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

fe17f72c7021d8205a27e05f2ecce89723852ecd

1,289

py

Python

qcloudsdkscaling/DescribeScheduledTaskRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkscaling/DescribeScheduledTaskRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkscaling/DescribeScheduledTaskRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from qcloudsdkcore.request import Request class DescribeScheduledTaskRequest(Request): def __init__(self): super(DescribeScheduledTaskRequest, self).__init__( 'scaling', 'qcloudcliV1', 'DescribeScheduledTask', 'scaling.api.qcloud.com') def get_limit(self): return self.get_params().get('limit') def set_limit(self, limit): self.add_param('limit', limit) def get_offset(self): return self.get_params().get('offset') def set_offset(self, offset): self.add_param('offset', offset) def get_scalingGroupId(self): return self.get_params().get('scalingGroupId') def set_scalingGroupId(self, scalingGroupId): self.add_param('scalingGroupId', scalingGroupId) def get_scalingScheduledTaskIds(self): return self.get_params().get('scalingScheduledTaskIds') def set_scalingScheduledTaskIds(self, scalingScheduledTaskIds): self.add_param('scalingScheduledTaskIds', scalingScheduledTaskIds) def get_scalingScheduledTaskName(self): return self.get_params().get('scalingScheduledTaskName') def set_scalingScheduledTaskName(self, scalingScheduledTaskName): self.add_param('scalingScheduledTaskName', scalingScheduledTaskName)

32.225

88

0.719162

20605e5821481f371f58f1260baf237fd327d9b5

5,619

py

Python

pyram/etc/rd_hal_pure.py

Hoseung/pyRamAn

f9386fa5a9f045f98590039988d3cd50bc488dc2

[ "MIT" ]

1

2021-11-25T16:11:56.000Z

pyram/etc/rd_hal_pure.py

Hoseung/pyRamAn

f9386fa5a9f045f98590039988d3cd50bc488dc2

[ "MIT" ]

6

2020-02-17T13:44:43.000Z

2020-06-25T15:35:05.000Z

pyram/etc/rd_hal_pure.py

Hoseung/pyRamAn

f9386fa5a9f045f98590039988d3cd50bc488dc2

[ "MIT" ]

1

2021-11-25T16:11:56.000Z

import numpy as np import struct def load_header(brick_data, double=False): offset = 4 if double: nbytes = 8 dtype_float="d" else: nbytes = 4 dtype_float="f" nbodies = struct.unpack("i", brick_data[4:8])[0] offset += 12 massp = struct.unpack(dtype_float, brick_data[offset:offset+nbytes])[0] offset += 8 + nbytes aexp = struct.unpack(dtype_float, brick_data[offset:offset+nbytes])[0] offset += 8 + nbytes omegat = struct.unpack(dtype_float, brick_data[offset:offset+nbytes])[0] offset += 8 + nbytes age = struct.unpack(dtype_float, brick_data[offset:offset+nbytes])[0] offset += 8 + nbytes halnum = struct.unpack("i", brick_data[offset:offset+4])[0] subnum = struct.unpack("i", brick_data[offset+4:offset+8])[0] return offset+16, halnum, subnum def load_a_halo(brick_data, offset, dd, is_gal=True, double=False): if double: nbytes = 8 dtype_float="d" else: nbytes = 4 dtype_float="f" npart = struct.unpack("i", brick_data[offset:offset+4])[0] dd["np"]=npart offset += 12 # 12 = 4 + 8 ids = struct.unpack_from("<{}i".format(npart), brick_data[offset:offset+4*npart]) offset += 4*npart + 8 dd["id"] = struct.unpack("i", brick_data[offset:offset+4])[0] offset += 24 dd["level"],dd["host"],dd["sub"],dd["nsub"],dd["nextsub"]\ = struct.unpack_from("<5i", brick_data[offset:offset+20]) offset += 28 dd["m"] = struct.unpack(dtype_float, brick_data[offset:offset+nbytes])[0] offset += 8 + nbytes dd["x"],dd["y"],dd["z"] = struct.unpack_from("<3"+dtype_float, brick_data[offset:offset+3*nbytes]) offset += 8 + 3*nbytes dd["vx"],dd["vy"],dd["vz"] = struct.unpack_from("<3"+dtype_float, brick_data[offset:offset+3*nbytes]) offset += 8 + 3*nbytes dd["ax"],dd["ay"],dd["az"] = struct.unpack_from("<3"+dtype_float, brick_data[offset:offset+3*nbytes]) offset += 8 + 3*nbytes radius= struct.unpack_from("<4"+dtype_float, brick_data[offset:offset+4*nbytes]) dd["r"],dd["abc"] = radius[0], radius[1:] offset += 8 + 4*nbytes dd["energy"] = struct.unpack_from("<3"+dtype_float, brick_data[offset:offset+3*nbytes]) offset += 8 + 3*nbytes dd["sp"] = struct.unpack(dtype_float, brick_data[offset:offset+nbytes])[0] offset += 8 + nbytes if is_gal: dd["sig"], dd["sigbulge"], dd["mbulge"]\ = struct.unpack_from("<3"+dtype_float, brick_data[offset:offset+3*nbytes]) offset += 8+ 3*nbytes dd["mvir"],dd["rvir"],dd["tvir"],dd["cvel"]\ = struct.unpack_from("<4"+dtype_float, brick_data[offset:offset+4*nbytes]) offset += 8+4*nbytes dd["p_rho"],dd["p_c"] = struct.unpack_from("<2"+dtype_float, brick_data[offset:offset+2*nbytes]) offset += 8+2*nbytes if is_gal: g_nbin = struct.unpack("i", brick_data[offset:offset+4])[0] dd["g_nbin"]=g_nbin offset += 12 dd["g_rr"] = struct.unpack_from("<{}".format(g_nbin)+dtype_float, brick_data[offset:offset+g_nbin*nbytes]) offset += 8 + g_nbin*nbytes dd["g_rho"] = struct.unpack_from("<{}".format(g_nbin)+dtype_float, brick_data[offset:offset+g_nbin*nbytes]) offset += 8 + g_nbin*nbytes return offset, ids def load_hm(fn, double=True, is_gal=True, return_idlists=[]): """ Return catalog in numpy array, and list of member particles in a list. >>> catalog, member_ids = load_hm("TREE_DM/tree_bricks500", is_gal=False, return_idlist=[1,3,5,7]) Paramters --------- double : logical if True, assume real are in double precision is_gal : logical If True, read GalaxyMaker output. If False, read HaloMaker output. return_idlists: sequence(list, array, range, tuple) Give halo/galaxy ids in a list(sequence) to retrieve member particle ID of the halos. NOTE ---- Reading tree_bricks in Fortranis 10x faster. But, maybe it's OK to be a bit slow. NH catalogues are small, anyways. """ if double: dtype_float = "<f8" else: dtype_float = "<f4" dtype_halo = [('np', '<i4'), ('id', '<i4'), ('level', '<i4'), ('host', '<i4'), ('sub', '<i4'), ('nsub', '<i4'), ('nextsub', '<i4'), ('m', dtype_float), ('mvir', dtype_float), ('r', dtype_float), ('rvir', dtype_float), ('tvir', dtype_float), ('cvel', dtype_float), ('x', dtype_float), ('y', dtype_float), ('z', dtype_float), ('vx', dtype_float), ('vy', dtype_float), ('vz', dtype_float), ('ax', dtype_float), ('ay', dtype_float), ('az', dtype_float), ('sp', dtype_float), ('idx', '<i4'), ('p_rho', dtype_float),('p_c', dtype_float), ('energy', '<f8', (3,)), ('abc', '<f8', (3,))] if is_gal: dtype_halo += [('sig', dtype_float), ('sigbulge', dtype_float), ('mbulge', dtype_float), ('hosthalo', '<i4'), ('g_nbin', '<i4'), ('g_rr', dtype_float, (100,)), ('g_rho', dtype_float, (100,))] idlists=[] f = open(fn, "rb") brick_data = f.read() offset, halnum, subnum = load_header(brick_data, double=double) gcat = np.zeros(halnum+subnum, dtype=dtype_halo) for i in range(halnum+subnum): offset,_ = load_a_halo(brick_data, offset, gcat[i], is_gal=is_gal, double=double) if gcat[i]["id"] in return_idlists: idlists.append(_) f.close() return gcat, idlists

40.42446

115

0.587649

c34eb1b6c536dd7f28acf602ddafddfa29b163f0

2,758

py

Python

MS5803.py

joachimlindborg/onion_omega

cd6e634fcf39796c790f41d09ab03139871c49a5

[ "MIT" ]

null

MS5803.py

joachimlindborg/onion_omega

cd6e634fcf39796c790f41d09ab03139871c49a5

[ "MIT" ]

null

MS5803.py

joachimlindborg/onion_omega

cd6e634fcf39796c790f41d09ab03139871c49a5

[ "MIT" ]

null

# Distributed with a free-will license. # Use it any way you want, profit or free, provided it fits in the licenses of its associated works. # MS5803_30BA # This code is designed to work with the MS5803_30BA_I2CS I2C Mini Module available from ControlEverything.com. # https://www.controleverything.com/content/Analog-Digital-Converters?sku=MS5803-30BA_I2CS#tabs-0-product_tabset-2 from OmegaExpansion import onionI2C import time # Get I2C bus i2c = onionI2C.OnionI2C(0) # MS5803_30BA address, 0x76(118) # 0x1E(30) Reset command data = [0x1E] i2c.write(0x76, data) time.sleep(0.5) # Read 12 bytes of calibration data # Read pressure sensitivity data = i2c.readBytes(0x76, 0xA2, 2) C1 = data[0] * 256 + data[1] # Read pressure offset data = i2c.readBytes(0x76, 0xA4, 2) C2 = data[0] * 256 + data[1] # Read temperature coefficient of pressure sensitivity data = i2c.readBytes(0x76, 0xA6, 2) C3 = data[0] * 256 + data[1] # Read temperature coefficient of pressure offset data = i2c.readBytes(0x76, 0xA8, 2) C4 = data[0] * 256 + data[1] # Read reference temperature data = i2c.readBytes(0x76, 0xAA, 2) C5 = data[0] * 256 + data[1] # Read temperature coefficient of the temperature data = i2c.readBytes(0x76, 0xAC, 2) C6 = data[0] * 256 + data[1] # MS5803_30BA address, 0x76(118) # 0x40(64) Pressure conversion(OSR = 256) command data = [0x40] i2c.write(0x76, data) time.sleep(0.5) # Read digital pressure value # Read data back from 0x00(0), 3 bytes # D1 MSB2, D1 MSB1, D1 LSB value = i2c.readBytes(0x76, 0x00, 3) D1 = value[0] * 65536 + value[1] * 256 + value[2] # MS5803_30BA address, 0x76(118) # 0x50(64) Temperature conversion(OSR = 256) command data = [0x50] i2c.write(0x76, data) time.sleep(0.5) # Read digital temperature value # Read data back from 0x00(0), 3 bytes # D2 MSB2, D2 MSB1, D2 LSB value = i2c.readBytes(0x76, 0x00, 3) D2 = value[0] * 65536 + value[1] * 256 + value[2] dT = D2 - C5 * 256 TEMP = 2000 + dT * C6 / 8388608 OFF = C2 * 65536 + (C4 * dT) / 128 SENS = C1 * 32768 + (C3 * dT ) / 256 T2 = 0 OFF2 = 0 SENS2 = 0 if TEMP >= 2000 : T2 = 7 * (dT * dT) / 137438953472 OFF2 = ((TEMP - 2000) * (TEMP - 2000)) / 16 SENS2= 0 elif TEMP < 2000 : T2 = 3 * (dT * dT) / 8589934592 OFF2= 3 * ((TEMP - 2000) * (TEMP - 2000)) / 2 SENS2= 5 * ((TEMP - 2000) * (TEMP - 2000)) / 8 if TEMP < -1500: OFF2 = OFF2 + 7 * ((TEMP + 1500) * (TEMP + 1500)) SENS2 = SENS2 + 4 * ((TEMP + 1500) * (TEMP +1500)) TEMP = TEMP - T2 OFF = OFF - OFF2 SENS = SENS - SENS2 pressure = ((((D1 * SENS2) / 2097152) - OFF2) / 8192.0) / 10.0 cTemp = TEMP / 100.0 fTemp = cTemp * 1.8 + 32 # Output data to screen print "Pressure : %.2f mbar" %pressure print "Temperature in Celsius : %.2f C" %cTemp print "Temperature in Fahrenheit : %.2f F" %fTemp

26.776699

114

0.668238

49ee47ce9bca2eabe80c879f6fbef5b8a68ca61d

5,684

py

Python

src/gt4sd/algorithms/controlled_sampling/tests/test_class_controlled_sampling.py

YoelShoshan/gt4sd-core

9ee86fc28634b43d69542159fe06a7a5132e23ae

[ "MIT" ]

1

2022-02-22T02:06:10.000Z

src/gt4sd/algorithms/controlled_sampling/tests/test_class_controlled_sampling.py

kwehden/gt4sd-core

ac907c1f6cfc6b0ff38b71325dd749001071c863

[ "MIT" ]

12

2022-02-21T12:59:24.000Z

2022-02-22T12:25:49.000Z

src/gt4sd/algorithms/controlled_sampling/tests/test_class_controlled_sampling.py

C-nit/gt4sd-core

01854438f2fdbf7f8123a322aeed5520beb1e696

[ "MIT" ]

null

"""CLaSS tests.""" import pickle from typing import ClassVar, Type import pytest from gt4sd.algorithms.core import AlgorithmConfiguration from gt4sd.algorithms.registry import ApplicationsRegistry from gt4sd.extras import EXTRAS_ENABLED if not EXTRAS_ENABLED: pytest.skip("Extras from custom PyPI disabled", allow_module_level=True) else: from gt4sd.algorithms.controlled_sampling.class_controlled_sampling import ( PAG, CLaSS, CogMol, ) from gt4sd.algorithms.controlled_sampling.class_controlled_sampling.implementation import ( UnsupportedTargetError, ) def get_classvar_type(class_var): """Extract type from ClassVar type annotation: `ClassVar[T]] -> T`.""" return class_var.__args__[0] MPRO = "SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDVVYCPRHVICTSEDMLNPNYEDLLIRKSNHNFLVQAGNVQLRVIGHSMQNCVLKLKVDTANPKTPKYKFVRIQPGQTFSVLACYNGSPSGVYQCAMRPNFTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGNFYGPFVDRQTAQAAGTDTTITVNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYEPLTQDHVDILGPLSAQTGIAVLDMCASLKELLQNGMNGRTILGSALLEDEFTPFDVVRQCSGVTFQ" @pytest.mark.parametrize( "config_class, algorithm_type, domain, algorithm_name", [ ( CogMol, "controlled_sampling", "materials", CLaSS.__name__, ), ( PAG, "controlled_sampling", "materials", CLaSS.__name__, ), ], ) def test_config_class( config_class: Type[AlgorithmConfiguration], algorithm_type: str, domain: str, algorithm_name: str, ): assert config_class.algorithm_type == algorithm_type assert config_class.domain == domain assert config_class.algorithm_name == algorithm_name for keyword, type_annotation in config_class.__annotations__.items(): if keyword in ("algorithm_type", "domain", "algorithm_name"): assert type_annotation.__origin__ is ClassVar # type: ignore assert str == get_classvar_type(type_annotation) @pytest.mark.parametrize( "config_class", [ (CogMol), (PAG), ], ) def test_config_instance(config_class: Type[AlgorithmConfiguration]): config = config_class() # type:ignore assert config.algorithm_application == config_class.__name__ @pytest.mark.parametrize( "config_class", [ (CogMol), (PAG), ], ) def test_available_versions(config_class: Type[AlgorithmConfiguration]): versions = config_class.list_versions() assert "v0" in versions @pytest.mark.parametrize( "config, example_target, algorithm, kwargs", [ ( CogMol, MPRO, CLaSS, { "samples_per_round": 173, "max_length": 40, "temperature": 0.8, "num_proteins_selectivity": 20, }, ), ( PAG, None, CLaSS, { "samples_per_round": 173, "max_length": 40, "temperature": 0.8, }, ), ], ) def test_generation_via_import(config, example_target, algorithm, kwargs): class_sampling = algorithm( configuration=config(**kwargs), target=example_target, ) items = list(class_sampling.sample(5)) assert len(items) == 5 @pytest.mark.parametrize( "algorithm_application, target", [ ( CogMol.__name__, MPRO, ), ( PAG.__name__, None, ), ], ) def test_generation_via_registry(target, algorithm_application): class_sampling = ApplicationsRegistry.get_application_instance( target=target, algorithm_type="controlled_sampling", domain="materials", algorithm_name=CLaSS.__name__, algorithm_application=algorithm_application, ) items = list(class_sampling.sample(5)) assert len(items) == 5 def test_unsupported_target(algorithm_application=CogMol.__name__, target=MPRO): invalid_target = target[:30] # assuming this makes it invalid # on construction with pytest.raises(UnsupportedTargetError): ApplicationsRegistry.get_application_instance( target=invalid_target, algorithm_type="controlled_sampling", domain="materials", algorithm_name=CLaSS.__name__, algorithm_application=algorithm_application, ) # on sampling with changed targed config = CogMol() implementation = config.get_class_instance( # type: ignore resources_path=config.ensure_artifacts(), target=target ) with pytest.raises(UnsupportedTargetError): implementation.sample_accepted(invalid_target) @pytest.mark.parametrize("config_class", [(CogMol), (PAG)]) def test_configuration_pickable(config_class: Type[AlgorithmConfiguration]): # implementation obj = config_class(algorithm_version="test") # --- import inspect inspect.getmodule(config_class) # --- pickled_obj = pickle.dumps(obj) restored_obj = pickle.loads(pickled_obj) assert restored_obj.algorithm_version == "test" assert restored_obj == obj # registered Config = ApplicationsRegistry.get_application( algorithm_type="controlled_sampling", domain="materials", algorithm_name=CLaSS.__name__, algorithm_application=config_class.__name__, ).configuration_class obj = Config(algorithm_version="test") pickled_obj = pickle.dumps(obj) restored_obj = pickle.loads(pickled_obj) assert restored_obj.algorithm_version == "test" assert restored_obj == obj

28.278607

315

0.663265

ed02c156168253705662bbce24bf570f0bdd2f1f

105,120

py

Python

venv/Lib/site-packages/numpy/core/tests/test_numeric.py

unbun/snake.ai

0c017357608dc7c06af0ca3ca57d870641461207

[ "MIT" ]

6,989

2017-07-18T06:23:18.000Z

2022-03-31T15:58:36.000Z

venv/Lib/site-packages/numpy/core/tests/test_numeric.py

unbun/snake.ai

0c017357608dc7c06af0ca3ca57d870641461207

[ "MIT" ]

1,978

2017-07-18T09:17:58.000Z

2022-03-31T14:28:43.000Z

venv/Lib/site-packages/numpy/core/tests/test_numeric.py

unbun/snake.ai

0c017357608dc7c06af0ca3ca57d870641461207

[ "MIT" ]

1,228

2017-07-18T09:03:13.000Z

2022-03-29T05:57:40.000Z

from __future__ import division, absolute_import, print_function import sys import warnings import itertools import platform import pytest from decimal import Decimal import numpy as np from numpy.core import umath from numpy.random import rand, randint, randn from numpy.testing import ( assert_, assert_equal, assert_raises, assert_raises_regex, assert_array_equal, assert_almost_equal, assert_array_almost_equal, HAS_REFCOUNT ) class TestResize(object): def test_copies(self): A = np.array([[1, 2], [3, 4]]) Ar1 = np.array([[1, 2, 3, 4], [1, 2, 3, 4]]) assert_equal(np.resize(A, (2, 4)), Ar1) Ar2 = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) assert_equal(np.resize(A, (4, 2)), Ar2) Ar3 = np.array([[1, 2, 3], [4, 1, 2], [3, 4, 1], [2, 3, 4]]) assert_equal(np.resize(A, (4, 3)), Ar3) def test_zeroresize(self): A = np.array([[1, 2], [3, 4]]) Ar = np.resize(A, (0,)) assert_array_equal(Ar, np.array([])) assert_equal(A.dtype, Ar.dtype) Ar = np.resize(A, (0, 2)) assert_equal(Ar.shape, (0, 2)) Ar = np.resize(A, (2, 0)) assert_equal(Ar.shape, (2, 0)) def test_reshape_from_zero(self): # See also gh-6740 A = np.zeros(0, dtype=[('a', np.float32, 1)]) Ar = np.resize(A, (2, 1)) assert_array_equal(Ar, np.zeros((2, 1), Ar.dtype)) assert_equal(A.dtype, Ar.dtype) class TestNonarrayArgs(object): # check that non-array arguments to functions wrap them in arrays def test_choose(self): choices = [[0, 1, 2], [3, 4, 5], [5, 6, 7]] tgt = [5, 1, 5] a = [2, 0, 1] out = np.choose(a, choices) assert_equal(out, tgt) def test_clip(self): arr = [-1, 5, 2, 3, 10, -4, -9] out = np.clip(arr, 2, 7) tgt = [2, 5, 2, 3, 7, 2, 2] assert_equal(out, tgt) def test_compress(self): arr = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] tgt = [[5, 6, 7, 8, 9]] out = np.compress([0, 1], arr, axis=0) assert_equal(out, tgt) def test_count_nonzero(self): arr = [[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]] tgt = np.array([2, 3]) out = np.count_nonzero(arr, axis=1) assert_equal(out, tgt) def test_cumproduct(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.all(np.cumproduct(A) == np.array([1, 2, 6, 24, 120, 720]))) def test_diagonal(self): a = [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] out = np.diagonal(a) tgt = [0, 5, 10] assert_equal(out, tgt) def test_mean(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.mean(A) == 3.5) assert_(np.all(np.mean(A, 0) == np.array([2.5, 3.5, 4.5]))) assert_(np.all(np.mean(A, 1) == np.array([2., 5.]))) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.mean([]))) assert_(w[0].category is RuntimeWarning) def test_ptp(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.ptp(a, axis=0), 15.0) def test_prod(self): arr = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] tgt = [24, 1890, 600] assert_equal(np.prod(arr, axis=-1), tgt) def test_ravel(self): a = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] tgt = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] assert_equal(np.ravel(a), tgt) def test_repeat(self): a = [1, 2, 3] tgt = [1, 1, 2, 2, 3, 3] out = np.repeat(a, 2) assert_equal(out, tgt) def test_reshape(self): arr = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] tgt = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]] assert_equal(np.reshape(arr, (2, 6)), tgt) def test_round(self): arr = [1.56, 72.54, 6.35, 3.25] tgt = [1.6, 72.5, 6.4, 3.2] assert_equal(np.around(arr, decimals=1), tgt) def test_searchsorted(self): arr = [-8, -5, -1, 3, 6, 10] out = np.searchsorted(arr, 0) assert_equal(out, 3) def test_size(self): A = [[1, 2, 3], [4, 5, 6]] assert_(np.size(A) == 6) assert_(np.size(A, 0) == 2) assert_(np.size(A, 1) == 3) def test_squeeze(self): A = [[[1, 1, 1], [2, 2, 2], [3, 3, 3]]] assert_(np.squeeze(A).shape == (3, 3)) def test_std(self): A = [[1, 2, 3], [4, 5, 6]] assert_almost_equal(np.std(A), 1.707825127659933) assert_almost_equal(np.std(A, 0), np.array([1.5, 1.5, 1.5])) assert_almost_equal(np.std(A, 1), np.array([0.81649658, 0.81649658])) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.std([]))) assert_(w[0].category is RuntimeWarning) def test_swapaxes(self): tgt = [[[0, 4], [2, 6]], [[1, 5], [3, 7]]] a = [[[0, 1], [2, 3]], [[4, 5], [6, 7]]] out = np.swapaxes(a, 0, 2) assert_equal(out, tgt) def test_sum(self): m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] tgt = [[6], [15], [24]] out = np.sum(m, axis=1, keepdims=True) assert_equal(tgt, out) def test_take(self): tgt = [2, 3, 5] indices = [1, 2, 4] a = [1, 2, 3, 4, 5] out = np.take(a, indices) assert_equal(out, tgt) def test_trace(self): c = [[1, 2], [3, 4], [5, 6]] assert_equal(np.trace(c), 5) def test_transpose(self): arr = [[1, 2], [3, 4], [5, 6]] tgt = [[1, 3, 5], [2, 4, 6]] assert_equal(np.transpose(arr, (1, 0)), tgt) def test_var(self): A = [[1, 2, 3], [4, 5, 6]] assert_almost_equal(np.var(A), 2.9166666666666665) assert_almost_equal(np.var(A, 0), np.array([2.25, 2.25, 2.25])) assert_almost_equal(np.var(A, 1), np.array([0.66666667, 0.66666667])) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_(np.isnan(np.var([]))) assert_(w[0].category is RuntimeWarning) class TestIsscalar(object): def test_isscalar(self): assert_(np.isscalar(3.1)) assert_(np.isscalar(np.int16(12345))) assert_(np.isscalar(False)) assert_(np.isscalar('numpy')) assert_(not np.isscalar([3.1])) assert_(not np.isscalar(None)) # PEP 3141 from fractions import Fraction assert_(np.isscalar(Fraction(5, 17))) from numbers import Number assert_(np.isscalar(Number())) class TestBoolScalar(object): def test_logical(self): f = np.False_ t = np.True_ s = "xyz" assert_((t and s) is s) assert_((f and s) is f) def test_bitwise_or(self): f = np.False_ t = np.True_ assert_((t | t) is t) assert_((f | t) is t) assert_((t | f) is t) assert_((f | f) is f) def test_bitwise_and(self): f = np.False_ t = np.True_ assert_((t & t) is t) assert_((f & t) is f) assert_((t & f) is f) assert_((f & f) is f) def test_bitwise_xor(self): f = np.False_ t = np.True_ assert_((t ^ t) is f) assert_((f ^ t) is t) assert_((t ^ f) is t) assert_((f ^ f) is f) class TestBoolArray(object): def setup(self): # offset for simd tests self.t = np.array([True] * 41, dtype=bool)[1::] self.f = np.array([False] * 41, dtype=bool)[1::] self.o = np.array([False] * 42, dtype=bool)[2::] self.nm = self.f.copy() self.im = self.t.copy() self.nm[3] = True self.nm[-2] = True self.im[3] = False self.im[-2] = False def test_all_any(self): assert_(self.t.all()) assert_(self.t.any()) assert_(not self.f.all()) assert_(not self.f.any()) assert_(self.nm.any()) assert_(self.im.any()) assert_(not self.nm.all()) assert_(not self.im.all()) # check bad element in all positions for i in range(256 - 7): d = np.array([False] * 256, dtype=bool)[7::] d[i] = True assert_(np.any(d)) e = np.array([True] * 256, dtype=bool)[7::] e[i] = False assert_(not np.all(e)) assert_array_equal(e, ~d) # big array test for blocked libc loops for i in list(range(9, 6000, 507)) + [7764, 90021, -10]: d = np.array([False] * 100043, dtype=bool) d[i] = True assert_(np.any(d), msg="%r" % i) e = np.array([True] * 100043, dtype=bool) e[i] = False assert_(not np.all(e), msg="%r" % i) def test_logical_not_abs(self): assert_array_equal(~self.t, self.f) assert_array_equal(np.abs(~self.t), self.f) assert_array_equal(np.abs(~self.f), self.t) assert_array_equal(np.abs(self.f), self.f) assert_array_equal(~np.abs(self.f), self.t) assert_array_equal(~np.abs(self.t), self.f) assert_array_equal(np.abs(~self.nm), self.im) np.logical_not(self.t, out=self.o) assert_array_equal(self.o, self.f) np.abs(self.t, out=self.o) assert_array_equal(self.o, self.t) def test_logical_and_or_xor(self): assert_array_equal(self.t | self.t, self.t) assert_array_equal(self.f | self.f, self.f) assert_array_equal(self.t | self.f, self.t) assert_array_equal(self.f | self.t, self.t) np.logical_or(self.t, self.t, out=self.o) assert_array_equal(self.o, self.t) assert_array_equal(self.t & self.t, self.t) assert_array_equal(self.f & self.f, self.f) assert_array_equal(self.t & self.f, self.f) assert_array_equal(self.f & self.t, self.f) np.logical_and(self.t, self.t, out=self.o) assert_array_equal(self.o, self.t) assert_array_equal(self.t ^ self.t, self.f) assert_array_equal(self.f ^ self.f, self.f) assert_array_equal(self.t ^ self.f, self.t) assert_array_equal(self.f ^ self.t, self.t) np.logical_xor(self.t, self.t, out=self.o) assert_array_equal(self.o, self.f) assert_array_equal(self.nm & self.t, self.nm) assert_array_equal(self.im & self.f, False) assert_array_equal(self.nm & True, self.nm) assert_array_equal(self.im & False, self.f) assert_array_equal(self.nm | self.t, self.t) assert_array_equal(self.im | self.f, self.im) assert_array_equal(self.nm | True, self.t) assert_array_equal(self.im | False, self.im) assert_array_equal(self.nm ^ self.t, self.im) assert_array_equal(self.im ^ self.f, self.im) assert_array_equal(self.nm ^ True, self.im) assert_array_equal(self.im ^ False, self.im) class TestBoolCmp(object): def setup(self): self.f = np.ones(256, dtype=np.float32) self.ef = np.ones(self.f.size, dtype=bool) self.d = np.ones(128, dtype=np.float64) self.ed = np.ones(self.d.size, dtype=bool) # generate values for all permutation of 256bit simd vectors s = 0 for i in range(32): self.f[s:s+8] = [i & 2**x for x in range(8)] self.ef[s:s+8] = [(i & 2**x) != 0 for x in range(8)] s += 8 s = 0 for i in range(16): self.d[s:s+4] = [i & 2**x for x in range(4)] self.ed[s:s+4] = [(i & 2**x) != 0 for x in range(4)] s += 4 self.nf = self.f.copy() self.nd = self.d.copy() self.nf[self.ef] = np.nan self.nd[self.ed] = np.nan self.inff = self.f.copy() self.infd = self.d.copy() self.inff[::3][self.ef[::3]] = np.inf self.infd[::3][self.ed[::3]] = np.inf self.inff[1::3][self.ef[1::3]] = -np.inf self.infd[1::3][self.ed[1::3]] = -np.inf self.inff[2::3][self.ef[2::3]] = np.nan self.infd[2::3][self.ed[2::3]] = np.nan self.efnonan = self.ef.copy() self.efnonan[2::3] = False self.ednonan = self.ed.copy() self.ednonan[2::3] = False self.signf = self.f.copy() self.signd = self.d.copy() self.signf[self.ef] *= -1. self.signd[self.ed] *= -1. self.signf[1::6][self.ef[1::6]] = -np.inf self.signd[1::6][self.ed[1::6]] = -np.inf self.signf[3::6][self.ef[3::6]] = -np.nan self.signd[3::6][self.ed[3::6]] = -np.nan self.signf[4::6][self.ef[4::6]] = -0. self.signd[4::6][self.ed[4::6]] = -0. def test_float(self): # offset for alignment test for i in range(4): assert_array_equal(self.f[i:] > 0, self.ef[i:]) assert_array_equal(self.f[i:] - 1 >= 0, self.ef[i:]) assert_array_equal(self.f[i:] == 0, ~self.ef[i:]) assert_array_equal(-self.f[i:] < 0, self.ef[i:]) assert_array_equal(-self.f[i:] + 1 <= 0, self.ef[i:]) r = self.f[i:] != 0 assert_array_equal(r, self.ef[i:]) r2 = self.f[i:] != np.zeros_like(self.f[i:]) r3 = 0 != self.f[i:] assert_array_equal(r, r2) assert_array_equal(r, r3) # check bool == 0x1 assert_array_equal(r.view(np.int8), r.astype(np.int8)) assert_array_equal(r2.view(np.int8), r2.astype(np.int8)) assert_array_equal(r3.view(np.int8), r3.astype(np.int8)) # isnan on amd64 takes the same code path assert_array_equal(np.isnan(self.nf[i:]), self.ef[i:]) assert_array_equal(np.isfinite(self.nf[i:]), ~self.ef[i:]) assert_array_equal(np.isfinite(self.inff[i:]), ~self.ef[i:]) assert_array_equal(np.isinf(self.inff[i:]), self.efnonan[i:]) assert_array_equal(np.signbit(self.signf[i:]), self.ef[i:]) def test_double(self): # offset for alignment test for i in range(2): assert_array_equal(self.d[i:] > 0, self.ed[i:]) assert_array_equal(self.d[i:] - 1 >= 0, self.ed[i:]) assert_array_equal(self.d[i:] == 0, ~self.ed[i:]) assert_array_equal(-self.d[i:] < 0, self.ed[i:]) assert_array_equal(-self.d[i:] + 1 <= 0, self.ed[i:]) r = self.d[i:] != 0 assert_array_equal(r, self.ed[i:]) r2 = self.d[i:] != np.zeros_like(self.d[i:]) r3 = 0 != self.d[i:] assert_array_equal(r, r2) assert_array_equal(r, r3) # check bool == 0x1 assert_array_equal(r.view(np.int8), r.astype(np.int8)) assert_array_equal(r2.view(np.int8), r2.astype(np.int8)) assert_array_equal(r3.view(np.int8), r3.astype(np.int8)) # isnan on amd64 takes the same code path assert_array_equal(np.isnan(self.nd[i:]), self.ed[i:]) assert_array_equal(np.isfinite(self.nd[i:]), ~self.ed[i:]) assert_array_equal(np.isfinite(self.infd[i:]), ~self.ed[i:]) assert_array_equal(np.isinf(self.infd[i:]), self.ednonan[i:]) assert_array_equal(np.signbit(self.signd[i:]), self.ed[i:]) class TestSeterr(object): def test_default(self): err = np.geterr() assert_equal(err, dict(divide='warn', invalid='warn', over='warn', under='ignore') ) def test_set(self): with np.errstate(): err = np.seterr() old = np.seterr(divide='print') assert_(err == old) new = np.seterr() assert_(new['divide'] == 'print') np.seterr(over='raise') assert_(np.geterr()['over'] == 'raise') assert_(new['divide'] == 'print') np.seterr(**old) assert_(np.geterr() == old) @pytest.mark.skipif(platform.machine() == "armv5tel", reason="See gh-413.") def test_divide_err(self): with np.errstate(divide='raise'): with assert_raises(FloatingPointError): np.array([1.]) / np.array([0.]) np.seterr(divide='ignore') np.array([1.]) / np.array([0.]) def test_errobj(self): olderrobj = np.geterrobj() self.called = 0 try: with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") with np.errstate(divide='warn'): np.seterrobj([20000, 1, None]) np.array([1.]) / np.array([0.]) assert_equal(len(w), 1) def log_err(*args): self.called += 1 extobj_err = args assert_(len(extobj_err) == 2) assert_("divide" in extobj_err[0]) with np.errstate(divide='ignore'): np.seterrobj([20000, 3, log_err]) np.array([1.]) / np.array([0.]) assert_equal(self.called, 1) np.seterrobj(olderrobj) with np.errstate(divide='ignore'): np.divide(1., 0., extobj=[20000, 3, log_err]) assert_equal(self.called, 2) finally: np.seterrobj(olderrobj) del self.called def test_errobj_noerrmask(self): # errmask = 0 has a special code path for the default olderrobj = np.geterrobj() try: # set errobj to something non default np.seterrobj([umath.UFUNC_BUFSIZE_DEFAULT, umath.ERR_DEFAULT + 1, None]) # call a ufunc np.isnan(np.array([6])) # same with the default, lots of times to get rid of possible # pre-existing stack in the code for i in range(10000): np.seterrobj([umath.UFUNC_BUFSIZE_DEFAULT, umath.ERR_DEFAULT, None]) np.isnan(np.array([6])) finally: np.seterrobj(olderrobj) class TestFloatExceptions(object): def assert_raises_fpe(self, fpeerr, flop, x, y): ftype = type(x) try: flop(x, y) assert_(False, "Type %s did not raise fpe error '%s'." % (ftype, fpeerr)) except FloatingPointError as exc: assert_(str(exc).find(fpeerr) >= 0, "Type %s raised wrong fpe error '%s'." % (ftype, exc)) def assert_op_raises_fpe(self, fpeerr, flop, sc1, sc2): # Check that fpe exception is raised. # # Given a floating operation `flop` and two scalar values, check that # the operation raises the floating point exception specified by # `fpeerr`. Tests all variants with 0-d array scalars as well. self.assert_raises_fpe(fpeerr, flop, sc1, sc2) self.assert_raises_fpe(fpeerr, flop, sc1[()], sc2) self.assert_raises_fpe(fpeerr, flop, sc1, sc2[()]) self.assert_raises_fpe(fpeerr, flop, sc1[()], sc2[()]) def test_floating_exceptions(self): # Test basic arithmetic function errors with np.errstate(all='raise'): # Test for all real and complex float types for typecode in np.typecodes['AllFloat']: ftype = np.obj2sctype(typecode) if np.dtype(ftype).kind == 'f': # Get some extreme values for the type fi = np.finfo(ftype) ft_tiny = fi.tiny ft_max = fi.max ft_eps = fi.eps underflow = 'underflow' divbyzero = 'divide by zero' else: # 'c', complex, corresponding real dtype rtype = type(ftype(0).real) fi = np.finfo(rtype) ft_tiny = ftype(fi.tiny) ft_max = ftype(fi.max) ft_eps = ftype(fi.eps) # The complex types raise different exceptions underflow = '' divbyzero = '' overflow = 'overflow' invalid = 'invalid' self.assert_raises_fpe(underflow, lambda a, b: a/b, ft_tiny, ft_max) self.assert_raises_fpe(underflow, lambda a, b: a*b, ft_tiny, ft_tiny) self.assert_raises_fpe(overflow, lambda a, b: a*b, ft_max, ftype(2)) self.assert_raises_fpe(overflow, lambda a, b: a/b, ft_max, ftype(0.5)) self.assert_raises_fpe(overflow, lambda a, b: a+b, ft_max, ft_max*ft_eps) self.assert_raises_fpe(overflow, lambda a, b: a-b, -ft_max, ft_max*ft_eps) self.assert_raises_fpe(overflow, np.power, ftype(2), ftype(2**fi.nexp)) self.assert_raises_fpe(divbyzero, lambda a, b: a/b, ftype(1), ftype(0)) self.assert_raises_fpe(invalid, lambda a, b: a/b, ftype(np.inf), ftype(np.inf)) self.assert_raises_fpe(invalid, lambda a, b: a/b, ftype(0), ftype(0)) self.assert_raises_fpe(invalid, lambda a, b: a-b, ftype(np.inf), ftype(np.inf)) self.assert_raises_fpe(invalid, lambda a, b: a+b, ftype(np.inf), ftype(-np.inf)) self.assert_raises_fpe(invalid, lambda a, b: a*b, ftype(0), ftype(np.inf)) def test_warnings(self): # test warning code path with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") with np.errstate(all="warn"): np.divide(1, 0.) assert_equal(len(w), 1) assert_("divide by zero" in str(w[0].message)) np.array(1e300) * np.array(1e300) assert_equal(len(w), 2) assert_("overflow" in str(w[-1].message)) np.array(np.inf) - np.array(np.inf) assert_equal(len(w), 3) assert_("invalid value" in str(w[-1].message)) np.array(1e-300) * np.array(1e-300) assert_equal(len(w), 4) assert_("underflow" in str(w[-1].message)) class TestTypes(object): def check_promotion_cases(self, promote_func): # tests that the scalars get coerced correctly. b = np.bool_(0) i8, i16, i32, i64 = np.int8(0), np.int16(0), np.int32(0), np.int64(0) u8, u16, u32, u64 = np.uint8(0), np.uint16(0), np.uint32(0), np.uint64(0) f32, f64, fld = np.float32(0), np.float64(0), np.longdouble(0) c64, c128, cld = np.complex64(0), np.complex128(0), np.clongdouble(0) # coercion within the same kind assert_equal(promote_func(i8, i16), np.dtype(np.int16)) assert_equal(promote_func(i32, i8), np.dtype(np.int32)) assert_equal(promote_func(i16, i64), np.dtype(np.int64)) assert_equal(promote_func(u8, u32), np.dtype(np.uint32)) assert_equal(promote_func(f32, f64), np.dtype(np.float64)) assert_equal(promote_func(fld, f32), np.dtype(np.longdouble)) assert_equal(promote_func(f64, fld), np.dtype(np.longdouble)) assert_equal(promote_func(c128, c64), np.dtype(np.complex128)) assert_equal(promote_func(cld, c128), np.dtype(np.clongdouble)) assert_equal(promote_func(c64, fld), np.dtype(np.clongdouble)) # coercion between kinds assert_equal(promote_func(b, i32), np.dtype(np.int32)) assert_equal(promote_func(b, u8), np.dtype(np.uint8)) assert_equal(promote_func(i8, u8), np.dtype(np.int16)) assert_equal(promote_func(u8, i32), np.dtype(np.int32)) assert_equal(promote_func(i64, u32), np.dtype(np.int64)) assert_equal(promote_func(u64, i32), np.dtype(np.float64)) assert_equal(promote_func(i32, f32), np.dtype(np.float64)) assert_equal(promote_func(i64, f32), np.dtype(np.float64)) assert_equal(promote_func(f32, i16), np.dtype(np.float32)) assert_equal(promote_func(f32, u32), np.dtype(np.float64)) assert_equal(promote_func(f32, c64), np.dtype(np.complex64)) assert_equal(promote_func(c128, f32), np.dtype(np.complex128)) assert_equal(promote_func(cld, f64), np.dtype(np.clongdouble)) # coercion between scalars and 1-D arrays assert_equal(promote_func(np.array([b]), i8), np.dtype(np.int8)) assert_equal(promote_func(np.array([b]), u8), np.dtype(np.uint8)) assert_equal(promote_func(np.array([b]), i32), np.dtype(np.int32)) assert_equal(promote_func(np.array([b]), u32), np.dtype(np.uint32)) assert_equal(promote_func(np.array([i8]), i64), np.dtype(np.int8)) assert_equal(promote_func(u64, np.array([i32])), np.dtype(np.int32)) assert_equal(promote_func(i64, np.array([u32])), np.dtype(np.uint32)) assert_equal(promote_func(np.int32(-1), np.array([u64])), np.dtype(np.float64)) assert_equal(promote_func(f64, np.array([f32])), np.dtype(np.float32)) assert_equal(promote_func(fld, np.array([f32])), np.dtype(np.float32)) assert_equal(promote_func(np.array([f64]), fld), np.dtype(np.float64)) assert_equal(promote_func(fld, np.array([c64])), np.dtype(np.complex64)) assert_equal(promote_func(c64, np.array([f64])), np.dtype(np.complex128)) assert_equal(promote_func(np.complex64(3j), np.array([f64])), np.dtype(np.complex128)) # coercion between scalars and 1-D arrays, where # the scalar has greater kind than the array assert_equal(promote_func(np.array([b]), f64), np.dtype(np.float64)) assert_equal(promote_func(np.array([b]), i64), np.dtype(np.int64)) assert_equal(promote_func(np.array([b]), u64), np.dtype(np.uint64)) assert_equal(promote_func(np.array([i8]), f64), np.dtype(np.float64)) assert_equal(promote_func(np.array([u16]), f64), np.dtype(np.float64)) # uint and int are treated as the same "kind" for # the purposes of array-scalar promotion. assert_equal(promote_func(np.array([u16]), i32), np.dtype(np.uint16)) # float and complex are treated as the same "kind" for # the purposes of array-scalar promotion, so that you can do # (0j + float32array) to get a complex64 array instead of # a complex128 array. assert_equal(promote_func(np.array([f32]), c128), np.dtype(np.complex64)) def test_coercion(self): def res_type(a, b): return np.add(a, b).dtype self.check_promotion_cases(res_type) # Use-case: float/complex scalar * bool/int8 array # shouldn't narrow the float/complex type for a in [np.array([True, False]), np.array([-3, 12], dtype=np.int8)]: b = 1.234 * a assert_equal(b.dtype, np.dtype('f8'), "array type %s" % a.dtype) b = np.longdouble(1.234) * a assert_equal(b.dtype, np.dtype(np.longdouble), "array type %s" % a.dtype) b = np.float64(1.234) * a assert_equal(b.dtype, np.dtype('f8'), "array type %s" % a.dtype) b = np.float32(1.234) * a assert_equal(b.dtype, np.dtype('f4'), "array type %s" % a.dtype) b = np.float16(1.234) * a assert_equal(b.dtype, np.dtype('f2'), "array type %s" % a.dtype) b = 1.234j * a assert_equal(b.dtype, np.dtype('c16'), "array type %s" % a.dtype) b = np.clongdouble(1.234j) * a assert_equal(b.dtype, np.dtype(np.clongdouble), "array type %s" % a.dtype) b = np.complex128(1.234j) * a assert_equal(b.dtype, np.dtype('c16'), "array type %s" % a.dtype) b = np.complex64(1.234j) * a assert_equal(b.dtype, np.dtype('c8'), "array type %s" % a.dtype) # The following use-case is problematic, and to resolve its # tricky side-effects requires more changes. # # Use-case: (1-t)*a, where 't' is a boolean array and 'a' is # a float32, shouldn't promote to float64 # # a = np.array([1.0, 1.5], dtype=np.float32) # t = np.array([True, False]) # b = t*a # assert_equal(b, [1.0, 0.0]) # assert_equal(b.dtype, np.dtype('f4')) # b = (1-t)*a # assert_equal(b, [0.0, 1.5]) # assert_equal(b.dtype, np.dtype('f4')) # # Probably ~t (bitwise negation) is more proper to use here, # but this is arguably less intuitive to understand at a glance, and # would fail if 't' is actually an integer array instead of boolean: # # b = (~t)*a # assert_equal(b, [0.0, 1.5]) # assert_equal(b.dtype, np.dtype('f4')) def test_result_type(self): self.check_promotion_cases(np.result_type) assert_(np.result_type(None) == np.dtype(None)) def test_promote_types_endian(self): # promote_types should always return native-endian types assert_equal(np.promote_types('<i8', '<i8'), np.dtype('i8')) assert_equal(np.promote_types('>i8', '>i8'), np.dtype('i8')) assert_equal(np.promote_types('>i8', '>U16'), np.dtype('U21')) assert_equal(np.promote_types('<i8', '<U16'), np.dtype('U21')) assert_equal(np.promote_types('>U16', '>i8'), np.dtype('U21')) assert_equal(np.promote_types('<U16', '<i8'), np.dtype('U21')) assert_equal(np.promote_types('<S5', '<U8'), np.dtype('U8')) assert_equal(np.promote_types('>S5', '>U8'), np.dtype('U8')) assert_equal(np.promote_types('<U8', '<S5'), np.dtype('U8')) assert_equal(np.promote_types('>U8', '>S5'), np.dtype('U8')) assert_equal(np.promote_types('<U5', '<U8'), np.dtype('U8')) assert_equal(np.promote_types('>U8', '>U5'), np.dtype('U8')) assert_equal(np.promote_types('<M8', '<M8'), np.dtype('M8')) assert_equal(np.promote_types('>M8', '>M8'), np.dtype('M8')) assert_equal(np.promote_types('<m8', '<m8'), np.dtype('m8')) assert_equal(np.promote_types('>m8', '>m8'), np.dtype('m8')) def test_promote_types_strings(self): assert_equal(np.promote_types('bool', 'S'), np.dtype('S5')) assert_equal(np.promote_types('b', 'S'), np.dtype('S4')) assert_equal(np.promote_types('u1', 'S'), np.dtype('S3')) assert_equal(np.promote_types('u2', 'S'), np.dtype('S5')) assert_equal(np.promote_types('u4', 'S'), np.dtype('S10')) assert_equal(np.promote_types('u8', 'S'), np.dtype('S20')) assert_equal(np.promote_types('i1', 'S'), np.dtype('S4')) assert_equal(np.promote_types('i2', 'S'), np.dtype('S6')) assert_equal(np.promote_types('i4', 'S'), np.dtype('S11')) assert_equal(np.promote_types('i8', 'S'), np.dtype('S21')) assert_equal(np.promote_types('bool', 'U'), np.dtype('U5')) assert_equal(np.promote_types('b', 'U'), np.dtype('U4')) assert_equal(np.promote_types('u1', 'U'), np.dtype('U3')) assert_equal(np.promote_types('u2', 'U'), np.dtype('U5')) assert_equal(np.promote_types('u4', 'U'), np.dtype('U10')) assert_equal(np.promote_types('u8', 'U'), np.dtype('U20')) assert_equal(np.promote_types('i1', 'U'), np.dtype('U4')) assert_equal(np.promote_types('i2', 'U'), np.dtype('U6')) assert_equal(np.promote_types('i4', 'U'), np.dtype('U11')) assert_equal(np.promote_types('i8', 'U'), np.dtype('U21')) assert_equal(np.promote_types('bool', 'S1'), np.dtype('S5')) assert_equal(np.promote_types('bool', 'S30'), np.dtype('S30')) assert_equal(np.promote_types('b', 'S1'), np.dtype('S4')) assert_equal(np.promote_types('b', 'S30'), np.dtype('S30')) assert_equal(np.promote_types('u1', 'S1'), np.dtype('S3')) assert_equal(np.promote_types('u1', 'S30'), np.dtype('S30')) assert_equal(np.promote_types('u2', 'S1'), np.dtype('S5')) assert_equal(np.promote_types('u2', 'S30'), np.dtype('S30')) assert_equal(np.promote_types('u4', 'S1'), np.dtype('S10')) assert_equal(np.promote_types('u4', 'S30'), np.dtype('S30')) assert_equal(np.promote_types('u8', 'S1'), np.dtype('S20')) assert_equal(np.promote_types('u8', 'S30'), np.dtype('S30')) def test_can_cast(self): assert_(np.can_cast(np.int32, np.int64)) assert_(np.can_cast(np.float64, complex)) assert_(not np.can_cast(complex, float)) assert_(np.can_cast('i8', 'f8')) assert_(not np.can_cast('i8', 'f4')) assert_(np.can_cast('i4', 'S11')) assert_(np.can_cast('i8', 'i8', 'no')) assert_(not np.can_cast('<i8', '>i8', 'no')) assert_(np.can_cast('<i8', '>i8', 'equiv')) assert_(not np.can_cast('<i4', '>i8', 'equiv')) assert_(np.can_cast('<i4', '>i8', 'safe')) assert_(not np.can_cast('<i8', '>i4', 'safe')) assert_(np.can_cast('<i8', '>i4', 'same_kind')) assert_(not np.can_cast('<i8', '>u4', 'same_kind')) assert_(np.can_cast('<i8', '>u4', 'unsafe')) assert_(np.can_cast('bool', 'S5')) assert_(not np.can_cast('bool', 'S4')) assert_(np.can_cast('b', 'S4')) assert_(not np.can_cast('b', 'S3')) assert_(np.can_cast('u1', 'S3')) assert_(not np.can_cast('u1', 'S2')) assert_(np.can_cast('u2', 'S5')) assert_(not np.can_cast('u2', 'S4')) assert_(np.can_cast('u4', 'S10')) assert_(not np.can_cast('u4', 'S9')) assert_(np.can_cast('u8', 'S20')) assert_(not np.can_cast('u8', 'S19')) assert_(np.can_cast('i1', 'S4')) assert_(not np.can_cast('i1', 'S3')) assert_(np.can_cast('i2', 'S6')) assert_(not np.can_cast('i2', 'S5')) assert_(np.can_cast('i4', 'S11')) assert_(not np.can_cast('i4', 'S10')) assert_(np.can_cast('i8', 'S21')) assert_(not np.can_cast('i8', 'S20')) assert_(np.can_cast('bool', 'S5')) assert_(not np.can_cast('bool', 'S4')) assert_(np.can_cast('b', 'U4')) assert_(not np.can_cast('b', 'U3')) assert_(np.can_cast('u1', 'U3')) assert_(not np.can_cast('u1', 'U2')) assert_(np.can_cast('u2', 'U5')) assert_(not np.can_cast('u2', 'U4')) assert_(np.can_cast('u4', 'U10')) assert_(not np.can_cast('u4', 'U9')) assert_(np.can_cast('u8', 'U20')) assert_(not np.can_cast('u8', 'U19')) assert_(np.can_cast('i1', 'U4')) assert_(not np.can_cast('i1', 'U3')) assert_(np.can_cast('i2', 'U6')) assert_(not np.can_cast('i2', 'U5')) assert_(np.can_cast('i4', 'U11')) assert_(not np.can_cast('i4', 'U10')) assert_(np.can_cast('i8', 'U21')) assert_(not np.can_cast('i8', 'U20')) assert_raises(TypeError, np.can_cast, 'i4', None) assert_raises(TypeError, np.can_cast, None, 'i4') # Also test keyword arguments assert_(np.can_cast(from_=np.int32, to=np.int64)) def test_can_cast_simple_to_structured(self): # Non-structured can only be cast to structured in 'unsafe' mode. assert_(not np.can_cast('i4', 'i4,i4')) assert_(not np.can_cast('i4', 'i4,i2')) assert_(np.can_cast('i4', 'i4,i4', casting='unsafe')) assert_(np.can_cast('i4', 'i4,i2', casting='unsafe')) # Even if there is just a single field which is OK. assert_(not np.can_cast('i2', [('f1', 'i4')])) assert_(not np.can_cast('i2', [('f1', 'i4')], casting='same_kind')) assert_(np.can_cast('i2', [('f1', 'i4')], casting='unsafe')) # It should be the same for recursive structured or subarrays. assert_(not np.can_cast('i2', [('f1', 'i4,i4')])) assert_(np.can_cast('i2', [('f1', 'i4,i4')], casting='unsafe')) assert_(not np.can_cast('i2', [('f1', '(2,3)i4')])) assert_(np.can_cast('i2', [('f1', '(2,3)i4')], casting='unsafe')) def test_can_cast_structured_to_simple(self): # Need unsafe casting for structured to simple. assert_(not np.can_cast([('f1', 'i4')], 'i4')) assert_(np.can_cast([('f1', 'i4')], 'i4', casting='unsafe')) assert_(np.can_cast([('f1', 'i4')], 'i2', casting='unsafe')) # Since it is unclear what is being cast, multiple fields to # single should not work even for unsafe casting. assert_(not np.can_cast('i4,i4', 'i4', casting='unsafe')) # But a single field inside a single field is OK. assert_(not np.can_cast([('f1', [('x', 'i4')])], 'i4')) assert_(np.can_cast([('f1', [('x', 'i4')])], 'i4', casting='unsafe')) # And a subarray is fine too - it will just take the first element # (arguably not very consistently; might also take the first field). assert_(not np.can_cast([('f0', '(3,)i4')], 'i4')) assert_(np.can_cast([('f0', '(3,)i4')], 'i4', casting='unsafe')) # But a structured subarray with multiple fields should fail. assert_(not np.can_cast([('f0', ('i4,i4'), (2,))], 'i4', casting='unsafe')) def test_can_cast_values(self): # gh-5917 for dt in np.sctypes['int'] + np.sctypes['uint']: ii = np.iinfo(dt) assert_(np.can_cast(ii.min, dt)) assert_(np.can_cast(ii.max, dt)) assert_(not np.can_cast(ii.min - 1, dt)) assert_(not np.can_cast(ii.max + 1, dt)) for dt in np.sctypes['float']: fi = np.finfo(dt) assert_(np.can_cast(fi.min, dt)) assert_(np.can_cast(fi.max, dt)) # Custom exception class to test exception propagation in fromiter class NIterError(Exception): pass class TestFromiter(object): def makegen(self): for x in range(24): yield x**2 def test_types(self): ai32 = np.fromiter(self.makegen(), np.int32) ai64 = np.fromiter(self.makegen(), np.int64) af = np.fromiter(self.makegen(), float) assert_(ai32.dtype == np.dtype(np.int32)) assert_(ai64.dtype == np.dtype(np.int64)) assert_(af.dtype == np.dtype(float)) def test_lengths(self): expected = np.array(list(self.makegen())) a = np.fromiter(self.makegen(), int) a20 = np.fromiter(self.makegen(), int, 20) assert_(len(a) == len(expected)) assert_(len(a20) == 20) assert_raises(ValueError, np.fromiter, self.makegen(), int, len(expected) + 10) def test_values(self): expected = np.array(list(self.makegen())) a = np.fromiter(self.makegen(), int) a20 = np.fromiter(self.makegen(), int, 20) assert_(np.alltrue(a == expected, axis=0)) assert_(np.alltrue(a20 == expected[:20], axis=0)) def load_data(self, n, eindex): # Utility method for the issue 2592 tests. # Raise an exception at the desired index in the iterator. for e in range(n): if e == eindex: raise NIterError('error at index %s' % eindex) yield e def test_2592(self): # Test iteration exceptions are correctly raised. count, eindex = 10, 5 assert_raises(NIterError, np.fromiter, self.load_data(count, eindex), dtype=int, count=count) def test_2592_edge(self): # Test iter. exceptions, edge case (exception at end of iterator). count = 10 eindex = count-1 assert_raises(NIterError, np.fromiter, self.load_data(count, eindex), dtype=int, count=count) class TestNonzero(object): def test_nonzero_trivial(self): assert_equal(np.count_nonzero(np.array([])), 0) assert_equal(np.count_nonzero(np.array([], dtype='?')), 0) assert_equal(np.nonzero(np.array([])), ([],)) assert_equal(np.count_nonzero(np.array(0)), 0) assert_equal(np.count_nonzero(np.array(0, dtype='?')), 0) assert_equal(np.nonzero(np.array(0)), ([],)) assert_equal(np.count_nonzero(np.array(1)), 1) assert_equal(np.count_nonzero(np.array(1, dtype='?')), 1) assert_equal(np.nonzero(np.array(1)), ([0],)) def test_nonzero_onedim(self): x = np.array([1, 0, 2, -1, 0, 0, 8]) assert_equal(np.count_nonzero(x), 4) assert_equal(np.count_nonzero(x), 4) assert_equal(np.nonzero(x), ([0, 2, 3, 6],)) x = np.array([(1, 2), (0, 0), (1, 1), (-1, 3), (0, 7)], dtype=[('a', 'i4'), ('b', 'i2')]) assert_equal(np.count_nonzero(x['a']), 3) assert_equal(np.count_nonzero(x['b']), 4) assert_equal(np.nonzero(x['a']), ([0, 2, 3],)) assert_equal(np.nonzero(x['b']), ([0, 2, 3, 4],)) def test_nonzero_twodim(self): x = np.array([[0, 1, 0], [2, 0, 3]]) assert_equal(np.count_nonzero(x), 3) assert_equal(np.nonzero(x), ([0, 1, 1], [1, 0, 2])) x = np.eye(3) assert_equal(np.count_nonzero(x), 3) assert_equal(np.nonzero(x), ([0, 1, 2], [0, 1, 2])) x = np.array([[(0, 1), (0, 0), (1, 11)], [(1, 1), (1, 0), (0, 0)], [(0, 0), (1, 5), (0, 1)]], dtype=[('a', 'f4'), ('b', 'u1')]) assert_equal(np.count_nonzero(x['a']), 4) assert_equal(np.count_nonzero(x['b']), 5) assert_equal(np.nonzero(x['a']), ([0, 1, 1, 2], [2, 0, 1, 1])) assert_equal(np.nonzero(x['b']), ([0, 0, 1, 2, 2], [0, 2, 0, 1, 2])) assert_(not x['a'].T.flags.aligned) assert_equal(np.count_nonzero(x['a'].T), 4) assert_equal(np.count_nonzero(x['b'].T), 5) assert_equal(np.nonzero(x['a'].T), ([0, 1, 1, 2], [1, 1, 2, 0])) assert_equal(np.nonzero(x['b'].T), ([0, 0, 1, 2, 2], [0, 1, 2, 0, 2])) def test_sparse(self): # test special sparse condition boolean code path for i in range(20): c = np.zeros(200, dtype=bool) c[i::20] = True assert_equal(np.nonzero(c)[0], np.arange(i, 200 + i, 20)) c = np.zeros(400, dtype=bool) c[10 + i:20 + i] = True c[20 + i*2] = True assert_equal(np.nonzero(c)[0], np.concatenate((np.arange(10 + i, 20 + i), [20 + i*2]))) def test_return_type(self): class C(np.ndarray): pass for view in (C, np.ndarray): for nd in range(1, 4): shape = tuple(range(2, 2+nd)) x = np.arange(np.prod(shape)).reshape(shape).view(view) for nzx in (np.nonzero(x), x.nonzero()): for nzx_i in nzx: assert_(type(nzx_i) is np.ndarray) assert_(nzx_i.flags.writeable) def test_count_nonzero_axis(self): # Basic check of functionality m = np.array([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]]) expected = np.array([1, 1, 1, 1, 1]) assert_equal(np.count_nonzero(m, axis=0), expected) expected = np.array([2, 3]) assert_equal(np.count_nonzero(m, axis=1), expected) assert_raises(ValueError, np.count_nonzero, m, axis=(1, 1)) assert_raises(TypeError, np.count_nonzero, m, axis='foo') assert_raises(np.AxisError, np.count_nonzero, m, axis=3) assert_raises(TypeError, np.count_nonzero, m, axis=np.array([[1], [2]])) def test_count_nonzero_axis_all_dtypes(self): # More thorough test that the axis argument is respected # for all dtypes and responds correctly when presented with # either integer or tuple arguments for axis msg = "Mismatch for dtype: %s" def assert_equal_w_dt(a, b, err_msg): assert_equal(a.dtype, b.dtype, err_msg=err_msg) assert_equal(a, b, err_msg=err_msg) for dt in np.typecodes['All']: err_msg = msg % (np.dtype(dt).name,) if dt != 'V': if dt != 'M': m = np.zeros((3, 3), dtype=dt) n = np.ones(1, dtype=dt) m[0, 0] = n[0] m[1, 0] = n[0] else: # np.zeros doesn't work for np.datetime64 m = np.array(['1970-01-01'] * 9) m = m.reshape((3, 3)) m[0, 0] = '1970-01-12' m[1, 0] = '1970-01-12' m = m.astype(dt) expected = np.array([2, 0, 0], dtype=np.intp) assert_equal_w_dt(np.count_nonzero(m, axis=0), expected, err_msg=err_msg) expected = np.array([1, 1, 0], dtype=np.intp) assert_equal_w_dt(np.count_nonzero(m, axis=1), expected, err_msg=err_msg) expected = np.array(2) assert_equal(np.count_nonzero(m, axis=(0, 1)), expected, err_msg=err_msg) assert_equal(np.count_nonzero(m, axis=None), expected, err_msg=err_msg) assert_equal(np.count_nonzero(m), expected, err_msg=err_msg) if dt == 'V': # There are no 'nonzero' objects for np.void, so the testing # setup is slightly different for this dtype m = np.array([np.void(1)] * 6).reshape((2, 3)) expected = np.array([0, 0, 0], dtype=np.intp) assert_equal_w_dt(np.count_nonzero(m, axis=0), expected, err_msg=err_msg) expected = np.array([0, 0], dtype=np.intp) assert_equal_w_dt(np.count_nonzero(m, axis=1), expected, err_msg=err_msg) expected = np.array(0) assert_equal(np.count_nonzero(m, axis=(0, 1)), expected, err_msg=err_msg) assert_equal(np.count_nonzero(m, axis=None), expected, err_msg=err_msg) assert_equal(np.count_nonzero(m), expected, err_msg=err_msg) def test_count_nonzero_axis_consistent(self): # Check that the axis behaviour for valid axes in # non-special cases is consistent (and therefore # correct) by checking it against an integer array # that is then casted to the generic object dtype from itertools import combinations, permutations axis = (0, 1, 2, 3) size = (5, 5, 5, 5) msg = "Mismatch for axis: %s" rng = np.random.RandomState(1234) m = rng.randint(-100, 100, size=size) n = m.astype(object) for length in range(len(axis)): for combo in combinations(axis, length): for perm in permutations(combo): assert_equal( np.count_nonzero(m, axis=perm), np.count_nonzero(n, axis=perm), err_msg=msg % (perm,)) def test_countnonzero_axis_empty(self): a = np.array([[0, 0, 1], [1, 0, 1]]) assert_equal(np.count_nonzero(a, axis=()), a.astype(bool)) def test_array_method(self): # Tests that the array method # call to nonzero works m = np.array([[1, 0, 0], [4, 0, 6]]) tgt = [[0, 1, 1], [0, 0, 2]] assert_equal(m.nonzero(), tgt) def test_nonzero_invalid_object(self): # gh-9295 a = np.array([np.array([1, 2]), 3]) assert_raises(ValueError, np.nonzero, a) class BoolErrors: def __bool__(self): raise ValueError("Not allowed") def __nonzero__(self): raise ValueError("Not allowed") assert_raises(ValueError, np.nonzero, np.array([BoolErrors()])) class TestIndex(object): def test_boolean(self): a = rand(3, 5, 8) V = rand(5, 8) g1 = randint(0, 5, size=15) g2 = randint(0, 8, size=15) V[g1, g2] = -V[g1, g2] assert_((np.array([a[0][V > 0], a[1][V > 0], a[2][V > 0]]) == a[:, V > 0]).all()) def test_boolean_edgecase(self): a = np.array([], dtype='int32') b = np.array([], dtype='bool') c = a[b] assert_equal(c, []) assert_equal(c.dtype, np.dtype('int32')) class TestBinaryRepr(object): def test_zero(self): assert_equal(np.binary_repr(0), '0') def test_positive(self): assert_equal(np.binary_repr(10), '1010') assert_equal(np.binary_repr(12522), '11000011101010') assert_equal(np.binary_repr(10736848), '101000111101010011010000') def test_negative(self): assert_equal(np.binary_repr(-1), '-1') assert_equal(np.binary_repr(-10), '-1010') assert_equal(np.binary_repr(-12522), '-11000011101010') assert_equal(np.binary_repr(-10736848), '-101000111101010011010000') def test_sufficient_width(self): assert_equal(np.binary_repr(0, width=5), '00000') assert_equal(np.binary_repr(10, width=7), '0001010') assert_equal(np.binary_repr(-5, width=7), '1111011') def test_neg_width_boundaries(self): # see gh-8670 # Ensure that the example in the issue does not # break before proceeding to a more thorough test. assert_equal(np.binary_repr(-128, width=8), '10000000') for width in range(1, 11): num = -2**(width - 1) exp = '1' + (width - 1) * '0' assert_equal(np.binary_repr(num, width=width), exp) class TestBaseRepr(object): def test_base3(self): assert_equal(np.base_repr(3**5, 3), '100000') def test_positive(self): assert_equal(np.base_repr(12, 10), '12') assert_equal(np.base_repr(12, 10, 4), '000012') assert_equal(np.base_repr(12, 4), '30') assert_equal(np.base_repr(3731624803700888, 36), '10QR0ROFCEW') def test_negative(self): assert_equal(np.base_repr(-12, 10), '-12') assert_equal(np.base_repr(-12, 10, 4), '-000012') assert_equal(np.base_repr(-12, 4), '-30') def test_base_range(self): with assert_raises(ValueError): np.base_repr(1, 1) with assert_raises(ValueError): np.base_repr(1, 37) class TestArrayComparisons(object): def test_array_equal(self): res = np.array_equal(np.array([1, 2]), np.array([1, 2])) assert_(res) assert_(type(res) is bool) res = np.array_equal(np.array([1, 2]), np.array([1, 2, 3])) assert_(not res) assert_(type(res) is bool) res = np.array_equal(np.array([1, 2]), np.array([3, 4])) assert_(not res) assert_(type(res) is bool) res = np.array_equal(np.array([1, 2]), np.array([1, 3])) assert_(not res) assert_(type(res) is bool) res = np.array_equal(np.array(['a'], dtype='S1'), np.array(['a'], dtype='S1')) assert_(res) assert_(type(res) is bool) res = np.array_equal(np.array([('a', 1)], dtype='S1,u4'), np.array([('a', 1)], dtype='S1,u4')) assert_(res) assert_(type(res) is bool) def test_none_compares_elementwise(self): a = np.array([None, 1, None], dtype=object) assert_equal(a == None, [True, False, True]) assert_equal(a != None, [False, True, False]) a = np.ones(3) assert_equal(a == None, [False, False, False]) assert_equal(a != None, [True, True, True]) def test_array_equiv(self): res = np.array_equiv(np.array([1, 2]), np.array([1, 2])) assert_(res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([1, 2, 3])) assert_(not res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([3, 4])) assert_(not res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([1, 3])) assert_(not res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 1]), np.array([1])) assert_(res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 1]), np.array([[1], [1]])) assert_(res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([2])) assert_(not res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([[1], [2]])) assert_(not res) assert_(type(res) is bool) res = np.array_equiv(np.array([1, 2]), np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])) assert_(not res) assert_(type(res) is bool) def assert_array_strict_equal(x, y): assert_array_equal(x, y) # Check flags, 32 bit arches typically don't provide 16 byte alignment if ((x.dtype.alignment <= 8 or np.intp().dtype.itemsize != 4) and sys.platform != 'win32'): assert_(x.flags == y.flags) else: assert_(x.flags.owndata == y.flags.owndata) assert_(x.flags.writeable == y.flags.writeable) assert_(x.flags.c_contiguous == y.flags.c_contiguous) assert_(x.flags.f_contiguous == y.flags.f_contiguous) assert_(x.flags.writebackifcopy == y.flags.writebackifcopy) # check endianness assert_(x.dtype.isnative == y.dtype.isnative) class TestClip(object): def setup(self): self.nr = 5 self.nc = 3 def fastclip(self, a, m, M, out=None): if out is None: return a.clip(m, M) else: return a.clip(m, M, out) def clip(self, a, m, M, out=None): # use slow-clip selector = np.less(a, m) + 2*np.greater(a, M) return selector.choose((a, m, M), out=out) # Handy functions def _generate_data(self, n, m): return randn(n, m) def _generate_data_complex(self, n, m): return randn(n, m) + 1.j * rand(n, m) def _generate_flt_data(self, n, m): return (randn(n, m)).astype(np.float32) def _neg_byteorder(self, a): a = np.asarray(a) if sys.byteorder == 'little': a = a.astype(a.dtype.newbyteorder('>')) else: a = a.astype(a.dtype.newbyteorder('<')) return a def _generate_non_native_data(self, n, m): data = randn(n, m) data = self._neg_byteorder(data) assert_(not data.dtype.isnative) return data def _generate_int_data(self, n, m): return (10 * rand(n, m)).astype(np.int64) def _generate_int32_data(self, n, m): return (10 * rand(n, m)).astype(np.int32) # Now the real test cases def test_simple_double(self): # Test native double input with scalar min/max. a = self._generate_data(self.nr, self.nc) m = 0.1 M = 0.6 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_simple_int(self): # Test native int input with scalar min/max. a = self._generate_int_data(self.nr, self.nc) a = a.astype(int) m = -2 M = 4 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_array_double(self): # Test native double input with array min/max. a = self._generate_data(self.nr, self.nc) m = np.zeros(a.shape) M = m + 0.5 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_simple_nonnative(self): # Test non native double input with scalar min/max. # Test native double input with non native double scalar min/max. a = self._generate_non_native_data(self.nr, self.nc) m = -0.5 M = 0.6 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_equal(ac, act) # Test native double input with non native double scalar min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 M = self._neg_byteorder(0.6) assert_(not M.dtype.isnative) ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_equal(ac, act) def test_simple_complex(self): # Test native complex input with native double scalar min/max. # Test native input with complex double scalar min/max. a = 3 * self._generate_data_complex(self.nr, self.nc) m = -0.5 M = 1. ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) # Test native input with complex double scalar min/max. a = 3 * self._generate_data(self.nr, self.nc) m = -0.5 + 1.j M = 1. + 2.j ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_clip_complex(self): # Address Issue gh-5354 for clipping complex arrays # Test native complex input without explicit min/max # ie, either min=None or max=None a = np.ones(10, dtype=complex) m = a.min() M = a.max() am = self.fastclip(a, m, None) aM = self.fastclip(a, None, M) assert_array_strict_equal(am, a) assert_array_strict_equal(aM, a) def test_clip_non_contig(self): # Test clip for non contiguous native input and native scalar min/max. a = self._generate_data(self.nr * 2, self.nc * 3) a = a[::2, ::3] assert_(not a.flags['F_CONTIGUOUS']) assert_(not a.flags['C_CONTIGUOUS']) ac = self.fastclip(a, -1.6, 1.7) act = self.clip(a, -1.6, 1.7) assert_array_strict_equal(ac, act) def test_simple_out(self): # Test native double input with scalar min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 M = 0.6 ac = np.zeros(a.shape) act = np.zeros(a.shape) self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_simple_int32_inout(self): # Test native int32 input with double min/max and int32 out. a = self._generate_int32_data(self.nr, self.nc) m = np.float64(0) M = np.float64(2) ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_simple_int64_out(self): # Test native int32 input with int32 scalar min/max and int64 out. a = self._generate_int32_data(self.nr, self.nc) m = np.int32(-1) M = np.int32(1) ac = np.zeros(a.shape, dtype=np.int64) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_simple_int64_inout(self): # Test native int32 input with double array min/max and int32 out. a = self._generate_int32_data(self.nr, self.nc) m = np.zeros(a.shape, np.float64) M = np.float64(1) ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_simple_int32_out(self): # Test native double input with scalar min/max and int out. a = self._generate_data(self.nr, self.nc) m = -1.0 M = 2.0 ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_simple_inplace_01(self): # Test native double input with array min/max in-place. a = self._generate_data(self.nr, self.nc) ac = a.copy() m = np.zeros(a.shape) M = 1.0 self.fastclip(a, m, M, a) self.clip(a, m, M, ac) assert_array_strict_equal(a, ac) def test_simple_inplace_02(self): # Test native double input with scalar min/max in-place. a = self._generate_data(self.nr, self.nc) ac = a.copy() m = -0.5 M = 0.6 self.fastclip(a, m, M, a) self.clip(ac, m, M, ac) assert_array_strict_equal(a, ac) def test_noncontig_inplace(self): # Test non contiguous double input with double scalar min/max in-place. a = self._generate_data(self.nr * 2, self.nc * 3) a = a[::2, ::3] assert_(not a.flags['F_CONTIGUOUS']) assert_(not a.flags['C_CONTIGUOUS']) ac = a.copy() m = -0.5 M = 0.6 self.fastclip(a, m, M, a) self.clip(ac, m, M, ac) assert_array_equal(a, ac) def test_type_cast_01(self): # Test native double input with scalar min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 M = 0.6 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_type_cast_02(self): # Test native int32 input with int32 scalar min/max. a = self._generate_int_data(self.nr, self.nc) a = a.astype(np.int32) m = -2 M = 4 ac = self.fastclip(a, m, M) act = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_type_cast_03(self): # Test native int32 input with float64 scalar min/max. a = self._generate_int32_data(self.nr, self.nc) m = -2 M = 4 ac = self.fastclip(a, np.float64(m), np.float64(M)) act = self.clip(a, np.float64(m), np.float64(M)) assert_array_strict_equal(ac, act) def test_type_cast_04(self): # Test native int32 input with float32 scalar min/max. a = self._generate_int32_data(self.nr, self.nc) m = np.float32(-2) M = np.float32(4) act = self.fastclip(a, m, M) ac = self.clip(a, m, M) assert_array_strict_equal(ac, act) def test_type_cast_05(self): # Test native int32 with double arrays min/max. a = self._generate_int_data(self.nr, self.nc) m = -0.5 M = 1. ac = self.fastclip(a, m * np.zeros(a.shape), M) act = self.clip(a, m * np.zeros(a.shape), M) assert_array_strict_equal(ac, act) def test_type_cast_06(self): # Test native with NON native scalar min/max. a = self._generate_data(self.nr, self.nc) m = 0.5 m_s = self._neg_byteorder(m) M = 1. act = self.clip(a, m_s, M) ac = self.fastclip(a, m_s, M) assert_array_strict_equal(ac, act) def test_type_cast_07(self): # Test NON native with native array min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 * np.ones(a.shape) M = 1. a_s = self._neg_byteorder(a) assert_(not a_s.dtype.isnative) act = a_s.clip(m, M) ac = self.fastclip(a_s, m, M) assert_array_strict_equal(ac, act) def test_type_cast_08(self): # Test NON native with native scalar min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 M = 1. a_s = self._neg_byteorder(a) assert_(not a_s.dtype.isnative) ac = self.fastclip(a_s, m, M) act = a_s.clip(m, M) assert_array_strict_equal(ac, act) def test_type_cast_09(self): # Test native with NON native array min/max. a = self._generate_data(self.nr, self.nc) m = -0.5 * np.ones(a.shape) M = 1. m_s = self._neg_byteorder(m) assert_(not m_s.dtype.isnative) ac = self.fastclip(a, m_s, M) act = self.clip(a, m_s, M) assert_array_strict_equal(ac, act) def test_type_cast_10(self): # Test native int32 with float min/max and float out for output argument. a = self._generate_int_data(self.nr, self.nc) b = np.zeros(a.shape, dtype=np.float32) m = np.float32(-0.5) M = np.float32(1) act = self.clip(a, m, M, out=b) ac = self.fastclip(a, m, M, out=b) assert_array_strict_equal(ac, act) def test_type_cast_11(self): # Test non native with native scalar, min/max, out non native a = self._generate_non_native_data(self.nr, self.nc) b = a.copy() b = b.astype(b.dtype.newbyteorder('>')) bt = b.copy() m = -0.5 M = 1. self.fastclip(a, m, M, out=b) self.clip(a, m, M, out=bt) assert_array_strict_equal(b, bt) def test_type_cast_12(self): # Test native int32 input and min/max and float out a = self._generate_int_data(self.nr, self.nc) b = np.zeros(a.shape, dtype=np.float32) m = np.int32(0) M = np.int32(1) act = self.clip(a, m, M, out=b) ac = self.fastclip(a, m, M, out=b) assert_array_strict_equal(ac, act) def test_clip_with_out_simple(self): # Test native double input with scalar min/max a = self._generate_data(self.nr, self.nc) m = -0.5 M = 0.6 ac = np.zeros(a.shape) act = np.zeros(a.shape) self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_clip_with_out_simple2(self): # Test native int32 input with double min/max and int32 out a = self._generate_int32_data(self.nr, self.nc) m = np.float64(0) M = np.float64(2) ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_clip_with_out_simple_int32(self): # Test native int32 input with int32 scalar min/max and int64 out a = self._generate_int32_data(self.nr, self.nc) m = np.int32(-1) M = np.int32(1) ac = np.zeros(a.shape, dtype=np.int64) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_clip_with_out_array_int32(self): # Test native int32 input with double array min/max and int32 out a = self._generate_int32_data(self.nr, self.nc) m = np.zeros(a.shape, np.float64) M = np.float64(1) ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_clip_with_out_array_outint32(self): # Test native double input with scalar min/max and int out a = self._generate_data(self.nr, self.nc) m = -1.0 M = 2.0 ac = np.zeros(a.shape, dtype=np.int32) act = ac.copy() self.fastclip(a, m, M, ac) self.clip(a, m, M, act) assert_array_strict_equal(ac, act) def test_clip_with_out_transposed(self): # Test that the out argument works when tranposed a = np.arange(16).reshape(4, 4) out = np.empty_like(a).T a.clip(4, 10, out=out) expected = self.clip(a, 4, 10) assert_array_equal(out, expected) def test_clip_with_out_memory_overlap(self): # Test that the out argument works when it has memory overlap a = np.arange(16).reshape(4, 4) ac = a.copy() a[:-1].clip(4, 10, out=a[1:]) expected = self.clip(ac[:-1], 4, 10) assert_array_equal(a[1:], expected) def test_clip_inplace_array(self): # Test native double input with array min/max a = self._generate_data(self.nr, self.nc) ac = a.copy() m = np.zeros(a.shape) M = 1.0 self.fastclip(a, m, M, a) self.clip(a, m, M, ac) assert_array_strict_equal(a, ac) def test_clip_inplace_simple(self): # Test native double input with scalar min/max a = self._generate_data(self.nr, self.nc) ac = a.copy() m = -0.5 M = 0.6 self.fastclip(a, m, M, a) self.clip(a, m, M, ac) assert_array_strict_equal(a, ac) def test_clip_func_takes_out(self): # Ensure that the clip() function takes an out=argument. a = self._generate_data(self.nr, self.nc) ac = a.copy() m = -0.5 M = 0.6 a2 = np.clip(a, m, M, out=a) self.clip(a, m, M, ac) assert_array_strict_equal(a2, ac) assert_(a2 is a) def test_clip_nan(self): d = np.arange(7.) assert_equal(d.clip(min=np.nan), d) assert_equal(d.clip(max=np.nan), d) assert_equal(d.clip(min=np.nan, max=np.nan), d) assert_equal(d.clip(min=-2, max=np.nan), d) assert_equal(d.clip(min=np.nan, max=10), d) class TestAllclose(object): rtol = 1e-5 atol = 1e-8 def setup(self): self.olderr = np.seterr(invalid='ignore') def teardown(self): np.seterr(**self.olderr) def tst_allclose(self, x, y): assert_(np.allclose(x, y), "%s and %s not close" % (x, y)) def tst_not_allclose(self, x, y): assert_(not np.allclose(x, y), "%s and %s shouldn't be close" % (x, y)) def test_ip_allclose(self): # Parametric test factory. arr = np.array([100, 1000]) aran = np.arange(125).reshape((5, 5, 5)) atol = self.atol rtol = self.rtol data = [([1, 0], [1, 0]), ([atol], [0]), ([1], [1+rtol+atol]), (arr, arr + arr*rtol), (arr, arr + arr*rtol + atol*2), (aran, aran + aran*rtol), (np.inf, np.inf), (np.inf, [np.inf])] for (x, y) in data: self.tst_allclose(x, y) def test_ip_not_allclose(self): # Parametric test factory. aran = np.arange(125).reshape((5, 5, 5)) atol = self.atol rtol = self.rtol data = [([np.inf, 0], [1, np.inf]), ([np.inf, 0], [1, 0]), ([np.inf, np.inf], [1, np.inf]), ([np.inf, np.inf], [1, 0]), ([-np.inf, 0], [np.inf, 0]), ([np.nan, 0], [np.nan, 0]), ([atol*2], [0]), ([1], [1+rtol+atol*2]), (aran, aran + aran*atol + atol*2), (np.array([np.inf, 1]), np.array([0, np.inf]))] for (x, y) in data: self.tst_not_allclose(x, y) def test_no_parameter_modification(self): x = np.array([np.inf, 1]) y = np.array([0, np.inf]) np.allclose(x, y) assert_array_equal(x, np.array([np.inf, 1])) assert_array_equal(y, np.array([0, np.inf])) def test_min_int(self): # Could make problems because of abs(min_int) == min_int min_int = np.iinfo(np.int_).min a = np.array([min_int], dtype=np.int_) assert_(np.allclose(a, a)) def test_equalnan(self): x = np.array([1.0, np.nan]) assert_(np.allclose(x, x, equal_nan=True)) def test_return_class_is_ndarray(self): # Issue gh-6475 # Check that allclose does not preserve subtypes class Foo(np.ndarray): def __new__(cls, *args, **kwargs): return np.array(*args, **kwargs).view(cls) a = Foo([1]) assert_(type(np.allclose(a, a)) is bool) class TestIsclose(object): rtol = 1e-5 atol = 1e-8 def setup(self): atol = self.atol rtol = self.rtol arr = np.array([100, 1000]) aran = np.arange(125).reshape((5, 5, 5)) self.all_close_tests = [ ([1, 0], [1, 0]), ([atol], [0]), ([1], [1 + rtol + atol]), (arr, arr + arr*rtol), (arr, arr + arr*rtol + atol), (aran, aran + aran*rtol), (np.inf, np.inf), (np.inf, [np.inf]), ([np.inf, -np.inf], [np.inf, -np.inf]), ] self.none_close_tests = [ ([np.inf, 0], [1, np.inf]), ([np.inf, -np.inf], [1, 0]), ([np.inf, np.inf], [1, -np.inf]), ([np.inf, np.inf], [1, 0]), ([np.nan, 0], [np.nan, -np.inf]), ([atol*2], [0]), ([1], [1 + rtol + atol*2]), (aran, aran + rtol*1.1*aran + atol*1.1), (np.array([np.inf, 1]), np.array([0, np.inf])), ] self.some_close_tests = [ ([np.inf, 0], [np.inf, atol*2]), ([atol, 1, 1e6*(1 + 2*rtol) + atol], [0, np.nan, 1e6]), (np.arange(3), [0, 1, 2.1]), (np.nan, [np.nan, np.nan, np.nan]), ([0], [atol, np.inf, -np.inf, np.nan]), (0, [atol, np.inf, -np.inf, np.nan]), ] self.some_close_results = [ [True, False], [True, False, False], [True, True, False], [False, False, False], [True, False, False, False], [True, False, False, False], ] def test_ip_isclose(self): self.setup() tests = self.some_close_tests results = self.some_close_results for (x, y), result in zip(tests, results): assert_array_equal(np.isclose(x, y), result) def tst_all_isclose(self, x, y): assert_(np.all(np.isclose(x, y)), "%s and %s not close" % (x, y)) def tst_none_isclose(self, x, y): msg = "%s and %s shouldn't be close" assert_(not np.any(np.isclose(x, y)), msg % (x, y)) def tst_isclose_allclose(self, x, y): msg = "isclose.all() and allclose aren't same for %s and %s" msg2 = "isclose and allclose aren't same for %s and %s" if np.isscalar(x) and np.isscalar(y): assert_(np.isclose(x, y) == np.allclose(x, y), msg=msg2 % (x, y)) else: assert_array_equal(np.isclose(x, y).all(), np.allclose(x, y), msg % (x, y)) def test_ip_all_isclose(self): self.setup() for (x, y) in self.all_close_tests: self.tst_all_isclose(x, y) def test_ip_none_isclose(self): self.setup() for (x, y) in self.none_close_tests: self.tst_none_isclose(x, y) def test_ip_isclose_allclose(self): self.setup() tests = (self.all_close_tests + self.none_close_tests + self.some_close_tests) for (x, y) in tests: self.tst_isclose_allclose(x, y) def test_equal_nan(self): assert_array_equal(np.isclose(np.nan, np.nan, equal_nan=True), [True]) arr = np.array([1.0, np.nan]) assert_array_equal(np.isclose(arr, arr, equal_nan=True), [True, True]) def test_masked_arrays(self): # Make sure to test the output type when arguments are interchanged. x = np.ma.masked_where([True, True, False], np.arange(3)) assert_(type(x) is type(np.isclose(2, x))) assert_(type(x) is type(np.isclose(x, 2))) x = np.ma.masked_where([True, True, False], [np.nan, np.inf, np.nan]) assert_(type(x) is type(np.isclose(np.inf, x))) assert_(type(x) is type(np.isclose(x, np.inf))) x = np.ma.masked_where([True, True, False], [np.nan, np.nan, np.nan]) y = np.isclose(np.nan, x, equal_nan=True) assert_(type(x) is type(y)) # Ensure that the mask isn't modified... assert_array_equal([True, True, False], y.mask) y = np.isclose(x, np.nan, equal_nan=True) assert_(type(x) is type(y)) # Ensure that the mask isn't modified... assert_array_equal([True, True, False], y.mask) x = np.ma.masked_where([True, True, False], [np.nan, np.nan, np.nan]) y = np.isclose(x, x, equal_nan=True) assert_(type(x) is type(y)) # Ensure that the mask isn't modified... assert_array_equal([True, True, False], y.mask) def test_scalar_return(self): assert_(np.isscalar(np.isclose(1, 1))) def test_no_parameter_modification(self): x = np.array([np.inf, 1]) y = np.array([0, np.inf]) np.isclose(x, y) assert_array_equal(x, np.array([np.inf, 1])) assert_array_equal(y, np.array([0, np.inf])) def test_non_finite_scalar(self): # GH7014, when two scalars are compared the output should also be a # scalar assert_(np.isclose(np.inf, -np.inf) is np.False_) assert_(np.isclose(0, np.inf) is np.False_) assert_(type(np.isclose(0, np.inf)) is np.bool_) class TestStdVar(object): def setup(self): self.A = np.array([1, -1, 1, -1]) self.real_var = 1 def test_basic(self): assert_almost_equal(np.var(self.A), self.real_var) assert_almost_equal(np.std(self.A)**2, self.real_var) def test_scalars(self): assert_equal(np.var(1), 0) assert_equal(np.std(1), 0) def test_ddof1(self): assert_almost_equal(np.var(self.A, ddof=1), self.real_var*len(self.A)/float(len(self.A)-1)) assert_almost_equal(np.std(self.A, ddof=1)**2, self.real_var*len(self.A)/float(len(self.A)-1)) def test_ddof2(self): assert_almost_equal(np.var(self.A, ddof=2), self.real_var*len(self.A)/float(len(self.A)-2)) assert_almost_equal(np.std(self.A, ddof=2)**2, self.real_var*len(self.A)/float(len(self.A)-2)) def test_out_scalar(self): d = np.arange(10) out = np.array(0.) r = np.std(d, out=out) assert_(r is out) assert_array_equal(r, out) r = np.var(d, out=out) assert_(r is out) assert_array_equal(r, out) r = np.mean(d, out=out) assert_(r is out) assert_array_equal(r, out) class TestStdVarComplex(object): def test_basic(self): A = np.array([1, 1.j, -1, -1.j]) real_var = 1 assert_almost_equal(np.var(A), real_var) assert_almost_equal(np.std(A)**2, real_var) def test_scalars(self): assert_equal(np.var(1j), 0) assert_equal(np.std(1j), 0) class TestCreationFuncs(object): # Test ones, zeros, empty and full. def setup(self): dtypes = {np.dtype(tp) for tp in itertools.chain(*np.sctypes.values())} # void, bytes, str variable_sized = {tp for tp in dtypes if tp.str.endswith('0')} self.dtypes = sorted(dtypes - variable_sized | {np.dtype(tp.str.replace("0", str(i))) for tp in variable_sized for i in range(1, 10)}, key=lambda dtype: dtype.str) self.orders = {'C': 'c_contiguous', 'F': 'f_contiguous'} self.ndims = 10 def check_function(self, func, fill_value=None): par = ((0, 1, 2), range(self.ndims), self.orders, self.dtypes) fill_kwarg = {} if fill_value is not None: fill_kwarg = {'fill_value': fill_value} for size, ndims, order, dtype in itertools.product(*par): shape = ndims * [size] # do not fill void type if fill_kwarg and dtype.str.startswith('|V'): continue arr = func(shape, order=order, dtype=dtype, **fill_kwarg) assert_equal(arr.dtype, dtype) assert_(getattr(arr.flags, self.orders[order])) if fill_value is not None: if dtype.str.startswith('|S'): val = str(fill_value) else: val = fill_value assert_equal(arr, dtype.type(val)) def test_zeros(self): self.check_function(np.zeros) def test_ones(self): self.check_function(np.zeros) def test_empty(self): self.check_function(np.empty) def test_full(self): self.check_function(np.full, 0) self.check_function(np.full, 1) @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts") def test_for_reference_leak(self): # Make sure we have an object for reference dim = 1 beg = sys.getrefcount(dim) np.zeros([dim]*10) assert_(sys.getrefcount(dim) == beg) np.ones([dim]*10) assert_(sys.getrefcount(dim) == beg) np.empty([dim]*10) assert_(sys.getrefcount(dim) == beg) np.full([dim]*10, 0) assert_(sys.getrefcount(dim) == beg) class TestLikeFuncs(object): '''Test ones_like, zeros_like, empty_like and full_like''' def setup(self): self.data = [ # Array scalars (np.array(3.), None), (np.array(3), 'f8'), # 1D arrays (np.arange(6, dtype='f4'), None), (np.arange(6), 'c16'), # 2D C-layout arrays (np.arange(6).reshape(2, 3), None), (np.arange(6).reshape(3, 2), 'i1'), # 2D F-layout arrays (np.arange(6).reshape((2, 3), order='F'), None), (np.arange(6).reshape((3, 2), order='F'), 'i1'), # 3D C-layout arrays (np.arange(24).reshape(2, 3, 4), None), (np.arange(24).reshape(4, 3, 2), 'f4'), # 3D F-layout arrays (np.arange(24).reshape((2, 3, 4), order='F'), None), (np.arange(24).reshape((4, 3, 2), order='F'), 'f4'), # 3D non-C/F-layout arrays (np.arange(24).reshape(2, 3, 4).swapaxes(0, 1), None), (np.arange(24).reshape(4, 3, 2).swapaxes(0, 1), '?'), ] def compare_array_value(self, dz, value, fill_value): if value is not None: if fill_value: try: z = dz.dtype.type(value) except OverflowError: pass else: assert_(np.all(dz == z)) else: assert_(np.all(dz == value)) def check_like_function(self, like_function, value, fill_value=False): if fill_value: fill_kwarg = {'fill_value': value} else: fill_kwarg = {} for d, dtype in self.data: # default (K) order, dtype dz = like_function(d, dtype=dtype, **fill_kwarg) assert_equal(dz.shape, d.shape) assert_equal(np.array(dz.strides)*d.dtype.itemsize, np.array(d.strides)*dz.dtype.itemsize) assert_equal(d.flags.c_contiguous, dz.flags.c_contiguous) assert_equal(d.flags.f_contiguous, dz.flags.f_contiguous) if dtype is None: assert_equal(dz.dtype, d.dtype) else: assert_equal(dz.dtype, np.dtype(dtype)) self.compare_array_value(dz, value, fill_value) # C order, default dtype dz = like_function(d, order='C', dtype=dtype, **fill_kwarg) assert_equal(dz.shape, d.shape) assert_(dz.flags.c_contiguous) if dtype is None: assert_equal(dz.dtype, d.dtype) else: assert_equal(dz.dtype, np.dtype(dtype)) self.compare_array_value(dz, value, fill_value) # F order, default dtype dz = like_function(d, order='F', dtype=dtype, **fill_kwarg) assert_equal(dz.shape, d.shape) assert_(dz.flags.f_contiguous) if dtype is None: assert_equal(dz.dtype, d.dtype) else: assert_equal(dz.dtype, np.dtype(dtype)) self.compare_array_value(dz, value, fill_value) # A order dz = like_function(d, order='A', dtype=dtype, **fill_kwarg) assert_equal(dz.shape, d.shape) if d.flags.f_contiguous: assert_(dz.flags.f_contiguous) else: assert_(dz.flags.c_contiguous) if dtype is None: assert_equal(dz.dtype, d.dtype) else: assert_equal(dz.dtype, np.dtype(dtype)) self.compare_array_value(dz, value, fill_value) # Test the 'subok' parameter class MyNDArray(np.ndarray): pass a = np.array([[1, 2], [3, 4]]).view(MyNDArray) b = like_function(a, **fill_kwarg) assert_(type(b) is MyNDArray) b = like_function(a, subok=False, **fill_kwarg) assert_(type(b) is not MyNDArray) def test_ones_like(self): self.check_like_function(np.ones_like, 1) def test_zeros_like(self): self.check_like_function(np.zeros_like, 0) def test_empty_like(self): self.check_like_function(np.empty_like, None) def test_filled_like(self): self.check_like_function(np.full_like, 0, True) self.check_like_function(np.full_like, 1, True) self.check_like_function(np.full_like, 1000, True) self.check_like_function(np.full_like, 123.456, True) self.check_like_function(np.full_like, np.inf, True) class TestCorrelate(object): def _setup(self, dt): self.x = np.array([1, 2, 3, 4, 5], dtype=dt) self.xs = np.arange(1, 20)[::3] self.y = np.array([-1, -2, -3], dtype=dt) self.z1 = np.array([ -3., -8., -14., -20., -26., -14., -5.], dtype=dt) self.z1_4 = np.array([-2., -5., -8., -11., -14., -5.], dtype=dt) self.z1r = np.array([-15., -22., -22., -16., -10., -4., -1.], dtype=dt) self.z2 = np.array([-5., -14., -26., -20., -14., -8., -3.], dtype=dt) self.z2r = np.array([-1., -4., -10., -16., -22., -22., -15.], dtype=dt) self.zs = np.array([-3., -14., -30., -48., -66., -84., -102., -54., -19.], dtype=dt) def test_float(self): self._setup(float) z = np.correlate(self.x, self.y, 'full') assert_array_almost_equal(z, self.z1) z = np.correlate(self.x, self.y[:-1], 'full') assert_array_almost_equal(z, self.z1_4) z = np.correlate(self.y, self.x, 'full') assert_array_almost_equal(z, self.z2) z = np.correlate(self.x[::-1], self.y, 'full') assert_array_almost_equal(z, self.z1r) z = np.correlate(self.y, self.x[::-1], 'full') assert_array_almost_equal(z, self.z2r) z = np.correlate(self.xs, self.y, 'full') assert_array_almost_equal(z, self.zs) def test_object(self): self._setup(Decimal) z = np.correlate(self.x, self.y, 'full') assert_array_almost_equal(z, self.z1) z = np.correlate(self.y, self.x, 'full') assert_array_almost_equal(z, self.z2) def test_no_overwrite(self): d = np.ones(100) k = np.ones(3) np.correlate(d, k) assert_array_equal(d, np.ones(100)) assert_array_equal(k, np.ones(3)) def test_complex(self): x = np.array([1, 2, 3, 4+1j], dtype=complex) y = np.array([-1, -2j, 3+1j], dtype=complex) r_z = np.array([3-1j, 6, 8+1j, 11+5j, -5+8j, -4-1j], dtype=complex) r_z = r_z[::-1].conjugate() z = np.correlate(y, x, mode='full') assert_array_almost_equal(z, r_z) class TestConvolve(object): def test_object(self): d = [1.] * 100 k = [1.] * 3 assert_array_almost_equal(np.convolve(d, k)[2:-2], np.full(98, 3)) def test_no_overwrite(self): d = np.ones(100) k = np.ones(3) np.convolve(d, k) assert_array_equal(d, np.ones(100)) assert_array_equal(k, np.ones(3)) class TestArgwhere(object): def test_2D(self): x = np.arange(6).reshape((2, 3)) assert_array_equal(np.argwhere(x > 1), [[0, 2], [1, 0], [1, 1], [1, 2]]) def test_list(self): assert_equal(np.argwhere([4, 0, 2, 1, 3]), [[0], [2], [3], [4]]) class TestStringFunction(object): def test_set_string_function(self): a = np.array([1]) np.set_string_function(lambda x: "FOO", repr=True) assert_equal(repr(a), "FOO") np.set_string_function(None, repr=True) assert_equal(repr(a), "array([1])") np.set_string_function(lambda x: "FOO", repr=False) assert_equal(str(a), "FOO") np.set_string_function(None, repr=False) assert_equal(str(a), "[1]") class TestRoll(object): def test_roll1d(self): x = np.arange(10) xr = np.roll(x, 2) assert_equal(xr, np.array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])) def test_roll2d(self): x2 = np.reshape(np.arange(10), (2, 5)) x2r = np.roll(x2, 1) assert_equal(x2r, np.array([[9, 0, 1, 2, 3], [4, 5, 6, 7, 8]])) x2r = np.roll(x2, 1, axis=0) assert_equal(x2r, np.array([[5, 6, 7, 8, 9], [0, 1, 2, 3, 4]])) x2r = np.roll(x2, 1, axis=1) assert_equal(x2r, np.array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]])) # Roll multiple axes at once. x2r = np.roll(x2, 1, axis=(0, 1)) assert_equal(x2r, np.array([[9, 5, 6, 7, 8], [4, 0, 1, 2, 3]])) x2r = np.roll(x2, (1, 0), axis=(0, 1)) assert_equal(x2r, np.array([[5, 6, 7, 8, 9], [0, 1, 2, 3, 4]])) x2r = np.roll(x2, (-1, 0), axis=(0, 1)) assert_equal(x2r, np.array([[5, 6, 7, 8, 9], [0, 1, 2, 3, 4]])) x2r = np.roll(x2, (0, 1), axis=(0, 1)) assert_equal(x2r, np.array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]])) x2r = np.roll(x2, (0, -1), axis=(0, 1)) assert_equal(x2r, np.array([[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]])) x2r = np.roll(x2, (1, 1), axis=(0, 1)) assert_equal(x2r, np.array([[9, 5, 6, 7, 8], [4, 0, 1, 2, 3]])) x2r = np.roll(x2, (-1, -1), axis=(0, 1)) assert_equal(x2r, np.array([[6, 7, 8, 9, 5], [1, 2, 3, 4, 0]])) # Roll the same axis multiple times. x2r = np.roll(x2, 1, axis=(0, 0)) assert_equal(x2r, np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])) x2r = np.roll(x2, 1, axis=(1, 1)) assert_equal(x2r, np.array([[3, 4, 0, 1, 2], [8, 9, 5, 6, 7]])) # Roll more than one turn in either direction. x2r = np.roll(x2, 6, axis=1) assert_equal(x2r, np.array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]])) x2r = np.roll(x2, -4, axis=1) assert_equal(x2r, np.array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]])) def test_roll_empty(self): x = np.array([]) assert_equal(np.roll(x, 1), np.array([])) class TestRollaxis(object): # expected shape indexed by (axis, start) for array of # shape (1, 2, 3, 4) tgtshape = {(0, 0): (1, 2, 3, 4), (0, 1): (1, 2, 3, 4), (0, 2): (2, 1, 3, 4), (0, 3): (2, 3, 1, 4), (0, 4): (2, 3, 4, 1), (1, 0): (2, 1, 3, 4), (1, 1): (1, 2, 3, 4), (1, 2): (1, 2, 3, 4), (1, 3): (1, 3, 2, 4), (1, 4): (1, 3, 4, 2), (2, 0): (3, 1, 2, 4), (2, 1): (1, 3, 2, 4), (2, 2): (1, 2, 3, 4), (2, 3): (1, 2, 3, 4), (2, 4): (1, 2, 4, 3), (3, 0): (4, 1, 2, 3), (3, 1): (1, 4, 2, 3), (3, 2): (1, 2, 4, 3), (3, 3): (1, 2, 3, 4), (3, 4): (1, 2, 3, 4)} def test_exceptions(self): a = np.arange(1*2*3*4).reshape(1, 2, 3, 4) assert_raises(np.AxisError, np.rollaxis, a, -5, 0) assert_raises(np.AxisError, np.rollaxis, a, 0, -5) assert_raises(np.AxisError, np.rollaxis, a, 4, 0) assert_raises(np.AxisError, np.rollaxis, a, 0, 5) def test_results(self): a = np.arange(1*2*3*4).reshape(1, 2, 3, 4).copy() aind = np.indices(a.shape) assert_(a.flags['OWNDATA']) for (i, j) in self.tgtshape: # positive axis, positive start res = np.rollaxis(a, axis=i, start=j) i0, i1, i2, i3 = aind[np.array(res.shape) - 1] assert_(np.all(res[i0, i1, i2, i3] == a)) assert_(res.shape == self.tgtshape[(i, j)], str((i,j))) assert_(not res.flags['OWNDATA']) # negative axis, positive start ip = i + 1 res = np.rollaxis(a, axis=-ip, start=j) i0, i1, i2, i3 = aind[np.array(res.shape) - 1] assert_(np.all(res[i0, i1, i2, i3] == a)) assert_(res.shape == self.tgtshape[(4 - ip, j)]) assert_(not res.flags['OWNDATA']) # positive axis, negative start jp = j + 1 if j < 4 else j res = np.rollaxis(a, axis=i, start=-jp) i0, i1, i2, i3 = aind[np.array(res.shape) - 1] assert_(np.all(res[i0, i1, i2, i3] == a)) assert_(res.shape == self.tgtshape[(i, 4 - jp)]) assert_(not res.flags['OWNDATA']) # negative axis, negative start ip = i + 1 jp = j + 1 if j < 4 else j res = np.rollaxis(a, axis=-ip, start=-jp) i0, i1, i2, i3 = aind[np.array(res.shape) - 1] assert_(np.all(res[i0, i1, i2, i3] == a)) assert_(res.shape == self.tgtshape[(4 - ip, 4 - jp)]) assert_(not res.flags['OWNDATA']) class TestMoveaxis(object): def test_move_to_end(self): x = np.random.randn(5, 6, 7) for source, expected in [(0, (6, 7, 5)), (1, (5, 7, 6)), (2, (5, 6, 7)), (-1, (5, 6, 7))]: actual = np.moveaxis(x, source, -1).shape assert_(actual, expected) def test_move_new_position(self): x = np.random.randn(1, 2, 3, 4) for source, destination, expected in [ (0, 1, (2, 1, 3, 4)), (1, 2, (1, 3, 2, 4)), (1, -1, (1, 3, 4, 2)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, expected) def test_preserve_order(self): x = np.zeros((1, 2, 3, 4)) for source, destination in [ (0, 0), (3, -1), (-1, 3), ([0, -1], [0, -1]), ([2, 0], [2, 0]), (range(4), range(4)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, (1, 2, 3, 4)) def test_move_multiples(self): x = np.zeros((0, 1, 2, 3)) for source, destination, expected in [ ([0, 1], [2, 3], (2, 3, 0, 1)), ([2, 3], [0, 1], (2, 3, 0, 1)), ([0, 1, 2], [2, 3, 0], (2, 3, 0, 1)), ([3, 0], [1, 0], (0, 3, 1, 2)), ([0, 3], [0, 1], (0, 3, 1, 2)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, expected) def test_errors(self): x = np.random.randn(1, 2, 3) assert_raises_regex(np.AxisError, 'source.*out of bounds', np.moveaxis, x, 3, 0) assert_raises_regex(np.AxisError, 'source.*out of bounds', np.moveaxis, x, -4, 0) assert_raises_regex(np.AxisError, 'destination.*out of bounds', np.moveaxis, x, 0, 5) assert_raises_regex(ValueError, 'repeated axis in `source`', np.moveaxis, x, [0, 0], [0, 1]) assert_raises_regex(ValueError, 'repeated axis in `destination`', np.moveaxis, x, [0, 1], [1, 1]) assert_raises_regex(ValueError, 'must have the same number', np.moveaxis, x, 0, [0, 1]) assert_raises_regex(ValueError, 'must have the same number', np.moveaxis, x, [0, 1], [0]) def test_array_likes(self): x = np.ma.zeros((1, 2, 3)) result = np.moveaxis(x, 0, 0) assert_(x.shape, result.shape) assert_(isinstance(result, np.ma.MaskedArray)) x = [1, 2, 3] result = np.moveaxis(x, 0, 0) assert_(x, list(result)) assert_(isinstance(result, np.ndarray)) class TestCross(object): def test_2x2(self): u = [1, 2] v = [3, 4] z = -2 cp = np.cross(u, v) assert_equal(cp, z) cp = np.cross(v, u) assert_equal(cp, -z) def test_2x3(self): u = [1, 2] v = [3, 4, 5] z = np.array([10, -5, -2]) cp = np.cross(u, v) assert_equal(cp, z) cp = np.cross(v, u) assert_equal(cp, -z) def test_3x3(self): u = [1, 2, 3] v = [4, 5, 6] z = np.array([-3, 6, -3]) cp = np.cross(u, v) assert_equal(cp, z) cp = np.cross(v, u) assert_equal(cp, -z) def test_broadcasting(self): # Ticket #2624 (Trac #2032) u = np.tile([1, 2], (11, 1)) v = np.tile([3, 4], (11, 1)) z = -2 assert_equal(np.cross(u, v), z) assert_equal(np.cross(v, u), -z) assert_equal(np.cross(u, u), 0) u = np.tile([1, 2], (11, 1)).T v = np.tile([3, 4, 5], (11, 1)) z = np.tile([10, -5, -2], (11, 1)) assert_equal(np.cross(u, v, axisa=0), z) assert_equal(np.cross(v, u.T), -z) assert_equal(np.cross(v, v), 0) u = np.tile([1, 2, 3], (11, 1)).T v = np.tile([3, 4], (11, 1)).T z = np.tile([-12, 9, -2], (11, 1)) assert_equal(np.cross(u, v, axisa=0, axisb=0), z) assert_equal(np.cross(v.T, u.T), -z) assert_equal(np.cross(u.T, u.T), 0) u = np.tile([1, 2, 3], (5, 1)) v = np.tile([4, 5, 6], (5, 1)).T z = np.tile([-3, 6, -3], (5, 1)) assert_equal(np.cross(u, v, axisb=0), z) assert_equal(np.cross(v.T, u), -z) assert_equal(np.cross(u, u), 0) def test_broadcasting_shapes(self): u = np.ones((2, 1, 3)) v = np.ones((5, 3)) assert_equal(np.cross(u, v).shape, (2, 5, 3)) u = np.ones((10, 3, 5)) v = np.ones((2, 5)) assert_equal(np.cross(u, v, axisa=1, axisb=0).shape, (10, 5, 3)) assert_raises(np.AxisError, np.cross, u, v, axisa=1, axisb=2) assert_raises(np.AxisError, np.cross, u, v, axisa=3, axisb=0) u = np.ones((10, 3, 5, 7)) v = np.ones((5, 7, 2)) assert_equal(np.cross(u, v, axisa=1, axisc=2).shape, (10, 5, 3, 7)) assert_raises(np.AxisError, np.cross, u, v, axisa=-5, axisb=2) assert_raises(np.AxisError, np.cross, u, v, axisa=1, axisb=-4) # gh-5885 u = np.ones((3, 4, 2)) for axisc in range(-2, 2): assert_equal(np.cross(u, u, axisc=axisc).shape, (3, 4)) def test_outer_out_param(): arr1 = np.ones((5,)) arr2 = np.ones((2,)) arr3 = np.linspace(-2, 2, 5) out1 = np.ndarray(shape=(5,5)) out2 = np.ndarray(shape=(2, 5)) res1 = np.outer(arr1, arr3, out1) assert_equal(res1, out1) assert_equal(np.outer(arr2, arr3, out2), out2) class TestRequire(object): flag_names = ['C', 'C_CONTIGUOUS', 'CONTIGUOUS', 'F', 'F_CONTIGUOUS', 'FORTRAN', 'A', 'ALIGNED', 'W', 'WRITEABLE', 'O', 'OWNDATA'] def generate_all_false(self, dtype): arr = np.zeros((2, 2), [('junk', 'i1'), ('a', dtype)]) arr.setflags(write=False) a = arr['a'] assert_(not a.flags['C']) assert_(not a.flags['F']) assert_(not a.flags['O']) assert_(not a.flags['W']) assert_(not a.flags['A']) return a def set_and_check_flag(self, flag, dtype, arr): if dtype is None: dtype = arr.dtype b = np.require(arr, dtype, [flag]) assert_(b.flags[flag]) assert_(b.dtype == dtype) # a further call to np.require ought to return the same array # unless OWNDATA is specified. c = np.require(b, None, [flag]) if flag[0] != 'O': assert_(c is b) else: assert_(c.flags[flag]) def test_require_each(self): id = ['f8', 'i4'] fd = [None, 'f8', 'c16'] for idtype, fdtype, flag in itertools.product(id, fd, self.flag_names): a = self.generate_all_false(idtype) self.set_and_check_flag(flag, fdtype, a) def test_unknown_requirement(self): a = self.generate_all_false('f8') assert_raises(KeyError, np.require, a, None, 'Q') def test_non_array_input(self): a = np.require([1, 2, 3, 4], 'i4', ['C', 'A', 'O']) assert_(a.flags['O']) assert_(a.flags['C']) assert_(a.flags['A']) assert_(a.dtype == 'i4') assert_equal(a, [1, 2, 3, 4]) def test_C_and_F_simul(self): a = self.generate_all_false('f8') assert_raises(ValueError, np.require, a, None, ['C', 'F']) def test_ensure_array(self): class ArraySubclass(np.ndarray): pass a = ArraySubclass((2, 2)) b = np.require(a, None, ['E']) assert_(type(b) is np.ndarray) def test_preserve_subtype(self): class ArraySubclass(np.ndarray): pass for flag in self.flag_names: a = ArraySubclass((2, 2)) self.set_and_check_flag(flag, None, a) class TestBroadcast(object): def test_broadcast_in_args(self): # gh-5881 arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)), np.empty((5, 1, 7))] mits = [np.broadcast(*arrs), np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])), np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])] for mit in mits: assert_equal(mit.shape, (5, 6, 7)) assert_equal(mit.ndim, 3) assert_equal(mit.nd, 3) assert_equal(mit.numiter, 4) for a, ia in zip(arrs, mit.iters): assert_(a is ia.base) def test_broadcast_single_arg(self): # gh-6899 arrs = [np.empty((5, 6, 7))] mit = np.broadcast(*arrs) assert_equal(mit.shape, (5, 6, 7)) assert_equal(mit.ndim, 3) assert_equal(mit.nd, 3) assert_equal(mit.numiter, 1) assert_(arrs[0] is mit.iters[0].base) def test_number_of_arguments(self): arr = np.empty((5,)) for j in range(35): arrs = [arr] * j if j < 1 or j > 32: assert_raises(ValueError, np.broadcast, *arrs) else: mit = np.broadcast(*arrs) assert_equal(mit.numiter, j) def test_broadcast_error_kwargs(self): #gh-13455 arrs = [np.empty((5, 6, 7))] mit = np.broadcast(*arrs) mit2 = np.broadcast(*arrs, **{}) assert_equal(mit.shape, mit2.shape) assert_equal(mit.ndim, mit2.ndim) assert_equal(mit.nd, mit2.nd) assert_equal(mit.numiter, mit2.numiter) assert_(mit.iters[0].base is mit2.iters[0].base) assert_raises(ValueError, np.broadcast, 1, **{'x': 1}) class TestKeepdims(object): class sub_array(np.ndarray): def sum(self, axis=None, dtype=None, out=None): return np.ndarray.sum(self, axis, dtype, out, keepdims=True) def test_raise(self): sub_class = self.sub_array x = np.arange(30).view(sub_class) assert_raises(TypeError, np.sum, x, keepdims=True) class TestTensordot(object): def test_zero_dimension(self): # Test resolution to issue #5663 a = np.ndarray((3,0)) b = np.ndarray((0,4)) td = np.tensordot(a, b, (1, 0)) assert_array_equal(td, np.dot(a, b)) assert_array_equal(td, np.einsum('ij,jk', a, b))

