fumiyau/python_comments_free_500000_v2 · Datasets at Hugging Face

f70bd2c627e3404c5ea43791336a763164c6fba4

1,318

py

Python

HDPython/ast/ast_classes/ast_op_stream_out.py

HardwareDesignWithPython/HDPython

aade03aaa092b1684fa12bffd17674cf1c45f5ac

[ "MIT" ]

null

HDPython/ast/ast_classes/ast_op_stream_out.py

HardwareDesignWithPython/HDPython

aade03aaa092b1684fa12bffd17674cf1c45f5ac

[ "MIT" ]

null

HDPython/ast/ast_classes/ast_op_stream_out.py

HardwareDesignWithPython/HDPython

aade03aaa092b1684fa12bffd17674cf1c45f5ac

[ "MIT" ]

1

2021-10-20T20:08:16.000Z

from HDPython.ast.ast_classes.ast_base import v_ast_base, add_class import HDPython.hdl_converter as hdl from HDPython.ast.ast_hdl_error import HDPython_error from HDPython.base import HDPython_base class v_re_assigne_rhsift(v_ast_base): def __init__(self,lhs, rhs,context=None, astParser=None): self.lhs = lhs self.rhs = rhs self.context =context self.astParser = astParser def __str__(self): if issubclass(type(self.lhs),HDPython_base): return hdl.impl_reasign_rshift_(self.lhs, self.rhs, astParser=self.astParser, context_str=self.context ) return str(self.lhs) + " := " + str(self.rhs) def body_RShift(astParser,Node): rhs = astParser.Unfold_body(Node.right) lhs = astParser.Unfold_body(Node.left) if issubclass( type(lhs),HDPython_base) and issubclass( type(rhs),HDPython_base): rhs.__Driver__ = astParser.ContextName[-1] return v_re_assigne_rhsift(lhs, rhs,context=astParser.ContextName[-1],astParser=astParser) err_msg = HDPython_error( astParser.sourceFileName, Node.lineno, Node.col_offset, type(lhs).__name__, "right shift is only supported for HDPyhon objects" ) raise Exception(err_msg,lhs) add_class("RShift",body_RShift)

33.794872

116

0.695751

from HDPython.ast.ast_classes.ast_base import v_ast_base, add_class import HDPython.hdl_converter as hdl from HDPython.ast.ast_hdl_error import HDPython_error from HDPython.base import HDPython_base class v_re_assigne_rhsift(v_ast_base): def __init__(self,lhs, rhs,context=None, astParser=None): self.lhs = lhs self.rhs = rhs self.context =context self.astParser = astParser def __str__(self): if issubclass(type(self.lhs),HDPython_base): return hdl.impl_reasign_rshift_(self.lhs, self.rhs, astParser=self.astParser, context_str=self.context ) return str(self.lhs) + " := " + str(self.rhs) def body_RShift(astParser,Node): rhs = astParser.Unfold_body(Node.right) lhs = astParser.Unfold_body(Node.left) if issubclass( type(lhs),HDPython_base) and issubclass( type(rhs),HDPython_base): rhs.__Driver__ = astParser.ContextName[-1] return v_re_assigne_rhsift(lhs, rhs,context=astParser.ContextName[-1],astParser=astParser) err_msg = HDPython_error( astParser.sourceFileName, Node.lineno, Node.col_offset, type(lhs).__name__, "right shift is only supported for HDPyhon objects" ) raise Exception(err_msg,lhs) add_class("RShift",body_RShift)

true

f70bd2ee450be0f82157aa65881304ad6a24cb47

1,884

py

Python

dask_kubernetes/conftest.py

ddelange/dask-kubernetes

42bcf9817ea963bf048f9dd06caec1622656302a

[ "BSD-3-Clause" ]

1

2022-01-20T12:38:27.000Z

dask_kubernetes/conftest.py

ddelange/dask-kubernetes

42bcf9817ea963bf048f9dd06caec1622656302a

[ "BSD-3-Clause" ]

null

dask_kubernetes/conftest.py

ddelange/dask-kubernetes

42bcf9817ea963bf048f9dd06caec1622656302a

[ "BSD-3-Clause" ]

null

import pytest import pathlib import os import subprocess import tempfile from kopf.testing import KopfRunner from dask_kubernetes.common.utils import check_dependency DIR = pathlib.Path(__file__).parent.absolute() check_dependency("helm") check_dependency("kubectl") check_dependency("docker") @pytest.fixture() async def kopf_runner(k8s_cluster): yield KopfRunner(["run", "-m", "dask_kubernetes.operator", "--verbose"]) @pytest.fixture(scope="session") def docker_image(): image_name = "dask-kubernetes:dev" subprocess.check_output(["docker", "build", "-t", image_name, "./ci/"]) return image_name @pytest.fixture(scope="session") def k8s_cluster(kind_cluster, docker_image): os.environ["KUBECONFIG"] = str(kind_cluster.kubeconfig_path) kind_cluster.load_docker_image(docker_image) yield kind_cluster del os.environ["KUBECONFIG"] @pytest.fixture(scope="session") def ns(k8s_cluster): return "default" def run_generate(crd_path, patch_path, temp_path): subprocess.run( ["k8s-crd-resolver", "-r", "-j", patch_path, crd_path, temp_path], check=True, env={**os.environ}, ) @pytest.fixture(scope="session", autouse=True) def customresources(k8s_cluster): temp_dir = tempfile.TemporaryDirectory() crd_path = os.path.join(DIR, "operator", "customresources") run_generate( os.path.join(crd_path, "daskcluster.yaml"), os.path.join(crd_path, "daskcluster.patch.yaml"), os.path.join(temp_dir.name, "daskcluster.yaml"), ) run_generate( os.path.join(crd_path, "daskworkergroup.yaml"), os.path.join(crd_path, "daskworkergroup.patch.yaml"), os.path.join(temp_dir.name, "daskworkergroup.yaml"), ) k8s_cluster.kubectl("apply", "-f", temp_dir.name) yield k8s_cluster.kubectl("delete", "-f", temp_dir.name) temp_dir.cleanup()

25.808219

76

0.701168

import pytest import pathlib import os import subprocess import tempfile from kopf.testing import KopfRunner from dask_kubernetes.common.utils import check_dependency DIR = pathlib.Path(__file__).parent.absolute() check_dependency("helm") check_dependency("kubectl") check_dependency("docker") @pytest.fixture() async def kopf_runner(k8s_cluster): yield KopfRunner(["run", "-m", "dask_kubernetes.operator", "--verbose"]) @pytest.fixture(scope="session") def docker_image(): image_name = "dask-kubernetes:dev" subprocess.check_output(["docker", "build", "-t", image_name, "./ci/"]) return image_name @pytest.fixture(scope="session") def k8s_cluster(kind_cluster, docker_image): os.environ["KUBECONFIG"] = str(kind_cluster.kubeconfig_path) kind_cluster.load_docker_image(docker_image) yield kind_cluster del os.environ["KUBECONFIG"] @pytest.fixture(scope="session") def ns(k8s_cluster): return "default" def run_generate(crd_path, patch_path, temp_path): subprocess.run( ["k8s-crd-resolver", "-r", "-j", patch_path, crd_path, temp_path], check=True, env={**os.environ}, ) @pytest.fixture(scope="session", autouse=True) def customresources(k8s_cluster): temp_dir = tempfile.TemporaryDirectory() crd_path = os.path.join(DIR, "operator", "customresources") run_generate( os.path.join(crd_path, "daskcluster.yaml"), os.path.join(crd_path, "daskcluster.patch.yaml"), os.path.join(temp_dir.name, "daskcluster.yaml"), ) run_generate( os.path.join(crd_path, "daskworkergroup.yaml"), os.path.join(crd_path, "daskworkergroup.patch.yaml"), os.path.join(temp_dir.name, "daskworkergroup.yaml"), ) k8s_cluster.kubectl("apply", "-f", temp_dir.name) yield k8s_cluster.kubectl("delete", "-f", temp_dir.name) temp_dir.cleanup()

true

f70bd49fe5654e00114c7d8e83bb1de6aef33e5b

1,019

py

Python

bigquery/samples/tests/test_query_to_arrow.py

ryanyuan/google-cloud-python

db481bfdd6816d020d99df0d4caa307358ab1141

[ "Apache-2.0" ]

2

2021-11-26T07:08:43.000Z

2022-03-07T20:20:04.000Z

bigquery/samples/tests/test_query_to_arrow.py

ryanyuan/google-cloud-python

db481bfdd6816d020d99df0d4caa307358ab1141

[ "Apache-2.0" ]

null

bigquery/samples/tests/test_query_to_arrow.py

ryanyuan/google-cloud-python

db481bfdd6816d020d99df0d4caa307358ab1141

[ "Apache-2.0" ]

null

# Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pyarrow from .. import query_to_arrow def test_query_to_arrow(capsys, client): arrow_table = query_to_arrow.query_to_arrow(client) out, err = capsys.readouterr() assert "Downloaded 8 rows, 2 columns." in out arrow_schema = arrow_table.schema assert arrow_schema.names == ["race", "participant"] assert pyarrow.types.is_string(arrow_schema.types[0]) assert pyarrow.types.is_struct(arrow_schema.types[1])

33.966667

74

0.75368

import pyarrow from .. import query_to_arrow def test_query_to_arrow(capsys, client): arrow_table = query_to_arrow.query_to_arrow(client) out, err = capsys.readouterr() assert "Downloaded 8 rows, 2 columns." in out arrow_schema = arrow_table.schema assert arrow_schema.names == ["race", "participant"] assert pyarrow.types.is_string(arrow_schema.types[0]) assert pyarrow.types.is_struct(arrow_schema.types[1])

true

f70bd5b228ad260502fad0f468efc7ec516cb86b

4,898

py

Python

src/oci/autoscaling/models/auto_scaling_policy_summary.py

Manny27nyc/oci-python-sdk

de60b04e07a99826254f7255e992f41772902df7

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

249

2017-09-11T22:06:05.000Z

2022-03-04T17:09:29.000Z

src/oci/autoscaling/models/auto_scaling_policy_summary.py

Manny27nyc/oci-python-sdk

de60b04e07a99826254f7255e992f41772902df7

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

228

2017-09-11T23:07:26.000Z

2022-03-23T10:58:50.000Z

src/oci/autoscaling/models/auto_scaling_policy_summary.py

Manny27nyc/oci-python-sdk

de60b04e07a99826254f7255e992f41772902df7

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

224

2017-09-27T07:32:43.000Z

2022-03-25T16:55:42.000Z

# coding: utf-8 # Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class AutoScalingPolicySummary(object): """ Summary information for an autoscaling policy. """ def __init__(self, **kwargs): """ Initializes a new AutoScalingPolicySummary object with values from keyword arguments. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param id: The value to assign to the id property of this AutoScalingPolicySummary. :type id: str :param display_name: The value to assign to the display_name property of this AutoScalingPolicySummary. :type display_name: str :param policy_type: The value to assign to the policy_type property of this AutoScalingPolicySummary. :type policy_type: str :param is_enabled: The value to assign to the is_enabled property of this AutoScalingPolicySummary. :type is_enabled: bool """ self.swagger_types = { 'id': 'str', 'display_name': 'str', 'policy_type': 'str', 'is_enabled': 'bool' } self.attribute_map = { 'id': 'id', 'display_name': 'displayName', 'policy_type': 'policyType', 'is_enabled': 'isEnabled' } self._id = None self._display_name = None self._policy_type = None self._is_enabled = None @property def id(self): """ **[Required]** Gets the id of this AutoScalingPolicySummary. The ID of the autoscaling policy that is assigned after creation. :return: The id of this AutoScalingPolicySummary. :rtype: str """ return self._id @id.setter def id(self, id): """ Sets the id of this AutoScalingPolicySummary. The ID of the autoscaling policy that is assigned after creation. :param id: The id of this AutoScalingPolicySummary. :type: str """ self._id = id @property def display_name(self): """ Gets the display_name of this AutoScalingPolicySummary. A user-friendly name. Does not have to be unique, and it's changeable. Avoid entering confidential information. :return: The display_name of this AutoScalingPolicySummary. :rtype: str """ return self._display_name @display_name.setter def display_name(self, display_name): """ Sets the display_name of this AutoScalingPolicySummary. A user-friendly name. Does not have to be unique, and it's changeable. Avoid entering confidential information. :param display_name: The display_name of this AutoScalingPolicySummary. :type: str """ self._display_name = display_name @property def policy_type(self): """ **[Required]** Gets the policy_type of this AutoScalingPolicySummary. The type of autoscaling policy. :return: The policy_type of this AutoScalingPolicySummary. :rtype: str """ return self._policy_type @policy_type.setter def policy_type(self, policy_type): """ Sets the policy_type of this AutoScalingPolicySummary. The type of autoscaling policy. :param policy_type: The policy_type of this AutoScalingPolicySummary. :type: str """ self._policy_type = policy_type @property def is_enabled(self): """ Gets the is_enabled of this AutoScalingPolicySummary. Whether the autoscaling policy is enabled. :return: The is_enabled of this AutoScalingPolicySummary. :rtype: bool """ return self._is_enabled @is_enabled.setter def is_enabled(self, is_enabled): """ Sets the is_enabled of this AutoScalingPolicySummary. Whether the autoscaling policy is enabled. :param is_enabled: The is_enabled of this AutoScalingPolicySummary. :type: bool """ self._is_enabled = is_enabled def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

29.865854

245

0.64067

from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class AutoScalingPolicySummary(object): def __init__(self, **kwargs): self.swagger_types = { 'id': 'str', 'display_name': 'str', 'policy_type': 'str', 'is_enabled': 'bool' } self.attribute_map = { 'id': 'id', 'display_name': 'displayName', 'policy_type': 'policyType', 'is_enabled': 'isEnabled' } self._id = None self._display_name = None self._policy_type = None self._is_enabled = None @property def id(self): return self._id @id.setter def id(self, id): self._id = id @property def display_name(self): return self._display_name @display_name.setter def display_name(self, display_name): self._display_name = display_name @property def policy_type(self): return self._policy_type @policy_type.setter def policy_type(self, policy_type): self._policy_type = policy_type @property def is_enabled(self): return self._is_enabled @is_enabled.setter def is_enabled(self, is_enabled): self._is_enabled = is_enabled def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f70bd624daea884c43b42cf57519b4e3a69a5311

1,413

py

Python

python/consumer.py

vishnuhd/getting-started-with-apache-kafka

7b900fc419cbcda8ab882121c2a72e63bdc8b2c4

[ "Apache-2.0" ]

14

2018-11-01T16:11:41.000Z

2019-06-03T14:52:03.000Z

python/consumer.py

vishnuhd/getting-started-with-apache-kafka

7b900fc419cbcda8ab882121c2a72e63bdc8b2c4

[ "Apache-2.0" ]

1

2018-10-31T15:39:24.000Z

2018-11-30T03:05:10.000Z

python/consumer.py

vishnuhd/getting-started-with-apache-kafka

7b900fc419cbcda8ab882121c2a72e63bdc8b2c4

[ "Apache-2.0" ]

8

2018-10-31T15:39:39.000Z

2019-06-06T12:21:55.000Z

from confluent_kafka import Consumer, KafkaException, KafkaError import sys import logging from pprint import pformat def print_assignment(consumer, partitions): print('Assignment:', partitions) if __name__ == '__main__': conf = { 'bootstrap.servers': 'localhost:9092', 'group.id': 'devnation-python', 'session.timeout.ms': 6000, 'auto.offset.reset': 'earliest' } c = Consumer(conf) c.subscribe(['devnation'], on_assign=print_assignment) # Read messages from Kafka, print to stdout try: while True: msg = c.poll(timeout=1.0) if msg is None: continue if msg.error(): if msg.error().code() == KafkaError._PARTITION_EOF: # Continue -> we reached the end of the partition continue else: sys.stderr.write('-E- Something went wrong: %s' % msg.error()) break else: # Proper message sys.stderr.write('-I- %s [%d] at offset %d with key %s: ' % (msg.topic(), msg.partition(), msg.offset(), str(msg.key()))) print(msg.value()) except KeyboardInterrupt: sys.stderr.write('%% Aborted by user\n') finally: c.close()

31.4

82

0.515924

from confluent_kafka import Consumer, KafkaException, KafkaError import sys import logging from pprint import pformat def print_assignment(consumer, partitions): print('Assignment:', partitions) if __name__ == '__main__': conf = { 'bootstrap.servers': 'localhost:9092', 'group.id': 'devnation-python', 'session.timeout.ms': 6000, 'auto.offset.reset': 'earliest' } c = Consumer(conf) c.subscribe(['devnation'], on_assign=print_assignment) try: while True: msg = c.poll(timeout=1.0) if msg is None: continue if msg.error(): if msg.error().code() == KafkaError._PARTITION_EOF: continue else: sys.stderr.write('-E- Something went wrong: %s' % msg.error()) break else: sys.stderr.write('-I- %s [%d] at offset %d with key %s: ' % (msg.topic(), msg.partition(), msg.offset(), str(msg.key()))) print(msg.value()) except KeyboardInterrupt: sys.stderr.write('%% Aborted by user\n') finally: c.close()

true

f70bd6f163acbc02bb3dfe9ad87f6d700204f840

73,983

py

Python

WrightTools/data/_data.py

untzag/WrightTools

05480d2f91ceeca422d9e5ac381fce1840207cb0

[ "MIT" ]

12

2017-07-11T15:58:12.000Z

2021-05-10T20:33:26.000Z

WrightTools/data/_data.py

untzag/WrightTools

05480d2f91ceeca422d9e5ac381fce1840207cb0

[ "MIT" ]

808

2015-04-12T00:36:08.000Z

2022-03-27T21:06:06.000Z

WrightTools/data/_data.py

untzag/WrightTools

05480d2f91ceeca422d9e5ac381fce1840207cb0

[ "MIT" ]

9

2017-07-22T18:54:23.000Z

2022-02-17T20:31:05.000Z

"""Central data class and associated.""" # --- import -------------------------------------------------------------------------------------- import collections import operator import functools import warnings import numpy as np import h5py import scipy from scipy.interpolate import griddata, interp1d from .._group import Group from .. import collection as wt_collection from .. import exceptions as wt_exceptions from .. import kit as wt_kit from .. import units as wt_units from ._axis import Axis, identifier_to_operator from ._channel import Channel from ._constant import Constant from ._variable import Variable # --- define -------------------------------------------------------------------------------------- __all__ = ["Data"] # --- class --------------------------------------------------------------------------------------- class Data(Group): """Multidimensional dataset.""" class_name = "Data" def __init__(self, *args, **kwargs): self._axes = [] self._constants = [] Group.__init__(self, *args, **kwargs) # populate axes, constants from attrs string for identifier in self.attrs.get("axes", []): if hasattr(identifier, "decode"): identifier = identifier.decode() expression, units = identifier.split("{") units = units.replace("}", "").strip() if units == "None": units = None # Should not be needed for wt5 >= 1.0.3, kept for opening older wt5 files. for i in identifier_to_operator.keys(): expression = expression.replace(i, identifier_to_operator[i]) expression = expression.replace(" ", "") # remove all whitespace axis = Axis(self, expression, units) self._axes.append(axis) for identifier in self.attrs.get("constants", []): if hasattr(identifier, "decode"): identifier = identifier.decode() expression, units = identifier.split("{") units = units.replace("}", "").strip() if units == "None": units = None for i in identifier_to_operator.keys(): expression = expression.replace(i, identifier_to_operator[i]) expression = expression.replace(" ", "") # remove all whitespace const = Constant(self, expression, units) self._constants.append(const) self._current_axis_identities_in_natural_namespace = [] if self.file.mode is not None and self.file.mode != "r": self._on_constants_updated() self._on_axes_updated() # the following are populated if not already recorded self.channel_names self.source self.variable_names def __repr__(self) -> str: return "<WrightTools.Data '{0}' {1} at {2}>".format( self.natural_name, str(self.axis_names), "::".join([self.filepath, self.name]) ) @property def axes(self) -> tuple: return tuple(self._axes) @property def axis_expressions(self) -> tuple: """Axis expressions.""" return tuple(a.expression for a in self._axes) @property def axis_names(self) -> tuple: """Axis names.""" return tuple(a.natural_name for a in self._axes) @property def constants(self) -> tuple: return tuple(self._constants) @property def constant_expressions(self) -> tuple: """Axis expressions.""" return tuple(a.expression for a in self._constants) @property def constant_names(self) -> tuple: """Axis names.""" return tuple(a.natural_name for a in self._constants) @property def channel_names(self) -> tuple: """Channel names.""" if "channel_names" not in self.attrs.keys(): self.attrs["channel_names"] = np.array([], dtype="S") return tuple(s.decode() for s in self.attrs["channel_names"]) @channel_names.setter def channel_names(self, value): """Set channel names.""" self.attrs["channel_names"] = np.array(value, dtype="S") @property def channels(self) -> tuple: """Channels.""" return tuple(self[n] for n in self.channel_names) @property def datasets(self) -> tuple: """Datasets.""" return tuple(v for _, v in self.items() if isinstance(v, h5py.Dataset)) @property def kind(self): """Kind.""" if "kind" not in self.attrs.keys(): self.attrs["kind"] = "None" value = self.attrs["kind"] return value if not value == "None" else None @property def ndim(self) -> int: """Get number of dimensions.""" try: assert self._ndim is not None except (AssertionError, AttributeError): if len(self.variables) == 0: self._ndim = 0 else: self._ndim = self.variables[0].ndim finally: return self._ndim @property def shape(self) -> tuple: """Shape.""" try: assert self._shape is not None except (AssertionError, AttributeError): self._shape = wt_kit.joint_shape(*self.variables) finally: return self._shape @property def size(self) -> int: """Size.""" return functools.reduce(operator.mul, self.shape) @property def source(self): """Source.""" if "source" not in self.attrs.keys(): self.attrs["source"] = "None" value = self.attrs["source"] return value if not value == "None" else None @property def units(self) -> tuple: """All axis units.""" return tuple(a.units for a in self._axes) @property def constant_units(self) -> tuple: """All constant units.""" return tuple(a.units for a in self._constants) @property def variable_names(self) -> tuple: """Variable names.""" if "variable_names" not in self.attrs.keys(): self.attrs["variable_names"] = np.array([], dtype="S") return tuple(s.decode() for s in self.attrs["variable_names"]) @variable_names.setter def variable_names(self, value): """Set variable names.""" self.attrs["variable_names"] = np.array(value, dtype="S") @property def variables(self) -> tuple: """Variables.""" try: assert self._variables is not None except (AssertionError, AttributeError): self._variables = [self[n] for n in self.variable_names] finally: return tuple(self._variables) @property def _leaf(self): return "{0} {1}".format(self.natural_name, self.shape) def _on_axes_updated(self): """Method to run when axes are changed in any way. Propagates updated axes properly. """ # update attrs self.attrs["axes"] = np.array([a.identity.encode() for a in self._axes], dtype="S") # remove old attributes while len(self._current_axis_identities_in_natural_namespace) > 0: key = self._current_axis_identities_in_natural_namespace.pop(0) try: delattr(self, key) except AttributeError: pass # already gone # populate new attributes for a in self._axes: key = a.natural_name setattr(self, key, a) self._current_axis_identities_in_natural_namespace.append(key) def _on_constants_updated(self): """Method to run when constants are changed in any way. Propagates updated constants properly. """ # update attrs self.attrs["constants"] = np.array( [a.identity.encode() for a in self._constants], dtype="S" ) def _print_branch(self, prefix, depth, verbose): def print_leaves(prefix, lis, vline=True): for i, item in enumerate(lis): if vline: a = "│ " else: a = " " if i + 1 == len(lis): b = "└── " else: b = "├── " s = prefix + a + b + "{0}: {1}".format(i, item._leaf) print(s) if verbose: # axes print(prefix + "├── axes") print_leaves(prefix, self.axes) # constants print(prefix + "├── constants") print_leaves(prefix, self.constants) # variables print(prefix + "├── variables") print_leaves(prefix, self.variables) # channels print(prefix + "└── channels") print_leaves(prefix, self.channels, vline=False) else: # axes s = "axes: " s += ", ".join(["{0} ({1})".format(a.expression, a.units) for a in self.axes]) print(prefix + "├── " + s) # constants s = "constants: " s += ", ".join( ["{0} ({1} {2})".format(a.expression, a.value, a.units) for a in self.constants] ) print(prefix + "├── " + s) # channels s = "channels: " s += ", ".join(self.channel_names) print(prefix + "└── " + s) def bring_to_front(self, channel): """Bring a specific channel to the zero-indexed position in channels. All other channels get pushed back but remain in order. Parameters ---------- channel : int or str Channel index or name. """ channel_index = wt_kit.get_index(self.channel_names, channel) new = list(self.channel_names) new.insert(0, new.pop(channel_index)) self.channel_names = new def chop(self, *args, at={}, parent=None, verbose=True) -> wt_collection.Collection: """Divide the dataset into its lower-dimensionality components. Parameters ---------- axis : str or int (args) Axes of the returned data objects. Strings refer to the names of axes in this object, integers refer to their index. Provide multiple axes to return multidimensional data objects. at : dict (optional) Choice of position along an axis. Keys are axis names, values are lists ``[position, input units]``. If exact position does not exist, the closest valid position is used. parent : WrightTools Collection instance (optional) Collection to place the new "chop" collection within. Default is None (new parent). verbose : bool (optional) Toggle talkback. Default is True. Returns ------- WrightTools Collection Collection of chopped data objects. Examples -------- >>> data.axis_names ['d2', 'w1', 'w2'] Get all w1 wigners. >>> datas = data.chop('d2', 'w1') >>> len(datas) 51 Get 2D frequency at d2=0 fs. >>> datas = data.chop('w1', 'w2', at={'d2': [0, 'fs']}) >>> len(datas) 0 >>> datas[0].axis_names ['w1', 'w2'] >>> datas[0].d2[:] 0. See Also -------- collapse Collapse the dataset along one axis. split Split the dataset while maintaining its dimensionality. """ from ._axis import operators, operator_to_identifier # parse args args = list(args) for i, arg in enumerate(args): if isinstance(arg, int): args[i] = self._axes[arg].natural_name elif isinstance(arg, str): # same normalization that occurs in the natural_name @property arg = arg.strip() for op in operators: arg = arg.replace(op, operator_to_identifier[op]) args[i] = wt_kit.string2identifier(arg) # normalize the at keys to the natural name for k in [ak for ak in at.keys() if type(ak) == str]: for op in operators: if op in k: nk = k.replace(op, operator_to_identifier[op]) at[nk] = at[k] at.pop(k) k = nk # get output collection out = wt_collection.Collection(name="chop", parent=parent) # get output shape kept = args + [ak for ak in at.keys() if type(ak) == str] kept_axes = [self._axes[self.axis_names.index(a)] for a in kept] removed_axes = [a for a in self._axes if a not in kept_axes] removed_shape = wt_kit.joint_shape(*removed_axes) if removed_shape == (): removed_shape = (1,) * self.ndim removed_shape = list(removed_shape) for i in at.keys(): if type(i) == int: removed_shape[i] = 1 for ax in kept_axes: if ax.shape.count(1) == ax.ndim - 1: removed_shape[ax.shape.index(ax.size)] = 1 removed_shape = tuple(removed_shape) # iterate i = 0 for idx in np.ndindex(removed_shape): idx = np.array(idx, dtype=object) idx[np.array(removed_shape) == 1] = slice(None) for axis, point in at.items(): if type(axis) == int: idx[axis] = point continue point, units = point destination_units = self._axes[self.axis_names.index(axis)].units point = wt_units.converter(point, units, destination_units) axis_index = self.axis_names.index(axis) axis = self._axes[axis_index] idx_index = np.array(axis.shape) > 1 if np.sum(idx_index) > 1: raise wt_exceptions.MultidimensionalAxisError("chop", axis.natural_name) idx_index = list(idx_index).index(True) idx[idx_index] = np.argmin(np.abs(axis[tuple(idx)] - point)) data = out.create_data(name="chop%03i" % i) for v in self.variables: kwargs = {} kwargs["name"] = v.natural_name kwargs["values"] = v[idx] kwargs["units"] = v.units kwargs["label"] = v.label kwargs.update(v.attrs) data.create_variable(**kwargs) for c in self.channels: kwargs = {} kwargs["name"] = c.natural_name kwargs["values"] = c[idx] kwargs["units"] = c.units kwargs["label"] = c.label kwargs["signed"] = c.signed kwargs.update(c.attrs) data.create_channel(**kwargs) new_axes = [a.expression for a in kept_axes if a.expression not in at.keys()] new_axis_units = [a.units for a in kept_axes if a.expression not in at.keys()] data.transform(*new_axes) for const in self.constant_expressions: data.create_constant(const, verbose=False) for ax in self.axis_expressions: if ax not in new_axes: data.create_constant(ax, verbose=False) for j, units in enumerate(new_axis_units): data.axes[j].convert(units) i += 1 out.flush() # return if verbose: print("chopped data into %d piece(s)" % len(out), "in", new_axes) return out def gradient(self, axis, *, channel=0): """ Compute the gradient along one axis. New channels have names ``<channel name>_<axis name>_gradient``. Parameters ---------- axis : int or str The axis to differentiate along. If given as an integer, the axis in the underlying array is used, and unitary spacing is assumed. If given as a string, the axis must exist, and be a 1D array-aligned axis. (i.e. have a shape with a single value which is not ``1``) The axis to collapse along is inferred from the shape of the axis. channel : int or str The channel to differentiate. Default is the first channel. """ # get axis index -------------------------------------------------------------------------- if isinstance(axis, int): axis_index = axis elif isinstance(axis, str): index = self.axis_names.index(axis) axes = [i for i in range(self.ndim) if self.axes[index].shape[i] > 1] if len(axes) > 1: raise wt_exceptions.MultidimensionalAxisError(axis, "collapse") elif len(axes) == 0: raise wt_exceptions.ValueError( "Axis '{}' is a single point, cannot compute gradient".format(axis) ) axis_index = axes[0] else: raise wt_exceptions.TypeError("axis: expected {int, str}, got %s" % type(axis)) channel_index = wt_kit.get_index(self.channel_names, channel) channel = self.channel_names[channel_index] if self[channel].shape[axis_index] == 1: raise wt_exceptions.ValueError( "Channel '{}' has a single point along Axis '{}', cannot compute gradient".format( channel, axis ) ) rtype = np.result_type(self[channel].dtype, float) new = self.create_channel( "{}_{}_gradient".format(channel, axis), values=np.empty(self[channel].shape, dtype=rtype), ) channel = self[channel] if axis == axis_index: new[:] = np.gradient(channel[:], axis=axis_index) else: new[:] = np.gradient(channel[:], self[axis].points, axis=axis_index) def moment(self, axis, channel=0, moment=1, *, resultant=None): """Take the nth moment the dataset along one axis, adding lower rank channels. New channels have names ``<channel name>_<axis name>_moment_<moment num>``. Moment 0 is the integral of the slice. Moment 1 is the weighted average or "Center of Mass", normalized by the integral Moment 2 is the variance, the central moment about the center of mass, normalized by the integral Moments 3+ are central moments about the center of mass, normalized by the integral and by the standard deviation to the power of the moment. Moments, especially higher order moments, are susceptible to noise and baseline. It is recommended when used with real data to use :meth:`WrightTools.data.Channel.clip` in conjunction with moments to reduce effects of noise. Parameters ---------- axis : int or str The axis to take the moment along. If given as an integer, the axis with that index is used. If given as a string, the axis with that name is used. The axis must exist, and be a 1D array-aligned axis. (i.e. have a shape with a single value which is not ``1``) The collapsed axis must be monotonic to produce correct results. The axis to collapse along is inferred from the shape of the axis. channel : int or str The channel to take the moment. If given as an integer, the channel with that index is used. If given as a string, the channel with that name is used. The channel must have values along the axis (i.e. its shape must not be ``1`` in the dimension for which the axis is not ``1``) Default is 0, the first channel. moment : int or tuple of int The moments to take. One channel will be created for each number given. Default is 1, the center of mass. resultant : tuple of int The resultant shape after the moment operation. By default, it is intuited by the axis along which the moment is being taken. This default only works if that axis is 1D, so resultant is required if a multidimensional axis is passed as the first argument. The requirement of monotonicity applies on a per pixel basis. See Also -------- collapse Reduce dimensionality by some mathematical operation clip Set values above/below a threshold to a particular value WrightTools.kit.joint_shape Useful for setting `resultant` kwarg based off of axes not collapsed. """ # get axis index -------------------------------------------------------------------------- axis_index = None if resultant is not None: for i, (s, r) in enumerate(zip(wt_kit.joint_shape(*self.axes), resultant)): if s != r and r == 1 and axis_index is None: axis_index = i elif s == r: continue else: raise wt_exceptions.ValueError( f"Invalid resultant shape '{resultant}' for shape {wt_kit.joint_shape(*self.axes)}. " + "Consider using `wt.kit.joint_shape` to join non-collapsed axes." ) index = wt_kit.get_index(self.axis_names, axis) if axis_index is None: axes = [i for i in range(self.ndim) if self.axes[index].shape[i] > 1] if len(axes) > 1: raise wt_exceptions.MultidimensionalAxisError(axis, "moment") elif len(axes) == 0: raise wt_exceptions.ValueError( "Axis {} is a single point, cannot compute moment".format(axis) ) axis_index = axes[0] warnings.warn("moment", category=wt_exceptions.EntireDatasetInMemoryWarning) channel_index = wt_kit.get_index(self.channel_names, channel) channel = self.channel_names[channel_index] if self[channel].shape[axis_index] == 1: raise wt_exceptions.ValueError( "Channel '{}' has a single point along Axis '{}', cannot compute moment".format( channel, axis ) ) new_shape = list(self[channel].shape) new_shape[axis_index] = 1 channel = self[channel] axis_inp = axis axis = self.axes[index] x = axis[:] if np.any(np.isnan(x)): raise wt_exceptions.ValueError("Axis '{}' includes NaN".format(axis_inp)) y = np.nan_to_num(channel[:]) try: moments = tuple(moment) except TypeError: moments = (moment,) multiplier = 1 if 0 in moments: # May be possible to optimize, probably doesn't need the sum # only matters for integral, all others normalize by integral multiplier = np.sign( np.sum(np.diff(x, axis=axis_index), axis=axis_index, keepdims=True) ) for moment in moments: about = 0 norm = 1 if moment > 0: norm = np.trapz(y, x, axis=axis_index) norm = np.array(norm) norm.shape = new_shape if moment > 1: about = np.trapz(x * y, x, axis=axis_index) about = np.array(about) about.shape = new_shape about /= norm if moment > 2: sigma = np.trapz((x - about) ** 2 * y, x, axis=axis_index) sigma = np.array(sigma) sigma.shape = new_shape sigma /= norm sigma **= 0.5 norm *= sigma ** moment values = np.trapz((x - about) ** moment * y, x, axis=axis_index) values = np.array(values) values.shape = new_shape values /= norm if moment == 0: values *= multiplier self.create_channel( "{}_{}_{}_{}".format(channel.natural_name, axis_inp, "moment", moment), values=values, ) def collapse(self, axis, method="sum"): """Collapse the dataset along one axis, adding lower rank channels. New channels have names ``<channel name>_<axis name>_<method>``. Parameters ---------- axis : int or str The axis to collapse along. If given as an integer, the axis in the underlying array is used. If given as a string, the axis must exist, and be a 1D array-aligned axis. (i.e. have a shape with a single value which is not ``1``) The axis to collapse along is inferred from the shape of the axis. method : {'average', 'sum', 'max', 'min'} (optional) The method of collapsing the given axis. Method may also be list of methods corresponding to the channels of the object. Default is sum. NaNs are ignored. Can also be a list, allowing for different treatment for varied channels. In this case, None indicates that no change to that channel should occur. See Also -------- chop Divide the dataset into its lower-dimensionality components. split Split the dataset while maintaining its dimensionality. moment Take the moment along a particular axis """ if method in ("int", "integrate"): warnings.warn( "integrate method of collapse is deprecated, use moment(moment=0) instead", wt_exceptions.VisibleDeprecationWarning, ) for channel in self.channel_names: try: self.moment(axis, channel, moment=0) self.rename_channels( **{self.channel_names[-1]: f"{channel}_{axis}_{method}"}, verbose=False ) except wt_exceptions.ValueError: pass # may have some channels which fail, do so silently return # get axis index -------------------------------------------------------------------------- if isinstance(axis, int): axis_index = axis elif isinstance(axis, str): index = self.axis_names.index(axis) axes = [i for i in range(self.ndim) if self.axes[index].shape[i] > 1] if len(axes) > 1: raise wt_exceptions.MultidimensionalAxisError(axis, "collapse") elif len(axes) == 0: raise wt_exceptions.ValueError( "Axis {} is a single point, cannot collapse".format(axis) ) axis_index = axes[0] else: raise wt_exceptions.TypeError("axis: expected {int, str}, got %s" % type(axis)) new_shape = list(self.shape) new_shape[axis_index] = 1 func = { "sum": np.nansum, "max": np.nanmax, "maximum": np.nanmax, "min": np.nanmin, "minimum": np.nanmin, "ave": np.nanmean, "average": np.nanmean, "mean": np.nanmean, } # methods --------------------------------------------------------------------------------- if isinstance(method, str): methods = [method for _ in self.channels] if isinstance(method, list): if len(method) == len(self.channels): methods = method else: raise wt_exceptions.ValueError( "method argument must have same number of elements as there are channels" ) for m in methods: if m not in func.keys(): raise wt_exceptions.ValueError("method '{}' not recognized".format(m)) warnings.warn("collapse", category=wt_exceptions.EntireDatasetInMemoryWarning) # collapse -------------------------------------------------------------------------------- for method, channel in zip(methods, self.channel_names): if method is None: continue if self[channel].shape[axis_index] == 1: continue # Cannot collapse any further, don't clutter data object new_shape = list(self[channel].shape) new_shape[axis_index] = 1 rtype = self[channel].dtype if method in ["ave", "average", "mean"]: rtype = np.result_type(self[channel].dtype, float) new = self.create_channel( "{}_{}_{}".format(channel, axis, method), values=np.empty(new_shape, dtype=rtype), units=self[channel].units, ) new[:] = func[method](self[channel], axis=axis_index, keepdims=True) def convert(self, destination_units, *, convert_variables=False, verbose=True): """Convert all compatable axes and constants to given units. Parameters ---------- destination_units : str Destination units. convert_variables : boolean (optional) Toggle conversion of stored arrays. Default is False verbose : bool (optional) Toggle talkback. Default is True. See Also -------- Axis.convert Convert a single axis object to compatable units. Call on an axis object in data.axes. """ # apply to all compatible axes for axis in self.axes: if wt_units.is_valid_conversion(axis.units, destination_units): orig = axis.units axis.convert(destination_units, convert_variables=convert_variables) if verbose: print( "axis {} converted from {} to {}".format( axis.expression, orig, destination_units ) ) # apply to all compatible constants for constant in self.constants: if wt_units.is_valid_conversion(constant.units, destination_units): orig = constant.units constant.convert(destination_units, convert_variables=convert_variables) if verbose: print( "constant {} converted from {} to {}".format( constant.expression, orig, destination_units ) ) if convert_variables: for var in self.variables: if wt_units.is_valid_conversion(var.units, destination_units): orig = var.units var.convert(destination_units) if verbose: print( "variable {} converted from {} to {}".format( var.natural_name, orig, destination_units ) ) self._on_axes_updated() self._on_constants_updated() def create_channel( self, name, values=None, *, shape=None, units=None, dtype=None, **kwargs ) -> Channel: """Append a new channel. Parameters ---------- name : string Unique name for this channel. values : array (optional) Array. If None, an empty array equaling the data shape is created. Default is None. shape : tuple of int Shape to use. Must broadcast with the full shape. Only used if `values` is None. Default is the full shape of self. units : string (optional) Channel units. Default is None. dtype : numpy.dtype (optional) dtype to use for dataset, default is np.float64. Only used if `values` is None. kwargs : dict Additional keyword arguments passed to Channel instantiation. Returns ------- Channel Created channel. """ if name in self.channel_names: warnings.warn(name, wt_exceptions.ObjectExistsWarning) return self[name] elif name in self.variable_names: raise wt_exceptions.NameNotUniqueError(name) require_kwargs = {"chunks": True} if values is None: if shape is None: require_kwargs["shape"] = self.shape else: require_kwargs["shape"] = shape if dtype is None: require_kwargs["dtype"] = np.dtype(np.float64) else: require_kwargs["dtype"] = dtype if require_kwargs["dtype"].kind in "fcmM": require_kwargs["fillvalue"] = np.nan else: require_kwargs["fillvalue"] = 0 else: require_kwargs["data"] = values require_kwargs["shape"] = values.shape require_kwargs["dtype"] = values.dtype if np.prod(require_kwargs["shape"]) == 1: require_kwargs["chunks"] = None # create dataset dataset_id = self.require_dataset(name=name, **require_kwargs).id channel = Channel(self, dataset_id, units=units, **kwargs) # finish self.attrs["channel_names"] = np.append(self.attrs["channel_names"], name.encode()) return channel def create_variable( self, name, values=None, *, shape=None, units=None, dtype=None, **kwargs ) -> Variable: """Add new child variable. Parameters ---------- name : string Unique identifier. values : array-like (optional) Array to populate variable with. If None, an variable will be filled with NaN. Default is None. shape : tuple of int Shape to use. must broadcast with the full shape. Only used if `values` is None. Default is the full shape of self. units : string (optional) Variable units. Default is None. dtype : numpy.dtype (optional) dtype to use for dataset, default is np.float64. Only used if `values` is None. kwargs Additional kwargs to variable instantiation. Returns ------- WrightTools Variable New child variable. """ if name in self.variable_names: warnings.warn(name, wt_exceptions.ObjectExistsWarning) return self[name] elif name in self.channel_names: raise wt_exceptions.NameNotUniqueError(name) if values is None: if shape is None: shape = self.shape if dtype is None: dtype = np.dtype(np.float64) if dtype.kind in "fcmM": fillvalue = np.nan else: fillvalue = 0 else: shape = values.shape dtype = values.dtype fillvalue = None # create dataset id = self.require_dataset( name=name, data=values, shape=shape, dtype=dtype, fillvalue=fillvalue ).id variable = Variable(self, id, units=units, **kwargs) # finish self._variables = None self.attrs["variable_names"] = np.append(self.attrs["variable_names"], name.encode()) return variable def get_nadir(self, channel=0) -> tuple: """Get the coordinates, in units, of the minimum in a channel. Parameters ---------- channel : int or str (optional) Channel. Default is 0. Returns ------- generator of numbers Coordinates in units for each axis. """ # get channel if isinstance(channel, int): channel_index = channel elif isinstance(channel, str): channel_index = self.channel_names.index(channel) else: raise TypeError("channel: expected {int, str}, got %s" % type(channel)) channel = self.channels[channel_index] # get indicies idx = channel.argmin() # finish return tuple(a[idx] for a in self._axes) def get_zenith(self, channel=0) -> tuple: """Get the coordinates, in units, of the maximum in a channel. Parameters ---------- channel : int or str (optional) Channel. Default is 0. Returns ------- generator of numbers Coordinates in units for each axis. """ # get channel if isinstance(channel, int): channel_index = channel elif isinstance(channel, str): channel_index = self.channel_names.index(channel) else: raise TypeError("channel: expected {int, str}, got %s" % type(channel)) channel = self.channels[channel_index] # get indicies idx = channel.argmax() # finish return tuple(a[idx] for a in self._axes) def heal(self, channel=0, method="linear", fill_value=np.nan, verbose=True): """ Remove nans from channel using interpolation. Parameters ---------- channel : int or str (optional) Channel to heal. Default is 0. method : {'linear', 'nearest', 'cubic'} (optional) The interpolation method. Note that cubic interpolation is only possible for 1D and 2D data. See `griddata`__ for more information. Default is linear. fill_value : number-like (optional) The value written to pixels that cannot be filled by interpolation. Default is nan. verbose : bool (optional) Toggle talkback. Default is True. __ http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html .. note:: Healing may take several minutes for large datasets. Interpolation time goes as nearest, linear, then cubic. """ warnings.warn("heal", category=wt_exceptions.EntireDatasetInMemoryWarning) timer = wt_kit.Timer(verbose=False) with timer: # channel if isinstance(channel, int): channel_index = channel elif isinstance(channel, str): channel_index = self.channel_names.index(channel) else: raise TypeError("channel: expected {int, str}, got %s" % type(channel)) channel = self.channels[channel_index] values = self.channels[channel_index][:] points = [axis[:] for axis in self._axes] xi = tuple(np.meshgrid(*points, indexing="ij")) # 'undo' gridding arr = np.zeros((len(self._axes) + 1, values.size)) for i in range(len(self._axes)): arr[i] = xi[i].flatten() arr[-1] = values.flatten() # remove nans arr = arr[:, ~np.isnan(arr).any(axis=0)] # grid data wants tuples tup = tuple([arr[i] for i in range(len(arr) - 1)]) # grid data out = griddata(tup, arr[-1], xi, method=method, fill_value=fill_value) self.channels[channel_index][:] = out # print if verbose: print( "channel {0} healed in {1} seconds".format( channel.name, np.around(timer.interval, decimals=3) ) ) def level(self, channel, axis, npts, *, verbose=True): """Subtract the average value of npts at the edge of a given axis. Parameters ---------- channel : int or str Channel to level. axis : int Axis to level along. npts : int Number of points to average for each slice. Positive numbers take points at leading indicies and negative numbers take points at trailing indicies. verbose : bool (optional) Toggle talkback. Default is True. """ warnings.warn("level", category=wt_exceptions.EntireDatasetInMemoryWarning) channel_index = wt_kit.get_index(self.channel_names, channel) channel = self.channels[channel_index] # verify npts not zero npts = int(npts) if npts == 0: raise wt_exceptions.ValueError("npts must not be zero") # get subtrahend ss = [slice(None)] * self.ndim if npts > 0: ss[axis] = slice(0, npts, None) else: ss[axis] = slice(npts, None, None) subtrahend = np.nanmean(channel[ss], axis=axis) if self.ndim > 1: subtrahend = np.expand_dims(subtrahend, axis=axis) # level channel -= subtrahend # finish channel._null = 0 if verbose: print("channel {0} leveled along axis {1}".format(channel.natural_name, axis)) def map_variable( self, variable, points, input_units="same", *, name=None, parent=None, verbose=True ) -> "Data": """Map points of an axis to new points using linear interpolation. Out-of-bounds points are written nan. Parameters ---------- variable : string The variable to map onto. points : array-like or int If array, the new points. If int, new points will have the same limits, with int defining the number of evenly spaced points between. input_units : str (optional) The units of the new points. Default is same, which assumes the new points have the same units as the axis. name : string (optional) The name of the new data object. If None, generated from natural_name. Default is None. parent : WrightTools.Collection (optional) Parent of new data object. If None, data is made at root of a new temporary file. verbose : bool (optional) Toggle talkback. Default is True. Returns ------- WrightTools.Data New data object. """ # get variable index variable_index = wt_kit.get_index(self.variable_names, variable) variable = self.variables[variable_index] # get points if isinstance(points, int): points = np.linspace(variable.min(), variable.max(), points) points = np.array(points) # points dimensionality if points.ndim < variable.ndim: for i, d in enumerate(variable.shape): if d == 1: points = np.expand_dims(points, axis=i) # convert points if input_units == "same": pass else: points = wt_units.converter(points, input_units, variable.units) # construct new data object special = ["name", "axes", "constants", "channel_names", "variable_names"] kwargs = {k: v for k, v in self.attrs.items() if k not in special} if name is None: name = "{0}_{1}_mapped".format(self.natural_name, variable.natural_name) kwargs["name"] = name kwargs["parent"] = parent out = Data(**kwargs) # mapped variable values = points out.create_variable(values=values, **variable.attrs) # orthogonal variables for v in self.variables: if wt_kit.orthogonal(v.shape, variable.shape): out.create_variable(values=v[:], **v.attrs) out.transform(*self.axis_expressions) # interpolate if self.ndim == 1: def interpolate(dataset, points): function = scipy.interpolate.interp1d(variable[:], dataset[:], bounds_error=False) return function(points) else: pts = np.array([a.full.flatten() for a in self.axes]).T out_pts = np.array([a.full.flatten() for a in out.axes]).T def interpolate(dataset, points): values = dataset.full.flatten() function = scipy.interpolate.LinearNDInterpolator(pts, values, rescale=True) new = function(out_pts) new.shape = out.shape return new for v in self.variables: if v.natural_name not in out.variable_names: out.create_variable(values=interpolate(v, points), **v.attrs) out.variable_names = self.variable_names # enforce old order out._variables = None # force regeneration of variables @property for channel in self.channels: out.create_channel(values=interpolate(channel, points), **channel.attrs) # finish if verbose: print("data mapped from {0} to {1}".format(self.shape, out.shape)) return out def offset( self, points, offsets, along, offset_axis, units="same", offset_units="same", mode="valid", method="linear", verbose=True, ): """Offset one axis based on another axis' values. Useful for correcting instrumental artifacts such as zerotune. Parameters ---------- points : 1D array-like Points. offsets : 1D array-like Offsets. along : str or int Axis that points array lies along. offset_axis : str or int Axis to offset using offsets. units : str (optional) Units of points array. offset_units : str (optional) Units of offsets aray. mode : {'valid', 'full', 'old'} (optional) Define how far the new axis will extend. Points outside of valid interpolation range will be written nan. method : {'linear', 'nearest', 'cubic'} (optional) The interpolation method. Note that cubic interpolation is only possible for 1D and 2D data. See `griddata`__ for more information. Default is linear. verbose : bool (optional) Toggle talkback. Default is True. __ http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html >>> points # an array of w1 points >>> offsets # an array of d1 corrections >>> data.offset(points, offsets, 'w1', 'd1') """ raise NotImplementedError # axis ------------------------------------------------------------------------------------ if isinstance(along, int): axis_index = along elif isinstance(along, str): axis_index = self.axis_names.index(along) else: raise TypeError("along: expected {int, str}, got %s" % type(along)) axis = self._axes[axis_index] # values & points ------------------------------------------------------------------------- # get values, points, units if units == "same": input_units = axis.units else: input_units = units # check offsets is 1D or 0D if len(offsets.shape) == 1: pass else: raise RuntimeError("values must be 1D or 0D in offset!") # check if units is compatible, convert dictionary = getattr(wt_units, axis.units_kind) if input_units in dictionary.keys(): pass else: raise RuntimeError("units incompatible in offset!") points = wt_units.converter(points, input_units, axis.units) # create correction array function = interp1d(points, offsets, bounds_error=False) corrections = function(axis[:]) # remove nans finite_indicies = np.where(np.isfinite(corrections))[0] left_pad_width = finite_indicies[0] right_pad_width = len(corrections) - finite_indicies[-1] - 1 corrections = np.pad( corrections[np.isfinite(corrections)], (int(left_pad_width), int(right_pad_width)), mode="edge", ) # do correction --------------------------------------------------------------------------- # transpose so axis is last transpose_order = np.arange(len(self._axes)) transpose_order[axis_index] = len(self._axes) - 1 transpose_order[-1] = axis_index self.transpose(transpose_order, verbose=False) # get offset axis index if isinstance(offset_axis, int): offset_axis_index = offset_axis elif isinstance(offset_axis, str): offset_axis_index = self.axis_names.index(offset_axis) else: raise TypeError("offset_axis: expected {int, str}, got %s" % type(offset_axis)) # new points new_points = [a[:] for a in self._axes] old_offset_axis_points = self._axes[offset_axis_index][:] spacing = abs( (old_offset_axis_points.max() - old_offset_axis_points.min()) / float(len(old_offset_axis_points)) ) if mode == "old": new_offset_axis_points = old_offset_axis_points elif mode == "valid": _max = old_offset_axis_points.max() + corrections.min() _min = old_offset_axis_points.min() + corrections.max() n = int(abs(np.ceil((_max - _min) / spacing))) new_offset_axis_points = np.linspace(_min, _max, n) elif mode == "full": _max = old_offset_axis_points.max() + corrections.max() _min = old_offset_axis_points.min() + corrections.min() n = np.ceil((_max - _min) / spacing) new_offset_axis_points = np.linspace(_min, _max, n) new_points[offset_axis_index] = new_offset_axis_points new_xi = tuple(np.meshgrid(*new_points, indexing="ij")) xi = tuple(np.meshgrid(*[a[:] for a in self._axes], indexing="ij")) for channel in self.channels: # 'undo' gridding arr = np.zeros((len(self._axes) + 1, channel[:].size)) for i in range(len(self._axes)): arr[i] = xi[i].flatten() arr[-1] = channel[:].flatten() # do corrections corrections = list(corrections) corrections = corrections * int((len(arr[0]) / len(corrections))) arr[offset_axis_index] += corrections # grid data tup = tuple([arr[i] for i in range(len(arr) - 1)]) # note that rescale is crucial in this operation out = griddata(tup, arr[-1], new_xi, method=method, fill_value=np.nan, rescale=True) channel[:] = out self._axes[offset_axis_index][:] = new_offset_axis_points # transpose out self.transpose(transpose_order, verbose=False) def print_tree(self, *, verbose=True): """Print a ascii-formatted tree representation of the data contents.""" print("{0} ({1})".format(self.natural_name, self.filepath)) self._print_branch("", depth=0, verbose=verbose) def prune(self, keep_channels=True, *, verbose=True): """Remove unused variables and (optionally) channels from the Data object. Unused variables are those that are not included in either axes or constants. Unused channels are those not specified in keep_channels, or the first channel. Parameters ---------- keep_channels : boolean or int or str or tuple If False, removes all but the first channel. If int or str, removes all but that index/name channel. If tuple, removes all channels except those in the tuple by index or name. Default is True: do not delete channels verbose : boolean Toggle talkback. Default is True. """ for v in self.variables: for var in wt_kit.flatten_list([ax.variables for ax in self._axes + self._constants]): if v == var: break else: self.remove_variable(v.natural_name, implied=False, verbose=verbose) if keep_channels is not True: try: if isinstance(keep_channels, str): raise TypeError indexes = tuple(keep_channels) except TypeError: indexes = (keep_channels,) for i, ch in enumerate(self.channels): if i not in indexes and not ch.natural_name in indexes: self.remove_channel(ch.natural_name, verbose=verbose) def remove_channel(self, channel, *, verbose=True): """Remove channel from data. Parameters ---------- channel : int or str Channel index or name to remove. verbose : boolean (optional) Toggle talkback. Default is True. """ channel_index = wt_kit.get_index(self.channel_names, channel) new = list(self.channel_names) name = new.pop(channel_index) del self[name] self.channel_names = new if verbose: print("channel {0} removed".format(name)) def remove_variable(self, variable, *, implied=True, verbose=True): """Remove variable from data. Parameters ---------- variable : int or str Variable index or name to remove. implied : boolean (optional) Toggle deletion of other variables that start with the same name. Default is True. verbose : boolean (optional) Toggle talkback. Default is True. """ if isinstance(variable, int): variable = self.variable_names[variable] # find all of the implied variables removed = [] if implied: for n in self.variable_names: if n.startswith(variable): removed.append(n) else: removed = [variable] # check that axes will not be ruined for n in removed: for a in self._axes: if n in [v.natural_name for v in a.variables]: message = "{0} is contained in axis {1}".format(n, a.expression) raise RuntimeError(message) for c in self._constants: if n in [v.natural_name for v in c.variables]: warnings.warn( "Variable being removed used in a constant", wt_exceptions.WrightToolsWarning, ) # do removal for n in removed: variable_index = wt_kit.get_index(self.variable_names, n) new = list(self.variable_names) name = new.pop(variable_index) del self[name] self.variable_names = new self._variables = None # finish if verbose: print("{0} variable(s) removed:".format(len(removed))) for n in removed: print(" {0}".format(n)) def rename_channels(self, *, verbose=True, **kwargs): """Rename a set of channels. Parameters ---------- kwargs Keyword arguments of the form current:'new'. verbose : boolean (optional) Toggle talkback. Default is True """ # ensure that items will remain unique changed = kwargs.keys() for k, v in kwargs.items(): if v not in changed and v in self.keys(): raise wt_exceptions.NameNotUniqueError(v) # compile references to items that are changing new = {} for k, v in kwargs.items(): obj = self[k] index = self.channel_names.index(k) # rename new[v] = obj, index Group._instances.pop(obj.fullpath, None) obj.natural_name = str(v) # remove old references del self[k] # apply new references names = list(self.channel_names) for v, value in new.items(): obj, index = value self[v] = obj names[index] = v self.channel_names = names # finish if verbose: print("{0} channel(s) renamed:".format(len(kwargs))) for k, v in kwargs.items(): print(" {0} --> {1}".format(k, v)) def rename_variables(self, *, implied=True, verbose=True, **kwargs): """Rename a set of variables. Parameters ---------- kwargs Keyword arguments of the form current:'new'. implied : boolean (optional) Toggle inclusion of other variables that start with the same name. Default is True. verbose : boolean (optional) Toggle talkback. Default is True """ # find all of the implied variables kwargs = collections.OrderedDict(kwargs) if implied: new = collections.OrderedDict() for k, v in kwargs.items(): for n in self.variable_names: if n.startswith(k): new[n] = n.replace(k, v, 1) kwargs = new # ensure that items will remain unique changed = kwargs.keys() for k, v in kwargs.items(): if v not in changed and v in self.keys(): raise wt_exceptions.NameNotUniqueError(v) # compile references to items that are changing new = {} for k, v in kwargs.items(): obj = self[k] index = self.variable_names.index(k) # rename new[v] = obj, index Group._instances.pop(obj.fullpath, None) obj.natural_name = str(v) # remove old references del self[k] # apply new references names = list(self.variable_names) for v, value in new.items(): obj, index = value self[v] = obj names[index] = v self.variable_names = names units = self.units new = list(self.axis_expressions) for i, v in enumerate(kwargs.keys()): for j, n in enumerate(new): new[j] = n.replace(v, "{%i}" % i) for i, n in enumerate(new): new[i] = n.format(*kwargs.values()) self.transform(*new) for a, u in zip(self._axes, units): a.convert(u) units = self.constant_units new = list(self.constant_expressions) for i, v in enumerate(kwargs.keys()): for j, n in enumerate(new): new[j] = n.replace(v, "{%i}" % i) for i, n in enumerate(new): new[i] = n.format(*kwargs.values()) self.set_constants(*new) for c, u in zip(self._constants, units): c.convert(u) # finish if verbose: print("{0} variable(s) renamed:".format(len(kwargs))) for k, v in kwargs.items(): print(" {0} --> {1}".format(k, v)) def share_nans(self): """Share not-a-numbers between all channels. If any channel is nan at a given index, all channels will be nan at that index after this operation. Uses the share_nans method found in wt.kit. """ def f(_, s, channels): outs = wt_kit.share_nans(*[c[s] for c in channels]) for c, o in zip(channels, outs): c[s] = o self.channels[0].chunkwise(f, self.channels) def smooth(self, factors, channel=None, verbose=True) -> "Data": """Smooth a channel using an n-dimenional kaiser window. Note, all arrays are loaded into memory. For more info see `Kaiser_window`__ wikipedia entry. __ https://en.wikipedia.org/wiki/Kaiser_window Parameters ---------- factors : int or list of int The smoothing factor. You may provide a list of smoothing factors for each axis. channel : int or str or None (optional) The channel to smooth. If None, all channels will be smoothed. Default is None. verbose : bool (optional) Toggle talkback. Default is True. """ warnings.warn("smooth", category=wt_exceptions.EntireDatasetInMemoryWarning) # get factors ----------------------------------------------------------------------------- if isinstance(factors, list): pass else: dummy = np.zeros(len(self._axes)) dummy[::] = factors factors = list(dummy) # get channels ---------------------------------------------------------------------------- if channel is None: channels = self.channels else: if isinstance(channel, int): channel_index = channel elif isinstance(channel, str): channel_index = self.channel_names.index(channel) else: raise TypeError("channel: expected {int, str}, got %s" % type(channel)) channels = [self.channels[channel_index]] # smooth ---------------------------------------------------------------------------------- for channel in channels: values = channel[:] for axis_index in range(len(factors)): factor = factors[axis_index] # transpose so the axis of interest is last transpose_order = range(len(values.shape)) # replace axis_index with zero transpose_order = [ len(values.shape) - 1 if i == axis_index else i for i in transpose_order ] transpose_order[len(values.shape) - 1] = axis_index values = values.transpose(transpose_order) # get kaiser window beta = 5.0 w = np.kaiser(2 * factor + 1, beta) # for all slices... for index in np.ndindex(values[..., 0].shape): current_slice = values[index] temp_slice = np.pad(current_slice, int(factor), mode=str("edge")) values[index] = np.convolve(temp_slice, w / w.sum(), mode=str("valid")) # transpose out values = values.transpose(transpose_order) # return array to channel object channel[:] = values if verbose: print("smoothed data") def split( self, expression, positions, *, units=None, parent=None, verbose=True ) -> wt_collection.Collection: """ Split the data object along a given expression, in units. Parameters ---------- expression : int or str The expression to split along. If given as an integer, the axis at that index is used. positions : number-type or 1D array-type The position(s) to split at, in units. units : str (optional) The units of the given positions. Default is same, which assumes input units are identical to first variable units. parent : WrightTools.Collection (optional) The parent collection in which to place the 'split' collection. Default is a new Collection. verbose : bool (optional) Toggle talkback. Default is True. Returns ------- WrightTools.collection.Collection A Collection of data objects. The order of the objects is such that the axis points retain their original order. See Also -------- chop Divide the dataset into its lower-dimensionality components. collapse Collapse the dataset along one axis. """ # axis ------------------------------------------------------------------------------------ old_expr = self.axis_expressions old_units = self.units out = wt_collection.Collection(name="split", parent=parent) if isinstance(expression, int): if units is None: units = self._axes[expression].units expression = self._axes[expression].expression elif isinstance(expression, str): pass else: raise TypeError("expression: expected {int, str}, got %s" % type(expression)) self.transform(expression) if units: self.convert(units, verbose=False) try: positions = [-np.inf] + sorted(list(positions)) + [np.inf] except TypeError: positions = [-np.inf, positions, np.inf] values = self._axes[0].full masks = [(values >= lo) & (values < hi) for lo, hi in wt_kit.pairwise(positions)] omasks = [] cuts = [] for mask in masks: try: omasks.append(wt_kit.mask_reduce(mask)) cuts.append([i == 1 for i in omasks[-1].shape]) # Ensure at least one axis is kept if np.all(cuts[-1]): cuts[-1][0] = False except ValueError: omasks.append(None) cuts.append(None) for i in range(len(positions) - 1): out.create_data("split%03i" % i) for var in self.variables: for i, (imask, omask, cut) in enumerate(zip(masks, omasks, cuts)): if omask is None: # Zero length split continue omask = wt_kit.enforce_mask_shape(omask, var.shape) omask.shape = tuple([s for s, c in zip(omask.shape, cut) if not c]) out_arr = np.full(omask.shape, np.nan) imask = wt_kit.enforce_mask_shape(imask, var.shape) out_arr[omask] = var[:][imask] out[i].create_variable(values=out_arr, **var.attrs) for ch in self.channels: for i, (imask, omask, cut) in enumerate(zip(masks, omasks, cuts)): if omask is None: # Zero length split continue omask = wt_kit.enforce_mask_shape(omask, ch.shape) omask.shape = tuple([s for s, c in zip(omask.shape, cut) if not c]) out_arr = np.full(omask.shape, np.nan) imask = wt_kit.enforce_mask_shape(imask, ch.shape) out_arr[omask] = ch[:][imask] out[i].create_channel(values=out_arr, **ch.attrs) if verbose: for d in out.values(): try: d.transform(expression) except IndexError: continue print("split data into {0} pieces along <{1}>:".format(len(positions) - 1, expression)) for i, (lo, hi) in enumerate(wt_kit.pairwise(positions)): new_data = out[i] if new_data.shape == (): print(" {0} : None".format(i)) else: new_axis = new_data.axes[0] print( " {0} : {1:0.2f} to {2:0.2f} {3} {4}".format( i, lo, hi, self.axes[0].units, new_axis.shape ) ) for d in out.values(): try: d.transform(*old_expr) keep = [] keep_units = [] for ax, u in zip(d.axes, old_units): if ax.size > 1: keep.append(ax.expression) keep_units.append(u) else: d.create_constant(ax.expression, verbose=False) d.transform(*keep) for ax, u in zip(d.axes, keep_units): ax.convert(u) except IndexError: continue tempax = Axis(d, expression) if all( np.all( np.sum(~np.isnan(tempax.masked), axis=tuple(set(range(tempax.ndim)) - {j})) <= 1 ) for j in range(tempax.ndim) ): d.create_constant(expression, verbose=False) self.transform(*old_expr) for ax, u in zip(self.axes, old_units): ax.convert(u) return out def transform(self, *axes, verbose=True): """Transform the data. Parameters ---------- axes : strings Expressions for the new set of axes. verbose : boolean (optional) Toggle talkback. Default is True See Also -------- set_constants Similar method except for constants """ # TODO: ensure that transform does not break data # create new = [] newt = "newt" in self.axis_expressions current = {a.expression: a for a in self._axes} for expression in axes: axis = current.get(expression, Axis(self, expression)) new.append(axis) self._axes = new # units for a in self._axes: if a.units is None: a.convert(a.variables[0].units) # finish self.flush() self._on_axes_updated() nownewt = "newt" in self.axis_expressions if verbose and nownewt and not newt: print("Look she turned me into a newt") elif verbose and newt and not nownewt: print("I got better") def set_constants(self, *constants, verbose=True): """Set the constants associated with the data. Parameters ---------- constants : str Expressions for the new set of constants. verbose : boolean (optional) Toggle talkback. Default is True See Also -------- transform Similar method except for axes. create_constant Add an individual constant. remove_constant Remove an individual constant. """ # create new = [] current = {c.expression: c for c in self._constants} for expression in constants: constant = current.get(expression, Constant(self, expression)) new.append(constant) self._constants = new # units for c in self._constants: if c.units is None: c.convert(c.variables[0].units) # finish self.flush() self._on_constants_updated() def create_constant(self, expression, *, verbose=True): """Append a constant to the stored list. Parameters ---------- expression : str Expression for the new constant. verbose : boolean (optional) Toggle talkback. Default is True See Also -------- set_constants Remove and replace all constants. remove_constant Remove an individual constant. """ if expression in self.constant_expressions: wt_exceptions.ObjectExistsWarning.warn(expression) return self.constants[self.constant_expressions.index(expression)] constant = Constant(self, expression) if constant.units is None: constant.convert(constant.variables[0].units) self._constants.append(constant) self.flush() self._on_constants_updated() if verbose: print("Constant '{}' added".format(constant.expression)) return constant def remove_constant(self, constant, *, verbose=True): """Remove a constant from the stored list. Parameters ---------- constant : str or Constant or int Expression for the new constant. verbose : boolean (optional) Toggle talkback. Default is True See Also -------- set_constants Remove and replace all constants. create_constant Add an individual constant. """ if isinstance(constant, (str, int)): constant_index = wt_kit.get_index(self.constant_expressions, constant) elif isinstance(constant, Constant): constant_index = wt_kit.get_index(self.constants, constant) constant = self._constants[constant_index] self._constants.pop(constant_index) self.flush() self._on_constants_updated() if verbose: print("Constant '{}' removed".format(constant.expression)) def zoom(self, factor, order=1, verbose=True): """Zoom the data array using spline interpolation of the requested order. The number of points along each axis is increased by factor. See `scipy ndimage`__ for more info. __ http://docs.scipy.org/doc/scipy/reference/ generated/scipy.ndimage.interpolation.zoom.html Parameters ---------- factor : float The number of points along each axis will increase by this factor. order : int (optional) The order of the spline used to interpolate onto new points. verbose : bool (optional) Toggle talkback. Default is True. """ raise NotImplementedError import scipy.ndimage # axes for axis in self._axes: axis[:] = scipy.ndimage.interpolation.zoom(axis[:], factor, order=order) # channels for channel in self.channels: channel[:] = scipy.ndimage.interpolation.zoom(channel[:], factor, order=order) # return if verbose: print("data zoomed to new shape:", self.shape)

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import collections import operator import functools import warnings import numpy as np import h5py import scipy from scipy.interpolate import griddata, interp1d from .._group import Group from .. import collection as wt_collection from .. import exceptions as wt_exceptions from .. import kit as wt_kit from .. import units as wt_units from ._axis import Axis, identifier_to_operator from ._channel import Channel from ._constant import Constant from ._variable import Variable __all__ = ["Data"] class Data(Group): class_name = "Data" def __init__(self, *args, **kwargs): self._axes = [] self._constants = [] Group.__init__(self, *args, **kwargs) for identifier in self.attrs.get("axes", []): if hasattr(identifier, "decode"): identifier = identifier.decode() expression, units = identifier.split("{") units = units.replace("}", "").strip() if units == "None": units = None for i in identifier_to_operator.keys(): expression = expression.replace(i, identifier_to_operator[i]) expression = expression.replace(" ", "") axis = Axis(self, expression, units) self._axes.append(axis) for identifier in self.attrs.get("constants", []): if hasattr(identifier, "decode"): identifier = identifier.decode() expression, units = identifier.split("{") units = units.replace("}", "").strip() if units == "None": units = None for i in identifier_to_operator.keys(): expression = expression.replace(i, identifier_to_operator[i]) expression = expression.replace(" ", "") const = Constant(self, expression, units) self._constants.append(const) self._current_axis_identities_in_natural_namespace = [] if self.file.mode is not None and self.file.mode != "r": self._on_constants_updated() self._on_axes_updated() self.channel_names self.source self.variable_names def __repr__(self) -> str: return "<WrightTools.Data '{0}' {1} at {2}>".format( self.natural_name, str(self.axis_names), "::".join([self.filepath, self.name]) ) @property def axes(self) -> tuple: return tuple(self._axes) @property def axis_expressions(self) -> tuple: return tuple(a.expression for a in self._axes) @property def axis_names(self) -> tuple: return tuple(a.natural_name for a in self._axes) @property def constants(self) -> tuple: return tuple(self._constants) @property def constant_expressions(self) -> tuple: return tuple(a.expression for a in self._constants) @property def constant_names(self) -> tuple: return tuple(a.natural_name for a in self._constants) @property def channel_names(self) -> tuple: if "channel_names" not in self.attrs.keys(): self.attrs["channel_names"] = np.array([], dtype="S") return tuple(s.decode() for s in self.attrs["channel_names"]) @channel_names.setter def channel_names(self, value): self.attrs["channel_names"] = np.array(value, dtype="S") @property def channels(self) -> tuple: return tuple(self[n] for n in self.channel_names) @property def datasets(self) -> tuple: return tuple(v for _, v in self.items() if isinstance(v, h5py.Dataset)) @property def kind(self): if "kind" not in self.attrs.keys(): self.attrs["kind"] = "None" value = self.attrs["kind"] return value if not value == "None" else None @property def ndim(self) -> int: try: assert self._ndim is not None except (AssertionError, AttributeError): if len(self.variables) == 0: self._ndim = 0 else: self._ndim = self.variables[0].ndim finally: return self._ndim @property def shape(self) -> tuple: try: assert self._shape is not None except (AssertionError, AttributeError): self._shape = wt_kit.joint_shape(*self.variables) finally: return self._shape @property def size(self) -> int: return functools.reduce(operator.mul, self.shape) @property def source(self): if "source" not in self.attrs.keys(): self.attrs["source"] = "None" value = self.attrs["source"] return value if not value == "None" else None @property def units(self) -> tuple: return tuple(a.units for a in self._axes) @property def constant_units(self) -> tuple: return tuple(a.units for a in self._constants) @property def variable_names(self) -> tuple: if "variable_names" not in self.attrs.keys(): self.attrs["variable_names"] = np.array([], dtype="S") return tuple(s.decode() for s in self.attrs["variable_names"]) @variable_names.setter def variable_names(self, value): self.attrs["variable_names"] = np.array(value, dtype="S") @property def variables(self) -> tuple: try: assert self._variables is not None except (AssertionError, AttributeError): self._variables = [self[n] for n in self.variable_names] finally: return tuple(self._variables) @property def _leaf(self): return "{0} {1}".format(self.natural_name, self.shape) def _on_axes_updated(self): self.attrs["axes"] = np.array([a.identity.encode() for a in self._axes], dtype="S") while len(self._current_axis_identities_in_natural_namespace) > 0: key = self._current_axis_identities_in_natural_namespace.pop(0) try: delattr(self, key) except AttributeError: pass for a in self._axes: key = a.natural_name setattr(self, key, a) self._current_axis_identities_in_natural_namespace.append(key) def _on_constants_updated(self): self.attrs["constants"] = np.array( [a.identity.encode() for a in self._constants], dtype="S" ) def _print_branch(self, prefix, depth, verbose): def print_leaves(prefix, lis, vline=True): for i, item in enumerate(lis): if vline: a = "│ " else: a = " " if i + 1 == len(lis): b = "└── " else: b = "├── " s = prefix + a + b + "{0}: {1}".format(i, item._leaf) print(s) if verbose: print(prefix + "├── axes") print_leaves(prefix, self.axes) print(prefix + "├── constants") print_leaves(prefix, self.constants) print(prefix + "├── variables") print_leaves(prefix, self.variables) print(prefix + "└── channels") print_leaves(prefix, self.channels, vline=False) else: s = "axes: " s += ", ".join(["{0} ({1})".format(a.expression, a.units) for a in self.axes]) print(prefix + "├── " + s) s = "constants: " s += ", ".join( ["{0} ({1} {2})".format(a.expression, a.value, a.units) for a in self.constants] ) print(prefix + "├── " + s) s = "channels: " s += ", ".join(self.channel_names) print(prefix + "└── " + s) def bring_to_front(self, channel): channel_index = wt_kit.get_index(self.channel_names, channel) new = list(self.channel_names) new.insert(0, new.pop(channel_index)) self.channel_names = new def chop(self, *args, at={}, parent=None, verbose=True) -> wt_collection.Collection: from ._axis import operators, operator_to_identifier args = list(args) for i, arg in enumerate(args): if isinstance(arg, int): args[i] = self._axes[arg].natural_name elif isinstance(arg, str): arg = arg.strip() for op in operators: arg = arg.replace(op, operator_to_identifier[op]) args[i] = wt_kit.string2identifier(arg) for k in [ak for ak in at.keys() if type(ak) == str]: for op in operators: if op in k: nk = k.replace(op, operator_to_identifier[op]) at[nk] = at[k] at.pop(k) k = nk out = wt_collection.Collection(name="chop", parent=parent) kept = args + [ak for ak in at.keys() if type(ak) == str] kept_axes = [self._axes[self.axis_names.index(a)] for a in kept] removed_axes = [a for a in self._axes if a not in kept_axes] removed_shape = wt_kit.joint_shape(*removed_axes) if removed_shape == (): removed_shape = (1,) * self.ndim removed_shape = list(removed_shape) for i in at.keys(): if type(i) == int: removed_shape[i] = 1 for ax in kept_axes: if ax.shape.count(1) == ax.ndim - 1: removed_shape[ax.shape.index(ax.size)] = 1 removed_shape = tuple(removed_shape) i = 0 for idx in np.ndindex(removed_shape): idx = np.array(idx, dtype=object) idx[np.array(removed_shape) == 1] = slice(None) for axis, point in at.items(): if type(axis) == int: idx[axis] = point continue point, units = point destination_units = self._axes[self.axis_names.index(axis)].units point = wt_units.converter(point, units, destination_units) axis_index = self.axis_names.index(axis) axis = self._axes[axis_index] idx_index = np.array(axis.shape) > 1 if np.sum(idx_index) > 1: raise wt_exceptions.MultidimensionalAxisError("chop", axis.natural_name) idx_index = list(idx_index).index(True) idx[idx_index] = np.argmin(np.abs(axis[tuple(idx)] - point)) data = out.create_data(name="chop%03i" % i) for v in self.variables: kwargs = {} kwargs["name"] = v.natural_name kwargs["values"] = v[idx] kwargs["units"] = v.units kwargs["label"] = v.label kwargs.update(v.attrs) data.create_variable(**kwargs) for c in self.channels: kwargs = {} kwargs["name"] = c.natural_name kwargs["values"] = c[idx] kwargs["units"] = c.units kwargs["label"] = c.label kwargs["signed"] = c.signed kwargs.update(c.attrs) data.create_channel(**kwargs) new_axes = [a.expression for a in kept_axes if a.expression not in at.keys()] new_axis_units = [a.units for a in kept_axes if a.expression not in at.keys()] data.transform(*new_axes) for const in self.constant_expressions: data.create_constant(const, verbose=False) for ax in self.axis_expressions: if ax not in new_axes: data.create_constant(ax, verbose=False) for j, units in enumerate(new_axis_units): data.axes[j].convert(units) i += 1 out.flush() if verbose: print("chopped data into %d piece(s)" % len(out), "in", new_axes) return out def gradient(self, axis, *, channel=0): if isinstance(axis, int): axis_index = axis elif isinstance(axis, str): index = self.axis_names.index(axis) axes = [i for i in range(self.ndim) if self.axes[index].shape[i] > 1] if len(axes) > 1: raise wt_exceptions.MultidimensionalAxisError(axis, "collapse") elif len(axes) == 0: raise wt_exceptions.ValueError( "Axis '{}' is a single point, cannot compute gradient".format(axis) ) axis_index = axes[0] else: raise wt_exceptions.TypeError("axis: expected {int, str}, got %s" % type(axis)) channel_index = wt_kit.get_index(self.channel_names, channel) channel = self.channel_names[channel_index] if self[channel].shape[axis_index] == 1: raise wt_exceptions.ValueError( "Channel '{}' has a single point along Axis '{}', cannot compute gradient".format( channel, axis ) ) rtype = np.result_type(self[channel].dtype, float) new = self.create_channel( "{}_{}_gradient".format(channel, axis), values=np.empty(self[channel].shape, dtype=rtype), ) channel = self[channel] if axis == axis_index: new[:] = np.gradient(channel[:], axis=axis_index) else: new[:] = np.gradient(channel[:], self[axis].points, axis=axis_index) def moment(self, axis, channel=0, moment=1, *, resultant=None): axis_index = None if resultant is not None: for i, (s, r) in enumerate(zip(wt_kit.joint_shape(*self.axes), resultant)): if s != r and r == 1 and axis_index is None: axis_index = i elif s == r: continue else: raise wt_exceptions.ValueError( f"Invalid resultant shape '{resultant}' for shape {wt_kit.joint_shape(*self.axes)}. " + "Consider using `wt.kit.joint_shape` to join non-collapsed axes." ) index = wt_kit.get_index(self.axis_names, axis) if axis_index is None: axes = [i for i in range(self.ndim) if self.axes[index].shape[i] > 1] if len(axes) > 1: raise wt_exceptions.MultidimensionalAxisError(axis, "moment") elif len(axes) == 0: raise wt_exceptions.ValueError( "Axis {} is a single point, cannot compute moment".format(axis) ) axis_index = axes[0] warnings.warn("moment", category=wt_exceptions.EntireDatasetInMemoryWarning) channel_index = wt_kit.get_index(self.channel_names, channel) channel = self.channel_names[channel_index] if self[channel].shape[axis_index] == 1: raise wt_exceptions.ValueError( "Channel '{}' has a single point along Axis '{}', cannot compute moment".format( channel, axis ) ) new_shape = list(self[channel].shape) new_shape[axis_index] = 1 channel = self[channel] axis_inp = axis axis = self.axes[index] x = axis[:] if np.any(np.isnan(x)): raise wt_exceptions.ValueError("Axis '{}' includes NaN".format(axis_inp)) y = np.nan_to_num(channel[:]) try: moments = tuple(moment) except TypeError: moments = (moment,) multiplier = 1 if 0 in moments: # only matters for integral, all others normalize by integral multiplier = np.sign( np.sum(np.diff(x, axis=axis_index), axis=axis_index, keepdims=True) ) for moment in moments: about = 0 norm = 1 if moment > 0: norm = np.trapz(y, x, axis=axis_index) norm = np.array(norm) norm.shape = new_shape if moment > 1: about = np.trapz(x * y, x, axis=axis_index) about = np.array(about) about.shape = new_shape about /= norm if moment > 2: sigma = np.trapz((x - about) ** 2 * y, x, axis=axis_index) sigma = np.array(sigma) sigma.shape = new_shape sigma /= norm sigma **= 0.5 norm *= sigma ** moment values = np.trapz((x - about) ** moment * y, x, axis=axis_index) values = np.array(values) values.shape = new_shape values /= norm if moment == 0: values *= multiplier self.create_channel( "{}_{}_{}_{}".format(channel.natural_name, axis_inp, "moment", moment), values=values, ) def collapse(self, axis, method="sum"): if method in ("int", "integrate"): warnings.warn( "integrate method of collapse is deprecated, use moment(moment=0) instead", wt_exceptions.VisibleDeprecationWarning, ) for channel in self.channel_names: try: self.moment(axis, channel, moment=0) self.rename_channels( **{self.channel_names[-1]: f"{channel}_{axis}_{method}"}, verbose=False ) except wt_exceptions.ValueError: pass # may have some channels which fail, do so silently return # get axis index -------------------------------------------------------------------------- if isinstance(axis, int): axis_index = axis elif isinstance(axis, str): index = self.axis_names.index(axis) axes = [i for i in range(self.ndim) if self.axes[index].shape[i] > 1] if len(axes) > 1: raise wt_exceptions.MultidimensionalAxisError(axis, "collapse") elif len(axes) == 0: raise wt_exceptions.ValueError( "Axis {} is a single point, cannot collapse".format(axis) ) axis_index = axes[0] else: raise wt_exceptions.TypeError("axis: expected {int, str}, got %s" % type(axis)) new_shape = list(self.shape) new_shape[axis_index] = 1 func = { "sum": np.nansum, "max": np.nanmax, "maximum": np.nanmax, "min": np.nanmin, "minimum": np.nanmin, "ave": np.nanmean, "average": np.nanmean, "mean": np.nanmean, } # methods --------------------------------------------------------------------------------- if isinstance(method, str): methods = [method for _ in self.channels] if isinstance(method, list): if len(method) == len(self.channels): methods = method else: raise wt_exceptions.ValueError( "method argument must have same number of elements as there are channels" ) for m in methods: if m not in func.keys(): raise wt_exceptions.ValueError("method '{}' not recognized".format(m)) warnings.warn("collapse", category=wt_exceptions.EntireDatasetInMemoryWarning) # collapse -------------------------------------------------------------------------------- for method, channel in zip(methods, self.channel_names): if method is None: continue if self[channel].shape[axis_index] == 1: continue # Cannot collapse any further, don't clutter data object new_shape = list(self[channel].shape) new_shape[axis_index] = 1 rtype = self[channel].dtype if method in ["ave", "average", "mean"]: rtype = np.result_type(self[channel].dtype, float) new = self.create_channel( "{}_{}_{}".format(channel, axis, method), values=np.empty(new_shape, dtype=rtype), units=self[channel].units, ) new[:] = func[method](self[channel], axis=axis_index, keepdims=True) def convert(self, destination_units, *, convert_variables=False, verbose=True): for axis in self.axes: if wt_units.is_valid_conversion(axis.units, destination_units): orig = axis.units axis.convert(destination_units, convert_variables=convert_variables) if verbose: print( "axis {} converted from {} to {}".format( axis.expression, orig, destination_units ) ) for constant in self.constants: if wt_units.is_valid_conversion(constant.units, destination_units): orig = constant.units constant.convert(destination_units, convert_variables=convert_variables) if verbose: print( "constant {} converted from {} to {}".format( constant.expression, orig, destination_units ) ) if convert_variables: for var in self.variables: if wt_units.is_valid_conversion(var.units, destination_units): orig = var.units var.convert(destination_units) if verbose: print( "variable {} converted from {} to {}".format( var.natural_name, orig, destination_units ) ) self._on_axes_updated() self._on_constants_updated() def create_channel( self, name, values=None, *, shape=None, units=None, dtype=None, **kwargs ) -> Channel: if name in self.channel_names: warnings.warn(name, wt_exceptions.ObjectExistsWarning) return self[name] elif name in self.variable_names: raise wt_exceptions.NameNotUniqueError(name) require_kwargs = {"chunks": True} if values is None: if shape is None: require_kwargs["shape"] = self.shape else: require_kwargs["shape"] = shape if dtype is None: require_kwargs["dtype"] = np.dtype(np.float64) else: require_kwargs["dtype"] = dtype if require_kwargs["dtype"].kind in "fcmM": require_kwargs["fillvalue"] = np.nan else: require_kwargs["fillvalue"] = 0 else: require_kwargs["data"] = values require_kwargs["shape"] = values.shape require_kwargs["dtype"] = values.dtype if np.prod(require_kwargs["shape"]) == 1: require_kwargs["chunks"] = None dataset_id = self.require_dataset(name=name, **require_kwargs).id channel = Channel(self, dataset_id, units=units, **kwargs) self.attrs["channel_names"] = np.append(self.attrs["channel_names"], name.encode()) return channel def create_variable( self, name, values=None, *, shape=None, units=None, dtype=None, **kwargs ) -> Variable: if name in self.variable_names: warnings.warn(name, wt_exceptions.ObjectExistsWarning) return self[name] elif name in self.channel_names: raise wt_exceptions.NameNotUniqueError(name) if values is None: if shape is None: shape = self.shape if dtype is None: dtype = np.dtype(np.float64) if dtype.kind in "fcmM": fillvalue = np.nan else: fillvalue = 0 else: shape = values.shape dtype = values.dtype fillvalue = None id = self.require_dataset( name=name, data=values, shape=shape, dtype=dtype, fillvalue=fillvalue ).id variable = Variable(self, id, units=units, **kwargs) self._variables = None self.attrs["variable_names"] = np.append(self.attrs["variable_names"], name.encode()) return variable def get_nadir(self, channel=0) -> tuple: if isinstance(channel, int): channel_index = channel elif isinstance(channel, str): channel_index = self.channel_names.index(channel) else: raise TypeError("channel: expected {int, str}, got %s" % type(channel)) channel = self.channels[channel_index] idx = channel.argmin() return tuple(a[idx] for a in self._axes) def get_zenith(self, channel=0) -> tuple: if isinstance(channel, int): channel_index = channel elif isinstance(channel, str): channel_index = self.channel_names.index(channel) else: raise TypeError("channel: expected {int, str}, got %s" % type(channel)) channel = self.channels[channel_index] idx = channel.argmax() return tuple(a[idx] for a in self._axes) def heal(self, channel=0, method="linear", fill_value=np.nan, verbose=True): warnings.warn("heal", category=wt_exceptions.EntireDatasetInMemoryWarning) timer = wt_kit.Timer(verbose=False) with timer: if isinstance(channel, int): channel_index = channel elif isinstance(channel, str): channel_index = self.channel_names.index(channel) else: raise TypeError("channel: expected {int, str}, got %s" % type(channel)) channel = self.channels[channel_index] values = self.channels[channel_index][:] points = [axis[:] for axis in self._axes] xi = tuple(np.meshgrid(*points, indexing="ij")) arr = np.zeros((len(self._axes) + 1, values.size)) for i in range(len(self._axes)): arr[i] = xi[i].flatten() arr[-1] = values.flatten() arr = arr[:, ~np.isnan(arr).any(axis=0)] tup = tuple([arr[i] for i in range(len(arr) - 1)]) out = griddata(tup, arr[-1], xi, method=method, fill_value=fill_value) self.channels[channel_index][:] = out if verbose: print( "channel {0} healed in {1} seconds".format( channel.name, np.around(timer.interval, decimals=3) ) ) def level(self, channel, axis, npts, *, verbose=True): warnings.warn("level", category=wt_exceptions.EntireDatasetInMemoryWarning) channel_index = wt_kit.get_index(self.channel_names, channel) channel = self.channels[channel_index] npts = int(npts) if npts == 0: raise wt_exceptions.ValueError("npts must not be zero") ss = [slice(None)] * self.ndim if npts > 0: ss[axis] = slice(0, npts, None) else: ss[axis] = slice(npts, None, None) subtrahend = np.nanmean(channel[ss], axis=axis) if self.ndim > 1: subtrahend = np.expand_dims(subtrahend, axis=axis) channel -= subtrahend channel._null = 0 if verbose: print("channel {0} leveled along axis {1}".format(channel.natural_name, axis)) def map_variable( self, variable, points, input_units="same", *, name=None, parent=None, verbose=True ) -> "Data": variable_index = wt_kit.get_index(self.variable_names, variable) variable = self.variables[variable_index] if isinstance(points, int): points = np.linspace(variable.min(), variable.max(), points) points = np.array(points) if points.ndim < variable.ndim: for i, d in enumerate(variable.shape): if d == 1: points = np.expand_dims(points, axis=i) if input_units == "same": pass else: points = wt_units.converter(points, input_units, variable.units) special = ["name", "axes", "constants", "channel_names", "variable_names"] kwargs = {k: v for k, v in self.attrs.items() if k not in special} if name is None: name = "{0}_{1}_mapped".format(self.natural_name, variable.natural_name) kwargs["name"] = name kwargs["parent"] = parent out = Data(**kwargs) values = points out.create_variable(values=values, **variable.attrs) for v in self.variables: if wt_kit.orthogonal(v.shape, variable.shape): out.create_variable(values=v[:], **v.attrs) out.transform(*self.axis_expressions) if self.ndim == 1: def interpolate(dataset, points): function = scipy.interpolate.interp1d(variable[:], dataset[:], bounds_error=False) return function(points) else: pts = np.array([a.full.flatten() for a in self.axes]).T out_pts = np.array([a.full.flatten() for a in out.axes]).T def interpolate(dataset, points): values = dataset.full.flatten() function = scipy.interpolate.LinearNDInterpolator(pts, values, rescale=True) new = function(out_pts) new.shape = out.shape return new for v in self.variables: if v.natural_name not in out.variable_names: out.create_variable(values=interpolate(v, points), **v.attrs) out.variable_names = self.variable_names out._variables = None for channel in self.channels: out.create_channel(values=interpolate(channel, points), **channel.attrs) if verbose: print("data mapped from {0} to {1}".format(self.shape, out.shape)) return out def offset( self, points, offsets, along, offset_axis, units="same", offset_units="same", mode="valid", method="linear", verbose=True, ): raise NotImplementedError if isinstance(along, int): axis_index = along elif isinstance(along, str): axis_index = self.axis_names.index(along) else: raise TypeError("along: expected {int, str}, got %s" % type(along)) axis = self._axes[axis_index] if units == "same": input_units = axis.units else: input_units = units if len(offsets.shape) == 1: pass else: raise RuntimeError("values must be 1D or 0D in offset!") dictionary = getattr(wt_units, axis.units_kind) if input_units in dictionary.keys(): pass else: raise RuntimeError("units incompatible in offset!") points = wt_units.converter(points, input_units, axis.units) function = interp1d(points, offsets, bounds_error=False) corrections = function(axis[:]) finite_indicies = np.where(np.isfinite(corrections))[0] left_pad_width = finite_indicies[0] right_pad_width = len(corrections) - finite_indicies[-1] - 1 corrections = np.pad( corrections[np.isfinite(corrections)], (int(left_pad_width), int(right_pad_width)), mode="edge", ) transpose_order = np.arange(len(self._axes)) transpose_order[axis_index] = len(self._axes) - 1 transpose_order[-1] = axis_index self.transpose(transpose_order, verbose=False) if isinstance(offset_axis, int): offset_axis_index = offset_axis elif isinstance(offset_axis, str): offset_axis_index = self.axis_names.index(offset_axis) else: raise TypeError("offset_axis: expected {int, str}, got %s" % type(offset_axis)) new_points = [a[:] for a in self._axes] old_offset_axis_points = self._axes[offset_axis_index][:] spacing = abs( (old_offset_axis_points.max() - old_offset_axis_points.min()) / float(len(old_offset_axis_points)) ) if mode == "old": new_offset_axis_points = old_offset_axis_points elif mode == "valid": _max = old_offset_axis_points.max() + corrections.min() _min = old_offset_axis_points.min() + corrections.max() n = int(abs(np.ceil((_max - _min) / spacing))) new_offset_axis_points = np.linspace(_min, _max, n) elif mode == "full": _max = old_offset_axis_points.max() + corrections.max() _min = old_offset_axis_points.min() + corrections.min() n = np.ceil((_max - _min) / spacing) new_offset_axis_points = np.linspace(_min, _max, n) new_points[offset_axis_index] = new_offset_axis_points new_xi = tuple(np.meshgrid(*new_points, indexing="ij")) xi = tuple(np.meshgrid(*[a[:] for a in self._axes], indexing="ij")) for channel in self.channels: arr = np.zeros((len(self._axes) + 1, channel[:].size)) for i in range(len(self._axes)): arr[i] = xi[i].flatten() arr[-1] = channel[:].flatten() corrections = list(corrections) corrections = corrections * int((len(arr[0]) / len(corrections))) arr[offset_axis_index] += corrections tup = tuple([arr[i] for i in range(len(arr) - 1)]) out = griddata(tup, arr[-1], new_xi, method=method, fill_value=np.nan, rescale=True) channel[:] = out self._axes[offset_axis_index][:] = new_offset_axis_points self.transpose(transpose_order, verbose=False) def print_tree(self, *, verbose=True): print("{0} ({1})".format(self.natural_name, self.filepath)) self._print_branch("", depth=0, verbose=verbose) def prune(self, keep_channels=True, *, verbose=True): for v in self.variables: for var in wt_kit.flatten_list([ax.variables for ax in self._axes + self._constants]): if v == var: break else: self.remove_variable(v.natural_name, implied=False, verbose=verbose) if keep_channels is not True: try: if isinstance(keep_channels, str): raise TypeError indexes = tuple(keep_channels) except TypeError: indexes = (keep_channels,) for i, ch in enumerate(self.channels): if i not in indexes and not ch.natural_name in indexes: self.remove_channel(ch.natural_name, verbose=verbose) def remove_channel(self, channel, *, verbose=True): channel_index = wt_kit.get_index(self.channel_names, channel) new = list(self.channel_names) name = new.pop(channel_index) del self[name] self.channel_names = new if verbose: print("channel {0} removed".format(name)) def remove_variable(self, variable, *, implied=True, verbose=True): if isinstance(variable, int): variable = self.variable_names[variable] removed = [] if implied: for n in self.variable_names: if n.startswith(variable): removed.append(n) else: removed = [variable] for n in removed: for a in self._axes: if n in [v.natural_name for v in a.variables]: message = "{0} is contained in axis {1}".format(n, a.expression) raise RuntimeError(message) for c in self._constants: if n in [v.natural_name for v in c.variables]: warnings.warn( "Variable being removed used in a constant", wt_exceptions.WrightToolsWarning, ) for n in removed: variable_index = wt_kit.get_index(self.variable_names, n) new = list(self.variable_names) name = new.pop(variable_index) del self[name] self.variable_names = new self._variables = None if verbose: print("{0} variable(s) removed:".format(len(removed))) for n in removed: print(" {0}".format(n)) def rename_channels(self, *, verbose=True, **kwargs): changed = kwargs.keys() for k, v in kwargs.items(): if v not in changed and v in self.keys(): raise wt_exceptions.NameNotUniqueError(v) new = {} for k, v in kwargs.items(): obj = self[k] index = self.channel_names.index(k) new[v] = obj, index Group._instances.pop(obj.fullpath, None) obj.natural_name = str(v) del self[k] names = list(self.channel_names) for v, value in new.items(): obj, index = value self[v] = obj names[index] = v self.channel_names = names if verbose: print("{0} channel(s) renamed:".format(len(kwargs))) for k, v in kwargs.items(): print(" {0} --> {1}".format(k, v)) def rename_variables(self, *, implied=True, verbose=True, **kwargs): kwargs = collections.OrderedDict(kwargs) if implied: new = collections.OrderedDict() for k, v in kwargs.items(): for n in self.variable_names: if n.startswith(k): new[n] = n.replace(k, v, 1) kwargs = new changed = kwargs.keys() for k, v in kwargs.items(): if v not in changed and v in self.keys(): raise wt_exceptions.NameNotUniqueError(v) new = {} for k, v in kwargs.items(): obj = self[k] index = self.variable_names.index(k) new[v] = obj, index Group._instances.pop(obj.fullpath, None) obj.natural_name = str(v) del self[k] names = list(self.variable_names) for v, value in new.items(): obj, index = value self[v] = obj names[index] = v self.variable_names = names units = self.units new = list(self.axis_expressions) for i, v in enumerate(kwargs.keys()): for j, n in enumerate(new): new[j] = n.replace(v, "{%i}" % i) for i, n in enumerate(new): new[i] = n.format(*kwargs.values()) self.transform(*new) for a, u in zip(self._axes, units): a.convert(u) units = self.constant_units new = list(self.constant_expressions) for i, v in enumerate(kwargs.keys()): for j, n in enumerate(new): new[j] = n.replace(v, "{%i}" % i) for i, n in enumerate(new): new[i] = n.format(*kwargs.values()) self.set_constants(*new) for c, u in zip(self._constants, units): c.convert(u) if verbose: print("{0} variable(s) renamed:".format(len(kwargs))) for k, v in kwargs.items(): print(" {0} --> {1}".format(k, v)) def share_nans(self): def f(_, s, channels): outs = wt_kit.share_nans(*[c[s] for c in channels]) for c, o in zip(channels, outs): c[s] = o self.channels[0].chunkwise(f, self.channels) def smooth(self, factors, channel=None, verbose=True) -> "Data": warnings.warn("smooth", category=wt_exceptions.EntireDatasetInMemoryWarning) if isinstance(factors, list): pass else: dummy = np.zeros(len(self._axes)) dummy[::] = factors factors = list(dummy) if channel is None: channels = self.channels else: if isinstance(channel, int): channel_index = channel elif isinstance(channel, str): channel_index = self.channel_names.index(channel) else: raise TypeError("channel: expected {int, str}, got %s" % type(channel)) channels = [self.channels[channel_index]] for channel in channels: values = channel[:] for axis_index in range(len(factors)): factor = factors[axis_index] transpose_order = range(len(values.shape)) transpose_order = [ len(values.shape) - 1 if i == axis_index else i for i in transpose_order ] transpose_order[len(values.shape) - 1] = axis_index values = values.transpose(transpose_order) beta = 5.0 w = np.kaiser(2 * factor + 1, beta) for index in np.ndindex(values[..., 0].shape): current_slice = values[index] temp_slice = np.pad(current_slice, int(factor), mode=str("edge")) values[index] = np.convolve(temp_slice, w / w.sum(), mode=str("valid")) values = values.transpose(transpose_order) channel[:] = values if verbose: print("smoothed data") def split( self, expression, positions, *, units=None, parent=None, verbose=True ) -> wt_collection.Collection: old_expr = self.axis_expressions old_units = self.units out = wt_collection.Collection(name="split", parent=parent) if isinstance(expression, int): if units is None: units = self._axes[expression].units expression = self._axes[expression].expression elif isinstance(expression, str): pass else: raise TypeError("expression: expected {int, str}, got %s" % type(expression)) self.transform(expression) if units: self.convert(units, verbose=False) try: positions = [-np.inf] + sorted(list(positions)) + [np.inf] except TypeError: positions = [-np.inf, positions, np.inf] values = self._axes[0].full masks = [(values >= lo) & (values < hi) for lo, hi in wt_kit.pairwise(positions)] omasks = [] cuts = [] for mask in masks: try: omasks.append(wt_kit.mask_reduce(mask)) cuts.append([i == 1 for i in omasks[-1].shape]) if np.all(cuts[-1]): cuts[-1][0] = False except ValueError: omasks.append(None) cuts.append(None) for i in range(len(positions) - 1): out.create_data("split%03i" % i) for var in self.variables: for i, (imask, omask, cut) in enumerate(zip(masks, omasks, cuts)): if omask is None: continue omask = wt_kit.enforce_mask_shape(omask, var.shape) omask.shape = tuple([s for s, c in zip(omask.shape, cut) if not c]) out_arr = np.full(omask.shape, np.nan) imask = wt_kit.enforce_mask_shape(imask, var.shape) out_arr[omask] = var[:][imask] out[i].create_variable(values=out_arr, **var.attrs) for ch in self.channels: for i, (imask, omask, cut) in enumerate(zip(masks, omasks, cuts)): if omask is None: continue omask = wt_kit.enforce_mask_shape(omask, ch.shape) omask.shape = tuple([s for s, c in zip(omask.shape, cut) if not c]) out_arr = np.full(omask.shape, np.nan) imask = wt_kit.enforce_mask_shape(imask, ch.shape) out_arr[omask] = ch[:][imask] out[i].create_channel(values=out_arr, **ch.attrs) if verbose: for d in out.values(): try: d.transform(expression) except IndexError: continue print("split data into {0} pieces along <{1}>:".format(len(positions) - 1, expression)) for i, (lo, hi) in enumerate(wt_kit.pairwise(positions)): new_data = out[i] if new_data.shape == (): print(" {0} : None".format(i)) else: new_axis = new_data.axes[0] print( " {0} : {1:0.2f} to {2:0.2f} {3} {4}".format( i, lo, hi, self.axes[0].units, new_axis.shape ) ) for d in out.values(): try: d.transform(*old_expr) keep = [] keep_units = [] for ax, u in zip(d.axes, old_units): if ax.size > 1: keep.append(ax.expression) keep_units.append(u) else: d.create_constant(ax.expression, verbose=False) d.transform(*keep) for ax, u in zip(d.axes, keep_units): ax.convert(u) except IndexError: continue tempax = Axis(d, expression) if all( np.all( np.sum(~np.isnan(tempax.masked), axis=tuple(set(range(tempax.ndim)) - {j})) <= 1 ) for j in range(tempax.ndim) ): d.create_constant(expression, verbose=False) self.transform(*old_expr) for ax, u in zip(self.axes, old_units): ax.convert(u) return out def transform(self, *axes, verbose=True): new = [] newt = "newt" in self.axis_expressions current = {a.expression: a for a in self._axes} for expression in axes: axis = current.get(expression, Axis(self, expression)) new.append(axis) self._axes = new for a in self._axes: if a.units is None: a.convert(a.variables[0].units) self.flush() self._on_axes_updated() nownewt = "newt" in self.axis_expressions if verbose and nownewt and not newt: print("Look she turned me into a newt") elif verbose and newt and not nownewt: print("I got better") def set_constants(self, *constants, verbose=True): new = [] current = {c.expression: c for c in self._constants} for expression in constants: constant = current.get(expression, Constant(self, expression)) new.append(constant) self._constants = new for c in self._constants: if c.units is None: c.convert(c.variables[0].units) self.flush() self._on_constants_updated() def create_constant(self, expression, *, verbose=True): if expression in self.constant_expressions: wt_exceptions.ObjectExistsWarning.warn(expression) return self.constants[self.constant_expressions.index(expression)] constant = Constant(self, expression) if constant.units is None: constant.convert(constant.variables[0].units) self._constants.append(constant) self.flush() self._on_constants_updated() if verbose: print("Constant '{}' added".format(constant.expression)) return constant def remove_constant(self, constant, *, verbose=True): if isinstance(constant, (str, int)): constant_index = wt_kit.get_index(self.constant_expressions, constant) elif isinstance(constant, Constant): constant_index = wt_kit.get_index(self.constants, constant) constant = self._constants[constant_index] self._constants.pop(constant_index) self.flush() self._on_constants_updated() if verbose: print("Constant '{}' removed".format(constant.expression)) def zoom(self, factor, order=1, verbose=True): raise NotImplementedError import scipy.ndimage for axis in self._axes: axis[:] = scipy.ndimage.interpolation.zoom(axis[:], factor, order=order) for channel in self.channels: channel[:] = scipy.ndimage.interpolation.zoom(channel[:], factor, order=order) if verbose: print("data zoomed to new shape:", self.shape)

true

f70bd75e36b14f005bee3275929a42154f09dfe5

54,196

py

Python

tensorflow/python/framework/func_graph.py

ahmedsabie/tensorflow

1c47355978f562a6a40cd8b0597e2638fb73e07d

[ "Apache-2.0" ]

2

2020-04-02T11:52:00.000Z

2020-05-29T09:02:00.000Z

tensorflow/python/framework/func_graph.py

sseung0703/tensorflow

be084bd7a4dd241eb781fc704f57bcacc5c9b6dd

[ "Apache-2.0" ]

1

2020-05-16T01:56:36.000Z

tensorflow/python/framework/func_graph.py

sseung0703/tensorflow

be084bd7a4dd241eb781fc704f57bcacc5c9b6dd

[ "Apache-2.0" ]

