Datasets:

PatrickHaller

/

the-stack-python-100k

like 0

17134739b8596a864d6e6e37035825d72ffbe045

py

Python

securetea/lib/antivirus/scanner/yara_scanner.py

neerajv18/SecureTea-Project

e999cbe7c8e497c69b76b4c886de0d063169ea03

2018-03-28T12:43:20.000Z

2022-03-29T07:07:23.000Z

securetea/lib/antivirus/scanner/yara_scanner.py

neerajv18/SecureTea-Project

e999cbe7c8e497c69b76b4c886de0d063169ea03

2018-03-31T14:57:46.000Z

2022-03-17T18:12:41.000Z

securetea/lib/antivirus/scanner/yara_scanner.py

neerajv18/SecureTea-Project

e999cbe7c8e497c69b76b4c886de0d063169ea03

2018-03-27T06:25:20.000Z

2022-03-28T11:32:45.000Z

# -*- coding: utf-8 -*- u"""Yara Scanner module for SecureTea AntiVirus. Project: ╔═╗┌─┐┌─┐┬ ┬┬─┐┌─┐╔╦╗┌─┐┌─┐ ╚═╗├┤ │ │ │├┬┘├┤ ║ ├┤ ├─┤ ╚═╝└─┘└─┘└─┘┴└─└─┘ ╩ └─┘┴ ┴ Author: Abhishek Sharma <abhishek_official@hotmail.com> , Jul 4 2019 Version: 1.4 Module: SecureTea """ from securetea.lib.antivirus.scanner.scanner_parent import Scanner import sys import os yara_status = True try: import yara except ImportError: yara_status = False print("[-] Yara not installed") except AttributeError: yara_status = False print("[-] Yara not configured: libyara.so not found") except Exception as e: yara_status = False print(e) class YaraScanner(Scanner): """YaraScanner class.""" def __init__(self, debug=False, config_path=None, vt_api_key=None, file_list=None): """ Initialize YaraEngine. Args: debug (bool): Log on terminal or not config_path (str): Configuration JSON file path vt_api_key (str): VirusTotal API Key file_list (list): List of files to scan Raises: None Returns: None """ # Initialize parent class super().__init__(debug, config_path, file_list, vt_api_key) if self.os_name: try: # Load threads self._WORKERS = self.config_dict[self.os_name]["scanner"]["yara"]["threads"] # Load Yara rules storage path self._YARA_STORAGE = self.config_dict[self.os_name]["update"]["yara"]["storage"] except KeyError: self.logger.log( "Could not load configuration for: {}".format(self.os_name), logtype="error" ) sys.exit(0) else: self.logger.log( "Could not determine the OS", logtype="error" ) sys.exit(0) def scan_file(self, file_path): """ Scan file using Yara rules. Args: file_path (str): Path of the file to scan Raises: None Returns: None """ if yara_status: yara_files_list = os.listdir(self._YARA_STORAGE) for yara_file in yara_files_list: if yara_file.endswith(".yar") or yara_file.endswith(".yara"): yara_file_path = os.path.join(self._YARA_STORAGE, yara_file) rule_compile = yara.compile(yara_file_path) matches = rule_compile.match(file_path) if matches: self.logger.log( "Possible malicious file detected: {0}".format(file_path), logtype="warning" ) if file_path not in self.malicious_file_list: self.malicious_file_list.append(file_path) super().check_virus_total(file_path) return return

20144ab955135ad084cf0fef939398cc14bdd008

py

Python

server/app/services/products/views/products.py

goodfree/ActorCloud

e8db470830ea6f6f208ad43c2e56a2e8976bc468

2019-06-10T07:14:49.000Z

2022-03-31T08:42:36.000Z

server/app/services/products/views/products.py

zlyz12345/ActorCloud

9c34b371c23464981323ef9865d9913bde1fe09c

2019-06-12T08:25:29.000Z

2022-02-26T11:37:15.000Z

server/app/services/products/views/products.py

zlyz12345/ActorCloud

9c34b371c23464981323ef9865d9913bde1fe09c

2019-06-10T08:40:05.000Z

2022-03-09T03:43:56.000Z

from flask import jsonify from sqlalchemy import func from sqlalchemy.exc import IntegrityError from actor_libs.database.orm import db from actor_libs.errors import ReferencedError from actor_libs.utils import get_delete_ids from app import auth from app.models import DataPoint, DataStream, Device, Product, User from app.schemas import ProductSchema, UpdateProductSchema from . import bp @bp.route('/products') @auth.login_required def list_products(): code_list = ['cloudProtocol', 'productType'] records = Product.query.pagination(code_list=code_list) # Count the number of devices, applications, # data points, and data streams of the product records_item = records['items'] records['items'] = records_item_count(records_item) return jsonify(records) @bp.route('/products/<int:product_id>') @auth.login_required def view_product(product_id): code_list = ['cloudProtocol', 'productType'] record = Product.query \ .outerjoin(Device, Device.productID == Product.productID) \ .join(User, User.id == Product.userIntID) \ .with_entities(Product, User.username.label('createUser'), func.count(Device.id).label('deviceCount')) \ .filter(Product.id == product_id) \ .group_by(Product.id, User.username).to_dict(code_list=code_list) return jsonify(record) @bp.route('/products', methods=['POST']) @auth.login_required def create_product(): request_dict = ProductSchema.validate_request() product = Product() created_product = product.create(request_dict) record = created_product.to_dict() return jsonify(record), 201 @bp.route('/products/<int:product_id>', methods=['PUT']) @auth.login_required def update_product(product_id): product = Product.query.filter(Product.id == product_id).first_or_404() request_dict = UpdateProductSchema.validate_request(obj=product) updated_product = product.update(request_dict) record = updated_product.to_dict() return jsonify(record) @bp.route('/products', methods=['DELETE']) @auth.login_required def delete_product(): delete_ids = get_delete_ids() query_results = Product.query \ .filter(Product.id.in_(delete_ids)) \ .many(allow_none=False, expect_result=len(delete_ids)) # check device is included in the delete product device_count = db.session.query(func.count(Device.id)) \ .join(Product, Device.productID == Product.productID) \ .filter(Product.id.in_(delete_ids)) \ .scalar() if device_count: raise ReferencedError(field='device') try: for product in query_results: db.session.delete(product) db.session.commit() except IntegrityError: raise ReferencedError() return '', 204 def records_item_count(records_item): product_dict = { item['productID']: item['cloudProtocol'] for item in records_item } product_uids = product_dict.keys() # Device count query = db.session \ .query(Product.productID, func.count(Device.id)) \ .outerjoin(Device, Device.productID == Product.productID) \ .group_by(Product.productID) \ .filter(Product.productID.in_(product_uids)).all() product_device_dict = dict(query) # data_point,data_stream or product_item(lwm2m) count query = db.session \ .query(Product.productID, func.count(DataPoint.id)) \ .outerjoin(DataPoint, DataPoint.productID == Product.productID) \ .group_by(Product.productID) \ .filter(Product.productID.in_(product_uids)) \ .all() product_point_dict = dict(query) query = db.session \ .query(Product.productID, func.count(DataStream.id)) \ .outerjoin(DataStream, DataStream.productID == Product.productID) \ .group_by(Product.productID) \ .filter(Product.productID.in_(product_uids)) \ .all() product_stream_dict = dict(query) for record in records_item: record_product_uid = record['productID'] record['deviceCount'] = product_device_dict.get(record_product_uid, 0) record['dataPointCount'] = product_point_dict.get(record_product_uid, 0) record['dataStreamCount'] = product_stream_dict.get(record_product_uid, 0) return records_item

a1f17c6dbe84da7d775bb580e842005296053491

py

Python

hooks/post_gen_project.py

BrianPugh/cookiecutter-pypackage

ec8b51cb59d2436d77ca1d802991103dd37c9a95

hooks/post_gen_project.py

BrianPugh/cookiecutter-pypackage

ec8b51cb59d2436d77ca1d802991103dd37c9a95

hooks/post_gen_project.py

BrianPugh/cookiecutter-pypackage

ec8b51cb59d2436d77ca1d802991103dd37c9a95

import os import os.path as osp import subprocess COOKIECUTTER_REPO_NAME = 'cookiecutter-pypackage' par_dir_path = osp.normpath(osp.join(osp.abspath(osp.curdir), osp.pardir)) if osp.basename(par_dir_path) == COOKIECUTTER_REPO_NAME: # This was most likely called `cookiecutter .` cookiecutter_repo_path = par_dir_path else: # This was most likely called as `cookeicutter git@bitbucket.org:geomagical/labtech-wrapper.git` # This is the canonical location for the cached cookiecutter template cookiecutter_repo_path = osp.join(os.environ['HOME'], '.cookiecutters', COOKIECUTTER_REPO_NAME) # Obtain Cookiecutter repo path cookiecutter_hash = subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=cookiecutter_repo_path) cookiecutter_hash = cookiecutter_hash.strip().decode('utf-8') cookiecutter_uri = subprocess.check_output(["git", "config", "--get", "remote.origin.url"], cwd=cookiecutter_repo_path) cookiecutter_uri = cookiecutter_uri.strip().decode('uft-8') ####################### # Setting up git repo # ####################### shell_cmds = [ """git init""", """git remote add origin git@github.com:{{cookiecutter.github_username}}/{{project_slug}}.git""", """git add *""", """git add .gitignore""", f'''git commit -m "Initial commit from cookiecutter {cookiecutter_uri} commit {cookiecutter_hash}"''', ] for cmd in shell_cmds: subprocess.call(cmd, shell=True) print("=======================================================================") print("Project setup complete. If you are happy with the setup, run:") print(" git push origin master")

272f9a294cfa1016c03db2b08daaf22b1cdf0748

py

Python

sdk/python/pulumi_azure_native/devtestlab/policy.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

sdk/python/pulumi_azure_native/devtestlab/policy.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

sdk/python/pulumi_azure_native/devtestlab/policy.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

# 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 ._enums import * __all__ = ['Policy'] class Policy(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, description: Optional[pulumi.Input[str]] = None, evaluator_type: Optional[pulumi.Input[Union[str, 'PolicyEvaluatorType']]] = None, fact_data: Optional[pulumi.Input[str]] = None, fact_name: Optional[pulumi.Input[Union[str, 'PolicyFactName']]] = None, lab_name: Optional[pulumi.Input[str]] = None, location: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, policy_set_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, status: Optional[pulumi.Input[Union[str, 'PolicyStatus']]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, threshold: Optional[pulumi.Input[str]] = None, __props__=None, __name__=None, __opts__=None): """ A Policy. API Version: 2018-09-15. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] description: The description of the policy. :param pulumi.Input[Union[str, 'PolicyEvaluatorType']] evaluator_type: The evaluator type of the policy (i.e. AllowedValuesPolicy, MaxValuePolicy). :param pulumi.Input[str] fact_data: The fact data of the policy. :param pulumi.Input[Union[str, 'PolicyFactName']] fact_name: The fact name of the policy (e.g. LabVmCount, LabVmSize, MaxVmsAllowedPerLab, etc. :param pulumi.Input[str] lab_name: The name of the lab. :param pulumi.Input[str] location: The location of the resource. :param pulumi.Input[str] name: The name of the policy. :param pulumi.Input[str] policy_set_name: The name of the policy set. :param pulumi.Input[str] resource_group_name: The name of the resource group. :param pulumi.Input[Union[str, 'PolicyStatus']] status: The status of the policy. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: The tags of the resource. :param pulumi.Input[str] threshold: The threshold of the policy (i.e. a number for MaxValuePolicy, and a JSON array of values for AllowedValuesPolicy). """ 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__['description'] = description __props__['evaluator_type'] = evaluator_type __props__['fact_data'] = fact_data __props__['fact_name'] = fact_name if lab_name is None and not opts.urn: raise TypeError("Missing required property 'lab_name'") __props__['lab_name'] = lab_name __props__['location'] = location __props__['name'] = name if policy_set_name is None and not opts.urn: raise TypeError("Missing required property 'policy_set_name'") __props__['policy_set_name'] = policy_set_name if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name __props__['status'] = status __props__['tags'] = tags __props__['threshold'] = threshold __props__['created_date'] = None __props__['provisioning_state'] = None __props__['type'] = None __props__['unique_identifier'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:devtestlab:Policy"), pulumi.Alias(type_="azure-native:devtestlab/latest:Policy"), pulumi.Alias(type_="azure-nextgen:devtestlab/latest:Policy"), pulumi.Alias(type_="azure-native:devtestlab/v20150521preview:Policy"), pulumi.Alias(type_="azure-nextgen:devtestlab/v20150521preview:Policy"), pulumi.Alias(type_="azure-native:devtestlab/v20160515:Policy"), pulumi.Alias(type_="azure-nextgen:devtestlab/v20160515:Policy"), pulumi.Alias(type_="azure-native:devtestlab/v20180915:Policy"), pulumi.Alias(type_="azure-nextgen:devtestlab/v20180915:Policy")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(Policy, __self__).__init__( 'azure-native:devtestlab:Policy', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'Policy': """ Get an existing Policy 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() __props__["created_date"] = None __props__["description"] = None __props__["evaluator_type"] = None __props__["fact_data"] = None __props__["fact_name"] = None __props__["location"] = None __props__["name"] = None __props__["provisioning_state"] = None __props__["status"] = None __props__["tags"] = None __props__["threshold"] = None __props__["type"] = None __props__["unique_identifier"] = None return Policy(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="createdDate") def created_date(self) -> pulumi.Output[str]: """ The creation date of the policy. """ return pulumi.get(self, "created_date") @property @pulumi.getter def description(self) -> pulumi.Output[Optional[str]]: """ The description of the policy. """ return pulumi.get(self, "description") @property @pulumi.getter(name="evaluatorType") def evaluator_type(self) -> pulumi.Output[Optional[str]]: """ The evaluator type of the policy (i.e. AllowedValuesPolicy, MaxValuePolicy). """ return pulumi.get(self, "evaluator_type") @property @pulumi.getter(name="factData") def fact_data(self) -> pulumi.Output[Optional[str]]: """ The fact data of the policy. """ return pulumi.get(self, "fact_data") @property @pulumi.getter(name="factName") def fact_name(self) -> pulumi.Output[Optional[str]]: """ The fact name of the policy (e.g. LabVmCount, LabVmSize, MaxVmsAllowedPerLab, etc. """ return pulumi.get(self, "fact_name") @property @pulumi.getter def location(self) -> pulumi.Output[Optional[str]]: """ The location of the resource. """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the resource. """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[str]: """ The provisioning status of the resource. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter def status(self) -> pulumi.Output[Optional[str]]: """ The status of the policy. """ return pulumi.get(self, "status") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: """ The tags of the resource. """ return pulumi.get(self, "tags") @property @pulumi.getter def threshold(self) -> pulumi.Output[Optional[str]]: """ The threshold of the policy (i.e. a number for MaxValuePolicy, and a JSON array of values for AllowedValuesPolicy). """ return pulumi.get(self, "threshold") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ The type of the resource. """ return pulumi.get(self, "type") @property @pulumi.getter(name="uniqueIdentifier") def unique_identifier(self) -> pulumi.Output[str]: """ The unique immutable identifier of a resource (Guid). """ return pulumi.get(self, "unique_identifier") 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

419ed8ebc49f720d92ff045b931926cee71ebb7f

py

Python

tests/compute/test_sampling.py

LunaBlack/dgl

bd1e48a51e348b0e8e25622325adeb5ddea1c0ea

2021-12-09T12:36:13.000Z

2022-03-01T21:22:36.000Z

tests/compute/test_sampling.py

LunaBlack/dgl

bd1e48a51e348b0e8e25622325adeb5ddea1c0ea

tests/compute/test_sampling.py

LunaBlack/dgl

bd1e48a51e348b0e8e25622325adeb5ddea1c0ea

2020-12-07T09:34:01.000Z

2020-12-13T06:18:58.000Z

import dgl import backend as F import numpy as np import unittest def check_random_walk(g, metapath, traces, ntypes, prob=None): traces = F.asnumpy(traces) ntypes = F.asnumpy(ntypes) for j in range(traces.shape[1] - 1): assert ntypes[j] == g.get_ntype_id(g.to_canonical_etype(metapath[j])[0]) assert ntypes[j + 1] == g.get_ntype_id(g.to_canonical_etype(metapath[j])[2]) for i in range(traces.shape[0]): for j in range(traces.shape[1] - 1): assert g.has_edge_between( traces[i, j], traces[i, j+1], etype=metapath[j]) if prob is not None and prob in g.edges[metapath[j]].data: p = F.asnumpy(g.edges[metapath[j]].data['p']) eids = g.edge_id(traces[i, j], traces[i, j+1], etype=metapath[j]) assert p[eids] != 0 @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU random walk not implemented") def test_random_walk(): g1 = dgl.heterograph({ ('user', 'follow', 'user'): [(0, 1), (1, 2), (2, 0)] }) g2 = dgl.heterograph({ ('user', 'follow', 'user'): [(0, 1), (1, 2), (1, 3), (2, 0), (3, 0)] }) g3 = dgl.heterograph({ ('user', 'follow', 'user'): [(0, 1), (1, 2), (2, 0)], ('user', 'view', 'item'): [(0, 0), (1, 1), (2, 2)], ('item', 'viewed-by', 'user'): [(0, 0), (1, 1), (2, 2)]}) g4 = dgl.heterograph({ ('user', 'follow', 'user'): [(0, 1), (1, 2), (1, 3), (2, 0), (3, 0)], ('user', 'view', 'item'): [(0, 0), (0, 1), (1, 1), (2, 2), (3, 2), (3, 1)], ('item', 'viewed-by', 'user'): [(0, 0), (1, 0), (1, 1), (2, 2), (2, 3), (1, 3)]}) g2.edata['p'] = F.tensor([3, 0, 3, 3, 3], dtype=F.float32) g2.edata['p2'] = F.tensor([[3], [0], [3], [3], [3]], dtype=F.float32) g4.edges['follow'].data['p'] = F.tensor([3, 0, 3, 3, 3], dtype=F.float32) g4.edges['viewed-by'].data['p'] = F.tensor([1, 1, 1, 1, 1, 1], dtype=F.float32) traces, ntypes = dgl.sampling.random_walk(g1, [0, 1, 2, 0, 1, 2], length=4) check_random_walk(g1, ['follow'] * 4, traces, ntypes) traces, ntypes = dgl.sampling.random_walk(g1, [0, 1, 2, 0, 1, 2], length=4, restart_prob=0.) check_random_walk(g1, ['follow'] * 4, traces, ntypes) traces, ntypes = dgl.sampling.random_walk( g1, [0, 1, 2, 0, 1, 2], length=4, restart_prob=F.zeros((4,), F.float32, F.cpu())) check_random_walk(g1, ['follow'] * 4, traces, ntypes) traces, ntypes = dgl.sampling.random_walk( g1, [0, 1, 2, 0, 1, 2], length=5, restart_prob=F.tensor([0, 0, 0, 0, 1], dtype=F.float32)) check_random_walk( g1, ['follow'] * 4, F.slice_axis(traces, 1, 0, 5), F.slice_axis(ntypes, 0, 0, 5)) assert (F.asnumpy(traces)[:, 5] == -1).all() traces, ntypes = dgl.sampling.random_walk( g2, [0, 1, 2, 3, 0, 1, 2, 3], length=4) check_random_walk(g2, ['follow'] * 4, traces, ntypes) traces, ntypes = dgl.sampling.random_walk( g2, [0, 1, 2, 3, 0, 1, 2, 3], length=4, prob='p') check_random_walk(g2, ['follow'] * 4, traces, ntypes, 'p') try: traces, ntypes = dgl.sampling.random_walk( g2, [0, 1, 2, 3, 0, 1, 2, 3], length=4, prob='p2') fail = False except dgl.DGLError: fail = True assert fail metapath = ['follow', 'view', 'viewed-by'] * 2 traces, ntypes = dgl.sampling.random_walk( g3, [0, 1, 2, 0, 1, 2], metapath=metapath) check_random_walk(g3, metapath, traces, ntypes) metapath = ['follow', 'view', 'viewed-by'] * 2 traces, ntypes = dgl.sampling.random_walk( g4, [0, 1, 2, 3, 0, 1, 2, 3], metapath=metapath) check_random_walk(g4, metapath, traces, ntypes) metapath = ['follow', 'view', 'viewed-by'] * 2 traces, ntypes = dgl.sampling.random_walk( g4, [0, 1, 2, 3, 0, 1, 2, 3], metapath=metapath, prob='p') check_random_walk(g4, metapath, traces, ntypes, 'p') traces, ntypes = dgl.sampling.random_walk( g4, [0, 1, 2, 3, 0, 1, 2, 3], metapath=metapath, prob='p', restart_prob=0.) check_random_walk(g4, metapath, traces, ntypes, 'p') traces, ntypes = dgl.sampling.random_walk( g4, [0, 1, 2, 3, 0, 1, 2, 3], metapath=metapath, prob='p', restart_prob=F.zeros((6,), F.float32, F.cpu())) check_random_walk(g4, metapath, traces, ntypes, 'p') traces, ntypes = dgl.sampling.random_walk( g4, [0, 1, 2, 3, 0, 1, 2, 3], metapath=metapath + ['follow'], prob='p', restart_prob=F.tensor([0, 0, 0, 0, 0, 0, 1], F.float32)) check_random_walk(g4, metapath, traces[:, :7], ntypes[:7], 'p') assert (F.asnumpy(traces[:, 7]) == -1).all() @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU pack traces not implemented") def test_pack_traces(): traces, types = (np.array( [[ 0, 1, -1, -1, -1, -1, -1], [ 0, 1, 1, 3, 0, 0, 0]], dtype='int64'), np.array([0, 0, 1, 0, 0, 1, 0], dtype='int64')) traces = F.zerocopy_from_numpy(traces) types = F.zerocopy_from_numpy(types) result = dgl.sampling.pack_traces(traces, types) assert F.array_equal(result[0], F.tensor([0, 1, 0, 1, 1, 3, 0, 0, 0], dtype=F.int64)) assert F.array_equal(result[1], F.tensor([0, 0, 0, 0, 1, 0, 0, 1, 0], dtype=F.int64)) assert F.array_equal(result[2], F.tensor([2, 7], dtype=F.int64)) assert F.array_equal(result[3], F.tensor([0, 2], dtype=F.int64)) def test_pinsage_sampling(): def _test_sampler(g, sampler, ntype): neighbor_g = sampler(F.tensor([0, 2], dtype=F.int64)) assert neighbor_g.ntypes == [ntype] u, v = neighbor_g.all_edges(form='uv', order='eid') uv = list(zip(F.asnumpy(u).tolist(), F.asnumpy(v).tolist())) assert (1, 0) in uv or (0, 0) in uv assert (2, 2) in uv or (3, 2) in uv g = dgl.heterograph({ ('item', 'bought-by', 'user'): [(0, 0), (0, 1), (1, 0), (1, 1), (2, 2), (2, 3), (3, 2), (3, 3)], ('user', 'bought', 'item'): [(0, 0), (1, 0), (0, 1), (1, 1), (2, 2), (3, 2), (2, 3), (3, 3)]}) sampler = dgl.sampling.PinSAGESampler(g, 'item', 'user', 4, 0.5, 3, 2) _test_sampler(g, sampler, 'item') sampler = dgl.sampling.RandomWalkNeighborSampler(g, 4, 0.5, 3, 2, ['bought-by', 'bought']) _test_sampler(g, sampler, 'item') sampler = dgl.sampling.RandomWalkNeighborSampler(g, 4, 0.5, 3, 2, [('item', 'bought-by', 'user'), ('user', 'bought', 'item')]) _test_sampler(g, sampler, 'item') g = dgl.graph([(0, 0), (0, 1), (1, 0), (1, 1), (2, 2), (2, 3), (3, 2), (3, 3)]) sampler = dgl.sampling.RandomWalkNeighborSampler(g, 4, 0.5, 3, 2) _test_sampler(g, sampler, g.ntypes[0]) g = dgl.heterograph({ ('A', 'AB', 'B'): [(0, 1), (2, 3)], ('B', 'BC', 'C'): [(1, 2), (3, 1)], ('C', 'CA', 'A'): [(2, 0), (1, 2)]}) sampler = dgl.sampling.RandomWalkNeighborSampler(g, 4, 0.5, 3, 2, ['AB', 'BC', 'CA']) _test_sampler(g, sampler, 'A') def _gen_neighbor_sampling_test_graph(hypersparse, reverse): if hypersparse: # should crash if allocated a CSR card = 1 << 50 card2 = (1 << 50, 1 << 50) else: card = None card2 = None if reverse: g = dgl.graph([(0,1),(0,2),(0,3),(1,0),(1,2),(1,3),(2,0)], 'user', 'follow', num_nodes=card) g.edata['prob'] = F.tensor([.5, .5, 0., .5, .5, 0., 1.], dtype=F.float32) g1 = dgl.bipartite([(0,0),(1,0),(2,1),(2,3)], 'game', 'play', 'user', num_nodes=card2) g1.edata['prob'] = F.tensor([.8, .5, .5, .5], dtype=F.float32) g2 = dgl.bipartite([(0,2),(1,2),(2,2),(0,1),(3,1),(0,0)], 'user', 'liked-by', 'game', num_nodes=card2) g2.edata['prob'] = F.tensor([.3, .5, .2, .5, .1, .1], dtype=F.float32) g3 = dgl.bipartite([(0,0),(0,1),(0,2),(0,3)], 'coin', 'flips', 'user', num_nodes=card2) hg = dgl.hetero_from_relations([g, g1, g2, g3]) else: g = dgl.graph([(1,0),(2,0),(3,0),(0,1),(2,1),(3,1),(0,2)], 'user', 'follow', num_nodes=card) g.edata['prob'] = F.tensor([.5, .5, 0., .5, .5, 0., 1.], dtype=F.float32) g1 = dgl.bipartite([(0,0),(0,1),(1,2),(3,2)], 'user', 'play', 'game', num_nodes=card2) g1.edata['prob'] = F.tensor([.8, .5, .5, .5], dtype=F.float32) g2 = dgl.bipartite([(2,0),(2,1),(2,2),(1,0),(1,3),(0,0)], 'game', 'liked-by', 'user', num_nodes=card2) g2.edata['prob'] = F.tensor([.3, .5, .2, .5, .1, .1], dtype=F.float32) g3 = dgl.bipartite([(0,0),(1,0),(2,0),(3,0)], 'user', 'flips', 'coin', num_nodes=card2) hg = dgl.hetero_from_relations([g, g1, g2, g3]) return g, hg def _gen_neighbor_topk_test_graph(hypersparse, reverse): if hypersparse: # should crash if allocated a CSR card = 1 << 50 card2 = (1 << 50, 1 << 50) else: card = None card2 = None if reverse: g = dgl.graph([(0,1),(0,2),(0,3),(1,0),(1,2),(1,3),(2,0)], 'user', 'follow') g.edata['weight'] = F.tensor([.5, .3, 0., -5., 22., 0., 1.], dtype=F.float32) g1 = dgl.bipartite([(0,0),(1,0),(2,1),(2,3)], 'game', 'play', 'user') g1.edata['weight'] = F.tensor([.8, .5, .4, .5], dtype=F.float32) g2 = dgl.bipartite([(0,2),(1,2),(2,2),(0,1),(3,1),(0,0)], 'user', 'liked-by', 'game') g2.edata['weight'] = F.tensor([.3, .5, .2, .5, .1, .1], dtype=F.float32) g3 = dgl.bipartite([(0,0),(0,1),(0,2),(0,3)], 'coin', 'flips', 'user') g3.edata['weight'] = F.tensor([10, 2, 13, -1], dtype=F.float32) hg = dgl.hetero_from_relations([g, g1, g2, g3]) else: g = dgl.graph([(1,0),(2,0),(3,0),(0,1),(2,1),(3,1),(0,2)], 'user', 'follow') g.edata['weight'] = F.tensor([.5, .3, 0., -5., 22., 0., 1.], dtype=F.float32) g1 = dgl.bipartite([(0,0),(0,1),(1,2),(3,2)], 'user', 'play', 'game') g1.edata['weight'] = F.tensor([.8, .5, .4, .5], dtype=F.float32) g2 = dgl.bipartite([(2,0),(2,1),(2,2),(1,0),(1,3),(0,0)], 'game', 'liked-by', 'user') g2.edata['weight'] = F.tensor([.3, .5, .2, .5, .1, .1], dtype=F.float32) g3 = dgl.bipartite([(0,0),(1,0),(2,0),(3,0)], 'user', 'flips', 'coin') g3.edata['weight'] = F.tensor([10, 2, 13, -1], dtype=F.float32) hg = dgl.hetero_from_relations([g, g1, g2, g3]) return g, hg def _test_sample_neighbors(hypersparse): g, hg = _gen_neighbor_sampling_test_graph(hypersparse, False) def _test1(p, replace): for i in range(10): subg = dgl.sampling.sample_neighbors(g, [0, 1], 2, prob=p, replace=replace) assert subg.number_of_nodes() == g.number_of_nodes() assert subg.number_of_edges() == 4 u, v = subg.edges() assert set(F.asnumpy(F.unique(v))) == {0, 1} assert F.array_equal(g.has_edges_between(u, v), F.ones((4,), dtype=F.int64)) assert F.array_equal(g.edge_ids(u, v), subg.edata[dgl.EID]) edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) if not replace: # check no duplication assert len(edge_set) == 4 if p is not None: assert not (3, 0) in edge_set assert not (3, 1) in edge_set _test1(None, True) # w/ replacement, uniform _test1(None, False) # w/o replacement, uniform _test1('prob', True) # w/ replacement _test1('prob', False) # w/o replacement def _test2(p, replace): # fanout > #neighbors for i in range(10): subg = dgl.sampling.sample_neighbors(g, [0, 2], 2, prob=p, replace=replace) assert subg.number_of_nodes() == g.number_of_nodes() num_edges = 4 if replace else 3 assert subg.number_of_edges() == num_edges u, v = subg.edges() assert set(F.asnumpy(F.unique(v))) == {0, 2} assert F.array_equal(g.has_edges_between(u, v), F.ones((num_edges,), dtype=F.int64)) assert F.array_equal(g.edge_ids(u, v), subg.edata[dgl.EID]) edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) if not replace: # check no duplication assert len(edge_set) == num_edges if p is not None: assert not (3, 0) in edge_set _test2(None, True) # w/ replacement, uniform _test2(None, False) # w/o replacement, uniform _test2('prob', True) # w/ replacement _test2('prob', False) # w/o replacement def _test3(p, replace): for i in range(10): subg = dgl.sampling.sample_neighbors(hg, {'user' : [0,1], 'game' : 0}, 2, prob=p, replace=replace) assert len(subg.ntypes) == 3 assert len(subg.etypes) == 4 assert subg['follow'].number_of_edges() == 4 assert subg['play'].number_of_edges() == 2 if replace else 1 assert subg['liked-by'].number_of_edges() == 4 if replace else 3 assert subg['flips'].number_of_edges() == 0 _test3(None, True) # w/ replacement, uniform _test3(None, False) # w/o replacement, uniform _test3('prob', True) # w/ replacement _test3('prob', False) # w/o replacement # test different fanouts for different relations for i in range(10): subg = dgl.sampling.sample_neighbors( hg, {'user' : [0,1], 'game' : 0}, {'follow': 1, 'play': 2, 'liked-by': 0, 'flips': 2}, replace=True) assert len(subg.ntypes) == 3 assert len(subg.etypes) == 4 assert subg['follow'].number_of_edges() == 2 assert subg['play'].number_of_edges() == 2 assert subg['liked-by'].number_of_edges() == 0 assert subg['flips'].number_of_edges() == 0 def _test_sample_neighbors_outedge(hypersparse): g, hg = _gen_neighbor_sampling_test_graph(hypersparse, True) def _test1(p, replace): for i in range(10): subg = dgl.sampling.sample_neighbors(g, [0, 1], 2, prob=p, replace=replace, edge_dir='out') assert subg.number_of_nodes() == g.number_of_nodes() assert subg.number_of_edges() == 4 u, v = subg.edges() assert set(F.asnumpy(F.unique(u))) == {0, 1} assert F.array_equal(g.has_edges_between(u, v), F.ones((4,), dtype=F.int64)) assert F.array_equal(g.edge_ids(u, v), subg.edata[dgl.EID]) edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) if not replace: # check no duplication assert len(edge_set) == 4 if p is not None: assert not (0, 3) in edge_set assert not (1, 3) in edge_set _test1(None, True) # w/ replacement, uniform _test1(None, False) # w/o replacement, uniform _test1('prob', True) # w/ replacement _test1('prob', False) # w/o replacement def _test2(p, replace): # fanout > #neighbors for i in range(10): subg = dgl.sampling.sample_neighbors(g, [0, 2], 2, prob=p, replace=replace, edge_dir='out') assert subg.number_of_nodes() == g.number_of_nodes() num_edges = 4 if replace else 3 assert subg.number_of_edges() == num_edges u, v = subg.edges() assert set(F.asnumpy(F.unique(u))) == {0, 2} assert F.array_equal(g.has_edges_between(u, v), F.ones((num_edges,), dtype=F.int64)) assert F.array_equal(g.edge_ids(u, v), subg.edata[dgl.EID]) edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) if not replace: # check no duplication assert len(edge_set) == num_edges if p is not None: assert not (0, 3) in edge_set _test2(None, True) # w/ replacement, uniform _test2(None, False) # w/o replacement, uniform _test2('prob', True) # w/ replacement _test2('prob', False) # w/o replacement def _test3(p, replace): for i in range(10): subg = dgl.sampling.sample_neighbors(hg, {'user' : [0,1], 'game' : 0}, 2, prob=p, replace=replace, edge_dir='out') assert len(subg.ntypes) == 3 assert len(subg.etypes) == 4 assert subg['follow'].number_of_edges() == 4 assert subg['play'].number_of_edges() == 2 if replace else 1 assert subg['liked-by'].number_of_edges() == 4 if replace else 3 assert subg['flips'].number_of_edges() == 0 _test3(None, True) # w/ replacement, uniform _test3(None, False) # w/o replacement, uniform _test3('prob', True) # w/ replacement _test3('prob', False) # w/o replacement def _test_sample_neighbors_topk(hypersparse): g, hg = _gen_neighbor_topk_test_graph(hypersparse, False) def _test1(): subg = dgl.sampling.select_topk(g, 2, 'weight', [0, 1]) assert subg.number_of_nodes() == g.number_of_nodes() assert subg.number_of_edges() == 4 u, v = subg.edges() edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) assert F.array_equal(g.edge_ids(u, v), subg.edata[dgl.EID]) assert edge_set == {(2,0),(1,0),(2,1),(3,1)} _test1() def _test2(): # k > #neighbors subg = dgl.sampling.select_topk(g, 2, 'weight', [0, 2]) assert subg.number_of_nodes() == g.number_of_nodes() assert subg.number_of_edges() == 3 u, v = subg.edges() assert F.array_equal(g.edge_ids(u, v), subg.edata[dgl.EID]) edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) assert edge_set == {(2,0),(1,0),(0,2)} _test2() def _test3(): subg = dgl.sampling.select_topk(hg, 2, 'weight', {'user' : [0,1], 'game' : 0}) assert len(subg.ntypes) == 3 assert len(subg.etypes) == 4 u, v = subg['follow'].edges() edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) assert F.array_equal(hg['follow'].edge_ids(u, v), subg['follow'].edata[dgl.EID]) assert edge_set == {(2,0),(1,0),(2,1),(3,1)} u, v = subg['play'].edges() edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) assert F.array_equal(hg['play'].edge_ids(u, v), subg['play'].edata[dgl.EID]) assert edge_set == {(0,0)} u, v = subg['liked-by'].edges() edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) assert F.array_equal(hg['liked-by'].edge_ids(u, v), subg['liked-by'].edata[dgl.EID]) assert edge_set == {(2,0),(2,1),(1,0)} assert subg['flips'].number_of_edges() == 0 _test3() # test different k for different relations subg = dgl.sampling.select_topk( hg, {'follow': 1, 'play': 2, 'liked-by': 0, 'flips': 2}, 'weight', {'user' : [0,1], 'game' : 0}) assert len(subg.ntypes) == 3 assert len(subg.etypes) == 4 assert subg['follow'].number_of_edges() == 2 assert subg['play'].number_of_edges() == 1 assert subg['liked-by'].number_of_edges() == 0 assert subg['flips'].number_of_edges() == 0 def _test_sample_neighbors_topk_outedge(hypersparse): g, hg = _gen_neighbor_topk_test_graph(hypersparse, True) def _test1(): subg = dgl.sampling.select_topk(g, 2, 'weight', [0, 1], edge_dir='out') assert subg.number_of_nodes() == g.number_of_nodes() assert subg.number_of_edges() == 4 u, v = subg.edges() edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) assert F.array_equal(g.edge_ids(u, v), subg.edata[dgl.EID]) assert edge_set == {(0,2),(0,1),(1,2),(1,3)} _test1() def _test2(): # k > #neighbors subg = dgl.sampling.select_topk(g, 2, 'weight', [0, 2], edge_dir='out') assert subg.number_of_nodes() == g.number_of_nodes() assert subg.number_of_edges() == 3 u, v = subg.edges() edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) assert F.array_equal(g.edge_ids(u, v), subg.edata[dgl.EID]) assert edge_set == {(0,2),(0,1),(2,0)} _test2() def _test3(): subg = dgl.sampling.select_topk(hg, 2, 'weight', {'user' : [0,1], 'game' : 0}, edge_dir='out') assert len(subg.ntypes) == 3 assert len(subg.etypes) == 4 u, v = subg['follow'].edges() edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) assert F.array_equal(hg['follow'].edge_ids(u, v), subg['follow'].edata[dgl.EID]) assert edge_set == {(0,2),(0,1),(1,2),(1,3)} u, v = subg['play'].edges() edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) assert F.array_equal(hg['play'].edge_ids(u, v), subg['play'].edata[dgl.EID]) assert edge_set == {(0,0)} u, v = subg['liked-by'].edges() edge_set = set(zip(list(F.asnumpy(u)), list(F.asnumpy(v)))) assert F.array_equal(hg['liked-by'].edge_ids(u, v), subg['liked-by'].edata[dgl.EID]) assert edge_set == {(0,2),(1,2),(0,1)} assert subg['flips'].number_of_edges() == 0 _test3() @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU sample neighbors not implemented") def test_sample_neighbors(): _test_sample_neighbors(False) _test_sample_neighbors(True) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU sample neighbors not implemented") def test_sample_neighbors_outedge(): _test_sample_neighbors_outedge(False) _test_sample_neighbors_outedge(True) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU sample neighbors not implemented") def test_sample_neighbors_topk(): _test_sample_neighbors_topk(False) _test_sample_neighbors_topk(True) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU sample neighbors not implemented") def test_sample_neighbors_topk_outedge(): _test_sample_neighbors_topk_outedge(False) _test_sample_neighbors_topk_outedge(True) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU sample neighbors not implemented") def test_sample_neighbors_with_0deg(): g = dgl.graph([], num_nodes=5) sg = dgl.sampling.sample_neighbors(g, F.tensor([1, 2], dtype=F.int64), 2, edge_dir='in', replace=False) assert sg.number_of_edges() == 0 sg = dgl.sampling.sample_neighbors(g, F.tensor([1, 2], dtype=F.int64), 2, edge_dir='in', replace=True) assert sg.number_of_edges() == 0 sg = dgl.sampling.sample_neighbors(g, F.tensor([1, 2], dtype=F.int64), 2, edge_dir='out', replace=False) assert sg.number_of_edges() == 0 sg = dgl.sampling.sample_neighbors(g, F.tensor([1, 2], dtype=F.int64), 2, edge_dir='out', replace=True) assert sg.number_of_edges() == 0 if __name__ == '__main__': test_random_walk() test_pack_traces() test_pinsage_sampling() test_sample_neighbors() test_sample_neighbors_outedge() test_sample_neighbors_topk() test_sample_neighbors_topk_outedge() test_sample_neighbors_with_0deg()

6042c12cfef6c41795afaee5e6f92c8ab2598325

py

Python

samples/roof_types/train_roof_model.py

mWollenhaupt/Mask_RCNN

40366f4fb6e4853467293bfeb657e0d69585024f

samples/roof_types/train_roof_model.py

mWollenhaupt/Mask_RCNN

40366f4fb6e4853467293bfeb657e0d69585024f

samples/roof_types/train_roof_model.py

mWollenhaupt/Mask_RCNN

40366f4fb6e4853467293bfeb657e0d69585024f

#!/usr/bin/env python # coding: utf-8 # In[1]: import os import sys import random import math import re import time import numpy as np import cv2 import matplotlib import matplotlib.pyplot as plt import skimage.io from imgaug import augmenters as iaa import imgaug as ia # Root directory of the project ROOT_DIR = os.path.abspath("../../") # Import Mask RCNN sys.path.append(ROOT_DIR) # To find local version of the library from mrcnn.config import Config from mrcnn import utils import mrcnn.model as modellib from mrcnn import visualize from mrcnn.model import log #get_ipython().run_line_magic('matplotlib', 'inline') # Directory to save logs and trained model MODEL_DIR = os.path.join(ROOT_DIR, "logs") # Local path to trained weights file COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5") # Download COCO trained weights from Releases if needed if not os.path.exists(COCO_MODEL_PATH): utils.download_trained_weights(COCO_MODEL_PATH) def get_ax(rows=1, cols=1, size=8): """Return a Matplotlib Axes array to be used in all visualizations in the notebook. Provide a central point to control graph sizes. Change the default size attribute to control the size of rendered images """ _, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows)) return ax class RoofTypeConfig(Config): """Configuration for training on the toy dataset. Derives from the base Config class and overrides some values. """ NAME = "roof_types" # Train on 1 GPU and 8 images per GPU. We can put multiple images on each # GPU because the images are small. Batch size is 8 (GPUs * images/GPU). GPU_COUNT = 1 IMAGES_PER_GPU = 4 BATCH_SIZE = IMAGES_PER_GPU * GPU_COUNT #RPN_NMS_THRESHOLD = 0.75 LEARNING_RATE = 0.001 DETECTION_MAX_INSTANCES = 400 MAX_GT_INSTANCES = 400 # Number of classes (including background) NUM_CLASSES = 1 + 7 # background + 16 roof types # Use small images for faster training. Set the limits of the small side # the large side, and that determines the image shape. IMAGE_MIN_DIM = 512 IMAGE_MAX_DIM = 512 IMAGE_CHANNEL_COUNT = 3 # Use smaller anchors because our image and objects are small RPN_ANCHOR_SCALES = (16, 32, 64, 128, 256) # anchor side in pixels #RPN_ANCHOR_SCALES = (10, 20, 40, 80, 160) # anchor side in pixels # Reduce training ROIs per image because the images are small and have # few objects. Aim to allow ROI sampling to pick 33% positive ROIs. TRAIN_ROIS_PER_IMAGE = 300 # Use a small epoch since the data is simple STEPS_PER_EPOCH = 100 # use small validation steps since the epoch is small VALIDATION_STEPS = 15 config = RoofTypeConfig() config.display() class DatasetLoader(): def __init__(self): self.roofs = [] self.dataset_dir = None def load_dataset(self, dataset_dir): self.dataset_dir = dataset_dir with open(os.path.join(dataset_dir, 'map.txt'), 'r') as file: lines = file.readlines() for line in lines: split = line.split() self.roofs.append(split) def split_train_val_data(self, _train=.8, _test=.1, _val=.1, SEED=101010): if not self.roofs: print('Load Dataset before try to split data!') return files = self.roofs count = len(files) train_files = self.split_indices(files, _train, SEED) validation_files = self.split_indices(files, _val/(len(files)/count), SEED) test_files = files dataset_train = RoofTypeDataset() dataset_train.load_roof_data(train_files, self.dataset_dir) dataset_train.prepare() dataset_val = RoofTypeDataset() dataset_val.load_roof_data(validation_files, self.dataset_dir) dataset_val.prepare() dataset_test = RoofTypeDataset() dataset_test.load_roof_data(test_files, self.dataset_dir) dataset_test.prepare() return (dataset_train, dataset_val, dataset_test) def split_indices(self, files, split, SEED=101010): random.seed(SEED) indices = random.sample(range(0, len(files)), int(len(files)*split)) indices.sort(reverse=True) result = [] for idx in indices: result.append(files.pop(idx)) return result class RoofTypeDataset(utils.Dataset): def __init__(self): super().__init__() self.roofs = [] self.types = { '1000':1, "2100":2, "3100":3, "3200":4, "3300":5, "3400":6, "3500":7 } def load_roof_data(self, data_list, dataset_dir): """Load a subset of the RoofType dataset. dataset_dir: Root directory of the dataset. subset: Subset to load: train or val """ # Add classes self.add_class("roof_types", 1, "1000") self.add_class("roof_types", 2, "2100") self.add_class("roof_types", 3, "3100") self.add_class("roof_types", 4, "3200") self.add_class("roof_types", 5, "3300") self.add_class("roof_types", 6, "3400") self.add_class("roof_types", 7, "3500") self.dataset_dir = dataset_dir for entry in data_list: self.add_image( "roof_types", image_id=len(self.roofs), path=os.path.join(dataset_dir, entry[0]) ) self.roofs.append(entry) def load_mask(self, image_id): """Load instance masks for an image. Returns: masks: A bool array of shape [height, width, instance count] with one mask per instance. class_ids: a 1D array of class IDs of the instance masks. """ image_info = self.image_info[image_id] if image_info["source"] != "roof_types": return super(self.__class__, self).load_mask(image_id) img = skimage.io.imread(image_info["path"]) mask_paths = self.roofs[image_id][1:] masks = [] lbls = np.empty(0).astype(np.int) for cnt, mask in enumerate(mask_paths): path = os.path.join(self.dataset_dir, mask) arr = skimage.io.imread(path).astype(np.bool) masks.append(arr) lbl = self.types[mask.split('\\')[1]] lbls = np.append(lbls, lbl) result = np.dstack(np.asarray(masks)) return result, lbls def image_reference(self, image_id): """Return the path of the image.""" info = self.image_info[image_id] if info["source"] == "roof_types": return info["path"] else: super(self.__class__, self).image_reference(image_id) dataset_dir = r'C:\Users\MoritzWollenhaupt\Desktop\ArcGIS_Rooftype_Detection\data\bochum\tif\train\512\mrcnn\single_instances_augmented_sobel_min_max_uint16' loader = DatasetLoader() loader.load_dataset(dataset_dir) dataset_train, dataset_val, dataset_test = loader.split_train_val_data() # Create model in training mode model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR) # Which weights to start with? #init_with = "coco" # imagenet, coco, or last init_with = "last" #init_with = "imagenet" if init_with == "imagenet": model.load_weights(model.get_imagenet_weights(), by_name=True) elif init_with == "coco": # Load weights trained on MS COCO, but skip layers that # are different due to the different number of classes # See README for instructions to download the COCO weights model.load_weights(COCO_MODEL_PATH, by_name=True, exclude=["mrcnn_class_logits", "mrcnn_bbox_fc", "mrcnn_bbox", "mrcnn_mask"]) elif init_with == "last": # Load the last model you trained and continue training model.load_weights(model.find_last(), by_name=True) # ### Augmentation sometimes = lambda aug: iaa.Sometimes(0.5, aug) seqAug = iaa.Sequential( [ # apply the following augmenters to most images iaa.Fliplr(0.5), # horizontally flip 50% of all images iaa.Flipud(0.2), # vertically flip 50% of all images iaa.LinearContrast((0.75, 1.5)), # crop images by -10% to 10% of their height/width sometimes(iaa.CropAndPad( percent=(-0.1, 0.1), #pad_mode=ia.ALL, pad_cval=0 )), sometimes(iaa.Affine( # scale images to 80-120% of their size, individually per axis scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, # # translate by -20 to +20 percent (per axis) translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, rotate=(-175, 175), # rotate by -175 to +175 degrees shear=(-16, 16), # shear by -16 to +16 degrees order=[0, 1], # use nearest neighbor or bilinear interpolation (fast) cval=0, # if mode is constant, use a cval = 0 #mode=ia.ALL # use any of scikit-image's warping modes )) ], random_order=True ) # ## Training epochs = 400 model.train(dataset_train, dataset_val, learning_rate=config.LEARNING_RATE/100, epochs=epochs, layers='all', #augmentation=seqAug )