37.569693

91

0.533685

d1a0982887a670d4106c82412f101ca5cc3acaaa

165,496

py

Python

python/ccxt/async_support/okex.py

fyesgo/ccxt

ba75b256fcdc0ba781ecdb9ae69c293bc0fa22ef

[ "MIT" ]

1

2022-01-20T10:47:10.000Z

python/ccxt/async_support/okex.py

fyesgo/ccxt

ba75b256fcdc0ba781ecdb9ae69c293bc0fa22ef

[ "MIT" ]

null

python/ccxt/async_support/okex.py

fyesgo/ccxt

ba75b256fcdc0ba781ecdb9ae69c293bc0fa22ef

[ "MIT" ]

null

# -*- coding: utf-8 -*- # PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN: # https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code from ccxt.async_support.base.exchange import Exchange # ----------------------------------------------------------------------------- try: basestring # Python 3 except NameError: basestring = str # Python 2 import hashlib from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import AccountSuspended from ccxt.base.errors import ArgumentsRequired from ccxt.base.errors import BadRequest from ccxt.base.errors import BadSymbol from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidAddress from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import CancelPending from ccxt.base.errors import NotSupported from ccxt.base.errors import DDoSProtection from ccxt.base.errors import RateLimitExceeded from ccxt.base.errors import ExchangeNotAvailable from ccxt.base.errors import OnMaintenance from ccxt.base.errors import InvalidNonce from ccxt.base.errors import RequestTimeout from ccxt.base.decimal_to_precision import TRUNCATE from ccxt.base.decimal_to_precision import TICK_SIZE from ccxt.base.precise import Precise class okex(Exchange): def describe(self): return self.deep_extend(super(okex, self).describe(), { 'id': 'okex', 'name': 'OKEX', 'countries': ['CN', 'US'], 'version': 'v3', 'rateLimit': 1000, # up to 3000 requests per 5 minutes ≈ 600 requests per minute ≈ 10 requests per second ≈ 100 ms 'pro': True, 'has': { 'cancelOrder': True, 'CORS': False, 'createOrder': True, 'fetchBalance': True, 'fetchClosedOrders': True, 'fetchCurrencies': False, # see below 'fetchDepositAddress': True, 'fetchDeposits': True, 'fetchLedger': True, 'fetchMarkets': True, 'fetchMyTrades': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchOrder': True, 'fetchOrderBook': True, 'fetchOrders': False, 'fetchOrderTrades': True, 'fetchTime': True, 'fetchTicker': True, 'fetchTickers': True, 'fetchTrades': True, 'fetchTransactions': False, 'fetchWithdrawals': True, 'futures': True, 'withdraw': True, }, 'timeframes': { '1m': '60', '3m': '180', '5m': '300', '15m': '900', '30m': '1800', '1h': '3600', '2h': '7200', '4h': '14400', '6h': '21600', '12h': '43200', '1d': '86400', '1w': '604800', '1M': '2678400', '3M': '8035200', '6M': '16070400', '1y': '31536000', }, 'hostname': 'okex.com', 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/32552768-0d6dd3c6-c4a6-11e7-90f8-c043b64756a7.jpg', 'api': { 'rest': 'https://www.{hostname}', }, 'www': 'https://www.okex.com', 'doc': 'https://www.okex.com/docs/en/', 'fees': 'https://www.okex.com/pages/products/fees.html', 'referral': 'https://www.okex.com/join/1888677', 'test': { 'rest': 'https://testnet.okex.com', }, }, 'api': { 'general': { 'get': [ 'time', ], }, 'account': { 'get': [ 'wallet', 'sub-account', 'asset-valuation', 'wallet/{currency}', 'withdrawal/history', 'withdrawal/history/{currency}', 'ledger', 'deposit/address', 'deposit/history', 'deposit/history/{currency}', 'currencies', 'withdrawal/fee', ], 'post': [ 'transfer', 'withdrawal', ], }, 'spot': { 'get': [ 'accounts', 'accounts/{currency}', 'accounts/{currency}/ledger', 'orders', 'amend_order/{instrument_id}', 'orders_pending', 'orders/{order_id}', 'orders/{client_oid}', 'trade_fee', 'fills', 'algo', # public 'instruments', 'instruments/{instrument_id}/book', 'instruments/ticker', 'instruments/{instrument_id}/ticker', 'instruments/{instrument_id}/trades', 'instruments/{instrument_id}/candles', 'instruments/{instrument_id}/history/candles', ], 'post': [ 'order_algo', 'orders', 'batch_orders', 'cancel_orders/{order_id}', 'cancel_orders/{client_oid}', 'cancel_batch_algos', 'cancel_batch_orders', ], }, 'margin': { 'get': [ 'accounts', 'accounts/{instrument_id}', 'accounts/{instrument_id}/ledger', 'accounts/availability', 'accounts/{instrument_id}/availability', 'accounts/borrowed', 'accounts/{instrument_id}/borrowed', 'orders', 'accounts/{instrument_id}/leverage', 'orders/{order_id}', 'orders/{client_oid}', 'orders_pending', 'fills', # public 'instruments/{instrument_id}/mark_price', ], 'post': [ 'accounts/borrow', 'accounts/repayment', 'orders', 'batch_orders', 'cancel_orders', 'cancel_orders/{order_id}', 'cancel_orders/{client_oid}', 'cancel_batch_orders', 'accounts/{instrument_id}/leverage', ], }, 'futures': { 'get': [ 'position', '{instrument_id}/position', 'accounts', 'accounts/{underlying}', 'accounts/{underlying}/leverage', 'accounts/{underlying}/ledger', 'order_algo/{instrument_id}', 'orders/{instrument_id}', 'orders/{instrument_id}/{order_id}', 'orders/{instrument_id}/{client_oid}', 'fills', 'trade_fee', 'accounts/{instrument_id}/holds', 'order_algo/{instrument_id}', # public 'instruments', 'instruments/{instrument_id}/book', 'instruments/ticker', 'instruments/{instrument_id}/ticker', 'instruments/{instrument_id}/trades', 'instruments/{instrument_id}/candles', 'instruments/{instrument_id}/history/candles', 'instruments/{instrument_id}/index', 'rate', 'instruments/{instrument_id}/estimated_price', 'instruments/{instrument_id}/open_interest', 'instruments/{instrument_id}/price_limit', 'instruments/{instrument_id}/mark_price', 'instruments/{instrument_id}/liquidation', ], 'post': [ 'accounts/{underlying}/leverage', 'order', 'amend_order/{instrument_id}', 'orders', 'cancel_order/{instrument_id}/{order_id}', 'cancel_order/{instrument_id}/{client_oid}', 'cancel_batch_orders/{instrument_id}', 'accounts/margin_mode', 'close_position', 'cancel_all', 'order_algo', 'cancel_algos', ], }, 'swap': { 'get': [ 'position', '{instrument_id}/position', 'accounts', '{instrument_id}/accounts', 'accounts/{instrument_id}/settings', 'accounts/{instrument_id}/ledger', 'orders/{instrument_id}', 'orders/{instrument_id}/{order_id}', 'orders/{instrument_id}/{client_oid}', 'fills', 'accounts/{instrument_id}/holds', 'trade_fee', 'order_algo/{instrument_id}', # public 'instruments', 'instruments/{instrument_id}/depth', 'instruments/ticker', 'instruments/{instrument_id}/ticker', 'instruments/{instrument_id}/trades', 'instruments/{instrument_id}/candles', 'instruments/{instrument_id}/history/candles', 'instruments/{instrument_id}/index', 'rate', 'instruments/{instrument_id}/open_interest', 'instruments/{instrument_id}/price_limit', 'instruments/{instrument_id}/liquidation', 'instruments/{instrument_id}/funding_time', 'instruments/{instrument_id}/mark_price', 'instruments/{instrument_id}/historical_funding_rate', ], 'post': [ 'accounts/{instrument_id}/leverage', 'order', 'amend_order/{instrument_id}', 'orders', 'cancel_order/{instrument_id}/{order_id}', 'cancel_order/{instrument_id}/{client_oid}', 'cancel_batch_orders/{instrument_id}', 'order_algo', 'cancel_algos', 'close_position', 'cancel_all', 'order_algo', 'cancel_algos', ], }, 'option': { 'get': [ 'accounts', 'position', '{underlying}/position', 'accounts/{underlying}', 'orders/{underlying}', 'fills/{underlying}', 'accounts/{underlying}/ledger', 'trade_fee', 'orders/{underlying}/{order_id}', 'orders/{underlying}/{client_oid}', # public 'underlying', 'instruments/{underlying}', 'instruments/{underlying}/summary', 'instruments/{underlying}/summary/{instrument_id}', 'instruments/{instrument_id}/book', 'instruments/{instrument_id}/trades', 'instruments/{instrument_id}/ticker', 'instruments/{instrument_id}/candles', ], 'post': [ 'order', 'orders', 'cancel_order/{underlying}/{order_id}', 'cancel_order/{underlying}/{client_oid}', 'cancel_batch_orders/{underlying}', 'amend_order/{underlying}', 'amend_batch_orders/{underlying}', ], }, 'index': { 'get': [ '{instrument_id}/constituents', ], }, }, 'fees': { 'trading': { 'taker': 0.0015, 'maker': 0.0010, }, 'spot': { 'taker': 0.0015, 'maker': 0.0010, }, 'futures': { 'taker': 0.0005, 'maker': 0.0002, }, 'swap': { 'taker': 0.00075, 'maker': 0.00020, }, }, 'requiredCredentials': { 'apiKey': True, 'secret': True, 'password': True, }, 'exceptions': { # http error codes # 400 Bad Request — Invalid request format # 401 Unauthorized — Invalid API Key # 403 Forbidden — You do not have access to the requested resource # 404 Not Found # 429 Client Error: Too Many Requests for url # 500 Internal Server Error — We had a problem with our server 'exact': { '1': ExchangeError, # {"code": 1, "message": "System error"} # undocumented 'failure to get a peer from the ring-balancer': ExchangeNotAvailable, # {"message": "failure to get a peer from the ring-balancer"} 'Server is busy, please try again.': ExchangeNotAvailable, # {"message": "Server is busy, please try again."} 'An unexpected error occurred': ExchangeError, # {"message": "An unexpected error occurred"} 'System error': ExchangeError, # {"error_message":"System error","message":"System error"} '4010': PermissionDenied, # {"code": 4010, "message": "For the security of your funds, withdrawals are not permitted within 24 hours after changing fund password / mobile number / Google Authenticator settings "} # common # '0': ExchangeError, # 200 successful,when the order placement / cancellation / operation is successful '4001': ExchangeError, # no data received in 30s '4002': ExchangeError, # Buffer full. cannot write data # -------------------------------------------------------- '30001': AuthenticationError, # {"code": 30001, "message": 'request header "OK_ACCESS_KEY" cannot be blank'} '30002': AuthenticationError, # {"code": 30002, "message": 'request header "OK_ACCESS_SIGN" cannot be blank'} '30003': AuthenticationError, # {"code": 30003, "message": 'request header "OK_ACCESS_TIMESTAMP" cannot be blank'} '30004': AuthenticationError, # {"code": 30004, "message": 'request header "OK_ACCESS_PASSPHRASE" cannot be blank'} '30005': InvalidNonce, # {"code": 30005, "message": "invalid OK_ACCESS_TIMESTAMP"} '30006': AuthenticationError, # {"code": 30006, "message": "invalid OK_ACCESS_KEY"} '30007': BadRequest, # {"code": 30007, "message": 'invalid Content_Type, please use "application/json" format'} '30008': RequestTimeout, # {"code": 30008, "message": "timestamp request expired"} '30009': ExchangeError, # {"code": 30009, "message": "system error"} '30010': AuthenticationError, # {"code": 30010, "message": "API validation failed"} '30011': PermissionDenied, # {"code": 30011, "message": "invalid IP"} '30012': AuthenticationError, # {"code": 30012, "message": "invalid authorization"} '30013': AuthenticationError, # {"code": 30013, "message": "invalid sign"} '30014': DDoSProtection, # {"code": 30014, "message": "request too frequent"} '30015': AuthenticationError, # {"code": 30015, "message": 'request header "OK_ACCESS_PASSPHRASE" incorrect'} '30016': ExchangeError, # {"code": 30015, "message": "you are using v1 apiKey, please use v1 endpoint. If you would like to use v3 endpoint, please subscribe to v3 apiKey"} '30017': ExchangeError, # {"code": 30017, "message": "apikey's broker id does not match"} '30018': ExchangeError, # {"code": 30018, "message": "apikey's domain does not match"} '30019': ExchangeNotAvailable, # {"code": 30019, "message": "Api is offline or unavailable"} '30020': BadRequest, # {"code": 30020, "message": "body cannot be blank"} '30021': BadRequest, # {"code": 30021, "message": "Json data format error"}, {"code": 30021, "message": "json data format error"} '30022': PermissionDenied, # {"code": 30022, "message": "Api has been frozen"} '30023': BadRequest, # {"code": 30023, "message": "{0} parameter cannot be blank"} '30024': BadSymbol, # {"code":30024,"message":"\"instrument_id\" is an invalid parameter"} '30025': BadRequest, # {"code": 30025, "message": "{0} parameter category error"} '30026': DDoSProtection, # {"code": 30026, "message": "requested too frequent"} '30027': AuthenticationError, # {"code": 30027, "message": "login failure"} '30028': PermissionDenied, # {"code": 30028, "message": "unauthorized execution"} '30029': AccountSuspended, # {"code": 30029, "message": "account suspended"} '30030': ExchangeNotAvailable, # {"code": 30030, "message": "endpoint request failed. Please try again"} '30031': BadRequest, # {"code": 30031, "message": "token does not exist"} '30032': BadSymbol, # {"code": 30032, "message": "pair does not exist"} '30033': BadRequest, # {"code": 30033, "message": "exchange domain does not exist"} '30034': ExchangeError, # {"code": 30034, "message": "exchange ID does not exist"} '30035': ExchangeError, # {"code": 30035, "message": "trading is not supported in self website"} '30036': ExchangeError, # {"code": 30036, "message": "no relevant data"} '30037': ExchangeNotAvailable, # {"code": 30037, "message": "endpoint is offline or unavailable"} # '30038': AuthenticationError, # {"code": 30038, "message": "user does not exist"} '30038': OnMaintenance, # {"client_oid":"","code":"30038","error_code":"30038","error_message":"Matching engine is being upgraded. Please try in about 1 minute.","message":"Matching engine is being upgraded. Please try in about 1 minute.","order_id":"-1","result":false} '30044': RequestTimeout, # {"code":30044, "message":"Endpoint request timeout"} # futures '32001': AccountSuspended, # {"code": 32001, "message": "futures account suspended"} '32002': PermissionDenied, # {"code": 32002, "message": "futures account does not exist"} '32003': CancelPending, # {"code": 32003, "message": "canceling, please wait"} '32004': ExchangeError, # {"code": 32004, "message": "you have no unfilled orders"} '32005': InvalidOrder, # {"code": 32005, "message": "max order quantity"} '32006': InvalidOrder, # {"code": 32006, "message": "the order price or trigger price exceeds USD 1 million"} '32007': InvalidOrder, # {"code": 32007, "message": "leverage level must be the same for orders on the same side of the contract"} '32008': InvalidOrder, # {"code": 32008, "message": "Max. positions to open(cross margin)"} '32009': InvalidOrder, # {"code": 32009, "message": "Max. positions to open(fixed margin)"} '32010': ExchangeError, # {"code": 32010, "message": "leverage cannot be changed with open positions"} '32011': ExchangeError, # {"code": 32011, "message": "futures status error"} '32012': ExchangeError, # {"code": 32012, "message": "futures order update error"} '32013': ExchangeError, # {"code": 32013, "message": "token type is blank"} '32014': ExchangeError, # {"code": 32014, "message": "your number of contracts closing is larger than the number of contracts available"} '32015': ExchangeError, # {"code": 32015, "message": "margin ratio is lower than 100% before opening positions"} '32016': ExchangeError, # {"code": 32016, "message": "margin ratio is lower than 100% after opening position"} '32017': ExchangeError, # {"code": 32017, "message": "no BBO"} '32018': ExchangeError, # {"code": 32018, "message": "the order quantity is less than 1, please try again"} '32019': ExchangeError, # {"code": 32019, "message": "the order price deviates from the price of the previous minute by more than 3%"} '32020': ExchangeError, # {"code": 32020, "message": "the price is not in the range of the price limit"} '32021': ExchangeError, # {"code": 32021, "message": "leverage error"} '32022': ExchangeError, # {"code": 32022, "message": "self function is not supported in your country or region according to the regulations"} '32023': ExchangeError, # {"code": 32023, "message": "self account has outstanding loan"} '32024': ExchangeError, # {"code": 32024, "message": "order cannot be placed during delivery"} '32025': ExchangeError, # {"code": 32025, "message": "order cannot be placed during settlement"} '32026': ExchangeError, # {"code": 32026, "message": "your account is restricted from opening positions"} '32027': ExchangeError, # {"code": 32027, "message": "cancelled over 20 orders"} '32028': ExchangeError, # {"code": 32028, "message": "account is suspended and liquidated"} '32029': ExchangeError, # {"code": 32029, "message": "order info does not exist"} '32030': InvalidOrder, # The order cannot be cancelled '32031': ArgumentsRequired, # client_oid or order_id is required. '32038': AuthenticationError, # User does not exist '32040': ExchangeError, # User have open contract orders or position '32044': ExchangeError, # {"code": 32044, "message": "The margin ratio after submitting self order is lower than the minimum requirement({0}) for your tier."} '32045': ExchangeError, # String of commission over 1 million '32046': ExchangeError, # Each user can hold up to 10 trade plans at the same time '32047': ExchangeError, # system error '32048': InvalidOrder, # Order strategy track range error '32049': ExchangeError, # Each user can hold up to 10 track plans at the same time '32050': InvalidOrder, # Order strategy rang error '32051': InvalidOrder, # Order strategy ice depth error '32052': ExchangeError, # String of commission over 100 thousand '32053': ExchangeError, # Each user can hold up to 6 ice plans at the same time '32057': ExchangeError, # The order price is zero. Market-close-all function cannot be executed '32054': ExchangeError, # Trade not allow '32055': InvalidOrder, # cancel order error '32056': ExchangeError, # iceberg per order average should between {0}-{1} contracts '32058': ExchangeError, # Each user can hold up to 6 initiative plans at the same time '32059': InvalidOrder, # Total amount should exceed per order amount '32060': InvalidOrder, # Order strategy type error '32061': InvalidOrder, # Order strategy initiative limit error '32062': InvalidOrder, # Order strategy initiative range error '32063': InvalidOrder, # Order strategy initiative rate error '32064': ExchangeError, # Time Stringerval of orders should set between 5-120s '32065': ExchangeError, # Close amount exceeds the limit of Market-close-all(999 for BTC, and 9999 for the rest tokens) '32066': ExchangeError, # You have open orders. Please cancel all open orders before changing your leverage level. '32067': ExchangeError, # Account equity < required margin in self setting. Please adjust your leverage level again. '32068': ExchangeError, # The margin for self position will fall short of the required margin in self setting. Please adjust your leverage level or increase your margin to proceed. '32069': ExchangeError, # Target leverage level too low. Your account balance is insufficient to cover the margin required. Please adjust the leverage level again. '32070': ExchangeError, # Please check open position or unfilled order '32071': ExchangeError, # Your current liquidation mode does not support self action. '32072': ExchangeError, # The highest available margin for your order’s tier is {0}. Please edit your margin and place a new order. '32073': ExchangeError, # The action does not apply to the token '32074': ExchangeError, # The number of contracts of your position, open orders, and the current order has exceeded the maximum order limit of self asset. '32075': ExchangeError, # Account risk rate breach '32076': ExchangeError, # Liquidation of the holding position(s) at market price will require cancellation of all pending close orders of the contracts. '32077': ExchangeError, # Your margin for self asset in futures account is insufficient and the position has been taken over for liquidation.(You will not be able to place orders, close positions, transfer funds, or add margin during self period of time. Your account will be restored after the liquidation is complete.) '32078': ExchangeError, # Please cancel all open orders before switching the liquidation mode(Please cancel all open orders before switching the liquidation mode) '32079': ExchangeError, # Your open positions are at high risk.(Please add margin or reduce positions before switching the mode) '32080': ExchangeError, # Funds cannot be transferred out within 30 minutes after futures settlement '32083': ExchangeError, # The number of contracts should be a positive multiple of %%. Please place your order again # token and margin trading '33001': PermissionDenied, # {"code": 33001, "message": "margin account for self pair is not enabled yet"} '33002': AccountSuspended, # {"code": 33002, "message": "margin account for self pair is suspended"} '33003': InsufficientFunds, # {"code": 33003, "message": "no loan balance"} '33004': ExchangeError, # {"code": 33004, "message": "loan amount cannot be smaller than the minimum limit"} '33005': ExchangeError, # {"code": 33005, "message": "repayment amount must exceed 0"} '33006': ExchangeError, # {"code": 33006, "message": "loan order not found"} '33007': ExchangeError, # {"code": 33007, "message": "status not found"} '33008': InsufficientFunds, # {"code": 33008, "message": "loan amount cannot exceed the maximum limit"} '33009': ExchangeError, # {"code": 33009, "message": "user ID is blank"} '33010': ExchangeError, # {"code": 33010, "message": "you cannot cancel an order during session 2 of call auction"} '33011': ExchangeError, # {"code": 33011, "message": "no new market data"} '33012': ExchangeError, # {"code": 33012, "message": "order cancellation failed"} '33013': InvalidOrder, # {"code": 33013, "message": "order placement failed"} '33014': OrderNotFound, # {"code": 33014, "message": "order does not exist"} '33015': InvalidOrder, # {"code": 33015, "message": "exceeded maximum limit"} '33016': ExchangeError, # {"code": 33016, "message": "margin trading is not open for self token"} '33017': InsufficientFunds, # {"code": 33017, "message": "insufficient balance"} '33018': ExchangeError, # {"code": 33018, "message": "self parameter must be smaller than 1"} '33020': ExchangeError, # {"code": 33020, "message": "request not supported"} '33021': BadRequest, # {"code": 33021, "message": "token and the pair do not match"} '33022': InvalidOrder, # {"code": 33022, "message": "pair and the order do not match"} '33023': ExchangeError, # {"code": 33023, "message": "you can only place market orders during call auction"} '33024': InvalidOrder, # {"code": 33024, "message": "trading amount too small"} '33025': InvalidOrder, # {"code": 33025, "message": "base token amount is blank"} '33026': ExchangeError, # {"code": 33026, "message": "transaction completed"} '33027': InvalidOrder, # {"code": 33027, "message": "cancelled order or order cancelling"} '33028': InvalidOrder, # {"code": 33028, "message": "the decimal places of the trading price exceeded the limit"} '33029': InvalidOrder, # {"code": 33029, "message": "the decimal places of the trading size exceeded the limit"} '33034': ExchangeError, # {"code": 33034, "message": "You can only place limit order after Call Auction has started"} '33035': ExchangeError, # This type of order cannot be canceled(This type of order cannot be canceled) '33036': ExchangeError, # Exceeding the limit of entrust order '33037': ExchangeError, # The buy order price should be lower than 130% of the trigger price '33038': ExchangeError, # The sell order price should be higher than 70% of the trigger price '33039': ExchangeError, # The limit of callback rate is 0 < x <= 5% '33040': ExchangeError, # The trigger price of a buy order should be lower than the latest transaction price '33041': ExchangeError, # The trigger price of a sell order should be higher than the latest transaction price '33042': ExchangeError, # The limit of price variance is 0 < x <= 1% '33043': ExchangeError, # The total amount must be larger than 0 '33044': ExchangeError, # The average amount should be 1/1000 * total amount <= x <= total amount '33045': ExchangeError, # The price should not be 0, including trigger price, order price, and price limit '33046': ExchangeError, # Price variance should be 0 < x <= 1% '33047': ExchangeError, # Sweep ratio should be 0 < x <= 100% '33048': ExchangeError, # Per order limit: Total amount/1000 < x <= Total amount '33049': ExchangeError, # Total amount should be X > 0 '33050': ExchangeError, # Time interval should be 5 <= x <= 120s '33051': ExchangeError, # cancel order number not higher limit: plan and track entrust no more than 10, ice and time entrust no more than 6 '33059': BadRequest, # {"code": 33059, "message": "client_oid or order_id is required"} '33060': BadRequest, # {"code": 33060, "message": "Only fill in either parameter client_oid or order_id"} '33061': ExchangeError, # Value of a single market price order cannot exceed 100,000 USD '33062': ExchangeError, # The leverage ratio is too high. The borrowed position has exceeded the maximum position of self leverage ratio. Please readjust the leverage ratio '33063': ExchangeError, # Leverage multiple is too low, there is insufficient margin in the account, please readjust the leverage ratio '33064': ExchangeError, # The setting of the leverage ratio cannot be less than 2, please readjust the leverage ratio '33065': ExchangeError, # Leverage ratio exceeds maximum leverage ratio, please readjust leverage ratio '33085': InvalidOrder, # The value of the position and buying order has reached the position limit, and no further buying is allowed. # account '21009': ExchangeError, # Funds cannot be transferred out within 30 minutes after swap settlement(Funds cannot be transferred out within 30 minutes after swap settlement) '34001': PermissionDenied, # {"code": 34001, "message": "withdrawal suspended"} '34002': InvalidAddress, # {"code": 34002, "message": "please add a withdrawal address"} '34003': ExchangeError, # {"code": 34003, "message": "sorry, self token cannot be withdrawn to xx at the moment"} '34004': ExchangeError, # {"code": 34004, "message": "withdrawal fee is smaller than minimum limit"} '34005': ExchangeError, # {"code": 34005, "message": "withdrawal fee exceeds the maximum limit"} '34006': ExchangeError, # {"code": 34006, "message": "withdrawal amount is lower than the minimum limit"} '34007': ExchangeError, # {"code": 34007, "message": "withdrawal amount exceeds the maximum limit"} '34008': InsufficientFunds, # {"code": 34008, "message": "insufficient balance"} '34009': ExchangeError, # {"code": 34009, "message": "your withdrawal amount exceeds the daily limit"} '34010': ExchangeError, # {"code": 34010, "message": "transfer amount must be larger than 0"} '34011': ExchangeError, # {"code": 34011, "message": "conditions not met"} '34012': ExchangeError, # {"code": 34012, "message": "the minimum withdrawal amount for NEO is 1, and the amount must be an integer"} '34013': ExchangeError, # {"code": 34013, "message": "please transfer"} '34014': ExchangeError, # {"code": 34014, "message": "transfer limited"} '34015': ExchangeError, # {"code": 34015, "message": "subaccount does not exist"} '34016': PermissionDenied, # {"code": 34016, "message": "transfer suspended"} '34017': AccountSuspended, # {"code": 34017, "message": "account suspended"} '34018': AuthenticationError, # {"code": 34018, "message": "incorrect trades password"} '34019': PermissionDenied, # {"code": 34019, "message": "please bind your email before withdrawal"} '34020': PermissionDenied, # {"code": 34020, "message": "please bind your funds password before withdrawal"} '34021': InvalidAddress, # {"code": 34021, "message": "Not verified address"} '34022': ExchangeError, # {"code": 34022, "message": "Withdrawals are not available for sub accounts"} '34023': PermissionDenied, # {"code": 34023, "message": "Please enable futures trading before transferring your funds"} '34026': RateLimitExceeded, # transfer too frequently(transfer too frequently) '34036': ExchangeError, # Parameter is incorrect, please refer to API documentation '34037': ExchangeError, # Get the sub-account balance interface, account type is not supported '34038': ExchangeError, # Since your C2C transaction is unusual, you are restricted from fund transfer. Please contact our customer support to cancel the restriction '34039': ExchangeError, # You are now restricted from transferring out your funds due to abnormal trades on C2C Market. Please transfer your fund on our website or app instead to verify your identity # swap '35001': ExchangeError, # {"code": 35001, "message": "Contract does not exist"} '35002': ExchangeError, # {"code": 35002, "message": "Contract settling"} '35003': ExchangeError, # {"code": 35003, "message": "Contract paused"} '35004': ExchangeError, # {"code": 35004, "message": "Contract pending settlement"} '35005': AuthenticationError, # {"code": 35005, "message": "User does not exist"} '35008': InvalidOrder, # {"code": 35008, "message": "Risk ratio too high"} '35010': InvalidOrder, # {"code": 35010, "message": "Position closing too large"} '35012': InvalidOrder, # {"code": 35012, "message": "Incorrect order size"} '35014': InvalidOrder, # {"code": 35014, "message": "Order price is not within limit"} '35015': InvalidOrder, # {"code": 35015, "message": "Invalid leverage level"} '35017': ExchangeError, # {"code": 35017, "message": "Open orders exist"} '35019': InvalidOrder, # {"code": 35019, "message": "Order size too large"} '35020': InvalidOrder, # {"code": 35020, "message": "Order price too high"} '35021': InvalidOrder, # {"code": 35021, "message": "Order size exceeded current tier limit"} '35022': BadRequest, # {"code": 35022, "message": "Contract status error"} '35024': BadRequest, # {"code": 35024, "message": "Contract not initialized"} '35025': InsufficientFunds, # {"code": 35025, "message": "No account balance"} '35026': BadRequest, # {"code": 35026, "message": "Contract settings not initialized"} '35029': OrderNotFound, # {"code": 35029, "message": "Order does not exist"} '35030': InvalidOrder, # {"code": 35030, "message": "Order size too large"} '35031': InvalidOrder, # {"code": 35031, "message": "Cancel order size too large"} '35032': ExchangeError, # {"code": 35032, "message": "Invalid user status"} '35037': ExchangeError, # No last traded price in cache '35039': InsufficientFunds, # {"code": 35039, "message": "Open order quantity exceeds limit"} '35040': InvalidOrder, # {"error_message":"Invalid order type","result":"true","error_code":"35040","order_id":"-1"} '35044': ExchangeError, # {"code": 35044, "message": "Invalid order status"} '35046': InsufficientFunds, # {"code": 35046, "message": "Negative account balance"} '35047': InsufficientFunds, # {"code": 35047, "message": "Insufficient account balance"} '35048': ExchangeError, # {"code": 35048, "message": "User contract is frozen and liquidating"} '35049': InvalidOrder, # {"code": 35049, "message": "Invalid order type"} '35050': InvalidOrder, # {"code": 35050, "message": "Position settings are blank"} '35052': InsufficientFunds, # {"code": 35052, "message": "Insufficient cross margin"} '35053': ExchangeError, # {"code": 35053, "message": "Account risk too high"} '35055': InsufficientFunds, # {"code": 35055, "message": "Insufficient account balance"} '35057': ExchangeError, # {"code": 35057, "message": "No last traded price"} '35058': ExchangeError, # {"code": 35058, "message": "No limit"} '35059': BadRequest, # {"code": 35059, "message": "client_oid or order_id is required"} '35060': BadRequest, # {"code": 35060, "message": "Only fill in either parameter client_oid or order_id"} '35061': BadRequest, # {"code": 35061, "message": "Invalid instrument_id"} '35062': InvalidOrder, # {"code": 35062, "message": "Invalid match_price"} '35063': InvalidOrder, # {"code": 35063, "message": "Invalid order_size"} '35064': InvalidOrder, # {"code": 35064, "message": "Invalid client_oid"} '35066': InvalidOrder, # Order interval error '35067': InvalidOrder, # Time-weighted order ratio error '35068': InvalidOrder, # Time-weighted order range error '35069': InvalidOrder, # Time-weighted single transaction limit error '35070': InvalidOrder, # Algo order type error '35071': InvalidOrder, # Order total must be larger than single order limit '35072': InvalidOrder, # Maximum 6 unfulfilled time-weighted orders can be held at the same time '35073': InvalidOrder, # Order price is 0. Market-close-all not available '35074': InvalidOrder, # Iceberg order single transaction average error '35075': InvalidOrder, # Failed to cancel order '35076': InvalidOrder, # LTC 20x leverage. Not allowed to open position '35077': InvalidOrder, # Maximum 6 unfulfilled iceberg orders can be held at the same time '35078': InvalidOrder, # Order amount exceeded 100,000 '35079': InvalidOrder, # Iceberg order price variance error '35080': InvalidOrder, # Callback rate error '35081': InvalidOrder, # Maximum 10 unfulfilled trail orders can be held at the same time '35082': InvalidOrder, # Trail order callback rate error '35083': InvalidOrder, # Each user can only hold a maximum of 10 unfulfilled stop-limit orders at the same time '35084': InvalidOrder, # Order amount exceeded 1 million '35085': InvalidOrder, # Order amount is not in the correct range '35086': InvalidOrder, # Price exceeds 100 thousand '35087': InvalidOrder, # Price exceeds 100 thousand '35088': InvalidOrder, # Average amount error '35089': InvalidOrder, # Price exceeds 100 thousand '35090': ExchangeError, # No stop-limit orders available for cancelation '35091': ExchangeError, # No trail orders available for cancellation '35092': ExchangeError, # No iceberg orders available for cancellation '35093': ExchangeError, # No trail orders available for cancellation '35094': ExchangeError, # Stop-limit order last traded price error '35095': BadRequest, # Instrument_id error '35096': ExchangeError, # Algo order status error '35097': ExchangeError, # Order status and order ID cannot exist at the same time '35098': ExchangeError, # An order status or order ID must exist '35099': ExchangeError, # Algo order ID error '35102': RateLimitExceeded, # {"error_message":"The operation that close all at market price is too frequent","result":"true","error_code":"35102","order_id":"-1"} # option '36001': BadRequest, # Invalid underlying index. '36002': BadRequest, # Instrument does not exist. '36005': ExchangeError, # Instrument status is invalid. '36101': AuthenticationError, # Account does not exist. '36102': PermissionDenied, # Account status is invalid. '36103': PermissionDenied, # Account is suspended due to ongoing liquidation. '36104': PermissionDenied, # Account is not enabled for options trading. '36105': PermissionDenied, # Please enable the account for option contract. '36106': PermissionDenied, # Funds cannot be transferred in or out, as account is suspended. '36107': PermissionDenied, # Funds cannot be transferred out within 30 minutes after option exercising or settlement. '36108': InsufficientFunds, # Funds cannot be transferred in or out, as equity of the account is less than zero. '36109': PermissionDenied, # Funds cannot be transferred in or out during option exercising or settlement. '36201': PermissionDenied, # New order function is blocked. '36202': PermissionDenied, # Account does not have permission to short option. '36203': InvalidOrder, # Invalid format for client_oid. '36204': ExchangeError, # Invalid format for request_id. '36205': BadRequest, # Instrument id does not match underlying index. '36206': BadRequest, # Order_id and client_oid can not be used at the same time. '36207': InvalidOrder, # Either order price or fartouch price must be present. '36208': InvalidOrder, # Either order price or size must be present. '36209': InvalidOrder, # Either order_id or client_oid must be present. '36210': InvalidOrder, # Either order_ids or client_oids must be present. '36211': InvalidOrder, # Exceeding max batch size for order submission. '36212': InvalidOrder, # Exceeding max batch size for oder cancellation. '36213': InvalidOrder, # Exceeding max batch size for order amendment. '36214': ExchangeError, # Instrument does not have valid bid/ask quote. '36216': OrderNotFound, # Order does not exist. '36217': InvalidOrder, # Order submission failed. '36218': InvalidOrder, # Order cancellation failed. '36219': InvalidOrder, # Order amendment failed. '36220': InvalidOrder, # Order is pending cancel. '36221': InvalidOrder, # Order qty is not valid multiple of lot size. '36222': InvalidOrder, # Order price is breaching highest buy limit. '36223': InvalidOrder, # Order price is breaching lowest sell limit. '36224': InvalidOrder, # Exceeding max order size. '36225': InvalidOrder, # Exceeding max open order count for instrument. '36226': InvalidOrder, # Exceeding max open order count for underlying. '36227': InvalidOrder, # Exceeding max open size across all orders for underlying '36228': InvalidOrder, # Exceeding max available qty for instrument. '36229': InvalidOrder, # Exceeding max available qty for underlying. '36230': InvalidOrder, # Exceeding max position limit for underlying. }, 'broad': { }, }, 'precisionMode': TICK_SIZE, 'options': { 'fetchOHLCV': { 'type': 'Candles', # Candles or HistoryCandles }, 'createMarketBuyOrderRequiresPrice': True, 'fetchMarkets': ['spot', 'futures', 'swap', 'option'], 'defaultType': 'spot', # 'account', 'spot', 'margin', 'futures', 'swap', 'option' 'auth': { 'time': 'public', 'currencies': 'private', 'instruments': 'public', 'rate': 'public', '{instrument_id}/constituents': 'public', }, }, 'commonCurrencies': { # OKEX refers to ERC20 version of Aeternity(AEToken) 'AE': 'AET', # https://github.com/ccxt/ccxt/issues/4981 'BOX': 'DefiBox', 'HOT': 'Hydro Protocol', 'HSR': 'HC', 'MAG': 'Maggie', 'SBTC': 'Super Bitcoin', 'YOYO': 'YOYOW', 'WIN': 'WinToken', # https://github.com/ccxt/ccxt/issues/5701 }, }) async def fetch_time(self, params={}): response = await self.generalGetTime(params) # # { # "iso": "2015-01-07T23:47:25.201Z", # "epoch": 1420674445.201 # } # return self.parse8601(self.safe_string(response, 'iso')) async def fetch_markets(self, params={}): types = self.safe_value(self.options, 'fetchMarkets') result = [] for i in range(0, len(types)): markets = await self.fetch_markets_by_type(types[i], params) result = self.array_concat(result, markets) return result def parse_markets(self, markets): result = [] for i in range(0, len(markets)): result.append(self.parse_market(markets[i])) return result def parse_market(self, market): # # spot markets # # { # base_currency: "EOS", # instrument_id: "EOS-OKB", # min_size: "0.01", # quote_currency: "OKB", # size_increment: "0.000001", # tick_size: "0.0001" # } # # futures markets # # { # instrument_id: "XRP-USD-200320", # underlying_index: "XRP", # quote_currency: "USD", # tick_size: "0.0001", # contract_val: "10", # listing: "2020-03-06", # delivery: "2020-03-20", # trade_increment: "1", # alias: "self_week", # underlying: "XRP-USD", # base_currency: "XRP", # settlement_currency: "XRP", # is_inverse: "true", # contract_val_currency: "USD", # } # # swap markets # # { # instrument_id: "BSV-USD-SWAP", # underlying_index: "BSV", # quote_currency: "USD", # coin: "BSV", # contract_val: "10", # listing: "2018-12-21T07:53:47.000Z", # delivery: "2020-03-14T08:00:00.000Z", # size_increment: "1", # tick_size: "0.01", # base_currency: "BSV", # underlying: "BSV-USD", # settlement_currency: "BSV", # is_inverse: "true", # contract_val_currency: "USD" # } # # options markets # # { # instrument_id: 'BTC-USD-200327-4000-C', # underlying: 'BTC-USD', # settlement_currency: 'BTC', # contract_val: '0.1000', # option_type: 'C', # strike: '4000', # tick_size: '0.0005', # lot_size: '1.0000', # listing: '2019-12-25T08:30:36.302Z', # delivery: '2020-03-27T08:00:00.000Z', # state: '2', # trading_start_time: '2019-12-25T08:30:36.302Z', # timestamp: '2020-03-13T08:05:09.456Z', # } # id = self.safe_string(market, 'instrument_id') marketType = 'spot' spot = True future = False swap = False option = False baseId = self.safe_string(market, 'base_currency') quoteId = self.safe_string(market, 'quote_currency') contractVal = self.safe_number(market, 'contract_val') if contractVal is not None: if 'option_type' in market: marketType = 'option' spot = False option = True underlying = self.safe_string(market, 'underlying') parts = underlying.split('-') baseId = self.safe_string(parts, 0) quoteId = self.safe_string(parts, 1) else: marketType = 'swap' spot = False swap = True futuresAlias = self.safe_string(market, 'alias') if futuresAlias is not None: swap = False future = True marketType = 'futures' baseId = self.safe_string(market, 'underlying_index') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = (base + '/' + quote) if spot else id lotSize = self.safe_number_2(market, 'lot_size', 'trade_increment') precision = { 'amount': self.safe_number(market, 'size_increment', lotSize), 'price': self.safe_number(market, 'tick_size'), } minAmount = self.safe_number_2(market, 'min_size', 'base_min_size') active = True fees = self.safe_value_2(self.fees, marketType, 'trading', {}) return self.extend(fees, { 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'baseId': baseId, 'quoteId': quoteId, 'info': market, 'type': marketType, 'spot': spot, 'futures': future, 'swap': swap, 'option': option, 'active': active, 'precision': precision, 'limits': { 'amount': { 'min': minAmount, 'max': None, }, 'price': { 'min': precision['price'], 'max': None, }, 'cost': { 'min': precision['price'], 'max': None, }, }, }) async def fetch_markets_by_type(self, type, params={}): if type == 'option': underlying = await self.optionGetUnderlying(params) result = [] for i in range(0, len(underlying)): response = await self.optionGetInstrumentsUnderlying({ 'underlying': underlying[i], }) # # options markets # # [ # { # instrument_id: 'BTC-USD-200327-4000-C', # underlying: 'BTC-USD', # settlement_currency: 'BTC', # contract_val: '0.1000', # option_type: 'C', # strike: '4000', # tick_size: '0.0005', # lot_size: '1.0000', # listing: '2019-12-25T08:30:36.302Z', # delivery: '2020-03-27T08:00:00.000Z', # state: '2', # trading_start_time: '2019-12-25T08:30:36.302Z', # timestamp: '2020-03-13T08:05:09.456Z', # }, # ] # result = self.array_concat(result, response) return self.parse_markets(result) elif (type == 'spot') or (type == 'futures') or (type == 'swap'): method = type + 'GetInstruments' response = await getattr(self, method)(params) # # spot markets # # [ # { # base_currency: "EOS", # instrument_id: "EOS-OKB", # min_size: "0.01", # quote_currency: "OKB", # size_increment: "0.000001", # tick_size: "0.0001" # } # ] # # futures markets # # [ # { # instrument_id: "XRP-USD-200320", # underlying_index: "XRP", # quote_currency: "USD", # tick_size: "0.0001", # contract_val: "10", # listing: "2020-03-06", # delivery: "2020-03-20", # trade_increment: "1", # alias: "self_week", # underlying: "XRP-USD", # base_currency: "XRP", # settlement_currency: "XRP", # is_inverse: "true", # contract_val_currency: "USD", # } # ] # # swap markets # # [ # { # instrument_id: "BSV-USD-SWAP", # underlying_index: "BSV", # quote_currency: "USD", # coin: "BSV", # contract_val: "10", # listing: "2018-12-21T07:53:47.000Z", # delivery: "2020-03-14T08:00:00.000Z", # size_increment: "1", # tick_size: "0.01", # base_currency: "BSV", # underlying: "BSV-USD", # settlement_currency: "BSV", # is_inverse: "true", # contract_val_currency: "USD" # } # ] # return self.parse_markets(response) else: raise NotSupported(self.id + ' fetchMarketsByType does not support market type ' + type) async def fetch_currencies(self, params={}): # has['fetchCurrencies'] is currently set to False # despite that their docs say these endpoints are public: # https://www.okex.com/api/account/v3/withdrawal/fee # https://www.okex.com/api/account/v3/currencies # it will still reply with {"code":30001, "message": "OK-ACCESS-KEY header is required"} # if you attempt to access it without authentication response = await self.accountGetCurrencies(params) # # [ # { # name: '', # currency: 'BTC', # can_withdraw: '1', # can_deposit: '1', # min_withdrawal: '0.0100000000000000' # }, # ] # result = {} for i in range(0, len(response)): currency = response[i] id = self.safe_string(currency, 'currency') code = self.safe_currency_code(id) precision = 0.00000001 # default precision, todo: fix "magic constants" name = self.safe_string(currency, 'name') canDeposit = self.safe_integer(currency, 'can_deposit') canWithdraw = self.safe_integer(currency, 'can_withdraw') active = True if (canDeposit and canWithdraw) else False result[code] = { 'id': id, 'code': code, 'info': currency, 'type': None, 'name': name, 'active': active, 'fee': None, # todo: redesign 'precision': precision, 'limits': { 'amount': {'min': None, 'max': None}, 'withdraw': { 'min': self.safe_number(currency, 'min_withdrawal'), 'max': None, }, }, } return result async def fetch_order_book(self, symbol, limit=None, params={}): await self.load_markets() market = self.market(symbol) method = market['type'] + 'GetInstrumentsInstrumentId' method += 'Depth' if (market['type'] == 'swap') else 'Book' request = { 'instrument_id': market['id'], } if limit is not None: request['size'] = limit # max 200 response = await getattr(self, method)(self.extend(request, params)) # # spot # # { asks: [["0.02685268", "0.242571", "1"], # ["0.02685493", "0.164085", "1"], # ... # ["0.02779", "1.039", "1"], # ["0.027813", "0.0876", "1"] ], # bids: [["0.02684052", "10.371849", "1"], # ["0.02684051", "3.707", "4"], # ... # ["0.02634963", "0.132934", "1"], # ["0.02634962", "0.264838", "2"] ], # timestamp: "2018-12-17T20:24:16.159Z" } # # swap # # { # "asks":[ # ["916.21","94","0","1"] # ], # "bids":[ # ["916.1","15","0","1"] # ], # "time":"2021-04-16T02:04:48.282Z" # } # timestamp = self.parse8601(self.safe_string_2(response, 'timestamp', 'time')) return self.parse_order_book(response, symbol, timestamp) def parse_ticker(self, ticker, market=None): # # { best_ask: "0.02665472", # best_bid: "0.02665221", # instrument_id: "ETH-BTC", # product_id: "ETH-BTC", # last: "0.02665472", # ask: "0.02665472", # missing in the docs # bid: "0.02665221", # not mentioned in the docs # open_24h: "0.02645482", # high_24h: "0.02714633", # low_24h: "0.02614109", # base_volume_24h: "572298.901923", # timestamp: "2018-12-17T21:20:07.856Z", # quote_volume_24h: "15094.86831261" } # timestamp = self.parse8601(self.safe_string(ticker, 'timestamp')) symbol = None marketId = self.safe_string(ticker, 'instrument_id') if marketId in self.markets_by_id: market = self.markets_by_id[marketId] symbol = market['symbol'] elif marketId is not None: parts = marketId.split('-') numParts = len(parts) if numParts == 2: baseId, quoteId = parts base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote else: symbol = marketId if (symbol is None) and (market is not None): symbol = market['symbol'] last = self.safe_number(ticker, 'last') open = self.safe_number(ticker, 'open_24h') return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_number(ticker, 'high_24h'), 'low': self.safe_number(ticker, 'low_24h'), 'bid': self.safe_number(ticker, 'best_bid'), 'bidVolume': self.safe_number(ticker, 'best_bid_size'), 'ask': self.safe_number(ticker, 'best_ask'), 'askVolume': self.safe_number(ticker, 'best_ask_size'), 'vwap': None, 'open': open, 'close': last, 'last': last, 'previousClose': None, 'change': None, 'percentage': None, 'average': None, 'baseVolume': self.safe_number(ticker, 'base_volume_24h'), 'quoteVolume': self.safe_number(ticker, 'quote_volume_24h'), 'info': ticker, } async def fetch_ticker(self, symbol, params={}): await self.load_markets() market = self.market(symbol) method = market['type'] + 'GetInstrumentsInstrumentIdTicker' request = { 'instrument_id': market['id'], } response = await getattr(self, method)(self.extend(request, params)) # # { best_ask: "0.02665472", # best_bid: "0.02665221", # instrument_id: "ETH-BTC", # product_id: "ETH-BTC", # last: "0.02665472", # ask: "0.02665472", # bid: "0.02665221", # open_24h: "0.02645482", # high_24h: "0.02714633", # low_24h: "0.02614109", # base_volume_24h: "572298.901923", # timestamp: "2018-12-17T21:20:07.856Z", # quote_volume_24h: "15094.86831261" } # return self.parse_ticker(response) async def fetch_tickers_by_type(self, type, symbols=None, params={}): await self.load_markets() method = type + 'GetInstrumentsTicker' response = await getattr(self, method)(params) result = {} for i in range(0, len(response)): ticker = self.parse_ticker(response[i]) symbol = ticker['symbol'] result[symbol] = ticker return self.filter_by_array(result, 'symbol', symbols) async def fetch_tickers(self, symbols=None, params={}): defaultType = self.safe_string_2(self.options, 'fetchTickers', 'defaultType') type = self.safe_string(params, 'type', defaultType) return await self.fetch_tickers_by_type(type, symbols, self.omit(params, 'type')) def parse_trade(self, trade, market=None): # # fetchTrades(public) # # spot trades # # { # time: "2018-12-17T23:31:08.268Z", # timestamp: "2018-12-17T23:31:08.268Z", # trade_id: "409687906", # price: "0.02677805", # size: "0.923467", # side: "sell" # } # # futures trades, swap trades # # { # trade_id: "1989230840021013", # side: "buy", # price: "92.42", # qty: "184", # missing in swap markets # size: "5", # missing in futures markets # timestamp: "2018-12-17T23:26:04.613Z" # } # # fetchOrderTrades(private) # # spot trades, margin trades # # { # "created_at":"2019-03-15T02:52:56.000Z", # "exec_type":"T", # whether the order is taker or maker # "fee":"0.00000082", # "instrument_id":"BTC-USDT", # "ledger_id":"3963052721", # "liquidity":"T", # whether the order is taker or maker # "order_id":"2482659399697408", # "price":"3888.6", # "product_id":"BTC-USDT", # "side":"buy", # "size":"0.00055306", # "timestamp":"2019-03-15T02:52:56.000Z" # }, # # futures trades, swap trades # # { # "trade_id":"197429674631450625", # "instrument_id":"EOS-USD-SWAP", # "order_id":"6a-7-54d663a28-0", # "price":"3.633", # "order_qty":"1.0000", # "fee":"-0.000551", # "created_at":"2019-03-21T04:41:58.0Z", # missing in swap trades # "timestamp":"2019-03-25T05:56:31.287Z", # missing in futures trades # "exec_type":"M", # whether the order is taker or maker # "side":"short", # "buy" in futures trades # } # symbol = None marketId = self.safe_string(trade, 'instrument_id') base = None quote = None if marketId in self.markets_by_id: market = self.markets_by_id[marketId] symbol = market['symbol'] base = market['base'] quote = market['quote'] elif marketId is not None: parts = marketId.split('-') numParts = len(parts) if numParts == 2: baseId, quoteId = parts base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote else: symbol = marketId if (symbol is None) and (market is not None): symbol = market['symbol'] base = market['base'] quote = market['quote'] timestamp = self.parse8601(self.safe_string_2(trade, 'timestamp', 'created_at')) priceString = self.safe_string(trade, 'price') amountString = self.safe_string_2(trade, 'size', 'qty') amountString = self.safe_string(trade, 'order_qty', amountString) price = self.parse_number(priceString) amount = self.parse_number(amountString) cost = self.parse_number(Precise.string_mul(priceString, amountString)) takerOrMaker = self.safe_string_2(trade, 'exec_type', 'liquidity') if takerOrMaker == 'M': takerOrMaker = 'maker' elif takerOrMaker == 'T': takerOrMaker = 'taker' side = self.safe_string(trade, 'side') feeCost = self.safe_number(trade, 'fee') fee = None if feeCost is not None: feeCurrency = base if (side == 'buy') else quote fee = { # fee is either a positive number(invitation rebate) # or a negative number(transaction fee deduction) # therefore we need to invert the fee # more about it https://github.com/ccxt/ccxt/issues/5909 'cost': -feeCost, 'currency': feeCurrency, } orderId = self.safe_string(trade, 'order_id') return { 'info': trade, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'id': self.safe_string_2(trade, 'trade_id', 'ledger_id'), 'order': orderId, 'type': None, 'takerOrMaker': takerOrMaker, 'side': side, 'price': price, 'amount': amount, 'cost': cost, 'fee': fee, } async def fetch_trades(self, symbol, since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) method = market['type'] + 'GetInstrumentsInstrumentIdTrades' if (limit is None) or (limit > 100): limit = 100 # maximum = default = 100 request = { 'instrument_id': market['id'], 'limit': limit, # from: 'id', # to: 'id', } response = await getattr(self, method)(self.extend(request, params)) # # spot markets # # [ # { # time: "2018-12-17T23:31:08.268Z", # timestamp: "2018-12-17T23:31:08.268Z", # trade_id: "409687906", # price: "0.02677805", # size: "0.923467", # side: "sell" # } # ] # # futures markets, swap markets # # [ # { # trade_id: "1989230840021013", # side: "buy", # price: "92.42", # qty: "184", # missing in swap markets # size: "5", # missing in futures markets # timestamp: "2018-12-17T23:26:04.613Z" # } # ] # return self.parse_trades(response, market, since, limit) def parse_ohlcv(self, ohlcv, market=None): # # spot markets # # { # close: "0.02684545", # high: "0.02685084", # low: "0.02683312", # open: "0.02683894", # time: "2018-12-17T20:28:00.000Z", # volume: "101.457222" # } # # futures markets # # [ # 1545072720000, # 0.3159, # 0.3161, # 0.3144, # 0.3149, # 22886, # 725179.26172331, # ] # if isinstance(ohlcv, list): numElements = len(ohlcv) volumeIndex = 6 if (numElements > 6) else 5 timestamp = self.safe_value(ohlcv, 0) if isinstance(timestamp, basestring): timestamp = self.parse8601(timestamp) return [ timestamp, # timestamp self.safe_number(ohlcv, 1), # Open self.safe_number(ohlcv, 2), # High self.safe_number(ohlcv, 3), # Low self.safe_number(ohlcv, 4), # Close # self.safe_number(ohlcv, 5), # Quote Volume # self.safe_number(ohlcv, 6), # Base Volume self.safe_number(ohlcv, volumeIndex), # Volume, okex will return base volume in the 7th element for future markets ] else: return [ self.parse8601(self.safe_string(ohlcv, 'time')), self.safe_number(ohlcv, 'open'), # Open self.safe_number(ohlcv, 'high'), # High self.safe_number(ohlcv, 'low'), # Low self.safe_number(ohlcv, 'close'), # Close self.safe_number(ohlcv, 'volume'), # Base Volume ] async def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): await self.load_markets() market = self.market(symbol) duration = self.parse_timeframe(timeframe) request = { 'instrument_id': market['id'], 'granularity': self.timeframes[timeframe], } options = self.safe_value(self.options, 'fetchOHLCV', {}) defaultType = self.safe_string(options, 'type', 'Candles') # Candles or HistoryCandles type = self.safe_string(params, 'type', defaultType) params = self.omit(params, 'type') method = market['type'] + 'GetInstrumentsInstrumentId' + type if type == 'Candles': if since is not None: if limit is not None: request['end'] = self.iso8601(self.sum(since, limit * duration * 1000)) request['start'] = self.iso8601(since) else: if limit is not None: now = self.milliseconds() request['start'] = self.iso8601(now - limit * duration * 1000) request['end'] = self.iso8601(now) elif type == 'HistoryCandles': if market['option']: raise NotSupported(self.id + ' fetchOHLCV does not have ' + type + ' for ' + market['type'] + ' markets') if since is not None: if limit is None: limit = 300 # default request['start'] = self.iso8601(self.sum(since, limit * duration * 1000)) request['end'] = self.iso8601(since) else: if limit is not None: now = self.milliseconds() request['end'] = self.iso8601(now - limit * duration * 1000) request['start'] = self.iso8601(now) response = await getattr(self, method)(self.extend(request, params)) # # spot markets # # [ # { # close: "0.02683401", # high: "0.02683401", # low: "0.02683401", # open: "0.02683401", # time: "2018-12-17T23:47:00.000Z", # volume: "0" # }, # { # close: "0.02684545", # high: "0.02685084", # low: "0.02683312", # open: "0.02683894", # time: "2018-12-17T20:28:00.000Z", # volume: "101.457222" # } # ] # # futures # # [ # [ # 1545090660000, # 0.3171, # 0.3174, # 0.3171, # 0.3173, # 1648, # 51930.38579450868 # ], # [ # 1545072720000, # 0.3159, # 0.3161, # 0.3144, # 0.3149, # 22886, # 725179.26172331 # ] # ] # return self.parse_ohlcvs(response, market, timeframe, since, limit) def parse_account_balance(self, response): # # account # # [ # { # balance: 0, # available: 0, # currency: "BTC", # hold: 0 # }, # { # balance: 0, # available: 0, # currency: "ETH", # hold: 0 # } # ] # # spot # # [ # { # frozen: "0", # hold: "0", # id: "2149632", # currency: "BTC", # balance: "0.0000000497717339", # available: "0.0000000497717339", # holds: "0" # }, # { # frozen: "0", # hold: "0", # id: "2149632", # currency: "ICN", # balance: "0.00000000925", # available: "0.00000000925", # holds: "0" # } # ] # result = {'info': response} for i in range(0, len(response)): balance = response[i] currencyId = self.safe_string(balance, 'currency') code = self.safe_currency_code(currencyId) account = self.account() account['total'] = self.safe_number(balance, 'balance') account['used'] = self.safe_number(balance, 'hold') account['free'] = self.safe_number(balance, 'available') result[code] = account return self.parse_balance(result) def parse_margin_balance(self, response): # # [ # { # "currency:BTC": { # "available":"0", # "balance":"0", # "borrowed":"0", # "can_withdraw":"0", # "frozen":"0", # "hold":"0", # "holds":"0", # "lending_fee":"0" # }, # "currency:USDT": { # "available":"100", # "balance":"100", # "borrowed":"0", # "can_withdraw":"100", # "frozen":"0", # "hold":"0", # "holds":"0", # "lending_fee":"0" # }, # "instrument_id":"BTC-USDT", # "liquidation_price":"0", # "product_id":"BTC-USDT", # "risk_rate":"" # }, # ] # result = {'info': response} for i in range(0, len(response)): balance = response[i] marketId = self.safe_string(balance, 'instrument_id') market = self.safe_value(self.markets_by_id, marketId) symbol = None if market is None: baseId, quoteId = marketId.split('-') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote else: symbol = market['symbol'] omittedBalance = self.omit(balance, [ 'instrument_id', 'liquidation_price', 'product_id', 'risk_rate', 'margin_ratio', 'maint_margin_ratio', 'tiers', ]) keys = list(omittedBalance.keys()) accounts = {} for k in range(0, len(keys)): key = keys[k] marketBalance = balance[key] if key.find(':') >= 0: parts = key.split(':') currencyId = parts[1] code = self.safe_currency_code(currencyId) account = self.account() account['total'] = self.safe_number(marketBalance, 'balance') account['used'] = self.safe_number(marketBalance, 'hold') account['free'] = self.safe_number(marketBalance, 'available') accounts[code] = account else: raise NotSupported(self.id + ' margin balance response format has changed!') result[symbol] = self.parse_balance(accounts) return result def parse_futures_balance(self, response): # # { # "info":{ # "eos":{ # "auto_margin":"0", # "contracts": [ # { # "available_qty":"40.37069445", # "fixed_balance":"0", # "instrument_id":"EOS-USD-190329", # "margin_for_unfilled":"0", # "margin_frozen":"0", # "realized_pnl":"0", # "unrealized_pnl":"0" # }, # { # "available_qty":"40.37069445", # "fixed_balance":"14.54895721", # "instrument_id":"EOS-USD-190628", # "margin_for_unfilled":"0", # "margin_frozen":"10.64042157", # "realized_pnl":"-3.90853564", # "unrealized_pnl":"-0.259" # }, # ], # "equity":"50.75220665", # "margin_mode":"fixed", # "total_avail_balance":"40.37069445" # }, # } # } # # their root field name is "info", so our info will contain their info result = {'info': response} info = self.safe_value(response, 'info', {}) ids = list(info.keys()) for i in range(0, len(ids)): id = ids[i] code = self.safe_currency_code(id) balance = self.safe_value(info, id, {}) account = self.account() totalAvailBalance = self.safe_number(balance, 'total_avail_balance') if self.safe_string(balance, 'margin_mode') == 'fixed': contracts = self.safe_value(balance, 'contracts', []) free = totalAvailBalance for i in range(0, len(contracts)): contract = contracts[i] fixedBalance = self.safe_number(contract, 'fixed_balance') realizedPnl = self.safe_number(contract, 'realized_pnl') marginFrozen = self.safe_number(contract, 'margin_frozen') marginForUnfilled = self.safe_number(contract, 'margin_for_unfilled') margin = self.sum(fixedBalance, realizedPnl) - marginFrozen - marginForUnfilled free = self.sum(free, margin) account['free'] = free else: realizedPnl = self.safe_number(balance, 'realized_pnl') unrealizedPnl = self.safe_number(balance, 'unrealized_pnl') marginFrozen = self.safe_number(balance, 'margin_frozen') marginForUnfilled = self.safe_number(balance, 'margin_for_unfilled') account['free'] = self.sum(totalAvailBalance, realizedPnl, unrealizedPnl) - marginFrozen - marginForUnfilled # it may be incorrect to use total, free and used for swap accounts account['total'] = self.safe_number(balance, 'equity') result[code] = account return self.parse_balance(result) def parse_swap_balance(self, response): # # { # "info": [ # { # "equity":"3.0139", # "fixed_balance":"0.0000", # "instrument_id":"EOS-USD-SWAP", # "margin":"0.5523", # "margin_frozen":"0.0000", # "margin_mode":"crossed", # "margin_ratio":"1.0913", # "realized_pnl":"-0.0006", # "timestamp":"2019-03-25T03:46:10.336Z", # "total_avail_balance":"3.0000", # "unrealized_pnl":"0.0145" # } # ] # } # # their root field name is "info", so our info will contain their info result = {'info': response} info = self.safe_value(response, 'info', []) for i in range(0, len(info)): balance = info[i] marketId = self.safe_string(balance, 'instrument_id') symbol = marketId if marketId in self.markets_by_id: symbol = self.markets_by_id[marketId]['symbol'] account = self.account() # it may be incorrect to use total, free and used for swap accounts account['total'] = self.safe_number(balance, 'equity') account['free'] = self.safe_number(balance, 'total_avail_balance') result[symbol] = account return self.parse_balance(result) async def fetch_balance(self, params={}): defaultType = self.safe_string_2(self.options, 'fetchBalance', 'defaultType') type = self.safe_string(params, 'type', defaultType) if type is None: raise ArgumentsRequired(self.id + " fetchBalance() requires a type parameter(one of 'account', 'spot', 'margin', 'futures', 'swap')") await self.load_markets() suffix = 'Wallet' if (type == 'account') else 'Accounts' method = type + 'Get' + suffix query = self.omit(params, 'type') response = await getattr(self, method)(query) # # account # # [ # { # balance: 0, # available: 0, # currency: "BTC", # hold: 0 # }, # { # balance: 0, # available: 0, # currency: "ETH", # hold: 0 # } # ] # # spot # # [ # { # frozen: "0", # hold: "0", # id: "2149632", # currency: "BTC", # balance: "0.0000000497717339", # available: "0.0000000497717339", # holds: "0" # }, # { # frozen: "0", # hold: "0", # id: "2149632", # currency: "ICN", # balance: "0.00000000925", # available: "0.00000000925", # holds: "0" # } # ] # # margin # # [ # { # "currency:BTC": { # "available":"0", # "balance":"0", # "borrowed":"0", # "can_withdraw":"0", # "frozen":"0", # "hold":"0", # "holds":"0", # "lending_fee":"0" # }, # "currency:USDT": { # "available":"100", # "balance":"100", # "borrowed":"0", # "can_withdraw":"100", # "frozen":"0", # "hold":"0", # "holds":"0", # "lending_fee":"0" # }, # "instrument_id":"BTC-USDT", # "liquidation_price":"0", # "product_id":"BTC-USDT", # "risk_rate":"" # }, # ] # # futures # # { # "info":{ # "eos":{ # "auto_margin":"0", # "contracts": [ # { # "available_qty":"40.37069445", # "fixed_balance":"0", # "instrument_id":"EOS-USD-190329", # "margin_for_unfilled":"0", # "margin_frozen":"0", # "realized_pnl":"0", # "unrealized_pnl":"0" # }, # { # "available_qty":"40.37069445", # "fixed_balance":"14.54895721", # "instrument_id":"EOS-USD-190628", # "margin_for_unfilled":"0", # "margin_frozen":"10.64042157", # "realized_pnl":"-3.90853564", # "unrealized_pnl":"-0.259" # }, # ], # "equity":"50.75220665", # "margin_mode":"fixed", # "total_avail_balance":"40.37069445" # }, # } # } # # swap # # { # "info": [ # { # "equity":"3.0139", # "fixed_balance":"0.0000", # "instrument_id":"EOS-USD-SWAP", # "margin":"0.5523", # "margin_frozen":"0.0000", # "margin_mode":"crossed", # "margin_ratio":"1.0913", # "realized_pnl":"-0.0006", # "timestamp":"2019-03-25T03:46:10.336Z", # "total_avail_balance":"3.0000", # "unrealized_pnl":"0.0145" # } # ] # } # return self.parse_balance_by_type(type, response) def parse_balance_by_type(self, type, response): if (type == 'account') or (type == 'spot'): return self.parse_account_balance(response) elif type == 'margin': return self.parse_margin_balance(response) elif type == 'futures': return self.parse_futures_balance(response) elif type == 'swap': return self.parse_swap_balance(response) raise NotSupported(self.id + " fetchBalance does not support the '" + type + "' type(the type must be one of 'account', 'spot', 'margin', 'futures', 'swap')") async def create_order(self, symbol, type, side, amount, price=None, params={}): await self.load_markets() market = self.market(symbol) request = { 'instrument_id': market['id'], # 'client_oid': 'abcdef1234567890', # [a-z0-9]{1,32} # 'order_type': '0', # 0 = Normal limit order, 1 = Post only, 2 = Fill Or Kill, 3 = Immediatel Or Cancel, 4 = Market for futures only } clientOrderId = self.safe_string_2(params, 'client_oid', 'clientOrderId') if clientOrderId is not None: request['client_oid'] = clientOrderId params = self.omit(params, ['client_oid', 'clientOrderId']) method = None if market['futures'] or market['swap']: size = self.number_to_string(amount) if market['futures'] else self.amount_to_precision(symbol, amount) request = self.extend(request, { 'type': type, # 1:open long 2:open short 3:close long 4:close short for futures 'size': size, # 'match_price': '0', # Order at best counter party price?(0:no 1:yes). The default is 0. If it is set as 1, the price parameter will be ignored. When posting orders at best bid price, order_type can only be 0(regular order). }) orderType = self.safe_string(params, 'order_type') # order_type == '4' means a market order isMarketOrder = (type == 'market') or (orderType == '4') if isMarketOrder: request['order_type'] = '4' else: request['price'] = self.price_to_precision(symbol, price) if market['futures']: request['leverage'] = '10' # or '20' method = market['type'] + 'PostOrder' else: marginTrading = self.safe_string(params, 'margin_trading', '1') # 1 = spot, 2 = margin request = self.extend(request, { 'side': side, 'type': type, # limit/market 'margin_trading': marginTrading, # 1 = spot, 2 = margin }) if type == 'limit': request['price'] = self.price_to_precision(symbol, price) request['size'] = self.amount_to_precision(symbol, amount) elif type == 'market': # for market buy it requires the amount of quote currency to spend if side == 'buy': notional = self.safe_number(params, 'notional') createMarketBuyOrderRequiresPrice = self.safe_value(self.options, 'createMarketBuyOrderRequiresPrice', True) if createMarketBuyOrderRequiresPrice: if price is not None: if notional is None: notional = amount * price elif notional is None: raise InvalidOrder(self.id + " createOrder() requires the price argument with market buy orders to calculate total order cost(amount to spend), where cost = amount * price. Supply a price argument to createOrder() call if you want the cost to be calculated for you from price and amount, or, alternatively, add .options['createMarketBuyOrderRequiresPrice'] = False and supply the total cost value in the 'amount' argument or in the 'notional' extra parameter(the exchange-specific behaviour)") else: notional = amount if (notional is None) else notional precision = market['precision']['price'] request['notional'] = self.decimal_to_precision(notional, TRUNCATE, precision, self.precisionMode) else: request['size'] = self.amount_to_precision(symbol, amount) method = 'marginPostOrders' if (marginTrading == '2') else 'spotPostOrders' response = await getattr(self, method)(self.extend(request, params)) # # { # "client_oid":"oktspot79", # "error_code":"", # "error_message":"", # "order_id":"2510789768709120", # "result":true # } # order = self.parse_order(response, market) return self.extend(order, { 'type': type, 'side': side, }) async def cancel_order(self, id, symbol=None, params={}): if symbol is None: raise ArgumentsRequired(self.id + ' cancelOrder() requires a symbol argument') await self.load_markets() market = self.market(symbol) type = None if market['futures'] or market['swap']: type = market['type'] else: defaultType = self.safe_string_2(self.options, 'cancelOrder', 'defaultType', market['type']) type = self.safe_string(params, 'type', defaultType) if type is None: raise ArgumentsRequired(self.id + " cancelOrder() requires a type parameter(one of 'spot', 'margin', 'futures', 'swap').") method = type + 'PostCancelOrder' request = { 'instrument_id': market['id'], } if market['futures'] or market['swap']: method += 'InstrumentId' else: method += 's' clientOrderId = self.safe_string_2(params, 'client_oid', 'clientOrderId') if clientOrderId is not None: method += 'ClientOid' request['client_oid'] = clientOrderId else: method += 'OrderId' request['order_id'] = id query = self.omit(params, ['type', 'client_oid', 'clientOrderId']) response = await getattr(self, method)(self.extend(request, query)) result = response if ('result' in response) else self.safe_value(response, market['id'], {}) # # spot, margin # # { # "btc-usdt": [ # { # "result":true, # "client_oid":"a123", # "order_id": "2510832677225473" # } # ] # } # # futures, swap # # { # "result": True, # "client_oid": "oktfuture10", # missing if requested by order_id # "order_id": "2517535534836736", # "instrument_id": "EOS-USD-190628" # } # return self.parse_order(result, market) def parse_order_status(self, status): statuses = { '-2': 'failed', '-1': 'canceled', '0': 'open', '1': 'open', '2': 'closed', '3': 'open', '4': 'canceled', } return self.safe_string(statuses, status, status) def parse_order_side(self, side): sides = { '1': 'buy', # open long '2': 'sell', # open short '3': 'sell', # close long '4': 'buy', # close short } return self.safe_string(sides, side, side) def parse_order(self, order, market=None): # # createOrder # # { # "client_oid":"oktspot79", # "error_code":"", # "error_message":"", # "order_id":"2510789768709120", # "result":true # } # # cancelOrder # # { # "result": True, # "client_oid": "oktfuture10", # missing if requested by order_id # "order_id": "2517535534836736", # # instrument_id is missing for spot/margin orders # # available in futures and swap orders only # "instrument_id": "EOS-USD-190628", # } # # fetchOrder, fetchOrdersByState, fetchOpenOrders, fetchClosedOrders # # # spot and margin orders # # { # "client_oid":"oktspot76", # "created_at":"2019-03-18T07:26:49.000Z", # "filled_notional":"3.9734", # "filled_size":"0.001", # filled_qty in futures and swap orders # "funds":"", # self is most likely the same as notional # "instrument_id":"BTC-USDT", # "notional":"", # "order_id":"2500723297813504", # "order_type":"0", # "price":"4013", # "product_id":"BTC-USDT", # missing in futures and swap orders # "side":"buy", # "size":"0.001", # "status":"filled", # "state": "2", # "timestamp":"2019-03-18T07:26:49.000Z", # "type":"limit" # } # # # futures and swap orders # # { # "instrument_id":"EOS-USD-190628", # "size":"10", # "timestamp":"2019-03-20T10:04:55.000Z", # "filled_qty":"10", # filled_size in spot and margin orders # "fee":"-0.00841043", # "order_id":"2512669605501952", # "price":"3.668", # "price_avg":"3.567", # missing in spot and margin orders # "status":"2", # "state": "2", # "type":"4", # "contract_val":"10", # "leverage":"10", # missing in swap, spot and margin orders # "client_oid":"", # "pnl":"1.09510794", # missing in swap, spo and margin orders # "order_type":"0" # } # id = self.safe_string(order, 'order_id') timestamp = self.parse8601(self.safe_string(order, 'timestamp')) side = self.safe_string(order, 'side') type = self.safe_string(order, 'type') if (side != 'buy') and (side != 'sell'): side = self.parse_order_side(type) symbol = None marketId = self.safe_string(order, 'instrument_id') if marketId in self.markets_by_id: market = self.markets_by_id[marketId] symbol = market['symbol'] else: symbol = marketId if market is not None: if symbol is None: symbol = market['symbol'] amount = self.safe_number(order, 'size') filled = self.safe_number_2(order, 'filled_size', 'filled_qty') remaining = None if amount is not None: if filled is not None: amount = max(amount, filled) remaining = max(0, amount - filled) if type == 'market': remaining = 0 cost = self.safe_number_2(order, 'filled_notional', 'funds') price = self.safe_number(order, 'price') average = self.safe_number(order, 'price_avg') if cost is None: if filled is not None and average is not None: cost = average * filled else: if (average is None) and (filled is not None) and (filled > 0): average = cost / filled status = self.parse_order_status(self.safe_string(order, 'state')) feeCost = self.safe_number(order, 'fee') fee = None if feeCost is not None: feeCurrency = None fee = { 'cost': feeCost, 'currency': feeCurrency, } clientOrderId = self.safe_string(order, 'client_oid') if (clientOrderId is not None) and (len(clientOrderId) < 1): clientOrderId = None # fix empty clientOrderId string stopPrice = self.safe_number(order, 'trigger_price') return { 'info': order, 'id': id, 'clientOrderId': clientOrderId, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': None, 'symbol': symbol, 'type': type, 'timeInForce': None, 'postOnly': None, 'side': side, 'price': price, 'stopPrice': stopPrice, 'average': average, 'cost': cost, 'amount': amount, 'filled': filled, 'remaining': remaining, 'status': status, 'fee': fee, 'trades': None, } async def fetch_order(self, id, symbol=None, params={}): if symbol is None: raise ArgumentsRequired(self.id + ' fetchOrder() requires a symbol argument') await self.load_markets() market = self.market(symbol) defaultType = self.safe_string_2(self.options, 'fetchOrder', 'defaultType', market['type']) type = self.safe_string(params, 'type', defaultType) if type is None: raise ArgumentsRequired(self.id + " fetchOrder() requires a type parameter(one of 'spot', 'margin', 'futures', 'swap').") instrumentId = 'InstrumentId' if (market['futures'] or market['swap']) else '' method = type + 'GetOrders' + instrumentId request = { 'instrument_id': market['id'], # 'client_oid': 'abcdef12345', # optional, [a-z0-9]{1,32} # 'order_id': id, } clientOid = self.safe_string(params, 'client_oid') if clientOid is not None: method += 'ClientOid' request['client_oid'] = clientOid else: method += 'OrderId' request['order_id'] = id query = self.omit(params, 'type') response = await getattr(self, method)(self.extend(request, query)) # # spot, margin # # { # "client_oid":"oktspot70", # "created_at":"2019-03-15T02:52:56.000Z", # "filled_notional":"3.8886", # "filled_size":"0.001", # "funds":"", # "instrument_id":"BTC-USDT", # "notional":"", # "order_id":"2482659399697408", # "order_type":"0", # "price":"3927.3", # "product_id":"BTC-USDT", # "side":"buy", # "size":"0.001", # "status":"filled", # "state": "2", # "timestamp":"2019-03-15T02:52:56.000Z", # "type":"limit" # } # # futures, swap # # { # "instrument_id":"EOS-USD-190628", # "size":"10", # "timestamp":"2019-03-20T02:46:38.000Z", # "filled_qty":"10", # "fee":"-0.0080819", # "order_id":"2510946213248000", # "price":"3.712", # "price_avg":"3.712", # "status":"2", # "state": "2", # "type":"2", # "contract_val":"10", # "leverage":"10", # "client_oid":"", # missing in swap orders # "pnl":"0", # missing in swap orders # "order_type":"0" # } # return self.parse_order(response) async def fetch_orders_by_state(self, state, symbol=None, since=None, limit=None, params={}): if symbol is None: raise ArgumentsRequired(self.id + ' fetchOrdersByState() requires a symbol argument') await self.load_markets() market = self.market(symbol) type = None if market['futures'] or market['swap']: type = market['type'] else: defaultType = self.safe_string_2(self.options, 'fetchOrder', 'defaultType', market['type']) type = self.safe_string(params, 'type', defaultType) if type is None: raise ArgumentsRequired(self.id + " fetchOrdersByState() requires a type parameter(one of 'spot', 'margin', 'futures', 'swap').") request = { 'instrument_id': market['id'], # '-2': failed, # '-1': cancelled, # '0': open , # '1': partially filled, # '2': fully filled, # '3': submitting, # '4': cancelling, # '6': incomplete（open+partially filled), # '7': complete（cancelled+fully filled), 'state': state, } method = type + 'GetOrders' if market['futures'] or market['swap']: method += 'InstrumentId' query = self.omit(params, 'type') response = await getattr(self, method)(self.extend(request, query)) # # spot, margin # # [ # # in fact, self documented API response does not correspond # # to their actual API response for spot markets # # OKEX v3 API returns a plain array of orders(see below) # [ # { # "client_oid":"oktspot76", # "created_at":"2019-03-18T07:26:49.000Z", # "filled_notional":"3.9734", # "filled_size":"0.001", # "funds":"", # "instrument_id":"BTC-USDT", # "notional":"", # "order_id":"2500723297813504", # "order_type":"0", # "price":"4013", # "product_id":"BTC-USDT", # "side":"buy", # "size":"0.001", # "status":"filled", # "state": "2", # "timestamp":"2019-03-18T07:26:49.000Z", # "type":"limit" # }, # ], # { # "before":"2500723297813504", # "after":"2500650881647616" # } # ] # # futures, swap # # { # "result":true, # missing in swap orders # "order_info": [ # { # "instrument_id":"EOS-USD-190628", # "size":"10", # "timestamp":"2019-03-20T10:04:55.000Z", # "filled_qty":"10", # "fee":"-0.00841043", # "order_id":"2512669605501952", # "price":"3.668", # "price_avg":"3.567", # "status":"2", # "state": "2", # "type":"4", # "contract_val":"10", # "leverage":"10", # missing in swap orders # "client_oid":"", # "pnl":"1.09510794", # missing in swap orders # "order_type":"0" # }, # ] # } # orders = None if market['swap'] or market['futures']: orders = self.safe_value(response, 'order_info', []) else: orders = response responseLength = len(response) if responseLength < 1: return [] # in fact, self documented API response does not correspond # to their actual API response for spot markets # OKEX v3 API returns a plain array of orders if responseLength > 1: before = self.safe_value(response[1], 'before') if before is not None: orders = response[0] return self.parse_orders(orders, market, since, limit) async def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): # '-2': failed, # '-1': cancelled, # '0': open , # '1': partially filled, # '2': fully filled, # '3': submitting, # '4': cancelling, # '6': incomplete（open+partially filled), # '7': complete（cancelled+fully filled), return await self.fetch_orders_by_state('6', symbol, since, limit, params) async def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): # '-2': failed, # '-1': cancelled, # '0': open , # '1': partially filled, # '2': fully filled, # '3': submitting, # '4': cancelling, # '6': incomplete（open+partially filled), # '7': complete（cancelled+fully filled), return await self.fetch_orders_by_state('7', symbol, since, limit, params) def parse_deposit_address(self, depositAddress, currency=None): # # { # address: '0x696abb81974a8793352cbd33aadcf78eda3cfdfa', # currency: 'eth' # tag: 'abcde12345', # will be missing if the token does not require a deposit tag # payment_id: 'abcde12345', # will not be returned if the token does not require a payment_id # # can_deposit: 1, # 0 or 1, documented but missing # # can_withdraw: 1, # 0 or 1, documented but missing # } # address = self.safe_string(depositAddress, 'address') tag = self.safe_string_2(depositAddress, 'tag', 'payment_id') tag = self.safe_string(depositAddress, 'memo', tag) currencyId = self.safe_string(depositAddress, 'currency') code = self.safe_currency_code(currencyId) self.check_address(address) return { 'currency': code, 'address': address, 'tag': tag, 'info': depositAddress, } async def fetch_deposit_address(self, code, params={}): await self.load_markets() parts = code.split('-') currency = self.currency(parts[0]) request = { 'currency': currency['id'], } response = await self.accountGetDepositAddress(self.extend(request, params)) # # [ # { # address: '0x696abb81974a8793352cbd33aadcf78eda3cfdfa', # currency: 'eth' # } # ] # addressesByCode = self.parse_deposit_addresses(response) address = self.safe_value(addressesByCode, code) if address is None: raise InvalidAddress(self.id + ' fetchDepositAddress cannot return nonexistent addresses, you should create withdrawal addresses with the exchange website first') return address async def withdraw(self, code, amount, address, tag=None, params={}): self.check_address(address) await self.load_markets() currency = self.currency(code) if tag: address = address + ':' + tag fee = self.safe_string(params, 'fee') if fee is None: raise ArgumentsRequired(self.id + " withdraw() requires a `fee` string parameter, network transaction fee must be ≥ 0. Withdrawals to OKCoin or OKEx are fee-free, please set '0'. Withdrawing to external digital asset address requires network transaction fee.") request = { 'currency': currency['id'], 'to_address': address, 'destination': '4', # 2 = OKCoin International, 3 = OKEx 4 = others 'amount': self.number_to_string(amount), 'fee': fee, # String. Network transaction fee ≥ 0. Withdrawals to OKCoin or OKEx are fee-free, please set as 0. Withdrawal to external digital asset address requires network transaction fee. } if 'password' in params: request['trade_pwd'] = params['password'] elif 'trade_pwd' in params: request['trade_pwd'] = params['trade_pwd'] elif self.password: request['trade_pwd'] = self.password query = self.omit(params, ['fee', 'password', 'trade_pwd']) if not ('trade_pwd' in request): raise ExchangeError(self.id + ' withdraw() requires self.password set on the exchange instance or a password / trade_pwd parameter') response = await self.accountPostWithdrawal(self.extend(request, query)) # # { # "amount":"0.1", # "withdrawal_id":"67485", # "currency":"btc", # "result":true # } # return { 'info': response, 'id': self.safe_string(response, 'withdrawal_id'), } async def fetch_deposits(self, code=None, since=None, limit=None, params={}): await self.load_markets() request = {} method = 'accountGetDepositHistory' currency = None if code is not None: currency = self.currency(code) request['currency'] = currency['id'] method += 'Currency' response = await getattr(self, method)(self.extend(request, params)) return self.parse_transactions(response, currency, since, limit, params) async def fetch_withdrawals(self, code=None, since=None, limit=None, params={}): await self.load_markets() request = {} method = 'accountGetWithdrawalHistory' currency = None if code is not None: currency = self.currency(code) request['currency'] = currency['id'] method += 'Currency' response = await getattr(self, method)(self.extend(request, params)) return self.parse_transactions(response, currency, since, limit, params) def parse_transaction_status(self, status): # # deposit statuses # # { # '0': 'waiting for confirmation', # '1': 'confirmation account', # '2': 'recharge success' # } # # withdrawal statues # # { # '-3': 'pending cancel', # '-2': 'cancelled', # '-1': 'failed', # '0': 'pending', # '1': 'sending', # '2': 'sent', # '3': 'email confirmation', # '4': 'manual confirmation', # '5': 'awaiting identity confirmation' # } # statuses = { '-3': 'pending', '-2': 'canceled', '-1': 'failed', '0': 'pending', '1': 'pending', '2': 'ok', '3': 'pending', '4': 'pending', '5': 'pending', } return self.safe_string(statuses, status, status) def parse_transaction(self, transaction, currency=None): # # withdraw # # { # "amount":"0.1", # "withdrawal_id":"67485", # "currency":"btc", # "result":true # } # # fetchWithdrawals # # { # amount: "4.72100000", # withdrawal_id: "1729116", # fee: "0.01000000eth", # txid: "0xf653125bbf090bcfe4b5e8e7b8f586a9d87aa7de94598702758c0802b…", # currency: "ETH", # from: "7147338839", # to: "0x26a3CB49578F07000575405a57888681249c35Fd", # timestamp: "2018-08-17T07:03:42.000Z", # status: "2" # } # # fetchDeposits # # { # "amount": "4.19511659", # "txid": "14c9a8c925647cdb7e5b2937ea9aefe2b29b2c273150ad3f44b3b8a4635ed437", # "currency": "XMR", # "from": "", # "to": "48PjH3ksv1fiXniKvKvyH5UtFs5WhfS2Vf7U3TwzdRJtCc7HJWvCQe56dRahyhQyTAViXZ8Nzk4gQg6o4BJBMUoxNy8y8g7", # "tag": "1234567", # "deposit_id": 11571659, <-- we can use self # "timestamp": "2019-10-01T14:54:19.000Z", # "status": "2" # } # type = None id = None address = None withdrawalId = self.safe_string(transaction, 'withdrawal_id') addressFrom = self.safe_string(transaction, 'from') addressTo = self.safe_string(transaction, 'to') tagTo = self.safe_string(transaction, 'tag') if withdrawalId is not None: type = 'withdrawal' id = withdrawalId address = addressTo else: # the payment_id will appear on new deposits but appears to be removed from the response after 2 months id = self.safe_string_2(transaction, 'payment_id', 'deposit_id') type = 'deposit' address = addressTo currencyId = self.safe_string(transaction, 'currency') code = self.safe_currency_code(currencyId) amount = self.safe_number(transaction, 'amount') status = self.parse_transaction_status(self.safe_string(transaction, 'status')) txid = self.safe_string(transaction, 'txid') timestamp = self.parse8601(self.safe_string(transaction, 'timestamp')) feeCost = None if type == 'deposit': feeCost = 0 else: if currencyId is not None: feeWithCurrencyId = self.safe_string(transaction, 'fee') if feeWithCurrencyId is not None: # https://github.com/ccxt/ccxt/pull/5748 lowercaseCurrencyId = currencyId.lower() feeWithoutCurrencyId = feeWithCurrencyId.replace(lowercaseCurrencyId, '') feeCost = float(feeWithoutCurrencyId) # todo parse tags return { 'info': transaction, 'id': id, 'currency': code, 'amount': amount, 'addressFrom': addressFrom, 'addressTo': addressTo, 'address': address, 'tagFrom': None, 'tagTo': tagTo, 'tag': tagTo, 'status': status, 'type': type, 'updated': None, 'txid': txid, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'fee': { 'currency': code, 'cost': feeCost, }, } def parse_my_trade(self, pair, market=None): # check that trading symbols match in both entries userTrade = self.safe_value(pair, 1) otherTrade = self.safe_value(pair, 0) firstMarketId = self.safe_string(otherTrade, 'instrument_id') secondMarketId = self.safe_string(userTrade, 'instrument_id') if firstMarketId != secondMarketId: raise NotSupported(self.id + ' parseMyTrade() received unrecognized response format, differing instrument_ids in one fill, the exchange API might have changed, paste your verbose output: https://github.com/ccxt/ccxt/wiki/FAQ#what-is-required-to-get-help') marketId = firstMarketId market = self.safe_market(marketId, market) symbol = market['symbol'] quoteId = market['quoteId'] side = None amount = None cost = None receivedCurrencyId = self.safe_string(userTrade, 'currency') feeCurrencyId = None if receivedCurrencyId == quoteId: side = self.safe_string(otherTrade, 'side') amount = self.safe_number(otherTrade, 'size') cost = self.safe_number(userTrade, 'size') feeCurrencyId = self.safe_string(otherTrade, 'currency') else: side = self.safe_string(userTrade, 'side') amount = self.safe_number(userTrade, 'size') cost = self.safe_number(otherTrade, 'size') feeCurrencyId = self.safe_string(userTrade, 'currency') id = self.safe_string(userTrade, 'trade_id') price = self.safe_number(userTrade, 'price') feeCostFirst = self.safe_number(otherTrade, 'fee') feeCostSecond = self.safe_number(userTrade, 'fee') feeCurrencyCodeFirst = self.safe_currency_code(self.safe_string(otherTrade, 'currency')) feeCurrencyCodeSecond = self.safe_currency_code(self.safe_string(userTrade, 'currency')) fee = None fees = None # fee is either a positive number(invitation rebate) # or a negative number(transaction fee deduction) # therefore we need to invert the fee # more about it https://github.com/ccxt/ccxt/issues/5909 if (feeCostFirst is not None) and (feeCostFirst != 0): if (feeCostSecond is not None) and (feeCostSecond != 0): fees = [ { 'cost': -feeCostFirst, 'currency': feeCurrencyCodeFirst, }, { 'cost': -feeCostSecond, 'currency': feeCurrencyCodeSecond, }, ] else: fee = { 'cost': -feeCostFirst, 'currency': feeCurrencyCodeFirst, } elif (feeCostSecond is not None) and (feeCostSecond != 0): fee = { 'cost': -feeCostSecond, 'currency': feeCurrencyCodeSecond, } else: fee = { 'cost': 0, 'currency': self.safe_currency_code(feeCurrencyId), } # # simplified structures to show the underlying semantics # # # market/limit sell # # { # "currency":"USDT", # "fee":"-0.04647925", # ←--- fee in received quote currency # "price":"129.13", # ←------ price # "size":"30.98616393", # ←-- cost # }, # { # "currency":"ETH", # "fee":"0", # "price":"129.13", # "size":"0.23996099", # ←--- amount # }, # # # market/limit buy # # { # "currency":"ETH", # "fee":"-0.00036049", # ←--- fee in received base currency # "price":"129.16", # ←------ price # "size":"0.240322", # ←----- amount # }, # { # "currency":"USDT", # "fee":"0", # "price":"129.16", # "size":"31.03998952", # ←-- cost # } # timestamp = self.parse8601(self.safe_string_2(userTrade, 'timestamp', 'created_at')) takerOrMaker = self.safe_string_2(userTrade, 'exec_type', 'liquidity') if takerOrMaker == 'M': takerOrMaker = 'maker' elif takerOrMaker == 'T': takerOrMaker = 'taker' orderId = self.safe_string(userTrade, 'order_id') result = { 'info': pair, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'id': id, 'order': orderId, 'type': None, 'takerOrMaker': takerOrMaker, 'side': side, 'price': price, 'amount': amount, 'cost': cost, 'fee': fee, } if fees is not None: result['fees'] = fees return result def parse_my_trades(self, trades, market=None, since=None, limit=None, params={}): grouped = self.group_by(trades, 'trade_id') tradeIds = list(grouped.keys()) result = [] for i in range(0, len(tradeIds)): tradeId = tradeIds[i] pair = grouped[tradeId] # make sure it has exactly 2 trades, no more, no less numTradesInPair = len(pair) if numTradesInPair == 2: trade = self.parse_my_trade(pair) result.append(trade) symbol = None if market is not None: symbol = market['symbol'] return self.filter_by_symbol_since_limit(result, symbol, since, limit) async def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}): # okex actually returns ledger entries instead of fills here, so each fill in the order # is represented by two trades with opposite buy/sell sides, not one :\ # self aspect renders the 'fills' endpoint unusable for fetchOrderTrades # until either OKEX fixes the API or we workaround self on our side somehow if symbol is None: raise ArgumentsRequired(self.id + ' fetchMyTrades() requires a symbol argument') await self.load_markets() market = self.market(symbol) if (limit is not None) and (limit > 100): limit = 100 request = { 'instrument_id': market['id'], # 'order_id': id, # string # 'after': '1', # pagination of data to return records earlier than the requested ledger_id # 'before': '1', # P=pagination of data to return records newer than the requested ledger_id # 'limit': limit, # optional, number of results per request, default = maximum = 100 } defaultType = self.safe_string_2(self.options, 'fetchMyTrades', 'defaultType') type = self.safe_string(params, 'type', defaultType) query = self.omit(params, 'type') method = type + 'GetFills' response = await getattr(self, method)(self.extend(request, query)) # # [ # # sell # { # "created_at":"2020-03-29T11:55:25.000Z", # "currency":"USDT", # "exec_type":"T", # "fee":"-0.04647925", # "instrument_id":"ETH-USDT", # "ledger_id":"10562924353", # "liquidity":"T", # "order_id":"4636470489136128", # "price":"129.13", # "product_id":"ETH-USDT", # "side":"buy", # "size":"30.98616393", # "timestamp":"2020-03-29T11:55:25.000Z", # "trade_id":"18551601" # }, # { # "created_at":"2020-03-29T11:55:25.000Z", # "currency":"ETH", # "exec_type":"T", # "fee":"0", # "instrument_id":"ETH-USDT", # "ledger_id":"10562924352", # "liquidity":"T", # "order_id":"4636470489136128", # "price":"129.13", # "product_id":"ETH-USDT", # "side":"sell", # "size":"0.23996099", # "timestamp":"2020-03-29T11:55:25.000Z", # "trade_id":"18551601" # }, # # buy # { # "created_at":"2020-03-29T11:55:16.000Z", # "currency":"ETH", # "exec_type":"T", # "fee":"-0.00036049", # "instrument_id":"ETH-USDT", # "ledger_id":"10562922669", # "liquidity":"T", # "order_id": "4636469894136832", # "price":"129.16", # "product_id":"ETH-USDT", # "side":"buy", # "size":"0.240322", # "timestamp":"2020-03-29T11:55:16.000Z", # "trade_id":"18551600" # }, # { # "created_at":"2020-03-29T11:55:16.000Z", # "currency":"USDT", # "exec_type":"T", # "fee":"0", # "instrument_id":"ETH-USDT", # "ledger_id":"10562922668", # "liquidity":"T", # "order_id":"4636469894136832", # "price":"129.16", # "product_id":"ETH-USDT", # "side":"sell", # "size":"31.03998952", # "timestamp":"2020-03-29T11:55:16.000Z", # "trade_id":"18551600" # } # ] # return self.parse_my_trades(response, market, since, limit, params) async def fetch_order_trades(self, id, symbol=None, since=None, limit=None, params={}): request = { # 'instrument_id': market['id'], 'order_id': id, # 'after': '1', # return the page after the specified page number # 'before': '1', # return the page before the specified page number # 'limit': limit, # optional, number of results per request, default = maximum = 100 } return await self.fetch_my_trades(symbol, since, limit, self.extend(request, params)) async def fetch_position(self, symbol, params={}): await self.load_markets() market = self.market(symbol) method = None request = { 'instrument_id': market['id'], # 'order_id': id, # string # 'after': '1', # pagination of data to return records earlier than the requested ledger_id # 'before': '1', # P=pagination of data to return records newer than the requested ledger_id # 'limit': limit, # optional, number of results per request, default = maximum = 100 } type = market['type'] if (type == 'futures') or (type == 'swap'): method = type + 'GetInstrumentIdPosition' elif type == 'option': underlying = self.safe_string(params, 'underlying') if underlying is None: raise ArgumentsRequired(self.id + ' fetchPosition() requires an underlying parameter for ' + type + ' market ' + symbol) method = type + 'GetUnderlyingPosition' else: raise NotSupported(self.id + ' fetchPosition() does not support ' + type + ' market ' + symbol + ', supported market types are futures, swap or option') response = await getattr(self, method)(self.extend(request, params)) # # futures # # crossed margin mode # # { # "result": True, # "holding": [ # { # "long_qty": "2", # "long_avail_qty": "2", # "long_avg_cost": "8260", # "long_settlement_price": "8260", # "realised_pnl": "0.00020928", # "short_qty": "2", # "short_avail_qty": "2", # "short_avg_cost": "8259.99", # "short_settlement_price": "8259.99", # "liquidation_price": "113.81", # "instrument_id": "BTC-USD-191227", # "leverage": "10", # "created_at": "2019-09-25T07:58:42.129Z", # "updated_at": "2019-10-08T14:02:51.029Z", # "margin_mode": "crossed", # "short_margin": "0.00242197", # "short_pnl": "6.63E-6", # "short_pnl_ratio": "0.002477997", # "short_unrealised_pnl": "6.63E-6", # "long_margin": "0.00242197", # "long_pnl": "-6.65E-6", # "long_pnl_ratio": "-0.002478", # "long_unrealised_pnl": "-6.65E-6", # "long_settled_pnl": "0", # "short_settled_pnl": "0", # "last": "8257.57" # } # ], # "margin_mode": "crossed" # } # # fixed margin mode # # { # "result": True, # "holding": [ # { # "long_qty": "4", # "long_avail_qty": "4", # "long_margin": "0.00323844", # "long_liqui_price": "7762.09", # "long_pnl_ratio": "0.06052306", # "long_avg_cost": "8234.43", # "long_settlement_price": "8234.43", # "realised_pnl": "-0.00000296", # "short_qty": "2", # "short_avail_qty": "2", # "short_margin": "0.00241105", # "short_liqui_price": "9166.74", # "short_pnl_ratio": "0.03318052", # "short_avg_cost": "8295.13", # "short_settlement_price": "8295.13", # "instrument_id": "BTC-USD-191227", # "long_leverage": "15", # "short_leverage": "10", # "created_at": "2019-09-25T07:58:42.129Z", # "updated_at": "2019-10-08T13:12:09.438Z", # "margin_mode": "fixed", # "short_margin_ratio": "0.10292507", # "short_maint_margin_ratio": "0.005", # "short_pnl": "7.853E-5", # "short_unrealised_pnl": "7.853E-5", # "long_margin_ratio": "0.07103743", # "long_maint_margin_ratio": "0.005", # "long_pnl": "1.9841E-4", # "long_unrealised_pnl": "1.9841E-4", # "long_settled_pnl": "0", # "short_settled_pnl": "0", # "last": "8266.99" # } # ], # "margin_mode": "fixed" # } # # swap # # crossed margin mode # # { # "margin_mode": "crossed", # "timestamp": "2019-09-27T03:49:02.018Z", # "holding": [ # { # "avail_position": "3", # "avg_cost": "59.49", # "instrument_id": "LTC-USD-SWAP", # "last": "55.98", # "leverage": "10.00", # "liquidation_price": "4.37", # "maint_margin_ratio": "0.0100", # "margin": "0.0536", # "position": "3", # "realized_pnl": "0.0000", # "unrealized_pnl": "0", # "settled_pnl": "-0.0330", # "settlement_price": "55.84", # "side": "long", # "timestamp": "2019-09-27T03:49:02.018Z" # }, # ] # } # # fixed margin mode # # { # "margin_mode": "fixed", # "timestamp": "2019-09-27T03:47:37.230Z", # "holding": [ # { # "avail_position": "20", # "avg_cost": "8025.0", # "instrument_id": "BTC-USD-SWAP", # "last": "8113.1", # "leverage": "15.00", # "liquidation_price": "7002.6", # "maint_margin_ratio": "0.0050", # "margin": "0.0454", # "position": "20", # "realized_pnl": "-0.0001", # "unrealized_pnl": "0", # "settled_pnl": "0.0076", # "settlement_price": "8279.2", # "side": "long", # "timestamp": "2019-09-27T03:47:37.230Z" # } # ] # } # # option # # { # "holding":[ # { # "instrument_id":"BTC-USD-190927-12500-C", # "position":"20", # "avg_cost":"3.26", # "avail_position":"20", # "settlement_price":"0.017", # "total_pnl":"50", # "pnl_ratio":"0.3", # "realized_pnl":"40", # "unrealized_pnl":"10", # "pos_margin":"100", # "option_value":"70", # "created_at":"2019-08-30T03:09:20.315Z", # "updated_at":"2019-08-30T03:40:18.318Z" # }, # { # "instrument_id":"BTC-USD-190927-12500-P", # "position":"20", # "avg_cost":"3.26", # "avail_position":"20", # "settlement_price":"0.019", # "total_pnl":"50", # "pnl_ratio":"0.3", # "realized_pnl":"40", # "unrealized_pnl":"10", # "pos_margin":"100", # "option_value":"70", # "created_at":"2019-08-30T03:09:20.315Z", # "updated_at":"2019-08-30T03:40:18.318Z" # } # ] # } # # todo unify parsePosition/parsePositions return response async def fetch_positions(self, symbols=None, params={}): await self.load_markets() method = None defaultType = self.safe_string_2(self.options, 'fetchPositions', 'defaultType') type = self.safe_string(params, 'type', defaultType) if (type == 'futures') or (type == 'swap'): method = type + 'GetPosition' elif type == 'option': underlying = self.safe_string(params, 'underlying') if underlying is None: raise ArgumentsRequired(self.id + ' fetchPositions() requires an underlying parameter for ' + type + ' markets') method = type + 'GetUnderlyingPosition' else: raise NotSupported(self.id + ' fetchPositions() does not support ' + type + ' markets, supported market types are futures, swap or option') params = self.omit(params, 'type') response = await getattr(self, method)(params) # # futures # # ... # # # swap # # ... # # option # # { # "holding":[ # { # "instrument_id":"BTC-USD-190927-12500-C", # "position":"20", # "avg_cost":"3.26", # "avail_position":"20", # "settlement_price":"0.017", # "total_pnl":"50", # "pnl_ratio":"0.3", # "realized_pnl":"40", # "unrealized_pnl":"10", # "pos_margin":"100", # "option_value":"70", # "created_at":"2019-08-30T03:09:20.315Z", # "updated_at":"2019-08-30T03:40:18.318Z" # }, # { # "instrument_id":"BTC-USD-190927-12500-P", # "position":"20", # "avg_cost":"3.26", # "avail_position":"20", # "settlement_price":"0.019", # "total_pnl":"50", # "pnl_ratio":"0.3", # "realized_pnl":"40", # "unrealized_pnl":"10", # "pos_margin":"100", # "option_value":"70", # "created_at":"2019-08-30T03:09:20.315Z", # "updated_at":"2019-08-30T03:40:18.318Z" # } # ] # } # # todo unify parsePosition/parsePositions return response async def fetch_ledger(self, code=None, since=None, limit=None, params={}): await self.load_markets() defaultType = self.safe_string_2(self.options, 'fetchLedger', 'defaultType') type = self.safe_string(params, 'type', defaultType) query = self.omit(params, 'type') suffix = '' if (type == 'account') else 'Accounts' argument = '' request = { # 'from': 'id', # 'to': 'id', } if limit is not None: request['limit'] = limit currency = None if type == 'spot': if code is None: raise ArgumentsRequired(self.id + " fetchLedger() requires a currency code argument for '" + type + "' markets") argument = 'Currency' currency = self.currency(code) request['currency'] = currency['id'] elif type == 'futures': if code is None: raise ArgumentsRequired(self.id + " fetchLedger() requires an underlying symbol for '" + type + "' markets") argument = 'Underlying' market = self.market(code) # we intentionally put a market inside here for the margin and swap ledgers marketInfo = self.safe_value(market, 'info', {}) settlementCurrencyId = self.safe_string(marketInfo, 'settlement_currency') settlementCurrencyСode = self.safe_currency_code(settlementCurrencyId) currency = self.currency(settlementCurrencyСode) underlyingId = self.safe_string(marketInfo, 'underlying') request['underlying'] = underlyingId elif (type == 'margin') or (type == 'swap'): if code is None: raise ArgumentsRequired(self.id + " fetchLedger() requires a code argument(a market symbol) for '" + type + "' markets") argument = 'InstrumentId' market = self.market(code) # we intentionally put a market inside here for the margin and swap ledgers currency = self.currency(market['base']) request['instrument_id'] = market['id'] # # if type == 'margin': # # # # 3. Borrow # # 4. Repayment # # 5. Interest # # 7. Buy # # 8. Sell # # 9. From capital account # # 10. From C2C # # 11. From Futures # # 12. From Spot # # 13. From ETT # # 14. To capital account # # 15. To C2C # # 16. To Spot # # 17. To Futures # # 18. To ETT # # 19. Mandatory Repayment # # 20. From Piggybank # # 21. To Piggybank # # 22. From Perpetual # # 23. To Perpetual # # 24. Liquidation Fee # # 54. Clawback # # 59. Airdrop Return. # # # request['type'] = 'number' # All types will be returned if self filed is left blank # } # elif type == 'account': if code is not None: currency = self.currency(code) request['currency'] = currency['id'] # # # # # 1. deposit # # 2. withdrawal # # 13. cancel withdrawal # # 18. into futures account # # 19. out of futures account # # 20. into sub account # # 21. out of sub account # # 28. claim # # 29. into ETT account # # 30. out of ETT account # # 31. into C2C account # # 32. out of C2C account # # 33. into margin account # # 34. out of margin account # # 37. into spot account # # 38. out of spot account # # # request['type'] = 'number' # else: raise NotSupported(self.id + " fetchLedger does not support the '" + type + "' type(the type must be one of 'account', 'spot', 'margin', 'futures', 'swap')") method = type + 'Get' + suffix + argument + 'Ledger' response = await getattr(self, method)(self.extend(request, query)) # # transfer funds transfer in/out # trade funds moved as a result of a trade, spot and margin accounts only # rebate fee rebate as per fee schedule, spot and margin accounts only # match open long/open short/close long/close short(futures) or a change in the amount because of trades(swap) # fee fee, futures only # settlement settlement/clawback/settle long/settle short # liquidation force close long/force close short/deliver close long/deliver close short # funding funding fee, swap only # margin a change in the amount after adjusting margin, swap only # # account # # [ # { # "amount":0.00051843, # "balance":0.00100941, # "currency":"BTC", # "fee":0, # "ledger_id":8987285, # "timestamp":"2018-10-12T11:01:14.000Z", # "typename":"Get from activity" # } # ] # # spot # # [ # { # "timestamp":"2019-03-18T07:08:25.000Z", # "ledger_id":"3995334780", # "created_at":"2019-03-18T07:08:25.000Z", # "currency":"BTC", # "amount":"0.0009985", # "balance":"0.0029955", # "type":"trade", # "details":{ # "instrument_id":"BTC-USDT", # "order_id":"2500650881647616", # "product_id":"BTC-USDT" # } # } # ] # # margin # # [ # [ # { # "created_at":"2019-03-20T03:45:05.000Z", # "ledger_id":"78918186", # "timestamp":"2019-03-20T03:45:05.000Z", # "currency":"EOS", # "amount":"0", # ? # "balance":"0.59957711", # "type":"transfer", # "details":{ # "instrument_id":"EOS-USDT", # "order_id":"787057", # "product_id":"EOS-USDT" # } # } # ], # { # "before":"78965766", # "after":"78918186" # } # ] # # futures # # [ # { # "ledger_id":"2508090544914461", # "timestamp":"2019-03-19T14:40:24.000Z", # "amount":"-0.00529521", # "balance":"0", # "currency":"EOS", # "type":"fee", # "details":{ # "order_id":"2506982456445952", # "instrument_id":"EOS-USD-190628" # } # } # ] # # swap # # [ # { # "amount":"0.004742", # "fee":"-0.000551", # "type":"match", # "instrument_id":"EOS-USD-SWAP", # "ledger_id":"197429674941902848", # "timestamp":"2019-03-25T05:56:31.286Z" # }, # ] # responseLength = len(response) if responseLength < 1: return [] isArray = isinstance(response[0], list) isMargin = (type == 'margin') entries = response[0] if (isMargin and isArray) else response if type == 'swap': ledgerEntries = self.parse_ledger(entries) return self.filter_by_symbol_since_limit(ledgerEntries, code, since, limit) return self.parse_ledger(entries, currency, since, limit) def parse_ledger_entry_type(self, type): types = { 'transfer': 'transfer', # # funds transfer in/out 'trade': 'trade', # funds moved as a result of a trade, spot and margin accounts only 'rebate': 'rebate', # fee rebate as per fee schedule, spot and margin accounts only 'match': 'trade', # open long/open short/close long/close short(futures) or a change in the amount because of trades(swap) 'fee': 'fee', # fee, futures only 'settlement': 'trade', # settlement/clawback/settle long/settle short 'liquidation': 'trade', # force close long/force close short/deliver close long/deliver close short 'funding': 'fee', # funding fee, swap only 'margin': 'margin', # a change in the amount after adjusting margin, swap only } return self.safe_string(types, type, type) def parse_ledger_entry(self, item, currency=None): # # # account # # { # "amount":0.00051843, # "balance":0.00100941, # "currency":"BTC", # "fee":0, # "ledger_id":8987285, # "timestamp":"2018-10-12T11:01:14.000Z", # "typename":"Get from activity" # } # # spot # # { # "timestamp":"2019-03-18T07:08:25.000Z", # "ledger_id":"3995334780", # "created_at":"2019-03-18T07:08:25.000Z", # "currency":"BTC", # "amount":"0.0009985", # "balance":"0.0029955", # "type":"trade", # "details":{ # "instrument_id":"BTC-USDT", # "order_id":"2500650881647616", # "product_id":"BTC-USDT" # } # } # # margin # # { # "created_at":"2019-03-20T03:45:05.000Z", # "ledger_id":"78918186", # "timestamp":"2019-03-20T03:45:05.000Z", # "currency":"EOS", # "amount":"0", # ? # "balance":"0.59957711", # "type":"transfer", # "details":{ # "instrument_id":"EOS-USDT", # "order_id":"787057", # "product_id":"EOS-USDT" # } # } # # futures # # { # "ledger_id":"2508090544914461", # "timestamp":"2019-03-19T14:40:24.000Z", # "amount":"-0.00529521", # "balance":"0", # "currency":"EOS", # "type":"fee", # "details":{ # "order_id":"2506982456445952", # "instrument_id":"EOS-USD-190628" # } # } # # swap # # { # "amount":"0.004742", # "fee":"-0.000551", # "type":"match", # "instrument_id":"EOS-USD-SWAP", # "ledger_id":"197429674941902848", # "timestamp":"2019-03-25T05:56:31.286Z" # }, # id = self.safe_string(item, 'ledger_id') account = None details = self.safe_value(item, 'details', {}) referenceId = self.safe_string(details, 'order_id') referenceAccount = None type = self.parse_ledger_entry_type(self.safe_string(item, 'type')) code = self.safe_currency_code(self.safe_string(item, 'currency'), currency) amount = self.safe_number(item, 'amount') timestamp = self.parse8601(self.safe_string(item, 'timestamp')) fee = { 'cost': self.safe_number(item, 'fee'), 'currency': code, } before = None after = self.safe_number(item, 'balance') status = 'ok' marketId = self.safe_string(item, 'instrument_id') symbol = None if marketId in self.markets_by_id: market = self.markets_by_id[marketId] symbol = market['symbol'] return { 'info': item, 'id': id, 'account': account, 'referenceId': referenceId, 'referenceAccount': referenceAccount, 'type': type, 'currency': code, 'symbol': symbol, 'amount': amount, 'before': before, # balance before 'after': after, # balance after 'status': status, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'fee': fee, } def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): isArray = isinstance(params, list) request = '/api/' + api + '/' + self.version + '/' request += path if isArray else self.implode_params(path, params) query = params if isArray else self.omit(params, self.extract_params(path)) url = self.implode_params(self.urls['api']['rest'], {'hostname': self.hostname}) + request type = self.get_path_authentication_type(path) if type == 'public': if query: url += '?' + self.urlencode(query) elif type == 'private': self.check_required_credentials() timestamp = self.iso8601(self.milliseconds()) headers = { 'OK-ACCESS-KEY': self.apiKey, 'OK-ACCESS-PASSPHRASE': self.password, 'OK-ACCESS-TIMESTAMP': timestamp, # 'OK-FROM': '', # 'OK-TO': '', # 'OK-LIMIT': '', } auth = timestamp + method + request if method == 'GET': if query: urlencodedQuery = '?' + self.urlencode(query) url += urlencodedQuery auth += urlencodedQuery else: if isArray or query: body = self.json(query) auth += body headers['Content-Type'] = 'application/json' signature = self.hmac(self.encode(auth), self.encode(self.secret), hashlib.sha256, 'base64') headers['OK-ACCESS-SIGN'] = signature return {'url': url, 'method': method, 'body': body, 'headers': headers} def get_path_authentication_type(self, path): # https://github.com/ccxt/ccxt/issues/6651 # a special case to handle the optionGetUnderlying interefering with # other endpoints containing self keyword if path == 'underlying': return 'public' auth = self.safe_value(self.options, 'auth', {}) key = self.find_broadly_matched_key(auth, path) return self.safe_string(auth, key, 'private') def handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody): if not response: return # fallback to default error handler feedback = self.id + ' ' + body if code == 503: # {"message":"name resolution failed"} raise ExchangeNotAvailable(feedback) # # {"error_message":"Order does not exist","result":"true","error_code":"35029","order_id":"-1"} # message = self.safe_string(response, 'message') errorCode = self.safe_string_2(response, 'code', 'error_code') nonEmptyMessage = ((message is not None) and (message != '')) nonZeroErrorCode = (errorCode is not None) and (errorCode != '0') if nonEmptyMessage: self.throw_exactly_matched_exception(self.exceptions['exact'], message, feedback) self.throw_broadly_matched_exception(self.exceptions['broad'], message, feedback) if nonZeroErrorCode: self.throw_exactly_matched_exception(self.exceptions['exact'], errorCode, feedback) if nonZeroErrorCode or nonEmptyMessage: raise ExchangeError(feedback) # unknown message

