PatrickHaller/the-stack-python-1M · Datasets at Hugging Face

a46995e40c568d9fab0090a1e0fdb13667f1d1ad

3,253

py

Python

data/p2DJ/New/program/qiskit/noisy/startQiskit_noisy378.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/p2DJ/New/program/qiskit/noisy/startQiskit_noisy378.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/p2DJ/New/program/qiskit/noisy/startQiskit_noisy378.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

# qubit number=2 # total number=13 import cirq import qiskit from qiskit.providers.aer import QasmSimulator from qiskit.test.mock import FakeVigo from qiskit import IBMQ from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister from qiskit import BasicAer, execute, transpile from pprint import pprint from qiskit.test.mock import FakeVigo from math import log2,floor, sqrt, pi import numpy as np import networkx as nx def build_oracle(n: int, f) -> QuantumCircuit: # implement the oracle O_f^\pm # NOTE: use U1 gate (P gate) with \lambda = 180 ==> CZ gate # or multi_control_Z_gate (issue #127) controls = QuantumRegister(n, "ofc") target = QuantumRegister(1, "oft") oracle = QuantumCircuit(controls, target, name="Of") for i in range(2 ** n): rep = np.binary_repr(i, n) if f(rep) == "1": for j in range(n): if rep[j] == "0": oracle.x(controls[j]) oracle.mct(controls, target[0], None, mode='noancilla') for j in range(n): if rep[j] == "0": oracle.x(controls[j]) # oracle.barrier() # oracle.draw('mpl', filename='circuit/deutsch-oracle.png') return oracle def make_circuit(n:int,f) -> QuantumCircuit: # circuit begin input_qubit = QuantumRegister(n, "qc") target = QuantumRegister(1, "qt") prog = QuantumCircuit(input_qubit, target) # inverse last one (can be omitted if using O_f^\pm) prog.x(target) # apply H to get superposition for i in range(n): prog.h(input_qubit[i]) prog.h(input_qubit[1]) # number=1 prog.x(input_qubit[1]) # number=8 prog.rx(-0.14765485471872042,input_qubit[1]) # number=9 prog.h(target) prog.barrier() # apply oracle O_f oracle = build_oracle(n, f) prog.append( oracle.to_gate(), [input_qubit[i] for i in range(n)] + [target]) # apply H back (QFT on Z_2^n) for i in range(n): prog.h(input_qubit[i]) prog.barrier() # measure #for i in range(n): # prog.measure(input_qubit[i], classicals[i]) prog.swap(input_qubit[1],input_qubit[0]) # number=2 prog.swap(input_qubit[1],input_qubit[0]) # number=3 prog.x(input_qubit[1]) # number=5 prog.h(input_qubit[1]) # number=10 prog.cz(input_qubit[0],input_qubit[1]) # number=11 prog.h(input_qubit[1]) # number=12 prog.rx(-2.73004401596953,input_qubit[1]) # number=6 prog.z(input_qubit[1]) # number=4 # circuit end return prog if __name__ == '__main__': n = 2 f = lambda rep: rep[-1] # f = lambda rep: "1" if rep[0:2] == "01" or rep[0:2] == "10" else "0" # f = lambda rep: "0" prog = make_circuit(n, f) sample_shot =2800 backend = FakeVigo() circuit1 = transpile(prog,FakeVigo()) circuit1.x(qubit=3) circuit1.x(qubit=3) circuit1.measure_all() prog = circuit1 info = execute(prog, backend=backend, shots=sample_shot).result().get_counts() writefile = open("../data/startQiskit_noisy378.csv","w") print(info,file=writefile) print("results end", file=writefile) print(circuit1.depth(),file=writefile) print(circuit1,file=writefile) writefile.close()

28.787611

82

0.631417

fc4bfecd2d5cf6ac9070193a6f82c0c3ab9e210d

527

py

Python

questions/migrations/0004_auto_20191127_1949.py

kemalayhan/kinda-stackoverflow

99052b78b18cbc562388c4fea87b5ec15cb47067

[ "MIT" ]

7

2021-02-08T19:40:57.000Z

2021-10-01T08:09:21.000Z

questions/migrations/0004_auto_20191127_1949.py

kemalayhan/kinda-stackoverflow

99052b78b18cbc562388c4fea87b5ec15cb47067

[ "MIT" ]

7

2021-04-08T19:57:02.000Z

2022-03-12T00:48:47.000Z

questions/migrations/0004_auto_20191127_1949.py

kemalayhan/kinda-stackoverflow

99052b78b18cbc562388c4fea87b5ec15cb47067

[ "MIT" ]

1

2020-12-21T19:01:52.000Z

# Generated by Django 2.2.7 on 2019-11-27 19:49 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('tags', '0001_initial'), ('questions', '0003_auto_20191121_1134'), ] operations = [ migrations.RemoveField( model_name='question', name='tag', ), migrations.AddField( model_name='question', name='tag', field=models.ManyToManyField(to='tags.Tag'), ), ]

21.958333

56

0.559772

7ee99d8de55bf14419641acc843a3ccb8ec89200

3,303

py

Python

trending.py

Lissy93/gh-trending-no-cors

c0f3234568e3e9421cb56bef49c92bd7a8e883a8

[ "MIT" ]

1

2021-11-27T00:17:11.000Z

trending.py

Lissy93/gh-trending-no-cors

c0f3234568e3e9421cb56bef49c92bd7a8e883a8

[ "MIT" ]

null

trending.py

Lissy93/gh-trending-no-cors

c0f3234568e3e9421cb56bef49c92bd7a8e883a8

[ "MIT" ]

5

2021-11-17T09:41:52.000Z

2021-11-24T01:04:04.000Z

from tornado.httpclient import AsyncHTTPClient, HTTPRequest from lxml import etree GITHUB_URL = 'https://github.com/' REPOSITORY = GITHUB_URL + 'trending/' DEVELOPER = REPOSITORY + 'developers/' USER_AGENT = 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 ' \ 'Safari/537.36 ' HEADER = {'User-Agent': USER_AGENT} TIMEOUT = 15 NO_RESULT = { 'count': 0, 'msg': 'Unavailable', 'items': [], } async def get_trending(url: str, params: dict = None) -> dict: html = await get_html(url, params) if html: is_blank = await has_trending(html) if not is_blank: if url.endswith(DEVELOPER): return await parse_developer(html) else: return await parse_repo(html) else: return NO_RESULT else: return NO_RESULT async def parse_repo(html) -> dict: items = [] articles = html.xpath('//article') for article in articles: item = {'repo': article.xpath('./h1/a/@href')[0][1:]} item['repo_link'] = GITHUB_URL + item['repo'] tmp = article.xpath('./p/text()') item['desc'] = tmp[0].replace('\n', '').strip() if len(tmp) > 0 else '' tmp = article.xpath('./div[last()]/span[1]/span[2]/text()') item['lang'] = tmp[0].replace('\n', '').strip() if len(tmp) > 0 else '' tmp = article.xpath('./div[last()]/a[1]/text()') item['stars'] = "".join(tmp).replace(' ', '').replace('\n', '') tmp = article.xpath('./div[last()]/a[2]/text()') item['forks'] = "".join(tmp).replace(' ', '').replace('\n', '') tmp = article.xpath('./div[last()]/span[3]/text()') item['added_stars'] = "".join(tmp).replace('\n', '').strip() item['avatars'] = article.xpath('./div[last()]/span[2]/a/img/@src') items.append(item) return { 'count': len(items), 'msg': 'suc', 'items': items } async def parse_developer(html) -> dict: items = [] articles = html.xpath('//article') for article in articles: item = {'user': article.xpath('./div[2]/div[1]/h1/a/@href')[0][1:]} item['user_link'] = GITHUB_URL + item['user'] item['full_name'] = article.xpath('./div[2]/div[1]/h1/a/text()')[0][1:] item['developer_avatar'] = article.xpath('./div[1]/a/img/@src')[0] items.append(item) return { 'count': len(items), 'msg': 'suc', 'items': items } async def has_trending(html): blank = html.xpath('//div[contains(@class,"blankslate")]') if blank or len(blank) > 0: return html.xpath('string(//div[contains(@class,"blankslate")]/h3)') \ .replace('\n', '').strip() else: return None async def get_html(url: str, params: dict = None): try: if params is not None: url = "{0}?since={1}".format(url, params.get('since')) req = HTTPRequest(url, headers=HEADER, request_timeout=TIMEOUT) response = await AsyncHTTPClient().fetch(req) except Exception: return None else: return etree.HTML(response.body) async def get_all_language(): html = await get_html(url=REPOSITORY) return html.xpath('//div[@class="select-menu-list"]/div/a/span/text()')

33.704082

108

0.565546

2eb771325a875f92b36c9d781d7422332007ae5b

353

py

Python

backintime/timeframes.py

akim-mukhtarov/backtesting

2d0491b919885eeddd62c4079c9c7292381cb4f9

[ "MIT" ]

null

backintime/timeframes.py

akim-mukhtarov/backtesting

2d0491b919885eeddd62c4079c9c7292381cb4f9

[ "MIT" ]

null

backintime/timeframes.py

akim-mukhtarov/backtesting

2d0491b919885eeddd62c4079c9c7292381cb4f9

[ "MIT" ]

null

from enum import Enum class Timeframes(Enum): # seconds S1 = 1 S5 = 5 S15 = 15 S30 = 30 # minutes M1 = 60 M3 = 180 M5 = 300 M15 = 900 M30 = 1800 M45 = 2700 # hours H1 = 3600 H2 = 7200 H3 = 10800 H4 = 14400 # day D1 = 86400 # week W1 = 604800

13.576923

24

0.436261

bf9628f988b986fae9d8ccc5fb20ae7b2a8d33de

7,944

py

Python

Apache-Spark-Programming-with-Databricks/ASP 3 - Functions/Labs/ASP 3.4L - Abandoned Carts Lab.py

sjuratov/apache-spark-programming-with-databricks

8059b5b8e754d4702c712646282a7592e8b4d53b

[ "CC0-1.0" ]

1

2022-03-20T05:23:50.000Z

Apache-Spark-Programming-with-Databricks/ASP 3 - Functions/Labs/ASP 3.4L - Abandoned Carts Lab.py

sjuratov/apache-spark-programming-with-databricks

8059b5b8e754d4702c712646282a7592e8b4d53b

[ "CC0-1.0" ]

null

Apache-Spark-Programming-with-Databricks/ASP 3 - Functions/Labs/ASP 3.4L - Abandoned Carts Lab.py

sjuratov/apache-spark-programming-with-databricks

8059b5b8e754d4702c712646282a7592e8b4d53b

[ "CC0-1.0" ]

null

# Databricks notebook source # MAGIC %md-sandbox # MAGIC # MAGIC <div style="text-align: center; line-height: 0; padding-top: 9px;"> # MAGIC <img src="https://databricks.com/wp-content/uploads/2018/03/db-academy-rgb-1200px.png" alt="Databricks Learning" style="width: 600px"> # MAGIC </div> # COMMAND ---------- # MAGIC %md # MAGIC # Abandoned Carts Lab # MAGIC Get abandoned cart items for email without purchases. # MAGIC 1. Get emails of converted users from transactions # MAGIC 2. Join emails with user IDs # MAGIC 3. Get cart item history for each user # MAGIC 4. Join cart item history with emails # MAGIC 5. Filter for emails with abandoned cart items # MAGIC # MAGIC ##### Methods # MAGIC - <a href="https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html" target="_blank">DataFrame</a>: `join` # MAGIC - <a href="https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql.html?#functions" target="_blank">Built-In Functions</a>: `collect_set`, `explode`, `lit` # MAGIC - <a href="https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrameNaFunctions.html" target="_blank">DataFrameNaFunctions</a>: `fill` # COMMAND ---------- # MAGIC %md # MAGIC ### Setup # MAGIC Run the cells below to create DataFrames **`sales_df`**, **`users_df`**, and **`events_df`**. # COMMAND ---------- # MAGIC %run ../../Includes/Classroom-Setup # COMMAND ---------- # sale transactions at BedBricks sales_df = spark.read.format("delta").load(sales_path) display(sales_df) # COMMAND ---------- # user IDs and emails at BedBricks users_df = spark.read.format("delta").load(users_path) display(users_df) # COMMAND ---------- # events logged on the BedBricks website events_df = spark.read.format("delta").load(events_path) display(events_df) # COMMAND ---------- # MAGIC %md ### 1: Get emails of converted users from transactions # MAGIC - Select the **`email`** column in **`sales_df`** and remove duplicates # MAGIC - Add a new column **`converted`** with the value **`True`** for all rows # MAGIC # MAGIC Save the result as **`converted_users_df`**. # COMMAND ---------- # TODO from pyspark.sql.functions import * converted_users_df = (sales_df.FILL_IN ) display(converted_users_df) # COMMAND ---------- # MAGIC %md #### 1.1: Check Your Work # MAGIC # MAGIC Run the following cell to verify that your solution works: # COMMAND ---------- expected_columns = ["email", "converted"] expected_count = 210370 assert converted_users_df.columns == expected_columns, "converted_users_df does not have the correct columns" assert converted_users_df.count() == expected_count, "converted_users_df does not have the correct number of rows" assert converted_users_df.select(col("converted")).first()[0] == True, "converted column not correct" # COMMAND ---------- # MAGIC %md # MAGIC ### 2: Join emails with user IDs # MAGIC - Perform an outer join on **`converted_users_df`** and **`users_df`** with the **`email`** field # MAGIC - Filter for users where **`email`** is not null # MAGIC - Fill null values in **`converted`** as **`False`** # MAGIC # MAGIC Save the result as **`conversions_df`**. # COMMAND ---------- # TODO conversions_df = (users_df.FILL_IN ) display(conversions_df) # COMMAND ---------- # MAGIC %md #### 2.1: Check Your Work # MAGIC # MAGIC Run the following cell to verify that your solution works: # COMMAND ---------- expected_columns = ["email", "user_id", "user_first_touch_timestamp", "converted"] expected_count = 782749 expected_false_count = 572379 assert conversions_df.columns == expected_columns, "Columns are not correct" assert conversions_df.filter(col("email").isNull()).count() == 0, "Email column contains null" assert conversions_df.count() == expected_count, "There is an incorrect number of rows" assert conversions_df.filter(col("converted") == False).count() == expected_false_count, "There is an incorrect number of false entries in converted column" # COMMAND ---------- # MAGIC %md # MAGIC ### 3: Get cart item history for each user # MAGIC - Explode the **`items`** field in **`events_df`** with the results replacing the existing **`items`** field # MAGIC - Group by **`user_id`** # MAGIC - Collect a set of all **`items.item_id`** objects for each user and alias the column to "cart" # MAGIC # MAGIC Save the result as **`carts_df`**. # COMMAND ---------- # TODO carts_df = (events_df.FILL_IN ) display(carts_df) # COMMAND ---------- # MAGIC %md #### 3.1: Check Your Work # MAGIC # MAGIC Run the following cell to verify that your solution works: # COMMAND ---------- expected_columns = ["user_id", "cart"] expected_count = 488403 assert carts_df.columns == expected_columns, "Incorrect columns" assert carts_df.count() == expected_count, "Incorrect number of rows" assert carts_df.select(col("user_id")).drop_duplicates().count() == expected_count, "Duplicate user_ids present" # COMMAND ---------- # MAGIC %md # MAGIC ### 4: Join cart item history with emails # MAGIC - Perform a left join on **`conversions_df`** and **`carts_df`** on the **`user_id`** field # MAGIC # MAGIC Save result as **`email_carts_df`**. # COMMAND ---------- # TODO email_carts_df = conversions_df.FILL_IN display(email_carts_df) # COMMAND ---------- # MAGIC %md #### 4.1: Check Your Work # MAGIC # MAGIC Run the following cell to verify that your solution works: # COMMAND ---------- expected_columns = ["user_id", "email", "user_first_touch_timestamp", "converted", "cart"] expected_count = 782749 expected_cart_null_count = 397799 assert email_carts_df.columns == expected_columns, "Columns do not match" assert email_carts_df.count() == expected_count, "Counts do not match" assert email_carts_df.filter(col("cart").isNull()).count() == expected_cart_null_count, "Cart null counts incorrect from join" # COMMAND ---------- # MAGIC %md # MAGIC ### 5: Filter for emails with abandoned cart items # MAGIC - Filter **`email_carts_df`** for users where **`converted`** is False # MAGIC - Filter for users with non-null carts # MAGIC # MAGIC Save result as **`abandoned_carts_df`**. # COMMAND ---------- # TODO abandoned_carts_df = (email_carts_df.FILL_IN ) display(abandoned_carts_df) # COMMAND ---------- # MAGIC %md #### 5.1: Check Your Work # MAGIC # MAGIC Run the following cell to verify that your solution works: # COMMAND ---------- expected_columns = ["user_id", "email", "user_first_touch_timestamp", "converted", "cart"] expected_count = 204272 assert abandoned_carts_df.columns == expected_columns, "Columns do not match" assert abandoned_carts_df.count() == expected_count, "Counts do not match" # COMMAND ---------- # MAGIC %md # MAGIC ### 6: Bonus Activity # MAGIC Plot number of abandoned cart items by product # COMMAND ---------- # TODO abandoned_items_df = (abandoned_carts_df.FILL_IN ) display(abandoned_items_df) # COMMAND ---------- # MAGIC %md #### 6.1: Check Your Work # MAGIC # MAGIC Run the following cell to verify that your solution works: # COMMAND ---------- abandoned_items_df.count() # COMMAND ---------- expected_columns = ["items", "count"] expected_count = 12 assert abandoned_items_df.count() == expected_count, "Counts do not match" assert abandoned_items_df.columns == expected_columns, "Columns do not match" # COMMAND ---------- # MAGIC %md ### Clean up classroom # COMMAND ---------- classroom_cleanup() # COMMAND ---------- # MAGIC %md-sandbox # MAGIC © 2022 Databricks, Inc. All rights reserved.<br/> # MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="https://www.apache.org/">Apache Software Foundation</a>.<br/> # MAGIC <br/> # MAGIC <a href="https://databricks.com/privacy-policy">Privacy Policy</a> | <a href="https://databricks.com/terms-of-use">Terms of Use</a> | <a href="https://help.databricks.com/">Support</a>

28.887273

192

0.68857

97e791cacee4deda9b69e647552f0426007d4227

4,619

py

Python

examples/apiary-scenario/asset_request_workflow.py

cloudblue/connect-python-sdk

82963ee62d8040804e6568af778d3fd0997af162

[ "Apache-2.0" ]

13

2019-12-21T05:03:43.000Z

2022-02-07T14:17:14.000Z

examples/apiary-scenario/asset_request_workflow.py

JaviCerveraIngram/connect-python-sdk

202e9ee8e9c2a0e77ff9343321f6fd946bc49ed6

[ "Apache-2.0" ]

76

2019-02-14T14:28:04.000Z

2019-11-29T11:02:01.000Z

examples/apiary-scenario/asset_request_workflow.py

ingrammicro/connect-python-sdk

656d653e4065637e2cc5768d7d554de17d0120eb

[ "Apache-2.0" ]