766190045266006ca75ef8f58ba1f3bb115e5859

py

Python

zvmsdk/tests/unit/test_api.py

wngzhe/feilong

43aeb9c002214e2b150cb1173cf4a2bae239aaa7

zvmsdk/tests/unit/test_api.py

wngzhe/feilong

43aeb9c002214e2b150cb1173cf4a2bae239aaa7

zvmsdk/tests/unit/test_api.py

wngzhe/feilong

43aeb9c002214e2b150cb1173cf4a2bae239aaa7

# Copyright 2017,2021 IBM Corp. # # 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 mock import six from zvmsdk import api from zvmsdk import exception from zvmsdk.tests.unit import base from zvmsdk import vmops class SDKAPITestCase(base.SDKTestCase): """Testcases for compute APIs.""" @classmethod def setUpClass(cls): super(SDKAPITestCase, cls).setUpClass() cls.userid = 'TESTUID' cls.userid_list = ["USERID1", "USERID2"] def setUp(self): super(SDKAPITestCase, self).setUp() vmops.VMOps.check_guests_exist_in_db = mock.MagicMock() self.api = api.SDKAPI() def test_init_ComputeAPI(self): self.assertTrue(isinstance(self.api, api.SDKAPI)) @mock.patch("zvmsdk.vmops.VMOps.get_power_state") def test_guest_get_power_state_real(self, gstate): self.api.guest_get_power_state_real(self.userid) gstate.assert_called_once_with(self.userid) @mock.patch("zvmsdk.utils.check_userid_exist") @mock.patch("zvmsdk.vmops.VMOps.get_power_state") def test_guest_get_power_state(self, gstate, chk_uid): chk_uid.return_value = True self.api.guest_get_power_state(self.userid) chk_uid.assert_called_once_with(self.userid) gstate.assert_called_once_with(self.userid) chk_uid.reset_mock() gstate.reset_mock() chk_uid.return_value = False self.assertRaises(exception.SDKObjectNotExistError, self.api.guest_get_power_state, self.userid) chk_uid.assert_called_once_with(self.userid) gstate.assert_not_called() @mock.patch("zvmsdk.vmops.VMOps.get_info") def test_guest_get_info(self, ginfo): self.api.guest_get_info(self.userid) ginfo.assert_called_once_with(self.userid) @mock.patch("zvmsdk.vmops.VMOps.get_definition_info") def test_guest_get_user_direct_(self, ginfo): ginfo.return_value = {'user_direct': ['CPU 00 BASE', 'USER USERID1 PASSWORD 4096m ']} expected_value = {'user_direct': ['CPU 00 BASE', 'USER USERID1 ****** 4096m ']} result = self.api.guest_get_user_direct(self.userid) ginfo.assert_called_once_with(self.userid) self.assertEqual(result, expected_value) @mock.patch("zvmsdk.vmops.VMOps.get_adapters_info") def test_guest_get_adapters_info(self, adapters_info): self.api.guest_get_adapters_info(self.userid) adapters_info.assert_called_once_with(self.userid) @mock.patch("zvmsdk.vmops.VMOps.guest_deploy") def test_guest_deploy(self, guest_deploy): user_id = 'fakevm' image_name = 'fakeimg' transportfiles = '/tmp/transport.tgz' vdev = '0100' self.api.guest_deploy(user_id, image_name, transportfiles=transportfiles, vdev=vdev) guest_deploy.assert_called_with(user_id.upper(), image_name, transportfiles, None, vdev, None, False) @mock.patch("zvmsdk.imageops.ImageOps.image_import") def test_image_import(self, image_import): image_name = '95a4da37-9f9b-4fb2-841f-f0bb441b7544' url = "file:///install/temp/test.img" image_meta = {'os_version': "rhel6.7"} self.api.image_import(image_name, url, image_meta) image_import.assert_called_once_with(image_name, url, image_meta, remote_host=None) @mock.patch("zvmsdk.imageops.ImageOps.image_export") def test_image_export(self, image_export): image_name = '95a4da37-9f9b-4fb2-841f-f0bb441b7544' dest_url = "file:///install/temp/test.img" self.api.image_export(image_name, dest_url) image_export.assert_called_once_with(image_name, dest_url, None) @mock.patch("zvmsdk.vmops.VMOps.create_vm") def test_guest_create(self, create_vm): vcpus = 1 memory = 1024 disk_list = [] user_profile = 'profile' max_cpu = 10 max_mem = '4G' self.api.guest_create(self.userid, vcpus, memory, disk_list, user_profile, max_cpu, max_mem) create_vm.assert_called_once_with(self.userid, vcpus, memory, disk_list, user_profile, max_cpu, max_mem, '', '', '', [], {}, '', None) @mock.patch("zvmsdk.vmops.VMOps.create_vm") def test_guest_create_with_account(self, create_vm): vcpus = 1 memory = 1024 disk_list = [] user_profile = 'profile' max_cpu = 10 max_mem = '4G' account = "dummy account" self.api.guest_create(self.userid, vcpus, memory, disk_list, user_profile, max_cpu, max_mem, account=account) create_vm.assert_called_once_with(self.userid, vcpus, memory, disk_list, user_profile, max_cpu, max_mem, '', '', '', [], {}, account, None) @mock.patch("zvmsdk.vmops.VMOps.create_vm") def test_guest_create_with_comment(self, create_vm): vcpus = 1 memory = 1024 disk_list = [] user_profile = 'profile' max_cpu = 10 max_mem = '4G' comment_list = ["dummy account", "this is a test"] self.api.guest_create(self.userid, vcpus, memory, disk_list, user_profile, max_cpu, max_mem, comment_list=comment_list) create_vm.assert_called_once_with(self.userid, vcpus, memory, disk_list, user_profile, max_cpu, max_mem, '', '', '', [], {}, '', comment_list) @mock.patch("zvmsdk.vmops.VMOps.create_vm") def test_guest_create_with_default_profile(self, create_vm): vcpus = 1 memory = 1024 disk_list = [] user_profile = '' max_cpu = 10 max_mem = '4G' base.set_conf('zvm', 'user_profile', 'abc') self.api.guest_create(self.userid, vcpus, memory, disk_list, user_profile, max_cpu, max_mem) create_vm.assert_called_once_with(self.userid, vcpus, memory, disk_list, 'abc', max_cpu, max_mem, '', '', '', [], {}, '', None) @mock.patch("zvmsdk.vmops.VMOps.create_vm") def test_guest_create_with_no_disk_pool(self, create_vm): disk_list = [{'size': '1g', 'is_boot_disk': True, 'disk_pool': 'ECKD: eckdpool1'}, {'size': '1g', 'format': 'ext3'}, {'size': '1g', 'format': 'swap'}] vcpus = 1 memory = 1024 user_profile = 'profile' max_cpu = 10 max_mem = '4G' base.set_conf('zvm', 'disk_pool', None) self.assertRaises(exception.SDKInvalidInputFormat, self.api.guest_create, self.userid, vcpus, memory, disk_list, user_profile, max_cpu, max_mem) create_vm.assert_not_called() @mock.patch("zvmsdk.vmops.VMOps.create_vm") def test_guest_create_with_no_disk_pool_swap_only(self, create_vm): disk_list = [{'size': '1g', 'format': 'swap'}] vcpus = 1 memory = 1024 user_profile = 'profile' base.set_conf('zvm', 'disk_pool', None) base.set_conf('zvm', 'swap_force_mdisk', False) self.api.guest_create(self.userid, vcpus, memory, disk_list, user_profile) create_vm.assert_called_once_with(self.userid, vcpus, memory, disk_list, user_profile, 32, '64G', '', '', '', [], {}, '', None) @mock.patch("zvmsdk.vmops.VMOps.create_vm") def test_guest_create_no_disk_pool_force_mdisk(self, create_vm): disk_list = [{'size': '1g', 'is_boot_disk': True, 'disk_pool': 'ECKD: eckdpool1'}, {'size': '1g', 'format': 'ext3'}, {'size': '1g', 'format': 'swap'}] vcpus = 1 memory = 1024 user_profile = 'profile' max_cpu = 10 max_mem = '4G' # should be no side effect at all base.set_conf('zvm', 'swap_force_mdisk', True) base.set_conf('zvm', 'disk_pool', None) self.assertRaises(exception.SDKInvalidInputFormat, self.api.guest_create, self.userid, vcpus, memory, disk_list, user_profile, max_cpu, max_mem) create_vm.assert_not_called() @mock.patch("zvmsdk.vmops.VMOps.create_vm") def test_guest_create_no_disk_pool_swap_only_force_mdisk(self, create_vm): disk_list = [{'size': '1g', 'format': 'swap'}] vcpus = 1 memory = 1024 user_profile = 'profile' base.set_conf('zvm', 'disk_pool', None) base.set_conf('zvm', 'swap_force_mdisk', True) self.assertRaises(exception.SDKInvalidInputFormat, self.api.guest_create, self.userid, vcpus, memory, disk_list, user_profile) @mock.patch("zvmsdk.vmops.VMOps.create_vm") def test_guest_create_with_default_max_cpu_memory(self, create_vm): vcpus = 1 memory = 1024 disk_list = [] user_profile = 'profile' self.api.guest_create(self.userid, vcpus, memory, disk_list, user_profile) create_vm.assert_called_once_with(self.userid, vcpus, memory, disk_list, user_profile, 32, '64G', '', '', '', [], {}, '', None) @mock.patch("zvmsdk.imageops.ImageOps.image_query") def test_image_query(self, image_query): imagekeyword = 'eae09a9f_7958_4024_a58c_83d3b2fc0aab' self.api.image_query(imagekeyword) image_query.assert_called_once_with(imagekeyword) @mock.patch("zvmsdk.vmops.VMOps.delete_vm") @mock.patch("zvmsdk.vmops.VMOps.check_guests_exist_in_db") def test_guest_delete(self, cge, delete_vm): cge.return_value = True self.api.guest_delete(self.userid) cge.assert_called_once_with(self.userid, raise_exc=False) delete_vm.assert_called_once_with(self.userid) @mock.patch("zvmsdk.vmops.VMOps.delete_vm") @mock.patch("zvmsdk.vmops.VMOps.check_guests_exist_in_db") def test_guest_delete_userid_in_lower_case(self, cge, delete_vm): cge.return_value = True self.api.guest_delete('testuid') cge.assert_called_once_with(self.userid, raise_exc=False) delete_vm.assert_called_once_with(self.userid) @mock.patch("zvmsdk.utils.check_userid_exist") @mock.patch("zvmsdk.vmops.VMOps.check_guests_exist_in_db") def test_guest_delete_not_exist(self, cge, cue): cge.return_value = False cue.return_value = False self.api.guest_delete(self.userid) cge.assert_called_once_with(self.userid, raise_exc=False) cue.assert_called_once_with(self.userid) @mock.patch("zvmsdk.utils.check_userid_exist") @mock.patch("zvmsdk.vmops.VMOps.check_guests_exist_in_db") def test_guest_delete_not_exist_in_db(self, cge, cue): cge.return_value = False cue.return_value = True self.assertRaises(exception.SDKObjectNotExistError, self.api.guest_delete, self.userid) cge.assert_called_once_with(self.userid, raise_exc=False) cue.assert_called_once_with(self.userid) @mock.patch("zvmsdk.monitor.ZVMMonitor.inspect_stats") def test_guest_inspect_cpus_list(self, inspect_stats): self.api.guest_inspect_stats(self.userid_list) inspect_stats.assert_called_once_with(self.userid_list) @mock.patch("zvmsdk.monitor.ZVMMonitor.inspect_stats") def test_guest_inspect_cpus_single(self, inspect_stats): self.api.guest_inspect_stats(self.userid) inspect_stats.assert_called_once_with([self.userid]) @mock.patch("zvmsdk.monitor.ZVMMonitor.inspect_vnics") def test_guest_inspect_vnics_list(self, inspect_vnics): self.api.guest_inspect_vnics(self.userid_list) inspect_vnics.assert_called_once_with(self.userid_list) @mock.patch("zvmsdk.monitor.ZVMMonitor.inspect_vnics") def test_guest_inspect_vnics_single(self, inspect_vnics): self.api.guest_inspect_vnics(self.userid) inspect_vnics.assert_called_once_with([self.userid]) @mock.patch("zvmsdk.vmops.VMOps.guest_stop") def test_guest_stop(self, gs): self.api.guest_stop(self.userid) gs.assert_called_once_with(self.userid) @mock.patch("zvmsdk.vmops.VMOps.guest_stop") def test_guest_stop_with_timeout(self, gs): self.api.guest_stop(self.userid, timeout=300) gs.assert_called_once_with(self.userid, timeout=300) @mock.patch("zvmsdk.vmops.VMOps.guest_softstop") def test_guest_softstop(self, gss): self.api.guest_softstop(self.userid, timeout=300) gss.assert_called_once_with(self.userid, timeout=300) @mock.patch("zvmsdk.vmops.VMOps.guest_pause") def test_guest_pause(self, gp): self.api.guest_pause(self.userid) gp.assert_called_once_with(self.userid) @mock.patch("zvmsdk.vmops.VMOps.guest_unpause") def test_guest_unpause(self, gup): self.api.guest_unpause(self.userid) gup.assert_called_once_with(self.userid) @mock.patch("zvmsdk.vmops.VMOps.guest_config_minidisks") def test_guest_process_additional_disks(self, config_disks): disk_list = [{'vdev': '0101', 'format': 'ext3', 'mntdir': '/mnt/0101'}] self.api.guest_config_minidisks(self.userid, disk_list) config_disks.assert_called_once_with(self.userid, disk_list) @mock.patch("zvmsdk.imageops.ImageOps.image_delete") def test_image_delete(self, image_delete): image_name = 'eae09a9f_7958_4024_a58c_83d3b2fc0aab' self.api.image_delete(image_name) image_delete.assert_called_once_with(image_name) def test_set_vswitch(self): self.assertRaises(exception.SDKInvalidInputFormat, self.api.vswitch_set, "vswitch_name", unknown='fake_id') @mock.patch("zvmsdk.vmops.VMOps.create_disks") def test_guest_add_disks(self, cds): disk_list = [{'size': '1g'}] self.api.guest_create_disks(self.userid, disk_list) cds.assert_called_once_with(self.userid, disk_list) @mock.patch("zvmsdk.vmops.VMOps.create_disks") def test_guest_add_disks_no_disk_pool(self, cds): disk_list = [{'size': '1g', 'is_boot_disk': True, 'disk_pool': 'ECKD: eckdpool1'}, {'size': '1g', 'format': 'ext3'}] base.set_conf('zvm', 'disk_pool', None) self.assertRaises(exception.SDKInvalidInputFormat, self.api.guest_create_disks, self.userid, disk_list) cds.ssert_not_called() @mock.patch("zvmsdk.vmops.VMOps.create_disks") def test_guest_add_disks_nothing_to_do(self, cds): self.api.guest_create_disks('userid', []) cds.assert_not_called() @mock.patch("zvmsdk.vmops.VMOps.delete_disks") def test_guest_delete_disks(self, dds): vdev_list = ['0102', '0103'] self.api.guest_delete_disks(self.userid, vdev_list) dds.assert_called_once_with(self.userid, vdev_list) @mock.patch("zvmsdk.vmops.VMOps.live_resize_cpus") def test_guest_live_resize_cpus(self, live_resize_cpus): cpu_cnt = 3 self.api.guest_live_resize_cpus(self.userid, cpu_cnt) live_resize_cpus.assert_called_once_with(self.userid, cpu_cnt) @mock.patch("zvmsdk.vmops.VMOps.resize_cpus") def test_guest_resize_cpus(self, resize_cpus): cpu_cnt = 3 self.api.guest_resize_cpus(self.userid, cpu_cnt) resize_cpus.assert_called_once_with(self.userid, cpu_cnt) @mock.patch("zvmsdk.vmops.VMOps.live_resize_memory") def test_guest_live_resize_mem(self, live_resize_memory): size = "1024m" self.api.guest_live_resize_mem(self.userid, size) live_resize_memory.assert_called_once_with(self.userid, size) @mock.patch("zvmsdk.vmops.VMOps.resize_memory") def test_guest_resize_mem(self, resize_memory): size = "2g" self.api.guest_resize_mem(self.userid, size) resize_memory.assert_called_once_with(self.userid, size) @mock.patch("zvmsdk.vmops.VMOps.guest_grow_root_volume") def test_guest_grow_root_volume(self, grow_root_volume): os_version = "RHEL7.8" self.api.guest_grow_root_volume(self.userid, os_version) grow_root_volume.assert_called_once_with(self.userid, os_version) @mock.patch("zvmsdk.networkops.NetworkOPS.grant_user_to_vswitch") def test_vswitch_grant_user(self, guv): self.api.vswitch_grant_user("testvsw", self.userid) guv.assert_called_once_with("testvsw", self.userid) @mock.patch("zvmsdk.volumeop.VolumeOperatorAPI.attach_volume_to_instance") def test_volume_attach(self, mock_attach): connection_info = {'platform': 'x86_64', 'ip': '1.2.3.4', 'os_version': 'rhel7', 'multipath': False, 'target_wwpn': '1111', 'target_lun': '2222', 'zvm_fcp': 'b83c', 'assigner_id': 'user1'} self.api.volume_attach(connection_info) mock_attach.assert_called_once_with(connection_info) @mock.patch("zvmsdk.volumeop.VolumeOperatorAPI.volume_refresh_bootmap") def test_refresh_bootmap(self, mock_attach): fcpchannel = ['5d71'] wwpn = ['5005076802100c1b', '5005076802200c1b'] lun = '01000000000000' self.api.volume_refresh_bootmap(fcpchannel, wwpn, lun) mock_attach.assert_called_once_with(fcpchannel, wwpn, lun, transportfiles=None, guest_networks=None) @mock.patch("zvmsdk.volumeop.VolumeOperatorAPI." "detach_volume_from_instance") def test_volume_detach(self, mock_detach): connection_info = {'platform': 'x86_64', 'ip': '1.2.3.4', 'os_version': 'rhel7', 'multipath': False, 'target_wwpn': '1111', 'target_lun': '2222', 'zvm_fcp': 'b83c', 'assigner_id': 'user1'} self.api.volume_detach(connection_info) mock_detach.assert_called_once_with(connection_info) @mock.patch("zvmsdk.utils.check_userid_exist") @mock.patch("zvmsdk.smtclient.SMTClient.get_adapters_info") @mock.patch("zvmsdk.database.GuestDbOperator.add_guest_registered") @mock.patch("zvmsdk.database.NetworkDbOperator.switch_add_record") def test_guest_register(self, networkdb_add, guestdb_reg, get_adapters_info, chk_usr): networkdb_add.return_value = '' guestdb_reg.return_value = '' adapters = [{'adapter_address': '1000', 'adapter_status': '02', 'lan_owner': 'SYSTEM', 'lan_name': 'VSC11590', 'mac_address': '02:55:36:EF:50:91', 'mac_ip_version': '4', 'mac_ip_address': '1.2.3.4'}] get_adapters_info.return_value = adapters chk_usr.return_value = True meta_data = 'rhel7' net_set = '1' port_macs = {'EF5091': '6e2ecc4f-14a2-4f33-9f12-5ac4a42f97e7', '69FCF1': '389dee5e-7b03-405c-b1e8-7c9c235d1425' } self.api.guest_register(self.userid, meta_data, net_set, port_macs) networkdb_add.assert_called_once_with(self.userid, '1000', '6e2ecc4f-14a2-4f33-9f12' '-5ac4a42f97e7', 'VSC11590') guestdb_reg.assert_called_once_with(self.userid, 'rhel7', '1') get_adapters_info.assert_called_once_with(self.userid) chk_usr.assert_called_once_with(self.userid) @mock.patch("zvmsdk.utils.check_userid_exist") @mock.patch("zvmsdk.smtclient.SMTClient.get_adapters_info") @mock.patch("zvmsdk.database.GuestDbOperator.add_guest_registered") @mock.patch("zvmsdk.database.NetworkDbOperator.switch_add_record") def test_guest_register_invalid_portmacs(self, networkdb_add, guestdb_reg, get_adapters_info, chk_usr): networkdb_add.return_value = '' guestdb_reg.return_value = '' adapters = [{'adapter_address': '1000', 'adapter_status': '02', 'lan_owner': 'SYSTEM', 'lan_name': 'VSC11590', 'mac_address': '02:55:36:EF:50:91', 'mac_ip_version': '4', 'mac_ip_address': '1.2.3.4'}] get_adapters_info.return_value = adapters chk_usr.return_value = True meta_data = 'rhel7' net_set = '1' port_macs = '6e2ecc4f-14a2-4f33-9f12-5ac4a42f97e7' self.assertRaises(exception.SDKInvalidInputFormat, self.api.guest_register, self.userid, meta_data, net_set, port_macs) @mock.patch("zvmsdk.utils.check_userid_exist") @mock.patch("zvmsdk.smtclient.SMTClient.get_adapters_info") @mock.patch("zvmsdk.database.GuestDbOperator.add_guest_registered") @mock.patch("zvmsdk.database.NetworkDbOperator.switch_add_record") def test_guest_register_no_port_macs(self, networkdb_add, guestdb_reg, get_adapters_info, chk_usr): networkdb_add.return_value = '' guestdb_reg.return_value = '' adapters = [{'adapter_address': '1000', 'adapter_status': '02', 'lan_owner': 'SYSTEM', 'lan_name': 'VSC11590', 'mac_address': '02:55:36:EF:50:91', 'mac_ip_version': '4', 'mac_ip_address': '1.2.3.4'}] get_adapters_info.return_value = adapters chk_usr.return_value = True meta_data = 'rhel7' net_set = '1' self.api.guest_register(self.userid, meta_data, net_set) networkdb_add.assert_called_once_with(self.userid, '1000', None, 'VSC11590') guestdb_reg.assert_called_once_with(self.userid, 'rhel7', '1') get_adapters_info.assert_called_once_with(self.userid) chk_usr.assert_called_once_with(self.userid) @mock.patch("zvmsdk.utils.check_userid_exist") @mock.patch("zvmsdk.smtclient.SMTClient.get_adapters_info") @mock.patch("zvmsdk.database.GuestDbOperator.add_guest_registered") @mock.patch("zvmsdk.database.NetworkDbOperator.switch_add_record") @mock.patch("zvmsdk.database.GuestDbOperator.update_guest_by_userid") @mock.patch("zvmsdk.database.GuestDbOperator.get_comments_by_userid") @mock.patch("zvmsdk.database.GuestDbOperator.get_migrated_guest_list") @mock.patch("zvmsdk.database.GuestDbOperator.get_guest_by_userid") def test_guest_register_guest_in_db(self, get_guest, get_mig_guest, get_comments, update_guest, networkdb_add, guestdb_reg, get_adapters_info, chk_usr): get_guest.return_value = 'fake_guest' get_mig_guest.return_value = self.userid + ' other info' get_comments.return_value = {'migrated': 1} update_guest.return_value = '' # Below mocks shall not be called networkdb_add.return_value = '' guestdb_reg.return_value = '' get_adapters_info.return_value = [] chk_usr.return_value = True meta_data = 'rhel7' net_set = '1' self.api.guest_register(self.userid, meta_data, net_set) get_guest.assert_called_once_with(self.userid) get_mig_guest.assert_called_once_with() get_comments.assert_called_once_with(self.userid) update_guest.assert_called_once_with(self.userid, comments={'migrated': 0}) chk_usr.assert_called_once_with(self.userid) networkdb_add.assert_not_called() guestdb_reg.assert_not_called() get_adapters_info.assert_not_called() @mock.patch("zvmsdk.vmops.VMOps.check_guests_exist_in_db") @mock.patch("zvmsdk.database.NetworkDbOperator." "switch_delete_record_for_userid") @mock.patch("zvmsdk.database.GuestDbOperator.delete_guest_by_userid") def test_guest_deregister(self, guestdb_del, networkdb_del, chk_db): guestdb_del.return_value = '' networkdb_del.return_value = '' chk_db.return_value = True self.api.guest_deregister(self.userid) guestdb_del.assert_called_once_with(self.userid) networkdb_del.assert_called_once_with(self.userid) chk_db.assert_called_once_with(self.userid, raise_exc=False) @mock.patch("zvmsdk.vmops.VMOps.check_guests_exist_in_db") @mock.patch("zvmsdk.database.NetworkDbOperator." "switch_delete_record_for_userid") @mock.patch("zvmsdk.database.GuestDbOperator.delete_guest_by_userid") def test_guest_deregister_not_exists(self, guestdb_del, networkdb_del, chk_db): guestdb_del.return_value = '' networkdb_del.return_value = '' chk_db.return_value = False self.api.guest_deregister(self.userid) guestdb_del.assert_called_once_with(self.userid) networkdb_del.assert_called_once_with(self.userid) chk_db.assert_called_once_with(self.userid, raise_exc=False) @mock.patch("zvmsdk.hostops.HOSTOps.guest_list") def test_host_get_guest_list(self, guest_list): self.api.host_get_guest_list() guest_list.assert_called_once_with() @mock.patch("zvmsdk.hostops.HOSTOps.diskpool_get_volumes") def test_host_get_diskpool_volumes(self, diskpool_vols): base.set_conf('zvm', 'disk_pool', None) disk_pool = 'ECKD:IAS1PL' result = self.api.host_get_diskpool_volumes(disk_pool) diskpool_vols.assert_called_once_with('IAS1PL') # Test disk_pool is None disk_pool = None try: self.api.host_get_diskpool_volumes(disk_pool) except Exception as exc: errmsg = ("Invalid disk_pool input None, disk_pool should be" " configured for sdkserver.") result = errmsg in six.text_type(exc) self.assertEqual(result, True) pass @mock.patch("zvmsdk.hostops.HOSTOps.get_volume_info") def test_host_get_volume_info(self, volume_info): volume = 'VOLUM1' result = self.api.host_get_volume_info(volume) volume_info.assert_called_once_with(volume) # Test volume is None volume = None try: self.api.host_get_volume_info(volume) except Exception as exc: errmsg = ("Invalid volume input None, volume" " must be specified.") result = errmsg in six.text_type(exc) self.assertEqual(result, True) pass @mock.patch("zvmsdk.hostops.HOSTOps.diskpool_get_info") def test_host_diskpool_get_info(self, dp_info): base.set_conf('zvm', 'disk_pool', None) results = self.api.host_diskpool_get_info() self.assertEqual(results['disk_total'], 0) self.assertEqual(results['disk_available'], 0) self.assertEqual(results['disk_used'], 0) dp_info.ssert_not_called()

08a44a4d5423bc24d10d60c34524642ea8970d9d

py

Python

persimmon/view/blocks/csvoutblock.py

AlvarBer/Persimmon

da08ed854dd0305d7e4684e97ee828acffd76b4d

2016-11-02T20:45:48.000Z

2022-02-07T05:43:18.000Z

persimmon/view/blocks/csvoutblock.py

mgbin088/Persimmon

da08ed854dd0305d7e4684e97ee828acffd76b4d

2016-11-06T19:16:01.000Z

2018-02-20T11:22:45.000Z

persimmon/view/blocks/csvoutblock.py

mgbin088/Persimmon

da08ed854dd0305d7e4684e97ee828acffd76b4d

2017-03-08T21:01:53.000Z

2020-12-29T16:43:56.000Z

from persimmon.view.pins import InputPin from persimmon.view.util import FileDialog from persimmon.view.blocks.block import Block from kivy.properties import ObjectProperty, StringProperty from kivy.lang import Builder import numpy as np import pandas as pd Builder.load_file('persimmon/view/blocks/csvoutblock.kv') class CSVOutBlock(Block): in_1 = ObjectProperty() path = StringProperty() file_dialog = ObjectProperty() def __init__(self, **kwargs): super().__init__(**kwargs) self.file_dialog = FileDialog(dir='~', filters=['*.csv'], size_hint=(0.8, 0.8)) # Let's bind two together self.file_dialog.bind(file_chosen=self.setter('path')) self.tainted = True self.tainted_msg = 'File not chosen in block {}!'.format(self.title) def function(self): if type(self.in_1.val) == np.ndarray: self.in_1.val = pd.DataFrame(self.in_1.val) self.in_1.val.to_csv(path_or_buf=self.path, index=False) def on_path(self, instance, value): self.tainted = not value.endswith('.csv')

37100c3d52b29cc3756f66df886a7f5479039646

py

Python

spacy_transformers/wrapper.py

maxtrem/spacy-transformers

7458fc3466af0800617c3c106a4ff86fc0285f4d

spacy_transformers/wrapper.py

maxtrem/spacy-transformers

7458fc3466af0800617c3c106a4ff86fc0285f4d

2020-07-11T14:08:04.000Z

2020-07-11T14:08:04.000Z

spacy_transformers/wrapper.py

maxtrem/spacy-transformers

7458fc3466af0800617c3c106a4ff86fc0285f4d

2020-06-04T18:38:34.000Z

2022-02-19T19:23:19.000Z

from thinc.extra.wrappers import PyTorchWrapper, xp2torch, torch2xp from transformers.optimization import AdamW import transformers import torch.autograd import torch.nn.utils.clip_grad import torch from typing import Tuple, Callable, Any from thinc.neural.optimizers import Optimizer import numpy import contextlib from thinc.compat import BytesIO from .util import get_model, Dropout from .activations import RaggedArray, Activations FINE_TUNE = True CONFIG = {"output_hidden_states": True, "output_attentions": True} class TransformersWrapper(PyTorchWrapper): """Wrap a Transformers model for use in Thinc. The model will take as input a spacy_transformers.util.RaggedArray object that will specify the input IDs and optionally the segment IDs. The RaggedArray is basically a tuple (ids, lengths), where ids is concatenated for a whole batch (this format allows the data to be contiguous even if the sequences are different lengths). The segment IDs should be coded as the different models expect them -- see https://github.com/huggingface/transformers/blob/master/examples/utils_glue.py """ _model: Any _optimizer: Any cfg: dict @classmethod def from_pretrained(cls, name): model_cls = get_model(name) model = model_cls.from_pretrained(name, **CONFIG) self = cls(name, model.config.to_dict(), model) self.cfg.update(self.transformers_model.config.to_dict()) return self def __init__(self, name, config, model): PyTorchWrapper.__init__(self, model) self.cfg = dict(config) @property def nO(self): if "hidden_size" in self.cfg: # BERT return self.cfg["hidden_size"] elif "hidden_dim" in self.cfg: # DistilBERT return self.cfg["hidden_dim"] // 4 elif "n_embd" in self.cfg: # GPT2 return self.cfg["n_embd"] elif "d_model" in self.cfg: # XLNet return self.cfg["d_model"] elif hasattr(self.transformers_model, "dim"): # XLM return self.transformers_model.dim else: keys = ", ".join(self.cfg.keys()) raise ValueError(f"Unexpected config. Keys: {keys}") @property def transformers_model(self): return self._model @property def max_length(self): # `n_positions` in GPT2 config return self.cfg.get("max_position_embeddings", self.cfg.get("n_positions", 128)) def predict(self, inputs: RaggedArray): self._model.eval() model_kwargs = self.get_model_kwargs(inputs) with torch.no_grad(): if hasattr(self._optimizer, "swap_swa_sgd"): self._optimizer.swap_swa_sgd() y_var = self._model(**model_kwargs) if hasattr(self._optimizer, "swap_swa_sgd"): self._optimizer.swap_swa_sgd() return self.make_activations(y_var, inputs.lengths) def begin_update( self, inputs: RaggedArray, drop: Dropout = 0.0 ) -> Tuple[Activations, Callable[..., None]]: if drop is None: # "drop is None" indicates prediction. It's one of the parts of # Thinc's API I'm least happy with... return self.predict(inputs), lambda dY, sgd=None: None max_original = max(inputs.lengths, default=0) model_kwargs = self.get_model_kwargs(inputs) self._model.train() # Prepare all the model arguments, including the attention mask y_var = self._model(**model_kwargs) output = self.make_activations(y_var, inputs.lengths) assert output.lh.data.shape[0] == inputs.data.shape[0], ( output.lh.data.shape, inputs.data.shape, ) def backward_pytorch(d_output: Activations, sgd: Optimizer = None) -> None: y_for_bwd = [] dy_for_bwd = [] if d_output.has_lh: assert d_output.lh.data.shape[0] == sum(d_output.lh.lengths) d_lh = d_output.lh.to_padded(to=max_original) if self.max_length and d_lh.shape[1] >= self.max_length: d_lh = d_lh[:, : self.max_length] dy_for_bwd.append(xp2torch(d_lh)) y_for_bwd.append(y_var[0]) if d_output.has_po: dy_for_bwd.append(xp2torch(d_output.po.data)) y_for_bwd.append(y_var[1]) if FINE_TUNE: torch.autograd.backward(y_for_bwd, grad_tensors=dy_for_bwd) if sgd is not None: if self._optimizer is None: self._optimizer = self._create_optimizer(sgd) if sgd.max_grad_norm: torch.nn.utils.clip_grad.clip_grad_norm_( self._model.parameters(), sgd.max_grad_norm ) optimizer = self._optimizer for group in optimizer.param_groups: group["lr"] = getattr(sgd, "trf_lr", sgd.alpha) optimizer.step() optimizer.zero_grad() self._update_pytorch_averages(sgd) return None self._model.eval() return output, backward_pytorch @contextlib.contextmanager def use_params(self, params): key_prefix = f"pytorch_{self.id}_" state_dict = {} for k, v in params.items(): if hasattr(k, "startswith") and k.startswith(key_prefix): state_dict[k.replace(key_prefix, "")] = xp2torch(v) if state_dict: backup = {k: v.clone() for k, v in self._model.state_dict().items()} self._model.load_state_dict(state_dict) yield self._model.load_state_dict(backup) else: yield def make_activations(self, fields, lengths) -> Activations: """Create Activations from the output tuples produced by PyTorch Transformers. Includes converting torch tensors to xp, and handling missing values. """ fields = list(fields) fields[0] = torch2xp(fields[0]) fields[0] = RaggedArray.from_padded(fields[0], lengths) assert fields[0].data.shape[0] == sum(lengths) # lh: last hidden # po: pooler_output # ah: all_hidden # aa: all_attention if len(fields) != 4: lh = fields[0] po = RaggedArray.blank() else: if isinstance(fields[1], tuple): fields[1] = RaggedArray.blank() else: fields[1] = RaggedArray(torch2xp(fields[1]), [1] * len(lengths)) lh, po, _, _2 = fields # Convert last_hidden_state to xp return Activations(lh, po) def get_model_kwargs(self, inputs): padded = inputs.to_padded(value=-1) if padded.ndim == 2: padded = padded.reshape(padded.shape + (1,)) ids = padded[:, :, 0] neg_idx = ids < 0 ids[neg_idx] = 0 ids = torch.as_tensor(ids, dtype=torch.int64) if padded.shape[2] == 2: segment_ids = padded[:, :, 1] numpy.place(segment_ids, segment_ids<0, 0) segment_ids = torch.as_tensor(segment_ids, dtype=torch.int64) else: segment_ids = torch.zeros_like(ids) # Calculate "attention mask" for BERT and XLNet, but not GPT2 (sigh) if isinstance(self._model, (transformers.BertModel, transformers.XLNetModel)): mask = self.ops.xp.ones(ids.shape, dtype=numpy.int_) mask[neg_idx] = 0 mask = xp2torch(mask) return { "input_ids": ids, "attention_mask": mask, "token_type_ids": segment_ids, } elif isinstance(self._model, (transformers.DistilBertModel)): # Mask, but no token type IDs for DistilBert (sigh again...) mask = self.ops.xp.ones(ids.shape, dtype=numpy.int_) mask[neg_idx] = 0 mask = xp2torch(mask) return {"input_ids": ids, "attention_mask": mask} else: return {"input_ids": ids, "token_type_ids": segment_ids} def _create_optimizer(self, sgd): optimizer = AdamW( self._model.parameters(), lr=getattr(sgd, "trf_lr", sgd.alpha), eps=sgd.eps, betas=(sgd.b1, sgd.b2), weight_decay=getattr(sgd, "trf_weight_decay", 0.0), ) optimizer.zero_grad() return optimizer def _update_pytorch_averages(self, sgd, *, init_steps=1): if sgd.averages is None: return # Collect parameters if we don't have them for name, param in self._model.state_dict().items(): key = f"pytorch_{self.id}_{name}" sgd.nr_update[key] += 1 xp_param = torch2xp(param) if key in sgd.averages: self.ops.update_averages( sgd.averages[key], xp_param, sgd.nr_update[key] ) else: sgd.averages[key] = xp_param.copy() sgd.nr_update[key] = init_steps def to_disk(self, path): torch.save(self._model.state_dict(), str(path)) def from_disk(self, path): if self.ops.device == "cpu": map_location = "cpu" else: map_location = "cuda:0" self._model.load_state_dict(torch.load(path, map_location=map_location)) self._model.to(map_location) def to_bytes(self): filelike = BytesIO() torch.save(self._model.state_dict(), filelike) filelike.seek(0) return filelike.getvalue() def from_bytes(self, data): filelike = BytesIO(data) filelike.seek(0) if self.ops.device == "cpu": map_location = "cpu" else: map_location = "cuda:0" self._model.load_state_dict(torch.load(filelike, map_location=map_location)) self._model.to(map_location)

eb5237ee19b1366d3f8afcf05da7a4a80734d3e4

py

Python

LED_Knight_Rider01.py

LekkerPrutsen/LED-matrix-experiments

62bd8b18be842df7648d5a09a87b203933541524

2017-01-27T15:08:05.000Z

2019-07-27T19:35:13.000Z

LED_Knight_Rider01.py

LekkerPrutsen/LED-matrix-experiments

62bd8b18be842df7648d5a09a87b203933541524

LED_Knight_Rider01.py

LekkerPrutsen/LED-matrix-experiments

62bd8b18be842df7648d5a09a87b203933541524

2018-03-31T13:09:00.000Z

2018-03-31T13:09:00.000Z

# -*- coding: utf-8 -*- import time from random import randint import max7219.led as led device = led.matrix(cascaded=4) device.orientation(90) print "Press Ctrl+C to stop" #ASCII codes symbol01 = 219 symbol02 = 178 symbol03 = 177 symbol04 = 176 duration = 0.2 try: while True: #position 1 device.letter(0, symbol01) device.letter(1, symbol02) device.letter(2, symbol04) device.letter(3, symbol04) time.sleep(duration) #position 2 device.letter(0, symbol02) device.letter(1, symbol01) device.letter(2, symbol04) device.clear(3) time.sleep(duration) #position 3 device.letter(0, symbol03) device.letter(1, symbol02) device.letter(2, symbol01) device.clear(3) time.sleep(duration) #position 4 device.letter(0, symbol04) device.letter(1, symbol03) device.letter(2, symbol02) device.letter(3, symbol01) time.sleep(duration) #position 5 device.clear(0) device.letter(1, symbol04) device.letter(2, symbol01) device.letter(3, symbol02) time.sleep(duration) #position 6 device.clear(0) device.letter(1, symbol01) device.letter(2, symbol02) device.letter(3, symbol03) time.sleep(duration) except KeyboardInterrupt: device.clear()

17abb5660d5577fd08a3c9ddab481c55cf7d159c

py

Python

tests/test_taskgroups.py

byronformwalt/anyio

35858dcd08d2522fee3f84b213d55d902ebbc2ff

tests/test_taskgroups.py

byronformwalt/anyio

35858dcd08d2522fee3f84b213d55d902ebbc2ff

tests/test_taskgroups.py

byronformwalt/anyio

35858dcd08d2522fee3f84b213d55d902ebbc2ff

import asyncio import re import sys import time import pytest import trio import anyio from anyio import ( CancelScope, ExceptionGroup, create_task_group, current_effective_deadline, current_time, fail_after, get_cancelled_exc_class, move_on_after, sleep, wait_all_tasks_blocked) if sys.version_info < (3, 7): current_task = asyncio.Task.current_task else: current_task = asyncio.current_task pytestmark = pytest.mark.anyio async def async_error(text, delay=0.1): try: if delay: await sleep(delay) finally: raise Exception(text) async def test_already_closed(): async with create_task_group() as tg: pass with pytest.raises(RuntimeError) as exc: tg.start_soon(async_error, 'fail') exc.match('This task group is not active; no new tasks can be started') async def test_success(): async def async_add(value): results.add(value) results = set() async with create_task_group() as tg: tg.start_soon(async_add, 'a') tg.start_soon(async_add, 'b') assert results == {'a', 'b'} @pytest.mark.parametrize('module', [ pytest.param(asyncio, id='asyncio'), pytest.param(trio, id='trio') ]) def test_run_natively(module): async def testfunc(): async with create_task_group() as tg: tg.start_soon(sleep, 0) if module is asyncio: from anyio._backends._asyncio import native_run try: native_run(testfunc()) finally: asyncio.set_event_loop(None) else: module.run(testfunc) async def test_start_soon_while_running(): async def task_func(): tg.start_soon(sleep, 0) async with create_task_group() as tg: tg.start_soon(task_func) async def test_start_soon_after_error(): with pytest.raises(ZeroDivisionError): async with create_task_group() as tg: a = 1 / 0 # noqa: F841 with pytest.raises(RuntimeError) as exc: tg.start_soon(sleep, 0) exc.match('This task group is not active; no new tasks can be started') async def test_start_no_value(): async def taskfunc(*, task_status): task_status.started() async with create_task_group() as tg: value = await tg.start(taskfunc) assert value is None async def test_start_with_value(): async def taskfunc(*, task_status): task_status.started('foo') async with create_task_group() as tg: value = await tg.start(taskfunc) assert value == 'foo' async def test_start_crash_before_started_call(): async def taskfunc(*, task_status): raise Exception('foo') async with create_task_group() as tg: with pytest.raises(Exception) as exc: await tg.start(taskfunc) exc.match('foo') async def test_start_crash_after_started_call(): async def taskfunc(*, task_status): task_status.started(2) raise Exception('foo') with pytest.raises(Exception) as exc: async with create_task_group() as tg: value = await tg.start(taskfunc) exc.match('foo') assert value == 2 async def test_start_no_started_call(): async def taskfunc(*, task_status): pass async with create_task_group() as tg: with pytest.raises(RuntimeError) as exc: await tg.start(taskfunc) exc.match('hild exited') async def test_start_cancelled(): async def taskfunc(*, task_status): nonlocal started, finished started = True await sleep(2) finished = True started = finished = False async with create_task_group() as tg: tg.cancel_scope.cancel() await tg.start(taskfunc) assert started assert not finished @pytest.mark.parametrize('anyio_backend', ['asyncio']) async def test_start_native_host_cancelled(): async def taskfunc(*, task_status): nonlocal started, finished started = True await sleep(2) finished = True async def start_another(): async with create_task_group() as tg: await tg.start(taskfunc) started = finished = False task = asyncio.get_event_loop().create_task(start_another()) await wait_all_tasks_blocked() task.cancel() with pytest.raises(asyncio.CancelledError): await task assert started assert not finished @pytest.mark.parametrize('anyio_backend', ['asyncio']) async def test_start_native_child_cancelled(): async def taskfunc(*, task_status): nonlocal task, finished task = current_task() await sleep(2) finished = True async def start_another(): async with create_task_group() as tg2: await tg2.start(taskfunc) task = None finished = False async with create_task_group() as tg: tg.start_soon(start_another) await wait_all_tasks_blocked() task.cancel() assert not finished async def test_start_exception_delivery(): def task_fn(*, task_status): task_status.started("hello") async with anyio.create_task_group() as tg: with pytest.raises(TypeError, match='to be synchronous$'): await tg.start(task_fn) async def test_host_exception(): async def set_result(value): nonlocal result await sleep(3) result = value result = None with pytest.raises(Exception) as exc: async with create_task_group() as tg: tg.start_soon(set_result, 'a') raise Exception('dummy error') exc.match('dummy error') assert result is None async def test_edge_cancellation(): async def dummy(): nonlocal marker marker = 1 # At this point the task has been cancelled so sleep() will raise an exception await sleep(0) # Execution should never get this far marker = 2 marker = None async with create_task_group() as tg: tg.start_soon(dummy) assert marker is None tg.cancel_scope.cancel() assert marker == 1 async def test_failing_child_task_cancels_host(): async def child(): await wait_all_tasks_blocked() raise Exception('foo') sleep_completed = False with pytest.raises(Exception) as exc: async with create_task_group() as tg: tg.start_soon(child) await sleep(0.5) sleep_completed = True exc.match('foo') assert not sleep_completed async def test_failing_host_task_cancels_children(): async def child(): nonlocal sleep_completed await sleep(1) sleep_completed = True sleep_completed = False with pytest.raises(Exception) as exc: async with create_task_group() as tg: tg.start_soon(child) await wait_all_tasks_blocked() raise Exception('foo') exc.match('foo') assert not sleep_completed async def test_cancel_scope_in_another_task(): async def child(): nonlocal result, local_scope with CancelScope() as local_scope: await sleep(2) result = True local_scope = None result = False async with create_task_group() as tg: tg.start_soon(child) while local_scope is None: await sleep(0) local_scope.cancel() assert not result async def test_cancel_propagation(): async def g(): async with create_task_group(): await sleep(1) assert False async with create_task_group() as tg: tg.start_soon(g) await sleep(0) tg.cancel_scope.cancel() async def test_cancel_twice(): """Test that the same task can receive two cancellations.""" async def cancel_group(): await wait_all_tasks_blocked() tg.cancel_scope.cancel() for _ in range(2): async with create_task_group() as tg: tg.start_soon(cancel_group) await sleep(1) pytest.fail('Execution should not reach this point') async def test_cancel_exiting_task_group(): """ Test that if a task group is waiting for subtasks to finish and it receives a cancellation, the subtasks are also cancelled and the waiting continues. """ async def waiter(): nonlocal cancel_received try: await sleep(5) finally: cancel_received = True async def subgroup(): async with create_task_group() as tg2: tg2.start_soon(waiter) cancel_received = False async with create_task_group() as tg: tg.start_soon(subgroup) await wait_all_tasks_blocked() tg.cancel_scope.cancel() assert cancel_received async def test_exception_group_children(): with pytest.raises(ExceptionGroup) as exc: async with create_task_group() as tg: tg.start_soon(async_error, 'task1') tg.start_soon(async_error, 'task2', 0.15) assert len(exc.value.exceptions) == 2 assert sorted(str(e) for e in exc.value.exceptions) == ['task1', 'task2'] assert exc.match('^2 exceptions were raised in the task group:\n') assert exc.match(r'Exception: task\d\n----') assert re.fullmatch( r"<ExceptionGroup: Exception\('task[12]',?\), Exception\('task[12]',?\)>", repr(exc.value)) async def test_exception_group_host(): with pytest.raises(ExceptionGroup) as exc: async with create_task_group() as tg: tg.start_soon(async_error, 'child', 2) await wait_all_tasks_blocked() raise Exception('host') assert len(exc.value.exceptions) == 2 assert sorted(str(e) for e in exc.value.exceptions) == ['child', 'host'] assert exc.match('^2 exceptions were raised in the task group:\n') assert exc.match(r'Exception: host\n----') async def test_escaping_cancelled_exception(): async with create_task_group() as tg: tg.cancel_scope.cancel() await sleep(0) async def test_cancel_scope_cleared(): with move_on_after(0.1): await sleep(1) await sleep(0) @pytest.mark.parametrize('delay', [0, 0.1], ids=['instant', 'delayed']) async def test_fail_after(delay): with pytest.raises(TimeoutError): with fail_after(delay) as scope: await sleep(1) assert scope.cancel_called async def test_fail_after_no_timeout(): with fail_after(None) as scope: assert scope.deadline == float('inf') await sleep(0.1) assert not scope.cancel_called async def test_fail_after_after_cancellation(): event = anyio.Event() async with anyio.create_task_group() as tg: tg.cancel_scope.cancel() await event.wait() block_complete = False with pytest.raises(TimeoutError): with fail_after(0.1): await anyio.sleep(0.5) block_complete = True assert not block_complete @pytest.mark.parametrize('delay', [0, 0.1], ids=['instant', 'delayed']) async def test_move_on_after(delay): result = False with move_on_after(delay) as scope: await sleep(1) result = True assert not result assert scope.cancel_called async def test_move_on_after_no_timeout(): result = False with move_on_after(None) as scope: assert scope.deadline == float('inf') await sleep(0.1) result = True assert result assert not scope.cancel_called async def test_nested_move_on_after(): sleep_completed = inner_scope_completed = False with move_on_after(0.1) as outer_scope: assert current_effective_deadline() == outer_scope.deadline with move_on_after(1) as inner_scope: assert current_effective_deadline() == outer_scope.deadline await sleep(2) sleep_completed = True inner_scope_completed = True assert not sleep_completed assert not inner_scope_completed assert outer_scope.cancel_called assert not inner_scope.cancel_called async def test_shielding(): async def cancel_when_ready(): await wait_all_tasks_blocked() tg.cancel_scope.cancel() inner_sleep_completed = outer_sleep_completed = False async with create_task_group() as tg: tg.start_soon(cancel_when_ready) with move_on_after(10, shield=True) as inner_scope: assert inner_scope.shield await sleep(0.1) inner_sleep_completed = True await sleep(1) outer_sleep_completed = True assert inner_sleep_completed assert not outer_sleep_completed assert tg.cancel_scope.cancel_called assert not inner_scope.cancel_called async def test_cancel_from_shielded_scope(): async with create_task_group() as tg: with CancelScope(shield=True) as inner_scope: assert inner_scope.shield tg.cancel_scope.cancel() with pytest.raises(get_cancelled_exc_class()): await sleep(0.01) with pytest.raises(get_cancelled_exc_class()): await sleep(0.01) @pytest.mark.parametrize('anyio_backend', ['asyncio']) async def test_cancel_host_asyncgen(): async def host_task(): nonlocal done async with create_task_group() as tg: with CancelScope(shield=True) as inner_scope: assert inner_scope.shield tg.cancel_scope.cancel() with pytest.raises(get_cancelled_exc_class()): await sleep(0) with pytest.raises(get_cancelled_exc_class()): await sleep(0) done = True async def host_agen_fn(): await host_task() yield pytest.fail("host_agen_fn should only be __anext__ed once") done = False host_agen = host_agen_fn() try: await asyncio.get_event_loop().create_task(host_agen.__anext__()) finally: await host_agen.aclose() assert done async def test_shielding_immediate_scope_cancelled(): async def cancel_when_ready(): await wait_all_tasks_blocked() scope.cancel() sleep_completed = False async with create_task_group() as tg: with CancelScope(shield=True) as scope: tg.start_soon(cancel_when_ready) await sleep(0.5) sleep_completed = True assert not sleep_completed async def test_cancel_scope_in_child_task(): async def child(): nonlocal child_scope with CancelScope() as child_scope: await sleep(2) child_scope = None host_done = False async with create_task_group() as tg: tg.start_soon(child) await wait_all_tasks_blocked() child_scope.cancel() await sleep(0.1) host_done = True assert host_done assert not tg.cancel_scope.cancel_called async def test_exception_cancels_siblings(): async def child(fail): if fail: raise Exception('foo') else: nonlocal sleep_completed await sleep(1) sleep_completed = True sleep_completed = False with pytest.raises(Exception) as exc: async with create_task_group() as tg: tg.start_soon(child, False) await wait_all_tasks_blocked() tg.start_soon(child, True) exc.match('foo') assert not sleep_completed async def test_cancel_cascade(): async def do_something(): async with create_task_group() as tg2: tg2.start_soon(sleep, 1) raise Exception('foo') async with create_task_group() as tg: tg.start_soon(do_something) await wait_all_tasks_blocked() tg.cancel_scope.cancel() async def test_cancelled_parent(): async def child(): with CancelScope(): await sleep(1) raise Exception('foo') async def parent(tg): await wait_all_tasks_blocked() tg.start_soon(child) async with create_task_group() as tg: tg.start_soon(parent, tg) tg.cancel_scope.cancel() async def test_shielded_deadline(): with move_on_after(10): with CancelScope(shield=True): with move_on_after(1000): assert current_effective_deadline() - current_time() > 900 async def test_deadline_reached_on_start(): with move_on_after(0): await sleep(0) pytest.fail('Execution should not reach this point') async def test_deadline_moved(): with fail_after(0.1) as scope: scope.deadline += 0.3 await sleep(0.2) async def test_timeout_error_with_multiple_cancellations(): with pytest.raises(TimeoutError): with fail_after(0.1): async with create_task_group() as tg: tg.start_soon(sleep, 2) await sleep(2) async def test_nested_fail_after(): async def killer(scope): await wait_all_tasks_blocked() scope.cancel() async with create_task_group() as tg: with CancelScope() as scope: with CancelScope(): tg.start_soon(killer, scope) with fail_after(1): await sleep(2) pytest.fail('Execution should not reach this point') pytest.fail('Execution should not reach this point either') pytest.fail('Execution should also not reach this point') assert scope.cancel_called async def test_nested_shield(): async def killer(scope): await wait_all_tasks_blocked() scope.cancel() with pytest.raises(TimeoutError): async with create_task_group() as tg: with CancelScope() as scope: with CancelScope(shield=True): tg.start_soon(killer, scope) with fail_after(0.2): await sleep(2) def test_task_group_in_generator(anyio_backend_name, anyio_backend_options): async def task_group_generator(): async with create_task_group(): yield gen = task_group_generator() anyio.run(gen.__anext__, backend=anyio_backend_name, backend_options=anyio_backend_options) pytest.raises(StopAsyncIteration, anyio.run, gen.__anext__, backend=anyio_backend_name, backend_options=anyio_backend_options) async def test_exception_group_filtering(): """Test that CancelledErrors are filtered out of nested exception groups.""" async def fail(name): try: await anyio.sleep(.1) finally: raise Exception('%s task failed' % name) async def fn(): async with anyio.create_task_group() as tg: tg.start_soon(fail, 'parent') async with anyio.create_task_group() as tg2: tg2.start_soon(fail, 'child') await anyio.sleep(1) with pytest.raises(ExceptionGroup) as exc: await fn() assert len(exc.value.exceptions) == 2 assert str(exc.value.exceptions[0]) == 'parent task failed' assert str(exc.value.exceptions[1]) == 'child task failed' async def test_cancel_propagation_with_inner_spawn(): async def g(): async with anyio.create_task_group() as tg2: tg2.start_soon(anyio.sleep, 10) await anyio.sleep(1) assert False async with anyio.create_task_group() as tg: tg.start_soon(g) await wait_all_tasks_blocked() tg.cancel_scope.cancel() async def test_escaping_cancelled_error_from_cancelled_task(): """Regression test for issue #88. No CancelledError should escape the outer scope.""" with CancelScope() as scope: with move_on_after(0.1): await sleep(1) scope.cancel() @pytest.mark.filterwarnings('ignore:"@coroutine" decorator is deprecated:DeprecationWarning') def test_cancel_generator_based_task(): from asyncio import coroutine async def native_coro_part(): with CancelScope() as scope: scope.cancel() @coroutine def generator_part(): yield from native_coro_part() anyio.run(generator_part, backend='asyncio') async def test_suppress_exception_context(): """ Test that the __context__ attribute has been cleared when the exception is re-raised in the exception group. This prevents recursive tracebacks. """ with pytest.raises(ValueError) as exc: async with create_task_group() as tg: tg.cancel_scope.cancel() async with create_task_group() as tg2: tg2.start_soon(sleep, 1) raise ValueError assert exc.value.__context__ is None @pytest.mark.parametrize('anyio_backend', ['asyncio']) async def test_cancel_native_future_tasks(): async def wait_native_future(): loop = asyncio.get_event_loop() await loop.create_future() async with anyio.create_task_group() as tg: tg.start_soon(wait_native_future) tg.cancel_scope.cancel() @pytest.mark.parametrize('anyio_backend', ['asyncio']) async def test_cancel_native_future_tasks_cancel_scope(): async def wait_native_future(): with anyio.CancelScope(): loop = asyncio.get_event_loop() await loop.create_future() async with anyio.create_task_group() as tg: tg.start_soon(wait_native_future) tg.cancel_scope.cancel() @pytest.mark.parametrize('anyio_backend', ['asyncio']) async def test_cancel_completed_task(): loop = asyncio.get_event_loop() old_exception_handler = loop.get_exception_handler() exceptions = [] def exception_handler(*args, **kwargs): exceptions.append((args, kwargs)) loop.set_exception_handler(exception_handler) try: async def noop(): pass async with anyio.create_task_group() as tg: tg.start_soon(noop) tg.cancel_scope.cancel() assert exceptions == [] finally: loop.set_exception_handler(old_exception_handler) async def test_task_in_sync_spawn_callback(): outer_task_id = anyio.get_current_task().id inner_task_id = None def task_wrap(): assert anyio.get_current_task().id == outer_task_id async def corofn(): nonlocal inner_task_id inner_task_id = anyio.get_current_task().id return corofn() async with create_task_group() as tg: tg.start_soon(task_wrap) assert inner_task_id is not None assert inner_task_id != outer_task_id async def test_shielded_cancel_sleep_time(): """Test that cancelling a shielded tasks spends more time sleeping than cancelling.""" event = anyio.Event() hang_time = 0.2 async def set_event(): await sleep(hang_time) event.set() async def never_cancel_task(): with CancelScope(shield=True): await sleep(0.2) await event.wait() async with create_task_group() as tg: tg.start_soon(set_event) async with create_task_group() as tg: tg.start_soon(never_cancel_task) tg.cancel_scope.cancel() process_time = time.process_time() assert (time.process_time() - process_time) < hang_time async def test_cancelscope_wrong_exit_order(): """ Test that a RuntimeError is raised if the task tries to exit cancel scopes in the wrong order. """ scope1 = CancelScope() scope2 = CancelScope() scope1.__enter__() scope2.__enter__() pytest.raises(RuntimeError, scope1.__exit__, None, None, None) async def test_cancelscope_exit_before_enter(): """Test that a RuntimeError is raised if one tries to exit a cancel scope before entering.""" scope = CancelScope() pytest.raises(RuntimeError, scope.__exit__, None, None, None) @pytest.mark.parametrize('anyio_backend', ['asyncio']) # trio does not check for this yet async def test_cancelscope_exit_in_wrong_task(): async def enter_scope(scope): scope.__enter__() async def exit_scope(scope): scope.__exit__(None, None, None) scope = CancelScope() async with create_task_group() as tg: tg.start_soon(enter_scope, scope) with pytest.raises(RuntimeError): async with create_task_group() as tg: tg.start_soon(exit_scope, scope)

ebef1c101324744cadab3ce6c3e39ccb05c9efed

py

Python

Splunk_TA_paloalto/bin/Splunk_TA_paloalto_rh_cortex_xdr.py

moshekaplan/Splunk-Apps

fc95334aa2ee2209b221bd5b2a6a520ad9ecab4a

2016-03-25T08:09:05.000Z

2020-07-23T05:04:16.000Z

Splunk_TA_paloalto/bin/Splunk_TA_paloalto_rh_cortex_xdr.py

moshekaplan/Splunk-Apps

fc95334aa2ee2209b221bd5b2a6a520ad9ecab4a

2020-08-07T21:56:58.000Z

2022-03-30T18:24:53.000Z

Splunk_TA_paloalto/bin/Splunk_TA_paloalto_rh_cortex_xdr.py

moshekaplan/Splunk-Apps

fc95334aa2ee2209b221bd5b2a6a520ad9ecab4a

2016-03-26T09:39:19.000Z

2020-07-27T21:17:55.000Z

import splunk_ta_paloalto_declare from splunktaucclib.rest_handler.endpoint import ( field, validator, RestModel, DataInputModel, ) from splunktaucclib.rest_handler import admin_external, util from splunk_aoblib.rest_migration import ConfigMigrationHandler util.remove_http_proxy_env_vars() fields = [ field.RestField( 'interval', required=True, encrypted=False, default=None, validator=validator.Pattern( regex=r"""^\-[1-9]\d*$|^\d*$""", ) ), field.RestField( 'index', required=True, encrypted=False, default='default', validator=validator.String( min_len=1, max_len=80, ) ), field.RestField( 'xdr_tenant', required=True, encrypted=False, default=None, validator=validator.String( min_len=0, max_len=8192, ) ), field.RestField( 'xdr_region', required=True, encrypted=False, default='us', validator=validator.String( min_len=0, max_len=8192, ) ), field.RestField( 'xdr_key_id', required=True, encrypted=True, default=None, validator=validator.String( min_len=0, max_len=8192, ) ), field.RestField( 'xdr_key', required=True, encrypted=True, default=None, validator=validator.String( min_len=0, max_len=8192, ) ), field.RestField( 'disabled', required=False, validator=None ) ] model = RestModel(fields, name=None) endpoint = DataInputModel( 'cortex_xdr', model, ) if __name__ == '__main__': admin_external.handle( endpoint, handler=ConfigMigrationHandler, )

430e28f567856a1115587b43a19d9f22689d789f

py

Python

Client/scraping.py

alejodiazg/DadaPoemGenerator

798d6e4a80b3b79201e65e394f11748a5d25ea8a

Client/scraping.py

alejodiazg/DadaPoemGenerator

798d6e4a80b3b79201e65e394f11748a5d25ea8a

Client/scraping.py

alejodiazg/DadaPoemGenerator

798d6e4a80b3b79201e65e394f11748a5d25ea8a

# -*- coding: utf-8 -*- from lxml import html import requests from random import shuffle from re import search print(" ") print("***** INSTRUCCIONES PARA REALIZAR UN POEMA DADAÍSTA *****") print("***** (Sin necesidad de periódicos o tijeras) *****") print(" ") url = raw_input("Introduzca el URL del artículo seleccionado: ") page = requests.get(url) tree = html.fromstring(page.content) texto = tree.xpath('//*[@class="entry-content"]/p/text()') text = [] for line in texto: aux = line.split() for word in aux: word = word.encode('utf8') objMatch = search(r'[\wÁÉÍÓÚÑáéíóúñ]+',word) if objMatch != None: text += [objMatch.group()] shuffle(text) shuffle(text) f = open('prueba', 'w') for word in text: f.write(word) f.write("\n")

5c1a11917529a867c7dfd9d48fa45816d5863d2a

py

Python

ML_and_DL/gpu_project.py

AshfakYeafi/AI_practice_code

3d8a0b9382f5903e840ce59218ebb95ca962ab01

ML_and_DL/gpu_project.py

AshfakYeafi/AI_practice_code

3d8a0b9382f5903e840ce59218ebb95ca962ab01

ML_and_DL/gpu_project.py

AshfakYeafi/AI_practice_code

3d8a0b9382f5903e840ce59218ebb95ca962ab01

import tensorflow as tf import cv2 from tensorflow import keras import matplotlib.pyplot as plt import numpy as np (x_train,y_train),(x_test,y_test)=tf.keras.datasets.cifar10.load_data() classes=["airplane", "automobile", "bird","cat","deer","dog","frog","horse","ship","truck"] x_train_scaled=x_train/255 x_test_scaled=x_test/255 print(x_train_scaled.shape) y_train_catargorical=keras.utils.to_categorical( y_train,num_classes=10 ) print(y_train_catargorical[0:5]) model=keras.Sequential([ keras.layers.Flatten(input_shape=(32,32,3)), keras.layers.Dense(3000,activation="relu"), keras.layers.Dense(1000,activation="relu"), keras.layers.Dense(10,activation="sigmoid") ]) model.compile(optimizer="SGD", loss="categorical_crossentropy", metrics=['accuracy']) model.fit(x_train_scaled,y_train_catargorical,epochs=50) print(np.argmax(model.predict(x_test_scaled)[0])) print(y_train[0][0])