47.352217

521

0.471667

5dfebdaaaf7e259a82f58fad3b83d632806686a0

4,475

py

Python

src/mixins/fixed_cone_mixin.py

cvxgrp/qcml

ff5e378cfeeebcf3f85a6e30c3449585f9af869f

[ "BSD-2-Clause-FreeBSD" ]

26

2015-02-06T02:59:17.000Z

2021-11-15T18:13:27.000Z

src/mixins/fixed_cone_mixin.py

cvxgrp/qcml

ff5e378cfeeebcf3f85a6e30c3449585f9af869f

[ "BSD-2-Clause-FreeBSD" ]

6

2015-06-14T04:43:43.000Z

2019-10-27T11:03:30.000Z

src/mixins/fixed_cone_mixin.py

cvxgrp/qcml

ff5e378cfeeebcf3f85a6e30c3449585f9af869f

[ "BSD-2-Clause-FreeBSD" ]

6

2015-03-14T07:40:56.000Z

2019-12-30T23:11:36.000Z

# TODO: (ECHU) presumably, this will work, but in actuality, i'm not sure... # Need to test this. # # This code doesn't work yet. Requires expression slices. # from .. ast.constraints import SOC, SOCProd from .. codes import SliceCoeff from variable_creation_mixin import VariableCreatorMixin class FixedConeMixin(VariableCreatorMixin): """ This implements the fixed cone size behavior. """ def __init__(self, cone_size = None, *args, **kwargs): super(FixedConeMixin, self).__init__(*args, **kwargs) if cone_size is not None: self.cone_size = max(3,cone_size) else: self.cone_size = 3 def visit_SOC(self, node): if self.cone_size is not None: # look at the size of the SOC cone_length = 1 for e in node.left: dim = e.shape.size(abstractdim_rewriter=self.abstractdim_rewriter) cone_length += dim while cone_length > self.cone_size: # maximum number of elements on the lhs max_lhs = self.cone_size - 1 # collect the new arguments new_args = [] old_args = [] cum = 0 create_new = True for e in node.left: if create_new: dim = e.shape.size(abstractdim_rewriter=self.abstractdim_rewriter) # if the dimension of the current expression doesn't # exceed the max allowable, just push onto argument stack if cum + dim <= max_lhs: new_args.append(e) else: # if it exceeds, only push the slice up to max_lhs new_args.append(SliceCoeff(e, 0, max_lhs - cum)) # save the rest of the expression for another cone old_args.append(SliceCoeff(e, max_lhs - cum, dim)) if cum + dim >= max_lhs: create_new = False else: # just push into the old args old_args.append(e) cum += dim # create a new variable new_var = self.create_variable(1) # process the new cone, which has the right size super(FixedConeMixin,self).visit_SOC(SOC(new_var, new_args)) # process the old cone old_args.append(new_var) node.left = old_args cone_length -= (max_lhs - 1) # the extra "1" is the rhs if cone_length < self.cone_size: # create a new variable and append to the node new_length = self.cone_size - cone_length new_var = self.create_variable(new_length) node.left.append(new_var) super(FixedConeMixin,self).visit_SOC(node) def visit_SOCProd(self, node): if self.cone_size is not None: # look at the size of the SOC n = node.shape.size(abstractdim_rewriter=self.abstractdim_rewriter) cone_length = 1 + node.nargs #print cone_length while cone_length > self.cone_size: # maximum number of elements on the lhs max_lhs = self.cone_size - 1 # collect the new arguments new_args = [] old_args = [] count = 0 for e in node.arglist: if count < max_lhs: new_args.append(e) else: old_args.append(e) count += 1 new_var = self.create_variable(n) # process the new cone, which has the right size super(FixedConeMixin,self).visit_SOCProd(SOCProd(new_var, new_args)) # process the old cone old_args.append(new_var) node.arglist = old_args cone_length -= (max_lhs - 1) # the extra "1" is the rhs if cone_length < self.cone_size: # create a new variable and append to the node new_length = self.cone_size - cone_length for i in range(new_length): new_var = self.create_variable(n) node.arglist.append(new_var) super(FixedConeMixin,self).visit_SOCProd(node)

38.577586

90

0.529832

d4bb40f31065712f45c03f3ba22440f9f1b89b19

3,508

py

Python

mne/viz/tests/test_evoked.py

jaeilepp/eggie

a7e812f27e33f9c43ac2e36c6b45a26a01530a06

[ "BSD-2-Clause" ]

null

mne/viz/tests/test_evoked.py

jaeilepp/eggie

a7e812f27e33f9c43ac2e36c6b45a26a01530a06

[ "BSD-2-Clause" ]

null

mne/viz/tests/test_evoked.py

jaeilepp/eggie

a7e812f27e33f9c43ac2e36c6b45a26a01530a06

[ "BSD-2-Clause" ]

null

# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr> # Denis Engemann <denis.engemann@gmail.com> # Martin Luessi <mluessi@nmr.mgh.harvard.edu> # Eric Larson <larson.eric.d@gmail.com> # Cathy Nangini <cnangini@gmail.com> # Mainak Jas <mainak@neuro.hut.fi> # # License: Simplified BSD import os.path as op import warnings import numpy as np from numpy.testing import assert_raises # Set our plotters to test mode import matplotlib matplotlib.use('Agg') # for testing don't use X server import matplotlib.pyplot as plt from mne import io, read_events, Epochs from mne import pick_types from mne.layouts import read_layout from mne.datasets import sample warnings.simplefilter('always') # enable b/c these tests throw warnings data_dir = sample.data_path(download=False) subjects_dir = op.join(data_dir, 'subjects') ecg_fname = op.join(data_dir, 'MEG', 'sample', 'sample_audvis_ecg_proj.fif') base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') evoked_fname = op.join(base_dir, 'test-ave.fif') raw_fname = op.join(base_dir, 'test_raw.fif') cov_fname = op.join(base_dir, 'test-cov.fif') event_name = op.join(base_dir, 'test-eve.fif') event_id, tmin, tmax = 1, -0.1, 0.1 n_chan = 6 layout = read_layout('Vectorview-all') def _get_raw(): return io.Raw(raw_fname, preload=False) def _get_events(): return read_events(event_name) def _get_picks(raw): return pick_types(raw.info, meg=True, eeg=False, stim=False, ecg=False, eog=False, exclude='bads') def _get_epochs(): raw = _get_raw() events = _get_events() picks = _get_picks(raw) # Use a subset of channels for plotting speed picks = np.round(np.linspace(0, len(picks) + 1, n_chan)).astype(int) epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks, baseline=(None, 0)) return epochs def _get_epochs_delayed_ssp(): raw = _get_raw() events = _get_events() picks = _get_picks(raw) reject = dict(mag=4e-12) epochs_delayed_ssp = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks, baseline=(None, 0), proj='delayed', reject=reject) return epochs_delayed_ssp def test_plot_evoked(): """Test plotting of evoked """ evoked = _get_epochs().average() with warnings.catch_warnings(record=True): evoked.plot(proj=True, hline=[1]) # plot with bad channels excluded evoked.plot(exclude='bads') evoked.plot(exclude=evoked.info['bads']) # does the same thing # test selective updating of dict keys is working. evoked.plot(hline=[1], units=dict(mag='femto foo')) evoked_delayed_ssp = _get_epochs_delayed_ssp().average() evoked_delayed_ssp.plot(proj='interactive') evoked_delayed_ssp.apply_proj() assert_raises(RuntimeError, evoked_delayed_ssp.plot, proj='interactive') evoked_delayed_ssp.info['projs'] = [] assert_raises(RuntimeError, evoked_delayed_ssp.plot, proj='interactive') assert_raises(RuntimeError, evoked_delayed_ssp.plot, proj='interactive', axes='foo') evoked.plot_image(proj=True) # plot with bad channels excluded evoked.plot_image(exclude='bads') evoked.plot_image(exclude=evoked.info['bads']) # does the same thing plt.close('all')

32.785047

77

0.661345

04c0dfee375fa10451792eb5dc0b3eb7e94a2830

54

py

Python

xuperchain/__init__.py

xuperchain/contract-sdk-py

190c1f80d055ea29c3cb16e4eeea46845e06cd88

[ "Apache-2.0" ]

null

xuperchain/__init__.py

xuperchain/contract-sdk-py

190c1f80d055ea29c3cb16e4eeea46845e06cd88

[ "Apache-2.0" ]

null

xuperchain/__init__.py

xuperchain/contract-sdk-py

190c1f80d055ea29c3cb16e4eeea46845e06cd88

[ "Apache-2.0" ]

1

2021-04-02T03:50:57.000Z

from xuperchain.contract_method import contract_method

54

0.925926

3a883431edaa642a8b96cac6844270b437843b04

5,548

py

Python

pruning/main.py

acnagle/optimal-lottery-tickets

9412547700c359339c819d8144e67c2f33a9e786

[ "Apache-2.0" ]

2

2020-11-26T00:37:23.000Z

2021-10-03T18:26:11.000Z

pruning/main.py

acnagle/optimal-lottery-tickets

9412547700c359339c819d8144e67c2f33a9e786

[ "Apache-2.0" ]

null

pruning/main.py

acnagle/optimal-lottery-tickets

9412547700c359339c819d8144e67c2f33a9e786

[ "Apache-2.0" ]

1

2021-06-24T11:36:04.000Z

from __future__ import print_function import os import math import random import numpy as np import sys import time import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import CosineAnnealingLR import torch.autograd as autograd from utils import data from utils.train_test import train, test import models from args import args def main(): print(args, '\n') random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) device = get_device(args) data = get_dataset(args) model = get_model(args, data, device) print('\n'+str(model)+'\n') # Only pass the parameters where p.requires_grad == True to the optimizer optimizer = optim.SGD( [p for p in model.parameters() if p.requires_grad], lr=args.lr, momentum=args.momentum, weight_decay=args.wd, ) criterion = nn.CrossEntropyLoss().to(device) scheduler = CosineAnnealingLR(optimizer, T_max=args.epochs) test_acc_arr = np.array([np.nan] * args.epochs) start = time.time() for epoch in range(args.epochs): train(model, device, data.train_loader, optimizer, criterion, epoch+1, args.log_interval) test_acc_arr[epoch] = test(model, device, data.test_loader, criterion, args.batch_size) scheduler.step() end = time.time() total_time = (end - start) / 60 num_weights = sum(x.numel() for x in model.parameters() if x.requires_grad) # This calcuation does not include the number of weights in convolutional layers, including baseline models, since we are interested in observing the number of parameters in fully connected layers only. Convolutional layers are randomly initialized and never trained/pruned in any of the models. Note that num_weights is equal to the number of parameters that are being updated in the network print('\nTotal time spent pruning/training: {:.2f} minutes'.format(total_time)) print('Total number of parameters in model:', num_weights) if args.arch not in ['TwoLayerFC', 'FourLayerFC', 'LeNet5']: num_params_pruned = int(num_weights * args.sparsity) num_params_remaining = num_weights - num_params_pruned print('Number of parameters in pruned model:', num_params_remaining) else: num_params_remaining = None if args.save_results or args.save_model: save(model, test_acc_arr, total_time, num_weights, num_params_remaining, args) def get_device(args): use_cuda = not args.no_cuda and torch.cuda.is_available() if args.gpu is None: device = torch.device('cuda:0' if use_cuda else 'cpu') else: device = 'cuda:' + str(args.gpu) if use_cuda: torch.cuda.device(device) print('Using device {} for training and testing'.format(device)) return device def get_dataset(args): print('Benchmarking with the {} dataset'.format(args.dataset)) dataset = getattr(data, args.dataset.upper())(args) return dataset def get_model(args, data, device): if args.redundancy <= 0: raise ValueError('Redundancy factor must be greater than or equal to 1') print('Creating model {}'.format(args.arch)) model = models.__dict__[args.arch](data.INPUT_SIZE, data.NUM_CLASSES, args) if not args.no_cuda: model.cuda(device) if args.freeze_weights: freeze_model_weights(model) return model def freeze_model_weights(model): print('\nFreezing model weights:') for weight_attr in ['weight', 'weight1', 'weight2']: for n, m in model.named_modules(): if hasattr(m, weight_attr) and getattr(m, weight_attr) is not None: print(f' No gradient to {n}.{weight_attr}') getattr(m, weight_attr).requires_grad = False if getattr(m, weight_attr).grad is not None: print(f' Setting gradient of {n}.{weight_attr} to None') getattr(m, weight_attr).grad = None if hasattr(m, "bias") and m.bias is not None: print(f' No gradient to {n}.bias') m.bias.requires_grad = False if m.bias.grad is not None: print(f' Setting gradient of {n}.bias to None') m.bias.grad = None def save(model, test_acc_arr, total_time, num_weights, num_params_remaining, args): if args.arch not in ['TwoLayerFC', 'FourLayerFC', 'LeNet5']: filename = 'r'+str(args.redundancy)+'_s'+str(args.sparsity)+'_' else: filename = '' filename += 'e'+str(args.epochs)+'_h'+str(args.hidden_size) if args.use_relu: filename += '_relu' if args.save_results: save_dir = './results/'+args.arch if not os.path.exists(save_dir): os.makedirs(save_dir) np.savez(save_dir+'/'+filename+'.npz', args=vars(args), test_acc=test_acc_arr, total_time=total_time, sparsity=args.sparsity, num_weights=num_weights, num_params_remaining=num_params_remaining ) if args.save_model: save_dir = './weights/'+args.arch if not os.path.exists(save_dir): os.makedirs(save_dir) torch.save(model.state_dict(), save_dir+'/'+filename+'.pt') if __name__ == '__main__': main()

32.828402

476

0.650324

822b1b8e47ebc1e15cce2fdbacb755fccd62f9b0

837

py

Python

tests/model_tasklog_tests.py

richard-ma/dress

86e892673635319c0a1860edb33cdba7ed22a7fb

[ "MIT" ]

2

2019-10-23T09:06:47.000Z

2019-11-07T12:52:42.000Z

tests/model_tasklog_tests.py

richard-ma/dress

86e892673635319c0a1860edb33cdba7ed22a7fb

[ "MIT" ]

4

2017-12-28T01:44:42.000Z

2017-12-31T13:08:18.000Z

tests/model_tasklog_tests.py

richard-ma/dress

86e892673635319c0a1860edb33cdba7ed22a7fb

[ "MIT" ]

2

2019-10-15T07:42:33.000Z

2019-10-24T06:49:22.000Z

#!/usr/bin/env python import unittest from flask_testing import TestCase import dress from dress.models import * from manager import seed class ModelTaskLogTestCase(TestCase): def create_app(self): app = dress.create_app() app.config.testing = True return app def setUp(self): seed() def tearDown(self): pass def test_create_setting_with_value(self): task_name = 'unknown' custom_data = { 'hello': 'world' } tl = TaskLog(task_name, custom_data) tl.create() query_tl = TaskLog.query.one() self.assertEqual(query_tl.task_name, task_name) self.assertEqual(query_tl.custom_data, custom_data) self.assertEqual(custom_data['hello'], 'world') if __name__ == '__main__': unittest.main()

21.461538

59

0.634409

3ff234037e4aabe49b84f6ae1ccc32bffeb82207

14,618

py

Python

ctapipe/reco/hillas_intersection.py

mpecimotika/ctapipe

ffd7930921f7139b761fbf1208da16dd302e97a6

[ "BSD-3-Clause" ]

null

ctapipe/reco/hillas_intersection.py

mpecimotika/ctapipe

ffd7930921f7139b761fbf1208da16dd302e97a6

[ "BSD-3-Clause" ]

null

ctapipe/reco/hillas_intersection.py

mpecimotika/ctapipe

ffd7930921f7139b761fbf1208da16dd302e97a6

[ "BSD-3-Clause" ]

null

# Licensed under a 3-clause BSD style license - see LICENSE.rst """ TODO: - Speed tests, need to be certain the looping on all telescopes is not killing performance - Introduce new weighting schemes - Make intersect_lines code more readable """ import numpy as np import itertools import astropy.units as u from ctapipe.reco.reco_algorithms import Reconstructor from ctapipe.io.containers import ReconstructedShowerContainer from ctapipe.coordinates import NominalFrame, HorizonFrame from ctapipe.coordinates import TiltedGroundFrame, project_to_ground from ctapipe.instrument import get_atmosphere_profile_functions __all__ = [ 'HillasIntersection' ] class HillasIntersection(Reconstructor): """ This class is a simple re-implementation of Hillas parameter based event reconstruction. e.g. https://arxiv.org/abs/astro-ph/0607333 In this case the Hillas parameters are all constructed in the shared angular ( Nominal) system. Direction reconstruction is performed by extrapolation of the major axes of the Hillas parameters in the nominal system and the weighted average of the crossing points is taken. Core reconstruction is performed by performing the same procedure in the tilted ground system. The height of maximum is reconstructed by the projection os the image centroid onto the shower axis, taking the weighted average of all images. Uncertainties on the positions are provided by taking the spread of the crossing points, however this means that no uncertainty can be provided for multiplicity 2 events. """ def __init__(self, atmosphere_profile_name="paranal"): # We need a conversion function from height above ground to depth of maximum # To do this we need the conversion table from CORSIKA _ = get_atmosphere_profile_functions(atmosphere_profile_name) self.thickness_profile, self.altitude_profile = _ def predict(self, hillas_parameters, tel_x, tel_y, array_direction): """ Parameters ---------- hillas_parameters: dict Dictionary containing Hillas parameters for all telescopes in reconstruction tel_x: dict Dictionary containing telescope position on ground for all telescopes in reconstruction tel_y: dict Dictionary containing telescope position on ground for all telescopes in reconstruction array_direction: HorizonFrame Pointing direction of the array Returns ------- ReconstructedShowerContainer: """ src_x, src_y, err_x, err_y = self.reconstruct_nominal(hillas_parameters) core_x, core_y, core_err_x, core_err_y = self.reconstruct_tilted( hillas_parameters, tel_x, tel_y) err_x *= u.rad err_y *= u.rad nom = NominalFrame(x=src_x * u.rad, y=src_y * u.rad, array_direction=array_direction) horiz = nom.transform_to(HorizonFrame()) result = ReconstructedShowerContainer() result.alt, result.az = horiz.alt, horiz.az tilt = TiltedGroundFrame(x=core_x * u.m, y=core_y * u.m, pointing_direction=array_direction) grd = project_to_ground(tilt) result.core_x = grd.x result.core_y = grd.y x_max = self.reconstruct_xmax(nom.x, nom.y, tilt.x, tilt.y, hillas_parameters, tel_x, tel_y, 90 * u.deg - array_direction.alt) result.core_uncert = np.sqrt(core_err_x * core_err_x + core_err_y * core_err_y) * u.m result.tel_ids = [h for h in hillas_parameters.keys()] result.average_size = np.mean([h.intensity for h in hillas_parameters.values()]) result.is_valid = True src_error = np.sqrt(err_x * err_x + err_y * err_y) result.alt_uncert = src_error.to(u.deg) result.az_uncert = src_error.to(u.deg) result.h_max = x_max result.h_max_uncert = np.nan result.goodness_of_fit = np.nan return result def reconstruct_nominal(self, hillas_parameters, weighting="Konrad"): """ Perform event reconstruction by simple Hillas parameter intersection in the nominal system Parameters ---------- hillas_parameters: dict Hillas parameter objects weighting: string Specify image weighting scheme used (HESS or Konrad style) Returns ------- Reconstructed event position in the nominal system """ if len(hillas_parameters) < 2: return None # Throw away events with < 2 images # Find all pairs of Hillas parameters combos = itertools.combinations(list(hillas_parameters.values()), 2) hillas_pairs = list(combos) # Copy parameters we need to a numpy array to speed things up h1 = list( map( lambda h: [h[0].psi.to(u.rad).value, h[0].x.value, h[0].y.value, h[0].intensity], hillas_pairs ) ) h1 = np.array(h1) h1 = np.transpose(h1) h2 = list( map(lambda h: [h[1].psi.to(u.rad).value, h[1].x.value, h[1].y.value, h[1].intensity], hillas_pairs) ) h2 = np.array(h2) h2 = np.transpose(h2) # Perform intersection sx, sy = self.intersect_lines(h1[1], h1[2], h1[0], h2[1], h2[2], h2[0]) if weighting == "Konrad": weight_fn = self.weight_konrad elif weighting == "HESS": weight_fn = self.weight_HESS # Weight by chosen method weight = weight_fn(h1[3], h2[3]) # And sin of interception angle weight *= self.weight_sin(h1[0], h2[0]) # Make weighted average of all possible pairs x_pos = np.average(sx, weights=weight) y_pos = np.average(sy, weights=weight) var_x = np.average((sx - x_pos) ** 2, weights=weight) var_y = np.average((sy - y_pos) ** 2, weights=weight) # Copy into nominal coordinate return x_pos, y_pos, np.sqrt(var_x), np.sqrt(var_y) def reconstruct_tilted(self, hillas_parameters, tel_x, tel_y, weighting="Konrad"): """ Core position reconstruction by image axis intersection in the tilted system Parameters ---------- hillas_parameters: dict Hillas parameter objects tel_x: dict Telescope X positions, tilted system tel_y: dict Telescope Y positions, tilted system weighting: str Weighting scheme for averaging of crossing points Returns ------- (float, float, float, float): core position X, core position Y, core uncertainty X, core uncertainty X """ if len(hillas_parameters) < 2: return None # Throw away events with < 2 images h = list() tx = list() ty = list() # Need to loop here as dict is unordered for tel in hillas_parameters.keys(): h.append(hillas_parameters[tel]) tx.append(tel_x[tel]) ty.append(tel_y[tel]) # Find all pairs of Hillas parameters hillas_pairs = list(itertools.combinations(h, 2)) tel_x = list(itertools.combinations(tx, 2)) tel_y = list(itertools.combinations(ty, 2)) tx = np.zeros((len(tel_x), 2)) ty = np.zeros((len(tel_y), 2)) for i, _ in enumerate(tel_x): tx[i][0], tx[i][1] = tel_x[i][0].value, tel_x[i][1].value ty[i][0], ty[i][1] = tel_y[i][0].value, tel_y[i][1].value tel_x = np.array(tx) tel_y = np.array(ty) # Copy parameters we need to a numpy array to speed things up h1 = map(lambda h: [h[0].psi.to(u.rad).value, h[0].intensity], hillas_pairs) h1 = np.array(list(h1)) h1 = np.transpose(h1) h2 = map(lambda h: [h[1].psi.to(u.rad).value, h[1].intensity], hillas_pairs) h2 = np.array(list(h2)) h2 = np.transpose(h2) # Perform intersection cx, cy = self.intersect_lines(tel_x[:, 0], tel_y[:, 0], h1[0], tel_x[:, 1], tel_y[:, 1], h2[0]) if weighting == "Konrad": weight_fn = self.weight_konrad elif weighting == "HESS": weight_fn = self.weight_HESS # Weight by chosen method weight = weight_fn(h1[1], h2[1]) # And sin of interception angle weight *= self.weight_sin(h1[0], h2[0]) # Make weighted average of all possible pairs x_pos = np.average(cx, weights=weight) y_pos = np.average(cy, weights=weight) var_x = np.average((cx - x_pos) ** 2, weights=weight) var_y = np.average((cy - y_pos) ** 2, weights=weight) return x_pos, y_pos, np.sqrt(var_x), np.sqrt(var_y) def reconstruct_xmax(self, source_x, source_y, core_x, core_y, hillas_parameters, tel_x, tel_y, zen): """ Geometrical depth of shower maximum reconstruction, assuming the shower maximum lies at the image centroid Parameters ---------- source_x: float Source X position in nominal system source_y: float Source Y position in nominal system core_x: float Core X position in nominal system core_y: float Core Y position in nominal system hillas_parameters: dict Dictionary of hillas parameters objects tel_x: dict Dictionary of telescope X positions tel_y: dict Dictionary of telescope X positions zen: float Zenith angle of shower Returns ------- float: Estimated depth of shower maximum """ cog_x = list() cog_y = list() amp = list() tx = list() ty = list() # Loops over telescopes in event for tel in hillas_parameters.keys(): cog_x.append(hillas_parameters[tel].x.to(u.rad).value) cog_y.append(hillas_parameters[tel].y.to(u.rad).value) amp.append(hillas_parameters[tel].intensity) tx.append(tel_x[tel].to(u.m).value) ty.append(tel_y[tel].to(u.m).value) height = get_shower_height(source_x.to(u.rad).value, source_y.to(u.rad).value, np.array(cog_x), np.array(cog_y), core_x.to(u.m).value, core_y.to(u.m).value, np.array(tx), np.array(ty)) weight = np.array(amp) mean_height = np.sum(height * weight) / np.sum(weight) # This value is height above telescope in the tilted system, # we should convert to height above ground mean_height *= np.cos(zen) # Add on the height of the detector above sea level mean_height += 2100 # TODO: replace with instrument info if mean_height > 100000 or np.isnan(mean_height): mean_height = 100000 mean_height *= u.m # Lookup this height in the depth tables, the convert Hmax to Xmax x_max = self.thickness_profile(mean_height.to(u.km)) # Convert to slant depth x_max /= np.cos(zen) return x_max @staticmethod def intersect_lines(xp1, yp1, phi1, xp2, yp2, phi2): """ Perform intersection of two lines. This code is borrowed from read_hess. Parameters ---------- xp1: ndarray X position of first image yp1: ndarray Y position of first image phi1: ndarray Rotation angle of first image xp2: ndarray X position of second image yp2: ndarray Y position of second image phi2: ndarray Rotation angle of second image Returns ------- ndarray of x and y crossing points for all pairs """ sin_1 = np.sin(phi1) cos_1 = np.cos(phi1) a1 = sin_1 b1 = -1 * cos_1 c1 = yp1 * cos_1 - xp1 * sin_1 sin_2 = np.sin(phi2) cos_2 = np.cos(phi2) a2 = sin_2 b2 = -1 * cos_2 c2 = yp2 * cos_2 - xp2 * sin_2 det_ab = (a1 * b2 - a2 * b1) det_bc = (b1 * c2 - b2 * c1) det_ca = (c1 * a2 - c2 * a1) # if math.fabs(det_ab) < 1e-14 : # /* parallel */ # return 0,0 xs = det_bc / det_ab ys = det_ca / det_ab return xs, ys @staticmethod def weight_konrad(p1, p2): return (p1 * p2) / (p1 + p2) @staticmethod def weight_hess(p1, p2): return 1 / ((1 / p1) + (1 / p2)) @staticmethod def weight_sin(phi1, phi2): return np.abs(np.sin(np.fabs(phi1 - phi2))) def get_shower_height(source_x, source_y, cog_x, cog_y, core_x, core_y, tel_pos_x, tel_pos_y): """ Function to calculate the depth of shower maximum geometrically under the assumption that the shower maximum lies at the brightest point of the camera image. Parameters ---------- source_x: float Event source position in nominal frame source_y: float Event source position in nominal frame core_x: float Event core position in telescope tilted frame core_y: float Event core position in telescope tilted frame zen: float Zenith angle of event Returns ------- float: Depth of maximum of air shower """ # Calculate displacement of image centroid from source position (in rad) disp = np.sqrt(np.power(cog_x - source_x, 2) + np.power(cog_y - source_y, 2)) # Calculate impact parameter of the shower impact = np.sqrt(np.power(tel_pos_x - core_x, 2) + np.power(tel_pos_y - core_y, 2)) # Distance above telescope is ration of these two (small angle) height = impact / disp return height

33.995349

88

0.577918

cfbb0558359bc109f34b756df9635c9844639ec7

2,350

py

Python

color-depth-reduction/CIFAR-10/CW_attack.py

jfc43/pixel-discretization

1543649e5172cb4f8226962a5ab5087091910418

[ "Apache-2.0" ]

6

2019-03-08T23:09:20.000Z

2021-07-29T19:23:58.000Z

color-depth-reduction/CIFAR-10/CW_attack.py

jfc43/pixel-discretization

1543649e5172cb4f8226962a5ab5087091910418

[ "Apache-2.0" ]

null

color-depth-reduction/CIFAR-10/CW_attack.py

jfc43/pixel-discretization

1543649e5172cb4f8226962a5ab5087091910418

[ "Apache-2.0" ]

1

2020-02-05T20:07:19.000Z

import tensorflow as tf import numpy as np from util import preprocess class CWAttack: def __init__(self, model, num_steps, step_size, epsilon, codes, batch_size, alpha): self.model = model self.num_steps = num_steps self.step_size = step_size self.codes = codes self.xs = tf.Variable(np.zeros((batch_size, 32, 32, 3), dtype=np.float32), name='modifier') self.orig_xs = tf.placeholder(tf.float32, [batch_size, 32, 32, 3]) self.ys = tf.placeholder(tf.int32, [batch_size]) self.epsilon = epsilon delta = tf.clip_by_value(self.xs, 0, 255) - self.orig_xs delta = tf.clip_by_value(delta, -self.epsilon, self.epsilon) self.do_clip_xs = tf.assign(self.xs, self.orig_xs+delta) w = [] cw = [] for i in range(codes.shape[0]): wt = tf.exp(-alpha*tf.abs(self.xs-codes[i])) w.append(wt) cw.append(codes[i]*wt) self.z = sum(cw)/(sum(w)) logits = self.model.forward(self.z) label_mask = tf.one_hot(self.ys, 10) correct_logit = tf.reduce_sum(label_mask * logits, axis=1) wrong_logit = tf.reduce_max((1-label_mask) * logits - 1e4*label_mask, axis=1) self.loss = (correct_logit - wrong_logit) start_vars = set(x.name for x in tf.global_variables()) optimizer = tf.train.AdamOptimizer(step_size*1) grad,var = optimizer.compute_gradients(self.loss, [self.xs])[0] self.train = optimizer.apply_gradients([(tf.sign(grad),var)]) end_vars = tf.global_variables() self.new_vars = [x for x in end_vars if x.name not in start_vars] self.new_vars_initializer = tf.variables_initializer(self.new_vars) def perturb(self, x, y, sess): sess.run(self.new_vars_initializer) sess.run(self.xs.initializer) sess.run(self.do_clip_xs, {self.orig_xs: x}) for i in range(self.num_steps): imgs = sess.run(self.xs) points = imgs.reshape((-1,3)) t = preprocess(imgs, self.codes) sess.run(self.train, feed_dict={self.ys: y, self.z: t}) sess.run(self.do_clip_xs, {self.orig_xs: x}) return sess.run(self.xs)

37.301587

87

0.58766

0d0d2ff8103a0b8723377d21503c48a8cc4f7b90

30,165

py

Python

built-in/TensorFlow/Official/cv/detection/MaskRcnn_ID0011_for_TensorFlow/dataloader_.py

Ascend/modelzoo

f018cfed33dbb1cc2110b9ea2e233333f71cc509

[ "Apache-2.0" ]

12

2020-12-13T08:34:24.000Z

2022-03-20T15:17:17.000Z

built-in/TensorFlow/Official/cv/detection/MaskRcnn_ID0011_for_TensorFlow/dataloader_.py

Ascend/modelzoo

f018cfed33dbb1cc2110b9ea2e233333f71cc509

[ "Apache-2.0" ]

1

2022-01-20T03:11:05.000Z

2022-01-20T06:53:39.000Z

built-in/TensorFlow/Official/cv/detection/MaskRcnn_ID0011_for_TensorFlow/dataloader_.py

Ascend/modelzoo

f018cfed33dbb1cc2110b9ea2e233333f71cc509

[ "Apache-2.0" ]