1

2021-12-06T17:11:35.000Z

# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """FuncGraph and related functionality.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections as py_collections import itertools import weakref import numpy as np from tensorflow.core.framework import attr_value_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import execute from tensorflow.python.eager import tape from tensorflow.python.eager.graph_only_ops import graph_placeholder from tensorflow.python.framework import auto_control_deps from tensorflow.python.framework import composite_tensor from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_spec from tensorflow.python.framework import tensor_util from tensorflow.python.framework import type_spec from tensorflow.python.ops import array_ops from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope from tensorflow.python.util import compat from tensorflow.python.util import memory from tensorflow.python.util import nest from tensorflow.python.util import object_identity from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator ALLOWLIST_COLLECTIONS = [ ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.TRAINABLE_VARIABLES, variable_scope._VARSTORE_KEY, # pylint: disable=protected-access variable_scope._VARSCOPESTORE_KEY # pylint: disable=protected-access ] _EAGER_CONST_THRESHOLD = 128 class UnknownArgument(object): """Signifies an argument which is not currently handled.""" pass def convert_structure_to_signature(structure, arg_names=None): """Convert a potentially nested structure to a signature. Args: structure: Structure to convert, where top level collection is a list or a tuple. arg_names: Optional list of arguments that has equal number of elements as `structure` and is used for naming corresponding TensorSpecs. Returns: Identical structure that has TensorSpec objects instead of Tensors and UnknownArgument instead of any unsupported types. """ def encode_arg(arg, path): """A representation for this argument, for converting into signatures.""" if isinstance(arg, ops.Tensor): user_specified_name = None try: user_specified_name = compat.as_str( arg.op.get_attr("_user_specified_name")) except ValueError: pass if path and user_specified_name and user_specified_name != path[0]: # The user has explicitly named the argument differently than the name # of the function argument. name = user_specified_name else: name = "/".join(str(p) for p in path) return tensor_spec.TensorSpec(arg.shape, arg.dtype, name) if isinstance(arg, composite_tensor.CompositeTensor): # TODO(b/133606651) Do we need to inject arg_name? return arg._type_spec # pylint: disable=protected-access if isinstance(arg, resource_variable_ops.BaseResourceVariable): name = "/".join(str(p) for p in path) return resource_variable_ops.VariableSpec(arg.shape, arg.dtype, name) if isinstance(arg, ( int, float, bool, str, type(None), dtypes.DType, tensor_spec.TensorSpec, type_spec.TypeSpec, )): return arg return UnknownArgument() # We are using the flattened paths to name the TensorSpecs. We need an # explicit name for them downstream. flattened = nest.flatten_with_tuple_paths(structure) if arg_names: if len(arg_names) != len(structure): raise ValueError( "Passed in arg_names don't match actual signature (%s)." % arg_names) # Replace all top-level names with their actual arg_names. If a path before # was "(2,'a',1)", it will become "(arg_names[2],'a',1)". flattened = [ ((arg_names[path[0]],) + path[1:], arg) for path, arg in flattened ] mapped = [encode_arg(arg, path) for path, arg in flattened] return nest.pack_sequence_as(structure, mapped) class FuncGraph(ops.Graph): """Graph representing a function body. Attributes: name: The name of the function. inputs: Placeholder tensors representing the inputs to this function. The tensors are in this FuncGraph. This represents "regular" inputs as well as captured inputs (i.e. the values of self.captures), with the regular inputs coming first. outputs: Tensors that will be returned by this function. The tensors are in this FuncGraph. control_outputs: Operations that must be executed before the function represented by this graph can be said to have been executed. structured_input_signature: A tuple of (args, kwargs), which are both possibly-nested python objects that were received by this function. Note that these structures might contain Python `None`s. structured_outputs: A possibly-nested python object which will be returned by this function. The Tensors in this structure are the same as those of self.outputs. Note that this structure might contain Python `None`s. variables: Variables that should be watched during function execution. outer_graph: The graph this function is defined in. May be another FuncGraph or the global default Graph. captures: Maps external tensor -> internal tensor (i.e. input placeholder). The entries are in the order they were captured. control_captures: Set of external ops on which this graph has a control dependency. seed: The graph-level random seed. capture_by_value: If True, the func graph will capture Variables by value instead of reference. """ def __init__(self, name, collections=None, capture_by_value=None): """Construct a new FuncGraph. The graph will inherit its graph key, collections, seed, and distribution strategy stack from the current context or graph. Args: name: the name of the function. collections: a dictionary of collections this FuncGraph should start with. If not specified (None), the FuncGraph will read (but not write to) the outer graph's collections that are not allowlisted, and both read and write to the outer graph's collections that are allowlisted. The current allowlisted collections are the global variables, the local variables, and the trainable variables. Defaults to None. capture_by_value: An optional boolean. If True, the func graph will capture Variables by value instead of reference. By default inherit from outer graphs, and failing that will default to False. """ super(FuncGraph, self).__init__() self.name = name self.inputs = [] self.outputs = [] self.control_outputs = [] self.control_captures = set() self.structured_input_signature = None self.structured_outputs = None self._weak_variables = [] self._watched_variables = object_identity.ObjectIdentityWeakSet() self.is_control_flow_graph = False outer_graph = ops.get_default_graph() self._weak_outer_graph = weakref.ref(outer_graph) while outer_graph.building_function: outer_graph = outer_graph.outer_graph # If self._weak_outer_graph is deleted, we revert to the outermost Graph # active when the FuncGraph was traced. This will not be a FuncGraph. self._fallback_outer_graph = outer_graph self._captures = py_collections.OrderedDict() # If not None, records the names of output args of this function. Used to # preserve the output names in the signature of a serialized+deserialized # function. Private at the moment mostly because it's often out of date. self._output_names = None # Maps arbitrary key -> (closure, nest of placeholders), where at function # call time the value of closure() will be used to feed the nest of # placeholders. self._deferred_captures = py_collections.OrderedDict() # Inherit capture-by-value from outer graph. if capture_by_value is not None: self.capture_by_value = capture_by_value elif self.outer_graph is not None and isinstance( self.outer_graph, FuncGraph): self.capture_by_value = self.outer_graph.capture_by_value else: self.capture_by_value = False self._building_function = True # Map from resource tensor name to last op (in program order) which uses # this tensor. Used to enforce that execution order matches program order # for resource tensors. self._last_op_using_resource_tensor = {} graph = self.outer_graph if context.executing_eagerly(): self.seed = context.global_seed() # [for tf-data user migration from TF1.0 to 2.0] seed_used keep track of # any None op_seed for random_op in the function, in which case we end up # using function seed, which could be unintended behavior for the op. self._seed_used = False else: self.seed = graph.seed self._seed_used = False # TODO(allenl): Figure out if we can remove colocation stack # specialization (currently used in cond_v2), here and in the cache key. self._colocation_stack = graph._colocation_stack.copy() # pylint: disable=protected-access if collections is None: for collection_name in graph.get_all_collection_keys(): if collection_name not in ALLOWLIST_COLLECTIONS: self._collections[collection_name] = graph.get_collection( collection_name) for collection_name in ALLOWLIST_COLLECTIONS: self._collections[collection_name] = graph.get_collection_ref( collection_name) else: self._collections = collections # Keep track of whether this FuncGraph is exportable to SavedModel. Use # `graph.mark_as_unsaveable(reason)` to mark this FuncGraph and any # dependent functions as unsaveable. self._saveable = True self._saving_errors = set() # Keep track of callbacks to run when this graph exits default scope self._scope_exit_callbacks = None def __str__(self): return "FuncGraph(name=%s, id=%s)" % (self.name, id(self)) def watch_variable(self, v): """Marks the variable v as accessed while building this graph.""" while self is not None and isinstance(self, FuncGraph): self._watched_variables.add(v) self = self.outer_graph def capture_call_time_value(self, closure, spec, key=None): """Creates a placeholder which at call time has the value closure(). Useful, for example, to respect TensorFlow context managers, which are often dynamically scoped. Args: closure: function which takes no arguments, to be evaluated at function call time, returning a nest of tensors compatible with `spec`. spec: nest of TypeSpec for the value to capture. key: optional. If not None, multiple calls to lazy_capture with the same key in the same graph will return the same placeholder, and the first closure will be used at function call time. Returns: Nest of placeholders which, at function call time, will be fed with the result of calling closure(). Raises: ValueError: at function call time, if the return value of closure() is not compatible with `spec`. """ if key is None: key = object() if key not in self._deferred_captures: def convert_to_placeholder(s): if not isinstance(s, tensor_spec.DenseSpec): raise TypeError( "Expected a nest of `TypeSpec` objects, found %s of type %s." % (s, type(s))) return array_ops.placeholder(dtype=s.dtype, shape=s.shape) placeholder = nest.map_structure( convert_to_placeholder, spec, expand_composites=True) def wrapped_closure(): ret_nest = closure() nest.assert_same_structure(spec, ret_nest, expand_composites=True) # This uses the tensor dtype defined in `spec` when converting values # in `ret_nest` to tensors. # pylint: disable=protected-access y = nest.map_structure(lambda s, r: s._to_components(r), spec, ret_nest, expand_composites=False) # pylint: enable=protected-access return nest.flatten(y, expand_composites=True) self._deferred_captures[key] = (wrapped_closure, placeholder) return self._deferred_captures[key][1] def control_dependencies(self, control_inputs): """Handles control dependencies. FuncGraph wraps Graph's control_dependencies logic by first filtering out any external tensors / operations and storing them in the graph's control_captures member. Any consumers of this function graph must then decide how to handle the control captures. Args: control_inputs: A list of `Operation` or `Tensor` objects which must be executed or computed before running the operations defined in the context. Can also be `None` to clear the control dependencies. Returns: A context manager that specifies control dependencies for all operations constructed within the context. Raises: TypeError: If `control_inputs` is not a list of `Operation` or `Tensor` objects. """ if control_inputs is None: return super(FuncGraph, self).control_dependencies(control_inputs) filtered_control_inputs = [] for c in control_inputs: # Check for _UnreadVariable if (isinstance(c, ops.IndexedSlices) or (hasattr(c, "_handle") and hasattr(c, "op"))): c = c.op graph_element = ops._as_graph_element(c) # pylint: disable=protected-access if graph_element is None: graph_element = c if graph_element is not None and getattr( graph_element, "graph", None) is not self: self.control_captures.add(graph_element) else: filtered_control_inputs.append(graph_element) return super(FuncGraph, self).control_dependencies(filtered_control_inputs) def as_default(self): outer_cm = super(FuncGraph, self).as_default() @tf_contextlib.contextmanager def inner_cm(): """Context manager for copying distribute.Strategy scope information.""" # pylint: disable=protected-access # TODO(b/112906995, nareshmodi): distribution strategy depends on # inheriting this stack from the default graph even in eager mode. Maybe # it should be part of the eager context? This would also allow us to # remove a get_default_graph() call from the function cache lookup. graph = ops.get_default_graph() old_strategy_stack = self._distribution_strategy_stack self._distribution_strategy_stack = list( graph._distribution_strategy_stack) # We ignore device placements from any outer scopes while tracing the # function when possible, to avoid hard-coding them in the function # graph. "Default" placements come from the PartitionedCallOp's placement, # so that the same trace of the Python function may be placed on several # different devices and saved functions may be placed on new devices when # restored. # However, we need to preserve the outer device stack in the following # cases in non eager context: # 1. device stack is callable # 2. When using distribution strategy with legacy graph mode. old_device_stack = self._device_function_stack if (not context.executing_eagerly() and (device_stack_has_callable(graph._device_function_stack) or (self._distribution_strategy_stack and not ops.executing_eagerly_outside_functions()))): # Hard-code devices from device functions in the function body self._device_function_stack = graph._device_function_stack.copy() old_creator_stack = self._variable_creator_stack self._variable_creator_stack = graph._variable_creator_stack # Inherit the graph key, since this is used for matching variables in # optimizers. old_graph_key = self._graph_key self._graph_key = graph._graph_key # pylint: enable=protected-access old_scope_exit_callbacks = self._scope_exit_callbacks self._scope_exit_callbacks = [] with outer_cm as g: try: yield g finally: try: for fn in self._scope_exit_callbacks: fn() finally: self._scope_exit_callbacks = old_scope_exit_callbacks self._distribution_strategy_stack = old_strategy_stack self._device_function_stack = old_device_stack self._variable_creator_stack = old_creator_stack self._graph_key = old_graph_key return inner_cm() @property def outer_graph(self): """The Graph this FuncGraph is nested in. Functions may capture Tensors from graphs they are nested in (transitive). Returns: A Graph object. Initially set to the current default graph when the FuncGraph was created. If the previous `outer_graph` was deleted because the function that owns it was deleted, `outer_graph` is reset to the outermost default graph active when the FuncGraph was created. This FuncGraph won't have captured anything from the new `outer_graph` (and likely not from the previous setting, since that would have created a strong reference), but it is returned so that FuncGraphs always have a parent. """ current = self._weak_outer_graph() if current is None: return self._fallback_outer_graph return current @outer_graph.setter def outer_graph(self, new_outer_graph): """Sets `outer_graph` to `new_outer_graph`.""" self._weak_outer_graph = weakref.ref(new_outer_graph) @property def output_types(self): return [t.dtype for t in self.outputs] @property def output_shapes(self): return [t.shape for t in self.outputs] @property def trainable_variables(self): """A sequence of trainable variables accessed by this FuncGraph. Note that functions keep only weak references to variables. Calling the function after a variable it accesses has been deleted is an error. Returns: Sequence of trainable variables for this func graph. """ return tuple(v for v in self.variables if v.trainable) @property def variables(self): """A sequence of variables accessed by this FuncGraph. Note that functions keep only weak references to variables. Calling the function after a variable it accesses has been deleted is an error. Returns: Sequence of variables for this func graph. """ def deref(weak_v): v = weak_v() if v is None: raise AssertionError( "Called a function referencing variables which have been deleted. " "This likely means that function-local variables were created and " "not referenced elsewhere in the program. This is generally a " "mistake; consider storing variables in an object attribute on " "first call.") return v return tuple(deref(v) for v in self._weak_variables) @variables.setter def variables(self, var_list): self._weak_variables = [weakref.ref(v) for v in var_list] def _capture_by_value( self, op_type, inputs, dtypes, # pylint: disable=redefined-outer-name input_types=None, name=None, attrs=None, op_def=None, compute_device=True): # When capturing by value, do the read outside reverse_captures = dict((id(v), k) for k, v in self.captures) uncaptured_inputs = [reverse_captures.get(id(t), t) for t in inputs] with ops.init_scope(): if context.executing_eagerly(): attr_list = ("dtype", int(attrs["dtype"].type)) value, = execute.execute( compat.as_bytes(op_type), 1, uncaptured_inputs, attr_list, context.context()) else: op = ops.get_default_graph()._create_op_internal( # pylint: disable=protected-access op_type, uncaptured_inputs, dtypes, input_types, name, attrs, op_def, compute_device) value = op.outputs[0] captured_value = self.capture(value) return captured_value.op def _create_op_internal( self, op_type, inputs, dtypes=None, # pylint: disable=redefined-outer-name input_types=None, name=None, attrs=None, op_def=None, compute_device=True): """Like Graph.create_op, except handles external input tensors. This overload adds functionality to create_op to "capture" any external input tensors, i.e. tensors from the eager context or outer function graphs if this is a nested function. See `capture` for more information. Args: op_type: The `Operation` type to create. This corresponds to the `OpDef.name` field for the proto that defines the operation. inputs: A list of `Tensor` objects that will be inputs to the `Operation`. dtypes: (Optional) A list of `DType` objects that will be the types of the tensors that the operation produces. input_types: (Optional.) A list of `DType`s that will be the types of the tensors that the operation consumes. By default, uses the base `DType` of each input in `inputs`. Operations that expect reference-typed inputs must specify `input_types` explicitly. name: (Optional.) A string name for the operation. If not specified, a name is generated based on `op_type`. attrs: (Optional.) A dictionary where the key is the attribute name (a string) and the value is the respective `attr` attribute of the `NodeDef` proto that will represent the operation (an `AttrValue` proto). op_def: (Optional.) The `OpDef` proto that describes the `op_type` that the operation will have. compute_device: (Optional.) If True, device functions will be executed to compute the device property of the Operation. Returns: An `Operation` object. """ if self.capture_by_value and op_type in ["ReadVariableOp", "ResourceGather"]: return self._capture_by_value(op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device) # This capturing logic interacts poorly with control flow contexts which # want to replace inputs of ops far too late in the process. This can lead # the context to get confused and try to create an Enter for an Enter. We # can detect this here and skip the additional Enter which can confuse loop # validation logic. if op_type == "Enter" and inputs[0].op.type == "Enter": if inputs[0].op.get_attr("frame_name") == attrs["frame_name"].s: return inputs[0].op # Calling AddValue on the control flow contexts to force creation of the # backward accumulators in the original graph before we create placeholders # to capture the inputs. ctxt = ops.get_default_graph()._control_flow_context # pylint: disable=protected-access # Use a different list to avoid modifying the original inputs list. captured_inputs = [] for inp in inputs: # TPU Estimator defines a control flow context with no AddValue method. if ctxt is not None and hasattr(ctxt, "AddValue"): inp = ctxt.AddValue(inp) inp = self.capture(inp) captured_inputs.append(inp) return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access op_type, captured_inputs, dtypes, input_types, name, attrs, op_def, compute_device) def capture(self, tensor, name=None, shape=None): """Captures `tensor` if it's external to this graph. If `tensor` is from a different graph, returns a placeholder for it. `tensor` and the placeholder will appear in self.captures, and the placeholder will appear in self.inputs. Multiple calls to this method with the same `tensor` argument will return the same placeholder. If `tensor` is from this graph, returns `tensor`. Args: tensor: Tensor. May be from this FuncGraph or a different graph. name: Optional name if a placeholder is created. shape: Optional shape if a placeholder is created. Returns: Tensor from this FuncGraph. Raises: InaccessibleTensorError: if any tensors are accessed in a manner that bypasses the mechanisms required for the data dependencies to be correctly wired. """ if isinstance(tensor, ops.EagerTensor): if name is None: name = str(ops.uid()) # Small EagerTensors are captured with Const ops if (tensor.dtype in dtypes.TF_VALUE_DTYPES and np.prod(tensor.shape) <= _EAGER_CONST_THRESHOLD): return self.capture_eager_tensor(tensor, name) # Large EagerTensors and resources are captured with Placeholder ops return self._capture_helper(tensor, name, shape) if tensor.graph is not self: if name is None: name = tensor.op.name inner_graph = tensor.graph while inner_graph is not None and isinstance(inner_graph, FuncGraph): if inner_graph is self: raise errors.InaccessibleTensorError( "The tensor '%s' cannot be accessed here: it is defined" " in another function or code block. Use return values," " explicit Python locals or TensorFlow collections to access" " it. Defined in: %s; accessed from: %s.\n" % (tensor, tensor.graph, self)) inner_graph = inner_graph.outer_graph return self._capture_helper(tensor, name) return tensor def _capture_helper(self, tensor, name, shape=None): capture = self._captures.get(id(tensor)) if capture is None: placeholder = _create_substitute_placeholder( tensor, name=name, dtype=tensor.dtype, shape=shape) # Record the composite device as an attribute to the placeholder. # This attribute would be propogated into the arg_attr of the FunctionDef. # Currently, a packed eager tensor is always placed on a CompositeDevice. if isinstance(tensor, ops.EagerTensor) and tensor.is_packed: placeholder.op._set_attr( # pylint: disable=protected-access "_composite_device", attr_value_pb2.AttrValue(s=compat.as_bytes(tensor.device))) self.add_capture(tensor, placeholder) else: placeholder = capture[1] tape.record_operation("captured_value", [placeholder], [tensor], backward_function=lambda x: [x], forward_function=lambda x: [x]) return placeholder @property def captures(self): """Order list of tuples containing external and internal captures.""" return self._captures.values() def add_capture(self, tensor, placeholder): """Capture a specific tensor and utilize the provided placeholder. Args: tensor: Tensor to captures. placeholder: Provided placeholder for the tensor. """ self._captures[id(tensor)] = (tensor, placeholder) self.inputs.append(placeholder) def replace_capture(self, tensor, placeholder): """Replace already existing capture.""" self._captures[id(tensor)] = (tensor, placeholder) def reset_captures(self, capture_list): """Set the captures with the provided list of captures & placeholder.""" self._captures = py_collections.OrderedDict() for tensor, placeholder in capture_list: self._captures[id(tensor)] = (tensor, placeholder) def pop_capture(self, tensor): """Remove the capture and return the generated placeholder.""" capture = self._captures.pop(id(tensor), None) if capture is None: return None return capture[1] def clear_captures(self): # TODO(b/115366440): Delete this method when a custom OrderedDict is added. # Clearing captures using clear() leaves some cycles around. while self._captures: self._captures.popitem() memory.dismantle_ordered_dict(self._captures) while self._deferred_captures: self._deferred_captures.popitem() memory.dismantle_ordered_dict(self._deferred_captures) def capture_distributed_variable(self, variable, placeholder): """Add given distributed variable to captures with given placeholder.""" self._captures[id(variable)] = (variable, placeholder) tape.record_operation("captured_value", [placeholder], [variable], backward_function=lambda x: [x], forward_function=lambda x: [x]) def capture_eager_tensor(self, tensor, name): capture = self._captures.get(id(tensor)) if capture is None: # We clear all control dependencies and place the Const op on the same # device as the source tensor. The device placement may be relaxed at # a later date. with ops.control_dependencies(None), self.device(tensor.device): constant_value = tensor_util.constant_value(tensor) if constant_value is None: # Some eager tensors, e.g. parallel tensors, are not convertible to a # single constant. We'll use a placeholder for this case. return self._capture_helper(tensor, name) graph_const = constant_op.constant(constant_value, dtype=tensor.dtype, shape=tensor.shape, name=name) self.add_capture(tensor, graph_const) else: graph_const = capture[1] tape.record_operation("captured_value", [graph_const], [tensor], backward_function=lambda x: [x], forward_function=lambda x: [x]) return graph_const def captured(self, tensor): """Check if the specified tensor has been captured.""" return id(tensor) in self._captures @property def external_captures(self): """External tensors captured by this function.""" return [c[0] for c in self._captures.values()] @property def internal_captures(self): """Placeholders in this function corresponding captured tensors.""" return [c[1] for c in self._captures.values()] @property def deferred_external_captures(self): """Ordered nest of tensors whose placeholders will be fed at call time.""" return [c[0] for c in self._deferred_captures.values()] @property def deferred_internal_captures(self): """List of nest of placeholders which at call time will be fed.""" return [c[1] for c in self._deferred_captures.values()] @property def variable_captures(self): """Map of python object ids of variables to variables which are captured.""" return { id(self._captures[id(v)][1]): v for v in self.variables if id(v) in self._captures } def mark_as_unsaveable(self, error_message): """Marks this FuncGraph as unsaveable. Any attempts to export this FuncGraph will raise an error with the specified message. Args: error_message: List or string containing the error message to be raised when saving this FuncGraph to SavedModel. """ self._saveable = False if isinstance(error_message, str): error_message = [error_message] self._saving_errors.update(error_message) @property def saveable(self): """Returns whether this FuncGraph is saveable.""" return self._saveable @property def saving_errors(self): """Returns set of errors preventing this FuncGraph from being saved.""" return self._saving_errors def _add_scope_exit_callback(self, fn): """Add a function to call when this graph exits the default scope.""" if not callable(fn): raise TypeError("fn is not callable: {}".format(fn)) if self._scope_exit_callbacks is None: raise RuntimeError( "Attempting to add a scope exit callback, but the default graph is " "not the context scope graph. Did you forget to call " "'with graph.as_default(): ...'?") self._scope_exit_callbacks.append(fn) def func_graph_from_py_func(name, python_func, args, kwargs, signature=None, func_graph=None, autograph=False, autograph_options=None, add_control_dependencies=True, arg_names=None, op_return_value=None, collections=None, capture_by_value=None, override_flat_arg_shapes=None): """Returns a `FuncGraph` generated from `python_func`. Args: name: an identifier for the function. python_func: the Python function to trace. args: the positional args with which the Python function should be called; ignored if a signature is provided. kwargs: the keyword args with which the Python function should be called; ignored if a signature is provided. signature: a possibly nested sequence of `TensorSpecs` specifying the shapes and dtypes of the arguments. When a signature is provided, `args` and `kwargs` are ignored, and `python_func` is traced with Tensors conforming to `signature`. If `None`, the shapes and dtypes are inferred from the inputs. func_graph: Optional. An instance of FuncGraph. If provided, we will use this graph else a new one is built and returned. autograph: whether to use autograph to compile `python_func`. See https://www.tensorflow.org/guide/autograph for more information. autograph_options: additional knobs to control when `autograph=True`. See https://www.tensorflow.org/guide/autograph for more information. add_control_dependencies: If True, automatically adds control dependencies to ensure program order matches execution order and stateful ops always execute. arg_names: Optional list of argument names, used to give input placeholders recognizable names. op_return_value: Optional. A Tensor. If set and `python_func` returns Operations, those return values will be replaced with this value. If not set, returning an Operation triggers an error. collections: a dictionary of collections this FuncGraph should start with. If not specified (None), the FuncGraph will read (but not write to) the outer graph's collections that are not allowlisted, and both read and write to the outer graph's collections that are allowlisted. The current allowlisted collections are the global variables, the local variables, and the trainable variables. Defaults to None. capture_by_value: An optional boolean. If True, the func graph will capture Variables by value instead of reference. By default inherit from outer graphs, and failing that will default to False. override_flat_arg_shapes: An optional list of instances that are either `None` or `TensorShape`. The length must match that of `nest.flatten((args, kwargs), expand_composites=True)`. The entries containing value `None` must match entries in flattened arguments containing non-tensors, while entries containing a `TensorShape` must match entries in the flattened arguments containing tensors. Returns: A FuncGraph. Raises: TypeError: If any of `python_func`'s return values is neither `None` nor a `Tensor`. ValueError: If both `signature` and `override_flat_arg_shapes` are passed in. """ if op_return_value is not None: assert isinstance(op_return_value, ops.Tensor), op_return_value if func_graph is None: func_graph = FuncGraph(name, collections=collections, capture_by_value=capture_by_value) assert isinstance(func_graph, FuncGraph) if add_control_dependencies: deps_control_manager = auto_control_deps.AutomaticControlDependencies() else: deps_control_manager = ops.NullContextmanager() with func_graph.as_default(), deps_control_manager as deps_ctx: current_scope = variable_scope.get_variable_scope() default_use_recource = current_scope.use_resource current_scope.set_use_resource(True) if signature is not None and override_flat_arg_shapes is not None: raise ValueError( "Passed both signature and override_flat_arg_shapes: %s and %s." % (signature, override_flat_arg_shapes)) if signature is not None: args = signature kwargs = {} # Creates and names placeholders for all arguments. if override_flat_arg_shapes is not None: flat_args = nest.flatten(args, expand_composites=True) arg_shapes = override_flat_arg_shapes[:len(flat_args)] kwarg_shapes = override_flat_arg_shapes[len(flat_args):] else: arg_shapes = None kwarg_shapes = None func_args = _get_defun_inputs_from_args( args, arg_names, flat_shapes=arg_shapes) func_kwargs = _get_defun_inputs_from_kwargs( kwargs, flat_shapes=kwarg_shapes) # Convert all Tensors into TensorSpecs before saving the structured inputs. # If storing pure concrete functions that are not called through polymorphic # functions, we don't have access to FunctionSpec, so we need to call the # TensorSpecs by their `arg_names` for later binding. func_graph.structured_input_signature = ( convert_structure_to_signature(func_args, arg_names), convert_structure_to_signature(func_kwargs)) flat_func_args = nest.flatten(func_args, expand_composites=True) flat_func_kwargs = nest.flatten(func_kwargs, expand_composites=True) # Temporarily set inputs to allow graph building code to inspect # them. Reassigned below. func_graph.inputs = [arg for arg in flat_func_args + flat_func_kwargs if isinstance(arg, ops.Tensor)] # Note: `nest.flatten` sorts by keys, as does `_deterministic_dict_values`. # Variables to help check whether mutation happens in calling the function # Copy the recursive list, tuple and map structure, but not base objects func_args_before = nest.pack_sequence_as(func_args, flat_func_args, expand_composites=True) func_kwargs_before = nest.pack_sequence_as( func_kwargs, flat_func_kwargs, expand_composites=True) def convert(x): """Converts a function output to a Tensor.""" if x is None: return None if op_return_value is not None and isinstance(x, ops.Operation): # TODO(b/79881896): we currently can't capture external control deps, so # this won't work if x needs to be captured (i.e. if python_func returns # captured Operations). with ops.control_dependencies([x]): x = array_ops.identity(op_return_value) elif not isinstance(x, tensor_array_ops.TensorArray): try: x = ops.convert_to_tensor_or_composite(x) except (ValueError, TypeError): raise TypeError( "To be compatible with tf.eager.defun, Python functions " "must return zero or more Tensors; in compilation of %s, found " "return value of type %s, which is not a Tensor." % (str(python_func), type(x))) if add_control_dependencies: x = deps_ctx.mark_as_return(x) return x try: if autograph: from tensorflow.python import autograph # pylint: disable=g-import-not-at-top _, original_func = tf_decorator.unwrap(python_func) def wrapper(*args, **kwargs): """Calls a converted version of original_func.""" # TODO(mdan): Push this block higher in tf.function's call stack. try: return autograph.converted_call( original_func, args, kwargs, options=autograph.ConversionOptions( recursive=True, optional_features=autograph_options, user_requested=True, )) except Exception as e: # pylint:disable=broad-except if hasattr(e, "ag_error_metadata"): raise e.ag_error_metadata.to_exception(e) else: raise # Wrapping around a decorator allows checks like tf_inspect.getargspec # to be accurate. converted_func = tf_decorator.make_decorator(original_func, wrapper) python_func = tf_decorator.rewrap(python_func, original_func, converted_func) else: _, original_func = tf_decorator.unwrap(python_func) func_outputs = python_func(*func_args, **func_kwargs) # invariant: `func_outputs` contains only Tensors, CompositeTensors, # TensorArrays and `None`s. func_outputs = nest.map_structure(convert, func_outputs, expand_composites=True) check_mutation(func_args_before, func_args, original_func) check_mutation(func_kwargs_before, func_kwargs, original_func) finally: current_scope.set_use_resource(default_use_recource) # Variables in `func_args`, `func_kwargs` should be explicit inputs # to the function, not captured inputs. graph_variables = list(func_graph._watched_variables) # pylint: disable=protected-access arg_variables = object_identity.ObjectIdentitySet() inputs = [] for arg in (nest.flatten(func_args, expand_composites=True) + nest.flatten(func_kwargs, expand_composites=True)): if isinstance(arg, resource_variable_ops.BaseResourceVariable): # Even if an argument variable was not used in the function, we've # already manually captured the resource Tensor when creating argument # placeholders. resource_placeholder = func_graph.pop_capture(arg.handle) if resource_placeholder is None: continue arg_variables.add(arg) inputs.append(resource_placeholder) elif isinstance(arg, ops.Tensor): inputs.append(arg) variables = [v for v in graph_variables if v not in arg_variables] func_graph.inputs = ( inputs + func_graph.internal_captures + nest.flatten( func_graph.deferred_internal_captures, expand_composites=True)) func_graph.structured_outputs = func_outputs # Returning a closed-over tensor does not trigger convert_to_tensor. func_graph.outputs.extend( func_graph.capture(x) for x in flatten(func_graph.structured_outputs) if x is not None) func_graph.variables = variables if add_control_dependencies: func_graph.control_outputs.extend(deps_control_manager.ops_which_must_run) func_graph.collective_manager_ids_used = ( deps_control_manager.collective_manager_ids_used) return func_graph def maybe_captured(tensor): """If t is a captured value placeholder, returns the original captured value. Args: tensor: Tensor. Returns: A tensor, potentially from a different Graph/FuncGraph. """ if (not isinstance(tensor, ops.EagerTensor) and tensor.op.graph.building_function and tensor.op.type == "Placeholder"): for input_t, placeholder_t in tensor.op.graph.captures: if tensor == placeholder_t: return maybe_captured(input_t) # pylint: enable=protected-access return tensor def device_stack_has_callable(device_stack): """Checks whether a device stack contains a callable.""" return any(callable(spec._device_name_or_function) # pylint: disable=protected-access for spec in device_stack.peek_objs()) def check_mutation(n1, n2, func): """Check if two list of arguments are exactly the same.""" func_name = getattr(func, "__name__", func) errmsg = ("{}() should not modify its Python input arguments." " Check if it modifies any lists or dicts passed as" " arguments. Modifying a copy is allowed.".format(func_name)) try: # TODO(mdan): Compare more robustly so that argument names can be reported. nest.assert_same_structure(n1, n2, expand_composites=True) except ValueError: raise ValueError(errmsg) for arg1, arg2 in zip(nest.flatten(n1, expand_composites=True), nest.flatten(n2, expand_composites=True)): if arg1 is not arg2: raise ValueError(errmsg) # TODO(edloper): If TensorArray becomes a CompositeTensor, then delete this. def flatten(sequence): """Like nest.flatten w/ expand_composites, but returns flow for TensorArrays. Args: sequence: A nested structure of Tensors, CompositeTensors, and TensorArrays. Returns: A list of tensors. """ flat_sequence = nest.flatten(sequence, expand_composites=True) return [ item.flow if isinstance(item, tensor_array_ops.TensorArray) else item for item in flat_sequence] # TODO(edloper): If TensorArray becomes a CompositeTensor, then delete this. def pack_sequence_as(structure, flat_sequence): """Like `nest.pack_sequence_as` but also builds TensorArrays from flows. Args: structure: The structure to pack into. May contain Tensors, CompositeTensors, or TensorArrays. flat_sequence: An iterable containing tensors. Returns: A nested structure. Raises: AssertionError if `structure` and `flat_sequence` are not compatible. """ flat_sequence = list(flat_sequence) flattened_structure = nest.flatten(structure, expand_composites=True) if len(flattened_structure) != len(flat_sequence): raise ValueError("Mismatch in element count") for i in range(len(flat_sequence)): if isinstance(flattened_structure[i], tensor_array_ops.TensorArray): flat_sequence[i] = tensor_array_ops.build_ta_with_new_flow( old_ta=flattened_structure[i], flow=flat_sequence[i]) return nest.pack_sequence_as(structure, flat_sequence, expand_composites=True) def _create_substitute_placeholder(value, name=None, dtype=None, shape=None): """Creates a placeholder for `value` and propagates shape info to it.""" # Note: setting ops.control_dependencies(None) ensures we always put # capturing placeholders outside of any control flow context. if shape is None: shape = value.shape with ops.control_dependencies(None): placeholder = graph_placeholder( dtype=dtype or value.dtype, shape=shape, name=name) custom_gradient.copy_handle_data(value, placeholder) return placeholder def _get_defun_inputs_from_args(args, names, flat_shapes=None): """Maps Python function positional args to graph-construction inputs.""" return _get_defun_inputs( args, names, structure=args, flat_shapes=flat_shapes) def _get_composite_tensor_spec(x): """Returns the TypeSpec for x if it's a composite tensor, or x otherwise.""" return (x._type_spec # pylint: disable=protected-access if isinstance(x, composite_tensor.CompositeTensor) else x) def _get_defun_inputs(args, names, structure, flat_shapes=None): """Maps python function args to graph-construction inputs. Args: args: A flat list of user-specified arguments. names: A list of strings with user-specified argument names, same length as `args`. May be `None`, in which case a generic name is used. structure: The original argument list or dictionary. flat_shapes: A flat list of values that are either `None` or instances of `TensorShape`. If provided, then length must match that of `nest.flatten(args, expand_composites=True)`; and locations where `args` are instances of `Tensor` must have a corresponding `TensorShape` in `flat_shapes`. May be `None`, in which case exact shapes are read directly from the args. Returns: Placeholders with the same structure as `structure`. Raises: RuntimeError: if `flat_shapes` is provided, but `len(flat_shapes) != len(nest.flatten(args, expand_composites=True))`. RuntimeError: if a shape from `flat_shapes` is not None for an argument that is not a `Tensor`, `TensorSpec`, or `ResourceVariable`. """ func_graph = ops.get_default_graph() function_inputs = [] if names is None: names = [None] * len(args) if flat_shapes is None: shapes_iter = itertools.repeat(None) else: len_flat_args = len(nest.flatten(args, expand_composites=True)) if len_flat_args != len(flat_shapes): raise RuntimeError( "Length of fully flat shapes (%d) must match that of " "flatten(args) (%d). args: %s, flat_shapes: %s" % (len(flat_shapes), len_flat_args, args, flat_shapes)) shapes_iter = iter(flat_shapes) for arg_value, name in zip(args, names): # Replace any composite tensors with their TypeSpecs. This is important # for ensuring that shape information that's not preserved by the TypeSpec # (such as the number of values in a SparseTensor) gets properly masked. arg_value = nest.map_structure(_get_composite_tensor_spec, arg_value) flattened = nest.flatten(arg_value, expand_composites=True) for arg in flattened: # We have a shape entry for each arg, regardless of whether it's a real # Tensor or not. For non-tensor entries it should be None. shape = next(shapes_iter) if isinstance(arg, (ops.Tensor, tensor_spec.TensorSpec)): arg_is_spec = isinstance(arg, tensor_spec.TensorSpec) if arg_is_spec and arg.name: requested_name = arg.name else: requested_name = name placeholder_shape = shape if shape is not None else arg.shape try: placeholder = graph_placeholder( arg.dtype, placeholder_shape, name=requested_name) except ValueError: # Sometimes parameter names are not valid op names, so fall back to # unnamed placeholders. placeholder = graph_placeholder(arg.dtype, placeholder_shape) if not arg_is_spec: custom_gradient.copy_handle_data(arg, placeholder) if name is not None: # Record the requested/user-specified name in case it's different than # the uniquified name, for validation when exporting signatures. placeholder.op._set_attr( # pylint: disable=protected-access "_user_specified_name", attr_value_pb2.AttrValue(s=compat.as_bytes(requested_name))) function_inputs.append(placeholder) elif isinstance(arg, (resource_variable_ops.BaseResourceVariable, resource_variable_ops.VariableSpec)): if isinstance(arg, resource_variable_ops.VariableSpec): name = arg.name or name with func_graph.outer_graph.as_default(): placeholder = graph_placeholder(dtypes.resource, arg.shape, name=name) arg = resource_variable_ops.BaseResourceVariable( name=name, shape=arg.shape, dtype=arg.dtype, handle=placeholder, handle_name=name) # Capture arg variables to create placeholders for them. These will be # removed as captures after the function is traced (since otherwise we'd # just add it back with a new placeholder when the variable was # referenced). placeholder = func_graph.capture(arg.handle, name=name) placeholder.op._set_attr( # pylint: disable=protected-access "_user_specified_name", attr_value_pb2.AttrValue(s=compat.as_bytes(name))) function_inputs.append(arg) else: if shape is not None: raise RuntimeError( "Expected provided shape override to be None for arg that isn't " "a Tensor, but saw arg: '%s', shape: '%s'. args: %s" % (arg, shape, args)) function_inputs.append(arg) return nest.pack_sequence_as(structure, function_inputs, expand_composites=True) def _get_defun_inputs_from_kwargs(kwargs, flat_shapes): """Maps Python function keyword args to graph-construction inputs.""" if kwargs: names, args = zip(*sorted(kwargs.items())) else: names = [] args = [] return _get_defun_inputs( args, names, structure=kwargs, flat_shapes=flat_shapes) def dismantle_func_graph(func_graph): """Removes reference cycles in `func_graph` FuncGraph. Helpful for making sure the garbage collector doesn't need to run when the FuncGraph goes out of scope, e.g. in tests using defun with @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True). Args: func_graph: A `FuncGraph` object to destroy. `func_graph` is unusable after this function. """ func_graph.clear_captures() ops.dismantle_graph(func_graph) def override_func_graph_name_scope(func_graph, name_scope): func_graph._name_stack = name_scope # pylint: disable=protected-access

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections as py_collections import itertools import weakref import numpy as np from tensorflow.core.framework import attr_value_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import execute from tensorflow.python.eager import tape from tensorflow.python.eager.graph_only_ops import graph_placeholder from tensorflow.python.framework import auto_control_deps from tensorflow.python.framework import composite_tensor from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_spec from tensorflow.python.framework import tensor_util from tensorflow.python.framework import type_spec from tensorflow.python.ops import array_ops from tensorflow.python.ops import custom_gradient from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import variable_scope from tensorflow.python.util import compat from tensorflow.python.util import memory from tensorflow.python.util import nest from tensorflow.python.util import object_identity from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator ALLOWLIST_COLLECTIONS = [ ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.TRAINABLE_VARIABLES, variable_scope._VARSTORE_KEY, variable_scope._VARSCOPESTORE_KEY ] _EAGER_CONST_THRESHOLD = 128 class UnknownArgument(object): pass def convert_structure_to_signature(structure, arg_names=None): def encode_arg(arg, path): if isinstance(arg, ops.Tensor): user_specified_name = None try: user_specified_name = compat.as_str( arg.op.get_attr("_user_specified_name")) except ValueError: pass if path and user_specified_name and user_specified_name != path[0]: name = user_specified_name else: name = "/".join(str(p) for p in path) return tensor_spec.TensorSpec(arg.shape, arg.dtype, name) if isinstance(arg, composite_tensor.CompositeTensor): return arg._type_spec if isinstance(arg, resource_variable_ops.BaseResourceVariable): name = "/".join(str(p) for p in path) return resource_variable_ops.VariableSpec(arg.shape, arg.dtype, name) if isinstance(arg, ( int, float, bool, str, type(None), dtypes.DType, tensor_spec.TensorSpec, type_spec.TypeSpec, )): return arg return UnknownArgument() flattened = nest.flatten_with_tuple_paths(structure) if arg_names: if len(arg_names) != len(structure): raise ValueError( "Passed in arg_names don't match actual signature (%s)." % arg_names) # Replace all top-level names with their actual arg_names. If a path before # was "(2,'a',1)", it will become "(arg_names[2],'a',1)". flattened = [ ((arg_names[path[0]],) + path[1:], arg) for path, arg in flattened ] mapped = [encode_arg(arg, path) for path, arg in flattened] return nest.pack_sequence_as(structure, mapped) class FuncGraph(ops.Graph): def __init__(self, name, collections=None, capture_by_value=None): super(FuncGraph, self).__init__() self.name = name self.inputs = [] self.outputs = [] self.control_outputs = [] self.control_captures = set() self.structured_input_signature = None self.structured_outputs = None self._weak_variables = [] self._watched_variables = object_identity.ObjectIdentityWeakSet() self.is_control_flow_graph = False outer_graph = ops.get_default_graph() self._weak_outer_graph = weakref.ref(outer_graph) while outer_graph.building_function: outer_graph = outer_graph.outer_graph # If self._weak_outer_graph is deleted, we revert to the outermost Graph # active when the FuncGraph was traced. This will not be a FuncGraph. self._fallback_outer_graph = outer_graph self._captures = py_collections.OrderedDict() # If not None, records the names of output args of this function. Used to # preserve the output names in the signature of a serialized+deserialized # function. Private at the moment mostly because it's often out of date. self._output_names = None self._deferred_captures = py_collections.OrderedDict() if capture_by_value is not None: self.capture_by_value = capture_by_value elif self.outer_graph is not None and isinstance( self.outer_graph, FuncGraph): self.capture_by_value = self.outer_graph.capture_by_value else: self.capture_by_value = False self._building_function = True self._last_op_using_resource_tensor = {} graph = self.outer_graph if context.executing_eagerly(): self.seed = context.global_seed() self._seed_used = False else: self.seed = graph.seed self._seed_used = False self._colocation_stack = graph._colocation_stack.copy() if collections is None: for collection_name in graph.get_all_collection_keys(): if collection_name not in ALLOWLIST_COLLECTIONS: self._collections[collection_name] = graph.get_collection( collection_name) for collection_name in ALLOWLIST_COLLECTIONS: self._collections[collection_name] = graph.get_collection_ref( collection_name) else: self._collections = collections self._saveable = True self._saving_errors = set() self._scope_exit_callbacks = None def __str__(self): return "FuncGraph(name=%s, id=%s)" % (self.name, id(self)) def watch_variable(self, v): while self is not None and isinstance(self, FuncGraph): self._watched_variables.add(v) self = self.outer_graph def capture_call_time_value(self, closure, spec, key=None): if key is None: key = object() if key not in self._deferred_captures: def convert_to_placeholder(s): if not isinstance(s, tensor_spec.DenseSpec): raise TypeError( "Expected a nest of `TypeSpec` objects, found %s of type %s." % (s, type(s))) return array_ops.placeholder(dtype=s.dtype, shape=s.shape) placeholder = nest.map_structure( convert_to_placeholder, spec, expand_composites=True) def wrapped_closure(): ret_nest = closure() nest.assert_same_structure(spec, ret_nest, expand_composites=True) y = nest.map_structure(lambda s, r: s._to_components(r), spec, ret_nest, expand_composites=False) return nest.flatten(y, expand_composites=True) self._deferred_captures[key] = (wrapped_closure, placeholder) return self._deferred_captures[key][1] def control_dependencies(self, control_inputs): if control_inputs is None: return super(FuncGraph, self).control_dependencies(control_inputs) filtered_control_inputs = [] for c in control_inputs: if (isinstance(c, ops.IndexedSlices) or (hasattr(c, "_handle") and hasattr(c, "op"))): c = c.op graph_element = ops._as_graph_element(c) if graph_element is None: graph_element = c if graph_element is not None and getattr( graph_element, "graph", None) is not self: self.control_captures.add(graph_element) else: filtered_control_inputs.append(graph_element) return super(FuncGraph, self).control_dependencies(filtered_control_inputs) def as_default(self): outer_cm = super(FuncGraph, self).as_default() @tf_contextlib.contextmanager def inner_cm(): graph = ops.get_default_graph() old_strategy_stack = self._distribution_strategy_stack self._distribution_strategy_stack = list( graph._distribution_strategy_stack) # so that the same trace of the Python function may be placed on several # different devices and saved functions may be placed on new devices when # restored. # However, we need to preserve the outer device stack in the following # cases in non eager context: # 1. device stack is callable # 2. When using distribution strategy with legacy graph mode. old_device_stack = self._device_function_stack if (not context.executing_eagerly() and (device_stack_has_callable(graph._device_function_stack) or (self._distribution_strategy_stack and not ops.executing_eagerly_outside_functions()))): # Hard-code devices from device functions in the function body self._device_function_stack = graph._device_function_stack.copy() old_creator_stack = self._variable_creator_stack self._variable_creator_stack = graph._variable_creator_stack # Inherit the graph key, since this is used for matching variables in # optimizers. old_graph_key = self._graph_key self._graph_key = graph._graph_key # pylint: enable=protected-access old_scope_exit_callbacks = self._scope_exit_callbacks self._scope_exit_callbacks = [] with outer_cm as g: try: yield g finally: try: for fn in self._scope_exit_callbacks: fn() finally: self._scope_exit_callbacks = old_scope_exit_callbacks self._distribution_strategy_stack = old_strategy_stack self._device_function_stack = old_device_stack self._variable_creator_stack = old_creator_stack self._graph_key = old_graph_key return inner_cm() @property def outer_graph(self): current = self._weak_outer_graph() if current is None: return self._fallback_outer_graph return current @outer_graph.setter def outer_graph(self, new_outer_graph): self._weak_outer_graph = weakref.ref(new_outer_graph) @property def output_types(self): return [t.dtype for t in self.outputs] @property def output_shapes(self): return [t.shape for t in self.outputs] @property def trainable_variables(self): return tuple(v for v in self.variables if v.trainable) @property def variables(self): def deref(weak_v): v = weak_v() if v is None: raise AssertionError( "Called a function referencing variables which have been deleted. " "This likely means that function-local variables were created and " "not referenced elsewhere in the program. This is generally a " "mistake; consider storing variables in an object attribute on " "first call.") return v return tuple(deref(v) for v in self._weak_variables) @variables.setter def variables(self, var_list): self._weak_variables = [weakref.ref(v) for v in var_list] def _capture_by_value( self, op_type, inputs, dtypes, # pylint: disable=redefined-outer-name input_types=None, name=None, attrs=None, op_def=None, compute_device=True): # When capturing by value, do the read outside reverse_captures = dict((id(v), k) for k, v in self.captures) uncaptured_inputs = [reverse_captures.get(id(t), t) for t in inputs] with ops.init_scope(): if context.executing_eagerly(): attr_list = ("dtype", int(attrs["dtype"].type)) value, = execute.execute( compat.as_bytes(op_type), 1, uncaptured_inputs, attr_list, context.context()) else: op = ops.get_default_graph()._create_op_internal( # pylint: disable=protected-access op_type, uncaptured_inputs, dtypes, input_types, name, attrs, op_def, compute_device) value = op.outputs[0] captured_value = self.capture(value) return captured_value.op def _create_op_internal( self, op_type, inputs, dtypes=None, # pylint: disable=redefined-outer-name input_types=None, name=None, attrs=None, op_def=None, compute_device=True): if self.capture_by_value and op_type in ["ReadVariableOp", "ResourceGather"]: return self._capture_by_value(op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device) # This capturing logic interacts poorly with control flow contexts which # want to replace inputs of ops far too late in the process. This can lead # the context to get confused and try to create an Enter for an Enter. We # can detect this here and skip the additional Enter which can confuse loop # validation logic. if op_type == "Enter" and inputs[0].op.type == "Enter": if inputs[0].op.get_attr("frame_name") == attrs["frame_name"].s: return inputs[0].op # Calling AddValue on the control flow contexts to force creation of the # backward accumulators in the original graph before we create placeholders # to capture the inputs. ctxt = ops.get_default_graph()._control_flow_context # pylint: disable=protected-access # Use a different list to avoid modifying the original inputs list. captured_inputs = [] for inp in inputs: # TPU Estimator defines a control flow context with no AddValue method. if ctxt is not None and hasattr(ctxt, "AddValue"): inp = ctxt.AddValue(inp) inp = self.capture(inp) captured_inputs.append(inp) return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access op_type, captured_inputs, dtypes, input_types, name, attrs, op_def, compute_device) def capture(self, tensor, name=None, shape=None): if isinstance(tensor, ops.EagerTensor): if name is None: name = str(ops.uid()) # Small EagerTensors are captured with Const ops if (tensor.dtype in dtypes.TF_VALUE_DTYPES and np.prod(tensor.shape) <= _EAGER_CONST_THRESHOLD): return self.capture_eager_tensor(tensor, name) # Large EagerTensors and resources are captured with Placeholder ops return self._capture_helper(tensor, name, shape) if tensor.graph is not self: if name is None: name = tensor.op.name inner_graph = tensor.graph while inner_graph is not None and isinstance(inner_graph, FuncGraph): if inner_graph is self: raise errors.InaccessibleTensorError( "The tensor '%s' cannot be accessed here: it is defined" " in another function or code block. Use return values," " explicit Python locals or TensorFlow collections to access" " it. Defined in: %s; accessed from: %s.\n" % (tensor, tensor.graph, self)) inner_graph = inner_graph.outer_graph return self._capture_helper(tensor, name) return tensor def _capture_helper(self, tensor, name, shape=None): capture = self._captures.get(id(tensor)) if capture is None: placeholder = _create_substitute_placeholder( tensor, name=name, dtype=tensor.dtype, shape=shape) # Record the composite device as an attribute to the placeholder. # This attribute would be propogated into the arg_attr of the FunctionDef. # Currently, a packed eager tensor is always placed on a CompositeDevice. if isinstance(tensor, ops.EagerTensor) and tensor.is_packed: placeholder.op._set_attr( # pylint: disable=protected-access "_composite_device", attr_value_pb2.AttrValue(s=compat.as_bytes(tensor.device))) self.add_capture(tensor, placeholder) else: placeholder = capture[1] tape.record_operation("captured_value", [placeholder], [tensor], backward_function=lambda x: [x], forward_function=lambda x: [x]) return placeholder @property def captures(self): return self._captures.values() def add_capture(self, tensor, placeholder): self._captures[id(tensor)] = (tensor, placeholder) self.inputs.append(placeholder) def replace_capture(self, tensor, placeholder): self._captures[id(tensor)] = (tensor, placeholder) def reset_captures(self, capture_list): self._captures = py_collections.OrderedDict() for tensor, placeholder in capture_list: self._captures[id(tensor)] = (tensor, placeholder) def pop_capture(self, tensor): capture = self._captures.pop(id(tensor), None) if capture is None: return None return capture[1] def clear_captures(self): # TODO(b/115366440): Delete this method when a custom OrderedDict is added. # Clearing captures using clear() leaves some cycles around. while self._captures: self._captures.popitem() memory.dismantle_ordered_dict(self._captures) while self._deferred_captures: self._deferred_captures.popitem() memory.dismantle_ordered_dict(self._deferred_captures) def capture_distributed_variable(self, variable, placeholder): self._captures[id(variable)] = (variable, placeholder) tape.record_operation("captured_value", [placeholder], [variable], backward_function=lambda x: [x], forward_function=lambda x: [x]) def capture_eager_tensor(self, tensor, name): capture = self._captures.get(id(tensor)) if capture is None: # We clear all control dependencies and place the Const op on the same # device as the source tensor. The device placement may be relaxed at # a later date. with ops.control_dependencies(None), self.device(tensor.device): constant_value = tensor_util.constant_value(tensor) if constant_value is None: # Some eager tensors, e.g. parallel tensors, are not convertible to a # single constant. We'll use a placeholder for this case. return self._capture_helper(tensor, name) graph_const = constant_op.constant(constant_value, dtype=tensor.dtype, shape=tensor.shape, name=name) self.add_capture(tensor, graph_const) else: graph_const = capture[1] tape.record_operation("captured_value", [graph_const], [tensor], backward_function=lambda x: [x], forward_function=lambda x: [x]) return graph_const def captured(self, tensor): return id(tensor) in self._captures @property def external_captures(self): return [c[0] for c in self._captures.values()] @property def internal_captures(self): return [c[1] for c in self._captures.values()] @property def deferred_external_captures(self): return [c[0] for c in self._deferred_captures.values()] @property def deferred_internal_captures(self): return [c[1] for c in self._deferred_captures.values()] @property def variable_captures(self): return { id(self._captures[id(v)][1]): v for v in self.variables if id(v) in self._captures } def mark_as_unsaveable(self, error_message): self._saveable = False if isinstance(error_message, str): error_message = [error_message] self._saving_errors.update(error_message) @property def saveable(self): return self._saveable @property def saving_errors(self): return self._saving_errors def _add_scope_exit_callback(self, fn): if not callable(fn): raise TypeError("fn is not callable: {}".format(fn)) if self._scope_exit_callbacks is None: raise RuntimeError( "Attempting to add a scope exit callback, but the default graph is " "not the context scope graph. Did you forget to call " "'with graph.as_default(): ...'?") self._scope_exit_callbacks.append(fn) def func_graph_from_py_func(name, python_func, args, kwargs, signature=None, func_graph=None, autograph=False, autograph_options=None, add_control_dependencies=True, arg_names=None, op_return_value=None, collections=None, capture_by_value=None, override_flat_arg_shapes=None): if op_return_value is not None: assert isinstance(op_return_value, ops.Tensor), op_return_value if func_graph is None: func_graph = FuncGraph(name, collections=collections, capture_by_value=capture_by_value) assert isinstance(func_graph, FuncGraph) if add_control_dependencies: deps_control_manager = auto_control_deps.AutomaticControlDependencies() else: deps_control_manager = ops.NullContextmanager() with func_graph.as_default(), deps_control_manager as deps_ctx: current_scope = variable_scope.get_variable_scope() default_use_recource = current_scope.use_resource current_scope.set_use_resource(True) if signature is not None and override_flat_arg_shapes is not None: raise ValueError( "Passed both signature and override_flat_arg_shapes: %s and %s." % (signature, override_flat_arg_shapes)) if signature is not None: args = signature kwargs = {} if override_flat_arg_shapes is not None: flat_args = nest.flatten(args, expand_composites=True) arg_shapes = override_flat_arg_shapes[:len(flat_args)] kwarg_shapes = override_flat_arg_shapes[len(flat_args):] else: arg_shapes = None kwarg_shapes = None func_args = _get_defun_inputs_from_args( args, arg_names, flat_shapes=arg_shapes) func_kwargs = _get_defun_inputs_from_kwargs( kwargs, flat_shapes=kwarg_shapes) # TensorSpecs by their `arg_names` for later binding. func_graph.structured_input_signature = ( convert_structure_to_signature(func_args, arg_names), convert_structure_to_signature(func_kwargs)) flat_func_args = nest.flatten(func_args, expand_composites=True) flat_func_kwargs = nest.flatten(func_kwargs, expand_composites=True) # Temporarily set inputs to allow graph building code to inspect # them. Reassigned below. func_graph.inputs = [arg for arg in flat_func_args + flat_func_kwargs if isinstance(arg, ops.Tensor)] # Note: `nest.flatten` sorts by keys, as does `_deterministic_dict_values`. # Variables to help check whether mutation happens in calling the function # Copy the recursive list, tuple and map structure, but not base objects func_args_before = nest.pack_sequence_as(func_args, flat_func_args, expand_composites=True) func_kwargs_before = nest.pack_sequence_as( func_kwargs, flat_func_kwargs, expand_composites=True) def convert(x): if x is None: return None if op_return_value is not None and isinstance(x, ops.Operation): # TODO(b/79881896): we currently can't capture external control deps, so # captured Operations). with ops.control_dependencies([x]): x = array_ops.identity(op_return_value) elif not isinstance(x, tensor_array_ops.TensorArray): try: x = ops.convert_to_tensor_or_composite(x) except (ValueError, TypeError): raise TypeError( "To be compatible with tf.eager.defun, Python functions " "must return zero or more Tensors; in compilation of %s, found " "return value of type %s, which is not a Tensor." % (str(python_func), type(x))) if add_control_dependencies: x = deps_ctx.mark_as_return(x) return x try: if autograph: from tensorflow.python import autograph # pylint: disable=g-import-not-at-top _, original_func = tf_decorator.unwrap(python_func) def wrapper(*args, **kwargs): # TODO(mdan): Push this block higher in tf.function's call stack. try: return autograph.converted_call( original_func, args, kwargs, options=autograph.ConversionOptions( recursive=True, optional_features=autograph_options, user_requested=True, )) except Exception as e: if hasattr(e, "ag_error_metadata"): raise e.ag_error_metadata.to_exception(e) else: raise converted_func = tf_decorator.make_decorator(original_func, wrapper) python_func = tf_decorator.rewrap(python_func, original_func, converted_func) else: _, original_func = tf_decorator.unwrap(python_func) func_outputs = python_func(*func_args, **func_kwargs) func_outputs = nest.map_structure(convert, func_outputs, expand_composites=True) check_mutation(func_args_before, func_args, original_func) check_mutation(func_kwargs_before, func_kwargs, original_func) finally: current_scope.set_use_resource(default_use_recource) graph_variables = list(func_graph._watched_variables) arg_variables = object_identity.ObjectIdentitySet() inputs = [] for arg in (nest.flatten(func_args, expand_composites=True) + nest.flatten(func_kwargs, expand_composites=True)): if isinstance(arg, resource_variable_ops.BaseResourceVariable): # already manually captured the resource Tensor when creating argument # placeholders. resource_placeholder = func_graph.pop_capture(arg.handle) if resource_placeholder is None: continue arg_variables.add(arg) inputs.append(resource_placeholder) elif isinstance(arg, ops.Tensor): inputs.append(arg) variables = [v for v in graph_variables if v not in arg_variables] func_graph.inputs = ( inputs + func_graph.internal_captures + nest.flatten( func_graph.deferred_internal_captures, expand_composites=True)) func_graph.structured_outputs = func_outputs # Returning a closed-over tensor does not trigger convert_to_tensor. func_graph.outputs.extend( func_graph.capture(x) for x in flatten(func_graph.structured_outputs) if x is not None) func_graph.variables = variables if add_control_dependencies: func_graph.control_outputs.extend(deps_control_manager.ops_which_must_run) func_graph.collective_manager_ids_used = ( deps_control_manager.collective_manager_ids_used) return func_graph def maybe_captured(tensor): if (not isinstance(tensor, ops.EagerTensor) and tensor.op.graph.building_function and tensor.op.type == "Placeholder"): for input_t, placeholder_t in tensor.op.graph.captures: if tensor == placeholder_t: return maybe_captured(input_t) # pylint: enable=protected-access return tensor def device_stack_has_callable(device_stack): return any(callable(spec._device_name_or_function) # pylint: disable=protected-access for spec in device_stack.peek_objs()) def check_mutation(n1, n2, func): func_name = getattr(func, "__name__", func) errmsg = ("{}() should not modify its Python input arguments." " Check if it modifies any lists or dicts passed as" " arguments. Modifying a copy is allowed.".format(func_name)) try: # TODO(mdan): Compare more robustly so that argument names can be reported. nest.assert_same_structure(n1, n2, expand_composites=True) except ValueError: raise ValueError(errmsg) for arg1, arg2 in zip(nest.flatten(n1, expand_composites=True), nest.flatten(n2, expand_composites=True)): if arg1 is not arg2: raise ValueError(errmsg) # TODO(edloper): If TensorArray becomes a CompositeTensor, then delete this. def flatten(sequence): flat_sequence = nest.flatten(sequence, expand_composites=True) return [ item.flow if isinstance(item, tensor_array_ops.TensorArray) else item for item in flat_sequence] # TODO(edloper): If TensorArray becomes a CompositeTensor, then delete this. def pack_sequence_as(structure, flat_sequence): flat_sequence = list(flat_sequence) flattened_structure = nest.flatten(structure, expand_composites=True) if len(flattened_structure) != len(flat_sequence): raise ValueError("Mismatch in element count") for i in range(len(flat_sequence)): if isinstance(flattened_structure[i], tensor_array_ops.TensorArray): flat_sequence[i] = tensor_array_ops.build_ta_with_new_flow( old_ta=flattened_structure[i], flow=flat_sequence[i]) return nest.pack_sequence_as(structure, flat_sequence, expand_composites=True) def _create_substitute_placeholder(value, name=None, dtype=None, shape=None): # Note: setting ops.control_dependencies(None) ensures we always put # capturing placeholders outside of any control flow context. if shape is None: shape = value.shape with ops.control_dependencies(None): placeholder = graph_placeholder( dtype=dtype or value.dtype, shape=shape, name=name) custom_gradient.copy_handle_data(value, placeholder) return placeholder def _get_defun_inputs_from_args(args, names, flat_shapes=None): return _get_defun_inputs( args, names, structure=args, flat_shapes=flat_shapes) def _get_composite_tensor_spec(x): return (x._type_spec # pylint: disable=protected-access if isinstance(x, composite_tensor.CompositeTensor) else x) def _get_defun_inputs(args, names, structure, flat_shapes=None): func_graph = ops.get_default_graph() function_inputs = [] if names is None: names = [None] * len(args) if flat_shapes is None: shapes_iter = itertools.repeat(None) else: len_flat_args = len(nest.flatten(args, expand_composites=True)) if len_flat_args != len(flat_shapes): raise RuntimeError( "Length of fully flat shapes (%d) must match that of " "flatten(args) (%d). args: %s, flat_shapes: %s" % (len(flat_shapes), len_flat_args, args, flat_shapes)) shapes_iter = iter(flat_shapes) for arg_value, name in zip(args, names): # Replace any composite tensors with their TypeSpecs. This is important # for ensuring that shape information that's not preserved by the TypeSpec arg_value = nest.map_structure(_get_composite_tensor_spec, arg_value) flattened = nest.flatten(arg_value, expand_composites=True) for arg in flattened: # Tensor or not. For non-tensor entries it should be None. shape = next(shapes_iter) if isinstance(arg, (ops.Tensor, tensor_spec.TensorSpec)): arg_is_spec = isinstance(arg, tensor_spec.TensorSpec) if arg_is_spec and arg.name: requested_name = arg.name else: requested_name = name placeholder_shape = shape if shape is not None else arg.shape try: placeholder = graph_placeholder( arg.dtype, placeholder_shape, name=requested_name) except ValueError: # Sometimes parameter names are not valid op names, so fall back to # unnamed placeholders. placeholder = graph_placeholder(arg.dtype, placeholder_shape) if not arg_is_spec: custom_gradient.copy_handle_data(arg, placeholder) if name is not None: # Record the requested/user-specified name in case it's different than placeholder.op._set_attr( "_user_specified_name", attr_value_pb2.AttrValue(s=compat.as_bytes(requested_name))) function_inputs.append(placeholder) elif isinstance(arg, (resource_variable_ops.BaseResourceVariable, resource_variable_ops.VariableSpec)): if isinstance(arg, resource_variable_ops.VariableSpec): name = arg.name or name with func_graph.outer_graph.as_default(): placeholder = graph_placeholder(dtypes.resource, arg.shape, name=name) arg = resource_variable_ops.BaseResourceVariable( name=name, shape=arg.shape, dtype=arg.dtype, handle=placeholder, handle_name=name) # just add it back with a new placeholder when the variable was # referenced). placeholder = func_graph.capture(arg.handle, name=name) placeholder.op._set_attr( # pylint: disable=protected-access "_user_specified_name", attr_value_pb2.AttrValue(s=compat.as_bytes(name))) function_inputs.append(arg) else: if shape is not None: raise RuntimeError( "Expected provided shape override to be None for arg that isn't " "a Tensor, but saw arg: '%s', shape: '%s'. args: %s" % (arg, shape, args)) function_inputs.append(arg) return nest.pack_sequence_as(structure, function_inputs, expand_composites=True) def _get_defun_inputs_from_kwargs(kwargs, flat_shapes): if kwargs: names, args = zip(*sorted(kwargs.items())) else: names = [] args = [] return _get_defun_inputs( args, names, structure=kwargs, flat_shapes=flat_shapes) def dismantle_func_graph(func_graph): func_graph.clear_captures() ops.dismantle_graph(func_graph) def override_func_graph_name_scope(func_graph, name_scope): func_graph._name_stack = name_scope

true

f70bd7df44af8f81da661aee94f5ea0c7b3f53cd

245

py

Python

scripts/project_package_name/setup.py

godzilla-but-nicer/SporeLoss

8159a628e5f17191254583c053891070ba3d6e7f

[ "MIT" ]

null

scripts/project_package_name/setup.py

godzilla-but-nicer/SporeLoss

8159a628e5f17191254583c053891070ba3d6e7f

[ "MIT" ]

null

scripts/project_package_name/setup.py

godzilla-but-nicer/SporeLoss

8159a628e5f17191254583c053891070ba3d6e7f

[ "MIT" ]

1

2022-01-10T00:40:05.000Z

#!/usr/bin/env python # encoding: utf-8 from distutils.core import setup setup(name='project_package_name', version='0.1', description = 'project description', author = '...', packages = ['project_package_name'], )

20.416667

43

0.636735

from distutils.core import setup setup(name='project_package_name', version='0.1', description = 'project description', author = '...', packages = ['project_package_name'], )

true

f70bda91f9c7115e3b24b393a5a89e703a6ef8f7

2,137

py

Python

model.py

JulianNovakovic/Vislice

061a252e6aafd60157b740cfcca9b2d76ff27926

[ "MIT" ]

null

model.py

JulianNovakovic/Vislice

061a252e6aafd60157b740cfcca9b2d76ff27926

[ "MIT" ]

null

model.py

JulianNovakovic/Vislice

061a252e6aafd60157b740cfcca9b2d76ff27926

[ "MIT" ]

null

STEVILO_DOVOLJENIH_NAPAK = 10 PRAVILNA_CRKA = '+' PONOVLJENA_CRKA = 'o' NAPACNA_CRKA = '-' ZMAGA = 'W' PORAZ = 'X' class Igra: def __init__(self, geslo, crke): self.geslo = geslo self.crke = crke[:] def napacne_crke(self): return [crka for crka in self.crke if crka not in self.geslo] def pravilne_crke(self): return [crka for crka in self.crke if crka in self.geslo] def stevilo_napak(self): return len(self.napacne_crke()) def zmaga(self): vse_crke = True for crka in self.geslo: if crka in self.pravilne_crke(): pass else: vse_crke = False break # vse_crke1 all(crka in self.crke for crka in self.geslo) return vse_crke and STEVILO_DOVOLJENIH_NAPAK >= self.stevilo_napak() def poraz(self): return STEVILO_DOVOLJENIH_NAPAK < self.stevilo_napak() def pravilni_del_gesla(self): delni = '' ugibanje = [crka.upper() for crka in self.crke] for crka in self.geslo: if crka.upper() in ugibanje: delni += crka else: delni += '_ ' return delni.strip() def nepravili_ugibi(self): return ' '.join(self.napacne_crke()) def ugibaj(self, crka): crka = crka.upper() if crka in self.crke: return PONOVLJENA_CRKA elif crka in self.geslo: self.crke.append(crka) if self.zmaga(): return ZMAGA else: return PRAVILNA_CRKA else: self.crke.append(crka) if self.poraz(): return PORAZ else: return NAPACNA_CRKA with open('Vislice/besede.txt', 'r') as f: bazen_besed = [beseda.strip().upper() for beseda in f.readlines()] import random def nova_igra(): geslo = random.choice(bazen_besed) return Igra(geslo, []) # testno_geslo = 'DEŽUJE' # testne_crke = ['A', 'E', 'I', 'O', 'U', 'D', 'J', 'K', 'Ž'] # igra = Igra(testno_geslo, testne_crke) # print(testno_geslo)

24.848837

76

0.560131

STEVILO_DOVOLJENIH_NAPAK = 10 PRAVILNA_CRKA = '+' PONOVLJENA_CRKA = 'o' NAPACNA_CRKA = '-' ZMAGA = 'W' PORAZ = 'X' class Igra: def __init__(self, geslo, crke): self.geslo = geslo self.crke = crke[:] def napacne_crke(self): return [crka for crka in self.crke if crka not in self.geslo] def pravilne_crke(self): return [crka for crka in self.crke if crka in self.geslo] def stevilo_napak(self): return len(self.napacne_crke()) def zmaga(self): vse_crke = True for crka in self.geslo: if crka in self.pravilne_crke(): pass else: vse_crke = False break return vse_crke and STEVILO_DOVOLJENIH_NAPAK >= self.stevilo_napak() def poraz(self): return STEVILO_DOVOLJENIH_NAPAK < self.stevilo_napak() def pravilni_del_gesla(self): delni = '' ugibanje = [crka.upper() for crka in self.crke] for crka in self.geslo: if crka.upper() in ugibanje: delni += crka else: delni += '_ ' return delni.strip() def nepravili_ugibi(self): return ' '.join(self.napacne_crke()) def ugibaj(self, crka): crka = crka.upper() if crka in self.crke: return PONOVLJENA_CRKA elif crka in self.geslo: self.crke.append(crka) if self.zmaga(): return ZMAGA else: return PRAVILNA_CRKA else: self.crke.append(crka) if self.poraz(): return PORAZ else: return NAPACNA_CRKA with open('Vislice/besede.txt', 'r') as f: bazen_besed = [beseda.strip().upper() for beseda in f.readlines()] import random def nova_igra(): geslo = random.choice(bazen_besed) return Igra(geslo, [])

true

f70bdaa5cd1ef8f895b12e61c5190d35da36ec24

9,996

py

Python

tools/yaml-nic-config-2-script.py

smolar/tripleo-heat-templates

6b858eb39f96cc2a81a115246fd4a2ef6a0b0097

[ "Apache-2.0" ]

null

tools/yaml-nic-config-2-script.py

smolar/tripleo-heat-templates

6b858eb39f96cc2a81a115246fd4a2ef6a0b0097

[ "Apache-2.0" ]

null

tools/yaml-nic-config-2-script.py

smolar/tripleo-heat-templates

6b858eb39f96cc2a81a115246fd4a2ef6a0b0097

[ "Apache-2.0" ]

null

#!/usr/bin/env python # 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 argparse import collections import datetime import os import re import shutil import six import sys import traceback import yaml def parse_opts(argv): parser = argparse.ArgumentParser( description='Convert an old style NIC config file into the new format ' 'using run-os-net-config.sh') parser.add_argument('--script-dir', metavar='<script directory>', help="Relative path to run-os-net-config.sh", default="network/scripts/run-os-net-config.sh") parser.add_argument('files', nargs="+", metavar='<file>', help='List of one or more NIC config files to convert') parser.add_argument('--yes', action='store_true', help=("Use --yes to skip the confirmation " "to overwrite the original config file "), ) opts = parser.parse_args(argv[1:]) return opts def to_commented_yaml(filename): """Convert comments into 'comments<num>: ...' YAML""" out_str = '' last_non_comment_spaces = '' with open(filename, 'r') as f: comment_count = 0 for line in f: # skip blank line if line.isspace(): continue char_count = 0 spaces = '' for char in line: char_count += 1 if char == ' ': spaces += ' ' next elif char == '#': last_non_comment_spaces = spaces comment_count += 1 comment = line[char_count:-1] out_str += "%scomment%i_%i: '%s'\n" % \ (last_non_comment_spaces, comment_count, len(spaces), comment) break else: last_non_comment_spaces = spaces out_str += line # inline comments check m = re.match(".*:.*#(.*)", line) if m: comment_count += 1 out_str += "%s inline_comment%i: '%s'\n" % \ (last_non_comment_spaces, comment_count, m.group(1)) break with open(filename, 'w') as f: f.write(out_str) return out_str def to_normal_yaml(filename): """Convert back to normal #commented YAML""" with open(filename, 'r') as f: data = f.read() out_str = '' next_line_break = False for line in data.split('\n'): # get_input not supported by run-os-net-config.sh script line = line.replace('get_input: ', '') # normal comments m = re.match(" +comment[0-9]+_([0-9]+): '(.*)'.*", line) # inline comments i = re.match(" +inline_comment[0-9]+: '(.*)'.*", line) if m: if next_line_break: out_str += '\n' next_line_break = False for x in range(0, int(m.group(1))): out_str += " " out_str += "#%s\n" % m.group(2) elif i: out_str += " #%s\n" % i.group(1) next_line_break = False else: if next_line_break: out_str += '\n' out_str += line next_line_break = True if next_line_break: out_str += '\n' with open(filename, 'w') as f: f.write(out_str) return out_str class description(six.text_type): pass # FIXME: Some of this duplicates code from build_endpoint_map.py, we should # refactor to share the common code class TemplateDumper(yaml.SafeDumper): def represent_ordered_dict(self, data): return self.represent_dict(data.items()) def description_presenter(self, data): if '\n' in data: style = '>' else: style = '' return self.represent_scalar( yaml.resolver.BaseResolver.DEFAULT_SCALAR_TAG, data, style=style) # We load mappings into OrderedDict to preserve their order class TemplateLoader(yaml.SafeLoader): def construct_mapping(self, node): self.flatten_mapping(node) return collections.OrderedDict(self.construct_pairs(node)) TemplateDumper.add_representer(description, TemplateDumper.description_presenter) TemplateDumper.add_representer(collections.OrderedDict, TemplateDumper.represent_ordered_dict) TemplateLoader.add_constructor(yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, TemplateLoader.construct_mapping) def write_template(template, filename=None): with open(filename, 'w') as f: yaml.dump(template, f, TemplateDumper, width=120, default_flow_style=False) def convert(filename, script_path): print('Converting %s' % filename) try: with open(filename, 'r') as f: tpl = yaml.load(f.read(), Loader=TemplateLoader) except Exception: print(traceback.format_exc()) return 0 for r in (tpl.get('resources', {})).items(): if (r[1].get('type') == 'OS::Heat::StructuredConfig' and r[1].get('properties', {}).get('group') == 'os-apply-config' and r[1].get('properties', {}).get('config', {}).get('os_net_config')): new_r = collections.OrderedDict() new_r['type'] = 'OS::Heat::SoftwareConfig' new_r['properties'] = collections.OrderedDict() new_r['properties']['group'] = 'script' old_net_config = r[1].get( 'properties', {}).get('config', {}).get('os_net_config') new_config = {'str_replace': collections.OrderedDict()} new_config['str_replace']['template'] = {'get_file': script_path} new_config['str_replace']['params'] = \ {'$network_config': old_net_config} new_r['properties']['config'] = new_config tpl['resources'][r[0]] = new_r else: print("No match %s" % r[0]) return 0 # Preserve typical HOT template key ordering od_result = collections.OrderedDict() # Need to bump the HOT version so str_replace supports serializing to json od_result['heat_template_version'] = "rocky" if tpl.get('description'): od_result['description'] = description(tpl['description']) od_result['parameters'] = tpl['parameters'] od_result['resources'] = tpl['resources'] od_result['outputs'] = tpl['outputs'] write_template(od_result, filename) return 1 def check_old_style(filename): with open(filename, 'r') as f: tpl = yaml.load(f.read(), Loader=yaml.SafeLoader) if isinstance(tpl.get('resources', {}), dict): for r in (tpl.get('resources', {})).items(): if (r[1].get('type') == 'OS::Heat::StructuredConfig' and r[1].get('properties', {}).get('group') == 'os-apply-config' and r[1].get('properties', {}).get('config', {}).get('os_net_config')): return True return False opts = parse_opts(sys.argv) exit_val = 0 num_converted = 0 for base_path in opts.files: if os.path.isfile(base_path) and base_path.endswith('.yaml'): if check_old_style(base_path): # Check for script in the user entered (or default) location or in # path relative to NIC config files script_paths = [opts.script_dir] script_paths.append('../../scripts/run-os-net-config.sh') script_paths.append('../network/scripts/run-os-net-config.sh') script_paths.append('/usr/share/openstack-tripleo-heat-templates/' 'network/scripts/run-os-net-config.sh') script_path = None for p in script_paths: if os.path.isfile(os.path.join(os.path.dirname(base_path), p)): script_path = p break if script_path is None: print("Error couldn't find run-os-net-config.sh relative " "to filename") sys.exit(1) print("Using script at %s" % script_path) extension = datetime.datetime.now().strftime('%Y%m%d%H%M%S') backup_filename = os.path.realpath(base_path) + '.' + extension print('The yaml file will be overwritten and the original saved ' 'as %s' % backup_filename) if not (opts.yes or input("Overwrite %s? [y/n] " % base_path).lower() == 'y'): print("Skipping file %s" % base_path) continue if os.path.exists(backup_filename): print("Backup file already exists, skipping file %s" % base_path) continue shutil.copyfile(base_path, backup_filename) to_commented_yaml(base_path) num_converted += convert(base_path, script_path) to_normal_yaml(base_path) else: print('File %s is not using old style NIC configuration' % base_path) else: print('Unexpected argument %s' % base_path) if num_converted == 0: exit_val = 1 sys.exit(exit_val)

33.884746

87

0.560524

import argparse import collections import datetime import os import re import shutil import six import sys import traceback import yaml def parse_opts(argv): parser = argparse.ArgumentParser( description='Convert an old style NIC config file into the new format ' 'using run-os-net-config.sh') parser.add_argument('--script-dir', metavar='<script directory>', help="Relative path to run-os-net-config.sh", default="network/scripts/run-os-net-config.sh") parser.add_argument('files', nargs="+", metavar='<file>', help='List of one or more NIC config files to convert') parser.add_argument('--yes', action='store_true', help=("Use --yes to skip the confirmation " "to overwrite the original config file "), ) opts = parser.parse_args(argv[1:]) return opts def to_commented_yaml(filename): out_str = '' last_non_comment_spaces = '' with open(filename, 'r') as f: comment_count = 0 for line in f: if line.isspace(): continue char_count = 0 spaces = '' for char in line: char_count += 1 if char == ' ': spaces += ' ' next elif char == '#': last_non_comment_spaces = spaces comment_count += 1 comment = line[char_count:-1] out_str += "%scomment%i_%i: '%s'\n" % \ (last_non_comment_spaces, comment_count, len(spaces), comment) break else: last_non_comment_spaces = spaces out_str += line m = re.match(".*:.*#(.*)", line) if m: comment_count += 1 out_str += "%s inline_comment%i: '%s'\n" % \ (last_non_comment_spaces, comment_count, m.group(1)) break with open(filename, 'w') as f: f.write(out_str) return out_str def to_normal_yaml(filename): with open(filename, 'r') as f: data = f.read() out_str = '' next_line_break = False for line in data.split('\n'): line = line.replace('get_input: ', '') m = re.match(" +comment[0-9]+_([0-9]+): '(.*)'.*", line) i = re.match(" +inline_comment[0-9]+: '(.*)'.*", line) if m: if next_line_break: out_str += '\n' next_line_break = False for x in range(0, int(m.group(1))): out_str += " " out_str += "#%s\n" % m.group(2) elif i: out_str += " #%s\n" % i.group(1) next_line_break = False else: if next_line_break: out_str += '\n' out_str += line next_line_break = True if next_line_break: out_str += '\n' with open(filename, 'w') as f: f.write(out_str) return out_str class description(six.text_type): pass class TemplateDumper(yaml.SafeDumper): def represent_ordered_dict(self, data): return self.represent_dict(data.items()) def description_presenter(self, data): if '\n' in data: style = '>' else: style = '' return self.represent_scalar( yaml.resolver.BaseResolver.DEFAULT_SCALAR_TAG, data, style=style) class TemplateLoader(yaml.SafeLoader): def construct_mapping(self, node): self.flatten_mapping(node) return collections.OrderedDict(self.construct_pairs(node)) TemplateDumper.add_representer(description, TemplateDumper.description_presenter) TemplateDumper.add_representer(collections.OrderedDict, TemplateDumper.represent_ordered_dict) TemplateLoader.add_constructor(yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, TemplateLoader.construct_mapping) def write_template(template, filename=None): with open(filename, 'w') as f: yaml.dump(template, f, TemplateDumper, width=120, default_flow_style=False) def convert(filename, script_path): print('Converting %s' % filename) try: with open(filename, 'r') as f: tpl = yaml.load(f.read(), Loader=TemplateLoader) except Exception: print(traceback.format_exc()) return 0 for r in (tpl.get('resources', {})).items(): if (r[1].get('type') == 'OS::Heat::StructuredConfig' and r[1].get('properties', {}).get('group') == 'os-apply-config' and r[1].get('properties', {}).get('config', {}).get('os_net_config')): new_r = collections.OrderedDict() new_r['type'] = 'OS::Heat::SoftwareConfig' new_r['properties'] = collections.OrderedDict() new_r['properties']['group'] = 'script' old_net_config = r[1].get( 'properties', {}).get('config', {}).get('os_net_config') new_config = {'str_replace': collections.OrderedDict()} new_config['str_replace']['template'] = {'get_file': script_path} new_config['str_replace']['params'] = \ {'$network_config': old_net_config} new_r['properties']['config'] = new_config tpl['resources'][r[0]] = new_r else: print("No match %s" % r[0]) return 0 od_result = collections.OrderedDict() od_result['heat_template_version'] = "rocky" if tpl.get('description'): od_result['description'] = description(tpl['description']) od_result['parameters'] = tpl['parameters'] od_result['resources'] = tpl['resources'] od_result['outputs'] = tpl['outputs'] write_template(od_result, filename) return 1 def check_old_style(filename): with open(filename, 'r') as f: tpl = yaml.load(f.read(), Loader=yaml.SafeLoader) if isinstance(tpl.get('resources', {}), dict): for r in (tpl.get('resources', {})).items(): if (r[1].get('type') == 'OS::Heat::StructuredConfig' and r[1].get('properties', {}).get('group') == 'os-apply-config' and r[1].get('properties', {}).get('config', {}).get('os_net_config')): return True return False opts = parse_opts(sys.argv) exit_val = 0 num_converted = 0 for base_path in opts.files: if os.path.isfile(base_path) and base_path.endswith('.yaml'): if check_old_style(base_path): script_paths = [opts.script_dir] script_paths.append('../../scripts/run-os-net-config.sh') script_paths.append('../network/scripts/run-os-net-config.sh') script_paths.append('/usr/share/openstack-tripleo-heat-templates/' 'network/scripts/run-os-net-config.sh') script_path = None for p in script_paths: if os.path.isfile(os.path.join(os.path.dirname(base_path), p)): script_path = p break if script_path is None: print("Error couldn't find run-os-net-config.sh relative " "to filename") sys.exit(1) print("Using script at %s" % script_path) extension = datetime.datetime.now().strftime('%Y%m%d%H%M%S') backup_filename = os.path.realpath(base_path) + '.' + extension print('The yaml file will be overwritten and the original saved ' 'as %s' % backup_filename) if not (opts.yes or input("Overwrite %s? [y/n] " % base_path).lower() == 'y'): print("Skipping file %s" % base_path) continue if os.path.exists(backup_filename): print("Backup file already exists, skipping file %s" % base_path) continue shutil.copyfile(base_path, backup_filename) to_commented_yaml(base_path) num_converted += convert(base_path, script_path) to_normal_yaml(base_path) else: print('File %s is not using old style NIC configuration' % base_path) else: print('Unexpected argument %s' % base_path) if num_converted == 0: exit_val = 1 sys.exit(exit_val)

true

f70bdc3a15e7b88c2514b1fcf2a401008c840d78

336

py

Python

geekshop/basketapp/urls.py

TonyBrother32/Django-shop

723a1eb9ff5b74fa968e8c09268bbcbb2fed857c

[ "MIT" ]

null

geekshop/basketapp/urls.py

TonyBrother32/Django-shop

723a1eb9ff5b74fa968e8c09268bbcbb2fed857c

[ "MIT" ]

null

geekshop/basketapp/urls.py

TonyBrother32/Django-shop

723a1eb9ff5b74fa968e8c09268bbcbb2fed857c

[ "MIT" ]

null

from django.urls import path from . import views app_name = 'basketapp' urlpatterns = [ path('', views.view, name='view'), path('add/<int:product_id>/', views.add, name='add'), path('remove/<int:basket_item_id>)/', views.remove, name='remove'), path('edit/<int:basket_item_id>/<int:quantity>/', views.edit, name='edit'), ]

30.545455

78

0.666667

from django.urls import path from . import views app_name = 'basketapp' urlpatterns = [ path('', views.view, name='view'), path('add/<int:product_id>/', views.add, name='add'), path('remove/<int:basket_item_id>)/', views.remove, name='remove'), path('edit/<int:basket_item_id>/<int:quantity>/', views.edit, name='edit'), ]

true

f70bddf32a2b465ec48d7297b542087af6cbef33

1,441

py

Python

google/cloud/monitoring_dashboard/v1/__init__.py

vam-google/python-monitoring-dashboards

effbff2703ade03269ad8ddacf4ab31637d8a799

[ "Apache-2.0" ]

null

google/cloud/monitoring_dashboard/v1/__init__.py

vam-google/python-monitoring-dashboards

effbff2703ade03269ad8ddacf4ab31637d8a799

[ "Apache-2.0" ]

null

google/cloud/monitoring_dashboard/v1/__init__.py

vam-google/python-monitoring-dashboards

effbff2703ade03269ad8ddacf4ab31637d8a799

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import import sys import warnings from google.cloud.monitoring_dashboard.v1 import types from google.cloud.monitoring_dashboard.v1.gapic import dashboards_service_client from google.cloud.monitoring_dashboard.v1.gapic import enums if sys.version_info[:2] == (2, 7): message = ( "A future version of this library will drop support for Python 2.7." "More details about Python 2 support for Google Cloud Client Libraries" "can be found at https://cloud.google.com/python/docs/python2-sunset/" ) warnings.warn(message, DeprecationWarning) class DashboardsServiceClient(dashboards_service_client.DashboardsServiceClient): __doc__ = dashboards_service_client.DashboardsServiceClient.__doc__ enums = enums __all__ = ("enums", "types", "DashboardsServiceClient")

34.309524

81

0.762665

from __future__ import absolute_import import sys import warnings from google.cloud.monitoring_dashboard.v1 import types from google.cloud.monitoring_dashboard.v1.gapic import dashboards_service_client from google.cloud.monitoring_dashboard.v1.gapic import enums if sys.version_info[:2] == (2, 7): message = ( "A future version of this library will drop support for Python 2.7." "More details about Python 2 support for Google Cloud Client Libraries" "can be found at https://cloud.google.com/python/docs/python2-sunset/" ) warnings.warn(message, DeprecationWarning) class DashboardsServiceClient(dashboards_service_client.DashboardsServiceClient): __doc__ = dashboards_service_client.DashboardsServiceClient.__doc__ enums = enums __all__ = ("enums", "types", "DashboardsServiceClient")

true

f70bdf701bbdb41d790f24af7996716a5faf0ff5

7,004

py

Python

python/GafferUI/Editor.py

sebaDesmet/gaffer

47b2d093c40452bd77947e3b5bd0722a366c8d59

[ "BSD-3-Clause" ]

null

python/GafferUI/Editor.py

sebaDesmet/gaffer

47b2d093c40452bd77947e3b5bd0722a366c8d59

[ "BSD-3-Clause" ]

null

python/GafferUI/Editor.py

sebaDesmet/gaffer

47b2d093c40452bd77947e3b5bd0722a366c8d59

[ "BSD-3-Clause" ]

null

########################################################################## # # Copyright (c) 2011-2012, John Haddon. All rights reserved. # Copyright (c) 2012-2013, Image Engine Design Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above # copyright notice, this list of conditions and the following # disclaimer. # # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided with # the distribution. # # * Neither the name of John Haddon nor the names of # any other contributors to this software may be used to endorse or # promote products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS # IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ########################################################################## import types import IECore import Gaffer import GafferUI from Qt import QtCore from Qt import QtWidgets class _EditorMetaclass( Gaffer.Trackable.__class__ ) : def __call__( cls, *args, **kw ) : instance = type.__call__( cls, *args, **kw ) while hasattr( cls, "instanceCreatedSignal" ) : cls.instanceCreatedSignal()( instance ) cls = cls.__bases__[0] return instance ## Base class for UI components which display or manipulate a ScriptNode # or its children. These make up the tabs in the UI layout. class Editor( GafferUI.Widget ) : __metaclass__ = _EditorMetaclass def __init__( self, topLevelWidget, scriptNode, **kw ) : GafferUI.Widget.__init__( self, topLevelWidget, **kw ) self._qtWidget().setFocusPolicy( QtCore.Qt.ClickFocus ) assert( isinstance( scriptNode, Gaffer.ScriptNode ) ) self.__scriptNode = scriptNode self.__context = None self.__title = "" self.__titleChangedSignal = GafferUI.WidgetSignal() self.enterSignal().connect( Gaffer.WeakMethod( self.__enter ), scoped = False ) self.leaveSignal().connect( Gaffer.WeakMethod( self.__leave ), scoped = False ) self.__setContextInternal( scriptNode.context(), callUpdate=False ) def scriptNode( self ) : return self.__scriptNode ## May be called to explicitly set the title for this editor. The # editor itself is not responsible for displaying the title - this # is left to the enclosing ui. def setTitle( self, title ) : if title == self.__title : return self.__title = title self.titleChangedSignal()( self ) ## May be overridden to provide sensible default behaviour for # the title, but must return BaseClass.getTitle() if it is non-empty. def getTitle( self ) : if self.__title : return self.__title # if there's no explicit title and a derived class # has overridden getTitle() then we return the empty # string to signify that the derived class is free # to return what it wants c = self.__class__ while c is not Editor : if "getTitle" in c.__dict__ : return "" c = c.__bases__[0] # otherwise we default to using the classname return IECore.CamelCase.toSpaced( self.__class__.__name__ ) ## A signal emitted whenever the title changes. def titleChangedSignal( self ) : return self.__titleChangedSignal ## By default Editors operate in the main context held by the script node. This function # allows an alternative context to be provided, making it possible for an editor to # display itself at a custom frame (or with any other context modification). def setContext( self, context ) : self.__setContextInternal( context, callUpdate=True ) def getContext( self ) : return self.__context def __setContextInternal( self, context, callUpdate ) : assert( isinstance( context, ( Gaffer.Context, types.NoneType ) ) ) previousContext = self.__context self.__context = context if self.__context is not None : self.__contextChangedConnection = self.__context.changedSignal().connect( Gaffer.WeakMethod( self.__contextChanged ) ) else : ## \todo I'm not sure why this code allows a None context - surely we # should always have a valid one? self.__contextChangedConnection = None if callUpdate : modifiedItems = set() if previousContext is not None : modifiedItems |= set( previousContext.names() ) if self.__context is not None : modifiedItems |= set( self.__context.names() ) self._updateFromContext( modifiedItems ) ## May be implemented by derived classes to update state based on a change of context. # To temporarily suspend calls to this function, use Gaffer.BlockedConnection( self._contextChangedConnection() ). def _updateFromContext( self, modifiedItems ) : pass def _contextChangedConnection( self ) : return self.__contextChangedConnection ## This must be implemented by all derived classes as it is used for serialisation of layouts. # It is not expected that the script being edited is also serialised as part of this operation - # instead the new script will be provided later as a variable named scriptNode. So a suitable # serialisation will look like "GafferUI.Editor( scriptNode )". def __repr__( self ) : raise NotImplementedError def __contextChanged( self, context, key ) : assert( context.isSame( self.getContext() ) ) self._updateFromContext( set( [ key ] ) ) @classmethod def types( cls ) : return cls.__namesToCreators.keys() @classmethod def create( cls, name, scriptNode ) : return cls.__namesToCreators[name]( scriptNode = scriptNode ) @classmethod def registerType( cls, name, creator ) : cls.__namesToCreators[name] = creator __namesToCreators = {} @classmethod def instanceCreatedSignal( cls ) : s = cls.__dict__.get( "__instanceCreatedSignal", None ) if s is not None : return s s = Gaffer.Signal1() setattr( cls, "__instanceCreatedSignal", s ) return s def __enter( self, widget ) : if not isinstance( QtWidgets.QApplication.focusWidget(), ( QtWidgets.QLineEdit, QtWidgets.QPlainTextEdit ) ) : self._qtWidget().setFocus() def __leave( self, widget ) : self._qtWidget().clearFocus()

32.276498

121

0.719446

# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, import types import IECore import Gaffer import GafferUI from Qt import QtCore from Qt import QtWidgets class _EditorMetaclass( Gaffer.Trackable.__class__ ) : def __call__( cls, *args, **kw ) : instance = type.__call__( cls, *args, **kw ) while hasattr( cls, "instanceCreatedSignal" ) : cls.instanceCreatedSignal()( instance ) cls = cls.__bases__[0] return instance class Editor( GafferUI.Widget ) : __metaclass__ = _EditorMetaclass def __init__( self, topLevelWidget, scriptNode, **kw ) : GafferUI.Widget.__init__( self, topLevelWidget, **kw ) self._qtWidget().setFocusPolicy( QtCore.Qt.ClickFocus ) assert( isinstance( scriptNode, Gaffer.ScriptNode ) ) self.__scriptNode = scriptNode self.__context = None self.__title = "" self.__titleChangedSignal = GafferUI.WidgetSignal() self.enterSignal().connect( Gaffer.WeakMethod( self.__enter ), scoped = False ) self.leaveSignal().connect( Gaffer.WeakMethod( self.__leave ), scoped = False ) self.__setContextInternal( scriptNode.context(), callUpdate=False ) def scriptNode( self ) : return self.__scriptNode def setTitle( self, title ) : if title == self.__title : return self.__title = title self.titleChangedSignal()( self ) def getTitle( self ) : if self.__title : return self.__title # has overridden getTitle() then we return the empty # string to signify that the derived class is free # to return what it wants c = self.__class__ while c is not Editor : if "getTitle" in c.__dict__ : return "" c = c.__bases__[0] # otherwise we default to using the classname return IECore.CamelCase.toSpaced( self.__class__.__name__ ) ## A signal emitted whenever the title changes. def titleChangedSignal( self ) : return self.__titleChangedSignal ## By default Editors operate in the main context held by the script node. This function # allows an alternative context to be provided, making it possible for an editor to # display itself at a custom frame (or with any other context modification). def setContext( self, context ) : self.__setContextInternal( context, callUpdate=True ) def getContext( self ) : return self.__context def __setContextInternal( self, context, callUpdate ) : assert( isinstance( context, ( Gaffer.Context, types.NoneType ) ) ) previousContext = self.__context self.__context = context if self.__context is not None : self.__contextChangedConnection = self.__context.changedSignal().connect( Gaffer.WeakMethod( self.__contextChanged ) ) else : ## \todo I'm not sure why this code allows a None context - surely we self.__contextChangedConnection = None if callUpdate : modifiedItems = set() if previousContext is not None : modifiedItems |= set( previousContext.names() ) if self.__context is not None : modifiedItems |= set( self.__context.names() ) self._updateFromContext( modifiedItems ) def _updateFromContext( self, modifiedItems ) : pass def _contextChangedConnection( self ) : return self.__contextChangedConnection def __repr__( self ) : raise NotImplementedError def __contextChanged( self, context, key ) : assert( context.isSame( self.getContext() ) ) self._updateFromContext( set( [ key ] ) ) @classmethod def types( cls ) : return cls.__namesToCreators.keys() @classmethod def create( cls, name, scriptNode ) : return cls.__namesToCreators[name]( scriptNode = scriptNode ) @classmethod def registerType( cls, name, creator ) : cls.__namesToCreators[name] = creator __namesToCreators = {} @classmethod def instanceCreatedSignal( cls ) : s = cls.__dict__.get( "__instanceCreatedSignal", None ) if s is not None : return s s = Gaffer.Signal1() setattr( cls, "__instanceCreatedSignal", s ) return s def __enter( self, widget ) : if not isinstance( QtWidgets.QApplication.focusWidget(), ( QtWidgets.QLineEdit, QtWidgets.QPlainTextEdit ) ) : self._qtWidget().setFocus() def __leave( self, widget ) : self._qtWidget().clearFocus()

true

f70be103caf3bbb85e059906041c636a547700ab

4,304

py

Python

tests/fixtures/pooldata.py

curvefi/deposit-and-stake-zap

2183cfa03d23b9a1e572d46332d73ad30b39845d

[ "MIT" ]

null

tests/fixtures/pooldata.py

curvefi/deposit-and-stake-zap

2183cfa03d23b9a1e572d46332d73ad30b39845d

[ "MIT" ]

null

tests/fixtures/pooldata.py

curvefi/deposit-and-stake-zap

2183cfa03d23b9a1e572d46332d73ad30b39845d

[ "MIT" ]

null

import pytest import brownie from brownie import Contract, ZERO_ADDRESS # gusd gusd_token_address = "0xD2967f45c4f384DEEa880F807Be904762a3DeA07" gusd_gauge_addresses = "0xC5cfaDA84E902aD92DD40194f0883ad49639b023" # susd susd_token_address = '0xC25a3A3b969415c80451098fa907EC722572917F' susd_gauge_address = '0xA90996896660DEcC6E997655E065b23788857849' @pytest.fixture(scope="module") def swap_address(pool_data): return pool_data['swap_address'] @pytest.fixture(scope="module") def token_address(pool_data): return pool_data['lp_token_address'] @pytest.fixture(scope="module") def gauge_address(pool_data): return pool_data['gauge_addresses'][0] @pytest.fixture(scope="module") def deposit_address(pool_data): return pool_data['zap_address'] if 'zap_address' in pool_data else pool_data['swap_address'] @pytest.fixture(scope="module") def other_token_address(pool_data): return gusd_token_address if gusd_token_address != pool_data["lp_token_address"] else susd_token_address @pytest.fixture(scope="module") def other_gauge_address(pool_data): return gusd_gauge_addresses if gusd_gauge_addresses != pool_data["gauge_addresses"][0] else susd_gauge_address @pytest.fixture(scope="module") def gauge(gauge_address): return Contract(gauge_address) @pytest.fixture(scope="module") def underlying_decimals(pool_data, base_pool_data): # number of decimal places for each underlying coin in the active pool decimals = [i.get("decimals", i.get("wrapped_decimals")) for i in pool_data["coins"]] if base_pool_data is None: return decimals base_decimals = [i.get("decimals", i.get("wrapped_decimals")) for i in base_pool_data["coins"]] return decimals[:-1] + base_decimals @pytest.fixture(scope="module") def wrapped_decimals(pool_data): # number of decimal places for each wrapped coin in the active pool yield [i.get("wrapped_decimals", i.get("decimals")) for i in pool_data["coins"]] @pytest.fixture(scope="module") def wrapped_amounts_to_mint(wrapped_decimals): return [100 * 10 ** i for i in wrapped_decimals] @pytest.fixture(scope="module") def underlying_amounts_to_mint(underlying_decimals): return [100 * 10 ** i for i in underlying_decimals] @pytest.fixture(scope="module") def wrong_amounts_to_mint(): return [100 * 10 ** 18] * 5 # Different amounts are needed to always pass test_wrong_order_of_coins @pytest.fixture(scope="module") def wrapped_amounts(wrapped_decimals, n_coins_wrapped): return [(10 + i) * 10 ** wrapped_decimals[i] for i in range(n_coins_wrapped)] + [0] * (5 - n_coins_wrapped) # Different amounts are needed to always pass test_wrong_order_of_coins @pytest.fixture(scope="module") def underlying_amounts(underlying_decimals, n_coins_underlying): return [(10 + i) * 10 ** underlying_decimals[i] for i in range(n_coins_underlying)] + [0] * (5 - n_coins_underlying) @pytest.fixture(scope="module") def n_coins_wrapped(wrapped_decimals): return len(wrapped_decimals) @pytest.fixture(scope="module") def n_coins_underlying(underlying_decimals): yield len(underlying_decimals) @pytest.fixture(scope="module") def value_wrapped(wrapped_amounts, wrapped_coins): return wrapped_amounts[wrapped_coins.index(brownie.ETH_ADDRESS)] if brownie.ETH_ADDRESS in wrapped_coins else 0 @pytest.fixture(scope="module") def value_underlying(underlying_amounts, underlying_coins): return underlying_amounts[underlying_coins.index(brownie.ETH_ADDRESS)] if brownie.ETH_ADDRESS in underlying_coins else 0 @pytest.fixture(scope="module") def use_underlying(pool_data): if pool_data['swap_address'] in [ "0xDeBF20617708857ebe4F679508E7b7863a8A8EeE", # aave "0xeb16ae0052ed37f479f7fe63849198df1765a733", # saave "0x2dded6Da1BF5DBdF597C45fcFaa3194e53EcfeAF", # ib "0x8301AE4fc9c624d1D396cbDAa1ed877821D7C511", # crveth (use_eth) "0xB576491F1E6e5E62f1d8F26062Ee822B40B0E0d4", # cvxeth (use_eth) ]: return True return False @pytest.fixture(scope="module") def is_meta(pool_data): return "meta" in pool_data.get("pool_types", []) @pytest.fixture(scope="module") def factory_pool_address(pool_data): return pool_data["swap_address"] if "factory" in pool_data.get("pool_types", []) else ZERO_ADDRESS

31.881481

124

0.766496

import pytest import brownie from brownie import Contract, ZERO_ADDRESS gusd_token_address = "0xD2967f45c4f384DEEa880F807Be904762a3DeA07" gusd_gauge_addresses = "0xC5cfaDA84E902aD92DD40194f0883ad49639b023" susd_token_address = '0xC25a3A3b969415c80451098fa907EC722572917F' susd_gauge_address = '0xA90996896660DEcC6E997655E065b23788857849' @pytest.fixture(scope="module") def swap_address(pool_data): return pool_data['swap_address'] @pytest.fixture(scope="module") def token_address(pool_data): return pool_data['lp_token_address'] @pytest.fixture(scope="module") def gauge_address(pool_data): return pool_data['gauge_addresses'][0] @pytest.fixture(scope="module") def deposit_address(pool_data): return pool_data['zap_address'] if 'zap_address' in pool_data else pool_data['swap_address'] @pytest.fixture(scope="module") def other_token_address(pool_data): return gusd_token_address if gusd_token_address != pool_data["lp_token_address"] else susd_token_address @pytest.fixture(scope="module") def other_gauge_address(pool_data): return gusd_gauge_addresses if gusd_gauge_addresses != pool_data["gauge_addresses"][0] else susd_gauge_address @pytest.fixture(scope="module") def gauge(gauge_address): return Contract(gauge_address) @pytest.fixture(scope="module") def underlying_decimals(pool_data, base_pool_data): decimals = [i.get("decimals", i.get("wrapped_decimals")) for i in pool_data["coins"]] if base_pool_data is None: return decimals base_decimals = [i.get("decimals", i.get("wrapped_decimals")) for i in base_pool_data["coins"]] return decimals[:-1] + base_decimals @pytest.fixture(scope="module") def wrapped_decimals(pool_data): yield [i.get("wrapped_decimals", i.get("decimals")) for i in pool_data["coins"]] @pytest.fixture(scope="module") def wrapped_amounts_to_mint(wrapped_decimals): return [100 * 10 ** i for i in wrapped_decimals] @pytest.fixture(scope="module") def underlying_amounts_to_mint(underlying_decimals): return [100 * 10 ** i for i in underlying_decimals] @pytest.fixture(scope="module") def wrong_amounts_to_mint(): return [100 * 10 ** 18] * 5 @pytest.fixture(scope="module") def wrapped_amounts(wrapped_decimals, n_coins_wrapped): return [(10 + i) * 10 ** wrapped_decimals[i] for i in range(n_coins_wrapped)] + [0] * (5 - n_coins_wrapped) @pytest.fixture(scope="module") def underlying_amounts(underlying_decimals, n_coins_underlying): return [(10 + i) * 10 ** underlying_decimals[i] for i in range(n_coins_underlying)] + [0] * (5 - n_coins_underlying) @pytest.fixture(scope="module") def n_coins_wrapped(wrapped_decimals): return len(wrapped_decimals) @pytest.fixture(scope="module") def n_coins_underlying(underlying_decimals): yield len(underlying_decimals) @pytest.fixture(scope="module") def value_wrapped(wrapped_amounts, wrapped_coins): return wrapped_amounts[wrapped_coins.index(brownie.ETH_ADDRESS)] if brownie.ETH_ADDRESS in wrapped_coins else 0 @pytest.fixture(scope="module") def value_underlying(underlying_amounts, underlying_coins): return underlying_amounts[underlying_coins.index(brownie.ETH_ADDRESS)] if brownie.ETH_ADDRESS in underlying_coins else 0 @pytest.fixture(scope="module") def use_underlying(pool_data): if pool_data['swap_address'] in [ "0xDeBF20617708857ebe4F679508E7b7863a8A8EeE", "0xeb16ae0052ed37f479f7fe63849198df1765a733", "0x2dded6Da1BF5DBdF597C45fcFaa3194e53EcfeAF", "0x8301AE4fc9c624d1D396cbDAa1ed877821D7C511", "0xB576491F1E6e5E62f1d8F26062Ee822B40B0E0d4", ]: return True return False @pytest.fixture(scope="module") def is_meta(pool_data): return "meta" in pool_data.get("pool_types", []) @pytest.fixture(scope="module") def factory_pool_address(pool_data): return pool_data["swap_address"] if "factory" in pool_data.get("pool_types", []) else ZERO_ADDRESS

true

f70be16b7667d5ef4e1fe1961c226707b73ee15d

795

py

Python

todo/task/repository.py

matiasjavierlucero/todo-challenge

4c0dbc518a5e8fc9d99b6034163be14246fd6666

[ "MIT" ]

null

todo/task/repository.py

matiasjavierlucero/todo-challenge

4c0dbc518a5e8fc9d99b6034163be14246fd6666

[ "MIT" ]

null

todo/task/repository.py

matiasjavierlucero/todo-challenge

4c0dbc518a5e8fc9d99b6034163be14246fd6666

[ "MIT" ]

null

from decimal import Decimal from django.db.models import Sum from django.shortcuts import get_object_or_404 from datetime import date, timedelta from .models import Task class TaskRepository: """Repository for tasks.""" def list(self): return Task.objects.all() def create(self, title: str, description: str, status: int): return Task.objects.create( title=title, description=description, status=status ) def detail(self, id): return get_object_or_404(Task, pk=id) def update(self, request, id): task = get_object_or_404(Task, pk=id) task.status = request.data.get('status') task.save() return task def destroy(self, pk=None): task = Task.objects.get(id=pk) task.delete()

25.645161

64

0.654088

from decimal import Decimal from django.db.models import Sum from django.shortcuts import get_object_or_404 from datetime import date, timedelta from .models import Task class TaskRepository: def list(self): return Task.objects.all() def create(self, title: str, description: str, status: int): return Task.objects.create( title=title, description=description, status=status ) def detail(self, id): return get_object_or_404(Task, pk=id) def update(self, request, id): task = get_object_or_404(Task, pk=id) task.status = request.data.get('status') task.save() return task def destroy(self, pk=None): task = Task.objects.get(id=pk) task.delete()

true

f70be1a781ef564608b08997e1b98fa857b29328

17,551

py

Python

sdk/python/pulumi_azure_nextgen/network/v20190401/express_route_circuit_peering.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_nextgen/network/v20190401/express_route_circuit_peering.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_nextgen/network/v20190401/express_route_circuit_peering.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables from . import outputs from ._inputs import * __all__ = ['ExpressRouteCircuitPeering'] class ExpressRouteCircuitPeering(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, azure_asn: Optional[pulumi.Input[int]] = None, circuit_name: Optional[pulumi.Input[str]] = None, connections: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExpressRouteCircuitConnectionArgs']]]]] = None, gateway_manager_etag: Optional[pulumi.Input[str]] = None, id: Optional[pulumi.Input[str]] = None, ipv6_peering_config: Optional[pulumi.Input[pulumi.InputType['Ipv6ExpressRouteCircuitPeeringConfigArgs']]] = None, last_modified_by: Optional[pulumi.Input[str]] = None, microsoft_peering_config: Optional[pulumi.Input[pulumi.InputType['ExpressRouteCircuitPeeringConfigArgs']]] = None, name: Optional[pulumi.Input[str]] = None, peer_asn: Optional[pulumi.Input[int]] = None, peering_name: Optional[pulumi.Input[str]] = None, peering_type: Optional[pulumi.Input[str]] = None, primary_azure_port: Optional[pulumi.Input[str]] = None, primary_peer_address_prefix: Optional[pulumi.Input[str]] = None, provisioning_state: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, route_filter: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None, secondary_azure_port: Optional[pulumi.Input[str]] = None, secondary_peer_address_prefix: Optional[pulumi.Input[str]] = None, shared_key: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input[str]] = None, stats: Optional[pulumi.Input[pulumi.InputType['ExpressRouteCircuitStatsArgs']]] = None, vlan_id: Optional[pulumi.Input[int]] = None, __props__=None, __name__=None, __opts__=None): """ Peering in an ExpressRouteCircuit resource. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[int] azure_asn: The Azure ASN. :param pulumi.Input[str] circuit_name: The name of the express route circuit. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExpressRouteCircuitConnectionArgs']]]] connections: The list of circuit connections associated with Azure Private Peering for this circuit. :param pulumi.Input[str] gateway_manager_etag: The GatewayManager Etag. :param pulumi.Input[str] id: Resource ID. :param pulumi.Input[pulumi.InputType['Ipv6ExpressRouteCircuitPeeringConfigArgs']] ipv6_peering_config: The IPv6 peering configuration. :param pulumi.Input[str] last_modified_by: Gets whether the provider or the customer last modified the peering. :param pulumi.Input[pulumi.InputType['ExpressRouteCircuitPeeringConfigArgs']] microsoft_peering_config: The Microsoft peering configuration. :param pulumi.Input[str] name: Gets name of the resource that is unique within a resource group. This name can be used to access the resource. :param pulumi.Input[int] peer_asn: The peer ASN. :param pulumi.Input[str] peering_name: The name of the peering. :param pulumi.Input[str] peering_type: The peering type. :param pulumi.Input[str] primary_azure_port: The primary port. :param pulumi.Input[str] primary_peer_address_prefix: The primary address prefix. :param pulumi.Input[str] provisioning_state: Gets the provisioning state of the public IP resource. Possible values are: 'Updating', 'Deleting', and 'Failed'. :param pulumi.Input[str] resource_group_name: The name of the resource group. :param pulumi.Input[pulumi.InputType['SubResourceArgs']] route_filter: The reference of the RouteFilter resource. :param pulumi.Input[str] secondary_azure_port: The secondary port. :param pulumi.Input[str] secondary_peer_address_prefix: The secondary address prefix. :param pulumi.Input[str] shared_key: The shared key. :param pulumi.Input[str] state: The peering state. :param pulumi.Input[pulumi.InputType['ExpressRouteCircuitStatsArgs']] stats: Gets peering stats. :param pulumi.Input[int] vlan_id: The VLAN ID. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['azure_asn'] = azure_asn if circuit_name is None: raise TypeError("Missing required property 'circuit_name'") __props__['circuit_name'] = circuit_name __props__['connections'] = connections __props__['gateway_manager_etag'] = gateway_manager_etag __props__['id'] = id __props__['ipv6_peering_config'] = ipv6_peering_config __props__['last_modified_by'] = last_modified_by __props__['microsoft_peering_config'] = microsoft_peering_config __props__['name'] = name __props__['peer_asn'] = peer_asn if peering_name is None: raise TypeError("Missing required property 'peering_name'") __props__['peering_name'] = peering_name __props__['peering_type'] = peering_type __props__['primary_azure_port'] = primary_azure_port __props__['primary_peer_address_prefix'] = primary_peer_address_prefix __props__['provisioning_state'] = provisioning_state if resource_group_name is None: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name __props__['route_filter'] = route_filter __props__['secondary_azure_port'] = secondary_azure_port __props__['secondary_peer_address_prefix'] = secondary_peer_address_prefix __props__['shared_key'] = shared_key __props__['state'] = state __props__['stats'] = stats __props__['vlan_id'] = vlan_id __props__['etag'] = None __props__['express_route_connection'] = None __props__['peered_connections'] = None __props__['type'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/latest:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20150501preview:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20150615:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20160330:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20160601:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20160901:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20161201:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20170301:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20170601:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20170801:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20170901:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20171001:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20171101:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180101:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180201:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180401:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180601:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180701:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180801:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20181001:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20181101:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20181201:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20190201:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20190601:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20190701:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20190801:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20190901:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20191101:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20191201:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20200301:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20200401:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20200501:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20200601:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20200701:ExpressRouteCircuitPeering")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(ExpressRouteCircuitPeering, __self__).__init__( 'azure-nextgen:network/v20190401:ExpressRouteCircuitPeering', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'ExpressRouteCircuitPeering': """ Get an existing ExpressRouteCircuitPeering resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() return ExpressRouteCircuitPeering(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="azureASN") def azure_asn(self) -> pulumi.Output[Optional[int]]: """ The Azure ASN. """ return pulumi.get(self, "azure_asn") @property @pulumi.getter def connections(self) -> pulumi.Output[Optional[Sequence['outputs.ExpressRouteCircuitConnectionResponse']]]: """ The list of circuit connections associated with Azure Private Peering for this circuit. """ return pulumi.get(self, "connections") @property @pulumi.getter def etag(self) -> pulumi.Output[str]: """ A unique read-only string that changes whenever the resource is updated. """ return pulumi.get(self, "etag") @property @pulumi.getter(name="expressRouteConnection") def express_route_connection(self) -> pulumi.Output[Optional['outputs.ExpressRouteConnectionIdResponse']]: """ The ExpressRoute connection. """ return pulumi.get(self, "express_route_connection") @property @pulumi.getter(name="gatewayManagerEtag") def gateway_manager_etag(self) -> pulumi.Output[Optional[str]]: """ The GatewayManager Etag. """ return pulumi.get(self, "gateway_manager_etag") @property @pulumi.getter(name="ipv6PeeringConfig") def ipv6_peering_config(self) -> pulumi.Output[Optional['outputs.Ipv6ExpressRouteCircuitPeeringConfigResponse']]: """ The IPv6 peering configuration. """ return pulumi.get(self, "ipv6_peering_config") @property @pulumi.getter(name="lastModifiedBy") def last_modified_by(self) -> pulumi.Output[Optional[str]]: """ Gets whether the provider or the customer last modified the peering. """ return pulumi.get(self, "last_modified_by") @property @pulumi.getter(name="microsoftPeeringConfig") def microsoft_peering_config(self) -> pulumi.Output[Optional['outputs.ExpressRouteCircuitPeeringConfigResponse']]: """ The Microsoft peering configuration. """ return pulumi.get(self, "microsoft_peering_config") @property @pulumi.getter def name(self) -> pulumi.Output[Optional[str]]: """ Gets name of the resource that is unique within a resource group. This name can be used to access the resource. """ return pulumi.get(self, "name") @property @pulumi.getter(name="peerASN") def peer_asn(self) -> pulumi.Output[Optional[int]]: """ The peer ASN. """ return pulumi.get(self, "peer_asn") @property @pulumi.getter(name="peeredConnections") def peered_connections(self) -> pulumi.Output[Sequence['outputs.PeerExpressRouteCircuitConnectionResponse']]: """ The list of peered circuit connections associated with Azure Private Peering for this circuit. """ return pulumi.get(self, "peered_connections") @property @pulumi.getter(name="peeringType") def peering_type(self) -> pulumi.Output[Optional[str]]: """ The peering type. """ return pulumi.get(self, "peering_type") @property @pulumi.getter(name="primaryAzurePort") def primary_azure_port(self) -> pulumi.Output[Optional[str]]: """ The primary port. """ return pulumi.get(self, "primary_azure_port") @property @pulumi.getter(name="primaryPeerAddressPrefix") def primary_peer_address_prefix(self) -> pulumi.Output[Optional[str]]: """ The primary address prefix. """ return pulumi.get(self, "primary_peer_address_prefix") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[Optional[str]]: """ Gets the provisioning state of the public IP resource. Possible values are: 'Updating', 'Deleting', and 'Failed'. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="routeFilter") def route_filter(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]: """ The reference of the RouteFilter resource. """ return pulumi.get(self, "route_filter") @property @pulumi.getter(name="secondaryAzurePort") def secondary_azure_port(self) -> pulumi.Output[Optional[str]]: """ The secondary port. """ return pulumi.get(self, "secondary_azure_port") @property @pulumi.getter(name="secondaryPeerAddressPrefix") def secondary_peer_address_prefix(self) -> pulumi.Output[Optional[str]]: """ The secondary address prefix. """ return pulumi.get(self, "secondary_peer_address_prefix") @property @pulumi.getter(name="sharedKey") def shared_key(self) -> pulumi.Output[Optional[str]]: """ The shared key. """ return pulumi.get(self, "shared_key") @property @pulumi.getter def state(self) -> pulumi.Output[Optional[str]]: """ The peering state. """ return pulumi.get(self, "state") @property @pulumi.getter def stats(self) -> pulumi.Output[Optional['outputs.ExpressRouteCircuitStatsResponse']]: """ Gets peering stats. """ return pulumi.get(self, "stats") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ Type of the resource. """ return pulumi.get(self, "type") @property @pulumi.getter(name="vlanId") def vlan_id(self) -> pulumi.Output[Optional[int]]: """ The VLAN ID. """ return pulumi.get(self, "vlan_id") def translate_output_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

51.469208

2,845

0.685089

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables from . import outputs from ._inputs import * __all__ = ['ExpressRouteCircuitPeering'] class ExpressRouteCircuitPeering(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, azure_asn: Optional[pulumi.Input[int]] = None, circuit_name: Optional[pulumi.Input[str]] = None, connections: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExpressRouteCircuitConnectionArgs']]]]] = None, gateway_manager_etag: Optional[pulumi.Input[str]] = None, id: Optional[pulumi.Input[str]] = None, ipv6_peering_config: Optional[pulumi.Input[pulumi.InputType['Ipv6ExpressRouteCircuitPeeringConfigArgs']]] = None, last_modified_by: Optional[pulumi.Input[str]] = None, microsoft_peering_config: Optional[pulumi.Input[pulumi.InputType['ExpressRouteCircuitPeeringConfigArgs']]] = None, name: Optional[pulumi.Input[str]] = None, peer_asn: Optional[pulumi.Input[int]] = None, peering_name: Optional[pulumi.Input[str]] = None, peering_type: Optional[pulumi.Input[str]] = None, primary_azure_port: Optional[pulumi.Input[str]] = None, primary_peer_address_prefix: Optional[pulumi.Input[str]] = None, provisioning_state: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, route_filter: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None, secondary_azure_port: Optional[pulumi.Input[str]] = None, secondary_peer_address_prefix: Optional[pulumi.Input[str]] = None, shared_key: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input[str]] = None, stats: Optional[pulumi.Input[pulumi.InputType['ExpressRouteCircuitStatsArgs']]] = None, vlan_id: Optional[pulumi.Input[int]] = None, __props__=None, __name__=None, __opts__=None): if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['azure_asn'] = azure_asn if circuit_name is None: raise TypeError("Missing required property 'circuit_name'") __props__['circuit_name'] = circuit_name __props__['connections'] = connections __props__['gateway_manager_etag'] = gateway_manager_etag __props__['id'] = id __props__['ipv6_peering_config'] = ipv6_peering_config __props__['last_modified_by'] = last_modified_by __props__['microsoft_peering_config'] = microsoft_peering_config __props__['name'] = name __props__['peer_asn'] = peer_asn if peering_name is None: raise TypeError("Missing required property 'peering_name'") __props__['peering_name'] = peering_name __props__['peering_type'] = peering_type __props__['primary_azure_port'] = primary_azure_port __props__['primary_peer_address_prefix'] = primary_peer_address_prefix __props__['provisioning_state'] = provisioning_state if resource_group_name is None: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name __props__['route_filter'] = route_filter __props__['secondary_azure_port'] = secondary_azure_port __props__['secondary_peer_address_prefix'] = secondary_peer_address_prefix __props__['shared_key'] = shared_key __props__['state'] = state __props__['stats'] = stats __props__['vlan_id'] = vlan_id __props__['etag'] = None __props__['express_route_connection'] = None __props__['peered_connections'] = None __props__['type'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/latest:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20150501preview:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20150615:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20160330:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20160601:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20160901:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20161201:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20170301:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20170601:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20170801:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20170901:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20171001:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20171101:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180101:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180201:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180401:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180601:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180701:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20180801:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20181001:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20181101:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20181201:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20190201:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20190601:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20190701:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20190801:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20190901:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20191101:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20191201:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20200301:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20200401:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20200501:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20200601:ExpressRouteCircuitPeering"), pulumi.Alias(type_="azure-nextgen:network/v20200701:ExpressRouteCircuitPeering")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(ExpressRouteCircuitPeering, __self__).__init__( 'azure-nextgen:network/v20190401:ExpressRouteCircuitPeering', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'ExpressRouteCircuitPeering': opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() return ExpressRouteCircuitPeering(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="azureASN") def azure_asn(self) -> pulumi.Output[Optional[int]]: return pulumi.get(self, "azure_asn") @property @pulumi.getter def connections(self) -> pulumi.Output[Optional[Sequence['outputs.ExpressRouteCircuitConnectionResponse']]]: return pulumi.get(self, "connections") @property @pulumi.getter def etag(self) -> pulumi.Output[str]: return pulumi.get(self, "etag") @property @pulumi.getter(name="expressRouteConnection") def express_route_connection(self) -> pulumi.Output[Optional['outputs.ExpressRouteConnectionIdResponse']]: return pulumi.get(self, "express_route_connection") @property @pulumi.getter(name="gatewayManagerEtag") def gateway_manager_etag(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "gateway_manager_etag") @property @pulumi.getter(name="ipv6PeeringConfig") def ipv6_peering_config(self) -> pulumi.Output[Optional['outputs.Ipv6ExpressRouteCircuitPeeringConfigResponse']]: return pulumi.get(self, "ipv6_peering_config") @property @pulumi.getter(name="lastModifiedBy") def last_modified_by(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "last_modified_by") @property @pulumi.getter(name="microsoftPeeringConfig") def microsoft_peering_config(self) -> pulumi.Output[Optional['outputs.ExpressRouteCircuitPeeringConfigResponse']]: return pulumi.get(self, "microsoft_peering_config") @property @pulumi.getter def name(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "name") @property @pulumi.getter(name="peerASN") def peer_asn(self) -> pulumi.Output[Optional[int]]: return pulumi.get(self, "peer_asn") @property @pulumi.getter(name="peeredConnections") def peered_connections(self) -> pulumi.Output[Sequence['outputs.PeerExpressRouteCircuitConnectionResponse']]: return pulumi.get(self, "peered_connections") @property @pulumi.getter(name="peeringType") def peering_type(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "peering_type") @property @pulumi.getter(name="primaryAzurePort") def primary_azure_port(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "primary_azure_port") @property @pulumi.getter(name="primaryPeerAddressPrefix") def primary_peer_address_prefix(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "primary_peer_address_prefix") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="routeFilter") def route_filter(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]: return pulumi.get(self, "route_filter") @property @pulumi.getter(name="secondaryAzurePort") def secondary_azure_port(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "secondary_azure_port") @property @pulumi.getter(name="secondaryPeerAddressPrefix") def secondary_peer_address_prefix(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "secondary_peer_address_prefix") @property @pulumi.getter(name="sharedKey") def shared_key(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "shared_key") @property @pulumi.getter def state(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "state") @property @pulumi.getter def stats(self) -> pulumi.Output[Optional['outputs.ExpressRouteCircuitStatsResponse']]: return pulumi.get(self, "stats") @property @pulumi.getter def type(self) -> pulumi.Output[str]: return pulumi.get(self, "type") @property @pulumi.getter(name="vlanId") def vlan_id(self) -> pulumi.Output[Optional[int]]: return pulumi.get(self, "vlan_id") def translate_output_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

true

f70be1eb045c7bfc67774263850d50bd2296ed60

1,394

py

Python

mdn/yari/testing/integration/conftest.py

private-face/webextensions-docset

9743056dbb3d3ccd8a20a665fcb0f98d388819a6

[ "MIT" ]

1

2021-11-22T20:01:26.000Z

mdn/yari/testing/integration/conftest.py

Kapeli/mdn-offline-build

e700fcc597be32dde7a2cdadeaa56343ccf0a678

[ "MIT" ]

1

2021-07-02T18:35:07.000Z

mdn/yari/testing/integration/conftest.py

Kapeli/mdn-offline-build

e700fcc597be32dde7a2cdadeaa56343ccf0a678

[ "MIT" ]

2

2021-06-21T12:09:37.000Z

2021-07-02T12:15:52.000Z

from urllib.parse import urlsplit, urlunsplit import pytest import requests _KUMA_STATUS = None def pytest_configure(config): """Configure pytest for the Kuma deployment under test.""" global _KUMA_STATUS # The pytest-base-url plugin adds --base-url, and sets the default from # environment variable PYTEST_BASE_URL. If still unset, force to staging. if config.option.base_url is None: config.option.base_url = "https://developer.allizom.org" base_url = config.getoption("base_url") # Process the server status from _kuma_status.json base_parts = urlsplit(base_url) kuma_status_url = urlunsplit( (base_parts.scheme, base_parts.netloc, "_kuma_status.json", "", "") ) response = requests.get(kuma_status_url, headers={"Accept": "application/json"}) response.raise_for_status() _KUMA_STATUS = response.json() _KUMA_STATUS["response"] = {"headers": response.headers} @pytest.fixture(scope="session") def kuma_status(base_url): return _KUMA_STATUS @pytest.fixture(scope="session") def is_behind_cdn(kuma_status): return "x-amz-cf-id" in kuma_status["response"]["headers"] @pytest.fixture(scope="session") def media_url(): return "https://media.prod.mdn.mozit.cloud" @pytest.fixture(scope="session") def attachment_url(kuma_status): return f'https://{kuma_status["settings"]["ATTACHMENT_HOST"]}'

27.88

84

0.721664

from urllib.parse import urlsplit, urlunsplit import pytest import requests _KUMA_STATUS = None def pytest_configure(config): global _KUMA_STATUS if config.option.base_url is None: config.option.base_url = "https://developer.allizom.org" base_url = config.getoption("base_url") base_parts = urlsplit(base_url) kuma_status_url = urlunsplit( (base_parts.scheme, base_parts.netloc, "_kuma_status.json", "", "") ) response = requests.get(kuma_status_url, headers={"Accept": "application/json"}) response.raise_for_status() _KUMA_STATUS = response.json() _KUMA_STATUS["response"] = {"headers": response.headers} @pytest.fixture(scope="session") def kuma_status(base_url): return _KUMA_STATUS @pytest.fixture(scope="session") def is_behind_cdn(kuma_status): return "x-amz-cf-id" in kuma_status["response"]["headers"] @pytest.fixture(scope="session") def media_url(): return "https://media.prod.mdn.mozit.cloud" @pytest.fixture(scope="session") def attachment_url(kuma_status): return f'https://{kuma_status["settings"]["ATTACHMENT_HOST"]}'

true

f70be1fc76ddddc85e3bee71647489d92784fa4f

74

py

Python

indoorair/foundations/urls.py

juby-gif/indoorair_webapp-b

51f8799e8b124748bec7f1e52a3b73bcb4c119a8

[ "BSD-3-Clause" ]

null

indoorair/foundations/urls.py

juby-gif/indoorair_webapp-b

51f8799e8b124748bec7f1e52a3b73bcb4c119a8

[ "BSD-3-Clause" ]

null

indoorair/foundations/urls.py

juby-gif/indoorair_webapp-b

51f8799e8b124748bec7f1e52a3b73bcb4c119a8

[ "BSD-3-Clause" ]

null

from django.urls import path from . import views urlpatterns = [ ]

9.25

28

0.675676

from django.urls import path from . import views urlpatterns = [ ]

true

f70be2546519bf0303806a830af9c2a53f69831e

3,946

py

Python

test/functional/feature_minchainwork.py

puzcoin/catcoin

dc3ad8d15b0c3303e8396514dffeb7685f0edf63

[ "MIT" ]

null

test/functional/feature_minchainwork.py

puzcoin/catcoin

dc3ad8d15b0c3303e8396514dffeb7685f0edf63

[ "MIT" ]

null

test/functional/feature_minchainwork.py

puzcoin/catcoin

dc3ad8d15b0c3303e8396514dffeb7685f0edf63

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2017 The Catcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test logic for setting nMinimumChainWork on command line. Nodes don't consider themselves out of "initial block download" until their active chain has more work than nMinimumChainWork. Nodes don't download blocks from a peer unless the peer's best known block has more work than nMinimumChainWork. While in initial block download, nodes won't relay blocks to their peers, so test that this parameter functions as intended by verifying that block relay only succeeds past a given node once its nMinimumChainWork has been exceeded. """ import time from test_framework.test_framework import CatcoinTestFramework from test_framework.util import connect_nodes, assert_equal # 2 hashes required per regtest block (with no difficulty adjustment) REGTEST_WORK_PER_BLOCK = 2 class MinimumChainWorkTest(CatcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 3 self.extra_args = [[], ["-minimumchainwork=0x65"], ["-minimumchainwork=0x65"]] self.node_min_work = [0, 101, 101] def setup_network(self): # This test relies on the chain setup being: # node0 <- node1 <- node2 # Before leaving IBD, nodes prefer to download blocks from outbound # peers, so ensure that we're mining on an outbound peer and testing # block relay to inbound peers. self.setup_nodes() for i in range(self.num_nodes-1): connect_nodes(self.nodes[i+1], i) def run_test(self): # Start building a chain on node0. node2 shouldn't be able to sync until node1's # minchainwork is exceeded starting_chain_work = REGTEST_WORK_PER_BLOCK # Genesis block's work self.log.info("Testing relay across node %d (minChainWork = %d)", 1, self.node_min_work[1]) starting_blockcount = self.nodes[2].getblockcount() num_blocks_to_generate = int((self.node_min_work[1] - starting_chain_work) / REGTEST_WORK_PER_BLOCK) self.log.info("Generating %d blocks on node0", num_blocks_to_generate) hashes = self.nodes[0].generate(num_blocks_to_generate) self.log.info("Node0 current chain work: %s", self.nodes[0].getblockheader(hashes[-1])['chainwork']) # Sleep a few seconds and verify that node2 didn't get any new blocks # or headers. We sleep, rather than sync_blocks(node0, node1) because # it's reasonable either way for node1 to get the blocks, or not get # them (since they're below node1's minchainwork). time.sleep(3) self.log.info("Verifying node 2 has no more blocks than before") self.log.info("Blockcounts: %s", [n.getblockcount() for n in self.nodes]) # Node2 shouldn't have any new headers yet, because node1 should not # have relayed anything. assert_equal(len(self.nodes[2].getchaintips()), 1) assert_equal(self.nodes[2].getchaintips()[0]['height'], 0) assert self.nodes[1].getbestblockhash() != self.nodes[0].getbestblockhash() assert_equal(self.nodes[2].getblockcount(), starting_blockcount) self.log.info("Generating one more block") self.nodes[0].generate(1) self.log.info("Verifying nodes are all synced") # Because nodes in regtest are all manual connections (eg using # addnode), node1 should not have disconnected node0. If not for that, # we'd expect node1 to have disconnected node0 for serving an # insufficient work chain, in which case we'd need to reconnect them to # continue the test. self.sync_all() self.log.info("Blockcounts: %s", [n.getblockcount() for n in self.nodes]) if __name__ == '__main__': MinimumChainWorkTest().main()