7e938db1a1cb23f0ac8b5d897b9240c86e4f9287

py

Python

stable_baselines3/common/recorder.py

offdroid/stable-baselines3

793bf44e11fe1e6735e8984add42442e5ab59d0f

stable_baselines3/common/recorder.py

offdroid/stable-baselines3

793bf44e11fe1e6735e8984add42442e5ab59d0f

stable_baselines3/common/recorder.py

offdroid/stable-baselines3

793bf44e11fe1e6735e8984add42442e5ab59d0f

from typing import List, Tuple from enum import Enum class ReplayMode(Enum): """No recording or replay""" IGNORE = 1 """Record the training buffer""" RECORDING = 2 """Replay a given buffer""" REPLAYING = 3 class Recording: def __init__(self, source: List) -> None: self.pos = 0 self._history = source def __iter__(self) -> "Recording": self.pos = 0 return self def __next__(self) -> Tuple: if self.pos <= len(self._history): x = self._history[self.pos] self.pos += 1 return x else: raise StopIteration class Recorder: def __init__(self) -> None: self._recording = [] def append(self, new_obs, rewards, dones, infos, buffer_actions) -> None: self._recording.append((new_obs, rewards, dones, infos, buffer_actions)) def freeze(self) -> Recording: return Recording(self._recording)

642cf8d3a51d63ae2db4a42fb7e03f804a6e2c42

py

Python

qlib/contrib/online/__init__.py

lpd6375/qlib

3a911bc09ba5136cd7c61c2c8dcca8a63339e738

2022-02-05T06:54:28.000Z

2022-02-05T06:54:28.000Z

qlib/contrib/online/__init__.py

lpd6375/qlib

3a911bc09ba5136cd7c61c2c8dcca8a63339e738

qlib/contrib/online/__init__.py

lpd6375/qlib

3a911bc09ba5136cd7c61c2c8dcca8a63339e738

2022-03-22T06:37:38.000Z

2022-03-22T06:37:38.000Z

# pylint: skip-file ''' TODO: - Online needs that the model have such method def get_data_with_date(self, date, **kwargs): """ Will be called in online module need to return the data that used to predict the label (score) of stocks at date. :param date: pd.Timestamp predict date :return: data: the input data that used to predict the label (score) of stocks at predict date. """ raise NotImplementedError("get_data_with_date for this model is not implemented.") '''

8cb519dd73ef2509e17d878d0ec8f2d5b5f35250

py

Python

test/functional/llmq-chainlocks.py

farsider350/AUTX-Core

6d00d1e027a5a6dffb3b0815a155e4515ced007b

test/functional/llmq-chainlocks.py

farsider350/AUTX-Core

6d00d1e027a5a6dffb3b0815a155e4515ced007b

test/functional/llmq-chainlocks.py

farsider350/AUTX-Core

6d00d1e027a5a6dffb3b0815a155e4515ced007b

2021-01-03T02:35:54.000Z

2021-01-03T02:35:54.000Z

#!/usr/bin/env python3 # Copyright (c) 2015-2020 The autx Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. import time from test_framework.mininode import * from test_framework.test_framework import autxTestFramework from test_framework.util import * ''' llmq-chainlocks.py Checks LLMQs based ChainLocks ''' class LLMQChainLocksTest(autxTestFramework): def set_test_params(self): self.set_autx_test_params(6, 5, fast_dip3_enforcement=True) def run_test(self): self.log.info("Wait for dip0008 activation") while self.nodes[0].getblockchaininfo()["bip9_softforks"]["dip0008"]["status"] != "active": self.nodes[0].generate(10) sync_blocks(self.nodes, timeout=60*5) self.nodes[0].spork("SPORK_17_QUORUM_DKG_ENABLED", 0) self.wait_for_sporks_same() self.log.info("Mining 4 quorums") for i in range(4): self.mine_quorum() self.nodes[0].spork("SPORK_19_CHAINLOCKS_ENABLED", 0) self.wait_for_sporks_same() self.log.info("Mine single block, wait for chainlock") self.nodes[0].generate(1) self.wait_for_chainlocked_block_all_nodes(self.nodes[0].getbestblockhash()) self.log.info("Mine many blocks, wait for chainlock") self.nodes[0].generate(20) # We need more time here due to 20 blocks being generated at once self.wait_for_chainlocked_block_all_nodes(self.nodes[0].getbestblockhash(), timeout=30) self.log.info("Assert that all blocks up until the tip are chainlocked") for h in range(1, self.nodes[0].getblockcount()): block = self.nodes[0].getblock(self.nodes[0].getblockhash(h)) assert(block['chainlock']) self.log.info("Isolate node, mine on another, and reconnect") isolate_node(self.nodes[0]) node0_mining_addr = self.nodes[0].getnewaddress() node0_tip = self.nodes[0].getbestblockhash() self.nodes[1].generatetoaddress(5, node0_mining_addr) self.wait_for_chainlocked_block(self.nodes[1], self.nodes[1].getbestblockhash()) assert(self.nodes[0].getbestblockhash() == node0_tip) reconnect_isolated_node(self.nodes[0], 1) self.nodes[1].generatetoaddress(1, node0_mining_addr) self.wait_for_chainlocked_block(self.nodes[0], self.nodes[1].getbestblockhash()) self.log.info("Isolate node, mine on both parts of the network, and reconnect") isolate_node(self.nodes[0]) self.nodes[0].generate(5) self.nodes[1].generatetoaddress(1, node0_mining_addr) good_tip = self.nodes[1].getbestblockhash() self.wait_for_chainlocked_block(self.nodes[1], good_tip) assert(not self.nodes[0].getblock(self.nodes[0].getbestblockhash())["chainlock"]) reconnect_isolated_node(self.nodes[0], 1) self.nodes[1].generatetoaddress(1, node0_mining_addr) self.wait_for_chainlocked_block(self.nodes[0], self.nodes[1].getbestblockhash()) assert(self.nodes[0].getblock(self.nodes[0].getbestblockhash())["previousblockhash"] == good_tip) assert(self.nodes[1].getblock(self.nodes[1].getbestblockhash())["previousblockhash"] == good_tip) self.log.info("Keep node connected and let it try to reorg the chain") good_tip = self.nodes[0].getbestblockhash() self.log.info("Restart it so that it forgets all the chainlocks from the past") self.stop_node(0) self.start_node(0) connect_nodes(self.nodes[0], 1) self.nodes[0].invalidateblock(self.nodes[0].getbestblockhash()) self.log.info("Now try to reorg the chain") self.nodes[0].generate(2) time.sleep(6) assert(self.nodes[1].getbestblockhash() == good_tip) self.nodes[0].generate(2) time.sleep(6) assert(self.nodes[1].getbestblockhash() == good_tip) self.log.info("Now let the node which is on the wrong chain reorg back to the locked chain") self.nodes[0].reconsiderblock(good_tip) assert(self.nodes[0].getbestblockhash() != good_tip) self.nodes[1].generatetoaddress(1, node0_mining_addr) self.wait_for_chainlocked_block(self.nodes[0], self.nodes[1].getbestblockhash()) assert(self.nodes[0].getbestblockhash() == self.nodes[1].getbestblockhash()) self.log.info("Enable LLMQ bases InstantSend, which also enables checks for \"safe\" transactions") self.nodes[0].spork("SPORK_2_INSTANTSEND_ENABLED", 0) self.nodes[0].spork("SPORK_3_INSTANTSEND_BLOCK_FILTERING", 0) self.wait_for_sporks_same() self.log.info("Isolate a node and let it create some transactions which won't get IS locked") isolate_node(self.nodes[0]) txs = [] for i in range(3): txs.append(self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 1)) txs += self.create_chained_txs(self.nodes[0], 1) self.log.info("Assert that after block generation these TXs are NOT included (as they are \"unsafe\")") self.nodes[0].generate(1) for txid in txs: tx = self.nodes[0].getrawtransaction(txid, 1) assert("confirmations" not in tx) time.sleep(1) assert(not self.nodes[0].getblock(self.nodes[0].getbestblockhash())["chainlock"]) self.log.info("Disable LLMQ based InstantSend for a very short time (this never gets propagated to other nodes)") self.nodes[0].spork("SPORK_2_INSTANTSEND_ENABLED", 4070908800) self.log.info("Now the TXs should be included") self.nodes[0].generate(1) self.nodes[0].spork("SPORK_2_INSTANTSEND_ENABLED", 0) self.log.info("Assert that TXs got included now") for txid in txs: tx = self.nodes[0].getrawtransaction(txid, 1) assert("confirmations" in tx and tx["confirmations"] > 0) # Enable network on first node again, which will cause the blocks to propagate and IS locks to happen retroactively # for the mined TXs, which will then allow the network to create a CLSIG self.log.info("Reenable network on first node and wait for chainlock") reconnect_isolated_node(self.nodes[0], 1) self.wait_for_chainlocked_block(self.nodes[0], self.nodes[0].getbestblockhash(), timeout=30) def create_chained_txs(self, node, amount): txid = node.sendtoaddress(node.getnewaddress(), amount) tx = node.getrawtransaction(txid, 1) inputs = [] valueIn = 0 for txout in tx["vout"]: inputs.append({"txid": txid, "vout": txout["n"]}) valueIn += txout["value"] outputs = { node.getnewaddress(): round(float(valueIn) - 0.0001, 6) } rawtx = node.createrawtransaction(inputs, outputs) rawtx = node.signrawtransaction(rawtx) rawtxid = node.sendrawtransaction(rawtx["hex"]) return [txid, rawtxid] if __name__ == '__main__': LLMQChainLocksTest().main()

8e5ee16141e43d4fa74f17f6c4f49a59d45501bc

py

Python

steps.py

undoingtech/Sticky-Steps

fdb3af0c4c7e0ccf3689204d298d0794c2f99dc6

steps.py

undoingtech/Sticky-Steps

fdb3af0c4c7e0ccf3689204d298d0794c2f99dc6

steps.py

undoingtech/Sticky-Steps

fdb3af0c4c7e0ccf3689204d298d0794c2f99dc6

""" TODO: cosmetics - get rid of the lines from html_label """ """ TODO: functionality - new temporary input file (opens editor) - save temporary input file (opens save dialog) - text resizing / zoom - remember text size / zoom on close - open file from url - copy button for codeblocks and code - remember file and step on close - remember window position and size on close - color preferences - add step before current step - add step after current step - add note to step """ """TODO: bug fixes """ """TODO: non-functionality - Readme - video link - description of each file - Help / tutorial default first open md file - Example md instruction files """ from tkinter import * from tkinter import filedialog from tkinter import simpledialog from tkinter import messagebox from tkhtmlview import HTMLLabel import markdown import editor # SOURCE: https://stackoverflow.com/questions/2632199/how-do-i-get-the-path-of-the-current-executed-file-in-python import inspect, os.path class Steps: def __init__(self, file_location): md_file = open(file_location) md_text = md_file.read() md_file.close() html = markdown.markdown(md_text) # variables that don't change after init self.step_list = html.split("<hr />") self.step_count = len(self.step_list) self.file_location = file_location # - blue #a9edf1 SOURCE: https://www.color-hex.com/color-palette/104537 # - yellow #f1f58f SOURCE: https://www.color-hex.com/color-palette/104537 # - purple #CB94FE - formerly #9985ff # - pink #e095f9 self.colors = ["#f1f58f", "#a9edf1", "#CB94FE", "#e095f9"] # variables that do change after init self.number = 1 self.html = self.step_list[0] self.color = self.colors[0] def goto_step_number(self, step_number): # if requested step number is invalid, return the current step if step_number in range(1, self.step_count + 1): self.number = step_number self.html = self.step_list[step_number - 1] self.color = self.get_step_color(step_number) return self.html def get_step_color(self, step_number): color_index = step_number - 1 if step_number >= len(self.colors): color_index = (step_number - 1) % len(self.colors) return self.colors[color_index] class StickySteps: root = Tk() root.title("Sticky Steps") widgets = dict() width = 300 height = 200 y = 10 step = None def __init__(self): # make the sticky sized window appear in the top right corner x = self.root.winfo_screenwidth() - self.width - 10 self.root.geometry("%dx%d+%d+%d" % (self.width, self.height, x, self.y)) # add gui elements self.widgets["counter"] = Label(self.root, text = "") self.widgets["counter"].pack() self.widgets["html_label"] = HTMLLabel(self.root, html="") self.widgets["html_label"].pack(fill="both", expand=True) self.widgets["html_label"].fit_height() self.widgets["bottomButtons"] = Frame(self.root) self.widgets["bottomButtons"].pack(side = BOTTOM) # make buttons to paginate through step list self.widgets["prev_button"] = Button(self.widgets["bottomButtons"], text="<", command=self.prev_step) self.widgets["prev_button"].grid(row = 0, column = 0) self.widgets["open_button"] = Button(self.widgets["bottomButtons"], text="o", command=self.open_file) self.widgets["open_button"].grid(row = 0, column = 1) self.widgets["next_button"] = Button(self.widgets["bottomButtons"], text=">", command=self.next_step) self.widgets["next_button"].grid(row = 0, column = 2) self.root["background"] = "#f1f58f" for widget in self.widgets: #print("widget: %s - widget type: %s" % (widget, type(widget))) self.widgets[widget].configure(bg="#f1f58f", bd=0, relief=FLAT) # because html_label only picks up color after the configure for some reason self.widgets["html_label"].set_html("") self.root.bind("<h>", lambda e:self.help_message()) self.root.bind("<o>", lambda e:self.open_file()) self.root.bind("<e>", lambda e:self.edit_file()) self.root.bind("<Right>", lambda e:self.next_step()) self.root.bind("<Left>", lambda e:self.prev_step()) self.root.bind("<g>", lambda e:self.goto_step_number()) self.root.bind("<Control-q>", lambda e:self.root.destroy()) self.keybindings = dict() self.keybindings["h"] = "Show keybindings" self.keybindings["o"] = "Open local file" self.keybindings["e"] = "Edit file" self.keybindings["Right"] = "Go to next step" self.keybindings["Left"] = "Go to previous step" self.keybindings["g"] = "Go to step [number]" self.keybindings["Control-q"] = "Quit" def help_message(self): # Oneliner SOURCE: https://stackoverflow.com/questions/44689546/how-to-print-out-a-dictionary-nicely-in-python message = "\n".join("{}\t{}".format(k, v) for k, v in self.keybindings.items()) messagebox.showinfo("Key bindings", message) def open_file(self, file_location=None): if file_location is None: file_location = filedialog.askopenfilename(filetypes=[("markdown files", "*.md")]) if type(file_location) is not str or file_location == "": return self.step = Steps(file_location) self.widgets["html_label"].set_html(self.step.html) self.update_counter() def update_counter(self): self.widgets["counter"].config(text = "%d / %d" % (self.step.number, self.step.step_count)) def update_color(self): self.root["background"] = self.step.color for widget in self.widgets: #print("widget: %s - widget type: %s" % (widget, type(widget))) self.widgets[widget].configure(bg=self.step.color) def update_widgets(self): self.update_counter() self.update_color() def goto_step_number(self): if self.step is None: return step_number = simpledialog.askinteger("Input", "Go to step", parent=self.root) html = self.step.goto_step_number(step_number) # must set html after update widgets so html has same color self.update_widgets() self.widgets["html_label"].set_html(html) def goto_step_increment(self, increment): if self.step is None: return html = self.step.goto_step_number(self.step.number + increment) # must set html after update widgets so html has same color self.update_widgets() self.widgets["html_label"].set_html(html) def prev_step(self): self.goto_step_increment(-1) def next_step(self): self.goto_step_increment(1) def edit_file(self): if self.step is None: return target_file = self.step.file_location editor(filename=target_file) self.open_file(target_file) def run(self): # SOURCE for getting file location: https://stackoverflow.com/questions/2632199/how-do-i-get-the-path-of-the-current-executed-file-in-python filename = inspect.getframeinfo(inspect.currentframe()).filename path = os.path.dirname(os.path.abspath(filename)) # SOURCE for joining path: https://stackoverflow.com/questions/7132861/build-the-full-path-filename-in-python test_file = os.path.join(path, "test.md") self.open_file(test_file) self.root.mainloop() stickysteps = StickySteps() stickysteps.run()

87ece73966885a4b54b7c9a3af968b9f263fb60d

py

Python

pyvac/tests/mocks/celery.py

sayoun/pyvac

45ade8de2f29864d500e0358e38ebcbd2674a06d

2015-11-19T17:36:46.000Z

2021-07-02T15:48:21.000Z

pyvac/tests/mocks/celery.py

sayoun/pyvac

45ade8de2f29864d500e0358e38ebcbd2674a06d

2015-07-03T07:54:48.000Z

2022-03-21T22:16:23.000Z

pyvac/tests/mocks/celery.py

sayoun/pyvac

45ade8de2f29864d500e0358e38ebcbd2674a06d

2015-07-03T07:30:04.000Z

2020-07-03T15:22:51.000Z

# -*- coding: utf-8 -*- """ Mock classses for Celery subtask method and Task class. """ def subtask(task): return task class DummyTask(object): _ids = { 'worker_approved': 10, 'worker_accepted': 20, 'worker_denied': 30, } def __init__(self, task=None): self.task = task @property def task_id(self): return self._ids[self.task] @property def name(self): return self.task def delay(self, **kwargs): return self def apply_async(self, **kwargs): return self def send(self, **kwargs): return True

6acba325333dfee5254429a311e85022dc800296

py

Python

ucsb/repository/asset_repository.py

jasunchen/agmonitor_backend

2eea26732dea09080af2a7e700c24ef7f9300f2c

ucsb/repository/asset_repository.py

jasunchen/agmonitor_backend

2eea26732dea09080af2a7e700c24ef7f9300f2c

ucsb/repository/asset_repository.py

jasunchen/agmonitor_backend

2eea26732dea09080af2a7e700c24ef7f9300f2c

from ucsb.models import user_asset, user from rest_framework.response import Response from rest_framework.decorators import api_view from ucsb.repository.asset_data_repository import delete_asset_data_helper from django.forms.models import model_to_dict from ucsb.repository.helpers import * @api_view(['POST']) def add_asset(request): params = ["email", "name", "description", "type_of_asset"] #Check for Required Fields for p in params: if request.data.get(p, None) == None: return Response( {"message": "Missing Required Parameters: {}".format(p)}, status = 400) email = request.data.get('email') tmp_user = user(user_email=email) name = request.data.get('name') desc = request.data.get('description') if request.data.get("type_of_asset", None) == "generation": params = ["declination", "azimuth", "modules_power"] #Check for Required Fields for p in params: if request.data.get(p, None) == None: return Response( {"message": "Missing Required Parameters: {}".format(p)}, status = 400) declination = request.data.get('declination') azimuth = request.data.get('azimuth') modules_power = request.data.get('modules_power') asset = user_asset(user=tmp_user, asset_name=name, description=desc, declination=declination, azimuth=azimuth, modules_power=modules_power, type_of_asset="generation") asset.save() elif request.data.get("type_of_asset", None) == "flexible": params = ["start_charge_time", "end_charge_time", "demand", "duration"] #Check for Required Fields for p in params: if request.data.get(p, None) == None: return Response( {"message": "Missing Required Parameters: {}".format(p)}, status = 400) start_time = request.data.get('start_charge_time') end_time = request.data.get('end_charge_time') dmd = request.data.get('demand') dur = request.data.get('duration') asset = user_asset(user=tmp_user, asset_name=name, description=desc, start_charge_time=start_time, end_charge_time=end_time, type_of_asset="flexible",demand=dmd, duration=dur) asset.save() else: asset = user_asset(user=tmp_user, asset_name=name, description=desc) asset.save() return Response({"detail":"Asset created successfully"}) @api_view(['POST']) def update_asset(request): params = ["id", "name", "description", "type_of_asset"] #Check for Required Fields for p in params: if request.data.get(p, None) == None: return Response( {"message": "Missing Required Parameters: {}".format(p)}, status = 400) id = request.data.get('id') name = request.data.get('name') desc = request.data.get('description') try: asset = user_asset.objects.get(id=id) except: return Response({"detail":"Asset does not exist"}, status=400) asset.asset_name = name asset.description = desc if asset.type_of_asset == "generation": params = ["declination", "azimuth", "modules_power"] #Check for Required Fields for p in params: if request.data.get(p, None) == None: return Response( {"message": "Missing Required Parameters: {}".format(p)}, status = 400) declination = request.data.get('declination') azimuth = request.data.get('azimuth') modules_power = request.data.get('modules_power') asset.declination = declination asset.azimuth = azimuth asset.modules_power = modules_power elif asset.type_of_asset == "flexible": params = ["start_charge_time", "end_charge_time", "demand", "duration"] #Check for Required Fields for p in params: if request.data.get(p, None) == None: return Response( {"message": "Missing Required Parameters: {}".format(p)}, status = 400) start_time = request.data.get('start_charge_time') end_time = request.data.get('end_charge_time') dmd = request.data.get('demand') dur = request.data.get('duration') asset.start_charge_time = start_time asset.end_charge_time = end_time asset.demand = dmd asset.duration = dur asset.save() return Response({"detail":"Asset updated successfully"}) @api_view(['DELETE']) def delete_asset(request): params = ["id"] #Check for Required Fields for p in params: if request.data.get(p, None) == None: return Response( {"message": "Missing Required Parameters: {}".format(p)}, status = 400) #Check for Invalid Parameters if verify(request.data, params): return Response( {"message": "Request has invalid parameter not in {}".format(params)}, status = 400) id = request.data.get('id') try: asset = user_asset.objects.get(id=id) except: return Response({"detail":"Asset does not exist"}, status=400) delete_asset_data_helper(id) user_asset.objects.filter(id=id).delete() return Response({"detail": "Asset deleted successfully"}) @api_view(['GET']) def get_all_assets(request): params = ["email"] #Check for Required Fields for p in params: if request.query_params.get(p, None) == None: return Response( {"message": "Missing Required Parameters: {}".format(p)}, status = 400) #Check for Invalid Parameters if verify(request.query_params, params): return Response( {"message": "Request has invalid parameter not in {}".format(params)}, status = 400) email = request.query_params.get('email') try: tmp_user = user.objects.get(user_email=email) except: return Response({"detail": "Error: User does not exist"}, status=400) bases = user_asset.objects.filter(user=tmp_user, type_of_asset='base').values('id', 'asset_name', 'description') generations = user_asset.objects.filter(user=tmp_user, type_of_asset='generation').values('id', 'asset_name', 'description', 'declination', 'azimuth', 'modules_power') felxible_assets = user_asset.objects.filter(user=tmp_user, type_of_asset='flexible').values('id', 'asset_name', 'description', 'start_charge_time', 'end_charge_time', 'duration', 'demand') result = {"base": bases, "generation": generations, "flexible": felxible_assets} return Response(result) @api_view(['GET']) def get_single_asset(request): id = request.query_params.get('id') try: asset = user_asset.objects.get(id=id) except: return Response({"detail":"Asset does not exist"}, status=400) return Response(model_to_dict(asset))

a574e9c80a438acde205cffa5ed7393322181f4a

py

Python

src/tic_tac_toe_game/__main__.py

alexistli/tic-tac-toe-game

f6689d5ea722c3ea52c06c8a3433cf052543297f

2021-12-11T09:10:33.000Z

2021-12-11T09:10:33.000Z

src/tic_tac_toe_game/__main__.py

alexistli/tic-tac-toe-game

f6689d5ea722c3ea52c06c8a3433cf052543297f

2021-11-01T23:18:43.000Z

2022-03-31T23:27:55.000Z

src/tic_tac_toe_game/__main__.py

alexistli/tic-tac-toe-game

f6689d5ea722c3ea52c06c8a3433cf052543297f

"""Command-line interface.""" import click from tic_tac_toe_game import engine @click.command() @click.version_option() def main() -> None: """Tic Tac Toe Game.""" click.secho("hello", fg="green") if click.confirm("Do you want to play a game?", abort=True): click.echo("Let's play a game...") # player_1_mark = click.prompt( # "Player 1, please pick your mark", # type=click.Choice(["X", "O"], case_sensitive=False), # default="X", # ) # # player_2_type = click.prompt( # "Player 2, are you Human (H) or a Bot (B)?", # type=click.Choice(["H", "B"], case_sensitive=False), # default="B", # ) game = engine.build_game() # game = engine.Engine(player_1_mark, player_2_type) # game.board = Board() # TODO: Upgrade with any possible strategy # game.players_match.update_ai_algorithm(move) finished = False while not finished: player = game.players_match.current() print("\n\n") print(f"{player.name}, it is your turn!") print("Current grid: \n") print(f"{game.board.framed_grid()}\n") if isinstance(player, engine.HumanPlayer): played_cell = click.prompt( "Please pick a cell xy", type=click.Tuple([int, int]) ) else: played_cell = game.get_move() game.board.make_move(coord=played_cell, value=player.get_mark()) if game.board.is_winning_move(played_cell, player.get_mark()): print(f"Player {player.name} won!") finished = True elif game.board.is_full(): print("Players tied!") finished = True else: game.players_match.switch() if __name__ == "__main__": main(prog_name="tic-tac-toe-game") # pragma: no cover

3359fef0e98cc83f7723cb3fe70bb7cba5b8a9ab

py

Python

movie.py

Roger-tn-su/javbus_crawler

ebc163a778df2210f8a01749f84057f8fdcb9229

movie.py

Roger-tn-su/javbus_crawler

ebc163a778df2210f8a01749f84057f8fdcb9229

movie.py

Roger-tn-su/javbus_crawler

ebc163a778df2210f8a01749f84057f8fdcb9229

2021-10-30T18:30:56.000Z

2021-10-30T18:30:56.000Z

class Movie: """ class for each movie in javbus website""" def __init__(self, avNum, title, coverImgUrl, date): self._avNum = avNum self._title = title self._cover_img = coverImgUrl self._release_date = date @property def avNum(self): return self._avNum @property def title(self): return self._title @property def cover_img(self): return self._cover_img @property def release_date(self): return self._release_date def __repr__(self): return '{} {} {} {}'.format(self._avNum, self._title, self._cover_img, self._release_date) class Link: """ Class for magnet link""" def __init__(self, avNum, magnet, size): self._avNum = avNum self._magnet = magnet self._size = size @property def size(self): return self._size @property def magnet(self): return self._magnet @property def av_num(self): return self._avNum def __repr__(self): return '{} {} {}'.format(self._avNum, self._magnet, self._size) class Counter: """ Class for counter""" def __init__(self): self._parsing_time = 0 self._page_skip = 0 self._movie_skip = 0 @property def parsing_time(self): return self._parsing_time @property def page_skip(self): return self._page_skip @property def movie_skip(self): return self._movie_skip def reset_movie_skip(self): self._movie_skip = 0 def increment_movie_skip(self): self._movie_skip += 1 def increment_page_skip(self): self._page_skip += 1 def increment_parse(self): self._parsing_time += 1

fa64d027993ad0602ee242b438a5dab7c4abc5aa

py

Python

setup.py

kadeve/assistant-sdk-python

d8f6143f03851a0c2f236e12c2f1051f8f77178c

setup.py

kadeve/assistant-sdk-python

d8f6143f03851a0c2f236e12c2f1051f8f77178c

setup.py

kadeve/assistant-sdk-python

d8f6143f03851a0c2f236e12c2f1051f8f77178c

# Copyright 2017 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from setuptools import setup, find_packages import io install_requires = [ 'googleapis-common-protos==1.5.2', 'grpcio==1.2.1', ] auth_helpers_requires = [ 'google-auth-oauthlib==0.0.1', 'urllib3[secure]==1.20', ] audio_helpers_requires = [ 'sounddevice==0.3.7', ] samples_requires = [ 'click==6.7', 'tenacity==4.1.0', ] + auth_helpers_requires + audio_helpers_requires with io.open('README.rst', 'r') as fh: long_description = fh.read() setup( name='google-assistant-sdk', version='0.2.1', author='Google Assistant SDK team', author_email='proppy+assistant-sdk@google.com', description='Samples and bindings for the Google Assistant API', long_description=long_description, url='https://github.com/googlesamples/assistant-sdk-python', packages=find_packages(exclude=['tests*']), namespace_packages=[ 'google', 'google.assistant', 'google.assistant.embedded', 'googlesamples', ], install_requires=install_requires, extras_require={ 'samples': samples_requires, 'auth_helpers': auth_helpers_requires, 'audio_helpers': audio_helpers_requires, }, entry_points={ 'console_scripts': [ 'googlesamples-assistant' '=googlesamples.assistant.__main__:main [samples]', 'googlesamples-assistant-auth' '=googlesamples.assistant.auth_helpers.__main__:main [samples]', ], }, license='Apache 2.0', keywords='google assistant api sdk sample', classifiers=( 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: Apache Software License', 'Operating System :: POSIX', 'Operating System :: Microsoft :: Windows', 'Operating System :: MacOS :: MacOS X', 'Operating System :: OS Independent', 'Topic :: Internet :: WWW/HTTP', ), )

621879993257357bd94ae2ab02c4f42840705bd9

py

Python

funsor/joint.py

bogiebro/funsor

c15eaf7019e34c647630ed3da89001e620a972fa

funsor/joint.py

bogiebro/funsor

c15eaf7019e34c647630ed3da89001e620a972fa

funsor/joint.py

bogiebro/funsor

c15eaf7019e34c647630ed3da89001e620a972fa

# Copyright Contributors to the Pyro project. # SPDX-License-Identifier: Apache-2.0 import math from collections import OrderedDict from functools import reduce from typing import Tuple, Union from multipledispatch import dispatch from multipledispatch.variadic import Variadic import funsor.ops as ops from funsor.cnf import Contraction, GaussianMixture from funsor.delta import Delta from funsor.domains import bint from funsor.gaussian import Gaussian, align_gaussian from funsor.ops import AssociativeOp from funsor.tensor import Tensor, align_tensor from funsor.terms import Funsor, Independent, Number, Reduce, Unary, eager, moment_matching, normalize @dispatch(str, str, Variadic[(Gaussian, GaussianMixture)]) def eager_cat_homogeneous(name, part_name, *parts): assert parts output = parts[0].output inputs = OrderedDict([(part_name, None)]) for part in parts: assert part.output == output assert part_name in part.inputs inputs.update(part.inputs) int_inputs = OrderedDict((k, v) for k, v in inputs.items() if v.dtype != "real") real_inputs = OrderedDict((k, v) for k, v in inputs.items() if v.dtype == "real") inputs = int_inputs.copy() inputs.update(real_inputs) discretes = [] info_vecs = [] precisions = [] for part in parts: inputs[part_name] = part.inputs[part_name] int_inputs[part_name] = inputs[part_name] shape = tuple(d.size for d in int_inputs.values()) if isinstance(part, Gaussian): discrete = None gaussian = part elif issubclass(type(part), GaussianMixture): # TODO figure out why isinstance isn't working discrete, gaussian = part.terms[0], part.terms[1] discrete = ops.expand(align_tensor(int_inputs, discrete), shape) else: raise NotImplementedError("TODO") discretes.append(discrete) info_vec, precision = align_gaussian(inputs, gaussian) info_vecs.append(ops.expand(info_vec, shape + (-1,))) precisions.append(ops.expand(precision, shape + (-1, -1))) if part_name != name: del inputs[part_name] del int_inputs[part_name] dim = 0 info_vec = ops.cat(dim, *info_vecs) precision = ops.cat(dim, *precisions) inputs[name] = bint(info_vec.shape[dim]) int_inputs[name] = inputs[name] result = Gaussian(info_vec, precision, inputs) if any(d is not None for d in discretes): for i, d in enumerate(discretes): if d is None: discretes[i] = ops.new_zeros(info_vecs[i], info_vecs[i].shape[:-1]) discrete = ops.cat(dim, *discretes) result = result + Tensor(discrete, int_inputs) return result ################################# # patterns for moment-matching ################################# @moment_matching.register(Contraction, AssociativeOp, AssociativeOp, frozenset, Variadic[object]) def moment_matching_contract_default(*args): return None @moment_matching.register(Contraction, ops.LogAddExpOp, ops.AddOp, frozenset, (Number, Tensor), Gaussian) def moment_matching_contract_joint(red_op, bin_op, reduced_vars, discrete, gaussian): approx_vars = frozenset(k for k in reduced_vars if k in gaussian.inputs and gaussian.inputs[k].dtype != 'real') exact_vars = reduced_vars - approx_vars if exact_vars and approx_vars: return Contraction(red_op, bin_op, exact_vars, discrete, gaussian).reduce(red_op, approx_vars) if approx_vars and not exact_vars: discrete += gaussian.log_normalizer new_discrete = discrete.reduce(ops.logaddexp, approx_vars.intersection(discrete.inputs)) new_discrete = discrete.reduce(ops.logaddexp, approx_vars.intersection(discrete.inputs)) num_elements = reduce(ops.mul, [ gaussian.inputs[k].num_elements for k in approx_vars.difference(discrete.inputs)], 1) if num_elements != 1: new_discrete -= math.log(num_elements) int_inputs = OrderedDict((k, d) for k, d in gaussian.inputs.items() if d.dtype != 'real') probs = (discrete - new_discrete.clamp_finite()).exp() old_loc = Tensor(ops.cholesky_solve(ops.unsqueeze(gaussian.info_vec, -1), gaussian._precision_chol).squeeze(-1), int_inputs) new_loc = (probs * old_loc).reduce(ops.add, approx_vars) old_cov = Tensor(ops.cholesky_inverse(gaussian._precision_chol), int_inputs) diff = old_loc - new_loc outers = Tensor(ops.unsqueeze(diff.data, -1) * ops.unsqueeze(diff.data, -2), diff.inputs) new_cov = ((probs * old_cov).reduce(ops.add, approx_vars) + (probs * outers).reduce(ops.add, approx_vars)) # Numerically stabilize by adding bogus precision to empty components. total = probs.reduce(ops.add, approx_vars) mask = ops.unsqueeze(ops.unsqueeze((total.data == 0), -1), -1) new_cov.data = new_cov.data + mask * ops.new_eye(new_cov.data, new_cov.data.shape[-1:]) new_precision = Tensor(ops.cholesky_inverse(ops.cholesky(new_cov.data)), new_cov.inputs) new_info_vec = (new_precision.data @ ops.unsqueeze(new_loc.data, -1)).squeeze(-1) new_inputs = new_loc.inputs.copy() new_inputs.update((k, d) for k, d in gaussian.inputs.items() if d.dtype == 'real') new_gaussian = Gaussian(new_info_vec, new_precision.data, new_inputs) new_discrete -= new_gaussian.log_normalizer return new_discrete + new_gaussian return None #################################################### # Patterns for normalizing #################################################### @eager.register(Reduce, ops.AddOp, Unary[ops.ExpOp, Funsor], frozenset) def eager_reduce_exp(op, arg, reduced_vars): # x.exp().reduce(ops.add) == x.reduce(ops.logaddexp).exp() log_result = arg.arg.reduce(ops.logaddexp, reduced_vars) if log_result is not normalize(Reduce, ops.logaddexp, arg.arg, reduced_vars): return log_result.exp() return None @eager.register(Independent, (Contraction[ops.NullOp, ops.AddOp, frozenset, Tuple[Delta, Union[Number, Tensor], Gaussian]], Contraction[ops.NullOp, ops.AddOp, frozenset, Tuple[Delta, Union[Number, Tensor, Gaussian]]]), str, str, str) def eager_independent_joint(joint, reals_var, bint_var, diag_var): if diag_var not in joint.terms[0].fresh: return None delta = Independent(joint.terms[0], reals_var, bint_var, diag_var) new_terms = (delta,) + tuple(t.reduce(ops.add, bint_var) for t in joint.terms[1:]) return reduce(joint.bin_op, new_terms)

b5cca9bc3246e5b87c5752b1652216acb8c65c7b

py

Python

yabul/__init__.py

timodonnell/yabul

d2ad2fbf934b375f9ef2b6f2d9c2ab9c157260d2

2021-03-01T20:09:20.000Z

2021-03-02T05:52:34.000Z

yabul/__init__.py

timodonnell/yabul

d2ad2fbf934b375f9ef2b6f2d9c2ab9c157260d2

yabul/__init__.py

timodonnell/yabul

d2ad2fbf934b375f9ef2b6f2d9c2ab9c157260d2

""" Yet Another Bioinformatics Utility Library """ __version__ = "0.0.3" from .fasta import read_fasta, write_fasta from .align import align_pair __all__ = [ "read_fasta", "write_fasta", "align_pair", ]

596289ba8a1eb9f05ef70c8af8d9347678a59cb9

py

Python

src/preprocess/displace_matrix.py

MelvinYin/Defined_Proteins

75da20be82a47d85d27176db29580ab87d52b670

2021-01-05T02:55:57.000Z

2021-04-16T15:49:08.000Z

src/preprocess/displace_matrix.py

MelvinYin/Defined_Proteins

75da20be82a47d85d27176db29580ab87d52b670

src/preprocess/displace_matrix.py

MelvinYin/Defined_Proteins

75da20be82a47d85d27176db29580ab87d52b670

2021-01-05T08:12:38.000Z

2021-01-05T08:12:38.000Z

""" Displace a matrix file to the left or right, filling remaining spots with the composition frequencies. """ import matplotlib.pyplot as plt import os from config import paths from utils import seq_logo from preprocess import crop_matrix def displace(displacement, matrix_file, output_file, composition_file=paths.COMPOSITION): matrix = [] with open(matrix_file, 'r') as file: for line in file: matrix.append((line.strip().split(" "))) composition = [] with open(composition_file, 'r') as file: for line in file: split_line = line.strip().split(" ") if len(split_line) != 2: continue composition.append(split_line[1]) output_matrix = [] assert len(composition) == len(matrix[0]) if displacement > 0: # shift right for i in range(displacement): output_matrix.append(composition) for i in range(len(matrix) - displacement): output_matrix.append(matrix[i]) else: # shift left displacement *= -1 for i in range(displacement, len(matrix)): output_matrix.append(matrix[i]) for i in range(displacement): output_matrix.append(composition) assert len(output_matrix) == len(matrix) with open(output_file, 'w') as file: for line in output_matrix: file.write(" ".join(line) + "\n") def test_displace(): direct_from_nbdb = os.path.join(paths.USER_INPUT, "GxxGxG_pssm.txt") cropped = os.path.join(paths.USER_INPUT, "GxxGxG_pssm_cropped.txt") test_output = os.path.join(paths.TEST, 'test.txt') crop_matrix.crop(direct_from_nbdb, cropped) displace(-2, cropped, test_output, paths.COMPOSITION) seq_logo.build_logo_nbdb(test_output) os.remove(cropped) os.remove(test_output) plt.show()

db91d1a194430276df4a1258b130857333228a1e

py

Python

test_net.py

R2D2oid/slowfast_feature_extractor

a5f2f0bdeee964ffd3b5e5950d84c22b93ab8467

2019-11-08T14:03:26.000Z

2022-03-06T05:28:40.000Z

test_net.py

xujinglin/slowfast_feature_extractor

be219c1dbf24511a189e458cc8d4ab5c0292a335

2019-11-11T09:16:30.000Z

2022-03-08T14:56:21.000Z

test_net.py

xujinglin/slowfast_feature_extractor

be219c1dbf24511a189e458cc8d4ab5c0292a335

2019-11-11T07:38:59.000Z

2021-11-19T19:29:09.000Z

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. # Modified to process a list of videos """Extract features for videos using pre-trained networks""" import numpy as np import torch import os import time from tqdm import tqdm import av from moviepy.video.io.VideoFileClip import VideoFileClip import slowfast.utils.checkpoint as cu import slowfast.utils.distributed as du import slowfast.utils.logging as logging import slowfast.utils.misc as misc from models import build_model from datasets import VideoSet logger = logging.get_logger(__name__) def calculate_time_taken(start_time, end_time): hours = int((end_time - start_time) / 3600) minutes = int((end_time - start_time) / 60) - (hours * 60) seconds = int((end_time - start_time) % 60) return hours, minutes, seconds @torch.no_grad() def perform_inference(test_loader, model, cfg): """ Perform mutli-view testing that samples a segment of frames from a video and extract features from a pre-trained model. Args: test_loader (loader): video testing loader. model (model): the pretrained video model to test. cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ # Enable eval mode. model.eval() feat_arr = None for inputs in tqdm(test_loader): # Transfer the data to the current GPU device. if isinstance(inputs, (list,)): for i in range(len(inputs)): inputs[i] = inputs[i].cuda(non_blocking=True) else: inputs = inputs.cuda(non_blocking=True) # Perform the forward pass. preds, feat = model(inputs) # Gather all the predictions across all the devices to perform ensemble. if cfg.NUM_GPUS > 1: preds, feat = du.all_gather([preds, feat]) feat = feat.cpu().numpy() if feat_arr is None: feat_arr = feat else: feat_arr = np.concatenate((feat_arr, feat), axis=0) return feat_arr def test(cfg): """ Perform multi-view testing/feature extraction on the pretrained video model. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ # Set random seed from configs. np.random.seed(cfg.RNG_SEED) torch.manual_seed(cfg.RNG_SEED) # Setup logging format. logging.setup_logging(cfg.OUTPUT_DIR) # Print config. logger.info("Test with config:") logger.info(cfg) # Build the video model and print model statistics. model = build_model(cfg) if du.is_master_proc() and cfg.LOG_MODEL_INFO: misc.log_model_info(model, cfg, use_train_input=False) cu.load_test_checkpoint(cfg, model) vid_root = os.path.join(cfg.DATA.PATH_TO_DATA_DIR, cfg.DATA.PATH_PREFIX) videos_list_file = os.path.join(cfg.DATA.PATH_TO_DATA_DIR, "vid_list.csv") print("Loading Video List ...") with open(videos_list_file) as f: videos = sorted([x.strip() for x in f.readlines() if len(x.strip()) > 0]) print("Done") print("----------------------------------------------------------") if cfg.DATA.READ_VID_FILE: rejected_vids = [] print("{} videos to be processed...".format(len(videos))) print("----------------------------------------------------------") start_time = time.time() for vid_no, vid in enumerate(videos): # Create video testing loaders. path_to_vid = os.path.join(vid_root, os.path.split(vid)[0]) vid_id = os.path.split(vid)[1] if cfg.DATA.READ_VID_FILE: try: _ = VideoFileClip( os.path.join(path_to_vid, vid_id) + cfg.DATA.VID_FILE_EXT, audio=False, fps_source="fps", ) except Exception as e: print("{}. {} cannot be read with error {}".format(vid_no, vid, e)) print("----------------------------------------------------------") rejected_vids.append(vid) continue out_path = os.path.join(cfg.OUTPUT_DIR, os.path.split(vid)[0]) out_file = vid_id.split(".")[0] + "_{}.npy".format(cfg.DATA.NUM_FRAMES) if os.path.exists(os.path.join(out_path, out_file)): print("{}. {} already exists".format(vid_no, out_file)) print("----------------------------------------------------------") continue print("{}. Processing {}...".format(vid_no, vid)) dataset = VideoSet( cfg, path_to_vid, vid_id, read_vid_file=cfg.DATA.READ_VID_FILE ) test_loader = torch.utils.data.DataLoader( dataset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False, sampler=None, num_workers=cfg.DATA_LOADER.NUM_WORKERS, pin_memory=cfg.DATA_LOADER.PIN_MEMORY, drop_last=False, ) # Perform multi-view test on the entire dataset. feat_arr = perform_inference(test_loader, model, cfg) os.makedirs(out_path, exist_ok=True) np.save(os.path.join(out_path, out_file), feat_arr) print("Done.") print("----------------------------------------------------------") if cfg.DATA.READ_VID_FILE: print("Rejected Videos: {}".format(rejected_vids)) end_time = time.time() hours, minutes, seconds = calculate_time_taken(start_time, end_time) print( "Time taken: {} hour(s), {} minute(s) and {} second(s)".format( hours, minutes, seconds ) ) print("----------------------------------------------------------")

3c3513a3ef5992c12d88cee0ae7da54721bec955

py

Python

train.py

ruyueshi/crnn.pytorch-1

2272d4defc328c033a889b9ca3279795f268b52d

2017-03-23T02:43:38.000Z

2022-03-30T22:48:43.000Z

train.py

Salamit/crnn.pytorch

d3a47f91691b31b8d5336e2ed5932e6cf65142f0

2017-04-02T07:05:24.000Z

2022-03-15T08:35:22.000Z

train.py

Salamit/crnn.pytorch

d3a47f91691b31b8d5336e2ed5932e6cf65142f0

2017-03-04T15:15:40.000Z

2022-03-30T02:32:48.000Z

from __future__ import print_function from __future__ import division import argparse import random import torch import torch.backends.cudnn as cudnn import torch.optim as optim import torch.utils.data from torch.autograd import Variable import numpy as np from warpctc_pytorch import CTCLoss import os import utils import dataset import models.crnn as crnn parser = argparse.ArgumentParser() parser.add_argument('--trainRoot', required=True, help='path to dataset') parser.add_argument('--valRoot', required=True, help='path to dataset') parser.add_argument('--workers', type=int, help='number of data loading workers', default=2) parser.add_argument('--batchSize', type=int, default=64, help='input batch size') parser.add_argument('--imgH', type=int, default=32, help='the height of the input image to network') parser.add_argument('--imgW', type=int, default=100, help='the width of the input image to network') parser.add_argument('--nh', type=int, default=256, help='size of the lstm hidden state') parser.add_argument('--nepoch', type=int, default=25, help='number of epochs to train for') # TODO(meijieru): epoch -> iter parser.add_argument('--cuda', action='store_true', help='enables cuda') parser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use') parser.add_argument('--pretrained', default='', help="path to pretrained model (to continue training)") parser.add_argument('--alphabet', type=str, default='0123456789abcdefghijklmnopqrstuvwxyz') parser.add_argument('--expr_dir', default='expr', help='Where to store samples and models') parser.add_argument('--displayInterval', type=int, default=500, help='Interval to be displayed') parser.add_argument('--n_test_disp', type=int, default=10, help='Number of samples to display when test') parser.add_argument('--valInterval', type=int, default=500, help='Interval to be displayed') parser.add_argument('--saveInterval', type=int, default=500, help='Interval to be displayed') parser.add_argument('--lr', type=float, default=0.01, help='learning rate for Critic, not used by adadealta') parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5') parser.add_argument('--adam', action='store_true', help='Whether to use adam (default is rmsprop)') parser.add_argument('--adadelta', action='store_true', help='Whether to use adadelta (default is rmsprop)') parser.add_argument('--keep_ratio', action='store_true', help='whether to keep ratio for image resize') parser.add_argument('--manualSeed', type=int, default=1234, help='reproduce experiemnt') parser.add_argument('--random_sample', action='store_true', help='whether to sample the dataset with random sampler') opt = parser.parse_args() print(opt) if not os.path.exists(opt.expr_dir): os.makedirs(opt.expr_dir) random.seed(opt.manualSeed) np.random.seed(opt.manualSeed) torch.manual_seed(opt.manualSeed) cudnn.benchmark = True if torch.cuda.is_available() and not opt.cuda: print("WARNING: You have a CUDA device, so you should probably run with --cuda") train_dataset = dataset.lmdbDataset(root=opt.trainroot) assert train_dataset if not opt.random_sample: sampler = dataset.randomSequentialSampler(train_dataset, opt.batchSize) else: sampler = None train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=opt.batchSize, shuffle=True, sampler=sampler, num_workers=int(opt.workers), collate_fn=dataset.alignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio=opt.keep_ratio)) test_dataset = dataset.lmdbDataset( root=opt.valroot, transform=dataset.resizeNormalize((100, 32))) nclass = len(opt.alphabet) + 1 nc = 1 converter = utils.strLabelConverter(opt.alphabet) criterion = CTCLoss() # custom weights initialization called on crnn def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: m.weight.data.normal_(0.0, 0.02) elif classname.find('BatchNorm') != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) crnn = crnn.CRNN(opt.imgH, nc, nclass, opt.nh) crnn.apply(weights_init) if opt.pretrained != '': print('loading pretrained model from %s' % opt.pretrained) crnn.load_state_dict(torch.load(opt.pretrained)) print(crnn) image = torch.FloatTensor(opt.batchSize, 3, opt.imgH, opt.imgH) text = torch.IntTensor(opt.batchSize * 5) length = torch.IntTensor(opt.batchSize) if opt.cuda: crnn.cuda() crnn = torch.nn.DataParallel(crnn, device_ids=range(opt.ngpu)) image = image.cuda() criterion = criterion.cuda() image = Variable(image) text = Variable(text) length = Variable(length) # loss averager loss_avg = utils.averager() # setup optimizer if opt.adam: optimizer = optim.Adam(crnn.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) elif opt.adadelta: optimizer = optim.Adadelta(crnn.parameters()) else: optimizer = optim.RMSprop(crnn.parameters(), lr=opt.lr) def val(net, dataset, criterion, max_iter=100): print('Start val') for p in crnn.parameters(): p.requires_grad = False net.eval() data_loader = torch.utils.data.DataLoader( dataset, shuffle=True, batch_size=opt.batchSize, num_workers=int(opt.workers)) val_iter = iter(data_loader) i = 0 n_correct = 0 loss_avg = utils.averager() max_iter = min(max_iter, len(data_loader)) for i in range(max_iter): data = val_iter.next() i += 1 cpu_images, cpu_texts = data batch_size = cpu_images.size(0) utils.loadData(image, cpu_images) t, l = converter.encode(cpu_texts) utils.loadData(text, t) utils.loadData(length, l) preds = crnn(image) preds_size = Variable(torch.IntTensor([preds.size(0)] * batch_size)) cost = criterion(preds, text, preds_size, length) / batch_size loss_avg.add(cost) _, preds = preds.max(2) preds = preds.squeeze(2) preds = preds.transpose(1, 0).contiguous().view(-1) sim_preds = converter.decode(preds.data, preds_size.data, raw=False) for pred, target in zip(sim_preds, cpu_texts): if pred == target.lower(): n_correct += 1 raw_preds = converter.decode(preds.data, preds_size.data, raw=True)[:opt.n_test_disp] for raw_pred, pred, gt in zip(raw_preds, sim_preds, cpu_texts): print('%-20s => %-20s, gt: %-20s' % (raw_pred, pred, gt)) accuracy = n_correct / float(max_iter * opt.batchSize) print('Test loss: %f, accuray: %f' % (loss_avg.val(), accuracy)) def trainBatch(net, criterion, optimizer): data = train_iter.next() cpu_images, cpu_texts = data batch_size = cpu_images.size(0) utils.loadData(image, cpu_images) t, l = converter.encode(cpu_texts) utils.loadData(text, t) utils.loadData(length, l) preds = crnn(image) preds_size = Variable(torch.IntTensor([preds.size(0)] * batch_size)) cost = criterion(preds, text, preds_size, length) / batch_size crnn.zero_grad() cost.backward() optimizer.step() return cost for epoch in range(opt.nepoch): train_iter = iter(train_loader) i = 0 while i < len(train_loader): for p in crnn.parameters(): p.requires_grad = True crnn.train() cost = trainBatch(crnn, criterion, optimizer) loss_avg.add(cost) i += 1 if i % opt.displayInterval == 0: print('[%d/%d][%d/%d] Loss: %f' % (epoch, opt.nepoch, i, len(train_loader), loss_avg.val())) loss_avg.reset() if i % opt.valInterval == 0: val(crnn, test_dataset, criterion) # do checkpointing if i % opt.saveInterval == 0: torch.save( crnn.state_dict(), '{0}/netCRNN_{1}_{2}.pth'.format(opt.expr_dir, epoch, i))

9a1837e2f9dd3fc1a3e0d38816c4c8288db421e0

py

Python

leonardo_package_index/apps/package_index_api.py

leonardo-modules/leonardo-package-index

ed4dd006055fe99d841ce09776507a2cd31c2b83

leonardo_package_index/apps/package_index_api.py

leonardo-modules/leonardo-package-index

ed4dd006055fe99d841ce09776507a2cd31c2b83

2015-08-28T23:00:15.000Z

2016-09-17T19:31:00.000Z

leonardo_package_index/apps/package_index_api.py

leonardo-modules/leonardo-package-index

ed4dd006055fe99d841ce09776507a2cd31c2b83

from django.conf.urls import * urlpatterns = patterns('leonardo_package_index.apps.views', url(r'^', include('leonardo_package_index.api.urls'),), )

69eb164733d6ec8bd04522102a81225cce0bf977

py

Python

src/utils/glove.py

LaudateCorpus1/hermes-5

d9b50452379fe636da96c2bad2d286afa15cd7b9

2015-11-17T09:04:37.000Z

2022-01-14T07:00:34.000Z

src/utils/glove.py

cacan/hermes

d9b50452379fe636da96c2bad2d286afa15cd7b9

2015-11-19T18:04:13.000Z

2016-11-19T00:30:12.000Z

src/utils/glove.py

cacan/hermes

d9b50452379fe636da96c2bad2d286afa15cd7b9

2015-11-13T22:51:57.000Z

2022-01-26T01:51:09.000Z

import numpy as np class Glove(object): """Load a GloVe model and provide access to the vectors and vector space. Provides access to word vectors as if it were a dictionary: glove_instance = Glove('file') glove['word'] Unrecognized words will return the Null vector (all 0). Also provides a way to find the closest word to a vector in the vector space. """ def __init__(self, glove_file): """Set up the GloVe class by reading in a vector file. Args: glove_file (str): Location of the plain text GloVe vector file. """ self.__model = {} self.__line_to_word = {} space = [] # Load the GloVe data from a file with open(glove_file, 'r') as open_file: for line_number, line in enumerate(open_file): sline = line.split() key_word = sline[0] vector = np.array([float(i) for i in sline[1:]]) self.__model[key_word] = vector self.__line_to_word[line_number] = key_word space.append(vector) # Set up a vector space so we can quickly find the closest vector self.__vector_space = np.array(space) # Null vector for unrecognized words self.vector_size = len(vector) self.__null_vector = np.zeros(self.vector_size) def __getitem__(self, key): """Return the vector representation of a word. Args: key (str): A word to locate in the vector space. Returns: numpy array: The location of the word in the vector space, or the null (0) vector if the word is not found. """ return self.__model.get(key, self.__null_vector) def closest_word(self, vector): """Return the closest word to a given vector. Args: vector (numpy array): A vector of the same dimension as the vector space. Returns: str: The closest word to the input vector in the vector space. """ squares = (self.__vector_space - vector)**2 distances = np.sum(squares, axis=1) line_number = np.argmin(distances) return self.__line_to_word[line_number]