2

2021-07-10T12:40:46.000Z

2021-12-17T07:55:15.000Z

# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # ============================================================================ # 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. # # ============================================================================== """Data loader and processing. Defines input_fn of Mask-RCNN for TF Estimator. The input_fn includes training data for category classification, bounding box regression, and number of positive examples to normalize the loss during training. """ import tensorflow.compat.v1 as tf import anchors import coco_utils import preprocess_ops import spatial_transform_ops from object_detection import tf_example_decoder from utils import box_utils from utils import dataloader_utils from utils import input_utils from dataloader import extract_objects_parser MAX_NUM_INSTANCES = 100 MAX_NUM_VERTICES_PER_INSTANCE = 1500 MAX_NUM_POLYGON_LIST_LEN = 2 * MAX_NUM_VERTICES_PER_INSTANCE * MAX_NUM_INSTANCES POLYGON_PAD_VALUE = coco_utils.POLYGON_PAD_VALUE def _prepare_labels_for_eval(data, target_num_instances=MAX_NUM_INSTANCES, target_polygon_list_len=MAX_NUM_POLYGON_LIST_LEN, use_instance_mask=False): """Create labels dict for infeed from data of tf.Example.""" image = data['image'] height = tf.shape(image)[0] width = tf.shape(image)[1] boxes = data['groundtruth_boxes'] classes = data['groundtruth_classes'] classes = tf.cast(classes, dtype=tf.float32) num_labels = tf.shape(classes)[0] boxes = preprocess_ops.pad_to_fixed_size(boxes, -1, [target_num_instances, 4]) classes = preprocess_ops.pad_to_fixed_size(classes, -1, [target_num_instances, 1]) is_crowd = data['groundtruth_is_crowd'] is_crowd = tf.cast(is_crowd, dtype=tf.float32) is_crowd = preprocess_ops.pad_to_fixed_size(is_crowd, 0, [target_num_instances, 1]) labels = {} labels['width'] = width labels['height'] = height labels['groundtruth_boxes'] = boxes labels['groundtruth_classes'] = classes labels['num_groundtruth_labels'] = num_labels labels['groundtruth_is_crowd'] = is_crowd if use_instance_mask: polygons = data['groundtruth_polygons'] polygons = preprocess_ops.pad_to_fixed_size(polygons, POLYGON_PAD_VALUE, [target_polygon_list_len, 1]) labels['groundtruth_polygons'] = polygons if 'groundtruth_area' in data: groundtruth_area = data['groundtruth_area'] groundtruth_area = preprocess_ops.pad_to_fixed_size( groundtruth_area, 0, [target_num_instances, 1]) labels['groundtruth_area'] = groundtruth_area return labels class InputReader(object): """Input reader for dataset.""" def __init__(self, file_pattern, mode=tf.estimator.ModeKeys.TRAIN, num_examples=0, use_fake_data=False, use_instance_mask=False, max_num_instances=MAX_NUM_INSTANCES, max_num_polygon_list_len=MAX_NUM_POLYGON_LIST_LEN): self._file_pattern = file_pattern self._max_num_instances = max_num_instances self._max_num_polygon_list_len = max_num_polygon_list_len self._mode = mode self._num_examples = num_examples self._use_fake_data = use_fake_data self._use_instance_mask = use_instance_mask self._include_mask = True self._skip_crowd_during_training = True self._aug_rand_hflip = True self._min_level = 2 self._max_level = 6 self._aug_scale_min = 0.5 self._aug_scale_max = 2 self._output_size = [1024, 1024] self._mask_crop_size = 112 self._copy_paste_occluded_obj_threshold = 300 self._copy_paste_box_update_threshold = 10 def _transform_mask(self, image_shape, scale, offset, mask): """Transform input mask according to the image info (scale, offset)""" image_scaled_shape = tf.round( tf.cast(image_shape, tf.float32) * scale ) image_scaled_shape = tf.cast(image_scaled_shape, tf.int32) offset = tf.cast(offset, tf.int32) mask_shape = tf.shape(mask) mask = tf.image.pad_to_bounding_box( mask, offset[0], offset[1], tf.maximum(image_scaled_shape[0], mask_shape[0]) + offset[0], tf.maximum(image_scaled_shape[1], mask_shape[1]) + offset[1], ) mask = mask[0:image_scaled_shape[0], 0:image_scaled_shape[1], :] mask = tf.image.resize(mask, image_shape) return mask def _get_occluded_bbox(self, updated_bbox, bbox): # finds bbox coordinates which are occluded by the new pasted objects. # if the difference between the bounding box coordinates of updated masks # and the original bounding box are larger than a threshold then those # coordinates are considered as occluded return tf.greater(tf.abs(updated_bbox - tf.cast(bbox, bbox.dtype)), self._copy_paste_box_update_threshold) def _get_visible_masks_indices(self, masks, boxes_, cropped_boxes): """return indices of not fully occluded objects""" occluded_objects = tf.reduce_any( self._get_occluded_bbox(boxes_, cropped_boxes) ) areas = tf.reduce_sum(masks, axis=[1, 2]) # among the occluded objects, find the objects that their mask area is # less than copy_paste_occluded_obj_threshold.These objects are considered # as fully occluded objects and will be removed from the ground truth indices = tf.where( tf.math.logical_or( tf.greater(areas, self._copy_paste_occluded_obj_threshold), tf.math.logical_not(occluded_objects) ) ) indices = tf.reshape(indices, [-1]) return indices def _compute_boxes_using_masks(self, masks, image_shape, image_info, image_scale, offset): """computes bounding boxes using masks""" masks = tf.cast(masks, tf.int8) x = tf.reduce_max(masks, axis=1) xmin = tf.cast(tf.argmax(x, 1), tf.int16) xmax = tf.cast(image_shape[1], tf.int16) - tf.cast(tf.argmax(tf.reverse(x, [1]), 1), tf.int16) y = tf.reduce_max(masks, axis=2) ymin = tf.cast(tf.argmax(y, 1), tf.int16) ymax = tf.cast(image_shape[0], tf.int16) - tf.cast(tf.argmax(tf.reverse(y, [1]), 1), tf.int16) bbox = tf.stack([ymin, xmin, ymax, xmax], -1) # clips boxes bbox = tf.cast(bbox, tf.float32) bbox = input_utils.resize_and_crop_boxes( bbox, image_scale, image_info[1, :], offset ) bbox += tf.tile(tf.expand_dims(offset, axis=0), [1, 2]) bbox /= tf.tile(tf.expand_dims(image_scale, axis=0), [1, 2]) return bbox def _parse_train_data_extractObjs(self, data): """ parses data for training. Args: data: the decoded tensor dictionary from tfexampledecoder returns: image: image tensor that is preprocessed to have normalized value and dimension [output_size[0], output_size[1], 4] labels: a dictionary of tensors used for training. """ classes = data['groundtruth_classes2'] boxes = data['groundtruth_boxes2'] if self._include_mask: masks = data['groundtruth_instance_masks2'] is_crowds = data['groundtruth_is_crowd2'] # skips annotation with 'is_crowd' = True if self._skip_crowd_during_training: num_groundtrtuhs = tf.shape(classes)[0] with tf.control_dependencies([num_groundtrtuhs, is_crowds]): indices = tf.cond( tf.greater(tf.size(data['groundtruth_is_crowd2']), 0), lambda: data['groundtruth_is_crowd2'], lambda: tf.zeros_like(data['groundtruth_classes2'], dtype=tf.bool) ) indices = tf.where(tf.logical_not(indices)) classes = tf.gather_nd(classes, indices) boxes = tf.gather_nd(boxes, indices) if self._include_mask: masks = tf.gather_nd(masks, indices) # gets original image and its size image = data['image2'] image_shape = tf.shape(image)[0:2] # normalizes image with mean and std pixel values image = input_utils.normalize_image(image) # flips image randomly during training if self._aug_rand_hflip: if self._include_mask: image, boxes, masks = input_utils.random_horizontal_flip( image, boxes, masks ) else: image, boxes = input_utils.random_horizontal_flip( image, boxes ) # converts boxes from normaliezd coordinates to pixel coordinates. # now the coordinates of boxes are w.r.t. the original image. boxes = box_utils.denormalize_boxes(boxes, image_shape) # resizes and crops image image, image_info, _ = input_utils.resize_and_crop_image( image, self._output_size, padded_size=input_utils.compute_padded_size( self._output_size, 2 ** self._max_level ), aug_scale_min=self._aug_scale_min, aug_scale_max=self._aug_scale_max ) # resizes and crops boxes # now the coordinates of boxes are w.r.t. the scaled image. image_scale = image_info[2, :] offset = image_info[3, :] boxes = input_utils.resize_and_crop_boxes( boxes, image_scale, image_info[1, :], offset ) # filters out groundtruth boxes that are all zeros indices = box_utils.get_non_empty_box_indices(boxes) boxes = tf.gather(boxes, indices) classes = tf.gather(classes, indices) if self._include_mask: masks = tf.gather(masks, indices) uncropped_masks = tf.cast(masks, tf.int8) uncropped_masks = tf.expand_dims(uncropped_masks, axis=3) uncropped_masks = input_utils.resize_and_crop_masks( uncropped_masks, image_scale, self._output_size, offset ) # transfer boxes to the original image space and do normalization cropped_boxes = boxes + tf.tile(tf.expand_dims(offset, axis=0), [1, 2]) cropped_boxes /= tf.tile(tf.expand_dims(image_scale, axis=0), [1, 2]) cropped_boxes = box_utils.normalize_boxes(cropped_boxes, image_shape) num_masks = tf.shape(masks)[0] masks = tf.image.crop_and_resize( tf.expand_dims(masks, axis=-1), cropped_boxes, box_indices = tf.range(num_masks, dtype=tf.int32), crop_size = [self._mask_crop_size, self._mask_crop_size], method='bilinear' ) masks = tf.squeeze(masks, axis=-1) indices = tf.range(start=0, limit=tf.shape(classes)[0], dtype=tf.int32) # samples the numbers of masks for pasting m = tf.random.uniform(shape=[], maxval=tf.shape(classes)[0]+1, dtype=tf.int32) m = tf.math.minimum(m, tf.shape(classes)[0]) # shuffle the indices of objects and keep the first m objects for pasting shuffled_indices = tf.random.shuffle(indices) shuffled_indices = tf.slice(shuffled_indices, [0], [m]) boxes = tf.gather(boxes, shuffled_indices) masks = tf.gather(masks, shuffled_indices) classes = tf.gather(classes, shuffled_indices) uncropped_masks = tf.gather(uncropped_masks, shuffled_indices) pasted_objects_mask = tf.reduce_max(uncropped_masks, 0) pasted_objects_mask = tf.cast(pasted_objects_mask, tf.bool) labels = { 'image':image, 'image_info':image_info, 'num_groundtrtuhs':tf.shape(classes)[0], 'boxes':boxes, 'masks':masks, 'classes':classes, 'pasted_objects_mask':pasted_objects_mask, } return labels def _create_dataset_fn(self): # Prefetch data from files. def _prefetch_dataset(filename): dataset = tf.data.TFRecordDataset(filename).prefetch(1) return dataset return _prefetch_dataset def _create_example_decoder(self): return tf_example_decoder.TfExampleDecoder( use_instance_mask=self._use_instance_mask) def _create_dataset_parser_fn(self, params): """Create parser for parsing input data (dictionary).""" example_decoder = self._create_example_decoder() def _dataset_parser(value, value2=None): """Parse data to a fixed dimension input image and learning targets. Args: value: A dictionary contains an image and groundtruth annotations. Returns: features: a dictionary that contains the image and auxiliary information. The following describes {key: value} pairs in the dictionary. image: Image tensor that is preproessed to have normalized value and fixed dimension [image_size, image_size, 3] image_info: image information that includes the original height and width, the scale of the proccessed image to the original image, and the scaled height and width. source_ids: Source image id. Default value -1 if the source id is empty in the groundtruth annotation. labels: a dictionary that contains auxiliary information plus (optional) labels. The following describes {key: value} pairs in the dictionary. `labels` is only for training. score_targets_dict: ordered dictionary with keys [min_level, min_level+1, ..., max_level]. The values are tensor with shape [height_l, width_l, num_anchors]. The height_l and width_l represent the dimension of objectiveness score at l-th level. box_targets_dict: ordered dictionary with keys [min_level, min_level+1, ..., max_level]. The values are tensor with shape [height_l, width_l, num_anchors * 4]. The height_l and width_l represent the dimension of bounding box regression output at l-th level. gt_boxes: Groundtruth bounding box annotations. The box is represented in [y1, x1, y2, x2] format. The tennsor is padded with -1 to the fixed dimension [self._max_num_instances, 4]. gt_classes: Groundtruth classes annotations. The tennsor is padded with -1 to the fixed dimension [self._max_num_instances]. cropped_gt_masks: groundtrugh masks cropped by the bounding box and resized to a fixed size determined by params['gt_mask_size'] """ with tf.name_scope('parser'): data = example_decoder.decode(value) # extract data2 objs here if value2 is None: data2 = self._parse_train_data_extractObjs(data) else: data2 = value2 data['groundtruth_is_crowd'] = tf.cond( tf.greater(tf.size(data['groundtruth_is_crowd']), 0), lambda: data['groundtruth_is_crowd'], lambda: tf.zeros_like(data['groundtruth_classes'], dtype=tf.bool)) image = data['image'] image = tf.image.convert_image_dtype(image, dtype=tf.float32) orig_image = image source_id = data['source_id'] source_id = tf.where(tf.equal(source_id, tf.constant('')), '-1', source_id) source_id = tf.string_to_number(source_id) if (self._mode == tf.estimator.ModeKeys.PREDICT or self._mode == tf.estimator.ModeKeys.EVAL): image = preprocess_ops.normalize_image(image) if params['resize_method'] == 'retinanet': image, image_info, _, _, _ = preprocess_ops.resize_crop_pad( image, params['image_size'], 2 ** params['max_level']) else: image, image_info, _, _, _ = preprocess_ops.resize_crop_pad_v2( image, params['short_side'], params['long_side'], 2 ** params['max_level']) if params['precision'] == 'bfloat16': image = tf.cast(image, dtype=tf.bfloat16) features = { 'images': image, 'image_info': image_info, 'source_ids': source_id, } if params['visualize_images_summary']: resized_image = tf.image.resize_images(orig_image, params['image_size']) features['orig_images'] = resized_image if (params['include_groundtruth_in_features'] or self._mode == tf.estimator.ModeKeys.EVAL): labels = _prepare_labels_for_eval( data, target_num_instances=self._max_num_instances, target_polygon_list_len=self._max_num_polygon_list_len, use_instance_mask=params['include_mask']) return {'features': features, 'labels': labels} else: return {'features': features} elif self._mode == tf.estimator.ModeKeys.TRAIN: instance_masks = None if self._use_instance_mask: instance_masks = data['groundtruth_instance_masks'] boxes = data['groundtruth_boxes'] classes = data['groundtruth_classes'] classes = tf.reshape(tf.cast(classes, dtype=tf.float32), [-1, 1]) if not params['use_category']: classes = tf.cast(tf.greater(classes, 0), dtype=tf.float32) if (params['skip_crowd_during_training'] and self._mode == tf.estimator.ModeKeys.TRAIN): indices = tf.where(tf.logical_not(data['groundtruth_is_crowd'])) classes = tf.gather_nd(classes, indices) boxes = tf.gather_nd(boxes, indices) if self._use_instance_mask: instance_masks = tf.gather_nd(instance_masks, indices) image = preprocess_ops.normalize_image(image) if params['input_rand_hflip']: flipped_results = ( preprocess_ops.random_horizontal_flip( image, boxes=boxes, masks=instance_masks)) if self._use_instance_mask: image, boxes, instance_masks = flipped_results else: image, boxes = flipped_results # Scaling, jittering and padding. if params['resize_method'] == 'retinanet': image_shape = tf.shape(image)[0:2] boxes = box_utils.denormalize_boxes(boxes, image_shape) image, image_info_copyPaste, image_info = input_utils.resize_and_crop_image( image, params['image_size'], padded_size=input_utils.compute_padded_size( params['image_size'], 2 ** self._max_level ), aug_scale_min=params['aug_scale_min'], aug_scale_max=params['aug_scale_max'] ) # resizes and crops boxes # now the coordinates of boxes are w.r.t. the scaled image image_scale = image_info_copyPaste[2, :] offset = image_info_copyPaste[3, :] boxes = input_utils.resize_and_crop_boxes( boxes, image_scale, image_info_copyPaste[1, :], offset ) indices = box_utils.get_non_empty_box_indices(boxes) boxes = tf.gather(boxes, indices) classes = tf.gather(classes, indices) else: image, image_info, boxes, classes, cropped_gt_masks = ( preprocess_ops.resize_crop_pad_v2( image, params['short_side'], params['long_side'], 2 ** params['max_level'], aug_scale_min=params['aug_scale_min'], aug_scale_max=params['aug_scale_max'], boxes=boxes, classes=classes, masks=instance_masks, crop_mask_size=params['gt_mask_size'])) _copy_paste_aug = True if _copy_paste_aug: # paste objects and creates a new composed image compose_mask = tf.cast(data2['pasted_objects_mask'],image.dtype) * tf.ones_like(image) image = image * (1 - compose_mask) + data2['image'] * compose_mask if self._include_mask: masks = tf.gather(instance_masks, indices) if _copy_paste_aug: pasted_objects_mask = self._transform_mask( image_shape, image_scale, offset, tf.cast(data2['pasted_objects_mask'], tf.int8) ) pasted_objects_mask = tf.cast(pasted_objects_mask, tf.int8) pasted_objects_mask = tf.expand_dims( tf.squeeze(pasted_objects_mask, -1), 0) * tf.ones(tf.shape(masks), dtype=pasted_objects_mask.dtype) masks = tf.where( tf.equal(pasted_objects_mask, 1), tf.zeros_like(masks), masks ) cropped_boxes = boxes + tf.tile(tf.expand_dims(offset, axis=0), [1, 2]) cropped_boxes /= tf.tile(tf.expand_dims(image_scale, axis=0), [1, 2]) if _copy_paste_aug: # computes bounding boxes of objects using updated masks boxes_ = self._compute_boxes_using_masks( masks, image_shape, image_info_copyPaste, image_scale, offset ) # filters out objects that are fully occluded in the new image indices = self._get_visible_masks_indices( masks, boxes_, cropped_boxes ) boxes_ = tf.gather(boxes_, indices) boxes = tf.gather(boxes, indices) cropped_boxes = tf.gather(cropped_boxes, indices) masks = tf.gather(masks, indices) classes = tf.gather(classes, indices) # update bounding boxes of which are occluded by new pasted objects def update_bboxes(boxes_, cropped_boxes): occluded_bbox = self._get_occluded_bbox(boxes_, cropped_boxes) cropped_boxes = tf.where( occluded_bbox, tf.cast(boxes_, cropped_boxes.dtype), cropped_boxes ) boxes = input_utils.resize_and_crop_boxes( cropped_boxes, image_scale, image_info_copyPaste[1, :], offset ) return boxes, cropped_boxes boxes, cropped_boxes = update_bboxes(boxes_, cropped_boxes) cropped_boxes = box_utils.normalize_boxes(cropped_boxes, image_shape) num_masks = tf.shape(masks)[0] masks = tf.image.crop_and_resize( tf.expand_dims(masks, axis=-1), cropped_boxes, box_indices=tf.range(num_masks, dtype=tf.int32), crop_size=[self._mask_crop_size, self._mask_crop_size], method='bilinear' ) masks = tf.squeeze(masks, axis=-1) cropped_gt_masks = masks else: cropped_gt_masks = None if _copy_paste_aug: if self._include_mask: masks = tf.concat([masks, data2['masks']], axis=0) data2['classes'] = tf.reshape(tf.cast(data2['classes'], dtype=tf.float32), [-1, 1]) boxes = tf.concat([boxes, data2['boxes']], axis=0) classes = tf.concat([classes, data2['classes']], axis=0) if cropped_gt_masks is not None: cropped_gt_masks = tf.pad( cropped_gt_masks, paddings=tf.constant([[0, 0,], [2, 2,], [2, 2]]), mode='CONSTANT', constant_values=0.) padded_height, padded_width, _ = image.get_shape().as_list() padded_image_size = (padded_height, padded_width) input_anchors = anchors.Anchors( params['min_level'], params['max_level'], params['num_scales'], params['aspect_ratios'], params['anchor_scale'], padded_image_size) anchor_labeler = anchors.AnchorLabeler( input_anchors, params['num_classes'], params['rpn_positive_overlap'], params['rpn_negative_overlap'], params['rpn_batch_size_per_im'], params['rpn_fg_fraction']) # Assign anchors. score_targets, box_targets = anchor_labeler.label_anchors( boxes, classes) # Pad groundtruth data. boxes = preprocess_ops.pad_to_fixed_size( boxes, -1, [self._max_num_instances, 4]) classes = preprocess_ops.pad_to_fixed_size( classes, -1, [self._max_num_instances, 1]) # Pads cropped_gt_masks. if self._use_instance_mask: cropped_gt_masks = tf.reshape( cropped_gt_masks, tf.stack([tf.shape(cropped_gt_masks)[0], -1])) cropped_gt_masks = preprocess_ops.pad_to_fixed_size( cropped_gt_masks, -1, [self._max_num_instances, (params['gt_mask_size'] + 4) ** 2]) cropped_gt_masks = tf.reshape( cropped_gt_masks, [self._max_num_instances, params['gt_mask_size'] + 4, params['gt_mask_size'] + 4]) if params['precision'] == 'bfloat16': image = tf.cast(image, dtype=tf.bfloat16) features = { 'images': image, 'image_info': image_info, 'source_ids': source_id, } labels = {} for level in range(params['min_level'], params['max_level'] + 1): labels['score_targets_%d' % level] = score_targets[level] labels['box_targets_%d' % level] = box_targets[level] labels['gt_boxes'] = boxes labels['gt_classes'] = classes if self._use_instance_mask: labels['cropped_gt_masks'] = cropped_gt_masks return features, labels return _dataset_parser def get_data(self, _file_pattern, dataset_fn, input_context=None): dataset = tf.data.Dataset.list_files( _file_pattern, shuffle=(self._mode == tf.estimator.ModeKeys.TRAIN), seed=0) if input_context is not None: dataset = dataset.shard(input_context.num_input_pipelines, input_context.input_pipeline_id) if self._mode == tf.estimator.ModeKeys.TRAIN: dataset = dataset.repeat() dataset = dataset.apply( tf.data.experimental.parallel_interleave( dataset_fn, cycle_length=32, sloppy=(self._mode == tf.estimator.ModeKeys.TRAIN))) if self._mode == tf.estimator.ModeKeys.TRAIN: dataset = dataset.shuffle(64, seed=0) return dataset def _create_dataset_parser_fn_pre(self, params=None): parse_pre = extract_objects_parser.Parser( [1024, 1024], params['min_level'], params['max_level'], aug_rand_hflip=True, aug_scale_min=0.1, aug_scale_max=2.0, skip_crowd_during_training=True, include_mask=True, mask_crop_size=112 ) return parse_pre def __call__(self, params, input_context=None): dataset_parser_fn = self._create_dataset_parser_fn(params) dataset_fn = self._create_dataset_fn() batch_size = params['batch_size'] if 'batch_size' in params else 1 dataset = self.get_data(self._file_pattern, dataset_fn, input_context) dataset_p = self.get_data(self._file_pattern, dataset_fn, input_context) pre_parser_fn = self._create_dataset_parser_fn_pre(params) dataset_p = dataset_p.map( pre_parser_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE ) dataset_p = dataset_p.prefetch(tf.data.experimental.AUTOTUNE) dataset_p = dataset_p.filter( lambda data:tf.greater(data['num_groundtrtuhs'], 0) ) dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE) dataset = tf.data.Dataset.zip((dataset, dataset_p)) # Parse the fetched records to input tensors for model function. dataset = dataset.map( dataset_parser_fn, num_parallel_calls=256) dataset = dataset.batch(batch_size, drop_remainder=True) # Enable TPU performance optimization: transpose input, space-to-depth # image transform, or both. if (self._mode == tf.estimator.ModeKeys.TRAIN and (params['transpose_input'] or (params['backbone'].startswith('resnet') and params['conv0_space_to_depth_block_size'] > 0))): def _transform_images(features, labels): """Transforms images.""" images = features['images'] if (params['backbone'].startswith('resnet') and params['conv0_space_to_depth_block_size'] > 0): # Transforms images for TPU performance. features['images'] = ( spatial_transform_ops.fused_transpose_and_space_to_depth( images, params['conv0_space_to_depth_block_size'], params['transpose_input'])) else: features['images'] = tf.transpose(features['images'], [1, 2, 3, 0]) return features, labels dataset = dataset.map(_transform_images, num_parallel_calls=256) dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE) if self._num_examples > 0: dataset = dataset.take(self._num_examples) if self._use_fake_data: # Turn this dataset into a semi-fake dataset which always loop at the # first batch. This reduces variance in performance and is useful in # testing. dataset = dataset.take(1).cache().repeat() return dataset

41.954103

121

0.641273

ededdc8cbb2813f08bc96cfb328b7084691608eb

3,223

py

Python

dnnf/model.py

dlshriver/DNNF

898e4df69be35312ace622ce2c47f7bf8d6a0ffe

[ "MIT" ]

5

2021-01-13T19:52:03.000Z

2021-12-10T02:54:35.000Z

dnnf/model.py

dlshriver/DNNF

898e4df69be35312ace622ce2c47f7bf8d6a0ffe

[ "MIT" ]

1

2021-09-15T20:45:17.000Z

2021-09-23T15:47:10.000Z

dnnf/model.py

dlshriver/DNNF

898e4df69be35312ace622ce2c47f7bf8d6a0ffe

[ "MIT" ]

1

2021-11-03T02:56:30.000Z

import numpy as np import torch import torch.nn.functional as F from .reduction import HPolyProperty class FalsificationModel: def __init__(self, prop: HPolyProperty): self.prop = prop self.op_graph = prop.suffixed_op_graph() self.input_details = self.op_graph.input_details self.input_shape = tuple( int(d) if d > 0 else 1 for d in self.input_details[0].shape ) self.input_dtype = self.input_details[0].dtype self.input_torch_dtype = torch.from_numpy( np.ones((1,), dtype=self.input_dtype) ).dtype self.model = self.as_pytorch() def __call__(self, *args, **kwargs): return self.model(*args, **kwargs) def __reduce__(self): return FalsificationModel, (self.prop,) @property def input_lower_bound(self): lower_bounds = self.prop.input_lower_bounds assert len(lower_bounds) == 1 lower_bound = lower_bounds[0] return torch.from_numpy(lower_bound.astype(self.input_dtype)).to( self.model.device ) @property def input_upper_bound(self): upper_bounds = self.prop.input_upper_bounds assert len(upper_bounds) == 1 upper_bound = upper_bounds[0] return torch.from_numpy(upper_bound.astype(self.input_dtype)).to( self.model.device ) def as_pytorch(self): from .pytorch import convert return convert(self.op_graph.output_operations).eval() def as_tf(self): return self.op_graph.as_tf() def loss(self, y): return F.cross_entropy( y.reshape((1, -1)), torch.Tensor([0]).long().to(y.device) ) - F.cross_entropy(y.reshape((1, -1)), torch.Tensor([1]).long().to(y.device)) def project_input(self, x): y = x.detach() lb = self.input_lower_bound ub = self.input_upper_bound lb_violations = y < lb ub_violations = y > ub y[lb_violations] = lb[lb_violations] y[ub_violations] = ub[ub_violations] return y.detach() def sample(self): x = ( torch.rand( self.input_shape, device=self.model.device, dtype=self.input_torch_dtype, ) * (self.input_upper_bound - self.input_lower_bound) + self.input_lower_bound ) return x.detach() def step(self, x, y, alpha=0.05): loss = self.loss(y) loss.backward() gradients = x.grad neg_grads = gradients < 0 pos_grads = gradients > 0 lb = self.input_lower_bound ub = self.input_upper_bound gradients[(x == lb) & neg_grads] = 0 gradients[(x == ub) & pos_grads] = 0 if gradients.abs().max().item() < 1e-12: return lb = self.input_lower_bound ub = self.input_upper_bound epsilon = (ub - lb) / 2 if len(gradients.shape) == 1: x = x + F.normalize(gradients.reshape(1, -1)).flatten() * epsilon else: x = x + F.normalize(gradients) * epsilon return x.detach() def validate(self, x): return self.prop.validate_counter_example(x)

30.990385

86

0.591374

0b4b37e1b8ff30adeafa90e83bcf4e3c97bf4770

12,240

py

Python

lib/python/treadmill/context.py

drienyov/treadmill

ce21537cd9a2fdb0567ac2aa3de1afcb2f6861de

[ "Apache-2.0" ]

null

lib/python/treadmill/context.py

drienyov/treadmill

ce21537cd9a2fdb0567ac2aa3de1afcb2f6861de

[ "Apache-2.0" ]

null

lib/python/treadmill/context.py

drienyov/treadmill

ce21537cd9a2fdb0567ac2aa3de1afcb2f6861de

[ "Apache-2.0" ]

null

"""Treadmill context. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import functools import logging import socket from treadmill import plugin_manager _LOGGER = logging.getLogger(__name__) class ContextError(Exception): """Raised when unable to connect to LDAP or Zookeeper. """ pass def required(msg): """Raises error if return value of function is None. """ def _decorator(func): """Actual decorator. """ @functools.wraps(func) def decorated_function(*args, **kwargs): """Decorated function, checks result is not None. """ result = func(*args, **kwargs) if result is None: raise ContextError(msg) return result return decorated_function return _decorator class DnsContext: """DNS context. """ __slots__ = ( '_context', '_dns', ) def __init__(self, ctx): self._context = ctx self._dns = None @property def _resolver(self): if self._dns is not None: return self._dns dns = plugin_manager.load('treadmill.context', 'dns') dns.init(self._context) self._dns = dns return self._dns def admin_api_srv(self): """Get Admin API SRV record data. """ (srv_entry, _proto) = self._resolver.lookup( self._context, 'admin_api' ) return srv_entry def state_api_srv(self, cell): """Get State API SRV record data. """ (srv_entry, _proto) = self._resolver.lookup( self._context, 'state_api', scope=self._resolver.cell_scope(cell) ) return srv_entry def cell_api_srv(self, cell): """Get Cell API SRV record data. """ (srv_entry, _proto) = self._resolver.lookup( self._context, 'cell_api', scope=self._resolver.cell_scope(cell) ) return srv_entry def ws_api_srv(self, cell): """Get Websocket API SRV record data. """ (srv_entry, _proto) = self._resolver.lookup( self._context, 'ws_api', scope=self._resolver.cell_scope(cell) ) return srv_entry class AdminContext: """Ldap context. """ __slots__ = ( '_context', '_conn', ) def __init__(self, ctx): self._context = ctx self._conn = None @property @required('Cannot resolve LDAP suffix.') def ldap_suffix(self): """LDAP suffix getter. """ return self._context.get('ldap_suffix', resolve=False) @property def user(self): """User, getter. """ return self._context.get('ldap_user', resolve=False) @user.setter def user(self, value): """User, setter. """ if value != self._context.get('ldap_user', resolve=False): self._conn = None self._context.set('ldap_user', value) @property def password(self): """Password, getter. """ return self._context.get('ldap_pwd', resolve=False) @password.setter def password(self, value): """Password, setter. """ self._context.set('ldap_pwd', value) if self.user is None: self.user = 'cn=Manager,%s' % self.ldap_suffix @property @required('Cannot resolve LDAP url.') def url(self): """URL, getter. """ return self._context.get('ldap_url', resolve=True) @url.setter def url(self, value): """Set URL, then nullify the connection. """ self._context.set('ldap_url', value) self._conn = None @property def write_url(self): """Get the LDAP server URL for write access. """ url = self._context.get('ldap_write_url', resolve=True) return url @write_url.setter def write_url(self, value): """Set the LDAP server URL for write access. """ self._context.set('ldap_write_url', value) self._conn = None @property def conn(self): """Lazily establishes connection to admin LDAP. """ if self._conn: return self._conn plugin = plugin_manager.load('treadmill.context', 'admin') self._conn = plugin.connect(self.url, self.write_url, self.ldap_suffix, self.user, self.password) return self._conn class ZkContext: """Zookeeper context. """ __slots__ = ( '_context', '_conn', '_listeners', ) def __init__(self, ctx): self._context = ctx self._conn = None self._listeners = [] def add_listener(self, listener): """Add a listener. """ self._listeners.append(listener) @property @required('Cannot resolve Zookeeper connection string.') def url(self): """Resolves and return context zk url. """ return self._context.get('zk_url', resolve=True) @url.setter def url(self, value): """Sets context zk url. """ self._context.set('zk_url', value) @property def conn(self): """Lazily creates Zookeeper client. """ if self._conn: return self._conn _LOGGER.debug('Connecting to Zookeeper %s', self.url) plugin = plugin_manager.load('treadmill.context', 'zookeeper') self._conn = plugin.connect(self.url) if self._listeners: for listener in self._listeners: self._conn.add_listener(listener) return self._conn @conn.setter def conn(self, zkclient): """Explicitely set connection.""" self._conn = zkclient class Context: """Global connection context. """ __slots__ = ( 'ldap', 'zk', 'dns', '_resolvers', '_plugins', '_profile', '_profile_name', '_defaults', '_stack', ) def __init__(self): self._profile_name = None self._profile = {} self._defaults = None self._plugins = [] # Protect against recursive gets self._stack = set() # Lazy connections to Zookeeper, LDAP and DNS self.zk = ZkContext(self) self.ldap = AdminContext(self) self.dns = DnsContext(self) def _load_profile(self): """Loads the profile. """ # Load once. if self._defaults is not None: return self._defaults = { 'dns_domain': '.'.join(socket.getfqdn().split('.')[1:]) } if self._profile_name: try: profile_mod = plugin_manager.load('treadmill.profiles', self._profile_name) self._defaults.update(profile_mod.PROFILE) except KeyError: _LOGGER.warning('Profile not found: %s', self._profile_name) def _init_plugins(self): """Initialize plugins. """ if self._plugins: return _LOGGER.debug('Loading plugins.') # TODO: Thsi is a hack, need a better way to determine if plugin # should be loaded. if self.get('dns_domain', resolve=False): _LOGGER.debug('Loading dns plugin.') dns = plugin_manager.load('treadmill.context', 'dns') dns.init(self) self._plugins.append(dns) if self.get('ldap_url', resolve=False): _LOGGER.debug('Loading admin plugin.') ldap = plugin_manager.load('treadmill.context', 'admin') ldap.init(self) self._plugins.append(ldap) def get(self, attr, default=None, resolve=True, volatile=False): """Get attribute from profile or defaults. """ if attr in self._profile: return self._profile[attr] self._load_profile() if resolve and attr not in self._stack: self._stack.add(attr) try: self._init_plugins() for plugin in self._plugins: try: self._profile[attr] = plugin.resolve(self, attr) except ContextError as err: _LOGGER.warning( 'Error resolving attribute %s in %s: %s', attr, plugin, err ) except KeyError: # Plugin is not responsible fot the attribute. pass finally: self._stack.discard(attr) if attr not in self._profile: # Attr was not found, look for it in _defaults if (self._defaults is not None and self._defaults.get(attr) is not None): self._profile[attr] = self._defaults[attr] if attr not in self._profile and default is not None: self._profile[attr] = default # The end of the function attribute is recorded in the profile and # never evaluated again. # # volatile attributes are evaluated all the time. if volatile: return self._profile.pop(attr, default) else: return self._profile.get(attr, default) def set(self, attr, value): """Set profile attribute. """ self._profile[attr] = value def set_profile_name(self, profile_name): """Sets current profile. """ self._profile_name = profile_name def get_profile_name(self): """Returns profile name. """ return self._profile_name @property def profile(self): """Returns the profile name. """ self._load_profile() return self._profile @property @required('Cannot resolve cell.') def cell(self): """Returns cell name. """ return self.get('cell', resolve=False) @cell.setter def cell(self, value): """Sets cell name. """ self.set('cell', value) @property @required('Cannot resolve DNS domain.') def dns_domain(self): """Returns DNS domain. """ return self.get('dns_domain', resolve=False) @dns_domain.setter def dns_domain(self, value): """Sets DNS domain. """ self.set('dns_domain', value) @property def dns_server(self): """Returns DNS server. """ return self.get('dns_server') @dns_server.setter def dns_server(self, value): """Sets DNS server. """ return self.set('dns_server', value) @property @required('Cannot resolve LDAP suffix.') def ldap_suffix(self): """Returns LDAP suffix. """ return self.get('ldap_suffix') @ldap_suffix.setter def ldap_suffix(self, value): """Sets DNS server. """ return self.set('ldap_suffix', value) def scopes(self): """Returns supported scopes. """ return self.get('scopes', ['cell']) @required('Cannot resolve admin api.') def admin_api(self, api=None): """Returns admin API. """ if api: return [api] return self.get('admin_api', volatile=True) @required('Cannot resolve cell api.') def cell_api(self, api=None): """Returns cell API. """ if api: return [api] return self.get('cell_api', volatile=True) @required('Cannot resolve websocket api.') def ws_api(self, api=None): """Returns cell API. """ if api: return [api] return self.get('ws_api', volatile=True) @required('Cannot resolve state api.') def state_api(self, api=None): """Returns cell API. """ if api: return [api] return self.get('state_api', volatile=True) GLOBAL = Context()

25.237113

79

0.547059

2a9eff59b0e3fd240bb114d6f792d334bc327d09

401

py

Python

innetProject/innetProject/innetProject/wsgi.py

stefanSuYiGuo/Django_case2

e0e06d95f747a4ada5416dae7d8064037bc5adf0

[ "MIT" ]

null

innetProject/innetProject/innetProject/wsgi.py

stefanSuYiGuo/Django_case2

e0e06d95f747a4ada5416dae7d8064037bc5adf0

[ "MIT" ]

null

innetProject/innetProject/innetProject/wsgi.py

stefanSuYiGuo/Django_case2

e0e06d95f747a4ada5416dae7d8064037bc5adf0

[ "MIT" ]

null

""" WSGI config for innetProject project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'innetProject.settings') application = get_wsgi_application()

23.588235

78

0.790524

6833c56e8f510a8ed2a2e0e955cb67003aa487cc

496

py

Python

examples/misc/djangotasks/todo/views.py

takipsizad/pyjs

54db0ba6747aca744f9f3c3e985a17e913dfb951

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

739

2015-01-01T02:05:11.000Z

2022-03-30T15:26:16.000Z

examples/misc/djangotasks/todo/views.py

takipsizad/pyjs

54db0ba6747aca744f9f3c3e985a17e913dfb951

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

33

2015-03-25T23:17:04.000Z

2021-08-19T08:25:22.000Z

examples/misc/djangotasks/todo/views.py

takipsizad/pyjs

54db0ba6747aca744f9f3c3e985a17e913dfb951

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

167

2015-01-01T22:27:47.000Z

2022-03-17T13:29:19.000Z

# Create your views here. from django.pimentech.network import * from todo.models import Todo service = JSONRPCService() @jsonremote(service) def getTasks (request): return [(str(task),task.id) for task in Todo.objects.all()] @jsonremote(service) def addTask (request, taskFromJson): t = Todo() t.task = taskFromJson t.save() return getTasks(request) @jsonremote(service) def deleteTask (request,idFromJson): t = Todo.objects.get(id=idFromJson) t.delete() return getTasks(request)

19.076923

60

0.743952

f2f123f9c7fbf276e4dc31df44a1df1630ae868b

43,126

py

Python

pyzoo/test/zoo/chronos/data/test_tsdataset.py

wangyoucaocxl/analytics-zoo

125d1c146f6552f3ceb38d78a2174af902535341

[ "Apache-2.0" ]

null

pyzoo/test/zoo/chronos/data/test_tsdataset.py

wangyoucaocxl/analytics-zoo

125d1c146f6552f3ceb38d78a2174af902535341

[ "Apache-2.0" ]

null

pyzoo/test/zoo/chronos/data/test_tsdataset.py

wangyoucaocxl/analytics-zoo

125d1c146f6552f3ceb38d78a2174af902535341

[ "Apache-2.0" ]

null

# # Copyright 2018 Analytics Zoo 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. # import pytest import numpy as np import pandas as pd import random import tempfile import os import shutil from test.zoo.pipeline.utils.test_utils import ZooTestCase from zoo.chronos.data import TSDataset from pandas.testing import assert_frame_equal from numpy.testing import assert_array_almost_equal def get_ts_df(): sample_num = np.random.randint(100, 200) train_df = pd.DataFrame({"datetime": pd.date_range('1/1/2019', periods=sample_num), "value": np.random.randn(sample_num), "id": np.array(['00']*sample_num), "extra feature": np.random.randn(sample_num)}) return train_df def get_multi_id_ts_df(): sample_num = 100 train_df = pd.DataFrame({"value": np.random.randn(sample_num), "id": np.array(['00']*50 + ['01']*50), "extra feature": np.random.randn(sample_num)}) train_df["datetime"] = pd.date_range('1/1/2019', periods=sample_num) train_df.loc[50:100, "datetime"] = pd.date_range('1/1/2019', periods=50) return train_df def get_ugly_ts_df(): data = np.random.random_sample((100, 5)) mask = np.random.random_sample((100, 5)) newmask = mask.copy() mask[newmask >= 0.4] = 2 mask[newmask < 0.4] = 1 mask[newmask < 0.2] = 0 data[mask == 0] = None data[mask == 1] = np.nan df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd', 'e']) df['a'][0] = np.nan # make sure column 'a' has a N/A df["datetime"] = pd.date_range('1/1/2019', periods=100) df.loc[50:100, "datetime"] = pd.date_range('1/1/2019', periods=50) df["id"] = np.array(['00']*50 + ['01']*50) return df def get_int_target_df(): sample_num = np.random.randint(100, 200) train_df = pd.DataFrame({"datetime": pd.date_range('1/1/2019', periods=sample_num), "value": np.array(sample_num), "id": np.array(['00']*sample_num), "extra feature": np.random.randn(sample_num)}) return train_df def get_non_dt(): df = pd.DataFrame({"datetime": np.arange(100), "id": np.array(['00']*100), "value": np.random.randn(100), "extra feature": np.random.randn(100)}) return df def get_not_aligned_df(): df_val = pd.DataFrame({"id": np.array(['00']*20+['01']*30+['02']*50), "value": np.random.randn(100), "extra feature": np.random.randn(100)}) data_sec = pd.DataFrame({"datetime": pd.date_range( start='1/1/2019 00:00:00', periods=20, freq='S')}) data_min = pd.DataFrame({"datetime": pd.date_range( start='1/2/2019 00:00:00', periods=30, freq='H')}) data_hou = pd.DataFrame({"datetime": pd.date_range( start='1/3/2019 00:00:00', periods=50, freq='D')}) dt_val = pd.concat([data_sec, data_min, data_hou], axis=0, ignore_index=True) df = pd.merge(left=dt_val, right=df_val, left_index=True, right_index=True) return df class TestTSDataset(ZooTestCase): def setup_method(self, method): pass def teardown_method(self, method): pass def test_tsdataset_initialization(self): df = get_ts_df() # legal input tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") assert tsdata._id_list == ['00'] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata._is_pd_datetime tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col=["value"], extra_feature_col="extra feature", id_col="id") assert tsdata._id_list == ['00'] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata._is_pd_datetime tsdata = TSDataset.from_pandas(df.drop(columns=["id"]), dt_col="datetime", target_col=["value"], extra_feature_col="extra feature") assert tsdata._id_list == ['0'] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata._is_pd_datetime # illegal input with pytest.raises(AssertionError): tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col=["value"], extra_feature_col="extra feature", id_col=0) with pytest.raises(AssertionError): tsdata = TSDataset.from_pandas(df, dt_col=0, target_col=["value"], extra_feature_col="extra feature", id_col="id") with pytest.raises(AssertionError): tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col=0, extra_feature_col="extra feature", id_col="id") with pytest.raises(AssertionError): tsdata = TSDataset.from_pandas(0, dt_col="datetime", target_col=["value"], extra_feature_col="extra feature", id_col="id") with pytest.raises(AssertionError): tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col=["value1"], extra_feature_col="extra feature", id_col="id") def test_tsdataset_from_parquet(self): df = get_ts_df() configs = dict(dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata_pd = TSDataset.from_pandas(df, **configs) temp = tempfile.mkdtemp() try: path = os.path.join(temp, "test.parquet") df.to_parquet(path) tsdata_pq = TSDataset.from_parquet(path, **configs) pd.testing.assert_frame_equal(tsdata_pd.to_pandas(), tsdata_pq.to_pandas(), check_like=True) finally: shutil.rmtree(temp) def test_tsdataset_initialization_multiple(self): df = get_multi_id_ts_df() # legal input tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") assert tsdata._id_list == ['00', '01'] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata._is_pd_datetime tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col=["value"], extra_feature_col="extra feature", id_col="id") assert tsdata._id_list == ['00', '01'] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata._is_pd_datetime tsdata = TSDataset.from_pandas(df.drop(columns=["id"]), dt_col="datetime", target_col=["value"], extra_feature_col="extra feature") assert tsdata._id_list == ['0'] assert tsdata.feature_col == ["extra feature"] assert tsdata.target_col == ["value"] assert tsdata.dt_col == "datetime" assert tsdata._is_pd_datetime # illegael input with pytest.raises(AssertionError): tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col=["value"], extra_feature_col="extra feature", id_col=0) with pytest.raises(AssertionError): tsdata = TSDataset.from_pandas(df, dt_col=0, target_col=["value"], extra_feature_col="extra feature", id_col="id") with pytest.raises(AssertionError): tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col=0, extra_feature_col="extra feature", id_col="id") with pytest.raises(AssertionError): tsdata = TSDataset.from_pandas(0, dt_col="datetime", target_col=["value"], extra_feature_col="extra feature", id_col="id") with pytest.raises(AssertionError): tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col=["value1"], extra_feature_col="extra feature", id_col="id") def test_tsdataset_roll_single_id(self): df = get_ts_df() horizon = random.randint(1, 10) lookback = random.randint(1, 20) tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") with pytest.raises(RuntimeError): tsdata.to_numpy() # roll train, diff input. tsdata.roll(lookback=lookback, horizon=horizon) x, y = tsdata.to_numpy() assert x.shape == (len(df)-lookback-horizon+1, lookback, 2) assert y.shape == (len(df)-lookback-horizon+1, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, id_sensitive=True) x, y = tsdata.to_numpy() assert x.shape == (len(df)-lookback-horizon+1, lookback, 2) assert y.shape == (len(df)-lookback-horizon+1, horizon, 1) # add extra_feature_col. tsdata.roll(lookback=lookback, horizon=horizon, feature_col=["extra feature"], target_col="value") x, y = tsdata.to_numpy() assert x.shape == (len(df)-lookback-horizon+1, lookback, 2) assert y.shape == (len(df)-lookback-horizon+1, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=["extra feature"], target_col="value", id_sensitive=True) x, y = tsdata.to_numpy() assert x.shape == (len(df)-lookback-horizon+1, lookback, 2) assert y.shape == (len(df)-lookback-horizon+1, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[], target_col="value") x, y = tsdata.to_numpy() assert x.shape == (len(df)-lookback-horizon+1, lookback, 1) assert y.shape == (len(df)-lookback-horizon+1, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, feature_col=[], target_col="value", id_sensitive=True) x, y = tsdata.to_numpy() assert x.shape == (len(df)-lookback-horizon+1, lookback, 1) assert y.shape == (len(df)-lookback-horizon+1, horizon, 1) # roll test. horizon = 0 lookback = random.randint(1, 20) tsdata.roll(lookback=lookback, horizon=horizon) x, y = tsdata.to_numpy() assert x.shape == (len(df)-lookback-horizon+1, lookback, 2) assert y is None tsdata.roll(lookback=lookback, horizon=horizon, id_sensitive=True) x, y = tsdata.to_numpy() assert x.shape == (len(df)-lookback-horizon+1, lookback, 2) assert y is None tsdata._check_basic_invariants() def test_tsdataset_roll_multi_id(self): df = get_multi_id_ts_df() horizon = random.randint(1, 10) lookback = random.randint(1, 20) tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") # test train tsdata.roll(lookback=lookback, horizon=horizon, id_sensitive=True) x, y = tsdata.to_numpy() assert x.shape == ((50-lookback-horizon+1), lookback, 4) assert y.shape == ((50-lookback-horizon+1), horizon, 2) tsdata.roll(lookback=lookback, horizon=horizon) x, y = tsdata.to_numpy() assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) # horizon list. horizon_list = [1, 3, 5] tsdata.roll(lookback=lookback, horizon=horizon_list) x, y = tsdata.to_numpy() assert x.shape == ((50-lookback-max(horizon_list)+1)*2, lookback, 2) assert y.shape == ((50-lookback-max(horizon_list)+1)*2, len(horizon_list), 1) horizon_list = [1, 5, 9] tsdata.roll(lookback=lookback, horizon=horizon_list, id_sensitive=True) x, y = tsdata.to_numpy() assert x.shape == ((50-lookback-max(horizon_list)+1), lookback, 4) assert y.shape == ((50-lookback-max(horizon_list)+1), len(horizon_list), 2) # target multi. tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col=["value", "extra feature"], id_col="id") tsdata.roll(lookback=lookback, horizon=horizon, id_sensitive=False) x, y = tsdata.to_numpy() assert x.shape == ((50-lookback-horizon+1)*2, lookback, 2) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 2) tsdata._check_basic_invariants() def test_tsdataset_roll_order(self): df = pd.DataFrame({"datetime": np.array(['1/1/2019', '1/1/2019', '1/2/2019', '1/2/2019']), "value": np.array([1.9, 2.3, 2.4, 2.6]), "id": np.array(['00', '01', '00', '01']), "extra feature1": np.array([1, 0, 3, 0]), "extra feature2": np.array([2, 9, 4, 2])}) tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature1", "extra feature2"], id_col="id") x, y = tsdata.roll(lookback=1, horizon=1, id_sensitive=False).to_numpy() assert x.shape == (2, 1, 3) and y.shape == (2, 1, 1) assert np.array_equal(x, np.array([[[1.9, 1, 2]], [[2.3, 0, 9]]], dtype=np.float32)) assert np.array_equal(y, np.array([[[2.4]], [[2.6]]], dtype=np.float32)) x, y = tsdata.roll(lookback=1, horizon=1, id_sensitive=True).to_numpy() assert x.shape == (1, 1, 6) and y.shape == (1, 1, 2) assert np.array_equal(x, np.array([[[1.9, 2.3, 1, 2, 0, 9]]], dtype=np.float32)) assert np.array_equal(y, np.array([[[2.4, 2.6]]], dtype=np.float32)) def test_tsdata_roll_int_target(self): horizon = random.randint(1, 10) lookback = random.randint(1, 20) df = get_int_target_df() tsdata = TSDataset.from_pandas(df, dt_col='datetime', target_col='value', extra_feature_col=['extra feature'], id_col="id") x, y = tsdata.roll(lookback=lookback, horizon=horizon).to_numpy() assert x.dtype == np.float32 assert y.dtype == np.float32 tsdata._check_basic_invariants() def test_tsdataset_to_torch_loader_roll(self): df_single_id = get_ts_df() df_multi_id = get_multi_id_ts_df() for df in [df_single_id, df_multi_id]: horizon = random.randint(1, 10) lookback = random.randint(1, 20) batch_size = random.randint(16, 32) tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") # train torch_loader = tsdata.to_torch_data_loader(batch_size=batch_size, roll=True, lookback=lookback, horizon=horizon) for x_batch, y_batch in torch_loader: assert tuple(x_batch.size()) == (batch_size, lookback, 2) assert tuple(y_batch.size()) == (batch_size, horizon, 1) break # test torch_loader = tsdata.to_torch_data_loader(batch_size=batch_size, roll=True, lookback=lookback, horizon=0) for x_batch in torch_loader: assert tuple(x_batch.size()) == (batch_size, lookback, 2) break # specify feature_col torch_loader = tsdata.to_torch_data_loader(batch_size=batch_size, roll=True, lookback=lookback, horizon=horizon, feature_col=[]) for x_batch, y_batch in torch_loader: assert tuple(x_batch.size()) == (batch_size, lookback, 1) assert tuple(y_batch.size()) == (batch_size, horizon, 1) break # Non-subset relationship with pytest.raises(ValueError): tsdata.to_torch_data_loader(batch_size=batch_size, roll=True, lookback=lookback, horizon=horizon, target_col=['value', 'extra feature']) # specify horizon_list horizon_list = [1, 3, 5] torch_loader = tsdata.to_torch_data_loader(batch_size=batch_size, roll=True, lookback=lookback, horizon=horizon_list) for x_batch, y_batch in torch_loader: assert tuple(x_batch.size()) == (batch_size, lookback, 2) assert tuple(y_batch.size()) == (batch_size, len(horizon_list), 1) break # multi target_col tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col=["value", "extra feature"], id_col="id") torch_loader = tsdata.to_torch_data_loader(batch_size=batch_size, roll=True, lookback=lookback, horizon=horizon) for x_batch, y_batch in torch_loader: assert tuple(x_batch.size()) == (batch_size, lookback, 2) assert tuple(y_batch.size()) == (batch_size, horizon, 2) break def test_tsdataset_to_torch_loader(self): df = get_ts_df() horizon = random.randint(1, 10) lookback = random.randint(1, 20) batch_size = random.randint(16, 32) tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") with pytest.raises(RuntimeError): tsdata.to_torch_data_loader() tsdata.roll(lookback=lookback, horizon=horizon) loader = tsdata.to_torch_data_loader(batch_size=batch_size, lookback=lookback, horizon=horizon) for x_batch, y_batch in loader: assert tuple(x_batch.size()) == (batch_size, lookback, 2) assert tuple(y_batch.size()) == (batch_size, horizon, 1) break def test_tsdata_multi_unscale_numpy_torch_load(self): lookback = random.randint(1, 10) horizon = random.randint(1, 20) batch_size = random.randint(16, 32) df = get_multi_id_ts_df() df_test = get_multi_id_ts_df() tsdata_train = TSDataset.from_pandas(df, target_col='value', dt_col='datetime', extra_feature_col='extra feature', id_col='id') tsdata_test = TSDataset.from_pandas(df_test, target_col='value', dt_col='datetime', extra_feature_col='extra feature', id_col='id') # roll is True. from sklearn.preprocessing import StandardScaler stand = StandardScaler() for tsdata in [tsdata_train, tsdata_test]: tsdata.scale(stand, fit=tsdata is tsdata_train) test_loader = tsdata_test.to_torch_data_loader(batch_size=batch_size, roll=True, lookback=lookback, horizon=horizon) import torch from torch.utils.data.dataloader import DataLoader test_loader = DataLoader(test_loader.dataset, batch_size=batch_size, shuffle=False) batch_load_list = [] for _, y_batch in test_loader: batch_load_list.append(y_batch) y_test = torch.cat(batch_load_list, dim=0) pred = np.copy(y_test.numpy()) # sanity check unscaled_pred = tsdata_train.unscale_numpy(pred) unscaled_y_test = tsdata_train.unscale_numpy(y_test.numpy()) _, unscaled_y_test_reproduce = tsdata_test.unscale()\ .roll(lookback=lookback, horizon=horizon)\ .to_numpy() assert_array_almost_equal(unscaled_pred, unscaled_y_test_reproduce) assert_array_almost_equal(unscaled_y_test, unscaled_y_test_reproduce) tsdata._check_basic_invariants() def test_tsdataset_imputation(self): for val in ["last", "const", "linear"]: df = get_ugly_ts_df() tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="e", extra_feature_col=["a", "b", "c", "d"], id_col="id") tsdata.impute(mode=val) assert tsdata.to_pandas().isna().sum().sum() == 0 assert len(tsdata.to_pandas()) == 100 tsdata._check_basic_invariants() def test_tsdataset_deduplicate(self): df = get_ugly_ts_df() for _ in range(20): df.loc[len(df)] = df.loc[np.random.randint(0, 99)] assert len(df) == 120 tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="e", extra_feature_col=["a", "b", "c", "d"], id_col="id") tsdata.deduplicate() assert len(tsdata.to_pandas()) == 100 tsdata._check_basic_invariants() def test_tsdataset_datetime_feature(self): df = get_ts_df() # interval = day tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.gen_dt_feature() assert set(tsdata.to_pandas().columns) == {'DAY', 'IS_WEEKEND', 'WEEKDAY', 'MONTH', 'DAYOFYEAR', 'WEEKOFYEAR', 'extra feature', 'value', 'datetime', 'id'} assert set(tsdata.feature_col) == {'DAY', 'IS_WEEKEND', 'WEEKDAY', 'MONTH', 'DAYOFYEAR', 'WEEKOFYEAR', 'extra feature'} tsdata._check_basic_invariants() # interval = day, one_hot = ["WEEKDAY"] tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.gen_dt_feature(one_hot_features=["WEEKDAY"]) assert set(tsdata.to_pandas().columns) == {'DAY', 'IS_WEEKEND', 'WEEKDAY_0', 'WEEKDAY_1', 'WEEKDAY_2', 'WEEKDAY_3', 'WEEKDAY_4', 'WEEKDAY_5', 'WEEKDAY_6', 'MONTH', 'DAYOFYEAR', 'WEEKOFYEAR', 'extra feature', 'value', 'datetime', 'id'} assert set(tsdata.feature_col) == {'DAY', 'IS_WEEKEND', 'WEEKDAY_0', 'WEEKDAY_1', 'WEEKDAY_2', 'WEEKDAY_3', 'WEEKDAY_4', 'WEEKDAY_5', 'WEEKDAY_6', 'MONTH', 'DAYOFYEAR', 'WEEKOFYEAR', 'extra feature'} tsdata._check_basic_invariants() def test_tsdataset_datetime_feature_multiple(self): df = get_multi_id_ts_df() # interval = day tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.gen_dt_feature() assert set(tsdata.to_pandas().columns) == {'DAY', 'IS_WEEKEND', 'WEEKDAY', 'MONTH', 'DAYOFYEAR', 'WEEKOFYEAR', 'extra feature', 'value', 'datetime', 'id'} assert set(tsdata.feature_col) == {'DAY', 'IS_WEEKEND', 'WEEKDAY', 'MONTH', 'DAYOFYEAR', 'WEEKOFYEAR', 'extra feature'} tsdata._check_basic_invariants() # interval = day, one_hot = ["WEEKDAY"] tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.gen_dt_feature(one_hot_features=["WEEKDAY"]) assert set(tsdata.to_pandas().columns) == {'DAY', 'IS_WEEKEND', 'WEEKDAY_0', 'WEEKDAY_1', 'WEEKDAY_2', 'WEEKDAY_3', 'WEEKDAY_4', 'WEEKDAY_5', 'WEEKDAY_6', 'MONTH', 'DAYOFYEAR', 'WEEKOFYEAR', 'extra feature', 'value', 'datetime', 'id'} assert set(tsdata.feature_col) == {'DAY', 'IS_WEEKEND', 'WEEKDAY_0', 'WEEKDAY_1', 'WEEKDAY_2', 'WEEKDAY_3', 'WEEKDAY_4', 'WEEKDAY_5', 'WEEKDAY_6', 'MONTH', 'DAYOFYEAR', 'WEEKOFYEAR', 'extra feature'} tsdata._check_basic_invariants() def test_tsdataset_scale_unscale(self): df = get_ts_df() df_test = get_ts_df() tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata_test = TSDataset.from_pandas(df_test, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler, RobustScaler scalers = [StandardScaler(), MaxAbsScaler(), MinMaxScaler(), RobustScaler()] for scaler in scalers: tsdata.scale(scaler) tsdata_test.scale(scaler, fit=False) with pytest.raises(AssertionError): assert_frame_equal(tsdata.to_pandas(), df) with pytest.raises(AssertionError): assert_frame_equal(tsdata_test.to_pandas(), df_test) tsdata.unscale() tsdata_test.unscale() assert_frame_equal(tsdata.to_pandas(), df) assert_frame_equal(tsdata_test.to_pandas(), df_test) tsdata._check_basic_invariants() def test_tsdataset_unscale_numpy(self): df = get_multi_id_ts_df() df_test = get_multi_id_ts_df() from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler, RobustScaler scalers = [StandardScaler(), StandardScaler(with_mean=False), StandardScaler(with_std=False), MaxAbsScaler(), MinMaxScaler(), MinMaxScaler(feature_range=(1, 3)), RobustScaler(), RobustScaler(with_centering=False), RobustScaler(with_scaling=False), RobustScaler(quantile_range=(20, 80))] for scaler in scalers: tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata_test = TSDataset.from_pandas(df_test, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.gen_dt_feature()\ .scale(scaler)\ .roll(lookback=5, horizon=4, id_sensitive=True) tsdata_test.gen_dt_feature()\ .scale(scaler, fit=False)\ .roll(lookback=5, horizon=4, id_sensitive=True) _, _ = tsdata.to_numpy() _, y_test = tsdata_test.to_numpy() pred = np.copy(y_test) # sanity check unscaled_pred = tsdata.unscale_numpy(pred) unscaled_y_test = tsdata.unscale_numpy(y_test) tsdata_test.unscale()\ .roll(lookback=5, horizon=4, id_sensitive=True) _, unscaled_y_test_reproduce = tsdata_test.to_numpy() assert_array_almost_equal(unscaled_pred, unscaled_y_test_reproduce) assert_array_almost_equal(unscaled_y_test, unscaled_y_test_reproduce) tsdata._check_basic_invariants() def test_tsdataset_resample(self): df = get_ts_df() tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.resample('2D', df["datetime"][0], df["datetime"][df.shape[0]-1]) assert len(tsdata.to_pandas()) == (df.shape[0] + 1) // 2 tsdata._check_basic_invariants() def test_tsdataset_resample_multiple(self): df = get_multi_id_ts_df() tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.resample('2D', df["datetime"][0], df["datetime"][df.shape[0]-1]) assert len(tsdata.to_pandas()) == df.shape[0] // 2 tsdata._check_basic_invariants() tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.resample('2D') assert len(tsdata.to_pandas()) == 50 tsdata._check_basic_invariants() def test_tsdataset_split(self): df = get_ts_df() # only train and test tsdata_train, tsdata_valid, tsdata_test =\ TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id", with_split=True, val_ratio=0, test_ratio=0.1) # standard split with all three sets tsdata_train, tsdata_valid, tsdata_test =\ TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id", with_split=True, val_ratio=0.1, test_ratio=0.1, largest_look_back=5, largest_horizon=2) assert set(np.unique(tsdata_train.to_pandas()["id"])) == {"00"} assert set(np.unique(tsdata_valid.to_pandas()["id"])) == {"00"} assert set(np.unique(tsdata_test.to_pandas()["id"])) == {"00"} assert len(tsdata_train.to_pandas()) == df[:-(int(df.shape[0]*0.1)*2)].shape[0] assert len(tsdata_valid.to_pandas()) == int(df.shape[0] * 0.1 + 5 + 2 - 1) assert len(tsdata_test.to_pandas()) == int(df.shape[0] * 0.1 + 5 + 2 - 1) tsdata_train.feature_col.append("new extra feature") assert len(tsdata_train.feature_col) == 2 assert len(tsdata_valid.feature_col) == 1 assert len(tsdata_test.feature_col) == 1 tsdata_train.target_col[0] = "new value" assert tsdata_train.target_col[0] == "new value" assert tsdata_valid.target_col[0] != "new value" assert tsdata_test.target_col[0] != "new value" def test_tsdataset_split_multiple(self): df = get_multi_id_ts_df() tsdata_train, tsdata_valid, tsdata_test =\ TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id", with_split=True, val_ratio=0.1, test_ratio=0.1, largest_look_back=5, largest_horizon=2) assert set(np.unique(tsdata_train.to_pandas()["id"])) == {"00", "01"} assert set(np.unique(tsdata_valid.to_pandas()["id"])) == {"00", "01"} assert set(np.unique(tsdata_test.to_pandas()["id"])) == {"00", "01"} assert len(tsdata_train.to_pandas()) == (50 * 0.8)*2 assert len(tsdata_valid.to_pandas()) == (50 * 0.1 + 5 + 2 - 1)*2 assert len(tsdata_test.to_pandas()) == (50 * 0.1 + 5 + 2 - 1)*2 assert tsdata_train.feature_col is not tsdata_valid.feature_col assert tsdata_train.feature_col is not tsdata_test.feature_col assert tsdata_train.target_col is not tsdata_valid.target_col assert tsdata_train.target_col is not tsdata_test.target_col tsdata_train.feature_col.append("new extra feature") assert len(tsdata_train.feature_col) == 2 assert len(tsdata_valid.feature_col) == 1 assert len(tsdata_test.feature_col) == 1 tsdata_train.target_col[0] = "new value" assert tsdata_train.target_col[0] == "new value" assert tsdata_valid.target_col[0] != "new value" assert tsdata_test.target_col[0] != "new value" def test_tsdataset_global_feature(self): for val in ["minimal"]: df = get_ts_df() tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.gen_global_feature(settings=val) tsdata._check_basic_invariants() def test_tsdataset_global_feature_multiple(self): df = get_multi_id_ts_df() tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.gen_global_feature(settings="minimal") tsdata._check_basic_invariants() tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.gen_global_feature(settings="minimal", n_jobs=2) tsdata._check_basic_invariants() def test_tsdataset_rolling_feature_multiple(self): df = get_multi_id_ts_df() horizon = random.randint(2, 10) lookback = random.randint(2, 20) tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.gen_rolling_feature(settings="minimal", window_size=lookback) tsdata._check_basic_invariants() tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=["extra feature"], id_col="id") tsdata.gen_rolling_feature(settings="minimal", window_size=lookback, n_jobs=2) tsdata._check_basic_invariants() # roll train tsdata.roll(lookback=lookback, horizon=horizon) x, y = tsdata.to_numpy() feature_num = len(tsdata.feature_col) + len(tsdata.target_col) assert x.shape == ((50-lookback-horizon+1)*2, lookback, feature_num) assert y.shape == ((50-lookback-horizon+1)*2, horizon, 1) tsdata.roll(lookback=lookback, horizon=horizon, id_sensitive=True) x, y = tsdata.to_numpy() assert x.shape == ((50-lookback-horizon+1), lookback, feature_num*2) assert y.shape == ((50-lookback-horizon+1), horizon, 2) tsdata._check_basic_invariants() def test_check_scale_sequence(self): df = get_multi_id_ts_df() # with split is True. td_train, td_valid, td_test = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col=[ "extra feature"], id_col="id", with_split=True, val_ratio=0.1, test_ratio=0.1) from sklearn.preprocessing import StandardScaler stand = StandardScaler() with pytest.raises(AssertionError): for tsdata in [td_train, td_valid, td_test]: tsdata.scale(stand, fit=False) tsdata._check_basic_invariants() # remove due to the possible large cost on test sys # with pytest.raises(AssertionError): # tsdata.gen_global_feature(settings="minimal")\ # .gen_rolling_feature(settings="minimal", window_size=5) def test_non_pd_datetime(self): df = get_non_dt() tsdata = TSDataset.from_pandas(df, dt_col="datetime", target_col="value", extra_feature_col="extra feature", id_col="id") with pytest.raises(AssertionError): tsdata.resample('2D') with pytest.raises(AssertionError): tsdata.gen_dt_feature() with pytest.raises(AssertionError): tsdata.gen_rolling_feature(settings="minimal", window_size=1000) tsdata._check_basic_invariants() def test_not_aligned(self): df = get_not_aligned_df() tsdata = TSDataset.from_pandas(df, target_col="value", dt_col="datetime", extra_feature_col="extra feature", id_col="id") with pytest.raises(AssertionError): tsdata.roll(lookback=5, horizon=2, id_sensitive=True) tsdata._check_basic_invariants()

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py

Python

manage.py

avinash795k/leaveProject

264818f052e0abfdd47a0b9e73b1a9fa28114da0

[ "MIT" ]

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manage.py

avinash795k/leaveProject

264818f052e0abfdd47a0b9e73b1a9fa28114da0

[ "MIT" ]

null

manage.py

avinash795k/leaveProject

264818f052e0abfdd47a0b9e73b1a9fa28114da0

[ "MIT" ]

null

#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "leaveProject.settings") try: from django.core.management import execute_from_command_line except ImportError: # The above import may fail for some other reason. Ensure that the # issue is really that Django is missing to avoid masking other # exceptions on Python 2. try: import django except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise execute_from_command_line(sys.argv)

35.217391

77

0.644444

bb712c5cfb785c0c0fa1b98d02fa48ffd2861c63

24,343

py

Python

keystone/identity/core.py

christophgysin/openstack-keystone

ab4f7473c34b8a94ed5a3aced01bf055d4453905

[ "Apache-2.0" ]

null

keystone/identity/core.py

christophgysin/openstack-keystone

ab4f7473c34b8a94ed5a3aced01bf055d4453905

[ "Apache-2.0" ]

null

keystone/identity/core.py

christophgysin/openstack-keystone

ab4f7473c34b8a94ed5a3aced01bf055d4453905

[ "Apache-2.0" ]

1

2021-08-29T16:53:06.000Z

# Copyright 2012 OpenStack Foundation # # 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. """Main entry point into the Identity service.""" import abc import functools import os from oslo.config import cfg import six from keystone import clean from keystone.common import dependency from keystone.common import driver_hints from keystone.common import manager from keystone import config from keystone import exception from keystone import notifications from keystone.openstack.common.gettextutils import _ from keystone.openstack.common import importutils from keystone.openstack.common import log CONF = config.CONF LOG = log.getLogger(__name__) DOMAIN_CONF_FHEAD = 'keystone.' DOMAIN_CONF_FTAIL = '.conf' def filter_user(user_ref): """Filter out private items in a user dict. 'password', 'tenants' and 'groups' are never returned. :returns: user_ref """ if user_ref: user_ref = user_ref.copy() user_ref.pop('password', None) user_ref.pop('tenants', None) user_ref.pop('groups', None) user_ref.pop('domains', None) try: user_ref['extra'].pop('password', None) user_ref['extra'].pop('tenants', None) except KeyError: pass return user_ref class DomainConfigs(dict): """Discover, store and provide access to domain specific configs. The setup_domain_drivers() call will be made via the wrapper from the first call to any driver function handled by this manager. This setup call it will scan the domain config directory for files of the form keystone.<domain_name>.conf For each file, the domain_name will be turned into a domain_id and then this class will: - Create a new config structure, adding in the specific additional options defined in this config file - Initialise a new instance of the required driver with this new config. """ configured = False driver = None def _load_driver(self, assignment_api, domain_id): domain_config = self[domain_id] domain_config['driver'] = ( importutils.import_object( domain_config['cfg'].identity.driver, domain_config['cfg'])) domain_config['driver'].assignment_api = assignment_api def _load_config(self, assignment_api, file_list, domain_name): try: domain_ref = assignment_api.get_domain_by_name(domain_name) except exception.DomainNotFound: LOG.warning( _('Invalid domain name (%s) found in config file name'), domain_name) return # Create a new entry in the domain config dict, which contains # a new instance of both the conf environment and driver using # options defined in this set of config files. Later, when we # service calls via this Manager, we'll index via this domain # config dict to make sure we call the right driver domain = domain_ref['id'] self[domain] = {} self[domain]['cfg'] = cfg.ConfigOpts() config.configure(conf=self[domain]['cfg']) self[domain]['cfg'](args=[], project='keystone', default_config_files=file_list) self._load_driver(assignment_api, domain) def setup_domain_drivers(self, standard_driver, assignment_api): # This is called by the api call wrapper self.configured = True self.driver = standard_driver conf_dir = CONF.identity.domain_config_dir if not os.path.exists(conf_dir): LOG.warning(_('Unable to locate domain config directory: %s'), conf_dir) return for r, d, f in os.walk(conf_dir): for fname in f: if (fname.startswith(DOMAIN_CONF_FHEAD) and fname.endswith(DOMAIN_CONF_FTAIL)): if fname.count('.') >= 2: self._load_config(assignment_api, [os.path.join(r, fname)], fname[len(DOMAIN_CONF_FHEAD): -len(DOMAIN_CONF_FTAIL)]) else: LOG.debug(_('Ignoring file (%s) while scanning domain ' 'config directory'), fname) def get_domain_driver(self, domain_id): if domain_id in self: return self[domain_id]['driver'] def get_domain_conf(self, domain_id): if domain_id in self: return self[domain_id]['cfg'] def reload_domain_driver(self, assignment_api, domain_id): # Only used to support unit tests that want to set # new config values. This should only be called once # the domains have been configured, since it relies on # the fact that the configuration files have already been # read. if self.configured: if domain_id in self: self._load_driver(assignment_api, domain_id) else: # The standard driver self.driver = self.driver() self.driver.assignment_api = assignment_api def domains_configured(f): """Wraps API calls to lazy load domain configs after init. This is required since the assignment manager needs to be initialized before this manager, and yet this manager's init wants to be able to make assignment calls (to build the domain configs). So instead, we check if the domains have been initialized on entry to each call, and if requires load them, """ @functools.wraps(f) def wrapper(self, *args, **kwargs): if (not self.domain_configs.configured and CONF.identity.domain_specific_drivers_enabled): LOG.warning(_( 'Running an experimental and unsupported configuration ' '(domain_specific_drivers_enabled = True); ' 'this will result in known issues.')) self.domain_configs.setup_domain_drivers( self.driver, self.assignment_api) return f(self, *args, **kwargs) return wrapper @dependency.provider('identity_api') @dependency.optional('revoke_api') @dependency.requires('assignment_api', 'credential_api', 'token_api') class Manager(manager.Manager): """Default pivot point for the Identity backend. See :mod:`keystone.common.manager.Manager` for more details on how this dynamically calls the backend. This class also handles the support of domain specific backends, by using the DomainConfigs class. The setup call for DomainConfigs is called from with the @domains_configured wrapper in a lazy loading fashion to get around the fact that we can't satisfy the assignment api it needs from within our __init__() function since the assignment driver is not itself yet initialized. Each of the identity calls are pre-processed here to choose, based on domain, which of the drivers should be called. The non-domain-specific driver is still in place, and is used if there is no specific driver for the domain in question. """ _USER = 'user' _GROUP = 'group' def __init__(self): super(Manager, self).__init__(CONF.identity.driver) self.domain_configs = DomainConfigs() # Domain ID normalization methods def _set_domain_id(self, ref, domain_id): if isinstance(ref, dict): ref = ref.copy() ref['domain_id'] = domain_id return ref elif isinstance(ref, list): return [self._set_domain_id(x, domain_id) for x in ref] else: raise ValueError(_('Expected dict or list: %s') % type(ref)) def _clear_domain_id(self, ref): # Clear the domain_id, and then check to ensure that if this # was not the default domain, it is being handled by its own # backend driver. ref = ref.copy() domain_id = ref.pop('domain_id', CONF.identity.default_domain_id) if (domain_id != CONF.identity.default_domain_id and domain_id not in self.domain_configs): raise exception.DomainNotFound(domain_id=domain_id) return ref def _normalize_scope(self, domain_scope): if domain_scope is None: return CONF.identity.default_domain_id else: return domain_scope def _select_identity_driver(self, domain_id): driver = self.domain_configs.get_domain_driver(domain_id) if driver: return driver else: self.assignment_api.get_domain(domain_id) return self.driver def _get_domain_id_and_driver(self, domain_scope): domain_id = self._normalize_scope(domain_scope) driver = self._select_identity_driver(domain_id) return (domain_id, driver) def _mark_domain_id_filter_satisfied(self, hints): if hints: for filter in hints.filters: if (filter['name'] == 'domain_id' and filter['comparator'] == 'equals'): hints.filters.remove(filter) # The actual driver calls - these are pre/post processed here as # part of the Manager layer to make sure we: # # - select the right driver for this domain # - clear/set domain_ids for drivers that do not support domains @notifications.emit_event('authenticate') @domains_configured def authenticate(self, context, user_id, password, domain_scope=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) ref = driver.authenticate(user_id, password) if not driver.is_domain_aware(): ref = self._set_domain_id(ref, domain_id) return ref @notifications.created(_USER) @domains_configured def create_user(self, user_id, user_ref): user = user_ref.copy() user['name'] = clean.user_name(user['name']) user.setdefault('enabled', True) user['enabled'] = clean.user_enabled(user['enabled']) # For creating a user, the domain is in the object itself domain_id = user_ref['domain_id'] driver = self._select_identity_driver(domain_id) if not driver.is_domain_aware(): user = self._clear_domain_id(user) ref = driver.create_user(user_id, user) if not driver.is_domain_aware(): ref = self._set_domain_id(ref, domain_id) return ref @domains_configured def get_user(self, user_id, domain_scope=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) ref = driver.get_user(user_id) if not driver.is_domain_aware(): ref = self._set_domain_id(ref, domain_id) return ref @domains_configured def get_user_by_name(self, user_name, domain_id): driver = self._select_identity_driver(domain_id) ref = driver.get_user_by_name(user_name, domain_id) if not driver.is_domain_aware(): ref = self._set_domain_id(ref, domain_id) return ref @manager.response_truncated @domains_configured def list_users(self, domain_scope=None, hints=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) if not driver.is_domain_aware(): # We are effectively satisfying any domain_id filter by the above # driver selection, so remove any such filter self._mark_domain_id_filter_satisfied(hints) ref_list = driver.list_users(hints or driver_hints.Hints()) if not driver.is_domain_aware(): ref_list = self._set_domain_id(ref_list, domain_id) return ref_list @notifications.updated(_USER) @domains_configured def update_user(self, user_id, user_ref, domain_scope=None): user = user_ref.copy() if 'name' in user: user['name'] = clean.user_name(user['name']) if 'enabled' in user: user['enabled'] = clean.user_enabled(user['enabled']) domain_id, driver = self._get_domain_id_and_driver(domain_scope) if not driver.is_domain_aware(): user = self._clear_domain_id(user) ref = driver.update_user(user_id, user) if user.get('enabled') is False or user.get('password') is not None: if self.revoke_api: self.revoke_api.revoke_by_user(user_id) self.token_api.delete_tokens_for_user(user_id) if not driver.is_domain_aware(): ref = self._set_domain_id(ref, domain_id) return ref @notifications.deleted(_USER) @domains_configured def delete_user(self, user_id, domain_scope=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) driver.delete_user(user_id) self.credential_api.delete_credentials_for_user(user_id) self.token_api.delete_tokens_for_user(user_id) @notifications.created(_GROUP) @domains_configured def create_group(self, group_id, group_ref): group = group_ref.copy() group.setdefault('description', '') # For creating a group, the domain is in the object itself domain_id = group_ref['domain_id'] driver = self._select_identity_driver(domain_id) if not driver.is_domain_aware(): group = self._clear_domain_id(group) ref = driver.create_group(group_id, group) if not driver.is_domain_aware(): ref = self._set_domain_id(ref, domain_id) return ref @domains_configured def get_group(self, group_id, domain_scope=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) ref = driver.get_group(group_id) if not driver.is_domain_aware(): ref = self._set_domain_id(ref, domain_id) return ref @notifications.updated(_GROUP) @domains_configured def update_group(self, group_id, group, domain_scope=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) if not driver.is_domain_aware(): group = self._clear_domain_id(group) ref = driver.update_group(group_id, group) if not driver.is_domain_aware(): ref = self._set_domain_id(ref, domain_id) return ref def revoke_tokens_for_group(self, group_id, domain_scope): # We get the list of users before we attempt the group # deletion, so that we can remove these tokens after we know # the group deletion succeeded. # TODO(ayoung): revoke based on group and roleids instead user_ids = [] for u in self.list_users_in_group(group_id, domain_scope): user_ids.append(u['id']) if self.revoke_api: self.revoke_api.revoke_by_user(u['id']) self.token_api.delete_tokens_for_users(user_ids) @notifications.deleted(_GROUP) @domains_configured def delete_group(self, group_id, domain_scope=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) # As well as deleting the group, we need to invalidate # any tokens for the users who are members of the group. self.revoke_tokens_for_group(group_id, domain_scope) driver.delete_group(group_id) @domains_configured def add_user_to_group(self, user_id, group_id, domain_scope=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) driver.add_user_to_group(user_id, group_id) self.token_api.delete_tokens_for_user(user_id) @domains_configured def remove_user_from_group(self, user_id, group_id, domain_scope=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) driver.remove_user_from_group(user_id, group_id) # TODO(ayoung) revoking all tokens for a user based on group # membership is overkill, as we only would need to revoke tokens # that had role assignments via the group. Calculating those # assignments would have to be done by the assignment backend. if self.revoke_api: self.revoke_api.revoke_by_user(user_id) self.token_api.delete_tokens_for_user(user_id) @manager.response_truncated @domains_configured def list_groups_for_user(self, user_id, domain_scope=None, hints=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) if not driver.is_domain_aware(): # We are effectively satisfying any domain_id filter by the above # driver selection, so remove any such filter self._mark_domain_id_filter_satisfied(hints) ref_list = driver.list_groups_for_user( user_id, hints or driver_hints.Hints()) if not driver.is_domain_aware(): ref_list = self._set_domain_id(ref_list, domain_id) return ref_list @manager.response_truncated @domains_configured def list_groups(self, domain_scope=None, hints=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) if not driver.is_domain_aware(): # We are effectively satisfying any domain_id filter by the above # driver selection, so remove any such filter self._mark_domain_id_filter_satisfied(hints) ref_list = driver.list_groups(hints or driver_hints.Hints()) if not driver.is_domain_aware(): ref_list = self._set_domain_id(ref_list, domain_id) return ref_list @manager.response_truncated @domains_configured def list_users_in_group(self, group_id, domain_scope=None, hints=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) if not driver.is_domain_aware(): # We are effectively satisfying any domain_id filter by the above # driver selection, so remove any such filter self._mark_domain_id_filter_satisfied(hints) ref_list = driver.list_users_in_group( group_id, hints or driver_hints.Hints()) if not driver.is_domain_aware(): ref_list = self._set_domain_id(ref_list, domain_id) return ref_list @domains_configured def check_user_in_group(self, user_id, group_id, domain_scope=None): domain_id, driver = self._get_domain_id_and_driver(domain_scope) driver.check_user_in_group(user_id, group_id) @domains_configured def change_password(self, context, user_id, original_password, new_password, domain_scope): # authenticate() will raise an AssertionError if authentication fails self.authenticate(context, user_id, original_password, domain_scope=domain_scope) update_dict = {'password': new_password} self.update_user(user_id, update_dict, domain_scope=domain_scope) @six.add_metaclass(abc.ABCMeta) class Driver(object): """Interface description for an Identity driver.""" def _get_list_limit(self): return CONF.identity.list_limit or CONF.list_limit @abc.abstractmethod def authenticate(self, user_id, password): """Authenticate a given user and password. :returns: user_ref :raises: AssertionError """ raise exception.NotImplemented() # user crud @abc.abstractmethod def create_user(self, user_id, user): """Creates a new user. :raises: keystone.exception.Conflict """ raise exception.NotImplemented() @abc.abstractmethod def list_users(self, hints): """List users in the system. :param hints: filter hints which the driver should implement if at all possible. :returns: a list of user_refs or an empty list. """ raise exception.NotImplemented() @abc.abstractmethod def list_users_in_group(self, group_id, hints): """List users in a group. :param group_id: the group in question :param hints: filter hints which the driver should implement if at all possible. :returns: a list of user_refs or an empty list. """ raise exception.NotImplemented() @abc.abstractmethod def get_user(self, user_id): """Get a user by ID. :returns: user_ref :raises: keystone.exception.UserNotFound """ raise exception.NotImplemented() @abc.abstractmethod def update_user(self, user_id, user): """Updates an existing user. :raises: keystone.exception.UserNotFound, keystone.exception.Conflict """ raise exception.NotImplemented() @abc.abstractmethod def add_user_to_group(self, user_id, group_id): """Adds a user to a group. :raises: keystone.exception.UserNotFound, keystone.exception.GroupNotFound """ raise exception.NotImplemented() @abc.abstractmethod def check_user_in_group(self, user_id, group_id): """Checks if a user is a member of a group. :raises: keystone.exception.UserNotFound, keystone.exception.GroupNotFound """ raise exception.NotImplemented() @abc.abstractmethod def remove_user_from_group(self, user_id, group_id): """Removes a user from a group. :raises: keystone.exception.NotFound """ raise exception.NotImplemented() @abc.abstractmethod def delete_user(self, user_id): """Deletes an existing user. :raises: keystone.exception.UserNotFound """ raise exception.NotImplemented() @abc.abstractmethod def get_user_by_name(self, user_name, domain_id): """Get a user by name. :returns: user_ref :raises: keystone.exception.UserNotFound """ raise exception.NotImplemented() # group crud @abc.abstractmethod def create_group(self, group_id, group): """Creates a new group. :raises: keystone.exception.Conflict """ raise exception.NotImplemented() @abc.abstractmethod def list_groups(self, hints): """List groups in the system. :param hints: filter hints which the driver should implement if at all possible. :returns: a list of group_refs or an empty list. """ raise exception.NotImplemented() @abc.abstractmethod def list_groups_for_user(self, user_id, hints): """List groups a user is in :param user_id: the user in question :param hints: filter hints which the driver should implement if at all possible. :returns: a list of group_refs or an empty list. """ raise exception.NotImplemented() @abc.abstractmethod def get_group(self, group_id): """Get a group by ID. :returns: group_ref :raises: keystone.exception.GroupNotFound """ raise exception.NotImplemented() @abc.abstractmethod def update_group(self, group_id, group): """Updates an existing group. :raises: keystone.exceptionGroupNotFound, keystone.exception.Conflict """ raise exception.NotImplemented() @abc.abstractmethod def delete_group(self, group_id): """Deletes an existing group. :raises: keystone.exception.GroupNotFound """ raise exception.NotImplemented() @abc.abstractmethod def is_domain_aware(self): """Indicates if Driver supports domains.""" raise exception.NotImplemented() # end of identity

35.693548

79

0.654192

d7de9b55370073145f653163d5da240ac4d7e133

25,441

py

Python

pytorch_toolkit/text_spotting/text_spotting/datasets/datasets.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

3

2020-12-29T02:47:32.000Z

2021-11-12T08:12:51.000Z

pytorch_toolkit/text_spotting/text_spotting/datasets/datasets.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

28

2020-09-25T22:40:36.000Z

2022-03-12T00:37:36.000Z

pytorch_toolkit/text_spotting/text_spotting/datasets/datasets.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