43.844444

108

0.701977

import time from test_framework.test_framework import CatcoinTestFramework from test_framework.util import connect_nodes, assert_equal REGTEST_WORK_PER_BLOCK = 2 class MinimumChainWorkTest(CatcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 3 self.extra_args = [[], ["-minimumchainwork=0x65"], ["-minimumchainwork=0x65"]] self.node_min_work = [0, 101, 101] def setup_network(self): # block relay to inbound peers. self.setup_nodes() for i in range(self.num_nodes-1): connect_nodes(self.nodes[i+1], i) def run_test(self): # Start building a chain on node0. node2 shouldn't be able to sync until node1's # minchainwork is exceeded starting_chain_work = REGTEST_WORK_PER_BLOCK # Genesis block's work self.log.info("Testing relay across node %d (minChainWork = %d)", 1, self.node_min_work[1]) starting_blockcount = self.nodes[2].getblockcount() num_blocks_to_generate = int((self.node_min_work[1] - starting_chain_work) / REGTEST_WORK_PER_BLOCK) self.log.info("Generating %d blocks on node0", num_blocks_to_generate) hashes = self.nodes[0].generate(num_blocks_to_generate) self.log.info("Node0 current chain work: %s", self.nodes[0].getblockheader(hashes[-1])['chainwork']) # or headers. We sleep, rather than sync_blocks(node0, node1) because # it's reasonable either way for node1 to get the blocks, or not get time.sleep(3) self.log.info("Verifying node 2 has no more blocks than before") self.log.info("Blockcounts: %s", [n.getblockcount() for n in self.nodes]) # have relayed anything. assert_equal(len(self.nodes[2].getchaintips()), 1) assert_equal(self.nodes[2].getchaintips()[0]['height'], 0) assert self.nodes[1].getbestblockhash() != self.nodes[0].getbestblockhash() assert_equal(self.nodes[2].getblockcount(), starting_blockcount) self.log.info("Generating one more block") self.nodes[0].generate(1) self.log.info("Verifying nodes are all synced") # Because nodes in regtest are all manual connections (eg using # addnode), node1 should not have disconnected node0. If not for that, # we'd expect node1 to have disconnected node0 for serving an # continue the test. self.sync_all() self.log.info("Blockcounts: %s", [n.getblockcount() for n in self.nodes]) if __name__ == '__main__': MinimumChainWorkTest().main()

true

f70be36c4203d27c522cdba5e3ca275e4037d7b3

15,955

py

Python

qzone/Qzone.py

lwpdzq/spiders

68f471f3dd92e1a59fe9ccc130fd529f1def3644

[ "MIT" ]

null

qzone/Qzone.py

lwpdzq/spiders

68f471f3dd92e1a59fe9ccc130fd529f1def3644

[ "MIT" ]

null

qzone/Qzone.py

lwpdzq/spiders

68f471f3dd92e1a59fe9ccc130fd529f1def3644

[ "MIT" ]

null

import time import re import random import requests from urllib import parse import qq_init as qq import pymongo from selenium import webdriver from selenium.webdriver.chrome.options import Options class Spider(object): def __init__(self): ''' 初始化 ''' chrome_options = Options() # chrome_options.add_argument('--headless') # chrome_options.add_argument('--disable-gpu') self.driver = webdriver.Chrome(chrome_options=chrome_options) self.driver.get('https://i.qq.com/') self.__username = qq.USERNAME self.__password = qq.PASSWORD self.headers = { 'host': 'h5.qzone.qq.com', 'accept-encoding': 'gzip, deflate, br', 'accept-language': 'zh-CN,zh;q=0.8', 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8', 'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36', 'connection': 'keep-alive' } self.req = requests.Session() self.cookies = {} self.client = pymongo.MongoClient(host=qq.HOST, port=qq.PORT) self.db = self.client[qq.DB] def login(self): ''' 登录、调用get_g_tk()、get_friends()函数 :return: ''' self.driver.switch_to.frame('login_frame') self.driver.find_element_by_id('switcher_plogin').click() self.driver.find_element_by_id('u').clear() self.driver.find_element_by_id('u').send_keys(self.__username) self.driver.find_element_by_id('p').clear() self.driver.find_element_by_id('p').send_keys(self.__password) self.driver.find_element_by_id('login_button').click() time.sleep(7) self.driver.get('http://user.qzone.qq.com/{}'.format(self.__username)) cookie = '' for item in self.driver.get_cookies(): cookie += item["name"] + '=' + item['value'] + ';' self.cookies = cookie self.get_g_tk() self.headers['Cookie'] = self.cookies self.get_friends() self.driver.quit() def get_friends(self): ''' 获取全部好友 :return: qq, name ''' url = 'https://user.qzone.qq.com/proxy/domain/r.qzone.qq.com/cgi-bin/tfriend/friend_hat_get.cgi?' params = { 'uin': self.__username, 'fupdate': 1, 'g_tk': self.g_tk } url = url + parse.urlencode(params) friends = self.req.get(url, headers=self.headers).text name, qq_num = [], [] for _qq, _name in zip(re.findall('"\d+"', friends), re.findall('"realname":.*"', friends)): name.append(re.sub('"|realname|:', '', _name)) qq_num.append(re.sub('"', '', _qq)) self.name, self.qq_num = name, qq_num def get_g_tk(self): ''' 获取g_tk() :return: 生成的g_tk ''' p_skey = self.cookies[self.cookies.find('p_skey=') + 7: self.cookies.find(';', self.cookies.find('p_skey='))] if len(p_skey) > 50: self.driver.quit() raise BaseException( '登录出错' ) h = 5381 for i in p_skey: h += (h << 5) + ord(i) print('g_tk', h & 2147483647) self.g_tk = h & 2147483647 def get_mood(self): ''' 构造说说请求链接对所有好友进行请求获取点赞好友信息正则解析存入数据库设置时长 5 秒，防封号 :return: ''' url = 'https://h5.qzone.qq.com/proxy/domain/taotao.qq.com/cgi-bin/emotion_cgi_msglist_v6?' params = { 'inCharset': 'utf-8', 'outCharset': 'utf-8', 'sort': 0, 'num': 20, 'repllyunm': 100, 'cgi_host': 'http://taotao.qq.com/cgi-bin/emotion_cgi_msglist_v6', 'callback': '_preloadCallback', 'code_version': 1, 'format': 'jsonp', 'need_private_comment': 1, 'g_tk': self.g_tk } url = url + parse.urlencode(params) for q in self.qq_num: num = 0 t1, pos = True, 0 url_ = url + '&uin=' + str(q) black, shuoshuo = self.db['black'], self.db['mood'] while(t1): url__ = url_ + '&pos=' + str(pos) mood = self.req.get(url=url__, headers=self.headers) if '\"msglist\":null' in mood.text or "\"message\":\"对不起,主人设置了保密,您没有权限查看\"" in mood.text: t1 = False if '\"message\":\"对不起,主人设置了保密,您没有权限查看\"' in mood.text: data = { 'name': self.name[self.qq_num.index(q)], 'qq': q } black.insert(data) else: created_time = re.findall('created_time":\d+', mood.text) source = re.findall('source_appid":".*?"source_name":".*?"', mood.text) contents = re.findall('],"content":".*?"', mood.text) forword = re.findall('fwdnum":\d+', mood.text) comment_content = re.findall('commentlist":(null|.*?],)', mood.text) comments = re.findall('cmtnum":\d+', mood.text) pics = re.findall('","pic(_template|".*?])', mood.text) like_url = 'https://user.qzone.qq.com/proxy/domain/users.qzone.qq.com/cgi-bin/likes/get_like_list_app?' tids = re.findall('tid":".*?"', mood.text) for _time, _source, _content, _forword, _comment_content, _comment, _pic, _tid in \ zip(created_time, source, contents, forword, comment_content, comments, pics, tids): param = { 'uin': self.__username, 'unikey': 'http://user.qzone.qq.com/{}/mood/'.format(q)+re.sub('tid":"|"', '', _tid)+'.1', 'begin_uin': 0, 'query_count': 60, 'if_first_page': 1, 'g_tk': self.g_tk } like_url_current = like_url + parse.urlencode(param) like = self.req.get(url=like_url_current, headers=self.headers) likers = like.text.encode(like.encoding).decode('utf-8') if likers is None: likers = [] fuin, nick, sex, constellation, address = re.findall('fuin":\d+', likers), re.findall('nick":".*?"', likers), re.findall('gender":".*?"', likers), re.findall('tion":".*?"', likers), re.findall('addr":".*?"', likers) infos = [] # 点赞信息 for _fuin, _nick, _sex, _constellation, _address in zip(fuin, nick, sex, constellation, address): info = { 'fuin': re.sub('fuin":', '', _fuin), 'nick': re.sub('nick":"|"', '', _nick), 'sex': re.sub('gender":"|"', '', _sex), 'constellation': re.sub('tion":"|"', '', _constellation), 'address': re.sub('addr":"|"', '', _address) } infos.append(info) num = num + 1 print(num) data = { # '_id': str(q) + '_' + str(random.random() * 10).replace('.', ''), '_id': str(q) + '_' + str(num), 'name': self.name[self.qq_num.index(q)], 'CreateTime': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(re.sub('created_time":', '', _time)))), 'source': re.sub('source_appid":".*?"source_name":"|"', '', _source), 'content': re.sub('],"content":"|"', '', _content), 'forward': re.sub('fwdnum":', '', _forword), 'comment_content': re.sub('null|commentlist":', '', _comment_content) if 'null' in _comment_content else str([(re.sub('content":"|"', '', x), re.sub('createTime2":"|"', '', y), re.sub('name":"|"', '', z), re.sub('uin":', '', zz)) for x, y, z, zz in zip(re.findall('content":".*?"', _comment_content), re.findall('createTime2":".*?"', _comment_content), re.findall('name":".*?"', _comment_content), re.findall('uin":\d+', _comment_content))]), 'comment': re.sub('cmtnum":', '', _comment), 'pic': [] if 'template' in _pic else [re.sub('url2":|"', '', i) for i in re.findall('url2":".*?"', _pic)], 'lick_url' : like_url_current } try: data['like'] = re.sub('number":', '', re.search('number":\d+', likers).group()) except Exception as identifier: print(identifier) data['like'] = 0 data['likers'] = infos if shuoshuo.insert(data): print('%s 的说说写入到数据库成功！' % self.name[self.qq_num.index(q)]) else: with open('filed', 'a+', encoding='utf-8') as f: f.write('%s 的说说爬取失败！\n' % self.name[self.qq_num.index(q)]) print('%s 的说说写入到数据库成功！' % self.name[self.qq_num.index(q)]) pos += 20 time.sleep(4) def get_board(self): ''' 获取留言，与获取说说大同小异 :return: ''' url = 'https://user.qzone.qq.com/proxy/domain/m.qzone.qq.com/cgi-bin/new/get_msgb?' params = { 'uin': self.__username, 'num': 10, 'hostword': 0, 'essence': 1, 'inCharset': 'utf-8', 'outCharset': 'utf-8', 'format': 'jsonp', 'g_tk': self.g_tk } url = url + parse.urlencode(params) for q in self.qq_num: num = 0 t2 = True url_ = url + '&hostUin=' + str(q) start = 0 boardb = self.db['board'] while(t2): url__ = url_ + '&start=' + str(start) board = self.req.get(url=url__, headers=self.headers) if '\"message":"空间主人设置了访问权限，您无法进行操作\"' in board.text or '\"message\":\"空间未开通\"' in board.text or '\"commentList\":[]' in board.text or '\"total\":0' in board.text: t2 = False else: text = board.text ids, nickname, uin, pubtime, content, replyList = \ re.findall('id":"\d+', text), re.findall('nickname":".*?"', text), re.findall('uin":\d+,\n"nick', text),\ re.findall('pubtime":".*?"', text), re.findall('ubbContent":".*?"', text), re.findall('"replyList":(\[\]|.*?\}\])', text, re.S) for _id, _nickname, _uin, _time, _content, _reply in zip(ids, nickname, uin, pubtime, content, replyList): num = num + 1 print(num) data = { # '_id': str(q) + '_' + re.sub('id":"', '', _id), '_id': str(q) + '_' + str(num), 'owner': self.name[self.qq_num.index(q)], 'total': re.sub('total":', '', re.search('total":\d+', board.text).group()), 'name': re.sub('nickname":"|"', '', _nickname), 'qq': re.sub('uin":|,\n"nick', '', _uin), 'time': re.sub('pubtime":"|"', '', _time), 'content': re.sub('ubbContent":"|"', '', _content), # 下行需要改动 'replyList': [] if '[]' in _reply else str([re.sub('nick":"|"', '', name) + re.sub('content"|"', '', con) for name, con in zip(re.findall('nick":".*?"', _reply), re.findall('content":".*?"', _reply))]) } if boardb.insert(data): print('%s 的留言存储到Mongodb成功！' % self.name[self.qq_num.index(q)]) start += 10 def get_information(self): ''' 构造请求，正则解析 :return: ''' url = 'https://h5.qzone.qq.com/proxy/domain/base.qzone.qq.com/cgi-bin/user/cgi_userinfo_get_all?' params = { 'vuin': self.__username, 'fupdate': 1, 'g_tk': self.g_tk } url = url + parse.urlencode(params) table = self.db['information'] for q in self.qq_num: t3 = True url_ = url + '&uin=' + str(q) while(t3): info = self.req.get(url=url_, headers=self.headers) if '\"message\":\"您无权访问\"' in info.text: t3 = False else: text = info.text sex = ['其他', '男', '女'] constellation = ['白羊座', '金牛座', '双子座', '巨蟹座', '狮子座', '处女座', '天秤座', '天蝎座', '射手座', '摩羯座', '水瓶座', '双鱼座', '未填写'] data = { '_id': str(q) + '_' + str(random.random() * 10).replace('.', ''), 'nickname': re.sub('nickname":"|"', '', re.search('nickname":".*?"', text).group()), 'spacename': re.sub('spacename":"|"', '', re.search('spacename":".*?"', text).group()), 'desc': re.sub('desc":"|"', '', re.search('desc":".*?"', text).group()), 'signature': re.sub('signature":"|"', '', re.search('signature":".*?"', text).group()), 'sex': sex[int(re.sub('sex":', '', re.search('sex":\d+', text).group()))], 'age': re.sub('"age":', '', re.search('"age":\d+', text).group()), 'birthday': re.sub('birthyear":', '', re.search('birthyear":\d+', text).group()) + '-' + re.sub('birthday":"|"', '', re.search('birthday":".*"', text).group()), 'constellation': constellation[int(re.sub('constellation":|,', '', re.search('constellation":.*,', text).group()).replace('-1', '12'))], 'country': re.sub('country":"|"', '', re.search('country":".*"', text).group()), 'province': re.sub('province":"|"', '', re.search('province":".*?"', text).group()), 'city': re.sub('city":"|"', '', re.search('city":".*?"', text).group()), 'hometown': re.sub('hco":"|"|,|\n|hc|hp|:', '', re.search('hco":".*\n".*\n".*', text).group()), # 'marriage': marriage[int(re.sub('marriage":', '', re.search('marriage":\d', text).group()))], 'career': re.sub('career":"|"', '', re.search('career":".*?"', text).group()), 'address': re.sub('cb":"|"', '', re.search('cb":".*?"', text).group()) } if table.insert(data): print('%s 的信息写入到数据库成功！' % self.name[self.qq_num.index(q)]) t3 = False if __name__ == '__main__': sp = Spider() sp.login() sp.get_information() t = time.perf_counter() sp.get_board() sp.get_mood() End = time.perf_counter() - t print('所有内容爬取完成！总用时%s!' % End)

51.302251

471

0.443936

import time import re import random import requests from urllib import parse import qq_init as qq import pymongo from selenium import webdriver from selenium.webdriver.chrome.options import Options class Spider(object): def __init__(self): chrome_options = Options() self.driver = webdriver.Chrome(chrome_options=chrome_options) self.driver.get('https://i.qq.com/') self.__username = qq.USERNAME self.__password = qq.PASSWORD self.headers = { 'host': 'h5.qzone.qq.com', 'accept-encoding': 'gzip, deflate, br', 'accept-language': 'zh-CN,zh;q=0.8', 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8', 'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36', 'connection': 'keep-alive' } self.req = requests.Session() self.cookies = {} self.client = pymongo.MongoClient(host=qq.HOST, port=qq.PORT) self.db = self.client[qq.DB] def login(self): self.driver.switch_to.frame('login_frame') self.driver.find_element_by_id('switcher_plogin').click() self.driver.find_element_by_id('u').clear() self.driver.find_element_by_id('u').send_keys(self.__username) self.driver.find_element_by_id('p').clear() self.driver.find_element_by_id('p').send_keys(self.__password) self.driver.find_element_by_id('login_button').click() time.sleep(7) self.driver.get('http://user.qzone.qq.com/{}'.format(self.__username)) cookie = '' for item in self.driver.get_cookies(): cookie += item["name"] + '=' + item['value'] + ';' self.cookies = cookie self.get_g_tk() self.headers['Cookie'] = self.cookies self.get_friends() self.driver.quit() def get_friends(self): url = 'https://user.qzone.qq.com/proxy/domain/r.qzone.qq.com/cgi-bin/tfriend/friend_hat_get.cgi?' params = { 'uin': self.__username, 'fupdate': 1, 'g_tk': self.g_tk } url = url + parse.urlencode(params) friends = self.req.get(url, headers=self.headers).text name, qq_num = [], [] for _qq, _name in zip(re.findall('"\d+"', friends), re.findall('"realname":.*"', friends)): name.append(re.sub('"|realname|:', '', _name)) qq_num.append(re.sub('"', '', _qq)) self.name, self.qq_num = name, qq_num def get_g_tk(self): p_skey = self.cookies[self.cookies.find('p_skey=') + 7: self.cookies.find(';', self.cookies.find('p_skey='))] if len(p_skey) > 50: self.driver.quit() raise BaseException( '登录出错' ) h = 5381 for i in p_skey: h += (h << 5) + ord(i) print('g_tk', h & 2147483647) self.g_tk = h & 2147483647 def get_mood(self): url = 'https://h5.qzone.qq.com/proxy/domain/taotao.qq.com/cgi-bin/emotion_cgi_msglist_v6?' params = { 'inCharset': 'utf-8', 'outCharset': 'utf-8', 'sort': 0, 'num': 20, 'repllyunm': 100, 'cgi_host': 'http://taotao.qq.com/cgi-bin/emotion_cgi_msglist_v6', 'callback': '_preloadCallback', 'code_version': 1, 'format': 'jsonp', 'need_private_comment': 1, 'g_tk': self.g_tk } url = url + parse.urlencode(params) for q in self.qq_num: num = 0 t1, pos = True, 0 url_ = url + '&uin=' + str(q) black, shuoshuo = self.db['black'], self.db['mood'] while(t1): url__ = url_ + '&pos=' + str(pos) mood = self.req.get(url=url__, headers=self.headers) if '\"msglist\":null' in mood.text or "\"message\":\"对不起,主人设置了保密,您没有权限查看\"" in mood.text: t1 = False if '\"message\":\"对不起,主人设置了保密,您没有权限查看\"' in mood.text: data = { 'name': self.name[self.qq_num.index(q)], 'qq': q } black.insert(data) else: created_time = re.findall('created_time":\d+', mood.text) source = re.findall('source_appid":".*?"source_name":".*?"', mood.text) contents = re.findall('],"content":".*?"', mood.text) forword = re.findall('fwdnum":\d+', mood.text) comment_content = re.findall('commentlist":(null|.*?],)', mood.text) comments = re.findall('cmtnum":\d+', mood.text) pics = re.findall('","pic(_template|".*?])', mood.text) like_url = 'https://user.qzone.qq.com/proxy/domain/users.qzone.qq.com/cgi-bin/likes/get_like_list_app?' tids = re.findall('tid":".*?"', mood.text) for _time, _source, _content, _forword, _comment_content, _comment, _pic, _tid in \ zip(created_time, source, contents, forword, comment_content, comments, pics, tids): param = { 'uin': self.__username, 'unikey': 'http://user.qzone.qq.com/{}/mood/'.format(q)+re.sub('tid":"|"', '', _tid)+'.1', 'begin_uin': 0, 'query_count': 60, 'if_first_page': 1, 'g_tk': self.g_tk } like_url_current = like_url + parse.urlencode(param) like = self.req.get(url=like_url_current, headers=self.headers) likers = like.text.encode(like.encoding).decode('utf-8') if likers is None: likers = [] fuin, nick, sex, constellation, address = re.findall('fuin":\d+', likers), re.findall('nick":".*?"', likers), re.findall('gender":".*?"', likers), re.findall('tion":".*?"', likers), re.findall('addr":".*?"', likers) infos = [] # 点赞信息 for _fuin, _nick, _sex, _constellation, _address in zip(fuin, nick, sex, constellation, address): info = { 'fuin': re.sub('fuin":', '', _fuin), 'nick': re.sub('nick":"|"', '', _nick), 'sex': re.sub('gender":"|"', '', _sex), 'constellation': re.sub('tion":"|"', '', _constellation), 'address': re.sub('addr":"|"', '', _address) } infos.append(info) num = num + 1 print(num) data = { '_id': str(q) + '_' + str(num), 'name': self.name[self.qq_num.index(q)], 'CreateTime': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(re.sub('created_time":', '', _time)))), 'source': re.sub('source_appid":".*?"source_name":"|"', '', _source), 'content': re.sub('],"content":"|"', '', _content), 'forward': re.sub('fwdnum":', '', _forword), 'comment_content': re.sub('null|commentlist":', '', _comment_content) if 'null' in _comment_content else str([(re.sub('content":"|"', '', x), re.sub('createTime2":"|"', '', y), re.sub('name":"|"', '', z), re.sub('uin":', '', zz)) for x, y, z, zz in zip(re.findall('content":".*?"', _comment_content), re.findall('createTime2":".*?"', _comment_content), re.findall('name":".*?"', _comment_content), re.findall('uin":\d+', _comment_content))]), 'comment': re.sub('cmtnum":', '', _comment), 'pic': [] if 'template' in _pic else [re.sub('url2":|"', '', i) for i in re.findall('url2":".*?"', _pic)], 'lick_url' : like_url_current } try: data['like'] = re.sub('number":', '', re.search('number":\d+', likers).group()) except Exception as identifier: print(identifier) data['like'] = 0 data['likers'] = infos if shuoshuo.insert(data): print('%s 的说说写入到数据库成功！' % self.name[self.qq_num.index(q)]) else: with open('filed', 'a+', encoding='utf-8') as f: f.write('%s 的说说爬取失败！\n' % self.name[self.qq_num.index(q)]) print('%s 的说说写入到数据库成功！' % self.name[self.qq_num.index(q)]) pos += 20 time.sleep(4) def get_board(self): url = 'https://user.qzone.qq.com/proxy/domain/m.qzone.qq.com/cgi-bin/new/get_msgb?' params = { 'uin': self.__username, 'num': 10, 'hostword': 0, 'essence': 1, 'inCharset': 'utf-8', 'outCharset': 'utf-8', 'format': 'jsonp', 'g_tk': self.g_tk } url = url + parse.urlencode(params) for q in self.qq_num: num = 0 t2 = True url_ = url + '&hostUin=' + str(q) start = 0 boardb = self.db['board'] while(t2): url__ = url_ + '&start=' + str(start) board = self.req.get(url=url__, headers=self.headers) if '\"message":"空间主人设置了访问权限，您无法进行操作\"' in board.text or '\"message\":\"空间未开通\"' in board.text or '\"commentList\":[]' in board.text or '\"total\":0' in board.text: t2 = False else: text = board.text ids, nickname, uin, pubtime, content, replyList = \ re.findall('id":"\d+', text), re.findall('nickname":".*?"', text), re.findall('uin":\d+,\n"nick', text),\ re.findall('pubtime":".*?"', text), re.findall('ubbContent":".*?"', text), re.findall('"replyList":(\[\]|.*?\}\])', text, re.S) for _id, _nickname, _uin, _time, _content, _reply in zip(ids, nickname, uin, pubtime, content, replyList): num = num + 1 print(num) data = { '_id': str(q) + '_' + str(num), 'owner': self.name[self.qq_num.index(q)], 'total': re.sub('total":', '', re.search('total":\d+', board.text).group()), 'name': re.sub('nickname":"|"', '', _nickname), 'qq': re.sub('uin":|,\n"nick', '', _uin), 'time': re.sub('pubtime":"|"', '', _time), 'content': re.sub('ubbContent":"|"', '', _content), # 下行需要改动 'replyList': [] if '[]' in _reply else str([re.sub('nick":"|"', '', name) + re.sub('content"|"', '', con) for name, con in zip(re.findall('nick":".*?"', _reply), re.findall('content":".*?"', _reply))]) } if boardb.insert(data): print('%s 的留言存储到Mongodb成功！' % self.name[self.qq_num.index(q)]) start += 10 def get_information(self): url = 'https://h5.qzone.qq.com/proxy/domain/base.qzone.qq.com/cgi-bin/user/cgi_userinfo_get_all?' params = { 'vuin': self.__username, 'fupdate': 1, 'g_tk': self.g_tk } url = url + parse.urlencode(params) table = self.db['information'] for q in self.qq_num: t3 = True url_ = url + '&uin=' + str(q) while(t3): info = self.req.get(url=url_, headers=self.headers) if '\"message\":\"您无权访问\"' in info.text: t3 = False else: text = info.text sex = ['其他', '男', '女'] constellation = ['白羊座', '金牛座', '双子座', '巨蟹座', '狮子座', '处女座', '天秤座', '天蝎座', '射手座', '摩羯座', '水瓶座', '双鱼座', '未填写'] data = { '_id': str(q) + '_' + str(random.random() * 10).replace('.', ''), 'nickname': re.sub('nickname":"|"', '', re.search('nickname":".*?"', text).group()), 'spacename': re.sub('spacename":"|"', '', re.search('spacename":".*?"', text).group()), 'desc': re.sub('desc":"|"', '', re.search('desc":".*?"', text).group()), 'signature': re.sub('signature":"|"', '', re.search('signature":".*?"', text).group()), 'sex': sex[int(re.sub('sex":', '', re.search('sex":\d+', text).group()))], 'age': re.sub('"age":', '', re.search('"age":\d+', text).group()), 'birthday': re.sub('birthyear":', '', re.search('birthyear":\d+', text).group()) + '-' + re.sub('birthday":"|"', '', re.search('birthday":".*"', text).group()), 'constellation': constellation[int(re.sub('constellation":|,', '', re.search('constellation":.*,', text).group()).replace('-1', '12'))], 'country': re.sub('country":"|"', '', re.search('country":".*"', text).group()), 'province': re.sub('province":"|"', '', re.search('province":".*?"', text).group()), 'city': re.sub('city":"|"', '', re.search('city":".*?"', text).group()), 'hometown': re.sub('hco":"|"|,|\n|hc|hp|:', '', re.search('hco":".*\n".*\n".*', text).group()), # 'marriage': marriage[int(re.sub('marriage":', '', re.search('marriage":\d', text).group()))], 'career': re.sub('career":"|"', '', re.search('career":".*?"', text).group()), 'address': re.sub('cb":"|"', '', re.search('cb":".*?"', text).group()) } if table.insert(data): print('%s 的信息写入到数据库成功！' % self.name[self.qq_num.index(q)]) t3 = False if __name__ == '__main__': sp = Spider() sp.login() sp.get_information() t = time.perf_counter() sp.get_board() sp.get_mood() End = time.perf_counter() - t print('所有内容爬取完成！总用时%s!' % End)

true

f70be413dfeca425596a01e87c4bbb46375b6a26

1,194

py

Python

src/napalm_digineo_procurve/device.py

digineo/napalm-digineo-procurve

477befcd09b0ce209c42f9742b2c4bb0986fceb8

[ "Apache-2.0" ]

4

2019-06-07T07:59:56.000Z

2020-12-09T19:27:56.000Z

src/napalm_digineo_procurve/device.py

digineo/napalm-digineo-procurve

477befcd09b0ce209c42f9742b2c4bb0986fceb8

[ "Apache-2.0" ]

1

2021-03-31T19:04:16.000Z

src/napalm_digineo_procurve/device.py

digineo/napalm-digineo-procurve

477befcd09b0ce209c42f9742b2c4bb0986fceb8

[ "Apache-2.0" ]

1

2019-12-24T11:05:24.000Z

import typing import netmiko import napalm_digineo_procurve.queries.interfaces import napalm_digineo_procurve.queries.lldp_neighbors import napalm_digineo_procurve.queries.device_info import napalm_digineo_procurve.queries.system_info import napalm_digineo_procurve.queries.uptime def get_uptime(device: netmiko.BaseConnection) -> float: return napalm_digineo_procurve.queries.uptime.query(device) def get_system_information( device: netmiko.BaseConnection ) -> napalm_digineo_procurve.queries.system_info.SystemInformation: return napalm_digineo_procurve.queries.system_info.query(device) def get_device_manufacturer_info( device: netmiko.BaseConnection ) -> napalm_digineo_procurve.queries.device_info.DeviceInformation: return napalm_digineo_procurve.queries.device_info.query(device) def get_interfaces( device: netmiko.BaseConnection ) -> typing.Sequence[napalm_digineo_procurve.queries.interfaces.Interface]: return napalm_digineo_procurve.queries.interfaces.query(device) def get_lldp_neighbors( device: netmiko.BaseConnection ) -> typing.List[typing.Mapping[str, str]]: return napalm_digineo_procurve.queries.lldp_neighbors.query(device)

31.421053

75

0.836683

import typing import netmiko import napalm_digineo_procurve.queries.interfaces import napalm_digineo_procurve.queries.lldp_neighbors import napalm_digineo_procurve.queries.device_info import napalm_digineo_procurve.queries.system_info import napalm_digineo_procurve.queries.uptime def get_uptime(device: netmiko.BaseConnection) -> float: return napalm_digineo_procurve.queries.uptime.query(device) def get_system_information( device: netmiko.BaseConnection ) -> napalm_digineo_procurve.queries.system_info.SystemInformation: return napalm_digineo_procurve.queries.system_info.query(device) def get_device_manufacturer_info( device: netmiko.BaseConnection ) -> napalm_digineo_procurve.queries.device_info.DeviceInformation: return napalm_digineo_procurve.queries.device_info.query(device) def get_interfaces( device: netmiko.BaseConnection ) -> typing.Sequence[napalm_digineo_procurve.queries.interfaces.Interface]: return napalm_digineo_procurve.queries.interfaces.query(device) def get_lldp_neighbors( device: netmiko.BaseConnection ) -> typing.List[typing.Mapping[str, str]]: return napalm_digineo_procurve.queries.lldp_neighbors.query(device)

true

f70be4df0b57c7639cd90df01dd882374cfb6959

413

py

Python

test.py

Oversize204/cvbpy_show_cases

ccd352761aa9bfab220feb888e6639f3cb9a5ad7

[ "MIT" ]

null

test.py

Oversize204/cvbpy_show_cases

ccd352761aa9bfab220feb888e6639f3cb9a5ad7

[ "MIT" ]

null

test.py

Oversize204/cvbpy_show_cases

ccd352761aa9bfab220feb888e6639f3cb9a5ad7

[ "MIT" ]

null

import os import cvb print("acquire images from CVMock.vin") device = cvb.DeviceFactory.open("/opt/cvb/drivers/CVMock.vin") stream = device.stream stream.start() for i in range(5): image, status = stream.wait() if status == cvb.WaitStatus.Ok: image_file = os.path.join(".", ".cvb", "test" + str(i) + ".jpg") image.save(image_file) print("saving: " + image_file) stream.abort()

22.944444

72

0.644068

import os import cvb print("acquire images from CVMock.vin") device = cvb.DeviceFactory.open("/opt/cvb/drivers/CVMock.vin") stream = device.stream stream.start() for i in range(5): image, status = stream.wait() if status == cvb.WaitStatus.Ok: image_file = os.path.join(".", ".cvb", "test" + str(i) + ".jpg") image.save(image_file) print("saving: " + image_file) stream.abort()

true

f70be525727b66a9997338896913e2083a32e400

222

py

Python

MinkowskiEngine/MinkowskiFunctional.py

dendisuhubdy/MinkowskiEngine

a1cdcba68ef925bfefed2fe161f62e1ec78573b9

[ "MIT" ]

1

2019-05-12T00:06:10.000Z

MinkowskiEngine/MinkowskiFunctional.py

dendisuhubdy/MinkowskiEngine

a1cdcba68ef925bfefed2fe161f62e1ec78573b9

[ "MIT" ]

null

MinkowskiEngine/MinkowskiFunctional.py

dendisuhubdy/MinkowskiEngine

a1cdcba68ef925bfefed2fe161f62e1ec78573b9

[ "MIT" ]

null

import torch.nn.functional as F from SparseTensor import SparseTensor def relu(input): output = F.relu(input.F) return SparseTensor( output, coords_key=input.coords_key, coords_manager=input.coords_man)

22.2

77

0.752252

import torch.nn.functional as F from SparseTensor import SparseTensor def relu(input): output = F.relu(input.F) return SparseTensor( output, coords_key=input.coords_key, coords_manager=input.coords_man)

true

f70be67a2d18174e2398b18aa0d130f82252a8f8

994

py

Python

app/core/models.py

shreyask543/Recipe-api

34c43db4ee6cdcd90cdcf8e88a536ef66452ddb6

[ "MIT" ]

null

app/core/models.py

shreyask543/Recipe-api

34c43db4ee6cdcd90cdcf8e88a536ef66452ddb6

[ "MIT" ]

null

app/core/models.py

shreyask543/Recipe-api

34c43db4ee6cdcd90cdcf8e88a536ef66452ddb6

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import AbstractBaseUser, BaseUserManager, PermissionsMixin class UserManager(BaseUserManager): def create_user(self, email, password=None, **extra_fields): if not email: raise ValueError('User must have an email address') user=self.model(email=self.normalize_email(email), **extra_fields) user.set_password(password) user.save(using=self._db) return user def create_superuser(self, email, password): user=self.create_user(email,password) user.is_staff=True user.is_superuser=True user.save(using=self._db) return user class User(AbstractBaseUser, PermissionsMixin): email=models.EmailField(max_length=255, unique=True) name=models.CharField(max_length=255) is_active = models.BooleanField(default=True) is_staff = models.BooleanField(default=False) objects=UserManager() USERNAME_FIELD= 'email'

29.235294

90

0.709256

from django.db import models from django.contrib.auth.models import AbstractBaseUser, BaseUserManager, PermissionsMixin class UserManager(BaseUserManager): def create_user(self, email, password=None, **extra_fields): if not email: raise ValueError('User must have an email address') user=self.model(email=self.normalize_email(email), **extra_fields) user.set_password(password) user.save(using=self._db) return user def create_superuser(self, email, password): user=self.create_user(email,password) user.is_staff=True user.is_superuser=True user.save(using=self._db) return user class User(AbstractBaseUser, PermissionsMixin): email=models.EmailField(max_length=255, unique=True) name=models.CharField(max_length=255) is_active = models.BooleanField(default=True) is_staff = models.BooleanField(default=False) objects=UserManager() USERNAME_FIELD= 'email'

true

f70be84014189656325b4993a31615b0adec7c88

3,617

py

Python

rbac/common/role/reject_owner.py

akgunkel/sawtooth-next-directory

a88833033ab30e9091479a38947f04c5e396ca46

[ "Apache-2.0" ]

null

rbac/common/role/reject_owner.py

akgunkel/sawtooth-next-directory

a88833033ab30e9091479a38947f04c5e396ca46

[ "Apache-2.0" ]

1

2018-09-10T19:12:31.000Z

rbac/common/role/reject_owner.py

akgunkel/sawtooth-next-directory

a88833033ab30e9091479a38947f04c5e396ca46

[ "Apache-2.0" ]

null

# Copyright 2019 Contributors to Hyperledger Sawtooth # # 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. # ----------------------------------------------------------------------------- """Implements the REJECT_ADD_ROLE_OWNER message usage: rbac.role.owner.reject.create()""" import logging from rbac.common import addresser from rbac.common.proposal.proposal_reject import ProposalReject LOGGER = logging.getLogger(__name__) class RejectAddRoleOwner(ProposalReject): """Implements the REJECT_ADD_ROLE_OWNER message usage: rbac.role.owner.reject.create()""" def __init__(self): super().__init__() self._register() @property def message_action_type(self): """The action type performed by this message""" return addresser.MessageActionType.REJECT @property def message_subaction_type(self): """The subsequent action performed or proposed by this message""" return addresser.MessageActionType.ADD @property def message_object_type(self): """The object type this message acts upon""" return addresser.ObjectType.ROLE @property def message_related_type(self): """the object type of the related object this message acts upon""" return addresser.ObjectType.USER @property def message_relationship_type(self): """The relationship type this message acts upon""" return addresser.RelationshipType.OWNER def make_addresses(self, message, signer_user_id): """Makes the appropriate inputs & output addresses for the message""" inputs, outputs = super().make_addresses(message, signer_user_id) # should be owner not admin signer_admin_address = addresser.role.admin.address( message.object_id, signer_user_id ) inputs.add(signer_admin_address) signer_owner_address = addresser.role.owner.address( message.object_id, signer_user_id ) inputs.add(signer_owner_address) proposal_address = self.address( object_id=message.object_id, related_id=message.related_id ) inputs.add(proposal_address) outputs.add(proposal_address) return inputs, outputs def validate_state(self, context, message, payload, input_state, store): """Validates that: 1. the signer is an owner of the role""" super().validate_state( context=context, message=message, payload=payload, input_state=input_state, store=store, ) # TODO: change to verify proposal assignment and hierarchy # TODO: should be owners # if not addresser.role.admin.exists_in_state_inputs( # inputs=payload.inputs, # input_state=input_state, # object_id=message.object_id, # related_id=payload.signer.user_id, # ): # raise ValueError( # "Signer {} must be an admin of the role {}".format( # payload.signer.user_id, message.object_id # ) # )

34.447619

79

0.656345

import logging from rbac.common import addresser from rbac.common.proposal.proposal_reject import ProposalReject LOGGER = logging.getLogger(__name__) class RejectAddRoleOwner(ProposalReject): def __init__(self): super().__init__() self._register() @property def message_action_type(self): return addresser.MessageActionType.REJECT @property def message_subaction_type(self): return addresser.MessageActionType.ADD @property def message_object_type(self): return addresser.ObjectType.ROLE @property def message_related_type(self): return addresser.ObjectType.USER @property def message_relationship_type(self): return addresser.RelationshipType.OWNER def make_addresses(self, message, signer_user_id): inputs, outputs = super().make_addresses(message, signer_user_id) signer_admin_address = addresser.role.admin.address( message.object_id, signer_user_id ) inputs.add(signer_admin_address) signer_owner_address = addresser.role.owner.address( message.object_id, signer_user_id ) inputs.add(signer_owner_address) proposal_address = self.address( object_id=message.object_id, related_id=message.related_id ) inputs.add(proposal_address) outputs.add(proposal_address) return inputs, outputs def validate_state(self, context, message, payload, input_state, store): super().validate_state( context=context, message=message, payload=payload, input_state=input_state, store=store, )

true

f70be9c29d438c8bd7ae0af6ada925b74f12119d

781

py

Python

src/c3nav/editor/tasks.py

johnjohndoe/c3nav

a17f863a3512e305595c16b0300796b6bae81241

[ "Apache-2.0" ]

132

2016-11-12T01:45:23.000Z

2022-03-08T15:17:10.000Z

src/c3nav/editor/tasks.py

johnjohndoe/c3nav

a17f863a3512e305595c16b0300796b6bae81241

[ "Apache-2.0" ]

66

2016-09-29T09:46:19.000Z

2022-03-11T23:26:18.000Z

src/c3nav/editor/tasks.py

johnjohndoe/c3nav

a17f863a3512e305595c16b0300796b6bae81241

[ "Apache-2.0" ]

42

2016-09-29T08:34:57.000Z

2022-03-08T15:17:15.000Z

import logging from django.conf import settings from django.contrib.auth.models import User from django.core.mail import send_mail from c3nav.celery import app logger = logging.getLogger('c3nav') @app.task(bind=True, max_retries=3) def send_changeset_proposed_notification(self, pk, author, title, description): subject = '[c3nav] New Changeset by %s: %s' % (author, title) for user in User.objects.filter(permissions__review_changesets=True): if not user.email: continue text = ( ('Hi %s!\n\n' % user.username) + ('A new Changeset has been proposed by %s:\n\n' % author) + ('---\n\n') + (title+'\n\n'+description) ) send_mail(subject, text, settings.MAIL_FROM, [user.email])

31.24

79

0.644046

import logging from django.conf import settings from django.contrib.auth.models import User from django.core.mail import send_mail from c3nav.celery import app logger = logging.getLogger('c3nav') @app.task(bind=True, max_retries=3) def send_changeset_proposed_notification(self, pk, author, title, description): subject = '[c3nav] New Changeset by %s: %s' % (author, title) for user in User.objects.filter(permissions__review_changesets=True): if not user.email: continue text = ( ('Hi %s!\n\n' % user.username) + ('A new Changeset has been proposed by %s:\n\n' % author) + ('---\n\n') + (title+'\n\n'+description) ) send_mail(subject, text, settings.MAIL_FROM, [user.email])

true

f70bea4fa1a8a385185194767936953325c64e31

5,772

py

Python

node_modules/clarifai/scripts/app_and_key_for_tests.py

seycileli/facerecognitionapp-api

c0c4b2bdb57cd73c9b58178438f033777f72bd5b

[ "MIT" ]

346

2016-05-26T20:02:41.000Z

2022-03-24T20:43:31.000Z

node_modules/clarifai/scripts/app_and_key_for_tests.py

seycileli/facerecognitionapp-api

c0c4b2bdb57cd73c9b58178438f033777f72bd5b

[ "MIT" ]

76

2015-10-25T13:03:47.000Z

2022-02-19T09:36:10.000Z

node_modules/clarifai/scripts/app_and_key_for_tests.py

seycileli/facerecognitionapp-api

c0c4b2bdb57cd73c9b58178438f033777f72bd5b

[ "MIT" ]

136

2015-09-04T13:48:27.000Z

2021-06-12T16:48:36.000Z

import json import os import sys try: from urllib.parse import urlparse, urlencode from urllib.request import urlopen, Request, build_opener, HTTPHandler from urllib.error import HTTPError except ImportError: from urlparse import urlparse from urllib import urlencode from urllib2 import urlopen, Request, HTTPError, build_opener, HTTPHandler EMAIL = os.environ['CLARIFAI_USER_EMAIL'] PASSWORD = os.environ['CLARIFAI_USER_PASSWORD'] BASE = 'https://api.clarifai.com/v2' def _request(method, url, payload={}, headers={}): opener = build_opener(HTTPHandler) full_url = '%s%s' % (BASE, url) request = Request(full_url, data=json.dumps(payload).encode()) for k in headers.keys(): request.add_header(k, headers[k]) request.get_method = lambda: method return json.loads(opener.open(request).read().decode()) def create_app(env_name): session_token, user_id = _login() url = '/users/%s/apps' % user_id payload = {'apps': [{'name': 'auto-created-in-%s-ci-test-run' % env_name}]} response = _request(method='POST', url=url, payload=payload, headers=_auth_headers(session_token)) _raise_on_http_error(response) data = response app_id = data['apps'][0]['id'] # This print needs to be present so we can read the value in CI. print(app_id) def create_key(app_id): session_token, user_id = _login() url = '/users/%s/keys' % user_id payload = { 'keys': [{ 'description': 'Auto-created in a CI test run', 'scopes': ['All'], 'apps': [{'id': app_id, 'user_id': user_id}] }] } response = _request(method='POST', url=url, payload=payload, headers=_auth_headers(session_token)) _raise_on_http_error(response) data = response key_id = data['keys'][0]['id'] # This print needs to be present so we can read the value in CI. print(key_id) def delete(app_id): session_token, user_id = _login() # All the related keys will be deleted automatically when the app is deleted _delete_app(session_token, user_id, app_id) def create_sample_workflow(api_key): url = '/workflows' payload = { 'workflows': [ { 'id': 'food-and-general', 'nodes': [ { 'id': 'food-workflow-node', 'model': { 'id': 'bd367be194cf45149e75f01d59f77ba7', 'model_version': { 'id': 'dfebc169854e429086aceb8368662641' } } }, { 'id': 'general-workflow-node', 'model': { 'id': 'aaa03c23b3724a16a56b629203edc62c', 'model_version': { 'id': 'aa9ca48295b37401f8af92ad1af0d91d' } } } ] } ] } response = _request(method='POST', url=url, payload=payload, headers=_auth_headers_for_api_key_key(api_key)) _raise_on_http_error(response) def _delete_app(session_token, user_id, app_id): url = '/users/%s/apps/%s' % (user_id, app_id) response = _request(method='DELETE', url=url, headers=_auth_headers(session_token)) _raise_on_http_error(response) def _auth_headers(session_token): headers = {'Content-Type': 'application/json', 'X-Clarifai-Session-Token': session_token} return headers def _auth_headers_for_api_key_key(api_key): headers = {'Content-Type': 'application/json', 'Authorization': 'Key ' + api_key} return headers def _login(): url = '/login' payload = {'email': EMAIL, 'password': PASSWORD} response = _request(method='POST', url=url, payload=payload) _raise_on_http_error(response) data = response user_id = data['v2_user_id'] session_token = data['session_token'] return session_token, user_id def _raise_on_http_error(response): # TODO: Make this work with urllib. # if int(response.status_code) // 100 != 2: # raise Exception('Unexpected response %s: %s' % (response.status_code, response.text)) pass def run(arguments): command = arguments[0] if arguments else '--help' if command == '--create-app': if len(arguments) != 2: raise Exception('--create-app takes one argument') env_name = arguments[1] create_app(env_name) elif command == '--create-key': if len(arguments) != 2: raise Exception('--create-key takes one argument') app_id = arguments[1] create_key(app_id) elif command == '--delete-app': if len(arguments) != 2: raise Exception('--delete-app takes one argument') app_id = arguments[1] delete(app_id) elif command == '--create-workflow': if len(arguments) != 2: raise Exception('--create-workflow takes one argument') api_key = arguments[1] create_sample_workflow(api_key) elif command == '--help': print('''DESCRIPTION: Creates and delete applications and API keys ARGUMENTS: --create-app [env_name] ... Creates a new application. --create-key [app_id] ... Creates a new API key. --delete-app [app_id] ... Deletes an application (API keys that use it are deleted as well). --create-workflow [api_key] ... Creates a sample workflow to be used in int. tests. --help ... This text.''') else: print('Unknown argument. Please see --help') exit(1) if __name__ == '__main__': run(arguments=sys.argv[1:])

31.889503

112

0.595461

import json import os import sys try: from urllib.parse import urlparse, urlencode from urllib.request import urlopen, Request, build_opener, HTTPHandler from urllib.error import HTTPError except ImportError: from urlparse import urlparse from urllib import urlencode from urllib2 import urlopen, Request, HTTPError, build_opener, HTTPHandler EMAIL = os.environ['CLARIFAI_USER_EMAIL'] PASSWORD = os.environ['CLARIFAI_USER_PASSWORD'] BASE = 'https://api.clarifai.com/v2' def _request(method, url, payload={}, headers={}): opener = build_opener(HTTPHandler) full_url = '%s%s' % (BASE, url) request = Request(full_url, data=json.dumps(payload).encode()) for k in headers.keys(): request.add_header(k, headers[k]) request.get_method = lambda: method return json.loads(opener.open(request).read().decode()) def create_app(env_name): session_token, user_id = _login() url = '/users/%s/apps' % user_id payload = {'apps': [{'name': 'auto-created-in-%s-ci-test-run' % env_name}]} response = _request(method='POST', url=url, payload=payload, headers=_auth_headers(session_token)) _raise_on_http_error(response) data = response app_id = data['apps'][0]['id'] print(app_id) def create_key(app_id): session_token, user_id = _login() url = '/users/%s/keys' % user_id payload = { 'keys': [{ 'description': 'Auto-created in a CI test run', 'scopes': ['All'], 'apps': [{'id': app_id, 'user_id': user_id}] }] } response = _request(method='POST', url=url, payload=payload, headers=_auth_headers(session_token)) _raise_on_http_error(response) data = response key_id = data['keys'][0]['id'] print(key_id) def delete(app_id): session_token, user_id = _login() _delete_app(session_token, user_id, app_id) def create_sample_workflow(api_key): url = '/workflows' payload = { 'workflows': [ { 'id': 'food-and-general', 'nodes': [ { 'id': 'food-workflow-node', 'model': { 'id': 'bd367be194cf45149e75f01d59f77ba7', 'model_version': { 'id': 'dfebc169854e429086aceb8368662641' } } }, { 'id': 'general-workflow-node', 'model': { 'id': 'aaa03c23b3724a16a56b629203edc62c', 'model_version': { 'id': 'aa9ca48295b37401f8af92ad1af0d91d' } } } ] } ] } response = _request(method='POST', url=url, payload=payload, headers=_auth_headers_for_api_key_key(api_key)) _raise_on_http_error(response) def _delete_app(session_token, user_id, app_id): url = '/users/%s/apps/%s' % (user_id, app_id) response = _request(method='DELETE', url=url, headers=_auth_headers(session_token)) _raise_on_http_error(response) def _auth_headers(session_token): headers = {'Content-Type': 'application/json', 'X-Clarifai-Session-Token': session_token} return headers def _auth_headers_for_api_key_key(api_key): headers = {'Content-Type': 'application/json', 'Authorization': 'Key ' + api_key} return headers def _login(): url = '/login' payload = {'email': EMAIL, 'password': PASSWORD} response = _request(method='POST', url=url, payload=payload) _raise_on_http_error(response) data = response user_id = data['v2_user_id'] session_token = data['session_token'] return session_token, user_id def _raise_on_http_error(response): pass def run(arguments): command = arguments[0] if arguments else '--help' if command == '--create-app': if len(arguments) != 2: raise Exception('--create-app takes one argument') env_name = arguments[1] create_app(env_name) elif command == '--create-key': if len(arguments) != 2: raise Exception('--create-key takes one argument') app_id = arguments[1] create_key(app_id) elif command == '--delete-app': if len(arguments) != 2: raise Exception('--delete-app takes one argument') app_id = arguments[1] delete(app_id) elif command == '--create-workflow': if len(arguments) != 2: raise Exception('--create-workflow takes one argument') api_key = arguments[1] create_sample_workflow(api_key) elif command == '--help': print('''DESCRIPTION: Creates and delete applications and API keys ARGUMENTS: --create-app [env_name] ... Creates a new application. --create-key [app_id] ... Creates a new API key. --delete-app [app_id] ... Deletes an application (API keys that use it are deleted as well). --create-workflow [api_key] ... Creates a sample workflow to be used in int. tests. --help ... This text.''') else: print('Unknown argument. Please see --help') exit(1) if __name__ == '__main__': run(arguments=sys.argv[1:])

true

f70beb1fa4370b3f14474cd5e4594b48d60c8bf8

7,308

py

Python

test/calibration/experiments/test_drag.py

QuantumHardware/qiskit-experiments

c09cf35bb922419354955abe8d536a97a9ea286b

[ "Apache-2.0" ]

null

test/calibration/experiments/test_drag.py

QuantumHardware/qiskit-experiments

c09cf35bb922419354955abe8d536a97a9ea286b

[ "Apache-2.0" ]

null

test/calibration/experiments/test_drag.py

QuantumHardware/qiskit-experiments

c09cf35bb922419354955abe8d536a97a9ea286b

[ "Apache-2.0" ]

null

# This code is part of Qiskit. # # (C) Copyright IBM 2021. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """Test drag calibration experiment.""" from test.base import QiskitExperimentsTestCase import unittest import numpy as np from qiskit.circuit import Parameter from qiskit.exceptions import QiskitError from qiskit.pulse import DriveChannel, Drag import qiskit.pulse as pulse from qiskit.qobj.utils import MeasLevel from qiskit import transpile from qiskit_experiments.exceptions import CalibrationError from qiskit_experiments.library import RoughDrag, RoughDragCal from qiskit_experiments.test.mock_iq_backend import DragBackend from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon from qiskit_experiments.calibration_management import Calibrations class TestDragEndToEnd(QiskitExperimentsTestCase): """Test the drag experiment.""" def setUp(self): """Setup some schedules.""" super().setUp() beta = Parameter("β") with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0)) self.x_plus = xp self.test_tol = 0.05 def test_reps(self): """Test that setting reps raises and error if reps is not of length three.""" drag = RoughDrag(0, self.x_plus) with self.assertRaises(CalibrationError): drag.set_experiment_options(reps=[1, 2, 3, 4]) def test_end_to_end(self): """Test the drag experiment end to end.""" backend = DragBackend(gate_name="Drag(xp)") drag = RoughDrag(1, self.x_plus) expdata = drag.run(backend) self.assertExperimentDone(expdata) result = expdata.analysis_results(1) self.assertTrue(abs(result.value.n - backend.ideal_beta) < self.test_tol) self.assertEqual(result.quality, "good") # Small leakage will make the curves very flat, in this case one should # rather increase beta. backend = DragBackend(error=0.0051, gate_name="Drag(xp)") drag = RoughDrag(0, self.x_plus) drag.analysis.set_options(p0={"beta": 1.2}) exp_data = drag.run(backend) self.assertExperimentDone(exp_data) result = exp_data.analysis_results(1) self.assertTrue(abs(result.value.n - backend.ideal_beta) < self.test_tol) self.assertEqual(result.quality, "good") # Large leakage will make the curves oscillate quickly. backend = DragBackend(error=0.05, gate_name="Drag(xp)") drag = RoughDrag(1, self.x_plus, betas=np.linspace(-4, 4, 31)) drag.set_run_options(shots=200) drag.analysis.set_options(p0={"beta": 1.8, "freq0": 0.08, "freq1": 0.16, "freq2": 0.32}) exp_data = drag.run(backend) self.assertExperimentDone(exp_data) result = exp_data.analysis_results(1) meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] self.assertEqual(meas_level, MeasLevel.CLASSIFIED) self.assertTrue(abs(result.value.n - backend.ideal_beta) < self.test_tol) self.assertEqual(result.quality, "good") class TestDragCircuits(QiskitExperimentsTestCase): """Test the circuits of the drag calibration.""" def setUp(self): """Setup some schedules.""" super().setUp() beta = Parameter("β") with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0)) self.x_plus = xp def test_default_circuits(self): """Test the default circuit.""" backend = DragBackend(error=0.005, gate_name="Drag(xp)") drag = RoughDrag(0, self.x_plus) drag.set_experiment_options(reps=[2, 4, 8]) drag.backend = DragBackend(gate_name="Drag(xp)") circuits = drag.circuits() for idx, expected in enumerate([4, 8, 16]): ops = transpile(circuits[idx * 51], backend).count_ops() self.assertEqual(ops["Drag(xp)"], expected) def test_raise_multiple_parameter(self): """Check that the experiment raises with unassigned parameters.""" beta = Parameter("β") amp = Parameter("amp") with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=amp, sigma=40, beta=beta), DriveChannel(0)) with self.assertRaises(QiskitError): RoughDrag(1, xp, betas=np.linspace(-3, 3, 21)) class TestRoughDragCalUpdate(QiskitExperimentsTestCase): """Test that a Drag calibration experiment properly updates the calibrations.""" def setUp(self): """Setup the tests""" super().setUp() library = FixedFrequencyTransmon() self.backend = DragBackend(gate_name="Drag(x)") self.cals = Calibrations.from_backend(self.backend, library) self.test_tol = 0.05 def test_update(self): """Test that running RoughDragCal updates the calibrations.""" qubit = 0 prev_beta = self.cals.get_parameter_value("β", (0,), "x") self.assertEqual(prev_beta, 0) expdata = RoughDragCal(qubit, self.cals, backend=self.backend).run() self.assertExperimentDone(expdata) new_beta = self.cals.get_parameter_value("β", (0,), "x") self.assertTrue(abs(new_beta - self.backend.ideal_beta) < self.test_tol) self.assertTrue(abs(new_beta) > self.test_tol) def test_dragcal_experiment_config(self): """Test RoughDragCal config can round trip""" exp = RoughDragCal(0, self.cals, backend=self.backend) loaded_exp = RoughDragCal.from_config(exp.config()) self.assertNotEqual(exp, loaded_exp) self.assertTrue(self.json_equiv(exp, loaded_exp)) @unittest.skip("Calibration experiments are not yet JSON serializable") def test_dragcal_roundtrip_serializable(self): """Test round trip JSON serialization""" exp = RoughDragCal(0, self.cals) self.assertRoundTripSerializable(exp, self.json_equiv) def test_drag_experiment_config(self): """Test RoughDrag config can roundtrip""" with pulse.build(name="xp") as sched: pulse.play(pulse.Drag(160, 0.5, 40, Parameter("β")), pulse.DriveChannel(0)) exp = RoughDrag(0, backend=self.backend, schedule=sched) loaded_exp = RoughDrag.from_config(exp.config()) self.assertNotEqual(exp, loaded_exp) self.assertTrue(self.json_equiv(exp, loaded_exp)) @unittest.skip("Schedules are not yet JSON serializable") def test_drag_roundtrip_serializable(self): """Test round trip JSON serialization""" with pulse.build(name="xp") as sched: pulse.play(pulse.Drag(160, 0.5, 40, Parameter("β")), pulse.DriveChannel(0)) exp = RoughDrag(0, backend=self.backend, schedule=sched) self.assertRoundTripSerializable(exp, self.json_equiv)

37.096447

96

0.676382

from test.base import QiskitExperimentsTestCase import unittest import numpy as np from qiskit.circuit import Parameter from qiskit.exceptions import QiskitError from qiskit.pulse import DriveChannel, Drag import qiskit.pulse as pulse from qiskit.qobj.utils import MeasLevel from qiskit import transpile from qiskit_experiments.exceptions import CalibrationError from qiskit_experiments.library import RoughDrag, RoughDragCal from qiskit_experiments.test.mock_iq_backend import DragBackend from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon from qiskit_experiments.calibration_management import Calibrations class TestDragEndToEnd(QiskitExperimentsTestCase): def setUp(self): super().setUp() beta = Parameter("β") with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0)) self.x_plus = xp self.test_tol = 0.05 def test_reps(self): drag = RoughDrag(0, self.x_plus) with self.assertRaises(CalibrationError): drag.set_experiment_options(reps=[1, 2, 3, 4]) def test_end_to_end(self): backend = DragBackend(gate_name="Drag(xp)") drag = RoughDrag(1, self.x_plus) expdata = drag.run(backend) self.assertExperimentDone(expdata) result = expdata.analysis_results(1) self.assertTrue(abs(result.value.n - backend.ideal_beta) < self.test_tol) self.assertEqual(result.quality, "good") backend = DragBackend(error=0.0051, gate_name="Drag(xp)") drag = RoughDrag(0, self.x_plus) drag.analysis.set_options(p0={"beta": 1.2}) exp_data = drag.run(backend) self.assertExperimentDone(exp_data) result = exp_data.analysis_results(1) self.assertTrue(abs(result.value.n - backend.ideal_beta) < self.test_tol) self.assertEqual(result.quality, "good") backend = DragBackend(error=0.05, gate_name="Drag(xp)") drag = RoughDrag(1, self.x_plus, betas=np.linspace(-4, 4, 31)) drag.set_run_options(shots=200) drag.analysis.set_options(p0={"beta": 1.8, "freq0": 0.08, "freq1": 0.16, "freq2": 0.32}) exp_data = drag.run(backend) self.assertExperimentDone(exp_data) result = exp_data.analysis_results(1) meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"] self.assertEqual(meas_level, MeasLevel.CLASSIFIED) self.assertTrue(abs(result.value.n - backend.ideal_beta) < self.test_tol) self.assertEqual(result.quality, "good") class TestDragCircuits(QiskitExperimentsTestCase): def setUp(self): super().setUp() beta = Parameter("β") with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=0.208519, sigma=40, beta=beta), DriveChannel(0)) self.x_plus = xp def test_default_circuits(self): backend = DragBackend(error=0.005, gate_name="Drag(xp)") drag = RoughDrag(0, self.x_plus) drag.set_experiment_options(reps=[2, 4, 8]) drag.backend = DragBackend(gate_name="Drag(xp)") circuits = drag.circuits() for idx, expected in enumerate([4, 8, 16]): ops = transpile(circuits[idx * 51], backend).count_ops() self.assertEqual(ops["Drag(xp)"], expected) def test_raise_multiple_parameter(self): beta = Parameter("β") amp = Parameter("amp") with pulse.build(name="xp") as xp: pulse.play(Drag(duration=160, amp=amp, sigma=40, beta=beta), DriveChannel(0)) with self.assertRaises(QiskitError): RoughDrag(1, xp, betas=np.linspace(-3, 3, 21)) class TestRoughDragCalUpdate(QiskitExperimentsTestCase): def setUp(self): super().setUp() library = FixedFrequencyTransmon() self.backend = DragBackend(gate_name="Drag(x)") self.cals = Calibrations.from_backend(self.backend, library) self.test_tol = 0.05 def test_update(self): qubit = 0 prev_beta = self.cals.get_parameter_value("β", (0,), "x") self.assertEqual(prev_beta, 0) expdata = RoughDragCal(qubit, self.cals, backend=self.backend).run() self.assertExperimentDone(expdata) new_beta = self.cals.get_parameter_value("β", (0,), "x") self.assertTrue(abs(new_beta - self.backend.ideal_beta) < self.test_tol) self.assertTrue(abs(new_beta) > self.test_tol) def test_dragcal_experiment_config(self): exp = RoughDragCal(0, self.cals, backend=self.backend) loaded_exp = RoughDragCal.from_config(exp.config()) self.assertNotEqual(exp, loaded_exp) self.assertTrue(self.json_equiv(exp, loaded_exp)) @unittest.skip("Calibration experiments are not yet JSON serializable") def test_dragcal_roundtrip_serializable(self): exp = RoughDragCal(0, self.cals) self.assertRoundTripSerializable(exp, self.json_equiv) def test_drag_experiment_config(self): with pulse.build(name="xp") as sched: pulse.play(pulse.Drag(160, 0.5, 40, Parameter("β")), pulse.DriveChannel(0)) exp = RoughDrag(0, backend=self.backend, schedule=sched) loaded_exp = RoughDrag.from_config(exp.config()) self.assertNotEqual(exp, loaded_exp) self.assertTrue(self.json_equiv(exp, loaded_exp)) @unittest.skip("Schedules are not yet JSON serializable") def test_drag_roundtrip_serializable(self): with pulse.build(name="xp") as sched: pulse.play(pulse.Drag(160, 0.5, 40, Parameter("β")), pulse.DriveChannel(0)) exp = RoughDrag(0, backend=self.backend, schedule=sched) self.assertRoundTripSerializable(exp, self.json_equiv)

true

f70beb38a18f8aaf4b1f040a4d2c358773707a65

1,278

py

Python

sa/profiles/ZTE/ZXDSL531/get_dot11_associations.py

xUndero/noc

9fb34627721149fcf7064860bd63887e38849131

[ "BSD-3-Clause" ]

1

2019-09-20T09:36:48.000Z

sa/profiles/ZTE/ZXDSL531/get_dot11_associations.py

ewwwcha/noc

aba08dc328296bb0e8e181c2ac9a766e1ec2a0bb

[ "BSD-3-Clause" ]

null

sa/profiles/ZTE/ZXDSL531/get_dot11_associations.py

ewwwcha/noc

aba08dc328296bb0e8e181c2ac9a766e1ec2a0bb

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # --------------------------------------------------------------------- # ZTE.ZXDSL531.get_dot11_associations # --------------------------------------------------------------------- # Copyright (C) 2007-2019 The NOC Project # See LICENSE for details # --------------------------------------------------------------------- # Python modules import re # NOC modules from noc.core.script.base import BaseScript from noc.sa.interfaces.igetdot11associations import IGetDot11Associations from noc.core.text import strip_html_tags rx_mac = re.compile( "(?P<mac>[0-9A-F]{2}:[0-9A-F]{2}:[0-9A-F]{2}:[0-9A-F]{2}:[0-9A-F]{2}:[0-9A-F]{2})" ) class Script(BaseScript): name = "ZTE.ZXDSL531.get_dot11_associations" interface = IGetDot11Associations def execute(self): if self.access_profile.scheme == self.TELNET: v = self.cli("wlctl authe_sta_list") elif self.access_profile.scheme == self.HTTP: v = self.http.get("/wlclientview.cmd") v = strip_html_tags(v) else: raise Exception("Unsupported access scheme") r = [] for l in v.split("\n"): m = rx_mac.search(l) if m: r.append({"mac": m.group("mac")}) return r

31.95

86

0.514085

import re from noc.core.script.base import BaseScript from noc.sa.interfaces.igetdot11associations import IGetDot11Associations from noc.core.text import strip_html_tags rx_mac = re.compile( "(?P<mac>[0-9A-F]{2}:[0-9A-F]{2}:[0-9A-F]{2}:[0-9A-F]{2}:[0-9A-F]{2}:[0-9A-F]{2})" ) class Script(BaseScript): name = "ZTE.ZXDSL531.get_dot11_associations" interface = IGetDot11Associations def execute(self): if self.access_profile.scheme == self.TELNET: v = self.cli("wlctl authe_sta_list") elif self.access_profile.scheme == self.HTTP: v = self.http.get("/wlclientview.cmd") v = strip_html_tags(v) else: raise Exception("Unsupported access scheme") r = [] for l in v.split("\n"): m = rx_mac.search(l) if m: r.append({"mac": m.group("mac")}) return r

true

f70bebf637a63567b98981f041f242cabd79d5e8

563

py

Python

apps/bulk/migrations/0007_auto_20150302_1935.py

Sult/daf

a4da9e8c96f70577e2490c05e82bdf7d0de1a563

[ "MIT" ]

null

apps/bulk/migrations/0007_auto_20150302_1935.py

Sult/daf

a4da9e8c96f70577e2490c05e82bdf7d0de1a563

[ "MIT" ]

null

apps/bulk/migrations/0007_auto_20150302_1935.py

Sult/daf

a4da9e8c96f70577e2490c05e82bdf7d0de1a563

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations import datetime from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('bulk', '0006_auto_20150302_1750'), ] operations = [ migrations.AlterField( model_name='sovereigntyholder', name='last_refresh', field=models.DateTimeField(default=datetime.datetime(2015, 3, 2, 19, 35, 37, 7218, tzinfo=utc)), preserve_default=True, ), ]

24.478261

108

0.64476

from __future__ import unicode_literals from django.db import models, migrations import datetime from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('bulk', '0006_auto_20150302_1750'), ] operations = [ migrations.AlterField( model_name='sovereigntyholder', name='last_refresh', field=models.DateTimeField(default=datetime.datetime(2015, 3, 2, 19, 35, 37, 7218, tzinfo=utc)), preserve_default=True, ), ]

true

f70bec598956cee8807a514f594b2be632fc271b

6,033

py

Python

pydotorg/settings/base.py

caputomarcos/pythondotorg

da96fee61bb5c92b7060bccb6ed467fe00136dd7

[ "Apache-2.0" ]

null

pydotorg/settings/base.py

caputomarcos/pythondotorg

da96fee61bb5c92b7060bccb6ed467fe00136dd7

[ "Apache-2.0" ]

null

pydotorg/settings/base.py

caputomarcos/pythondotorg

da96fee61bb5c92b7060bccb6ed467fe00136dd7

[ "Apache-2.0" ]

null

import os import dj_database_url ### Basic config BASE = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) DEBUG = TEMPLATE_DEBUG = True SITE_ID = 1 SECRET_KEY = 'its-a-secret-to-everybody' # Until Sentry works on Py3, do errors the old-fashioned way. ADMINS = [] # General project information # These are available in the template as SITE_INFO.<title> SITE_VARIABLES = { 'site_name': 'Python.org', 'site_descript': 'The official home of the Python Programming Language', } ### Databases DATABASES = { 'default': dj_database_url.config(default='postgres:///python.org') } ### Locale settings TIME_ZONE = 'UTC' LANGUAGE_CODE = 'en-us' USE_I18N = True USE_L10N = True USE_TZ = True DATE_FORMAT = 'Y-m-d' ### Files (media and static) MEDIA_ROOT = os.path.join(BASE, 'media') MEDIA_URL = '/m/' # Absolute path to the directory static files should be collected to. # Don't put anything in this directory yourself; store your static files # in apps' "static/" subdirectories and in STATICFILES_DIRS. # Example: "/var/www/example.com/static/" STATIC_ROOT = os.path.join(BASE, 'static-root') STATIC_URL = '/static/' STATICFILES_DIRS = [ os.path.join(BASE, 'static'), ] STATICFILES_STORAGE = 'pipeline.storage.PipelineStorage' ### Authentication AUTHENTICATION_BACKENDS = ( # Needed to login by username in Django admin, regardless of `allauth` "django.contrib.auth.backends.ModelBackend", # `allauth` specific authentication methods, such as login by e-mail "allauth.account.auth_backends.AuthenticationBackend", ) LOGIN_REDIRECT_URL = 'home' ACCOUNT_LOGOUT_REDIRECT_URL = 'home' ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_UNIQUE_EMAIL = True ACCOUNT_EMAIL_VERIFICATION = 'mandatory' SOCIALACCOUNT_EMAIL_REQUIRED = True SOCIALACCOUNT_EMAIL_VERIFICATION = True SOCIALACCOUNT_QUERY_EMAIL = True ### Templates TEMPLATE_DIRS = [ os.path.join(BASE, 'templates') ] TEMPLATE_CONTEXT_PROCESSORS = [ "django.contrib.auth.context_processors.auth", "django.core.context_processors.debug", "django.core.context_processors.i18n", "django.core.context_processors.media", "django.core.context_processors.static", "django.core.context_processors.tz", "django.core.context_processors.request", "django.contrib.messages.context_processors.messages", "pydotorg.context_processors.site_info", "pydotorg.context_processors.url_name", "pydotorg.context_processors.get_host_with_scheme", ] ### URLs, WSGI, middleware, etc. ROOT_URLCONF = 'pydotorg.urls' MIDDLEWARE_CLASSES = ( 'pydotorg.middleware.AdminNoCaching', 'django.middleware.common.CommonMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'waffle.middleware.WaffleMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'pages.middleware.PageFallbackMiddleware', 'django.contrib.redirects.middleware.RedirectFallbackMiddleware', ) AUTH_USER_MODEL = 'users.User' WSGI_APPLICATION = 'pydotorg.wsgi.application' ### Apps INSTALLED_APPS = [ 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.redirects', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.comments', 'django.contrib.admin', 'django.contrib.admindocs', 'django_comments_xtd', 'jsonfield', 'pipeline', 'sitetree', 'timedelta', 'imagekit', 'haystack', 'honeypot', 'waffle', 'users', 'boxes', 'cms', 'companies', 'feedbacks', 'community', 'jobs', 'pages', 'sponsors', 'successstories', 'events', 'minutes', 'peps', 'blogs', 'downloads', 'codesamples', 'work_groups', 'allauth', 'allauth.account', 'allauth.socialaccount', #'allauth.socialaccount.providers.facebook', #'allauth.socialaccount.providers.github', #'allauth.socialaccount.providers.openid', #'allauth.socialaccount.providers.twitter', # Tastypie needs the `users` app to be already loaded. 'tastypie', 'debug_toolbar', ] # Fixtures FIXTURE_DIRS = ( os.path.join(BASE, 'fixtures'), ) ### Testing SKIP_NETWORK_TESTS = True ### Logging LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'filters': { 'require_debug_false': { '()': 'django.utils.log.RequireDebugFalse' } }, 'handlers': { 'mail_admins': { 'level': 'ERROR', 'filters': ['require_debug_false'], 'class': 'django.utils.log.AdminEmailHandler' } }, 'loggers': { 'django.request': { 'handlers': ['mail_admins'], 'level': 'ERROR', 'propagate': True, }, } } ### Development DEV_FIXTURE_URL = 'https://www.python.org/m/fixtures/dev-fixtures.json.gz' ### Comments COMMENTS_APP = 'django_comments_xtd' COMMENTS_XTD_MAX_THREAD_LEVEL = 0 COMMENTS_XTD_FORM_CLASS = "jobs.forms.JobCommentForm" ### Honeypot HONEYPOT_FIELD_NAME = 'email_body_text' HONEYPOT_VALUE = 'write your message' ### Blog Feed URL PYTHON_BLOG_FEED_URL = "http://feeds.feedburner.com/PythonInsider" PYTHON_BLOG_URL = "http://blog.python.org" ### Registration mailing lists MAILING_LIST_PSF_MEMBERS = "psf-members-announce-request@python.org" ### PEP Repo Location PEP_REPO_PATH = '' ### Fastly ### FASTLY_API_KEY = False # Set to Fastly API key in production to allow pages to # be purged on save # Jobs JOB_THRESHOLD_DAYS = 90 JOB_FROM_EMAIL = 'jobs@python.org' ### Pipeline from .pipeline import ( PIPELINE_CSS, PIPELINE_JS, PIPELINE_COMPILERS, PIPELINE_SASS_BINARY, PIPELINE_SASS_ARGUMENTS, PIPELINE_CSS_COMPRESSOR, PIPELINE_JS_COMPRESSOR, ) ### django-waffle settings WAFFLE_OVERRIDE = True

24.326613

79

0.697331

import os import dj_database_url BASE = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) DEBUG = TEMPLATE_DEBUG = True SITE_ID = 1 SECRET_KEY = 'its-a-secret-to-everybody' ADMINS = [] SITE_VARIABLES = { 'site_name': 'Python.org', 'site_descript': 'The official home of the Python Programming Language', } DATABASES = { 'default': dj_database_url.config(default='postgres:///python.org') } TIME_ZONE = 'UTC' LANGUAGE_CODE = 'en-us' USE_I18N = True USE_L10N = True USE_TZ = True DATE_FORMAT = 'Y-m-d' MEDIA_ROOT = os.path.join(BASE, 'media') MEDIA_URL = '/m/' # in apps' "static/" subdirectories and in STATICFILES_DIRS. STATIC_ROOT = os.path.join(BASE, 'static-root') STATIC_URL = '/static/' STATICFILES_DIRS = [ os.path.join(BASE, 'static'), ] STATICFILES_STORAGE = 'pipeline.storage.PipelineStorage' AUTHENTICATION_BACKENDS = ( "django.contrib.auth.backends.ModelBackend", "allauth.account.auth_backends.AuthenticationBackend", ) LOGIN_REDIRECT_URL = 'home' ACCOUNT_LOGOUT_REDIRECT_URL = 'home' ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_UNIQUE_EMAIL = True ACCOUNT_EMAIL_VERIFICATION = 'mandatory' SOCIALACCOUNT_EMAIL_REQUIRED = True SOCIALACCOUNT_EMAIL_VERIFICATION = True SOCIALACCOUNT_QUERY_EMAIL = True TEMPLATE_DIRS = [ os.path.join(BASE, 'templates') ] TEMPLATE_CONTEXT_PROCESSORS = [ "django.contrib.auth.context_processors.auth", "django.core.context_processors.debug", "django.core.context_processors.i18n", "django.core.context_processors.media", "django.core.context_processors.static", "django.core.context_processors.tz", "django.core.context_processors.request", "django.contrib.messages.context_processors.messages", "pydotorg.context_processors.site_info", "pydotorg.context_processors.url_name", "pydotorg.context_processors.get_host_with_scheme", ] ROOT_URLCONF = 'pydotorg.urls' MIDDLEWARE_CLASSES = ( 'pydotorg.middleware.AdminNoCaching', 'django.middleware.common.CommonMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'waffle.middleware.WaffleMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'pages.middleware.PageFallbackMiddleware', 'django.contrib.redirects.middleware.RedirectFallbackMiddleware', ) AUTH_USER_MODEL = 'users.User' WSGI_APPLICATION = 'pydotorg.wsgi.application' INSTALLED_APPS = [ 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.redirects', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.comments', 'django.contrib.admin', 'django.contrib.admindocs', 'django_comments_xtd', 'jsonfield', 'pipeline', 'sitetree', 'timedelta', 'imagekit', 'haystack', 'honeypot', 'waffle', 'users', 'boxes', 'cms', 'companies', 'feedbacks', 'community', 'jobs', 'pages', 'sponsors', 'successstories', 'events', 'minutes', 'peps', 'blogs', 'downloads', 'codesamples', 'work_groups', 'allauth', 'allauth.account', 'allauth.socialaccount', 'tastypie', 'debug_toolbar', ] FIXTURE_DIRS = ( os.path.join(BASE, 'fixtures'), ) SKIP_NETWORK_TESTS = True LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'filters': { 'require_debug_false': { '()': 'django.utils.log.RequireDebugFalse' } }, 'handlers': { 'mail_admins': { 'level': 'ERROR', 'filters': ['require_debug_false'], 'class': 'django.utils.log.AdminEmailHandler' } }, 'loggers': { 'django.request': { 'handlers': ['mail_admins'], 'level': 'ERROR', 'propagate': True, }, } } DEV_FIXTURE_URL = 'https://www.python.org/m/fixtures/dev-fixtures.json.gz' COMMENTS_APP = 'django_comments_xtd' COMMENTS_XTD_MAX_THREAD_LEVEL = 0 COMMENTS_XTD_FORM_CLASS = "jobs.forms.JobCommentForm" HONEYPOT_FIELD_NAME = 'email_body_text' HONEYPOT_VALUE = 'write your message' PYTHON_BLOG_FEED_URL = "http://feeds.feedburner.com/PythonInsider" PYTHON_BLOG_URL = "http://blog.python.org" MAILING_LIST_PSF_MEMBERS = "psf-members-announce-request@python.org" PEP_REPO_PATH = '' FASTLY_API_KEY = False JOB_THRESHOLD_DAYS = 90 JOB_FROM_EMAIL = 'jobs@python.org' from .pipeline import ( PIPELINE_CSS, PIPELINE_JS, PIPELINE_COMPILERS, PIPELINE_SASS_BINARY, PIPELINE_SASS_ARGUMENTS, PIPELINE_CSS_COMPRESSOR, PIPELINE_JS_COMPRESSOR, ) WAFFLE_OVERRIDE = True

true

f70beccc8167dc8794e9af202f98dc483d904d5e

464

py

Python

ecom/api/migrations/0001_initial.py

Lisgevan/FULL_STACK_DEVELOMENT_DJANGO_REACT

9e87db526a4126a6e3cbac9dd2b88b8ea88a2318

[ "MIT" ]

null

ecom/api/migrations/0001_initial.py

Lisgevan/FULL_STACK_DEVELOMENT_DJANGO_REACT

9e87db526a4126a6e3cbac9dd2b88b8ea88a2318

[ "MIT" ]

null

ecom/api/migrations/0001_initial.py

Lisgevan/FULL_STACK_DEVELOMENT_DJANGO_REACT

9e87db526a4126a6e3cbac9dd2b88b8ea88a2318

[ "MIT" ]

null

from django.db import migrations from api.user.models import CustomUser class Migration(migrations.Migration): def seed_data(apps, schema_editor): user = CustomUser( name = 'admin', email = 'admin@admin.dev', is_staff = True, is_superuser = True, phone = "9876554321", gender = 'Male' ) user.set_password('qwerty') user.save() dependencies = [ ] operations = [ migrations.RunPython(seed_data), ]

22.095238

38

0.640086

from django.db import migrations from api.user.models import CustomUser class Migration(migrations.Migration): def seed_data(apps, schema_editor): user = CustomUser( name = 'admin', email = 'admin@admin.dev', is_staff = True, is_superuser = True, phone = "9876554321", gender = 'Male' ) user.set_password('qwerty') user.save() dependencies = [ ] operations = [ migrations.RunPython(seed_data), ]

true

f70bed112c601c3087cd5a890d8455f8167600dd

13,335

py

Python

tests/test_type.py

thyneb19/lux

07a282d6a5f60c05942d866fa6f33636c3428abc

[ "Apache-2.0" ]

null

tests/test_type.py

thyneb19/lux

07a282d6a5f60c05942d866fa6f33636c3428abc

[ "Apache-2.0" ]

null

tests/test_type.py

thyneb19/lux

07a282d6a5f60c05942d866fa6f33636c3428abc

[ "Apache-2.0" ]

null

# Copyright 2019-2020 The Lux Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .context import lux import pytest import random import pandas as pd import warnings # Suite of test that checks if data_type inferred correctly by Lux def test_check_cars(): lux.config.set_SQL_connection("") df = pd.read_csv("lux/data/car.csv") df.maintain_metadata() assert df.data_type["Name"] == "nominal" assert df.data_type["MilesPerGal"] == "quantitative" assert df.data_type["Cylinders"] == "nominal" assert df.data_type["Displacement"] == "quantitative" assert df.data_type["Horsepower"] == "quantitative" assert df.data_type["Weight"] == "quantitative" assert df.data_type["Acceleration"] == "quantitative" assert df.data_type["Year"] == "temporal" assert df.data_type["Origin"] == "nominal" def test_check_int_id(): df = pd.read_csv( "https://github.com/lux-org/lux-datasets/blob/master/data/instacart_sample.csv?raw=true" ) df._repr_html_() inverted_data_type = lux.config.executor.invert_data_type(df.data_type) assert len(inverted_data_type["id"]) == 3 assert ( "<code>order_id</code>, <code>product_id</code>, <code>user_id</code> is not visualized since it resembles an ID field." in df._message.to_html() ) def test_check_str_id(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/churn.csv?raw=true") df._repr_html_() assert ( "<code>customerID</code> is not visualized since it resembles an ID field.</li>" in df._message.to_html() ) def test_check_hpi(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/hpi.csv?raw=true") df.maintain_metadata() assert df.data_type == { "HPIRank": "quantitative", "Country": "geographical", "SubRegion": "nominal", "AverageLifeExpectancy": "quantitative", "AverageWellBeing": "quantitative", "HappyLifeYears": "quantitative", "Footprint": "quantitative", "InequalityOfOutcomes": "quantitative", "InequalityAdjustedLifeExpectancy": "quantitative", "InequalityAdjustedWellbeing": "quantitative", "HappyPlanetIndex": "quantitative", "GDPPerCapita": "quantitative", "Population": "quantitative", } def test_check_airbnb(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/airbnb_nyc.csv?raw=true") df.maintain_metadata() assert df.data_type == { "id": "id", "name": "nominal", "host_id": "id", "host_name": "nominal", "neighbourhood_group": "nominal", "neighbourhood": "nominal", "latitude": "quantitative", "longitude": "quantitative", "room_type": "nominal", "price": "quantitative", "minimum_nights": "quantitative", "number_of_reviews": "quantitative", "last_review": "temporal", "reviews_per_month": "quantitative", "calculated_host_listings_count": "quantitative", "availability_365": "quantitative", } def test_check_airports(): df = pd.read_csv( "https://raw.githubusercontent.com/altair-viz/vega_datasets/master/vega_datasets/_data/airports.csv" ) df.maintain_metadata() assert df.data_type == { "iata": "id", "name": "nominal", "city": "nominal", "state": "geographical", "country": "geographical", "latitude": "quantitative", "longitude": "quantitative", } def test_check_datetime(): df = pd.DataFrame( { "a": ["2020-01-01"], "b": ["20-01-01"], "c": ["20-jan-01"], "d": ["20-january-01"], "e": ["2020 January 01"], "f": ["2020 January 01 00:00:00 pm PT"], "g": ["2020 January 01 13:00:00"], "h": ["2020 January 01 23:59:59 GTC-6"], } ) df.maintain_metadata() assert df.data_type == { "a": "temporal", "b": "temporal", "c": "temporal", "d": "temporal", "e": "temporal", "f": "temporal", "g": "temporal", "h": "temporal", } def test_check_datetime_numeric_values(): car_df = pd.read_csv("lux/data/car.csv") car_df = car_df.rename(columns={"Year": "blah"}) car_df.maintain_metadata() assert car_df.data_type["blah"] == "temporal" spotify_df = pd.read_csv( "https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/spotify.csv" ) spotify_df = spotify_df.rename(columns={"year": "blah"}) spotify_df.maintain_metadata() assert spotify_df.data_type["blah"] == "temporal" assert spotify_df.data_type["release_date"] == "temporal" def test_check_stock(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/stocks.csv?raw=true") df.maintain_metadata() assert df.data_type == { "symbol": "nominal", "monthdate": "temporal", "price": "quantitative", }, "Stock dataset type detection error" def test_check_college(): df = pd.read_csv("lux/data/college.csv") df.maintain_metadata() assert df.data_type == { "Name": "nominal", "PredominantDegree": "nominal", "HighestDegree": "nominal", "FundingModel": "nominal", "Region": "nominal", "Geography": "nominal", "AdmissionRate": "quantitative", "ACTMedian": "quantitative", "SATAverage": "quantitative", "AverageCost": "quantitative", "Expenditure": "quantitative", "AverageFacultySalary": "quantitative", "MedianDebt": "quantitative", "AverageAgeofEntry": "quantitative", "MedianFamilyIncome": "quantitative", "MedianEarnings": "quantitative", } def test_float_categorical(): values = [ {"A": 6.0, "B": 1.0, "C": 1.0, "D": 3.0, "E": 2.0, "F": 5.0}, {"A": 5.0, "B": 2.0, "C": 2.0, "D": 2.0, "E": 2.0, "F": 3.0}, {"A": 3.0, "B": 6.0, "C": 3.0, "D": 3.0, "E": 2.0, "F": 5.0}, {"A": 6.0, "B": 3.0, "C": 3.0, "D": 2.0, "E": 2.0, "F": 2.0}, {"A": 7.0, "B": 4.0, "C": 2.0, "D": 2.0, "E": 2.0, "F": 4.0}, {"A": 5.0, "B": 3.0, "C": 6.0, "D": 3.0, "E": 3.0, "F": 4.0}, {"A": 3.0, "B": 4.0, "C": 3.0, "D": 6.0, "E": 5.0, "F": 5.0}, {"A": 3.0, "B": 3.0, "C": 2.0, "D": 2.0, "E": 4.0, "F": 5.0}, {"A": 3.0, "B": 2.0, "C": 2.0, "D": 2.0, "E": 2.0, "F": 4.0}, {"A": 1.0, "B": 2.0, "C": 2.0, "D": 2.0, "E": 2.0, "F": 6.0}, {"A": 3.0, "B": 3.0, "C": 2.0, "D": 3.0, "E": 3.0, "F": 5.0}, {"A": 7.0, "B": 1.0, "C": 1.0, "D": 2.0, "E": 2.0, "F": 3.0}, {"A": 6.0, "B": 2.0, "C": 2.0, "D": 2.0, "E": 2.0, "F": 3.0}, {"A": 2.0, "B": 3.0, "C": 2.0, "D": 3.0, "E": 3.0, "F": 4.0}, {"A": 6.0, "B": 2.0, "C": 3.0, "D": 3.0, "E": 3.0, "F": 5.0}, ] df = pd.DataFrame(values) df.maintain_metadata() inverted_data_type = lux.config.executor.invert_data_type(df.data_type) assert inverted_data_type["nominal"] == [ "A", "B", "C", "D", "E", "F", ], "Float column should be detected as categorical" for x in list(df.dtypes): assert x == "float64", "Source dataframe preserved as float dtype" def test_set_data_type(): df = pd.read_csv( "https://github.com/lux-org/lux-datasets/blob/master/data/real_estate_tutorial.csv?raw=true" ) with pytest.warns(UserWarning) as w: df._repr_html_() assert "starter template that you can use" in str(w[-1].message) assert "df.set_data_type" in str(w[-1].message) df.set_data_type({"Month": "nominal", "Year": "nominal"}) assert df.data_type["Month"] == "nominal" assert df.data_type["Year"] == "nominal" with warnings.catch_warnings() as w: warnings.simplefilter("always") df._repr_html_() assert not w def test_set_data_type_invalid(): df = pd.read_csv( "https://github.com/lux-org/lux-datasets/blob/master/data/real_estate_tutorial.csv?raw=true" ) with pytest.raises(ValueError): df.set_data_type({"Month": "nomnal", "Year": "nomnal"}) def test_set_wrong_data_type(): df = pd.read_csv( "https://github.com/lux-org/lux-datasets/blob/master/data/real_estate_tutorial.csv?raw=true" ) df.set_data_type({"Year": "quantitative"}) assert df.data_type["Year"] == "quantitative" def test_id_with_label(): df = pd.read_csv( "https://github.com/lux-org/lux-datasets/blob/master/data/state_timeseries.csv?raw=true" ) df.maintain_metadata() assert df.data_type == {"Date": "temporal", "State": "geographical", "Value": "quantitative"} def test_ID_random(): """Tests whether a ID column not satisfying other properties of an ID gets recognized.""" values = [ {"ID": random.randint(0, 1000), "A": 6.0, "B": 1.0, "C": 1.0, "D": 3.0, "E": 2.0, "F": 5.0} for x in range(1000) ] df = pd.DataFrame(values) df.maintain_metadata() assert df.data_type == { "ID": "quantitative", "A": "nominal", "B": "nominal", "C": "nominal", "D": "nominal", "E": "nominal", "F": "nominal", } def test_ID(): """Tests different ways of writing id""" values = [{"ID": x, "A": 6.0, "B": 1.0, "C": 1.0, "D": 3.0, "E": 2.0, "F": 5.0} for x in range(1000)] df = pd.DataFrame(values) df.maintain_metadata() assert df.data_type == { "ID": "id", "A": "nominal", "B": "nominal", "C": "nominal", "D": "nominal", "E": "nominal", "F": "nominal", } def test_id_aug_test(): """Tests in a different dataset Reference: https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists """ df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/aug_test.csv?raw=true") df.maintain_metadata() assert df.data_type == { "enrollee_id": "id", "city": "nominal", "city_development_index": "quantitative", "gender": "nominal", "relevent_experience": "nominal", "enrolled_university": "nominal", "education_level": "nominal", "major_discipline": "nominal", "experience": "nominal", "company_size": "nominal", "company_type": "nominal", "last_new_job": "nominal", "training_hours": "quantitative", } def test_id_music_data(): """Tests in a different dataset if a column not named as an ID is recognized as an identification. Reference: https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-160k-tracks """ df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/spotify.csv?raw=true") df["unique_num"] = df["id"] df.drop(columns=["id"]) df.maintain_metadata() assert df.data_type == { "valence": "quantitative", "year": "temporal", "acousticness": "quantitative", "artists": "nominal", "danceability": "quantitative", "duration_ms": "quantitative", "energy": "quantitative", "explicit": "nominal", "unique_num": "id", "instrumentalness": "quantitative", "key": "nominal", "liveness": "quantitative", "loudness": "quantitative", "mode": "nominal", "name": "nominal", "popularity": "quantitative", "release_date": "temporal", "speechiness": "quantitative", "tempo": "quantitative", "id": "id", } def test_id_absenteeism_data(): """ Tests whether an id named column is not recognized because even though it is named an id, it is not with its nature. """ df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/absenteeism.csv?raw=true") df.maintain_metadata() assert df.data_type == { "ID": "quantitative", "Reason for absence": "quantitative", "Month of absence": "nominal", "Day of the week": "nominal", "Seasons": "nominal", "Transportation expense": "quantitative", "Distance from Residence to Work": "quantitative", "Service time": "nominal", "Age": "quantitative", "Work load Average/day ": "quantitative", "Hit target": "nominal", "Disciplinary failure": "nominal", "Education": "nominal", "Son": "nominal", "Social drinker": "nominal", "Social smoker": "nominal", "Pet": "nominal", "Weight": "quantitative", "Height": "nominal", "Body mass index": "nominal", "Absenteeism time in hours": "nominal", }

34.546632

128

0.580427

from .context import lux import pytest import random import pandas as pd import warnings def test_check_cars(): lux.config.set_SQL_connection("") df = pd.read_csv("lux/data/car.csv") df.maintain_metadata() assert df.data_type["Name"] == "nominal" assert df.data_type["MilesPerGal"] == "quantitative" assert df.data_type["Cylinders"] == "nominal" assert df.data_type["Displacement"] == "quantitative" assert df.data_type["Horsepower"] == "quantitative" assert df.data_type["Weight"] == "quantitative" assert df.data_type["Acceleration"] == "quantitative" assert df.data_type["Year"] == "temporal" assert df.data_type["Origin"] == "nominal" def test_check_int_id(): df = pd.read_csv( "https://github.com/lux-org/lux-datasets/blob/master/data/instacart_sample.csv?raw=true" ) df._repr_html_() inverted_data_type = lux.config.executor.invert_data_type(df.data_type) assert len(inverted_data_type["id"]) == 3 assert ( "<code>order_id</code>, <code>product_id</code>, <code>user_id</code> is not visualized since it resembles an ID field." in df._message.to_html() ) def test_check_str_id(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/churn.csv?raw=true") df._repr_html_() assert ( "<code>customerID</code> is not visualized since it resembles an ID field.</li>" in df._message.to_html() ) def test_check_hpi(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/hpi.csv?raw=true") df.maintain_metadata() assert df.data_type == { "HPIRank": "quantitative", "Country": "geographical", "SubRegion": "nominal", "AverageLifeExpectancy": "quantitative", "AverageWellBeing": "quantitative", "HappyLifeYears": "quantitative", "Footprint": "quantitative", "InequalityOfOutcomes": "quantitative", "InequalityAdjustedLifeExpectancy": "quantitative", "InequalityAdjustedWellbeing": "quantitative", "HappyPlanetIndex": "quantitative", "GDPPerCapita": "quantitative", "Population": "quantitative", } def test_check_airbnb(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/airbnb_nyc.csv?raw=true") df.maintain_metadata() assert df.data_type == { "id": "id", "name": "nominal", "host_id": "id", "host_name": "nominal", "neighbourhood_group": "nominal", "neighbourhood": "nominal", "latitude": "quantitative", "longitude": "quantitative", "room_type": "nominal", "price": "quantitative", "minimum_nights": "quantitative", "number_of_reviews": "quantitative", "last_review": "temporal", "reviews_per_month": "quantitative", "calculated_host_listings_count": "quantitative", "availability_365": "quantitative", } def test_check_airports(): df = pd.read_csv( "https://raw.githubusercontent.com/altair-viz/vega_datasets/master/vega_datasets/_data/airports.csv" ) df.maintain_metadata() assert df.data_type == { "iata": "id", "name": "nominal", "city": "nominal", "state": "geographical", "country": "geographical", "latitude": "quantitative", "longitude": "quantitative", } def test_check_datetime(): df = pd.DataFrame( { "a": ["2020-01-01"], "b": ["20-01-01"], "c": ["20-jan-01"], "d": ["20-january-01"], "e": ["2020 January 01"], "f": ["2020 January 01 00:00:00 pm PT"], "g": ["2020 January 01 13:00:00"], "h": ["2020 January 01 23:59:59 GTC-6"], } ) df.maintain_metadata() assert df.data_type == { "a": "temporal", "b": "temporal", "c": "temporal", "d": "temporal", "e": "temporal", "f": "temporal", "g": "temporal", "h": "temporal", } def test_check_datetime_numeric_values(): car_df = pd.read_csv("lux/data/car.csv") car_df = car_df.rename(columns={"Year": "blah"}) car_df.maintain_metadata() assert car_df.data_type["blah"] == "temporal" spotify_df = pd.read_csv( "https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/spotify.csv" ) spotify_df = spotify_df.rename(columns={"year": "blah"}) spotify_df.maintain_metadata() assert spotify_df.data_type["blah"] == "temporal" assert spotify_df.data_type["release_date"] == "temporal" def test_check_stock(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/stocks.csv?raw=true") df.maintain_metadata() assert df.data_type == { "symbol": "nominal", "monthdate": "temporal", "price": "quantitative", }, "Stock dataset type detection error" def test_check_college(): df = pd.read_csv("lux/data/college.csv") df.maintain_metadata() assert df.data_type == { "Name": "nominal", "PredominantDegree": "nominal", "HighestDegree": "nominal", "FundingModel": "nominal", "Region": "nominal", "Geography": "nominal", "AdmissionRate": "quantitative", "ACTMedian": "quantitative", "SATAverage": "quantitative", "AverageCost": "quantitative", "Expenditure": "quantitative", "AverageFacultySalary": "quantitative", "MedianDebt": "quantitative", "AverageAgeofEntry": "quantitative", "MedianFamilyIncome": "quantitative", "MedianEarnings": "quantitative", } def test_float_categorical(): values = [ {"A": 6.0, "B": 1.0, "C": 1.0, "D": 3.0, "E": 2.0, "F": 5.0}, {"A": 5.0, "B": 2.0, "C": 2.0, "D": 2.0, "E": 2.0, "F": 3.0}, {"A": 3.0, "B": 6.0, "C": 3.0, "D": 3.0, "E": 2.0, "F": 5.0}, {"A": 6.0, "B": 3.0, "C": 3.0, "D": 2.0, "E": 2.0, "F": 2.0}, {"A": 7.0, "B": 4.0, "C": 2.0, "D": 2.0, "E": 2.0, "F": 4.0}, {"A": 5.0, "B": 3.0, "C": 6.0, "D": 3.0, "E": 3.0, "F": 4.0}, {"A": 3.0, "B": 4.0, "C": 3.0, "D": 6.0, "E": 5.0, "F": 5.0}, {"A": 3.0, "B": 3.0, "C": 2.0, "D": 2.0, "E": 4.0, "F": 5.0}, {"A": 3.0, "B": 2.0, "C": 2.0, "D": 2.0, "E": 2.0, "F": 4.0}, {"A": 1.0, "B": 2.0, "C": 2.0, "D": 2.0, "E": 2.0, "F": 6.0}, {"A": 3.0, "B": 3.0, "C": 2.0, "D": 3.0, "E": 3.0, "F": 5.0}, {"A": 7.0, "B": 1.0, "C": 1.0, "D": 2.0, "E": 2.0, "F": 3.0}, {"A": 6.0, "B": 2.0, "C": 2.0, "D": 2.0, "E": 2.0, "F": 3.0}, {"A": 2.0, "B": 3.0, "C": 2.0, "D": 3.0, "E": 3.0, "F": 4.0}, {"A": 6.0, "B": 2.0, "C": 3.0, "D": 3.0, "E": 3.0, "F": 5.0}, ] df = pd.DataFrame(values) df.maintain_metadata() inverted_data_type = lux.config.executor.invert_data_type(df.data_type) assert inverted_data_type["nominal"] == [ "A", "B", "C", "D", "E", "F", ], "Float column should be detected as categorical" for x in list(df.dtypes): assert x == "float64", "Source dataframe preserved as float dtype" def test_set_data_type(): df = pd.read_csv( "https://github.com/lux-org/lux-datasets/blob/master/data/real_estate_tutorial.csv?raw=true" ) with pytest.warns(UserWarning) as w: df._repr_html_() assert "starter template that you can use" in str(w[-1].message) assert "df.set_data_type" in str(w[-1].message) df.set_data_type({"Month": "nominal", "Year": "nominal"}) assert df.data_type["Month"] == "nominal" assert df.data_type["Year"] == "nominal" with warnings.catch_warnings() as w: warnings.simplefilter("always") df._repr_html_() assert not w def test_set_data_type_invalid(): df = pd.read_csv( "https://github.com/lux-org/lux-datasets/blob/master/data/real_estate_tutorial.csv?raw=true" ) with pytest.raises(ValueError): df.set_data_type({"Month": "nomnal", "Year": "nomnal"}) def test_set_wrong_data_type(): df = pd.read_csv( "https://github.com/lux-org/lux-datasets/blob/master/data/real_estate_tutorial.csv?raw=true" ) df.set_data_type({"Year": "quantitative"}) assert df.data_type["Year"] == "quantitative" def test_id_with_label(): df = pd.read_csv( "https://github.com/lux-org/lux-datasets/blob/master/data/state_timeseries.csv?raw=true" ) df.maintain_metadata() assert df.data_type == {"Date": "temporal", "State": "geographical", "Value": "quantitative"} def test_ID_random(): values = [ {"ID": random.randint(0, 1000), "A": 6.0, "B": 1.0, "C": 1.0, "D": 3.0, "E": 2.0, "F": 5.0} for x in range(1000) ] df = pd.DataFrame(values) df.maintain_metadata() assert df.data_type == { "ID": "quantitative", "A": "nominal", "B": "nominal", "C": "nominal", "D": "nominal", "E": "nominal", "F": "nominal", } def test_ID(): values = [{"ID": x, "A": 6.0, "B": 1.0, "C": 1.0, "D": 3.0, "E": 2.0, "F": 5.0} for x in range(1000)] df = pd.DataFrame(values) df.maintain_metadata() assert df.data_type == { "ID": "id", "A": "nominal", "B": "nominal", "C": "nominal", "D": "nominal", "E": "nominal", "F": "nominal", } def test_id_aug_test(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/aug_test.csv?raw=true") df.maintain_metadata() assert df.data_type == { "enrollee_id": "id", "city": "nominal", "city_development_index": "quantitative", "gender": "nominal", "relevent_experience": "nominal", "enrolled_university": "nominal", "education_level": "nominal", "major_discipline": "nominal", "experience": "nominal", "company_size": "nominal", "company_type": "nominal", "last_new_job": "nominal", "training_hours": "quantitative", } def test_id_music_data(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/spotify.csv?raw=true") df["unique_num"] = df["id"] df.drop(columns=["id"]) df.maintain_metadata() assert df.data_type == { "valence": "quantitative", "year": "temporal", "acousticness": "quantitative", "artists": "nominal", "danceability": "quantitative", "duration_ms": "quantitative", "energy": "quantitative", "explicit": "nominal", "unique_num": "id", "instrumentalness": "quantitative", "key": "nominal", "liveness": "quantitative", "loudness": "quantitative", "mode": "nominal", "name": "nominal", "popularity": "quantitative", "release_date": "temporal", "speechiness": "quantitative", "tempo": "quantitative", "id": "id", } def test_id_absenteeism_data(): df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/absenteeism.csv?raw=true") df.maintain_metadata() assert df.data_type == { "ID": "quantitative", "Reason for absence": "quantitative", "Month of absence": "nominal", "Day of the week": "nominal", "Seasons": "nominal", "Transportation expense": "quantitative", "Distance from Residence to Work": "quantitative", "Service time": "nominal", "Age": "quantitative", "Work load Average/day ": "quantitative", "Hit target": "nominal", "Disciplinary failure": "nominal", "Education": "nominal", "Son": "nominal", "Social drinker": "nominal", "Social smoker": "nominal", "Pet": "nominal", "Weight": "quantitative", "Height": "nominal", "Body mass index": "nominal", "Absenteeism time in hours": "nominal", }

true

f70bed892a69b0a979a1236c72b4e27848a2e38e

4,455

py

Python

others/median_two_sorted.py

sumitsk/leetcode

bb3527b08ca794dea2c9d071efc24b4276bd1c05

[ "MIT" ]

null

others/median_two_sorted.py

sumitsk/leetcode

bb3527b08ca794dea2c9d071efc24b4276bd1c05

[ "MIT" ]

null

others/median_two_sorted.py

sumitsk/leetcode

bb3527b08ca794dea2c9d071efc24b4276bd1c05

[ "MIT" ]

null

# INCOMPLETE / UNSUCCESSFUL # find median of two sorted arrays import ipdb class Solution(object): def findMedianSortedArrays(self, nums1, nums2): """ :type nums1: List[int] :type nums2: List[int] :rtype: float """ n1 = len(nums1) n2 = len(nums2) # special cases if n1==0: return self.median_sorted_array(nums2) if n2==0: return self.median_sorted_array(nums1) N = n1 + n2 l1, r1 = 0, n1-1 l2, r2 = 0, n2-1 while True: idx1 = (l1+r1)//2 # find index of largest element in nums2 smaller than v1 idx2 = self.find_largest_elem(nums2[l2:r2+1], nums1[idx1]) t2 = l2 + -1 if idx2 is None else idx2 # arr1[idx1] is at index 'n' in the joint array n = idx1 + 1 + t2 if n < N//2 - 1: # this should not be done if idx2 is None next_l1 = (l1+r1)//2 next_l2 = (l2+r2)//2 if idx2 is not None else l2 next_r1, next_r2 = r1, r2 elif n == N//2 - 1: next_val = self.next_num(nums1, nums2, idx1, t2) if N%2==1: return next_val else: return (nums1[idx1] + next_val)/2 elif n == N//2: if N%2==1: return nums1[idx1] else: prev_val = self.prev_num(nums1, nums2, idx1-1, t2) return (prev_val + nums1[idx1])/2 else: next_r1 = (l1+r1)//2 next_r2 = (l2+r2)//2 if idx2 is not None else r2 next_l1, next_l2 = l1, l2 l1, l2, r1, r2 = next_l1, next_l2, next_r1, next_r2 # if (l1,l2,r1,r2) == (next_l1,next_l2,next_r1,next_r2): if r1-l1<=1 and r2-l2<=1: # ipdb.set_trace() # sort them until median index is reached if n<=N//2-1: while n!=N//2-1: idx1, t2, val = self.next_indices_and_num(nums1, nums2, idx1, t2) n += 1 next_val = self.next_num(nums1, nums2, idx1, t2) if N%2==1: return next_val else: return (val + next_val)/2 else: while n!=N//2-1: idx1, t2, next_val = self.prev_indices_and_num(nums1, nums2, idx1, t2) n -= 1 if N%2==1: return next_val else: val = self.prev_num(nums1, nums2, idx1, t2) return (val + next_val)/2 ipdb.set_trace() def median_sorted_array(self, arr): # median of a sorted array n = len(arr) if n%2==1: return arr[n//2] return (arr[n//2-1] + arr[n//2])/2 def find_largest_elem(self, arr, val): li = 0 ri = len(arr) done = False while not done: if arr[(li+ri)//2] >= val: ri = (li+ri)//2 else: li = (li+ri)//2 done = li==ri or li+1==ri if arr[li]<val: return li return None def next_indices_and_num(self, arr1, arr2, idx1, idx2): if idx1>=len(arr1)-1: return idx1, idx2+1, arr2[idx2+1] if idx2>=len(arr2)-1: return idx1+1, idx2, arr1[idx1+1] if arr1[idx1+1] < arr2[idx2+1]: return idx1+1, idx2, arr1[idx1+1] else: return idx1, idx2+1, arr2[idx2+1] def prev_indices_and_num(self, arr1, arr2, idx1, idx2): if idx1<0: return idx1, idx2-1, arr2[idx2] if idx2<0: return idx1-1, idx2, arr1[idx1] if arr1[idx1] >= arr2[idx2]: return idx1-1, idx2, arr1[idx1] else: return idx1, idx2-1, arr2[idx2] def next_num(self, arr1, arr2, idx1, idx2): return self.next_indices_and_num(arr1, arr2, idx1, idx2)[-1] def prev_num(self, arr1, arr2, idx1, idx2): return self.prev_indices_and_num(arr1, arr2, idx1, idx2)[-1] nums1 = [1] nums2 = [2,3,4,5,6] sol = Solution() median = sol.findMedianSortedArrays(nums1, nums2) print(median)

33.246269

94

0.464198

import ipdb class Solution(object): def findMedianSortedArrays(self, nums1, nums2): n1 = len(nums1) n2 = len(nums2) if n1==0: return self.median_sorted_array(nums2) if n2==0: return self.median_sorted_array(nums1) N = n1 + n2 l1, r1 = 0, n1-1 l2, r2 = 0, n2-1 while True: idx1 = (l1+r1)//2 idx2 = self.find_largest_elem(nums2[l2:r2+1], nums1[idx1]) t2 = l2 + -1 if idx2 is None else idx2 n = idx1 + 1 + t2 if n < N//2 - 1: next_l1 = (l1+r1)//2 next_l2 = (l2+r2)//2 if idx2 is not None else l2 next_r1, next_r2 = r1, r2 elif n == N//2 - 1: next_val = self.next_num(nums1, nums2, idx1, t2) if N%2==1: return next_val else: return (nums1[idx1] + next_val)/2 elif n == N//2: if N%2==1: return nums1[idx1] else: prev_val = self.prev_num(nums1, nums2, idx1-1, t2) return (prev_val + nums1[idx1])/2 else: next_r1 = (l1+r1)//2 next_r2 = (l2+r2)//2 if idx2 is not None else r2 next_l1, next_l2 = l1, l2 l1, l2, r1, r2 = next_l1, next_l2, next_r1, next_r2 if r1-l1<=1 and r2-l2<=1: if n<=N//2-1: while n!=N//2-1: idx1, t2, val = self.next_indices_and_num(nums1, nums2, idx1, t2) n += 1 next_val = self.next_num(nums1, nums2, idx1, t2) if N%2==1: return next_val else: return (val + next_val)/2 else: while n!=N//2-1: idx1, t2, next_val = self.prev_indices_and_num(nums1, nums2, idx1, t2) n -= 1 if N%2==1: return next_val else: val = self.prev_num(nums1, nums2, idx1, t2) return (val + next_val)/2 ipdb.set_trace() def median_sorted_array(self, arr): n = len(arr) if n%2==1: return arr[n//2] return (arr[n//2-1] + arr[n//2])/2 def find_largest_elem(self, arr, val): li = 0 ri = len(arr) done = False while not done: if arr[(li+ri)//2] >= val: ri = (li+ri)//2 else: li = (li+ri)//2 done = li==ri or li+1==ri if arr[li]<val: return li return None def next_indices_and_num(self, arr1, arr2, idx1, idx2): if idx1>=len(arr1)-1: return idx1, idx2+1, arr2[idx2+1] if idx2>=len(arr2)-1: return idx1+1, idx2, arr1[idx1+1] if arr1[idx1+1] < arr2[idx2+1]: return idx1+1, idx2, arr1[idx1+1] else: return idx1, idx2+1, arr2[idx2+1] def prev_indices_and_num(self, arr1, arr2, idx1, idx2): if idx1<0: return idx1, idx2-1, arr2[idx2] if idx2<0: return idx1-1, idx2, arr1[idx1] if arr1[idx1] >= arr2[idx2]: return idx1-1, idx2, arr1[idx1] else: return idx1, idx2-1, arr2[idx2] def next_num(self, arr1, arr2, idx1, idx2): return self.next_indices_and_num(arr1, arr2, idx1, idx2)[-1] def prev_num(self, arr1, arr2, idx1, idx2): return self.prev_indices_and_num(arr1, arr2, idx1, idx2)[-1] nums1 = [1] nums2 = [2,3,4,5,6] sol = Solution() median = sol.findMedianSortedArrays(nums1, nums2) print(median)

true

f70bee32aa74ac635fab5ef016db1d5deccb1d1a

594

py

Python

webscraper/__main__.py

neerajhp/neighboorhoodgems-webscraper

714e34d808225c9d7fac2da8fbfca64ab62a2534

[ "MIT" ]

null

webscraper/__main__.py

neerajhp/neighboorhoodgems-webscraper

714e34d808225c9d7fac2da8fbfca64ab62a2534

[ "MIT" ]

null

webscraper/__main__.py

neerajhp/neighboorhoodgems-webscraper

714e34d808225c9d7fac2da8fbfca64ab62a2534

[ "MIT" ]

null

import siteScripts.timeout.scraper as timeoutScraper import logging from webscraper.models.landmark import Landmark from webscraper.services.csv import saveLandmarksCSV def main(): # File to save landmarks f = "landmarks.csv" # Scrapers timeOutLandmarks = timeoutScraper.scrape() # Save Data saveLandmarksCSV(timeOutLandmarks, f) if __name__ == '__main__': logging.config.fileConfig(fname="./logs/logging.conf", disable_existing_loggers=False) logger = logging.getLogger(__name__) logger.info("Let's Begin") main()

22

61

0.700337

import siteScripts.timeout.scraper as timeoutScraper import logging from webscraper.models.landmark import Landmark from webscraper.services.csv import saveLandmarksCSV def main(): f = "landmarks.csv" timeOutLandmarks = timeoutScraper.scrape() saveLandmarksCSV(timeOutLandmarks, f) if __name__ == '__main__': logging.config.fileConfig(fname="./logs/logging.conf", disable_existing_loggers=False) logger = logging.getLogger(__name__) logger.info("Let's Begin") main()

true

f70beebd89d334569db63ed5fc6d13fbd127389a

9,667

py

Python

src/sv-pipeline/pre_SVCalling_and_QC/raw_vcf_qc/calc_num_svs_pick_outlier.py

leipzig/gatk-sv

96566cbbaf0f8f9c8452517b38eea1e5dd6ed33a

[ "BSD-3-Clause" ]

76

2020-06-18T21:31:43.000Z

2022-03-02T18:42:58.000Z

src/sv-pipeline/pre_SVCalling_and_QC/raw_vcf_qc/calc_num_svs_pick_outlier.py

iamh2o/gatk-sv

bf3704bd1d705339577530e267cd4d1b2f77a17f

[ "BSD-3-Clause" ]

195

2020-06-22T15:12:28.000Z

2022-03-28T18:06:46.000Z

src/sv-pipeline/pre_SVCalling_and_QC/raw_vcf_qc/calc_num_svs_pick_outlier.py

iamh2o/gatk-sv

bf3704bd1d705339577530e267cd4d1b2f77a17f

[ "BSD-3-Clause" ]

39

2020-07-03T06:47:18.000Z

2022-03-03T03:47:25.000Z

#!/usr/bin/env python import sys from typing import Sequence, Set import argparse import numpy import pandas _zero_svs_are_outliers = True _outlier_std_threshold = 5.0 _column_order = ["CHROM", "SVTYPE", "Mean", "Median", "STD", "Outlier_Sample", "Outlier_Number", "Outlier_Cate"] def read_statfile(statfile: str) -> pandas.DataFrame: """ Special function needed to read in stats data table because a) pandas doesn't understand that the '#' means header b) there are multiple stats files concatenated together, resulting in headers being randomly mixed in Args: statfile: str File name with concatenated tab-separated tables of variant stats Returns: stats_data: pandas.DataFrame Table of variant stats """ with open(statfile, 'r') as f_in: # get column header from first line, stripping '#' columns = f_in.readline().lstrip('#').split() # read rest of tsv file, using these columns as header and ignoring any future lines starting with '#' return pandas.read_csv(statfile, sep='\t', comment='#', names=columns) def pick_outliers_by_group( chrom: str, sv_type: str, check_stats: pandas.DataFrame, all_samples: Set[str], zero_svs_are_outliers: bool = _zero_svs_are_outliers, outlier_std_threshold: float = _outlier_std_threshold ) -> pandas.DataFrame: """ For given combination of contig and SV type, find samples that have outlier number of SVs. Return table of outliers along with statistics about SV count. Args: chrom: str Contig for checking SV counts sv_type: str SV type for checking SV counts check_stats: pandas.DataFrame Table with SV counts on this contig with this sv_type all_samples: Set[str] Set of all sample IDs in cohort zero_svs_are_outliers: bool Whether to treat samples with no counts as automatic outliers, or explicitly code as zero counts outlier_std_threshold: float Threshold for outlier status as multiple of standard deviation of SV counts Returns: outliers: pandas.DataFrame Table of outliers """ # find samples that are missing: they have 0 SVs of this type on this contig missing_samples = pandas.DataFrame( tuple( {"CHROM": chrom, "SVTYPE": sv_type, "SAMPLE": sample_id, "NUM": 0} for sample_id in all_samples.difference(check_stats["SAMPLE"]) ) ) if zero_svs_are_outliers: # THIS IS THE ORIGINAL PIPELINE BEHAVIOR # compute basic stats about observed nonzero SV counts count_mean = check_stats["NUM"].mean() count_median = check_stats["NUM"].median() count_std = check_stats["NUM"].std() # Amongst samples that have SVs, find counts deviating by more than set multiple of std from the median is_outlier = numpy.abs( check_stats["NUM"] - count_median) > outlier_std_threshold * count_std # Treat missing samples as outliers. outliers = pandas.concat( (missing_samples, check_stats.loc[is_outlier]), axis=0) else: # THIS FINDS FEWER, MORE MEANINGFUL OUTLIERS # Which samples are missing / included but have zero counts is unpredictable. # 1) concatenate all samples together check_stats = pandas.concat((check_stats, missing_samples), axis=0) # 2) compute stats from non-zero SV counts nonzero = check_stats["NUM"] > 0 count_mean = check_stats.loc[nonzero, "NUM"].mean() count_median = check_stats.loc[nonzero, "NUM"].median() count_std = check_stats.loc[nonzero, "NUM"].std() # 3) check outliers by usual means from those stats # Set threshold to be set multiple of greater of: std of counts, sqrt(median of counts) # (i.e. greater of std or expected Poisson std) # Find counts those deviating by more than threshold from the median (including zeros) is_outlier = ( numpy.abs(check_stats["NUM"] - count_median) > outlier_std_threshold * numpy.maximum(count_std, numpy.sqrt(count_median)) ) outliers = check_stats.loc[is_outlier].copy() if outliers.empty: return pandas.DataFrame([], columns=_column_order) # augment outlier table with some statistics outliers["Mean"] = count_mean outliers["Median"] = count_median outliers["STD"] = count_std outliers["Outlier_Cate"] = numpy.where( outliers["NUM"] > count_median, "high", "low") # rename and re-order columns return outliers.rename({"NUM": "Outlier_Number", "SAMPLE": "Outlier_Sample"}, axis=1).reindex(_column_order, axis=1) def pick_outliers( stats_data: pandas.DataFrame, zero_svs_are_outliers: bool = _zero_svs_are_outliers, outlier_std_threshold: float = _outlier_std_threshold ) -> pandas.DataFrame: """ Find samples that have outlier number of SVs when broken down by contig and SV type. Return table of outliers along with statistics about SV count. Args: stats_data: pandas.DataFrame Table with SV counts zero_svs_are_outliers: bool Whether to treat samples with no counts as automatic outliers, or explicitly code as zero counts outlier_std_threshold: float Threshold for outlier status as multiple of standard deviation of SV counts Returns: outliers: pandas.DataFrame Table of outliers """ # get set of all samples in stats data all_samples = set(stats_data["SAMPLE"]) # loop over unique combinations of contig and sv type # find outliers from each unique combination # and concatenate those outliers into one table outliers = pandas.concat( tuple( pick_outliers_by_group( chrom=chrom, sv_type=sv_type, check_stats=check_stats, all_samples=all_samples, zero_svs_are_outliers=zero_svs_are_outliers, outlier_std_threshold=outlier_std_threshold ) for (chrom, sv_type), check_stats in stats_data.groupby( ["CHROM", "SVTYPE"], sort=False, as_index=False, group_keys=False ) ), axis=0 ) return outliers def write_outliers_file( outliers: pandas.DataFrame, outname: str, outlier_type: str ): """ Write outliers of the appropriate type ("low" or "high") to TSV file. Args: outliers: pandas.DataFrame Table of outlier data outname: str Base name of outlier TSV file. Final file name will have ".low" or ".high" appended to it. outlier_type: str "low" or "high". """ # write outliers to tsv. Add "#" in front of header with open(outname + "." + outlier_type, 'w') as f_out: f_out.write("#") # add '#' in front of header outlier_wanted = outliers["Outlier_Cate"] == outlier_type outliers.loc[outlier_wanted].to_csv(f_out, sep='\t', index=False) def calc_num_svs_pick_outlier( statfile: str, outname: str, zero_svs_are_outliers: bool = _zero_svs_are_outliers, outlier_std_threshold: float = _outlier_std_threshold ): """ Find samples that have outlier number of SVs when broken down by contig and SV type. Write two tables of outliers, along with statistics about SV count: one for those with above-median counts ("high") and one for those at median or below ("low"). Args: statfile: str TSV file with table with SV counts outname: str Base name for saving outlier files. Low file will have ".low" appended to the name, and high file will have ".high" zero_svs_are_outliers: bool Whether to treat samples with no counts as automatic outliers, or explicitly code as zero counts outlier_std_threshold: float Threshold for outlier status as multiple of standard deviation of SV counts """ stats_data = read_statfile(statfile) outliers = pick_outliers(stats_data, zero_svs_are_outliers=zero_svs_are_outliers, outlier_std_threshold=outlier_std_threshold) write_outliers_file(outliers, outname, "low") write_outliers_file(outliers, outname, "high") def _parse_arguments(argv: Sequence[str]) -> argparse.Namespace: # noinspection PyTypeChecker parser = argparse.ArgumentParser( description="Find outliers in SV counts broken down by contig and SV type", formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument("statfile", type=str, help="name of stats concatinated from all samples") parser.add_argument("outname", type=str, help="name of output file") parser.add_argument("-z", "--zero-counts-are-not-outliers", action="store_true", help="don't make zero SV counts an automatic outlier, check deviation from median as usual") parser.add_argument("-t", "--outlier-std-threshold", type=float, default=_outlier_std_threshold, help="threshold multiple of std of counts for outliers") return parser.parse_args(argv[1:]) if __name__ == "__main__": args = _parse_arguments(sys.argv) calc_num_svs_pick_outlier(statfile=args.statfile, outname=args.outname, zero_svs_are_outliers=not args.zero_counts_are_not_outliers, outlier_std_threshold=args.outlier_std_threshold)

