terminal.helper_funcs import ( valid_date, plot_view_stock, parse_known_args_and_warn, check_ohlc, lett_to_num, check_sources, plot_autoscale, ) from gamestonk_terminal import config_terminal as cfg from gamestonk_terminal import feature_flags as gtff from gamestonk_terminal.technical_analysis import trendline_api as trend def print_help(s_ticker, s_start, s_interval, b_is_market_open): """Print help""" print("What do you want to do?") print(" help help to see this menu again") print(" quit to abandon the program") print("") print(" clear clear a specific stock ticker from analysis") print(" load load a specific stock ticker for analysis") print(" candle view a candle chart for a specific stock ticker") print(" view view and load a specific stock ticker for technical analysis") if s_ticker: print( " export export the currently loaded dataframe to a file or stdout" ) s_intraday = (f"Intraday {s_interval}", "Daily")[s_interval == "1440min"] if s_ticker and s_start: print(f"\n{s_intraday} Stock: {s_ticker} (from {s_start.strftime('%Y-%m-%d')})") elif s_ticker: print(f"\n{s_intraday} Stock: {s_ticker}") else: print("\nStock: ?") print(f"Market {('CLOSED', 'OPEN')[b_is_market_open]}.\n") print( " > disc discover trending stocks, \t e.g. map, sectors, high short interest" ) print( " > scr screener stocks, \t\t e.g. overview/performance, using preset filters" ) print(" > mill papermill menu, \t\t menu to generate notebook reports") print(" > econ economic data, \t\t e.g.: FRED, events") print( " > pa portfolio analysis, \t\t supports: robinhood, alpaca, ally " ) print( " > crypto cryptocurrencies, \t\t from: coingecko, coinmarketcap, binance" ) print( " > po portfolio optimization, \t optimal portfolio weights from pyportfolioopt" ) print(" > fx forex menu, \t\t\t forex support through Oanda") print(" > rc resource collection, \t\t e.g. hf letters") if s_ticker: print( " > ba behavioural analysis, \t from: reddit, stocktwits, twitter, google" ) print( " > res research web page, \t e.g.: macroaxis, yahoo finance, fool" ) print( " > ca comparison analysis, \t e.g.: historical, correlation, financials" ) print( " > fa fundamental analysis, \t e.g.: income, balance, cash, earnings" ) print( " > ta technical analysis, \t e.g.: ema, macd, rsi, adx, bbands, obv" ) print( " > bt strategy backtester, \t e.g.: simple ema, ema cross, rsi strategies" ) print( " > dd in-depth due-diligence, \t e.g.: news, analyst, shorts, insider, sec" ) print( " > eda exploratory data analysis,\t e.g.: decompose, cusum, residuals analysis" ) print( " > pred prediction techniques, \t e.g.: regression, arima, rnn, lstm, prophet" ) print( " > ra residuals analysis, \t e.g.: model fit, qqplot, hypothesis test" ) print( " > op options info , \t e.g.: volume and open interest" ) print("") def clear(l_args, s_ticker, s_start, s_interval, df_stock): parser = argparse.ArgumentParser( add_help=False, prog="clear", description="""Clear previously loaded stock ticker.""", ) try: ns_parser = parse_known_args_and_warn(parser, l_args) if not ns_parser: return "", "", "", pd.DataFrame() print("Clearing stock ticker to be used for analysis\n") return "", "", "", pd.DataFrame() except SystemExit: print("") return s_ticker, s_start, s_interval, df_stock def load(l_args, s_ticker, s_start, s_interval, df_stock): parser = argparse.ArgumentParser( add_help=False, prog="load", description="Load stock ticker to perform analysis on. When the data source is 'yf', an Indian ticker can be" " loaded by using '.NS' at the end, e.g. 'SBIN.NS'. See available market in" " https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html.", ) parser.add_argument( "-t", "--ticker", action="store", dest="s_ticker", required=True, help="Stock ticker", ) parser.add_argument( "-s", "--start", type=valid_date, default="2015-01-01", dest="s_start_date", help="The starting date (format YYYY-MM-DD) of the stock", ) parser.add_argument( "-i", "--interval", action="store", dest="n_interval", type=int, default=1440, choices=[1, 5, 15, 30, 60], help="Intraday stock minutes", ) parser.add_argument( "--source", action="store", dest="source", type=check_sources, default="yf", help="Source of historical data. 'yf' and 'av' available.", ) parser.add_argument( "-p", "--prepost", action="store_true", default=False, dest="b_prepost", help="Pre/After market hours. Only works for 'yf' source, and intraday data", ) try: # For the case where a user uses: 'load BB' if l_args: if "-" not in l_args[0]: l_args.insert(0, "-t") ns_parser = parse_known_args_and_warn(parser, l_args) if not ns_parser: return [s_ticker, s_start, s_interval, df_stock] # Daily if ns_parser.n_interval == 1440: # Alpha Vantage Source if ns_parser.source == "av": ts = TimeSeries(key=cfg.API_KEY_ALPHAVANTAGE, output_format="pandas") # pylint: disable=unbalanced-tuple-unpacking df_stock_candidate, _ = ts.get_daily_adjusted( symbol=ns_parser.s_ticker, outputsize="full" ) # Check that loading a stock was not successful if df_stock_candidate.empty: print("") return [s_ticker, s_start, s_interval, df_stock] # pylint: disable=no-member df_stock_candidate.sort_index(ascending=True, inplace=True) # Slice dataframe from the starting date YYYY-MM-DD selected df_stock_candidate = df_stock_candidate[ns_parser.s_start_date :] # Yahoo Finance Source elif ns_parser.source == "yf": df_stock_candidate = yf.download( ns_parser.s_ticker, start=ns_parser.s_start_date, progress=False ) # Check that loading a stock was not successful if df_stock_candidate.empty: print("") return [s_ticker, s_start, s_interval, df_stock] df_stock_candidate = df_stock_candidate.rename( columns={ "Open": "1. open", "High": "2. high", "Low": "3. low", "Close": "4. close", "Adj Close": "5. adjusted close", "Volume": "6. volume", } ) df_stock_candidate.index.name = "date" # Check if start time from dataframe is more recent than specified if df_stock_candidate.index[0] > pd.to_datetime(ns_parser.s_start_date): s_start = df_stock_candidate.index[0] else: s_start = ns_parser.s_start_date # Intraday else: # Alpha Vantage Source if ns_parser.source == "av": ts = TimeSeries(key=cfg.API_KEY_ALPHAVANTAGE, output_format="pandas") # pylint: disable=unbalanced-tuple-unpacking df_stock_candidate, _ = ts.get_intraday( symbol=ns_parser.s_ticker, outputsize="full", interval=str(ns_parser.n_interval) + "min", ) # Check that loading a stock was not successful if df_stock_candidate.empty: print("") return [s_ticker, s_start, s_interval, df_stock] # pylint: disable=no-member df_stock_candidate.sort_index(ascending=True, inplace=True) # Slice dataframe from the starting date YYYY-MM-DD selected df_stock_candidate = df_stock_candidate[ns_parser.s_start_date :] # Check if start time from dataframe is more recent than specified if df_stock_candidate.index[0] > pd.to_datetime(ns_parser.s_start_date): s_start = df_stock_candidate.index[0] else: s_start = ns_parser.s_start_date # Yahoo Finance Source elif ns_parser.source == "yf": s_int = str(ns_parser.n_interval) + "m" d_granularity = {"1m": 6, "5m": 59, "15m": 59, "30m": 59, "60m": 729} s_start_dt = datetime.utcnow() - timedelta(days=d_granularity[s_int]) s_date_start = s_start_dt.strftime("%Y-%m-%d") if s_start_dt > ns_parser.s_start_date: # Using Yahoo Finance with granularity {s_int} the starting date is set to: {s_date_start} df_stock_candidate = yf.download( ns_parser.s_ticker, start=s_date_start, progress=False, interval=s_int, prepost=ns_parser.b_prepost, ) else: df_stock_candidate = yf.download( ns_parser.s_ticker, start=ns_parser.s_start_date.strftime("%Y-%m-%d"), progress=False, interval=s_int, prepost=ns_parser.b_prepost, ) # Check that loading a stock was not successful if df_stock_candidate.empty: print("") return [s_ticker, s_start, s_interval, df_stock] if s_start_dt > ns_parser.s_start_date: s_start = pytz.utc.localize(s_start_dt) else: s_start = ns_parser.s_start_date df_stock_candidate = df_stock_candidate.rename( columns={ "Open": "1. open", "High": "2. high", "Low": "3. low", "Close": "4. close", "Adj Close": "5. adjusted close", "Volume": "6. volume", } ) df_stock_candidate.index.name = "date" s_intraday = (f"Intraday {s_interval}", "Daily")[ns_parser.n_interval == 1440] print( f"Loading {s_intraday} {ns_parser.s_ticker.upper()} stock " f"with starting period {s_start.strftime('%Y-%m-%d')} for analysis.\n" ) return [ ns_parser.s_ticker.upper(), s_start, str(ns_parser.n_interval) + "min", df_stock_candidate, ] except Exception as e: print(e, "\nEither the ticker or the API_KEY are invalids. Try again!\n") return [s_ticker, s_start, s_interval, df_stock] except SystemExit: print("") return [s_ticker, s_start, s_interval, df_stock] def candle(s_ticker: str, s_start: str): df_stock = trend.load_ticker(s_ticker, s_start) df_stock = trend.find_trendline(df_stock, "OC_High", "high") df_stock = trend.find_trendline(df_stock, "OC_Low", "low") mc = mpf.make_marketcolors( up="green", down="red", edge="black", wick="black", volume="in", ohlc="i" ) s = mpf.make_mpf_style(marketcolors=mc, gridstyle=":", y_on_right=True) ap0 = [] if "OC_High_trend" in df_stock.columns: ap0.append( mpf.make_addplot(df_stock["OC_High_trend"], color="g"), ) if "OC_Low_trend" in df_stock.columns: ap0.append( mpf.make_addplot(df_stock["OC_Low_trend"], color="b"), ) if gtff.USE_ION: plt.ion() mpf.plot( df_stock, type="candle", mav=(20, 50), volume=True, title=f"\n{s_ticker} - Last 6 months", addplot=ap0, xrotation=10, style=s, figratio=(10, 7), figscale=1.10, figsize=(plot_autoscale()), update_width_config=dict( candle_linewidth=1.0, candle_width=0.8, volume_linewidth=1.0 ), ) print("") def view(l_args, s_ticker, s_start, s_interval, df_stock): parser = argparse.ArgumentParser( add_help=False, prog="view", description="Visualize historical data of a stock. An alpha_vantage key is necessary.", ) if s_ticker: parser.add_argument( "-t", "--ticker", action="store", dest="s_ticker", default=s_ticker, help="Stock ticker", ) else: parser.add_argument( "-t", "--ticker", action="store", dest="s_ticker", required=True, help="Stock ticker", ) parser.add_argument( "-s", "--start", type=valid_date, dest="s_start_date", default=s_start, help="The starting date (format YYYY-MM-DD) of the stock", ) parser.add_argument( "-i", "--interval", action="store", dest="n_interval", type=int, default=0, choices=[1, 5, 15, 30, 60], help="Intraday stock minutes", ) parser.add_argument( "--type", action="store", dest="type", type=check_ohlc, default="a", # in case it's adjusted close help=( "ohlc corresponds to types: open; high; low; close; " "while oc corresponds to types: open; close" ), ) try: ns_parser = parse_known_args_and_warn(parser, l_args) if not ns_parser: return except SystemExit: print("") return # Update values: s_ticker = ns_parser.s_ticker # A new interval intraday period was given if ns_parser.n_interval != 0: s_interval = str(ns_parser.n_interval) + "min" type_candles = lett_to_num(ns_parser.type) df_stock.sort_index(ascending=True, inplace=True) # Slice dataframe from the starting date YYYY-MM-DD selected df_stock = df_stock[ns_parser.s_start_date :] # Daily if s_interval == "1440min": # The default doesn't exist for intradaily data ln_col_idx = [int(x) - 1 for x in list(type_candles)] # Check that the types given are not bigger than 4, as there are only 5 types (0-4) # pylint: disable=len-as-condition if len([i for i in ln_col_idx if i > 4]) > 0: print("An index bigger than 4 was given, which is wrong. Try again") return # Append last column of df to be filtered which corresponds to: 6. Volume ln_col_idx.append(5) # Intraday else: # The default doesn't exist for intradaily data if ns_parser.type == "a": ln_col_idx = [3] else: ln_col_idx = [int(x) - 1 for x in list(type_candles)] # Check that the types given are not bigger than 3, as there are only 4 types (0-3) # pylint: disable=len-as-condition if len([i for i in ln_col_idx if i > 3]) > 0: print("An index bigger than 3 was given, which is wrong. Try again") return # Append last column of df to be filtered which corresponds to: 5. Volume ln_col_idx.append(4) # Plot view of the stock plot_view_stock(df_stock.iloc[:, ln_col_idx], ns_parser.s_ticker) def export(l_args, df_stock): parser = argparse.ArgumentParser( add_help=False, prog="export", description="Exports the historical data from this ticker to a file or stdout.", ) parser.add_argument( "-f", "--filename", type=str, dest="s_filename", default=stdout, help="Name of file to save the historical data exported (stdout if unspecified)", ) parser.add_argument( "-F", "--format", dest="s_format", type=str, default="csv", help="Export historical data into following formats: csv, json, excel, clipboard", ) try: ns_parser = parse_known_args_and_warn(parser, l_args) if not ns_parser: return except SystemExit: print("") return if df_stock.empty: print("No data loaded yet to export.") return if ns_parser.s_format == "csv": df_stock.to_csv(ns_parser.s_filename) elif ns_parser.s_format == "json": df_stock.to_json(ns_parser.s_filename) elif ns_parser.s_format == "excel": df_stock.to_excel(ns_parser.s_filename) elif ns_parser.s_format == "clipboard": df_stock.to_clipboard() print("") def print_goodbye(): goodbye_msg = [ "An informed ape, is a strong ape. ", "Remember that stonks only go up. ", "Diamond hands. ", "Apes together strong. ", "This is our way. ", "Keep the spacesuit ape, we haven't reached the moon yet. ", "I am not a cat. I'm an ape. ", "We like the terminal. ", ] goodbye_hr = datetime.now().hour if goodbye_hr < 5: goodbye_msg_time = "Go get some rest soldier!" elif goodbye_hr < 11: goodbye_msg_time = "Rise and shine baby!" elif goodbye_hr < 17: goodbye_msg_time = "Enjoy your day!" elif goodbye_hr < 23: goodbye_msg_time = "Tomorrow's another day!" else: goodbye_msg_time = "Go get some rest soldier!" print( goodbye_msg[random.randint(0, len(goodbye_msg) - 1)] + goodbye_msg_time + "\n" )

33.34904

117

0.550788

a1e4312150f4ab81f7905b4b1833b62393b50863

596

py

Python

theory/2nd_sprint/oop_baes04.py

abi83/YaPractice

1c3a5670ee2f872d4f872623a392755318b893b5

[ "MIT" ]

3

2020-11-18T05:16:30.000Z

2021-03-08T06:36:01.000Z

theory/2nd_sprint/oop_baes04.py

abi83/YaPractice

1c3a5670ee2f872d4f872623a392755318b893b5

[ "MIT" ]

null

theory/2nd_sprint/oop_baes04.py

abi83/YaPractice

1c3a5670ee2f872d4f872623a392755318b893b5

[ "MIT" ]