35b101f2c6f3c7d3d6daf2d23908f82c5f60f15f

py

Python

python/cuda_linux_demo/model_test.py

windstamp/Paddle-Inference-Demo

de773a0864eb12911d2cdcbc8f1f036911541c60

2020-05-06T09:47:08.000Z

2022-03-31T08:47:18.000Z

python/cuda_linux_demo/model_test.py

windstamp/Paddle-Inference-Demo

de773a0864eb12911d2cdcbc8f1f036911541c60

2020-05-06T09:51:45.000Z

2022-03-27T00:23:29.000Z

python/cuda_linux_demo/model_test.py

windstamp/Paddle-Inference-Demo

de773a0864eb12911d2cdcbc8f1f036911541c60

2020-05-06T09:47:11.000Z

2022-03-23T07:29:32.000Z

import numpy as np import argparse import cv2 from paddle.inference import Config from paddle.inference import create_predictor from paddle.inference import PrecisionType from img_preprocess import preprocess def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( "--model_dir", type=str, default="", help= "Model dir, If you load a non-combined model, specify the directory of the model." ) parser.add_argument( "--model_file", type=str, default="", help="Model filename, Specify this when your model is a combined model." ) parser.add_argument( "--params_file", type=str, default="", help= "Parameter filename, Specify this when your model is a combined model." ) parser.add_argument("--img_path", type=str, default="", help="Input image path.") parser.add_argument("--threads", type=int, default=1, help="Whether use gpu.") return parser.parse_args() if __name__ == '__main__': args = parse_args() assert (args.model_dir != "") or \ (args.model_file != "" and args.params_file != ""), \ "Set model path error." assert args.img_path != "", "Set img_path error." # Init config if args.model_dir == "": config = Config(args.model_file, args.params_file) else: config = Config(args.model_dir) config.enable_use_gpu(500, 0) config.switch_ir_optim() config.enable_memory_optim() config.enable_tensorrt_engine(workspace_size=1 << 30, precision_mode=PrecisionType.Float32,max_batch_size=1, min_subgraph_size=5, use_static=False, use_calib_mode=False) # Create predictor predictor = create_predictor(config) # Set input img = cv2.imread(args.img_path) img = preprocess(img) input_names = predictor.get_input_names() input_tensor = predictor.get_input_handle(input_names[0]) input_tensor.reshape(img.shape) input_tensor.copy_from_cpu(img.copy()) # Run predictor.run() # Set output output_names = predictor.get_output_names() output_tensor = predictor.get_output_handle(output_names[0]) output_data = output_tensor.copy_to_cpu() print("Predict class index: ", np.argmax(output_data))

2c26c233741c290e55f23f5fb8856be3cd70b909

py

Python

motorodm/documents/meta_document.py

rob-blackbourn/motorodm

c77e79bd2b11c896b9a971ba6a9c4947dce96163

motorodm/documents/meta_document.py

rob-blackbourn/motorodm

c77e79bd2b11c896b9a971ba6a9c4947dce96163

motorodm/documents/meta_document.py

rob-blackbourn/motorodm

c77e79bd2b11c896b9a971ba6a9c4947dce96163

from ..fields.field import Field from ..fields import ObjectIdField from ..query_sets.query_set import QuerySet class MetaEmbeddedDocument(type): def __new__(cls, name, bases, dct): dct['_fields'] = {} dct['_db_name_map'] = {} dct['_indices'] = [] for base in bases: for field_name, field in filter(lambda x: isinstance(x[1], Field), base.__dict__.items()): cls.add_field(dct, field_name, field) for field_name, field in filter(lambda x: isinstance(x[1], Field), dct.items()): field.name = field_name cls.add_field(dct, field_name, field) dct['_values'] = {} dct['_dirty_fields'] = set() return super().__new__(cls, name, bases, dct) @classmethod def add_field(cls, dct, field_name, field): if field_name in dct['_fields']: raise KeyError(f"Field '{field_name}' already exists") if not field.db_name: field.db_name = field_name if field.db_name in dct['_db_name_map']: raise KeyError(f"Field '{field_name}' already exists") field.name = field_name dct['_fields'][field_name] = field dct['_db_name_map'][field.db_name] = field_name if field.unique: dct['_indices'].append(field_name) class MetaDocument(MetaEmbeddedDocument): def __new__(cls, name, bases, dct): if '_root' in dct and dct['_root']: return super().__new__(cls, name, bases, dct) if dct.get('__collection__', None) is None: dct['__collection__'] = name klass = super().__new__(cls, name, bases, dct) if '_id' not in klass._db_name_map: if 'id' in klass.__dict__: raise Exception('Unable to set id field - already exists') field = ObjectIdField(name='id', db_name='_id') klass.id = field klass._fields[field.name] = field klass._db_name_map[field.db_name] = field.name klass.qs = QuerySet() return klass

d27a7cd8fb1a33aefe639f54dcde93489cfbe955

py

Python

JobScript/wind_to_db/wind_stock_daily_import.py

zuoziji/transaction

7a59817a699d9df32e13d43edda630520af7860d

JobScript/wind_to_db/wind_stock_daily_import.py

zuoziji/transaction

7a59817a699d9df32e13d43edda630520af7860d

2021-02-08T20:19:53.000Z

2022-03-11T23:16:46.000Z

JobScript/wind_to_db/wind_stock_daily_import.py

zuoziji/transaction

7a59817a699d9df32e13d43edda630520af7860d

2019-03-03T14:27:54.000Z

2019-07-22T09:00:35.000Z

from datetime import date, datetime, timedelta import pandas as pd import numpy as np from config_fh import get_db_engine, get_db_session, STR_FORMAT_DATE, UN_AVAILABLE_DATE, WIND_REST_URL from fh_tools.windy_utils_rest import WindRest from fh_tools.fh_utils import get_last, get_first import logging from sqlalchemy.types import String, Date, Float, Integer DATE_BASE = datetime.strptime('2005-01-01', STR_FORMAT_DATE).date() ONE_DAY = timedelta(days=1) def get_datelist(startdate, enddate): datelist = w.tdays(startdate, enddate) datelist = datelist.Data[0] datelist = [i.strftime(STR_FORMAT_DATE) for i in datelist] return datelist def get_stockcodes(targerdate): codesinfo = w.wset("sectorconstituent", "date=%s;windcode=881001.WI" % targerdate) codes = codesinfo.Data[1] names = codesinfo.Data[2] return codes, names def get_tradeinfo(stockcode, stockname, startdate, enddate): wind_indictor_str = "open,high,low,close,adjfactor,volume,amt,pct_chg,maxupordown," + \ "swing,turn,free_turn,trade_status,susp_days" stock_tradeinfo = w.wsd(stockcode, wind_indictor_str, startdate, enddate) stock_times = stock_tradeinfo.Times stock_data = stock_tradeinfo.Data stockre = pd.DataFrame() stockre['Trade_Date'] = [i.strftime('%Y-%m-%d') for i in stock_times] stockcode_list = [stockcode] * len(stock_data[0]) stockname_list = [stockname] * len(stock_data[0]) stockre['Stock_Code'] = stockcode_list stockre['Stock_Name'] = stockname_list wind_list = wind_indictor_str.split(',') for index, wincode in enumerate(wind_list): stockre[wincode] = stock_data[index] # 去除nan数据 open_tmp = stockre['close'] open_tmp_nan = np.isnan(open_tmp) stockre = stockre[open_tmp_nan != 1] return stockre def save_df2db(stockre, indexnames, conn): stockre.to_sql('stock_tradeinfo', conn, if_exists='append', flavor='mysql', index_label=['Trade_Date', 'Stock_Code', 'Stock_Name']) def import_stock_daily(): w = WindRest(WIND_REST_URL) engine = get_db_engine() with get_db_session(engine) as session: # 获取每只股票最新交易日数据 sql_str = 'select wind_code, max(Trade_date) from wind_stock_daily group by wind_code' table = session.execute(sql_str) stock_trade_date_latest_dic = dict(table.fetchall()) # 获取市场有效交易日数据 sql_str = "select trade_date from wind_trade_date where trade_date > '2005-1-1'" table = session.execute(sql_str) trade_date_sorted_list = [t[0] for t in table.fetchall()] trade_date_sorted_list.sort() # 获取每只股票上市日期、退市日期 table = session.execute('SELECT wind_code, ipo_date, delist_date FROM wind_stock_info') stock_date_dic = {wind_code: (ipo_date, delist_date if delist_date is None or delist_date > UN_AVAILABLE_DATE else None) for wind_code, ipo_date, delist_date in table.fetchall()} today_t_1 = date.today() - ONE_DAY data_df_list = [] try: for wind_code, date_pair in stock_date_dic.items(): date_ipo, date_delist = date_pair # 获取 date_from if wind_code in stock_trade_date_latest_dic: date_latest_t1 = stock_trade_date_latest_dic[wind_code] + ONE_DAY date_from = max([date_latest_t1, DATE_BASE, date_ipo]) else: date_from = max([DATE_BASE, date_ipo]) date_from = get_first(trade_date_sorted_list, lambda x: x >= date_from) # 获取 date_to if date_delist is None: date_to = today_t_1 else: date_to = min([date_delist, today_t_1]) date_to = get_last(trade_date_sorted_list, lambda x: x <= date_to) if date_from is None or date_to is None or date_from > date_to: continue # 获取股票量价等行情数据 wind_indictor_str = "open,high,low,close,adjfactor,volume,amt,pct_chg,maxupordown," + \ "swing,turn,free_turn,trade_status,susp_days" data_df = w.wsd(wind_code, wind_indictor_str, date_from, date_to) if data_df is None: logging.warning('%s has no data during %s %s', wind_code, date_from, date_to) continue logging.info('%d data of %s', data_df.shape[0], wind_code) data_df['wind_code'] = wind_code data_df_list.append(data_df) finally: # 导入数据库 if len(data_df_list) > 0: data_df_all = pd.concat(data_df_list) data_df_all.index.rename('trade_date', inplace=True) data_df_all.reset_index(inplace=True) data_df_all.set_index(['wind_code', 'trade_date'], inplace=True) data_df_all.to_sql('wind_stock_daily', engine, if_exists='append', dtype={ 'wind_code': String(20), 'trade_date': Date, 'open': Float, 'high': Float, 'low': Float, 'close': Float, 'adjfactor': Float, 'volume': Float, 'amt': Float, 'pct_chg': Float, 'maxupordown': Integer, 'swing': Float, 'turn': Float, 'free_turn': Float, 'trade_status': String(20), 'susp_days': Integer, } ) logging.info('%d data imported', data_df_all.shape[0]) if __name__ == '__main__': import_stock_daily() # startdate = '2005-01-03' # enddate = '2014-12-31' # stockcodes, stocknames = get_stockcodes(enddate) # stockloc = 1085 # costtime = 0 # stockcodes = stockcodes[stockloc:] # stocknames = stocknames[stockloc:] # with get_db_session() as session: # for stockcode, stockname in zip(stockcodes, stocknames): # timestart = time.time() # stockre = get_tradeinfo(stockcode, stockname, startdate, enddate) # stockre.set_index(['Trade_Date', 'Stock_Code', 'Stock_Name'], inplace=True) # # indexnames = ['Trade_Date', 'Stock_Code', 'Stock_Name'] # save_df2db(stockre, indexnames, session) # timeend = time.time() # costtime = costtime + timeend - timestart # # conn.close() # print('Success Transfer %s, %s' % (stockcode, stockname), # "本次耗时：%d" % round(timeend - timestart), "累计耗时：%d" % costtime)

7e99ec6791ef449bd15d88bc40e47be5465426ce

py

Python

bot_4u/shop.py

Gametz/Helper_Bot

c48c8258a71782ecc4621b0a0eac3f9de4d461b7

bot_4u/shop.py

Gametz/Helper_Bot

c48c8258a71782ecc4621b0a0eac3f9de4d461b7

bot_4u/shop.py

Gametz/Helper_Bot

c48c8258a71782ecc4621b0a0eac3f9de4d461b7

import json def shop(): return "Магазин:" \ "\n" \ "\n&#12288;🚗 Машины" \ "\n&#12288;📱 Телефоны" \ "\n&#12288;🏡 Дома" \ "\n&#12288;🎞 Видеокарты" \ "\n&#12288;₿ Биткоины" \ "\n" \ "\n📌 Для просмотра категории используйте ее название" def sell(): return "Продажа:" \ "\n" \ "\n&#12288;🚗 Пмашину - Продать свою машину" \ "\n&#12288;📱 Птел - Продать свой телефон" \ "\n&#12288;🏡 Пдом - Продать свой дом" \ "\n&#12288;🎞 Пкарту - Продать свю видеокарту" def cars(): return "🚗 Машины:" \ "\n" \ "\n&#12288;💎 1. ВАЗ 2115 | 2.000$" \ "\n&#12288;💎 2. LADA Vesta | 4.000$" \ "\n&#12288;💎 3. Audi Q7 | 8.000$" \ "\n&#12288;💎 4. BMW M8 | 15.000$" \ "\n&#12288;💎 5. Range Rover | 50.000$" \ "\n&#12288;💎 6. Rolls-Royce | 150.000$" \ "\n" \ "\n📌 Для покупки транспорта используйте 'кмашину [номер]'\n" \ "Например: кмашину 1" def phones(): return "📱 Телефоны:" \ "\n" \ "\n&#12288;💎 1. Fly Ezzy Flip | 200$" \ "\n&#12288;💎 2. Sony Xperia XA1 | 1.000$" \ "\n&#12288;💎 3. Xiaomi Mi 11 | 10.000$" \ "\n&#12288;💎 4. Samsung Galaxy S21 | 50.000$" \ "\n&#12288;💎 5. iPhone 12 | 200.000$" \ "\n" \ "\n📌 Для покупки телефона используйте 'ктел [номер]'\n" \ "Например: ктел 1" def homes(): return "🏡 Дома:" \ "\n" \ "\n&#12288;💎 1. Картонная коробка | 100$" \ "\n&#12288;💎 2. Дом на дереве | 2.000$" \ "\n&#12288;💎 3. Деревянный дом | 10.000$" \ "\n&#12288;💎 4. Квартира в новостройке | 50.000$" \ "\n&#12288;💎 5. Особняк | 150.000$" \ "\n&#12288;💎 6. Дом на Рублёвке | 300.000$" \ "\n&#12288;💎 7. Личный остров | 500.000$" \ "\n&#12288;💎 8. Дворец в Геленджике | 1.000.000$" \ "\n" \ "\n📌 Для покупки транспорта используйте 'кдом [номер]'\n" \ "Например: кдом 1"

02f488e6ce3fe201ae71bfea566b158c06d9e203

py

Python

dataset_processing/process_modelnet.py

Mingy2018/SwitchVAE

cf9c06ce3af50a559d79b9cba14851472e43a70b

2021-07-22T00:46:06.000Z

2021-07-22T00:46:06.000Z

dataset_processing/process_modelnet.py

Mingy2018/SwitchVAE

cf9c06ce3af50a559d79b9cba14851472e43a70b

dataset_processing/process_modelnet.py

Mingy2018/SwitchVAE

cf9c06ce3af50a559d79b9cba14851472e43a70b

2021-12-07T17:10:19.000Z

2021-12-07T17:10:19.000Z

import os import numpy as np from utils import binvox_rw import glob if __name__ == '__main__': ModelNet10_ROOT = '/home/zmy/Datasets/ModelNet10/ModelNet10' ModelNet40_ROOT = '/home/zmy/Datasets/ModelNet40' image_ROOT = '/home/zmy/mmi_dataset/ModelNet40_images/modelnet40_images_new_12x' ModelNet10_CLASSES = ['bathtub', 'bed', 'chair', 'desk', 'dresser', 'monitor', 'night_stand', 'sofa', 'table', 'toilet'] ModelNet40_CLASSES = [ 'airplane', 'bowl', 'table', 'chair', 'vase', 'glass_box', 'bathtub', 'toilet', 'range_hood', 'flower_pot', 'laptop', 'plant', 'cup', 'person', 'tent', 'sofa', 'monitor', 'keyboard', 'desk', 'mantel', 'curtain', 'bed', 'lamp', 'bench', 'dresser','car', 'sink', 'night_stand', 'stool', 'door', 'guitar', 'stairs', 'radio', 'tv_stand', 'cone', 'xbox', 'wardrobe', 'bookshelf', 'bottle', 'piano'] # ---------------Block1--------------------------- # X = {'train': [], 'test': []} # y = {'train': [], 'test': []} # # for label, cl in enumerate(ModelNet10_CLASSES): # for split in ['train', 'test']: # examples_dir = os.path.join(ModelNet10_ROOT, cl, split) # for example in os.listdir(examples_dir): # if 'binvox' in example: # Ignore OFF files # with open(os.path.join(examples_dir, example), 'rb') as file: # data = np.int32(binvox_rw.read_as_3d_array(file).data) # X[split].append(data) # y[split].append(label) # X['train']=np.expand_dims(X['train'], axis=1) # X['test'] = np.expand_dims(X['test'], axis=1) # # np.savez_compressed('/home/zmy/Datasets/modelnet10.npz', # X_train=X['train'], # X_test=X['test'], # y_train=y['train'], # y_test=y['test']) #---------------------------------------------------- # -----------------------Block2-------------------------- # X = {'train': [], 'test': []} # y = {'train': [], 'test': []} # for label, cl in enumerate(ModelNet40_CLASSES): # for split in ['train', 'test']: # examples_dir = os.path.join(ModelNet40_ROOT, cl, split) # for example in os.listdir(examples_dir): # if 'binvox' in example: # Ignore OFF files # with open(os.path.join(examples_dir, example), 'rb') as file: # data = np.int32(binvox_rw.read_as_3d_array(file).data) # X[split].append(data) # y[split].append(label) # # X['train'] = np.expand_dims(X['train'], axis=1) # X['test'] = np.expand_dims(X['test'], axis=1) # np.savez_compressed('/home/zmy/Datasets/modelnet40.npz', # X_train=X['train'], # X_test=X['test'], # y_train=y['train'], # y_test=y['test']) #------------------------------------------------------- #------------------------------------------------------------- # X = {'train': [], 'test': []} # y = {'train': [], 'test': []} # # for label, cl in enumerate(ModelNet10_CLASSES): # for split in ['train', 'test']: # examples_dir = os.path.join(image_ROOT, cl, split) # file_list = os.listdir(examples_dir) # id_list = [name.split('.')[0] for name in file_list if not name.startswith('.')] # unique_id_list = list(set(id_list)) # X[split]+= unique_id_list # y[split]+= [label] * len(unique_id_list) # # np.savez_compressed('/home/zmy/mmi_dataset/modelnet10_image.npz', # X_train=X['train'], # X_test=X['test'], # y_train=y['train'], # y_test=y['test']) #------------------------------------------------------------------------------------- #------------------------------------------------------------- X = {'train': [], 'test': []} y = {'train': [], 'test': []} for label, cl in enumerate(ModelNet40_CLASSES): for split in ['train', 'test']: examples_dir = os.path.join(image_ROOT, cl, split) file_list = os.listdir(examples_dir) id_list = [name.split('.')[0] for name in file_list if not name.startswith('.')] unique_id_list = list(set(id_list)) X[split]+= unique_id_list y[split]+= [label] * len(unique_id_list) np.savez_compressed('/home/zmy/mmi_dataset/modelnet40_image.npz', X_train=X['train'], X_test=X['test'], y_train=y['train'], y_test=y['test']) #-------------------------------------------------------------------------------------

1c51aa15373779b06273296a27d913c070079f41

py

Python

python/paddle/fluid/contrib/slim/quantization/__init__.py

ysh329/Paddle

50ad9046c9a440564d104eaa354eb9df83a35678

2022-03-26T11:44:07.000Z

2022-03-26T11:44:07.000Z

python/paddle/fluid/contrib/slim/quantization/__init__.py

ysh329/Paddle

50ad9046c9a440564d104eaa354eb9df83a35678

python/paddle/fluid/contrib/slim/quantization/__init__.py

ysh329/Paddle

50ad9046c9a440564d104eaa354eb9df83a35678

2022-03-26T11:44:12.000Z

2022-03-26T11:44:12.000Z

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function from . import quantization_pass from .quantization_pass import * from . import quantization_strategy from .quantization_strategy import * __all__ = quantization_pass.__all__ + quantization_strategy.__all__

7be7a563b31adbb4c9c6a38a319557746106dc98

py

Python

day20/part2.py

mtn/advent18

634ad20f02c321d6a38583077ee6b7f84a8848e5

2018-12-01T20:58:37.000Z

2018-12-01T20:58:37.000Z

day20/part2.py

mtn/advent18

634ad20f02c321d6a38583077ee6b7f84a8848e5

day20/part2.py

mtn/advent18

634ad20f02c321d6a38583077ee6b7f84a8848e5

#!/usr/bin/env python3 from collections import defaultdict open_parens = [] start_end = {} # open paren to close paren ind alt_starts = {} # alternation to opening "(" alts = {} # maps "(" ind to [alternations] with open("input.txt") as f: inp = f.read().strip() for i, ch in enumerate(inp): if ch == "$": break if ch == "(": open_parens.append(i) elif ch == ")": last_open = open_parens.pop() start_end[last_open] = i if ch == "|": alt_starts[i] = open_parens[-1] if open_parens[-1] in alts: alts[open_parens[-1]].append(i) else: alts[open_parens[-1]] = [i] assert not open_parens # all opened parens should be closed g = defaultdict(set) # graph represented as adjacency lists visited = set() # what we've visited so we don't cycle in inp def run(point, ind): global g while True: if inp[ind] == "$" or (point, ind) in visited: break visited.add((point, ind)) if inp[ind] == "N": new = (point[0], point[1] - 1) g[point].add(new) g[new].add(point) ind += 1 point = new elif inp[ind] == "E": new = (point[0] + 1, point[1]) g[point].add(new) g[new].add(point) ind += 1 point = new elif inp[ind] == "S": new = (point[0], point[1] + 1) g[point].add(new) g[new].add(point) ind += 1 point = new elif inp[ind] == "W": new = (point[0] - 1, point[1]) g[point].add(new) g[new].add(point) ind += 1 point = new elif inp[ind] == "|": # jump to the end of the alternation ind = start_end[alt_starts[ind]] + 1 elif inp[ind] == "(": for alt in alts[ind]: run(point, alt + 1) ind += 1 # the first branch wasn't parsed as part of the alts elif inp[ind] == ")": ind += 1 run((0, 0), 1) q = [((0, 0), 0)] distances = {} count = 0 while q: (x, y), dist = q.pop() if (x, y) in distances and distances[(x, y)] <= dist: continue if dist >= 1000: count += 1 distances[(x, y)] = dist for neighbor in g[(x, y)]: q.append((neighbor, dist + 1)) print(count)

c045e6c776ebf0200b59b2bd61fbe2c462c08bca

py

Python

pydown/downloader.py

qorost/pydown

b8ab1ff12c20f0e9e3f7af30bea31f4288025d7e

pydown/downloader.py

qorost/pydown

b8ab1ff12c20f0e9e3f7af30bea31f4288025d7e

pydown/downloader.py

qorost/pydown

b8ab1ff12c20f0e9e3f7af30bea31f4288025d7e

import sys import urllib import urllib2 #import requests import os.path import thread import threading import argparse def print_progress(iteration,total,prefix='Progress: ',suffix='Complete',decimals = 2, barlen = 100): """ http://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console Call in a loop to create terminal progress bar @params: iteration - Required : current iteration (Int) total - Required : total iterations (Int) prefix - Optional : prefix string (Str) suffix - Optional : suffix string (Str) """ filledlen = int(round(barlen*iteration)/float(total)) percents = round(100.00*(iteration/float(total)),decimals) bar = '#' * filledlen + '-' * (barlen - filledlen) try: sys.stdout.write("%s [%s] %s%s %s\r" % (prefix,bar,percents,'%',suffix)) sys.stdout.flush() except Exception,e: print str(e) print prefix,bar,percents,suffix print type(percents),type(bar) if iteration == total: print("\n") class LinkFile(): def __init__(self,filename="links.txt"): self.filename = filename def saveto(self,links): try: fp = open(self.filename,"w") for i in links: fp.write(i) fp.write("\n") fp.close() except Exception,e: sys.stderr.write("Error in Writing: " + str(e)) def extractfrom(self,filename=None): links = set() if filename is not None: tmp = filename else: tmp = self.filename try: fp = open(tmp,"r") texts = fp.read().splitlines() for i in texts: links.add(i) fp.close() return links except Exception,e: sys.stderr.write("Error while reading: " + str(e)) class Downloader(): def __init__(self,url,filename,overite=False): self.url = url self.filename = filename def run(self,overite=False) : msg= self.url i = 0 url = self.url file_name = self.filename if os.path.exists(os.path.abspath(file_name)) and overite == False: print "File Already Existed, skip downloading..." return 1 try: u = urllib2.urlopen(url) f = open(file_name, 'wb') meta = u.info() file_size = int(meta.getheaders("Content-Length")[0]) print "Downloading: %s Bytes: %s" % (file_name, file_size) file_size_dl = 0 block_sz = 8192 while True: buffer = u.read(block_sz) if not buffer: break file_size_dl += len(buffer) f.write(buffer) print_progress(file_size_dl,file_size) #status = r"%10d [%3.2f%%]" % (file_size_dl, file_size_dl * 100. / file_size) #status = status + chr(8)*(len(status)+1) #print status f.close() return 0 except Exception,e: print 'Exceptiion in %s',url,': ',str(e) return -1 class MyDownLoadThread(threading.Thread) : def __init__(self,url, filename = None): threading.Thread.__init__(self) self.fileurl = url if filename is not None: self.filename = filename else: if url.find('/') >= 0: self.filename = url.split('/')[-1] else : self.filename ='test.pdf' def run(self) : msg= 'Thread downloading %s started!\n From url: %s' %(self.filename,self.fileurl) try : urllib.urlretrieve(self.fileurl, self.filename,None) msg= 'File %s downloaded!' % self.filename except: msg= 'failed to download' class MyFilesDownloader(): def __init__(self, urls, dir='.'): self.downurls = urls self.threads = [] self.dir = dir def startDownloadingFiles(self, multiThreading = False): if multiThreading == True : msg= 'In MULTITHREAD mode \nStart Downloading file into directory %s...' % self.dir if self.downurls is not None: for url in self.downurls : if url.find('/') >= 0: filename = url.split('/')[-1] else : filename ="test.pdf" filename =os.path.join(self.dir, filename) t = MyDownLoadThread(url, filename) self.threads.append(t) t.start() else : msg= 'In NORMAL mode \nStart Downloading file into directory %s...' % self.dir if self.downurls is not None: i = 1 failures = 0 skipped = 0 success = 0 num = len(self.downurls) for url in self.downurls : if url.find('/') >= 0: filename = url.split('/')[-1] else : filename ="test.pdf" print "(%d/%d) URL: %s" %(i,num,url) filename =os.path.join(self.dir, filename) filedownloader = Downloader(url, filename) result = filedownloader.run() i += 1 if result == 1: skipped += 1 elif result == 0: success += 1 else: failures += 1 print "\nDownloading finished, (Suc:%d,Fails:%d,Skipped,%d,Total:%d)" %(success,failures,skipped,num) def test_download(): filename = "5MB5MB.zip" url = "http://download.thinkbroadband.com/5MB.zip" xdown = Downloader(url,filename) xdown.run() if __name__ == '__main__': test_download() #

d8ffa1d38fd38bef012c335812ecb17304bf1ace

py

Python

raymon/types.py

pbonte/raymon

83912d7a5ff22d61289688828169a7178fa34a2d

2021-06-14T08:37:22.000Z

2022-03-08T05:41:54.000Z

raymon/types.py

pbonte/raymon

83912d7a5ff22d61289688828169a7178fa34a2d

2021-01-30T08:45:13.000Z

2022-02-21T16:15:00.000Z

raymon/types.py

pbonte/raymon

83912d7a5ff22d61289688828169a7178fa34a2d

2021-06-18T09:53:58.000Z

2021-06-18T09:53:58.000Z

import json import io from abc import ABC, abstractmethod from pydoc import locate import msgpack import numpy as np import pandas as pd import base64 import ast from PIL import Image as PILImage from raymon.globals import Serializable class RaymonDataType(Serializable, ABC): def to_json(self): return json.dumps(self.to_jcr()) def to_msgpack(self): return msgpack.packb(self.to_jcr()) def class2str(self): module = str(self.__class__.__module__) classname = str(self.__class__.__name__) return f"{module}.{classname}" class Image(RaymonDataType): def __init__(self, data, lossless=False): self.validate(data=data, lossless=lossless) self.data = data self.lossless = lossless def validate(self, data, lossless): # Validate 3 channels if not isinstance(data, PILImage.Image): raise ValueError("Image shoud be a PIL Image") if not isinstance(lossless, bool): raise ValueError("lossless should be boolean") return True def to_jcr(self): img_byte_arr = io.BytesIO() if self.lossless: self.data.save(img_byte_arr, format="png") else: # We'll save the image as JPEG. This is not lossless, but it is saves as the highest JPEG quality. This is 25 times faster than dumping as lossless PNG, and results in a size of only 1/5th the size, before b64 encoding. # Measurements: PNG: 3.767667055130005s, 4008037 bytes -- PNG: 3.767667055130005s, 4008037 bytes # For impact on algorithms see "On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures" (https://arxiv.org/abs/2007.14314), although this paper takes jpeg quality 95 as highest quality. self.data.save(img_byte_arr, format="jpeg", quality=95) img_byte_arr = img_byte_arr.getvalue() b64 = base64.b64encode(img_byte_arr).decode() data = {"type": self.class2str(), "params": {"data": b64, "lossless": self.lossless}} return data @classmethod def from_jcr(cls, params): b64 = params["data"] img_byte_arr = io.BytesIO(base64.decodebytes(b64.encode())) img = PILImage.open(img_byte_arr) return cls(data=img) class Numpy(RaymonDataType): def __init__(self, data): self.validate(data) self.data = data def validate(self, data): if not isinstance(data, np.ndarray): raise ValueError(f"Data must bu of type numpy.ndarray, not {type(data)}.") return True def to_jcr(self): b64 = base64.b64encode(self.data).decode() shape = self.data.shape dtype = self.data.dtype data = {"type": self.class2str(), "params": {"data": b64, "shape": str(shape), "dtype": str(dtype)}} return data @classmethod def from_jcr(cls, params): shape = ast.literal_eval(params["shape"]) dtype = params["dtype"] b64 = params["data"] nprest = np.frombuffer(base64.decodebytes(b64.encode()), dtype=str(dtype)).reshape(shape) return cls(data=nprest) class Series(RaymonDataType): def __init__(self, data): self.validate(data) self.data = data def validate(self, data): if not isinstance(data, pd.Series): raise ValueError("Data should be a Pandas Series") return True def to_jcr(self): data = { "type": self.class2str(), "params": { "data": json.loads(self.data.to_json()), }, } return data @classmethod def from_jcr(cls, jcr): series = pd.Series(**jcr) return cls(series) class DataFrame(RaymonDataType): def __init__(self, data): self.validate(data) self.data = data def validate(self, data): if not isinstance(data, pd.DataFrame): raise ValueError("Data should be a Pandas DataFrame") return True def to_jcr(self): data = { "type": self.class2str(), "params": { "data": json.loads(self.data.to_json()), }, } return data @classmethod def from_jcr(cls, jcr): frame = pd.read_json(json.dumps(jcr["data"])) return cls(frame) class Native(RaymonDataType): def __init__(self, data): self.validate(data) self.data = data def validate(self, data): try: json.dumps(data) except TypeError as exc: raise ValueError(f"{exc}") return True def to_jcr(self): data = { "type": self.class2str(), "params": { "data": self.data, }, } return data @classmethod def from_jcr(cls, jcr): return cls(jcr["data"]) def load_jcr(jcr): params = jcr["params"] dtype = jcr["type"] type_class = locate(dtype) if type_class is None: raise NameError(f"Could not locate {dtype}") loaded = type_class.from_jcr(params) return loaded def from_msgpack(data): loaded_data = msgpack.unpackb(data, raw=False) return load_jcr(loaded_data)

4e04e12ca888d485002ae7196472f3182c8a4d8a

py

Python

release/cs_submitter/mainform/views.py

kvswim/kv_jhucs_coursesubmit

a00f2d1ca52204857bdf34271e13d97b424fcfca

release/cs_submitter/mainform/views.py

kvswim/kv_jhucs_coursesubmit

a00f2d1ca52204857bdf34271e13d97b424fcfca

release/cs_submitter/mainform/views.py

kvswim/kv_jhucs_coursesubmit

a00f2d1ca52204857bdf34271e13d97b424fcfca

#Kyle Verdeyen #Independent Study, Summer 2017 #Joanne Selinski #View for the mainform. Very basic validation checks. from django.shortcuts import render, render_to_response, redirect from django.http import HttpResponse from .forms import MainFormModelForm from django.urls import reverse # Create your views here. def index(request): if request.method == "POST": form = MainFormModelForm(request.POST) if form.is_valid(): post = form.save(commit = False) post.save() return redirect('/table') else: form = MainFormModelForm() return render(request, 'form/index.html', {'form' : form}) #return reverse('table:table')

ae70b469d9ff51872b0638d41b15693be95ad731

py

Python

mars/services/scheduling/supervisor/tests/test_queue_balance.py

ConanoutlooklvTBS/mars

7030566fd9e9fc02b6b4064ef7bd86f6c24a2f60

2018-12-06T09:37:11.000Z

2022-03-30T15:47:39.000Z

mars/services/scheduling/supervisor/tests/test_queue_balance.py

ConanoutlooklvTBS/mars

7030566fd9e9fc02b6b4064ef7bd86f6c24a2f60

2018-12-07T03:06:18.000Z

2022-03-31T11:45:57.000Z

mars/services/scheduling/supervisor/tests/test_queue_balance.py

ConanoutlooklvTBS/mars

7030566fd9e9fc02b6b4064ef7bd86f6c24a2f60

2018-12-07T03:12:41.000Z

2022-03-29T21:49:57.000Z

# 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 asyncio import pytest from typing import Tuple, List from ..... import oscar as mo from ....cluster import ClusterAPI from ....cluster.core import NodeRole, NodeStatus from ....cluster.uploader import NodeInfoUploaderActor from ....cluster.supervisor.locator import SupervisorPeerLocatorActor from ....cluster.supervisor.node_info import NodeInfoCollectorActor from ....subtask import Subtask from ...supervisor import AssignerActor, \ SubtaskManagerActor, SubtaskQueueingActor, GlobalSlotManagerActor class MockNodeInfoCollectorActor(NodeInfoCollectorActor): def __init__(self, timeout=None, check_interval=None): super().__init__(timeout=timeout, check_interval=check_interval) self.ready_nodes = {('address0', 'numa-0'): 2, ('address1', 'numa-0'): 2, ('address2', 'numa-0'): 2} async def update_node_info(self, address, role, env=None, resource=None, detail=None, status=None): if 'address' in address and status == NodeStatus.STOPPING: del self.ready_nodes[(address, 'numa-0')] await super().update_node_info(address, role, env, resource, detail, status) def get_all_bands(self, role=None, statuses=None): if statuses == {NodeStatus.READY}: return self.ready_nodes else: return {('address0', 'numa-0'): 2, ('address1', 'numa-0'): 2, ('address2', 'numa-0'): 2} class FakeClusterAPI(ClusterAPI): @classmethod async def create(cls, address: str, **kw): dones, _ = await asyncio.wait([ mo.create_actor(SupervisorPeerLocatorActor, 'fixed', address, uid=SupervisorPeerLocatorActor.default_uid(), address=address), mo.create_actor(MockNodeInfoCollectorActor, uid=NodeInfoCollectorActor.default_uid(), address=address), mo.create_actor(NodeInfoUploaderActor, NodeRole.WORKER, interval=kw.get('upload_interval'), band_to_slots=kw.get('band_to_slots'), use_gpu=kw.get('use_gpu', False), uid=NodeInfoUploaderActor.default_uid(), address=address), ]) for task in dones: try: task.result() except mo.ActorAlreadyExist: # pragma: no cover pass api = await super().create(address=address) await api.mark_node_ready() return api class MockSlotsActor(mo.Actor): def apply_subtask_slots(self, band: Tuple, session_id: str, subtask_ids: List[str], subtask_slots: List[int]): return subtask_ids class MockAssignerActor(mo.Actor): def assign_subtasks(self, subtasks: List[Subtask]): return [subtask.expect_bands[0] for subtask in subtasks] def reassign_subtasks(self, band_num_queued_subtasks): if len(band_num_queued_subtasks.keys()) == 1: [(band, _)] = band_num_queued_subtasks.items() return {band: 0} return {('address1', 'numa-0'): -8, ('address0', 'numa-0'): 0, ('address2', 'numa-0'): 8} class MockSubtaskManagerActor(mo.Actor): def __init__(self): self._subtask_ids, self._bands = [], [] @mo.extensible def submit_subtask_to_band(self, subtask_id: str, band: Tuple): self._subtask_ids.append(subtask_id) self._bands.append(band) def dump_data(self): return self._subtask_ids, self._bands @pytest.fixture async def actor_pool(): pool = await mo.create_actor_pool('127.0.0.1', n_process=0) async with pool: session_id = 'test_session' cluster_api = await FakeClusterAPI.create(pool.external_address) # create assigner actor await mo.create_actor(MockAssignerActor, uid=AssignerActor.gen_uid(session_id), address=pool.external_address) # create queueing actor manager_ref = await mo.create_actor(MockSubtaskManagerActor, uid=SubtaskManagerActor.gen_uid(session_id), address=pool.external_address) # create slots actor slots_ref = await mo.create_actor(MockSlotsActor, uid=GlobalSlotManagerActor.default_uid(), address=pool.external_address) # create queueing actor queueing_ref = await mo.create_actor(SubtaskQueueingActor, session_id, 1, uid=SubtaskQueueingActor.gen_uid(session_id), address=pool.external_address) yield pool, session_id, cluster_api, queueing_ref, slots_ref, manager_ref await mo.destroy_actor(queueing_ref) async def _queue_subtasks(num_subtasks, expect_bands, queueing_ref): if not num_subtasks: return subtasks = [Subtask(expect_bands[0] + '-' + str(i)) for i in range(num_subtasks)] for subtask in subtasks: subtask.expect_bands = [expect_bands] priorities = [(i,) for i in range(num_subtasks)] await queueing_ref.add_subtasks(subtasks, priorities) @pytest.mark.asyncio async def test_subtask_queueing(actor_pool): _pool, session_id, cluster_api, queueing_ref, slots_ref, manager_ref = actor_pool nums_subtasks = [9, 8, 1] expects_bands = [('address0', 'numa-0'), ('address1', 'numa-0'), ('address2', 'numa-0')] for num_subtasks, expect_bands in zip(nums_subtasks, expects_bands): await _queue_subtasks(num_subtasks, expect_bands, queueing_ref) await cluster_api.set_node_status( node='address1', role=NodeRole.WORKER, status=NodeStatus.STOPPING) # 9 subtasks on ('address0', 'numa-0') await queueing_ref.submit_subtasks(band=('address0', 'numa-0'), limit=10) commited_subtask_ids, _commited_bands = await manager_ref.dump_data() assert len(commited_subtask_ids) == 9 # 0 subtasks on ('address1', 'numa-0') await queueing_ref.submit_subtasks(band=('address1', 'numa-0'), limit=10) commited_subtask_ids, _commited_bands = await manager_ref.dump_data() assert len(commited_subtask_ids) == 9 # 9 subtasks on ('address2', 'numa-0') await queueing_ref.submit_subtasks(band=('address2', 'numa-0'), limit=10) commited_subtask_ids, _commited_bands = await manager_ref.dump_data() assert len(commited_subtask_ids) == 18

1bd69ceec19c6af6752dbe20cdaa51daebdac417

py

Python

mindhome_alpha/erpnext/patches/v5_0/rename_customer_issue.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

2021-04-29T14:55:29.000Z

2021-04-29T14:55:29.000Z

mindhome_alpha/erpnext/patches/v5_0/rename_customer_issue.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

mindhome_alpha/erpnext/patches/v5_0/rename_customer_issue.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

2021-04-29T14:39:01.000Z

2021-04-29T14:39:01.000Z

from __future__ import unicode_literals import frappe def execute(): if frappe.db.table_exists("Customer Issue"): frappe.rename_doc("DocType", "Customer Issue", "Warranty Claim")

3385ae91238131c0f2ff872285f9cab3f21c3557

py

Python

fynance/models/xgb.py

ArthurBernard/Fynance

efd9a2e6f8eddcff017d828972236312f6f24084

2018-12-13T18:52:51.000Z

2021-09-03T00:33:47.000Z

fynance/models/xgb.py

ArthurBernard/Fynance

efd9a2e6f8eddcff017d828972236312f6f24084

fynance/models/xgb.py

ArthurBernard/Fynance

efd9a2e6f8eddcff017d828972236312f6f24084

2019-05-31T16:51:51.000Z

2021-07-29T21:31:25.000Z

#!/usr/bin/env python3 # coding: utf-8 # @Author: ArthurBernard # @Email: arthur.bernard.92@gmail.com # @Date: 2019-04-23 19:15:05 # @Last modified by: ArthurBernard # @Last modified time: 2019-09-25 14:14:47 # Built-in packages # Third party packages # import xgboost as xgb # Local packages __all__ = ['XGB', 'XGBData'] class XGB: # TODO : train method, predict method def __init__(self, X, y, **kwargs): """ Setting data to XGBoot model. Parameters ---------- X, y : np.ndarray[ndim=2, dtype=np.float64] Respectively features with shape `(T, N)` and target with shape `(T, 1)` of the model. kwargs : dict, optional Parameters of DMatrix object, cf XGBoost documentation [1]_. References ---------- .. [1] https://xgboost.readthedocs.io/en/latest/python/python_api.html """ self.data = XGBData(X, label=y, **kwargs) def run(self, n, s, **params): # TODO : to remove train = self.data[:-n] estim = self.data[: s] # bst = xgb.train(params, train) # return bst.predict(estim) class XGBData: # (xgb.DMatrix): """ Set data for XGBoost models. """ def __getitem__(self, key): """ Slice the DMatrix and return a new DMatrix that only contains `key`. Parameters ---------- key : slice Slice to be selected. Returns ------- res : DMatrix A new DMatrix containing only selected indices. """ start = 0 if key.start is None else key.start step = 1 if key.step is None else key.step stop = self.num_row() if key.stop is None else key.stop if step < 0: stop, start = start - 1, stop + 1 if stop < 0: stop += self.num_row() + 1 return self.slice(list(range(start, stop, step))) def train_xgb(params, dtrain, bst=None, **kwargs): """ Train a XGBoost model """ if bst is None: pass # return xgb.train(params, dtrain, **kwargs) else: pass # return xgb.train(params, dtrain, xgb_model=bst, **kwargs)

