xszheng2020/the_stack_dedup_python · Datasets at Hugging Face

d8bd8bd8d533ca30262aba2bf7317396c7dab909

443

py

Python

blender/arm/logicnode/native/LN_read_storage.py

onelsonic/armory

55cfead0844923d419d75bf4bd677ebed714b4b5

[ "Zlib" ]

2,583

2016-07-27T08:25:47.000Z

2022-03-31T10:42:17.000Z

blender/arm/logicnode/native/LN_read_storage.py

onelsonic/armory

55cfead0844923d419d75bf4bd677ebed714b4b5

[ "Zlib" ]

2,122

2016-07-31T14:20:04.000Z

2022-03-31T20:44:14.000Z

blender/arm/logicnode/native/LN_read_storage.py

onelsonic/armory

55cfead0844923d419d75bf4bd677ebed714b4b5

[ "Zlib" ]

451

2016-08-12T05:52:58.000Z

2022-03-31T01:33:07.000Z

from arm.logicnode.arm_nodes import * class ReadStorageNode(ArmLogicTreeNode): """Reads a stored content. @seeNode Write Storage""" bl_idname = 'LNReadStorageNode' bl_label = 'Read Storage' arm_section = 'file' arm_version = 1 def arm_init(self, context): self.add_input('ArmStringSocket', 'Key') self.add_input('ArmStringSocket', 'Default') self.add_output('ArmDynamicSocket', 'Value')

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443

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0.205418

443

16

53

27.6875

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null

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1

0

2

d8bfc1ac4a0ba5561449846faf72e97b38a7b701

320

py

Python

apolo/apolo/apps/adopcion/models.py

XeresRed/TrabajoGrado

3d7135732ea76410995ed3b5c3332eb83881677c

[ "MIT" ]

null

apolo/apolo/apps/adopcion/models.py

XeresRed/TrabajoGrado

3d7135732ea76410995ed3b5c3332eb83881677c

[ "MIT" ]

null

apolo/apolo/apps/adopcion/models.py

XeresRed/TrabajoGrado

3d7135732ea76410995ed3b5c3332eb83881677c

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import AbstractUser # Create your models here. class Persona(AbstractUser): activos = models.CharField(blank=True, max_length=100) numeroEmpl = models.IntegerField(blank=True, null=True) tipo = models.CharField(blank=True, max_length=100)

26.666667

59

0.759375

42

320

5.738095

0.571429

0.112033

0.165975

0.19917

0.298755

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0.021978

0.146875

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1

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1

0

3

d8c08d175a1cbd3180bab825774c67e642b794d4

1,731

py

Python

generator/framework/util/cfg_generator.py

sinsay/ds_generator

9365e22e8730418caf29b8ed6ada1f30f936a297

[ "Apache-2.0" ]

null

generator/framework/util/cfg_generator.py

sinsay/ds_generator

9365e22e8730418caf29b8ed6ada1f30f936a297

[ "Apache-2.0" ]

null

generator/framework/util/cfg_generator.py

sinsay/ds_generator

9365e22e8730418caf29b8ed6ada1f30f936a297

[ "Apache-2.0" ]

null

class HasIdent(object): """ 避免 CfgGenerator 交叉引用所使用的基类 """ def __init__(self, ident: int = 0): self.ident = ident def increase_ident(self): self.ident += 1 def decrease_ident(self): self.ident -= 1 class CfgGeneratorIdent(object): def __init__(self, gen: HasIdent): self.gen = gen def __enter__(self): self.gen.increase_ident() def __exit__(self, exc_type, exc_val, exc_tb): self.gen.decrease_ident() class CfgGenerator(HasIdent): def __init__(self, step: int = 4): super(CfgGenerator, self).__init__() self.step = step self.conf = [] def to_cfg_string(self): """ 获取生成好的配置信息 :return: """ return "".join(self.conf) def with_ident(self): """ 返回一个保存了递进状态的资源对象, 用于 with 语句 :return: """ return CfgGeneratorIdent(self) def increase_ident(self, ident: int = 1): """ 手动增加或减少递进状态 :param ident: :return: """ self.ident += ident def append_with(self, conf: str = "", new_line: bool = True, with_ident: bool = True): """ 添加配置信息，传递了 new_line 或 ident 参数时可为其添加 ident 及新行 :param conf: :param new_line: :param with_ident: :return: """ ident = "" if not with_ident else self.ident_str() if str: self.conf.append("%s%s" % (ident, conf)) if new_line: self.conf.append("\n") def ident_str(self): return self.ident * self.step * " " def str_with_ident(self, s) -> str: """ 使用当前的缩进来创建字符串 """ return "%s%s" % (self.ident_str(), s)

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1,731

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null

0

null

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1

0

2

d8c1279c1f035fd1c0ca93502531ba20b1cf610a

2,323

py

Python

app/product/tests/test_product_api.py

RamzeyXD/varanus-ecommerce-api

4688fc393b73d70a4923d471006caee2ec624f68

[ "MIT" ]

null

app/product/tests/test_product_api.py

RamzeyXD/varanus-ecommerce-api

4688fc393b73d70a4923d471006caee2ec624f68

[ "MIT" ]

5

2021-03-19T04:52:44.000Z

2021-09-22T19:12:07.000Z

app/product/tests/test_product_api.py

RamzeyXD/varanus-ecommerce-api

4688fc393b73d70a4923d471006caee2ec624f68

[ "MIT" ]

null

from django.contrib.auth import get_user_model from django.urls import reverse from django.test import TestCase from rest_framework import status from rest_framework.test import APIClient from core.models import Product from product.serializers import ProductSerializer PRODUCTS_URL = reverse('product:product-list') def detail_url(product_slug): """Return product detail URL""" return reverse('product:product-detail', args=[product_slug]) def sample_product(**params): """Create and return sample product""" defaults = { 'name': 'TestNameCase', 'description': "test description for test Product", 'cost': 45 } defaults.update(params) return Product.objects.create(**defaults) class PublicProductsApiTests(TestCase): """Test the publicly available products API""" def setUp(self): self.client = APIClient() def test_login_required(self): """Test that login is required to access the endpoint""" res = self.client.get(PRODUCTS_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateProductApiTests(TestCase): """Test products can be retrieved by authorized user""" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( email='TestMail@gmail.com', password='TestPassword123' ) self.client.force_authenticate(self.user) def test_retrieve_product_list(self): """Test retrieving list of products""" params = { 'name': 'TestProduct', 'description': 'Test description for second test product', 'cost': 5.00 } sample_product(**params) sample_product() products = Product.objects.all() serializer = ProductSerializer(products, many=True) res = self.client.get(PRODUCTS_URL) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_view_product_detail(self): """Test viewing product detail""" product = sample_product() url = detail_url(product.slug) res = self.client.get(url) serializer = ProductSerializer(product) self.assertEqual(serializer.data, res.data)

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0.226862

2,323

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0.352941

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null

0

null

0

1

0

d8c141a49a479e74699dc9b65661ce60383e9e67

4,686

py

Python

src/face_feature.py

ryota0051/facial_expressions

763f1108fc56f5360fbd6603e0dc3e40c27a3d1b

[ "MIT" ]

null

src/face_feature.py

ryota0051/facial_expressions

763f1108fc56f5360fbd6603e0dc3e40c27a3d1b

[ "MIT" ]

null

src/face_feature.py

ryota0051/facial_expressions

763f1108fc56f5360fbd6603e0dc3e40c27a3d1b

[ "MIT" ]

null

import os from typing import Dict, Tuple, List import json import time import tensorflow as tf import numpy as np from type_def import BOUNDARY_BOX_TYPE, PERSONAL_INFO_TYPE class FaceFeatureExtractor(): def __init__(self, base_model_path: str, nationality_model_path: str, label_path: str) -> None: '''必要なファイルを読み込むインスタンスメソッド Parameter ---------- base_model_path: mobilenetV2の畳み込み部分のみのモデルパス nationality_model_path: 国籍モデルのパス label_path: 各モデルが出力する数字が表す文字列を格納したファイルのパスファイル内容の例: { "gender": { 0: "female", 1: "male" }, ... } ''' self.base_model = self.__load_model(base_model_path) self.nationality_model = self.__load_model(nationality_model_path) self.labels = self.__load_labels(label_path) def get_personal_data_from_faces(self, img_batch: np.array, rect_list: BOUNDARY_BOX_TYPE) -> PERSONAL_INFO_TYPE: '''顔画像データから性別、年齢、人種を判別するメソッド Parameter ---------- img_batch: バッチ画像 rect_list: 顔座標 Returns ---------- 例: [ { "coodinate": [x, y, W, H], "attrributes": { "nationality": "japanese" } }, ... ] ''' features = self.get_feature_batch(img_batch) features = features.reshape(len(features), -1) # 国籍判定 nationality_list = self.predict_facial_expression(features, self.nationality_model) result_list = [None] * len(rect_list) assert len(rect_list) == len(nationality_list) for i, (rect, nationality) in enumerate(zip(rect_list, nationality_list)): result = {'coodinate': None, 'attribute': {}} result['coodinate'] = list(rect) result['attribute']['nationality'] = self.labels['nationality'][str(nationality)] result_list[i] = result return result_list def get_feature_batch(self, img_batch: np.array) -> np.array: '''ベースとなるモバイルネットからバッチ画像ごとに特徴量を抽出するメソッド Parameter --------- img_batch: バッチ画像 Returns --------- モバイルネットが出力する特徴量 ''' assert isinstance(img_batch, np.ndarray) assert img_batch.ndim == 4 x = tf.keras.applications.mobilenet_v2.preprocess_input(img_batch) features = self.base_model.predict(x) return features def predict_facial_expression( self, features: np.array, model: '学習済み予測部分モデル') -> List[int]: '''指定モデルにおける顔の属性を予測するメソッド Parameter --------- features: modelに入力する特徴量 model: 属性予測モデル(kerasのクラスラベルを返すメソッドである predict_classesを用いているので、別のフレームワークを使う場合は、 classなどでラッパーする。) Returns --------- 要素として、予測結果の数値ラベルをもつリスト ''' return model.predict_classes(features).tolist() def __load_labels(self, label_path: str) -> Dict[str, Dict[str, str]]: '''json形式で記述されたファイルからone-hot-vectorが表す文字列辞書を取得するメソッド Parameter ---------- label_path: 各モデルが出力するラベルが表す文字列辞書が記述されたjsonファイルのパス Returns ---------- one-hot-vectorが表す文字列辞書例: { "gender": { "0": "female", "1": "male" }, "age": { "0": "10代", "1": "20代", "2": "30代", "3": "40代", "4": "50代" }, "race": { "0": "Asian", "1": "Black", "2": "Indian", "3": "others", "4": "White" } } ''' self.__check_file_exists(label_path) with open(label_path, 'r') as f: labels = json.load(f) return labels def __load_model(self, model_path:str) -> 'kerasのmodel': '''kerasモデルを読み込むメソッド Parameter ---------- model_path: 読み込みモデルパス Returns ---------- tf.keras.models.load_modelの返り値 ''' self.__check_file_exists(model_path) return tf.keras.models.load_model(model_path) def __check_file_exists(self, file_path: str) -> None: '''ファイルが存在するかを確かめるメソッド(ファイルが存在しない場合は、例外を出力する。) Parameter ---------- file_path: 存在を確かめるファイル ''' if not os.path.exists(file_path): raise FileNotFoundError('[{}]が存在しません。'.format(file_path))

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0

d8c15c388c58bbae49aac02c97bdee96b885e94e

3,234

py

Python

app/main/routes.py

Tsolmon1/company

270d88e40e0c709247a7338cd41942b0ceb67c5e

[ "MIT" ]

null

app/main/routes.py

Tsolmon1/company

270d88e40e0c709247a7338cd41942b0ceb67c5e

[ "MIT" ]

null

app/main/routes.py

Tsolmon1/company

270d88e40e0c709247a7338cd41942b0ceb67c5e

[ "MIT" ]

null

from datetime import datetime from flask import render_template, flash, redirect, url_for, request, g, \ jsonify, current_app from flask_login import current_user, login_required from flask_babel import _, get_locale #from guess_language import guess_language from app import db from app.main.forms import CompanyForm from app.models import Company_list from app.main import bp @bp.route("/company", methods=['GET']) def company_namelist(): """ List all company """ #loan_requests = Loan_request.query.all() page = request.args.get('page', 1, type=int) companys = Company_list.query.order_by(Company_list.id.asc()).paginate( page, current_app.config['POSTS_PER_PAGE'], False) next_url = url_for('main.company_namelist', page=companys.next_num) \ if companys.has_next else None prev_url = url_for('main.company_namelist', page=companys.prev_num) \ if companys.has_prev else None return render_template('company/company_namelists.html', companys=companys.items, title="companys", next_url=next_url, prev_url=prev_url) @bp.route('/company/add', methods=['GET', 'POST']) def add_company(): form = CompanyForm() if form.validate_on_submit(): company = Company_list(names_one=form.names_one.data, names_two=form.names_two.data, names_three=form.names_three.data, branches=form.branches.data) # add employee to the database db.session.add(company) db.session.commit() flash('You have successfully registered!') # redirect to the login page return redirect(url_for('main.company_namelist')) # load registration template return render_template('company/company_add.html', form=form, title='LoanTypeAdd') @bp.route('/companys/edit/<int:id>', methods=['GET', 'POST']) def edit_company(id): """ Edit a user """ add_company = False companys = Company_list.query.get_or_404(id) form = CompanyForm(obj=companys) if form.validate_on_submit(): companys.names_one = form.names_one.data companys.names_two = form.names_two.data companys.names_three = form.names_three.data companys.branches = form.branches.data db.session.add(companys) db.session.commit() flash('You have successfully edited the companys.') # redirect to the roles page return redirect(url_for('main.company_namelist')) form.names_one.data = companys.names_one form.names_two.data = companys.names_two form.names_three.data = companys.names_three form.branches.data = companys.branches return render_template('company/company_edit.html', add_company=add_company, form=form, title="Edit company") @bp.route('/company/delete/<int:id>', methods=['GET', 'POST']) def delete_company(id): """ Delete a employee from the database """ companyss = Company_list.query.get_or_404(id) db.session.delete(companyss) db.session.commit() flash('You have successfully deleted the company.') # redirect to the roles page return redirect(url_for('main.company_namelist'))

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null

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null

0

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0

d8c4609c13c1b5b024cb78f178101d21b07a60ae

31,034

py

Python

opentisim/containers/container_defaults.py

TUDelft-CITG/OpenTISim

443b20572eb2aae2f1909a8a01e95e31be53b675

[ "MIT" ]

7

2020-02-15T01:34:29.000Z

2022-02-28T01:24:05.000Z

opentisim/containers/container_defaults.py

TUDelft-CITG/OpenTISim

443b20572eb2aae2f1909a8a01e95e31be53b675

[ "MIT" ]

2

2020-02-14T18:44:31.000Z

2020-04-06T15:39:17.000Z

opentisim/containers/container_defaults.py

TUDelft-CITG/OpenTISim

443b20572eb2aae2f1909a8a01e95e31be53b675

[ "MIT" ]

2

2019-07-19T08:50:31.000Z

2020-02-05T11:14:07.000Z

""" Main generic object classes: - 1. Quay_wall - 2. Berth - 3. Cyclic_Unloader - STS crane - 4. Horizontal transport - Tractor trailer - 5. Commodity - TEU - 6. Containers - Laden - Reefer - Empty - OOG - 7. Laden and reefer stack - 8. Stack equipment - 9. Empty stack - 10. OOG stack - 11. Gates - 12. Empty handler - 13. Vessel - 14. Labour - 15. Energy - 16. General - 17. Indirect Costs """ # package(s) for data handling import pandas as pd # *** Default inputs: Quay_Wall class *** todo add values of RHDHV or general (e.g. PIANC) quay_wall_data = {"name": 'Quay', "ownership": 'Port authority', "delivery_time": 2, "lifespan": 50, "mobilisation_min": 2_500_000, "mobilisation_perc": 0.02, "maintenance_perc": 0.01, "insurance_perc": 0.01, "berthing_gap": 15, # see PIANC (2014), p 98 "freeboard": 4, # m "Gijt_constant": 753.24, # Source: (J. de Gijt, 2011) Figure 2 ; USD/m (if 1.0 EUR = 1.12 USD, 670.45 EUR = 757.8 USD) "Gijt_coefficient": 1.2729, # Source: (J. de Gijt, 2011) Figure 2 "max_sinkage": 0.5, "wave_motion": 0.5, "safety_margin": 0.5, "apron_width": 65.5, # see PIANC (2014b), p 62 "apron_pavement": 125} # all values from Ijzermans, 2019, P 91 # *** Default inputs: Berth class *** berth_data = {"name": 'Berth', "crane_type": 'Mobile cranes', "delivery_time": 2, "max_cranes": 3} # STS cranes # *** Default inputs: Crane class *** todo check sources sts_crane_data and check small sts_crane_data for the barge berths sts_crane_data = {"name": 'STS_crane', "ownership": 'Terminal operator', "delivery_time": 1, # years "lifespan": 40, # years "unit_rate": 10_000_000, # USD per unit "mobilisation_perc": 0.15, # percentage "maintenance_perc": 0.02, # percentage "insurance_perc": 0.01, # percentage "consumption": 8, # Source: Peter Beamish (RHDHV) "crew": 5.5, # 1.5 crane driver, 2 quay staff, 2 twistlock handler (per shift) "crane_type": 'STS crane', "lifting_capacity": 2.13, # weighted average of TEU per lift "hourly_cycles": 25, # PIANC wg135 "eff_fact": 0.75} # *** Default inputs: Barge_Berth class *** barge_berth_data = {"name": 'Barge_Berth', "delivery_time": 2, # years "max_cranes": 1.0} # barge_cranes/barge_berth (Source: RHDHV) barge_quay_wall_data = {"name": 'Barge_Quay', "ownership": "Terminal operator", "delivery_time": 2, # years "lifespan": 50, # equal to quay wall OGV "mobilisation_min": 1_000_000, # todo add source "mobilisation_perc": 0.02, "maintenance_perc": 0.01, "insurance_perc": 0.01, "berthing_gap": 15, # see PIANC (2014), p 98 "freeboard": 4, # m "Gijt_constant": 753.24, # Source: (J. de Gijt, 2011) Figure 2 ; USD/m (if 1.0 EUR = 1.12 USD, 670.45 EUR = 757.8 USD) "Gijt_coefficient": 1.2729, # Source: (J. de Gijt, 2011) Figure 2 "max_sinkage": 0.5, "wave_motion": 0.5, "safety_margin": 0.5, "apron_width": 30, # todo add source, check PIANC 2014b "apron_pavement": 125} # all values from Ijzermans, 2019, P 91 barge_crane_data = {"name": 'Barge Crane', "ownership": 'Terminal operator', "delivery_time": 1, # years "lifespan": 40, # years "unit_rate": 5_000_000, # USD per unit "mobilisation_perc": 0.15, # percentage "maintenance_perc": 0.02, # percentage "insurance_perc": 0.01, # percentage "consumption": 4, # RHDHV "crew": 1.5, # 1.5 crane driver (per shift) "lifting_capacity": 1.60, # RHDHV, weighted average of TEU per lift "avg_utilisation": 0.9, # RHDHV "nom_crane_productivity": 15.0, # moves per hour "utilisation": 0.90, # rate "efficiency": 0.75, # rate "handling_time_ratio": 0.90, # handling time to berthing time ratio "peak_factor": 1.10} # RHDHV # *** Default inputs: *** channel_data = {"name": 'Channel', "ownership": 'Port authority', "delivery_time": 2, # years "lifespan": 50, # years "capital_dredging_rate": 7.0, # USD per m3 (source: Payra, $6.82) "infill_dredging_rate": 5.5, # USD per m3 (source: Payra, $5.25) "maintenance_dredging_rate": 4.5, # USD per m3 (source: Payra, $4.43) "mobilisation_min": 2_500_000, "mobilisation_perc": 0.02, "maintenance_perc": 0.10, "insurance_perc": 0.01} bridge_data = {"name": 'Bridge', "ownership": 'Port authority', "delivery_time": 3, "lifespan": 50, # years "unit_rate": 100_000_000, # USD per km "maintenance_perc": 0.025, "insurance_perc": 0.01} reclamation_data = {"name": 'Reclamation', "ownership": 'Port authority', "delivery_time": 2, # years "lifespan": 50, # years "reclamation_rate": 12.50, # USD per m3 "maintenance_perc": 0.02, "insurance_perc": 0.00} revetment_data = {"name": 'Revetment', "ownership": 'Port authority', "delivery_time": 2, # years "lifespan": 50, # years "revetment_rate": 180_000, # USD per m "quay_length_rate": 1.5, "maintenance_perc": 0.01, "insurance_perc": 0.00} breakwater_data = {"name": 'Breakwater', "ownership": 'Port authority', "delivery_time": 2, # years "lifespan": 50, # years "breakwater_rate": 275_000, # USD per m "quay_length_rate": 1.5, "maintenance_perc": 0.01, "insurance_perc": 0.00} # Default inputs: Horizontal_Transport class *** #todo add sources tractor_trailer_data = {"name": 'Tractor-trailer', "type": 'tractor_trailer', "ownership": 'Terminal operator', "delivery_time": 0, "lifespan": 10, "mobilisation": 1_000, "unit_rate": 85_000, "maintenance_perc": 0.10, "insurance_perc": 0.01, "crew": 1, "salary": 30_000, # dummy "utilisation": 0.80, "fuel_consumption": 2, # liter per box move "productivity": 1, "required": 5, # typical 3 - 6 see PIANC 2014b, p 58 "non_essential_moves": 1.2} # todo input value for tractor productivity # *** Default inputs: Container class #todo add sources laden_container_data = {"name": 'Laden container', "type": 'laden_container', "teu_factor": 1.60, "dwell_time": 3, # days, PIANC (2014b) p 64 (5 - 10) "peak_factor": 1.2, "stack_ratio": 0.7, "stack_occupancy": 0.8, # acceptable occupancy rate (0.65 to 0.70), Quist and Wijdeven (2014), p 49 "width": 48, # TEU "height": 4, # TEU "length": 20 # TEU } reefer_container_data = {"name": 'Reefer container', "type": 'reefer_container', "teu_factor": 1.75, "dwell_time": 3, # days, PIANC (2014b) p 64 (5 - 10) "peak_factor": 1.2, "stack_ratio": 0.7, "stack_occupancy": 0.8, # acceptable occupancy rate (0.65 to 0.70), Quist and Wijdeven (2014), p 49 "width": 21, # TEU "height": 4, # TEU "length": 4 # TEU } empty_container_data = {"name": 'Empty container', "type": 'empty_container', "teu_factor": 1.55, "dwell_time": 10, # days, PIANC (2014b) p 64 (10 - 20) "peak_factor": 1.2, "stack_ratio": 1, # looking for a good reference for this value "stack_occupancy": 0.7, # acceptable occupancy rate (0.65 to 0.70), Quist and Wijdeven (2014), p 49 "width": 48, # TEU "height": 4, # TEU "length": 20 # TEU } oog_container_data = {"name": 'OOG container', "type": 'oog_container', "teu_factor": 1.55, "dwell_time": 4, # days, PIANC (2014b) p 64 (5 - 10) "peak_factor": 1.2, "stack_ratio": 1, # by definition the H of oog stacks is 1 "stack_occupancy": 0.9, # acceptable occupancy rate (0.65 to 0.70), Quist and Wijdeven (2014), p 49 "width": 48, # TEU "height": 4, # TEU "length": 20 # TEU } # *** Default inputs: Laden_Stack class within the stacks rtg_stack_data = {"name": 'RTG Stack', "ownership": 'Terminal operator', "delivery_time": 1, # years "lifespan": 40, # years "mobilisation": 25_000, # USD "maintenance_perc": 0.1, # "width": 6, # TEU # "height": 5, # TEU # "length": 30, # TEU # "capacity": 900, # TEU "gross_tgs": 18, # TEU Ground Slot [m2/teu] "area_factor": 2.04, # m2/TEU (based on grasshopper layout P. Koster) "pavement": 200, # m2 DUMMY "drainage": 50, # m2 DUMMY "household": 0.1, # moves "digout_margin": 1.2, # percentage "reefer_factor": 2.33, # RHDHV "consumption": 4, # kWh per active reefer "reefer_rack": 3500, # USD "reefers_present": 0.5} # per reefer spot rmg_stack_data = {"name": 'RMG Stack', "ownership": 'Terminal operator', "delivery_time": 1, # years "lifespan": 40, # years "mobilisation": 50_000, # USD "maintenance_perc": 0.1, # "width": 6, # TEU # "height": 5, # TEU # "length": 40, # TEU # "capacity": 1200, # TEU "gross_tgs": 18.67, # TEU Ground Slot [m2/teu] "area_factor": 2.79, # m2/TEU (based on grasshopper layout P. Koster) "pavement": 200, # m2 DUMMY "drainage": 50, # m2 DUMMY "household": 0.1, # moves "digout_margin": 1.2, # percentage "reefer_factor": 2.33, # RHDHV "consumption": 4, # kWh per active reefer "reefer_rack": 3500, # USD "reefers_present": 0.5} # per reefer spot sc_stack_data = {"name": 'SC Stack', "ownership": 'Terminal operator', "delivery_time": 1, # years "lifespan": 40, # years "mobilisation": 50_000, # USD "maintenance_perc": 0.1, # "width": 45, # TEU # "height": 3, # TEU # "length": 22, # TEU # "capacity": 1200, # TEU "gross_tgs": 27.3, # TEU Ground Slot [m2/teu] "area_factor": 1.45, # m2/TEU (based on grasshopper layout P. Koster) "pavement": 200, # DUMMY "drainage": 50, # DUMMY "household": 0.1, # moves "digout_margin": 1.2, # percentage "reefer_factor": 2.33, # RHDHV "consumption": 4, # kWh per active reefer "reefer_rack": 3500, # USD "reefers_present": 0.5} # per reefer spot rs_stack_data = {"name": 'RS Stack', "ownership": 'Terminal operator', "delivery_time": 1, # years "lifespan": 40, # years "mobilisation": 10_000, # USD "maintenance_perc": 0.1, # "width": 4, # TEU # "height": 4, # TEU # "length": 20, # TEU # "capacity": 320, # TEU "gross_tgs": 18, # TEU Ground Slot [m2/teu] "area_factor": 3.23, # m2/TEU (based on grasshopper layout P. Koster) "pavement": 200, # m2 DUMMY "drainage": 50, # m2 DUMMY "household": 0.1, # moves "digout_margin": 1.2, # percentage "reefer_factor": 2.33, # RHDHV "consumption": 4, # kWh per active reefer "reefer_rack": 3500, # USD "reefers_present": 0.5} # per reefer spot # *** Default inputs: Other_Stack class empty_stack_data = {"name": 'Empty Stack', "ownership": 'Terminal operator', "delivery_time": 1, "lifespan": 40, "mobilisation": 25_000, "maintenance_perc": 0.1, "width": 8, # TEU "height": 6, # TEU "length": 10, # TEU "capacity": 480, # TEU "gross_tgs": 18, # TEU Ground Slot "area_factor": 2.04, # Based on grasshopper layout "pavement": 200, # DUMMY "drainage": 50, "household": 1.05, "digout": 1.05} # DUMMY oog_stack_data = {"name": 'OOG Stack', "ownership": 'Terminal operator', "delivery_time": 1, "lifespan": 40, "mobilisation": 25_000, "maintenance_perc": 0.1, "width": 10, # TEU "height": 1, # TEU "length": 10, # TEU "capacity": 100, # TEU "gross_tgs": 64, # TEU Ground Slot "area_factor": 1.05, # m2/TEU (based on grasshopper layout P. Koster) "pavement": 200, # DUMMY "drainage": 50} # DUMMY # *** Default inputs: Stack_Equipment class rtg_data = {"name": 'RTG', "type": 'rtg', "ownership": 'Terminal operator', "delivery_time": 0, "lifespan": 10, "unit_rate": 1_400_000, "mobilisation": 5000, "maintenance_perc": 0.1, # dummy "insurance_perc": 0, "crew": 1, # dummy "salary": 50_000, # dummy "required": 3, "fuel_consumption": 1, # dummy "power_consumption": 0 } rmg_data = {"name": 'RMG', "type": 'rmg', "ownership": 'Terminal operator', "delivery_time": 0, "lifespan": 10, "unit_rate": 2_500_000, "mobilisation": 5000, "maintenance_perc": 0.1, # dummy "insurance_perc": 0, "crew": 0, # dummy "salary": 50_000, # dummy "required": 1, # one per stack "fuel_consumption": 0, # dummy "power_consumption": 15 # kWh/box move } sc_data = {"name": 'Straddle carrier', "type": 'sc', "ownership": 'Terminal operator', "delivery_time": 0, "lifespan": 10, "unit_rate": 2_000_000, # dummy "mobilisation": 5000, "maintenance_perc": 0.1, # dummy "insurance_perc": 0, "crew": 0, # dummy "salary": 50_000, # dummy "required": 5, "fuel_consumption": 0, # dummy "power_consumption": 30 } rs_data = {"name": 'Reach stacker', "type": 'rs', "ownership": 'Terminal operator', "delivery_time": 0, "lifespan": 10, "unit_rate": 500_000, "mobilisation": 5000, "maintenance_perc": 0.1, # dummy "insurance_perc": 0, "crew": 2, # dummy "salary": 50_000, # dummy "required": 4, "fuel_consumption": 1, # dummy "power_consumption": 0 } # *** Default inputs: Gate class *** gate_data = {"name": 'Gate', "type": 'gate', "ownership": "Terminal operator", "delivery_time": 1, # years "lifespan": 15, # years "unit_rate": 30_000, # USD/gate "mobilisation": 5000, # USD/gate "maintenance_perc": 0.02, "crew": 2, # crew "salary": 30_000, # Dummy "canopy_costs": 250, # USD/m2 # Dummy "area": 288.75, # PIANC WG135 "staff_gates": 1, # "service_gates": 1, # "design_capacity": 0.98, # "exit_inspection_time": 3, # min #dummy "entry_inspection_time": 2, # min #dummy "peak_hour": 0.125, # dummy "peak_day": 0.25, # dummy "peak_factor": 1.2, "truck_moves": 0.75, "operating_days": 7, "capacity": 60} # *** Default inputs: ECH class*** empty_handler_data = {"name": 'Empty Handler', "type": 'empty_handler', "ownership": "Terminal operator", "delivery_time": 1, "lifespan": 15, "unit_rate": 500_000, "mobilisation": 5000, "maintenance_perc": 0.02, "crew": 1, "salary": 35_000, # dummy "fuel_consumption": 1.5, "required": 5} # *** Default inputs: Commodity class *** container_data = {"name": 'Laden', "handling_fee": 150, "fully_cellular_perc": 0, "panamax_perc": 0, "panamax_max_perc": 0, "post_panamax_I_perc": 0, "post_panamax_II_perc": 0, "new_panamax_perc": 100, "VLCS_perc": 0, "ULCS_perc": 0} # *** Default inputs: Vessel class *** (Source: i) The Geography of Transport Systems, Jean-Paul Rodrigue (2017), ii) UNCTAD) fully_cellular_data = {"name": 'Fully_Cellular_1', "type": 'Fully_Cellular', "delivery_time": 0, # years "call_size": 2500 / 8, # TEU "LOA": 215, # m "draught": 10.0, # m "beam": 20.0, # m "max_cranes": 4, # STS cranes "all_turn_time": 31, # todo source "mooring_time": 6, # berthing + deberthing time "demurrage_rate": 730, # USD todo edit "transport_costs": 200, # USD per TEU, RHDHV "all_in_transport_costs": 2128 # USD per TEU, Ports and Terminals p.158 } panamax_data = {"name": 'Panamax_1', "type": 'Panamax', "delivery_time": 0, # years "call_size": 3400 / 8, # TEU "LOA": 250, # m "draught": 12.5, # m "beam": 32.2, # m "max_cranes": 4, # STS cranes "all_turn_time": 31, # todo source [hr] "mooring_time": 6, # berthing + deberthing time [hr] "demurrage_rate": 730, # USD todo edit "transport_costs": 180, # USD per TEU, RHDHV "all_in_transport_costs": 1881 # USD per TEU, Ports and Terminals p.158 } panamax_max_data = {"name": 'Panamax_Max_1', "type": 'Panamax_Max', "delivery_time": 0, # years "call_size": 4500 / 8, # TEU "LOA": 290, # m "draught": 12.5, # m "beam": 32.0, # m "max_cranes": 4, # STS cranes "all_turn_time": 31, # todo source [hr] "mooring_time": 2, # berthing + deberthing time [hr] "demurrage_rate": 730, # USD todo edit "transport_costs": 160, # USD per TEU, RHDHV "all_in_transport_costs": 1682 # USD per TEU, Ports and Terminals p.158 } post_panamax_I_data = {"name": 'Post_Panamax_I_1', "type": 'Post_Panamax_I', "delivery_time": 0, # years "call_size": 6000 / 8, # TEU "LOA": 300, # m "draught": 13.0, # m "beam": 40.0, # m "max_cranes": 4, # STS cranes "all_turn_time": 31, # todo source [hr] "mooring_time": 2, # berthing + deberthing time [hr] "demurrage_rate": 730, # USD todo edit "transport_costs": 150, # USD per TEU, RHDHV "all_in_transport_costs": 1499 # USD per TEU, Ports and Terminals p.158 } post_panamax_II_data = {"name": 'Post_Panamax_II_1', "type": 'Post_Panamax_II', "delivery_time": 0, # years "call_size": 8500 / 8, # TEU "LOA": 340, # m "draught": 14.5, # m "beam": 43.0, # m "max_cranes": 4, # STS cranes "all_turn_time": 31, # todo source [hr] "mooring_time": 2, # berthing + deberthing time [hr] "demurrage_rate": 730, # USD todo edit "transport_costs": 140, # USD per TEU, RHDHV "all_in_transport_costs": 1304 # USD per TEU, Ports and Terminals p.158 } new_panamax_data = {"name": 'New_Panamax_1', "type": 'New_Panamax', "delivery_time": 0, # years "call_size": 12500 / 8, # TEU "LOA": 366, # m "draught": 15.2, # m "beam": 49.0, # m "max_cranes": 4, # STS cranes "all_turn_time": 31, # todo source [hr] "mooring_time": 6, # berthing + deberthing time [hr] "demurrage_rate": 730, # USD todo edit "transport_costs": 120, # USD per TEU, RHDHV "all_in_transport_costs": 1118 # USD per TEU, Ports and Terminals p.158 } VLCS_data = {"name": 'VLCS_1', "type": 'VLCS', "delivery_time": 0, # years "call_size": 15000 / 8, # TEU "LOA": 397, # m "draught": 15.5, # m "beam": 56.0, # m "max_cranes": 4, # STS cranes "all_turn_time": 31, # todo source [hr] "mooring_time": 4, # berthing + deberthing time [hr] "demurrage_rate": 730, # USD todo edit "transport_costs": 80, # USD per TEU, RHDHV "all_in_transport_costs": 2128 # USD per TEU, Ports and Terminals p.158 } ULCS_data = {"name": 'ULCS_1', "type": 'ULCS', "delivery_time": 0, # years "call_size": 21000 / 8, # TEU "LOA": 400, # m "draught": 16.0, # m "beam": 59.0, # m "max_cranes": 4, # STS cranes "all_turn_time": 31, # todo source [hr] "mooring_time": 4, # berthing + deberthing time [hr] "demurrage_rate": 730, # USD todo edit "transport_costs": 60, # USD per TEU, RHDHV "all_in_transport_costs": 908 # USD per TEU, Ports and Terminals p.158 } # *** Default inputs: Barge class *** # todo add sources small_barge_data = {"name": 'Small_Barge_1', "type": 'small', "ownership": 'Port authority', "delivery_time": 1, # years "lifespan": 10, # years "call_size": 200, # TEU "LOA": 90, # m "draught": 4.5, # m "beam": 12.0, # m "unit_rate": 1_000_000, # USD per barge "operations_perc": 0.10, "maintenance_perc": 0.10, "insurance_perc": 0.01, "mooring_time": 6, # berthing + deberthing time "transport_costs": 200} # USD per TEU medium_barge_data = {"name": 'Medium_Barge_1', "type": 'medium', "ownership": 'Port authority', "delivery_time": 1, # years "lifespan": 10, # years "call_size": 250, # TEU "LOA": 100, # m "draught": 5.0, # m "beam": 13.0, # m "unit_rate": 1_000_000, # USD per barge "operations_perc": 0.10, "maintenance_perc": 0.10, "insurance_perc": 0.01, "mooring_time": 6, # berthing + deberthing time "transport_costs": 200} # USD per TEU large_barge_data = {"name": 'Large_Barge_1', "type": 'large', "ownership": 'Port authority', "delivery_time": 1, # years "lifespan": 10, # years "call_size": 300, # TEU "LOA": 120, # m "draught": 5.5, # m "beam": 14.0, # m "unit_rate": 1_000_000, # USD per barge "operations_perc": 0.10, "maintenance_perc": 0.10, "insurance_perc": 0.01, "mooring_time": 6, # berthing + deberthing time "transport_costs": 200} # USD per TEU truck_data = {"name": 'Truck', "ownership": 'Port authority', "delivery_time": 1, "lifespan": 10, "unit_rate": 10_000, # USD per truck "operations_perc": 0.10, "maintenance_perc": 0.10, "insurance_perc": 0.01} # *** Default inputs: Labour class *** labour_data = {"name": 'Labour', "international_salary": 105_000, "international_staff": 4, "local_salary": 18_850, "local_staff": 10, "operational_salary": 16_750, "shift_length": 6.5, # hr per shift "annual_shifts": 200, "daily_shifts": 5, # shifts per day "blue_collar_salary": 25_000, # USD per crew per day "white_collar_salary": 35_000} # USD per crew per day # *** Default inputs: Energy class *** energy_data = {"name": 'Energy', "price": 0.10} # *** Default inputs: General_Services class *** general_services_data = {"name": 'General_Services"', "type": 'general_services', "office": 2400, "office_cost": 1500, "workshop": 2400, "workshop_cost": 1000, "fuel_station_cost": 500_000, "scanning_inspection_area": 2700, "scanning_inspection_area_cost": 1000, "lighting_mast_required": 1.2, # masts per ha "lighting_mast_cost": 30_000, "firefight_cost": 2_000_000, "maintenance_tools_cost": 10_000_000, "terminal_operating_software_cost": 10_000_000, "electrical_station_cost": 2_000_000, "repair_building": 100, "repair_building_cost": 1000, "ceo": 1, # FTE per 500 k TEU "secretary": 1, # FTE per 500 k TEU "administration": 3, # FTE per 500 k TEU "hr": 2, # FTE per 500 k TEU "commercial": 1, # FTE per 500 k TEU "operations": 4, # FTE/shirt per 500 k TEU "engineering": 2, # FTE/shift per 500 k TEU "security": 2, # FTE/shift per 500 k TEU "general_maintenance": 0.015, "crew_required": 500_000, # for each 500_k TEU an additional crew team is added "delivery_time": 1, "lighting_consumption": 1, "general_consumption": 1000} # *** Default inputs: Indirect_Costs class *** indirect_costs_data = {"name": 'Indirect_Costs', "preliminaries": 0.15, "engineering": 0.05, "miscellaneous": 0.15, "electrical_works_fuel_terminal": 0.12, "electrical_works_power_terminal": 0.15}

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d8c6bd57ec5db427c92f2222ca7c809d32503546

571

py

Python

src/arrays/combination-sum-2.py

vighnesh153/ds-algo

79c401dad2d2e575ce1913184ca8665f2712a5b8

[ "MIT" ]

null

src/arrays/combination-sum-2.py

vighnesh153/ds-algo

79c401dad2d2e575ce1913184ca8665f2712a5b8

[ "MIT" ]

null

src/arrays/combination-sum-2.py

vighnesh153/ds-algo

79c401dad2d2e575ce1913184ca8665f2712a5b8

[ "MIT" ]

1

2020-08-09T06:37:21.000Z

def recursion(arr, target, index, solutions, current): if target == 0: solutions.add(tuple(current)) return if target < 0 or index >= len(arr): return current.append(arr[index]) recursion(arr, target - arr[index], index + 1, solutions, current) current.pop() recursion(arr, target, index + 1, solutions, current) def solve(arr, target): solutions = set() arr.sort() recursion(arr, target, 0, solutions, []) return [list(item) for item in solutions] A = [10, 1, 2, 7, 6, 1, 5] B = 8 print(solve(A, B))

22.84

70

0.607706

79

571

4.392405

0.417722

0.129683

0.207493

0.132565

0

0.032558

0.246935

571

24

71

23.791667

0.774419

0

0.111111

0

1

0.111111

false

0

0.277778

0.055556

0

null

0

1

0

null

0

1

d8c794bc8aa2a91941f9265c3fe17da6aef9d56a

472

py

Python

mmhuman3d/core/visualization/__init__.py

ttxskk/mmhuman3d

f6d39e24a2d5cc216448fc3bd82832ff45eee436

[ "Apache-2.0" ]

1

2021-12-03T04:17:52.000Z

mmhuman3d/core/visualization/__init__.py

ttxskk/mmhuman3d

f6d39e24a2d5cc216448fc3bd82832ff45eee436

[ "Apache-2.0" ]

null

mmhuman3d/core/visualization/__init__.py

ttxskk/mmhuman3d

f6d39e24a2d5cc216448fc3bd82832ff45eee436

[ "Apache-2.0" ]

null

from .visualize_keypoints2d import visualize_kp2d from .visualize_keypoints3d import visualize_kp3d from .visualize_smpl import ( render_smpl, visualize_smpl_calibration, visualize_smpl_hmr, visualize_smpl_pose, visualize_smpl_vibe, visualize_T_pose, ) __all__ = [ 'visualize_kp2d', 'visualize_kp3d', 'visualize_smpl_pose', 'visualize_T_pose', 'render_smpl', 'visualize_smpl_vibe', 'visualize_smpl_calibration', 'visualize_smpl_hmr' ]

27.764706

62

0.777542

55

472

6.072727

0.272727

0.350299

0.113772

0.137725

0.239521

0

0.014851

0.144068

472

16

63

29.5

0.811881

0

0.290254

0.055085

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false

0

0.2

0

0.2

0

null

1

0

null

0

1

d8c9e071e19e41968b2a38fb82cb08379e2983f3

12,413

py

Python

pyoogle/preprocessing/crawl/crawler.py

DanDits/Pyoogle

f860dffb574f8629d3e894074450fdcb76547a03

[ "Apache-2.0" ]

null

pyoogle/preprocessing/crawl/crawler.py

DanDits/Pyoogle

f860dffb574f8629d3e894074450fdcb76547a03

[ "Apache-2.0" ]

null

pyoogle/preprocessing/crawl/crawler.py

DanDits/Pyoogle

f860dffb574f8629d3e894074450fdcb76547a03

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Sat Feb 6 12:49:02 2016 @author: daniel """ import logging import threading # For main processing thread import urllib # For downloading websites import urllib.error import urllib.request from concurrent.futures import ThreadPoolExecutor # each downloads a website from http.client import RemoteDisconnected from queue import Queue, Empty # For processing downloaded websites from socket import timeout as socket_timeout from pyoogle.config import LOGGING_LEVEL from pyoogle.preprocessing.crawl.linkconstraint import LinkConstraint # constraint to which links are allowed from pyoogle.preprocessing.web.net import WebNet from pyoogle.preprocessing.web.node import WebNode from pyoogle.preprocessing.web.nodestore import WebNodeStore # for permanently saving created WebNodes from pyoogle.preprocessing.web.parser import WebParser # parses the downloaded html site and extracts info logging.getLogger().setLevel(LOGGING_LEVEL) NotResolvable = "NOT_RESOLVABLE_LINK" class Crawler: # Initializes the Crawler. If max_sites is greater than zero it will only # download this many sites and stop afterwards, else until no new site is found. def __init__(self, store_path, link_constraint, max_sites=0, max_workers=2, timeout=30): self.store_path = store_path self.pending_links = Queue() self.pending_websites = Queue() self.web_net = None self.link_constraint = link_constraint if self.link_constraint is None: raise ValueError("No link constraint given!") self.already_processed_links = set() self.already_processed_websites = set() self.is_crawling = False self.max_sites = max_sites self.processed_sites_count = 0 self.max_workers = max_workers self.timeout = timeout self.starting_processor = None self.links_processor = None self.websites_processor = None def _is_finished(self): return not self.is_crawling or self.has_maximum_sites_processed() def has_maximum_sites_processed(self): return 0 < self.max_sites <= self.processed_sites_count def process_link(self, link): if self._is_finished(): return website = Crawler.download_website(link, self.timeout) if website is None: logging.debug("Website %s not downloaded", link) if website is NotResolvable: logging.debug("Website %s not resolvable and not trying again.", link) return return self, link, website @staticmethod def link_got_processed(future): if future.done() and future.result() is not None: self, link, website = future.result() if self._is_finished(): return if website is None: # revert and try later logging.debug("Website %s not downloaded, retrying later ", link) self.add_link(link) return if not self.has_maximum_sites_processed(): self.pending_websites.put((link, website)) def obtain_new_link(self): link = None while link is None and not self._is_finished(): try: link = self.pending_links.get(timeout=self.timeout) except Empty: logging.info("No more links found to process!") return if link in self.already_processed_links: link = None continue # already processed if link is not None: self.already_processed_links.add(link) return link def process_links(self): logging.info("Starting to process links") try: with ThreadPoolExecutor(max_workers=self.max_workers) as executor: while not self._is_finished(): # this will submit many many futures when testing with limited maxsites(>0) # but they will be ignored! link = self.obtain_new_link() if link is None: return future = executor.submit(self.process_link, link) future.add_done_callback(Crawler.link_got_processed) finally: self.stop() # ensure crawler is really stopped def process_website(self, link, website): logging.debug("Starting to parse %s pending links %d", link, self.pending_links.qsize()) try: webparser = WebParser(link, website) except ValueError: logging.debug("Website %s not parsable, ignored but out link kept", link) return web_hash = hash(webparser) if web_hash in self.already_processed_websites: # Already processed but with a different url, add this url to node so we know this in the future! logging.debug("Website %s already processed (with different url)!", link) node = self.web_net.get_by_content_hash(web_hash) if node is not None: node.add_url(link) return logging.info("Processed %d.link %s pending websites %d", self.processed_sites_count + 1, link, self.pending_websites.qsize()) self.already_processed_websites.add(web_hash) self.processed_sites_count += 1 builder = WebNode.Builder(self.link_constraint) builder.init_from_webparser(webparser) webnode = builder.make_node() self.web_net.add_node(webnode) for link in webnode.get_out_links(): self.add_link(link) def process_websites(self, clear_store): # We are required to open the store in the same thread the store is modified in logging.info("Starting to process websites") with WebNodeStore(self.store_path, clear_store) as node_store: try: while not self._is_finished(): data = self.pending_websites.get(block=True) if data is None: break link, website = data self.process_website(link, website) node_store.save_webnodes(self.web_net.get_nodes()) finally: self.stop() # ensure crawler is really stopped def _init_net(self, clear_store): self.web_net = WebNet() if not clear_store: # Do not clear the store but add new nodes to it, load and add existing to webnet with WebNodeStore(self.store_path, clear=False) as node_store: for node in node_store.load_webnodes(True): self.already_processed_websites.add(node.get_content_hash()) for link in node.get_urls(): self.already_processed_links.add(link) self.web_net.add_node(node) # After we marked all already processed links, add new outgoings to restart restart_link_count = 0 total_link_out = 0 for node in self.web_net: for link in node.get_out_links(): total_link_out += 1 if link not in self.already_processed_links: self.add_link(link) restart_link_count += 1 logging.info("Restarting with %d links of %d", restart_link_count, total_link_out) def _start_async(self, clear_store): self._init_net(clear_store) self.links_processor = threading.Thread(target=self.process_links) self.links_processor.start() self.websites_processor = threading.Thread(target=Crawler.process_websites, args=[self, clear_store]) self.websites_processor.start() def join(self): try: self.starting_processor.join() # If this stops blocking, the other processors are valid self.websites_processor.join() self.links_processor.join() except KeyboardInterrupt: self.stop() def start(self, start_url, clear_store=True): logging.info("Starting crawling at %s", start_url) self.is_crawling = True self.add_link(start_url) self.starting_processor = threading.Thread(target=Crawler._start_async, args=[self, clear_store]) self.starting_processor.start() def add_link(self, link): link = self.link_constraint.get_valid(link) if link is None: return self.pending_links.put(link) def stop(self): if self.is_crawling: # Race condition safe (could be executed multiple times) logging.info("Stopping crawling") self.is_crawling = False self.pending_websites.put(None) # Ensure threads do not wait forever and exit self.pending_links.put(None) @staticmethod def download_website(url, timeout): # Download and read website logging.debug("Downloading website %s", url) try: website = urllib.request.urlopen(url, timeout=timeout).read() except socket_timeout: logging.debug("Timeout error when downloading %s", url) website = None except urllib.error.HTTPError as err: if int(err.code / 100) == 4: logging.debug("Client http error when downloading %s %s", url, err) website = NotResolvable # 404 Not Found or other Client Error, ignore link in future else: logging.debug("HTTP Error when downloading %d %s %s", err.code, url, err) website = None except urllib.error.URLError as err: logging.debug("Url error when downloading %s %s", url, err) website = None except RemoteDisconnected as disc: logging.debug("(RemoteDisconnect) error when downloading %s %s", url, disc) website = NotResolvable except UnicodeEncodeError: logging.debug("(UnicodeEncodeError) error when downloading %s", url) website = NotResolvable return website def crawl_mathy(): # Build constraint that describes which outgoing WebNode links to follow constraint = LinkConstraint('http', 'www.math.kit.edu') # Prevent downloading links with these endings # Frequent candidates: '.png', '.jpg', '.jpeg', '.pdf', '.ico', '.doc', '.txt', '.gz', '.zip', '.tar','.ps', # '.docx', '.tex', 'gif', '.ppt', '.m', '.mw', '.mp3', '.wav', '.mp4' forbidden_endings = ['.pdf', '.png', '.ico', '#top'] # for fast exclusion constraint.add_rule(lambda link: all((not link.lower().endswith(ending) for ending in forbidden_endings))) # Forbid every point in the last path segment as this likely is a file and we are not interested in it def rule_no_point_in_last_path_segment(link_parsed): split = link_parsed.path.split("/") return len(split) == 0 or "." not in split[-1] constraint.add_rule(rule_no_point_in_last_path_segment, parsed_link=True) # Start the crawler from a start domain, optionally loading already existing nodes from pyoogle.config import DATABASE_PATH path = DATABASE_PATH c = Crawler(path, constraint) c.start("http://www.math.kit.edu", clear_store=False) # Wait for the crawler to finish c.join() webnet = c.web_net logging.info("DONE, webnet contains %d nodes", len(webnet)) return path, webnet def crawl_spon(): constraint = LinkConstraint('', 'www.spiegel.de') # Forbid every point in the last path segment as this likely is a file and we are not interested in it def rule_no_point_in_last_path_segment(link_parsed): split = link_parsed.path.split("/") return len(split) == 0 or ("." not in split[-1] or split[-1].lower().endswith(".html") or split[-1].lower().endswith(".htm")) constraint.add_rule(rule_no_point_in_last_path_segment, parsed_link=True) path = "/home/daniel/PycharmProjects/PageRank/spon.db" c = Crawler(path, constraint) c.start("http://www.spiegel.de", clear_store=False) # Wait for the crawler to finish c.join() webnet = c.web_net logging.info("DONE, webnet contains %d nodes", len(webnet)) return path, webnet if __name__ == "__main__": crawl_spon()

42.077966

112

0.634174

1,546

12,413

4.930789

0.205692

0.027286

0.023613

0.016398

0.283878

0.173554

0.122262

0.103896

0.091827

0

0.004483

0.281157

12,413

294

113

42.221088

0.849826

0.152501

0

0.265217

0

0.092098

0.004295

0

1

0.086957

false

0

0.069565

0.008696

0.234783

0

null

0

null

0

1

0

d8caaf44d7f053ff6f28f609749087b123ec4b34

2,965

py

Python

13.part2.py

elp2/advent_of_code_2018

0d359422dd04b0849481796005e97d05c30e9eb4

[ "Apache-2.0" ]

1

2021-12-02T15:19:36.000Z

13.part2.py

elp2/advent_of_code_2018

0d359422dd04b0849481796005e97d05c30e9eb4

[ "Apache-2.0" ]

null

13.part2.py

elp2/advent_of_code_2018

0d359422dd04b0849481796005e97d05c30e9eb4

[ "Apache-2.0" ]

null

from collections import defaultdict def return_default(): return 0 REAL=open("13.txt").readlines() SAMPLE=open("13.sample2").readlines() def parse_lines(lines): return list(map(list, lines)) CARTS = "^>v<" DIRS = [(0, -1), (1, 0), (0, 1), (-1, 0)] def cart_positions(start, facing, board): poses = [] pos = start corners = 0 fidx = DIRS.index(facing) while True: poses.append(pos) x, y = pos here = board[y][x] delta = 0 if here == "\\": delta = [-1, 1, -1, 1][fidx] elif here == "/": delta = [1, -1, 1, -1][fidx] elif here == "+": cmod = corners % 3 if cmod == 0: delta = -1 elif cmod == 1: delta = 0 elif cmod == 2: delta = 1 corners += 1 else: assert here in CARTS or here in "|-+" fidx = (fidx + len(DIRS) + delta) % len(DIRS) facing = DIRS[fidx] dx, dy = facing x += dx y += dy pos = (x, y) if pos == start and corners % 3 == 0: break return poses def solve(lines): carts = [] parsed = parse_lines(lines) ats = {} for y in range(len(lines)): for x in range(len(lines[y])): here = parsed[y][x] if here in CARTS: facing = DIRS[CARTS.index(here)] pos = (x, y) carts.append(cart_positions(pos, facing, parsed)) ats[pos] = len(carts) - 1 t = 0 dead_carts = set() while True: moved = set() for y in range(len(parsed)): for x in range(len(parsed[y])): pos = (x, y) if pos not in ats: continue cidx = ats[pos] if cidx in moved: continue moved.add(cidx) cart = carts[cidx] cart_next = cart[(t + 1) % len(cart)] if cart_next in ats: dead_carts.add(cidx) dead2 = ats[cart_next] dead_carts.add(dead2) print("Crash at ", cart_next, " from ", pos, cidx, dead2) del ats[cart_next] del ats[pos] if len(ats) == 1: at = list(ats.keys())[0] print("EARLY: " + str(at[0]) + "," + str(at[1])) else: ats[cart_next] = cidx del ats[pos] # assert len(ats) + len(dead_carts) == len(carts) # assert len(set(ats.keys()).intersection(dead_carts)) == 0 if len(ats) == 1: at = list(ats.keys())[0] return str(at[0]) + "," + str(at[1]) t += 1 sample = solve(SAMPLE) assert sample == "6,4" print("*** SAMPLE PASSED ***") print(solve(REAL)) # not 93,59

27.201835

77

0.43204

355

2,965

3.56338

0.230986

0.012648

0.01581

0.006324

0.151779

0.0917

0.072727

0

0.033175

0.430691

2,965

108

78

27.453704

0.716232

0.038786

0

0.186813

0

0.026353

0

0.021978

1

0.043956

false

0.010989

0.021978

0.098901

0.043956

0

null

0

null

0

1

0

d8cb54d17428f4a861ab1eb4f8524561f2936c44

844

py

Python

docs/_downloads/485d1a22616717976d2f85cbaf046db3/plot__jitterdodge_position.py

IKupriyanov-HORIS/lets-plot-docs

30fd31cb03dc649a03518b0c9348639ebfe09d53

[ "MIT" ]

null

docs/_downloads/485d1a22616717976d2f85cbaf046db3/plot__jitterdodge_position.py

IKupriyanov-HORIS/lets-plot-docs

30fd31cb03dc649a03518b0c9348639ebfe09d53

[ "MIT" ]

null

docs/_downloads/485d1a22616717976d2f85cbaf046db3/plot__jitterdodge_position.py

IKupriyanov-HORIS/lets-plot-docs

30fd31cb03dc649a03518b0c9348639ebfe09d53

[ "MIT" ]

null

""" Jitterdodge Position ==================== Position adjustments determine how to arrange geoms that would otherwise occupy the same space. Simultaneously dodge and jitter in one function: ``position_jitterdodge()``. See `position_jitterdodge() <https://jetbrains.github.io/lets-plot-docs/pages/api/lets_plot.position_jitterdodge.html#lets_plot.position_jitterdodge>`__. """ # sphinx_gallery_thumbnail_path = "gallery_py\_position_adjustments\_jitterdodge_position.png" import pandas as pd from lets_plot import * LetsPlot.setup_html() # %% df = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv') # %% ggplot(df, aes('cyl', 'hwy', group='drv', fill='drv')) + \ geom_boxplot() + \ geom_point(position='jitterdodge', shape=21, color='black')

27.225806

151

0.703791

103

844

5.563107

0.669903

0.165794

0.041885

0.094241

0

0.00277

0.14455

844

31

152

27.225806

0.790859

0.569905

0

0.142857

0.329193

0

1

0

false

0

0.285714

0

0.285714

0

null

0

null

0

1

0

d8ceaa47207dcd451d3a6b75d0d1b483e1ba9218

2,537

py

Python

mask_example/classification_vars.py

ami-a/MaskDetection

9df329a24a987e63331c17db154319b3ebcaad74

[ "MIT" ]

1

2021-04-09T09:08:33.000Z

mask_example/classification_vars.py

ami-a/MaskDetection

9df329a24a987e63331c17db154319b3ebcaad74

[ "MIT" ]

null

mask_example/classification_vars.py

ami-a/MaskDetection

9df329a24a987e63331c17db154319b3ebcaad74

[ "MIT" ]

null

"""loading the classification model variables for the detector object""" import numpy as np import cv2 from TrackEverything.tool_box import ClassificationVars def get_class_vars(class_model_path): """loading the classification model variables for the detector object We define here the model interpolation function so the detector can use the classification model Args: class_model_path (str): classification model path Returns: ClassificationVars: classification variables for the detector """ #custom classification model interpolation def custom_classify_detection(model,det_images,size=(224,224)): """Classify a batch of images Args: model (tensorflow model): classification model det_images (np.array): batch of images in numpy array to classify size (tuple, optional): size to resize to, 1-D int32 Tensor of 2 elements: new_height, new_width (if None then no resizing). (In custom function you can use model.inputs[0].shape.as_list() and set size to default) Returns: Numpy NxM vector where N num of images, M num of classes and filled with scores. For example two images (car,plan) with three possible classes (car,plan,lion) that are identify currectly with 90% in the currect category and the rest is devided equally will return [[0.9,0.05,0.05],[0.05,0.9,0.05]]. """ #resize bounding box capture to fit classification model if size is not None: det_images=np.asarray( [ cv2.resize(img, size, interpolation = cv2.INTER_LINEAR) for img in det_images ] ) predictions=model.predict(det_images/255.) #if class is binary make sure size is 2 if len(predictions)>0 and len(predictions[0])<2: reshaped_pred=np.ones((len(predictions),2)) #size of classification list is 1 so turn it to 2 for ind,pred in enumerate(predictions): reshaped_pred[ind,:]=pred,1-pred #print(reshaped_pred) predictions=reshaped_pred return predictions #providing only the classification model path for ClassificationVars #since the default loding method #tf.keras.models.load_model(path) will work return ClassificationVars( class_model_path=class_model_path, class_proccessing=custom_classify_detection )

41.590164

97

0.658652

330

2,537

4.972727

0.421212

0.092626

0.053626

0.042048

0.076782

0.070689

0

0.023433

0.276705

2,537

60

98

42.283333

0.870845

0.564446

0

1

0.090909

false

0

0.136364

0

0.318182

0

null

0

null

0

1

0

d8d26259abf1d70bfe1abffb2493230cee42b319

668

py

Python

detector/urls.py

SPIN-RD/data_analysis

b2ec9ca008781f3015ec3780a858de0dac4549b9

[ "MIT" ]

null

detector/urls.py

SPIN-RD/data_analysis

b2ec9ca008781f3015ec3780a858de0dac4549b9

[ "MIT" ]

null

detector/urls.py

SPIN-RD/data_analysis

b2ec9ca008781f3015ec3780a858de0dac4549b9

[ "MIT" ]

null

from django.urls import path from .views import ( MeasurementCreateView, MeasurementRetrieveView, energy_spectrum_analysis, half_life_analysis, index, ) urlpatterns = [ path("api/measurements/", MeasurementCreateView.as_view()), path( "api/measurements/<str:device_id>/<str:mode>", MeasurementRetrieveView.as_view() ), path("", index, name="index"), path( "detector/half-life/<str:device_id>", half_life_analysis, name="half-life-analysis", ), path( "detector/energy-spectrum/<str:device_id>", energy_spectrum_analysis, name="energy-spectrum-analysis", ), ]

23.857143

88

0.646707

67

668

6.253731

0.373134

0.133652

0.157518

0

0.22006

668

27

89

24.740741

0.804223

0

0.4

0

0.270958

0.211078

0

1

0

false

0

0.08

0

0.08

0

null

0

null

0

1

0

d8d3a26759da05b4ac662c9a2c7f47a9af51214a

166

py

Python

site_repo/utils/requests.py

Aviah/one-click-django-server

ddce7181f025b7f8d0979d725f85f8124add6adf

[ "MIT" ]

10

2016-03-22T22:14:40.000Z

2021-07-23T22:00:02.000Z

site_repo/utils/requests.py

Aviah/one-click-django-server

ddce7181f025b7f8d0979d725f85f8124add6adf

[ "MIT" ]

null

site_repo/utils/requests.py

Aviah/one-click-django-server

ddce7181f025b7f8d0979d725f85f8124add6adf

[ "MIT" ]

4

2016-04-05T05:41:15.000Z

2017-01-08T10:03:25.000Z

# requests utils def get_ip(request): ip = request.META['REMOTE_ADDR'] ip = request.META.get('HTTP_X_FORWARDED_FOR',ip) return ip

16.6

52

0.596386

22

166

4.272727

0.636364

0.287234

0.276596

0

0.295181

166

10

53

16.6

0.803419

0.084337

0

0.205298

0

1

0.25

false

0

0.5

0

1

0

null

1

0

null

0

1

0

4

d8d3a3c4c4228610d78f5c857adca30a66a1762a

142

py

Python

backend/app/views/GetConstructsAsGenbankView/__init__.py

Edinburgh-Genome-Foundry/dab

7eabf76adf3a0b9332c3651b5d0e5e6d98237d2b

[ "MIT" ]

7

2019-04-11T20:36:07.000Z

2020-03-24T07:12:13.000Z

backend/app/views/GetConstructsAsGenbankView/__init__.py

Edinburgh-Genome-Foundry/dab

7eabf76adf3a0b9332c3651b5d0e5e6d98237d2b

[ "MIT" ]

null

backend/app/views/GetConstructsAsGenbankView/__init__.py

Edinburgh-Genome-Foundry/dab

7eabf76adf3a0b9332c3651b5d0e5e6d98237d2b

[ "MIT" ]

null

from .GetConstructsAsGenbank import (GetConstructsAsGenbankView, construct_data_to_assemblies_sequences)

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1

0

null

0

1

0

1

0

5

d8d3e8f353c9df0eca02ab9d7ac5d28b32d4bbb1

1,287

py

Python

newsletter/models.py

doctsystems/newsletter

7355ad7bca5f2d082abf9703a20fc91a0c325454

[ "MIT" ]

null

newsletter/models.py

doctsystems/newsletter

7355ad7bca5f2d082abf9703a20fc91a0c325454

[ "MIT" ]

null

newsletter/models.py

doctsystems/newsletter

7355ad7bca5f2d082abf9703a20fc91a0c325454

[ "MIT" ]

null

from django.db import models class Newsletter(models.Model): name=models.CharField(max_length=100) description=models.TextField() created_at=models.DateTimeField(auto_now_add=True) updated_at=models.DateTimeField(auto_now=True) def __str__(self): return self.name class Subscriber(models.Model): name = models.CharField(max_length=100) email = models.CharField(max_length=100) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) newsletter = models.ManyToManyField(Newsletter, through='Subscription') def __str__(self): return self.name class Issue(models.Model): content = models.TextField() title = models.CharField(max_length=100) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) newsletter = models.ForeignKey(Newsletter, on_delete=models.CASCADE) def __str__(self): return self.title class Subscription(models.Model): newsletter = models.ForeignKey(Newsletter, on_delete=models.CASCADE) subscriber = models.ForeignKey(Subscriber, on_delete=models.CASCADE) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) def __str__(self): return f"{self.subscriber} Subscribed to {self.newsletter}"

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null

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d8d46c16be9da8396527eb259a0965644da2d48d

37

py

Python

veintitres/__init__.py

joelalejandro/veintitres-python

18fa7aa66688cce3f2c42ebc96ddb780bcd6d4bf

[ "MIT" ]

null

veintitres/__init__.py

joelalejandro/veintitres-python

18fa7aa66688cce3f2c42ebc96ddb780bcd6d4bf

[ "MIT" ]

null

veintitres/__init__.py

joelalejandro/veintitres-python

18fa7aa66688cce3f2c42ebc96ddb780bcd6d4bf

[ "MIT" ]

null

from .client import VeintitresClient

18.5

36

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1

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6

d8d586caec5e48f58983b527adfdcf89eb123054

6,604

py

Python

bin/pylint_runner.py

PickBas/meta-social

f6fb0a50c30e240086a75917b705dfdc71dbebf9

[ "MIT" ]

null

bin/pylint_runner.py

PickBas/meta-social

f6fb0a50c30e240086a75917b705dfdc71dbebf9

[ "MIT" ]

15

2020-06-07T07:58:05.000Z

2022-01-19T16:53:47.000Z

bin/pylint_runner.py

PickBas/meta-social

f6fb0a50c30e240086a75917b705dfdc71dbebf9

[ "MIT" ]

null

''' The MIT License (MIT) Copyright (c) 2015 Matthew Peveler Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' # https://github.com/MasterOdin/pylint_runner from argparse import ArgumentParser import configparser import os import sys import colorama import pylint import pylint.lint PYTHON_VERSION = ".".join([str(x) for x in sys.version_info[0:3]]) class Runner: """ A pylint runner that will lint all files recursively from the CWD. """ DEFAULT_IGNORE_FOLDERS = [".git", ".idea", "__pycache__"] DEFAULT_ARGS = ["--reports=n", "--output-format=colorized"] DEFAULT_RCFILE = ".pylintrc" def __init__(self, args=None): colorama.init(autoreset=True) self.verbose = False self.args = self.DEFAULT_ARGS self.rcfile = self.DEFAULT_RCFILE self.ignore_folders = self.DEFAULT_IGNORE_FOLDERS self._parse_args(args or sys.argv[1:]) self._parse_ignores() def _parse_args(self, args): """Parses any supplied command-line args and provides help text. """ parser = ArgumentParser(description="Runs pylint recursively on a directory") parser.add_argument( "-v", "--verbose", dest="verbose", action="store_true", default=False, help="Verbose mode (report which files were found for testing).", ) parser.add_argument( "--rcfile", dest="rcfile", action="store", default=".pylintrc", help="A relative or absolute path to your pylint rcfile. Defaults to\ `.pylintrc` at the current working directory", ) options, _ = parser.parse_known_args(args) self.verbose = options.verbose if options.rcfile: if not os.path.isfile(options.rcfile): options.rcfile = os.getcwd() + "/" + options.rcfile self.rcfile = options.rcfile return options def _parse_ignores(self): """ Parse the ignores setting from the pylintrc file if available. """ error_message = ( colorama.Fore.RED + "{} does not appear to be a valid pylintrc file".format(self.rcfile) + colorama.Fore.RESET ) if not os.path.isfile(self.rcfile): if not self._is_using_default_rcfile(): print(error_message) sys.exit(1) else: return config = configparser.ConfigParser() try: config.read(self.rcfile) except configparser.MissingSectionHeaderError: print(error_message) sys.exit(1) if config.has_section("MASTER") and config.get("MASTER", "ignore"): self.ignore_folders += config.get("MASTER", "ignore").split(",") def _is_using_default_rcfile(self): return self.rcfile == os.getcwd() + "/" + self.DEFAULT_RCFILE def _print_line(self, line): """ Print output only with verbose flag. """ if self.verbose and line != 'pylint_runner.py' and 'test_settings' not in line and 'test' not in line and 'migrations' not in line: print(line) def get_files_from_dir(self, current_dir): """ Recursively walk through a directory and get all python files and then walk through any potential directories that are found off current directory, so long as not within self.IGNORE_FOLDERS :return: all python files that were found off current_dir """ if current_dir[-1] != "/" and current_dir != ".": current_dir += "/" files = [] for dir_file in os.listdir(current_dir): if current_dir != ".": file_path = current_dir + dir_file else: file_path = dir_file if os.path.isfile(file_path): file_split = os.path.splitext(dir_file) if len(file_split) == 2 and file_split[0] != "" \ and file_split[1] == ".py": files.append(file_path) elif (os.path.isdir(dir_file) or os.path.isdir(file_path)) \ and dir_file not in self.ignore_folders: path = dir_file + os.path.sep if current_dir not in ["", "."]: path = os.path.join(current_dir.rstrip(os.path.sep), path) files += self.get_files_from_dir(path) return files def run(self, output=None, error=None): """ Runs pylint on all python files in the current directory """ pylint_output = output if output is not None else sys.stdout pylint_error = error if error is not None else sys.stderr savedout, savederr = sys.__stdout__, sys.__stderr__ sys.stdout = pylint_output sys.stderr = pylint_error pylint_files = self.get_files_from_dir(os.curdir) for pylint_file in pylint_files: # we need to recast this as a string, else pylint enters an endless recursion split_file = str(pylint_file).split("/") split_file[-1] = colorama.Fore.CYAN + split_file[-1] + colorama.Fore.RESET pylint_file = "/".join(split_file) if 'pylint' not in pylint_file: self._print_line(pylint_file) def main(output=None, error=None, verbose=False): """ The main (cli) interface for the pylint runner. """ runner = Runner(args=["--verbose"] if verbose is not False else None) runner.run(output, error) if __name__ == "__main__": main(verbose=True)

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0.045872

0

null

0

null

0

1

0

d8d6b40e4e24a3388d3de220686239dd259ccfe1

2,141

py

Python

util/intensity_scaling.py

LeHenschel/DeepFCD

494c92dfd80b55aa2369095853bfea84b770acdc

[ "MIT" ]

3

2020-03-24T11:16:50.000Z

2022-01-21T14:50:41.000Z

util/intensity_scaling.py

LeHenschel/DeepFCD

494c92dfd80b55aa2369095853bfea84b770acdc

[ "MIT" ]

null

util/intensity_scaling.py

LeHenschel/DeepFCD

494c92dfd80b55aa2369095853bfea84b770acdc

[ "MIT" ]

3

2022-01-07T10:53:45.000Z

2022-03-02T15:31:29.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Apr 7 17:24:20 2020 Rescaling exported synthetic PNGs to the intensity range of real PNGs. MinMax-Scaling not recommended. Better use Histogram Matching TODO: Integrate directly into apply_GAN notebook @author: bdavid """ import os import numpy as np import glob from PIL import Image #from matplotlib import pyplot as plt from skimage.exposure import match_histograms SYNTHPATH='/home/bdavid/Deep_Learning/playground/fake_flair_2d/png_cor/synth_T1/' REALPATH='/home/bdavid/Deep_Learning/playground/fake_flair_2d/png_cor/T1/' OUTPATH='/home/bdavid/Deep_Learning/playground/intensity_rescaled/T1_synth/test' def intensity_rescale(synth_img, real_img): real_img=np.array(Image.open(real_img)) synth_img=np.array(Image.open(synth_img)) min_real=np.min(real_img) max_real=np.max(real_img) scale=(max_real-min_real)/(np.max(synth_img)-np.min(synth_img)) offset=max_real - scale*np.max(synth_img) synth_img_scaled=scale*synth_img+offset # plt.figure() # plt.imshow(synth_img,cmap='gray',vmin=0,vmax=255) # plt.figure() # plt.imshow(real_img,cmap='gray',vmin=0,vmax=255) # plt.figure() # plt.imshow(synth_img_scaled.astype(int),cmap='gray',vmin=0,vmax=255) return Image.fromarray(np.uint8(synth_img_scaled)) def histo_matching(synth_img, real_img): real_img=np.array(Image.open(real_img)) synth_img=np.array(Image.open(synth_img)) synth_img_scaled=match_histograms(synth_img,real_img) return Image.fromarray(np.uint8(synth_img_scaled)) synth_list=glob.glob(os.path.join(SYNTHPATH,"test/*.png")) real_list=glob.glob(os.path.join(REALPATH,"test/*.png")) os.makedirs(OUTPATH, exist_ok=True) for synth_img, real_img in zip(synth_list, real_list): # synth_img_minmax_scaled= intensity_rescale(synth_img, real_img) # synth_img_minmax_scaled.save(os.path.join(OUTPATH,'test','minmax',synth_img.split('/')[-1])) synth_img_histo_scaled = histo_matching(synth_img, real_img) synth_img_histo_scaled.save(os.path.join(OUTPATH,synth_img.split('/')[-1]))