1

2021-01-20T12:41:48.000Z

import math class Planet: def __init__(self, name, radius, temp_celsius): self.name = name self.surface_area = 4 * math.pi * radius**2 self.average_temp_celcius = temp_celsius self.average_temp_fahrenheit = temp_celsius * 9 / 5 + 32 def show_info(self): print(f"Планета {self.name} имеет площадь поверхности {self.surface_area} кв.км.") print(f"Средняя температура поверхности планеты: {self.average_temp_fahrenheit}° по Фаренгейту.") jupiter = Planet('Юпитер', 69911, -108) # вызовите метод show_info для Юпитера jupiter.show_info()

33.111111

105

0.696309

543670570d9752d5d2b139b8b8e10443396ab612

10,660

py

Python

website/views.py

hackerspace-ntnu/website

0c296cb39759778aaf0c296027345a658414b397

[ "MIT" ]

25

2016-04-13T20:25:37.000Z

2021-11-26T14:41:00.000Z

website/views.py

hackerspace-ntnu/website

0c296cb39759778aaf0c296027345a658414b397

[ "MIT" ]

358

2016-02-20T21:13:27.000Z

2022-03-31T20:06:03.000Z

website/views.py

hackerspace-ntnu/website

0c296cb39759778aaf0c296027345a658414b397

[ "MIT" ]

7

2016-04-18T14:03:15.000Z

2022-02-04T14:19:47.000Z

from datetime import datetime from random import randint from urllib import parse as urlparse from django.contrib.auth.mixins import LoginRequiredMixin, PermissionRequiredMixin from django.shortcuts import get_object_or_404, redirect, render from django.utils import timezone from django.views.generic import DetailView, RedirectView, TemplateView from applications.models import ApplicationPeriod from committees.models import Committee from door.models import DoorStatus from inventory.models import ItemLoan from news.models import Article, Event from userprofile.models import Profile, TermsOfService from .models import Card, FaqQuestion, Rule from .settings import INTRANET_GREETINGS class AcceptTosView(TemplateView): template_name = "website/tos-returningls.html" def get(self, request, *args, **kwargs): if ( not self.request.user.is_authenticated or self.request.user.profile.has_accepted_most_recent_tos() ): # No user logged in, or user has already accepted TOS, return to main page return redirect("/") # Get originally visited page before TOS "pop-up" refererUrl = request.META.get("HTTP_REFERER") # Make sure page is valid before storing it for later if refererUrl: # Save users pre-TOS page path in session variable # Parse converts from absolute to relative path request.session["redirect_after_tos_accept"] = urlparse.urlparse( refererUrl ).path return super().get(self, request, *args, **kwargs) class AcceptTosRedirectView(LoginRequiredMixin, RedirectView): pattern_name = "index" def get_redirect_url(self, *args, **kwargs): profile = get_object_or_404(Profile, pk=self.request.user.profile.id) if profile is not None: most_recent_tos = TermsOfService.objects.order_by("-pub_date").first() profile.accepted_tos = most_recent_tos profile.save() # Pop and redirect to pre-TOS path stored in session variable # Redirects to '/' if pop fails return self.request.session.pop( "redirect_after_tos_accept", super().get_redirect_url(*args, **kwargs) ) class AboutView(TemplateView): template_name = "website/about.html" def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context["committees"] = Committee.objects.filter(active=True).order_by( "-priority" ) context["faq"] = FaqQuestion.objects.all() return context class RulesView(TemplateView): template_name = "website/rules.html" def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) if not self.request.user.has_perm("website.can_view_internal_rule"): context["rules"] = Rule.objects.order_by("-priority").filter(internal=False) else: context["rules"] = Rule.objects.order_by("-priority") return context class RuleDetailsView(DetailView): model = Rule template_name = "website/rule_details.html" def dispatch(self, request, *args, **kwargs): rule = self.get_object() if rule.internal and not request.user.has_perm( "website.can_view_internal_rule" ): return redirect("/") return super(RuleDetailsView, self).dispatch(request, *args, **kwargs) def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context["rule"] = Rule.objects.get(id=self.object.pk) return context class AdminView(PermissionRequiredMixin, TemplateView): template_name = "website/admin.html" permission_required = "userprofile.can_view_admin" def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) # Get all users belonging to a committee as well as pang committee_array = list(Committee.objects.values_list("name", flat=True)) committee_array.append("Pang") profiles = Profile.objects.filter( user__groups__name__in=committee_array ).order_by("user__first_name") context["profiles"] = profiles return context class IndexView(TemplateView): template_name = "website/index.html" def get_internal_articles_indicator(self): # Determine number of hidden internal articles if not self.request.user.has_perm("news.can_view_internal_article"): internal_articles_count = len( Article.objects.filter(internal=True, draft=False) ) else: internal_articles_count = 0 badge_text = { "plural": { "large": "interne artikler skjult", "medium": "interne skjult", "small": "skjult", }, "singular": { "large": "intern artikkel skjult", "medium": "intern skjult", "small": "skjult", }, } return { "count": internal_articles_count, "badge_text": badge_text, "tooltip_text": "Trykk for å logge på og se interne artikler", } def get_internal_events_indicator(self): current_date = datetime.now() # Determine number of hidden internal events if not self.request.user.has_perm("news.can_view_internal_event"): upcoming_internal_events_count = len( Event.objects.filter(internal=True, draft=False).filter( time_start__gte=current_date ) ) else: upcoming_internal_events_count = 0 badge_text = { "plural": { "large": "interne arrangementer skjult", "medium": "interne skjult", "small": "skjult", }, "singular": { "large": "internt arrangement skjult", "medium": "internt skjult", "small": "skjult", }, } return { "count": upcoming_internal_events_count, "badge_text": badge_text, "tooltip_text": "Trykk for å logge på og se interne arrangementer", } def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) # Sjekk om bruker har medlemskap og kan se interne elementer can_access_internal_article = self.request.user.has_perm( "news.can_view_internal_article" ) can_access_internal_event = self.request.user.has_perm( "news.can_view_internal_event" ) # Get the 5 events closest to starting event_list = list( Event.objects.filter( time_start__gt=timezone.now(), internal__lte=can_access_internal_event, draft=False, ).order_by("time_start")[:5] ) # Add expired events if we couldn't fill the 5 slots if len(event_list) < 5: to_fill = 5 - len(event_list) expired_events = Event.objects.filter( time_start__lte=timezone.now(), internal__lte=can_access_internal_event, draft=False, ).order_by("-time_start")[:to_fill] event_list += list(expired_events) current_date = datetime.now() # Get five published articles article_list = Article.objects.filter( internal__lte=can_access_internal_article, draft=False ).order_by("-pub_date")[:5] # Få dørstatus try: door_status = DoorStatus.objects.get(name="hackerspace").status except DoorStatus.DoesNotExist: door_status = True # hvis det ikke eksisterer en ApplicationPeriod, lag en. if not ApplicationPeriod.objects.filter(name="Opptak"): ApplicationPeriod.objects.create( name="Opptak", period_start=datetime(2018, 1, 1), period_end=datetime(2018, 1, 2), ).save() app_start_date = ApplicationPeriod.objects.get(name="Opptak").period_start app_end_date = ApplicationPeriod.objects.get(name="Opptak").period_end if (current_date < app_start_date) or (current_date > app_end_date): is_application = False else: is_application = True context = { "article_list": article_list, "event_list": event_list, "internal_articles_indicator": self.get_internal_articles_indicator(), "internal_events_indicator": self.get_internal_events_indicator(), "door_status": door_status, "app_start_date": app_start_date, "app_end_date": app_end_date, "is_application": is_application, "index_cards": Card.objects.all(), "current_date": current_date, } return context class IntranetView(PermissionRequiredMixin, TemplateView): template_name = "website/intranet.html" permission_required = "userprofile.is_active_member" def get_context_data(self, *args, **kwargs): context = super().get_context_data(*args, **kwargs) context["current_date"] = datetime.now() # Random greeting for the intranet header banner. Just for fun greeting = INTRANET_GREETINGS[randint(0, len(INTRANET_GREETINGS) - 1)] # cba doing a regex or some other fancy stuff to check if the string has formatting # just break it till it works try: context["greeting"] = greeting.format(self.request.user.first_name) except IndexError: context["greeting"] = greeting # Find the 5 loan apps that have gone unapproved the longest context["loan_app_list"] = ItemLoan.objects.filter( approver__isnull=True, ).order_by("-loan_from")[:5] # Same as in the index view context["event_list"] = Event.objects.filter(internal=True).order_by( "-time_start" )[:5] context["article_list"] = Article.objects.filter( internal=True, draft=False ).order_by("-pub_date")[:5] return context def handler404(request, exception=None): return render(request, "website/404.html", status=404) def handler403(request, exception=None): return render(request, "website/403.html", status=403) def handler500(request, exception=None): return render(request, "website/500.html", status=500)

34.387097

91

0.627767

f6279995d8ae4ab4661421c6369daffe851796a2

1,114

py

Python

students/k3343/laboratory_works/Andreeva_Ekaterina/laboratory_work_2/hotel/laboratory_work_2/urls.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

10

2020-03-20T09:06:12.000Z

2021-07-27T13:06:02.000Z

students/k3343/laboratory_works/Andreeva_Ekaterina/laboratory_work_2/hotel/laboratory_work_2/urls.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

134

2020-03-23T09:47:48.000Z

2022-03-12T01:05:19.000Z

students/k3343/laboratory_works/Andreeva_Ekaterina/laboratory_work_2/hotel/laboratory_work_2/urls.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

71

2020-03-20T12:45:56.000Z

2021-10-31T19:22:25.000Z

"""laboratory_work_2 URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.conf import settings from django.conf.urls.static import static from django.contrib import admin from django.urls import path, include urlpatterns = [ path('admin/', admin.site.urls), path('api-auth/', include('rest_framework.urls')), path('api/v1/', include('hotel.urls')), path('auth/', include('djoser.urls')), path('auth/', include('djoser.urls.authtoken')), path('auth/', include('djoser.urls.jwt')), ]

38.413793

78

0.682226

d7f28a7c63a717e4d287a6f956c5786663ce3386

1,407

py

Python

app/NinePatch.py

JoeyGaojingxing/QtLearn

c6f03c5e3af13d93a622442620584493f792f20f

[ "MIT" ]

null

app/NinePatch.py

JoeyGaojingxing/QtLearn

c6f03c5e3af13d93a622442620584493f792f20f

[ "MIT" ]

null

app/NinePatch.py

JoeyGaojingxing/QtLearn

c6f03c5e3af13d93a622442620584493f792f20f

[ "MIT" ]

null

import os from ctypes import CDLL, c_char_p from utils.utils import convert_byte from UI.ui_nine_patch import Ui_NinePatchFrame from PySide2.QtWidgets import QFrame, QTableWidgetItem from PySide2.QtCore import SIGNAL, Slot path = os.path.join(os.getcwd(), r'build\NinePatch.so') # path = r'D:\Programming\QtLearn\build\NinePatch.so' nine_patch_go = CDLL(path) def nine_patch(edge: int) -> list: res = nine_patch_go.NinePatch res.restype = c_char_p return convert_byte(res(edge))[:edge**2] class NinePatchWindow(QFrame, Ui_NinePatchFrame): def __init__(self, main): QFrame.__init__(self) self.setupUi(self) self.main = main self.edge = 3 self.spinBoxEdge.setValue(self.edge) self.connect(self.spinBoxEdge, SIGNAL("valueChanged(int)"), self.set_edge) self.connect(self.pushButtonCommit, SIGNAL("clicked()"), self.run) def closeEvent(self, event=None): self.main.show() @Slot(int) def set_edge(self, val): self.edge = val @Slot() def run(self): self.tableWidgetResult.setColumnCount(self.edge) self.tableWidgetResult.setRowCount(self.edge) res = nine_patch(self.edge) num = 0 for i in range(self.edge): for j in range(self.edge): self.tableWidgetResult.setItem(i, j, QTableWidgetItem(str(res[num]))) num += 1

29.93617

85

0.666667

011c3a09851e9b1925dfff5df2e44ca1c0e7da79

100,790

py

Python

modules/templates/RLPPTM/controllers.py

krypt0x/eden

63679c36d627b5d0be5858759217408e09aa4ef0

[ "MIT" ]

1

2018-06-06T12:11:25.000Z

modules/templates/RLPPTM/controllers.py

krypt0x/eden

63679c36d627b5d0be5858759217408e09aa4ef0

[ "MIT" ]

null

modules/templates/RLPPTM/controllers.py

krypt0x/eden

63679c36d627b5d0be5858759217408e09aa4ef0

[ "MIT" ]