79e4955fbc2717ea605a117d3b035dbae454bceb

py

Python

tensorflow/python/autograph/impl/api_test.py

AdaAlarm/tensorflow

e0db063159751276a92d88a4ad6d481b1199318c

2021-05-25T17:43:04.000Z

2022-03-08T10:46:09.000Z

tensorflow/python/autograph/impl/api_test.py

AdaAlarm/tensorflow

e0db063159751276a92d88a4ad6d481b1199318c

2019-12-15T01:20:31.000Z

2022-02-10T02:06:28.000Z

tensorflow/python/autograph/impl/api_test.py

AdaAlarm/tensorflow

e0db063159751276a92d88a4ad6d481b1199318c

2016-09-07T04:00:15.000Z

2022-01-12T01:47:38.000Z

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for api module.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc import collections import contextlib import functools import gc import imp import os import re import sys import textwrap import types import numpy as np import six from tensorflow.python.autograph.core import ag_ctx from tensorflow.python.autograph.core import converter from tensorflow.python.autograph.core import converter_testing from tensorflow.python.autograph.impl import api from tensorflow.python.autograph.impl import conversion from tensorflow.python.autograph.pyct import errors from tensorflow.python.autograph.pyct import inspect_utils from tensorflow.python.autograph.pyct import parser from tensorflow.python.autograph.utils import ag_logging from tensorflow.python.data.ops import dataset_ops from tensorflow.python.eager import def_function from tensorflow.python.eager import function from tensorflow.python.framework import _errors_test_helper from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors as tf_errors from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import math_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.util import function_utils from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect global_n = 2 DEFAULT_RECURSIVE = converter.ConversionOptions(recursive=True) class TestResource(object): def __init__(self): self.x = 3 class ApiTest(test.TestCase): @contextlib.contextmanager def assertPrints(self, expected, not_expected): try: out_capturer = six.StringIO() sys.stdout = out_capturer yield self.assertIn(expected, out_capturer.getvalue()) self.assertNotIn(not_expected, out_capturer.getvalue()) finally: sys.stdout = sys.__stdout__ def assertNoMemoryLeaks(self, f): object_ids_before = {id(o) for o in gc.get_objects()} f() gc.collect() objects_after = tuple( o for o in gc.get_objects() if id(o) not in object_ids_before) self.assertEmpty( tuple(o for o in objects_after if isinstance(o, TestResource))) def test_converted_call_kwonly_args(self): def test_fn(*, a): return a x = api.converted_call( test_fn, (), {'a': constant_op.constant(-1)}, options=DEFAULT_RECURSIVE) self.assertEqual(-1, self.evaluate(x)) def test_super_with_no_arg(self): test_case_self = self class TestBase: def plus_three(self, x): return x + 3 class TestSubclass(TestBase): def plus_three(self, x): test_case_self.fail('This should never be called.') def no_arg(self, x): return super().plus_three(x) tc = api.converted_call(TestSubclass, (), None, options=DEFAULT_RECURSIVE) self.assertEqual(5, tc.no_arg(2)) def test_converted_call_avoids_triggering_operators(self): test_self = self class Pair(collections.namedtuple('Pair', ['a', 'b'])): def __call__(self): return self.a + self.b def __eq__(self, other): test_self.fail('Triggered operator') p = Pair(constant_op.constant(1), constant_op.constant(2)) x = api.converted_call(p, (), {}, options=DEFAULT_RECURSIVE) self.assertIsNotNone(self.evaluate(x), 3) @test_util.run_deprecated_v1 def test_decorator_recursive(self): class TestClass(object): def called_member(self, a): if a < 0: a = -a return a @api.convert(recursive=True) def test_method(self, x, s, a): while math_ops.reduce_sum(x) > s: x //= self.called_member(a) return x tc = TestClass() x = tc.test_method( constant_op.constant([2, 4]), constant_op.constant(1), constant_op.constant(-2)) self.assertListEqual([0, 1], self.evaluate(x).tolist()) @test_util.run_deprecated_v1 def test_decorator_not_recursive(self): class TestClass(object): def called_member(self, a): return math_ops.negative(a) @api.convert(recursive=False) def test_method(self, x, s, a): while math_ops.reduce_sum(x) > s: x //= self.called_member(a) return x tc = TestClass() x = tc.test_method( constant_op.constant([2, 4]), constant_op.constant(1), constant_op.constant(-2)) self.assertListEqual([0, 1], self.evaluate(x).tolist()) @test_util.run_deprecated_v1 def test_convert_then_do_not_convert(self): class TestClass(object): @api.do_not_convert def called_member(self, a): return math_ops.negative(a) @api.convert(recursive=True) def test_method(self, x, s, a): while math_ops.reduce_sum(x) > s: x //= self.called_member(a) return x tc = TestClass() x = tc.test_method( constant_op.constant((2, 4)), constant_op.constant(1), constant_op.constant(-2)) self.assertAllEqual((0, 1), self.evaluate(x)) @test_util.run_deprecated_v1 def test_decorator_calls_decorated(self): class TestClass(object): @api.convert() def called_member(self, a): if a < 0: a = -a return a @api.convert(recursive=True) def test_method(self, x, s, a): while math_ops.reduce_sum(x) > s: x //= self.called_member(a) return x tc = TestClass() x = tc.test_method( constant_op.constant([2, 4]), constant_op.constant(1), constant_op.constant(-2)) self.assertListEqual([0, 1], self.evaluate(x).tolist()) def test_decorator_preserves_argspec(self): class TestClass(object): def test_method(self, a): if a < 0: a = -a return a test_method_converted = api.convert()(test_method) tc = TestClass() self.assertListEqual( list(tf_inspect.getfullargspec(tc.test_method)), list(tf_inspect.getfullargspec(tc.test_method_converted))) def test_do_not_convert_argspec(self): class TestClass(object): def test_method(self, x, y): z = x + y return z test_method_allowlisted = api.do_not_convert(test_method) tc = TestClass() self.assertTrue(tf_inspect.ismethod(tc.test_method_allowlisted)) # Because the wrapped function is not generated, we can't preserve its # arg spec. self.assertEqual((), tuple(function_utils.fn_args(tc.test_method_allowlisted))) def test_do_not_convert_callable_object(self): class TestClass(object): def __call__(self): return 1 tc = TestClass() self.assertEqual(1, api.do_not_convert(tc)()) @test_util.run_deprecated_v1 def test_convert_call_site_decorator(self): class TestClass(object): def called_member(self, a): if a < 0: a = -a return a @api.convert(recursive=True) def test_method(self, x, s, a): while math_ops.reduce_sum(x) > s: x //= api.converted_call( self.called_member, (a,), None, options=DEFAULT_RECURSIVE) return x tc = TestClass() x = tc.test_method( constant_op.constant([2, 4]), constant_op.constant(1), constant_op.constant(-2)) self.assertListEqual([0, 1], self.evaluate(x).tolist()) def test_converted_call_builtin(self): x = api.converted_call(range, (3,), None, options=DEFAULT_RECURSIVE) self.assertEqual((0, 1, 2), tuple(x)) x = api.converted_call( re.compile, ('mnas_v4_a.*\\/.*(weights|kernel):0$',), None, options=DEFAULT_RECURSIVE) self.assertIsNotNone(x.match('mnas_v4_a/weights:0')) def test_converted_call_function(self): def test_fn(x): if x < 0: return -x return x x = api.converted_call( test_fn, (constant_op.constant(-1),), None, options=DEFAULT_RECURSIVE) self.assertEqual(1, self.evaluate(x)) @test_util.run_v1_only('b/120545219') def test_converted_call_functools_partial(self): def test_fn(x, y, z): if x < 0: return -x, -y, -z return x, y, z x = api.converted_call( functools.partial(test_fn, constant_op.constant(-1), z=-3), (constant_op.constant(-2),), None, options=DEFAULT_RECURSIVE) self.assertEqual((1, 2, 3), self.evaluate(x)) x = api.converted_call( functools.partial( functools.partial(test_fn, constant_op.constant(-1)), z=-3), (constant_op.constant(-2),), None, options=DEFAULT_RECURSIVE) self.assertEqual((1, 2, 3), self.evaluate(x)) @test_util.run_v1_only('b/120545219') def test_converted_call_functools_partial_kwarg_mutation(self): def test_fn(x, y, z): if x < 0: return -x, -y, -z return x, y, z partial_fn = functools.partial(test_fn, constant_op.constant(-1), z=-3) # Call using kwargs to assign y first to ensure that partial_fn.keywords is # not mutated for subsequent calls (where y is assign through args). x = api.converted_call( partial_fn, args=(), kwargs={ 'y': constant_op.constant(-2), }, options=DEFAULT_RECURSIVE) self.assertEqual((1, 2, 3), self.evaluate(x)) x = api.converted_call( partial_fn, args=(constant_op.constant(-4),), kwargs=None, options=DEFAULT_RECURSIVE) self.assertEqual((1, 4, 3), self.evaluate(x)) def test_converted_call_method(self): class TestClass(object): def __init__(self, x): self.x = x def test_method(self): if self.x < 0: return -self.x return self.x tc = TestClass(constant_op.constant(-1)) x = api.converted_call(tc.test_method, (), None, options=DEFAULT_RECURSIVE) self.assertEqual(1, self.evaluate(x)) def test_converted_call_synthetic_method(self): class TestClass(object): def __init__(self, x): self.x = x def test_function(self): if self.x < 0: return -self.x return self.x tc = TestClass(constant_op.constant(-1)) test_method = types.MethodType(test_function, tc) x = api.converted_call(test_method, (), None, options=DEFAULT_RECURSIVE) self.assertEqual(1, self.evaluate(x)) def test_converted_call_method_wrapper(self): class TestClass(object): def foo(self): pass tc = TestClass() # `method.__get__()` returns a so-called method-wrapper. wrapper = api.converted_call( tc.foo.__get__, (tc,), None, options=DEFAULT_RECURSIVE) self.assertEqual(wrapper, tc.foo) def test_converted_call_method_as_object_attribute(self): class AnotherClass(object): def __init__(self): self.another_class_attr = constant_op.constant(1) def method(self): if self.another_class_attr > 0: return self.another_class_attr + 1 return self.another_class_attr + 10 class TestClass(object): def __init__(self, another_obj_method): self.another_obj_method = another_obj_method obj = AnotherClass() tc = TestClass(obj.method) x = api.converted_call( tc.another_obj_method, (), None, options=DEFAULT_RECURSIVE) self.assertEqual(self.evaluate(x), 2) def test_converted_call_method_converts_recursively(self): class TestClass(object): def __init__(self, x): self.x = x def other_method(self): if self.x < 0: return -self.x return self.x def test_method(self): return self.other_method() tc = TestClass(constant_op.constant(-1)) x = api.converted_call(tc.test_method, (), None, options=DEFAULT_RECURSIVE) self.assertEqual(1, self.evaluate(x)) def test_converted_call_method_by_class(self): class TestClass(object): def __init__(self, x): self.x = x def test_method(self): if self.x < 0: return -self.x return self.x tc = TestClass(constant_op.constant(-1)) x = api.converted_call( TestClass.test_method, (tc,), None, options=DEFAULT_RECURSIVE) self.assertEqual(1, self.evaluate(x)) def test_converted_call_callable_object(self): class TestClass(object): def __init__(self, x): self.x = x def __call__(self): if self.x < 0: return -self.x return self.x tc = TestClass(constant_op.constant(-1)) x = api.converted_call(tc, (), None, options=DEFAULT_RECURSIVE) self.assertEqual(1, self.evaluate(x)) def test_converted_call_callable_metaclass(self): test_self = self class TestMetaclass(type): def __call__(cls): self.assertTrue(converter_testing.is_inside_generated_code()) inst = object.__new__(cls) inst.__init__() def instance_call(unused_self): test_self.fail( 'The class-bound __call__ should be called, not the instance' ' bound one.') inst.__call__ = instance_call return inst tmc = TestMetaclass('TestClass', (), {}) tc = api.converted_call(tmc, (), None, options=DEFAULT_RECURSIVE) self.assertIsInstance(tc, tmc) def test_converted_call_callable_abc(self): test_self = self @six.add_metaclass(abc.ABCMeta) class TestBase(object): @abc.abstractmethod def __call__(self): test_self.fail('This should not be called') class TestSubclass(TestBase): def __init__(self): test_self.assertFalse(converter_testing.is_inside_generated_code()) def __call__(self, expected): test_self.assertTrue(expected) test_self.assertTrue(converter_testing.is_inside_generated_code()) tc = api.converted_call(TestSubclass, (), None, options=DEFAULT_RECURSIVE) api.converted_call(tc, (True,), None, options=DEFAULT_RECURSIVE) @test_util.run_deprecated_v1 def test_converted_call_constructor(self): test_self = self class TestClass(object): def __init__(self): test_self.assertFalse(converter_testing.is_inside_generated_code()) tc = api.converted_call(TestClass, (), None, options=DEFAULT_RECURSIVE) self.assertIsInstance(tc, TestClass) def test_converted_call_mangled_properties(self): class TestClass(object): def __init__(self): self.__private = constant_op.constant(-1) def test_method(self): return self.__private tc = TestClass() with self.assertRaisesRegex( errors.UnsupportedLanguageElementError, 'mangled names'): api.converted_call(tc.test_method, (), None, options=DEFAULT_RECURSIVE) # TODO(mdan): Refactor to avoid this use of global state. ag_logging.set_verbosity(0, True) os.environ['AUTOGRAPH_STRICT_CONVERSION'] = '0' with self.assertPrints('could not transform', 'bug'): api.converted_call(tc.test_method, (), None, options=DEFAULT_RECURSIVE) ag_logging.set_verbosity(0, False) os.environ['AUTOGRAPH_STRICT_CONVERSION'] = '1' def test_converted_call_partial_of_allowlisted_function(self): def test_fn(_): self.assertFalse(converter_testing.is_inside_generated_code()) converter_testing.allowlist(test_fn) api.converted_call( functools.partial(test_fn, None), (), None, options=DEFAULT_RECURSIVE) def test_converted_call_already_converted(self): def f(x): return x == 0 x = api.converted_call( f, (constant_op.constant(0),), None, options=DEFAULT_RECURSIVE) self.assertTrue(self.evaluate(x)) converted_f = api.to_graph( f, experimental_optional_features=converter.Feature.ALL) x = api.converted_call( converted_f, (constant_op.constant(0),), None, options=DEFAULT_RECURSIVE) self.assertTrue(self.evaluate(x)) def test_converted_call_then_already_converted_dynamic(self): @api.convert() def g(x): if x > 0: return x else: return -x def f(g, x): return g(x) x = api.converted_call( f, (g, constant_op.constant(1)), None, options=DEFAULT_RECURSIVE) self.assertEqual(self.evaluate(x), 1) def test_converted_call_forced_when_explicitly_allowlisted(self): @api.do_not_convert() def f(x): return x + 1 opts = converter.ConversionOptions(recursive=True, user_requested=True) x = api.converted_call(f, (constant_op.constant(0),), None, options=opts) self.assertTrue(self.evaluate(x)) converted_f = api.to_graph( f, experimental_optional_features=converter.Feature.ALL) x = api.converted_call(converted_f, (0,), None, options=DEFAULT_RECURSIVE) self.assertEqual(x, 1) @test_util.run_deprecated_v1 def test_converted_call_no_user_code(self): def f(x): return len(x) opts = converter.ConversionOptions(internal_convert_user_code=False) # f should not be converted, causing len to error out. with self.assertRaisesRegex(Exception, 'len is not well defined'): api.converted_call(f, (constant_op.constant([0]),), None, options=opts) # len on the other hand should work fine. x = api.converted_call( len, (constant_op.constant([0]),), None, options=opts) # The constant has static shape so the result is a primitive not a Tensor. self.assertEqual(x, 1) def test_converted_call_no_kwargs_allowed(self): def f(*args): # Note: np.broadcast rejects any **kwargs, even *{} return np.broadcast(args[:1]) opts = converter.ConversionOptions(internal_convert_user_code=False) self.assertIsNotNone( api.converted_call(f, (1, 2, 3, 4), None, options=opts)) def test_converted_call_allowlisted_method(self): class TestClass(object): def method(self): return converter_testing.is_inside_generated_code() obj = TestClass() converter_testing.allowlist(obj.method.__func__) self.assertFalse( api.converted_call(obj.method, (), {}, options=DEFAULT_RECURSIVE)) def test_converted_call_allowlisted_method_via_owner(self): class TestClass(object): def method(self): return converter_testing.is_inside_generated_code() converter_testing.allowlist(TestClass) obj = TestClass() self.assertFalse( api.converted_call(obj.method, (), {}, options=DEFAULT_RECURSIVE)) def test_converted_call_numpy(self): x = api.converted_call(np.arange, (5,), None, options=DEFAULT_RECURSIVE) self.assertAllEqual(x, list(range(5))) def test_converted_call_tf_op_forced(self): # TODO(mdan): Add the missing level of support to LOGICAL_EXPRESSIONS. opts = converter.ConversionOptions( user_requested=True, optional_features=None) x = api.converted_call(math_ops.add, (1, 1), None, options=opts) self.assertAllEqual(self.evaluate(x), 2) def test_converted_call_exec_generated_code(self): temp_mod = imp.new_module('test_module') dynamic_code = """ def foo(x): return x + 1 """ exec(textwrap.dedent(dynamic_code), temp_mod.__dict__) # pylint:disable=exec-used opts = converter.ConversionOptions(optional_features=None) x = api.converted_call(temp_mod.foo, (1,), None, options=opts) self.assertAllEqual(x, 2) def test_converted_call_namedtuple(self): x = api.converted_call( collections.namedtuple, ('TestNamedtuple', ('a', 'b')), None, options=DEFAULT_RECURSIVE) self.assertTrue(inspect_utils.isnamedtuple(x)) def test_converted_call_namedtuple_via_collections(self): x = api.converted_call( collections.namedtuple, ('TestNamedtuple', ('a', 'b')), None, options=DEFAULT_RECURSIVE) self.assertTrue(inspect_utils.isnamedtuple(x)) def test_converted_call_namedtuple_subclass_bound_method(self): class TestClass(collections.namedtuple('TestNamedtuple', ('a', 'b'))): def test_method(self, x): while math_ops.reduce_sum(x) > self.a: x //= self.b return x obj = TestClass(5, 2) x = api.converted_call( obj.test_method, (constant_op.constant([2, 4]),), None, options=DEFAULT_RECURSIVE) self.assertAllEqual(self.evaluate(x), [1, 2]) def test_converted_call_namedtuple_method(self): class TestClass(collections.namedtuple('TestNamedtuple', ('a', 'b'))): pass obj = TestClass(5, 2) # _asdict is a documented method of namedtuple. x = api.converted_call(obj._asdict, (), None, options=DEFAULT_RECURSIVE) self.assertDictEqual(x, {'a': 5, 'b': 2}) def test_converted_call_namedtuple_subclass_unbound_method(self): class TestClass(collections.namedtuple('TestNamedtuple', ('a', 'b'))): def test_method(self, x): while math_ops.reduce_sum(x) > self.a: x //= self.b return x obj = TestClass(5, 2) x = api.converted_call( TestClass.test_method, (obj, constant_op.constant([2, 4])), None, options=DEFAULT_RECURSIVE) self.assertAllEqual(self.evaluate(x), [1, 2]) def test_converted_call_lambda(self): l = lambda x: x == 0 x = api.converted_call( l, (constant_op.constant(0),), None, options=DEFAULT_RECURSIVE) self.evaluate(variables.global_variables_initializer()) self.assertAllEqual(True, self.evaluate(x)) def test_converted_call_defun_object_method(self): # pylint:disable=method-hidden class TestClass(object): def method(self): return 1 def prepare(self): self.method = function.defun(self.method) # pylint:enable=method-hidden tc = TestClass() tc.prepare() x = api.converted_call(tc.method, (), None, options=DEFAULT_RECURSIVE) self.assertAllEqual(1, self.evaluate(x)) def test_converted_call_native_binding(self): x = api.converted_call(np.power, (2, 2), None, options=DEFAULT_RECURSIVE) self.assertAllEqual(x, 4) def test_converted_call_native_binding_errorneous(self): class FaultyBinding(object): def __array__(self): raise ValueError('fault') bad_obj = FaultyBinding() def fail_if_warning(*_): self.fail('No warning should be issued') with test.mock.patch.object(ag_logging, 'warn', fail_if_warning): with self.assertRaisesRegex(ValueError, 'fault'): api.converted_call( np.power, (bad_obj, 2), None, options=DEFAULT_RECURSIVE) def test_converted_call_through_tf_dataset(self): def other_fn(x): if x > 0: return x return -x def f(): return dataset_ops.Dataset.range(-3, 3).map(other_fn) # Dataset iteration only works inside math_ops. @def_function.function def graph_fn(): ds = api.converted_call(f, (), None, options=DEFAULT_RECURSIVE) itr = iter(ds) return next(itr), next(itr), next(itr) self.assertAllEqual(self.evaluate(graph_fn()), (3, 2, 1)) def test_converted_call_no_leaks_via_closure(self): def test_fn(): res = TestResource() def f(y): return res.x + y api.converted_call(f, (1,), None, options=DEFAULT_RECURSIVE) self.assertNoMemoryLeaks(test_fn) def test_converted_call_no_leaks_via_inner_function_closure(self): def test_fn(): res = TestResource() def f(y): def inner_f(): return res.x + y return inner_f api.converted_call(f, (1,), None, options=DEFAULT_RECURSIVE)() self.assertNoMemoryLeaks(test_fn) def test_converted_call_no_caching_on_abort(self): def test_fn(needs_autograph): if needs_autograph: if constant_op.constant(True): x = constant_op.constant(1) else: x = constant_op.constant(2) else: x = 3 return x def call_in_disabled_context(): with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED): return api.converted_call( test_fn, (False,), None, options=DEFAULT_RECURSIVE) def call_in_default_context(): with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.ENABLED): return api.converted_call( test_fn, (True,), None, options=DEFAULT_RECURSIVE) # Note: this is an invariant, not a test (see above). assert call_in_disabled_context() == 3 # If api.convert placed test_fn in the unconverted cache, this second # invocation would fail. self.assertEqual(self.evaluate(call_in_default_context()), 1) def test_converted_call_caching_of_allowlisted_bound_methods(self): class TestClass(object): def __init__(self): self.__private = constant_op.constant(-1) def test_method(self): return self.__private # TODO(mdan): Refactor to avoid this use of global state. cache_size_before = len(conversion._ALLOWLIST_CACHE) # First invocation with fallback on, to allow recording it into cache. os.environ['AUTOGRAPH_STRICT_CONVERSION'] = '0' tc = TestClass() api.converted_call(tc.test_method, (), None, options=DEFAULT_RECURSIVE) os.environ['AUTOGRAPH_STRICT_CONVERSION'] = '1' # Entry should be added to the allowlist cache. self.assertEqual(len(conversion._ALLOWLIST_CACHE), cache_size_before + 1) # A second invocation should go through even with fallback off. tc = TestClass() api.converted_call(tc.test_method, (), None, options=DEFAULT_RECURSIVE) # No new entries should appear in the allowlist cache. self.assertEqual(len(conversion._ALLOWLIST_CACHE), cache_size_before + 1) def test_context_tracking_direct_calls(self): @api.do_not_convert() def unconverted_fn(): self.assertEqual(ag_ctx.control_status_ctx().status, ag_ctx.Status.DISABLED) @api.convert() def converted_fn(): self.assertEqual(ag_ctx.control_status_ctx().status, ag_ctx.Status.ENABLED) unconverted_fn() self.assertEqual(ag_ctx.control_status_ctx().status, ag_ctx.Status.ENABLED) self.assertEqual(ag_ctx.control_status_ctx().status, ag_ctx.Status.UNSPECIFIED) converted_fn() self.assertEqual(ag_ctx.control_status_ctx().status, ag_ctx.Status.UNSPECIFIED) @api.call_with_unspecified_conversion_status def unspecified_fn(): self.assertEqual(ag_ctx.control_status_ctx().status, ag_ctx.Status.UNSPECIFIED) unspecified_fn() def test_to_graph_basic(self): def test_fn(x, s): while math_ops.reduce_sum(x) > s: x //= 2 return x compiled_fn = api.to_graph(test_fn) with ops.Graph().as_default(): x = compiled_fn(constant_op.constant((4, 8)), 4) self.assertAllEqual(self.evaluate(x), (1, 2)) @test_util.run_deprecated_v1 def test_to_graph_with_defaults(self): foo = 4 def test_fn(x, s=foo): while math_ops.reduce_sum(x) > s: x //= 2 return x compiled_fn = api.to_graph(test_fn) x = compiled_fn(constant_op.constant([4, 8])) self.assertListEqual([1, 2], self.evaluate(x).tolist()) def test_to_graph_with_globals(self): def test_fn(x): global global_n global_n = x + global_n return global_n converted_fn = api.to_graph(test_fn) prev_val = global_n converted_fn(10) self.assertGreater(global_n, prev_val) def test_to_graph_with_kwargs_clashing_converted_call(self): def called_fn(**kwargs): return kwargs['f'] + kwargs['owner'] def test_fn(): # These arg names intentionally match converted_call's return called_fn(f=1, owner=2) compiled_fn = api.to_graph(test_fn) self.assertEqual(compiled_fn(), 3) def test_to_graph_with_kwargs_clashing_unconverted_call(self): @api.do_not_convert def called_fn(**kwargs): return kwargs['f'] + kwargs['owner'] def test_fn(): # These arg names intentionally match _call_unconverted's return called_fn(f=1, owner=2) compiled_fn = api.to_graph(test_fn) self.assertEqual(compiled_fn(), 3) def test_to_graph_caching(self): def test_fn(x): if x > 0: return x else: return -x converted_functions = tuple(api.to_graph(test_fn) for _ in (-1, 0, 1)) # All outputs are from the same module. We can't use __module__ because # that's reset when we instantiate the function (see conversion.py). # TODO(mdan): Can and should we overwrite __module__ instead? module_names = frozenset(f.ag_module for f in converted_functions) self.assertEqual(len(module_names), 1) self.assertNotIn('__main__', module_names) self.assertEqual(len(frozenset(id(f) for f in converted_functions)), 3) def test_to_graph_caching_different_options(self): def called_fn(): pass def test_fn(): return called_fn() converted_recursive = api.to_graph(test_fn, recursive=True) converted_non_recursive = api.to_graph(test_fn, recursive=False) self.assertNotEqual(converted_recursive.ag_module, converted_non_recursive.ag_module) self.assertRegex( tf_inspect.getsource(converted_recursive), 'FunctionScope(.*recursive=True.*)') self.assertRegex( tf_inspect.getsource(converted_non_recursive), 'FunctionScope(.*recursive=False.*)') def test_to_graph_preserves_bindings(self): y = 3 def test_fn(): return y converted = api.to_graph(test_fn) self.assertEqual(converted(), 3) y = 7 self.assertEqual(converted(), 7) def test_to_graph_source_map(self): def test_fn(y): return y**2 self.assertTrue(hasattr(api.to_graph(test_fn), 'ag_source_map')) def test_to_graph_sets_conversion_context(self): def g(): self.assertEqual(ag_ctx.control_status_ctx().status, ag_ctx.Status.ENABLED) return 0 # Note: the autograph=False sets the connect to Status.DISABLED. The test # verifies that to_graph overrides that. @def_function.function(autograph=False) def f(): converted_g = api.to_graph(g) converted_g() f() def test_to_code_basic(self): def test_fn(x, s): while math_ops.reduce_sum(x) > s: x /= 2 return x # Just check that the output is parseable Python code. self.assertIsNotNone(parser.parse(api.to_code(test_fn))) def test_to_code_with_wrapped_function(self): @def_function.function def test_fn(x, s): while math_ops.reduce_sum(x) > s: x /= 2 return x with self.assertRaisesRegex(Exception, 'try passing.*python_function'): api.to_code(test_fn) def test_tf_convert_overrides_current_context(self): def f(expect_converted): self.assertEqual( converter_testing.is_inside_generated_code(), expect_converted) @api.do_not_convert def test_fn(ctx, expect_converted): return api.tf_convert(f, ctx)(expect_converted) test_fn( ag_ctx.ControlStatusCtx(status=ag_ctx.Status.ENABLED), True) test_fn( ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED), False) def test_tf_convert_unspecified_not_converted_by_default(self): def f(): self.assertEqual(ag_ctx.control_status_ctx().status, ag_ctx.Status.UNSPECIFIED) self.assertFalse(converter_testing.is_inside_generated_code()) @def_function.function def test_fn(ctx): return api.tf_convert(f, ctx, convert_by_default=False)() test_fn(ag_ctx.ControlStatusCtx(status=ag_ctx.Status.UNSPECIFIED)) def test_tf_convert_allowlisted_method(self): if six.PY2: self.skipTest('Test bank not comptible with Python 2.') class TestClass(object): def method(self): return converter_testing.is_inside_generated_code() converter_testing.allowlist(TestClass.method) obj = TestClass() converted_call = api.tf_convert( obj.method, ag_ctx.ControlStatusCtx(status=ag_ctx.Status.ENABLED)) _, converted_target = tf_decorator.unwrap(converted_call) self.assertIs(converted_target.__func__, obj.method.__func__) def test_tf_convert_tf_decorator_unwrapping_context_enabled(self): def f(): self.assertTrue(converter_testing.is_inside_generated_code()) @functools.wraps(f) def wrapper(*args, **kwargs): return wrapper.__wrapped__(*args, **kwargs) decorated_f = tf_decorator.make_decorator(f, wrapper) def test_fn(ctx): return api.tf_convert(decorated_f, ctx)() test_fn(ag_ctx.ControlStatusCtx(status=ag_ctx.Status.ENABLED)) def test_tf_convert_tf_decorator_unwrapping_context_disabled(self): def f(): self.assertFalse(converter_testing.is_inside_generated_code()) @functools.wraps(f) def wrapper(*args, **kwargs): return wrapper.__wrapped__(*args, **kwargs) decorated_f = tf_decorator.make_decorator(f, wrapper) def test_fn(ctx): return api.tf_convert(decorated_f, ctx)() test_fn(ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED)) def test_tf_convert_tf_decorator_allowlist_method(self): def wrap(f): def wrapper(*args, **kwargs): return wrapper.__wrapped__(*args, **kwargs) return tf_decorator.make_decorator(f, wrapper) class TestClass(object): @wrap def method(self): return converter_testing.is_inside_generated_code() converter_testing.allowlist(TestClass.method) obj = TestClass() # It's intended that tf_convert modifies the original method in this case. # This is not desirable, but options are limited. converted = api.tf_convert( obj.method, ag_ctx.ControlStatusCtx(status=ag_ctx.Status.ENABLED)) self.assertTrue(converted()) self.assertTrue(obj.method()) def test_super_with_one_arg(self): test_case_self = self class TestBase(object): def plus_three(self, x): return x + 3 class TestSubclass(TestBase): def plus_three(self, x): test_case_self.fail('This should never be called.') def one_arg(self, x): test_base_unbound = super(TestSubclass) test_base = test_base_unbound.__get__(self, TestSubclass) return test_base.plus_three(x) tc = api.converted_call(TestSubclass, (), None, options=DEFAULT_RECURSIVE) self.assertEqual(5, tc.one_arg(2)) def test_super_with_two_args(self): test_case_self = self class TestBase(object): def plus_three(self, x): return x + 3 class TestSubclass(TestBase): def plus_three(self, x): test_case_self.fail('This should never be called.') def two_args(self, x): return super(TestSubclass, self).plus_three(x) tc = api.converted_call(TestSubclass, (), None, options=DEFAULT_RECURSIVE) self.assertEqual(5, tc.two_args(2)) def test_raise_from_func_graph(self): @def_function.function def raise_from_tf_function(n): _errors_test_helper.TestRaiseFromStatus(n) for code, expected_exception in [ (1, tf_errors.CancelledError), (2, tf_errors.UnknownError), (3, tf_errors.InvalidArgumentError), (4, tf_errors.DeadlineExceededError), (5, tf_errors.NotFoundError), (6, tf_errors.AlreadyExistsError), (7, tf_errors.PermissionDeniedError), (16, tf_errors.UnauthenticatedError), (8, tf_errors.ResourceExhaustedError), (9, tf_errors.FailedPreconditionError), (10, tf_errors.AbortedError), (11, tf_errors.OutOfRangeError), (12, tf_errors.UnimplementedError), (13, tf_errors.InternalError), (14, tf_errors.UnavailableError), (15, tf_errors.DataLossError), ]: with self.assertRaises(expected_exception) as error: raise_from_tf_function(code) self.assertEqual(error.exception.experimental_payloads['key1'], 'value1') self.assertEqual(error.exception.experimental_payloads['key2'], 'value2') if __name__ == '__main__': os.environ['AUTOGRAPH_STRICT_CONVERSION'] = '1' test.main()

dc2fc7e67bc880962a42b7655e4b7d26a8f72a0d

py

Python

com/xiumei/etl/process_finance_data.py

struggle3014/aqf

d0477075bd6d25d0de82acd9796a5a4e9e056b2e

2020-02-07T15:08:12.000Z

2020-04-14T09:48:07.000Z

com/xiumei/etl/process_finance_data.py

struggle3014/aqf

d0477075bd6d25d0de82acd9796a5a4e9e056b2e

com/xiumei/etl/process_finance_data.py

struggle3014/aqf

d0477075bd6d25d0de82acd9796a5a4e9e056b2e

# -*- coding=utf-8 -*- import pandas as pd import numpy as np import datetime import matplotlib.pyplot as plt import seaborn import tushare as ts import scipy.stats as stats # 1.1- 获取单个证券的股价数据 def get_single_stock_data(stock, start_date, end_date): # 获取证券的股价。数据类型为 pandas.core.frame.DataFrame data = ts.get_k_data(stock, start=start_date, end=end_date) # 1- 将数据 date 列指定为索引 data.set_index('date', inplace=True) # 将字符串格式的 date 转换为日期格式 data.index = pd.to_datetime(data.index) return data # 1.2- 获取多只证券的股价信息 def get_multi_stock_data(stocks, start_date, end_date): datas = map(get_single_stock_data, stocks, fill_list(start_date, len(stocks)), fill_list(end_date, len(stocks))) return pd.concat(datas, keys=stocks, names=['Ticker', 'Date']) # 填充指定长度的 list def fill_list(thing, length): result = [] for i in range(length): result.append(thing) return result # 2.1- 金融数据可视化 def finance_data_visual(): stocks = get_multi_stock_data(['600030', '000001', '600426'], '2019-05-05', '2019-06-06') # 1- 重置索引 close_price = stocks[['close']].reset_index() print(close_price.head()) # 2- 数据透视表，将所有股价信息显示在一张表中 daily_close = close_price.pivot(index='Date', columns='Ticker', values='close') print(daily_close.head()) # 3- 画图 daily_close.plot(subplots=True, figsize=(10, 8)) plt.show() # 3.1- 金融数据计算，每日收益 def calculate_daily_profit(): stocks = get_multi_stock_data(['600030', '000001', '600426'], '2019-05-05', '2019-06-06') # 1- 重置索引 close_price = stocks[['close']].reset_index() # 2- 数据透视表，将所有股价信息显示在一张表中 daily_close = close_price.pivot(index='Date', columns='Ticker', values='close') # 3- 使用 shift 方法，计算收益。shift 将每列下移 n 格。 price_change = daily_close / daily_close.shift(1) - 1 # print(price_change.ix[:, 0:4].head()) print(price_change.head()) # 4- 将 NaN 替换为 0 price_change.fillna(0, inplace=True) print(price_change.head()) # 3.2- 金融数据计算，累计收益 def calculate_accu_profit(): stocks = get_multi_stock_data(['600030', '000001', '600426'], '2019-05-05', '2019-06-06') # 1- 重置索引 close_price = stocks[['close']].reset_index() # 2- 数据透视表，将所有股价信息显示在一张表中 daily_close = close_price.pivot(index='Date', columns='Ticker', values='close') # 3- 使用 shift 方法，计算收益。shift 将每列下移 n 格。 price_change = daily_close / daily_close.shift(1) - 1 # print(price_change.ix[:, 0:4].head()) # 4- 将 NaN 替换为 0 price_change.fillna(0, inplace=True) cum_daily_return = (1 + price_change).cumprod() print(cum_daily_return.head()) cum_daily_return.plot(figsize=(8, 6)) plt.show() # 4- 分析 return 分布 # 4.1- 直方图 def plot_hist(): # stocks = get_multi_stock_data(['600030', '000001', '600426'], '2019-05-05', '2019-06-06') stocks = get_multi_stock_data(['600030', '600426'], '2019-05-05', '2019-06-06') # 1- 重置索引 close_price = stocks[['close']].reset_index() # 2- 数据透视表，将所有股价信息显示在一张表中 daily_close = close_price.pivot(index='Date', columns='Ticker', values='close') # 3- 使用 shift 方法，计算收益。shift 将每列下移 n 格。 price_change = daily_close / daily_close.shift(1) - 1 # print(price_change.ix[:, 0:4].head()) # 4- 将 NaN 替换为 0 price_change.fillna(0, inplace=True) # 5- 画出 600030 股价直方图 price_change['600030'].hist(bins=30, figsize=(4, 3)) plt.show() # 6- 画出所有股票的股价分布图 price_change.hist(bins=20, sharex=True, figsize=(12, 8)) plt.show() # 4.2- QQ-Plots # 使用 QQ 图验证股价 return 分布 def plot_qq(): # stocks = get_multi_stock_data(['600030', '000001', '600426'], '2019-05-05', '2019-06-06') stocks = get_multi_stock_data(['600030', '600426'], '2019-05-05', '2019-06-06') # 1- 重置索引 close_price = stocks[['close']].reset_index() # 2- 数据透视表，将所有股价信息显示在一张表中 daily_close = close_price.pivot(index='Date', columns='Ticker', values='close') # 3- 使用 shift 方法，计算收益。shift 将每列下移 n 格。 price_change = daily_close / daily_close.shift(1) - 1 # print(price_change.ix[:, 0:4].head()) # 4- 将 NaN 替换为 0 price_change.fillna(0, inplace=True) # 5- 绘制 QQ 图 fig = plt.figure(figsize=(7, 5)) stats.probplot(price_change['600030'], dist='norm', plot=fig.add_subplot(111)) plt.show() # 5- 股价相关性 def plot_stocks_coors(): # 1- 获取 hs300 股价信息 hs300_data = get_single_stock_data('hs300', '2016-01-01', '2017-07-01') hs300_return = hs300_data.close.pct_change().fillna(0) # 2- 获取其他股票股价信息 stocks = get_multi_stock_data(['600030', '000001', '600426'], '2016-01-01', '2017-07-01') close_price = stocks[['close']].reset_index() # 数据透视表，将所有股价信息显示在一张表中 daily_close = close_price.pivot(index='Date', columns='Ticker', values='close') # 3- 数据合并 return_all = pd.concat([hs300_return, daily_close.pct_change().fillna(0)], axis=1) return_all.rename(columns={'close': 'hs300'}, inplace=True) print(return_all.head()) # 4- 计算累计收益 cum_return_all = (1 + return_all).cumprod() print(cum_return_all.head()) # 5- 累计收益作图 cum_return_all[['hs300', '600030', '600426']].plot(figsize=(8, 6)) # plt.show() # 6- 计算相关性，corr 协方差计算 corrs = return_all.corr() seaborn.heatmap(corrs) plt.show() if __name__ == '__main__': # get_single_stock_data() # result = get_multi_stock_data(['600030', '000001'], '2019-06-05', '2019-06-06') # finance_data_visual() # calculate_daily_profit() # calculate_accu_profit() # plot_hist() # plot_qq() plot_stocks_coors()

8ada3766ccb0b1de8ad1debab6da32648e1e988f

py

Python

myshkin/mixins/model.py

jakesnell/myshkin

cea0a625b1913627e27d66d0ada9155402f57d33

myshkin/mixins/model.py

jakesnell/myshkin

cea0a625b1913627e27d66d0ada9155402f57d33

myshkin/mixins/model.py

jakesnell/myshkin

cea0a625b1913627e27d66d0ada9155402f57d33

import os import glob import yaml import numpy as np import keras class Model(object): def save_conf(self, out_file): with open(out_file, 'w') as f: conf_dict = {'model': self.__class__.__name__, 'opts': dict(self.opts._asdict())} f.write(yaml.dump(conf_dict, default_flow_style=False)) def save_weights(self, out_dir, verbose=False): if not os.path.isdir(out_dir): os.mkdir(out_dir) def _save_weights(out_dir, name, component): if isinstance(component, keras.models.Model): if verbose: print "saving {:s}...".format(name) component.save_weights(os.path.join(out_dir, name + ".h5"), overwrite=True) else: for k, subcomponent in component.components.iteritems(): _save_weights(out_dir, name + "." + k, subcomponent) for k, component in self.components.iteritems(): _save_weights(out_dir, k, component) def get_component(self, specs): cur = self for spec in specs: cur = cur.components[spec] return cur def load_weights(self, weights_dir, verbose=False): weight_files = glob.glob(os.path.join(weights_dir, '*.h5')) for weight_file in weight_files: component = self.get_component(os.path.basename(weight_file).split(".")[:-1]) if verbose: print "loading from {:s}...".format(os.path.basename(weight_file)) component.load_weights(weight_file) def __repr__(self): return "{:s}({:s})".format(self.__class__.__name__, self.opts)

4a84eff221ec4d84cf0a858d30cc9ef4061428fe

py

Python

commands.py

Bocom/LSP-typescript

c2fbd5f756ff0fe36142b00a31100bf7505bdbc1

commands.py

Bocom/LSP-typescript

c2fbd5f756ff0fe36142b00a31100bf7505bdbc1

commands.py

Bocom/LSP-typescript

c2fbd5f756ff0fe36142b00a31100bf7505bdbc1

from .protocol import Call, CallsDirection, CallsRequestParams, CallsResponse from LSP.plugin import Request from LSP.plugin import Session from LSP.plugin.core.protocol import LocationLink from LSP.plugin.core.registry import LspTextCommand from LSP.plugin.core.typing import Optional from LSP.plugin.core.views import text_document_position_params from LSP.plugin.locationpicker import LocationPicker import functools import sublime SESSION_NAME = "LSP-typescript" class LspTypescriptCallsCommand(LspTextCommand): session_name = SESSION_NAME def is_enabled(self) -> bool: selection = self.view.sel() return len(selection) > 0 and super().is_enabled() def run(self, edit: sublime.Edit, direction: CallsDirection) -> None: session = self.session_by_name(self.session_name) if session is None: return position_params = text_document_position_params(self.view, self.view.sel()[0].b) params = { 'textDocument': position_params['textDocument'], 'position': position_params['position'], 'direction': direction } # type: CallsRequestParams session.send_request(Request("textDocument/calls", params), functools.partial(self.on_result_async, session)) def on_result_async(self, session: Session, result: Optional[CallsResponse]) -> None: if not result: return def to_location_link(call: Call) -> LocationLink: return { 'targetUri': call['location']['uri'], 'targetSelectionRange': call['location']['range'], } locations = list(map(to_location_link, result['calls'])) self.view.run_command("add_jump_record", {"selection": [(r.a, r.b) for r in self.view.sel()]}) LocationPicker(self.view, session, locations, side_by_side=False)

020af2a1bce83b829f5f549f2229002dcd2274fc

py

Python

ambari-server/src/main/resources/common-services/AMBARI_METRICS/0.1.0/package/scripts/hbase.py

hmcl/ambari-apache

87423d64f54d896c62d1a9245eb03a97763e35a4

2021-05-06T06:24:04.000Z

2021-05-06T06:24:04.000Z

ambari-server/src/main/resources/common-services/AMBARI_METRICS/0.1.0/package/scripts/hbase.py

hmcl/ambari-apache

87423d64f54d896c62d1a9245eb03a97763e35a4

ambari-server/src/main/resources/common-services/AMBARI_METRICS/0.1.0/package/scripts/hbase.py

hmcl/ambari-apache

87423d64f54d896c62d1a9245eb03a97763e35a4

#!/usr/bin/env python """ Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import os from ambari_commons import OSConst from resource_management.core.resources.system import Directory, Execute, File from resource_management.libraries.resources.xml_config import XmlConfig from resource_management.libraries.resources.template_config import TemplateConfig from resource_management.libraries.functions.format import format from resource_management.core.source import Template, InlineTemplate from ambari_commons.os_family_impl import OsFamilyFuncImpl, OsFamilyImpl @OsFamilyFuncImpl(os_family=OSConst.WINSRV_FAMILY) def hbase(name=None, action = None): import params Directory(params.hbase_conf_dir, owner = params.hadoop_user, create_parents = True ) Directory(params.hbase_tmp_dir, create_parents = True, owner = params.hadoop_user ) Directory (os.path.join(params.local_dir, "jars"), owner = params.hadoop_user, create_parents = True ) XmlConfig("hbase-site.xml", conf_dir = params.hbase_conf_dir, configurations = params.config['configurations']['ams-hbase-site'], configuration_attributes=params.config['configuration_attributes']['ams-hbase-site'], owner = params.hadoop_user ) if 'ams-hbase-policy' in params.config['configurations']: XmlConfig("hbase-policy.xml", conf_dir = params.hbase_conf_dir, configurations = params.config['configurations']['ams-hbase-policy'], configuration_attributes=params.config['configuration_attributes']['ams-hbase-policy'], owner = params.hadoop_user ) # Manually overriding ownership of file installed by hadoop package else: File(os.path.join(params.hbase_conf_dir, "hbase-policy.xml"), owner = params.hadoop_user ) # Metrics properties File(os.path.join(params.hbase_conf_dir, "hadoop-metrics2-hbase.properties"), owner = params.hbase_user, content=Template("hadoop-metrics2-hbase.properties.j2") ) hbase_TemplateConfig('regionservers', user=params.hadoop_user) if params.security_enabled: hbase_TemplateConfig(format("hbase_{name}_jaas.conf"), user=params.hadoop_user) if name != "client": Directory (params.hbase_log_dir, owner = params.hadoop_user, create_parents = True ) if (params.hbase_log4j_props != None): File(os.path.join(params.hbase_conf_dir, "log4j.properties"), owner=params.hadoop_user, content=params.hbase_log4j_props ) elif (os.path.exists(os.path.join(params.hbase_conf_dir,"log4j.properties"))): File(os.path.join(params.hbase_conf_dir,"log4j.properties"), owner=params.hadoop_user ) @OsFamilyFuncImpl(os_family=OsFamilyImpl.DEFAULT) def hbase(name=None # 'master' or 'regionserver' or 'client' , action=None): import params Directory(params.hbase_conf_dir, owner = params.hbase_user, group = params.user_group, create_parents = True, recursive_ownership = True, ) Directory (params.hbase_tmp_dir, owner = params.hbase_user, cd_access="a", create_parents = True, recursive_ownership = True, ) Directory (os.path.join(params.local_dir, "jars"), owner = params.hbase_user, group = params.user_group, cd_access="a", mode=0775, create_parents = True ) merged_ams_hbase_site = {} merged_ams_hbase_site.update(params.config['configurations']['ams-hbase-site']) if params.security_enabled: merged_ams_hbase_site.update(params.config['configurations']['ams-hbase-security-site']) if not params.is_hbase_distributed: File(format("{hbase_conf_dir}/core-site.xml"), action='delete', owner=params.hbase_user) File(format("{hbase_conf_dir}/hdfs-site.xml"), action='delete', owner=params.hbase_user) XmlConfig("hbase-site.xml", conf_dir = params.hbase_conf_dir, configurations = merged_ams_hbase_site, configuration_attributes=params.config['configuration_attributes']['ams-hbase-site'], owner = params.hbase_user, group = params.user_group ) # Phoenix spool file dir if not /tmp if not os.path.exists(params.phoenix_server_spool_dir): Directory(params.phoenix_server_spool_dir, owner=params.ams_user, mode = 0755, group=params.user_group, cd_access="a", create_parents = True ) pass if 'ams-hbase-policy' in params.config['configurations']: XmlConfig("hbase-policy.xml", conf_dir = params.hbase_conf_dir, configurations = params.config['configurations']['ams-hbase-policy'], configuration_attributes=params.config['configuration_attributes']['ams-hbase-policy'], owner = params.hbase_user, group = params.user_group ) # Manually overriding ownership of file installed by hadoop package else: File( format("{params.hbase_conf_dir}/hbase-policy.xml"), owner = params.hbase_user, group = params.user_group ) File(format("{hbase_conf_dir}/hbase-env.sh"), owner = params.hbase_user, content=InlineTemplate(params.hbase_env_sh_template) ) # Metrics properties File(os.path.join(params.hbase_conf_dir, "hadoop-metrics2-hbase.properties"), owner = params.hbase_user, group = params.user_group, content=Template("hadoop-metrics2-hbase.properties.j2") ) # hbase_TemplateConfig( params.metric_prop_file_name, # tag = 'GANGLIA-MASTER' if name == 'master' else 'GANGLIA-RS' # ) hbase_TemplateConfig('regionservers', user=params.hbase_user) if params.security_enabled: hbase_TemplateConfig( format("hbase_{name}_jaas.conf"), user=params.hbase_user) hbase_TemplateConfig( format("hbase_client_jaas.conf"), user=params.hbase_user) hbase_TemplateConfig( format("ams_zookeeper_jaas.conf"), user=params.hbase_user) if name != "client": Directory( params.hbase_pid_dir, owner = params.hbase_user, create_parents = True, cd_access = "a", mode = 0755, ) Directory (params.hbase_log_dir, owner = params.hbase_user, create_parents = True, cd_access = "a", mode = 0755, ) if name == "master": if not params.is_local_fs_rootdir: # If executing Stop All, HDFS is probably down if action != 'stop': params.HdfsResource(params.hbase_root_dir, type="directory", action="create_on_execute", owner=params.hbase_user, mode=0775, dfs_type=params.dfs_type ) params.HdfsResource(params.hbase_staging_dir, type="directory", action="create_on_execute", owner=params.hbase_user, mode=0711, dfs_type=params.dfs_type ) params.HdfsResource(None, action="execute") if params.is_hbase_distributed: #Workaround for status commands not aware of operating mode File(format("{params.hbase_pid_dir}/distributed_mode"), action="create", mode=0644, owner=params.hbase_user) pass else: local_root_dir = params.hbase_root_dir #cut protocol name if local_root_dir.startswith("file://"): local_root_dir = local_root_dir[7:] #otherwise assume dir name is provided as is Directory(local_root_dir, owner = params.hbase_user, cd_access="a", create_parents = True, recursive_ownership = True ) File(format("{params.hbase_pid_dir}/distributed_mode"), action="delete", owner=params.hbase_user) if params.hbase_log4j_props is not None: File(format("{params.hbase_conf_dir}/log4j.properties"), mode=0644, group=params.user_group, owner=params.hbase_user, content=params.hbase_log4j_props ) elif os.path.exists(format("{params.hbase_conf_dir}/log4j.properties")): File(format("{params.hbase_conf_dir}/log4j.properties"), mode=0644, group=params.user_group, owner=params.hbase_user ) def hbase_TemplateConfig(name, tag=None, user=None): import params TemplateConfig( os.path.join(params.hbase_conf_dir, name), owner = user, template_tag = tag )

cf922ee9a428e5e2e14e71ebf5fb49ed68e89d24

py

Python

setup.py

laterpay/djtranslationchecker

6589ed3472193a795d78504a9f2337cd045b29f2

2015-03-25T09:30:55.000Z

2015-03-25T09:30:55.000Z

setup.py

laterpay/djtranslationchecker

6589ed3472193a795d78504a9f2337cd045b29f2

setup.py

laterpay/djtranslationchecker

6589ed3472193a795d78504a9f2337cd045b29f2

# -*- coding: UTF-8 -*- from setuptools import find_packages, setup import codecs import os #import time #_version = "0.10.dev%s" % int(time.time()) _version = "0.10.0" _packages = find_packages('.', exclude=["*.tests", "*.tests.*", "tests.*", "tests"]) if os.path.exists('README.rst'): _long_description = codecs.open('README.rst', 'r', 'utf-8').read() else: _long_description = "" setup( name='djtranslationchecker', version=_version, description="Check your Django translation files", long_description=_long_description, author="LaterPay GmbH", author_email="support@laterpay.net", url="https://github.com/laterpay/djtranslationchecker", license='MIT', keywords="Django translation check gettext", #test_suite="tests", packages=_packages, classifiers=( "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Topic :: Software Development :: Libraries :: Python Modules", ), )

ca071c293474dd0ee380b182a0684ea4dd7fde3b

py

Python

persist.py

leosantos16/sample-market-maker

4006b1a7fadf0db3821202cfb5681768af33d711

persist.py

leosantos16/sample-market-maker

4006b1a7fadf0db3821202cfb5681768af33d711

persist.py

leosantos16/sample-market-maker

4006b1a7fadf0db3821202cfb5681768af33d711

from subprocess import Popen import sys filename = 'run.py' while True: print("\nStarting " + filename) p = Popen("python " + filename, shell=True) p.wait()

9e511e5d1d9bdf7f3fa6fac8215605f4269c1137

py

Python

cafe/drivers/unittest/config.py

melissa-kam/opencafe

af90c228084d479afa60b8b06a6b5d4d1adf2b8e

cafe/drivers/unittest/config.py

melissa-kam/opencafe

af90c228084d479afa60b8b06a6b5d4d1adf2b8e

cafe/drivers/unittest/config.py

melissa-kam/opencafe

af90c228084d479afa60b8b06a6b5d4d1adf2b8e

# Copyright 2016 Rackspace # 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 cafe.engine.models.data_interfaces import ( ConfigSectionInterface, _get_path_from_env) class DriverConfig(ConfigSectionInterface): """ Unittest driver configuration values. This config section is intended to supply values and configuration that can not be programatically identified to the unittest driver. """ SECTION_NAME = 'drivers.unittest' def __init__(self, config_file_path=None): config_file_path = config_file_path or _get_path_from_env( 'CAFE_ENGINE_CONFIG_FILE_PATH') super(DriverConfig, self).__init__(config_file_path=config_file_path) @property def ignore_empty_datasets(self): """ Identify whether empty datasets should change suite results. A dataset provided to a suite should result in the suite failing. This value provides a mechanism to modify that behavior in the case of suites with intensionally included empty datasets. If this is set to 'True' empty datasets will not cause suite failures. This defaults to 'False'. """ return self.get_boolean( item_name="ignore_empty_datasets", default=False)

d28f307af02ce3bac54ba47c296eef107a99687c

py

Python

src/103_signal_processing.py

j20232/kaggle_earthquake

47fac5f2e8d2ad4fab82426a0b6af18b71e4b57b

src/103_signal_processing.py

j20232/kaggle_earthquake

47fac5f2e8d2ad4fab82426a0b6af18b71e4b57b

src/103_signal_processing.py

j20232/kaggle_earthquake

47fac5f2e8d2ad4fab82426a0b6af18b71e4b57b

"""Extract signal processing features Reference: https://www.kaggle.com/gpreda/lanl-earthquake-eda-and-prediction """ import sys import numpy as np import pandas as pd from pathlib import Path from sklearn.linear_model import LinearRegression from tqdm import tqdm import competition as cc from common import stop_watch TRAIN_CSV_DIRECTORY_PATH = cc.INPUT_PATH / sys.argv[1] TRAIN_CSV_LIST = list(TRAIN_CSV_DIRECTORY_PATH.glob('**/*.csv')) @stop_watch def extract_features(csv_list, feature_dir_path): df = pd.DataFrame() Path.mkdir(feature_dir_path, exist_ok=True, parents=True) for index, each_csv in enumerate(tqdm(sorted(csv_list))): seg = pd.read_csv(each_csv, dtype=cc.DTYPES) seg_id = each_csv.split("/")[-1].split(".")[0] df.loc[index, "seg_id"] = seg_id xc = pd.Series(seg['acoustic_data'].values) # Regression df.loc[index, 'trend'] = add_trend_feature(xc) df.loc[index, 'abs_trend'] = add_trend_feature(xc, abs_values=True) # classic_sta_lta (the definition is written in this file) df.loc[index, 'classic_sta_lta1_mean'] = classic_sta_lta(xc, 500, 10000).mean() df.loc[index, 'classic_sta_lta2_mean'] = classic_sta_lta(xc, 5000, 100000).mean() df.loc[index, 'classic_sta_lta3_mean'] = classic_sta_lta(xc, 3333, 6666).mean() df.loc[index, 'classic_sta_lta4_mean'] = classic_sta_lta(xc, 10000, 25000).mean() # moving average df.loc[index, 'Moving_average_700_mean'] = xc.rolling(window=700).mean().mean(skipna=True) df.loc[index, 'Moving_average_1500_mean'] = xc.rolling(window=1500).mean().mean(skipna=True) df.loc[index, 'Moving_average_3000_mean'] = xc.rolling(window=3000).mean().mean(skipna=True) df.loc[index, 'Moving_average_6000_mean'] = xc.rolling(window=6000).mean().mean(skipna=True) # ema moving average ewma = pd.Series.ewm df.loc[index, 'exp_Moving_average_300_mean'] = ewma(xc, span=300).mean().mean(skipna=True) df.loc[index, 'exp_Moving_average_3000_mean'] = ewma(xc, span=3000).mean().mean(skipna=True) df.loc[index, 'exp_Moving_average_30000_mean'] = ewma(xc, span=6000).mean().mean(skipna=True) # moving average by correction with std no_of_std = 2 df.loc[index, 'MA_400MA_std_mean'] = xc.rolling(window=400).std().mean() df.loc[index, 'MA_400MA_BB_high_mean'] = (df.loc[index, 'Moving_average_700_mean'] + no_of_std * df.loc[index, 'MA_400MA_std_mean']).mean() df.loc[index, 'MA_400MA_BB_low_mean'] = (df.loc[index, 'Moving_average_700_mean'] - no_of_std * df.loc[index, 'MA_400MA_std_mean']).mean() df.loc[index, 'MA_700MA_std_mean'] = xc.rolling(window=700).std().mean() df.loc[index, 'MA_700MA_BB_high_mean'] = (df.loc[index, 'Moving_average_700_mean'] + no_of_std * df.loc[index, 'MA_700MA_std_mean']).mean() df.loc[index, 'MA_700MA_BB_low_mean'] = (df.loc[index, 'Moving_average_700_mean'] - no_of_std * df.loc[index, 'MA_700MA_std_mean']).mean() df.loc[index, 'MA_1000MA_std_mean'] = xc.rolling(window=1000).std().mean() print("Aggregation output is belows:") print(df.head(3)) df.to_csv(feature_dir_path / "{}.csv".format(cc.PREF), index=False) def add_trend_feature(arr, abs_values=False): idx = np.array(range(len(arr))) if abs_values: arr = np.abs(arr) lr = LinearRegression() lr.fit(idx.reshape(-1, 1), arr) return lr.coef_[0] def classic_sta_lta(x, length_sta, length_lta): sta = np.cumsum(x ** 2) # Convert to float sta = np.require(sta, dtype=np.float) # Copy for LTA lta = sta.copy() # Compute the STA and the LTA sta[length_sta:] = sta[length_sta:] - sta[:-length_sta] sta /= length_sta lta[length_lta:] = lta[length_lta:] - lta[:-length_lta] lta /= length_lta # Pad zeros sta[:length_lta - 1] = 0 # Avoid division by zero by setting zero values to tiny float dtiny = np.finfo(0.0).tiny idx = lta < dtiny lta[idx] = dtiny return sta / lta if __name__ == "__main__": train_csv_path = cc.FEATURE_PATH / "{}".format(sys.argv[1]) train_csv_l = [str(item) for item in TRAIN_CSV_LIST] extract_features(train_csv_l, train_csv_path) test_csv_path = cc.FEATURE_PATH / "test" test_csv_l = [str(item) for item in cc.TEST_CSV_LIST] extract_features(test_csv_l, test_csv_path)

7d6fa3b3374606aeeb424c0e983aa60cabcb2ff0

py

Python

dgi_gcn/models/dgi.py

Guo-lab/Graph

c4c5fbc8fb5d645c16da20351b9746019cf75aab

dgi_gcn/models/dgi.py

Guo-lab/Graph

c4c5fbc8fb5d645c16da20351b9746019cf75aab

dgi_gcn/models/dgi.py

Guo-lab/Graph

c4c5fbc8fb5d645c16da20351b9746019cf75aab

import torch import torch.nn as nn from layers import GCN, AvgReadout, Discriminator class DGI(nn.Module): def __init__(self, n_in, n_h, activation): super(DGI, self).__init__() self.gcn = GCN(n_in, n_h, activation) self.read = AvgReadout() self.sigm = nn.Sigmoid() self.disc = Discriminator(n_h) def forward(self, seq1, seq2, adj, sparse, msk, samp_bias1, samp_bias2): h_1 = self.gcn(seq1, adj, sparse) #//print("h1 gat OK") #//print("h1 shape", h_1.shape) #//print("seq shape, adj shape", seq1.shape, adj.shape) c = self.read(h_1, msk) #//print("AvgReadout OK") #//print("c shape", c.shape) c = self.sigm(c) #//print("seq2 shape, adj shape", seq2.shape, adj.shape) h_2 = self.gcn(seq2, adj, sparse) #//print(h_2.shape) #//print("h2 gat OK") ret = self.disc(c, h_1, h_2, samp_bias1, samp_bias2) return ret # Detach the return variables def embed(self, seq, adj, sparse, msk): h_1 = self.gcn(seq, adj, sparse) c = self.read(h_1, msk) return h_1.detach(), c.detach()

c570097cf8e08e24570debaf9bdf54543aca13c6

py

Python

lab/weather.py

AbdullahNoori/replace

1dffd3668ca467a1e5acf20c5fe6f992e483eb49

lab/weather.py

AbdullahNoori/replace

1dffd3668ca467a1e5acf20c5fe6f992e483eb49

lab/weather.py

AbdullahNoori/replace

1dffd3668ca467a1e5acf20c5fe6f992e483eb49

class Subject: # Both of the following two methods take an # observer as an argument; that is, the observer # to be registered ore removed. def registerObserver(observer): pass def removeObserver(observer): pass # This method is called to notify all observers # when the Subject's state (measurements) have changed. def notifyObservers(): pass # The observer class is implemented by all observers, # so they all have to implemented the update() method. Here # we're following Mary and Sue's lead and # passing the measurements to the observers. class Observer: def update(self, temp, humidity, pressure): pass # WeatherData now implements the subject interface. class WeatherData(Subject): def __init__(self): self.observers = [] self.temperature = 0 self.humidity = 0 self.pressure = 0 def registerObserver(self, observer): # When an observer registers, we just # add it to the end of the list. self.observers.append(observer) def removeObserver(self, observer): # When an observer wants to un-register, # we just take it off the list. self.observers.remove(observer) def notifyObservers(self): # We notify the observers when we get updated measurements # from the Weather Station. for ob in self.observers: ob.update(self.temperature, self.humidity, self.pressure) def measurementsChanged(self): self.notifyObservers() def setMeasurements(self, temperature, humidity, pressure): self.temperature = temperature self.humidity = humidity self.pressure = pressure self.measurementsChanged() # other WeatherData methods here. class CurrentConditionsDisplay(Observer): def __init__(self, weatherData): self.temperature = 0 self.humidity = 0 self.pressure = 0 self.weatherData = weatherData # save the ref in an attribute. weatherData.registerObserver(self) # register the observer # so it gets data updates. def update(self, temperature, humidity, pressure): self.temeprature = temperature self.humidity = humidity self.pressure = pressure self.display() def display(self): print("Current conditions:", self.temperature, "F degrees and", self.humidity,"[%] humidity", "and pressure", self.pressure) # TODO: implement StatisticsDisplay class and ForecastDisplay class. # The StatisticsDisplay class should keep track of the min/average/max # measurements and display them. class StatisticsDisplay(Observer): def __init__(self, weatherData): self.temperatures = [] self.humidities = [] self.pressures = [] self.weatherData = weatherData weatherData.registerObserver(self) def update(self, temperature, humidity, pressure): self.temperatures.append(temperature) self.humidities.append(humidity) self.pressures.append(pressure) self.display() def getStats(self, units): """Returns min, max, and avg of a list""" if units == "[%]": values = self.temperatures measurement = "temp" elif units == "F degrees": values = self.humidities measurement = "humidity" else: values = self.pressures measurement = "pressure" # calculate results result = ( f"Min: {measurement}: {min(values)} {units}, " + f"Avg {measurement}: {sum(values) / len(values)} {units}, " + f"Max {measurement}: {max(values)} {units}" ) return result def display(self): # displays temparatures if self.temperatures: print(self.getStats("F degrees")) else: print("No temperature stats") # displays humidities if self.humidities: print(self.getStats("[%]")) else: print("No humidity stats") # displays pressures if self.pressures: print(self.getStats(""), '\n') else: print("No pressure stats", '\n') # ForecastDisplay class. Also, we register them to the concrete instance # of the Subject class so the they retrieve the measurements' updates. class ForecastDisplay(Observer): def __init__(self, weatherData): self.weatherData = weatherData weatherData.registerObserver(self) self.forecast_temp = 0 self.forecast_humidity = 0 self.forecast_pressure = 0 # The ForecastDisplay class shows the weather forecast based on the current # temperature, humidity and pressure. Use the following formulas : def update(self, temperature, humidity, pressure): self.forecast_temp = temperature + 0.11 * humidity + 0.2 * pressure self.forecast_humidity = humidity - 0.9 * humidity self.forecast_pressure = pressure + 0.1 * temperature - 0.21 * pressure self.display() def display(self): print("Forecast conditions:", self.forecast_temp, "F degrees and", self.forecast_humidity, "[%] humidity", "and pressure", self.forecast_pressure,) class WeatherStation: def main(self): weatherData = WeatherData() current_display = CurrentConditionsDisplay(weatherData) # TODO: Create two objects from StatisticsDisplay class and # ForecastDisplay class. Also, register them to the concrete instance # of the Subject class so they get the measurements' updates. stats_display = StatisticsDisplay(weatherData) forecast_display = ForecastDisplay(weatherData) weatherData.setMeasurements(80, 65,30.4) weatherData.setMeasurements(82, 70,29.2) weatherData.setMeasurements(78, 90,29.2) # un-register the observer weatherData.removeObserver(current_display) weatherData.setMeasurements(120, 100,1000) if __name__ == "__main__": w = WeatherStation() w.main()

dfa52052899c80b0f08aa247e02dd629e0153ad3

py

Python

start_notebook_server.py

ftschindler-work/proceedings-mbour-2017-lrbms-control

0fa8b63b223f3ce8bdfa0f010266a7719a574091

start_notebook_server.py

ftschindler-work/proceedings-mbour-2017-lrbms-control

0fa8b63b223f3ce8bdfa0f010266a7719a574091

start_notebook_server.py

ftschindler-work/proceedings-mbour-2017-lrbms-control

0fa8b63b223f3ce8bdfa0f010266a7719a574091

2020-09-23T12:51:00.000Z

2020-09-23T12:51:00.000Z

#!/bin/bash export NOTEBOOK_PATH=$PWD export NOTEBOOK_PORT=${EXPOSED_PORT:-18881} jupyter-notebook --ip 0.0.0.0 --no-browser --notebook-dir=$NOTEBOOK_PATH --port=$NOTEBOOK_PORT