8

2019-02-11T08:12:02.000Z

2019-11-19T10:32:08.000Z

# -*- coding: utf-8 -*- # This file is part of the Ingram Micro Cloud Blue Connect SDK. # Copyright (c) 2019-2020 Ingram Micro. All Rights Reserved. """ This is part of the example of the implementation between connect and a Vendor System API. The detail of this scenario is documented in the documentation portal https://connect.cloudblue.com/community/sdk/ This microservice search all of the Purchase Request in status pending in Connect and creates the request in the Vendor System using Vendor System API. """ import warnings import requests from connect.config import Config from connect.logger import logger from connect.models import AssetRequest from connect.resources.automation_engine import AutomationEngine from connect.resources.fulfillment import Fulfillment # URL of the Vendor API, in this case the apiary.io scenario VENDOR_API_URL = 'https://SET_YOUR_OWN_SAMPLE.apiary-mock.com/' # Enable processing of deprecation warnings warnings.simplefilter('default') # Set logger level / default level ERROR logger.setLevel('DEBUG') # If we remove this line, it is done implicitly Config(file='examples/apiary-scenario/config.json') class AssetRequest(AutomationEngine): resource = 'requests' model_class = AssetRequest def __init__(self, config=None): super().__init__(config=config) self._fulfillment = Fulfillment(config) def dispatch(self, request): return self.process_request(request) def process_request(self, request): if (request.type == 'purchase'): if (len(request.asset.items) == 1): tenant_param_id = '' for param in request.asset.params: if (param.name == 'tenantId'): tenant_param_id = param.value if (tenant_param_id == ''): self.create_request(request) else: logger.info('Skip process') else: logger.info('Request malformed, too many items') else: logger.info('This processor not handle this type of request') return False def create_request(self, request): for item in request.asset.items: mpn = item.mpn quantity = item.quantity break url = VENDOR_API_URL + "tenant?externalId=" + mpn response = requests.get(url, data='').json() first_name = request.asset.tiers.customer.contact_info.contact.first_name last_name = request.asset.tiers.customer.contact_info.contact.last_name address = request.asset.tiers.customer.contact_info.address_line1 postal_code = request.asset.tiers.customer.contact_info.postal_code country = request.asset.tiers.customer.contact_info.country email = request.asset.tiers.customer.contact_info.contact.email account_phone = request.asset.tiers.customer.contact_info.contact.phone_number.phone_number if response['externalId'] != request.asset.id: url = VENDOR_API_URL + 'tenant' payload = { 'Attributes': { 'product': { 'item': mpn, 'quantity': quantity }, 'account': { 'accountFirstName': first_name, 'accountLastName': last_name, 'accountCompany': request.asset.tiers.customer.name, 'accountAddress': address, 'accountCity': request.asset.tiers.customer.contact_info.city, 'accountState': request.asset.tiers.customer.contact_info.state, 'accountPostalCode': postal_code, 'accountCountry': country, 'accountEmail': email, 'accountPhone': account_phone } } } response = requests.post(url, data=payload).json() if (response['tenantId'] != ''): data = { "params": [{ "id": "tenantId", "value": response['tenantId'] }] } self._fulfillment.update_param_asset_request(request.id, data, 'vendor system Id') return response else: logger.info('Error in Vendor System') return False if __name__ == '__main__': asset_request_example = AssetRequest() asset_request_example.process()

39.144068

99

0.599047

b1b302b70789fd34acbf973ac2df2c9b2b4314aa

5,195

py

Python

src/posts/views.py

vikasvmads/Post-With-Django

8c088c847cc82f42a0d477166a3d5a0c7512b688

[ "MIT" ]

1

2019-04-30T09:07:56.000Z

src/posts/views.py

vikasvmads/Post-With-Django

8c088c847cc82f42a0d477166a3d5a0c7512b688

[ "MIT" ]

null

src/posts/views.py

vikasvmads/Post-With-Django

8c088c847cc82f42a0d477166a3d5a0c7512b688

[ "MIT" ]

null

try: from urllib import quote_plus #python 2 except: pass try: from urllib.parse import quote_plus #python 3 except: pass from django.contrib import messages from django.contrib.contenttypes.models import ContentType from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger from django.db.models import Q from django.http import HttpResponse, HttpResponseRedirect, Http404 from django.shortcuts import render, get_object_or_404, redirect from django.utils import timezone from comments.models import comments from comments.forms import CommentForm from .forms import PostForm from .models import Post def post_create(request): if not request.user.is_staff or not request.user.is_superuser: raise Http404 form = PostForm(request.POST or None, request.FILES or None) if form.is_valid(): instance = form.save(commit=False) instance.user = request.user instance.save() # message success messages.success(request, "Successfully Created") return HttpResponseRedirect(instance.get_absolute_url()) context = { "form": form, } return render(request, "post_form.html", context) from django.views.generic import DetailView class PostDetailView(DetailView): template_name = 'post_detail.html' def get_object(self, *args, **kwargs): slug = self.kwargs.get("slug") instance = get_object_or_404(Post, slug=slug) if instance.publish > timezone.now().date() or instance.draft: if not self.request.user.is_staff or not self.request.user.is_superuser: raise Http404 return instance def get_context_data(self, *args, **kwargs): context = super(PostDetailView, self).get_context_data(*args, **kwargs) instance = context['object'] context['share_string'] = quote_plus(instance.content) return context # in urls.py --> PostDetailView.as_view() instead of post_detail def post_detail(request, slug=None): instance = get_object_or_404(Post, slug=slug) if instance.publish > timezone.now().date() or instance.draft: if not request.user.is_staff or not request.user.is_superuser: raise Http404 share_string = quote_plus(instance.content) instance_data = { 'content_type' : instance.get_content_type, 'object_id' : instance.id, } form = CommentForm(request.POST or None , initial=instance_data) if form.is_valid(): c_type = form.cleaned_data.get('content_type') obj_id = form.cleaned_data.get('object_id') content_type = ContentType.objects.get(model=c_type) content_data = form.cleaned_data.get('content') parent_obj = None try: parent_id= int(request.POST.get("parent_id")) except: parent_id= None if parent_id: qs = comments.objects.filter(id=parent_id) if qs.exists(): parent_obj = qs.first() new_comment , created = comments.objects.get_or_create( user = request.user, content_type = content_type, object_id = obj_id , content = content_data, parent = parent_obj ) return HttpResponseRedirect(new_comment.content_object.get_absolute_url()) Comments = comments.objects.filter_by_instance(instance) context = { "title": instance.title, "instance": instance, "share_string": share_string, "comment_form":form, "comments":Comments } return render(request, "post_detail.html", context) def post_list(request): today = timezone.now().date() queryset_list = Post.objects.active() #.order_by("-timestamp") if request.user.is_staff or request.user.is_superuser: queryset_list = Post.objects.all() query = request.GET.get("q") if query: queryset_list = queryset_list.filter( Q(title__icontains=query)| Q(content__icontains=query)| Q(user__first_name__icontains=query) | Q(user__last_name__icontains=query) ).distinct() paginator = Paginator(queryset_list, 8) # Show 25 contacts per page page_request_var = "page" page = request.GET.get(page_request_var) try: queryset = paginator.page(page) except PageNotAnInteger: # If page is not an integer, deliver first page. queryset = paginator.page(1) except EmptyPage: # If page is out of range (e.g. 9999), deliver last page of results. queryset = paginator.page(paginator.num_pages) context = { "object_list": queryset, "title": "List", "page_request_var": page_request_var, "today": today, } return render(request, "post_list.html", context) def post_update(request, slug=None): if not request.user.is_staff or not request.user.is_superuser: raise Http404 instance = get_object_or_404(Post, slug=slug) form = PostForm(request.POST or None, request.FILES or None, instance=instance) if form.is_valid(): instance = form.save(commit=False) instance.save() messages.success(request, "<a href='#'>Item</a> Saved", extra_tags='html_safe') return HttpResponseRedirect(instance.get_absolute_url()) context = { "title": instance.title, "instance": instance, "form":form, } return render(request, "post_form.html", context) def post_delete(request, slug=None): if not request.user.is_staff or not request.user.is_superuser: raise Http404 instance = get_object_or_404(Post, slug=slug) instance.delete() messages.success(request, "Successfully deleted") return redirect("posts:list")

29.856322

81

0.744177

9d6633cf565c433921fb4878e1c06b22fc99c4d3

2,380

py

Python

Machine-Learning-with-Python- From-LM-to-DL/Unit 1.Linear Classifiers and Generalizations/my_functions.py

andresdelarosa1887/Public-Projects

db8d8e0c0f5f0f7326346462fcdfe21ce8142a12

[ "Unlicense" ]

1

2020-09-29T17:29:34.000Z

Machine-Learning-with-Python- From-LM-to-DL/Unit 1.Linear Classifiers and Generalizations/my_functions.py

andresdelarosa1887/Public-Projects

db8d8e0c0f5f0f7326346462fcdfe21ce8142a12

[ "Unlicense" ]

null

Machine-Learning-with-Python- From-LM-to-DL/Unit 1.Linear Classifiers and Generalizations/my_functions.py

andresdelarosa1887/Public-Projects

db8d8e0c0f5f0f7326346462fcdfe21ce8142a12

[ "Unlicense" ]

null

msg= "hello_world" import numpy as np ##Classifier- Im using pegasos from string import punctuation, digits import numpy as np import random from matplotlib import pyplot as plt def get_order(n_samples): try: with open(str(n_samples) + '.txt') as fp: line = fp.readline() return list(map(int, line.split(','))) except FileNotFoundError: random.seed(1) indices = list(range(n_samples)) random.shuffle(indices) return indices def pegasos_single_step_update( feature_vector, label, L, eta, current_theta, current_theta_0): if label*(feature_vector@current_theta + current_theta_0) <= 1: current_theta = (1 - eta*L)*current_theta + eta*label*feature_vector current_theta_0 = current_theta_0 + eta*label else: current_theta = (1 - eta*L)*current_theta return (current_theta, current_theta_0) def classifier(feature_matrix, labels, T, L): pegasos_theta = np.zeros(len(feature_matrix[0])) pegasos_theta_0 = 0 update_counter = 0 # updating perceptrons for t in range(T): for i in get_order(feature_matrix.shape[0]): update_counter += 1 eta = 1/(np.sqrt(update_counter)) pegasos_theta, pegasos_theta_0 = pegasos_single_step_update(feature_matrix[i], labels[i], L, eta, pegasos_theta, pegasos_theta_0) return (pegasos_theta, pegasos_theta_0) ##Classification Function## def classify(feature_matrix, theta, theta_0): result= [] for i, x in enumerate(feature_matrix): if (np.dot(feature_matrix[i], theta) + theta_0) >0: classification= 1 else: classification= -1 result.append(classification) return(np.array(result)) def accuracy(preds, targets): """ Given length-N vectors containing predicted and target labels, returns the percentage and number of correct predictions. """ return (preds == targets).mean()

33.521127

90

0.555882

b404a70e7dc272f2ee71a5c99f4226879351975c

1,702

py

Python

test/functional/feature_uacomment.py

PeterL73/veil

2825d735275cd592b1fd5207b0dfdca2d4e3e78c

[ "MIT" ]

124

2018-12-25T00:01:18.000Z

2021-12-26T19:38:43.000Z

test/functional/feature_uacomment.py

PeterL73/veil

2825d735275cd592b1fd5207b0dfdca2d4e3e78c

[ "MIT" ]

702

2018-12-16T18:07:18.000Z

2022-03-18T16:52:14.000Z

test/functional/feature_uacomment.py

PeterL73/veil

2825d735275cd592b1fd5207b0dfdca2d4e3e78c

[ "MIT" ]

151

2018-12-13T07:33:34.000Z

2022-01-29T11:35:23.000Z

#!/usr/bin/env python3 # Copyright (c) 2017-2019 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test the -uacomment option.""" import re from test_framework.test_framework import BitcoinTestFramework from test_framework.test_node import ErrorMatch from test_framework.util import assert_equal class UacommentTest(BitcoinTestFramework): def set_test_params(self): self.num_nodes = 1 self.setup_clean_chain = True def run_test(self): self.log.info("test multiple -uacomment") test_uacomment = self.nodes[0].getnetworkinfo()["subversion"][-12:-1] assert_equal(test_uacomment, "(testnode0)") self.restart_node(0, ["-uacomment=foo"]) foo_uacomment = self.nodes[0].getnetworkinfo()["subversion"][-17:-1] assert_equal(foo_uacomment, "(testnode0; foo)") self.log.info("test -uacomment max length") self.stop_node(0) expected = "Error: Total length of network version string $[0-9]+$ exceeds maximum length $256$. Reduce the number or size of uacomments." self.nodes[0].assert_start_raises_init_error(["-uacomment=" + 'a' * 256], expected, match=ErrorMatch.FULL_REGEX) self.log.info("test -uacomment unsafe characters") for unsafe_char in ['/', ':', '(', ')']: expected = "Error: User Agent comment $" + re.escape(unsafe_char) + "$ contains unsafe characters." self.nodes[0].assert_start_raises_init_error(["-uacomment=" + unsafe_char], expected, match=ErrorMatch.FULL_REGEX) if __name__ == '__main__': UacommentTest().main()

41.512195

150

0.692714

6bcd9345c3167dada174c7d901d05f38caabf0c6

115

py

Python

src/tools/inspector/resetOnTargetDestroyed.py

t3kt/raytk

e0e2b3643b2f536d597c5db64f02d17f7e8f23ac

[ "CC-BY-4.0" ]

108

2020-11-23T01:22:37.000Z

2022-03-29T09:27:32.000Z

src/tools/inspector/resetOnTargetDestroyed.py

t3kt/raytk

e0e2b3643b2f536d597c5db64f02d17f7e8f23ac

[ "CC-BY-4.0" ]

794

2020-11-21T22:27:37.000Z

2022-03-24T06:41:19.000Z

src/tools/inspector/resetOnTargetDestroyed.py

t3kt/raytk

e0e2b3643b2f536d597c5db64f02d17f7e8f23ac

[ "CC-BY-4.0" ]

3

2021-06-19T00:57:54.000Z

2021-11-01T11:55:07.000Z

def onDestroy(): ext.inspector.Reset() def onNameChange(changeOp): return def onPathChange(changeOp): return

12.777778

27

0.756522

d61b525b3f398dacd0049fded9924e079ff583a9

814

py

Python

licenses/migrations/0010_auto_20201105_1404.py

ayushmankumar7/cc-licenses

51f0dd26c17c7fcff0008630452cc75505977408

[ "MIT" ]

null

licenses/migrations/0010_auto_20201105_1404.py

ayushmankumar7/cc-licenses

51f0dd26c17c7fcff0008630452cc75505977408

[ "MIT" ]

null

licenses/migrations/0010_auto_20201105_1404.py

ayushmankumar7/cc-licenses

51f0dd26c17c7fcff0008630452cc75505977408

[ "MIT" ]

null

# Generated by Django 2.2.13 on 2020-11-05 19:04 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("licenses", "0009_auto_20201029_1453"), ] operations = [ migrations.AddField( model_name="legalcode", name="deed_url", field=models.URLField(default=None), preserve_default=False, ), migrations.AddField( model_name="legalcode", name="license_url", field=models.URLField(default=None), preserve_default=False, ), migrations.AddField( model_name="legalcode", name="plain_text_url", field=models.URLField(default=None), preserve_default=False, ), ]

25.4375

48

0.570025

40c28d9bee34c66cb0a749e7e883ac7d7820799f

1,503

py

Python

dev/potential/ThreeBodyPotential/three_body_utils.py

eragasa/pypospack

21cdecaf3b05c87acc532d992be2c04d85bfbc22

[ "MIT" ]

4

2018-01-18T19:59:56.000Z

2020-08-25T11:56:52.000Z

dev/potential/ThreeBodyPotential/three_body_utils.py

eragasa/pypospack

21cdecaf3b05c87acc532d992be2c04d85bfbc22

[ "MIT" ]

1

2018-04-22T23:02:13.000Z

dev/potential/ThreeBodyPotential/three_body_utils.py

eragasa/pypospack

21cdecaf3b05c87acc532d992be2c04d85bfbc22

[ "MIT" ]

1

2019-09-14T07:04:42.000Z

import pytest def get_2body_pairs(symbols): pair = [] for i,s_i in enumerate(symbols): for j,s_j in enumerate(symbols): if i<=j: pair.append("{}{}".format(s_i,s_j)) return pair def get_3body_triples(symbols): triples = [] for i,s_i in enumerate(symbols): for j,s_j in enumerate(symbols): for k,s_k in enumerate(symbols): triples.append("{}{}{}".format(s_i,s_j,s_k)) return triples def dev__get_2body_pairs__1_element(): symbols = ['Si'] pairs = get_2body_pairs(symbols) print(pairs) def dev__get_2body_pairs__2_element(): symbols = ['Si','Ge'] pairs = get_2body_pairs(symbols) print(pairs) def dev__get_2body_pairs__3_element(): symbols = ['Si','Ge','Sn'] pairs = get_2body_pairs(symbols) print(pairs) def dev__get_3body_triples__1_element(): symbols = ['Si'] triples = get_3body_triples(symbols) print(triples) def dev__get_3body_triples__2_element(): symbols = ['Si','Ge'] triples = get_3body_triples(symbols) print(triples) def dev__get_3body_triples__3_element(): symbols = ['Si','Ge','Sn'] triples = get_3body_triples(symbols) print(triples) if __name__ == "__main__": dev__get_2body_pairs__1_element() dev__get_2body_pairs__2_element() dev__get_2body_pairs__3_element() dev__get_3body_triples__1_element() dev__get_3body_triples__2_element() dev__get_3body_triples__3_element()

21.471429

60

0.667332

77a915d9c9619650b756c00ed070590d72b8be7d

612

py

Python

netbox/utilities/exceptions.py

esljaz/netbox

e7f64334c06748b4b85c54d881f5e2b03b9463b5

[ "Apache-2.0" ]

2

2021-06-02T03:00:05.000Z

2021-07-30T18:52:32.000Z

netbox/utilities/exceptions.py

emersonfelipesp/netbox

fecca5ad83fb6b48a2f15982dfd3242653f105f9

[ "Apache-2.0" ]

25

2019-09-17T19:40:50.000Z

2022-03-11T04:01:55.000Z

netbox/utilities/exceptions.py

emersonfelipesp/netbox

fecca5ad83fb6b48a2f15982dfd3242653f105f9

[ "Apache-2.0" ]

1

2020-09-16T11:31:25.000Z

from rest_framework import status from rest_framework.exceptions import APIException class AbortTransaction(Exception): """ A dummy exception used to trigger a database transaction rollback. """ pass class RQWorkerNotRunningException(APIException): """ Indicates the temporary inability to enqueue a new task (e.g. custom script execution) because no RQ worker processes are currently running. """ status_code = status.HTTP_503_SERVICE_UNAVAILABLE default_detail = 'Unable to process request: RQ worker process not running.' default_code = 'rq_worker_not_running'

30.6

111

0.75817

8ac483c2426701e883ef8115677e7214048bcf53

68,561

py

Python

fiftyone/utils/coco.py

Pascal0902/fiftyone

94ce6bebac6df848547228869a7825a9e8e67595

[ "Apache-2.0" ]

null

fiftyone/utils/coco.py

Pascal0902/fiftyone

94ce6bebac6df848547228869a7825a9e8e67595

[ "Apache-2.0" ]