42.774336

120

0.663701

import sys from typing import Sequence, Set import argparse import numpy import pandas _zero_svs_are_outliers = True _outlier_std_threshold = 5.0 _column_order = ["CHROM", "SVTYPE", "Mean", "Median", "STD", "Outlier_Sample", "Outlier_Number", "Outlier_Cate"] def read_statfile(statfile: str) -> pandas.DataFrame: with open(statfile, 'r') as f_in: columns = f_in.readline().lstrip('#').split() return pandas.read_csv(statfile, sep='\t', comment='#', names=columns) def pick_outliers_by_group( chrom: str, sv_type: str, check_stats: pandas.DataFrame, all_samples: Set[str], zero_svs_are_outliers: bool = _zero_svs_are_outliers, outlier_std_threshold: float = _outlier_std_threshold ) -> pandas.DataFrame: missing_samples = pandas.DataFrame( tuple( {"CHROM": chrom, "SVTYPE": sv_type, "SAMPLE": sample_id, "NUM": 0} for sample_id in all_samples.difference(check_stats["SAMPLE"]) ) ) if zero_svs_are_outliers: count_mean = check_stats["NUM"].mean() count_median = check_stats["NUM"].median() count_std = check_stats["NUM"].std() is_outlier = numpy.abs( check_stats["NUM"] - count_median) > outlier_std_threshold * count_std outliers = pandas.concat( (missing_samples, check_stats.loc[is_outlier]), axis=0) else: check_stats = pandas.concat((check_stats, missing_samples), axis=0) nonzero = check_stats["NUM"] > 0 count_mean = check_stats.loc[nonzero, "NUM"].mean() count_median = check_stats.loc[nonzero, "NUM"].median() count_std = check_stats.loc[nonzero, "NUM"].std() is_outlier = ( numpy.abs(check_stats["NUM"] - count_median) > outlier_std_threshold * numpy.maximum(count_std, numpy.sqrt(count_median)) ) outliers = check_stats.loc[is_outlier].copy() if outliers.empty: return pandas.DataFrame([], columns=_column_order) outliers["Mean"] = count_mean outliers["Median"] = count_median outliers["STD"] = count_std outliers["Outlier_Cate"] = numpy.where( outliers["NUM"] > count_median, "high", "low") return outliers.rename({"NUM": "Outlier_Number", "SAMPLE": "Outlier_Sample"}, axis=1).reindex(_column_order, axis=1) def pick_outliers( stats_data: pandas.DataFrame, zero_svs_are_outliers: bool = _zero_svs_are_outliers, outlier_std_threshold: float = _outlier_std_threshold ) -> pandas.DataFrame: all_samples = set(stats_data["SAMPLE"]) outliers = pandas.concat( tuple( pick_outliers_by_group( chrom=chrom, sv_type=sv_type, check_stats=check_stats, all_samples=all_samples, zero_svs_are_outliers=zero_svs_are_outliers, outlier_std_threshold=outlier_std_threshold ) for (chrom, sv_type), check_stats in stats_data.groupby( ["CHROM", "SVTYPE"], sort=False, as_index=False, group_keys=False ) ), axis=0 ) return outliers def write_outliers_file( outliers: pandas.DataFrame, outname: str, outlier_type: str ): with open(outname + "." + outlier_type, 'w') as f_out: f_out.write("#") # add '#' in front of header outlier_wanted = outliers["Outlier_Cate"] == outlier_type outliers.loc[outlier_wanted].to_csv(f_out, sep='\t', index=False) def calc_num_svs_pick_outlier( statfile: str, outname: str, zero_svs_are_outliers: bool = _zero_svs_are_outliers, outlier_std_threshold: float = _outlier_std_threshold ): stats_data = read_statfile(statfile) outliers = pick_outliers(stats_data, zero_svs_are_outliers=zero_svs_are_outliers, outlier_std_threshold=outlier_std_threshold) write_outliers_file(outliers, outname, "low") write_outliers_file(outliers, outname, "high") def _parse_arguments(argv: Sequence[str]) -> argparse.Namespace: parser = argparse.ArgumentParser( description="Find outliers in SV counts broken down by contig and SV type", formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument("statfile", type=str, help="name of stats concatinated from all samples") parser.add_argument("outname", type=str, help="name of output file") parser.add_argument("-z", "--zero-counts-are-not-outliers", action="store_true", help="don't make zero SV counts an automatic outlier, check deviation from median as usual") parser.add_argument("-t", "--outlier-std-threshold", type=float, default=_outlier_std_threshold, help="threshold multiple of std of counts for outliers") return parser.parse_args(argv[1:]) if __name__ == "__main__": args = _parse_arguments(sys.argv) calc_num_svs_pick_outlier(statfile=args.statfile, outname=args.outname, zero_svs_are_outliers=not args.zero_counts_are_not_outliers, outlier_std_threshold=args.outlier_std_threshold)

true

f70bef908070d3279c1f2b01765777a4e765f230

1,997

py

Python

model-optimizer/mo/front/caffe/extractors/inner_product_test.py

shinh/dldt

693ab4e79a428e0801f17f4511b129a3fa8f4a62

[ "Apache-2.0" ]

1

2021-02-20T21:48:36.000Z

model-optimizer/mo/front/caffe/extractors/inner_product_test.py

erinpark33/dldt

edd86d090592f7779f4dbb2681546e1f4e81284f

[ "Apache-2.0" ]

null

model-optimizer/mo/front/caffe/extractors/inner_product_test.py

erinpark33/dldt

edd86d090592f7779f4dbb2681546e1f4e81284f

[ "Apache-2.0" ]

1

2018-12-14T07:52:51.000Z

""" Copyright (c) 2018-2019 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 unittest import numpy as np from mo.front.caffe.extractors.inner_product import inner_product_ext from mo.front.common.partial_infer.inner_product import caffe_inner_product from mo.utils.unittest.extractors import FakeMultiParam, FakeModelLayer class FakeProtoLayer: def __init__(self, val): self.inner_product_param = val class TestInnerProduct(unittest.TestCase): def test_inner_product_ext(self): params = { 'num_output': 10, 'bias_term': True } mean_blob = np.array([1., 2.]) variance_blob = np.array([3., 4.]) blobs = [mean_blob, variance_blob] res = inner_product_ext(FakeProtoLayer(FakeMultiParam(params)), FakeModelLayer(blobs)) exp_res = { 'type': 'FullyConnected', 'out-size': 10, 'infer': caffe_inner_product, 'weights': mean_blob, 'biases': variance_blob, 'embedded_inputs': [ (1, 'weights', { 'bin': 'weights' }), (2, 'biases', { 'bin': 'biases' }) ] } for i in exp_res: if i in ('weights', 'biases'): np.testing.assert_array_equal(res[i], exp_res[i]) else: self.assertEqual(res[i], exp_res[i])

32.209677

75

0.608413

import unittest import numpy as np from mo.front.caffe.extractors.inner_product import inner_product_ext from mo.front.common.partial_infer.inner_product import caffe_inner_product from mo.utils.unittest.extractors import FakeMultiParam, FakeModelLayer class FakeProtoLayer: def __init__(self, val): self.inner_product_param = val class TestInnerProduct(unittest.TestCase): def test_inner_product_ext(self): params = { 'num_output': 10, 'bias_term': True } mean_blob = np.array([1., 2.]) variance_blob = np.array([3., 4.]) blobs = [mean_blob, variance_blob] res = inner_product_ext(FakeProtoLayer(FakeMultiParam(params)), FakeModelLayer(blobs)) exp_res = { 'type': 'FullyConnected', 'out-size': 10, 'infer': caffe_inner_product, 'weights': mean_blob, 'biases': variance_blob, 'embedded_inputs': [ (1, 'weights', { 'bin': 'weights' }), (2, 'biases', { 'bin': 'biases' }) ] } for i in exp_res: if i in ('weights', 'biases'): np.testing.assert_array_equal(res[i], exp_res[i]) else: self.assertEqual(res[i], exp_res[i])

true

f70befa47bcbbb2f37b411233346de8ecbc85bc3

57

py

Python

Tester/concurrentDL.py

garff/pyTorrent

fe8ff606ea0c146517e44ee6d475ebee58996d03

[ "MIT" ]

null

Tester/concurrentDL.py

garff/pyTorrent

fe8ff606ea0c146517e44ee6d475ebee58996d03

[ "MIT" ]

null

Tester/concurrentDL.py

garff/pyTorrent

fe8ff606ea0c146517e44ee6d475ebee58996d03

[ "MIT" ]

null

import urllib import concurrent.futures import threading

19

25

0.877193

import urllib import concurrent.futures import threading

true

f70bf15027e783d8f8206b2b5debcae15150d1b6

25,199

py

Python

pysteps/io/exporters.py

savelov/nowcast

9c1168b1ba642f15bc4ffb000bdbca6db27c29b1

[ "BSD-3-Clause" ]

6

2019-01-06T07:42:55.000Z

2021-02-03T13:59:50.000Z

pysteps/io/exporters.py

savelov/nowcast

9c1168b1ba642f15bc4ffb000bdbca6db27c29b1

[ "BSD-3-Clause" ]

5

2018-12-23T15:10:27.000Z

2021-01-06T15:03:03.000Z

pysteps/io/exporters.py

savelov/nowcast

9c1168b1ba642f15bc4ffb000bdbca6db27c29b1

[ "BSD-3-Clause" ]

2

2019-08-06T14:16:43.000Z

2019-08-13T00:36:31.000Z

""" pysteps.io.exporter =================== Methods for exporting forecasts of 2d precipitation fields into various file formats. Each exporter method in this module has its own initialization function that implements the following interface:: initialize_forecast_exporter_xxx(filename, startdate, timestep, num_timesteps, shape, num_ens_members, metadata, incremental=None) where xxx is the name (or abbreviation) of the file format. This function creates the file and writes the metadata. The datasets are written by calling :py:func:`pysteps.io.exporters.export_forecast_dataset`, and the file is closed by calling :py:func:`pysteps.io.exporters.close_forecast_file`. The arguments in the above are defined as follows: .. tabularcolumns:: |p{2cm}|p{2cm}|L| +---------------+-------------------+-----------------------------------------+ | Argument | Type/values | Description | +===============+===================+=========================================+ | filename | str | name of the output file | +---------------+-------------------+-----------------------------------------+ | startdate | datetime.datetime | start date of the forecast | +---------------+-------------------+-----------------------------------------+ | timestep | int | time step of the forecast (minutes) | +---------------+-------------------+-----------------------------------------+ | n_timesteps | int | number of time steps in the forecast | | | | this argument is ignored if | | | | incremental is set to 'timestep'. | +---------------+-------------------+-----------------------------------------+ | shape | tuple | two-element tuple defining the shape | | | | (height,width) of the forecast grids | +---------------+-------------------+-----------------------------------------+ | n_ens_members | int | number of ensemble members in the | | | | forecast. This argument is ignored if | | | | incremental is set to 'member' | +---------------+-------------------+-----------------------------------------+ | metadata | dict | metadata dictionary containing the | | | | projection,x1,x2,y1,y2 and unit | | | | attributes described in the | | | | documentation of pysteps.io.importers | +---------------+-------------------+-----------------------------------------+ | incremental | {None, 'timestep',| Allow incremental writing of datasets | | | 'member'} | into the netCDF file | | | | the available options are: | | | | 'timestep' = write a forecast or a | | | | forecast ensemble for a given | | | | time step | | | | 'member' = write a forecast sequence | | | | for a given ensemble member | +---------------+-------------------+-----------------------------------------+ The return value is a dictionary containing an exporter object. This can be used with :py:func:`pysteps.io.exporters.export_forecast_dataset` to write datasets into the given file format. Available Exporters ------------------- .. autosummary:: :toctree: ../generated/ initialize_forecast_exporter_kineros initialize_forecast_exporter_netcdf Generic functions ----------------- .. autosummary:: :toctree: ../generated/ export_forecast_dataset close_forecast_file """ from datetime import datetime import numpy as np import os from pysteps.exceptions import MissingOptionalDependency try: import netCDF4 netcdf4_imported = True except ImportError: netcdf4_imported = False try: import pyproj pyproj_imported = True except ImportError: pyproj_imported = False # TODO(exporters): This is a draft version of the kineros exporter. # Revise the variable names and # the structure of the file if necessary. def initialize_forecast_exporter_kineros(filename, startdate, timestep, n_timesteps, shape, n_ens_members, metadata, incremental=None): """Initialize a KINEROS2 Rainfall .pre file as specified in https://www.tucson.ars.ag.gov/kineros/. Grid points are treated as individual rain gauges and a separate file is produced for each ensemble member. Parameters ---------- filename : str Name of the output file. startdate : datetime.datetime Start date of the forecast as datetime object. timestep : int Time step of the forecast (minutes). n_timesteps : int Number of time steps in the forecast this argument is ignored if incremental is set to 'timestep'. shape : tuple of int Two-element tuple defining the shape (height,width) of the forecast grids. n_ens_members : int Number of ensemble members in the forecast. This argument is ignored if incremental is set to 'member'. metadata: dict Metadata dictionary containing the projection,x1,x2,y1,y2 and unit attributes described in the documentation of :py:mod:`pysteps.io.importers`. incremental : {None}, optional Currently not implemented for this method. Returns ------- exporter : dict The return value is a dictionary containing an exporter object. This c an be used with :py:func:`pysteps.io.exporters.export_forecast_dataset` to write datasets into the given file format. """ if incremental is not None: raise ValueError("unknown option %s: incremental writing is not supported" % incremental) exporter = {} basefn, extfn = os.path.splitext(filename) if extfn == "": extfn = ".pre" # one file for each member n_ens_members = np.min((99, n_ens_members)) fns = [] for i in range(n_ens_members): fn = "%s_N%02d%s" % (basefn, i, extfn) with open(fn, "w") as fd: # write header fd.writelines("! pysteps-generated nowcast.\n") fd.writelines("! created the %s.\n" % datetime.now().strftime("%c")) # TODO(exporters): Add pySTEPS version here fd.writelines("! Member = %02d.\n" % i) fd.writelines("! Startdate = %s.\n" % startdate.strftime("%c")) fns.append(fn) fd.close() h, w = shape if metadata["unit"] == "mm/h": var_name = "Intensity" var_long_name = "Intensity in mm/hr" var_unit = "mm/hr" elif metadata["unit"] == "mm": var_name = "Depth" var_long_name = "Accumulated depth in mm" var_unit = "mm" else: raise ValueError("unsupported unit %s" % metadata["unit"]) xr = np.linspace(metadata["x1"], metadata["x2"], w+1)[:-1] xr += 0.5 * (xr[1] - xr[0]) yr = np.linspace(metadata["y1"], metadata["y2"], h+1)[:-1] yr += 0.5 * (yr[1] - yr[0]) X, Y = np.meshgrid(xr, yr) XY_coords = np.stack([X, Y]) exporter["method"] = "kineros" exporter["ncfile"] = fns exporter["XY_coords"] = XY_coords exporter["var_name"] = var_name exporter["var_long_name"] = var_long_name exporter["var_unit"] = var_unit exporter["startdate"] = startdate exporter["timestep"] = timestep exporter["metadata"] = metadata exporter["incremental"] = incremental exporter["num_timesteps"] = n_timesteps exporter["num_ens_members"] = n_ens_members exporter["shape"] = shape return exporter # TODO(exporters): This is a draft version of the netcdf exporter. # Revise the variable names and # the structure of the file if necessary. def initialize_forecast_exporter_netcdf(filename, startdate, timestep, n_timesteps, shape, n_ens_members, metadata, product='precip_intensity', incremental=None): """Initialize a netCDF forecast exporter. Parameters ---------- filename : str Name of the output file. startdate : datetime.datetime Start date of the forecast as datetime object. timestep : int Time step of the forecast (minutes). n_timesteps : int Number of time steps in the forecast this argument is ignored if incremental is set to 'timestep'. shape : tuple of int Two-element tuple defining the shape (height,width) of the forecast grids. n_ens_members : int Number of ensemble members in the forecast. This argument is ignored if incremental is set to 'member'. metadata: dict Metadata dictionary containing the projection,x1,x2,y1,y2 and unit attributes described in the documentation of :py:mod:`pysteps.io.importers`. product: str product name can be 'precip_intensity' for intensity export, 'precip_probability' for probability export. incremental : {None,'timestep','member'}, optional Allow incremental writing of datasets into the netCDF file.\n The available options are: 'timestep' = write a forecast or a forecast ensemble for a given time step; 'member' = write a forecast sequence for a given ensemble member. If set to None, incremental writing is disabled. Returns ------- exporter : dict The return value is a dictionary containing an exporter object. This c an be used with :py:func:`pysteps.io.exporters.export_forecast_dataset` to write datasets into the given file format. """ if not netcdf4_imported: raise MissingOptionalDependency( "netCDF4 package is required for netcdf " "exporters but it is not installed") if not pyproj_imported: raise MissingOptionalDependency( "pyproj package is required for netcdf " "exporters but it is not installed") if incremental not in [None, "timestep", "member"]: raise ValueError("unknown option %s: incremental must be 'timestep' or 'member'" % incremental) if incremental == "timestep": n_timesteps = None elif incremental == "member": n_ens_members = None elif incremental is not None: raise ValueError("unknown argument value incremental='%s': must be 'timestep' or 'member'" % str(incremental)) exporter = {} filename = os.path.realpath(filename) if not os.path.exists(os.path.dirname(filename)): os.mkdir(os.path.dirname(filename)) ncf = netCDF4.Dataset(filename, 'w', format="NETCDF4") ncf.Conventions = "CF-1.7" ncf.title = "pysteps-generated nowcast" ncf.institution = "the pySTEPS community (https://pysteps.github.io)" ncf.source = "pysteps" # TODO(exporters): Add pySTEPS version here ncf.history = "" ncf.references = "" ncf.comment = "" h, w = shape # if product != 'precip_probability': # ncf.createDimension("ens_number", size=n_ens_members) ncf.createDimension("time", size=n_timesteps) ncf.createDimension("y", size=h) ncf.createDimension("x", size=w) # necessary settings for probability nowcasting ncf.datetime = str(startdate) if product == 'precip_probability': #TODO: Add this metadata unit percent in the source metadata["unit"] = "percent" if metadata["unit"] == "mm/h": var_name = "precip_intensity" var_standard_name = None var_long_name = "instantaneous precipitation rate" var_unit = "mm h-1" elif metadata["unit"] == "percent": var_name = "precip_probability" var_standard_name = None var_long_name = "probablistic precipitation" var_unit = "percent" elif metadata["unit"] == "mm": var_name = "precip_accum" var_standard_name = None var_long_name = "accumulated precipitation" var_unit = "mm" elif metadata["unit"] == "dBZ": var_name = "reflectivity" var_long_name = "equivalent reflectivity factor" var_standard_name = "equivalent_reflectivity_factor" var_unit = "dBZ" else: raise ValueError("unknown unit %s" % metadata["unit"]) xr = np.linspace(metadata["x1"], metadata["x2"], w+1)[:-1] xr += 0.5 * (xr[1] - xr[0]) yr = np.linspace(metadata["y1"], metadata["y2"], h+1)[:-1] yr += 0.5 * (yr[1] - yr[0]) var_xc = ncf.createVariable("xc", np.float32, dimensions=("x",)) var_xc[:] = xr var_xc.axis = 'X' var_xc.standard_name = "projection_x_coordinate" var_xc.long_name = "x-coordinate in Cartesian system" # TODO(exporters): Don't hard-code the unit. var_xc.units = 'm' var_yc = ncf.createVariable("yc", np.float32, dimensions=("y",)) var_yc[:] = yr var_yc.axis = 'Y' var_yc.standard_name = "projection_y_coordinate" var_yc.long_name = "y-coordinate in Cartesian system" # TODO(exporters): Don't hard-code the unit. var_yc.units = 'm' X, Y = np.meshgrid(xr, yr) pr = pyproj.Proj(metadata["projection"]) lon,lat = pr(X.flatten(), Y.flatten(), inverse=True) lon, lat = pr(X.flatten(), Y.flatten(), inverse=True) new_long, new_lat = np.zeros((h, w), dtype=np.float), np.zeros((h, w), dtype=np.float) idx = 0 for row in range(h): for col in range(w): new_long[row][col] = lon[idx] idx += 1 idx = 0 for row in range(h): for col in range(w): new_lat[row][col] = lat[idx] idx += 1 var_lon = ncf.createVariable("lon", np.float32, dimensions=("y", "x")) var_lon[:] = new_long var_lon.standard_name = "longitude" var_lon.long_name = "longitude coordinate" # TODO(exporters): Don't hard-code the unit. var_lon.units = "degrees_east" var_lat = ncf.createVariable("lat", np.float, dimensions=("y", "x")) var_lat[:] = new_lat var_lat.standard_name = "latitude" var_lat.long_name = "latitude coordinate" # TODO(exporters): Don't hard-code the unit. var_lat.units = "degrees_north" ncf.projection = metadata["projection"] grid_mapping_var_name, grid_mapping_name, grid_mapping_params = \ _convert_proj4_to_grid_mapping(metadata["projection"]) # skip writing the grid mapping if a matching name was not found if grid_mapping_var_name is not None: var_gm = ncf.createVariable(grid_mapping_var_name, np.int, dimensions=()) var_gm.grid_mapping_name = grid_mapping_name for i in grid_mapping_params.items(): var_gm.setncattr(i[0], i[1]) # if product != 'precip_probability': # var_ens_num = ncf.createVariable("ens_number", np.int, # dimensions=("ens_number",)) # if incremental != "member": # var_ens_num[:] = list(range(1, n_ens_members+1)) # var_ens_num.long_name = "ensemble member" # var_ens_num.units = "" var_time = ncf.createVariable("time", np.int, dimensions=("time",)) if incremental != "timestep": if product == 'precip_probability': var_time[:] = [i*timestep for i in range(1, n_timesteps+1)] else: var_time[:] = [i*timestep*60 for i in range(1, n_timesteps+1)] var_time.long_name = "forecast time" startdate_str = datetime.strftime(startdate, "%Y-%m-%d %H:%M:%S") var_time.units = "minutes since %s" % startdate_str if product == 'precip_probability' \ else "seconds since %s" % startdate_str dimensions = ("time", "y", "x") var_F = ncf.createVariable(var_name, np.float32, dimensions=dimensions, zlib=True, complevel=9) if var_standard_name is not None: var_F.standard_name = var_standard_name var_F.long_name = var_long_name var_F.coordinates = "y x" var_F.units = var_unit exporter["method"] = "netcdf" exporter["ncfile"] = ncf exporter["var_F"] = var_F # if product != 'precip_probability': # exporter["var_ens_num"] = var_ens_num exporter["var_time"] = var_time exporter["var_name"] = var_name exporter["startdate"] = startdate exporter["timestep"] = timestep exporter["metadata"] = metadata exporter["incremental"] = incremental exporter["num_timesteps"] = n_timesteps exporter["num_ens_members"] = n_ens_members exporter["shape"] = shape return exporter def export_forecast_dataset(F, exporter, mask=None): """Write a forecast array into a file. The written dataset has dimensions (num_ens_members,num_timesteps,shape[0],shape[1]), where shape refers to the shape of the two-dimensional forecast grids. If the exporter was initialized with incremental!=None, the array is appended to the existing dataset either along the ensemble member or time axis. Parameters ---------- exporter : dict An exporter object created with any initialization method implemented in :py:mod:`pysteps.io.exporters`. F : array_like The array to write. The required shape depends on the choice of the 'incremental' parameter the exporter was initialized with: :TODO: Update this table incorporating 'precip_probability' +-----------------+---------------------------------------------------+ | incremental | required shape | +=================+===================================================+ | None | (num_ens_members,num_timesteps,shape[0],shape[1]) | +-----------------+---------------------------------------------------+ | 'timestep' | (num_ens_members,shape[0],shape[1]) | +-----------------+---------------------------------------------------+ | 'member' | (num_timesteps,shape[0],shape[1]) | +-----------------+---------------------------------------------------+ """ if exporter["method"] == "netcdf" and not netcdf4_imported: raise MissingOptionalDependency( "netCDF4 package is required for netcdf " "exporters but it is not installed") if exporter["incremental"] is None: shp = (exporter["num_timesteps"], exporter["shape"][0], exporter["shape"][1]) if F.shape != shp: raise ValueError("F has invalid shape: %s != %s" % (str(F.shape),str(shp))) elif exporter["incremental"] == "timestep": shp = (exporter["num_ens_members"], exporter["shape"][0], exporter["shape"][1]) if F.shape != shp: raise ValueError("F has invalid shape: %s != %s" % (str(F.shape),str(shp))) elif exporter["incremental"] == "member": shp = (exporter["num_timesteps"], exporter["shape"][0], exporter["shape"][1]) if F.shape != shp: raise ValueError("F has invalid shape: %s != %s" % (str(F.shape),str(shp))) if exporter["method"] == "netcdf": _export_netcdf(F, exporter, mask) elif exporter["method"] == "kineros": _export_kineros(F, exporter) else: raise ValueError("unknown exporter method %s" % exporter["method"]) def close_forecast_file(exporter): """Close the file associated with a forecast exporter. Finish writing forecasts and close the file associated with a forecast exporter. Parameters ---------- exporter : dict An exporter object created with any initialization method implemented in :py:mod:`pysteps.io.exporters`. """ if exporter["method"] == "kineros": pass # no need to close the file else: exporter["ncfile"].close() def _export_kineros(F, exporter): num_timesteps = exporter["num_timesteps"] num_ens_members = exporter["num_ens_members"] startdate = exporter["startdate"] timestep = exporter["timestep"] xgrid = exporter["XY_coords"][0, :, :].flatten() ygrid = exporter["XY_coords"][1, :, :].flatten() timemin = [(t + 1)*timestep for t in range(num_timesteps)] for n in range(num_ens_members): fn = exporter["ncfile"][n] F_ = F[n, :, :, :].reshape((num_timesteps, -1)) if exporter["var_name"] == "Depth": F_ = np.cumsum(F_, axis=0) with open(fn, "a") as fd: for m in range(F_.shape[1]): fd.writelines("BEGIN RG%03d\n" % (m + 1)) fd.writelines(" X = %.2f, Y = %.2f\n" % (xgrid[m], ygrid[m])) fd.writelines(" N = %i\n" % num_timesteps) fd.writelines(" TIME %s\n" % exporter["var_name"].upper()) fd.writelines("! (min) (%s)\n" % exporter["var_unit"]) for t in range(num_timesteps): line_new = "{:6.1f} {:11.2f}\n".format(timemin[t], F_[t, m]) fd.writelines(line_new) fd.writelines("END\n\n") def _export_netcdf(F, exporter, mask=None): var_F = exporter["var_F"] if exporter["incremental"] is None: var_F[:] = F[:,::-1,:] elif exporter["incremental"] == "timestep": var_F[:, var_F.shape[1], :, :] = F var_time = exporter["var_time"] var_time[len(var_time)-1] = len(var_time) * exporter["timestep"] * 60 else: var_F[var_F.shape[0], :, :, :] = F var_ens_num = exporter["var_time"] var_ens_num[len(var_ens_num)-1] = len(var_ens_num) # TODO(exporters): Write methods for converting Proj.4 projection definitions # into CF grid mapping attributes. Currently this has been implemented for # the stereographic projection. # The conversions implemented here are take from: # https://github.com/cf-convention/cf-convention.github.io/blob/master/wkt-proj-4.md def _convert_proj4_to_grid_mapping(proj4str): tokens = proj4str.split('+') d = {} for t in tokens[1:]: t = t.split('=') if len(t) > 1: d[t[0]] = t[1].strip() params = {} # TODO(exporters): implement more projection types here if d["proj"] == "stere": grid_mapping_var_name = "polar_stereographic" grid_mapping_name = "polar_stereographic" v = d["lon_0"] if d["lon_0"][-1] not in ["E", "W"] else d["lon_0"][:-1] params["straight_vertical_longitude_from_pole"] = float(v) v = d["lat_0"] if d["lat_0"][-1] not in ["N", "S"] else d["lat_0"][:-1] params["latitude_of_projection_origin"] = float(v) if "lat_ts" in list(d.keys()): params["standard_parallel"] = float(d["lat_ts"]) elif "k_0" in list(d.keys()): params["scale_factor_at_projection_origin"] = float(d["k_0"]) params["false_easting"] = float(d["x_0"]) params["false_northing"] = float(d["y_0"]) elif d["proj"] == "sterea": grid_mapping_var_name = "oblique_stereographic" grid_mapping_name = "oblique_stereographic" v = d["lon_0"] if d["lon_0"][-1] not in ["E", "W"] else d["lon_0"][:-1] params["longitude_of_projection_origin"] = float(v) v = d["lat_0"] if d["lat_0"][-1] not in ["N", "S"] else d["lat_0"][:-1] params["latitude_of_projection_origin"] = float(v) if "lat_ts" in list(d.keys()): params["standard_parallel"] = float(d["lat_ts"]) elif "k_0" in list(d.keys()): params["scale_factor_at_projection_origin"] = float(d["k_0"]) params["false_easting"] = float(d["x_0"]) params["false_northing"] = float(d["y_0"]) elif d["proj"] == "aea": # Albers Conical Equal Area grid_mapping_var_name = "proj" grid_mapping_name = "albers_conical_equal_area" params["false_easting"] = float(d["x_0"]) if "x_0" in d else float(0) params["false_northing"] = float(d["y_0"]) if "y_0" in d else float(0) v = d["lon_0"] if "lon_0" in d else float(0) params["longitude_of_central_meridian"] = float(v) v = d["lat_0"] if "lat_0" in d else float(0) params["latitude_of_projection_origin"] = float(v) v1 = d["lat_1"] if "lat_1" in d else float(0) v2 = d["lat_2"] if "lat_2" in d else float(0) params["standard_parallel"] = (float(v1), float(v2)) else: print('unknown projection', d["proj"]) return None, None, None return grid_mapping_var_name, grid_mapping_name, params

39.55887

118

0.5703

from datetime import datetime import numpy as np import os from pysteps.exceptions import MissingOptionalDependency try: import netCDF4 netcdf4_imported = True except ImportError: netcdf4_imported = False try: import pyproj pyproj_imported = True except ImportError: pyproj_imported = False def initialize_forecast_exporter_kineros(filename, startdate, timestep, n_timesteps, shape, n_ens_members, metadata, incremental=None): if incremental is not None: raise ValueError("unknown option %s: incremental writing is not supported" % incremental) exporter = {} basefn, extfn = os.path.splitext(filename) if extfn == "": extfn = ".pre" n_ens_members = np.min((99, n_ens_members)) fns = [] for i in range(n_ens_members): fn = "%s_N%02d%s" % (basefn, i, extfn) with open(fn, "w") as fd: fd.writelines("! pysteps-generated nowcast.\n") fd.writelines("! created the %s.\n" % datetime.now().strftime("%c")) fd.writelines("! Member = %02d.\n" % i) fd.writelines("! Startdate = %s.\n" % startdate.strftime("%c")) fns.append(fn) fd.close() h, w = shape if metadata["unit"] == "mm/h": var_name = "Intensity" var_long_name = "Intensity in mm/hr" var_unit = "mm/hr" elif metadata["unit"] == "mm": var_name = "Depth" var_long_name = "Accumulated depth in mm" var_unit = "mm" else: raise ValueError("unsupported unit %s" % metadata["unit"]) xr = np.linspace(metadata["x1"], metadata["x2"], w+1)[:-1] xr += 0.5 * (xr[1] - xr[0]) yr = np.linspace(metadata["y1"], metadata["y2"], h+1)[:-1] yr += 0.5 * (yr[1] - yr[0]) X, Y = np.meshgrid(xr, yr) XY_coords = np.stack([X, Y]) exporter["method"] = "kineros" exporter["ncfile"] = fns exporter["XY_coords"] = XY_coords exporter["var_name"] = var_name exporter["var_long_name"] = var_long_name exporter["var_unit"] = var_unit exporter["startdate"] = startdate exporter["timestep"] = timestep exporter["metadata"] = metadata exporter["incremental"] = incremental exporter["num_timesteps"] = n_timesteps exporter["num_ens_members"] = n_ens_members exporter["shape"] = shape return exporter def initialize_forecast_exporter_netcdf(filename, startdate, timestep, n_timesteps, shape, n_ens_members, metadata, product='precip_intensity', incremental=None): if not netcdf4_imported: raise MissingOptionalDependency( "netCDF4 package is required for netcdf " "exporters but it is not installed") if not pyproj_imported: raise MissingOptionalDependency( "pyproj package is required for netcdf " "exporters but it is not installed") if incremental not in [None, "timestep", "member"]: raise ValueError("unknown option %s: incremental must be 'timestep' or 'member'" % incremental) if incremental == "timestep": n_timesteps = None elif incremental == "member": n_ens_members = None elif incremental is not None: raise ValueError("unknown argument value incremental='%s': must be 'timestep' or 'member'" % str(incremental)) exporter = {} filename = os.path.realpath(filename) if not os.path.exists(os.path.dirname(filename)): os.mkdir(os.path.dirname(filename)) ncf = netCDF4.Dataset(filename, 'w', format="NETCDF4") ncf.Conventions = "CF-1.7" ncf.title = "pysteps-generated nowcast" ncf.institution = "the pySTEPS community (https://pysteps.github.io)" ncf.source = "pysteps" ncf.history = "" ncf.references = "" ncf.comment = "" h, w = shape ncf.createDimension("time", size=n_timesteps) ncf.createDimension("y", size=h) ncf.createDimension("x", size=w) ncf.datetime = str(startdate) if product == 'precip_probability': metadata["unit"] = "percent" if metadata["unit"] == "mm/h": var_name = "precip_intensity" var_standard_name = None var_long_name = "instantaneous precipitation rate" var_unit = "mm h-1" elif metadata["unit"] == "percent": var_name = "precip_probability" var_standard_name = None var_long_name = "probablistic precipitation" var_unit = "percent" elif metadata["unit"] == "mm": var_name = "precip_accum" var_standard_name = None var_long_name = "accumulated precipitation" var_unit = "mm" elif metadata["unit"] == "dBZ": var_name = "reflectivity" var_long_name = "equivalent reflectivity factor" var_standard_name = "equivalent_reflectivity_factor" var_unit = "dBZ" else: raise ValueError("unknown unit %s" % metadata["unit"]) xr = np.linspace(metadata["x1"], metadata["x2"], w+1)[:-1] xr += 0.5 * (xr[1] - xr[0]) yr = np.linspace(metadata["y1"], metadata["y2"], h+1)[:-1] yr += 0.5 * (yr[1] - yr[0]) var_xc = ncf.createVariable("xc", np.float32, dimensions=("x",)) var_xc[:] = xr var_xc.axis = 'X' var_xc.standard_name = "projection_x_coordinate" var_xc.long_name = "x-coordinate in Cartesian system" var_xc.units = 'm' var_yc = ncf.createVariable("yc", np.float32, dimensions=("y",)) var_yc[:] = yr var_yc.axis = 'Y' var_yc.standard_name = "projection_y_coordinate" var_yc.long_name = "y-coordinate in Cartesian system" # TODO(exporters): Don't hard-code the unit. var_yc.units = 'm' X, Y = np.meshgrid(xr, yr) pr = pyproj.Proj(metadata["projection"]) lon,lat = pr(X.flatten(), Y.flatten(), inverse=True) lon, lat = pr(X.flatten(), Y.flatten(), inverse=True) new_long, new_lat = np.zeros((h, w), dtype=np.float), np.zeros((h, w), dtype=np.float) idx = 0 for row in range(h): for col in range(w): new_long[row][col] = lon[idx] idx += 1 idx = 0 for row in range(h): for col in range(w): new_lat[row][col] = lat[idx] idx += 1 var_lon = ncf.createVariable("lon", np.float32, dimensions=("y", "x")) var_lon[:] = new_long var_lon.standard_name = "longitude" var_lon.long_name = "longitude coordinate" var_lon.units = "degrees_east" var_lat = ncf.createVariable("lat", np.float, dimensions=("y", "x")) var_lat[:] = new_lat var_lat.standard_name = "latitude" var_lat.long_name = "latitude coordinate" # TODO(exporters): Don't hard-code the unit. var_lat.units = "degrees_north" ncf.projection = metadata["projection"] grid_mapping_var_name, grid_mapping_name, grid_mapping_params = \ _convert_proj4_to_grid_mapping(metadata["projection"]) if grid_mapping_var_name is not None: var_gm = ncf.createVariable(grid_mapping_var_name, np.int, dimensions=()) var_gm.grid_mapping_name = grid_mapping_name for i in grid_mapping_params.items(): var_gm.setncattr(i[0], i[1]) var_time = ncf.createVariable("time", np.int, dimensions=("time",)) if incremental != "timestep": if product == 'precip_probability': var_time[:] = [i*timestep for i in range(1, n_timesteps+1)] else: var_time[:] = [i*timestep*60 for i in range(1, n_timesteps+1)] var_time.long_name = "forecast time" startdate_str = datetime.strftime(startdate, "%Y-%m-%d %H:%M:%S") var_time.units = "minutes since %s" % startdate_str if product == 'precip_probability' \ else "seconds since %s" % startdate_str dimensions = ("time", "y", "x") var_F = ncf.createVariable(var_name, np.float32, dimensions=dimensions, zlib=True, complevel=9) if var_standard_name is not None: var_F.standard_name = var_standard_name var_F.long_name = var_long_name var_F.coordinates = "y x" var_F.units = var_unit exporter["method"] = "netcdf" exporter["ncfile"] = ncf exporter["var_F"] = var_F exporter["var_time"] = var_time exporter["var_name"] = var_name exporter["startdate"] = startdate exporter["timestep"] = timestep exporter["metadata"] = metadata exporter["incremental"] = incremental exporter["num_timesteps"] = n_timesteps exporter["num_ens_members"] = n_ens_members exporter["shape"] = shape return exporter def export_forecast_dataset(F, exporter, mask=None): if exporter["method"] == "netcdf" and not netcdf4_imported: raise MissingOptionalDependency( "netCDF4 package is required for netcdf " "exporters but it is not installed") if exporter["incremental"] is None: shp = (exporter["num_timesteps"], exporter["shape"][0], exporter["shape"][1]) if F.shape != shp: raise ValueError("F has invalid shape: %s != %s" % (str(F.shape),str(shp))) elif exporter["incremental"] == "timestep": shp = (exporter["num_ens_members"], exporter["shape"][0], exporter["shape"][1]) if F.shape != shp: raise ValueError("F has invalid shape: %s != %s" % (str(F.shape),str(shp))) elif exporter["incremental"] == "member": shp = (exporter["num_timesteps"], exporter["shape"][0], exporter["shape"][1]) if F.shape != shp: raise ValueError("F has invalid shape: %s != %s" % (str(F.shape),str(shp))) if exporter["method"] == "netcdf": _export_netcdf(F, exporter, mask) elif exporter["method"] == "kineros": _export_kineros(F, exporter) else: raise ValueError("unknown exporter method %s" % exporter["method"]) def close_forecast_file(exporter): if exporter["method"] == "kineros": pass else: exporter["ncfile"].close() def _export_kineros(F, exporter): num_timesteps = exporter["num_timesteps"] num_ens_members = exporter["num_ens_members"] startdate = exporter["startdate"] timestep = exporter["timestep"] xgrid = exporter["XY_coords"][0, :, :].flatten() ygrid = exporter["XY_coords"][1, :, :].flatten() timemin = [(t + 1)*timestep for t in range(num_timesteps)] for n in range(num_ens_members): fn = exporter["ncfile"][n] F_ = F[n, :, :, :].reshape((num_timesteps, -1)) if exporter["var_name"] == "Depth": F_ = np.cumsum(F_, axis=0) with open(fn, "a") as fd: for m in range(F_.shape[1]): fd.writelines("BEGIN RG%03d\n" % (m + 1)) fd.writelines(" X = %.2f, Y = %.2f\n" % (xgrid[m], ygrid[m])) fd.writelines(" N = %i\n" % num_timesteps) fd.writelines(" TIME %s\n" % exporter["var_name"].upper()) fd.writelines("! (min) (%s)\n" % exporter["var_unit"]) for t in range(num_timesteps): line_new = "{:6.1f} {:11.2f}\n".format(timemin[t], F_[t, m]) fd.writelines(line_new) fd.writelines("END\n\n") def _export_netcdf(F, exporter, mask=None): var_F = exporter["var_F"] if exporter["incremental"] is None: var_F[:] = F[:,::-1,:] elif exporter["incremental"] == "timestep": var_F[:, var_F.shape[1], :, :] = F var_time = exporter["var_time"] var_time[len(var_time)-1] = len(var_time) * exporter["timestep"] * 60 else: var_F[var_F.shape[0], :, :, :] = F var_ens_num = exporter["var_time"] var_ens_num[len(var_ens_num)-1] = len(var_ens_num) def _convert_proj4_to_grid_mapping(proj4str): tokens = proj4str.split('+') d = {} for t in tokens[1:]: t = t.split('=') if len(t) > 1: d[t[0]] = t[1].strip() params = {} if d["proj"] == "stere": grid_mapping_var_name = "polar_stereographic" grid_mapping_name = "polar_stereographic" v = d["lon_0"] if d["lon_0"][-1] not in ["E", "W"] else d["lon_0"][:-1] params["straight_vertical_longitude_from_pole"] = float(v) v = d["lat_0"] if d["lat_0"][-1] not in ["N", "S"] else d["lat_0"][:-1] params["latitude_of_projection_origin"] = float(v) if "lat_ts" in list(d.keys()): params["standard_parallel"] = float(d["lat_ts"]) elif "k_0" in list(d.keys()): params["scale_factor_at_projection_origin"] = float(d["k_0"]) params["false_easting"] = float(d["x_0"]) params["false_northing"] = float(d["y_0"]) elif d["proj"] == "sterea": grid_mapping_var_name = "oblique_stereographic" grid_mapping_name = "oblique_stereographic" v = d["lon_0"] if d["lon_0"][-1] not in ["E", "W"] else d["lon_0"][:-1] params["longitude_of_projection_origin"] = float(v) v = d["lat_0"] if d["lat_0"][-1] not in ["N", "S"] else d["lat_0"][:-1] params["latitude_of_projection_origin"] = float(v) if "lat_ts" in list(d.keys()): params["standard_parallel"] = float(d["lat_ts"]) elif "k_0" in list(d.keys()): params["scale_factor_at_projection_origin"] = float(d["k_0"]) params["false_easting"] = float(d["x_0"]) params["false_northing"] = float(d["y_0"]) elif d["proj"] == "aea": grid_mapping_var_name = "proj" grid_mapping_name = "albers_conical_equal_area" params["false_easting"] = float(d["x_0"]) if "x_0" in d else float(0) params["false_northing"] = float(d["y_0"]) if "y_0" in d else float(0) v = d["lon_0"] if "lon_0" in d else float(0) params["longitude_of_central_meridian"] = float(v) v = d["lat_0"] if "lat_0" in d else float(0) params["latitude_of_projection_origin"] = float(v) v1 = d["lat_1"] if "lat_1" in d else float(0) v2 = d["lat_2"] if "lat_2" in d else float(0) params["standard_parallel"] = (float(v1), float(v2)) else: print('unknown projection', d["proj"]) return None, None, None return grid_mapping_var_name, grid_mapping_name, params

true

f70bf420f2d3ab317f714627b80d6cfd01d77b6b

3,345

py

Python

update_sheetinRange.py

akifislam/CodeforcesAutoTracker

d147f6b6639d74a029208bb6e1407aec89212f27

[ "Apache-2.0" ]

null

update_sheetinRange.py

akifislam/CodeforcesAutoTracker

d147f6b6639d74a029208bb6e1407aec89212f27

[ "Apache-2.0" ]

null

update_sheetinRange.py

akifislam/CodeforcesAutoTracker

d147f6b6639d74a029208bb6e1407aec89212f27

[ "Apache-2.0" ]

1

2022-02-15T20:21:47.000Z

from bs4 import BeautifulSoup import requests import test import gspread from oauth2client.service_account import ServiceAccountCredentials import datetime def update_inRange(): print("Today's Date : ",datetime.date.today()) today = datetime.date.today() - datetime.timedelta(1) yesterday_month = today.strftime("%b") yesterday_dayno = today.strftime("%d") yesterday_full_Date = today.strftime("%d %B, %Y") compareable_date = today.strftime("%b/%d") print(compareable_date) print() print("----- Start -----") print() # Test # todays_month = 'Sep' # todays_day = '15' # Test print("Yesterday was : ",yesterday_full_Date) scope = ["https://spreadsheets.google.com/feeds", 'https://www.googleapis.com/auth/spreadsheets', "https://www.googleapis.com/auth/drive.file", "https://www.googleapis.com/auth/drive"] creds = ServiceAccountCredentials.from_json_keyfile_name("CodeforcesAutoTracker-b2030a7afa6c.json", scope); client = gspread.authorize(creds) sheet = client.open("Codeforces Auto Tracker - Akif Islam").worksheet('Sheet2') data = sheet.get_all_records() # pprint(data) date_column = sheet.col_values(1) no_of_total_submission_column = sheet.col_values(2) no_of_total_accepted_column = sheet.col_values(3) source_link = "https://codeforces.com/submissions/miss.progga" source = requests.get(source_link).text soup = BeautifulSoup(source, "lxml").find('table', class_="status-frame-datatable") submission_time = [] # 1. Collecting all dates from 50 submission of First Page of Codeforces Submission for data in soup.findAll('span', class_="format-time"): submission_time.append(data.text[0:6]) print("Submission's Time : ", submission_time) print("OK !") print() # Execution submission_count = int(0) total_accepted = [] accepted_count = int(0) accpeted_nonduplicate_set = [] # Total Accepted Count from 50s : for data in soup.findAll('span', class_="submissionVerdictWrapper"): total_accepted.append(data.text) print(total_accepted) print(len(total_accepted)) print(len(submission_time)) #Total Submission Count for i in range(0,len(submission_time),1): if submission_time[i][0:3] == yesterday_month and submission_time[i][4:6] == yesterday_dayno: submission_count += 1 if(total_accepted[i]== "Accepted"): str = test.get_problemlist()[i] + " Accepted" accpeted_nonduplicate_set.append(str) # Total Submission Count accpeted_nonduplicate_set = set(accpeted_nonduplicate_set) print("Accepted List : ",accpeted_nonduplicate_set) accepted_count = len(accpeted_nonduplicate_set) print("Total Submission : ", submission_count) print("Total Accepted : ", accepted_count) insert_list = [yesterday_full_Date, submission_count, accepted_count] print(insert_list) previous_date = sheet.cell(len(date_column), 1).value[0:2] # if sheet.cell(len(date_column),1)[0:1] != todays_day : if previous_date != yesterday_dayno: sheet.insert_row(insert_list, (len(date_column) + 1)) else: print("Duplicate Date Found ! ") print() print("----- Finished !-----") print() update_inRange()

32.163462

111

0.681614

from bs4 import BeautifulSoup import requests import test import gspread from oauth2client.service_account import ServiceAccountCredentials import datetime def update_inRange(): print("Today's Date : ",datetime.date.today()) today = datetime.date.today() - datetime.timedelta(1) yesterday_month = today.strftime("%b") yesterday_dayno = today.strftime("%d") yesterday_full_Date = today.strftime("%d %B, %Y") compareable_date = today.strftime("%b/%d") print(compareable_date) print() print("----- Start -----") print() # Test # todays_month = 'Sep' # todays_day = '15' # Test print("Yesterday was : ",yesterday_full_Date) scope = ["https://spreadsheets.google.com/feeds", 'https://www.googleapis.com/auth/spreadsheets', "https://www.googleapis.com/auth/drive.file", "https://www.googleapis.com/auth/drive"] creds = ServiceAccountCredentials.from_json_keyfile_name("CodeforcesAutoTracker-b2030a7afa6c.json", scope); client = gspread.authorize(creds) sheet = client.open("Codeforces Auto Tracker - Akif Islam").worksheet('Sheet2') data = sheet.get_all_records() # pprint(data) date_column = sheet.col_values(1) no_of_total_submission_column = sheet.col_values(2) no_of_total_accepted_column = sheet.col_values(3) source_link = "https://codeforces.com/submissions/miss.progga" source = requests.get(source_link).text soup = BeautifulSoup(source, "lxml").find('table', class_="status-frame-datatable") submission_time = [] # 1. Collecting all dates from 50 submission of First Page of Codeforces Submission for data in soup.findAll('span', class_="format-time"): submission_time.append(data.text[0:6]) print("Submission's Time : ", submission_time) print("OK !") print() submission_count = int(0) total_accepted = [] accepted_count = int(0) accpeted_nonduplicate_set = [] for data in soup.findAll('span', class_="submissionVerdictWrapper"): total_accepted.append(data.text) print(total_accepted) print(len(total_accepted)) print(len(submission_time)) for i in range(0,len(submission_time),1): if submission_time[i][0:3] == yesterday_month and submission_time[i][4:6] == yesterday_dayno: submission_count += 1 if(total_accepted[i]== "Accepted"): str = test.get_problemlist()[i] + " Accepted" accpeted_nonduplicate_set.append(str) accpeted_nonduplicate_set = set(accpeted_nonduplicate_set) print("Accepted List : ",accpeted_nonduplicate_set) accepted_count = len(accpeted_nonduplicate_set) print("Total Submission : ", submission_count) print("Total Accepted : ", accepted_count) insert_list = [yesterday_full_Date, submission_count, accepted_count] print(insert_list) previous_date = sheet.cell(len(date_column), 1).value[0:2] if previous_date != yesterday_dayno: sheet.insert_row(insert_list, (len(date_column) + 1)) else: print("Duplicate Date Found ! ") print() print("----- Finished !-----") print() update_inRange()

true

f70bf444aeed4ac27e527b05648fdf6fe9dd813e

302

py

Python

Linkedin/linkedin-become-a-programmer-foundations/1.programming-foundations-fundamentals-3/challenge_1.py

mohammedelzanaty/myRoad2BeFullStack

eea3a5edb6c6a999136b04fdaea6ce0c81137a58

[ "MIT" ]

2

2021-04-21T12:05:01.000Z

2022-01-19T09:58:38.000Z

Linkedin/linkedin-become-a-programmer-foundations/1.programming-foundations-fundamentals-3/challenge_1.py

mohammedelzanaty/myRoad2BeFullStack

eea3a5edb6c6a999136b04fdaea6ce0c81137a58

[ "MIT" ]

34

2019-12-26T11:21:42.000Z

2022-02-27T19:55:10.000Z

Linkedin/linkedin-become-a-programmer-foundations/1.programming-foundations-fundamentals-3/challenge_1.py

mohammedelzanaty/myRoad2BeFullStack

eea3a5edb6c6a999136b04fdaea6ce0c81137a58

[ "MIT" ]

2

2021-08-15T07:59:36.000Z

2022-01-16T06:17:32.000Z

print("Challenge 1:") # A message for user message = "This is goind to be tricky ;" Message = "Very tricky!" print(message) # show the message on the screen # Perform mathematical operations result = 2**3 print("2**3 =", result) result = 5 - 3 print("5 - 3 =", result) print("Challenge complete!")

18.875

47

0.678808

print("Challenge 1:") message = "This is goind to be tricky ;" Message = "Very tricky!" print(message) result = 2**3 print("2**3 =", result) result = 5 - 3 print("5 - 3 =", result) print("Challenge complete!")

true

f70bf45fbcab8216b7333ea95959f5208d7eb563

2,726

py

Python

blogs/models.py

6ba/bbgo

dfa9b55b8d40c53940105333c2e03a3c6abddb88

[ "MIT" ]

22

2017-07-13T04:07:03.000Z

2021-06-10T05:39:29.000Z

blogs/models.py

genonfire/bbgo

5f374f0b620f4dc3e106de5969f26f4585044605

[ "MIT" ]

7

2017-08-25T06:33:45.000Z

2019-10-14T05:49:32.000Z

blogs/models.py

6ba/bbgo

dfa9b55b8d40c53940105333c2e03a3c6abddb88

[ "MIT" ]

9

2017-12-31T02:45:58.000Z

2021-01-22T03:09:02.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.conf import settings from django.db import models from django.urls import reverse_lazy from django.utils.translation import ugettext as _ class Blog(models.Model): """Blog of blogs""" BLOG_STATUS = { ('1normal', _('status_published')), ('2temp', _('status_draft')), ('5hidden', _('status_pending')), ('6deleted', _('status_deleted')), } status = models.CharField( max_length=10, choices=BLOG_STATUS, default='1normal') user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE) created_at = models.DateTimeField(auto_now_add=True) modified_at = models.DateTimeField(auto_now_add=True) ip = models.GenericIPAddressField() category = models.CharField(max_length=23, blank=True) title = models.CharField(max_length=41) content = models.TextField() view_count = models.IntegerField(default=0) comment_count = models.IntegerField(default=0) like_count = models.IntegerField(default=0) like_users = models.TextField(default='', blank=True) image = models.ImageField( upload_to="featured_images/%Y-%m/", blank=True) tags = models.TextField(default='', blank=True) def get_absolute_url(self): """Back to list""" return reverse_lazy('blogs:show_blogs', args=[1]) def get_post_url(self): """Back to post""" return reverse_lazy('blogs:show_post', args=[self.id]) def get_edit_url(self): """Stay editing""" return reverse_lazy('blogs:edit_post', args=[self.id]) def get_status_text(self): """Get status text""" if self.status == '1normal': return _('status_normal') elif self.status == '2temp': return _('status_draft') elif self.status == '5hidden': return _('status_pending') elif self.status == '6deleted': return _('status_deleted') class Comment(models.Model): """Comment of blogs""" COMMENT_STATUS = { ('1normal', _('status_normal')), ('6deleted', _('status_deleted')), ('7spam', _('status_spam')), } post_id = models.IntegerField(default=0) comment_id = models.IntegerField(default=0) status = models.CharField( max_length=10, choices=COMMENT_STATUS, default='1normal') userid = models.CharField(max_length=settings.ID_MAX_LENGTH, blank=True) username = models.CharField(max_length=settings.USERNAME_MAX, blank=True) created_at = models.DateTimeField(auto_now_add=True) ip = models.GenericIPAddressField() content = models.TextField(max_length=settings.COMMENT_TEXT_MAX)

34.075

77

0.662509

from __future__ import unicode_literals from django.conf import settings from django.db import models from django.urls import reverse_lazy from django.utils.translation import ugettext as _ class Blog(models.Model): BLOG_STATUS = { ('1normal', _('status_published')), ('2temp', _('status_draft')), ('5hidden', _('status_pending')), ('6deleted', _('status_deleted')), } status = models.CharField( max_length=10, choices=BLOG_STATUS, default='1normal') user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE) created_at = models.DateTimeField(auto_now_add=True) modified_at = models.DateTimeField(auto_now_add=True) ip = models.GenericIPAddressField() category = models.CharField(max_length=23, blank=True) title = models.CharField(max_length=41) content = models.TextField() view_count = models.IntegerField(default=0) comment_count = models.IntegerField(default=0) like_count = models.IntegerField(default=0) like_users = models.TextField(default='', blank=True) image = models.ImageField( upload_to="featured_images/%Y-%m/", blank=True) tags = models.TextField(default='', blank=True) def get_absolute_url(self): return reverse_lazy('blogs:show_blogs', args=[1]) def get_post_url(self): return reverse_lazy('blogs:show_post', args=[self.id]) def get_edit_url(self): return reverse_lazy('blogs:edit_post', args=[self.id]) def get_status_text(self): if self.status == '1normal': return _('status_normal') elif self.status == '2temp': return _('status_draft') elif self.status == '5hidden': return _('status_pending') elif self.status == '6deleted': return _('status_deleted') class Comment(models.Model): COMMENT_STATUS = { ('1normal', _('status_normal')), ('6deleted', _('status_deleted')), ('7spam', _('status_spam')), } post_id = models.IntegerField(default=0) comment_id = models.IntegerField(default=0) status = models.CharField( max_length=10, choices=COMMENT_STATUS, default='1normal') userid = models.CharField(max_length=settings.ID_MAX_LENGTH, blank=True) username = models.CharField(max_length=settings.USERNAME_MAX, blank=True) created_at = models.DateTimeField(auto_now_add=True) ip = models.GenericIPAddressField() content = models.TextField(max_length=settings.COMMENT_TEXT_MAX)

true

f70bf471cf08b34e3769f50e1b418e61f0ca8aa4

2,823

py

Python

python3/koans/about_string_manipulation.py

OriginalTsynn/python_koans

f35ced3ebbf2c9c19f56183b2997beeb18aae9a9

[ "MIT" ]

null

python3/koans/about_string_manipulation.py

OriginalTsynn/python_koans

f35ced3ebbf2c9c19f56183b2997beeb18aae9a9

[ "MIT" ]

null

python3/koans/about_string_manipulation.py

OriginalTsynn/python_koans

f35ced3ebbf2c9c19f56183b2997beeb18aae9a9

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from runner.koan import * class AboutStringManipulation(Koan): def test_use_format_to_interpolate_variables(self): value1 = 'one' value2 = 2 string = "The values are {0} and {1}".format(value1, value2) self.assertEqual("The values are one and 2", string) def test_formatted_values_can_be_shown_in_any_order_or_be_repeated(self): value1 = 'doh' value2 = 'DOH' string = "The values are {1}, {0}, {0} and {1}!".format(value1, value2) self.assertEqual("The values are DOH, doh, doh and DOH!", string) def test_any_python_expression_may_be_interpolated(self): import math # import a standard python module with math functions decimal_places = 4 string = "The square root of 5 is {0:.{1}f}".format(math.sqrt(5), decimal_places) self.assertEqual("The square root of 5 is 2.2361", string) def test_you_can_get_a_substring_from_a_string(self): string = "Bacon, lettuce and tomato" self.assertEqual("let", string[7:10]) def test_you_can_get_a_single_character_from_a_string(self): string = "Bacon, lettuce and tomato" self.assertEqual("a", string[1]) def test_single_characters_can_be_represented_by_integers(self): self.assertEqual(97, ord('a')) self.assertEqual(True, ord('b') == (ord('a') + 1)) def test_strings_can_be_split(self): string = "Sausage Egg Cheese" words = string.split() self.assertListEqual(["Sausage", "Egg", "Cheese"], words) def test_strings_can_be_split_with_different_patterns(self): import re # import python regular expression library string = "the,rain;in,spain" pattern = re.compile(',|;') words = pattern.split(string) self.assertListEqual(["the", "rain", "in", "spain"], words) # Pattern is a Python regular expression pattern which matches ',' or ';' def test_raw_strings_do_not_interpret_escape_characters(self): string = r'\n' self.assertNotEqual('\n', string) self.assertEqual('\\n', string) self.assertEqual(2, len(string)) # Useful in regular expressions, file paths, URLs, etc. def test_strings_can_be_joined(self): words = ["Now", "is", "the", "time"] self.assertEqual("Now is the time", ' '.join(words)) def test_strings_can_change_case(self): self.assertEqual("Guido", 'guido'.capitalize()) self.assertEqual("GUIDO", 'guido'.upper()) self.assertEqual("timbot", 'TimBot'.lower()) self.assertEqual("Guido Van Rossum", 'guido van rossum'.title()) self.assertEqual("tOtAlLy AwEsOmE", 'ToTaLlY aWeSoMe'.swapcase())

37.144737

81

0.636557

from runner.koan import * class AboutStringManipulation(Koan): def test_use_format_to_interpolate_variables(self): value1 = 'one' value2 = 2 string = "The values are {0} and {1}".format(value1, value2) self.assertEqual("The values are one and 2", string) def test_formatted_values_can_be_shown_in_any_order_or_be_repeated(self): value1 = 'doh' value2 = 'DOH' string = "The values are {1}, {0}, {0} and {1}!".format(value1, value2) self.assertEqual("The values are DOH, doh, doh and DOH!", string) def test_any_python_expression_may_be_interpolated(self): import math decimal_places = 4 string = "The square root of 5 is {0:.{1}f}".format(math.sqrt(5), decimal_places) self.assertEqual("The square root of 5 is 2.2361", string) def test_you_can_get_a_substring_from_a_string(self): string = "Bacon, lettuce and tomato" self.assertEqual("let", string[7:10]) def test_you_can_get_a_single_character_from_a_string(self): string = "Bacon, lettuce and tomato" self.assertEqual("a", string[1]) def test_single_characters_can_be_represented_by_integers(self): self.assertEqual(97, ord('a')) self.assertEqual(True, ord('b') == (ord('a') + 1)) def test_strings_can_be_split(self): string = "Sausage Egg Cheese" words = string.split() self.assertListEqual(["Sausage", "Egg", "Cheese"], words) def test_strings_can_be_split_with_different_patterns(self): import re string = "the,rain;in,spain" pattern = re.compile(',|;') words = pattern.split(string) self.assertListEqual(["the", "rain", "in", "spain"], words) def test_raw_strings_do_not_interpret_escape_characters(self): string = r'\n' self.assertNotEqual('\n', string) self.assertEqual('\\n', string) self.assertEqual(2, len(string)) def test_strings_can_be_joined(self): words = ["Now", "is", "the", "time"] self.assertEqual("Now is the time", ' '.join(words)) def test_strings_can_change_case(self): self.assertEqual("Guido", 'guido'.capitalize()) self.assertEqual("GUIDO", 'guido'.upper()) self.assertEqual("timbot", 'TimBot'.lower()) self.assertEqual("Guido Van Rossum", 'guido van rossum'.title()) self.assertEqual("tOtAlLy AwEsOmE", 'ToTaLlY aWeSoMe'.swapcase())

true

f70bf669380d96903bd4e90137b76c926924b501

15,957

py

Python

build/android/pylib/chrome_test_server_spawner.py

GnorTech/chromium

e1c7731d5bd099ca5544fcf8eda3867d4ce5bab5

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

1

2018-03-10T13:08:49.000Z

build/android/pylib/chrome_test_server_spawner.py

GnorTech/chromium

e1c7731d5bd099ca5544fcf8eda3867d4ce5bab5

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

null

build/android/pylib/chrome_test_server_spawner.py

GnorTech/chromium

e1c7731d5bd099ca5544fcf8eda3867d4ce5bab5

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

1

2020-11-04T07:19:31.000Z

# Copyright 2013 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """A "Test Server Spawner" that handles killing/stopping per-test test servers. It's used to accept requests from the device to spawn and kill instances of the chrome test server on the host. """ import BaseHTTPServer import json import logging import os import select import struct import subprocess import threading import time import urlparse import constants from forwarder import Forwarder import ports # Path that are needed to import necessary modules when launching a testserver. os.environ['PYTHONPATH'] = os.environ.get('PYTHONPATH', '') + (':%s:%s:%s:%s:%s' % (os.path.join(constants.CHROME_DIR, 'third_party'), os.path.join(constants.CHROME_DIR, 'third_party', 'tlslite'), os.path.join(constants.CHROME_DIR, 'third_party', 'pyftpdlib', 'src'), os.path.join(constants.CHROME_DIR, 'net', 'tools', 'testserver'), os.path.join(constants.CHROME_DIR, 'sync', 'tools', 'testserver'))) SERVER_TYPES = { 'http': '', 'ftp': '-f', 'sync': '', # Sync uses its own script, and doesn't take a server type arg. 'tcpecho': '--tcp-echo', 'udpecho': '--udp-echo', } # The timeout (in seconds) of starting up the Python test server. TEST_SERVER_STARTUP_TIMEOUT = 10 def _CheckPortStatus(port, expected_status): """Returns True if port has expected_status. Args: port: the port number. expected_status: boolean of expected status. Returns: Returns True if the status is expected. Otherwise returns False. """ for timeout in range(1, 5): if ports.IsHostPortUsed(port) == expected_status: return True time.sleep(timeout) return False def _GetServerTypeCommandLine(server_type): """Returns the command-line by the given server type. Args: server_type: the server type to be used (e.g. 'http'). Returns: A string containing the command-line argument. """ if server_type not in SERVER_TYPES: raise NotImplementedError('Unknown server type: %s' % server_type) if server_type == 'udpecho': raise Exception('Please do not run UDP echo tests because we do not have ' 'a UDP forwarder tool.') return SERVER_TYPES[server_type] class TestServerThread(threading.Thread): """A thread to run the test server in a separate process.""" def __init__(self, ready_event, arguments, adb, tool, build_type): """Initialize TestServerThread with the following argument. Args: ready_event: event which will be set when the test server is ready. arguments: dictionary of arguments to run the test server. adb: instance of AndroidCommands. tool: instance of runtime error detection tool. build_type: 'Release' or 'Debug'. """ threading.Thread.__init__(self) self.wait_event = threading.Event() self.stop_flag = False self.ready_event = ready_event self.ready_event.clear() self.arguments = arguments self.adb = adb self.tool = tool self.test_server_process = None self.is_ready = False self.host_port = self.arguments['port'] assert isinstance(self.host_port, int) self._test_server_forwarder = None # The forwarder device port now is dynamically allocated. self.forwarder_device_port = 0 # Anonymous pipe in order to get port info from test server. self.pipe_in = None self.pipe_out = None self.command_line = [] self.build_type = build_type def _WaitToStartAndGetPortFromTestServer(self): """Waits for the Python test server to start and gets the port it is using. The port information is passed by the Python test server with a pipe given by self.pipe_out. It is written as a result to |self.host_port|. Returns: Whether the port used by the test server was successfully fetched. """ assert self.host_port == 0 and self.pipe_out and self.pipe_in (in_fds, _, _) = select.select([self.pipe_in, ], [], [], TEST_SERVER_STARTUP_TIMEOUT) if len(in_fds) == 0: logging.error('Failed to wait to the Python test server to be started.') return False # First read the data length as an unsigned 4-byte value. This # is _not_ using network byte ordering since the Python test server packs # size as native byte order and all Chromium platforms so far are # configured to use little-endian. # TODO(jnd): Change the Python test server and local_test_server_*.cc to # use a unified byte order (either big-endian or little-endian). data_length = os.read(self.pipe_in, struct.calcsize('=L')) if data_length: (data_length,) = struct.unpack('=L', data_length) assert data_length if not data_length: logging.error('Failed to get length of server data.') return False port_json = os.read(self.pipe_in, data_length) if not port_json: logging.error('Failed to get server data.') return False logging.info('Got port json data: %s', port_json) port_json = json.loads(port_json) if port_json.has_key('port') and isinstance(port_json['port'], int): self.host_port = port_json['port'] return _CheckPortStatus(self.host_port, True) logging.error('Failed to get port information from the server data.') return False def _GenerateCommandLineArguments(self): """Generates the command line to run the test server. Note that all options are processed by following the definitions in testserver.py. """ if self.command_line: return # The following arguments must exist. type_cmd = _GetServerTypeCommandLine(self.arguments['server-type']) if type_cmd: self.command_line.append(type_cmd) self.command_line.append('--port=%d' % self.host_port) # Use a pipe to get the port given by the instance of Python test server # if the test does not specify the port. if self.host_port == 0: (self.pipe_in, self.pipe_out) = os.pipe() self.command_line.append('--startup-pipe=%d' % self.pipe_out) self.command_line.append('--host=%s' % self.arguments['host']) data_dir = self.arguments['data-dir'] or 'chrome/test/data' if not os.path.isabs(data_dir): data_dir = os.path.join(constants.CHROME_DIR, data_dir) self.command_line.append('--data-dir=%s' % data_dir) # The following arguments are optional depending on the individual test. if self.arguments.has_key('log-to-console'): self.command_line.append('--log-to-console') if self.arguments.has_key('auth-token'): self.command_line.append('--auth-token=%s' % self.arguments['auth-token']) if self.arguments.has_key('https'): self.command_line.append('--https') if self.arguments.has_key('cert-and-key-file'): self.command_line.append('--cert-and-key-file=%s' % os.path.join( constants.CHROME_DIR, self.arguments['cert-and-key-file'])) if self.arguments.has_key('ocsp'): self.command_line.append('--ocsp=%s' % self.arguments['ocsp']) if self.arguments.has_key('https-record-resume'): self.command_line.append('--https-record-resume') if self.arguments.has_key('ssl-client-auth'): self.command_line.append('--ssl-client-auth') if self.arguments.has_key('tls-intolerant'): self.command_line.append('--tls-intolerant=%s' % self.arguments['tls-intolerant']) if self.arguments.has_key('ssl-client-ca'): for ca in self.arguments['ssl-client-ca']: self.command_line.append('--ssl-client-ca=%s' % os.path.join(constants.CHROME_DIR, ca)) if self.arguments.has_key('ssl-bulk-cipher'): for bulk_cipher in self.arguments['ssl-bulk-cipher']: self.command_line.append('--ssl-bulk-cipher=%s' % bulk_cipher) def run(self): logging.info('Start running the thread!') self.wait_event.clear() self._GenerateCommandLineArguments() command = constants.CHROME_DIR if self.arguments['server-type'] == 'sync': command = [os.path.join(command, 'sync', 'tools', 'testserver', 'sync_testserver.py')] + self.command_line else: command = [os.path.join(command, 'net', 'tools', 'testserver', 'testserver.py')] + self.command_line logging.info('Running: %s', command) self.process = subprocess.Popen(command) if self.process: if self.pipe_out: self.is_ready = self._WaitToStartAndGetPortFromTestServer() else: self.is_ready = _CheckPortStatus(self.host_port, True) if self.is_ready: self._test_server_forwarder = Forwarder(self.adb, self.build_type) self._test_server_forwarder.Run( [(0, self.host_port)], self.tool, '127.0.0.1') # Check whether the forwarder is ready on the device. self.is_ready = False device_port = self._test_server_forwarder.DevicePortForHostPort( self.host_port) if device_port: for timeout in range(1, 5): if ports.IsDevicePortUsed(self.adb, device_port, 'LISTEN'): self.is_ready = True self.forwarder_device_port = device_port break time.sleep(timeout) # Wake up the request handler thread. self.ready_event.set() # Keep thread running until Stop() gets called. while not self.stop_flag: time.sleep(1) if self.process.poll() is None: self.process.kill() if self._test_server_forwarder: self._test_server_forwarder.Close() self.process = None self.is_ready = False if self.pipe_out: os.close(self.pipe_in) os.close(self.pipe_out) self.pipe_in = None self.pipe_out = None logging.info('Test-server has died.') self.wait_event.set() def Stop(self): """Blocks until the loop has finished. Note that this must be called in another thread. """ if not self.process: return self.stop_flag = True self.wait_event.wait() class SpawningServerRequestHandler(BaseHTTPServer.BaseHTTPRequestHandler): """A handler used to process http GET/POST request.""" def _SendResponse(self, response_code, response_reason, additional_headers, contents): """Generates a response sent to the client from the provided parameters. Args: response_code: number of the response status. response_reason: string of reason description of the response. additional_headers: dict of additional headers. Each key is the name of the header, each value is the content of the header. contents: string of the contents we want to send to client. """ self.send_response(response_code, response_reason) self.send_header('Content-Type', 'text/html') # Specify the content-length as without it the http(s) response will not # be completed properly (and the browser keeps expecting data). self.send_header('Content-Length', len(contents)) for header_name in additional_headers: self.send_header(header_name, additional_headers[header_name]) self.end_headers() self.wfile.write(contents) self.wfile.flush() def _StartTestServer(self): """Starts the test server thread.""" logging.info('Handling request to spawn a test server.') content_type = self.headers.getheader('content-type') if content_type != 'application/json': raise Exception('Bad content-type for start request.') content_length = self.headers.getheader('content-length') if not content_length: content_length = 0 try: content_length = int(content_length) except: raise Exception('Bad content-length for start request.') logging.info(content_length) test_server_argument_json = self.rfile.read(content_length) logging.info(test_server_argument_json) assert not self.server.test_server_instance ready_event = threading.Event() self.server.test_server_instance = TestServerThread( ready_event, json.loads(test_server_argument_json), self.server.adb, self.server.tool, self.server.build_type) self.server.test_server_instance.setDaemon(True) self.server.test_server_instance.start() ready_event.wait() if self.server.test_server_instance.is_ready: self._SendResponse(200, 'OK', {}, json.dumps( {'port': self.server.test_server_instance.forwarder_device_port, 'message': 'started'})) logging.info('Test server is running on port: %d.', self.server.test_server_instance.host_port) else: self.server.test_server_instance.Stop() self.server.test_server_instance = None self._SendResponse(500, 'Test Server Error.', {}, '') logging.info('Encounter problem during starting a test server.') def _KillTestServer(self): """Stops the test server instance.""" # There should only ever be one test server at a time. This may do the # wrong thing if we try and start multiple test servers. if not self.server.test_server_instance: return port = self.server.test_server_instance.host_port logging.info('Handling request to kill a test server on port: %d.', port) self.server.test_server_instance.Stop() # Make sure the status of test server is correct before sending response. if _CheckPortStatus(port, False): self._SendResponse(200, 'OK', {}, 'killed') logging.info('Test server on port %d is killed', port) else: self._SendResponse(500, 'Test Server Error.', {}, '') logging.info('Encounter problem during killing a test server.') self.server.test_server_instance = None def do_POST(self): parsed_path = urlparse.urlparse(self.path) action = parsed_path.path logging.info('Action for POST method is: %s.', action) if action == '/start': self._StartTestServer() else: self._SendResponse(400, 'Unknown request.', {}, '') logging.info('Encounter unknown request: %s.', action) def do_GET(self): parsed_path = urlparse.urlparse(self.path) action = parsed_path.path params = urlparse.parse_qs(parsed_path.query, keep_blank_values=1) logging.info('Action for GET method is: %s.', action) for param in params: logging.info('%s=%s', param, params[param][0]) if action == '/kill': self._KillTestServer() elif action == '/ping': # The ping handler is used to check whether the spawner server is ready # to serve the requests. We don't need to test the status of the test # server when handling ping request. self._SendResponse(200, 'OK', {}, 'ready') logging.info('Handled ping request and sent response.') else: self._SendResponse(400, 'Unknown request', {}, '') logging.info('Encounter unknown request: %s.', action) class SpawningServer(object): """The class used to start/stop a http server.""" def __init__(self, test_server_spawner_port, adb, tool, build_type): logging.info('Creating new spawner on port: %d.', test_server_spawner_port) self.server = BaseHTTPServer.HTTPServer(('', test_server_spawner_port), SpawningServerRequestHandler) self.port = test_server_spawner_port self.server.adb = adb self.server.tool = tool self.server.test_server_instance = None self.server.build_type = build_type def _Listen(self): logging.info('Starting test server spawner') self.server.serve_forever() def Start(self): listener_thread = threading.Thread(target=self._Listen) listener_thread.setDaemon(True) listener_thread.start() time.sleep(1) def Stop(self): if self.server.test_server_instance: self.server.test_server_instance.Stop() self.server.shutdown()

39.01467

80

0.682522

import BaseHTTPServer import json import logging import os import select import struct import subprocess import threading import time import urlparse import constants from forwarder import Forwarder import ports os.environ['PYTHONPATH'] = os.environ.get('PYTHONPATH', '') + (':%s:%s:%s:%s:%s' % (os.path.join(constants.CHROME_DIR, 'third_party'), os.path.join(constants.CHROME_DIR, 'third_party', 'tlslite'), os.path.join(constants.CHROME_DIR, 'third_party', 'pyftpdlib', 'src'), os.path.join(constants.CHROME_DIR, 'net', 'tools', 'testserver'), os.path.join(constants.CHROME_DIR, 'sync', 'tools', 'testserver'))) SERVER_TYPES = { 'http': '', 'ftp': '-f', 'sync': '', 'tcpecho': '--tcp-echo', 'udpecho': '--udp-echo', } # The timeout (in seconds) of starting up the Python test server. TEST_SERVER_STARTUP_TIMEOUT = 10 def _CheckPortStatus(port, expected_status): for timeout in range(1, 5): if ports.IsHostPortUsed(port) == expected_status: return True time.sleep(timeout) return False def _GetServerTypeCommandLine(server_type): if server_type not in SERVER_TYPES: raise NotImplementedError('Unknown server type: %s' % server_type) if server_type == 'udpecho': raise Exception('Please do not run UDP echo tests because we do not have ' 'a UDP forwarder tool.') return SERVER_TYPES[server_type] class TestServerThread(threading.Thread): def __init__(self, ready_event, arguments, adb, tool, build_type): threading.Thread.__init__(self) self.wait_event = threading.Event() self.stop_flag = False self.ready_event = ready_event self.ready_event.clear() self.arguments = arguments self.adb = adb self.tool = tool self.test_server_process = None self.is_ready = False self.host_port = self.arguments['port'] assert isinstance(self.host_port, int) self._test_server_forwarder = None # The forwarder device port now is dynamically allocated. self.forwarder_device_port = 0 # Anonymous pipe in order to get port info from test server. self.pipe_in = None self.pipe_out = None self.command_line = [] self.build_type = build_type def _WaitToStartAndGetPortFromTestServer(self): assert self.host_port == 0 and self.pipe_out and self.pipe_in (in_fds, _, _) = select.select([self.pipe_in, ], [], [], TEST_SERVER_STARTUP_TIMEOUT) if len(in_fds) == 0: logging.error('Failed to wait to the Python test server to be started.') return False # First read the data length as an unsigned 4-byte value. This # is _not_ using network byte ordering since the Python test server packs # size as native byte order and all Chromium platforms so far are # configured to use little-endian. # TODO(jnd): Change the Python test server and local_test_server_*.cc to # use a unified byte order (either big-endian or little-endian). data_length = os.read(self.pipe_in, struct.calcsize('=L')) if data_length: (data_length,) = struct.unpack('=L', data_length) assert data_length if not data_length: logging.error('Failed to get length of server data.') return False port_json = os.read(self.pipe_in, data_length) if not port_json: logging.error('Failed to get server data.') return False logging.info('Got port json data: %s', port_json) port_json = json.loads(port_json) if port_json.has_key('port') and isinstance(port_json['port'], int): self.host_port = port_json['port'] return _CheckPortStatus(self.host_port, True) logging.error('Failed to get port information from the server data.') return False def _GenerateCommandLineArguments(self): if self.command_line: return # The following arguments must exist. type_cmd = _GetServerTypeCommandLine(self.arguments['server-type']) if type_cmd: self.command_line.append(type_cmd) self.command_line.append('--port=%d' % self.host_port) # Use a pipe to get the port given by the instance of Python test server # if the test does not specify the port. if self.host_port == 0: (self.pipe_in, self.pipe_out) = os.pipe() self.command_line.append('--startup-pipe=%d' % self.pipe_out) self.command_line.append('--host=%s' % self.arguments['host']) data_dir = self.arguments['data-dir'] or 'chrome/test/data' if not os.path.isabs(data_dir): data_dir = os.path.join(constants.CHROME_DIR, data_dir) self.command_line.append('--data-dir=%s' % data_dir) # The following arguments are optional depending on the individual test. if self.arguments.has_key('log-to-console'): self.command_line.append('--log-to-console') if self.arguments.has_key('auth-token'): self.command_line.append('--auth-token=%s' % self.arguments['auth-token']) if self.arguments.has_key('https'): self.command_line.append('--https') if self.arguments.has_key('cert-and-key-file'): self.command_line.append('--cert-and-key-file=%s' % os.path.join( constants.CHROME_DIR, self.arguments['cert-and-key-file'])) if self.arguments.has_key('ocsp'): self.command_line.append('--ocsp=%s' % self.arguments['ocsp']) if self.arguments.has_key('https-record-resume'): self.command_line.append('--https-record-resume') if self.arguments.has_key('ssl-client-auth'): self.command_line.append('--ssl-client-auth') if self.arguments.has_key('tls-intolerant'): self.command_line.append('--tls-intolerant=%s' % self.arguments['tls-intolerant']) if self.arguments.has_key('ssl-client-ca'): for ca in self.arguments['ssl-client-ca']: self.command_line.append('--ssl-client-ca=%s' % os.path.join(constants.CHROME_DIR, ca)) if self.arguments.has_key('ssl-bulk-cipher'): for bulk_cipher in self.arguments['ssl-bulk-cipher']: self.command_line.append('--ssl-bulk-cipher=%s' % bulk_cipher) def run(self): logging.info('Start running the thread!') self.wait_event.clear() self._GenerateCommandLineArguments() command = constants.CHROME_DIR if self.arguments['server-type'] == 'sync': command = [os.path.join(command, 'sync', 'tools', 'testserver', 'sync_testserver.py')] + self.command_line else: command = [os.path.join(command, 'net', 'tools', 'testserver', 'testserver.py')] + self.command_line logging.info('Running: %s', command) self.process = subprocess.Popen(command) if self.process: if self.pipe_out: self.is_ready = self._WaitToStartAndGetPortFromTestServer() else: self.is_ready = _CheckPortStatus(self.host_port, True) if self.is_ready: self._test_server_forwarder = Forwarder(self.adb, self.build_type) self._test_server_forwarder.Run( [(0, self.host_port)], self.tool, '127.0.0.1') # Check whether the forwarder is ready on the device. self.is_ready = False device_port = self._test_server_forwarder.DevicePortForHostPort( self.host_port) if device_port: for timeout in range(1, 5): if ports.IsDevicePortUsed(self.adb, device_port, 'LISTEN'): self.is_ready = True self.forwarder_device_port = device_port break time.sleep(timeout) # Wake up the request handler thread. self.ready_event.set() # Keep thread running until Stop() gets called. while not self.stop_flag: time.sleep(1) if self.process.poll() is None: self.process.kill() if self._test_server_forwarder: self._test_server_forwarder.Close() self.process = None self.is_ready = False if self.pipe_out: os.close(self.pipe_in) os.close(self.pipe_out) self.pipe_in = None self.pipe_out = None logging.info('Test-server has died.') self.wait_event.set() def Stop(self): if not self.process: return self.stop_flag = True self.wait_event.wait() class SpawningServerRequestHandler(BaseHTTPServer.BaseHTTPRequestHandler): def _SendResponse(self, response_code, response_reason, additional_headers, contents): self.send_response(response_code, response_reason) self.send_header('Content-Type', 'text/html') # Specify the content-length as without it the http(s) response will not # be completed properly (and the browser keeps expecting data). self.send_header('Content-Length', len(contents)) for header_name in additional_headers: self.send_header(header_name, additional_headers[header_name]) self.end_headers() self.wfile.write(contents) self.wfile.flush() def _StartTestServer(self): logging.info('Handling request to spawn a test server.') content_type = self.headers.getheader('content-type') if content_type != 'application/json': raise Exception('Bad content-type for start request.') content_length = self.headers.getheader('content-length') if not content_length: content_length = 0 try: content_length = int(content_length) except: raise Exception('Bad content-length for start request.') logging.info(content_length) test_server_argument_json = self.rfile.read(content_length) logging.info(test_server_argument_json) assert not self.server.test_server_instance ready_event = threading.Event() self.server.test_server_instance = TestServerThread( ready_event, json.loads(test_server_argument_json), self.server.adb, self.server.tool, self.server.build_type) self.server.test_server_instance.setDaemon(True) self.server.test_server_instance.start() ready_event.wait() if self.server.test_server_instance.is_ready: self._SendResponse(200, 'OK', {}, json.dumps( {'port': self.server.test_server_instance.forwarder_device_port, 'message': 'started'})) logging.info('Test server is running on port: %d.', self.server.test_server_instance.host_port) else: self.server.test_server_instance.Stop() self.server.test_server_instance = None self._SendResponse(500, 'Test Server Error.', {}, '') logging.info('Encounter problem during starting a test server.') def _KillTestServer(self): # There should only ever be one test server at a time. This may do the # wrong thing if we try and start multiple test servers. if not self.server.test_server_instance: return port = self.server.test_server_instance.host_port logging.info('Handling request to kill a test server on port: %d.', port) self.server.test_server_instance.Stop() # Make sure the status of test server is correct before sending response. if _CheckPortStatus(port, False): self._SendResponse(200, 'OK', {}, 'killed') logging.info('Test server on port %d is killed', port) else: self._SendResponse(500, 'Test Server Error.', {}, '') logging.info('Encounter problem during killing a test server.') self.server.test_server_instance = None def do_POST(self): parsed_path = urlparse.urlparse(self.path) action = parsed_path.path logging.info('Action for POST method is: %s.', action) if action == '/start': self._StartTestServer() else: self._SendResponse(400, 'Unknown request.', {}, '') logging.info('Encounter unknown request: %s.', action) def do_GET(self): parsed_path = urlparse.urlparse(self.path) action = parsed_path.path params = urlparse.parse_qs(parsed_path.query, keep_blank_values=1) logging.info('Action for GET method is: %s.', action) for param in params: logging.info('%s=%s', param, params[param][0]) if action == '/kill': self._KillTestServer() elif action == '/ping': # The ping handler is used to check whether the spawner server is ready # to serve the requests. We don't need to test the status of the test self._SendResponse(200, 'OK', {}, 'ready') logging.info('Handled ping request and sent response.') else: self._SendResponse(400, 'Unknown request', {}, '') logging.info('Encounter unknown request: %s.', action) class SpawningServer(object): def __init__(self, test_server_spawner_port, adb, tool, build_type): logging.info('Creating new spawner on port: %d.', test_server_spawner_port) self.server = BaseHTTPServer.HTTPServer(('', test_server_spawner_port), SpawningServerRequestHandler) self.port = test_server_spawner_port self.server.adb = adb self.server.tool = tool self.server.test_server_instance = None self.server.build_type = build_type def _Listen(self): logging.info('Starting test server spawner') self.server.serve_forever() def Start(self): listener_thread = threading.Thread(target=self._Listen) listener_thread.setDaemon(True) listener_thread.start() time.sleep(1) def Stop(self): if self.server.test_server_instance: self.server.test_server_instance.Stop() self.server.shutdown()

true

f70bf6ee38f2719e916cda8cb70d9a8dda8c9666

8,303

py

Python

whoville/cloudbreak/models/reinstall_request_v2.py

mikchaos/whoville

6eabaea4b74ac0b632c03db8252590131c6ce63b

[ "Apache-2.0" ]

null

whoville/cloudbreak/models/reinstall_request_v2.py

mikchaos/whoville

6eabaea4b74ac0b632c03db8252590131c6ce63b

[ "Apache-2.0" ]

null

whoville/cloudbreak/models/reinstall_request_v2.py

mikchaos/whoville

6eabaea4b74ac0b632c03db8252590131c6ce63b

[ "Apache-2.0" ]

null

# coding: utf-8 """ Cloudbreak API Cloudbreak is a powerful left surf that breaks over a coral reef, a mile off southwest the island of Tavarua, Fiji. Cloudbreak is a cloud agnostic Hadoop as a Service API. Abstracts the provisioning and ease management and monitoring of on-demand clusters. SequenceIQ's Cloudbreak is a RESTful application development platform with the goal of helping developers to build solutions for deploying Hadoop YARN clusters in different environments. Once it is deployed in your favourite servlet container it exposes a REST API allowing to span up Hadoop clusters of arbitary sizes and cloud providers. Provisioning Hadoop has never been easier. Cloudbreak is built on the foundation of cloud providers API (Amazon AWS, Microsoft Azure, Google Cloud Platform, Openstack), Apache Ambari, Docker lightweight containers, Swarm and Consul. For further product documentation follow the link: <a href=\"http://hortonworks.com/apache/cloudbreak/\">http://hortonworks.com/apache/cloudbreak/</a> OpenAPI spec version: 2.7.1 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class ReinstallRequestV2(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 = { 'instance_groups': 'list[InstanceGroupsV2]', 'ambari_stack_details': 'AmbariStackDetails', 'blueprint_name': 'str', 'kerberos_password': 'str', 'kerberos_principal': 'str' } attribute_map = { 'instance_groups': 'instanceGroups', 'ambari_stack_details': 'ambariStackDetails', 'blueprint_name': 'blueprintName', 'kerberos_password': 'kerberosPassword', 'kerberos_principal': 'kerberosPrincipal' } def __init__(self, instance_groups=None, ambari_stack_details=None, blueprint_name=None, kerberos_password=None, kerberos_principal=None): """ ReinstallRequestV2 - a model defined in Swagger """ self._instance_groups = None self._ambari_stack_details = None self._blueprint_name = None self._kerberos_password = None self._kerberos_principal = None if instance_groups is not None: self.instance_groups = instance_groups if ambari_stack_details is not None: self.ambari_stack_details = ambari_stack_details self.blueprint_name = blueprint_name if kerberos_password is not None: self.kerberos_password = kerberos_password if kerberos_principal is not None: self.kerberos_principal = kerberos_principal @property def instance_groups(self): """ Gets the instance_groups of this ReinstallRequestV2. collection of instance groupst :return: The instance_groups of this ReinstallRequestV2. :rtype: list[InstanceGroupsV2] """ return self._instance_groups @instance_groups.setter def instance_groups(self, instance_groups): """ Sets the instance_groups of this ReinstallRequestV2. collection of instance groupst :param instance_groups: The instance_groups of this ReinstallRequestV2. :type: list[InstanceGroupsV2] """ self._instance_groups = instance_groups @property def ambari_stack_details(self): """ Gets the ambari_stack_details of this ReinstallRequestV2. details of the Ambari stack :return: The ambari_stack_details of this ReinstallRequestV2. :rtype: AmbariStackDetails """ return self._ambari_stack_details @ambari_stack_details.setter def ambari_stack_details(self, ambari_stack_details): """ Sets the ambari_stack_details of this ReinstallRequestV2. details of the Ambari stack :param ambari_stack_details: The ambari_stack_details of this ReinstallRequestV2. :type: AmbariStackDetails """ self._ambari_stack_details = ambari_stack_details @property def blueprint_name(self): """ Gets the blueprint_name of this ReinstallRequestV2. blueprint name for the cluster :return: The blueprint_name of this ReinstallRequestV2. :rtype: str """ return self._blueprint_name @blueprint_name.setter def blueprint_name(self, blueprint_name): """ Sets the blueprint_name of this ReinstallRequestV2. blueprint name for the cluster :param blueprint_name: The blueprint_name of this ReinstallRequestV2. :type: str """ if blueprint_name is None: raise ValueError("Invalid value for `blueprint_name`, must not be `None`") self._blueprint_name = blueprint_name @property def kerberos_password(self): """ Gets the kerberos_password of this ReinstallRequestV2. kerberos admin password :return: The kerberos_password of this ReinstallRequestV2. :rtype: str """ return self._kerberos_password @kerberos_password.setter def kerberos_password(self, kerberos_password): """ Sets the kerberos_password of this ReinstallRequestV2. kerberos admin password :param kerberos_password: The kerberos_password of this ReinstallRequestV2. :type: str """ if kerberos_password is not None and len(kerberos_password) > 50: raise ValueError("Invalid value for `kerberos_password`, length must be less than or equal to `50`") if kerberos_password is not None and len(kerberos_password) < 5: raise ValueError("Invalid value for `kerberos_password`, length must be greater than or equal to `5`") self._kerberos_password = kerberos_password @property def kerberos_principal(self): """ Gets the kerberos_principal of this ReinstallRequestV2. kerberos principal :return: The kerberos_principal of this ReinstallRequestV2. :rtype: str """ return self._kerberos_principal @kerberos_principal.setter def kerberos_principal(self, kerberos_principal): """ Sets the kerberos_principal of this ReinstallRequestV2. kerberos principal :param kerberos_principal: The kerberos_principal of this ReinstallRequestV2. :type: str """ self._kerberos_principal = kerberos_principal def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in 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 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, ReinstallRequestV2): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

34.168724

984

0.648802

from pprint import pformat from six import iteritems import re class ReinstallRequestV2(object): swagger_types = { 'instance_groups': 'list[InstanceGroupsV2]', 'ambari_stack_details': 'AmbariStackDetails', 'blueprint_name': 'str', 'kerberos_password': 'str', 'kerberos_principal': 'str' } attribute_map = { 'instance_groups': 'instanceGroups', 'ambari_stack_details': 'ambariStackDetails', 'blueprint_name': 'blueprintName', 'kerberos_password': 'kerberosPassword', 'kerberos_principal': 'kerberosPrincipal' } def __init__(self, instance_groups=None, ambari_stack_details=None, blueprint_name=None, kerberos_password=None, kerberos_principal=None): self._instance_groups = None self._ambari_stack_details = None self._blueprint_name = None self._kerberos_password = None self._kerberos_principal = None if instance_groups is not None: self.instance_groups = instance_groups if ambari_stack_details is not None: self.ambari_stack_details = ambari_stack_details self.blueprint_name = blueprint_name if kerberos_password is not None: self.kerberos_password = kerberos_password if kerberos_principal is not None: self.kerberos_principal = kerberos_principal @property def instance_groups(self): return self._instance_groups @instance_groups.setter def instance_groups(self, instance_groups): self._instance_groups = instance_groups @property def ambari_stack_details(self): return self._ambari_stack_details @ambari_stack_details.setter def ambari_stack_details(self, ambari_stack_details): self._ambari_stack_details = ambari_stack_details @property def blueprint_name(self): return self._blueprint_name @blueprint_name.setter def blueprint_name(self, blueprint_name): if blueprint_name is None: raise ValueError("Invalid value for `blueprint_name`, must not be `None`") self._blueprint_name = blueprint_name @property def kerberos_password(self): return self._kerberos_password @kerberos_password.setter def kerberos_password(self, kerberos_password): if kerberos_password is not None and len(kerberos_password) > 50: raise ValueError("Invalid value for `kerberos_password`, length must be less than or equal to `50`") if kerberos_password is not None and len(kerberos_password) < 5: raise ValueError("Invalid value for `kerberos_password`, length must be greater than or equal to `5`") self._kerberos_password = kerberos_password @property def kerberos_principal(self): return self._kerberos_principal @kerberos_principal.setter def kerberos_principal(self, kerberos_principal): self._kerberos_principal = kerberos_principal def to_dict(self): result = {} for attr, _ in 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): return pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, ReinstallRequestV2): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f70bf6f1dc5f4faa2e79e0059d5d2beeba7eb784

41,081

py

Python

venv/Lib/site-packages/matplotlib/backends/backend_qt5.py

StewSchrieff/riddlerHoopGame

3d63f494aa803c7571ace83f87a40ce5d6b0dfc1

[ "MIT" ]

69

2020-03-31T06:40:17.000Z

2022-02-25T11:48:18.000Z

venv/Lib/site-packages/matplotlib/backends/backend_qt5.py

StewSchrieff/riddlerHoopGame

3d63f494aa803c7571ace83f87a40ce5d6b0dfc1

[ "MIT" ]

6

2018-08-28T12:33:14.000Z

2019-05-07T20:32:42.000Z

venv/Lib/site-packages/matplotlib/backends/backend_qt5.py

StewSchrieff/riddlerHoopGame

3d63f494aa803c7571ace83f87a40ce5d6b0dfc1

[ "MIT" ]

28

2020-04-15T15:24:17.000Z

2021-12-26T04:05:02.000Z

from __future__ import (absolute_import, division, print_function, unicode_literals) import six import functools import os import re import signal import sys from six import unichr import traceback import matplotlib from matplotlib._pylab_helpers import Gcf from matplotlib.backend_bases import ( _Backend, FigureCanvasBase, FigureManagerBase, NavigationToolbar2, TimerBase, cursors, ToolContainerBase, StatusbarBase) import matplotlib.backends.qt_editor.figureoptions as figureoptions from matplotlib.backends.qt_editor.formsubplottool import UiSubplotTool from matplotlib.figure import Figure from matplotlib.backend_managers import ToolManager from matplotlib import backend_tools from .qt_compat import ( QtCore, QtGui, QtWidgets, _getSaveFileName, is_pyqt5, __version__, QT_API) backend_version = __version__ # SPECIAL_KEYS are keys that do *not* return their unicode name # instead they have manually specified names SPECIAL_KEYS = {QtCore.Qt.Key_Control: 'control', QtCore.Qt.Key_Shift: 'shift', QtCore.Qt.Key_Alt: 'alt', QtCore.Qt.Key_Meta: 'super', QtCore.Qt.Key_Return: 'enter', QtCore.Qt.Key_Left: 'left', QtCore.Qt.Key_Up: 'up', QtCore.Qt.Key_Right: 'right', QtCore.Qt.Key_Down: 'down', QtCore.Qt.Key_Escape: 'escape', QtCore.Qt.Key_F1: 'f1', QtCore.Qt.Key_F2: 'f2', QtCore.Qt.Key_F3: 'f3', QtCore.Qt.Key_F4: 'f4', QtCore.Qt.Key_F5: 'f5', QtCore.Qt.Key_F6: 'f6', QtCore.Qt.Key_F7: 'f7', QtCore.Qt.Key_F8: 'f8', QtCore.Qt.Key_F9: 'f9', QtCore.Qt.Key_F10: 'f10', QtCore.Qt.Key_F11: 'f11', QtCore.Qt.Key_F12: 'f12', QtCore.Qt.Key_Home: 'home', QtCore.Qt.Key_End: 'end', QtCore.Qt.Key_PageUp: 'pageup', QtCore.Qt.Key_PageDown: 'pagedown', QtCore.Qt.Key_Tab: 'tab', QtCore.Qt.Key_Backspace: 'backspace', QtCore.Qt.Key_Enter: 'enter', QtCore.Qt.Key_Insert: 'insert', QtCore.Qt.Key_Delete: 'delete', QtCore.Qt.Key_Pause: 'pause', QtCore.Qt.Key_SysReq: 'sysreq', QtCore.Qt.Key_Clear: 'clear', } # define which modifier keys are collected on keyboard events. # elements are (mpl names, Modifier Flag, Qt Key) tuples SUPER = 0 ALT = 1 CTRL = 2 SHIFT = 3 MODIFIER_KEYS = [('super', QtCore.Qt.MetaModifier, QtCore.Qt.Key_Meta), ('alt', QtCore.Qt.AltModifier, QtCore.Qt.Key_Alt), ('ctrl', QtCore.Qt.ControlModifier, QtCore.Qt.Key_Control), ('shift', QtCore.Qt.ShiftModifier, QtCore.Qt.Key_Shift), ] if sys.platform == 'darwin': # in OSX, the control and super (aka cmd/apple) keys are switched, so # switch them back. SPECIAL_KEYS.update({QtCore.Qt.Key_Control: 'cmd', # cmd/apple key QtCore.Qt.Key_Meta: 'control', }) MODIFIER_KEYS[0] = ('cmd', QtCore.Qt.ControlModifier, QtCore.Qt.Key_Control) MODIFIER_KEYS[2] = ('ctrl', QtCore.Qt.MetaModifier, QtCore.Qt.Key_Meta) cursord = { cursors.MOVE: QtCore.Qt.SizeAllCursor, cursors.HAND: QtCore.Qt.PointingHandCursor, cursors.POINTER: QtCore.Qt.ArrowCursor, cursors.SELECT_REGION: QtCore.Qt.CrossCursor, cursors.WAIT: QtCore.Qt.WaitCursor, } # make place holder qApp = None def _create_qApp(): """ Only one qApp can exist at a time, so check before creating one. """ global qApp if qApp is None: app = QtWidgets.QApplication.instance() if app is None: # check for DISPLAY env variable on X11 build of Qt if is_pyqt5(): try: from PyQt5 import QtX11Extras is_x11_build = True except ImportError: is_x11_build = False else: is_x11_build = hasattr(QtGui, "QX11Info") if is_x11_build: display = os.environ.get('DISPLAY') if display is None or not re.search(r':\d', display): raise RuntimeError('Invalid DISPLAY variable') qApp = QtWidgets.QApplication([b"matplotlib"]) qApp.lastWindowClosed.connect(qApp.quit) else: qApp = app if is_pyqt5(): try: qApp.setAttribute(QtCore.Qt.AA_UseHighDpiPixmaps) qApp.setAttribute(QtCore.Qt.AA_EnableHighDpiScaling) except AttributeError: pass def _allow_super_init(__init__): """ Decorator for ``__init__`` to allow ``super().__init__`` on PyQt4/PySide2. """ if QT_API == "PyQt5": return __init__ else: # To work around lack of cooperative inheritance in PyQt4, PySide, # and PySide2, when calling FigureCanvasQT.__init__, we temporarily # patch QWidget.__init__ by a cooperative version, that first calls # QWidget.__init__ with no additional arguments, and then finds the # next class in the MRO with an __init__ that does support cooperative # inheritance (i.e., not defined by the PyQt4, PySide, PySide2, sip # or Shiboken packages), and manually call its `__init__`, once again # passing the additional arguments. qwidget_init = QtWidgets.QWidget.__init__ def cooperative_qwidget_init(self, *args, **kwargs): qwidget_init(self) mro = type(self).__mro__ next_coop_init = next( cls for cls in mro[mro.index(QtWidgets.QWidget) + 1:] if cls.__module__.split(".")[0] not in [ "PyQt4", "sip", "PySide", "PySide2", "Shiboken"]) next_coop_init.__init__(self, *args, **kwargs) @functools.wraps(__init__) def wrapper(self, **kwargs): try: QtWidgets.QWidget.__init__ = cooperative_qwidget_init __init__(self, **kwargs) finally: # Restore __init__ QtWidgets.QWidget.__init__ = qwidget_init return wrapper class TimerQT(TimerBase): ''' Subclass of :class:`backend_bases.TimerBase` that uses Qt timer events. Attributes ---------- interval : int The time between timer events in milliseconds. Default is 1000 ms. single_shot : bool Boolean flag indicating whether this timer should operate as single shot (run once and then stop). Defaults to False. callbacks : list Stores list of (func, args) tuples that will be called upon timer events. This list can be manipulated directly, or the functions `add_callback` and `remove_callback` can be used. ''' def __init__(self, *args, **kwargs): TimerBase.__init__(self, *args, **kwargs) # Create a new timer and connect the timeout() signal to the # _on_timer method. self._timer = QtCore.QTimer() self._timer.timeout.connect(self._on_timer) self._timer_set_interval() def _timer_set_single_shot(self): self._timer.setSingleShot(self._single) def _timer_set_interval(self): self._timer.setInterval(self._interval) def _timer_start(self): self._timer.start() def _timer_stop(self): self._timer.stop() class FigureCanvasQT(QtWidgets.QWidget, FigureCanvasBase): # map Qt button codes to MouseEvent's ones: buttond = {QtCore.Qt.LeftButton: 1, QtCore.Qt.MidButton: 2, QtCore.Qt.RightButton: 3, # QtCore.Qt.XButton1: None, # QtCore.Qt.XButton2: None, } @_allow_super_init def __init__(self, figure): _create_qApp() super(FigureCanvasQT, self).__init__(figure=figure) self.figure = figure # We don't want to scale up the figure DPI more than once. # Note, we don't handle a signal for changing DPI yet. figure._original_dpi = figure.dpi self._update_figure_dpi() # In cases with mixed resolution displays, we need to be careful if the # dpi_ratio changes - in this case we need to resize the canvas # accordingly. We could watch for screenChanged events from Qt, but # the issue is that we can't guarantee this will be emitted *before* # the first paintEvent for the canvas, so instead we keep track of the # dpi_ratio value here and in paintEvent we resize the canvas if # needed. self._dpi_ratio_prev = None self._draw_pending = False self._is_drawing = False self._draw_rect_callback = lambda painter: None self.setAttribute(QtCore.Qt.WA_OpaquePaintEvent) self.setMouseTracking(True) self.resize(*self.get_width_height()) # Key auto-repeat enabled by default self._keyautorepeat = True palette = QtGui.QPalette(QtCore.Qt.white) self.setPalette(palette) def _update_figure_dpi(self): dpi = self._dpi_ratio * self.figure._original_dpi self.figure._set_dpi(dpi, forward=False) @property def _dpi_ratio(self): # Not available on Qt4 or some older Qt5. try: # self.devicePixelRatio() returns 0 in rare cases return self.devicePixelRatio() or 1 except AttributeError: return 1 def _update_dpi(self): # As described in __init__ above, we need to be careful in cases with # mixed resolution displays if dpi_ratio is changing between painting # events. # Return whether we triggered a resizeEvent (and thus a paintEvent) # from within this function. if self._dpi_ratio != self._dpi_ratio_prev: # We need to update the figure DPI. self._update_figure_dpi() self._dpi_ratio_prev = self._dpi_ratio # The easiest way to resize the canvas is to emit a resizeEvent # since we implement all the logic for resizing the canvas for # that event. event = QtGui.QResizeEvent(self.size(), self.size()) self.resizeEvent(event) # resizeEvent triggers a paintEvent itself, so we exit this one # (after making sure that the event is immediately handled). return True return False def get_width_height(self): w, h = FigureCanvasBase.get_width_height(self) return int(w / self._dpi_ratio), int(h / self._dpi_ratio) def enterEvent(self, event): FigureCanvasBase.enter_notify_event(self, guiEvent=event) def leaveEvent(self, event): QtWidgets.QApplication.restoreOverrideCursor() FigureCanvasBase.leave_notify_event(self, guiEvent=event) def mouseEventCoords(self, pos): """Calculate mouse coordinates in physical pixels Qt5 use logical pixels, but the figure is scaled to physical pixels for rendering. Transform to physical pixels so that all of the down-stream transforms work as expected. Also, the origin is different and needs to be corrected. """ dpi_ratio = self._dpi_ratio x = pos.x() # flip y so y=0 is bottom of canvas y = self.figure.bbox.height / dpi_ratio - pos.y() return x * dpi_ratio, y * dpi_ratio def mousePressEvent(self, event): x, y = self.mouseEventCoords(event.pos()) button = self.buttond.get(event.button()) if button is not None: FigureCanvasBase.button_press_event(self, x, y, button, guiEvent=event) def mouseDoubleClickEvent(self, event): x, y = self.mouseEventCoords(event.pos()) button = self.buttond.get(event.button()) if button is not None: FigureCanvasBase.button_press_event(self, x, y, button, dblclick=True, guiEvent=event) def mouseMoveEvent(self, event): x, y = self.mouseEventCoords(event) FigureCanvasBase.motion_notify_event(self, x, y, guiEvent=event) def mouseReleaseEvent(self, event): x, y = self.mouseEventCoords(event) button = self.buttond.get(event.button()) if button is not None: FigureCanvasBase.button_release_event(self, x, y, button, guiEvent=event) if is_pyqt5(): def wheelEvent(self, event): x, y = self.mouseEventCoords(event) # from QWheelEvent::delta doc if event.pixelDelta().x() == 0 and event.pixelDelta().y() == 0: steps = event.angleDelta().y() / 120 else: steps = event.pixelDelta().y() if steps: FigureCanvasBase.scroll_event( self, x, y, steps, guiEvent=event) else: def wheelEvent(self, event): x = event.x() # flipy so y=0 is bottom of canvas y = self.figure.bbox.height - event.y() # from QWheelEvent::delta doc steps = event.delta() / 120 if event.orientation() == QtCore.Qt.Vertical: FigureCanvasBase.scroll_event( self, x, y, steps, guiEvent=event) def keyPressEvent(self, event): key = self._get_key(event) if key is not None: FigureCanvasBase.key_press_event(self, key, guiEvent=event) def keyReleaseEvent(self, event): key = self._get_key(event) if key is not None: FigureCanvasBase.key_release_event(self, key, guiEvent=event) @property def keyAutoRepeat(self): """ If True, enable auto-repeat for key events. """ return self._keyautorepeat @keyAutoRepeat.setter def keyAutoRepeat(self, val): self._keyautorepeat = bool(val) def resizeEvent(self, event): # _dpi_ratio_prev will be set the first time the canvas is painted, and # the rendered buffer is useless before anyways. if self._dpi_ratio_prev is None: return w = event.size().width() * self._dpi_ratio h = event.size().height() * self._dpi_ratio dpival = self.figure.dpi winch = w / dpival hinch = h / dpival self.figure.set_size_inches(winch, hinch, forward=False) # pass back into Qt to let it finish QtWidgets.QWidget.resizeEvent(self, event) # emit our resize events FigureCanvasBase.resize_event(self) def sizeHint(self): w, h = self.get_width_height() return QtCore.QSize(w, h) def minumumSizeHint(self): return QtCore.QSize(10, 10) def _get_key(self, event): if not self._keyautorepeat and event.isAutoRepeat(): return None event_key = event.key() event_mods = int(event.modifiers()) # actually a bitmask # get names of the pressed modifier keys # bit twiddling to pick out modifier keys from event_mods bitmask, # if event_key is a MODIFIER, it should not be duplicated in mods mods = [name for name, mod_key, qt_key in MODIFIER_KEYS if event_key != qt_key and (event_mods & mod_key) == mod_key] try: # for certain keys (enter, left, backspace, etc) use a word for the # key, rather than unicode key = SPECIAL_KEYS[event_key] except KeyError: # unicode defines code points up to 0x0010ffff # QT will use Key_Codes larger than that for keyboard keys that are # are not unicode characters (like multimedia keys) # skip these # if you really want them, you should add them to SPECIAL_KEYS MAX_UNICODE = 0x10ffff if event_key > MAX_UNICODE: return None key = unichr(event_key) # qt delivers capitalized letters. fix capitalization # note that capslock is ignored if 'shift' in mods: mods.remove('shift') else: key = key.lower() mods.reverse() return '+'.join(mods + [key]) def new_timer(self, *args, **kwargs): """ Creates a new backend-specific subclass of :class:`backend_bases.Timer`. This is useful for getting periodic events through the backend's native event loop. Implemented only for backends with GUIs. Other Parameters ---------------- interval : scalar Timer interval in milliseconds callbacks : list Sequence of (func, args, kwargs) where ``func(*args, **kwargs)`` will be executed by the timer every *interval*. """ return TimerQT(*args, **kwargs) def flush_events(self): qApp.processEvents() def start_event_loop(self, timeout=0): if hasattr(self, "_event_loop") and self._event_loop.isRunning(): raise RuntimeError("Event loop already running") self._event_loop = event_loop = QtCore.QEventLoop() if timeout: timer = QtCore.QTimer.singleShot(timeout * 1000, event_loop.quit) event_loop.exec_() def stop_event_loop(self, event=None): if hasattr(self, "_event_loop"): self._event_loop.quit() def draw(self): """Render the figure, and queue a request for a Qt draw. """ # The renderer draw is done here; delaying causes problems with code # that uses the result of the draw() to update plot elements. if self._is_drawing: return self._is_drawing = True try: super(FigureCanvasQT, self).draw() finally: self._is_drawing = False self.update() def draw_idle(self): """Queue redraw of the Agg buffer and request Qt paintEvent. """ # The Agg draw needs to be handled by the same thread matplotlib # modifies the scene graph from. Post Agg draw request to the # current event loop in order to ensure thread affinity and to # accumulate multiple draw requests from event handling. # TODO: queued signal connection might be safer than singleShot if not (self._draw_pending or self._is_drawing): self._draw_pending = True QtCore.QTimer.singleShot(0, self._draw_idle) def _draw_idle(self): if self.height() < 0 or self.width() < 0: self._draw_pending = False if not self._draw_pending: return try: self.draw() except Exception: # Uncaught exceptions are fatal for PyQt5, so catch them instead. traceback.print_exc() finally: self._draw_pending = False def drawRectangle(self, rect): # Draw the zoom rectangle to the QPainter. _draw_rect_callback needs # to be called at the end of paintEvent. if rect is not None: def _draw_rect_callback(painter): pen = QtGui.QPen(QtCore.Qt.black, 1 / self._dpi_ratio, QtCore.Qt.DotLine) painter.setPen(pen) painter.drawRect(*(pt / self._dpi_ratio for pt in rect)) else: def _draw_rect_callback(painter): return self._draw_rect_callback = _draw_rect_callback self.update() class MainWindow(QtWidgets.QMainWindow): closing = QtCore.Signal() def closeEvent(self, event): self.closing.emit() QtWidgets.QMainWindow.closeEvent(self, event) class FigureManagerQT(FigureManagerBase): """ Attributes ---------- canvas : `FigureCanvas` The FigureCanvas instance num : int or str The Figure number toolbar : qt.QToolBar The qt.QToolBar window : qt.QMainWindow The qt.QMainWindow """ def __init__(self, canvas, num): FigureManagerBase.__init__(self, canvas, num) self.canvas = canvas self.window = MainWindow() self.window.closing.connect(canvas.close_event) self.window.closing.connect(self._widgetclosed) self.window.setWindowTitle("Figure %d" % num) image = os.path.join(matplotlib.rcParams['datapath'], 'images', 'matplotlib.svg') self.window.setWindowIcon(QtGui.QIcon(image)) # Give the keyboard focus to the figure instead of the # manager; StrongFocus accepts both tab and click to focus and # will enable the canvas to process event w/o clicking. # ClickFocus only takes the focus is the window has been # clicked # on. http://qt-project.org/doc/qt-4.8/qt.html#FocusPolicy-enum or # http://doc.qt.digia.com/qt/qt.html#FocusPolicy-enum self.canvas.setFocusPolicy(QtCore.Qt.StrongFocus) self.canvas.setFocus() self.window._destroying = False self.toolmanager = self._get_toolmanager() self.toolbar = self._get_toolbar(self.canvas, self.window) self.statusbar = None if self.toolmanager: backend_tools.add_tools_to_manager(self.toolmanager) if self.toolbar: backend_tools.add_tools_to_container(self.toolbar) self.statusbar = StatusbarQt(self.window, self.toolmanager) if self.toolbar is not None: self.window.addToolBar(self.toolbar) if not self.toolmanager: # add text label to status bar statusbar_label = QtWidgets.QLabel() self.window.statusBar().addWidget(statusbar_label) self.toolbar.message.connect(statusbar_label.setText) tbs_height = self.toolbar.sizeHint().height() else: tbs_height = 0 # resize the main window so it will display the canvas with the # requested size: cs = canvas.sizeHint() sbs = self.window.statusBar().sizeHint() self._status_and_tool_height = tbs_height + sbs.height() height = cs.height() + self._status_and_tool_height self.window.resize(cs.width(), height) self.window.setCentralWidget(self.canvas) if matplotlib.is_interactive(): self.window.show() self.canvas.draw_idle() def notify_axes_change(fig): # This will be called whenever the current axes is changed if self.toolbar is not None: self.toolbar.update() self.canvas.figure.add_axobserver(notify_axes_change) self.window.raise_() def full_screen_toggle(self): if self.window.isFullScreen(): self.window.showNormal() else: self.window.showFullScreen() def _widgetclosed(self): if self.window._destroying: return self.window._destroying = True try: Gcf.destroy(self.num) except AttributeError: pass # It seems that when the python session is killed, # Gcf can get destroyed before the Gcf.destroy # line is run, leading to a useless AttributeError. def _get_toolbar(self, canvas, parent): # must be inited after the window, drawingArea and figure # attrs are set if matplotlib.rcParams['toolbar'] == 'toolbar2': toolbar = NavigationToolbar2QT(canvas, parent, False) elif matplotlib.rcParams['toolbar'] == 'toolmanager': toolbar = ToolbarQt(self.toolmanager, self.window) else: toolbar = None return toolbar def _get_toolmanager(self): if matplotlib.rcParams['toolbar'] == 'toolmanager': toolmanager = ToolManager(self.canvas.figure) else: toolmanager = None return toolmanager def resize(self, width, height): 'set the canvas size in pixels' self.window.resize(width, height + self._status_and_tool_height) def show(self): self.window.show() self.window.activateWindow() self.window.raise_() def destroy(self, *args): # check for qApp first, as PySide deletes it in its atexit handler if QtWidgets.QApplication.instance() is None: return if self.window._destroying: return self.window._destroying = True if self.toolbar: self.toolbar.destroy() self.window.close() def get_window_title(self): return six.text_type(self.window.windowTitle()) def set_window_title(self, title): self.window.setWindowTitle(title) class NavigationToolbar2QT(NavigationToolbar2, QtWidgets.QToolBar): message = QtCore.Signal(str) def __init__(self, canvas, parent, coordinates=True): """ coordinates: should we show the coordinates on the right? """ self.canvas = canvas self.parent = parent self.coordinates = coordinates self._actions = {} """A mapping of toolitem method names to their QActions""" QtWidgets.QToolBar.__init__(self, parent) NavigationToolbar2.__init__(self, canvas) def _icon(self, name): if is_pyqt5(): name = name.replace('.png', '_large.png') pm = QtGui.QPixmap(os.path.join(self.basedir, name)) if hasattr(pm, 'setDevicePixelRatio'): pm.setDevicePixelRatio(self.canvas._dpi_ratio) return QtGui.QIcon(pm) def _init_toolbar(self): self.basedir = os.path.join(matplotlib.rcParams['datapath'], 'images') for text, tooltip_text, image_file, callback in self.toolitems: if text is None: self.addSeparator() else: a = self.addAction(self._icon(image_file + '.png'), text, getattr(self, callback)) self._actions[callback] = a if callback in ['zoom', 'pan']: a.setCheckable(True) if tooltip_text is not None: a.setToolTip(tooltip_text) if text == 'Subplots': a = self.addAction(self._icon("qt4_editor_options.png"), 'Customize', self.edit_parameters) a.setToolTip('Edit axis, curve and image parameters') self.buttons = {} # Add the x,y location widget at the right side of the toolbar # The stretch factor is 1 which means any resizing of the toolbar # will resize this label instead of the buttons. if self.coordinates: self.locLabel = QtWidgets.QLabel("", self) self.locLabel.setAlignment( QtCore.Qt.AlignRight | QtCore.Qt.AlignTop) self.locLabel.setSizePolicy( QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Ignored)) labelAction = self.addWidget(self.locLabel) labelAction.setVisible(True) # reference holder for subplots_adjust window self.adj_window = None # Esthetic adjustments - we need to set these explicitly in PyQt5 # otherwise the layout looks different - but we don't want to set it if # not using HiDPI icons otherwise they look worse than before. if is_pyqt5(): self.setIconSize(QtCore.QSize(24, 24)) self.layout().setSpacing(12) if is_pyqt5(): # For some reason, self.setMinimumHeight doesn't seem to carry over to # the actual sizeHint, so override it instead in order to make the # aesthetic adjustments noted above. def sizeHint(self): size = super(NavigationToolbar2QT, self).sizeHint() size.setHeight(max(48, size.height())) return size def edit_parameters(self): allaxes = self.canvas.figure.get_axes() if not allaxes: QtWidgets.QMessageBox.warning( self.parent, "Error", "There are no axes to edit.") return elif len(allaxes) == 1: axes, = allaxes else: titles = [] for axes in allaxes: name = (axes.get_title() or " - ".join(filter(None, [axes.get_xlabel(), axes.get_ylabel()])) or "<anonymous {} (id: {:#x})>".format( type(axes).__name__, id(axes))) titles.append(name) item, ok = QtWidgets.QInputDialog.getItem( self.parent, 'Customize', 'Select axes:', titles, 0, False) if ok: axes = allaxes[titles.index(six.text_type(item))] else: return figureoptions.figure_edit(axes, self) def _update_buttons_checked(self): # sync button checkstates to match active mode self._actions['pan'].setChecked(self._active == 'PAN') self._actions['zoom'].setChecked(self._active == 'ZOOM') def pan(self, *args): super(NavigationToolbar2QT, self).pan(*args) self._update_buttons_checked() def zoom(self, *args): super(NavigationToolbar2QT, self).zoom(*args) self._update_buttons_checked() def set_message(self, s): self.message.emit(s) if self.coordinates: self.locLabel.setText(s) def set_cursor(self, cursor): self.canvas.setCursor(cursord[cursor]) def draw_rubberband(self, event, x0, y0, x1, y1): height = self.canvas.figure.bbox.height y1 = height - y1 y0 = height - y0 rect = [int(val) for val in (x0, y0, x1 - x0, y1 - y0)] self.canvas.drawRectangle(rect) def remove_rubberband(self): self.canvas.drawRectangle(None) def configure_subplots(self): image = os.path.join(matplotlib.rcParams['datapath'], 'images', 'matplotlib.png') dia = SubplotToolQt(self.canvas.figure, self.parent) dia.setWindowIcon(QtGui.QIcon(image)) dia.exec_() def save_figure(self, *args): filetypes = self.canvas.get_supported_filetypes_grouped() sorted_filetypes = sorted(six.iteritems(filetypes)) default_filetype = self.canvas.get_default_filetype() startpath = os.path.expanduser( matplotlib.rcParams['savefig.directory']) start = os.path.join(startpath, self.canvas.get_default_filename()) filters = [] selectedFilter = None for name, exts in sorted_filetypes: exts_list = " ".join(['*.%s' % ext for ext in exts]) filter = '%s (%s)' % (name, exts_list) if default_filetype in exts: selectedFilter = filter filters.append(filter) filters = ';;'.join(filters) fname, filter = _getSaveFileName(self.parent, "Choose a filename to save to", start, filters, selectedFilter) if fname: # Save dir for next time, unless empty str (i.e., use cwd). if startpath != "": matplotlib.rcParams['savefig.directory'] = ( os.path.dirname(six.text_type(fname))) try: self.canvas.figure.savefig(six.text_type(fname)) except Exception as e: QtWidgets.QMessageBox.critical( self, "Error saving file", six.text_type(e), QtWidgets.QMessageBox.Ok, QtWidgets.QMessageBox.NoButton) class SubplotToolQt(UiSubplotTool): def __init__(self, targetfig, parent): UiSubplotTool.__init__(self, None) self._figure = targetfig for lower, higher in [("bottom", "top"), ("left", "right")]: self._widgets[lower].valueChanged.connect( lambda val: self._widgets[higher].setMinimum(val + .001)) self._widgets[higher].valueChanged.connect( lambda val: self._widgets[lower].setMaximum(val - .001)) self._attrs = ["top", "bottom", "left", "right", "hspace", "wspace"] self._defaults = {attr: vars(self._figure.subplotpars)[attr] for attr in self._attrs} # Set values after setting the range callbacks, but before setting up # the redraw callbacks. self._reset() for attr in self._attrs: self._widgets[attr].valueChanged.connect(self._on_value_changed) for action, method in [("Export values", self._export_values), ("Tight layout", self._tight_layout), ("Reset", self._reset), ("Close", self.close)]: self._widgets[action].clicked.connect(method) def _export_values(self): # Explicitly round to 3 decimals (which is also the spinbox precision) # to avoid numbers of the form 0.100...001. dialog = QtWidgets.QDialog() layout = QtWidgets.QVBoxLayout() dialog.setLayout(layout) text = QtWidgets.QPlainTextEdit() text.setReadOnly(True) layout.addWidget(text) text.setPlainText( ",\n".join("{}={:.3}".format(attr, self._widgets[attr].value()) for attr in self._attrs)) # Adjust the height of the text widget to fit the whole text, plus # some padding. size = text.maximumSize() size.setHeight( QtGui.QFontMetrics(text.document().defaultFont()) .size(0, text.toPlainText()).height() + 20) text.setMaximumSize(size) dialog.exec_() def _on_value_changed(self): self._figure.subplots_adjust(**{attr: self._widgets[attr].value() for attr in self._attrs}) self._figure.canvas.draw_idle() def _tight_layout(self): self._figure.tight_layout() for attr in self._attrs: widget = self._widgets[attr] widget.blockSignals(True) widget.setValue(vars(self._figure.subplotpars)[attr]) widget.blockSignals(False) self._figure.canvas.draw_idle() def _reset(self): for attr, value in self._defaults.items(): self._widgets[attr].setValue(value) class ToolbarQt(ToolContainerBase, QtWidgets.QToolBar): def __init__(self, toolmanager, parent): ToolContainerBase.__init__(self, toolmanager) QtWidgets.QToolBar.__init__(self, parent) self._toolitems = {} self._groups = {} self._last = None @property def _icon_extension(self): if is_pyqt5(): return '_large.png' return '.png' def add_toolitem( self, name, group, position, image_file, description, toggle): button = QtWidgets.QToolButton(self) button.setIcon(self._icon(image_file)) button.setText(name) if description: button.setToolTip(description) def handler(): self.trigger_tool(name) if toggle: button.setCheckable(True) button.toggled.connect(handler) else: button.clicked.connect(handler) self._last = button self._toolitems.setdefault(name, []) self._add_to_group(group, name, button, position) self._toolitems[name].append((button, handler)) def _add_to_group(self, group, name, button, position): gr = self._groups.get(group, []) if not gr: sep = self.addSeparator() gr.append(sep) before = gr[position] widget = self.insertWidget(before, button) gr.insert(position, widget) self._groups[group] = gr def _icon(self, name): pm = QtGui.QPixmap(name) if hasattr(pm, 'setDevicePixelRatio'): pm.setDevicePixelRatio(self.toolmanager.canvas._dpi_ratio) return QtGui.QIcon(pm) def toggle_toolitem(self, name, toggled): if name not in self._toolitems: return for button, handler in self._toolitems[name]: button.toggled.disconnect(handler) button.setChecked(toggled) button.toggled.connect(handler) def remove_toolitem(self, name): for button, handler in self._toolitems[name]: button.setParent(None) del self._toolitems[name] class StatusbarQt(StatusbarBase, QtWidgets.QLabel): def __init__(self, window, *args, **kwargs): StatusbarBase.__init__(self, *args, **kwargs) QtWidgets.QLabel.__init__(self) window.statusBar().addWidget(self) def set_message(self, s): self.setText(s) class ConfigureSubplotsQt(backend_tools.ConfigureSubplotsBase): def trigger(self, *args): image = os.path.join(matplotlib.rcParams['datapath'], 'images', 'matplotlib.png') parent = self.canvas.manager.window dia = SubplotToolQt(self.figure, parent) dia.setWindowIcon(QtGui.QIcon(image)) dia.exec_() class SaveFigureQt(backend_tools.SaveFigureBase): def trigger(self, *args): filetypes = self.canvas.get_supported_filetypes_grouped() sorted_filetypes = sorted(six.iteritems(filetypes)) default_filetype = self.canvas.get_default_filetype() startpath = os.path.expanduser( matplotlib.rcParams['savefig.directory']) start = os.path.join(startpath, self.canvas.get_default_filename()) filters = [] selectedFilter = None for name, exts in sorted_filetypes: exts_list = " ".join(['*.%s' % ext for ext in exts]) filter = '%s (%s)' % (name, exts_list) if default_filetype in exts: selectedFilter = filter filters.append(filter) filters = ';;'.join(filters) parent = self.canvas.manager.window fname, filter = _getSaveFileName(parent, "Choose a filename to save to", start, filters, selectedFilter) if fname: # Save dir for next time, unless empty str (i.e., use cwd). if startpath != "": matplotlib.rcParams['savefig.directory'] = ( os.path.dirname(six.text_type(fname))) try: self.canvas.figure.savefig(six.text_type(fname)) except Exception as e: QtWidgets.QMessageBox.critical( self, "Error saving file", six.text_type(e), QtWidgets.QMessageBox.Ok, QtWidgets.QMessageBox.NoButton) class SetCursorQt(backend_tools.SetCursorBase): def set_cursor(self, cursor): self.canvas.setCursor(cursord[cursor]) class RubberbandQt(backend_tools.RubberbandBase): def draw_rubberband(self, x0, y0, x1, y1): height = self.canvas.figure.bbox.height y1 = height - y1 y0 = height - y0 rect = [int(val) for val in (x0, y0, x1 - x0, y1 - y0)] self.canvas.drawRectangle(rect) def remove_rubberband(self): self.canvas.drawRectangle(None) backend_tools.ToolSaveFigure = SaveFigureQt backend_tools.ToolConfigureSubplots = ConfigureSubplotsQt backend_tools.ToolSetCursor = SetCursorQt backend_tools.ToolRubberband = RubberbandQt def error_msg_qt(msg, parent=None): if not isinstance(msg, six.string_types): msg = ','.join(map(str, msg)) QtWidgets.QMessageBox.warning(None, "Matplotlib", msg, QtGui.QMessageBox.Ok) def exception_handler(type, value, tb): """Handle uncaught exceptions It does not catch SystemExit """ msg = '' # get the filename attribute if available (for IOError) if hasattr(value, 'filename') and value.filename is not None: msg = value.filename + ': ' if hasattr(value, 'strerror') and value.strerror is not None: msg += value.strerror else: msg += six.text_type(value) if len(msg): error_msg_qt(msg) @_Backend.export class _BackendQT5(_Backend): FigureCanvas = FigureCanvasQT FigureManager = FigureManagerQT @staticmethod def trigger_manager_draw(manager): manager.canvas.draw_idle() @staticmethod def mainloop(): # allow KeyboardInterrupt exceptions to close the plot window. signal.signal(signal.SIGINT, signal.SIG_DFL) qApp.exec_()