32.439394

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0

null

0

null

0

1

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1

d8d6b4d53e13b0fd18dcd2609163a130f5b31c93

1,311

py

Python

mysite/polls/migrations/0007_auto_20150314_0332.py

aaronkrolik/rule46

20d3e384768caced5b76f37e8fdefc2e9fb129d6

[ "Apache-2.0" ]

null

mysite/polls/migrations/0007_auto_20150314_0332.py

aaronkrolik/rule46

20d3e384768caced5b76f37e8fdefc2e9fb129d6

[ "Apache-2.0" ]

null

mysite/polls/migrations/0007_auto_20150314_0332.py

aaronkrolik/rule46

20d3e384768caced5b76f37e8fdefc2e9fb129d6

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('polls', '0006_auto_20150314_0320'), ] operations = [ migrations.CreateModel( name='Accolade', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('title', models.CharField(max_length=200)), ('accolade_text', models.TextField()), ('player', models.ForeignKey(to='polls.Player')), ], options={ }, bases=(models.Model,), ), migrations.AddField( model_name='player', name='position', field=models.CharField(default='x', max_length=200), preserve_default=False, ), migrations.AddField( model_name='player', name='salary', field=models.IntegerField(default=0), preserve_default=True, ), migrations.AddField( model_name='player', name='team', field=models.CharField(default='x', max_length=200), preserve_default=False, ), ]

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null

0

null

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0

d8d72c5cdaafc3330e3c8acc96c27ff888c380fc

2,549

py

Python

ceraon/api/v1/meals/schema.py

Rdbaker/Mealbound

37cec6b45a632ac26a5341a0c9556279b6229ea8

[ "BSD-3-Clause" ]

1

2018-11-03T17:48:50.000Z

ceraon/api/v1/meals/schema.py

Rdbaker/Mealbound

37cec6b45a632ac26a5341a0c9556279b6229ea8

[ "BSD-3-Clause" ]

3

2021-03-09T09:47:04.000Z

2022-02-12T13:04:41.000Z

ceraon/api/v1/meals/schema.py

Rdbaker/Mealbound

37cec6b45a632ac26a5341a0c9556279b6229ea8

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """Meal schema.""" from datetime import datetime as dt from marshmallow import Schema, ValidationError, fields, validates from ceraon.api.v1.locations.schema import LocationSchema from ceraon.api.v1.users.schema import UserSchema from ceraon.api.v1.tags.schema import TagSchema from ceraon.constants import Errors class MealSchema(Schema): """A schema for a Meal model.""" id = fields.UUID(dump_only=True) name = fields.String(required=True) description = fields.String() scheduled_for = fields.DateTime(required=True) price = fields.Float(required=True, places=2) host = fields.Nested(UserSchema, dump_only=True) location = fields.Nested(LocationSchema, dump_only=True, load_from='user.location') my_review = fields.Nested('ReviewSchema', dump_only=True, exclude=('meal',)) num_reviews = fields.Int(dump_only=True) num_guests = fields.Int(dump_only=True) max_guests = fields.Int() avg_rating = fields.Float(places=2, dump_only=True) tags = fields.Nested(TagSchema, many=True) guest_fields = ['my_review'] private_fields = ['location.{}'.format(field) for field in LocationSchema.private_fields] + \ ['host.{}'.format(field) for field in UserSchema.private_fields] class Meta: """Meta class for Meal schema.""" type_ = 'meal' strict = True @validates('name') def validate_name(self, value): """Validate the name field.""" if not value: raise ValidationError(Errors.MEAL_NAME_MISSING[1]) @validates('price') def validate_price(self, value): """Validate the price field.""" if not value: raise ValidationError(Errors.MEAL_PRICE_MISSING[1]) if value < 0: raise ValidationError(Errors.MEAL_PRICE_NEGATIVE[1]) @validates('scheduled_for') def validate_scheduled_for(self, value): """Validate the scheduled_for field.""" # if there is no timezone if value.tzinfo is None: # assign the server's timezone value = value.astimezone() if value <= dt.now().astimezone(): raise ValidationError(Errors.MEAL_CREATE_IN_PAST[1]) @validates('max_guests') def validate_max_guests(self, value): """Validate the max_guests field.""" if value is not None and value < 1: raise ValidationError(Errors.BAD_MAX_GUESTS)

34.445946

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null

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1

d8d7c06e5b65b400c9d18a38d0705a519de0d5e6

506

py

Python

Vbox_postgres_conn.py

RajasKhokle/Drug_Web

e59496fc4af69e34a5b0b4d3acb498a038ad831c

[ "BSD-3-Clause" ]

null

Vbox_postgres_conn.py

RajasKhokle/Drug_Web

e59496fc4af69e34a5b0b4d3acb498a038ad831c

[ "BSD-3-Clause" ]

null

Vbox_postgres_conn.py

RajasKhokle/Drug_Web

e59496fc4af69e34a5b0b4d3acb498a038ad831c

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Mar 29 08:14:44 2019 Purpose: Connect to the Postgresql Drug Database on local virtual machine on UBUNTU @author: Rajas Khokle """ import pandas as pd from sqlalchemy import create_engine # Create Connection to the database engine = create_engine('postgres://postgres:raj_drug_2019@10.37.17.10:5432/diabetes') df = pd.read_csv('P:\GA Capstone\Health Forcast\Capstone Deliverables\Tableau\Capstone.csv') df.to_sql('df',engine,if_exists = 'append',index=True)

28.111111

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null

0

null

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0

1

d8d80406f757e14704187e04f0b5d07b32575e58

1,071

py

Python

core/objs/zona.py

aanacleto/erp-

9c2d5388248cfe4b8cdb8454f6f47df4cb521f0e

[ "MIT" ]

null

core/objs/zona.py

aanacleto/erp-

9c2d5388248cfe4b8cdb8454f6f47df4cb521f0e

[ "MIT" ]

null

core/objs/zona.py

aanacleto/erp-

9c2d5388248cfe4b8cdb8454f6f47df4cb521f0e

[ "MIT" ]

2

2017-12-04T14:59:22.000Z

2018-12-06T18:50:29.000Z

# !/usr/bin/env python3 # -*- encoding: utf-8 -*- """ ERP+ """ __author__ = 'António Anacleto' __credits__ = [] __version__ = "1.0" __maintainer__ = "António Anacleto" __status__ = "Development" __model_name__ = 'zona.Zona' import auth, base_models from orm import * from form import * class Zona(Model, View): def __init__(self, **kargs): Model.__init__(self, **kargs) self.__name__ = 'zona' self.__title__ = 'Zonas de Distribuição' self.__model_name__ = __model_name__ self.__list_edit_mode__ = 'inline' self.__order_by__ = 'zona.nome' self.__auth__ = { 'read':['All'], 'write':['Gestor'], 'create':['Gestor'], 'delete':['Gestor'], 'full_access':['Gestor'] } self.__get_options__ = ['nome'] self.nome = string_field(view_order=1 , name='Nome', size=80) self.contratos = list_field(view_order=2 , name='Contratos', model_name='contrato.Contrato', condition="zona='{id}'", list_edit_mode='edit', onlist = False)

29.75

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0

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0.238095

1,071

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false

0

0.111111

0

0.185185

0

null

0

null

0

1

0

d8d85cecefde2c0134f937fbe84f1d254b9a273b

4,383

py

Python

biothings/hub/upgrade.py

sirloon/biothings.api

8a981fa2151e368d0ca76aaf226eb565d794d4fb

[ "Apache-2.0" ]

null

biothings/hub/upgrade.py

sirloon/biothings.api

8a981fa2151e368d0ca76aaf226eb565d794d4fb

[ "Apache-2.0" ]

null

biothings/hub/upgrade.py

sirloon/biothings.api

8a981fa2151e368d0ca76aaf226eb565d794d4fb

[ "Apache-2.0" ]

null

import sys from biothings.utils.hub_db import get_src_dump, get_data_plugin, get_hub_db_conn, backup, restore from biothings import config logging = config.logger def migrate_0dot1_to_0dot2(): """ mongodb src_dump/data_plugin changed: 1. "data_folder" and "release" under "download" 2. "data_folder" and "release" in upload.jobs[subsrc] taken from "download" 3. no more "err" under "upload" 4. no more "status" under "upload" 5. "pending_to_upload" is now "pending": ["upload"] """ src_dump = get_src_dump() data_plugin = get_data_plugin() for srccol in [src_dump,data_plugin]: logging.info("Converting collection %s" % srccol) srcs = [src for src in srccol.find()] wasdue = False for src in srcs: logging.info("\tConverting '%s'" % src["_id"]) # 1. for field in ["data_folder","release"]: if field in src: logging.debug("\t\t%s: found '%s' in document, moving under 'download'" % (src["_id"],field)) try: src["download"][field] = src.pop(field) wasdue = True except KeyError as e: logging.warning("\t\t%s: no such field '%s' found, skip it (error: %s)" % (src["_id"],field,e)) # 2. for subsrc_name in src.get("upload",{}).get("jobs",{}): for field in ["data_folder","release"]: if not field in src["upload"]["jobs"][subsrc_name]: logging.debug("\t\t%s: no '%s' found in upload jobs, taking it from 'download' (or from root keys)" % (src["_id"],field)) try: src["upload"]["jobs"][subsrc_name][field] = src["download"][field] wasdue = True except KeyError: try: src["upload"]["jobs"][subsrc_name][field] = src[field] wasdue = True except KeyError: logging.warning("\t\t%s: no such field '%s' found, skip it" % (src["_id"],field)) # 3. & 4. for field in ["err","status"]: if field in src.get("upload",{}): logging.debug("\t\t%s: removing '%s' key from 'upload'" % (src["_id"],field)) src["upload"].pop(field) wasdue = True # 5. if "pending_to_upload" in src: logging.debug("\t%s: found 'pending_to_upload' field, moving to 'pending' list" % src["_id"]) src.pop("pending_to_upload") wasdue = True if not "upload" in src.get("pending",[]): src.setdefault("pending",[]).append("upload") if wasdue: logging.info("\tFinishing converting document for '%s'" % src["_id"]) srccol.save(src) else: logging.info("\tDocument for '%s' already converted" % src["_id"]) def migrate(from_version, to_version,restore_if_failure=True): func_name = "migrate_%s_to_%s" % (from_version.replace(".","dot"), to_version.replace(".","dot")) # backup db = get_hub_db_conn()[config.DATA_HUB_DB_DATABASE] logging.info("Backing up %s" % db.name) path = backup() logging.info("Backup file: %s" % path) thismodule = sys.modules[__name__] try: func = getattr(thismodule,func_name) except AttributeError: logging.error("Can't upgrade, no such function to migrate from '%s' to '%s'" % (from_version, to_version)) raise # resolve A->C = A->B then B->C logging.info("Start upgrading from '%s' to '%s'" % (from_version, to_version)) try: func() except Exception as e: logging.exception("Failed upgrading: %s") if restore_if_failure: logging.info("Now restoring original database from '%s" % path) restore(db,path,drop=True) logging.info("Done. If you want to keep converted data for inspection, use restore_if_failure=False") else: logging.info("*not* restoring original data. It can still be restored using file '%s'" % path)

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4.293333

0.257143

0.048802

0.006655

0.022626

0.213398

0.116238

0.090506

0.035492

0

0.004806

0.335387

4,383

97

146

45.185567

0.768967

0.080995

0

0.216216

0

0.027027

0.254395

0.006027

0

1

0.027027

false

0

0.040541

0

0.067568

0

null

0

null

0

1

0

d8d8d4bab6bca93fe7ec5b879bc940d20a949497

22,052

py

Python

capirca/lib/gce.py

supertylerc/capirca

31235e964c9893f3f3432d84604fbaa727384047

[ "Apache-2.0" ]

null

capirca/lib/gce.py

supertylerc/capirca

31235e964c9893f3f3432d84604fbaa727384047

[ "Apache-2.0" ]

null

capirca/lib/gce.py

supertylerc/capirca

31235e964c9893f3f3432d84604fbaa727384047

[ "Apache-2.0" ]

null

# Copyright 2015 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """Google Compute Engine firewall generator. More information about GCE networking and firewalls: https://cloud.google.com/compute/docs/networking https://cloud.google.com/compute/docs/reference/latest/firewalls """ import copy import datetime import ipaddress import json import logging import re from typing import Dict, Any from capirca.lib import gcp from capirca.lib import nacaddr import six class Error(Exception): """Generic error class.""" class GceFirewallError(Error): """Raised with problems in formatting for GCE firewall.""" class ExceededAttributeCountError(Error): """Raised when the total attribute count of a policy is above the maximum.""" def IsDefaultDeny(term): """Returns true if a term is a default deny without IPs, ports, etc.""" skip_attrs = ['flattened', 'flattened_addr', 'flattened_saddr', 'flattened_daddr', 'action', 'comment', 'name', 'logging'] if 'deny' not in term.action: return False # This lc will look through all methods and attributes of the object. # It returns only the attributes that need to be looked at to determine if # this is a default deny. for i in [a for a in dir(term) if not a.startswith('__') and a.islower() and not callable(getattr(term, a))]: if i in skip_attrs: continue v = getattr(term, i) if isinstance(v, str) and v: return False if isinstance(v, list) and v: return False return True def GetNextPriority(priority): """Get the priority for the next rule.""" return priority class Term(gcp.Term): """Creates the term for the GCE firewall.""" ACTION_MAP = {'accept': 'allowed', 'deny': 'denied'} # Restrict the number of addresses per term to 256. # Similar restrictions apply to source and target tags, and ports. # Details: https://cloud.google.com/vpc/docs/quota#per_network_2 _TERM_ADDRESS_LIMIT = 256 _TERM_SOURCE_TAGS_LIMIT = 30 _TERM_TARGET_TAGS_LIMIT = 70 _TERM_PORTS_LIMIT = 256 # Firewall rule name has to match specific RE: # The first character must be a lowercase letter, and all following characters # must be a dash, lowercase letter, or digit, except the last character, which # cannot be a dash. # Details: https://cloud.google.com/compute/docs/reference/latest/firewalls _TERM_NAME_RE = re.compile(r'^[a-z]([-a-z0-9]*[a-z0-9])?$') # Protocols allowed by name from: # https://cloud.google.com/vpc/docs/firewalls#protocols_and_ports _ALLOW_PROTO_NAME = frozenset( ['tcp', 'udp', 'icmp', 'esp', 'ah', 'ipip', 'sctp', 'all' # Needed for default deny, do not use in policy file. ]) # Any protocol not in _ALLOW_PROTO_NAME must be passed by number. ALWAYS_PROTO_NUM = set(gcp.Term.PROTO_MAP.keys()) - _ALLOW_PROTO_NAME def __init__(self, term, inet_version='inet', policy_inet_version='inet'): super().__init__(term) self.term = term self.inet_version = inet_version # This is to handle mixed, where the policy_inet_version is mixed, # but the term inet version is either inet/inet6. # This is only useful for term name and priority. self.policy_inet_version = policy_inet_version self._validateDirection() if self.term.source_address_exclude and not self.term.source_address: raise GceFirewallError( 'GCE firewall does not support address exclusions without a source ' 'address list.') # The reason for the error below isn't because of a GCE restriction, but # because we don't want to use a bad default of GCE that allows talking # to anything when there's no source address, source tag, or source service # account. if (not self.term.source_address and not self.term.source_tag) and self.term.direction == 'INGRESS': raise GceFirewallError( 'GCE firewall needs either to specify source address or source tags.') if self.term.source_port: raise GceFirewallError( 'GCE firewall does not support source port restrictions.') if (self.term.source_address_exclude and self.term.source_address or self.term.destination_address_exclude and self.term.destination_address): self.term.FlattenAll() if not self.term.source_address and self.term.direction == 'INGRESS': raise GceFirewallError( 'GCE firewall rule no longer contains any source addresses after ' 'the prefixes in source_address_exclude were removed.') # Similarly to the comment above, the reason for this error is also # because we do not want to use the bad default of GCE that allows for # talking to anything when there is no IP address provided for this field. if not self.term.destination_address and self.term.direction == 'EGRESS': raise GceFirewallError( 'GCE firewall rule no longer contains any destination addresses ' 'after the prefixes in destination_address_exclude were removed.') def __str__(self): """Convert term to a string.""" json.dumps(self.ConvertToDict(), indent=2, separators=(six.ensure_str(','), six.ensure_str(': '))) def _validateDirection(self): if self.term.direction == 'INGRESS': if not self.term.source_address and not self.term.source_tag: raise GceFirewallError( 'Ingress rule missing required field oneof "sourceRanges" or ' '"sourceTags".') if self.term.destination_address: raise GceFirewallError('Ingress rules cannot include ' '"destinationRanges.') elif self.term.direction == 'EGRESS': if self.term.source_address: raise GceFirewallError( 'Egress rules cannot include "sourceRanges".') if not self.term.destination_address: raise GceFirewallError( 'Egress rule missing required field "destinationRanges".') if self.term.destination_tag: raise GceFirewallError( 'GCE Egress rule cannot have destination tag.') def ConvertToDict(self): """Convert term to a dictionary. This is used to get a dictionary describing this term which can be output easily as a JSON blob. Returns: A dictionary that contains all fields necessary to create or update a GCE firewall. Raises: GceFirewallError: The term name is too long. """ if self.term.owner: self.term.comment.append('Owner: %s' % self.term.owner) term_dict = { 'description': ' '.join(self.term.comment), 'name': self.term.name, 'direction': self.term.direction } if self.term.network: term_dict['network'] = self.term.network term_dict['name'] = '%s-%s' % ( self.term.network.split('/')[-1], term_dict['name']) # Identify if this is inet6 processing for a term under a mixed policy. mixed_policy_inet6_term = False if self.policy_inet_version == 'mixed' and self.inet_version == 'inet6': mixed_policy_inet6_term = True # Update term name to have the IPv6 suffix for the inet6 rule. if mixed_policy_inet6_term: term_dict['name'] = gcp.GetIpv6TermName(term_dict['name']) # Checking counts of tags, and ports to see if they exceeded limits. if len(self.term.source_tag) > self._TERM_SOURCE_TAGS_LIMIT: raise GceFirewallError( 'GCE firewall rule exceeded number of source tags per rule: %s' % self.term.name) if len(self.term.destination_tag) > self._TERM_TARGET_TAGS_LIMIT: raise GceFirewallError( 'GCE firewall rule exceeded number of target tags per rule: %s' % self.term.name) if self.term.source_tag: if self.term.direction == 'INGRESS': term_dict['sourceTags'] = self.term.source_tag elif self.term.direction == 'EGRESS': term_dict['targetTags'] = self.term.source_tag if self.term.destination_tag and self.term.direction == 'INGRESS': term_dict['targetTags'] = self.term.destination_tag if self.term.priority: term_dict['priority'] = self.term.priority # Update term priority for the inet6 rule. if mixed_policy_inet6_term: term_dict['priority'] = GetNextPriority(term_dict['priority']) rules = [] # If 'mixed' ends up in indvidual term inet_version, something has gone # horribly wrong. The only valid values are inet/inet6. term_af = self.AF_MAP.get(self.inet_version) if self.inet_version == 'mixed': raise GceFirewallError( 'GCE firewall rule has incorrect inet_version for rule: %s' % self.term.name) # Exit early for inet6 processing of mixed rules that have only tags, # and no IP addresses, since this is handled in the inet processing. if mixed_policy_inet6_term: if not self.term.source_address and not self.term.destination_address: if 'targetTags' in term_dict or 'sourceTags' in term_dict: return [] saddrs = sorted(self.term.GetAddressOfVersion('source_address', term_af), key=ipaddress.get_mixed_type_key) daddrs = sorted( self.term.GetAddressOfVersion('destination_address', term_af), key=ipaddress.get_mixed_type_key) # If the address got filtered out and is empty due to address family, we # don't render the term. At this point of term processing, the direction # has already been validated, so we can just log and return empty rule. if self.term.source_address and not saddrs: logging.warning( 'WARNING: Term %s is not being rendered for %s, ' 'because there are no addresses of that family.', self.term.name, self.inet_version) return [] if self.term.destination_address and not daddrs: logging.warning( 'WARNING: Term %s is not being rendered for %s, ' 'because there are no addresses of that family.', self.term.name, self.inet_version) return [] if not self.term.protocol: raise GceFirewallError( 'GCE firewall rule contains no protocol, it must be specified.') proto_dict = copy.deepcopy(term_dict) if self.term.logging: proto_dict['logConfig'] = {'enable': True} filtered_protocols = [] for proto in self.term.protocol: # ICMP filtering by inet_version # Since each term has inet_version, 'mixed' is correctly processed here. # Convert protocol to number for uniformity of comparison. # PROTO_MAP always returns protocol number. if proto in self._ALLOW_PROTO_NAME: proto_num = self.PROTO_MAP[proto] else: proto_num = proto if proto_num == self.PROTO_MAP['icmp'] and self.inet_version == 'inet6': logging.warning( 'WARNING: Term %s is being rendered for inet6, ICMP ' 'protocol will not be rendered.', self.term.name) continue if proto_num == self.PROTO_MAP['icmpv6'] and self.inet_version == 'inet': logging.warning( 'WARNING: Term %s is being rendered for inet, ICMPv6 ' 'protocol will not be rendered.', self.term.name) continue if proto_num == self.PROTO_MAP['igmp'] and self.inet_version == 'inet6': logging.warning( 'WARNING: Term %s is being rendered for inet6, IGMP ' 'protocol will not be rendered.', self.term.name) continue filtered_protocols.append(proto) # If there is no protocol left after ICMP/IGMP filtering, drop this term. if not filtered_protocols: return [] for proto in filtered_protocols: # If the protocol name is not supported, protocol number is used. # This is done by default in policy.py. if proto not in self._ALLOW_PROTO_NAME: logging.info( 'INFO: Term %s is being rendered using protocol number', self.term.name) dest = { 'IPProtocol': proto } if self.term.destination_port: ports = [] for start, end in self.term.destination_port: if start == end: ports.append(str(start)) else: ports.append('%d-%d' % (start, end)) if len(ports) > self._TERM_PORTS_LIMIT: raise GceFirewallError( 'GCE firewall rule exceeded number of ports per rule: %s' % self.term.name) dest['ports'] = ports action = self.ACTION_MAP[self.term.action[0]] dict_val = [] if action in proto_dict: dict_val = proto_dict[action] if not isinstance(dict_val, list): dict_val = [dict_val] dict_val.append(dest) proto_dict[action] = dict_val # There's a limit of 256 addresses each term can contain. # If we're above that limit, we're breaking it down in more terms. if saddrs: source_addr_chunks = [ saddrs[x:x+self._TERM_ADDRESS_LIMIT] for x in range( 0, len(saddrs), self._TERM_ADDRESS_LIMIT)] for i, chunk in enumerate(source_addr_chunks): rule = copy.deepcopy(proto_dict) if len(source_addr_chunks) > 1: rule['name'] = '%s-%d' % (rule['name'], i+1) rule['sourceRanges'] = [str(saddr) for saddr in chunk] rules.append(rule) elif daddrs: dest_addr_chunks = [ daddrs[x:x+self._TERM_ADDRESS_LIMIT] for x in range( 0, len(daddrs), self._TERM_ADDRESS_LIMIT)] for i, chunk in enumerate(dest_addr_chunks): rule = copy.deepcopy(proto_dict) if len(dest_addr_chunks) > 1: rule['name'] = '%s-%d' % (rule['name'], i+1) rule['destinationRanges'] = [str(daddr) for daddr in chunk] rules.append(rule) else: rules.append(proto_dict) # Sanity checking term name lengths. long_rules = [rule['name'] for rule in rules if len(rule['name']) > 63] if long_rules: raise GceFirewallError( 'GCE firewall name ended up being too long: %s' % long_rules) return rules class GCE(gcp.GCP): """A GCE firewall policy object.""" _PLATFORM = 'gce' SUFFIX = '.gce' _SUPPORTED_AF = frozenset(('inet', 'inet6', 'mixed')) _ANY_IP = { 'inet': nacaddr.IP('0.0.0.0/0'), 'inet6': nacaddr.IP('::/0'), } # Supported is 63 but we need to account for dynamic updates when the term # is rendered (which can add proto and a counter). _TERM_MAX_LENGTH = 53 _GOOD_DIRECTION = ['INGRESS', 'EGRESS'] _OPTIONAL_SUPPORTED_KEYWORDS = set(['expiration', 'destination_tag', 'source_tag']) def _BuildTokens(self): """Build supported tokens for platform. Returns: tuple containing both supported tokens and sub tokens """ supported_tokens, _ = super()._BuildTokens() # add extra things supported_tokens |= {'destination_tag', 'expiration', 'owner', 'priority', 'source_tag'} # remove unsupported things supported_tokens -= {'icmp_type', 'platform', 'platform_exclude', 'verbatim'} # easier to make a new structure supported_sub_tokens = {'action': {'accept', 'deny'}} return supported_tokens, supported_sub_tokens def _TranslatePolicy(self, pol, exp_info): self.gce_policies = [] max_attribute_count = 0 total_attribute_count = 0 total_rule_count = 0 current_date = datetime.datetime.utcnow().date() exp_info_date = current_date + datetime.timedelta(weeks=exp_info) for header, terms in pol.filters: if self._PLATFORM not in header.platforms: continue filter_options = header.FilterOptions(self._PLATFORM) filter_name = header.FilterName(self._PLATFORM) network = '' direction = 'INGRESS' if filter_options: for i in self._GOOD_DIRECTION: if i in filter_options: direction = i filter_options.remove(i) # Get the address family if set. address_family = 'inet' for i in self._SUPPORTED_AF: if i in filter_options: address_family = i filter_options.remove(i) for opt in filter_options: try: max_attribute_count = int(opt) logging.info( 'Checking policy for max attribute count %d', max_attribute_count) filter_options.remove(opt) break except ValueError: continue if filter_options: network = filter_options[0] else: logging.warning('GCE filter does not specify a network.') term_names = set() if IsDefaultDeny(terms[-1]): terms[-1].protocol = ['all'] terms[-1].priority = 65534 if direction == 'EGRESS': if address_family != 'mixed': # Default deny also gets processed as part of terms processing. # The name and priority get updated there. terms[-1].destination_address = [self._ANY_IP[address_family]] else: terms[-1].destination_address = [self._ANY_IP['inet'], self._ANY_IP['inet6']] else: if address_family != 'mixed': terms[-1].source_address = [self._ANY_IP[address_family]] else: terms[-1].source_address = [ self._ANY_IP['inet'], self._ANY_IP['inet6'] ] for term in terms: if term.stateless_reply: logging.warning('WARNING: Term %s in policy %s is a stateless reply ' 'term and will not be rendered.', term.name, filter_name) continue term.network = network if not term.comment: term.comment = header.comment if direction == 'EGRESS': term.name += '-e' term.name = self.FixTermLength(term.name) if term.name in term_names: raise GceFirewallError('Duplicate term name %s' % term.name) term_names.add(term.name) term.direction = direction if term.expiration: if term.expiration <= exp_info_date: logging.info('INFO: Term %s in policy %s expires ' 'in less than two weeks.', term.name, filter_name) if term.expiration <= current_date: logging.warning('WARNING: Term %s in policy %s is expired and ' 'will not be rendered.', term.name, filter_name) continue if term.option: raise GceFirewallError( 'GCE firewall does not support term options.') # Handle mixed for each indvidual term as inet and inet6. # inet/inet6 are treated the same. term_address_families = [] if address_family == 'mixed': term_address_families = ['inet', 'inet6'] else: term_address_families = [address_family] for term_af in term_address_families: for rules in Term(term, term_af, address_family).ConvertToDict(): logging.debug('Attribute count of rule %s is: %d', term.name, GetAttributeCount(rules)) total_attribute_count += GetAttributeCount(rules) total_rule_count += 1 if max_attribute_count and total_attribute_count > max_attribute_count: # Stop processing rules as soon as the attribute count is over the # limit. raise ExceededAttributeCountError( 'Attribute count (%d) for %s exceeded the maximum (%d)' % (total_attribute_count, filter_name, max_attribute_count)) self.gce_policies.append(rules) logging.info('Total rule count of policy %s is: %d', filter_name, total_rule_count) logging.info('Total attribute count of policy %s is: %d', filter_name, total_attribute_count) def __str__(self): out = '%s\n\n' % (json.dumps(self.gce_policies, indent=2, separators=(six.ensure_str(','), six.ensure_str(': ')), sort_keys=True)) return out def GetAttributeCount(dict_term: Dict[str, Any]) -> int: """Calculate the attribute count of a term in its dictionary form. The attribute count of a rule is the sum of the number of ports, protocols, IP ranges, tags and target service account. Note: The goal of this function is not to determine if a term is valid, but to calculate its attribute count regardless of correctness. Args: dict_term: A dict object. Returns: int: The attribute count of the term. """ addresses = (len(dict_term.get('destinationRanges', [])) or len(dict_term.get('sourceRanges', []))) proto_ports = 0 for allowed in dict_term.get('allowed', []): proto_ports += len(allowed.get('ports', [])) + 1 # 1 for ipProtocol for denied in dict_term.get('denied', []): proto_ports += len(denied.get('ports', [])) + 1 # 1 for ipProtocol tags = 0 for _ in dict_term.get('sourceTags', []): tags += 1 for _ in dict_term.get('targetTags', []): tags += 1 service_accounts = 0 for _ in dict_term.get('targetServiceAccount', []): service_accounts += 1 return addresses + proto_ports + tags + service_accounts

38.02069

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0.640169

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0.043725

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0.080601

0

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null

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null

0

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0

d8dab0f4aacf85ce7a8eb87b58a351fa764a3691

134,339

py

Python

myhabitatagent.py

karkuspeter/habitat-challenge

4b61be2b24b43d03246c94435febc691b6172ab6

[ "MIT" ]

null

myhabitatagent.py

karkuspeter/habitat-challenge

4b61be2b24b43d03246c94435febc691b6172ab6

[ "MIT" ]

null

myhabitatagent.py

karkuspeter/habitat-challenge

4b61be2b24b43d03246c94435febc691b6172ab6

[ "MIT" ]