1

2021-04-02T07:51:01.000Z

""" Copyright (c) 2020 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import copy import json import os from collections import defaultdict import cv2 import imagesize import numpy as np from tqdm import tqdm class TextOnlyCocoAnnotation: """ Class for working with MSCOCO-like annotation for text. """ def __init__(self, path=None, root=''): self.label_map = {'text': 1} self.annotation = { "type": "instances", "images": [], "categories": [], "annotations": [], } self.annotation['categories'] = [{"supercategory": "none", "name": key, "id": value} for key, value in self.label_map.items()] self.annotation['categories'] = sorted(self.annotation['categories'], key=lambda x: x["id"]) if path is not None: assert os.path.exists(path), path with open(path) as read_file: self.annotation = json.load(read_file) if root: for image_info in self.annotation['images']: image_info['file_name'] = os.path.join(root, image_info['file_name']) self.img_id_2_ann_id = defaultdict(list) for index, ann in enumerate(self.annotation['annotations']): assert index == ann['id'] self.img_id_2_ann_id[ann['image_id']].append(ann['id']) self.img_path_2_img_id = dict() for index, img in enumerate(self.annotation['images']): assert index == img['id'] self.img_path_2_img_id[img['file_name']] = index def add_bbox(self, image_path, image_size, obj): """ Adds new text object to annotation. """ if image_path not in self.img_path_2_img_id: self.img_path_2_img_id[image_path] = len(self.img_path_2_img_id) self.annotation['images'].append({ "file_name": image_path, "height": image_size[1], "width": image_size[0], "id": self.img_path_2_img_id[image_path] }) new_ann_id = len(self.annotation['annotations']) self.img_id_2_ann_id[self.img_path_2_img_id[image_path]].append(new_ann_id) self.annotation['annotations'].append({ "bbox": obj['bbox'], # x, y, w, h "segmentation": obj['segmentation'], "text": obj['text'], "ignore": 0, "id": new_ann_id, "image_id": self.img_path_2_img_id[image_path], "area": obj['bbox'][2] * obj['bbox'][3], "iscrowd": 1 - int(obj['text']['legible']), "category_id": self.label_map['text'] }) def __iadd__(self, other): for image_info in other.annotation['images']: ann_ids = other.img_id_2_ann_id[image_info['id']] for ann_id in ann_ids: ann = other.annotation['annotations'][ann_id] self.add_bbox(image_info['file_name'], (image_info['width'], image_info['height']), copy.deepcopy(ann)) return self def write(self, path): """ Writes annotation as json file. """ annotation = copy.deepcopy(self.annotation) for image_info in annotation['images']: image_info['file_name'] = os.path.relpath(image_info['file_name'], os.path.dirname(path)) with open(path, 'w') as read_file: json.dump(annotation, read_file) @staticmethod def _check_object_consistency(obj): assert obj['iscrowd'] == 1 - obj['text']['legible'] def visualize(self, put_text, imshow_delay=1): """ Visualizes annotation using cv2.imshow from OpenCV. Press `Esc` to exit. """ max_image_size = 1280, 768 for frame in tqdm(self.annotation['images']): image_path = frame['file_name'] image = cv2.imread(image_path) for ann_id in self.img_id_2_ann_id[frame['id']]: obj = self.annotation['annotations'][ann_id] lwd = 2 color = (0, 255, 0) if obj['iscrowd']: color = (128, 128, 128) bbox = obj['bbox'] if put_text: cv2.putText(image, obj['text']['transcription'], tuple(bbox[0:2]), 1, 1.0, color) cv2.rectangle(image, (bbox[0], bbox[1]), (bbox[0] + bbox[2], bbox[1] + bbox[3]), color, lwd) contours = np.array(obj['segmentation']) contours = contours.reshape([contours.shape[0], contours.shape[1] // 2, 2]) cv2.drawContours(image, contours, 0, color, 1) try: if image.shape[1] > max_image_size[0] or image.shape[0] > max_image_size[1]: print('resized') image = cv2.resize(image, max_image_size) cv2.imshow('image', image) k = cv2.waitKey(imshow_delay) if k == 27: break except: print('Error: image is empty or corrupted: ', frame['file_name']) def extract_text_recognition_dataset(self, path): """ Crops text instances and saves as another dataset. """ os.makedirs(os.path.join(path, 'images')) annotation = [] for frame in tqdm(self.annotation['images']): image = cv2.imread(frame['file_name'], cv2.IMREAD_IGNORE_ORIENTATION | cv2.IMREAD_COLOR) for ann_id in self.img_id_2_ann_id[frame['id']]: obj = self.annotation['annotations'][ann_id] if obj['text']['legible']: bbox = obj['bbox'] try: transcription = obj['text']['transcription'] if transcription.isalnum(): coord_x1, coord_y1, coord_x2, coord_y2 = bbox[0], bbox[1], bbox[0] + \ bbox[2], bbox[1] + bbox[3] coord_x1 = max(0, coord_x1) coord_x2 = min(image.shape[1] - 1, coord_x2) coord_y1 = max(0, coord_y1) coord_y2 = min(image.shape[0] - 1, coord_y2) crop_path = os.path.join(path, 'images', f'image{len(annotation)}.jpg') annotation.append(f'{crop_path} {transcription}') cv2.imwrite(crop_path, image[coord_y1:coord_y2, coord_x1:coord_x2]) except: print('Something went wrong with', frame['file_name']) break with open(os.path.join(path, 'annotation.txt'), 'w') as file: file.write('\n'.join(annotation)) class ICDAR2013DatasetConverter: """ Class for conversion of ICDAR2013 to TextOnlyCocoAnnotation. """ def __init__(self, images_folder, annotations_folder, is_train, root=''): self.images_folder = images_folder self.annotations_folder = annotations_folder self.is_train = is_train if root: self.annotations_folder = os.path.join(root, self.annotations_folder) self.images_folder = os.path.join(root, self.images_folder) def __call__(self, *args, **kwargs): dataset = TextOnlyCocoAnnotation() begin, end = (100, 328 + 1) if self.is_train else (1, 233 + 1) gt_format = 'gt_{}.txt' if self.is_train else 'gt_img_{}.txt' img_format = '{}.jpg' if self.is_train else 'img_{}.jpg' for i in range(begin, end): image_path = os.path.join(self.images_folder, img_format.format(i)) annotation_path = os.path.join(self.annotations_folder, gt_format.format(i)) with open(annotation_path, encoding='utf-8-sig') as read_file: for line in [line.strip() for line in read_file.readlines()]: image_size = imagesize.get(image_path) dataset.add_bbox(image_path, image_size, self.parse_line(line)) return dataset def parse_line(self, line): """ Parses line of ICDAR2013 annotation. """ sep = ' ' if self.is_train else ', ' line = line.split(sep) xmin, ymin, xmax, ymax = [int(x) for x in line[:4]] assert xmin < xmax assert ymin < ymax transcription = (sep.join(line[4:]))[1:-1] word_annotation = { 'bbox': [xmin, ymin, xmax - xmin + 1, ymax - ymin + 1], 'segmentation': [[xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax]], 'text': { 'transcription': transcription, 'legible': 1, 'language': 'english', } } return word_annotation class ICDAR2015DatasetConverter: """ Class for conversion of ICDAR2015 to TextOnlyCocoAnnotation. """ def __init__(self, images_folder, annotations_folder, is_train, root=''): self.images_folder = images_folder self.annotations_folder = annotations_folder self.is_train = is_train if root: self.annotations_folder = os.path.join(root, self.annotations_folder) self.images_folder = os.path.join(root, self.images_folder) @staticmethod def parse_line(line): """ Parses line of ICDAR2015 annotation. """ line = line.split(',') quadrilateral = [int(x) for x in line[:8]] transcription = ','.join(line[8:]) legible = 1 language = 'english' if transcription == '###': transcription = '' legible = 0 language = '' xmin = min(quadrilateral[0::2]) xmax = max(quadrilateral[0::2]) ymin = min(quadrilateral[1::2]) ymax = max(quadrilateral[1::2]) word_annotation = { 'bbox': [xmin, ymin, xmax - xmin + 1, ymax - ymin + 1], 'segmentation': [quadrilateral], 'text': { 'transcription': transcription, 'legible': legible, 'language': language, } } return word_annotation def __call__(self, *args, **kwargs): """ Converts annotation from ICDAR 2015 format to internal format. """ dataset = TextOnlyCocoAnnotation() n_images = 1000 if self.is_train else 500 for i in range(1, n_images + 1): image_path = os.path.join(self.images_folder, 'img_{}.jpg'.format(i)) annotation_path = os.path.join(self.annotations_folder, 'gt_img_{}.txt'.format(i)) with open(annotation_path, encoding='utf-8-sig') as read_file: content = [line.strip() for line in read_file.readlines()] for line in content: dataset.add_bbox(image_path, imagesize.get(image_path), self.parse_line(line)) return dataset class ICDAR2017MLTDatasetConverter: """ Class for conversion of ICDAR2017 to TextOnlyCocoAnnotation. """ def __init__(self, folder, subset, is_latin_required, root=''): ''' Converts ICDAR2017 MLT to TextOnlyCocoAnnotation :param folder: Folder with extracted zip archives containing images and annotation. :param subset: 'train' or 'val' :param is_latin_required: if it is True than images that do not contain latin text will be filtered out. ''' self.folder = folder self.subset = subset self.is_latin_required = is_latin_required if root: self.folder = os.path.join(root, self.folder) assert self.subset in ['train', 'val'] if self.subset == 'train': for i in range(1, 9): assert os.path.exists(os.path.join(self.folder, f'ch8_training_images_{i}')) assert os.path.exists( os.path.join(self.folder, 'ch8_training_localization_transcription_gt_v2')) elif self.subset == 'val': assert os.path.exists( os.path.join(self.folder, 'ch8_validation_images')) assert os.path.exists( os.path.join(self.folder, 'ch8_validation_localization_transcription_gt_v2')) @staticmethod def parse_line(line): """ Parses line of ICDAR2015 annotation. """ line = line.split(',') quadrilateral = [int(x) for x in line[:8]] language = line[8] transcription = ','.join(line[9:]) legible = 1 if transcription == '###': transcription = '' legible = 0 language = '' xmin = min(quadrilateral[0::2]) xmax = max(quadrilateral[0::2]) ymin = min(quadrilateral[1::2]) ymax = max(quadrilateral[1::2]) word_annotation = { 'bbox': [xmin, ymin, xmax - xmin + 1, ymax - ymin + 1], 'segmentation': [quadrilateral], 'text': { 'transcription': transcription, 'legible': legible, 'language': language, } } return word_annotation def collect_train_paths(self): """ Collects images and annotations paths for training set. """ image_paths = [] annotation_paths = [] n_images = 7200 for i in range(1, n_images + 1): added = False for extension in ['jpg', 'png']: image_path = os.path.join(self.folder, f'ch8_training_images_{(i - 1) // 1000 + 1}', f'img_{i}.{extension}') if os.path.exists(image_path): image_paths.append(image_path) added = True break if added: annotation_paths.append( os.path.join(self.folder, 'ch8_training_localization_transcription_gt_v2', f'gt_img_{i}.txt') ) else: print(f'Could not find: {image_path[:-3]}*') return image_paths, annotation_paths def collect_val_paths(self): """ Collects images and annotations paths for validation set. """ image_paths = [] annotation_paths = [] n_images = 1800 for i in range(1, n_images + 1): added = False for extension in ['jpg', 'png']: image_path = os.path.join(self.folder, 'ch8_validation_images', f'img_{i}.{extension}') if os.path.exists(image_path): image_paths.append(image_path) added = True break if added: annotation_paths.append( os.path.join(self.folder, 'ch8_validation_localization_transcription_gt_v2', f'gt_img_{i}.txt') ) else: print(f'Could not find: {image_path[:-3]}*') return image_paths, annotation_paths def __call__(self, *args, **kwargs): """ Converts annotation from ICDAR 2017 format to internal format. """ dataset = TextOnlyCocoAnnotation() if self.subset == 'train': image_paths, annotation_paths = self.collect_train_paths() elif self.subset == 'val': image_paths, annotation_paths = self.collect_val_paths() for image_path, annotation_path in zip(image_paths, annotation_paths): word_annotations = [] with open(annotation_path, encoding='utf-8-sig') as read_file: content = [line.strip() for line in read_file.readlines()] for line in content: word_annotations.append(self.parse_line(line)) should_add = not self.is_latin_required if self.is_latin_required: for word_annotation in word_annotations: if word_annotation['text']['language'].lower() == 'latin': should_add = True break if should_add: for word_annotation in word_annotations: dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation) return dataset class ICDAR2019MLTDatasetConverter: """ Class for conversion of ICDAR2019 to TextOnlyCocoAnnotation. """ def __init__(self, folder, is_latin_required, root=''): ''' Converts ICDAR2017 MLT to TextOnlyCocoAnnotation :param folder: Folder with extracted zip archives containing images and annotation. :param is_latin_required: if it is True than images that do not contain latin text will be filtered out. ''' self.folder = folder self.is_latin_required = is_latin_required if root: self.folder = os.path.join(root, self.folder) assert os.path.exists(os.path.join(self.folder, 'ImagesPart1')) assert os.path.exists(os.path.join(self.folder, 'ImagesPart2')) assert os.path.exists(os.path.join(self.folder, 'train_gt_t13')) @staticmethod def parse_line(line): """ Parses line of ICDAR2019 annotation. """ line = line.split(',') quadrilateral = [int(x) for x in line[:8]] language = line[8] transcription = ','.join(line[9:]) legible = 1 if transcription == '###': transcription = '' legible = 0 language = '' xmin = min(quadrilateral[0::2]) xmax = max(quadrilateral[0::2]) ymin = min(quadrilateral[1::2]) ymax = max(quadrilateral[1::2]) word_annotation = { 'bbox': [xmin, ymin, xmax - xmin + 1, ymax - ymin + 1], 'segmentation': [quadrilateral], 'text': { 'transcription': transcription, 'legible': legible, 'language': language, } } return word_annotation def collect_train_paths(self): """ Collects images and annotations paths for training set. """ image_paths = [] annotation_paths = [] n_images = 10000 for i in range(1, n_images + 1): added = False for extension in ['jpg', 'png']: image_path = os.path.join(self.folder, f'ImagesPart{(i - 1) // 5000 + 1}', f'tr_img_{i:05}.{extension}') if os.path.exists(image_path): image_paths.append(image_path) added = True break if added: annotation_paths.append( os.path.join(self.folder, 'train_gt_t13', f'tr_img_{i:05}.txt') ) else: print(f'Could not find: {image_path[:-3]}*') return image_paths, annotation_paths def __call__(self, *args, **kwargs): """ Converts annotation from ICDAR 2019 format to internal format. """ dataset = TextOnlyCocoAnnotation() image_paths, annotation_paths = self.collect_train_paths() for image_path, annotation_path in zip(image_paths, annotation_paths): word_annotations = [] with open(annotation_path, encoding='utf-8-sig') as read_file: content = [line.strip() for line in read_file.readlines()] for line in content: word_annotations.append(self.parse_line(line)) should_add = not self.is_latin_required if self.is_latin_required: for word_annotation in word_annotations: if word_annotation['text']['language'].lower() == 'latin': should_add = True break if should_add: for word_annotation in word_annotations: dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation) return dataset class MSRATD500DatasetConverter: """ Class for conversion of MSRA-TD500 to TextOnlyCocoAnnotation. """ def __init__(self, folder, root=''): self.folder = folder if root: self.folder = os.path.join(root, self.folder) @staticmethod def parse_line(line): """ Parses line of MSRA-TD500 annotation. """ line = line.split(' ') _, _, top_left_x, top_left_y, width, height, rotation = [float(x) for x in line] box = cv2.boxPoints(((top_left_x + width / 2, top_left_y + height / 2), (width, height), rotation * 57.2958)) quadrilateral = [int(x) for x in box.reshape([-1])] xmin = min(quadrilateral[0::2]) xmax = max(quadrilateral[0::2]) ymin = min(quadrilateral[1::2]) ymax = max(quadrilateral[1::2]) word_annotation = { 'bbox': [xmin, ymin, xmax - xmin + 1, ymax - ymin + 1], 'segmentation': [quadrilateral], 'text': { 'transcription': '', 'legible': 1, 'language': '', } } return word_annotation def __call__(self, *args, **kwargs): """ Converts annotation from MSRA-TD500 format to internal format. """ dataset = TextOnlyCocoAnnotation() for image_name in sorted(os.listdir(self.folder)): if image_name.endswith('JPG'): image_path = os.path.join(self.folder, image_name) annotation_path = os.path.join(self.folder, image_name.replace('.JPG', '.gt')) with open(annotation_path, encoding='utf-8-sig') as read_file: content = [line.strip() for line in read_file.readlines()] for line in content: dataset.add_bbox(image_path, imagesize.get(image_path), self.parse_line(line)) return dataset class COCOTextDatasetConverter: """ Class for conversion of COCO-Text to TextOnlyCocoAnnotation. """ def __init__(self, path, sets=None, root=''): self.path = path if root: self.path = os.path.join(root, self.path) self.sets = sets if self.sets is None: self.sets = ['train'] # 'val @staticmethod def parse_annotation_instance(annotation): """ Parses annotation instance of COCO-Text dataset. """ text = annotation['utf8_string'] language = annotation['language'] legible = int(annotation['legibility'] == 'legible') mask = np.reshape(np.array(annotation['mask'], np.int32), (-1, 2)) box = cv2.boxPoints(cv2.minAreaRect(mask)) quadrilateral = [int(x) for x in box.reshape([-1])] xmin = min(quadrilateral[0::2]) xmax = max(quadrilateral[0::2]) ymin = min(quadrilateral[1::2]) ymax = max(quadrilateral[1::2]) word_annotation = { 'bbox': [xmin, ymin, xmax - xmin + 1, ymax - ymin + 1], 'segmentation': [quadrilateral], 'text': { 'transcription': text, 'legible': legible, 'language': language, } } return word_annotation def __call__(self): """ Converts annotation from COCO-TEXT format to internal format. """ dataset = TextOnlyCocoAnnotation() with open(self.path) as read_file: json_loaded = json.load(read_file) for i, value in json_loaded['imgs'].items(): image_path = os.path.join(os.path.dirname(self.path), 'train2014', value['file_name']) dataset_type = value['set'] if dataset_type not in self.sets: print(dataset_type) continue for annotation_id in json_loaded['imgToAnns'][i]: annotation_value = json_loaded['anns'][str(annotation_id)] word_annotation = self.parse_annotation_instance(annotation_value) dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation) return dataset str_to_class = { 'ICDAR2013DatasetConverter': ICDAR2013DatasetConverter, 'ICDAR2015DatasetConverter': ICDAR2015DatasetConverter, 'ICDAR2019MLTDatasetConverter': ICDAR2019MLTDatasetConverter, 'MSRATD500DatasetConverter': MSRATD500DatasetConverter, 'COCOTextDatasetConverter': COCOTextDatasetConverter, }

37.579025

100

0.552966

0a06a6e077bdec81e2d5cc5f0593b79585aecc8b

77

py

Python

tests/development/conftest.py

denssk/backup

292d5f1b1a3765ce0ea8d3cab8bd1ae0c583f72e

[ "Apache-2.0" ]

69

2016-06-29T16:13:55.000Z

2022-03-21T06:38:37.000Z

tests/development/conftest.py

denssk/backup

292d5f1b1a3765ce0ea8d3cab8bd1ae0c583f72e

[ "Apache-2.0" ]

237

2016-09-28T02:12:34.000Z

2022-03-25T13:32:23.000Z

tests/development/conftest.py

denssk/backup

292d5f1b1a3765ce0ea8d3cab8bd1ae0c583f72e

[ "Apache-2.0" ]

45

2017-01-04T21:20:27.000Z

2021-12-29T10:42:22.000Z

from twindb_backup import setup_logging, LOG setup_logging(LOG, debug=True)

19.25

44

0.831169

652e166f64b376d9e6ea0b8ee4469ffb49db67de

4,046

py

Python

laikarestd.py

sandialabs/laikaboss

3064ac1176911651d61c5176e9bd83eacec36b16

[ "Apache-2.0" ]

2

2019-11-02T23:40:23.000Z

2019-12-01T22:24:57.000Z

laikarestd.py

sandialabs/laikaboss

3064ac1176911651d61c5176e9bd83eacec36b16

[ "Apache-2.0" ]

null

laikarestd.py

sandialabs/laikaboss

3064ac1176911651d61c5176e9bd83eacec36b16

[ "Apache-2.0" ]

3

2017-08-09T23:58:40.000Z

2019-12-01T22:25:06.000Z

#!/usr/bin/env python # Copyright 2020 National Technology & Engineering Solutions of Sandia, LLC # (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. # Government retains certain rights in this software. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import logging from flask import g from flask import Flask from flask_cors import CORS from flask import g from werkzeug.middleware.proxy_fix import ProxyFix from laikaboss.lbconfigparser import LBConfigParser from laikaboss.storage_utils import redisclient_from_url from laikarest import routes def create_flask_app(): """ Creates a flask web server """ app = Flask(__name__) app.wsgi_app = ProxyFix(app.wsgi_app) CORS(app, supports_credentials=True) # Allow requests from different origin return app def setupLoggers(laikarest_config, app): logFormatter = logging.Formatter("%(asctime)s - %(process)d [%(levelname)-5.5s] %(message)s") rootLogger = logging.getLogger() log_file_path = laikarest_config["log_file"] debug = True if laikarest_config.get("debug", '').lower() == 'true' else False fileHandler = logging.FileHandler(log_file_path) fileHandler.setFormatter(logFormatter) app.logger.addHandler(fileHandler) rootLogger.addHandler(fileHandler) consoleHandler = logging.StreamHandler() consoleHandler.setFormatter(logFormatter) app.logger.addHandler(consoleHandler) rootLogger.addHandler(consoleHandler) if debug: app.logger.setLevel(logging.DEBUG) rootLogger.setLevel(logging.DEBUG) else: if __name__ != "__main__": # if running under gunicorn set the loggers to that level gunicorn_logger = logging.getLogger("gunicorn.error") app.logger.setLevel(gunicorn_logger.level) rootLogger.setLevel(gunicorn_logger.level) else: app.logger.setLevel(logging.INFO) rootLogger.setLevel(logging.INFO) # create Flask application app = create_flask_app() # path to config config_file = app.config.get("CONFIG_FILE", "/etc/laikaboss/laikarestd.conf") # environmental variable which if present must contain the client secret, and this overrides config file value CLIENT_SECRET_VAR = "CLIENT_SECRET" lb_api_client_id = "laikaboss-api" lb_api_client_secret_file = "/etc/laikaboss/secrets/client_secret" default_config = { "submission_dir": "/var/laikaboss/submission-queue", "lb_client_secret_file": lb_api_client_secret_file, "lb_client": lb_api_client_id, "lb_grant_type": "unset", "jwt_enabled": "False", "redis_url" : "redis://127.0.0.1:6379/0", "max_submission_size": 100 * 1024 * 1024 } # Read config file into a dict # Read config file into a dict config = LBConfigParser() config.read(config_file) laikarest_config = default_config.copy() laikarest_config.update(config.items("General")) laikarest_config.update(config.items("laikarestd")) storage_gui_config = default_config.copy() storage_gui_config.update(config.items("General")) storage_gui_config.update(config.items("storage-gui")) # Setup logging setupLoggers(laikarest_config, app) # Register the routes pertaining to this application routes.init_app(app, laikarest_config, storage_gui_config) if __name__ == "__main__": # Start Flask web server # It should be okay to bind to all interfaces because gunicorn is # running on production and doesn't expose port 8123 to the world # (e.g. binding to all interfaces is convenient for dev work) app.run(host="0.0.0.0", port=8123, debug=False)

35.80531

110

0.749135

67c808f4ed62f104cc9a60d4442724dac655b0ff

3,406

py

Python

sdv/docker/sdvstate/tools/conf/__init__.py

opnfv/cirv-sdv

31fb310d3fd1c9c1f12cfe0c654870e24f5efab6

[ "Apache-2.0" ]

2

2021-09-16T06:31:45.000Z

2022-03-09T19:59:55.000Z

sdv/docker/sdvstate/tools/conf/__init__.py

opnfv/cirv-sdv

31fb310d3fd1c9c1f12cfe0c654870e24f5efab6

[ "Apache-2.0" ]

null

sdv/docker/sdvstate/tools/conf/__init__.py

opnfv/cirv-sdv

31fb310d3fd1c9c1f12cfe0c654870e24f5efab6

[ "Apache-2.0" ]

2

2021-05-11T14:41:01.000Z

2021-05-14T05:59:38.000Z

# Copyright 2020 University Of Delhi. # # 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. """Settings and configuration handlers. Settings will be loaded from several .yaml or .yml files and any user provided settings file. """ import os import ast import yaml # pylint: disable=invalid-name class Settings(): """Holding class for settings. """ def __init__(self): pass def getValue(self, attr): """ Return a settings item value """ try: attr = attr.lower() return getattr(self, attr) except AttributeError: raise AttributeError("{obj} object has no attribute \ {attr}".format(obj=self.__class__, attr=attr)) def setValue(self, name, value): """Set a value """ if name is not None and value is not None: super(Settings, self).__setattr__(name.lower(), value) def load_from_file(self, path): """Update ``settings`` with values found in module at ``path``. """ with open(path) as file: configs = yaml.load_all(file, Loader=yaml.SafeLoader) for conf in configs: for name, value in conf.items(): self.setValue(name, value) def load_from_env(self): """ Update ``settings`` with values found in the environment. """ for key in os.environ: value = os.environ[key] #evaluate string to python type try: value = ast.literal_eval(os.environ[key]) except (ValueError, SyntaxError): pass #already string self.setValue(key, value) def load_from_dir(self, dir_path): """Update ``settings`` with contents of the yaml files at ``path``. Files are read in ascending order, hence if a configuration item exists in more than one file, then the setting in the file that occurs in the last read file will have high precedence and overwrite previous values. Same precedence logic for sub-directories. Also, child directory will have more precedence than it's parent :param dir_path: The full path to the dir from which to load the yaml files. :returns: None """ files = list_yamls(dir_path) for file in files: self.load_from_file(file) settings = Settings() def list_yamls(dir_path): """Get all yaml files recursively in ``dir_path`` """ files = [] dir_list = [x[0] for x in os.walk(dir_path)] dir_list.sort() for path in dir_list: dir_files = [path+'/'+f for f in os.listdir(path) if f.endswith('.yaml') or f.endswith('.yml')] if dir_files is not None: dir_files.sort() files.extend(dir_files) return files

28.14876

79

0.611861

d178cbddbc46bdb4b04a594ebd401f53bbcd4a85

453

py

Python

zezere/migrations/0008_runrequest_raw_settings.py

Rintsi/zezere

1a49476d3d9cef26d65c7dcab2c4abb47938b934

[ "MIT" ]

32

2020-02-16T21:37:22.000Z

2022-03-29T06:34:28.000Z

zezere/migrations/0008_runrequest_raw_settings.py

Rintsi/zezere

1a49476d3d9cef26d65c7dcab2c4abb47938b934

[ "MIT" ]

44

2019-12-18T14:03:22.000Z

2022-03-31T11:54:21.000Z

zezere/migrations/0008_runrequest_raw_settings.py

Rintsi/zezere

1a49476d3d9cef26d65c7dcab2c4abb47938b934

[ "MIT" ]

15

2019-12-05T18:46:35.000Z

2022-03-29T12:21:33.000Z

# Generated by Django 2.2.6 on 2019-10-29 12:48 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [("zezere", "0007_auto_20191021_1444")] operations = [ migrations.AddField( model_name="runrequest", name="raw_settings", field=models.TextField( blank=True, null=True, verbose_name="JSON-encoded settings" ), ) ]

23.842105

75

0.604857

8aeef6c8143075baaddb2fc79089598e98ac648c

953

py

Python

tests/v1/test_timeseries_widget_definition_type.py

MichaelTROEHLER/datadog-api-client-python

12c46626622fb1277bb1e172753b342c671348bd

[ "Apache-2.0" ]

null

tests/v1/test_timeseries_widget_definition_type.py

MichaelTROEHLER/datadog-api-client-python

12c46626622fb1277bb1e172753b342c671348bd

[ "Apache-2.0" ]

null

tests/v1/test_timeseries_widget_definition_type.py

MichaelTROEHLER/datadog-api-client-python

12c46626622fb1277bb1e172753b342c671348bd

[ "Apache-2.0" ]

null

# coding: utf-8 # Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License. # This product includes software developed at Datadog (https://www.datadoghq.com/). # Copyright 2019-Present Datadog, Inc. from __future__ import absolute_import import sys import unittest import datadog_api_client.v1 from datadog_api_client.v1.model.timeseries_widget_definition_type import TimeseriesWidgetDefinitionType class TestTimeseriesWidgetDefinitionType(unittest.TestCase): """TimeseriesWidgetDefinitionType unit test stubs""" def setUp(self): pass def tearDown(self): pass def testTimeseriesWidgetDefinitionType(self): """Test TimeseriesWidgetDefinitionType""" # FIXME: construct object with mandatory attributes with example values # model = TimeseriesWidgetDefinitionType() # noqa: E501 pass if __name__ == '__main__': unittest.main()

28.029412

108

0.756558

248aeefc8743bf6be9be72cfb51fb2c4bf80ab8e

130

py

Python

pypro/base/views.py

limberger/curso-django

9b099a9934871c221be2018d2e80331e90bee40f

[ "Apache-2.0" ]

null

pypro/base/views.py

limberger/curso-django

9b099a9934871c221be2018d2e80331e90bee40f

[ "Apache-2.0" ]

1,012

2020-06-22T21:43:39.000Z

2022-03-31T22:09:32.000Z

pypro/base/views.py

limberger/curso-django

9b099a9934871c221be2018d2e80331e90bee40f

[ "Apache-2.0" ]

1

2020-08-06T19:50:33.000Z

# Create your views here. from django.shortcuts import render def home(request): return render(request, 'base/home.html')

14.444444

44

0.730769

e3273fcb87325d9e3c5c093beacf6a58fc5c00f4

174

py

Python

02/00/getreader.py

pylangstudy/201709

53d868786d7327a83bfa7f4149549c6f9855a6c6

[ "CC0-1.0" ]

null

02/00/getreader.py

pylangstudy/201709

53d868786d7327a83bfa7f4149549c6f9855a6c6

[ "CC0-1.0" ]

32

2017-09-01T00:52:17.000Z

2017-10-01T00:30:02.000Z

02/00/getreader.py

pylangstudy/201709

53d868786d7327a83bfa7f4149549c6f9855a6c6

[ "CC0-1.0" ]

null

#!python3.6 #encoding:utf-8 import codecs text = '日本語' for enc in ['utf-8', 'utf-16LE', 'utf-16BE', 'utf-32', 'shift-jis', 'euc-jp']: print(enc, codecs.getreader(enc))

19.333333

78

0.626437

3abd6121a2045489e599f3d8fb6fcb1e7db9ab8b

10,424

py

Python

utils/dataloaders.py

adrift00/imagenet_pretrain

fb824a860b105aad0bda1c4dcc0b9bffea5fb418

[ "Apache-2.0" ]

null

utils/dataloaders.py

adrift00/imagenet_pretrain

fb824a860b105aad0bda1c4dcc0b9bffea5fb418

[ "Apache-2.0" ]

null

utils/dataloaders.py

adrift00/imagenet_pretrain

fb824a860b105aad0bda1c4dcc0b9bffea5fb418

[ "Apache-2.0" ]

null

import os import torch import numpy as np import torchvision.datasets as datasets import torchvision.transforms as transforms DATA_BACKEND_CHOICES = ['pytorch'] try: from nvidia.dali.plugin.pytorch import DALIClassificationIterator from nvidia.dali.pipeline import Pipeline import nvidia.dali.ops as ops import nvidia.dali.types as types DATA_BACKEND_CHOICES.append('dali-gpu') DATA_BACKEND_CHOICES.append('dali-cpu') except ImportError: print("Please install DALI from https://www.github.com/NVIDIA/DALI to run this example.") class HybridTrainPipe(Pipeline): def __init__(self, batch_size, num_threads, device_id, data_dir, crop, dali_cpu=False): super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed = 12 + device_id) if torch.distributed.is_initialized(): local_rank = torch.distributed.get_rank() world_size = torch.distributed.get_world_size() else: local_rank = 0 world_size = 1 self.input = ops.FileReader( file_root = data_dir, shard_id = local_rank, num_shards = world_size, random_shuffle = True) if dali_cpu: dali_device = "cpu" self.decode = ops.HostDecoderRandomCrop(device=dali_device, output_type=types.RGB, random_aspect_ratio=[0.75, 4./3.], random_area=[0.08, 1.0], num_attempts=100) else: dali_device = "gpu" # This padding sets the size of the internal nvJPEG buffers to be able to handle all images from full-sized ImageNet # without additional reallocations self.decode = ops.nvJPEGDecoderRandomCrop(device="mixed", output_type=types.RGB, device_memory_padding=211025920, host_memory_padding=140544512, random_aspect_ratio=[0.75, 4./3.], random_area=[0.08, 1.0], num_attempts=100) self.res = ops.Resize(device=dali_device, resize_x=crop, resize_y=crop, interp_type=types.INTERP_TRIANGULAR) self.cmnp = ops.CropMirrorNormalize(device = "gpu", output_dtype = types.FLOAT, output_layout = types.NCHW, crop = (crop, crop), image_type = types.RGB, mean = [0.485 * 255,0.456 * 255,0.406 * 255], std = [0.229 * 255,0.224 * 255,0.225 * 255]) self.coin = ops.CoinFlip(probability = 0.5) def define_graph(self): rng = self.coin() self.jpegs, self.labels = self.input(name = "Reader") images = self.decode(self.jpegs) images = self.res(images) output = self.cmnp(images.gpu(), mirror = rng) return [output, self.labels] class HybridValPipe(Pipeline): def __init__(self, batch_size, num_threads, device_id, data_dir, crop, size): super(HybridValPipe, self).__init__(batch_size, num_threads, device_id, seed = 12 + device_id) if torch.distributed.is_initialized(): local_rank = torch.distributed.get_rank() world_size = torch.distributed.get_world_size() else: local_rank = 0 world_size = 1 self.input = ops.FileReader( file_root = data_dir, shard_id = local_rank, num_shards = world_size, random_shuffle = False) self.decode = ops.nvJPEGDecoder(device = "mixed", output_type = types.RGB) self.res = ops.Resize(device = "gpu", resize_shorter = size) self.cmnp = ops.CropMirrorNormalize(device = "gpu", output_dtype = types.FLOAT, output_layout = types.NCHW, crop = (crop, crop), image_type = types.RGB, mean = [0.485 * 255,0.456 * 255,0.406 * 255], std = [0.229 * 255,0.224 * 255,0.225 * 255]) def define_graph(self): self.jpegs, self.labels = self.input(name = "Reader") images = self.decode(self.jpegs) images = self.res(images) output = self.cmnp(images) return [output, self.labels] class DALIWrapper(object): def gen_wrapper(dalipipeline): for data in dalipipeline: input = data[0]["data"] target = data[0]["label"].squeeze().cuda().long() yield input, target dalipipeline.reset() def __init__(self, dalipipeline): self.dalipipeline = dalipipeline def __iter__(self): return DALIWrapper.gen_wrapper(self.dalipipeline) def get_dali_train_loader(dali_cpu=False): def gdtl(data_path, batch_size, workers=5, _worker_init_fn=None): if torch.distributed.is_initialized(): local_rank = torch.distributed.get_rank() world_size = torch.distributed.get_world_size() else: local_rank = 0 world_size = 1 traindir = os.path.join(data_path, 'train') pipe = HybridTrainPipe(batch_size=batch_size, num_threads=workers, device_id = local_rank, data_dir = traindir, crop = 224, dali_cpu=dali_cpu) pipe.build() test_run = pipe.run() train_loader = DALIClassificationIterator(pipe, size = int(pipe.epoch_size("Reader") / world_size)) return DALIWrapper(train_loader), int(pipe.epoch_size("Reader") / (world_size * batch_size)) return gdtl def get_dali_val_loader(): def gdvl(data_path, batch_size, workers=5, _worker_init_fn=None): if torch.distributed.is_initialized(): local_rank = torch.distributed.get_rank() world_size = torch.distributed.get_world_size() else: local_rank = 0 world_size = 1 valdir = os.path.join(data_path, 'val') pipe = HybridValPipe(batch_size=batch_size, num_threads=workers, device_id = local_rank, data_dir = valdir, crop = 224, size = 256) pipe.build() test_run = pipe.run() val_loader = DALIClassificationIterator(pipe, size = int(pipe.epoch_size("Reader") / world_size), fill_last_batch=False) return DALIWrapper(val_loader), int(pipe.epoch_size("Reader") / (world_size * batch_size)) return gdvl def fast_collate(batch): imgs = [img[0] for img in batch] targets = torch.tensor([target[1] for target in batch], dtype=torch.int64) w = imgs[0].size[0] h = imgs[0].size[1] tensor = torch.zeros( (len(imgs), 3, h, w), dtype=torch.uint8 ) for i, img in enumerate(imgs): nump_array = np.asarray(img, dtype=np.uint8) tens = torch.from_numpy(nump_array) if(nump_array.ndim < 3): nump_array = np.expand_dims(nump_array, axis=-1) nump_array = np.rollaxis(nump_array, 2) tensor[i] += torch.from_numpy(nump_array) return tensor, targets class PrefetchedWrapper(object): def prefetched_loader(loader): mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1,3,1,1) std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1,3,1,1) stream = torch.cuda.Stream() first = True for next_input, next_target in loader: with torch.cuda.stream(stream): next_input = next_input.cuda() next_target = next_target.cuda() next_input = next_input.float() # next_input = next_input.sub_(mean).div_(std) # don't use normolization because siamrpn doesn't use it. if not first: yield input, target else: first = False torch.cuda.current_stream().wait_stream(stream) input = next_input target = next_target yield input, target def __init__(self, dataloader): self.dataloader = dataloader self.epoch = 0 def __iter__(self): if (self.dataloader.sampler is not None and isinstance(self.dataloader.sampler, torch.utils.data.distributed.DistributedSampler)): self.dataloader.sampler.set_epoch(self.epoch) self.epoch += 1 return PrefetchedWrapper.prefetched_loader(self.dataloader) def get_pytorch_train_loader(data_path, batch_size, workers=5, _worker_init_fn=None, input_size=224): traindir = os.path.join(data_path, 'train') train_dataset = datasets.ImageFolder( traindir, transforms.Compose([ transforms.RandomResizedCrop(input_size), transforms.RandomHorizontalFlip(), ])) if torch.distributed.is_initialized(): train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) else: train_sampler = None train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=batch_size, shuffle=(train_sampler is None), num_workers=workers, worker_init_fn=_worker_init_fn, pin_memory=True, sampler=train_sampler, collate_fn=fast_collate) return PrefetchedWrapper(train_loader), len(train_loader) def get_pytorch_val_loader(data_path, batch_size, workers=5, _worker_init_fn=None, input_size=224): valdir = os.path.join(data_path, 'val') val_dataset = datasets.ImageFolder( valdir, transforms.Compose([ transforms.Resize(int(input_size / 0.875)), transforms.CenterCrop(input_size), ])) if torch.distributed.is_initialized(): val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset) else: val_sampler = None val_loader = torch.utils.data.DataLoader( val_dataset, sampler=val_sampler, batch_size=batch_size, shuffle=False, num_workers=workers, worker_init_fn=_worker_init_fn, pin_memory=True, collate_fn=fast_collate) return PrefetchedWrapper(val_loader), len(val_loader)

39.78626

156

0.601784

06c3f5dbf1ed4e6e30448f5a857e377a1753c66e

4,115

py

Python

amqp/abstract_channel.py

smurfix/py-amqp

583e5c8f2b6fc37070654e68efcdc6ed681b87ea

[ "BSD-3-Clause" ]

null

amqp/abstract_channel.py

smurfix/py-amqp

583e5c8f2b6fc37070654e68efcdc6ed681b87ea

[ "BSD-3-Clause" ]

null

amqp/abstract_channel.py

smurfix/py-amqp

583e5c8f2b6fc37070654e68efcdc6ed681b87ea

[ "BSD-3-Clause" ]

null

"""Code common to Connection and Channel objects.""" # Copyright (C) 2007-2008 Barry Pederson <bp@barryp.org>) from __future__ import absolute_import, unicode_literals from vine import ensure_promise, promise from .exceptions import AMQPNotImplementedError, RecoverableConnectionError from .serialization import dumps, loads __all__ = ['AbstractChannel'] class AbstractChannel(object): """Superclass for Connection and Channel. The connection is treated as channel 0, then comes user-created channel objects. The subclasses must have a _METHOD_MAP class property, mapping between AMQP method signatures and Python methods. """ def __init__(self, connection, channel_id): self.connection = connection self.channel_id = channel_id connection.channels[channel_id] = self self.method_queue = [] # Higher level queue for methods self.auto_decode = False self._pending = {} self._callbacks = {} self._setup_listeners() def __enter__(self): return self def __exit__(self, *exc_info): self.close() def send_method(self, sig, format=None, args=None, content=None, wait=None, callback=None, returns_tuple=False): p = promise() conn = self.connection if conn is None: raise RecoverableConnectionError('connection already closed') args = dumps(format, args) if format else '' try: conn.frame_writer(1, self.channel_id, sig, args, content) except StopIteration: raise RecoverableConnectionError('connection already closed') # TODO temp: callback should be after write_method ... ;) if callback: p.then(callback) p() if wait: return self.wait(wait, returns_tuple=returns_tuple) return p def close(self): """Close this Channel or Connection.""" raise NotImplementedError('Must be overriden in subclass') def wait(self, method, callback=None, timeout=None, returns_tuple=False): p = ensure_promise(callback) pending = self._pending prev_p = [] if not isinstance(method, list): method = [method] for m in method: prev_p.append(pending.get(m)) pending[m] = p try: while not p.ready: self.connection.drain_events(timeout=timeout) if p.value: args, kwargs = p.value return args if returns_tuple else (args and args[0]) finally: for i, m in enumerate(method): if prev_p[i] is not None: pending[m] = prev_p[i] else: pending.pop(m, None) def dispatch_method(self, method_sig, payload, content): if content and \ self.auto_decode and \ hasattr(content, 'content_encoding'): try: content.body = content.body.decode(content.content_encoding) except Exception: pass try: amqp_method = self._METHODS[method_sig] except KeyError: raise AMQPNotImplementedError( 'Unknown AMQP method {0!r}'.format(method_sig)) try: listeners = [self._callbacks[method_sig]] except KeyError: listeners = None try: one_shot = self._pending.pop(method_sig) except KeyError: if not listeners: return else: if listeners is None: listeners = [one_shot] else: listeners.append(one_shot) args = [] if amqp_method.args: args, _ = loads(amqp_method.args, payload, 4) if amqp_method.content: args.append(content) for listener in listeners: listener(*args) #: Placeholder, the concrete implementations will have to #: supply their own versions of _METHOD_MAP _METHODS = {}

31.174242

77

0.590765

8d3bf08c426df034576d4813300d6f8a05caf297

1,713

py

Python

xen/xen-4.2.2/tools/xm-test/tests/block-list/06_block-list_checkremove_pos.py

zhiming-shen/Xen-Blanket-NG

47e59d9bb92e8fdc60942df526790ddb983a5496

[ "Apache-2.0" ]

1

2018-02-02T00:15:26.000Z

xen/xen-4.2.2/tools/xm-test/tests/block-list/06_block-list_checkremove_pos.py

zhiming-shen/Xen-Blanket-NG

47e59d9bb92e8fdc60942df526790ddb983a5496

[ "Apache-2.0" ]

null

xen/xen-4.2.2/tools/xm-test/tests/block-list/06_block-list_checkremove_pos.py

zhiming-shen/Xen-Blanket-NG

47e59d9bb92e8fdc60942df526790ddb983a5496

[ "Apache-2.0" ]

1

2019-05-27T09:47:18.000Z

#!/usr/bin/python # Copyright (C) International Business Machines Corp., 2005 # Author: Dan Smith <danms@us.ibm.com> from XmTestLib import * from XmTestLib.block_utils import * if ENABLE_HVM_SUPPORT: SKIP("Block-list not supported for HVM domains") domain = XmTestDomain() try: domain.start(noConsole=True) except DomainError, e: FAIL(str(e)) s, o = traceCommand("xm block-list %s" % domain.getName()) if s != 0: FAIL("block-list returned !0 when no devices attached") if o: FAIL("block-list without devices reported something!") block_attach(domain, "phy:/dev/ram0", "xvda1") s, o = traceCommand("xm block-list %s" % domain.getName()) if s != 0: FAIL("block-list failed") if o.find("51713") == -1: FAIL("block-list didn't show the block device I just attached!") block_attach(domain, "phy:/dev/ram1", "xvda2") s, o = traceCommand("xm block-list %s" % domain.getName()) if s != 0: FAIL("block-list failed") if o.find("51714") == -1: FAIL("block-list didn't show the other block device I just attached!") block_detach(domain, "xvda1") s, o = traceCommand("xm block-list %s" % domain.getName()) if s != 0: FAIL("block-list failed after detaching a device") if o.find("51713") != -1: FAIL("xvda1 still shown in block-list after detach!") if o.find("51714") == -1: FAIL("xvda2 not shown after detach of xvda1!") block_detach(domain, "xvda2") s, o = traceCommand("xm block-list %s" % domain.getName()) if s != 0: FAIL("block-list failed after detaching another device") if o.find("51714") != -1: FAIL("xvda2 still shown in block-list after detach!") if o: FAIL("block-list still shows something after all devices detached!") domain.stop()

27.629032

74

0.676007

e976e750a39493a6066a1cef2c31e77767cc2a11

4,094

py

Python

python/graphscope/tests/unittest/test_java_app.py

luoxiaojian/GraphScope-1

97785684f2b2495c41dc079aed64198b5a6e1331

[ "Apache-2.0" ]

2

2021-04-07T07:57:13.000Z

2021-11-19T09:44:01.000Z

python/graphscope/tests/unittest/test_java_app.py

luoxiaojian/GraphScope-1

97785684f2b2495c41dc079aed64198b5a6e1331

[ "Apache-2.0" ]

16

2021-12-22T09:19:25.000Z

2022-03-29T02:43:34.000Z

python/graphscope/tests/unittest/test_java_app.py

luoxiaojian/GraphScope-1

97785684f2b2495c41dc079aed64198b5a6e1331

[ "Apache-2.0" ]

2

2022-01-25T10:16:51.000Z

2022-02-07T11:51:20.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # Copyright 2020 Alibaba Group Holding Limited. 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 logging import os from pathlib import Path import numpy as np import pandas as pd import pytest from graphscope import JavaApp @pytest.fixture(scope="module") def not_exist_jar(): path = os.path.join("not_exist_dir", "not_exist.jar") return path @pytest.fixture(scope="module") def not_jar_file(): return os.path.expandvars("${GS_TEST_DIR}/p2p-31.e") @pytest.fixture(scope="module") def a_gar_file(): return os.path.expandvars("${GS_TEST_DIR}/gars/sssp_pie.gar") @pytest.fixture(scope="module") def empty_jar(): return os.path.expandvars("${GS_TEST_DIR}/jars/empty.jar") @pytest.fixture(scope="module") def demo_jar(): return os.path.expandvars("${USER_JAR_PATH}") @pytest.fixture(scope="module") def property_graph_sssp_vertex_data_class(): return "com.alibaba.graphscope.example.property.sssp.ParallelPropertySSSPVertexData" @pytest.fixture(scope="module") def non_exist_java_class(): return "com.alibaba.graphscope.example.non.existing.java.class" @pytest.mark.skipif( os.environ.get("RUN_JAVA_TESTS") != "ON", reason="Java SDK is disabled, skip this test.", ) def test_load_non_existing_jar( not_exist_jar, property_graph_sssp_vertex_data_class, non_exist_java_class ): with pytest.raises(FileNotFoundError): sssp = JavaApp(not_exist_jar, property_graph_sssp_vertex_data_class) with pytest.raises(FileNotFoundError): sssp = JavaApp(not_exist_jar, non_exist_java_class) @pytest.mark.skipif( os.environ.get("RUN_JAVA_TESTS") != "ON", reason="Java SDK is disabled, skip this test.", ) def test_load_not_a_jar( not_jar_file, property_graph_sssp_vertex_data_class, non_exist_java_class ): with pytest.raises(KeyError): sssp = JavaApp(not_jar_file, property_graph_sssp_vertex_data_class) with pytest.raises(KeyError): sssp = JavaApp(not_jar_file, non_exist_java_class) @pytest.mark.skipif( os.environ.get("RUN_JAVA_TESTS") != "ON", reason="Java SDK is disabled, skip this test.", ) def test_load_gar_file( a_gar_file, property_graph_sssp_vertex_data_class, non_exist_java_class ): with pytest.raises(KeyError): sssp = JavaApp(a_gar_file, property_graph_sssp_vertex_data_class) with pytest.raises(KeyError): sssp = JavaApp(a_gar_file, non_exist_java_class) @pytest.mark.skipif( os.environ.get("RUN_JAVA_TESTS") != "ON", reason="Java SDK is disabled, skip this test.", ) def test_load_empty_jar( empty_jar, property_graph_sssp_vertex_data_class, non_exist_java_class ): with pytest.raises(KeyError): sssp = JavaApp(empty_jar, property_graph_sssp_vertex_data_class) with pytest.raises(KeyError): sssp = JavaApp(empty_jar, non_exist_java_class) @pytest.mark.skipif( os.environ.get("RUN_JAVA_TESTS") != "ON", reason="Java SDK is disabled, skip this test.", ) def test_load_correct_jar(property_graph_sssp_vertex_data_class, demo_jar): sssp = JavaApp(demo_jar, property_graph_sssp_vertex_data_class) @pytest.mark.skipif( os.environ.get("RUN_JAVA_TESTS") != "ON", reason="Java SDK is disabled, skip this test.", ) def test_sssp_property_vertex_data( demo_jar, graphscope_session, p2p_property_graph, property_graph_sssp_vertex_data_class, ): sssp = JavaApp( full_jar_path=demo_jar, java_app_class=property_graph_sssp_vertex_data_class ) sssp(p2p_property_graph, src=6)

29.242857

88

0.74597

94592d0c5d4dff58a268ddc1cd719d067e9701fd

1,824

py

Python

objectModel/Python/cdm/resolvedmodel/resolved_entity_reference_set.py

rt112000/CDM

34bd34f9260140a8f8aa02bd87c23033f3daad4c

[ "CC-BY-4.0", "MIT" ]

884

2019-05-10T02:09:10.000Z

2022-03-31T14:02:00.000Z

objectModel/Python/cdm/resolvedmodel/resolved_entity_reference_set.py

spbast/CDM

bf97a3720c97ee4c9df3625084cf8b3bc65ff9c7

[ "CC-BY-4.0", "MIT" ]

171

2019-06-10T11:34:37.000Z

2022-03-31T22:50:12.000Z

objectModel/Python/cdm/resolvedmodel/resolved_entity_reference_set.py

spbast/CDM

bf97a3720c97ee4c9df3625084cf8b3bc65ff9c7

[ "CC-BY-4.0", "MIT" ]

340

2019-05-07T18:00:16.000Z

2022-03-31T12:00:15.000Z

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. from typing import List, Optional, TYPE_CHECKING if TYPE_CHECKING: from cdm.objectmodel import CdmEntityDefinition, SpewCatcher from cdm.resolvedmodel import ResolvedEntityReference from cdm.utilities import ResolveOptions class ResolvedEntityReferenceSet: def __init__(self, res_opt: 'ResolveOptions', rer_set: List['ResolvedEntityReference'] = None) -> None: self.res_opt = res_opt # type: ResolveOptions self.rer_set = rer_set or [] # type: List[ResolvedEntityReference] def add(self, to_add: 'ResolvedEntityReferenceSet') -> None: if to_add and to_add.rer_set: self.rer_set += to_add.rer_set def copy(self) -> 'ResolvedEntityReferenceSet': return ResolvedEntityReferenceSet(self.res_opt, [rer.copy() for rer in self.rer_set]) def find_entity(self, ent_other: 'CdmEntityDefinition') -> Optional['ResolvedEntityReferenceSet']: # Make an array of just the refs that include the requested. filtered_set = [rer for rer in self.rer_set if any(rers.entity == ent_other for rers in rer.referenced)] return None if filtered_set else ResolvedEntityReferenceSet(self.res_opt, filtered_set) def spew(self, res_opt: 'ResolveOptions', to: 'SpewCatcher', indent: str, name_sort: bool) -> None: if name_sort: rer_list = sorted( self.rer_set, key=lambda rer: rer.referenced[0].entity.entity_name.casefold() if rer and rer.referenced else '') else: rer_list = self.rer_set for idx, rer in enumerate(rer_list): rer.spew(res_opt, to, indent + '(rer[' + str(idx) + '])', name_sort)

46.769231

114

0.697368

92ad9de318fd5152094b8872d8254796ff8f6e08

1,003

py

Python

tests/test_feedback.py

voyagegroup/apns-proxy-server

5858d1b33d37b9333ca153cd92f091bad9537455

[ "BSD-2-Clause" ]

16

2015-01-20T22:54:43.000Z

2021-07-07T03:33:04.000Z

tests/test_feedback.py

voyagegroup/apns-proxy-server

5858d1b33d37b9333ca153cd92f091bad9537455

[ "BSD-2-Clause" ]

null

tests/test_feedback.py

voyagegroup/apns-proxy-server

5858d1b33d37b9333ca153cd92f091bad9537455

[ "BSD-2-Clause" ]

6

2015-01-22T05:00:36.000Z

2022-03-03T15:20:00.000Z

# -*- coding: utf-8 -*- """ Tests for apns_proxy_server.feedback """ from datetime import datetime from nose.tools import ok_, eq_ import simplejson as json from apns_proxy_server.feedback import FeedbackProxy def test_instance(): proxy = FeedbackProxy(True, '/path/to/cert', '/path/to/key') ok_(proxy) ok_(proxy.use_sandbox) eq_(proxy.cert_file, '/path/to/cert') eq_(proxy.key_file, '/path/to/key') def test_get(): disabled_datetime = datetime(1988, 4, 23, 2, 0, 0) proxy = FeedbackProxy(True, '/path/to/cert', '/path/to/key') proxy._apns = type('MockApns', (object,), { 'feedback_server': { 'token_value': disabled_datetime, }, }) result = proxy.get() json_result = json.loads(result) ok_(result) ok_(json_result) ok_(isinstance(result, basestring)) ok_(isinstance(json_result, dict)) ok_('token_value' in json_result) eq_(datetime.fromtimestamp(json_result['token_value']), disabled_datetime)

23.880952

78

0.666002

c261420a300c4ce53735f0cd1ba088344d58a79a

7,926

py

Python

aiida_vasp/workchains/tests/test_vasp_wc.py

MichaelWolloch/aiida-vasp

315b79bf874b8449dd702f1f3bc48c55aa89683b

[ "MIT" ]

28

2019-03-06T11:33:01.000Z

2022-02-25T22:29:12.000Z

aiida_vasp/workchains/tests/test_vasp_wc.py

MichaelWolloch/aiida-vasp

315b79bf874b8449dd702f1f3bc48c55aa89683b

[ "MIT" ]

386

2018-09-04T15:05:51.000Z

2022-03-04T12:18:39.000Z

aiida_vasp/workchains/tests/test_vasp_wc.py

MichaelWolloch/aiida-vasp

315b79bf874b8449dd702f1f3bc48c55aa89683b

[ "MIT" ]

35

2019-01-14T17:12:08.000Z

2022-02-24T18:52:11.000Z

""" Test submitting a VaspWorkChain. This does not seem to work, for `submit` the daemon will not pick up the workchain and `run` just seems to get stuck after a while. """ # pylint: disable=unused-import,wildcard-import,unused-wildcard-import,unused-argument,redefined-outer-name, import-outside-toplevel from __future__ import print_function import pytest import numpy as np from aiida.common.extendeddicts import AttributeDict from aiida_vasp.utils.fixtures import * from aiida_vasp.utils.fixtures.data import POTCAR_FAMILY_NAME, POTCAR_MAP from aiida_vasp.utils.aiida_utils import get_data_node, aiida_version, cmp_version, create_authinfo @pytest.mark.parametrize(['vasp_structure', 'vasp_kpoints'], [('str', 'mesh')], indirect=True) def test_vasp_wc(fresh_aiida_env, run_vasp_process): """Test submitting only, not correctness, with mocked vasp code.""" results, node = run_vasp_process(process_type='workchain') assert node.exit_status == 0 assert 'retrieved' in results assert 'misc' in results assert 'remote_folder' in results misc = results['misc'].get_dict() assert misc['maximum_stress'] == pytest.approx(22.8499295) assert misc['total_energies']['energy_extrapolated'] == pytest.approx(-14.16209692) @pytest.mark.parametrize(['vasp_structure', 'vasp_kpoints'], [('str', 'mesh')], indirect=True) def test_vasp_wc_chgcar(fresh_aiida_env, run_vasp_process): """Test submitting only, not correctness, with mocked vasp code, test fetching of the CHGCAR.""" settings = {'ADDITIONAL_RETRIEVE_LIST': ['CHGCAR'], 'parser_settings': {'add_chgcar': True}} results, node = run_vasp_process(settings=settings, process_type='workchain') assert node.exit_status == 0 assert 'chgcar' in results assert results['chgcar'].get_content() == 'This is a test CHGCAR file.\n' ### COMPLEX WORHCAIN TEST ### def si_structure(): """ Setup a silicon structure in a displaced FCC setting """ from aiida.plugins import DataFactory structure_data = DataFactory('structure') alat = 3.9 lattice = np.array([[.5, .5, 0], [0, .5, .5], [.5, 0, .5]]) * alat structure = structure_data(cell=lattice) positions = [[0.1, 0.0, 0.0]] for pos_direct in positions: pos_cartesian = np.dot(pos_direct, lattice) structure.append_atom(position=pos_cartesian, symbols='Si') return structure # TEST INPUT FOR AUTOMATIC correction of NELM # calculation should finish in the second run where the calculation INCAR_ELEC_CONV = { 'encut': 240, 'ismear': 0, 'sigma': 0.1, 'ediff': 1e-9, 'nelm': 7, 'ibrion': -1, 'potim': 0.01, 'nsw': -1, 'isif': 3, # 'ediffg': -0.01 } INCAR_IONIC_CONV = { 'encut': 240, 'ismear': 0, 'sigma': 0.1, 'ediff': 1e-9, 'nelm': 15, 'ibrion': 1, 'potim': 0.1, 'nsw': 5, 'isif': 3, } # Parameters for test handling unfinished VASP. The first iteration was killed manually. INCAR_IONIC_UNFINISHED = { 'encut': 500, 'ismear': 0, 'isym': 0, 'sigma': 0.1, 'ediff': 1e-9, 'nelm': 15, 'ibrion': 1, 'potim': 0.1, 'nsw': 20, 'isif': 3, } def setup_vasp_workchain(structure, incar, nkpts): """ Setup the inputs for a VaspWorkChain. """ from aiida.orm import Code inputs = AttributeDict() inputs.structure = structure inputs.parameters = get_data_node('dict', dict={'incar': incar}) kpoints = get_data_node('array.kpoints') kpoints.set_kpoints_mesh((nkpts, nkpts, nkpts)) inputs.kpoints = kpoints inputs.potential_family = get_data_node('str', POTCAR_FAMILY_NAME) inputs.potential_mapping = get_data_node('dict', dict=POTCAR_MAP) inputs.options = get_data_node('dict', dict={ 'withmpi': False, 'queue_name': 'None', 'resources': { 'num_machines': 1, 'num_mpiprocs_per_machine': 1 }, 'max_wallclock_seconds': 3600 }) inputs.settings = get_data_node('dict', dict={'parser_settings': {'add_structure': True}}) mock = Code.get_from_string('mock-vasp-strict@localhost') inputs.code = mock return inputs def test_vasp_wc_nelm(fresh_aiida_env, potentials, mock_vasp_strict): """Test with mocked vasp code for handling electronic convergence issues""" from aiida.orm import Code from aiida.plugins import WorkflowFactory from aiida.engine import run from aiida.cmdline.utils.common import get_calcjob_report, get_workchain_report workchain = WorkflowFactory('vasp.vasp') mock_vasp_strict.store() create_authinfo(computer=mock_vasp_strict.computer, store=True) inputs = setup_vasp_workchain(si_structure(), INCAR_ELEC_CONV, 8) inputs.verbose = get_data_node('bool', True) results, node = run.get_node(workchain, **inputs) called_nodes = list(node.called) called_nodes.sort(key=lambda x: x.ctime) print(get_workchain_report(node, 'DEBUG')) for child in called_nodes: print(get_calcjob_report(child)) child = called_nodes[0] print(child.get_object_content('INCAR')) print(child.get_object_content('POSCAR')) print(child.get_object_content('KPOINTS')) print(child.outputs.retrieved.get_object_content('vasp_output')) print(child.outputs.retrieved.list_object_names()) print(child.outputs.misc.get_dict()) print(child.exit_status) child = called_nodes[1] print(child.get_object_content('INCAR')) print(child.get_object_content('POSCAR')) print(child.get_object_content('KPOINTS')) print(child.outputs.retrieved.get_object_content('vasp_output')) print(child.outputs.retrieved.list_object_names()) print(child.outputs.misc.get_dict()) print(child.exit_status) assert node.exit_status == 0 assert 'retrieved' in results assert 'misc' in results assert 'remote_folder' in results assert results['misc']['total_energies']['energy_extrapolated'] == pytest.approx(-4.82467802) # Sort the called nodes by creation time called_nodes = list(node.called) called_nodes.sort(key=lambda x: x.ctime) assert called_nodes[0].exit_status == 701 assert called_nodes[1].exit_status == 0 @pytest.mark.parametrize('incar,nkpts,exit_codes', [[INCAR_IONIC_CONV, 8, [702, 0]], [INCAR_IONIC_UNFINISHED, 16, [700, 0]]]) def test_vasp_wc_ionic_continue(fresh_aiida_env, potentials, mock_vasp_strict, incar, nkpts, exit_codes): """Test with mocked vasp code for handling ionic convergence issues""" from aiida.orm import Code from aiida.plugins import WorkflowFactory from aiida.engine import run workchain = WorkflowFactory('vasp.vasp') mock_vasp_strict.store() create_authinfo(computer=mock_vasp_strict.computer, store=True) inputs = setup_vasp_workchain(si_structure(), incar, nkpts) inputs.verbose = get_data_node('bool', True) # The test calculation contain NELM breaches during the relaxation - set to ignore it. inputs.handler_overrides = get_data_node('dict', dict={'ignore_nelm_breach_relax': True}) results, node = run.get_node(workchain, **inputs) assert node.exit_status == 0 assert 'retrieved' in results assert 'misc' in results assert 'remote_folder' in results assert results['misc']['run_status']['ionic_converged'] # Sort the called nodes by creation time called_nodes = list(node.called) called_nodes.sort(key=lambda x: x.ctime) # Check the child status - here the first calculation is not finished but the second one is for idx, code in enumerate(exit_codes): assert called_nodes[idx].exit_status == code

35.070796

132

0.676634

35186bb87f7e509bca13d96949b0ab808bd786ad

6,384

py

Python

openstack_dashboard/dashboards/project/database_backups/tests.py

maofutian/horizon

dab92e7d2f576caea8f81c8e22a516fb45633794

[ "Apache-2.0" ]

null

openstack_dashboard/dashboards/project/database_backups/tests.py

maofutian/horizon

dab92e7d2f576caea8f81c8e22a516fb45633794

[ "Apache-2.0" ]

null

openstack_dashboard/dashboards/project/database_backups/tests.py

maofutian/horizon

dab92e7d2f576caea8f81c8e22a516fb45633794

[ "Apache-2.0" ]

null

# Copyright 2013 Mirantis Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from django.core.urlresolvers import reverse from django import http from mox import IsA # noqa from openstack_dashboard import api from openstack_dashboard.test import helpers as test INDEX_URL = reverse('horizon:project:database_backups:index') BACKUP_URL = reverse('horizon:project:database_backups:create') DETAILS_URL = reverse('horizon:project:database_backups:detail', args=['id']) class DatabasesBackupsTests(test.TestCase): @test.create_stubs({api.trove: ('backup_list', 'instance_get')}) def test_index(self): api.trove.backup_list(IsA(http.HttpRequest))\ .AndReturn(self.database_backups.list()) api.trove.instance_get(IsA(http.HttpRequest), IsA(str))\ .MultipleTimes()\ .AndReturn(self.databases.first()) self.mox.ReplayAll() res = self.client.get(INDEX_URL) self.assertTemplateUsed(res, 'project/database_backups/index.html') @test.create_stubs({api.trove: ('backup_list',)}) def test_index_exception(self): api.trove.backup_list(IsA(http.HttpRequest))\ .AndRaise(self.exceptions.trove) self.mox.ReplayAll() res = self.client.get(INDEX_URL) self.assertTemplateUsed( res, 'project/database_backups/index.html') self.assertEqual(res.status_code, 200) self.assertMessageCount(res, error=1) @test.create_stubs({api.trove: ('instance_list', 'backup_list', 'backup_create')}) def test_launch_backup(self): api.trove.instance_list(IsA(http.HttpRequest))\ .AndReturn(self.databases.list()) api.trove.backup_list(IsA(http.HttpRequest)) \ .AndReturn(self.database_backups.list()) database = self.databases.first() backupName = "NewBackup" backupDesc = "Backup Description" api.trove.backup_create( IsA(http.HttpRequest), backupName, database.id, backupDesc, "") self.mox.ReplayAll() post = { 'name': backupName, 'instance': database.id, 'description': backupDesc, 'parent': "" } res = self.client.post(BACKUP_URL, post) self.assertNoFormErrors(res) self.assertRedirectsNoFollow(res, INDEX_URL) @test.create_stubs({api.trove: ('instance_list', 'backup_list')}) def test_launch_backup_exception(self): api.trove.instance_list(IsA(http.HttpRequest))\ .AndRaise(self.exceptions.trove) api.trove.backup_list(IsA(http.HttpRequest)) \ .AndReturn(self.database_backups.list()) self.mox.ReplayAll() res = self.client.get(BACKUP_URL) self.assertMessageCount(res, error=1) self.assertTemplateUsed(res, 'project/database_backups/backup.html') @test.create_stubs({api.trove: ('instance_list', 'backup_list', 'backup_create')}) def test_launch_backup_incr(self): api.trove.instance_list(IsA(http.HttpRequest)) \ .AndReturn(self.databases.list()) api.trove.backup_list(IsA(http.HttpRequest)) \ .AndReturn(self.database_backups.list()) database = self.databases.first() backupName = "NewBackup" backupDesc = "Backup Description" backupParent = self.database_backups.first() api.trove.backup_create( IsA(http.HttpRequest), backupName, database.id, backupDesc, backupParent.id) self.mox.ReplayAll() post = { 'name': backupName, 'instance': database.id, 'description': backupDesc, 'parent': backupParent.id, } res = self.client.post(BACKUP_URL, post) self.assertNoFormErrors(res) self.assertRedirectsNoFollow(res, INDEX_URL) @test.create_stubs({api.trove: ('backup_get', 'instance_get')}) def test_detail_backup(self): api.trove.backup_get(IsA(http.HttpRequest), IsA(unicode))\ .AndReturn(self.database_backups.first()) api.trove.instance_get(IsA(http.HttpRequest), IsA(str))\ .AndReturn(self.databases.first()) self.mox.ReplayAll() res = self.client.get(DETAILS_URL) self.assertTemplateUsed(res, 'project/database_backups/details.html') @test.create_stubs({api.trove: ('backup_get',)}) def test_detail_backup_notfound(self): api.trove.backup_get(IsA(http.HttpRequest), IsA(unicode))\ .AndRaise(self.exceptions.trove) self.mox.ReplayAll() res = self.client.get(DETAILS_URL) self.assertRedirectsNoFollow(res, INDEX_URL) @test.create_stubs({api.trove: ('backup_get', 'instance_get')}) def test_detail_backup_incr(self): incr_backup = self.database_backups.list()[2] parent_backup = self.database_backups.list()[1] api.trove.backup_get(IsA(http.HttpRequest), IsA(unicode))\ .AndReturn(incr_backup) api.trove.backup_get(IsA(http.HttpRequest), incr_backup.parent_id) \ .AndReturn(parent_backup) api.trove.instance_get(IsA(http.HttpRequest), IsA(str))\ .AndReturn(self.databases.list()[1]) self.mox.ReplayAll() url = reverse('horizon:project:database_backups:detail', args=[incr_backup.id]) res = self.client.get(url) self.assertTemplateUsed(res, 'project/database_backups/details.html')

34.885246

78

0.620771

c2e39d13ced0835f16408cb680517001e90f1caa

188

py

Python

redhawk/test/files/python/z004.py

spranesh/Redhawk

e2be5a6553df8449acecee2239b60c7bca0f22bc

[ "BSD-2-Clause-FreeBSD" ]

2

2016-10-04T11:46:32.000Z

2017-07-09T15:23:55.000Z

redhawk/test/files/python/z004.py

spranesh/Redhawk

e2be5a6553df8449acecee2239b60c7bca0f22bc

[ "BSD-2-Clause-FreeBSD" ]

4

2016-03-07T13:16:48.000Z

2018-03-21T00:25:04.000Z

redhawk/test/files/python/z004.py

spranesh/Redhawk

e2be5a6553df8449acecee2239b60c7bca0f22bc

[ "BSD-2-Clause-FreeBSD" ]

3

2016-04-06T08:04:34.000Z

2020-03-17T20:59:47.000Z

# Test Try, Except try: x = a.x y = a.y z = a.array[0] except AttributeError as e: x = 1 y = 1 z = 1 except IndexError as e: x = 0 y = 0 z = 0 finally: print(x, y, z)

11.058824

27

0.526596

4b457f80788111d80cb0fb6b5838695f237fc5f6

4,733

py

Python

python/pyspark/pandas/tests/plot/test_frame_plot.py

akhalymon-cv/spark

76191b9151b6a7804f8894e53eef74106f98b787

[ "Apache-2.0" ]

35,083

2015-01-01T03:05:13.000Z

2022-03-31T21:57:40.000Z

python/pyspark/pandas/tests/plot/test_frame_plot.py

akhalymon-cv/spark

76191b9151b6a7804f8894e53eef74106f98b787

[ "Apache-2.0" ]

32,117

2015-01-01T00:00:24.000Z

2022-03-31T23:54:58.000Z

python/pyspark/pandas/tests/plot/test_frame_plot.py

akhalymon-cv/spark

76191b9151b6a7804f8894e53eef74106f98b787

[ "Apache-2.0" ]

29,687

2015-01-01T02:40:43.000Z

2022-03-31T16:49:33.000Z

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import pandas as pd import numpy as np from pyspark import pandas as ps from pyspark.pandas.config import set_option, reset_option, option_context from pyspark.pandas.plot import TopNPlotBase, SampledPlotBase, HistogramPlotBase from pyspark.pandas.exceptions import PandasNotImplementedError from pyspark.testing.pandasutils import PandasOnSparkTestCase class DataFramePlotTest(PandasOnSparkTestCase): @classmethod def setUpClass(cls): super().setUpClass() set_option("plotting.max_rows", 2000) set_option("plotting.sample_ratio", None) @classmethod def tearDownClass(cls): reset_option("plotting.max_rows") reset_option("plotting.sample_ratio") super().tearDownClass() def test_missing(self): psdf = ps.DataFrame(np.random.rand(2500, 4), columns=["a", "b", "c", "d"]) unsupported_functions = ["box", "hexbin"] for name in unsupported_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*DataFrame.*{}.*not implemented".format(name) ): getattr(psdf.plot, name)() def test_topn_max_rows(self): pdf = pd.DataFrame(np.random.rand(2500, 4), columns=["a", "b", "c", "d"]) psdf = ps.from_pandas(pdf) data = TopNPlotBase().get_top_n(psdf) self.assertEqual(len(data), 2000) def test_sampled_plot_with_ratio(self): with option_context("plotting.sample_ratio", 0.5): pdf = pd.DataFrame(np.random.rand(2500, 4), columns=["a", "b", "c", "d"]) psdf = ps.from_pandas(pdf) data = SampledPlotBase().get_sampled(psdf) self.assertEqual(round(len(data) / 2500, 1), 0.5) def test_sampled_plot_with_max_rows(self): # 'plotting.max_rows' is 2000 pdf = pd.DataFrame(np.random.rand(2000, 4), columns=["a", "b", "c", "d"]) psdf = ps.from_pandas(pdf) data = SampledPlotBase().get_sampled(psdf) self.assertEqual(round(len(data) / 2000, 1), 1) def test_compute_hist_single_column(self): psdf = ps.DataFrame( {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 50]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9, 10, 10] ) expected_bins = np.linspace(1, 50, 11) bins = HistogramPlotBase.get_bins(psdf[["a"]].to_spark(), 10) expected_histogram = np.array([5, 4, 1, 0, 0, 0, 0, 0, 0, 1]) histogram = HistogramPlotBase.compute_hist(psdf[["a"]], bins)[0] self.assert_eq(pd.Series(expected_bins), pd.Series(bins)) self.assert_eq(pd.Series(expected_histogram, name="a"), histogram, almost=True) def test_compute_hist_multi_columns(self): expected_bins = np.linspace(1, 50, 11) psdf = ps.DataFrame( { "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 50], "b": [50, 50, 30, 30, 30, 24, 10, 5, 4, 3, 1], } ) bins = HistogramPlotBase.get_bins(psdf.to_spark(), 10) self.assert_eq(pd.Series(expected_bins), pd.Series(bins)) expected_histograms = [ np.array([5, 4, 1, 0, 0, 0, 0, 0, 0, 1]), np.array([4, 1, 0, 0, 1, 3, 0, 0, 0, 2]), ] histograms = HistogramPlotBase.compute_hist(psdf, bins) expected_names = ["a", "b"] for histogram, expected_histogram, expected_name in zip( histograms, expected_histograms, expected_names ): self.assert_eq( pd.Series(expected_histogram, name=expected_name), histogram, almost=True ) if __name__ == "__main__": import unittest from pyspark.pandas.tests.plot.test_frame_plot import * # noqa: F401 try: import xmlrunner # type: ignore[import] testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) except ImportError: testRunner = None unittest.main(testRunner=testRunner, verbosity=2)

37.864

97

0.640397

4f51ecb40dc2f9b80a3a072d784295c5e4c5925f

456

py

Python

Section 19/9.Document-complie-operations.py

airbornum/-Complete-Python-Scripting-for-Automation

bc053444f8786259086269ca1713bdb10144dd74

[ "MIT" ]

18

2020-04-13T03:14:06.000Z

2022-03-09T18:54:41.000Z

Section 19/9.Document-complie-operations.py

airbornum/-Complete-Python-Scripting-for-Automation

bc053444f8786259086269ca1713bdb10144dd74

[ "MIT" ]

null

Section 19/9.Document-complie-operations.py

airbornum/-Complete-Python-Scripting-for-Automation

bc053444f8786259086269ca1713bdb10144dd74

[ "MIT" ]

22

2020-04-29T21:12:42.000Z

2022-03-17T18:19:54.000Z

import re my_str="This is about python. Python is easy to learn and we have two major versions: python2 and python3 " my_pat=r'\bPython[23]?\b' #print(re.search(my_pat,my_str)) #print(re.findall(my_pat,my_str,flags=re.I)) #print(re.split(my_pat,my_str)) pat_ob=re.compile(my_pat,flags=re.I) print(pat_ob) print(pat_ob.search(my_str)) print(pat_ob.findall(my_str)) #re.findall(my_pat,my_str)===> re.complie(my_pat).findall(my_str)

24

109

0.719298

314a5c1a65f462d0ca7d5fecec70069e9f0c97a0

439

py

Python

api/cloud_provider/migrations/0007_auto_20190731_0424.py

240325184/KubeOperator

777774050b236abf938a5a9ef505124c26e4916e

[ "Apache-2.0" ]

3

2019-11-29T03:49:08.000Z

2020-07-29T02:52:51.000Z

api/cloud_provider/migrations/0007_auto_20190731_0424.py

240325184/KubeOperator

777774050b236abf938a5a9ef505124c26e4916e

[ "Apache-2.0" ]

27

2021-05-05T02:51:26.000Z

2022-01-04T21:30:21.000Z

api/cloud_provider/migrations/0007_auto_20190731_0424.py

240325184/KubeOperator

777774050b236abf938a5a9ef505124c26e4916e

[ "Apache-2.0" ]

1

2020-07-06T04:53:51.000Z

# Generated by Django 2.1.2 on 2019-07-31 04:24 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('cloud_provider', '0006_region_cloud_region'), ] operations = [ migrations.RemoveField( model_name='region', name='connected', ), migrations.RemoveField( model_name='region', name='msg', ), ]

19.954545

55

0.567198

0aa24044bdd1197df87c9f5bf04ff6b0db185e3f

285

py

Python

week03/CodingSkills/coding_skills_test.py

PreslavaKuzova/Python101

716cdd2d818f7eef45a1cdafdfb85a208daec750

[ "MIT" ]

4

2019-04-06T20:06:19.000Z

2020-03-31T20:51:56.000Z

week03/CodingSkills/coding_skills_test.py

PreslavaKuzova/Python101

716cdd2d818f7eef45a1cdafdfb85a208daec750

[ "MIT" ]

null

week03/CodingSkills/coding_skills_test.py

PreslavaKuzova/Python101

716cdd2d818f7eef45a1cdafdfb85a208daec750

[ "MIT" ]

1

2020-03-21T00:49:56.000Z

import unittest from coding_skills import coding_skills, read_json class TestCodingSkills(unittest.TestCase): def test_when_no_system_argument_is_given_are_throw_an_exception(self): self.assertRaises(Exception, read_json()) if __name__ == '__main__': unittest.main()

31.666667

75

0.796491

3f0ca4b1e1fddb156f8bba69b9d8acd0fc9b31a1

1,493

py

Python

todo/routes.py

hhao99/flask-todo

dc0bc6ffcdde206e05b4ea7636324efa26315241

[ "CNRI-Python" ]

3

2020-02-01T11:25:48.000Z

2020-02-04T14:11:50.000Z

todo/routes.py

hhao99/flask-todo

dc0bc6ffcdde206e05b4ea7636324efa26315241

[ "CNRI-Python" ]

1

2021-06-02T01:11:26.000Z

todo/routes.py

hhao99/flask-todo

dc0bc6ffcdde206e05b4ea7636324efa26315241

[ "CNRI-Python" ]

null

from flask import ( render_template, redirect, render_template, request, g, flash, url_for ) from .forms import TodoForm from .models import Todo from .models import db def init_route(app): @app.route('/') def index(): todos = Todo.query.all() return render_template('index.html',todos = todos) @app.route('/new',methods=['GET','POST']) def new(): if request.method == 'POST': form = TodoForm() print(form) if form.validate_on_submit(): task = form.task.data isDone = form.isDone.data t = Todo(task=task,isDone = isDone) print(t.isDone) db.session.add(t) db.session.commit() return redirect(url_for('index')) form = TodoForm() return render_template('edit.html',form=form,action=url_for('new')) @app.route('/delete/<int:id>') def delete(id): print(f"delete the todo with id: {id}") todo = db.session.query(Todo).get(id) db.session.delete(todo) db.session.commit() return redirect(url_for('index')) @app.route('/update/<int:id>') def update(id): print(f"update the todo with id: {id}") todo = db.session.query(Todo).get(id) todo.isDone = not todo.isDone db.session.add(todo) db.session.commit() return redirect(url_for('index'))

29.27451

75

0.54789

e175189fdb42e853767f0afbb683b6553f8c27eb

828

py

Python

collatelogs/metahandlers.py

tchamberlin/collatelogs

00335099a2a0a893bec1f927b3541d2acb5fb932

[ "MIT" ]

null

collatelogs/metahandlers.py

tchamberlin/collatelogs

00335099a2a0a893bec1f927b3541d2acb5fb932

[ "MIT" ]

1

2018-05-04T14:54:42.000Z

2018-05-04T15:48:22.000Z

collatelogs/metahandlers.py

tchamberlin/collatelogs

00335099a2a0a893bec1f927b3541d2acb5fb932

[ "MIT" ]

null

# -*- coding: utf-8 -*- """File metadata handlers: provide metadata based on a given path These are used to populated keywords in the line_output_format, elsewhere""" import os try: # For unix import pwd except ImportError: # For windows import win32security import sys def get_owner_from_path(path): """Get the username of the owner of the given file""" if "pwd" in sys.modules: # On unix return pwd.getpwuid(os.stat(path).st_uid).pw_name # On Windows f = win32security.GetFileSecurity(path, win32security.OWNER_SECURITY_INFORMATION) username, _, _ = win32security.LookupAccountSid( None, f.GetSecurityDescriptorOwner() ) return username # All available meta handlers all_meta_handlers = {"owner": get_owner_from_path, "filename": os.path.basename}

23.657143

85

0.707729

d9b8172a25a0d56e4aceb18f37a936063e1bc2df

242

py

Python

exampleproject/lectures/admin.py

rishikesh67/django-shared-schema-tenants

ed3bcddf80a7838979fe1be2045dfa16b545beed

[ "MIT" ]

20

2017-08-29T02:36:32.000Z

2021-12-06T21:29:46.000Z

exampleproject/lectures/admin.py

rishikesh67/django-shared-schema-tenants

ed3bcddf80a7838979fe1be2045dfa16b545beed

[ "MIT" ]

35

2017-08-18T06:28:31.000Z

2021-09-02T01:53:09.000Z

exampleproject/lectures/admin.py

rishikesh67/django-shared-schema-tenants

ed3bcddf80a7838979fe1be2045dfa16b545beed

[ "MIT" ]

9

2018-06-17T22:04:13.000Z

2022-03-18T09:27:18.000Z

from django.contrib import admin from shared_schema_tenants_custom_data.admin import TenantSpecificModelAdmin from .models import Lecture class LectureAdmin(TenantSpecificModelAdmin): pass admin.sites.register(Lecture, LectureAdmin)

20.166667

76

0.847107

be5ef80622566f1ace154f2c6c86d4d4f30a9442

10,355

py

Python

tests/unit/pynwb_tests/test_ophys.py

VBaratham/pynwb

a9429c93f29763b9ebe9022b099afcffbc6be493

[ "BSD-3-Clause-LBNL" ]

null

tests/unit/pynwb_tests/test_ophys.py

VBaratham/pynwb

a9429c93f29763b9ebe9022b099afcffbc6be493

[ "BSD-3-Clause-LBNL" ]

null

tests/unit/pynwb_tests/test_ophys.py

VBaratham/pynwb

a9429c93f29763b9ebe9022b099afcffbc6be493

[ "BSD-3-Clause-LBNL" ]

null

import unittest from pynwb.ophys import TwoPhotonSeries, RoiResponseSeries, DfOverF, Fluorescence, PlaneSegmentation, \ ImageSegmentation, OpticalChannel, ImagingPlane, MotionCorrection, CorrectedImageStack from pynwb.image import ImageSeries from pynwb.base import TimeSeries from pynwb.device import Device from pynwb.base import ProcessingModule import numpy as np def CreatePlaneSegmentation(): w, h = 5, 5 img_mask = [[[1.0 for x in range(w)] for y in range(h)], [[2.0 for x in range(w)] for y in range(h)]] pix_mask = [[1, 2, 1.0], [3, 4, 1.0], [5, 6, 1.0], [7, 8, 2.0], [9, 10, 2.0]] iSS = ImageSeries(name='test_iS', data=np.ones((2, 2, 2)), unit='unit', external_file=['external_file'], starting_frame=[1, 2, 3], format='tiff', timestamps=[1., 2.]) oc = OpticalChannel('test_optical_channel', 'description', 500.) device = Device(name='device_name') ip = ImagingPlane('test_imaging_plane', oc, 'description', device, 600., 300., 'indicator', 'location', (1, 2, 1, 2, 3), 4.0, 'unit', 'reference_frame') pS = PlaneSegmentation('description', ip, 'test_name', iSS) pS.add_roi(pixel_mask=pix_mask[0:3], image_mask=img_mask[0]) pS.add_roi(pixel_mask=pix_mask[3:5], image_mask=img_mask[1]) return pS class TwoPhotonSeriesConstructor(unittest.TestCase): def test_init(self): oc = OpticalChannel('test_name', 'description', 500.) self.assertEqual(oc.description, 'description') self.assertEqual(oc.emission_lambda, 500.) device = Device(name='device_name') ip = ImagingPlane('test_imaging_plane', oc, 'description', device, 600., 300., 'indicator', 'location', (50, 100, 3), 4.0, 'unit', 'reference_frame') self.assertEqual(ip.optical_channel[0], oc) self.assertEqual(ip.device, device) self.assertEqual(ip.excitation_lambda, 600.) self.assertEqual(ip.imaging_rate, 300.) self.assertEqual(ip.indicator, 'indicator') self.assertEqual(ip.location, 'location') self.assertEqual(ip.manifold, (50, 100, 3)) self.assertEqual(ip.conversion, 4.0) self.assertEqual(ip.unit, 'unit') self.assertEqual(ip.reference_frame, 'reference_frame') tPS = TwoPhotonSeries('test_tPS', unit='unit', field_of_view=[2., 3.], imaging_plane=ip, pmt_gain=1.0, scan_line_rate=2.0, external_file=['external_file'], starting_frame=[1, 2, 3], format='tiff', timestamps=list()) self.assertEqual(tPS.name, 'test_tPS') self.assertEqual(tPS.unit, 'unit') self.assertEqual(tPS.field_of_view, [2., 3.]) self.assertEqual(tPS.imaging_plane, ip) self.assertEqual(tPS.pmt_gain, 1.0) self.assertEqual(tPS.scan_line_rate, 2.0) self.assertEqual(tPS.external_file, ['external_file']) self.assertEqual(tPS.starting_frame, [1, 2, 3]) self.assertEqual(tPS.format, 'tiff') self.assertEqual(tPS.dimension, [np.nan]) def test_args(self): oc = OpticalChannel('test_name', 'description', 500.) device = Device(name='device_name') ip = ImagingPlane('test_imaging_plane', oc, 'description', device, 600., 300., 'indicator', 'location', (50, 100, 3), 4.0, 'unit', 'reference_frame') with self.assertRaises(ValueError): # no data or external file TwoPhotonSeries('test_tPS', unit='unit', field_of_view=[2., 3.], imaging_plane=ip, pmt_gain=1.0, scan_line_rate=2.0, starting_frame=[1, 2, 3], format='tiff', timestamps=[1., 2.]) class MotionCorrectionConstructor(unittest.TestCase): def test_init(self): MotionCorrection(list()) class CorrectedImageStackConstructor(unittest.TestCase): def test_init(self): is1 = ImageSeries(name='is1', data=np.ones((2, 2, 2)), unit='unit', external_file=['external_file'], starting_frame=[1, 2, 3], format='tiff', timestamps=[1., 2.]) is2 = ImageSeries(name='is2', data=np.ones((2, 2, 2)), unit='unit', external_file=['external_file'], starting_frame=[1, 2, 3], format='tiff', timestamps=[1., 2.]) tstamps = np.arange(1.0, 100.0, 0.1, dtype=np.float) ts = TimeSeries("test_ts", list(range(len(tstamps))), 'unit', timestamps=tstamps) cis = CorrectedImageStack(is1, is2, ts) ProcessingModule('name', 'description').add_container(cis) self.assertEqual(cis.corrected, is1) self.assertEqual(cis.original, is2) self.assertEqual(cis.xy_translation, ts) class RoiResponseSeriesConstructor(unittest.TestCase): def test_init(self): ip = CreatePlaneSegmentation() rt_region = ip.create_roi_table_region('the second ROI', region=[0]) ts = RoiResponseSeries('test_ts', list(), 'unit', rt_region, timestamps=list()) self.assertEqual(ts.name, 'test_ts') self.assertEqual(ts.unit, 'unit') self.assertEqual(ts.rois, rt_region) class DfOverFConstructor(unittest.TestCase): def test_init(self): ip = CreatePlaneSegmentation() rt_region = ip.create_roi_table_region('the second ROI', region=[1]) rrs = RoiResponseSeries('test_ts', list(), 'unit', rt_region, timestamps=list()) dof = DfOverF(rrs) self.assertEqual(dof.roi_response_series['test_ts'], rrs) class FluorescenceConstructor(unittest.TestCase): def test_init(self): ip = CreatePlaneSegmentation() rt_region = ip.create_roi_table_region('the second ROI', region=[1]) ts = RoiResponseSeries('test_ts', list(), 'unit', rt_region, timestamps=list()) ff = Fluorescence(ts) self.assertEqual(ff.roi_response_series['test_ts'], ts) self.assertEqual(ff.roi_response_series['test_ts'], ts) class ImageSegmentationConstructor(unittest.TestCase): def test_init(self): ps = CreatePlaneSegmentation() iS = ImageSegmentation(ps, name='test_iS') self.assertEqual(iS.name, 'test_iS') self.assertEqual(iS.plane_segmentations[ps.name], ps) self.assertEqual(iS[ps.name], iS.plane_segmentations[ps.name]) class PlaneSegmentationConstructor(unittest.TestCase): def getBoilerPlateObjects(self): iSS = ImageSeries(name='test_iS', data=np.ones((2, 2, 2)), unit='unit', external_file=['external_file'], starting_frame=[1, 2, 3], format='tiff', timestamps=list()) device = Device(name='device_name') oc = OpticalChannel('test_optical_channel', 'description', 500.) ip = ImagingPlane('test_imaging_plane', oc, 'description', device, 600., 300., 'indicator', 'location', (1, 2, 1, 2, 3), 4.0, 'unit', 'reference_frame') return iSS, ip def test_init(self): w, h = 5, 5 img_mask = [[[1.0 for x in range(w)] for y in range(h)], [[2.0 for x in range(w)] for y in range(h)]] pix_mask = [[1, 2, 1.0], [3, 4, 1.0], [5, 6, 1.0], [7, 8, 2.0], [9, 10, 2.0]] iSS, ip = self.getBoilerPlateObjects() pS = PlaneSegmentation('description', ip, 'test_name', iSS) pS.add_roi(pixel_mask=pix_mask[0:3], image_mask=img_mask[0]) pS.add_roi(pixel_mask=pix_mask[3:5], image_mask=img_mask[1]) self.assertEqual(pS.description, 'description') self.assertEqual(pS.imaging_plane, ip) self.assertEqual(pS.reference_images, (iSS,)) self.assertEqual(pS['pixel_mask'].target.data, pix_mask) self.assertEqual(pS['pixel_mask'][0], pix_mask[0:3]) self.assertEqual(pS['pixel_mask'][1], pix_mask[3:5]) self.assertEqual(pS['image_mask'].data, img_mask) def test_init_pixel_mask(self): pix_mask = [[1, 2, 1.0], [3, 4, 1.0], [5, 6, 1.0], [7, 8, 2.0], [9, 10, 2.0]] iSS, ip = self.getBoilerPlateObjects() pS = PlaneSegmentation('description', ip, 'test_name', iSS) pS.add_roi(pixel_mask=pix_mask[0:3]) pS.add_roi(pixel_mask=pix_mask[3:5]) self.assertEqual(pS.description, 'description') self.assertEqual(pS.imaging_plane, ip) self.assertEqual(pS.reference_images, (iSS,)) self.assertEqual(pS['pixel_mask'].target.data, pix_mask) self.assertEqual(pS['pixel_mask'][0], pix_mask[0:3]) self.assertEqual(pS['pixel_mask'][1], pix_mask[3:5]) def test_init_voxel_mask(self): vox_mask = [[1, 2, 3, 1.0], [3, 4, 1, 1.0], [5, 6, 3, 1.0], [7, 8, 3, 2.0], [9, 10, 2, 2.0]] iSS, ip = self.getBoilerPlateObjects() pS = PlaneSegmentation('description', ip, 'test_name', iSS) pS.add_roi(voxel_mask=vox_mask[0:3]) pS.add_roi(voxel_mask=vox_mask[3:5]) self.assertEqual(pS.description, 'description') self.assertEqual(pS.imaging_plane, ip) self.assertEqual(pS.reference_images, (iSS,)) self.assertEqual(pS['voxel_mask'].target.data, vox_mask) self.assertEqual(pS['voxel_mask'][0], vox_mask[0:3]) self.assertEqual(pS['voxel_mask'][1], vox_mask[3:5]) def test_init_3d_image_mask(self): img_masks = np.random.randn(2, 20, 30, 4) iSS, ip = self.getBoilerPlateObjects() pS = PlaneSegmentation('description', ip, 'test_name', iSS) pS.add_roi(image_mask=img_masks[0]) pS.add_roi(image_mask=img_masks[1]) self.assertTrue(np.allclose(pS['image_mask'][0], img_masks[0])) self.assertTrue(np.allclose(pS['image_mask'][1], img_masks[1])) def test_init_image_mask(self): w, h = 5, 5 img_mask = [[[1.0 for x in range(w)] for y in range(h)], [[2.0 for x in range(w)] for y in range(h)]] iSS, ip = self.getBoilerPlateObjects() pS = PlaneSegmentation('description', ip, 'test_name', iSS) pS.add_roi(image_mask=img_mask[0]) pS.add_roi(image_mask=img_mask[1]) self.assertEqual(pS.description, 'description') self.assertEqual(pS.imaging_plane, ip) self.assertEqual(pS.reference_images, (iSS,)) self.assertEqual(pS['image_mask'].data, img_mask) if __name__ == '__main__': unittest.main()