1

2022-03-25T19:27:53.000Z

fiftyone/utils/coco.py

cnheider/fiftyone

9fa8d64f913b0cdf59efd10f72d8c6d37864ce33

[ "Apache-2.0" ]

null

""" Utilities for working with datasets in `COCO format <https://cocodataset.org/#format-data>`_. | Copyright 2017-2021, Voxel51, Inc. | `voxel51.com <https://voxel51.com/>`_ | """ from collections import defaultdict import csv from datetime import datetime from itertools import groupby import logging import multiprocessing import os import random import shutil import warnings import numpy as np from skimage import measure import eta.core.image as etai import eta.core.serial as etas import eta.core.utils as etau import eta.core.web as etaw import fiftyone.core.labels as fol import fiftyone.core.metadata as fom import fiftyone.core.utils as fou import fiftyone.utils.data as foud mask_utils = fou.lazy_import( "pycocotools.mask", callback=lambda: fou.ensure_import("pycocotools") ) logger = logging.getLogger(__name__) def add_coco_labels( sample_collection, label_field, labels_or_path, label_type="detections", coco_id_field="coco_id", classes=None, extra_attrs=None, use_polylines=False, tolerance=None, ): """Adds the given COCO labels to the collection. The ``labels_or_path`` argument can be either a list of COCO annotations in the format below, or the path to a JSON file containing such data on disk. When ``label_type="detections"``, the labels should have format:: [ { "id": 1, "image_id": 1, "category_id": 2, "bbox": [260, 177, 231, 199], # optional "score": 0.95, "area": 45969, "iscrowd": 0, # extra attrs ... }, ... ] When ``label_type="segmentations"``, the labels should have format:: [ { "id": 1, "image_id": 1, "category_id": 2, "bbox": [260, 177, 231, 199], "segmentation": [...], # optional "score": 0.95, "area": 45969, "iscrowd": 0, # extra attrs ... }, ... ] When ``label_type="keypoints"``, the labels should have format:: [ { "id": 1, "image_id": 1, "category_id": 2, "keypoints": [224, 226, 2, ...], "num_keypoints": 10, # extra attrs ... }, ... ] See `this page <https://cocodataset.org/#format-data>`_ for more information about the COCO data format. Args: sample_collection: a :class:`fiftyone.core.collections.SampleCollection` label_field: the label field in which to store the labels. The field will be created if necessary labels_or_path: a list of COCO annotations or the path to a JSON file containing such data on disk label_type ("detections"): the type of labels to load. Supported values are ``("detections", "segmentations", "keypoints")`` coco_id_field ("coco_id"): the field of ``sample_collection`` containing the COCO IDs for the samples classes (None): the list of class label strings. If not provided, these must be available from :meth:`classes <fiftyone.core.collections.SampleCollection.classes>` or :meth:`default_classes <fiftyone.core.collections.SampleCollection.default_classes>` extra_attrs (None): whether to load extra annotation attributes onto the imported labels. Supported values are: - ``None``/``False``: do not load extra attributes - ``True``: load all extra attributes found - a name or list of names of specific attributes to load use_polylines (False): whether to represent segmentations as :class:`fiftyone.core.labels.Polylines` instances rather than :class:`fiftyone.core.labels.Detections` with dense masks tolerance (None): a tolerance, in pixels, when generating approximate polylines for instance masks. Typical values are 1-3 pixels """ if classes is None: if label_field in sample_collection.classes: classes = sample_collection.classes[label_field] elif sample_collection.default_classes: classes = sample_collection.default_classes if not classes: raise ValueError( "You must provide `classes` in order to load COCO labels" ) if etau.is_str(labels_or_path): labels = etas.load_json(labels_or_path) else: labels = labels_or_path coco_objects_map = defaultdict(list) for d in labels: coco_obj = COCOObject.from_anno_dict(d, extra_attrs=extra_attrs) coco_objects_map[coco_obj.image_id].append(coco_obj) id_map = { k: v for k, v in zip(*sample_collection.values([coco_id_field, "id"])) } coco_ids = sorted(coco_objects_map.keys()) bad_ids = set(coco_ids) - set(id_map.keys()) if bad_ids: coco_ids = [_id for _id in coco_ids if _id not in bad_ids] logger.warning( "Ignoring labels with %d nonexistent COCO IDs: %s", len(bad_ids), sorted(bad_ids), ) view = sample_collection.select( [id_map[coco_id] for coco_id in coco_ids], ordered=True ) view.compute_metadata() heights, widths = view.values(["metadata.height", "metadata.width"]) labels = [] for coco_id, height, width in zip(coco_ids, heights, widths): coco_objects = coco_objects_map[coco_id] frame_size = (height, width) if label_type == "detections": _labels = _coco_objects_to_detections( coco_objects, frame_size, classes, None, False ) elif label_type == "segmentations": if use_polylines: _labels = _coco_objects_to_polylines( coco_objects, frame_size, classes, None, tolerance ) else: _labels = _coco_objects_to_detections( coco_objects, frame_size, classes, None, True ) elif label_type == "keypoints": _labels = _coco_objects_to_keypoints( coco_objects, frame_size, classes ) else: raise ValueError( "Unsupported label_type='%s'. Supported values are %s" % (label_type, ("detections", "segmentations", "keypoints")) ) labels.append(_labels) view.set_values(label_field, labels) class COCODetectionDatasetImporter( foud.LabeledImageDatasetImporter, foud.ImportPathsMixin ): """Importer for COCO detection datasets stored on disk. See :class:`fiftyone.types.dataset_types.COCODetectionDataset` for format details. Args: dataset_dir (None): the dataset directory data_path (None): an optional parameter that enables explicit control over the location of the media. Can be any of the following: - a folder name like ``"data"`` or ``"data/"`` specifying a subfolder of ``dataset_dir`` where the media files reside - an absolute directory path where the media files reside. In this case, the ``dataset_dir`` has no effect on the location of the data - a filename like ``"data.json"`` specifying the filename of the JSON data manifest file in ``dataset_dir`` - an absolute filepath specifying the location of the JSON data manifest. In this case, ``dataset_dir`` has no effect on the location of the data If None, this parameter will default to whichever of ``data/`` or ``data.json`` exists in the dataset directory labels_path (None): an optional parameter that enables explicit control over the location of the labels. Can be any of the following: - a filename like ``"labels.json"`` specifying the location of the labels in ``dataset_dir`` - an absolute filepath to the labels. In this case, ``dataset_dir`` has no effect on the location of the labels If None, the parameter will default to ``labels.json`` label_types (None): a label type or list of label types to load. The supported values are ``("detections", "segmentations", "keypoints")``. By default, only "detections" are loaded classes (None): a string or list of strings specifying required classes to load. Only samples containing at least one instance of a specified class will be loaded image_ids (None): an optional list of specific image IDs to load. Can be provided in any of the following formats: - a list of ``<image-id>`` ints or strings - a list of ``<split>/<image-id>`` strings - the path to a text (newline-separated), JSON, or CSV file containing the list of image IDs to load in either of the first two formats include_id (False): whether to include the COCO ID of each sample in the loaded labels extra_attrs (None): whether to load extra annotation attributes onto the imported labels. Supported values are: - ``None``/``False``: do not load extra attributes - ``True``: load all extra attributes found - a name or list of names of specific attributes to load only_matching (False): whether to only load labels that match the ``classes`` requirement that you provide (True), or to load all labels for samples that match the requirements (False) use_polylines (False): whether to represent segmentations as :class:`fiftyone.core.labels.Polylines` instances rather than :class:`fiftyone.core.labels.Detections` with dense masks tolerance (None): a tolerance, in pixels, when generating approximate polylines for instance masks. Typical values are 1-3 pixels shuffle (False): whether to randomly shuffle the order in which the samples are imported seed (None): a random seed to use when shuffling max_samples (None): a maximum number of samples to load. If ``label_types`` and/or ``classes`` are also specified, first priority will be given to samples that contain all of the specified label types and/or classes, followed by samples that contain at least one of the specified labels types or classes. The actual number of samples loaded may be less than this maximum value if the dataset does not contain sufficient samples matching your requirements. By default, all matching samples are loaded """ def __init__( self, dataset_dir=None, data_path=None, labels_path=None, label_types=None, classes=None, image_ids=None, include_id=False, extra_attrs=None, only_matching=False, use_polylines=False, tolerance=None, shuffle=False, seed=None, max_samples=None, ): data_path = self._parse_data_path( dataset_dir=dataset_dir, data_path=data_path, default="data/", ) labels_path = self._parse_labels_path( dataset_dir=dataset_dir, labels_path=labels_path, default="labels.json", ) label_types = _parse_label_types(label_types) if include_id: label_types.append("coco_id") super().__init__( dataset_dir=dataset_dir, shuffle=shuffle, seed=seed, max_samples=max_samples, ) self.data_path = data_path self.labels_path = labels_path self.label_types = label_types self.classes = classes self.image_ids = image_ids self.extra_attrs = extra_attrs self.only_matching = only_matching self.use_polylines = use_polylines self.tolerance = tolerance self._info = None self._classes = None self._supercategory_map = None self._image_paths_map = None self._image_dicts_map = None self._annotations = None self._filenames = None self._iter_filenames = None def __iter__(self): self._iter_filenames = iter(self._filenames) return self def __len__(self): return len(self._filenames) def __next__(self): filename = next(self._iter_filenames) if os.path.isabs(filename): image_path = filename else: image_path = self._image_paths_map[filename] image_dict = self._image_dicts_map.get(filename, None) if image_dict is None: image_metadata = fom.ImageMetadata.build_for(image_path) return image_path, image_metadata, None image_id = image_dict["id"] width = image_dict["width"] height = image_dict["height"] image_metadata = fom.ImageMetadata(width=width, height=height) if self._annotations is None: return image_path, image_metadata, None coco_objects = self._annotations.get(image_id, []) frame_size = (width, height) if self.classes is not None and self.only_matching: coco_objects = _get_matching_objects( coco_objects, self.classes, self._classes ) label = {} if "detections" in self.label_types: detections = _coco_objects_to_detections( coco_objects, frame_size, self._classes, self._supercategory_map, False, # no segmentations ) if detections is not None: label["detections"] = detections if "segmentations" in self.label_types: if self.use_polylines: segmentations = _coco_objects_to_polylines( coco_objects, frame_size, self._classes, self._supercategory_map, self.tolerance, ) else: segmentations = _coco_objects_to_detections( coco_objects, frame_size, self._classes, self._supercategory_map, True, # load segmentations ) if segmentations is not None: label["segmentations"] = segmentations if "keypoints" in self.label_types: keypoints = _coco_objects_to_keypoints( coco_objects, frame_size, self._classes ) if keypoints is not None: label["keypoints"] = keypoints if "coco_id" in self.label_types: label["coco_id"] = image_id if self._has_scalar_labels: label = next(iter(label.values())) if label else None return image_path, image_metadata, label @property def has_dataset_info(self): return True @property def has_image_metadata(self): return True @property def _has_scalar_labels(self): return len(self.label_types) == 1 @property def label_cls(self): seg_type = fol.Polylines if self.use_polylines else fol.Detections types = { "detections": fol.Detections, "segmentations": seg_type, "keypoints": fol.Keypoints, "coco_id": int, } if self._has_scalar_labels: return types[self.label_types[0]] return {k: v for k, v in types.items() if k in self.label_types} def setup(self): self._image_paths_map = self._load_data_map(self.data_path) if self.labels_path is not None and os.path.isfile(self.labels_path): ( info, classes, supercategory_map, images, annotations, ) = load_coco_detection_annotations( self.labels_path, extra_attrs=self.extra_attrs ) if classes is not None: info["classes"] = classes image_ids = _get_matching_image_ids( classes, images, annotations, image_ids=self.image_ids, classes=self.classes, shuffle=self.shuffle, seed=self.seed, max_samples=self.max_samples, ) filenames = [images[_id]["file_name"] for _id in image_ids] _image_ids = set(image_ids) image_dicts_map = { i["file_name"]: i for _id, i in images.items() if _id in _image_ids } else: info = {} classes = None supercategory_map = None image_dicts_map = {} annotations = None filenames = [] self._info = info self._classes = classes self._supercategory_map = supercategory_map self._image_dicts_map = image_dicts_map self._annotations = annotations self._filenames = filenames def get_dataset_info(self): return self._info class COCODetectionDatasetExporter( foud.LabeledImageDatasetExporter, foud.ExportPathsMixin ): """Exporter that writes COCO detection datasets to disk. This class currently only supports exporting detections and instance segmentations. See :class:`fiftyone.types.dataset_types.COCODetectionDataset` for format details. Args: export_dir (None): the directory to write the export. This has no effect if ``data_path`` and ``labels_path`` are absolute paths data_path (None): an optional parameter that enables explicit control over the location of the exported media. Can be any of the following: - a folder name like ``"data"`` or ``"data/"`` specifying a subfolder of ``export_dir`` in which to export the media - an absolute directory path in which to export the media. In this case, the ``export_dir`` has no effect on the location of the data - a JSON filename like ``"data.json"`` specifying the filename of the manifest file in ``export_dir`` generated when ``export_media`` is ``"manifest"`` - an absolute filepath specifying the location to write the JSON manifest file when ``export_media`` is ``"manifest"``. In this case, ``export_dir`` has no effect on the location of the data If None, the default value of this parameter will be chosen based on the value of the ``export_media`` parameter labels_path (None): an optional parameter that enables explicit control over the location of the exported labels. Can be any of the following: - a filename like ``"labels.json"`` specifying the location in ``export_dir`` in which to export the labels - an absolute filepath to which to export the labels. In this case, the ``export_dir`` has no effect on the location of the labels If None, the labels will be exported into ``export_dir`` using the default filename export_media (None): controls how to export the raw media. The supported values are: - ``True``: copy all media files into the output directory - ``False``: don't export media - ``"move"``: move all media files into the output directory - ``"symlink"``: create symlinks to the media files in the output directory - ``"manifest"``: create a ``data.json`` in the output directory that maps UUIDs used in the labels files to the filepaths of the source media, rather than exporting the actual media If None, the default value of this parameter will be chosen based on the value of the ``data_path`` parameter image_format (None): the image format to use when writing in-memory images to disk. By default, ``fiftyone.config.default_image_ext`` is used classes (None): the list of possible class labels. If not provided, this list will be extracted when :meth:`log_collection` is called, if possible info (None): a dict of info as returned by :meth:`load_coco_detection_annotations`. If not provided, this info will be extracted when :meth:`log_collection` is called, if possible extra_attrs (None): an optional field name or list of field names of extra label attributes to include in the exported annotations iscrowd ("iscrowd"): the name of a detection attribute that indicates whether an object is a crowd (only used if present) num_decimals (None): an optional number of decimal places at which to round bounding box pixel coordinates. By default, no rounding is done tolerance (None): a tolerance, in pixels, when generating approximate polylines for instance masks. Typical values are 1-3 pixels """ def __init__( self, export_dir=None, data_path=None, labels_path=None, export_media=None, image_format=None, classes=None, info=None, extra_attrs=None, iscrowd="iscrowd", num_decimals=None, tolerance=None, ): data_path, export_media = self._parse_data_path( export_dir=export_dir, data_path=data_path, export_media=export_media, default="data/", ) labels_path = self._parse_labels_path( export_dir=export_dir, labels_path=labels_path, default="labels.json", ) if etau.is_str(extra_attrs): extra_attrs = [extra_attrs] super().__init__(export_dir=export_dir) self.data_path = data_path self.labels_path = labels_path self.export_media = export_media self.image_format = image_format self.classes = classes self.info = info self.extra_attrs = extra_attrs self.iscrowd = iscrowd self.num_decimals = num_decimals self.tolerance = tolerance self._labels_map_rev = None self._image_id = None self._anno_id = None self._images = None self._annotations = None self._classes = None self._has_labels = None self._media_exporter = None @property def requires_image_metadata(self): return True @property def label_cls(self): return fol.Detections def setup(self): self._image_id = 0 self._anno_id = 0 self._images = [] self._annotations = [] self._classes = set() self._has_labels = False self._parse_classes() self._media_exporter = foud.ImageExporter( self.export_media, export_path=self.data_path, default_ext=self.image_format, ) self._media_exporter.setup() def log_collection(self, sample_collection): if self.classes is None: if sample_collection.default_classes: self.classes = sample_collection.default_classes self._parse_classes() elif sample_collection.classes: self.classes = next(iter(sample_collection.classes.values())) self._parse_classes() elif "classes" in sample_collection.info: self.classes = sample_collection.info["classes"] self._parse_classes() if self.info is None: self.info = sample_collection.info def export_sample(self, image_or_path, detections, metadata=None): out_image_path, _ = self._media_exporter.export(image_or_path) if metadata is None: metadata = fom.ImageMetadata.build_for(out_image_path) self._image_id += 1 self._images.append( { "id": self._image_id, "file_name": os.path.basename(out_image_path), "height": metadata.height, "width": metadata.width, "license": None, "coco_url": None, } ) if detections is None: return self._has_labels = True for detection in detections.detections: self._anno_id += 1 self._classes.add(detection.label) obj = COCOObject.from_detection( detection, metadata, labels_map_rev=self._labels_map_rev, extra_attrs=self.extra_attrs, iscrowd=self.iscrowd, num_decimals=self.num_decimals, tolerance=self.tolerance, ) obj.id = self._anno_id obj.image_id = self._image_id self._annotations.append(obj.to_anno_dict()) def close(self, *args): if self.classes is None: classes = sorted(self._classes) labels_map_rev = _to_labels_map_rev(classes) for anno in self._annotations: anno["category_id"] = labels_map_rev[anno["category_id"]] else: classes = self.classes date_created = datetime.now().replace(microsecond=0).isoformat() info = { "year": self.info.get("year", ""), "version": self.info.get("version", ""), "description": self.info.get("year", "Exported from FiftyOne"), "contributor": self.info.get("contributor", ""), "url": self.info.get("url", "https://voxel51.com/fiftyone"), "date_created": self.info.get("date_created", date_created), } licenses = self.info.get("licenses", []) categories = self.info.get("categories", None) if categories is None: categories = [ {"id": i, "name": l, "supercategory": None} for i, l in enumerate(classes) ] labels = { "info": info, "licenses": licenses, "categories": categories, "images": self._images, } if self._has_labels: labels["annotations"] = self._annotations etas.write_json(labels, self.labels_path) self._media_exporter.close() def _parse_classes(self): if self.classes is not None: self._labels_map_rev = _to_labels_map_rev(self.classes) class COCOObject(object): """An object in COCO detection format. Args: id (None): the ID of the annotation image_id (None): the ID of the image in which the annotation appears category_id (None): the category ID of the object bbox (None): a bounding box for the object in ``[xmin, ymin, width, height]`` format segmentation (None): the segmentation data for the object keypoints (None): the keypoints data for the object score (None): a confidence score for the object area (None): the area of the bounding box, in pixels iscrowd (None): whether the detection is a crowd **attributes: additional custom attributes """ def __init__( self, id=None, image_id=None, category_id=None, bbox=None, segmentation=None, keypoints=None, score=None, area=None, iscrowd=None, **attributes, ): self.id = id self.image_id = image_id self.category_id = category_id self.bbox = bbox self.segmentation = segmentation self.keypoints = keypoints self.score = score self.area = area self.iscrowd = iscrowd self.attributes = attributes def to_polyline( self, frame_size, classes=None, supercategory_map=None, tolerance=None ): """Returns a :class:`fiftyone.core.labels.Polyline` representation of the object. Args: frame_size: the ``(width, height)`` of the image classes (None): the list of classes supercategory_map (None): a dict mapping class names to category dicts tolerance (None): a tolerance, in pixels, when generating approximate polylines for instance masks. Typical values are 1-3 pixels Returns: a :class:`fiftyone.core.labels.Polyline`, or None if no segmentation data is available """ if self.segmentation is None: return None label, attributes = self._get_object_label_and_attributes( classes, supercategory_map ) attributes.update(self.attributes) points = _get_polygons_for_segmentation( self.segmentation, frame_size, tolerance ) return fol.Polyline( label=label, points=points, confidence=self.score, closed=False, filled=True, **attributes, ) def to_keypoints(self, frame_size, classes=None): """Returns a :class:`fiftyone.core.labels.Keypoint` representation of the object. Args: frame_size: the ``(width, height)`` of the image classes (None): the list of classes Returns: a :class:`fiftyone.core.labels.Keypoint`, or None if no keypoints data is available """ if self.keypoints is None: return None width, height = frame_size label = self._get_label(classes) points = [] for x, y, v in fou.iter_batches(self.keypoints, 3): if v == 0: continue points.append((x / width, y / height)) return fol.Keypoint( label=label, points=points, confidence=self.score, **self.attributes, ) def to_detection( self, frame_size, classes=None, supercategory_map=None, load_segmentation=False, ): """Returns a :class:`fiftyone.core.labels.Detection` representation of the object. Args: frame_size: the ``(width, height)`` of the image classes (None): the list of classes supercategory_map (None): a dict mapping class names to category dicts load_segmentation (False): whether to load the segmentation mask for the object, if available Returns: a :class:`fiftyone.core.labels.Detection` """ label, attributes = self._get_object_label_and_attributes( classes, supercategory_map ) attributes.update(self.attributes) width, height = frame_size x, y, w, h = self.bbox bounding_box = [x / width, y / height, w / width, h / height] mask = None if load_segmentation and self.segmentation is not None: mask = _coco_segmentation_to_mask( self.segmentation, self.bbox, frame_size ) return fol.Detection( label=label, bounding_box=bounding_box, mask=mask, confidence=self.score, **attributes, ) def to_anno_dict(self): """Returns a COCO annotation dictionary representation of the object. Returns: a COCO annotation dict """ d = { "id": self.id, "image_id": self.image_id, "category_id": self.category_id, } if self.bbox is not None: d["bbox"] = self.bbox if self.keypoints is not None: d["keypoints"] = self.keypoints d["num_keypoints"] = len(self.keypoints) // 3 if self.segmentation is not None: d["segmentation"] = self.segmentation if self.score is not None: d["score"] = self.score if self.area is not None: d["area"] = self.area if self.iscrowd is not None: d["iscrowd"] = self.iscrowd if self.attributes: d.update(self.attributes) return d @classmethod def from_detection( cls, detection, metadata, keypoint=None, labels_map_rev=None, extra_attrs=None, iscrowd="iscrowd", num_decimals=None, tolerance=None, ): """Creates a :class:`COCOObject` from a :class:`fiftyone.core.labels.Detection`. Args: detection: a :class:`fiftyone.core.labels.Detection` metadata: a :class:`fiftyone.core.metadata.ImageMetadata` for the image keypoint (None): an optional :class:`fiftyone.core.labels.Keypoint` containing keypoints to include for the object labels_map_rev (None): an optional dict mapping labels to category IDs extra_attrs (None): an optional list of extra attributes to include iscrowd ("iscrowd"): the name of the crowd attribute (used if present) num_decimals (None): an optional number of decimal places at which to round bounding box pixel coordinates. By default, no rounding is done tolerance (None): a tolerance, in pixels, when generating approximate polylines for instance masks. Typical values are 1-3 pixels Returns: a :class:`COCOObject` """ if labels_map_rev: category_id = labels_map_rev[detection.label] else: category_id = detection.label width = metadata.width height = metadata.height x, y, w, h = detection.bounding_box bbox = [x * width, y * height, w * width, h * height] if num_decimals is not None: bbox = [round(p, num_decimals) for p in bbox] area = bbox[2] * bbox[3] try: _iscrowd = int(detection[iscrowd]) except KeyError: # @todo remove Attribute usage if