null

import argparse import habitat import random import numpy as np import scipy import os import cv2 import time from habitat.tasks.nav.shortest_path_follower import ShortestPathFollower from habitat.utils.visualizations import maps from gibsonagents.expert import Expert from gibsonagents.pathplanners import Dstar_planner, Astar3D, VI_planner from gibsonagents.classic_mapping import rotate_2d, ClassicMapping, map_path_for_sim from utils.dotdict import dotdict from utils.tfrecordfeatures import tf_bytes_feature, tf_int64_feature, sequence_feature_wrapper # tf_bytelist_feature from habitat_utils import load_map_from_file, encode_image_to_png, get_model_id_from_episode, get_floor_from_json from vin import grid_actions_from_trajectory, project_state_and_goal_to_smaller_map import quaternion from multiprocessing import Queue, Process import atexit import platform from arguments import parse_args import tensorflow as tf from train import get_brain, get_tf_config from common_net import load_from_file, count_number_trainable_params from visualize.visualize_habitat_training import plot_viewpoints, plot_target_and_path, mapping_visualizer from gen_habitat_data import actions_from_trajectory from gen_planner_data import rotate_map_and_poses, Transform2D import matplotlib.pyplot as plt import matplotlib.animation as animation import matplotlib.gridspec as gridspec try: import ipdb as pdb except: import pdb # # Fix multiprocessing on mac OSX # if platform.system() == "Darwin": # import multiprocessing # multiprocessing.set_start_method('spawn') ACTION_SOURCE = "plan" #"expert" # "plan" # START_WITH_SPIN = False # True SPIN_TARGET = np.deg2rad(370) # np.deg2rad(270) # np.deg2rad(360 - 70) SPIN_DIRECTION = 1 # 1 for same direction as target, -1 for opposite direction. Opposite is better if target < 360 PLANNER_SINGLE_THREAD = False PLANNER_STOP_THREAD_EACH_EPISODE = False # COST_SETTING = 0 # 2 # SOFT_COST_MAP = True PLANNER2D_TIMEOUT = 200 # 200. # 0.08 # PLANNER3D_TIMEOUT = 2.5 # 1.5 # 200. # 0.08 - ------------------ RECOVER_ON_COLLISION = True COLLISION_DISTANCE_THRESHOLD = 0.6 # 0.8 MAX_SHORTCUT_TURNS = 2 # was 1 in submission NEAR_TARGET_COLLISION_STOP_DISTANCE = 5. # when colliding withing this radius to the goal, stop instead # # Patch map with collisions and around target TARGET_MAP_MARGIN = 2 OBSTACLE_DOWNWEIGHT_DISTANCE = 20 # from top, smaller the further OBSTACLE_DOWNWEIGHT_SCALARS = (0.3, 0.8) # (0.3, 0.8) EXTRA_STEPS_WHEN_EXPANDING_MAP = 30 # !!!!!! SUPRESS_EXCEPTIONS = False INTERACTIVE_ON_EXCEPTIONS = True PLOT_EVERY_N_STEP = -1 PRINT_TIMES = True INTERACTIVE_PLOT = True PLOT_PROCESS = False # True SAVE_VIDEO = True # will save params.num_video number of videos, or all if interactive USE_ASSERTS = False # 42 * 60 * 60 - 3 * 60 * 60 # 30 * 60 - 5 * 60 # TOTAL_TIME_LIMIT = 42 * 60 * 60 - 30 * 60 # challenge gave up at 38h and finished at 39h so 120 minutes should be enough. Even more recently. all giveup finished in 6 mins. # 42 hours = 2520 mins for 1000-2000 episodes. # Average episode time should be < 75.6 sec ERROR_ON_TIMEOUT = False # True SKIP_FIRST_N_FOR_TEST = -1 # 10 # 10 # 10 VIDEO_FRAME_SKIP = 1 # 6` VIDEO_FPS = 5 # 5 # 30 VIDEO_LARGE_PLOT = False VIDEO_DETAILED = True DEBUG_DUMMY_ACTIONS_ONLY = False SKIP_FIRST_N = -1 # 1000 SKIP_AFTER_N = -1 # 1500 SKIP_MAP_SHAPE_MISMATCH = True # !!!!!!! REPLACE_WITH_RANDOM_ACTIONS = False EXIT_AFTER_N_STEPS_FOR_SPEED_TEST = -1 FAKE_INPUT_FOR_SPEED_TEST = False MAX_MAP_SIZE_FOR_SPEED_TEST = False # DATA GENERATION - for both sim scenarios and real spot DATA_TYPE = "scenario" SAVE_DATA_EVERY_N = 1 # 4 DATA_FIRST_STEP_ONLY = True DATA_MAX_TRAJLEN = 50 # when DATA_FIRST_STEP_ONLY == False DATA_INCLUDE_NONPLANNED_ACTIONS = False DATA_USE_LAST_SEGMENT = False # when map is smaller use either the last or the first trajectory segment # DATA_SEPARATE_FILES = True # for real spot data DATA_SEPARATE_FILES = False # for simulated scenario data def giveup_settings(giveup_setting): # # Give up settings - submission if giveup_setting == "1": GIVE_UP_NO_PROGRESS_STEPS = 90 # 100 NO_PROGRESS_THRESHOLD = 15 GIVE_UP_NUM_COLLISIONS = 6 # 100 # TODO increase TODO increase later distances GIVE_UP_STEP_AND_DISTANCE = [[0, 340], [150, 220], [300, 150], [400, 100]] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [[3.5, 100], [4., 120], [5., 300], [6., 400]] # in minutes ! and distance reduction from beginning # Give up settings - more agressive for submission2 elif giveup_setting == "2": GIVE_UP_NO_PROGRESS_STEPS = 90 # 100 NO_PROGRESS_THRESHOLD = 15 GIVE_UP_NUM_COLLISIONS = 6 GIVE_UP_STEP_AND_DISTANCE = [[0, 340], [150, 220], [300, 100], [400, 50]] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [[3.5, 100], [4., 120], [5., 300], [6., 400]] # in minutenum_wrong_frees ! and distance reduction from beginning # Relaxed giveup settings for local evaluation (3) elif giveup_setting == "2": GIVE_UP_NO_PROGRESS_STEPS = 100 # 100 NO_PROGRESS_THRESHOLD = 12 GIVE_UP_NUM_COLLISIONS = 8 # 100 # TODO increase TODO increase later distances GIVE_UP_STEP_AND_DISTANCE = [[0, 440], [150, 320], [300, 250], [400, 150]] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [[10., 100], [15., 120], [20., 300], [30., 400]] # in minutes ! and distance reduction from beginning # Almost never give up -- august submission4 elif giveup_setting == "4": GIVE_UP_NO_PROGRESS_STEPS = 100 NO_PROGRESS_THRESHOLD = 12 GIVE_UP_NUM_COLLISIONS = 20 GIVE_UP_STEP_AND_DISTANCE = [[0, 440], [150, 300], [200, 250], [250, 200], [300, 150], [350, 100], [400, 40]] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [] #[10., 100], [15., 120], [20., 300], [30., 400]] # in minutes ! and distance reduction from beginning elif giveup_setting == "5": # # Almost never give up -- sept submission5 GIVE_UP_NO_PROGRESS_STEPS = 100 NO_PROGRESS_THRESHOLD = 10 GIVE_UP_NUM_COLLISIONS = 1000 GIVE_UP_STEP_AND_DISTANCE = [[200, 300], [300, 200], [400, 100]] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [] # # Almost never give up -- sept submission6 elif giveup_setting == "6": GIVE_UP_NO_PROGRESS_STEPS = 120 NO_PROGRESS_THRESHOLD = 10 GIVE_UP_NUM_COLLISIONS = 1000 GIVE_UP_STEP_AND_DISTANCE = [[200, 400], [300, 250], [400, 150], [450, 100]] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [] # # Almost never give up -- sept submission7 elif giveup_setting == "7": GIVE_UP_NO_PROGRESS_STEPS = 120 NO_PROGRESS_THRESHOLD = 10 GIVE_UP_NUM_COLLISIONS = 1000 GIVE_UP_STEP_AND_DISTANCE = [[200, 500], [300, 300], [400, 175], [450, 100]] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [] # # Almost never give up -- nov submission8 elif giveup_setting == "8": GIVE_UP_NO_PROGRESS_STEPS = 1000 NO_PROGRESS_THRESHOLD = 1 GIVE_UP_NUM_COLLISIONS = 1000 GIVE_UP_STEP_AND_DISTANCE = [[200, 400], [300, 240], [400, 160]] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [] # # no giveup but 300 limit for data generation elif giveup_setting == "data300": GIVE_UP_NO_PROGRESS_STEPS = 1000 NO_PROGRESS_THRESHOLD = 1 GIVE_UP_NUM_COLLISIONS = 1000 GIVE_UP_STEP_AND_DISTANCE = [[300, 1], ] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [] # in minutes ! and distance reduction from beginning # # No giveup elif giveup_setting == "never": GIVE_UP_NO_PROGRESS_STEPS = 1000 # 100 NO_PROGRESS_THRESHOLD = 1 GIVE_UP_NUM_COLLISIONS = 1000 GIVE_UP_STEP_AND_DISTANCE = [] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [] # in minutes ! and distance reduction from beginning # # No giveup elif giveup_setting == "always": GIVE_UP_NO_PROGRESS_STEPS = 1 # 100 NO_PROGRESS_THRESHOLD = 1 GIVE_UP_NUM_COLLISIONS = 1 GIVE_UP_STEP_AND_DISTANCE = [[0, 1]] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [] # in minutes ! and distance reduction from beginning # # Very agressive for fast testing elif giveup_setting == "fast": GIVE_UP_NO_PROGRESS_STEPS = 50 # 100 NO_PROGRESS_THRESHOLD = 15 GIVE_UP_NUM_COLLISIONS = 1 GIVE_UP_STEP_AND_DISTANCE = [[0, 340], [100, 200], [200, 100], [300, 50]] # NOTE if changing first threshold also change max map size. GIVE_UP_TIME_AND_REDUCTION = [[3.5, 100], [4., 120], [5., 300], [6., 400]] # in minutes ! and distance reduction from beginning else: raise ValueError('Unknown giveup_setting %s'%giveup_setting) return GIVE_UP_NO_PROGRESS_STEPS, NO_PROGRESS_THRESHOLD, GIVE_UP_NUM_COLLISIONS, GIVE_UP_STEP_AND_DISTANCE, GIVE_UP_TIME_AND_REDUCTION class DSLAMAgent(habitat.Agent): def __init__(self, task_config, params, env=None, logdir='./temp/', tfwriters=()): self.start_time = time.time() self._POSSIBLE_ACTIONS = task_config.TASK.POSSIBLE_ACTIONS self.step_i = 0 self.episode_i = -2 self.env = env self.task_config = task_config self.tfwriters = tfwriters self.num_data_entries = 0 if env is None: self.follower = None assert ACTION_SOURCE != "expert" else: self.follower = ShortestPathFollower(env._sim, 0.36/2., False) # if len(params.gpu) > 0 and int(params.gpu[0]) > 4: # print ("Try to explicitly disable gpu") # try: # tf.config.experimental.set_visible_devices([], 'GPU') # except Exception as e: # print("Exception " + str(e)) print (params) self.params = params # Giveup setting self.GIVE_UP_NO_PROGRESS_STEPS, self.NO_PROGRESS_THRESHOLD, self.GIVE_UP_NUM_COLLISIONS, \ self.GIVE_UP_STEP_AND_DISTANCE, self.GIVE_UP_TIME_AND_REDUCTION = giveup_settings(params.giveup) if INTERACTIVE_PLOT or self.params.interactive_video: plt.ion() assert params.sim in ['habitat', 'spot', 'spotsmall', 'spotsmall2', 'habitat2021'] self.map_source = self.params.agent_map_source self.pose_source = self.params.agent_pose_source self.action_source = ACTION_SOURCE self.max_confidence = 0.96 # 0.98 self.confidence_threshold = None # (0.2, 0.01) # (0.35, 0.05) self.use_custom_visibility = (self.params.visibility_mask in [2, 20, 21]) assert self.params.agent_map_source in ['true', 'true-saved', 'true-saved-sampled', 'true-saved-hrsampled', 'true-partial', 'true-partial-sampled', 'pred'] assert self.params.agent_pose_source in ['slam', 'slam-truestart', 'true'] _, gpuname = get_tf_config(devices=params.gpu) # sets CUDA_VISIBLE_DEVICES if params.skip_slam: print ("SKIP SLAM overwritting particles and removing noise.") assert self.pose_source == 'true' assert params.num_particles == 1 assert params.odom_source == 'relmotion' self.accumulated_spin = 0. self.spin_direction = None self.map_ch = 2 # slam_map_ch = 1 self.max_map_size = (self.params.global_map_size, self.params.global_map_size) # (360, 360) params.batchsize = 1 params.trajlen = 1 sensor_ch = (1 if params.mode == 'depth' else (3 if params.mode == 'rgb' else 4)) batchsize = params.batchsize if params.seed is not None and params.seed > 0: print("Fix Numpy and TF seed to %d" % params.seed) tf.set_random_seed(params.seed) np.random.seed(params.seed) random.seed(params.seed) # Build graph for slam and planner with tf.Graph().as_default(): with tf.variable_scope(tf.get_variable_scope(), reuse=False): # Choose planner if self.params.planner == 'astar3d': self.max_map_size = (370, 370) # also change giveup setting when changing this self.fixed_map_size = True self.planner_needs_cont_map = False self.allow_shrink_map = True assert self.params.agent_map_downscale == 1 # assert MAP_SOURCE != "true" self.pathplanner = Astar3D(single_thread=PLANNER_SINGLE_THREAD, max_map_size=self.max_map_size, timeout=self.params.planner_timeout) self.need_to_stop_planner_thread = PLANNER_STOP_THREAD_EACH_EPISODE elif self.params.planner in ['dstar_track_fixsize', 'dstar4_track_fixsize']: self.fixed_map_size = True self.planner_needs_cont_map = False self.allow_shrink_map = True assert self.params.agent_map_downscale == 1 if self.params.planner in ['dstar4_track_fixsize']: assert not self.params.connect8 else: assert self.params.connect8 self.pathplanner = Dstar_planner(single_thread=PLANNER_SINGLE_THREAD, max_map_size=self.max_map_size, connect8=self.params.connect8) self.need_to_stop_planner_thread = PLANNER_STOP_THREAD_EACH_EPISODE elif self.params.planner in ['dstar_track', 'dstar2d']: self.max_map_size = (900, 900) self.fixed_map_size = False self.planner_needs_cont_map = False self.allow_shrink_map = False assert self.params.agent_map_downscale == 1 assert self.params.connect8 # add to def config self.pathplanner = Dstar_planner(single_thread=PLANNER_SINGLE_THREAD, max_map_size=self.max_map_size, connect8=self.params.connect8) self.need_to_stop_planner_thread = PLANNER_STOP_THREAD_EACH_EPISODE elif self.params.planner in ['vi4', 'vi8', 'vi4-e1', 'vi8-e1', 'vi4-noshrink', 'vi8-noshrink']: self.fixed_map_size = True self.planner_needs_cont_map = False self.allow_shrink_map = (self.params.planner not in ['vi4-noshrink', 'vi8-noshrink']) if self.params.planner in ['vi4', 'vi4-e1', 'vi4-noshrink']: assert not self.params.connect8 else: assert self.params.connect8 self.pathplanner = VI_planner(max_map_size=(None, None), brain="trueplanner", params=self.params, connect8=self.params.connect8, downscale=self.params.agent_map_downscale) self.need_to_stop_planner_thread = False elif self.params.planner in ['vin', 'vin-e1', 'vinpred']: self.fixed_map_size = True self.planner_needs_cont_map = (self.params.planner in ['vinpred']) self.allow_shrink_map = False self.pathplanner = VI_planner(max_map_size=(None, None), brain=self.params.agent_planner_brain, params=self.params, connect8=self.params.connect8, downscale=self.params.agent_map_downscale) self.need_to_stop_planner_thread = False else: raise ValueError("Unknown planner %s"%self.params.planner) # Test data and network assert params.target in ["traj"] train_brain = get_brain(params.brain, params) req = train_brain.requirements() self.brain_requirements = req self.local_map_shape = req.local_map_size # Build slam brain with placeholder inputs # global_map_input = tf.placeholder(shape=(batchsize, None, None, slam_map_ch,), dtype=tf.float32) # self.images_input = tf.placeholder(shape=(batchsize, None) + req.sensor_shape + (sensor_ch,), # dtype=tf.float32) # self.visibility_input = ( # tf.placeholder(shape=(batchsize, None) + tuple(req.local_map_size) + (1,), dtype=tf.float32) # if params.visibility_mask == 2 # else tf.zeros((batchsize, None, 0, 0, 1))) self.new_images_input = tf.placeholder(shape=(batchsize, 1) + req.sensor_shape + (sensor_ch,), dtype=tf.float32) self.last_images_input = tf.placeholder(shape=(batchsize, 1) + req.sensor_shape + (sensor_ch,), dtype=tf.float32) self.past_visibility_input = tf.placeholder(shape=(batchsize, None) + tuple(req.local_map_size) + (1,), dtype=tf.float32) self.visibility_input = tf.placeholder(shape=(batchsize, 1) + tuple(req.local_map_size) + (1,), dtype=tf.float32) self.past_local_maps_input = tf.placeholder(shape=(batchsize, None) + tuple(req.local_map_size) + (1,), dtype=tf.float32) self.past_needed_image_features_input = tf.placeholder(shape=(batchsize, None) + tuple(req.local_map_size) + (req.latent_map_ch,), dtype=tf.float32) self.particle_xy_input = tf.placeholder(shape=(batchsize, None, params.num_particles, 2,), dtype=tf.float32) self.particle_yaw_input = tf.placeholder(shape=(batchsize, None, params.num_particles, 1,), dtype=tf.float32) self.last_step_particle_logits_input = tf.placeholder(shape=(batchsize, params.num_particles), dtype=tf.float32) self.new_action_input = tf.placeholder(shape=(batchsize, 1, 1,), dtype=tf.int32) self.new_rel_xy_input = tf.placeholder(shape=(batchsize, 1, 2,), dtype=tf.float32) self.new_rel_yaw_input = tf.placeholder(shape=(batchsize, 1, 1,), dtype=tf.float32) self.true_xy_input = tf.placeholder(shape=(batchsize, None, 2,), dtype=tf.float32) self.true_yaw_input = tf.placeholder(shape=(batchsize, None, 1,), dtype=tf.float32) self.inference_timesteps_input = tf.placeholder(shape=(batchsize, None), dtype=tf.int32) # indexes history to be used for slam update self.global_map_shape_input = tf.placeholder(shape=(2, ), dtype=tf.int32) if self.params.obstacle_downweight: custom_obstacle_prediction_weight = Expert.get_obstacle_prediction_weight(OBSTACLE_DOWNWEIGHT_DISTANCE, OBSTACLE_DOWNWEIGHT_SCALARS, self.local_map_shape) else: custom_obstacle_prediction_weight = None if FAKE_INPUT_FOR_SPEED_TEST: self.inference_outputs = train_brain.sequential_localization_with_past_and_pred_maps( tf.zeros_like(self.past_local_maps_input), tf.ones_like(self.past_visibility_input), tf.zeros_like(self.past_needed_image_features_input), tf.zeros_like(self.new_images_input), tf.zeros_like(self.true_xy_input), tf.zeros_like(self.true_yaw_input), tf.zeros_like(self.visibility_input), tf.zeros_like(self.particle_xy_input), tf.zeros_like(self.particle_yaw_input), tf.zeros_like(self.new_action_input), tf.zeros_like(self.new_rel_xy_input), tf.zeros_like(self.new_rel_yaw_input), particle_logits_acc=tf.zeros_like(self.last_step_particle_logits_input), global_map_shape=self.global_map_shape_input, max_confidence=self.max_confidence) else: ### # THIS IS USED NORMALLY ### self.inference_outputs = train_brain.sequential_localization_with_past_and_pred_maps( self.past_local_maps_input, self.past_visibility_input, self.past_needed_image_features_input, self.new_images_input, self.true_xy_input, self.true_yaw_input, self.visibility_input, self.particle_xy_input, self.particle_yaw_input, self.new_action_input, self.new_rel_xy_input, self.new_rel_yaw_input, inference_timesteps=self.inference_timesteps_input, particle_logits_acc=self.last_step_particle_logits_input, global_map_shape=(tuple(self.max_map_size) if self.fixed_map_size else self.global_map_shape_input), # self.global_map_shape_input, tuple(self.max_map_size), max_confidence=self.max_confidence, custom_obstacle_prediction_weight=custom_obstacle_prediction_weight, last_images=self.last_images_input, use_true_pose_instead_of_slam=(self.params.agent_pose_source == 'true'), ) if PLOT_EVERY_N_STEP < 0: self.inference_outputs = self.drop_output(self.inference_outputs, drop_names=['tiled_visibility_mask']) self.inference_outputs_without_map = self.drop_output(self.inference_outputs, drop_names=['global_map_logodds']) # self.inference_outputs = train_brain.sequential_localization_with_map_prediction( # self.images_input, self.true_xy_input, self.true_yaw_input, self.visibility_input, # self.particle_xy_input, self.particle_yaw_input, # self.new_action_input, self.new_rel_xy_input, self.new_rel_yaw_input, # particle_logits_acc=self.last_step_particle_logits_input) # self.inference_outputs = train_brain.sequential_localization_with_past_and_pred_maps( # self.past_local_maps_input, self.past_visibility_input, NEED_IMAGES, # self.new_images_input, self.true_xy_input, self.true_yaw_input, self.visibility_input, # self.particle_xy_input, self.particle_yaw_input, # self.new_action_input, self.new_rel_xy_input, self.new_rel_yaw_input, # particle_logits_acc=self.last_step_particle_logits_input) # # TODO pass in map inference inputs. Could produce one processed and one unprocess map for slam. # self.true_map_input = tf.placeholder(shape=self.max_map_size + (1, ), dtype=tf.uint8) # self.images_input = tf.placeholder(shape=req.sensor_shape + (sensor_ch,), dtype=tf.float32) # self.xy_input = tf.placeholder(shape=(2,), dtype=tf.float32) # self.yaw_input = tf.placeholder(shape=(1, ), dtype=tf.float32) # # self.action_input = tf.placeholder(shape=(2,), dtype=tf.float32) # actions = tf.zeros((1, 1, 2), dtype=tf.float32) # self.global_map_input = tf.placeholder(shape=self.max_map_size + (self.map_ch, ), dtype=tf.float32) # self.visibility_input = tf.placeholder(shape=self.local_map_shape + (1, ), dtype=tf.uint8) if self.use_custom_visibility else None # local_obj_map_labels = tf.zeros((1, 1, ) + self.local_map_shape + (1, ), dtype=np.uint8) # # self.inference_outputs = train_brain.sequential_inference( # self.true_map_input[None], self.images_input[None, None], self.xy_input[None, None], self.yaw_input[None, None], # actions, prev_global_map_logodds=self.global_map_input[None], # local_obj_maps=local_obj_map_labels, # confidence_threshold=self.confidence_threshold, # max_confidence=self.max_confidence, # max_obj_confidence=0.8, # custom_visibility_maps=None if self.visibility_input is None else self.visibility_input[None, None], # is_training=True) # self.true_map_input = tf.zeros(shape=self.max_map_size + (1, ), dtype=tf.uint8) # self.images_input = tf.zeros(shape=req.sensor_shape + (sensor_ch,), dtype=tf.float32) # self.xy_input = tf.ones(shape=(2,), dtype=tf.float32) # self.yaw_input = tf.zeros(shape=(1, ), dtype=tf.float32) # # self.action_input = tf.placeholder(shape=(2,), dtype=tf.float32) # actions = tf.ones((1, 1, 2), dtype=tf.float32) # self.global_map_input = tf.ones(shape=self.max_map_size + (self.map_ch, ), dtype=tf.float32) # self.visibility_input = tf.ones(shape=self.local_map_shape + (1, ), dtype=tf.uint8) if self.use_custom_visibility else None # local_obj_map_labels = tf.zeros((1, 1, ) + self.local_map_shape + (1, ), dtype=np.uint8) # # self.inference_outputs = train_brain.sequential_inference( # self.true_map_input[None], self.images_input[None, None], self.xy_input[None, None], self.yaw_input[None, None], # actions, prev_global_map_logodds=self.global_map_input[None], # local_obj_maps=local_obj_map_labels, # confidence_threshold=self.confidence_threshold, # max_confidence=self.max_confidence, # max_obj_confidence=0.8, # custom_visibility_maps=None if self.visibility_input is None else self.visibility_input[None, None], # is_training=True) # Add the variable initializer Op. init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) count_number_trainable_params(verbose=True) # training session gpuconfig, gpuname = get_tf_config(devices=params.gpu) self.sess = tf.Session(config=gpuconfig) # # Debug # self.sess.run(init) # withouth init if a variable is not loaded we get an error # outputs = self.sess.run(self.inference_outputs) # print ("Success") # pdb.set_trace() # self.sess.run(init) # withouth init if a variable is not loaded we get an error load_from_file(self.sess, params.load, partialload=params.partialload, loadcenter=[], skip=params.loadskip, autorename=False) self.global_map_logodds = None self.xy = None self.yaw = None self.target_xy = None self.step_i = -1 self.t = time.time() # datafile self.scenario_traj_data = [] # video self.frame_traj_data = [] self.num_videos_saved = 0 self.summary_str = "" self.filename_addition = "" self.logdir = logdir self.saved_map_i = 0 if self.params.interactive_video and PLOT_PROCESS: print ("Starting plot process.. ANY PLOT IN THIS THREAD WILL LEAD TO A CRASH") atexit.register(self.cleanup) # to stop process self.plot_queue = Queue() self.plot_process = Process(target=self.plot_loop, args=(self.plot_queue, )) self.plot_process.start() # # plotting should be only done in one thread... any plot in this thread will lead to a crash # plt.show = lambda *args: None # plt.figure = lambda *args: None # plt.imshow = lambda *args: None # plt.plot = lambda *args: None # # import matplotlib # # matplotlib.use('Agg') # # plt.figure = lambda *args: None else: self.plot_queue = None self.plot_process = None self.reset() def cleanup(self): try: if not self.plot_process or not self.plot_process.is_alive(): return print("Stopping plot process") self.plot_queue.put(("exit", None), block=False) self.plot_process.join(timeout=4.0) self.plot_process.terminate() except Exception as e: print ("Destructor had an exception. %s" % str(e)) def get_scene_name(self): scene_path = "unknown" if self.env is None else self.env._sim._current_scene scene_name = scene_path.split('/')[-1].split('.')[0] return scene_name def _to_grid_pos(self, agent_pos_0, agent_pos_2, top_down_map_dict): if not hasattr(maps, 'COORDINATE_MAX'): grid_pos = maps.to_grid( agent_pos_2, # not the order! agent_pos_0, top_down_map_dict['map'].shape[0:2], sim=self.env.sim, keep_float=True, ) else: del top_down_map_dict map_shape = (self.task_config.TASK.TOP_DOWN_MAP.MAP_RESOLUTION, self.task_config.TASK.TOP_DOWN_MAP.MAP_RESOLUTION) grid_pos = maps.to_grid( agent_pos_0, agent_pos_2, # order does not matter here maps.COORDINATE_MIN, maps.COORDINATE_MAX, map_shape, keep_float=True) return np.array(grid_pos, np.float32) def reset(self, last_success=None): self.step_i = -1 self.episode_i += 1 self.t = time.time() self.episode_t = self.t self.accumulated_spin = 0. self.spin_direction = None self.distance_history = [] self.raw_xy_transform = lambda xys: xys self.raw_yaw_offset = 0. self.recover_step_i = 0 self.num_collisions = 0 self.num_shortcut_actions = 0 self.num_wrong_obstacle = 0 self.num_wrong_free = 0 self.num_wrong_free_area = 0 self.num_wrong_free_area2 = 0 self.num_wrong_free_area3 = 0 self.map_mismatch_count = 0 self.plan_times = [] assert self.params.recoverpolicy in ['back5', 'back1'] num_recover_back_steps = (5 if self.params.recoverpolicy == 'back5' else 1) self.recover_policy = [3] * 6 + [1] * num_recover_back_steps self.global_map_logodds = None # will initialize in act() np.zeros((1, 1) + (1, ), np.float32) self.collision_timesteps = [] for tfwriter in self.tfwriters: tfwriter.flush() self.pathplanner.reset() self.reset_scenario_data_writer() self.reset_video_writer(last_success=last_success) print ("Resetting agent %d. Scene %s."%(self.episode_i, self.get_scene_name())) self.last_call_time = time.time() def drop_output(self, outputs, drop_names): return dotdict({key: val for key, val in outputs.items() if key not in drop_names}) def plan_and_control(self, xy, yaw, target_xy, global_map_pred, ang_vel, target_fi, allow_shrink_map=False, cont_global_map_pred=None): if self.params.start_with_spin and np.abs(self.accumulated_spin) < SPIN_TARGET and self.step_i < 40: if self.spin_direction is None: self.spin_direction = SPIN_DIRECTION * np.sign(target_fi) # spin opposite direction to the goal self.accumulated_spin += ang_vel # spin status_message = "%d: spin %f: %f"%(self.step_i, self.spin_direction, self.accumulated_spin) action = (2 if self.spin_direction > 0 else 3) planned_path = np.zeros((0, 2)) return action, planned_path, status_message, (global_map_pred * 255.).astype(np.uint8), None assert global_map_pred.dtype == np.float32 assert cont_global_map_pred is None or cont_global_map_pred.dtype == np.float32 if self.params.soft_cost_map: assert not allow_shrink_map assert cont_global_map_pred is None # keep global_map as continuous input elif allow_shrink_map: assert cont_global_map_pred is None # otherwise would need to shrink it global_map_pred = (global_map_pred * 255.).astype(np.uint8) xy, target_xy, global_map_pred, offset_xy = self.shrink_map(xy, target_xy, global_map_pred) else: global_map_pred = (global_map_pred * 255.).astype(np.uint8) offset_xy = None # Scan map and cost graph. scan_graph, eroded_scan_map, normal_scan_map, costmap = Expert.get_graph_and_eroded_map( raw_trav_map=global_map_pred[..., :1], trav_map_for_simulator=global_map_pred[..., :1], raw_scan_map=global_map_pred, rescale_scan_map=1., erosion=self.params.map_erosion_for_planning, build_graph=False, interactive_channel=False, cost_setting=self.params.cost_setting, soft_cost_map=self.params.soft_cost_map, ) # plt.figure() # plt.imshow(costmap) # plt.show() # pdb.set_trace() # scan_map = global_map_pred[..., :1] # # scan_map = cv2.erode(scan_map, kernel=np.ones((3, 3))) # scan_map[scan_map<255] = 0 # # costmap = np.zeros_like(global_map_pred, dtype=np.float32) # costmap[global_map_pred == 0] = 1000. # # temp_map1 = scan_map # temp_map2 = cv2.erode(temp_map1, kernel=np.ones((3, 3))) # temp_filter = np.logical_and(temp_map2 < 255, temp_map1 == 255) # costmap[temp_filter] = 100. # # temp_map1 = scan_map # temp_map2 = cv2.erode(temp_map1, kernel=np.ones((7, 7))) # temp_filter = np.logical_and(temp_map2 < 255, temp_map1 == 255) # costmap[temp_filter] = 1. assert self.params.goalpolicy in ['twostep', 'none'] start_time = time.time() if self.params.planner == 'astar3d': assert self.params.goalpolicy in ['twostep'] # need to add support below for removing twostep action, obstacle_distance, planned_path, status_message = Expert.discrete3d_policy( scan_map=eroded_scan_map, pos_map_float=xy, yaw=yaw, target_map_float=target_xy, cost_map=costmap, pathplanner=self.pathplanner) elif self.params.planner == 'dstar2d': assert self.params.goalpolicy in ['twostep'] # need to add support below for removing twostep action, obstacle_distance, planned_path, status_message = Expert.discrete_policy( scan_map=eroded_scan_map, pos_map_float=xy, yaw=yaw, target_map_float=target_xy, cost_map=costmap, use_asserts=True, shortest_path_fn=lambda _, source_tuple, target_tuple, cost_map, scan_map: self.pathplanner.dstar_path( cost_map, source_tuple, target_tuple, timeout=PLANNER2D_TIMEOUT)) elif self.params.planner in ['dstar_track', 'dstar_track_fixsize', 'dstar4_track_fixsize']: action, obstacle_distance, planned_path, status_message = Expert.discrete_tracking_policy( scan_map=eroded_scan_map, pos_map_float=xy, yaw=yaw, target_map_float=target_xy, cost_map=costmap, use_lookahead=True, use_twostep_approach=(self.params.goalpolicy == 'twostep'), shortest_path_fn=lambda _, source_tuple, target_tuple, cost_map, scan_map: self.pathplanner.dstar_path( cost_map, source_tuple, target_tuple, timeout=PLANNER2D_TIMEOUT)) elif self.params.planner in ['vi4', 'vi8', 'vin', 'vi4-noshrink', 'vi8-noshrink']: assert not self.params.soft_cost_map # because expert policy checks traversability assuming uint map action, obstacle_distance, planned_path, status_message = Expert.discrete_tracking_policy( scan_map=eroded_scan_map, pos_map_float=xy, yaw=yaw, target_map_float=target_xy, cost_map=costmap, use_lookahead=True, use_twostep_approach=(self.params.goalpolicy == 'twostep'), shortest_path_fn=lambda _, source_tuple, target_tuple, cost_map, scan_map: self.pathplanner.vi_path( scan_map, cost_map, source_tuple, target_tuple, sess=self.sess)) elif self.params.planner in ['vinpred']: assert not allow_shrink_map # because we are shortcutting map, not using the shrunk map assert not self.params.soft_cost_map # because expert policy checks traversability assuming uint map assert cont_global_map_pred is not None # Shortcut map input to path planner directly action, obstacle_distance, planned_path, status_message = Expert.discrete_tracking_policy( scan_map=eroded_scan_map, pos_map_float=xy, yaw=yaw, target_map_float=target_xy, cost_map=costmap, use_lookahead=True, use_twostep_approach=(self.params.goalpolicy == 'twostep'), shortest_path_fn=lambda _, source_tuple, target_tuple, cost_map, scan_map: self.pathplanner.vi_path( cont_global_map_pred * 255., # this is intentionally shortcutting the scan_map input cost_map, source_tuple, target_tuple, sess=self.sess)) elif self.params.planner in ['vi4-e1', 'vi8-e1', 'vin-e1']: assert not self.params.soft_cost_map # because expert policy checks traversability assuming uint map action, obstacle_distance, planned_path, status_message = Expert.discrete_tracking_policy( scan_map=eroded_scan_map, pos_map_float=xy, yaw=yaw, target_map_float=target_xy, cost_map=costmap, use_lookahead=True, use_twostep_approach=(self.params.goalpolicy == 'twostep'), shortest_path_fn=lambda _, source_tuple, target_tuple, cost_map, scan_map: self.pathplanner.vi_path( normal_scan_map, cost_map, source_tuple, target_tuple, sess=self.sess)) else: raise ValueError("Unknown planner %s"%(self.params.planner)) status_message = "%d/%d: %.2f %s"%(self.episode_i, self.step_i, time.time()-self.t, status_message) self.t = time.time() self.plan_times.append(time.time() - start_time) if allow_shrink_map: planned_path = planned_path + offset_xy[None] return action, planned_path, status_message, eroded_scan_map, offset_xy def act(self, observations): if SUPRESS_EXCEPTIONS or INTERACTIVE_ON_EXCEPTIONS: try: return_val = self.wrapped_act(observations) if self.need_to_stop_planner_thread and return_val['action'] == 0: self.pathplanner.stop_thread() self.last_call_time = time.time() return return_val except Exception as e: print ("Exception " + str(e)) if INTERACTIVE_ON_EXCEPTIONS: self.reset_scenario_data_writer() self.reset_video_writer(last_success=False) print ("Data and video saved. Continue?") pdb.set_trace() self.last_call_time = time.time() return {"action": 0, "xy_error": 0.} else: return_val = self.wrapped_act(observations) if self.need_to_stop_planner_thread and return_val['action'] == 0: self.pathplanner.stop_thread() self.last_call_time = time.time() return return_val def wrapped_act(self, observations): time_sim = time.time() - self.last_call_time time_last = time.time() time_now = time.time() time_since_beginning = time_now - self.start_time if REPLACE_WITH_RANDOM_ACTIONS and self.episode_i > 2: self.step_i += 1 if self.step_i > 100: action = 0 else: action = np.random.choice([1, 2, 3]) return {"action": action, "xy_error": 0.} initial_target_r_meters, initial_target_fi = observations['pointgoal'] if self.step_i == -1: self.step_i += 1 # self.initial_xy = np.zeros((2, ), np.float32) # self.initial_yaw = np.zeros((1, ), np.float32) self.episode_t = time_now if self.env is not None: # info = self.env.get_metrics() # print (info['top_down_map']['agent_map_coord']) # pdb.set_trace() stay_action = self.task_config.SIMULATOR.get('STAY_ACTION', 3) # turn right by default return {"action": stay_action} # turn right, because first step does not provide the top down map # otherwise continue below if TOTAL_TIME_LIMIT > 0 and time_since_beginning > TOTAL_TIME_LIMIT: print ("Giving up because total time limit of %d sec reached."%TOTAL_TIME_LIMIT) if ERROR_ON_TIMEOUT: raise ValueError("Timeout.. only for minival!") return {"action": 0, "xy_error": 0.} if (SKIP_FIRST_N_FOR_TEST > 0 and self.episode_i < SKIP_FIRST_N_FOR_TEST) or (SKIP_FIRST_N > 0 and self.episode_i < SKIP_FIRST_N) or (SKIP_AFTER_N > 0 and self.episode_i >= SKIP_AFTER_N): print ("Skip") return {"action": 0, "xy_error": 0.} # if EXIT_AFTER_N_STEPS_FOR_SPEED_TEST > 0 and self.step_i > EXIT_AFTER_N_STEPS_FOR_SPEED_TEST: # raise SystemExit # Check for possible shortcut shortcut_action = None need_map = True if self.params.skip_plan_when_turning and len(self.pathplanner.cached_action_path) >= 2 and self.num_shortcut_actions < MAX_SHORTCUT_TURNS: cached_next_action = self.pathplanner.cached_action_path[1] if cached_next_action in [2, 3]: # turning print ("Shortcut turn action") shortcut_action = cached_next_action need_map = False self.num_shortcut_actions += 1 self.pathplanner.num_timeouts += 1 self.pathplanner.cached_action_path = self.pathplanner.cached_action_path[1:] else: self.num_shortcut_actions = 0 else: self.num_shortcut_actions = 0 if RECOVER_ON_COLLISION and self.recover_step_i > 0: need_map = False # True pose and map if self.env is not None: # When using slam, must run with --habitat_eval local not localtest. # Thats because with --localtest we skip the first step, but that ruins the goal observation. assert self.pose_source != 'slam' info = self.env.get_metrics() agent_pos = self.env.sim.get_agent_state().position goal_pos = self.env.current_episode.goals[0].position # First deal with observed map and optionally samplre random rotation true_global_map = info['top_down_map']['map'] true_global_map = (true_global_map > 0).astype(np.uint8) * 255 true_global_map = np.atleast_3d(true_global_map) if self.step_i > 0: # Count pixels that used to be free but not in the latest map if not self.params.random_rotations: new_map_mismatch_count = np.count_nonzero(np.logical_and(true_global_map == 0, self.last_true_global_map)) if new_map_mismatch_count > 200: print ("TOO MANY MAP MISMATCHES %d"%new_map_mismatch_count) # assert False self.map_mismatch_count += new_map_mismatch_count # assert true_global_map.shape == self.last_true_global_map.shape true_global_map = self.last_true_global_map # keep the first else: if self.map_source in ['true-saved', 'true-saved-sampled', 'true-saved-hrsampled', 'true-partial-sampled']: saved_global_map = load_map_from_file(scene_id=self.get_scene_name(), height=agent_pos[1], map_name=( "map" if self.map_source == 'true-saved' else ('sampledmap' if self.map_source in [ 'true-saved-sampled', 'true-partial-sampled'] else 'hrsampledmap')), basepath=map_path_for_sim(self.params.sim)) assert saved_global_map.dtype == np.uint8 if saved_global_map.shape != true_global_map.shape: # This can happen if floors are not perfectly aligned, etc. Its a problem as we cannot recover # map pose anymore. # Save log print ('Map shapes mismatch for %s. Exmple saved under ./temp/failures/'%self.get_scene_name()) if not os.path.exists('./temp/failures'): os.mkdir('./temp/failures') cv2.imwrite('./temp/failures/%s_ep%d_envmap.png'%(self.get_scene_name(), int(self.env.current_episode.episode_id)), true_global_map) cv2.imwrite('./temp/failures/%s_ep%d_savedmap.png'%(self.get_scene_name(), int(self.env.current_episode.episode_id)), saved_global_map) if SKIP_MAP_SHAPE_MISMATCH: print("Skip because of map mismatch") return {"action": 0, "xy_error": 0.} else: raise ValueError('Map shapes mismatch. Exmple saved under ./temp/failures/') self.map_mismatch_count = np.count_nonzero(np.logical_and(saved_global_map, true_global_map)) true_global_map = saved_global_map else: self.map_mismatch_count = 0 # Rotate true map once and keep this for the episode. Remember the transformation and apply it from all raw observations if self.params.random_rotations: self.raw_yaw_offset = np.random.rand() * 2. * np.pi true_global_map, new_poses, transform = rotate_map_and_poses(true_global_map, self.raw_yaw_offset, poses=[np.zeros((1, 2), np.float32)], constant_value=0) assert true_global_map.dtype == np.uint8 # Reapply threshold true_global_map = (true_global_map > 128).astype(np.uint8) * 255 self.raw_xy_transform = transform self.last_true_global_map = true_global_map # Deal with observed pose # TODO this might be wrong here if map shapes don't match and/or change during episode. true_xy = np.array(info['top_down_map']['agent_map_coord']) # x: downwards; y: rightwars if np.any(true_xy < 0.): raise ValueError("Map coordinates are less than zero. On spot this happens if the dummy environment " "happens to be smaller than the real world.") true_xy = self.raw_xy_transform(true_xy[None])[0] true_yaw = info['top_down_map']['agent_angle'] # 0 downwards, positive ccw. Forms stanard coord system with x and y. true_yaw = true_yaw + self.raw_yaw_offset true_yaw = np.array((true_yaw, ), np.float32) true_yaw = (true_yaw + np.pi) % (2 * np.pi) - np.pi # normalize # Recover from simulator pos true_xy_from_pos = self._to_grid_pos(agent_pos[0], agent_pos[2], info['top_down_map']) true_xy_from_pos = self.raw_xy_transform(true_xy_from_pos[None])[0] offset_xy = true_xy - true_xy_from_pos global_true_target_xy = self._to_grid_pos(goal_pos[0], goal_pos[2], info['top_down_map']) true_target_xy = self.raw_xy_transform(global_true_target_xy[None])[0] true_target_xy += offset_xy del offset_xy # # Debug pose # print("position", true_xy, self.env.sim.get_agent_state().position) # rot_euler = quaternion.as_euler_angles(self.env.sim.get_agent_state().rotation) # print("rotation", np.rad2deg(true_yaw), np.rad2deg(rot_euler)) # # quat = quaternion.from_euler_angles(np.pi, true_yaw - np.pi, np.pi) # print (np.rad2deg(quaternion.as_euler_angles(quat))) # # pdb.set_trace() else: true_xy = np.zeros((2,), np.float32) true_yaw = np.zeros((1,), np.float32) true_target_xy = np.zeros((2,), np.float32) global_true_target_xy = None info = None true_global_map = np.zeros([self.max_map_size[0], self.max_map_size[1], 1], np.float32) # Initialize everything if self.step_i == 0: if self.pose_source in ['true', 'slam-truestart']: # Initialize with true things. Only makes sense if we access it assert self.env is not None self.true_xy_offset = -true_xy.astype(np.int32) # self.true_xy_transform = Transform2D # self.true_xy_transform.add_translation() if self.fixed_map_size: self.global_map_logodds = np.zeros((self.max_map_size[0], self.max_map_size[1], 1), np.float32) # np.zeros(true_global_map.shape, np.float32) else: self.global_map_logodds = np.zeros((1, 1, 1), np.float32) # np.zeros(true_global_map.shape, np.float32) self.prev_yaw = true_yaw self.xy = true_xy + self.true_xy_offset self.yaw = true_yaw particle_xy0 = np.tile((self.xy)[None], [self.params.num_particles, 1]) particle_yaw0 = np.tile(self.yaw[None], [self.params.num_particles, 1]) # Target from observed distance. Can only use it after reset initial_target_r_meters, initial_target_fi = observations['pointgoal'] initial_target_r = initial_target_r_meters / 0.05 # meters to grid cells # assumes initial pose is 0.0 initial_target_xy = rotate_2d(np.array([initial_target_r, 0.], np.float32), initial_target_fi + true_yaw + np.deg2rad(30)) + true_xy + self.true_xy_offset # Target from observed distance. Can only use it after reset initial_target_r_meters, initial_target_fi = observations['pointgoal_with_gps_compass'] target_r = initial_target_r_meters / 0.05 # meters to grid cells # assumes initial pose is 0.0 observed_target_xy = rotate_2d(np.array([target_r, 0.], np.float32), initial_target_fi + true_yaw) + true_xy + self.true_xy_offset print ("Target observed: (%d, %d) true: (%d, %d) initial (%d, %d)"%( observed_target_xy[0], observed_target_xy[1], true_target_xy[0] + self.true_xy_offset[0], true_target_xy[1] + self.true_xy_offset[1], initial_target_xy[0], initial_target_xy[1])) if np.linalg.norm(observed_target_xy - (true_target_xy + self.true_xy_offset)) > 0.001: pdb.set_trace() self.target_xy = observed_target_xy elif self.pose_source == "slam": self.true_xy_offset = np.zeros((2,), np.int32) # we dont know if self.fixed_map_size: self.global_map_logodds = np.zeros((self.max_map_size[0], self.max_map_size[1], 1), np.float32) # np.zeros(true_global_map.shape, np.float32) else: self.global_map_logodds = np.zeros((1, 1, 1), np.float32) # np.zeros(true_global_map.shape, np.float32) self.prev_yaw = 0. self.xy = np.zeros((2, ), np.float32) self.yaw = np.zeros((1, ), np.float32) particle_xy0 = np.zeros((self.params.num_particles, 2), np.float32) particle_yaw0 = np.zeros((self.params.num_particles, 1), np.float32) # Target from observed distance. Can only use it after reset initial_target_r_meters, initial_target_fi = observations['pointgoal'] target_r = initial_target_r_meters / 0.05 # meters to grid cells # assumes initial pose is 0.0 observed_target_xy = rotate_2d(np.array([target_r, 0.], np.float32), initial_target_fi) self.target_xy = observed_target_xy else: raise ValueError("Unknown pose estimation source.") self.particle_xy_list = [particle_xy0] self.particle_yaw_list = [particle_yaw0] self.particle_logit_acc_list = [np.zeros((self.params.num_particles,), np.float32)] self.xy_loss_list = [0.] self.yaw_loss_list = [0.] self.true_xy_traj = [true_xy] self.true_yaw_traj = [true_yaw] self.action_traj = [] # Resize map and add offset map_shape = self.global_map_logodds.shape if self.fixed_map_size: # xy_map_margin = 10 # this is before slam update. a single step can move 6 cells plus estimation may change. # # TODO xy_map_margin was occasionally too small. Expose as param and increase # # # Keep a fixed map size. Dont even update it, only move the offset, such that center point is between current pose and goal # assert map_shape[:2] == self.max_map_size # assert self.max_map_size[0] == self.max_map_size[1] # # center_xy = (self.xy + self.target_xy) * 0.5 # desired_center_xy = np.array(self.max_map_size, np.float32) * 0.5 # offset_xy = (desired_center_xy - center_xy).astype(np.int) # # new_xy = self.xy + offset_xy # # # Handle the case when xy would fall out of the map area or would be too near the edge. # # These will be only nonzero if xy is outside the allowed area # # if np.any(new_xy < xy_map_margin): # offset_xy += np.ceil(np.maximum(xy_map_margin - new_xy, 0.)).astype(np.int32) # # if np.any(new_xy >= self.max_map_size[0] - xy_map_margin): # offset_xy -= np.ceil(np.maximum(new_xy - (self.max_map_size[0] - xy_map_margin), 0.)).astype(np.int32) # # self.particle_xy_list = [xy + offset_xy for xy in self.particle_xy_list] # self.target_xy += offset_xy # self.true_xy_offset += offset_xy # self.xy += offset_xy # # # Handle the case when target is outside of the map area # if np.any(self.target_xy < TARGET_MAP_MARGIN) or np.any(self.target_xy >= self.max_map_size[0] - TARGET_MAP_MARGIN): # # Find the free map cell closest to the target # global_map_pred = ClassicMapping.inverse_logodds(self.global_map_logodds) # # TODO this should use the same threshold instead of 0.5 # free_x, free_y = np.nonzero(np.squeeze(global_map_pred[TARGET_MAP_MARGIN:-TARGET_MAP_MARGIN, TARGET_MAP_MARGIN:-TARGET_MAP_MARGIN], axis=-1) >= 0.5) # free_xy = np.stack([free_x, free_y], axis=-1) # free_xy = free_xy.astype(np.float32) # free_xy += 0.5 # free_xy += TARGET_MAP_MARGIN # dist = np.linalg.norm(free_xy - self.target_xy[None], axis=1) # # pretend the closest free cell is the target # self.target_xy_for_planning = free_xy[np.argmin(dist)] # print ("Moving target within the map: %s --> %s"%(str(self.target_xy), str(self.target_xy_for_planning))) # else: # self.target_xy_for_planning = self.target_xy.copy() # Keep a fixed map size. Dont even update it, only move the offset, such that center point is between current pose and goal assert map_shape[:2] == self.max_map_size # Find the free map cell closest to the target global_map_pred = ClassicMapping.inverse_logodds(self.global_map_logodds) is_free_map = (np.squeeze(global_map_pred, axis=-1) >= 0.5) # TODO this should use the same threshold instead of 0.5 # TODO xy_map_margin was occasionally too small. Expose as param and increase offset_ij, projected_target_xy = project_state_and_goal_to_smaller_map( self.max_map_size, self.xy, self.target_xy, is_free_map, xy_map_margin=10, target_map_margin=TARGET_MAP_MARGIN) self.particle_xy_list = [xy + offset_ij for xy in self.particle_xy_list] self.target_xy += offset_ij self.true_xy_offset += offset_ij self.xy += offset_ij self.target_xy_for_planning = projected_target_xy if np.any(self.target_xy != self.target_xy_for_planning): print("Moving target within the map: %s --> %s" % (str(self.target_xy), str(self.target_xy_for_planning))) else: # Expand map and offset pose if needed, such that target and the surrounding of current pose are all in the map. if MAX_MAP_SIZE_FOR_SPEED_TEST: offset_ij = np.array(((self.max_map_size[0]-map_shape[0])//2, (self.max_map_size[1]-map_shape[1])//2), np.int32) expand_xy = offset_ij.copy() else: local_map_max_extent = 110 # TODO need to adjust to local map size and scaler local_map_max_extent += 10 # to account for how much the robot may move in one step, including max overshooting target_margin = 8 min_particle_xy = self.particle_xy_list[-1].min(axis=0) # last is step is enough because earliers could arleady fit on map max_particle_xy = self.particle_xy_list[-1].max(axis=0) min_x = int(min(self.target_xy[0] - target_margin, min_particle_xy[0] - local_map_max_extent) - 1) min_y = int(min(self.target_xy[1] - target_margin, min_particle_xy[1] - local_map_max_extent) - 1) max_x = int(max(self.target_xy[0] + target_margin, max_particle_xy[0] + local_map_max_extent) + 1) max_y = int(max(self.target_xy[1] + target_margin, max_particle_xy[1] + local_map_max_extent) + 1) offset_ij = np.array([max(0, -min_x), max(0, -min_y)]) expand_xy = np.array([max(0, max_x+1-map_shape[0]), max(0, max_y+1-map_shape[1])]) is_offset = np.any(offset_ij > 0) is_expand = np.any(expand_xy > 0) if is_offset: offset_ij += 0 if MAX_MAP_SIZE_FOR_SPEED_TEST else EXTRA_STEPS_WHEN_EXPANDING_MAP self.particle_xy_list = [xy + offset_ij for xy in self.particle_xy_list] self.target_xy += offset_ij self.true_xy_offset += offset_ij if is_expand: expand_xy += 0 if MAX_MAP_SIZE_FOR_SPEED_TEST else EXTRA_STEPS_WHEN_EXPANDING_MAP if is_offset or is_expand: prev_shape = self.global_map_logodds.shape self.global_map_logodds = np.pad( self.global_map_logodds, [[offset_ij[0], expand_xy[0]], [offset_ij[1], expand_xy[1]], [0, 0]], mode='constant', constant_values=0.) print ("Increasing map size: (%d, %d) --> (%d, %d) offset (%d, %d), expand (%d, %d)"%( prev_shape[0], prev_shape[1], self.global_map_logodds.shape[0], self.global_map_logodds.shape[1], offset_ij[0], offset_ij[1], expand_xy[0], expand_xy[1])) excess_xy = np.array(self.global_map_logodds.shape[:2], np.int32) - np.array(self.max_map_size[:2], np.int32) excess_xy = np.maximum(excess_xy, np.zeros_like(excess_xy)) if np.any(excess_xy > 0): print ("Reducing map to fit max size (%d, %d)"%(excess_xy[0], excess_xy[1])) if self.target_xy[0] > self.global_map_logodds.shape[0] // 2: self.global_map_logodds = self.global_map_logodds[excess_xy[0]:] else: self.global_map_logodds = self.global_map_logodds[:-excess_xy[0]] if self.target_xy[1] > self.global_map_logodds.shape[1] // 2: self.global_map_logodds = self.global_map_logodds[:, excess_xy[1]:] else: self.global_map_logodds = self.global_map_logodds[:, :-excess_xy[1]] self.target_xy_for_planning = self.target_xy.copy() map_shape = self.global_map_logodds.shape # Offset true map if self.env is not None: reduce_xy = np.maximum(-self.true_xy_offset, np.zeros((2,), np.int32)).astype(np.int32) extend_xy = np.maximum(self.true_xy_offset, np.zeros((2,), np.int32)).astype(np.int32) global_map_label = true_global_map * (1./255.) global_map_label = global_map_label[reduce_xy[0]:, reduce_xy[1]:] global_map_label = np.pad(global_map_label, [[extend_xy[0], 0], [extend_xy[1], 0], [0, 0]]) global_map_label = np.pad(global_map_label, [[0, max(map_shape[0]-global_map_label.shape[0], 0)], [0, max(map_shape[1]-global_map_label.shape[1], 0)], [0, 0]]) global_map_label = global_map_label[:map_shape[0], :map_shape[1]] assert global_map_label.shape == map_shape else: global_map_label = None # Get image observations rgb = observations['rgb'] depth = observations['depth'] if USE_ASSERTS: assert rgb.dtype == np.uint8 assert depth.dtype == np.float32 and np.all(depth <= 1.) rgb = cv2.resize(rgb, (160, 90), ) rgb = rgb.astype(np.float32) * 255. depth = cv2.resize(depth, (160, 90), ) # interpolation=cv2.INTER_NEAREST) depth = np.atleast_3d(depth) if self.params.mode == 'both': images = np.concatenate([depth, rgb], axis=-1) # these are 0..1 float format elif self.params.mode == 'depth': images = depth else: images = rgb images = (images * 255).astype(np.uint8) images = np.array(images, np.float32) # images = images * 255 # to unit8 0..255 format images = images * (2. / 255.) - 1. # to network input -1..1 format # Get visibility map from depth if needed if self.use_custom_visibility: visibility_map_input = ClassicMapping.is_visible_from_depth(depth, self.local_map_shape, sim=self.params.sim, zoom_factor=self.brain_requirements.transform_window_scaler, fix_habitat_depth=self.params.fix_habitat_depth) visibility_map_input = visibility_map_input[:, :, None].astype(np.float32) assert np.all(visibility_map_input <= 1.) else: visibility_map_input = np.zeros(self.visibility_input.shape[2:], dtype=np.float32) # # Map prediction only, using known pose # last_global_map_input = np.zeros(self.max_map_size + (self.map_ch, ), np.float32) # last_global_map_input[:map_shape[0], :map_shape[1]] = self.global_map_logodds # true_map_input = np.zeros(self.max_map_size + (1, ), np.uint8) # true_map_input[:global_map_label.shape[0], :global_map_label.shape[1]] = global_map_label # # feed_dict = { # self.images_input: images, self.xy_input: true_xy, self.yaw_input: np.array((true_yaw, )), # self.global_map_input: last_global_map_input, # self.true_map_input: true_map_input, # } # if self.visibility_input is not None: # visibility_map_input = ClassicMapping.is_visible_from_depth(depth, self.local_map_shape, sim=self.params.sim, zoom_factor=self.brain_requirements.transform_window_scaler) # visibility_map_input = visibility_map_input[:, :, None].astype(np.uint8) # feed_dict[self.visibility_input] = visibility_map_input # # mapping_output = self.run_inference(feed_dict) # global_map_logodds = np.array(mapping_output.global_map_logodds[0, -1]) # squeeze batch and traj # global_map_logodds = global_map_logodds[:map_shape[0], :map_shape[1]] # self.global_map_logodds = global_map_logodds time_prepare = time.time() - time_last time_last = time.time() # SLAM prediction if self.step_i == 0: # For the first step we dont do pose update, but we need to obtain local maps and image features self.image_traj = [images.copy()] # Get local maps for first feed_dict = { self.new_images_input: images[None, None], self.visibility_input: visibility_map_input[None, None], } # TODO we should predict global map as well with a single local map added to it new_local_maps, new_visibility_maps, new_image_features = self.sess.run([self.inference_outputs['new_local_maps'], self.inference_outputs['new_visibility_maps'], self.inference_outputs['new_image_features']], feed_dict=feed_dict) self.local_map_traj = [new_local_maps[0, 0]] self.visibility_traj = [new_visibility_maps[0, 0]] self.image_features_traj = [new_image_features[0, 0]] slam_outputs = None # Transform predictions global_map_true_partial = None assert self.global_map_logodds.shape[-1] == 1 global_map_pred = ClassicMapping.inverse_logodds(self.global_map_logodds) slam_xy = np.mean(self.particle_xy_list[-1], axis=0) slam_yaw = np.mean(self.particle_yaw_list[-1], axis=0) slam_mean_xy = slam_xy slam_mean_yaw = slam_yaw slam_mean2_xy = slam_xy slam_mean2_yaw = slam_yaw slam_ml_xy = slam_xy slam_ml_yaw = slam_yaw slam_traj_xy = None slam_traj_yaw = None else: assert len(self.action_traj) > 0 assert len(self.particle_xy_list) == len(self.action_traj) assert self.visibility_traj[-1].dtype == np.float32 assert np.all(self.visibility_traj[-1] <= 1.) inference_trajlen = self.params.inference_trajlen self.image_traj.append(images.copy()) self.true_xy_traj.append(true_xy) self.true_yaw_traj.append(true_yaw) new_action = np.array((self.action_traj[-1], ), np.int32)[None] new_rel_xy, new_rel_yaw = actions_from_trajectory( np.stack([self.true_xy_traj[-2], self.true_xy_traj[-1]], axis=0), np.stack([self.true_yaw_traj[-2], self.true_yaw_traj[-1]], axis=0)) # Pick best segment of the trajectory based on how much viewing areas overlap current_trajlen = len(self.particle_xy_list) + 1 assert len(self.true_xy_traj) == current_trajlen and len(self.image_features_traj) == current_trajlen - 1 if self.params.slam_use_best_steps: mean_traj_xy, mean_traj_yaw = ClassicMapping.mean_particle_traj( np.array(self.particle_xy_list), np.array(self.particle_yaw_list), self.particle_logit_acc_list[-1][None, :, None]) mean_traj_xy, mean_traj_yaw = ClassicMapping.propage_trajectory_with_action(mean_traj_xy, mean_traj_yaw, self.action_traj[-1]) segment_steps = ClassicMapping.get_steps_with_largest_overlapping_view( mean_traj_xy, mean_traj_yaw, segment_len=inference_trajlen, view_distance=30*self.brain_requirements.transform_window_scaler) else: segment_steps = np.arange(max(current_trajlen-inference_trajlen, 0), current_trajlen) assert segment_steps.ndim == 1 past_particle_xy = np.stack(self.particle_xy_list, axis=0) past_particle_yaw = np.stack(self.particle_yaw_list, axis=0) true_xy_seg = np.stack([self.true_xy_traj[i] for i in segment_steps], axis=0) + self.true_xy_offset[None] true_yaw_seg = np.stack([self.true_yaw_traj[i] for i in segment_steps], axis=0) past_image_features_seg = np.stack([self.image_features_traj[i] for i in segment_steps[:-1]], axis=0) past_local_maps = np.stack(self.local_map_traj, axis=0) past_visibility = np.stack(self.visibility_traj, axis=0) feed_dict = { self.inference_timesteps_input: segment_steps[None], self.new_images_input: images[None, None], self.last_images_input: self.image_traj[-2][None, None], self.visibility_input: visibility_map_input[None, None], self.past_local_maps_input: past_local_maps[None], self.past_visibility_input: past_visibility[None], self.past_needed_image_features_input: past_image_features_seg[None], self.global_map_shape_input: np.array(map_shape[:2], np.int32), # global_map_input: global_map, # self.images_input: images_seg[None], # always input both images and global map, only one will be connected self.true_xy_input: true_xy_seg[None], # used for global to local transition and loss self.true_yaw_input: true_yaw_seg[None], # self.visibility_input: visibility_seg[None], # self.particle_xy_input: particle_xy_seg[None], # self.particle_yaw_input: particle_yaw_seg[None], self.particle_xy_input: past_particle_xy[None], self.particle_yaw_input: past_particle_yaw[None], self.new_action_input: new_action[None], self.new_rel_xy_input: new_rel_xy[None], self.new_rel_yaw_input: new_rel_yaw[None], self.last_step_particle_logits_input: self.particle_logit_acc_list[-1][None], } slam_outputs = self.run_inference(feed_dict, need_map=need_map) # Deal with resampling self.particle_xy_list = [particle[slam_outputs.particle_indices[0]] for particle in self.particle_xy_list] self.particle_yaw_list = [particle[slam_outputs.particle_indices[0]] for particle in self.particle_yaw_list] self.particle_logit_acc_list = [particle[slam_outputs.particle_indices[0]] for particle in self.particle_logit_acc_list] # Store new particles self.particle_xy_list.append(slam_outputs.particle_xy_t[0]) self.particle_yaw_list.append(slam_outputs.particle_yaw_t[0]) self.particle_logit_acc_list.append(slam_outputs.particle_logits_acc[0]) if FAKE_INPUT_FOR_SPEED_TEST: self.particle_xy_list[-1] = self.particle_xy_list[-1] * 0 + true_xy[None] + self.true_xy_offset[None] # Store local map prediction self.local_map_traj.append(slam_outputs.new_local_maps[0, 0]) self.visibility_traj.append(slam_outputs.new_visibility_maps[0, 0]) self.image_features_traj.append(slam_outputs.new_image_features[0, 0]) print (self.image_features_traj[-1].shape) # Store losses. only meaningful if true state was input self.xy_loss_list.append(slam_outputs.loss_xy_all[0]) self.yaw_loss_list.append(slam_outputs.loss_yaw_all[0]) # Update map if need_map: global_map_logodds = np.array(slam_outputs.global_map_logodds[0]) # squeeze batch and traj # if global_map_logodds.shape != self.global_map_logodds.shape: # raise ValueError("Unexpected global map shape output from slam net.") if not self.fixed_map_size: global_map_logodds = global_map_logodds[:map_shape[0], :map_shape[1]] self.global_map_logodds = global_map_logodds # Transform predictions global_map_true_partial = None assert self.global_map_logodds.shape[-1] == 1 global_map_pred = ClassicMapping.inverse_logodds(self.global_map_logodds) slam_mean_xy = slam_outputs.mean_xy[0, -1] slam_mean_yaw = slam_outputs.mean_yaw[0, -1] slam_mean2_xy = slam_outputs.mean2_xy[0, -1] slam_mean2_yaw = slam_outputs.mean2_yaw[0, -1] slam_ml_xy = slam_outputs.ml_xy[0, -1] slam_ml_yaw = slam_outputs.ml_yaw[0, -1] slam_traj_xy = slam_outputs.xy[0, :] # the one used for mapping slam_traj_yaw = slam_outputs.yaw[0, :] # the one used for mapping slam_xy = slam_outputs.xy[0, -1] # the one used for mapping slam_yaw = slam_outputs.yaw[0, -1] # TODO should separate reassemble the map for the whole trajectory for the mean particle trajectory # do NOT use most likely particle, its meaningless after resampling. Density is what matters. # need to implement reasonable sequential averaging of yaws.. # Compute mean separately here if self.params.brain == 'habslambrain_v1' and USE_ASSERTS: mean_xy_from_np, mean_yaw_from_np = ClassicMapping.mean_particle_traj(self.particle_xy_list[-1], self.particle_yaw_list[-1], self.particle_logit_acc_list[-1][:, None]) xy_diff = np.abs(mean_xy_from_np - slam_mean_xy) yaw_diff = np.abs(mean_yaw_from_np - slam_mean_yaw) yaw_diff = (yaw_diff + np.pi) % (2 * np.pi) - np.pi if not np.all(xy_diff < 1.) or not np.all(yaw_diff < np.deg2rad(10.)): raise ValueError("SLAM mean and numpy mean dont match. Mean difference: %s vs %s | %s vs. %s" % ( str(mean_xy_from_np), str(slam_mean_xy), str(mean_yaw_from_np), str(slam_mean_yaw))) # Pose source if self.pose_source == 'true': xy = true_xy + self.true_xy_offset yaw = true_yaw traj_xy = np.array(self.true_xy_traj) + self.true_xy_offset[None] traj_yaw = np.array(self.true_yaw_traj) assert slam_traj_xy is None or traj_xy.shape[0] == slam_traj_xy.shape[0] elif self.pose_source in ["slam-truestart", "slam"]: xy = slam_xy yaw = slam_yaw traj_xy = slam_traj_xy traj_yaw = slam_traj_yaw # TODO weighted mean of particles else: raise NotImplementedError self.xy = xy self.yaw = yaw # Verify true pose if USE_ASSERTS and self.params.agent_pose_source == 'true': assert np.all(np.isclose(traj_xy[:, None], np.array(self.particle_xy_list), atol=1e-3)) assert np.all(np.isclose(traj_yaw[:, None], np.array(self.particle_yaw_list), atol=1e-3)) # last_action = self.action_traj[-1] # if last_action == 1: # nominal_xy = traj_xy[-2] + rotate_2d(np.array([5., 0.], np.float32), traj_yaw[-2]) # else: # nominal_xy = traj_xy[-2] # move_error = np.linalg.norm(xy - nominal_xy) # move_amount = np.linalg.norm(xy - traj_xy[-2]) # print ("Act %d. Moved %f. Error %f"%(last_action, move_amount, move_error)) # if move_error > 3.: # pdb.set_trace() local_map_label = None # local_map_label = slam_outputs.local_map_label[0, 0, :, :, 0] # local_map_pred = slam_outputs.combined_local_map_pred[0, 0, :, :, 0] ang_vel = yaw - self.prev_yaw ang_vel = (ang_vel + np.pi) % (2*np.pi) - np.pi target_dist = np.linalg.norm(self.target_xy - xy) true_target_dist = np.linalg.norm(true_target_xy - true_xy) xy_error, yaw_error = self.pose_error(slam_xy, slam_yaw, true_xy, true_yaw) mean_xy_error, mean_yaw_error = self.pose_error(slam_mean_xy, slam_mean_yaw, true_xy, true_yaw) mean2_xy_error, _ = self.pose_error(slam_mean2_xy, slam_mean2_yaw, true_xy, true_yaw) ml_xy_error, _ = self.pose_error(slam_ml_xy, slam_ml_yaw, true_xy, true_yaw) self.distance_history.append(target_dist) if self.pose_source != 'slam' and not FAKE_INPUT_FOR_SPEED_TEST: assert np.abs(np.sqrt(self.xy_loss_list[-1]) - xy_error) < 2. # one is before resampling, other is after # Detect collision is_colliding = False if self.step_i > 2 and self.action_traj[-1] == 1 and self.recover_step_i == 0: # moved forward last_step_len = np.linalg.norm(traj_xy[-2] - traj_xy[-1], axis=0) if last_step_len < COLLISION_DISTANCE_THRESHOLD: is_colliding = True self.collision_timesteps.append(self.step_i) self.num_collisions += 1 if self.recover_step_i >= len(self.recover_policy): self.recover_step_i = 0 # done with recovery dist_hist = np.array(self.distance_history[-self.GIVE_UP_NO_PROGRESS_STEPS:]) time_slam = time.time() - time_last time_last = time.time() should_give_up = False # Modify state if its out of bounds, or give up if goal is out of bounds if (np.any(self.target_xy_for_planning < TARGET_MAP_MARGIN) or np.any(self.target_xy_for_planning + TARGET_MAP_MARGIN >= np.array(self.max_map_size))): should_give_up = True if USE_ASSERTS and self.fixed_map_size: raise ValueError("Target is outside of map area -- this should not happen for fixed size map.") elif (np.any(self.xy < 0) or np.any(self.xy >= np.array(self.max_map_size))): print ("State is outside of map area -- this can happen for fixed size map because its cropped before the slam update.") if self.fixed_map_size: new_xy = np.clip(xy, [0., 0.], np.array(self.max_map_size, np.float32) - 0.001) print ("moving state.. %s --> %s"%(str(xy), str(new_xy))) xy = new_xy self.xy = new_xy else: print ("Giving up") should_give_up = True # Check for time and distance limits try: for time_thres, dist_thres in self.GIVE_UP_STEP_AND_DISTANCE: if self.step_i >= time_thres and target_dist >= dist_thres: should_give_up = True break except Exception as e: print ("Exception " + str(e)) # Give up if no progress for too long wallclock time try: mins_since_ep_start = (time.time() - self.episode_t) / 60 reduction_since_beginning = self.distance_history[0] - self.distance_history[-1] for time_thres, reduct_thres in self.GIVE_UP_TIME_AND_REDUCTION: if mins_since_ep_start >= time_thres and reduction_since_beginning < reduct_thres: print ("Give up because of wallclock time and reduction t=%f reduct=%f"%(mins_since_ep_start, reduction_since_beginning)) should_give_up = True break except Exception as e: print ("Exception " + str(e)) giving_up_collision = False giving_up_distance = False giving_up_progress = False is_done = False # Plan planned_path = np.zeros([0, 2], dtype=np.float32) # Choose which map to use for planning global_map_for_planning, cont_global_map_for_planning = self.get_global_map_for_planning(global_map_pred, global_map_label, traj_xy, traj_yaw, map_shape, self.map_source, keep_soft=self.params.soft_cost_map) shrunk_map_offset_xy = None if self.params.interactive_action: while True: ans = input("Manual action: ") try: if ans and int(ans) >= 0 and int(ans) <= 3: action = int(ans) break except: pass plan_status_msg = "Manual %d"%action elif target_dist < self.params.agent_stop_near_target_dist: # Close enough to target. Normal requirement is 0.36/0.05 = 7.2 plan_status_msg = "Manual stop" is_done = True action = 0 elif should_give_up: plan_status_msg = "Giving up because target is too far (or state was outside of map)" giving_up_distance = True action = 0 elif shortcut_action is not None: # NOTE must be before recover on collision - because we already incremented recover policy plan_status_msg = "Shortcut action" action = shortcut_action elif RECOVER_ON_COLLISION and (is_colliding or self.recover_step_i > 0): plan_status_msg = ("Recover from collision %d / %d."%(self.recover_step_i, len(self.recover_policy))) action = self.recover_policy[self.recover_step_i] self.recover_step_i += 1 self.pathplanner.reset() # to clear out its cache if target_dist < NEAR_TARGET_COLLISION_STOP_DISTANCE: plan_status_msg += " --> Attempt to stop instead, near target" is_done = True action = 0 elif self.GIVE_UP_NUM_COLLISIONS > 0 and self.num_collisions >= self.GIVE_UP_NUM_COLLISIONS: plan_status_msg = "Too many collisions (%d). Giving up.."%(self.num_collisions, ) giving_up_collision = True action = 0 elif self.GIVE_UP_NO_PROGRESS_STEPS > 0 and self.step_i > self.GIVE_UP_NO_PROGRESS_STEPS and self.step_i > 100 and np.max(dist_hist) - np.min(dist_hist) < self.NO_PROGRESS_THRESHOLD: plan_status_msg = "No progress for %d steps. Giving up.."%(self.GIVE_UP_NO_PROGRESS_STEPS, ) giving_up_progress = True action = 0 else: action, planned_path, plan_status_msg, processed_map_for_planning, shrunk_map_offset_xy = self.plan_and_control( xy, yaw, self.target_xy_for_planning, global_map_for_planning, ang_vel, initial_target_fi, allow_shrink_map=self.allow_shrink_map, cont_global_map_pred=cont_global_map_for_planning if self.planner_needs_cont_map else None) is_done = (action == 0) # Visualize agent if self.step_i % PLOT_EVERY_N_STEP == 0 and PLOT_EVERY_N_STEP > 0 and slam_outputs is not None: local_map_pred = self.local_map_traj[-1][:, :, 0] self.visualize_agent(slam_outputs.tiled_visibility_mask[0, 0, :, :, 0], images, global_map_pred, global_map_for_planning, # processed_map_for_planning.astype(np.float32)/255., global_map_label, global_map_true_partial, local_map_pred, local_map_label, planned_path, sim_rgb=observations['rgb'], # uint xy=xy, yaw=yaw, true_xy=true_xy + self.true_xy_offset, true_yaw=true_yaw, target_xy=self.target_xy_for_planning) # pdb.set_trace() # Overwrite with expert if self.action_source == 'expert': best_action = self.follower.get_next_action(goal_pos) action = best_action if action == 0 and EXIT_AFTER_N_STEPS_FOR_SPEED_TEST > 0: print ("Sping instead of stopping.") action = 3 is_done = (action == 0) if DEBUG_DUMMY_ACTIONS_ONLY: action = 1 # # Overwrite with manual actions # if self.params.interactive_action: # ans = input("Overwrite %d: "%action) # if ans and int(ans) >= 0 and int(ans) <= 3: # action = int(ans) time_plan = time.time() - time_last time_last = time.time() # Save data if len(self.tfwriters) > 0 and self.step_i % SAVE_DATA_EVERY_N == 0: is_using_planner = planned_path.shape[0] > 0 and target_dist >= 10.5 if DATA_TYPE == "planinstance": assert self.map_source != "pred" if not is_using_planner: # two step strategy for <= 10 if DATA_INCLUDE_NONPLANNED_ACTIONS: raise NotImplementedError else: pred_map_for_planning, _ = self.get_global_map_for_planning( global_map_pred, global_map_label, traj_xy, traj_yaw, map_shape, "pred", keep_soft=True) self.write_datapoint(global_map_for_planning, pred_map_for_planning, self.target_xy_for_planning, planned_path.astype(np.int32), action, shrunk_map_offset_xy) elif DATA_TYPE == "scenario": assert SAVE_DATA_EVERY_N == 1 # need to save all steps for meaningful slam and image data pred_map_for_planning, _ = self.get_global_map_for_planning( global_map_pred, global_map_label, traj_xy, traj_yaw, map_shape, "pred", keep_soft=True) # TODO for predmap the maps we save will not necessarily be meaningful. Never tested. # convert maps assert global_map_for_planning.dtype == np.float32 and pred_map_for_planning.dtype == np.float32 assert global_map_for_planning.shape == pred_map_for_planning.shape assert global_true_target_xy is not None # unchanged goal coordinated on map data_true_map_png = encode_image_to_png((global_map_for_planning * 255.).astype(np.uint8)) data_pred_map_png = encode_image_to_png((pred_map_for_planning * 255.).astype(np.uint8)) # encode predicted probability as uint8 depth_data = np.atleast_3d(observations['depth']) if self.params.data_highres_images else depth rgb_data = observations['rgb'] if self.params.data_highres_images else (rgb * 255.).astype(np.uint8) depth_png = encode_image_to_png((depth_data * 255.).astype(np.uint8)) rgb_png = encode_image_to_png(rgb_data) global_xy = np.array(info['top_down_map']['agent_map_coord']) # x: downwards; y: rightwars global_yaw = np.array(info['top_down_map']['agent_angle']) # 0 downwards, positive ccw. Forms stanard coord system with x and y. self.scenario_traj_data.append({ 'action': np.array(action, np.int32), 'local_est_xy': xy.copy(), 'local_est_yaw': yaw.copy(), 'local_true_xy': (true_xy + self.true_xy_offset).copy(), 'local_true_yaw': true_yaw.copy(), 'local_goal_xy': self.target_xy_for_planning.copy(), 'true_map_png': data_true_map_png, 'pred_map_png': data_pred_map_png, 'depth_png': depth_png, 'rgb_png': rgb_png, 'global_xy': global_xy.copy(), 'global_yaw': global_yaw.copy(), 'is_using_planner': is_using_planner, 'is_colliding': is_colliding, }) # Metadata if len(self.scenario_traj_data) == 1: ep = self.env.current_episode episode_id = ep.episode_id model_id = get_model_id_from_episode(ep) height = ep.start_position[1] floor = get_floor_from_json(model_id, height, map_path_for_sim(self.params.sim)) self.scenario_traj_data[0].update({ 'global_goal_xy': global_true_target_xy.copy(), 'model_id': str(model_id), 'floor': int(floor), 'episode_id': int(episode_id), }) else: raise NotImplementedError(DATA_TYPE) # pdb.set_trace() # if self.episode_i == 0: # cv2.imwrite('./temp/ep%d-step%d.png'%(self.episode_i, self.step_i), observations['rgb']) # if self.step_i == 0: # top_down_map = maps.get_topdown_map( # self.env.sim, map_resolution=(5000, 5000) # ) # plt.imshow(top_down_map) # plt.show() self.prev_yaw = yaw self.action_traj.append(action) self.step_i += 1 slam_status_msg = "Pose errors mean=%.1f mean2=%.1f ml=%.1f yaw=%.1f. Loss=%.1f "%( mean_xy_error, mean2_xy_error, ml_xy_error, np.rad2deg(mean_yaw_error), np.sqrt(self.xy_loss_list[-1])) act_status_msg = "Est dist=%.1f. True dist=%.1f Act=%d %s"%( target_dist, true_target_dist, action, "COL" if is_colliding else "") print (plan_status_msg) print (slam_status_msg + act_status_msg) # Get map statistics if global_map_label is not None: ij = xy.astype(np.int32) self.num_wrong_obstacle += 1. if not is_colliding and global_map_label[ij[0], ij[1]] < 0.5 else 0. self.num_wrong_free += 1. if is_colliding and global_map_label[ij[0], ij[1]] >= 0.5 else 0. self.num_wrong_free_area += 1. if is_colliding and np.all(global_map_label[max(ij[0]-1, 0):ij[0]+2, max(ij[1]-1, 0):ij[1]+2] >= 0.5) else 0. self.num_wrong_free_area2 += 1. if is_colliding and np.all(global_map_label[max(ij[0]-2, 0):ij[0]+3, max(ij[1]-2, 0):ij[1]+3] >= 0.5) else 0. self.num_wrong_free_area3 += 1. if is_colliding and np.all(global_map_label[max(ij[0]-3, 0):ij[0]+4, max(ij[1]-3, 0):ij[1]+4] >= 0.5) else 0. # Video output if self.params.interactive_video or self.params.save_video > self.num_videos_saved: if not isinstance(planned_path, np.ndarray) or planned_path.ndim != 2: print ("planned path has an unexpected format") pdb.set_trace() # Set outcome text if (giving_up_collision or giving_up_progress or giving_up_distance): outcome = 'giveup' elif is_done: outcome = 'done' else: outcome = 'timeout' frame_data = dict( rgb=observations['rgb'], depth=observations['depth'], global_map=global_map_pred.copy(), global_map_for_planning=global_map_for_planning.copy(), cont_global_map_for_planning=cont_global_map_for_planning.copy(), true_global_map=global_map_label.copy(), xy=self.xy.copy(), yaw=self.yaw.copy(), target_xy=self.target_xy_for_planning.copy(), path=planned_path.copy(), # subgoal=planned_subgoal.copy(), target_status=slam_status_msg, control_status=plan_status_msg, act_status=act_status_msg, outcome=outcome) if self.plot_process: while not self.plot_queue.empty(): time.sleep(0.01) self.plot_queue.put(("step", frame_data)) else: self.frame_traj_data.append(frame_data) if self.params.interactive_video: self.video_update(VIDEO_FRAME_SKIP * len(self.frame_traj_data)) time_output = time.time() - time_last time_last = time.time() if PRINT_TIMES: print ("Time sim %.3f prep %.3f slam %.3f plan %.3f output %.3f"%(time_sim, time_prepare, time_slam, time_plan, time_output)) # Pause for interactive run every n steps if self.params.interactive_step > 0 and self.step_i > 0 and self.step_i % self.params.interactive_step == 0: print ("pause..") pdb.set_trace() return {"action": action, "has_collided": float(self.num_collisions > 0), "num_collisions": self.num_collisions, "xy_error": xy_error, # "mean_xy_error": mean_xy_error, "mean2_xy_error": mean2_xy_error, "ml_xy_error": ml_xy_error, 'mean_yaw_error': mean_yaw_error, 'target_dist': target_dist, 'num_wrong_obstacle': self.num_wrong_obstacle, 'num_wrong_free': self.num_wrong_free, 'num_wrong_free_area': self.num_wrong_free_area, 'num_wrong_free_area2': self.num_wrong_free_area2, 'num_wrong_free_area3': self.num_wrong_free_area3, 'time_plan': 0. if len(self.plan_times) == 0 else np.mean(self.plan_times), 'map_mismatch_count': float(self.map_mismatch_count) / self.step_i, # TODO remove 'giveup_collision': float(giving_up_collision), 'giveup_progress': float(giving_up_progress), 'giveup_distance': float(giving_up_distance), 'is_done: ': is_done} # 0: stop, forward, left, right # return {"action": numpy.random.choice(self._POSSIBLE_ACTIONS)} def reset_scenario_data_writer(self): if len(self.scenario_traj_data) == 0: return assert len(self.tfwriters) == len(self.params.data_map_sizes) == 1 metadata = self.scenario_traj_data[0] trajdata = self.scenario_traj_data context_features = { 'trajlen': tf_int64_feature(len(trajdata)), 'goal_xy': tf_bytes_feature(metadata['global_goal_xy'].astype(np.float32).tobytes()), 'model_id': tf_bytes_feature(str(metadata['model_id']).encode()), 'floor': tf_int64_feature(metadata['floor']), 'episode_id': tf_int64_feature(metadata['episode_id']), # int(ep.episode_id) 'map_id': tf_int64_feature(self.saved_map_i), # tf_bytes_feature(np.array((,), np.int32).tobytes()), } sequence_features = { 'actions': sequence_feature_wrapper([stepdata['action'].astype(np.int32) for stepdata in trajdata]), # global map coordinates 'xys': sequence_feature_wrapper([stepdata['global_xy'].astype(np.float32) for stepdata in trajdata]), 'yaws': sequence_feature_wrapper([stepdata['global_yaw'].astype(np.float32) for stepdata in trajdata]), 'is_using_planner': sequence_feature_wrapper([np.array(stepdata['is_using_planner'], dtype=np.bool) for stepdata in trajdata]), 'is_colliding': sequence_feature_wrapper([np.array(stepdata['is_colliding'], dtype=np.bool) for stepdata in trajdata]), # coordinates in rotated local coordinate frame for planning. cropped compared to global pose 'local_true_xys': sequence_feature_wrapper([stepdata['local_true_xy'].astype(np.float32) for stepdata in trajdata]), 'local_true_yaws': sequence_feature_wrapper([stepdata['local_true_yaw'].astype(np.float32) for stepdata in trajdata]), 'local_est_xys': sequence_feature_wrapper([stepdata['local_est_xy'].astype(np.float32) for stepdata in trajdata]), 'local_est_yaws': sequence_feature_wrapper([stepdata['local_est_yaw'].astype(np.float32) for stepdata in trajdata]), 'local_goal_xys': sequence_feature_wrapper([stepdata['local_goal_xy'].astype(np.float32) for stepdata in trajdata]), # maps used for planning (typically true-partial) and (accumulated) predicted map 'true_maps': sequence_feature_wrapper([stepdata['true_map_png'] for stepdata in trajdata]), 'pred_maps': sequence_feature_wrapper([stepdata['pred_map_png'] for stepdata in trajdata]), 'depths':sequence_feature_wrapper([stepdata['depth_png'] for stepdata in trajdata]), 'rgbs': sequence_feature_wrapper([stepdata['rgb_png'] for stepdata in trajdata]), } # store example = tf.train.SequenceExample(context=tf.train.Features(feature=context_features), feature_lists=tf.train.FeatureLists(feature_list=sequence_features)) if DATA_SEPARATE_FILES: data_filename = os.path.join( self.logdir, "habscenarios.episode.m%d.tfrecords.%d" % (self.params.data_map_sizes[0], self.num_data_entries)) with tf.python_io.TFRecordWriter(data_filename) as tfwriter: tfwriter.write(example.SerializeToString()) else: self.tfwriters[0].write(example.SerializeToString()) self.saved_map_i += 1 self.num_data_entries += 1 self.scenario_traj_data = [] def write_datapoint(self, map_for_planning, pred_map, target_xy, planned_path, action, shrunk_map_offset_xy): assert planned_path.shape[0] > 0 assert planned_path.dtype == np.int32 planned_actions = grid_actions_from_trajectory(planned_path, connect8=self.params.connect8) target_ij = target_xy.astype(np.int32) if planned_path.shape[0] < self.params.trainlen: return if planned_path[-1][0] != target_ij[0] or planned_path[-1][1] != target_ij[1]: print ("Skip because path does not reach goal") print (planned_path) print (target_ij) return # convert maps assert map_for_planning.dtype == np.float32 and pred_map.dtype == np.float32 assert map_for_planning.shape == pred_map.shape map_for_planning = (map_for_planning * 255.).astype(np.uint8) pred_map = (pred_map * 255.).astype(np.uint8) # encode predicted probability as uint8 if np.any(map_for_planning[planned_path[:, 0], planned_path[:, 1]] < 127): print ("Skip because path is not collision free") return # Q values. Assumes planner is a VI and it was called in this time step, otherwise path would be None qs = self.pathplanner.last_qs_value if shrunk_map_offset_xy is not None: shrunk_map_offset_xy = shrunk_map_offset_xy.astype(np.int) qs = np.pad(qs, [[shrunk_map_offset_xy[0], pred_map.shape[0]-qs.shape[0]-shrunk_map_offset_xy[0]], [shrunk_map_offset_xy[1], pred_map.shape[1]-qs.shape[1]-shrunk_map_offset_xy[1]], [0, 0]]) assert qs.shape[:2] == pred_map.shape[:2] assert len(self.tfwriters) == len(self.params.data_map_sizes) for tfwriter, map_size in zip(self.tfwriters, self.params.data_map_sizes): self.write_data_for_map_size(map_for_planning, pred_map, qs, planned_actions, planned_path, target_xy, tfwriter, map_size) self.saved_map_i += 1 def write_data_for_map_size(self, map_for_planning, pred_map, qs, planned_actions, planned_path, target_xy, tfwriter, map_size): segment_len = self.params.trainlen if map_size < map_for_planning.shape[0]: assert False # We need to replan for q values to be valid. if DATA_USE_LAST_SEGMENT: # Find last trajectory segment that is still within the map size margin = 2 for start_i in range(len(planned_path)): range_ij = np.max(planned_path[start_i:], axis=0) - np.min(planned_path[start_i:], axis=0) if np.all(range_ij < map_size - 2 * margin): break planned_path = planned_path[start_i:] planned_actions = planned_actions[start_i:] else: # Find first trajectory segment that is within the map size and change goal margin = 2 for end_i in range(len(planned_path), 0, -1): # go backwards range_ij = np.max(planned_path[:end_i], axis=0) - np.min(planned_path[:end_i], axis=0) if np.all(range_ij < map_size - 2 * margin): break planned_path = planned_path[:end_i] planned_actions = planned_actions[:end_i] target_xy = planned_path[-1].astype(np.float32) + 0.5 # Crop map offset_ij = np.min(planned_path, axis=0) range_ij = np.max(planned_path, axis=0) - offset_ij # add half of the remaining spacing to the beginning topleft_space = (map_size - range_ij) // 2 offset_ij = offset_ij - topleft_space offset_ij = np.maximum(offset_ij, np.zeros((2, ), np.int32)) # cannot be less than zero offset_ij = np.minimum(offset_ij, np.array(map_for_planning.shape[:2], np.int32) - map_size) # crop the given size starting from offset_ij map_for_planning = map_for_planning[offset_ij[0]:offset_ij[0]+map_size, offset_ij[1]:offset_ij[1]+map_size] pred_map = pred_map[offset_ij[0]:offset_ij[0]+map_size, offset_ij[1]:offset_ij[1]+map_size] qs = qs[offset_ij[0]:offset_ij[0]+map_size, offset_ij[1]:offset_ij[1]+map_size] qs = qs.astype(np.float32) # Move poses to cropped frame planned_path = planned_path - offset_ij[None] target_xy = target_xy - offset_ij.astype(np.float32) if planned_path.shape[0] < self.params.trainlen: return assert map_for_planning.shape[0] == map_size and map_for_planning.shape[1] == map_size assert np.all(planned_path >= 0) and np.all(planned_path < map_size) # Limit trajlen so we only save the trajectory segment near the current pose if DATA_FIRST_STEP_ONLY: max_trajlen = segment_len # there will be only one segment else: max_trajlen = DATA_MAX_TRAJLEN // segment_len * segment_len assert max_trajlen >= 2 planned_path = planned_path[:max_trajlen] planned_actions = planned_actions[:max_trajlen-1] # Abstract Q values along trajectory and make sure they are consistent with the action choices q_traj = qs[planned_path[:, 0].astype(np.int), planned_path[:, 1].astype(np.int), :] q_for_actions = q_traj[np.arange(q_traj.shape[0]-1), planned_actions] assert np.all(np.isclose(q_for_actions, q_traj[:-1].max(axis=1))) planned_xy = planned_path.astype(np.float32) + 0.5 true_map_png = cv2.imencode('.png', map_for_planning)[1].tobytes() pred_map_png = cv2.imencode('.png', pred_map)[1].tobytes() # segments traj_segments = [] overlap = 1 # use extra step because last action will be dropped assert overlap < segment_len start_i = 0 while start_i < planned_path.shape[0] - overlap: # include all steps # for incomplete last segment, start earlier overlapping with previous segment if start_i + segment_len > planned_path.shape[0]: start_i = planned_path.shape[0] - segment_len overlap = -1 # this is to triger break at the end of this iteration segment = tuple(range(start_i, start_i + segment_len)) traj_segments.append(segment) start_i += segment_len - overlap del overlap assert not DATA_FIRST_STEP_ONLY or len(traj_segments) == 1 # store each segment goal_xy = target_xy.copy() for segment_i, segment in enumerate(traj_segments): # repeat multiple times xy_segment = planned_xy[segment, :] grid_action_segment = planned_actions[segment[:-1],] q_segment = q_traj[segment[:-1],] # dummy yaw and action yaw_segment = np.ones((segment_len, 1), np.float32) * -1 action_segment = np.ones((segment_len - 1, 1), np.int32) * -1 # tfrecord features context_features = { 'true_map': tf_bytes_feature(true_map_png), 'pred_map': tf_bytes_feature(pred_map_png), 'trajlen': tf_int64_feature(len(segment)), 'goal_xy': tf_bytes_feature(goal_xy.astype(np.float32).tobytes()), 'xy': tf_bytes_feature(xy_segment.astype(np.float32).tobytes()), 'yaw': tf_bytes_feature(yaw_segment.astype(np.float32).tobytes()), 'action': tf_bytes_feature(action_segment.astype(np.int32).tobytes()), 'grid_q_values': tf_bytes_feature(q_segment.astype(np.float32).tobytes()), 'grid_action': tf_bytes_feature(grid_action_segment.astype(np.int32).tobytes()), 'qs': tf_bytes_feature(qs.tobytes()), 'episode_id': tf_bytes_feature(np.array((self.episode_i + self.params.skip_first_n, ), np.int32).tobytes()), 'map_id': tf_bytes_feature(np.array((self.saved_map_i, ), np.int32).tobytes()), 'segment_i': tf_bytes_feature(np.array((segment_i, ), np.int32).tobytes()), } sequence_features = { # 'local_map': tf.train.FeatureList(feature=[tf.train.Feature(bytes_list=tf.train.BytesList(value=[local_map_pngs[i]])) for i in segment]), # 'visibility': tf.train.FeatureList(feature=[tf.train.Feature(bytes_list=tf.train.BytesList(value=[visibility_map_pngs[i]])) for i in segment]), } # store example = tf.train.SequenceExample(context=tf.train.Features(feature=context_features), feature_lists=tf.train.FeatureLists(feature_list=sequence_features)) tfwriter.write(example.SerializeToString()) self.num_data_entries += 1 def get_global_map_for_planning(self, global_map_pred, global_map_label, traj_xy, traj_yaw, map_shape, map_source, keep_soft): if map_source in ['true', 'true-saved', 'true-saved-sampled', 'true-saved-hrsampled']: assert global_map_label.ndim == 3 global_map_for_planning = global_map_label.copy() assert global_map_for_planning.shape == map_shape elif map_source in ['true-partial', 'true-partial-sampled']: global_map_for_planning = global_map_label.copy() # Overwrite unseen areas with 0.5 unseen_mask = np.isclose(global_map_pred, 0.5) global_map_for_planning[unseen_mask] = 0.5 else: global_map_for_planning = global_map_pred.copy() # Erode with float values before thresholding and before adding patches for collision. # This is used to account for larger robot than used in training data, like spot. if self.params.map_erosion_pre_planning > 1: global_map_for_planning = np.squeeze(global_map_for_planning, axis=-1) global_map_for_planning = cv2.erode( global_map_for_planning, Expert.get_kernel_for_erosion(self.params.map_erosion_pre_planning)) global_map_for_planning = global_map_for_planning[..., None] if self.params.collision_patch_radius > 0 and self.step_i > 1: global_map_for_planning = self.patch_map_with_collisions(global_map_for_planning, traj_xy, traj_yaw, self.collision_timesteps, self.params.collision_patch_radius) if self.params.agent_clear_target_radius > 0: try: min_xy = self.target_xy_for_planning.astype(np.int32) - self.params.agent_clear_target_radius max_xy = self.target_xy_for_planning.astype(np.int32) + self.params.agent_clear_target_radius + 1 global_map_for_planning[min_xy[0]:max_xy[0], min_xy[1]:max_xy[1]] = 1. except Exception as e: print ("Exception clearing target. " + str(e)) raise e # # if self.step_i == 1: # print ("DEBUG !!!!!!! REMOVE !!!!!!!") # self.collision_timesteps.append(1) # threshold cont_global_map_for_planning = global_map_for_planning if not keep_soft: traversable_threshold = self.params.traversable_threshold # higher than this is traversable object_treshold = 0. # treat everything as non-object threshold_const = np.array((traversable_threshold, object_treshold))[None, None, :self.map_ch - 1] global_map_for_planning = np.array(global_map_for_planning >= threshold_const, np.float32) return global_map_for_planning, cont_global_map_for_planning @staticmethod def shrink_map(xy, target_xy, global_map, margin=8): assert margin > 6 # one step in each direction requires at least 6 margin assert global_map.dtype == np.uint8 obst_i, obst_j, _ = np.nonzero(global_map == 0) if obst_i.shape[0] == 0: obst_i = np.array([xy[0]], np.int) obst_j = np.array([xy[1]], np.int) min_i = min(int(xy[0]), int(target_xy[0]), np.min(obst_i)) - margin min_j = min(int(xy[1]), int(target_xy[1]), np.min(obst_j)) - margin max_i = max(int(xy[0]), int(target_xy[0]), np.max(obst_i)) + margin + 1 max_j = max(int(xy[1]), int(target_xy[1]), np.max(obst_j)) + margin + 1 min_i = max(min_i, 0) min_j = max(min_j, 0) max_i = min(max_i, global_map.shape[0]) max_j = min(max_j, global_map.shape[1]) offset_xy = np.array([min_i, min_j], np.float32) if min_i > 0 or min_j > 0 or max_i < global_map.shape[0] or max_j < global_map.shape[1]: global_map = global_map[min_i:max_i, min_j:max_j] xy = xy - offset_xy target_xy = target_xy - offset_xy return xy, target_xy, global_map, offset_xy @staticmethod def patch_map_with_collisions(global_map_for_planning, traj_xy, traj_yaw, collision_timesteps, patch_radius): for timestep in collision_timesteps: xy = traj_xy[timestep] yaw = traj_yaw[timestep] if patch_radius > 0.5: num_samples = max(int(2 * patch_radius), 6) ego_x, ego_y = np.meshgrid( np.linspace(0, 2 * patch_radius, num_samples) - 0.4, np.linspace(-patch_radius, patch_radius, num_samples), indexing='ij') ego_xy = np.stack((ego_x.flatten(), ego_y.flatten()), axis=-1) abs_xy = xy[None] + rotate_2d(ego_xy, yaw[None]) abs_ij = abs_xy.astype(np.int32) else: abs_ij = xy[None].astype(np.int32) # Filter out of range abs_ij = abs_ij[np.logical_and.reduce([ abs_ij[:, 0] >= 0, abs_ij[:, 1] >= 0, abs_ij[:, 0] < global_map_for_planning.shape[0], abs_ij[:, 1] < global_map_for_planning.shape[1] ])] # Set map not traversable (0.) global_map_for_planning[abs_ij[:, 0], abs_ij[:, 1]] = 0. return global_map_for_planning def pose_error(self, slam_xy, slam_yaw, true_xy, true_yaw): xy_error = np.linalg.norm(true_xy + self.true_xy_offset - slam_xy) yaw_error = true_yaw - slam_yaw yaw_error = np.abs((yaw_error + np.pi) % (2 * np.pi) - np.pi) return xy_error, yaw_error def run_inference(self, feed_dict, need_map=True): outputs = self.sess.run((self.inference_outputs if need_map else self.inference_outputs_without_map), feed_dict=feed_dict) return outputs def video_update(self, frame_i): # frame skip of 3 if frame_i % VIDEO_FRAME_SKIP == 0: ind = min(frame_i // VIDEO_FRAME_SKIP, len(self.frame_traj_data)-1) self.video_image_ax.set_data(self.frame_traj_data[ind]['rgb']) self.video_image_ax2.set_data(1.-self.frame_traj_data[ind]['depth'][..., 0]) # self.video_text_ax1.set_text(self.frame_traj_data[ind]['target_status']) split_str = self.frame_traj_data[ind]['control_status'] + " " + self.frame_traj_data[ind]['act_status'] # Attempt to break lines segs = split_str.split("[") if len(segs) > 1: split_str = segs[0] + "\n["+"[".join(segs[1:]) segs = split_str.split(" v=") if len(segs) > 1: split_str = segs[0] + "\nv=" + " v=".join(segs[1:]) # self.video_text_ax2.set_text(split_str) self.video_text_ax1.set_text("t = %d"%(frame_i // VIDEO_FRAME_SKIP + 1)) if self.video_global_map_ax is not None: xy = self.frame_traj_data[ind]['xy'] target_xy = self.frame_traj_data[ind]['target_xy'] #subgoal = self.frame_traj_data[ind]['subgoal'] path = self.frame_traj_data[ind]['path'].copy() if len(path) == 0: path = xy[None] path = np.array(path)[:, :2] global_map = np.atleast_3d(self.frame_traj_data[ind]['global_map']) global_map = np.tile(global_map[:, :, :1], [1, 1, 3]) true_map = np.atleast_3d(self.frame_traj_data[ind]['true_global_map']) true_map = np.tile(true_map[:, :, :1], [1, 1, 3]) # map_for_planning = np.atleast_3d(self.frame_traj_data[ind]['global_map_for_planning']) map_for_planning = np.atleast_3d(self.frame_traj_data[ind]['cont_global_map_for_planning']) map_for_planning = np.tile(map_for_planning[:, :, :1], [1, 1, 3]) if self.fixed_map_size: # Fix window to full map window_size = self.max_map_size[0] map_for_planning_crop, path_crop, target_xy_crop, xy_crop = self.crop_experience_window( window_size, map_for_planning, path, target_xy, xy) # Use a fixed global view combined_map = map_for_planning_crop combined_map2 = global_map combined_map3 = true_map else: # Follow agent with a window window_size = 220 map_for_planning_crop, path_crop, target_xy_crop, xy_crop = self.crop_experience_window( window_size, map_for_planning, path, target_xy, xy) combined_map = map_for_planning_crop # global_map_crop if MAP_SOURCE == 'pred' else true_map_crop global_map_crop, _, _, temp_xy_crop = self.crop_experience_window(window_size, global_map, path, target_xy, xy) assert np.all(temp_xy_crop == xy_crop) combined_map2 = global_map_crop true_map_crop, _, _, temp_xy_crop = self.crop_experience_window(window_size, true_map, path, target_xy, xy) assert np.all(temp_xy_crop == xy_crop) combined_map3 = true_map_crop xy = xy_crop target_xy = target_xy_crop path = path_crop planned_path_skip = 4 # global_map = global_map[:map_size-map_offset_xy[0], :map_size-map_offset_xy[1]] # combined_map[map_offset_xy[0]:map_offset_xy[0]+global_map.shape[0], map_offset_xy[1]:map_offset_xy[1]+global_map.shape[1]] = global_map # TODO add mild colors to cont_global_map_for_planning combined_map[int(xy_crop[0])-1:int(xy_crop[0])+2, int(xy_crop[1])-1:int(xy_crop[1]+2)] = (1., 0., 1.) combined_map2[int(xy[0])-1:int(xy[0])+2, int(xy[1])-1:int(xy[1]+2)] = (1., 0., 1.) combined_map3[int(xy[0])-1:int(xy[0])+2, int(xy[1])-1:int(xy[1]+2)] = (1., 0., 1.) # print (self.video_ax.get_xlim()) self.video_ax.set_xlim(-0.5, combined_map.shape[1]-0.5) self.video_ax.set_ylim(combined_map.shape[0]-0.5, -0.5) self.video_global_map_ax.set_data(combined_map) self.video_global_map_ax.set_extent([-0.5, combined_map.shape[1]-0.5, combined_map.shape[0]-0.5, -0.5]) self.video_path_scatter.set_offsets(np.flip(path_crop[planned_path_skip::planned_path_skip], axis=-1)) self.video_target_scatter.set_offsets([np.flip(target_xy_crop, axis=-1)]) if VIDEO_LARGE_PLOT: self.video_ax2.set_xlim(-0.5, combined_map2.shape[1]-0.5) self.video_ax2.set_ylim(combined_map2.shape[0]-0.5, -0.5) self.video_global_map_ax2.set_data(combined_map2) self.video_global_map_ax2.set_extent([-0.5, combined_map2.shape[1]-0.5, combined_map2.shape[0]-0.5, -0.5]) self.video_path_scatter2.set_offsets(np.flip(path[planned_path_skip::planned_path_skip], axis=-1)) self.video_target_scatter2.set_offsets([np.flip(target_xy, axis=-1)]) self.video_ax3.set_xlim(-0.5, combined_map3.shape[1]-0.5) self.video_ax3.set_ylim(combined_map3.shape[0]-0.5, -0.5) self.video_global_map_ax3.set_data(combined_map3) self.video_global_map_ax3.set_extent([-0.5, combined_map3.shape[1]-0.5, combined_map3.shape[0]-0.5, -0.5]) self.video_path_scatter3.set_offsets(np.flip(path[planned_path_skip::planned_path_skip], axis=-1)) self.video_target_scatter3.set_offsets([np.flip(target_xy, axis=-1)]) if VIDEO_DETAILED: # View angle half_fov = 0.5 * np.deg2rad(70) for ang_i, angle in enumerate([half_fov, -half_fov]): angle = angle - float(self.frame_traj_data[ind]['yaw']) + np.pi/2 # angle = angle + yaw[batch_i, traj_i, 0] v = np.array([np.cos(angle), np.sin(angle)]) * 10. x1 = np.array([xy[1], xy[0]]) # need to be flipped for display x2 = v + x1 self.video_view_angle_lines[ang_i].set_data([x1[0], x2[0]], [x1[1], x2[1]]) # # # pdb.set_trace() # Path # # print(self.frame_traj_data[ind]['xy'], path[0]) # for i in range(len(self.video_path_circles)-2): # path_i = min(i * 4, len(path)-1) # xy = path[path_i] # self.video_path_circles[i].center = ([xy[1], xy[0]]) # # Sub-goal # xy = self.frame_traj_data[ind]['subgoal'] # self.video_path_circles[-2].center = ([xy[1], xy[0]]) # # Target # xy = path[-1] # self.video_path_circles[-1].center = ([xy[1], xy[0]]) # self.video_text_ax2.set_data(self.summary_str) if self.params.interactive_video: plt.draw() plt.show() plt.waitforbuttonpress(0.01) return self.video_image_ax def crop_experience_window(self, map_size, global_map, path, target_xy, xy): # Cut it to fixed size 300 x 300 center_xy = (xy + target_xy) * 0.5 desired_center_xy = np.array(map_size, np.float32) * 0.5 center_xy = center_xy.astype(np.int) desired_center_xy = desired_center_xy.astype(np.int) offset_xy = (desired_center_xy - center_xy).astype(np.int) xy = xy + offset_xy target_xy = target_xy + offset_xy # subgoal += offset_xy path = path + offset_xy[None] map_start_xy = np.maximum(center_xy - map_size // 2, 0) map_cutoff_xy = -np.minimum(center_xy - map_size // 2, 0) global_map = global_map[map_start_xy[0]:map_start_xy[0] + map_size - map_cutoff_xy[0], map_start_xy[1]:map_start_xy[1] + map_size - map_cutoff_xy[1]] global_map_crop = np.ones((map_size, map_size, 3), np.float32) * 0.5 global_map_crop[map_cutoff_xy[0]:map_cutoff_xy[0] + global_map.shape[0], map_cutoff_xy[1]:global_map.shape[1] + map_cutoff_xy[1]] = global_map return global_map_crop, path, target_xy, xy def plot_loop(self, queue): # Infinite loop that takes frame data or reset request from queue and does plotting in a separate thread. plt.ion() print ("plot loop") while True: cmd, frame_data = queue.get(block=True) # print ("plot command %s"%cmd) if cmd == "reset": self.reset_video_writer(called_from_plot_process=True) elif cmd == "exit": # self.reset_video_writer(called_from_plot_process=True) # TODO could save it here, but usually trying to create a new figure in this thread raises excpetion plt.close('all') return elif cmd == "step": self.frame_traj_data.append(frame_data) self.video_update(VIDEO_FRAME_SKIP * len(self.frame_traj_data)) # hack to plot last frame else: raise ValueError("Unknown plot command") def reset_video_writer(self, last_success=None, called_from_plot_process=False): if not called_from_plot_process and self.plot_process: self.plot_queue.put(("reset", None)) return if self.params.interactive_video or (SAVE_VIDEO and len(self.frame_traj_data) > 0): # Save video if False: fig = plt.figure() ax = fig.add_subplot(111) ax.set_aspect('equal') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) self.video_image_ax = ax.imshow(np.zeros((90, 160, 3))) self.video_global_map_ax = None self.video_text_ax0 = fig.text(0.04, 0.9, self.summary_str, transform=fig.transFigure, fontsize=10, verticalalignment='top') # bottom left self.video_text_ax1 = fig.text(0.96, 0.9, "Target", transform=fig.transFigure, fontsize=10, verticalalignment='top', horizontalalignment='right') self.video_text_ax2 = fig.text(0.04, 0.05, "Status2", transform=fig.transFigure, fontsize=10, verticalalignment='bottom', wrap=True) else: # fig = plt.figure(figsize=(6, 9)) # aspect ratio # ax = fig.add_subplot(221 if VIDEO_LARGE_PLOT else 121) # # ax.set_aspect('equal') # ax.get_xaxis().set_visible(False) # ax.get_yaxis().set_visible(False) # self.video_image_ax = ax.imshow(np.zeros((90, 160, 3))) if self.params.interactive_video and not called_from_plot_process: plt.close('all') fig = plt.figure(constrained_layout=True, figsize=(9, 5)) # figsize overwritten later gs = gridspec.GridSpec(20, 30) ax = plt.subplot(gs[:9, :15]) # image ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) self.video_image_ax = ax.imshow(np.zeros((90, 160, 3))) ax = plt.subplot(gs[9:18, 0:15]) # depth ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) self.video_image_ax2 = ax.imshow(np.zeros((90, 160)), cmap='Greys', vmin=0., vmax=1.) ax = plt.subplot(gs[:18, 15:]) # map window # ax.set_aspect('equal') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) self.video_global_map_ax = ax.imshow(np.zeros((1200, 1200, 3))) self.video_ax = ax self.video_path_scatter = ax.scatter([0.], [1.], s=2., c='green', marker='o') self.video_target_scatter = ax.scatter([0.], [1.], s=2., c='red', marker='o') if VIDEO_LARGE_PLOT: ax = fig.add_subplot(223) # ax.set_aspect('equal') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) self.video_global_map_ax2 = ax.imshow(np.zeros((1200, 1200, 3))) self.video_ax2 = ax self.video_path_scatter2 = ax.scatter([0.], [1.], s=2., c='green', marker='o') self.video_target_scatter2 = ax.scatter([0.], [1.], s=2., c='red', marker='o') ax = fig.add_subplot(224) # ax.set_aspect('equal') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) self.video_global_map_ax3 = ax.imshow(np.zeros((1200, 1200, 3))) self.video_ax3 = ax self.video_path_scatter3 = ax.scatter([0.], [1.], s=2., c='green', marker='o') self.video_target_scatter3 = ax.scatter([0.], [1.], s=2., c='red', marker='o') if VIDEO_DETAILED: # self.video_view_angle_lines = [mlines.Line2D([0., 0.], [10., 10.,], color='green') for _ in range(2)] # ax.add_line(self.video_view_angle_lines[0]) # ax.add_line(self.video_view_angle_lines[1]) # self.video_path_circles = [] # for i in range(20): # circle = plt.Circle((0., 0.), 2., color=('red' if i >= 18 else 'orange'), fill=False, transform='data') # ax.add_artist(circle) # self.video_path_circles.append(circle) self.video_view_angle_lines = [] for _ in range(2): self.video_view_angle_lines.extend(ax.plot([0., 1.], [0., 1.], '-', color='blue')) # plot returns a list of lines # self.video_text_ax0 = fig.text(0.04, 0.9, self.summary_str, transform=fig.transFigure, fontsize=9, # verticalalignment='top') # bottom left # self.video_text_ax1 = fig.text(0.96, 0.9, "Target", transform=fig.transFigure, fontsize=9, # verticalalignment='top', horizontalalignment='right') # self.video_text_ax2 = fig.text(0.04, 0.05, "Status2", transform=fig.transFigure, fontsize=9, # verticalalignment='bottom', wrap=True) self.video_text_ax1 = fig.text(0.5, 0.05, "Status2", transform=fig.transFigure, fontsize=9, verticalalignment='bottom', horizontalalignment='center', wrap=False) # im.set_clim([0, 1]) if self.params.interactive_video: fig.set_size_inches([9., 5.]) else: fig.set_size_inches([9./2, 5./2]) plt.tight_layout() if SAVE_VIDEO and len(self.frame_traj_data) > 0: ani = animation.FuncAnimation(fig, self.video_update, len(self.frame_traj_data) * VIDEO_FRAME_SKIP + 21, interval=100) # time between frames in ms. overwritten by fps below writer = animation.writers['ffmpeg'](fps=VIDEO_FPS) # default h264 is lossless, but could not find in docker) if last_success is not None: outcome_str = '_S' if last_success else '_F' else: outcome_str = '' outcome_str = outcome_str + '_' + self.frame_traj_data[-1]['outcome'] video_filename = os.path.join(self.logdir, '%s_%d%s%s.mp4'%(self.get_scene_name(), self.episode_i, outcome_str, self.filename_addition)) ani.save(video_filename, writer=writer, dpi=200) print ("Video saved to "+video_filename) self.num_videos_saved += 1 self.frame_traj_data = [] def visualize_agent(self, visibility_mask, images, global_map_pred, global_map_for_planning, global_map_label, global_map_true_partial, local_map_pred, local_map_label, planned_path, sim_rgb=None, local_obj_map_pred=None, xy=None, yaw=None, true_xy=None, true_yaw=None, target_xy=None): # Coordinate systems dont match the ones assumed in these plot functions, but all cancells out except for yaw yaw = yaw - np.pi/2 if true_yaw is not None: true_yaw = true_yaw - np.pi/2 status_msg = "step %d" % (self.step_i,) if global_map_label is not None: # assert global_map_label.shape[-1] == 3 global_map_label = np.concatenate( [global_map_label, np.zeros_like(global_map_label), np.zeros_like(global_map_label)], axis=-1) plt.figure("Global map label") plt.imshow(global_map_label) plot_viewpoints(xy[0], xy[1], yaw) if true_xy is not None and true_yaw is not None: plot_viewpoints(true_xy[0], true_xy[1], true_yaw, color='green') plot_target_and_path(target_xy=target_xy, path=planned_path, every_n=1) plt.title(status_msg) plt.savefig('./temp/global-map-label.png') plt.figure("Global map (%d)" % self.step_i) map_to_plot = global_map_pred[..., :1] map_to_plot = np.pad(map_to_plot, [[0, 0], [0, 0], [0, 3-map_to_plot.shape[-1]]]) plt.imshow(map_to_plot) plot_viewpoints(xy[0], xy[1], yaw) plot_target_and_path(target_xy=target_xy, path=planned_path, every_n=1) # plot_target_and_path(target_xy=target_xy_vel, path=np.array(self.hist2)[:, :2]) plt.title(status_msg) plt.savefig('./temp/global-map-pred.png') if global_map_pred.shape[-1] == 2: map_to_plot = global_map_pred[..., 1:2] map_to_plot = np.pad(map_to_plot, [[0, 0], [0, 0], [0, 3-map_to_plot.shape[-1]]]) plt.imshow(map_to_plot) plot_viewpoints(xy[0], xy[1], yaw) plot_target_and_path(target_xy=target_xy, path=planned_path, every_n=1) plt.title(status_msg) plt.savefig('./temp/global-obj-map-pred.png') # if global_map_true_partial is not None: # plt.figure("Global map true (%d)" % self.step_i) # map_to_plot = global_map_true_partial # map_to_plot = np.pad(map_to_plot, [[0, 0], [0, 0], [0, 3-map_to_plot.shape[-1]]]) # plt.imshow(map_to_plot) # plot_viewpoints(xy[0], xy[1], yaw) # plot_target_and_path(target_xy=self.target_xy, path=planned_path) # # plot_target_and_path(target_xy=self.target_xy, path=np.array(self.hist1)[:, :2]) # # plot_target_and_path(target_xy=self.target_xy_vel, path=np.array(self.hist2)[:, :2]) # plt.title(status_msg) # plt.savefig('./temp/global-map-true.png') # plt.figure("Global map plan (%d)" % self.step_i) map_to_plot = global_map_for_planning map_to_plot = np.pad(map_to_plot, [[0, 0], [0, 0], [0, 3-map_to_plot.shape[-1]]]) plt.imshow(map_to_plot) plot_viewpoints(xy[0], xy[1], yaw) plot_target_and_path(target_xy=target_xy, path=planned_path, every_n=1) plt.title(status_msg) plt.savefig('./temp/global-map-plan.png') depth, rgb = mapping_visualizer.recover_depth_and_rgb(images) if self.params.mode == 'depth' and sim_rgb is not None: rgb = sim_rgb rgb[:5, :5, :] = 0 # indicate this is not observed images_fig, images_axarr = plt.subplots(2, 2, squeeze=True) plt.title(status_msg) plt.axes(images_axarr[0, 0]) plt.imshow(depth) plt.axes(images_axarr[0, 1]) plt.imshow(rgb) plt.axes(images_axarr[1, 0]) if local_map_pred is not None: plt.imshow(local_map_pred * visibility_mask + (1 - visibility_mask) * 0.5, vmin=0., vmax=1.) plt.axes(images_axarr[1, 1]) if local_obj_map_pred is not None: plt.imshow(local_obj_map_pred * visibility_mask + (1 - visibility_mask) * 0.5, vmin=0, vmax=1.) elif local_map_label is not None: plt.imshow(local_map_label * visibility_mask + (1 - visibility_mask) * 0.5, vmin=0., vmax=1.) plt.savefig('./temp/inputs.png') # if INTERACTIVE_PLOT: plt.figure('step') plt.show() # pdb.set_trace() plt.waitforbuttonpress(0.01) # True for keyboard, False for mouse, None for timeout if button_res: print ('pause') pdb.set_trace() else: plt.close('all') # def main(): # params = parse_args(default_files=('./gibson_submission.conf', )) # is_submission = (params.gibson_mode == 'submission') # # parser = argparse.ArgumentParser() # parser.add_argument("--evaluation", type=str, required=True, choices=["local", "remote"]) # args = parser.parse_args() # # config_paths = os.environ["CHALLENGE_CONFIG_FILE"] # config = habitat.get_config(config_paths) # # # agent = RandomAgent(task_config=config) # # if args.evaluation == "local": # challenge = habitat.Challenge(eval_remote=False) # else: # challenge = habitat.Challenge(eval_remote=True) # # env = challenge._env # agent = DSLAMAgent(task_config=config, env=env) # # challenge.submit(agent) # # # if __name__ == "__main__": # main()