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from __future__ import (absolute_import, division, print_function, unicode_literals) import six import functools import os import re import signal import sys from six import unichr import traceback import matplotlib from matplotlib._pylab_helpers import Gcf from matplotlib.backend_bases import ( _Backend, FigureCanvasBase, FigureManagerBase, NavigationToolbar2, TimerBase, cursors, ToolContainerBase, StatusbarBase) import matplotlib.backends.qt_editor.figureoptions as figureoptions from matplotlib.backends.qt_editor.formsubplottool import UiSubplotTool from matplotlib.figure import Figure from matplotlib.backend_managers import ToolManager from matplotlib import backend_tools from .qt_compat import ( QtCore, QtGui, QtWidgets, _getSaveFileName, is_pyqt5, __version__, QT_API) backend_version = __version__ SPECIAL_KEYS = {QtCore.Qt.Key_Control: 'control', QtCore.Qt.Key_Shift: 'shift', QtCore.Qt.Key_Alt: 'alt', QtCore.Qt.Key_Meta: 'super', QtCore.Qt.Key_Return: 'enter', QtCore.Qt.Key_Left: 'left', QtCore.Qt.Key_Up: 'up', QtCore.Qt.Key_Right: 'right', QtCore.Qt.Key_Down: 'down', QtCore.Qt.Key_Escape: 'escape', QtCore.Qt.Key_F1: 'f1', QtCore.Qt.Key_F2: 'f2', QtCore.Qt.Key_F3: 'f3', QtCore.Qt.Key_F4: 'f4', QtCore.Qt.Key_F5: 'f5', QtCore.Qt.Key_F6: 'f6', QtCore.Qt.Key_F7: 'f7', QtCore.Qt.Key_F8: 'f8', QtCore.Qt.Key_F9: 'f9', QtCore.Qt.Key_F10: 'f10', QtCore.Qt.Key_F11: 'f11', QtCore.Qt.Key_F12: 'f12', QtCore.Qt.Key_Home: 'home', QtCore.Qt.Key_End: 'end', QtCore.Qt.Key_PageUp: 'pageup', QtCore.Qt.Key_PageDown: 'pagedown', QtCore.Qt.Key_Tab: 'tab', QtCore.Qt.Key_Backspace: 'backspace', QtCore.Qt.Key_Enter: 'enter', QtCore.Qt.Key_Insert: 'insert', QtCore.Qt.Key_Delete: 'delete', QtCore.Qt.Key_Pause: 'pause', QtCore.Qt.Key_SysReq: 'sysreq', QtCore.Qt.Key_Clear: 'clear', } SUPER = 0 ALT = 1 CTRL = 2 SHIFT = 3 MODIFIER_KEYS = [('super', QtCore.Qt.MetaModifier, QtCore.Qt.Key_Meta), ('alt', QtCore.Qt.AltModifier, QtCore.Qt.Key_Alt), ('ctrl', QtCore.Qt.ControlModifier, QtCore.Qt.Key_Control), ('shift', QtCore.Qt.ShiftModifier, QtCore.Qt.Key_Shift), ] if sys.platform == 'darwin': SPECIAL_KEYS.update({QtCore.Qt.Key_Control: 'cmd', QtCore.Qt.Key_Meta: 'control', }) MODIFIER_KEYS[0] = ('cmd', QtCore.Qt.ControlModifier, QtCore.Qt.Key_Control) MODIFIER_KEYS[2] = ('ctrl', QtCore.Qt.MetaModifier, QtCore.Qt.Key_Meta) cursord = { cursors.MOVE: QtCore.Qt.SizeAllCursor, cursors.HAND: QtCore.Qt.PointingHandCursor, cursors.POINTER: QtCore.Qt.ArrowCursor, cursors.SELECT_REGION: QtCore.Qt.CrossCursor, cursors.WAIT: QtCore.Qt.WaitCursor, } qApp = None def _create_qApp(): global qApp if qApp is None: app = QtWidgets.QApplication.instance() if app is None: if is_pyqt5(): try: from PyQt5 import QtX11Extras is_x11_build = True except ImportError: is_x11_build = False else: is_x11_build = hasattr(QtGui, "QX11Info") if is_x11_build: display = os.environ.get('DISPLAY') if display is None or not re.search(r':\d', display): raise RuntimeError('Invalid DISPLAY variable') qApp = QtWidgets.QApplication([b"matplotlib"]) qApp.lastWindowClosed.connect(qApp.quit) else: qApp = app if is_pyqt5(): try: qApp.setAttribute(QtCore.Qt.AA_UseHighDpiPixmaps) qApp.setAttribute(QtCore.Qt.AA_EnableHighDpiScaling) except AttributeError: pass def _allow_super_init(__init__): if QT_API == "PyQt5": return __init__ else: qwidget_init = QtWidgets.QWidget.__init__ def cooperative_qwidget_init(self, *args, **kwargs): qwidget_init(self) mro = type(self).__mro__ next_coop_init = next( cls for cls in mro[mro.index(QtWidgets.QWidget) + 1:] if cls.__module__.split(".")[0] not in [ "PyQt4", "sip", "PySide", "PySide2", "Shiboken"]) next_coop_init.__init__(self, *args, **kwargs) @functools.wraps(__init__) def wrapper(self, **kwargs): try: QtWidgets.QWidget.__init__ = cooperative_qwidget_init __init__(self, **kwargs) finally: QtWidgets.QWidget.__init__ = qwidget_init return wrapper class TimerQT(TimerBase): def __init__(self, *args, **kwargs): TimerBase.__init__(self, *args, **kwargs) self._timer = QtCore.QTimer() self._timer.timeout.connect(self._on_timer) self._timer_set_interval() def _timer_set_single_shot(self): self._timer.setSingleShot(self._single) def _timer_set_interval(self): self._timer.setInterval(self._interval) def _timer_start(self): self._timer.start() def _timer_stop(self): self._timer.stop() class FigureCanvasQT(QtWidgets.QWidget, FigureCanvasBase): buttond = {QtCore.Qt.LeftButton: 1, QtCore.Qt.MidButton: 2, QtCore.Qt.RightButton: 3, # QtCore.Qt.XButton1: None, # QtCore.Qt.XButton2: None, } @_allow_super_init def __init__(self, figure): _create_qApp() super(FigureCanvasQT, self).__init__(figure=figure) self.figure = figure # We don't want to scale up the figure DPI more than once. figure._original_dpi = figure.dpi self._update_figure_dpi() # In cases with mixed resolution displays, we need to be careful if the # dpi_ratio changes - in this case we need to resize the canvas # accordingly. We could watch for screenChanged events from Qt, but # the issue is that we can't guarantee this will be emitted *before* self._dpi_ratio_prev = None self._draw_pending = False self._is_drawing = False self._draw_rect_callback = lambda painter: None self.setAttribute(QtCore.Qt.WA_OpaquePaintEvent) self.setMouseTracking(True) self.resize(*self.get_width_height()) self._keyautorepeat = True palette = QtGui.QPalette(QtCore.Qt.white) self.setPalette(palette) def _update_figure_dpi(self): dpi = self._dpi_ratio * self.figure._original_dpi self.figure._set_dpi(dpi, forward=False) @property def _dpi_ratio(self): try: return self.devicePixelRatio() or 1 except AttributeError: return 1 def _update_dpi(self): if self._dpi_ratio != self._dpi_ratio_prev: self._update_figure_dpi() self._dpi_ratio_prev = self._dpi_ratio event = QtGui.QResizeEvent(self.size(), self.size()) self.resizeEvent(event) return True return False def get_width_height(self): w, h = FigureCanvasBase.get_width_height(self) return int(w / self._dpi_ratio), int(h / self._dpi_ratio) def enterEvent(self, event): FigureCanvasBase.enter_notify_event(self, guiEvent=event) def leaveEvent(self, event): QtWidgets.QApplication.restoreOverrideCursor() FigureCanvasBase.leave_notify_event(self, guiEvent=event) def mouseEventCoords(self, pos): dpi_ratio = self._dpi_ratio x = pos.x() y = self.figure.bbox.height / dpi_ratio - pos.y() return x * dpi_ratio, y * dpi_ratio def mousePressEvent(self, event): x, y = self.mouseEventCoords(event.pos()) button = self.buttond.get(event.button()) if button is not None: FigureCanvasBase.button_press_event(self, x, y, button, guiEvent=event) def mouseDoubleClickEvent(self, event): x, y = self.mouseEventCoords(event.pos()) button = self.buttond.get(event.button()) if button is not None: FigureCanvasBase.button_press_event(self, x, y, button, dblclick=True, guiEvent=event) def mouseMoveEvent(self, event): x, y = self.mouseEventCoords(event) FigureCanvasBase.motion_notify_event(self, x, y, guiEvent=event) def mouseReleaseEvent(self, event): x, y = self.mouseEventCoords(event) button = self.buttond.get(event.button()) if button is not None: FigureCanvasBase.button_release_event(self, x, y, button, guiEvent=event) if is_pyqt5(): def wheelEvent(self, event): x, y = self.mouseEventCoords(event) if event.pixelDelta().x() == 0 and event.pixelDelta().y() == 0: steps = event.angleDelta().y() / 120 else: steps = event.pixelDelta().y() if steps: FigureCanvasBase.scroll_event( self, x, y, steps, guiEvent=event) else: def wheelEvent(self, event): x = event.x() y = self.figure.bbox.height - event.y() steps = event.delta() / 120 if event.orientation() == QtCore.Qt.Vertical: FigureCanvasBase.scroll_event( self, x, y, steps, guiEvent=event) def keyPressEvent(self, event): key = self._get_key(event) if key is not None: FigureCanvasBase.key_press_event(self, key, guiEvent=event) def keyReleaseEvent(self, event): key = self._get_key(event) if key is not None: FigureCanvasBase.key_release_event(self, key, guiEvent=event) @property def keyAutoRepeat(self): return self._keyautorepeat @keyAutoRepeat.setter def keyAutoRepeat(self, val): self._keyautorepeat = bool(val) def resizeEvent(self, event): if self._dpi_ratio_prev is None: return w = event.size().width() * self._dpi_ratio h = event.size().height() * self._dpi_ratio dpival = self.figure.dpi winch = w / dpival hinch = h / dpival self.figure.set_size_inches(winch, hinch, forward=False) QtWidgets.QWidget.resizeEvent(self, event) FigureCanvasBase.resize_event(self) def sizeHint(self): w, h = self.get_width_height() return QtCore.QSize(w, h) def minumumSizeHint(self): return QtCore.QSize(10, 10) def _get_key(self, event): if not self._keyautorepeat and event.isAutoRepeat(): return None event_key = event.key() event_mods = int(event.modifiers()) mods = [name for name, mod_key, qt_key in MODIFIER_KEYS if event_key != qt_key and (event_mods & mod_key) == mod_key] try: key = SPECIAL_KEYS[event_key] except KeyError: MAX_UNICODE = 0x10ffff if event_key > MAX_UNICODE: return None key = unichr(event_key) if 'shift' in mods: mods.remove('shift') else: key = key.lower() mods.reverse() return '+'.join(mods + [key]) def new_timer(self, *args, **kwargs): return TimerQT(*args, **kwargs) def flush_events(self): qApp.processEvents() def start_event_loop(self, timeout=0): if hasattr(self, "_event_loop") and self._event_loop.isRunning(): raise RuntimeError("Event loop already running") self._event_loop = event_loop = QtCore.QEventLoop() if timeout: timer = QtCore.QTimer.singleShot(timeout * 1000, event_loop.quit) event_loop.exec_() def stop_event_loop(self, event=None): if hasattr(self, "_event_loop"): self._event_loop.quit() def draw(self): if self._is_drawing: return self._is_drawing = True try: super(FigureCanvasQT, self).draw() finally: self._is_drawing = False self.update() def draw_idle(self): if not (self._draw_pending or self._is_drawing): self._draw_pending = True QtCore.QTimer.singleShot(0, self._draw_idle) def _draw_idle(self): if self.height() < 0 or self.width() < 0: self._draw_pending = False if not self._draw_pending: return try: self.draw() except Exception: traceback.print_exc() finally: self._draw_pending = False def drawRectangle(self, rect): if rect is not None: def _draw_rect_callback(painter): pen = QtGui.QPen(QtCore.Qt.black, 1 / self._dpi_ratio, QtCore.Qt.DotLine) painter.setPen(pen) painter.drawRect(*(pt / self._dpi_ratio for pt in rect)) else: def _draw_rect_callback(painter): return self._draw_rect_callback = _draw_rect_callback self.update() class MainWindow(QtWidgets.QMainWindow): closing = QtCore.Signal() def closeEvent(self, event): self.closing.emit() QtWidgets.QMainWindow.closeEvent(self, event) class FigureManagerQT(FigureManagerBase): def __init__(self, canvas, num): FigureManagerBase.__init__(self, canvas, num) self.canvas = canvas self.window = MainWindow() self.window.closing.connect(canvas.close_event) self.window.closing.connect(self._widgetclosed) self.window.setWindowTitle("Figure %d" % num) image = os.path.join(matplotlib.rcParams['datapath'], 'images', 'matplotlib.svg') self.window.setWindowIcon(QtGui.QIcon(image)) self.canvas.setFocusPolicy(QtCore.Qt.StrongFocus) self.canvas.setFocus() self.window._destroying = False self.toolmanager = self._get_toolmanager() self.toolbar = self._get_toolbar(self.canvas, self.window) self.statusbar = None if self.toolmanager: backend_tools.add_tools_to_manager(self.toolmanager) if self.toolbar: backend_tools.add_tools_to_container(self.toolbar) self.statusbar = StatusbarQt(self.window, self.toolmanager) if self.toolbar is not None: self.window.addToolBar(self.toolbar) if not self.toolmanager: statusbar_label = QtWidgets.QLabel() self.window.statusBar().addWidget(statusbar_label) self.toolbar.message.connect(statusbar_label.setText) tbs_height = self.toolbar.sizeHint().height() else: tbs_height = 0 cs = canvas.sizeHint() sbs = self.window.statusBar().sizeHint() self._status_and_tool_height = tbs_height + sbs.height() height = cs.height() + self._status_and_tool_height self.window.resize(cs.width(), height) self.window.setCentralWidget(self.canvas) if matplotlib.is_interactive(): self.window.show() self.canvas.draw_idle() def notify_axes_change(fig): if self.toolbar is not None: self.toolbar.update() self.canvas.figure.add_axobserver(notify_axes_change) self.window.raise_() def full_screen_toggle(self): if self.window.isFullScreen(): self.window.showNormal() else: self.window.showFullScreen() def _widgetclosed(self): if self.window._destroying: return self.window._destroying = True try: Gcf.destroy(self.num) except AttributeError: pass def _get_toolbar(self, canvas, parent): if matplotlib.rcParams['toolbar'] == 'toolbar2': toolbar = NavigationToolbar2QT(canvas, parent, False) elif matplotlib.rcParams['toolbar'] == 'toolmanager': toolbar = ToolbarQt(self.toolmanager, self.window) else: toolbar = None return toolbar def _get_toolmanager(self): if matplotlib.rcParams['toolbar'] == 'toolmanager': toolmanager = ToolManager(self.canvas.figure) else: toolmanager = None return toolmanager def resize(self, width, height): self.window.resize(width, height + self._status_and_tool_height) def show(self): self.window.show() self.window.activateWindow() self.window.raise_() def destroy(self, *args): if QtWidgets.QApplication.instance() is None: return if self.window._destroying: return self.window._destroying = True if self.toolbar: self.toolbar.destroy() self.window.close() def get_window_title(self): return six.text_type(self.window.windowTitle()) def set_window_title(self, title): self.window.setWindowTitle(title) class NavigationToolbar2QT(NavigationToolbar2, QtWidgets.QToolBar): message = QtCore.Signal(str) def __init__(self, canvas, parent, coordinates=True): self.canvas = canvas self.parent = parent self.coordinates = coordinates self._actions = {} QtWidgets.QToolBar.__init__(self, parent) NavigationToolbar2.__init__(self, canvas) def _icon(self, name): if is_pyqt5(): name = name.replace('.png', '_large.png') pm = QtGui.QPixmap(os.path.join(self.basedir, name)) if hasattr(pm, 'setDevicePixelRatio'): pm.setDevicePixelRatio(self.canvas._dpi_ratio) return QtGui.QIcon(pm) def _init_toolbar(self): self.basedir = os.path.join(matplotlib.rcParams['datapath'], 'images') for text, tooltip_text, image_file, callback in self.toolitems: if text is None: self.addSeparator() else: a = self.addAction(self._icon(image_file + '.png'), text, getattr(self, callback)) self._actions[callback] = a if callback in ['zoom', 'pan']: a.setCheckable(True) if tooltip_text is not None: a.setToolTip(tooltip_text) if text == 'Subplots': a = self.addAction(self._icon("qt4_editor_options.png"), 'Customize', self.edit_parameters) a.setToolTip('Edit axis, curve and image parameters') self.buttons = {} if self.coordinates: self.locLabel = QtWidgets.QLabel("", self) self.locLabel.setAlignment( QtCore.Qt.AlignRight | QtCore.Qt.AlignTop) self.locLabel.setSizePolicy( QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Ignored)) labelAction = self.addWidget(self.locLabel) labelAction.setVisible(True) self.adj_window = None # not using HiDPI icons otherwise they look worse than before. if is_pyqt5(): self.setIconSize(QtCore.QSize(24, 24)) self.layout().setSpacing(12) if is_pyqt5(): # For some reason, self.setMinimumHeight doesn't seem to carry over to def sizeHint(self): size = super(NavigationToolbar2QT, self).sizeHint() size.setHeight(max(48, size.height())) return size def edit_parameters(self): allaxes = self.canvas.figure.get_axes() if not allaxes: QtWidgets.QMessageBox.warning( self.parent, "Error", "There are no axes to edit.") return elif len(allaxes) == 1: axes, = allaxes else: titles = [] for axes in allaxes: name = (axes.get_title() or " - ".join(filter(None, [axes.get_xlabel(), axes.get_ylabel()])) or "<anonymous {} (id: {:#x})>".format( type(axes).__name__, id(axes))) titles.append(name) item, ok = QtWidgets.QInputDialog.getItem( self.parent, 'Customize', 'Select axes:', titles, 0, False) if ok: axes = allaxes[titles.index(six.text_type(item))] else: return figureoptions.figure_edit(axes, self) def _update_buttons_checked(self): self._actions['pan'].setChecked(self._active == 'PAN') self._actions['zoom'].setChecked(self._active == 'ZOOM') def pan(self, *args): super(NavigationToolbar2QT, self).pan(*args) self._update_buttons_checked() def zoom(self, *args): super(NavigationToolbar2QT, self).zoom(*args) self._update_buttons_checked() def set_message(self, s): self.message.emit(s) if self.coordinates: self.locLabel.setText(s) def set_cursor(self, cursor): self.canvas.setCursor(cursord[cursor]) def draw_rubberband(self, event, x0, y0, x1, y1): height = self.canvas.figure.bbox.height y1 = height - y1 y0 = height - y0 rect = [int(val) for val in (x0, y0, x1 - x0, y1 - y0)] self.canvas.drawRectangle(rect) def remove_rubberband(self): self.canvas.drawRectangle(None) def configure_subplots(self): image = os.path.join(matplotlib.rcParams['datapath'], 'images', 'matplotlib.png') dia = SubplotToolQt(self.canvas.figure, self.parent) dia.setWindowIcon(QtGui.QIcon(image)) dia.exec_() def save_figure(self, *args): filetypes = self.canvas.get_supported_filetypes_grouped() sorted_filetypes = sorted(six.iteritems(filetypes)) default_filetype = self.canvas.get_default_filetype() startpath = os.path.expanduser( matplotlib.rcParams['savefig.directory']) start = os.path.join(startpath, self.canvas.get_default_filename()) filters = [] selectedFilter = None for name, exts in sorted_filetypes: exts_list = " ".join(['*.%s' % ext for ext in exts]) filter = '%s (%s)' % (name, exts_list) if default_filetype in exts: selectedFilter = filter filters.append(filter) filters = ';;'.join(filters) fname, filter = _getSaveFileName(self.parent, "Choose a filename to save to", start, filters, selectedFilter) if fname: if startpath != "": matplotlib.rcParams['savefig.directory'] = ( os.path.dirname(six.text_type(fname))) try: self.canvas.figure.savefig(six.text_type(fname)) except Exception as e: QtWidgets.QMessageBox.critical( self, "Error saving file", six.text_type(e), QtWidgets.QMessageBox.Ok, QtWidgets.QMessageBox.NoButton) class SubplotToolQt(UiSubplotTool): def __init__(self, targetfig, parent): UiSubplotTool.__init__(self, None) self._figure = targetfig for lower, higher in [("bottom", "top"), ("left", "right")]: self._widgets[lower].valueChanged.connect( lambda val: self._widgets[higher].setMinimum(val + .001)) self._widgets[higher].valueChanged.connect( lambda val: self._widgets[lower].setMaximum(val - .001)) self._attrs = ["top", "bottom", "left", "right", "hspace", "wspace"] self._defaults = {attr: vars(self._figure.subplotpars)[attr] for attr in self._attrs} self._reset() for attr in self._attrs: self._widgets[attr].valueChanged.connect(self._on_value_changed) for action, method in [("Export values", self._export_values), ("Tight layout", self._tight_layout), ("Reset", self._reset), ("Close", self.close)]: self._widgets[action].clicked.connect(method) def _export_values(self): dialog = QtWidgets.QDialog() layout = QtWidgets.QVBoxLayout() dialog.setLayout(layout) text = QtWidgets.QPlainTextEdit() text.setReadOnly(True) layout.addWidget(text) text.setPlainText( ",\n".join("{}={:.3}".format(attr, self._widgets[attr].value()) for attr in self._attrs)) size = text.maximumSize() size.setHeight( QtGui.QFontMetrics(text.document().defaultFont()) .size(0, text.toPlainText()).height() + 20) text.setMaximumSize(size) dialog.exec_() def _on_value_changed(self): self._figure.subplots_adjust(**{attr: self._widgets[attr].value() for attr in self._attrs}) self._figure.canvas.draw_idle() def _tight_layout(self): self._figure.tight_layout() for attr in self._attrs: widget = self._widgets[attr] widget.blockSignals(True) widget.setValue(vars(self._figure.subplotpars)[attr]) widget.blockSignals(False) self._figure.canvas.draw_idle() def _reset(self): for attr, value in self._defaults.items(): self._widgets[attr].setValue(value) class ToolbarQt(ToolContainerBase, QtWidgets.QToolBar): def __init__(self, toolmanager, parent): ToolContainerBase.__init__(self, toolmanager) QtWidgets.QToolBar.__init__(self, parent) self._toolitems = {} self._groups = {} self._last = None @property def _icon_extension(self): if is_pyqt5(): return '_large.png' return '.png' def add_toolitem( self, name, group, position, image_file, description, toggle): button = QtWidgets.QToolButton(self) button.setIcon(self._icon(image_file)) button.setText(name) if description: button.setToolTip(description) def handler(): self.trigger_tool(name) if toggle: button.setCheckable(True) button.toggled.connect(handler) else: button.clicked.connect(handler) self._last = button self._toolitems.setdefault(name, []) self._add_to_group(group, name, button, position) self._toolitems[name].append((button, handler)) def _add_to_group(self, group, name, button, position): gr = self._groups.get(group, []) if not gr: sep = self.addSeparator() gr.append(sep) before = gr[position] widget = self.insertWidget(before, button) gr.insert(position, widget) self._groups[group] = gr def _icon(self, name): pm = QtGui.QPixmap(name) if hasattr(pm, 'setDevicePixelRatio'): pm.setDevicePixelRatio(self.toolmanager.canvas._dpi_ratio) return QtGui.QIcon(pm) def toggle_toolitem(self, name, toggled): if name not in self._toolitems: return for button, handler in self._toolitems[name]: button.toggled.disconnect(handler) button.setChecked(toggled) button.toggled.connect(handler) def remove_toolitem(self, name): for button, handler in self._toolitems[name]: button.setParent(None) del self._toolitems[name] class StatusbarQt(StatusbarBase, QtWidgets.QLabel): def __init__(self, window, *args, **kwargs): StatusbarBase.__init__(self, *args, **kwargs) QtWidgets.QLabel.__init__(self) window.statusBar().addWidget(self) def set_message(self, s): self.setText(s) class ConfigureSubplotsQt(backend_tools.ConfigureSubplotsBase): def trigger(self, *args): image = os.path.join(matplotlib.rcParams['datapath'], 'images', 'matplotlib.png') parent = self.canvas.manager.window dia = SubplotToolQt(self.figure, parent) dia.setWindowIcon(QtGui.QIcon(image)) dia.exec_() class SaveFigureQt(backend_tools.SaveFigureBase): def trigger(self, *args): filetypes = self.canvas.get_supported_filetypes_grouped() sorted_filetypes = sorted(six.iteritems(filetypes)) default_filetype = self.canvas.get_default_filetype() startpath = os.path.expanduser( matplotlib.rcParams['savefig.directory']) start = os.path.join(startpath, self.canvas.get_default_filename()) filters = [] selectedFilter = None for name, exts in sorted_filetypes: exts_list = " ".join(['*.%s' % ext for ext in exts]) filter = '%s (%s)' % (name, exts_list) if default_filetype in exts: selectedFilter = filter filters.append(filter) filters = ';;'.join(filters) parent = self.canvas.manager.window fname, filter = _getSaveFileName(parent, "Choose a filename to save to", start, filters, selectedFilter) if fname: if startpath != "": matplotlib.rcParams['savefig.directory'] = ( os.path.dirname(six.text_type(fname))) try: self.canvas.figure.savefig(six.text_type(fname)) except Exception as e: QtWidgets.QMessageBox.critical( self, "Error saving file", six.text_type(e), QtWidgets.QMessageBox.Ok, QtWidgets.QMessageBox.NoButton) class SetCursorQt(backend_tools.SetCursorBase): def set_cursor(self, cursor): self.canvas.setCursor(cursord[cursor]) class RubberbandQt(backend_tools.RubberbandBase): def draw_rubberband(self, x0, y0, x1, y1): height = self.canvas.figure.bbox.height y1 = height - y1 y0 = height - y0 rect = [int(val) for val in (x0, y0, x1 - x0, y1 - y0)] self.canvas.drawRectangle(rect) def remove_rubberband(self): self.canvas.drawRectangle(None) backend_tools.ToolSaveFigure = SaveFigureQt backend_tools.ToolConfigureSubplots = ConfigureSubplotsQt backend_tools.ToolSetCursor = SetCursorQt backend_tools.ToolRubberband = RubberbandQt def error_msg_qt(msg, parent=None): if not isinstance(msg, six.string_types): msg = ','.join(map(str, msg)) QtWidgets.QMessageBox.warning(None, "Matplotlib", msg, QtGui.QMessageBox.Ok) def exception_handler(type, value, tb): msg = '' if hasattr(value, 'filename') and value.filename is not None: msg = value.filename + ': ' if hasattr(value, 'strerror') and value.strerror is not None: msg += value.strerror else: msg += six.text_type(value) if len(msg): error_msg_qt(msg) @_Backend.export class _BackendQT5(_Backend): FigureCanvas = FigureCanvasQT FigureManager = FigureManagerQT @staticmethod def trigger_manager_draw(manager): manager.canvas.draw_idle() @staticmethod def mainloop(): signal.signal(signal.SIGINT, signal.SIG_DFL) qApp.exec_()

true

f70bf89b51c9499b4cc42a25db5b53866df60828

262

py

Python

src/baseClients.py

tokarzmaciej/testing-simple-app-online-shop

446d063585f50b96a57bf6e7c23d2042df9eecc0

[ "MIT" ]

null

src/baseClients.py

tokarzmaciej/testing-simple-app-online-shop

446d063585f50b96a57bf6e7c23d2042df9eecc0

[ "MIT" ]

null

src/baseClients.py

tokarzmaciej/testing-simple-app-online-shop

446d063585f50b96a57bf6e7c23d2042df9eecc0

[ "MIT" ]

null

class ClientStorage: def getAllClients(self): pass def postClient(self, name, surname, email): pass def delClient(self, id_client): pass def patchClient(self, id_client, new_name, new_surname, new_email): pass

18.714286

71

0.637405

class ClientStorage: def getAllClients(self): pass def postClient(self, name, surname, email): pass def delClient(self, id_client): pass def patchClient(self, id_client, new_name, new_surname, new_email): pass

true

f70bf8bff2b888f43bbda54839a20ead612a3997

453

py

Python

PythonChallenge/Ex06/06_02.py

YorkFish/git_study

6e023244daaa22e12b24e632e76a13e5066f2947

[ "MIT" ]

null

PythonChallenge/Ex06/06_02.py

YorkFish/git_study

6e023244daaa22e12b24e632e76a13e5066f2947

[ "MIT" ]

null

PythonChallenge/Ex06/06_02.py

YorkFish/git_study

6e023244daaa22e12b24e632e76a13e5066f2947

[ "MIT" ]

null

#!/usr/bin/env python3 # coding:utf-8 from zipfile import ZipFile comments = [] filename = "90052" channel = ZipFile("channel.zip", 'r') while filename.isdigit(): filename += ".txt" f = channel.open(filename, 'r') line = f.readline() f.close() t = channel.getinfo(filename).comment comments.append(str(t, encoding="utf-8")) # bytes -> str filename = bytes.decode(line.split()[-1]) # bytes -> str print(''.join(comments))

25.166667

61

0.637969

from zipfile import ZipFile comments = [] filename = "90052" channel = ZipFile("channel.zip", 'r') while filename.isdigit(): filename += ".txt" f = channel.open(filename, 'r') line = f.readline() f.close() t = channel.getinfo(filename).comment comments.append(str(t, encoding="utf-8")) filename = bytes.decode(line.split()[-1]) print(''.join(comments))

true

f70bf8e6fb1f119b8653e64d00e8152613bdc1fe

208

py

Python

pythran/tests/cases/fibo_seq.py

davidbrochart/pythran

24b6c8650fe99791a4091cbdc2c24686e86aa67c

[ "BSD-3-Clause" ]

1,647

2015-01-13T01:45:38.000Z

2022-03-28T01:23:41.000Z

pythran/tests/cases/fibo_seq.py

davidbrochart/pythran

24b6c8650fe99791a4091cbdc2c24686e86aa67c

[ "BSD-3-Clause" ]

1,116

2015-01-01T09:52:05.000Z

2022-03-18T21:06:40.000Z

pythran/tests/cases/fibo_seq.py

davidbrochart/pythran

24b6c8650fe99791a4091cbdc2c24686e86aa67c

[ "BSD-3-Clause" ]

180

2015-02-12T02:47:28.000Z

2022-03-14T10:28:18.000Z

""" Nom recursive version of fibo. """ # pythran export fibo(int) # runas fibo(7) def fibo(n): """ fibonaccie compuation. """ a, b = 1, 1 for _ in range(n): a, b = a + b, a return a

17.333333

38

0.533654

def fibo(n): a, b = 1, 1 for _ in range(n): a, b = a + b, a return a

true

f70bf9092d547f667f305a39ab84d812eb782f20

5,060

py

Python

common/blockchain_util.py

vinthedark/snet-marketplace-service

66ed9d093b00f09d3e28ef4d86c4e4c125037d06

[ "MIT" ]

null

common/blockchain_util.py

vinthedark/snet-marketplace-service

66ed9d093b00f09d3e28ef4d86c4e4c125037d06

[ "MIT" ]

null

common/blockchain_util.py

vinthedark/snet-marketplace-service

66ed9d093b00f09d3e28ef4d86c4e4c125037d06

[ "MIT" ]

null

import json import uuid from enum import Enum import web3 from eth_account.messages import defunct_hash_message from web3 import Web3 from common.logger import get_logger logger = get_logger(__name__) class ContractType(Enum): REGISTRY = "REGISTRY" MPE = "MPE" RFAI = "RFAI" class BlockChainUtil(object): def __init__(self, provider_type, provider): if provider_type == "HTTP_PROVIDER": self.provider = Web3.HTTPProvider(provider) elif provider_type == "WS_PROVIDER": self.provider = web3.providers.WebsocketProvider(provider) else: raise Exception("Only HTTP_PROVIDER and WS_PROVIDER provider type are supported.") self.web3_object = Web3(self.provider) def load_contract(self, path): with open(path) as f: contract = json.load(f) return contract def read_contract_address(self, net_id, path, key): contract = self.load_contract(path) return Web3.toChecksumAddress(contract[str(net_id)][key]) def contract_instance(self, contract_abi, address): return self.web3_object.eth.contract(abi=contract_abi, address=address) def get_contract_instance(self, base_path, contract_name, net_id): contract_network_path, contract_abi_path = self.get_contract_file_paths(base_path, contract_name) contract_address = self.read_contract_address(net_id=net_id, path=contract_network_path, key='address') contract_abi = self.load_contract(contract_abi_path) logger.debug(f"contract address is {contract_address}") contract_instance = self.contract_instance(contract_abi=contract_abi, address=contract_address) return contract_instance def generate_signature(self, data_types, values, signer_key): signer_key = "0x" + signer_key if not signer_key.startswith("0x") else signer_key message = web3.Web3.soliditySha3(data_types, values) signature = self.web3_object.eth.account.signHash(defunct_hash_message(message), signer_key) return signature.signature.hex() def generate_signature_bytes(self, data_types, values, signer_key): signer_key = "0x" + signer_key if not signer_key.startswith("0x") else signer_key message = web3.Web3.soliditySha3(data_types, values) signature = self.web3_object.eth.account.signHash(defunct_hash_message(message), signer_key) return bytes(signature.signature) def get_nonce(self, address): """ transaction count includes pending transaction also. """ nonce = self.web3_object.eth.getTransactionCount(address) return nonce def sign_transaction_with_private_key(self, private_key, transaction_object): return self.web3_object.eth.account.signTransaction(transaction_object, private_key).rawTransaction def create_transaction_object(self, *positional_inputs, method_name, address, contract_path, contract_address_path, net_id): nonce = self.get_nonce(address=address) self.contract = self.load_contract(path=contract_path) self.contract_address = self.read_contract_address(net_id=net_id, path=contract_address_path, key='address') self.contract_instance = self.contract_instance(contract_abi=self.contract, address=self.contract_address) print("gas_price == ", self.web3_object.eth.gasPrice) print("nonce == ", nonce) gas_price = 3 * (self.web3_object.eth.gasPrice) transaction_object = getattr(self.contract_instance.functions, method_name)( *positional_inputs).buildTransaction({ "from": address, "nonce": nonce, "gasPrice": gas_price, "chainId": net_id }) return transaction_object def process_raw_transaction(self, raw_transaction): return self.web3_object.eth.sendRawTransaction(raw_transaction).hex() def create_account(self): account = self.web3_object.eth.account.create(uuid.uuid4().hex) return account.address, account.privateKey.hex() def get_current_block_no(self): return self.web3_object.eth.blockNumber def get_transaction_receipt_from_blockchain(self, transaction_hash): return self.web3_object.eth.getTransactionReceipt(transaction_hash) def get_contract_file_paths(self, base_path, contract_name): if contract_name == ContractType.REGISTRY.value: json_file = "Registry.json" elif contract_name == ContractType.MPE.value: json_file = "MultiPartyEscrow.json" elif contract_name == ContractType.RFAI.value: json_file = "ServiceRequest.json" else: raise Exception("Invalid contract Type {}".format(contract_name)) contract_network_path = base_path + "/{}/{}".format("networks", json_file) contract_abi_path = base_path + "/{}/{}".format("abi", json_file) return contract_network_path, contract_abi_path

42.166667

119

0.701186

import json import uuid from enum import Enum import web3 from eth_account.messages import defunct_hash_message from web3 import Web3 from common.logger import get_logger logger = get_logger(__name__) class ContractType(Enum): REGISTRY = "REGISTRY" MPE = "MPE" RFAI = "RFAI" class BlockChainUtil(object): def __init__(self, provider_type, provider): if provider_type == "HTTP_PROVIDER": self.provider = Web3.HTTPProvider(provider) elif provider_type == "WS_PROVIDER": self.provider = web3.providers.WebsocketProvider(provider) else: raise Exception("Only HTTP_PROVIDER and WS_PROVIDER provider type are supported.") self.web3_object = Web3(self.provider) def load_contract(self, path): with open(path) as f: contract = json.load(f) return contract def read_contract_address(self, net_id, path, key): contract = self.load_contract(path) return Web3.toChecksumAddress(contract[str(net_id)][key]) def contract_instance(self, contract_abi, address): return self.web3_object.eth.contract(abi=contract_abi, address=address) def get_contract_instance(self, base_path, contract_name, net_id): contract_network_path, contract_abi_path = self.get_contract_file_paths(base_path, contract_name) contract_address = self.read_contract_address(net_id=net_id, path=contract_network_path, key='address') contract_abi = self.load_contract(contract_abi_path) logger.debug(f"contract address is {contract_address}") contract_instance = self.contract_instance(contract_abi=contract_abi, address=contract_address) return contract_instance def generate_signature(self, data_types, values, signer_key): signer_key = "0x" + signer_key if not signer_key.startswith("0x") else signer_key message = web3.Web3.soliditySha3(data_types, values) signature = self.web3_object.eth.account.signHash(defunct_hash_message(message), signer_key) return signature.signature.hex() def generate_signature_bytes(self, data_types, values, signer_key): signer_key = "0x" + signer_key if not signer_key.startswith("0x") else signer_key message = web3.Web3.soliditySha3(data_types, values) signature = self.web3_object.eth.account.signHash(defunct_hash_message(message), signer_key) return bytes(signature.signature) def get_nonce(self, address): nonce = self.web3_object.eth.getTransactionCount(address) return nonce def sign_transaction_with_private_key(self, private_key, transaction_object): return self.web3_object.eth.account.signTransaction(transaction_object, private_key).rawTransaction def create_transaction_object(self, *positional_inputs, method_name, address, contract_path, contract_address_path, net_id): nonce = self.get_nonce(address=address) self.contract = self.load_contract(path=contract_path) self.contract_address = self.read_contract_address(net_id=net_id, path=contract_address_path, key='address') self.contract_instance = self.contract_instance(contract_abi=self.contract, address=self.contract_address) print("gas_price == ", self.web3_object.eth.gasPrice) print("nonce == ", nonce) gas_price = 3 * (self.web3_object.eth.gasPrice) transaction_object = getattr(self.contract_instance.functions, method_name)( *positional_inputs).buildTransaction({ "from": address, "nonce": nonce, "gasPrice": gas_price, "chainId": net_id }) return transaction_object def process_raw_transaction(self, raw_transaction): return self.web3_object.eth.sendRawTransaction(raw_transaction).hex() def create_account(self): account = self.web3_object.eth.account.create(uuid.uuid4().hex) return account.address, account.privateKey.hex() def get_current_block_no(self): return self.web3_object.eth.blockNumber def get_transaction_receipt_from_blockchain(self, transaction_hash): return self.web3_object.eth.getTransactionReceipt(transaction_hash) def get_contract_file_paths(self, base_path, contract_name): if contract_name == ContractType.REGISTRY.value: json_file = "Registry.json" elif contract_name == ContractType.MPE.value: json_file = "MultiPartyEscrow.json" elif contract_name == ContractType.RFAI.value: json_file = "ServiceRequest.json" else: raise Exception("Invalid contract Type {}".format(contract_name)) contract_network_path = base_path + "/{}/{}".format("networks", json_file) contract_abi_path = base_path + "/{}/{}".format("abi", json_file) return contract_network_path, contract_abi_path

true

f70bfa4b74f327eaff6e74deeff7234d2edf2d5a

4,717

py

Python

quantum/common/config.py

cuiwow/quantum

ce11b62046a0501e9fcd8442524d3c151d315dfb

[ "Apache-2.0" ]

1

2019-04-11T10:27:47.000Z

quantum/common/config.py

cuiwow/quantum

ce11b62046a0501e9fcd8442524d3c151d315dfb

[ "Apache-2.0" ]

null

quantum/common/config.py

cuiwow/quantum

ce11b62046a0501e9fcd8442524d3c151d315dfb

[ "Apache-2.0" ]

null

# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright 2011 Nicira Networks, Inc. # 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. """ Routines for configuring Quantum """ import os from paste import deploy from quantum.api.v2 import attributes from quantum.common import utils from quantum.openstack.common import cfg from quantum.openstack.common import log as logging from quantum.openstack.common import rpc from quantum.version import version_info as quantum_version LOG = logging.getLogger(__name__) core_opts = [ cfg.StrOpt('bind_host', default='0.0.0.0', help=_("The host IP to bind to")), cfg.IntOpt('bind_port', default=9696, help=_("The port to bind to")), cfg.StrOpt('api_paste_config', default="api-paste.ini", help=_("The API paste config file to use")), cfg.StrOpt('api_extensions_path', default="", help=_("The path for API extensions")), cfg.StrOpt('policy_file', default="policy.json", help=_("The policy file to use")), cfg.StrOpt('auth_strategy', default='keystone', help=_("The type of authentication to use")), cfg.StrOpt('core_plugin', help=_("The core plugin Quantum will use")), cfg.ListOpt('service_plugins', default=[], help=_("The service plugins Quantum will use")), cfg.StrOpt('base_mac', default="fa:16:3e:00:00:00", help=_("The base MAC address Quantum will use for VIFs")), cfg.IntOpt('mac_generation_retries', default=16, help=_("How many times Quantum will retry MAC generation")), cfg.BoolOpt('allow_bulk', default=True, help=_("Allow the usage of the bulk API")), cfg.IntOpt('max_dns_nameservers', default=5, help=_("Maximum number of DNS nameservers")), cfg.IntOpt('max_subnet_host_routes', default=20, help=_("Maximum number of host routes per subnet")), cfg.IntOpt('dhcp_lease_duration', default=120, help=_("DHCP lease duration")), cfg.BoolOpt('allow_overlapping_ips', default=False, help=_("Allow overlapping IP support in Quantum")), cfg.StrOpt('host', default=utils.get_hostname(), help=_("The hostname Quantum is running on")), cfg.BoolOpt('force_gateway_on_subnet', default=False, help=_("Ensure that configured gateway is on subnet")), ] core_cli_opts = [ cfg.StrOpt('state_path', default='/var/lib/quantum'), ] # Register the configuration options cfg.CONF.register_opts(core_opts) cfg.CONF.register_cli_opts(core_cli_opts) # Ensure that the control exchange is set correctly rpc.set_defaults(control_exchange='quantum') def parse(args): cfg.CONF(args=args, project='quantum', version='%%prog %s' % quantum_version.version_string_with_vcs()) # Validate that the base_mac is of the correct format msg = attributes._validate_regex(cfg.CONF.base_mac, attributes.MAC_PATTERN) if msg: msg = _("Base MAC: %s") % msg raise Exception(msg) def setup_logging(conf): """ Sets up the logging options for a log with supplied name :param conf: a cfg.ConfOpts object """ product_name = "quantum" logging.setup(product_name) log_root = logging.getLogger(product_name).logger log_root.propagate = 0 LOG.info(_("Logging enabled!")) def load_paste_app(app_name): """ Builds and returns a WSGI app from a paste config file. :param app_name: Name of the application to load :raises RuntimeError when config file cannot be located or application cannot be loaded from config file """ config_path = os.path.abspath(cfg.CONF.find_file( cfg.CONF.api_paste_config)) LOG.info(_("Config paste file: %s"), config_path) try: app = deploy.loadapp("config:%s" % config_path, name=app_name) except (LookupError, ImportError): msg = _("Unable to load %(app_name)s from " "configuration file %(config_path)s.") % locals() LOG.exception(msg) raise RuntimeError(msg) return app

36.007634

78

0.665889

import os from paste import deploy from quantum.api.v2 import attributes from quantum.common import utils from quantum.openstack.common import cfg from quantum.openstack.common import log as logging from quantum.openstack.common import rpc from quantum.version import version_info as quantum_version LOG = logging.getLogger(__name__) core_opts = [ cfg.StrOpt('bind_host', default='0.0.0.0', help=_("The host IP to bind to")), cfg.IntOpt('bind_port', default=9696, help=_("The port to bind to")), cfg.StrOpt('api_paste_config', default="api-paste.ini", help=_("The API paste config file to use")), cfg.StrOpt('api_extensions_path', default="", help=_("The path for API extensions")), cfg.StrOpt('policy_file', default="policy.json", help=_("The policy file to use")), cfg.StrOpt('auth_strategy', default='keystone', help=_("The type of authentication to use")), cfg.StrOpt('core_plugin', help=_("The core plugin Quantum will use")), cfg.ListOpt('service_plugins', default=[], help=_("The service plugins Quantum will use")), cfg.StrOpt('base_mac', default="fa:16:3e:00:00:00", help=_("The base MAC address Quantum will use for VIFs")), cfg.IntOpt('mac_generation_retries', default=16, help=_("How many times Quantum will retry MAC generation")), cfg.BoolOpt('allow_bulk', default=True, help=_("Allow the usage of the bulk API")), cfg.IntOpt('max_dns_nameservers', default=5, help=_("Maximum number of DNS nameservers")), cfg.IntOpt('max_subnet_host_routes', default=20, help=_("Maximum number of host routes per subnet")), cfg.IntOpt('dhcp_lease_duration', default=120, help=_("DHCP lease duration")), cfg.BoolOpt('allow_overlapping_ips', default=False, help=_("Allow overlapping IP support in Quantum")), cfg.StrOpt('host', default=utils.get_hostname(), help=_("The hostname Quantum is running on")), cfg.BoolOpt('force_gateway_on_subnet', default=False, help=_("Ensure that configured gateway is on subnet")), ] core_cli_opts = [ cfg.StrOpt('state_path', default='/var/lib/quantum'), ] cfg.CONF.register_opts(core_opts) cfg.CONF.register_cli_opts(core_cli_opts) rpc.set_defaults(control_exchange='quantum') def parse(args): cfg.CONF(args=args, project='quantum', version='%%prog %s' % quantum_version.version_string_with_vcs()) msg = attributes._validate_regex(cfg.CONF.base_mac, attributes.MAC_PATTERN) if msg: msg = _("Base MAC: %s") % msg raise Exception(msg) def setup_logging(conf): product_name = "quantum" logging.setup(product_name) log_root = logging.getLogger(product_name).logger log_root.propagate = 0 LOG.info(_("Logging enabled!")) def load_paste_app(app_name): config_path = os.path.abspath(cfg.CONF.find_file( cfg.CONF.api_paste_config)) LOG.info(_("Config paste file: %s"), config_path) try: app = deploy.loadapp("config:%s" % config_path, name=app_name) except (LookupError, ImportError): msg = _("Unable to load %(app_name)s from " "configuration file %(config_path)s.") % locals() LOG.exception(msg) raise RuntimeError(msg) return app

true

f70bfbfd82da3d2f03a5deda6f9ab6cf8be40de4

529

py

Python

Code_Socke/test.py

Jugendhackt/Maladidea

dfee3f2ee6006c0d2bcb4117d62afb1404f4bdee

[ "MIT" ]

null

Code_Socke/test.py

Jugendhackt/Maladidea

dfee3f2ee6006c0d2bcb4117d62afb1404f4bdee

[ "MIT" ]

null

Code_Socke/test.py

Jugendhackt/Maladidea

dfee3f2ee6006c0d2bcb4117d62afb1404f4bdee

[ "MIT" ]

null

import RPi.GPIO as G import time as t G.setmode(G.BCM) G.setup(19, G.OUT) G.setup(26, G.IN)# pull_up_down=G.PUD_UP) G.setup(21, G.OUT) G.setup(20, G.IN, pull_up_down=G.PUD_UP) print("setup done") G.output(21, True) print("output on") while True: input_sensor = G.input(26) if input_sensor == False: print("sensor triggered") G.output(19, True) t.sleep(.5) G.output(19, False) input_taster = G.input(20) if input_taster == False: print("break") break G.cleanup()

20.346154

41

0.621928

import RPi.GPIO as G import time as t G.setmode(G.BCM) G.setup(19, G.OUT) G.setup(26, G.IN)G.setup(21, G.OUT) G.setup(20, G.IN, pull_up_down=G.PUD_UP) print("setup done") G.output(21, True) print("output on") while True: input_sensor = G.input(26) if input_sensor == False: print("sensor triggered") G.output(19, True) t.sleep(.5) G.output(19, False) input_taster = G.input(20) if input_taster == False: print("break") break G.cleanup()

true

f70bfcff5507413149e7bbff65a7ae2fd88753fc

12,293

py

Python

postfinancecheckout/api/bank_account_service_api.py

pfpayments/python-sdk

b8ef159ea3c843a8d0361d1e0b122a9958adbcb4

[ "Apache-2.0" ]

1

2022-03-08T12:51:53.000Z

postfinancecheckout/api/bank_account_service_api.py

pfpayments/python-sdk

b8ef159ea3c843a8d0361d1e0b122a9958adbcb4

[ "Apache-2.0" ]

null

postfinancecheckout/api/bank_account_service_api.py

pfpayments/python-sdk

b8ef159ea3c843a8d0361d1e0b122a9958adbcb4

[ "Apache-2.0" ]

null

# coding: utf-8 from __future__ import absolute_import import six from postfinancecheckout.api_client import ApiClient class BankAccountServiceApi: def __init__(self, configuration): self.api_client = ApiClient(configuration=configuration) def count(self, space_id, **kwargs): """Count Counts the number of items in the database as restricted by the given filter. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.count(space_id, async_req=True) >>> result = thread.get() :param async_req bool :param int space_id: (required) :param EntityQueryFilter filter: The filter which restricts the entities which are used to calculate the count. :return: int If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.count_with_http_info(space_id, **kwargs) else: (data) = self.count_with_http_info(space_id, **kwargs) return data def count_with_http_info(self, space_id, **kwargs): """Count Counts the number of items in the database as restricted by the given filter. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.count_with_http_info(space_id, async_req=True) >>> result = thread.get() :param async_req bool :param int space_id: (required) :param EntityQueryFilter filter: The filter which restricts the entities which are used to calculate the count. :return: int If the method is called asynchronously, returns the request thread. """ all_params = ['space_id', 'filter'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method count" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'space_id' is set if ('space_id' not in params or params['space_id'] is None): raise ValueError("Missing the required parameter `space_id` when calling `count`") collection_formats = {} path_params = {} query_params = [] if 'space_id' in params: query_params.append(('spaceId', params['space_id'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'filter' in params: body_params = params['filter'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json;charset=utf-8']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json;charset=utf-8']) # Authentication setting auth_settings = [] return self.api_client.call_api( '/bank-account/count', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='int', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def read(self, space_id, id, **kwargs): """Read Reads the entity with the given 'id' and returns it. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.read(space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param int space_id: (required) :param int id: The ID of the bank account which should be returned. (required) :return: BankAccount If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.read_with_http_info(space_id, id, **kwargs) else: (data) = self.read_with_http_info(space_id, id, **kwargs) return data def read_with_http_info(self, space_id, id, **kwargs): """Read Reads the entity with the given 'id' and returns it. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.read_with_http_info(space_id, id, async_req=True) >>> result = thread.get() :param async_req bool :param int space_id: (required) :param int id: The ID of the bank account which should be returned. (required) :return: BankAccount If the method is called asynchronously, returns the request thread. """ all_params = ['space_id', 'id'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method read" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'space_id' is set if ('space_id' not in params or params['space_id'] is None): raise ValueError("Missing the required parameter `space_id` when calling `read`") # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `read`") collection_formats = {} path_params = {} query_params = [] if 'space_id' in params: query_params.append(('spaceId', params['space_id'])) if 'id' in params: query_params.append(('id', params['id'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json;charset=utf-8']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['*/*']) # Authentication setting auth_settings = [] return self.api_client.call_api( '/bank-account/read', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BankAccount', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def search(self, space_id, query, **kwargs): """Search Searches for the entities as specified by the given query. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.search(space_id, query, async_req=True) >>> result = thread.get() :param async_req bool :param int space_id: (required) :param EntityQuery query: The query restricts the bank accounts which are returned by the search. (required) :return: list[BankAccount] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.search_with_http_info(space_id, query, **kwargs) else: (data) = self.search_with_http_info(space_id, query, **kwargs) return data def search_with_http_info(self, space_id, query, **kwargs): """Search Searches for the entities as specified by the given query. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.search_with_http_info(space_id, query, async_req=True) >>> result = thread.get() :param async_req bool :param int space_id: (required) :param EntityQuery query: The query restricts the bank accounts which are returned by the search. (required) :return: list[BankAccount] If the method is called asynchronously, returns the request thread. """ all_params = ['space_id', 'query'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method search" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'space_id' is set if ('space_id' not in params or params['space_id'] is None): raise ValueError("Missing the required parameter `space_id` when calling `search`") # verify the required parameter 'query' is set if ('query' not in params or params['query'] is None): raise ValueError("Missing the required parameter `query` when calling `search`") collection_formats = {} path_params = {} query_params = [] if 'space_id' in params: query_params.append(('spaceId', params['space_id'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'query' in params: body_params = params['query'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json;charset=utf-8']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json;charset=utf-8']) # Authentication setting auth_settings = [] return self.api_client.call_api( '/bank-account/search', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[BankAccount]', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

36.58631

119

0.602619

from __future__ import absolute_import import six from postfinancecheckout.api_client import ApiClient class BankAccountServiceApi: def __init__(self, configuration): self.api_client = ApiClient(configuration=configuration) def count(self, space_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.count_with_http_info(space_id, **kwargs) else: (data) = self.count_with_http_info(space_id, **kwargs) return data def count_with_http_info(self, space_id, **kwargs): all_params = ['space_id', 'filter'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method count" % key ) params[key] = val del params['kwargs'] if ('space_id' not in params or params['space_id'] is None): raise ValueError("Missing the required parameter `space_id` when calling `count`") collection_formats = {} path_params = {} query_params = [] if 'space_id' in params: query_params.append(('spaceId', params['space_id'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'filter' in params: body_params = params['filter'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json;charset=utf-8']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json;charset=utf-8']) auth_settings = [] return self.api_client.call_api( '/bank-account/count', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='int', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def read(self, space_id, id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.read_with_http_info(space_id, id, **kwargs) else: (data) = self.read_with_http_info(space_id, id, **kwargs) return data def read_with_http_info(self, space_id, id, **kwargs): all_params = ['space_id', 'id'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method read" % key ) params[key] = val del params['kwargs'] if ('space_id' not in params or params['space_id'] is None): raise ValueError("Missing the required parameter `space_id` when calling `read`") if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `read`") collection_formats = {} path_params = {} query_params = [] if 'space_id' in params: query_params.append(('spaceId', params['space_id'])) if 'id' in params: query_params.append(('id', params['id'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json;charset=utf-8']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['*/*']) auth_settings = [] return self.api_client.call_api( '/bank-account/read', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BankAccount', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def search(self, space_id, query, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.search_with_http_info(space_id, query, **kwargs) else: (data) = self.search_with_http_info(space_id, query, **kwargs) return data def search_with_http_info(self, space_id, query, **kwargs): all_params = ['space_id', 'query'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method search" % key ) params[key] = val del params['kwargs'] if ('space_id' not in params or params['space_id'] is None): raise ValueError("Missing the required parameter `space_id` when calling `search`") if ('query' not in params or params['query'] is None): raise ValueError("Missing the required parameter `query` when calling `search`") collection_formats = {} path_params = {} query_params = [] if 'space_id' in params: query_params.append(('spaceId', params['space_id'])) header_params = {} form_params = [] local_var_files = {} body_params = None if 'query' in params: body_params = params['query'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json;charset=utf-8']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json;charset=utf-8']) auth_settings = [] return self.api_client.call_api( '/bank-account/search', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[BankAccount]', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

true

f70bfd48b4dffc44103f3819522ad40aac7dc5c2

759

py

Python

assignments/a2/containerWithMostWater.py

jcdiv47/geekbang-algorithms

38dae85aeadb684b2c44945bd07a32cdede4ad5a

[ "MIT" ]

null

assignments/a2/containerWithMostWater.py

jcdiv47/geekbang-algorithms

38dae85aeadb684b2c44945bd07a32cdede4ad5a

[ "MIT" ]

null

assignments/a2/containerWithMostWater.py

jcdiv47/geekbang-algorithms

38dae85aeadb684b2c44945bd07a32cdede4ad5a

[ "MIT" ]

null

import unittest """ Leetcode(https://leetcode.com/problems/container-with-most-water/solution/) """ def maxArea(height): ans = 0 left, right = 0, len(height) - 1 while left < right: ans = max(ans, min(height[left], height[right]) * (right - left)) if height[left] < height[right]: left += 1 else: right -= 1 return ans class MyTestCase(unittest.TestCase): def test1(self): height = [4, 3, 2, 1, 4] self.assertEqual(maxArea(height), 16) def test2(self): height = [1, 1] self.assertEqual(maxArea(height), 1) def test3(self): height = [1, 2, 1] self.assertEqual(maxArea(height), 2) if __name__ == '__main__': unittest.main()

21.083333

75

0.56917

import unittest def maxArea(height): ans = 0 left, right = 0, len(height) - 1 while left < right: ans = max(ans, min(height[left], height[right]) * (right - left)) if height[left] < height[right]: left += 1 else: right -= 1 return ans class MyTestCase(unittest.TestCase): def test1(self): height = [4, 3, 2, 1, 4] self.assertEqual(maxArea(height), 16) def test2(self): height = [1, 1] self.assertEqual(maxArea(height), 1) def test3(self): height = [1, 2, 1] self.assertEqual(maxArea(height), 2) if __name__ == '__main__': unittest.main()

true

f70bfec06d09bcd09179f6c36f86fd581e0bfd1d

1,357

py

Python

test/test_flake8.py

Theosakamg/colcon-powershell

86657800695097ec4d5f1cd0035d15fd5cde2eb0

[ "Apache-2.0" ]

null

test/test_flake8.py

Theosakamg/colcon-powershell

86657800695097ec4d5f1cd0035d15fd5cde2eb0

[ "Apache-2.0" ]

null

test/test_flake8.py

Theosakamg/colcon-powershell

86657800695097ec4d5f1cd0035d15fd5cde2eb0

[ "Apache-2.0" ]

null

# Copyright 2016-2018 Dirk Thomas # Licensed under the Apache License, Version 2.0 import logging from pathlib import Path import sys from flake8 import LOG from flake8.api.legacy import get_style_guide # avoid debug and info messages from flake8 internals LOG.setLevel(logging.WARN) def test_flake8(): style_guide = get_style_guide( ignore=['D100', 'D104'], show_source=True, ) style_guide_tests = get_style_guide( ignore=['D100', 'D101', 'D102', 'D103', 'D104', 'D105', 'D107'], show_source=True, ) stdout = sys.stdout sys.stdout = sys.stderr # implicitly calls report_errors() report = style_guide.check_files([ str(Path(__file__).parents[1] / 'colcon_powershell'), ]) report_tests = style_guide_tests.check_files([ str(Path(__file__).parents[1] / 'test'), ]) sys.stdout = stdout total_errors = report.total_errors + report_tests.total_errors if total_errors: # pragma: no cover # output summary with per-category counts print() report._application.formatter.show_statistics(report._stats) print( 'flake8 reported {total_errors} errors' .format_map(locals()), file=sys.stderr) assert not report.total_errors, \ 'flake8 reported {total_errors} errors'.format_map(locals())

28.270833

72

0.66986

import logging from pathlib import Path import sys from flake8 import LOG from flake8.api.legacy import get_style_guide LOG.setLevel(logging.WARN) def test_flake8(): style_guide = get_style_guide( ignore=['D100', 'D104'], show_source=True, ) style_guide_tests = get_style_guide( ignore=['D100', 'D101', 'D102', 'D103', 'D104', 'D105', 'D107'], show_source=True, ) stdout = sys.stdout sys.stdout = sys.stderr report = style_guide.check_files([ str(Path(__file__).parents[1] / 'colcon_powershell'), ]) report_tests = style_guide_tests.check_files([ str(Path(__file__).parents[1] / 'test'), ]) sys.stdout = stdout total_errors = report.total_errors + report_tests.total_errors if total_errors: print() report._application.formatter.show_statistics(report._stats) print( 'flake8 reported {total_errors} errors' .format_map(locals()), file=sys.stderr) assert not report.total_errors, \ 'flake8 reported {total_errors} errors'.format_map(locals())

true

f70bffe4d8ea3902244e43a68f75def149a1c37f

1,705

py

Python

lib/sim/tests/test_onebit_counter.py

pp-mo/bbc

33c20ab511a88a9e7236e82477fae3256d41e38a

[ "BSD-3-Clause" ]

2

2020-10-01T09:05:01.000Z

2021-05-30T17:34:46.000Z

lib/sim/tests/test_onebit_counter.py

pp-mo/bbc

33c20ab511a88a9e7236e82477fae3256d41e38a

[ "BSD-3-Clause" ]

null

lib/sim/tests/test_onebit_counter.py

pp-mo/bbc

33c20ab511a88a9e7236e82477fae3256d41e38a

[ "BSD-3-Clause" ]

null

from sim.signal import Signal, SIG_UNDEF from sim.sequencer import DEFAULT_SEQUENCER as SEQ from sim.tests import okeq, okin, setsig, fails from sim.device.arith import CounterOnebit counter = CounterOnebit( 'c1b', t_toggle_0_to_1=3., t_toggle_1_to_0=3., t_out_2_carry=1., t_clear_2_carry=2., t_clear_onoff=4., t_eor_onoff=2.) din = Signal('d_in') clr = Signal('clr') ore = Signal('ore') counter.connect('input', din) counter.connect('clear', clr) counter.connect('enable_or', ore) din.trace() clr.trace() counter.output.trace() counter.x_carry_out.trace() COUNTER_CARRYOUTS_COUNT = 0 def carry_out_callback(time, signal): global COUNTER_CARRYOUTS_COUNT COUNTER_CARRYOUTS_COUNT += 1 counter.x_carry_out.add_connection(carry_out_callback) SEQ.addall([ setsig(100.0, din, 1), setsig(110.0, din, 1), setsig(120.0, din, 1), setsig(150.0, clr, 1), setsig(160.0, clr, 0), setsig(180.0, clr, 1), setsig(181.0, ore, 1), setsig(190.0, clr, 0), setsig(200.0, din, 1), setsig(210.0, din, 1), setsig(220.0, din, 1), setsig(223.0, clr, 0), ]) okeq(counter.output.state, 0) okeq(COUNTER_CARRYOUTS_COUNT, 0) SEQ.run(101.) okeq(counter.output.state, SIG_UNDEF) okeq(COUNTER_CARRYOUTS_COUNT, 0) SEQ.run(109.) okeq(counter.output.state, 1) okeq(COUNTER_CARRYOUTS_COUNT, 0) SEQ.run(111.) okeq(counter.output.state, SIG_UNDEF) okeq(COUNTER_CARRYOUTS_COUNT, 0) SEQ.run(119.) okeq(counter.output.state, 0) okeq(COUNTER_CARRYOUTS_COUNT, 1) SEQ.run() # Check we get an error when clear active period is too short. SEQ.addall([ setsig(250.0, clr, 1), setsig(251.0, clr, 0) ]) with fails(ValueError): SEQ.run()

20.792683

62

0.695015

from sim.signal import Signal, SIG_UNDEF from sim.sequencer import DEFAULT_SEQUENCER as SEQ from sim.tests import okeq, okin, setsig, fails from sim.device.arith import CounterOnebit counter = CounterOnebit( 'c1b', t_toggle_0_to_1=3., t_toggle_1_to_0=3., t_out_2_carry=1., t_clear_2_carry=2., t_clear_onoff=4., t_eor_onoff=2.) din = Signal('d_in') clr = Signal('clr') ore = Signal('ore') counter.connect('input', din) counter.connect('clear', clr) counter.connect('enable_or', ore) din.trace() clr.trace() counter.output.trace() counter.x_carry_out.trace() COUNTER_CARRYOUTS_COUNT = 0 def carry_out_callback(time, signal): global COUNTER_CARRYOUTS_COUNT COUNTER_CARRYOUTS_COUNT += 1 counter.x_carry_out.add_connection(carry_out_callback) SEQ.addall([ setsig(100.0, din, 1), setsig(110.0, din, 1), setsig(120.0, din, 1), setsig(150.0, clr, 1), setsig(160.0, clr, 0), setsig(180.0, clr, 1), setsig(181.0, ore, 1), setsig(190.0, clr, 0), setsig(200.0, din, 1), setsig(210.0, din, 1), setsig(220.0, din, 1), setsig(223.0, clr, 0), ]) okeq(counter.output.state, 0) okeq(COUNTER_CARRYOUTS_COUNT, 0) SEQ.run(101.) okeq(counter.output.state, SIG_UNDEF) okeq(COUNTER_CARRYOUTS_COUNT, 0) SEQ.run(109.) okeq(counter.output.state, 1) okeq(COUNTER_CARRYOUTS_COUNT, 0) SEQ.run(111.) okeq(counter.output.state, SIG_UNDEF) okeq(COUNTER_CARRYOUTS_COUNT, 0) SEQ.run(119.) okeq(counter.output.state, 0) okeq(COUNTER_CARRYOUTS_COUNT, 1) SEQ.run() SEQ.addall([ setsig(250.0, clr, 1), setsig(251.0, clr, 0) ]) with fails(ValueError): SEQ.run()

true

f70c01b6d44231ba57c5bf3a9a4f685924e7a5d2

5,927

py

Python

server.py

project-anuvaad/OpenNMT-py

267d097b9e90d59709fe1c26ea8b8e2c43c755c9

[ "MIT" ]

null

server.py

project-anuvaad/OpenNMT-py

267d097b9e90d59709fe1c26ea8b8e2c43c755c9

[ "MIT" ]

29

2019-07-18T10:21:57.000Z

2019-10-24T11:41:59.000Z

server.py

project-anuvaad/OpenNMT-py

267d097b9e90d59709fe1c26ea8b8e2c43c755c9

[ "MIT" ]

null

#!/usr/bin/env python from __future__ import unicode_literals import configargparse import sys from config.config import statusCode,benchmark_types, language_supported, file_location import config.bleu_results as bleu_results import tools.sp_enc_dec as sp import ancillary_functions_anuvaad.ancillary_functions as ancillary_functions import ancillary_functions_anuvaad.sc_preface_handler as sc_preface_handler import ancillary_functions_anuvaad.handle_date_url as date_url_util from flask import Flask, jsonify, request,send_file,abort,send_from_directory from flask_cors import CORS from onmt.translate import TranslationServer, ServerModelError from itertools import repeat from onmt.utils.logging import init_logger,logger,entry_exit_log,LOG_TAGS from onmt.utils.misc import split_corpus from onmt.translate.translator import build_translator import os import onmt.opts as opts from onmt.utils.parse import ArgumentParser from config.mongo_model import db,Benchmarks import datetime from kafka_utils.document_translator import doc_translator import threading import translation_util.translate_util as translate_util import translation_util.interactive_translate as interactive_translation from config.kafka_topics import consumer_topics,producer_topics,kafka_topic STATUS_OK = "ok" STATUS_ERROR = "error" mongo_config_dir = "config/mongo_config.py" IS_RUN_KAFKA = 'IS_RUN_KAFKA' IS_RUN_KAFKA_DEFAULT_VALUE = False bootstrap_server_boolean = os.environ.get(IS_RUN_KAFKA, IS_RUN_KAFKA_DEFAULT_VALUE) def start(config_file, url_root="/translator", host="0.0.0.0", port=3003, debug=True): def prefix_route(route_function, prefix='', mask='{0}{1}'): def newroute(route, *args, **kwargs): return route_function(mask.format(prefix, route), *args, **kwargs) return newroute app = Flask(__name__) CORS(app) app.config.from_pyfile(mongo_config_dir) db.init_app(app) app.route = prefix_route(app.route, url_root) translation_server = TranslationServer() translation_server.start(config_file) def kafka_function(): logger.info('starting kafka from nmt-server on thread-1') doc_translator(translation_server,[kafka_topic[0]['consumer'],kafka_topic[1]['consumer'],kafka_topic[2]['consumer']]) if bootstrap_server_boolean: t1 = threading.Thread(target=kafka_function) # t1.start() @app.route('/models', methods=['GET']) def get_models(): out = {} try: out['status'] = statusCode["SUCCESS"] out['response_body'] = translation_server.list_models() except: out['status'] = statusCode["SYSTEM_ERR"] logger.info("Unexpected error: %s"% sys.exc_info()[0]) return jsonify(out) @app.route('/clone_model/<int:model_id>', methods=['POST']) def clone_model(model_id): out = {} data = request.get_json(force=True) timeout = -1 if 'timeout' in data: timeout = data['timeout'] del data['timeout'] opt = data.get('opt', None) try: model_id, load_time = translation_server.clone_model( model_id, opt, timeout) except ServerModelError as e: out['status'] = STATUS_ERROR out['error'] = str(e) else: out['status'] = STATUS_OK out['model_id'] = model_id out['load_time'] = load_time return jsonify(out) @app.route('/unload_model/<int:model_id>', methods=['GET']) def unload_model(model_id): out = {"model_id": model_id} try: translation_server.unload_model(model_id) out['status'] = STATUS_OK except Exception as e: out['status'] = STATUS_ERROR out['error'] = str(e) return jsonify(out) @app.route('/translate-anuvaad', methods=['POST']) def translate(): inputs = request.get_json(force=True) if len(inputs)>0: logger.info("Making translate-anuvaad API call") logger.info(entry_exit_log(LOG_TAGS["input"],inputs)) out = translate_util.translate_func(inputs, translation_server) logger.info("out from translate_func-trans_util done{}".format(out)) logger.info(entry_exit_log(LOG_TAGS["output"],out)) return jsonify(out) else: logger.info("null inputs in request in translate-anuvaad API") return jsonify({'status':statusCode["INVALID_API_REQUEST"]}) @app.route('/to_cpu/<int:model_id>', methods=['GET']) def to_cpu(model_id): out = {'model_id': model_id} translation_server.models[model_id].to_cpu() out['status'] = STATUS_OK return jsonify(out) @app.route('/to_gpu/<int:model_id>', methods=['GET']) def to_gpu(model_id): out = {'model_id': model_id} translation_server.models[model_id].to_gpu() out['status'] = STATUS_OK return jsonify(out) app.run(debug=debug, host=host, port=port, use_reloader=False, threaded=True) def _get_parser(): parser = configargparse.ArgumentParser( config_file_parser_class=configargparse.YAMLConfigFileParser, description="OpenNMT-py REST Server") parser.add_argument("--ip", type=str, default="0.0.0.0") parser.add_argument("--port", type=int, default="3003") parser.add_argument("--url_root", type=str, default="/translator") parser.add_argument("--debug", "-d", action="store_true") parser.add_argument("--config", "-c", type=str, default="./available_models/conf.json") return parser if __name__ == '__main__': parser = _get_parser() args = parser.parse_args() start(args.config, url_root=args.url_root, host=args.ip, port=args.port, debug=args.debug)

35.704819

130

0.672516

from __future__ import unicode_literals import configargparse import sys from config.config import statusCode,benchmark_types, language_supported, file_location import config.bleu_results as bleu_results import tools.sp_enc_dec as sp import ancillary_functions_anuvaad.ancillary_functions as ancillary_functions import ancillary_functions_anuvaad.sc_preface_handler as sc_preface_handler import ancillary_functions_anuvaad.handle_date_url as date_url_util from flask import Flask, jsonify, request,send_file,abort,send_from_directory from flask_cors import CORS from onmt.translate import TranslationServer, ServerModelError from itertools import repeat from onmt.utils.logging import init_logger,logger,entry_exit_log,LOG_TAGS from onmt.utils.misc import split_corpus from onmt.translate.translator import build_translator import os import onmt.opts as opts from onmt.utils.parse import ArgumentParser from config.mongo_model import db,Benchmarks import datetime from kafka_utils.document_translator import doc_translator import threading import translation_util.translate_util as translate_util import translation_util.interactive_translate as interactive_translation from config.kafka_topics import consumer_topics,producer_topics,kafka_topic STATUS_OK = "ok" STATUS_ERROR = "error" mongo_config_dir = "config/mongo_config.py" IS_RUN_KAFKA = 'IS_RUN_KAFKA' IS_RUN_KAFKA_DEFAULT_VALUE = False bootstrap_server_boolean = os.environ.get(IS_RUN_KAFKA, IS_RUN_KAFKA_DEFAULT_VALUE) def start(config_file, url_root="/translator", host="0.0.0.0", port=3003, debug=True): def prefix_route(route_function, prefix='', mask='{0}{1}'): def newroute(route, *args, **kwargs): return route_function(mask.format(prefix, route), *args, **kwargs) return newroute app = Flask(__name__) CORS(app) app.config.from_pyfile(mongo_config_dir) db.init_app(app) app.route = prefix_route(app.route, url_root) translation_server = TranslationServer() translation_server.start(config_file) def kafka_function(): logger.info('starting kafka from nmt-server on thread-1') doc_translator(translation_server,[kafka_topic[0]['consumer'],kafka_topic[1]['consumer'],kafka_topic[2]['consumer']]) if bootstrap_server_boolean: t1 = threading.Thread(target=kafka_function) @app.route('/models', methods=['GET']) def get_models(): out = {} try: out['status'] = statusCode["SUCCESS"] out['response_body'] = translation_server.list_models() except: out['status'] = statusCode["SYSTEM_ERR"] logger.info("Unexpected error: %s"% sys.exc_info()[0]) return jsonify(out) @app.route('/clone_model/<int:model_id>', methods=['POST']) def clone_model(model_id): out = {} data = request.get_json(force=True) timeout = -1 if 'timeout' in data: timeout = data['timeout'] del data['timeout'] opt = data.get('opt', None) try: model_id, load_time = translation_server.clone_model( model_id, opt, timeout) except ServerModelError as e: out['status'] = STATUS_ERROR out['error'] = str(e) else: out['status'] = STATUS_OK out['model_id'] = model_id out['load_time'] = load_time return jsonify(out) @app.route('/unload_model/<int:model_id>', methods=['GET']) def unload_model(model_id): out = {"model_id": model_id} try: translation_server.unload_model(model_id) out['status'] = STATUS_OK except Exception as e: out['status'] = STATUS_ERROR out['error'] = str(e) return jsonify(out) @app.route('/translate-anuvaad', methods=['POST']) def translate(): inputs = request.get_json(force=True) if len(inputs)>0: logger.info("Making translate-anuvaad API call") logger.info(entry_exit_log(LOG_TAGS["input"],inputs)) out = translate_util.translate_func(inputs, translation_server) logger.info("out from translate_func-trans_util done{}".format(out)) logger.info(entry_exit_log(LOG_TAGS["output"],out)) return jsonify(out) else: logger.info("null inputs in request in translate-anuvaad API") return jsonify({'status':statusCode["INVALID_API_REQUEST"]}) @app.route('/to_cpu/<int:model_id>', methods=['GET']) def to_cpu(model_id): out = {'model_id': model_id} translation_server.models[model_id].to_cpu() out['status'] = STATUS_OK return jsonify(out) @app.route('/to_gpu/<int:model_id>', methods=['GET']) def to_gpu(model_id): out = {'model_id': model_id} translation_server.models[model_id].to_gpu() out['status'] = STATUS_OK return jsonify(out) app.run(debug=debug, host=host, port=port, use_reloader=False, threaded=True) def _get_parser(): parser = configargparse.ArgumentParser( config_file_parser_class=configargparse.YAMLConfigFileParser, description="OpenNMT-py REST Server") parser.add_argument("--ip", type=str, default="0.0.0.0") parser.add_argument("--port", type=int, default="3003") parser.add_argument("--url_root", type=str, default="/translator") parser.add_argument("--debug", "-d", action="store_true") parser.add_argument("--config", "-c", type=str, default="./available_models/conf.json") return parser if __name__ == '__main__': parser = _get_parser() args = parser.parse_args() start(args.config, url_root=args.url_root, host=args.ip, port=args.port, debug=args.debug)

true

f70c02307b230273bb150b0e7528be384ec81c1c

284

py

Python

CellState.py

WilliamPJSmith/CM4-A

bf2a0f2a49ea7e77454bacba25e6cbb2f282572f

[ "Unlicense" ]

null

CellState.py

WilliamPJSmith/CM4-A

bf2a0f2a49ea7e77454bacba25e6cbb2f282572f

[ "Unlicense" ]

null

CellState.py

WilliamPJSmith/CM4-A

bf2a0f2a49ea7e77454bacba25e6cbb2f282572f

[ "Unlicense" ]

null

class CellState: # Don't show these attributes in gui (not used any more?) excludeAttr = ['divideFlag'] excludeAttr = ['deathFlag'] def __init__(self, cid): self.id = cid self.growthRate = 1.0 self.color = [0.5,0.5,0.5] self.divideFlag = False self.deathFlag = False

20.285714

58

0.676056

class CellState: excludeAttr = ['divideFlag'] excludeAttr = ['deathFlag'] def __init__(self, cid): self.id = cid self.growthRate = 1.0 self.color = [0.5,0.5,0.5] self.divideFlag = False self.deathFlag = False

true

f70c02e67d29d64736ba5ecb710a7508719ba359

1,184

py

Python

pyside/lesson_08_main.py

LueyEscargot/pyGuiTest

c072fe29a7c94dc60ec54344a5d4a91253d25f3f

[ "MIT" ]

null

pyside/lesson_08_main.py

LueyEscargot/pyGuiTest

c072fe29a7c94dc60ec54344a5d4a91253d25f3f

[ "MIT" ]

null

pyside/lesson_08_main.py

LueyEscargot/pyGuiTest

c072fe29a7c94dc60ec54344a5d4a91253d25f3f

[ "MIT" ]

null

import sys import argparse import pandas as pd from PySide2.QtCore import QDateTime, QTimeZone from PySide2.QtWidgets import QApplication from lesson_08_main_window import MainWindow from lesson_08_mainWidget import Widget def transform_date(utc, timezone=None): utc_fmt = "yyyy-MM-ddTHH:mm:ss.zzzZ" new_date = QDateTime().fromString(utc, utc_fmt) if timezone: new_date.setTimeZone(timezone) return new_date def read_data(fname): # Read the CSV content df = pd.read_csv(fname) # Remove wrong magnitudes df = df.drop(df[df.mag < 0].index) magnitudes = df["mag"] # My local timezone timezone = QTimeZone(b"Aisa/ShangHai") # Get timestamp transformed to our timezone times = df["time"].apply(lambda x: transform_date(x, timezone)) return times, magnitudes if __name__ == "__main__": options = argparse.ArgumentParser() options.add_argument("-f", "--file", type=str, required=True) args = options.parse_args() data = read_data(args.file) # Qt Application app = QApplication(sys.argv) widget = Widget(data) window = MainWindow(widget) window.show() sys.exit(app.exec_())

23.68

67

0.69848

import sys import argparse import pandas as pd from PySide2.QtCore import QDateTime, QTimeZone from PySide2.QtWidgets import QApplication from lesson_08_main_window import MainWindow from lesson_08_mainWidget import Widget def transform_date(utc, timezone=None): utc_fmt = "yyyy-MM-ddTHH:mm:ss.zzzZ" new_date = QDateTime().fromString(utc, utc_fmt) if timezone: new_date.setTimeZone(timezone) return new_date def read_data(fname): df = pd.read_csv(fname) df = df.drop(df[df.mag < 0].index) magnitudes = df["mag"] timezone = QTimeZone(b"Aisa/ShangHai") times = df["time"].apply(lambda x: transform_date(x, timezone)) return times, magnitudes if __name__ == "__main__": options = argparse.ArgumentParser() options.add_argument("-f", "--file", type=str, required=True) args = options.parse_args() data = read_data(args.file) app = QApplication(sys.argv) widget = Widget(data) window = MainWindow(widget) window.show() sys.exit(app.exec_())

true

f70c042f92fd9bc243b435404e81962654d0d10f

5,051

py

Python

mars/tensor/fft/ifftn.py

wjsi/mars

a69fb19edfe748d4393b90ff2c4941a76c084596

[ "Apache-2.0" ]

2,413

2018-12-06T09:37:11.000Z

2022-03-30T15:47:39.000Z

mars/tensor/fft/ifftn.py

wjsi/mars

a69fb19edfe748d4393b90ff2c4941a76c084596

[ "Apache-2.0" ]

1,335

2018-12-07T03:06:18.000Z

2022-03-31T11:45:57.000Z

mars/tensor/fft/ifftn.py

wjsi/mars

a69fb19edfe748d4393b90ff2c4941a76c084596

[ "Apache-2.0" ]

329

2018-12-07T03:12:41.000Z

2022-03-29T21:49:57.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2021 Alibaba Group Holding 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. import numpy as np from ... import opcodes as OperandDef from ..datasource import tensor as astensor from .core import TensorComplexFFTNMixin, validate_fftn, TensorStandardFFTN class TensorIFFTN(TensorStandardFFTN, TensorComplexFFTNMixin): _op_type_ = OperandDef.IFFTN def __init__(self, shape=None, axes=None, norm=None, **kw): super().__init__(_shape=shape, _axes=axes, _norm=norm, **kw) def ifftn(a, s=None, axes=None, norm=None): """ Compute the N-dimensional inverse discrete Fourier Transform. This function computes the inverse of the N-dimensional discrete Fourier Transform over any number of axes in an M-dimensional tensor by means of the Fast Fourier Transform (FFT). In other words, ``ifftn(fftn(a)) == a`` to within numerical accuracy. For a description of the definitions and conventions used, see `mt.fft`. The input, analogously to `ifft`, should be ordered in the same way as is returned by `fftn`, i.e. it should have the term for zero frequency in all axes in the low-order corner, the positive frequency terms in the first half of all axes, the term for the Nyquist frequency in the middle of all axes and the negative frequency terms in the second half of all axes, in order of decreasingly negative frequency. Parameters ---------- a : array_like Input tensor, can be complex. s : sequence of ints, optional Shape (length of each transformed axis) of the output (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.). This corresponds to ``n`` for ``ifft(x, n)``. Along any axis, if the given shape is smaller than that of the input, the input is cropped. If it is larger, the input is padded with zeros. if `s` is not given, the shape of the input along the axes specified by `axes` is used. See notes for issue on `ifft` zero padding. axes : sequence of ints, optional Axes over which to compute the IFFT. If not given, the last ``len(s)`` axes are used, or all axes if `s` is also not specified. Repeated indices in `axes` means that the inverse transform over that axis is performed multiple times. norm : {None, "ortho"}, optional Normalization mode (see `mt.fft`). Default is None. Returns ------- out : complex Tensor The truncated or zero-padded input, transformed along the axes indicated by `axes`, or by a combination of `s` or `a`, as explained in the parameters section above. Raises ------ ValueError If `s` and `axes` have different length. IndexError If an element of `axes` is larger than than the number of axes of `a`. See Also -------- mt.fft : Overall view of discrete Fourier transforms, with definitions and conventions used. fftn : The forward *n*-dimensional FFT, of which `ifftn` is the inverse. ifft : The one-dimensional inverse FFT. ifft2 : The two-dimensional inverse FFT. ifftshift : Undoes `fftshift`, shifts zero-frequency terms to beginning of tensor. Notes ----- See `mt.fft` for definitions and conventions used. Zero-padding, analogously with `ifft`, is performed by appending zeros to the input along the specified dimension. Although this is the common approach, it might lead to surprising results. If another form of zero padding is desired, it must be performed before `ifftn` is called. Examples -------- >>> import mars.tensor as mt >>> a = mt.eye(4) >>> mt.fft.ifftn(mt.fft.fftn(a, axes=(0,)), axes=(1,)).execute() array([[ 1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], [ 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j], [ 0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j], [ 0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j]]) Create and plot an image with band-limited frequency content: >>> import matplotlib.pyplot as plt >>> n = mt.zeros((200,200), dtype=complex) >>> n[60:80, 20:40] = mt.exp(1j*mt.random.uniform(0, 2*mt.pi, (20, 20))) >>> im = mt.fft.ifftn(n).real >>> plt.imshow(im.execute()) <matplotlib.image.AxesImage object at 0x...> >>> plt.show() """ a = astensor(a) axes = validate_fftn(a, s=s, axes=axes, norm=norm) op = TensorIFFTN(shape=s, axes=axes, norm=norm, dtype=np.dtype(np.complex_)) return op(a)

39.460938

80

0.659077

import numpy as np from ... import opcodes as OperandDef from ..datasource import tensor as astensor from .core import TensorComplexFFTNMixin, validate_fftn, TensorStandardFFTN class TensorIFFTN(TensorStandardFFTN, TensorComplexFFTNMixin): _op_type_ = OperandDef.IFFTN def __init__(self, shape=None, axes=None, norm=None, **kw): super().__init__(_shape=shape, _axes=axes, _norm=norm, **kw) def ifftn(a, s=None, axes=None, norm=None): a = astensor(a) axes = validate_fftn(a, s=s, axes=axes, norm=norm) op = TensorIFFTN(shape=s, axes=axes, norm=norm, dtype=np.dtype(np.complex_)) return op(a)

true

f70c0476b617936a3fedaa107dd5a21cbd494991

4,387

py

Python

research/cv/Neighbor2Neighbor/src/dataset.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

null

research/cv/Neighbor2Neighbor/src/dataset.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

null

research/cv/Neighbor2Neighbor/src/dataset.py

leelige/mindspore

5199e05ba3888963473f2b07da3f7bca5b9ef6dc

[ "Apache-2.0" ]

2

2019-09-01T06:17:04.000Z

2019-10-04T08:39:45.000Z

# 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. # ============================================================================ '''dataloader''' import os import glob import numpy as np import PIL.Image as Image import mindspore.dataset as ds import mindspore.dataset.vision.c_transforms as CV class DataLoader_Imagenet_val: '''DataLoader_Imagenet_val''' def __init__(self, data_dir, patch=256, noise_style="gauss25", batch_size=4): super(DataLoader_Imagenet_val, self).__init__() self.data_dir = data_dir self.patch = patch self.train_fns = glob.glob(os.path.join(self.data_dir, "*")) self.train_fns.sort() print('fetch {} samples for training'.format(len(self.train_fns))) self.noise_generator = AugmentNoise(noise_style) self.batch_size = batch_size self.test = 1 def __getitem__(self, index): # fetch image fn = self.train_fns[index] im = Image.open(fn) im = np.array(im, dtype=np.float32) # random crop H = im.shape[0] W = im.shape[1] if H - self.patch > 0: xx = np.random.randint(0, H - self.patch) im = im[xx:xx + self.patch, :, :] if W - self.patch > 0: yy = np.random.randint(0, W - self.patch) im = im[:, yy:yy + self.patch, :] im /= 255.0 #clean image noisy = self.noise_generator.add_noise(im) return im, noisy def __len__(self): return len(self.train_fns) class AugmentNoise(): '''AugmentNoise''' def __init__(self, style): if style.startswith('gauss'): self.params = [ float(p) / 255.0 for p in style.replace('gauss', '').split('_') ] if len(self.params) == 1: self.style = "gauss_fix" elif len(self.params) == 2: self.style = "gauss_range" elif style.startswith('poisson'): self.params = [ float(p) for p in style.replace('poisson', '').split('_') ] if len(self.params) == 1: self.style = "poisson_fix" elif len(self.params) == 2: self.style = "poisson_range" def add_noise(self, x): '''add_noise''' shape = x.shape if self.style == "gauss_fix": std = self.params[0] return np.array(x + np.random.normal(size=shape) * std, dtype=np.float32) if self.style == "gauss_range": min_std, max_std = self.params std = np.random.uniform(low=min_std, high=max_std, size=(1, 1, 1)) return np.array(x + np.random.normal(size=shape) * std, dtype=np.float32) if self.style == "poisson_fix": lam = self.params[0] return np.array(np.random.poisson(lam * x) / lam, dtype=np.float32) assert self.style == "poisson_range" min_lam, max_lam = self.params lam = np.random.uniform(low=min_lam, high=max_lam, size=(1, 1, 1)) return np.array(np.random.poisson(lam * x) / lam, dtype=np.float32) def create_Dataset(data_dir, patch, noise_style, batch_size, device_num, rank, shuffle): dataset = DataLoader_Imagenet_val(data_dir, patch, noise_style, batch_size) hwc_to_chw = CV.HWC2CHW() data_set = ds.GeneratorDataset(dataset, column_names=["image", "noisy"], \ num_parallel_workers=8, shuffle=shuffle, num_shards=device_num, shard_id=rank) data_set = data_set.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=8) data_set = data_set.map(input_columns=["noisy"], operations=hwc_to_chw, num_parallel_workers=8) data_set = data_set.batch(batch_size, drop_remainder=True) return data_set, data_set.get_dataset_size()

40.62037

99

0.605197

import os import glob import numpy as np import PIL.Image as Image import mindspore.dataset as ds import mindspore.dataset.vision.c_transforms as CV class DataLoader_Imagenet_val: def __init__(self, data_dir, patch=256, noise_style="gauss25", batch_size=4): super(DataLoader_Imagenet_val, self).__init__() self.data_dir = data_dir self.patch = patch self.train_fns = glob.glob(os.path.join(self.data_dir, "*")) self.train_fns.sort() print('fetch {} samples for training'.format(len(self.train_fns))) self.noise_generator = AugmentNoise(noise_style) self.batch_size = batch_size self.test = 1 def __getitem__(self, index): fn = self.train_fns[index] im = Image.open(fn) im = np.array(im, dtype=np.float32) H = im.shape[0] W = im.shape[1] if H - self.patch > 0: xx = np.random.randint(0, H - self.patch) im = im[xx:xx + self.patch, :, :] if W - self.patch > 0: yy = np.random.randint(0, W - self.patch) im = im[:, yy:yy + self.patch, :] im /= 255.0 noisy = self.noise_generator.add_noise(im) return im, noisy def __len__(self): return len(self.train_fns) class AugmentNoise(): def __init__(self, style): if style.startswith('gauss'): self.params = [ float(p) / 255.0 for p in style.replace('gauss', '').split('_') ] if len(self.params) == 1: self.style = "gauss_fix" elif len(self.params) == 2: self.style = "gauss_range" elif style.startswith('poisson'): self.params = [ float(p) for p in style.replace('poisson', '').split('_') ] if len(self.params) == 1: self.style = "poisson_fix" elif len(self.params) == 2: self.style = "poisson_range" def add_noise(self, x): shape = x.shape if self.style == "gauss_fix": std = self.params[0] return np.array(x + np.random.normal(size=shape) * std, dtype=np.float32) if self.style == "gauss_range": min_std, max_std = self.params std = np.random.uniform(low=min_std, high=max_std, size=(1, 1, 1)) return np.array(x + np.random.normal(size=shape) * std, dtype=np.float32) if self.style == "poisson_fix": lam = self.params[0] return np.array(np.random.poisson(lam * x) / lam, dtype=np.float32) assert self.style == "poisson_range" min_lam, max_lam = self.params lam = np.random.uniform(low=min_lam, high=max_lam, size=(1, 1, 1)) return np.array(np.random.poisson(lam * x) / lam, dtype=np.float32) def create_Dataset(data_dir, patch, noise_style, batch_size, device_num, rank, shuffle): dataset = DataLoader_Imagenet_val(data_dir, patch, noise_style, batch_size) hwc_to_chw = CV.HWC2CHW() data_set = ds.GeneratorDataset(dataset, column_names=["image", "noisy"], \ num_parallel_workers=8, shuffle=shuffle, num_shards=device_num, shard_id=rank) data_set = data_set.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=8) data_set = data_set.map(input_columns=["noisy"], operations=hwc_to_chw, num_parallel_workers=8) data_set = data_set.batch(batch_size, drop_remainder=True) return data_set, data_set.get_dataset_size()

true

f70c04e3484409a9478edc1a27fe0c1fc78a80c3

402

py

Python

app/route/route_sales.py

LifeLaboratory/skb_sudo_su

92f29cc8e7fbd30624ee0d8634d61b8ecbcace35

[ "MIT" ]

null

app/route/route_sales.py

LifeLaboratory/skb_sudo_su

92f29cc8e7fbd30624ee0d8634d61b8ecbcace35

[ "MIT" ]

null

app/route/route_sales.py

LifeLaboratory/skb_sudo_su

92f29cc8e7fbd30624ee0d8634d61b8ecbcace35

[ "MIT" ]

null

# coding=utf-8 from app.api.base import base_name as names from app.api.src.sales import * from app.api.base.base_router import BaseRouter class Sales(BaseRouter): def __init__(self): super().__init__() self.args = [names.LOGIN, names.PASSWORD] def get(self, id_user): args = { names.ID_USER: id_user } return get_sales_user(args) or {}

21.157895

49

0.636816

from app.api.base import base_name as names from app.api.src.sales import * from app.api.base.base_router import BaseRouter class Sales(BaseRouter): def __init__(self): super().__init__() self.args = [names.LOGIN, names.PASSWORD] def get(self, id_user): args = { names.ID_USER: id_user } return get_sales_user(args) or {}

true

f70c0525ea06688b28847ae0a8658955c449019c

15,211

py

Python

salt/states/cloud.py

ContinuumIO/salt

91c0955cfc24b13f07f4917d3d44a9fd9636347a

[ "Apache-2.0" ]

2

2017-09-17T21:10:35.000Z

2019-08-26T03:00:12.000Z

salt/states/cloud.py

ContinuumIO/salt

91c0955cfc24b13f07f4917d3d44a9fd9636347a

[ "Apache-2.0" ]

null

salt/states/cloud.py

ContinuumIO/salt

91c0955cfc24b13f07f4917d3d44a9fd9636347a

[ "Apache-2.0" ]

3

2021-02-23T08:12:48.000Z

2021-02-23T08:13:13.000Z

# -*- coding: utf-8 -*- ''' Using states instead of maps to deploy clouds ============================================= .. versionadded:: 2014.1.0 (Hydrogen) Use this minion to spin up a cloud instance: .. code-block:: yaml my-ec2-instance: cloud.profile: my-ec2-config ''' import pprint from salt._compat import string_types import salt.utils.cloud as suc def __virtual__(): ''' Only load if the cloud module is available in __salt__ ''' return 'cloud.profile' in __salt__ def _check_name(name): ret = {'name': name, 'changes': {}, 'result': None, 'comment': ''} if suc.check_name(name, 'a-zA-Z0-9._-'): ret['comment'] = 'Invalid characters in name.' ret['result'] = False return ret else: ret['result'] = True return ret def _valid(name, comment='', changes=None): if not changes: changes = {} return {'name': name, 'result': True, 'changes': changes, 'comment': comment} def present(name, cloud_provider, onlyif=None, unless=None, **kwargs): ''' Spin up a single instance on a cloud provider, using salt-cloud. This state does not take a profile argument; rather, it takes the arguments that would normally be configured as part of the state. Note that while this function does take any configuration argument that would normally be used to create an instance, it will not verify the state of any of those arguments on an existing instance. Stateful properties of an instance should be configured using their own individual state (i.e., cloud.tagged, cloud.untagged, etc). name The name of the instance to create cloud_provider The name of the cloud provider to use onlyif Do run the state only if is unless succeed unless Do not run the state at least unless succeed ''' ret = {'name': name, 'changes': {}, 'result': None, 'comment': ''} instance = __salt__['cloud.action']( fun='show_instance', names=[name]) retcode = __salt__['cmd.retcode'] prov = str([a for a in instance][0]) if onlyif is not None: if not isinstance(onlyif, string_types): if not onlyif: return _valid(name, comment='onlyif execution failed') elif isinstance(onlyif, string_types): if retcode(onlyif) != 0: return _valid(name, comment='onlyif execution failed') if unless is not None: if not isinstance(unless, string_types): if unless: return _valid(name, comment='unless execution succeeded') elif isinstance(unless, string_types): if retcode(unless) == 0: return _valid(name, comment='unless execution succeeded') if instance and 'Not Actioned' not in prov: ret['result'] = True ret['comment'] = 'Instance {0} already exists in {1}'.format(name, prov) return ret if __opts__['test']: ret['comment'] = 'Instance {0} needs to be created'.format(name) return ret info = __salt__['cloud.create'](cloud_provider, name, **kwargs) if info and not 'Error' in info: ret['changes'] = info ret['result'] = True ret['comment'] = ('Created instance {0} using provider {1}' ' and the following options: {2}').format( name, cloud_provider, pprint.pformat(kwargs) ) elif info and not 'Error' in info: ret['result'] = False ret['comment'] = ('Failed to create instance {0}' 'using profile {1}: {2}').format( name, profile, info['Error'], ) else: ret['result'] = False ret['comment'] = ('Failed to create instance {0}' ' using profile {1},' ' please check your configuration').format(name, profile) return ret def absent(name, onlyif=None, unless=None): ''' Ensure that no instances with the specified names exist. CAUTION: This is a destructive state, which will search all configured cloud providers for the named instance, and destroy it. name The name of the instance to destroy onlyif Do run the state only if is unless succeed unless Do not run the state at least unless succeed ''' ret = {'name': name, 'changes': {}, 'result': None, 'comment': ''} retcode = __salt__['cmd.retcode'] instance = __salt__['cloud.action'](fun='show_instance', names=[name]) if not instance or \ ('Not Actioned/Not Running' in ret and name in ret['Not Actioned/Not Running']): ret['result'] = True ret['comment'] = 'Instance {0} already absent'.format(name) return ret if __opts__['test']: ret['comment'] = 'Instance {0} needs to be destroyed'.format(name) return ret if onlyif is not None: if not isinstance(onlyif, string_types): if not onlyif: return _valid(name, comment='onlyif execution failed') elif isinstance(onlyif, string_types): if retcode(onlyif) != 0: return _valid(name, comment='onlyif execution failed') if unless is not None: if not isinstance(unless, string_types): if unless: return _valid(name, comment='unless execution succeeded') elif isinstance(unless, string_types): if retcode(unless) == 0: return _valid(name, comment='unless execution succeeded') info = __salt__['cloud.destroy'](name) if info and not 'Error' in info: ret['changes'] = info ret['result'] = True ret['comment'] = ('Destroyed instance {0}').format( name, ) elif 'Error' in info: ret['result'] = False ret['comment'] = ('Failed to destroy instance {0}: {1}').format( name, info['Error'], ) else: ret['result'] = False ret['comment'] = 'Failed to destroy instance {0}'.format(name) return ret def profile(name, profile, onlyif=None, unless=None, **kwargs): ''' Create a single instance on a cloud provider, using a salt-cloud profile. Note that while profiles used this function do take any configuration argument that would normally be used to create an instance using a profile, this state will not verify the state of any of those arguments on an existing instance. Stateful properties of an instance should be configured using their own individual state (i.e., cloud.tagged, cloud.untagged, etc). name The name of the instance to create profile The name of the cloud profile to use onlyif Do run the state only if is unless succeed unless Do not run the state at least unless succeed kwargs Any profile override or addition ''' ret = {'name': name, 'changes': {}, 'result': None, 'comment': ''} retcode = __salt__['cmd.retcode'] if onlyif is not None: if not isinstance(onlyif, string_types): if not onlyif: return _valid(name, comment='onlyif execution failed') elif isinstance(onlyif, string_types): if retcode(onlyif) != 0: return _valid(name, comment='onlyif execution failed') if unless is not None: if not isinstance(unless, string_types): if unless: return _valid(name, comment='unless execution succeeded') elif isinstance(unless, string_types): if retcode(unless) == 0: return _valid(name, comment='unless execution succeeded') instance = __salt__['cloud.action'](fun='show_instance', names=[name]) prov = str(instance.keys()[0]) if instance and 'Not Actioned' not in prov: ret['result'] = True ret['comment'] = 'Instance {0} already exists in {1}'.format( name, prov) return ret if __opts__['test']: ret['comment'] = 'Instance {0} needs to be created'.format(name) return ret info = __salt__['cloud.profile'](profile, name, vm_overrides=kwargs) # get either {Error: ''} or {namestring: {Error: ''}} # which is what we can get from providers returns main_error = info.get('Error', '') name_error = '' if isinstance(info, dict): subinfo = info.get(name, {}) if isinstance(subinfo, dict): name_error = subinfo.get('Error', None) error = main_error or name_error if info and not error: node_info = info.get(name) ret['result'] = True default_msg = 'Created instance {0} using profile {1}'.format( name, profile,) # some providers support changes if 'changes' in node_info: ret['changes'] = node_info['changes'] ret['comment'] = node_info.get('comment', default_msg) else: ret['changes'] = info ret['comment'] = default_msg elif error: ret['result'] = False ret['comment'] = ('Failed to create instance {0}' ' using profile {1}: {2}').format( name, profile, '{0}\n{1}\n'.format(main_error, name_error).strip(), ) else: ret['result'] = False ret['comment'] = ('Failed to create instance {0}' 'using profile {1}').format( name, profile, ) return ret def volume_present(name, provider=None, **kwargs): ''' Check that a block volume exists. ''' ret = _check_name(name) if not ret['result']: return ret volumes = __salt__['cloud.volume_list'](provider=provider) if name in volumes.keys(): ret['comment'] = 'Volume exists: {0}'.format(name) ret['result'] = True return ret elif __opts__['test']: ret['comment'] = 'Volume {0} will be created.'.format(name) ret['result'] = None return ret response = __salt__['cloud.volume_create']( names=name, provider=provider, **kwargs ) if response: ret['result'] = True ret['comment'] = 'Volume {0} was created'.format(name) ret['changes'] = {'old': None, 'new': response} else: ret['result'] = False ret['comment'] = 'Volume {0} failed to create.'.format(name) return ret def volume_absent(name, provider=None, **kwargs): ''' Check that a block volume exists. ''' ret = _check_name(name) if not ret['result']: return ret volumes = __salt__['cloud.volume_list'](provider=provider) if not name in volumes.keys(): ret['comment'] = 'Volume is absent.' ret['result'] = True return ret elif __opts__['test']: ret['comment'] = 'Volume {0} will be deleted.'.format(name) ret['result'] = None return ret response = __salt__['cloud.volume_delete']( names=name, provider=provider, **kwargs ) if response: ret['result'] = True ret['comment'] = 'Volume {0} was deleted'.format(name) ret['changes'] = {'old': volumes[name], 'new': response} else: ret['result'] = False ret['comment'] = 'Volume {0} failed to delete.'.format(name) return ret def volume_attached(name, server_name, provider=None, **kwargs): ''' Check if a block volume is attached. ''' ret = _check_name(name) if not ret['result']: return ret ret = _check_name(server_name) if not ret['result']: return ret volumes = __salt__['cloud.volume_list'](provider=provider) instance = __salt__['cloud.action']( fun='show_instance', names=server_name ) if name in volumes.keys() and volumes[name]['attachments']: volume = volumes[name] ret['comment'] = ('Volume {name} is already' 'attached: {attachments}').format(**volumes[name]) ret['result'] = True return ret elif not name in volumes.keys(): ret['comment'] = 'Volume {0} does not exist'.format(name) ret['result'] = False return ret elif not instance: ret['comment'] = 'Server {0} does not exist'.format(server_name) ret['result'] = False return ret elif __opts__['test']: ret['comment'] = 'Volume {0} will be will be attached.'.format( name ) ret['result'] = None return ret response = __salt__['cloud.volume_attach']( provider=provider, names=name, server_name=server_name, **kwargs ) if response: ret['result'] = True ret['comment'] = 'Volume {0} was created'.format(name) ret['changes'] = {'old': volumes[name], 'new': response} else: ret['result'] = False ret['comment'] = 'Volume {0} failed to attach.'.format(name) return ret def volume_detached(name, server_name=None, provider=None, **kwargs): ''' Check if a block volume is attached. Returns True if server or Volume do not exist. ''' ret = _check_name(name) if not ret['result']: return ret if not server_name is None: ret = _check_name(server_name) if not ret['result']: return ret volumes = __salt__['cloud.volume_list'](provider=provider) if server_name: instance = __salt__['cloud.action'](fun='show_instance', names=[name]) else: instance = None if name in volumes.keys() and not volumes[name]['attachments']: volume = volumes[name] ret['comment'] = ( 'Volume {name} is not currently attached to anything.' ).format(**volumes[name]) ret['result'] = True return ret elif not name in volumes.keys(): ret['comment'] = 'Volume {0} does not exist'.format(name) ret['result'] = True return ret elif not instance and not server_name is None: ret['comment'] = 'Server {0} does not exist'.format(server_name) ret['result'] = True return ret elif __opts__['test']: ret['comment'] = 'Volume {0} will be will be detached.'.format( name ) ret['result'] = None return ret response = __salt__['cloud.volume_detach']( provider=provider, names=name, server_name=server_name, **kwargs ) if response: ret['result'] = True ret['comment'] = 'Volume {0} was created'.format(name) ret['changes'] = {'old': volumes[name], 'new': response} else: ret['result'] = False ret['comment'] = 'Volume {0} failed to detach.'.format(name) return ret