detection.has_attribute(iscrowd): _iscrowd = int(detection.get_attribute_value(iscrowd)) else: _iscrowd = None frame_size = (width, height) segmentation = _make_coco_segmentation( detection, frame_size, _iscrowd, tolerance ) keypoints = _make_coco_keypoints(keypoint, frame_size) if extra_attrs: attributes = {f: getattr(detection, f, None) for f in extra_attrs} else: attributes = {} return cls( id=None, image_id=None, category_id=category_id, bbox=bbox, segmentation=segmentation, keypoints=keypoints, score=detection.confidence, area=area, iscrowd=_iscrowd, **attributes, ) @classmethod def from_anno_dict(cls, d, extra_attrs=None): """Creates a :class:`COCOObject` from a COCO annotation dict. Args: d: a COCO annotation dict extra_attrs (None): whether to load extra annotation attributes. Supported values are: - ``None``/``False``: do not load extra attributes - ``True``: load all extra attributes - a name or list of names of specific attributes to load Returns: a :class:`COCOObject` """ if extra_attrs is True: return cls(**d) if etau.is_str(extra_attrs): extra_attrs = [extra_attrs] if extra_attrs: attributes = {f: d.get(f, None) for f in extra_attrs} else: attributes = {} return cls( id=d.get("id", None), image_id=d.get("image_id", None), category_id=d.get("category_id", None), bbox=d.get("bbox", None), segmentation=d.get("segmentation", None), keypoints=d.get("keypoints", None), score=d.get("score", None), area=d.get("area", None), iscrowd=d.get("iscrowd", None), **attributes, ) def _get_label(self, classes): if classes: return classes[self.category_id] return str(self.category_id) def _get_object_label_and_attributes(self, classes, supercategory_map): if classes: label = classes[self.category_id] else: label = str(self.category_id) attributes = {} if supercategory_map is not None and label in supercategory_map: supercategory = supercategory_map[label].get("supercategory", None) else: supercategory = None if supercategory is not None: attributes["supercategory"] = supercategory if self.iscrowd is not None: attributes["iscrowd"] = self.iscrowd return label, attributes def load_coco_detection_annotations(json_path, extra_attrs=None): """Loads the COCO annotations from the given JSON file. See :class:`fiftyone.types.dataset_types.COCODetectionDataset` for format details. Args: json_path: the path to the annotations JSON file extra_attrs (None): whether to load extra annotation attributes. Supported values are: - ``None``/``False``: do not load extra attributes - ``True``: load all extra attributes found - a name or list of names of specific attributes to load Returns: a tuple of - info: a dict of dataset info - classes: a list of classes - supercategory_map: a dict mapping class labels to category dicts - images: a dict mapping image IDs to image dicts - annotations: a dict mapping image IDs to list of :class:`COCOObject` instances, or ``None`` for unlabeled datasets """ d = etas.load_json(json_path) # Load info info = d.get("info", {}) licenses = d.get("licenses", None) categories = d.get("categories", None) if licenses is not None: info["licenses"] = licenses if categories is not None: info["categories"] = categories # Load classes if categories is not None: classes, supercategory_map = parse_coco_categories(categories) else: classes = None supercategory_map = None # Load image metadata images = {i["id"]: i for i in d.get("images", [])} # Load annotations _annotations = d.get("annotations", None) if _annotations is not None: annotations = defaultdict(list) for a in _annotations: annotations[a["image_id"]].append( COCOObject.from_anno_dict(a, extra_attrs=extra_attrs) ) annotations = dict(annotations) else: annotations = None return info, classes, supercategory_map, images, annotations def parse_coco_categories(categories): """Parses the COCO categories list. The returned ``classes`` contains all class IDs from ``[0, max_id]``, inclusive. Args: categories: a dict of the form:: [ ... { "id": 2, "name": "cat", "supercategory": "animal", "keypoints": ["nose", "head", ...], "skeleton": [[12, 14], [14, 16], ...] }, ... ] Returns: a tuple of - classes: a list of classes - supercategory_map: a dict mapping class labels to category dicts """ cat_map = {c["id"]: c for c in categories} classes = [] supercategory_map = {} for cat_id in range(max(cat_map) + 1): category = cat_map.get(cat_id, None) try: name = category["name"] except: name = str(cat_id) classes.append(name) if category is not None: supercategory_map[name] = category return classes, supercategory_map def is_download_required( dataset_dir, split, year="2017", label_types=None, classes=None, image_ids=None, max_samples=None, raw_dir=None, ): """Checks whether :meth:`download_coco_dataset_split` must be called in order for the given directory to contain enough samples to satisfy the given requirements. See :class:`fiftyone.types.dataset_types.COCODetectionDataset` for the format in which ``dataset_dir`` must be arranged. Args: dataset_dir: the directory to download the dataset split: the split to download. Supported values are ``("train", "validation", "test")`` year ("2017"): the dataset year to download. Supported values are ``("2014", "2017")`` label_types (None): a label type or list of label types to load. The supported values are ``("detections", "segmentations")``. By default, only "detections" are loaded classes (None): a string or list of strings specifying required classes to load. Only samples containing at least one instance of a specified class will be loaded image_ids (None): an optional list of specific image IDs to load. Can be provided in any of the following formats: - a list of ``<image-id>`` ints or strings - a list of ``<split>/<image-id>`` strings - the path to a text (newline-separated), JSON, or CSV file containing the list of image IDs to load in either of the first two formats max_samples (None): the maximum number of samples desired raw_dir (None): a directory in which full annotations files may be stored to avoid re-downloads in the future Returns: True/False """ logging.disable(logging.CRITICAL) try: _download_coco_dataset_split( dataset_dir, split, year=year, label_types=label_types, classes=classes, image_ids=image_ids, max_samples=max_samples, raw_dir=raw_dir, dry_run=True, ) return False # everything was downloaded except: return True # something needs to be downloaded finally: logging.disable(logging.NOTSET) def download_coco_dataset_split( dataset_dir, split, year="2017", label_types=None, classes=None, image_ids=None, num_workers=None, shuffle=None, seed=None, max_samples=None, raw_dir=None, scratch_dir=None, ): """Utility that downloads full or partial splits of the `COCO dataset <https://cocodataset.org>`_. See :class:`fiftyone.types.dataset_types.COCODetectionDataset` for the format in which ``dataset_dir`` will be arranged. Any existing files are not re-downloaded. Args: dataset_dir: the directory to download the dataset split: the split to download. Supported values are ``("train", "validation", "test")`` year ("2017"): the dataset year to download. Supported values are ``("2014", "2017")`` label_types (None): a label type or list of label types to load. The supported values are ``("detections", "segmentations")``. By default, only "detections" are loaded classes (None): a string or list of strings specifying required classes to load. Only samples containing at least one instance of a specified class will be loaded image_ids (None): an optional list of specific image IDs to load. Can be provided in any of the following formats: - a list of ``<image-id>`` ints or strings - a list of ``<split>/<image-id>`` strings - the path to a text (newline-separated), JSON, or CSV file containing the list of image IDs to load in either of the first two formats num_workers (None): the number of processes to use when downloading individual images. By default, ``multiprocessing.cpu_count()`` is used shuffle (False): whether to randomly shuffle the order in which samples are chosen for partial downloads seed (None): a random seed to use when shuffling max_samples (None): a maximum number of samples to load. If ``label_types`` and/or ``classes`` are also specified, first priority will be given to samples that contain all of the specified label types and/or classes, followed by samples that contain at least one of the specified labels types or classes. The actual number of samples loaded may be less than this maximum value if the dataset does not contain sufficient samples matching your requirements. By default, all matching samples are loaded raw_dir (None): a directory in which full annotations files may be stored to avoid re-downloads in the future scratch_dir (None): a scratch directory to use to download any necessary temporary files Returns: a tuple of: - num_samples: the total number of downloaded images - classes: the list of all classes """ return _download_coco_dataset_split( dataset_dir, split, year=year, label_types=label_types, classes=classes, image_ids=image_ids, num_workers=num_workers, shuffle=shuffle, seed=seed, max_samples=max_samples, raw_dir=raw_dir, scratch_dir=scratch_dir, dry_run=False, ) def _download_coco_dataset_split( dataset_dir, split, year="2017", label_types=None, classes=None, image_ids=None, num_workers=None, shuffle=None, seed=None, max_samples=None, raw_dir=None, scratch_dir=None, dry_run=False, ): if year not in _IMAGE_DOWNLOAD_LINKS: raise ValueError( "Unsupported year '%s'; supported values are %s" % (year, tuple(_IMAGE_DOWNLOAD_LINKS.keys())) ) if split not in _IMAGE_DOWNLOAD_LINKS[year]: raise ValueError( "Unsupported split '%s'; supported values are %s" % (year, tuple(_IMAGE_DOWNLOAD_LINKS[year].keys())) ) if classes is not None and split == "test": logger.warning("Test split is unlabeled; ignoring `classes`") classes = None if scratch_dir is None: scratch_dir = os.path.join(dataset_dir, "scratch") anno_path = os.path.join(dataset_dir, "labels.json") images_dir = os.path.join(dataset_dir, "data") split_size = _SPLIT_SIZES[year][split] etau.ensure_dir(images_dir) # # Download annotations to `raw_dir`, if necessary # if raw_dir is None: raw_dir = os.path.join(dataset_dir, "raw") etau.ensure_dir(raw_dir) if split != "test": src_path = _ANNOTATION_DOWNLOAD_LINKS[year] rel_path = _ANNOTATION_PATHS[year][split] subdir = "trainval" anno_type = "annotations" else: src_path = _TEST_INFO_DOWNLOAD_LINKS[year] rel_path = _TEST_INFO_PATHS[year] subdir = "test" anno_type = "test info" zip_path = os.path.join(scratch_dir, os.path.basename(src_path)) unzip_dir = os.path.join(scratch_dir, subdir) content_dir = os.path.join(unzip_dir, os.path.dirname(rel_path)) full_anno_path = os.path.join(raw_dir, os.path.basename(rel_path)) if not os.path.isfile(full_anno_path): if dry_run: raise ValueError("%s is not downloaded" % src_path) logger.info("Downloading %s to '%s'", anno_type, zip_path) etaw.download_file(src_path, path=zip_path) logger.info("Extracting %s to '%s'", anno_type, full_anno_path) etau.extract_zip(zip_path, outdir=unzip_dir, delete_zip=False) _merge_dir(content_dir, raw_dir) else: logger.info("Found %s at '%s'", anno_type, full_anno_path) ( _, all_classes, _, images, annotations, ) = load_coco_detection_annotations(full_anno_path, extra_attrs=True) # # Download images to `images_dir`, if necessary # images_src_path = _IMAGE_DOWNLOAD_LINKS[year][split] images_zip_path = os.path.join( scratch_dir, os.path.basename(images_src_path) ) unzip_images_dir = os.path.splitext(images_zip_path)[0] if classes is None and image_ids is None and max_samples is None: # Full image download num_downloaded = len(etau.list_files(images_dir)) if num_downloaded < split_size: if dry_run: raise ValueError("%s is not downloaded" % images_src_path) if num_downloaded > 0: logger.info( "Found %d (< %d) downloaded images; must download full " "image zip", num_downloaded, split_size, ) logger.info("Downloading images to '%s'", images_zip_path) etaw.download_file(images_src_path, path=images_zip_path) logger.info("Extracting images to '%s'", images_dir) etau.extract_zip(images_zip_path, delete_zip=False) etau.move_dir(unzip_images_dir, images_dir) else: logger.info("Images already downloaded") else: # Partial image download if image_ids is not None: # Start with specific images image_ids = _parse_image_ids(image_ids, images, split=split) else: # Start with all images image_ids = list(images.keys()) if classes is not None: # Filter by specified classes all_ids, any_ids = _get_images_with_classes( image_ids, annotations, classes, all_classes ) else: all_ids = image_ids any_ids = [] all_ids = sorted(all_ids) any_ids = sorted(any_ids) if shuffle: if seed is not None: random.seed(seed) random.shuffle(all_ids) random.shuffle(any_ids) image_ids = all_ids + any_ids # Determine IDs to download existing_ids, downloadable_ids = _get_existing_ids( images_dir, images, image_ids ) if max_samples is not None: num_existing = len(existing_ids) num_downloadable = len(downloadable_ids) num_available = num_existing + num_downloadable if num_available < max_samples: logger.warning( "Only found %d (<%d) samples matching your " "requirements", num_available, max_samples, ) if max_samples > num_existing: num_download = max_samples - num_existing download_ids = downloadable_ids[:num_download] else: download_ids = [] else: download_ids = downloadable_ids # Download necessary images num_existing = len(existing_ids) num_download = len(download_ids) if num_existing > 0: if num_download > 0: logger.info( "%d images found; downloading the remaining %d", num_existing, num_download, ) else: logger.info("Sufficient images already downloaded") elif num_download > 0: logger.info("Downloading %d images", num_download) if num_download > 0: if dry_run: raise ValueError("%d images must be downloaded" % num_download) _download_images(images_dir, download_ids, images, num_workers) if dry_run: return None, None # # Write usable annotations file to `anno_path` # downloaded_filenames = etau.list_files(images_dir) num_samples = len(downloaded_filenames) # total downloaded if num_samples >= split_size: logger.info("Writing annotations to '%s'", anno_path) etau.copy_file(full_anno_path, anno_path) else: logger.info( "Writing annotations for %d downloaded samples to '%s'", num_samples, anno_path, ) _write_partial_annotations( full_anno_path, anno_path, split, downloaded_filenames ) return num_samples, all_classes def _merge_dir(indir, outdir): etau.ensure_dir(outdir) for filename in os.listdir(indir): inpath = os.path.join(indir, filename) outpath = os.path.join(outdir, filename) shutil.move(inpath, outpath) def _write_partial_annotations(inpath, outpath, split, filenames): d = etas.load_json(inpath) id_map = {i["file_name"]: i["id"] for i in d["images"]} filenames = set(filenames) image_ids = {id_map[f] for f in filenames} d["images"] = [i for i in d["images"] if i["file_name"] in filenames] if split != "test": d["annotations"] = [ a for a in d["annotations"] if a["image_id"] in image_ids ] else: d.pop("annotations", None) etas.write_json(d, outpath) def _parse_label_types(label_types): if label_types is None: return ["detections"] if etau.is_str(label_types): label_types = [label_types] else: label_types = list(label_types) bad_types = [l for l in label_types if l not in _SUPPORTED_LABEL_TYPES] if len(bad_types) == 1: raise ValueError( "Unsupported label type '%s'. Supported types are %s" % (bad_types[0], _SUPPORTED_LABEL_TYPES) ) if len(bad_types) > 1: raise ValueError( "Unsupported label types %s. Supported types are %s" % (bad_types, _SUPPORTED_LABEL_TYPES) ) return label_types def _get_matching_image_ids( all_classes, images, annotations, image_ids=None, classes=None, shuffle=False, seed=None, max_samples=None, ): if image_ids is not None: image_ids = _parse_image_ids(image_ids, images) else: image_ids = list(images.keys()) if classes is not None: all_ids, any_ids = _get_images_with_classes( image_ids, annotations, classes, all_classes ) else: all_ids = image_ids any_ids = [] all_ids = sorted(all_ids) any_ids = sorted(any_ids) if shuffle: if seed is not None: random.seed(seed) random.shuffle(all_ids) random.shuffle(any_ids) image_ids = all_ids + any_ids if max_samples is not None: return image_ids[:max_samples] return image_ids def _get_existing_ids(images_dir, images, image_ids): filenames = set(etau.list_files(images_dir)) existing_ids = [] downloadable_ids = [] for _id in image_ids: if images[_id]["file_name"] in filenames: existing_ids.append(_id) else: downloadable_ids.append(_id) return existing_ids, downloadable_ids def _download_images(images_dir, image_ids, images, num_workers): if num_workers is None or num_workers < 1: num_workers = multiprocessing.cpu_count() tasks = [] for image_id in image_ids: image_dict = images[image_id] url = image_dict["coco_url"] path = os.path.join(images_dir, image_dict["file_name"]) tasks.append((url, path)) if not tasks: return if num_workers == 1: with fou.ProgressBar() as pb: for task in pb(tasks): _do_download(task) else: with fou.ProgressBar(total=len(tasks)) as pb: with multiprocessing.Pool(num_workers) as pool: for _ in pool.imap_unordered(_do_download, tasks): pb.update() def _do_download(args): url, path = args etaw.download_file(url, path=path, quiet=True) def _get_images_with_classes( image_ids, annotations, target_classes, all_classes ): if etau.is_str(target_classes): target_classes = [target_classes] bad_classes = [c for c in target_classes if c not in all_classes] if bad_classes: raise ValueError("Unsupported classes: %s" % bad_classes) labels_map_rev = _to_labels_map_rev(all_classes) class_ids = {labels_map_rev[c] for c in target_classes} all_ids = [] any_ids = [] for image_id in image_ids: coco_objects = annotations.get(image_id, None) if not coco_objects: continue oids = set(o.category_id for o in coco_objects) if class_ids.issubset(oids): all_ids.append(image_id) elif class_ids & oids: any_ids.append(image_id) return all_ids, any_ids def _parse_image_ids(raw_image_ids, images, split=None): # Load IDs from file if etau.is_str(raw_image_ids): image_ids_path = raw_image_ids ext = os.path.splitext(image_ids_path)[-1] if ext == ".txt": raw_image_ids = _load_image_ids_txt(image_ids_path) elif ext == ".json": raw_image_ids = _load_image_ids_json(image_ids_path) elif ext == ".csv": raw_image_ids = _load_image_ids_csv(image_ids_path) else: raise ValueError( "Invalid image ID file '%s'. Supported formats are .txt, " ".csv, and .json" % ext ) image_ids = [] for raw_id in raw_image_ids: if etau.is_str(raw_id): if "/" in raw_id: _split, raw_id = raw_id.split("/") if split and _split != split: continue raw_id = int(raw_id.strip()) image_ids.append(raw_id) # Validate that IDs exist invalid_ids = [_id for _id in image_ids if _id not in images] if invalid_ids: raise ValueError( "Found %d invalid IDs, ex: %s" % (len(invalid_ids), invalid_ids[0]) ) return image_ids def _load_image_ids_txt(txt_path): with open(txt_path, "r") as f: return [l.strip() for l in f.readlines()] def _load_image_ids_csv(csv_path): with open(csv_path, "r", newline="") as f: dialect = csv.Sniffer().sniff(f.read(10240)) f.seek(0) if dialect.delimiter in _CSV_DELIMITERS: reader = csv.reader(f, dialect) else: reader = csv.reader(f) image_ids = [row for row in reader] if isinstance(image_ids[0], list): # Flatten list image_ids = [_id for ids in image_ids for _id in ids] return image_ids def _load_image_ids_json(json_path): return [_id for _id in etas.load_json(json_path)] def _make_images_list(images_dir): logger.info("Computing image metadata for '%s'", images_dir) image_paths = foud.parse_images_dir(images_dir) images = [] with fou.ProgressBar() as pb: for idx, image_path in pb(enumerate(image_paths)): metadata = fom.ImageMetadata.build_for(image_path) images.append( { "id": idx, "file_name": os.path.basename(image_path), "height": metadata.height, "width": metadata.width, "license": None, "coco_url": None, } ) return images def _to_labels_map_rev(classes): return {c: i for i, c in enumerate(classes)} def _get_matching_objects(coco_objects, target_classes, all_classes): if etau.is_str(target_classes): target_classes = [target_classes] labels_map_rev = _to_labels_map_rev(all_classes) class_ids = {labels_map_rev[c] for c in target_classes} return [obj for obj in coco_objects if obj.category_id in class_ids] def _coco_objects_to_polylines( coco_objects, frame_size, classes, supercategory_map, tolerance ): polylines = [] for coco_obj in coco_objects: polyline = coco_obj.to_polyline( frame_size, classes=classes, supercategory_map=supercategory_map, tolerance=tolerance, ) if polyline is not None: polylines.append(polyline) else: msg = "Skipping object with no segmentation mask" warnings.warn(msg) return fol.Polylines(polylines=polylines) def _coco_objects_to_detections( coco_objects, frame_size, classes, supercategory_map, load_segmentations ): detections = [] for coco_obj in coco_objects: detection = coco_obj.to_detection( frame_size, classes=classes, supercategory_map=supercategory_map, load_segmentation=load_segmentations, ) if load_segmentations and detection.mask is None: msg = "Skipping object with no segmentation mask" warnings.warn(msg) else: detections.append(detection) return fol.Detections(detections=detections) def _coco_objects_to_keypoints(coco_objects, frame_size, classes): keypoints = [] for coco_obj in coco_objects: keypoints.append(coco_obj.to_keypoints(frame_size, classes=classes)) return fol.Keypoints(keypoints=keypoints) # # The methods below are taken, in part, from: # https://github.com/waspinator/pycococreator/blob/207b4fa8bbaae22ebcdeb3bbf00b724498e026a7/pycococreatortools/pycococreatortools.py # def _get_polygons_for_segmentation(segmentation, frame_size, tolerance): width, height = frame_size # Convert to [[x1, y1, x2, y2, ...]] polygons if isinstance(segmentation, list): abs_points = segmentation else: if isinstance(segmentation["counts"], list): # Uncompressed RLE rle = mask_utils.frPyObjects(segmentation, height, width) else: # RLE rle = segmentation mask = mask_utils.decode(rle) abs_points = _mask_to_polygons(mask, tolerance) # Convert to [[(x1, y1), (x2, y2), ...]] in relative coordinates rel_points = [] for apoints in abs_points: rel_points.append( [(x / width, y / height) for x, y, in _pairwise(apoints)] ) return rel_points def _pairwise(x): y = iter(x) return zip(y, y) def _coco_segmentation_to_mask(segmentation, bbox, frame_size): if segmentation is None: return None x, y, w, h = bbox width, height = frame_size if isinstance(segmentation, list): # Polygon -- a single object might consist of multiple parts, so merge # all parts into one mask RLE code rle = mask_utils.merge( mask_utils.frPyObjects(segmentation, height, width) ) elif isinstance(segmentation["counts"], list): # Uncompressed RLE rle = mask_utils.frPyObjects(segmentation, height, width) else: # RLE rle = segmentation mask = mask_utils.decode(rle).astype(bool) return mask[ int(round(y)) : int(round(y + h)), int(round(x)) : int(round(x + w)), ] def _make_coco_segmentation(detection, frame_size, iscrowd, tolerance): if detection.mask is None: return None dobj = detection.to_detected_object() mask = etai.render_instance_image(dobj.mask, dobj.bounding_box, frame_size) if iscrowd: return _mask_to_rle(mask) return _mask_to_polygons(mask, tolerance) def _make_coco_keypoints(keypoint, frame_size): if keypoint is None: return None width, height = frame_size # @todo true COCO format would set v = 1/2 based on whether the keypoints # lie within the object's segmentation, but we'll be lazy for now keypoints = [] for x, y in keypoint.points: keypoints.extend((int(x * width), int(y * height), 2)) return keypoints def _mask_to_rle(mask): counts = [] for i, (value, elements) in enumerate(groupby(mask.ravel(order="F"))): if i == 0 and value == 1: counts.append(0) counts.append(len(list(elements))) return {"counts": counts, "size": list(mask.shape)} def _mask_to_polygons(mask, tolerance): if tolerance is None: tolerance = 2 # Pad mask to close contours of shapes which start and end at an edge padded_mask = np.pad(mask, pad_width=1, mode="constant", constant_values=0) contours = measure.find_contours(padded_mask, 0.5) contours = np.subtract(contours, 1) # undo padding polygons = [] for contour in contours: contour = _close_contour(contour) contour = measure.approximate_polygon(contour, tolerance) if len(contour) < 3: continue contour = np.flip(contour, axis=1) segmentation = contour.ravel().tolist() # After padding and subtracting 1 there may be -0.5 points segmentation = [0 if i < 0 else i for i in segmentation] polygons.append(segmentation) return polygons def _close_contour(contour): if not np.array_equal(contour[0], contour[-1]): contour = np.vstack((contour, contour[0])) return contour _IMAGE_DOWNLOAD_LINKS = { "2014": { "train": "http://images.cocodataset.org/zips/train2014.zip", "validation": "http://images.cocodataset.org/zips/val2014.zip", "test": "http://images.cocodataset.org/zips/test2014.zip", }, "2017": { "train": "http://images.cocodataset.org/zips/train2017.zip", "validation": "http://images.cocodataset.org/zips/val2017.zip", "test": "http://images.cocodataset.org/zips/test2017.zip", }, } _SPLIT_SIZES = { "2014": {"train": 82783, "test": 40775, "validation": 40504}, "2017": {"train": 118287, "test": 40670, "validation": 5000}, } _ANNOTATION_DOWNLOAD_LINKS = { "2014": "http://images.cocodataset.org/annotations/annotations_trainval2014.zip", "2017": "http://images.cocodataset.org/annotations/annotations_trainval2017.zip", } _ANNOTATION_PATHS = { "2014": { "train": "annotations/instances_train2014.json", "validation": "annotations/instances_val2014.json", }, "2017": { "train": "annotations/instances_train2017.json", "validation": "annotations/instances_val2017.json", }, } _KEYPOINTS_PATHS = { "2014": { "train": "annotations/person_keypoints_train2014.json", "validation": "annotations/person_keypoints_val2014.json", }, "2017": { "train": "annotations/person_keypoints_train2017.json", "validation": "annotations/person_keypoints_val2017.json", }, } _TEST_INFO_DOWNLOAD_LINKS = { "2014": "http://images.cocodataset.org/annotations/image_info_test2014.zip", "2017": "http://images.cocodataset.org/annotations/image_info_test2017.zip", } _TEST_INFO_PATHS = { "2014": "annotations/image_info_test2014.json", "2017": "annotations/image_info_test2017.json", } _SUPPORTED_LABEL_TYPES = ["detections", "segmentations", "keypoints"] _SUPPORTED_SPLITS = ["train", "validation", "test"] _CSV_DELIMITERS = [",", ";", ":", " ", "\t", "\n"]