41.09127

120

0.627523

3bbcf7b77edc894d07497e30200a30fbfc8201c5

137

py

Python

apis/raw/021_raw/021_cleaner_bs4_version.py

sighill/shade_app

2b42d6411bc6e292b112a5e6be3598de8edadee1

[ "MIT" ]

null

apis/raw/021_raw/021_cleaner_bs4_version.py

sighill/shade_app

2b42d6411bc6e292b112a5e6be3598de8edadee1

[ "MIT" ]

null

apis/raw/021_raw/021_cleaner_bs4_version.py

sighill/shade_app

2b42d6411bc6e292b112a5e6be3598de8edadee1

[ "MIT" ]

null

from requests import get from bs4 import BeautifulSoup file = '/home/common/shade/apis/raw/021_raw/src' soup = BeautifulSoup(file)

22.833333

49

0.759124

1e5fe405209094bcd713d6efce48b34fd054c594

6,827

py

Python

rioxarray/raster_writer.py

spestana/rioxarray

a96c6083ee15b090ffe15b2beb34047777e90ecf

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

null

rioxarray/raster_writer.py

spestana/rioxarray

a96c6083ee15b090ffe15b2beb34047777e90ecf

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

null

rioxarray/raster_writer.py

spestana/rioxarray

a96c6083ee15b090ffe15b2beb34047777e90ecf

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

null

""" This module contains a dataset writer for Dask. Credits: RasterioWriter dask write functionality was adopted from https://github.com/dymaxionlabs/dask-rasterio # noqa: E501 Source file: - https://github.com/dymaxionlabs/dask-rasterio/blob/8dd7fdece7ad094a41908c0ae6b4fe6ca49cf5e1/dask_rasterio/write.py # noqa: E501 """ import rasterio from rasterio.windows import Window from xarray.conventions import encode_cf_variable from rioxarray.exceptions import RioXarrayError try: import dask.array from dask import is_dask_collection except ImportError: def is_dask_collection(_): """ Replacement method to check if it is a dask collection """ # if you cannot import dask, then it cannot be a dask array return False FILL_VALUE_NAMES = ("_FillValue", "missing_value", "fill_value", "nodata") UNWANTED_RIO_ATTRS = ("nodatavals", "crs", "is_tiled", "res") def _write_metatata_to_raster(raster_handle, xarray_dataset, tags): """ Write the metadata stored in the xarray object to raster metadata """ tags = xarray_dataset.attrs if tags is None else {**xarray_dataset.attrs, **tags} # write scales and offsets try: raster_handle.scales = tags["scales"] except KeyError: scale_factor = tags.get( "scale_factor", xarray_dataset.encoding.get("scale_factor") ) if scale_factor is not None: raster_handle.scales = (scale_factor,) * raster_handle.count try: raster_handle.offsets = tags["offsets"] except KeyError: add_offset = tags.get("add_offset", xarray_dataset.encoding.get("add_offset")) if add_offset is not None: raster_handle.offsets = (add_offset,) * raster_handle.count # filter out attributes that should be written in a different location skip_tags = ( UNWANTED_RIO_ATTRS + FILL_VALUE_NAMES + ( "transform", "scales", "scale_factor", "add_offset", "offsets", "grid_mapping", ) ) # this is for when multiple values are used # in this case, it will be stored in the raster description if not isinstance(tags.get("long_name"), str): skip_tags += ("long_name",) tags = {key: value for key, value in tags.items() if key not in skip_tags} raster_handle.update_tags(**tags) # write band name information long_name = xarray_dataset.attrs.get("long_name") if isinstance(long_name, (tuple, list)): if len(long_name) != raster_handle.count: raise RioXarrayError( "Number of names in the 'long_name' attribute does not equal " "the number of bands." ) for iii, band_description in enumerate(long_name): raster_handle.set_band_description(iii + 1, band_description) else: band_description = long_name or xarray_dataset.name if band_description: for iii in range(raster_handle.count): raster_handle.set_band_description(iii + 1, band_description) class RasterioWriter: """ ..versionadded:: 0.2 Rasterio wrapper to allow dask.array.store to do window saving or to save using the rasterio write method. """ def __init__(self, raster_path): """ raster_path: str The path to output the raster to. """ # https://github.com/dymaxionlabs/dask-rasterio/issues/3#issuecomment-514781825 # Rasterio datasets can't be pickled and can't be shared between # processes or threads. The work around is to distribute dataset # identifiers (paths or URIs) and then open them in new threads. # See mapbox/rasterio#1731. self.raster_path = raster_path def __setitem__(self, key, item): """Put the data chunk in the image""" if len(key) == 3: index_range, yyy, xxx = key indexes = list( range( index_range.start + 1, index_range.stop + 1, index_range.step or 1 ) ) else: indexes = 1 yyy, xxx = key chy_off = yyy.start chy = yyy.stop - yyy.start chx_off = xxx.start chx = xxx.stop - xxx.start with rasterio.open(self.raster_path, "r+") as rds: rds.write(item, window=Window(chx_off, chy_off, chx, chy), indexes=indexes) def to_raster(self, xarray_dataarray, tags, windowed, lock, compute, **kwargs): """ This method writes to the raster on disk. xarray_dataarray: xarray.DataArray The input data array to write to disk. tags: dict, optional A dictionary of tags to write to the raster. windowed: bool If True and the data array is not a dask array, it will write the data to disk using rasterio windows. lock: boolean or Lock, optional Lock to use to write data using dask. If not supplied, it will use a single process. compute: bool If True (default) and data is a dask array, then compute and save the data immediately. If False, return a dask Delayed object. Call ".compute()" on the Delayed object to compute the result later. Call ``dask.compute(delayed1, delayed2)`` to save multiple delayed files at once. **kwargs Keyword arguments to pass into writing the raster. """ dtype = kwargs["dtype"] # generate initial output file with rasterio.open(self.raster_path, "w", **kwargs) as rds: _write_metatata_to_raster(rds, xarray_dataarray, tags) if not (lock and is_dask_collection(xarray_dataarray.data)): # write data to raster immmediately if not dask array if windowed: window_iter = rds.block_windows(1) else: window_iter = [(None, None)] for _, window in window_iter: if window is not None: out_data = xarray_dataarray.rio.isel_window(window) else: out_data = xarray_dataarray data = encode_cf_variable(out_data).values.astype(dtype) if data.ndim == 2: rds.write(data, 1, window=window) else: rds.write(data, window=window) if lock and is_dask_collection(xarray_dataarray.data): return dask.array.store( encode_cf_variable(xarray_dataarray).data.astype(dtype), self, lock=lock, compute=compute, )

36.704301

130

0.609345

8daa7d5624c694926e07552e655edd1742be7468

6,871

py

Python

webWeixin/webWeixin.py

awesome-archive/awesome-python-login-model

98aecab631a717934efc308c873fd00cbc6ef930

[ "MIT" ]

2

2019-04-12T15:10:02.000Z

2019-04-12T15:11:18.000Z

webWeixin/webWeixin.py

masdude/awesome-python-login-model

aa67e633c0be8634081bae9fa1ed218c1f9fb75c

[ "MIT" ]

null

webWeixin/webWeixin.py

masdude/awesome-python-login-model

aa67e633c0be8634081bae9fa1ed218c1f9fb75c

[ "MIT" ]

1

2019-03-31T15:45:36.000Z

import os import re import time import sys import subprocess import requests import xml.dom.minidom import json """ info: author:CriseLYJ github:https://github.com/CriseLYJ/ update_time:2019-3-6 """ session = requests.session() headers = { 'User-Agent' : 'Mozilla/5.0 (Windows NT 5.1; rv:33.0) Gecko/20100101 Firefox/33.0' } QRImgPath = os.path.split(os.path.realpath(__file__))[0] + os.sep + 'webWeixinQr.jpg' uuid = '' tip = 0 base_uri = '' redirect_uri = '' skey = '' wxsid = '' wxuin = '' pass_ticket = '' deviceId = 'e000000000000000' BaseRequest = {} ContactList = [] My = [] SyncKey = '' def getUUID(): global uuid,session url = 'https://login.weixin.qq.com/jslogin' params = { 'appid': 'wx782c26e4c19acffb', 'fun': 'new', 'lang': 'zh_CN', '_': int(time.time()), } response = session.get(url, params=params) data = response.content.decode('utf-8') # print(data) >>> window.QRLogin.code = 200; window.QRLogin.uuid = "oZwt_bFfRg=="; regx = r'window.QRLogin.code = (\d+); window.QRLogin.uuid = "(\S+?)"' pm = re.search(regx, data) code = pm.group(1) uuid = pm.group(2) if code == '200': return True return False def showQRImage(): global tip url = 'https://login.weixin.qq.com/qrcode/' + uuid params = { 't': 'webwx', '_': int(time.time()), } response = session.get(url, params=params) tip = 1 with open(QRImgPath, 'wb') as f: f.write(response.content) f.close() if sys.platform.find('darwin') >= 0: subprocess.call(['open', QRImgPath]) elif sys.platform.find('linux') >= 0: subprocess.call(['xdg-open', QRImgPath]) else: os.startfile(QRImgPath) print('请使用微信扫描二维码以登录') def waitForLogin(): global tip, base_uri, redirect_uri url = 'https://login.weixin.qq.com/cgi-bin/mmwebwx-bin/login?tip=%s&uuid=%s&_=%s' % ( tip, uuid, int(time.time())) response = session.get(url) data = response.content.decode('utf-8') # print(data) # window.code=500; regx = r'window.code=(\d+);' pm = re.search(regx, data) code = pm.group(1) if code == '201': # 已扫描 print('成功扫描,请在手机上点击确认以登录') tip = 0 elif code == '200': # 已登录 print('正在登录...') regx = r'window.redirect_uri="(\S+?)";' pm = re.search(regx, data) redirect_uri = pm.group(1) + '&fun=new' base_uri = redirect_uri[:redirect_uri.rfind('/')] # closeQRImage if sys.platform.find('darwin') >= 0: # for OSX with Preview os.system("osascript -e 'quit app \"Preview\"'") elif code == '408': # 超时 pass # elif code == '400' or code == '500': return code def login(): global skey, wxsid, wxuin, pass_ticket, BaseRequest response = session.get(redirect_uri) data = response.content.decode('utf-8') # print(data) ''' <error> <ret>0</ret> <message>OK</message> <skey>xxx</skey> <wxsid>xxx</wxsid> <wxuin>xxx</wxuin> <pass_ticket>xxx</pass_ticket> <isgrayscale>1</isgrayscale> </error> ''' xml.dom doc = xml.dom.minidom.parseString(data) root = doc.documentElement for node in root.childNodes: if node.nodeName == 'skey': skey = node.childNodes[0].data elif node.nodeName == 'wxsid': wxsid = node.childNodes[0].data elif node.nodeName == 'wxuin': wxuin = node.childNodes[0].data elif node.nodeName == 'pass_ticket': pass_ticket = node.childNodes[0].data # print('skey: %s, wxsid: %s, wxuin: %s, pass_ticket: %s' % (skey, wxsid, # wxuin, pass_ticket)) if not all((skey, wxsid, wxuin, pass_ticket)): return False BaseRequest = { 'Uin': int(wxuin), 'Sid': wxsid, 'Skey': skey, 'DeviceID': deviceId, } return True def webwxinit(): url = base_uri + \ '/webwxinit?pass_ticket=%s&skey=%s&r=%s' % ( pass_ticket, skey, int(time.time())) params = { 'BaseRequest': BaseRequest } h = headers h['ContentType'] = 'application/json; charset=UTF-8' response = session.post(url, data=json.dumps(params), headers=h) data = response.content.decode('utf-8') #print(data) global ContactList, My, SyncKey dic = json.loads(data) ContactList = dic['ContactList'] My = dic['User'] SyncKeyList = [] for item in dic['SyncKey']['List']: SyncKeyList.append('%s_%s' % (item['Key'], item['Val'])) SyncKey = '|'.join(SyncKeyList) ErrMsg = dic['BaseResponse']['ErrMsg'] Ret = dic['BaseResponse']['Ret'] if Ret != 0: return False return True def webwxgetcontact(): url = base_uri + \ '/webwxgetcontact?pass_ticket=%s&skey=%s&r=%s' % ( pass_ticket, skey, int(time.time())) h = headers h['ContentType'] = 'application/json; charset=UTF-8' response = session.get(url, headers=h) data = response.content.decode('utf-8') # print(data) dic = json.loads(data) MemberList = dic['MemberList'] # 倒序遍历,不然删除的时候出问题.. SpecialUsers = ["newsapp", "fmessage", "filehelper", "weibo", "qqmail", "tmessage", "qmessage", "qqsync", "floatbottle", "lbsapp", "shakeapp", "medianote", "qqfriend", "readerapp", "blogapp", "facebookapp", "masssendapp", "meishiapp", "feedsapp", "voip", "blogappweixin", "weixin", "brandsessionholder", "weixinreminder", "wxid_novlwrv3lqwv11", "gh_22b87fa7cb3c", "officialaccounts", "notification_messages", "wxitil", "userexperience_alarm"] for i in range(len(MemberList) - 1, -1, -1): Member = MemberList[i] if Member['VerifyFlag'] & 8 != 0: # 公众号/服务号 MemberList.remove(Member) elif Member['UserName'] in SpecialUsers: # 特殊账号 MemberList.remove(Member) elif Member['UserName'].find('@@') != -1: # 群聊 MemberList.remove(Member) elif Member['UserName'] == My['UserName']: # 自己 MemberList.remove(Member) return MemberList def main(): if not getUUID(): print('获取uuid失败') return showQRImage() time.sleep(1) while waitForLogin() != '200': pass os.remove(QRImgPath) if not login(): print('登录失败') return #登录完成, 下面查询好友 if not webwxinit(): print('初始化失败') return MemberList = webwxgetcontact() print('通讯录共%s位好友' % len(MemberList)) for x in MemberList : sex = '未知' if x['Sex'] == 0 else '男' if x['Sex'] == 1 else '女' print('昵称:%s, 性别:%s, 备注:%s, 签名:%s' % (x['NickName'], sex, x['RemarkName'], x['Signature'])) if __name__ == '__main__': print('开始') main()

24.539286

240

0.572988

2207f92aa9a28e0454a7ba0b7e5d54d108fa1f3b

5,454

py

Python

external/configure_panorama.py

jabielecki/azure-vmseries-terraform

338337c347c54b1a07b5c6f0f0a38efd54f26d08

[ "Apache-2.0" ]

null

external/configure_panorama.py

jabielecki/azure-vmseries-terraform

338337c347c54b1a07b5c6f0f0a38efd54f26d08

[ "Apache-2.0" ]

null

external/configure_panorama.py

jabielecki/azure-vmseries-terraform

338337c347c54b1a07b5c6f0f0a38efd54f26d08

[ "Apache-2.0" ]

null

from terraform_external_data import terraform_external_data from panosxml import Panos import re import urllib3 urllib3.disable_warnings() from xml.etree import ElementTree from constants import * import os import subprocess import time OUTPUT_DIR="output" REQUIRED_ARGS=[ "panorama_ip", "username", "password", "panorama_private_ip", "storage_account_name", "storage_account_key", "inbound_storage_share_name", "outbound_storage_share_name", ] OPTIONAL_ARGS={ "outbound_hostname": "outside-fw", "outbound_device_group": "OUTBOUND", "outbound_template_stack": "OUTBOUND", "inbound_hostname": "inside-fw", "inbound_device_group": "INBOUND", "inbound_template_stack": "INBOUND", "dns_server": "8.8.8.8", } def connect(query: dict): connected = False failures = 0 # Retry for 10 minutes max_failures = 20 while not connected: if failures >= max_failures: raise PanoramaError("Failed to connect to panorama at {}".format(query["panorama_ip"])) try: p = Panos(query["panorama_ip"], user=query["username"], pw=query["password"]) connected = True except: failures = failures +1 time.sleep(30) pass return p def gen_inbound_init_cfgs(query: dict, vm_auth_key:str): inbound_config = init_cfg( hostname=query["inbound_hostname"], vm_auth_key=vm_auth_key, device_group_name=query["inbound_device_group"], template_name=query["inbound_template_stack"], panorama_ip=query["panorama_private_ip"], dns_ip=query["dns_server"] ) fp = os.path.join(query["output_dir"], OUTPUT_DIR, "init-cfg-inbound.txt") fd = os.path.join(query["output_dir"], OUTPUT_DIR) if not os.path.isdir(fd): os.mkdir(fd) fh = open(fp, mode="w") fh.write(inbound_config) fh.close() return fp def gen_outbound_init_cfgs(query: dict, vm_auth_key:str): outbound_config = init_cfg( hostname=query["outbound_hostname"], vm_auth_key=vm_auth_key, device_group_name=query["outbound_device_group"], template_name=query["outbound_template_stack"], panorama_ip=query["panorama_private_ip"], dns_ip=query["dns_server"] ) fp = os.path.join(query["output_dir"], OUTPUT_DIR, "init-cfg-outbound.txt") fd = os.path.join(query["output_dir"], OUTPUT_DIR) if not os.path.isdir(fd): os.mkdir(fd) fh = open(fp, mode="w") fh.write(outbound_config) fh.close() return fp def upload_cfgs(path, storage_account_name, primary_access_key, storage_share_name ): results = [] cmd = f"az storage file upload --account-name {storage_account_name} --account-key {primary_access_key} --share-name {storage_share_name} --source {path} --path config/init-cfg.txt" r = subprocess.run(cmd.split(), shell=True, capture_output=True) results.append(r) return results def gen_bootstrap(p: Panos, lifetime: str): """ Gen a new Bootstrap key """ params = { "type": "op", "cmd": "<request><bootstrap><vm-auth-key><generate><lifetime>{}</lifetime></generate></vm-auth-key></bootstrap></request>".format(lifetime) } r = p.send(params) if not p.check_resp(r): raise PanoramaError("Failed to generate Bootstrap key {}".format(r.content)) regex_result = re.search("VM auth key\s+(\d+)\s+", r.content.decode()) key = regex_result.group(1) return key def show_bootstrap(p: Panos): """ Get the most recently generated bootstrap key """ params = { "type": "op", "cmd": "<request><bootstrap><vm-auth-key><show></show></vm-auth-key></bootstrap></request>" } r = p.send(params) if not p.check_resp(r): raise PanoramaError("Failed to show Bootstrap key.") root = ElementTree.fromstring(r.content.decode()) keys = root.findall("./result/bootstrap-vm-auth-keys/entry/vm-auth-key") if len(keys) == 0: return return keys[0].text def bootstrap(query): p = connect(query) key = show_bootstrap(p) # never yet bootstratpped if not key: key = gen_bootstrap(p, query["key_lifetime"]) inbound_config = gen_inbound_init_cfgs(query, key) outbound_config = gen_outbound_init_cfgs(query, key) upload_cfgs( inbound_config, storage_account_name=query["storage_account_name"], storage_share_name=query["inbound_storage_share_name"], primary_access_key=query["storage_account_key"] ) upload_cfgs( outbound_config, storage_account_name=query["storage_account_name"], storage_share_name=query["outbound_storage_share_name"], primary_access_key=query["storage_account_key"] ) return key def parse_args(query: dict): for a in REQUIRED_ARGS: if a not in query: raise ValueError("Missing required argument {}".format(a)) for k, v in OPTIONAL_ARGS.items(): if k not in query: query[k] = v return query @terraform_external_data def main(query): r = {} query = parse_args(query) r['vm-auth-key'] = bootstrap(query) r['status'] = "OK" return r class PanoramaError(Exception): pass if __name__ == '__main__': main()

28.259067

185

0.642831

827b8015080165a574795a1927545e7e66951a64

15,556

py

Python

scipy/sparse/linalg/isolve/_gcrotmk.py

EverLookNeverSee/scipy

5ffd20ab831b3bc46bc5692c8624c01f8df09a9b

[ "BSD-3-Clause" ]

1

2021-08-16T09:32:42.000Z

scipy/sparse/linalg/isolve/_gcrotmk.py

EverLookNeverSee/scipy

5ffd20ab831b3bc46bc5692c8624c01f8df09a9b

[ "BSD-3-Clause" ]

44

2019-06-27T15:56:14.000Z

2022-03-15T22:21:10.000Z

scipy/sparse/linalg/isolve/_gcrotmk.py

EverLookNeverSee/scipy

5ffd20ab831b3bc46bc5692c8624c01f8df09a9b

[ "BSD-3-Clause" ]

4

2020-06-13T10:32:25.000Z

2021-12-03T15:48:16.000Z

# Copyright (C) 2015, Pauli Virtanen <pav@iki.fi> # Distributed under the same license as SciPy. import warnings import numpy as np from numpy.linalg import LinAlgError from scipy.linalg import (get_blas_funcs, qr, solve, svd, qr_insert, lstsq) from scipy.sparse.linalg.isolve.utils import make_system __all__ = ['gcrotmk'] def _fgmres(matvec, v0, m, atol, lpsolve=None, rpsolve=None, cs=(), outer_v=(), prepend_outer_v=False): """ FGMRES Arnoldi process, with optional projection or augmentation Parameters ---------- matvec : callable Operation A*x v0 : ndarray Initial vector, normalized to nrm2(v0) == 1 m : int Number of GMRES rounds atol : float Absolute tolerance for early exit lpsolve : callable Left preconditioner L rpsolve : callable Right preconditioner R CU : list of (ndarray, ndarray) Columns of matrices C and U in GCROT outer_v : list of ndarrays Augmentation vectors in LGMRES prepend_outer_v : bool, optional Whether augmentation vectors come before or after Krylov iterates Raises ------ LinAlgError If nans encountered Returns ------- Q, R : ndarray QR decomposition of the upper Hessenberg H=QR B : ndarray Projections corresponding to matrix C vs : list of ndarray Columns of matrix V zs : list of ndarray Columns of matrix Z y : ndarray Solution to ||H y - e_1||_2 = min! res : float The final (preconditioned) residual norm """ if lpsolve is None: lpsolve = lambda x: x if rpsolve is None: rpsolve = lambda x: x axpy, dot, scal, nrm2 = get_blas_funcs(['axpy', 'dot', 'scal', 'nrm2'], (v0,)) vs = [v0] zs = [] y = None res = np.nan m = m + len(outer_v) # Orthogonal projection coefficients B = np.zeros((len(cs), m), dtype=v0.dtype) # H is stored in QR factorized form Q = np.ones((1, 1), dtype=v0.dtype) R = np.zeros((1, 0), dtype=v0.dtype) eps = np.finfo(v0.dtype).eps breakdown = False # FGMRES Arnoldi process for j in range(m): # L A Z = C B + V H if prepend_outer_v and j < len(outer_v): z, w = outer_v[j] elif prepend_outer_v and j == len(outer_v): z = rpsolve(v0) w = None elif not prepend_outer_v and j >= m - len(outer_v): z, w = outer_v[j - (m - len(outer_v))] else: z = rpsolve(vs[-1]) w = None if w is None: w = lpsolve(matvec(z)) else: # w is clobbered below w = w.copy() w_norm = nrm2(w) # GCROT projection: L A -> (1 - C C^H) L A # i.e. orthogonalize against C for i, c in enumerate(cs): alpha = dot(c, w) B[i,j] = alpha w = axpy(c, w, c.shape[0], -alpha) # w -= alpha*c # Orthogonalize against V hcur = np.zeros(j+2, dtype=Q.dtype) for i, v in enumerate(vs): alpha = dot(v, w) hcur[i] = alpha w = axpy(v, w, v.shape[0], -alpha) # w -= alpha*v hcur[i+1] = nrm2(w) with np.errstate(over='ignore', divide='ignore'): # Careful with denormals alpha = 1/hcur[-1] if np.isfinite(alpha): w = scal(alpha, w) if not (hcur[-1] > eps * w_norm): # w essentially in the span of previous vectors, # or we have nans. Bail out after updating the QR # solution. breakdown = True vs.append(w) zs.append(z) # Arnoldi LSQ problem # Add new column to H=Q*R, padding other columns with zeros Q2 = np.zeros((j+2, j+2), dtype=Q.dtype, order='F') Q2[:j+1,:j+1] = Q Q2[j+1,j+1] = 1 R2 = np.zeros((j+2, j), dtype=R.dtype, order='F') R2[:j+1,:] = R Q, R = qr_insert(Q2, R2, hcur, j, which='col', overwrite_qru=True, check_finite=False) # Transformed least squares problem # || Q R y - inner_res_0 * e_1 ||_2 = min! # Since R = [R'; 0], solution is y = inner_res_0 (R')^{-1} (Q^H)[:j,0] # Residual is immediately known res = abs(Q[0,-1]) # Check for termination if res < atol or breakdown: break if not np.isfinite(R[j,j]): # nans encountered, bail out raise LinAlgError() # -- Get the LSQ problem solution # The problem is triangular, but the condition number may be # bad (or in case of breakdown the last diagonal entry may be # zero), so use lstsq instead of trtrs. y, _, _, _, = lstsq(R[:j+1,:j+1], Q[0,:j+1].conj()) B = B[:,:j+1] return Q, R, B, vs, zs, y, res def gcrotmk(A, b, x0=None, tol=1e-5, maxiter=1000, M=None, callback=None, m=20, k=None, CU=None, discard_C=False, truncate='oldest', atol=None): """ Solve a matrix equation using flexible GCROT(m,k) algorithm. Parameters ---------- A : {sparse matrix, ndarray, LinearOperator} The real or complex N-by-N matrix of the linear system. Alternatively, ``A`` can be a linear operator which can produce ``Ax`` using, e.g., ``scipy.sparse.linalg.LinearOperator``. b : ndarray Right hand side of the linear system. Has shape (N,) or (N,1). x0 : ndarray Starting guess for the solution. tol, atol : float, optional Tolerances for convergence, ``norm(residual) <= max(tol*norm(b), atol)``. The default for ``atol`` is `tol`. .. warning:: The default value for `atol` will be changed in a future release. For future compatibility, specify `atol` explicitly. maxiter : int, optional Maximum number of iterations. Iteration will stop after maxiter steps even if the specified tolerance has not been achieved. M : {sparse matrix, ndarray, LinearOperator}, optional Preconditioner for A. The preconditioner should approximate the inverse of A. gcrotmk is a 'flexible' algorithm and the preconditioner can vary from iteration to iteration. Effective preconditioning dramatically improves the rate of convergence, which implies that fewer iterations are needed to reach a given error tolerance. callback : function, optional User-supplied function to call after each iteration. It is called as callback(xk), where xk is the current solution vector. m : int, optional Number of inner FGMRES iterations per each outer iteration. Default: 20 k : int, optional Number of vectors to carry between inner FGMRES iterations. According to [2]_, good values are around m. Default: m CU : list of tuples, optional List of tuples ``(c, u)`` which contain the columns of the matrices C and U in the GCROT(m,k) algorithm. For details, see [2]_. The list given and vectors contained in it are modified in-place. If not given, start from empty matrices. The ``c`` elements in the tuples can be ``None``, in which case the vectors are recomputed via ``c = A u`` on start and orthogonalized as described in [3]_. discard_C : bool, optional Discard the C-vectors at the end. Useful if recycling Krylov subspaces for different linear systems. truncate : {'oldest', 'smallest'}, optional Truncation scheme to use. Drop: oldest vectors, or vectors with smallest singular values using the scheme discussed in [1,2]. See [2]_ for detailed comparison. Default: 'oldest' Returns ------- x : ndarray The solution found. info : int Provides convergence information: * 0 : successful exit * >0 : convergence to tolerance not achieved, number of iterations References ---------- .. [1] E. de Sturler, ''Truncation strategies for optimal Krylov subspace methods'', SIAM J. Numer. Anal. 36, 864 (1999). .. [2] J.E. Hicken and D.W. Zingg, ''A simplified and flexible variant of GCROT for solving nonsymmetric linear systems'', SIAM J. Sci. Comput. 32, 172 (2010). .. [3] M.L. Parks, E. de Sturler, G. Mackey, D.D. Johnson, S. Maiti, ''Recycling Krylov subspaces for sequences of linear systems'', SIAM J. Sci. Comput. 28, 1651 (2006). """ A,M,x,b,postprocess = make_system(A,M,x0,b) if not np.isfinite(b).all(): raise ValueError("RHS must contain only finite numbers") if truncate not in ('oldest', 'smallest'): raise ValueError("Invalid value for 'truncate': %r" % (truncate,)) if atol is None: warnings.warn("scipy.sparse.linalg.gcrotmk called without specifying `atol`. " "The default value will change in the future. To preserve " "current behavior, set ``atol=tol``.", category=DeprecationWarning, stacklevel=2) atol = tol matvec = A.matvec psolve = M.matvec if CU is None: CU = [] if k is None: k = m axpy, dot, scal = None, None, None r = b - matvec(x) axpy, dot, scal, nrm2 = get_blas_funcs(['axpy', 'dot', 'scal', 'nrm2'], (x, r)) b_norm = nrm2(b) if b_norm == 0: x = b return (postprocess(x), 0) if discard_C: CU[:] = [(None, u) for c, u in CU] # Reorthogonalize old vectors if CU: # Sort already existing vectors to the front CU.sort(key=lambda cu: cu[0] is not None) # Fill-in missing ones C = np.empty((A.shape[0], len(CU)), dtype=r.dtype, order='F') us = [] j = 0 while CU: # More memory-efficient: throw away old vectors as we go c, u = CU.pop(0) if c is None: c = matvec(u) C[:,j] = c j += 1 us.append(u) # Orthogonalize Q, R, P = qr(C, overwrite_a=True, mode='economic', pivoting=True) del C # C := Q cs = list(Q.T) # U := U P R^-1, back-substitution new_us = [] for j in range(len(cs)): u = us[P[j]] for i in range(j): u = axpy(us[P[i]], u, u.shape[0], -R[i,j]) if abs(R[j,j]) < 1e-12 * abs(R[0,0]): # discard rest of the vectors break u = scal(1.0/R[j,j], u) new_us.append(u) # Form the new CU lists CU[:] = list(zip(cs, new_us))[::-1] if CU: axpy, dot = get_blas_funcs(['axpy', 'dot'], (r,)) # Solve first the projection operation with respect to the CU # vectors. This corresponds to modifying the initial guess to # be # # x' = x + U y # y = argmin_y || b - A (x + U y) ||^2 # # The solution is y = C^H (b - A x) for c, u in CU: yc = dot(c, r) x = axpy(u, x, x.shape[0], yc) r = axpy(c, r, r.shape[0], -yc) # GCROT main iteration for j_outer in range(maxiter): # -- callback if callback is not None: callback(x) beta = nrm2(r) # -- check stopping condition beta_tol = max(atol, tol * b_norm) if beta <= beta_tol and (j_outer > 0 or CU): # recompute residual to avoid rounding error r = b - matvec(x) beta = nrm2(r) if beta <= beta_tol: j_outer = -1 break ml = m + max(k - len(CU), 0) cs = [c for c, u in CU] try: Q, R, B, vs, zs, y, pres = _fgmres(matvec, r/beta, ml, rpsolve=psolve, atol=max(atol, tol*b_norm)/beta, cs=cs) y *= beta except LinAlgError: # Floating point over/underflow, non-finite result from # matmul etc. -- report failure. break # # At this point, # # [A U, A Z] = [C, V] G; G = [ I B ] # [ 0 H ] # # where [C, V] has orthonormal columns, and r = beta v_0. Moreover, # # || b - A (x + Z y + U q) ||_2 = || r - C B y - V H y - C q ||_2 = min! # # from which y = argmin_y || beta e_1 - H y ||_2, and q = -B y # # # GCROT(m,k) update # # Define new outer vectors # ux := (Z - U B) y ux = zs[0]*y[0] for z, yc in zip(zs[1:], y[1:]): ux = axpy(z, ux, ux.shape[0], yc) # ux += z*yc by = B.dot(y) for cu, byc in zip(CU, by): c, u = cu ux = axpy(u, ux, ux.shape[0], -byc) # ux -= u*byc # cx := V H y hy = Q.dot(R.dot(y)) cx = vs[0] * hy[0] for v, hyc in zip(vs[1:], hy[1:]): cx = axpy(v, cx, cx.shape[0], hyc) # cx += v*hyc # Normalize cx, maintaining cx = A ux # This new cx is orthogonal to the previous C, by construction try: alpha = 1/nrm2(cx) if not np.isfinite(alpha): raise FloatingPointError() except (FloatingPointError, ZeroDivisionError): # Cannot update, so skip it continue cx = scal(alpha, cx) ux = scal(alpha, ux) # Update residual and solution gamma = dot(cx, r) r = axpy(cx, r, r.shape[0], -gamma) # r -= gamma*cx x = axpy(ux, x, x.shape[0], gamma) # x += gamma*ux # Truncate CU if truncate == 'oldest': while len(CU) >= k and CU: del CU[0] elif truncate == 'smallest': if len(CU) >= k and CU: # cf. [1,2] D = solve(R[:-1,:].T, B.T).T W, sigma, V = svd(D) # C := C W[:,:k-1], U := U W[:,:k-1] new_CU = [] for j, w in enumerate(W[:,:k-1].T): c, u = CU[0] c = c * w[0] u = u * w[0] for cup, wp in zip(CU[1:], w[1:]): cp, up = cup c = axpy(cp, c, c.shape[0], wp) u = axpy(up, u, u.shape[0], wp) # Reorthogonalize at the same time; not necessary # in exact arithmetic, but floating point error # tends to accumulate here for cp, up in new_CU: alpha = dot(cp, c) c = axpy(cp, c, c.shape[0], -alpha) u = axpy(up, u, u.shape[0], -alpha) alpha = nrm2(c) c = scal(1.0/alpha, c) u = scal(1.0/alpha, u) new_CU.append((c, u)) CU[:] = new_CU # Add new vector to CU CU.append((cx, ux)) # Include the solution vector to the span CU.append((None, x.copy())) if discard_C: CU[:] = [(None, uz) for cz, uz in CU] return postprocess(x), j_outer + 1

31.682281

86

0.5144

0432bffe20765a015ff93b81699d46ffae4d736f

21,654

py

Python

AtomicASTChangeMining/src/test/resources/ASTConversion/main.py

maldil/CPATMiner2.0

743aa8a5b638a1963e621f59f63d794728ab0c79

[ "Apache-2.0" ]

4

2021-11-04T02:47:31.000Z

2022-01-25T02:04:05.000Z

AtomicASTChangeMining/src/test/resources/ASTConversion/main.py

maldil/R-CPATMiner

88b96a5af438a9c2ea2dab351cb8b210119132a2

[ "Apache-2.0" ]

null

AtomicASTChangeMining/src/test/resources/ASTConversion/main.py

maldil/R-CPATMiner

88b96a5af438a9c2ea2dab351cb8b210119132a2

[ "Apache-2.0" ]

1

2021-09-11T06:52:39.000Z

#!/usr/bin/env python3 ############################################################################### # # # RMG - Reaction Mechanism Generator # # # # Copyright (c) 2002-2020 Prof. William H. Green (whgreen@mit.edu), # # Prof. Richard H. West (r.west@neu.edu) and the RMG Team (rmg_dev@mit.edu) # # # # 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. # # # ############################################################################### """ This module contains the :class:`Arkane` class, the main class used to run Arkane. """ import argparse import csv import logging import os import os.path import sys import time import numpy as np try: import matplotlib matplotlib.rc('mathtext', default='regular') except ImportError: pass from rmgpy import __version__ from rmgpy.chemkin import write_elements_section from rmgpy.data.thermo import ThermoLibrary from rmgpy.data.base import Entry from rmgpy.data.kinetics.library import KineticsLibrary from rmgpy.exceptions import InputError from arkane.common import is_pdep from arkane.encorr.ae import AEJob from arkane.encorr.bac import BACJob from arkane.explorer import ExplorerJob from arkane.input import load_input_file from arkane.kinetics import KineticsJob from arkane.output import save_thermo_lib, save_kinetics_lib from arkane.pdep import PressureDependenceJob from arkane.statmech import StatMechJob from arkane.thermo import ThermoJob ################################################################################ class Arkane(object): """ The :class:`Arkane` class represents an instance of Arkane, a tool for computing properties of chemical species and reactions. The attributes are: =================== ======================================================== Attribute Description =================== ======================================================== `job_list` A list of the jobs to execute `input_file` The path of the input file defining the jobs to execute `output_directory` The directory in which to write the output files `verbose` The level of detail in the generated logging messages =================== ======================================================== The output directory defaults to the same directory as the input file if not explicitly specified. To use this class programmatically, create an instance and set its attributes using either the :meth:`__init__()` method or by directly accessing the attributes, and then invoke the :meth:`execute()` method. You can also populate the attributes from the command line using the :meth:`parse_command_line_arguments()` method before running :meth:`execute()`. """ def __init__(self, input_file=None, output_directory=None, verbose=logging.INFO, save_rmg_libraries=True): self.job_list = [] self.input_file = input_file self.output_directory = output_directory self.verbose = verbose self.save_rmg_libraries = save_rmg_libraries def parse_command_line_arguments(self): """ Parse the command-line arguments being passed to Arkane. This uses the :mod:`argparse` module, which ensures that the command-line arguments are sensible, parses them, and returns them. """ parser = argparse.ArgumentParser(description=""" Arkane is a Python toolkit for computing chemical reaction rates and other properties used in detailed kinetics models using various methodologies and theories. """) parser.add_argument('file', metavar='FILE', type=str, nargs=1, help='a file describing the job to execute') # Options for controlling the amount of information printed to the console # By default a moderate level of information is printed; you can either # ask for less (quiet), more (verbose), or much more (debug) group = parser.add_mutually_exclusive_group() group.add_argument('-q', '--quiet', action='store_const', const=logging.WARNING, default=logging.INFO, dest='verbose', help='only print warnings and errors') group.add_argument('-v', '--verbose', action='store_const', const=logging.DEBUG, default=logging.INFO, dest='verbose', help='print more verbose output') group.add_argument('-d', '--debug', action='store_const', const=0, default=logging.INFO, dest='verbose', help='print debug information') # Add options for controlling what directories files are written to parser.add_argument('-o', '--output-directory', type=str, nargs=1, default='', metavar='DIR', help='use DIR as output directory') # Add options for controlling generation of plots parser.add_argument('-p', '--no-plot', action='store_false', default=True, help='prevent generating plots', dest='plot') args = parser.parse_args() # Extract the input file self.input_file = args.file[0] # Extract the log verbosity self.verbose = args.verbose # Extract the plot settings self.plot = args.plot # Determine the output directory # By default the directory containing the input file is used, unless an # alternate directory is specified using the -o flag if args.output_directory and os.path.isdir(args.output_directory[0]): self.output_directory = os.path.abspath(args.output_directory[0]) else: self.output_directory = os.path.dirname(os.path.abspath(args.file[0])) def load_input_file(self, input_file): """ Load a set of jobs from the given `input_file` on disk. Returns the loaded set of jobs as a list. """ self.input_file = input_file self.job_list, self.reaction_dict, self.species_dict, self.transition_state_dict, self.network_dict, \ self.level_of_theory = load_input_file(self.input_file) logging.info('') return self.job_list def execute(self): """ Execute, in order, the jobs found in input file specified by the `input_file` attribute. """ # Initialize the logging system (both to the console and to a file in the # output directory) initialize_log(self.verbose, os.path.join(self.output_directory, 'arkane.log')) # Print some information to the beginning of the log log_header() # Load the input file for the job self.job_list = self.load_input_file(self.input_file) logging.info('') # Initialize (and clear!) the output files for the job if self.output_directory is None: self.output_directory = os.path.dirname(os.path.abspath(self.input_file)) output_file = os.path.join(self.output_directory, 'output.py') with open(output_file, 'w'): pass chemkin_file = os.path.join(self.output_directory, 'chem.inp') # write the chemkin files and run the thermo and then kinetics jobs with open(chemkin_file, 'w') as f: write_elements_section(f) f.write('SPECIES\n\n') # write each species in species block for job in self.job_list: if isinstance(job, ThermoJob): f.write(job.species.to_chemkin()) f.write('\n') f.write('\nEND\n\n\n\n') f.write('THERM ALL\n') f.write(' 300.000 1000.000 5000.000\n\n') # run thermo and statmech jobs (also writes thermo blocks to Chemkin file) supporting_info = [] hindered_rotor_info = [] bacjob_num = 1 for job in self.job_list: if isinstance(job, ThermoJob): job.execute(output_directory=self.output_directory, plot=self.plot) if isinstance(job, StatMechJob): job.execute(output_directory=self.output_directory, plot=self.plot, pdep=is_pdep(self.job_list)) if hasattr(job, 'supporting_info'): supporting_info.append(job.supporting_info) if hasattr(job, 'raw_hindered_rotor_data'): for hr_info in job.raw_hindered_rotor_data: hindered_rotor_info.append(hr_info) if isinstance(job, BACJob): job.execute(output_directory=self.output_directory, plot=self.plot, jobnum=bacjob_num) bacjob_num += 1 if isinstance(job, AEJob): job.execute(output_file=output_file) with open(chemkin_file, 'a') as f: f.write('\n') f.write('END\n\n\n\n') f.write('REACTIONS KCAL/MOLE MOLES\n\n') if supporting_info: # write supporting_info.csv for statmech jobs supporting_info_file = os.path.join(self.output_directory, 'supporting_information.csv') with open(supporting_info_file, 'w') as csvfile: writer = csv.writer(csvfile, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) writer.writerow(['Label', 'Symmetry Number', 'Number of optical isomers', 'Symmetry Group', 'Rotational constant (cm-1)', 'Calculated Frequencies (unscaled and prior to projection, cm^-1)', 'Electronic energy (J/mol)', 'E0 (electronic energy + ZPE, J/mol)', 'E0 with atom and bond corrections (J/mol)', 'Atom XYZ coordinates (angstrom)', 'T1 diagnostic', 'D1 diagnostic']) for row in supporting_info: label = row[0] rot = '-' freq = '-' if row[4] is not None and isinstance(row[4].rotationalConstant.value, float): # diatomic species have a single rotational constant rot = '{0:.2f}'.format(row[4].rotationalConstant.value) elif row[4] is not None: rot = ', '.join(['{0:.2f}'.format(s) for s in row[4].rotationalConstant.value]) if row[5] is not None: freq = '' if row[6] is not None: # there is a negative frequency freq = '{0:.1f}'.format(abs(row[6])) + 'i, ' freq += ', '.join(['{0:.1f}'.format(s) for s in row[5]]) atoms = ', '.join(["{0} {1}".format(atom, " ".join([str(c) for c in coords])) for atom, coords in zip(row[10], row[11])]) writer.writerow([label, row[1], row[2], row[3], rot, freq, row[7], row[8], row[9], atoms, row[12], row[13]]) if hindered_rotor_info: hr_file = os.path.join(self.output_directory, 'hindered_rotor_scan_data.csv') # find longest length to set column number for energies max_energy_length = max([len(hr[4]) for hr in hindered_rotor_info]) with open(hr_file, 'w') as csvfile: writer = csv.writer(csvfile, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) writer.writerow(['species', 'rotor_number', 'symmetry', 'resolution (degrees)', 'pivot_atoms', 'frozen_atoms'] + ['energy (J/mol) {}'.format(i) for i in range(max_energy_length)]) for row in hindered_rotor_info: writer.writerow([row[0], row[1], row[2], row[3][1] * 180 / np.pi, row[5], row[6]] + [a for a in row[4]]) # run kinetics and pdep jobs (also writes reaction blocks to Chemkin file) for job in self.job_list: if isinstance(job, KineticsJob): job.execute(output_directory=self.output_directory, plot=self.plot) elif isinstance(job, PressureDependenceJob) and not any([isinstance(job, ExplorerJob) for job in self.job_list]): # if there is an explorer job the pdep job will be run in the explorer job if job.network is None: raise InputError( 'No network matched the label of the pressureDependence block and there is no explorer block ' 'to generate a network') job.execute(output_file=output_file, plot=self.plot) elif isinstance(job, ExplorerJob): thermo_library, kinetics_library, species_list = self.get_libraries() job.execute(output_file=output_file, plot=self.plot, species_list=species_list, thermo_library=thermo_library, kinetics_library=kinetics_library) with open(chemkin_file, 'a') as f: f.write('END\n\n') # Print some information to the end of the log log_footer() if self.save_rmg_libraries: # save RMG thermo and kinetics libraries species, reactions = list(), list() for job in self.job_list: if isinstance(job, ThermoJob) and len(job.species.molecule): species.append(job.species) elif isinstance(job, KineticsJob) \ and all([len(species.molecule) for species in job.reaction.reactants + job.reaction.products]): reactions.append(job.reaction) elif isinstance(job, PressureDependenceJob): for reaction in job.network.path_reactions: if all([len(species.molecule) for species in reaction.reactants + reaction.products]): reactions.append(reaction) lib_path = os.path.join(self.output_directory, 'RMG_libraries') level_of_theory = f' using {self.level_of_theory}' if self.level_of_theory is not None else '' lib_long_desc = f'Calculated using Arkane v{__version__}{level_of_theory}.' save_thermo_lib(species_list=species, path=lib_path, name='thermo', lib_long_desc=lib_long_desc) save_kinetics_lib(rxn_list=reactions, path=lib_path, name='kinetics', lib_long_desc=lib_long_desc) def get_libraries(self): """Get RMG kinetics and thermo libraries""" name = 'kineticsjobs' species_list = list(self.species_dict.values()) reaction_list = list(self.reaction_dict.values()) # remove duplicate species for rxn in reaction_list: for i, rspc in enumerate(rxn.reactants): for spc in species_list: if spc.is_isomorphic(rspc): rxn.reactants[i] = spc break for i, rspc in enumerate(rxn.products): for spc in species_list: if spc.is_isomorphic(rspc): rxn.products[i] = spc break del_inds = [] for i, spc1 in enumerate(species_list): for j, spc2 in enumerate(species_list): if j > i and spc1.is_isomorphic(spc2): del_inds.append(j) for j in sorted(del_inds)[::-1]: del species_list[j] thermo_library = ThermoLibrary(name=name) for i, species in enumerate(species_list): if species.thermo: thermo_library.load_entry(index=i + 1, label=species.label, molecule=species.molecule[0].to_adjacency_list(), thermo=species.thermo, shortDesc=species.thermo.comment) else: logging.warning( 'Species {0} did not contain any thermo data and was omitted from the thermo library.'.format( str(species))) # load kinetics library entries kinetics_library = KineticsLibrary(name=name, auto_generated=True) kinetics_library.entries = {} for i, reaction in enumerate(reaction_list): entry = Entry( index=i + 1, label=reaction.to_labeled_str(), item=reaction, data=reaction.kinetics) if reaction.kinetics is not None: if hasattr(reaction, 'library') and reaction.library: entry.long_desc = 'Originally from reaction library: ' + \ reaction.library + "\n" + reaction.kinetics.comment else: entry.long_desc = reaction.kinetics.comment kinetics_library.entries[i + 1] = entry kinetics_library.label = name return thermo_library, kinetics_library, species_list def initialize_log(verbose=logging.INFO, log_file=None): """ Set up a logger for Arkane to use to print output to stdout. The `verbose` parameter is an integer specifying the amount of log text seen at the console; the levels correspond to those of the :data:`logging` module. """ # Create logger logger = logging.getLogger() logger.setLevel(verbose) # Use custom level names for cleaner log output logging.addLevelName(logging.CRITICAL, 'Critical: ') logging.addLevelName(logging.ERROR, 'Error: ') logging.addLevelName(logging.WARNING, 'Warning: ') logging.addLevelName(logging.INFO, '') logging.addLevelName(logging.DEBUG, '') logging.addLevelName(0, '') # Create formatter and add to handlers formatter = logging.Formatter('%(levelname)s%(message)s') # Remove old handlers before adding ours while logger.handlers: logger.removeHandler(logger.handlers[0]) # Create console handler; send everything to stdout rather than stderr ch = logging.StreamHandler(sys.stdout) ch.setLevel(verbose) ch.setFormatter(formatter) logger.addHandler(ch) # Create file handler; always be at least verbose in the file if log_file: fh = logging.FileHandler(filename=log_file) fh.setLevel(min(logging.DEBUG, verbose)) fh.setFormatter(formatter) logger.addHandler(fh) def log_header(level=logging.INFO): """ Output a header containing identifying information about Arkane to the log. """ from rmgpy import __version__ logging.log(level, 'Arkane execution initiated at {0}'.format(time.asctime())) logging.log(level, '') logging.log(level, '################################################################') logging.log(level, '# #') logging.log(level, '# Automated Reaction Kinetics and Network Exploration (Arkane) #') logging.log(level, '# #') logging.log(level, '# Version: {0:49s} #'.format(__version__)) logging.log(level, '# Authors: RMG Developers (rmg_dev@mit.edu) #') logging.log(level, '# P.I.s: William H. Green (whgreen@mit.edu) #') logging.log(level, '# Richard H. West (r.west@neu.edu) #') logging.log(level, '# Website: http://reactionmechanismgenerator.github.io/ #') logging.log(level, '# #') logging.log(level, '################################################################') logging.log(level, '') def log_footer(level=logging.INFO): """ Output a footer to the log. """ logging.log(level, '') logging.log(level, 'Arkane execution terminated at {0}'.format(time.asctime()))

48.55157

119

0.573012

518a563ab73b988bb3feb87130aa6da3ec7f7bf6

1,608

py

Python

accounts/api.py

notrealanurag/curezo_old

c3fd350750a799ae975ed6a89f6db2b39a22fbd0

[ "MIT" ]

null

accounts/api.py

notrealanurag/curezo_old

c3fd350750a799ae975ed6a89f6db2b39a22fbd0

[ "MIT" ]

5

2021-03-19T11:01:39.000Z

2021-09-22T19:35:40.000Z

accounts/api.py

notrealanurag/curezo_old

c3fd350750a799ae975ed6a89f6db2b39a22fbd0

[ "MIT" ]

null

from rest_framework import generics, permissions, viewsets from rest_framework.response import Response from knox.models import AuthToken from knox.auth import TokenAuthentication from .serializers import UserSerializer, RegisterSerializer, LoginSerializer from django.contrib.auth.models import User # Register Viewset class RegisterAPI(generics.GenericAPIView): serializer_class = RegisterSerializer def post(self, request, *args, **kwargs): serializer = self.get_serializer(data=request.data) serializer.is_valid(raise_exception=True) user = serializer.save() return Response({ # "user": UserSerializer(user, context=self.get_serializer_context()).data, # "token": AuthToken.objects.create(user)[1] "creation" : "Account Created. Please Login" }) class LoginAPI(generics.GenericAPIView): serializer_class = LoginSerializer def post(self, request, *args, **kwargs): serializer = self.get_serializer(data=request.data) serializer.is_valid(raise_exception=True) user = serializer.validated_data _, token = AuthToken.objects.create(user) return Response({ "user": UserSerializer(user, context=self.get_serializer_context()).data, "token": token }) class UserAPI(generics.RetrieveUpdateDestroyAPIView): authentication_classes = (TokenAuthentication,) permission_classes = [ permissions.IsAuthenticated, ] serializer_class = UserSerializer def get_object(self, *args, **kwargs): return self.request.user

35.733333

87

0.709577

f5a5d9548048d51e81e6caa7f88042c87c428fad

214

py

Python

setup.py

ioliveros/github-api-client

2bbc832af0a7e744958e4741b9f6419cdabc4eb0

[ "MIT" ]

null

setup.py

ioliveros/github-api-client

2bbc832af0a7e744958e4741b9f6419cdabc4eb0

[ "MIT" ]

1

2021-06-02T03:15:56.000Z

setup.py

ioliveros/github-api-client

2bbc832af0a7e744958e4741b9f6419cdabc4eb0

[ "MIT" ]

null

from setuptools import setup, find_packages setup( name='github_api_client', version='1.1', author='Ian Oliveros', author_email='ioliveros.dev@gmail.com', packages=find_packages(), python_requires='>=3.6' )

19.454545

43

0.747664

19cc55f8af9df936d3b09e882b98454ba4361514

7,482

py

Python

models/networks_basic.py

nistath/PerceptualSimilarity

29c5c71a6b09557ea0d049f27ec44f02d9ba7937

[ "BSD-2-Clause" ]

null

models/networks_basic.py

nistath/PerceptualSimilarity

29c5c71a6b09557ea0d049f27ec44f02d9ba7937

[ "BSD-2-Clause" ]

null

models/networks_basic.py

nistath/PerceptualSimilarity

29c5c71a6b09557ea0d049f27ec44f02d9ba7937

[ "BSD-2-Clause" ]

null

from __future__ import absolute_import import sys import torch import torch.nn as nn import torch.nn.init as init from torch.autograd import Variable import numpy as np from pdb import set_trace as st from skimage import color from IPython import embed from . import pretrained_networks as pn from .. import models as util def spatial_average(in_tens, keepdim=True): return in_tens.mean([2,3],keepdim=keepdim) def upsample(in_tens, out_H=64): # assumes scale factor is same for H and W in_H = in_tens.shape[2] scale_factor = 1.*out_H/in_H return nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False)(in_tens) # Learned perceptual metric class PNetLin(nn.Module): def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False, version='0.1', lpips=True): super(PNetLin, self).__init__() self.pnet_type = pnet_type self.pnet_tune = pnet_tune self.pnet_rand = pnet_rand self.spatial = spatial self.lpips = lpips self.version = version self.scaling_layer = ScalingLayer() if(self.pnet_type in ['vgg','vgg16']): net_type = pn.vgg16 self.chns = [64,128,256,512,512] elif(self.pnet_type=='alex'): net_type = pn.alexnet self.chns = [64,192,384,256,256] elif(self.pnet_type=='squeeze'): net_type = pn.squeezenet self.chns = [64,128,256,384,384,512,512] self.L = len(self.chns) self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune) if(lpips): self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4] if(self.pnet_type=='squeeze'): # 7 layers for squeezenet self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout) self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout) self.lins+=[self.lin5,self.lin6] def forward(self, in0, in1, retPerLayer=False): # v0.0 - original release had a bug, where input was not scaled in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version=='0.1' else (in0, in1) outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input) feats0, feats1, diffs = {}, {}, {} for kk in range(self.L): feats0[kk], feats1[kk] = util.normalize_tensor(outs0[kk]), util.normalize_tensor(outs1[kk]) diffs[kk] = (feats0[kk]-feats1[kk])**2 if(self.lpips): if(self.spatial): res = [upsample(self.lins[kk].model(diffs[kk]), out_H=in0.shape[2]) for kk in range(self.L)] else: res = [spatial_average(self.lins[kk].model(diffs[kk]), keepdim=True) for kk in range(self.L)] else: if(self.spatial): res = [upsample(diffs[kk].sum(dim=1,keepdim=True), out_H=in0.shape[2]) for kk in range(self.L)] else: res = [spatial_average(diffs[kk].sum(dim=1,keepdim=True), keepdim=True) for kk in range(self.L)] val = res[0] for l in range(1,self.L): val += res[l] if(retPerLayer): return (val, res) else: return val class ScalingLayer(nn.Module): def __init__(self): super(ScalingLayer, self).__init__() self.register_buffer('shift', torch.Tensor([-.030,-.088,-.188])[None,:,None,None]) self.register_buffer('scale', torch.Tensor([.458,.448,.450])[None,:,None,None]) def forward(self, inp): return (inp - self.shift) / self.scale class NetLinLayer(nn.Module): ''' A single linear layer which does a 1x1 conv ''' def __init__(self, chn_in, chn_out=1, use_dropout=False): super(NetLinLayer, self).__init__() layers = [nn.Dropout(),] if(use_dropout) else [] layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),] self.model = nn.Sequential(*layers) class Dist2LogitLayer(nn.Module): ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) ''' def __init__(self, chn_mid=32, use_sigmoid=True): super(Dist2LogitLayer, self).__init__() layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True),] layers += [nn.LeakyReLU(0.2,True),] layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True),] layers += [nn.LeakyReLU(0.2,True),] layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True),] if(use_sigmoid): layers += [nn.Sigmoid(),] self.model = nn.Sequential(*layers) def forward(self,d0,d1,eps=0.1): return self.model.forward(torch.cat((d0,d1,d0-d1,d0/(d1+eps),d1/(d0+eps)),dim=1)) class BCERankingLoss(nn.Module): def __init__(self, chn_mid=32): super(BCERankingLoss, self).__init__() self.net = Dist2LogitLayer(chn_mid=chn_mid) # self.parameters = list(self.net.parameters()) self.loss = torch.nn.BCELoss() def forward(self, d0, d1, judge): per = (judge+1.)/2. self.logit = self.net.forward(d0,d1) return self.loss(self.logit, per) # L2, DSSIM metrics class FakeNet(nn.Module): def __init__(self, use_gpu=True, colorspace='Lab'): super(FakeNet, self).__init__() self.use_gpu = use_gpu self.colorspace=colorspace class L2(FakeNet): def forward(self, in0, in1, retPerLayer=None): assert(in0.size()[0]==1) # currently only supports batchSize 1 if(self.colorspace=='RGB'): (N,C,X,Y) = in0.size() value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N) return value elif(self.colorspace=='Lab'): value = util.l2(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)), util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float') ret_var = Variable( torch.Tensor((value,) ) ) if(self.use_gpu): ret_var = ret_var.cuda() return ret_var class DSSIM(FakeNet): def forward(self, in0, in1, retPerLayer=None): assert(in0.size()[0]==1) # currently only supports batchSize 1 if(self.colorspace=='RGB'): value = util.dssim(1.*util.tensor2im(in0.data), 1.*util.tensor2im(in1.data), range=255.).astype('float') elif(self.colorspace=='Lab'): value = util.dssim(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)), util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float') ret_var = Variable( torch.Tensor((value,) ) ) if(self.use_gpu): ret_var = ret_var.cuda() return ret_var def print_network(net): num_params = 0 for param in net.parameters(): num_params += param.numel() print('Network',net) print('Total number of parameters: %d' % num_params)

39.797872

134

0.623095

d22b875fd1a5d832cd2a299573b03a9270994d6f

194

py

Python

search/settings/settings_api.py

JinHai-CN/phantoscope

1148a30bd379691220e46520248f76615f1d86d3

[ "Apache-2.0" ]

null

search/settings/settings_api.py

JinHai-CN/phantoscope

1148a30bd379691220e46520248f76615f1d86d3

[ "Apache-2.0" ]

null

search/settings/settings_api.py

JinHai-CN/phantoscope

1148a30bd379691220e46520248f76615f1d86d3

[ "Apache-2.0" ]

null

from flask import Blueprint from common.common import json_response settings = Blueprint("settings", __name__) @settings.route("/ping") @json_response def settings_ping(): return "pong"

16.166667

42

0.762887

701e74b0b13c6bd090e81c58ec45e7a2bd6dc2fa

1,830

py

Python

examples/fona_simpletest.py

FoamyGuy/Adafruit_CircuitPython_FONA

0343cb590901c78af4f0510f25c63e4ef50d351f

[ "MIT" ]

null

examples/fona_simpletest.py

FoamyGuy/Adafruit_CircuitPython_FONA

0343cb590901c78af4f0510f25c63e4ef50d351f

[ "MIT" ]

null

examples/fona_simpletest.py

FoamyGuy/Adafruit_CircuitPython_FONA

0343cb590901c78af4f0510f25c63e4ef50d351f

[ "MIT" ]

null

# pylint: disable=unused-import import time import board import busio import digitalio import adafruit_requests as requests from adafruit_fona.adafruit_fona import FONA from adafruit_fona.fona_3g import FONA3G import adafruit_fona.adafruit_fona_network as network import adafruit_fona.adafruit_fona_socket as cellular_socket print("FONA Webclient Test") TEXT_URL = "http://wifitest.adafruit.com/testwifi/index.html" JSON_URL = "http://api.coindesk.com/v1/bpi/currentprice/USD.json" # Get GPRS details and more from a secrets.py file try: from secrets import secrets except ImportError: print("GPRS secrets are kept in secrets.py, please add them there!") raise # Create a serial connection for the FONA connection uart = busio.UART(board.TX, board.RX) rst = digitalio.DigitalInOut(board.D4) # Use this for FONA800 and FONA808 fona = FONA(uart, rst) # Use this for FONA3G # fona = FONA3G(uart, rst) # Initialize cellular data network network = network.CELLULAR( fona, (secrets["apn"], secrets["apn_username"], secrets["apn_password"]) ) while not network.is_attached: print("Attaching to network...") time.sleep(0.5) print("Attached!") while not network.is_connected: print("Connecting to network...") network.connect() time.sleep(0.5) print("Network Connected!") print("My IP address is:", fona.local_ip) print("IP lookup adafruit.com: %s" % fona.get_host_by_name("adafruit.com")) # Initialize a requests object with a socket and cellular interface requests.set_socket(cellular_socket, fona) # fona._debug = True print("Fetching text from", TEXT_URL) r = requests.get(TEXT_URL) print("-" * 40) print(r.text) print("-" * 40) r.close() print() print("Fetching json from", JSON_URL) r = requests.get(JSON_URL) print("-" * 40) print(r.json()) print("-" * 40) r.close() print("Done!")

25.068493

76

0.743169

11cd643eae0d7845c378b6833d48874d4c20506d

309

py

Python

tests/test_dataset.py

elifesciences/elife-crossref-xml-generation

1cd7b3981d9c78032d2d0ffb68b651de40a8d622

[ "MIT" ]

3

2018-03-01T01:14:14.000Z

2021-01-19T13:04:42.000Z

tests/test_dataset.py

elifesciences/elife-crossref-xml-generation

1cd7b3981d9c78032d2d0ffb68b651de40a8d622

[ "MIT" ]

88

2017-07-20T00:13:47.000Z

2021-11-29T04:58:01.000Z

tests/test_dataset.py

elifesciences/elife-crossref-xml-generation

1cd7b3981d9c78032d2d0ffb68b651de40a8d622

[ "MIT" ]

4

2017-06-28T22:22:20.000Z

2021-02-17T23:06:39.000Z

import unittest from elifecrossref import dataset class TestDataset(unittest.TestCase): def test_choose_dataset_identifier_none(self): """test when an object has no attributes""" self.assertIsNone(dataset.choose_dataset_identifier(None)) if __name__ == "__main__": unittest.main()

23.769231

66

0.747573

7d4a70d474b2b52ac32a7a02bef47ac4a3f2e3db

1,691

py

Python

escea/discover.py

snikch/escea

9678b8dbec81b67e61e8f9fb62578ec5870af61e

[ "MIT" ]

3

2016-09-18T02:39:07.000Z

2019-09-02T03:07:09.000Z

escea/discover.py

snikch/escea

9678b8dbec81b67e61e8f9fb62578ec5870af61e

[ "MIT" ]

3

2019-03-24T04:56:27.000Z

2020-09-29T10:00:33.000Z

escea/discover.py

snikch/escea

9678b8dbec81b67e61e8f9fb62578ec5870af61e

[ "MIT" ]

3

2019-09-05T06:46:32.000Z

2022-03-29T05:32:37.000Z

import socket import binascii from escea.message import ( ) from escea.error import (ConnectionTimeout) class Fire(object): UDP_PORT = 3300 def __init__(self, ip): super(Fire, self).__init__() self._ip = ip self._prefix = '47' self._suffix = '46' def start(self): self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) self.sock.bind(('0.0.0.0', Fire.UDP_PORT)) def stop(self): self.sock.close() def send(self, message): data = '' try: self.sock.sendto(message.payload(), (self._ip, Fire.UDP_PORT)) self.sock.settimeout(2) data, _ = self.sock.recvfrom(1024) data = binascii.hexlify(data) except socket.timeout: raise ConnectionTimeout response = Response(data) message.assert_code(response.get(1)) return response def status(self): return StatusResponse(self.send(StatusRequest(self._prefix, self._suffix))) def set_temp(self, target): self.send(SetTempRequest(self._prefix, self._suffix, target)) def power_on(self): self.send(PowerOnRequest(self._prefix, self._suffix)) def power_off(self): self.send(PowerOffRequest(self._prefix, self._suffix)) def flame_effect_on(self): self.send(FlameEffectOnRequest(self._prefix, self._suffix)) def flame_effect_off(self): self.send(FlameEffectOffRequest(self._prefix, self._suffix)) def fan_boost_on(self): self.send(FanBoostOnRequest(self._prefix, self._suffix)) def fan_boost_off(self): self.send(FanBoostOffRequest(self._prefix, self._suffix))

26.84127

83

0.643998

58edfcb94d4e5fc3120d5f013f575d59aeb4dd9f

1,026

py

Python

codes/a_config/c_pybullet_parameters/parameters_ant_ddpg.py

linklab/link_rl

e3d3196dcd49fd71b45941e07fc0d8a27d1d8c99

[ "MIT" ]

null

codes/a_config/c_pybullet_parameters/parameters_ant_ddpg.py

linklab/link_rl

e3d3196dcd49fd71b45941e07fc0d8a27d1d8c99

[ "MIT" ]

null

codes/a_config/c_pybullet_parameters/parameters_ant_ddpg.py

linklab/link_rl

e3d3196dcd49fd71b45941e07fc0d8a27d1d8c99

[ "MIT" ]

1

2021-11-23T12:30:37.000Z

from codes.a_config._rl_parameters.off_policy.parameter_ddpg import PARAMETERS_DDPG from codes.e_utils.names import * from codes.a_config.parameters_general import PARAMETERS_GENERAL # https://github.com/bulletphysics/bullet3/blob/master/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_gym_env.py class PARAMETERS_ANT_DDPG(PARAMETERS_GENERAL, PARAMETERS_DDPG): ENVIRONMENT_ID = EnvironmentName.PYBULLET_ANT_V0 DEEP_LEARNING_MODEL = DeepLearningModelName.CONTINUOUS_DETERMINISTIC_ACTOR_CRITIC_MLP RL_ALGORITHM = RLAlgorithmName.DDPG_V0 OPTIMIZER = OptimizerName.ADAM TRAIN_STOP_EPISODE_REWARD = 2000.0 TRAIN_STOP_EPISODE_REWARD_STD = 50.0 STOP_PATIENCE_COUNT = 10 REPLAY_BUFFER_SIZE = 1000000 TARGET_NET_SYNC_STEP_PERIOD = 10000 MAX_GLOBAL_STEP = 10000000 EPSILON_INIT = 1.0 EPSILON_MIN = 0.01 EPSILON_MIN_STEP = 3000000 LEARNING_RATE = 0.00025 GAMMA = 0.99 BATCH_SIZE = 32 TRAIN_STEP_FREQ = 1 AVG_EPISODE_SIZE_FOR_STAT = 50 N_STEP = 1

35.37931

124

0.792398

e0ba0a82781711224640c363d3caf75836714486

644

py

Python

setup.py

python-diamond/diamond-redis

f55d747e5853b92b17b917d4616eb2c723e6f09b

[ "MIT" ]

2

2015-09-08T05:24:45.000Z

2017-03-14T08:46:59.000Z

setup.py

python-diamond/diamond-redis

f55d747e5853b92b17b917d4616eb2c723e6f09b

[ "MIT" ]

1

2020-09-25T06:29:00.000Z

2020-09-28T06:22:50.000Z

setup.py

python-diamond/diamond-redis

f55d747e5853b92b17b917d4616eb2c723e6f09b

[ "MIT" ]

1

2021-02-21T10:58:21.000Z

#!/usr/bin/env python from setuptools import setup install_requires = [ 'diamond', 'redis', ] setup( name='diamond-redis', version='0.0.1', author='Matt Robenolt', author_email='matt@ydekproductons.com', url='https://github.com/python-diamond/diamond-redis', description='', long_description='', license='MIT License', py_modules=['diamond_redis'], zip_safe=False, install_requires=install_requires, include_package_data=True, entry_points={ 'diamond.collectors': [ 'redis = diamond_redis', ], }, classifiers=[ 'DO NOT UPLOAD', ], )

20.125

58

0.61646

226bb9ddf543214d178c457597840963294d3dfa

3,374

py

Python

web/api/get_mp_function.py

spudmind/spud

86e44bca4efd3cd6358467e1511048698a45edbc

[ "MIT" ]

2

2015-04-11T12:22:41.000Z

2016-08-18T11:12:06.000Z

web/api/get_mp_function.py

spudmind/spud

86e44bca4efd3cd6358467e1511048698a45edbc

[ "MIT" ]

84

2015-01-22T14:33:49.000Z

2015-04-01T23:15:29.000Z

web/api/get_mp_function.py

spudmind/spud

86e44bca4efd3cd6358467e1511048698a45edbc

[ "MIT" ]

1

2015-04-16T03:10:39.000Z

from web.api import BaseAPI from data_models import government_models from utils import mongo class MpApi(BaseAPI): def __init__(self): BaseAPI.__init__(self) self._db = mongo.MongoInterface() self._db_table = 'api_mps' def request(self, args): name = args['name'] result, _ = self._db.query(self._db_table, query=args) if len(result) > 0: mp = government_models.MemberOfParliament(name) meetings = self._influencer_urls(mp.meetings) #interests = self._nest_category(self._interest_urls(mp.interests)) interests = self._interest_urls(mp.interests) donations = self._donor_urls(mp.donations) result = { 'name': result[0]['name'], 'party': result[0]['party'], 'influences_summary': result[0]['influences'], 'influences_detail': { "register_of_interests": interests, "electoral_commission": donations, "meetings": meetings }, "government_departments": self._department_detail_urls( result[0]["government_departments"] ), "government_positions": result[0]["government_positions"], "government_committees": self._committee_detail_urls( result[0]["government_committees"] ), 'mp': mp.mp_website, 'wikipedia': mp.wikipedia, 'guardian': mp.guardian, 'bbc': mp.bbc, } return result def _interest_urls(self, interests): results = [] for category in interests: updated_interests = [] for interest in category["interests"]: updated = interest interest_name = interest["interest"]["name"] interest_labels = interest["interest"]["labels"] urls = self.named_entity_resources(interest_name, interest_labels) updated["interest"]["details_url"] = urls[0] updated["interest"]["api_url"] = urls[1] updated_interests.append(updated) if len(updated_interests) > 0: category["interests"] = updated_interests results.append(category) return results def _donor_urls(self, donations): results = [] for donation in donations: updated = donation donor_name = donation["donor"]["name"] donor_labels = donation["donor"]["labels"] urls = self.named_entity_resources(donor_name, donor_labels) updated["donor"]["details_url"] = urls[0] updated["donor"]["api_url"] = urls[1] results.append(updated) return results def _influencer_urls(self, meetings): results = [] for meeting in meetings: updated = meeting attendee_name = {"name": meeting["attendee"], "details_url": None} if meeting["attendee"]: urls = self.named_entity_resources(meeting["attendee"], "influencer") attendee_name["details_url"] = urls[0] updated["attendee"] = attendee_name results.append(updated) return results

38.781609

85

0.557499

360fabb24d52b432a80272fe5b616de7b9c63233

1,610

py

Python

setup.py

howl-anderson/tf_summary_reader

a88d6aeeb325405f91c011c74c04c5efb641a06c

[ "MIT" ]

null

setup.py

howl-anderson/tf_summary_reader

a88d6aeeb325405f91c011c74c04c5efb641a06c

[ "MIT" ]

null

setup.py

howl-anderson/tf_summary_reader

a88d6aeeb325405f91c011c74c04c5efb641a06c

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """The setup script.""" from setuptools import setup, find_packages with open('README.rst') as readme_file: readme = readme_file.read() with open('HISTORY.rst') as history_file: history = history_file.read() requirements = ["pandas", "tensorflow>=1.15.0,<2.0.0"] setup_requirements = ['pytest-runner', ] test_requirements = ['pytest>=3', ] setup( author="Xiaoquan Kong", author_email='u1mail2me@gmail.com', python_requires='>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*', classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', "Programming Language :: Python :: 2", 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', ], description="A package for read data from tensorflow summary files", install_requires=requirements, license="MIT license", long_description=readme + '\n\n' + history, include_package_data=True, keywords='tf_summary_reader', name='tf_summary_reader', packages=find_packages(include=['tf_summary_reader', 'tf_summary_reader.*']), setup_requires=setup_requirements, test_suite='tests', tests_require=test_requirements, url='https://github.com/howl-anderson/tf_summary_reader', version='0.2.0', zip_safe=False, )

31.568627

81

0.642236

8563ebfc903383a99eb74a9bfc8ecd9e64dd9f3f

393

py

Python

cha_bebe/wsgi.py

intelektos/Cha_bebe

23df4af3901413c9c50e73bd305ade165c81001b

[ "MIT" ]

null

cha_bebe/wsgi.py

intelektos/Cha_bebe

23df4af3901413c9c50e73bd305ade165c81001b

[ "MIT" ]

9

2020-06-08T03:31:08.000Z

2022-01-13T02:44:42.000Z

cha_bebe/wsgi.py

intelektos/Cha_bebe

23df4af3901413c9c50e73bd305ade165c81001b

[ "MIT" ]

1

2020-06-01T17:43:20.000Z

""" WSGI config for cha_bebe project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'cha_bebe.settings') application = get_wsgi_application()

23.117647

78

0.78626

0595e0cbf847c9acb45d88519985a86f3413f473

8,885

py

Python

tests/pouw/nods/decentralized/test_worker.py

projectpai/pouw-main-iteration

e2505f63e11bbf80648c8cbe56b6d6f3e3a8546e

[ "MIT" ]

11

2020-06-22T05:31:18.000Z

2022-03-29T16:50:21.000Z

tests/pouw/nods/decentralized/test_worker.py

AIIJJII/pouw-main-iteration

e2505f63e11bbf80648c8cbe56b6d6f3e3a8546e

[ "MIT" ]

3

2020-06-23T18:20:09.000Z

2021-07-06T23:28:24.000Z

tests/pouw/nods/decentralized/test_worker.py

AIIJJII/pouw-main-iteration

e2505f63e11bbf80648c8cbe56b6d6f3e3a8546e

[ "MIT" ]

3

2020-09-02T11:03:16.000Z

2022-03-29T16:50:00.000Z

from copy import copy import mxnet as mx import pytest from mock import MagicMock from mxnet.gluon import nn from pai.pouw.nodes.decentralized.worker import create_network, get_layer_parameters_from_config, WorkerNode def test_create_network_fc_dnn(): model_data = { 'type': 'FC-DNN', 'hidden-units': [ { 'id': 'layer1', 'type': 'Dense', 'nodes': 128, 'activation': 'relu' }, { 'id': 'layer2', 'type': 'Dense', 'nodes': 64, 'activation': 'relu' }, { 'id': 'output', 'type': 'Dense', 'nodes': 10 }, ], 'loss': 'SoftmaxCrossEntropyLoss' } network = create_network(model_data) assert type(network) == nn.Sequential @pytest.mark.parametrize('layer_number', range(1, 10)) def test_create_network_hidden_units_number_properly_initialized(layer_number): model_data = { 'type': 'FC-DNN', 'hidden-units': [ { 'id': 'output', 'type': 'Dense', 'nodes': 10 }, ], 'loss': 'SoftmaxCrossEntropyLoss' } layer = { 'id': 'layer', 'type': 'Dense', 'nodes': 128, 'activation': 'relu' } for index in range(layer_number): new_layer = copy(layer) new_layer['id'] += str(index) model_data['hidden-units'].insert(0, new_layer) network = create_network(model_data) assert len(network) == layer_number + 1 @pytest.mark.parametrize('node_number', (2 ** n for n in (4, 11))) def test_create_network_node_number_in_dense_layer(node_number): model_data = { 'type': 'FC-DNN', 'hidden-units': [ { 'id': 'layer1', 'type': 'Dense', 'nodes': node_number, 'activation': 'relu' }, { 'id': 'output', 'type': 'Dense', 'nodes': 10 }, ], 'loss': 'SoftmaxCrossEntropyLoss' } network = create_network(model_data) assert network[0]._units == node_number def test_get_layer_parameters_from_config_simple(): raw_conf = { 'id': 'layer1', 'type': 'Dense', 'nodes': 10, 'activation': 'relu' } layer_config = get_layer_parameters_from_config(raw_conf) assert layer_config == { 'units': 10, 'activation': 'relu' } def test_create_network_dropout_layer(): model_data = { 'type': 'FC-DNN', 'hidden-units': [ { 'id': 'layer1', 'type': 'Dense', 'nodes': 128, 'activation': 'relu' }, { 'id': 'dropout1', 'type': 'Dropout', 'rate': 0.5, }, { 'id': 'output', 'type': 'Dense', 'nodes': 10 }, ], 'loss': 'SoftmaxCrossEntropyLoss' } network = create_network(model_data) assert type(network[1]) == nn.Dropout def test_create_network_batch_normalization_layer(): model_data = { 'type': 'FC-DNN', 'hidden-units': [ { 'id': 'layer1', 'type': 'Dense', 'nodes': 128, 'activation': 'relu' }, { 'id': 'batch1', 'type': 'BatchNorm' }, { 'id': 'output', 'type': 'Dense', 'nodes': 10 }, ], 'loss': 'SoftmaxCrossEntropyLoss' } network = create_network(model_data) assert type(network[1]) == nn.BatchNorm def test_create_network_instance_normalization_layer(): model_data = { 'type': 'FC-DNN', 'hidden-units': [ { 'id': 'layer1', 'type': 'Dense', 'nodes': 128, 'activation': 'relu' }, { 'id': 'batch1', 'type': 'InstanceNorm' }, { 'id': 'output', 'type': 'Dense', 'nodes': 10 }, ], 'loss': 'SoftmaxCrossEntropyLoss' } network = create_network(model_data) assert type(network[1]) == nn.InstanceNorm def test_create_network_layer_normalization(): model_data = { 'type': 'FC-DNN', 'hidden-units': [ { 'id': 'layer1', 'type': 'Dense', 'nodes': 128, 'activation': 'relu' }, { 'id': 'batch1', 'type': 'LayerNorm' }, { 'id': 'output', 'type': 'Dense', 'nodes': 10 }, ], 'loss': 'SoftmaxCrossEntropyLoss' } network = create_network(model_data) assert type(network[1]) == nn.LayerNorm def test_create_network_embedding_layer(): model_data = { 'type': 'FC-DNN', 'hidden-units': [ { 'id': 'layer1', 'type': 'Dense', 'nodes': 128, 'activation': 'relu' }, { 'id': 'embedding', 'type': 'Embedding', 'input_dim': 64, 'output_dim': 32 }, { 'id': 'output', 'type': 'Dense', 'nodes': 10 }, ], 'loss': 'SoftmaxCrossEntropyLoss' } network = create_network(model_data) assert type(network[1]) == nn.Embedding def test_create_network_flatten_layer(): model_data = { 'type': 'FC-DNN', 'hidden-units': [ { 'id': 'layer1', 'type': 'Dense', 'nodes': 128, 'activation': 'relu' }, { 'id': 'embedding', 'type': 'Flatten', }, { 'id': 'output', 'type': 'Dense', 'nodes': 10 }, ], 'loss': 'SoftmaxCrossEntropyLoss' } network = create_network(model_data) assert type(network[1]) == nn.Flatten def test_initialize_network_passing_parameters_to_optimizer(client_task_definition_data, mocker): mocker.patch('redis.Redis', MagicMock()) ctx = mx.cpu(0) node = WorkerNode(redis_host=None, redis_port=None, context=ctx) node.task_data = client_task_definition_data node.initialize_network() @pytest.mark.parametrize('init_settings', [{'name': 'Xavier', 'parameters': {}}, {'name': 'Bilinear', 'parameters': {}}, {'name': 'Constant', 'parameters': {'value': 0}}, {'name': 'FusedRNN', 'parameters': {'init': None, 'num_hidden': 1, 'num_layers': 1, 'mode': 'test'}}, {'name': 'LSTMBias', 'parameters': {}}, {'name': 'MSRAPrelu', 'parameters': {}}, {'name': 'Normal', 'parameters': {}}, {'name': 'One', 'parameters': {}}, {'name': 'Orthogonal', 'parameters': {}}, {'name': 'Uniform', 'parameters': {}}, {'name': 'Zero', 'parameters': {}}]) def test_initialize_network_passing_parameters_to_optimizer_inicializator(client_task_definition_data, mocker, init_settings): mocker.patch('redis.Redis', MagicMock()) ctx = mx.cpu(0) node = WorkerNode(redis_host=None, redis_port=None, context=ctx) node.task_data = client_task_definition_data node.task_data['ml']['optimizer']['initializer'] = init_settings node.initialize_network() def test_initialize_network_passing_parameters_to_optimizer_no_parameters(client_task_definition_data, mocker): mocker.patch('redis.Redis', MagicMock()) ctx = mx.cpu(0) node = WorkerNode(redis_host=None, redis_port=None, context=ctx) node.task_data = client_task_definition_data del node.task_data['ml']['optimizer']['initializer']['parameters'] node.initialize_network()

28.477564

111

0.448171

6ada85e40f050c2e639ce1d4f0b808bef81bde9b

106

py

Python

ex2-21.py

ppedraum/infosatc-lp-avaliativo-01

aa548868ada4a98727587da3a4c6452a4042c199

[ "MIT" ]

null

ex2-21.py

ppedraum/infosatc-lp-avaliativo-01

aa548868ada4a98727587da3a4c6452a4042c199

[ "MIT" ]

null

ex2-21.py

ppedraum/infosatc-lp-avaliativo-01

aa548868ada4a98727587da3a4c6452a4042c199

[ "MIT" ]

null

#21 l = float(input("Digite uma massa em libras: ")) k = l*0.45 print("{:.2f}lb2 = {:.2f}kg".format(l, k))

26.5

48

0.584906

6917711c32e4be05cbef44b881d3c2e33fbff33c

4,701

py

Python

iunets/baseline_networks.py

YoelShoshan/iunets

9789da07e2ef932c5ea612737066ba88f4f26977

[ "MIT" ]

86

2020-05-12T06:33:43.000Z

2022-03-29T13:56:30.000Z

iunets/baseline_networks.py

YoelShoshan/iunets

9789da07e2ef932c5ea612737066ba88f4f26977

[ "MIT" ]

8

2020-05-19T08:08:01.000Z

2022-02-25T09:04:14.000Z

iunets/baseline_networks.py

YoelShoshan/iunets

9789da07e2ef932c5ea612737066ba88f4f26977

[ "MIT" ]

13

2020-05-12T06:33:55.000Z

2021-12-20T07:59:43.000Z

import torch from torch import nn from .utils import get_num_channels from .layers import StandardBlock class StandardUNet(nn.Module): def __init__(self, input_shape_or_channels, dim=None, architecture=[2,2,2,2], base_filters=32, skip_connection=False, block_type=StandardBlock, zero_init=False, *args, **kwargs): super(StandardUNet, self).__init__() self.input_channels = get_num_channels(input_shape_or_channels) self.base_filters = base_filters self.architecture = architecture self.n_levels = len(self.architecture) self.dim = dim self.skip_connection = skip_connection self.block_type = block_type pool_ops = [nn.MaxPool1d, nn.MaxPool2d, nn.MaxPool3d] pool_op = pool_ops[dim-1] upsampling_ops = [nn.ConvTranspose1d, nn.ConvTranspose2d, nn.ConvTranspose3d] upsampling_op = upsampling_ops[dim-1] filters = self.base_filters filters_list = [filters] self.module_L = nn.ModuleList() self.module_R = nn.ModuleList() self.downsampling_layers = nn.ModuleList() self.upsampling_layers = nn.ModuleList() # Left side of the U-Net for i in range(self.n_levels): self.module_L.append(nn.ModuleList()) self.downsampling_layers.append( pool_op(kernel_size=2) ) depth = architecture[i] for j in range(depth): if i == 0 and j == 0: in_channels = self.input_channels else: in_channels = self.base_filters * (2**i) if j == depth-1: out_channels = self.base_filters * (2**(i+1)) else: out_channels = self.base_filters * (2**i) self.module_L[i].append( self.block_type(self.dim, in_channels, out_channels, zero_init, *args, **kwargs) ) # Right side of the U-Net for i in range(self.n_levels-1): self.module_R.append(nn.ModuleList()) depth = architecture[i] for j in range(depth): if j == 0: in_channels = 3*self.base_filters * (2**(i+1)) else: in_channels = self.base_filters * (2**(i+1)) out_channels = self.base_filters * (2**(i+1)) self.module_R[i].append( self.block_type(self.dim, in_channels, out_channels, zero_init, *args, **kwargs) ) self.upsampling_layers.append( upsampling_op(self.base_filters * (2**(i+2)), self.base_filters * (2**(i+2)), kernel_size=2, stride=2) ) if self.skip_connection: # We have to convert back to the original number of channels if # we want a skip connection. We do this with an appropriate # convolution. conv_ops = [nn.Conv1d, nn.Conv2d, nn.Conv3d] conv_op = conv_ops[self.dim-1] self.output_layer = conv_op(self.base_filters*2, self.input_channels, 3, padding=1) def forward(self, input, *args, **kwargs): # FORWARD skip_inputs = [] x = input # Left side for i in range(self.n_levels): depth = self.architecture[i] # Left side for j in range(depth): x = self.module_L[i][j](x) # Downsampling L if i < self.n_levels - 1: skip_inputs.append(x) x = self.downsampling_layers[i](x) # Right side for i in range(self.n_levels - 2, -1, -1): depth = self.architecture[i] # Upsampling R x = self.upsampling_layers[i](x) y = skip_inputs.pop() x = torch.cat((x,y),dim=1) for j in range(depth): x = self.module_R[i][j](x) if self.skip_connection: x = self.output_layer(x) + input return x