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241

0.615592

18,049

134,339

4.278741

0.066319

0.034379

0.014503

0.012172

0.545975

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0.367514

0.328279

0.298354

0.261877

0

0.026285

0.283224

134,339

2,451

242

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0.002415

0.04629

0.002715

0

0.000408

0.049517

1

0.013889

false

0.001208

0.019928

0.000604

0.052536

0.024155

0

null

0

null

0

1

0

d8db1da409aa926ae0d4a1dd1326712356ef588d

2,890

py

Python

examples/nightlight/nightlight.py

pimoroni/breakout-garden

15f6886a1d011363cc660df1a350fd23d6cf4b78

[ "MIT" ]

68

2018-08-20T21:45:01.000Z

2022-03-17T20:45:47.000Z

examples/nightlight/nightlight.py

pimoroni/breakout-garden

15f6886a1d011363cc660df1a350fd23d6cf4b78

[ "MIT" ]

24

2018-08-20T14:04:13.000Z

2022-03-09T12:26:24.000Z

examples/nightlight/nightlight.py

pimoroni/breakout-garden

15f6886a1d011363cc660df1a350fd23d6cf4b78

[ "MIT" ]

14

2018-08-25T13:33:49.000Z

2021-12-09T09:02:35.000Z

#!/usr/bin/env python3 import time from ltr559 import LTR559 from rgbmatrix5x5 import RGBMatrix5x5 print("""This Pimoroni Breakout Garden example requires an LTR-559 Light and Proximity Breakout and a 5x5 RGB Matrix Breakout. This example creates a little nightlight that can be toggled on or off by tapping the proximity sensor with your finger, or triggered automatically when it's dark. Press Ctrl+C to exit. """) # Set up the LTR-559 sensor ltr559 = LTR559() # Set up the 5x5 RGB matrix rgbmatrix5x5 = RGBMatrix5x5() rgbmatrix5x5.set_clear_on_exit() rgbmatrix5x5.set_brightness(0.8) # Initial variables to keep track of state of light state = False last_state = False toggled = False light_threshold = 100 # Low-light trigger level prox_threshold = 1000 # Proximity trigger level colour = (255, 165, 0) # Orange-ish # Function to toggle the RGB matrix on or off depending on state def toggle_matrix(): global state, last_state if state is True and last_state is False: rgbmatrix5x5.set_all(*colour) rgbmatrix5x5.show() elif state is False and last_state is True: rgbmatrix5x5.clear() rgbmatrix5x5.show() last_state = state # Read the sensor once, as the first values are always squiffy ltr559.update_sensor() lux = ltr559.get_lux() prox =ltr559. get_proximity() time.sleep(1) try: while True: # Read the light and proximity sensor ltr559.update_sensor() lux = ltr559.get_lux() prox = ltr559.get_proximity() # If it's dark and the light isn't toggled on, turn on if lux < light_threshold and not toggled: state = True if state != last_state: print("It's dark! Turning light ON") toggle_matrix() # If it's light and the light isn't on, turn off elif lux >= light_threshold and not toggled: state = False if state != last_state: print("It's light! Turning light OFF") toggle_matrix() # If there's a tap on the sensor if prox > prox_threshold: # Toggle it off if it's currently on if toggled: state = False toggled = False if state != last_state: print("Toggling light OFF") toggle_matrix() # Toggle it on if it's currently off else: state = True toggled = True if state != last_state: print("Toggling light ON") toggle_matrix() # Wait a short while to prevent the on/off switch # from immediately re-triggering time.sleep(0.5) elif prox < prox_threshold and lux >= light_threshold: state = False time.sleep(0.05) except KeyboardInterrupt: pass

27.788462

67

0.623529

386

2,890

4.585492

0.331606

0.045763

0.039548

0.036158

0.19435

0.177401

0.167232

0.062147

0

0.041667

0.310727

2,890

103

68

28.058252

0.846888

0.215225

0

0.358209

0

0.175922

0

1

0.014925

false

0.014925

0.044776

0

0.059701

0.074627

0

null

0

null

0

1

0

d8dd3e6986ec9de4d911cd084dbaeaf781af98b0

186

py

Python

python_for_everybody/for_in_range.py

timothyyu/p4e-prac

f978b71ce147b6e9058372929f2666c2e67d0741

[ "BSD-3-Clause" ]

null

python_for_everybody/for_in_range.py

timothyyu/p4e-prac

f978b71ce147b6e9058372929f2666c2e67d0741

[ "BSD-3-Clause" ]

null

python_for_everybody/for_in_range.py

timothyyu/p4e-prac

f978b71ce147b6e9058372929f2666c2e67d0741

[ "BSD-3-Clause" ]

1

2020-04-18T16:09:04.000Z

x = 0 for y in range(5): print(x,y) x = x + y # x = 0 + 0 # x = 0 + 1 ==> x = 1 # x = 1 + 2 ==> x = 3 # x = 3 + 3 ==> x = 6 # x = 6 + 4 ==> x = 10 print(x)

15.5

27

0.290323

38

186

1.421053

0.368421

0.111111

0

0.177083

0.483871

186

11

28

16.909091

0.385417

0.483871

0

1

0

false

0

0.4

0

1

null

0

1

0

null

0

2

d8de1a0557c16a820290ec65f2861645cf8269e4

6,595

py

Python

leaguedirector/sequence/sequenceTrackView.py

santutu/league-director

631ab416e31a0391ab207f9b657638c8e350a48c

[ "Apache-2.0" ]

null

leaguedirector/sequence/sequenceTrackView.py

santutu/league-director

631ab416e31a0391ab207f9b657638c8e350a48c

[ "Apache-2.0" ]

null

leaguedirector/sequence/sequenceTrackView.py

santutu/league-director

631ab416e31a0391ab207f9b657638c8e350a48c

[ "Apache-2.0" ]

null

import copy import statistics from operator import attrgetter from PySide2.QtCore import Signal, Qt, QEvent from PySide2.QtGui import QPen, QMouseEvent from PySide2.QtWidgets import QGraphicsView, QGraphicsScene, QAbstractScrollArea, QApplication, QGraphicsItem from leaguedirector.libs.memoryCache import MemoryCache from leaguedirector.sequence.constant import PRECISION, ADJACENT from leaguedirector.sequence.sequenceKeyframe import SequenceKeyframe from leaguedirector.sequence.sequenceTime import SequenceTime from leaguedirector.sequence.sequenceTrack import SequenceTrack from leaguedirector.widgets import schedule class SequenceTrackView(QGraphicsView): selectionChanged = Signal() def __init__(self, api, headers): self.api = api self.scene = QGraphicsScene() QGraphicsView.__init__(self, self.scene) self.tracks = {} self.timer = schedule(10, self.animate) self.scale(1.0 / PRECISION, 1.0) self.setDragMode(QGraphicsView.NoDrag) self.setAlignment(Qt.AlignLeft | Qt.AlignTop) self.setTransformationAnchor(QGraphicsView.AnchorUnderMouse) self.setSizeAdjustPolicy(QAbstractScrollArea.AdjustToContents) for index, name in enumerate(self.api.sequence.keys()): track = SequenceTrack(self.api, name, index) self.scene.addItem(track) self.tracks[name] = track self.time = SequenceTime(0, 1, 0, self.scene.height() - 2) self.time.setPen(QPen(QApplication.palette().highlight(), 1)) self.time.setFlags(QGraphicsItem.ItemIgnoresTransformations) self.scene.addItem(self.time) self.api.playback.updated.connect(self.update) self.api.sequence.updated.connect(self.update) self.api.sequence.dataLoaded.connect(self.reload) headers.addKeyframe.connect(self.addKeyframe) headers.verticalScrollBar().valueChanged.connect(lambda value: self.verticalScrollBar().setValue(value)) self.verticalScrollBar().valueChanged.connect(lambda value: headers.verticalScrollBar().setValue(value)) self.scene.selectionChanged.connect(self.selectionChanged.emit) self.clipboard = MemoryCache() self.clipboard.set('copied_key_frames', []) def copyKeyframes(self): self.clipboard.set('copied_key_frames', [(keyframe.track.name, copy.deepcopy(keyframe.item)) for keyframe in self.selectedKeyframes()]) return self def pasteKeyframes(self): keyframes = self.clipboard.get('copied_key_frames') for keyframe in keyframes: [name, item] = keyframe item = copy.deepcopy(item) self.api.sequence.appendKeyframe(name, item) SequenceKeyframe(self.api, item, self.tracks[name]) def reload(self): for track in self.tracks.values(): track.reload() def selectedKeyframes(self): return [key for key in self.scene.selectedItems() if isinstance(key, SequenceKeyframe)] def allKeyframes(self): return [key for key in self.scene.items() if isinstance(key, SequenceKeyframe)] def addKeyframe(self, name): self.tracks[name].addKeyframe() def clearKeyframes(self): for track in self.tracks.values(): track.clearKeyframes() def deleteSelectedKeyframes(self): for selected in self.selectedKeyframes(): selected.delete() def selectAllKeyframes(self): for child in self.allKeyframes(): child.setSelected(True) def selectAdjacentKeyframes(self): for selected in self.selectedKeyframes(): for child in self.allKeyframes(): if abs(child.time - selected.time) < ADJACENT: child.setSelected(True) def selectNextKeyframe(self): selectionSorted = sorted(self.selectedKeyframes(), key=attrgetter('time')) trackSelection = {key.track: key for key in selectionSorted} for track, selected in trackSelection.items(): for child in sorted(track.childItems(), key=attrgetter('time')): if child.time > selected.time: trackSelection[track] = child break self.scene.clearSelection() for item in trackSelection.values(): item.setSelected(True) def selectPrevKeyframe(self): selectionSorted = sorted(self.selectedKeyframes(), key=attrgetter('time'), reverse=True) trackSelection = {key.track: key for key in selectionSorted} for track, selected in trackSelection.items(): for child in sorted(track.childItems(), key=attrgetter('time'), reverse=True): if child.time < selected.time: trackSelection[track] = child break self.scene.clearSelection() for item in trackSelection.values(): item.setSelected(True) def seekSelectedKeyframe(self): selected = [key.time for key in self.selectedKeyframes()] if selected: self.api.playback.pause(statistics.mean(selected)) def update(self): for track in self.tracks.values(): track.update() def mousePressEvent(self, event): if event.button() == Qt.RightButton: self.setDragMode(QGraphicsView.ScrollHandDrag) QGraphicsView.mousePressEvent(self, QMouseEvent( QEvent.GraphicsSceneMousePress, event.pos(), Qt.MouseButton.LeftButton, Qt.MouseButton.LeftButton, Qt.KeyboardModifier.NoModifier )) elif event.button() == Qt.LeftButton: if event.modifiers() == Qt.ShiftModifier: self.setDragMode(QGraphicsView.RubberBandDrag) QGraphicsView.mousePressEvent(self, event) QGraphicsView.mousePressEvent(self, event) def mouseDoubleClickEvent(self, event): QGraphicsView.mouseDoubleClickEvent(self, event) if not self.scene.selectedItems() and not event.isAccepted(): self.api.playback.pause(self.mapToScene(event.pos()).x() / PRECISION) def mouseReleaseEvent(self, event): QGraphicsView.mouseReleaseEvent(self, event) self.setDragMode(QGraphicsView.NoDrag) def wheelEvent(self, event): if event.angleDelta().y() > 0: self.scale(1.1, 1.0) else: self.scale(0.9, 1.0) def animate(self): self.time.setPos(self.api.playback.currentTime * PRECISION, 0)

40.962733

112

0.660197

660

6,595

6.575758

0.239394

0.019355

0.009217

0.010138

0.290553

0.236175

0.204378

0.186406

0.119355

0

0.004796

0.241243

6,595

160

113

41.21875

0.86251

0

0.214815

0

0.010159

0

1

0.148148

false

0

0.088889

0.014815

0.274074

0

null

0

null

0

1

0

d8de56f1954539d2d33e25fa9d9007b69553e370

23,746

py

Python

annealed_flow_transport/flows.py

LaudateCorpus1/annealed_flow_transport

28f348bb41e3acec5bc925355063d476f2e2aea2

[ "Apache-2.0" ]

23

2021-08-13T14:00:10.000Z

2022-02-15T12:44:20.000Z

annealed_flow_transport/flows.py

deepmind/annealed_flow_transport

28f348bb41e3acec5bc925355063d476f2e2aea2

[ "Apache-2.0" ]

1

2021-10-05T16:19:25.000Z

annealed_flow_transport/flows.py

LaudateCorpus1/annealed_flow_transport

28f348bb41e3acec5bc925355063d476f2e2aea2

[ "Apache-2.0" ]

4

2021-10-05T16:14:58.000Z

2022-01-03T15:17:36.000Z

# Copyright 2020 DeepMind Technologies Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Code for normalizing flows. For a review of normalizing flows see: https://arxiv.org/abs/1912.02762 The abstract base class ConfigurableFlow demonstrates our minimal interface. Although the standard change of variables formula requires that normalizing flows are invertible, none of the algorithms in train.py require evaluating that inverse explicitly so inverses are not implemented. """ import abc from typing import Callable, List, Tuple import annealed_flow_transport.aft_types as tp import chex import haiku as hk import jax import jax.numpy as jnp import numpy as np Array = tp.Array ConfigDict = tp.ConfigDict class ConfigurableFlow(hk.Module, abc.ABC): """Abstract base clase for configurable normalizing flows. This is the interface expected by all flow based algorithms called in train.py """ def __init__(self, config: ConfigDict): super().__init__() self._check_configuration(config) self._config = config def _check_input(self, x: Array) -> Array: chex.assert_rank(x, 1) def _check_outputs(self, x: Array, transformed_x: Array, log_abs_det_jac: Array) -> Array: chex.assert_rank(x, 1) chex.assert_equal_shape([x, transformed_x]) chex.assert_shape(log_abs_det_jac, ()) def _check_members_types(self, config: ConfigDict, expected_members_types): for elem, elem_type in expected_members_types: if elem not in config: raise ValueError('Flow config element not found: ', elem) if not isinstance(config[elem], elem_type): msg = 'Flow config element '+elem+' is not of type '+str(elem_type) raise TypeError(msg) def __call__(self, x: Array) -> Tuple[Array, Array]: """Call transform_and_log abs_det_jac with automatic shape checking. This calls transform_and_log_abs_det_jac which needs to be implemented in derived classes. Args: x: Array size (num_dim,) containing input to flow. Returns: Array size (num_dim,) containing output and Scalar log abs det Jacobian. """ self._check_input(x) output, log_abs_det_jac = self.transform_and_log_abs_det_jac(x) self._check_outputs(x, output, log_abs_det_jac) return output, log_abs_det_jac @abc.abstractmethod def transform_and_log_abs_det_jac(self, x: Array) -> Tuple[Array, Array]: """Transform x through the flow and compute log abs determinant of Jacobian. Args: x: (num_dim,) input to the flow. Returns: Array size (num_dim,) containing output and Scalar log abs det Jacobian. """ @abc.abstractmethod def _check_configuration(self, config: ConfigDict): """Check the configuration includes the necessary fields. Will typically raise Assertion like errors. Args: config: A ConfigDict include the fields required by the flow. """ class DiagonalAffine(ConfigurableFlow): """An affine transformation with a positive diagonal matrix.""" def _check_configuration(self, unused_config: ConfigDict): pass def transform_and_log_abs_det_jac(self, x: Array) -> Tuple[Array, Array]: num_elem = x.shape[0] unconst_diag_init = hk.initializers.Constant(jnp.zeros((num_elem,))) bias_init = hk.initializers.Constant(jnp.zeros((num_elem,))) unconst_diag = hk.get_parameter('unconst_diag', shape=[num_elem], dtype=x.dtype, init=unconst_diag_init) bias = hk.get_parameter('bias', shape=[num_elem], dtype=x.dtype, init=bias_init) output = jnp.exp(unconst_diag)*x + bias log_abs_det = jnp.sum(unconst_diag) return output, log_abs_det def rational_quadratic_spline(x: Array, bin_positions: Array, bin_heights: Array, derivatives: Array) -> Tuple[Array, Array]: """Compute a rational quadratic spline. See https://arxiv.org/abs/1906.04032 Args: x: A single real number. bin_positions: A sorted array of bin positions of length num_bins+1. bin_heights: An array of bin heights of length num_bins+1. derivatives: An array of derivatives at bin positions of length num_bins+1. Returns: Value of the rational quadratic spline at x. Derivative with respect to x of rational quadratic spline at x. """ bin_index = jnp.searchsorted(bin_positions, x) array_index = bin_index % len(bin_positions) lower_x = bin_positions[array_index-1] upper_x = bin_positions[array_index] lower_y = bin_heights[array_index-1] upper_y = bin_heights[array_index] lower_deriv = derivatives[array_index-1] upper_deriv = derivatives[array_index] delta_x = upper_x - lower_x delta_y = upper_y - lower_y slope = delta_y / delta_x alpha = (x - lower_x)/delta_x alpha_squared = jnp.square(alpha) beta = alpha * (1.-alpha) gamma = jnp.square(1.-alpha) epsilon = upper_deriv+lower_deriv -2. *slope numerator_quadratic = delta_y * (slope*alpha_squared + lower_deriv*beta) denominator_quadratic = slope + epsilon*beta interp_x = lower_y + numerator_quadratic/denominator_quadratic # now compute derivative numerator_deriv = jnp.square(slope) * ( upper_deriv * alpha_squared + 2. * slope * beta + lower_deriv * gamma) sqrt_denominator_deriv = slope + epsilon*beta denominator_deriv = jnp.square(sqrt_denominator_deriv) deriv = numerator_deriv / denominator_deriv return interp_x, deriv def identity_padded_rational_quadratic_spline( x: Array, bin_positions: Array, bin_heights: Array, derivatives: Array) -> Tuple[Array, Array]: """An identity padded rational quadratic spline. Args: x: the value to evaluate the spline at. bin_positions: sorted values of bin x positions of length num_bins+1. bin_heights: absolute height of bin of length num_bins-1. derivatives: derivatives at internal bin edge of length num_bins-1. Returns: The value of the spline at x. The derivative with respect to x of the spline at x. """ lower_limit = bin_positions[0] upper_limit = bin_positions[-1] bin_height_sequence = (jnp.atleast_1d(jnp.array(lower_limit)), bin_heights, jnp.atleast_1d(jnp.array(upper_limit))) full_bin_heights = jnp.concatenate(bin_height_sequence) derivative_sequence = (jnp.ones((1,)), derivatives, jnp.ones((1,))) full_derivatives = jnp.concatenate(derivative_sequence) in_range = jnp.logical_and(jnp.greater(x, lower_limit), jnp.less(x, upper_limit)) multiplier = in_range*1. multiplier_complement = jnp.logical_not(in_range)*1. spline_val, spline_deriv = rational_quadratic_spline(x, bin_positions, full_bin_heights, full_derivatives) identity_val = x identity_deriv = 1. val = spline_val*multiplier + multiplier_complement*identity_val deriv = spline_deriv*multiplier + multiplier_complement*identity_deriv return val, deriv class AutoregressiveMLP(hk.Module): """An MLP which is constrained to have autoregressive dependency.""" def __init__(self, num_hiddens_per_input_dim: List[int], include_self_links: bool, non_linearity, zero_final: bool, bias_last: bool, name=None): super().__init__(name=name) self._num_hiddens_per_input_dim = num_hiddens_per_input_dim self._include_self_links = include_self_links self._non_linearity = non_linearity self._zero_final = zero_final self._bias_last = bias_last def __call__(self, x: Array) -> Array: input_dim = x.shape[0] hidden_representation = jnp.atleast_2d(x).T prev_hid_per_dim = 1 num_hidden_layers = len(self._num_hiddens_per_input_dim) final_index = num_hidden_layers-1 for layer_index in range(num_hidden_layers): is_last_layer = (final_index == layer_index) hid_per_dim = self._num_hiddens_per_input_dim[layer_index] name_stub = '_'+str(layer_index) layer_shape = (input_dim, prev_hid_per_dim, input_dim, hid_per_dim) in_degree = prev_hid_per_dim * input_dim if is_last_layer and self._zero_final: w_init = jnp.zeros else: w_init = hk.initializers.TruncatedNormal(1. / np.sqrt(in_degree)) bias_init = hk.initializers.Constant(jnp.zeros((input_dim, hid_per_dim,))) weights = hk.get_parameter(name='weights'+name_stub, shape=layer_shape, dtype=x.dtype, init=w_init) if is_last_layer and not self._bias_last: biases = jnp.zeros((input_dim, hid_per_dim,)) else: biases = hk.get_parameter(name='biases'+name_stub, shape=(input_dim, hid_per_dim), dtype=x.dtype, init=bias_init) if not(self._include_self_links) and is_last_layer: k = -1 else: k = 0 mask = jnp.tril(jnp.ones((input_dim, input_dim)), k=k) masked_weights = mask[:, None, :, None] * weights new_hidden_representation = jnp.einsum('ijkl,ij->kl', masked_weights, hidden_representation) + biases prev_hid_per_dim = hid_per_dim if not is_last_layer: hidden_representation = self._non_linearity(new_hidden_representation) else: hidden_representation = new_hidden_representation return hidden_representation class InverseAutogressiveFlow(object): """A generic inverse autoregressive flow. See https://arxiv.org/abs/1606.04934 Takes two functions as input. 1) autoregressive_func takes array of (num_dim,) and returns array (num_dim, num_features) it is autoregressive in the sense that the output[i, :] depends only on the input[:i]. This is not checked. 2) transform_func takes array of (num_dim, num_features) and an array of (num_dim,) and returns output of shape (num_dim,) and a single log_det_jacobian value. The represents the transformation acting on the inputs with given parameters. """ def __init__(self, autoregressive_func: Callable[[Array], Array], transform_func: Callable[[Array, Array], Tuple[Array, Array]]): self._autoregressive_func = autoregressive_func self._transform_func = transform_func def __call__(self, x: Array) -> Tuple[Array, Array]: """x is of shape (num_dim,).""" transform_features = self._autoregressive_func(x) output, log_abs_det = self._transform_func(transform_features, x) return output, log_abs_det class SplineInverseAutoregressiveFlow(ConfigurableFlow): """An inverse autoregressive flow with spline transformer. config must contain the following fields: num_spline_bins: Number of bins for rational quadratic spline. intermediate_hids_per_dim: See AutoregresiveMLP. num_layers: Number of layers for AutoregressiveMLP. identity_init: Whether to initalize the flow to the identity. bias_last: Whether to include biases on the last later of AutoregressiveMLP lower_lim: Lower limit of active region for rational quadratic spline. upper_lim: Upper limit of active region for rational quadratic spline. min_bin_size: Minimum bin size for rational quadratic spline. min_derivative: Minimum derivative for rational quadratic spline. """ def __init__(self, config: ConfigDict): super().__init__(config) self._num_spline_bins = config.num_spline_bins num_spline_parameters = 3 * config.num_spline_bins - 1 num_hids_per_input_dim = [config.intermediate_hids_per_dim ] * config.num_layers + [ num_spline_parameters ] self._autoregressive_mlp = AutoregressiveMLP( num_hids_per_input_dim, include_self_links=False, non_linearity=jax.nn.leaky_relu, zero_final=config.identity_init, bias_last=config.bias_last) self._lower_lim = config.lower_lim self._upper_lim = config.upper_lim self._min_bin_size = config.min_bin_size self._min_derivative = config.min_derivative def _check_configuration(self, config: ConfigDict): expected_members_types = [ ('num_spline_bins', int), ('intermediate_hids_per_dim', int), ('num_layers', int), ('identity_init', bool), ('bias_last', bool), ('lower_lim', float), ('upper_lim', float), ('min_bin_size', float), ('min_derivative', float) ] self._check_members_types(config, expected_members_types) def _unpack_spline_params(self, raw_param_vec) -> Tuple[Array, Array, Array]: unconst_bin_size_x = raw_param_vec[:self._num_spline_bins] unconst_bin_size_y = raw_param_vec[self._num_spline_bins:2 * self._num_spline_bins] unconst_derivs = raw_param_vec[2 * self._num_spline_bins:( 3 * self._num_spline_bins - 1)] return unconst_bin_size_x, unconst_bin_size_y, unconst_derivs def _transform_raw_to_spline_params( self, raw_param_vec: Array) -> Tuple[Array, Array, Array]: unconst_bin_size_x, unconst_bin_size_y, unconst_derivs = self._unpack_spline_params( raw_param_vec) def normalize_bin_sizes(unconst_bin_sizes: Array) -> Array: bin_range = self._upper_lim - self._lower_lim reduced_bin_range = ( bin_range - self._num_spline_bins * self._min_bin_size) return jax.nn.softmax( unconst_bin_sizes) * reduced_bin_range + self._min_bin_size bin_size_x = normalize_bin_sizes(unconst_bin_size_x) bin_size_y = normalize_bin_sizes(unconst_bin_size_y) # get the x bin positions. array_sequence = (jnp.ones((1,))*self._lower_lim, bin_size_x) x_bin_pos = jnp.cumsum(jnp.concatenate(array_sequence)) # get the y bin positions, ignoring redundant terms. stripped_y_bin_pos = self._lower_lim + jnp.cumsum(bin_size_y[:-1]) def forward_positive_transform(unconst_value: Array, min_value: Array) -> Array: return jax.nn.softplus(unconst_value) + min_value def inverse_positive_transform(const_value: Array, min_value: Array) -> Array: return jnp.log(jnp.expm1(const_value-min_value)) inverted_one = inverse_positive_transform(1., self._min_derivative) derivatives = forward_positive_transform(unconst_derivs + inverted_one, self._min_derivative) return x_bin_pos, stripped_y_bin_pos, derivatives def _get_spline_values(self, raw_parameters: Array, x: Array) -> Tuple[Array, Array]: bat_get_parameters = jax.vmap(self._transform_raw_to_spline_params) bat_x_bin_pos, bat_stripped_y, bat_derivatives = bat_get_parameters( raw_parameters) # Vectorize spline over data and parameters. bat_get_spline_vals = jax.vmap(identity_padded_rational_quadratic_spline, in_axes=[0, 0, 0, 0]) spline_vals, derivs = bat_get_spline_vals(x, bat_x_bin_pos, bat_stripped_y, bat_derivatives) log_abs_det = jnp.sum(jnp.log(jnp.abs(derivs))) return spline_vals, log_abs_det def transform_and_log_abs_det_jac(self, x: Array) -> Tuple[Array, Array]: iaf = InverseAutogressiveFlow(self._autoregressive_mlp, self._get_spline_values) return iaf(x) class AffineInverseAutoregressiveFlow(ConfigurableFlow): """An inverse autoregressive flow with affine transformer. config must contain the following fields: intermediate_hids_per_dim: See AutoregresiveMLP. num_layers: Number of layers for AutoregressiveMLP. identity_init: Whether to initalize the flow to the identity. bias_last: Whether to include biases on the last later of AutoregressiveMLP """ def __init__(self, config: ConfigDict): super().__init__(config) num_affine_params = 2 num_hids_per_input_dim = [config.intermediate_hids_per_dim ] * config.num_layers + [num_affine_params] self._autoregressive_mlp = AutoregressiveMLP( num_hids_per_input_dim, include_self_links=False, non_linearity=jax.nn.leaky_relu, zero_final=config.identity_init, bias_last=config.bias_last) def _check_configuration(self, config: ConfigDict): expected_members_types = [('intermediate_hids_per_dim', int), ('num_layers', int), ('identity_init', bool), ('bias_last', bool) ] self._check_members_types(config, expected_members_types) def _get_affine_transformation(self, raw_parameters: Array, x: Array) -> Tuple[Array, Array]: shifts = raw_parameters[:, 0] scales = raw_parameters[:, 1] + jnp.ones_like(raw_parameters[:, 1]) log_abs_det = jnp.sum(jnp.log(jnp.abs(scales))) output = x * scales + shifts return output, log_abs_det def transform_and_log_abs_det_jac(self, x: Array) -> Tuple[Array, Array]: iaf = InverseAutogressiveFlow(self._autoregressive_mlp, self._get_affine_transformation) return iaf(x) def affine_transformation(params: Array, x: Array) -> Tuple[Array, Array]: shift = params[0] # Assuming params start as zero adding 1 to scale gives identity transform. scale = params[1] + 1. output = x * scale + shift return output, jnp.log(jnp.abs(scale)) class RationalQuadraticSpline(ConfigurableFlow): """A learnt monotonic rational quadratic spline with identity padding. Each input dimension is operated on by a separate spline. The spline is initialized to the identity. config must contain the following fields: num_bins: Number of bins for rational quadratic spline. lower_lim: Lower limit of active region for rational quadratic spline. upper_lim: Upper limit of active region for rational quadratic spline. min_bin_size: Minimum bin size for rational quadratic spline. min_derivative: Minimum derivative for rational quadratic spline. """ def __init__(self, config: ConfigDict): super().__init__(config) self._num_bins = config.num_bins self._lower_lim = config.lower_lim self._upper_lim = config.upper_lim self._min_bin_size = config.min_bin_size self._min_derivative = config.min_derivative def _check_configuration(self, config: ConfigDict): expected_members_types = [ ('num_bins', int), ('lower_lim', float), ('upper_lim', float), ('min_bin_size', float), ('min_derivative', float) ] self._check_members_types(config, expected_members_types) def transform_and_log_abs_det_jac(self, x: Array) -> Tuple[Array, Array]: """Apply the spline transformation. Args: x: (num_dim,) DeviceArray representing flow input. Returns: output: (num_dim,) transformed sample through flow. log_prob_out: new Scalar representing log_probability of output. """ num_dim = x.shape[0] bin_parameter_shape = (num_dim, self._num_bins) # Setup the bin position and height parameters. bin_init = hk.initializers.Constant(jnp.ones(bin_parameter_shape)) unconst_bin_size_x = hk.get_parameter( 'unconst_bin_size_x', shape=bin_parameter_shape, dtype=x.dtype, init=bin_init) unconst_bin_size_y = hk.get_parameter( 'unconst_bin_size_y', shape=bin_parameter_shape, dtype=x.dtype, init=bin_init) def normalize_bin_sizes(unconst_bin_sizes): bin_range = self._upper_lim - self._lower_lim reduced_bin_range = (bin_range - self._num_bins * self._min_bin_size) return jax.nn.softmax( unconst_bin_sizes) * reduced_bin_range + self._min_bin_size batched_normalize = jax.vmap(normalize_bin_sizes) bin_size_x = batched_normalize(unconst_bin_size_x) bin_size_y = batched_normalize(unconst_bin_size_y) array_sequence = (jnp.ones((num_dim, 1)) * self._lower_lim, bin_size_x) bin_positions = jnp.cumsum(jnp.concatenate(array_sequence, axis=1), axis=1) # Don't include the redundant bin heights. stripped_bin_heights = self._lower_lim + jnp.cumsum( bin_size_y[:, :-1], axis=1) # Setup the derivative parameters. def forward_positive_transform(unconst_value, min_value): return jax.nn.softplus(unconst_value) + min_value def inverse_positive_transform(const_value, min_value): return jnp.log(jnp.expm1(const_value - min_value)) deriv_parameter_shape = (num_dim, self._num_bins - 1) inverted_one = inverse_positive_transform(1., self._min_derivative) deriv_init = hk.initializers.Constant( jnp.ones(deriv_parameter_shape) * inverted_one) unconst_deriv = hk.get_parameter( 'unconst_deriv', shape=deriv_parameter_shape, dtype=x.dtype, init=deriv_init) batched_positive_transform = jax.vmap( forward_positive_transform, in_axes=[0, None]) deriv = batched_positive_transform(unconst_deriv, self._min_derivative) # Setup batching then apply the spline. batch_padded_rq_spline = jax.vmap( identity_padded_rational_quadratic_spline, in_axes=[0, 0, 0, 0]) output, jac_terms = batch_padded_rq_spline(x, bin_positions, stripped_bin_heights, deriv) log_abs_det_jac = jnp.sum(jnp.log(jac_terms)) return output, log_abs_det_jac class ComposedFlows(ConfigurableFlow): """Class to compose flows based on a list of configs. config should contain flow_configs a list of flow configs to compose. """ def __init__(self, config: ConfigDict): super().__init__(config) self._flows = [] for flow_config in self._config.flow_configs: base_flow_class = globals()[flow_config.type] flow = base_flow_class(flow_config) self._flows.append(flow) def _check_configuration(self, config: ConfigDict): expected_members_types = [ ('flow_configs', list), ] self._check_members_types(config, expected_members_types) def transform_and_log_abs_det_jac(self, x: Array) -> Tuple[Array, Array]: log_abs_det = 0. progress = x for flow in self._flows: progress, log_abs_det_increment = flow(progress) log_abs_det += log_abs_det_increment return progress, log_abs_det

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d8debc26659ca852de091f669c8c62e62434b95f

3,208

py

Python

EMD_ensembled_data_prepare_Test_server.py

NahianHasan/Cardiovascular_Disease_Classification_Employing_EMD

57bf6425808bcff4f1c54e6be2e1df9c14b61313

[ "MIT" ]

6

2019-10-10T18:53:13.000Z

2020-08-13T08:39:43.000Z

EMD_ensembled_data_prepare_Test_server.py

NahianHasan/Cardiovascular_Disease_Classification_Employing_EMD

57bf6425808bcff4f1c54e6be2e1df9c14b61313

[ "MIT" ]

null

EMD_ensembled_data_prepare_Test_server.py

NahianHasan/Cardiovascular_Disease_Classification_Employing_EMD

57bf6425808bcff4f1c54e6be2e1df9c14b61313

[ "MIT" ]

1

2020-04-22T07:50:49.000Z

import pyhht from pyhht.emd import EMD from pyhht.visualization import plot_imfs import numpy as np import math from pyhht.utils import extr from pyhht.utils import get_envelops import matplotlib.pyplot as plt from pyhht.utils import inst_freq import wfdb import os import sys import glob def EMD_data_preparation(csv_folder,samplenumber,test_list): ########### Trining and Test Data Spliting ###################### Ensembled_test = open(csv_folder+'Ensembled_test.csv', 'w') Total_data = 0 #Testing data preparation G = open(test_list,'r') line = G.readline() while line: Original_signal = [] splitted = line.split(',') for h in range(0,samplenumber): Original_signal.append(float(splitted[h])) disease = splitted[-1][:-1] Original_signal = np.asarray(Original_signal) try: decomposer = EMD(Original_signal,n_imfs=3,maxiter=3000) imfs = decomposer.decompose() ensembled_data = [] for h in range(0,samplenumber): ensembled_data.append(imfs[0][h]+imfs[1][h]+imfs[2][h]) Total_data = Total_data+1 string = str(float("{0:.8f}".format(ensembled_data[0]))) for h in range(1,samplenumber): string = string +','+str(float("{0:.8f}".format(ensembled_data[h]))) string = string+','+disease+'\n' Ensembled_test.write(string) print 'Test Data = ',Total_data,'---Disease = ',disease line = G.readline() except: print 'Could not write' line = G.readline() #Ensembled_train.close() Ensembled_test.close() #F.close() G.close() def Main(): parser = argparse.ArgumentParser(description='ECG data training using EMD Data with separate threading', usage='Classifying EMD Data', epilog='Give proper arguments') parser.add_argument('-p',"--data_path",metavar='', help="Path to the main database",default=C.data_path) parser.add_argument('-c',"--csv_path",metavar='',help="Path to the CSV Folder of EMD Data",default=C.IMF_csv_path) Training",default=C.initial_epoch) parser.add_argument('-rc',"--patient_data_path",metavar='',help="Path to the Patient file RECORD.txt",default=C.patient_data_path) parser.add_argument('-pd',"--problem_data_path",metavar='',help="Path to the text file where problematic data to be stored",default=C.preoblem_data_path) parser.add_argument('-s',"--sample_number",metavar='',help="Number of samples to be taken by each record",type=int,default=C.samplenumber) parser.add_argument('-imf',"--number_of_IMFs",metavar='',help="Number of IMFs to be extracted",default=C.number_of_IMFs,type=int,choices=[2,3,4,5,6]) parser.add_argument('-spl',"--split_perc",metavar='',help="Splitting percentage of train and test(upper limit)",type=float,default=C.split_perc) parser.add_argument('-tel',"--te_list",metavar='',help="A csv file containing the list of test files") args = parser.parse_args() file_path=args.data_path csv_path=args.csv_path test_list = args.te_list patient_data=args.patient_data_path problem_data_file=args.problem_data_path samplenumber=int(args.sample_number) number_of_IMFs=int(args.number_of_IMFs) EMD_data_preparation(csv_path,samplenumber,test_list) Main()

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3,208

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0.029851

0

null

0

null

0

1

0

1

d8df2a64ed17e68830f228cf62337f3dea5df521

7,373

py

Python

2.ReinforcementLearning/CartPole/CartPole-PPO/cartpole_ppo.py

link-kut/deeplink_public

688c379bfeb63156e865d78d0428f97d7d203cc1

[ "MIT" ]

null

2.ReinforcementLearning/CartPole/CartPole-PPO/cartpole_ppo.py

link-kut/deeplink_public

688c379bfeb63156e865d78d0428f97d7d203cc1

[ "MIT" ]

11

2020-01-28T22:33:49.000Z

2022-03-11T23:41:08.000Z

2.ReinforcementLearning/CartPole/CartPole-PPO/cartpole_ppo.py

link-kut/deeplink_public

688c379bfeb63156e865d78d0428f97d7d203cc1

[ "MIT" ]

2

2019-06-01T04:14:52.000Z

2020-05-31T08:13:23.000Z

# Initial framework taken from https://github.com/OctThe16th/PPO-Keras/blob/master/Main.py import numpy as np import gym from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Dense from tensorflow.keras import backend as K from tensorflow.keras.optimizers import Adam import tensorflow as tf import random from tensorflow.keras.models import Sequential from tensorflow.keras.optimizers import RMSprop from tensorflow.keras.utils import to_categorical import matplotlib.pyplot as plt print(tf.__version__) env = gym.make(ENV) CONTINUOUS = False # num_states = env.observation_space.shape[0] LOSS_CLIPPING = 0.2 # Only implemented clipping for the surrogate loss, paper said it was best NOISE = 1.0 # Exploration noise GAMMA = 0.99 BUFFER_SIZE = 256 BATCH_SIZE = 64 NUM_ACTIONS = 2 NUM_STATE = 4 HIDDEN_SIZE = 128 NUM_LAYERS = 2 ENTROPY_LOSS = 1e-3 LR = 1e-4 # Lower lr stabilises training greatly '''def exponential_average(old, new, b1): return old * b1 + (1-b1) * new''' def proximal_policy_optimization_loss(advantage, old_prediction): def loss(y_true, y_pred): prob = y_true * y_pred old_prob = y_true * old_prediction r = prob / (old_prob + 1e-10) return -K.mean( K.minimum( r * advantage, K.clip(r, min_value=1 - LOSS_CLIPPING, max_value=1 + LOSS_CLIPPING) * advantage ) + ENTROPY_LOSS * (prob * K.log(prob + 1e-10)) ) return loss class Agent: def __init__(self): self.critic = self.build_critic() self.actor = self.build_actor() self.env = gym.make(ENV) print(self.env.action_space, 'action_space', self.env.observation_space, 'observation_space') self.episode = 0 self.observation = self.env.reset() self.val = False self.reward = [] self.reward_over_time = [] self.name = self.get_name() self.scores = [] self.episode_reward = 0 def get_name(self): name = 'AllRuns/' name += 'discrete/' name += ENV return name def build_actor(self): state_input = Input(shape=(NUM_STATE,)) advantage = Input(shape=(1,)) old_prediction = Input(shape=(NUM_ACTIONS,)) x = Dense(units=HIDDEN_SIZE, activation='tanh')(state_input) for _ in range(NUM_LAYERS - 1): x = Dense(HIDDEN_SIZE, activation='tanh')(x) out_actions = Dense(units=NUM_ACTIONS, activation='softmax', name='output')(x) model = Model( inputs=[state_input, advantage, old_prediction], outputs=[out_actions], name="actor_model" ) model.compile( optimizer=Adam(lr=LR), loss=[proximal_policy_optimization_loss(advantage=advantage, old_prediction=old_prediction)] ) model.summary() return model def build_critic(self): state_input = Input(shape=(NUM_STATE,)) x = Dense(units=HIDDEN_SIZE, activation='tanh')(state_input) for _ in range(NUM_LAYERS - 1): x = Dense(units=HIDDEN_SIZE, activation='tanh')(x) out_value = Dense(units=1)(x) model = Model( inputs=[state_input], outputs=[out_value], name="critic_model" ) model.compile( optimizer=Adam(lr=LR), loss='mse' ) model.summary() return model def reset_env(self): self.episode += 1 if self.episode % 100 == 0: self.val = True else: self.val = False self.observation = self.env.reset() self.reward = [] self.episode_reward = 0 def get_action(self): DUMMY_VALUE = np.zeros((1, 1)) DUMMY_ACTION = np.zeros((1, NUM_ACTIONS)) p = self.actor.predict([self.observation.reshape(1, NUM_STATE), DUMMY_VALUE, DUMMY_ACTION]) if self.val is False: action = np.random.choice(NUM_ACTIONS, p=np.nan_to_num(p[0])) else: action = np.argmax(p[0]) action_matrix = np.zeros(NUM_ACTIONS) action_matrix[action] = 1 return action, action_matrix, p def transform_reward(self): for j in range(len(self.reward) - 2, -1, -1): self.reward[j] += self.reward[j + 1] * GAMMA def get_batch(self): batch = [[], [], [], []] tmp_batch = [[], [], []] while len(batch[0]) < BUFFER_SIZE: action, action_matrix, actor_p = self.get_action() observation, reward, done, info = self.env.step(action) self.reward.append(reward) self.episode_reward = self.episode_reward + reward tmp_batch[0].append(self.observation) tmp_batch[1].append(action_matrix) tmp_batch[2].append(actor_p) self.observation = observation if done: self.transform_reward() if self.val is False: for i in range(len(tmp_batch[0])): obs, action, pred = tmp_batch[0][i], tmp_batch[1][i], tmp_batch[2][i] r = self.reward[i] batch[0].append(obs) batch[1].append(action) batch[2].append(pred) batch[3].append(r) tmp_batch = [[], [], []] #print("EPISODE REWARD ", self.episode_reward) self.scores.append(self.episode_reward) self.reset_env() obs = np.array(batch[0]) action = np.array(batch[1]) pred = np.array(batch[2]) pred = np.reshape(pred, (pred.shape[0], pred.shape[2])) reward = np.reshape(np.array(batch[3]), (len(batch[3]), 1)) return obs[:BUFFER_SIZE], action[:BUFFER_SIZE], pred[:BUFFER_SIZE], reward[:BUFFER_SIZE] def run(self): total_episodes = 100000 epochs = 10 while self.episode < total_episodes: if len(self.scores) > 1: print("EPISODE ", self.episode, self.scores[-1]) obs, action, pred, reward = self.get_batch() old_prediction = pred pred_values = self.critic.predict(obs) advantage = reward - pred_values # advantage = (advantage - advantage.mean()) / advantage.std() actor_loss = self.actor.fit( x=[obs, advantage, old_prediction], y=[action], batch_size=BATCH_SIZE, shuffle=True, epochs=epochs, verbose=0 ) critic_loss = self.critic.fit( x=[obs], y=[reward], batch_size=BATCH_SIZE, shuffle=True, epochs=epochs, verbose=0 ) if self.episode % 10 == 0: print('(episode, score) = ' + str((self.episode, self.episode_reward))) # Solved condition if len(self.scores) >= 110: if np.mean(self.scores[-100:]) >= 195.0: print(' \ Solved after ' + str(self.episode - 100) + ' episodes') break plt.plot(self.scores) if __name__ == '__main__': ag = Agent() ag.run()

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0.041137

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7,373

240

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30.720833

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0.027322

0

null

0

null

0

1

0

d8df48a2c6778c32363c444430a9dcd1859230a7

8,721

py

Python

models/san_lowrank.py

LegionChang/CoTNet

b1bc456c0b13b282b807d1082a1598b71014b4fe

[ "Apache-2.0" ]

360

2021-07-26T07:23:29.000Z

2022-03-16T03:03:25.000Z

python_developer_tools/cv/bases/conv/CoTNet/CoTNet-master/models/san_lowrank.py

HonestyBrave/python_developer_tools

fc0dcf5c4ef088e2e535206dc82f09bbfd01f280

[ "Apache-2.0" ]

22

2021-07-29T15:05:00.000Z

2022-03-17T04:28:14.000Z

python_developer_tools/cv/bases/conv/CoTNet/CoTNet-master/models/san_lowrank.py

HonestyBrave/python_developer_tools

fc0dcf5c4ef088e2e535206dc82f09bbfd01f280

[ "Apache-2.0" ]

47

2021-07-27T02:14:21.000Z

2022-02-25T09:15:12.000Z

import math import numpy as np import torch from torch import nn as nn from config import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import build_model_with_cfg from .layers import SelectiveKernelConv, ConvBnAct, create_attn from .registry import register_model from .resnet import ResNet from .layers import Shiftlution from cupy_layers.aggregation_zeropad import LocalConvolution def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'crop_pct': 0.875, 'interpolation': 'bicubic', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'fc', **kwargs } default_cfgs = { 'san19': _cfg( url='',), } def conv1x1(in_planes, out_planes, stride=1): return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) class SAM(nn.Module): def __init__(self, in_planes, rel_planes, out_planes, share_planes, kernel_size=3, stride=1, dilation=1): super(SAM, self).__init__() self.kernel_size, self.stride = kernel_size, stride self.conv1 = nn.Conv2d(in_planes, rel_planes, kernel_size=1) self.conv2 = nn.Conv2d(in_planes, rel_planes, kernel_size=1) self.conv3 = nn.Conv2d(in_planes, out_planes, kernel_size=1) self.conv_w = nn.Sequential(nn.BatchNorm2d(rel_planes * (pow(kernel_size, 2) + 1)), nn.ReLU(inplace=True), nn.Conv2d(rel_planes * (pow(kernel_size, 2) + 1), out_planes // share_planes, kernel_size=1, bias=False), nn.BatchNorm2d(out_planes // share_planes), nn.ReLU(inplace=True), nn.Conv2d(out_planes // share_planes, pow(kernel_size, 2) * out_planes // share_planes, kernel_size=1)) self.unfold_j = nn.Unfold(kernel_size=kernel_size, dilation=dilation, padding=0, stride=stride) self.pad = nn.ReflectionPad2d(kernel_size // 2) #self.aggregation = Aggregation(kernel_size, stride, (dilation * (kernel_size - 1) + 1) // 2, dilation, pad_mode=1) self.local_conv = LocalConvolution(out_planes, out_planes, kernel_size=self.kernel_size, stride=1, padding=(self.kernel_size - 1) // 2, dilation=1) def forward(self, x): x1, x2, x3 = self.conv1(x), self.conv2(x), self.conv3(x) x2 = self.unfold_j(self.pad(x2)).view(x.shape[0], -1, x1.shape[2], x1.shape[3]) w = self.conv_w(torch.cat([x1, x2], 1)) w = w.view(x1.shape[0], -1, self.kernel_size*self.kernel_size, x1.shape[2], x1.shape[3]) w = w.unsqueeze(1) #x = self.aggregation(x3, w) x = self.local_conv(x3, w) return x class SAM_lowRank(nn.Module): def __init__(self, in_planes, rel_planes, out_planes, share_planes, kernel_size=3, stride=1, dilation=1): super(SAM_lowRank, self).__init__() self.rel_planes = rel_planes self.out_planes = out_planes self.kernel_size, self.stride = kernel_size, stride self.pool_size = min(512 // out_planes, 4) self.down = nn.AvgPool2d(self.pool_size, self.pool_size, padding=0) if self.pool_size > 1 else None self.unfold_j = nn.Unfold(kernel_size=kernel_size, dilation=dilation, padding=0, stride=stride) self.pad = nn.ReflectionPad2d(kernel_size // 2) self.conv = nn.Sequential( nn.Conv2d(in_planes, out_planes + 2*rel_planes, kernel_size=1, bias=False), #nn.BatchNorm2d(out_planes + rel_planes), #nn.ReLU(inplace=True), ) self.key_embed = nn.Sequential( nn.BatchNorm2d(rel_planes * self.kernel_size * self.kernel_size), nn.ReLU(inplace=True), nn.Conv2d(rel_planes * self.kernel_size * self.kernel_size, rel_planes, 1, bias=False), ) self.conv_w = nn.Sequential( nn.BatchNorm2d(rel_planes * 2), nn.ReLU(inplace=True), nn.Conv2d(rel_planes * 2, out_planes * self.kernel_size * 2, kernel_size=1, bias=False) ) self.local_conv = LocalConvolution(out_planes, out_planes, kernel_size=self.kernel_size, stride=1, padding=(self.kernel_size - 1) // 2, dilation=1) def forward(self, x): x = self.conv(x) q, k, x = torch.split(x, [self.rel_planes, self.rel_planes, self.out_planes], 1) x2 = self.unfold_j(self.pad(k)) x2 = x2.view(x.shape[0], -1, x.shape[2], x.shape[3]) x2 = self.key_embed(x2) qk = torch.cat([q, x2], 1) if self.pool_size > 1: qk = self.down(qk) b, c, qk_hh, qk_ww = qk.size() embed = self.conv_w(qk) embed_h, embed_w = torch.split(embed, embed.shape[1] // 2, dim=1) embed_h = embed_h.view(b, -1, self.kernel_size, 1, qk_hh, qk_ww) embed_w = embed_w.view(b, -1, 1, self.kernel_size, qk_hh, qk_ww) w = embed_h * embed_w w = w.view(x.shape[0], -1, self.kernel_size*self.kernel_size, qk_hh, qk_ww) if self.pool_size > 1: w = w.view(b, -1, self.kernel_size*self.kernel_size, qk_hh, 1, qk_ww, 1) w = w.expand(b, -1, self.kernel_size*self.kernel_size, qk_hh, self.pool_size, qk_ww, self.pool_size).contiguous() w = w.view(b, -1, self.kernel_size*self.kernel_size, x.shape[2], x.shape[3]) w = w.unsqueeze(1) x = self.local_conv(x, w) return x class Bottleneck(nn.Module): def __init__(self, in_planes, rel_planes, mid_planes, out_planes, share_planes=8, kernel_size=7, stride=1): super(Bottleneck, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.sam = SAM(in_planes, rel_planes, mid_planes, share_planes, kernel_size, stride) self.bn2 = nn.BatchNorm2d(mid_planes) self.conv = nn.Conv2d(mid_planes, out_planes, kernel_size=1) self.relu = nn.ReLU(inplace=True) self.stride = stride def forward(self, x): identity = x out = self.relu(self.bn1(x)) out = self.relu(self.bn2(self.sam(out))) out = self.conv(out) out += identity return out class SAN(nn.Module): def __init__(self, in_chans, block, layers, kernels, num_classes, **kwargs): super(SAN, self).__init__() c = 64 self.conv_in, self.bn_in = conv1x1(3, c), nn.BatchNorm2d(c) self.conv0, self.bn0 = conv1x1(c, c), nn.BatchNorm2d(c) self.layer0 = self._make_layer(block, c, layers[0], kernels[0]) c *= 4 self.conv1, self.bn1 = conv1x1(c // 4, c), nn.BatchNorm2d(c) self.layer1 = self._make_layer(block, c, layers[1], kernels[1]) c *= 2 self.conv2, self.bn2 = conv1x1(c // 2, c), nn.BatchNorm2d(c) self.layer2 = self._make_layer(block, c, layers[2], kernels[2]) c *= 2 self.conv3, self.bn3 = conv1x1(c // 2, c), nn.BatchNorm2d(c) self.layer3 = self._make_layer(block, c, layers[3], kernels[3]) c *= 2 self.conv4, self.bn4 = conv1x1(c // 2, c), nn.BatchNorm2d(c) self.layer4 = self._make_layer(block, c, layers[4], kernels[4]) self.relu = nn.ReLU(inplace=True) self.pool = nn.MaxPool2d(kernel_size=2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(c, num_classes) def _make_layer(self, block, planes, blocks, kernel_size=7, stride=1): layers = [] for _ in range(0, blocks): layers.append(block(planes, planes // 16, planes // 4, planes, 8, kernel_size, stride)) return nn.Sequential(*layers) def forward(self, x): x = self.relu(self.bn_in(self.conv_in(x))) x = self.relu(self.bn0(self.layer0(self.conv0(self.pool(x))))) x = self.relu(self.bn1(self.layer1(self.conv1(self.pool(x))))) x = self.relu(self.bn2(self.layer2(self.conv2(self.pool(x))))) x = self.relu(self.bn3(self.layer3(self.conv3(self.pool(x))))) x = self.relu(self.bn4(self.layer4(self.conv4(self.pool(x))))) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x def _create_san(variant, pretrained=False, **kwargs): return build_model_with_cfg( SAN, variant, default_cfg=default_cfgs[variant], pretrained=pretrained, **kwargs) @register_model def san19(pretrained=False, **kwargs): #model_args = dict(block=Bottleneck, layers=[3, 3, 4, 6, 3], kernels = [3, 3, 3, 3, 3], **kwargs) #model_args = dict(block=Bottleneck, layers=[3, 3, 4, 6, 3], kernels = [3, 5, 5, 5, 5], **kwargs) model_args = dict(block=Bottleneck, layers=[3, 3, 4, 6, 3], kernels=[3, 7, 7, 7, 7], **kwargs) return _create_san('san19', pretrained, **model_args)

44.269036

155

0.624928

1,306

8,721

3.981623

0.124809

0.103846

0.061923

0.031154

0.551346

0.470962

0.395769

0.338077

0.280962

0.2125

0

0.040728

0.231395

8,721

197

156

44.269036

0.735044

0.045522

0

0.178344

0

0.009737

0

1

0.082803

false

0

0.070064

0.019108

0.235669

0

null

0

null

0

1

0

d8e095732d43409e05f872611e0a25bf01b9a971

471

py

Python

d1lod/d1lod/__init__.py

DataONEorg/slinky

1f0f774b7b5556126d75524ac9fd328ad0fc1ba2

[ "Apache-2.0" ]

2

2019-03-07T21:14:27.000Z

2021-03-30T00:24:13.000Z

d1lod/d1lod/__init__.py

DataONEorg/slinky

1f0f774b7b5556126d75524ac9fd328ad0fc1ba2

[ "Apache-2.0" ]

64

2021-03-11T22:28:45.000Z

2022-03-17T18:41:08.000Z

d1lod/d1lod/__init__.py

DataONEorg/slinky

1f0f774b7b5556126d75524ac9fd328ad0fc1ba2

[ "Apache-2.0" ]

2

2018-09-05T16:38:42.000Z

2021-03-12T18:07:20.000Z

from . import people from . import dataone from . import util from . import validator from . import metadata from .graph import Graph from .interface import Interface # Set default logging handler to avoid "No handler found" warnings. import logging try: # Python 2.7+ from logging import NullHandler except ImportError: class NullHandler(logging.Handler): def emit(self, record): pass logging.getLogger(__name__).addHandler(NullHandler())

24.789474

67

0.7431

59

471

5.864407

0.576271

0.144509

0

0.005236

0.18896

471

18

68

26.166667

0.900524

0.163482

0

1

0.066667

false

0.066667

0.666667

0

0.8

0

null

0

null

0

1

0

1

0

3

d8e1107cf7ccb8c88d2d79f53d1ffccc5940049b

1,262

py

Python

qa/admin.py

thebenwaters/openclickio

c5e08d89b37c5f415810dca088803dba25af5e1a

[ "MIT" ]

null

qa/admin.py

thebenwaters/openclickio

c5e08d89b37c5f415810dca088803dba25af5e1a

[ "MIT" ]

1

2017-10-21T19:29:18.000Z

qa/admin.py

thebenwaters/openclickio

c5e08d89b37c5f415810dca088803dba25af5e1a

[ "MIT" ]

null

from django.contrib import admin from .models import Answer, AnswerOption, AnswerInstance, Question,\ OpenEndedResponse, ClosedEndedQuestion # Register your models here. @admin.register(AnswerOption) class AnswerOptionAdmin(admin.ModelAdmin): list_display = ('id', 'text') @admin.register(Answer) class AnswerAdmin(admin.ModelAdmin): list_display = ('id', 'created', 'owner', 'correct_answer') @admin.register(AnswerInstance) class AnswerInstanceAdmin(admin.ModelAdmin): list_display = ('id', 'created', 'student', 'question', 'answer_option', 'was_correct') def was_correct(self, obj): my_question = ClosedEndedQuestion.objects.get(pk=obj.question.pk) if obj.answer_option == my_question.answer.correct_answer: return True return False def activate(modeladmin, request, queryset): for obj in queryset: obj.activate() def deactivate(modeladmin,request, queryset): for obj in queryset: obj.deactivate() @admin.register(Question) class QuestionAdmin(admin.ModelAdmin): list_display = ('id', 'owner', 'text', 'active') actions = [activate, deactivate] @admin.register(ClosedEndedQuestion) class ClosedEndedQuestionAdmin(admin.ModelAdmin): list_display = ('id', 'owner', 'text', 'answer', 'active') actions = [activate, deactivate]

29.348837

88

0.759113

142

1,262

6.65493

0.359155

0.068783

0.100529

0.137566

0.275132

0.245503

0.171429

0.093122

0

0.108558

1,262

43

89

29.348837

0.84

0.020602

0

0.129032

0

0.098785

0

1

0.096774

false

0

0.064516

0

0.612903

0

null

0

null

0

1

0

d8e180bf8b4b157c9e27b0c8c553c612b8e2d1ec

6,212

py

Python

Bot/Cogs/jisho.py

No767/Rin-Bot

b4c64e0ebccc9465100006ec2cb023eecb425570

[ "Apache-2.0" ]

null

Bot/Cogs/jisho.py

No767/Rin-Bot

b4c64e0ebccc9465100006ec2cb023eecb425570

[ "Apache-2.0" ]

null

Bot/Cogs/jisho.py

No767/Rin-Bot

b4c64e0ebccc9465100006ec2cb023eecb425570

[ "Apache-2.0" ]

null

import re import discord import requests import ujson from discord.ext import commands from dotenv import load_dotenv from jamdict import Jamdict load_dotenv() jam = Jamdict() # Use Array Loop Instead def kanjiv2(search): res = jam.lookup(search.replace("\n", " ")) for c in res.chars: return str(c).replace("\n", " ") def hiragana(search): result = jam.lookup(search) for word in result.entries: m = re.findall("[ぁ-ん]", str(word)) r = str(m).replace("'", "").replace(",", "").replace(" ", "") return str(r) def katakana(search): result = jam.lookup(search.replace("\n", " ")) for entry in result.entries: m = re.findall("[ァ-ン]", str(entry)) r = ( str(m) .replace("[", " ") .replace("]", " ") .replace("'", " ") .replace(",", "") .replace(" ", "") ) return str(r) # old kanji lookup system. use the function kanjiv2 instead def kanji(search): result = jam.lookup(search) result_search = result.text(separator=" | ", with_chars=False, no_id=True) m = re.findall(".[一-龯]", result_search) all_kanji = str(m).replace(",", "")[1:-1] all_kanjiv2 = all_kanji.replace("'", "").replace(" ", "").replace("", ", ") return all_kanjiv2 def searcher(search): result = jam.lookup(search) for word in result.entries: return str(word[4:10]) def better_hiragana(search): searcher(search) def tag(search): search = search.replace(" ", "%20") link = f"https://jisho.org/api/v1/search/words?keyword={search}" r = requests.get(link) jisho = ujson.loads(r.text) jisho_tag = str(jisho["data"][0]["tags"]) return jisho_tag.replace("[", " ").replace("]", " ").replace("'", " ") def jlpt(search): search = search.replace(" ", "%20") link = f"https://jisho.org/api/v1/search/words?keyword={search}" r = requests.get(link) jisho = ujson.loads(r.text) jisho_jlpt = str(jisho["data"][0]["tags"]) return jisho_jlpt.replace("[", " ").replace("]", " ").replace("'", " ") def is_common(search): search = search.replace(" ", "%20") link = f"https://jisho.org/api/v1/search/words?keyword={search}" r = requests.get(link) jisho = ujson.loads(r.text) jishov1 = str(jisho["data"][0]["is_common"]) return jishov1.replace("[", " ").replace("]", " ") def pos(search): search = search.replace(" ", "%20") link = f"https://jisho.org/api/v1/search/words?keyword={search}" r = requests.get(link) jisho = ujson.loads(r.text) jisho_sorted = jisho["data"][0]["senses"][0]["parts_of_speech"] return str(jisho_sorted).replace("[", "").replace("]", "").replace("'", "") def see_also(search): search = search.replace(" ", "%20") link = f"https://jisho.org/api/v1/search/words?keyword={search}" r = requests.get(link) jisho = ujson.loads(r.text) jisho_sorted = jisho["data"][0]["senses"][0]["see_also"] return str(jisho_sorted).replace("[", "").replace("]", "").replace("'", "") def antonyms(search): search = search.replace(" ", "%20") link = f"https://jisho.org/api/v1/search/words?keyword={search}" r = requests.get(link) jisho = ujson.loads(r.text) jisho_sorted = jisho["data"][0]["senses"][0]["antonyms"] return str(jisho_sorted).replace("[", "").replace("]", "").replace("'", "") def links(search): search = search.replace(" ", "%20") link = f"https://jisho.org/api/v1/search/words?keyword={search}" r = requests.get(link) jisho = ujson.loads(r.text) jisho_sorted = jisho["data"][0]["senses"][0]["links"] return str(jisho_sorted).replace("[", "").replace("]", "").replace("'", "") class jisho_dict(commands.Cog): def __init__(self, bot): self.bot = bot @commands.command(name="jisho") async def jisho(self, ctx, search: str): try: result = jam.lookup(search) link = f"https://jisho.org/api/v1/search/words?keyword={search}" r = requests.get(link) jisho = ujson.loads(r.text) res = jam.lookup(search.replace("\n", " ")) embedVar = discord.Embed() embedVar.add_field( name="Kanji", value=[str(c).replace("'", "") for c in res.chars], inline=False, ) embedVar.add_field( name="Position of Speech (POS)", value=pos(search), inline=False ) embedVar.add_field(name="Is Common?", value=is_common(search), inline=False) embedVar.add_field( name="Other Info", value=f"Tags >> {tag(search)}\nJLPT >> {jlpt(search)}\nAntonyms >> {antonyms(search)}\nSee Also >> {see_also(search)}\nLinks >> {links(search)}", inline=False, ) embedVar.add_field( name="Attributions", value=f"JMDict >> {jisho['data'][0]['attribution']['jmdict']}\nJMNEDict >> {jisho['data'][0]['attribution']['jmnedict']}\nDBPedia >> {jisho['data'][0]['attribution']['dbpedia']}", inline=False, ) embedVar.add_field( name="HTTP Status (Jisho API)", value=f"{jisho['meta']['status']}", inline=False, ) embedVar.description = str([str(word[0]) for word in result.entries]) await ctx.send(embed=embedVar) except Exception as e: embed_discord = discord.Embed() embed_discord.description = ( f"An error has occurred. Please try again\nReason: {e}" ) await ctx.send(embed=embed_discord) @jisho.error async def on_message_error( self, ctx: commands.Context, error: commands.CommandError ): if isinstance(error, commands.MissingRequiredArgument): embed_discord = discord.Embed() embed_discord.description = f"Missing a requireed argument: {error.param}" await ctx.send(embed=embed_discord) def setup(bot): bot.add_cog(jisho_dict(bot))

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null

0

null

0

1

0

d8e1c9d0e3a88657f33d1a06132b17c2fdbfdca9

40

py

Python

cs-224n/assn2/utils/__init__.py

PranjalGupta2199/nlp-dl

7e2290c82602cb2ff863f2513c54dfb0412affd0

[ "MIT" ]

null

cs-224n/assn2/utils/__init__.py

PranjalGupta2199/nlp-dl

7e2290c82602cb2ff863f2513c54dfb0412affd0

[ "MIT" ]

null

cs-224n/assn2/utils/__init__.py

PranjalGupta2199/nlp-dl

7e2290c82602cb2ff863f2513c54dfb0412affd0

[ "MIT" ]

null

from . import gradcheck, treebank, utils

40

0.8

5

40

6.4

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0

1

0

1

0

1

0

6

d8e326a0842502832eb889a12acb3aa9cc0f3dbc

679

py

Python

simple_import/forms.py

smcoll/django-simple-import

fcdaaf61196fb7c596c3383159d49f11a8dd518e

[ "BSD-3-Clause" ]

null

simple_import/forms.py

smcoll/django-simple-import

fcdaaf61196fb7c596c3383159d49f11a8dd518e

[ "BSD-3-Clause" ]

null

simple_import/forms.py

smcoll/django-simple-import

fcdaaf61196fb7c596c3383159d49f11a8dd518e

[ "BSD-3-Clause" ]

null

from django import forms from django.contrib.contenttypes.models import ContentType from simple_import.models import ImportLog, ColumnMatch, RelationalMatch class ImportForm(forms.ModelForm): class Meta: model = ImportLog fields = ('name', 'import_file', 'import_type') model = forms.ModelChoiceField(ContentType.objects.all()) class MatchForm(forms.ModelForm): class Meta: model = ColumnMatch exclude = ['header_position'] class MatchRelationForm(forms.ModelForm): class Meta: model = RelationalMatch widgets = { 'related_field_name': forms.Select(choices=(('', '---------'),)) }

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76

0.664212

65

679

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0.12809

0.155056

0.188764

0

0.222386

679

24

77

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0.166667

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null

0

null

0

1

0

1

0

2

d8e4dee2464dfe2fbd5f649c5d1fede75cf0bf21

3,198

py

Python

src/main/python/utils/handle_null.py

meowpunch/bobsim-research

4411ac6eaf5b760611f689b0a9e290546e2f5435

[ "MIT" ]

2

2020-03-01T17:42:44.000Z

2020-03-09T06:13:34.000Z

src/main/python/utils/handle_null.py

meowpunch/bobsim-research

4411ac6eaf5b760611f689b0a9e290546e2f5435

[ "MIT" ]

2

2020-04-01T16:48:06.000Z

2020-04-04T11:04:10.000Z

src/main/python/utils/handle_null.py

meowpunch/bobsim-research

4411ac6eaf5b760611f689b0a9e290546e2f5435

[ "MIT" ]

null

import sys from functools import reduce import pandas as pd from utils.logging import init_logger from utils.s3_manager.manage import S3Manager class NullHandler: def __init__(self, strategy=None, df=None): self.logger = init_logger() self.input_df = df self.strategy = strategy self.fillna_method = { "drop": self.fillna_with_drop, "zero": self.fillna_with_zero, "linear": self.fillna_with_linear, } @staticmethod def fillna_with_linear(df: pd.DataFrame): # fill nan by linear formula. return df.interpolate(method='linear', limit_direction='both') @staticmethod def fillna_with_zero(df: pd.DataFrame): return df.fillna(value=0) @staticmethod def fillna_with_drop(df: pd.DataFrame): return df.dropna(axis=0) @staticmethod def missing_values(df: pd.DataFrame): # TODO: specify df_null = df.isna().sum() if df_null.sum() > 0: filtered = df_null[df_null.map(lambda x: x > 0)] # self.logger.info("missing values: \n {}".format(filtered)) return filtered else: # self.logger.info("no missing value at raw material price") return None def get_columns_list(self): # TODO: in order not to scan df twice, combine this method with fillnan if len(self.strategy.values()) is 1: return list(self.strategy.values())[0] else: return reduce( lambda x, y: x + y, self.strategy.values() ) @staticmethod def fill_by_method(method, df): filled = method(df) if isinstance(filled, pd.Series): return filled.to_frame() return filled def fill_nan(self): """ :return: list of pd DataFrame filled nan by each method """ method_list = self.strategy.keys() return list(map( lambda name: self.fill_by_method( self.fillna_method[name], self.input_df.filter(items=self.strategy[name]) ), method_list )) def process(self): """ by strategy, fill nan in df at once :return: pd DataFrame after fill nan """ df_list = self.fill_nan() return pd.concat( df_list + [self.input_df.drop(columns=self.get_columns_list(), axis=1)], axis=1, join="inner" ) def load(filename="2014-2020"): """ fetch DataFrame and astype and filter by columns :return: pd DataFrame """ manager = S3Manager(bucket_name="production-bobsim") df = manager.fetch_df_from_csv( key="public_data/open_data_terrestrial_weather/origin/csv/{filename}.csv".format(filename=filename) ) # TODO: no use index to get first element. return df[0] def main(): """ df, key = build_origin_weather(date="201908") print(df.info()) t = NullHandler( strategy={ "linear": ["최대 풍속(m/s)"], "drop": ["최저기온(°C)"] }, df=df ) print(t.process()) """ pass if __name__ == '__main__': main()