null

# -*- coding: utf-8 -*- import json from uuid import uuid4 from gluon import Field, HTTP, SQLFORM, URL, current, redirect, \ CRYPT, IS_EMAIL, IS_EMPTY_OR, IS_EXPR, IS_IN_SET, IS_LENGTH, \ IS_LOWER, IS_NOT_EMPTY, IS_NOT_IN_DB, \ A, BR, DIV, FORM, H3, H4, I, INPUT, LI, TAG, TABLE, TD, TR, UL, XML from gluon.storage import Storage from s3 import FS, IS_PHONE_NUMBER_MULTI, JSONERRORS, S3CRUD, S3CustomController, \ S3GroupedOptionsWidget, S3LocationSelector, S3Represent, S3Request, \ S3WithIntro, s3_comments_widget, s3_get_extension, s3_mark_required, \ s3_str, s3_text_represent, s3_truncate from .config import TESTSTATIONS from .helpers import applicable_org_types from .notifications import formatmap TEMPLATE = "RLPPTM" THEME = "RLP" # ============================================================================= class index(S3CustomController): """ Custom Home Page """ def __call__(self): output = {} T = current.T s3 = current.response.s3 auth = current.auth settings = current.deployment_settings # Defaults login_form = None login_div = None announcements = None announcements_title = None roles = current.session.s3.roles sr = auth.get_system_roles() if sr.AUTHENTICATED in roles: # Logged-in user # => display announcements from s3 import S3DateTime dtrepr = lambda dt: S3DateTime.datetime_represent(dt, utc=True) filter_roles = roles if sr.ADMIN not in roles else None posts = self.get_announcements(roles=filter_roles) # Render announcements list announcements = UL(_class="announcements") if posts: announcements_title = T("Announcements") priority_classes = {2: "announcement-important", 3: "announcement-critical", } priority_icons = {2: "fa-exclamation-circle", 3: "fa-exclamation-triangle", } for post in posts: # The header header = H4(post.name) # Priority priority = post.priority # Add icon to header? icon_class = priority_icons.get(post.priority) if icon_class: header = TAG[""](I(_class="fa %s announcement-icon" % icon_class), header, ) # Priority class for the box prio = priority_classes.get(priority, "") row = LI(DIV(DIV(DIV(dtrepr(post.date), _class = "announcement-date", ), _class="fright", ), DIV(DIV(header, _class = "announcement-header", ), DIV(XML(post.body), _class = "announcement-body", ), _class="announcement-text", ), _class = "announcement-box %s" % prio, ), ) announcements.append(row) else: # Anonymous user # => provide a login box login_div = DIV(H3(T("Login")), ) auth.messages.submit_button = T("Login") login_form = auth.login(inline=True) output = {"login_div": login_div, "login_form": login_form, "announcements": announcements, "announcements_title": announcements_title, } # Custom view and homepage styles s3.stylesheets.append("../themes/%s/homepage.css" % THEME) self._view(settings.get_theme_layouts(), "index.html") return output # ------------------------------------------------------------------------- @staticmethod def get_announcements(roles=None): """ Get current announcements @param roles: filter announcement by these roles @returns: any announcements (Rows) """ db = current.db s3db = current.s3db # Look up all announcements ptable = s3db.cms_post stable = s3db.cms_series join = stable.on((stable.id == ptable.series_id) & \ (stable.name == "Announcements") & \ (stable.deleted == False)) query = (ptable.date <= current.request.utcnow) & \ (ptable.expired == False) & \ (ptable.deleted == False) if roles: # Filter posts by roles ltable = s3db.cms_post_role q = (ltable.group_id.belongs(roles)) & \ (ltable.deleted == False) rows = db(q).select(ltable.post_id, cache = s3db.cache, groupby = ltable.post_id, ) post_ids = {row.post_id for row in rows} query = (ptable.id.belongs(post_ids)) & query posts = db(query).select(ptable.name, ptable.body, ptable.date, ptable.priority, join = join, orderby = (~ptable.priority, ~ptable.date), limitby = (0, 5), ) return posts # ============================================================================= class privacy(S3CustomController): """ Custom Page """ def __call__(self): output = {} # Allow editing of page content from browser using CMS module ADMIN = current.auth.s3_has_role("ADMIN") s3db = current.s3db table = s3db.cms_post ltable = s3db.cms_post_module module = "default" resource = "Privacy" query = (ltable.module == module) & \ (ltable.resource == resource) & \ (ltable.post_id == table.id) & \ (table.deleted != True) item = current.db(query).select(table.body, table.id, limitby=(0, 1)).first() if item: if ADMIN: content = DIV(XML(item.body), BR(), A(current.T("Edit"), _href = URL(c="cms", f="post", args = [item.id, "update"], vars = {"module": module, "resource": resource, }, ), _class="action-btn", ), ) else: content = DIV(XML(item.body)) elif ADMIN: content = A(current.T("Edit"), _href = URL(c="cms", f="post", args="create", vars = {"module": module, "resource": resource, }, ), _class="action-btn cms-edit", ) else: content = "" output["item"] = content self._view(THEME, "cmspage.html") return output # ============================================================================= class legal(S3CustomController): """ Custom Page """ def __call__(self): output = {} # Allow editing of page content from browser using CMS module ADMIN = current.auth.s3_has_role("ADMIN") s3db = current.s3db table = s3db.cms_post ltable = s3db.cms_post_module module = "default" resource = "Legal" query = (ltable.module == module) & \ (ltable.resource == resource) & \ (ltable.post_id == table.id) & \ (table.deleted != True) item = current.db(query).select(table.body, table.id, limitby = (0, 1) ).first() if item: if ADMIN: content = DIV(XML(item.body), BR(), A(current.T("Edit"), _href = URL(c="cms", f="post", args = [item.id, "update"], vars = {"module": module, "resource": resource, }, ), _class="action-btn", ), ) else: content = DIV(XML(item.body)) elif ADMIN: content = A(current.T("Edit"), _href = URL(c="cms", f="post", args="create", vars = {"module": module, "resource": resource, }, ), _class="action-btn cms-edit", ) else: content = "" output["item"] = content self._view(THEME, "cmspage.html") return output # ============================================================================= class approve(S3CustomController): """ Custom Approval Page """ def __call__(self): T = current.T auth = current.auth db = current.db s3db = current.s3db session = current.session ogtable = s3db.org_group org_group = db(ogtable.name == TESTSTATIONS).select(ogtable.id, ogtable.pe_id, limitby = (0, 1) ).first() try: org_group_pe_id = org_group.pe_id except: raise RuntimeError("Cannot approve user account as Org Group '%s' is missing " % TESTSTATIONS) has_role = auth.s3_has_role if has_role("ORG_GROUP_ADMIN", for_pe = org_group_pe_id): ORG_ADMIN = False elif has_role("ORG_ADMIN"): ORG_ADMIN = True else: session.error = T("Not Permitted!") redirect(URL(c = "default", f = "index", args = None, )) utable = db.auth_user request = current.request response = current.response org_group_id = org_group.id # Single User or List? if len(request.args) > 1: user_id = request.args[1] user = db(utable.id == user_id).select(utable.id, utable.first_name, utable.last_name, utable.email, utable.organisation_id, utable.org_group_id, utable.registration_key, utable.link_user_to, # Needed for s3_approve_user utable.site_id, # Needed for s3_link_to_human_resource (calledfrom s3_approve_user) limitby = (0, 1) ).first() if not user or user.org_group_id != org_group_id: session.error = T("Invalid Account!") redirect(URL(c = "default", f = "index", args = ["approve"], )) otable = s3db.org_organisation organisation_id = user.organisation_id if organisation_id: org = db(otable.id == organisation_id).select(otable.name, otable.pe_id, limitby = (0, 1) ).first() if ORG_ADMIN: if not organisation_id or \ not has_role("ORG_ADMIN", for_pe = org.pe_id): session.error = T("Account not within your Organisation!") redirect(URL(c = "default", f = "index", args = ["approve"], )) person = "%(first_name)s %(last_name)s <%(email)s>" % {"first_name": user.first_name, "last_name": user.last_name, "email": user.email, } ttable = s3db.auth_user_temp temp = db(ttable.user_id == user_id).select(ttable.custom, limitby = (0, 1) ).first() try: custom = json.loads(temp.custom) except JSONERRORS: custom = {} # Test Station (Organisation) custom_get = custom.get organisation = custom_get("organisation") if organisation: test_station = TR(TD("%s:" % T("Test Station")), TD(organisation), ) else: test_station = None # Org type selector selected_type = custom_get("organisation_type") org_types = applicable_org_types(None, group=TESTSTATIONS, represent=True) if selected_type and selected_type not in org_types: selected_type = None field = Field("organisation_type", "integer", label = T("Organization Type"), requires = IS_IN_SET(org_types), ) field.tablename = "approve" from gluon.sqlhtml import OptionsWidget type_selector = OptionsWidget.widget(field, selected_type) # Address location = custom_get("location") location_get = location.get addr_street = location_get("addr_street") addr_postcode = location_get("addr_postcode") L1 = location_get("L1") L2 = location_get("L2") L3 = location_get("L3") L4 = location_get("L4") represent = S3Represent(lookup = "gis_location") address = TABLE(TR(addr_street or ""), TR(addr_postcode or ""), TR(represent(L4) if L4 else ""), TR(represent(L3) if L3 else ""), TR(represent(L2) if L2 else ""), TR(represent(L1) if L1 else ""), ) # Service Offer opening_times = custom_get("opening_times") service_modes = register.selectable_services_modes() service_mode_id = custom_get("service_mode") if service_mode_id: try: service_mode_id = int(service_mode_id) except (ValueError, TypeError): service_mode_id = None if service_mode_id in service_modes: service_mode = service_modes[service_mode_id] else: service_mode = service_mode_id = None # Map selected services to the services selectable at time of approval selectable_services = register.selectable_services() service_ids, service_names = [], [] selected = custom_get("services") if selected: if not isinstance(selected, list): selected = [selected] for v in selected: try: service_id = int(v) except (ValueError, TypeError): continue if service_id in selectable_services: service_ids.append(service_id) service_names.append(selectable_services[service_id]) services = ", ".join(service_names) comments = custom_get("comments") # Contact and Appointments facility_phone = custom_get("facility_phone") or custom_get("office_phone") facility_email = custom_get("facility_email") facility_website = custom_get("facility_website") booking_modes = register.selectable_booking_modes() booking_mode_id = custom_get("booking_mode") if booking_mode_id: try: booking_mode_id = int(booking_mode_id) except (ValueError, TypeError): booking_mode_id = None if booking_mode_id in booking_modes: booking_mode = booking_modes[booking_mode_id] else: booking_mode = booking_mode_id = None # Administrative # Map selected projects to the projects selectable at time of approval selectable_projects = register.selectable_projects() projects = [] selected = custom_get("projects") if not isinstance(selected, (tuple, list)): selected = [selected] for v in selected: try: project_id = int(v) except (ValueError, TypeError): continue if project_id in selectable_projects: projects.append(project_id) # Add project selector field = Field("projects", "list:integer", label = T("Programs"), requires = [IS_IN_SET(selectable_projects, multiple = True, zero = None, ), IS_NOT_EMPTY(), ], ) field.tablename = "approve" # Dummy to make widget work projects_selector = S3GroupedOptionsWidget(cols=1)(field, projects) if user.registration_key is None: response.warning = T("Registration has previously been Approved") elif user.registration_key == "rejected": response.warning = T("Registration has previously been Rejected") elif user.registration_key != "pending": response.warning = T("User hasn't verified their email") approve = INPUT(_value = T("Approve"), _type = "submit", _name = "approve-btn", _id = "approve-btn", _class = "small primary button", ) reject = INPUT(_value = T("Reject"), _type = "submit", _name = "reject-btn", _id = "reject-btn", _class = "small alert button", ) strrepr = lambda v: v if v else "-" form = FORM(TABLE(TR(approve, reject, ), TR(TD("%s:" % T("Person")), TD(person), ), test_station, TR(TD("%s:" % T("Organization Type")), TD(type_selector), ), TR(TD("%s:" % T("Address")), TD(address), ), TR(TD("%s:" % T("Opening Hours")), TD(strrepr(opening_times)), ), TR(TD("%s:" % T("Service Mode")), TD(strrepr(service_mode)), ), TR(TD("%s:" % T("Services")), TD(strrepr(services)), ), TR(TD("%s:" % T("Telephone")), TD(strrepr(facility_phone)), ), TR(TD("%s:" % T("Email")), TD(strrepr(facility_email)), ), TR(TD("%s:" % T("Website")), TD(strrepr(facility_website)), ), TR(TD("%s:" % T("Appointments via")), TD(strrepr(booking_mode)), ), TR(TD("%s:" % T("Projects")), TD(projects_selector), ), TR(TD("%s:" % T("Comments")), TD(s3_text_represent(strrepr(comments))), ), ), _class = "approve-form", ) if form.accepts(request.post_vars, session, formname="approve"): form_vars = form.vars rejected = bool(form_vars.get("reject-btn")) approved = bool(form_vars.get("approve-btn")) and not rejected if approved: set_record_owner = auth.s3_set_record_owner s3db_onaccept = s3db.onaccept update_super = s3db.update_super if not organisation_id: # Create organisation org = {"name": organisation} org["id"] = organisation_id = otable.insert(**org) update_super(otable, org) set_record_owner(otable, org, owned_by_user=user_id) s3db_onaccept(otable, org, method="create") # Link organisation to TESTSTATIONS group mtable = s3db.org_group_membership membership = {"group_id": org_group_id, "organisation_id": organisation_id, } membership["id"] = mtable.insert(**membership) set_record_owner(mtable, membership) s3db_onaccept(mtable, membership, method="create") # Link organisation to selected organisation type type_id = form_vars.get("organisation_type") if type_id: ltable = s3db.org_organisation_organisation_type type_id = int(type_id) link = {"organisation_id": organisation_id, "organisation_type_id": type_id, } link["id"] = ltable.insert(**link) set_record_owner(ltable, link) s3db_onaccept(ltable, link, method="create") # Link organisation to selected projects selected = form_vars.get("projects") if isinstance(selected, (tuple, list)): ltable = s3db.project_organisation for item in selected: try: project_id = int(item) except (ValueError, TypeError): continue link = {"project_id": project_id, "organisation_id": organisation_id, "role": 2, } link["id"] = ltable.insert(**link) set_record_owner(ltable, link) s3db_onaccept(ltable, link, method="create") # Add default tags from .helpers import add_organisation_default_tags add_organisation_default_tags(organisation_id) # Update user user.update_record(organisation_id = organisation_id, registration_key = None, ) # Grant ORG_ADMIN and PROVIDER_ACCOUNTANT auth.s3_assign_role(user_id, "ORG_ADMIN", for_pe=org["pe_id"]) auth.s3_assign_role(user_id, "PROVIDER_ACCOUNTANT") else: # Update user user.update_record(registration_key = None) # Grant VOUCHER_PROVIDER auth.s3_assign_role(user_id, "VOUCHER_PROVIDER") location_id = location_get("id") if not location_id: # Create location ltable = s3db.gis_location del location["wkt"] # Will get created during onaccept & we don't want the 'Source WKT has been cleaned by Shapely" warning location["id"] = location_id = ltable.insert(**location) set_record_owner(ltable, location, owned_by_user=user_id) s3db_onaccept(ltable, location, method="create") # Create facility ftable = s3db.org_facility facility_name = organisation if organisation else org.name facility = {"name": s3_truncate(facility_name), "organisation_id": organisation_id, "location_id": location_id, "phone1": facility_phone, "email": facility_email, "website": facility_website, "opening_times": opening_times, "comments": comments, } facility_id = facility["id"] = ftable.insert(**facility) update_super(ftable, facility) set_record_owner(ftable, facility, owned_by_user=user_id) s3db_onaccept(ftable, facility, method="create") site_id = facility["site_id"] # Capture site details dtable = s3db.org_site_details details = {"site_id": site_id, "service_mode_id": service_mode_id, "booking_mode_id": booking_mode_id, } details["id"] = dtable.insert(**details) update_super(dtable, details) set_record_owner(dtable, details, owned_by_user=user_id) s3db_onaccept(dtable, details, method="create") # Link facility to facility type fttable = s3db.org_facility_type tltable = s3db.org_site_facility_type facility_type = db(fttable.name == "Infection Test Station") \ .select(fttable.id, limitby=(0, 1)).first() if facility_type: tltable.insert(site_id = site_id, facility_type_id = facility_type.id, ) # Link facility to services if service_ids: sltable = s3db.org_service_site for service_id in service_ids: link = {"service_id": service_id, "site_id": site_id, } link["id"] = sltable.insert(**link) set_record_owner(sltable, link, owned_by_user=user_id) s3db_onaccept(sltable, link, method="create") # Add default tags for facility from .helpers import add_facility_default_tags add_facility_default_tags(facility_id) # Approve user auth.s3_approve_user(user) # Send welcome email settings = current.deployment_settings from .notifications import CMSNotifications error = CMSNotifications.send(user.email, "WelcomeProvider", {"name": organisation or org.name, "homepage": settings.get_base_public_url(), "profile": URL("default", "person", host=True), }, module = "auth", resource = "user", ) if error: session.warning = "%s: %s" % (T("Welcome Email NOT sent"), error) session.confirmation = T("Registration approved") redirect(URL(c = "default", f = "index", args = ["approve"], )) elif rejected: user.update_record(registration_key = "rejected") session.confirmation = T("Registration rejected") redirect(URL(c = "default", f = "index", args = ["approve"], )) output = {"form": form, "title": T("Approve Test Station"), } # Custom View self._view("RLPPTM", "approve.html") else: # List View if ORG_ADMIN: # Filter to just their users gtable = db.auth_group mtable = db.auth_membership query = (mtable.user_id == auth.user.id) & \ (mtable.group_id == gtable.id) & \ (gtable.uuid == "ORG_ADMIN") memberships = db(query).select(mtable.pe_id) pe_id = [m.pe_id for m in memberships] otable = s3db.org_organisation orgs = db(otable.pe_id.belongs(pe_id)).select(otable.id) organisation_id = [org.id for org in orgs] accounts_filter = FS("organisation_id").belongs(organisation_id) else: # Filter to all for the ORG_GROUP accounts_filter = FS("org_group_id") == org_group_id # Only include pending accounts accounts_filter &= FS("registration_key") == "pending" resource = s3db.resource("auth_user", filter=accounts_filter) list_id = "datatable" # List fields list_fields = resource.list_fields() orderby = None s3 = response.s3 representation = s3_get_extension(request) or \ S3Request.DEFAULT_REPRESENTATION # Pagination get_vars = request.get_vars if representation == "aadata": start, limit = S3CRUD._limits(get_vars) else: # Initial page request always uses defaults (otherwise # filtering and pagination would have to be relative to # the initial limits, but there is no use-case for that) start = None limit = None if s3.no_sspag else 0 left = [] distinct = False dtargs = {} if representation in S3Request.INTERACTIVE_FORMATS: # How many records per page? if s3.dataTable_pageLength: display_length = s3.dataTable_pageLength else: display_length = 25 # Server-side pagination? if not s3.no_sspag: dt_pagination = "true" if not limit: limit = 2 * display_length session.s3.filter = get_vars if orderby is None: dt_sorting = {"iSortingCols": "1", "sSortDir_0": "asc" } if len(list_fields) > 1: dt_sorting["bSortable_0"] = "false" dt_sorting["iSortCol_0"] = "1" else: dt_sorting["bSortable_0"] = "true" dt_sorting["iSortCol_0"] = "0" orderby, left = resource.datatable_filter(list_fields, dt_sorting, )[1:3] else: dt_pagination = "false" # Disable exports s3.no_formats = True # Get the data table dt, totalrows = resource.datatable(fields = list_fields, start = start, limit = limit, left = left, orderby = orderby, distinct = distinct, ) displayrows = totalrows # Always show table, otherwise it can't be Ajax-filtered # @todo: need a better algorithm to determine total_rows # (which excludes URL filters), so that datatables # shows the right empty-message (ZeroRecords instead # of EmptyTable) dtargs["dt_pagination"] = dt_pagination dtargs["dt_pageLength"] = display_length dtargs["dt_base_url"] = URL(c="default", f="index", args="approve") dtargs["dt_permalink"] = URL(c="default", f="index", args="approve") datatable = dt.html(totalrows, displayrows, id = list_id, **dtargs) # Action Buttons s3.actions = [{"label": s3_str(T("Review")), "url": URL(args = ["approve", "[id]"], ), "_class": "action-btn", }, ] output = {"items": datatable, "title": T("Test Stations to be Approved"), } # Custom View self._view(TEMPLATE, "approve_list.html") elif representation == "aadata": # Apply datatable filters searchq, orderby, left = resource.datatable_filter(list_fields, get_vars) if searchq is not None: totalrows = resource.count() resource.add_filter(searchq) else: totalrows = None # Get a data table if totalrows != 0: dt, displayrows = resource.datatable(fields = list_fields, start = start, limit = limit, left = left, orderby = orderby, distinct = distinct, ) else: dt, displayrows = None, 0 if totalrows is None: totalrows = displayrows # Echo draw = int(get_vars.get("draw", 0)) # Representation if dt is not None: output = dt.json(totalrows, displayrows, list_id, draw, **dtargs) else: output = '{"recordsTotal":%s,' \ '"recordsFiltered":0,' \ '"dataTable_id":"%s",' \ '"draw":%s,' \ '"data":[]}' % (totalrows, list_id, draw) else: S3Request("auth", "user").error(415, current.ERROR.BAD_FORMAT) return output # ============================================================================= class register(S3CustomController): """ Custom Registration Page """ def __call__(self): auth = current.auth if auth.s3_logged_in(): # Redirect if already logged-in redirect(URL(c="default", f="index")) auth_settings = auth.settings auth_messages = auth.messages self.customise_auth_messages() T = current.T db = current.db s3db = current.s3db request = current.request response = current.response session = current.session settings = current.deployment_settings if not settings.get_custom(key="test_station_registration"): session.error = T("Function not available") redirect(URL(c="default", f="index")) utable = auth_settings.table_user # Page title and intro text title = T("Register Test Station") # Get intro text from CMS db = current.db s3db = current.s3db ctable = s3db.cms_post ltable = s3db.cms_post_module join = ltable.on((ltable.post_id == ctable.id) & \ (ltable.module == "auth") & \ (ltable.resource == "user") & \ (ltable.deleted == False)) query = (ctable.name == "SelfRegistrationIntro") & \ (ctable.deleted == False) row = db(query).select(ctable.body, join = join, cache = s3db.cache, limitby = (0, 1), ).first() intro = row.body if row else None # Form Fields formfields, required_fields, subheadings = self.formfields() # Generate labels (and mark required fields in the process) labels, has_required = s3_mark_required(formfields, mark_required = required_fields, ) response.s3.has_required = has_required # Form buttons REGISTER = T("Register") buttons = [INPUT(_type = "submit", _value = REGISTER, ), # TODO cancel-button? ] # Construct the form response.form_label_separator = "" form = SQLFORM.factory(table_name = utable._tablename, record = None, hidden = {"_next": request.vars._next}, labels = labels, separator = "", showid = False, submit_button = REGISTER, delete_label = auth_messages.delete_label, formstyle = settings.get_ui_formstyle(), buttons = buttons, *formfields) # Identify form for CSS & JS Validation form.add_class("auth_register") # Add Subheadings if subheadings: for pos, heading in subheadings[::-1]: form[0].insert(pos, DIV(heading, _class="subheading")) # Inject client-side Validation auth.s3_register_validation() # Set default registration key, so new users are prevented # from logging in until approved key = str(uuid4()) code = uuid4().hex[-6:].upper() utable.registration_key.default = self.keyhash(key, code) if form.accepts(request.vars, session, formname = "register", onvalidation = auth_settings.register_onvalidation, ): formvars = form.vars organisation = formvars.get("organisation") # Check if organisation already exists organisation_id = self.lookup_organisation(formvars) if organisation_id: formvars["organisation_id"] = organisation_id # Create the user record user_id = utable.insert(**utable._filter_fields(formvars, id=False)) formvars.id = user_id # Set org_group ogtable = s3db.org_group org_group = db(ogtable.name == TESTSTATIONS).select(ogtable.id, limitby = (0, 1) ).first() try: org_group_id = org_group.id except: raise RuntimeError("Cannot register user account as Org Group '%s' is missing " % TESTSTATIONS) db(utable.id == user_id).update(org_group_id = org_group_id) # Save temporary user fields in s3db.auth_user_temp temptable = s3db.auth_user_temp record = {"user_id": user_id} record["consent"] = formvars.consent # Store Custom fields custom = {"location": formvars.location, "opening_times": formvars.opening_times, "service_mode": formvars.service_mode, "services": formvars.services, "facility_phone": formvars.facility_phone, "facility_email": formvars.facility_email, "facility_website": formvars.facility_website, "booking_mode": formvars.booking_mode, "comments": formvars.comments, "projects": formvars.projects, } if not organisation_id: custom["organisation"] = organisation custom["organisation_type"] = formvars.organisation_type record["custom"] = json.dumps(custom) temptable.insert(**record) # Post-process the new user record users = db(utable.id > 0).select(utable.id, limitby=(0, 2)) if len(users) == 1: # 1st user to register doesn't need verification/approval auth.s3_approve_user(form.vars) session.confirmation = auth_messages.registration_successful # 1st user gets Admin rights admin_group_id = 1 auth.add_membership(admin_group_id, users.first().id) # Log them in if "language" not in form.vars: # Was missing from login form form.vars.language = T.accepted_language user = Storage(utable._filter_fields(form.vars, id=True)) auth.login_user(user) # Send welcome email auth.s3_send_welcome_email(form.vars) # Where to go next? register_next = request.vars._next or auth_settings.register_next else: # Request User Verify their Email # System Details for Verification Email verify_url = URL(c = "default", f = "index", args = ["verify_email", key], scheme = "https" if request.is_https else "http", ) system = {"system_name": settings.get_system_name(), "url": verify_url, #"url": "%s/default/index/verify_email/%s" % (response.s3.base_url, key), "code": code, } # Try to send the Verification Email if not auth_settings.mailer or \ not auth_settings.mailer.settings.server or \ not auth_settings.mailer.send(to = form.vars.email, subject = auth_messages.verify_email_subject % system, message = auth_messages.verify_email % system, ): response.error = auth_messages.email_verification_failed # Custom View self._view(THEME, "register.html") return {"title": title, "form": form, } # Redirect to Verification Info page register_next = URL(c = "default", f = "message", args = ["verify_email_sent"], vars = {"email": form.vars.email}, ) # Log action auth.log_event(auth_messages.register_log, form.vars) # Redirect redirect(register_next) elif form.errors: response.error = T("There are errors in the form, please check your input") # Custom View self._view(THEME, "register.html") return {"title": title, "intro": intro, "form": form, } # ------------------------------------------------------------------------- @classmethod def formfields(cls): """ Generate the form fields for the registration form @returns: a tuple (formfields, required_fields, subheadings) - formfields = list of form fields - required_fields = list of field names of required fields - subheadings = list of tuples (position, heading) to insert into the form """ T = current.T request = current.request #db = current.db s3db = current.s3db auth = current.auth auth_settings = auth.settings auth_messages = auth.messages utable = auth_settings.table_user passfield = auth_settings.password_field # Instantiate Consent Tracker consent = s3db.auth_Consent(processing_types=["SHARE", "RULES_PRO"]) # Last name is required utable.last_name.requires = IS_NOT_EMPTY(error_message=T("input required")) #ltable = s3db.gis_location # Lookup projects with provider self-registration projects = cls.selectable_projects() # Lookup site services services = cls.selectable_services() # Lookup applicable organisation types org_types = applicable_org_types(None, group=TESTSTATIONS, represent=True) # Form fields formfields = [# -- User account --- utable.first_name, utable.last_name, utable.email, utable[passfield], # Password Verification Field Field("password_two", "password", label = auth_messages.verify_password, requires = IS_EXPR("value==%s" % \ repr(request.vars.get(passfield)), error_message = auth_messages.mismatched_password, ), comment = DIV(_class = "tooltip", _title = "%s|%s" % (auth_messages.verify_password, T("Enter the same password again"), ), ), ), # -- Test Station --- Field("organisation", label = T("Name"), requires = [IS_NOT_EMPTY(), IS_LENGTH(60)], comment = DIV(_class = "tooltip", _title = "%s|%s" % (T("Test Station Name"), T("Specify the name of the test station (max 60 characters)"), ), ), ), Field("organisation_type", "integer", label = T("Organization Type"), requires = IS_IN_SET(org_types), ), # -- Address -- Field("location", "json", widget = S3LocationSelector( levels = ("L1", "L2", "L3", "L4"), required_levels = ("L1", "L2", "L3"), show_address = True, address_required = True, show_postcode = True, postcode_required = True, show_map = True, ), ), # -- Service Offer -- Field("opening_times", label = T("Opening Hours"), requires = IS_NOT_EMPTY(), ), Field("service_mode", "integer", label = T("Service Mode"), requires = IS_IN_SET(cls.selectable_services_modes()), ), Field("services", "list:integer", label = T("Services"), requires = IS_IN_SET(services, multiple = True, zero = None, ), widget = S3WithIntro(S3GroupedOptionsWidget(cols=1), # Widget intro from CMS intro = ("org", "facility", "SiteServiceIntro", ), ), ), # -- Contact and Appointments -- Field("facility_phone", label = T("Telephone"), requires = IS_EMPTY_OR(IS_PHONE_NUMBER_MULTI()), ), Field("facility_email", label = T("Email"), requires = IS_EMPTY_OR(IS_EMAIL()), ), Field("facility_website", label = T("Website"), ), Field("booking_mode", "integer", label = T("Appointments via"), requires = IS_EMPTY_OR(IS_IN_SET( cls.selectable_booking_modes(), )), ), Field("comments", "text", label = T("Comments"), widget = s3_comments_widget, ), # -- Administrative -- Field("projects", "list:integer", label = T("Programs"), requires = [IS_IN_SET(projects, multiple = True, zero = None, ), IS_NOT_EMPTY(), ], widget = S3WithIntro(S3GroupedOptionsWidget(cols=1), # Widget intro from CMS intro = ("org", "organisation", "ProjectParticipationIntro", ), ), ), # -- Privacy and Consent -- Field("consent", label = T("Consent"), widget = consent.widget, ), ] # Required fields required_fields = ["first_name", "last_name", ] # Subheadings subheadings = ((0, T("User Account")), (5, T("Test Station")), (7, T("Address")), (8, T("Service Offer")), (11, T("Contact and Appointments")), (16, T("Administrative")), (17, "%s / %s" % (T("Privacy"), T("Terms of Service"))), ) # Geocoder current.response.s3.scripts.append("/%s/static/themes/RLP/js/geocoderPlugin.js" % request.application) return formfields, required_fields, subheadings # ------------------------------------------------------------------------- @staticmethod def keyhash(key, code): """ Generate a hash of the activation code using the registration key @param key: the registration key @param code: the activation code @returns: the hash as string """ crypt = CRYPT(key=key, digest_alg="sha512", salt=None) return str(crypt(code.upper())[0]) # ------------------------------------------------------------------------- @staticmethod def customise_auth_messages(): """ Customise auth messages: - verification email - welcome email """ messages = current.auth.messages messages.verify_email_subject = "%(system_name)s - Verify Email" messages.verify_email = \ """Click on the link %(url)s to verify your email. Your Activation Code: %(code)s """ messages.welcome_email_subject = "Welcome to the %(system_name)s Portal" messages.welcome_email = \ """Welcome to the %(system_name)s Portal - To edit your profile go to: %(url)s%(profile)s Thank you """ # ------------------------------------------------------------------------- @staticmethod def lookup_organisation(formvars): """ Identify the organisation the user attempts to register for, by name, facility Lx and if necessary facility email address @param formvars: the FORM vars @returns: organisation_id if found, or None if this is a new organisation """ orgname = formvars.get("organisation") if not orgname: return None db = current.db s3db = current.s3db otable = s3db.org_organisation ftable = s3db.org_facility ltable = s3db.gis_location gtable = s3db.org_group mtable = s3db.org_group_membership # Search by name among test stations query = (otable.name == orgname) & \ (otable.deleted == False) join = [mtable.on(mtable.organisation_id == otable.id), gtable.on((gtable.id == mtable.group_id) & \ (gtable.name == TESTSTATIONS)), ftable.on(ftable.organisation_id == otable.id), ] # Do we have a selected location (should have since mandatory) location = formvars.get("location") if isinstance(location, str): try: location = json.loads(location) except JSONERRORS: location = None if location: # Include the Lx ancestor in the lookup ancestor = None for level in ("L4", "L3", "L2"): ancestor = location.get(level) if ancestor: break if ancestor: join.append(ltable.on(ltable.id == ftable.location_id)) query &= ((ltable.level == None) & (ltable.parent == ancestor)) | \ (ltable.id == ancestor) rows = db(query).select(otable.id, join = join) organisation_id = None if len(rows) > 1: # Multiple matches => try using facility email to reduce facility_email = formvars.get("facility_email") if facility_email: candidates = {row.id for row in rows} query = (ftable.organisation_id.belongs(candidates)) & \ (ftable.email == facility_email) & \ (ftable.deleted == False) match = db(query).select(ftable.organisation_id, limitby = (0, 2), ) if len(match) == 1: organisation_id = match.first().organisation_id elif rows: # Single match - this organisation already exists organisation_id = rows.first().organisation_id return organisation_id # ------------------------------------------------------------------------- @staticmethod def selectable_projects(): """ Projects the user can select during test station registration => all projects that are tagged with APPLY=Y @returns: list of project_ids """ db = current.db s3db = current.s3db # Lookup projects with provider self-registration ptable = s3db.project_project ttable = s3db.project_project_tag join = ttable.on((ttable.project_id == ptable.id) & \ (ttable.tag == "APPLY") & \ (ttable.value == "Y") & \ (ttable.deleted == False)) query = (ptable.deleted == False) rows = db(query).select(ptable.id, ptable.name, join = join, ) projects = {row.id: row.name for row in rows} return projects # ------------------------------------------------------------------------- @staticmethod def selectable_services(): """ Services the user can select during test station registration @returns: list of service_ids """ db = current.db s3db = current.s3db stable = s3db.org_service query = (stable.deleted == False) rows = db(query).select(stable.id, stable.name, ) services = {row.id: row.name for row in rows} return services # ------------------------------------------------------------------------- @staticmethod def selectable_services_modes(): """ Service modes the user can select during test station registration @returns: list of service_ids """ db = current.db s3db = current.s3db mtable = s3db.org_service_mode query = (mtable.deleted == False) rows = db(query).select(mtable.id, mtable.name, ) modes = {row.id: row.name for row in rows} return modes # ------------------------------------------------------------------------- @staticmethod def selectable_booking_modes(): """ Booking modes the user can select during test station registration @returns: list of service_ids """ db = current.db s3db = current.s3db mtable = s3db.org_booking_mode query = (mtable.deleted == False) rows = db(query).select(mtable.id, mtable.name, ) modes = {row.id: row.name for row in rows} return modes # ============================================================================= class verify_email(S3CustomController): """ Custom verify_email Page """ def __call__(self): T = current.T request = current.request response = current.response session = current.session settings = current.deployment_settings # Get the registration key if request.env.request_method == "POST": key = request.post_vars.registration_key elif len(request.args) > 1: key = request.args[-1] else: key = None if not key: session.error = T("Missing registration key") redirect(URL(c="default", f="index")) formfields = [Field("activation_code", label = T("Please enter your Activation Code"), requires = IS_NOT_EMPTY(), ), ] # Construct the form response.form_label_separator = "" form = SQLFORM.factory(table_name = "auth_user", record = None, hidden = {"_next": request.vars._next, "registration_key": key, }, separator = ":", showid = False, submit_button = T("Submit"), formstyle = settings.get_ui_formstyle(), #buttons = buttons, *formfields) if form.accepts(request.vars, session, formname = "register_confirm", ): db = current.db s3db = current.s3db auth = current.auth auth_settings = auth.settings register.customise_auth_messages() # Get registration key from URL code = form.vars.activation_code # Find the pending user account utable = auth_settings.table_user query = (utable.registration_key == register.keyhash(key, code)) user = db(query).select(limitby = (0, 1), ).first() if not user: session.error = T("Registration not found") redirect(auth_settings.verify_email_next) user_id = user.id db(utable.id == user_id).update(registration_key = "pending") auth.log_event(auth.messages.verify_email_log, user) # Lookup the Approver(s) gtable = db.auth_group mtable = db.auth_membership pe_id = None # Is this an existing Org? organisation_id = user.organisation_id if organisation_id: role_required = "ORG_ADMIN" # Get org_name and pe_id from organisation otable = s3db.org_organisation row = db(otable.id == organisation_id).select(otable.name, otable.pe_id, limitby = (0, 1) ).first() if row: org_name = row.name pe_id = row.pe_id subject = """%(system_name)s - New User Approval Pending""" message = """Your action is required to approve a New User for %(org_name)s: %(user_name)s Please go to %(url)s to approve this user.""" if not pe_id: role_required = "ORG_GROUP_ADMIN" subject = """%(system_name)s - New Test Station Approval Pending""" message = """Your action is required to approve a New Test Station for %(system_name)s: %(org_name)s Please go to %(url)s to approve this station.""" # Get org_name from auth_user_temp ttable= s3db.auth_user_temp temp = db(ttable.user_id == user_id).select(ttable.custom, limitby = (0, 1) ).first() try: custom = json.loads(temp.custom) except JSONERRORS: custom = {} org_name = custom.get("organisation") # Get pe_id of TESTSTATIONS group ogtable = s3db.org_group query = (ogtable.name == TESTSTATIONS) & \ (ogtable.deleted == False) row = db(query).select(ogtable.pe_id, limitby=(0, 1)).first() if row: pe_id = row.pe_id query = (mtable.pe_id == 0) if pe_id: query |= (mtable.pe_id == pe_id) join = [mtable.on((mtable.user_id == utable.id) & \ (mtable.deleted == False)), gtable.on((gtable.id == mtable.group_id) & \ (gtable.uuid == role_required)), ] approvers = db(query).select(utable.email, utable.language, join = join, ) # Ensure that we send out the mails in the language that the approver(s) want languages = {} for approver in approvers: language = approver.language if language not in languages: languages[language] = [approver.email] else: languages[language].append(approver.email) subjects = {} messages = {} system_name = settings.get_system_name() base_url = response.s3.base_url url = "%s/default/index/approve/%s" % (base_url, user_id) for language in languages: subjects[language] = s3_str(T(subject, language=language) % {"system_name": system_name, }) messages[language] = s3_str(T(message, language=language) % {"org_name": org_name, "system_name": system_name, "user_name": user.email, "url": url, }) result = None mailer = auth_settings.mailer if mailer.settings.server: send_email = mailer.send for approver in approvers: language = approver["language"] result = send_email(to = approver["email"], subject = subjects[language], message = messages[language] ) session = current.session if result: session.confirmation = settings.get_auth_registration_pending_approval() else: # Don't prevent registration just because email not configured #db.rollback() session.error = auth.messages.email_send_failed redirect(URL(c="default", f="index")) self._view(THEME, "register.html") return {"title": T("Confirm Registration"), "form": form, } # ------------------------------------------------------------------------- @staticmethod def send_welcome_email(user): """ Send a welcome email to the new user @param user: the auth_user Row """ register.customise_auth_messages() auth_messages = current.auth.messages try: recipient = user["email"] except (KeyError, TypeError): recipient = None if not recipient: current.response.error = auth_messages.unable_send_email return # Look up CMS template for welcome email db = current.db s3db = current.s3db settings = current.deployment_settings # Define join ctable = s3db.cms_post ltable = s3db.cms_post_module join = ltable.on((ltable.post_id == ctable.id) & \ (ltable.module == "auth") & \ (ltable.resource == "user") & \ (ltable.deleted == False)) # Get message template query = (ctable.name == "WelcomeMessage") & \ (ctable.deleted == False) row = db(query).select(ctable.doc_id, ctable.body, join = join, limitby = (0, 1), ).first() if row: message_template = row.body else: # Disabled return # Look up attachments dtable = s3db.doc_document query = (dtable.doc_id == row.doc_id) & \ (dtable.file != None) & (dtable.file != "") & \ (dtable.deleted == False) rows = db(query).select(dtable.file) attachments = [] for row in rows: filename, stream = dtable.file.retrieve(row.file) attachments.append(current.mail.Attachment(stream, filename=filename)) # Default subject from auth.messages system_name = s3_str(settings.get_system_name()) subject = s3_str(auth_messages.welcome_email_subject % \ {"system_name": system_name}) # Custom message body data = {"system_name": system_name, "url": settings.get_base_public_url(), "profile": URL("default", "person", host=True), } message = formatmap(message_template, data) # Send email success = current.msg.send_email(to = recipient, subject = subject, message = message, attachments = attachments, ) if not success: current.response.error = auth_messages.unable_send_email # ============================================================================= class register_invited(S3CustomController): """ Custom Registration Page """ def __call__(self): auth = current.auth # Redirect if already logged-in if auth.s3_logged_in(): redirect(URL(c="default", f="index")) T = current.T settings = current.deployment_settings request = current.request response = current.response session = current.session # Get the registration key if len(request.args) > 1: key = request.args[-1] session.s3.invite_key = key redirect(URL(c="default", f="index", args = ["register_invited"])) else: key = session.s3.invite_key if not key: session.error = T("Missing registration key") redirect(URL(c="default", f="index")) # Page title and intro text title = T("Registration") # Get intro text from CMS db = current.db s3db = current.s3db ctable = s3db.cms_post ltable = s3db.cms_post_module join = ltable.on((ltable.post_id == ctable.id) & \ (ltable.module == "auth") & \ (ltable.resource == "user") & \ (ltable.deleted == False)) query = (ctable.name == "InvitedRegistrationIntro") & \ (ctable.deleted == False) row = db(query).select(ctable.body, join = join, cache = s3db.cache, limitby = (0, 1), ).first() intro = row.body if row else None # Customise Auth Messages auth_settings = auth.settings auth_messages = auth.messages self.customise_auth_messages() # Form Fields formfields, required_fields = self.formfields() # Generate labels (and mark required fields in the process) labels, has_required = s3_mark_required(formfields, mark_required = required_fields, ) response.s3.has_required = has_required # Form buttons REGISTER = T("Register") buttons = [INPUT(_type = "submit", _value = REGISTER, ), # TODO cancel-button? ] # Construct the form utable = auth_settings.table_user response.form_label_separator = "" form = SQLFORM.factory(table_name = utable._tablename, record = None, hidden = {"_next": request.vars._next}, labels = labels, separator = "", showid = False, submit_button = REGISTER, delete_label = auth_messages.delete_label, formstyle = settings.get_ui_formstyle(), buttons = buttons, *formfields) # Identify form for CSS & JS Validation form.add_class("auth_register") # Inject client-side Validation auth.s3_register_validation() if form.accepts(request.vars, session, formname = "register", onvalidation = self.validate(key), ): form_vars = form.vars # Get the account account = self.account(key, form_vars.code) account.update_record(**utable._filter_fields(form_vars, id=False)) del session.s3["invite_key"] # Post-process the new user record s3db.configure("auth_user", register_onaccept = self.register_onaccept, ) # Store consent response (for approve_user to register it) consent = form_vars.consent if consent: ttable = s3db.auth_user_temp record = {"user_id": account.id, "consent": form_vars.consent } ttable.insert(**record) # Approve and link user auth.s3_approve_user(account) # Send welcome email (custom) self.send_welcome_email(account) # Log them in user = Storage(utable._filter_fields(account, id=True)) auth.login_user(user) auth_messages = auth.messages auth.log_event(auth_messages.register_log, user) session.flash = auth_messages.registration_successful # TODO redirect to the org instead? redirect(URL(c="default", f="person")) elif form.errors: response.error = T("There are errors in the form, please check your input") # Custom View self._view(TEMPLATE, "register_invited.html") return {"title": title, "intro": intro, "form": form, } # ------------------------------------------------------------------------- @classmethod def validate(cls, key): """ Custom validation of registration form - check the registration code - check for duplicate email """ T = current.T def register_onvalidation(form): code = form.vars.get("code") account = cls.account(key, code) if not account: form.errors["code"] = T("Invalid Registration Code") return email = form.vars.get("email") from gluon.validators import ValidationError auth = current.auth utable = auth.settings.table_user dbset = current.db(utable.id != account.id) requires = IS_NOT_IN_DB(dbset, "%s.email" % utable._tablename) try: requires.validate(email) except ValidationError: form.errors["email"] = auth.messages.duplicate_email return onvalidation = current.auth.settings.register_onvalidation if onvalidation: from gluon.tools import callback callback(onvalidation, form, tablename="auth_user") return register_onvalidation # ------------------------------------------------------------------------- @staticmethod def register_onaccept(user_id): """ Process Registration @param user_id: the user ID """ auth = current.auth assign_role = auth.s3_assign_role assign_role(user_id, "ORG_ADMIN") assign_role(user_id, "VOUCHER_ISSUER") # ------------------------------------------------------------------------- @classmethod def send_welcome_email(cls, user): """ Send a welcome email to the new user @param user: the auth_user Row """ cls.customise_auth_messages() auth_messages = current.auth.messages # Look up CMS template for welcome email try: recipient = user["email"] except (KeyError, TypeError): recipient = None if not recipient: current.response.error = auth_messages.unable_send_email return db = current.db s3db = current.s3db settings = current.deployment_settings # Define join ctable = s3db.cms_post ltable = s3db.cms_post_module join = ltable.on((ltable.post_id == ctable.id) & \ (ltable.module == "auth") & \ (ltable.resource == "user") & \ (ltable.deleted == False)) # Get message template query = (ctable.name == "WelcomeMessageInvited") & \ (ctable.deleted == False) row = db(query).select(ctable.doc_id, ctable.body, join = join, limitby = (0, 1), ).first() if row: message_template = row.body else: # Disabled return # Look up attachments dtable = s3db.doc_document query = (dtable.doc_id == row.doc_id) & \ (dtable.file != None) & (dtable.file != "") & \ (dtable.deleted == False) rows = db(query).select(dtable.file) attachments = [] for row in rows: filename, stream = dtable.file.retrieve(row.file) attachments.append(current.mail.Attachment(stream, filename=filename)) # Default subject from auth.messages system_name = s3_str(settings.get_system_name()) subject = s3_str(auth_messages.welcome_email_subject % \ {"system_name": system_name}) # Custom message body data = {"system_name": system_name, "url": settings.get_base_public_url(), "profile": URL("default", "person", host=True), } message = formatmap(message_template, data) # Send email success = current.msg.send_email(to = recipient, subject = subject, message = message, attachments = attachments, ) if not success: current.response.error = auth_messages.unable_send_email # ------------------------------------------------------------------------- @classmethod def account(cls, key, code): """ Find the account matching registration key and code @param key: the registration key (from URL args) @param code: the registration code (from form) """ if key and code: utable = current.auth.settings.table_user query = (utable.registration_key == cls.keyhash(key, code)) account = current.db(query).select(utable.ALL, limitby=(0, 1)).first() else: account = None return account # ------------------------------------------------------------------------- @staticmethod def formfields(): """ Generate the form fields for the registration form @returns: a tuple (formfields, required_fields) - formfields = list of form fields - required_fields = list of field names of required fields """ T = current.T request = current.request s3db = current.s3db auth = current.auth auth_settings = auth.settings auth_messages = auth.messages utable = auth_settings.table_user passfield = auth_settings.password_field # Last name is required utable.last_name.requires = IS_NOT_EMPTY(error_message=T("input required")) # Don't check for duplicate email (will be done in onvalidation) # => user might choose to use the current email address of the account # => if registration key or code are invalid, we don't want to give away # any existing email addresses utable.email.requires = [IS_EMAIL(error_message = auth_messages.invalid_email), IS_LOWER(), ] # Instantiate Consent Tracker consent = s3db.auth_Consent(processing_types=["STORE", "RULES_ISS"]) # Form fields formfields = [utable.first_name, utable.last_name, utable.email, utable[passfield], Field("password_two", "password", label = auth_messages.verify_password, requires = IS_EXPR("value==%s" % \ repr(request.vars.get(passfield)), error_message = auth_messages.mismatched_password, ), comment = DIV(_class = "tooltip", _title = "%s|%s" % (auth_messages.verify_password, T("Enter the same password again"), ), ), ), Field("code", label = T("Registration Code"), requires = IS_NOT_EMPTY(), ), Field("consent", label = T("Consent"), widget = consent.widget, ), ] # Required fields required_fields = ["first_name", "last_name", ] return formfields, required_fields # ------------------------------------------------------------------------- @staticmethod def keyhash(key, code): """ Generate a hash of the activation code using the registration key @param key: the registration key @param code: the activation code @returns: the hash as string """ crypt = CRYPT(key=key, digest_alg="sha512", salt=None) return str(crypt(code.upper())[0]) # ------------------------------------------------------------------------- @staticmethod def customise_auth_messages(): """ Customise auth messages: - welcome email subject """ messages = current.auth.messages messages.welcome_email_subject = "Welcome to the %(system_name)s Portal" # ============================================================================= class geocode(S3CustomController): """ Custom Geocoder - looks up Lat/Lon from Postcode &/or Address - looks up Lx from Lat/Lon """ def __call__(self): vars_get = current.request.post_vars.get # Validate the formkey formkey = vars_get("k") keyname = "_formkey[geocode]" if not formkey or formkey not in current.session.get(keyname, []): status = 403 message = current.ERROR.NOT_PERMITTED headers = {"Content-Type": "application/json"} current.log.error(message) raise HTTP(status, body = current.xml.json_message(success = False, statuscode = status, message = message), web2py_error = message, **headers) gis = current.gis postcode = vars_get("postcode") address = vars_get("address") if address: full_address = "%s %s" %(postcode, address) else: full_address = postcode latlon = gis.geocode(full_address) if not isinstance(latlon, dict): output = "{}" else: lat = latlon["lat"] lon = latlon["lon"] results = gis.geocode_r(lat, lon) results["lat"] = lat results["lon"] = lon from s3.s3xml import SEPARATORS output = json.dumps(results, separators=SEPARATORS) current.response.headers["Content-Type"] = "application/json" return output # ============================================================================= class ocert(S3CustomController): """ Custom controller to certify the eligibility of an organisation to perform certain actions in an external application Process similar to OAuth: - external app presents the user a link/redirect to a Sahana URL containing a purpose code and a one-off token, e.g. /default/index/ocert?p=XY&t=0123456789ABCDEF - user follows the link, and is asked by Sahana to login - once logged-in, Sahana verifies that the user is OrgAdmin for an organisation that qualifies for the specified purpose, and if it does, redirects the user back to a URL in the external app, with a verification hash ("certificate") as a URL parameter - the external app requests the OrgID from the user, and generates a hash of the that OrgID with an appkey and the session token, and if both hashes match, access to the intended function is granted """ def __call__(self): db = current.db s3db = current.s3db auth = current.auth session = current.session # Handle purpose code and token in URL get_vars = current.request.get_vars purpose = get_vars.get("p") token = get_vars.get("t") if purpose or token: if not purpose or not token: self._error("Invalid Request - Missing Parameter") session.s3.ocert = {"p": purpose, "t": token} redirect(URL(args=["ocert"], vars={})) # Check that function is configured ocert = current.deployment_settings.get_custom(key="ocert") if not isinstance(ocert, dict): self._error("Function not available") # Read key from session, extract purpose code and token keys = session.s3.get("ocert") try: purpose, token = keys.get("p"), keys.get("t") except (TypeError, AttributeError): self._error("Invalid Request - Missing Parameter") # Verify purpose try: appkey, redirect_uri = ocert.get(purpose) except (TypeError, ValueError): appkey, redirect_uri = None, None if not appkey or not redirect_uri: self._error("Invalid Parameter") # Validate token # - must be a 64 to 256 bit hex-encoded number token_value = None if 16 <= len(token) <= 64: try: token_value = int(token, 16) except (TypeError, ValueError): pass if not token_value: self._error("Invalid Parameter") # Determine the organisation to check organisation_id = None if auth.s3_logged_in(): # Must be ORG_ADMIN if not auth.s3_has_role("ORG_ADMIN"): self._error("Insufficient Privileges") # Must manage at least one organisation managed_orgs = None user = auth.user sr = auth.get_system_roles() realms = user.realms.get(sr.ORG_ADMIN) if not realms: realms = s3db.pr_realm(user.pe_id) if realms: # Look up managed organisations otable = s3db.org_organisation query = (otable.pe_id.belongs(realms)) & \ (otable.deleted == False) managed_orgs = db(query).select(otable.id, otable.name, ) if not managed_orgs: self._error("No Managed Organizations") elif len(managed_orgs) == 1: # Only one managed org organisation_id = managed_orgs.first().id else: # Let user select the organisation form = self._org_select(managed_orgs) if form.accepts(current.request.vars, session, formname = "org_select", ): organisation_id = form.vars.organisation_id else: self._view(THEME, "register.html") output = {"title": current.T("Select Organization"), "intro": None, "form": form, } else: # Go to login, then return here redirect(URL(c = "default", f = "user", args = ["login"], vars = {"_next": URL(args=["ocert"], vars={})}, )) if organisation_id: # Remove ocert key from session del session.s3.ocert # Generate verification hash vhash = self._vhash(organisation_id, purpose, token, appkey) if vhash: from s3compat import urllib_quote url = redirect_uri % {"token": urllib_quote(token), "vhash": urllib_quote(vhash), } redirect(url) else: # Organisation is not authorized for the purpose self._error("Organization not authorized") return output # ------------------------------------------------------------------------- @staticmethod def _vhash(organisation_id, purpose, token, appkey): """ Verify the qualification of the organisation for the purpose, and generate a verification hash (=encrypt the OrgID with the appkey, salted with the token) if successful @param organisation_id: the organisation_id @param purpose: the purpose code @param token: the token @param appkey: the appkey @returns: the encrypted certificate if the organisation qualifies, otherwise None """ if not all((purpose, token, appkey)): return None db = current.db s3db = current.s3db # Look up the organisation ID tag ttable = s3db.org_organisation_tag query = (ttable.organisation_id == organisation_id) & \ (ttable.tag == "OrgID") & \ (ttable.deleted == False) orgid = db(query).select(ttable.value, limitby = (0, 1), ).first() if not orgid or not orgid.value: return None if purpose == "KVREG": # Must be a TESTSTATIONS organisation gtable = s3db.org_group mtable = s3db.org_group_membership query = (mtable.organisation_id == organisation_id) & \ (mtable.deleted == False) & \ (gtable.id == mtable.group_id) & \ (gtable.name == TESTSTATIONS) row = db(query).select(mtable.id, limitby=(0, 1)).first() if not row: return None # Must be partner in the TESTS-PUBLIC project ptable = s3db.project_project ltable = s3db.project_organisation query = (ltable.organisation_id == organisation_id) & \ (ltable.deleted == False) & \ (ptable.id == ltable.project_id) & \ (ptable.code == "TESTS-PUBLIC") row = db(query).select(ltable.id, limitby=(0, 1)).first() if not row: return None # Generate vhash crypt = CRYPT(key = appkey, digest_alg = "sha512", salt = token, ) return str(crypt(orgid.value)[0]).rsplit("$")[-1] # ------------------------------------------------------------------------- @staticmethod def _org_select(organisations): """ Render a form for the user to select one of their managed organisations @param organisations: the managed organisations, Rows {id, name} @returns: a FORM """ T = current.T response = current.response settings = current.deployment_settings options = {row.id: row.name for row in organisations} formfields = [Field("organisation_id", label = T("Organization"), requires = IS_IN_SET(options), ), ] # Generate labels (and mark required fields in the process) labels = s3_mark_required(formfields)[0] response.s3.has_required = False # Form buttons SUBMIT = T("Continue") buttons = [INPUT(_type = "submit", _value = SUBMIT, ), ] # Construct the form response.form_label_separator = "" form = SQLFORM.factory(table_name = "organisation", record = None, labels = labels, separator = "", showid = False, submit_button = SUBMIT, formstyle = settings.get_ui_formstyle(), buttons = buttons, *formfields) return form # ------------------------------------------------------------------------- @staticmethod def _error(message): """ Redirect to home page with error message @param message: the error message """ current.session.error = current.T(message) redirect(URL(c="default", f="index")) # END =========================================================================