32.023158

79

0.571297

import pprint from salt._compat import string_types import salt.utils.cloud as suc def __virtual__(): return 'cloud.profile' in __salt__ def _check_name(name): ret = {'name': name, 'changes': {}, 'result': None, 'comment': ''} if suc.check_name(name, 'a-zA-Z0-9._-'): ret['comment'] = 'Invalid characters in name.' ret['result'] = False return ret else: ret['result'] = True return ret def _valid(name, comment='', changes=None): if not changes: changes = {} return {'name': name, 'result': True, 'changes': changes, 'comment': comment} def present(name, cloud_provider, onlyif=None, unless=None, **kwargs): ret = {'name': name, 'changes': {}, 'result': None, 'comment': ''} instance = __salt__['cloud.action']( fun='show_instance', names=[name]) retcode = __salt__['cmd.retcode'] prov = str([a for a in instance][0]) if onlyif is not None: if not isinstance(onlyif, string_types): if not onlyif: return _valid(name, comment='onlyif execution failed') elif isinstance(onlyif, string_types): if retcode(onlyif) != 0: return _valid(name, comment='onlyif execution failed') if unless is not None: if not isinstance(unless, string_types): if unless: return _valid(name, comment='unless execution succeeded') elif isinstance(unless, string_types): if retcode(unless) == 0: return _valid(name, comment='unless execution succeeded') if instance and 'Not Actioned' not in prov: ret['result'] = True ret['comment'] = 'Instance {0} already exists in {1}'.format(name, prov) return ret if __opts__['test']: ret['comment'] = 'Instance {0} needs to be created'.format(name) return ret info = __salt__['cloud.create'](cloud_provider, name, **kwargs) if info and not 'Error' in info: ret['changes'] = info ret['result'] = True ret['comment'] = ('Created instance {0} using provider {1}' ' and the following options: {2}').format( name, cloud_provider, pprint.pformat(kwargs) ) elif info and not 'Error' in info: ret['result'] = False ret['comment'] = ('Failed to create instance {0}' 'using profile {1}: {2}').format( name, profile, info['Error'], ) else: ret['result'] = False ret['comment'] = ('Failed to create instance {0}' ' using profile {1},' ' please check your configuration').format(name, profile) return ret def absent(name, onlyif=None, unless=None): ret = {'name': name, 'changes': {}, 'result': None, 'comment': ''} retcode = __salt__['cmd.retcode'] instance = __salt__['cloud.action'](fun='show_instance', names=[name]) if not instance or \ ('Not Actioned/Not Running' in ret and name in ret['Not Actioned/Not Running']): ret['result'] = True ret['comment'] = 'Instance {0} already absent'.format(name) return ret if __opts__['test']: ret['comment'] = 'Instance {0} needs to be destroyed'.format(name) return ret if onlyif is not None: if not isinstance(onlyif, string_types): if not onlyif: return _valid(name, comment='onlyif execution failed') elif isinstance(onlyif, string_types): if retcode(onlyif) != 0: return _valid(name, comment='onlyif execution failed') if unless is not None: if not isinstance(unless, string_types): if unless: return _valid(name, comment='unless execution succeeded') elif isinstance(unless, string_types): if retcode(unless) == 0: return _valid(name, comment='unless execution succeeded') info = __salt__['cloud.destroy'](name) if info and not 'Error' in info: ret['changes'] = info ret['result'] = True ret['comment'] = ('Destroyed instance {0}').format( name, ) elif 'Error' in info: ret['result'] = False ret['comment'] = ('Failed to destroy instance {0}: {1}').format( name, info['Error'], ) else: ret['result'] = False ret['comment'] = 'Failed to destroy instance {0}'.format(name) return ret def profile(name, profile, onlyif=None, unless=None, **kwargs): ret = {'name': name, 'changes': {}, 'result': None, 'comment': ''} retcode = __salt__['cmd.retcode'] if onlyif is not None: if not isinstance(onlyif, string_types): if not onlyif: return _valid(name, comment='onlyif execution failed') elif isinstance(onlyif, string_types): if retcode(onlyif) != 0: return _valid(name, comment='onlyif execution failed') if unless is not None: if not isinstance(unless, string_types): if unless: return _valid(name, comment='unless execution succeeded') elif isinstance(unless, string_types): if retcode(unless) == 0: return _valid(name, comment='unless execution succeeded') instance = __salt__['cloud.action'](fun='show_instance', names=[name]) prov = str(instance.keys()[0]) if instance and 'Not Actioned' not in prov: ret['result'] = True ret['comment'] = 'Instance {0} already exists in {1}'.format( name, prov) return ret if __opts__['test']: ret['comment'] = 'Instance {0} needs to be created'.format(name) return ret info = __salt__['cloud.profile'](profile, name, vm_overrides=kwargs) main_error = info.get('Error', '') name_error = '' if isinstance(info, dict): subinfo = info.get(name, {}) if isinstance(subinfo, dict): name_error = subinfo.get('Error', None) error = main_error or name_error if info and not error: node_info = info.get(name) ret['result'] = True default_msg = 'Created instance {0} using profile {1}'.format( name, profile,) if 'changes' in node_info: ret['changes'] = node_info['changes'] ret['comment'] = node_info.get('comment', default_msg) else: ret['changes'] = info ret['comment'] = default_msg elif error: ret['result'] = False ret['comment'] = ('Failed to create instance {0}' ' using profile {1}: {2}').format( name, profile, '{0}\n{1}\n'.format(main_error, name_error).strip(), ) else: ret['result'] = False ret['comment'] = ('Failed to create instance {0}' 'using profile {1}').format( name, profile, ) return ret def volume_present(name, provider=None, **kwargs): ret = _check_name(name) if not ret['result']: return ret volumes = __salt__['cloud.volume_list'](provider=provider) if name in volumes.keys(): ret['comment'] = 'Volume exists: {0}'.format(name) ret['result'] = True return ret elif __opts__['test']: ret['comment'] = 'Volume {0} will be created.'.format(name) ret['result'] = None return ret response = __salt__['cloud.volume_create']( names=name, provider=provider, **kwargs ) if response: ret['result'] = True ret['comment'] = 'Volume {0} was created'.format(name) ret['changes'] = {'old': None, 'new': response} else: ret['result'] = False ret['comment'] = 'Volume {0} failed to create.'.format(name) return ret def volume_absent(name, provider=None, **kwargs): ret = _check_name(name) if not ret['result']: return ret volumes = __salt__['cloud.volume_list'](provider=provider) if not name in volumes.keys(): ret['comment'] = 'Volume is absent.' ret['result'] = True return ret elif __opts__['test']: ret['comment'] = 'Volume {0} will be deleted.'.format(name) ret['result'] = None return ret response = __salt__['cloud.volume_delete']( names=name, provider=provider, **kwargs ) if response: ret['result'] = True ret['comment'] = 'Volume {0} was deleted'.format(name) ret['changes'] = {'old': volumes[name], 'new': response} else: ret['result'] = False ret['comment'] = 'Volume {0} failed to delete.'.format(name) return ret def volume_attached(name, server_name, provider=None, **kwargs): ret = _check_name(name) if not ret['result']: return ret ret = _check_name(server_name) if not ret['result']: return ret volumes = __salt__['cloud.volume_list'](provider=provider) instance = __salt__['cloud.action']( fun='show_instance', names=server_name ) if name in volumes.keys() and volumes[name]['attachments']: volume = volumes[name] ret['comment'] = ('Volume {name} is already' 'attached: {attachments}').format(**volumes[name]) ret['result'] = True return ret elif not name in volumes.keys(): ret['comment'] = 'Volume {0} does not exist'.format(name) ret['result'] = False return ret elif not instance: ret['comment'] = 'Server {0} does not exist'.format(server_name) ret['result'] = False return ret elif __opts__['test']: ret['comment'] = 'Volume {0} will be will be attached.'.format( name ) ret['result'] = None return ret response = __salt__['cloud.volume_attach']( provider=provider, names=name, server_name=server_name, **kwargs ) if response: ret['result'] = True ret['comment'] = 'Volume {0} was created'.format(name) ret['changes'] = {'old': volumes[name], 'new': response} else: ret['result'] = False ret['comment'] = 'Volume {0} failed to attach.'.format(name) return ret def volume_detached(name, server_name=None, provider=None, **kwargs): ret = _check_name(name) if not ret['result']: return ret if not server_name is None: ret = _check_name(server_name) if not ret['result']: return ret volumes = __salt__['cloud.volume_list'](provider=provider) if server_name: instance = __salt__['cloud.action'](fun='show_instance', names=[name]) else: instance = None if name in volumes.keys() and not volumes[name]['attachments']: volume = volumes[name] ret['comment'] = ( 'Volume {name} is not currently attached to anything.' ).format(**volumes[name]) ret['result'] = True return ret elif not name in volumes.keys(): ret['comment'] = 'Volume {0} does not exist'.format(name) ret['result'] = True return ret elif not instance and not server_name is None: ret['comment'] = 'Server {0} does not exist'.format(server_name) ret['result'] = True return ret elif __opts__['test']: ret['comment'] = 'Volume {0} will be will be detached.'.format( name ) ret['result'] = None return ret response = __salt__['cloud.volume_detach']( provider=provider, names=name, server_name=server_name, **kwargs ) if response: ret['result'] = True ret['comment'] = 'Volume {0} was created'.format(name) ret['changes'] = {'old': volumes[name], 'new': response} else: ret['result'] = False ret['comment'] = 'Volume {0} failed to detach.'.format(name) return ret

true

f70c054e7f9c75daf40ce7a574ccf0b3546d13eb

3,655

py

Python

iotronic_lightningrod/modules/utils.py

Zakaria-Ben/iotronic-lightning-rod

4a3eff68bd1db2d57beee0e8c51fbb14fcc0877a

[ "Apache-2.0" ]

null

iotronic_lightningrod/modules/utils.py

Zakaria-Ben/iotronic-lightning-rod

4a3eff68bd1db2d57beee0e8c51fbb14fcc0877a

[ "Apache-2.0" ]

null

iotronic_lightningrod/modules/utils.py

Zakaria-Ben/iotronic-lightning-rod

4a3eff68bd1db2d57beee0e8c51fbb14fcc0877a

[ "Apache-2.0" ]

1

2018-05-18T13:01:03.000Z

# Copyright 2017 MDSLAB - University of Messina # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. __author__ = "Nicola Peditto <npeditto@unime.it" import asyncio import inspect import pkg_resources from six import moves from stevedore import extension import sys from iotronic_lightningrod.config import entry_points_name from iotronic_lightningrod.lightningrod import SESSION from iotronic_lightningrod.modules import Module from oslo_log import log as logging LOG = logging.getLogger(__name__) def getFuncName(): return inspect.stack()[1][3] def refresh_stevedore(namespace=None): """Trigger reload of entry points. Useful to have dynamic loading/unloading of stevedore modules. """ # NOTE(sheeprine): pkg_resources doesn't support reload on python3 due to # defining basestring which is still there on reload hence executing # python2 related code. try: del sys.modules['pkg_resources'].basestring except AttributeError: # python2, do nothing pass # Force working_set reload moves.reload_module(sys.modules['pkg_resources']) # Clear stevedore cache cache = extension.ExtensionManager.ENTRY_POINT_CACHE if namespace: if namespace in cache: del cache[namespace] else: cache.clear() class Utility(Module.Module): def __init__(self, board, session): super(Utility, self).__init__("Utility", board) def finalize(self): pass def restore(self): pass async def hello(self, client_name, message): import random s = random.uniform(0.5, 3.0) await asyncio.sleep(s) result = "Hello by board to Conductor " + client_name + \ " that said me " + message + " - Time: " + '%.2f' % s LOG.info("DEVICE hello result: " + str(result)) return result async def plug_and_play(self, new_module, new_class): LOG.info("LR modules loaded:\n\t" + new_module) # Updating entry_points with open(entry_points_name, 'a') as entry_points: entry_points.write( new_module + '= iotronic_lightningrod.modules.' + new_module + ':' + new_class ) # Reload entry_points refresh_stevedore('s4t.modules') LOG.info("New entry_points loaded!") # Reading updated entry_points named_objects = {} for ep in pkg_resources.iter_entry_points(group='s4t.modules'): named_objects.update({ep.name: ep.load()}) await named_objects SESSION.disconnect() return str(named_objects) async def changeConf(self, conf): await self.board.getConf(conf) self.board.setUpdateTime() result = "Board configuration changed!" LOG.info("PROVISIONING RESULT: " + str(result)) return result async def destroyNode(self, conf): await self.board.setConf(conf) result = "Board configuration cleaned!" LOG.info("DESTROY RESULT: " + str(result)) return result

28.554688

78

0.661012

__author__ = "Nicola Peditto <npeditto@unime.it" import asyncio import inspect import pkg_resources from six import moves from stevedore import extension import sys from iotronic_lightningrod.config import entry_points_name from iotronic_lightningrod.lightningrod import SESSION from iotronic_lightningrod.modules import Module from oslo_log import log as logging LOG = logging.getLogger(__name__) def getFuncName(): return inspect.stack()[1][3] def refresh_stevedore(namespace=None): # defining basestring which is still there on reload hence executing # python2 related code. try: del sys.modules['pkg_resources'].basestring except AttributeError: # python2, do nothing pass # Force working_set reload moves.reload_module(sys.modules['pkg_resources']) # Clear stevedore cache cache = extension.ExtensionManager.ENTRY_POINT_CACHE if namespace: if namespace in cache: del cache[namespace] else: cache.clear() class Utility(Module.Module): def __init__(self, board, session): super(Utility, self).__init__("Utility", board) def finalize(self): pass def restore(self): pass async def hello(self, client_name, message): import random s = random.uniform(0.5, 3.0) await asyncio.sleep(s) result = "Hello by board to Conductor " + client_name + \ " that said me " + message + " - Time: " + '%.2f' % s LOG.info("DEVICE hello result: " + str(result)) return result async def plug_and_play(self, new_module, new_class): LOG.info("LR modules loaded:\n\t" + new_module) # Updating entry_points with open(entry_points_name, 'a') as entry_points: entry_points.write( new_module + '= iotronic_lightningrod.modules.' + new_module + ':' + new_class ) # Reload entry_points refresh_stevedore('s4t.modules') LOG.info("New entry_points loaded!") # Reading updated entry_points named_objects = {} for ep in pkg_resources.iter_entry_points(group='s4t.modules'): named_objects.update({ep.name: ep.load()}) await named_objects SESSION.disconnect() return str(named_objects) async def changeConf(self, conf): await self.board.getConf(conf) self.board.setUpdateTime() result = "Board configuration changed!" LOG.info("PROVISIONING RESULT: " + str(result)) return result async def destroyNode(self, conf): await self.board.setConf(conf) result = "Board configuration cleaned!" LOG.info("DESTROY RESULT: " + str(result)) return result

true

f70c0553ea487881fbca1e06b6c31bb278cd4251

4,069

py

Python

Project/EnhancedDeepPath/scripts/sl_policy.py

iust-projects/Data-Mining-IUST

88f7a5541278f1fe907ca9b70c990a27f60900b2

[ "Apache-2.0" ]

null

Project/EnhancedDeepPath/scripts/sl_policy.py

iust-projects/Data-Mining-IUST

88f7a5541278f1fe907ca9b70c990a27f60900b2

[ "Apache-2.0" ]

2

2020-07-10T17:58:07.000Z

2020-12-22T09:02:39.000Z

Project/EnhancedDeepPath/scripts/sl_policy.py

iust-projects/Data-Mining-IUST

88f7a5541278f1fe907ca9b70c990a27f60900b2

[ "Apache-2.0" ]

null

from __future__ import division from __future__ import print_function import tensorflow as tf import numpy as np from itertools import count import sys from networks import policy_nn from utils import * from env import Env from BFS.KB import KB from BFS.BFS import BFS import time relation = sys.argv[1] # episodes = int(sys.argv[2]) graphpath = dataPath + 'tasks/' + relation + '/' + 'graph.txt' relationPath = dataPath + 'tasks/' + relation + '/' + 'train_pos' class SupervisedPolicy(object): """docstring for SupervisedPolicy""" def __init__(self, learning_rate = 0.001): self.initializer = tf.contrib.layers.xavier_initializer() with tf.variable_scope('supervised_policy'): self.state = tf.placeholder(tf.float32, [None, state_dim], name = 'state') self.action = tf.placeholder(tf.int32, [None], name = 'action') self.action_prob = policy_nn(self.state, state_dim, action_space, self.initializer) action_mask = tf.cast(tf.one_hot(self.action, depth = action_space), tf.bool) self.picked_action_prob = tf.boolean_mask(self.action_prob, action_mask) self.loss = tf.reduce_sum(-tf.log(self.picked_action_prob)) + sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope = 'supervised_policy')) self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate) self.train_op = self.optimizer.minimize(self.loss) def predict(self, state, sess = None): sess = sess or tf.get_default_session() return sess.run(self.action_prob, {self.state: state}) def update(self, state, action, sess = None): sess = sess or tf.get_default_session() _, loss = sess.run([self.train_op, self.loss], {self.state: state, self.action: action}) return loss def train(): tf.reset_default_graph() policy_nn = SupervisedPolicy() f = open(relationPath) train_data = f.readlines() f.close() num_samples = len(train_data) saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) if num_samples > 500: num_samples = 500 else: num_episodes = num_samples for episode in range(num_samples): print("Episode %d" % episode) print('Training Sample:', train_data[episode%num_samples][:-1]) env = Env(dataPath, train_data[episode%num_samples]) sample = train_data[episode%num_samples].split() try: good_episodes = teacher(sample[0], sample[1], 5, env, graphpath) except Exception as e: print('Cannot find a path') continue for item in good_episodes: state_batch = [] action_batch = [] for t, transition in enumerate(item): state_batch.append(transition.state) action_batch.append(transition.action) state_batch = np.squeeze(state_batch) state_batch = np.reshape(state_batch, [-1, state_dim]) policy_nn.update(state_batch, action_batch) saver.save(sess, 'models/policy_supervised_' + relation) print('Model saved') def test(test_episodes): tf.reset_default_graph() policy_nn = SupervisedPolicy() f = open(relationPath) test_data = f.readlines() f.close() test_num = len(test_data) test_data = test_data[-test_episodes:] print(len(test_data)) success = 0 saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, 'models/policy_supervised_'+ relation) print('Model reloaded') for episode in range(len(test_data)): print('Test sample %d: %s' % (episode,test_data[episode][:-1])) env = Env(dataPath, test_data[episode]) sample = test_data[episode].split() state_idx = [env.entity2id_[sample[0]], env.entity2id_[sample[1]], 0] for t in count(): state_vec = env.idx_state(state_idx) action_probs = policy_nn.predict(state_vec) action_chosen = np.random.choice(np.arange(action_space), p = np.squeeze(action_probs)) reward, new_state, done = env.interact(state_idx, action_chosen) if done or t == max_steps_test: if done: print('Success') success += 1 print('Episode ends\n') break state_idx = new_state print('Success persentage:', success/test_episodes) if __name__ == "__main__": train() # test(50)

30.593985

152

0.717375

from __future__ import division from __future__ import print_function import tensorflow as tf import numpy as np from itertools import count import sys from networks import policy_nn from utils import * from env import Env from BFS.KB import KB from BFS.BFS import BFS import time relation = sys.argv[1] graphpath = dataPath + 'tasks/' + relation + '/' + 'graph.txt' relationPath = dataPath + 'tasks/' + relation + '/' + 'train_pos' class SupervisedPolicy(object): def __init__(self, learning_rate = 0.001): self.initializer = tf.contrib.layers.xavier_initializer() with tf.variable_scope('supervised_policy'): self.state = tf.placeholder(tf.float32, [None, state_dim], name = 'state') self.action = tf.placeholder(tf.int32, [None], name = 'action') self.action_prob = policy_nn(self.state, state_dim, action_space, self.initializer) action_mask = tf.cast(tf.one_hot(self.action, depth = action_space), tf.bool) self.picked_action_prob = tf.boolean_mask(self.action_prob, action_mask) self.loss = tf.reduce_sum(-tf.log(self.picked_action_prob)) + sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope = 'supervised_policy')) self.optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate) self.train_op = self.optimizer.minimize(self.loss) def predict(self, state, sess = None): sess = sess or tf.get_default_session() return sess.run(self.action_prob, {self.state: state}) def update(self, state, action, sess = None): sess = sess or tf.get_default_session() _, loss = sess.run([self.train_op, self.loss], {self.state: state, self.action: action}) return loss def train(): tf.reset_default_graph() policy_nn = SupervisedPolicy() f = open(relationPath) train_data = f.readlines() f.close() num_samples = len(train_data) saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) if num_samples > 500: num_samples = 500 else: num_episodes = num_samples for episode in range(num_samples): print("Episode %d" % episode) print('Training Sample:', train_data[episode%num_samples][:-1]) env = Env(dataPath, train_data[episode%num_samples]) sample = train_data[episode%num_samples].split() try: good_episodes = teacher(sample[0], sample[1], 5, env, graphpath) except Exception as e: print('Cannot find a path') continue for item in good_episodes: state_batch = [] action_batch = [] for t, transition in enumerate(item): state_batch.append(transition.state) action_batch.append(transition.action) state_batch = np.squeeze(state_batch) state_batch = np.reshape(state_batch, [-1, state_dim]) policy_nn.update(state_batch, action_batch) saver.save(sess, 'models/policy_supervised_' + relation) print('Model saved') def test(test_episodes): tf.reset_default_graph() policy_nn = SupervisedPolicy() f = open(relationPath) test_data = f.readlines() f.close() test_num = len(test_data) test_data = test_data[-test_episodes:] print(len(test_data)) success = 0 saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, 'models/policy_supervised_'+ relation) print('Model reloaded') for episode in range(len(test_data)): print('Test sample %d: %s' % (episode,test_data[episode][:-1])) env = Env(dataPath, test_data[episode]) sample = test_data[episode].split() state_idx = [env.entity2id_[sample[0]], env.entity2id_[sample[1]], 0] for t in count(): state_vec = env.idx_state(state_idx) action_probs = policy_nn.predict(state_vec) action_chosen = np.random.choice(np.arange(action_space), p = np.squeeze(action_probs)) reward, new_state, done = env.interact(state_idx, action_chosen) if done or t == max_steps_test: if done: print('Success') success += 1 print('Episode ends\n') break state_idx = new_state print('Success persentage:', success/test_episodes) if __name__ == "__main__": train()

true

f70c06fd558229c1a63658ce7eb7a0987e13c526

415

py

Python

students/K33422/Iskhakova_Emina/labs/lab3/admin.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

null

students/K33422/Iskhakova_Emina/labs/lab3/admin.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

null

students/K33422/Iskhakova_Emina/labs/lab3/admin.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

null

from django.contrib import admin from .models import * admin.site.register(Client) admin.site.register(ServicesPL) admin.site.register(MaterialsPL) admin.site.register(Request) admin.site.register(ChosenServices) admin.site.register(ChosenMaterials) admin.site.register(WorkGroup) admin.site.register(Executor) admin.site.register(Invoice) admin.site.register(PaymentOrder) admin.site.register(User)

27.666667

37

0.804819

from django.contrib import admin from .models import * admin.site.register(Client) admin.site.register(ServicesPL) admin.site.register(MaterialsPL) admin.site.register(Request) admin.site.register(ChosenServices) admin.site.register(ChosenMaterials) admin.site.register(WorkGroup) admin.site.register(Executor) admin.site.register(Invoice) admin.site.register(PaymentOrder) admin.site.register(User)

true

f70c0739ded8c4ed003bf1865ab7f1e637ca68d0

1,378

py

Python

pytorch-frontend/caffe2/python/operator_test/glu_op_test.py

AndreasKaratzas/stonne

2915fcc46cc94196303d81abbd1d79a56d6dd4a9

[ "MIT" ]

40

2021-06-01T07:37:59.000Z

2022-03-25T01:42:09.000Z

pytorch-frontend/caffe2/python/operator_test/glu_op_test.py

AndreasKaratzas/stonne

2915fcc46cc94196303d81abbd1d79a56d6dd4a9

[ "MIT" ]

14

2021-06-01T11:52:46.000Z

2022-03-25T02:13:08.000Z

pytorch-frontend/caffe2/python/operator_test/glu_op_test.py

AndreasKaratzas/stonne

2915fcc46cc94196303d81abbd1d79a56d6dd4a9

[ "MIT" ]

7

2021-07-20T19:34:26.000Z

2022-03-13T21:07:36.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from caffe2.python import core import caffe2.python.hypothesis_test_util as hu import caffe2.python.serialized_test.serialized_test_util as serial from hypothesis import assume, given, settings, HealthCheck import hypothesis.strategies as st import numpy as np import unittest @st.composite def _glu_old_input(draw): dims = draw(st.lists(st.integers(min_value=1, max_value=5), min_size=1, max_size=3)) axis = draw(st.integers(min_value=0, max_value=len(dims))) # The axis dimension must be divisible by two axis_dim = 2 * draw(st.integers(min_value=1, max_value=2)) dims.insert(axis, axis_dim) X = draw(hu.arrays(dims, np.float32, None)) return (X, axis) class TestGlu(serial.SerializedTestCase): @given( X_axis=_glu_old_input(), **hu.gcs ) @settings(deadline=10000) def test_glu_old(self, X_axis, gc, dc): X, axis = X_axis def glu_ref(X): x1, x2 = np.split(X, [X.shape[axis] // 2], axis=axis) Y = x1 * (1. / (1. + np.exp(-x2))) return [Y] op = core.CreateOperator("Glu", ["X"], ["Y"], dim=axis) self.assertReferenceChecks(gc, op, [X], glu_ref) if __name__ == "__main__": unittest.main()

29.956522

88

0.681422

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from caffe2.python import core import caffe2.python.hypothesis_test_util as hu import caffe2.python.serialized_test.serialized_test_util as serial from hypothesis import assume, given, settings, HealthCheck import hypothesis.strategies as st import numpy as np import unittest @st.composite def _glu_old_input(draw): dims = draw(st.lists(st.integers(min_value=1, max_value=5), min_size=1, max_size=3)) axis = draw(st.integers(min_value=0, max_value=len(dims))) axis_dim = 2 * draw(st.integers(min_value=1, max_value=2)) dims.insert(axis, axis_dim) X = draw(hu.arrays(dims, np.float32, None)) return (X, axis) class TestGlu(serial.SerializedTestCase): @given( X_axis=_glu_old_input(), **hu.gcs ) @settings(deadline=10000) def test_glu_old(self, X_axis, gc, dc): X, axis = X_axis def glu_ref(X): x1, x2 = np.split(X, [X.shape[axis] // 2], axis=axis) Y = x1 * (1. / (1. + np.exp(-x2))) return [Y] op = core.CreateOperator("Glu", ["X"], ["Y"], dim=axis) self.assertReferenceChecks(gc, op, [X], glu_ref) if __name__ == "__main__": unittest.main()

true

f70c079999af8cc5d3c6169652b547016bc9d133

20,765

py

Python

diofant/tests/matrices/test_sparse.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

null

diofant/tests/matrices/test_sparse.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

null

diofant/tests/matrices/test_sparse.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

null

import pytest from diofant import (I, Matrix, MutableDenseMatrix, MutableSparseMatrix, PurePoly, Rational, ShapeError, SparseMatrix, eye, ones, zeros) from diofant.abc import x, y, z __all__ = () def test_sparse_matrix(): def sparse_eye(n): return SparseMatrix.eye(n) def sparse_zeros(n): return SparseMatrix.zeros(n) # creation args pytest.raises(TypeError, lambda: SparseMatrix(1, 2)) pytest.raises(ValueError, lambda: SparseMatrix(2, 2, (1, 3, 4, 5, 6))) a = SparseMatrix(( (1, 0), (0, 1) )) assert SparseMatrix(a) == a a = MutableSparseMatrix([]) b = MutableDenseMatrix([1, 2]) assert a.row_join(b) == b assert a.col_join(b) == b assert type(a.row_join(b)) == type(a) assert type(a.col_join(b)) == type(a) # test element assignment a = SparseMatrix(( (1, 0), (0, 1) )) a[3] = 4 assert a[1, 1] == 4 a[3] = 1 a[0, 0] = 2 assert a == SparseMatrix(( (2, 0), (0, 1) )) a[1, 0] = 5 assert a == SparseMatrix(( (2, 0), (5, 1) )) a[1, 1] = 0 assert a == SparseMatrix(( (2, 0), (5, 0) )) assert a._smat == {(0, 0): 2, (1, 0): 5} # test_multiplication a = SparseMatrix(( (1, 2), (3, 1), (0, 6), )) b = SparseMatrix(( (1, 2), (3, 0), )) c = a*b assert c[0, 0] == 7 assert c[0, 1] == 2 assert c[1, 0] == 6 assert c[1, 1] == 6 assert c[2, 0] == 18 assert c[2, 1] == 0 c = b * x assert isinstance(c, SparseMatrix) assert c[0, 0] == x assert c[0, 1] == 2*x assert c[1, 0] == 3*x assert c[1, 1] == 0 c = 5 * b assert isinstance(c, SparseMatrix) assert c[0, 0] == 5 assert c[0, 1] == 2*5 assert c[1, 0] == 3*5 assert c[1, 1] == 0 # test_power A = SparseMatrix([[2, 3], [4, 5]]) assert (A**5)[:] == [6140, 8097, 10796, 14237] A = SparseMatrix([[2, 1, 3], [4, 2, 4], [6, 12, 1]]) assert (A**3)[:] == [290, 262, 251, 448, 440, 368, 702, 954, 433] # test_creation a = SparseMatrix([[x, 0], [0, 0]]) m = a assert m.cols == m.rows assert m.cols == 2 assert m[:] == [x, 0, 0, 0] b = SparseMatrix(2, 2, [x, 0, 0, 0]) m = b assert m.cols == m.rows assert m.cols == 2 assert m[:] == [x, 0, 0, 0] assert a == b S = sparse_eye(3) del S[1, :] assert S == SparseMatrix([ [1, 0, 0], [0, 0, 1]]) S = sparse_eye(3) del S[:, 1] assert S == SparseMatrix([ [1, 0], [0, 0], [0, 1]]) S = SparseMatrix.eye(3) S[2, 1] = 2 S.col_swap(1, 0) assert S == SparseMatrix([[0, 1, 0], [1, 0, 0], [2, 0, 1]]) S.row_swap(0, 1) assert S == SparseMatrix([[1, 0, 0], [0, 1, 0], [2, 0, 1]]) S.col_swap(0, 1) assert S == SparseMatrix([[0, 1, 0], [1, 0, 0], [0, 2, 1]]) S.row_swap(0, 2) assert S == SparseMatrix([[0, 2, 1], [1, 0, 0], [0, 1, 0]]) S.col_swap(0, 2) assert S == SparseMatrix([[1, 2, 0], [0, 0, 1], [0, 1, 0]]) a = SparseMatrix(1, 2, [1, 2]) b = a.copy() c = a.copy() assert a[0] == 1 del a[0, :] assert a == SparseMatrix(0, 2, []) del b[:, 1] assert b == SparseMatrix(1, 1, [1]) # test_determinant assert SparseMatrix(1, 1, [0]).det() == 0 assert SparseMatrix([[1]]).det() == 1 assert SparseMatrix(((-3, 2), (8, -5))).det() == -1 assert SparseMatrix(((x, 1), (y, 2*y))).det() == 2*x*y - y assert SparseMatrix(( (1, 1, 1), (1, 2, 3), (1, 3, 6) )).det() == 1 assert SparseMatrix(( ( 3, -2, 0, 5), (-2, 1, -2, 2), ( 0, -2, 5, 0), ( 5, 0, 3, 4) )).det() == -289 assert SparseMatrix(( ( 1, 2, 3, 4), ( 5, 6, 7, 8), ( 9, 10, 11, 12), (13, 14, 15, 16) )).det() == 0 assert SparseMatrix(( (3, 2, 0, 0, 0), (0, 3, 2, 0, 0), (0, 0, 3, 2, 0), (0, 0, 0, 3, 2), (2, 0, 0, 0, 3) )).det() == 275 assert SparseMatrix(( (1, 0, 1, 2, 12), (2, 0, 1, 1, 4), (2, 1, 1, -1, 3), (3, 2, -1, 1, 8), (1, 1, 1, 0, 6) )).det() == -55 assert SparseMatrix(( (-5, 2, 3, 4, 5), ( 1, -4, 3, 4, 5), ( 1, 2, -3, 4, 5), ( 1, 2, 3, -2, 5), ( 1, 2, 3, 4, -1) )).det() == 11664 assert SparseMatrix(( ( 2, 7, -1, 3, 2), ( 0, 0, 1, 0, 1), (-2, 0, 7, 0, 2), (-3, -2, 4, 5, 3), ( 1, 0, 0, 0, 1) )).det() == 123 # test_slicing m0 = sparse_eye(4) assert m0[:3, :3] == sparse_eye(3) assert m0[2:4, 0:2] == sparse_zeros(2) m1 = SparseMatrix(3, 3, lambda i, j: i + j) assert m1[0, :] == SparseMatrix(1, 3, (0, 1, 2)) assert m1[1:3, 1] == SparseMatrix(2, 1, (2, 3)) m2 = SparseMatrix( [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]) assert m2[:, -1] == SparseMatrix(4, 1, [3, 7, 11, 15]) assert m2[-2:, :] == SparseMatrix([[8, 9, 10, 11], [12, 13, 14, 15]]) assert SparseMatrix([[1, 2], [3, 4]])[[1], [1]] == Matrix([[4]]) # test_submatrix_assignment m = sparse_zeros(4) m[2:4, 2:4] = sparse_eye(2) assert m == SparseMatrix([(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 0, 0, 1)]) assert len(m._smat) == 2 m[:2, :2] = sparse_eye(2) assert m == sparse_eye(4) m[:, 0] = SparseMatrix(4, 1, (1, 2, 3, 4)) assert m == SparseMatrix([(1, 0, 0, 0), (2, 1, 0, 0), (3, 0, 1, 0), (4, 0, 0, 1)]) m[:, :] = sparse_zeros(4) assert m == sparse_zeros(4) m[:, :] = ((1, 2, 3, 4), (5, 6, 7, 8), (9, 10, 11, 12), (13, 14, 15, 16)) assert m == SparseMatrix((( 1, 2, 3, 4), ( 5, 6, 7, 8), ( 9, 10, 11, 12), (13, 14, 15, 16))) m[:2, 0] = [0, 0] assert m == SparseMatrix((( 0, 2, 3, 4), ( 0, 6, 7, 8), ( 9, 10, 11, 12), (13, 14, 15, 16))) # test_reshape m0 = sparse_eye(3) assert m0.reshape(1, 9) == SparseMatrix(1, 9, (1, 0, 0, 0, 1, 0, 0, 0, 1)) m1 = SparseMatrix(3, 4, lambda i, j: i + j) assert m1.reshape(4, 3) == \ SparseMatrix([(0, 1, 2), (3, 1, 2), (3, 4, 2), (3, 4, 5)]) assert m1.reshape(2, 6) == \ SparseMatrix([(0, 1, 2, 3, 1, 2), (3, 4, 2, 3, 4, 5)]) # test_applyfunc m0 = sparse_eye(3) assert m0.applyfunc(lambda x: 2*x) == sparse_eye(3)*2 assert m0.applyfunc(lambda x: 0 ) == sparse_zeros(3) # test_LUdecomp testmat = SparseMatrix([[ 0, 2, 5, 3], [ 3, 3, 7, 4], [ 8, 4, 0, 2], [-2, 6, 3, 4]]) L, U, p = testmat.LUdecomposition() assert L.is_lower assert U.is_upper assert (L*U).permuteBkwd(p) - testmat == sparse_zeros(4) testmat = SparseMatrix([[ 6, -2, 7, 4], [ 0, 3, 6, 7], [ 1, -2, 7, 4], [-9, 2, 6, 3]]) L, U, p = testmat.LUdecomposition() assert L.is_lower assert U.is_upper assert (L*U).permuteBkwd(p) - testmat == sparse_zeros(4) M = Matrix(((1, x, 1), (2, y, 0), (y, 0, z))) L, U, p = M.LUdecomposition() assert L.is_lower assert U.is_upper assert (L*U).permuteBkwd(p) - M == sparse_zeros(3) # test_LUsolve A = SparseMatrix([[2, 3, 5], [3, 6, 2], [8, 3, 6]]) B = SparseMatrix(3, 1, [3, 7, 5]) b = A*B soln = A.LUsolve(b) assert soln == B A = SparseMatrix([[0, -1, 2], [5, 10, 7], [8, 3, 4]]) B = SparseMatrix(3, 1, [-1, 2, 5]) b = A*B soln = A.LUsolve(b) assert soln == B # test_inverse A = sparse_eye(4) assert A.inv() == sparse_eye(4) assert A.inv(method='CH') == sparse_eye(4) assert A.inv(method='LDL') == sparse_eye(4) A = SparseMatrix([[2, 3, 5], [3, 6, 2], [7, 2, 6]]) Ainv = SparseMatrix(Matrix(A).inv()) assert A*Ainv == sparse_eye(3) assert A.inv(method='CH') == Ainv assert A.inv(method='LDL') == Ainv A = SparseMatrix([[2, 3, 5], [3, 6, 2], [5, 2, 6]]) Ainv = SparseMatrix(Matrix(A).inv()) assert A*Ainv == sparse_eye(3) assert A.inv(method='CH') == Ainv assert A.inv(method='LDL') == Ainv # test_cross v1 = Matrix(1, 3, [1, 2, 3]) v2 = Matrix(1, 3, [3, 4, 5]) assert v1.cross(v2) == Matrix(1, 3, [-2, 4, -2]) assert v1.norm(2)**2 == 14 # conjugate a = SparseMatrix(((1, 2 + I), (3, 4))) assert a.C == SparseMatrix([ [1, 2 - I], [3, 4] ]) # mul assert a*Matrix(2, 2, [1, 0, 0, 1]) == a assert a + Matrix(2, 2, [1, 1, 1, 1]) == SparseMatrix([ [2, 3 + I], [4, 5] ]) assert a*0 == Matrix([[0, 0], [0, 0]]) # col join assert a.col_join(sparse_eye(2)) == SparseMatrix([ [1, 2 + I], [3, 4], [1, 0], [0, 1] ]) A = SparseMatrix(ones(3)) B = eye(3) assert A.col_join(B) == Matrix([[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1]]) # row join A = SparseMatrix(((1, 0, 1), (0, 1, 0), (1, 1, 0))) B = Matrix(((1, 0, 0), (0, 1, 0), (0, 0, 1))) assert A.row_join(B) == Matrix([[1, 0, 1, 1, 0, 0], [0, 1, 0, 0, 1, 0], [1, 1, 0, 0, 0, 1]]) # symmetric assert not a.is_symmetric(simplify=False) assert sparse_eye(3).is_symmetric(simplify=False) # test_cofactor assert sparse_eye(3) == sparse_eye(3).cofactorMatrix() test = SparseMatrix([[1, 3, 2], [2, 6, 3], [2, 3, 6]]) assert test.cofactorMatrix() == \ SparseMatrix([[27, -6, -6], [-12, 2, 3], [-3, 1, 0]]) test = SparseMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) assert test.cofactorMatrix() == \ SparseMatrix([[-3, 6, -3], [6, -12, 6], [-3, 6, -3]]) # test_jacobian L = SparseMatrix(1, 2, [x**2*y, 2*y**2 + x*y]) syms = [x, y] assert L.jacobian(syms) == Matrix([[2*x*y, x**2], [y, 4*y + x]]) L = SparseMatrix(1, 2, [x, x**2*y**3]) assert L.jacobian(syms) == SparseMatrix([[1, 0], [2*x*y**3, x**2*3*y**2]]) # test_QR A = Matrix([[1, 2], [2, 3]]) Q, S = A.QRdecomposition() R = Rational assert Q == Matrix([ [ 5**R(-1, 2), (R(2)/5)*(R(1)/5)**R(-1, 2)], [2*5**R(-1, 2), (-R(1)/5)*(R(1)/5)**R(-1, 2)]]) assert S == Matrix([ [5**R(1, 2), 8*5**R(-1, 2)], [ 0, (R(1)/5)**R(1, 2)]]) assert Q*S == A assert Q.T * Q == sparse_eye(2) R = Rational # test nullspace # first test reduced row-ech form M = SparseMatrix([[5, 7, 2, 1], [1, 6, 2, -1]]) out, tmp = M.rref() assert out == Matrix([[1, 0, -R(2)/23, R(13)/23], [0, 1, R(8)/23, R(-6)/23]]) M = SparseMatrix([[ 1, 3, 0, 2, 6, 3, 1], [-2, -6, 0, -2, -8, 3, 1], [ 3, 9, 0, 0, 6, 6, 2], [-1, -3, 0, 1, 0, 9, 3]]) out, tmp = M.rref() assert out == Matrix([[1, 3, 0, 0, 2, 0, 0], [0, 0, 0, 1, 2, 0, 0], [0, 0, 0, 0, 0, 1, R(1)/3], [0, 0, 0, 0, 0, 0, 0]]) # now check the vectors basis = M.nullspace() assert basis[0] == Matrix([-3, 1, 0, 0, 0, 0, 0]) assert basis[1] == Matrix([0, 0, 1, 0, 0, 0, 0]) assert basis[2] == Matrix([-2, 0, 0, -2, 1, 0, 0]) assert basis[3] == Matrix([0, 0, 0, 0, 0, R(-1)/3, 1]) # test eigen sparse_eye3 = sparse_eye(3) assert sparse_eye3.charpoly(x) == PurePoly(((x - 1)**3)) assert sparse_eye3.charpoly(y) == PurePoly(((y - 1)**3)) # test values M = Matrix([( 0, 1, -1), ( 1, 1, 0), (-1, 0, 1)]) vals = M.eigenvals() assert sorted(vals) == [-1, 1, 2] R = Rational M = Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) assert M.eigenvects() == [(1, 3, [ Matrix([1, 0, 0]), Matrix([0, 1, 0]), Matrix([0, 0, 1])])] M = Matrix([[5, 0, 2], [3, 2, 0], [0, 0, 1]]) assert M.eigenvects() == [(1, 1, [Matrix([R(-1)/2, R(3)/2, 1])]), (2, 1, [Matrix([0, 1, 0])]), (5, 1, [Matrix([1, 1, 0])])] assert M.zeros(3, 5) == SparseMatrix(3, 5, {}) A = SparseMatrix(10, 10, {(0, 0): 18, (0, 9): 12, (1, 4): 18, (2, 7): 16, (3, 9): 12, (4, 2): 19, (5, 7): 16, (6, 2): 12, (9, 7): 18}) assert A.row_list() == [(0, 0, 18), (0, 9, 12), (1, 4, 18), (2, 7, 16), (3, 9, 12), (4, 2, 19), (5, 7, 16), (6, 2, 12), (9, 7, 18)] assert A.col_list() == [(0, 0, 18), (4, 2, 19), (6, 2, 12), (1, 4, 18), (2, 7, 16), (5, 7, 16), (9, 7, 18), (0, 9, 12), (3, 9, 12)] assert SparseMatrix.eye(2).nnz() == 2 M = SparseMatrix.eye(3)*2 M[1, 0] = -1 M.col_op(1, lambda v, i: v + 2*M[i, 0]) assert M == Matrix([[ 2, 4, 0], [-1, 0, 0], [ 0, 0, 2]]) M = SparseMatrix.zeros(3) M.fill(1) assert M == ones(3) assert SparseMatrix(ones(0, 3)).tolist() == [] def test_eq(): A = SparseMatrix(((1, 2), (3, 4))) assert A != 1 assert A != zeros(2, 1) def test_transpose(): assert SparseMatrix(((1, 2), (3, 4))).transpose() == \ SparseMatrix(((1, 3), (2, 4))) def test_trace(): assert SparseMatrix(((1, 2), (3, 4))).trace() == 5 assert SparseMatrix(((0, 0), (0, 4))).trace() == 4 def test_CL_RL(): assert SparseMatrix(((1, 2), (3, 4))).row_list() == \ [(0, 0, 1), (0, 1, 2), (1, 0, 3), (1, 1, 4)] assert SparseMatrix(((1, 2), (3, 4))).col_list() == \ [(0, 0, 1), (1, 0, 3), (0, 1, 2), (1, 1, 4)] def test_add(): assert SparseMatrix(((1, 0), (0, 1))) + SparseMatrix(((0, 1), (1, 0))) == \ SparseMatrix(((1, 1), (1, 1))) a = SparseMatrix(100, 100, lambda i, j: int(j != 0 and i % j == 0)) b = SparseMatrix(100, 100, lambda i, j: int(i != 0 and j % i == 0)) assert (len(a._smat) + len(b._smat) - len((a + b)._smat) > 0) def test_errors(): pytest.raises(ValueError, lambda: SparseMatrix(1.4, 2, lambda i, j: 0)) pytest.raises(ValueError, lambda: SparseMatrix(2, 2, 1)) pytest.raises(TypeError, lambda: SparseMatrix([1, 2, 3], [1, 2])) pytest.raises(ValueError, lambda: SparseMatrix([[1, 2], [3, 4]])[(1, 2, 3)]) pytest.raises(IndexError, lambda: SparseMatrix([[1, 2], [3, 4]])[5]) pytest.raises(ValueError, lambda: SparseMatrix([[1, 2], [3, 4]])[1, 2, 3]) pytest.raises(TypeError, lambda: SparseMatrix([[1, 2], [3, 4]]).copyin_list([0, 1], set())) pytest.raises(IndexError, lambda: SparseMatrix([[1, 2], [3, 4]])[1, 2]) pytest.raises(TypeError, lambda: SparseMatrix([1, 2, 3]).cross(1)) pytest.raises(IndexError, lambda: SparseMatrix(1, 2, [1, 2])[3]) pytest.raises(ShapeError, lambda: SparseMatrix(1, 2, [1, 2]) + SparseMatrix(2, 1, [2, 1])) pytest.raises(IndexError, lambda: SparseMatrix([1, 2, 3])[3, 0]) pytest.raises(TypeError, lambda: SparseMatrix([1, 2, 3]).applyfunc(1)) pytest.raises(ValueError, lambda: SparseMatrix([1, 2, 3]).reshape(2, 2)) pytest.raises(ValueError, lambda: SparseMatrix([[2, 3], [4, 1]]).cholesky()) pytest.raises(ValueError, lambda: SparseMatrix([[2, 3], [4, 1]]).LDLdecomposition()) pytest.raises(ValueError, lambda: SparseMatrix([[2, 3], [4, 1]]).add(1)) pytest.raises(ShapeError, lambda: SparseMatrix([[1, 2], [3, 4]]).row_join(Matrix([[1, 2]]))) pytest.raises(ShapeError, lambda: SparseMatrix([[1, 2], [3, 4]]).col_join(Matrix([1, 2]))) pytest.raises(ShapeError, lambda: SparseMatrix([[1, 2], [3, 4]]).copyin_matrix([1, 0], Matrix([1, 2]))) def test_len(): assert not SparseMatrix() assert SparseMatrix() == SparseMatrix([]) assert SparseMatrix() == SparseMatrix([[]]) def test_sparse_zeros_sparse_eye(): assert SparseMatrix.eye(3) == eye(3, cls=SparseMatrix) assert len(SparseMatrix.eye(3)._smat) == 3 assert SparseMatrix.zeros(3) == zeros(3, cls=SparseMatrix) assert len(SparseMatrix.zeros(3)._smat) == 0 def test_copyin(): s = SparseMatrix(3, 3, {}) s[1, 0] = 1 assert s[:, 0] == SparseMatrix(Matrix([0, 1, 0])) assert s[3] == 1 assert s[3: 4] == [1] s[1, 1] = 42 assert s[1, 1] == 42 assert s[1, 1:] == SparseMatrix([[42, 0]]) s[1, 1:] = Matrix([[5, 6]]) assert s[1, :] == SparseMatrix([[1, 5, 6]]) s[1, 1:] = [[42, 43]] assert s[1, :] == SparseMatrix([[1, 42, 43]]) s[0, 0] = 17 assert s[:, :1] == SparseMatrix([17, 1, 0]) s[0, 0] = [1, 1, 1] assert s[:, 0] == SparseMatrix([1, 1, 1]) s[0, 0] = Matrix([1, 1, 1]) assert s[:, 0] == SparseMatrix([1, 1, 1]) s[0, 0] = SparseMatrix([1, 1, 1]) assert s[:, 0] == SparseMatrix([1, 1, 1]) def test_sparse_solve(): A = SparseMatrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11))) assert A.cholesky() == Matrix([ [ 5, 0, 0], [ 3, 3, 0], [-1, 1, 3]]) assert A.cholesky() * A.cholesky().T == Matrix([ [25, 15, -5], [15, 18, 0], [-5, 0, 11]]) A = SparseMatrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11))) L, D = A.LDLdecomposition() assert 15*L == Matrix([ [15, 0, 0], [ 9, 15, 0], [-3, 5, 15]]) assert D == Matrix([ [25, 0, 0], [ 0, 9, 0], [ 0, 0, 9]]) assert L * D * L.T == A A = SparseMatrix(((3, 0, 2), (0, 0, 1), (1, 2, 0))) assert A.inv() * A == SparseMatrix(eye(3)) A = SparseMatrix([ [ 2, -1, 0], [-1, 2, -1], [ 0, 0, 2]]) ans = SparseMatrix([ [Rational(2, 3), Rational(1, 3), Rational(1, 6)], [Rational(1, 3), Rational(2, 3), Rational(1, 3)], [ 0, 0, Rational(1, 2)]]) assert A.inv(method='CH') == ans assert A.inv(method='LDL') == ans assert A * ans == SparseMatrix(eye(3)) s = A.solve(A[:, 0], 'LDL') assert A*s == A[:, 0] s = A.solve(A[:, 0], 'CH') assert A*s == A[:, 0] A = A.col_join(A) s = A.solve_least_squares(A[:, 0], 'CH') assert A*s == A[:, 0] s = A.solve_least_squares(A[:, 0], 'LDL') assert A*s == A[:, 0] pytest.raises(ValueError, lambda: SparseMatrix([[1, 0, 1], [0, 0, 1]]).solve([1, 1])) pytest.raises(ValueError, lambda: SparseMatrix([[1, 0], [0, 0], [2, 1]]).solve([1, 1, 1])) def test_hermitian(): a = SparseMatrix([[0, I], [-I, 0]]) assert a.is_hermitian a = SparseMatrix([[1, I], [-I, 1]]) assert a.is_hermitian a[0, 0] = 2*I assert a.is_hermitian is False a[0, 0] = x assert a.is_hermitian is None a[0, 1] = a[1, 0]*I assert a.is_hermitian is False def test_fill(): a = SparseMatrix([[0, I], [-I, 0]]) a.fill(0) assert a == Matrix([[0, 0], [0, 0]])

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import pytest from diofant import (I, Matrix, MutableDenseMatrix, MutableSparseMatrix, PurePoly, Rational, ShapeError, SparseMatrix, eye, ones, zeros) from diofant.abc import x, y, z __all__ = () def test_sparse_matrix(): def sparse_eye(n): return SparseMatrix.eye(n) def sparse_zeros(n): return SparseMatrix.zeros(n) pytest.raises(TypeError, lambda: SparseMatrix(1, 2)) pytest.raises(ValueError, lambda: SparseMatrix(2, 2, (1, 3, 4, 5, 6))) a = SparseMatrix(( (1, 0), (0, 1) )) assert SparseMatrix(a) == a a = MutableSparseMatrix([]) b = MutableDenseMatrix([1, 2]) assert a.row_join(b) == b assert a.col_join(b) == b assert type(a.row_join(b)) == type(a) assert type(a.col_join(b)) == type(a) a = SparseMatrix(( (1, 0), (0, 1) )) a[3] = 4 assert a[1, 1] == 4 a[3] = 1 a[0, 0] = 2 assert a == SparseMatrix(( (2, 0), (0, 1) )) a[1, 0] = 5 assert a == SparseMatrix(( (2, 0), (5, 1) )) a[1, 1] = 0 assert a == SparseMatrix(( (2, 0), (5, 0) )) assert a._smat == {(0, 0): 2, (1, 0): 5} a = SparseMatrix(( (1, 2), (3, 1), (0, 6), )) b = SparseMatrix(( (1, 2), (3, 0), )) c = a*b assert c[0, 0] == 7 assert c[0, 1] == 2 assert c[1, 0] == 6 assert c[1, 1] == 6 assert c[2, 0] == 18 assert c[2, 1] == 0 c = b * x assert isinstance(c, SparseMatrix) assert c[0, 0] == x assert c[0, 1] == 2*x assert c[1, 0] == 3*x assert c[1, 1] == 0 c = 5 * b assert isinstance(c, SparseMatrix) assert c[0, 0] == 5 assert c[0, 1] == 2*5 assert c[1, 0] == 3*5 assert c[1, 1] == 0 A = SparseMatrix([[2, 3], [4, 5]]) assert (A**5)[:] == [6140, 8097, 10796, 14237] A = SparseMatrix([[2, 1, 3], [4, 2, 4], [6, 12, 1]]) assert (A**3)[:] == [290, 262, 251, 448, 440, 368, 702, 954, 433] a = SparseMatrix([[x, 0], [0, 0]]) m = a assert m.cols == m.rows assert m.cols == 2 assert m[:] == [x, 0, 0, 0] b = SparseMatrix(2, 2, [x, 0, 0, 0]) m = b assert m.cols == m.rows assert m.cols == 2 assert m[:] == [x, 0, 0, 0] assert a == b S = sparse_eye(3) del S[1, :] assert S == SparseMatrix([ [1, 0, 0], [0, 0, 1]]) S = sparse_eye(3) del S[:, 1] assert S == SparseMatrix([ [1, 0], [0, 0], [0, 1]]) S = SparseMatrix.eye(3) S[2, 1] = 2 S.col_swap(1, 0) assert S == SparseMatrix([[0, 1, 0], [1, 0, 0], [2, 0, 1]]) S.row_swap(0, 1) assert S == SparseMatrix([[1, 0, 0], [0, 1, 0], [2, 0, 1]]) S.col_swap(0, 1) assert S == SparseMatrix([[0, 1, 0], [1, 0, 0], [0, 2, 1]]) S.row_swap(0, 2) assert S == SparseMatrix([[0, 2, 1], [1, 0, 0], [0, 1, 0]]) S.col_swap(0, 2) assert S == SparseMatrix([[1, 2, 0], [0, 0, 1], [0, 1, 0]]) a = SparseMatrix(1, 2, [1, 2]) b = a.copy() c = a.copy() assert a[0] == 1 del a[0, :] assert a == SparseMatrix(0, 2, []) del b[:, 1] assert b == SparseMatrix(1, 1, [1]) assert SparseMatrix(1, 1, [0]).det() == 0 assert SparseMatrix([[1]]).det() == 1 assert SparseMatrix(((-3, 2), (8, -5))).det() == -1 assert SparseMatrix(((x, 1), (y, 2*y))).det() == 2*x*y - y assert SparseMatrix(( (1, 1, 1), (1, 2, 3), (1, 3, 6) )).det() == 1 assert SparseMatrix(( ( 3, -2, 0, 5), (-2, 1, -2, 2), ( 0, -2, 5, 0), ( 5, 0, 3, 4) )).det() == -289 assert SparseMatrix(( ( 1, 2, 3, 4), ( 5, 6, 7, 8), ( 9, 10, 11, 12), (13, 14, 15, 16) )).det() == 0 assert SparseMatrix(( (3, 2, 0, 0, 0), (0, 3, 2, 0, 0), (0, 0, 3, 2, 0), (0, 0, 0, 3, 2), (2, 0, 0, 0, 3) )).det() == 275 assert SparseMatrix(( (1, 0, 1, 2, 12), (2, 0, 1, 1, 4), (2, 1, 1, -1, 3), (3, 2, -1, 1, 8), (1, 1, 1, 0, 6) )).det() == -55 assert SparseMatrix(( (-5, 2, 3, 4, 5), ( 1, -4, 3, 4, 5), ( 1, 2, -3, 4, 5), ( 1, 2, 3, -2, 5), ( 1, 2, 3, 4, -1) )).det() == 11664 assert SparseMatrix(( ( 2, 7, -1, 3, 2), ( 0, 0, 1, 0, 1), (-2, 0, 7, 0, 2), (-3, -2, 4, 5, 3), ( 1, 0, 0, 0, 1) )).det() == 123 m0 = sparse_eye(4) assert m0[:3, :3] == sparse_eye(3) assert m0[2:4, 0:2] == sparse_zeros(2) m1 = SparseMatrix(3, 3, lambda i, j: i + j) assert m1[0, :] == SparseMatrix(1, 3, (0, 1, 2)) assert m1[1:3, 1] == SparseMatrix(2, 1, (2, 3)) m2 = SparseMatrix( [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]]) assert m2[:, -1] == SparseMatrix(4, 1, [3, 7, 11, 15]) assert m2[-2:, :] == SparseMatrix([[8, 9, 10, 11], [12, 13, 14, 15]]) assert SparseMatrix([[1, 2], [3, 4]])[[1], [1]] == Matrix([[4]]) m = sparse_zeros(4) m[2:4, 2:4] = sparse_eye(2) assert m == SparseMatrix([(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 1, 0), (0, 0, 0, 1)]) assert len(m._smat) == 2 m[:2, :2] = sparse_eye(2) assert m == sparse_eye(4) m[:, 0] = SparseMatrix(4, 1, (1, 2, 3, 4)) assert m == SparseMatrix([(1, 0, 0, 0), (2, 1, 0, 0), (3, 0, 1, 0), (4, 0, 0, 1)]) m[:, :] = sparse_zeros(4) assert m == sparse_zeros(4) m[:, :] = ((1, 2, 3, 4), (5, 6, 7, 8), (9, 10, 11, 12), (13, 14, 15, 16)) assert m == SparseMatrix((( 1, 2, 3, 4), ( 5, 6, 7, 8), ( 9, 10, 11, 12), (13, 14, 15, 16))) m[:2, 0] = [0, 0] assert m == SparseMatrix((( 0, 2, 3, 4), ( 0, 6, 7, 8), ( 9, 10, 11, 12), (13, 14, 15, 16))) m0 = sparse_eye(3) assert m0.reshape(1, 9) == SparseMatrix(1, 9, (1, 0, 0, 0, 1, 0, 0, 0, 1)) m1 = SparseMatrix(3, 4, lambda i, j: i + j) assert m1.reshape(4, 3) == \ SparseMatrix([(0, 1, 2), (3, 1, 2), (3, 4, 2), (3, 4, 5)]) assert m1.reshape(2, 6) == \ SparseMatrix([(0, 1, 2, 3, 1, 2), (3, 4, 2, 3, 4, 5)]) m0 = sparse_eye(3) assert m0.applyfunc(lambda x: 2*x) == sparse_eye(3)*2 assert m0.applyfunc(lambda x: 0 ) == sparse_zeros(3) testmat = SparseMatrix([[ 0, 2, 5, 3], [ 3, 3, 7, 4], [ 8, 4, 0, 2], [-2, 6, 3, 4]]) L, U, p = testmat.LUdecomposition() assert L.is_lower assert U.is_upper assert (L*U).permuteBkwd(p) - testmat == sparse_zeros(4) testmat = SparseMatrix([[ 6, -2, 7, 4], [ 0, 3, 6, 7], [ 1, -2, 7, 4], [-9, 2, 6, 3]]) L, U, p = testmat.LUdecomposition() assert L.is_lower assert U.is_upper assert (L*U).permuteBkwd(p) - testmat == sparse_zeros(4) M = Matrix(((1, x, 1), (2, y, 0), (y, 0, z))) L, U, p = M.LUdecomposition() assert L.is_lower assert U.is_upper assert (L*U).permuteBkwd(p) - M == sparse_zeros(3) A = SparseMatrix([[2, 3, 5], [3, 6, 2], [8, 3, 6]]) B = SparseMatrix(3, 1, [3, 7, 5]) b = A*B soln = A.LUsolve(b) assert soln == B A = SparseMatrix([[0, -1, 2], [5, 10, 7], [8, 3, 4]]) B = SparseMatrix(3, 1, [-1, 2, 5]) b = A*B soln = A.LUsolve(b) assert soln == B A = sparse_eye(4) assert A.inv() == sparse_eye(4) assert A.inv(method='CH') == sparse_eye(4) assert A.inv(method='LDL') == sparse_eye(4) A = SparseMatrix([[2, 3, 5], [3, 6, 2], [7, 2, 6]]) Ainv = SparseMatrix(Matrix(A).inv()) assert A*Ainv == sparse_eye(3) assert A.inv(method='CH') == Ainv assert A.inv(method='LDL') == Ainv A = SparseMatrix([[2, 3, 5], [3, 6, 2], [5, 2, 6]]) Ainv = SparseMatrix(Matrix(A).inv()) assert A*Ainv == sparse_eye(3) assert A.inv(method='CH') == Ainv assert A.inv(method='LDL') == Ainv v1 = Matrix(1, 3, [1, 2, 3]) v2 = Matrix(1, 3, [3, 4, 5]) assert v1.cross(v2) == Matrix(1, 3, [-2, 4, -2]) assert v1.norm(2)**2 == 14 a = SparseMatrix(((1, 2 + I), (3, 4))) assert a.C == SparseMatrix([ [1, 2 - I], [3, 4] ]) assert a*Matrix(2, 2, [1, 0, 0, 1]) == a assert a + Matrix(2, 2, [1, 1, 1, 1]) == SparseMatrix([ [2, 3 + I], [4, 5] ]) assert a*0 == Matrix([[0, 0], [0, 0]]) assert a.col_join(sparse_eye(2)) == SparseMatrix([ [1, 2 + I], [3, 4], [1, 0], [0, 1] ]) A = SparseMatrix(ones(3)) B = eye(3) assert A.col_join(B) == Matrix([[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 0, 0], [0, 1, 0], [0, 0, 1]]) A = SparseMatrix(((1, 0, 1), (0, 1, 0), (1, 1, 0))) B = Matrix(((1, 0, 0), (0, 1, 0), (0, 0, 1))) assert A.row_join(B) == Matrix([[1, 0, 1, 1, 0, 0], [0, 1, 0, 0, 1, 0], [1, 1, 0, 0, 0, 1]]) assert not a.is_symmetric(simplify=False) assert sparse_eye(3).is_symmetric(simplify=False) assert sparse_eye(3) == sparse_eye(3).cofactorMatrix() test = SparseMatrix([[1, 3, 2], [2, 6, 3], [2, 3, 6]]) assert test.cofactorMatrix() == \ SparseMatrix([[27, -6, -6], [-12, 2, 3], [-3, 1, 0]]) test = SparseMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) assert test.cofactorMatrix() == \ SparseMatrix([[-3, 6, -3], [6, -12, 6], [-3, 6, -3]]) L = SparseMatrix(1, 2, [x**2*y, 2*y**2 + x*y]) syms = [x, y] assert L.jacobian(syms) == Matrix([[2*x*y, x**2], [y, 4*y + x]]) L = SparseMatrix(1, 2, [x, x**2*y**3]) assert L.jacobian(syms) == SparseMatrix([[1, 0], [2*x*y**3, x**2*3*y**2]]) A = Matrix([[1, 2], [2, 3]]) Q, S = A.QRdecomposition() R = Rational assert Q == Matrix([ [ 5**R(-1, 2), (R(2)/5)*(R(1)/5)**R(-1, 2)], [2*5**R(-1, 2), (-R(1)/5)*(R(1)/5)**R(-1, 2)]]) assert S == Matrix([ [5**R(1, 2), 8*5**R(-1, 2)], [ 0, (R(1)/5)**R(1, 2)]]) assert Q*S == A assert Q.T * Q == sparse_eye(2) R = Rational M = SparseMatrix([[5, 7, 2, 1], [1, 6, 2, -1]]) out, tmp = M.rref() assert out == Matrix([[1, 0, -R(2)/23, R(13)/23], [0, 1, R(8)/23, R(-6)/23]]) M = SparseMatrix([[ 1, 3, 0, 2, 6, 3, 1], [-2, -6, 0, -2, -8, 3, 1], [ 3, 9, 0, 0, 6, 6, 2], [-1, -3, 0, 1, 0, 9, 3]]) out, tmp = M.rref() assert out == Matrix([[1, 3, 0, 0, 2, 0, 0], [0, 0, 0, 1, 2, 0, 0], [0, 0, 0, 0, 0, 1, R(1)/3], [0, 0, 0, 0, 0, 0, 0]]) basis = M.nullspace() assert basis[0] == Matrix([-3, 1, 0, 0, 0, 0, 0]) assert basis[1] == Matrix([0, 0, 1, 0, 0, 0, 0]) assert basis[2] == Matrix([-2, 0, 0, -2, 1, 0, 0]) assert basis[3] == Matrix([0, 0, 0, 0, 0, R(-1)/3, 1]) sparse_eye3 = sparse_eye(3) assert sparse_eye3.charpoly(x) == PurePoly(((x - 1)**3)) assert sparse_eye3.charpoly(y) == PurePoly(((y - 1)**3)) M = Matrix([( 0, 1, -1), ( 1, 1, 0), (-1, 0, 1)]) vals = M.eigenvals() assert sorted(vals) == [-1, 1, 2] R = Rational M = Matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) assert M.eigenvects() == [(1, 3, [ Matrix([1, 0, 0]), Matrix([0, 1, 0]), Matrix([0, 0, 1])])] M = Matrix([[5, 0, 2], [3, 2, 0], [0, 0, 1]]) assert M.eigenvects() == [(1, 1, [Matrix([R(-1)/2, R(3)/2, 1])]), (2, 1, [Matrix([0, 1, 0])]), (5, 1, [Matrix([1, 1, 0])])] assert M.zeros(3, 5) == SparseMatrix(3, 5, {}) A = SparseMatrix(10, 10, {(0, 0): 18, (0, 9): 12, (1, 4): 18, (2, 7): 16, (3, 9): 12, (4, 2): 19, (5, 7): 16, (6, 2): 12, (9, 7): 18}) assert A.row_list() == [(0, 0, 18), (0, 9, 12), (1, 4, 18), (2, 7, 16), (3, 9, 12), (4, 2, 19), (5, 7, 16), (6, 2, 12), (9, 7, 18)] assert A.col_list() == [(0, 0, 18), (4, 2, 19), (6, 2, 12), (1, 4, 18), (2, 7, 16), (5, 7, 16), (9, 7, 18), (0, 9, 12), (3, 9, 12)] assert SparseMatrix.eye(2).nnz() == 2 M = SparseMatrix.eye(3)*2 M[1, 0] = -1 M.col_op(1, lambda v, i: v + 2*M[i, 0]) assert M == Matrix([[ 2, 4, 0], [-1, 0, 0], [ 0, 0, 2]]) M = SparseMatrix.zeros(3) M.fill(1) assert M == ones(3) assert SparseMatrix(ones(0, 3)).tolist() == [] def test_eq(): A = SparseMatrix(((1, 2), (3, 4))) assert A != 1 assert A != zeros(2, 1) def test_transpose(): assert SparseMatrix(((1, 2), (3, 4))).transpose() == \ SparseMatrix(((1, 3), (2, 4))) def test_trace(): assert SparseMatrix(((1, 2), (3, 4))).trace() == 5 assert SparseMatrix(((0, 0), (0, 4))).trace() == 4 def test_CL_RL(): assert SparseMatrix(((1, 2), (3, 4))).row_list() == \ [(0, 0, 1), (0, 1, 2), (1, 0, 3), (1, 1, 4)] assert SparseMatrix(((1, 2), (3, 4))).col_list() == \ [(0, 0, 1), (1, 0, 3), (0, 1, 2), (1, 1, 4)] def test_add(): assert SparseMatrix(((1, 0), (0, 1))) + SparseMatrix(((0, 1), (1, 0))) == \ SparseMatrix(((1, 1), (1, 1))) a = SparseMatrix(100, 100, lambda i, j: int(j != 0 and i % j == 0)) b = SparseMatrix(100, 100, lambda i, j: int(i != 0 and j % i == 0)) assert (len(a._smat) + len(b._smat) - len((a + b)._smat) > 0) def test_errors(): pytest.raises(ValueError, lambda: SparseMatrix(1.4, 2, lambda i, j: 0)) pytest.raises(ValueError, lambda: SparseMatrix(2, 2, 1)) pytest.raises(TypeError, lambda: SparseMatrix([1, 2, 3], [1, 2])) pytest.raises(ValueError, lambda: SparseMatrix([[1, 2], [3, 4]])[(1, 2, 3)]) pytest.raises(IndexError, lambda: SparseMatrix([[1, 2], [3, 4]])[5]) pytest.raises(ValueError, lambda: SparseMatrix([[1, 2], [3, 4]])[1, 2, 3]) pytest.raises(TypeError, lambda: SparseMatrix([[1, 2], [3, 4]]).copyin_list([0, 1], set())) pytest.raises(IndexError, lambda: SparseMatrix([[1, 2], [3, 4]])[1, 2]) pytest.raises(TypeError, lambda: SparseMatrix([1, 2, 3]).cross(1)) pytest.raises(IndexError, lambda: SparseMatrix(1, 2, [1, 2])[3]) pytest.raises(ShapeError, lambda: SparseMatrix(1, 2, [1, 2]) + SparseMatrix(2, 1, [2, 1])) pytest.raises(IndexError, lambda: SparseMatrix([1, 2, 3])[3, 0]) pytest.raises(TypeError, lambda: SparseMatrix([1, 2, 3]).applyfunc(1)) pytest.raises(ValueError, lambda: SparseMatrix([1, 2, 3]).reshape(2, 2)) pytest.raises(ValueError, lambda: SparseMatrix([[2, 3], [4, 1]]).cholesky()) pytest.raises(ValueError, lambda: SparseMatrix([[2, 3], [4, 1]]).LDLdecomposition()) pytest.raises(ValueError, lambda: SparseMatrix([[2, 3], [4, 1]]).add(1)) pytest.raises(ShapeError, lambda: SparseMatrix([[1, 2], [3, 4]]).row_join(Matrix([[1, 2]]))) pytest.raises(ShapeError, lambda: SparseMatrix([[1, 2], [3, 4]]).col_join(Matrix([1, 2]))) pytest.raises(ShapeError, lambda: SparseMatrix([[1, 2], [3, 4]]).copyin_matrix([1, 0], Matrix([1, 2]))) def test_len(): assert not SparseMatrix() assert SparseMatrix() == SparseMatrix([]) assert SparseMatrix() == SparseMatrix([[]]) def test_sparse_zeros_sparse_eye(): assert SparseMatrix.eye(3) == eye(3, cls=SparseMatrix) assert len(SparseMatrix.eye(3)._smat) == 3 assert SparseMatrix.zeros(3) == zeros(3, cls=SparseMatrix) assert len(SparseMatrix.zeros(3)._smat) == 0 def test_copyin(): s = SparseMatrix(3, 3, {}) s[1, 0] = 1 assert s[:, 0] == SparseMatrix(Matrix([0, 1, 0])) assert s[3] == 1 assert s[3: 4] == [1] s[1, 1] = 42 assert s[1, 1] == 42 assert s[1, 1:] == SparseMatrix([[42, 0]]) s[1, 1:] = Matrix([[5, 6]]) assert s[1, :] == SparseMatrix([[1, 5, 6]]) s[1, 1:] = [[42, 43]] assert s[1, :] == SparseMatrix([[1, 42, 43]]) s[0, 0] = 17 assert s[:, :1] == SparseMatrix([17, 1, 0]) s[0, 0] = [1, 1, 1] assert s[:, 0] == SparseMatrix([1, 1, 1]) s[0, 0] = Matrix([1, 1, 1]) assert s[:, 0] == SparseMatrix([1, 1, 1]) s[0, 0] = SparseMatrix([1, 1, 1]) assert s[:, 0] == SparseMatrix([1, 1, 1]) def test_sparse_solve(): A = SparseMatrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11))) assert A.cholesky() == Matrix([ [ 5, 0, 0], [ 3, 3, 0], [-1, 1, 3]]) assert A.cholesky() * A.cholesky().T == Matrix([ [25, 15, -5], [15, 18, 0], [-5, 0, 11]]) A = SparseMatrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11))) L, D = A.LDLdecomposition() assert 15*L == Matrix([ [15, 0, 0], [ 9, 15, 0], [-3, 5, 15]]) assert D == Matrix([ [25, 0, 0], [ 0, 9, 0], [ 0, 0, 9]]) assert L * D * L.T == A A = SparseMatrix(((3, 0, 2), (0, 0, 1), (1, 2, 0))) assert A.inv() * A == SparseMatrix(eye(3)) A = SparseMatrix([ [ 2, -1, 0], [-1, 2, -1], [ 0, 0, 2]]) ans = SparseMatrix([ [Rational(2, 3), Rational(1, 3), Rational(1, 6)], [Rational(1, 3), Rational(2, 3), Rational(1, 3)], [ 0, 0, Rational(1, 2)]]) assert A.inv(method='CH') == ans assert A.inv(method='LDL') == ans assert A * ans == SparseMatrix(eye(3)) s = A.solve(A[:, 0], 'LDL') assert A*s == A[:, 0] s = A.solve(A[:, 0], 'CH') assert A*s == A[:, 0] A = A.col_join(A) s = A.solve_least_squares(A[:, 0], 'CH') assert A*s == A[:, 0] s = A.solve_least_squares(A[:, 0], 'LDL') assert A*s == A[:, 0] pytest.raises(ValueError, lambda: SparseMatrix([[1, 0, 1], [0, 0, 1]]).solve([1, 1])) pytest.raises(ValueError, lambda: SparseMatrix([[1, 0], [0, 0], [2, 1]]).solve([1, 1, 1])) def test_hermitian(): a = SparseMatrix([[0, I], [-I, 0]]) assert a.is_hermitian a = SparseMatrix([[1, I], [-I, 1]]) assert a.is_hermitian a[0, 0] = 2*I assert a.is_hermitian is False a[0, 0] = x assert a.is_hermitian is None a[0, 1] = a[1, 0]*I assert a.is_hermitian is False def test_fill(): a = SparseMatrix([[0, I], [-I, 0]]) a.fill(0) assert a == Matrix([[0, 0], [0, 0]])

true

f70c087df6136cae52ef50e6b06ba60de3007853

1,505

py

Python

tests/test_resourcerelease.py

asears/moviepy

6ab3efba36cf7fc5d3245f0ee0dc9244cb141c9e

[ "MIT" ]

1

2020-12-20T20:38:52.000Z

tests/test_resourcerelease.py

asears/moviepy

6ab3efba36cf7fc5d3245f0ee0dc9244cb141c9e

[ "MIT" ]

1

2022-03-12T01:04:31.000Z

tests/test_resourcerelease.py

asears/moviepy

6ab3efba36cf7fc5d3245f0ee0dc9244cb141c9e

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Tool tests meant to be run with pytest. Testing whether issue #596 has been repaired. Note: Platform dependent test. Will only fail on Windows > NT. """ import time from os import remove from os.path import join from moviepy.video.compositing.CompositeVideoClip import clips_array from moviepy.video.io.VideoFileClip import VideoFileClip from moviepy.video.VideoClip import ColorClip from tests.test_helper import TMP_DIR def test_release_of_file_via_close(): # Create a random video file. red = ColorClip((256, 200), color=(255, 0, 0)) green = ColorClip((256, 200), color=(0, 255, 0)) blue = ColorClip((256, 200), color=(0, 0, 255)) red.fps = green.fps = blue.fps = 10 # Repeat this so we can see no conflicts. for i in range(3): # Get the name of a temporary file we can use. local_video_filename = join( TMP_DIR, "test_release_of_file_via_close_%s.mp4" % int(time.time()) ) clip = clips_array([[red, green, blue]]).with_duration(0.5) clip.write_videofile(local_video_filename) # Open it up with VideoFileClip. video = VideoFileClip(local_video_filename) video.close() clip.close() # Now remove the temporary file. # This would fail on Windows if the file is still locked. # This should succeed without exceptions. remove(local_video_filename) red.close() green.close() blue.close()

28.396226

79

0.662458

import time from os import remove from os.path import join from moviepy.video.compositing.CompositeVideoClip import clips_array from moviepy.video.io.VideoFileClip import VideoFileClip from moviepy.video.VideoClip import ColorClip from tests.test_helper import TMP_DIR def test_release_of_file_via_close(): red = ColorClip((256, 200), color=(255, 0, 0)) green = ColorClip((256, 200), color=(0, 255, 0)) blue = ColorClip((256, 200), color=(0, 0, 255)) red.fps = green.fps = blue.fps = 10 for i in range(3): local_video_filename = join( TMP_DIR, "test_release_of_file_via_close_%s.mp4" % int(time.time()) ) clip = clips_array([[red, green, blue]]).with_duration(0.5) clip.write_videofile(local_video_filename) video = VideoFileClip(local_video_filename) video.close() clip.close() remove(local_video_filename) red.close() green.close() blue.close()

true

f70c0a607295a9f836d9c828a3c177e182d6a1d4

416

py

Python

report_builder/migrations/0007_auto_20190214_1405.py

nazmizorlu/django-report-builder

0b37cd0c94af15531e487554c774a01dad3b5500

[ "BSD-3-Clause" ]

560

2015-01-05T07:14:50.000Z

2022-03-11T13:27:42.000Z

report_builder/migrations/0007_auto_20190214_1405.py

nazmizorlu/django-report-builder

0b37cd0c94af15531e487554c774a01dad3b5500

[ "BSD-3-Clause" ]

189

2015-01-15T16:55:55.000Z

2020-10-29T07:36:51.000Z

report_builder/migrations/0007_auto_20190214_1405.py

nazmizorlu/django-report-builder

0b37cd0c94af15531e487554c774a01dad3b5500

[ "BSD-3-Clause" ]

235

2015-01-10T16:56:17.000Z

2022-03-29T15:57:03.000Z

# Generated by Django 2.1 on 2019-02-14 14:05 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('report_builder', '0006_auto_20180413_0747'), ] operations = [ migrations.AlterField( model_name='filterfield', name='filter_value', field=models.CharField(blank=True, max_length=2000), ), ]

21.894737

64

0.622596

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('report_builder', '0006_auto_20180413_0747'), ] operations = [ migrations.AlterField( model_name='filterfield', name='filter_value', field=models.CharField(blank=True, max_length=2000), ), ]

true

f70c0b2de377270eaba5653ab6d25d86078e095d

1,810

py

Python

Bot tiles.py

Santiagorich/Piano-tiles-bot

bc71c331c4350bfc1949840674ba48a957617686

[ "MIT" ]

null

Bot tiles.py

Santiagorich/Piano-tiles-bot

bc71c331c4350bfc1949840674ba48a957617686

[ "MIT" ]

null

Bot tiles.py

Santiagorich/Piano-tiles-bot

bc71c331c4350bfc1949840674ba48a957617686

[ "MIT" ]

null

from pyautogui import * import pyautogui import time import keyboard import random import win32api, win32con px = 0 py = 0 class Tile(): def __init__(self,px,py): self.x = px self.y = py def click(x,y): win32api.SetCursorPos((x,y)) win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN,0,0) time.sleep(0.1) win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP,0,0) tiles = int(input("How many tiles?\n")) tilesp = [] for tile in range(tiles): print("Press Q on tile " + str(tile)) while True: if keyboard.read_key() == "q": px = position().x py = position().y tilesp.append(Tile(px,py)) time.sleep(0.1) break print("Press Q on the color we want to snipe") while True: if keyboard.read_key() == "q": px = position().x py = position().y color = pyautogui.pixel(px, py) time.sleep(0.1) break print(color) print("Hold K to snipe or J to reverse snipe! - Press P to stop the bot") toggle = False while True: #if keyboard.read_key() == "l": # toggle = not toggle # print(toggle) # time.sleep(0.1) #if toggle: # for tile in tilesp: # if pyautogui.pixel(tile.x,tile.y) == color: # click(tile.x,tile.y) #else: if keyboard.is_pressed("k") == True: for tile in tilesp: if pyautogui.pixel(tile.x,tile.y) == color: click(tile.x,tile.y) if keyboard.is_pressed("j") == True: for tile in tilesp: if pyautogui.pixel(tile.x,tile.y) != color: click(tile.x,tile.y) if keyboard.is_pressed("p") == True: print("Exiting!") break

27.424242

74

0.545856

from pyautogui import * import pyautogui import time import keyboard import random import win32api, win32con px = 0 py = 0 class Tile(): def __init__(self,px,py): self.x = px self.y = py def click(x,y): win32api.SetCursorPos((x,y)) win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN,0,0) time.sleep(0.1) win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP,0,0) tiles = int(input("How many tiles?\n")) tilesp = [] for tile in range(tiles): print("Press Q on tile " + str(tile)) while True: if keyboard.read_key() == "q": px = position().x py = position().y tilesp.append(Tile(px,py)) time.sleep(0.1) break print("Press Q on the color we want to snipe") while True: if keyboard.read_key() == "q": px = position().x py = position().y color = pyautogui.pixel(px, py) time.sleep(0.1) break print(color) print("Hold K to snipe or J to reverse snipe! - Press P to stop the bot") toggle = False while True: if keyboard.is_pressed("k") == True: for tile in tilesp: if pyautogui.pixel(tile.x,tile.y) == color: click(tile.x,tile.y) if keyboard.is_pressed("j") == True: for tile in tilesp: if pyautogui.pixel(tile.x,tile.y) != color: click(tile.x,tile.y) if keyboard.is_pressed("p") == True: print("Exiting!") break

true

f70c0cda81ea85e278d39235993b2c823742b388

937

py

Python

isi_sdk_9_0_0/test/test_cluster_node_hardware.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

24

2018-06-22T14:13:23.000Z

2022-03-23T01:21:26.000Z

isi_sdk_9_0_0/test/test_cluster_node_hardware.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

46

2018-04-30T13:28:22.000Z

2022-03-21T21:11:07.000Z

isi_sdk_9_0_0/test/test_cluster_node_hardware.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

29

2018-06-19T00:14:04.000Z

2022-02-08T17:51:19.000Z

# coding: utf-8 """ Isilon SDK Isilon SDK - Language bindings for the OneFS API # noqa: E501 OpenAPI spec version: 10 Contact: sdk@isilon.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import isi_sdk_9_0_0 from isi_sdk_9_0_0.models.cluster_node_hardware import ClusterNodeHardware # noqa: E501 from isi_sdk_9_0_0.rest import ApiException class TestClusterNodeHardware(unittest.TestCase): """ClusterNodeHardware unit test stubs""" def setUp(self): pass def tearDown(self): pass def testClusterNodeHardware(self): """Test ClusterNodeHardware""" # FIXME: construct object with mandatory attributes with example values # model = isi_sdk_9_0_0.models.cluster_node_hardware.ClusterNodeHardware() # noqa: E501 pass if __name__ == '__main__': unittest.main()

22.853659

96

0.716115

from __future__ import absolute_import import unittest import isi_sdk_9_0_0 from isi_sdk_9_0_0.models.cluster_node_hardware import ClusterNodeHardware from isi_sdk_9_0_0.rest import ApiException class TestClusterNodeHardware(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testClusterNodeHardware(self): pass if __name__ == '__main__': unittest.main()

true

f70c0dcdfb66f70f05cade13558581da90e8fcba

2,032

py

Python

test/functional/rpc_help.py

VeriBlock/b

1c2dccb1f87251b72049b75cc4db630c4da1b5c9

[ "MIT" ]

4

2020-05-14T11:49:20.000Z

2022-01-19T19:54:54.000Z

test/functional/rpc_help.py

VeriBlock/b

1c2dccb1f87251b72049b75cc4db630c4da1b5c9

[ "MIT" ]

125

2020-01-16T11:02:04.000Z

2022-03-24T12:27:13.000Z

test/functional/rpc_help.py

VeriBlock/b

1c2dccb1f87251b72049b75cc4db630c4da1b5c9

[ "MIT" ]

9

2020-04-06T14:31:16.000Z

2021-09-30T07:50:29.000Z

#!/usr/bin/env python3 # Copyright (c) 2018 The Bitcoin Core developers # Copyright (c) 2019-2021 Xenios SEZC # https://www.veriblock.org # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test RPC help output.""" from test_framework.test_framework import BitcoinTestFramework from test_framework.util import assert_equal, assert_raises_rpc_error import os class HelpRpcTest(BitcoinTestFramework): def set_test_params(self): self.num_nodes = 1 self.supports_cli = False def run_test(self): self.test_categories() self.dump_help() def test_categories(self): node = self.nodes[0] # wrong argument count assert_raises_rpc_error(-1, 'help', node.help, 'foo', 'bar') # invalid argument assert_raises_rpc_error(-1, 'JSON value is not a string as expected', node.help, 0) # help of unknown command assert_equal(node.help('foo'), 'help: unknown command: foo') # command titles titles = [line[3:-3] for line in node.help().splitlines() if line.startswith('==')] components = ['Blockchain', 'Control', 'Generating', 'Mining', 'Network', 'Pop_mining', 'Rawtransactions', 'Util'] if self.is_wallet_compiled(): components.append('Wallet') if self.is_zmq_compiled(): components.append('Zmq') assert_equal(titles, components) def dump_help(self): dump_dir = os.path.join(self.options.tmpdir, 'rpc_help_dump') os.mkdir(dump_dir) calls = [line.split(' ', 1)[0] for line in self.nodes[0].help().splitlines() if line and not line.startswith('==')] for call in calls: with open(os.path.join(dump_dir, call), 'w', encoding='utf-8') as f: # Make sure the node can generate the help at runtime without crashing f.write(self.nodes[0].help(call)) if __name__ == '__main__': HelpRpcTest().main()

33.311475

123

0.652067

from test_framework.test_framework import BitcoinTestFramework from test_framework.util import assert_equal, assert_raises_rpc_error import os class HelpRpcTest(BitcoinTestFramework): def set_test_params(self): self.num_nodes = 1 self.supports_cli = False def run_test(self): self.test_categories() self.dump_help() def test_categories(self): node = self.nodes[0] assert_raises_rpc_error(-1, 'help', node.help, 'foo', 'bar') assert_raises_rpc_error(-1, 'JSON value is not a string as expected', node.help, 0) assert_equal(node.help('foo'), 'help: unknown command: foo') titles = [line[3:-3] for line in node.help().splitlines() if line.startswith('==')] components = ['Blockchain', 'Control', 'Generating', 'Mining', 'Network', 'Pop_mining', 'Rawtransactions', 'Util'] if self.is_wallet_compiled(): components.append('Wallet') if self.is_zmq_compiled(): components.append('Zmq') assert_equal(titles, components) def dump_help(self): dump_dir = os.path.join(self.options.tmpdir, 'rpc_help_dump') os.mkdir(dump_dir) calls = [line.split(' ', 1)[0] for line in self.nodes[0].help().splitlines() if line and not line.startswith('==')] for call in calls: with open(os.path.join(dump_dir, call), 'w', encoding='utf-8') as f: f.write(self.nodes[0].help(call)) if __name__ == '__main__': HelpRpcTest().main()

true

f70c0e3404bc51df606439e4d7d3727526d29eb8

8,667

py

Python

astropy/table/serialize.py

jbkalmbach/astropy

88ae8c615533efd1e60de4aded204943f66f881c

[ "BSD-3-Clause" ]

1

2022-03-02T17:07:20.000Z

astropy/table/serialize.py

jbkalmbach/astropy

88ae8c615533efd1e60de4aded204943f66f881c

[ "BSD-3-Clause" ]

1

2017-09-22T21:10:10.000Z

astropy/table/serialize.py

jbkalmbach/astropy

88ae8c615533efd1e60de4aded204943f66f881c

[ "BSD-3-Clause" ]

1

2019-10-09T21:30:57.000Z

from importlib import import_module import re from copy import deepcopy from ..utils.data_info import MixinInfo from .column import Column from .table import Table, QTable, has_info_class from ..units.quantity import QuantityInfo __construct_mixin_classes = ('astropy.time.core.Time', 'astropy.time.core.TimeDelta', 'astropy.units.quantity.Quantity', 'astropy.coordinates.angles.Latitude', 'astropy.coordinates.angles.Longitude', 'astropy.coordinates.angles.Angle', 'astropy.coordinates.distances.Distance', 'astropy.coordinates.earth.EarthLocation', 'astropy.coordinates.sky_coordinate.SkyCoord', 'astropy.table.table.NdarrayMixin') class SerializedColumn(dict): """ Subclass of dict that is a used in the representation to contain the name (and possible other info) for a mixin attribute (either primary data or an array-like attribute) that is serialized as a column in the table. Normally contains the single key ``name`` with the name of the column in the table. """ pass def _represent_mixin_as_column(col, name, new_cols, mixin_cols, exclude_classes=()): """Convert a mixin column to a plain columns or a set of mixin columns.""" # If not a mixin, or if class in ``exclude_classes`` tuple then # treat as a normal column. Excluded sub-classes must be explicitly # specified. if not has_info_class(col, MixinInfo) or col.__class__ in exclude_classes: new_cols.append(col) return # Subtlety here is handling mixin info attributes. The basic list of such # attributes is: 'name', 'unit', 'dtype', 'format', 'description', 'meta'. # - name: handled directly [DON'T store] # - unit: DON'T store if this is a parent attribute # - dtype: captured in plain Column if relevant [DON'T store] # - format: possibly irrelevant but settable post-object creation [DO store] # - description: DO store # - meta: DO store info = {} for attr, nontrivial, xform in (('unit', lambda x: x not in (None, ''), str), ('format', lambda x: x is not None, None), ('description', lambda x: x is not None, None), ('meta', lambda x: x, None)): col_attr = getattr(col.info, attr) if nontrivial(col_attr): info[attr] = xform(col_attr) if xform else col_attr obj_attrs = col.info._represent_as_dict() ordered_keys = col.info._represent_as_dict_attrs data_attrs = [key for key in ordered_keys if key in obj_attrs and getattr(obj_attrs[key], 'shape', ())[:1] == col.shape[:1]] for data_attr in data_attrs: data = obj_attrs[data_attr] if len(data_attrs) == 1 and not has_info_class(data, MixinInfo): # For one non-mixin attribute, we need only one serialized column. # We can store info there, and keep the column name as is. new_cols.append(Column(data, name=name, **info)) obj_attrs[data_attr] = SerializedColumn({'name': name}) # Remove attributes that are already on the serialized column. for attr in info: if attr in obj_attrs: del obj_attrs[attr] else: # New column name combines the old name and attribute # (e.g. skycoord.ra, skycoord.dec). new_name = name + '.' + data_attr # TODO masking, MaskedColumn if not has_info_class(data, MixinInfo): new_cols.append(Column(data, name=new_name)) obj_attrs[data_attr] = SerializedColumn({'name': new_name}) else: # recurse. This will define obj_attrs[new_name]. _represent_mixin_as_column(data, new_name, new_cols, obj_attrs) obj_attrs[data_attr] = SerializedColumn(obj_attrs.pop(new_name)) # Strip out from info any attributes defined by the parent for attr in col.info.attrs_from_parent: if attr in info: del info[attr] if info: obj_attrs['__info__'] = info # Store the fully qualified class name obj_attrs['__class__'] = col.__module__ + '.' + col.__class__.__name__ mixin_cols[name] = obj_attrs def _represent_mixins_as_columns(tbl, exclude_classes=()): """ Convert any mixin columns to plain Column or MaskedColumn and return a new table. Exclude any mixin columns in ``exclude_classes``, which must be a tuple of classes. """ if not tbl.has_mixin_columns: return tbl mixin_cols = {} new_cols = [] for col in tbl.itercols(): _represent_mixin_as_column(col, col.info.name, new_cols, mixin_cols, exclude_classes=exclude_classes) meta = deepcopy(tbl.meta) meta['__serialized_columns__'] = mixin_cols out = Table(new_cols, meta=meta, copy=False) return out def _construct_mixin_from_obj_attrs_and_info(obj_attrs, info): cls_full_name = obj_attrs.pop('__class__') # If this is a supported class then import the class and run # the _construct_from_col method. Prevent accidentally running # untrusted code by only importing known astropy classes. if cls_full_name not in __construct_mixin_classes: raise ValueError('unsupported class for construct {}'.format(cls_full_name)) mod_name, cls_name = re.match(r'(.+)\.(\w+)', cls_full_name).groups() module = import_module(mod_name) cls = getattr(module, cls_name) for attr, value in info.items(): if attr in cls.info.attrs_from_parent: obj_attrs[attr] = value mixin = cls.info._construct_from_dict(obj_attrs) for attr, value in info.items(): if attr not in obj_attrs: setattr(mixin.info, attr, value) return mixin def _construct_mixin_from_columns(new_name, obj_attrs, out): data_attrs_map = {} for name, val in obj_attrs.items(): if isinstance(val, SerializedColumn): if 'name' in val: data_attrs_map[val['name']] = name else: _construct_mixin_from_columns(name, val, out) data_attrs_map[name] = name for name in data_attrs_map.values(): del obj_attrs[name] # Get the index where to add new column idx = min(out.colnames.index(name) for name in data_attrs_map) # Name is the column name in the table (e.g. "coord.ra") and # data_attr is the object attribute name (e.g. "ra"). A different # example would be a formatted time object that would have (e.g.) # "time_col" and "value", respectively. for name, data_attr in data_attrs_map.items(): col = out[name] obj_attrs[data_attr] = col del out[name] info = obj_attrs.pop('__info__', {}) if len(data_attrs_map) == 1: # col is the first and only serialized column; in that case, use info # stored on the column. for attr, nontrivial in (('unit', lambda x: x not in (None, '')), ('format', lambda x: x is not None), ('description', lambda x: x is not None), ('meta', lambda x: x)): col_attr = getattr(col.info, attr) if nontrivial(col_attr): info[attr] = col_attr info['name'] = new_name col = _construct_mixin_from_obj_attrs_and_info(obj_attrs, info) out.add_column(col, index=idx) def _construct_mixins_from_columns(tbl): if '__serialized_columns__' not in tbl.meta: return tbl # Don't know final output class but assume QTable so no columns get # downgraded. out = QTable(tbl, copy=False) mixin_cols = out.meta.pop('__serialized_columns__') for new_name, obj_attrs in mixin_cols.items(): _construct_mixin_from_columns(new_name, obj_attrs, out) # If no quantity subclasses are in the output then output as Table. # For instance ascii.read(file, format='ecsv') doesn't specify an # output class and should return the minimal table class that # represents the table file. has_quantities = any(isinstance(col.info, QuantityInfo) for col in out.itercols()) if not has_quantities: out = Table(out, copy=False) return out

39.756881

84

0.62063

from importlib import import_module import re from copy import deepcopy from ..utils.data_info import MixinInfo from .column import Column from .table import Table, QTable, has_info_class from ..units.quantity import QuantityInfo __construct_mixin_classes = ('astropy.time.core.Time', 'astropy.time.core.TimeDelta', 'astropy.units.quantity.Quantity', 'astropy.coordinates.angles.Latitude', 'astropy.coordinates.angles.Longitude', 'astropy.coordinates.angles.Angle', 'astropy.coordinates.distances.Distance', 'astropy.coordinates.earth.EarthLocation', 'astropy.coordinates.sky_coordinate.SkyCoord', 'astropy.table.table.NdarrayMixin') class SerializedColumn(dict): pass def _represent_mixin_as_column(col, name, new_cols, mixin_cols, exclude_classes=()): if not has_info_class(col, MixinInfo) or col.__class__ in exclude_classes: new_cols.append(col) return # - unit: DON'T store if this is a parent attribute # - format: possibly irrelevant but settable post-object creation [DO store] # - description: DO store # - meta: DO store info = {} for attr, nontrivial, xform in (('unit', lambda x: x not in (None, ''), str), ('format', lambda x: x is not None, None), ('description', lambda x: x is not None, None), ('meta', lambda x: x, None)): col_attr = getattr(col.info, attr) if nontrivial(col_attr): info[attr] = xform(col_attr) if xform else col_attr obj_attrs = col.info._represent_as_dict() ordered_keys = col.info._represent_as_dict_attrs data_attrs = [key for key in ordered_keys if key in obj_attrs and getattr(obj_attrs[key], 'shape', ())[:1] == col.shape[:1]] for data_attr in data_attrs: data = obj_attrs[data_attr] if len(data_attrs) == 1 and not has_info_class(data, MixinInfo): # For one non-mixin attribute, we need only one serialized column. # We can store info there, and keep the column name as is. new_cols.append(Column(data, name=name, **info)) obj_attrs[data_attr] = SerializedColumn({'name': name}) # Remove attributes that are already on the serialized column. for attr in info: if attr in obj_attrs: del obj_attrs[attr] else: # New column name combines the old name and attribute # (e.g. skycoord.ra, skycoord.dec). new_name = name + '.' + data_attr # TODO masking, MaskedColumn if not has_info_class(data, MixinInfo): new_cols.append(Column(data, name=new_name)) obj_attrs[data_attr] = SerializedColumn({'name': new_name}) else: # recurse. This will define obj_attrs[new_name]. _represent_mixin_as_column(data, new_name, new_cols, obj_attrs) obj_attrs[data_attr] = SerializedColumn(obj_attrs.pop(new_name)) # Strip out from info any attributes defined by the parent for attr in col.info.attrs_from_parent: if attr in info: del info[attr] if info: obj_attrs['__info__'] = info # Store the fully qualified class name obj_attrs['__class__'] = col.__module__ + '.' + col.__class__.__name__ mixin_cols[name] = obj_attrs def _represent_mixins_as_columns(tbl, exclude_classes=()): if not tbl.has_mixin_columns: return tbl mixin_cols = {} new_cols = [] for col in tbl.itercols(): _represent_mixin_as_column(col, col.info.name, new_cols, mixin_cols, exclude_classes=exclude_classes) meta = deepcopy(tbl.meta) meta['__serialized_columns__'] = mixin_cols out = Table(new_cols, meta=meta, copy=False) return out def _construct_mixin_from_obj_attrs_and_info(obj_attrs, info): cls_full_name = obj_attrs.pop('__class__') # If this is a supported class then import the class and run # the _construct_from_col method. Prevent accidentally running # untrusted code by only importing known astropy classes. if cls_full_name not in __construct_mixin_classes: raise ValueError('unsupported class for construct {}'.format(cls_full_name)) mod_name, cls_name = re.match(r'(.+)\.(\w+)', cls_full_name).groups() module = import_module(mod_name) cls = getattr(module, cls_name) for attr, value in info.items(): if attr in cls.info.attrs_from_parent: obj_attrs[attr] = value mixin = cls.info._construct_from_dict(obj_attrs) for attr, value in info.items(): if attr not in obj_attrs: setattr(mixin.info, attr, value) return mixin def _construct_mixin_from_columns(new_name, obj_attrs, out): data_attrs_map = {} for name, val in obj_attrs.items(): if isinstance(val, SerializedColumn): if 'name' in val: data_attrs_map[val['name']] = name else: _construct_mixin_from_columns(name, val, out) data_attrs_map[name] = name for name in data_attrs_map.values(): del obj_attrs[name] # Get the index where to add new column idx = min(out.colnames.index(name) for name in data_attrs_map) # Name is the column name in the table (e.g. "coord.ra") and # data_attr is the object attribute name (e.g. "ra"). A different # example would be a formatted time object that would have (e.g.) # "time_col" and "value", respectively. for name, data_attr in data_attrs_map.items(): col = out[name] obj_attrs[data_attr] = col del out[name] info = obj_attrs.pop('__info__', {}) if len(data_attrs_map) == 1: # col is the first and only serialized column; in that case, use info # stored on the column. for attr, nontrivial in (('unit', lambda x: x not in (None, '')), ('format', lambda x: x is not None), ('description', lambda x: x is not None), ('meta', lambda x: x)): col_attr = getattr(col.info, attr) if nontrivial(col_attr): info[attr] = col_attr info['name'] = new_name col = _construct_mixin_from_obj_attrs_and_info(obj_attrs, info) out.add_column(col, index=idx) def _construct_mixins_from_columns(tbl): if '__serialized_columns__' not in tbl.meta: return tbl # Don't know final output class but assume QTable so no columns get out = QTable(tbl, copy=False) mixin_cols = out.meta.pop('__serialized_columns__') for new_name, obj_attrs in mixin_cols.items(): _construct_mixin_from_columns(new_name, obj_attrs, out) # output class and should return the minimal table class that # represents the table file. has_quantities = any(isinstance(col.info, QuantityInfo) for col in out.itercols()) if not has_quantities: out = Table(out, copy=False) return out

true

f70c0f56841d3e49809f2c21b43b9bac19f6cda2

389

py

Python

social_network/urls.py

zareisajad/social-network-django

991c8075a9fb51b7fbdb17704325ebc4c9d2e0fa

[ "MIT" ]

null

social_network/urls.py

zareisajad/social-network-django

991c8075a9fb51b7fbdb17704325ebc4c9d2e0fa

[ "MIT" ]

null

social_network/urls.py

zareisajad/social-network-django

991c8075a9fb51b7fbdb17704325ebc4c9d2e0fa

[ "MIT" ]

null

from django.contrib import admin from django.urls import path, include from django.conf import settings from django.conf.urls.static import static urlpatterns = [ path('admin/', admin.site.urls), path('', include('posts.urls')), path('accounts/', include('accounts.urls')), ] if settings.DEBUG: urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

29.923077

80

0.737789

from django.contrib import admin from django.urls import path, include from django.conf import settings from django.conf.urls.static import static urlpatterns = [ path('admin/', admin.site.urls), path('', include('posts.urls')), path('accounts/', include('accounts.urls')), ] if settings.DEBUG: urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

true

f70c0f57d7596b54aedf93375dc3a812baaafc4a

1,126

py

Python

semseg_vaihingen/tests/test_unit_model.py

SilkeDH/semseg_vaihingen

0a8bed71836fa892b8a13b7d2c5109dbcae3c549

[ "MIT" ]

3

2020-02-03T16:55:50.000Z

2020-12-12T15:29:49.000Z

semseg_vaihingen/tests/test_unit_model.py

SilkeDH/semseg_vaihingen

0a8bed71836fa892b8a13b7d2c5109dbcae3c549

[ "MIT" ]

8

2020-03-24T17:39:59.000Z

2022-02-10T00:20:46.000Z

semseg_vaihingen/tests/test_unit_model.py

SilkeDH/semseg_vaihingen

0a8bed71836fa892b8a13b7d2c5109dbcae3c549

[ "MIT" ]

1

2020-02-27T09:48:53.000Z

# -*- coding: utf-8 -*- # # Copyright (c) 2017 - 2019 Karlsruhe Institute of Technology - Steinbuch Centre for Computing # This code is distributed under the MIT License # Please, see the LICENSE file # """ Created on Sat Aug 10 08:47:51 2019 @author: vykozlov """ import unittest import semseg_vaihingen.models.deepaas_api as deepaas_api class TestModelMethods(unittest.TestCase): def setUp(self): self.meta = deepaas_api.get_metadata() def test_model_metadata_type(self): """ Test that get_metadata() returns dict """ self.assertTrue(type(self.meta) is dict) def test_model_metadata_values(self): """ Test that get_metadata() returns right values (subset) """ self.assertEqual(self.meta['Name'].replace('-','_'), 'semseg_vaihingen'.replace('-','_')) self.assertEqual(self.meta['Author'], 'G.Cavallaro (FZJ), M.Goetz (KIT), V.Kozlov (KIT), A.Grupp (KIT)') self.assertEqual(self.meta['Author-email'], 'valentin.kozlov@kit.edu') if __name__ == '__main__': unittest.main()

29.631579

112

0.637655

import unittest import semseg_vaihingen.models.deepaas_api as deepaas_api class TestModelMethods(unittest.TestCase): def setUp(self): self.meta = deepaas_api.get_metadata() def test_model_metadata_type(self): self.assertTrue(type(self.meta) is dict) def test_model_metadata_values(self): self.assertEqual(self.meta['Name'].replace('-','_'), 'semseg_vaihingen'.replace('-','_')) self.assertEqual(self.meta['Author'], 'G.Cavallaro (FZJ), M.Goetz (KIT), V.Kozlov (KIT), A.Grupp (KIT)') self.assertEqual(self.meta['Author-email'], 'valentin.kozlov@kit.edu') if __name__ == '__main__': unittest.main()

true

f70c10807ab4db35e019d3e3323ba978f7699588

47,600

py

Python

cvxpy/problems/problem.py

adshieh/cvxpy

73b696b71dbb2ceb66a805798c922461e33afc6b

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

2

2021-12-21T03:11:12.000Z

2022-03-02T16:56:24.000Z

cvxpy/problems/problem.py

adshieh/cvxpy

73b696b71dbb2ceb66a805798c922461e33afc6b

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

null

cvxpy/problems/problem.py

adshieh/cvxpy

73b696b71dbb2ceb66a805798c922461e33afc6b

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

null

""" Copyright 2013 Steven Diamond, 2017 Akshay Agrawal 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 cvxpy import settings as s from cvxpy import error from cvxpy.problems.objective import Minimize, Maximize from cvxpy.reductions.chain import Chain from cvxpy.reductions.dgp2dcp.dgp2dcp import Dgp2Dcp from cvxpy.reductions.dqcp2dcp import dqcp2dcp from cvxpy.reductions.eval_params import EvalParams from cvxpy.reductions.flip_objective import FlipObjective from cvxpy.reductions.solvers.solving_chain import construct_solving_chain from cvxpy.interface.matrix_utilities import scalar_value from cvxpy.reductions.solvers import bisection from cvxpy.reductions.solvers import defines as slv_def from cvxpy.utilities.deterministic import unique_list import cvxpy.utilities.performance_utils as perf from cvxpy.constraints import Equality, Inequality, NonPos, Zero, NonNeg import cvxpy.utilities as u from collections import namedtuple import numpy as np import time SolveResult = namedtuple( 'SolveResult', ['opt_value', 'status', 'primal_values', 'dual_values']) class Cache(object): def __init__(self): self.key = None self.solving_chain = None self.param_prog = None self.inverse_data = None def invalidate(self): self.key = None self.solving_chain = None self.param_prog = None self.inverse_data = None def make_key(self, solver, gp): return (solver, gp) def gp(self): return self.key is not None and self.key[1] class Problem(u.Canonical): """A convex optimization problem. Problems are immutable, save for modification through the specification of :class:`~cvxpy.expressions.constants.parameters.Parameter` Arguments --------- objective : Minimize or Maximize The problem's objective. constraints : list The constraints on the problem variables. """ # The solve methods available. REGISTERED_SOLVE_METHODS = {} def __init__(self, objective, constraints=None): if constraints is None: constraints = [] # Check that objective is Minimize or Maximize. if not isinstance(objective, (Minimize, Maximize)): raise error.DCPError("Problem objective must be Minimize or Maximize.") # Constraints and objective are immutable. self._objective = objective self._constraints = [c for c in constraints] self._value = None self._status = None self._solution = None self._cache = Cache() self._solver_cache = {} # Information about the shape of the problem and its constituent parts self._size_metrics = None # Benchmarks reported by the solver: self._solver_stats = None self.args = [self._objective, self._constraints] @property def value(self): """float : The value from the last time the problem was solved (or None if not solved). """ if self._value is None: return None else: return scalar_value(self._value) @property def status(self): """str : The status from the last time the problem was solved; one of optimal, infeasible, or unbounded (with or without suffix inaccurate). """ return self._status @property def solution(self): """Solution : The solution from the last time the problem was solved. """ return self._solution @property def objective(self): """Minimize or Maximize : The problem's objective. Note that the objective cannot be reassigned after creation, and modifying the objective after creation will result in undefined behavior. """ return self._objective @property def constraints(self): """A shallow copy of the problem's constraints. Note that constraints cannot be reassigned, appended to, or otherwise modified after creation, except through parameters. """ return self._constraints[:] @perf.compute_once def is_dcp(self, dpp=False): """Does the problem satisfy DCP rules? Arguments --------- dpp : bool, optional If True, enforce the disciplined parametrized programming (DPP) ruleset; only relevant when the problem involves Parameters. DPP is a mild restriction of DCP. When a problem involving Parameters is DPP, subsequent solves can be much faster than the first one. For more information, consult the documentation at https://www.cvxpy.org/tutorial/advanced/index.html#disciplined-parametrized-programming Returns ------- bool True if the Expression is DCP, False otherwise. """ return all( expr.is_dcp(dpp) for expr in self.constraints + [self.objective]) @perf.compute_once def is_dgp(self, dpp=False): """Does the problem satisfy DGP rules? Arguments --------- dpp : bool, optional If True, enforce the disciplined parametrized programming (DPP) ruleset; only relevant when the problem involves Parameters. DPP is a mild restriction of DGP. When a problem involving Parameters is DPP, subsequent solves can be much faster than the first one. For more information, consult the documentation at https://www.cvxpy.org/tutorial/advanced/index.html#disciplined-parametrized-programming Returns ------- bool True if the Expression is DGP, False otherwise. """ return all( expr.is_dgp(dpp) for expr in self.constraints + [self.objective]) @perf.compute_once def is_dqcp(self): """Does the problem satisfy the DQCP rules? """ return all( expr.is_dqcp() for expr in self.constraints + [self.objective]) @perf.compute_once def is_dpp(self, context='dcp'): """Does the problem satisfy DPP rules? DPP is a mild restriction of DGP. When a problem involving Parameters is DPP, subsequent solves can be much faster than the first one. For more information, consult the documentation at https://www.cvxpy.org/tutorial/advanced/index.html#disciplined-parametrized-programming Arguments --------- context : str Whether to check DPP-compliance for DCP or DGP; ``context`` should be either ``'dcp'`` or ``'dgp'``. Calling ``problem.is_dpp('dcp')`` is equivalent to ``problem.is_dcp(dpp=True)``, and `problem.is_dpp('dgp')`` is equivalent to `problem.is_dgp(dpp=True)`. Returns ------- bool Whether the problem satisfies the DPP rules. """ if context.lower() == 'dcp': return self.is_dcp(dpp=True) elif context.lower() == 'dgp': return self.is_dgp(dpp=True) else: raise ValueError("Unsupported context ", context) @perf.compute_once def is_qp(self): """Is problem a quadratic program? """ for c in self.constraints: if not (isinstance(c, (Equality, Zero)) or c.args[0].is_pwl()): return False for var in self.variables(): if var.is_psd() or var.is_nsd(): return False return (self.is_dcp() and self.objective.args[0].is_qpwa()) @perf.compute_once def is_mixed_integer(self): return any(v.attributes['boolean'] or v.attributes['integer'] for v in self.variables()) @perf.compute_once def variables(self): """Accessor method for variables. Returns ------- list of :class:`~cvxpy.expressions.variable.Variable` A list of the variables in the problem. """ vars_ = self.objective.variables() for constr in self.constraints: vars_ += constr.variables() return unique_list(vars_) @perf.compute_once def parameters(self): """Accessor method for parameters. Returns ------- list of :class:`~cvxpy.expressions.constants.parameter.Parameter` A list of the parameters in the problem. """ params = self.objective.parameters() for constr in self.constraints: params += constr.parameters() return unique_list(params) @perf.compute_once def constants(self): """Accessor method for constants. Returns ------- list of :class:`~cvxpy.expressions.constants.constant.Constant` A list of the constants in the problem. """ const_dict = {} constants_ = self.objective.constants() for constr in self.constraints: constants_ += constr.constants() # Note that numpy matrices are not hashable, so we use the built-in # function "id" const_dict = {id(constant): constant for constant in constants_} return list(const_dict.values()) def atoms(self): """Accessor method for atoms. Returns ------- list of :class:`~cvxpy.atoms.Atom` A list of the atom types in the problem; note that this list contains classes, not instances. """ atoms = self.objective.atoms() for constr in self.constraints: atoms += constr.atoms() return unique_list(atoms) @property def size_metrics(self): """:class:`~cvxpy.problems.problem.SizeMetrics` : Information about the problem's size. """ if self._size_metrics is None: self._size_metrics = SizeMetrics(self) return self._size_metrics @property def solver_stats(self): """:class:`~cvxpy.problems.problem.SolverStats` : Information returned by the solver. """ return self._solver_stats def solve(self, *args, **kwargs): """Solves the problem using the specified method. Populates the :code:`status` and :code:`value` attributes on the problem object as a side-effect. Arguments --------- solver : str, optional The solver to use. For example, 'ECOS', 'SCS', or 'OSQP'. verbose : bool, optional Overrides the default of hiding solver output. gp : bool, optional If True, parses the problem as a disciplined geometric program instead of a disciplined convex program. qcp : bool, optional If True, parses the problem as a disciplined quasiconvex program instead of a disciplined convex program. requires_grad : bool, optional Makes it possible to compute gradients of a solution with respect to Parameters by calling ``problem.backward()`` after solving, or to compute perturbations to the variables given perturbations to Parameters by calling ``problem.derivative()``. Gradients are only supported for DCP and DGP problems, not quasiconvex problems. When computing gradients (i.e., when this argument is True), the problem must satisfy the DPP rules. enforce_dpp : bool, optional When True, a DPPError will be thrown when trying to solve a non-DPP problem (instead of just a warning). Only relevant for problems involving Parameters. Defaults to False. method : function, optional A custom solve method to use. kwargs : keywords, optional Additional solver specific arguments. See Notes below. Notes ------ CVXPY interfaces with a wide range of solvers; the algorithms used by these solvers have arguments relating to stopping criteria, and strategies to improve solution quality. There is no one choice of arguments which is perfect for every problem. If you are not getting satisfactory results from a solver, you can try changing its arguments. The exact way this is done depends on the specific solver. Here are some examples: prob.solve(solver='ECOS', abstol=1e-6) prob.solve(solver='OSQP', max_iter=10000). mydict = {"MSK_DPAR_INTPNT_CO_TOL_NEAR_REL": 10} prob.solve(solver='MOSEK', mosek_params=mydict). You should refer to CVXPY's web documentation for details on how to pass solver solver arguments, available at https://www.cvxpy.org/tutorial/advanced/index.html#setting-solver-options Returns ------- float The optimal value for the problem, or a string indicating why the problem could not be solved. Raises ------ cvxpy.error.DCPError Raised if the problem is not DCP and `gp` is False. cvxpy.error.DGPError Raised if the problem is not DGP and `gp` is True. cvxpy.error.SolverError Raised if no suitable solver exists among the installed solvers, or if an unanticipated error is encountered. """ func_name = kwargs.pop("method", None) if func_name is not None: solve_func = Problem.REGISTERED_SOLVE_METHODS[func_name] else: solve_func = Problem._solve return solve_func(self, *args, **kwargs) @classmethod def register_solve(cls, name, func): """Adds a solve method to the Problem class. Arguments --------- name : str The keyword for the method. func : function The function that executes the solve method. This function must take as its first argument the problem instance to solve. """ cls.REGISTERED_SOLVE_METHODS[name] = func def get_problem_data(self, solver, gp=False, enforce_dpp=False): """Returns the problem data used in the call to the solver. When a problem is solved, CVXPY creates a chain of reductions enclosed in a :class:`~cvxpy.reductions.solvers.solving_chain.SolvingChain`, and compiles it to some low-level representation that is compatible with the targeted solver. This method returns that low-level representation. For some solving chains, this low-level representation is a dictionary that contains exactly those arguments that were supplied to the solver; however, for other solving chains, the data is an intermediate representation that is compiled even further by the solver interfaces. A solution to the equivalent low-level problem can be obtained via the data by invoking the `solve_via_data` method of the returned solving chain, a thin wrapper around the code external to CVXPY that further processes and solves the problem. Invoke the unpack_results method to recover a solution to the original problem. For example: :: objective = ... constraints = ... problem = cp.Problem(objective, constraints) data, chain, inverse_data = problem.get_problem_data(cp.SCS) # calls SCS using `data` soln = chain.solve_via_data(problem, data) # unpacks the solution returned by SCS into `problem` problem.unpack_results(soln, chain, inverse_data) Alternatively, the `data` dictionary returned by this method contains enough information to bypass CVXPY and call the solver directly. For example: :: problem = cp.Problem(objective, constraints) data, _, _ = problem.get_problem_data(cp.SCS) import scs probdata = { 'A': data['A'], 'b': data['b'], 'c': data['c'], } cone_dims = data['dims'] cones = { "f": cone_dims.zero, "l": cone_dims.nonpos, "q": cone_dims.soc, "ep": cone_dims.exp, "s": cone_dims.psd, } soln = scs.solve(data, cones) The structure of the data dict that CVXPY returns depends on the solver. For details, consult the solver interfaces in `cvxpy/reductions/solvers`. Arguments --------- solver : str The solver the problem data is for. gp : bool, optional If True, then parses the problem as a disciplined geometric program instead of a disciplined convex program. enforce_dpp : bool, optional When True, a DPPError will be thrown when trying to parse a non-DPP problem (instead of just a warning). Defaults to False. Returns ------- dict or object lowest level representation of problem SolvingChain The solving chain that created the data. list The inverse data generated by the chain. """ key = self._cache.make_key(solver, gp) if key != self._cache.key: self._cache.invalidate() solving_chain = self._construct_chain( solver=solver, gp=gp, enforce_dpp=enforce_dpp) self._cache.key = key self._cache.solving_chain = solving_chain self._solver_cache = {} else: solving_chain = self._cache.solving_chain if self._cache.param_prog is not None: # fast path, bypasses application of reductions if gp: dgp2dcp = self._cache.solving_chain.get(Dgp2Dcp) # Parameters in the param cone prog are the logs # of parameters in the original problem (with one exception: # parameters appearing as exponents (in power and gmatmul # atoms) are unchanged. old_params_to_new_params = dgp2dcp.canon_methods._parameters for param in self.parameters(): if param in old_params_to_new_params: old_params_to_new_params[param].value = np.log( param.value) data, solver_inverse_data = solving_chain.solver.apply( self._cache.param_prog) inverse_data = self._cache.inverse_data + [solver_inverse_data] else: data, inverse_data = solving_chain.apply(self) safe_to_cache = ( isinstance(data, dict) and s.PARAM_PROB in data and not any(isinstance(reduction, EvalParams) for reduction in solving_chain.reductions) ) if safe_to_cache: self._cache.param_prog = data[s.PARAM_PROB] # the last datum in inverse_data corresponds to the solver, # so we shouldn't cache it self._cache.inverse_data = inverse_data[:-1] return data, solving_chain, inverse_data def _find_candidate_solvers(self, solver=None, gp=False): """ Find candiate solvers for the current problem. If solver is not None, it checks if the specified solver is compatible with the problem passed. Arguments --------- solver : string The name of the solver with which to solve the problem. If no solver is supplied (i.e., if solver is None), then the targeted solver may be any of those that are installed. If the problem is variable-free, then this parameter is ignored. gp : bool If True, the problem is parsed as a Disciplined Geometric Program instead of as a Disciplined Convex Program. Returns ------- dict A dictionary of compatible solvers divided in `qp_solvers` and `conic_solvers`. Raises ------ cvxpy.error.SolverError Raised if the problem is not DCP and `gp` is False. cvxpy.error.DGPError Raised if the problem is not DGP and `gp` is True. """ candidates = {'qp_solvers': [], 'conic_solvers': []} if solver is not None: if solver not in slv_def.INSTALLED_SOLVERS: raise error.SolverError("The solver %s is not installed." % solver) if solver in slv_def.CONIC_SOLVERS: candidates['conic_solvers'] += [solver] if solver in slv_def.QP_SOLVERS: candidates['qp_solvers'] += [solver] else: candidates['qp_solvers'] = [s for s in slv_def.INSTALLED_SOLVERS if s in slv_def.QP_SOLVERS] candidates['conic_solvers'] = [s for s in slv_def.INSTALLED_SOLVERS if s in slv_def.CONIC_SOLVERS] # If gp we must have only conic solvers if gp: if solver is not None and solver not in slv_def.CONIC_SOLVERS: raise error.SolverError( "When `gp=True`, `solver` must be a conic solver " "(received '%s'); try calling " % solver + " `solve()` with `solver=cvxpy.ECOS`." ) elif solver is None: candidates['qp_solvers'] = [] # No QP solvers allowed if self.is_mixed_integer(): if len(slv_def.INSTALLED_MI_SOLVERS) == 0: msg = """ CVXPY needs additional software (a `mixed-integer solver`) to handle this model. The web documentation https://www.cvxpy.org/tutorial/advanced/index.html#mixed-integer-programs reviews open-source and commercial options for mixed-integer solvers. Quick fix: if you install the python package CVXOPT (pip install cvxopt), then CVXPY can use the open-source mixed-integer solver `GLPK`. """ raise error.SolverError(msg) candidates['qp_solvers'] = [ s for s in candidates['qp_solvers'] if slv_def.SOLVER_MAP_QP[s].MIP_CAPABLE] candidates['conic_solvers'] = [ s for s in candidates['conic_solvers'] if slv_def.SOLVER_MAP_CONIC[s].MIP_CAPABLE] if not candidates['conic_solvers'] and \ not candidates['qp_solvers']: raise error.SolverError( "Problem is mixed-integer, but candidate " "QP/Conic solvers (%s) are not MIP-capable." % (candidates['qp_solvers'] + candidates['conic_solvers'])) return candidates def _construct_chain(self, solver=None, gp=False, enforce_dpp=False): """ Construct the chains required to reformulate and solve the problem. In particular, this function # finds the candidate solvers # constructs the solving chain that performs the numeric reductions and solves the problem. Arguments --------- solver : str, optional The solver to use. Defaults to ECOS. gp : bool, optional If True, the problem is parsed as a Disciplined Geometric Program instead of as a Disciplined Convex Program. enforce_dpp : bool, optional Whether to error on DPP violations. Returns ------- A solving chain """ candidate_solvers = self._find_candidate_solvers(solver=solver, gp=gp) return construct_solving_chain(self, candidate_solvers, gp=gp, enforce_dpp=enforce_dpp) def _invalidate_cache(self): self._cache_key = None self._solving_chain = None self._param_prog = None self._inverse_data = None def _solve(self, solver=None, warm_start=True, verbose=False, gp=False, qcp=False, requires_grad=False, enforce_dpp=False, **kwargs): """Solves a DCP compliant optimization problem. Saves the values of primal and dual variables in the variable and constraint objects, respectively. Arguments --------- solver : str, optional The solver to use. Defaults to ECOS. warm_start : bool, optional Should the previous solver result be used to warm start? verbose : bool, optional Overrides the default of hiding solver output. gp : bool, optional If True, parses the problem as a disciplined geometric program. qcp : bool, optional If True, parses the problem as a disciplined quasiconvex program. requires_grad : bool, optional Makes it possible to compute gradients with respect to parameters by calling `backward()` after solving, or to compute perturbations to the variables by calling `derivative()`. When True, the solver must be SCS, and dqcp must be False. A DPPError is thrown when problem is not DPP. enforce_dpp : bool, optional When True, a DPPError will be thrown when trying to solve a non-DPP problem (instead of just a warning). Defaults to False. kwargs : dict, optional A dict of options that will be passed to the specific solver. In general, these options will override any default settings imposed by cvxpy. Returns ------- float The optimal value for the problem, or a string indicating why the problem could not be solved. """ for parameter in self.parameters(): if parameter.value is None: raise error.ParameterError( "A Parameter (whose name is '%s') does not have a value " "associated with it; all Parameter objects must have " "values before solving a problem." % parameter.name()) if requires_grad: dpp_context = 'dgp' if gp else 'dcp' if qcp: raise ValueError("Cannot compute gradients of DQCP problems.") elif not self.is_dpp(dpp_context): raise error.DPPError("Problem is not DPP (when requires_grad " "is True, problem must be DPP).") elif solver is not None and solver not in [s.SCS, s.DIFFCP]: raise ValueError("When requires_grad is True, the only " "supported solver is SCS " "(received %s)." % solver) elif s.DIFFCP not in slv_def.INSTALLED_SOLVERS: raise ImportError( "The Python package diffcp must be installed to " "differentiate through problems. Please follow the " "installation instructions at " "https://github.com/cvxgrp/diffcp") else: solver = s.DIFFCP else: if gp and qcp: raise ValueError("At most one of `gp` and `qcp` can be True.") if qcp and not self.is_dcp(): if not self.is_dqcp(): raise error.DQCPError("The problem is not DQCP.") reductions = [dqcp2dcp.Dqcp2Dcp()] if type(self.objective) == Maximize: reductions = [FlipObjective()] + reductions chain = Chain(problem=self, reductions=reductions) soln = bisection.bisect( chain.reduce(), solver=solver, verbose=verbose, **kwargs) self.unpack(chain.retrieve(soln)) return self.value data, solving_chain, inverse_data = self.get_problem_data( solver, gp, enforce_dpp) solution = solving_chain.solve_via_data( self, data, warm_start, verbose, kwargs) self.unpack_results(solution, solving_chain, inverse_data) return self.value def backward(self): """Compute the gradient of a solution with respect to Parameters. This method differentiates through the solution map of the problem, obtaining the gradient of a solution with respect to the Parameters. In other words, it calculates the sensitivities of the Parameters with respect to perturbations in the optimal Variable values. This can be useful for integrating CVXPY into automatic differentation toolkits. ``backward()`` populates the ``gradient`` attribute of each Parameter in the problem as a side-effect. It can only be called after calling ``solve()`` with ``requires_grad=True``. Below is a simple example: :: import cvxpy as cp import numpy as np p = cp.Parameter() x = cp.Variable() quadratic = cp.square(x - 2 * p) problem = cp.Problem(cp.Minimize(quadratic), [x >= 0]) p.value = 3.0 problem.solve(requires_grad=True, eps=1e-10) # backward() populates the gradient attribute of the parameters problem.backward() # Because x* = 2 * p, dx*/dp = 2 np.testing.assert_allclose(p.gradient, 2.0) In the above example, the gradient could easily be computed by hand. The ``backward()`` is useful because for almost all problems, the gradient cannot be computed analytically. This method can be used to differentiate through any DCP or DGP problem, as long as the problem is DPP compliant (i.e., ``problem.is_dcp(dpp=True)`` or ``problem.is_dgp(dpp=True)`` evaluates to ``True``). This method uses the chain rule to evaluate the gradients of a scalar-valued function of the Variables with respect to the Parameters. For example, let x be a variable and p a Parameter; x and p might be scalars, vectors, or matrices. Let f be a scalar-valued function, with z = f(x). Then this method computes dz/dp = (dz/dx) (dx/p). dz/dx is chosen as the all-ones vector by default, corresponding to choosing f to be the sum function. You can specify a custom value for dz/dx by setting the ``gradient`` attribute on your variables. For example, :: import cvxpy as cp import numpy as np b = cp.Parameter() x = cp.Variable() quadratic = cp.square(x - 2 * b) problem = cp.Problem(cp.Minimize(quadratic), [x >= 0]) b.value = 3. problem.solve(requires_grad=True, eps=1e-10) x.gradient = 4. problem.backward() # dz/dp = dz/dx dx/dp = 4. * 2. == 8. np.testing.assert_allclose(b.gradient, 8.) The ``gradient`` attribute on a variable can also be interpreted as a perturbation to its optimal value. Raises ------ ValueError if solve was not called with ``requires_grad=True`` SolverError if the problem is infeasible or unbounded """ if s.DIFFCP not in self._solver_cache: raise ValueError("backward can only be called after calling " "solve with `requires_grad=True`") elif self.status not in s.SOLUTION_PRESENT: raise error.SolverError("Backpropagating through " "infeasible/unbounded problems is not " "yet supported. Please file an issue on " "Github if you need this feature.") # TODO(akshayka): Backpropagate through dual variables as well. backward_cache = self._solver_cache[s.DIFFCP] DT = backward_cache["DT"] zeros = np.zeros(backward_cache["s"].shape) del_vars = {} gp = self._cache.gp() for variable in self.variables(): if variable.gradient is None: del_vars[variable.id] = np.ones(variable.shape) else: del_vars[variable.id] = np.asarray(variable.gradient, dtype=np.float64) if gp: # x_gp = exp(x_cone_program), # dx_gp/d x_cone_program = exp(x_cone_program) = x_gp del_vars[variable.id] *= variable.value dx = self._cache.param_prog.split_adjoint(del_vars) start = time.time() dA, db, dc = DT(dx, zeros, zeros) end = time.time() backward_cache['DT_TIME'] = end - start dparams = self._cache.param_prog.apply_param_jac(dc, -dA, db) if not gp: for param in self.parameters(): param.gradient = dparams[param.id] else: dgp2dcp = self._cache.solving_chain.get(Dgp2Dcp) old_params_to_new_params = dgp2dcp.canon_methods._parameters for param in self.parameters(): # Note: if param is an exponent in a power or gmatmul atom, # then the parameter passes through unchanged to the DCP # program; if the param is also used elsewhere (not as an # exponent), then param will also be in # old_params_to_new_params. Therefore, param.gradient = # dparams[param.id] (or 0) + 1/param*dparams[new_param.id] # # Note that param.id is in dparams if and only if # param was used as an exponent (because this means that # the parameter entered the DCP problem unchanged.) grad = 0.0 if param.id not in dparams else dparams[param.id] if param in old_params_to_new_params: new_param = old_params_to_new_params[param] # new_param.value == log(param), apply chain rule grad += (1.0 / param.value) * dparams[new_param.id] param.gradient = grad def derivative(self): """Apply the derivative of the solution map to perturbations in the Parameters This method applies the derivative of the solution map to perturbations in the Parameters to obtain perturbations in the optimal values of the Variables. In other words, it tells you how the optimal values of the Variables would be changed by small changes to the Parameters. You can specify perturbations in a Parameter by setting its ``delta`` attribute (if unspecified, the perturbation defaults to 0). This method populates the ``delta`` attribute of the Variables as a side-effect. This method can only be called after calling ``solve()`` with ``requires_grad=True``. It is compatible with both DCP and DGP problems (that are also DPP-compliant). Below is a simple example: :: import cvxpy as cp import numpy as np p = cp.Parameter() x = cp.Variable() quadratic = cp.square(x - 2 * p) problem = cp.Problem(cp.Minimize(quadratic), [x >= 0]) p.value = 3.0 problem.solve(requires_grad=True, eps=1e-10) # derivative() populates the delta attribute of the variables problem.derivative() p.delta = 1e-3 # Because x* = 2 * p, dx*/dp = 2, so (dx*/dp)(p.delta) == 2e-3 np.testing.assert_allclose(x.delta, 2e-3) Raises ------ ValueError if solve was not called with ``requires_grad=True`` SolverError if the problem is infeasible or unbounded """ if s.DIFFCP not in self._solver_cache: raise ValueError("derivative can only be called after calling " "solve with `requires_grad=True`") elif self.status not in s.SOLUTION_PRESENT: raise ValueError("Differentiating through infeasible/unbounded " "problems is not yet supported. Please file an " "issue on Github if you need this feature.") # TODO(akshayka): Forward differentiate dual variables as well backward_cache = self._solver_cache[s.DIFFCP] param_prog = self._cache.param_prog D = backward_cache["D"] param_deltas = {} gp = self._cache.gp() if gp: dgp2dcp = self._cache.solving_chain.get(Dgp2Dcp) if not self.parameters(): for variable in self.variables(): variable.delta = np.zeros(variable.shape) return for param in self.parameters(): delta = param.delta if param.delta is not None else np.zeros(param.shape) if gp: if param in dgp2dcp.canon_methods._parameters: new_param_id = dgp2dcp.canon_methods._parameters[param].id else: new_param_id = param.id param_deltas[new_param_id] = ( 1.0/param.value * np.asarray(delta, dtype=np.float64)) if param.id in param_prog.param_id_to_col: # here, param generated a new parameter and also # passed through to the param cone prog unchanged # (because it was an exponent of a power) param_deltas[param.id] = np.asarray(delta, dtype=np.float64) else: param_deltas[param.id] = np.asarray(delta, dtype=np.float64) dc, _, dA, db = param_prog.apply_parameters(param_deltas, zero_offset=True) start = time.time() dx, _, _ = D(-dA, db, dc) end = time.time() backward_cache['D_TIME'] = end - start dvars = param_prog.split_solution( dx, [v.id for v in self.variables()]) for variable in self.variables(): variable.delta = dvars[variable.id] if gp: # x_gp = exp(x_cone_program), # dx_gp/d x_cone_program = exp(x_cone_program) = x_gp variable.delta *= variable.value def _clear_solution(self): for v in self.variables(): v.save_value(None) for c in self.constraints: for dv in c.dual_variables: dv.save_value(None) self._value = None self._status = None self._solution = None def unpack(self, solution): """Updates the problem state given a Solution. Updates problem.status, problem.value and value of primal and dual variables. If solution.status is in cvxpy.settins.ERROR, this method is a no-op. Arguments _________ solution : cvxpy.Solution A Solution object. Raises ------ ValueError If the solution object has an invalid status """ if solution.status in s.SOLUTION_PRESENT: for v in self.variables(): v.save_value(solution.primal_vars[v.id]) for c in self.constraints: if c.id in solution.dual_vars: c.save_dual_value(solution.dual_vars[c.id]) elif solution.status in s.INF_OR_UNB: for v in self.variables(): v.save_value(None) for constr in self.constraints: for dv in constr.dual_variables: dv.save_value(None) else: raise ValueError("Cannot unpack invalid solution: %s" % solution) self._value = solution.opt_val self._status = solution.status self._solution = solution def unpack_results(self, solution, chain, inverse_data): """Updates the problem state given the solver results. Updates problem.status, problem.value and value of primal and dual variables. Arguments _________ solution : object The solution returned by applying the chain to the problem and invoking the solver on the resulting data. chain : SolvingChain A solving chain that was used to solve the problem. inverse_data : list The inverse data returned by applying the chain to the problem. Raises ------ cvxpy.error.SolverError If the solver failed """ solution = chain.invert(solution, inverse_data) if solution.status in s.ERROR: raise error.SolverError( "Solver '%s' failed. " % chain.solver.name() + "Try another solver, or solve with verbose=True for more " "information.") self.unpack(solution) self._solver_stats = SolverStats(self._solution.attr, chain.solver.name()) def __str__(self): if len(self.constraints) == 0: return str(self.objective) else: subject_to = "subject to " lines = [str(self.objective), subject_to + str(self.constraints[0])] for constr in self.constraints[1:]: lines += [len(subject_to) * " " + str(constr)] return '\n'.join(lines) def __repr__(self): return "Problem(%s, %s)" % (repr(self.objective), repr(self.constraints)) def __neg__(self): return Problem(-self.objective, self.constraints) def __add__(self, other): if other == 0: return self elif not isinstance(other, Problem): return NotImplemented return Problem(self.objective + other.objective, unique_list(self.constraints + other.constraints)) def __radd__(self, other): if other == 0: return self else: return NotImplemented def __sub__(self, other): if not isinstance(other, Problem): return NotImplemented return Problem(self.objective - other.objective, unique_list(self.constraints + other.constraints)) def __rsub__(self, other): if other == 0: return -self else: return NotImplemented def __mul__(self, other): if not isinstance(other, (int, float)): return NotImplemented return Problem(self.objective * other, self.constraints) __rmul__ = __mul__ def __div__(self, other): if not isinstance(other, (int, float)): return NotImplemented return Problem(self.objective * (1.0 / other), self.constraints) def is_constant(self): return False __truediv__ = __div__ class SolverStats(object): """Reports some of the miscellaneous information that is returned by the solver after solving but that is not captured directly by the Problem instance. Attributes ---------- solve_time : double The time (in seconds) it took for the solver to solve the problem. setup_time : double The time (in seconds) it took for the solver to setup the problem. num_iters : int The number of iterations the solver had to go through to find a solution. """ def __init__(self, results_dict, solver_name): self.solver_name = solver_name self.solve_time = None self.setup_time = None self.num_iters = None if s.SOLVE_TIME in results_dict: self.solve_time = results_dict[s.SOLVE_TIME] if s.SETUP_TIME in results_dict: self.setup_time = results_dict[s.SETUP_TIME] if s.NUM_ITERS in results_dict: self.num_iters = results_dict[s.NUM_ITERS] class SizeMetrics(object): """Reports various metrics regarding the problem. Attributes ---------- num_scalar_variables : integer The number of scalar variables in the problem. num_scalar_data : integer The number of scalar constants and parameters in the problem. The number of constants used across all matrices, vectors, in the problem. Some constants are not apparent when the problem is constructed: for example, The sum_squares expression is a wrapper for a quad_over_lin expression with a constant 1 in the denominator. num_scalar_eq_constr : integer The number of scalar equality constraints in the problem. num_scalar_leq_constr : integer The number of scalar inequality constraints in the problem. max_data_dimension : integer The longest dimension of any data block constraint or parameter. max_big_small_squared : integer The maximum value of (big)(small)^2 over all data blocks of the problem, where (big) is the larger dimension and (small) is the smaller dimension for each data block. """ def __init__(self, problem): # num_scalar_variables self.num_scalar_variables = 0 for var in problem.variables(): self.num_scalar_variables += var.size # num_scalar_data, max_data_dimension, and max_big_small_squared self.max_data_dimension = 0 self.num_scalar_data = 0 self.max_big_small_squared = 0 for const in problem.constants()+problem.parameters(): big = 0 # Compute number of data self.num_scalar_data += const.size big = 1 if len(const.shape) == 0 else max(const.shape) small = 1 if len(const.shape) == 0 else min(const.shape) # Get max data dimension: if self.max_data_dimension < big: self.max_data_dimension = big max_big_small_squared = float(big)*(float(small)**2) if self.max_big_small_squared < max_big_small_squared: self.max_big_small_squared = max_big_small_squared # num_scalar_eq_constr self.num_scalar_eq_constr = 0 for constraint in problem.constraints: if isinstance(constraint, (Equality, Zero)): self.num_scalar_eq_constr += constraint.expr.size # num_scalar_leq_constr self.num_scalar_leq_constr = 0 for constraint in problem.constraints: if isinstance(constraint, (Inequality, NonPos, NonNeg)): self.num_scalar_leq_constr += constraint.expr.size