32.203382

132

0.597643

c80ea624772fc2018213361af067895999948f09

15,076

py

Python

detectron2/structures/boxes.py

hhy-ee/PedestrianDetection-NohNMS

482078a6bd0ff8cf03fbf7f6988e475f75c56e57

[ "Apache-2.0" ]

null

detectron2/structures/boxes.py

hhy-ee/PedestrianDetection-NohNMS

482078a6bd0ff8cf03fbf7f6988e475f75c56e57

[ "Apache-2.0" ]

null

detectron2/structures/boxes.py

hhy-ee/PedestrianDetection-NohNMS

482078a6bd0ff8cf03fbf7f6988e475f75c56e57

[ "Apache-2.0" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import math import numpy as np from enum import IntEnum, unique from typing import Iterator, List, Tuple, Union import torch from detectron2.layers import cat _RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.ndarray] @unique class BoxMode(IntEnum): """ Enum of different ways to represent a box. Attributes: XYXY_ABS: (x0, y0, x1, y1) in absolute floating points coordinates. The coordinates in range [0, width or height]. XYWH_ABS: (x0, y0, w, h) in absolute floating points coordinates. XYXY_REL: (x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image. XYWH_REL: (x0, y0, w, h) in range [0, 1]. They are relative to the size of the image. XYWHA_ABS: (xc, yc, w, h, a) in absolute floating points coordinates. (xc, yc) is the center of the rotated box, and the angle a is in degrees ccw. """ XYXY_ABS = 0 XYWH_ABS = 1 XYXY_REL = 2 XYWH_REL = 3 XYWHA_ABS = 4 @staticmethod def convert(box: _RawBoxType, from_mode: "BoxMode", to_mode: "BoxMode") -> _RawBoxType: """ Args: box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5 from_mode, to_mode (BoxMode) Returns: The converted box of the same type. """ if from_mode == to_mode: return box original_type = type(box) is_numpy = isinstance(box, np.ndarray) single_box = isinstance(box, (list, tuple)) if single_box: assert len(box) == 4 or len(box) == 5, ( "BoxMode.convert takes either a k-tuple/list or an Nxk array/tensor," " where k == 4 or 5" ) arr = torch.tensor(box)[None, :] else: # avoid modifying the input box if is_numpy: arr = torch.from_numpy(np.asarray(box)).clone() else: arr = box.clone() assert to_mode.value not in [ BoxMode.XYXY_REL, BoxMode.XYWH_REL, ] and from_mode.value not in [ BoxMode.XYXY_REL, BoxMode.XYWH_REL, ], "Relative mode not yet supported!" if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS: assert ( arr.shape[-1] == 5 ), "The last dimension of input shape must be 5 for XYWHA format" original_dtype = arr.dtype arr = arr.double() w = arr[:, 2] h = arr[:, 3] a = arr[:, 4] c = torch.abs(torch.cos(a * math.pi / 180.0)) s = torch.abs(torch.sin(a * math.pi / 180.0)) # This basically computes the horizontal bounding rectangle of the rotated box new_w = c * w + s * h new_h = c * h + s * w # convert center to top-left corner arr[:, 0] -= new_w / 2.0 arr[:, 1] -= new_h / 2.0 # bottom-right corner arr[:, 2] = arr[:, 0] + new_w arr[:, 3] = arr[:, 1] + new_h arr = arr[:, :4].to(dtype=original_dtype) elif from_mode == BoxMode.XYWH_ABS and to_mode == BoxMode.XYWHA_ABS: original_dtype = arr.dtype arr = arr.double() arr[:, 0] += arr[:, 2] / 2.0 arr[:, 1] += arr[:, 3] / 2.0 angles = torch.zeros((arr.shape[0], 1), dtype=arr.dtype) arr = torch.cat((arr, angles), axis=1).to(dtype=original_dtype) else: if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS: arr[:, 2] += arr[:, 0] arr[:, 3] += arr[:, 1] elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS: arr[:, 2] -= arr[:, 0] arr[:, 3] -= arr[:, 1] else: raise NotImplementedError( "Conversion from BoxMode {} to {} is not supported yet".format( from_mode, to_mode ) ) if single_box: return original_type(arr.flatten()) if is_numpy: return arr.numpy() else: return arr class Boxes: """ This structure stores a list of boxes as a Nx4 torch.Tensor. It supports some common methods about boxes (`area`, `clip`, `nonempty`, etc), and also behaves like a Tensor (support indexing, `to(device)`, `.device`, and iteration over all boxes) Attributes: tensor (torch.Tensor): float matrix of Nx4. """ BoxSizeType = Union[List[int], Tuple[int, int]] def __init__(self, tensor: torch.Tensor): """ Args: tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2). """ device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu") tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device) if tensor.numel() == 0: # Use reshape, so we don't end up creating a new tensor that does not depend on # the inputs (and consequently confuses jit) tensor = tensor.reshape((0, 4)).to(dtype=torch.float32, device=device) assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size() self.tensor = tensor def clone(self) -> "Boxes": """ Clone the Boxes. Returns: Boxes """ return Boxes(self.tensor.clone()) def to(self, device: str) -> "Boxes": return Boxes(self.tensor.to(device)) def area(self) -> torch.Tensor: """ Computes the area of all the boxes. Returns: torch.Tensor: a vector with areas of each box. """ box = self.tensor area = (box[:, 2] - box[:, 0] + 1) * (box[:, 3] - box[:, 1] + 1) return area def clip(self, box_size: BoxSizeType) -> None: """ Clip (in place) the boxes by limiting x coordinates to the range [0, width] and y coordinates to the range [0, height]. Args: box_size (height, width): The clipping box's size. """ assert torch.isfinite(self.tensor).all(), "Box tensor contains infinite or NaN!" h, w = box_size self.tensor[:, 0].clamp_(min=0, max=w) self.tensor[:, 1].clamp_(min=0, max=h) self.tensor[:, 2].clamp_(min=0, max=w) self.tensor[:, 3].clamp_(min=0, max=h) def nonempty(self, threshold: int = 0) -> torch.Tensor: """ Find boxes that are non-empty. A box is considered empty, if either of its side is no larger than threshold. Returns: Tensor: a binary vector which represents whether each box is empty (False) or non-empty (True). """ box = self.tensor widths = box[:, 2] - box[:, 0] heights = box[:, 3] - box[:, 1] keep = (widths > threshold) & (heights > threshold) return keep def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Boxes": """ Returns: Boxes: Create a new :class:`Boxes` by indexing. The following usage are allowed: 1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box. 2. `new_boxes = boxes[2:10]`: return a slice of boxes. 3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor with `length = len(boxes)`. Nonzero elements in the vector will be selected. Note that the returned Boxes might share storage with this Boxes, subject to Pytorch's indexing semantics. """ if isinstance(item, int): return Boxes(self.tensor[item].view(1, -1)) b = self.tensor[item] assert b.dim() == 2, "Indexing on Boxes with {} failed to return a matrix!".format(item) return Boxes(b) def __len__(self) -> int: return self.tensor.shape[0] def __repr__(self) -> str: return "Boxes(" + str(self.tensor) + ")" def inside_box(self, box_size: BoxSizeType, boundary_threshold: int = 0) -> torch.Tensor: """ Args: box_size (height, width): Size of the reference box. boundary_threshold (int): Boxes that extend beyond the reference box boundary by more than boundary_threshold are considered "outside". Returns: a binary vector, indicating whether each box is inside the reference box. """ height, width = box_size inds_inside = ( (self.tensor[..., 0] >= -boundary_threshold) & (self.tensor[..., 1] >= -boundary_threshold) & (self.tensor[..., 2] < width + boundary_threshold) & (self.tensor[..., 3] < height + boundary_threshold) ) return inds_inside def get_centers(self) -> torch.Tensor: """ Returns: The box centers in a Nx2 array of (x, y). """ return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2 def scale(self, scale_x: float, scale_y: float) -> None: """ Scale the box with horizontal and vertical scaling factors """ self.tensor[:, 0::2] *= scale_x self.tensor[:, 1::2] *= scale_y @staticmethod def cat(boxes_list: List["Boxes"]) -> "Boxes": """ Concatenates a list of Boxes into a single Boxes Arguments: boxes_list (list[Boxes]) Returns: Boxes: the concatenated Boxes """ assert isinstance(boxes_list, (list, tuple)) assert len(boxes_list) > 0 assert all(isinstance(box, Boxes) for box in boxes_list) cat_boxes = type(boxes_list[0])(cat([b.tensor for b in boxes_list], dim=0)) return cat_boxes @property def device(self) -> torch.device: return self.tensor.device def __iter__(self) -> Iterator[torch.Tensor]: """ Yield a box as a Tensor of shape (4,) at a time. """ yield from self.tensor # implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py # with slight modifications def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: """ Given two lists of boxes of size N and M, compute the IoU (intersection over union) between __all__ N x M pairs of boxes. The box order must be (xmin, ymin, xmax, ymax). Args: boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. Returns: Tensor: IoU, sized [N,M]. """ area1 = boxes1.area() area2 = boxes2.area() boxes1, boxes2 = boxes1.tensor, boxes2.tensor width_height = ( torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max(boxes1[:, None, :2], boxes2[:, :2]) + 1 ) # [N,M,2] width_height.clamp_(min=0) # [N,M,2] inter = width_height.prod(dim=2) # [N,M] del width_height # handle empty boxes iou = torch.where( inter > 0, inter / (area1[:, None] + area2 - inter), torch.zeros(1, dtype=inter.dtype, device=inter.device), ) return iou def pairwise_ioa(boxes1: Boxes, anchor: Boxes) -> torch.Tensor: """ Given two lists of boxes of size N and M, compute the IoU (intersection over union) between __all__ N x M pairs of boxes. The box order must be (xmin, ymin, xmax, ymax). Args: boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. Returns: Tensor: IoU, sized [N,M]. """ area_anchor = anchor.area() boxes1, anchor = boxes1.tensor, anchor.tensor width_height = ( torch.min(boxes1[:, None, 2:], anchor[:, 2:]) - torch.max(boxes1[:, None, :2], anchor[:, :2]) + 1 ) # [N,M,2] width_height.clamp_(min=0) # [N,M,2] inter = width_height.prod(dim=2) # [N,M] del width_height # handle empty boxes iou = torch.where( inter > 0, inter / area_anchor[None, :], torch.zeros(1, dtype=inter.dtype, device=inter.device), ) return iou def calculate_iou(boxes1: torch.Tensor, boxes2: torch.Tensor) -> torch.Tensor: """ Given two lists of boxes of size N and N, compute the IoU (intersection over union) The box order must be (xmin, ymin, xmax, ymax). Args: boxes1,boxes2 (Tensor): two `Boxes`. Both contains N boxes. Returns: Tensor: IoU, sized [N]. """ area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1]) area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1]) width_height = torch.min(boxes1[:, 2:], boxes2[:, 2:]) - torch.max( boxes1[:, :2], boxes2[:, :2] ) # [N,2] width_height.clamp_(min=0) # [N,2] inter = width_height.prod(dim=1) # [N] del width_height # handle empty boxes iou = torch.where( inter > 0, inter / (area1 + area2 - inter), torch.zeros(1, dtype=inter.dtype, device=inter.device), ) return iou def calculate_iog(proposals: torch.Tensor, gts: torch.Tensor) -> torch.Tensor: """ Given two lists of boxes of size N and N, compute the IoG (intersection over GT box) The box order must be (xmin, ymin, xmax, ymax). Args: proposals,gts (Tensor): two `Boxes`. Contains N & M boxes, respectively. Returns: Tensor: IoU, sized [N,M]. """ gt_area = (gts[:, 2] - gts[:, 0]) * (gts[:, 3] - gts[:, 1]) boxes1, boxes2 = proposals, gts width_height = torch.min(boxes1[:, 2:], boxes2[:, 2:]) - torch.max( boxes1[:, :2], boxes2[:, :2] ) # [N,M,2] width_height.clamp_(min=0) # [N,M,2] inter = width_height.prod(dim=1) # [N,M] del width_height # handle empty boxes iog = torch.where( inter > 0, inter / (gt_area), torch.zeros(1, dtype=inter.dtype, device=inter.device) ) return iog def matched_boxlist_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor: """ Compute pairwise intersection over union (IOU) of two sets of matched boxes. The box order must be (xmin, ymin, xmax, ymax). Similar to boxlist_iou, but computes only diagonal elements of the matrix Arguments: boxes1: (Boxes) bounding boxes, sized [N,4]. boxes2: (Boxes) bounding boxes, sized [N,4]. Returns: (tensor) iou, sized [N]. """ assert len(boxes1) == len(boxes2), ( "boxlists should have the same" "number of entries, got {}, {}".format(len(boxes1), len(boxes2)) ) area1 = boxes1.area() # [N] area2 = boxes2.area() # [N] box1, box2 = boxes1.tensor, boxes2.tensor lt = torch.max(box1[:, :2], box2[:, :2]) # [N,2] rb = torch.min(box1[:, 2:], box2[:, 2:]) # [N,2] wh = (rb - lt).clamp(min=0) # [N,2] inter = wh[:, 0] * wh[:, 1] # [N] iou = inter / (area1 + area2 - inter) # [N] return iou