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0.016529

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0.15942

false

0.014493

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0.42029

0

null

0

null

0

1

0

1

d8e67ae78b6e8735abac8eb28c78858b399f444d

1,207

py

Python

scripts/executor_action.py

rezhajulio/azkaban

974e2e45f4e2f1cd14a3e160f9326aa067b606c2

[ "Apache-2.0" ]

3

2019-12-19T00:04:36.000Z

2020-05-07T02:54:56.000Z

scripts/executor_action.py

rezhajulio/azkaban

974e2e45f4e2f1cd14a3e160f9326aa067b606c2

[ "Apache-2.0" ]

null

scripts/executor_action.py

rezhajulio/azkaban

974e2e45f4e2f1cd14a3e160f9326aa067b606c2

[ "Apache-2.0" ]

3

2018-03-15T04:54:50.000Z

2019-07-15T06:33:58.000Z

#!/usr/bin/python3 import requests import sys import time from wait_for_port_ready import wait_for_port_ready import traceback import json action = sys.argv[1] assert action in ('activate', 'deactivate', 'getStatus', 'shutdown') url = 'http://localhost:12321/executor?action={action}'.format(action=action) if action == 'getStatus': r = requests.post(url, timeout=5) assert r.status_code == 200 assert json.loads(r.text)['isActive'] == 'true' else: wait_for_port_ready(12321, 15) retries = 0 retry_count = 15 success = False while not success: try: r = requests.post(url, timeout=5) print(r.status_code) print(r.text) if r.json()['status'] == 'success': success = True if not success: raise Exception('Attempt to ' + action + ' executor failed') except Exception as ex: print(traceback.format_exc()) sys.stdout.flush() retries += 1 if retries > retry_count: raise Exception('Attempt to ' + action + ' executor failed') print('waiting for 1 seconds...') time.sleep(1)

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0.191429

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0.293289

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0.114286

0

null

0

null

0

1

0

d8e730bb375940f9f3582e34ab7d4585a0799ed3

80

py

Python

T26-04/program.py

tiagomendes7/SSof-Project1920

9b026763a492683ab982aa769f2a381ed02930aa

[ "MIT" ]

2

2019-11-20T19:26:07.000Z

2019-11-22T00:42:23.000Z

T26-04/program.py

tiagomendes7/SSof-Project1920

9b026763a492683ab982aa769f2a381ed02930aa

[ "MIT" ]

2

2019-11-28T05:21:24.000Z

2019-11-28T05:21:58.000Z

T26-04/program.py

tiagomendes7/SSof-Project1920

9b026763a492683ab982aa769f2a381ed02930aa

[ "MIT" ]

25

2019-11-27T01:40:56.000Z

2019-12-04T23:38:59.000Z

src = source() src2 = src src3 = src2 src5 = src2 src4 = src3 src4 = sink(src4)

11.428571

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0

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false

0

1

0

null

0

1

0

null

0

2

d8e84bbf2e79e843bed9f808b75020d51a48d30f

3,855

py

Python

faker/providers/barcode/__init__.py

StabbarN/faker

57882ff73255cb248d8f995b2abfce5cfee45ab3

[ "MIT" ]

4

2020-09-23T15:48:00.000Z

2021-02-25T07:55:23.000Z

faker/providers/barcode/__init__.py

StabbarN/faker

57882ff73255cb248d8f995b2abfce5cfee45ab3

[ "MIT" ]

7

2021-03-05T23:08:02.000Z

2022-03-12T00:47:19.000Z

faker/providers/barcode/__init__.py

StabbarN/faker

57882ff73255cb248d8f995b2abfce5cfee45ab3

[ "MIT" ]

1

2022-03-10T16:25:33.000Z

from .. import BaseProvider localized = True class Provider(BaseProvider): # Source of GS1 country codes: https://gs1.org/standards/id-keys/company-prefix local_prefixes = () def _ean(self, length=13, prefixes=()): if length not in (8, 13): raise AssertionError("length can only be 8 or 13") code = [self.random_digit() for _ in range(length - 1)] if prefixes: prefix = self.random_element(prefixes) code[:len(prefix)] = map(int, prefix) if length == 8: weights = [3, 1, 3, 1, 3, 1, 3] elif length == 13: weights = [1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3] weighted_sum = sum(x * y for x, y in zip(code, weights)) check_digit = (10 - weighted_sum % 10) % 10 code.append(check_digit) return ''.join(str(x) for x in code) def ean(self, length=13, prefixes=()): """Generate an EAN barcode of the specified ``length``. The value of ``length`` can only be ``8`` or ``13`` (default) which will create an EAN-8 or an EAN-13 barcode respectively. If ``prefixes`` are specified, the result will begin with one of the sequence in ``prefixes`` :sample: length=13 :sample: length=8 :sample: prefixes=('00',) :sample: prefixes=('45', '49') """ return self._ean(length, prefixes=prefixes) def ean8(self, prefixes=()): """Generate an EAN-8 barcode. This method uses :meth:`ean() <faker.providers.barcode.Provider.ean>` under the hood with the ``length`` argument explicitly set to ``8``. If ``prefixes`` are specified, the result will begin with one of the sequence in ``prefixes`` :sample: :sample: prefixes=('00',) :sample: prefixes=('45', '49') """ return self._ean(8, prefixes=prefixes) def ean13(self, prefixes=()): """Generate an EAN-13 barcode. If ``prefixes`` are specified, the result will begin with one of the sequence in ``prefixes``. This method uses :meth:`ean() <faker.providers.barcode.Provider.ean>` under the hood with the ``length`` argument explicitly set to ``13``. .. note:: Codes starting with a leading zero are treated specially in some barcode readers. For more information about compatibility with UPC-A codes, see :meth:`en_US.ean13() <faker.providers.barcode.en_US.Provider.ean13>` :sample: :sample: prefixes=('00',) :sample: prefixes=('45', '49') """ return self._ean(13, prefixes=prefixes) def localized_ean(self, length=13): """Generate a localized EAN barcode of the specified ``length``. The value of ``length`` can only be ``8`` or ``13`` (default) which will create an EAN-8 or an EAN-13 barcode respectively. This method uses :meth:`ean() <faker.providers.barcode.Provider.ean>` under the hood with the ``prefixes`` argument explicitly set to ``self.local_prefixes``. :sample: :sample: length=13 :sample: length=8 """ return self._ean(length, prefixes=self.local_prefixes) def localized_ean8(self): """Generate a localized EAN-8 barcode. This method uses :meth:`localized_ean() <faker.providers.barcode.Provider.ean>` under the hood with the ``length`` argument explicitly set to ``8``. :sample: """ return self.localized_ean(8) def localized_ean13(self): """Generate a localized EAN-13 barcode. This method uses :meth:`localized_ean() <faker.providers.barcode.Provider.ean>` under the hood with the ``length`` argument explicitly set to ``13``. :sample: """ return self.localized_ean(13)

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null

0

null

0

1

0

1

0

2

d8e8a2245da2f5f3c3aaee9fd554b9ee96a551e9

22,494

py

Python

axonius_api_client/http.py

geransmith/axonius_api_client

09fd564d62f0ddf7aa44db14a509eaafaf0c930f

[ "MIT" ]

null

axonius_api_client/http.py

geransmith/axonius_api_client

09fd564d62f0ddf7aa44db14a509eaafaf0c930f

[ "MIT" ]

null

axonius_api_client/http.py

geransmith/axonius_api_client

09fd564d62f0ddf7aa44db14a509eaafaf0c930f

[ "MIT" ]

null

# -*- coding: utf-8 -*- """HTTP client.""" import logging import warnings from urllib.parse import urlparse, urlunparse import requests from .constants import ( LOG_LEVEL_HTTP, MAX_BODY_LEN, REQUEST_ATTR_MAP, RESPONSE_ATTR_MAP, TIMEOUT_CONNECT, TIMEOUT_RESPONSE, ) from .exceptions import HttpError from .logs import get_obj_log, set_log_level from .tools import join_url, json_reload, listify, path_read from .version import __version__ InsecureRequestWarning = requests.urllib3.exceptions.InsecureRequestWarning class Http: """HTTP client wrapper around :obj:`requests.Session`.""" def __init__( self, url, connect_timeout=TIMEOUT_CONNECT, response_timeout=TIMEOUT_RESPONSE, certpath=None, certwarn=True, certverify=False, cert_client_both=None, cert_client_cert=None, cert_client_key=None, http_proxy=None, https_proxy=None, save_last=True, save_history=False, log_level=LOG_LEVEL_HTTP, log_level_urllib="warning", log_request_attrs=None, log_response_attrs=None, log_request_body=False, log_response_body=False, ): """HTTP client wrapper around :obj:`requests.Session`. Notes: * If certpath is supplied, certverify is ignored * private key supplied to cert_client_key or cert_client_both can **NOT** be password encrypted Args: url (:obj:`str` or :obj:`ParserUrl`): URL to connect to connect_timeout (:obj:`int`, optional): default :data:`TIMEOUT_CONNECT` - seconds to wait for connections to open to :attr:`url` response_timeout (:obj:`int`, optional): default :data:`TIMEOUT_RESPONSE` - seconds to wait for responses from :attr:`url` certpath (:obj:`str` or :obj:`pathlib.Path`, optional): default ``None`` - path to CA bundle file to use when verifing certs offered by :attr:`url` instead of the system CA bundle certwarn (:obj:`bool`, optional): default ``True`` - show warnings from requests about certs offered by :attr:`url` that are self signed: * if ``True`` show warning only the first time it happens * if ``False`` never show warning * if ``None`` show warning every time it happens certverify (:obj:`bool`, optional): default ``False`` - control validation of certs offered by :attr:`url`: * if ``True`` raise exception if cert is invalid/self-signed * if ``False`` only raise exception if cert is invalid cert_client_both (:obj:`str` or :obj:`pathlib.Path`, optional): default ``None`` - path to cert file containing both the private key and cert to offer to :attr:`url` cert_client_cert (:obj:`str` or :obj:`pathlib.Path`, optional): default ``None`` - path to cert file to offer to :attr:`url` (*must also supply cert_client_key*) cert_client_key (:obj:`str` or :obj:`pathlib.Path`, optional): default ``None`` - path to private key file for cert_client_cert to offer to :attr:`url` (*must also supply cert_client_cert*) http_proxy (:obj:`str`, optional): default ``None`` - proxy to use when making http requests to :attr:`url` https_proxy (:obj:`str`, optional): default ``None`` - proxy to use when making https requests to :attr:`url` save_last (:obj:`bool`, optional): default ``True`` - * if ``True`` save request to :attr:`LAST_REQUEST` and response to :attr:`LAST_RESPONSE` * if ``False`` do not save request to :attr:`LAST_REQUEST` and response to :attr:`LAST_RESPONSE` save_history (:obj:`bool`, optional): default ``True`` - * if ``True`` append responses to :attr:`HISTORY` * if ``False`` do not append responses to :attr:`HISTORY` log_level (:obj:`str`): default :data:`axonius_api_client.LOG_LEVEL_HTTP` - logging level to use for this objects logger log_level_urllib (:obj:`str`): default ``"warning"`` - logging level to use for this urllib logger log_request_attrs (:obj:`bool`): default ``None`` - control logging of request attributes: * if ``True``, log request attributes defined in :data:`axonius_api_client.LOG_REQUEST_ATTRS_VERBOSE` * if ``False``, log request attributes defined in :data:`axonius_api_client.LOG_REQUEST_ATTRS_BRIEF` * if ``None``, do not log any request attributes log_response_attrs (:obj:`bool`): default ``None`` - control logging of response attributes: * if ``True``, log response attributes defined in :data:`axonius_api_client.LOG_RESPONSE_ATTRS_VERBOSE` * if ``False``, log response attributes defined in :data:`axonius_api_client.LOG_RESPONSE_ATTRS_BRIEF` * if ``None``, do not log any response attributes log_request_body (:obj:`bool`): default ``False`` - control logging of request bodies: * if ``True``, log request bodies * if ``False``, do not log request bodies log_response_body (:obj:`bool`): default ``False`` - control logging of response bodies: * if ``True``, log response bodies * if ``False``, do not log response bodies Raises: :exc:`HttpError`: if either cert_client_cert or cert_client_key are supplied, and the other is not supplied :exc:`HttpError`: if any of cert_path, cert_client_cert, cert_client_key, or cert_client_both are supplied and the file does not exist """ self.LOG = get_obj_log(obj=self, level=log_level) """:obj:`logging.Logger`: Logger for this object.""" if isinstance(url, ParserUrl): self.URLPARSED = url else: self.URLPARSED = ParserUrl(url=url, default_scheme="https") self.url = self.URLPARSED.url """:obj:`str`: URL to connect to""" self.LAST_REQUEST = None """:obj:`requests.PreparedRequest`: last request sent""" self.LAST_RESPONSE = None """:obj:`requests.Response`: last response received""" self.HISTORY = [] """:obj:`list` of :obj:`requests.Response`: all responses received.""" self.SAVE_LAST = save_last """:obj:`bool`: save requests to :attr:`LAST_REQUEST` and responses to :attr:`LAST_RESPONSE`""" self.SAVEHISTORY = save_history """:obj:`bool`: Append all responses to :attr:`HISTORY`""" self.CONNECT_TIMEOUT = connect_timeout """:obj:`int`: seconds to wait for connections to open to :attr:`url`""" self.RESPONSE_TIMEOUT = response_timeout """:obj:`int`: seconds to wait for responses from :attr:`url`""" self.session = requests.Session() """:obj:`requests.Session`: session object to use""" self.LOG_REQUEST_BODY = log_request_body """:obj:`bool`: Log the full request body.""" self.LOG_RESPONSE_BODY = log_response_body """:obj:`bool`: Log the full response body.""" self.log_request_attrs = log_request_attrs self.log_response_attrs = log_response_attrs self.session.proxies = {} self.session.proxies["https"] = https_proxy self.session.proxies["http"] = http_proxy if certpath: path_read(obj=certpath, binary=True) self.session.verify = certpath else: self.session.verify = certverify if cert_client_both: path_read(obj=cert_client_both, binary=True) self.session.cert = str(cert_client_both) elif cert_client_cert or cert_client_key: if not all([cert_client_cert, cert_client_key]): error = ( "You must supply both a 'cert_client_cert' and 'cert_client_key'" " or use 'cert_client_both'!" ) raise HttpError(error) path_read(obj=cert_client_cert, binary=True) path_read(obj=cert_client_key, binary=True) self.session.cert = (str(cert_client_cert), str(cert_client_key)) if certwarn is True: warnings.simplefilter("once", InsecureRequestWarning) elif certwarn is False: warnings.simplefilter("ignore", InsecureRequestWarning) urllog = logging.getLogger("urllib3.connectionpool") set_log_level(obj=urllog, level=log_level_urllib) def __call__( self, path=None, route=None, method="get", data=None, params=None, headers=None, json=None, files=None, # fmt: off **kwargs # fmt: on ): """Create, prepare, and then send a request using :attr:`session`. Args: path (:obj:`str`, optional): default ``None`` - path to append to :attr:`url` route (:obj:`str`, optional): default ``None`` - route to append to :attr:`url` method (:obj:`str`, optional): default ``"get"`` - method to use data (:obj:`str`, optional): default ``None`` - body to send params (:obj:`dict`, optional): default ``None`` - parameters to url encode headers (:obj:`dict`, optional): default ``None`` - headers to send json (:obj:`dict`, optional): default ``None`` - obj to encode as json files (:obj:`tuple` of :obj:`tuple`, optional): default ``None`` - files to send **kwargs: overrides for object attributes * connect_timeout (:obj:`int`): default :attr:`CONNECT_TIMEOUT` - seconds to wait for connection to open to :attr:`url` * response_timeout (:obj:`int`): default :attr:`RESPONSE_TIMEOUT` - seconds to wait for for response from :attr:`url` * proxies (:obj:`dict`): default ``None`` - use custom proxies instead of proxies defined in :attr:`session` * verify (:obj:`bool` or :obj:`str`): default ``None`` - use custom verification of cert offered by :attr:`url` instead of verification defined in :attr:`session` * cert (:obj:`str`): default ``None`` - use custom client cert to offer to :attr:`url` cert defined in :attr:`session` Returns: :obj:`requests.Response`: raw response object """ url = join_url(self.url, path, route) headers = headers or {} headers.setdefault("User-Agent", self.user_agent) request = requests.Request( url=url, method=method, data=data, headers=headers, params=params, json=json, files=files or [], ) prepped_request = self.session.prepare_request(request=request) prepped_request.body_size = len(prepped_request.body or "") if self.SAVE_LAST: self.LAST_REQUEST = prepped_request if self.log_request_attrs: lattrs = ", ".join(self.log_request_attrs).format(request=prepped_request) self.LOG.debug(f"REQUEST ATTRS: {lattrs}") send_args = self.session.merge_environment_settings( url=prepped_request.url, proxies=kwargs.get("proxies", {}), stream=kwargs.get("stream", None), verify=kwargs.get("verify", None), cert=kwargs.get("cert", None), ) send_args["request"] = prepped_request send_args["timeout"] = ( kwargs.get("connect_timeout", self.CONNECT_TIMEOUT), kwargs.get("response_timeout", self.RESPONSE_TIMEOUT), ) if self.LOG_REQUEST_BODY: self.log_body(body=prepped_request.body, body_type="REQUEST") response = self.session.send(**send_args) response.body_size = len(response.text or "") if self.SAVE_LAST: self.LAST_RESPONSE = response if self.SAVEHISTORY: self.HISTORY.append(response) if self.log_response_attrs: lattrs = ", ".join(self.log_response_attrs).format(response=response) self.LOG.debug(f"RESPONSE ATTRS: {lattrs}") if self.LOG_RESPONSE_BODY: self.log_body(body=response.text, body_type="RESPONSE") return response def __str__(self): """Show object info. Returns: :obj:`str` """ return "{c.__module__}.{c.__name__}(url={url!r})".format( c=self.__class__, url=self.url ) def __repr__(self): """Show object info. Returns: :obj:`str` """ return self.__str__() @property def user_agent(self): """Value to use in User-Agent header. Returns: :obj:`str`: user agent string """ return f"{__name__}.{self.__class__.__name__}/{__version__}" @property def log_request_attrs(self): """Get the request attributes that should be logged.""" return self._get_log_attrs("request") @log_request_attrs.setter def log_request_attrs(self, value): """Set the request attributes that should be logged.""" attr_map = REQUEST_ATTR_MAP attr_type = "request" self._set_log_attrs(attr_map=attr_map, attr_type=attr_type, value=value) @property def log_response_attrs(self): """Get the response attributes that should be logged.""" return self._get_log_attrs("response") @log_response_attrs.setter def log_response_attrs(self, value): """Set the response attributes that should be logged.""" attr_map = RESPONSE_ATTR_MAP attr_type = "response" self._set_log_attrs(attr_map=attr_map, attr_type=attr_type, value=value) def _get_log_attrs(self, attr_type): return getattr(self, "_LOG_ATTRS", {}).get(attr_type, []) def _set_log_attrs(self, attr_map, attr_type, value): if not hasattr(self, "_LOG_ATTRS"): self._LOG_ATTRS = {"response": [], "request": []} value = [x.lower().strip() for x in listify(value)] if not value: self._LOG_ATTRS[attr_type] = [] return log_attrs = self._LOG_ATTRS[attr_type] if "all" in value: for k, v in attr_map.items(): entry = f"{k}={v}" if entry not in log_attrs: log_attrs.append(entry) return for item in value: if item in attr_map: value = attr_map[item] entry = f"{item}={value}" if entry not in log_attrs: log_attrs.append(entry) def log_body(self, body, body_type): """Pass.""" body = body or "" body = json_reload(obj=body, error=False, trim=MAX_BODY_LEN) self.LOG.debug(f"{body_type} BODY:\n{body}") class ParserUrl: """Parse a URL and ensure it has the neccessary bits.""" def __init__(self, url, default_scheme="https"): """Parse a URL and ensure it has the neccessary bits. Args: url (:obj:`str`): URL to parse default_scheme (:obj:`str`, optional): default ``"https"`` - default scheme to use if url does not contain a scheme Raises: :exc:`HttpError`: if parsed URL winds up without a hostname, port, or scheme. """ self._init_url = url """:obj:`str`: initial URL provided""" self._init_scheme = default_scheme """:obj:`str`: default scheme provided""" self._init_parsed = urlparse(url) """:obj:`urllib.parse.ParseResult`: first pass of parsing URL""" self.parsed = self.reparse( parsed=self._init_parsed, default_scheme=default_scheme ) """:obj:`urllib.parse.ParseResult`: second pass of parsing URL""" for part in ["hostname", "port", "scheme"]: if not getattr(self.parsed, part, None): error = ( f"Parsed URL into {self.parsed_str!r} and no {part!r} provided" f" in URL {url!r}" ) raise HttpError(error) def __str__(self): """Show object info. Returns: :obj:`str` """ cls = self.__class__ return f"{cls.__module__}.{cls.__name__}({self.parsed_str})" def __repr__(self): """Show object info. Returns: :obj:`str` """ return self.__str__() @property def hostname(self): """Hostname part from :attr:`ParserUrl.parsed`. Returns: :obj:`str`: hostname value """ return self.parsed.hostname @property def port(self): """Port part from :attr:`ParserUrl.parsed`. Returns :obj:`int`: port value """ return int(self.parsed.port) @property def scheme(self): """Scheme part from :attr:`ParserUrl.parsed`. Returns: :obj:`str`: scheme value """ return self.parsed.scheme @property def url(self): """Get scheme, hostname, and port from :attr:`ParserUrl.parsed`. Returns: :obj:`str`: schema, hostname, and port unparsed values """ return self.unparse_base(parsed_result=self.parsed) @property def url_full(self): """Get full URL from :attr:`ParserUrl.parsed`. Returns: :obj:`str`: full unparsed url """ return self.unparse_all(parsed_result=self.parsed) @property def parsed_str(self): """Get a str value of :attr:`ParserUrl.parsed`. Returns: :obj:`str`: str value of :attr:`ParserUrl.parsed` """ parsed = getattr(self, "parsed", None) attrs = [ "scheme", "netloc", "hostname", "port", "path", "params", "query", "fragment", ] atmpl = "{a}={v!r}".format attrs = [atmpl(a=a, v="{}".format(getattr(parsed, a, "")) or "") for a in attrs] return ", ".join(attrs) def make_netloc(self, host, port): """Create netloc from host and port. Args: host (:obj:`str`): host part to use in netloc port (:obj:`str`): port part to use in netloc Returns: :obj:`str`: host and port values joined by : """ return ":".join([x for x in [host, port] if x]) def reparse(self, parsed, default_scheme=""): """Reparse a parsed URL into a parsed URL with values fixed. Args: parsed (:obj:`urllib.parse.ParseResult`): parsed URL to reparse default_scheme (:obj:`str`, optional): default ``""`` - default scheme to use if URL does not contain a scheme Returns: :obj:`urllib.parse.ParseResult`: reparsed result """ scheme, netloc, path, params, query, fragment = parsed host = parsed.hostname port = format(parsed.port or "") if not netloc and scheme and path and path.split("/")[0].isdigit(): """For case: >>> urllib.parse.urlparse('host:443/') ParseResult( scheme='host', netloc='', path='443/', params='', query='', fragment='' ) """ host = scheme # switch host from scheme to host port = path.split("/")[0] # remove / from path and assign to port path = "" # empty out path scheme = default_scheme netloc = ":".join([host, port]) if not netloc and path: """For cases: >>> urllib.parse.urlparse('host:443') ParseResult( scheme='', netloc='', path='host:443', params='', query='', fragment='' ) >>> urllib.parse.urlparse('host') ParseResult( scheme='', netloc='', path='host', params='', query='', fragment='' ) """ netloc, path = path, netloc if ":" in netloc: # pragma: no cover # can't get this to trigger anymore, ignore test coverage host, port = netloc.split(":", 1) netloc = ":".join([host, port]) if port else host else: host = netloc scheme = scheme or default_scheme if not scheme and port: if format(port) == "443": scheme = "https" elif format(port) == "80": scheme = "http" if not port: if scheme == "https": netloc = self.make_netloc(host, "443") elif scheme == "http": netloc = self.make_netloc(host, "80") pass2 = urlunparse((scheme, netloc, path, params, query, fragment)) return urlparse(pass2) def unparse_base(self, parsed_result): """Unparse a parsed URL into just the scheme, hostname, and port parts. Args: parsed (:obj:`urllib.parse.ParseResult`): parsed URL to unparse Returns: :obj:`str`: unparsed url """ # only unparse self.parsed into url with scheme and netloc bits = (parsed_result.scheme, parsed_result.netloc, "", "", "", "") return urlunparse(bits) def unparse_all(self, parsed_result): """Unparse a parsed URL with all the parts. Args: parsed (:obj:`urllib.parse.ParseResult`): parsed URL to unparse Returns: :obj:`str`: unparsed url """ return urlunparse(parsed_result)

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0

null

0

null

0

1

0

d8edf7cbcf7cedddc71ad9cf461c4f588b745f8c

427

py

Python

tests/test.py

alex-panda/PDFCompiler

3454ee01a6e5ebb2d2bccdcdc32678bf1def895d

[ "MIT" ]

null

tests/test.py

alex-panda/PDFCompiler

3454ee01a6e5ebb2d2bccdcdc32678bf1def895d

[ "MIT" ]

null

tests/test.py

alex-panda/PDFCompiler

3454ee01a6e5ebb2d2bccdcdc32678bf1def895d

[ "MIT" ]

null

from fpdf import FPDF import os pdf = FPDF() pdf.add_page() #pdf.add_font('CMUSerif-UprightItalic', fname=os.path.abspath('./src/Fonts/Computer Modern/cmunui.ttf'), uni=True) #pdf.set_font('CMUSerif-UprightItalic', size=16) pdf.add_font('BerlinSansFB-Bold', fname='C:\\Windows\\Fonts\\VINERITC.TTF', uni=True) pdf.set_font('BerlinSansFB-Bold') pdf.cell(40, 10, "Hello World! (It's a great day today!)") pdf.output("test.pdf")

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427

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0

0.25

0

null

0

null

0

1

0

d8ee816a339e470ff77545c7b6f55ec86374feb8

362

py

Python

examinations/serializers.py

CASDON-MYSTERY/studentapp

0fd942e963a10a02a6c9f358dd362cfd646eecc3

[ "MIT" ]

null

examinations/serializers.py

CASDON-MYSTERY/studentapp

0fd942e963a10a02a6c9f358dd362cfd646eecc3

[ "MIT" ]

null

examinations/serializers.py

CASDON-MYSTERY/studentapp

0fd942e963a10a02a6c9f358dd362cfd646eecc3

[ "MIT" ]

null

from rest_framework import serializers from .models import Examination, Exam_day_and_venue class Examination_Serializer(serializers.ModelSerializer): class Meta: model=Examination fields= "__all__" class Exam_day_and_venue_Serializer(serializers.ModelSerializer): class Meta: model=Exam_day_and_venue fields= "__all__"

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0.117188

0.175781

0.390625

0

0.18232

362

14

66

25.857143

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0.4

0

0.038567

0

1

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false

0

0.2

0

0.6

0

null

0

1

0

null

0

1

d8f1c63452f9e657da2dce69409e74cc04262524

2,008

py

Python

server/settings.py

codingismycraft/pinta

bbeb721f5d365cf2643d01e1f3196e90e2658d05

[ "MIT" ]

null

server/settings.py

codingismycraft/pinta

bbeb721f5d365cf2643d01e1f3196e90e2658d05

[ "MIT" ]

null

server/settings.py

codingismycraft/pinta

bbeb721f5d365cf2643d01e1f3196e90e2658d05

[ "MIT" ]

null

"""Exposes the configuration settings.""" import os import json class _Settings: """Exposes the configuration settings.""" _project_root = None _include_root = None _dependencies_filename = None _pinta_executable = None _history_db = None def __init__(self): """Initializer. Loads the settings using the pinta configuration file. """ home_dir = os.path.expanduser("~") filename = os.path.join(home_dir, ".pinta", "pinta_conf.json") with open(filename) as json_file: data = json.load(json_file) self._project_root = data["project_root"] self._include_root = data["include_root"] self._dependencies_filename = data["dependencies_filename"] self._history_db = data["history_db"] self._pinta_executable = os.path.join(home_dir, ".pinta", "pinta") @property def project_root(self): """Returns the project root. :return: The project root. :rtype: str. """ return self._project_root @property def include_root(self): """Returns the include root for all python imports. :return: The include root. :rtype: str. """ return self._include_root @property def dependencies_filename(self): """Returns the filename that containg the dependencies as csv. :return: The filename that containg the dependencies as csv. :rtype: str. """ return self._dependencies_filename @property def pinta_executable(self): """Returns the path to the pinta executable. :return: The path to the pinta executable. :rtype: str. """ return self._pinta_executable @property def history_db(self): """Returns the path to the history database. :return: The path to the history database. :rtype: str. """ return self._history_db settings = _Settings()

25.417722

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0.616036

224

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5.294643

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0.274874

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0

null

0

null

0

1

0

1

d8f2c334a4aedf8eab7e4c82a7180f26920b4989

2,533

py

Python

pages/header_page.py

Morjus/kinoposk_ui_tests

0e5c0660593731529db7df5912e10a81e80a55ea

[ "MIT" ]

null

pages/header_page.py

Morjus/kinoposk_ui_tests

0e5c0660593731529db7df5912e10a81e80a55ea

[ "MIT" ]

null

pages/header_page.py

Morjus/kinoposk_ui_tests

0e5c0660593731529db7df5912e10a81e80a55ea

[ "MIT" ]

null

import allure import logging from selenium.webdriver.common.by import By from pages.base_page import BasePage class HeaderPage(BasePage): LOGIN_BUTTON = (By.XPATH, '//button[contains(text(), "Войти")]') EMAIL_FIELD = (By.CSS_SELECTOR, '#passp-field-login') PASSW_FIELD = (By.CSS_SELECTOR, '#passp-field-passwd') SUBMIT_BUTTON = (By.XPATH, '//button[@type="submit"]') SKIP_PHONE_BUTTON = (By.XPATH, '//button[@type="button"]') SEARCH_FIELD = (By.CSS_SELECTOR, "input[type='text'][name='kp_query']") SEARCH_BUTTON = (By.CSS_SELECTOR, 'button[type="submit"]') HD_LINK = (By.XPATH, '//a[contains(text(), "Онлайн-кинотеатр")]') MY_BUYS = (By.XPATH, '//a[contains(text(), "Мои покупки")]') def __init__(self, driver, url): super().__init__(driver, url) self.driver = driver self.base_url = url self.logger = logging.getLogger(type(self).__name__) def _set_email(self, email): with allure.step(f"Отправка {email} в {self.EMAIL_FIELD}"): self.find(locator=self.EMAIL_FIELD).send_keys(email) def _set_passw(self, passw): with allure.step(f"Отправка {passw} в {self.PASSW_FIELD}"): self.find(locator=self.PASSW_FIELD).send_keys(passw) def login(self, email, passw): with allure.step(f"Нажатие на 'Войти' на главной странице"): self.find(locator=self.LOGIN_BUTTON).click() self._set_email(email) with allure.step(f"Нажимаю кнопку {self.SUBMIT_BUTTON}"): self.find(locator=self.SUBMIT_BUTTON).click() self._set_passw(passw) with allure.step(f"Нажимаю кнопку {self.SUBMIT_BUTTON}"): self.find(locator=self.SUBMIT_BUTTON).click() with allure.step("Жду появления поисковой строки"): self.find(locator=self.SEARCH_FIELD, time=60) # with allure.step(f"Пропуск предложения привязать телефон"): # self.find(locator=self.SKIP_PHONE_BUTTON).click() def go_to_hd(self): with allure.step("Перехожу в онлайн кинотеатр"): self.find(locator=self.HD_LINK).click() with allure.step("Смотрю, что на странице есть вкладка 'Мои покупки'"): self.find(locator=self.MY_BUYS) def search_movie(self, movie_name): with allure.step(f"Ввожу в поиск фильм: {movie_name}"): self.find(locator=self.SEARCH_FIELD).send_keys(movie_name) with allure.step(f"Запускаю поиск фильма: {movie_name}"): self.find(locator=self.SEARCH_BUTTON).click()

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0.099751

0

0.000982

0.19621

2,533

58

80

43.672414

0.786837

0.044611

0

0.086957

0

0.252275

0.05335

0

1

0.130435

false

0.152174

0.086957

0

0.434783

0

null

0

null

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0

1

d8f4b8a4abb68a6dab26dbeb1dd2c937dba8e5c5

1,911

py

Python

modules/data/warehouse/tools/import_record.py

Shokoofeh/apollo

71d6ea753b4595eb38cc54d6650c8de677b173df

[ "Apache-2.0" ]

7

2017-07-07T07:56:13.000Z

2019-03-06T06:27:00.000Z

modules/data/warehouse/tools/import_record.py

Shokoofeh/apollo

71d6ea753b4595eb38cc54d6650c8de677b173df

[ "Apache-2.0" ]

null

modules/data/warehouse/tools/import_record.py

Shokoofeh/apollo

71d6ea753b4595eb38cc54d6650c8de677b173df

[ "Apache-2.0" ]

2

2017-07-07T07:56:15.000Z

2018-08-10T17:13:34.000Z

#!/usr/bin/env python # -*- coding: UTF-8-*- ############################################################################### # Copyright 2018 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. ############################################################################### """ Import Apollo record into a MongoDB. Use as command tool: import_record.py <record> Use as util lib: RecordImporter.Import(<record>) """ import os import sys import gflags import glog from mongo_util import Mongo from parse_record import RecordParser gflags.DEFINE_string('mongo_collection_name', 'records', 'MongoDB collection name.') class RecordImporter(object): """Import Apollo record files.""" @staticmethod def Import(record_file): """Import one record.""" parser = RecordParser(record_file) if not parser.ParseMeta(): glog.error('Fail to parse record {}'.format(record_file)) return parser.ParseMessages() doc = Mongo.pb_to_doc(parser.record) collection = Mongo.collection(gflags.FLAGS.mongo_collection_name) collection.replace_one({'path': parser.record.path}, doc, upsert=True) glog.info('Imported record {}'.format(record_file)) if __name__ == '__main__': gflags.FLAGS(sys.argv) if len(sys.argv) > 0: RecordImporter.Import(sys.argv[-1])

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d8f50d17f9a52ee96a7037b851918cf13a2ca3ae

96

py

Python

enthought/mayavi/core/ui/mayavi_scene.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/mayavi/core/ui/mayavi_scene.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/mayavi/core/ui/mayavi_scene.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from mayavi.core.ui.mayavi_scene import *

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5

d8f564b8365eed4a07a4dd31237eb8da98838a5f

3,064

py

Python

docs/talks/xdc2016/compare_cairo.py

juhapekka/ezbench_work

ac0cb9ccbc205746d4790a9e33e598fbd5732741

[ "BSD-3-Clause" ]

3

2019-06-25T16:49:25.000Z

2021-04-30T06:36:54.000Z

docs/talks/xdc2016/compare_cairo.py

juhapekka/ezbench_work

ac0cb9ccbc205746d4790a9e33e598fbd5732741

[ "BSD-3-Clause" ]

4

2019-12-10T00:50:49.000Z

2022-03-10T06:18:42.000Z

docs/talks/xdc2016/compare_cairo.py

juhapekka/ezbench_work

ac0cb9ccbc205746d4790a9e33e598fbd5732741

[ "BSD-3-Clause" ]

1

2021-04-30T06:36:36.000Z

#!/usr/bin/env python3 """ Copyright (c) 2015, Intel Corporation Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import sys import os # Import ezbench from the utils/ folder ezbench_dir = os.path.abspath(sys.path[0]+'/../') sys.path.append(ezbench_dir+'/utils/') sys.path.append(ezbench_dir+'/utils/env_dump') from ezbench import * from env_dump_parser import * if __name__ == "__main__": import argparse # parse the options parser = argparse.ArgumentParser() parser.add_argument("log_folder") args = parser.parse_args() report = Report(args.log_folder, silentMode=True) report.enhance_report([]) print("Test name, cairo image perf, xlib perf, cairo image energy, xlib energy") for result in report.commits[0].results: test_name = result.test.full_name if not test_name.startswith("x11:cairo:xlib:"): continue img_res = report.find_result_by_name(report.commits[0], test_name.replace("x11:cairo:xlib:", "x11:cairo:image:")) if img_res is None: img_res = report.find_result_by_name(report.commits[0], test_name.replace("x11:cairo:xlib:", "x11:cairo:ximage:")) test_name = test_name.replace(":xlib:", ':') if img_res is None: print("could not find the cpu result for test '{}'".format(test_name)) perf_cpu = img_res.result().mean() perf_gpu = result.result().mean() pwr_cpu = img_res.result("metric_rapl0.package-0:energy").mean() pwr_gpu = result.result("metric_rapl0.package-0:energy").mean() print("{},{},{},{},{}".format(test_name, perf_cpu, perf_gpu, pwr_cpu, pwr_gpu))

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null

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0

d8f89ca57ebf1d8154f7f2629edeea9594a44b41

9,541

py

Python

generator/blocks/write_back/base/memory_block_base.py

biarmic/OpenCache

bb9e110e434deb83900de328cc76b63901ba582f

[ "BSD-3-Clause" ]

null

generator/blocks/write_back/base/memory_block_base.py

biarmic/OpenCache

bb9e110e434deb83900de328cc76b63901ba582f

[ "BSD-3-Clause" ]

null

generator/blocks/write_back/base/memory_block_base.py

biarmic/OpenCache

bb9e110e434deb83900de328cc76b63901ba582f

[ "BSD-3-Clause" ]

null

# See LICENSE for licensing information. # # Copyright (c) 2021 Regents of the University of California and The Board # of Regents for the Oklahoma Agricultural and Mechanical College # (acting for and on behalf of Oklahoma State University) # All rights reserved. # from block_base import block_base from amaranth import Cat, C from state import state class memory_block_base(block_base): """ This is the base class of memory controller always block modules. Methods of this class can be overridden for specific implementation of different cache designs. In this block, cache communicates with memory components such as tag array, data array, use array, and DRAM. """ def __init__(self): super().__init__() def add_reset(self, c, m): """ Add statements for the RESET state. """ # In the RESET state, cache sends write request to the tag array to reset # the current set. # set register is incremented by the Request Block. # When set register reaches the end, state switches to IDLE. with m.Case(state.RESET): c.tag_array.write(c.set, 0) c.data_array.write(c.set, 0) def add_flush(self, c, m): """ Add statements for the FLUSH state. """ # In the FLUSH state, cache sends write request to DRAM. # set register is incremented by the Request Block. # way register is incremented by the Replacement Block. # When set and way registers reach the end, state switches to IDLE. with m.Case(state.FLUSH): c.tag_array.read(c.set) c.data_array.read(c.set) with m.Switch(c.way): for i in range(c.num_ways): with m.Case(i): # Check if current set is clean or DRAM is available, # and all ways of the set are checked if i == c.num_ways - 1: with m.If(~c.tag_array.output().dirty(i) | ~c.dram.stall()): # Request the next tag and data lines from SRAMs c.tag_array.read(c.set + 1) c.data_array.read(c.set + 1) # Check if current set is dirty and DRAM is available with m.If(c.tag_array.output().dirty(i) & ~c.dram.stall()): # Update dirty bits in the tag line c.tag_array.write(c.set, Cat(c.tag_array.output().tag(i), C(2, 2)), i) # Send the write request to DRAM c.dram.write(Cat(c.set, c.tag_array.output().tag(i)), c.data_array.output(i)) def add_idle(self, c, m): """ Add statements for the IDLE state. """ # In the IDLE state, cache waits for CPU to send a new request. # Until there is a new request from the cache, stall is low. # When there is a new request from the cache stall is asserted, request # is decoded and corresponding tag, data, and use array lines are read # from internal SRAMs. with m.Case(state.IDLE): # Read next lines from SRAMs even though CPU is not sending a new # request since read is non-destructive. c.tag_array.read(c.addr.parse_set()) c.data_array.read(c.addr.parse_set()) def add_compare(self, c, m): """ Add statements for the COMPARE state. """ # In the COMPARE state, cache compares tags. with m.Case(state.COMPARE): c.tag_array.read(c.set) c.data_array.read(c.set) # Assuming that current request is miss, check if it is dirty miss with c.check_dirty_miss(m): # If DRAM is available, switch to WAIT_WRITE and wait for DRAM to # complete writing with m.If(~c.dram.stall()): c.dram.write(Cat(c.set, c.tag_array.output().tag()), c.data_array.output()) # Else, assume that current request is clean miss with c.check_clean_miss(m): # If DRAM is busy, switch to READ and wait for DRAM to be available # If DRAM is available, switch to WAIT_READ and wait for DRAM to # complete reading with m.If(~c.dram.stall()): c.dram.read(Cat(c.set, c.tag)) # Check if current request is hit with c.check_hit(m): # Set DRAM's csb to 1 again since it could be set 0 above c.dram.disable() # Perform the write request with m.If(~c.web_reg): # Update dirty bit c.tag_array.write(c.set, Cat(c.tag, C(3, 2))) # Perform write request c.data_array.write(c.set, c.data_array.output()) c.data_array.write_input(0, c.offset, c.din_reg, c.wmask_reg if c.num_masks else None) # Read next lines from SRAMs even though the CPU is not sending # a new request since read is non-destructive. c.tag_array.read(c.addr.parse_set()) c.data_array.read(c.addr.parse_set()) def add_write(self, c, m): """ Add statements for the WRITE state. """ # In the WRITE state, cache waits for DRAM to be available. # When DRAM is available, write request is sent. with m.Case(state.WRITE): c.tag_array.read(c.set) c.data_array.read(c.set) # If DRAM is busy, wait in this state. # If DRAM is available, switch to WAIT_WRITE and wait for DRAM to # complete writing. with m.If(~c.dram.stall()): with m.Switch(c.way): for i in range(c.num_ways): with m.Case(i): c.dram.write(Cat(c.set, c.tag_array.output().tag(c.way)), c.data_array.output(i)) def add_wait_write(self, c, m): """ Add statements for the WAIT_WRITE state. """ # In the WAIT_WRITE state, cache waits for DRAM to complete writing. # When DRAM completes writing, read request is sent. with m.Case(state.WAIT_WRITE): c.tag_array.read(c.set) c.data_array.read(c.set) # If DRAM is busy, wait in this state. # If DRAM completes writing, switch to WAIT_READ and wait for DRAM to # complete reading. with m.If(~c.dram.stall()): c.dram.read(Cat(c.set, c.tag)) def add_read(self, c, m): """ Add statements for the READ state. """ # In the READ state, cache waits for DRAM to be available. # When DRAM is available, read request is sent. # TODO: Is this state really necessary? WAIT_WRITE state may be used instead with m.Case(state.READ): c.tag_array.read(c.set) c.data_array.read(c.set) # If DRAM is busy, wait in this state. # If DRAM completes writing, switch to WAIT_READ and wait for DRAM to # complete reading. with m.If(~c.dram.stall()): c.dram.read(Cat(c.set, c.tag)) def add_wait_read(self, c, m): """ Add statements for the WAIT_READ state. """ # In the WAIT_READ state, cache waits for DRAM to complete reading # When DRAM completes reading, request is completed. with m.Case(state.WAIT_READ): c.tag_array.read(c.set) c.data_array.read(c.set) # If DRAM is busy, cache waits in this state. # If DRAM completes reading, cache switches to: # IDLE if CPU isn't sending a new request # COMPARE if CPU is sending a new request with m.If(~c.dram.stall()): # Update tag line c.tag_array.write(c.set, Cat(c.tag, ~c.web_reg, C(1, 1)), c.way) # Update data line c.data_array.write(c.set, c.dram.output(), c.way) # Perform the write request with m.If(~c.web_reg): c.data_array.write_input(c.way, c.offset, c.din_reg, c.wmask_reg if c.num_masks else None) # Read next lines from SRAMs even though the CPU is not sending # a new request since read is non-destructive c.tag_array.read(c.addr.parse_set()) c.data_array.read(c.addr.parse_set()) def add_flush_hazard(self, c, m): """ Add statements for the FLUSH_HAZARD state. """ # In the FLUSH_HAZARD state, cache waits in this state for 1 cycle. # Read requests are sent to tag and data arrays. with m.Case(state.FLUSH_HAZARD): c.tag_array.read(0) c.data_array.read(0) def add_wait_hazard(self, c, m): """ Add statements for the WAIT_HAZARD state. """ # In the WAIT_HAZARD state, cache waits in this state for 1 cycle. # Read requests are sent to tag and data arrays. with m.Case(state.WAIT_HAZARD): c.tag_array.read(c.set) c.data_array.read(c.set) def add_flush_sig(self, c, m): """ Add flush signal control. """ # If flush is high, state switches to FLUSH. # In the FLUSH state, cache will write all data lines back to DRAM. with m.If(c.flush): c.tag_array.read(0) c.data_array.read(0)

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d8f8b4873e2a9d706b1b4b89de4220f8b9ddd61d

731

py

Python

concordia/migrations/0002_auto_20181004_1848.py

juliecentofanti172/juliecentofanti.github.io

446ea8522b9f4a6709124ebb6e0f675acf7fe205

[ "CC0-1.0" ]

134

2018-05-23T14:00:29.000Z

2022-03-10T15:47:53.000Z

concordia/migrations/0002_auto_20181004_1848.py

ptrourke/concordia

56ff364dbf38cb8a763df489479821fe43b76d69

[ "CC0-1.0" ]

1,104

2018-05-22T20:18:22.000Z

2022-03-31T17:28:40.000Z

concordia/migrations/0002_auto_20181004_1848.py

ptrourke/concordia

56ff364dbf38cb8a763df489479821fe43b76d69

[ "CC0-1.0" ]

32

2018-05-22T20:22:38.000Z

2021-12-21T14:11:44.000Z

# Generated by Django 2.0.9 on 2018-10-04 18:48 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [("concordia", "0001_squashed_0040_remove_campaign_is_active")] operations = [ migrations.AlterField( model_name="pageinuse", name="created_on", field=models.DateTimeField(auto_now_add=True), ), migrations.AlterField( model_name="pageinuse", name="page_url", field=models.URLField(max_length=768), ), migrations.AlterField( model_name="pageinuse", name="updated_on", field=models.DateTimeField(auto_now=True), ), ]

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null

0

2

d8fb835e064c6068c174aaab9d60c797f66b3c26

319

py

Python

combinatorics/p11069.py

sajjadt/competitive-programming

fb0844afba95383441f0c4c0c3b1a38078d24ec9

[ "MIT" ]

10

2019-03-29T08:37:10.000Z

2021-12-29T14:06:57.000Z

combinatorics/p11069.py

sajjadt/competitive-programming

fb0844afba95383441f0c4c0c3b1a38078d24ec9

[ "MIT" ]

1

2020-07-03T08:25:38.000Z

combinatorics/p11069.py

sajjadt/competitive-programming

fb0844afba95383441f0c4c0c3b1a38078d24ec9

[ "MIT" ]

4

2019-05-30T16:04:48.000Z

2020-10-22T21:42:25.000Z

# f(n) = number of valid sequencess with n items # f(n) = {"attaching n to"} f(n-2) + {"attaching n-1 to "} f(n-3) LIMIT = 76 + 1 f_table = [0, 1, 2, 2] for i in range(LIMIT): f_table.append(f_table[-2] + f_table[-3]) while True: try: n = int(input()) print(f_table[n]) except(EOFError): break

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d8fbe81e9f1a510748c80f8dc8a1b42b637b2f92

207

py

Python

kartverket_tide_api/exceptions/__init__.py

matsjp/kartverket_tide_api

b4be15e9c8f077ef6ec0747fe67f0a64383cfa30

[ "MIT" ]

null

kartverket_tide_api/exceptions/__init__.py

matsjp/kartverket_tide_api

b4be15e9c8f077ef6ec0747fe67f0a64383cfa30

[ "MIT" ]

null

kartverket_tide_api/exceptions/__init__.py

matsjp/kartverket_tide_api

b4be15e9c8f077ef6ec0747fe67f0a64383cfa30

[ "MIT" ]

null

from .apierrorexception import NoTideDataErrorException, UnknownApiErrorException,\ ApiErrorException, InvalidStationTypeErrorException from .cannotfindelementexception import CannotFindElementException

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6

d8fee123a93215beee41ff7185b11c6c92c2b7c1

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py

Python

aita/api/course.py

ze-lin/AITA

0f2fe4e630c37fcc566a54621880b78ec67eefa6

[ "MIT" ]

null

aita/api/course.py

ze-lin/AITA

0f2fe4e630c37fcc566a54621880b78ec67eefa6

[ "MIT" ]

null

aita/api/course.py

ze-lin/AITA

0f2fe4e630c37fcc566a54621880b78ec67eefa6

[ "MIT" ]

1

2020-12-29T19:45:28.000Z

import datetime, time, os from flask import Blueprint, jsonify, request, g from aita.auth import login_required from aita.db import get_collection from werkzeug.utils import secure_filename bp = Blueprint('course', __name__, url_prefix='/course') @bp.route('/getall', methods=['GET']) def get_all_course(): COURSE = get_collection('course') result = COURSE.find() json_body = {} for i, document in enumerate(result): json_body[i] = document json_body[i].pop('_id') return jsonify(json_body) @bp.route('/getall-teacher', methods=['GET']) @login_required def get_all_course_teacher(): COURSE = get_collection('course') if g.usr['role'] == 'student': return 'student' result = COURSE.find({ 'teacher': g.usr['usr'] }) json_body = {} for i, document in enumerate(result): json_body[i] = document json_body[i].pop('_id') return jsonify(json_body) @bp.route('/create', methods=['GET']) @login_required def create_course(): COURSE = get_collection('course') document = { 'genre': request.args.get('genre'), 'title': request.args.get('title'), 'exam': request.args.get('exam'), 'time': request.args.get('time'), 'teacher': g.usr['usr'], 'video': secure_filename(request.args.get('video')), 'article': secure_filename(request.args.get('article')), 'date': str(datetime.date.today()), 'id': str(time.time()), 'view': 0 } COURSE.insert_one(document) return 'Success!' @bp.route('/delete', methods=['GET']) @login_required def delete_course(): # 级联删除 COURSE = get_collection('course') COLLECTION = get_collection('collection') # delete all course_id = request.args.get('id') COLLECTION.delete_many({ 'id': course_id }) COURSE.delete_one({'id': course_id}) return 'Success!' @bp.route('/uploadfile', methods=['POST']) @login_required def upload(): """ 存数据库留给submit_class做 """ file = request.files['file'] file_name = secure_filename(file.filename) if not os.path.exists(os.path.join('aita/static', file_name)): file.save(os.path.join('aita/static', file_name)) return 'Success!' @bp.route('/getreading', methods=['GET']) @login_required def get_reading(): COURSE = get_collection('course') result = COURSE.find_one({'id': request.args.get('id')}) file_name = result['article'] file_path = os.path.join('aita/static', file_name) content = '' with open(file_path, 'r') as f: for line in f: content += line return content @bp.route('/getvideo', methods=['GET']) @login_required def get_video(): COURSE = get_collection('course') result = COURSE.find_one({'id': request.args.get('id')}) file_name = result['video'] return file_name @bp.route('/getexam', methods=['GET']) @login_required def get_exam(): COURSE = get_collection('course') result = COURSE.find_one({'id': request.args.get('id')}) return result['exam'] @bp.route('/view', methods=['GET']) @login_required def view(): COURSE = get_collection('course') course_id = request.args.get('id') result = COURSE.find_one({'id': course_id }) result['view'] += 1 COURSE.replace_one({'id': course_id }, result) COLLECTION = get_collection('collection') document = { 'id': course_id, 'usr': g.usr['usr'] } result = COLLECTION.find_one(document) if not result: COLLECTION.insert_one(document) return 'Success!'

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null

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0

d8ff3f9b9d076df28a9f625cded5efea0322cd15

16,309

py

Python

apps/dash-baseball-statistics/layouts.py

JeroenvdSande/dash-sample-apps

106fa24693cfdaf47c06466a0aed78e642344f91

[ "MIT" ]

2,332

2019-05-10T18:24:20.000Z

2022-03-30T21:46:29.000Z

apps/dash-baseball-statistics/layouts.py

JeroenvdSande/dash-sample-apps

106fa24693cfdaf47c06466a0aed78e642344f91

[ "MIT" ]

384

2019-05-09T19:19:56.000Z

2022-03-12T00:58:24.000Z

apps/dash-baseball-statistics/layouts.py

JeroenvdSande/dash-sample-apps

106fa24693cfdaf47c06466a0aed78e642344f91

[ "MIT" ]

3,127

2019-05-16T17:20:45.000Z

2022-03-31T17:59:07.000Z

# Dash components, html, and dash tables import dash_core_components as dcc import dash_html_components as html import dash_table # Import Bootstrap components import dash_bootstrap_components as dbc # Import custom data.py import data # Import data from data.py file teams_df = data.teams # Hardcoded list that contain era names and marks era_list = data.era_list era_marks = data.era_marks # Main applicaiton menu appMenu = html.Div( [ dbc.Row( [ dbc.Col( html.H4(style={"text-align": "center"}, children="Select Era:"), xs={"size": "auto", "offset": 0}, sm={"size": "auto", "offset": 0}, md={"size": "auto", "offset": 3}, lg={"size": "auto", "offset": 0}, xl={"size": "auto", "offset": 0}, ), dbc.Col( dcc.Dropdown( style={ "text-align": "center", "font-size": "18px", "width": "210px", }, id="era-dropdown", options=era_list, value=era_list[0]["value"], clearable=False, ), xs={"size": "auto", "offset": 0}, sm={"size": "auto", "offset": 0}, md={"size": "auto", "offset": 0}, lg={"size": "auto", "offset": 0}, xl={"size": "auto", "offset": 0}, ), dbc.Col( html.H4( style={"text-align": "center", "justify-self": "right"}, children="Select Team:", ), xs={"size": "auto", "offset": 0}, sm={"size": "auto", "offset": 0}, md={"size": "auto", "offset": 3}, lg={"size": "auto", "offset": 0}, xl={"size": "auto", "offset": 1}, ), dbc.Col( dcc.Dropdown( style={ "text-align": "center", "font-size": "18px", "width": "210px", }, id="team-dropdown", clearable=False, ), xs={"size": "auto", "offset": 0}, sm={"size": "auto", "offset": 0}, md={"size": "auto", "offset": 0}, lg={"size": "auto", "offset": 0}, xl={"size": "auto", "offset": 0}, ), ], form=True, ), dbc.Row( dbc.Col( html.P( style={"font-size": "16px", "opacity": "70%"}, children="""For continuity, some teams historical names where changed to match """ """their modern counterpart. Available teams are updated based on Era selection.""", ) ) ), ], className="menu", ) # Menu slider used, NOT independent, MUST be used with main menu menuSlider = html.Div( [ dbc.Row( dbc.Col( dcc.RangeSlider( id="era-slider", min=1903, max=teams_df["year"].max(), marks=era_marks, tooltip={"always_visible": False, "placement": "bottom"}, ) ) ), dbc.Row( dbc.Col( html.P( style={"font-size": "16px", "opacity": "70%"}, children="Adjust slider to desired range.", ) ) ), ], className="era-slider", ) # Layout for Team Analysis page teamLayout = html.Div( [ dbc.Row(dbc.Col(html.H3(children="Team Accolades"))), # Display Championship titles in datatable dbc.Row( dbc.Col( html.Div(id="team-data"), xs={"size": "auto", "offset": 0}, sm={"size": "auto", "offset": 0}, md={"size": 7, "offset": 0}, lg={"size": "auto", "offset": 0}, xl={"size": "auto", "offset": 0}, ), justify="center", ), ### Graphs of Historical Team statistics ### dbc.Row(dbc.Col(html.H3(children="Team Analysis"))), # Bar Chart of Wins and Losses dbc.Row( dbc.Col( dcc.Graph(id="wl-bar", config={"displayModeBar": False}), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 12, "offset": 0}, lg={"size": 12, "offset": 0}, ) ), # Line Chart of Batting Average, BABIP, and Strikeout Rate dbc.Row( dbc.Col( dcc.Graph(id="batting-line", config={"displayModeBar": False}), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 12, "offset": 0}, lg={"size": 12, "offset": 0}, ) ), # Bar Char of Errors and Double Plays dbc.Row( dbc.Col( dcc.Graph(id="feild-line", config={"displayModeBar": False}), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 12, "offset": 0}, lg={"size": 12, "offset": 0}, ) ), dbc.Row(dbc.Col(html.H4(children="Pitching Performance"))), dbc.Row( [ # Line graph of K/BB ratio with ERA bubbles dbc.Col( dcc.Graph(id="pitch-bubble", config={"displayModeBar": False}), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 12, "offset": 0}, lg={"size": 6, "offset": 0}, ), # Pie Chart, % of Completed Games, Shutouts, and Saves of Total Games played dbc.Col( dcc.Graph(id="pitch-pie", config={"displayModeBar": False}), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 12, "offset": 0}, lg={"size": 6, "offset": 0}, ), ], no_gutters=True, ), ], className="app-page", ) # Player menu used to select players after era and team are set playerMenu = html.Div( [ dbc.Row(dbc.Col(html.H3(children="Player Profile and Statistics"))), dbc.Row( dbc.Col( html.P( style={"font-size": "16px", "opacity": "70%"}, children="Available players are updated based on team selection.", ) ) ), dbc.Row( [ dbc.Row( dbc.Col( html.H4( style={"text-align": "center"}, children="Select Player:" ), xs={"size": "auto", "offset": 0}, sm={"size": "auto", "offset": 0}, md={"size": "auto", "offset": 0}, lg={"size": "auto", "offset": 0}, xl={"size": "auto", "offset": 0}, ) ), dbc.Row( dbc.Col( dcc.Dropdown( style={ "margin-left": "2%", "text-align": "center", "font-size": "18px", "width": "218px", }, id="player-dropdown", clearable=False, ), xs={"size": "auto", "offset": 0}, sm={"size": "auto", "offset": 0}, md={"size": "auto", "offset": 0}, lg={"size": "auto", "offset": 0}, xl={"size": "auto", "offset": 0}, ) ), ], no_gutters=True, ), html.Br(), dbc.Row( dbc.Col( dash_table.DataTable( id="playerProfile", style_as_list_view=True, editable=False, style_table={ "overflowY": "scroll", "width": "100%", "minWidth": "100%", }, style_header={"backgroundColor": "#f8f5f0", "fontWeight": "bold"}, style_cell={"textAlign": "center", "padding": "8px"}, ), xs={"size": "auto", "offset": 0}, sm={"size": "auto", "offset": 0}, md={"size": 8, "offset": 0}, lg={"size": "auto", "offset": 0}, xl={"size": "auto", "offset": 0}, ), justify="center", ), html.Br(), ], className="app-page", ) # Batting statistics battingLayout = html.Div( [ # Batting datatable dbc.Row( dbc.Col( dash_table.DataTable( id="batterTable", style_as_list_view=True, editable=False, style_table={ "overflowY": "scroll", "width": "100%", "minWidth": "100%", }, style_header={"backgroundColor": "#f8f5f0", "fontWeight": "bold"}, style_cell={"textAlign": "center", "padding": "8px"}, ), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 10, "offset": 0}, lg={"size": 10, "offset": 0}, xl={"size": 10, "offset": 0}, ), justify="center", ), dbc.Row( dbc.Col( html.H3( style={"margin-top": "1%", "margin-bottom": "1%"}, children="Player Analysis", ) ) ), dbc.Row( dbc.Col( html.P( style={"font-size": "16px", "opacity": "70%"}, children="Some statistics where not tracked until the 1950s, graphs may not always reflect certain plots.", ) ) ), dbc.Row( [ # Line/Bar Chart of On-Base Percentage, features; H BB HBP SF dbc.Col( dcc.Graph(id="obp-line", config={"displayModeBar": False}), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 12, "offset": 0}, lg={"size": 6, "offset": 0}, ), # Line/Bar Chart of Slugging Average, features; 2B 3B HR dbc.Col( dcc.Graph(id="slg-line", config={"displayModeBar": False}), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 12, "offset": 0}, lg={"size": 6, "offset": 0}, ), ], no_gutters=True, ), # Line Chart of OPS, Features; OBP SLG dbc.Row( dbc.Col( dcc.Graph(id="ops-line", config={"displayModeBar": False}), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 12, "offset": 0}, lg={"size": 12, "offset": 0}, ) ), ], className="app-page", ) # Feilding Statistics fieldingLayout = html.Div( [ dbc.Row(dbc.Col(html.H3(style={"margin-bottom": "1%"}, children="Feilding"))), # Feilding Datatable dbc.Row( dbc.Col( dash_table.DataTable( id="fieldTable", style_as_list_view=True, editable=False, style_table={ "overflowY": "scroll", "width": "100%", "minWidth": "100%", }, style_header={"backgroundColor": "#f8f5f0", "fontWeight": "bold"}, style_cell={"textAlign": "center", "padding": "8px"}, ), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 8, "offset": 0}, lg={"size": 8, "offset": 0}, xl={"size": 8, "offset": 0}, ), justify="center", ), html.Br(), dbc.Row(dbc.Col(html.H3(style={"margin-bottom": "1%"}, children="Pitching"))), dbc.Row( dbc.Col( html.Div(id="pitch-data"), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 10, "offset": 0}, lg={"size": 10, "offset": 0}, xl={"size": 10, "offset": 0}, ), justify="center", ), html.Br(), dbc.Row(dbc.Col(html.H3(children="Player Analysis"))), # Player dropdown menu dbc.Row( [ dbc.Row( dbc.Col( html.H4( style={"text-align": "center"}, children="Select Position:" ), xs={"size": "auto", "offset": 0}, sm={"size": "auto", "offset": 0}, md={"size": "auto", "offset": 0}, lg={"size": "auto", "offset": 0}, xl={"size": "auto", "offset": 0}, ) ), dbc.Row( dbc.Col( dcc.Dropdown( style={ "margin-left": "5px", "text-align": "center", "font-size": "18px", "width": "100px", }, id="pos-dropdown", clearable=False, ), xs={"size": "auto", "offset": 0}, sm={"size": "auto", "offset": 0}, md={"size": "auto", "offset": 0}, lg={"size": "auto", "offset": 0}, xl={"size": "auto", "offset": 0}, ) ), ], no_gutters=True, ), dbc.Row(dbc.Col(html.H4(children="Pitching Performance"))), # Pitching and Fielding graphs, Pitching graphs are set in a subplot dcc.Graph(id='field-graphs', config={'displayModeBar': False}) dbc.Row( dbc.Col( html.Div(id="pitch-graphs"), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 12, "offset": 0}, lg={"size": 12, "offset": 0}, xl={"size": 12, "offset": 0}, ) ), dbc.Row( dbc.Col( dcc.Graph(id="field-graph", config={"displayModeBar": False}), xs={"size": 12, "offset": 0}, sm={"size": 12, "offset": 0}, md={"size": 12, "offset": 0}, lg={"size": 12, "offset": 0}, xl={"size": 12, "offset": 0}, ) ), ], className="app-page", )

35.843956

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16,309

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0.160903

0.122973

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0.691216

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0

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16,309

454

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35.922907

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null

0

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null

0

1

2b028209a00a7c4331c52f1ca61c13b4b8eaf902

107

py

Python

parquet_metadata/test_parquet_metadata.py

dzamo/parquet-metadata

221bff0253bcaefc7c95e6e16ae376e3bba6ee9f

[ "Apache-2.0" ]

11

2018-09-11T02:56:32.000Z

2022-02-16T18:49:39.000Z

parquet_metadata/test_parquet_metadata.py

dzamo/parquet-metadata

221bff0253bcaefc7c95e6e16ae376e3bba6ee9f

[ "Apache-2.0" ]

null

parquet_metadata/test_parquet_metadata.py

dzamo/parquet-metadata

221bff0253bcaefc7c95e6e16ae376e3bba6ee9f

[ "Apache-2.0" ]

4

2019-05-30T22:44:33.000Z

2022-02-16T18:49:40.000Z

from . import parquet_metadata def test_smoke_test(): parquet_metadata.dump('parquets/types.parquet')

21.4

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null

0

1

0

1

0

1

0

7

2b05abd8c972d299aef12d9ffb9c918cfcc45604

5,956

py

Python

src/pylightnix/repl.py

stagedml/pylightnix

b475057f03db1d0b975500d079745c4de9c42069

[ "Apache-2.0" ]

9

2020-01-25T12:03:12.000Z

2021-10-12T08:22:33.000Z

src/pylightnix/repl.py

stagedml/pylightnix

b475057f03db1d0b975500d079745c4de9c42069

[ "Apache-2.0" ]

1

2020-02-16T10:47:34.000Z

2020-02-20T17:11:45.000Z

src/pylightnix/repl.py

stagedml/pylightnix

b475057f03db1d0b975500d079745c4de9c42069

[ "Apache-2.0" ]

null

# Copyright 2020, Sergey Mironov # # 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. """ Repl module defines variants of `instantiate` and `realize1` functions, which are suitable for REPL shells. Repl-friendly wrappers (see `repl_realize`) could pause the computation, save the Pylightnix state into a variable and return to the REPL's main loop. At this point user could alter the state of the whole system. Finally, `repl_continue` or `repl_cancel` could be called to either continue or cancel the realization. """ from pylightnix.utils import ( dirrm, timestring, concat ) from pylightnix.types import (Dict, Closure, Context, Derivation, RRef, DRef, List, Tuple, Optional, Generator, Path, Build, Union, Any, BuildArgs, RealizeArg, SPath, StorageSettings) from pylightnix.core import (realizeSeq, RealizeSeqGen, mkrealization) class ReplHelper: def __init__(self, gen:RealizeSeqGen)->None: self.gen:Optional[RealizeSeqGen]=gen self.S:Optional[StorageSettings]=None self.dref:Optional[DRef]=None self.context:Optional[Context]=None self.drv:Optional[Derivation]=None self.result:Optional[Context]=None self.rarg:Optional[RealizeArg]=None ERR_INVALID_RH="Neither global, nor user-defined ReplHelper is valid" ERR_INACTIVE_RH="REPL session is not paused or was already unpaused" PYLIGHTNIX_REPL_HELPER:Optional[ReplHelper]=None def repl_continueAll(out_paths:Optional[List[Path]]=None, out_rrefs:Optional[List[RRef]]=None, rh:Optional[ReplHelper]=None)->Optional[Context]: global PYLIGHTNIX_REPL_HELPER if rh is None: rh=PYLIGHTNIX_REPL_HELPER assert rh is not None, ERR_INVALID_RH assert rh.gen is not None, ERR_INACTIVE_RH assert rh.dref is not None, ERR_INACTIVE_RH assert rh.context is not None, ERR_INACTIVE_RH assert rh.drv is not None, ERR_INACTIVE_RH assert rh.S is not None, ERR_INACTIVE_RH try: rrefs:Optional[List[RRef]] if out_paths is not None: assert out_rrefs is None rrefs=[mkrealization(rh.dref,rh.context,p,rh.S) for p in out_paths] elif out_rrefs is not None: assert out_paths is None rrefs=out_rrefs else: rrefs=None rh.S,rh.dref,rh.context,rh.drv,rh.rarg=rh.gen.send((rrefs,False)) except StopIteration as e: rh.gen=None rh.result=e.value return repl_result(rh) def repl_continueMany(out_paths:Optional[List[Path]]=None, out_rrefs:Optional[List[RRef]]=None, rh:Optional[ReplHelper]=None)->Optional[List[RRef]]: ctx=repl_continueAll(out_paths,out_rrefs,rh) if ctx is None: return None assert len(ctx)==1, f"Expected a single-targeted closure" return ctx[list(ctx.keys())[0]] def repl_continue(out_paths:Optional[List[Path]]=None, out_rrefs:Optional[List[RRef]]=None, rh:Optional[ReplHelper]=None)->Optional[RRef]: rrefs=repl_continueMany(out_paths,out_rrefs,rh) if rrefs is None: return None assert len(rrefs)==1, f"Expected a single-result derivation" return rrefs[0] def repl_realize(closure:Union[Closure,Tuple[Any,Closure]], force_interrupt:Union[List[DRef],bool]=True, realize_args:Dict[DRef,RealizeArg]={})->ReplHelper: """ TODO Example: ```python rh=repl_realize(instantiate(mystage), force_interrupt=True) # ^^^ `repl_realize` returnes the `ReplHelper` object which holds the state of # incomplete realization b:Build=repl_build() # ^^^ Access it's build object. Now we may think that we are inside the # realization function. Lets do some hacks. with open(join(build_outpath(b),'artifact.txt'), 'w') as f: f.write("Fooo") repl_continueBuild(b) rref=repl_rref(rh) # ^^^ Since we didn't program any other pasues, we should get the usual RRef # holding the result of our hacks. ``` """ # FIXME: define a Closure as a datatype and simplify the below check closure_:Closure if isinstance(closure,tuple) and len(closure)==2: closure_=closure[1] # type:ignore else: closure_=closure # type:ignore global PYLIGHTNIX_REPL_HELPER force_interrupt_:List[DRef]=[] if isinstance(force_interrupt,bool): if force_interrupt: force_interrupt_=closure_.targets elif isinstance(force_interrupt,list): force_interrupt_=force_interrupt else: assert False, "Invalid argument" rh=ReplHelper(realizeSeq(closure_,force_interrupt_,realize_args=realize_args)) PYLIGHTNIX_REPL_HELPER=rh assert rh.gen is not None, ERR_INACTIVE_RH try: rh.S,rh.dref,rh.context,rh.drv,rh.rarg=next(rh.gen) except StopIteration as e: rh.gen=None rh.result=e.value return rh def repl_result(rh:ReplHelper)->Optional[Context]: return rh.result def repl_rrefs(rh:ReplHelper)->Optional[List[RRef]]: ctx=repl_result(rh) if ctx is None: return None assert len(ctx)==1 return ctx[list(ctx.keys())[0]] def repl_rref(rh:ReplHelper)->Optional[RRef]: rrefs=repl_rrefs(rh) if rrefs is None: return None assert len(rrefs)==1 return rrefs[0] def repl_cancel(rh:Optional[ReplHelper]=None)->None: global PYLIGHTNIX_REPL_HELPER if rh is None: rh=PYLIGHTNIX_REPL_HELPER assert rh is not None, ERR_INVALID_RH try: assert rh.gen is not None while True: rh.gen.send((None,True)) except StopIteration as e: rh.gen=None