39.209226

100

0.597017

from cvxpy import settings as s from cvxpy import error from cvxpy.problems.objective import Minimize, Maximize from cvxpy.reductions.chain import Chain from cvxpy.reductions.dgp2dcp.dgp2dcp import Dgp2Dcp from cvxpy.reductions.dqcp2dcp import dqcp2dcp from cvxpy.reductions.eval_params import EvalParams from cvxpy.reductions.flip_objective import FlipObjective from cvxpy.reductions.solvers.solving_chain import construct_solving_chain from cvxpy.interface.matrix_utilities import scalar_value from cvxpy.reductions.solvers import bisection from cvxpy.reductions.solvers import defines as slv_def from cvxpy.utilities.deterministic import unique_list import cvxpy.utilities.performance_utils as perf from cvxpy.constraints import Equality, Inequality, NonPos, Zero, NonNeg import cvxpy.utilities as u from collections import namedtuple import numpy as np import time SolveResult = namedtuple( 'SolveResult', ['opt_value', 'status', 'primal_values', 'dual_values']) class Cache(object): def __init__(self): self.key = None self.solving_chain = None self.param_prog = None self.inverse_data = None def invalidate(self): self.key = None self.solving_chain = None self.param_prog = None self.inverse_data = None def make_key(self, solver, gp): return (solver, gp) def gp(self): return self.key is not None and self.key[1] class Problem(u.Canonical): REGISTERED_SOLVE_METHODS = {} def __init__(self, objective, constraints=None): if constraints is None: constraints = [] if not isinstance(objective, (Minimize, Maximize)): raise error.DCPError("Problem objective must be Minimize or Maximize.") self._objective = objective self._constraints = [c for c in constraints] self._value = None self._status = None self._solution = None self._cache = Cache() self._solver_cache = {} self._size_metrics = None self._solver_stats = None self.args = [self._objective, self._constraints] @property def value(self): if self._value is None: return None else: return scalar_value(self._value) @property def status(self): return self._status @property def solution(self): return self._solution @property def objective(self): return self._objective @property def constraints(self): return self._constraints[:] @perf.compute_once def is_dcp(self, dpp=False): return all( expr.is_dcp(dpp) for expr in self.constraints + [self.objective]) @perf.compute_once def is_dgp(self, dpp=False): return all( expr.is_dgp(dpp) for expr in self.constraints + [self.objective]) @perf.compute_once def is_dqcp(self): return all( expr.is_dqcp() for expr in self.constraints + [self.objective]) @perf.compute_once def is_dpp(self, context='dcp'): if context.lower() == 'dcp': return self.is_dcp(dpp=True) elif context.lower() == 'dgp': return self.is_dgp(dpp=True) else: raise ValueError("Unsupported context ", context) @perf.compute_once def is_qp(self): for c in self.constraints: if not (isinstance(c, (Equality, Zero)) or c.args[0].is_pwl()): return False for var in self.variables(): if var.is_psd() or var.is_nsd(): return False return (self.is_dcp() and self.objective.args[0].is_qpwa()) @perf.compute_once def is_mixed_integer(self): return any(v.attributes['boolean'] or v.attributes['integer'] for v in self.variables()) @perf.compute_once def variables(self): vars_ = self.objective.variables() for constr in self.constraints: vars_ += constr.variables() return unique_list(vars_) @perf.compute_once def parameters(self): params = self.objective.parameters() for constr in self.constraints: params += constr.parameters() return unique_list(params) @perf.compute_once def constants(self): const_dict = {} constants_ = self.objective.constants() for constr in self.constraints: constants_ += constr.constants() const_dict = {id(constant): constant for constant in constants_} return list(const_dict.values()) def atoms(self): atoms = self.objective.atoms() for constr in self.constraints: atoms += constr.atoms() return unique_list(atoms) @property def size_metrics(self): if self._size_metrics is None: self._size_metrics = SizeMetrics(self) return self._size_metrics @property def solver_stats(self): return self._solver_stats def solve(self, *args, **kwargs): func_name = kwargs.pop("method", None) if func_name is not None: solve_func = Problem.REGISTERED_SOLVE_METHODS[func_name] else: solve_func = Problem._solve return solve_func(self, *args, **kwargs) @classmethod def register_solve(cls, name, func): cls.REGISTERED_SOLVE_METHODS[name] = func def get_problem_data(self, solver, gp=False, enforce_dpp=False): key = self._cache.make_key(solver, gp) if key != self._cache.key: self._cache.invalidate() solving_chain = self._construct_chain( solver=solver, gp=gp, enforce_dpp=enforce_dpp) self._cache.key = key self._cache.solving_chain = solving_chain self._solver_cache = {} else: solving_chain = self._cache.solving_chain if self._cache.param_prog is not None: if gp: dgp2dcp = self._cache.solving_chain.get(Dgp2Dcp) old_params_to_new_params = dgp2dcp.canon_methods._parameters for param in self.parameters(): if param in old_params_to_new_params: old_params_to_new_params[param].value = np.log( param.value) data, solver_inverse_data = solving_chain.solver.apply( self._cache.param_prog) inverse_data = self._cache.inverse_data + [solver_inverse_data] else: data, inverse_data = solving_chain.apply(self) safe_to_cache = ( isinstance(data, dict) and s.PARAM_PROB in data and not any(isinstance(reduction, EvalParams) for reduction in solving_chain.reductions) ) if safe_to_cache: self._cache.param_prog = data[s.PARAM_PROB] self._cache.inverse_data = inverse_data[:-1] return data, solving_chain, inverse_data def _find_candidate_solvers(self, solver=None, gp=False): candidates = {'qp_solvers': [], 'conic_solvers': []} if solver is not None: if solver not in slv_def.INSTALLED_SOLVERS: raise error.SolverError("The solver %s is not installed." % solver) if solver in slv_def.CONIC_SOLVERS: candidates['conic_solvers'] += [solver] if solver in slv_def.QP_SOLVERS: candidates['qp_solvers'] += [solver] else: candidates['qp_solvers'] = [s for s in slv_def.INSTALLED_SOLVERS if s in slv_def.QP_SOLVERS] candidates['conic_solvers'] = [s for s in slv_def.INSTALLED_SOLVERS if s in slv_def.CONIC_SOLVERS] # If gp we must have only conic solvers if gp: if solver is not None and solver not in slv_def.CONIC_SOLVERS: raise error.SolverError( "When `gp=True`, `solver` must be a conic solver " "(received '%s'); try calling " % solver + " `solve()` with `solver=cvxpy.ECOS`." ) elif solver is None: candidates['qp_solvers'] = [] # No QP solvers allowed if self.is_mixed_integer(): if len(slv_def.INSTALLED_MI_SOLVERS) == 0: msg = """ CVXPY needs additional software (a `mixed-integer solver`) to handle this model. The web documentation https://www.cvxpy.org/tutorial/advanced/index.html#mixed-integer-programs reviews open-source and commercial options for mixed-integer solvers. Quick fix: if you install the python package CVXOPT (pip install cvxopt), then CVXPY can use the open-source mixed-integer solver `GLPK`. """ raise error.SolverError(msg) candidates['qp_solvers'] = [ s for s in candidates['qp_solvers'] if slv_def.SOLVER_MAP_QP[s].MIP_CAPABLE] candidates['conic_solvers'] = [ s for s in candidates['conic_solvers'] if slv_def.SOLVER_MAP_CONIC[s].MIP_CAPABLE] if not candidates['conic_solvers'] and \ not candidates['qp_solvers']: raise error.SolverError( "Problem is mixed-integer, but candidate " "QP/Conic solvers (%s) are not MIP-capable." % (candidates['qp_solvers'] + candidates['conic_solvers'])) return candidates def _construct_chain(self, solver=None, gp=False, enforce_dpp=False): candidate_solvers = self._find_candidate_solvers(solver=solver, gp=gp) return construct_solving_chain(self, candidate_solvers, gp=gp, enforce_dpp=enforce_dpp) def _invalidate_cache(self): self._cache_key = None self._solving_chain = None self._param_prog = None self._inverse_data = None def _solve(self, solver=None, warm_start=True, verbose=False, gp=False, qcp=False, requires_grad=False, enforce_dpp=False, **kwargs): for parameter in self.parameters(): if parameter.value is None: raise error.ParameterError( "A Parameter (whose name is '%s') does not have a value " "associated with it; all Parameter objects must have " "values before solving a problem." % parameter.name()) if requires_grad: dpp_context = 'dgp' if gp else 'dcp' if qcp: raise ValueError("Cannot compute gradients of DQCP problems.") elif not self.is_dpp(dpp_context): raise error.DPPError("Problem is not DPP (when requires_grad " "is True, problem must be DPP).") elif solver is not None and solver not in [s.SCS, s.DIFFCP]: raise ValueError("When requires_grad is True, the only " "supported solver is SCS " "(received %s)." % solver) elif s.DIFFCP not in slv_def.INSTALLED_SOLVERS: raise ImportError( "The Python package diffcp must be installed to " "differentiate through problems. Please follow the " "installation instructions at " "https://github.com/cvxgrp/diffcp") else: solver = s.DIFFCP else: if gp and qcp: raise ValueError("At most one of `gp` and `qcp` can be True.") if qcp and not self.is_dcp(): if not self.is_dqcp(): raise error.DQCPError("The problem is not DQCP.") reductions = [dqcp2dcp.Dqcp2Dcp()] if type(self.objective) == Maximize: reductions = [FlipObjective()] + reductions chain = Chain(problem=self, reductions=reductions) soln = bisection.bisect( chain.reduce(), solver=solver, verbose=verbose, **kwargs) self.unpack(chain.retrieve(soln)) return self.value data, solving_chain, inverse_data = self.get_problem_data( solver, gp, enforce_dpp) solution = solving_chain.solve_via_data( self, data, warm_start, verbose, kwargs) self.unpack_results(solution, solving_chain, inverse_data) return self.value def backward(self): if s.DIFFCP not in self._solver_cache: raise ValueError("backward can only be called after calling " "solve with `requires_grad=True`") elif self.status not in s.SOLUTION_PRESENT: raise error.SolverError("Backpropagating through " "infeasible/unbounded problems is not " "yet supported. Please file an issue on " "Github if you need this feature.") # TODO(akshayka): Backpropagate through dual variables as well. backward_cache = self._solver_cache[s.DIFFCP] DT = backward_cache["DT"] zeros = np.zeros(backward_cache["s"].shape) del_vars = {} gp = self._cache.gp() for variable in self.variables(): if variable.gradient is None: del_vars[variable.id] = np.ones(variable.shape) else: del_vars[variable.id] = np.asarray(variable.gradient, dtype=np.float64) if gp: # x_gp = exp(x_cone_program), # dx_gp/d x_cone_program = exp(x_cone_program) = x_gp del_vars[variable.id] *= variable.value dx = self._cache.param_prog.split_adjoint(del_vars) start = time.time() dA, db, dc = DT(dx, zeros, zeros) end = time.time() backward_cache['DT_TIME'] = end - start dparams = self._cache.param_prog.apply_param_jac(dc, -dA, db) if not gp: for param in self.parameters(): param.gradient = dparams[param.id] else: dgp2dcp = self._cache.solving_chain.get(Dgp2Dcp) old_params_to_new_params = dgp2dcp.canon_methods._parameters for param in self.parameters(): # Note: if param is an exponent in a power or gmatmul atom, # then the parameter passes through unchanged to the DCP # program; if the param is also used elsewhere (not as an # exponent), then param will also be in # old_params_to_new_params. Therefore, param.gradient = # dparams[param.id] (or 0) + 1/param*dparams[new_param.id] # # Note that param.id is in dparams if and only if # param was used as an exponent (because this means that # the parameter entered the DCP problem unchanged.) grad = 0.0 if param.id not in dparams else dparams[param.id] if param in old_params_to_new_params: new_param = old_params_to_new_params[param] # new_param.value == log(param), apply chain rule grad += (1.0 / param.value) * dparams[new_param.id] param.gradient = grad def derivative(self): if s.DIFFCP not in self._solver_cache: raise ValueError("derivative can only be called after calling " "solve with `requires_grad=True`") elif self.status not in s.SOLUTION_PRESENT: raise ValueError("Differentiating through infeasible/unbounded " "problems is not yet supported. Please file an " "issue on Github if you need this feature.") # TODO(akshayka): Forward differentiate dual variables as well backward_cache = self._solver_cache[s.DIFFCP] param_prog = self._cache.param_prog D = backward_cache["D"] param_deltas = {} gp = self._cache.gp() if gp: dgp2dcp = self._cache.solving_chain.get(Dgp2Dcp) if not self.parameters(): for variable in self.variables(): variable.delta = np.zeros(variable.shape) return for param in self.parameters(): delta = param.delta if param.delta is not None else np.zeros(param.shape) if gp: if param in dgp2dcp.canon_methods._parameters: new_param_id = dgp2dcp.canon_methods._parameters[param].id else: new_param_id = param.id param_deltas[new_param_id] = ( 1.0/param.value * np.asarray(delta, dtype=np.float64)) if param.id in param_prog.param_id_to_col: # here, param generated a new parameter and also # passed through to the param cone prog unchanged # (because it was an exponent of a power) param_deltas[param.id] = np.asarray(delta, dtype=np.float64) else: param_deltas[param.id] = np.asarray(delta, dtype=np.float64) dc, _, dA, db = param_prog.apply_parameters(param_deltas, zero_offset=True) start = time.time() dx, _, _ = D(-dA, db, dc) end = time.time() backward_cache['D_TIME'] = end - start dvars = param_prog.split_solution( dx, [v.id for v in self.variables()]) for variable in self.variables(): variable.delta = dvars[variable.id] if gp: # x_gp = exp(x_cone_program), # dx_gp/d x_cone_program = exp(x_cone_program) = x_gp variable.delta *= variable.value def _clear_solution(self): for v in self.variables(): v.save_value(None) for c in self.constraints: for dv in c.dual_variables: dv.save_value(None) self._value = None self._status = None self._solution = None def unpack(self, solution): if solution.status in s.SOLUTION_PRESENT: for v in self.variables(): v.save_value(solution.primal_vars[v.id]) for c in self.constraints: if c.id in solution.dual_vars: c.save_dual_value(solution.dual_vars[c.id]) elif solution.status in s.INF_OR_UNB: for v in self.variables(): v.save_value(None) for constr in self.constraints: for dv in constr.dual_variables: dv.save_value(None) else: raise ValueError("Cannot unpack invalid solution: %s" % solution) self._value = solution.opt_val self._status = solution.status self._solution = solution def unpack_results(self, solution, chain, inverse_data): solution = chain.invert(solution, inverse_data) if solution.status in s.ERROR: raise error.SolverError( "Solver '%s' failed. " % chain.solver.name() + "Try another solver, or solve with verbose=True for more " "information.") self.unpack(solution) self._solver_stats = SolverStats(self._solution.attr, chain.solver.name()) def __str__(self): if len(self.constraints) == 0: return str(self.objective) else: subject_to = "subject to " lines = [str(self.objective), subject_to + str(self.constraints[0])] for constr in self.constraints[1:]: lines += [len(subject_to) * " " + str(constr)] return '\n'.join(lines) def __repr__(self): return "Problem(%s, %s)" % (repr(self.objective), repr(self.constraints)) def __neg__(self): return Problem(-self.objective, self.constraints) def __add__(self, other): if other == 0: return self elif not isinstance(other, Problem): return NotImplemented return Problem(self.objective + other.objective, unique_list(self.constraints + other.constraints)) def __radd__(self, other): if other == 0: return self else: return NotImplemented def __sub__(self, other): if not isinstance(other, Problem): return NotImplemented return Problem(self.objective - other.objective, unique_list(self.constraints + other.constraints)) def __rsub__(self, other): if other == 0: return -self else: return NotImplemented def __mul__(self, other): if not isinstance(other, (int, float)): return NotImplemented return Problem(self.objective * other, self.constraints) __rmul__ = __mul__ def __div__(self, other): if not isinstance(other, (int, float)): return NotImplemented return Problem(self.objective * (1.0 / other), self.constraints) def is_constant(self): return False __truediv__ = __div__ class SolverStats(object): def __init__(self, results_dict, solver_name): self.solver_name = solver_name self.solve_time = None self.setup_time = None self.num_iters = None if s.SOLVE_TIME in results_dict: self.solve_time = results_dict[s.SOLVE_TIME] if s.SETUP_TIME in results_dict: self.setup_time = results_dict[s.SETUP_TIME] if s.NUM_ITERS in results_dict: self.num_iters = results_dict[s.NUM_ITERS] class SizeMetrics(object): def __init__(self, problem): # num_scalar_variables self.num_scalar_variables = 0 for var in problem.variables(): self.num_scalar_variables += var.size # num_scalar_data, max_data_dimension, and max_big_small_squared self.max_data_dimension = 0 self.num_scalar_data = 0 self.max_big_small_squared = 0 for const in problem.constants()+problem.parameters(): big = 0 # Compute number of data self.num_scalar_data += const.size big = 1 if len(const.shape) == 0 else max(const.shape) small = 1 if len(const.shape) == 0 else min(const.shape) # Get max data dimension: if self.max_data_dimension < big: self.max_data_dimension = big max_big_small_squared = float(big)*(float(small)**2) if self.max_big_small_squared < max_big_small_squared: self.max_big_small_squared = max_big_small_squared # num_scalar_eq_constr self.num_scalar_eq_constr = 0 for constraint in problem.constraints: if isinstance(constraint, (Equality, Zero)): self.num_scalar_eq_constr += constraint.expr.size # num_scalar_leq_constr self.num_scalar_leq_constr = 0 for constraint in problem.constraints: if isinstance(constraint, (Inequality, NonPos, NonNeg)): self.num_scalar_leq_constr += constraint.expr.size

true

f70c10ef1da6e4113a7dda715729c1f83bb1a8dc

2,545

py

Python

models/pointnet2_part_seg_msg.py

Danielznn16/RoboticHand-in-KG

27e4eee97ea4ecab40fbd13b24a97e1f94c10258

[ "MIT" ]

null

models/pointnet2_part_seg_msg.py

Danielznn16/RoboticHand-in-KG

27e4eee97ea4ecab40fbd13b24a97e1f94c10258

[ "MIT" ]

null

models/pointnet2_part_seg_msg.py

Danielznn16/RoboticHand-in-KG

27e4eee97ea4ecab40fbd13b24a97e1f94c10258

[ "MIT" ]

null

import torch.nn as nn import torch import torch.nn.functional as F from models.pointnet_util import PointNetSetAbstractionMsg,PointNetSetAbstraction,PointNetFeaturePropagation class get_model(nn.Module): def __init__(self, num_classes, normal_channel=False): super(get_model, self).__init__() if normal_channel: additional_channel = 3 else: additional_channel = 0 self.normal_channel = normal_channel self.sa1 = PointNetSetAbstractionMsg(512, [0.1, 0.2, 0.4], [32, 64, 128], 3+additional_channel, [[32, 32, 64], [64, 64, 128], [64, 96, 128]]) self.sa2 = PointNetSetAbstractionMsg(128, [0.4,0.8], [64, 128], 128+128+64, [[128, 128, 256], [128, 196, 256]]) self.sa3 = PointNetSetAbstraction(npoint=None, radius=None, nsample=None, in_channel=512 + 3, mlp=[256, 512, 1024], group_all=True) self.fp3 = PointNetFeaturePropagation(in_channel=1536, mlp=[256, 256]) self.fp2 = PointNetFeaturePropagation(in_channel=576, mlp=[256, 128]) self.fp1 = PointNetFeaturePropagation(in_channel=150+additional_channel, mlp=[128, 128]) self.conv1 = nn.Conv1d(128, 128, 1) self.bn1 = nn.BatchNorm1d(128) self.drop1 = nn.Dropout(0.5) self.conv2 = nn.Conv1d(128, num_classes, 1) def forward(self, xyz, cls_label): # Set Abstraction layers B,C,N = xyz.shape if self.normal_channel: l0_points = xyz l0_xyz = xyz[:,:3,:] else: l0_points = xyz l0_xyz = xyz l1_xyz, l1_points = self.sa1(l0_xyz, l0_points) l2_xyz, l2_points = self.sa2(l1_xyz, l1_points) l3_xyz, l3_points = self.sa3(l2_xyz, l2_points) # Feature Propagation layers l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points) l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points) cls_label_one_hot = cls_label.view(B,16,1).repeat(1,1,N) # print(cls_label_one_hot) l0_points = self.fp1(l0_xyz, l1_xyz, torch.cat([cls_label_one_hot,l0_xyz,l0_points],1), l1_points) # FC layers feat = F.relu(self.bn1(self.conv1(l0_points))) x = self.drop1(feat) x = self.conv2(x) x = F.log_softmax(x, dim=1) x = x.permute(0, 2, 1) return x, l3_points class get_loss(nn.Module): def __init__(self): super(get_loss, self).__init__() def forward(self, pred, target, trans_feat): total_loss = F.nll_loss(pred, target) return total_loss

42.416667

149

0.6389

import torch.nn as nn import torch import torch.nn.functional as F from models.pointnet_util import PointNetSetAbstractionMsg,PointNetSetAbstraction,PointNetFeaturePropagation class get_model(nn.Module): def __init__(self, num_classes, normal_channel=False): super(get_model, self).__init__() if normal_channel: additional_channel = 3 else: additional_channel = 0 self.normal_channel = normal_channel self.sa1 = PointNetSetAbstractionMsg(512, [0.1, 0.2, 0.4], [32, 64, 128], 3+additional_channel, [[32, 32, 64], [64, 64, 128], [64, 96, 128]]) self.sa2 = PointNetSetAbstractionMsg(128, [0.4,0.8], [64, 128], 128+128+64, [[128, 128, 256], [128, 196, 256]]) self.sa3 = PointNetSetAbstraction(npoint=None, radius=None, nsample=None, in_channel=512 + 3, mlp=[256, 512, 1024], group_all=True) self.fp3 = PointNetFeaturePropagation(in_channel=1536, mlp=[256, 256]) self.fp2 = PointNetFeaturePropagation(in_channel=576, mlp=[256, 128]) self.fp1 = PointNetFeaturePropagation(in_channel=150+additional_channel, mlp=[128, 128]) self.conv1 = nn.Conv1d(128, 128, 1) self.bn1 = nn.BatchNorm1d(128) self.drop1 = nn.Dropout(0.5) self.conv2 = nn.Conv1d(128, num_classes, 1) def forward(self, xyz, cls_label): B,C,N = xyz.shape if self.normal_channel: l0_points = xyz l0_xyz = xyz[:,:3,:] else: l0_points = xyz l0_xyz = xyz l1_xyz, l1_points = self.sa1(l0_xyz, l0_points) l2_xyz, l2_points = self.sa2(l1_xyz, l1_points) l3_xyz, l3_points = self.sa3(l2_xyz, l2_points) l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points) l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points) cls_label_one_hot = cls_label.view(B,16,1).repeat(1,1,N) l0_points = self.fp1(l0_xyz, l1_xyz, torch.cat([cls_label_one_hot,l0_xyz,l0_points],1), l1_points) feat = F.relu(self.bn1(self.conv1(l0_points))) x = self.drop1(feat) x = self.conv2(x) x = F.log_softmax(x, dim=1) x = x.permute(0, 2, 1) return x, l3_points class get_loss(nn.Module): def __init__(self): super(get_loss, self).__init__() def forward(self, pred, target, trans_feat): total_loss = F.nll_loss(pred, target) return total_loss

true

f70c11f8a5b4f0874903f8ba8e3d38d1b62f1537

12,511

py

Python

shared/common.py

jonnyCodev/cloudmapper

10fd533e318f0a18f58929f1759e32005347254e

[ "BSD-3-Clause" ]

null

shared/common.py

jonnyCodev/cloudmapper

10fd533e318f0a18f58929f1759e32005347254e

[ "BSD-3-Clause" ]

null

shared/common.py

jonnyCodev/cloudmapper

10fd533e318f0a18f58929f1759e32005347254e

[ "BSD-3-Clause" ]

1

2021-12-23T12:42:14.000Z

from __future__ import print_function import argparse import json import datetime import pyjq import yaml import sys from netaddr import IPNetwork from shared.nodes import Account, Region from shared.query import query_aws, get_parameter_file class Severity: # For logging DEBUG = 0 INFO = 1 WARN = 2 ERROR = 3 @classmethod def str_to_int(cls, level): if level == "DEBUG": return cls.DEBUG elif level == "INFO": return cls.INFO elif level == "WARN": return cls.WARN elif level == "ERROR": return cls.ERROR else: raise Exception("Unknown log level {}".format(level)) @staticmethod def string(severity_level): if severity_level == Severity.DEBUG: return "DEBUG" elif severity_level == Severity.INFO: return "INFO" elif severity_level == Severity.WARN: return "WARN" elif severity_level == Severity.ERROR: return "ERROR" else: raise Exception("Unknown severity level") LOG_LEVEL = Severity.INFO def log_debug(msg, location=None, reasons=[]): log_issue(Severity.DEBUG, msg, location, reasons) def log_info(msg, location=None, reasons=[]): log_issue(Severity.INFO, msg, location, reasons) def log_warning(msg, location=None, reasons=[]): log_issue(Severity.WARN, msg, location, reasons) def log_error(msg, location=None, reasons=[]): log_issue(Severity.ERROR, msg, location, reasons) def log_issue(severity, msg, location=None, reasons=[]): if severity >= LOG_LEVEL: json_issue = { "Severity": Severity.string(severity), "Issue": msg, "Location": location, "Reasons": reasons, } print(json.dumps(json_issue, sort_keys=True), file=sys.stderr) class Finding(object): """Used for auditing""" region = None issue_id = None resource_id = None resource_details = None def __init__(self, region, issue_id, resource_id, resource_details=None): self.region = region self.issue_id = issue_id self.resource_id = resource_id self.resource_details = resource_details def __str__(self): return json.dumps( { "account_id": self.region.account.local_id, "account_name": self.region.account.name, "region": self.region.name, "issue": self.issue_id, "resource": self.resource_id, "details": self.resource_details, } ) @property def account_name(self): return self.region.account.name def custom_serializer(x): if isinstance(x, datetime.datetime): return x.isoformat() elif isinstance(x, bytes): return x.decode() raise TypeError("Unknown type") def make_list(v): if not isinstance(v, list): return [v] return v def is_external_cidr(cidr): ipnetwork = IPNetwork(cidr) if ( ipnetwork in IPNetwork("10.0.0.0/8") or ipnetwork in IPNetwork("172.16.0.0/12") or ipnetwork in IPNetwork("192.168.0.0/16") ): return False return True def is_unblockable_cidr(cidr): ipnetwork = IPNetwork(cidr) if ( ipnetwork in IPNetwork("169.254.0.0/16") or ipnetwork in IPNetwork("127.0.0.0/8") # link local or ipnetwork in IPNetwork("192.0.2.0/24") # loopback or ipnetwork in IPNetwork("198.51.100.0/24") # Test network from RFC 5737 or ipnetwork in IPNetwork("203.0.113.0/24") # Test network or ipnetwork in IPNetwork("224.0.0.0/4") # Test network or ipnetwork in IPNetwork("240.0.0.0/5") # class D multicast or ipnetwork in IPNetwork("248.0.0.0/5") # class E reserved or ipnetwork in IPNetwork("255.255.255.255/32") # reserved # broadcast ): return True return False def get_regions(account, outputfilter={}): # aws ec2 describe-regions region_data = query_aws(account, "describe-regions") region_filter = "" if "regions" in outputfilter: region_filter = "| select(.RegionName | contains({}))".format( outputfilter["regions"] ) regions = pyjq.all(".Regions[]{}".format(region_filter), region_data) return regions def get_account(account_name, config=None, config_filename="config.json.demo"): if config is None: config = json.load(open(config_filename)) for account in config["accounts"]: if account["name"] == account_name: return account if account_name is None and account.get("default", False): return account # Else could not find account if account_name is None: exit( "ERROR: Must specify an account, or set one in {} as a default".format( config_filename ) ) exit( 'ERROR: Account named "{}" not found in {}'.format( account_name, config_filename ) ) def parse_arguments(arguments, parser=None): """Returns (args, accounts, config)""" if parser is None: parser = argparse.ArgumentParser() parser.add_argument( "--config", help="Config file name", default="config.json", type=str ) parser.add_argument( "--accounts", help="Accounts to collect from", required=True, type=str ) parser.add_argument( "--log_level", help="Log level to record (DEBUG, INFO, WARN, ERROR)", default="INFO", required=False, type=str, ) args = parser.parse_args(arguments) global LOG_LEVEL LOG_LEVEL = Severity.str_to_int(args.log_level) # Read accounts file try: config = json.load(open(args.config)) except IOError: exit('ERROR: Unable to load config file "{}"'.format(args.config)) except ValueError as e: exit( 'ERROR: Config file "{}" could not be loaded ({}), see config.json.demo for an example'.format( args.config, e ) ) # Get accounts account_names = args.accounts.split(",") accounts = [] # TODO Need to be able to tag accounts into sets (ex. Prod, or by business unit) so the tag can be referenced # as opposed to the individual account names. for account_name in account_names: if account_name == "all": for account in config["accounts"]: accounts.append(account) break accounts.append(get_account(account_name, config, args.config)) return (args, accounts, config) def get_account_stats(account, all_resources=False): """Returns stats for an account""" with open("stats_config.yaml", "r") as f: resources = yaml.safe_load(f) account = Account(None, account) log_debug( "Collecting stats in account {} ({})".format(account.name, account.local_id) ) stats = {} stats["keys"] = [] for resource in resources: # If the resource is marked as verbose, and we're not showing all resources, skip it. if resource.get("verbose", False) and not all_resources: continue stats["keys"].append(resource["name"]) stats[resource["name"]] = {} for region_json in get_regions(account): region = Region(account, region_json) for resource in resources: if resource.get("verbose", False) and not all_resources: continue # Skip global services (just CloudFront) if ("region" in resource) and (resource["region"] != region.name): continue # S3 buckets require special code to identify their location if resource["name"] == "S3 buckets": if region.name == "us-east-1": buckets = pyjq.all( ".Buckets[].Name", query_aws(region.account, "s3-list-buckets", region), ) for bucket in buckets: # Get the bucket's location bucket_region = get_parameter_file( region, "s3", "get-bucket-location", bucket )["LocationConstraint"] # Convert the value to a name. # See https://docs.aws.amazon.com/general/latest/gr/rande.html#s3_region if bucket_region is None: bucket_region = "us-east-1" elif bucket_region == "EU": bucket_region = "eu-west-1" # Increment the count tmp = stats[resource["name"]].get(bucket_region, 0) stats[resource["name"]][bucket_region] = tmp + 1 else: if region.name != 'ap-east-1': # Normal path stats[resource["name"]][region.name] = sum( pyjq.all( resource["query"], query_aws(region.account, resource["source"], region), ) ) return stats def get_us_east_1(account): for region_json in get_regions(account): region = Region(account, region_json) if region.name == "us-east-1": return region raise Exception("us-east-1 not found") def iso_date(d): """ Convert ISO format date string such as 2018-04-08T23:33:20+00:00""" time_format = "%Y-%m-%dT%H:%M:%S" return datetime.datetime.strptime(d.split("+")[0], time_format) def days_between(s1, s2): """s1 and s2 are date strings""" d1 = iso_date(s1) d2 = iso_date(s2) return abs((d1 - d2).days) def get_collection_date(account): if type(account) is not Account: account = Account(None, account) account_struct = account json_blob = query_aws( account_struct, "iam-get-credential-report", get_us_east_1(account_struct) ) if not json_blob: raise Exception( "File iam-get-credential-report.json does not exist or is not well-formed. Likely cause is you did not run the collect command for this account." ) # GeneratedTime looks like "2019-01-30T15:43:24+00:00" return json_blob["GeneratedTime"] def get_access_advisor_active_counts(account, max_age=90): region = get_us_east_1(account) json_account_auth_details = query_aws( region.account, "iam-get-account-authorization-details", region ) account_stats = { "users": {"active": 0, "inactive": 0}, "roles": {"active": 0, "inactive": 0}, } for principal_auth in [ *json_account_auth_details["UserDetailList"], *json_account_auth_details["RoleDetailList"], ]: stats = {} stats["auth"] = principal_auth principal_type = "roles" if "UserName" in principal_auth: principal_type = "users" job_id = get_parameter_file( region, "iam", "generate-service-last-accessed-details", principal_auth["Arn"], )["JobId"] json_last_access_details = get_parameter_file( region, "iam", "get-service-last-accessed-details", job_id ) stats["last_access"] = json_last_access_details stats["is_inactive"] = True job_completion_date = datetime.datetime.strptime( json_last_access_details["JobCompletionDate"][0:10], "%Y-%m-%d" ) for service in json_last_access_details["ServicesLastAccessed"]: if "LastAuthenticated" in service: last_access_date = datetime.datetime.strptime( service["LastAuthenticated"][0:10], "%Y-%m-%d" ) if (job_completion_date - last_access_date).days < max_age: stats["is_inactive"] = False break if stats["is_inactive"]: account_stats[principal_type]["inactive"] += 1 else: account_stats[principal_type]["active"] += 1 return account_stats def get_current_policy_doc(policy): for doc in policy["PolicyVersionList"]: if doc["IsDefaultVersion"]: return doc["Document"] raise Exception("No default document version in policy {}".format(policy["Arn"]))

31.044665

157

0.587963

from __future__ import print_function import argparse import json import datetime import pyjq import yaml import sys from netaddr import IPNetwork from shared.nodes import Account, Region from shared.query import query_aws, get_parameter_file class Severity: DEBUG = 0 INFO = 1 WARN = 2 ERROR = 3 @classmethod def str_to_int(cls, level): if level == "DEBUG": return cls.DEBUG elif level == "INFO": return cls.INFO elif level == "WARN": return cls.WARN elif level == "ERROR": return cls.ERROR else: raise Exception("Unknown log level {}".format(level)) @staticmethod def string(severity_level): if severity_level == Severity.DEBUG: return "DEBUG" elif severity_level == Severity.INFO: return "INFO" elif severity_level == Severity.WARN: return "WARN" elif severity_level == Severity.ERROR: return "ERROR" else: raise Exception("Unknown severity level") LOG_LEVEL = Severity.INFO def log_debug(msg, location=None, reasons=[]): log_issue(Severity.DEBUG, msg, location, reasons) def log_info(msg, location=None, reasons=[]): log_issue(Severity.INFO, msg, location, reasons) def log_warning(msg, location=None, reasons=[]): log_issue(Severity.WARN, msg, location, reasons) def log_error(msg, location=None, reasons=[]): log_issue(Severity.ERROR, msg, location, reasons) def log_issue(severity, msg, location=None, reasons=[]): if severity >= LOG_LEVEL: json_issue = { "Severity": Severity.string(severity), "Issue": msg, "Location": location, "Reasons": reasons, } print(json.dumps(json_issue, sort_keys=True), file=sys.stderr) class Finding(object): region = None issue_id = None resource_id = None resource_details = None def __init__(self, region, issue_id, resource_id, resource_details=None): self.region = region self.issue_id = issue_id self.resource_id = resource_id self.resource_details = resource_details def __str__(self): return json.dumps( { "account_id": self.region.account.local_id, "account_name": self.region.account.name, "region": self.region.name, "issue": self.issue_id, "resource": self.resource_id, "details": self.resource_details, } ) @property def account_name(self): return self.region.account.name def custom_serializer(x): if isinstance(x, datetime.datetime): return x.isoformat() elif isinstance(x, bytes): return x.decode() raise TypeError("Unknown type") def make_list(v): if not isinstance(v, list): return [v] return v def is_external_cidr(cidr): ipnetwork = IPNetwork(cidr) if ( ipnetwork in IPNetwork("10.0.0.0/8") or ipnetwork in IPNetwork("172.16.0.0/12") or ipnetwork in IPNetwork("192.168.0.0/16") ): return False return True def is_unblockable_cidr(cidr): ipnetwork = IPNetwork(cidr) if ( ipnetwork in IPNetwork("169.254.0.0/16") or ipnetwork in IPNetwork("127.0.0.0/8") or ipnetwork in IPNetwork("192.0.2.0/24") or ipnetwork in IPNetwork("198.51.100.0/24") or ipnetwork in IPNetwork("203.0.113.0/24") or ipnetwork in IPNetwork("224.0.0.0/4") or ipnetwork in IPNetwork("240.0.0.0/5") or ipnetwork in IPNetwork("248.0.0.0/5") or ipnetwork in IPNetwork("255.255.255.255/32") ): return True return False def get_regions(account, outputfilter={}): region_data = query_aws(account, "describe-regions") region_filter = "" if "regions" in outputfilter: region_filter = "| select(.RegionName | contains({}))".format( outputfilter["regions"] ) regions = pyjq.all(".Regions[]{}".format(region_filter), region_data) return regions def get_account(account_name, config=None, config_filename="config.json.demo"): if config is None: config = json.load(open(config_filename)) for account in config["accounts"]: if account["name"] == account_name: return account if account_name is None and account.get("default", False): return account if account_name is None: exit( "ERROR: Must specify an account, or set one in {} as a default".format( config_filename ) ) exit( 'ERROR: Account named "{}" not found in {}'.format( account_name, config_filename ) ) def parse_arguments(arguments, parser=None): if parser is None: parser = argparse.ArgumentParser() parser.add_argument( "--config", help="Config file name", default="config.json", type=str ) parser.add_argument( "--accounts", help="Accounts to collect from", required=True, type=str ) parser.add_argument( "--log_level", help="Log level to record (DEBUG, INFO, WARN, ERROR)", default="INFO", required=False, type=str, ) args = parser.parse_args(arguments) global LOG_LEVEL LOG_LEVEL = Severity.str_to_int(args.log_level) try: config = json.load(open(args.config)) except IOError: exit('ERROR: Unable to load config file "{}"'.format(args.config)) except ValueError as e: exit( 'ERROR: Config file "{}" could not be loaded ({}), see config.json.demo for an example'.format( args.config, e ) ) account_names = args.accounts.split(",") accounts = [] for account_name in account_names: if account_name == "all": for account in config["accounts"]: accounts.append(account) break accounts.append(get_account(account_name, config, args.config)) return (args, accounts, config) def get_account_stats(account, all_resources=False): with open("stats_config.yaml", "r") as f: resources = yaml.safe_load(f) account = Account(None, account) log_debug( "Collecting stats in account {} ({})".format(account.name, account.local_id) ) stats = {} stats["keys"] = [] for resource in resources: if resource.get("verbose", False) and not all_resources: continue stats["keys"].append(resource["name"]) stats[resource["name"]] = {} for region_json in get_regions(account): region = Region(account, region_json) for resource in resources: if resource.get("verbose", False) and not all_resources: continue # Skip global services (just CloudFront) if ("region" in resource) and (resource["region"] != region.name): continue # S3 buckets require special code to identify their location if resource["name"] == "S3 buckets": if region.name == "us-east-1": buckets = pyjq.all( ".Buckets[].Name", query_aws(region.account, "s3-list-buckets", region), ) for bucket in buckets: # Get the bucket's location bucket_region = get_parameter_file( region, "s3", "get-bucket-location", bucket )["LocationConstraint"] if bucket_region is None: bucket_region = "us-east-1" elif bucket_region == "EU": bucket_region = "eu-west-1" tmp = stats[resource["name"]].get(bucket_region, 0) stats[resource["name"]][bucket_region] = tmp + 1 else: if region.name != 'ap-east-1': stats[resource["name"]][region.name] = sum( pyjq.all( resource["query"], query_aws(region.account, resource["source"], region), ) ) return stats def get_us_east_1(account): for region_json in get_regions(account): region = Region(account, region_json) if region.name == "us-east-1": return region raise Exception("us-east-1 not found") def iso_date(d): time_format = "%Y-%m-%dT%H:%M:%S" return datetime.datetime.strptime(d.split("+")[0], time_format) def days_between(s1, s2): d1 = iso_date(s1) d2 = iso_date(s2) return abs((d1 - d2).days) def get_collection_date(account): if type(account) is not Account: account = Account(None, account) account_struct = account json_blob = query_aws( account_struct, "iam-get-credential-report", get_us_east_1(account_struct) ) if not json_blob: raise Exception( "File iam-get-credential-report.json does not exist or is not well-formed. Likely cause is you did not run the collect command for this account." ) return json_blob["GeneratedTime"] def get_access_advisor_active_counts(account, max_age=90): region = get_us_east_1(account) json_account_auth_details = query_aws( region.account, "iam-get-account-authorization-details", region ) account_stats = { "users": {"active": 0, "inactive": 0}, "roles": {"active": 0, "inactive": 0}, } for principal_auth in [ *json_account_auth_details["UserDetailList"], *json_account_auth_details["RoleDetailList"], ]: stats = {} stats["auth"] = principal_auth principal_type = "roles" if "UserName" in principal_auth: principal_type = "users" job_id = get_parameter_file( region, "iam", "generate-service-last-accessed-details", principal_auth["Arn"], )["JobId"] json_last_access_details = get_parameter_file( region, "iam", "get-service-last-accessed-details", job_id ) stats["last_access"] = json_last_access_details stats["is_inactive"] = True job_completion_date = datetime.datetime.strptime( json_last_access_details["JobCompletionDate"][0:10], "%Y-%m-%d" ) for service in json_last_access_details["ServicesLastAccessed"]: if "LastAuthenticated" in service: last_access_date = datetime.datetime.strptime( service["LastAuthenticated"][0:10], "%Y-%m-%d" ) if (job_completion_date - last_access_date).days < max_age: stats["is_inactive"] = False break if stats["is_inactive"]: account_stats[principal_type]["inactive"] += 1 else: account_stats[principal_type]["active"] += 1 return account_stats def get_current_policy_doc(policy): for doc in policy["PolicyVersionList"]: if doc["IsDefaultVersion"]: return doc["Document"] raise Exception("No default document version in policy {}".format(policy["Arn"]))

true

f70c127f5f194424081088d7da6167ddefd1d0fc

3,957

py

Python

monitorrent/plugins/clients/transmission.py

mortifactor/monitorrent

2388ec5b82af5d078fa7e37930d3b66b4a797954

[ "WTFPL" ]

465

2015-08-31T09:16:41.000Z

2022-03-12T10:33:04.000Z

monitorrent/plugins/clients/transmission.py

mortifactor/monitorrent

2388ec5b82af5d078fa7e37930d3b66b4a797954

[ "WTFPL" ]

340

2015-07-18T17:31:54.000Z

2022-03-30T15:16:25.000Z

monitorrent/plugins/clients/transmission.py

mortifactor/monitorrent

2388ec5b82af5d078fa7e37930d3b66b4a797954

[ "WTFPL" ]

87

2015-07-18T10:52:24.000Z

2022-03-27T09:52:35.000Z

import six import transmissionrpc from pytz import reference, utc from sqlalchemy import Column, Integer, String from monitorrent.db import Base, DBSession from monitorrent.plugin_managers import register_plugin import base64 class TransmissionCredentials(Base): __tablename__ = "transmission_credentials" id = Column(Integer, primary_key=True) host = Column(String, nullable=False) port = Column(Integer, nullable=False) username = Column(String, nullable=True) password = Column(String, nullable=True) class TransmissionClientPlugin(object): name = "transmission" form = [{ 'type': 'row', 'content': [{ 'type': 'text', 'label': 'Host', 'model': 'host', 'flex': 80 }, { 'type': 'text', 'label': 'Port', 'model': 'port', 'flex': 20 }] }, { 'type': 'row', 'content': [{ 'type': 'text', 'label': 'Username', 'model': 'username', 'flex': 50 }, { 'type': 'password', 'label': 'Password', 'model': 'password', 'flex': 50 }] }] DEFAULT_PORT = 9091 SUPPORTED_FIELDS = ['download_dir'] def get_settings(self): with DBSession() as db: cred = db.query(TransmissionCredentials).first() if not cred: return None return {'host': cred.host, 'port': cred.port, 'username': cred.username} def set_settings(self, settings): with DBSession() as db: cred = db.query(TransmissionCredentials).first() if not cred: cred = TransmissionCredentials() db.add(cred) cred.host = settings['host'] cred.port = settings.get('port', self.DEFAULT_PORT) cred.username = settings.get('username', None) cred.password = settings.get('password', None) def check_connection(self): with DBSession() as db: cred = db.query(TransmissionCredentials).first() if not cred: return False client = transmissionrpc.Client(address=cred.host, port=cred.port, user=cred.username, password=cred.password) return client def find_torrent(self, torrent_hash): client = self.check_connection() if not client: return False try: torrent = client.get_torrent(torrent_hash.lower(), ['id', 'hashString', 'addedDate', 'name']) return { "name": torrent.name, "date_added": torrent.date_added.replace(tzinfo=reference.LocalTimezone()).astimezone(utc) } except KeyError: return False def get_download_dir(self): client = self.check_connection() if not client: return None session = client.get_session() return six.text_type(session.download_dir) def add_torrent(self, torrent, torrent_settings): """ :type torrent: str :type torrent_settings: clients.TopicSettings | None """ client = self.check_connection() if not client: return False torrent_settings_dict = {} if torrent_settings is not None: if torrent_settings.download_dir is not None: torrent_settings_dict['download_dir'] = torrent_settings.download_dir client.add_torrent(base64.b64encode(torrent).decode('utf-8'), **torrent_settings_dict) return True def remove_torrent(self, torrent_hash): client = self.check_connection() if not client: return False client.remove_torrent(torrent_hash.lower(), delete_data=False) return True register_plugin('client', 'transmission', TransmissionClientPlugin())

32.434426

106

0.573414

import six import transmissionrpc from pytz import reference, utc from sqlalchemy import Column, Integer, String from monitorrent.db import Base, DBSession from monitorrent.plugin_managers import register_plugin import base64 class TransmissionCredentials(Base): __tablename__ = "transmission_credentials" id = Column(Integer, primary_key=True) host = Column(String, nullable=False) port = Column(Integer, nullable=False) username = Column(String, nullable=True) password = Column(String, nullable=True) class TransmissionClientPlugin(object): name = "transmission" form = [{ 'type': 'row', 'content': [{ 'type': 'text', 'label': 'Host', 'model': 'host', 'flex': 80 }, { 'type': 'text', 'label': 'Port', 'model': 'port', 'flex': 20 }] }, { 'type': 'row', 'content': [{ 'type': 'text', 'label': 'Username', 'model': 'username', 'flex': 50 }, { 'type': 'password', 'label': 'Password', 'model': 'password', 'flex': 50 }] }] DEFAULT_PORT = 9091 SUPPORTED_FIELDS = ['download_dir'] def get_settings(self): with DBSession() as db: cred = db.query(TransmissionCredentials).first() if not cred: return None return {'host': cred.host, 'port': cred.port, 'username': cred.username} def set_settings(self, settings): with DBSession() as db: cred = db.query(TransmissionCredentials).first() if not cred: cred = TransmissionCredentials() db.add(cred) cred.host = settings['host'] cred.port = settings.get('port', self.DEFAULT_PORT) cred.username = settings.get('username', None) cred.password = settings.get('password', None) def check_connection(self): with DBSession() as db: cred = db.query(TransmissionCredentials).first() if not cred: return False client = transmissionrpc.Client(address=cred.host, port=cred.port, user=cred.username, password=cred.password) return client def find_torrent(self, torrent_hash): client = self.check_connection() if not client: return False try: torrent = client.get_torrent(torrent_hash.lower(), ['id', 'hashString', 'addedDate', 'name']) return { "name": torrent.name, "date_added": torrent.date_added.replace(tzinfo=reference.LocalTimezone()).astimezone(utc) } except KeyError: return False def get_download_dir(self): client = self.check_connection() if not client: return None session = client.get_session() return six.text_type(session.download_dir) def add_torrent(self, torrent, torrent_settings): client = self.check_connection() if not client: return False torrent_settings_dict = {} if torrent_settings is not None: if torrent_settings.download_dir is not None: torrent_settings_dict['download_dir'] = torrent_settings.download_dir client.add_torrent(base64.b64encode(torrent).decode('utf-8'), **torrent_settings_dict) return True def remove_torrent(self, torrent_hash): client = self.check_connection() if not client: return False client.remove_torrent(torrent_hash.lower(), delete_data=False) return True register_plugin('client', 'transmission', TransmissionClientPlugin())

true

f70c12954517a7ab92741620556d0e5dde45046e

3,476

py

Python

examples/generate_notices_report_for_project_version.py

AvneetKhaira/hub-rest-api-python

d9fac065d8cae72aded87f7326477b03f52f45f8

[ "Apache-2.0" ]

null

examples/generate_notices_report_for_project_version.py

AvneetKhaira/hub-rest-api-python

d9fac065d8cae72aded87f7326477b03f52f45f8

[ "Apache-2.0" ]

null

examples/generate_notices_report_for_project_version.py

AvneetKhaira/hub-rest-api-python

d9fac065d8cae72aded87f7326477b03f52f45f8

[ "Apache-2.0" ]

null

''' Created on Dec 19, 2018 @author: gsnyder Generate notices report for a given project-version ''' from blackduck.HubRestApi import HubInstance import argparse import json import logging import sys import time import zipfile parser = argparse.ArgumentParser("A program to generate the notices file for a given project-version") parser.add_argument("project_name") parser.add_argument("version_name") # TODO: Add the copyright checkbox option parser.add_argument('-f', "--file_name_base", default="notices_report", help="Base file name to write the report data into. If the report format is TEXT a .zip file will be created, otherwise a .json file") parser.add_argument('-r', '--report_format', default='TEXT', choices=["JSON", "TEXT"], help="Report format - choices are TEXT or HTML") parser.add_argument('-c', '--include_copyright_info', action='store_true', help="Set this option to have additional copyright information from the Black Duck KB included in the notices file report.") args = parser.parse_args() hub = HubInstance() logging.basicConfig(format='%(asctime)s:%(levelname)s:%(message)s', stream=sys.stderr, level=logging.DEBUG) class FailedReportDownload(Exception): pass def download_report(location, file_name_base, retries=10): report_id = location.split("/")[-1] if retries: logging.debug("Retrieving generated report from {}".format(location)) # response = hub.download_report(report_id) response, report_format = hub.download_notification_report(location) if response.status_code == 200: if report_format == "TEXT": filename = file_name_base + ".zip" with open(filename, "wb") as f: f.write(response.content) else: # JSON format filename = file_name_base + ".json" with open(filename, "w") as f: json.dump(response.json(), f, indent=3) logging.info("Successfully downloaded json file to {} for report {}".format( filename, report_id)) else: logging.warning("Failed to retrieve report {}".format(report_id)) logging.warning("Probably not ready yet, waiting 5 seconds then retrying (remaining retries={}".format(retries)) time.sleep(5) retries -= 1 download_report(location, file_name_base, retries) else: raise FailedReportDownload("Failed to retrieve report {} after multiple retries".format(report_id)) project = hub.get_project_by_name(args.project_name) if project: version = hub.get_version_by_name(project, args.version_name) response = hub.create_version_notices_report(version, args.report_format, include_copyright_info=args.include_copyright_info) if response.status_code == 201: logging.info("Successfully created notices report in {} format for project {} and version {}".format( args.report_format, args.project_name, args.version_name)) location = response.headers['Location'] download_report(location, args.file_name_base) # Showing how you can interact with the downloaded zip and where to find the # output content. Uncomment the lines below to see how it works. # with zipfile.ZipFile(zip_file_name_base, 'r') as zipf: # with zipf.open("{}/{}/version-license.txt".format(args.project_name, args.version_name), "r") as license_file: # print(license_file.read()) else: logging.error("Failed to create reports for project {} version {}, status code returned {}".format( args.project_name, args.version_name, response.status_code)) else: logging.warning("Did not find project with name {}".format(args.project_name))

39.05618

206

0.750575

from blackduck.HubRestApi import HubInstance import argparse import json import logging import sys import time import zipfile parser = argparse.ArgumentParser("A program to generate the notices file for a given project-version") parser.add_argument("project_name") parser.add_argument("version_name") parser.add_argument('-f', "--file_name_base", default="notices_report", help="Base file name to write the report data into. If the report format is TEXT a .zip file will be created, otherwise a .json file") parser.add_argument('-r', '--report_format', default='TEXT', choices=["JSON", "TEXT"], help="Report format - choices are TEXT or HTML") parser.add_argument('-c', '--include_copyright_info', action='store_true', help="Set this option to have additional copyright information from the Black Duck KB included in the notices file report.") args = parser.parse_args() hub = HubInstance() logging.basicConfig(format='%(asctime)s:%(levelname)s:%(message)s', stream=sys.stderr, level=logging.DEBUG) class FailedReportDownload(Exception): pass def download_report(location, file_name_base, retries=10): report_id = location.split("/")[-1] if retries: logging.debug("Retrieving generated report from {}".format(location)) response, report_format = hub.download_notification_report(location) if response.status_code == 200: if report_format == "TEXT": filename = file_name_base + ".zip" with open(filename, "wb") as f: f.write(response.content) else: filename = file_name_base + ".json" with open(filename, "w") as f: json.dump(response.json(), f, indent=3) logging.info("Successfully downloaded json file to {} for report {}".format( filename, report_id)) else: logging.warning("Failed to retrieve report {}".format(report_id)) logging.warning("Probably not ready yet, waiting 5 seconds then retrying (remaining retries={}".format(retries)) time.sleep(5) retries -= 1 download_report(location, file_name_base, retries) else: raise FailedReportDownload("Failed to retrieve report {} after multiple retries".format(report_id)) project = hub.get_project_by_name(args.project_name) if project: version = hub.get_version_by_name(project, args.version_name) response = hub.create_version_notices_report(version, args.report_format, include_copyright_info=args.include_copyright_info) if response.status_code == 201: logging.info("Successfully created notices report in {} format for project {} and version {}".format( args.report_format, args.project_name, args.version_name)) location = response.headers['Location'] download_report(location, args.file_name_base) else: logging.error("Failed to create reports for project {} version {}, status code returned {}".format( args.project_name, args.version_name, response.status_code)) else: logging.warning("Did not find project with name {}".format(args.project_name))

true

f70c13aa147eaeb39388692f9ff8fa426bc19476

1,735

py

Python

napari_assistant/_gui/_button_grid.py

Cryaaa/napari-assistant

efdde41368885ccc6cc0e40c4eba236e3883215c

[ "BSD-3-Clause" ]

null

napari_assistant/_gui/_button_grid.py

Cryaaa/napari-assistant

efdde41368885ccc6cc0e40c4eba236e3883215c

[ "BSD-3-Clause" ]

8

2022-03-07T20:38:01.000Z

2022-03-20T14:50:52.000Z

napari_assistant/_gui/_button_grid.py

Cryaaa/napari-assistant

efdde41368885ccc6cc0e40c4eba236e3883215c

[ "BSD-3-Clause" ]

null

from qtpy.QtCore import QSize from qtpy.QtGui import QIcon from qtpy.QtWidgets import QListWidget, QListWidgetItem from pathlib import Path ICON_ROOT = Path(__file__).parent / "icons" STYLES = r""" QListWidget{ min-width: 294; background: none; font-size: 8pt; color: #eee; } QListWidget::item { width: 68; height: 85; border-radius: 0; margin: 1; padding: 4; background: #414851; } QListWidget::item::hover { background: #5A626C; } """ def _get_icon(name): path = ICON_ROOT / f'{name.lower().replace(" ", "_")}.png' if not path.exists(): return "" return str(path) class ButtonGrid(QListWidget): def __init__(self, parent=None): super().__init__(parent=parent) self.setMovement(self.Static) # The items cannot be moved by the user. self.setViewMode(self.IconMode) # make items icons self.setResizeMode(self.Adjust) # relayout when view is resized. self.setUniformItemSizes(True) # better performance self.setIconSize(QSize(64, 44)) self.setWordWrap(True) self.setStyleSheet(STYLES) def addItem(self, label : str, tool_tip : str = None): if isinstance(label, QListWidgetItem): super().addItem(label) item = QListWidgetItem(QIcon(_get_icon(label)), label) if tool_tip is not None: item.setToolTip(tool_tip) super().addItem(item) def addItems(self, labels) -> None: for label in labels: if hasattr(labels[label], "tool_tip"): self.addItem(label, labels[label].tool_tip) else: self.addItem(label)

27.983871

79

0.605764

from qtpy.QtCore import QSize from qtpy.QtGui import QIcon from qtpy.QtWidgets import QListWidget, QListWidgetItem from pathlib import Path ICON_ROOT = Path(__file__).parent / "icons" STYLES = r""" QListWidget{ min-width: 294; background: none; font-size: 8pt; color: #eee; } QListWidget::item { width: 68; height: 85; border-radius: 0; margin: 1; padding: 4; background: #414851; } QListWidget::item::hover { background: #5A626C; } """ def _get_icon(name): path = ICON_ROOT / f'{name.lower().replace(" ", "_")}.png' if not path.exists(): return "" return str(path) class ButtonGrid(QListWidget): def __init__(self, parent=None): super().__init__(parent=parent) self.setMovement(self.Static) self.setViewMode(self.IconMode) self.setResizeMode(self.Adjust) self.setUniformItemSizes(True) self.setIconSize(QSize(64, 44)) self.setWordWrap(True) self.setStyleSheet(STYLES) def addItem(self, label : str, tool_tip : str = None): if isinstance(label, QListWidgetItem): super().addItem(label) item = QListWidgetItem(QIcon(_get_icon(label)), label) if tool_tip is not None: item.setToolTip(tool_tip) super().addItem(item) def addItems(self, labels) -> None: for label in labels: if hasattr(labels[label], "tool_tip"): self.addItem(label, labels[label].tool_tip) else: self.addItem(label)

true

f70c144e6451a175f12753b62c7803ebe9b46b98

3,156

py

Python

day14/day14.py

elp2/advent_of_code_2019

af3ce232fb6597dbc80e96bdfd5a6248f07aa3c6

[ "Apache-2.0" ]

1

2021-12-02T15:19:36.000Z

day14/day14.py

elp2/advent_of_code_2019

af3ce232fb6597dbc80e96bdfd5a6248f07aa3c6

[ "Apache-2.0" ]

null

day14/day14.py

elp2/advent_of_code_2019

af3ce232fb6597dbc80e96bdfd5a6248f07aa3c6

[ "Apache-2.0" ]

null

from collections import defaultdict from copy import copy from math import ceil, floor def parse_item(item): [num, name] = item.strip().split(' ') return {} def filter_zeroes(d): ret = defaultdict(lambda: 0) for k, v in d.items(): if v != 0: ret[k] = v return ret output_to_formula = {} def parse_input(): lines = open('input').readlines() for line in lines: [input_string, output_string] = line.split('=>') [output_number, output_chemical] = output_string.strip().split(' ') formula = {'num': int(output_number)} input_formula = defaultdict(lambda: 0) for inp in input_string.strip().split(','): [num, name] = inp.strip().split(' ') input_formula[name] = int(num) formula['inputs'] = input_formula output_to_formula[output_chemical] = formula def subtract_from_extras(extras, chem, num): ret = num if chem in extras: from_extras = min(num, extras[chem]) ret -= from_extras extras[chem] -= from_extras return ret def expand_one(chem, needed, extras): if chem == 'ORE': return {chem: needed} formula = output_to_formula[chem] fnum = formula['num'] scaling = ceil(needed / fnum) extra = fnum * scaling - needed if extra != 0: extras[chem] += extra ins = copy(formula['inputs']) for key in ins.keys(): ins[key] *= scaling ins[key] = subtract_from_extras(extras, key, ins[key]) return ins def expand(chemicals): extras = defaultdict(lambda: 0) while list(chemicals.keys()) != ['ORE']: new = defaultdict(lambda: 0) for chem, num in chemicals.items(): num = subtract_from_extras(extras, chem, num) expanded = expand_one(chem, num, extras) for key in expanded.keys(): new[key] += expanded[key] print('Round! ', chemicals, '->', new) chemicals = new ret = defaultdict(lambda: 0) for key, value in extras.items(): if value != 0: ret[key] = value for key, value in chemicals.items(): if value != 0: ret[key] = value return chemicals def part1(): parse_input() chemicals = defaultdict(lambda: 0) chemicals['FUEL'] = 1 while list(chemicals.keys()) != ['ORE']: chemicals = expand(chemicals) print('Expanded: ', chemicals) # part1() # 892207 ONE_TRILLION = 1_000_000_000_000 START_FUELS = floor(ONE_TRILLION / 892207) START_STEP = floor(START_FUELS / 2) def part2(): parse_input() fuels = START_FUELS step = START_STEP while True: chemicals = defaultdict(lambda: 0) chemicals['FUEL'] = fuels + step while list(chemicals.keys()) != ['ORE']: chemicals = expand(chemicals) ores = chemicals['ORE'] if ores == ONE_TRILLION or step == 0: print('FUELS = ', fuels) break elif ores < ONE_TRILLION: fuels += step elif ores > ONE_TRILLION: step = floor(step / 2) print(ores - ONE_TRILLION, step) part2() # 1935265

28.178571

75

0.585234

from collections import defaultdict from copy import copy from math import ceil, floor def parse_item(item): [num, name] = item.strip().split(' ') return {} def filter_zeroes(d): ret = defaultdict(lambda: 0) for k, v in d.items(): if v != 0: ret[k] = v return ret output_to_formula = {} def parse_input(): lines = open('input').readlines() for line in lines: [input_string, output_string] = line.split('=>') [output_number, output_chemical] = output_string.strip().split(' ') formula = {'num': int(output_number)} input_formula = defaultdict(lambda: 0) for inp in input_string.strip().split(','): [num, name] = inp.strip().split(' ') input_formula[name] = int(num) formula['inputs'] = input_formula output_to_formula[output_chemical] = formula def subtract_from_extras(extras, chem, num): ret = num if chem in extras: from_extras = min(num, extras[chem]) ret -= from_extras extras[chem] -= from_extras return ret def expand_one(chem, needed, extras): if chem == 'ORE': return {chem: needed} formula = output_to_formula[chem] fnum = formula['num'] scaling = ceil(needed / fnum) extra = fnum * scaling - needed if extra != 0: extras[chem] += extra ins = copy(formula['inputs']) for key in ins.keys(): ins[key] *= scaling ins[key] = subtract_from_extras(extras, key, ins[key]) return ins def expand(chemicals): extras = defaultdict(lambda: 0) while list(chemicals.keys()) != ['ORE']: new = defaultdict(lambda: 0) for chem, num in chemicals.items(): num = subtract_from_extras(extras, chem, num) expanded = expand_one(chem, num, extras) for key in expanded.keys(): new[key] += expanded[key] print('Round! ', chemicals, '->', new) chemicals = new ret = defaultdict(lambda: 0) for key, value in extras.items(): if value != 0: ret[key] = value for key, value in chemicals.items(): if value != 0: ret[key] = value return chemicals def part1(): parse_input() chemicals = defaultdict(lambda: 0) chemicals['FUEL'] = 1 while list(chemicals.keys()) != ['ORE']: chemicals = expand(chemicals) print('Expanded: ', chemicals) ONE_TRILLION = 1_000_000_000_000 START_FUELS = floor(ONE_TRILLION / 892207) START_STEP = floor(START_FUELS / 2) def part2(): parse_input() fuels = START_FUELS step = START_STEP while True: chemicals = defaultdict(lambda: 0) chemicals['FUEL'] = fuels + step while list(chemicals.keys()) != ['ORE']: chemicals = expand(chemicals) ores = chemicals['ORE'] if ores == ONE_TRILLION or step == 0: print('FUELS = ', fuels) break elif ores < ONE_TRILLION: fuels += step elif ores > ONE_TRILLION: step = floor(step / 2) print(ores - ONE_TRILLION, step) part2()

true

f70c15bf0053d4434cfa71056c4b147777a06236

547

py

Python

ufdl-image-segmentation-app/src/ufdl/image_segmentation_app/migrations/0006_job_templates.py

waikato-ufdl/ufdl-backend

776fc906c61eba6c2f2e6324758e7b8a323e30d7

[ "Apache-2.0" ]

null

ufdl-image-segmentation-app/src/ufdl/image_segmentation_app/migrations/0006_job_templates.py

waikato-ufdl/ufdl-backend

776fc906c61eba6c2f2e6324758e7b8a323e30d7

[ "Apache-2.0" ]

85

2020-07-24T00:04:28.000Z

2022-02-10T10:35:15.000Z

ufdl-image-segmentation-app/src/ufdl/image_segmentation_app/migrations/0006_job_templates.py

waikato-ufdl/ufdl-backend

776fc906c61eba6c2f2e6324758e7b8a323e30d7

[ "Apache-2.0" ]

null

from django.db import migrations from ufdl.core_app.migrations import DataMigration from ufdl.core_app.migrations.job_templates import get_python_job_template_migration from .job_templates import iterate_job_templates class Migration(migrations.Migration): """ Migration inserting the pre-trained model presets into the database. """ dependencies = [ ('ufdl-image-segmentation', '0005_pretrained_models') ] operations = [ DataMigration(get_python_job_template_migration(iterate_job_templates())) ]

27.35

84

0.764168

from django.db import migrations from ufdl.core_app.migrations import DataMigration from ufdl.core_app.migrations.job_templates import get_python_job_template_migration from .job_templates import iterate_job_templates class Migration(migrations.Migration): dependencies = [ ('ufdl-image-segmentation', '0005_pretrained_models') ] operations = [ DataMigration(get_python_job_template_migration(iterate_job_templates())) ]

true

f70c15fdf850b63bd6c15c2688bbc3eb9b82f421

13,299

py

Python

qucumber/nn_states/density_matrix.py

ZvonimirBandic/QuCumber

81f0291951e89346fd8ab5c35cc90341fd8acf35

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

163

2018-07-18T15:00:57.000Z

2022-03-31T09:05:06.000Z

qucumber/nn_states/density_matrix.py

ZvonimirBandic/QuCumber

81f0291951e89346fd8ab5c35cc90341fd8acf35

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

101

2018-07-17T17:36:06.000Z

2021-10-19T01:40:10.000Z

qucumber/nn_states/density_matrix.py

ZvonimirBandic/QuCumber

81f0291951e89346fd8ab5c35cc90341fd8acf35

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

32

2018-08-18T21:56:02.000Z

2022-03-12T22:04:16.000Z

# Copyright 2019 PIQuIL - 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 warnings import torch from torch.nn import functional as F from qucumber import _warn_on_missing_gpu from qucumber.utils import cplx, unitaries from qucumber.rbm import PurificationRBM from .neural_state import NeuralStateBase class DensityMatrix(NeuralStateBase): r""" :param num_visible: The number of visible units, i.e. the size of the system :type num_visible: int :param num_hidden: The number of units in the hidden layer :type num_hidden: int :param num_aux: The number of units in the purification layer :type num_aux: int :param unitary_dict: A dictionary associating bases with their unitary rotations :type unitary_dict: dict[str, torch.Tensor] :param gpu: Whether to perform computations on the default gpu. :type gpu: bool """ _rbm_am = None _rbm_ph = None _device = None def __init__( self, num_visible, num_hidden=None, num_aux=None, unitary_dict=None, gpu=False, module=None, ): if gpu and torch.cuda.is_available(): warnings.warn( "Using DensityMatrix on GPU is not recommended due to poor performance compared to CPU.", ResourceWarning, 2, ) self.device = torch.device("cuda") else: self.device = torch.device("cpu") if module is None: self.rbm_am = PurificationRBM(num_visible, num_hidden, num_aux, gpu=gpu) self.rbm_ph = PurificationRBM(num_visible, num_hidden, num_aux, gpu=gpu) else: _warn_on_missing_gpu(gpu) self.rbm_am = module.to(self.device) self.rbm_am.device = self.device self.rbm_ph = module.to(self.device).clone() self.rbm_ph.device = self.device self.num_visible = self.rbm_am.num_visible self.num_hidden = self.rbm_am.num_hidden self.num_aux = self.rbm_am.num_aux self.device = self.rbm_am.device self.unitary_dict = unitary_dict if unitary_dict else unitaries.create_dict() self.unitary_dict = { k: v.to(device=self.device) for k, v in self.unitary_dict.items() } @property def networks(self): return ["rbm_am", "rbm_ph"] @property def rbm_am(self): return self._rbm_am @rbm_am.setter def rbm_am(self, new_val): self._rbm_am = new_val @property def rbm_ph(self): """RBM used to learn the wavefunction phase.""" return self._rbm_ph @rbm_ph.setter def rbm_ph(self, new_val): self._rbm_ph = new_val @property def device(self): return self._device @device.setter def device(self, new_val): self._device = new_val def pi(self, v, vp, expand=True): r"""Calculates elements of the :math:`\Pi` matrix. If `expand` is `True`, will return a complex matrix :math:`A_{ij} = \langle\sigma_i|\Pi|\sigma'_j\rangle`. Otherwise will return a complex vector :math:`A_{i} = \langle\sigma_i|\Pi|\sigma'_i\rangle`. :param v: A batch of visible states, :math:`\sigma`. :type v: torch.Tensor :param vp: The other batch of visible state, :math:`\sigma'`. :type vp: torch.Tensor :param expand: Whether to return a matrix (`True`) or a vector (`False`). :type expand: bool :returns: The matrix elements given by :math:`\langle\sigma|\Pi|\sigma'\rangle` :rtype: torch.Tensor """ m_am = F.linear(v, self.rbm_am.weights_U, self.rbm_am.aux_bias) mp_am = F.linear(vp, self.rbm_am.weights_U, self.rbm_am.aux_bias) m_ph = F.linear(v, self.rbm_ph.weights_U) mp_ph = F.linear(vp, self.rbm_ph.weights_U) if expand and v.dim() >= 2: m_am = m_am.unsqueeze_(1) m_ph = m_ph.unsqueeze_(1) if expand and vp.dim() >= 2: mp_am = mp_am.unsqueeze_(0) mp_ph = mp_ph.unsqueeze_(0) exp_arg = (m_am + mp_am) / 2 phase = (m_ph - mp_ph) / 2 real = ( (1 + 2 * exp_arg.exp() * phase.cos() + (2 * exp_arg).exp()) .sqrt() .log() .sum(-1) ) imag = torch.atan2( (exp_arg.exp() * phase.sin()), (1 + exp_arg.exp() * phase.cos()) ).sum(-1) return cplx.make_complex(real, imag) def pi_grad(self, v, vp, phase=False, expand=False): r"""Calculates the gradient of the :math:`\Pi` matrix with respect to the amplitude RBM parameters for two input states :param v: One of the visible states, :math:`\sigma` :type v: torch.Tensor :param vp: The other visible state, :math`\sigma'` :type vp: torch.Tensor :param phase: Whether to compute the gradients for the phase RBM (`True`) or the amplitude RBM (`False`) :type phase: bool :returns: The matrix element of the gradient given by :math:`\langle\sigma|\nabla_\lambda\Pi|\sigma'\rangle` :rtype: torch.Tensor """ unsqueezed = v.dim() < 2 or vp.dim() < 2 v = (v.unsqueeze(0) if v.dim() < 2 else v).to(self.rbm_am.weights_W) vp = (vp.unsqueeze(0) if vp.dim() < 2 else vp).to(self.rbm_am.weights_W) if expand: arg_real = 0.5 * ( F.linear(v, self.rbm_am.weights_U, self.rbm_am.aux_bias).unsqueeze_(1) + F.linear(vp, self.rbm_am.weights_U, self.rbm_am.aux_bias).unsqueeze_( 0 ) ) arg_imag = 0.5 * ( F.linear(v, self.rbm_ph.weights_U).unsqueeze_(1) - F.linear(vp, self.rbm_ph.weights_U).unsqueeze_(0) ) else: arg_real = self.rbm_am.mixing_term(v + vp) arg_imag = self.rbm_ph.mixing_term(v - vp) sig = cplx.sigmoid(arg_real, arg_imag) batch_sizes = ( (v.shape[0], vp.shape[0], *v.shape[1:-1]) if expand else (*v.shape[:-1],) ) W_grad = torch.zeros_like(self.rbm_am.weights_W).expand(*batch_sizes, -1, -1) vb_grad = torch.zeros_like(self.rbm_am.visible_bias).expand(*batch_sizes, -1) hb_grad = torch.zeros_like(self.rbm_am.hidden_bias).expand(*batch_sizes, -1) if phase: temp = (v.unsqueeze(1) - vp.unsqueeze(0)) if expand else (v - vp) sig = cplx.scalar_mult(sig, cplx.I) ab_grad_real = torch.zeros_like(self.rbm_ph.aux_bias).expand( *batch_sizes, -1 ) ab_grad_imag = ab_grad_real.clone() else: temp = (v.unsqueeze(1) + vp.unsqueeze(0)) if expand else (v + vp) ab_grad_real = cplx.real(sig) ab_grad_imag = cplx.imag(sig) U_grad = 0.5 * torch.einsum("c...j,...k->c...jk", sig, temp) U_grad_real = cplx.real(U_grad) U_grad_imag = cplx.imag(U_grad) vec_real = [ W_grad.view(*batch_sizes, -1), U_grad_real.view(*batch_sizes, -1), vb_grad, hb_grad, ab_grad_real, ] vec_imag = [ W_grad.view(*batch_sizes, -1).clone(), U_grad_imag.view(*batch_sizes, -1), vb_grad.clone(), hb_grad.clone(), ab_grad_imag, ] if unsqueezed and not expand: vec_real = [grad.squeeze_(0) for grad in vec_real] vec_imag = [grad.squeeze_(0) for grad in vec_imag] return cplx.make_complex( torch.cat(vec_real, dim=-1), torch.cat(vec_imag, dim=-1) ) def rho(self, v, vp=None, expand=True): r"""Computes the matrix elements of the (unnormalized) density matrix. If `expand` is `True`, will return a complex matrix :math:`A_{ij} = \langle\sigma_i|\widetilde{\rho}|\sigma'_j\rangle`. Otherwise will return a complex vector :math:`A_{i} = \langle\sigma_i|\widetilde{\rho}|\sigma'_i\rangle`. :param v: One of the visible states, :math:`\sigma`. :type v: torch.Tensor :param vp: The other visible state, :math:`\sigma'`. If `None`, will be set to `v`. :type vp: torch.Tensor :param expand: Whether to return a matrix (`True`) or a vector (`False`). :type expand: bool :returns: The elements of the current density matrix :math:`\langle\sigma|\widetilde{\rho}|\sigma'\rangle` :rtype: torch.Tensor """ if expand is False and vp is None: return cplx.make_complex(self.probability(v)) elif vp is None: vp = v pi_ = self.pi(v, vp, expand=expand) amp = (self.rbm_am.gamma(v, vp, eta=+1, expand=expand) + cplx.real(pi_)).exp() phase = self.rbm_ph.gamma(v, vp, eta=-1, expand=expand) + cplx.imag(pi_) return cplx.make_complex(amp * phase.cos(), amp * phase.sin()) def importance_sampling_numerator(self, vp, v): return self.rho(vp, v, expand=False) def importance_sampling_denominator(self, v): return cplx.make_complex(self.probability(v)) def rotated_gradient(self, basis, sample): r"""Computes the gradients rotated into the measurement basis :param basis: The bases in which the measurement is made :type basis: numpy.ndarray :param sample: The measurement (either 0 or 1) :type sample: torch.Tensor :returns: A list of two tensors, representing the rotated gradients of the amplitude and phase RBMs :rtype: list[torch.Tensor, torch.Tensor] """ UrhoU, UrhoU_v, v = unitaries.rotate_rho_probs( self, basis, sample, include_extras=True ) inv_UrhoU = 1 / (UrhoU + 1e-8) # avoid dividing by zero raw_grads = [self.am_grads(v), self.ph_grads(v)] rotated_grad = [ -cplx.einsum("ijb,ijbg->bg", UrhoU_v, g, imag_part=False) for g in raw_grads ] return [torch.einsum("b,bg->g", inv_UrhoU, g) for g in rotated_grad] def am_grads(self, v): r"""Computes the gradients of the amplitude RBM for given input states :param v: The first input state, :math:`\sigma` :type v: torch.Tensor :returns: The gradients of all amplitude RBM parameters :rtype: torch.Tensor """ return self.rbm_am.gamma_grad(v, v, eta=+1, expand=True) + self.pi_grad( v, v, phase=False, expand=True ) def ph_grads(self, v): r"""Computes the gradients of the phase RBM for given input states :param v: The first input state, :math:`\sigma` :type v: torch.Tensor :returns: The gradients of all phase RBM parameters :rtype: torch.Tensor """ return cplx.scalar_mult( # need to multiply Gamma- by i self.rbm_ph.gamma_grad(v, v, eta=-1, expand=True), cplx.I ) + self.pi_grad(v, v, phase=True, expand=True) def fit( self, data, epochs=100, pos_batch_size=100, neg_batch_size=None, k=1, lr=1, input_bases=None, progbar=False, starting_epoch=1, time=False, callbacks=None, optimizer=torch.optim.SGD, optimizer_args=None, scheduler=None, scheduler_args=None, **kwargs, ): if input_bases is None: raise ValueError("input_bases must be provided to train a DensityMatrix!") else: super().fit( data=data, epochs=epochs, pos_batch_size=pos_batch_size, neg_batch_size=neg_batch_size, k=k, lr=lr, input_bases=input_bases, progbar=progbar, starting_epoch=starting_epoch, time=time, callbacks=callbacks, optimizer=optimizer, optimizer_args=optimizer_args, scheduler=scheduler, scheduler_args=scheduler_args, **kwargs, ) @staticmethod def autoload(location, gpu=False): state_dict = torch.load(location) nn_state = DensityMatrix( unitary_dict=state_dict["unitary_dict"], num_visible=len(state_dict["rbm_am"]["visible_bias"]), num_hidden=len(state_dict["rbm_am"]["hidden_bias"]), num_aux=len(state_dict["rbm_am"]["aux_bias"]), gpu=gpu, ) nn_state.load(location) return nn_state

34.542857

105

0.585608

import warnings import torch from torch.nn import functional as F from qucumber import _warn_on_missing_gpu from qucumber.utils import cplx, unitaries from qucumber.rbm import PurificationRBM from .neural_state import NeuralStateBase class DensityMatrix(NeuralStateBase): _rbm_am = None _rbm_ph = None _device = None def __init__( self, num_visible, num_hidden=None, num_aux=None, unitary_dict=None, gpu=False, module=None, ): if gpu and torch.cuda.is_available(): warnings.warn( "Using DensityMatrix on GPU is not recommended due to poor performance compared to CPU.", ResourceWarning, 2, ) self.device = torch.device("cuda") else: self.device = torch.device("cpu") if module is None: self.rbm_am = PurificationRBM(num_visible, num_hidden, num_aux, gpu=gpu) self.rbm_ph = PurificationRBM(num_visible, num_hidden, num_aux, gpu=gpu) else: _warn_on_missing_gpu(gpu) self.rbm_am = module.to(self.device) self.rbm_am.device = self.device self.rbm_ph = module.to(self.device).clone() self.rbm_ph.device = self.device self.num_visible = self.rbm_am.num_visible self.num_hidden = self.rbm_am.num_hidden self.num_aux = self.rbm_am.num_aux self.device = self.rbm_am.device self.unitary_dict = unitary_dict if unitary_dict else unitaries.create_dict() self.unitary_dict = { k: v.to(device=self.device) for k, v in self.unitary_dict.items() } @property def networks(self): return ["rbm_am", "rbm_ph"] @property def rbm_am(self): return self._rbm_am @rbm_am.setter def rbm_am(self, new_val): self._rbm_am = new_val @property def rbm_ph(self): return self._rbm_ph @rbm_ph.setter def rbm_ph(self, new_val): self._rbm_ph = new_val @property def device(self): return self._device @device.setter def device(self, new_val): self._device = new_val def pi(self, v, vp, expand=True): m_am = F.linear(v, self.rbm_am.weights_U, self.rbm_am.aux_bias) mp_am = F.linear(vp, self.rbm_am.weights_U, self.rbm_am.aux_bias) m_ph = F.linear(v, self.rbm_ph.weights_U) mp_ph = F.linear(vp, self.rbm_ph.weights_U) if expand and v.dim() >= 2: m_am = m_am.unsqueeze_(1) m_ph = m_ph.unsqueeze_(1) if expand and vp.dim() >= 2: mp_am = mp_am.unsqueeze_(0) mp_ph = mp_ph.unsqueeze_(0) exp_arg = (m_am + mp_am) / 2 phase = (m_ph - mp_ph) / 2 real = ( (1 + 2 * exp_arg.exp() * phase.cos() + (2 * exp_arg).exp()) .sqrt() .log() .sum(-1) ) imag = torch.atan2( (exp_arg.exp() * phase.sin()), (1 + exp_arg.exp() * phase.cos()) ).sum(-1) return cplx.make_complex(real, imag) def pi_grad(self, v, vp, phase=False, expand=False): unsqueezed = v.dim() < 2 or vp.dim() < 2 v = (v.unsqueeze(0) if v.dim() < 2 else v).to(self.rbm_am.weights_W) vp = (vp.unsqueeze(0) if vp.dim() < 2 else vp).to(self.rbm_am.weights_W) if expand: arg_real = 0.5 * ( F.linear(v, self.rbm_am.weights_U, self.rbm_am.aux_bias).unsqueeze_(1) + F.linear(vp, self.rbm_am.weights_U, self.rbm_am.aux_bias).unsqueeze_( 0 ) ) arg_imag = 0.5 * ( F.linear(v, self.rbm_ph.weights_U).unsqueeze_(1) - F.linear(vp, self.rbm_ph.weights_U).unsqueeze_(0) ) else: arg_real = self.rbm_am.mixing_term(v + vp) arg_imag = self.rbm_ph.mixing_term(v - vp) sig = cplx.sigmoid(arg_real, arg_imag) batch_sizes = ( (v.shape[0], vp.shape[0], *v.shape[1:-1]) if expand else (*v.shape[:-1],) ) W_grad = torch.zeros_like(self.rbm_am.weights_W).expand(*batch_sizes, -1, -1) vb_grad = torch.zeros_like(self.rbm_am.visible_bias).expand(*batch_sizes, -1) hb_grad = torch.zeros_like(self.rbm_am.hidden_bias).expand(*batch_sizes, -1) if phase: temp = (v.unsqueeze(1) - vp.unsqueeze(0)) if expand else (v - vp) sig = cplx.scalar_mult(sig, cplx.I) ab_grad_real = torch.zeros_like(self.rbm_ph.aux_bias).expand( *batch_sizes, -1 ) ab_grad_imag = ab_grad_real.clone() else: temp = (v.unsqueeze(1) + vp.unsqueeze(0)) if expand else (v + vp) ab_grad_real = cplx.real(sig) ab_grad_imag = cplx.imag(sig) U_grad = 0.5 * torch.einsum("c...j,...k->c...jk", sig, temp) U_grad_real = cplx.real(U_grad) U_grad_imag = cplx.imag(U_grad) vec_real = [ W_grad.view(*batch_sizes, -1), U_grad_real.view(*batch_sizes, -1), vb_grad, hb_grad, ab_grad_real, ] vec_imag = [ W_grad.view(*batch_sizes, -1).clone(), U_grad_imag.view(*batch_sizes, -1), vb_grad.clone(), hb_grad.clone(), ab_grad_imag, ] if unsqueezed and not expand: vec_real = [grad.squeeze_(0) for grad in vec_real] vec_imag = [grad.squeeze_(0) for grad in vec_imag] return cplx.make_complex( torch.cat(vec_real, dim=-1), torch.cat(vec_imag, dim=-1) ) def rho(self, v, vp=None, expand=True): if expand is False and vp is None: return cplx.make_complex(self.probability(v)) elif vp is None: vp = v pi_ = self.pi(v, vp, expand=expand) amp = (self.rbm_am.gamma(v, vp, eta=+1, expand=expand) + cplx.real(pi_)).exp() phase = self.rbm_ph.gamma(v, vp, eta=-1, expand=expand) + cplx.imag(pi_) return cplx.make_complex(amp * phase.cos(), amp * phase.sin()) def importance_sampling_numerator(self, vp, v): return self.rho(vp, v, expand=False) def importance_sampling_denominator(self, v): return cplx.make_complex(self.probability(v)) def rotated_gradient(self, basis, sample): UrhoU, UrhoU_v, v = unitaries.rotate_rho_probs( self, basis, sample, include_extras=True ) inv_UrhoU = 1 / (UrhoU + 1e-8) raw_grads = [self.am_grads(v), self.ph_grads(v)] rotated_grad = [ -cplx.einsum("ijb,ijbg->bg", UrhoU_v, g, imag_part=False) for g in raw_grads ] return [torch.einsum("b,bg->g", inv_UrhoU, g) for g in rotated_grad] def am_grads(self, v): return self.rbm_am.gamma_grad(v, v, eta=+1, expand=True) + self.pi_grad( v, v, phase=False, expand=True ) def ph_grads(self, v): return cplx.scalar_mult( self.rbm_ph.gamma_grad(v, v, eta=-1, expand=True), cplx.I ) + self.pi_grad(v, v, phase=True, expand=True) def fit( self, data, epochs=100, pos_batch_size=100, neg_batch_size=None, k=1, lr=1, input_bases=None, progbar=False, starting_epoch=1, time=False, callbacks=None, optimizer=torch.optim.SGD, optimizer_args=None, scheduler=None, scheduler_args=None, **kwargs, ): if input_bases is None: raise ValueError("input_bases must be provided to train a DensityMatrix!") else: super().fit( data=data, epochs=epochs, pos_batch_size=pos_batch_size, neg_batch_size=neg_batch_size, k=k, lr=lr, input_bases=input_bases, progbar=progbar, starting_epoch=starting_epoch, time=time, callbacks=callbacks, optimizer=optimizer, optimizer_args=optimizer_args, scheduler=scheduler, scheduler_args=scheduler_args, **kwargs, ) @staticmethod def autoload(location, gpu=False): state_dict = torch.load(location) nn_state = DensityMatrix( unitary_dict=state_dict["unitary_dict"], num_visible=len(state_dict["rbm_am"]["visible_bias"]), num_hidden=len(state_dict["rbm_am"]["hidden_bias"]), num_aux=len(state_dict["rbm_am"]["aux_bias"]), gpu=gpu, ) nn_state.load(location) return nn_state

true

f70c16978f371e597599b34590bfb00c27d46526

1,589

py

Python

venv/lib/python3.8/site-packages/vsts/task_agent/v4_1/models/task_group_definition.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

null

venv/lib/python3.8/site-packages/vsts/task_agent/v4_1/models/task_group_definition.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

null

venv/lib/python3.8/site-packages/vsts/task_agent/v4_1/models/task_group_definition.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

2

2021-05-23T16:46:31.000Z

2021-05-26T23:51:09.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # Generated file, DO NOT EDIT # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------------------------- from msrest.serialization import Model class TaskGroupDefinition(Model): """TaskGroupDefinition. :param display_name: :type display_name: str :param is_expanded: :type is_expanded: bool :param name: :type name: str :param tags: :type tags: list of str :param visible_rule: :type visible_rule: str """ _attribute_map = { 'display_name': {'key': 'displayName', 'type': 'str'}, 'is_expanded': {'key': 'isExpanded', 'type': 'bool'}, 'name': {'key': 'name', 'type': 'str'}, 'tags': {'key': 'tags', 'type': '[str]'}, 'visible_rule': {'key': 'visibleRule', 'type': 'str'} } def __init__(self, display_name=None, is_expanded=None, name=None, tags=None, visible_rule=None): super(TaskGroupDefinition, self).__init__() self.display_name = display_name self.is_expanded = is_expanded self.name = name self.tags = tags self.visible_rule = visible_rule

37.833333

102

0.512901

from msrest.serialization import Model class TaskGroupDefinition(Model): _attribute_map = { 'display_name': {'key': 'displayName', 'type': 'str'}, 'is_expanded': {'key': 'isExpanded', 'type': 'bool'}, 'name': {'key': 'name', 'type': 'str'}, 'tags': {'key': 'tags', 'type': '[str]'}, 'visible_rule': {'key': 'visibleRule', 'type': 'str'} } def __init__(self, display_name=None, is_expanded=None, name=None, tags=None, visible_rule=None): super(TaskGroupDefinition, self).__init__() self.display_name = display_name self.is_expanded = is_expanded self.name = name self.tags = tags self.visible_rule = visible_rule

true

f70c16f14cc1f04dbb6d4ae81ba7699c84f9eca6

2,431

py

Python

apps/core/views.py

RobertArzolaC/base_django

3fc368000b418d387ccb57b30fa223ac916f2895

[ "MIT" ]

null

apps/core/views.py

RobertArzolaC/base_django

3fc368000b418d387ccb57b30fa223ac916f2895

[ "MIT" ]

7

2020-02-12T00:30:41.000Z

2022-02-10T08:03:46.000Z

apps/core/views.py

RobertArzolaC/base_django

3fc368000b418d387ccb57b30fa223ac916f2895

[ "MIT" ]

2

2020-09-21T23:32:11.000Z

2021-01-10T17:29:24.000Z

from django.contrib.auth.models import User from django.contrib.auth import authenticate, login from rest_framework import generics from rest_framework import permissions from rest_framework.views import status from rest_framework.response import Response from rest_framework_jwt.settings import api_settings from .serializers import TokenSerializer, UserSerializer # Get the JWT settings jwt_payload_handler = api_settings.JWT_PAYLOAD_HANDLER jwt_encode_handler = api_settings.JWT_ENCODE_HANDLER # Create your views here. class LoginView(generics.CreateAPIView): """ POST auth/login/ """ # This permission class will over ride the global permission # class setting permission_classes = (permissions.AllowAny,) queryset = User.objects.all() def post(self, request, *args, **kwargs): username = request.data.get("username", "") password = request.data.get("password", "") user = authenticate(request, username=username, password=password) if user is not None: # login saves the user’s ID in the session, # using Django’s session framework. login(request, user) serializer = TokenSerializer(data={ # using drf jwt utility functions to generate a token "token": jwt_encode_handler( jwt_payload_handler(user) )}) serializer.is_valid() return Response(data=serializer.data, status=status.HTTP_200_OK) return Response(status=status.HTTP_401_UNAUTHORIZED) class RegisterUsers(generics.CreateAPIView): """ POST auth/register/ """ permission_classes = (permissions.AllowAny,) def post(self, request, *args, **kwargs): username = request.data.get("username", "") password = request.data.get("password", "") email = request.data.get("email", "") if not username and not password and not email: return Response( data={ "message": "username, password and email is required to register a user" }, status=status.HTTP_400_BAD_REQUEST ) new_user = User.objects.create_user( username=username, password=password, email=email ) return Response( data=UserSerializer(new_user).data, status=status.HTTP_201_CREATED )

33.763889

92

0.653229

from django.contrib.auth.models import User from django.contrib.auth import authenticate, login from rest_framework import generics from rest_framework import permissions from rest_framework.views import status from rest_framework.response import Response from rest_framework_jwt.settings import api_settings from .serializers import TokenSerializer, UserSerializer jwt_payload_handler = api_settings.JWT_PAYLOAD_HANDLER jwt_encode_handler = api_settings.JWT_ENCODE_HANDLER class LoginView(generics.CreateAPIView): permission_classes = (permissions.AllowAny,) queryset = User.objects.all() def post(self, request, *args, **kwargs): username = request.data.get("username", "") password = request.data.get("password", "") user = authenticate(request, username=username, password=password) if user is not None: login(request, user) serializer = TokenSerializer(data={ "token": jwt_encode_handler( jwt_payload_handler(user) )}) serializer.is_valid() return Response(data=serializer.data, status=status.HTTP_200_OK) return Response(status=status.HTTP_401_UNAUTHORIZED) class RegisterUsers(generics.CreateAPIView): permission_classes = (permissions.AllowAny,) def post(self, request, *args, **kwargs): username = request.data.get("username", "") password = request.data.get("password", "") email = request.data.get("email", "") if not username and not password and not email: return Response( data={ "message": "username, password and email is required to register a user" }, status=status.HTTP_400_BAD_REQUEST ) new_user = User.objects.create_user( username=username, password=password, email=email ) return Response( data=UserSerializer(new_user).data, status=status.HTTP_201_CREATED )

true

f70c1a0ce799afb0a85a95fec93286da54ddca94

4,890

py

Python

src/primaires/scripting/actions/remplir.py

vlegoff/tsunami

36b3b974f6eefbf15cd5d5f099fc14630e66570b

[ "BSD-3-Clause" ]

14

2015-08-21T19:15:21.000Z

2017-11-26T13:59:17.000Z

src/primaires/scripting/actions/remplir.py

vincent-lg/tsunami

36b3b974f6eefbf15cd5d5f099fc14630e66570b

[ "BSD-3-Clause" ]

20

2015-09-29T20:50:45.000Z

2018-06-21T12:58:30.000Z

src/primaires/scripting/actions/remplir.py

vlegoff/tsunami

36b3b974f6eefbf15cd5d5f099fc14630e66570b

[ "BSD-3-Clause" ]

3

2015-05-02T19:42:03.000Z

2018-09-06T10:55:00.000Z

# -*-coding:Utf-8 -* # Copyright (c) 2010-2017 LE GOFF Vincent # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # * Neither the name of 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 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. """Fichier contenant l'action remplir.""" from primaires.scripting.action import Action from primaires.scripting.instruction import ErreurExecution class ClasseAction(Action): """Remplit un conteneur de nourriture ou de potion.""" @classmethod def init_types(cls): cls.ajouter_types(cls.remplir_objet, "Objet", "Objet") cls.ajouter_types(cls.remplir_proto_nb, "Objet", "str", "Fraction") @staticmethod def remplir_objet(conteneur, objet): """Met l'objet dans le conteneur de nourriture. Attention, l'objet conteneur ne peut en aucun cas être "flottant" mais doit lui-même être contenu quelque part (sol d'une salle, inventaire d'un personnage, autre conteneur...). """ if not conteneur.contenu: raise ErreurExecution("{} n'est contenu nul part".format( conteneur.get_nom())) if conteneur.est_de_type("conteneur de potion"): if conteneur.potion: raise ErreurExecution("{} est plein".format( conteneur.get_nom())) if objet.contenu: objet.contenu.retirer(objet) conteneur.potion = objet conteneur.onces = conteneur.onces_max return if not conteneur.est_de_type("conteneur de nourriture"): raise ErreurExecution("{} n'est pas un conteneur".format( conteneur.get_nom())) if objet.poids_unitaire > conteneur.poids_max: raise ErreurExecution("{} est plein".format(conteneur.get_nom())) if objet.contenu: objet.contenu.retirer(objet) conteneur.nourriture.append(objet) @staticmethod def remplir_proto_nb(conteneur, prototype, nb): """Pose dans le conteneur nb objets du prototype précisé. Attention, l'objet conteneur ne peut en aucun cas être "flottant" mais doit lui-même être contenu quelque part (sol d'une salle, inventaire d'un personnage, autre conteneur...). """ nb = int(nb) if not prototype in importeur.objet.prototypes: raise ErreurExecution("prototype {} introuvable".format(prototype)) prototype = importeur.objet.prototypes[prototype] if not conteneur.contenu: raise ErreurExecution("{} n'est contenu nul part".format( conteneur.get_nom())) if conteneur.est_de_type("conteneur de potion"): if conteneur.potion: raise ErreurExecution("{} est plein".format( conteneur.get_nom())) objet = importeur.objet.creer_objet(prototype) conteneur.potion = objet conteneur.onces = conteneur.onces_max return if not conteneur.est_de_type("conteneur de nourriture"): raise ErreurExecution("{} n'est pas un conteneur".format( conteneur.get_nom())) poids_total = 0 for i in range(nb): poids_total += prototype.poids if poids_total > conteneur.poids_max: raise ErreurExecution("{} est plein".format( conteneur.get_nom())) objet = importeur.objet.creer_objet(prototype) conteneur.nourriture.append(objet)

44.054054

79

0.666871

from primaires.scripting.action import Action from primaires.scripting.instruction import ErreurExecution class ClasseAction(Action): @classmethod def init_types(cls): cls.ajouter_types(cls.remplir_objet, "Objet", "Objet") cls.ajouter_types(cls.remplir_proto_nb, "Objet", "str", "Fraction") @staticmethod def remplir_objet(conteneur, objet): if not conteneur.contenu: raise ErreurExecution("{} n'est contenu nul part".format( conteneur.get_nom())) if conteneur.est_de_type("conteneur de potion"): if conteneur.potion: raise ErreurExecution("{} est plein".format( conteneur.get_nom())) if objet.contenu: objet.contenu.retirer(objet) conteneur.potion = objet conteneur.onces = conteneur.onces_max return if not conteneur.est_de_type("conteneur de nourriture"): raise ErreurExecution("{} n'est pas un conteneur".format( conteneur.get_nom())) if objet.poids_unitaire > conteneur.poids_max: raise ErreurExecution("{} est plein".format(conteneur.get_nom())) if objet.contenu: objet.contenu.retirer(objet) conteneur.nourriture.append(objet) @staticmethod def remplir_proto_nb(conteneur, prototype, nb): nb = int(nb) if not prototype in importeur.objet.prototypes: raise ErreurExecution("prototype {} introuvable".format(prototype)) prototype = importeur.objet.prototypes[prototype] if not conteneur.contenu: raise ErreurExecution("{} n'est contenu nul part".format( conteneur.get_nom())) if conteneur.est_de_type("conteneur de potion"): if conteneur.potion: raise ErreurExecution("{} est plein".format( conteneur.get_nom())) objet = importeur.objet.creer_objet(prototype) conteneur.potion = objet conteneur.onces = conteneur.onces_max return if not conteneur.est_de_type("conteneur de nourriture"): raise ErreurExecution("{} n'est pas un conteneur".format( conteneur.get_nom())) poids_total = 0 for i in range(nb): poids_total += prototype.poids if poids_total > conteneur.poids_max: raise ErreurExecution("{} est plein".format( conteneur.get_nom())) objet = importeur.objet.creer_objet(prototype) conteneur.nourriture.append(objet)

true

f70c1a7906ca430885a53e98cf618a681cf5345c

1,871

py

Python

nova/tests/unit/objects/test_numa.py

gabriel-samfira/nova

5ef07cc04dbf0216452ae358e57d9ddac51f1803

[ "Apache-2.0" ]

null

nova/tests/unit/objects/test_numa.py

gabriel-samfira/nova

5ef07cc04dbf0216452ae358e57d9ddac51f1803

[ "Apache-2.0" ]

null

nova/tests/unit/objects/test_numa.py

gabriel-samfira/nova

5ef07cc04dbf0216452ae358e57d9ddac51f1803

[ "Apache-2.0" ]

null

# 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 nova import objects from nova.tests.unit.objects import test_objects fake_obj_numa = objects.NUMATopology( cells=[ objects.NUMACell( id=0, cpuset=set([1, 2]), memory=512, cpu_usage=2, memory_usage=256), objects.NUMACell( id=1, cpuset=set([3, 4]), memory=512, cpu_usage=1, memory_usage=128)]) class _TestNUMA(object): def test_convert_wipe(self): d1 = fake_obj_numa._to_dict() d2 = objects.NUMATopology.obj_from_primitive(d1)._to_dict() self.assertEqual(d1, d2) def test_pinning_logic(self): obj = objects.NUMATopology(cells=[ objects.NUMACell( id=0, cpuset=set([1, 2]), memory=512, cpu_usage=2, memory_usage=256, pinned_cpus=set([1])), objects.NUMACell( id=1, cpuset=set([3, 4]), memory=512, cpu_usage=1, memory_usage=128, pinned_cpus=set([])) ] ) self.assertEqual(set([2]), obj.cells[0].free_cpus) self.assertEqual(set([3, 4]), obj.cells[1].free_cpus) class TestNUMA(test_objects._LocalTest, _TestNUMA): pass class TestNUMARemote(test_objects._RemoteTest, _TestNUMA): pass

31.711864

78

0.621058

from nova import objects from nova.tests.unit.objects import test_objects fake_obj_numa = objects.NUMATopology( cells=[ objects.NUMACell( id=0, cpuset=set([1, 2]), memory=512, cpu_usage=2, memory_usage=256), objects.NUMACell( id=1, cpuset=set([3, 4]), memory=512, cpu_usage=1, memory_usage=128)]) class _TestNUMA(object): def test_convert_wipe(self): d1 = fake_obj_numa._to_dict() d2 = objects.NUMATopology.obj_from_primitive(d1)._to_dict() self.assertEqual(d1, d2) def test_pinning_logic(self): obj = objects.NUMATopology(cells=[ objects.NUMACell( id=0, cpuset=set([1, 2]), memory=512, cpu_usage=2, memory_usage=256, pinned_cpus=set([1])), objects.NUMACell( id=1, cpuset=set([3, 4]), memory=512, cpu_usage=1, memory_usage=128, pinned_cpus=set([])) ] ) self.assertEqual(set([2]), obj.cells[0].free_cpus) self.assertEqual(set([3, 4]), obj.cells[1].free_cpus) class TestNUMA(test_objects._LocalTest, _TestNUMA): pass class TestNUMARemote(test_objects._RemoteTest, _TestNUMA): pass

true

f70c1a9e14e34afc1f891dc6e43eba44f38e5062

6,564

py

Python

tests/shell/test_configadmin.py

svidoso/ipopo

1d4b81207e67890dfccc8f562336c7104f194c17

[ "Apache-2.0" ]

65

2015-04-21T10:41:18.000Z

2022-01-02T16:25:40.000Z

tests/shell/test_configadmin.py

svidoso/ipopo

1d4b81207e67890dfccc8f562336c7104f194c17

[ "Apache-2.0" ]

85

2015-01-20T14:23:52.000Z

2022-02-19T17:08:46.000Z

tests/shell/test_configadmin.py

svidoso/ipopo

1d4b81207e67890dfccc8f562336c7104f194c17

[ "Apache-2.0" ]