32.989059

96

0.559432

d7e94f5a48e5e61ce9acf99db81a56fbef0a7330

984

py

Python

hello.py

maxq210/googlePython

f25337b7dc55bb9c9ac4d936dfff555647d59ec2

[ "Apache-2.0" ]

null

hello.py

maxq210/googlePython

f25337b7dc55bb9c9ac4d936dfff555647d59ec2

[ "Apache-2.0" ]

null

hello.py

maxq210/googlePython

f25337b7dc55bb9c9ac4d936dfff555647d59ec2

[ "Apache-2.0" ]

null

#!/usr/bin/python -tt # Copyright 2010 Google Inc. # Licensed under the Apache License, Version 2.0 # http://www.apache.org/licenses/LICENSE-2.0 # Google's Python Class # http://code.google.com/edu/languages/google-python-class/ """A tiny Python program to check that Python is working. Try running this program from the command line like this: python hello.py python hello.py Alice That should print: Hello World -or- Hello Alice Try changing the 'Hello' to 'Howdy' and run again. Once you have that working, you're ready for class -- you can edit and run Python code; now you just need to learn Python! """ import sys # Define a main() function that prints a little greeting. def main(): # Get the name from the command line, using 'World' as a fallback. if len(sys.argv) >= 2: name = sys.argv[1] else: name = 'World' print 'Howdy', name print 'yay' # This is the standard boilerplate that calls the main() function. if __name__ == '__main__': main()

28.114286

68

0.714431

ca39526b747dec3d6817303f52997bc5a76a14b1

4,699

py

Python

aiormq/base.py

vknrk/aiormq

735594b3291db005ed59bd17d91ddaecc42f6762

[ "Apache-2.0" ]

null

aiormq/base.py

vknrk/aiormq

735594b3291db005ed59bd17d91ddaecc42f6762

[ "Apache-2.0" ]

null

aiormq/base.py

vknrk/aiormq

735594b3291db005ed59bd17d91ddaecc42f6762

[ "Apache-2.0" ]

null

import abc import asyncio from contextlib import suppress from functools import wraps from typing import Any, Callable, Coroutine, Optional, Set, TypeVar, Union from weakref import WeakSet from .abc import ( AbstractBase, AbstractFutureStore, CoroutineType, ExceptionType, TaskType, TaskWrapper, ) from .tools import shield T = TypeVar("T") class FutureStore(AbstractFutureStore): __slots__ = "futures", "loop", "parent" futures: Set[Union[asyncio.Future, TaskType]] weak_futures: WeakSet loop: asyncio.AbstractEventLoop def __init__(self, loop: asyncio.AbstractEventLoop): self.futures = set() self.loop = loop self.parent: Optional[FutureStore] = None def __on_task_done( self, future: Union[asyncio.Future, TaskWrapper], ) -> Callable[..., Any]: def remover(*_: Any) -> None: nonlocal future if future in self.futures: self.futures.remove(future) return remover def add(self, future: Union[asyncio.Future, TaskWrapper]) -> None: self.futures.add(future) future.add_done_callback(self.__on_task_done(future)) if self.parent: self.parent.add(future) @shield async def reject_all(self, exception: Optional[ExceptionType]) -> None: tasks = [] while self.futures: future: Union[TaskType, asyncio.Future] = self.futures.pop() if future.done(): continue if isinstance(future, TaskWrapper): future.throw(exception or Exception) tasks.append(future) elif isinstance(future, asyncio.Future): future.set_exception(exception or Exception) if tasks: await asyncio.gather(*tasks, return_exceptions=True) def create_task(self, coro: CoroutineType) -> TaskType: task: TaskWrapper = TaskWrapper(self.loop.create_task(coro)) self.add(task) return task def create_future(self, weak: bool = False) -> asyncio.Future: future = self.loop.create_future() self.add(future) return future def get_child(self) -> "FutureStore": store = FutureStore(self.loop) store.parent = self return store class Base(AbstractBase): __slots__ = "loop", "__future_store", "closing" def __init__( self, *, loop: asyncio.AbstractEventLoop, parent: Optional[AbstractBase] = None ): self.loop: asyncio.AbstractEventLoop = loop if parent: self.__future_store = parent._future_store_child() else: self.__future_store = FutureStore(loop=self.loop) self.closing = self._create_closing_future() def _create_closing_future(self) -> asyncio.Future: future = self.__future_store.create_future() future.add_done_callback(lambda x: x.exception()) return future def _cancel_tasks( self, exc: ExceptionType = None, ) -> Coroutine[Any, Any, None]: return self.__future_store.reject_all(exc) def _future_store_child(self) -> AbstractFutureStore: return self.__future_store.get_child() def create_task(self, coro: CoroutineType) -> TaskType: return self.__future_store.create_task(coro) def create_future(self) -> asyncio.Future: return self.__future_store.create_future() @abc.abstractmethod async def _on_close( self, exc: Optional[ExceptionType] = None ) -> None: # pragma: no cover return async def __closer(self, exc: Optional[ExceptionType]) -> None: if self.is_closed: # pragma: no cover return with suppress(Exception): await self._on_close(exc) with suppress(Exception): await self._cancel_tasks(exc) async def close( self, exc: Optional[ExceptionType] = asyncio.CancelledError ) -> None: if self.is_closed: return None await self.loop.create_task(self.__closer(exc)) def __repr__(self) -> str: cls_name = self.__class__.__name__ return '<{0}: "{1}" at 0x{2:02x}>'.format( cls_name, str(self), id(self), ) @abc.abstractmethod def __str__(self) -> str: # pragma: no cover raise NotImplementedError @property def is_closed(self) -> bool: return self.closing.done() TaskFunctionType = Callable[..., T] def task(func: TaskFunctionType) -> TaskFunctionType: @wraps(func) async def wrap(self: Base, *args: Any, **kwargs: Any) -> Any: return await self.create_task(func(self, *args, **kwargs)) return wrap

28.478788

78

0.635242

6b1d1c880a6ace4ac95a72fb37515c8abba9a44b

745

py

Python

examples/Decimal2HexConversion.py

Ellis0817/Introduction-to-Programming-Using-Python

1882a2a846162d5ff56d4d56c3940b638ef408bd

[ "MIT" ]

null

examples/Decimal2HexConversion.py

Ellis0817/Introduction-to-Programming-Using-Python

1882a2a846162d5ff56d4d56c3940b638ef408bd

[ "MIT" ]

4

2019-11-07T12:32:19.000Z

2020-07-19T14:04:44.000Z

examples/Decimal2HexConversion.py

Ellis0817/Introduction-to-Programming-Using-Python

1882a2a846162d5ff56d4d56c3940b638ef408bd

[ "MIT" ]

5

2019-12-04T15:56:55.000Z

2022-01-14T06:19:18.000Z

# Convert a decimal to a hex as a string def decimalToHex(decimalValue): hex = "" while decimalValue != 0: hexValue = decimalValue % 16 hex = toHexChar(hexValue) + hex decimalValue = decimalValue // 16 return hex # Convert an integer to a single hex digit in a character def toHexChar(hexValue): if 0 <= hexValue <= 9: return chr(hexValue + ord('0')) else: # 10 <= hexValue <= 15 return chr(hexValue - 10 + ord('A')) def main(): # Prompt the user to enter a decimal integer decimalValue = eval(input("Enter a decimal number: ")) print("The hex number for decimal", decimalValue, "is", decimalToHex(decimalValue)) main() # Call the main function

27.592593

58

0.624161

8b82118f9bc0984705a095fe1e9caa181039e33b

128

py

Python

Aula13/ex04.py

danicon/MD2-Curso_Python

77a2eb2d123eb1359dd7c84360c83bf3b3033ab4

[ "MIT" ]

1

2020-11-28T14:48:03.000Z

Aula13/ex04.py

danicon/MD2-Curso_Python

77a2eb2d123eb1359dd7c84360c83bf3b3033ab4

[ "MIT" ]

null

Aula13/ex04.py

danicon/MD2-Curso_Python

77a2eb2d123eb1359dd7c84360c83bf3b3033ab4

[ "MIT" ]

null

s = 0 for c in range(0, 4): n = int(input('Digite um valor: ')) s += n print(f'O somatório de todos os valores foi {s}')

25.6

49

0.585938

6bd37de6c078abdd5398202ad2825a8be301e750

4,053

gyp

Python

Xcode/node-sdl2_mixer.gyp

flyover/node-sdl2_mixer

e886ce7057c780374fd3e1eeeebb33f3601a8540

[ "MIT" ]

null

Xcode/node-sdl2_mixer.gyp

flyover/node-sdl2_mixer

e886ce7057c780374fd3e1eeeebb33f3601a8540

[ "MIT" ]

null

Xcode/node-sdl2_mixer.gyp

flyover/node-sdl2_mixer

e886ce7057c780374fd3e1eeeebb33f3601a8540

[ "MIT" ]

null

{ 'variables': { 'OS%': 'ios', 'NODE_PATH': '../../../node-v0.x-archive', 'SDL2_PATH': '../../../SDL', 'SDL2_MIXER_PATH': '../../../SDL_mixer', }, 'xcode_settings': { 'ALWAYS_SEARCH_USER_PATHS': 'NO', 'USE_HEADERMAP': 'NO', }, 'conditions': [ [ 'OS=="ios"', { 'xcode_settings': { 'SDKROOT': 'iphoneos', 'ARCHS': '$(ARCHS_STANDARD)', 'TARGETED_DEVICE_FAMILY': '1,2', 'CODE_SIGN_IDENTITY': 'iPhone Developer', } }], [ 'OS=="osx"', { 'xcode_settings': { 'SDKROOT': 'macosx', 'ARCHS': '$(ARCHS_STANDARD_32_64_BIT)', } }], ], 'target_defaults': { 'defines': [ '__POSIX__', '_LARGEFILE_SOURCE', '_LARGEFILE64_SOURCE', '_FILE_OFFSET_BITS=64', '_DARWIN_USE_64_BIT_INODE=1', ], 'configurations': { 'Debug': { 'defines': [ '_DEBUG', 'DEBUG=1' ], 'cflags': [ '-g', '-O0', '-fno-strict-aliasing' '-fwrapv' ], }, 'Release': { 'defines': [ 'NDEBUG=1' ], 'cflags': [ '-O3', '-fstrict-aliasing', '-fomit-frame-pointer', '-fdata-sections', '-ffunction-sections', ], }, }, 'xcode_settings': { 'GCC_C_LANGUAGE_STANDARD': 'c99', # -std=c99 'GCC_CW_ASM_SYNTAX': 'NO', # No -fasm-blocks 'GCC_DYNAMIC_NO_PIC': 'NO', # No -mdynamic-no-pic (Equivalent to -fPIC) 'GCC_ENABLE_CPP_EXCEPTIONS': 'NO', # -fno-exceptions 'GCC_ENABLE_CPP_RTTI': 'NO', # -fno-rtti 'GCC_ENABLE_PASCAL_STRINGS': 'NO', # No -mpascal-strings 'GCC_INLINES_ARE_PRIVATE_EXTERN': 'YES', # -fvisibility-inlines-hidden 'GCC_SYMBOLS_PRIVATE_EXTERN': 'YES', # -fvisibility=hidden 'GCC_THREADSAFE_STATICS': 'NO', # -fno-threadsafe-statics 'GCC_WARN_NON_VIRTUAL_DESTRUCTOR': 'YES', # -Wnon-virtual-dtor 'PREBINDING': 'NO', # No -Wl,-prebind 'WARNING_CFLAGS': [ '-Wall', '-Wendif-labels', '-W', '-Wno-unused-parameter', ], 'OTHER_CFLAGS[arch=armv7]': [ '-marm' ], 'OTHER_CFLAGS[arch=armv7s]': [ '-marm' ], 'OTHER_CFLAGS[arch=arm64]': [ '-marm' ], }, }, 'targets': [ { 'target_name': 'libnode-sdl2_mixer-<(OS)', 'type': 'static_library', 'defines': [ 'NODE_WANT_INTERNALS=1', ], 'include_dirs': [ '.', '<!(node -e "require(\'@flyover/node-sdl2/include\')")', '<!(node -e "require(\'nan\')")', '<(NODE_PATH)/src', '<(NODE_PATH)/deps/uv/include', '<(NODE_PATH)/deps/v8/include', '<(NODE_PATH)/deps/debugger-agent/include', '<(NODE_PATH)/deps/cares/include', '<(SDL2_PATH)/include', '<(SDL2_MIXER_PATH)', ], 'direct_dependent_settings': { 'include_dirs': [ '.', '<!(node -e "require(\'@flyover/node-sdl2/include\')")', '<!(node -e "require(\'nan\')")', '<(SDL2_PATH)/include', '<(SDL2_MIXER_PATH)', ] }, 'dependencies': [ ], 'sources': [ 'node-sdl2_mixer.h', 'node-sdl2_mixer.cc', ], }, ], }

34.641026

97

0.399457

124c4fa79dd56065e728e4fe323d8227f930d210

1,576

py

Python

keras/integration_test/vectorized_map_test.py

Halo9Pan/dive-keras

7d4c5572fa3a9fc2542a1314d06c555f67575cb0

[ "Apache-2.0" ]

11

2015-11-27T18:33:56.000Z

2020-08-12T22:51:57.000Z

tensorflow/python/keras/integration_test/vectorized_map_test.py

CaptainGizzy21/tensorflow

3457a2b122e50b4d44ceaaed5a663d635e5c22df

[ "Apache-2.0" ]

1,056

2019-12-15T01:20:31.000Z

2022-02-10T02:06:28.000Z

tensorflow/python/keras/integration_test/vectorized_map_test.py

CaptainGizzy21/tensorflow

3457a2b122e50b4d44ceaaed5a663d635e5c22df

[ "Apache-2.0" ]

6

2016-09-07T04:00:15.000Z

2022-01-12T01:47:38.000Z

# Copyright 2020 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== import tensorflow as tf class VectorizedMapTest(tf.test.TestCase): def test_vectorized_map(self): batch_size = 10 num_features = 32 layer = tf.keras.layers.Dense(1) def model_fn(arg): with tf.GradientTape() as g: inp, label = arg inp = tf.expand_dims(inp, 0) label = tf.expand_dims(label, 0) prediction = layer(inp) loss = tf.nn.l2_loss(label - prediction) return g.gradient(loss, (layer.kernel, layer.bias)) inputs = tf.random.uniform([batch_size, num_features]) labels = tf.random.uniform([batch_size, 1]) per_example_gradients = tf.vectorized_map(model_fn, (inputs, labels)) self.assertEqual(per_example_gradients[0].shape, (batch_size, num_features, 1)) self.assertEqual(per_example_gradients[1].shape, (batch_size, 1)) if __name__ == "__main__": tf.test.main()

35.022222

80

0.668147

43a6496451937e9f118507bc9d1d771e0f3123e1

7,088

py

Python

python/pyarrow/tests/conftest.py

stspyder/arrow

16b2a44be2b71bc1a7c95df70795664b4d450b6d

[ "Apache-2.0" ]

2

2021-09-28T01:36:21.000Z

2021-12-22T08:24:17.000Z

python/pyarrow/tests/conftest.py

stspyder/arrow

16b2a44be2b71bc1a7c95df70795664b4d450b6d

[ "Apache-2.0" ]

6

2020-07-01T20:18:37.000Z

2021-01-07T16:22:13.000Z

python/pyarrow/tests/conftest.py

stspyder/arrow

16b2a44be2b71bc1a7c95df70795664b4d450b6d

[ "Apache-2.0" ]