13bd82f006456ce483eba276ce72a8df6bcd6f25

py

Python

solutions/day11/p1/main.py

tosmun/AdventOfCode

62f4f3a8cc3761ee5d5eaf682ae9c2c985cd80b5

2017-07-15T19:01:03.000Z

2017-07-15T19:01:03.000Z

solutions/day11/p1/main.py

tosmun/Python-AdventOfCode

62f4f3a8cc3761ee5d5eaf682ae9c2c985cd80b5

solutions/day11/p1/main.py

tosmun/Python-AdventOfCode

62f4f3a8cc3761ee5d5eaf682ae9c2c985cd80b5

import copy, re encoding="UTF-8" input=bytearray('hepxcrrq',encoding) max_byte=bytearray('z', encoding)[0] min_byte=bytearray('a', encoding)[0] invalid_chars=bytearray('iol', encoding) double_char_pattern=re.compile(r'(.|^)(?!\1)(.)\2(?!\2)') new=copy.copy(input) while True: #Work backwards for i in range(len(new)-1,-1,-1): if new[i] == max_byte: new[i] = min_byte #Detect rollover if i == 0: #TODO more pythonic way? for i in range(0, len(new)): new[i] = min_byte break else: new[i] = new[i] + 1 #Ensure valid char while(new[i] in invalid_chars): new[i] = new[i] + 1 break #Check for two overlapping pairs new_str = new.decode(encoding) if len(double_char_pattern.findall(new_str)) < 2: continue buffer = [ new[0] ] for i in range(1, len(new)): if len(buffer) == 3: break elif buffer[-1] != new[i]-1: buffer = [ new[i] ] else: buffer.append(new[i]) if len(buffer) == 3: print(new.decode(encoding)) break if new == input: raise Exception("No suitable new password found")

a89b95f6ef51c58e817351f96c53f0273a56013a

py

Python

zfused_maya/zfused_maya/tool/rigging/transferuv.py

qinningfx/zfused_outsource

bfc5558f05e3d6005653794a47bd863b61b009b1

2019-02-22T03:33:26.000Z

2019-02-23T03:29:26.000Z

zfused_maya/zfused_maya/tool/rigging/transferuv.py

qinningfx/zfused_outsource

bfc5558f05e3d6005653794a47bd863b61b009b1

zfused_maya/zfused_maya/tool/rigging/transferuv.py

qinningfx/zfused_outsource

bfc5558f05e3d6005653794a47bd863b61b009b1

# coding:utf-8 # --author-- 张伟 # cmds.polyTransfer('zero:p_zero_luoxuanjiang_1', v = 0, vc = 0, uv = 1, ao = 'zero_172:p_zero_luoxuanjiang_1') from PySide2 import QtWidgets, QtCore import maya.OpenMayaUI as oi import shiboken2 as shiboken import zfused_maya.widgets.window as win import maya.cmds as cmds class PolyTransfer(): def __init__(self): self.showUI() self.__uiwindow = oi.MQtUtil.findWindow(u"传递UV") self.__uiwindow = shiboken.wrapInstance(long(self.__uiwindow), QtWidgets.QWidget) # pass def _returnwindow(self): return self.__uiwindow def showUI(self): windowName = u'传递UV' if cmds.window(windowName, q = True, exists = True): cmds.deleteUI(windowName) if cmds.windowPref(windowName, exists = True) == True: cmds.windowPref(windowName,remove = True) cmds.window(windowName,t = windowName,sizeable = True,w = 250) PrimaryLayout = cmds.columnLayout(adj = True, bgc = [0.15,0.15,0.15]) cmds.separator(h = 5) cmds.text(l = u'方 法 一', bgc = [0.2,0.2,0.2], height = 22) cmds.text(l = u'选择模型，单击\'<<<\'选定', h = 20) true_InfoGatherLayout = cmds.rowLayout(nc = 3, adjustableColumn = 2, p = PrimaryLayout) cmds.text(l = u"UV正确模型 : ") trueUV_Name = cmds.textField(w = 200) trueUV_Assign = cmds.button(l = '<<<', c = lambda *args: self.true_AssignBtnCmd()) cmds.setParent(PrimaryLayout) false_InfoGatherLayout = cmds.rowLayout(nc = 3, adjustableColumn = 2, p = PrimaryLayout) cmds.text(l = u"UV错误模型 : ") falseUV_Name = cmds.textField(w = 200) falseUV_Assign = cmds.button(l = '<<<', c = lambda *agrs: self.false_AssignBtnCmd()) cmds.setParent(PrimaryLayout) assignButton = cmds.button(l = u'传递', c = lambda *args: self.transferUV()) cmds.setParent(PrimaryLayout) cmds.separator(h = 10, bgc = [0.2,0.2,0.2]) cmds.separator(h = 10, bgc = [0.2,0.2,0.2]) cmds.text(l = u'方 法 二', bgc = [0.2,0.2,0.2], height = 22) cmds.text(l = u'先选正确UV模型，后选错误UV模型', h = 20) cmds.button(l = u'传递', c = lambda *args: self.secTransferUV()) cmds.separator(h = 5) self.trueUV_Name = trueUV_Name self.falseUV_Name = falseUV_Name def setTextField(self, textFieldToSet, Value): cmds.textField(textFieldToSet, e = True, text = Value) def getTrueModel(self): selection = cmds.ls(sl = True)[0] return selection def true_AssignBtnCmd(self): trueUV_Name = self.trueUV_Name trueModelName = self.getTrueModel() self.setTextField(trueUV_Name, trueModelName) def getFalseModel(self): selection = cmds.ls(sl = True)[0] return selection def false_AssignBtnCmd(self): falseUV_Name = self.falseUV_Name falseModelName = self.getFalseModel() self.setTextField(falseUV_Name, falseModelName) def transferUV(self): trueUV_Name = self.trueUV_Name falseUV_Name = self.falseUV_Name trueName = cmds.textField(trueUV_Name, q = True, text = True) falseName = cmds.textField(falseUV_Name, q = True, text = True) cmds.polyTransfer(falseName, v = 0, vc = 0, uv = 1, ao = trueName) def secTransferUV(self): selects = cmds.ls(sl = True) cmds.polyTransfer(selects[1], v = 0, vc = 0, uv = 1, ao = selects[0]) def UI(self): # self.showUI() # _uiwindow = oi.MQtUtil.findWindow(u"传递UV") # _uiwindow = shiboken.wrapInstance(long(_uiwindow), QtWidgets.QWidget) mainWindow = win.Window() mainWindow.set_central_widget(self._returnwindow()) mainWindow.set_title_name(u"传递UV") #mainWindow.setFixedSize(500,286) mainWindow.resize(500,286) mainWindow.show() if __name__ == "__main__": polytransfer = PolyTransfer() polytransfer.UI()

af1f95eda2e8845d622db272b4eee154b56d8c9d

py

Python

test/record/parser/test_response_ccwhois_verisign_grs_com_cc_status_registered.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

test/record/parser/test_response_ccwhois_verisign_grs_com_cc_status_registered.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

test/record/parser/test_response_ccwhois_verisign_grs_com_cc_status_registered.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

# This file is autogenerated. Do not edit it manually. # If you want change the content of this file, edit # # spec/fixtures/responses/ccwhois.verisign-grs.com/cc/status_registered # # and regenerate the tests with the following script # # $ scripts/generate_tests.py # from nose.tools import * from dateutil.parser import parse as time_parse import yawhois class TestCcwhoisVerisignGrsComCcStatusRegistered(object): def setUp(self): fixture_path = "spec/fixtures/responses/ccwhois.verisign-grs.com/cc/status_registered.txt" host = "ccwhois.verisign-grs.com" part = yawhois.record.Part(open(fixture_path, "r").read(), host) self.record = yawhois.record.Record(None, [part]) def test_status(self): eq_(self.record.status, 'registered') def test_available(self): eq_(self.record.available, False) def test_domain(self): eq_(self.record.domain, "google.cc") def test_nameservers(self): eq_(self.record.nameservers.__class__.__name__, 'list') eq_(len(self.record.nameservers), 4) eq_(self.record.nameservers[0].__class__.__name__, 'Nameserver') eq_(self.record.nameservers[0].name, "ns1.google.com") eq_(self.record.nameservers[0].ipv4, None) eq_(self.record.nameservers[0].ipv6, None) eq_(self.record.nameservers[1].__class__.__name__, 'Nameserver') eq_(self.record.nameservers[1].name, "ns2.google.com") eq_(self.record.nameservers[1].ipv4, None) eq_(self.record.nameservers[1].ipv6, None) eq_(self.record.nameservers[2].__class__.__name__, 'Nameserver') eq_(self.record.nameservers[2].name, "ns3.google.com") eq_(self.record.nameservers[2].ipv4, None) eq_(self.record.nameservers[2].ipv6, None) eq_(self.record.nameservers[3].__class__.__name__, 'Nameserver') eq_(self.record.nameservers[3].name, "ns4.google.com") eq_(self.record.nameservers[3].ipv4, None) eq_(self.record.nameservers[3].ipv6, None) def test_registered(self): eq_(self.record.registered, True) def test_referral_whois(self): eq_(self.record.referral_whois, "whois.markmonitor.com") def test_created_on(self): eq_(self.record.created_on.__class__.__name__, 'datetime') eq_(self.record.created_on, time_parse('1999-06-07 00:00:00 UTC')) def test_registrar(self): eq_(self.record.registrar.__class__.__name__, 'Registrar') eq_(self.record.registrar.id, "292") eq_(self.record.registrar.name, "MARKMONITOR INC.") eq_(self.record.registrar.organization, None) eq_(self.record.registrar.url, "http://www.markmonitor.com") def test_referral_url(self): eq_(self.record.referral_url, "http://www.markmonitor.com") def test_updated_on(self): eq_(self.record.updated_on.__class__.__name__, 'datetime') eq_(self.record.updated_on, time_parse('2013-05-06 05:17:44 UTC')) def test_domain_id(self): eq_(self.record.domain_id, "86420657") def test_expires_on(self): eq_(self.record.expires_on.__class__.__name__, 'datetime') eq_(self.record.expires_on, time_parse('2014-06-07 00:00:00 UTC')) def test_disclaimer(self): eq_(self.record.disclaimer, "TERMS OF USE: You are not authorized to access or query our Whois database through the use of electronic processes that are high-volume and automated except as reasonably necessary to register domain names or modify existing registrations; the Data in VeriSign's (\"VeriSign\") Whois database is provided by VeriSign for information purposes only, and to assist persons in obtaining information about or related to a domain name registration record. VeriSign does not guarantee its accuracy. By submitting a Whois query, you agree to abide by the following terms of use: You agree that you may use this Data only for lawful purposes and that under no circumstances will you use this Data to: (1) allow, enable, or otherwise support the transmission of mass unsolicited, commercial advertising or solicitations via e-mail, telephone, or facsimile; or (2) enable high volume, automated, electronic processes that apply to VeriSign (or its computer systems). The compilation, repackaging, dissemination or other use of this Data is expressly prohibited without the prior written consent of VeriSign. You agree not to use electronic processes that are automated and high-volume to access or query the Whois database except as reasonably necessary to register domain names or modify existing registrations. VeriSign reserves the right to restrict your access to the Whois database in its sole discretion to ensure operational stability. VeriSign may restrict or terminate your access to the Whois database for failure to abide by these terms of use. VeriSign reserves the right to modify these terms at any time.")

d762a376f57716369303f18337fb37d8543ff2f8

py

Python

arithmetic_analysis/in_static_equilibrium.py

Study-Repos-Forks/Python

c86aa72cfa0467bd9a5711d7b5a77ed8243e49f1

2022-03-18T12:11:26.000Z

2022-03-18T12:11:26.000Z

arithmetic_analysis/in_static_equilibrium.py

abdussalam02/Python

a80e5aadf30817251989378e8d908ca18f733a2f

arithmetic_analysis/in_static_equilibrium.py

abdussalam02/Python

a80e5aadf30817251989378e8d908ca18f733a2f

""" Checks if a system of forces is in static equilibrium. """ from __future__ import annotations from numpy import array, cos, cross, float64, radians, sin from numpy.typing import NDArray def polar_force( magnitude: float, angle: float, radian_mode: bool = False ) -> list[float]: """ Resolves force along rectangular components. (force, angle) => (force_x, force_y) >>> import math >>> force = polar_force(10, 45) >>> math.isclose(force[0], 7.071067811865477) True >>> math.isclose(force[1], 7.0710678118654755) True >>> polar_force(10, 3.14, radian_mode=True) [-9.999987317275396, 0.01592652916486828] """ if radian_mode: return [magnitude * cos(angle), magnitude * sin(angle)] return [magnitude * cos(radians(angle)), magnitude * sin(radians(angle))] def in_static_equilibrium( forces: NDArray[float64], location: NDArray[float64], eps: float = 10**-1 ) -> bool: """ Check if a system is in equilibrium. It takes two numpy.array objects. forces ==> [ [force1_x, force1_y], [force2_x, force2_y], ....] location ==> [ [x1, y1], [x2, y2], ....] >>> force = array([[1, 1], [-1, 2]]) >>> location = array([[1, 0], [10, 0]]) >>> in_static_equilibrium(force, location) False """ # summation of moments is zero moments: NDArray[float64] = cross(location, forces) sum_moments: float = sum(moments) return abs(sum_moments) < eps if __name__ == "__main__": # Test to check if it works forces = array( [ polar_force(718.4, 180 - 30), polar_force(879.54, 45), polar_force(100, -90), ] ) location: NDArray[float64] = array([[0, 0], [0, 0], [0, 0]]) assert in_static_equilibrium(forces, location) # Problem 1 in image_data/2D_problems.jpg forces = array( [ polar_force(30 * 9.81, 15), polar_force(215, 180 - 45), polar_force(264, 90 - 30), ] ) location = array([[0, 0], [0, 0], [0, 0]]) assert in_static_equilibrium(forces, location) # Problem in image_data/2D_problems_1.jpg forces = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]]) location = array([[0, 0], [6, 0], [10, 0], [12, 0]]) assert in_static_equilibrium(forces, location) import doctest doctest.testmod()

03c0e45d005d4bdc28d9e1488c2e81136cd0e8d7

py

Python

modules/tools/common/message_manager.py

zarmars/apollo

2c71e68118fdfc8ea4327e6a0fdc93b428882a8b

2021-12-04T08:02:09.000Z

2021-12-04T08:02:09.000Z

modules/tools/common/message_manager.py

Mrrabbitan/apollo

ff6bb065eb343689603a0827828728ed4fa1a699

modules/tools/common/message_manager.py

Mrrabbitan/apollo

ff6bb065eb343689603a0827828728ed4fa1a699

#!/usr/bin/env python3 ############################################################################### # Copyright 2017 The Apollo 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. ############################################################################### from modules.localization.proto import localization_pb2 from modules.perception.proto import perception_obstacle_pb2 from modules.perception.proto import traffic_light_detection_pb2 from modules.planning.proto import planning_internal_pb2 from modules.planning.proto import planning_pb2 from modules.prediction.proto import prediction_obstacle_pb2 from modules.routing.proto import routing_pb2 from modules.control.proto import control_cmd_pb2 from modules.canbus.proto import chassis_pb2 from modules.common.proto import drive_event_pb2 from modules.map.relative_map.proto import navigation_pb2 from modules.guardian.proto import guardian_pb2 from modules.tools.common import proto_utils class MessageType: def __init__(self, name, topic, msg_type): self.name = name self.topic = topic self.msg_type = msg_type def instance(self): return self.__msg_type() def parse_file(self, filename): value = self.instance() if not proto_utils.get_pb_from_file(filename, value): print("Failed to parse file %s" % filename) return None else: return value topic_pb_list = [ MessageType("planning", "/apollo/planning", planning_pb2.ADCTrajectory), MessageType("control", "/apollo/control", control_cmd_pb2.ControlCommand), MessageType("chassis", "/apollo/canbus/chassis", chassis_pb2.Chassis), MessageType("prediction", "/apollo/prediction", prediction_obstacle_pb2.PredictionObstacles), MessageType("perception", "/apollo/perception/obstacles", perception_obstacle_pb2.PerceptionObstacles), MessageType("routing_response", "/apollo/routing_response", routing_pb2.RoutingResponse), MessageType("routing_request", "/apollo/routing_request", routing_pb2.RoutingRequest), MessageType("localization", "/apollo/localization/pose", localization_pb2.LocalizationEstimate), MessageType("traffic_light", "/apollo/perception/traffic_light", traffic_light_detection_pb2.TrafficLightDetection), MessageType("drive_event", "/apollo/drive_event", drive_event_pb2.DriveEvent), MessageType("relative_map", "/apollo/relative_map", navigation_pb2.MapMsg), MessageType("navigation", "/apollo/navigation", navigation_pb2.NavigationInfo), MessageType("guardian", "/apollo/guardian", guardian_pb2.GuardianCommand), ] class PbMessageManager: def __init__(self): self.__topic_dict = {} self.__name_dict = {} for msg in topic_pb_list: self.__topic_dict[msg.topic] = msg self.__name_dict[msg.name] = msg def topic_dict(self): return self.__topic_dict def get_msg_meta_by_topic(self, topic): if topic in self.__topic_dict: return self.__topic_dict[topic] else: return None def get_msg_meta_by_name(self, name): if name in self.__name_dict: return self.__name_dict[name] else: return None def name_dict(self): return self.__name_dict def parse_topic_file(self, topic, filename): if topic not in self.__topic_dict: print("topic %s is not registered in topic_pb_list" % topic) return None meta_msg = self.__topic_dict[topic] return meta_msg.parse_file(filename) def parse_file(self, filename): """parse a file by guessing topic type""" for topic, meta_msg in self.__topic_dict.items(): try: message = meta_msg.parse_file(filename) if message: print("identified topic %s" % topic) return (meta_msg, message) except text_format.ParseError as e: print("Tried %s, failed" % (topic)) continue return (None, None)

73c11134fd43b5ccb5c236067011b23c61ff39d3

py

Python

niftynet/application/regression_application.py

josemariamoreira/NiftyNet

d3ed1404fed9b8b69a3b60ae5b398045ae121d34

2018-05-13T14:54:47.000Z

2018-05-13T14:54:47.000Z

niftynet/application/regression_application.py

josemariamoreira/NiftyNet

d3ed1404fed9b8b69a3b60ae5b398045ae121d34

niftynet/application/regression_application.py

josemariamoreira/NiftyNet

d3ed1404fed9b8b69a3b60ae5b398045ae121d34

2018-05-13T14:54:48.000Z

2018-05-26T16:08:09.000Z

import tensorflow as tf import os from niftynet.application.base_application import BaseApplication from niftynet.engine.application_factory import ApplicationNetFactory from niftynet.engine.application_factory import OptimiserFactory from niftynet.engine.application_variables import CONSOLE from niftynet.engine.application_variables import NETWORK_OUTPUT from niftynet.engine.application_variables import TF_SUMMARIES from niftynet.engine.sampler_grid import GridSampler from niftynet.engine.sampler_resize import ResizeSampler from niftynet.engine.sampler_uniform import UniformSampler from niftynet.engine.sampler_weighted import WeightedSampler from niftynet.engine.sampler_balanced import BalancedSampler from niftynet.engine.windows_aggregator_grid import GridSamplesAggregator from niftynet.engine.windows_aggregator_resize import ResizeSamplesAggregator from niftynet.io.image_reader import ImageReader from niftynet.layer.crop import CropLayer from niftynet.layer.histogram_normalisation import \ HistogramNormalisationLayer from niftynet.layer.loss_regression import LossFunction from niftynet.layer.mean_variance_normalisation import \ MeanVarNormalisationLayer from niftynet.layer.pad import PadLayer from niftynet.layer.post_processing import PostProcessingLayer from niftynet.layer.rand_flip import RandomFlipLayer from niftynet.layer.rand_rotation import RandomRotationLayer from niftynet.layer.rand_spatial_scaling import RandomSpatialScalingLayer from niftynet.evaluation.regression_evaluator import RegressionEvaluator SUPPORTED_INPUT = set(['image', 'output', 'weight', 'sampler', 'inferred']) class RegressionApplication(BaseApplication): REQUIRED_CONFIG_SECTION = "REGRESSION" def __init__(self, net_param, action_param, action): BaseApplication.__init__(self) tf.logging.info('starting regression application') self.action = action self.net_param = net_param self.action_param = action_param self.regression_param = None self.data_param = None self.SUPPORTED_SAMPLING = { 'uniform': (self.initialise_uniform_sampler, self.initialise_grid_sampler, self.initialise_grid_aggregator), 'weighted': (self.initialise_weighted_sampler, self.initialise_grid_sampler, self.initialise_grid_aggregator), 'resize': (self.initialise_resize_sampler, self.initialise_resize_sampler, self.initialise_resize_aggregator), 'balanced': (self.initialise_balanced_sampler, self.initialise_grid_sampler, self.initialise_grid_aggregator), } def initialise_dataset_loader( self, data_param=None, task_param=None, data_partitioner=None): self.data_param = data_param self.regression_param = task_param file_lists = self.get_file_lists(data_partitioner) # read each line of csv files into an instance of Subject if self.is_training: self.readers = [] for file_list in file_lists: reader = ImageReader({'image', 'output', 'weight', 'sampler'}) reader.initialise(data_param, task_param, file_list) self.readers.append(reader) elif self.is_inference: inference_reader = ImageReader(['image']) file_list = data_partitioner.inference_files inference_reader.initialise(data_param, task_param, file_lists[0]) self.readers = [inference_reader] elif self.is_evaluation: file_list = data_partitioner.inference_files reader = ImageReader({'image', 'output', 'inferred'}) reader.initialise(data_param, task_param, file_lists[0]) self.readers = [reader] else: raise ValueError('Action `{}` not supported. Expected one of {}' .format(self.action, self.SUPPORTED_ACTIONS)) mean_var_normaliser = MeanVarNormalisationLayer( image_name='image') histogram_normaliser = None if self.net_param.histogram_ref_file: histogram_normaliser = HistogramNormalisationLayer( image_name='image', modalities=vars(task_param).get('image'), model_filename=self.net_param.histogram_ref_file, norm_type=self.net_param.norm_type, cutoff=self.net_param.cutoff, name='hist_norm_layer') normalisation_layers = [] if self.net_param.normalisation: normalisation_layers.append(histogram_normaliser) if self.net_param.whitening: normalisation_layers.append(mean_var_normaliser) augmentation_layers = [] if self.is_training: if self.action_param.random_flipping_axes != -1: augmentation_layers.append(RandomFlipLayer( flip_axes=self.action_param.random_flipping_axes)) if self.action_param.scaling_percentage: augmentation_layers.append(RandomSpatialScalingLayer( min_percentage=self.action_param.scaling_percentage[0], max_percentage=self.action_param.scaling_percentage[1])) if self.action_param.rotation_angle: augmentation_layers.append(RandomRotationLayer()) augmentation_layers[-1].init_uniform_angle( self.action_param.rotation_angle) volume_padding_layer = [] if self.net_param.volume_padding_size: volume_padding_layer.append(PadLayer( image_name=SUPPORTED_INPUT, border=self.net_param.volume_padding_size)) for reader in self.readers: reader.add_preprocessing_layers(volume_padding_layer + normalisation_layers + augmentation_layers) def initialise_uniform_sampler(self): self.sampler = [[UniformSampler( reader=reader, data_param=self.data_param, batch_size=self.net_param.batch_size, windows_per_image=self.action_param.sample_per_volume, queue_length=self.net_param.queue_length) for reader in self.readers]] def initialise_weighted_sampler(self): self.sampler = [[WeightedSampler( reader=reader, data_param=self.data_param, batch_size=self.net_param.batch_size, windows_per_image=self.action_param.sample_per_volume, queue_length=self.net_param.queue_length) for reader in self.readers]] def initialise_resize_sampler(self): self.sampler = [[ResizeSampler( reader=reader, data_param=self.data_param, batch_size=self.net_param.batch_size, shuffle_buffer=self.is_training, queue_length=self.net_param.queue_length) for reader in self.readers]] def initialise_grid_sampler(self): self.sampler = [[GridSampler( reader=reader, data_param=self.data_param, batch_size=self.net_param.batch_size, spatial_window_size=self.action_param.spatial_window_size, window_border=self.action_param.border, queue_length=self.net_param.queue_length) for reader in self.readers]] def initialise_balanced_sampler(self): self.sampler = [[BalancedSampler( reader=reader, data_param=self.data_param, batch_size=self.net_param.batch_size, windows_per_image=self.action_param.sample_per_volume, queue_length=self.net_param.queue_length) for reader in self.readers]] def initialise_grid_aggregator(self): self.output_decoder = GridSamplesAggregator( image_reader=self.readers[0], output_path=self.action_param.save_seg_dir, window_border=self.action_param.border, interp_order=self.action_param.output_interp_order) def initialise_resize_aggregator(self): self.output_decoder = ResizeSamplesAggregator( image_reader=self.readers[0], output_path=self.action_param.save_seg_dir, window_border=self.action_param.border, interp_order=self.action_param.output_interp_order) def initialise_sampler(self): if self.is_training: self.SUPPORTED_SAMPLING[self.net_param.window_sampling][0]() elif self.is_inference: self.SUPPORTED_SAMPLING[self.net_param.window_sampling][1]() def initialise_aggregator(self): self.SUPPORTED_SAMPLING[self.net_param.window_sampling][2]() def initialise_network(self): w_regularizer = None b_regularizer = None reg_type = self.net_param.reg_type.lower() decay = self.net_param.decay if reg_type == 'l2' and decay > 0: from tensorflow.contrib.layers.python.layers import regularizers w_regularizer = regularizers.l2_regularizer(decay) b_regularizer = regularizers.l2_regularizer(decay) elif reg_type == 'l1' and decay > 0: from tensorflow.contrib.layers.python.layers import regularizers w_regularizer = regularizers.l1_regularizer(decay) b_regularizer = regularizers.l1_regularizer(decay) self.net = ApplicationNetFactory.create(self.net_param.name)( num_classes=1, w_regularizer=w_regularizer, b_regularizer=b_regularizer, acti_func=self.net_param.activation_function) def connect_data_and_network(self, outputs_collector=None, gradients_collector=None): def switch_sampler(for_training): with tf.name_scope('train' if for_training else 'validation'): sampler = self.get_sampler()[0][0 if for_training else -1] return sampler.pop_batch_op() if self.is_training: if self.action_param.validation_every_n > 0: data_dict = tf.cond(tf.logical_not(self.is_validation), lambda: switch_sampler(True), lambda: switch_sampler(False)) else: data_dict = switch_sampler(for_training=True) image = tf.cast(data_dict['image'], tf.float32) net_out = self.net(image, is_training=self.is_training) with tf.name_scope('Optimiser'): optimiser_class = OptimiserFactory.create( name=self.action_param.optimiser) self.optimiser = optimiser_class.get_instance( learning_rate=self.action_param.lr) loss_func = LossFunction( loss_type=self.action_param.loss_type) crop_layer = CropLayer( border=self.regression_param.loss_border, name='crop-88') prediction = crop_layer(net_out) ground_truth = crop_layer(data_dict.get('output', None)) weight_map = None if data_dict.get('weight', None) is None \ else crop_layer(data_dict.get('weight', None)) data_loss = loss_func(prediction=prediction, ground_truth=ground_truth, weight_map=weight_map) reg_losses = tf.get_collection( tf.GraphKeys.REGULARIZATION_LOSSES) if self.net_param.decay > 0.0 and reg_losses: reg_loss = tf.reduce_mean( [tf.reduce_mean(reg_loss) for reg_loss in reg_losses]) loss = data_loss + reg_loss else: loss = data_loss grads = self.optimiser.compute_gradients(loss) # collecting gradients variables gradients_collector.add_to_collection([grads]) # collecting output variables outputs_collector.add_to_collection( var=data_loss, name='Loss', average_over_devices=False, collection=CONSOLE) outputs_collector.add_to_collection( var=data_loss, name='Loss', average_over_devices=True, summary_type='scalar', collection=TF_SUMMARIES) elif self.is_inference: data_dict = switch_sampler(for_training=False) image = tf.cast(data_dict['image'], tf.float32) net_out = self.net(image, is_training=self.is_training) crop_layer = CropLayer(border=0, name='crop-88') post_process_layer = PostProcessingLayer('IDENTITY') net_out = post_process_layer(crop_layer(net_out)) outputs_collector.add_to_collection( var=net_out, name='window', average_over_devices=False, collection=NETWORK_OUTPUT) outputs_collector.add_to_collection( var=data_dict['image_location'], name='location', average_over_devices=False, collection=NETWORK_OUTPUT) self.initialise_aggregator() def interpret_output(self, batch_output): if self.is_inference: return self.output_decoder.decode_batch( batch_output['window'], batch_output['location']) else: return True def initialise_evaluator(self, eval_param): self.eval_param = eval_param self.evaluator = RegressionEvaluator(self.readers[0], self.regression_param, eval_param) def add_inferred_output(self, data_param, task_param): return self.add_inferred_output_like(data_param, task_param, 'output')

13acb83397ddc86f7d6751b0180565940b1d779a

py

Python

cplex_gurobi_projects/00_Initial/first_example.py

hpaucar/autonomous-system-repo

b86b62c23fe9a05694fcb5a106457454ff9976fb

cplex_gurobi_projects/00_Initial/first_example.py

hpaucar/autonomous-system-repo

b86b62c23fe9a05694fcb5a106457454ff9976fb

cplex_gurobi_projects/00_Initial/first_example.py

hpaucar/autonomous-system-repo

b86b62c23fe9a05694fcb5a106457454ff9976fb

# -*- coding: utf-8 -*- """ First Example from CPLEX """ import pyomo.environ as pyo from pyomo.environ import * from pyomo.opt import SolverFactory model = pyo.ConcreteModel() model.c = pyo.Var(bounds=(0, None), domain=Integers) model.p = pyo.Var(bounds=(0, None), domain=Integers) c = model.c p = model.p model.obj = pyo.Objective(expr = 3000*c + 5000*p, sense=maximize) model.C1 = pyo.Constraint(expr = 2*c + 3*p <= 30) model.C2 = pyo.Constraint(expr = 4*c + 8*p <= 70) opt = SolverFactory('gurobi') opt.solve(model) model.pprint() print('------------------------------------------------------') print('N casas: ', pyo.value(c)) print('N predios: ', pyo.value(p)) print('Lucro: ', pyo.value(model.obj))

5fb99e5517c1f752028d731a23e50fc1001ed510

py

Python

lib/core/function.py

kuldeepbrd1/deep-high-resolution-net.pytorch

aece4d855edb4f43c968218a294a72e23304b4b1

2021-12-17T08:37:38.000Z

2021-12-17T08:37:38.000Z

lib/core/function.py

kuldeepbrd1/HRNet-spacecraft-pose

13992450423449b9abf3b02741e699d5e2ae2875

lib/core/function.py

kuldeepbrd1/HRNet-spacecraft-pose

13992450423449b9abf3b02741e699d5e2ae2875

# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao (Bin.Xiao@microsoft.com) # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function import time import logging import os import numpy as np import torch from core.evaluate import accuracy from core.inference import get_final_preds from utils.transforms import flip_back from utils.vis import save_debug_images # ---- Added all heatmap save util function ---- 28/02 from sat_pose_utils.utils import save_all_val_heatmaps logger = logging.getLogger(__name__) def train(config, train_loader, model, criterion, optimizer, epoch, output_dir, tb_log_dir, writer_dict): batch_time = AverageMeter() data_time = AverageMeter() losses = AverageMeter() acc = AverageMeter() # switch to train mode model.train() end = time.time() for i, (input, target, target_weight, meta) in enumerate(train_loader): # measure data loading time data_time.update(time.time() - end) # compute output outputs = model(input) target = target.cuda(non_blocking=True) target_weight = target_weight.cuda(non_blocking=True) if isinstance(outputs, list): loss = criterion(outputs[0], target, target_weight) for output in outputs[1:]: loss += criterion(output, target, target_weight) else: output = outputs loss = criterion(output, target, target_weight) # loss = criterion(output, target, target_weight) # compute gradient and do update step optimizer.zero_grad() loss.backward() optimizer.step() # measure accuracy and record loss losses.update(loss.item(), input.size(0)) _, avg_acc, cnt, pred = accuracy(output.detach().cpu().numpy(), target.detach().cpu().numpy()) acc.update(avg_acc, cnt) # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % config.PRINT_FREQ == 0: msg = 'Epoch: [{0}][{1}/{2}]\t' \ 'Time {batch_time.val:.3f}s ({batch_time.avg:.3f}s)\t' \ 'Speed {speed:.1f} samples/s\t' \ 'Data {data_time.val:.3f}s ({data_time.avg:.3f}s)\t' \ 'Loss {loss.val:.5f} ({loss.avg:.5f})\t' \ 'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format( epoch, i, len(train_loader), batch_time=batch_time, speed=input.size(0)/batch_time.val, data_time=data_time, loss=losses, acc=acc) logger.info(msg) writer = writer_dict['writer'] global_steps = writer_dict['train_global_steps'] writer.add_scalar('train_loss', losses.val, global_steps) writer.add_scalar('train_acc', acc.val, global_steps) writer_dict['train_global_steps'] = global_steps + 1 prefix = '{}_{}'.format(os.path.join(output_dir, 'train'), i) save_debug_images(config, input, meta, target, pred*4, output, prefix) def validate(config, val_loader, val_dataset, model, criterion, output_dir, tb_log_dir, writer_dict=None): batch_time = AverageMeter() losses = AverageMeter() acc = AverageMeter() # switch to evaluate mode model.eval() num_samples = len(val_dataset) all_preds = np.zeros( (num_samples, config.MODEL.NUM_JOINTS, 3), dtype=np.float32 ) all_boxes = np.zeros((num_samples, 6)) image_path = [] filenames = [] imgnums = [] idx = 0 with torch.no_grad(): end = time.time() for i, (input, target, target_weight, meta) in enumerate(val_loader): # compute output outputs = model(input) if isinstance(outputs, list): output = outputs[-1] else: output = outputs if config.TEST.FLIP_TEST: input_flipped = input.flip(3) outputs_flipped = model(input_flipped) if isinstance(outputs_flipped, list): output_flipped = outputs_flipped[-1] else: output_flipped = outputs_flipped output_flipped = flip_back(output_flipped.cpu().numpy(), val_dataset.flip_pairs) output_flipped = torch.from_numpy(output_flipped.copy()).cuda() # feature is not aligned, shift flipped heatmap for higher accuracy if config.TEST.SHIFT_HEATMAP: output_flipped[:, :, :, 1:] = \ output_flipped.clone()[:, :, :, 0:-1] output = (output + output_flipped) * 0.5 target = target.cuda(non_blocking=True) target_weight = target_weight.cuda(non_blocking=True) loss = criterion(output, target, target_weight) num_images = input.size(0) # measure accuracy and record loss losses.update(loss.item(), num_images) _, avg_acc, cnt, pred = accuracy(output.cpu().numpy(), target.cpu().numpy()) acc.update(avg_acc, cnt) # measure elapsed time batch_time.update(time.time() - end) end = time.time() c = meta['center'].numpy() s = meta['scale'].numpy() score = meta['score'].numpy() #print(f"idx: {i}") preds, maxvals = get_final_preds( config, output.clone().cpu().numpy(), c, s) all_preds[idx:idx + num_images, :, 0:2] = preds[:, :, 0:2] all_preds[idx:idx + num_images, :, 2:3] = maxvals # double check this all_boxes parts all_boxes[idx:idx + num_images, 0:2] = c[:, 0:2] all_boxes[idx:idx + num_images, 2:4] = s[:, 0:2] all_boxes[idx:idx + num_images, 4] = np.prod(s*200, 1) all_boxes[idx:idx + num_images, 5] = score image_path.extend(meta['image']) idx += num_images # ----------------Save all heatmaps from test/val : 28/02 ----- save_all_val_heatmaps(config, input, meta, target, pred*4, output,output_dir) if i % config.PRINT_FREQ == 0: msg = 'Test: [{0}/{1}]\t' \ 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' \ 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' \ 'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format( i, len(val_loader), batch_time=batch_time, loss=losses, acc=acc) logger.info(msg) prefix = '{}_{}'.format( os.path.join(output_dir, config.DATASET.TEST_SET), i ) save_debug_images(config, input, meta, target, pred*4, output, prefix) name_values, perf_indicator = val_dataset.evaluate( config, all_preds, output_dir, all_boxes, image_path, filenames, imgnums ) model_name = config.MODEL.NAME if isinstance(name_values, list): for name_value in name_values: _print_name_value(name_value, model_name) else: _print_name_value(name_values, model_name) if writer_dict: writer = writer_dict['writer'] global_steps = writer_dict['valid_global_steps'] writer.add_scalar( 'valid_loss', losses.avg, global_steps ) writer.add_scalar( 'valid_acc', acc.avg, global_steps ) if isinstance(name_values, list): for name_value in name_values: writer.add_scalars( 'valid', dict(name_value), global_steps ) else: writer.add_scalars( 'valid', dict(name_values), global_steps ) writer_dict['valid_global_steps'] = global_steps + 1 return perf_indicator # markdown format output def _print_name_value(name_value, full_arch_name): names = name_value.keys() values = name_value.values() num_values = len(name_value) logger.info( '| Arch ' + ' '.join(['| {}'.format(name) for name in names]) + ' |' ) logger.info('|---' * (num_values+1) + '|') if len(full_arch_name) > 15: full_arch_name = full_arch_name[:8] + '...' logger.info( '| ' + full_arch_name + ' ' + ' '.join(['| {:.3f}'.format(value) for value in values]) + ' |' ) class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count if self.count != 0 else 0

86cd1aac404ea65ff2fa02d832bb61f296deba69

py

Python

venv/lib/python3.6/site-packages/ansible_collections/community/general/plugins/modules/rax_cdb_database.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

2021-07-16T08:11:22.000Z

2022-03-31T07:15:34.000Z

venv/lib/python3.6/site-packages/ansible_collections/community/general/plugins/modules/rax_cdb_database.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

2020-02-21T07:24:52.000Z

2020-04-14T09:54:32.000Z

venv/lib/python3.6/site-packages/ansible_collections/community/general/plugins/modules/rax_cdb_database.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

2021-07-05T02:31:42.000Z

2022-03-31T02:46:03.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- # Copyright: Ansible Project # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = ''' module: rax_cdb_database short_description: 'create / delete a database in the Cloud Databases' description: - create / delete a database in the Cloud Databases. options: cdb_id: type: str description: - The databases server UUID required: yes name: type: str description: - Name to give to the database required: yes character_set: type: str description: - Set of symbols and encodings default: 'utf8' collate: type: str description: - Set of rules for comparing characters in a character set default: 'utf8_general_ci' state: type: str description: - Indicate desired state of the resource choices: ['present', 'absent'] default: present author: "Simon JAILLET (@jails)" extends_documentation_fragment: - community.general.rackspace - community.general.rackspace.openstack ''' EXAMPLES = ''' - name: Build a database in Cloud Databases tasks: - name: Database build request local_action: module: rax_cdb_database credentials: ~/.raxpub region: IAD cdb_id: 323e7ce0-9cb0-11e3-a5e2-0800200c9a66 name: db1 state: present register: rax_db_database ''' try: import pyrax HAS_PYRAX = True except ImportError: HAS_PYRAX = False from ansible.module_utils.basic import AnsibleModule from ansible_collections.community.general.plugins.module_utils.rax import rax_argument_spec, rax_required_together, rax_to_dict, setup_rax_module def find_database(instance, name): try: database = instance.get_database(name) except Exception: return False return database def save_database(module, cdb_id, name, character_set, collate): cdb = pyrax.cloud_databases try: instance = cdb.get(cdb_id) except Exception as e: module.fail_json(msg='%s' % e.message) changed = False database = find_database(instance, name) if not database: try: database = instance.create_database(name=name, character_set=character_set, collate=collate) except Exception as e: module.fail_json(msg='%s' % e.message) else: changed = True module.exit_json(changed=changed, action='create', database=rax_to_dict(database)) def delete_database(module, cdb_id, name): cdb = pyrax.cloud_databases try: instance = cdb.get(cdb_id) except Exception as e: module.fail_json(msg='%s' % e.message) changed = False database = find_database(instance, name) if database: try: database.delete() except Exception as e: module.fail_json(msg='%s' % e.message) else: changed = True module.exit_json(changed=changed, action='delete', database=rax_to_dict(database)) def rax_cdb_database(module, state, cdb_id, name, character_set, collate): # act on the state if state == 'present': save_database(module, cdb_id, name, character_set, collate) elif state == 'absent': delete_database(module, cdb_id, name) def main(): argument_spec = rax_argument_spec() argument_spec.update( dict( cdb_id=dict(type='str', required=True), name=dict(type='str', required=True), character_set=dict(type='str', default='utf8'), collate=dict(type='str', default='utf8_general_ci'), state=dict(default='present', choices=['present', 'absent']) ) ) module = AnsibleModule( argument_spec=argument_spec, required_together=rax_required_together(), ) if not HAS_PYRAX: module.fail_json(msg='pyrax is required for this module') cdb_id = module.params.get('cdb_id') name = module.params.get('name') character_set = module.params.get('character_set') collate = module.params.get('collate') state = module.params.get('state') setup_rax_module(module, pyrax) rax_cdb_database(module, state, cdb_id, name, character_set, collate) if __name__ == '__main__': main()

a82f4fe5cd0e915c94eb16e20bf17f63bc9186f1

py

Python

kernal_rom.py

folkertvanheusden/pyc64

90d7fc117427fc3f7b7a65a5a26ed0d1d2ad8941

kernal_rom.py

folkertvanheusden/pyc64

90d7fc117427fc3f7b7a65a5a26ed0d1d2ad8941

kernal_rom.py

folkertvanheusden/pyc64

90d7fc117427fc3f7b7a65a5a26ed0d1d2ad8941

# (C) 2020 by Folkert van Heusden <mail@vanheusden.com> # License: Apache License v2.0 from bus_device import bus_device class kernal_rom(bus_device): def __init__(self): fh = open('kernal.901227-01.bin', 'rb') self.rom: List[int] = [ int(b) for b in fh.read() ] fh.close() def write_through(self): return True def read(self, addr): return self.rom[addr - 0xe000]