32

2015-03-13T07:43:05.000Z

2020-04-24T07:56:53.000Z

#!/usr/bin/env python # -- Content-Encoding: UTF-8 -- """ Tests the ConfigurationAdmin shell commands :author: Thomas Calmant """ # Pelix import pelix.framework import pelix.services import pelix.shell import pelix.shell.beans as beans # Standard library import os try: from StringIO import StringIO except ImportError: from io import StringIO try: import unittest2 as unittest except ImportError: import unittest # ------------------------------------------------------------------------------ __version_info__ = (1, 0, 1) __version__ = ".".join(str(x) for x in __version_info__) # ------------------------------------------------------------------------------ class ConfigAdminShellTest(unittest.TestCase): """ Tests the EventAdmin shell commands """ def setUp(self): """ Prepares a framework and a registers a service to export """ # Use a local configuration folder conf_folder = os.path.join(os.path.dirname(__file__), "conf") # Create the framework self.framework = pelix.framework.create_framework( ('pelix.ipopo.core', 'pelix.shell.core', 'pelix.services.configadmin', 'pelix.shell.configadmin'), {'configuration.folder': conf_folder}) self.framework.start() # Get the Shell service context = self.framework.get_bundle_context() svc_ref = context.get_service_reference(pelix.shell.SERVICE_SHELL) self.shell = context.get_service(svc_ref) # Instantiate the EventAdmin component context = self.framework.get_bundle_context() # Get the service self.config_ref = context.get_service_reference( pelix.services.SERVICE_CONFIGURATION_ADMIN) self.config = context.get_service(self.config_ref) # Remove existing configurations for config in self.config.list_configurations(): config.delete() def _run_command(self, command, *args): """ Runs the given shell command """ # String output str_output = StringIO() # Format command if args: command = command.format(*args) # Add the namespace prefix command = 'config.{0}'.format(command) # Run command session = beans.ShellSession(beans.IOHandler(None, str_output)) self.shell.execute(command, session) return str_output.getvalue() def tearDown(self): """ Cleans up for next test """ # Remove existing configurations for config in self.config.list_configurations(): config.delete() # Stop the framework pelix.framework.FrameworkFactory.delete_framework() self.framework = None def testLifeCycle(self): """ Tests a configuration life cycle """ # Create a factory configuration key = "testConfig" first_value = "first" factory_name = "testFactory" output = self._run_command("create {0} {1}={2}", factory_name, key, first_value) # Get the generated configuration config = next(iter(self.config.list_configurations())) # Check validity self.assertIn(config.get_pid(), output) self.assertEqual(factory_name, config.get_factory_pid()) self.assertDictContainsSubset({key: first_value}, config.get_properties()) # Update it second_value = "second" self._run_command("update {0} {1}={2}", config.get_pid(), key, second_value) self.assertDictContainsSubset({key: second_value}, config.get_properties()) # Reload it self._run_command("reload {0}", config.get_pid()) # List it output = self._run_command('list') self.assertIn(config.get_pid(), output) output = self._run_command('list {0}', config.get_pid()) self.assertIn(config.get_pid(), output) # Delete it self._run_command("delete {0}", config.get_pid()) self.assertEqual(self.config.list_configurations(), set()) def testInvalidPid(self): """ Tests commands with invalid PIDs """ self._run_command("delete <invalid>") self._run_command("list <invalid>") self._run_command("reload <invalid>") def testUpdate(self): """ Tests the update command """ pid = "testPid" key = "testConfig" value = "testValue" # Create the configuration, with no property self._run_command("update {0}", pid) # Get the generated configuration config = next(iter(self.config.list_configurations())) self.assertEqual(config.get_pid(), pid) self.assertIsNone(config.get_properties()) # Set a key self._run_command("update {0} {1}={2}", pid, key, value) self.assertDictContainsSubset({key: value}, config.get_properties()) # Remove a key self._run_command("update {0} {1}=None", pid, key) self.assertNotIn(key, config.get_properties()) def testList(self): """ Other tests for the list command """ pid = "testPid" pid2 = "testPidBis" key = "testConfig" value = "testValue" # Nothing at first output = self._run_command("list") self.assertIn("No configuration", output) # List inexistent PID output = self._run_command("list {0}", pid) self.assertIn("No configuration", output) # Create a configuration without properties config = self.config.get_configuration(pid) # List it output = self._run_command("list {0}", pid) self.assertIn("Not yet updated", output) # Update it config.update({key: value}) output = self._run_command("list {0}", pid) self.assertIn(pid, output) self.assertIn(key, output) self.assertIn(value, output) # Create a second one config2 = self.config.get_configuration(pid2) # Delete the first one config.delete() self.assertNotIn(config, self.config.list_configurations()) self.assertIn(config2, self.config.list_configurations()) # List it output = self._run_command("list {0}", pid) self.assertIn("No configuration", output) self.assertIn(pid, output)

30.248848

80

0.591865

import pelix.framework import pelix.services import pelix.shell import pelix.shell.beans as beans import os try: from StringIO import StringIO except ImportError: from io import StringIO try: import unittest2 as unittest except ImportError: import unittest __version_info__ = (1, 0, 1) __version__ = ".".join(str(x) for x in __version_info__) class ConfigAdminShellTest(unittest.TestCase): def setUp(self): conf_folder = os.path.join(os.path.dirname(__file__), "conf") self.framework = pelix.framework.create_framework( ('pelix.ipopo.core', 'pelix.shell.core', 'pelix.services.configadmin', 'pelix.shell.configadmin'), {'configuration.folder': conf_folder}) self.framework.start() context = self.framework.get_bundle_context() svc_ref = context.get_service_reference(pelix.shell.SERVICE_SHELL) self.shell = context.get_service(svc_ref) context = self.framework.get_bundle_context() self.config_ref = context.get_service_reference( pelix.services.SERVICE_CONFIGURATION_ADMIN) self.config = context.get_service(self.config_ref) for config in self.config.list_configurations(): config.delete() def _run_command(self, command, *args): str_output = StringIO() if args: command = command.format(*args) command = 'config.{0}'.format(command) session = beans.ShellSession(beans.IOHandler(None, str_output)) self.shell.execute(command, session) return str_output.getvalue() def tearDown(self): for config in self.config.list_configurations(): config.delete() pelix.framework.FrameworkFactory.delete_framework() self.framework = None def testLifeCycle(self): key = "testConfig" first_value = "first" factory_name = "testFactory" output = self._run_command("create {0} {1}={2}", factory_name, key, first_value) config = next(iter(self.config.list_configurations())) self.assertIn(config.get_pid(), output) self.assertEqual(factory_name, config.get_factory_pid()) self.assertDictContainsSubset({key: first_value}, config.get_properties()) second_value = "second" self._run_command("update {0} {1}={2}", config.get_pid(), key, second_value) self.assertDictContainsSubset({key: second_value}, config.get_properties()) self._run_command("reload {0}", config.get_pid()) output = self._run_command('list') self.assertIn(config.get_pid(), output) output = self._run_command('list {0}', config.get_pid()) self.assertIn(config.get_pid(), output) self._run_command("delete {0}", config.get_pid()) self.assertEqual(self.config.list_configurations(), set()) def testInvalidPid(self): self._run_command("delete <invalid>") self._run_command("list <invalid>") self._run_command("reload <invalid>") def testUpdate(self): pid = "testPid" key = "testConfig" value = "testValue" self._run_command("update {0}", pid) config = next(iter(self.config.list_configurations())) self.assertEqual(config.get_pid(), pid) self.assertIsNone(config.get_properties()) self._run_command("update {0} {1}={2}", pid, key, value) self.assertDictContainsSubset({key: value}, config.get_properties()) self._run_command("update {0} {1}=None", pid, key) self.assertNotIn(key, config.get_properties()) def testList(self): pid = "testPid" pid2 = "testPidBis" key = "testConfig" value = "testValue" output = self._run_command("list") self.assertIn("No configuration", output) output = self._run_command("list {0}", pid) self.assertIn("No configuration", output) config = self.config.get_configuration(pid) output = self._run_command("list {0}", pid) self.assertIn("Not yet updated", output) config.update({key: value}) output = self._run_command("list {0}", pid) self.assertIn(pid, output) self.assertIn(key, output) self.assertIn(value, output) config2 = self.config.get_configuration(pid2) config.delete() self.assertNotIn(config, self.config.list_configurations()) self.assertIn(config2, self.config.list_configurations()) output = self._run_command("list {0}", pid) self.assertIn("No configuration", output) self.assertIn(pid, output)

true

f70c1c985b94a376cb5ae57f8f742ff430e60c99

8,056

py

Python

src/models/densenet/model.py

gsc2001/ConvexNet

a17609bd5bca0a02b6330b1ad8035f2b280109f0

[ "MIT" ]

null

src/models/densenet/model.py

gsc2001/ConvexNet

a17609bd5bca0a02b6330b1ad8035f2b280109f0

[ "MIT" ]

null

src/models/densenet/model.py

gsc2001/ConvexNet

a17609bd5bca0a02b6330b1ad8035f2b280109f0

[ "MIT" ]

null

""" Vanilla DenseNet implementation Paper: https://arxiv.org/abs/1608.06993 Implementation taken from: https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py """ import re from collections import OrderedDict from functools import partial from typing import Any, List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as cp from torch import Tensor class _DenseLayer(nn.Module): def __init__( self, num_input_features: int, growth_rate: int, bn_size: int, drop_rate: float, memory_efficient: bool = False ) -> None: super().__init__() self.norm1: nn.BatchNorm2d self.add_module("norm1", nn.BatchNorm2d(num_input_features)) self.relu1: nn.ReLU self.add_module("relu1", nn.ReLU(inplace=True)) self.conv1: nn.Conv2d self.add_module( "conv1", nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False) ) self.norm2: nn.BatchNorm2d self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate)) self.relu2: nn.ReLU self.add_module("relu2", nn.ReLU(inplace=True)) self.conv2: nn.Conv2d self.add_module( "conv2", nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False) ) self.drop_rate = float(drop_rate) self.memory_efficient = memory_efficient def bn_function(self, inputs: List[Tensor]) -> Tensor: concated_features = torch.cat(inputs, 1) bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) # noqa: T484 return bottleneck_output # todo: rewrite when torchscript supports any def any_requires_grad(self, input: List[Tensor]) -> bool: for tensor in input: if tensor.requires_grad: return True return False @torch.jit.unused # noqa: T484 def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor: def closure(*inputs): return self.bn_function(inputs) return cp.checkpoint(closure, *input) @torch.jit._overload_method # noqa: F811 def forward(self, input: List[Tensor]) -> Tensor: # noqa: F811 pass @torch.jit._overload_method # noqa: F811 def forward(self, input: Tensor) -> Tensor: # noqa: F811 pass # torchscript does not yet support *args, so we overload method # allowing it to take either a List[Tensor] or single Tensor def forward(self, input: Tensor) -> Tensor: # noqa: F811 if isinstance(input, Tensor): prev_features = [input] else: prev_features = input if self.memory_efficient and self.any_requires_grad(prev_features): if torch.jit.is_scripting(): raise Exception("Memory Efficient not supported in JIT") bottleneck_output = self.call_checkpoint_bottleneck(prev_features) else: bottleneck_output = self.bn_function(prev_features) new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) if self.drop_rate > 0: new_features = F.dropout(new_features, p=self.drop_rate, training=self.training) return new_features class _DenseBlock(nn.ModuleDict): _version = 2 def __init__( self, num_layers: int, num_input_features: int, bn_size: int, growth_rate: int, drop_rate: float, memory_efficient: bool = False, ) -> None: super().__init__() for i in range(num_layers): layer = _DenseLayer( num_input_features + i * growth_rate, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate, memory_efficient=memory_efficient, ) self.add_module("denselayer%d" % (i + 1), layer) def forward(self, init_features: Tensor) -> Tensor: features = [init_features] for name, layer in self.items(): new_features = layer(features) features.append(new_features) return torch.cat(features, 1) class _Transition(nn.Sequential): def __init__(self, num_input_features: int, num_output_features: int) -> None: super().__init__() self.add_module("norm", nn.BatchNorm2d(num_input_features)) self.add_module("relu", nn.ReLU(inplace=True)) self.add_module("conv", nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False)) self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2)) class DenseNet(nn.Module): r"""Densenet-BC model class, based on `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_. Args: growth_rate (int) - how many filters to add each layer (`k` in paper) block_config (list of 4 ints) - how many layers in each pooling block num_init_features (int) - the number of filters to learn in the first convolution layer bn_size (int) - multiplicative factor for number of bottle neck layers (i.e. bn_size * k features in the bottleneck layer) drop_rate (float) - dropout rate after each dense layer num_classes (int) - number of classification classes memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_. """ def __init__( self, growth_rate: int = 32, block_config: Tuple[int, int, int, int] = (6, 12, 24, 16), num_init_features: int = 64, bn_size: int = 4, drop_rate: float = 0, num_classes: int = 1000, memory_efficient: bool = False, ) -> None: super().__init__() # First convolution self.features = nn.Sequential( OrderedDict( [ ("conv0", nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)), ("norm0", nn.BatchNorm2d(num_init_features)), ("relu0", nn.ReLU(inplace=True)), ("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), ] ) ) # Each denseblock num_features = num_init_features for i, num_layers in enumerate(block_config): block = _DenseBlock( num_layers=num_layers, num_input_features=num_features, bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate, memory_efficient=memory_efficient, ) self.features.add_module("denseblock%d" % (i + 1), block) num_features = num_features + num_layers * growth_rate if i != len(block_config) - 1: trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2) self.features.add_module("transition%d" % (i + 1), trans) num_features = num_features // 2 # Final batch norm self.features.add_module("norm5", nn.BatchNorm2d(num_features)) # Linear layer self.classifier = nn.Linear(num_features, num_classes) # Official init from torch repo. for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.constant_(m.bias, 0) def forward(self, x: Tensor) -> Tensor: features = self.features(x) out = F.relu(features, inplace=True) out = F.adaptive_avg_pool2d(out, (1, 1)) out = torch.flatten(out, 1) out = self.classifier(out) return out

38.180095

120

0.620531

import re from collections import OrderedDict from functools import partial from typing import Any, List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as cp from torch import Tensor class _DenseLayer(nn.Module): def __init__( self, num_input_features: int, growth_rate: int, bn_size: int, drop_rate: float, memory_efficient: bool = False ) -> None: super().__init__() self.norm1: nn.BatchNorm2d self.add_module("norm1", nn.BatchNorm2d(num_input_features)) self.relu1: nn.ReLU self.add_module("relu1", nn.ReLU(inplace=True)) self.conv1: nn.Conv2d self.add_module( "conv1", nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False) ) self.norm2: nn.BatchNorm2d self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate)) self.relu2: nn.ReLU self.add_module("relu2", nn.ReLU(inplace=True)) self.conv2: nn.Conv2d self.add_module( "conv2", nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False) ) self.drop_rate = float(drop_rate) self.memory_efficient = memory_efficient def bn_function(self, inputs: List[Tensor]) -> Tensor: concated_features = torch.cat(inputs, 1) bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) return bottleneck_output def any_requires_grad(self, input: List[Tensor]) -> bool: for tensor in input: if tensor.requires_grad: return True return False @torch.jit.unused def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor: def closure(*inputs): return self.bn_function(inputs) return cp.checkpoint(closure, *input) @torch.jit._overload_method def forward(self, input: List[Tensor]) -> Tensor: pass @torch.jit._overload_method def forward(self, input: Tensor) -> Tensor: pass def forward(self, input: Tensor) -> Tensor: if isinstance(input, Tensor): prev_features = [input] else: prev_features = input if self.memory_efficient and self.any_requires_grad(prev_features): if torch.jit.is_scripting(): raise Exception("Memory Efficient not supported in JIT") bottleneck_output = self.call_checkpoint_bottleneck(prev_features) else: bottleneck_output = self.bn_function(prev_features) new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) if self.drop_rate > 0: new_features = F.dropout(new_features, p=self.drop_rate, training=self.training) return new_features class _DenseBlock(nn.ModuleDict): _version = 2 def __init__( self, num_layers: int, num_input_features: int, bn_size: int, growth_rate: int, drop_rate: float, memory_efficient: bool = False, ) -> None: super().__init__() for i in range(num_layers): layer = _DenseLayer( num_input_features + i * growth_rate, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate, memory_efficient=memory_efficient, ) self.add_module("denselayer%d" % (i + 1), layer) def forward(self, init_features: Tensor) -> Tensor: features = [init_features] for name, layer in self.items(): new_features = layer(features) features.append(new_features) return torch.cat(features, 1) class _Transition(nn.Sequential): def __init__(self, num_input_features: int, num_output_features: int) -> None: super().__init__() self.add_module("norm", nn.BatchNorm2d(num_input_features)) self.add_module("relu", nn.ReLU(inplace=True)) self.add_module("conv", nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False)) self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2)) class DenseNet(nn.Module): def __init__( self, growth_rate: int = 32, block_config: Tuple[int, int, int, int] = (6, 12, 24, 16), num_init_features: int = 64, bn_size: int = 4, drop_rate: float = 0, num_classes: int = 1000, memory_efficient: bool = False, ) -> None: super().__init__() self.features = nn.Sequential( OrderedDict( [ ("conv0", nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)), ("norm0", nn.BatchNorm2d(num_init_features)), ("relu0", nn.ReLU(inplace=True)), ("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), ] ) ) num_features = num_init_features for i, num_layers in enumerate(block_config): block = _DenseBlock( num_layers=num_layers, num_input_features=num_features, bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate, memory_efficient=memory_efficient, ) self.features.add_module("denseblock%d" % (i + 1), block) num_features = num_features + num_layers * growth_rate if i != len(block_config) - 1: trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2) self.features.add_module("transition%d" % (i + 1), trans) num_features = num_features // 2 self.features.add_module("norm5", nn.BatchNorm2d(num_features)) self.classifier = nn.Linear(num_features, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.constant_(m.bias, 0) def forward(self, x: Tensor) -> Tensor: features = self.features(x) out = F.relu(features, inplace=True) out = F.adaptive_avg_pool2d(out, (1, 1)) out = torch.flatten(out, 1) out = self.classifier(out) return out

true

f70c1d6a132dc5c4f214794bc2ddfb198c8735bf

10,736

py

Python

modules/andforensics_connector.py

KimVegetable/carpe

8325b680898970c02e1fcfc1929490bf31b9ea49

[ "Apache-2.0" ]

null

modules/andforensics_connector.py

KimVegetable/carpe

8325b680898970c02e1fcfc1929490bf31b9ea49

[ "Apache-2.0" ]

null

modules/andforensics_connector.py

KimVegetable/carpe

8325b680898970c02e1fcfc1929490bf31b9ea49

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """module for android forensics.""" import os import io import subprocess import sqlite3 from datetime import datetime from modules import logger from modules import manager from modules import interface class AndForensicsConnector(interface.ModuleConnector): NAME = 'andforensics_connector' DESCRIPTION = 'Module for android' TABLE_NAME = 'lv1_os_android_andforensics' _plugin_classes = {} def __init__(self): super(AndForensicsConnector, self).__init__() def Connect(self, par_id, configuration, source_path_spec, knowledge_base): """Connector to connect to AndForensics. Args: par_id: partition id. configuration: configuration values. source_path_spec (dfvfs.PathSpec): path specification of the source file. knowledge_base (KnowledgeBase): knowledge base. """ # 이미지를 복사해와야함 andforensics if os.path.exists(configuration.source_path): cmd = 'python3.6 /home/byeongchan/modules/andForensics/andForensics.py -i \'{0:s}\' -o \'{1:s}\' ' \ '-proc {2:d}'.format(os.path.dirname(configuration.source_path), configuration.tmp_path + os.sep + 'andForensics', 10) proc = subprocess.Popen(cmd, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE) ret_code = proc.stdout.read() f = io.StringIO(str(ret_code)) result_msg = f.readline() print(result_msg) f.close() if result_msg[-14:-3] == 'Process End': base_name = os.path.basename(configuration.source_path) output_path = configuration.tmp_path + os.sep + 'andForensics' + os.sep \ + os.path.basename(configuration.source_path) analysis_db_path = output_path + os.sep + 'analysis_' + base_name + '.db' load_db_path = output_path + os.sep + 'loaddb_' + base_name + '.db' preprocess_db_path = output_path + os.sep + 'preprocess_' + base_name + '.db' this_file_path = os.path.dirname( os.path.abspath(__file__)) + os.sep + 'schema' + os.sep + 'android' + os.sep yaml_list = [this_file_path + 'lv1_os_and_app_list.yaml', this_file_path + 'lv1_os_and_call_history.yaml', this_file_path + 'lv1_os_and_emb_file.yaml', this_file_path + 'lv1_os_and_file_history.yaml', this_file_path + 'lv1_os_and_geodata.yaml', this_file_path + 'lv1_os_and_id_pw_hash.yaml', this_file_path + 'lv1_os_and_web_browser_history.yaml'] old_table_list = ['application_list', 'call_history', 'embedded_file', 'file_history', 'geodata', 'id_password_hash', 'web_browser_history'] new_table_list = ['lv1_os_and_app_list', 'lv1_os_and_call_history', 'lv1_os_and_emb_file', 'lv1_os_and_file_history', 'lv1_os_and_geodata', 'lv1_os_and_id_pw_hash', 'lv1_os_and_web_browser_history'] if not self.check_table_from_yaml(configuration, yaml_list, new_table_list): return False info = tuple([par_id, configuration.case_id, configuration.evidence_id]) try: conn = sqlite3.connect(analysis_db_path) cursor = conn.cursor() for idx, table in enumerate(old_table_list): cursor.execute(f'select * from {table}') rows = cursor.fetchall() rows_list = [] for row in rows: if table is 'application_list': row = row[:5] + _convert_timestamp(row[5:13]) + row[13:] rows_list.append(info + row) print(rows_list) query = "" if table is 'application_list': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, %s, " \ f"%s, %s, %s, %s, %s, %s, %s, %s, %s);" if table is 'call_history': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, %s, " \ f"%s, %s, %s)" elif table is 'embedded_file': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, %s, " \ f"%s, %s, %s, %s, %s)" elif table is 'file_history' or table is 'id_password_hash': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)" elif table is 'geodata': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, %s)" elif table is 'web_browser_history': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, " \ f"%s, %s, %s)" configuration.cursor.bulk_execute(query, rows_list) self.mask_table(configuration, 'call_history') except Exception as exception: logger.error('Database error : {0!s}'.format(exception)) finally: conn.close() else: logger.info('') def mask_table(self, configuration, table_name): if table_name is 'call_history': query = "update lv1_os_and_call_history set timestamp = regexp_replace(timestamp, " \ "'(\\\\d{2,3}-)\\\\d{1,2}(\\\\d{2}-)\\\\d{2}(\\\\d{2})', " \ "'\\\\1**\\\\2**\\\\3');" configuration.cursor.execute_query(query) query = "update lv1_os_and_call_history set phonenumber = regexp_replace(phonenumber, " \ "'((?:(?:0|\\\\+82)(?:10|2|3[1-3]|4[1-4]|5[0-5]|6[1-4]|70)-?)\\\\d{1,2})\\\\d{2}(-?)\\\\d{2}(\\\\d{2})', " \ "'\\\\1**\\\\2**\\\\3')" configuration.cursor.execute_query(query) query = "update lv1_os_and_call_history set file = regexp_replace(file, " \ "'(통화 녹음 )([가-힣]|(?:\\\\d{6}))(?:\\\\s|\\\\S)*(_\\\\d{6}_\\\\d{6})', " \ "'\\\\1\\\\2*\\\\3')" configuration.cursor.execute_query(query) query = "update lv1_os_and_call_history SET contents = if(CHAR_LENGTH(contents)-CHAR_LENGTH(REPLACE(contents,'|',''))=2," \ " CONCAT_WS('|'," \ " REGEXP_REPLACE(SUBSTRING_INDEX(contents, '|', 1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$contact_name:string_num\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 2), '|', -1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$contact_name:string\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 3), '|', -1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$contact_name:string_num_mixed\\\$)', '\\\\1*\\\\2')" \ " )," \ " CONCAT_WS('|'," \ " SUBSTRING_INDEX(contents, '|', 1)," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 2), '|', -1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$name:string\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 3), '|', -1)," \ " '(^(?:\\\\S|\\\\s){2})(?:\\\\S|\\\\s)*(\\\$m_subject:string_num\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 4), '|', -1)," \ " '(^(?:\\\\S|\\\\s){2})(?:\\\\S|\\\\s)*(\\\$m_subject:string\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 5), '|', -1)," \ " '(^(?:\\\\S|\\\\s){2})(?:\\\\S|\\\\s)*(\\\$m_subject:string_num_mixed\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 6), '|', -1)," \ " '(^(?:\\\\S|\\\\s){3})(?:\\\\S|\\\\s)*(\\\$m_content:string_num_mixed\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 7), '|', -1)," \ " '(^(?:\\\\S|\\\\s){3})(?:\\\\S|\\\\s)*(\\\$m_content:string_num\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 8), '|', -1)," \ " '(^(?:\\\\S|\\\\s){3})(?:\\\\S|\\\\s)*(\\\$m_content:string\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 9), '|', -1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$cnap_name:string_num_mixed\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 10), '|', -1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$cnap_name:string\\\$)', '\\\\1*\\\\2')" \ " )" \ ")" configuration.cursor.execute_query(query) manager.ModulesManager.RegisterModule(AndForensicsConnector) def _convert_timestamp(timestamp): if timestamp is None: return 'N/A' if isinstance(timestamp, tuple): to_timestamp = [] for t in timestamp: to_timestamp.append(datetime.fromtimestamp(t).strftime('%Y-%m-%dT%H:%M:%SZ')) return tuple(to_timestamp) else: return datetime.fromtimestamp(timestamp).strftime('%Y-%m-%dT%H:%M:%SZ')

59.644444

147

0.454359

import os import io import subprocess import sqlite3 from datetime import datetime from modules import logger from modules import manager from modules import interface class AndForensicsConnector(interface.ModuleConnector): NAME = 'andforensics_connector' DESCRIPTION = 'Module for android' TABLE_NAME = 'lv1_os_android_andforensics' _plugin_classes = {} def __init__(self): super(AndForensicsConnector, self).__init__() def Connect(self, par_id, configuration, source_path_spec, knowledge_base): if os.path.exists(configuration.source_path): cmd = 'python3.6 /home/byeongchan/modules/andForensics/andForensics.py -i \'{0:s}\' -o \'{1:s}\' ' \ '-proc {2:d}'.format(os.path.dirname(configuration.source_path), configuration.tmp_path + os.sep + 'andForensics', 10) proc = subprocess.Popen(cmd, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE) ret_code = proc.stdout.read() f = io.StringIO(str(ret_code)) result_msg = f.readline() print(result_msg) f.close() if result_msg[-14:-3] == 'Process End': base_name = os.path.basename(configuration.source_path) output_path = configuration.tmp_path + os.sep + 'andForensics' + os.sep \ + os.path.basename(configuration.source_path) analysis_db_path = output_path + os.sep + 'analysis_' + base_name + '.db' load_db_path = output_path + os.sep + 'loaddb_' + base_name + '.db' preprocess_db_path = output_path + os.sep + 'preprocess_' + base_name + '.db' this_file_path = os.path.dirname( os.path.abspath(__file__)) + os.sep + 'schema' + os.sep + 'android' + os.sep yaml_list = [this_file_path + 'lv1_os_and_app_list.yaml', this_file_path + 'lv1_os_and_call_history.yaml', this_file_path + 'lv1_os_and_emb_file.yaml', this_file_path + 'lv1_os_and_file_history.yaml', this_file_path + 'lv1_os_and_geodata.yaml', this_file_path + 'lv1_os_and_id_pw_hash.yaml', this_file_path + 'lv1_os_and_web_browser_history.yaml'] old_table_list = ['application_list', 'call_history', 'embedded_file', 'file_history', 'geodata', 'id_password_hash', 'web_browser_history'] new_table_list = ['lv1_os_and_app_list', 'lv1_os_and_call_history', 'lv1_os_and_emb_file', 'lv1_os_and_file_history', 'lv1_os_and_geodata', 'lv1_os_and_id_pw_hash', 'lv1_os_and_web_browser_history'] if not self.check_table_from_yaml(configuration, yaml_list, new_table_list): return False info = tuple([par_id, configuration.case_id, configuration.evidence_id]) try: conn = sqlite3.connect(analysis_db_path) cursor = conn.cursor() for idx, table in enumerate(old_table_list): cursor.execute(f'select * from {table}') rows = cursor.fetchall() rows_list = [] for row in rows: if table is 'application_list': row = row[:5] + _convert_timestamp(row[5:13]) + row[13:] rows_list.append(info + row) print(rows_list) query = "" if table is 'application_list': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, %s, " \ f"%s, %s, %s, %s, %s, %s, %s, %s, %s);" if table is 'call_history': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, %s, " \ f"%s, %s, %s)" elif table is 'embedded_file': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, %s, " \ f"%s, %s, %s, %s, %s)" elif table is 'file_history' or table is 'id_password_hash': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)" elif table is 'geodata': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, %s)" elif table is 'web_browser_history': query = f"Insert into {new_table_list[idx]} values (%s, %s, %s, %s, %s, %s, %s, %s, " \ f"%s, %s, %s)" configuration.cursor.bulk_execute(query, rows_list) self.mask_table(configuration, 'call_history') except Exception as exception: logger.error('Database error : {0!s}'.format(exception)) finally: conn.close() else: logger.info('') def mask_table(self, configuration, table_name): if table_name is 'call_history': query = "update lv1_os_and_call_history set timestamp = regexp_replace(timestamp, " \ "'(\\\\d{2,3}-)\\\\d{1,2}(\\\\d{2}-)\\\\d{2}(\\\\d{2})', " \ "'\\\\1**\\\\2**\\\\3');" configuration.cursor.execute_query(query) query = "update lv1_os_and_call_history set phonenumber = regexp_replace(phonenumber, " \ "'((?:(?:0|\\\\+82)(?:10|2|3[1-3]|4[1-4]|5[0-5]|6[1-4]|70)-?)\\\\d{1,2})\\\\d{2}(-?)\\\\d{2}(\\\\d{2})', " \ "'\\\\1**\\\\2**\\\\3')" configuration.cursor.execute_query(query) query = "update lv1_os_and_call_history set file = regexp_replace(file, " \ "'(통화 녹음 )([가-힣]|(?:\\\\d{6}))(?:\\\\s|\\\\S)*(_\\\\d{6}_\\\\d{6})', " \ "'\\\\1\\\\2*\\\\3')" configuration.cursor.execute_query(query) query = "update lv1_os_and_call_history SET contents = if(CHAR_LENGTH(contents)-CHAR_LENGTH(REPLACE(contents,'|',''))=2," \ " CONCAT_WS('|'," \ " REGEXP_REPLACE(SUBSTRING_INDEX(contents, '|', 1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$contact_name:string_num\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 2), '|', -1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$contact_name:string\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 3), '|', -1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$contact_name:string_num_mixed\\\$)', '\\\\1*\\\\2')" \ " )," \ " CONCAT_WS('|'," \ " SUBSTRING_INDEX(contents, '|', 1)," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 2), '|', -1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$name:string\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 3), '|', -1)," \ " '(^(?:\\\\S|\\\\s){2})(?:\\\\S|\\\\s)*(\\\$m_subject:string_num\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 4), '|', -1)," \ " '(^(?:\\\\S|\\\\s){2})(?:\\\\S|\\\\s)*(\\\$m_subject:string\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 5), '|', -1)," \ " '(^(?:\\\\S|\\\\s){2})(?:\\\\S|\\\\s)*(\\\$m_subject:string_num_mixed\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 6), '|', -1)," \ " '(^(?:\\\\S|\\\\s){3})(?:\\\\S|\\\\s)*(\\\$m_content:string_num_mixed\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 7), '|', -1)," \ " '(^(?:\\\\S|\\\\s){3})(?:\\\\S|\\\\s)*(\\\$m_content:string_num\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 8), '|', -1)," \ " '(^(?:\\\\S|\\\\s){3})(?:\\\\S|\\\\s)*(\\\$m_content:string\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 9), '|', -1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$cnap_name:string_num_mixed\\\$)', '\\\\1*\\\\2')," \ " REGEXP_REPLACE(SUBSTRING_INDEX(SUBSTRING_INDEX(contents, '|', 10), '|', -1)," \ " '(^\\\\S|\\\\s)(?:\\\\S|\\\\s)*(\\\$cnap_name:string\\\$)', '\\\\1*\\\\2')" \ " )" \ ")" configuration.cursor.execute_query(query) manager.ModulesManager.RegisterModule(AndForensicsConnector) def _convert_timestamp(timestamp): if timestamp is None: return 'N/A' if isinstance(timestamp, tuple): to_timestamp = [] for t in timestamp: to_timestamp.append(datetime.fromtimestamp(t).strftime('%Y-%m-%dT%H:%M:%SZ')) return tuple(to_timestamp) else: return datetime.fromtimestamp(timestamp).strftime('%Y-%m-%dT%H:%M:%SZ')

true

f70c1db71c0d85aad1438342c2764a0e1cfb70f9

48,153

py

Python

python/ccxt/bitz.py

atommy1966/ccxt

928243ed26a268659723c0965c4c5d6ee128d70a

[ "MIT" ]

1

2020-12-21T04:04:24.000Z

python/ccxt/bitz.py

atommy1966/ccxt

928243ed26a268659723c0965c4c5d6ee128d70a

[ "MIT" ]

1

2020-05-08T09:19:46.000Z

2020-09-12T14:55:58.000Z

python/ccxt/bitz.py

atommy1966/ccxt

928243ed26a268659723c0965c4c5d6ee128d70a

[ "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.base.exchange import Exchange import math from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import ArgumentsRequired from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import DDoSProtection from ccxt.base.errors import RateLimitExceeded from ccxt.base.errors import ExchangeNotAvailable from ccxt.base.errors import OnMaintenance class bitz(Exchange): def describe(self): return self.deep_extend(super(bitz, self).describe(), { 'id': 'bitz', 'name': 'Bit-Z', 'countries': ['HK'], 'rateLimit': 2000, 'version': 'v2', 'userAgent': self.userAgents['chrome'], 'has': { 'fetchTickers': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchClosedOrders': True, 'fetchOrders': True, 'fetchOrder': True, 'createMarketOrder': False, 'fetchDeposits': True, 'fetchWithdrawals': True, 'fetchTransactions': False, }, 'timeframes': { '1m': '1min', '5m': '5min', '15m': '15min', '30m': '30min', '1h': '60min', '4h': '4hour', '1d': '1day', '5d': '5day', '1w': '1week', '1M': '1mon', }, 'hostname': 'apiv2.bitz.com', 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/35862606-4f554f14-0b5d-11e8-957d-35058c504b6f.jpg', 'api': { 'market': 'https://{hostname}', 'trade': 'https://{hostname}', 'assets': 'https://{hostname}', }, 'www': 'https://www.bitz.com', 'doc': 'https://apidoc.bitz.com/en/', 'fees': 'https://www.bitz.com/fee?type=1', 'referral': 'https://u.bitz.com/register?invite_code=1429193', }, 'api': { 'market': { 'get': [ 'ticker', 'depth', 'order', # trades 'tickerall', 'kline', 'symbolList', 'currencyRate', 'currencyCoinRate', 'coinRate', ], }, 'trade': { 'post': [ 'addEntrustSheet', 'cancelEntrustSheet', 'cancelAllEntrustSheet', 'getUserHistoryEntrustSheet', # closed orders 'getUserNowEntrustSheet', # open orders 'getEntrustSheetInfo', # order 'depositOrWithdraw', # transactions ], }, 'assets': { 'post': [ 'getUserAssets', ], }, }, 'fees': { 'trading': { 'maker': 0.002, 'taker': 0.002, }, 'funding': { 'withdraw': { 'BTC': '0.5%', 'DKKT': '0.5%', 'ETH': 0.01, 'USDT': '0.5%', 'LTC': '0.5%', 'FCT': '0.5%', 'LSK': '0.5%', 'HXI': '0.8%', 'ZEC': '0.5%', 'DOGE': '0.5%', 'MZC': '0.5%', 'ETC': '0.5%', 'GXS': '0.5%', 'XPM': '0.5%', 'PPC': '0.5%', 'BLK': '0.5%', 'XAS': '0.5%', 'HSR': '0.5%', 'NULS': 5.0, 'VOISE': 350.0, 'PAY': 1.5, 'EOS': 0.6, 'YBCT': 35.0, 'OMG': 0.3, 'OTN': 0.4, 'BTX': '0.5%', 'QTUM': '0.5%', 'DASH': '0.5%', 'GAME': '0.5%', 'BCH': '0.5%', 'GNT': 9.0, 'SSS': 1500.0, 'ARK': '0.5%', 'PART': '0.5%', 'LEO': '0.5%', 'DGB': '0.5%', 'ZSC': 130.0, 'VIU': 350.0, 'BTG': '0.5%', 'ARN': 10.0, 'VTC': '0.5%', 'BCD': '0.5%', 'TRX': 200.0, 'HWC': '0.5%', 'UNIT': '0.5%', 'OXY': '0.5%', 'MCO': 0.3500, 'SBTC': '0.5%', 'BCX': '0.5%', 'ETF': '0.5%', 'PYLNT': 0.4000, 'XRB': '0.5%', 'ETP': '0.5%', }, }, }, 'precision': { 'amount': 8, 'price': 8, }, 'options': { 'fetchOHLCVVolume': True, 'fetchOHLCVWarning': True, 'lastNonceTimestamp': 0, }, 'commonCurrencies': { # https://github.com/ccxt/ccxt/issues/3881 # https://support.bit-z.pro/hc/en-us/articles/360007500654-BOX-BOX-Token- 'BOX': 'BOX Token', 'LEO': 'LeoCoin', 'XRB': 'NANO', 'PXC': 'Pixiecoin', 'VTC': 'VoteCoin', 'TTC': 'TimesChain', }, 'exceptions': { # '200': Success '-102': ExchangeError, # Invalid parameter '-103': AuthenticationError, # Verification failed '-104': ExchangeNotAvailable, # Network Error-1 '-105': AuthenticationError, # Invalid api signature '-106': ExchangeNotAvailable, # Network Error-2 '-109': AuthenticationError, # Invalid scretKey '-110': DDoSProtection, # The number of access requests exceeded '-111': PermissionDenied, # Current IP is not in the range of trusted IP '-112': OnMaintenance, # Service is under maintenance '-114': RateLimitExceeded, # The number of daily requests has reached the limit '-117': AuthenticationError, # The apikey expires '-100015': AuthenticationError, # Trade password error '-100044': ExchangeError, # Fail to request data '-100101': ExchangeError, # Invalid symbol '-100201': ExchangeError, # Invalid symbol '-100301': ExchangeError, # Invalid symbol '-100401': ExchangeError, # Invalid symbol '-100302': ExchangeError, # Type of K-line error '-100303': ExchangeError, # Size of K-line error '-200003': AuthenticationError, # Please set trade password '-200005': PermissionDenied, # This account can not trade '-200025': ExchangeNotAvailable, # Temporary trading halt '-200027': InvalidOrder, # Price Error '-200028': InvalidOrder, # Amount must be greater than 0 '-200029': InvalidOrder, # Number must be between %s and %d '-200030': InvalidOrder, # Over price range '-200031': InsufficientFunds, # Insufficient assets '-200032': ExchangeError, # System error. Please contact customer service '-200033': ExchangeError, # Fail to trade '-200034': OrderNotFound, # The order does not exist '-200035': OrderNotFound, # Cancellation error, order filled '-200037': InvalidOrder, # Trade direction error '-200038': ExchangeError, # Trading Market Error '-200055': OrderNotFound, # Order record does not exist '-300069': AuthenticationError, # api_key is illegal '-300101': ExchangeError, # Transaction type error '-300102': InvalidOrder, # Price or number cannot be less than 0 '-300103': AuthenticationError, # Trade password error '-301001': ExchangeNotAvailable, # Network Error-3 }, }) def fetch_markets(self, params={}): response = self.marketGetSymbolList(params) # # { status: 200, # msg: "", # data: { ltc_btc: { id: "1", # name: "ltc_btc", # coinFrom: "ltc", # coinTo: "btc", # numberFloat: "4", # priceFloat: "8", # status: "1", # minTrade: "0.010", # maxTrade: "500000000.000"}, # qtum_usdt: { id: "196", # name: "qtum_usdt", # coinFrom: "qtum", # coinTo: "usdt", # numberFloat: "4", # priceFloat: "2", # status: "1", # minTrade: "0.100", # maxTrade: "500000000.000"}, }, # time: 1535969146, # microtime: "0.66955600 1535969146", # source: "api" } # markets = self.safe_value(response, 'data') ids = list(markets.keys()) result = [] for i in range(0, len(ids)): id = ids[i] market = markets[id] numericId = self.safe_string(market, 'id') baseId = self.safe_string(market, 'coinFrom') quoteId = self.safe_string(market, 'coinTo') base = baseId.upper() quote = quoteId.upper() base = self.safe_currency_code(base) quote = self.safe_currency_code(quote) symbol = base + '/' + quote precision = { 'amount': self.safe_integer(market, 'numberFloat'), 'price': self.safe_integer(market, 'priceFloat'), } result.append({ 'info': market, 'id': id, 'numericId': numericId, 'symbol': symbol, 'base': base, 'quote': quote, 'baseId': baseId, 'quoteId': quoteId, 'active': True, 'precision': precision, 'limits': { 'amount': { 'min': self.safe_float(market, 'minTrade'), 'max': self.safe_float(market, 'maxTrade'), }, 'price': { 'min': math.pow(10, -precision['price']), 'max': None, }, 'cost': { 'min': None, 'max': None, }, }, }) return result def fetch_balance(self, params={}): self.load_markets() response = self.assetsPostGetUserAssets(params) # # { # status: 200, # msg: "", # data: { # cny: 0, # usd: 0, # btc_total: 0, # info: [{ # "name": "zpr", # "num": "37.49067275", # "over": "37.49067275", # "lock": "0.00000000", # "btc": "0.00000000", # "usd": "0.00000000", # "cny": "0.00000000", # }], # }, # time: 1535983966, # microtime: "0.70400500 1535983966", # source: "api", # } # balances = self.safe_value(response['data'], 'info') result = {'info': response} for i in range(0, len(balances)): balance = balances[i] currencyId = self.safe_string(balance, 'name') code = self.safe_currency_code(currencyId) account = self.account() account['used'] = self.safe_float(balance, 'lock') account['total'] = self.safe_float(balance, 'num') account['free'] = self.safe_float(balance, 'over') result[code] = account return self.parse_balance(result) def parse_ticker(self, ticker, market=None): # # { symbol: "eth_btc", # quoteVolume: "3905.72", # volume: "97058.21", # priceChange: "-1.72", # priceChange24h: "-1.65", # askPrice: "0.03971272", # askQty: "0.0663", # bidPrice: "0.03961469", # bidQty: "19.5451", # open: "0.04036769", # high: "0.04062988", # low: "0.03956123", # now: "0.03970100", # firstId: 115567767, # lastId: 115795316, # dealCount: 14078, # numberPrecision: 4, # pricePrecision: 8, # cny: "1959.05", # usd: "287.10", # krw: "318655.82" } # timestamp = None symbol = None if market is None: marketId = self.safe_string(ticker, 'symbol') market = self.safe_value(self.markets_by_id, marketId) if market is not None: symbol = market['symbol'] last = self.safe_float(ticker, 'now') open = self.safe_float(ticker, 'open') change = None average = None if last is not None and open is not None: change = last - open average = self.sum(last, open) / 2 return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'high'), 'low': self.safe_float(ticker, 'low'), 'bid': self.safe_float(ticker, 'bidPrice'), 'bidVolume': self.safe_float(ticker, 'bidQty'), 'ask': self.safe_float(ticker, 'askPrice'), 'askVolume': self.safe_float(ticker, 'askQty'), 'vwap': None, 'open': open, 'close': last, 'last': last, 'previousClose': None, 'change': change, 'percentage': self.safe_float(ticker, 'priceChange24h'), 'average': average, 'baseVolume': self.safe_float(ticker, 'volume'), 'quoteVolume': self.safe_float(ticker, 'quoteVolume'), 'info': ticker, } def parse_microtime(self, microtime): if microtime is None: return microtime parts = microtime.split(' ') milliseconds = float(parts[0]) seconds = int(parts[1]) total = self.sum(seconds, milliseconds) return int(total * 1000) def fetch_ticker(self, symbol, params={}): self.load_markets() market = self.market(symbol) request = { 'symbol': market['id'], } response = self.marketGetTicker(self.extend(request, params)) # # { status: 200, # msg: "", # data: { symbol: "eth_btc", # quoteVolume: "3905.72", # volume: "97058.21", # priceChange: "-1.72", # priceChange24h: "-1.65", # askPrice: "0.03971272", # askQty: "0.0663", # bidPrice: "0.03961469", # bidQty: "19.5451", # open: "0.04036769", # high: "0.04062988", # low: "0.03956123", # now: "0.03970100", # firstId: 115567767, # lastId: 115795316, # dealCount: 14078, # numberPrecision: 4, # pricePrecision: 8, # cny: "1959.05", # usd: "287.10", # krw: "318655.82" }, # time: 1535970397, # microtime: "0.76341900 1535970397", # source: "api" } # ticker = self.parse_ticker(response['data'], market) timestamp = self.parse_microtime(self.safe_string(response, 'microtime')) return self.extend(ticker, { 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), }) def fetch_tickers(self, symbols=None, params={}): self.load_markets() request = {} if symbols is not None: ids = self.market_ids(symbols) request['symbols'] = ','.join(ids) response = self.marketGetTickerall(self.extend(request, params)) # # { status: 200, # msg: "", # data: { ela_btc: { symbol: "ela_btc", # quoteVolume: "0.00", # volume: "3.28", # priceChange: "0.00", # priceChange24h: "0.00", # askPrice: "0.00147984", # askQty: "5.4580", # bidPrice: "0.00120230", # bidQty: "12.5384", # open: "0.00149078", # high: "0.00149078", # low: "0.00149078", # now: "0.00149078", # firstId: 115581219, # lastId: 115581219, # dealCount: 1, # numberPrecision: 4, # pricePrecision: 8, # cny: "73.66", # usd: "10.79", # krw: "11995.03" } }, # time: 1535971578, # microtime: "0.39854200 1535971578", # source: "api" } # tickers = self.safe_value(response, 'data') timestamp = self.parse_microtime(self.safe_string(response, 'microtime')) result = {} ids = list(tickers.keys()) for i in range(0, len(ids)): id = ids[i] ticker = tickers[id] market = None if id in self.markets_by_id: market = self.markets_by_id[id] ticker = self.parse_ticker(tickers[id], market) symbol = ticker['symbol'] if symbol is None: if market is not None: symbol = market['symbol'] else: baseId, quoteId = id.split('_') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote if symbol is not None: result[symbol] = self.extend(ticker, { 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), }) return result def fetch_order_book(self, symbol, limit=None, params={}): self.load_markets() request = { 'symbol': self.market_id(symbol), } response = self.marketGetDepth(self.extend(request, params)) # # { status: 200, # msg: "", # data: { asks: [["10.00000000", "0.4426", "4.4260"], # ["1.00000000", "0.8339", "0.8339"], # ["0.91700000", "0.0500", "0.0458"], # ["0.20000000", "0.1000", "0.0200"], # ["0.03987120", "16.1262", "0.6429"], # ["0.03986120", "9.7523", "0.3887"] ], # bids: [["0.03976145", "0.0359", "0.0014"], # ["0.03973401", "20.9493", "0.8323"], # ["0.03967970", "0.0328", "0.0013"], # ["0.00000002", "10000.0000", "0.0002"], # ["0.00000001", "231840.7500", "0.0023"]], # coinPair: "eth_btc" }, # time: 1535974778, # microtime: "0.04017400 1535974778", # source: "api" } # orderbook = self.safe_value(response, 'data') timestamp = self.parse_microtime(self.safe_string(response, 'microtime')) return self.parse_order_book(orderbook, timestamp) def parse_trade(self, trade, market=None): # # fetchTrades(public) # # {id: 115807453, # t: "19:36:24", # T: 1535974584, # p: "0.03983296", # n: "0.1000", # s: "buy" }, # id = self.safe_string(trade, 'id') timestamp = self.safe_timestamp(trade, 'T') symbol = None if market is not None: symbol = market['symbol'] price = self.safe_float(trade, 'p') amount = self.safe_float(trade, 'n') cost = None if price is not None: if amount is not None: cost = self.price_to_precision(symbol, amount * price) side = self.safe_string(trade, 's') return { 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'id': id, 'order': None, 'type': 'limit', 'side': side, 'takerOrMaker': None, 'price': price, 'amount': amount, 'cost': cost, 'fee': None, 'info': trade, } def fetch_trades(self, symbol, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'symbol': market['id'], } response = self.marketGetOrder(self.extend(request, params)) # # { status: 200, # msg: "", # data: [{id: 115807453, # t: "19:36:24", # T: 1535974584, # p: "0.03983296", # n: "0.1000", # s: "buy" }, # {id: 115806811, # t: "19:33:19", # T: 1535974399, # p: "0.03981135", # n: "9.4612", # s: "sell" } ], # time: 1535974583, # microtime: "0.57118100 1535974583", # source: "api" } # return self.parse_trades(response['data'], market, since, limit) def parse_ohlcv(self, ohlcv, market=None, timeframe='1m', since=None, limit=None): # # { # time: "1535973420000", # open: "0.03975084", # high: "0.03975084", # low: "0.03967700", # close: "0.03967700", # volume: "12.4733", # datetime: "2018-09-03 19:17:00" # } # return [ self.safe_integer(ohlcv, 'time'), self.safe_float(ohlcv, 'open'), self.safe_float(ohlcv, 'high'), self.safe_float(ohlcv, 'low'), self.safe_float(ohlcv, 'close'), self.safe_float(ohlcv, 'volume'), ] def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): self.load_markets() duration = self.parse_timeframe(timeframe) * 1000 market = self.market(symbol) request = { 'symbol': market['id'], 'resolution': self.timeframes[timeframe], } if limit is not None: request['size'] = min(limit, 300) # 1-300 if since is not None: request['to'] = self.sum(since, limit * duration * 1000) else: if since is not None: raise ArgumentsRequired(self.id + ' fetchOHLCV requires a limit argument if the since argument is specified') response = self.marketGetKline(self.extend(request, params)) # # { # status: 200, # msg: "", # data: { # bars: [ # {time: "1535973420000", open: "0.03975084", high: "0.03975084", low: "0.03967700", close: "0.03967700", volume: "12.4733", datetime: "2018-09-03 19:17:00"}, # {time: "1535955480000", open: "0.04009900", high: "0.04016745", low: "0.04009900", close: "0.04012074", volume: "74.4803", datetime: "2018-09-03 14:18:00"}, # ], # resolution: "1min", # symbol: "eth_btc", # from: "1535973420000", # to: "1535955480000", # size: 300 # }, # time: 1535973435, # microtime: "0.56462100 1535973435", # source: "api" # } # data = self.safe_value(response, 'data', {}) bars = self.safe_value(data, 'bars', []) return self.parse_ohlcvs(bars, market, timeframe, since, limit) def parse_order_status(self, status): statuses = { '0': 'open', '1': 'open', # partially filled '2': 'closed', # filled '3': 'canceled', } return self.safe_string(statuses, status, status) def parse_order(self, order, market=None): # # createOrder # # { # "id": "693248739", # order id # "uId": "2074056", # uid # "price": "100", # price # "number": "10", # number # "numberOver": "10", # undealed # "flag": "sale", # flag # "status": "0", # unfilled # "coinFrom": "vtc", # "coinTo": "dkkt", # "numberDeal": "0" # dealed # } # id = self.safe_string(order, 'id') symbol = None if market is None: baseId = self.safe_string(order, 'coinFrom') quoteId = self.safe_string(order, 'coinTo') if (baseId is not None) and (quoteId is not None): marketId = baseId + '_' + quoteId if marketId in self.markets_by_id: market = self.safe_value(self.markets_by_id, marketId) else: base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote if market is not None: symbol = market['symbol'] side = self.safe_string(order, 'flag') if side is not None: side = 'sell' if (side == 'sale') else 'buy' price = self.safe_float(order, 'price') amount = self.safe_float(order, 'number') remaining = self.safe_float(order, 'numberOver') filled = self.safe_float(order, 'numberDeal') timestamp = self.safe_integer(order, 'timestamp') if timestamp is None: timestamp = self.safe_timestamp(order, 'created') cost = self.safe_float(order, 'orderTotalPrice') if price is not None: if filled is not None: cost = filled * price status = self.parse_order_status(self.safe_string(order, 'status')) return { 'id': id, 'clientOrderId': None, 'datetime': self.iso8601(timestamp), 'timestamp': timestamp, 'lastTradeTimestamp': None, 'status': status, 'symbol': symbol, 'type': 'limit', 'side': side, 'price': price, 'cost': cost, 'amount': amount, 'filled': filled, 'remaining': remaining, 'trades': None, 'fee': None, 'info': order, 'average': None, } def create_order(self, symbol, type, side, amount, price=None, params={}): self.load_markets() if type != 'limit': raise ExchangeError(self.id + ' createOrder allows limit orders only') market = self.market(symbol) orderType = '1' if (side == 'buy') else '2' if not self.password: raise ExchangeError(self.id + ' createOrder() requires you to set exchange.password = "YOUR_TRADING_PASSWORD"(a trade password is NOT THE SAME as your login password)') request = { 'symbol': market['id'], 'type': orderType, 'price': self.price_to_precision(symbol, price), 'number': self.amount_to_precision(symbol, amount), 'tradePwd': self.password, } response = self.tradePostAddEntrustSheet(self.extend(request, params)) # # { # "status": 200, # "msg": "", # "data": { # "id": "693248739", # order id # "uId": "2074056", # uid # "price": "100", # price # "number": "10", # number # "numberOver": "10", # undealed # "flag": "sale", # flag # "status": "0", # unfilled # "coinFrom": "vtc", # "coinTo": "dkkt", # "numberDeal": "0" # dealed # }, # "time": "1533035297", # "microtime": "0.41892000 1533035297", # "source": "api", # } # timestamp = self.parse_microtime(self.safe_string(response, 'microtime')) order = self.extend({ 'timestamp': timestamp, }, response['data']) return self.parse_order(order, market) def cancel_order(self, id, symbol=None, params={}): self.load_markets() request = { 'entrustSheetId': id, } response = self.tradePostCancelEntrustSheet(self.extend(request, params)) # # { # "status":200, # "msg":"", # "data":{ # "updateAssetsData":{ # "coin":"bz", # "over":"1000.00000000", # "lock":"-1000.00000000" # }, # "assetsInfo":{ # "coin":"bz", # "over":"9999.99999999", # "lock":"9999.99999999" # } # }, # "time":"1535464383", # "microtime":"0.91558000 1535464383", # "source":"api" # } # return response def cancel_orders(self, ids, symbol=None, params={}): self.load_markets() request = { 'ids': ','.join(ids), } response = self.tradePostCancelEntrustSheet(self.extend(request, params)) # # { # "status":200, # "msg":"", # "data":{ # "744173808":{ # "updateAssetsData":{ # "coin":"bz", # "over":"100.00000000", # "lock":"-100.00000000" # }, # "assetsInfo":{ # "coin":"bz", # "over":"899.99999999", # "lock":"19099.99999999" # } # }, # "744173809":{ # "updateAssetsData":{ # "coin":"bz", # "over":"100.00000000", # "lock":"-100.00000000" # }, # "assetsInfo":{ # "coin":"bz", # "over":"999.99999999", # "lock":"18999.99999999" # } # } # }, # "time":"1535525649", # "microtime":"0.05009400 1535525649", # "source":"api" # } # return response def fetch_order(self, id, symbol=None, params={}): self.load_markets() request = { 'entrustSheetId': id, } response = self.tradePostGetEntrustSheetInfo(self.extend(request, params)) # # { # "status":200, # "msg":"", # "data":{ # "id":"708279852", # "uId":"2074056", # "price":"100.00000000", # "number":"10.0000", # "total":"0.00000000", # "numberOver":"10.0000", # "numberDeal":"0.0000", # "flag":"sale", # "status":"0", #0:unfilled, 1:partial deal, 2:all transactions, 3:already cancelled # "coinFrom":"bz", # "coinTo":"usdt", # "orderTotalPrice":"0", # "created":"1533279876" # }, # "time":"1533280294", # "microtime":"0.36859200 1533280294", # "source":"api" # } # return self.parse_order(response['data']) def fetch_orders_with_method(self, method, symbol=None, since=None, limit=None, params={}): if symbol is None: raise ArgumentsRequired(self.id + ' fetchOpenOrders requires a symbol argument') self.load_markets() market = self.market(symbol) request = { 'coinFrom': market['baseId'], 'coinTo': market['quoteId'], # 'type': 1, # optional integer, 1 = buy, 2 = sell # 'page': 1, # optional integer # 'pageSize': 100, # optional integer, max 100 # 'startTime': 1510235730, # optional integer timestamp in seconds # 'endTime': 1510235730, # optional integer timestamp in seconds } if limit is not None: request['page'] = 1 request['pageSize'] = limit if since is not None: request['startTime'] = int(since / 1000) # request['endTime'] = int(since / 1000) response = getattr(self, method)(self.extend(request, params)) # # { # "status": 200, # "msg": "", # "data": { # "data": [ # { # "id": "693248739", # "uid": "2074056", # "price": "100.00000000", # "number": "10.0000", # "total": "0.00000000", # "numberOver": "0.0000", # "numberDeal": "0.0000", # "flag": "sale", # "status": "3", # 0:unfilled, 1:partial deal, 2:all transactions, 3:already cancelled # "isNew": "N", # "coinFrom": "vtc", # "coinTo": "dkkt", # "created": "1533035300", # }, # { # "id": "723086996", # "uid": "2074056", # "price": "100.00000000", # "number": "10.0000", # "total": "0.00000000", # "numberOver": "0.0000", # "numberDeal": "0.0000", # "flag": "sale", # "status": "3", # "isNew": "N", # "coinFrom": "bz", # "coinTo": "usdt", # "created": "1533523568", # }, # ], # "pageInfo": { # "limit": "10", # "offest": "0", # "current_page": "1", # "page_size": "10", # "total_count": "17", # "page_count": "2", # } # }, # "time": "1533279329", # "microtime": "0.15305300 1533279329", # "source": "api" # } # orders = self.safe_value(response['data'], 'data', []) return self.parse_orders(orders, None, since, limit) def fetch_orders(self, symbol=None, since=None, limit=None, params={}): return self.fetch_orders_with_method('tradePostGetUserHistoryEntrustSheet', symbol, since, limit, params) def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): return self.fetch_orders_with_method('tradePostGetUserNowEntrustSheet', symbol, since, limit, params) def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): return self.fetch_orders_with_method('tradePostGetUserHistoryEntrustSheet', symbol, since, limit, params) def parse_transaction_status(self, status): statuses = { '1': 'pending', '2': 'pending', '3': 'pending', '4': 'ok', '5': 'canceled', } return self.safe_string(statuses, status, status) def parse_transaction(self, transaction, currency=None): # # { # "id": '96275', # "uid": '2109073', # "wallet": '0xf4c4141c0127bc37b1d0c409a091920eba13ada7', # "txid": '0xb7adfa52aa566f9ac112e3c01f77bd91179b19eab12092a9a5a8b33d5086e31d', # "confirm": '12', # "number": '0.50000000', # "status": 4, # "updated": '1534944168605', # "addressUrl": 'https://etherscan.io/address/', # "txidUrl": 'https://etherscan.io/tx/', # "description": 'Ethereum', # "coin": 'eth', # "memo": '' # } # # { # "id":"397574", # "uid":"2033056", # "wallet":"1AG1gZvQAYu3WBvgg7p4BMMghQD2gE693k", # "txid":"", # "confirm":"0", # "number":"1000.00000000", # "status":1, # "updated":"0", # "addressUrl":"http://omniexplorer.info/lookupadd.aspx?address=", # "txidUrl":"http://omniexplorer.info/lookuptx.aspx?txid=", # "description":"Tether", # "coin":"usdt", # "memo":"" # } # # { # "id":"153606", # "uid":"2033056", # "wallet":"1AG1gZvQAYu3WBvgg7p4BMMghQD2gE693k", # "txid":"aa2b179f84cd6dedafd41845e0fbf7f01e14c0d71ea3140d03d6f5a9ccd93199", # "confirm":"0", # "number":"761.11110000", # "status":4, # "updated":"1536726133579", # "addressUrl":"http://omniexplorer.info/lookupadd.aspx?address=", # "txidUrl":"http://omniexplorer.info/lookuptx.aspx?txid=", # "description":"Tether", # "coin":"usdt", # "memo":"" # } # timestamp = self.safe_integer(transaction, 'updated') if timestamp == 0: timestamp = None currencyId = self.safe_string(transaction, 'coin') code = self.safe_currency_code(currencyId, currency) type = self.safe_string_lower(transaction, 'type') status = self.parse_transaction_status(self.safe_string(transaction, 'status')) return { 'id': self.safe_string(transaction, 'id'), 'txid': self.safe_string(transaction, 'txid'), 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'address': self.safe_string(transaction, 'wallet'), 'tag': self.safe_string(transaction, 'memo'), 'type': type, 'amount': self.safe_float(transaction, 'number'), 'currency': code, 'status': status, 'updated': timestamp, 'fee': None, 'info': transaction, } def parse_transactions_by_type(self, type, transactions, code=None, since=None, limit=None): result = [] for i in range(0, len(transactions)): transaction = self.parse_transaction(self.extend({ 'type': type, }, transactions[i])) result.append(transaction) return self.filter_by_currency_since_limit(result, code, since, limit) def parse_transaction_type(self, type): types = { 'deposit': 1, 'withdrawal': 2, } return self.safe_integer(types, type, type) def fetch_deposits(self, code=None, since=None, limit=None, params={}): return self.fetch_transactions_for_type('deposit', code, since, limit, params) def fetch_withdrawals(self, code=None, since=None, limit=None, params={}): return self.fetch_transactions_for_type('withdrawal', code, since, limit, params) def fetch_transactions_for_type(self, type, code=None, since=None, limit=None, params={}): if code is None: raise ArgumentsRequired(self.id + ' fetchTransactions() requires a currency `code` argument') self.load_markets() currency = self.currency(code) request = { 'coin': currency['id'], 'type': self.parse_transaction_type(type), } if since is not None: request['startTime'] = int(since / str(1000)) if limit is not None: request['page'] = 1 request['pageSize'] = limit response = self.tradePostDepositOrWithdraw(self.extend(request, params)) transactions = self.safe_value(response['data'], 'data', []) return self.parse_transactions_by_type(type, transactions, code, since, limit) def nonce(self): currentTimestamp = self.seconds() if currentTimestamp > self.options['lastNonceTimestamp']: self.options['lastNonceTimestamp'] = currentTimestamp self.options['lastNonce'] = 100000 self.options['lastNonce'] = self.sum(self.options['lastNonce'], 1) return self.options['lastNonce'] def sign(self, path, api='market', method='GET', params={}, headers=None, body=None): baseUrl = self.implode_params(self.urls['api'][api], {'hostname': self.hostname}) url = baseUrl + '/' + self.capitalize(api) + '/' + path query = None if api == 'market': query = self.urlencode(params) if len(query): url += '?' + query else: self.check_required_credentials() body = self.rawencode(self.keysort(self.extend({ 'apiKey': self.apiKey, 'timeStamp': self.seconds(), 'nonce': self.nonce(), }, params))) body += '&sign=' + self.hash(self.encode(body + self.secret)) headers = {'Content-type': 'application/x-www-form-urlencoded'} return {'url': url, 'method': method, 'body': body, 'headers': headers} def handle_errors(self, httpCode, reason, url, method, headers, body, response, requestHeaders, requestBody): if response is None: return # fallback to default error handler status = self.safe_string(response, 'status') if status is not None: feedback = self.id + ' ' + body # # {"status":-107,"msg":"","data":"","time":1535968848,"microtime":"0.89092200 1535968848","source":"api"} # if status == '200': # # {"status":200,"msg":"","data":-200031,"time":1535999806,"microtime":"0.85476800 1535999806","source":"api"} # code = self.safe_integer(response, 'data') if code is not None: self.throw_exactly_matched_exception(self.exceptions, code, feedback) raise ExchangeError(feedback) else: return # no error self.throw_exactly_matched_exception(self.exceptions, status, feedback) raise ExchangeError(feedback)

41.981691

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0.415114

from ccxt.base.exchange import Exchange import math from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import ArgumentsRequired from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import DDoSProtection from ccxt.base.errors import RateLimitExceeded from ccxt.base.errors import ExchangeNotAvailable from ccxt.base.errors import OnMaintenance class bitz(Exchange): def describe(self): return self.deep_extend(super(bitz, self).describe(), { 'id': 'bitz', 'name': 'Bit-Z', 'countries': ['HK'], 'rateLimit': 2000, 'version': 'v2', 'userAgent': self.userAgents['chrome'], 'has': { 'fetchTickers': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchClosedOrders': True, 'fetchOrders': True, 'fetchOrder': True, 'createMarketOrder': False, 'fetchDeposits': True, 'fetchWithdrawals': True, 'fetchTransactions': False, }, 'timeframes': { '1m': '1min', '5m': '5min', '15m': '15min', '30m': '30min', '1h': '60min', '4h': '4hour', '1d': '1day', '5d': '5day', '1w': '1week', '1M': '1mon', }, 'hostname': 'apiv2.bitz.com', 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/35862606-4f554f14-0b5d-11e8-957d-35058c504b6f.jpg', 'api': { 'market': 'https://{hostname}', 'trade': 'https://{hostname}', 'assets': 'https://{hostname}', }, 'www': 'https://www.bitz.com', 'doc': 'https://apidoc.bitz.com/en/', 'fees': 'https://www.bitz.com/fee?type=1', 'referral': 'https://u.bitz.com/register?invite_code=1429193', }, 'api': { 'market': { 'get': [ 'ticker', 'depth', 'order', 'tickerall', 'kline', 'symbolList', 'currencyRate', 'currencyCoinRate', 'coinRate', ], }, 'trade': { 'post': [ 'addEntrustSheet', 'cancelEntrustSheet', 'cancelAllEntrustSheet', 'getUserHistoryEntrustSheet', 'getUserNowEntrustSheet', 'getEntrustSheetInfo', 'depositOrWithdraw', ], }, 'assets': { 'post': [ 'getUserAssets', ], }, }, 'fees': { 'trading': { 'maker': 0.002, 'taker': 0.002, }, 'funding': { 'withdraw': { 'BTC': '0.5%', 'DKKT': '0.5%', 'ETH': 0.01, 'USDT': '0.5%', 'LTC': '0.5%', 'FCT': '0.5%', 'LSK': '0.5%', 'HXI': '0.8%', 'ZEC': '0.5%', 'DOGE': '0.5%', 'MZC': '0.5%', 'ETC': '0.5%', 'GXS': '0.5%', 'XPM': '0.5%', 'PPC': '0.5%', 'BLK': '0.5%', 'XAS': '0.5%', 'HSR': '0.5%', 'NULS': 5.0, 'VOISE': 350.0, 'PAY': 1.5, 'EOS': 0.6, 'YBCT': 35.0, 'OMG': 0.3, 'OTN': 0.4, 'BTX': '0.5%', 'QTUM': '0.5%', 'DASH': '0.5%', 'GAME': '0.5%', 'BCH': '0.5%', 'GNT': 9.0, 'SSS': 1500.0, 'ARK': '0.5%', 'PART': '0.5%', 'LEO': '0.5%', 'DGB': '0.5%', 'ZSC': 130.0, 'VIU': 350.0, 'BTG': '0.5%', 'ARN': 10.0, 'VTC': '0.5%', 'BCD': '0.5%', 'TRX': 200.0, 'HWC': '0.5%', 'UNIT': '0.5%', 'OXY': '0.5%', 'MCO': 0.3500, 'SBTC': '0.5%', 'BCX': '0.5%', 'ETF': '0.5%', 'PYLNT': 0.4000, 'XRB': '0.5%', 'ETP': '0.5%', }, }, }, 'precision': { 'amount': 8, 'price': 8, }, 'options': { 'fetchOHLCVVolume': True, 'fetchOHLCVWarning': True, 'lastNonceTimestamp': 0, }, 'commonCurrencies': { 'BOX': 'BOX Token', 'LEO': 'LeoCoin', 'XRB': 'NANO', 'PXC': 'Pixiecoin', 'VTC': 'VoteCoin', 'TTC': 'TimesChain', }, 'exceptions': { '-102': ExchangeError, '-103': AuthenticationError, '-104': ExchangeNotAvailable, '-105': AuthenticationError, '-106': ExchangeNotAvailable, '-109': AuthenticationError, '-110': DDoSProtection, '-111': PermissionDenied, '-112': OnMaintenance, '-114': RateLimitExceeded, '-117': AuthenticationError, '-100015': AuthenticationError, '-100044': ExchangeError, '-100101': ExchangeError, '-100201': ExchangeError, '-100301': ExchangeError, '-100401': ExchangeError, '-100302': ExchangeError, '-100303': ExchangeError, '-200003': AuthenticationError, '-200005': PermissionDenied, '-200025': ExchangeNotAvailable, '-200027': InvalidOrder, '-200028': InvalidOrder, '-200029': InvalidOrder, '-200030': InvalidOrder, '-200031': InsufficientFunds, '-200032': ExchangeError, '-200033': ExchangeError, '-200034': OrderNotFound, '-200035': OrderNotFound, '-200037': InvalidOrder, '-200038': ExchangeError, '-200055': OrderNotFound, '-300069': AuthenticationError, '-300101': ExchangeError, '-300102': InvalidOrder, '-300103': AuthenticationError, '-301001': ExchangeNotAvailable, }, }) def fetch_markets(self, params={}): response = self.marketGetSymbolList(params) markets = self.safe_value(response, 'data') ids = list(markets.keys()) result = [] for i in range(0, len(ids)): id = ids[i] market = markets[id] numericId = self.safe_string(market, 'id') baseId = self.safe_string(market, 'coinFrom') quoteId = self.safe_string(market, 'coinTo') base = baseId.upper() quote = quoteId.upper() base = self.safe_currency_code(base) quote = self.safe_currency_code(quote) symbol = base + '/' + quote precision = { 'amount': self.safe_integer(market, 'numberFloat'), 'price': self.safe_integer(market, 'priceFloat'), } result.append({ 'info': market, 'id': id, 'numericId': numericId, 'symbol': symbol, 'base': base, 'quote': quote, 'baseId': baseId, 'quoteId': quoteId, 'active': True, 'precision': precision, 'limits': { 'amount': { 'min': self.safe_float(market, 'minTrade'), 'max': self.safe_float(market, 'maxTrade'), }, 'price': { 'min': math.pow(10, -precision['price']), 'max': None, }, 'cost': { 'min': None, 'max': None, }, }, }) return result def fetch_balance(self, params={}): self.load_markets() response = self.assetsPostGetUserAssets(params) balances = self.safe_value(response['data'], 'info') result = {'info': response} for i in range(0, len(balances)): balance = balances[i] currencyId = self.safe_string(balance, 'name') code = self.safe_currency_code(currencyId) account = self.account() account['used'] = self.safe_float(balance, 'lock') account['total'] = self.safe_float(balance, 'num') account['free'] = self.safe_float(balance, 'over') result[code] = account return self.parse_balance(result) def parse_ticker(self, ticker, market=None): timestamp = None symbol = None if market is None: marketId = self.safe_string(ticker, 'symbol') market = self.safe_value(self.markets_by_id, marketId) if market is not None: symbol = market['symbol'] last = self.safe_float(ticker, 'now') open = self.safe_float(ticker, 'open') change = None average = None if last is not None and open is not None: change = last - open average = self.sum(last, open) / 2 return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'high'), 'low': self.safe_float(ticker, 'low'), 'bid': self.safe_float(ticker, 'bidPrice'), 'bidVolume': self.safe_float(ticker, 'bidQty'), 'ask': self.safe_float(ticker, 'askPrice'), 'askVolume': self.safe_float(ticker, 'askQty'), 'vwap': None, 'open': open, 'close': last, 'last': last, 'previousClose': None, 'change': change, 'percentage': self.safe_float(ticker, 'priceChange24h'), 'average': average, 'baseVolume': self.safe_float(ticker, 'volume'), 'quoteVolume': self.safe_float(ticker, 'quoteVolume'), 'info': ticker, } def parse_microtime(self, microtime): if microtime is None: return microtime parts = microtime.split(' ') milliseconds = float(parts[0]) seconds = int(parts[1]) total = self.sum(seconds, milliseconds) return int(total * 1000) def fetch_ticker(self, symbol, params={}): self.load_markets() market = self.market(symbol) request = { 'symbol': market['id'], } response = self.marketGetTicker(self.extend(request, params)) ticker = self.parse_ticker(response['data'], market) timestamp = self.parse_microtime(self.safe_string(response, 'microtime')) return self.extend(ticker, { 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), }) def fetch_tickers(self, symbols=None, params={}): self.load_markets() request = {} if symbols is not None: ids = self.market_ids(symbols) request['symbols'] = ','.join(ids) response = self.marketGetTickerall(self.extend(request, params)) tickers = self.safe_value(response, 'data') timestamp = self.parse_microtime(self.safe_string(response, 'microtime')) result = {} ids = list(tickers.keys()) for i in range(0, len(ids)): id = ids[i] ticker = tickers[id] market = None if id in self.markets_by_id: market = self.markets_by_id[id] ticker = self.parse_ticker(tickers[id], market) symbol = ticker['symbol'] if symbol is None: if market is not None: symbol = market['symbol'] else: baseId, quoteId = id.split('_') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote if symbol is not None: result[symbol] = self.extend(ticker, { 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), }) return result def fetch_order_book(self, symbol, limit=None, params={}): self.load_markets() request = { 'symbol': self.market_id(symbol), } response = self.marketGetDepth(self.extend(request, params)) orderbook = self.safe_value(response, 'data') timestamp = self.parse_microtime(self.safe_string(response, 'microtime')) return self.parse_order_book(orderbook, timestamp) def parse_trade(self, trade, market=None): id = self.safe_string(trade, 'id') timestamp = self.safe_timestamp(trade, 'T') symbol = None if market is not None: symbol = market['symbol'] price = self.safe_float(trade, 'p') amount = self.safe_float(trade, 'n') cost = None if price is not None: if amount is not None: cost = self.price_to_precision(symbol, amount * price) side = self.safe_string(trade, 's') return { 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'id': id, 'order': None, 'type': 'limit', 'side': side, 'takerOrMaker': None, 'price': price, 'amount': amount, 'cost': cost, 'fee': None, 'info': trade, } def fetch_trades(self, symbol, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'symbol': market['id'], } response = self.marketGetOrder(self.extend(request, params)) return self.parse_trades(response['data'], market, since, limit) def parse_ohlcv(self, ohlcv, market=None, timeframe='1m', since=None, limit=None): return [ self.safe_integer(ohlcv, 'time'), self.safe_float(ohlcv, 'open'), self.safe_float(ohlcv, 'high'), self.safe_float(ohlcv, 'low'), self.safe_float(ohlcv, 'close'), self.safe_float(ohlcv, 'volume'), ] def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): self.load_markets() duration = self.parse_timeframe(timeframe) * 1000 market = self.market(symbol) request = { 'symbol': market['id'], 'resolution': self.timeframes[timeframe], } if limit is not None: request['size'] = min(limit, 300) if since is not None: request['to'] = self.sum(since, limit * duration * 1000) else: if since is not None: raise ArgumentsRequired(self.id + ' fetchOHLCV requires a limit argument if the since argument is specified') response = self.marketGetKline(self.extend(request, params)) data = self.safe_value(response, 'data', {}) bars = self.safe_value(data, 'bars', []) return self.parse_ohlcvs(bars, market, timeframe, since, limit) def parse_order_status(self, status): statuses = { '0': 'open', '1': 'open', '2': 'closed', '3': 'canceled', } return self.safe_string(statuses, status, status) def parse_order(self, order, market=None): id = self.safe_string(order, 'id') symbol = None if market is None: baseId = self.safe_string(order, 'coinFrom') quoteId = self.safe_string(order, 'coinTo') if (baseId is not None) and (quoteId is not None): marketId = baseId + '_' + quoteId if marketId in self.markets_by_id: market = self.safe_value(self.markets_by_id, marketId) else: base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote if market is not None: symbol = market['symbol'] side = self.safe_string(order, 'flag') if side is not None: side = 'sell' if (side == 'sale') else 'buy' price = self.safe_float(order, 'price') amount = self.safe_float(order, 'number') remaining = self.safe_float(order, 'numberOver') filled = self.safe_float(order, 'numberDeal') timestamp = self.safe_integer(order, 'timestamp') if timestamp is None: timestamp = self.safe_timestamp(order, 'created') cost = self.safe_float(order, 'orderTotalPrice') if price is not None: if filled is not None: cost = filled * price status = self.parse_order_status(self.safe_string(order, 'status')) return { 'id': id, 'clientOrderId': None, 'datetime': self.iso8601(timestamp), 'timestamp': timestamp, 'lastTradeTimestamp': None, 'status': status, 'symbol': symbol, 'type': 'limit', 'side': side, 'price': price, 'cost': cost, 'amount': amount, 'filled': filled, 'remaining': remaining, 'trades': None, 'fee': None, 'info': order, 'average': None, } def create_order(self, symbol, type, side, amount, price=None, params={}): self.load_markets() if type != 'limit': raise ExchangeError(self.id + ' createOrder allows limit orders only') market = self.market(symbol) orderType = '1' if (side == 'buy') else '2' if not self.password: raise ExchangeError(self.id + ' createOrder() requires you to set exchange.password = "YOUR_TRADING_PASSWORD"(a trade password is NOT THE SAME as your login password)') request = { 'symbol': market['id'], 'type': orderType, 'price': self.price_to_precision(symbol, price), 'number': self.amount_to_precision(symbol, amount), 'tradePwd': self.password, } response = self.tradePostAddEntrustSheet(self.extend(request, params)) timestamp = self.parse_microtime(self.safe_string(response, 'microtime')) order = self.extend({ 'timestamp': timestamp, }, response['data']) return self.parse_order(order, market) def cancel_order(self, id, symbol=None, params={}): self.load_markets() request = { 'entrustSheetId': id, } response = self.tradePostCancelEntrustSheet(self.extend(request, params)) return response def cancel_orders(self, ids, symbol=None, params={}): self.load_markets() request = { 'ids': ','.join(ids), } response = self.tradePostCancelEntrustSheet(self.extend(request, params)) return response def fetch_order(self, id, symbol=None, params={}): self.load_markets() request = { 'entrustSheetId': id, } response = self.tradePostGetEntrustSheetInfo(self.extend(request, params)) return self.parse_order(response['data']) def fetch_orders_with_method(self, method, symbol=None, since=None, limit=None, params={}): if symbol is None: raise ArgumentsRequired(self.id + ' fetchOpenOrders requires a symbol argument') self.load_markets() market = self.market(symbol) request = { 'coinFrom': market['baseId'], 'coinTo': market['quoteId'], } if limit is not None: request['page'] = 1 request['pageSize'] = limit if since is not None: request['startTime'] = int(since / 1000) response = getattr(self, method)(self.extend(request, params)) orders = self.safe_value(response['data'], 'data', []) return self.parse_orders(orders, None, since, limit) def fetch_orders(self, symbol=None, since=None, limit=None, params={}): return self.fetch_orders_with_method('tradePostGetUserHistoryEntrustSheet', symbol, since, limit, params) def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): return self.fetch_orders_with_method('tradePostGetUserNowEntrustSheet', symbol, since, limit, params) def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): return self.fetch_orders_with_method('tradePostGetUserHistoryEntrustSheet', symbol, since, limit, params) def parse_transaction_status(self, status): statuses = { '1': 'pending', '2': 'pending', '3': 'pending', '4': 'ok', '5': 'canceled', } return self.safe_string(statuses, status, status) def parse_transaction(self, transaction, currency=None): timestamp = self.safe_integer(transaction, 'updated') if timestamp == 0: timestamp = None currencyId = self.safe_string(transaction, 'coin') code = self.safe_currency_code(currencyId, currency) type = self.safe_string_lower(transaction, 'type') status = self.parse_transaction_status(self.safe_string(transaction, 'status')) return { 'id': self.safe_string(transaction, 'id'), 'txid': self.safe_string(transaction, 'txid'), 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'address': self.safe_string(transaction, 'wallet'), 'tag': self.safe_string(transaction, 'memo'), 'type': type, 'amount': self.safe_float(transaction, 'number'), 'currency': code, 'status': status, 'updated': timestamp, 'fee': None, 'info': transaction, } def parse_transactions_by_type(self, type, transactions, code=None, since=None, limit=None): result = [] for i in range(0, len(transactions)): transaction = self.parse_transaction(self.extend({ 'type': type, }, transactions[i])) result.append(transaction) return self.filter_by_currency_since_limit(result, code, since, limit) def parse_transaction_type(self, type): types = { 'deposit': 1, 'withdrawal': 2, } return self.safe_integer(types, type, type) def fetch_deposits(self, code=None, since=None, limit=None, params={}): return self.fetch_transactions_for_type('deposit', code, since, limit, params) def fetch_withdrawals(self, code=None, since=None, limit=None, params={}): return self.fetch_transactions_for_type('withdrawal', code, since, limit, params) def fetch_transactions_for_type(self, type, code=None, since=None, limit=None, params={}): if code is None: raise ArgumentsRequired(self.id + ' fetchTransactions() requires a currency `code` argument') self.load_markets() currency = self.currency(code) request = { 'coin': currency['id'], 'type': self.parse_transaction_type(type), } if since is not None: request['startTime'] = int(since / str(1000)) if limit is not None: request['page'] = 1 request['pageSize'] = limit response = self.tradePostDepositOrWithdraw(self.extend(request, params)) transactions = self.safe_value(response['data'], 'data', []) return self.parse_transactions_by_type(type, transactions, code, since, limit) def nonce(self): currentTimestamp = self.seconds() if currentTimestamp > self.options['lastNonceTimestamp']: self.options['lastNonceTimestamp'] = currentTimestamp self.options['lastNonce'] = 100000 self.options['lastNonce'] = self.sum(self.options['lastNonce'], 1) return self.options['lastNonce'] def sign(self, path, api='market', method='GET', params={}, headers=None, body=None): baseUrl = self.implode_params(self.urls['api'][api], {'hostname': self.hostname}) url = baseUrl + '/' + self.capitalize(api) + '/' + path query = None if api == 'market': query = self.urlencode(params) if len(query): url += '?' + query else: self.check_required_credentials() body = self.rawencode(self.keysort(self.extend({ 'apiKey': self.apiKey, 'timeStamp': self.seconds(), 'nonce': self.nonce(), }, params))) body += '&sign=' + self.hash(self.encode(body + self.secret)) headers = {'Content-type': 'application/x-www-form-urlencoded'} return {'url': url, 'method': method, 'body': body, 'headers': headers} def handle_errors(self, httpCode, reason, url, method, headers, body, response, requestHeaders, requestBody): if response is None: return status = self.safe_string(response, 'status') if status is not None: feedback = self.id + ' ' + body if status == '200': code = self.safe_integer(response, 'data') if code is not None: self.throw_exactly_matched_exception(self.exceptions, code, feedback) raise ExchangeError(feedback) else: return self.throw_exactly_matched_exception(self.exceptions, status, feedback) raise ExchangeError(feedback)

true

f70c1e0a237bb4b5225ab23400b8c3d6fa0b725c

3,648

py

Python

_scripts/nblint.py

dfreeman06/ipyradiant

6298889eb0d28c0dda01c4fc9d422814b9858878

[ "BSD-3-Clause" ]

null

_scripts/nblint.py

dfreeman06/ipyradiant

6298889eb0d28c0dda01c4fc9d422814b9858878

[ "BSD-3-Clause" ]

null

_scripts/nblint.py

dfreeman06/ipyradiant

6298889eb0d28c0dda01c4fc9d422814b9858878

[ "BSD-3-Clause" ]

null

""" linter and formatter of notebooks """ # Copyright (c) 2020 ipyradiant contributors. # Distributed under the terms of the Modified BSD License. import json import shutil import subprocess import sys from hashlib import sha256 from pathlib import Path import black import isort import nbformat from . import project as P NODE = [shutil.which("node") or shutil.which("node.exe") or shutil.which("node.cmd")] NB_METADATA_KEYS = ["kernelspec", "language_info"] def blacken(source): """apply black to a source string""" return black.format_str(source, mode=black.FileMode(line_length=88)) def nblint_one(nb_node): """format/lint one notebook""" changes = 0 has_empty = 0 nb_metadata_keys = list(nb_node.metadata.keys()) for key in nb_metadata_keys: if key not in NB_METADATA_KEYS: nb_node.metadata.pop(key) for cell in nb_node.cells: cell_type = cell["cell_type"] source = "".join(cell["source"]) if not source.strip(): has_empty += 1 if cell_type == "markdown": args = [ *P.PRETTIER, "--stdin-filepath", "foo.md", "--prose-wrap", "always", ] prettier = subprocess.Popen( list(map(str, args)), stdin=subprocess.PIPE, stdout=subprocess.PIPE, ) out, _err = prettier.communicate(source.encode("utf-8")) new = out.decode("utf-8").rstrip() if new != source: cell["source"] = new.splitlines(True) changes += 1 elif cell_type == "code": if cell["outputs"] or cell["execution_count"]: cell["outputs"] = [] cell["execution_count"] = None changes += 1 if [line for line in source.splitlines() if line.strip().startswith("!")]: continue if source.startswith("%"): continue new = isort.SortImports(file_contents=source).output new = blacken(new).rstrip() if new != source: cell["source"] = new.splitlines(True) changes += 1 if has_empty: changes += 1 nb_node.cells = [ cell for cell in nb_node.cells if "".join(cell["source"]).strip() ] return nb_node def nb_hash(nb_text): """hash one notebook""" return sha256(nb_text.encode("utf-8")).hexdigest() def nblint(nb_paths): """lint a number of notebook paths""" nb_hashes = {} if P.NBLINT_HASHES.exists(): nb_hashes = json.loads(P.NBLINT_HASHES.read_text()) len_paths = len(nb_paths) for i, nb_path in enumerate(nb_paths): hash_key = f"{nb_path}" log_hash = nb_hashes.get(hash_key) nb_text = nb_path.read_text() pre_hash = nb_hash(nb_text) print(f"[{i + 1} of {len_paths}] {nb_path}") if log_hash == pre_hash: continue nb_node = nblint_one(nbformat.reads(nb_text, 4)) with nb_path.open("w") as fpt: nbformat.write(nb_node, fpt) post_hash = nb_hash(nb_path.read_text()) if post_hash != pre_hash: print("\tformatted") else: print("\tno change") nb_hashes[hash_key] = post_hash P.NBLINT_HASHES.parent.mkdir(exist_ok=True, parents=True) P.NBLINT_HASHES.write_text(json.dumps(nb_hashes, indent=2, sort_keys=True)) return 0 if __name__ == "__main__": sys.exit(nblint([Path(p) for p in sys.argv[1:]] or P.EXAMPLE_IPYNB))

27.847328

86

0.573739

import json import shutil import subprocess import sys from hashlib import sha256 from pathlib import Path import black import isort import nbformat from . import project as P NODE = [shutil.which("node") or shutil.which("node.exe") or shutil.which("node.cmd")] NB_METADATA_KEYS = ["kernelspec", "language_info"] def blacken(source): return black.format_str(source, mode=black.FileMode(line_length=88)) def nblint_one(nb_node): changes = 0 has_empty = 0 nb_metadata_keys = list(nb_node.metadata.keys()) for key in nb_metadata_keys: if key not in NB_METADATA_KEYS: nb_node.metadata.pop(key) for cell in nb_node.cells: cell_type = cell["cell_type"] source = "".join(cell["source"]) if not source.strip(): has_empty += 1 if cell_type == "markdown": args = [ *P.PRETTIER, "--stdin-filepath", "foo.md", "--prose-wrap", "always", ] prettier = subprocess.Popen( list(map(str, args)), stdin=subprocess.PIPE, stdout=subprocess.PIPE, ) out, _err = prettier.communicate(source.encode("utf-8")) new = out.decode("utf-8").rstrip() if new != source: cell["source"] = new.splitlines(True) changes += 1 elif cell_type == "code": if cell["outputs"] or cell["execution_count"]: cell["outputs"] = [] cell["execution_count"] = None changes += 1 if [line for line in source.splitlines() if line.strip().startswith("!")]: continue if source.startswith("%"): continue new = isort.SortImports(file_contents=source).output new = blacken(new).rstrip() if new != source: cell["source"] = new.splitlines(True) changes += 1 if has_empty: changes += 1 nb_node.cells = [ cell for cell in nb_node.cells if "".join(cell["source"]).strip() ] return nb_node def nb_hash(nb_text): return sha256(nb_text.encode("utf-8")).hexdigest() def nblint(nb_paths): nb_hashes = {} if P.NBLINT_HASHES.exists(): nb_hashes = json.loads(P.NBLINT_HASHES.read_text()) len_paths = len(nb_paths) for i, nb_path in enumerate(nb_paths): hash_key = f"{nb_path}" log_hash = nb_hashes.get(hash_key) nb_text = nb_path.read_text() pre_hash = nb_hash(nb_text) print(f"[{i + 1} of {len_paths}] {nb_path}") if log_hash == pre_hash: continue nb_node = nblint_one(nbformat.reads(nb_text, 4)) with nb_path.open("w") as fpt: nbformat.write(nb_node, fpt) post_hash = nb_hash(nb_path.read_text()) if post_hash != pre_hash: print("\tformatted") else: print("\tno change") nb_hashes[hash_key] = post_hash P.NBLINT_HASHES.parent.mkdir(exist_ok=True, parents=True) P.NBLINT_HASHES.write_text(json.dumps(nb_hashes, indent=2, sort_keys=True)) return 0 if __name__ == "__main__": sys.exit(nblint([Path(p) for p in sys.argv[1:]] or P.EXAMPLE_IPYNB))

true

f70c1e5e6020c7c8e558bc2ed17aaf6cfa5c8b3f

812

py

Python

CraftProtocol/Protocol/v1_12_2/Packet/Play/KeepAliveServerPacket.py

Toranktto/CraftProtocol

a6f4a67756c3868820ab76df5e148d76b020d990

[ "MIT" ]

21

2018-05-12T20:18:02.000Z

2022-02-18T17:33:50.000Z

CraftProtocol/Protocol/v1_12_2/Packet/Play/KeepAliveServerPacket.py

Toranktto/CraftProtocol

a6f4a67756c3868820ab76df5e148d76b020d990

[ "MIT" ]

1

2018-06-23T09:13:39.000Z

2018-06-27T01:22:27.000Z

CraftProtocol/Protocol/v1_12_2/Packet/Play/KeepAliveServerPacket.py

Toranktto/CraftProtocol

a6f4a67756c3868820ab76df5e148d76b020d990

[ "MIT" ]

2

2018-05-19T21:36:00.000Z

2020-10-02T03:23:13.000Z

#!/usr/bin/env python from CraftProtocol.Protocol.Packet.BasePacket import BasePacket from CraftProtocol.Protocol.Packet.PacketDirection import PacketDirection from CraftProtocol.StreamIO import StreamIO class KeepAliveServerPacket(BasePacket): PACKET_ID = 0x0B PACKET_DIRECTION = PacketDirection.SERVERBOUND def __init__(self, keepalive_id): BasePacket.__init__(self) self._id = long(keepalive_id) def get_id(self): return self._id def set_id(self, keepalive_id): self._id = long(keepalive_id) @staticmethod def write(stream, packet): StreamIO.write_long(stream, packet.get_id()) @staticmethod def read(stream, packet_size): keepalive_id = StreamIO.read_long(stream) return KeepAliveServerPacket(keepalive_id)

26.193548

73

0.730296

from CraftProtocol.Protocol.Packet.BasePacket import BasePacket from CraftProtocol.Protocol.Packet.PacketDirection import PacketDirection from CraftProtocol.StreamIO import StreamIO class KeepAliveServerPacket(BasePacket): PACKET_ID = 0x0B PACKET_DIRECTION = PacketDirection.SERVERBOUND def __init__(self, keepalive_id): BasePacket.__init__(self) self._id = long(keepalive_id) def get_id(self): return self._id def set_id(self, keepalive_id): self._id = long(keepalive_id) @staticmethod def write(stream, packet): StreamIO.write_long(stream, packet.get_id()) @staticmethod def read(stream, packet_size): keepalive_id = StreamIO.read_long(stream) return KeepAliveServerPacket(keepalive_id)

true

f70c1fe0165b3d3a65f17e6891bf1b312cb2442d

7,155

py

Python

src/oci/log_analytics/models/classify_command_descriptor.py

xjuarez/oci-python-sdk

3c1604e4e212008fb6718e2f68cdb5ef71fd5793

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

3

2020-09-10T22:09:45.000Z

2021-12-24T17:00:07.000Z

src/oci/log_analytics/models/classify_command_descriptor.py

xjuarez/oci-python-sdk

3c1604e4e212008fb6718e2f68cdb5ef71fd5793

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

null

src/oci/log_analytics/models/classify_command_descriptor.py

xjuarez/oci-python-sdk

3c1604e4e212008fb6718e2f68cdb5ef71fd5793

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

null

# coding: utf-8 # Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. from .abstract_command_descriptor import AbstractCommandDescriptor from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class ClassifyCommandDescriptor(AbstractCommandDescriptor): """ Command descriptor for querylanguage CLASSIFY command. """ def __init__(self, **kwargs): """ Initializes a new ClassifyCommandDescriptor object with values from keyword arguments. The default value of the :py:attr:`~oci.log_analytics.models.ClassifyCommandDescriptor.name` attribute of this class is ``CLASSIFY`` and it should not be changed. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param name: The value to assign to the name property of this ClassifyCommandDescriptor. Allowed values for this property are: "COMMAND", "SEARCH", "STATS", "GEO_STATS", "TIME_STATS", "SORT", "FIELDS", "ADD_FIELDS", "LINK", "LINK_DETAILS", "CLUSTER", "CLUSTER_DETAILS", "CLUSTER_SPLIT", "EVAL", "EXTRACT", "JSON_EXTRACT", "XML_EXTRACT", "EVENT_STATS", "BUCKET", "CLASSIFY", "TOP", "BOTTOM", "HEAD", "TAIL", "FIELD_SUMMARY", "REGEX", "RENAME", "TIME_COMPARE", "WHERE", "CLUSTER_COMPARE", "DELETE", "DELTA", "DISTINCT", "SEARCH_LOOKUP", "LOOKUP", "DEMO_MODE", "MACRO", "MULTI_SEARCH", "HIGHLIGHT", "HIGHLIGHT_ROWS", "HIGHLIGHT_GROUPS", "CREATE_VIEW", "MAP", "NLP", "COMPARE" :type name: str :param display_query_string: The value to assign to the display_query_string property of this ClassifyCommandDescriptor. :type display_query_string: str :param internal_query_string: The value to assign to the internal_query_string property of this ClassifyCommandDescriptor. :type internal_query_string: str :param category: The value to assign to the category property of this ClassifyCommandDescriptor. :type category: str :param referenced_fields: The value to assign to the referenced_fields property of this ClassifyCommandDescriptor. :type referenced_fields: list[oci.log_analytics.models.AbstractField] :param declared_fields: The value to assign to the declared_fields property of this ClassifyCommandDescriptor. :type declared_fields: list[oci.log_analytics.models.AbstractField] :param top_count: The value to assign to the top_count property of this ClassifyCommandDescriptor. :type top_count: int :param bottom_count: The value to assign to the bottom_count property of this ClassifyCommandDescriptor. :type bottom_count: int :param correlate: The value to assign to the correlate property of this ClassifyCommandDescriptor. :type correlate: list[oci.log_analytics.models.FieldsAddRemoveField] """ self.swagger_types = { 'name': 'str', 'display_query_string': 'str', 'internal_query_string': 'str', 'category': 'str', 'referenced_fields': 'list[AbstractField]', 'declared_fields': 'list[AbstractField]', 'top_count': 'int', 'bottom_count': 'int', 'correlate': 'list[FieldsAddRemoveField]' } self.attribute_map = { 'name': 'name', 'display_query_string': 'displayQueryString', 'internal_query_string': 'internalQueryString', 'category': 'category', 'referenced_fields': 'referencedFields', 'declared_fields': 'declaredFields', 'top_count': 'topCount', 'bottom_count': 'bottomCount', 'correlate': 'correlate' } self._name = None self._display_query_string = None self._internal_query_string = None self._category = None self._referenced_fields = None self._declared_fields = None self._top_count = None self._bottom_count = None self._correlate = None self._name = 'CLASSIFY' @property def top_count(self): """ Gets the top_count of this ClassifyCommandDescriptor. Value specified in CLASSIFY command in queryString if set limits the results returned to top N. :return: The top_count of this ClassifyCommandDescriptor. :rtype: int """ return self._top_count @top_count.setter def top_count(self, top_count): """ Sets the top_count of this ClassifyCommandDescriptor. Value specified in CLASSIFY command in queryString if set limits the results returned to top N. :param top_count: The top_count of this ClassifyCommandDescriptor. :type: int """ self._top_count = top_count @property def bottom_count(self): """ Gets the bottom_count of this ClassifyCommandDescriptor. Value specified in CLASSIFY command in queryString if set limits the results returned to bottom N. :return: The bottom_count of this ClassifyCommandDescriptor. :rtype: int """ return self._bottom_count @bottom_count.setter def bottom_count(self, bottom_count): """ Sets the bottom_count of this ClassifyCommandDescriptor. Value specified in CLASSIFY command in queryString if set limits the results returned to bottom N. :param bottom_count: The bottom_count of this ClassifyCommandDescriptor. :type: int """ self._bottom_count = bottom_count @property def correlate(self): """ Gets the correlate of this ClassifyCommandDescriptor. Fields specified in CLASSIFY command in queryString if set include / exclude fields in correlate results. :return: The correlate of this ClassifyCommandDescriptor. :rtype: list[oci.log_analytics.models.FieldsAddRemoveField] """ return self._correlate @correlate.setter def correlate(self, correlate): """ Sets the correlate of this ClassifyCommandDescriptor. Fields specified in CLASSIFY command in queryString if set include / exclude fields in correlate results. :param correlate: The correlate of this ClassifyCommandDescriptor. :type: list[oci.log_analytics.models.FieldsAddRemoveField] """ self._correlate = correlate def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

40.196629

595

0.672397

from .abstract_command_descriptor import AbstractCommandDescriptor from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class ClassifyCommandDescriptor(AbstractCommandDescriptor): def __init__(self, **kwargs): self.swagger_types = { 'name': 'str', 'display_query_string': 'str', 'internal_query_string': 'str', 'category': 'str', 'referenced_fields': 'list[AbstractField]', 'declared_fields': 'list[AbstractField]', 'top_count': 'int', 'bottom_count': 'int', 'correlate': 'list[FieldsAddRemoveField]' } self.attribute_map = { 'name': 'name', 'display_query_string': 'displayQueryString', 'internal_query_string': 'internalQueryString', 'category': 'category', 'referenced_fields': 'referencedFields', 'declared_fields': 'declaredFields', 'top_count': 'topCount', 'bottom_count': 'bottomCount', 'correlate': 'correlate' } self._name = None self._display_query_string = None self._internal_query_string = None self._category = None self._referenced_fields = None self._declared_fields = None self._top_count = None self._bottom_count = None self._correlate = None self._name = 'CLASSIFY' @property def top_count(self): return self._top_count @top_count.setter def top_count(self, top_count): self._top_count = top_count @property def bottom_count(self): return self._bottom_count @bottom_count.setter def bottom_count(self, bottom_count): self._bottom_count = bottom_count @property def correlate(self): return self._correlate @correlate.setter def correlate(self, correlate): self._correlate = correlate def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f70c229a3106b39a3efca6e2d4e211e7c73f62f0

1,626

py

Python

projects/pub_utils.py

ChameleonCloud/portal

92a06fb926dc36e997b94fb8dcd22b7e0d24d3ee

[ "Apache-2.0" ]

3

2015-08-04T20:53:41.000Z

2020-02-14T22:58:20.000Z

projects/pub_utils.py

ChameleonCloud/portal

92a06fb926dc36e997b94fb8dcd22b7e0d24d3ee

[ "Apache-2.0" ]

103

2015-01-15T14:21:00.000Z

2022-03-31T19:14:20.000Z

projects/pub_utils.py

ChameleonCloud/portal

92a06fb926dc36e997b94fb8dcd22b7e0d24d3ee

[ "Apache-2.0" ]

4

2016-02-22T16:48:20.000Z

2021-01-08T17:13:21.000Z

import datetime import re class PublicationUtils: @staticmethod def get_month(bibtex_entry): month = bibtex_entry.get("month") m = None try: m = int(month) except Exception: pass try: m = datetime.datetime.strptime(month, "%b").month except Exception: pass try: m = datetime.datetime.strptime(month, "%B").month except Exception: pass return m @staticmethod def get_forum(bibtex_entry): forum = [] if "journal" in bibtex_entry: forum.append(bibtex_entry["journal"]) if "booktitle" in bibtex_entry: forum.append(bibtex_entry["booktitle"]) if "series" in bibtex_entry: forum.append(bibtex_entry["series"]) if "publisher" in bibtex_entry: forum.append(bibtex_entry["publisher"]) if "school" in bibtex_entry: forum.append(bibtex_entry["school"]) if "institution" in bibtex_entry: forum.append(bibtex_entry["institution"]) if "address" in bibtex_entry: forum.append(bibtex_entry["address"]) return ",".join(forum) @staticmethod def get_link(bibtex_entry): if "note" in bibtex_entry: m = re.search("^\\\\url{(.+?)}$", bibtex_entry["note"]) if m: return m.group(1) if "howpublished" in bibtex_entry: m = re.search("^\\\\url{(.+?)}$", bibtex_entry["howpublished"]) if m: return m.group(1) return None

29.563636

75

0.54797

import datetime import re class PublicationUtils: @staticmethod def get_month(bibtex_entry): month = bibtex_entry.get("month") m = None try: m = int(month) except Exception: pass try: m = datetime.datetime.strptime(month, "%b").month except Exception: pass try: m = datetime.datetime.strptime(month, "%B").month except Exception: pass return m @staticmethod def get_forum(bibtex_entry): forum = [] if "journal" in bibtex_entry: forum.append(bibtex_entry["journal"]) if "booktitle" in bibtex_entry: forum.append(bibtex_entry["booktitle"]) if "series" in bibtex_entry: forum.append(bibtex_entry["series"]) if "publisher" in bibtex_entry: forum.append(bibtex_entry["publisher"]) if "school" in bibtex_entry: forum.append(bibtex_entry["school"]) if "institution" in bibtex_entry: forum.append(bibtex_entry["institution"]) if "address" in bibtex_entry: forum.append(bibtex_entry["address"]) return ",".join(forum) @staticmethod def get_link(bibtex_entry): if "note" in bibtex_entry: m = re.search("^\\\\url{(.+?)}$", bibtex_entry["note"]) if m: return m.group(1) if "howpublished" in bibtex_entry: m = re.search("^\\\\url{(.+?)}$", bibtex_entry["howpublished"]) if m: return m.group(1) return None

true

f70c238c07f15da6c86f56503d1173d7e0edad0d

498

py

Python

blog/migrations/0019_user_avatar.py

dijiudu/django_blog-django2.0.3

b18889c4b9053b2a39c734c10a3bed84554d4303

[ "MIT" ]

137

2017-05-05T11:57:11.000Z

2021-01-06T18:56:56.000Z

blog/migrations/0019_user_avatar.py

dijiudu/django_blog-django2.0.3

b18889c4b9053b2a39c734c10a3bed84554d4303

[ "MIT" ]

10

2018-05-20T06:36:10.000Z

2022-03-11T23:19:21.000Z

blog/migrations/0019_user_avatar.py

wangchaocc21/django_blog

3fe8215e627960e933abe9548eda123987e94f13

[ "MIT" ]

24

2017-06-19T18:08:59.000Z

2019-02-02T04:15:13.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.2 on 2017-06-25 08:45 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blog', '0018_auto_20170625_1616'), ] operations = [ migrations.AddField( model_name='user', name='avatar', field=models.ImageField(blank=True, default='avatar/default.png', upload_to='avatar/%Y/%m'), ), ]

23.714286

104

0.620482

from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blog', '0018_auto_20170625_1616'), ] operations = [ migrations.AddField( model_name='user', name='avatar', field=models.ImageField(blank=True, default='avatar/default.png', upload_to='avatar/%Y/%m'), ), ]

true

f70c259df5a8437d1f62a29d06994107e133cb19

7,245

py

Python

seamseg/utils/coco_ap.py

gladcolor/seamseg

9e6c7e2828f32b311a7b0c16b279ac194e8aaf94

[ "BSD-3-Clause" ]

2

2021-01-11T08:57:40.000Z

2021-01-11T08:57:44.000Z

seamseg/utils/coco_ap.py

gladcolor/seamseg

9e6c7e2828f32b311a7b0c16b279ac194e8aaf94

[ "BSD-3-Clause" ]

null

seamseg/utils/coco_ap.py

gladcolor/seamseg

9e6c7e2828f32b311a7b0c16b279ac194e8aaf94

[ "BSD-3-Clause" ]

1

2020-09-28T07:55:50.000Z

import json import tempfile import time from collections import defaultdict from os import path, remove import numpy as np import torch import torch.distributed as dist from PIL import Image from pycocotools.coco import COCO as _COCO from pycocotools.cocoeval import COCOeval from pycocotools.mask import encode as mask_encode from .bbx import invert_roi_bbx, extract_boxes from .parallel import PackedSequence from .roi_sampling import roi_sampling def process_prediction(bbx_pred, cls_pred, obj_pred, msk_pred, img_size, idx, original_size): # Move everything to CPU bbx_pred, cls_pred, obj_pred = (t.cpu() for t in (bbx_pred, cls_pred, obj_pred)) msk_pred = msk_pred.cpu() if msk_pred is not None else None if msk_pred is not None: if isinstance(msk_pred, torch.Tensor): # ROI-stile prediction bbx_inv = invert_roi_bbx(bbx_pred, list(msk_pred.shape[-2:]), list(img_size)) bbx_idx = torch.arange(0, msk_pred.size(0), dtype=torch.long) msk_pred = roi_sampling(msk_pred.unsqueeze(1).sigmoid(), bbx_inv, bbx_idx, list(img_size), padding="zero") msk_pred = msk_pred.squeeze(1) > 0.5 elif isinstance(msk_pred, PackedSequence): # Seeds-style prediction msk_pred.data = msk_pred.data > 0.5 msk_pred_exp = msk_pred.data.new_zeros(len(msk_pred), img_size[0], img_size[1]) for it, (msk_pred_i, bbx_pred_i) in enumerate(zip(msk_pred, bbx_pred)): i, j = int(bbx_pred_i[0].item()), int(bbx_pred_i[1].item()) msk_pred_exp[it, i:i + msk_pred_i.size(0), j:j + msk_pred_i.size(1)] = msk_pred_i msk_pred = msk_pred_exp # Convert bbx and redo clamping bbx_pred[:, [0, 2]] = (bbx_pred[:, [0, 2]] / img_size[0] * original_size[0]).clamp(min=0, max=original_size[0]) bbx_pred[:, [1, 3]] = (bbx_pred[:, [1, 3]] / img_size[1] * original_size[1]).clamp(min=0, max=original_size[1]) bbx_pred_size = bbx_pred[:, 2:] - bbx_pred[:, :2] outs = [] for i, (bbx_pred_i, bbx_pred_size_i, cls_pred_i, obj_pred_i) in \ enumerate(zip(bbx_pred, bbx_pred_size, cls_pred, obj_pred)): out = dict(image_id=idx, category_id=int(cls_pred_i.item()), score=float(obj_pred_i.item())) out["bbox"] = [ float(bbx_pred_i[1].item()), float(bbx_pred_i[0].item()), float(bbx_pred_size_i[1].item()), float(bbx_pred_size_i[0].item()), ] # Expand and convert mask if present if msk_pred is not None: segmentation = Image.fromarray(msk_pred[i].numpy()).resize(original_size[::-1], Image.NEAREST) out["segmentation"] = mask_encode(np.asfortranarray(np.array(segmentation))) out["segmentation"]["counts"] = str(out["segmentation"]["counts"], "utf-8") outs.append(out) return outs def process_panoptic_prediction(panoptic_pred, num_stuff, idx, img_size, original_size): # Extract panoptic prediction msk_pred, cat_pred, obj_pred, iscrowd_pred = panoptic_pred bbx_pred = extract_boxes(msk_pred, cat_pred.numel()) # Convert bbx and redo clamping bbx_pred[:, [0, 2]] = (bbx_pred[:, [0, 2]] / img_size[0] * original_size[0]).clamp(min=0, max=original_size[0]) bbx_pred[:, [1, 3]] = (bbx_pred[:, [1, 3]] / img_size[1] * original_size[1]).clamp(min=0, max=original_size[1]) bbx_pred_size = bbx_pred[:, 2:] - bbx_pred[:, :2] outs = [] for i, (obj_i, cat_i, bbx_i, iscrowd_i, bbx_size_i) in enumerate(zip( obj_pred, cat_pred, bbx_pred, iscrowd_pred, bbx_pred_size)): if iscrowd_i.item() == 1 or cat_i.item() < num_stuff or cat_i.item() == 255: continue out = dict(image_id=idx, category_id=int(cat_i.item()), score=float(obj_i.item())) out["bbox"] = [ float(bbx_i[1].item()), float(bbx_i[0].item()), float(bbx_size_i[1].item()), float(bbx_size_i[0].item()), ] segmentation = msk_pred == i segmentation = Image.fromarray(segmentation.numpy()).resize(original_size[::-1], Image.NEAREST) out["segmentation"] = mask_encode(np.asfortranarray(np.array(segmentation))) out["segmentation"]["counts"] = str(out["segmentation"]["counts"], "utf-8") outs.append(out) return outs def summarize(predictions, annotations_file, img_list, mask=False): msk_map = 0 with tempfile.NamedTemporaryFile("w") as fid: json.dump(predictions, fid) fid.flush() # Detection gt = COCO(annotations_file, img_list) pred = gt.loadRes(fid.name) pred_eval = COCOeval(gt, pred, "bbox") pred_eval.evaluate() pred_eval.accumulate() pred_eval.summarize() det_map = pred_eval.stats[0] if mask: pred_eval = COCOeval(gt, pred, "segm") pred_eval.evaluate() pred_eval.accumulate() pred_eval.summarize() msk_map = pred_eval.stats[0] return det_map, msk_map def summarize_mp(predictions, annotations_file, img_list, log_dir, mask=False): # Write partial results to file (all workers) rank = dist.get_rank() with open(path.join(log_dir, "coco_ap_{:02d}.json".format(rank)), "w") as fid: json.dump(predictions, fid) with open(path.join(log_dir, "img_list_{:02d}.json".format(rank)), "w") as fid: json.dump(img_list, fid) dist.barrier() # Merge results from all workers and run evaluation (only rank 0) if rank == 0: predictions = [] img_list = [] for i in range(dist.get_world_size()): coco_ap_file = path.join(log_dir, "coco_ap_{:02d}.json".format(i)) with open(coco_ap_file) as fid: predictions += json.load(fid) remove(coco_ap_file) img_list_file = path.join(log_dir, "img_list_{:02d}.json".format(i)) with open(img_list_file) as fid: img_list += json.load(fid) remove(img_list_file) det_map, msk_map = summarize(predictions, annotations_file, img_list, mask) else: det_map, msk_map = 0, 0 dist.barrier() return det_map, msk_map class COCO(_COCO): """Modified COCO class that loads only a subset of""" def __init__(self, annotation_file, img_list): # load dataset self.dataset, self.anns, self.cats, self.imgs = dict(), dict(), dict(), dict() self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list) print('loading annotations into memory...') tic = time.time() dataset = json.load(open(annotation_file, 'r')) assert type(dataset) == dict, 'annotation file format {} not supported'.format(type(dataset)) print('Done (t={:0.2f}s)'.format(time.time() - tic)) # Clean-up dataset, removing all images and annotations that are not in img_list img_list = set(img_list) dataset["images"] = [img for img in dataset["images"] if img["id"] in img_list] dataset["annotations"] = [ann for ann in dataset["annotations"] if ann["image_id"] in img_list] self.dataset = dataset self.createIndex()

38.537234

118

0.633402

import json import tempfile import time from collections import defaultdict from os import path, remove import numpy as np import torch import torch.distributed as dist from PIL import Image from pycocotools.coco import COCO as _COCO from pycocotools.cocoeval import COCOeval from pycocotools.mask import encode as mask_encode from .bbx import invert_roi_bbx, extract_boxes from .parallel import PackedSequence from .roi_sampling import roi_sampling def process_prediction(bbx_pred, cls_pred, obj_pred, msk_pred, img_size, idx, original_size): bbx_pred, cls_pred, obj_pred = (t.cpu() for t in (bbx_pred, cls_pred, obj_pred)) msk_pred = msk_pred.cpu() if msk_pred is not None else None if msk_pred is not None: if isinstance(msk_pred, torch.Tensor): bbx_inv = invert_roi_bbx(bbx_pred, list(msk_pred.shape[-2:]), list(img_size)) bbx_idx = torch.arange(0, msk_pred.size(0), dtype=torch.long) msk_pred = roi_sampling(msk_pred.unsqueeze(1).sigmoid(), bbx_inv, bbx_idx, list(img_size), padding="zero") msk_pred = msk_pred.squeeze(1) > 0.5 elif isinstance(msk_pred, PackedSequence): msk_pred.data = msk_pred.data > 0.5 msk_pred_exp = msk_pred.data.new_zeros(len(msk_pred), img_size[0], img_size[1]) for it, (msk_pred_i, bbx_pred_i) in enumerate(zip(msk_pred, bbx_pred)): i, j = int(bbx_pred_i[0].item()), int(bbx_pred_i[1].item()) msk_pred_exp[it, i:i + msk_pred_i.size(0), j:j + msk_pred_i.size(1)] = msk_pred_i msk_pred = msk_pred_exp bbx_pred[:, [0, 2]] = (bbx_pred[:, [0, 2]] / img_size[0] * original_size[0]).clamp(min=0, max=original_size[0]) bbx_pred[:, [1, 3]] = (bbx_pred[:, [1, 3]] / img_size[1] * original_size[1]).clamp(min=0, max=original_size[1]) bbx_pred_size = bbx_pred[:, 2:] - bbx_pred[:, :2] outs = [] for i, (bbx_pred_i, bbx_pred_size_i, cls_pred_i, obj_pred_i) in \ enumerate(zip(bbx_pred, bbx_pred_size, cls_pred, obj_pred)): out = dict(image_id=idx, category_id=int(cls_pred_i.item()), score=float(obj_pred_i.item())) out["bbox"] = [ float(bbx_pred_i[1].item()), float(bbx_pred_i[0].item()), float(bbx_pred_size_i[1].item()), float(bbx_pred_size_i[0].item()), ] if msk_pred is not None: segmentation = Image.fromarray(msk_pred[i].numpy()).resize(original_size[::-1], Image.NEAREST) out["segmentation"] = mask_encode(np.asfortranarray(np.array(segmentation))) out["segmentation"]["counts"] = str(out["segmentation"]["counts"], "utf-8") outs.append(out) return outs def process_panoptic_prediction(panoptic_pred, num_stuff, idx, img_size, original_size): msk_pred, cat_pred, obj_pred, iscrowd_pred = panoptic_pred bbx_pred = extract_boxes(msk_pred, cat_pred.numel()) bbx_pred[:, [0, 2]] = (bbx_pred[:, [0, 2]] / img_size[0] * original_size[0]).clamp(min=0, max=original_size[0]) bbx_pred[:, [1, 3]] = (bbx_pred[:, [1, 3]] / img_size[1] * original_size[1]).clamp(min=0, max=original_size[1]) bbx_pred_size = bbx_pred[:, 2:] - bbx_pred[:, :2] outs = [] for i, (obj_i, cat_i, bbx_i, iscrowd_i, bbx_size_i) in enumerate(zip( obj_pred, cat_pred, bbx_pred, iscrowd_pred, bbx_pred_size)): if iscrowd_i.item() == 1 or cat_i.item() < num_stuff or cat_i.item() == 255: continue out = dict(image_id=idx, category_id=int(cat_i.item()), score=float(obj_i.item())) out["bbox"] = [ float(bbx_i[1].item()), float(bbx_i[0].item()), float(bbx_size_i[1].item()), float(bbx_size_i[0].item()), ] segmentation = msk_pred == i segmentation = Image.fromarray(segmentation.numpy()).resize(original_size[::-1], Image.NEAREST) out["segmentation"] = mask_encode(np.asfortranarray(np.array(segmentation))) out["segmentation"]["counts"] = str(out["segmentation"]["counts"], "utf-8") outs.append(out) return outs def summarize(predictions, annotations_file, img_list, mask=False): msk_map = 0 with tempfile.NamedTemporaryFile("w") as fid: json.dump(predictions, fid) fid.flush() gt = COCO(annotations_file, img_list) pred = gt.loadRes(fid.name) pred_eval = COCOeval(gt, pred, "bbox") pred_eval.evaluate() pred_eval.accumulate() pred_eval.summarize() det_map = pred_eval.stats[0] if mask: pred_eval = COCOeval(gt, pred, "segm") pred_eval.evaluate() pred_eval.accumulate() pred_eval.summarize() msk_map = pred_eval.stats[0] return det_map, msk_map def summarize_mp(predictions, annotations_file, img_list, log_dir, mask=False): rank = dist.get_rank() with open(path.join(log_dir, "coco_ap_{:02d}.json".format(rank)), "w") as fid: json.dump(predictions, fid) with open(path.join(log_dir, "img_list_{:02d}.json".format(rank)), "w") as fid: json.dump(img_list, fid) dist.barrier() if rank == 0: predictions = [] img_list = [] for i in range(dist.get_world_size()): coco_ap_file = path.join(log_dir, "coco_ap_{:02d}.json".format(i)) with open(coco_ap_file) as fid: predictions += json.load(fid) remove(coco_ap_file) img_list_file = path.join(log_dir, "img_list_{:02d}.json".format(i)) with open(img_list_file) as fid: img_list += json.load(fid) remove(img_list_file) det_map, msk_map = summarize(predictions, annotations_file, img_list, mask) else: det_map, msk_map = 0, 0 dist.barrier() return det_map, msk_map class COCO(_COCO): def __init__(self, annotation_file, img_list): self.dataset, self.anns, self.cats, self.imgs = dict(), dict(), dict(), dict() self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list) print('loading annotations into memory...') tic = time.time() dataset = json.load(open(annotation_file, 'r')) assert type(dataset) == dict, 'annotation file format {} not supported'.format(type(dataset)) print('Done (t={:0.2f}s)'.format(time.time() - tic)) img_list = set(img_list) dataset["images"] = [img for img in dataset["images"] if img["id"] in img_list] dataset["annotations"] = [ann for ann in dataset["annotations"] if ann["image_id"] in img_list] self.dataset = dataset self.createIndex()

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examples/quotes_avro/quotes_avro/pipelines.py

ZuInnoTe/scrapy-contrib-bigexporters

45428fcfc2c1531ac93a66d381f46ef70ccef1fe

[ "MIT" ]

9

2020-10-11T16:48:26.000Z

2022-03-22T22:49:55.000Z

examples/quotes_avro/quotes_avro/pipelines.py

ZuInnoTe/scrapy-contrib-bigexporters

45428fcfc2c1531ac93a66d381f46ef70ccef1fe

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2020-10-11T18:23:16.000Z

2022-03-24T16:50:34.000Z

examples/quotes_avro/quotes_avro/pipelines.py

ZuInnoTe/scrapy-contrib-bigexporters

45428fcfc2c1531ac93a66d381f46ef70ccef1fe

[ "MIT" ]

1

2022-03-31T20:00:04.000Z

# Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html # useful for handling different item types with a single interface from itemadapter import ItemAdapter class QuotesAvroPipeline: def process_item(self, item, spider): return item

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# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html # useful for handling different item types with a single interface from itemadapter import ItemAdapter class QuotesAvroPipeline: def process_item(self, item, spider): return item

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