33.578571

100

0.478196

c8338493827b6156d8a392083666d8439c7104bf

562

py

Python

var/spack/repos/builtin/packages/libinih/package.py

player1537-forks/spack

822b7632222ec5a91dc7b7cda5fc0e08715bd47c

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

11

2015-10-04T02:17:46.000Z

2018-02-07T18:23:00.000Z

var/spack/repos/builtin/packages/libinih/package.py

player1537-forks/spack

822b7632222ec5a91dc7b7cda5fc0e08715bd47c

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

22

2017-08-01T22:45:10.000Z

2022-03-10T07:46:31.000Z

var/spack/repos/builtin/packages/libinih/package.py

player1537-forks/spack

822b7632222ec5a91dc7b7cda5fc0e08715bd47c

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

4

2016-06-10T17:57:39.000Z

2018-09-11T04:59:38.000Z

# Copyright 2013-2022 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) from spack import * class Libinih(MesonPackage): """ inih (INI Not Invented Here) is a simple .INI file parser written in C. """ homepage = "https://github.com/benhoyt/inih" url = "https://github.com/benhoyt/inih/archive/refs/tags/r53.tar.gz" git = "https://github.com/benhoyt/inih.git" version('master', branch="master")

29.578947

77

0.688612

300031c71c91ad83d18767a77b2cc0ee03e6ed16

6,428

py

Python

invtorch/nn/modules/module.py

xmodar/invtorch

74b80be3b4126925e583282b6f78171b99788b37

[ "Apache-2.0" ]

14

2021-11-18T11:26:11.000Z

2022-01-20T13:29:52.000Z

invtorch/nn/modules/module.py

xmodar/invtorch

74b80be3b4126925e583282b6f78171b99788b37

[ "Apache-2.0" ]

null

invtorch/nn/modules/module.py

xmodar/invtorch

74b80be3b4126925e583282b6f78171b99788b37

[ "Apache-2.0" ]

null

"""Base Invertible Modules""" import itertools from contextlib import contextmanager import torch from torch import nn from ...autograd.grad_mode import backward_mode, dry_mode from ...utils.checkpoint import checkpoint from ...utils.tools import pack __all__ = ['Module'] class Module(nn.Module): """Base invertible module""" def __init__(self): super().__init__() self.seed = False # preserve RNG state in backward self.checkpoint = True # enables or disables checkpointing self.invertible = True # use inverse if checkpointing is enabled self._reversed = False # switch function and inverse def forward(self, *args, **kwargs): """Perform the forward pass""" private = { 'seed': self.seed, 'enabled': self.checkpoint, 'inverse': self.call_inverse if self.invertible else None, } assert all(k not in kwargs for k in private), 'got an illegal argument' kwargs.update(private) return self.process(checkpoint(self.call_function, *args, **kwargs)) def function(self, *args): """Compute the outputs of the function given the inputs""" raise NotImplementedError def inverse(self, *args): """Compute the inputs of the function given the outputs""" raise NotImplementedError @property def call_function(self): """Current function (according to `self.reversed`)""" return self.inverse if self.reversed else self.function @property def call_inverse(self): """Current inverse (according to `self.reversed`)""" return self.function if self.reversed else self.inverse @property def reversible(self): """Whether function and inverse can be switched""" return False def reverse(self, mode=None): """Switch function and inverse""" if not self.reversed if mode is None else mode: assert self.reversible, 'module is not reversible' self._reversed = True else: self._reversed = False return self @property def checkpoint(self): """Whether the module is in checkpoint or pass_through mode""" return self._checkpoint @checkpoint.setter def checkpoint(self, value): if value: self._checkpoint = True else: self._checkpoint = self._invertible = False @property def invertible(self): """Whether the module is in invertible or simple checkpoint mode""" return self._checkpoint and self._invertible @invertible.setter def invertible(self, value): if value: self._invertible = self._checkpoint = True else: self._invertible = False @property def reversed(self): """Whether function and inverse should be switched""" return self._reversed @reversed.setter def reversed(self, value): self.reverse(value) @property def num_outputs(self): """End index to slice `call_function()`'s outputs in `forward()`""" return None @property def num_inputs(self): """End index to slice `call_inverse()`'s outputs in `forward()`""" return None def process(self, args, inverse=False): """Process the outputs of `call_function()` or `call_inverse()`""" args = pack(args) assert isinstance(args, tuple), 'should only output a `tuple`' num_args = self.num_inputs if inverse else self.num_outputs if num_args is None: num_args = len(args) elif num_args < 0: num_args += len(args) assert 0 < num_args <= len(args), f'needs {num_args} args' return args[0] if num_args == 1 else args[:num_args] def check(self, *args, rtol=1e-3, atol=1e-5): """Check invertability and second forward pass consistency""" def check(args1, args2, message): for arg1, arg2 in itertools.zip_longest(args1, args2): is_tensor = torch.is_tensor(arg1) assert is_tensor == torch.is_tensor(arg2), message same = not is_tensor or torch.allclose(arg1, arg2, rtol, atol) assert same, message with dry_mode(): outputs = pack(self.call_function(*args)) with torch.inference_mode(): inputs = pack(self.call_inverse(*outputs)) check(args, inputs, 'inverted tensors mismatch (try double precision)') with backward_mode(): second = pack(self.call_function(*args)) if self.seed: message = 'second forward pass mismatched despite `self.seed=True`' else: message = 'second forward pass mismatched (try `self.seed=True`)' check(outputs, second, message) return True def get_extra_state(self): return { 'seed': self.seed, 'checkpoint': self.checkpoint, 'invertible': self.invertible, 'reversed': self.reversed, } def set_extra_state(self, state): self.seed = state['seed'] self.checkpoint = state['checkpoint'] self.invertible = state['invertible'] self.reversed = state['reversed'] @contextmanager def temp_mode(self, **kwargs): """Set, temporarily, the mode of the model""" state = {} for key in ('seed', 'checkpoint', 'invertible', 'reversed'): state[key] = getattr(self, key) if key in kwargs and state[key] == bool(kwargs[key]): kwargs.pop(key) assert all(k in state for k in kwargs), 'got an illegal argument' if 'checkpoint' in kwargs and 'invertible' in kwargs: assert kwargs['checkpoint'] or not kwargs['invertible'], ( 'set either `checkpoint` or `invertible` or avoid conflict') try: for key, value in kwargs.items(): setattr(self, key, value) yield self finally: for key, value in state.items(): setattr(self, key, value) def extra_repr(self): extra = f'reversed={self.reversed}, checkpoint={self.checkpoint}' if self.checkpoint: extra += f', invertible={self.invertible}, seed={self.seed}' return extra def __repr__(self): return 'Inv' + super().__repr__()

34.374332

79

0.60781

f3f3b0001b880c6b44df6ecf5d674e70a6eb0283

1,420

py

Python

custom_components/samsungtv_encrypted/PySmartCrypto/command_encryption.py

MizterB/ha-samsungtv-encrypted

6b81a311b5d40b4a7f3311917ba7eade91bb6cd5

[ "Apache-2.0" ]

41

2020-03-08T23:49:29.000Z

2022-01-25T01:33:57.000Z

custom_components/samsungtv_encrypted/PySmartCrypto/command_encryption.py

MizterB/ha-samsungtv-encrypted

6b81a311b5d40b4a7f3311917ba7eade91bb6cd5

[ "Apache-2.0" ]

88

2020-03-08T23:11:36.000Z

2022-03-15T01:32:21.000Z

custom_components/samsungtv_encrypted/PySmartCrypto/command_encryption.py

MizterB/ha-samsungtv-encrypted

6b81a311b5d40b4a7f3311917ba7eade91bb6cd5

[ "Apache-2.0" ]

29

2020-03-16T09:24:41.000Z

2022-03-14T06:44:46.000Z

from hashlib import md5 from base64 import b64decode from base64 import b64encode from Crypto.Cipher import AES import binascii # Padding for the input string --not # related to encryption itself. BLOCK_SIZE = 16 # Bytes pad = lambda s: s + (BLOCK_SIZE - len(s) % BLOCK_SIZE) * \ chr(BLOCK_SIZE - len(s) % BLOCK_SIZE) unpad = lambda s: s[:-ord(s[len(s) - 1:])] class AESCipher: """ Usage: c = AESCipher('password').encrypt('message') m = AESCipher('password').decrypt(c) Tested under Python 3 and PyCrypto 2.6.1. """ def __init__(self, key, session_id): self.key = binascii.unhexlify(key) self.session_id = session_id def decrypt(self, enc): cipher = AES.new(self.key, AES.MODE_ECB) return unpad(cipher.decrypt(binascii.unhexlify(enc))) def encrypt(self, raw): cipher = AES.new(self.key, AES.MODE_ECB) return cipher.encrypt(bytes(pad(raw), encoding = "utf8")) def generate_command(self,key_press): command_bytes = self.encrypt(self.generate_json(key_press)) int_array = ','.join((list(map(str, command_bytes)))) return '5::/com.samsung.companion:{"name":"callCommon","args":[{"Session_Id":' + str(self.session_id) + ',"body":"[' + int_array + ']"}]}' def generate_json(self,key_press): return '{"method":"POST","body":{"plugin":"RemoteControl","param1":"uuid:12345","param2":"Click","param3":"' + key_press + '","param4":false,"api":"SendRemoteKey","version":"1.000"}}'

32.272727

185

0.689437

b69420463a7e079e9c7dc894a50509a0273a42c2

548

py

Python

tutorial/matplotlib-tutorial/image_clip_path.py

zixia/python-facenet

d86e0c49a9ce413bef6e58a19a9f723aadcef968

[ "MIT" ]

4

2018-06-11T03:02:49.000Z

2018-07-11T07:18:52.000Z

tutorial/matplotlib-tutorial/image_clip_path.py

zixia/python-facenet

d86e0c49a9ce413bef6e58a19a9f723aadcef968

[ "MIT" ]

null

tutorial/matplotlib-tutorial/image_clip_path.py

zixia/python-facenet

d86e0c49a9ce413bef6e58a19a9f723aadcef968

[ "MIT" ]

2

2017-08-31T05:35:36.000Z

2018-10-11T16:42:15.000Z

""" http://matplotlib.org/examples/images_contours_and_fields/image_demo_clip_path.html Demo of image that's been clipped by a circular patch. """ # %% import matplotlib.pyplot as plt import matplotlib.patches as patches import matplotlib.cbook as cbook image_file = cbook.get_sample_data('grace_hopper.png') image = plt.imread(image_file) fig, ax = plt.subplots() im = ax.imshow(image) patch = patches.Circle( (260, 200), radius=200, transform=ax.transData ) im.set_clip_path(patch) # ax.add_patch(patch) ax.axis('off') plt.show()

21.076923

83

0.748175

5bfc9e528b1e39f02302fc0e428e17c864c94289

5,612

py

Python

mosdef_code/spectra/check_line_broadness.py

brianlorenz/code

e24277bbb1deb2f0488f7b6e1f28c7b633c2c12b

[ "MIT" ]

null

mosdef_code/spectra/check_line_broadness.py

brianlorenz/code

e24277bbb1deb2f0488f7b6e1f28c7b633c2c12b

[ "MIT" ]

null

mosdef_code/spectra/check_line_broadness.py

brianlorenz/code

e24277bbb1deb2f0488f7b6e1f28c7b633c2c12b

[ "MIT" ]

1

2021-12-08T01:20:12.000Z

import initialize_mosdef_dirs as imd from astropy.io import ascii import pandas as pd import numpy as np from scipy import interpolate import scipy.integrate as integrate from scipy.optimize import curve_fit import os import matplotlib.pyplot as plt def check_broadness(groupID, run_name, rest_wave, width=15): """Makes a plot of how broad a line is by convolving it with all of the filters Parameters: groupID (int): ID of the group to convolve run_name (str): Name of the prospector run that you are looking at to convolve rest_wave (int): Wavelength closest to the line width (int): Angstroms on either side of the line to consider in the convolution """ # Read in the spectrum spec_df = ascii.read(imd.prospector_fit_csvs_dir + f'/{run_name}_csvs/{groupID}_spec.csv').to_pandas() spec_df_cut = spec_df[np.logical_and(spec_df['rest_wavelength']>rest_wave-width, spec_df['rest_wavelength']<rest_wave+1+width)] spec_interp = interpolate.interp1d(spec_df_cut['rest_wavelength'], spec_df_cut['spec50_flambda'], bounds_error=False, fill_value=0) # Test plot, looks good, it grabs the line # wave_plot = np.arange(6553, 6573, 0.2) # plt.plot(wave_plot, spec_interp(wave_plot)) # plt.show() # Find the filters filt_folder = imd.composite_filter_csvs_dir + f'/{groupID}_filter_csvs/' filt_files = [file for file in os.listdir(filt_folder) if '.csv' in file] # loop over each point, storing both the point and the integrated flux value at that point points = [] fluxes = [] for i in range(len(filt_files)): filt_file = filt_files[i] point = filt_file.split('.')[0].split('_')[1] print(f'Reading in filter for point {point}...') filt = ascii.read(filt_folder + filt_file).to_pandas() filt_interp = interpolate.interp1d(filt['rest_wavelength'], filt['transmission'], bounds_error=False) # Test plot, looks good, it grabs the filter # wave_plot = np.arange(20000, 33000, 0.73) # plt.plot(wave_plot, filt_interp(wave_plot)) # plt.show() def flux_func_numerator(wave): """Function that you need to integrate to get the flux""" return spec_interp(wave)*filt_interp(wave)*wave*10**18 def flux_func_denominator(wave): """Function that you need to integrate to get the flux""" return filt_interp(wave)*wave # numerator = integrate.quad(flux_func_numerator, 801, 25000)[0] # denominator = integrate.quad(flux_func_denominator, 801, 25000)[0] # Testing trapz integration wave_array = np.arange(801, 39999, 0.1) numerator = integrate.trapz(flux_func_numerator(wave_array)) denominator = integrate.trapz(flux_func_denominator(wave_array)) flux = numerator / denominator print(f'Num: {numerator}') print(f'Dem: {denominator}') print(f'-----------------') points.append(int(point)) fluxes.append(flux / 10**18) line_width_df = pd.DataFrame(zip(points, fluxes), columns=['rest_wavelength', 'flux']) line_width_df.to_csv(imd.line_widths_dir + f'/group{groupID}_{rest_wave}_broadness.csv', index=False) def plot_broadness(groupID, rest_waves): '''Plots the broadness of all of the provided lines on one axis Parameters: groupID (int): ID of the composite rest_waves (list): List of peak wavelengths in angtroms, rounded ''' colors = ['black','blue'] run_count = 0 min_bounds = [] max_bounds = [] fig, ax = plt.subplots(figsize = (8,8)) for rest_wave in rest_waves: line_width_df = ascii.read(imd.line_width_csvs_dir + f'/group{groupID}_{rest_wave}_broadness.csv').to_pandas() line_width_df['flux'] = line_width_df['flux']*10**18 guess = [6563, 3000, 50] def gaussian(x, mu, sig, amp): return amp * np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.))) popt, pcov = curve_fit(gaussian, line_width_df['rest_wavelength'], line_width_df['flux'], p0=guess) mean = popt[0] sigma = popt[1] amp = popt[2] gauss_waves = np.arange(rest_wave-4000, rest_wave+4000, 1) gauss_ys = gaussian(gauss_waves, mean, sigma, amp) ax.plot(line_width_df['rest_wavelength'], line_width_df['flux'], marker='o', ls='None', color=colors[run_count]) ax.plot(gauss_waves, gauss_ys, marker='None', ls='-', color='red') if run_count==0: ylim = ax.get_ylim() min_bound = mean-2*np.abs(sigma) max_bound = mean+2*np.abs(sigma) min_bounds.append(min_bound) max_bounds.append(max_bound) ax.plot([min_bound, min_bound], [-1000, 1000], ls='--', marker='None', color=colors[run_count]) ax.plot([max_bound, max_bound], [-1000, 1000], ls='--', marker='None', color=colors[run_count]) run_count += 1 ax.set_xscale('log') ax.set_ylabel('Flux (*10^18)') ax.set_xlabel('Wavelength ($\AA$)') ax.set_ylim(ylim) fig.savefig(imd.line_width_images_dir + f'/{groupID}_widths.pdf') # Save the bounds bounds_df = pd.DataFrame(zip(rest_waves, min_bounds, max_bounds), columns=['rest_wavelength', 'min_bound', 'max_bound']) bounds_df.to_csv(imd.line_widths_dir + f'/{groupID}_bounds.csv', index=False) for groupID in range(0, 29): try: plot_broadness(groupID, [6563,5007]) except: pass # for groupID in range(0, 29): # try: # check_broadness(groupID, 'redshift_maggies', 5007) # except: # pass

37.413333

135

0.656272

a8a6be3d2b5cea8702763c75c0ac12ef819b87a2

10,868

py

Python

applications/camera_calibration/scripts/create_calibration_pattern.py

xiesc/camera_calibration

8bd0071a1175894101f6dd204345297010756c09

[ "BSD-3-Clause" ]

1

2020-11-03T13:25:49.000Z

applications/camera_calibration/scripts/create_calibration_pattern.py

xiesc/camera_calibration

8bd0071a1175894101f6dd204345297010756c09

[ "BSD-3-Clause" ]

null

applications/camera_calibration/scripts/create_calibration_pattern.py

xiesc/camera_calibration

8bd0071a1175894101f6dd204345297010756c09

[ "BSD-3-Clause" ]

1

2020-12-05T07:41:04.000Z

# Copyright 2019 ETH Zürich, Thomas Schöps # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import argparse import os import math import sys import numpy as np from scipy.misc import imread # This requires reportlab, installed like this: # sudo pip3 install reportlab from reportlab.pdfgen import canvas from reportlab.lib.pagesizes import letter, A4 from reportlab.lib.units import inch, cm, mm def GetStarCoord(square_length, i, num_star_segments, center_x, center_y): angle = (2 * math.pi) * i / num_star_segments x = math.sin(angle) y = math.cos(angle) max_abs_x = max(abs(x), abs(y)) x /= max_abs_x y /= max_abs_x return (center_x - 0.5 * square_length * x, center_y + 0.5 * square_length * y) if __name__ == '__main__': # Define arguments parser = argparse.ArgumentParser(description="Create calibration patterns.") parser.add_argument("--tag36h11_path", required=True, help="Path to a folder containing the 36h11 AprilTag images. May be downloaded from: https://github.com/AprilRobotics/apriltag-imgs") parser.add_argument("--output_base_path", required=True, help="Base path to the PDF and YAML output files (excluding the file extensions).") parser.add_argument("--paper_size", default="A4", help="Paper size; supported values: A4, letter.") parser.add_argument("--num_star_segments", default="16", help="Number of segments of each star in the pattern. Refers to the sum of black and white segments. 4 would give a checkerboard.") parser.add_argument("--apriltag_index", default="0", help="Index of the AprilTag to use for the pattern.") parser.add_argument("--margin_in_cm", default="0.4", help="Page margin in centimeters.") parser.add_argument("--approx_square_length_in_cm", default="1.2", help="Approximate star square length in centimeters. May get slightly modified such that the squares exactly fit into the print area.") parser.add_argument("--apriltag_length_in_squares", default="4", help="Length of the AprilTag measured in star squares.") # Parse and check arguments args = parser.parse_args() num_star_segments = int(args.num_star_segments) apriltag_index = int(args.apriltag_index) margin_in_cm = float(args.margin_in_cm) approx_square_length_in_cm = float(args.approx_square_length_in_cm) apriltag_length_in_squares = int(args.apriltag_length_in_squares) pagesize = A4 if args.paper_size == "A4": pagesize = A4 elif args.paper_size == "letter": pagesize = letter else: print("Error: The given paper size (" + args.paper_size + ") must be either A4 or letter.") sys.exit(1) pdf_path = args.output_base_path + '.pdf' metadata_path = args.output_base_path + '.yaml' tag_path = os.path.join(args.tag36h11_path, 'tag36_11_{:0>5d}.png'.format(apriltag_index)) if num_star_segments < 4: print('Error: The number of star segments must be larger or equal to four.') sys.exit(1) if num_star_segments % 4 != 0: print('Warning: The number of star segments must be divisible by four for the symmetry-based detector.') if not os.path.exists(tag_path): print('Error: Required file does not exist: ' + tag_path) sys.exit(1) # Set up page. (0, 0) is at the bottom-left of the page. c = canvas.Canvas(pdf_path, pagesize=pagesize) c.setFillColorRGB(0, 0, 0) width, height = pagesize margin = margin_in_cm * cm start_x = margin end_x = width - margin start_y = height - margin end_y = margin print_area_width = abs(end_x - start_x) print_area_height = abs(end_y - start_y) # Determine the checkerboard resolution approx_square_length = approx_square_length_in_cm * cm squares_length_1 = print_area_width / round(print_area_width / approx_square_length) squares_length_2 = print_area_height / round(print_area_height / approx_square_length) square_length = min(squares_length_1, squares_length_2) squares_x = math.floor(print_area_width / square_length) squares_y = math.floor(print_area_height / square_length) unused_x = print_area_width - squares_x * square_length pattern_start_x = start_x + 0.5 * unused_x unused_y = print_area_height - squares_y * square_length pattern_start_y = start_y - 0.5 * unused_y # Draw AprilTag in the middle clip_path = c.beginPath() im = imread(tag_path).astype(np.uint8) tag_width = im.shape[0] tag_height = im.shape[1] if tag_width != tag_height: print('Non-square tags are not supported') sys.exit(1) tag_x = squares_x // 2 - apriltag_length_in_squares // 2 tag_start_x = pattern_start_x + tag_x * square_length tag_y = squares_y // 2 - apriltag_length_in_squares // 2 tag_start_y = pattern_start_y - tag_y * square_length tag_square_length = apriltag_length_in_squares * square_length / tag_width for x in range(0, tag_width): for y in range(0, tag_height): if im[y][x][0] < 127: c.rect(tag_start_x + x * tag_square_length, tag_start_y - y * tag_square_length - tag_square_length, tag_square_length, tag_square_length, stroke=0, fill=1) clip_path.moveTo(tag_start_x, tag_start_y) clip_path.lineTo(tag_start_x + tag_width * tag_square_length, tag_start_y) clip_path.lineTo(tag_start_x + tag_width * tag_square_length, tag_start_y - tag_height * tag_square_length) clip_path.lineTo(tag_start_x, tag_start_y - tag_height * tag_square_length) clip_path.lineTo(tag_start_x, tag_start_y) pattern_end_x = end_x - 0.5 * unused_x pattern_end_y = end_y + 0.5 * unused_y clip_path.moveTo(pattern_start_x, pattern_start_y) clip_path.lineTo(pattern_end_x, pattern_start_y) clip_path.lineTo(pattern_end_x, pattern_end_y) clip_path.lineTo(pattern_start_x, pattern_end_y) clip_path.lineTo(pattern_start_x, pattern_start_y) # Draw checkerboard c.clipPath(clip_path, stroke=0, fill=0) for x in range(-1, squares_x): for y in range(0, squares_y + 1): center_x = pattern_start_x + (x + 1) * square_length center_y = pattern_start_y - y * square_length path = c.beginPath() # Draw all black segments for segment in range(0, num_star_segments, 2): path.moveTo(center_x, center_y) sc1 = GetStarCoord(square_length, segment, num_star_segments, center_x, center_y) path.lineTo(sc1[0], sc1[1]) # Add point at the square corner? angle1 = (2 * math.pi) * (segment) / num_star_segments angle2 = (2 * math.pi) * (segment + 1) / num_star_segments if math.floor((angle1 - math.pi / 4) / (math.pi / 2)) != math.floor((angle2 - math.pi / 4) / (math.pi / 2)): corner_angle = (math.pi / 4) + (math.pi / 2) * math.floor((angle2 - math.pi / 4) / (math.pi / 2)) corner_x = math.sin(corner_angle) corner_y = math.cos(corner_angle) normalizer = abs(corner_x) corner_x /= normalizer corner_y /= normalizer corner_coord = (center_x - 0.5 * square_length * corner_x, center_y + 0.5 * square_length * corner_y) path.lineTo(corner_coord[0], corner_coord[1]) sc2 = GetStarCoord(square_length, segment + 1, num_star_segments, center_x, center_y) path.lineTo(sc2[0], sc2[1]) path.lineTo(center_x, center_y) c.drawPath(path, stroke=0, fill=1) # Write metadata with open(metadata_path, 'wb') as metadata_file: metadata_file.write(bytes('num_star_segments: ' + str(num_star_segments) + '\n', 'UTF-8')) metadata_file.write(bytes('squares_x: ' + str(squares_x) + '\n', 'UTF-8')) metadata_file.write(bytes('squares_y: ' + str(squares_y) + '\n', 'UTF-8')) metadata_file.write(bytes('square_length_in_meters: ' + str(0.01 * square_length / cm) + '\n', 'UTF-8')) metadata_file.write(bytes('page:\n', 'UTF-8')) metadata_file.write(bytes(' width_mm: ' + str(width / mm) + '\n', 'UTF-8')) metadata_file.write(bytes(' height_mm: ' + str(height / mm) + '\n', 'UTF-8')) metadata_file.write(bytes(' pattern_start_x_mm: ' + str(pattern_start_x / mm) + '\n', 'UTF-8')) metadata_file.write(bytes(' pattern_start_y_mm: ' + str((height - pattern_start_y) / mm) + '\n', 'UTF-8')) metadata_file.write(bytes(' pattern_end_x_mm: ' + str(pattern_end_x / mm) + '\n', 'UTF-8')) metadata_file.write(bytes(' pattern_end_y_mm: ' + str((height - pattern_end_y) / mm) + '\n', 'UTF-8')) metadata_file.write(bytes('apriltags:\n', 'UTF-8')) metadata_file.write(bytes(' - tag_x: ' + str(tag_x) + '\n', 'UTF-8')) metadata_file.write(bytes(' tag_y: ' + str(tag_y) + '\n', 'UTF-8')) metadata_file.write(bytes(' width: ' + str(apriltag_length_in_squares) + '\n', 'UTF-8')) metadata_file.write(bytes(' height: ' + str(apriltag_length_in_squares) + '\n', 'UTF-8')) metadata_file.write(bytes(' index: ' + str(apriltag_index) + '\n', 'UTF-8')) # Save the page c.setTitle('Calibration pattern #' + str(apriltag_index)) c.setAuthor('Calibration pattern generation script') c.showPage() c.save() print('Successfully generated pattern:\n' + pdf_path + '\nwith metadata:\n' + metadata_path)

43.822581

157

0.688075

5957f574864cfc36583d783bd5ac791643dbe615

605

py

Python

haokan.py

squ33ker/Dlink_Parse

b8ea35e64e480720fff5f466c3959e631b379abf

[ "MIT" ]

142

2021-04-10T01:54:06.000Z

2022-03-29T11:22:43.000Z

haokan.py

squ33ker/Dlink_Parse

b8ea35e64e480720fff5f466c3959e631b379abf

[ "MIT" ]

4

2021-04-11T00:50:30.000Z

2021-09-14T13:00:56.000Z

haokan.py

squ33ker/Dlink_Parse

b8ea35e64e480720fff5f466c3959e631b379abf

[ "MIT" ]

57

2021-05-21T09:58:12.000Z

2022-03-31T06:49:01.000Z

import requests import re import json class haokan: def __init__(self, url): self.url = url self.headers = { "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 11_2_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36¬", } def getJson(self, text): res = re.findall("PRELOADED_STATE__\s=\s(.*?);", text)[0] res = json.loads(res) return res def start(self): res = requests.get(self.url, headers=self.headers).text return self.getJson(res) if __name__ == '__main__': haokan().start()

24.2

149

0.601653

a7a404ccde43b5c0cef09258b3ff7c189c0ea9a1

2,234

py

Python

euca2ools/commands/iam/listmfadevices.py

sjones4/euca2ools

03b0e421eeebd8f402422a0ad6994bd6ee4e4127

[ "BSD-2-Clause" ]

null

euca2ools/commands/iam/listmfadevices.py

sjones4/euca2ools

03b0e421eeebd8f402422a0ad6994bd6ee4e4127

[ "BSD-2-Clause" ]

null

euca2ools/commands/iam/listmfadevices.py

sjones4/euca2ools

03b0e421eeebd8f402422a0ad6994bd6ee4e4127

[ "BSD-2-Clause" ]

2

2016-06-24T20:19:40.000Z

2020-02-05T10:50:19.000Z

# Copyright 2009-2013 Eucalyptus Systems, Inc. # # Redistribution and use of this software 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 # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. from euca2ools.commands.iam import IAMRequest, AS_ACCOUNT from requestbuilder import Arg from requestbuilder.response import PaginatedResponse class ListMFADevices(IAMRequest): DESCRIPTION = "List a user's MFA devices" ARGS = [Arg('-u', '--user-name', dest='UserName', metavar='USER', help='user to list MFA devices for (default: current user)'), AS_ACCOUNT] LIST_TAGS = ['MFADevices'] def main(self): return PaginatedResponse(self, (None,), ('MFADevices',)) def prepare_for_page(self, page): # Pages are defined by markers self.params['Marker'] = page def get_next_page(self, response): if response.get('IsTruncated') == 'true': return response['Marker'] def print_result(self, result): for device in result.get('MFADevices', []): print device['SerialNumber']

42.961538

77

0.732319

ccc97c5de0ae0a6490739b53580e96de914abfa2

355

py

Python

python/148-SortList.py

vermouth1992/Leetcode

0d7dda52b12f9e01d88fc279243742cd8b4bcfd1

[ "MIT" ]

null

python/148-SortList.py

vermouth1992/Leetcode

0d7dda52b12f9e01d88fc279243742cd8b4bcfd1

[ "MIT" ]

null

python/148-SortList.py

vermouth1992/Leetcode

0d7dda52b12f9e01d88fc279243742cd8b4bcfd1

[ "MIT" ]

null

""" Sort a linked list in O(n log n) time using constant space complexity. """ # Definition for singly-linked list. class ListNode(object): def __init__(self, x): self.val = x self.next = None class Solution(object): def sortList(self, head): """ :type head: ListNode :rtype: ListNode """

20.882353

70

0.580282

c9fb10966cd84268c6d017f0795fb65c86504e1d

3,033

py

Python

pyOCD/flash/flash_stm32f031.py

mesheven/mesh-pyocd-old

99ecfeeac95820dacab52a1280b0fba6d4f51fc9

[ "Apache-2.0" ]

null

pyOCD/flash/flash_stm32f031.py

mesheven/mesh-pyocd-old

99ecfeeac95820dacab52a1280b0fba6d4f51fc9

[ "Apache-2.0" ]

null

pyOCD/flash/flash_stm32f031.py

mesheven/mesh-pyocd-old

99ecfeeac95820dacab52a1280b0fba6d4f51fc9

[ "Apache-2.0" ]

null

""" mbed CMSIS-DAP debugger Copyright (c) 2006-2013 ARM Limited 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 flash import Flash flash_algo = { 'load_address' : 0x20000000, 'instructions' : [ 0xE00ABE00, 0x062D780D, 0x24084068, 0xD3000040, 0x1E644058, 0x1C49D1FA, 0x2A001E52, 0x4770D1F2, 0x49544853, 0x48546048, 0x20006048, 0xb5104770, 0x20344603, 0x60e04c4f, 0xbd102000, 0x20004601, 0xb5004770, 0x23002200, 0x6902484a, 0x40102080, 0xd1012880, 0xffe4f7ff, 0x4846bf00, 0x07d868c3, 0xd1fa0fc0, 0x69024843, 0x43022004, 0x61024841, 0x20406902, 0x483f4302, 0xbf006102, 0x68c3483d, 0x0fc007d8, 0x483bd1fa, 0x21046902, 0x43884610, 0x48384602, 0x20006102, 0xb510bd00, 0x22004603, 0x48342400, 0x20806902, 0x28804010, 0xf7ffd101, 0xbf00ffb7, 0x68c4482f, 0x0fc007e0, 0x482dd1fa, 0x20026902, 0x482b4302, 0x61436102, 0x20406902, 0x48284302, 0xbf006102, 0x68c44826, 0x0fc007e0, 0x4824d1fa, 0x21026902, 0x43884610, 0x48214602, 0x20006102, 0xb5f7bd10, 0x22004615, 0x27002600, 0x462c9b00, 0x6902481b, 0x40102080, 0xd1012880, 0xff86f7ff, 0x4817bf00, 0x07f068c6, 0xd1fa0fc0, 0x4814e01b, 0x20016902, 0x48124302, 0x88206102, 0xbf008018, 0x68c6480f, 0x0fc007f0, 0x8820d1fa, 0x42888819, 0x480bd006, 0x08526902, 0x61020052, 0xbdfe2001, 0x1ca41c9b, 0x98011c7f, 0x42b80840, 0x4804d8df, 0x08526902, 0x61020052, 0xe7f02000, 0x45670123, 0x40022000, 0xcdef89ab, 0x00000000, ], 'pc_init' : 0x2000002F, 'pc_eraseAll' : 0x20000043, 'pc_erase_sector' : 0x2000009B, 'pc_program_page' : 0x200000F7, 'static_base' : 0x200001A0, 'begin_data' : 0x20000600, # Analyzer uses a max of 256 B data (32 pages * 4 bytes / page) 'begin_stack' : 0x20000600, 'min_program_length' : 2, 'analyzer_supported' : True, 'analyzer_address' : 0x20000A00 # Analyzer, 0x20000A00--0x20001000 }; class Flash_stm32f031(Flash): def __init__(self, target): super(Flash_stm32f031, self).__init__(target, flash_algo)

54.160714

130

0.616551

ebe7869b55db4736175b00562c3b025ff0c9dd11

3,136

py

Python

rstem/projects/space_invaders/space_invaders_2.py

readysetstem/readysetstem-api

01e1360f4a28a6783ee1e0fa1bc239dd999de6be

[ "Apache-2.0" ]

1

2018-02-23T20:20:45.000Z

rstem/projects/space_invaders/space_invaders_2.py

readysetstem/readysetstem-api

01e1360f4a28a6783ee1e0fa1bc239dd999de6be

[ "Apache-2.0" ]

1

2016-10-25T18:00:15.000Z

rstem/projects/space_invaders/space_invaders_2.py

readysetstem/readysetstem-api

01e1360f4a28a6783ee1e0fa1bc239dd999de6be

[ "Apache-2.0" ]

null

# # Copyright (c) 2014, Scott Silver Labs, 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. # #This is the second project in the "Space invaders" series. #This project adds missles to the game when the player #presses the button. #Imports, need sys for exit function from rstem import led_matrix from rstem import accel import RPi.GPIO as GPIO import time #Initialize matrix, accelerometer, and GPIO, the matrix layout and accelerometer channel may changes from user to user led_matrix.init_grid(2) accel.init(1) GPIO.setmode(GPIO.BCM) GPIO.setup(4, GPIO.IN, pull_up_down = GPIO.PUD_UP) #Game entity data player_pos = [7, 0] missles = [] #Game timing data, missles get updated and therefore move roughly sixty times faster than enemies initialy game_tick = 0 game_tick_max = 64 enemy_tick = 60 start_time = time.time() #Function to add missle at the players position to set of current missles def fire_missle(channel): missles.append(Missle([int(round(player_pos[0])), int(round(player_pos[1]))],[0, 1])) #Call fire_missle when fire button is pressed GPIO.add_event_detect(4, GPIO.FALLING, callback=fire_missle, bouncetime = 300) #Useful clamp function to make sure the data passed to point is on the matrix def clamp(x): return max(0, min(x, 15)) #Missle keeps track of its current position and current direction class Missle: def __init__(self, position, direction): self.pos = position self.dir = direction # Move missle on missle update tick def update(self): self.pos[0] = self.pos[0] + self.dir[0] self.pos[1] = self.pos[1] + self.dir[1] if self.pos[1] > 15 or self.pos[1] < 0 or self.pos[0] < 0 or self.pos[1] > 15: missles.remove(self) try: # Start game while True: # Clear previous framebuffer led_matrix.fill(0) # Update and redraw missles for m in missles: m.update() led_matrix.point(m.pos[0], m.pos[1]) # Get angles from accelerometer data = accel.angles() # Generate smooth movement data using IIR filter, and make a 1/4 turn move # the player to the edge of the screen player_pos[0] = player_pos[0] + (clamp(data[0]*8*4/90 + 7) - player_pos[0])*0.1 # Draw player led_matrix.point(int(round(player_pos[0])), int(round(player_pos[1]))) # Show framebuffer led_matrix.show() # Delay one game tick, in this case 1ms time.sleep(0.001) #Stop if player hits Ctrl-C except KeyboardInterrupt: pass #Clean everything up finally: GPIO.cleanup() led_matrix.cleanup()

30.745098

118

0.691008

5d505065709ebdb10e4155b2df0e09bd1687992b

2,545

py

Python

user.py

Brian-M-code/password-locker

12ae594f310baa72dd7df6b7f91749418f0b1925

[ "MIT" ]

null

user.py

Brian-M-code/password-locker

12ae594f310baa72dd7df6b7f91749418f0b1925

[ "MIT" ]

null

user.py

Brian-M-code/password-locker

12ae594f310baa72dd7df6b7f91749418f0b1925

[ "MIT" ]

null

import string import secrets import pyperclip import random class User: # Class Variables # global users_list users_list = [] def __init__(self,first_name,last_name,password): ''' Method to define the properties for each user object will hold. ''' # instance variables self.first_name = first_name self.last_name = last_name self.password = password def save_user(self): ''' Function to save a newly created user instance ''' User.users_list.append(self) class Credential: ''' Class to create account credentials, generate passwords and save their information ''' # Class Variables credentials_list =[] user_credentials_list = [] @classmethod def check_user(cls,first_name,password): ''' Method that checks if the name and password entered match entries in the users_list ''' current_user = '' for user in User.users_list: if (user.first_name == first_name and user.password == password): current_user = user.first_name return current_user def __init__(self,user_name,site_name,account_name,password): ''' Method to define the properties for each user object will hold. ''' # instance variables self.user_name = user_name self.site_name = site_name self.account_name = account_name self.password = password def save_credentials(self): ''' Function to save a newly created user instance ''' # global users_list Credential.credentials_list.append(self) def generate_password(self, char=string.ascii_uppercase+string.ascii_lowercase+string.digits): ''' Function to generate an 8 character password for a credential ''' gen_pass=''.join(random.choice(char) for _ in range()) return gen_pass @classmethod def display_credentials(cls,user_name): ''' Class method to display the list of credentials saved ''' user_credentials_list = [] for credential in cls.credentials_list: if credential.user_name == user_name: user_credentials_list.append(credential) return user_credentials_list @classmethod def find_by_site_name(cls, site_name): ''' Method that takes in a site_name and returns a credential that matches that site_name. ''' for credential in cls.credentials_list: if credential.site_name == site_name: return credential @classmethod def copy_credential(cls,site_name): ''' Class method that copies a credential's info after the credential's site name is entered ''' find_credential = Credential.find_by_site_name(site_name) return pyperclip.copy(find_credential.password)

24.95098

95

0.743811

ece5985dc043465f4b258f6d25cdc513fb0bf48d

16,355

py

Python

tools/test_files/test_vault/less_than_10/rd53_133.py

Astlaan/OpenQL

404b3edf4406071992e9ad190303b12e143689a0

[ "Apache-2.0" ]

null

tools/test_files/test_vault/less_than_10/rd53_133.py

Astlaan/OpenQL

404b3edf4406071992e9ad190303b12e143689a0

[ "Apache-2.0" ]

null

tools/test_files/test_vault/less_than_10/rd53_133.py

Astlaan/OpenQL

404b3edf4406071992e9ad190303b12e143689a0

[ "Apache-2.0" ]

null

from openql import openql as ql import os import argparse def circuit(config_file, new_scheduler='yes', scheduler='ASAP', uniform_sched= 'no', sched_commute = 'yes', mapper='base', moves='no', maptiebreak='random', initial_placement='no', output_dir_name='test_output', optimize='no', measurement=True, log_level='LOG_WARNING'): curdir = os.path.dirname(__file__) output_dir = os.path.join(curdir, output_dir_name) ql.set_option('output_dir', output_dir) ql.set_option('optimize', optimize) ql.set_option('scheduler', scheduler) ql.set_option('scheduler_uniform', uniform_sched) ql.set_option('mapper', mapper) ql.set_option('initialplace', initial_placement) ql.set_option('log_level', log_level) ql.set_option('scheduler_post179', new_scheduler) ql.set_option('scheduler_commute', sched_commute) ql.set_option('mapusemoves', moves) ql.set_option('maptiebreak', maptiebreak) config_fn = os.path.join(curdir, config_file) # platform = ql.Platform('platform_none', config_fn) platform = ql.Platform('starmon', config_fn) num_circuits = 1 num_qubits = 7 p = ql.Program('rd53_133', platform, num_qubits) k = ql.Kernel('rd53_133', platform, num_qubits) k.gate('h',[6]) k.gate('t',[3]) k.gate('t',[5]) k.gate('t',[6]) k.gate('cnot',[5,3]) k.gate('cnot',[6,5]) k.gate('cnot',[3,6]) k.gate('tdag',[5]) k.gate('cnot',[3,5]) k.gate('tdag',[3]) k.gate('tdag',[5]) k.gate('t',[6]) k.gate('cnot',[6,5]) k.gate('cnot',[3,6]) k.gate('cnot',[5,3]) k.gate('h',[6]) k.gate('h',[5]) k.gate('t',[2]) k.gate('t',[4]) k.gate('t',[5]) k.gate('cnot',[4,2]) k.gate('cnot',[5,4]) k.gate('cnot',[2,5]) k.gate('tdag',[4]) k.gate('cnot',[2,4]) k.gate('tdag',[2]) k.gate('tdag',[4]) k.gate('t',[5]) k.gate('cnot',[5,4]) k.gate('cnot',[2,5]) k.gate('cnot',[4,2]) k.gate('h',[5]) k.gate('h',[4]) k.gate('t',[0]) k.gate('t',[1]) k.gate('t',[4]) k.gate('cnot',[1,0]) k.gate('cnot',[4,1]) k.gate('cnot',[0,4]) k.gate('tdag',[1]) k.gate('cnot',[0,1]) k.gate('tdag',[0]) k.gate('tdag',[1]) k.gate('t',[4]) k.gate('cnot',[4,1]) k.gate('cnot',[0,4]) k.gate('cnot',[1,0]) k.gate('h',[4]) k.gate('h',[5]) k.gate('t',[2]) k.gate('t',[4]) k.gate('t',[5]) k.gate('cnot',[4,2]) k.gate('cnot',[5,4]) k.gate('cnot',[2,5]) k.gate('tdag',[4]) k.gate('cnot',[2,4]) k.gate('tdag',[2]) k.gate('tdag',[4]) k.gate('t',[5]) k.gate('cnot',[5,4]) k.gate('cnot',[2,5]) k.gate('cnot',[4,2]) k.gate('h',[5]) k.gate('h',[6]) k.gate('t',[3]) k.gate('t',[5]) k.gate('t',[6]) k.gate('cnot',[5,3]) k.gate('cnot',[6,5]) k.gate('cnot',[3,6]) k.gate('tdag',[5]) k.gate('cnot',[3,5]) k.gate('tdag',[3]) k.gate('tdag',[5]) k.gate('t',[6]) k.gate('cnot',[6,5]) k.gate('cnot',[3,6]) k.gate('cnot',[5,3]) k.gate('h',[6]) k.gate('h',[5]) k.gate('t',[2]) k.gate('t',[4]) k.gate('t',[5]) k.gate('cnot',[4,2]) k.gate('cnot',[5,4]) k.gate('cnot',[2,5]) k.gate('tdag',[4]) k.gate('cnot',[2,4]) k.gate('tdag',[2]) k.gate('tdag',[4]) k.gate('t',[5]) k.gate('cnot',[5,4]) k.gate('cnot',[2,5]) k.gate('cnot',[4,2]) k.gate('h',[5]) k.gate('h',[4]) k.gate('t',[0]) k.gate('t',[1]) k.gate('t',[4]) k.gate('cnot',[1,0]) k.gate('cnot',[4,1]) k.gate('cnot',[0,4]) k.gate('tdag',[1]) k.gate('cnot',[0,1]) k.gate('tdag',[0]) k.gate('tdag',[1]) k.gate('t',[4]) k.gate('cnot',[4,1]) k.gate('cnot',[0,4]) k.gate('cnot',[1,0]) k.gate('h',[4]) k.gate('h',[5]) k.gate('t',[2]) k.gate('t',[4]) k.gate('t',[5]) k.gate('cnot',[4,2]) k.gate('cnot',[5,4]) k.gate('cnot',[2,5]) k.gate('tdag',[4]) k.gate('cnot',[2,4]) k.gate('tdag',[2]) k.gate('tdag',[4]) k.gate('t',[5]) k.gate('cnot',[5,4]) k.gate('cnot',[2,5]) k.gate('cnot',[4,2]) k.gate('h',[5]) k.gate('h',[6]) k.gate('t',[4]) k.gate('t',[5]) k.gate('t',[6]) k.gate('cnot',[5,4]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('tdag',[5]) k.gate('cnot',[4,5]) k.gate('tdag',[4]) k.gate('tdag',[5]) k.gate('t',[6]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('cnot',[5,4]) k.gate('h',[6]) k.gate('h',[5]) k.gate('t',[2]) k.gate('t',[3]) k.gate('t',[5]) k.gate('cnot',[3,2]) k.gate('cnot',[5,3]) k.gate('cnot',[2,5]) k.gate('tdag',[3]) k.gate('cnot',[2,3]) k.gate('tdag',[2]) k.gate('tdag',[3]) k.gate('t',[5]) k.gate('cnot',[5,3]) k.gate('cnot',[2,5]) k.gate('cnot',[3,2]) k.gate('h',[5]) k.gate('h',[3]) k.gate('t',[0]) k.gate('t',[1]) k.gate('t',[3]) k.gate('cnot',[1,0]) k.gate('cnot',[3,1]) k.gate('cnot',[0,3]) k.gate('tdag',[1]) k.gate('cnot',[0,1]) k.gate('tdag',[0]) k.gate('tdag',[1]) k.gate('t',[3]) k.gate('cnot',[3,1]) k.gate('cnot',[0,3]) k.gate('cnot',[1,0]) k.gate('h',[3]) k.gate('h',[5]) k.gate('t',[2]) k.gate('t',[3]) k.gate('t',[5]) k.gate('cnot',[3,2]) k.gate('cnot',[5,3]) k.gate('cnot',[2,5]) k.gate('tdag',[3]) k.gate('cnot',[2,3]) k.gate('tdag',[2]) k.gate('tdag',[3]) k.gate('t',[5]) k.gate('cnot',[5,3]) k.gate('cnot',[2,5]) k.gate('cnot',[3,2]) k.gate('h',[5]) k.gate('h',[6]) k.gate('t',[4]) k.gate('t',[5]) k.gate('t',[6]) k.gate('cnot',[5,4]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('tdag',[5]) k.gate('cnot',[4,5]) k.gate('tdag',[4]) k.gate('tdag',[5]) k.gate('t',[6]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('cnot',[5,4]) k.gate('h',[6]) k.gate('h',[5]) k.gate('t',[2]) k.gate('t',[3]) k.gate('t',[5]) k.gate('cnot',[3,2]) k.gate('cnot',[5,3]) k.gate('cnot',[2,5]) k.gate('tdag',[3]) k.gate('cnot',[2,3]) k.gate('tdag',[2]) k.gate('tdag',[3]) k.gate('t',[5]) k.gate('cnot',[5,3]) k.gate('cnot',[2,5]) k.gate('cnot',[3,2]) k.gate('h',[5]) k.gate('h',[3]) k.gate('t',[0]) k.gate('t',[1]) k.gate('t',[3]) k.gate('cnot',[1,0]) k.gate('cnot',[3,1]) k.gate('cnot',[0,3]) k.gate('tdag',[1]) k.gate('cnot',[0,1]) k.gate('tdag',[0]) k.gate('tdag',[1]) k.gate('t',[3]) k.gate('cnot',[3,1]) k.gate('cnot',[0,3]) k.gate('cnot',[1,0]) k.gate('h',[3]) k.gate('h',[5]) k.gate('t',[2]) k.gate('t',[3]) k.gate('t',[5]) k.gate('cnot',[3,2]) k.gate('cnot',[5,3]) k.gate('cnot',[2,5]) k.gate('tdag',[3]) k.gate('cnot',[2,3]) k.gate('tdag',[2]) k.gate('tdag',[3]) k.gate('t',[5]) k.gate('cnot',[5,3]) k.gate('cnot',[2,5]) k.gate('cnot',[3,2]) k.gate('h',[5]) k.gate('h',[6]) k.gate('t',[4]) k.gate('t',[5]) k.gate('t',[6]) k.gate('cnot',[5,4]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('tdag',[5]) k.gate('cnot',[4,5]) k.gate('tdag',[4]) k.gate('tdag',[5]) k.gate('t',[6]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('cnot',[5,4]) k.gate('h',[6]) k.gate('h',[5]) k.gate('t',[3]) k.gate('t',[0]) k.gate('t',[5]) k.gate('cnot',[0,3]) k.gate('cnot',[5,0]) k.gate('cnot',[3,5]) k.gate('tdag',[0]) k.gate('cnot',[3,0]) k.gate('tdag',[3]) k.gate('tdag',[0]) k.gate('t',[5]) k.gate('cnot',[5,0]) k.gate('cnot',[3,5]) k.gate('cnot',[0,3]) k.gate('h',[5]) k.gate('h',[0]) k.gate('t',[1]) k.gate('t',[2]) k.gate('t',[0]) k.gate('cnot',[2,1]) k.gate('cnot',[0,2]) k.gate('cnot',[1,0]) k.gate('tdag',[2]) k.gate('cnot',[1,2]) k.gate('tdag',[1]) k.gate('tdag',[2]) k.gate('t',[0]) k.gate('cnot',[0,2]) k.gate('cnot',[1,0]) k.gate('cnot',[2,1]) k.gate('h',[0]) k.gate('h',[5]) k.gate('t',[3]) k.gate('t',[0]) k.gate('t',[5]) k.gate('cnot',[0,3]) k.gate('cnot',[5,0]) k.gate('cnot',[3,5]) k.gate('tdag',[0]) k.gate('cnot',[3,0]) k.gate('tdag',[3]) k.gate('tdag',[0]) k.gate('t',[5]) k.gate('cnot',[5,0]) k.gate('cnot',[3,5]) k.gate('cnot',[0,3]) k.gate('h',[5]) k.gate('h',[6]) k.gate('t',[4]) k.gate('t',[5]) k.gate('t',[6]) k.gate('cnot',[5,4]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('tdag',[5]) k.gate('cnot',[4,5]) k.gate('tdag',[4]) k.gate('tdag',[5]) k.gate('t',[6]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('cnot',[5,4]) k.gate('h',[6]) k.gate('h',[5]) k.gate('t',[3]) k.gate('t',[0]) k.gate('t',[5]) k.gate('cnot',[0,3]) k.gate('cnot',[5,0]) k.gate('cnot',[3,5]) k.gate('tdag',[0]) k.gate('cnot',[3,0]) k.gate('tdag',[3]) k.gate('tdag',[0]) k.gate('t',[5]) k.gate('cnot',[5,0]) k.gate('cnot',[3,5]) k.gate('cnot',[0,3]) k.gate('h',[5]) k.gate('h',[0]) k.gate('t',[1]) k.gate('t',[2]) k.gate('t',[0]) k.gate('cnot',[2,1]) k.gate('cnot',[0,2]) k.gate('cnot',[1,0]) k.gate('tdag',[2]) k.gate('cnot',[1,2]) k.gate('tdag',[1]) k.gate('tdag',[2]) k.gate('t',[0]) k.gate('cnot',[0,2]) k.gate('cnot',[1,0]) k.gate('cnot',[2,1]) k.gate('h',[0]) k.gate('h',[5]) k.gate('t',[3]) k.gate('t',[0]) k.gate('t',[5]) k.gate('cnot',[0,3]) k.gate('cnot',[5,0]) k.gate('cnot',[3,5]) k.gate('tdag',[0]) k.gate('cnot',[3,0]) k.gate('tdag',[3]) k.gate('tdag',[0]) k.gate('t',[5]) k.gate('cnot',[5,0]) k.gate('cnot',[3,5]) k.gate('cnot',[0,3]) k.gate('h',[5]) k.gate('h',[5]) k.gate('t',[1]) k.gate('t',[2]) k.gate('t',[5]) k.gate('cnot',[2,1]) k.gate('cnot',[5,2]) k.gate('cnot',[1,5]) k.gate('tdag',[2]) k.gate('cnot',[1,2]) k.gate('tdag',[1]) k.gate('tdag',[2]) k.gate('t',[5]) k.gate('cnot',[5,2]) k.gate('cnot',[1,5]) k.gate('cnot',[2,1]) k.gate('h',[5]) k.gate('cnot',[2,1]) k.gate('h',[6]) k.gate('t',[4]) k.gate('t',[5]) k.gate('t',[6]) k.gate('cnot',[5,4]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('tdag',[5]) k.gate('cnot',[4,5]) k.gate('tdag',[4]) k.gate('tdag',[5]) k.gate('t',[6]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('cnot',[5,4]) k.gate('h',[6]) k.gate('h',[5]) k.gate('t',[3]) k.gate('t',[2]) k.gate('t',[5]) k.gate('cnot',[2,3]) k.gate('cnot',[5,2]) k.gate('cnot',[3,5]) k.gate('tdag',[2]) k.gate('cnot',[3,2]) k.gate('tdag',[3]) k.gate('tdag',[2]) k.gate('t',[5]) k.gate('cnot',[5,2]) k.gate('cnot',[3,5]) k.gate('cnot',[2,3]) k.gate('h',[5]) k.gate('h',[2]) k.gate('t',[0]) k.gate('t',[1]) k.gate('t',[2]) k.gate('cnot',[1,0]) k.gate('cnot',[2,1]) k.gate('cnot',[0,2]) k.gate('tdag',[1]) k.gate('cnot',[0,1]) k.gate('tdag',[0]) k.gate('tdag',[1]) k.gate('t',[2]) k.gate('cnot',[2,1]) k.gate('cnot',[0,2]) k.gate('cnot',[1,0]) k.gate('h',[2]) k.gate('h',[5]) k.gate('t',[3]) k.gate('t',[2]) k.gate('t',[5]) k.gate('cnot',[2,3]) k.gate('cnot',[5,2]) k.gate('cnot',[3,5]) k.gate('tdag',[2]) k.gate('cnot',[3,2]) k.gate('tdag',[3]) k.gate('tdag',[2]) k.gate('t',[5]) k.gate('cnot',[5,2]) k.gate('cnot',[3,5]) k.gate('cnot',[2,3]) k.gate('h',[5]) k.gate('h',[6]) k.gate('t',[4]) k.gate('t',[5]) k.gate('t',[6]) k.gate('cnot',[5,4]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('tdag',[5]) k.gate('cnot',[4,5]) k.gate('tdag',[4]) k.gate('tdag',[5]) k.gate('t',[6]) k.gate('cnot',[6,5]) k.gate('cnot',[4,6]) k.gate('cnot',[5,4]) k.gate('h',[6]) k.gate('h',[5]) k.gate('t',[3]) k.gate('t',[2]) k.gate('t',[5]) k.gate('cnot',[2,3]) k.gate('cnot',[5,2]) k.gate('cnot',[3,5]) k.gate('tdag',[2]) k.gate('cnot',[3,2]) k.gate('tdag',[3]) k.gate('tdag',[2]) k.gate('t',[5]) k.gate('cnot',[5,2]) k.gate('cnot',[3,5]) k.gate('cnot',[2,3]) k.gate('h',[5]) k.gate('h',[2]) k.gate('t',[0]) k.gate('t',[1]) k.gate('t',[2]) k.gate('cnot',[1,0]) k.gate('cnot',[2,1]) k.gate('cnot',[0,2]) k.gate('tdag',[1]) k.gate('cnot',[0,1]) k.gate('tdag',[0]) k.gate('tdag',[1]) k.gate('t',[2]) k.gate('cnot',[2,1]) k.gate('cnot',[0,2]) k.gate('cnot',[1,0]) k.gate('h',[2]) k.gate('h',[5]) k.gate('t',[3]) k.gate('t',[2]) k.gate('t',[5]) k.gate('cnot',[2,3]) k.gate('cnot',[5,2]) k.gate('cnot',[3,5]) k.gate('tdag',[2]) k.gate('cnot',[3,2]) k.gate('tdag',[3]) k.gate('tdag',[2]) k.gate('t',[5]) k.gate('cnot',[5,2]) k.gate('cnot',[3,5]) k.gate('cnot',[2,3]) k.gate('h',[5]) k.gate('h',[5]) k.gate('t',[0]) k.gate('t',[1]) k.gate('t',[5]) k.gate('cnot',[1,0]) k.gate('cnot',[5,1]) k.gate('cnot',[0,5]) k.gate('tdag',[1]) k.gate('cnot',[0,1]) k.gate('tdag',[0]) k.gate('tdag',[1]) k.gate('t',[5]) k.gate('cnot',[5,1]) k.gate('cnot',[0,5]) k.gate('cnot',[1,0]) k.gate('h',[5]) k.gate('cnot',[1,0]) k.gate('h',[5]) k.gate('t',[0]) k.gate('t',[3]) k.gate('t',[5]) k.gate('cnot',[3,0]) k.gate('cnot',[5,3]) k.gate('cnot',[0,5]) k.gate('tdag',[3]) k.gate('cnot',[0,3]) k.gate('tdag',[0]) k.gate('tdag',[3]) k.gate('t',[5]) k.gate('cnot',[5,3]) k.gate('cnot',[0,5]) k.gate('cnot',[3,0]) k.gate('h',[5]) k.gate('cnot',[0,3]) k.gate('h',[5]) k.gate('t',[3]) k.gate('t',[4]) k.gate('t',[5]) k.gate('cnot',[4,3]) k.gate('cnot',[5,4]) k.gate('cnot',[3,5]) k.gate('tdag',[4]) k.gate('cnot',[3,4]) k.gate('tdag',[3]) k.gate('tdag',[4]) k.gate('t',[5]) k.gate('cnot',[5,4]) k.gate('cnot',[3,5]) k.gate('cnot',[4,3]) k.gate('h',[5]) k.gate('cnot',[3,4]) if measurement: for q in range(num_qubits): k.gate('measure', [q]) p.add_kernel(k) p.compile() ql.set_option('mapper', 'no') if __name__ == '__main__': parser = argparse.ArgumentParser(description='OpenQL compilation of a Quantum Algorithm') parser.add_argument('config_file', help='Path to the OpenQL configuration file to compile this algorithm') parser.add_argument('--new_scheduler', nargs='?', default='yes', help='Scheduler defined by Hans') parser.add_argument('--scheduler', nargs='?', default='ASAP', help='Scheduler specification (ASAP (default), ALAP, ...)') parser.add_argument('--uniform_sched', nargs='?', default='no', help='Uniform shceduler actication (yes or no)') parser.add_argument('--sched_commute', nargs='?', default='yes', help='Permits two-qubit gates to be commutable') parser.add_argument('--mapper', nargs='?', default='base', help='Mapper specification (base, minextend, minextendrc)') parser.add_argument('--moves', nargs='?', default='no', help='Let the use of moves') parser.add_argument('--maptiebreak', nargs='?', default='random', help='') parser.add_argument('--initial_placement', nargs='?', default='no', help='Initial placement specification (yes or no)') parser.add_argument('--out_dir', nargs='?', default='test_output', help='Folder name to store the compilation') parser.add_argument('--measurement', nargs='?', default=True, help='Add measurement to all the qubits in the end of the algorithm') args = parser.parse_args() try: circuit(args.config_file, args.new_scheduler, args.scheduler, args.uniform_sched, args.sched_commute, args.mapper, args.moves, args.maptiebreak, args.initial_placement, args.out_dir) except TypeError: print('\nCompiled, but some gate is not defined in the configuration file. \nThe gate will be invoked like it is.') raise

25.755906

270

0.467441

d02c5af1eb177db138aeed094c68cd69db10c397

5,860

py

Python

nbdev/sync.py

theccalderon/nbdev

59a49fbc587894d7ef73970e762cca8c92cf5a13

[ "Apache-2.0" ]

1

2021-02-15T05:48:35.000Z

nbdev/sync.py

bhoov/nbdev

0e071dc35c7cafebd7945367badb5894cab21c2e

[ "Apache-2.0" ]

2

2021-09-28T01:11:23.000Z

2022-02-26T06:50:19.000Z

nbdev/sync.py

bhoov/nbdev

0e071dc35c7cafebd7945367badb5894cab21c2e

[ "Apache-2.0" ]

null

# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/01_sync.ipynb (unless otherwise specified). __all__ = ['get_name', 'qual_name', 'source_nb', 'relimport2name', 'script2notebook', 'diff_nb_script'] # Cell from .imports import * from .export import * # Cell def _get_property_name(p): "Get the name of property `p`" if hasattr(p, 'fget'): return p.fget.func.__qualname__ if hasattr(p.fget, 'func') else p.fget.__qualname__ else: return next(iter(re.findall(r'\'(.*)\'', str(p)))).split('.')[-1] def get_name(obj): "Get the name of `obj`" if hasattr(obj, '__name__'): return obj.__name__ elif getattr(obj, '_name', False): return obj._name elif hasattr(obj,'__origin__'): return str(obj.__origin__).split('.')[-1] #for types elif type(obj)==property: return _get_property_name(obj) else: return str(obj).split('.')[-1] # Cell def qual_name(obj): "Get the qualified name of `obj`" if hasattr(obj,'__qualname__'): return obj.__qualname__ if inspect.ismethod(obj): return f"{get_name(obj.__self__)}.{get_name(fn)}" return get_name(obj) # Cell def source_nb(func, is_name=None, return_all=False, mod=None): "Return the name of the notebook where `func` was defined" is_name = is_name or isinstance(func, str) if mod is None: mod = get_nbdev_module() index = mod.index name = func if is_name else qual_name(func) while len(name) > 0: if name in index: return (name,index[name]) if return_all else index[name] name = '.'.join(name.split('.')[:-1]) # Cell _re_cell = re.compile(r'^# Cell|^# Comes from\s+(\S+), cell') # Cell def _split(code): lines = code.split('\n') nbs_path = Config().nbs_path.relative_to(Config().config_file.parent) prefix = '' if nbs_path == Path('.') else f'{nbs_path}/' default_nb = re.search(f'File to edit: {prefix}(\\S+)\\s+', lines[0]).groups()[0] s,res = 1,[] while _re_cell.search(lines[s]) is None: s += 1 e = s+1 while e < len(lines): while e < len(lines) and _re_cell.search(lines[e]) is None: e += 1 grps = _re_cell.search(lines[s]).groups() nb = grps[0] or default_nb content = lines[s+1:e] while len(content) > 1 and content[-1] == '': content = content[:-1] res.append((nb, '\n'.join(content))) s,e = e,e+1 return res # Cell def relimport2name(name, mod_name): "Unwarps a relative import in `name` according to `mod_name`" if mod_name.endswith('.py'): mod_name = mod_name[:-3] mods = mod_name.split(os.path.sep) i = last_index(Config().lib_name, mods) mods = mods[i:] if name=='.': return '.'.join(mods[:-1]) i = 0 while name[i] == '.': i += 1 return '.'.join(mods[:-i] + [name[i:]]) # Cell #Catches any from .bla import something and catches .bla in group 1, the imported thing(s) in group 2. _re_loc_import = re.compile(r'(^\s*)from (\.\S*) import (.*)$') _re_loc_import1 = re.compile(r'(^\s*)import (\.\S*)(.*)$') # Cell def _deal_loc_import(code, fname): def _replace(m): sp,mod,obj = m.groups() return f"{sp}from {relimport2name(mod, fname)} import {obj}" def _replace1(m): sp,mod,end = m.groups() return f"{sp}import {relimport2name(mod, fname)}{end}" return '\n'.join([_re_loc_import1.sub(_replace1, _re_loc_import.sub(_replace,line)) for line in code.split('\n')]) # Cell def _script2notebook(fname, dic, silent=False): "Put the content of `fname` back in the notebooks it came from." if os.environ.get('IN_TEST',0): return # don't export if running tests fname = Path(fname) with open(fname, encoding='utf8') as f: code = f.read() splits = _split(code) rel_name = fname.absolute().resolve().relative_to(Config().lib_path) key = str(rel_name.with_suffix('')) assert len(splits)==len(dic[key]), f"Exported file from notebooks should have {len(dic[fname])} cells but has {len(splits)}." assert all([c1[0]==c2[1]] for c1,c2 in zip(splits, dic[key])) splits = [(c2[0],c1[0],c1[1]) for c1,c2 in zip(splits, dic[key])] nb_fnames = {Config().nbs_path/s[1] for s in splits} for nb_fname in nb_fnames: nb = read_nb(nb_fname) for i,f,c in splits: c = _deal_loc_import(c, str(fname)) if f == nb_fname.name: l = nb['cells'][i]['source'].split('\n')[0] nb['cells'][i]['source'] = l + '\n' + c NotebookNotary().sign(nb) nbformat.write(nb, str(nb_fname), version=4) if not silent: print(f"Converted {rel_name}.") # Cell def script2notebook(fname=None, silent=False): "Update the notebooks from any changes made in the modules corresponding to `fname`" if os.environ.get('IN_TEST',0): return dic = notebook2script(silent=True, to_dict=True) exported = get_nbdev_module().modules if fname is None: files = [f for f in Config().lib_path.glob('**/*.py') if str(f.relative_to(Config().lib_path)) in exported] else: files = glob.glob(fname) [ _script2notebook(f, dic, silent=silent) for f in files] # Cell import subprocess from distutils.dir_util import copy_tree # Cell def diff_nb_script(): "Print the diff between the notebooks and the library in lib_path" lib_folder = Config().lib_path with tempfile.TemporaryDirectory() as d1, tempfile.TemporaryDirectory() as d2: copy_tree(Config().lib_path, d1) notebook2script(silent=True) copy_tree(Config().lib_path, d2) shutil.rmtree(Config().lib_path) shutil.copytree(d1, str(Config().lib_path)) for d in [d1, d2]: if (Path(d)/'__pycache__').exists(): shutil.rmtree(Path(d)/'__pycache__') res = subprocess.run(['diff', '-ru', d1, d2], stdout=subprocess.PIPE) print(res.stdout.decode('utf-8'))

40.413793

129

0.630546

e56185c5f39178ae4229787ab6a81a31bde8ef49

1,167

py

Python

week1/ex6.py

kingpeen/My_Pynet

328b5b3441ace4e3bbd524c726833a077c2e2dd6

[ "Apache-2.0" ]

null

week1/ex6.py

kingpeen/My_Pynet

328b5b3441ace4e3bbd524c726833a077c2e2dd6

[ "Apache-2.0" ]

null

week1/ex6.py

kingpeen/My_Pynet

328b5b3441ace4e3bbd524c726833a077c2e2dd6

[ "Apache-2.0" ]

null

#!/usr/bin/env python ''' Write a Python program that creates a list. One of the elements of the list should be a dictionary with at least two keys. Write this list out to a file using both YAML and JSON formats. The YAML file should be in the expanded form. ''' import yaml import json def main(): ''' Write a Python program that creates a list. One of the elements of the list should be a dictionary with at least two keys. Write this list out to a file using both YAML and JSON formats. The YAML file should be in the expanded form. ''' yaml_file = 'my_test.yml' json_file = 'my_test.json' my_dict = { 'ip_addr': '172.31.200.1', 'platform': 'cisco_ios', 'vendor': 'cisco', 'model': '1921' } my_list = [ 'some string', 99, 18, my_dict, 'another string', 'final string' ] with open(yaml_file, "w") as f: f.write(yaml.dump(my_list, default_flow_style=False)) f.write("############ \n ") yaml.dump(my_list, f) with open(json_file, "w") as f: json.dump(my_list, f) if __name__ == "__main__": main()

23.34

80

0.60497

c831f8ff1cdc1e72723d6a5ac532a984bf6fd3aa

6,498

py

Python

lib/services/vloadbalancer/ncloud_vloadbalancer/model/set_target_group_description_response.py

NaverCloudPlatform/ncloud-sdk-python

5976dfabd205c615fcf57ac2f0ab67313ee6953c

[ "MIT" ]

12

2018-11-20T04:30:49.000Z

2021-11-09T12:34:26.000Z

lib/services/vloadbalancer/ncloud_vloadbalancer/model/set_target_group_description_response.py

NaverCloudPlatform/ncloud-sdk-python

5976dfabd205c615fcf57ac2f0ab67313ee6953c

[ "MIT" ]

1

2019-01-24T15:56:15.000Z

2019-05-31T07:56:55.000Z

lib/services/vloadbalancer/ncloud_vloadbalancer/model/set_target_group_description_response.py

NaverCloudPlatform/ncloud-sdk-python

5976dfabd205c615fcf57ac2f0ab67313ee6953c

[ "MIT" ]

6

2018-06-29T03:45:50.000Z

2022-03-18T01:51:45.000Z

# coding: utf-8 """ vloadbalancer Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six from ncloud_vloadbalancer.model.target_group import TargetGroup # noqa: F401,E501 class SetTargetGroupDescriptionResponse(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 = { 'request_id': 'str', 'return_code': 'str', 'return_message': 'str', 'total_rows': 'int', 'target_group_list': 'list[TargetGroup]' } attribute_map = { 'request_id': 'requestId', 'return_code': 'returnCode', 'return_message': 'returnMessage', 'total_rows': 'totalRows', 'target_group_list': 'targetGroupList' } def __init__(self, request_id=None, return_code=None, return_message=None, total_rows=None, target_group_list=None): # noqa: E501 """SetTargetGroupDescriptionResponse - a model defined in Swagger""" # noqa: E501 self._request_id = None self._return_code = None self._return_message = None self._total_rows = None self._target_group_list = None self.discriminator = None if request_id is not None: self.request_id = request_id if return_code is not None: self.return_code = return_code if return_message is not None: self.return_message = return_message if total_rows is not None: self.total_rows = total_rows if target_group_list is not None: self.target_group_list = target_group_list @property def request_id(self): """Gets the request_id of this SetTargetGroupDescriptionResponse. # noqa: E501 :return: The request_id of this SetTargetGroupDescriptionResponse. # noqa: E501 :rtype: str """ return self._request_id @request_id.setter def request_id(self, request_id): """Sets the request_id of this SetTargetGroupDescriptionResponse. :param request_id: The request_id of this SetTargetGroupDescriptionResponse. # noqa: E501 :type: str """ self._request_id = request_id @property def return_code(self): """Gets the return_code of this SetTargetGroupDescriptionResponse. # noqa: E501 :return: The return_code of this SetTargetGroupDescriptionResponse. # noqa: E501 :rtype: str """ return self._return_code @return_code.setter def return_code(self, return_code): """Sets the return_code of this SetTargetGroupDescriptionResponse. :param return_code: The return_code of this SetTargetGroupDescriptionResponse. # noqa: E501 :type: str """ self._return_code = return_code @property def return_message(self): """Gets the return_message of this SetTargetGroupDescriptionResponse. # noqa: E501 :return: The return_message of this SetTargetGroupDescriptionResponse. # noqa: E501 :rtype: str """ return self._return_message @return_message.setter def return_message(self, return_message): """Sets the return_message of this SetTargetGroupDescriptionResponse. :param return_message: The return_message of this SetTargetGroupDescriptionResponse. # noqa: E501 :type: str """ self._return_message = return_message @property def total_rows(self): """Gets the total_rows of this SetTargetGroupDescriptionResponse. # noqa: E501 :return: The total_rows of this SetTargetGroupDescriptionResponse. # noqa: E501 :rtype: int """ return self._total_rows @total_rows.setter def total_rows(self, total_rows): """Sets the total_rows of this SetTargetGroupDescriptionResponse. :param total_rows: The total_rows of this SetTargetGroupDescriptionResponse. # noqa: E501 :type: int """ self._total_rows = total_rows @property def target_group_list(self): """Gets the target_group_list of this SetTargetGroupDescriptionResponse. # noqa: E501 :return: The target_group_list of this SetTargetGroupDescriptionResponse. # noqa: E501 :rtype: list[TargetGroup] """ return self._target_group_list @target_group_list.setter def target_group_list(self, target_group_list): """Sets the target_group_list of this SetTargetGroupDescriptionResponse. :param target_group_list: The target_group_list of this SetTargetGroupDescriptionResponse. # noqa: E501 :type: list[TargetGroup] """ self._target_group_list = target_group_list 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 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, SetTargetGroupDescriptionResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

30.083333

134

0.628347

4395c2d4e74cdfd6ac4fe48c550ffe4ae4f0b5be

5,174

py

Python

dead_hosts/launcher/updater/pyfunceble_config.py

dead-hosts/infrastructure-launcher

23287ef68007532958a3385703e01fbab651a0dc

[ "MIT" ]

4

2020-04-20T01:15:44.000Z

2021-06-17T07:55:11.000Z

dead_hosts/launcher/updater/pyfunceble_config.py

dead-hosts/infrastructure-launcher

23287ef68007532958a3385703e01fbab651a0dc

[ "MIT" ]

2

2020-05-21T02:49:24.000Z

2020-06-06T13:06:44.000Z

dead_hosts/launcher/updater/pyfunceble_config.py

dead-hosts/infrastructure-launcher

23287ef68007532958a3385703e01fbab651a0dc

[ "MIT" ]

2

2020-05-21T05:48:35.000Z

2021-07-05T06:47:20.000Z

""" Dead Hosts's launcher - The launcher of the Dead-Hosts infrastructure. Provides the updater of the PyFunceble configuration. Author: Nissar Chababy, @funilrys, contactTATAfunilrysTODTODcom Project link: https://github.com/dead-hosts/infrastructure-launcher License: :: MIT License Copyright (c) 2019, 2020, 2021 Dead Hosts Copyright (c) 2019, 2020. 2021 Nissar Chababy Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import importlib.resources import logging import os from typing import Optional from PyFunceble.helpers.dict import DictHelper from PyFunceble.helpers.file import FileHelper from PyFunceble.helpers.merge import Merge import dead_hosts.launcher.defaults.links import dead_hosts.launcher.defaults.paths import dead_hosts.launcher.defaults.pyfunceble from dead_hosts.launcher.info_manager import InfoManager from dead_hosts.launcher.updater.base import UpdaterBase class PyFuncebleConfigUpdater(UpdaterBase): """ Provides the updated of the PyFunceble configuration. """ def __init__(self, info_manager: InfoManager) -> None: self.pyfunceble_config_file_instance = FileHelper( os.path.join(info_manager.PYFUNCEBLE_CONFIG_DIR, ".PyFunceble.yaml") ) super().__init__(info_manager) @property def authorized(self) -> bool: return not self.info_manager.own_management @staticmethod def get_commit_message(message: str, ping: Optional[str] = None) -> str: """ Provides the commit message to use. """ if ping: return f"{message} | cc {ping} | " return message def pre(self) -> "PyFuncebleConfigUpdater": logging.info( "Started to update %r.", self.pyfunceble_config_file_instance.path, ) return self def post(self) -> "PyFuncebleConfigUpdater": logging.info( "Finished to update %r", self.pyfunceble_config_file_instance.path, ) return self def start(self) -> "PyFuncebleConfigUpdater": with importlib.resources.path( "PyFunceble.data.infrastructure", ".PyFunceble_production.yaml" ) as file_path: local_version = DictHelper( DictHelper().from_yaml_file(str(file_path)) ).flatten() local_version = Merge( dead_hosts.launcher.defaults.pyfunceble.CONFIGURATION ).into(local_version, strict=True) if self.info_manager.custom_pyfunceble_config and isinstance( self.info_manager.custom_pyfunceble_config, dict ): logging.info( "Custom PyFunceble configuration given, " "appending them to the local configuration file." ) local_version = Merge(self.info_manager.custom_pyfunceble_config).into( local_version, strict=True ) if self.info_manager.ping: logging.info("Ping names given, appending them to the commit message.") local_version[ "cli_testing.ci.end_commit_message" ] = self.get_commit_message( local_version["cli_testing.ci.end_commit_message"], ping=self.info_manager.get_ping_for_commit(), ) local_version = Merge( dead_hosts.launcher.defaults.pyfunceble.PERSISTENT_CONFIG ).into(local_version, strict=True) if FileHelper( os.path.join( self.info_manager.WORKSPACE_DIR, dead_hosts.launcher.defaults.paths.EXAMPLE_INFO_FILENAME, ) ).exists(): local_version["cli_testing.ci.active"] = False # Default behavior of PyFunceble since 4.0.0b12. local_version["cli_testing.autocontinue"] = False local_version = DictHelper(local_version).unflatten() DictHelper(local_version).to_yaml_file( self.pyfunceble_config_file_instance.path ) logging.debug("Configuration:\n%s", self.pyfunceble_config_file_instance.read()) return self

33.597403

88

0.676846

5750afa4293cdacab8dd00966128e781c96f2a36

19,271

py

Python

robot/transform.py

sgowda/brain-python-interface

708e2a5229d0496a8ce9de32bda66f0925d366d9

[ "Apache-2.0" ]

7

2015-08-25T00:28:49.000Z

2020-04-14T22:58:51.000Z

robot/transform.py

sgowda/brain-python-interface

708e2a5229d0496a8ce9de32bda66f0925d366d9

[ "Apache-2.0" ]

89

2020-08-03T16:54:08.000Z

2022-03-09T19:56:19.000Z

robot/transform.py

sgowda/brain-python-interface

708e2a5229d0496a8ce9de32bda66f0925d366d9

[ "Apache-2.0" ]

4

2016-10-05T17:54:26.000Z

2020-08-06T15:37:09.000Z

""" Primitive operations for 3x3 orthonormal and 4x4 homogeneous matrices. @author: Peter Corke @copyright: Peter Corke """ from numpy import * from robot.utility import * from numpy.linalg import norm from .Quaternion import * def rotx(theta): """ Rotation about X-axis @type theta: number @param theta: the rotation angle @rtype: 3x3 orthonormal matrix @return: rotation about X-axis @see: L{roty}, L{rotz}, L{rotvec} """ ct = cos(theta) st = sin(theta) return mat([[1, 0, 0], [0, ct, -st], [0, st, ct]]) def roty(theta): """ Rotation about Y-axis @type theta: number @param theta: the rotation angle @rtype: 3x3 orthonormal matrix @return: rotation about Y-axis @see: L{rotx}, L{rotz}, L{rotvec} """ ct = cos(theta) st = sin(theta) return mat([[ct, 0, st], [0, 1, 0], [-st, 0, ct]]) def rotz(theta): """ Rotation about Z-axis @type theta: number @param theta: the rotation angle @rtype: 3x3 orthonormal matrix @return: rotation about Z-axis @see: L{rotx}, L{roty}, L{rotvec} """ ct = cos(theta) st = sin(theta) return mat([[ct, -st, 0], [st, ct, 0], [ 0, 0, 1]]) def trotx(theta): """ Rotation about X-axis @type theta: number @param theta: the rotation angle @rtype: 4x4 homogeneous matrix @return: rotation about X-axis @see: L{troty}, L{trotz}, L{rotx} """ return r2t(rotx(theta)) def troty(theta): """ Rotation about Y-axis @type theta: number @param theta: the rotation angle @rtype: 4x4 homogeneous matrix @return: rotation about Y-axis @see: L{troty}, L{trotz}, L{roty} """ return r2t(roty(theta)) def trotz(theta): """ Rotation about Z-axis @type theta: number @param theta: the rotation angle @rtype: 4x4 homogeneous matrix @return: rotation about Z-axis @see: L{trotx}, L{troty}, L{rotz} """ return r2t(rotz(theta)) ##################### Euler angles def tr2eul(m): """ Extract Euler angles. Returns a vector of Euler angles corresponding to the rotational part of the homogeneous transform. The 3 angles correspond to rotations about the Z, Y and Z axes respectively. @type m: 3x3 or 4x4 matrix @param m: the rotation matrix @rtype: 1x3 matrix @return: Euler angles [S{theta} S{phi} S{psi}] @see: L{eul2tr}, L{tr2rpy} """ try: m = mat(m) if ishomog(m): euler = mat(zeros((1,3))) if norm(m[0,2])<finfo(float).eps and norm(m[1,2])<finfo(float).eps: # singularity euler[0,0] = 0 sp = 0 cp = 1 euler[0,1] = arctan2(cp*m[0,2] + sp*m[1,2], m[2,2]) euler[0,2] = arctan2(-sp*m[0,0] + cp*m[1,0], -sp*m[0,1] + cp*m[1,1]) return euler else: euler[0,0] = arctan2(m[1,2],m[0,2]) sp = sin(euler[0,0]) cp = cos(euler[0,0]) euler[0,1] = arctan2(cp*m[0,2] + sp*m[1,2], m[2,2]) euler[0,2] = arctan2(-sp*m[0,0] + cp*m[1,0], -sp*m[0,1] + cp*m[1,1]) return euler except ValueError: euler = [] for i in range(0,len(m)): euler.append(tr2eul(m[i])) return euler def eul2r(phi, theta=None, psi=None): """ Rotation from Euler angles. Two call forms: - R = eul2r(S{theta}, S{phi}, S{psi}) - R = eul2r([S{theta}, S{phi}, S{psi}]) These correspond to rotations about the Z, Y, Z axes respectively. @type phi: number or list/array/matrix of angles @param phi: the first Euler angle, or a list/array/matrix of angles @type theta: number @param theta: the second Euler angle @type psi: number @param psi: the third Euler angle @rtype: 3x3 orthonormal matrix @return: R([S{theta} S{phi} S{psi}]) @see: L{tr2eul}, L{eul2tr}, L{tr2rpy} """ n = 1 if theta == None and psi==None: # list/array/matrix argument phi = mat(phi) if numcols(phi) != 3: error('bad arguments') else: n = numrows(phi) psi = phi[:,2] theta = phi[:,1] phi = phi[:,0] elif (theta!=None and psi==None) or (theta==None and psi!=None): error('bad arguments') elif not isinstance(phi,(int,int32,float,float64)): # all args are vectors phi = mat(phi) n = numrows(phi) theta = mat(theta) psi = mat(psi) if n>1: R = [] for i in range(0,n): r = rotz(phi[i,0]) * roty(theta[i,0]) * rotz(psi[i,0]) R.append(r) return R try: r = rotz(phi[0,0]) * roty(theta[0,0]) * rotz(psi[0,0]) return r except: r = rotz(phi) * roty(theta) * rotz(psi) return r def eul2tr(phi,theta=None,psi=None): """ Rotation from Euler angles. Two call forms: - R = eul2tr(S{theta}, S{phi}, S{psi}) - R = eul2tr([S{theta}, S{phi}, S{psi}]) These correspond to rotations about the Z, Y, Z axes respectively. @type phi: number or list/array/matrix of angles @param phi: the first Euler angle, or a list/array/matrix of angles @type theta: number @param theta: the second Euler angle @type psi: number @param psi: the third Euler angle @rtype: 4x4 homogenous matrix @return: R([S{theta} S{phi} S{psi}]) @see: L{tr2eul}, L{eul2r}, L{tr2rpy} """ return r2t( eul2r(phi, theta, psi) ) ################################## RPY angles def tr2rpy(m,zyx=False): """ Extract RPY angles. Returns a vector of RPY angles corresponding to the rotational part of the homogeneous transform. The 3 angles correspond to rotations about the Z, Y and X axes respectively. @type m: 3x3 or 4x4 matrix @param m: the rotation matrix @rtype: 1x3 matrix @return: RPY angles [S{theta} S{phi} S{psi}] @see: L{rpy2tr}, L{tr2eul} """ try: if ~zyx: m = mat(m) if ishomog(m): rpy = mat(zeros((1,3))) if norm(m[2,2])<finfo(float).eps and norm(m[1,2])<finfo(float).eps: # singularity rpy[0,0] = 0 rpy[0,1] = arctan2(m[0,2], m[2,2]) # pitch rpy[0,2] = arctan2(m[1,0], m[1,1]) # yaw is sum of roll+yaw return rpy else: rpy[0,0] = arctan2(-m[1,3],m[2,2]) sp = sin(rpy[0,0]) cp = cos(rpy[0,0]) rpy[0,1] = arctan2(m[0,2], cp*m[2,2] - sp*m[1,2])# pitch rpy[0,2] = arctan2(-m[0,1], m[0,0])# yaw return rpy else: m = mat(m) if ishomog(m): rpy = mat(zeros((1,3))) if norm(m[0,0])<finfo(float).eps and norm(m[1,0])<finfo(float).eps: # singularity rpy[0,0] = 0 rpy[0,1] = arctan2(-m[2,0], m[0,0]) rpy[0,2] = arctan2(-m[1,2], m[1,1]) return rpy else: rpy[0,0] = arctan2(m[1,0],m[0,0]) sp = sin(rpy[0,0]) cp = cos(rpy[0,0]) rpy[0,1] = arctan2(-m[2,0], cp*m[0,0] + sp*m[1,0]) rpy[0,2] = arctan2(sp*m[0,2] - cp*m[1,2], cp*m[1,1] - sp*m[0,1]) return rpy except ValueError: rpy = [] for i in range(0,len(m)): rpy.append(tr2rpy(m[i])) return rpy def rpy2r(roll, pitch=None,yaw=None,zyx=False,deg=False): """ Rotation from RPY angles. Two call forms: - R = rpy2r(S{theta}, S{phi}, S{psi}) - R = rpy2r([S{theta}, S{phi}, S{psi}]) These correspond to rotations about the Z, Y, X axes respectively. @type roll: number or list/array/matrix of angles @param roll: roll angle, or a list/array/matrix of angles @type pitch: number @param pitch: pitch angle @type yaw: number @param yaw: yaw angle @rtype: 4x4 homogenous matrix @return: R([S{theta} S{phi} S{psi}]) @see: L{tr2rpy}, L{rpy2r}, L{tr2eul} """ n=1 if pitch==None and yaw==None: roll= mat(roll) if numcols(roll) != 3: error('bad arguments') n = numrows(roll) pitch = roll[:,1] yaw = roll[:,2] roll = roll[:,0] if deg: #convert to degrees d2r = pi/180.0 roll = roll * d2r; pitch = pitch * d2r; yaw = yaw * d2r; if ~zyx: # XYZ order if n>1: R = [] for i in range(0,n): r = rotz(roll[i,0]) * roty(pitch[i,0]) * rotx(yaw[i,0]) R.append(r) return R try: r = rotz(roll[0,0]) * roty(pitch[0,0]) * rotx(yaw[0,0]) return r except: r = rotx(roll) * roty(pitch) * rotz(yaw) return r else: # XYZ order if n>1: R = [] for i in range(0,n): r = rotz(roll[i,0]) * roty(pitch[i,0]) * rotx(yaw[i,0]) R.append(r) return R try: r = rotz(roll[0,0]) * roty(pitch[0,0]) * rotx(yaw[0,0]) return r except: r = rotz(roll) * roty(pitch) * rotx(yaw) return r def rpy2tr(roll, pitch=None, yaw=None, zyx=False, deg=False): """ Rotation from RPY angles. Two call forms: - R = rpy2tr(r, p, y) - R = rpy2tr([r, p, y]) These correspond to rotations about the Z, Y, X axes respectively. @type roll: number or list/array/matrix of angles @param roll: roll angle, or a list/array/matrix of angles @type pitch: number @param pitch: pitch angle @type yaw: number @param yaw: yaw angle @rtype: 4x4 homogenous matrix @return: R([S{theta} S{phi} S{psi}]) @see: L{tr2rpy}, L{rpy2r}, L{tr2eul} """ return r2t( rpy2r(roll, pitch, yaw, zxy, deg) ) ###################################### OA vector form def oa2r(o,a): """Rotation from 2 vectors. The matrix is formed from 3 vectors such that:: R = [N O A] and N = O x A. In robotics A is the approach vector, along the direction of the robot's gripper, and O is the orientation vector in the direction between the fingertips. The submatrix is guaranteed to be orthonormal so long as O and A are not parallel. @type o: 3-vector @param o: The orientation vector. @type a: 3-vector @param a: The approach vector @rtype: 3x3 orthonormal rotation matrix @return: Rotatation matrix @see: L{rpy2r}, L{eul2r} """ n = crossp(o, a) n = unit(n) o = crossp(a, n); o = unit(o).reshape(3,1) a = unit(a).reshape(3,1) return bmat('n o a') def oa2tr(o,a): """otation from 2 vectors. The rotation submatrix is formed from 3 vectors such that:: R = [N O A] and N = O x A. In robotics A is the approach vector, along the direction of the robot's gripper, and O is the orientation vector in the direction between the fingertips. The submatrix is guaranteed to be orthonormal so long as O and A are not parallel. @type o: 3-vector @param o: The orientation vector. @type a: 3-vector @param a: The approach vector @rtype: 4x4 homogeneous transformation matrix @return: Transformation matrix @see: L{rpy2tr}, L{eul2tr} """ return r2t(oa2r(o,a)) ###################################### angle/vector form def rotvec2r(theta, v): """ Rotation about arbitrary axis. Compute a rotation matrix representing a rotation of C{theta} about the vector C{v}. @type v: 3-vector @param v: rotation vector @type theta: number @param theta: the rotation angle @rtype: 3x3 orthonormal matrix @return: rotation @see: L{rotx}, L{roty}, L{rotz} """ v = arg2array(v); ct = cos(theta) st = sin(theta) vt = 1-ct r = mat([[ct, -v[2]*st, v[1]*st],\ [v[2]*st, ct, -v[0]*st],\ [-v[1]*st, v[0]*st, ct]]) return v*v.T*vt+r def rotvec2tr(theta, v): """ Rotation about arbitrary axis. Compute a rotation matrix representing a rotation of C{theta} about the vector C{v}. @type v: 3-vector @param v: rotation vector @type theta: number @param theta: the rotation angle @rtype: 4x4 homogeneous matrix @return: rotation @see: L{trotx}, L{troty}, L{trotz} """ return r2t(rotvec2r(theta, v)) ###################################### translational transform def transl(x, y=None, z=None): """ Create or decompose translational homogeneous transformations. Create a homogeneous transformation =================================== - T = transl(v) - T = transl(vx, vy, vz) The transformation is created with a unit rotation submatrix. The translational elements are set from elements of v which is a list, array or matrix, or from separate passed elements. Decompose a homogeneous transformation ====================================== - v = transl(T) Return the translation vector """ if y==None and z==None: x=mat(x) try: if ishomog(x): return x[0:3,3].reshape(3,1) else: return concatenate((concatenate((eye(3),x.reshape(3,1)),1),mat([0,0,0,1]))) except AttributeError: n=len(x) r = [[],[],[]] for i in range(n): r = concatenate((r,x[i][0:3,3]),1) return r elif y!=None and z!=None: return concatenate((concatenate((eye(3),mat([x,y,z]).T),1),mat([0,0,0,1]))) ###################################### Skew symmetric transform def skew(*args): """ Convert to/from skew-symmetric form. A skew symmetric matrix is a matrix such that M = -M' Two call forms -ss = skew(v) -v = skew(ss) The first form builds a 3x3 skew-symmetric from a 3-element vector v. The second form takes a 3x3 skew-symmetric matrix and returns the 3 unique elements that it contains. """ def ss(b): return matrix([ [0, -b[2], b[1]], [b[2], 0, -b[0]], [-b[1], b[0], 0]]); if len(args) == 1: # convert matrix to skew vector b = args[0]; if isrot(b): return 0.5*matrix( [b[2,1]-b[1,2], b[0,2]-b[2,0], b[1,0]-b[0,1]] ); elif ishomog(b): return vstack( (b[0:3,3], 0.5*matrix( [b[2,1]-b[1,2], b[0,2]-b[2,0], b[1,0]-b[0,1]] ).T) ); # build skew-symmetric matrix b = arg2array(b); if len(b) == 3: return ss(b); elif len(b) == 6: r = hstack( (ss(b[3:6]), mat(b[0:3]).T) ); r = vstack( (r, mat([0, 0, 0, 1])) ); return r; elif len(args) == 3: return ss(args); elif len(args) == 6: r = hstack( (ss(args[3:6]), mat(args[0:3]).T) ); r = vstack( (r, mat([0, 0, 0, 1])) ); return r; else: raise ValueError; def tr2diff(t1, t2): """ Convert a transform difference to differential representation. Returns the 6-element differential motion required to move from T1 to T2 in base coordinates. @type t1: 4x4 homogeneous transform @param t1: Initial value @type t2: 4x4 homogeneous transform @param t2: Final value @rtype: 6-vector @return: Differential motion [dx dy dz drx dry drz] @see: L{skew} """ t1 = mat(t1) t2 = mat(t2) d = concatenate( (t2[0:3,3]-t1[0:3,3], 0.5*( crossp(t1[0:3,0], t2[0:3,0]) + crossp(t1[0:3,1], t2[0:3,1]) + crossp(t1[0:3,2], t2[0:3,2]) ) )) return d ################################## Utility def trinterp(T0, T1, r): """ Interpolate homogeneous transformations. Compute a homogeneous transform interpolation between C{T0} and C{T1} as C{r} varies from 0 to 1 such that:: trinterp(T0, T1, 0) = T0 trinterp(T0, T1, 1) = T1 Rotation is interpolated using quaternion spherical linear interpolation. @type T0: 4x4 homogeneous transform @param T0: Initial value @type T1: 4x4 homogeneous transform @param T1: Final value @type r: number @param r: Interpolation index, in the range 0 to 1 inclusive @rtype: 4x4 homogeneous transform @return: Interpolated value @see: L{quaternion}, L{ctraj} """ q0 = Quaternion(T0) q1 = Quaternion(T1) p0 = transl(T0) p1 = transl(T1) qr = q0.interp(q1, r) pr = p0*(1-r) + r*p1 return vstack( (concatenate((qr.R(),pr),1), mat([0,0,0,1])) ) def trnorm(t): """ Normalize a homogeneous transformation. Finite word length arithmetic can cause transforms to become `unnormalized', that is the rotation submatrix is no longer orthonormal (det(R) != 1). The rotation submatrix is re-orthogonalized such that the approach vector (third column) is unchanged in direction:: N = O x A O = A x N @type t: 4x4 homogeneous transformation @param t: the transform matrix to convert @rtype: 3x3 orthonormal rotation matrix @return: rotation submatrix @see: L{oa2tr} @bug: Should work for 3x3 matrix as well. """ t = mat(t) # N O A n = crossp(t[0:3,1],t[0:3,2]) # N = O X A o = crossp(t[0:3,2],t[0:3,0]) # O = A x N return concatenate(( concatenate((unit(n),unit(t[0:3,1]),unit(t[0:3,2]),t[0:3,3]),1), mat([0,0,0,1]))) def t2r(T): """ Return rotational submatrix of a homogeneous transformation. @type T: 4x4 homogeneous transformation @param T: the transform matrix to convert @rtype: 3x3 orthonormal rotation matrix @return: rotation submatrix """ if ishomog(T)==False: error( 'input must be a homogeneous transform') return T[0:3,0:3] def r2t(R): """ Convert a 3x3 orthonormal rotation matrix to a 4x4 homogeneous transformation:: T = | R 0 | | 0 1 | @type R: 3x3 orthonormal rotation matrix @param R: the rotation matrix to convert @rtype: 4x4 homogeneous matrix @return: homogeneous equivalent """ return concatenate( (concatenate( (R, zeros((3,1))),1), mat([0,0,0,1])) )