39.681102

147

0.43676

3390c19a4677efe819b596dadc40b2f2972cdfb7

9,310

py

Python

exporter/opentelemetry-exporter-zipkin-json/src/opentelemetry/exporter/zipkin/encoder/__init__.py

mariojonke/opentelemetry-python

300ce1bc601440e5560523296b426592f54bf7c5

[ "Apache-2.0" ]

1

2021-08-20T04:34:28.000Z

exporter/opentelemetry-exporter-zipkin-json/src/opentelemetry/exporter/zipkin/encoder/__init__.py

harshita19244/opentelemetry-python

e2a5b0b7439acfc16d474fdd0adb3fca41308e80

[ "Apache-2.0" ]

null

exporter/opentelemetry-exporter-zipkin-json/src/opentelemetry/exporter/zipkin/encoder/__init__.py

harshita19244/opentelemetry-python

e2a5b0b7439acfc16d474fdd0adb3fca41308e80

[ "Apache-2.0" ]

null

# Copyright The OpenTelemetry 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. """Zipkin Exporter Transport Encoder Base module and abstract class for concrete transport encoders to extend. """ import abc import json import logging from enum import Enum from typing import Any, Dict, List, Optional, Sequence, TypeVar from opentelemetry.exporter.zipkin.node_endpoint import NodeEndpoint from opentelemetry.sdk.trace import Event from opentelemetry.trace import ( Span, SpanContext, StatusCode, format_span_id, format_trace_id, ) EncodedLocalEndpointT = TypeVar("EncodedLocalEndpointT") DEFAULT_MAX_TAG_VALUE_LENGTH = 128 NAME_KEY = "otel.library.name" VERSION_KEY = "otel.library.version" logger = logging.getLogger(__name__) class Protocol(Enum): """Enum of supported protocol formats. Values are human-readable strings so that they can be easily used by the OS environ var OTEL_EXPORTER_ZIPKIN_PROTOCOL (reserved for future usage). """ V1 = "v1" V2 = "v2" # pylint: disable=W0223 class Encoder(abc.ABC): """Base class for encoders that are used by the exporter. Args: max_tag_value_length: maximum length of an exported tag value. Values will be truncated to conform. Since values are serialized to a JSON list string, max_tag_value_length is honored at the element boundary. """ def __init__( self, max_tag_value_length: int = DEFAULT_MAX_TAG_VALUE_LENGTH ): self.max_tag_value_length = max_tag_value_length @staticmethod @abc.abstractmethod def content_type() -> str: pass @abc.abstractmethod def serialize( self, spans: Sequence[Span], local_endpoint: NodeEndpoint ) -> str: pass @abc.abstractmethod def _encode_span( self, span: Span, encoded_local_endpoint: EncodedLocalEndpointT ) -> Any: """ Per spec Zipkin fields that can be absent SHOULD be omitted from the payload when they are empty in the OpenTelemetry Span. https://github.com/open-telemetry/opentelemetry-specification/blob/master/specification/trace/sdk_exporters/zipkin.md#request-payload """ @staticmethod @abc.abstractmethod def _encode_local_endpoint( local_endpoint: NodeEndpoint, ) -> EncodedLocalEndpointT: pass @staticmethod def _encode_debug(span_context) -> Any: return span_context.trace_flags.sampled @staticmethod @abc.abstractmethod def _encode_span_id(span_id: int) -> Any: pass @staticmethod @abc.abstractmethod def _encode_trace_id(trace_id: int) -> Any: pass @staticmethod def _get_parent_id(span_context) -> Optional[int]: if isinstance(span_context, Span): parent_id = span_context.parent.span_id elif isinstance(span_context, SpanContext): parent_id = span_context.span_id else: parent_id = None return parent_id def _extract_tags_from_dict( self, tags_dict: Optional[Dict] ) -> Dict[str, str]: tags = {} if not tags_dict: return tags for attribute_key, attribute_value in tags_dict.items(): if isinstance(attribute_value, bool): value = str(attribute_value).lower() elif isinstance(attribute_value, (int, float, str)): value = str(attribute_value) elif isinstance(attribute_value, Sequence): value = self._extract_tag_value_string_from_sequence( attribute_value ) if not value: logger.warning("Could not serialize tag %s", attribute_key) continue else: logger.warning("Could not serialize tag %s", attribute_key) continue if ( self.max_tag_value_length is not None and self.max_tag_value_length > 0 ): value = value[: self.max_tag_value_length] tags[attribute_key] = value return tags def _extract_tag_value_string_from_sequence(self, sequence: Sequence): if self.max_tag_value_length and self.max_tag_value_length == 1: return None tag_value_elements = [] running_string_length = ( 2 # accounts for array brackets in output string ) defined_max_tag_value_length = ( self.max_tag_value_length is not None and self.max_tag_value_length > 0 ) for element in sequence: if isinstance(element, bool): tag_value_element = str(element).lower() elif isinstance(element, (int, float, str)): tag_value_element = str(element) elif element is None: tag_value_element = None else: continue if defined_max_tag_value_length: if tag_value_element is None: running_string_length += 4 # null with no quotes else: # + 2 accounts for string quotation marks running_string_length += len(tag_value_element) + 2 if tag_value_elements: # accounts for ',' item separator running_string_length += 1 if running_string_length > self.max_tag_value_length: break tag_value_elements.append(tag_value_element) return json.dumps(tag_value_elements, separators=(",", ":")) def _extract_tags_from_span(self, span: Span) -> Dict[str, str]: tags = self._extract_tags_from_dict(span.attributes) if span.resource: tags.update(self._extract_tags_from_dict(span.resource.attributes)) if span.instrumentation_info is not None: tags.update( { NAME_KEY: span.instrumentation_info.name, VERSION_KEY: span.instrumentation_info.version, } ) if span.status.status_code is not StatusCode.UNSET: tags.update({"otel.status_code": span.status.status_code.name}) if span.status.status_code is StatusCode.ERROR: tags.update({"error": span.status.description or ""}) return tags def _extract_annotations_from_events( self, events: Optional[List[Event]] ) -> Optional[List[Dict]]: if not events: return None annotations = [] for event in events: attrs = {} for key, value in event.attributes.items(): if ( isinstance(value, str) and self.max_tag_value_length is not None and self.max_tag_value_length > 0 ): value = value[: self.max_tag_value_length] attrs[key] = value annotations.append( { "timestamp": self._nsec_to_usec_round(event.timestamp), "value": json.dumps({event.name: attrs}, sort_keys=True), } ) return annotations @staticmethod def _nsec_to_usec_round(nsec: int) -> int: """Round nanoseconds to microseconds Timestamp in zipkin spans is int of microseconds. See: https://zipkin.io/pages/instrumenting.html """ return (nsec + 500) // 10 ** 3 class JsonEncoder(Encoder): @staticmethod def content_type(): return "application/json" def serialize( self, spans: Sequence[Span], local_endpoint: NodeEndpoint ) -> str: encoded_local_endpoint = self._encode_local_endpoint(local_endpoint) encoded_spans = [] for span in spans: encoded_spans.append( self._encode_span(span, encoded_local_endpoint) ) return json.dumps(encoded_spans) @staticmethod def _encode_local_endpoint(local_endpoint: NodeEndpoint) -> Dict: encoded_local_endpoint = {"serviceName": local_endpoint.service_name} if local_endpoint.ipv4 is not None: encoded_local_endpoint["ipv4"] = str(local_endpoint.ipv4) if local_endpoint.ipv6 is not None: encoded_local_endpoint["ipv6"] = str(local_endpoint.ipv6) if local_endpoint.port is not None: encoded_local_endpoint["port"] = local_endpoint.port return encoded_local_endpoint @staticmethod def _encode_span_id(span_id: int) -> str: return format_span_id(span_id) @staticmethod def _encode_trace_id(trace_id: int) -> str: return format_trace_id(trace_id)

33.014184

141

0.627497

0ff81139fa6617afd981b05ff182a2e1f27c1768

2,928

py

Python

dpnp/dpnp_iface_libmath.py

mfkiwl/dpnp

a963646c3082b7ec6fe4b40c4df2a95d3a59fc95

[ "BSD-2-Clause" ]

37

2020-09-08T00:38:52.000Z

2022-03-18T01:44:10.000Z

dpnp/dpnp_iface_libmath.py

mfkiwl/dpnp

a963646c3082b7ec6fe4b40c4df2a95d3a59fc95

[ "BSD-2-Clause" ]

432

2020-09-07T09:48:41.000Z

2022-03-25T17:50:55.000Z

dpnp/dpnp_iface_libmath.py

mfkiwl/dpnp

a963646c3082b7ec6fe4b40c4df2a95d3a59fc95

[ "BSD-2-Clause" ]