1

2020-12-08T10:36:30.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import os import pathlib import subprocess from tempfile import TemporaryDirectory import pytest import hypothesis as h from pyarrow.util import find_free_port # setup hypothesis profiles h.settings.register_profile('ci', max_examples=1000) h.settings.register_profile('dev', max_examples=10) h.settings.register_profile('debug', max_examples=10, verbosity=h.Verbosity.verbose) # load default hypothesis profile, either set HYPOTHESIS_PROFILE environment # variable or pass --hypothesis-profile option to pytest, to see the generated # examples try: # pytest pyarrow -sv --enable-hypothesis --hypothesis-profile=debug h.settings.load_profile(os.environ.get('HYPOTHESIS_PROFILE', 'dev')) groups = [ 'cython', 'dataset', 'hypothesis', 'fastparquet', 'gandiva', 'hdfs', 'large_memory', 'memory_leak', 'nopandas', 'orc', 'pandas', 'parquet', 'plasma', 's3', 'tensorflow', 'flight', 'slow', 'requires_testing_data', ] defaults = { 'cython': False, 'dataset': False, 'fastparquet': False, 'hypothesis': False, 'gandiva': False, 'hdfs': False, 'large_memory': False, 'memory_leak': False, 'orc': False, 'nopandas': False, 'pandas': False, 'parquet': False, 'plasma': False, 's3': False, 'tensorflow': False, 'flight': False, 'slow': False, 'requires_testing_data': True, } try: import cython # noqa defaults['cython'] = True except ImportError: pass try: import fastparquet # noqa defaults['fastparquet'] = True except ImportError: pass try: import pyarrow.gandiva # noqa defaults['gandiva'] = True except ImportError: pass try: import pyarrow.dataset # noqa defaults['dataset'] = True except ImportError: pass try: import pyarrow.orc # noqa defaults['orc'] = True except ImportError: pass try: import pandas # noqa defaults['pandas'] = True except ImportError: defaults['nopandas'] = True try: import pyarrow.parquet # noqa defaults['parquet'] = True except ImportError: pass try: import pyarrow.plasma # noqa defaults['plasma'] = True except ImportError: pass try: import tensorflow # noqa defaults['tensorflow'] = True except ImportError: pass try: import pyarrow.flight # noqa defaults['flight'] = True except ImportError: pass try: from pyarrow.fs import S3FileSystem # noqa defaults['s3'] = True except ImportError: pass try: from pyarrow.fs import HadoopFileSystem # noqa defaults['hdfs'] = True except ImportError: pass def pytest_addoption(parser): # Create options to selectively enable test groups def bool_env(name, default=None): value = os.environ.get(name.upper()) if value is None: return default value = value.lower() if value in {'1', 'true', 'on', 'yes', 'y'}: return True elif value in {'0', 'false', 'off', 'no', 'n'}: return False else: raise ValueError('{}={} is not parsable as boolean' .format(name.upper(), value)) for group in groups: default = bool_env('PYARROW_TEST_{}'.format(group), defaults[group]) parser.addoption('--enable-{}'.format(group), action='store_true', default=default, help=('Enable the {} test group'.format(group))) parser.addoption('--disable-{}'.format(group), action='store_true', default=False, help=('Disable the {} test group'.format(group))) class PyArrowConfig: def __init__(self): self.is_enabled = {} def apply_mark(self, mark): group = mark.name if group in groups: self.requires(group) def requires(self, group): if not self.is_enabled[group]: pytest.skip('{} NOT enabled'.format(group)) def pytest_configure(config): # Apply command-line options to initialize PyArrow-specific config object config.pyarrow = PyArrowConfig() for mark in groups: config.addinivalue_line( "markers", mark, ) enable_flag = '--enable-{}'.format(mark) disable_flag = '--disable-{}'.format(mark) is_enabled = (config.getoption(enable_flag) and not config.getoption(disable_flag)) config.pyarrow.is_enabled[mark] = is_enabled def pytest_runtest_setup(item): # Apply test markers to skip tests selectively for mark in item.iter_markers(): item.config.pyarrow.apply_mark(mark) @pytest.fixture def tempdir(tmpdir): # convert pytest's LocalPath to pathlib.Path return pathlib.Path(tmpdir.strpath) @pytest.fixture(scope='session') def datadir(): return pathlib.Path(__file__).parent / 'data' # TODO(kszucs): move the following fixtures to test_fs.py once the previous # parquet dataset implementation and hdfs implementation are removed. @pytest.mark.hdfs @pytest.fixture(scope='session') def hdfs_connection(): host = os.environ.get('ARROW_HDFS_TEST_HOST', 'default') port = int(os.environ.get('ARROW_HDFS_TEST_PORT', 0)) user = os.environ.get('ARROW_HDFS_TEST_USER', 'hdfs') return host, port, user @pytest.mark.s3 @pytest.fixture(scope='session') def s3_connection(): host, port = 'localhost', find_free_port() access_key, secret_key = 'arrow', 'apachearrow' return host, port, access_key, secret_key @pytest.fixture(scope='session') def s3_server(s3_connection): host, port, access_key, secret_key = s3_connection address = '{}:{}'.format(host, port) env = os.environ.copy() env.update({ 'MINIO_ACCESS_KEY': access_key, 'MINIO_SECRET_KEY': secret_key }) with TemporaryDirectory() as tempdir: args = ['minio', '--compat', 'server', '--quiet', '--address', address, tempdir] proc = None try: proc = subprocess.Popen(args, env=env) except OSError: pytest.skip('`minio` command cannot be located') else: yield proc finally: if proc is not None: proc.kill()

25.96337

78

0.645316

77c3ae0408d83382e76637d7cef3a4d06d21618b

2,745

py

Python

utest/py3270/test_command.py

MichaelSeeburger/Robot-Framework-Mainframe-3270-Library

76b589d58c55a39f96c027a8ae28c41fa37ed445

[ "MIT" ]

3

2018-10-02T14:32:06.000Z

2018-10-02T14:33:32.000Z

utest/py3270/test_command.py

MichaelSeeburger/Robot-Framework-Mainframe-3270-Library

76b589d58c55a39f96c027a8ae28c41fa37ed445

[ "MIT" ]

null

utest/py3270/test_command.py

MichaelSeeburger/Robot-Framework-Mainframe-3270-Library

76b589d58c55a39f96c027a8ae28c41fa37ed445

[ "MIT" ]

null

import warnings import pytest from pytest_mock import MockerFixture from Mainframe3270.py3270 import Command, CommandError, ExecutableAppLinux def test_command_default(mocker: MockerFixture): mocker.patch("subprocess.Popen") app = ExecutableAppLinux() under_test = Command(app, b"abc") assert under_test.app == app assert under_test.cmdstr == b"abc" assert under_test.status_line is None assert under_test.data == [] def test_command_with_text_type(mocker: MockerFixture): mocker.patch("subprocess.Popen") mocker.patch("warnings.warn") app = ExecutableAppLinux() under_test = Command(app, "abc") warnings.warn.assert_called_with("Commands should be byte strings", stacklevel=3) assert isinstance(under_test.cmdstr, bytes) def test_execute(mocker: MockerFixture): mocker.patch("subprocess.Popen") mocker.patch( "Mainframe3270.py3270.ExecutableAppLinux.readline", side_effect=[ b"data: abc", b"U U U C(pub400.com) C 4 43 80 4 24 0x0 0.000", b"ok", ], ) app = ExecutableAppLinux() under_test = Command(app, b"abc") under_test.execute() assert under_test.data == [b"abc"] def test_handle_result_quit(mocker: MockerFixture): mocker.patch("subprocess.Popen") mocker.patch("Mainframe3270.py3270.ExecutableAppLinux.readline", return_value=b"") app = ExecutableAppLinux() under_test = Command(app, b"Quit") under_test.execute() def test_handle_result_error(mocker: MockerFixture): mocker.patch("subprocess.Popen") mocker.patch( "Mainframe3270.py3270.ExecutableAppLinux.readline", return_value=b"error" ) app = ExecutableAppLinux() under_test = Command(app, b"abc") with pytest.raises(CommandError, match="[no data message]"): under_test.execute() def test_handle_result_with_data(mocker: MockerFixture): mocker.patch("subprocess.Popen") mocker.patch( "Mainframe3270.py3270.ExecutableAppLinux.readline", side_effect=[ b"data: abc", b"U U U C(pub400.com) C 4 43 80 4 24 0x0 0.000", b"error", ], ) app = ExecutableAppLinux() under_test = Command(app, b"abc") with pytest.raises(CommandError, match="abc"): under_test.execute() def test_handle_result_not_ok_or_error(mocker: MockerFixture): mocker.patch("subprocess.Popen") mocker.patch( "Mainframe3270.py3270.ExecutableAppLinux.readline", return_value=b"abc" ) app = ExecutableAppLinux() under_test = Command(app, b"abc") with pytest.raises( ValueError, match='expected "ok" or "error" result, but received: abc' ): under_test.execute()

27.45

86

0.680146

b20f2c523e4a6c47ec8daeffa4345ae166a1099c

39,111

py

Python

core/domain/stats_services.py

aadilmehdis/oppia

afa9e55c1de88f6898fd31e8ed452f4a9157050b

[ "Apache-2.0" ]

null

core/domain/stats_services.py

aadilmehdis/oppia

afa9e55c1de88f6898fd31e8ed452f4a9157050b

[ "Apache-2.0" ]