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0.023873

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0

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5,956

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0

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false

0

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0.00885

0.212389

0

null

0

null

0

1

0

1

2b078da4ba018d0ed23b38cf26025965f628a808

3,658

py

Python

main.py

AuroraBTH/minecraft-modpack-randomizer

797fb6a438a3365da69fbcbc22d856668a90ed27

[ "MIT" ]

null

main.py

AuroraBTH/minecraft-modpack-randomizer

797fb6a438a3365da69fbcbc22d856668a90ed27

[ "MIT" ]

null

main.py

AuroraBTH/minecraft-modpack-randomizer

797fb6a438a3365da69fbcbc22d856668a90ed27

[ "MIT" ]

null

from bs4 import BeautifulSoup from requests import get import json def get_amount_of_pages(minecraft_version): initial_site_response = get("https://www.curseforge.com/minecraft/mc-mods?filter-game-version=" + minecraft_version + "&filter-sort=5&") soup = BeautifulSoup(initial_site_response.text, "html.parser") amount_of_pages = soup.find('li', class_="dots").find_next_sibling().text return int(amount_of_pages) def get_project_and_author_id(mod): mod_id = mod.find("a", class_="button--download").get("data-nurture-data") author_id = mod.find("a", class_="button--download").get("data-nurture-data") if mod_id is None: mod_id = json.loads(mod.find("a", class_="button--download").get("data-exp-nurture"))["ProjectID"] author_id = json.loads(mod.find("a", class_="button--download").get("data-exp-nurture"))["AuthorID"] else: mod_id = json.loads(mod_id)["ProjectID"] author_id = json.loads(author_id)["AuthorID"] return [mod_id, author_id] def write_mods_to_json(minecraft_version, file_name): domain = "https://www.curseforge.com" page_number = 1 amount_of_pages = get_amount_of_pages(minecraft_version) mod_list = [] while page_number <= amount_of_pages: url = "https://www.curseforge.com/minecraft/mc-mods?filter-game-version=" + minecraft_version + "&filter-sort=5&page=" + str(page_number) response = get(url) data = BeautifulSoup(response.text, "html.parser") list_of_mods = data.find_all("li", class_="project-list-item") for mod in list_of_mods: id_list = get_project_and_author_id(mod) project_name = mod.find("h2", class_="list-item__title").text.strip() project_id = id_list[0] project_author_id = id_list[1] project_category = mod.find("a", class_="category__item")["title"].strip() project_description = mod.find("div", class_="list-item__description").p.text.strip() project_downloads = int(mod.find("span", class_="count--download").text.strip().replace(",", "")) project_link = mod.find("div", class_="list-item__details").a["href"] mod_data = {} mod_data["id"] = project_id mod_data["name"] = project_name mod_data["author_id"] = project_author_id mod_data["category"] = project_category mod_data["description"] = project_description mod_data["downloads"] = project_downloads mod_data["link"] = domain + project_link mod_list.append(mod_data) progress_percent = round((page_number / amount_of_pages) * 100, 2) print("Done with page " + str(page_number) + "/" + str(amount_of_pages) + " (" + str(progress_percent) + "%)") page_number = page_number + 1 with open(file_name, "w") as f: amount_of_mods = len(mod_list) pretty_json = json.loads(json.JSONEncoder().encode(mod_list)) f.write(json.dumps(pretty_json, indent=4)) print("Done indexing " + str(amount_of_mods) + " mods, see " + file_name + " for more details.") user_version = input("0: 1.7.10\n1: 1.12.2\n_________\n->") file_name = input("Name on file output (default is data.json):\n->") if file_name == "": file_name = "data.json" if user_version == "0": minecraft_1_7_10 = "2020709689%3A4449" write_mods_to_json(minecraft_1_7_10, file_name) elif user_version == "1": minecraft_1_12_2 = "2020709689%3A6756" write_mods_to_json(minecraft_1_12_2, file_name)

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0.642701

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0.252033

0.03653

0.047489

0.02968

0.321005

0.261187

0.16621

0

0.024721

0.214872

3,658

83

147

44.072289

0.737813

0

0.21035

0.012028

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1

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0

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0.03125

0

null

0

null

0

1

0

2b09394715e3c0dcf590faefc51ab0a74f18287b

540

py

Python

product/views/brand_details.py

Rafeen/Inventory-Management-and-POS

c6b93fd83e76d8cdee1bdbe1042a29b23bfc36ac

[ "MIT" ]

null

product/views/brand_details.py

Rafeen/Inventory-Management-and-POS

c6b93fd83e76d8cdee1bdbe1042a29b23bfc36ac

[ "MIT" ]

10

2019-07-03T21:28:41.000Z

2022-01-13T01:13:35.000Z

product/views/brand_details.py

Rafeen/Inventory-Management-and-POS

c6b93fd83e76d8cdee1bdbe1042a29b23bfc36ac

[ "MIT" ]

null

from django.shortcuts import render, redirect, get_object_or_404 from product.models.brand_model import Brand from django.contrib.auth.decorators import login_required @login_required(login_url='/login/') def brand_detail_view(request, id): """ This view renders User Detail page with a details of selected user """ brand_obj = get_object_or_404(Brand, brand_id=id) context = { "brand": brand_obj, "title": "Category Details" } return render(request, "brand_details.html", context)

21.6

74

0.709259

72

540

5.097222

0.555556

0.054496

0.059946

0.076294

0

0.013857

0.198148

540

24

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22.5

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0.454545

0

null

0

null

0

1

0

2b0a6f1bbc8afafe4db77b3247308ff00dd67a64

1,264

py

Python

1-100q/40.py

rampup01/Leetcode

8450a95a966ef83b24ffe6450f06ce8de92b3efb

[ "MIT" ]

990

2018-06-05T11:49:22.000Z

2022-03-31T08:59:17.000Z

1-100q/40.py

rampup01/Leetcode

8450a95a966ef83b24ffe6450f06ce8de92b3efb

[ "MIT" ]

1

2021-11-01T01:29:38.000Z

1-100q/40.py

rampup01/Leetcode

8450a95a966ef83b24ffe6450f06ce8de92b3efb

[ "MIT" ]

482

2018-06-12T22:16:53.000Z

2022-03-29T00:23:29.000Z

''' Given a collection of candidate numbers (candidates) and a target number (target), find all unique combinations in candidates where the candidate numbers sums to target. Each number in candidates may only be used once in the combination. Note: All numbers (including target) will be positive integers. The solution set must not contain duplicate combinations. Example 1: Input: candidates = [10,1,2,7,6,1,5], target = 8, A solution set is: [ [1, 7], [1, 2, 5], [2, 6], [1, 1, 6] ] ''' class Solution(object): def combinationSum2(self, candidates, target): """ :type candidates: List[int] :type target: int :rtype: List[List[int]] """ result = [] candidates.sort() def recursive(candidates, target, currList, index): if target < 0: return if target == 0: result.append(currList) return previous = -1 for start in range(index, len(candidates)): if previous != candidates[start]: recursive(candidates, target - candidates[start], currList + [candidates[start]], start+1) previous = candidates[start] recursive(candidates, target, [], 0) return result

26.893617

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152

1,264

5.072368

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0.283228

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0.352941

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null

0

null

0

1

0

2b0caaaf1b41e4b4941d55d16c265dd9df819b1f

8,651

py

Python

src/clustar_project/clustarray.py

jz5jx/Test_Repo

8796f45021943984ed02232fd34ff02e17123d71

[ "MIT" ]

1

2021-04-24T21:52:53.000Z

src/clustar_project/clustarray.py

jz5jx/Test_Repo

8796f45021943984ed02232fd34ff02e17123d71

[ "MIT" ]

null

src/clustar_project/clustarray.py

jz5jx/Test_Repo

8796f45021943984ed02232fd34ff02e17123d71

[ "MIT" ]

null

import warnings import numpy as np import itertools class ClustArray: ''' Class for working with data from FITS images Initialized from a numpy array from an image Methods for denoising images ''' def __init__(self, np_array): self.im_array = np_array self.noise_est = None self.denoised_arr = None def circle_crop(self, rad_factor = 1.0): '''Function to crop square images to a circle Params ------ rad_factor: float multiple allowing change to size of circle_crop default is 1 value equal to 0.7 crops to a circle with radius that is 70% as large as the max image radius values < 0 not allowed values >= sqrt(2) will return original image Outputs ------- new_imdata: np array of same size as image data array, but with values outside radius set to nan; sets self.denoised_arr to equal this array ''' if rad_factor < 0: raise ValueError('rad_factor must be >= 0') if self.denoised_arr is None: new_imdata = self.im_array.copy() else: new_imdata = self.denoised_arr.copy() rad = (new_imdata.shape[0]/2) rad_sq = (rad*rad_factor)**2 for ix,iy in np.ndindex(new_imdata.shape): if (ix - rad)**2 + (iy - rad)**2 > rad_sq: new_imdata[ix, iy] = np.nan self.denoised_arr = new_imdata return new_imdata def pb_multiply(self, pb_array): '''Function to multiply a FITS image by a .pb file to deemphasize edges Inputs ------ pb_array: numpy array from a .pb file Outputs ------- new_imdata: np array of same size as image data array consisting of elementwise multiplication of image and pb file; sets self.denoised_arr to equal this array ''' if self.denoised_arr is None: imdata = self.im_array.copy() else: imdata = self.denoised_arr.copy() new_imdata = np.multiply(imdata, pb_array) self.denoised_arr = new_imdata return new_imdata def get_noise_level(self, nchunks = 3, rms_quantile = 0): '''Calculates estimated noise level in image intensity Stores value in FitsImage object noise attribute Arguments --------- nchunks: int number of chunks to use in grid, must be odd rms_quantile: float in range [0, 1] indicating quantile of chunk RMS to use for noise level (0 = min RMS, 0.5 = median, etc) Returns ------- noise: float estimated noise in image intensity values; sets self.noise_est to this value ''' if self.denoised_arr is None: imdata = self.im_array.copy() warnings.warn('Calculating noise level from uncleaned image') else: imdata = self.denoised_arr.copy() #now break the image into chunks and do the same analysis; # one of the chunks should have no signal in and give you an estimate of the noise (= rms).# number of chunks in each direction: # an odd value is used so that the centre of the image does not correspond to the edge of chunks; # when you ask for observations with ALMA, you usually specify that the object of interest be in the # center of your image. size = [i//nchunks for i in imdata.shape] remain = [i % nchunks for i in imdata.shape] chunks = dict() k = 0 for j,i in itertools.product(range(nchunks),range(nchunks)): chunks[k] = size.copy() k += 1# next, account for when the image dimensions are not evenly divisible by `nchunks`. row_remain, column_remain = 0, 0 for k in chunks: if k % nchunks < remain[0]: row_remain = 1 if k // nchunks < remain[1]: column_remain = 1 if row_remain > 0: chunks[k][0] += 1 row_remain -= 1 if column_remain > 0: chunks[k][1] += 1 column_remain -= 1# with that in hand, calculate the lower left corner indices of each chunk indices = dict() for k in chunks: indices[k] = chunks[k].copy() if k % nchunks == 0: indices[k][0] = 0 elif k % nchunks != 0: indices[k][0] = indices[k-1][0] + chunks[k][0] if k >= nchunks: indices[k][1] = indices[k-nchunks][1] + chunks[k][1] else: indices[k][1] = 0 stddev_chunk = dict() rms_chunk = dict() for k in chunks: i,j = indices[k] di,dj = chunks[k] x = imdata[i:i+di,j:j+dj] stddev_this = np.nanstd(x) rms_this = np.sqrt(np.nanmean(x**2)) stddev_chunk[k] = stddev_this rms_chunk[k] = rms_this noise = np.quantile(list(rms_chunk.values()), q = rms_quantile) self.noise_est = noise return(noise) def denoise(self, pb_array = None, rad_factor = 1.0, rms_quantile = 0, grid_chunks = 3): '''Wrapper function to perform entire denoising process Crops image to a circle, multiplies by a pb file (if desired), and calculates RMS noise level Inputs ------ im_array: 2d array representing a FITS image data pb_array: optional numpy array from a .pb file Params ------ rad_factor: float multiple allowing change to size of circle_crop default is 1 value equal to 0.7 crops to a circle with radius that is 70% as large as the max image radius values < 0 not allowed values >= sqrt(2) will return original image grid_chunks: int number of chunks to use in grid, must be odd rms_quantile: float in range [0, 1] indicating quantile of chunk RMS to use for noise level (0 = min RMS, 0.5 = median, etc) Outputs ------- ''' self.circle_crop(rad_factor) if pb_array is not None: self.pb_multiply(pb_array) noise_lvl = self.get_noise_level() return(noise_lvl) def extract_subgroup(self, group_indices, square = True, buffer = 0.0): '''Function for extracting a subgroup of an image Inputs ------ group_indices: list containing indices of subgroup [row_min, row_max, col_min, col_max] Params ------ square: if True, widen shorter axis range to make subgroup a square buffer: fraction to add to each dimension (e.g. if subgroup is 200x200 pixels, buffer = 0.1 will return 220x220 pixels) ''' row_min = group_indices[0] row_max = group_indices[1] col_min = group_indices[2] col_max = group_indices[3] if square: diff = (row_max - row_min) - (col_max - col_min) if diff == 0: #already square pass elif diff < 0: #adjust row min/max row_min += int(np.floor(diff/2)) row_max -= int(np.ceil(diff/2)) else: #adjust col min/max col_min -= int(np.floor(diff/2)) col_max += int(np.ceil(diff/2)) buffer_width = int(buffer*(col_max - col_min)/2) buffer_height = int(buffer*(row_max - row_min)/2) row_min -= buffer_height row_max += buffer_height col_min -= buffer_width col_max += buffer_width subgroup = self.im_array[row_min:row_max, col_min:col_max] return subgroup def plot_subgroup(self, group_indices, square = True, buffer = 0.0, colorbar = True): '''Function for plotting a subgroup of an image Inputs ------ group_indices: list containing indices of subgroup [row_min, row_max, col_min, col_max] Params ------ square: if True, widen shorter axis range to make subgroup a square buffer: fraction to add to each dimension (e.g. if subgroup is 200x200 pixels, buffer = 0.1 will return 220x220 pixels) colorbar: boolean indicating whether or not to include a colorbar with the plot ''' subgroup = self.extract_subgroup(group_indices, square, buffer) plt.imshow(subgroup, origin='lower') if colorbar: plt.colorbar()

35.310204

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0.204953

0.022416

0.034247

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0.401619

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0.337069

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0

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8,651

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false

0.00885

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0

null

0

null

0

1

0

2b0d1ad6e91ffdcea74efa0272a18d860ad0c2ae

7,151

py

Python

rpa_logger/task.py

kangasta/rpa_logger

63fb9d2472cc8039b6d794c5a09f4fbb77f5ac23

[ "MIT" ]

null

rpa_logger/task.py

kangasta/rpa_logger

63fb9d2472cc8039b6d794c5a09f4fbb77f5ac23

[ "MIT" ]

null

rpa_logger/task.py

kangasta/rpa_logger

63fb9d2472cc8039b6d794c5a09f4fbb77f5ac23

[ "MIT" ]

null

'''Constants and helpers for describing RPA tasks and their status. ''' from collections import Counter from dataclasses import dataclass from typing import Any, Dict, Hashable, List, Union from uuid import uuid4 from .utils import timestamp from .utils.output import OutputText STARTED = 'STARTED' SUCCESS = 'SUCCESS' IGNORED = 'IGNORED' FAILURE = 'FAILURE' ERROR = 'ERROR' SKIPPED = 'SKIPPED' STATUSES = (STARTED, SUCCESS, IGNORED, FAILURE, ERROR, SKIPPED,) @dataclass class BaseTask: '''Base class to define common functionality of `rpa_logger.task.Task` and `rpa_logger.task.TaskSuite` ''' type: str '''Used to identify task type, when task is presented as dict''' name: Union[str, None] '''Human-readable name of the task.''' status: str '''Describes state of the task. For example `SUCCESS` or `ERROR`.''' started: str '''UTC ISO-8601 timestamp that stores the start time of the task. Defined automatically when instance is created. ''' finished: Union[str, None] '''UTC ISO-8601 timestamp that stores the finish time of the task. Defined automatically when `rpa_logger.task.BaseTask.finish` method is called. ''' metadata: Dict[str, Any] '''Container for any other data stored in the task. Could, for example, contain information about the execution environment or data that was processed in the task. ''' def __init__(self, name: Union[str, None], status: str = STARTED) -> None: ''' Args: name: Name of the task. status: Status to use for the started task. ''' self.status = status self.name = name self.started = timestamp() self.finished = None self.metadata = dict() def finish(self, status) -> None: '''Set finished timestamp and end status of the task Args: status: Status to use for the finished task. ''' self.status = status self.finished = timestamp() def log_metadata(self, key: str, value: Any) -> None: '''Log metadata for the task. Args: key: Key for the metadata item. value: Value for the metadata item. If task data is saved as json or yaml, this value must be serializable. ''' self.metadata[key] = value @dataclass class Task(BaseTask): '''Defines single task and stores its output and metadata ''' output: List[OutputText] def __init__(self, name: str, status: str = STARTED): ''' Args: name: Name of the task. status: Status to use for the started task. ''' super().__init__(name, status) self.output = list() @property def type(self): return 'TASK' def log_output(self, text: str, stream: str = 'stdout') -> None: '''Append new `rpa_logger.utils.output.OutputText` to task output. Args: text: Output text content. stream: Output stream. Defaults to `stdout`. ''' self.output.append(OutputText(text, stream)) @dataclass class TaskSuite(BaseTask): '''Defines task suite and stores its tasks and metadata ''' description: Union[str, None] tasks: List[Task] def __init__( self, name: Union[str, None], description: str = None, status: str = STARTED): ''' Args: name: Name of the task suite. description: Description of the task suite. status: Status to use for the started task suite. ''' super().__init__(name, status) self.description = description self._tasks: Dict[Hashable, Task] = dict() @property def type(self): return 'SUITE' @property def tasks(self) -> List[Task]: # pylint: disable=function-redefined '''Return suites tasks as list sorted by the started time. ''' tasks = list(self._tasks.values()) tasks.sort(key=lambda i: i.started) return tasks @property def active_tasks(self) -> List[Task]: '''Return suites active tasks as list sorted by the started time. Task is active until it is finished; Task is active, if its finished variable is None. ''' return [i for i in self.tasks if i.finished is None] @property def task_status_counter(self) -> Counter: '''Return `Counter` instance initialized with suites task statuses. ''' return Counter(i.status for i in self._tasks.values()) def create_task( self, name: str, key: Hashable = None, status: str = STARTED): '''Create new task and store it in the suite tasks. Args: name: Name of the task. key: Key to identify the created task with. status: Status to use for the started task. Returns: Key of the created task. ''' if not key: key = uuid4() self._tasks[key] = Task(name, status) return key def log_task(self, status: str, name: str) -> None: '''Create and finish a new task. Args: name: Name of the task. status: Status to use for the finished task. Returns: Key of the created task. ''' key = self.create_task(name) self.finish_task(key, status) return key def finish_task(self, key: Hashable, status: str) -> None: '''Set finished timestamp and end status of the task Args: key: Key of the task to finish status: Status to use for the finished task. ''' return self._tasks[key].finish(status) def get_task(self, key: Hashable) -> Task: '''Get `rpa_logger.task.Task` with given key. Args: key: Key to try to find from suite. Returns: Task with matching key. ''' return self._tasks.get(key) def log_metadata( self, key: str, value: Any, task_key: Hashable = None) -> None: '''Log metadata into the task suite or any of its tasks. Args: key: Key for the metadata item. value: Value for the metadata item. If task data is saved as json or yaml, this value must be serializable. task_key: Key of a task to log metadata into. If None, metadata is logged to the suite. ''' if task_key: self._tasks[task_key].log_metadata(key, value) return super().log_metadata(key, value) def log_output(self, key: Hashable, text: str, stream: str = 'stdout') -> None: '''Append new `rpa_logger.utils.output.OutputText` to task output. Args: key: Key of the task to log output to. text: Output text content. stream: Output stream. Defaults to `stdout`. ''' self._tasks[key].log_output(text, stream)

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7,151

4.654668

0.173228

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0.028758

0.389319

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0

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0.322193

7,151

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false

0

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0.020408

0.459184

0

null

0

null

0

1

0

2b0d8b35ff7e943b202f21481e50e5769f2ff2f4

13,760

py

Python

src/graph_construction.py

chrisdxie/rice

c3e42822226af9ac28d95d434cd582386122b679

[ "MIT" ]

16

2021-07-01T16:18:26.000Z

2022-02-21T05:19:39.000Z

src/graph_construction.py

chrisdxie/rice

c3e42822226af9ac28d95d434cd582386122b679

[ "MIT" ]

1

2022-02-22T22:46:37.000Z

src/graph_construction.py

chrisdxie/rice

c3e42822226af9ac28d95d434cd582386122b679

[ "MIT" ]

1

2021-11-08T19:52:40.000Z

import sys, os import numpy as np import cv2 import torch import torch.nn.functional as F from torch_geometric.data import Data, Batch import torchvision.transforms as transforms from . import constants from .util import utilities as util_ def get_resnet50_fpn_model(pretrained=True, trainable_layer_names=[]): """Load ResNet50 + FPN model, pre-trained on COCO 2017.""" import torchvision.models.detection.backbone_utils as backbone_utils from torch.utils.model_zoo import load_url as load_url pretrained_backbone=False rn50_fpn = backbone_utils.resnet_fpn_backbone('resnet50', pretrained_backbone) # This is an instance of BackboneWithFPN: https://github.com/pytorch/vision/blob/master/torchvision/models/detection/backbone_utils.py#L11 if pretrained: model_urls = { 'maskrcnn_resnet50_fpn_coco': 'https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth', } pretrained_state_dict = load_url(model_urls['maskrcnn_resnet50_fpn_coco'], progress=True) # Hack to load only the backbone weights to the model, instead of all of MaskRCNN rn50_fpn_dict = rn50_fpn.state_dict() pretrained_dict = {k : pretrained_state_dict['backbone.' + k] for k in rn50_fpn_dict.keys()} rn50_fpn_dict.update(pretrained_dict) rn50_fpn.load_state_dict(rn50_fpn_dict) rn50_fpn = rn50_fpn.to(constants.DEVICE) # Freeze layers unless specified for name, parameter in rn50_fpn.named_parameters(): parameter.requires_grad_(False) for layer_name in trainable_layer_names: if layer_name in name: parameter.requires_grad_(True) return rn50_fpn def extract_rgb_img_features(model, img): """Run model (COCO2017 pre-trained ResNet50+FPN) on image. Args: model: output from get_resnet50_fpn_model() img: a [3 x H x W] torch.FloatTensor. Should have been standardized already Returns: an OrderedDict of torch.FloatTensors of shape [1, 256, H, W]. """ H,W = img.shape[1:] features = model(img.unsqueeze(0).to(constants.DEVICE)) for key in features.keys(): if key == 'pool': del features[key] continue features[key] = F.interpolate(features[key], size=(H,W), mode='bilinear') return features def FPN_feature_key(mask): """Compute which FPN layer to use. Args: mask: a [H x W] torch tensor with values in {0,1} Returns: a string """ x_min, y_min, x_max, y_max = util_.mask_to_tight_box(mask) roi_w = x_max-x_min+1; roi_h = y_max-y_min+1; roi_w = roi_w.float(); roi_h = roi_h.float() k = torch.floor(4 + torch.log2(torch.sqrt(roi_w*roi_h)/224)) # Taken from FPN paper k = min(max(int(k), 2), 5) features_key = str(k-2) # P2 -> '0', P3 -> '1', P4 -> '2', P5 -> '3' return features_key def crop_tensor_to_nchw(tensor, x_min, y_min, x_max, y_max, img_size=(64,64), mode='bilinear'): """Crop a tensor and reshape. Args: tensor: a torch.Tensor of shape [H x W], [C x H x W], or [N x C x H x W] x_min: int y_min: int x_max: int y_max: int x_axis: int y_axis: int img_size: tuple of (H, W) Returns: a torch.Tensor of shape [N x C x img_size[0] x img_size[1]] """ y_axis = tensor.ndim - 2 x_axis = tensor.ndim - 1 crop = torch.narrow(tensor, y_axis, y_min, y_max - y_min + 1) crop = torch.narrow(crop, x_axis, x_min, x_max - x_min + 1) while crop.ndim < 4: # NCHW crop.unsqueeze_(0) crop = F.interpolate(crop, img_size, mode=mode) return crop def construct_segmentation_graph(rgb_img_features, xyz_img, masks, create_edge_indices=True, compute_bg_node=True, neighbor_dist=10, padding_config=None, device=None): """Construct Graph from img + masks. Args: rgb_img_features: an OrderedDict of image features. Output of extract_rgb_img_features() xyz_img: a [3 x H x W] torch.FloatTensor. 3D point cloud from camera frame of reference masks: a [H x W] torch.FloatTensor of masks in {0, 1, ..., K-1}. HW OR a [N x H x W] torch.FloatTensor of masks in {0,1}. NHW compute_bg_node: bool. create_edge_indices: bool. neighbor_dist: int. Used to create edge indices. padding_config: a Python dictionary with padding parameters. Returns: graph: a torch_geometric.data.Data instance with keys: - rgb: a [N, 256, h, w] torch.FloatTensor of ResnNet50+FPN rgb image features - depth: a [N, 3, h, w] torch.FloatTensor. XYZ image - mask: a [N, h, w] torch.FloatTensor of values in {0, 1} - orig_masks: a [N, H, W] torch.FloatTensor of values in {0, 1}. Original image size. - crop_indices: a [N, 4] torch.LongTensor. xmin, ymin, xmax, ymax. """ if device is None: device = constants.DEVICE H, W = xyz_img.shape[1:] if padding_config is None: padding_config = { 'inference' : True, 'padding_percentage' : 0.25, 'new_H' : 64, 'new_W' : 64, } new_H = padding_config['new_H'] new_W = padding_config['new_W'] # Get relevant masks if masks.ndim == 2: orig_masks = util_.convert_mask_HW_to_NHW(masks, to_ignore=range(0,constants.OBJECTS_LABEL)) # [N x H x W] elif masks.ndim == 3: orig_masks = masks masks = util_.convert_mask_NHW_to_HW(orig_masks, start_label=constants.OBJECTS_LABEL) else: raise Exception(f"<masks> MUST be in HW or NHW format. Got shape: {masks.shape}...") N = orig_masks.shape[0] # Number of objects, and nodes in graph # Crop/Resize Masks/Depth rgb_channels_dim = 256 # hard-coded based on ResNet50+FPN output rgb_cr = torch.zeros((N, rgb_channels_dim, new_H, new_W), dtype=torch.float32, device=device) # + 1 for background depth_cr = torch.zeros((N, 3, new_H, new_W), dtype=torch.float32, device=device) mask_cr = torch.zeros((N, 1, new_H, new_W), dtype=torch.float32, device=device) crop_indices = torch.zeros((N, 4), dtype=torch.long, device=device) for i, mask in enumerate(orig_masks): x_min, y_min, x_max, y_max = util_.crop_indices_with_padding(mask, padding_config, inference=padding_config['inference']) crop_indices[i] = torch.stack([x_min, y_min, x_max, y_max]) features_key = FPN_feature_key(mask) layer_features = rgb_img_features[features_key] # Shape: [1 x C x h x w]. C = 256 rgb_cr[i] = crop_tensor_to_nchw(layer_features, x_min, y_min, x_max, y_max, img_size=(new_H, new_W))[0] depth_cr[i] = crop_tensor_to_nchw(xyz_img, x_min, y_min, x_max, y_max, img_size=(new_H, new_W), mode='nearest')[0] mask_cr[i] = crop_tensor_to_nchw(mask, x_min, y_min, x_max, y_max, img_size=(new_H, new_W), mode='nearest')[0] # Background node if compute_bg_node: crop_indices = torch.cat([torch.LongTensor([[0, 0, W-1, H-1]]).to(device), crop_indices], axis=0) rgb_cr = torch.cat([crop_tensor_to_nchw(rgb_img_features['3'], *crop_indices[0]), # deepest layer. Semantic features rgb_cr], axis=0) depth_cr = torch.cat([crop_tensor_to_nchw(xyz_img, *crop_indices[0]).to(device), depth_cr], axis=0) bg_orig_mask = (masks == 0).float().unsqueeze(0) # [1, H, W] orig_masks = torch.cat([bg_orig_mask, orig_masks], axis=0) mask_cr = torch.cat([crop_tensor_to_nchw(bg_orig_mask, *crop_indices[0]), mask_cr], axis=0) N += 1 # Check to make sure no masks are 0 valid_indices = [] for i in range(N): if torch.sum(mask_cr[i]) > 0: valid_indices.append(i) valid_indices = np.array(valid_indices) N = len(valid_indices) graph = Data(rgb=rgb_cr[valid_indices], depth=depth_cr[valid_indices], mask=mask_cr[valid_indices], orig_masks=orig_masks[valid_indices], crop_indices=crop_indices[valid_indices], ) if create_edge_indices: build_edge_index(graph, neighbor_dist=neighbor_dist) graph = graph.to(device) return graph def build_edge_index(graph, neighbor_dist): edge_index = util_.neighboring_mask_indices(graph.orig_masks, reduction_factor=1, neighbor_dist=neighbor_dist) edge_index = torch.cat([edge_index, edge_index.flip([1])], dim=0).T # Shape: [2 x E] graph.edge_index = edge_index.to(graph.mask.device) def remove_bg_node(data_list): """Return a list of new graphs with background node removed. Note: the RGB/Depth/Mask is not copied over, but assigned. Thus, losses can be applied to the new graphs (w/out BG nodes) and gradients will still flow through the old graphs. Args: graph: Can be a torch_geometric.Data instance, torch_geometric.Batch instance, or a List of torch_geometric.Data instances. Returns: Same data type as input. A copy of graphs, but without background nodes and update edge_indices. """ if isinstance(data_list, Data): input_type = 'Data' data_list = [data_list] elif isinstance(data_list, Batch): data_list = Batch.to_data_list(data_list) input_type = 'Batch' elif isinstance(data_list, list): input_type = 'list' else: raise NotImplementedError() # Note: data_list is now of type list new_data_list = [] for graph in data_list: # Double check to make sure background node hasn't already been removed if 'background_removed' in graph: raise Exception("Cannot remove background node if it has already been removed...") new_graph = Data() new_graph.rgb = graph.rgb[1:] new_graph.depth = graph.depth[1:] new_graph.mask = graph.mask[1:] new_graph.orig_masks = graph.orig_masks[1:] new_graph.crop_indices = graph.crop_indices[1:] new_graph.background_removed = True # Special cases if 'edge_index' in graph: edge_mask = torch.all(graph.edge_index != 0, dim=0) # [E] new_graph.edge_index = graph.edge_index[:, edge_mask] - 1 # -1 since we removed background if 'paths' in graph and 'split' in graph: # Splitting is stored new_graph.paths = {k - 1: graph.paths[k] for k in graph.paths.keys()} new_graph.split = graph.split[1:] new_data_list.append(new_graph) if input_type == 'Data': return new_data_list[0] elif input_type == 'Batch': return convert_list_to_batch(new_data_list) elif input_type == 'list': return new_data_list def convert_list_to_batch(graph_list, external_key='crop_indices'): """Convert list of graphs into a Batch(Data) instance. Args: graph_list: a Python list of torch_geometric.data.Data instances Returns: a torch_geometric.data.Batch instance """ for graph in graph_list: if 'x' not in graph.keys: # Batch.from_data_list needs 'x' to run correctly (to compute graph.num_nodes) graph.x = graph[external_key] return Batch.from_data_list(graph_list) def convert_batch_to_list(batch_graph): """Convert Batch(Data) instance into a list of Data instances. Undoes the convert_list_to_batch() function. Args: batch_graph: a torch_geometric.Batch instance Returns: a Python list of torch_geometric.data.Data instances. """ return Batch.to_data_list(batch_graph) def get_edge_graph(graph, rgb_img_features, xyz_img, padding_config=None): """Compute graph where each node is an edge of original graph. Creates a new graph such that each node in the new graph corresponds to an edge in the original graph. The new graph is constructed in the same way, but the crop_indices cover the union of the masks. This graph has no edges. Args: graph: a torch_geometric.data.Data instance rgb_img_features: an OrderedDict of image features. Output of extract_rgb_img_features() xyz_img: a [3 x H x W] torch.FloatTensor. 3D point cloud from camera frame of reference padding_config: a Python dictionary. Returns: a torch_geometric.Data instance """ union_orig_masks = torch.clamp(graph.orig_masks[graph.edge_index[0]] + \ graph.orig_masks[graph.edge_index[1]], max=1) # Shape: [E x H x W] return construct_segmentation_graph( rgb_img_features, xyz_img, union_orig_masks, compute_bg_node=False, create_edge_indices=False, padding_config=padding_config ) def add_zero_channel_to_masks(graph): """Add an empty channel of 0's to graph.mask.""" graph.mask = torch.cat([graph.mask, torch.zeros_like(graph.mask)], dim=1)

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null

0

null

0

1

0

2b0eda6bd56eca2e8afd3f6bc6ca556e0abd44cd

374

py

Python

Learning+Misc/Quantum-Circuits/deutsch_oracle.py

Gregory-Eales/QA-Reimplementations

bef0b3e67397a73c468e539c426c6629d398433b

[ "MIT" ]

1

2019-05-03T21:48:29.000Z

Learning+Misc/Quantum-Circuits/deutsch_oracle.py

Gregory-Eales/QA-Reimplementations

bef0b3e67397a73c468e539c426c6629d398433b

[ "MIT" ]

null

Learning+Misc/Quantum-Circuits/deutsch_oracle.py

Gregory-Eales/QA-Reimplementations

bef0b3e67397a73c468e539c426c6629d398433b

[ "MIT" ]

null

import cirq import numpy as np class DeutschOracle(object): def __init__(self): self.circuit = cirq.Circuit() self.x_gate = cirq.X self.hadmard_gate = cirq.H self.measure_gate = cirq.MeasurementGate def init_qubits(self, length=3): q = [cirq.GridQubit(i, j) for i in range(length) for j in range(length)] return q

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0.103896

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374

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0.545455

0

null

0

null

0

1

0

1

2b0f54ec033255faf4b2fe939082f385da0b5246

263

py

Python

example.py

pnsn/python_template

1e09b87149407ee8e5f5ed78f57244470cb1415d

[ "MIT" ]

null

example.py

pnsn/python_template

1e09b87149407ee8e5f5ed78f57244470cb1415d

[ "MIT" ]

null

example.py

pnsn/python_template

1e09b87149407ee8e5f5ed78f57244470cb1415d

[ "MIT" ]

null

#!/usr/bin/env python3 ''' All projects should use python 3 virtualenv symlinks python, python3, python3.x to environments python envoke with either python example.py or ./example.py ''' def main(): print("Hello PNSN") if __name__ == "__main__": main()

18.785714

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0.673004

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0.227848

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null

0

null

0

1

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2

2b1231e92f21f35984d0911fe8b686f6f0ee27b3

4,272

py

Python

python/tests/wind_settings_test.py

anth-dj/geog5003m_project

51caa4255a04cc7043dde9ff94e654c41fc1620c

[ "MIT" ]

null

python/tests/wind_settings_test.py

anth-dj/geog5003m_project

51caa4255a04cc7043dde9ff94e654c41fc1620c

[ "MIT" ]

null

python/tests/wind_settings_test.py

anth-dj/geog5003m_project

51caa4255a04cc7043dde9ff94e654c41fc1620c

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Wind Settings unit tests @author: Anthony Jarrett """ import unittest from python.src.simulation import particleframework class WindSettingsTestCase(unittest.TestCase): def test_init(self): # Initialize parameters north_percentage = 5 east_percentage = 10 south_percentage = 20 west_percentage = 65 # Create wind settings wind_settings = particleframework.WindSettings( north_percentage, east_percentage, south_percentage, west_percentage ) # Verify the wind settings self.assertIsNotNone(wind_settings) self.assertEqual(wind_settings.north_percentage, north_percentage) self.assertEqual(wind_settings.east_percentage, east_percentage) self.assertEqual(wind_settings.south_percentage, south_percentage) self.assertEqual(wind_settings.west_percentage, west_percentage) def test_sum_too_large(self): # Initialize parameters north_percentage = 5 east_percentage = 10 south_percentage = 20 west_percentage = 75 try: # Create wind settings particleframework.WindSettings( north_percentage, east_percentage, south_percentage, west_percentage ) self.fail() except Exception as e: self.assertIsNotNone(e) def test_sum_too_small(self): # Initialize parameters north_percentage = 5 east_percentage = 10 south_percentage = 20 west_percentage = 15 try: # Create wind settings particleframework.WindSettings( north_percentage, east_percentage, south_percentage, west_percentage ) self.fail() except Exception as e: self.assertIsNotNone(e) def test_get_next_north(self): # Initialize parameters north_percentage = 100 east_percentage = 0 south_percentage = 0 west_percentage = 0 # Create wind settings wind_settings = particleframework.WindSettings( north_percentage, east_percentage, south_percentage, west_percentage ) # Verify the next direction next_direction = wind_settings.get_next() self.assertEqual(next_direction, particleframework.Direction.NORTH) def test_get_next_east(self): # Initialize parameters north_percentage = 0 east_percentage = 100 south_percentage = 0 west_percentage = 0 # Create wind settings wind_settings = particleframework.WindSettings( north_percentage, east_percentage, south_percentage, west_percentage ) # Verify the next direction next_direction = wind_settings.get_next() self.assertEqual(next_direction, particleframework.Direction.EAST) def test_get_next_south(self): # Initialize parameters north_percentage = 0 east_percentage = 0 south_percentage = 100 west_percentage = 0 # Create wind settings wind_settings = particleframework.WindSettings( north_percentage, east_percentage, south_percentage, west_percentage ) # Verify the next direction next_direction = wind_settings.get_next() self.assertEqual(next_direction, particleframework.Direction.SOUTH) def test_get_next_west(self): # Initialize parameters north_percentage = 0 east_percentage = 0 south_percentage = 0 west_percentage = 100 # Create wind settings wind_settings = particleframework.WindSettings( north_percentage, east_percentage, south_percentage, west_percentage ) # Verify the next direction next_direction = wind_settings.get_next() self.assertEqual(next_direction, particleframework.Direction.WEST)

27.037975

75

0.617041

393

4,272

6.430025

0.157761

0.10922

0.075979

0.080332

0.815196

0.755837

0.722596

0

0.016445

0.330993

4,272

157

76

27.210191

0.86774

0.122659

0

0.673267

0

0.108911

1

0.069307

false

0

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0

0.09901

0

null

0

1

0

null

0

6

2b12bcc09d43893147348ccc3696625e690b010c

3,817

py

Python

src/views/botones/informacion/boton_informacion.py

julianVelandia/UI_RETEDECON

87b707f5c1553446fc92265db9da50f292e2f2d1

[ "MIT" ]

3

2022-02-27T02:15:52.000Z

2022-02-28T15:16:40.000Z

src/views/botones/informacion/boton_informacion.py

julianVelandia/UI_RETEDECON

87b707f5c1553446fc92265db9da50f292e2f2d1

[ "MIT" ]

null

src/views/botones/informacion/boton_informacion.py

julianVelandia/UI_RETEDECON

87b707f5c1553446fc92265db9da50f292e2f2d1

[ "MIT" ]

null

from PyQt5.QtWidgets import * from PyQt5.QtCore import * from PyQt5.QtGui import * #locals from .funciones_informacion import Funcion_informacion from src.views.botones.inicio.funciones import * class Boton_informacion(Funcion_informacion): def boton_informacion_manual(self, widget): self.informacion_manual = QToolButton(widget) self.informacion_manual.setText('Manual de Usuario') self.informacion_manual.setObjectName("button") # nombre de enlace a css self.informacion_manual.setIcon(QIcon('src/views/static/icons/icono_manual_usuario')) # icono self.informacion_manual.setIconSize(QSize(self.height/11, self.height/11)) self.informacion_manual.setToolButtonStyle(Qt.ToolButtonTextUnderIcon) self.informacion_manual.setGeometry(self.width/4.5, self.height/2.8, self.width/4, self.height/3.9) self.informacion_manual.clicked.connect(self.InformacionManual) self.informacion_manual.setVisible(False) def boton_informacion_fabricante(self, widget): self.informacion_fabricante = QToolButton(widget) self.informacion_fabricante.setText('Información del\nFabricante') self.informacion_fabricante.setObjectName("button") # nombre de enlace a css self.informacion_fabricante.setIcon(QIcon('src/views/static/icons/favicon3')) # icono self.informacion_fabricante.setIconSize(QSize(self.height/11, self.height/11)) self.informacion_fabricante.setToolButtonStyle(Qt.ToolButtonTextUnderIcon) self.informacion_fabricante.clicked.connect(self.InformacionFabricante) self.informacion_fabricante.setGeometry(self.width/1.9, self.height/2.8, self.width/4, self.height/3.9) self.informacion_fabricante.setVisible(False) def qr_informacion_qr(self, widget): self.informacion_qr = QToolButton(widget) self.informacion_qr.setObjectName("button_trasnparente") # nombre de enlace a css self.informacion_qr.setIcon(QIcon('src/views/static/icons/QRDRIVE.png')) # icono self.informacion_qr.setIconSize(QSize(self.height/5, self.height/5)) self.informacion_qr.setGeometry((self.width/2) - (self.height/7), (self.height/2) - (self.height/7), self.height/5, self.height/5) self.informacion_qr.setVisible(False) def label_informacion_label(self, widget): self.informacion_label = QLabel(widget) self.informacion_label.setObjectName("FabInfo") # nombre de enlace a css self.informacion_label.setText("GRACIAS POR USAR RETEDECON\n" "\n" "RETEDECON es fabricado por:\n" " - Julián C. Velandia\n" " - Sebastian Cubides\n" " - Brayan Guevara\n" " - Jhon B. Muñoz\n" "Con la coolaboración de: \n" " - Diego A. Tibaduiza\n" "Bajo la supervición y sustento de la Unidad De Gestion De La Innovación,\n" "Facultad De Ingeniería (Ingnova), de La Universidad Nacional De Colombia.\n\n" "Si desea contactarse con nosotros puede hacerlo a través de los siguientes medios:\n" " - Celular/Whatsapp: +57 313 8244012\n" " - E-Mail: scubidest@unal.edu.co\n\n" "Versión del Software: 1.0") self.informacion_label.setGeometry((self.width / 6), (self.height/9), self.width / 1.2, self.height/1.2) self.informacion_label.setVisible(False)

59.640625

113

0.632172

420

3,817

5.640476

0.309524

0.183622

0.079781

0.042212

0.295061

0.246095

0.192486

0.164626

0.087801

0

0.019445

0.272465

3,817

64

114

59.640625

0.833633

0.03039

0

0.035088

0

0.193557

0.036004

0

1

0.070175

false

0

0.087719

0

0.175439

0

null

0

null

0

1

0

2b13c9bdd22e18cff242d5292bbf3eb9e6c0efa1

263

py

Python

1030 Brick Layout.py

ansabgillani/binarysearchcomproblems

12fe8632f8cbb5058c91a55bae53afa813a3247e

[ "MIT" ]

null

1030 Brick Layout.py

ansabgillani/binarysearchcomproblems

12fe8632f8cbb5058c91a55bae53afa813a3247e

[ "MIT" ]

null

1030 Brick Layout.py

ansabgillani/binarysearchcomproblems

12fe8632f8cbb5058c91a55bae53afa813a3247e

[ "MIT" ]

null

class Solution: def solve(self, bricks, width, height): dp = [0]*(width+1) dp[0] = 1 for i in range(len(dp)): for brick in bricks: dp[i] += dp[i-brick] if i-brick >= 0 else 0 return dp[-1]**height

23.909091

59

0.48289

40

263

3.175

0.5

0.047244

0

0.042424

0.372624

263

10

60

26.3

0.727273

0

1

0.125

false

0

0.375

0

null

0

null

0

1

0

2b14e1f101cb9c3f4df93b0023935c69cc41ca60

6,801

py

Python

Malt/SourceTranspiler.py

panthavma/Malt

918bce21a131e472db7a136a3cb851bda9237a7f

[ "MIT-0", "MIT" ]

null

Malt/SourceTranspiler.py

panthavma/Malt

918bce21a131e472db7a136a3cb851bda9237a7f

[ "MIT-0", "MIT" ]

null

Malt/SourceTranspiler.py

panthavma/Malt

918bce21a131e472db7a136a3cb851bda9237a7f

[ "MIT-0", "MIT" ]

null

import textwrap #TODO: Send transpiler along graph types class SourceTranspiler(): @classmethod def get_source_name(self, name): name = name.replace('.','_').replace(' ', '_') name = '_' + ''.join(char for char in name if char.isalnum() or char == '_') while '__' in name: name = name.replace('__','_') return name @classmethod def asignment(self, name, asignment): pass @classmethod def declaration(self, type, size, name, initialization=None): pass @classmethod def global_reference(self, node_name, parameter_name): pass @classmethod def global_declaration(self, type, size, name, initialization=None): pass @classmethod def custom_io_reference(self, io, graph_io_type, name): pass @classmethod def preprocessor_wrap(self, define, content): return content @classmethod def custom_output_declaration(self, type, name, index, graph_io_type): pass @classmethod def parameter_reference(self, node_name, parameter_name, io_type): pass @classmethod def io_parameter_reference(self, parameter_name, io_type): return parameter_name @classmethod def is_instantiable_type(self, type): return True @classmethod def call(self, name, parameters=[], full_statement=False): pass @classmethod def result(self, result): pass @classmethod def scoped(self, code): pass class GLSLTranspiler(SourceTranspiler): @classmethod def asignment(self, name, asignment): return f'{name} = {asignment};\n' @classmethod def declaration(self, type, size, name, initialization=None): array = '' if size == 0 else f'[{size}]' asignment = f' = {initialization}' if initialization else '' return f'{type} {name}{array}{asignment};\n' @classmethod def global_reference(self, node_name, parameter_name): return f"U_0{node_name}_0_{self.get_source_name(parameter_name)}".replace('__','_') @classmethod def global_declaration(self, type, size, name, initialization=None): return 'uniform ' + self.declaration(type, size, name, initialization) @classmethod def custom_io_reference(self, io, graph_io_type, name): return f"{io.upper()}_{graph_io_type.upper()}_{''.join(char.upper() for char in name if char.isalnum())}" @classmethod def preprocessor_wrap(self, define, content): if define is None: return content return textwrap.dedent('''\ #ifdef {} {} #endif //{} ''').format(define, content.strip(), define) @classmethod def custom_output_declaration(self, type, name, index, graph_io_type): return f"layout (location = {index}) out {type} {self.custom_io_reference('OUT', graph_io_type, name)};\n" @classmethod def parameter_reference(self, node_name, parameter_name, io_type): return f'{node_name}_0_{parameter_name}' @classmethod def is_instantiable_type(self, type): return type.startswith('sampler') == False @classmethod def call(self, function, name, parameters=[], post_parameter_initialization = ''): src = '' for i, parameter in enumerate(function['parameters']): if parameter['io'] in ['out','inout']: initialization = parameters[i] src_reference = self.parameter_reference(name, parameter['name'], parameter['io']) src += self.declaration(parameter['type'], parameter['size'], src_reference, initialization) parameters[i] = src_reference src += post_parameter_initialization initialization = f'{function["name"]}({",".join(parameters)})' if function['type'] != 'void' and self.is_instantiable_type(function['type']): src += self.declaration(function['type'], 0, self.parameter_reference(name, 'result', 'out'), initialization) else: src += initialization + ';\n' return src @classmethod def result(self, result): return f'return {result};\n' @classmethod def scoped(self, code): import textwrap code = textwrap.indent(code, '\t') return f'{{\n{code}}}\n' class PythonTranspiler(SourceTranspiler): @classmethod def asignment(self, name, asignment): return f'{name} = {asignment}\n' @classmethod def declaration(self, type, size, name, initialization=None): if initialization is None: initialization = 'None' return self.asignment(name, initialization) @classmethod def global_reference(self, node_name, parameter_name): return f'PARAMETERS["{node_name}"]["{parameter_name}"]' @classmethod def global_declaration(self, type, size, name, initialization=None): return '' return self.declaration(type, size, name, initialization) @classmethod def custom_io_reference(self, io, graph_io_type, name): return self.io_parameter_reference(name, io) @classmethod def custom_output_declaration(self, type, name, index, graph_io_type): return self.declaration(type, 0, self.io_parameter_reference(name, 'out')) @classmethod def parameter_reference(self, node_name, parameter_name, io_type): if io_type: return f'{node_name}_parameters["{io_type.upper()}"]["{parameter_name}"]' else: return f'{node_name}_parameters["{parameter_name}"]' @classmethod def io_parameter_reference(self, parameter_name, io_type): return f'{io_type.upper()}["{parameter_name}"]' @classmethod def call(self, function, name, parameters=[], post_parameter_initialization = ''): import textwrap src = '' src += textwrap.dedent(f''' {name}_parameters = {{ 'IN' : {{}}, 'OUT' : {{}}, }} ''') for i, parameter in enumerate(function['parameters']): initialization = parameters[i] if initialization is None: initialization = 'None' parameter_reference = self.parameter_reference(name, parameter['name'], parameter['io']) src += f'{parameter_reference} = {initialization}\n' src += post_parameter_initialization src += f'run_node("{name}", "{function["name"]}", {name}_parameters)\n' return src @classmethod def result(self, result): return f'return {result}\n' @classmethod def scoped(self, code): import textwrap code = textwrap.indent(code, '\t') return f'if True:\n{code}'

32.385714

121

0.624761

737

6,801

5.583446

0.118046

0.125881

0.041312

0.050547

0.680194

0.614581

0.576428

0.520535

0.507412

0

0.001179

0.251728

6,801

209

122

32.54067

0.807428

0.005734

0

0.618182

0

0.012121

0.157077

0.06996

0.006061

0

0.004785

0

1

0.224242

false

0.060606

0.024242

0.10303

0.448485

0

null

0

null

0

1

0

1

0

1

0

3

2b157c7b02563c277a3b074729ba1a44f8eb0df3

113

py

Python

endpoints/projects.py

FLUX-SE/TrackedHQ_python_wrapper

d35f868698d0ba0cb2fdb820317f7460b154a6d0

[ "MIT" ]

null

endpoints/projects.py

FLUX-SE/TrackedHQ_python_wrapper

d35f868698d0ba0cb2fdb820317f7460b154a6d0

[ "MIT" ]

null

endpoints/projects.py

FLUX-SE/TrackedHQ_python_wrapper

d35f868698d0ba0cb2fdb820317f7460b154a6d0

[ "MIT" ]

null

from .base import Resource class Projects(Resource): def list(self): return self._get("/projects")

16.142857

37

0.672566

14

113

5.357143

0.785714

0

0.212389

113

6

38

18.833333

0.842697

0

0.079646

0

1

0.25

false

0

0.25

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0

1

0

null

0

1

0

null

0

1

0

1

0

5

2b15e166bdadb8379f269e4e1a5eb613b13e1d82

3,173

py

Python

src/feature_creation.py

aswain571/m5_forecasting

3b7fccd56a4c14c38bbcff6b11f82cd440132730

[ "MIT" ]

null

src/feature_creation.py

aswain571/m5_forecasting

3b7fccd56a4c14c38bbcff6b11f82cd440132730

[ "MIT" ]

null

src/feature_creation.py

aswain571/m5_forecasting

3b7fccd56a4c14c38bbcff6b11f82cd440132730

[ "MIT" ]

null

import pandas as pd import numpy as np import pickle from preprocess import process_ds from sklearn.preprocessing import LabelEncoder def transform_cat_feats(df): """makes null columns into unknown and cat columns are label encoded Args: df (pd.DataFrame): Dataframe with the sales data. Returns: Dataframe with the sales data including lag and rolling features. """ # nan_features = [ #'event_name_1', #'event_type_1', #'event_name_2', #'event_type_2',] # for feature in nan_features: # df[feature].fillna('unknown', inplace = True) cat = [ "item_id", "dept_id", "cat_id", "store_id", "state_id", "event_name_1", "event_type_1", "event_name_2", "event_type_2", ] for feature in cat: encoder = LabelEncoder() df[feature] = encoder.fit_transform(df[feature]) return df def calculate_time_features(df): """Clagged and rolling mean features of the sales data. Args: df (pd.DataFrame): Dataframe with the sales data. Returns: Dataframe with the sales data including lag and rolling features. """ dayLags = [28] lagSalesCols = [f"lag_{dayLag}" for dayLag in dayLags] for dayLag, lagSalesCol in zip(dayLags, lagSalesCols): df[lagSalesCol] = ( df[["id", "item_sales"]].groupby("id")["item_sales"].shift(dayLag) ) windows = [7, 28] for window in windows: for dayLag, lagSalesCol in zip(dayLags, lagSalesCols): df[f"rmean_{dayLag}_{window}"] = ( df[["id", lagSalesCol]] .groupby("id")[lagSalesCol] .transform(lambda x: x.rolling(window).mean()) ) return df def cat_ts_feats(df): """Build categorical and time series feats. Args: df (pd.Dataframe) : Dataframe with sales data Returns: Dataframe with sales data including categorical features and lag/rolling mean features """ df = transform_cat_feats(df) df = calculate_time_features(df) return df def get_test_train_data(): """Build train and test dataset. Test is used for inference Args: None Returns: train and test dataframes """ df = process_ds() df = cat_ts_feats(df) df = df.reset_index().set_index("date") # remove unused columns cols_not_used = ["id", "weekday", "d", "index"] df.drop(columns=cols_not_used, inplace=True) df.dropna(inplace=True) # convert T/F to boolean - lightgbm throws error otherwise df["is_weekend"] = df["is_weekend"].astype(int) df["no_sell_price"] = df["no_sell_price"].astype(int) print(df) train_start_date = "2014-04-24" train_end_date = "2016-04-23" test_start_date = "2016-04-24" test_end_date = "2016-05-23" df_train = df.loc[train_start_date:train_end_date] df_test = df.loc[test_start_date:test_end_date] # save train and test dataframes for later use df_train.to_pickle("../data/df_train.pkl") df_test.to_pickle("../data/df_test.pkl") if __name__ == "__main__": get_test_train_data()

25.58871

78

0.632524

423

3,173

4.524823

0.316785

0.032915

0.031348

0.043887

0.241902

0.22675

0.211076

0.163009

0

0.019027

0.254649

3,173

123

79

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0.790275

0.300347

0

0.081967

0

0.143612

0.011047

0

1

0.065574

false

0

0.081967

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0.196721

0.016393

0

null

0

null

0

1

0

2b175fc4f0131469f592b2c384a3aa753931cdfb

294

py

Python

setup.py

bzdvdn/sipuni-api-wrapper

44c799244388e65e9f455ed21dd16481be36f5f6

[ "MIT" ]

1

2019-12-05T14:28:31.000Z

setup.py

bzdvdn/sipuni-api-wrapper

44c799244388e65e9f455ed21dd16481be36f5f6

[ "MIT" ]

3

2020-03-24T17:55:25.000Z

2021-02-02T22:22:17.000Z

setup.py

bzdvdn/sipuni-api-wrapper

44c799244388e65e9f455ed21dd16481be36f5f6

[ "MIT" ]

null

from setuptools import setup, find_packages setup( name='sipuni-api', version='0.0.1.1', packages=find_packages(), install_requires=[ 'requests' ], author='bzdvdn', author_email='bzdv.dn@gmail.com', url='https://github.com/bzdvdn/sipuni-api-wrapper', )

21

55

0.642857

37

294

5

0.702703

0.12973

0

0.016878

0.193878

294

13

56

22.615385

0.763713

0

0.312925

0

1

0

true

0

0.083333

0

0.083333

0

null

0

null

0

1

0

1

2b178eeae032ec25548f56cb6c96df9b289d22b5

6,545

py

Python

cosmosis/output/fits_output.py

annis/cosmosis

55efc1bc2260ca39298c584ae809fa2a8e72a38e

[ "BSD-2-Clause" ]

2

2021-06-18T14:11:59.000Z

2022-02-23T19:19:36.000Z

cosmosis/output/fits_output.py

annis/cosmosis

55efc1bc2260ca39298c584ae809fa2a8e72a38e

[ "BSD-2-Clause" ]

2

2021-11-02T12:44:24.000Z

2022-03-30T15:09:48.000Z

cosmosis/output/fits_output.py

annis/cosmosis

55efc1bc2260ca39298c584ae809fa2a8e72a38e

[ "BSD-2-Clause" ]

2

2022-03-25T21:26:27.000Z

2022-03-29T06:37:46.000Z

from .output_base import OutputBase from . import utils import numpy as np import os from glob import glob from collections import OrderedDict try: import fitsio except ImportError: fitsio = None comment_indicator = "_cosmosis_comment_indicator_" final_metadata_indicator = "FINALMETA" unreserve_indicator = "UNRES" reserved_keys = [ "XTENSION", "BITPIX", "NAXIS", "NAXIS1", "NAXIS2", "PCOUNT", "GCOUNT", "TFIELDS", "TTYPE1", "COMMENT", ] def check_fitsio(): if fitsio is None: raise RuntimeError("You need to have the fitsio library installed to output FITS files. Try running: pip install --install-option=\"--use-system-fitsio\" git+git://github.com/joezuntz/fitsio") class FitsOutput(OutputBase): FILE_EXTENSION = ".fits" _aliases = ["fits"] def __init__(self, filename, rank=0, nchain=1, clobber=True): super(FitsOutput, self).__init__() #If filename already ends in .txt then remove it for a moment if filename.endswith(self.FILE_EXTENSION): filename = filename[:-len(self.FILE_EXTENSION)] if nchain > 1: filename = filename + "_{}".format(rank+1) self._filename = filename + self.FILE_EXTENSION self.filename_base = filename check_fitsio() self._fits = fitsio.FITS(self._filename, "rw", clobber=clobber) self._hdu = None #also used to store comments: self._metadata = OrderedDict() self._final_metadata = OrderedDict() def _close(self): self._flush_metadata(self._final_metadata) self._final_metadata={} self._fits.close() def _flush_metadata(self, metadata): for (key,(value,comment)) in list(metadata.items()): if key.startswith(comment_indicator): self._hdu.write_comment(value) elif comment: self._hdu.write_key(key, value, comment) else: self._hdu.write_key(key, value) def _begun_sampling(self, params): #write the name line self._fits.create_table_hdu(data=params, names=[c[0] for c in self.columns]) self._hdu = self._fits[-1] self._dtype = self._hdu.get_rec_dtype()[0] self._flush_metadata(self._metadata) self._metadata={} @staticmethod def is_reserved_fits_keyword(key): for k in reserved_keys: if key.upper().startswith(k): return True return False def _write_metadata(self, key, value, comment=''): #We save the metadata until we get the first #parameters since up till then the columns can #be changed if self.is_reserved_fits_keyword(key): key=unreserve_indicator + key self._metadata[key]= (value, comment) def _write_comment(self, comment): #save comments along with the metadata - nice as #preserves order self._metadata[comment_indicator + "_%d" % (len(self._metadata))] = (comment,None) def _write_parameters(self, params): row = np.core.records.fromarrays(params, dtype=self._dtype) row=np.atleast_1d(row) self._hdu.append(row) def _write_final(self, key, value, comment=''): #I suppose we can put this at the end - why not? if self.is_reserved_fits_keyword(key): key=unreserve_indicator + key self._final_metadata[key]= (value, final_metadata_indicator+comment) def name_for_sampler_resume_info(self): return self.filename_base + '.sampler_status' @classmethod def from_options(cls, options, resume=False): #look something up required parameters in the ini file. #how this looks will depend on the ini if resume: raise ValueError("Cannot resume from FITS output") filename = options['filename'] delimiter = options.get('delimiter', '\t') rank = options.get('rank', 0) nchain = options.get('parallel', 1) clobber = utils.boolean_string(options.get('clobber', True)) return cls(filename, rank, nchain, clobber=clobber) @classmethod def load_from_options(cls, options): check_fitsio() filename = options['filename'] cut = False if filename.endswith(cls.FILE_EXTENSION): filename = filename[:-len(cls.FILE_EXTENSION)] cut = True # first look for serial file if os.path.exists(filename+cls.FILE_EXTENSION): datafiles = [filename+cls.FILE_EXTENSION] elif os.path.exists(filename) and not cut: datafiles = [filename] else: datafiles = glob(filename+"_[0-9]*"+cls.FILE_EXTENSION) if not datafiles: raise RuntimeError("No datafiles found starting with %s!"%filename) #Read the metadata metadata = [] final_metadata = [] data = [] comments = [] column_names = None for datafile in datafiles: print('LOADING CHAIN FROM FILE: ', datafile) chain = [] chain_metadata = {} chain_final_metadata = {} chain_comments = [] f = fitsio.FITS(datafile, "r") hdu = f[1] chain = f[1].read() #convert to unstructured format chain = chain.view((chain.dtype[0], len(chain.dtype.names))) column_names = hdu.get_colnames() hdr = hdu.read_header() chain_comments = [r['comment'] for r in hdr.records() if r['name'].lower()=="comment"] for r in hdr.records(): key = r['name'] if key=='COMMENT': continue if key.startswith(unreserve_indicator): key = key[len(unreserve_indicator):] value = r['value'] key=key.lower() if r['comment'].startswith(final_metadata_indicator): chain_final_metadata[key] = value else: chain_metadata[key] = value data.append(np.array(chain)) metadata.append(chain_metadata) final_metadata.append(chain_final_metadata) comments.append(chain_comments) if column_names is None: raise ValueError("Could not find column names header in file starting %s"%filename) return column_names, data, metadata, comments, final_metadata

33.055556

201

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6,545

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0.28229

0.04438

0.019695

0.016544

0.089811

0.054622

0.030462

0

0.003902

0.295187

6,545

197

202

33.22335

0.821591

0.073644

0

0.087838

0

0.006757

0.086957

0.01058

0

1

0.087838

false

0

0.054054

0.006757

0.195946

0.006757

0

null

0

null

0

1

0

2b17cbd8a9488937054f8a24306f05f58eb7a8b6

2,757

py

Python

micromath/fibonacci/test_fibonacci.py

hedrox/micromath

0f300da914c844e5ff0775f25119909f748de635

[ "MIT" ]

null

micromath/fibonacci/test_fibonacci.py

hedrox/micromath

0f300da914c844e5ff0775f25119909f748de635

[ "MIT" ]

null

micromath/fibonacci/test_fibonacci.py

hedrox/micromath

0f300da914c844e5ff0775f25119909f748de635

[ "MIT" ]

null

import logging import json from server import app app.testing = True logging.disable(logging.ERROR) class TestFibonacci: def test_correct_fibonacci(self): with app.test_client() as client: body = {'number': 16} result = client.post('/api/v1/fibonacci', json=body) assert result.status_code == 200 data = json.loads(result.data) assert int(data['result']) == 610 assert data['error'] is None def test_invalid_attribute_type(self): with app.test_client() as client: body = {'number': None} result = client.post('/api/v1/fibonacci', json=body) assert result.status_code == 500 data = json.loads(result.data) assert data['result'] is None assert 'name' in data['error'] assert data['error']['name'] == 'ValidationError' body = {'number': '2'} result = client.post('/api/v1/fibonacci', json=body) assert result.status_code == 500 data = json.loads(result.data) assert data['result'] is None assert 'name' in data['error'] assert data['error']['name'] == 'ValidationError' def test_empty_body(self): with app.test_client() as client: body = {} result = client.post('/api/v1/fibonacci', json=body) assert result.status_code == 500 data = json.loads(result.data) assert data['result'] is None assert 'name' in data['error'] assert data['error']['name'] == 'ValidationError' def test_extra_attribute(self): with app.test_client() as client: body = {'number': 32, 'extra_key': 32} result = client.post('/api/v1/fibonacci', json=body) assert result.status_code == 500 data = json.loads(result.data) assert data['result'] is None assert 'name' in data['error'] assert data['error']['name'] == 'ValidationError' def test_no_body(self): with app.test_client() as client: result = client.post('/api/v1/fibonacci') assert result.status_code == 400 data = json.loads(result.data) assert data['result'] is None assert data['error'] == 'Data not provided' def test_invalid_api_version(self): with app.test_client() as client: body = {'number': 32} result = client.post('/api/v2/fibonacci', json=body) assert result.status_code == 404 data = json.loads(result.data) assert data['result'] is None assert data['error'] == 'API version v2 not found'

33.621951

64

0.564019

322

2,757

4.742236

0.177019

0.085134

0.073346

0.087099

0.787164

0.773412

0.734774

0.709234

0.663392

0.612312

0

0.021602

0.311571

2,757

81

65

34.037037

0.782929

0

0.571429

0

0.141095

0

0.396825

1

0.095238

false

0

0.047619

0

0.15873

0

null

0

1

0

1

0

null

0

4

2b1c1953cad2c24ae38087460d540f5ab88ef710

278

py

Python

app.py

M3nin0/selectToTex

423cfdafdd0bd391c30cbbf70386f74e93844c2f

[ "BSD-2-Clause" ]

4

2018-06-06T15:35:51.000Z

2020-01-19T15:47:23.000Z

app.py

M3nin0/selectToTex

423cfdafdd0bd391c30cbbf70386f74e93844c2f

[ "BSD-2-Clause" ]

null

app.py

M3nin0/selectToTex

423cfdafdd0bd391c30cbbf70386f74e93844c2f

[ "BSD-2-Clause" ]

null

from selecttotex.totex import Totex # Criando instância do SelectToTex tt = Totex() # Comandos que serão utilizados commands = ['SELECT * FROM aluno;', 'SELECT * FROM materia;', 'SELECT * FROM matricula;'] # Chama a função para a conversão tt.to_tex(commands, 'tabelas.txt')

25.272727

89

0.733813

37

278

5.486486

0.702703

0.147783

0

0.154676

278

10

90

27.8

0.86383

0.33813

0

0.427778

0

1

0

false

0

0.25

0

0.25

0

null

0

null

0

1

0

2b1d50c991de2a9641935aabe9ff1cac8d06aaa8

506

py

Python

demo/blog/migrations/0003_auto_20200424_2258.py

erdem/django-marshmallow

3b9f3cefd70d98e9348f5a69fa61837ca28be7a6

[ "MIT" ]

3

2020-05-05T16:17:34.000Z

2021-05-13T19:05:12.000Z

demo/blog/migrations/0003_auto_20200424_2258.py

erdem/django-marshmallow

3b9f3cefd70d98e9348f5a69fa61837ca28be7a6

[ "MIT" ]

3

2021-09-08T02:12:25.000Z

2022-03-12T00:36:59.000Z

demo/blog/migrations/0003_auto_20200424_2258.py

erdem/django-marshmallow

3b9f3cefd70d98e9348f5a69fa61837ca28be7a6

[ "MIT" ]

null

# Generated by Django 2.2.11 on 2020-04-24 22:58 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blog', '0002_tag_related_tags'), ] operations = [ migrations.RemoveField( model_name='tag', name='related_tags', ), migrations.AddField( model_name='tag', name='related_tags', field=models.ManyToManyField(null=True, to='blog.Tag'), ), ]

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2b1da2d3ed1a52018f6ec06f4c582bd00a0d9184

6,682

py

Python

python/vtool/maya_lib/ui.py

louisVottero/vtool

4e2592df5841829e790251dc6923e45c8d013091

[ "MIT" ]

3

2022-02-22T01:00:59.000Z

2022-03-07T16:19:27.000Z

python/vtool/maya_lib/ui.py

louisVottero/vtool

4e2592df5841829e790251dc6923e45c8d013091

[ "MIT" ]

4

2022-03-04T05:25:44.000Z

2022-03-11T04:51:35.000Z

python/vtool/maya_lib/ui.py

louisVottero/vtool

4e2592df5841829e790251dc6923e45c8d013091

[ "MIT" ]

1

2022-03-31T23:07:09.000Z

# Copyright (C) 2022 Louis Vottero louis.vot@gmail.com All rights reserved. from __future__ import absolute_import import maya.cmds as cmds import maya.utils import maya.mel as mel from maya.app.general.mayaMixin import MayaQWidgetBaseMixin, MayaQWidgetDockableMixin from maya import OpenMayaUI as omui from .. import qt_ui, qt from .. import util, util_file from .ui_lib import ui_fx, ui_shape_combo, ui_corrective from .ui_lib import ui_rig from .ui_lib import ui_anim from .ui_lib import ui_model from . import ui_core from ..process_manager import process from . import core from . import attr from . import space from . import geo from . import deform from . import rigs_util def load_into_tool_manager(window): if ToolManager._last_instance: parent_name = ToolManager._last_instance.parent().objectName() if parent_name.find('WorkspaceControl') > -1: window.show() window_name = window.parent().objectName() cmds.workspaceControl(window_name, e = True, tabToControl = (parent_name,-1))#, uiScript = command, li = False, retain = False) if not ToolManager._last_instance: window.show() #window_name = window.parent().objectName() #cmds.workspaceControl(window_name, e = True)#, tabToControl = (parent_name,-1))#, uiScript = command, li = False, retain = False) if hasattr(window, 'initialize_settings'): window.show() window.initialize_settings() def pose_manager(shot_sculpt_only = False): window = ui_rig.pose_manager(shot_sculpt_only) load_into_tool_manager(window) def shape_combo(): window = ui_rig.shape_combo() load_into_tool_manager(window) def picker(): window = ui_rig.picker() if ToolManager._last_instance: ToolManager._last_instance.add_tab(window, window.title) def tool_manager(name = None, directory = None): workspace_name = ToolManager.title + 'WorkspaceControl' ui_core.delete_workspace_control(workspace_name) manager = ToolManager(name) workspace_control = manager.title + 'WorkspaceControl' if not ui_core.was_floating(manager.title): tab_name = ui_core.get_stored_tab(manager.title) manager.show() ui_core.add_tab(workspace_control, tab_name) else: manager.show() if directory: manager.set_directory(directory) return manager def process_manager(directory = None): ui_core.delete_workspace_control(ui_rig.ProcessMayaWindow.title + 'WorkspaceControl') window = ui_rig.ProcessMayaWindow() if directory: window.set_directory(directory) window.show() return window def ramen(): ui_core.delete_workspace_control(ui_rig.RamenMayaWindow.title + 'WorkspaceControl') window = ui_rig.RamenMayaWindow() window.show() return window def script_manager(directory): ui_core.delete_workspace_control(ui_rig.ScriptMayaWindow.title + 'WorkspaceControl') window = ui_rig.ScriptMayaWindow() window.set_directory(directory) window.show() return window class ToolManager(ui_core.MayaDirectoryWindowMixin): #class ToolManager(ui_core.MayaDockMixin, qt_ui.BasicWidget): #class ToolManager(ui_core.MayaDockMixin,qt.QWidget): title = (util.get_custom('vetala_name', 'VETALA') + ' HUB') #_last_instance = None def __init__(self,name = None): if name: self.title = name self.default_docks = [] self.docks = [] super(ToolManager, self).__init__() self.setWindowTitle(self.title) ui_core.new_tool_signal.signal.connect(load_into_tool_manager) def _build_widgets(self): self.main_layout.setAlignment(qt.QtCore.Qt.AlignTop) header_layout = qt.QHBoxLayout() version = qt.QLabel('%s' % util_file.get_vetala_version()) version.setMaximumHeight(30) header_layout.addWidget(version) self.main_layout.addLayout(header_layout) self.rigging_widget = ui_rig.RigManager() self.main_layout.addWidget(self.rigging_widget) def add_tab(self, widget, name): self.add_dock(widget, name) def add_dock(self, widget , name): self.dock_window.add_dock(widget, name) def set_directory(self, directory): super(ToolManager, self).set_directory(directory) self.rigging_widget.set_directory(directory) class Dock(ui_core.MayaBasicMixin,qt_ui.BasicWindow): def __init__(self, name = None): self.docks = [] super(Dock, self).__init__() def _get_dock_widgets(self): children = self.children() found = [] for child in children: if isinstance(child, qt.QDockWidget): found.append(child) return found def _build_widgets(self): self.main_widget.setSizePolicy(qt.QSizePolicy.Minimum, qt.QSizePolicy.Minimum) self.centralWidget().hide() self.setTabPosition(qt.QtCore.Qt.TopDockWidgetArea, qt.QTabWidget.West) self.setDockOptions( self.AllowTabbedDocks) def add_dock(self, widget , name): docks = self._get_dock_widgets() for dock in docks: if dock.windowTitle() == name: dock.deleteLater() dock.close() old_parent = widget.parent() old_parent_name = None if old_parent: old_parent_name = old_parent.objectName() dock_widget = ui_core.MayaDockWidget(self) dock_widget.setWindowTitle(name) dock_widget.setWidget(widget) if old_parent_name and old_parent_name.find('Mixin') > -1: old_parent.close() cmds.deleteUI(old_parent_name) self.addDockWidget(qt.QtCore.Qt.TopDockWidgetArea, dock_widget) if docks: self.tabifyDockWidget( docks[-1], dock_widget) dock_widget.show() dock_widget.raise_() return dock_widget

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6,682

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null

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null

0

1

0

2b1e286ea315966366a86b5a9f5142b3ebdb896b

4,748

py

Python

xtbservice/models.py

cheminfo-py/xtbservice

d9227ea9e4647fe302cc3c1e9d57838fff938cd4

[ "MIT" ]