17

2020-09-07T10:00:34.000Z

2022-03-25T13:53:43.000Z

# cython: language_level=3 # distutils: language = c++ # -*- coding: utf-8 -*- # ***************************************************************************** # Copyright (c) 2016-2020, Intel Corporation # 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. # ***************************************************************************** """ Interface of the function from Python Math library Notes ----- This module is a face or public interface file for the library it contains: - Interface functions - documentation for the functions - The functions parameters check """ import math from dpnp.dpnp_algo import * from dpnp.dpnp_utils import * import dpnp __all__ = [ "erf" ] def erf(in_array1): """ Returns the error function of complex argument. For full documentation refer to :obj:`scipy.special.erf`. Limitations ----------- Parameter ``in_array1`` is supported as :obj:`dpnp.ndarray`. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. .. seealso:: :obj:`math.erf` Examples -------- >>> import dpnp as np >>> x = np.linspace(2.0, 3.0, num=5) >>> [i for i in x] [2.0, 2.25, 2.5, 2.75, 3.0] >>> out = np.erf(x) >>> [i for i in out] [0.99532227, 0.99853728, 0.99959305, 0.99989938, 0.99997791] """ x1_desc = dpnp.get_dpnp_descriptor(in_array1) if x1_desc: return dpnp_erf(x1_desc).get_pyobj() result = create_output_descriptor_py(in_array1.shape, in_array1.dtype, None).get_pyobj() for i in range(result.size): result[i] = math.erf(in_array1[i]) return result

32.898876

92

0.677254

2d37b27339dd27c534e2a0daf44ce0129bc8d8a8

11,400

py

Python

pay-api/src/pay_api/models/payment.py

stevenc987/sbc-pay

04f02f362f88a30c082b0643583b8d0ebff6063f

[ "Apache-2.0" ]

null

pay-api/src/pay_api/models/payment.py

stevenc987/sbc-pay

04f02f362f88a30c082b0643583b8d0ebff6063f

[ "Apache-2.0" ]

null

pay-api/src/pay_api/models/payment.py

stevenc987/sbc-pay

04f02f362f88a30c082b0643583b8d0ebff6063f

[ "Apache-2.0" ]

null

# Copyright © 2019 Province of British Columbia # # 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. """Model to handle all operations related to Payment data.""" from datetime import datetime from typing import Dict import pytz from flask import current_app from marshmallow import fields from sqlalchemy import ForeignKey from sqlalchemy import func, or_ from sqlalchemy.orm import relationship from pay_api.utils.enums import InvoiceReferenceStatus, PaymentMethod as PaymentMethodEnum, PaymentStatus from pay_api.utils.util import get_first_and_last_dates_of_month, get_str_by_path, get_week_start_and_end_date from .base_model import BaseModel from .base_schema import BaseSchema from .db import db from .invoice import Invoice from .payment_account import PaymentAccount from .payment_method import PaymentMethod from .payment_status_code import PaymentStatusCode from .payment_system import PaymentSystem from .invoice_reference import InvoiceReference class Payment(BaseModel): # pylint: disable=too-many-instance-attributes """This class manages all of the base data about Payment .""" __tablename__ = 'payments' id = db.Column(db.Integer, primary_key=True, autoincrement=True) payment_system_code = db.Column(db.String(10), ForeignKey('payment_systems.code'), nullable=False) payment_account_id = db.Column(db.Integer, ForeignKey('payment_accounts.id'), nullable=True) payment_method_code = db.Column(db.String(15), ForeignKey('payment_methods.code'), nullable=False) payment_status_code = db.Column(db.String(20), ForeignKey('payment_status_codes.code'), nullable=True) invoice_number = db.Column(db.String(50), nullable=True, index=True) receipt_number = db.Column(db.String(50), nullable=True, index=True) cons_inv_number = db.Column(db.String(50), nullable=True, index=True) invoice_amount = db.Column(db.Numeric(), nullable=True) paid_amount = db.Column(db.Numeric(), nullable=True) created_by = db.Column(db.String(50), default='SYSTEM') completed_on = db.Column(db.DateTime, nullable=True) payment_system = relationship(PaymentSystem, foreign_keys=[payment_system_code], lazy='select', innerjoin=True) payment_status = relationship(PaymentStatusCode, foreign_keys=[payment_status_code], lazy='select', innerjoin=True) @classmethod def find_payment_method_by_payment_id(cls, identifier: int): """Return a Payment by id.""" query = db.session.query(PaymentMethod) \ .join(Payment) \ .filter(PaymentMethod.code == Payment.payment_method_code) \ .filter(Payment.id == identifier) return query.one_or_none() @classmethod def find_payment_by_invoice_number_and_status(cls, inv_number: str, payment_status: str): """Return a Payment by invoice_number and status.""" query = db.session.query(Payment) \ .filter(Payment.invoice_number == inv_number) \ .filter(Payment.payment_status_code == payment_status) return query.all() @classmethod def find_payment_by_receipt_number(cls, receipt_number: str): """Return a Payment by receipt_number.""" return db.session.query(Payment).filter(Payment.receipt_number == receipt_number).one_or_none() @classmethod def find_payment_for_invoice(cls, invoice_id: int): """Find payment records created for the invoice.""" query = db.session.query(Payment) \ .join(InvoiceReference, InvoiceReference.invoice_number == Payment.invoice_number) \ .join(Invoice, InvoiceReference.invoice_id == Invoice.id) \ .filter(Invoice.id == invoice_id) \ .filter(InvoiceReference.status_code. in_([InvoiceReferenceStatus.ACTIVE.value, InvoiceReferenceStatus.COMPLETED.value])) return query.one_or_none() @classmethod def search_account_payments(cls, auth_account_id: str, payment_status: str, page: int, limit: int): """Search payment records created for the account.""" query = db.session.query(Payment, Invoice) \ .join(PaymentAccount, PaymentAccount.id == Payment.payment_account_id) \ .outerjoin(InvoiceReference, InvoiceReference.invoice_number == Payment.invoice_number) \ .outerjoin(Invoice, InvoiceReference.invoice_id == Invoice.id) \ .filter(PaymentAccount.auth_account_id == auth_account_id) # TODO handle other status and conditions gracefully. if payment_status: query = query.filter(Payment.payment_status_code == payment_status) if payment_status == PaymentStatus.FAILED.value: consolidated_inv_subquery = db.session.query(Payment.invoice_number) \ .filter(Payment.payment_status_code == PaymentStatus.CREATED.value) \ .filter(Payment.payment_method_code == PaymentMethodEnum.CC.value) \ .subquery() # If call is to get NSF payments, get only active failed payments. # Exclude any payments which failed first and paid later. query = query.filter(or_(InvoiceReference.status_code == InvoiceReferenceStatus.ACTIVE.value, Payment.cons_inv_number.in_(consolidated_inv_subquery))) query = query.order_by(Payment.id.asc()) pagination = query.paginate(per_page=limit, page=page) result, count = pagination.items, pagination.total return result, count @classmethod def find_payments_to_consolidate(cls, auth_account_id: str): """Find payments to be consolidated.""" consolidated_inv_subquery = db.session.query(Payment.cons_inv_number)\ .filter(Payment.payment_status_code == PaymentStatus.FAILED.value)\ .filter(Payment.payment_method_code == PaymentMethodEnum.PAD.value)\ .subquery() query = db.session.query(Payment) \ .join(PaymentAccount, PaymentAccount.id == Payment.payment_account_id) \ .outerjoin(InvoiceReference, InvoiceReference.invoice_number == Payment.invoice_number) \ .filter(InvoiceReference.status_code == InvoiceReferenceStatus.ACTIVE.value) \ .filter(PaymentAccount.auth_account_id == auth_account_id) \ .filter(or_(Payment.payment_status_code == PaymentStatus.FAILED.value, Payment.invoice_number.in_(consolidated_inv_subquery))) return query.all() @classmethod def search_purchase_history(cls, # pylint:disable=too-many-arguments, too-many-locals, too-many-branches auth_account_id: str, search_filter: Dict, page: int, limit: int, return_all: bool, max_no_records: int = 0): """Search for purchase history.""" query = db.session.query(Invoice) \ .outerjoin(PaymentAccount, Invoice.payment_account_id == PaymentAccount.id) \ .filter(PaymentAccount.auth_account_id == auth_account_id) if search_filter.get('status', None): query = query.filter(Invoice.invoice_status_code == search_filter.get('status')) if search_filter.get('folioNumber', None): query = query.filter(Invoice.folio_number == search_filter.get('folioNumber')) if search_filter.get('businessIdentifier', None): query = query.filter(Invoice.business_identifier == search_filter.get('businessIdentifier')) if search_filter.get('createdBy', None): # pylint: disable=no-member query = query.filter( Invoice.created_name.ilike('%' + search_filter.get('createdBy') + '%')) # pylint: disable=no-member # Find start and end dates created_from: datetime = None created_to: datetime = None if get_str_by_path(search_filter, 'dateFilter/startDate'): created_from = datetime.strptime(get_str_by_path(search_filter, 'dateFilter/startDate'), '%m/%d/%Y') if get_str_by_path(search_filter, 'dateFilter/endDate'): created_to = datetime.strptime(get_str_by_path(search_filter, 'dateFilter/endDate'), '%m/%d/%Y') if get_str_by_path(search_filter, 'weekFilter/index'): created_from, created_to = get_week_start_and_end_date( int(get_str_by_path(search_filter, 'weekFilter/index'))) if get_str_by_path(search_filter, 'monthFilter/month') and get_str_by_path(search_filter, 'monthFilter/year'): month = int(get_str_by_path(search_filter, 'monthFilter/month')) year = int(get_str_by_path(search_filter, 'monthFilter/year')) created_from, created_to = get_first_and_last_dates_of_month(month=month, year=year) if created_from and created_to: # Truncate time for from date and add max time for to date tz_name = current_app.config['LEGISLATIVE_TIMEZONE'] tz_local = pytz.timezone(tz_name) created_from = created_from.replace(hour=0, minute=0, second=0, microsecond=0).astimezone(tz_local) created_to = created_to.replace(hour=23, minute=59, second=59, microsecond=999999).astimezone(tz_local) query = query.filter( func.timezone(tz_name, func.timezone('UTC', Invoice.created_on)).between(created_from, created_to)) # Add ordering query = query.order_by(Invoice.created_on.desc()) if not return_all: # Add pagination pagination = query.paginate(per_page=limit, page=page) result, count = pagination.items, pagination.total # If maximum number of records is provided, return it as total if max_no_records > 0: count = max_no_records if max_no_records < count else count else: # If maximum number of records is provided, set the page with that number if max_no_records > 0: pagination = query.paginate(per_page=max_no_records, page=1) result, count = pagination.items, max_no_records else: result = query.all() count = len(result) return result, count class PaymentSchema(BaseSchema): # pylint: disable=too-many-ancestors """Main schema used to serialize the Payment.""" class Meta: # pylint: disable=too-few-public-methods """Returns all the fields from the SQLAlchemy class.""" model = Payment exclude = ['payment_system', 'payment_status', 'payment_account_id', 'cons_inv_number'] payment_system_code = fields.String(data_key='payment_system') payment_method_code = fields.String(data_key='payment_method') payment_status_code = fields.String(data_key='status_code') invoice_amount = fields.Float(data_key='invoice_amount') paid_amount = fields.Float(data_key='paid_amount')

51.351351

119

0.69307

f8a9eaafd6d169c227b680d9a44f503018ff7617

8,222

py

Python

reference/train.py

BoChenYS/ROPE

3e50f134259b555cf547e4a3ef8b14cf5cda4e00

[ "BSD-3-Clause" ]

6

2022-01-12T05:59:20.000Z

2022-03-31T02:33:23.000Z

reference/train.py

BoChenYS/ROPE

3e50f134259b555cf547e4a3ef8b14cf5cda4e00

[ "BSD-3-Clause" ]

null

reference/train.py

BoChenYS/ROPE

3e50f134259b555cf547e4a3ef8b14cf5cda4e00

[ "BSD-3-Clause" ]

null

r"""PyTorch Detection Training. To run in a multi-gpu environment, use the distributed launcher:: python -m torch.distributed.launch --nproc_per_node=$NGPU --use_env \ train.py ... --world-size $NGPU The default hyperparameters are tuned for training on 8 gpus and 2 images per gpu. --lr 0.02 --batch-size 2 --world-size 8 If you use different number of gpus, the learning rate should be changed to 0.02/8*$NGPU. On top of that, for training Faster/Mask R-CNN, the default hyperparameters are --epochs 26 --lr-steps 16 22 --aspect-ratio-group-factor 3 Also, if you train Keypoint R-CNN, the default hyperparameters are --epochs 46 --lr-steps 36 43 --aspect-ratio-group-factor 3 Because the number of images is smaller in the person keypoint subset of COCO, the number of epochs should be adapted so that we have the same number of iterations. """ import datetime import os import time import torch import torch.utils.data from torch import nn import torchvision import torchvision.models.detection import torchvision.models.detection.mask_rcnn from coco_utils import get_coco, get_coco_kp from group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups from engine import train_one_epoch, evaluate from . import utils from . import transforms as T def get_dataset(name, image_set, transform, data_path): paths = { "coco": (data_path, get_coco, 91), "coco_kp": (data_path, get_coco_kp, 2) } p, ds_fn, num_classes = paths[name] ds = ds_fn(p, image_set=image_set, transforms=transform) return ds, num_classes def get_transform(train): transforms = [] transforms.append(T.ToTensor()) if train: transforms.append(T.RandomHorizontalFlip(0.5)) return T.Compose(transforms) def main(args): utils.init_distributed_mode(args) print(args) device = torch.device(args.device) # Data loading code print("Loading data") dataset, num_classes = get_dataset(args.dataset, "train", get_transform(train=True), args.data_path) dataset_test, _ = get_dataset(args.dataset, "val", get_transform(train=False), args.data_path) print("Creating data loaders") if args.distributed: train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test) else: train_sampler = torch.utils.data.RandomSampler(dataset) test_sampler = torch.utils.data.SequentialSampler(dataset_test) if args.aspect_ratio_group_factor >= 0: group_ids = create_aspect_ratio_groups(dataset, k=args.aspect_ratio_group_factor) train_batch_sampler = GroupedBatchSampler(train_sampler, group_ids, args.batch_size) else: train_batch_sampler = torch.utils.data.BatchSampler( train_sampler, args.batch_size, drop_last=True) data_loader = torch.utils.data.DataLoader( dataset, batch_sampler=train_batch_sampler, num_workers=args.workers, collate_fn=utils.collate_fn) data_loader_test = torch.utils.data.DataLoader( dataset_test, batch_size=1, sampler=test_sampler, num_workers=args.workers, collate_fn=utils.collate_fn) print("Creating model") model = torchvision.models.detection.__dict__[args.model](num_classes=num_classes, pretrained=args.pretrained) model.to(device) model_without_ddp = model if args.distributed: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) model_without_ddp = model.module params = [p for p in model.parameters() if p.requires_grad] optimizer = torch.optim.SGD( params, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) # lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma) lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_steps, gamma=args.lr_gamma) if args.resume: checkpoint = torch.load(args.resume, map_location='cpu') model_without_ddp.load_state_dict(checkpoint['model']) optimizer.load_state_dict(checkpoint['optimizer']) lr_scheduler.load_state_dict(checkpoint['lr_scheduler']) args.start_epoch = checkpoint['epoch'] + 1 if args.test_only: evaluate(model, data_loader_test, device=device) return print("Start training") start_time = time.time() for epoch in range(args.start_epoch, args.epochs): if args.distributed: train_sampler.set_epoch(epoch) train_one_epoch(model, optimizer, data_loader, device, epoch, args.print_freq) lr_scheduler.step() if args.output_dir: utils.save_on_master({ 'model': model_without_ddp.state_dict(), 'optimizer': optimizer.state_dict(), 'lr_scheduler': lr_scheduler.state_dict(), 'args': args, 'epoch': epoch}, os.path.join(args.output_dir, 'model_{}.pth'.format(epoch))) # evaluate after every epoch evaluate(model, data_loader_test, device=device) total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print('Training time {}'.format(total_time_str)) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser( description=__doc__) parser.add_argument('--data-path', default='/datasets01/COCO/022719/', help='dataset') parser.add_argument('--dataset', default='coco', help='dataset') parser.add_argument('--model', default='maskrcnn_resnet50_fpn', help='model') parser.add_argument('--device', default='cuda', help='device') parser.add_argument('-b', '--batch-size', default=2, type=int, help='images per gpu, the total batch size is $NGPU x batch_size') parser.add_argument('--epochs', default=26, type=int, metavar='N', help='number of total epochs to run') parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', help='number of data loading workers (default: 4)') parser.add_argument('--lr', default=0.02, type=float, help='initial learning rate, 0.02 is the default value for training ' 'on 8 gpus and 2 images_per_gpu') parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum') parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)', dest='weight_decay') parser.add_argument('--lr-step-size', default=8, type=int, help='decrease lr every step-size epochs') parser.add_argument('--lr-steps', default=[16, 22], nargs='+', type=int, help='decrease lr every step-size epochs') parser.add_argument('--lr-gamma', default=0.1, type=float, help='decrease lr by a factor of lr-gamma') parser.add_argument('--print-freq', default=20, type=int, help='print frequency') parser.add_argument('--output-dir', default='.', help='path where to save') parser.add_argument('--resume', default='', help='resume from checkpoint') parser.add_argument('--start_epoch', default=0, type=int, help='start epoch') parser.add_argument('--aspect-ratio-group-factor', default=3, type=int) parser.add_argument( "--test-only", dest="test_only", help="Only test the model", action="store_true", ) parser.add_argument( "--pretrained", dest="pretrained", help="Use pre-trained models from the modelzoo", action="store_true", ) # distributed training parameters parser.add_argument('--world-size', default=1, type=int, help='number of distributed processes') parser.add_argument('--dist-url', default='env://', help='url used to set up distributed training') args = parser.parse_args() if args.output_dir: utils.mkdir(args.output_dir) main(args)