0b1440e94e2481ce39569cfabc7511ea13f6f245

py

Python

taxcalc/utils.py

ClarePan/Tax-Calculator

d2d6cb4b551f34017db7166d91d982b5c4670816

2021-02-23T21:03:43.000Z

2021-02-23T21:03:43.000Z

taxcalc/utils.py

ClarePan/Tax-Calculator

d2d6cb4b551f34017db7166d91d982b5c4670816

taxcalc/utils.py

ClarePan/Tax-Calculator

d2d6cb4b551f34017db7166d91d982b5c4670816

""" PUBLIC low-level utility functions for Tax-Calculator. """ # CODING-STYLE CHECKS: # pycodestyle utils.py # pylint --disable=locally-disabled utils.py # # pylint: disable=too-many-lines import os import math import json import collections import pkg_resources import six import numpy as np import pandas as pd import bokeh.io as bio import bokeh.plotting as bp from bokeh.models import PrintfTickFormatter from taxcalc.utilsprvt import (weighted_count_lt_zero, weighted_count_gt_zero, weighted_count, weighted_mean, wage_weighted, agi_weighted, expanded_income_weighted) # Items in the DIST_TABLE_COLUMNS list below correspond to the items in the # DIST_TABLE_LABELS list below; this correspondence allows us to use this # labels list to map a label to the correct column in a distribution table. DIST_VARIABLES = ['expanded_income', 'c00100', 'aftertax_income', 'standard', 'c04470', 'c04600', 'c04800', 'taxbc', 'c62100', 'c09600', 'c05800', 'othertaxes', 'refund', 'c07100', 'iitax', 'payrolltax', 'combined', 's006', 'ubi', 'benefit_cost_total', 'benefit_value_total'] DIST_TABLE_COLUMNS = ['s006', 'c00100', 'num_returns_StandardDed', 'standard', 'num_returns_ItemDed', 'c04470', 'c04600', 'c04800', 'taxbc', 'c62100', 'num_returns_AMT', 'c09600', 'c05800', 'c07100', 'othertaxes', 'refund', 'iitax', 'payrolltax', 'combined', 'ubi', 'benefit_cost_total', 'benefit_value_total', 'expanded_income', 'aftertax_income'] DIST_TABLE_LABELS = ['Returns', 'AGI', 'Standard Deduction Filers', 'Standard Deduction', 'Itemizers', 'Itemized Deduction', 'Personal Exemption', 'Taxable Income', 'Regular Tax', 'AMTI', 'AMT Filers', 'AMT', 'Tax before Credits', 'Non-refundable Credits', 'Other Taxes', 'Refundable Credits', 'Individual Income Tax Liabilities', 'Payroll Tax Liablities', 'Combined Payroll and Individual Income Tax Liabilities', 'Universal Basic Income', 'Total Cost of Benefits', 'Consumption Value of Benefits', 'Expanded Income', 'After-Tax Expanded Income'] # Items in the DIFF_TABLE_COLUMNS list below correspond to the items in the # DIFF_TABLE_LABELS list below; this correspondence allows us to use this # labels list to map a label to the correct column in a difference table. DIFF_VARIABLES = ['expanded_income', 'c00100', 'aftertax_income', 'iitax', 'payrolltax', 'combined', 's006', 'ubi', 'benefit_cost_total', 'benefit_value_total'] DIFF_TABLE_COLUMNS = ['count', 'tax_cut', 'perc_cut', 'tax_inc', 'perc_inc', 'mean', 'tot_change', 'share_of_change', 'ubi', 'benefit_cost_total', 'benefit_value_total', 'pc_aftertaxinc'] DIFF_TABLE_LABELS = ['All Tax Units', 'Tax Units with Tax Cut', 'Percent with Tax Cut', 'Tax Units with Tax Increase', 'Percent with Tax Increase', 'Average Tax Change', 'Total Tax Difference', 'Share of Overall Change', 'Universal Basic Income', 'Total Cost of Benefits', 'Consumption Value of Benefits', '% Change in After-Tax Income'] DECILE_ROW_NAMES = ['0-10n', '0-10z', '0-10p', '10-20', '20-30', '30-40', '40-50', '50-60', '60-70', '70-80', '80-90', '90-100', 'ALL', '90-95', '95-99', 'Top 1%'] STANDARD_ROW_NAMES = ['<$0K', '=$0K', '$0-10K', '$10-20K', '$20-30K', '$30-40K', '$40-50K', '$50-75K', '$75-100K', '$100-200K', '$200-500K', '$500-1000K', '>$1000K', 'ALL'] STANDARD_INCOME_BINS = [-9e99, -1e-9, 1e-9, 10e3, 20e3, 30e3, 40e3, 50e3, 75e3, 100e3, 200e3, 500e3, 1e6, 9e99] SOI_AGI_BINS = [-9e99, 1.0, 5e3, 10e3, 15e3, 20e3, 25e3, 30e3, 40e3, 50e3, 75e3, 100e3, 200e3, 500e3, 1e6, 1.5e6, 2e6, 5e6, 10e6, 9e99] def unweighted_sum(pdf, col_name): """ Return unweighted sum of Pandas DataFrame col_name items. """ return pdf[col_name].sum() def weighted_sum(pdf, col_name): """ Return weighted sum of Pandas DataFrame col_name items. """ return (pdf[col_name] * pdf['s006']).sum() def add_quantile_table_row_variable(pdf, income_measure, num_quantiles, decile_details=False, weight_by_income_measure=False): """ Add a variable to specified Pandas DataFrame, pdf, that specifies the table row and is called 'table_row'. The rows hold equal number of filing units when weight_by_income_measure=False or equal number of income dollars when weight_by_income_measure=True. Assumes that specified pdf contains columns for the specified income_measure and for sample weights, s006. When num_quantiles is 10 and decile_details is True, the bottom decile is broken up into three subgroups (neg, zero, and pos income_measure ) and the top decile is broken into three subgroups (90-95, 95-99, and top 1%). """ assert isinstance(pdf, pd.DataFrame) assert income_measure in pdf if decile_details and num_quantiles != 10: msg = 'decile_details is True when num_quantiles is {}' raise ValueError(msg.format(num_quantiles)) pdf.sort_values(by=income_measure, inplace=True) if weight_by_income_measure: pdf['cumsum_temp'] = np.cumsum(np.multiply(pdf[income_measure].values, pdf['s006'].values)) min_cumsum = pdf['cumsum_temp'].values[0] else: pdf['cumsum_temp'] = np.cumsum(pdf['s006'].values) min_cumsum = 0. # because s006 values are non-negative max_cumsum = pdf['cumsum_temp'].values[-1] cumsum_range = max_cumsum - min_cumsum bin_width = cumsum_range / float(num_quantiles) bin_edges = list(min_cumsum + np.arange(0, (num_quantiles + 1)) * bin_width) bin_edges[-1] = 9e99 # raise top of last bin to include all observations bin_edges[0] = -9e99 # lower bottom of 1st bin to include all observations num_bins = num_quantiles if decile_details: assert bin_edges[1] > 1e-9 # bin_edges[1] is top of bottom decile bin_edges.insert(1, 1e-9) # top of zeros bin_edges.insert(1, -1e-9) # top of negatives bin_edges.insert(-1, bin_edges[-2] + 0.5 * bin_width) # top of 90-95 bin_edges.insert(-1, bin_edges[-2] + 0.4 * bin_width) # top of 95-99 num_bins += 4 labels = range(1, (num_bins + 1)) pdf['table_row'] = pd.cut(pdf['cumsum_temp'], bin_edges, right=False, labels=labels) pdf.drop('cumsum_temp', axis=1, inplace=True) return pdf def add_income_table_row_variable(pdf, income_measure, bin_edges): """ Add a variable to specified Pandas DataFrame, pdf, that specifies the table row and is called 'table_row'. The rows are defined by the specified bin_edges function argument. Note that the bin groupings are LEFT INCLUSIVE, which means that bin_edges=[1,2,3,4] implies these three bin groupings: [1,2), [2,3), [3,4). Parameters ---------- pdf: Pandas DataFrame the object to which we are adding bins income_measure: String specifies income variable used to construct bins bin_edges: list of scalar bin edges Returns ------- pdf: Pandas DataFrame the original input plus the added 'table_row' column """ assert isinstance(pdf, pd.DataFrame) assert income_measure in pdf assert isinstance(bin_edges, list) pdf['table_row'] = pd.cut(pdf[income_measure], bin_edges, right=False) return pdf def get_sums(pdf): """ Compute unweighted sum of items in each column of Pandas DataFrame, pdf. Returns ------- Pandas Series object containing column sums indexed by pdf column names. """ sums = dict() for col in pdf.columns.values.tolist(): if col != 'table_row': sums[col] = pdf[col].sum() return pd.Series(sums, name='ALL') def create_distribution_table(vdf, groupby, income_measure): """ Get results from vdf, sort them by expanded_income based on groupby, and return them as a table. Parameters ---------- vdf : Pandas DataFrame including columns named in DIST_TABLE_COLUMNS list for example, an object returned from the Calculator class distribution_table_dataframe method groupby : String object options for input: 'weighted_deciles' or 'standard_income_bins' or 'soi_agi_bins' determines how the rows in the resulting Pandas DataFrame are sorted income_measure: String object options for input: 'expanded_income' or 'expanded_income_baseline' determines which variable is used to sort rows Returns ------- distribution table as a Pandas DataFrame with DIST_TABLE_COLUMNS and groupby rows. NOTE: when groupby is 'weighted_deciles', the returned table has three extra rows containing top-decile detail consisting of statistics for the 0.90-0.95 quantile range (bottom half of top decile), for the 0.95-0.99 quantile range, and for the 0.99-1.00 quantile range (top one percent); and the returned table splits the bottom decile into filing units with negative (denoted by a 0-10n row label), zero (denoted by a 0-10z row label), and positive (denoted by a 0-10p row label) values of the specified income_measure. """ # pylint: disable=too-many-statements,too-many-branches # nested function that returns calculated column statistics as a DataFrame def stat_dataframe(gpdf): """ Returns calculated distribution table column statistics derived from the specified grouped Dataframe object, gpdf. """ unweighted_columns = ['s006', 'num_returns_StandardDed', 'num_returns_ItemDed', 'num_returns_AMT'] sdf = pd.DataFrame() for col in DIST_TABLE_COLUMNS: if col in unweighted_columns: sdf[col] = gpdf.apply(unweighted_sum, col) else: sdf[col] = gpdf.apply(weighted_sum, col) return sdf # main logic of create_distribution_table assert isinstance(vdf, pd.DataFrame) assert (groupby == 'weighted_deciles' or groupby == 'standard_income_bins' or groupby == 'soi_agi_bins') assert (income_measure == 'expanded_income' or income_measure == 'expanded_income_baseline') assert income_measure in vdf assert 'table_row' not in list(vdf.columns.values) # sort the data given specified groupby and income_measure if groupby == 'weighted_deciles': pdf = add_quantile_table_row_variable(vdf, income_measure, 10, decile_details=True) elif groupby == 'standard_income_bins': pdf = add_income_table_row_variable(vdf, income_measure, STANDARD_INCOME_BINS) elif groupby == 'soi_agi_bins': pdf = add_income_table_row_variable(vdf, income_measure, SOI_AGI_BINS) # construct grouped DataFrame gpdf = pdf.groupby('table_row', as_index=False) dist_table = stat_dataframe(gpdf) del pdf['table_row'] # compute sum row sum_row = get_sums(dist_table)[dist_table.columns] # handle placement of sum_row in table if groupby == 'weighted_deciles': # compute top-decile row lenindex = len(dist_table.index) assert lenindex == 14 # rows should be indexed from 0 to 13 topdec_row = get_sums(dist_table[11:lenindex])[dist_table.columns] # move top-decile detail rows to make room for topdec_row and sum_row dist_table = dist_table.reindex(index=range(0, lenindex + 2)) dist_table.iloc[15] = dist_table.iloc[13] dist_table.iloc[14] = dist_table.iloc[12] dist_table.iloc[13] = dist_table.iloc[11] dist_table.iloc[12] = sum_row dist_table.iloc[11] = topdec_row del topdec_row else: dist_table = dist_table.append(sum_row) del sum_row # set print display format for float table elements pd.options.display.float_format = '{:8,.0f}'.format # ensure dist_table columns are in correct order assert dist_table.columns.values.tolist() == DIST_TABLE_COLUMNS # add row names to table if using weighted_deciles or standard_income_bins if groupby == 'weighted_deciles': rownames = DECILE_ROW_NAMES elif groupby == 'standard_income_bins': rownames = STANDARD_ROW_NAMES else: rownames = None if rownames: assert len(dist_table.index) == len(rownames) dist_table.index = rownames del rownames # delete intermediate Pandas DataFrame objects del gpdf del pdf # return table as Pandas DataFrame vdf.sort_index(inplace=True) return dist_table def create_difference_table(vdf1, vdf2, groupby, tax_to_diff): """ Get results from two different vdf, construct tax difference results, and return the difference statistics as a table. Parameters ---------- vdf1 : Pandas DataFrame including columns named in DIFF_VARIABLES list for example, object returned from a dataframe(DIFF_VARIABLE) call on the basesline Calculator object vdf2 : Pandas DataFrame including columns in the DIFF_VARIABLES list for example, object returned from a dataframe(DIFF_VARIABLE) call on the reform Calculator object groupby : String object options for input: 'weighted_deciles' or 'standard_income_bins' or 'soi_agi_bins' determines how the rows in the resulting Pandas DataFrame are sorted tax_to_diff : String object options for input: 'iitax', 'payrolltax', 'combined' specifies which tax to difference Returns ------- difference table as a Pandas DataFrame with DIFF_TABLE_COLUMNS and groupby rows. NOTE: when groupby is 'weighted_deciles', the returned table has three extra rows containing top-decile detail consisting of statistics for the 0.90-0.95 quantile range (bottom half of top decile), for the 0.95-0.99 quantile range, and for the 0.99-1.00 quantile range (top one percent); and the returned table splits the bottom decile into filing units with negative (denoted by a 0-10n row label), zero (denoted by a 0-10z row label), and positive (denoted by a 0-10p row label) values of the specified income_measure. """ # pylint: disable=too-many-statements,too-many-locals # nested function that creates dataframe containing additive statistics def additive_stats_dataframe(gpdf): """ Nested function that returns additive stats DataFrame derived from gpdf """ sdf = pd.DataFrame() sdf['count'] = gpdf.apply(weighted_count) sdf['tax_cut'] = gpdf.apply(weighted_count_lt_zero, 'tax_diff') sdf['tax_inc'] = gpdf.apply(weighted_count_gt_zero, 'tax_diff') sdf['tot_change'] = gpdf.apply(weighted_sum, 'tax_diff') sdf['ubi'] = gpdf.apply(weighted_sum, 'ubi') sdf['benefit_cost_total'] = gpdf.apply(weighted_sum, 'benefit_cost_total') sdf['benefit_value_total'] = gpdf.apply(weighted_sum, 'benefit_value_total') sdf['atinc1'] = gpdf.apply(weighted_sum, 'atinc1') sdf['atinc2'] = gpdf.apply(weighted_sum, 'atinc2') return sdf # main logic of create_difference_table assert isinstance(vdf1, pd.DataFrame) assert isinstance(vdf2, pd.DataFrame) assert np.allclose(vdf1['s006'], vdf2['s006']) # check rows in same order assert (groupby == 'weighted_deciles' or groupby == 'standard_income_bins' or groupby == 'soi_agi_bins') assert 'expanded_income' in vdf1 assert (tax_to_diff == 'iitax' or tax_to_diff == 'payrolltax' or tax_to_diff == 'combined') assert 'table_row' not in list(vdf1.columns.values) assert 'table_row' not in list(vdf2.columns.values) baseline_expanded_income = 'expanded_income_baseline' vdf2[baseline_expanded_income] = vdf1['expanded_income'] vdf2['tax_diff'] = vdf2[tax_to_diff] - vdf1[tax_to_diff] vdf2['atinc1'] = vdf1['aftertax_income'] vdf2['atinc2'] = vdf2['aftertax_income'] # add table_row column to vdf2 given specified groupby and income_measure if groupby == 'weighted_deciles': pdf = add_quantile_table_row_variable(vdf2, baseline_expanded_income, 10, decile_details=True) elif groupby == 'standard_income_bins': pdf = add_income_table_row_variable(vdf2, baseline_expanded_income, STANDARD_INCOME_BINS) elif groupby == 'soi_agi_bins': pdf = add_income_table_row_variable(vdf2, baseline_expanded_income, SOI_AGI_BINS) # create grouped Pandas DataFrame gpdf = pdf.groupby('table_row', as_index=False) del pdf['table_row'] # create additive difference table statistics from gpdf diff_table = additive_stats_dataframe(gpdf) # calculate additive statistics on sums row sum_row = get_sums(diff_table)[diff_table.columns] # handle placement of sum_row in table if groupby == 'weighted_deciles': # compute top-decile row lenindex = len(diff_table.index) assert lenindex == 14 # rows should be indexed from 0 to 13 topdec_row = get_sums(diff_table[11:lenindex])[diff_table.columns] # move top-decile detail rows to make room for topdec_row and sum_row diff_table = diff_table.reindex(index=range(0, lenindex + 2)) diff_table.iloc[15] = diff_table.iloc[13] diff_table.iloc[14] = diff_table.iloc[12] diff_table.iloc[13] = diff_table.iloc[11] diff_table.iloc[12] = sum_row diff_table.iloc[11] = topdec_row del topdec_row else: diff_table = diff_table.append(sum_row) # delete intermediate Pandas DataFrame objects del gpdf del pdf # compute non-additive stats in each table cell count = diff_table['count'] diff_table['perc_cut'] = np.where(count > 0., 100 * diff_table['tax_cut'] / count, 0.) diff_table['perc_inc'] = np.where(count > 0., 100 * diff_table['tax_inc'] / count, 0.) diff_table['mean'] = np.where(count > 0., diff_table['tot_change'] / count, 0.) total_change = sum_row['tot_change'] diff_table['share_of_change'] = np.where(total_change == 0., np.nan, (100 * diff_table['tot_change'] / total_change)) diff_table['pc_aftertaxinc'] = np.where(diff_table['atinc1'] == 0., np.nan, (100 * (diff_table['atinc2'] / diff_table['atinc1'] - 1))) # delete intermediate Pandas DataFrame objects del diff_table['atinc1'] del diff_table['atinc2'] del count del sum_row # set print display format for float table elements pd.options.display.float_format = '{:10,.2f}'.format # put diff_table columns in correct order diff_table = diff_table.reindex(columns=DIFF_TABLE_COLUMNS) # add row names to table if using weighted_deciles or standard_income_bins if groupby == 'weighted_deciles': rownames = DECILE_ROW_NAMES elif groupby == 'standard_income_bins': rownames = STANDARD_ROW_NAMES else: rownames = None if rownames: assert len(diff_table.index) == len(rownames) diff_table.index = rownames del rownames # return table as Pandas DataFrame vdf1.sort_index(inplace=True) vdf2.sort_index(inplace=True) return diff_table def create_diagnostic_table(vdf, year): """ Extract single-year diagnostic table from Pandas DataFrame object derived from a Calculator object using the dataframe(DIST_VARIABLES) method. Parameters ---------- vdf : Pandas DataFrame object containing the variables year : calendar year for which variables were drawn from Calculator object Returns ------- Pandas DataFrame object containing the diagnostic table """ # pylint: disable=too-many-statements def diagnostic_table_odict(recs): """ Nested function that extracts diagnostic table dictionary from the specified Pandas DataFrame object, vdf. Parameters ---------- vdf : Pandas DataFrame object containing the variables Returns ------- ordered dictionary of variable names and aggregate weighted values """ # aggregate weighted values expressed in millions or billions in_millions = 1.0e-6 in_billions = 1.0e-9 odict = collections.OrderedDict() # total number of filing units wghts = vdf['s006'] odict['Returns (#m)'] = wghts.sum() * in_millions # adjusted gross income agi = vdf['c00100'] odict['AGI ($b)'] = (agi * wghts).sum() * in_billions # number of itemizers num = (wghts[vdf['c04470'] > 0.].sum()) odict['Itemizers (#m)'] = num * in_millions # itemized deduction ided1 = vdf['c04470'] * wghts val = ided1[vdf['c04470'] > 0.].sum() odict['Itemized Deduction ($b)'] = val * in_billions # number of standard deductions num = wghts[vdf['standard'] > 0.].sum() odict['Standard Deduction Filers (#m)'] = num * in_millions # standard deduction sded1 = recs.standard * wghts val = sded1[vdf['standard'] > 0.].sum() odict['Standard Deduction ($b)'] = val * in_billions # personal exemption val = (vdf['c04600'] * wghts).sum() odict['Personal Exemption ($b)'] = val * in_billions # taxable income val = (vdf['c04800'] * wghts).sum() odict['Taxable Income ($b)'] = val * in_billions # regular tax liability val = (vdf['taxbc'] * wghts).sum() odict['Regular Tax ($b)'] = val * in_billions # AMT taxable income odict['AMT Income ($b)'] = ((vdf['c62100'] * wghts).sum() * in_billions) # total AMT liability odict['AMT Liability ($b)'] = ((vdf['c09600'] * wghts).sum() * in_billions) # number of people paying AMT odict['AMT Filers (#m)'] = (wghts[vdf['c09600'] > 0.].sum() * in_millions) # tax before credits val = (vdf['c05800'] * wghts).sum() odict['Tax before Credits ($b)'] = val * in_billions # refundable credits val = (vdf['refund'] * wghts).sum() odict['Refundable Credits ($b)'] = val * in_billions # nonrefundable credits val = (vdf['c07100'] * wghts).sum() odict['Nonrefundable Credits ($b)'] = val * in_billions # reform surtaxes (part of federal individual income tax liability) val = (vdf['surtax'] * wghts).sum() odict['Reform Surtaxes ($b)'] = val * in_billions # other taxes on Form 1040 val = (vdf['othertaxes'] * wghts).sum() odict['Other Taxes ($b)'] = val * in_billions # federal individual income tax liability val = (vdf['iitax'] * wghts).sum() odict['Ind Income Tax ($b)'] = val * in_billions # OASDI+HI payroll tax liability (including employer share) val = (vdf['payrolltax'] * wghts).sum() odict['Payroll Taxes ($b)'] = val * in_billions # combined income and payroll tax liability val = (vdf['combined'] * wghts).sum() odict['Combined Liability ($b)'] = val * in_billions # number of tax units with non-positive income tax liability num = (wghts[vdf['iitax'] <= 0]).sum() odict['With Income Tax <= 0 (#m)'] = num * in_millions # number of tax units with non-positive combined tax liability num = (wghts[vdf['combined'] <= 0]).sum() odict['With Combined Tax <= 0 (#m)'] = num * in_millions return odict # tabulate diagnostic table odict = diagnostic_table_odict(vdf) pdf = pd.DataFrame(data=odict, index=[year], columns=odict.keys()) pdf = pdf.transpose() pd.options.display.float_format = '{:8,.1f}'.format del odict return pdf def mtr_graph_data(vdf, year, mars='ALL', mtr_measure='combined', mtr_variable='e00200p', alt_e00200p_text='', mtr_wrt_full_compen=False, income_measure='expanded_income', dollar_weighting=False): """ Prepare marginal tax rate data needed by xtr_graph_plot utility function. Parameters ---------- vdf : a Pandas DataFrame object containing variables and marginal tax rates (See Calculator.mtr_graph method for required elements of vdf.) year : integer specifies calendar year of the data in vdf mars : integer or string specifies which filing status subgroup to show in the graph - 'ALL': include all filing units in sample - 1: include only single filing units - 2: include only married-filing-jointly filing units - 3: include only married-filing-separately filing units - 4: include only head-of-household filing units mtr_measure : string specifies which marginal tax rate to show on graph's y axis - 'itax': marginal individual income tax rate - 'ptax': marginal payroll tax rate - 'combined': sum of marginal income and payroll tax rates mtr_variable : string any string in the Calculator.VALID_MTR_VARS set specifies variable to change in order to compute marginal tax rates alt_e00200p_text : string text to use in place of mtr_variable when mtr_variable is 'e00200p'; if empty string then use 'e00200p' mtr_wrt_full_compen : boolean see documentation of Calculator.mtr() argument wrt_full_compensation (value has an effect only if mtr_variable is 'e00200p') income_measure : string specifies which income variable to show on the graph's x axis - 'wages': wage and salary income (e00200) - 'agi': adjusted gross income, AGI (c00100) - 'expanded_income': sum of AGI, non-taxable interest income, non-taxable social security benefits, and employer share of FICA taxes. dollar_weighting : boolean False implies both income_measure percentiles on x axis and mtr values for each percentile on the y axis are computed without using dollar income_measure weights (just sampling weights); True implies both income_measure percentiles on x axis and mtr values for each percentile on the y axis are computed using dollar income_measure weights (in addition to sampling weights). Specifying True produces a graph x axis that shows income_measure (not filing unit) percentiles. Returns ------- dictionary object suitable for passing to xtr_graph_plot utility function """ # pylint: disable=too-many-arguments,too-many-statements # pylint: disable=too-many-locals,too-many-branches # check validity of function arguments # . . check income_measure value weighting_function = weighted_mean if income_measure == 'wages': income_var = 'e00200' income_str = 'Wage' if dollar_weighting: weighting_function = wage_weighted elif income_measure == 'agi': income_var = 'c00100' income_str = 'AGI' if dollar_weighting: weighting_function = agi_weighted elif income_measure == 'expanded_income': income_var = 'expanded_income' income_str = 'Expanded-Income' if dollar_weighting: weighting_function = expanded_income_weighted else: msg = ('income_measure="{}" is neither ' '"wages", "agi", nor "expanded_income"') raise ValueError(msg.format(income_measure)) # . . check mars value if isinstance(mars, six.string_types): if mars != 'ALL': msg = 'string value of mars="{}" is not "ALL"' raise ValueError(msg.format(mars)) elif isinstance(mars, int): if mars < 1 or mars > 4: msg = 'integer mars="{}" is not in [1,4] range' raise ValueError(msg.format(mars)) else: msg = 'mars="{}" is neither a string nor an integer' raise ValueError(msg.format(mars)) # . . check mars value if mtr_variable is e00200s if mtr_variable == 'e00200s' and mars != 2: msg = 'mtr_variable == "e00200s" but mars != 2' raise ValueError(msg) # . . check mtr_measure value if mtr_measure == 'itax': mtr_str = 'Income-Tax' elif mtr_measure == 'ptax': mtr_str = 'Payroll-Tax' elif mtr_measure == 'combined': mtr_str = 'Income+Payroll-Tax' else: msg = ('mtr_measure="{}" is neither ' '"itax" nor "ptax" nor "combined"') raise ValueError(msg.format(mtr_measure)) # . . check vdf assert isinstance(vdf, pd.DataFrame) # create 'table_row' column given specified income_var and dollar_weighting dfx = add_quantile_table_row_variable( vdf, income_var, 100, weight_by_income_measure=dollar_weighting) # split dfx into groups specified by 'table_row' column gdfx = dfx.groupby('table_row', as_index=False) # apply the weighting_function to percentile-grouped mtr values mtr1_series = gdfx.apply(weighting_function, 'mtr1') mtr2_series = gdfx.apply(weighting_function, 'mtr2') # construct DataFrame containing the two mtr?_series lines = pd.DataFrame() lines['base'] = mtr1_series lines['reform'] = mtr2_series # construct dictionary containing merged data and auto-generated labels data = dict() data['lines'] = lines if dollar_weighting: income_str = 'Dollar-weighted {}'.format(income_str) mtr_str = 'Dollar-weighted {}'.format(mtr_str) data['ylabel'] = '{} MTR'.format(mtr_str) xlabel_str = 'Baseline {} Percentile'.format(income_str) if mars != 'ALL': xlabel_str = '{} for MARS={}'.format(xlabel_str, mars) data['xlabel'] = xlabel_str var_str = '{}'.format(mtr_variable) if mtr_variable == 'e00200p' and alt_e00200p_text != '': var_str = '{}'.format(alt_e00200p_text) if mtr_variable == 'e00200p' and mtr_wrt_full_compen: var_str = '{} wrt full compensation'.format(var_str) title_str = 'Mean Marginal Tax Rate for {} by Income Percentile' title_str = title_str.format(var_str) if mars != 'ALL': title_str = '{} for MARS={}'.format(title_str, mars) title_str = '{} for {}'.format(title_str, year) data['title'] = title_str return data def atr_graph_data(vdf, year, mars='ALL', atr_measure='combined'): """ Prepare average tax rate data needed by xtr_graph_plot utility function. Parameters ---------- vdf : a Pandas DataFrame object containing variables and tax liabilities (See Calculator.atr_graph method for required elements of vdf.) year : integer specifies calendar year of the data in vdf mars : integer or string specifies which filing status subgroup to show in the graph - 'ALL': include all filing units in sample - 1: include only single filing units - 2: include only married-filing-jointly filing units - 3: include only married-filing-separately filing units - 4: include only head-of-household filing units atr_measure : string specifies which average tax rate to show on graph's y axis - 'itax': average individual income tax rate - 'ptax': average payroll tax rate - 'combined': sum of average income and payroll tax rates Returns ------- dictionary object suitable for passing to xtr_graph_plot utility function """ # pylint: disable=too-many-locals,too-many-statements # check validity of function arguments # . . check mars value if isinstance(mars, six.string_types): if mars != 'ALL': msg = 'string value of mars="{}" is not "ALL"' raise ValueError(msg.format(mars)) elif isinstance(mars, int): if mars < 1 or mars > 4: msg = 'integer mars="{}" is not in [1,4] range' raise ValueError(msg.format(mars)) else: msg = 'mars="{}" is neither a string nor an integer' raise ValueError(msg.format(mars)) # . . check atr_measure value if atr_measure == 'combined': atr_str = 'Income+Payroll-Tax' elif atr_measure == 'itax': atr_str = 'Income-Tax' elif atr_measure == 'ptax': atr_str = 'Payroll-Tax' else: msg = ('atr_measure="{}" is neither ' '"itax" nor "ptax" nor "combined"') raise ValueError(msg.format(atr_measure)) # . . check vdf object assert isinstance(vdf, pd.DataFrame) # determine last bin that contains non-positive expanded_income values weights = vdf['s006'] nonpos = np.array(vdf['expanded_income'] <= 0, dtype=bool) nonpos_frac = weights[nonpos].sum() / weights.sum() num_bins_with_nonpos = int(math.ceil(100 * nonpos_frac)) # create 'table_row' column dfx = add_quantile_table_row_variable(vdf, 'expanded_income', 100) # specify which 'table_row' are included include = [0] * num_bins_with_nonpos + [1] * (100 - num_bins_with_nonpos) included = np.array(include, dtype=bool) # split dfx into groups specified by 'table_row' column gdfx = dfx.groupby('table_row', as_index=False) # apply weighted_mean function to percentile-grouped values avginc_series = gdfx.apply(weighted_mean, 'expanded_income') avgtax1_series = gdfx.apply(weighted_mean, 'tax1') avgtax2_series = gdfx.apply(weighted_mean, 'tax2') # compute average tax rates for each included income percentile atr1_series = np.zeros_like(avginc_series) atr1_series[included] = avgtax1_series[included] / avginc_series[included] atr2_series = np.zeros_like(avginc_series) atr2_series[included] = avgtax2_series[included] / avginc_series[included] # construct DataFrame containing the two atr?_series lines = pd.DataFrame() lines['base'] = atr1_series lines['reform'] = atr2_series # include only percentiles with average income no less than min_avginc lines = lines[included] # construct dictionary containing plot lines and auto-generated labels data = dict() data['lines'] = lines data['ylabel'] = '{} Average Tax Rate'.format(atr_str) xlabel_str = 'Baseline Expanded-Income Percentile' if mars != 'ALL': xlabel_str = '{} for MARS={}'.format(xlabel_str, mars) data['xlabel'] = xlabel_str title_str = 'Average Tax Rate by Income Percentile' if mars != 'ALL': title_str = '{} for MARS={}'.format(title_str, mars) title_str = '{} for {}'.format(title_str, year) data['title'] = title_str return data def xtr_graph_plot(data, width=850, height=500, xlabel='', ylabel='', title='', legendloc='bottom_right'): """ Plot marginal/average tax rate graph using data returned from either the mtr_graph_data function or the atr_graph_data function. Parameters ---------- data : dictionary object returned from ?tr_graph_data() utility function width : integer width of plot expressed in pixels height : integer height of plot expressed in pixels xlabel : string x-axis label; if '', then use label generated by ?tr_graph_data ylabel : string y-axis label; if '', then use label generated by ?tr_graph_data title : string graph title; if '', then use title generated by ?tr_graph_data legendloc : string options: 'top_right', 'top_left', 'bottom_left', 'bottom_right' specifies location of the legend in the plot Returns ------- bokeh.plotting figure object containing a raster graphics plot Notes ----- USAGE EXAMPLE:: gdata = mtr_graph_data(...) gplot = xtr_graph_plot(gdata) THEN when working interactively in a Python notebook:: bp.show(gplot) OR when executing script using Python command-line interpreter:: bio.output_file('graph-name.html', title='?TR by Income Percentile') bio.show(gplot) [OR bio.save(gplot) WILL JUST WRITE FILE TO DISK] WILL VISUALIZE GRAPH IN BROWSER AND WRITE GRAPH TO SPECIFIED HTML FILE To convert the visualized graph into a PNG-formatted file, click on the "Save" icon on the Toolbar (located in the top-right corner of the visualized graph) and a PNG-formatted file will written to your Download directory. The ONLY output option the bokeh.plotting figure has is HTML format, which (as described above) can be converted into a PNG-formatted raster graphics file. There is no option to make the bokeh.plotting figure generate a vector graphics file such as an EPS file. """ # pylint: disable=too-many-arguments if title == '': title = data['title'] fig = bp.figure(plot_width=width, plot_height=height, title=title) fig.title.text_font_size = '12pt' lines = data['lines'] fig.line(lines.index, lines.base, line_color='blue', line_width=3, legend='Baseline') fig.line(lines.index, lines.reform, line_color='red', line_width=3, legend='Reform') fig.circle(0, 0, visible=False) # force zero to be included on y axis if xlabel == '': xlabel = data['xlabel'] fig.xaxis.axis_label = xlabel fig.xaxis.axis_label_text_font_size = '12pt' fig.xaxis.axis_label_text_font_style = 'normal' if ylabel == '': ylabel = data['ylabel'] fig.yaxis.axis_label = ylabel fig.yaxis.axis_label_text_font_size = '12pt' fig.yaxis.axis_label_text_font_style = 'normal' fig.legend.location = legendloc fig.legend.label_text_font = 'times' fig.legend.label_text_font_style = 'italic' fig.legend.label_width = 2 fig.legend.label_height = 2 fig.legend.label_standoff = 2 fig.legend.glyph_width = 14 fig.legend.glyph_height = 14 fig.legend.spacing = 5 fig.legend.padding = 5 return fig def pch_graph_data(vdf, year): """ Prepare percentage change in after-tax expanded income data needed by pch_graph_plot utility function. Parameters ---------- vdf : a Pandas DataFrame object containing variables (See Calculator.pch_graph method for required elements of vdf.) year : integer specifies calendar year of the data in vdf Returns ------- dictionary object suitable for passing to pch_graph_plot utility function """ # pylint: disable=too-many-locals # check validity of function arguments assert isinstance(vdf, pd.DataFrame) # determine last bin that contains non-positive expanded_income values weights = vdf['s006'] nonpos = np.array(vdf['expanded_income'] <= 0, dtype=bool) nonpos_frac = weights[nonpos].sum() / weights.sum() num_bins_with_nonpos = int(math.ceil(100 * nonpos_frac)) # create 'table_row' column dfx = add_quantile_table_row_variable(vdf, 'expanded_income', 100) # specify which 'table_row' are included include = [0] * num_bins_with_nonpos + [1] * (100 - num_bins_with_nonpos) included = np.array(include, dtype=bool) # split dfx into groups specified by 'table_row' column gdfx = dfx.groupby('table_row', as_index=False) # apply weighted_mean function to percentile-grouped values avginc_series = gdfx.apply(weighted_mean, 'expanded_income') change_series = gdfx.apply(weighted_mean, 'chg_aftinc') # compute percentage change statistic each included income percentile pch_series = np.zeros_like(avginc_series) pch_series[included] = change_series[included] / avginc_series[included] # construct DataFrame containing the pch_series expressed as percent line = pd.DataFrame() line['pch'] = pch_series * 100 # include only percentiles with average income no less than min_avginc line = line[included] # construct dictionary containing plot line and auto-generated labels data = dict() data['line'] = line data['ylabel'] = 'Change in After-Tax Expanded Income' data['xlabel'] = 'Baseline Expanded-Income Percentile' title_str = ('Percentage Change in After-Tax Expanded Income ' 'by Income Percentile') title_str = '{} for {}'.format(title_str, year) data['title'] = title_str return data def pch_graph_plot(data, width=850, height=500, xlabel='', ylabel='', title=''): """ Plot percentage change in after-tax expanded income using data returned from the pch_graph_data function. Parameters ---------- data : dictionary object returned from ?tr_graph_data() utility function width : integer width of plot expressed in pixels height : integer height of plot expressed in pixels xlabel : string x-axis label; if '', then use label generated by pch_graph_data ylabel : string y-axis label; if '', then use label generated by pch_graph_data title : string graph title; if '', then use title generated by pch_graph_data Returns ------- bokeh.plotting figure object containing a raster graphics plot Notes ----- See Notes to xtr_graph_plot function. """ # pylint: disable=too-many-arguments if title == '': title = data['title'] fig = bp.figure(plot_width=width, plot_height=height, title=title) fig.title.text_font_size = '12pt' fig.line(data['line'].index, data['line'].pch, line_color='blue', line_width=3) fig.circle(0, 0, visible=False) # force zero to be included on y axis zero_grid_line_range = range(0, 101) zero_grid_line_height = [0] * len(zero_grid_line_range) fig.line(zero_grid_line_range, zero_grid_line_height, line_color='black', line_width=1) if xlabel == '': xlabel = data['xlabel'] fig.xaxis.axis_label = xlabel fig.xaxis.axis_label_text_font_size = '12pt' fig.xaxis.axis_label_text_font_style = 'normal' if ylabel == '': ylabel = data['ylabel'] fig.yaxis.axis_label = ylabel fig.yaxis.axis_label_text_font_size = '12pt' fig.yaxis.axis_label_text_font_style = 'normal' fig.yaxis[0].formatter = PrintfTickFormatter(format='%+.1f%%') return fig def write_graph_file(figure, filename, title): """ Write HTML file named filename containing figure. The title is the text displayed in the browser tab. Parameters ---------- figure : bokeh.plotting figure object filename : string name of HTML file to which figure is written; should end in .html title : string text displayed in browser tab when HTML file is displayed in browser Returns ------- Nothing """ delete_file(filename) # work around annoying 'already exists' bokeh msg bio.output_file(filename=filename, title=title) bio.save(figure) def isoelastic_utility_function(consumption, crra, cmin): """ Calculate and return utility of consumption. Parameters ---------- consumption : float consumption for a filing unit crra : non-negative float constant relative risk aversion parameter cmin : positive float consumption level below which marginal utility is assumed to be constant Returns ------- utility of consumption """ if consumption >= cmin: if crra == 1.0: return math.log(consumption) return math.pow(consumption, (1.0 - crra)) / (1.0 - crra) else: # if consumption < cmin if crra == 1.0: tu_at_cmin = math.log(cmin) else: tu_at_cmin = math.pow(cmin, (1.0 - crra)) / (1.0 - crra) mu_at_cmin = math.pow(cmin, -crra) tu_at_c = tu_at_cmin + mu_at_cmin * (consumption - cmin) return tu_at_c def expected_utility(consumption, probability, crra, cmin): """ Calculate and return expected utility of consumption. Parameters ---------- consumption : numpy array consumption for each filing unit probability : numpy array samplying probability of each filing unit crra : non-negative float constant relative risk aversion parameter of isoelastic utility function cmin : positive float consumption level below which marginal utility is assumed to be constant Returns ------- expected utility of consumption array """ utility = consumption.apply(isoelastic_utility_function, args=(crra, cmin,)) return np.inner(utility, probability) def certainty_equivalent(exputil, crra, cmin): """ Calculate and return certainty-equivalent of exputil of consumption assuming an isoelastic utility function with crra and cmin as parameters. Parameters ---------- exputil : float expected utility value crra : non-negative float constant relative risk aversion parameter of isoelastic utility function cmin : positive float consumption level below which marginal utility is assumed to be constant Returns ------- certainty-equivalent of specified expected utility, exputil """ if crra == 1.0: tu_at_cmin = math.log(cmin) else: tu_at_cmin = math.pow(cmin, (1.0 - crra)) / (1.0 - crra) if exputil >= tu_at_cmin: if crra == 1.0: return math.exp(exputil) return math.pow((exputil * (1.0 - crra)), (1.0 / (1.0 - crra))) mu_at_cmin = math.pow(cmin, -crra) return ((exputil - tu_at_cmin) / mu_at_cmin) + cmin def ce_aftertax_expanded_income(df1, df2, custom_params=None, require_no_agg_tax_change=True): """ Return dictionary that contains certainty-equivalent of the expected utility of after-tax expanded income computed for several constant-relative-risk-aversion parameter values for each of two Pandas DataFrame objects: df1, which represents the pre-reform situation, and df2, which represents the post-reform situation. Both DataFrame objects must contain 's006', 'combined', and 'expanded_income' columns. IMPORTANT NOTES: These normative welfare calculations are very simple. It is assumed that utility is a function of only consumption, and that consumption is equal to after-tax income. This means that any assumed behavioral responses that change work effort will not affect utility via the correpsonding change in leisure. And any saving response to changes in after-tax income do not affect consumption. The cmin value is the consumption level below which marginal utility is considered to be constant. This allows the handling of filing units with very low or even negative after-tax expanded income in the expected-utility and certainty-equivalent calculations. """ # pylint: disable=too-many-locals # check consistency of the two DataFrame objects assert isinstance(df1, pd.DataFrame) assert isinstance(df2, pd.DataFrame) assert df1.shape == df2.shape # specify utility function parameters if custom_params: crras = custom_params['crra_list'] for crra in crras: assert crra >= 0 cmin = custom_params['cmin_value'] assert cmin > 0 else: crras = [0, 1, 2, 3, 4] cmin = 1000 # compute aggregate combined tax revenue and aggregate after-tax income billion = 1.0e-9 cedict = dict() cedict['tax1'] = weighted_sum(df1, 'combined') * billion cedict['tax2'] = weighted_sum(df2, 'combined') * billion if require_no_agg_tax_change: diff = cedict['tax2'] - cedict['tax1'] if abs(diff) >= 0.0005: msg = 'Aggregate taxes not equal when required_... arg is True:' msg += '\n taxes1= {:9.3f}' msg += '\n taxes2= {:9.3f}' msg += '\n txdiff= {:9.3f}' msg += ('\n(adjust _LST or other parameter to bracket txdiff=0 ' 'and then interpolate)') raise ValueError(msg.format(cedict['tax1'], cedict['tax2'], diff)) cedict['inc1'] = weighted_sum(df1, 'expanded_income') * billion cedict['inc2'] = weighted_sum(df2, 'expanded_income') * billion # calculate sample-weighted probability of each filing unit prob_raw = np.divide(df1['s006'], # pylint: disable=no-member df1['s006'].sum()) prob = np.divide(prob_raw, # pylint: disable=no-member prob_raw.sum()) # handle any rounding error # calculate after-tax income of each filing unit in df1 and df2 ati1 = df1['expanded_income'] - df1['combined'] ati2 = df2['expanded_income'] - df2['combined'] # calculate certainty-equivaluent after-tax income in df1 and df2 cedict['crra'] = crras ce1 = list() ce2 = list() for crra in crras: eu1 = expected_utility(ati1, prob, crra, cmin) ce1.append(certainty_equivalent(eu1, crra, cmin)) eu2 = expected_utility(ati2, prob, crra, cmin) ce2.append(certainty_equivalent(eu2, crra, cmin)) cedict['ceeu1'] = ce1 cedict['ceeu2'] = ce2 # ... return cedict return cedict def read_egg_csv(fname, index_col=None): """ Read from egg the file named fname that contains CSV data and return pandas DataFrame containing the data. """ try: path_in_egg = os.path.join('taxcalc', fname) vdf = pd.read_csv( pkg_resources.resource_stream( pkg_resources.Requirement.parse('taxcalc'), path_in_egg), index_col=index_col ) except Exception: raise ValueError('could not read {} data from egg'.format(fname)) # cannot call read_egg_ function in unit tests return vdf # pragma: no cover def read_egg_json(fname): """ Read from egg the file named fname that contains JSON data and return dictionary containing the data. """ try: path_in_egg = os.path.join('taxcalc', fname) pdict = json.loads( pkg_resources.resource_stream( pkg_resources.Requirement.parse('taxcalc'), path_in_egg).read().decode('utf-8'), object_pairs_hook=collections.OrderedDict ) except Exception: raise ValueError('could not read {} data from egg'.format(fname)) # cannot call read_egg_ function in unit tests return pdict # pragma: no cover def delete_file(filename): """ Remove specified file if it exists. """ if os.path.isfile(filename): os.remove(filename) def bootstrap_se_ci(data, seed, num_samples, statistic, alpha): """ Return bootstrap estimate of standard error of statistic and bootstrap estimate of 100*(1-2*alpha)% confidence interval for statistic in a dictionary along with specified seed and nun_samples (B) and alpha. """ assert isinstance(data, np.ndarray) assert isinstance(seed, int) assert isinstance(num_samples, int) assert callable(statistic) # function that computes statistic from data assert isinstance(alpha, float) bsest = dict() bsest['seed'] = seed np.random.seed(seed) # pylint: disable=no-member dlen = len(data) idx = np.random.randint(low=0, high=dlen, # pylint: disable=no-member size=(num_samples, dlen)) samples = data[idx] stat = statistic(samples, axis=1) bsest['B'] = num_samples bsest['se'] = np.std(stat, ddof=1) stat = np.sort(stat) bsest['alpha'] = alpha bsest['cilo'] = stat[int(round(alpha * num_samples)) - 1] bsest['cihi'] = stat[int(round((1 - alpha) * num_samples)) - 1] return bsest def dec_graph_data(dist_table1, dist_table2, year, include_zero_incomes, include_negative_incomes): """ Prepare data needed by dec_graph_plot utility function. Parameters ---------- dist_table1 : a Pandas DataFrame object returned from the Calculator class distribution_tables method for baseline dist_table2 : a Pandas DataFrame object returned from the Calculator class distribution_tables method for reform year : integer specifies calendar year of the data in the diff_table include_zero_incomes : boolean if True, the bottom decile does contain filing units with zero expanded_income; if False, the bottom decile does not contain filing units with zero expanded_income. include_negative_incomes : boolean if True, the bottom decile does contain filing units with negative expanded_income; if False, the bottom decile does not contain filing units with negative expanded_income. Returns ------- dictionary object suitable for passing to dec_graph_plot utility function """ # pylint: disable=too-many-locals # check that the two distribution tables are consistent assert len(dist_table1.index) == len(DECILE_ROW_NAMES) assert len(dist_table2.index) == len(DECILE_ROW_NAMES) assert np.allclose(dist_table1['s006'], dist_table2['s006']) # compute bottom bar width and statistic value wght = dist_table1['s006'] total_wght = wght[2] + wght[1] + wght[0] included_wght = wght[2] included_val1 = dist_table1['aftertax_income'][2] * wght[2] included_val2 = dist_table2['aftertax_income'][2] * wght[2] if include_zero_incomes: included_wght += wght[1] included_val1 += dist_table1['aftertax_income'][1] * wght[1] included_val2 += dist_table2['aftertax_income'][1] * wght[1] if include_negative_incomes: included_wght += wght[0] included_val1 += dist_table1['aftertax_income'][0] * wght[0] included_val2 += dist_table2['aftertax_income'][0] * wght[0] bottom_bar_width = included_wght / total_wght bottom_bar_value = (included_val2 / included_val1 - 1.) * 100. # construct dictionary containing the bar data required by dec_graph_plot bars = dict() # ... bottom bar info = dict() if include_zero_incomes and include_negative_incomes: info['label'] = '0-10' elif include_zero_incomes and not include_negative_incomes: info['label'] = '0-10zp' if not include_zero_incomes and include_negative_incomes: info['label'] = '0-10np' if not include_zero_incomes and not include_negative_incomes: info['label'] = '0-10p' info['value'] = bottom_bar_value bars[0] = info # ... other bars offset = 2 for idx in range(offset + 1, len(DECILE_ROW_NAMES)): info = dict() info['label'] = DECILE_ROW_NAMES[idx] val1 = dist_table1['aftertax_income'][idx] * wght[idx] val2 = dist_table2['aftertax_income'][idx] * wght[idx] info['value'] = (val2 / val1 - 1.) * 100. if info['label'] == 'ALL': info['label'] = '---------' info['value'] = 0 bars[idx - offset] = info # construct dictionary containing bar data and auto-generated labels data = dict() data['bottom_bar_width'] = bottom_bar_width data['bars'] = bars xlabel = 'Reform-Induced Percentage Change in After-Tax Expanded Income' data['xlabel'] = xlabel ylabel = 'Expanded Income Percentile Group' data['ylabel'] = ylabel title_str = 'Change in After-Tax Income by Income Percentile Group' data['title'] = '{} for {}'.format(title_str, year) return data def dec_graph_plot(data, width=850, height=500, xlabel='', ylabel='', title=''): """ Plot stacked decile graph using data returned from dec_graph_data function. Parameters ---------- data : dictionary object returned from dec_graph_data() utility function width : integer width of plot expressed in pixels height : integer height of plot expressed in pixels xlabel : string x-axis label; if '', then use label generated by dec_graph_data ylabel : string y-axis label; if '', then use label generated by dec_graph_data title : string graph title; if '', then use title generated by dec_graph_data Returns ------- bokeh.plotting figure object containing a raster graphics plot Notes ----- USAGE EXAMPLE:: gdata = dec_graph_data(...) gplot = dec_graph_plot(gdata) THEN when working interactively in a Python notebook:: bp.show(gplot) OR when executing script using Python command-line interpreter:: bio.output_file('graph-name.html', title='Change in After-Tax Income') bio.show(gplot) [OR bio.save(gplot) WILL JUST WRITE FILE TO DISK] WILL VISUALIZE GRAPH IN BROWSER AND WRITE GRAPH TO SPECIFIED HTML FILE To convert the visualized graph into a PNG-formatted file, click on the "Save" icon on the Toolbar (located in the top-right corner of the visualized graph) and a PNG-formatted file will written to your Download directory. The ONLY output option the bokeh.plotting figure has is HTML format, which (as described above) can be converted into a PNG-formatted raster graphics file. There is no option to make the bokeh.plotting figure generate a vector graphics file such as an EPS file. """ # pylint: disable=too-many-arguments,too-many-locals if title == '': title = data['title'] bar_keys = sorted(data['bars'].keys()) bar_labels = [data['bars'][key]['label'] for key in bar_keys] fig = bp.figure(plot_width=width, plot_height=height, title=title, y_range=bar_labels) fig.title.text_font_size = '12pt' fig.outline_line_color = None fig.axis.axis_line_color = None fig.axis.minor_tick_line_color = None fig.axis.axis_label_text_font_size = '12pt' fig.axis.axis_label_text_font_style = 'normal' fig.axis.major_label_text_font_size = '12pt' if xlabel == '': xlabel = data['xlabel'] fig.xaxis.axis_label = xlabel fig.xaxis[0].formatter = PrintfTickFormatter(format='%+.1f%%') if ylabel == '': ylabel = data['ylabel'] fig.yaxis.axis_label = ylabel fig.ygrid.grid_line_color = None # plot thick x-axis grid line at zero fig.line(x=[0, 0], y=[0, 14], line_width=1, line_color='black') # plot bars barheight = 0.8 bcolor = 'blue' yidx = 0 for idx in bar_keys: bval = data['bars'][idx]['value'] blabel = data['bars'][idx]['label'] bheight = barheight if blabel == '0-10': bheight *= data['bottom_bar_width'] elif blabel == '90-95': bheight *= 0.5 bcolor = 'red' elif blabel == '95-99': bheight *= 0.4 elif blabel == 'Top 1%': bheight *= 0.1 fig.rect(x=(bval / 2.0), # x-coordinate of center of the rectangle y=(yidx + 0.5), # y-coordinate of center of the rectangle width=abs(bval), # width of the rectangle height=bheight, # height of the rectangle color=bcolor) yidx += 1 return fig def nonsmall_diffs(linelist1, linelist2, small=0.0): """ Return True if line lists differ significantly; otherwise return False. Significant difference means one or more numbers differ (between linelist1 and linelist2) by more than the small amount. NOTE: this function is meant to be used only in the unit tests to handle small differences in floating point values generated by Python 2.7 and 3.6, where a nonzero small amount is used only under Python 3.6. """ # embedded function used only in nonsmall_diffs function def isfloat(value): """ Return True if value can be cast to float; otherwise return False. """ try: float(value) return True except ValueError: return False # begin nonsmall_diffs logic assert isinstance(linelist1, list) assert isinstance(linelist2, list) if len(linelist1) != len(linelist2): return True assert small >= 0.0 and small <= 1.0 epsilon = 1e-6 smallamt = small + epsilon for line1, line2 in zip(linelist1, linelist2): if line1 == line2: continue else: tokens1 = line1.replace(',', '').split() tokens2 = line2.replace(',', '').split() for tok1, tok2 in zip(tokens1, tokens2): tok1_isfloat = isfloat(tok1) tok2_isfloat = isfloat(tok2) if tok1_isfloat and tok2_isfloat: if abs(float(tok1) - float(tok2)) <= smallamt: continue else: return True elif not tok1_isfloat and not tok2_isfloat: if tok1 == tok2: continue else: return True else: return True return False def quantity_response(quantity, price_elasticity, aftertax_price1, aftertax_price2, income_elasticity, aftertax_income1, aftertax_income2): """ Calculate dollar change in quantity using a log-log response equation, which assumes that the proportional change in the quantity is equal to the sum of two terms: (1) the proportional change in the quanitity's marginal aftertax price times an assumed price elasticity, and (2) the proportional change in aftertax income times an assumed income elasticity. Parameters ---------- quantity: numpy array pre-response quantity whose response is being calculated price_elasticity: float coefficient of the percentage change in aftertax price of the quantity in the log-log response equation aftertax_price1: numpy array marginal aftertax price of the quanitity under baseline policy Note that this function forces prices to be in [0.01, inf] range, but the caller of this function may want to constrain negative or very small prices to be somewhat larger in order to avoid extreme proportional changes in price. Note this is NOT an array of marginal tax rates (MTR), but rather usually 1-MTR (or in the case of quantities, like charitable giving, whose MTR values are non-positive, 1+MTR). aftertax_price2: numpy array marginal aftertax price of the quantity under reform policy Note that this function forces prices to be in [0.01, inf] range, but the caller of this function may want to constrain negative or very small prices to be somewhat larger in order to avoid extreme proportional changes in price. Note this is NOT an array of marginal tax rates (MTR), but rather usually 1-MTR (or in the case of quantities, like charitable giving, whose MTR values are non-positive, 1+MTR). income_elasticity: float coefficient of the percentage change in aftertax income in the log-log response equation aftertax_income1: numpy array aftertax income under baseline policy Note that this function forces income to be in [1, inf] range, but the caller of this function may want to constrain negative or small incomes to be somewhat larger in order to avoid extreme proportional changes in aftertax income. aftertax_income2: numpy array aftertax income under reform policy Note that this function forces income to be in [1, inf] range, but the caller of this function may want to constrain negative or small incomes to be somewhat larger in order to avoid extreme proportional changes in aftertax income. Returns ------- response: numpy array dollar change in quantity calculated from log-log response equation """ # pylint: disable=too-many-arguments # compute price term in log-log response equation if price_elasticity == 0.: pch_price = np.zeros(quantity.shape) else: atp1 = np.where(aftertax_price1 < 0.01, 0.01, aftertax_price1) atp2 = np.where(aftertax_price2 < 0.01, 0.01, aftertax_price2) pch_price = atp2 / atp1 - 1. # compute income term in log-log response equation if income_elasticity == 0.: pch_income = np.zeros(quantity.shape) else: ati1 = np.where(aftertax_income1 < 1.0, 1.0, aftertax_income1) ati2 = np.where(aftertax_income2 < 1.0, 1.0, aftertax_income2) pch_income = ati2 / ati1 - 1. # compute response pch_q = price_elasticity * pch_price + income_elasticity * pch_income response = pch_q * quantity return response

8185a8d9f5d71cb35eecdb1605c61aa54a9f4f9f

py

Python

code/calcUpstreamLength.py

hishivshah/waterSourceHeatMap

5f5e3ace8d7ede88ec543132b48c9c3f21f66593

code/calcUpstreamLength.py

hishivshah/waterSourceHeatMap

5f5e3ace8d7ede88ec543132b48c9c3f21f66593

code/calcUpstreamLength.py

hishivshah/waterSourceHeatMap

5f5e3ace8d7ede88ec543132b48c9c3f21f66593

import sqlite3 import logging import networkx import shapely import shapely.wkt def searchUpOrDownStream( graph, startNode, gaugedEdgeId, gaugedEdgeUpLen, searchDirection ): if searchDirection == "upstream": # Find upstream edges searchNodes = graph.predecessors(startNode) elif searchDirection == "downstream": # Find downstream edges searchNodes = graph.successors(startNode) searchEdges = graph.edges(searchNodes, keys=True, data=True) for sEdge in searchEdges: if sEdge[3].get("nearestGaugedEdge") is None: sEdge[3]["nearestGaugedEdge"] = gaugedEdgeId sEdge[3]["upstreamLengthRatio"] = ( sEdge[3]["upstreamLength"] / gaugedEdgeUpLen ) searchUpOrDownStream( graph, sEdge[0], gaugedEdgeId, gaugedEdgeUpLen, searchDirection ) if __name__ == "__main__": # Logging set-up logging.basicConfig(format="%(asctime)s|%(levelname)s|%(message)s", level=logging.INFO) # Database path sqliteDb = "../results/results.sqlite" # Create Directed Graph with multiple edges logging.info("Creating graph object") G = networkx.MultiDiGraph() # Connect to database logging.info("Connecting to database") with sqlite3.connect(sqliteDb) as db: db.enable_load_extension(True) db.load_extension("mod_spatialite") cur = db.cursor() cur.execute("SELECT InitSpatialMetaData(1);") # Add river nodes to graph logging.info("Adding river nodes to graph") cur.execute("SELECT id, ST_ASText(geometry) from riverNodes;") for row in cur: id = row[0] geometry = shapely.wkt.loads(row[1]) G.add_node(id, geometry=geometry) # Add river edges to graph logging.info("Adding river edges to graph") cur.execute("""SELECT id, startNodeId, endNodeId, ST_ASText(geometry) FROM riverEdges WHERE startNodeId IS NOT NULL AND endNodeId IS NOT NULL;""") for row in cur: id = row[0] startNodeId = row[1] endNodeId = row[2] geometry = shapely.wkt.loads(row[3]) G.add_edge(startNodeId, endNodeId, key=id, geometry=geometry) # Calculate upstream river length logging.info("Calculating upstream river lengths") for startNode, endNode, key, attr in G.edges_iter( data=True, keys=True ): preNodes = networkx.ancestors(G, startNode) preEdges = G.edges(preNodes, keys=True, data=True) upstreamLength = ( attr["geometry"].length + sum([e[3]["geometry"].length for e in preEdges]) ) G.edge[startNode][endNode][key]["upstreamLength"] = upstreamLength # Find river reaches with gauging station cur.execute("""SELECT id FROM riverEdges e WHERE e.id IN (SELECT riverId FROM nrfaStations) AND startNodeId IS NOT NULL AND endNodeId IS NOT NULL;""") gEdgeIds = [row[0] for row in cur.fetchall()] gEdges = [ e for e in G.edges(keys=True, data=True) if edge[2] in gEdgeIds ] for gEdge in gEdges: gEdge[3]["nearestGaugedEdge"] = gEdge[2] gEdge[3]["upstreamLengthRatio"] = 1 # Find upstream edges for each gauged edge for gEdge in gEdges: gEdgeStart = gEdge[0] gEdgeId = gEdge[2] gEdgeUpLen = gEdge[3]["upstreamLength"] searchUpOrDownStream( G, gEdgeStart, gEdgeId, gEdgeUpLen, "upstream" ) # Find downstream edges for each gauged edge for gEdge in gEdges: gEdgeStart = gEdge[0] gEdgeId = gEdge[2] gEdgeUpLen = gEdge[3]["upstreamLength"] searchUpOrDownStream( G, gEdgeStart, gEdgeId, gEdgeUpLen, "downstream" ) # Update riverEdges tables for e in G.edges_iter(data=True, keys=True): if e[3].get("nearestGaugedEdge") is not None: cur.execute(""" UPDATE riverEdges SET nearestGaugedEdge = '%s', upstreamLengthRatio = %s WHERE id = '%s'; """ % ( e[3].get("nearestGaugedEdge"), e[3].get("upstreamLengthRatio"), e[2] )) # Commit changes db.commit()