2

2022-01-28T02:59:28.000Z

2022-01-31T15:47:30.000Z

xtbservice/models.py

cheminfo-py/xtbservice

d9227ea9e4647fe302cc3c1e9d57838fff938cd4

[ "MIT" ]

17

2021-09-13T12:26:57.000Z

2022-01-31T22:35:49.000Z

xtbservice/models.py

cheminfo-py/xtbservice

d9227ea9e4647fe302cc3c1e9d57838fff938cd4

[ "MIT" ]

1

2022-01-26T08:17:50.000Z

# -*- coding: utf-8 -*- from dataclasses import dataclass from typing import Dict, List, Optional import numpy as np from ase import Atoms from pydantic import BaseModel, Field, validator ALLOWED_METHODS = ("GFNFF", "GFN2xTB", "GFN1xTB") ALLOWED_FF = ("uff", "mmff94", "mmff94s") @dataclass class OptimizationResult: atoms: Atoms forces: np.ndarray energy: float class IRResult(BaseModel): wavenumbers: List[float] = Field(None, description="List of wavenumbers in cm^-1") intensities: List[float] = Field( None, description="List of IR intensities in (D/Å)^2 amu^-1" ) ramanIntensities: List[float] = Field( None, description="List of Raman intensities in (D/Å)^2 amu^-1, computed using Placzek and Bond Polarization (using values from Lippincott/Stuttman) approximation", ) zeroPointEnergy: float = Field(None, description="Zero point energy in a.u.") modes: Optional[List[dict]] = Field( None, description="List of dictionaries with the keys `number` - number of the mode (zero indexed), `displacements` - xyz file with the displacement vectors, `intensity` - IR intensity of the mode in D/Å)^2 amu^-1, `ramanIntensity` - Raman intensity of mode, `imaginary` - true if mode is imaginary, `mostDisplaceAtoms` - sorted list of atom indices (zero indiced) according to they displacement (Euclidean norm), `mostContributingAtoms` - most contributing atoms according to a distance criterion.", ) mostRelevantModesOfAtoms: Optional[Dict[int, List[int]]] = Field( None, description="Dictionary indexed with atom indices (zero indexed) and mode indices (zero indexed) as values that is most relevant for a given", ) mostRelevantModesOfBonds: Optional[List[dict]] = Field( None, description="List of dictionaries with the key `startAtom`, `endAtom` and `mode`", ) hasImaginaryFrequency: bool = Field( None, description="True if there is any mode with imaginary frequency" ) isLinear: bool = Field(None, description="True if the molecule is linear.") momentsOfInertia: List[float] = Field( None, description="Moments of inertia around principal axes. For a linear molecule one only expects two non-zero components.", ) hasLargeImaginaryFrequency: bool = Field( None, description="True if there is a large imaginary frequency, indicating a failed geometry optimization.", ) class IRRequest(BaseModel): smiles: Optional[str] = Field( None, description="SMILES string of input molecule. The service will add implicit hydrogens", ) molFile: Optional[str] = Field( None, description="String with molfile with expanded hydrogens. The service will not attempt to add implicit hydrogens to ensure that the atom ordering is preserved.", ) method: Optional[str] = Field( "GFNFF", description="String with method that is used for geometry optimization and calculation of the vibrational frequencies. Allowed values are `GFNFF`, `GFN2xTB`, and `GFN1xTB`. `GFNFF` is the computationally most inexpensive method, but can be less accurate than the xTB methods", ) @validator("method") def method_match(cls, v): if not v in ALLOWED_METHODS: raise ValueError(f"method must be in {ALLOWED_METHODS}") return v class ConformerRequest(BaseModel): smiles: Optional[str] = Field( None, description="SMILES string of input molecule. The service will add implicit hydrogens", ) molFile: Optional[str] = Field( None, description="String with molfile with expanded hydrogens. The service will not attempt to add implicit hydrogens to ensure that the atom ordering is preserved.", ) forceField: Optional[str] = Field( "uff", description="String with method force field that is used for energy minimization. Options are 'uff', 'mmff94', and 'mmff94s'", ) rmsdThreshold: Optional[float] = Field( 0.5, description="RMSD threshold that is used to prune conformer library." ) maxConformers: Optional[int] = Field( 1, description="Maximum number of conformers that are generated (after pruning).", ) @validator("forceField") def method_match(cls, v): if not v in ALLOWED_FF: raise ValueError(f"forceField must be in {ALLOWED_FF}") return v class Conformer(BaseModel): molFile: str = Field( None, description="String with molfile.", ) energy: str = Field( None, description="Final energy after energy minimization.", ) class ConformerLibrary(BaseModel): conformers: List[Conformer]

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0.30092

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0.221472

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4,748

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0.875812

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0.004868

0

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false

0

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0.408163

0

null

0

null

0

1

0

2b1f7bce8113efac8c0084ee01c17879a8efac3c

4,590

py

Python

heat/tests/test_common_param_utils.py

noironetworks/heat

7cdadf1155f4d94cf8f967635b98e4012a7acfb7

[ "Apache-2.0" ]

265

2015-01-02T09:33:22.000Z

2022-03-26T23:19:54.000Z

heat/tests/test_common_param_utils.py

noironetworks/heat

7cdadf1155f4d94cf8f967635b98e4012a7acfb7

[ "Apache-2.0" ]

8

2015-09-01T15:43:19.000Z

2021-12-14T05:18:23.000Z

heat/tests/test_common_param_utils.py

noironetworks/heat

7cdadf1155f4d94cf8f967635b98e4012a7acfb7

[ "Apache-2.0" ]

295

2015-01-06T07:00:40.000Z

2021-09-06T08:05:06.000Z

# # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from heat.common import param_utils from heat.tests import common class TestExtractBool(common.HeatTestCase): def test_extract_bool(self): for value in ('True', 'true', 'TRUE', True): self.assertTrue(param_utils.extract_bool('bool', value)) for value in ('False', 'false', 'FALSE', False): self.assertFalse(param_utils.extract_bool('bool', value)) for value in ('foo', 't', 'f', 'yes', 'no', 'y', 'n', '1', '0', None): self.assertRaises(ValueError, param_utils.extract_bool, 'bool', value) class TestExtractInt(common.HeatTestCase): def test_extract_int(self): # None case self.assertIsNone(param_utils.extract_int('num', None)) # 0 case self.assertEqual(0, param_utils.extract_int('num', 0)) self.assertEqual(0, param_utils.extract_int('num', 0, allow_zero=True)) self.assertEqual(0, param_utils.extract_int('num', '0')) self.assertEqual(0, param_utils.extract_int('num', '0', allow_zero=True)) self.assertRaises(ValueError, param_utils.extract_int, 'num', 0, allow_zero=False) self.assertRaises(ValueError, param_utils.extract_int, 'num', '0', allow_zero=False) # positive values self.assertEqual(1, param_utils.extract_int('num', 1)) self.assertEqual(1, param_utils.extract_int('num', '1')) self.assertRaises(ValueError, param_utils.extract_int, 'num', '1.1') self.assertRaises(ValueError, param_utils.extract_int, 'num', 1.1) # negative values self.assertEqual(-1, param_utils.extract_int('num', -1, allow_negative=True)) self.assertEqual(-1, param_utils.extract_int('num', '-1', allow_negative=True)) self.assertRaises(ValueError, param_utils.extract_int, 'num', '-1.1', allow_negative=True) self.assertRaises(ValueError, param_utils.extract_int, 'num', -1.1, allow_negative=True) self.assertRaises(ValueError, param_utils.extract_int, 'num', -1) self.assertRaises(ValueError, param_utils.extract_int, 'num', '-1') self.assertRaises(ValueError, param_utils.extract_int, 'num', '-1.1') self.assertRaises(ValueError, param_utils.extract_int, 'num', -1.1) self.assertRaises(ValueError, param_utils.extract_int, 'num', -1, allow_negative=False) self.assertRaises(ValueError, param_utils.extract_int, 'num', '-1', allow_negative=False) self.assertRaises(ValueError, param_utils.extract_int, 'num', '-1.1', allow_negative=False) self.assertRaises(ValueError, param_utils.extract_int, 'num', -1.1, allow_negative=False) # Non-int value self.assertRaises(ValueError, param_utils.extract_int, 'num', 'abc') self.assertRaises(ValueError, param_utils.extract_int, 'num', '') self.assertRaises(ValueError, param_utils.extract_int, 'num', 'true') self.assertRaises(ValueError, param_utils.extract_int, 'num', True) class TestExtractTags(common.HeatTestCase): def test_extract_tags(self): self.assertRaises(ValueError, param_utils.extract_tags, "aaaaaaaaaaaaa" "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" "aaaaaaaaaaaaaaaaa,a") self.assertEqual(["foo", "bar"], param_utils.extract_tags('foo,bar'))

45.9

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0

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99

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null

0

1

0

null

0

1

0

3

2b202431420e6ee7ee27a4ade6e7bbf3a50a0b94

7,741

py

Python

commander/thirdparty/covertutils/shells/multi/shell.py

how2how/ToyHome

4457b1d28e21ed6fd4ab980a0f7fed345c570ae3

[ "Apache-2.0" ]

1

2020-07-26T01:08:30.000Z

commander/thirdparty/covertutils/shells/multi/shell.py

how2how/ToyHome

4457b1d28e21ed6fd4ab980a0f7fed345c570ae3

[ "Apache-2.0" ]

null

commander/thirdparty/covertutils/shells/multi/shell.py

how2how/ToyHome

4457b1d28e21ed6fd4ab980a0f7fed345c570ae3

[ "Apache-2.0" ]

null

import cmd import sys import argparse import threading from covertutils.handlers.multi import MultiHandler # # The idea is to pass a list of handlers to start with, and create a MultiHandler object from this list. # # The initial arguments for this should be a list of Shells. From shells, the handlers will be retrieved and from there, the MultiHandler object will be created # try: raw_input # Python 2 except NameError: raw_input = input # Python 3 class CLIArgumentParser( argparse.ArgumentParser ) : def exit(self, ex_code = 1, message = "Unrecognised") : # print message # print '[!] [EXIT] - ', ex_code, message return def error(self, message) : print (message) class MultiShell( cmd.Cmd ) : def __init__( self, shells = [], output = None ) : cmd.Cmd.__init__(self) self.shells = {} for shell in shells : self.__add_handler_shell( shell ) self.prompt = 'covertpreter> ' def __add_handler_shell( self, shell ) : self.shells[shell.handler.getOrchestrator().getIdentity()] = shell def list_sessions( self, verbose = False ) : handlers = [shell.handler for shell in self.shells.values()] to_show = [] for orch_id, shell in self.shells.iteritems() : # print shell.sysinfo try : sysinfo = shell.sysinfo sysinfo = ' - '.join([sysinfo[i] for i in (0,4,3,8)]) # hostname, distro, locale, user except Exception as e: # print e sysinfo = None # Could dispatch selectively a 'SI' command handler = shell.handler if verbose : streams = handler.getOrchestrator().getStreams() else : streams = [] to_show.append( {'row' : (orch_id, handler.__class__) , 'streams' : streams, 'info' : sysinfo } ) print ("\tCurrent Sessions:") for i, sess_dict in enumerate(to_show) : # print row row = sess_dict['row'] streams = sess_dict['streams'] num_row = "%d) {:16} - {}" % (i) print ( num_row.format(*row) ) if sess_dict['info'] : print sess_dict['info'] else : print "System Info: N/A" for stream in streams : print ("\t-> {}".format(stream)) print ('\n') def default(self, line) : if not line : return arg_parser = CLIArgumentParser(prog = "\n%s" % self.prompt) arg_parser.add_argument("SESSIONS", nargs = '*', help = 'The SESSIONS IDs that the MESSAGE must be sent to. If not provided, it defaults to ALL SESSIONS', default = None) arg_parser.add_argument("STREAM", type = str, help = 'The STREAM to send the MESSAGE. If a SESSION does not support the provided STREAM, it will be omitted') arg_parser.add_argument("MESSAGE", type = str, default = None, help = "The MESSAGE to send to the selected SESSIONS") try : args = arg_parser.parse_args( line.split() ) except Exception as e : print e return if args.MESSAGE == None : # print arg_parser.print_help() return # print args if not args.SESSIONS : print "No sessions selected, ALL sessions will be commanded" # Warning # [y/n] thing resp = raw_input("Are you sure? [y/N]: ") if resp.lower() != 'y' : return else : args.SESSIONS = self.shells.keys() for session_id in args.SESSIONS : if session_id in self.shells.keys() : shell = self.shells[session_id] command = "{stream_char}{stream} {message}".format( stream_char = shell.stream_preamp_char, stream = args.STREAM, message = args.MESSAGE, ) print ( "'%s' -> <%s>" % (command, session_id) ) shell.onecmd( command ) def do_session( self, line ) : arg_parser = CLIArgumentParser(prog = "session") arg_parser.add_argument("-i", "--session_num", help = "Jumps to the shell designated by the SESSION_NUM", type = int, required = False) arg_parser.add_argument("-s", "--session_id", help = "Jumps to the shell designated by the SESSIONS_ID", type = str, required = False) arg_parser.add_argument("-l", "--list", help = "Lists all current Session Shells", action = 'store_true' ) arg_parser.add_argument("-v", help = "Verbose output of '-l'", action = 'store_true' ) try : args = arg_parser.parse_args( line.split() ) except Exception as e : print e return # print args if args.list : return self.list_sessions( args.v ) try : i = args.session_num # print list(self.shells)[i] shell = self.shells.values()[i] return shell.start(False) except Exception as e: # print e pass if args.session_id in self.shells.keys() : shell = self.shells[args.session_id] return shell.start(False) def do_handler( self, line ) : arg_parser = CLIArgumentParser(prog='handler') subparsers = arg_parser.add_subparsers(help='command for the handler', dest="command") parser_add = subparsers.add_parser('add', help='Add a Handler in a new Thread and start a session') parser_add.add_argument("SCRIPT", help = "The file that contains the Handler in Python 'covertutils' code", type = str) parser_add.add_argument("ARGUMENTS", help = "The arguments passed to the Python 'covertutils' handler script", type = str, default = '', nargs = '*') parser_add.add_argument("--shell", '-s', help = "The argument in the Python code that contains the 'covertutils.shell.baseshell.BaseShell' implementation", type = str, default = 'shell') parser_del = subparsers.add_parser('del', help='Delete a Handler') parser_del.add_argument("SESSION_ID", help = "The ID of the SESSION to purge", type = str) parser_del.add_argument("--kill", '-k', help = "Send 'KILL' command to the corresponding Agent [TODO]", action = 'store_true', default = False) args = arg_parser.parse_args(line.split()) # print args if args.command == 'add' : if args.SCRIPT == None : print arg_parser.print_help() return filename = args.SCRIPT arguments = args.ARGUMENTS shell_var = args.shell mount_thread = threading.Thread( target = self.mount_new_handler, args = ( filename, arguments, shell_var ) ) mount_thread.daemon = True mount_thread.start() elif args.command == 'del' : if args.SESSION_ID == None : print arg_parser.print_help() return self.unmount_handler(args.SESSION_ID, args.kill) def unmount_handler( self, orch_id, kill = False ) : if orch_id in self.shells.keys() : if kill : self.shells[orch_id].onecmd("!control kill") self.shells[orch_id].handler.stop() # self.shells[orch_id].handler.receive_function = None del self.shells[orch_id] def mount_new_handler( self, filename, arguments, shell_var = 'shell' ) : # handler add examples/tcp_reverse_handler.py 8080 Pa55phra531 variables = ['handler_filaneme.py'] + arguments sys.argv = variables print variables with open(filename, 'r') as handler_codefile : handler_code = handler_codefile.read() # namespace_dict = locals() namespace_dict = {} handler_code = handler_code.replace("%s.start()" % shell_var, 'pass') # Replace the blocking command of the shell # exec( handler_code ) exec( handler_code, namespace_dict ) print namespace_dict[shell_var] self.__add_handler_shell( namespace_dict[shell_var] ) print "Added Session!" def emptyline( self ) : return def do_EOF( self, *args ) : return def do_exit( self, *args ) : return self.do_q( *args ) def do_quit( self, *args ) : return self.do_q( *args ) def do_q( self, *args ) : return self.quitPrompt() def quitPrompt( self, *args ) : # print( args ) exit_input = raw_input("[!]\tQuit shell? [y/N] ") if exit_input.lower() == 'y' : print( "Aborted by the user..." ) # sys.exit(0) return True return False def start( self, warn = True ) : # try : while True : ret = None try : ret = self.cmdloop() # if ret : break except KeyboardInterrupt : print ("\n[!] For exiting use [q|quit|exit]")

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0

null

0

null

0

1

0

1

2b219b5d2c6acf165fc3fb183df871cbdfc2a9e9

3,068

py

Python

Aprior.py

zhangmingming-chb/Aprior

69bea22f34d20bdc9984faf1fa021fac6e60ef38

[ "MIT" ]

null

Aprior.py

zhangmingming-chb/Aprior

69bea22f34d20bdc9984faf1fa021fac6e60ef38

[ "MIT" ]

null

Aprior.py

zhangmingming-chb/Aprior

69bea22f34d20bdc9984faf1fa021fac6e60ef38

[ "MIT" ]

null

#-*-coding:utf-8-*- from typing import List from itertools import chain class Aprior(): def __init__(self, support, confidence): self.support = support self.confidence = confidence def set_transactions(self, transactions: List[List[str]]) -> None: self.transactions = transactions def get_I(self) -> List[str]: return sorted(set(chain(*self.transactions))) def F(self, items: List[List[str]] or List[str]) -> List[List[str]]: # 统计集合出现次数 records = {} query_table = {} for i in items: for j in self.transactions: query_table[id(i)] = i if set(i).issubset(set(j)): if id(i) not in records: records[id(i)] = 1 else: records[id(i)] += 1 # 选出k-频繁项集 item = [] for k, v in records.items(): if v / len(self.transactions) >= self.support: item.append(query_table[k]) item = list(map(lambda x: sorted(list(x)), item)) return item def generate_1_items(self, I: List[str]) -> List[List[str]]: return self.F(I) def generate_k_items(self, k_last_items: List[List[str]]) -> List[List[str]]: prefix = [] for i in k_last_items: prefix.append(",".join(i[:-1])) records = {} for i in prefix: records[i] = [] for i in k_last_items: # 将只有最后一个元素不同的集合分组 current_prefix = ",".join(i[:-1]) if current_prefix in records.keys(): records[current_prefix].append(i) items = [] for v in records.values(): for i in range(len(v)): for j in range(i + 1, len(v)): temp = sorted(list(set(v[i]).union(v[j]))) # 判断集合中的后k-1是否在k-1-频繁项集中 if temp[1:] in k_last_items: items.append(temp) return self.F(items) def k_items_result(self) -> List[List[str]]: I = self.get_I() items = self.generate_1_items(I) item_max_length = len(sorted(self.transactions,key=lambda x:len(x))[-1]) while True: if len(items) == 1 or len(items[0]) > item_max_length: break last_items = items[::] items = self.generate_k_items(items) # 无符合的频繁项集，返回上次计算结果 if len(items) == 0: return last_items return items tran = [ ['1','2','3'], ['1','2','4'], ['1','3','4'], ['1','2','3','5'], ['1','3','5'], ['2','4','5'], ['1','2','3','4'] ] # tran = [ # ['apple','banana','orange'], # ['apple','banana','peer'], # ['apple','orange','peer'], # ['apple','banana','orange','mongo'], # ['apple','orange','mongo'], # ['banana','peer','mongo'], # ['apple','banana','orange','peer'] # ] ap = Aprior(support=3/7,confidence=5/7) ap.set_transactions(tran) print(ap.k_items_result()) # [['1', '2', '3']]

29.5

81

0.496415

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3,068

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0.013889

0

null

0

null

0

1

0

2b23c62c9bf29b77cf256e932af29e6d9da15c7b

686

py

Python

dlex/tf/models/base_v2.py

dvtrung/dl-torch

b49e57d10d32bb223e2d7643f2579ccc32c63a9a

[ "MIT" ]

null

dlex/tf/models/base_v2.py

dvtrung/dl-torch

b49e57d10d32bb223e2d7643f2579ccc32c63a9a

[ "MIT" ]

null

dlex/tf/models/base_v2.py

dvtrung/dl-torch

b49e57d10d32bb223e2d7643f2579ccc32c63a9a

[ "MIT" ]

null

import tensorflow as tf from dlex import Params from dlex.datasets.tf import Dataset class BaseModel(tf.keras.Model): def __init__(self, params: Params, dataset: Dataset): super().__init__() self.params = params self.dataset = dataset self._optimizer = None self._loss = None @property def model(self): raise NotImplemented def compile(self): super().compile( optimizer=self.optimizer, loss=self.loss, metrics=self.metrics) return self.model @property def optimizer(self): return tf.keras.optimizers.SGD(learning_rate=0.02)

25.407407

58

0.603499

76

686

5.302632

0.421053

0.039702

0.069479

0.099256

0

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686

27

59

25.407407

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0

0.090909

0

1

0.181818

false

0

0.136364

0.045455

0.454545

0

null

0

null

0

1

0

2b240ebab74f79c8c466f67dcad71acc3ab5e267

67

py

Python

slack_bolt/context/respond/__init__.py

hirosassa/bolt-python

befc3a1463f3ac8dbb780d66decc304e2bdf3e7a

[ "MIT" ]

504

2020-08-07T05:02:57.000Z

2022-03-31T14:32:46.000Z

slack_bolt/context/respond/__init__.py

hirosassa/bolt-python

befc3a1463f3ac8dbb780d66decc304e2bdf3e7a

[ "MIT" ]

560

2020-08-07T01:16:06.000Z

2022-03-30T00:40:56.000Z

slack_bolt/context/respond/__init__.py

hirosassa/bolt-python

befc3a1463f3ac8dbb780d66decc304e2bdf3e7a

[ "MIT" ]

150

2020-08-07T09:41:14.000Z

2022-03-30T04:54:51.000Z

# Don't add async module imports here from .respond import Respond

22.333333

37

0.791045

11

67

4.818182

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0

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67

2

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33.5

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0

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null

0

1

0

1

0

4

2b2476addfb055d48f5d5ac598a8041fdc9fee29

1,259

py

Python

pi/commands/token/reset.py

pan-net-security/pi-bundle

1819caede77357331465216e0355eb2499d09cb4

[ "MIT" ]

2

2017-12-15T20:50:58.000Z

2020-10-21T15:48:48.000Z

pi/commands/token/reset.py

pan-net-security/pi-bundle

1819caede77357331465216e0355eb2499d09cb4

[ "MIT" ]

1

2017-10-26T09:28:30.000Z

2017-10-26T10:33:41.000Z

pi/commands/token/reset.py

pan-net-security/pi-bundle

1819caede77357331465216e0355eb2499d09cb4

[ "MIT" ]

null

from pi.commands.token.base import TokenBase import json import re class Reset(TokenBase): def __init__(self): super().__init__() def run(self): handler = self.parse_subcommand_ handler() def reset(self): results = [] # currently supporting just one argument arg_user = self.request.args[0] # options not yet supported # future implementation: serial - to reset one specific token failcounter # self.request.options) user = {'name': arg_user} try: reset_tokens = self.reset_tokens(user=arg_user) if reset_tokens: reset_tokens = json.loads(reset_tokens.content) #print(json.dumps(reset_tokens, indent=4, sort_keys=True)) user['result'] = reset_tokens['result']['status'] else: user['result'] = False except Exception as e: self.fail(e) results.append(user) self.response.content(results, template='token_reset').send() @property def parse_subcommand_(self): if self.request.args: return self.reset self.fail("This command requires at least one argument and none was passed.")

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47

85

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0

0.310345

0

null

0

null

0

1

0

2b25709c41a264855b79fbdf3a37d395af6fdc3b

4,500

py

Python

canaries/canaries.py

wyatt-howe/canaries

0bd0783e388dcee21fd3addd09a9299940627536

[ "MIT" ]

null

canaries/canaries.py

wyatt-howe/canaries

0bd0783e388dcee21fd3addd09a9299940627536

[ "MIT" ]

null

canaries/canaries.py

wyatt-howe/canaries

0bd0783e388dcee21fd3addd09a9299940627536

[ "MIT" ]

null

"""Library for loading dynamic library files. Python library for choosing and loading dynamic library files compatible with the operating environment. """ import doctest import sys import os.path import platform from ctypes import cdll, create_string_buffer from multiprocessing import Pool class canaries(): """ Wrapper class for static methods. """ @staticmethod def _xdll(path): """ Load a library using the appropriate method. """ system = platform.system() xdll = cdll if system == 'Windows': # pylint: disable=import-outside-toplevel from ctypes import windll as xdll # pragma: no cover return xdll.LoadLibrary(path) @staticmethod def _probe(lib): """ Probe whether a library has a correctly implemented verification method. """ # Build input and output buffers. treat = create_string_buffer(5) for (i, c) in enumerate('treat'): try: treat[i] = c except: treat[i] = ord(c) chirp = create_string_buffer(5) # Attempt to invoke the canary method. r = lib.canary(chirp, treat) # Decode results. chirp = chirp.raw if isinstance(chirp, bytes): chirp = chirp.decode() # Check that results are correct. return r == 0 and chirp == 'chirp' @staticmethod def _isolated(path): """ Method to be used by isolated probe process. """ return canaries._probe(canaries._xdll(path)) @staticmethod def canary(system, path): """ Single-path wrapper method for convenience. """ paths = {} paths[system] = [path] obj = canaries(paths) return obj.lib if hasattr(obj, 'lib') else None @staticmethod def load(paths): """ Wrapper method for backwards compatibility. """ obj = canaries(paths) return obj.lib if hasattr(obj, 'lib') else None def __init__(self, paths): """ Attempt to load a library at one of the supplied paths based on the platform. Retains state in order to record all exceptions and incorrect outputs. """ if not isinstance(paths, (str, list, dict)): raise TypeError( "input must be a string, list, or dictionary" ) if isinstance(paths, dict) and\ not all(isinstance(p, (str, list)) for p in paths.values()): raise TypeError( "path values in dictionary must be strings or lists of strings" ) self.lib = None self.exceptions = [] self.outputs = [] system = platform.system() if isinstance(paths, str): self.lib = self._canary(system, paths) elif isinstance(paths, list): for path in paths: self.lib = self._canary(system, path) if self.lib is not None: break elif isinstance(paths, dict): if system in paths: ps = paths[system] for path in [ps] if isinstance(ps, str) else ps: self.lib = self._canary(system, path) if self.lib is not None: break def _canary(self, system, path): """ Attempt to load a library file at the supplied path and verify that its exported functions work. """ lib = None # Only attempt to load object files that exist. if os.path.exists(path): # Confirm that the library's exported functions work. try: # Invoke compatibility validation method. with Pool(1) as p: task = p.imap(canaries._isolated, [path]) if task.next(5): # Process has five seconds to succeedd. lib = canaries._xdll(path) except: self.exceptions.append(( (system, path), ( sys.exc_info()[0], sys.exc_info()[1], sys.exc_info()[2].tb_lineno ) )) return lib # Provide direct access to static methods. canary = canaries.canary load = canaries.load if __name__ == "__main__": doctest.testmod() # pragma: no cover

29.220779

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496

4,500

4.866935

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0.02237

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0

0.002829

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4,500

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0

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0

0.233333

0

null

0

null

0

1

0

2b25b21c76b9e5d09cd75db1cb54b32efea42278

906

py

Python

examples/wsgi.py

eliziario/django-websocket-redis

e36449a622230dfc7af090471e0ae59b51526767

[ "MIT" ]

815

2015-01-04T03:02:03.000Z

2022-03-18T21:48:48.000Z

examples/wsgi.py

eliziario/django-websocket-redis

e36449a622230dfc7af090471e0ae59b51526767

[ "MIT" ]

213

2015-01-06T14:15:32.000Z

2022-01-28T17:35:45.000Z

examples/wsgi.py

eliziario/django-websocket-redis

e36449a622230dfc7af090471e0ae59b51526767

[ "MIT" ]

253

2015-01-02T16:38:15.000Z

2022-03-30T20:43:58.000Z

# Django with Websocket for Redis under a single uWSGI server # (for sites with low traffic) # # uwsgi --virtualenv /path/to/virtualenv --http :9090 --gevent 100 --http-websockets --module wsgi # # See: http://django-websocket-redis.readthedocs.io/en/latest/running.html#django-with-websockets-for-redis-as-a-stand-alone-uwsgi-server import os import sys sys.path.insert(0, os.path.abspath('..')) os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'chatserver.settings') from django.core.wsgi import get_wsgi_application from django.conf import settings from ws4redis.uwsgi_runserver import uWSGIWebsocketServer _django_app = get_wsgi_application() _websocket_app = uWSGIWebsocketServer() def application(environ, start_response): if environ.get('PATH_INFO').startswith(settings.WEBSOCKET_URL): return _websocket_app(environ, start_response) return _django_app(environ, start_response)

36.24

137

0.788079

122

906

5.688525

0.491803

0.051873

0.086455

0.066282

0

0.01107

0.102649

906

24

138

37.75

0.842558

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0

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false

0

0.384615

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0

null

0

null

0

1

0

1

0

2

2b25d82fa6f0f77480a8c0271786df4755236b12

136

py

Python

src/py/twitter_credentials.py

franciscomoura/social-media-analysis

e277ad7b81952ec5d01c0801133e226f430daa6e

[ "Apache-2.0" ]

null

src/py/twitter_credentials.py

franciscomoura/social-media-analysis

e277ad7b81952ec5d01c0801133e226f430daa6e

[ "Apache-2.0" ]

null

src/py/twitter_credentials.py

franciscomoura/social-media-analysis

e277ad7b81952ec5d01c0801133e226f430daa6e

[ "Apache-2.0" ]

null

consumer_key = '<your key>' consumer_secret = '<your secret>' access_token = '<your token>' access_token_secret = '<your token secret>'

27.2

43

0.727941

18

136

5.222222

0.333333

0.212766

0

0.125

136

4

44

34

0.789916

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0.397059

0

1

0

false

0

1

0

null

1

0

1

0

null

0

3

2b275e20cb5d8a0781ea95c53cddfe783077cf6e

1,820

py

Python

pmaf/pipe/agents/miners/_metakit.py

mmtechslv/PhyloMAF

bab43dd4a4d2812951b1fdf4f1abb83edb79ea88

[ "BSD-3-Clause" ]

1

2021-07-02T06:24:17.000Z

pmaf/pipe/agents/miners/_metakit.py

mmtechslv/PhyloMAF

bab43dd4a4d2812951b1fdf4f1abb83edb79ea88

[ "BSD-3-Clause" ]

1

2021-06-28T12:02:46.000Z

pmaf/pipe/agents/miners/_metakit.py

mmtechslv/PhyloMAF

bab43dd4a4d2812951b1fdf4f1abb83edb79ea88

[ "BSD-3-Clause" ]

null

from abc import ABC, abstractmethod class MinerBackboneMetabase(ABC): """ """ @abstractmethod def verify_docker(self,docker): """ Parameters ---------- docker : Returns ------- """ pass @abstractmethod def yield_accession_by_identifier(self, docker, **kwargs): """ Parameters ---------- docker : **kwargs : Returns ------- """ pass @abstractmethod def yield_sequence_by_identifier(self, docker, **kwargs): """ Parameters ---------- docker : **kwargs : Returns ------- """ pass @abstractmethod def yield_phylogeny_by_identifier(self, docker, **kwargs): """ Parameters ---------- docker : **kwargs : Returns ------- """ pass @abstractmethod def yield_taxonomy_by_identifier(self, docker, **kwargs): """ Parameters ---------- docker : **kwargs : Returns ------- """ pass @abstractmethod def yield_identifier_by_docker(self, docker, **kwargs): """ Parameters ---------- docker : **kwargs : Returns ------- """ pass @property @abstractmethod def factor(self): """ """ pass @property @abstractmethod def mediator(self): """ """ pass @property @abstractmethod def state(self): """ """ pass

14.108527

62

0.392308

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1,820

6.584906

0.235849

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0.687679

0.545845

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0.471978

1,820

128

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14.21875

0.726327

0.218132

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0.28125

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0.34375

0

null

0

1

0

null

0

1

0

1

0

3

2b27627202fcf16a1bd8bdf2adc4734f515109a8

45

py

Python

01_hello/hello02_comment.py

rebeckaflynn/tiny_python_projects

692f24dd00769438e7aaa1c45223b701b20a1192

[ "MIT" ]

null

01_hello/hello02_comment.py

rebeckaflynn/tiny_python_projects

692f24dd00769438e7aaa1c45223b701b20a1192

[ "MIT" ]

null

01_hello/hello02_comment.py

rebeckaflynn/tiny_python_projects

692f24dd00769438e7aaa1c45223b701b20a1192

[ "MIT" ]

null

# Purpose: Say hello print('Hello, World!')

11.25

22

0.666667

6

45

5

0.833333

0

0.155556

45

3

23

15

0.789474

0.4

0

0.541667

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

2b28b58e2579cbe5e2ab26c5528edcabd5571c91

1,074

py

Python

docs/end-to-end/library/GeocontribOnCoordinatesLibrary.py

hcharp/geocontrib

87ee241c737aae23eff358d2550bddba714f9c7b

[ "Apache-2.0" ]

3

2020-12-02T09:44:41.000Z

2021-04-17T13:05:30.000Z

docs/end-to-end/library/GeocontribOnCoordinatesLibrary.py

hcharp/geocontrib

87ee241c737aae23eff358d2550bddba714f9c7b

[ "Apache-2.0" ]

14

2020-01-27T09:49:33.000Z

2021-06-14T08:04:10.000Z

docs/end-to-end/library/GeocontribOnCoordinatesLibrary.py

hcharp/geocontrib

87ee241c737aae23eff358d2550bddba714f9c7b

[ "Apache-2.0" ]

9

2020-01-16T12:37:39.000Z

2021-04-22T09:57:59.000Z

# Copyright (c) 2017-2021 Neogeo-Technologies. # 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 selenium.webdriver.common.action_chains import ActionChains from utils import get_driver def geocontrib_click_at_coordinates(pos_x, pos_y): actions = ActionChains(get_driver()) my_map = get_driver().find_element_by_xpath("//html/body/main/div/div/form/div[3]/div/div/div[1]/div[4]/div") actions.move_to_element_with_offset(my_map, pos_x, pos_y).click().perform() get_driver().find_element_by_xpath("//button[@type='submit']").click()

42.96

113

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1,074

4.732143

0.630952

0.075472

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0.067925

0

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1,074

24

114

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0

0.285714

0

0.428571

0

null

0

null

0

1

0

2b2938cdd0a73902522794999f575e5ff3fb8b89

3,341

py

Python

torch/quantization/fx/qconfig_utils.py

deltabravozulu/pytorch

c6eef589971e45bbedacc7f65533d1b8f80a6895

[ "Intel" ]

1

2021-06-17T13:02:45.000Z

torch/quantization/fx/qconfig_utils.py

deltabravozulu/pytorch

c6eef589971e45bbedacc7f65533d1b8f80a6895

[ "Intel" ]

1

2022-01-18T12:17:29.000Z

torch/quantization/fx/qconfig_utils.py

deltabravozulu/pytorch

c6eef589971e45bbedacc7f65533d1b8f80a6895

[ "Intel" ]

2

2021-07-02T10:18:21.000Z

2021-08-18T10:10:28.000Z

import torch from collections import OrderedDict from typing import Union, Callable, Any, Dict import re from .utils import _parent_name QConfigAny = Union[torch.quantization.QConfig, torch.quantization.QConfigDynamic, None] def get_flattened_qconfig_dict(qconfig_dict): """ flatten the global, object_type and module_name qconfig to the same qconfig_dict so that it can be used by propagate_qconfig_ function. "module_name_regex" is ignored for now since it's not supported in propagate_qconfig_, but it can be fixed later. For example: Input: { "": qconfig, "object_type": [ (torch.add, qconfig) ], "module_name": [ ("conv", qconfig) ] } Output: { "": qconfig, torch.add: qconfig, "conv": qconfig } """ flattened = dict() if '' in qconfig_dict: flattened[''] = qconfig_dict[''] def flatten_key(key): if key in qconfig_dict: for (obj, qconfig) in qconfig_dict[key].items(): flattened[obj] = qconfig flatten_key('object_type') flatten_key('module_name') return flattened def convert_dict_to_ordered_dict(qconfig_dict: Any) -> Dict[str, Dict[Any, Any]]: """ Convert dict in qconfig_dict to ordered dict """ # convert a qconfig list for a type to OrderedDict def _convert_to_ordered_dict(key, qconfig_dict): qconfig_dict[key] = OrderedDict(qconfig_dict.get(key, [])) _convert_to_ordered_dict('object_type', qconfig_dict) _convert_to_ordered_dict('module_name_regex', qconfig_dict) _convert_to_ordered_dict('module_name', qconfig_dict) return qconfig_dict def get_object_type_qconfig( qconfig_dict: Any, object_type: Union[Callable, str], fallback_qconfig: QConfigAny) -> QConfigAny: # object_type can be # 1. module type (call_module) # 2. function (call_function) # 3. string (call_method) return qconfig_dict['object_type'].get( object_type, fallback_qconfig) def get_module_name_regex_qconfig(qconfig_dict, module_name, fallback_qconfig): for regex_pattern, qconfig in \ qconfig_dict['module_name_regex'].items(): if re.match(regex_pattern, module_name): # first match wins return qconfig return fallback_qconfig def get_module_name_qconfig(qconfig_dict, module_name, fallback_qconfig): if module_name == '': # module name qconfig not found return fallback_qconfig if module_name in qconfig_dict['module_name']: return qconfig_dict['module_name'][module_name] else: parent, _ = _parent_name(module_name) return get_module_name_qconfig(qconfig_dict, parent, fallback_qconfig) # get qconfig for module_name, # fallback to module_name_regex_qconfig, module_type_qconfig, # global_qconfig if necessary def get_qconfig(qconfig_dict, module_type, module_name, global_qconfig): module_type_qconfig = get_object_type_qconfig( qconfig_dict, module_type, global_qconfig) module_name_regex_qconfig = get_module_name_regex_qconfig( qconfig_dict, module_name, module_type_qconfig) module_name_qconfig = get_module_name_qconfig( qconfig_dict, module_name, module_name_regex_qconfig) return module_name_qconfig

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0.141553

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0.116895

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0

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0

1

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false

0

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0

null

0

null

0

1

0

2b2a9c9a4b580fa5f2d5bbe38b2d93f37f8e19c1

3,062

py

Python

researchmap/wrapper.py

RTa-technology/researchmap.py

6aa427e1564644b20ba2001dfecf63457ef40463

[ "MIT" ]

null

researchmap/wrapper.py

RTa-technology/researchmap.py

6aa427e1564644b20ba2001dfecf63457ef40463

[ "MIT" ]

null

researchmap/wrapper.py

RTa-technology/researchmap.py

6aa427e1564644b20ba2001dfecf63457ef40463

[ "MIT" ]

null

from typing import List import urllib.parse from .adapter import Adapter __all__ = ['Wrapper'] class Wrapper: """Wrapper class for the Adapter class. This class is used to wrap the Adapter class and provide a more convenient interface for the user. """ def __init__(self, adapter: Adapter) -> None: self._adapter = adapter def get_bulk(self, params=None) -> dict: """Get a list of researchers from the API. Parameters ---------- params : :class:`dict` A dictionary containing the parameters to be passed to the API. The payload to send to the API. Defaults to None. Returns ------- :class:`dict` """ return self._adapter.get_bulk(params=params) def set_bulk(self, jsondata=None, params=None) -> dict: """Get a list of researchers from the API. Parameters ---------- jsondata : :class:`dict` A dictionary containing the parameters to be passed to the API. The payload to send to the API. Defaults to None. params : :class:`dict` A dictionary containing the parameters to be passed to the API. Returns ------- :class:`dict` """ if params is None: params = {} if jsondata is None: jsondata = {} data = self._adapter.set_bulk(params=params, jsondata=jsondata) print(data) bulk_data = {} bulk_data['id'] = urllib.parse.parse_qs(urllib.parse.urlparse(data['url']).query)['id'][0] error = self._adapter.get_bulk_results(bulk_data) bulk_data['display_type'] = "success" print(bulk_data) succeed = self._adapter.get_bulk_results(bulk_data) print(succeed) print(error) return self._adapter.get_bulk_results(bulk_data) def set_bulk_apply(self, params=None) -> dict: """Get a list of researchers from the API. Parameters ---------- params : :class:`dict` A dictionary containing the parameters to be passed to the API. Returns ------- :class:`dict` """ if params is None: params = {} return self._adapter.set_bulk_apply(params=params) def get_bulk_results(self, params=None) -> dict: """Get a list of researchers from the API. Parameters ---------- params : :class:`dict` A dictionary containing the parameters to be passed to the API. Returns ------- :class:`dict` """ if params is None: params = {} return self._adapter.get_bulk_results(params=params) def search_researcher(self, payload=None) -> dict: """Search for a researcher in the API. Parameters ---------- payload : :class:`dict` A dictionary containing the parameters to be passed to the API. The payload to send to the API. Defaults to None. Returns ------- :class:`dict` """ if payload is None: payload = {} return self._adapter.search_researcher(payload) def usage(self) -> dict: return self._adapter.get_usage()

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4.726098

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0

0.000448

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3,062

116

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26.396552

0.81863

0.423253

0

0.153846

0

0.023125

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1

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false

0

0.076923

0.025641

0.435897

0.102564

0

null

0

null

0

1

0

2b2dc91a67e56678c390a41ec58ff7af3ed3237a

2,888

py

Python

demo/MagicMind/python/calibrator_custom_data.py

huismiling/YOLOX

d9d1c1e8c6362c71703d34e25765a2dfe8618e4a

[ "Apache-2.0" ]

null

demo/MagicMind/python/calibrator_custom_data.py

huismiling/YOLOX

d9d1c1e8c6362c71703d34e25765a2dfe8618e4a

[ "Apache-2.0" ]

null

demo/MagicMind/python/calibrator_custom_data.py

huismiling/YOLOX

d9d1c1e8c6362c71703d34e25765a2dfe8618e4a

[ "Apache-2.0" ]

null

from typing import List import cv2 import numpy import magicmind.python.runtime as mm from magicmind.python.common.types import get_numpy_dtype_by_datatype import os import sys def preprocess(img, input_size, swap=(2, 0, 1)): if len(img.shape) == 3: padded_img = numpy.ones((input_size[0], input_size[1], 3), dtype=numpy.uint8) * 114 else: padded_img = numpy.ones(input_size, dtype=numpy.uint8) * 114 r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) resized_img = cv2.resize( img, (int(img.shape[1] * r), int(img.shape[0] * r)), interpolation=cv2.INTER_LINEAR, ).astype(numpy.uint8) padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img padded_img = padded_img.transpose(swap) padded_img = numpy.ascontiguousarray(padded_img, dtype=numpy.float32) return padded_img, r def load_multi_image(data_paths: List[str], input_wh = List[int], target_dtype: mm.DataType = mm.DataType.FLOAT32) -> numpy.ndarray: # Load multiple pre-processed image into a NCHW style ndarray images = [] for path in data_paths: img = cv2.imread(path) images.append(preprocess(img, input_wh)[0][numpy.newaxis, :]) ret = numpy.concatenate(tuple(images), axis = 0) return numpy.ascontiguousarray( ret.astype(dtype = get_numpy_dtype_by_datatype(target_dtype))) class FixedCalibData(mm.CalibDataInterface): def __init__(self, shape: mm.Dims, data_type: mm.DataType, max_samples: int, data_paths: str): super().__init__() self.shape_ = shape self.data_type_ = data_type self.batch_size_ = shape.GetDimValue(0) self.input_wh = [shape.GetDimValue(3), shape.GetDimValue(2)] data_lines = [itd.strip() for itd in open(data_paths).readlines() if os.path.isfile(itd.strip())] self.max_samples_ = min(max_samples, len(data_lines)) self.data_paths_ = data_lines self.current_sample_ = None self.outputed_sample_count = 0 def get_shape(self): return self.shape_ def get_data_type(self): return self.data_type_ def get_sample(self): return self.current_sample_ def next(self): beg_ind = self.outputed_sample_count end_ind = self.outputed_sample_count + self.batch_size_ if end_ind > self.max_samples_: return mm.Status(mm.Code.OUT_OF_RANGE, "End reached") self.current_sample_ = load_multi_image(self.data_paths_[beg_ind:end_ind], input_wh = self.input_wh, target_dtype = self.data_type_) self.outputed_sample_count = end_ind return mm.Status.OK() def reset(self): self.current_sample_ = None self.outputed_sample_count = 0 return mm.Status.OK()

35.219512

132

0.655125

400

2,888

4.465

0.28

0.040314

0.050392

0.06439

0.182531

0.113102

0.050392

0

0.017671

0.235803

2,888

81

133

35.654321

0.791572

0.020429

0

0.096774

0

0.003891

0

1

0.129032

false

0

0.112903

0.048387

0.387097

0

null

0

null

0

1

0

2b2eb592acc995c4132c3288aeaefe49afa5e490

66,478

py

Python

probreg/main.py

albertvisser/probreg

5f685616221e3261afe0d8ae8506cad9a719fa82

[ "MIT" ]

null

probreg/main.py

albertvisser/probreg

5f685616221e3261afe0d8ae8506cad9a719fa82

[ "MIT" ]

null

probreg/main.py

albertvisser/probreg

5f685616221e3261afe0d8ae8506cad9a719fa82

[ "MIT" ]

null

#! usr/bin/env python """Actie (was: problemen) Registratie, GUI toolkit onafhankelijke code """ import os # import sys import pathlib import functools import probreg.gui as gui import probreg.shared as shared # import DataError, et_projnames import probreg.dml_django as dmls import probreg.dml_xml as dmlx LIN = True if os.name == 'posix' else False class Page(): "base class for notebook page" def __init__(self, parent, pageno, standard=True): self.parent = parent self.pageno = pageno self.is_text_page = standard if standard: self.gui = gui.PageGui(parent, self) def get_toolbar_data(self, textfield): "return texts, shortcuts and picture names for setting op toolbar" return (('&Bold', 'Ctrl+B', 'icons/sc_bold', 'Toggle Bold', textfield.text_bold, textfield.update_bold), ('&Italic', 'Ctrl+I', 'icons/sc_italic', 'Toggle Italic', textfield.text_italic, textfield.update_italic), ('&Underline', 'Ctrl+U', 'icons/sc_underline', 'Toggle Underline', textfield.text_underline, textfield.update_underline), ('Strike&through', 'Ctrl+~', 'icons/sc_strikethrough', 'Toggle Strikethrough', textfield.text_strikethrough), # ("Toggle &Monospace", 'Shift+Ctrl+M', 'icons/text', # 'Switch using proportional font off/on', textfield.toggle_monospace), (), ("&Enlarge text", 'Ctrl+Up', 'icons/sc_grow', 'Use bigger letters', textfield.enlarge_text), ("&Shrink text", 'Ctrl+Down', 'icons/sc_shrink', 'Use smaller letters', textfield.shrink_text), (), ('To &Lower Case', 'Shift+Ctrl+L', 'icons/sc_changecasetolower', 'Use lower case letters', textfield.case_lower), ('To &Upper Case', 'Shift+Ctrl+U', 'icons/sc_changecasetoupper', 'Use upper case letters', textfield.case_upper), (), ("Indent &More", 'Ctrl+]', 'icons/sc_incrementindent', 'Increase indentation', textfield.indent_more), ("Indent &Less", 'Ctrl+[', 'icons/sc_decrementindent', 'Decrease indentation', textfield.indent_less), (), # ("Normal Line Spacing", '', 'icons/sc_spacepara1', # 'Set line spacing to 1', textfield.linespacing_1), # ("1.5 Line Spacing", '', 'icons/sc_spacepara15', # 'Set line spacing to 1.5', textfield.linespacing_15), # ("Double Line Spacing", '', 'icons/sc_spacepara2', # 'Set line spacing to 2', textfield.linespacing_2), # (), ("Increase Paragraph &Spacing", '', 'icons/sc_paraspaceincrease', 'Increase spacing between paragraphs', textfield.increase_paragraph_spacing), ("Decrease &Paragraph Spacing", '', 'icons/sc_paraspacedecrease', 'Decrease spacing between paragraphs', textfield.decrease_paragraph_spacing)) def vulp(self): """te tonen gegevens invullen in velden e.a. initialisaties methode aan te roepen voorafgaand aan het tonen van de pagina""" self.initializing = True self.parent.parent.enable_settingsmenu() if self.parent.current_tab == 0: text = self.seltitel else: state = True if self.parent.current_tab == 1 and self.parent.newitem else False self.enable_buttons(state) text = self.parent.tabs[self.parent.current_tab].split(None, 1) if self.parent.pagedata: text = str(self.parent.pagedata.id) + ' ' + self.parent.pagedata.titel self.parent.parent.set_windowtitle("{} | {}".format(self.parent.parent.title, text)) self.parent.parent.set_statusmessage() if 1 < self.parent.current_tab < 6: self.oldbuf = '' is_readonly = False if self.parent.pagedata is not None: if self.parent.current_tab == 2 and self.parent.pagedata.melding: self.oldbuf = self.parent.pagedata.melding if self.parent.current_tab == 3 and self.parent.pagedata.oorzaak: self.oldbuf = self.parent.pagedata.oorzaak if self.parent.current_tab == 4 and self.parent.pagedata.oplossing: self.oldbuf = self.parent.pagedata.oplossing if self.parent.current_tab == 5 and self.parent.pagedata.vervolg: self.oldbuf = self.parent.pagedata.vervolg # self.text1.setReadOnly(self.parent.pagedata.arch) is_readonly = self.parent.pagedata.arch # print('in Page.vulp, setting text:', self.oldbuf) self.gui.set_textarea_contents(self.oldbuf) # print('in Page.vulp, set text') if not is_readonly: is_readonly = not self.parent.parent.is_user self.gui.set_text_readonly(is_readonly) self.gui.enable_toolbar(self.parent.parent.is_user) # print('in Page.vulp, getting text') self.oldbuf = self.gui.get_textarea_contents() # make sure it's rich text # print('in Page.vulp, got text:', self.oldbuf) self.gui.move_cursor_to_end() # print(' set cursor to end') self.initializing = False # self.parent.checked_for_leaving = True - alleen voor wx versie, hoort bij gui # print('end of Page.vulp') def readp(self, pid): "lezen van een actie" if self.parent.pagedata: # spul van de vorige actie opruimen self.parent.pagedata.clear() self.parent.pagedata = shared.Actie[self.parent.parent.datatype](self.parent.fnaam, pid, self.parent.parent.user) self.parent.parent.imagelist = self.parent.pagedata.imagelist self.parent.old_id = self.parent.pagedata.id self.parent.newitem = False def nieuwp(self, *args): """voorbereiden opvoeren nieuwe actie""" shared.log('opvoeren nieuwe actie') self.parent.newitem = True if self.leavep(): if self.parent.current_tab == 0: self.parent.parent.gui.enable_book_tabs(True, tabfrom=1) self.parent.pagedata = shared.Actie[self.parent.parent.datatype](self.parent.fnaam, 0, self.parent.parent.user) self.parent.pagedata.events.append((shared.get_dts(), 'Actie opgevoerd')) self.parent.parent.imagelist = self.parent.pagedata.imagelist if self.parent.current_tab == 1: self.vulp() # om de velden leeg te maken self.gui.set_focus() else: self.goto_page(1, check=False) else: self.parent.newitem = False shared.log("leavep() geeft False: nog niet klaar met huidige pagina") def leavep(self): "afsluitende acties uit te voeren alvorens de pagina te verlaten" newbuf = [] if self.parent.current_tab > 0: newbuf = self.oldbuf newbuf = self.gui.build_newbuf() ok_to_leave = True if self.parent.current_tab == 0: pass elif self.parent.changed_item: message = "\n".join(("De gegevens op de pagina zijn gewijzigd, ", "wilt u de wijzigingen opslaan voordat u verder gaat?")) ok, cancel = gui.ask_cancel_question(self.gui, message) if ok: ok_to_leave = self.savep() elif cancel: # self.parent.checked_for_leaving = ok_to_leave = False ok_to_leave = False if not cancel: self.parent.parent.gui.enable_all_other_tabs(True) return ok_to_leave def savep(self, *args): "gegevens van een actie opslaan afhankelijk van pagina" if not self.gui.can_save: return False self.enable_buttons(False) if self.parent.current_tab <= 1 or self.parent.current_tab == 6: return False text = self.gui.get_textarea_contents() event_text = '' if self.parent.current_tab == 2 and text != self.parent.pagedata.melding: self.oldbuf = self.parent.pagedata.melding = text event_text = "Meldingtekst aangepast" if self.parent.current_tab == 3 and text != self.parent.pagedata.oorzaak: self.oldbuf = self.parent.pagedata.oorzaak = text event_text = "Beschrijving oorzaak aangepast" if self.parent.current_tab == 4 and text != self.parent.pagedata.oplossing: self.oldbuf = self.parent.pagedata.oplossing = text event_text = "Beschrijving oplossing aangepast" if self.parent.current_tab == 5 and text != self.parent.pagedata.vervolg: self.oldbuf = self.parent.pagedata.vervolg = text event_text = "Tekst vervolgactie aangepast" if event_text: self.parent.pagedata.events.append((shared.get_dts(), event_text)) self.update_actie() self.parent.pages[0].gui.set_item_text(self.parent.pages[0].gui.get_selection(), 3, self.parent.pagedata.updated) return True def savepgo(self, *args): "opslaan en naar de volgende pagina" if not self.gui.can_saveandgo(): return if self.savep(): self.goto_next() else: self.enable_buttons() def restorep(self, *args): "oorspronkelijke (laatst opgeslagen) inhoud van de pagina herstellen" # reset font - are these also needed: case? indent? linespacing? paragraphspacing? if self.parent.current_tab > 1: self.gui.reset_font() self.vulp() def on_text(self, *args): """callback voor EVT_TEXT e.d. de initializing flag wordt uitgevraagd omdat deze event ook tijdens vulp() en tijdens vul_combos plaatsvindt""" if not self.initializing: newbuf = self.gui.build_newbuf() changed = newbuf != self.oldbuf self.enable_buttons(changed) def on_choice(self): "callback voor combobox (? wordt on_text hier niet gewoon voor gebruikt?)" self.enable_buttons() def update_actie(self): """pass page data from the GUI to the internal storage """ self.parent.pagedata.imagecount = self.parent.parent.imagecount self.parent.pagedata.imagelist = self.parent.parent.imagelist if self.parent.parent.datatype == shared.DataType.SQL.name: self.parent.pagedata.write(self.parent.parent.user) else: self.parent.pagedata.write() self.parent.pagedata.read() # om "updated" attribuut op te halen if self.parent.newitem: # nieuwe entry maken in de tabel voor panel 0 newindex = len(self.parent.data) # + 1 pagegui = self.parent.pages[1].gui itemdata = (pagegui.get_text('date'), " - ".join((pagegui.get_text('proc'), pagegui.get_text('desc'))), pagegui.get_choice_data('stat')[0], pagegui.get_choice_data('cat')[0], pagegui.get_text('id')) self.parent.data[newindex] = itemdata # waarom niet append? # ook nieuwe entry maken in de visuele tree page = self.parent.pages[0] self.parent.current_item = page.gui.add_listitem(itemdata[0].split(' ')[0]) page.gui.set_selection() self.parent.newitem = False self.parent.rereadlist = True def enable_buttons(self, state=True): "buttons wel of niet bruikbaar maken" self.gui.enable_buttons(state) self.parent.changed_item = state if self.parent.current_tab > 0: self.parent.parent.gui.enable_all_other_tabs(not state) def goto_actie(self, *args): "naar startpagina actie gaan" self.goto_page(1) def goto_next(self, *args): "naar de volgende pagina gaan" if not self.leavep(): return next = self.parent.current_tab + 1 if next >= len(self.parent.pages): next = 0 self.parent.parent.gui.set_page(next) def goto_prev(self, *args): "naar de vorige pagina gaan" if not self.leavep(): return next = self.parent.current_tab - 1 if next < 0: next = len(self.parent.pages) - 1 self.parent.parent.gui.set_page(next) def goto_page(self, page_num, check=True): "naar de aangegeven pagina gaan" if check and not self.leavep(): return if 0 <= page_num <= len(self.parent.pages): self.parent.parent.gui.set_page(page_num) def get_textarea_contents(self): "get the page text" return self.gui.get_textarea_contents() class Page0(Page): "pagina 0: overzicht acties" def __init__(self, parent): self.parent = parent super().__init__(parent, pageno=0, standard=False) self.selection = 'excl. gearchiveerde' self.sel_args = {} self.sorted = (0, "A") widths = [94, 24, 146, 90, 400] if LIN else [64, 24, 114, 72, 292] if self.parent.parent.datatype == shared.DataType.SQL.name: widths[4] = 90 if LIN else 72 extra = 310 if LIN else 220 widths.append(extra) self.gui = gui.Page0Gui(parent, self, widths) self.gui.enable_buttons() self.sort_via_options = False def vulp(self): """te tonen gegevens invullen in velden e.a. initialisaties methode aan te roepen voorafgaand aan het tonen van de pagina """ # print('in Page0.vulp') self.saved_sortopts = None if (self.parent.parent.datatype == shared.DataType.SQL.name and self.parent.parent.filename): if self.parent.parent.is_user: self.saved_sortopts = dmls.SortOptions(self.parent.parent.filename) test = self.saved_sortopts.load_options() test = bool(test) self.sort_via_options = test value = not test else: value = False self.gui.enable_sorting(value) self.seltitel = 'alle meldingen ' + self.selection super().vulp() msg = '' if self.parent.rereadlist: self.parent.data = {} select = self.sel_args.copy() arch = "" # "alles" if "arch" in select: arch = select.pop("arch") data = shared.get_acties[self.parent.parent.datatype](self.parent.fnaam, select, arch, self.parent.parent.user) for idx, item in enumerate(data): if self.parent.parent.datatype == shared.DataType.XML.name: self.parent.data[idx] = (item[0], item[1], ".".join((item[3][1], item[3][0])), ".".join((item[2][1], item[2][0])), item[5], item[4], True if item[6] == 'arch' else False) elif self.parent.parent.datatype == shared.DataType.SQL.name: self.parent.data[idx] = (item[0], item[1], ".".join((item[5], item[4])), ".".join((str(item[3]), item[2])), item[8], item[6], item[7], item[9]) msg = self.populate_list() # nodig voor sorteren? Geen idee maar als het ergens goed voor is dan moet dit # naar de gui module want sortItems is een qt methode # if self.parent.parent.datatype == shared.DataType.XML.name: # self.gui.p0list.sortItems(self.sorted[0], sortorder[self.sorted[1]]) # , True) # self.parent.current_item = self.gui.get_first_item() self.parent.parent.enable_all_book_tabs(False) self.gui.enable_buttons() if self.gui.has_selection(): self.parent.parent.enable_all_book_tabs(True) self.gui.set_selection() self.gui.ensure_visible(self.parent.current_item) self.parent.parent.set_statusmessage(msg) def populate_list(self): "list control vullen" self.gui.clear_list() self.parent.rereadlist = False items = self.parent.data.items() if items is None: self.parent.parent.set_statusmessage('Selection is None?') if not items: return for _, data in items: new_item = self.gui.add_listitem(data[0]) self.gui.set_listitem_values(new_item, [data[0]] + list(data[2:])) def change_selected(self, item_n): """callback voor wijzigen geselecteerd item, o.a. door verplaatsen van de cursor of door klikken """ self.parent.current_item = item_n self.gui.set_selection() if not self.parent.newitem: selindx = self.gui.get_selected_action() self.readp(selindx) hlp = "&Herleef" if self.parent.pagedata.arch else "&Archiveer" self.gui.set_archive_button_text(hlp) def activate_item(self): """callback voor activeren van item, door doubleclick of enter """ self.goto_actie() def select_items(self, event=None): """tonen van de selectie dialoog niet alleen selecteren op tekst(deel) maar ook op status, soort etc """ args = self.sel_args, None if self.parent.parent.datatype == shared.DataType.SQL.name: data = dmls.SelectOptions(self.parent.fnaam, self.parent.parent.user) args, sel_args = data.load_options(), {} for key, value in args.items(): if key == 'nummer': for item in value: # splitsen in idgt, id en idlt if len(item) == 1: sel_args['id'] = 'and' if item[0] == 'en' else 'or' elif item[1] == 'GT': sel_args['idgt'] = item[0] elif item[1] == 'LT': sel_args['idlt'] = item[0] # elif key == 'arch': # sel_args[key] = {0: 'narch', 1: 'arch', 2: 'alles'}[value] elif value: sel_args[key] = value args = sel_args, data while True: test = gui.show_dialog(self.gui, gui.SelectOptionsDialog, args) if not test: break self.parent.rereadlist = True try: self.vulp() except (dmlx.DataError, dmls.DataError) as msg: self.parent.rereadlist = False gui.show_message(self, str(msg)) else: break def sort_items(self, *args): """tonen van de sorteer-opties dialoog sortering mogelijk op datum/tijd, soort, titel, status via schermpje met 2x4 comboboxjes waarin je de volgorde van de rubrieken en de sorteervolgorde per rubriek kunt aangeven""" sortopts, sortlist = {}, [] if self.parent.parent.datatype == shared.DataType.XML.name: gui.show_message(self.gui, 'Sorry, multi-column sorteren werkt nog niet') return if self.parent.parent.datatype == shared.DataType.SQL.name: sortopts = self.saved_sortopts.load_options() try: sortlist = [x[0] for x in dmls.SORTFIELDS] except AttributeError: pass if not sortlist: sortlist = [x for x in self.parent.ctitels] sortlist[1] = "Soort" sortlist.insert(0, "(geen)") args = sortopts, sortlist test = gui.show_dialog(self.gui, gui.SortOptionsDialog, args) if not test: return if self.sort_via_options: self.gui.enable_sorting(False) self.parent.rereadlist = True try: self.vulp() # moet hier soms nog het daadwerkelijke sorteren tussen (bij XML)? except (dmlx.DataError, dmls.DataError) as msg: self.parent.rereadlist = False gui.show_message(self, str(msg)) else: self.gui.enable_sorting(True) def archiveer(self, *args): "archiveren of herleven van het geselecteerde item" selindx = self.gui.get_selected_action() if self.parent.parent.datatype == shared.DataType.XML.name: selindx = shared.data2str(selindx) else: selindx = shared.data2int(selindx) self.readp(selindx) if self.parent.parent.datatype == shared.DataType.XML.name: self.parent.pagedata.arch = not self.parent.pagedata.arch hlp = "gearchiveerd" if self.parent.pagedata.arch else "herleefd" self.parent.pagedata.events.append((shared.get_dts(), "Actie {0}".format(hlp))) elif self.parent.parent.datatype == shared.DataType.SQL.name: self.parent.pagedata.set_arch(not self.parent.pagedata.arch) self.update_actie() # self.parent.pagedata.write() self.parent.rereadlist = True self.vulp() self.parent.parent.gui.set_tabfocus(0) # het navolgende geldt alleen voor de selectie "gearchiveerd en actief" if self.sel_args.get("arch", "") == "alles": self.gui.ensure_visible(self.parent.current_item) hlp = "&Herleef" if self.parent.pagedata.arch else "&Archiveer" self.gui.set_archive_button_text(hlp) def enable_buttons(self, value=None): "buttons wel of niet bruikbaar maken" if value is not None: self.gui.enable_buttons(value) else: self.gui.enable_buttons() def get_items(self): "retrieve all listitems" return self.gui.get_items() def get_item_text(self, item_or_index, column): "get the item's text for a specified column" return self.gui.get_item_text(item_or_index, column) def clear_selection(self): "initialize selection criteria" self.sel_args = {} class Page1(Page): "pagina 1: startscherm actie" def __init__(self, parent): self.parent = parent super().__init__(parent, pageno=1, standard=False) self.gui = gui.Page1Gui(parent, self) def vulp(self): """te tonen gegevens invullen in velden e.a. initialisaties methode aan te roepen voorafgaand aan het tonen van de pagina""" super().vulp() self.initializing = True self.gui.init_fields() self.parch = False if self.parent.pagedata is not None: # and not self.parent.newitem: self.gui.set_text('id', str(self.parent.pagedata.id)) self.gui.set_text('date', self.parent.pagedata.datum) self.parch = self.parent.pagedata.arch if self.parent.parent.datatype == shared.DataType.XML.name: if self.parent.pagedata.titel is not None: if " - " in self.parent.pagedata.titel: hlp = self.parent.pagedata.titel.split(" - ", 1) else: hlp = self.parent.pagedata.titel.split(": ", 1) self.gui.set_text('proc', hlp[0]) if len(hlp) > 1: self.gui.set_text('desc', hlp[1]) elif self.parent.parent.datatype == shared.DataType.SQL.name: self.gui.set_text('proc', self.parent.pagedata.over) self.gui.set_text('desc', self.parent.pagedata.titel) self.gui.set_choice('stat', self.parent.pagedata.status) self.gui.set_choice('cat', self.parent.pagedata.soort) self.oldbuf = self.gui.set_oldbuf() if self.parch: aanuit = False if self.parent.parent.datatype == shared.DataType.XML.name: if self.parent.pagedata.titel is not None: if " - " in self.parent.pagedata.titel: hlp = self.parent.pagedata.titel.split(" - ", 1) else: hlp = self.parent.pagedata.titel.split(": ", 1) self.gui.set_text('proc', hlp[0]) if len(hlp) > 1: self.gui.set_text('desc', hlp[1]) elif self.parent.parent.datatype == shared.DataType.SQL.name: self.gui.set_text('proc', self.parent.pagedata.over) self.gui.set_text('desc', self.parent.pagedata.titel) self.gui.set_text('arch', "Deze actie is gearchiveerd") self.gui.set_archive_button_text("Herleven") else: aanuit = True self.gui.set_text('arch', '') self.gui.set_archive_button_text("Archiveren") if not self.parent.parent.is_user: aanuit = False self.gui.enable_fields(aanuit) self.initializing = False def savep(self, *args): "opslaan van de paginagegevens" super().savep() proc = self.gui.get_text('proc') self.gui.set_text('proc', proc.capitalize()) self.enable_buttons(False) desc = self.gui.get_text('desc') if proc == "" or desc == "": gui.show_message(self.gui, "Beide tekstrubrieken moeten worden ingevuld") return False wijzig = False procdesc = " - ".join((proc, desc)) if procdesc != self.parent.pagedata.titel: if self.parent.parent.datatype == shared.DataType.XML.name: self.parent.pagedata.titel = procdesc elif self.parent.parent.datatype == shared.DataType.SQL.name: self.parent.pagedata.over = proc self.parent.pagedata.events.append( (shared.get_dts(), 'Onderwerp gewijzigd in "{0}"'.format(proc))) self.parent.pagedata.titel = procdesc = desc self.parent.pagedata.events.append( (shared.get_dts(), 'Titel gewijzigd in "{0}"'.format(procdesc))) wijzig = True newstat, sel = self.gui.get_choice_data('stat') if newstat != self.parent.pagedata.status: self.parent.pagedata.status = newstat self.parent.pagedata.events.append( (shared.get_dts(), 'Status gewijzigd in "{0}"'.format(sel))) wijzig = True newcat, sel = self.gui.get_choice_data('cat') if newcat != self.parent.pagedata.soort: self.parent.pagedata.soort = newcat self.parent.pagedata.events.append( (shared.get_dts(), 'Categorie gewijzigd in "{0}"'.format(sel))) wijzig = True if self.parch != self.parent.pagedata.arch: self.parent.pagedata.set_arch(self.parch) hlp = "gearchiveerd" if self.parch else "herleefd" self.parent.pagedata.events.append( (shared.get_dts(), "Actie {0}".format(hlp))) wijzig = True if wijzig: self.update_actie() # teksten op panel 0 bijwerken pagegui = self.parent.pages[0].gui item = pagegui.get_selection() pagegui.set_item_text(item, 1, self.parent.pagedata.get_soorttext()[0].upper()) pagegui.set_item_text(item, 2, self.parent.pagedata.get_statustext()) pagegui.set_item_text(item, 3, self.parent.pagedata.updated) if self.parent.parent.datatype == shared.DataType.XML.name: pagegui.set_item_text(item, 4, self.parent.pagedata.titel) elif self.parent.parent.datatype == shared.DataType.SQL.name: pagegui.set_item_text(item, 4, self.parent.pagedata.over) pagegui.set_item_text(item, 5, self.parent.pagedata.titel) self.oldbuf = self.gui.set_oldbuf() return True def archiveer(self, *args): "archiveren/herleven" self.parch = not self.parch self.savep() self.parent.rereadlist = True self.vulp() def vul_combos(self): "vullen comboboxen" self.initializing = True self.gui.clear_stats() self.gui.clear_cats() for key in sorted(self.parent.stats.keys()): text, value = self.parent.stats[key][:2] self.gui.add_stat_choice(text, value) for key in sorted(self.parent.cats.keys()): text, value = self.parent.cats[key][:2] self.gui.add_cat_choice(text, value) self.initializing = False def get_field_text(self, entry_type): "return a screen field's text" return self.gui.get_field_text(entry_type) class Page6(Page): "pagina 6: voortgang" def __init__(self, parent): super().__init__(parent, pageno=6, standard=False) self.current_item = 0 self.oldtext = "" self.event_list, self.event_data, self.old_list, self.old_data = [], [], [], [] self.gui = gui.Page6Gui(parent, self) def vulp(self): """te tonen gegevens invullen in velden e.a. initialisaties methode aan te roepen voorafgaand aan het tonen van de pagina""" super().vulp() self.initializing = True self.gui.init_textfield() # self.progress_text.clear() # self.progress_text.setReadOnly(True) if self.parent.pagedata: self.event_list = [x[0] for x in self.parent.pagedata.events] self.event_list.reverse() self.old_list = self.event_list[:] self.event_data = [x[1] for x in self.parent.pagedata.events] self.event_data.reverse() self.old_data = self.event_data[:] if self.parent.parent.is_user: text = '-- doubleclick or press Shift-Ctrl-N to add new item --' else: text = '-- adding new items is disabled --' self.gui.init_list(text) for idx, datum in enumerate(self.event_list): self.gui.add_item_to_list(idx, datum) if self.parent.parent.datatype == shared.DataType.SQL.name: self.gui.set_list_callback() # self.gui.clear_textfield() - zit al in init_textfield self.oldbuf = (self.old_list, self.old_data) self.oldtext = '' self.initializing = False def savep(self, *args): "opslaan van de paginagegevens" super().savep() # voor het geval er na het aanpassen van een tekst direkt "sla op" gekozen is # nog even kijken of de tekst al in self.event_data is aangepast. idx = self.current_item hlp = self.gui.get_textfield_contents() if idx > 0: idx -= 1 if self.event_data[idx] != hlp: self.event_data[idx] = hlp self.oldtext = hlp short_text = hlp.split("\n")[0] if len(short_text) < 80: short_text = short_text[:80] + "..." if self.parent.parent.datatype == shared.DataType.XML.name: short_text = short_text.encode('latin-1') self.gui.set_listitem_text(idx + 1, "{} - {}".format(self.event_list[idx], short_text)) self.gui.set_listitem_data(idx + 1) wijzig = False if self.event_list != self.old_list or self.event_data != self.old_data: wijzig = True hlp = len(self.event_list) - 1 for idx, data in enumerate(self.parent.pagedata.events): if data != (self.event_list[hlp - idx], self.event_data[hlp - idx]): self.parent.pagedata.events[idx] = (self.event_list[hlp - idx], self.event_data[hlp - idx]) for idx in range(len(self.parent.pagedata.events), hlp + 1): if self.event_data[hlp - idx]: self.parent.pagedata.events.append((self.event_list[hlp - idx], self.event_data[hlp - idx])) if wijzig: self.update_actie() # waar is deze voor (self.book.current_item.setText) ? # self.parent.current_item = self.parent.page0.p0list.topLevelItem(x) # self.parent.current_item.setText(4, self.parent.pagedata.updated) self.parent.pages[0].gui.set_item_text(self.parent.current_item, 3, self.parent.pagedata.updated) # dit was self.parent.page0.p0list.currentItem().setText( -- is dat niet hetzelfde? self.old_list = self.event_list[:] self.old_data = self.event_data[:] self.oldbuf = (self.old_list, self.old_data) return True def goto_prev(self, *args): "set the selection to the previous row, if possible" test = self.gui.get_list_row() - 1 if test > 0: self.gui.set_list_row(test) def goto_next(self, *args): "set the selection to the next row, if possible" test = self.gui.get_list_row() + 1 if test < self.gui.get_list_rowcount(): self.gui.set_list_row(test) def on_text(self, *args): """callback voor wanneer de tekst gewijzigd is de initializing flag wordt uitgevraagd omdat deze event ook tijdens vulp() en wijzigen van list positie plaatsvindt """ if self.initializing: return # lees de inhoud van het tekstveld en vergelijk deze met de buffer tekst = self.gui.get_textfield_contents() # str(self.progress_text.get_contents()) # self.progress_list.GetItemText(ix) if tekst != self.oldtext: # stel de buffer in op de nieuwe tekst self.oldtext = tekst # maak er platte tekst van om straks in de listbox bij te werken tekst_plat = self.gui.convert_text(self.oldtext, to='plain') # stel in dat we niet van dit scherm af kunnen zonder te updaten if self.parent.parent.is_user: self.enable_buttons() self.current_item = self.gui.get_list_row() if self.current_item > 0: indx = self.current_item - 1 self.event_data[indx] = tekst # item = self.progress_list.currentItem() # datum = str(item.text()).split(' - ')[0] datum = self.gui.get_listitem_text(self.current_item).split(' - ')[0] short_text = ' - '.join((datum, tekst_plat.split("\n")[0])) if len(short_text) >= 80: short_text = short_text[:80] + "..." # item.setText(short_text) self.gui.set_listitem_text(self.current_item, short_text) class TabOptions: "hulp klasse bij dialoog voor mogelijke tab headers" def initstuff(self, parent): "aanvullende initialisatie" self.titel = "Tab titels" self.data = [] for key in sorted(parent.master.book.tabs.keys()): tab_text = parent.master.book.tabs[key].split(" ", 1)[1] self.data.append(tab_text) self.tekst = ["De tab titels worden getoond in de volgorde", "zoals ze van links naar rechts staan.", "Er kunnen geen tabs worden verwijderd of toegevoegd."] self.editable = False def leesuit(self, parent, optionslist): "wijzigingen doorvoeren" self.newtabs = {} for idx, item in enumerate(optionslist): self.newtabs[str(idx)] = str(item) parent.master.save_settings("tab", self.newtabs) class StatOptions: "hulp klasse bij dialoog voor de mogelijke statussen" def initstuff(self, parent): "aanvullende initialisatie" self.titel = "Status codes en waarden" self.data = [] for key in sorted(parent.master.book.stats.keys()): if parent.master.datatype == shared.DataType.XML.name: item_text, item_value = parent.master.book.stats[key] self.data.append(": ".join((item_value, item_text))) elif parent.master.datatype == shared.DataType.SQL.name: item_text, item_value, row_id = parent.master.book.stats[key] self.data.append(": ".join((item_value, item_text, row_id))) self.tekst = ["De waarden voor de status worden getoond in dezelfde volgorde", "als waarin ze in de combobox staan.", "Vóór de dubbele punt staat de code, erachter de waarde.", "Denk erom dat als je codes wijzigt of statussen verwijdert, deze", "ook niet meer getoond en gebruikt kunnen worden in de registratie.", "Omschrijvingen kun je rustig aanpassen"] self.editable = True def leesuit(self, parent, optionslist): "wijzigingen doorvoeren" self.newstats = {} for sortkey, item in enumerate(optionslist): try: value, text = str(item).split(": ") except ValueError: return 'Foutieve waarde: bevat geen dubbele punt' self.newstats[value] = (text, sortkey) parent.master.save_settings("stat", self.newstats) return '' class CatOptions: "hulp klasse bij dialoog voor de mogelijke categorieen" def initstuff(self, parent): "aanvullende initialisatie" self.titel = "Soort codes en waarden" self.data = [] for key in sorted(parent.master.book.cats.keys()): if parent.master.datatype == shared.DataType.XML.name: item_value, item_text = parent.master.book.cats[key] self.data.append(": ".join((item_text, item_value))) elif parent.master.datatype == shared.DataType.SQL.name: item_value, item_text, row_id = parent.master.book.cats[key] self.data.append(": ".join((item_text, item_value, str(row_id)))) self.tekst = ["De waarden voor de soorten worden getoond in dezelfde volgorde", "als waarin ze in de combobox staan.", "Vóór de dubbele punt staat de code, erachter de waarde.", "Denk erom dat als je codes wijzigt of soorten verwijdert, deze", "ook niet meer getoond en gebruikt kunnen worden in de registratie.", "Omschrijvingen kun je rustig aanpassen"] self.editable = True def leesuit(self, parent, optionslist): "wijzigingen doorvoeren" self.newcats = {} for sortkey, item in enumerate(optionslist): try: value, text = str(item).split(": ") except ValueError: return 'Foutieve waarde: bevat geen dubbele punt' self.newcats[value] = (text, sortkey) parent.master.save_settings("cat", self.newcats) return '' class MainWindow(): """Hoofdscherm met menu, statusbalk, notebook en een "quit" button""" def __init__(self, parent, fnaam="", version=None): # if not version: # raise ValueError('No data method specified') self.parent = parent self.datatype = version self.dirname, self.filename = '', '' self.title = 'Actieregistratie' self.initializing = True self.exiting = False self.helptext = '' # self.pagedata = None # self.oldbuf = None self.is_newfile = False self.oldsort = -1 self.idlist = self.actlist = self.alist = [] shared.log('fnaam is %s', fnaam) self.projnames = dmls.get_projnames() if fnaam: if fnaam == 'xml' or os.path.exists(fnaam): self.datatype = shared.DataType.XML.name if fnaam != 'xml': test = pathlib.Path(fnaam) self.dirname, self.filename = test.parent, test.name shared.log('XML: %s %s', self.dirname, self.filename) elif fnaam == 'sql' or fnaam.lower() in [x[0] for x in self.projnames]: self.datatype = shared.DataType.SQL.name if fnaam == 'basic': self.filename = '_basic' elif fnaam != 'sql': self.filename = fnaam.lower() shared.log('SQL: %s', self.filename) else: fnaam = '' self.gui = gui.MainGui(self) if not self.datatype: self.filename = '' choice = gui.get_choice_item(None, 'Select Mode', ['XML', 'SQL']) if choice == 'XML': self.datatype = shared.DataType.XML.name elif choice == 'SQL': self.datatype = shared.DataType.SQL.name else: raise SystemExit('No datatype selected') self.user = None # start without user self.is_user = self.is_admin = False if self.datatype == shared.DataType.XML.name: self.user = 1 # pretend user self.is_user = self.is_admin = True # force editability for XML mode self.create_book() self.gui.create_menu() self.gui.create_actions() self.create_book_pages() if self.datatype == shared.DataType.XML.name: if self.filename == "": self.open_xml() else: self.startfile() elif self.datatype == shared.DataType.SQL.name: if self.filename: self.open_sql(do_sel=False) else: self.open_sql() self.initializing = False def get_menu_data(self): """Define application menu """ data = [("&File", [("&Open", self.open_xml, 'Ctrl+O', " Open a new file"), ("&New", self.new_file, 'Ctrl+N', " Create a new file"), ('',), ("&Print", (("Dit &Scherm", self.print_scherm, 'Shift+Ctrl+P', "Print the contents of the current screen"), ("Deze &Actie", self.print_actie, 'Alt+Ctrl+P', "Print the contents of the current issue"))), ('',), ("&Quit", self.exit_app, 'Ctrl+Q', " Terminate the program")]), ("&Login", [("&Go", self.sign_in, 'Ctrl+L', " Sign in to the database")]), ("&Settings", (("&Applicatie", (("&Lettertype", self.font_settings, '', " Change the size and font of the text"), ("&Kleuren", self.colour_settings, '', " Change the colours of various items"))), ("&Data", (("&Tabs", self.tab_settings, '', " Change the titles of the tabs"), ("&Soorten", self.cat_settings, '', " Add/change type categories"), ("St&atussen", self.stat_settings, '', " Add/change status categories"))), ("&Het leven", self.silly_menu, '', " Change the way you look at life"))), ("&View", []), ("&Help", (("&About", self.about_help, 'F1', " Information about this program"), ("&Keys", self.hotkey_help, 'Ctrl+H', " List of shortcut keys")))] for tabnum, tabtitle in self.book.tabs.items(): data[3][1].append(('&{}'.format(tabtitle), functools.partial(self.gui.go_to, int(tabnum)), 'Alt+{}'.format(tabnum), "switch to tab")) if self.datatype == shared.DataType.XML.name: data.pop(1) elif self.datatype == shared.DataType.SQL.name: data[0][1][0] = ("&Other project", self.open_sql, 'Ctrl+O', " Select a project") data[0][1][1] = ("&New", self.new_file, 'Ctrl+N', " Create a new project") return data def create_book(self): """define the tabbed interface and its subclasses """ self.book = self.gui.get_bookwidget() self.book.parent = self self.book.fnaam = "" if self.filename and self.datatype == shared.DataType.SQL.name: self.book.fnaam = self.filename self.book.current_item = None self.book.data = {} self.book.rereadlist = True self.lees_settings() # print('in create book na lees_settings: book.tabs is', self.book.tabs) self.book.ctitels = ["actie", " ", "status", "L.wijz."] if self.datatype == shared.DataType.XML.name: self.book.ctitels.append("titel") elif self.datatype == shared.DataType.SQL.name: self.book.ctitels.extend(("betreft", "omschrijving")) self.book.current_tab = -1 self.book.pages = [] self.book.newitem = False self.book.changed_item = True self.book.pagedata = None def create_book_pages(self): "add the pages to the tabbed widget" self.book.pages.append(Page0(self.book)) self.book.pages.append(Page1(self.book)) self.book.pages.append(Page(self.book, 2)) self.book.pages.append(Page(self.book, 3)) self.book.pages.append(Page(self.book, 4)) self.book.pages.append(Page(self.book, 5)) self.book.pages.append(Page6(self.book)) # print('in create_book_pages: book.tabs is', self.book.tabs) for i, page in enumerate(self.book.pages): self.gui.add_book_tab(page, "&" + self.book.tabs[i]) self.enable_all_book_tabs(False) def not_implemented_message(self): "information" gui.show_message(self.gui, "Sorry, werkt nog niet") def new_file(self, event=None): "Menukeuze: nieuw file" if self.datatype == shared.DataType.SQL.name: self.not_implemented_message() return self.is_newfile = False # self.dirname = str(self.dirname) # defaults to '.' so no need for `or os.getcwd()` fname = gui.get_save_filename(self.gui, start=self.dirname) if fname: test = pathlib.Path(fname) if test.suffix != '.xml': gui.show_message(self.gui, 'Naam voor nieuw file moet wel extensie .xml hebben') return self.dirname, self.filename = test.parent, test.name self.is_newfile = True self.startfile() self.is_newfile = False self.enable_all_book_tabs(False) def open_xml(self, event=None): "Menukeuze: open file" shared.log('in open_xml: %s', self.filename) self.dirname = self.dirname or os.getcwd() fname = gui.get_open_filename(self.gui, start=self.dirname) if fname: test = pathlib.Path(fname) self.dirname, self.filename = test.parent, test.name self.startfile() def open_sql(self, event=None, do_sel=True): "Menukeuze: open project" shared.log('in open_sql: %s', self.filename) current = choice = 0 data = self.projnames if self.filename in data: current = data.index(self.filename) if do_sel: choice = gui.get_choice_item(self.gui, 'Kies een project om te openen', [": ".join((h[0], h[2])) for h in data], current) else: for h in data: shared.log(h) if h[0] == self.filename or (h[0] == 'basic' and self.filename == "_basic"): choice = h[0] break if choice: self.filename = choice.split(': ')[0] if self.filename in ("Demo", 'basic'): self.filename = "_basic" self.startfile() def print_something(self, event=None): """callback voor ctrl-P(rint) vraag om printen scherm of actie, bv. met een InputDialog """ choices = ['huidig scherm', 'huidige actie'] choice = gui.get_choice_item(self, 'Wat wil je afdrukken?', choices) if choice == choices[0]: self.print_scherm() elif choice == choices[1]: self.print_actie() def print_scherm(self, event=None): "Menukeuze: print dit scherm" self.printdict = {'lijst': [], 'actie': [], 'sections': [], 'events': []} self.hdr = "Actie: {} {}".format(self.book.pagedata.id, self.book.pagedata.titel) if self.book.current_tab == 0: self.hdr = "Overzicht acties uit " + self.filename lijst = [] page = self.book.pages[0] for item in page.get_items(): actie = page.get_item_text(item, 0) started = '' soort = page.get_item_text(item, 1) for x in self.book.cats.values(): oms, code = x[0], x[1] if code == soort: soort = oms break status = page.get_item_text(item, 2) l_wijz = page.get_item_text(item, 3) titel = page.get_item_text(item, 4) if self.datatype == shared.DataType.SQL.name: over = titel titel = page.get_item_text(item, 5) l_wijz = l_wijz[:19] actie = actie + " - " + over started = started[:19] if status != self.book.stats[0][0]: if l_wijz: l_wijz = ", laatst behandeld op " + l_wijz l_wijz = "status: {}{}".format(status, l_wijz) else: hlp = "status: {}".format(status) if l_wijz and not started: hlp += ' op {}'.format(l_wijz) l_wijz = hlp lijst.append((actie, titel, soort, started, l_wijz)) self.printdict['lijst'] = lijst elif self.book.current_tab == 1: data = {x: self.book.pages[1].get_field_text(x) for x in ('actie', 'datum', 'oms', 'tekst', 'soort', 'status')} self.hdr = "Informatie over actie {}: samenvatting".format(data["actie"]) self.printdict.update(data) elif 2 <= self.book.current_tab <= 5: title = self.book.tabs[self.book.current_tab].split(None, 1)[1] # if self.book.current_tab == 2: text = self.book.pages[self.book.current_tab].get_textarea_contents() # elif self.book.current_tab == 3: # text = self.book.page3.get_textarea_contents() # elif self.book.current_tab == 4: # text = self.book.page4.get_textarea_contents() # elif self.book.current_tab == 5: # text = self.book.page5.get_textarea_contents() self.printdict['sections'] = [(title, text)] elif self.book.current_tab == 6: events = [] for idx, data in enumerate(self.book.pages[6].event_list): if self.datatype == shared.DataType.SQL.name: data = data[:19] events.append((data, self.book.pages[6].event_data[idx])) self.printdict['events'] = events self.gui.preview() def print_actie(self, event=None): "Menukeuze: print deze actie" if self.book.pagedata is None: # or self.book.newitem: gui.show_message(self.gui, "Wel eerst een actie kiezen om te printen") return self.hdr = ("Actie: {} {}".format(self.book.pagedata.id, self.book.pagedata.titel)) tekst = self.book.pagedata.titel try: oms, tekst = tekst.split(" - ", 1) except ValueError: try: oms, tekst = tekst.split(": ", 1) except ValueError: oms = '' srt = "(onbekende soort)" for srtoms, srtcode in self.book.cats.values(): if srtcode == self.book.pagedata.soort: srt = srtoms break stat = "(onbekende status)" for statoms, statcode in self.book.stats.values(): if statcode == self.book.pagedata.status: stat = statoms break self.printdict = {'lijst': [], 'actie': self.book.pagedata.id, 'datum': self.book.pagedata.datum, 'oms': oms, 'tekst': tekst, 'soort': srt, 'status': stat} empty = "(nog niet beschreven)" sections = [[title.split(None, 1)[1], ''] for key, title in self.book.tabs.items() if key > 2] sections[0][1] = self.book.pagedata.melding or empty sections[1][1] = self.book.pagedata.oorzaak or empty sections[2][1] = self.book.pagedata.oplossing or empty sections[3][1] = self.book.pagedata.vervolg or '' if not sections[3][1]: sections.pop() self.printdict['sections'] = sections self.printdict['events'] = [(x, y) for x, y in self.book.pagedata.events] or [] self.gui.preview() def exit_app(self, event=None): "Menukeuze: exit applicatie" self.exiting = True ok_to_leave = True # while we don't have pages yet if self.book.current_tab > -1: ok_to_leave = self.book.pages[self.book.current_tab].leavep() if ok_to_leave: self.gui.exit() def sign_in(self, *args): """aanloggen in SQL/Django mode """ logged_in = False while not logged_in: ok = gui.show_dialog(self.gui, gui.LoginBox) if not ok: break test = dmls.validate_user(*self.gui.dialog_data) if test: text = 'Login accepted' logged_in = True else: text = 'Login failed' gui.show_message(self.gui, text) if logged_in: self.user, self.is_user, self.is_admin = test # print('in signin:', self.user, self.is_user) self.book.rereadlist = True self.gui.refresh_page() def tab_settings(self, event=None): "Menukeuze: settings - data - tab titels" gui.show_dialog(self.gui, gui.SettOptionsDialog, args=(TabOptions, "Wijzigen tab titels")) def stat_settings(self, event=None): "Menukeuze: settings - data - statussen" gui.show_dialog(self.gui, gui.SettOptionsDialog, args=(StatOptions, "Wijzigen statussen")) def cat_settings(self, event=None): "Menukeuze: settings - data - soorten" gui.show_dialog(self.gui, gui.SettOptionsDialog, args=(CatOptions, "Wijzigen categorieën")) def font_settings(self, event=None): "Menukeuze: settings - applicatie - lettertype" self.not_implemented_message() def colour_settings(self, event=None): "Menukeuze: settings - applicatie - kleuren" self.not_implemented_message() def hotkey_settings(self, event=None): "Menukeuze: settings - applicatie- hotkeys (niet geactiveerd)" self.not_implemented_message() def about_help(self, event=None): "Menukeuze: help - about" gui.show_message(self.gui, "PyQt versie van mijn actiebox") def hotkey_help(self, event=None): "menukeuze: help - keys" if not self.helptext: lines = ["=== Albert's actiebox ===\n", "Keyboard shortcuts:", " Alt left/right: verder - terug", " Alt-0 t/m Alt-6: naar betreffende pagina", " Alt-O op tab 1: S_o_rteren", " Alt-I op tab 1: F_i_lteren", " Alt-G of Enter op tab 1: _G_a naar aangegeven actie", " Alt-N op elke tab: _N_ieuwe actie opvoeren", " Ctrl-P: _p_rinten (scherm of actie)", " Shift-Ctrl-P: print scherm", " Alt-Ctrl-P: print actie", " Ctrl-Q: _q_uit actiebox", " Ctrl-H: _h_elp (dit scherm)", " Ctrl-S: gegevens in het scherm op_s_laan", " Ctrl-G: oplaan en _g_a door naar volgende tab", " Ctrl-Z in een tekstveld: undo", " Shift-Ctrl-Z in een tekstveld: redo", " Alt-Ctrl-Z overal: wijzigingen ongedaan maken", " Shift-Ctrl-N op tab 6: nieuwe regel opvoeren", " Ctrl-up/down op tab 6: move in list"] if self.datatype == shared.DataType.XML.name: lines.insert(8, " Ctrl-O: _o_pen een (ander) actiebestand") lines.insert(8, " Ctrl-N: maak een _n_ieuw actiebestand") elif self.datatype == shared.DataType.SQL.name: lines.insert(8, " Ctrl-O: selecteer een (ander) pr_o_ject") self.helptext = "\n".join(lines) gui.show_message(self.gui, self.helptext) def silly_menu(self, event=None): "Menukeuze: settings - het leven" gui.show_message(self.gui, "Yeah you wish...\nHet leven is niet in te stellen helaas") def startfile(self): "initialisatie t.b.v. nieuw bestand" if self.datatype == shared.DataType.XML.name: fullname = self.dirname / self.filename retval = dmlx.checkfile(fullname, self.is_newfile) if retval != '': gui.show_message(self.gui, retval) return retval self.book.fnaam = fullname self.title = self.filename elif self.datatype == shared.DataType.SQL.name: self.book.fnaam = self.title = self.filename self.book.rereadlist = True self.book.sorter = None self.lees_settings() self.gui.set_tab_titles(self.book.tabs) self.book.pages[0].clear_selection() self.book.pages[1].vul_combos() if self.book.current_tab == 0: self.book.pages[0].vulp() else: self.gui.select_first_tab() self.book.changed_item = True return '' def lees_settings(self): """instellingen (tabnamen, actiesoorten en actiestatussen) inlezen""" self.book.stats = {0: ('dummy,', 0, 0)} self.book.cats = {0: ('dummy,', ' ', 0)} self.book.tabs = {0: '0 start'} data = shared.Settings[self.datatype](self.book.fnaam) ## print(data.meld) # "Standaard waarden opgehaald" self.imagecount = data.imagecount self.book.stats = {} self.book.cats = {} self.book.tabs = {} self.book.pagehelp = ["Overzicht van alle acties", "Identificerende gegevens van de actie", "Beschrijving van het probleem of wens", "Analyse van het probleem of wens", "Voorgestelde oplossing", "Eventuele vervolgactie(s)", "Overzicht stand van zaken"] for item_value, item in data.stat.items(): if self.datatype == shared.DataType.XML.name: item_text, sortkey = item self.book.stats[int(sortkey)] = (item_text, item_value) elif self.datatype == shared.DataType.SQL.name: item_text, sortkey, row_id = item self.book.stats[int(sortkey)] = (item_text, item_value, row_id) for item_value, item in data.cat.items(): if self.datatype == shared.DataType.XML.name: item_text, sortkey = item self.book.cats[int(sortkey)] = (item_text, item_value) elif self.datatype == shared.DataType.SQL.name: item_text, sortkey, row_id = item self.book.cats[int(sortkey)] = (item_text, item_value, row_id) for tab_num, tab_text in data.kop.items(): if self.datatype == shared.DataType.XML.name: self.book.tabs[int(tab_num)] = " ".join((tab_num, tab_text)) elif self.datatype == shared.DataType.SQL.name: tab_text = tab_text[0] # , tab_adr = tab_text self.book.tabs[int(tab_num)] = " ".join((tab_num, tab_text.title())) # print('in lees_settings voor', self.book.fnaam, 'book.tabs is', self.book.tabs) def save_settings(self, srt, data): """instellingen (tabnamen, actiesoorten of actiestatussen) terugschrijven argumenten: soort, data data is een dictionary die in een van de dialogen TabOptions, CatOptions of StatOptions wordt opgebouwd""" settings = shared.Settings[self.datatype](self.book.fnaam) if srt == "tab": settings.kop = data settings.write() self.book.tabs = {} for item_value, item_text in data.items(): item = " ".join((item_value, item_text)) self.book.tabs[int(item_value)] = item self.gui.set_page_title(int(item_value), item) elif srt == "stat": settings.stat = data settings.write() self.book.stats = {} for item_value, item in data.items(): if self.datatype == shared.DataType.XML.name: item_text, sortkey = item self.book.stats[sortkey] = (item_text, item_value) elif self.datatype == shared.DataType.SQL.name: item_text, sortkey, row_id = item self.book.stats[sortkey] = (item_text, item_value, row_id) elif srt == "cat": settings.cat = data settings.write() self.book.cats = {} for item_value, item in data.items(): if self.datatype == shared.DataType.XML.name: item_text, sortkey = item self.book.cats[sortkey] = (item_text, item_value) elif self.datatype == shared.DataType.SQL.name: item_text, sortkey, row_id = item self.book.cats[sortkey] = (item_text, item_value, row_id) self.book.pages[1].vul_combos() def goto_next(self, *args): """redirect to the method of the current page """ Page.goto_next(self.book.pages[self.book.current_tab]) def goto_prev(self, *args): """redirect to the method of the current page """ Page.goto_prev(self.book.pages[self.book.current_tab]) def goto_page(self, page): """redirect to the method of the current page """ # print('in MainWindow.goto_page naar page', page, 'van page', self.book.current_tab) Page.goto_page(self.book.pages[self.book.current_tab], page) def enable_settingsmenu(self): "instellen of gebruik van settingsmenu mogelijk is" self.gui.enable_settingsmenu() def set_windowtitle(self, text): "build title for window" self.gui.set_window_title(text) def set_statusmessage(self, msg=''): """stel tekst in statusbar in """ if not msg: msg = self.book.pagehelp[self.book.current_tab] if self.book.current_tab == 0: msg += ' - {} items'.format(len(self.book.data)) self.gui.set_statusmessage(msg) if self.datatype == shared.DataType.SQL.name: if self.user: msg = 'Aangemeld als {}'.format(self.user.username) else: msg = 'Niet aangemeld' self.gui.show_username(msg) def get_focus_widget_for_tab(self, tabno): "determine field to set focus on" return (self.book.pages[0].gui.p0list, self.book.pages[1].gui.proc_entry, self.book.pages[2].gui.text1, self.book.pages[3].gui.text1, self.book.pages[4].gui.text1, self.book.pages[5].gui.text1, self.book.pages[6].gui.progress_list)[tabno] def enable_all_book_tabs(self, state): "make all tabs (in)accessible" self.gui.enable_book_tabs(state, tabfrom=1) def main(arg=None): "opstart routine" # if arg is None: # version = shared.DataType.SQL.name # else: # version = shared.DataType.XML.name # try: frame = MainWindow(None, arg) # , version) frame.gui.go() # except ValueError as err: # print(err)