40.70297

119

0.677816

8d161b72fa47a578395dc5e0b2dde178f56298ac

2,107

py

Python

lib/spack/spack/hooks/monitor.py

HigherOrderMethods/spack

87ed3fcc59fc25ce250042338d082925e3a3610b

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2

2019-02-10T13:47:48.000Z

2019-04-17T13:05:17.000Z

lib/spack/spack/hooks/monitor.py

HigherOrderMethods/spack

87ed3fcc59fc25ce250042338d082925e3a3610b

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

15

2021-04-14T12:34:46.000Z

2022-03-02T19:08:00.000Z

lib/spack/spack/hooks/monitor.py

Kerilk/spack

e027942b55407a4a5fe323b93d8e57200c873a43

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2

2018-04-06T09:04:11.000Z

2020-01-24T12:52:12.000Z

# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) import llnl.util.tty as tty import spack.monitor def on_install_start(spec): """On start of an install, we want to ping the server if it exists """ if not spack.monitor.cli: return tty.debug("Running on_install_start for %s" % spec) build_id = spack.monitor.cli.new_build(spec) tty.verbose("Build created with id %s" % build_id) def on_install_success(spec): """On the success of an install (after everything is complete) """ if not spack.monitor.cli: return tty.debug("Running on_install_success for %s" % spec) result = spack.monitor.cli.update_build(spec, status="SUCCESS") tty.verbose(result.get('message')) def on_install_failure(spec): """Triggered on failure of an install """ if not spack.monitor.cli: return tty.debug("Running on_install_failure for %s" % spec) result = spack.monitor.cli.fail_task(spec) tty.verbose(result.get('message')) def on_phase_success(pkg, phase_name, log_file): """Triggered on a phase success """ if not spack.monitor.cli: return tty.debug("Running on_phase_success %s, phase %s" % (pkg.name, phase_name)) result = spack.monitor.cli.send_phase(pkg, phase_name, log_file, "SUCCESS") tty.verbose(result.get('message')) def on_phase_error(pkg, phase_name, log_file): """Triggered on a phase error """ if not spack.monitor.cli: return tty.debug("Running on_phase_error %s, phase %s" % (pkg.name, phase_name)) result = spack.monitor.cli.send_phase(pkg, phase_name, log_file, "ERROR") tty.verbose(result.get('message')) def on_analyzer_save(pkg, result): """given a package and a result, if we have a spack monitor, upload the result to it. """ if not spack.monitor.cli: return # This hook runs after a save result spack.monitor.cli.send_analyze_metadata(pkg, result)

28.472973

79

0.68486

5e2b2f0e5c9cdd14dd3e3052e5392fdfa1eb0061

1,115

py

Python

talipp/indicators/CHOP.py

solocarrie/talipp

a35bbc33444c56683d4e26439f4878e92b937d7f

[ "MIT" ]

54

2020-11-19T02:27:04.000Z

2022-02-22T06:31:05.000Z

talipp/indicators/CHOP.py

justin-pierce/talipp

f5296381e3f4270b7743694e2ab5a0da301bdaf3

[ "MIT" ]

24

2020-11-01T17:56:28.000Z

2021-09-15T18:40:04.000Z

talipp/indicators/CHOP.py

justin-pierce/talipp

f5296381e3f4270b7743694e2ab5a0da301bdaf3

[ "MIT" ]

14

2020-12-10T22:43:37.000Z

2022-01-15T22:23:42.000Z

from math import log10 from typing import List, Any from talipp.indicators.Indicator import Indicator from talipp.indicators.ATR import ATR from talipp.ohlcv import OHLCV class CHOP(Indicator): """ Choppiness Index Output: a list of OHLCV objects """ def __init__(self, period: int, input_values: List[OHLCV] = None): super().__init__() self.period = period self.atr = ATR(1) self.add_sub_indicator(self.atr) self.initialize(input_values) def _calculate_new_value(self) -> Any: if len(self.atr) < self.period or len(self.input_values) < self.period: return None max_high = max(self.input_values[-self.period:], key = lambda x: x.high).high min_low = min(self.input_values[-self.period:], key = lambda x: x.low).low if max_high != min_low: return 100.0 * log10(sum(self.atr[-self.period:]) / (max_high - min_low) ) / log10(self.period) else: if len(self.output_values) > 0: return self.output_values[-1] else: return None

28.589744

107

0.622422

f20e5ce021f8a4b59cc652737f5aac27463109fa

11,212

py

Python

mezzanine/generic/fields.py

shangzhikeji/mezzanine

4dfd078d64236dfd4a9d67587ad20562801d0932

[ "BSD-2-Clause" ]

2

2019-01-19T09:00:20.000Z

2019-03-13T01:09:36.000Z

mezzanine/generic/fields.py

shangzhikeji/mezzanine

4dfd078d64236dfd4a9d67587ad20562801d0932

[ "BSD-2-Clause" ]

null

mezzanine/generic/fields.py

shangzhikeji/mezzanine

4dfd078d64236dfd4a9d67587ad20562801d0932

[ "BSD-2-Clause" ]

null

from __future__ import division, unicode_literals from future.builtins import str from copy import copy from django.contrib.contenttypes.fields import GenericRelation from django.core.exceptions import ImproperlyConfigured, AppRegistryNotReady from django.db.models import IntegerField, CharField, FloatField from django.db.models.signals import post_save, post_delete from mezzanine.utils.deprecation import get_related_model class BaseGenericRelation(GenericRelation): """ Extends ``GenericRelation`` to: - Add a consistent default value for ``object_id_field`` and check for a ``default_related_model`` attribute which can be defined on subclasses as a default for the ``to`` argument. - Add one or more custom fields to the model that the relation field is applied to, and then call a ``related_items_changed`` method each time related items are saved or deleted, so that a calculated value can be stored against the custom fields since aggregates aren't available for GenericRelation instances. """ # Mapping of field names to model fields that will be added. fields = {} def __init__(self, *args, **kwargs): """ Set up some defaults and check for a ``default_related_model`` attribute for the ``to`` argument. """ kwargs.setdefault("object_id_field", "object_pk") to = getattr(self, "default_related_model", None) # Avoid having both a positional arg and a keyword arg for # the parameter ``to`` if to and not args: kwargs.setdefault("to", to) try: # Check if ``related_model`` has been modified by a subclass self.related_model except (AppRegistryNotReady, AttributeError): # if not, all is good super(BaseGenericRelation, self).__init__(*args, **kwargs) else: # otherwise, warn the user to stick to the new (as of 4.0) # ``default_related_model`` attribute raise ImproperlyConfigured("BaseGenericRelation changed the " "way it handled a default ``related_model`` in mezzanine " "4.0. Please override ``default_related_model`` instead " "and do not tamper with django's ``related_model`` " "property anymore.") def contribute_to_class(self, cls, name): """ Add each of the names and fields in the ``fields`` attribute to the model the relationship field is applied to, and set up the related item save and delete signals for calling ``related_items_changed``. """ for field in cls._meta.many_to_many: if isinstance(field, self.__class__): e = "Multiple %s fields are not supported (%s.%s, %s.%s)" % ( self.__class__.__name__, cls.__name__, cls.__name__, name, field.name) raise ImproperlyConfigured(e) self.related_field_name = name super(BaseGenericRelation, self).contribute_to_class(cls, name) # Not applicable to abstract classes, and in fact will break. if not cls._meta.abstract: for (name_string, field) in self.fields.items(): if "%s" in name_string: name_string = name_string % name extant_fields = cls._meta._forward_fields_map if name_string in extant_fields: continue if field.verbose_name is None: field.verbose_name = self.verbose_name cls.add_to_class(name_string, copy(field)) # Add a getter function to the model we can use to retrieve # the field/manager by name. getter_name = "get_%s_name" % self.__class__.__name__.lower() cls.add_to_class(getter_name, lambda self: name) sender = get_related_model(self) post_save.connect(self._related_items_changed, sender=sender) post_delete.connect(self._related_items_changed, sender=sender) def _related_items_changed(self, **kwargs): """ Ensure that the given related item is actually for the model this field applies to, and pass the instance to the real ``related_items_changed`` handler. """ for_model = kwargs["instance"].content_type.model_class() if for_model and issubclass(for_model, self.model): instance_id = kwargs["instance"].object_pk try: instance = for_model.objects.get(id=instance_id) except self.model.DoesNotExist: # Instance itself was deleted - signals are irrelevant. return if hasattr(instance, "get_content_model"): instance = instance.get_content_model() related_manager = getattr(instance, self.related_field_name) self.related_items_changed(instance, related_manager) def related_items_changed(self, instance, related_manager): """ Can be implemented by subclasses - called whenever the state of related items change, eg they're saved or deleted. The instance for this field and the related manager for the field are passed as arguments. """ pass def value_from_object(self, obj): """ Returns the value of this field in the given model instance. See: https://code.djangoproject.com/ticket/22552 """ return getattr(obj, self.attname).all() class CommentsField(BaseGenericRelation): """ Stores the number of comments against the ``COMMENTS_FIELD_NAME_count`` field when a comment is saved or deleted. """ default_related_model = "generic.ThreadedComment" fields = {"%s_count": IntegerField(editable=False, default=0)} def related_items_changed(self, instance, related_manager): """ Stores the number of comments. A custom ``count_filter`` queryset gets checked for, allowing managers to implement custom count logic. """ try: count = related_manager.count_queryset() except AttributeError: count = related_manager.count() count_field_name = list(self.fields.keys())[0] % \ self.related_field_name setattr(instance, count_field_name, count) instance.save() class KeywordsField(BaseGenericRelation): """ Stores the keywords as a single string into the ``KEYWORDS_FIELD_NAME_string`` field for convenient access when searching. """ default_related_model = "generic.AssignedKeyword" fields = {"%s_string": CharField(editable=False, blank=True, max_length=500)} def __init__(self, *args, **kwargs): """ Mark the field as editable so that it can be specified in admin class fieldsets and pass validation, and also so that it shows up in the admin form. """ super(KeywordsField, self).__init__(*args, **kwargs) self.editable = True def formfield(self, **kwargs): """ Provide the custom form widget for the admin, since there isn't a form field mapped to ``GenericRelation`` model fields. """ from mezzanine.generic.forms import KeywordsWidget kwargs["widget"] = KeywordsWidget return super(KeywordsField, self).formfield(**kwargs) def save_form_data(self, instance, data): """ The ``KeywordsWidget`` field will return data as a string of comma separated IDs for the ``Keyword`` model - convert these into actual ``AssignedKeyword`` instances. Also delete ``Keyword`` instances if their last related ``AssignedKeyword`` instance is being removed. """ from mezzanine.generic.models import Keyword related_manager = getattr(instance, self.name) # Get a list of Keyword IDs being removed. old_ids = [str(a.keyword_id) for a in related_manager.all()] new_ids = data.split(",") removed_ids = set(old_ids) - set(new_ids) # Remove current AssignedKeyword instances. related_manager.all().delete() # Convert the data into AssignedKeyword instances. if data: data = [related_manager.create(keyword_id=i) for i in new_ids] # Remove keywords that are no longer assigned to anything. Keyword.objects.delete_unused(removed_ids) #super(KeywordsField, self).save_form_data(instance, data) def contribute_to_class(self, cls, name): """ Swap out any reference to ``KeywordsField`` with the ``KEYWORDS_FIELD_string`` field in ``search_fields``. """ super(KeywordsField, self).contribute_to_class(cls, name) string_field_name = list(self.fields.keys())[0] % \ self.related_field_name if hasattr(cls, "search_fields") and name in cls.search_fields: try: weight = cls.search_fields[name] except TypeError: # search_fields is a sequence. index = cls.search_fields.index(name) search_fields_type = type(cls.search_fields) cls.search_fields = list(cls.search_fields) cls.search_fields[index] = string_field_name cls.search_fields = search_fields_type(cls.search_fields) else: del cls.search_fields[name] cls.search_fields[string_field_name] = weight def related_items_changed(self, instance, related_manager): """ Stores the keywords as a single string for searching. """ assigned = related_manager.select_related("keyword") keywords = " ".join([str(a.keyword) for a in assigned]) string_field_name = list(self.fields.keys())[0] % \ self.related_field_name if getattr(instance, string_field_name) != keywords: setattr(instance, string_field_name, keywords) instance.save() class RatingField(BaseGenericRelation): """ Stores the rating count and average against the ``RATING_FIELD_NAME_count`` and ``RATING_FIELD_NAME_average`` fields when a rating is saved or deleted. """ default_related_model = "generic.Rating" fields = {"%s_count": IntegerField(default=0, editable=False), "%s_sum": IntegerField(default=0, editable=False), "%s_average": FloatField(default=0, editable=False)} def related_items_changed(self, instance, related_manager): """ Calculates and saves the average rating. """ ratings = [r.value for r in related_manager.all()] count = len(ratings) _sum = sum(ratings) average = _sum / count if count > 0 else 0 setattr(instance, "%s_count" % self.related_field_name, count) setattr(instance, "%s_sum" % self.related_field_name, _sum) setattr(instance, "%s_average" % self.related_field_name, average) instance.save()

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