27.490728

103

0.518032

5c3adfda9d1f2b50a8ab55b3a95b306407ca1588

24,286

py

Python

python_modules/dagster/dagster/core/workspace/context.py

dehume/dagster

3b55c4e864775b7a70ed8ff539629317a1202505

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/core/workspace/context.py

dehume/dagster

3b55c4e864775b7a70ed8ff539629317a1202505

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/core/workspace/context.py

dehume/dagster

3b55c4e864775b7a70ed8ff539629317a1202505

[ "Apache-2.0" ]

null

import sys import threading import time import warnings from abc import ABC, abstractmethod from collections import OrderedDict from contextlib import ExitStack from typing import TYPE_CHECKING, Dict, List, Optional, Union, cast from dagster import check from dagster.core.errors import DagsterInvariantViolationError, DagsterRepositoryLocationLoadError from dagster.core.execution.plan.state import KnownExecutionState from dagster.core.host_representation import ( ExternalExecutionPlan, ExternalPipeline, GrpcServerRepositoryLocation, PipelineSelector, RepositoryHandle, RepositoryLocation, RepositoryLocationOrigin, ) from dagster.core.host_representation.grpc_server_registry import ( GrpcServerRegistry, ProcessGrpcServerRegistry, ) from dagster.core.host_representation.grpc_server_state_subscriber import ( LocationStateChangeEvent, LocationStateChangeEventType, LocationStateSubscriber, ) from dagster.core.host_representation.origin import GrpcServerRepositoryLocationOrigin from dagster.core.instance import DagsterInstance from dagster.grpc.server_watcher import create_grpc_watch_thread from dagster.utils.error import SerializableErrorInfo, serializable_error_info_from_exc_info from .load_target import WorkspaceLoadTarget from .permissions import get_user_permissions from .workspace import IWorkspace, WorkspaceLocationEntry, WorkspaceLocationLoadStatus if TYPE_CHECKING: from rx.subjects import Subject from dagster.core.host_representation import ( ExternalPartitionConfigData, ExternalPartitionExecutionErrorData, ExternalPartitionNamesData, ExternalPartitionSetExecutionParamData, ExternalPartitionTagsData, ) DAGIT_GRPC_SERVER_HEARTBEAT_TTL = 45 class BaseWorkspaceRequestContext(IWorkspace): """ This class is a request-scoped object that stores (1) a reference to all repository locations that exist on the `IWorkspaceProcessContext` at the start of the request and (2) a snapshot of the workspace at the start of the request. This object is needed because a process context and the repository locations on that context can be updated (for example, from a thread on the process context). If a request is accessing a repository location at the same time the repository location was being cleaned up, we would run into errors. """ @property @abstractmethod def instance(self) -> DagsterInstance: pass @abstractmethod def get_workspace_snapshot(self) -> Dict[str, WorkspaceLocationEntry]: pass @abstractmethod def get_location_entry(self, name: str) -> Optional[WorkspaceLocationEntry]: pass @property @abstractmethod def process_context(self) -> "IWorkspaceProcessContext": pass @property @abstractmethod def version(self) -> Optional[str]: pass @property @abstractmethod def permissions(self) -> Dict[str, bool]: pass @abstractmethod def has_permission(self, permission: str) -> bool: pass @property def show_instance_config(self) -> bool: return True def get_location(self, location_name: str): location_entry = self.get_location_entry(location_name) if not location_entry: raise DagsterInvariantViolationError( f"Location {location_name} does not exist in workspace" ) if location_entry.repository_location: return location_entry.repository_location error_info = cast(SerializableErrorInfo, location_entry.load_error) raise DagsterRepositoryLocationLoadError( f"Failure loading {location_name}: {error_info.to_string()}", load_error_infos=[error_info], ) @property def repository_locations(self) -> List[RepositoryLocation]: return [ entry.repository_location for entry in self.get_workspace_snapshot().values() if entry.repository_location ] @property def repository_location_names(self) -> List[str]: return list(self.get_workspace_snapshot()) def repository_location_errors(self) -> List[SerializableErrorInfo]: return [ entry.load_error for entry in self.get_workspace_snapshot().values() if entry.load_error ] def get_repository_location(self, name: str) -> RepositoryLocation: location_entry = self.get_location_entry(name) if not location_entry: raise Exception(f"Location {name} not in workspace") if location_entry.load_error: raise Exception(f"Error loading location {name}: {location_entry.load_error}") return cast(RepositoryLocation, location_entry.repository_location) def has_repository_location_error(self, name: str) -> bool: return self.get_repository_location_error(name) != None def get_repository_location_error(self, name: str) -> Optional[SerializableErrorInfo]: entry = self.get_location_entry(name) return entry.load_error if entry else None def has_repository_location_name(self, name: str) -> bool: return bool(self.get_location_entry(name)) def has_repository_location(self, name: str) -> bool: location_entry = self.get_location_entry(name) return bool(location_entry and location_entry.repository_location != None) def is_reload_supported(self, name: str) -> bool: entry = self.get_location_entry(name) return entry.origin.is_reload_supported if entry else False def is_shutdown_supported(self, name: str) -> bool: entry = self.get_location_entry(name) return entry.origin.is_shutdown_supported if entry else False def reload_repository_location(self, name: str) -> "BaseWorkspaceRequestContext": # This method reloads the location on the process context, and returns a new # request context created from the updated process context self.process_context.reload_repository_location(name) return self.process_context.create_request_context() def shutdown_repository_location(self, name: str): self.process_context.shutdown_repository_location(name) def reload_workspace(self) -> "BaseWorkspaceRequestContext": self.process_context.reload_workspace() return self.process_context.create_request_context() def has_external_pipeline(self, selector: PipelineSelector) -> bool: check.inst_param(selector, "selector", PipelineSelector) loc = self.get_repository_location(selector.location_name) return ( loc is not None and loc.has_repository(selector.repository_name) and loc.get_repository(selector.repository_name).has_external_pipeline( selector.pipeline_name ) ) def get_full_external_pipeline(self, selector: PipelineSelector) -> ExternalPipeline: return ( self.get_repository_location(selector.location_name) .get_repository(selector.repository_name) .get_full_external_pipeline(selector.pipeline_name) ) def get_external_execution_plan( self, external_pipeline: ExternalPipeline, run_config: dict, mode: str, step_keys_to_execute: List[str], known_state: KnownExecutionState, ) -> ExternalExecutionPlan: return self.get_repository_location( external_pipeline.handle.location_name ).get_external_execution_plan( external_pipeline=external_pipeline, run_config=run_config, mode=mode, step_keys_to_execute=step_keys_to_execute, known_state=known_state, instance=self.instance, ) def get_external_partition_config( self, repository_handle: RepositoryHandle, partition_set_name: str, partition_name: str ) -> Union["ExternalPartitionConfigData", "ExternalPartitionExecutionErrorData"]: return self.get_repository_location( repository_handle.location_name ).get_external_partition_config( repository_handle=repository_handle, partition_set_name=partition_set_name, partition_name=partition_name, ) def get_external_partition_tags( self, repository_handle: RepositoryHandle, partition_set_name: str, partition_name: str ) -> Union["ExternalPartitionTagsData", "ExternalPartitionExecutionErrorData"]: return self.get_repository_location( repository_handle.location_name ).get_external_partition_tags( repository_handle=repository_handle, partition_set_name=partition_set_name, partition_name=partition_name, ) def get_external_partition_names( self, repository_handle: RepositoryHandle, partition_set_name: str ) -> Union["ExternalPartitionNamesData", "ExternalPartitionExecutionErrorData"]: return self.get_repository_location( repository_handle.location_name ).get_external_partition_names(repository_handle, partition_set_name) def get_external_partition_set_execution_param_data( self, repository_handle: RepositoryHandle, partition_set_name: str, partition_names: List[str], ) -> Union["ExternalPartitionSetExecutionParamData", "ExternalPartitionExecutionErrorData"]: return self.get_repository_location( repository_handle.location_name ).get_external_partition_set_execution_param_data( repository_handle=repository_handle, partition_set_name=partition_set_name, partition_names=partition_names, ) def get_external_notebook_data(self, repository_location_name, notebook_path: str): check.str_param(repository_location_name, "repository_location_name") check.str_param(notebook_path, "notebook_path") repository_location = self.get_repository_location(repository_location_name) return repository_location.get_external_notebook_data(notebook_path=notebook_path) class WorkspaceRequestContext(BaseWorkspaceRequestContext): def __init__( self, instance: DagsterInstance, workspace_snapshot: Dict[str, WorkspaceLocationEntry], process_context: "WorkspaceProcessContext", version: Optional[str], source: Optional[object], ): self._instance = instance self._workspace_snapshot = workspace_snapshot self._process_context = process_context self._version = version self._source = source @property def instance(self) -> DagsterInstance: return self._instance def get_workspace_snapshot(self) -> Dict[str, WorkspaceLocationEntry]: return self._workspace_snapshot def get_location_entry(self, name) -> Optional[WorkspaceLocationEntry]: return self._workspace_snapshot.get(name) @property def process_context(self) -> "IWorkspaceProcessContext": return self._process_context @property def version(self) -> Optional[str]: return self._version @property def read_only(self) -> bool: return self._process_context.read_only @property def permissions(self) -> Dict[str, bool]: return self._process_context.permissions def has_permission(self, permission: str) -> bool: permissions = self._process_context.permissions check.invariant( permission in permissions, f"Permission {permission} not listed in permissions map" ) return permissions[permission] @property def source(self) -> Optional[object]: """ The source of the request this WorkspaceRequestContext originated from. For example in Dagit this object represents the web request. """ return self._source class IWorkspaceProcessContext(ABC): """ Class that stores process-scoped information about a dagit session. In most cases, you will want to create an `BaseWorkspaceRequestContext` to create a request-scoped object. """ @abstractmethod def create_request_context(self, source=None) -> BaseWorkspaceRequestContext: """ Create a usable fixed context for the scope of a request. Args: source (Optional[Any]): The source of the request, such as an object representing the web request or http connection. """ @property @abstractmethod def version(self) -> str: pass @property @abstractmethod def location_state_events(self) -> "Subject": pass @abstractmethod def reload_repository_location(self, name: str) -> None: pass def shutdown_repository_location(self, name: str) -> None: raise NotImplementedError @abstractmethod def reload_workspace(self) -> None: pass @property @abstractmethod def instance(self): pass def __enter__(self): return self def __exit__(self, exception_type, exception_value, traceback): pass class WorkspaceProcessContext(IWorkspaceProcessContext): """ This class is a process-scoped object that: 1. Maintain an update-to-date dictionary of repository locations 1. Create a `WorkspaceRequestContext` to be the workspace for each request 2. Run watch thread processes that monitor repository locations To access a RepositoryLocation, you should create a `WorkspaceRequestContext` using `create_request_context`. """ def __init__( self, instance: DagsterInstance, workspace_load_target: Optional[WorkspaceLoadTarget], version: str = "", read_only: bool = False, grpc_server_registry=None, ): self._stack = ExitStack() check.opt_str_param(version, "version") check.bool_param(read_only, "read_only") # lazy import for perf from rx.subjects import Subject self._instance = check.inst_param(instance, "instance", DagsterInstance) self._workspace_load_target = check.opt_inst_param( workspace_load_target, "workspace_load_target", WorkspaceLoadTarget ) self._location_state_events = Subject() self._location_state_subscriber = LocationStateSubscriber( self._location_state_events_handler ) self._read_only = read_only self._version = version # Guards changes to _location_dict, _location_error_dict, and _location_origin_dict self._lock = threading.Lock() # Only ever set up by main thread self._watch_thread_shutdown_events: Dict[str, threading.Event] = {} self._watch_threads: Dict[str, threading.Thread] = {} self._state_subscribers: List[LocationStateSubscriber] = [] self.add_state_subscriber(self._location_state_subscriber) if grpc_server_registry: self._grpc_server_registry: GrpcServerRegistry = check.inst_param( grpc_server_registry, "grpc_server_registry", GrpcServerRegistry ) else: self._grpc_server_registry = self._stack.enter_context( ProcessGrpcServerRegistry( reload_interval=0, heartbeat_ttl=DAGIT_GRPC_SERVER_HEARTBEAT_TTL, startup_timeout=instance.code_server_process_startup_timeout, ) ) self._location_entry_dict: Dict[str, WorkspaceLocationEntry] = OrderedDict() with self._lock: self._load_workspace() @property def workspace_load_target(self): return self._workspace_load_target def add_state_subscriber(self, subscriber): self._state_subscribers.append(subscriber) def _load_workspace(self): assert self._lock.locked() repository_location_origins = ( self._workspace_load_target.create_origins() if self._workspace_load_target else [] ) check.list_param( repository_location_origins, "repository_location_origins", of_type=RepositoryLocationOrigin, ) self._location_entry_dict = OrderedDict() for origin in repository_location_origins: check.invariant( self._location_entry_dict.get(origin.location_name) is None, 'Cannot have multiple locations with the same name, got multiple "{name}"'.format( name=origin.location_name, ), ) if origin.supports_server_watch: self._start_watch_thread(origin) self._location_entry_dict[origin.location_name] = self._load_location(origin) def _create_location_from_origin( self, origin: RepositoryLocationOrigin ) -> Optional[RepositoryLocation]: if not self._grpc_server_registry.supports_origin(origin): return origin.create_location() else: endpoint = ( self._grpc_server_registry.reload_grpc_endpoint(origin) if self._grpc_server_registry.supports_reload else self._grpc_server_registry.get_grpc_endpoint(origin) ) return GrpcServerRepositoryLocation( origin=origin, server_id=endpoint.server_id, port=endpoint.port, socket=endpoint.socket, host=endpoint.host, heartbeat=True, watch_server=False, grpc_server_registry=self._grpc_server_registry, ) @property def instance(self): return self._instance @property def read_only(self): return self._read_only @property def permissions(self) -> Dict[str, bool]: return get_user_permissions(self) @property def version(self) -> str: return self._version def _send_state_event_to_subscribers(self, event: LocationStateChangeEvent) -> None: check.inst_param(event, "event", LocationStateChangeEvent) for subscriber in self._state_subscribers: subscriber.handle_event(event) def _start_watch_thread(self, origin: GrpcServerRepositoryLocationOrigin) -> None: location_name = origin.location_name check.invariant(location_name not in self._watch_thread_shutdown_events) client = origin.create_client() shutdown_event, watch_thread = create_grpc_watch_thread( location_name, client, on_updated=lambda location_name, new_server_id: self._send_state_event_to_subscribers( LocationStateChangeEvent( LocationStateChangeEventType.LOCATION_UPDATED, location_name=location_name, message="Server has been updated.", server_id=new_server_id, ) ), on_error=lambda location_name: self._send_state_event_to_subscribers( LocationStateChangeEvent( LocationStateChangeEventType.LOCATION_ERROR, location_name=location_name, message="Unable to reconnect to server. You can reload the server once it is " "reachable again", ) ), ) self._watch_thread_shutdown_events[location_name] = shutdown_event self._watch_threads[location_name] = watch_thread watch_thread.start() def _load_location(self, origin): assert self._lock.locked() location_name = origin.location_name location = None error = None try: location = self._create_location_from_origin(origin) except Exception: error = serializable_error_info_from_exc_info(sys.exc_info()) warnings.warn( "Error loading repository location {location_name}:{error_string}".format( location_name=location_name, error_string=error.to_string() ) ) return WorkspaceLocationEntry( origin=origin, repository_location=location, load_error=error, load_status=WorkspaceLocationLoadStatus.LOADED, display_metadata=location.get_display_metadata() if location else origin.get_display_metadata(), update_timestamp=time.time(), ) def create_snapshot(self): with self._lock: return self._location_entry_dict.copy() @property def repository_locations_count(self): with self._lock: return len(self._location_entry_dict) @property def repository_location_names(self): with self._lock: return list(self._location_entry_dict) def has_repository_location(self, location_name): check.str_param(location_name, "location_name") with self._lock: return ( location_name in self._location_entry_dict and self._location_entry_dict[location_name].repository_location ) def has_repository_location_error(self, location_name): check.str_param(location_name, "location_name") with self._lock: return ( location_name in self._location_entry_dict and self._location_entry_dict[location_name].load_error ) def reload_repository_location(self, name: str) -> None: # Can be called from a background thread with self._lock: # Relying on GC to clean up the old location once nothing else # is referencing it self._location_entry_dict[name] = self._load_location( self._location_entry_dict[name].origin ) def shutdown_repository_location(self, name: str): with self._lock: self._location_entry_dict[name].origin.shutdown_server() def reload_workspace(self): # Can be called from a background thread with self._lock: self._cleanup_locations() self._load_workspace() def _cleanup_locations(self): assert self._lock.locked() for _, event in self._watch_thread_shutdown_events.items(): event.set() for _, watch_thread in self._watch_threads.items(): watch_thread.join() self._watch_thread_shutdown_events = {} self._watch_threads = {} for entry in self._location_entry_dict.values(): if entry.repository_location: entry.repository_location.cleanup() self._location_entry_dict = OrderedDict() def create_request_context(self, source=None) -> WorkspaceRequestContext: return WorkspaceRequestContext( instance=self._instance, workspace_snapshot=self.create_snapshot(), process_context=self, version=self.version, source=source, ) @property def location_state_events(self) -> "Subject": return self._location_state_events def _location_state_events_handler(self, event: LocationStateChangeEvent) -> None: # If the server was updated or we were not able to reconnect, we immediately reload the # location handle if event.event_type in ( LocationStateChangeEventType.LOCATION_UPDATED, LocationStateChangeEventType.LOCATION_ERROR, ): # In case of an updated location, reload the handle to get updated repository data and # re-attach a subscriber # In case of a location error, just reload the handle in order to update the workspace # with the correct error messages self.reload_repository_location(event.location_name) self._location_state_events.on_next(event) def __enter__(self): return self def __exit__(self, exception_type, exception_value, traceback): with self._lock: self._cleanup_locations() self._stack.close()

35.767305

102

0.680474

a4fd95cdbbffd86f4c4e7ef02992638073ae43c5

1,109

py

Python

quickstart.py

erickmartinez/pydlcp

611eceeb0816af432e1c06ee171376af2bc13a0e

[ "BSD-3-Clause" ]

null

quickstart.py

erickmartinez/pydlcp

611eceeb0816af432e1c06ee171376af2bc13a0e

[ "BSD-3-Clause" ]

null

quickstart.py

erickmartinez/pydlcp

611eceeb0816af432e1c06ee171376af2bc13a0e

[ "BSD-3-Clause" ]

null

import pydlcp.arduino_board as ard import pydlcp.controller as controller import configparser import os settings = r'G:\Shared drives\FenningLab2\LabData\ImpedanceAnalyzer\DLCP\20200922_training\D69_clean_low_frequency.ini' arduino_com = 'COM8' unit_name = 'HP1' pin = 1 pinMappings = { 'keithley': 'A0', 'fan': 'A1', 'thermocouple': '10', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6', 6: ' 7', 7: '8', 8: '9' } if __name__ == '__main__': if not os.path.exists(settings): raise FileExistsError('Settings file: \'{0}\' does not exist!'.format(settings)) config = configparser.ConfigParser() config.read(settings) a = ard.ArduinoBoard(address=arduino_com, name=unit_name, pin_mappings=pinMappings) a.connect() dlcp_controller = controller.Controller() dlcp_controller.connect_devices() dlcp_controller.load_test_config(config=config) # a.connect_keithley() a.pin_on(2) try: dlcp_controller.start_dlcp() except Exception as e: print(e) finally: a.pin_off(2) dlcp_controller.disconnect_devices() a.disconnect()

30.805556

120

0.679892

7ed9cae26cadfbf52b8679b7edb377dff7bd90d7

320

py

Python

2018/day-01/part2.py

amochtar/adventofcode

292e7f00a1e19d2149d00246b0a77fedfcd3bd08

[ "MIT" ]

1

2019-12-27T22:36:30.000Z

2018/day-01/part2.py

amochtar/adventofcode

292e7f00a1e19d2149d00246b0a77fedfcd3bd08

[ "MIT" ]

null

2018/day-01/part2.py

amochtar/adventofcode

292e7f00a1e19d2149d00246b0a77fedfcd3bd08

[ "MIT" ]

null

def solve(input): f = 0 ff = set([f]) frs = list(map(int, input)) while True: for fr in frs: f += fr if f in ff: print(f) return ff.add(f) with open('input.txt', 'r') as f: input = f.read().splitlines() solve(input)

16.842105

33

0.425

9212a5939104a181dcdd48fe7d37ab61603d3868

593

gyp

Python

deps/libgdal/gyp-formats/mrsid_lidar.gyp

seraph144/node-gdal

c6987705ced2b4eba8be123ececa40be80e56694

[ "Apache-2.0" ]

null

deps/libgdal/gyp-formats/mrsid_lidar.gyp

seraph144/node-gdal

c6987705ced2b4eba8be123ececa40be80e56694

[ "Apache-2.0" ]

null

deps/libgdal/gyp-formats/mrsid_lidar.gyp

seraph144/node-gdal

c6987705ced2b4eba8be123ececa40be80e56694

[ "Apache-2.0" ]

null

{ "includes": [ "../common.gypi" ], "targets": [ { "target_name": "libgdal_mrsid_lidar_frmt", "type": "static_library", "sources": [ "../gdal/frmts/mrsid_lidar/gdal_MG4Lidar.cpp" ], "include_dirs": [ "../gdal/frmts/mrsid_lidar", "../gdal/frmts/gtiff/libgeotiff", # The mrsid_include variable needs to be set to the full path of your local lizard tech libs "<(mrsid_include)/Lidar_DSDK/include" ] } ] }

29.65

108

0.473862

c94b8eaa127adee64848c9105b93aad25d5c9b8b

2,573

py

Python

generate_flashcards.py

district10/shuangpin-heatmap

0a299d4f567673648e5ca08db7744b0be1d90951

[ "MIT" ]

9

2020-03-09T14:27:10.000Z

2022-01-11T13:57:53.000Z

generate_flashcards.py

district10/shuangpin-heatmap

0a299d4f567673648e5ca08db7744b0be1d90951

[ "MIT" ]

null

generate_flashcards.py

district10/shuangpin-heatmap

0a299d4f567673648e5ca08db7744b0be1d90951

[ "MIT" ]

2

2021-01-13T11:27:36.000Z

2022-03-07T16:37:52.000Z

import pygal import pypinyin from typing import Union, Set, Dict, List, Any, Tuple, Optional import os import sys import json from collections import defaultdict import numpy as np import re from pprint import pprint from shuangpin_heatmap import pinyin2shuangpin, mkdir_p import shutil PWD = os.path.abspath(os.path.dirname(__file__)) if __name__ == '__main__': path = f'{PWD}/data/sample3.txt' output_directory = '/home/tzx/git/blog/notes/cards_shuangpin' mkdir_p(output_directory) with open(path) as f: lines = f.readlines() cards = [] for line in lines: line = line.strip() if not line: continue pinyin = [k[0] for k in pypinyin.pinyin(line, style=pypinyin.Style.NORMAL, errors='ignore')] cache = {} trans = {} shuangpin = [ pinyin2shuangpin( py, shuangpin_schema_name='ziranma', cache=cache, translated=trans, ) for py in pinyin ] py2sp = [[py, sp] for py, sp in trans.items() if py != sp] if not py2sp: continue cards.append(f'{output_directory}/card_{len(cards):08d}.md') with open(cards[-1], 'w') as f: if len(line) < 40: prefix = f' ' line = line.replace('\n', ';') f.write(f'- "{line}" -<\n\n : ') else: prefix = '' f.write(f'{line}\n') if py2sp: f.write(f'| 拼音 | 双拼 |\n') f.write(f'{prefix}| :--- | :--: |\n') for (py, sp) in py2sp: if py == sp: continue f.write(f'{prefix}| {py} | {sp} |\n') f.write(f'\n{prefix}```') f.write(f'\n{prefix}{"".join(shuangpin)}') f.write(f'\n{prefix}{line}') # ziranma = [ # pinyin2shuangpin( # py, # shuangpin_schema_name='ziranma', # cache=cache, # translated=trans, # ) for py in pinyin # ] # f.write(f'\n\n\n\n{prefix}{"".join(ziranma)}') f.write(f'\n{prefix}```\n') with open(f'{output_directory}/index.md', 'w') as f: f.write('# Cards\n') for card in cards: basename = os.path.basename(card) f.write(f'\n- [{basename}]({basename})') print(f'done, wrote #{len(cards)} cards to {output_directory}')

31.765432

100

0.487369

7ffe8debb982828d2a4bdc7cbc20ff9c992e3bbb

36,292

py

Python

research/object_detection/utils/config_util.py

Santhanalakshmimano/SpeedBump_detection_usingCV

7b68f260cf1351d757983a48c5a62e063df807c9

[ "Apache-2.0" ]

null

research/object_detection/utils/config_util.py

Santhanalakshmimano/SpeedBump_detection_usingCV

7b68f260cf1351d757983a48c5a62e063df807c9

[ "Apache-2.0" ]

null

research/object_detection/utils/config_util.py

Santhanalakshmimano/SpeedBump_detection_usingCV

7b68f260cf1351d757983a48c5a62e063df807c9

[ "Apache-2.0" ]

1

2021-07-13T01:22:08.000Z

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Functions for reading and updating configuration files.""" import os import tensorflow as tf from google.protobuf import text_format from tensorflow.python.lib.io import file_io from protos import eval_pb2 from protos import graph_rewriter_pb2 from protos import input_reader_pb2 from protos import model_pb2 from protos import pipeline_pb2 from protos import train_pb2 def get_image_resizer_config(model_config): """Returns the image resizer config from a model config. Args: model_config: A model_pb2.DetectionModel. Returns: An image_resizer_pb2.ImageResizer. Raises: ValueError: If the model type is not recognized. """ meta_architecture = model_config.WhichOneof("model") if meta_architecture == "faster_rcnn": return model_config.faster_rcnn.image_resizer if meta_architecture == "ssd": return model_config.ssd.image_resizer raise ValueError("Unknown model type: {}".format(meta_architecture)) def get_spatial_image_size(image_resizer_config): """Returns expected spatial size of the output image from a given config. Args: image_resizer_config: An image_resizer_pb2.ImageResizer. Returns: A list of two integers of the form [height, width]. `height` and `width` are set -1 if they cannot be determined during graph construction. Raises: ValueError: If the model type is not recognized. """ if image_resizer_config.HasField("fixed_shape_resizer"): return [ image_resizer_config.fixed_shape_resizer.height, image_resizer_config.fixed_shape_resizer.width ] if image_resizer_config.HasField("keep_aspect_ratio_resizer"): if image_resizer_config.keep_aspect_ratio_resizer.pad_to_max_dimension: return [image_resizer_config.keep_aspect_ratio_resizer.max_dimension] * 2 else: return [-1, -1] raise ValueError("Unknown image resizer type.") def get_configs_from_pipeline_file(pipeline_config_path, config_override=None): """Reads config from a file containing pipeline_pb2.TrainEvalPipelineConfig. Args: pipeline_config_path: Path to pipeline_pb2.TrainEvalPipelineConfig text proto. config_override: A pipeline_pb2.TrainEvalPipelineConfig text proto to override pipeline_config_path. Returns: Dictionary of configuration objects. Keys are `model`, `train_config`, `train_input_config`, `eval_config`, `eval_input_config`. Value are the corresponding config objects. """ pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.gfile.GFile(pipeline_config_path, "r") as f: proto_str = f.read() text_format.Merge(proto_str, pipeline_config) if config_override: text_format.Merge(config_override, pipeline_config) return create_configs_from_pipeline_proto(pipeline_config) def create_configs_from_pipeline_proto(pipeline_config): """Creates a configs dictionary from pipeline_pb2.TrainEvalPipelineConfig. Args: pipeline_config: pipeline_pb2.TrainEvalPipelineConfig proto object. Returns: Dictionary of configuration objects. Keys are `model`, `train_config`, `train_input_config`, `eval_config`, `eval_input_configs`. Value are the corresponding config objects or list of config objects (only for eval_input_configs). """ configs = {} configs["model"] = pipeline_config.model configs["train_config"] = pipeline_config.train_config configs["train_input_config"] = pipeline_config.train_input_reader configs["eval_config"] = pipeline_config.eval_config configs["eval_input_configs"] = pipeline_config.eval_input_reader # Keeps eval_input_config only for backwards compatibility. All clients should # read eval_input_configs instead. if configs["eval_input_configs"]: configs["eval_input_config"] = configs["eval_input_configs"][0] if pipeline_config.HasField("graph_rewriter"): configs["graph_rewriter_config"] = pipeline_config.graph_rewriter return configs def get_graph_rewriter_config_from_file(graph_rewriter_config_file): """Parses config for graph rewriter. Args: graph_rewriter_config_file: file path to the graph rewriter config. Returns: graph_rewriter_pb2.GraphRewriter proto """ graph_rewriter_config = graph_rewriter_pb2.GraphRewriter() with tf.gfile.GFile(graph_rewriter_config_file, "r") as f: text_format.Merge(f.read(), graph_rewriter_config) return graph_rewriter_config def create_pipeline_proto_from_configs(configs): """Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary. This function performs the inverse operation of create_configs_from_pipeline_proto(). Args: configs: Dictionary of configs. See get_configs_from_pipeline_file(). Returns: A fully populated pipeline_pb2.TrainEvalPipelineConfig. """ pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() pipeline_config.model.CopyFrom(configs["model"]) pipeline_config.train_config.CopyFrom(configs["train_config"]) pipeline_config.train_input_reader.CopyFrom(configs["train_input_config"]) pipeline_config.eval_config.CopyFrom(configs["eval_config"]) pipeline_config.eval_input_reader.extend(configs["eval_input_configs"]) if "graph_rewriter_config" in configs: pipeline_config.graph_rewriter.CopyFrom(configs["graph_rewriter_config"]) return pipeline_config def save_pipeline_config(pipeline_config, directory): """Saves a pipeline config text file to disk. Args: pipeline_config: A pipeline_pb2.TrainEvalPipelineConfig. directory: The model directory into which the pipeline config file will be saved. """ if not file_io.file_exists(directory): file_io.recursive_create_dir(directory) pipeline_config_path = os.path.join(directory, "pipeline.config") config_text = text_format.MessageToString(pipeline_config) with tf.gfile.Open(pipeline_config_path, "wb") as f: tf.logging.info("Writing pipeline config file to %s", pipeline_config_path) f.write(config_text) def get_configs_from_multiple_files(model_config_path="", train_config_path="", train_input_config_path="", eval_config_path="", eval_input_config_path="", graph_rewriter_config_path=""): """Reads training configuration from multiple config files. Args: model_config_path: Path to model_pb2.DetectionModel. train_config_path: Path to train_pb2.TrainConfig. train_input_config_path: Path to input_reader_pb2.InputReader. eval_config_path: Path to eval_pb2.EvalConfig. eval_input_config_path: Path to input_reader_pb2.InputReader. graph_rewriter_config_path: Path to graph_rewriter_pb2.GraphRewriter. Returns: Dictionary of configuration objects. Keys are `model`, `train_config`, `train_input_config`, `eval_config`, `eval_input_config`. Key/Values are returned only for valid (non-empty) strings. """ configs = {} if model_config_path: model_config = model_pb2.DetectionModel() with tf.gfile.GFile(model_config_path, "r") as f: text_format.Merge(f.read(), model_config) configs["model"] = model_config if train_config_path: train_config = train_pb2.TrainConfig() with tf.gfile.GFile(train_config_path, "r") as f: text_format.Merge(f.read(), train_config) configs["train_config"] = train_config if train_input_config_path: train_input_config = input_reader_pb2.InputReader() with tf.gfile.GFile(train_input_config_path, "r") as f: text_format.Merge(f.read(), train_input_config) configs["train_input_config"] = train_input_config if eval_config_path: eval_config = eval_pb2.EvalConfig() with tf.gfile.GFile(eval_config_path, "r") as f: text_format.Merge(f.read(), eval_config) configs["eval_config"] = eval_config if eval_input_config_path: eval_input_config = input_reader_pb2.InputReader() with tf.gfile.GFile(eval_input_config_path, "r") as f: text_format.Merge(f.read(), eval_input_config) configs["eval_input_configs"] = [eval_input_config] if graph_rewriter_config_path: configs["graph_rewriter_config"] = get_graph_rewriter_config_from_file( graph_rewriter_config_path) return configs def get_number_of_classes(model_config): """Returns the number of classes for a detection model. Args: model_config: A model_pb2.DetectionModel. Returns: Number of classes. Raises: ValueError: If the model type is not recognized. """ meta_architecture = model_config.WhichOneof("model") if meta_architecture == "faster_rcnn": return model_config.faster_rcnn.num_classes if meta_architecture == "ssd": return model_config.ssd.num_classes raise ValueError("Expected the model to be one of 'faster_rcnn' or 'ssd'.") def get_optimizer_type(train_config): """Returns the optimizer type for training. Args: train_config: A train_pb2.TrainConfig. Returns: The type of the optimizer """ return train_config.optimizer.WhichOneof("optimizer") def get_learning_rate_type(optimizer_config): """Returns the learning rate type for training. Args: optimizer_config: An optimizer_pb2.Optimizer. Returns: The type of the learning rate. """ return optimizer_config.learning_rate.WhichOneof("learning_rate") def _is_generic_key(key): """Determines whether the key starts with a generic config dictionary key.""" for prefix in [ "graph_rewriter_config", "model", "train_input_config", "train_config", "eval_config"]: if key.startswith(prefix + "."): return True return False def _check_and_convert_legacy_input_config_key(key): """Checks key and converts legacy input config update to specific update. Args: key: string indicates the target of update operation. Returns: is_valid_input_config_key: A boolean indicating whether the input key is to update input config(s). key_name: 'eval_input_configs' or 'train_input_config' string if is_valid_input_config_key is true. None if is_valid_input_config_key is false. input_name: always returns None since legacy input config key never specifies the target input config. Keeping this output only to match the output form defined for input config update. field_name: the field name in input config. `key` itself if is_valid_input_config_key is false. """ key_name = None input_name = None field_name = key is_valid_input_config_key = True if field_name == "train_shuffle": key_name = "train_input_config" field_name = "shuffle" elif field_name == "eval_shuffle": key_name = "eval_input_configs" field_name = "shuffle" elif field_name == "train_input_path": key_name = "train_input_config" field_name = "input_path" elif field_name == "eval_input_path": key_name = "eval_input_configs" field_name = "input_path" elif field_name == "append_train_input_path": key_name = "train_input_config" field_name = "input_path" elif field_name == "append_eval_input_path": key_name = "eval_input_configs" field_name = "input_path" else: is_valid_input_config_key = False return is_valid_input_config_key, key_name, input_name, field_name def check_and_parse_input_config_key(configs, key): """Checks key and returns specific fields if key is valid input config update. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). key: string indicates the target of update operation. Returns: is_valid_input_config_key: A boolean indicate whether the input key is to update input config(s). key_name: 'eval_input_configs' or 'train_input_config' string if is_valid_input_config_key is true. None if is_valid_input_config_key is false. input_name: the name of the input config to be updated. None if is_valid_input_config_key is false. field_name: the field name in input config. `key` itself if is_valid_input_config_key is false. Raises: ValueError: when the input key format doesn't match any known formats. ValueError: if key_name doesn't match 'eval_input_configs' or 'train_input_config'. ValueError: if input_name doesn't match any name in train or eval input configs. ValueError: if field_name doesn't match any supported fields. """ key_name = None input_name = None field_name = None fields = key.split(":") if len(fields) == 1: field_name = key return _check_and_convert_legacy_input_config_key(key) elif len(fields) == 3: key_name = fields[0] input_name = fields[1] field_name = fields[2] else: raise ValueError("Invalid key format when overriding configs.") # Checks if key_name is valid for specific update. if key_name not in ["eval_input_configs", "train_input_config"]: raise ValueError("Invalid key_name when overriding input config.") # Checks if input_name is valid for specific update. For train input config it # should match configs[key_name].name, for eval input configs it should match # the name field of one of the eval_input_configs. if isinstance(configs[key_name], input_reader_pb2.InputReader): is_valid_input_name = configs[key_name].name == input_name else: is_valid_input_name = input_name in [ eval_input_config.name for eval_input_config in configs[key_name] ] if not is_valid_input_name: raise ValueError("Invalid input_name when overriding input config.") # Checks if field_name is valid for specific update. if field_name not in [ "input_path", "label_map_path", "shuffle", "mask_type", "sample_1_of_n_examples" ]: raise ValueError("Invalid field_name when overriding input config.") return True, key_name, input_name, field_name def merge_external_params_with_configs(configs, hparams=None, kwargs_dict=None): """Updates `configs` dictionary based on supplied parameters. This utility is for modifying specific fields in the object detection configs. Say that one would like to experiment with different learning rates, momentum values, or batch sizes. Rather than creating a new config text file for each experiment, one can use a single base config file, and update particular values. There are two types of field overrides: 1. Strategy-based overrides, which update multiple relevant configuration options. For example, updating `learning_rate` will update both the warmup and final learning rates. In this case key can be one of the following formats: 1. legacy update: single string that indicates the attribute to be updated. E.g. 'label_map_path', 'eval_input_path', 'shuffle'. Note that when updating fields (e.g. eval_input_path, eval_shuffle) in eval_input_configs, the override will only be applied when eval_input_configs has exactly 1 element. 2. specific update: colon separated string that indicates which field in which input_config to update. It should have 3 fields: - key_name: Name of the input config we should update, either 'train_input_config' or 'eval_input_configs' - input_name: a 'name' that can be used to identify elements, especially when configs[key_name] is a repeated field. - field_name: name of the field that you want to override. For example, given configs dict as below: configs = { 'model': {...} 'train_config': {...} 'train_input_config': {...} 'eval_config': {...} 'eval_input_configs': [{ name:"eval_coco", ...}, { name:"eval_voc", ... }] } Assume we want to update the input_path of the eval_input_config whose name is 'eval_coco'. The `key` would then be: 'eval_input_configs:eval_coco:input_path' 2. Generic key/value, which update a specific parameter based on namespaced configuration keys. For example, `model.ssd.loss.hard_example_miner.max_negatives_per_positive` will update the hard example miner configuration for an SSD model config. Generic overrides are automatically detected based on the namespaced keys. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). hparams: A `HParams`. kwargs_dict: Extra keyword arguments that are treated the same way as attribute/value pairs in `hparams`. Note that hyperparameters with the same names will override keyword arguments. Returns: `configs` dictionary. Raises: ValueError: when the key string doesn't match any of its allowed formats. """ if kwargs_dict is None: kwargs_dict = {} if hparams: kwargs_dict.update(hparams.values()) for key, value in kwargs_dict.items(): tf.logging.info("Maybe overwriting %s: %s", key, value) # pylint: disable=g-explicit-bool-comparison if value == "" or value is None: continue # pylint: enable=g-explicit-bool-comparison elif _maybe_update_config_with_key_value(configs, key, value): continue elif _is_generic_key(key): _update_generic(configs, key, value) else: tf.logging.info("Ignoring config override key: %s", key) return configs def _maybe_update_config_with_key_value(configs, key, value): """Checks key type and updates `configs` with the key value pair accordingly. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). key: String indicates the field(s) to be updated. value: Value used to override existing field value. Returns: A boolean value that indicates whether the override succeeds. Raises: ValueError: when the key string doesn't match any of the formats above. """ is_valid_input_config_key, key_name, input_name, field_name = ( check_and_parse_input_config_key(configs, key)) if is_valid_input_config_key: update_input_reader_config( configs, key_name=key_name, input_name=input_name, field_name=field_name, value=value) elif field_name == "learning_rate": _update_initial_learning_rate(configs, value) elif field_name == "batch_size": _update_batch_size(configs, value) elif field_name == "momentum_optimizer_value": _update_momentum_optimizer_value(configs, value) elif field_name == "classification_localization_weight_ratio": # Localization weight is fixed to 1.0. _update_classification_localization_weight_ratio(configs, value) elif field_name == "focal_loss_gamma": _update_focal_loss_gamma(configs, value) elif field_name == "focal_loss_alpha": _update_focal_loss_alpha(configs, value) elif field_name == "train_steps": _update_train_steps(configs, value) elif field_name == "label_map_path": _update_label_map_path(configs, value) elif field_name == "mask_type": _update_mask_type(configs, value) elif field_name == "sample_1_of_n_eval_examples": _update_all_eval_input_configs(configs, "sample_1_of_n_examples", value) elif field_name == "eval_num_epochs": _update_all_eval_input_configs(configs, "num_epochs", value) elif field_name == "eval_with_moving_averages": _update_use_moving_averages(configs, value) elif field_name == "retain_original_images_in_eval": _update_retain_original_images(configs["eval_config"], value) elif field_name == "use_bfloat16": _update_use_bfloat16(configs, value) else: return False return True def _update_tf_record_input_path(input_config, input_path): """Updates input configuration to reflect a new input path. The input_config object is updated in place, and hence not returned. Args: input_config: A input_reader_pb2.InputReader. input_path: A path to data or list of paths. Raises: TypeError: if input reader type is not `tf_record_input_reader`. """ input_reader_type = input_config.WhichOneof("input_reader") if input_reader_type == "tf_record_input_reader": input_config.tf_record_input_reader.ClearField("input_path") if isinstance(input_path, list): input_config.tf_record_input_reader.input_path.extend(input_path) else: input_config.tf_record_input_reader.input_path.append(input_path) else: raise TypeError("Input reader type must be `tf_record_input_reader`.") def update_input_reader_config(configs, key_name=None, input_name=None, field_name=None, value=None, path_updater=_update_tf_record_input_path): """Updates specified input reader config field. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). key_name: Name of the input config we should update, either 'train_input_config' or 'eval_input_configs' input_name: String name used to identify input config to update with. Should be either None or value of the 'name' field in one of the input reader configs. field_name: Field name in input_reader_pb2.InputReader. value: Value used to override existing field value. path_updater: helper function used to update the input path. Only used when field_name is "input_path". Raises: ValueError: when input field_name is None. ValueError: when input_name is None and number of eval_input_readers does not equal to 1. """ if isinstance(configs[key_name], input_reader_pb2.InputReader): # Updates singular input_config object. target_input_config = configs[key_name] if field_name == "input_path": path_updater(input_config=target_input_config, input_path=value) else: setattr(target_input_config, field_name, value) elif input_name is None and len(configs[key_name]) == 1: # Updates first (and the only) object of input_config list. target_input_config = configs[key_name][0] if field_name == "input_path": path_updater(input_config=target_input_config, input_path=value) else: setattr(target_input_config, field_name, value) elif input_name is not None and len(configs[key_name]): # Updates input_config whose name matches input_name. update_count = 0 for input_config in configs[key_name]: if input_config.name == input_name: setattr(input_config, field_name, value) update_count = update_count + 1 if not update_count: raise ValueError( "Input name {} not found when overriding.".format(input_name)) elif update_count > 1: raise ValueError("Duplicate input name found when overriding.") else: key_name = "None" if key_name is None else key_name input_name = "None" if input_name is None else input_name field_name = "None" if field_name is None else field_name raise ValueError("Unknown input config overriding: " "key_name:{}, input_name:{}, field_name:{}.".format( key_name, input_name, field_name)) def _update_initial_learning_rate(configs, learning_rate): """Updates `configs` to reflect the new initial learning rate. This function updates the initial learning rate. For learning rate schedules, all other defined learning rates in the pipeline config are scaled to maintain their same ratio with the initial learning rate. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). learning_rate: Initial learning rate for optimizer. Raises: TypeError: if optimizer type is not supported, or if learning rate type is not supported. """ optimizer_type = get_optimizer_type(configs["train_config"]) if optimizer_type == "rms_prop_optimizer": optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer elif optimizer_type == "momentum_optimizer": optimizer_config = configs["train_config"].optimizer.momentum_optimizer elif optimizer_type == "adam_optimizer": optimizer_config = configs["train_config"].optimizer.adam_optimizer else: raise TypeError("Optimizer %s is not supported." % optimizer_type) learning_rate_type = get_learning_rate_type(optimizer_config) if learning_rate_type == "constant_learning_rate": constant_lr = optimizer_config.learning_rate.constant_learning_rate constant_lr.learning_rate = learning_rate elif learning_rate_type == "exponential_decay_learning_rate": exponential_lr = ( optimizer_config.learning_rate.exponential_decay_learning_rate) exponential_lr.initial_learning_rate = learning_rate elif learning_rate_type == "manual_step_learning_rate": manual_lr = optimizer_config.learning_rate.manual_step_learning_rate original_learning_rate = manual_lr.initial_learning_rate learning_rate_scaling = float(learning_rate) / original_learning_rate manual_lr.initial_learning_rate = learning_rate for schedule in manual_lr.schedule: schedule.learning_rate *= learning_rate_scaling elif learning_rate_type == "cosine_decay_learning_rate": cosine_lr = optimizer_config.learning_rate.cosine_decay_learning_rate learning_rate_base = cosine_lr.learning_rate_base warmup_learning_rate = cosine_lr.warmup_learning_rate warmup_scale_factor = warmup_learning_rate / learning_rate_base cosine_lr.learning_rate_base = learning_rate cosine_lr.warmup_learning_rate = warmup_scale_factor * learning_rate else: raise TypeError("Learning rate %s is not supported." % learning_rate_type) def _update_batch_size(configs, batch_size): """Updates `configs` to reflect the new training batch size. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). batch_size: Batch size to use for training (Ideally a power of 2). Inputs are rounded, and capped to be 1 or greater. """ configs["train_config"].batch_size = max(1, int(round(batch_size))) def _validate_message_has_field(message, field): if not message.HasField(field): raise ValueError("Expecting message to have field %s" % field) def _update_generic(configs, key, value): """Update a pipeline configuration parameter based on a generic key/value. Args: configs: Dictionary of pipeline configuration protos. key: A string key, dot-delimited to represent the argument key. e.g. "model.ssd.train_config.batch_size" value: A value to set the argument to. The type of the value must match the type for the protocol buffer. Note that setting the wrong type will result in a TypeError. e.g. 42 Raises: ValueError if the message key does not match the existing proto fields. TypeError the value type doesn't match the protobuf field type. """ fields = key.split(".") first_field = fields.pop(0) last_field = fields.pop() message = configs[first_field] for field in fields: _validate_message_has_field(message, field) message = getattr(message, field) _validate_message_has_field(message, last_field) setattr(message, last_field, value) def _update_momentum_optimizer_value(configs, momentum): """Updates `configs` to reflect the new momentum value. Momentum is only supported for RMSPropOptimizer and MomentumOptimizer. For any other optimizer, no changes take place. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). momentum: New momentum value. Values are clipped at 0.0 and 1.0. Raises: TypeError: If the optimizer type is not `rms_prop_optimizer` or `momentum_optimizer`. """ optimizer_type = get_optimizer_type(configs["train_config"]) if optimizer_type == "rms_prop_optimizer": optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer elif optimizer_type == "momentum_optimizer": optimizer_config = configs["train_config"].optimizer.momentum_optimizer else: raise TypeError("Optimizer type must be one of `rms_prop_optimizer` or " "`momentum_optimizer`.") optimizer_config.momentum_optimizer_value = min(max(0.0, momentum), 1.0) def _update_classification_localization_weight_ratio(configs, ratio): """Updates the classification/localization weight loss ratio. Detection models usually define a loss weight for both classification and objectness. This function updates the weights such that the ratio between classification weight to localization weight is the ratio provided. Arbitrarily, localization weight is set to 1.0. Note that in the case of Faster R-CNN, this same ratio is applied to the first stage objectness loss weight relative to localization loss weight. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). ratio: Desired ratio of classification (and/or objectness) loss weight to localization loss weight. """ meta_architecture = configs["model"].WhichOneof("model") if meta_architecture == "faster_rcnn": model = configs["model"].faster_rcnn model.first_stage_localization_loss_weight = 1.0 model.first_stage_objectness_loss_weight = ratio model.second_stage_localization_loss_weight = 1.0 model.second_stage_classification_loss_weight = ratio if meta_architecture == "ssd": model = configs["model"].ssd model.loss.localization_weight = 1.0 model.loss.classification_weight = ratio def _get_classification_loss(model_config): """Returns the classification loss for a model.""" meta_architecture = model_config.WhichOneof("model") if meta_architecture == "faster_rcnn": model = model_config.faster_rcnn classification_loss = model.second_stage_classification_loss elif meta_architecture == "ssd": model = model_config.ssd classification_loss = model.loss.classification_loss else: raise TypeError("Did not recognize the model architecture.") return classification_loss def _update_focal_loss_gamma(configs, gamma): """Updates the gamma value for a sigmoid focal loss. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). gamma: Exponent term in focal loss. Raises: TypeError: If the classification loss is not `weighted_sigmoid_focal`. """ classification_loss = _get_classification_loss(configs["model"]) classification_loss_type = classification_loss.WhichOneof( "classification_loss") if classification_loss_type != "weighted_sigmoid_focal": raise TypeError("Classification loss must be `weighted_sigmoid_focal`.") classification_loss.weighted_sigmoid_focal.gamma = gamma def _update_focal_loss_alpha(configs, alpha): """Updates the alpha value for a sigmoid focal loss. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). alpha: Class weight multiplier for sigmoid loss. Raises: TypeError: If the classification loss is not `weighted_sigmoid_focal`. """ classification_loss = _get_classification_loss(configs["model"]) classification_loss_type = classification_loss.WhichOneof( "classification_loss") if classification_loss_type != "weighted_sigmoid_focal": raise TypeError("Classification loss must be `weighted_sigmoid_focal`.") classification_loss.weighted_sigmoid_focal.alpha = alpha def _update_train_steps(configs, train_steps): """Updates `configs` to reflect new number of training steps.""" configs["train_config"].num_steps = int(train_steps) def _update_eval_steps(configs, eval_steps): """Updates `configs` to reflect new number of eval steps per evaluation.""" configs["eval_config"].num_examples = int(eval_steps) def _update_all_eval_input_configs(configs, field, value): """Updates the content of `field` with `value` for all eval input configs.""" for eval_input_config in configs["eval_input_configs"]: setattr(eval_input_config, field, value) def _update_label_map_path(configs, label_map_path): """Updates the label map path for both train and eval input readers. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). label_map_path: New path to `StringIntLabelMap` pbtxt file. """ configs["train_input_config"].label_map_path = label_map_path _update_all_eval_input_configs(configs, "label_map_path", label_map_path) def _update_mask_type(configs, mask_type): """Updates the mask type for both train and eval input readers. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). mask_type: A string name representing a value of input_reader_pb2.InstanceMaskType """ configs["train_input_config"].mask_type = mask_type _update_all_eval_input_configs(configs, "mask_type", mask_type) def _update_use_moving_averages(configs, use_moving_averages): """Updates the eval config option to use or not use moving averages. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). use_moving_averages: Boolean indicating whether moving average variables should be loaded during evaluation. """ configs["eval_config"].use_moving_averages = use_moving_averages def _update_retain_original_images(eval_config, retain_original_images): """Updates eval config with option to retain original images. The eval_config object is updated in place, and hence not returned. Args: eval_config: A eval_pb2.EvalConfig. retain_original_images: Boolean indicating whether to retain original images in eval mode. """ eval_config.retain_original_images = retain_original_images def _update_use_bfloat16(configs, use_bfloat16): """Updates `configs` to reflect the new setup on whether to use bfloat16. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). use_bfloat16: A bool, indicating whether to use bfloat16 for training. """ configs["train_config"].use_bfloat16 = use_bfloat16

38.814973

80

0.7473

f66aa47124f47c307ae7f2ee24b939df77566559

2,648

py

Python

app/models.py

tinabayi/blogs

690db73b8f2b9976217e19ab432cc42dd0fd83fd

[ "MIT" ]

null

app/models.py

tinabayi/blogs

690db73b8f2b9976217e19ab432cc42dd0fd83fd

[ "MIT" ]

null

app/models.py

tinabayi/blogs

690db73b8f2b9976217e19ab432cc42dd0fd83fd

[ "MIT" ]

null

from . import db from werkzeug.security import generate_password_hash,check_password_hash from flask_login import UserMixin from . import login_manager class User(UserMixin,db.Model): __tablename__ = 'users' id = db.Column(db.Integer,primary_key = True) username = db.Column(db.String(255),index = True) blogs = db.relationship('Blog',backref = 'user',lazy="dynamic") email = db.Column(db.String(255),unique = True,index = True) bio = db.Column(db.String(255)) profile_pic_path = db.Column(db.String()) pass_secure = db.Column(db.String(255)) @property def password(self): raise AttributeError('You cannot read the password attribute') @password.setter def password(self, password): self.pass_secure = generate_password_hash(password) def verify_password(self,password): return check_password_hash(self.pass_secure,password) def __repr__(self): return f'User {self.username}' @login_manager.user_loader def load_user(user_id): return User.query.get(int(user_id)) class Blog(db.Model): __tablename__ = 'blogs' id = db.Column(db.Integer,primary_key = True) user_id = db.Column(db.Integer,db.ForeignKey('users.id')) description = db.Column(db.String(255)) comments = db.relationship('Comment',backref = 'blog',lazy="dynamic") def save_blogs(self): db.session.add(self) db.session.commit() @classmethod def clear_blogs(cls): Blog.all_blogs.clear() @classmethod def get_blogs(id): blogs=Blog.query.all() return blogs class Comment(db.Model): __tablename__ = 'comments' id = db.Column(db.Integer,primary_key = True) blog_id = db.Column(db.Integer,db.ForeignKey('blogs.id')) comment = db.Column(db.String(255)) def save_comments(self): db.session.add(self) db.session.commit() @classmethod def clear_blogs(cls): Blog.all_blogs.clear() @classmethod def get_comments(id): all_comments=Comment.query.all() return all_comments def delete_comment(self): db.session.delete(self) db.session.commit() class Subscribe(db.Model): __tablename__ = 'subsribes' id = db.Column(db.Integer,primary_key = True) email = db.Column(db.String(255)) def __repr__(self): return f'User {self.username}' class Quote: ''' Quote class to define Quote Objects ''' def __init__(self,author,id,quote): self.author=author self.id =id self.quote=quote

24.072727

74

0.646903

eeda4fc09d510ab7c3a8844b50360f132d3934eb

1,374

py

Python

cred-append.py

BlackDiverX/CredCompilator

737cc3e7e7992cd7c49dd5f5222371d275f1d4a1

[ "Apache-2.0" ]

6

2017-11-02T16:26:10.000Z

2021-06-07T10:01:29.000Z

cred-append.py

BlackDiverX/CredCompilator

737cc3e7e7992cd7c49dd5f5222371d275f1d4a1

[ "Apache-2.0" ]

null

cred-append.py

BlackDiverX/CredCompilator

737cc3e7e7992cd7c49dd5f5222371d275f1d4a1

[ "Apache-2.0" ]

1

2019-09-04T12:03:09.000Z

#!/usr/bin/python # cred-append.py # Version: 1.0 # License: Apache License Version 2.0 # Author: Georgii Starostin # E-mail# blackdiverx@gmail.com # Site: http://BlackDiver.net import sys if len (sys.argv) != 6: print ("Утилита для добавления текста в начало или конец строки.") print ("Синтаксис:") print ("python cred-append.py <InFile> <append-text> <position> <format> <OutFile>") print ("<InFile> - входной файл;") print ("<append-text> - текст для добавления в конец строки;") print ("<position> - добавление в начало [start] или конец [end] строки;") print ("<format> - формат вывода[unix|win|mac]. Устаналивает формат перевода строки;") print ("<OutFile> - файл результатов.") print ("") print ("Пример:") print ("python cred-append.py Logins.txt a end unix Result.txt") exit(); def formattype(x): return{ "unix":"\n", "win":"\r\n", "mac":"\r" }.get(x) with open(sys.argv[1] , "r") as ins: Farray = [] for line in ins: Farray.append((line)) f = open(sys.argv[5],'w') if sys.argv[3] == 'end': i = 0 while i<len(Farray): f.write((Farray[i]).rstrip('\r').rstrip('\n')+sys.argv[2]+formattype(sys.argv[4])) i=i+1 if sys.argv[3] == 'start': i = 0 while i<len(Farray): f.write(sys.argv[2]+(Farray[i]).rstrip('\r').rstrip('\n')+formattype(sys.argv[4])) i=i+1 f.close()

28.040816

88

0.614993

32a46fe7ceb6cfa9bbefaad5a1bdfa42c1fccef5

7,253

py

Python

src/audio/aubio/python/tests/test_specdesc.py

vrushank-agrawal/video_editor_BX23

3a458114f499e0ba3d1c61afde2b9d30bc76459b

[ "Apache-2.0" ]

null

src/audio/aubio/python/tests/test_specdesc.py

vrushank-agrawal/video_editor_BX23

3a458114f499e0ba3d1c61afde2b9d30bc76459b

[ "Apache-2.0" ]

null

src/audio/aubio/python/tests/test_specdesc.py

vrushank-agrawal/video_editor_BX23

3a458114f499e0ba3d1c61afde2b9d30bc76459b

[ "Apache-2.0" ]

null

#! /usr/bin/env python from numpy.testing import TestCase, assert_equal, assert_almost_equal from numpy import random, arange, log, zeros from aubio import specdesc, cvec, float_type methods = ["default", "energy", "hfc", "complex", "phase", "specdiff", "kl", "mkl", "specflux", "centroid", "spread", "skewness", "kurtosis", "slope", "decrease", "rolloff"] buf_size = 2048 class aubio_specdesc(TestCase): def test_members(self): o = specdesc() for method in methods: o = specdesc(method, buf_size) assert_equal ([o.buf_size, o.method], [buf_size, method]) spec = cvec(buf_size) spec.norm[0] = 1 spec.norm[1] = 1./2. #print "%20s" % method, str(o(spec)) o(spec) spec.norm = random.random_sample((len(spec.norm),)).astype(float_type) spec.phas = random.random_sample((len(spec.phas),)).astype(float_type) #print "%20s" % method, str(o(spec)) assert (o(spec) != 0.) def test_phase(self): o = specdesc("phase", buf_size) spec = cvec(buf_size) # phase of zeros is zero assert_equal (o(spec), 0.) spec.phas = random.random_sample((len(spec.phas),)).astype(float_type) # phase of random is not zero spec.norm[:] = 1 assert (o(spec) != 0.) def test_specdiff(self): o = specdesc("phase", buf_size) spec = cvec(buf_size) # specdiff of zeros is zero assert_equal (o(spec), 0.) spec.phas = random.random_sample((len(spec.phas),)).astype(float_type) # phase of random is not zero spec.norm[:] = 1 assert (o(spec) != 0.) def test_hfc(self): o = specdesc("hfc") c = cvec() assert_equal( 0., o(c)) a = arange(c.length, dtype=float_type) c.norm = a assert_equal (a, c.norm) assert_equal ( sum(a*(a+1)), o(c)) def test_complex(self): o = specdesc("complex") c = cvec() assert_equal( 0., o(c)) a = arange(c.length, dtype=float_type) c.norm = a assert_equal (a, c.norm) # the previous run was on zeros, so previous frames are still 0 # so we have sqrt ( abs ( r2 ^ 2) ) == r2 assert_equal ( sum(a), o(c)) # second time. c.norm = a, so, r1 = r2, and the euclidian distance is 0 assert_equal ( 0, o(c)) def test_kl(self): o = specdesc("kl") c = cvec() assert_equal( 0., o(c)) a = arange(c.length, dtype=float_type) c.norm = a assert_almost_equal( sum(a * log(1.+ a/1.e-1 ) ) / o(c), 1., decimal=6) def test_mkl(self): o = specdesc("mkl") c = cvec() assert_equal( 0., o(c)) a = arange(c.length, dtype=float_type) c.norm = a assert_almost_equal( sum(log(1.+ a/1.e-1 ) ) / o(c), 1, decimal=6) def test_specflux(self): o = specdesc("specflux") c = cvec() assert_equal( 0., o(c)) a = arange(c.length, dtype=float_type) c.norm = a assert_equal( sum(a), o(c)) assert_equal( 0, o(c)) c.norm = zeros(c.length, dtype=float_type) assert_equal( 0, o(c)) def test_centroid(self): o = specdesc("centroid") c = cvec() # make sure centroid of zeros is zero assert_equal( 0., o(c)) a = arange(c.length, dtype=float_type) c.norm = a centroid = sum(a*a) / sum(a) assert_almost_equal (centroid, o(c), decimal = 2) c.norm = a * .5 assert_almost_equal (centroid, o(c), decimal = 2) def test_spread(self): o = specdesc("spread") c = cvec(1024) ramp = arange(c.length, dtype=float_type) assert_equal( 0., o(c)) a = ramp c.norm = a centroid = sum(a*a) / sum(a) spread = sum( a * pow(ramp - centroid, 2.) ) / sum(a) assert_almost_equal (o(c), spread, decimal = 1) def test_skewness(self): o = specdesc("skewness") c = cvec() assert_equal( 0., o(c)) a = arange(c.length, dtype=float_type) c.norm = a centroid = sum(a*a) / sum(a) spread = sum( (a - centroid)**2 *a) / sum(a) skewness = sum( (a - centroid)**3 *a) / sum(a) / spread **1.5 assert_almost_equal (skewness, o(c), decimal = 2) c.norm = a * 3 assert_almost_equal (skewness, o(c), decimal = 2) def test_kurtosis(self): o = specdesc("kurtosis") c = cvec() assert_equal( 0., o(c)) a = arange(c.length, dtype=float_type) c.norm = a centroid = sum(a*a) / sum(a) spread = sum( (a - centroid)**2 *a) / sum(a) kurtosis = sum( (a - centroid)**4 *a) / sum(a) / spread **2 assert_almost_equal (kurtosis, o(c), decimal = 2) def test_slope(self): o = specdesc("slope") c = cvec() assert_equal( 0., o(c)) a = arange(c.length * 2, 0, -2, dtype=float_type) k = arange(c.length, dtype=float_type) c.norm = a num = len(a) * sum(k*a) - sum(k)*sum(a) den = (len(a) * sum(k**2) - sum(k)**2) slope = num/den/sum(a) assert_almost_equal (slope, o(c), decimal = 5) a = arange(0, c.length * 2, +2, dtype=float_type) c.norm = a num = len(a) * sum(k*a) - sum(k)*sum(a) den = (len(a) * sum(k**2) - sum(k)**2) slope = num/den/sum(a) assert_almost_equal (slope, o(c), decimal = 5) a = arange(0, c.length * 2, +2, dtype=float_type) c.norm = a * 2 assert_almost_equal (slope, o(c), decimal = 5) def test_decrease(self): o = specdesc("decrease") c = cvec() assert_equal( 0., o(c)) a = arange(c.length * 2, 0, -2, dtype=float_type) k = arange(c.length, dtype=float_type) c.norm = a decrease = sum((a[1:] - a [0]) / k[1:]) / sum(a[1:]) assert_almost_equal (decrease, o(c), decimal = 5) a = arange(0, c.length * 2, +2, dtype=float_type) c.norm = a decrease = sum((a[1:] - a [0]) / k[1:]) / sum(a[1:]) assert_almost_equal (decrease, o(c), decimal = 5) a = arange(0, c.length * 2, +2, dtype=float_type) c.norm = a * 2 decrease = sum((a[1:] - a [0]) / k[1:]) / sum(a[1:]) assert_almost_equal (decrease, o(c), decimal = 5) def test_rolloff(self): o = specdesc("rolloff") c = cvec() assert_equal( 0., o(c)) a = arange(c.length * 2, 0, -2, dtype=float_type) c.norm = a cumsum = .95*sum(a*a) i = 0; rollsum = 0 while rollsum < cumsum: rollsum += a[i]*a[i] i+=1 rolloff = i assert_equal (rolloff, o(c)) class aubio_specdesc_wrong(TestCase): def test_negative(self): with self.assertRaises(ValueError): specdesc("default", -10) def test_unknown(self): with self.assertRaises(RuntimeError): specdesc("unknown", 512) if __name__ == '__main__': from unittest import main main()

31.128755

82

0.524886

ddc83e627025704c5e7fbc401781c975fad633b4

648

py

Python

e2e/scripts/st_info.py

kamito/streamlit

af68a915b3a1f37ddd411d081e430dad70869c45

[ "Apache-2.0" ]

19,099

2019-08-25T14:00:15.000Z

2022-03-31T21:00:28.000Z

e2e/scripts/st_info.py

linzhou-zhong/streamlit

fde1b548e4bf2d2e5a97b5c3fcf655d43134b342

[ "Apache-2.0" ]

3,078

2019-08-25T19:50:14.000Z

2022-03-31T23:26:14.000Z

e2e/scripts/st_info.py

linzhou-zhong/streamlit

fde1b548e4bf2d2e5a97b5c3fcf655d43134b342

[ "Apache-2.0" ]

1,892

2019-08-26T04:44:24.000Z

2022-03-30T16:11:51.000Z

# Copyright 2018-2021 Streamlit Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import streamlit as st st.info("This info message is awesome!")

36

74

0.762346

20d9a4c2812c63b13e845105f6b7111076c05881

1,471

py

Python

machinelearn2.py

varnaugj/Python-Early-Codes

3b659529c65dc608eaf41ec5d5ffaa4c18704946

[ "MIT" ]

null

machinelearn2.py

varnaugj/Python-Early-Codes

3b659529c65dc608eaf41ec5d5ffaa4c18704946

[ "MIT" ]

null

machinelearn2.py

varnaugj/Python-Early-Codes

3b659529c65dc608eaf41ec5d5ffaa4c18704946

[ "MIT" ]

null

#News Article specific reader #import nltk #from nltk import send_tokenize #from nltk import word_tokenize import requests import newspaper from bs4 import BeautifulSoup as bs from newspaper import Article def news(): #target we want to open url = 'https://www.cnn.com' papertest = newspaper.build(url) for article in papertest.articles: print(article.url) #open with GET method resp = requests.get(url) #http_respone 200 means link works if resp.status_code==200: print("Successfully opened the web page") print("the news are as follow :-\n") # article = Article(url) # article.download() # article.parse() # article.nlp() # text = article.text # print (type(text)) # print("\n") # print(text) # print("\n") # print(len(text)) # print(article.keywords) else: print("Error") #news() def news2(): #url = 'https://www.cnn.com/politics' url = 'https://www.foxnews.com/politics' #open with GET method resp = requests.get(url) soup = bs(resp.text, 'html.parser') rawtext = soup.get_text() #http_respone 200 means link works if resp.status_code==200: print("Successfully opened the web page") print("the news are as follow :-\n") for link in soup.find_all('a'): print(link.get('href')) else: print("Error") news2()

21.014286

49

0.600272

44609016798ad96c96c6d1b464e4a38f819977d8

11,761

py

Python

pandadoc_client/model/document_create_request_content_placeholders.py

PandaDoc/pandadoc-api-python-client

a707c540e788eee485cc338f29ca363acca4973e

[ "MIT" ]

27

2021-11-16T11:30:13.000Z

2022-03-17T08:56:18.000Z

pandadoc_client/model/document_create_request_content_placeholders.py

PandaDoc/pandadoc-api-python-client

a707c540e788eee485cc338f29ca363acca4973e

[ "MIT" ]

null

pandadoc_client/model/document_create_request_content_placeholders.py

PandaDoc/pandadoc-api-python-client

a707c540e788eee485cc338f29ca363acca4973e

[ "MIT" ]

2

2021-12-16T13:38:15.000Z

2022-01-09T00:38:00.000Z

""" PandaDoc Public API PandaDoc Public API documentation # noqa: E501 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from pandadoc_client.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from pandadoc_client.exceptions import ApiAttributeError def lazy_import(): from pandadoc_client.model.document_create_request_content_library_items import DocumentCreateRequestContentLibraryItems globals()['DocumentCreateRequestContentLibraryItems'] = DocumentCreateRequestContentLibraryItems class DocumentCreateRequestContentPlaceholders(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ lazy_import() return { 'block_id': (str,), # noqa: E501 'content_library_items': ([DocumentCreateRequestContentLibraryItems],), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'block_id': 'block_id', # noqa: E501 'content_library_items': 'content_library_items', # noqa: E501 } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 """DocumentCreateRequestContentPlaceholders - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) block_id (str): Content placeholder block id. [optional] # noqa: E501 content_library_items ([DocumentCreateRequestContentLibraryItems]): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """DocumentCreateRequestContentPlaceholders - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) block_id (str): Content placeholder block id. [optional] # noqa: E501 content_library_items ([DocumentCreateRequestContentLibraryItems]): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

44.381132

124

0.583539

13139a39a5b5ddcc410924d54dba2ad031ca1239

1,057

py

Python

data-handling-scripts/split_rid_newendo.py

bolero2/DeepLearning-dc

680266128d5a7aff590e2d6b9b71cb340b95c2ab

[ "Apache-2.0" ]

2

2021-04-23T03:49:30.000Z

2021-04-23T03:49:33.000Z

data-handling-scripts/split_rid_newendo.py

bolero2/DeepLearning-dc

680266128d5a7aff590e2d6b9b71cb340b95c2ab

[ "Apache-2.0" ]

null

data-handling-scripts/split_rid_newendo.py

bolero2/DeepLearning-dc

680266128d5a7aff590e2d6b9b71cb340b95c2ab

[ "Apache-2.0" ]

null

import pandas as pd import glob import os import random import shutil as sh df = pd.read_csv("RID_for_test_newENDO.csv") rid = list(df.loc[df['GUBUN'] == "TRAIN"]['RID']) print(f'RID= {rid}') root = os.getcwd() print(root) os.chdir(f'{root}/yolo_dataset/') for imagename in glob.glob('*.jpg'): three = imagename[0:3] if three[-1] == '내': idnum = three[0] elif three[-1] == '_': idnum = three[0:2] else: idnum = three if int(idnum) in rid: print(f'Target= {idnum}') sh.copy(f'{root}/yolo_dataset/{imagename}', f'{root}/rid/train/') """ if len(idx) == 1: os.chdir(root + "/" + idx) full_path = f'{root}/{idx}/' # print(os.getcwd()) filelist = os.listdir() for filename in filelist: file_rid = filename[0:8] if file_rid in rid8: shutil.copy(f'{full_path}{filename}', f'{root}/test/{idx}/') else: shutil.copy(f'{full_path}{filename}', f'{root}/train/{idx}/') """

25.166667

77

0.535478

7d07851c3aa3f4962dd40b267f3fdf81c60126f3

3,492

py

Python

src/mykrobe/typing/models/variant.py

chamilaadikaram/mykrobe

2bcebf7b37f1c1416f397374da6ebfd02ce1aead

[ "MIT" ]

1

2020-08-08T01:08:01.000Z

src/mykrobe/typing/models/variant.py

chamilaadikaram/mykrobe

2bcebf7b37f1c1416f397374da6ebfd02ce1aead

[ "MIT" ]

null

src/mykrobe/typing/models/variant.py

chamilaadikaram/mykrobe

2bcebf7b37f1c1416f397374da6ebfd02ce1aead

[ "MIT" ]

null

import datetime import json import logging logger = logging.getLogger(__name__) class VariantProbeCoverage(object): def __init__(self, reference_coverages, alternate_coverages, var_name=None, params={}): self.reference_coverages = reference_coverages self.alternate_coverages = alternate_coverages self.var_name = var_name self.params = params if self.reference_coverages and self.alternate_coverages: self.best_alternate_coverage = self._choose_best_alternate_coverage() self.best_reference_coverage = self._choose_best_reference_coverage() def _choose_best_coverage(self, coverages): coverages.sort( key=lambda x: x.k_count, reverse=True) current_best = coverages[0] for probe_coverage in coverages[1:]: if probe_coverage.k_count < current_best.k_count: current_best = current_best else: if probe_coverage.percent_coverage > current_best.percent_coverage: current_best = probe_coverage elif probe_coverage.min_depth > current_best.min_depth: current_best = probe_coverage elif probe_coverage.min_depth <= current_best.min_depth: if probe_coverage.median_depth > current_best.median_depth: current_best = probe_coverage return current_best def _choose_best_alternate_coverage(self): return self._choose_best_coverage(self.alternate_coverages) def _choose_best_reference_coverage(self): best_reference_coverage = self._choose_best_coverage( self.reference_coverages) return best_reference_coverage @property def coverage_dict(self): return {"reference": self.best_reference_coverage.coverage_dict, "alternate": self.best_alternate_coverage.coverage_dict } def __str__(self): d = self.coverage_dict d['variant'] = self.var_name return json.dumps(d) def __repr__(self): return self.__str__() @property def reference_coverage(self): return self.best_reference_coverage @property def reference_percent_coverage(self): return self.best_reference_coverage.percent_coverage @property def reference_kmer_count(self): return self.best_reference_coverage.k_count @property def reference_median_depth(self): return self.best_reference_coverage.median_depth @property def reference_min_depth(self): return self.best_reference_coverage.min_depth @property def reference_klen(self): return self.best_reference_coverage.klen @property def alternate_percent_coverage(self): return self.best_alternate_coverage.percent_coverage @alternate_percent_coverage.setter def alternate_percent_coverage(self, value): self.best_alternate_coverage.percent_coverage = value @property def alternate_median_depth(self): return self.best_alternate_coverage.median_depth @property def alternate_kmer_count(self): return self.best_alternate_coverage.k_count @property def alternate_min_depth(self): return self.best_alternate_coverage.min_depth @property def alternate_klen(self): return self.best_alternate_coverage.klen

32.333333

83

0.684994

0a61e3a9ac1bd1b8232eb8bb893b97a4327bc46c

8,166

py

Python

Lib/concurrent/futures/thread.py

MaxNoe/cpython

29d018aa63b72161cfc67602dc3dbd386272da64

[ "CNRI-Python-GPL-Compatible" ]

1

2021-03-26T10:54:41.000Z

Lib/concurrent/futures/thread.py

MaxNoe/cpython

29d018aa63b72161cfc67602dc3dbd386272da64

[ "CNRI-Python-GPL-Compatible" ]

4

2022-03-30T01:50:22.000Z

2022-03-30T01:50:28.000Z

Lib/concurrent/futures/thread.py

MaxNoe/cpython

29d018aa63b72161cfc67602dc3dbd386272da64

[ "CNRI-Python-GPL-Compatible" ]

1

2021-02-01T20:44:21.000Z

# Copyright 2009 Brian Quinlan. All Rights Reserved. # Licensed to PSF under a Contributor Agreement. """Implements ThreadPoolExecutor.""" __author__ = 'Brian Quinlan (brian@sweetapp.com)' import atexit from concurrent.futures import _base import itertools import queue import threading import weakref import os # Workers are created as daemon threads. This is done to allow the interpreter # to exit when there are still idle threads in a ThreadPoolExecutor's thread # pool (i.e. shutdown() was not called). However, allowing workers to die with # the interpreter has two undesirable properties: # - The workers would still be running during interpreter shutdown, # meaning that they would fail in unpredictable ways. # - The workers could be killed while evaluating a work item, which could # be bad if the callable being evaluated has external side-effects e.g. # writing to a file. # # To work around this problem, an exit handler is installed which tells the # workers to exit when their work queues are empty and then waits until the # threads finish. _threads_queues = weakref.WeakKeyDictionary() _shutdown = False def _python_exit(): global _shutdown _shutdown = True items = list(_threads_queues.items()) for t, q in items: q.put(None) for t, q in items: t.join() atexit.register(_python_exit) class _WorkItem(object): def __init__(self, future, fn, args, kwargs): self.future = future self.fn = fn self.args = args self.kwargs = kwargs def run(self): if not self.future.set_running_or_notify_cancel(): return try: result = self.fn(*self.args, **self.kwargs) except BaseException as exc: self.future.set_exception(exc) # Break a reference cycle with the exception 'exc' self = None else: self.future.set_result(result) def _worker(executor_reference, work_queue, initializer, initargs): if initializer is not None: try: initializer(*initargs) except BaseException: _base.LOGGER.critical('Exception in initializer:', exc_info=True) executor = executor_reference() if executor is not None: executor._initializer_failed() return try: while True: work_item = work_queue.get(block=True) if work_item is not None: work_item.run() # Delete references to object. See issue16284 del work_item continue executor = executor_reference() # Exit if: # - The interpreter is shutting down OR # - The executor that owns the worker has been collected OR # - The executor that owns the worker has been shutdown. if _shutdown or executor is None or executor._shutdown: # Flag the executor as shutting down as early as possible if it # is not gc-ed yet. if executor is not None: executor._shutdown = True # Notice other workers work_queue.put(None) return del executor except BaseException: _base.LOGGER.critical('Exception in worker', exc_info=True) class BrokenThreadPool(_base.BrokenExecutor): """ Raised when a worker thread in a ThreadPoolExecutor failed initializing. """ class ThreadPoolExecutor(_base.Executor): # Used to assign unique thread names when thread_name_prefix is not supplied. _counter = itertools.count().__next__ def __init__(self, max_workers=None, thread_name_prefix='', initializer=None, initargs=()): """Initializes a new ThreadPoolExecutor instance. Args: max_workers: The maximum number of threads that can be used to execute the given calls. thread_name_prefix: An optional name prefix to give our threads. initializer: An callable used to initialize worker threads. initargs: A tuple of arguments to pass to the initializer. """ if max_workers is None: # Use this number because ThreadPoolExecutor is often # used to overlap I/O instead of CPU work. max_workers = (os.cpu_count() or 1) * 5 if max_workers <= 0: raise ValueError("max_workers must be greater than 0") if initializer is not None and not callable(initializer): raise TypeError("initializer must be a callable") self._max_workers = max_workers self._work_queue = queue.SimpleQueue() self._threads = set() self._broken = False self._shutdown = False self._shutdown_lock = threading.Lock() self._thread_name_prefix = (thread_name_prefix or ("ThreadPoolExecutor-%d" % self._counter())) self._initializer = initializer self._initargs = initargs def submit(*args, **kwargs): if len(args) >= 2: self, fn, *args = args elif not args: raise TypeError("descriptor 'submit' of 'ThreadPoolExecutor' object " "needs an argument") elif 'fn' in kwargs: fn = kwargs.pop('fn') self, *args = args import warnings warnings.warn("Passing 'fn' as keyword argument is deprecated", DeprecationWarning, stacklevel=2) else: raise TypeError('submit expected at least 1 positional argument, ' 'got %d' % (len(args)-1)) with self._shutdown_lock: if self._broken: raise BrokenThreadPool(self._broken) if self._shutdown: raise RuntimeError('cannot schedule new futures after shutdown') if _shutdown: raise RuntimeError('cannot schedule new futures after ' 'interpreter shutdown') f = _base.Future() w = _WorkItem(f, fn, args, kwargs) self._work_queue.put(w) self._adjust_thread_count() return f submit.__doc__ = _base.Executor.submit.__doc__ def _adjust_thread_count(self): # When the executor gets lost, the weakref callback will wake up # the worker threads. def weakref_cb(_, q=self._work_queue): q.put(None) # TODO(bquinlan): Should avoid creating new threads if there are more # idle threads than items in the work queue. num_threads = len(self._threads) if num_threads < self._max_workers: thread_name = '%s_%d' % (self._thread_name_prefix or self, num_threads) t = threading.Thread(name=thread_name, target=_worker, args=(weakref.ref(self, weakref_cb), self._work_queue, self._initializer, self._initargs)) t.daemon = True t.start() self._threads.add(t) _threads_queues[t] = self._work_queue def _initializer_failed(self): with self._shutdown_lock: self._broken = ('A thread initializer failed, the thread pool ' 'is not usable anymore') # Drain work queue and mark pending futures failed while True: try: work_item = self._work_queue.get_nowait() except queue.Empty: break if work_item is not None: work_item.future.set_exception(BrokenThreadPool(self._broken)) def shutdown(self, wait=True): with self._shutdown_lock: self._shutdown = True self._work_queue.put(None) if wait: for t in self._threads: t.join() shutdown.__doc__ = _base.Executor.shutdown.__doc__

36.950226

82

0.59613