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0.307882

0

0.12979

0.002902

0

1

0.067323

false

0.003284

0.005747

0

0.10509

0.01642

0

null

0

null

0

1

0

2b2fdb3f5840708a53911e04a2280ed67c225b83

6,055

py

Python

back-end/python-scripts/add_vm.py

matteomitico/vtc-senior-project

778b59537edae54fbcb284a862bad587469b96af

[ "MIT" ]

null

back-end/python-scripts/add_vm.py

matteomitico/vtc-senior-project

778b59537edae54fbcb284a862bad587469b96af

[ "MIT" ]

null

back-end/python-scripts/add_vm.py

matteomitico/vtc-senior-project

778b59537edae54fbcb284a862bad587469b96af

[ "MIT" ]

null

#!/usr/bin/env python import json import sys, os import virtualbox from os.path import expanduser from pprint import pprint from subprocess import call #create virtual machine from the repository # arg1 = vmType # arg2 = vmName # return 1|0 success or failure # list of all VMs gateway = '155.42.97.220' #gateway = '155.42.13.162' vms_repo_path = expanduser("~") + '/cluster/VMs' vm_repo = { 'nodejs' : { 'folder' : '/nodejs/nodejs-13.0-i386/', 'file' : 'nodejs.ovf' , 'vm_name' : 'nodejs-13.0-i386', 'ports': { 'http' : {'rule': '10.0.2.15,8000' , 'note': 'http://{{the-gatway}}' }, 'https' : {'rule': '10.0.2.15,443' , 'note': 'https://{{the-gatway}}' }, 'webshell' : {'rule': '10.0.2.15,12320', 'note': 'https://{{the-gatway}}' }, 'webmin' : {'rule': '10.0.2.15,12321', 'note': 'https://{{the-gatway}}' }, 'ssh' : {'rule': '10.0.2.15,22' , 'note': 'root@{{the-gatway}} port'} }, 'notes' : [ 'Webmin, SSH: username:root', 'default password:root (remember to change!)' ] }, 'postgresql' : { 'folder' : '/postgresql/postgresql-i386/', 'file' : 'postgresql-i386.ovf' , 'vm_name' : 'postgres', 'ports': { 'PHPPgAdmin' : {'rule': '10.0.2.15,443', 'note': 'https://{{the-gatway}}' } , 'webshell' : {'rule': '10.0.2.15,12320', 'note': 'https://{{the-gatway}}' } , 'webmin' : {'rule': '10.0.2.15,12321', 'note': 'https://{{the-gatway}}' } , 'ssh' : {'rule': '10.0.2.15,22', 'note': 'root@{{the-gatway}} port'} }, 'notes' : [ 'Webmin, SSH: username:root, password:root(remember to change!)', 'postgresql, phppgadmin: username:postgres, password:postgresroot(remember to change!)', 'PostgreSQL: psql -U postgres -h 10.0.2.15' ] }, 'lamp' : { 'folder' : '/lamp/lamp-13.0-i386/', 'file' : 'lamp-13.0.ovf' , 'vm_name' : 'lamp-13.0', 'ports': { 'http' : {'rule': '10.0.2.15,80' , 'note': 'http://{{the-gatway}}' }, 'https' : {'rule': '10.0.2.15,443' , 'note': 'https://{{the-gatway}}' }, 'webshell' : {'rule': '10.0.2.15,12320', 'note': 'https://{{the-gatway}}' }, 'webmin' : {'rule': '10.0.2.15,12321', 'note': 'https://{{the-gatway}}' }, 'ssh' : {'rule': '10.0.2.15,22' , 'note': 'root@{{the-gatway}} port'}, 'PHPMyAdmin' : {'rule': '10.0.2.15,12322', 'note': 'https://{{the-gatway}}' } , }, 'notes' : [ 'Webmin, SSH: username:root, password:root(remember to change!)', 'MySQL, phpMyAdmin: username:root, password:mysql(remember to change!)' ] }, 'rails' : { 'folder' : '/rails/lamp-13.0-i386/', 'file' : 'lamp-13.0.ovf' , 'vm_name' : 'lamp-13.0', 'ports': { 'http' : {'rule': '10.0.2.15,80' , 'note': 'http://{{the-gatway}}' }, 'https' : {'rule': '10.0.2.15,443' , 'note': 'https://{{the-gatway}}' }, 'webshell' : {'rule': '10.0.2.15,12320', 'note': 'https://{{the-gatway}}' }, 'webmin' : {'rule': '10.0.2.15,12321', 'note': 'https://{{the-gatway}}' }, 'ssh' : {'rule': '10.0.2.15,22' , 'note': 'root@{{the-gatway}} port'}, 'PHPMyAdmin' : {'rule': '10.0.2.15,12322', 'note': 'https://{{the-gatway}}' } , }, 'notes' : [ 'Webmin, SSH: username:root, password:root(remember to change!)', 'MySQL: username:root, password:mysql(remember to change!)' ] } } port_ranges = {'http' : 8000, 'https' : 4000, 'ssh' : 22000, 'webshell' : 12000, 'webmin' : 13000, 'PHPPgAdmin': 14000, 'PHPMyAdmin': 15000, } vm_type = sys.argv[1] vm_name = sys.argv[2] vbox = virtualbox.VirtualBox() notes = vm_repo[vm_type]['notes'] # Create new IAppliance and read the exported machine # called 'vmName'. path = os.path.normpath(vms_repo_path + vm_repo[vm_type]['folder'] + vm_repo[vm_type]['file']) #print path #adding port forwardings if os.stat("usedports.json").st_size == 0 : used_ports = {} else: used_ports = json.load(open("usedports.json")) ports_to_forward = [] for port_forw_rule in vm_repo[vm_type]['ports']: #print "checking: " + port_forw_rule + str(port_ranges[port_forw_rule]) for the_port in range(port_ranges[port_forw_rule]+1, port_ranges[port_forw_rule] + 1000): #print the_port #print used_ports if not str(the_port) in used_ports: #print the_port #print port_forw_rule + ",tcp," + gateway + "," + str(the_port) + "," + vm_repo[vm_type]['ports'][port_forw_rule] #print 'making notes' note = port_forw_rule + ": " + vm_repo[vm_type]['ports'][port_forw_rule]['note'] + ":" + str(the_port) #print note note = note.replace("{{the-gatway}}", gateway); #print note notes.append(note) #pprint (["VBoxManage", 'modifyvm', vm_name , '--natpf1', port_forw_rule + ",tcp," + gateway + "," + str(the_port) + "," + vm_repo[vm_type]['ports'][port_forw_rule]['rule']]) ports_to_forward.append(["VBoxManage", 'modifyvm', vm_name , '--natpf1', port_forw_rule + ",tcp," + gateway + "," + str(the_port) + "," + vm_repo[vm_type]['ports'][port_forw_rule]['rule'] ]) #call(["VBoxManage", 'modifyvm', vm_name , '--natpf1', port_forw_rule + ",tcp," + gateway + "," + str(the_port) + "," + vm_repo[vm_type]['ports'][port_forw_rule]['rule'] ]) #print 'done' used_ports[the_port] = vm_name + ":" + vm_type break json.dump(used_ports, open("usedports.json",'w')) appliance = vbox.create_appliance() appliance.read(path) # Extract the IVirtualSystemDescription object # for 'ubuntu' and set its name to 'foobar' and cpu '2'. #desc = appliance.interpret() # for some reason this returns empty so I go with find_description(name of the vm when it is imported) #pprint (dir(desc)) desc = appliance.find_description(vm_repo[vm_type]['vm_name']) #pprint (dir(desc)) desc.set_name(vm_name) my_desc = ''; for n in notes: my_desc += n + ',\n' #print my_desc desc.set_final_value(9, my_desc ) try: # perform import progress = appliance.import_machines() progress.wait_for_completion() new_vm = vbox.find_machine(vm_name) for pf in ports_to_forward: call(pf) result = {'status' : 1, 'note' : notes} pprint (result) except: pprint (vars(sys.exc_info()[0])) #print 0

34.798851

193

0.605945

850

6,055

4.184706

0.215294

0.018555

0.02474

0.03711

0.467529

0.444195

0.4307

0.402305

0.394152

0

0.06442

0.166804

6,055

173

194

35

0.640634

0.196367

0

0.278689

0

0.435728

0.049265

0

1

0

false

0.057377

0

0.057377

0.02459

0

null

0

null

0

1

0

1

2b30462f4e15d1e7277002cce72ced8525343755

982

py

Python

dailyblink/media.py

ptrstn/dailyblink

16fe482552b101d83412bfbb662b8754682ba7d2

[ "MIT" ]

25

2020-05-01T16:34:11.000Z

2022-02-19T09:39:20.000Z

dailyblink/media.py

ptrstn/dailyblink

16fe482552b101d83412bfbb662b8754682ba7d2

[ "MIT" ]

24

2020-12-07T21:07:11.000Z

2022-03-15T18:18:00.000Z

dailyblink/media.py

ptrstn/dailyblink

16fe482552b101d83412bfbb662b8754682ba7d2

[ "MIT" ]

6

2021-03-05T09:19:37.000Z

2022-01-01T08:25:14.000Z

import pathlib from mutagen.mp4 import MP4 def create_file(content, path, mode): pathlib.Path(path).parent.mkdir(parents=True, exist_ok=True) with open(path, mode) as file: file.write(content) def save_media(media, file_path): create_file(content=media, path=file_path, mode="wb") def save_text(text, file_path): create_file(content=text, path=file_path, mode="w+") def set_m4a_meta_data( filename, artist=None, title=None, album=None, track_number=None, total_track_number=None, genre=None, ): mp4_file = MP4(filename) if not mp4_file.tags: mp4_file.add_tags() tags = mp4_file.tags if artist: tags["\xa9ART"] = artist if title: tags["\xa9alb"] = album if album: tags["\xa9nam"] = title if track_number and total_track_number: tags["trkn"] = [(track_number, total_track_number)] if genre: tags["\xa9gen"] = genre tags.save(filename)

20.458333

64

0.647658

137

982

4.445255

0.357664

0.108374

0.083744

0.059113

0.082102

0

0.015915

0.232179

982

47

65

20.893617

0.791777

0

0.03666

0

1

0.117647

false

0

0.058824

0

0.176471

0

null

0

null

0

1

0

2b307d66d8ef01ec6b560b96a1fec0c928cc9a2d

22,878

py

Python

src/server/server.py

HanseMerkur/cassh

947023ad7971a0922d56aaaee5afcdf9294334e3

[ "Apache-2.0" ]

null

src/server/server.py

HanseMerkur/cassh

947023ad7971a0922d56aaaee5afcdf9294334e3

[ "Apache-2.0" ]

null

src/server/server.py

HanseMerkur/cassh

947023ad7971a0922d56aaaee5afcdf9294334e3

[ "Apache-2.0" ]

null

#!/usr/bin/env python """ Sign a user's SSH public key. """ from argparse import ArgumentParser from json import dumps from os import remove from re import compile as re_compile, IGNORECASE from tempfile import NamedTemporaryFile from urllib.parse import unquote_plus # Third party library imports from configparser import ConfigParser, NoOptionError from ldap import initialize, SCOPE_SUBTREE from web import application, config, data, httpserver from web.wsgiserver import CherryPyWSGIServer # Own library from ssh_utils import get_fingerprint from tools import get_principals, get_pubkey, random_string, response_render, timestamp, unquote_custom, Tools # DEBUG # from pdb import set_trace as st STATES = { 0: 'ACTIVE', 1: 'REVOKED', 2: 'PENDING', } URLS = ( '/admin/([a-z]+)', 'Admin', '/ca', 'Ca', '/client', 'Client', '/client/status', 'ClientStatus', '/cluster/status', 'ClusterStatus', '/health', 'Health', '/krl', 'Krl', '/ping', 'Ping', '/test_auth', 'TestAuth', ) VERSION = '1.9.2' PARSER = ArgumentParser() PARSER.add_argument('-c', '--config', action='store', help='Configuration file') PARSER.add_argument('-v', '--verbose', action='store_true', default=False, help='Add verbosity') ARGS = PARSER.parse_args() if not ARGS.config: PARSER.error('--config argument is required !') CONFIG = ConfigParser() CONFIG.read(ARGS.config) SERVER_OPTS = {} SERVER_OPTS['ca'] = CONFIG.get('main', 'ca') SERVER_OPTS['krl'] = CONFIG.get('main', 'krl') SERVER_OPTS['port'] = CONFIG.get('main', 'port') try: SERVER_OPTS['admin_db_failover'] = CONFIG.get('main', 'admin_db_failover') except NoOptionError: SERVER_OPTS['admin_db_failover'] = False SERVER_OPTS['ldap'] = False SERVER_OPTS['ssl'] = False if CONFIG.has_section('postgres'): try: SERVER_OPTS['db_host'] = CONFIG.get('postgres', 'host') SERVER_OPTS['db_name'] = CONFIG.get('postgres', 'dbname') SERVER_OPTS['db_user'] = CONFIG.get('postgres', 'user') SERVER_OPTS['db_password'] = CONFIG.get('postgres', 'password') except NoOptionError: if ARGS.verbose: print('Option reading error (postgres).') exit(1) if CONFIG.has_section('ldap'): try: SERVER_OPTS['ldap'] = True SERVER_OPTS['ldap_host'] = CONFIG.get('ldap', 'host') SERVER_OPTS['ldap_bind_dn'] = CONFIG.get('ldap', 'bind_dn') SERVER_OPTS['ldap_admin_cn'] = CONFIG.get('ldap', 'admin_cn') SERVER_OPTS['filterstr'] = CONFIG.get('ldap', 'filterstr') except NoOptionError: if ARGS.verbose: print('Option reading error (ldap).') exit(1) if CONFIG.has_section('ssl'): try: SERVER_OPTS['ssl'] = True SERVER_OPTS['ssl_private_key'] = CONFIG.get('ssl', 'private_key') SERVER_OPTS['ssl_public_key'] = CONFIG.get('ssl', 'public_key') except NoOptionError: if ARGS.verbose: print('Option reading error (ssl).') exit(1) # Cluster mode is used for revocation try: SERVER_OPTS['cluster'] = CONFIG.get('main', 'cluster').split(',') except NoOptionError: # Standalone mode PROTO = 'http' if SERVER_OPTS['ssl']: PROTO = 'https' SERVER_OPTS['cluster'] = ['%s://localhost:%s' % (PROTO, SERVER_OPTS['port'])] try: SERVER_OPTS['clustersecret'] = CONFIG.get('main', 'clustersecret') except NoOptionError: # Standalone mode SERVER_OPTS['clustersecret'] = random_string(32) try: SERVER_OPTS['debug'] = bool(CONFIG.get('main', 'debug') != 'False') except NoOptionError: SERVER_OPTS['debug'] = False TOOLS = Tools(SERVER_OPTS, STATES, VERSION) def data2map(): """ Returns a map from data POST """ data_map = {} data_str = data().decode('utf-8') if data_str == '': return data_map for key in data_str.split('&'): data_map[key.split('=')[0]] = '='.join(key.split('=')[1:]) return data_map def ldap_authentification(admin=False): """ Return True if user is well authentified realname=xxxxx@domain.fr password=xxxxx """ if SERVER_OPTS['ldap']: credentials = data2map() if 'realname' in credentials: realname = unquote_plus(credentials['realname']) else: return False, 'Error: No realname option given.' if 'password' in credentials: password = unquote_plus(credentials['password']) else: return False, 'Error: No password option given.' if password == '': return False, 'Error: password is empty.' ldap_conn = initialize("ldap://"+SERVER_OPTS['ldap_host']) try: ldap_conn.bind_s(realname, password) except Exception as e: return False, 'Error: %s' % e if admin: memberof_admin_list = ldap_conn.search_s( SERVER_OPTS['ldap_bind_dn'], SCOPE_SUBTREE, filterstr='(&(%s=%s)(memberOf=%s))' % ( SERVER_OPTS['filterstr'], realname, SERVER_OPTS['ldap_admin_cn'])) if not memberof_admin_list: return False, 'Error: user %s is not an admin.' % realname return True, 'OK' class Admin(): """ Class admin to action or revoke keys. """ def POST(self, username): """ Revoke or Active keys. /admin/<username> revoke=true/false => Revoke user status=true/false => Display status """ # LDAP authentication is_admin_auth, message = ldap_authentification(admin=True) if not is_admin_auth: return response_render(message, http_code='401 Unauthorized') payload = data2map() if 'revoke' in payload: do_revoke = payload['revoke'].lower() == 'true' else: do_revoke = False if 'status' in payload: do_status = payload['status'].lower() == 'true' else: do_status = False pg_conn, message = TOOLS.pg_connection() if pg_conn is None: return response_render(message, http_code='503 Service Unavailable') cur = pg_conn.cursor() if username == 'all' and do_status: return response_render( TOOLS.list_keys(), content_type='application/json') # Search if key already exists cur.execute('SELECT * FROM USERS WHERE NAME=(%s)', (username,)) user = cur.fetchone() # If user dont exist if user is None: cur.close() pg_conn.close() message = 'User does not exists.' elif do_revoke: cur.execute('UPDATE USERS SET STATE=1 WHERE NAME=(%s)', (username,)) pg_conn.commit() pubkey = get_pubkey(username, pg_conn) cur.execute('INSERT INTO REVOCATION VALUES \ ((%s), (%s), (%s))', \ (pubkey, timestamp(), username)) pg_conn.commit() message = 'Revoke user=%s.' % username cur.close() pg_conn.close() # Display status elif do_status: return response_render( TOOLS.list_keys(username=username), content_type='application/json') # If user is in PENDING state elif user[2] == 2: cur.execute('UPDATE USERS SET STATE=0 WHERE NAME=(%s)', (username,)) pg_conn.commit() cur.close() pg_conn.close() message = 'Active user=%s. SSH Key active but need to be signed.' % username # If user is in REVOKE state elif user[2] == 1: cur.execute('UPDATE USERS SET STATE=0 WHERE NAME=(%s)', (username,)) pg_conn.commit() cur.close() pg_conn.close() message = 'Active user=%s. SSH Key active but need to be signed.' % username else: cur.close() pg_conn.close() message = 'user=%s already active. Nothing done.' % username return response_render(message) def PATCH(self, username): """ Set the first founded value. /admin/<username> key=value => Set the key value. Keys are in status output. """ # LDAP authentication is_admin_auth, message = ldap_authentification(admin=True) if not is_admin_auth: return response_render(message, http_code='401 Unauthorized') pg_conn, message = TOOLS.pg_connection() if pg_conn is None: return response_render(message, http_code='503 Service Unavailable') cur = pg_conn.cursor() payload = data2map() for key, value in payload.items(): if key == 'expiry': pattern = re_compile('^\\+([0-9]+)+[dh]$') if pattern.match(value) is None: return response_render( 'ERROR: Value %s is malformed. Should match pattern ^\\+([0-9]+)+[dh]$' \ % value, http_code='400 Bad Request') cur.execute('UPDATE USERS SET EXPIRY=(%s) WHERE NAME=(%s)', (value, username)) pg_conn.commit() cur.close() pg_conn.close() return response_render('OK: %s=%s for %s' % (key, value, username)) elif key == 'principals': value = unquote_plus(value) pattern = re_compile("^([a-zA-Z-]+)$") for principal in value.split(','): if pattern.match(principal) is None: return response_render( 'ERROR: Value %s is malformed. Should match pattern ^([a-zA-Z-]+)$' \ % principal, http_code='400 Bad Request') cur.execute('UPDATE USERS SET PRINCIPALS=(%s) WHERE NAME=(%s)', (value, username)) pg_conn.commit() cur.close() pg_conn.close() return response_render('OK: %s=%s for %s' % (key, value, username)) return response_render('WARNING: No key found...') def DELETE(self, username): """ Delete keys (but DOESN'T REVOKE) /admin/<username> """ # LDAP authentication is_admin_auth, message = ldap_authentification(admin=True) if not is_admin_auth: return response_render(message, http_code='401 Unauthorized') pg_conn, message = TOOLS.pg_connection() if pg_conn is None: return response_render(message, http_code='503 Service Unavailable') cur = pg_conn.cursor() # Search if key already exists cur.execute('DELETE FROM USERS WHERE NAME=(%s)', (username,)) pg_conn.commit() cur.close() pg_conn.close() return response_render('OK') class Ca(): """ Class CA. """ def GET(self): """ Return ca. """ return response_render( open(SERVER_OPTS['ca'] + '.pub', 'rb'), content_type='application/octet-stream') class ClientStatus(): """ ClientStatus main class. """ def POST(self): """ Get client key status. /client/status """ # LDAP authentication is_auth, message = ldap_authentification() if not is_auth: return response_render(message, http_code='401 Unauthorized') payload = data2map() if 'realname' in payload: realname = unquote_plus(payload['realname']) else: return response_render( 'Error: No realname option given.', http_code='400 Bad Request') return response_render( TOOLS.list_keys(realname=realname), content_type='application/json') class Client(): """ Client main class. """ def POST(self): """ Ask to sign pub key. /client username=xxxxxx => Unique username. Used by default to connect on server. realname=xxxxx@domain.fr => This LDAP/AD user. # Optionnal admin_force=true|false """ # LDAP authentication is_auth, message = ldap_authentification() if not is_auth: return response_render(message, http_code='401 Unauthorized') # Check if user is an admin and want to force signature when db fail force_sign = False # LDAP ADMIN authentication is_admin_auth, _ = ldap_authentification(admin=True) payload = data2map() if is_admin_auth and SERVER_OPTS['admin_db_failover'] \ and 'admin_force' in payload and payload['admin_force'].lower() == 'true': force_sign = True # Get username if 'username' in payload: username = payload['username'] else: return response_render( 'Error: No username option given. Update your CASSH >= 1.3.0', http_code='400 Bad Request') username_pattern = re_compile("^([a-z]+)$") if username_pattern.match(username) is None or username == 'all': return response_render( "Error: Username doesn't match pattern %s" \ % username_pattern.pattern, http_code='400 Bad Request') # Get realname if 'realname' in payload: realname = unquote_plus(payload['realname']) else: return response_render( 'Error: No realname option given.', http_code='400 Bad Request') # Get public key if 'pubkey' in payload: pubkey = unquote_custom(payload['pubkey']) else: return response_render( 'Error: No pubkey given.', http_code='400 Bad Request') tmp_pubkey = NamedTemporaryFile(delete=False) tmp_pubkey.write(bytes(pubkey, 'utf-8')) tmp_pubkey.close() pubkey_fingerprint = get_fingerprint(tmp_pubkey.name) if pubkey_fingerprint == 'Unknown': remove(tmp_pubkey.name) return response_render( 'Error : Public key unprocessable', http_code='422 Unprocessable Entity') pg_conn, message = TOOLS.pg_connection() # Admin force signature case if pg_conn is None and force_sign: cert_contents = TOOLS.sign_key(tmp_pubkey.name, username, '+12h', username) remove(tmp_pubkey.name) return response_render(cert_contents, content_type='application/octet-stream') # Else, if db is down it fails. elif pg_conn is None: remove(tmp_pubkey.name) return response_render(message, http_code='503 Service Unavailable') cur = pg_conn.cursor() # Search if key already exists cur.execute('SELECT * FROM USERS WHERE SSH_KEY=(%s) AND NAME=lower(%s)', (pubkey, username)) user = cur.fetchone() if user is None: cur.close() pg_conn.close() remove(tmp_pubkey.name) return response_render( 'Error : User or Key absent, add your key again.', http_code='400 Bad Request') if username != user[0] or realname != user[1]: cur.close() pg_conn.close() remove(tmp_pubkey.name) return response_render( 'Error : (username, realname) couple mismatch.', http_code='401 Unauthorized') status = user[2] expiry = user[6] principals = get_principals(user[7], username, shell=True) if status > 0: cur.close() pg_conn.close() remove(tmp_pubkey.name) return response_render("Status: %s" % STATES[user[2]]) cert_contents = TOOLS.sign_key(tmp_pubkey.name, username, expiry, principals, db_cursor=cur) remove(tmp_pubkey.name) pg_conn.commit() cur.close() pg_conn.close() return response_render( cert_contents, content_type='application/octet-stream') def PUT(self): """ This function permit to add or update a ssh public key. /client username=xxxxxx => Unique username. Used by default to connect on server. realname=xxxxx@domain.fr => This LDAP/AD user. """ # LDAP authentication is_auth, message = ldap_authentification() if not is_auth: return response_render(message, http_code='401 Unauthorized') payload = data2map() if 'username' in payload: username = payload['username'] else: return response_render( 'Error: No username option given.', http_code='400 Bad Request') username_pattern = re_compile("^([a-z]+)$") if username_pattern.match(username) is None or username == 'all': return response_render( "Error: Username doesn't match pattern %s" \ % username_pattern.pattern, http_code='400 Bad Request') if 'realname' in payload: realname = unquote_plus(payload['realname']) else: return response_render( 'Error: No realname option given.', http_code='400 Bad Request') realname_pattern = re_compile( r"(^[-!#$%&'*+/=?^_`{}|~0-9A-Z]+(\.[-!#$%&'*+/=?^_`{}|~0-9A-Z]+)*" r'|^"([\001-\010\013\014\016-\037!#-\[\]-\177]|\\[\001-011\013\014\016-\177])*"' r')@(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+[A-Z]{2,6}\.?$', IGNORECASE) if realname_pattern.match(realname) is None: return response_render( "Error: Realname doesn't match pattern", http_code='400 Bad Request') # Get public key if 'pubkey' in payload: pubkey = unquote_custom(payload['pubkey']) else: return response_render( 'Error: No pubkey given.', http_code='400 Bad Request') tmp_pubkey = NamedTemporaryFile(delete=False) tmp_pubkey.write(bytes(pubkey, 'utf-8')) tmp_pubkey.close() pubkey_fingerprint = get_fingerprint(tmp_pubkey.name) if pubkey_fingerprint == 'Unknown': remove(tmp_pubkey.name) return response_render( 'Error : Public key unprocessable', http_code='422 Unprocessable Entity') pg_conn, message = TOOLS.pg_connection() if pg_conn is None: remove(tmp_pubkey.name) return response_render(message, http_code='503 Service Unavailable') cur = pg_conn.cursor() # Search if key already exists cur.execute('SELECT * FROM USERS WHERE NAME=(%s)', (username,)) user = cur.fetchone() # CREATE NEW USER if user is None: cur.execute('INSERT INTO USERS VALUES \ ((%s), (%s), (%s), (%s), (%s), (%s), (%s), (%s))', \ (username, realname, 2, 0, pubkey_fingerprint, pubkey, '+12h', '')) pg_conn.commit() cur.close() pg_conn.close() remove(tmp_pubkey.name) return response_render( 'Create user=%s. Pending request.' % username, http_code='201 Created') else: # Check if realname is the same cur.execute('SELECT * FROM USERS WHERE NAME=(%s) AND REALNAME=lower((%s))', \ (username, realname)) if cur.fetchone() is None: pg_conn.commit() cur.close() pg_conn.close() remove(tmp_pubkey.name) return response_render( 'Error : (username, realname) couple mismatch.', http_code='401 Unauthorized') # Update entry into database cur.execute('UPDATE USERS SET SSH_KEY=(%s), SSH_KEY_HASH=(%s), STATE=2, EXPIRATION=0 \ WHERE NAME=(%s)', (pubkey, pubkey_fingerprint, username)) pg_conn.commit() cur.close() pg_conn.close() remove(tmp_pubkey.name) return response_render('Update user=%s. Pending request.' % username) class ClusterStatus(): """ ClusterStatus main class. """ def GET(self): """ /cluster/status """ message = dict() alive_nodes, dead_nodes = TOOLS.cluster_alived() for node in alive_nodes: message.update({node: {'status': 'OK'}}) for node in dead_nodes: message.update({node: {'status': 'KO'}}) return response_render( dumps(message), content_type='application/json') class Health(): """ Class Health """ def GET(self): """ Return a health check """ health = {} health['name'] = 'cassh' health['version'] = VERSION return response_render( dumps(health, indent=4, sort_keys=True), content_type='application/json') class Krl(): """ Class KRL. """ def GET(self): """ Return krl. """ return TOOLS.get_last_krl() class Ping(): """ Class Ping """ def GET(self): """ Return a pong """ return response_render('pong') class TestAuth(): """ Test authentication """ def POST(self): """ Test authentication """ # LDAP authentication is_auth, message = ldap_authentification() if not is_auth: return response_render(message, http_code='401 Unauthorized') return response_render('OK') class MyApplication(application): """ Can change port or other stuff """ def run(self, port=int(SERVER_OPTS['port']), *middleware): func = self.wsgifunc(*middleware) return httpserver.runsimple(func, ('0.0.0.0', port)) if __name__ == "__main__": if SERVER_OPTS['ssl']: CherryPyWSGIServer.ssl_certificate = SERVER_OPTS['ssl_public_key'] CherryPyWSGIServer.ssl_private_key = SERVER_OPTS['ssl_private_key'] if ARGS.verbose: print('SSL: %s' % SERVER_OPTS['ssl']) print('LDAP: %s' % SERVER_OPTS['ldap']) print('Admin DB Failover: %s' % SERVER_OPTS['admin_db_failover']) APP = MyApplication(URLS, globals()) config.debug = SERVER_OPTS['debug'] if SERVER_OPTS['debug']: print('Debug mode on') APP.run()

33.447368

110

0.563992

2,549

22,878

4.910553

0.137309

0.053687

0.075098

0.033954

0.526244

0.468483

0.44835

0.439722

0.430375

0.405928

0

0.01281

0.314145

22,878

683

111

33.49634

0.784909

0.085978

0

0.528421

0

0.006316

0.188702

0.014493

0

1

0.031579

false

0.014737

0.025263

0

0.197895

0.029474

0

null

0

null

0

1

0

2b31dd8ff5a66ac8bf0442f51e45b1fcb61fee3b

23,469

py

Python

src/test/test_data.py

opploans/cbc-syslog

72a203b1dbe6ddd97f02dc87f36631d758564022

[ "MIT" ]

14

2020-04-28T12:52:50.000Z

2021-08-25T00:36:51.000Z

src/test/test_data.py

opploans/cbc-syslog

72a203b1dbe6ddd97f02dc87f36631d758564022

[ "MIT" ]

21

2016-10-24T20:16:39.000Z

2020-02-11T21:30:50.000Z

src/test/test_data.py

opploans/cbc-syslog

72a203b1dbe6ddd97f02dc87f36631d758564022

[ "MIT" ]

15

2016-12-19T20:39:24.000Z

2020-01-02T16:26:34.000Z

# -*- coding: utf-8 -*- null = "" true = "true" false = "false" raw_notifications = { "notifications": [{ "threatInfo": { "incidentId": "Z7NG6", "score": 7, "summary": "A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.", "indicators": [{ "indicatorName": "PACKED_CALL", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "TARGET_MALWARE_APP", "applicationName": "explorer.exe", "sha256Hash": "1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455" }, { "indicatorName": "HAS_PACKED_CODE", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "KNOWN_DOWNLOADER", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "ENUMERATE_PROCESSES", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "SET_SYSTEM_SECURITY", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "MODIFY_MEMORY_PROTECTION", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "KNOWN_PASSWORD_STEALER", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "RUN_MALWARE_APP", "applicationName": "explorer.exe", "sha256Hash": "1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455" }, { "indicatorName": "MODIFY_PROCESS", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "MALWARE_APP", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" } ], "time": 1460703240678 }, "url": "https://testserver.company.net/ui#investigate/events/device/2004118/incident/Z7NG6", "eventTime": 1460703240678, "eventId": "f279d0e6035211e6be8701df2c083974", "eventDescription": "[syslog alert] [Cb Defense has detected a threat against your company.] [https://testserver.company.net/ui#device/2004118/incident/Z7NG6] [A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.] [Incident id: Z7NG6] [Threat score: 7] [Group: default] [Email: FirstName.LastName@company.net.demo] [Name: Demo_CaretoPC] [Type and OS: WINDOWS XP x86 SP: 0]\n", "deviceInfo": { "email": "COMPANY\\FirstName.LastName", "groupName": "default", "internalIpAddress": null, "externalIpAddress": null, "deviceType": "WINDOWS", "deviceVersion": "XP x86 SP: 0", "targetPriorityType": "MEDIUM", "deviceId": 2004118, "deviceName": "COMPANY\\Demo_CaretoPC", "deviceHostName": null, "targetPriorityCode": 0 }, "ruleName": "syslog alert", "type": "THREAT" }, { "policyAction": { "sha256Hash": "2552332222112552332222112552332222112552332222112552332222112552", "action": "TERMINATE", "reputation": "KNOWN_MALWARE", "applicationName": "firefox.exe" }, "type": "POLICY_ACTION", "eventTime": 1423163263482, "eventId": "EV1", "url": "http://carbonblack.com/ui#device/100/hash/2552332222112552332222112552332222112552332222112552332222112552/app/firefox.exe/keyword/terminate policy action", "deviceInfo": { "deviceType": "WINDOWS", "email": "tester@carbonblack.com", "deviceId": 100, "deviceName": "testers-pc", "deviceHostName": null, "deviceVersion": "7 SP1", "targetPriorityType": "HIGH", "targetPriorityCode": 0, "internalIpAddress": "55.33.22.11", "groupName": "Executives", "externalIpAddress": "255.233.222.211" }, "eventDescription": "Policy action 1", "ruleName": "Alert Rule 1" }, { "threatHunterInfo": { "incidentId": "WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660", "score": 1, "summary": "PowerShell - File and Directory Discovery Enumeration", "time": 1554652050250, "indicators": [ { "applicationName": "powershell.exe", "sha256Hash": "ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436", "indicatorName": "565660-0" } ], "watchLists": [ { "id": "a3xW2ZiaRyAqRtuVES8Q", "name": "ATT&CK Framework", "alert": true } ], "iocId": "565660-0", "count": 0, "dismissed": false, "documentGuid": "7a9fQEsTRfuFmXcogI8CMQ", "firstActivityTime": 1554651811577, "md5": "097ce5761c89434367598b34fe32893b", "policyId": 9815, "processGuid": "WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf", "processPath": "c:\\windows\\system32\\windowspowershell\\v1.0\\powershell.exe", "reportName": "PowerShell - File and Directory Discovery Enumeration", "reportId": "j0MkcneCQXy1fIbhber6rw-565660", "reputation": "TRUSTED_WHITE_LIST", "responseAlarmId": "WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660", "responseSeverity": 1, "runState": "RAN", "sha256": "ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436", "status": "UNRESOLVED", "tags": null, "targetPriority": "MEDIUM", "threatCause": { "reputation": "TRUSTED_WHITE_LIST", "actor": "ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436", "actorName": "powershell.exe", "reason": "Process powershell.exe was detected by the report \"PowerShell - File and Directory Discovery Enumeration\" in watchlist \"ATT&CK Framework\"", "actorType": null, "threatCategory": "RESPONSE_WATCHLIST", "actorProcessPPid": null, "causeEventId": null, "originSourceType": "UNKNOWN" }, "threatId": "a2b724aa094af97c06c758d325240460", "lastUpdatedTime": 0, "orgId": 428 }, "eventDescription": "[sm-sentinel-notification] [Carbon Black has detected a threat against your company.] [https://defense-eap01.conferdeploy.net#device/18900/incident/WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660] [PowerShell - File and Directory Discovery Enumeration] [Incident id: WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660] [Threat score: 1] [Group: sm-detection] [Email: smultani@carbonblack.com] [Name: win-559j1nqvfgj] [Type and OS: WINDOWS pscr-sensor] [Severity: 1]\n", "eventTime": 1554651811577, "deviceInfo": { "deviceId": 18900, "targetPriorityCode": 0, "groupName": "sm-detection", "deviceName": "win-559j1nqvfgj", "deviceType": "WINDOWS", "email": "smultani@carbonblack.com", "deviceHostName": null, "deviceVersion": "pscr-sensor", "targetPriorityType": "MEDIUM", "uemId": null, "internalIpAddress": "192.168.81.148", "externalIpAddress": "73.69.152.214" }, "url": "https://defense-eap01.conferdeploy.net/investigate?s[searchWindow]=ALL&s[c][DEVICE_ID][0]=18900&s[c][INCIDENT_ID][0]=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660", "ruleName": "sm-sentinel-notification", "type": "THREAT_HUNTER" }], "success": true, "message": "Success" } cef_notifications = ['test CEF:0|CarbonBlack|CbDefense_Syslog_Connector|2.0|Active_Threat|A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.|7|rt="Apr 15 2016 06:54:00" sntdom=COMPANY dvchost=Demo_CaretoPC duser=FirstName.LastName dvc= cs3Label="Link" cs3="https://testserver.company.net/ui#investigate/events/device/2004118/incident/Z7NG6" cs4Label="Threat_ID" cs4="Z7NG6" act=Alert', 'test CEF:0|CarbonBlack|CbDefense_Syslog_Connector|2.0|Policy_Action|Confer Sensor Policy Action|1|rt="Feb 05 2015 19:07:43" dvchost=testers-pc duser=tester@carbonblack.com dvc=55.33.22.11 cs3Label="Link" cs3="http://carbonblack.com/ui#device/100/hash/2552332222112552332222112552332222112552332222112552332222112552/app/firefox.exe/keyword/terminate policy action" act=TERMINATE hash=2552332222112552332222112552332222112552332222112552332222112552 deviceprocessname=firefox.exe', 'test CEF:0|CarbonBlack|CbDefense_Syslog_Connector|2.0|Threat_Hunter|PowerShell - File and Directory Discovery Enumeration|1|rt="Apr 07 2019 15:43:31" dvchost=win-559j1nqvfgj duser=smultani@carbonblack.com dvc=192.168.81.148 cs3Label="Link" cs3="https://defense-eap01.conferdeploy.net/investigate?s[searchWindow]=ALL&s[c][DEVICE_ID][0]=18900&s[c][INCIDENT_ID][0]=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660" cs4Label="Threat_ID" cs4="WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660" hash=ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436'] leef_notifications = ['LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=PACKED_CALL\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=explorer.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=TARGET_MALWARE_APP\tsev=Z7NG6\tsha256Hash=1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=HAS_PACKED_CODE\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=KNOWN_DOWNLOADER\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=ENUMERATE_PROCESSES\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=SET_SYSTEM_SECURITY\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=MODIFY_MEMORY_PROTECTION\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=KNOWN_PASSWORD_STEALER\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=explorer.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=RUN_MALWARE_APP\tsev=Z7NG6\tsha256Hash=1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=MODIFY_PROCESS\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=MALWARE_APP\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|THREAT|x09|cat=THREAT\tdevTime=Apr-15-2016 06:54:00 GMT\tdevTimeFormat=MMM dd yyyy HH:mm:ss z\tdeviceId=2004118\tdeviceType=WINDOWS\teventId=f279d0e6035211e6be8701df2c083974\tidentHostName=\tidentSrc=\tincidentId=Z7NG6\trealm=default\tresource=COMPANY\\Demo_CaretoPC\truleName=syslog alert\tsev=7\tsummary=A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.\ttargetPriorityType=MEDIUM\turl=https://testserver.company.net/ui#investigate/events/device/2004118/incident/Z7NG6', 'LEEF:2.0|CarbonBlack|Cloud|1.0|POLICY_ACTION|x09|action=TERMINATE\tapplicationName=firefox.exe\tcat=POLICY_ACTION\tdevTime=Feb-05-2015 19:07:43 GMT\tdevTimeFormat=MMM dd yyyy HH:mm:ss z\tdeviceId=100\tdeviceType=WINDOWS\teventId=EV1\tidentHostName=\tidentSrc=55.33.22.11\trealm=Executives\treputation=KNOWN_MALWARE\tresource=testers-pc\truleName=Alert Rule 1\tsev=1\tsha256=2552332222112552332222112552332222112552332222112552332222112552\tsummary=\ttargetPriorityType=HIGH\turl=http://carbonblack.com/ui#device/100/hash/2552332222112552332222112552332222112552332222112552332222112552/app/firefox.exe/keyword/terminate policy action', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=powershell.exe\tcat=INDICATOR\tincidentId=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660\tindicatorName=565660-0\tsev=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660\tsha256Hash=ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436', 'LEEF:2.0|CarbonBlack|Cloud|1.0|THREAT_HUNTER|x09|cat=THREAT_HUNTER\tdevTime=Apr-07-2019 15:43:31 GMT\tdevTimeFormat=MMM dd yyyy HH:mm:ss z\tdeviceId=18900\tdeviceType=WINDOWS\teventId=None\tidentHostName=\tidentSrc=192.168.81.148\tincidentId=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660\tprocessGuid=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf\tprocessPath=c:\\windows\\system32\\windowspowershell\\v1.0\\powershell.exe\trealm=sm-detection\treportName=PowerShell - File and Directory Discovery Enumeration\treputation=TRUSTED_WHITE_LIST\tresource=win-559j1nqvfgj\truleName=sm-sentinel-notification\trunState=RAN\tsev=1\tsha256=ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436\tsummary=PowerShell - File and Directory Discovery Enumeration\ttargetPriorityType=MEDIUM\turl=https://defense-eap01.conferdeploy.net/investigate?s[searchWindow]=ALL&s[c][DEVICE_ID][0]=18900&s[c][INCIDENT_ID][0]=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660\twatchlists=ATT&CK Framework'] json_notifications = [{'threatInfo': {'incidentId': 'Z7NG6', 'score': 7, 'summary': 'A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.', 'indicators': [{'indicatorName': 'PACKED_CALL', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'TARGET_MALWARE_APP', 'applicationName': 'explorer.exe', 'sha256Hash': '1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455'}, {'indicatorName': 'HAS_PACKED_CODE', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'KNOWN_DOWNLOADER', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'ENUMERATE_PROCESSES', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'SET_SYSTEM_SECURITY', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'MODIFY_MEMORY_PROTECTION', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'KNOWN_PASSWORD_STEALER', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'RUN_MALWARE_APP', 'applicationName': 'explorer.exe', 'sha256Hash': '1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455'}, {'indicatorName': 'MODIFY_PROCESS', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'MALWARE_APP', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}], 'time': 1460703240678}, 'url': 'https://testserver.company.net/ui#investigate/events/device/2004118/incident/Z7NG6', 'eventTime': 1460703240678, 'eventId': 'f279d0e6035211e6be8701df2c083974', 'eventDescription': '[syslog alert] [Cb Defense has detected a threat against your company.] [https://testserver.company.net/ui#device/2004118/incident/Z7NG6] [A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.] [Incident id: Z7NG6] [Threat score: 7] [Group: default] [Email: FirstName.LastName@company.net.demo] [Name: Demo_CaretoPC] [Type and OS: WINDOWS XP x86 SP: 0]\n', 'deviceInfo': {'email': 'COMPANY\\FirstName.LastName', 'groupName': 'default', 'internalIpAddress': '', 'externalIpAddress': '', 'deviceType': 'WINDOWS', 'deviceVersion': 'XP x86 SP: 0', 'targetPriorityType': 'MEDIUM', 'deviceId': 2004118, 'deviceName': 'COMPANY\\Demo_CaretoPC', 'deviceHostName': '', 'targetPriorityCode': 0}, 'ruleName': 'syslog alert', 'type': 'THREAT', 'source': 'test'}, {'policyAction': {'sha256Hash': '2552332222112552332222112552332222112552332222112552332222112552', 'action': 'TERMINATE', 'reputation': 'KNOWN_MALWARE', 'applicationName': 'firefox.exe'}, 'type': 'POLICY_ACTION', 'eventTime': 1423163263482, 'eventId': 'EV1', 'url': 'http://carbonblack.com/ui#device/100/hash/2552332222112552332222112552332222112552332222112552332222112552/app/firefox.exe/keyword/terminate policy action', 'deviceInfo': {'deviceType': 'WINDOWS', 'email': 'tester@carbonblack.com', 'deviceId': 100, 'deviceName': 'testers-pc', 'deviceHostName': '', 'deviceVersion': '7 SP1', 'targetPriorityType': 'HIGH', 'targetPriorityCode': 0, 'internalIpAddress': '55.33.22.11', 'groupName': 'Executives', 'externalIpAddress': '255.233.222.211'}, 'eventDescription': 'Policy action 1', 'ruleName': 'Alert Rule 1', 'source': 'test'}, {'threatHunterInfo': {'incidentId': 'WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660', 'score': 1, 'summary': 'PowerShell - File and Directory Discovery Enumeration', 'time': 1554652050250, 'indicators': [{'applicationName': 'powershell.exe', 'sha256Hash': 'ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436', 'indicatorName': '565660-0'}], 'watchLists': [{'id': 'a3xW2ZiaRyAqRtuVES8Q', 'name': 'ATT&CK Framework', 'alert': 'true'}], 'iocId': '565660-0', 'count': 0, 'dismissed': 'false', 'documentGuid': '7a9fQEsTRfuFmXcogI8CMQ', 'firstActivityTime': 1554651811577, 'md5': '097ce5761c89434367598b34fe32893b', 'policyId': 9815, 'processGuid': 'WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf', 'processPath': 'c:\\windows\\system32\\windowspowershell\\v1.0\\powershell.exe', 'reportName': 'PowerShell - File and Directory Discovery Enumeration', 'reportId': 'j0MkcneCQXy1fIbhber6rw-565660', 'reputation': 'TRUSTED_WHITE_LIST', 'responseAlarmId': 'WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660', 'responseSeverity': 1, 'runState': 'RAN', 'sha256': 'ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436', 'status': 'UNRESOLVED', 'tags': '', 'targetPriority': 'MEDIUM', 'threatCause': {'reputation': 'TRUSTED_WHITE_LIST', 'actor': 'ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436', 'actorName': 'powershell.exe', 'reason': 'Process powershell.exe was detected by the report "PowerShell - File and Directory Discovery Enumeration" in watchlist "ATT&CK Framework"', 'actorType': '', 'threatCategory': 'RESPONSE_WATCHLIST', 'actorProcessPPid': '', 'causeEventId': '', 'originSourceType': 'UNKNOWN'}, 'threatId': 'a2b724aa094af97c06c758d325240460', 'lastUpdatedTime': 0, 'orgId': 428}, 'eventDescription': '[sm-sentinel-notification] [Carbon Black has detected a threat against your company.] [https://defense-eap01.conferdeploy.net#device/18900/incident/WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660] [PowerShell - File and Directory Discovery Enumeration] [Incident id: WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660] [Threat score: 1] [Group: sm-detection] [Email: smultani@carbonblack.com] [Name: win-559j1nqvfgj] [Type and OS: WINDOWS pscr-sensor] [Severity: 1]\n', 'eventTime': 1554651811577, 'deviceInfo': {'deviceId': 18900, 'targetPriorityCode': 0, 'groupName': 'sm-detection', 'deviceName': 'win-559j1nqvfgj', 'deviceType': 'WINDOWS', 'email': 'smultani@carbonblack.com', 'deviceHostName': '', 'deviceVersion': 'pscr-sensor', 'targetPriorityType': 'MEDIUM', 'uemId': '', 'internalIpAddress': '192.168.81.148', 'externalIpAddress': '73.69.152.214'}, 'url': 'https://defense-eap01.conferdeploy.net/investigate?s[searchWindow]=ALL&s[c][DEVICE_ID][0]=18900&s[c][INCIDENT_ID][0]=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660', 'ruleName': 'sm-sentinel-notification', 'type': 'THREAT_HUNTER', 'source': 'test'}]

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