dbcd6fa121b8b855845c9a235a8511266164b058

py

Python

cinder/tests/unit/test_rbd.py

scottdangelo/RemoveVolumeMangerLocks

a448e6981f00ee068e29f3daac33d2d2d3820b4d

2019-02-08T05:24:58.000Z

2019-02-08T05:24:58.000Z

cinder/tests/unit/test_rbd.py

scottdangelo/RemoveVolumeMangerLocks

a448e6981f00ee068e29f3daac33d2d2d3820b4d

cinder/tests/unit/test_rbd.py

scottdangelo/RemoveVolumeMangerLocks

a448e6981f00ee068e29f3daac33d2d2d3820b4d

# Copyright 2012 Josh Durgin # Copyright 2013 Canonical Ltd. # 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 math import os import tempfile import mock from oslo_utils import timeutils from oslo_utils import units from cinder import db from cinder import exception from cinder.i18n import _ from cinder.image import image_utils from cinder import test from cinder.tests.unit.image import fake as fake_image from cinder.tests.unit import test_volume from cinder.tests.unit import utils from cinder.volume import configuration as conf import cinder.volume.drivers.rbd as driver from cinder.volume.flows.manager import create_volume # This is used to collect raised exceptions so that tests may check what was # raised. # NOTE: this must be initialised in test setUp(). RAISED_EXCEPTIONS = [] class MockException(Exception): def __init__(self, *args, **kwargs): RAISED_EXCEPTIONS.append(self.__class__) class MockImageNotFoundException(MockException): """Used as mock for rbd.ImageNotFound.""" class MockImageBusyException(MockException): """Used as mock for rbd.ImageBusy.""" class MockImageExistsException(MockException): """Used as mock for rbd.ImageExists.""" def common_mocks(f): """Decorator to set mocks common to all tests. The point of doing these mocks here is so that we don't accidentally set mocks that can't/don't get unset. """ def _common_inner_inner1(inst, *args, **kwargs): @mock.patch('cinder.volume.drivers.rbd.RBDVolumeProxy') @mock.patch('cinder.volume.drivers.rbd.RADOSClient') @mock.patch('cinder.backup.drivers.ceph.rbd') @mock.patch('cinder.backup.drivers.ceph.rados') def _common_inner_inner2(mock_rados, mock_rbd, mock_client, mock_proxy): inst.mock_rbd = mock_rbd inst.mock_rados = mock_rados inst.mock_client = mock_client inst.mock_proxy = mock_proxy inst.mock_rbd.RBD.Error = Exception inst.mock_rados.Error = Exception inst.mock_rbd.ImageBusy = MockImageBusyException inst.mock_rbd.ImageNotFound = MockImageNotFoundException inst.mock_rbd.ImageExists = MockImageExistsException inst.driver.rbd = inst.mock_rbd inst.driver.rados = inst.mock_rados return f(inst, *args, **kwargs) return _common_inner_inner2() return _common_inner_inner1 CEPH_MON_DUMP = """dumped monmap epoch 1 { "epoch": 1, "fsid": "33630410-6d93-4d66-8e42-3b953cf194aa", "modified": "2013-05-22 17:44:56.343618", "created": "2013-05-22 17:44:56.343618", "mons": [ { "rank": 0, "name": "a", "addr": "[::1]:6789\/0"}, { "rank": 1, "name": "b", "addr": "[::1]:6790\/0"}, { "rank": 2, "name": "c", "addr": "[::1]:6791\/0"}, { "rank": 3, "name": "d", "addr": "127.0.0.1:6792\/0"}, { "rank": 4, "name": "e", "addr": "example.com:6791\/0"}], "quorum": [ 0, 1, 2]} """ class RBDTestCase(test.TestCase): def setUp(self): global RAISED_EXCEPTIONS RAISED_EXCEPTIONS = [] super(RBDTestCase, self).setUp() self.cfg = mock.Mock(spec=conf.Configuration) self.cfg.volume_tmp_dir = None self.cfg.image_conversion_dir = None self.cfg.rbd_cluster_name = 'nondefault' self.cfg.rbd_pool = 'rbd' self.cfg.rbd_ceph_conf = None self.cfg.rbd_secret_uuid = None self.cfg.rbd_user = None self.cfg.volume_dd_blocksize = '1M' self.cfg.rbd_store_chunk_size = 4 mock_exec = mock.Mock() mock_exec.return_value = ('', '') self.driver = driver.RBDDriver(execute=mock_exec, configuration=self.cfg) self.driver.set_initialized() self.volume_name = u'volume-00000001' self.snapshot_name = u'snapshot-00000001' self.volume_size = 1 self.volume = dict(name=self.volume_name, size=self.volume_size) self.snapshot = dict(volume_name=self.volume_name, name=self.snapshot_name) @common_mocks def test_create_volume(self): client = self.mock_client.return_value client.__enter__.return_value = client self.driver.create_volume(self.volume) chunk_size = self.cfg.rbd_store_chunk_size * units.Mi order = int(math.log(chunk_size, 2)) args = [client.ioctx, str(self.volume_name), self.volume_size * units.Gi, order] kwargs = {'old_format': False, 'features': client.features} self.mock_rbd.RBD.return_value.create.assert_called_once_with( *args, **kwargs) client.__enter__.assert_called_once_with() client.__exit__.assert_called_once_with(None, None, None) @common_mocks def test_manage_existing_get_size(self): with mock.patch.object(self.driver.rbd.Image(), 'size') as \ mock_rbd_image_size: with mock.patch.object(self.driver.rbd.Image(), 'close') \ as mock_rbd_image_close: mock_rbd_image_size.return_value = 2 * units.Gi existing_ref = {'source-name': self.volume_name} return_size = self.driver.manage_existing_get_size( self.volume, existing_ref) self.assertEqual(2, return_size) mock_rbd_image_size.assert_called_once_with() mock_rbd_image_close.assert_called_once_with() @common_mocks def test_manage_existing_get_invalid_size(self): with mock.patch.object(self.driver.rbd.Image(), 'size') as \ mock_rbd_image_size: with mock.patch.object(self.driver.rbd.Image(), 'close') \ as mock_rbd_image_close: mock_rbd_image_size.return_value = 'abcd' existing_ref = {'source-name': self.volume_name} self.assertRaises(exception.VolumeBackendAPIException, self.driver.manage_existing_get_size, self.volume, existing_ref) mock_rbd_image_size.assert_called_once_with() mock_rbd_image_close.assert_called_once_with() @common_mocks def test_manage_existing(self): client = self.mock_client.return_value client.__enter__.return_value = client with mock.patch.object(self.driver.rbd.RBD(), 'rename') as \ mock_rbd_image_rename: exist_volume = 'vol-exist' existing_ref = {'source-name': exist_volume} mock_rbd_image_rename.return_value = 0 self.driver.manage_existing(self.volume, existing_ref) mock_rbd_image_rename.assert_called_with( client.ioctx, exist_volume, self.volume_name) @common_mocks def test_manage_existing_with_exist_rbd_image(self): client = self.mock_client.return_value client.__enter__.return_value = client self.mock_rbd.RBD.return_value.rename.side_effect = ( MockImageExistsException) exist_volume = 'vol-exist' existing_ref = {'source-name': exist_volume} self.assertRaises(self.mock_rbd.ImageExists, self.driver.manage_existing, self.volume, existing_ref) # Make sure the exception was raised self.assertEqual(RAISED_EXCEPTIONS, [self.mock_rbd.ImageExists]) @common_mocks def test_delete_backup_snaps(self): self.driver.rbd.Image.remove_snap = mock.Mock() with mock.patch.object(self.driver, '_get_backup_snaps') as \ mock_get_backup_snaps: mock_get_backup_snaps.return_value = [{'name': 'snap1'}] rbd_image = self.driver.rbd.Image() self.driver._delete_backup_snaps(rbd_image) mock_get_backup_snaps.assert_called_once_with(rbd_image) self.assertTrue( self.driver.rbd.Image.return_value.remove_snap.called) @common_mocks def test_delete_volume(self): client = self.mock_client.return_value self.driver.rbd.Image.return_value.list_snaps.return_value = [] with mock.patch.object(self.driver, '_get_clone_info') as \ mock_get_clone_info: with mock.patch.object(self.driver, '_delete_backup_snaps') as \ mock_delete_backup_snaps: mock_get_clone_info.return_value = (None, None, None) self.driver.delete_volume(self.volume) mock_get_clone_info.assert_called_once_with( self.mock_rbd.Image.return_value, self.volume_name, None) (self.driver.rbd.Image.return_value .list_snaps.assert_called_once_with()) client.__enter__.assert_called_once_with() client.__exit__.assert_called_once_with(None, None, None) mock_delete_backup_snaps.assert_called_once_with( self.mock_rbd.Image.return_value) self.assertFalse( self.driver.rbd.Image.return_value.unprotect_snap.called) self.assertEqual( 1, self.driver.rbd.RBD.return_value.remove.call_count) @common_mocks def delete_volume_not_found(self): self.mock_rbd.Image.side_effect = self.mock_rbd.ImageNotFound self.assertIsNone(self.driver.delete_volume(self.volume)) self.mock_rbd.Image.assert_called_once_with() # Make sure the exception was raised self.assertEqual(RAISED_EXCEPTIONS, [self.mock_rbd.ImageNotFound]) @common_mocks def test_delete_busy_volume(self): self.mock_rbd.Image.return_value.list_snaps.return_value = [] self.mock_rbd.RBD.return_value.remove.side_effect = ( self.mock_rbd.ImageBusy) with mock.patch.object(self.driver, '_get_clone_info') as \ mock_get_clone_info: mock_get_clone_info.return_value = (None, None, None) with mock.patch.object(self.driver, '_delete_backup_snaps') as \ mock_delete_backup_snaps: with mock.patch.object(driver, 'RADOSClient') as \ mock_rados_client: self.assertRaises(exception.VolumeIsBusy, self.driver.delete_volume, self.volume) mock_get_clone_info.assert_called_once_with( self.mock_rbd.Image.return_value, self.volume_name, None) (self.mock_rbd.Image.return_value.list_snaps .assert_called_once_with()) mock_rados_client.assert_called_once_with(self.driver) mock_delete_backup_snaps.assert_called_once_with( self.mock_rbd.Image.return_value) self.assertFalse( self.mock_rbd.Image.return_value.unprotect_snap.called) self.assertEqual( 3, self.mock_rbd.RBD.return_value.remove.call_count) self.assertEqual(3, len(RAISED_EXCEPTIONS)) # Make sure the exception was raised self.assertIn(self.mock_rbd.ImageBusy, RAISED_EXCEPTIONS) @common_mocks def test_delete_volume_not_found(self): self.mock_rbd.Image.return_value.list_snaps.return_value = [] self.mock_rbd.RBD.return_value.remove.side_effect = ( self.mock_rbd.ImageNotFound) with mock.patch.object(self.driver, '_get_clone_info') as \ mock_get_clone_info: mock_get_clone_info.return_value = (None, None, None) with mock.patch.object(self.driver, '_delete_backup_snaps') as \ mock_delete_backup_snaps: with mock.patch.object(driver, 'RADOSClient') as \ mock_rados_client: self.assertIsNone(self.driver.delete_volume(self.volume)) mock_get_clone_info.assert_called_once_with( self.mock_rbd.Image.return_value, self.volume_name, None) (self.mock_rbd.Image.return_value.list_snaps .assert_called_once_with()) mock_rados_client.assert_called_once_with(self.driver) mock_delete_backup_snaps.assert_called_once_with( self.mock_rbd.Image.return_value) self.assertFalse( self.mock_rbd.Image.return_value.unprotect_snap.called) self.assertEqual( 1, self.mock_rbd.RBD.return_value.remove.call_count) # Make sure the exception was raised self.assertEqual(RAISED_EXCEPTIONS, [self.mock_rbd.ImageNotFound]) @common_mocks def test_create_snapshot(self): proxy = self.mock_proxy.return_value proxy.__enter__.return_value = proxy self.driver.create_snapshot(self.snapshot) args = [str(self.snapshot_name)] proxy.create_snap.assert_called_with(*args) proxy.protect_snap.assert_called_with(*args) @common_mocks def test_delete_snapshot(self): proxy = self.mock_proxy.return_value proxy.__enter__.return_value = proxy self.driver.delete_snapshot(self.snapshot) proxy.remove_snap.assert_called_with(self.snapshot_name) proxy.unprotect_snap.assert_called_with(self.snapshot_name) @common_mocks def test_delete_busy_snapshot(self): proxy = self.mock_proxy.return_value proxy.__enter__.return_value = proxy proxy.unprotect_snap.side_effect = ( self.mock_rbd.ImageBusy) with mock.patch.object(self.driver, '_get_children_info') as \ mock_get_children_info: mock_get_children_info.return_value = [('pool', 'volume2')] with mock.patch.object(driver, 'LOG') as \ mock_log: self.assertRaises(exception.SnapshotIsBusy, self.driver.delete_snapshot, self.snapshot) mock_get_children_info.assert_called_once_with( proxy, self.snapshot_name) self.assertTrue(mock_log.info.called) self.assertTrue(proxy.unprotect_snap.called) self.assertFalse(proxy.remove_snap.called) @common_mocks def test_get_children_info(self): volume = self.mock_proxy volume.set_snap = mock.Mock() volume.list_children = mock.Mock() list_children = [('pool', 'volume2')] volume.list_children.return_value = list_children info = self.driver._get_children_info(volume, self.snapshot_name) self.assertEqual(list_children, info) @common_mocks def test_get_clone_info(self): volume = self.mock_rbd.Image() volume.set_snap = mock.Mock() volume.parent_info = mock.Mock() parent_info = ('a', 'b', '%s.clone_snap' % (self.volume_name)) volume.parent_info.return_value = parent_info info = self.driver._get_clone_info(volume, self.volume_name) self.assertEqual(parent_info, info) self.assertFalse(volume.set_snap.called) volume.parent_info.assert_called_once_with() @common_mocks def test_get_clone_info_w_snap(self): volume = self.mock_rbd.Image() volume.set_snap = mock.Mock() volume.parent_info = mock.Mock() parent_info = ('a', 'b', '%s.clone_snap' % (self.volume_name)) volume.parent_info.return_value = parent_info snapshot = self.mock_rbd.ImageSnapshot() info = self.driver._get_clone_info(volume, self.volume_name, snap=snapshot) self.assertEqual(parent_info, info) self.assertEqual(2, volume.set_snap.call_count) volume.parent_info.assert_called_once_with() @common_mocks def test_get_clone_info_w_exception(self): volume = self.mock_rbd.Image() volume.set_snap = mock.Mock() volume.parent_info = mock.Mock() volume.parent_info.side_effect = self.mock_rbd.ImageNotFound snapshot = self.mock_rbd.ImageSnapshot() info = self.driver._get_clone_info(volume, self.volume_name, snap=snapshot) self.assertEqual((None, None, None), info) self.assertEqual(2, volume.set_snap.call_count) volume.parent_info.assert_called_once_with() # Make sure the exception was raised self.assertEqual(RAISED_EXCEPTIONS, [self.mock_rbd.ImageNotFound]) @common_mocks def test_get_clone_info_deleted_volume(self): volume = self.mock_rbd.Image() volume.set_snap = mock.Mock() volume.parent_info = mock.Mock() parent_info = ('a', 'b', '%s.clone_snap' % (self.volume_name)) volume.parent_info.return_value = parent_info info = self.driver._get_clone_info(volume, "%s.deleted" % (self.volume_name)) self.assertEqual(parent_info, info) self.assertFalse(volume.set_snap.called) volume.parent_info.assert_called_once_with() @common_mocks def test_create_cloned_volume_same_size(self): src_name = u'volume-00000001' dst_name = u'volume-00000002' self.cfg.rbd_max_clone_depth = 2 with mock.patch.object(self.driver, '_get_clone_depth') as \ mock_get_clone_depth: # Try with no flatten required with mock.patch.object(self.driver, '_resize') as mock_resize: mock_get_clone_depth.return_value = 1 self.driver.create_cloned_volume({'name': dst_name, 'size': 10}, {'name': src_name, 'size': 10}) (self.mock_rbd.Image.return_value.create_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) (self.mock_rbd.Image.return_value.protect_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) self.assertEqual( 1, self.mock_rbd.RBD.return_value.clone.call_count) self.mock_rbd.Image.return_value.close \ .assert_called_once_with() self.assertTrue(mock_get_clone_depth.called) self.assertEqual( 0, mock_resize.call_count) @common_mocks def test_create_cloned_volume_different_size(self): src_name = u'volume-00000001' dst_name = u'volume-00000002' self.cfg.rbd_max_clone_depth = 2 with mock.patch.object(self.driver, '_get_clone_depth') as \ mock_get_clone_depth: # Try with no flatten required with mock.patch.object(self.driver, '_resize') as mock_resize: mock_get_clone_depth.return_value = 1 self.driver.create_cloned_volume({'name': dst_name, 'size': 20}, {'name': src_name, 'size': 10}) (self.mock_rbd.Image.return_value.create_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) (self.mock_rbd.Image.return_value.protect_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) self.assertEqual( 1, self.mock_rbd.RBD.return_value.clone.call_count) self.mock_rbd.Image.return_value.close \ .assert_called_once_with() self.assertTrue(mock_get_clone_depth.called) self.assertEqual( 1, mock_resize.call_count) @common_mocks def test_create_cloned_volume_w_flatten(self): src_name = u'volume-00000001' dst_name = u'volume-00000002' self.cfg.rbd_max_clone_depth = 1 with mock.patch.object(self.driver, '_get_clone_info') as \ mock_get_clone_info: mock_get_clone_info.return_value = ( ('fake_pool', dst_name, '.'.join((dst_name, 'clone_snap')))) with mock.patch.object(self.driver, '_get_clone_depth') as \ mock_get_clone_depth: # Try with no flatten required mock_get_clone_depth.return_value = 1 self.assertRaises(self.mock_rbd.RBD.Error, self.driver.create_cloned_volume, dict(name=dst_name), dict(name=src_name)) (self.mock_rbd.Image.return_value.create_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) (self.mock_rbd.Image.return_value.protect_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) self.assertEqual( 1, self.mock_rbd.RBD.return_value.clone.call_count) (self.mock_rbd.Image.return_value.unprotect_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) (self.mock_rbd.Image.return_value.remove_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) # We expect the driver to close both volumes, so 2 is expected self.assertEqual( 2, self.mock_rbd.Image.return_value.close.call_count) self.assertTrue(mock_get_clone_depth.called) @common_mocks def test_create_cloned_volume_w_clone_exception(self): src_name = u'volume-00000001' dst_name = u'volume-00000002' self.cfg.rbd_max_clone_depth = 2 self.mock_rbd.RBD.return_value.clone.side_effect = ( self.mock_rbd.RBD.Error) with mock.patch.object(self.driver, '_get_clone_depth') as \ mock_get_clone_depth: # Try with no flatten required mock_get_clone_depth.return_value = 1 self.assertRaises(self.mock_rbd.RBD.Error, self.driver.create_cloned_volume, {'name': dst_name}, {'name': src_name}) (self.mock_rbd.Image.return_value.create_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) (self.mock_rbd.Image.return_value.protect_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) self.assertEqual( 1, self.mock_rbd.RBD.return_value.clone.call_count) (self.mock_rbd.Image.return_value.unprotect_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) (self.mock_rbd.Image.return_value.remove_snap .assert_called_once_with('.'.join((dst_name, 'clone_snap')))) self.mock_rbd.Image.return_value.close.assert_called_once_with() @common_mocks def test_good_locations(self): locations = ['rbd://fsid/pool/image/snap', 'rbd://%2F/%2F/%2F/%2F', ] map(self.driver._parse_location, locations) @common_mocks def test_bad_locations(self): locations = ['rbd://image', 'http://path/to/somewhere/else', 'rbd://image/extra', 'rbd://image/', 'rbd://fsid/pool/image/', 'rbd://fsid/pool/image/snap/', 'rbd://///', ] for loc in locations: self.assertRaises(exception.ImageUnacceptable, self.driver._parse_location, loc) self.assertFalse( self.driver._is_cloneable(loc, {'disk_format': 'raw'})) @common_mocks def test_cloneable(self): with mock.patch.object(self.driver, '_get_fsid') as mock_get_fsid: mock_get_fsid.return_value = 'abc' location = 'rbd://abc/pool/image/snap' info = {'disk_format': 'raw'} self.assertTrue(self.driver._is_cloneable(location, info)) self.assertTrue(mock_get_fsid.called) @common_mocks def test_uncloneable_different_fsid(self): with mock.patch.object(self.driver, '_get_fsid') as mock_get_fsid: mock_get_fsid.return_value = 'abc' location = 'rbd://def/pool/image/snap' self.assertFalse( self.driver._is_cloneable(location, {'disk_format': 'raw'})) self.assertTrue(mock_get_fsid.called) @common_mocks def test_uncloneable_unreadable(self): with mock.patch.object(self.driver, '_get_fsid') as mock_get_fsid: mock_get_fsid.return_value = 'abc' location = 'rbd://abc/pool/image/snap' self.driver.rbd.Error = Exception self.mock_proxy.side_effect = Exception args = [location, {'disk_format': 'raw'}] self.assertFalse(self.driver._is_cloneable(*args)) self.assertEqual(1, self.mock_proxy.call_count) self.assertTrue(mock_get_fsid.called) @common_mocks def test_uncloneable_bad_format(self): with mock.patch.object(self.driver, '_get_fsid') as mock_get_fsid: mock_get_fsid.return_value = 'abc' location = 'rbd://abc/pool/image/snap' formats = ['qcow2', 'vmdk', 'vdi'] for f in formats: self.assertFalse( self.driver._is_cloneable(location, {'disk_format': f})) self.assertTrue(mock_get_fsid.called) def _copy_image(self): with mock.patch.object(tempfile, 'NamedTemporaryFile'): with mock.patch.object(os.path, 'exists') as mock_exists: mock_exists.return_value = True with mock.patch.object(image_utils, 'fetch_to_raw'): with mock.patch.object(self.driver, 'delete_volume'): with mock.patch.object(self.driver, '_resize'): mock_image_service = mock.MagicMock() args = [None, {'name': 'test', 'size': 1}, mock_image_service, None] self.driver.copy_image_to_volume(*args) @common_mocks def test_copy_image_no_volume_tmp(self): self.cfg.volume_tmp_dir = None self.cfg.image_conversion_dir = None self._copy_image() @common_mocks def test_copy_image_volume_tmp(self): self.cfg.volume_tmp_dir = None self.cfg.image_conversion_dir = '/var/run/cinder/tmp' self._copy_image() @common_mocks def test_update_volume_stats(self): client = self.mock_client.return_value client.__enter__.return_value = client client.cluster = mock.Mock() client.cluster.mon_command = mock.Mock() client.cluster.mon_command.return_value = ( 0, '{"stats":{"total_bytes":64385286144,' '"total_used_bytes":3289628672,"total_avail_bytes":61095657472},' '"pools":[{"name":"rbd","id":2,"stats":{"kb_used":1510197,' '"bytes_used":1546440971,"max_avail":28987613184,"objects":412}},' '{"name":"volumes","id":3,"stats":{"kb_used":0,"bytes_used":0,' '"max_avail":28987613184,"objects":0}}]}\n', '') self.driver.configuration.safe_get = mock.Mock() self.driver.configuration.safe_get.return_value = 'RBD' expected = dict( volume_backend_name='RBD', vendor_name='Open Source', driver_version=self.driver.VERSION, storage_protocol='ceph', total_capacity_gb=27, free_capacity_gb=26, reserved_percentage=0) actual = self.driver.get_volume_stats(True) client.cluster.mon_command.assert_called_once_with( '{"prefix":"df", "format":"json"}', '') self.assertDictMatch(expected, actual) @common_mocks def test_update_volume_stats_error(self): client = self.mock_client.return_value client.__enter__.return_value = client client.cluster = mock.Mock() client.cluster.mon_command = mock.Mock() client.cluster.mon_command.return_value = (22, '', '') self.driver.configuration.safe_get = mock.Mock() self.driver.configuration.safe_get.return_value = 'RBD' expected = dict(volume_backend_name='RBD', vendor_name='Open Source', driver_version=self.driver.VERSION, storage_protocol='ceph', total_capacity_gb='unknown', free_capacity_gb='unknown', reserved_percentage=0) actual = self.driver.get_volume_stats(True) client.cluster.mon_command.assert_called_once_with( '{"prefix":"df", "format":"json"}', '') self.assertDictMatch(expected, actual) @common_mocks def test_get_mon_addrs(self): with mock.patch.object(self.driver, '_execute') as mock_execute: mock_execute.return_value = (CEPH_MON_DUMP, '') hosts = ['::1', '::1', '::1', '127.0.0.1', 'example.com'] ports = ['6789', '6790', '6791', '6792', '6791'] self.assertEqual((hosts, ports), self.driver._get_mon_addrs()) @common_mocks def test_initialize_connection(self): hosts = ['::1', '::1', '::1', '127.0.0.1', 'example.com'] ports = ['6789', '6790', '6791', '6792', '6791'] with mock.patch.object(self.driver, '_get_mon_addrs') as \ mock_get_mon_addrs: mock_get_mon_addrs.return_value = (hosts, ports) volume_id = '0a83f0a3-ef6e-47b6-a8aa-20436bc9ed01' expected = { 'driver_volume_type': 'rbd', 'data': { 'name': '%s/%s' % (self.cfg.rbd_pool, self.volume_name), 'hosts': hosts, 'ports': ports, 'auth_enabled': False, 'auth_username': None, 'secret_type': 'ceph', 'secret_uuid': None, 'volume_id': volume_id } } volume = dict(name=self.volume_name, id=volume_id) actual = self.driver.initialize_connection(volume, None) self.assertDictMatch(expected, actual) self.assertTrue(mock_get_mon_addrs.called) @common_mocks def test_clone(self): src_pool = u'images' src_image = u'image-name' src_snap = u'snapshot-name' client_stack = [] def mock__enter__(inst): def _inner(): client_stack.append(inst) return inst return _inner client = self.mock_client.return_value # capture both rados client used to perform the clone client.__enter__.side_effect = mock__enter__(client) self.driver._clone(self.volume, src_pool, src_image, src_snap) args = [client_stack[0].ioctx, str(src_image), str(src_snap), client_stack[1].ioctx, str(self.volume_name)] kwargs = {'features': client.features} self.mock_rbd.RBD.return_value.clone.assert_called_once_with( *args, **kwargs) self.assertEqual(2, client.__enter__.call_count) @common_mocks def test_extend_volume(self): fake_size = '20' fake_vol = {'project_id': 'testprjid', 'name': self.volume_name, 'size': fake_size, 'id': 'a720b3c0-d1f0-11e1-9b23-0800200c9a66'} self.mox.StubOutWithMock(self.driver, '_resize') size = int(fake_size) * units.Gi self.driver._resize(fake_vol, size=size) self.mox.ReplayAll() self.driver.extend_volume(fake_vol, fake_size) self.mox.VerifyAll() @common_mocks def test_retype(self): context = {} diff = {'encryption': {}, 'extra_specs': {}} fake_volume = {'name': 'testvolume', 'host': 'currenthost'} fake_type = 'high-IOPS' # no support for migration host = {'host': 'anotherhost'} self.assertFalse(self.driver.retype(context, fake_volume, fake_type, diff, host)) host = {'host': 'currenthost'} # no support for changing encryption diff['encryption'] = {'non-empty': 'non-empty'} self.assertFalse(self.driver.retype(context, fake_volume, fake_type, diff, host)) diff['encryption'] = {} # no support for changing extra_specs diff['extra_specs'] = {'non-empty': 'non-empty'} self.assertFalse(self.driver.retype(context, fake_volume, fake_type, diff, host)) diff['extra_specs'] = {} self.assertTrue(self.driver.retype(context, fake_volume, fake_type, diff, host)) @common_mocks def test_update_migrated_volume(self): client = self.mock_client.return_value client.__enter__.return_value = client with mock.patch.object(self.driver.rbd.RBD(), 'rename') as mock_rename: context = {} current_volume = {'id': 'curr_id', 'name': 'curr_name', 'provider_location': 'curr_provider_location'} original_volume = {'id': 'orig_id', 'name': 'orig_name', 'provider_location': 'orig_provider_location'} mock_rename.return_value = 0 model_update = self.driver.update_migrated_volume(context, original_volume, current_volume, 'available') mock_rename.assert_called_with(client.ioctx, 'volume-%s' % current_volume['id'], 'volume-%s' % original_volume['id']) self.assertEqual({'_name_id': None, 'provider_location': None}, model_update) def test_rbd_volume_proxy_init(self): mock_driver = mock.Mock(name='driver') mock_driver._connect_to_rados.return_value = (None, None) with driver.RBDVolumeProxy(mock_driver, self.volume_name): self.assertEqual(1, mock_driver._connect_to_rados.call_count) self.assertFalse(mock_driver._disconnect_from_rados.called) self.assertEqual(1, mock_driver._disconnect_from_rados.call_count) mock_driver.reset_mock() snap = u'snapshot-name' with driver.RBDVolumeProxy(mock_driver, self.volume_name, snapshot=snap): self.assertEqual(1, mock_driver._connect_to_rados.call_count) self.assertFalse(mock_driver._disconnect_from_rados.called) self.assertEqual(1, mock_driver._disconnect_from_rados.call_count) @common_mocks @mock.patch('time.sleep') def test_connect_to_rados(self, sleep_mock): # Default self.cfg.rados_connect_timeout = -1 self.mock_rados.Rados.return_value.open_ioctx.return_value = \ self.mock_rados.Rados.return_value.ioctx # default configured pool ret = self.driver._connect_to_rados() self.assertTrue(self.mock_rados.Rados.return_value.connect.called) # Expect no timeout if default is used self.mock_rados.Rados.return_value.connect.assert_called_once_with() self.assertTrue(self.mock_rados.Rados.return_value.open_ioctx.called) self.assertEqual(self.mock_rados.Rados.return_value.ioctx, ret[1]) self.mock_rados.Rados.return_value.open_ioctx.assert_called_with( self.cfg.rbd_pool) # different pool ret = self.driver._connect_to_rados('alt_pool') self.assertTrue(self.mock_rados.Rados.return_value.connect.called) self.assertTrue(self.mock_rados.Rados.return_value.open_ioctx.called) self.assertEqual(self.mock_rados.Rados.return_value.ioctx, ret[1]) self.mock_rados.Rados.return_value.open_ioctx.assert_called_with( 'alt_pool') # With timeout self.cfg.rados_connect_timeout = 1 self.mock_rados.Rados.return_value.connect.reset_mock() self.driver._connect_to_rados() self.mock_rados.Rados.return_value.connect.assert_called_once_with( timeout=1) # error self.mock_rados.Rados.return_value.open_ioctx.reset_mock() self.mock_rados.Rados.return_value.shutdown.reset_mock() self.mock_rados.Rados.return_value.open_ioctx.side_effect = ( self.mock_rados.Error) self.assertRaises(exception.VolumeBackendAPIException, self.driver._connect_to_rados) self.assertTrue(self.mock_rados.Rados.return_value.open_ioctx.called) self.assertEqual( 3, self.mock_rados.Rados.return_value.shutdown.call_count) class RBDImageIOWrapperTestCase(test.TestCase): def setUp(self): super(RBDImageIOWrapperTestCase, self).setUp() self.meta = mock.Mock() self.meta.user = 'mock_user' self.meta.conf = 'mock_conf' self.meta.pool = 'mock_pool' self.meta.image = mock.Mock() self.meta.image.read = mock.Mock() self.meta.image.write = mock.Mock() self.meta.image.size = mock.Mock() self.mock_rbd_wrapper = driver.RBDImageIOWrapper(self.meta) self.data_length = 1024 self.full_data = 'abcd' * 256 def test_init(self): self.assertEqual(self.mock_rbd_wrapper._rbd_meta, self.meta) self.assertEqual(0, self.mock_rbd_wrapper._offset) def test_inc_offset(self): self.mock_rbd_wrapper._inc_offset(10) self.mock_rbd_wrapper._inc_offset(10) self.assertEqual(20, self.mock_rbd_wrapper._offset) def test_rbd_image(self): self.assertEqual(self.mock_rbd_wrapper.rbd_image, self.meta.image) def test_rbd_user(self): self.assertEqual(self.mock_rbd_wrapper.rbd_user, self.meta.user) def test_rbd_pool(self): self.assertEqual(self.mock_rbd_wrapper.rbd_conf, self.meta.conf) def test_rbd_conf(self): self.assertEqual(self.mock_rbd_wrapper.rbd_pool, self.meta.pool) def test_read(self): def mock_read(offset, length): return self.full_data[offset:length] self.meta.image.read.side_effect = mock_read self.meta.image.size.return_value = self.data_length data = self.mock_rbd_wrapper.read() self.assertEqual(self.full_data, data) data = self.mock_rbd_wrapper.read() self.assertEqual('', data) self.mock_rbd_wrapper.seek(0) data = self.mock_rbd_wrapper.read() self.assertEqual(self.full_data, data) self.mock_rbd_wrapper.seek(0) data = self.mock_rbd_wrapper.read(10) self.assertEqual(self.full_data[:10], data) def test_write(self): self.mock_rbd_wrapper.write(self.full_data) self.assertEqual(1024, self.mock_rbd_wrapper._offset) def test_seekable(self): self.assertTrue(self.mock_rbd_wrapper.seekable) def test_seek(self): self.assertEqual(0, self.mock_rbd_wrapper._offset) self.mock_rbd_wrapper.seek(10) self.assertEqual(10, self.mock_rbd_wrapper._offset) self.mock_rbd_wrapper.seek(10) self.assertEqual(10, self.mock_rbd_wrapper._offset) self.mock_rbd_wrapper.seek(10, 1) self.assertEqual(20, self.mock_rbd_wrapper._offset) self.mock_rbd_wrapper.seek(0) self.mock_rbd_wrapper.write(self.full_data) self.meta.image.size.return_value = self.data_length self.mock_rbd_wrapper.seek(0) self.assertEqual(0, self.mock_rbd_wrapper._offset) self.mock_rbd_wrapper.seek(10, 2) self.assertEqual(self.data_length + 10, self.mock_rbd_wrapper._offset) self.mock_rbd_wrapper.seek(-10, 2) self.assertEqual(self.data_length - 10, self.mock_rbd_wrapper._offset) # test exceptions. self.assertRaises(IOError, self.mock_rbd_wrapper.seek, 0, 3) self.assertRaises(IOError, self.mock_rbd_wrapper.seek, -1) # offset should not have been changed by any of the previous # operations. self.assertEqual(self.data_length - 10, self.mock_rbd_wrapper._offset) def test_tell(self): self.assertEqual(0, self.mock_rbd_wrapper.tell()) self.mock_rbd_wrapper._inc_offset(10) self.assertEqual(10, self.mock_rbd_wrapper.tell()) def test_flush(self): with mock.patch.object(driver, 'LOG') as mock_logger: self.meta.image.flush = mock.Mock() self.mock_rbd_wrapper.flush() self.meta.image.flush.assert_called_once_with() self.meta.image.flush.reset_mock() # this should be caught and logged silently. self.meta.image.flush.side_effect = AttributeError self.mock_rbd_wrapper.flush() self.meta.image.flush.assert_called_once_with() msg = _("flush() not supported in this version of librbd") mock_logger.warning.assert_called_with(msg) def test_fileno(self): self.assertRaises(IOError, self.mock_rbd_wrapper.fileno) def test_close(self): self.mock_rbd_wrapper.close() class ManagedRBDTestCase(test_volume.DriverTestCase): driver_name = "cinder.volume.drivers.rbd.RBDDriver" def setUp(self): super(ManagedRBDTestCase, self).setUp() # TODO(dosaboy): need to remove dependency on mox stubs here once # image.fake has been converted to mock. fake_image.stub_out_image_service(self.stubs) self.volume.driver.set_initialized() self.volume.stats = {'allocated_capacity_gb': 0, 'pools': {}} self.called = [] def _create_volume_from_image(self, expected_status, raw=False, clone_error=False): """Try to clone a volume from an image, and check status afterwards. NOTE: if clone_error is True we force the image type to raw otherwise clone_image is not called """ volume_id = 1 # See tests.image.fake for image types. if raw: image_id = '155d900f-4e14-4e4c-a73d-069cbf4541e6' else: image_id = 'c905cedb-7281-47e4-8a62-f26bc5fc4c77' # creating volume testdata db.volume_create(self.context, {'id': volume_id, 'updated_at': timeutils.utcnow(), 'display_description': 'Test Desc', 'size': 20, 'status': 'creating', 'instance_uuid': None, 'host': 'dummy'}) try: if not clone_error: self.volume.create_volume(self.context, volume_id, request_spec={'image_id': image_id}) else: self.assertRaises(exception.CinderException, self.volume.create_volume, self.context, volume_id, request_spec={'image_id': image_id}) volume = db.volume_get(self.context, volume_id) self.assertEqual(expected_status, volume['status']) finally: # cleanup db.volume_destroy(self.context, volume_id) def test_create_vol_from_image_status_available(self): """Clone raw image then verify volume is in available state.""" def _mock_clone_image(context, volume, image_location, image_meta, image_service): return {'provider_location': None}, True with mock.patch.object(self.volume.driver, 'clone_image') as \ mock_clone_image: mock_clone_image.side_effect = _mock_clone_image with mock.patch.object(self.volume.driver, 'create_volume') as \ mock_create: with mock.patch.object(create_volume.CreateVolumeFromSpecTask, '_copy_image_to_volume') as mock_copy: self._create_volume_from_image('available', raw=True) self.assertFalse(mock_copy.called) self.assertTrue(mock_clone_image.called) self.assertFalse(mock_create.called) @mock.patch('cinder.image.image_utils.TemporaryImages.fetch') def test_create_vol_from_non_raw_image_status_available(self, mock_fetch): """Clone non-raw image then verify volume is in available state.""" def _mock_clone_image(context, volume, image_location, image_meta, image_service): return {'provider_location': None}, False mock_fetch.return_value = mock.MagicMock(spec=utils.get_file_spec()) with mock.patch.object(self.volume.driver, 'clone_image') as \ mock_clone_image: mock_clone_image.side_effect = _mock_clone_image with mock.patch.object(self.volume.driver, 'create_volume') as \ mock_create: with mock.patch.object(create_volume.CreateVolumeFromSpecTask, '_copy_image_to_volume') as mock_copy: self._create_volume_from_image('available', raw=False) self.assertTrue(mock_copy.called) self.assertTrue(mock_clone_image.called) self.assertTrue(mock_create.called) def test_create_vol_from_image_status_error(self): """Fail to clone raw image then verify volume is in error state.""" with mock.patch.object(self.volume.driver, 'clone_image') as \ mock_clone_image: mock_clone_image.side_effect = exception.CinderException with mock.patch.object(self.volume.driver, 'create_volume'): with mock.patch.object(create_volume.CreateVolumeFromSpecTask, '_copy_image_to_volume') as mock_copy: self._create_volume_from_image('error', raw=True, clone_error=True) self.assertFalse(mock_copy.called) self.assertTrue(mock_clone_image.called) self.assertFalse(self.volume.driver.create_volume.called) def test_clone_failure(self): driver = self.volume.driver with mock.patch.object(driver, '_is_cloneable', lambda *args: False): image_loc = (mock.Mock(), None) actual = driver.clone_image(mock.Mock(), mock.Mock(), image_loc, {}, mock.Mock()) self.assertEqual(({}, False), actual) self.assertEqual(({}, False), driver.clone_image('', object(), None, {}, '')) def test_clone_success(self): expected = ({'provider_location': None}, True) driver = self.volume.driver with mock.patch.object(self.volume.driver, '_is_cloneable') as \ mock_is_cloneable: mock_is_cloneable.return_value = True with mock.patch.object(self.volume.driver, '_clone') as \ mock_clone: with mock.patch.object(self.volume.driver, '_resize') as \ mock_resize: image_loc = ('rbd://fee/fi/fo/fum', None) volume = {'name': 'vol1'} actual = driver.clone_image(mock.Mock(), volume, image_loc, {'disk_format': 'raw', 'id': 'id.foo'}, mock.Mock()) self.assertEqual(expected, actual) mock_clone.assert_called_once_with(volume, 'fi', 'fo', 'fum') mock_resize.assert_called_once_with(volume) def test_clone_multilocation_success(self): expected = ({'provider_location': None}, True) driver = self.volume.driver def cloneable_side_effect(url_location, image_meta): return url_location == 'rbd://fee/fi/fo/fum' with mock.patch.object(self.volume.driver, '_is_cloneable') \ as mock_is_cloneable, \ mock.patch.object(self.volume.driver, '_clone') as mock_clone, \ mock.patch.object(self.volume.driver, '_resize') \ as mock_resize: mock_is_cloneable.side_effect = cloneable_side_effect image_loc = ('rbd://bee/bi/bo/bum', [{'url': 'rbd://bee/bi/bo/bum'}, {'url': 'rbd://fee/fi/fo/fum'}]) volume = {'name': 'vol1'} image_meta = mock.sentinel.image_meta image_service = mock.sentinel.image_service actual = driver.clone_image(self.context, volume, image_loc, image_meta, image_service) self.assertEqual(expected, actual) self.assertEqual(2, mock_is_cloneable.call_count) mock_clone.assert_called_once_with(volume, 'fi', 'fo', 'fum') mock_is_cloneable.assert_called_with('rbd://fee/fi/fo/fum', image_meta) mock_resize.assert_called_once_with(volume) def test_clone_multilocation_failure(self): expected = ({}, False) driver = self.volume.driver with mock.patch.object(driver, '_is_cloneable', return_value=False) \ as mock_is_cloneable, \ mock.patch.object(self.volume.driver, '_clone') as mock_clone, \ mock.patch.object(self.volume.driver, '_resize') \ as mock_resize: image_loc = ('rbd://bee/bi/bo/bum', [{'url': 'rbd://bee/bi/bo/bum'}, {'url': 'rbd://fee/fi/fo/fum'}]) volume = {'name': 'vol1'} image_meta = mock.sentinel.image_meta image_service = mock.sentinel.image_service actual = driver.clone_image(self.context, volume, image_loc, image_meta, image_service) self.assertEqual(expected, actual) self.assertEqual(2, mock_is_cloneable.call_count) mock_is_cloneable.assert_any_call('rbd://bee/bi/bo/bum', image_meta) mock_is_cloneable.assert_any_call('rbd://fee/fi/fo/fum', image_meta) self.assertFalse(mock_clone.called) self.assertFalse(mock_resize.called)

8514a26670ff04da73c9c99ba2907300ac8a2757

py

Python

ubb/fop/lab05-07/main.py

AlexanderChristian/private_courses

c80f3526af539e35f93b460f3909f669aaef573c

ubb/fop/lab05-07/main.py

AlexanderChristian/private_courses

c80f3526af539e35f93b460f3909f669aaef573c

2020-03-04T20:52:39.000Z

2022-03-31T00:33:07.000Z

ubb/fop/lab05-07/main.py

AlexanderChristian/private_courses

c80f3526af539e35f93b460f3909f669aaef573c

import atexit from tests.tester import Tester from ui.LibraryApplication import LibraryApplication from controllers.LibraryController import LibraryController from repository.LibraryRepository import LibraryRepository from model.sort import gnomeSort from model.sort import testSort from model.book import Book from model.client import Client __author__ = 'cosmin' if __name__ == '__main__': tester = Tester() tester.testAll() testSort() repo = LibraryRepository() controller = LibraryController(repo) atexit.register(repo.saveHistory) app = LibraryApplication(controller) app.run()

40b15e77b786a0dd62c833f18e16386fc67b5932

py

Python

mne/parallel.py

libertyh/mne-python

bf03e17f323341a877dea62963c86cf140757896

2020-07-28T16:09:54.000Z

2020-07-28T16:09:54.000Z

mne/parallel.py

gkmaro634/mne-python

5409a89233b764f3f3f3136cf9bf6b8d5fb0a4fe

2019-08-16T13:59:53.000Z

2019-08-19T16:37:35.000Z

mne/parallel.py

gkmaro634/mne-python

5409a89233b764f3f3f3136cf9bf6b8d5fb0a4fe

2019-12-10T02:59:18.000Z

2019-12-10T02:59:18.000Z

"""Parallel util function.""" # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr> # # License: Simplified BSD import logging import os from . import get_config from .utils import logger, verbose, warn, ProgressBar from .fixes import _get_args if 'MNE_FORCE_SERIAL' in os.environ: _force_serial = True else: _force_serial = None @verbose def parallel_func(func, n_jobs, max_nbytes='auto', pre_dispatch='n_jobs', total=None, prefer=None, verbose=None): """Return parallel instance with delayed function. Util function to use joblib only if available Parameters ---------- func: callable A function n_jobs: int Number of jobs to run in parallel max_nbytes : int, str, or None Threshold on the minimum size of arrays passed to the workers that triggers automated memory mapping. Can be an int in Bytes, or a human-readable string, e.g., '1M' for 1 megabyte. Use None to disable memmaping of large arrays. Use 'auto' to use the value set using mne.set_memmap_min_size. pre_dispatch : int, or string, optional See :class:`joblib.Parallel`. total : int | None If int, use a progress bar to display the progress of dispatched jobs. This should only be used when directly iterating, not when using ``split_list`` or :func:`np.array_split`. If None (default), do not add a progress bar. prefer : str | None If str, can be "processes" or "threads". See :class:`joblib.Parallel`. Ignored if the joblib version is too old to support this. .. versionadded:: 0.18 %(verbose)s INFO or DEBUG will print parallel status, others will not. Returns ------- parallel: instance of joblib.Parallel or list The parallel object my_func: callable func if not parallel or delayed(func) n_jobs: int Number of jobs >= 0 """ should_print = (logger.level <= logging.INFO) # for a single job, we don't need joblib if n_jobs != 1: try: from joblib import Parallel, delayed except ImportError: try: from sklearn.externals.joblib import Parallel, delayed except ImportError: warn('joblib not installed. Cannot run in parallel.') n_jobs = 1 if n_jobs == 1: n_jobs = 1 my_func = func parallel = list else: # check if joblib is recent enough to support memmaping p_args = _get_args(Parallel.__init__) joblib_mmap = ('temp_folder' in p_args and 'max_nbytes' in p_args) cache_dir = get_config('MNE_CACHE_DIR', None) if isinstance(max_nbytes, str) and max_nbytes == 'auto': max_nbytes = get_config('MNE_MEMMAP_MIN_SIZE', None) if max_nbytes is not None: if not joblib_mmap and cache_dir is not None: warn('"MNE_CACHE_DIR" is set but a newer version of joblib is ' 'needed to use the memmapping pool.') if joblib_mmap and cache_dir is None: logger.info( 'joblib supports memapping pool but "MNE_CACHE_DIR" ' 'is not set in MNE-Python config. To enable it, use, ' 'e.g., mne.set_cache_dir(\'/tmp/shm\'). This will ' 'store temporary files under /dev/shm and can result ' 'in large memory savings.') # create keyword arguments for Parallel kwargs = {'verbose': 5 if should_print and total is None else 0} kwargs['pre_dispatch'] = pre_dispatch if 'prefer' in p_args: kwargs['prefer'] = prefer if joblib_mmap: if cache_dir is None: max_nbytes = None # disable memmaping kwargs['temp_folder'] = cache_dir kwargs['max_nbytes'] = max_nbytes n_jobs = check_n_jobs(n_jobs) parallel = Parallel(n_jobs, **kwargs) my_func = delayed(func) if total is not None: def parallel_progress(op_iter): pb = ProgressBar(total, verbose_bool=should_print) return parallel(pb(op_iter)) parallel_out = parallel_progress else: parallel_out = parallel return parallel_out, my_func, n_jobs def check_n_jobs(n_jobs, allow_cuda=False): """Check n_jobs in particular for negative values. Parameters ---------- n_jobs : int The number of jobs. allow_cuda : bool Allow n_jobs to be 'cuda'. Default: False. Returns ------- n_jobs : int The checked number of jobs. Always positive (or 'cuda' if applicable.) """ if not isinstance(n_jobs, int): if not allow_cuda: raise ValueError('n_jobs must be an integer') elif not isinstance(n_jobs, str) or n_jobs != 'cuda': raise ValueError('n_jobs must be an integer, or "cuda"') # else, we have n_jobs='cuda' and this is okay, so do nothing elif _force_serial: n_jobs = 1 logger.info('... MNE_FORCE_SERIAL set. Processing in forced ' 'serial mode.') elif n_jobs <= 0: try: import multiprocessing n_cores = multiprocessing.cpu_count() n_jobs = min(n_cores + n_jobs + 1, n_cores) if n_jobs <= 0: raise ValueError('If n_jobs has a negative value it must not ' 'be less than the number of CPUs present. ' 'You\'ve got %s CPUs' % n_cores) except ImportError: # only warn if they tried to use something other than 1 job if n_jobs != 1: warn('multiprocessing not installed. Cannot run in parallel.') n_jobs = 1 return n_jobs

7e653828f0538d63d5a0053be206b584f1b6d857

py

Python

feature_engine/base_transformers.py

iahsanujunda/feature_engine

46c6bd5a06626b0789fcc1367069d065010794a1

2020-11-15T13:15:28.000Z

2020-11-15T13:15:28.000Z

feature_engine/base_transformers.py

myamullaciencia/feature_engine

46c6bd5a06626b0789fcc1367069d065010794a1

feature_engine/base_transformers.py

myamullaciencia/feature_engine

46c6bd5a06626b0789fcc1367069d065010794a1

# Transformation methods are shared by most transformer groups. # Each transformer can inherit the transform method from these base classes. import warnings from sklearn.base import BaseEstimator, TransformerMixin from sklearn.utils.validation import check_is_fitted from feature_engine.dataframe_checks import ( _is_dataframe, _check_input_matches_training_df, _check_contains_na, ) from feature_engine.variable_manipulation import _find_numerical_variables class BaseImputer(BaseEstimator, TransformerMixin): # Common transformation procedure for most feature imputers def transform(self, X): """ Replaces missing data with the learned parameters. Parameters ---------- X : pandas dataframe of shape = [n_samples, n_features] The input samples. Returns ------- X_transformed : pandas dataframe of shape = [n_samples, n_features] The dataframe without missing values in the selected variables. """ # Check method fit has been called check_is_fitted(self) # check that input is a dataframe X = _is_dataframe(X) # Check that input data contains same number of columns than # the dataframe used to fit the imputer. _check_input_matches_training_df(X, self.input_shape_[1]) # replaces missing data with the learned parameters for variable in self.imputer_dict_: X[variable].fillna(self.imputer_dict_[variable], inplace=True) return X class BaseCategoricalTransformer(BaseEstimator, TransformerMixin): # Common transformation procedure for most variable encoders def transform(self, X): """ Replaces categories with the learned parameters. Parameters ---------- X : pandas dataframe of shape = [n_samples, n_features]. The input samples. Returns ------- X_transformed : pandas dataframe of shape = [n_samples, n_features]. The dataframe containing categories replaced by numbers. """ # Check method fit has been called check_is_fitted(self) # check that input is a dataframe X = _is_dataframe(X) # check if dataset contains na _check_contains_na(X, self.variables) # Check that the dataframe contains the same number of columns # than the dataframe # used to fit the imputer. _check_input_matches_training_df(X, self.input_shape_[1]) # replace categories by the learned parameters for feature in self.encoder_dict_.keys(): X[feature] = X[feature].map(self.encoder_dict_[feature]) # check if NaN values were introduced by the encoding if X[self.encoder_dict_.keys()].isnull().sum().sum() > 0: warnings.warn( "NaN values were introduced in the returned dataframe by the encoder." "This means that some of the categories in the input dataframe were " "not present in the training set used when the fit method was called. " "Thus, mappings for those categories does not exist. Try using the " "RareLabelCategoricalEncoder to remove infrequent categories before " "calling this encoder." ) return X class BaseNumericalTransformer(BaseEstimator, TransformerMixin): # shared set-up procedures across numerical transformers, i.e., # variable transformers, discretisers, outlier handlers def fit(self, X, y=None): # check input dataframe X = _is_dataframe(X) # find or check for numerical variables self.variables = _find_numerical_variables(X, self.variables) # check if dataset contains na _check_contains_na(X, self.variables) return X def transform(self, X): # Check method fit has been called check_is_fitted(self) # check that input is a dataframe X = _is_dataframe(X) # check if dataset contains na _check_contains_na(X, self.variables) # Check that the dataframe contains the same number of columns # than the dataframe used to fit the imputer. _check_input_matches_training_df(X, self.input_shape_[1]) return X

b7ec5f45c56d3759b8ab7bfc00162a5371be0bb7

py

Python

tests/sentry/receivers/test_featureadoption.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

tests/sentry/receivers/test_featureadoption.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

tests/sentry/receivers/test_featureadoption.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

from __future__ import absolute_import from django.utils import timezone from sentry.models import FeatureAdoption, GroupAssignee, GroupTombstone, Rule from sentry.plugins.bases import IssueTrackingPlugin2, NotificationPlugin from sentry.signals import ( alert_rule_created, event_processed, first_event_received, project_created, member_joined, plugin_enabled, user_feedback_received, issue_assigned, issue_resolved, advanced_search, save_search_created, inbound_filter_toggled, sso_enabled, data_scrubber_enabled, ) from sentry.receivers.rules import DEFAULT_RULE_DATA from sentry.testutils import SnubaTestCase, TestCase class FeatureAdoptionTest(TestCase, SnubaTestCase): def setUp(self): super(FeatureAdoptionTest, self).setUp() self.now = timezone.now() self.owner = self.create_user() self.organization = self.create_organization(owner=self.owner) self.team = self.create_team(organization=self.organization) self.project = self.create_project(teams=[self.team]) def test_bad_feature_slug(self): FeatureAdoption.objects.record(self.organization.id, "xxx") feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="first_event" ) assert feature_complete is None def test_all_passed_feature_slugs_are_complete(self): event1 = self.store_event( data={"tags": {"environment": "prod"}}, project_id=self.project.id ) event2 = self.store_event( data={"tags": {"environment": "prod"}}, project_id=self.project.id ) event_processed.send(project=self.project, event=event1, sender=type(self.project)) event_processed.send(project=self.project, event=event2, sender=type(self.project)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="environment_tracking" ) assert feature_complete.complete def test_first_event(self): event = self.store_event( data={"platform": "javascript", "message": "javascript error message"}, project_id=self.project.id, ) first_event_received.send(project=self.project, event=event, sender=type(self.project)) first_event = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="first_event" ) assert first_event.complete def test_javascript(self): event = self.store_event(data={"platform": "javascript"}, project_id=self.project.id) event_processed.send(project=self.project, event=event, sender=type(self.project)) js = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="javascript") assert js.complete def test_python(self): event = self.store_event( data={"platform": "python", "message": "python error message"}, project_id=self.project.id, ) event_processed.send(project=self.project, event=event, sender=type(self.project)) python = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="python") assert python.complete def test_node(self): event = self.store_event( data={"platform": "node", "message": "node error message"}, project_id=self.project.id ) event_processed.send(project=self.project, event=event, sender=type(self.project)) node = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="node") assert node.complete def test_ruby(self): event = self.store_event( data={"platform": "ruby", "message": "ruby error message"}, project_id=self.project.id ) event_processed.send(project=self.project, event=event, sender=type(self.project)) ruby = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="ruby") assert ruby.complete def test_java(self): event = self.store_event( data={"platform": "java", "message": "java error message"}, project_id=self.project.id ) event_processed.send(project=self.project, event=event, sender=type(self.project)) java = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="java") assert java.complete def test_cocoa(self): event = self.store_event( data={"platform": "cocoa", "message": "cocoa error message"}, project_id=self.project.id ) event_processed.send(project=self.project, event=event, sender=type(self.project)) cocoa = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="cocoa") assert cocoa.complete def test_objc(self): event = self.store_event( data={"platform": "objc", "message": "objc error message"}, project_id=self.project.id ) event_processed.send(project=self.project, event=event, sender=type(self.project)) objc = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="objc") assert objc.complete def test_php(self): event = self.store_event( data={"platform": "php", "message": "php error message"}, project_id=self.project.id ) event_processed.send(project=self.project, event=event, sender=type(self.project)) php = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="php") assert php.complete def test_go(self): event = self.store_event( data={"platform": "go", "message": "go error message"}, project_id=self.project.id ) event_processed.send(project=self.project, event=event, sender=type(self.project)) go = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="go") assert go.complete def test_csharp(self): event = self.store_event( data={"platform": "csharp", "message": "csharp error message"}, project_id=self.project.id, ) event_processed.send(project=self.project, event=event, sender=type(self.project)) csharp = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="csharp") assert csharp.complete def test_perl(self): event = self.store_event( data={"platform": "perl", "message": "perl error message"}, project_id=self.project.id ) event_processed.send(project=self.project, event=event, sender=type(self.project)) perl = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="perl") assert perl.complete def test_elixir(self): event = self.store_event( data={"platform": "elixir", "message": "elixir error message"}, project_id=self.project.id, ) event_processed.send(project=self.project, event=event, sender=type(self.project)) elixir = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="elixir") assert elixir.complete def test_cfml(self): event = self.store_event( data={"platform": "cfml", "message": "cfml error message"}, project_id=self.project.id ) event_processed.send(project=self.project, event=event, sender=type(self.project)) cfml = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="cfml") assert cfml.complete def test_groovy(self): event = self.store_event( data={"platform": "groovy", "message": "groovy error message"}, project_id=self.project.id, ) event_processed.send(project=self.project, event=event, sender=type(self.project)) groovy = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="groovy") assert groovy.complete def test_release_tracking(self): event = self.store_event(data={"tags": {"sentry:release": "1"}}, project_id=self.project.id) event_processed.send(project=self.project, event=event, sender=type(self.project)) release_tracking = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="release_tracking" ) assert release_tracking def test_environment_tracking(self): event = self.store_event(data={"environment": "prod"}, project_id=self.project.id) event_processed.send(project=self.project, event=event, sender=type(self.project)) environment_tracking = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="environment_tracking" ) assert environment_tracking def test_bulk_create(self): event = self.store_event( data={ "platform": "javascript", "environment": "prod", "tags": {"sentry:release": "abc"}, "user": {"id": "123"}, }, project_id=self.project.id, ) event_processed.send(project=self.project, event=event, sender=type(self.project)) javascript = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="javascript" ) assert javascript environment_tracking = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="environment_tracking" ) assert environment_tracking release_tracking = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="release_tracking" ) assert release_tracking feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="user_tracking" ) assert feature_complete def test_user_tracking(self): event = self.store_event(data={"user": {"id": "123"}}, project_id=self.project.id) event_processed.send(project=self.project, event=event, sender=type(self.project)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="user_tracking" ) assert feature_complete def test_no_user_tracking_for_ip_address_only(self): """test to see if just sending ip address doesn't check the user tracking box""" userless_event = self.store_event( data={"user": {"ip_address": "0.0.0.0"}}, project_id=self.project.id ) event_processed.send(project=self.project, event=userless_event, sender=type(self.project)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="user_tracking" ) assert feature_complete is None def test_no_env_tracking(self): envless_event = self.store_event( data={"platform": "javascript"}, project_id=self.project.id ) event_processed.send(project=self.project, event=envless_event, sender=type(self.project)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="environment_tracking" ) assert feature_complete is None def test_custom_tags(self): event = self.store_event(data={}, project_id=self.project.id) event.data["tags"].append(("foo", "bar")) assert event.get_tag("foo") == "bar" event_processed.send(project=self.project, event=event, sender=type(self.project)) custom_tags = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="custom_tags" ) assert custom_tags def test_source_maps(self): event = self.store_event( data={ "platform": "javascript", "exception": { "values": [ { "stacktrace": { "frames": [ { "data": { "sourcemap": "https://media.sentry.io/_static/29e365f8b0d923bc123e8afa38d890c3/sentry/dist/vendor.js.map" } } ] }, "type": "TypeError", } ] }, }, project_id=self.project.id, ) event_processed.send(project=self.project, event=event, sender=type(self.project)) source_maps = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="source_maps" ) assert source_maps def test_breadcrumbs(self): event = self.store_event( data={ "breadcrumbs": { "values": [ { "category": "xhr", "timestamp": 1496395011.63, "type": "http", "data": { "url": "/api/path/here", "status_code": "500", "method": "POST", }, } ] } }, project_id=self.project.id, ) event_processed.send(project=self.project, event=event, sender=type(self.project)) breadcrumbs = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="breadcrumbs" ) assert breadcrumbs def test_multiple_events(self): simple_event = self.store_event( data={"message": "javascript error message", "platform": "javascript"}, project_id=self.project.id, ) first_event_received.send( project=self.project, event=simple_event, sender=type(self.project) ) event_processed.send(project=self.project, event=simple_event, sender=type(self.project)) first_event = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="first_event" ) assert first_event.complete js = FeatureAdoption.objects.get_by_slug(organization=self.organization, slug="javascript") assert js.complete full_event = self.store_event( data={ "message": "javascript error message", "platform": "javascript", "environment": "prod", "tags": {"sentry:release": "abc"}, "user": {"id": "123"}, "exception": { "values": [ { "stacktrace": { "frames": [ { "data": { "sourcemap": "https://media.sentry.io/_static/29e365f8b0d923bc123e8afa38d890c3/sentry/dist/vendor.js.map" } } ] }, "type": "TypeError", } ] }, "breadcrumbs": { "values": [ { "category": "xhr", "timestamp": 1496395011.63, "type": "http", "data": { "url": "/api/path/here", "status_code": "500", "method": "POST", }, } ] }, }, project_id=self.project.id, ) event_processed.send(project=self.project, event=full_event, sender=type(self.project)) release_tracking = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="release_tracking" ) assert release_tracking environment_tracking = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="environment_tracking" ) assert environment_tracking feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="user_tracking" ) assert feature_complete source_maps = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="source_maps" ) assert source_maps breadcrumbs = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="breadcrumbs" ) assert breadcrumbs def test_user_feedback(self): user_feedback_received.send(project=self.project, sender=type(self.project)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="user_feedback" ) assert feature_complete def test_project_created(self): project_created.send(project=self.project, user=self.owner, sender=type(self.project)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="first_project" ) assert feature_complete def test_member_joined(self): member = self.create_member( organization=self.organization, teams=[self.team], user=self.create_user() ) member_joined.send(member=member, organization=self.organization, sender=type(self.project)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="invite_team" ) assert feature_complete def test_assignment(self): GroupAssignee.objects.create( group_id=self.group.id, user_id=self.user.id, project_id=self.project.id ) issue_assigned.send( project=self.project, group=self.group, user=self.user, sender="something" ) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="assignment" ) assert feature_complete def test_resolved_in_release(self): issue_resolved.send( organization_id=self.organization.id, project=self.project, group=self.group, user=self.user, resolution_type="in_next_release", sender=type(self.project), ) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="resolved_in_release" ) assert feature_complete def test_resolved_manually(self): issue_resolved.send( organization_id=self.organization.id, project=self.project, group=self.group, user=self.user, resolution_type="now", sender=type(self.project), ) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="resolved_in_release" ) assert not feature_complete def test_advanced_search(self): advanced_search.send(project=self.project, sender=type(self.project)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="advanced_search" ) assert feature_complete def test_save_search(self): save_search_created.send(project=self.project, user=self.user, sender=type(self.project)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="saved_search" ) assert feature_complete def test_inbound_filters(self): inbound_filter_toggled.send(project=self.project, sender=type(self.project)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="inbound_filters" ) assert feature_complete def test_alert_rules(self): rule = Rule.objects.create( project=self.project, label="Trivially modified rule", data=DEFAULT_RULE_DATA ) alert_rule_created.send( user=self.owner, project=self.project, rule=rule, rule_type="issue", sender=type(self.project), ) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="alert_rules" ) assert feature_complete def test_issue_tracker_plugin(self): plugin_enabled.send( plugin=IssueTrackingPlugin2(), project=self.project, user=self.owner, sender=type(self.project), ) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="issue_tracker_integration" ) assert feature_complete def test_notification_plugin(self): plugin_enabled.send( plugin=NotificationPlugin(), project=self.project, user=self.owner, sender=type(self.project), ) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="notification_integration" ) assert feature_complete def test_sso(self): sso_enabled.send( organization=self.organization, user=self.user, provider="google", sender=type(self.organization), ) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="sso" ) assert feature_complete def test_data_scrubber(self): data_scrubber_enabled.send(organization=self.organization, sender=type(self.organization)) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="data_scrubbers" ) assert feature_complete def test_delete_and_discard(self): GroupTombstone.objects.create(previous_group_id=self.group.id, project=self.project) feature_complete = FeatureAdoption.objects.get_by_slug( organization=self.organization, slug="delete_and_discard" ) assert feature_complete

5c6ad587928af26fe0259f211e65b6b49220a490

py

Python

krux/object.py

SpectrumIO/python-krux-stdlib

cb99e5dbd52711a76eb4fbb68a90fc1373616c07

2016-02-05T22:46:11.000Z

2017-07-15T03:23:41.000Z

krux/object.py

SpectrumIO/python-krux-stdlib

cb99e5dbd52711a76eb4fbb68a90fc1373616c07

2015-01-13T00:59:05.000Z

2019-10-16T01:14:02.000Z

krux/object.py

SpectrumIO/python-krux-stdlib