null

core/domain/stats_services.py

aadilmehdis/oppia

afa9e55c1de88f6898fd31e8ed452f4a9157050b

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2014 The Oppia 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. """Services for exploration-related statistics.""" import collections import copy import itertools from core.domain import exp_domain from core.domain import interaction_registry from core.domain import stats_domain from core.platform import models import feconf (stats_models,) = models.Registry.import_models([models.NAMES.statistics]) transaction_services = models.Registry.import_transaction_services() # Counts contributions from all versions. VERSION_ALL = 'all' def _migrate_to_latest_issue_schema(exp_issue_dict): """Holds the responsibility of performing a step-by-step sequential update of an exploration issue dict based on its schema version. If the current issue schema version changes (stats_models.CURRENT_ISSUE_SCHEMA_VERSION), a new conversion function must be added and some code appended to this function to account for that new version. Args: exp_issue_dict: dict. Dict representing the exploration issue. Raises: Exception. The issue_schema_version is invalid. """ issue_schema_version = exp_issue_dict['schema_version'] if issue_schema_version is None or issue_schema_version < 1: issue_schema_version = 0 if not (0 <= issue_schema_version <= stats_models.CURRENT_ISSUE_SCHEMA_VERSION): raise Exception( 'Sorry, we can only process v1-v%d and unversioned issue schemas at' 'present.' % stats_models.CURRENT_ISSUE_SCHEMA_VERSION) while issue_schema_version < stats_models.CURRENT_ISSUE_SCHEMA_VERSION: stats_domain.ExplorationIssue.update_exp_issue_from_model( exp_issue_dict) issue_schema_version += 1 def _migrate_to_latest_action_schema(learner_action_dict): """Holds the responsibility of performing a step-by-step sequential update of an learner action dict based on its schema version. If the current action schema version changes (stats_models.CURRENT_ACTION_SCHEMA_VERSION), a new conversion function must be added and some code appended to this function to account for that new version. Args: learner_action_dict: dict. Dict representing the learner action. Raises: Exception. The action_schema_version is invalid. """ action_schema_version = learner_action_dict['schema_version'] if action_schema_version is None or action_schema_version < 1: action_schema_version = 0 if not (0 <= action_schema_version <= stats_models.CURRENT_ACTION_SCHEMA_VERSION): raise Exception( 'Sorry, we can only process v1-v%d and unversioned action schemas ' 'at present.' % stats_models.CURRENT_ACTION_SCHEMA_VERSION) while action_schema_version < stats_models.CURRENT_ACTION_SCHEMA_VERSION: stats_domain.LearnerAction.update_learner_action_from_model( learner_action_dict) action_schema_version += 1 def get_exploration_stats(exp_id, exp_version): """Retrieves the ExplorationStats domain instance. Args: exp_id: str. ID of the exploration. exp_version: int. Version of the exploration. Returns: ExplorationStats. The exploration stats domain object. """ exploration_stats = get_exploration_stats_by_id(exp_id, exp_version) if exploration_stats is None: exploration_stats = stats_domain.ExplorationStats.create_default( exp_id, exp_version, {}) return exploration_stats def update_stats(exp_id, exp_version, aggregated_stats): """Updates ExplorationStatsModel according to the dict containing aggregated stats. Args: exp_id: str. ID of the exploration. exp_version: int. Version of the exploration. aggregated_stats: dict. Dict representing an ExplorationStatsModel instance with stats aggregated in the frontend. """ exploration_stats = get_exploration_stats_by_id( exp_id, exp_version) exploration_stats.num_starts_v2 += aggregated_stats['num_starts'] exploration_stats.num_completions_v2 += aggregated_stats['num_completions'] exploration_stats.num_actual_starts_v2 += aggregated_stats[ 'num_actual_starts'] for state_name in aggregated_stats['state_stats_mapping']: exploration_stats.state_stats_mapping[ state_name].total_answers_count_v2 += aggregated_stats[ 'state_stats_mapping'][state_name]['total_answers_count'] exploration_stats.state_stats_mapping[ state_name].useful_feedback_count_v2 += aggregated_stats[ 'state_stats_mapping'][state_name]['useful_feedback_count'] exploration_stats.state_stats_mapping[ state_name].total_hit_count_v2 += aggregated_stats[ 'state_stats_mapping'][state_name]['total_hit_count'] exploration_stats.state_stats_mapping[ state_name].first_hit_count_v2 += aggregated_stats[ 'state_stats_mapping'][state_name]['first_hit_count'] exploration_stats.state_stats_mapping[ state_name].num_times_solution_viewed_v2 += aggregated_stats[ 'state_stats_mapping'][state_name]['num_times_solution_viewed'] exploration_stats.state_stats_mapping[ state_name].num_completions_v2 += aggregated_stats[ 'state_stats_mapping'][state_name]['num_completions'] save_stats_model_transactional(exploration_stats) def handle_stats_creation_for_new_exploration(exp_id, exp_version, state_names): """Creates ExplorationStatsModel for the freshly created exploration and sets all initial values to zero. Args: exp_id: str. ID of the exploration. exp_version: int. Version of the exploration. state_names: list(str). State names of the exploration. """ state_stats_mapping = { state_name: stats_domain.StateStats.create_default() for state_name in state_names } exploration_stats = stats_domain.ExplorationStats.create_default( exp_id, exp_version, state_stats_mapping) create_stats_model(exploration_stats) def handle_stats_creation_for_new_exp_version( exp_id, exp_version, state_names, change_list): """Retrieves the ExplorationStatsModel for the old exp_version and makes any required changes to the structure of the model. Then, a new ExplorationStatsModel is created for the new exp_version. Args: exp_id: str. ID of the exploration. exp_version: int. Version of the exploration. state_names: list(str). State names of the exploration. change_list: list(ExplorationChange). A list of changes introduced in this commit. """ old_exp_version = exp_version - 1 new_exp_version = exp_version exploration_stats = get_exploration_stats_by_id( exp_id, old_exp_version) if exploration_stats is None: handle_stats_creation_for_new_exploration( exp_id, new_exp_version, state_names) return # Handling state additions, deletions and renames. for change in change_list: if change.cmd == exp_domain.CMD_ADD_STATE: exploration_stats.state_stats_mapping[ change.state_name ] = stats_domain.StateStats.create_default() elif change.cmd == exp_domain.CMD_DELETE_STATE: exploration_stats.state_stats_mapping.pop(change.state_name) elif change.cmd == exp_domain.CMD_RENAME_STATE: exploration_stats.state_stats_mapping[ change.new_state_name ] = exploration_stats.state_stats_mapping.pop(change.old_state_name) exploration_stats.exp_version = new_exp_version # Create new statistics model. create_stats_model(exploration_stats) def create_exp_issues_for_new_exploration(exp_id, exp_version): """Creates the ExplorationIssuesModel instance for the exploration. Args: exp_id: str. ID of the exploration. exp_version: int. Version of the exploration. """ stats_models.ExplorationIssuesModel.create(exp_id, exp_version, []) def _handle_exp_issues_after_state_deletion( state_name, exp_issue, deleted_state_names): """Checks if the exploration issue's concerned state is a deleted state and invalidates the exploration issue accoridngly. Args: state_name: str. The issue's concerened state name. exp_issue: ExplorationIssue. The exploration issue domain object. deleted_state_names: list(str). The list of deleted state names in this commit. Returns: ExplorationIssue. The exploration issue domain object. """ if state_name in deleted_state_names: exp_issue.is_valid = False return exp_issue def _handle_exp_issues_after_state_rename( state_name, exp_issue, old_to_new_state_names, playthrough_ids_by_state_name): """Checks if the exploration issue's concerned state is a renamed state and modifies the exploration issue accoridngly. Args: state_name: str. The issue's concerened state name. exp_issue: ExplorationIssue. The exploration issue domain object. old_to_new_state_names: dict. The dict mapping state names to their renamed versions. This mapping contains state names only if it is actually renamed. playthrough_ids_by_state_name: dict. The dict mapping old state names to their new ones Returns: ExplorationIssue. The exploration issue domain object. """ if state_name not in old_to_new_state_names: return exp_issue, playthrough_ids_by_state_name old_state_name = state_name new_state_name = old_to_new_state_names[old_state_name] if stats_models.ISSUE_TYPE_KEYNAME_MAPPING[ exp_issue.issue_type] == 'state_names': state_names = exp_issue.issue_customization_args['state_names'][ 'value'] exp_issue.issue_customization_args['state_names']['value'] = [ new_state_name if state_name == old_state_name else state_name for state_name in state_names] else: exp_issue.issue_customization_args['state_name']['value'] = ( new_state_name) playthrough_ids_by_state_name[old_state_name].extend( exp_issue.playthrough_ids) return exp_issue, playthrough_ids_by_state_name def update_exp_issues_for_new_exp_version( exploration, exp_versions_diff, revert_to_version): """Retrieves the ExplorationIssuesModel for the old exp_version and makes any required changes to the structure of the model. Args: exploration: Exploration. Domain object for the exploration. exp_versions_diff: ExplorationVersionsDiff|None. The domain object for the exploration versions difference, None if it is a revert. revert_to_version: int|None. If the change is a revert, the version. Otherwise, None. """ exp_issues = get_exp_issues(exploration.id, exploration.version - 1) if exp_issues is None: create_exp_issues_for_new_exploration( exploration.id, exploration.version - 1) return # Handling reverts. if revert_to_version: old_exp_issues = get_exp_issues(exploration.id, revert_to_version) # If the old exploration issues model doesn't exist, the current model # is carried over (this is a fallback case for some tests, and can # never happen in production.) if old_exp_issues: exp_issues.unresolved_issues = old_exp_issues.unresolved_issues exp_issues.exp_version = exploration.version + 1 create_exp_issues_model(exp_issues) return playthrough_ids_by_state_name = collections.defaultdict(list) for i_idx, exp_issue in enumerate(exp_issues.unresolved_issues): keyname = stats_models.ISSUE_TYPE_KEYNAME_MAPPING[exp_issue.issue_type] if keyname == 'state_names': state_names = exp_issue.issue_customization_args[keyname]['value'] for state_name in state_names: # Handle exp issues changes for deleted states. exp_issues.unresolved_issues[i_idx] = ( _handle_exp_issues_after_state_deletion( state_name, exp_issue, exp_versions_diff.deleted_state_names)) # Handle exp issues changes for renamed states. exp_issues.unresolved_issues[ i_idx], playthrough_ids_by_state_name = ( _handle_exp_issues_after_state_rename( state_name, exp_issue, exp_versions_diff.old_to_new_state_names, playthrough_ids_by_state_name)) else: state_name = exp_issue.issue_customization_args[keyname]['value'] # Handle exp issues changes for deleted states. exp_issues.unresolved_issues[i_idx] = ( _handle_exp_issues_after_state_deletion( state_name, exp_issue, exp_versions_diff.deleted_state_names)) # Handle exp issues changes for renamed states. exp_issues.unresolved_issues[ i_idx], playthrough_ids_by_state_name = ( _handle_exp_issues_after_state_rename( state_name, exp_issue, exp_versions_diff.old_to_new_state_names, playthrough_ids_by_state_name)) # Handling changes to playthrough instances. all_playthrough_ids = [] all_playthroughs = [] for old_state_name in playthrough_ids_by_state_name: new_state_name = exp_versions_diff.old_to_new_state_names[ old_state_name] playthrough_ids = playthrough_ids_by_state_name[old_state_name] playthroughs = get_playthroughs_multi(playthrough_ids) for p_idx, playthrough in enumerate(playthroughs): if stats_models.ISSUE_TYPE_KEYNAME_MAPPING[ playthrough.issue_type] == 'state_names': state_names = playthrough.issue_customization_args[ 'state_names']['value'] playthrough.issue_customization_args['state_names']['value'] = [ new_state_name if state_name == old_state_name else state_name for state_name in state_names] else: playthrough.issue_customization_args['state_name']['value'] = ( new_state_name) for a_idx, action in enumerate(playthrough.actions): if action.action_customization_args['state_name']['value'] == ( old_state_name): playthroughs[p_idx].actions[ a_idx].action_customization_args['state_name'][ 'value'] = new_state_name all_playthrough_ids.extend(playthrough_ids) all_playthroughs.extend(playthroughs) update_playthroughs_multi(all_playthrough_ids, all_playthroughs) exp_issues.exp_version += 1 create_exp_issues_model(exp_issues) def get_exp_issues(exp_id, exp_version): """Retrieves the ExplorationIssues domain object. Args: exp_id: str. ID of the exploration. exp_version: int. Version of the exploration. Returns: ExplorationIssues|None: The domain object for exploration issues or None if the exp_id is invalid. """ exp_issues = None exp_issues_model = stats_models.ExplorationIssuesModel.get_model( exp_id, exp_version) if exp_issues_model is not None: exp_issues = get_exp_issues_from_model(exp_issues_model) return exp_issues def get_playthrough_by_id(playthrough_id): """Retrieves the Playthrough domain object. Args: playthrough_id: str. ID of the playthrough. Returns: Playthrough|None: The domain object for the playthrough or None if the playthrough_id is invalid. """ playthrough = None playthrough_model = stats_models.PlaythroughModel.get( playthrough_id, strict=False) if playthrough_model is not None: playthrough = get_playthrough_from_model(playthrough_model) return playthrough def get_playthroughs_multi(playthrough_ids): """Retrieves multiple Playthrough domain objects. Args: playthrough_ids: list(str). List of playthrough IDs. Returns: list(Playthrough). List of playthrough domain objects. """ playthrough_instances = stats_models.PlaythroughModel.get_multi( playthrough_ids) playthroughs = [ get_playthrough_from_model(playthrough_instance) for playthrough_instance in playthrough_instances] return playthroughs def update_playthroughs_multi(playthrough_ids, playthroughs): """Updates the playthrough instances. Args: playthrough_ids: list(str). List of playthrough IDs. playthroughs: list(Playthrough). List of playthrough domain objects. """ playthrough_instances = stats_models.PlaythroughModel.get_multi( playthrough_ids) updated_instances = [] for idx, playthrough_instance in enumerate(playthrough_instances): playthrough_dict = playthroughs[idx].to_dict() playthrough_instance.issue_type = playthrough_dict['issue_type'] playthrough_instance.issue_customization_args = ( playthrough_dict['issue_customization_args']) playthrough_instance.actions = playthrough_dict['actions'] updated_instances.append(playthrough_instance) stats_models.PlaythroughModel.put_multi(updated_instances) def get_exploration_stats_by_id(exp_id, exp_version): """Retrieves the ExplorationStats domain object. Args: exp_id: str. ID of the exploration. exp_version: int. Version of the exploration. Returns: ExplorationStats. The domain object for exploration statistics. Raises: Exception: Entity for class ExplorationStatsModel with id not found. """ exploration_stats = None exploration_stats_model = stats_models.ExplorationStatsModel.get_model( exp_id, exp_version) if exploration_stats_model is not None: exploration_stats = get_exploration_stats_from_model( exploration_stats_model) return exploration_stats def get_multiple_exploration_stats_by_version(exp_id, version_numbers): """Returns a list of ExplorationStats domain objects corresponding to the specified versions. Args: exp_id: str. ID of the exploration. version_numbers: list(int). List of version numbers. Returns: list(ExplorationStats|None). List of ExplorationStats domain class instances. """ exploration_stats = [] exploration_stats_models = ( stats_models.ExplorationStatsModel.get_multi_versions( exp_id, version_numbers)) for exploration_stats_model in exploration_stats_models: if exploration_stats_model is None: exploration_stats.append(None) else: exploration_stats.append(get_exploration_stats_from_model( exploration_stats_model)) return exploration_stats def get_exp_issues_from_model(exp_issues_model): """Gets an ExplorationIssues domain object from an ExplorationIssuesModel instance. Args: exp_issues_model: ExplorationIssuesModel. Exploration issues model in datastore. Returns: ExplorationIssues. The domain object for exploration issues. """ unresolved_issues = [] for unresolved_issue_dict in exp_issues_model.unresolved_issues: _migrate_to_latest_issue_schema(copy.deepcopy(unresolved_issue_dict)) unresolved_issues.append( stats_domain.ExplorationIssue.from_dict(unresolved_issue_dict)) return stats_domain.ExplorationIssues( exp_issues_model.exp_id, exp_issues_model.exp_version, unresolved_issues) def get_exploration_stats_from_model(exploration_stats_model): """Gets an ExplorationStats domain object from an ExplorationStatsModel instance. Args: exploration_stats_model: ExplorationStatsModel. Exploration statistics model in datastore. Returns: ExplorationStats. The domain object for exploration statistics. """ new_state_stats_mapping = { state_name: stats_domain.StateStats.from_dict( exploration_stats_model.state_stats_mapping[state_name]) for state_name in exploration_stats_model.state_stats_mapping } return stats_domain.ExplorationStats( exploration_stats_model.exp_id, exploration_stats_model.exp_version, exploration_stats_model.num_starts_v1, exploration_stats_model.num_starts_v2, exploration_stats_model.num_actual_starts_v1, exploration_stats_model.num_actual_starts_v2, exploration_stats_model.num_completions_v1, exploration_stats_model.num_completions_v2, new_state_stats_mapping) def get_playthrough_from_model(playthrough_model): """Gets a PlaythroughModel domain object from a PlaythroughModel instance. Args: playthrough_model: PlaythroughModel. Playthrough model in datastore. Returns: Playthrough. The domain object for a playthrough. """ actions = [] for action_dict in playthrough_model.actions: _migrate_to_latest_action_schema(action_dict) actions.append(stats_domain.LearnerAction.from_dict(action_dict)) return stats_domain.Playthrough( playthrough_model.exp_id, playthrough_model.exp_version, playthrough_model.issue_type, playthrough_model.issue_customization_args, actions) def create_stats_model(exploration_stats): """Creates an ExplorationStatsModel in datastore given an ExplorationStats domain object. Args: exploration_stats: ExplorationStats. The domain object for exploration statistics. Returns: str. ID of the datastore instance for ExplorationStatsModel. """ new_state_stats_mapping = { state_name: exploration_stats.state_stats_mapping[state_name].to_dict() for state_name in exploration_stats.state_stats_mapping } instance_id = stats_models.ExplorationStatsModel.create( exploration_stats.exp_id, exploration_stats.exp_version, exploration_stats.num_starts_v1, exploration_stats.num_starts_v2, exploration_stats.num_actual_starts_v1, exploration_stats.num_actual_starts_v2, exploration_stats.num_completions_v1, exploration_stats.num_completions_v2, new_state_stats_mapping ) return instance_id def _save_stats_model(exploration_stats): """Updates the ExplorationStatsModel datastore instance with the passed ExplorationStats domain object. Args: exploration_stats. ExplorationStats. The exploration statistics domain object. """ new_state_stats_mapping = { state_name: exploration_stats.state_stats_mapping[state_name].to_dict() for state_name in exploration_stats.state_stats_mapping } exploration_stats_model = stats_models.ExplorationStatsModel.get_model( exploration_stats.exp_id, exploration_stats.exp_version) exploration_stats_model.num_starts_v1 = exploration_stats.num_starts_v1 exploration_stats_model.num_starts_v2 = exploration_stats.num_starts_v2 exploration_stats_model.num_actual_starts_v1 = ( exploration_stats.num_actual_starts_v1) exploration_stats_model.num_actual_starts_v2 = ( exploration_stats.num_actual_starts_v2) exploration_stats_model.num_completions_v1 = ( exploration_stats.num_completions_v1) exploration_stats_model.num_completions_v2 = ( exploration_stats.num_completions_v2) exploration_stats_model.state_stats_mapping = new_state_stats_mapping exploration_stats_model.put() def save_stats_model_transactional(exploration_stats): """Updates the ExplorationStatsModel datastore instance with the passed ExplorationStats domain object in a transaction. Args: exploration_stats. ExplorationStats. The exploration statistics domain object. """ transaction_services.run_in_transaction( _save_stats_model, exploration_stats) def create_exp_issues_model(exp_issues): """Creates a new ExplorationIssuesModel in the datastore. Args: exp_issues: ExplorationIssues. The exploration issues domain object. """ unresolved_issues_dicts = [ unresolved_issue.to_dict() for unresolved_issue in exp_issues.unresolved_issues] stats_models.ExplorationIssuesModel.create( exp_issues.exp_id, exp_issues.exp_version, unresolved_issues_dicts) def _save_exp_issues_model(exp_issues): """Updates the ExplorationIssuesModel datastore instance with the passed ExplorationIssues domain object. Args: exp_issues: ExplorationIssues. The exploration issues domain object. """ unresolved_issues_dicts = [ unresolved_issue.to_dict() for unresolved_issue in exp_issues.unresolved_issues] exp_issues_model = stats_models.ExplorationIssuesModel.get_model( exp_issues.exp_id, exp_issues.exp_version) exp_issues_model.exp_version = exp_issues.exp_version exp_issues_model.unresolved_issues = unresolved_issues_dicts exp_issues_model.put() def save_exp_issues_model_transactional(exp_issues): """Updates the ExplorationIssuesModel datastore instance with the passed ExplorationIssues domain object in a transaction. Args: exp_issues: ExplorationIssues. The exploration issues domain object. """ transaction_services.run_in_transaction( _save_exp_issues_model, exp_issues) def get_exploration_stats_multi(exp_version_references): """Retrieves the exploration stats for the given explorations. Args: exp_version_references: list(ExpVersionReference). List of exploration version reference domain objects. Returns: list(ExplorationStats). The list of exploration stats domain objects. """ exploration_stats_models = ( stats_models.ExplorationStatsModel.get_multi_stats_models( exp_version_references)) exploration_stats_list = [] for index, exploration_stats_model in enumerate(exploration_stats_models): if exploration_stats_model is None: exploration_stats_list.append( stats_domain.ExplorationStats.create_default( exp_version_references[index].exp_id, exp_version_references[index].version, {})) else: exploration_stats_list.append( get_exploration_stats_from_model(exploration_stats_model)) return exploration_stats_list def delete_playthroughs_multi(playthrough_ids): """Deletes multiple playthrough instances. Args: playthrough_ids: list(str). List of playthrough IDs to be deleted. """ stats_models.PlaythroughModel.delete_playthroughs_multi(playthrough_ids) def get_visualizations_info(exp_id, state_name, interaction_id): """Returns a list of visualization info. Each item in the list is a dict with keys 'data' and 'options'. Args: exp_id: str. The ID of the exploration. state_name: str. Name of the state. interaction_id: str. The interaction type. Returns: list(dict). Each item in the list is a dict with keys representing - 'id': str. The visualization ID. - 'data': list(dict). A list of answer/frequency dicts. - 'options': dict. The visualization options. An example of the returned value may be: [{'options': {'y_axis_label': 'Count', 'x_axis_label': 'Answer'}, 'id': 'BarChart', 'data': [{u'frequency': 1, u'answer': 0}]}] """ if interaction_id is None: return [] visualizations = interaction_registry.Registry.get_interaction_by_id( interaction_id).answer_visualizations calculation_ids = set([ visualization.calculation_id for visualization in visualizations]) calculation_ids_to_outputs = {} for calculation_id in calculation_ids: # Don't show top unresolved answers calculation ouutput in stats of # exploration. if calculation_id == 'TopNUnresolvedAnswersByFrequency': continue # This is None if the calculation job has not yet been run for this # state. calc_output_domain_object = _get_calc_output( exp_id, state_name, calculation_id) # If the calculation job has not yet been run for this state, we simply # exclude the corresponding visualization results. if calc_output_domain_object is None: continue # If the output was associated with a different interaction ID, skip the # results. This filtering step is needed since the same calculation_id # can be shared across multiple interaction types. if calc_output_domain_object.interaction_id != interaction_id: continue calculation_ids_to_outputs[calculation_id] = ( calc_output_domain_object.calculation_output.to_raw_type()) return [{ 'id': visualization.id, 'data': calculation_ids_to_outputs[visualization.calculation_id], 'options': visualization.options, 'addressed_info_is_supported': ( visualization.addressed_info_is_supported), } for visualization in visualizations if visualization.calculation_id in calculation_ids_to_outputs] def record_answer( exploration_id, exploration_version, state_name, interaction_id, submitted_answer): """Record an answer by storing it to the corresponding StateAnswers entity. Args: exploration_id: str. The exploration ID. exploration_version: int. The version of the exploration. state_name: str. The name of the state. interaction_id: str. The ID of the interaction. submitted_answer: SubmittedAnswer. The submitted answer. """ record_answers( exploration_id, exploration_version, state_name, interaction_id, [submitted_answer]) def record_answers( exploration_id, exploration_version, state_name, interaction_id, submitted_answer_list): """Optimally record a group of answers using an already loaded exploration.. The submitted_answer_list is a list of SubmittedAnswer domain objects. Args: exploration_id: str. The exploration ID. exploration_version: int. The version of the exploration. state_name: str. The name of the state. interaction_id: str. The ID of the interaction. submitted_answer_list: list(SubmittedAnswer). The list of answers to be recorded. """ state_answers = stats_domain.StateAnswers( exploration_id, exploration_version, state_name, interaction_id, submitted_answer_list) for submitted_answer in submitted_answer_list: submitted_answer.validate() stats_models.StateAnswersModel.insert_submitted_answers( state_answers.exploration_id, state_answers.exploration_version, state_answers.state_name, state_answers.interaction_id, state_answers.get_submitted_answer_dict_list()) def get_state_answers(exploration_id, exploration_version, state_name): """Returns a StateAnswers object containing all answers associated with the specified exploration state, or None if no such answers have yet been submitted. Args: exploration_id: str. The exploration ID. exploration_version: int. The version of the exploration to fetch answers for. state_name: str. The name of the state to fetch answers for. Returns: StateAnswers or None. A StateAnswers object containing all answers associated with the state, or None if no such answers exist. """ state_answers_models = stats_models.StateAnswersModel.get_all_models( exploration_id, exploration_version, state_name) if state_answers_models: main_state_answers_model = state_answers_models[0] submitted_answer_dict_list = itertools.chain.from_iterable([ state_answers_model.submitted_answer_list for state_answers_model in state_answers_models]) return stats_domain.StateAnswers( exploration_id, exploration_version, state_name, main_state_answers_model.interaction_id, [stats_domain.SubmittedAnswer.from_dict(submitted_answer_dict) for submitted_answer_dict in submitted_answer_dict_list], schema_version=main_state_answers_model.schema_version) else: return None def get_sample_answers(exploration_id, exploration_version, state_name): """Fetches a list of sample answers that were submitted to the specified exploration state (at the given version of the exploration). Args: exploration_id: str. The exploration ID. exploration_version: int. The version of the exploration to fetch answers for. state_name: str. The name of the state to fetch answers for. Returns: list(*). A list of some sample raw answers. At most 100 answers are returned. """ answers_model = stats_models.StateAnswersModel.get_master_model( exploration_id, exploration_version, state_name) if answers_model is None: return [] # Return at most 100 answers, and only answers from the initial shard (If # we needed to use subsequent shards then the answers are probably too big # anyway). sample_answers = answers_model.submitted_answer_list[:100] return [ stats_domain.SubmittedAnswer.from_dict(submitted_answer_dict).answer for submitted_answer_dict in sample_answers] def get_top_state_answer_stats(exploration_id, state_name): """Fetches the top (at most) 10 answers from the given state_name in the corresponding exploration. Only answers that occur with frequency >= STATE_ANSWER_STATS_MIN_FREQUENCY are returned. Args: exploration_id: str. The exploration ID. state_name: str. The name of the state to fetch answers for. Returns: list(*). A list of the top 10 answers, sorted by decreasing frequency. """ calc_output = ( _get_calc_output(exploration_id, state_name, 'Top10AnswerFrequencies')) raw_calc_output = ( [] if calc_output is None else calc_output.calculation_output.to_raw_type()) return [ {'answer': output['answer'], 'frequency': output['frequency']} for output in raw_calc_output if output['frequency'] >= feconf.STATE_ANSWER_STATS_MIN_FREQUENCY ] def get_top_state_unresolved_answers(exploration_id, state_name): """Fetches the top unresolved answers for the given state_name in the corresponding exploration. Only answers that occur with frequency >= STATE_ANSWER_STATS_MIN_FREQUENCY are returned. Args: exploration_id: str. The exploration ID. state_name: str. The name of the state to fetch answers for. Returns: list(*). A list of the top 10 answers, sorted by decreasing frequency. """ calculation_output = ( _get_calc_output( exploration_id, state_name, 'TopNUnresolvedAnswersByFrequency') .calculation_output.to_raw_type()) return [ {'answer': output['answer'], 'frequency': output['frequency']} for output in calculation_output if output['frequency'] >= feconf.STATE_ANSWER_STATS_MIN_FREQUENCY ] def get_top_state_answer_stats_multi(exploration_id, state_names): """Fetches the top (at most) 10 answers from each given state_name in the corresponding exploration. Only answers that occur with frequency >= STATE_ANSWER_STATS_MIN_FREQUENCY are returned. Args: exploration_id: str. The exploration ID. state_names: list(str). The name of the state to fetch answers for. Returns: dict(str: list(*)). Dict mapping each state name to the list of its top (at most) 10 answers, sorted by decreasing frequency. """ return { state_name: get_top_state_answer_stats(exploration_id, state_name) for state_name in state_names } def _get_calc_output(exploration_id, state_name, calculation_id): """Get state answers calculation output domain object obtained from StateAnswersCalcOutputModel instance stored in the data store. The calculation ID comes from the name of the calculation class used to compute aggregate data from submitted user answers. This returns aggregated output for all versions of the specified state and exploration. Args: exploration_id: str. ID of the exploration. state_name: str. Name of the state. calculation_id: str. Name of the calculation class. Returns: StateAnswersCalcOutput|None. The state answers calculation output domain object or None. """ calc_output_model = stats_models.StateAnswersCalcOutputModel.get_model( exploration_id, VERSION_ALL, state_name, calculation_id) if calc_output_model: calculation_output = None if (calc_output_model.calculation_output_type == stats_domain.CALC_OUTPUT_TYPE_ANSWER_FREQUENCY_LIST): calculation_output = ( stats_domain.AnswerFrequencyList.from_raw_type( calc_output_model.calculation_output)) elif (calc_output_model.calculation_output_type == stats_domain.CALC_OUTPUT_TYPE_CATEGORIZED_ANSWER_FREQUENCY_LISTS): calculation_output = ( stats_domain.CategorizedAnswerFrequencyLists.from_raw_type( calc_output_model.calculation_output)) return stats_domain.StateAnswersCalcOutput( exploration_id, VERSION_ALL, state_name, calc_output_model.interaction_id, calculation_id, calculation_output) else: return None

38.955179

80

0.71292

132cfac5f59e599e9c94a50f7c18af239737e0a3

21,740

py

Python

hug/routing.py

Asteur/hug_api_maker

7da43cee1d1298a25417b12770b382464a8fe389

[ "MIT" ]

null

hug/routing.py

Asteur/hug_api_maker

7da43cee1d1298a25417b12770b382464a8fe389

[ "MIT" ]

null

hug/routing.py

Asteur/hug_api_maker

7da43cee1d1298a25417b12770b382464a8fe389

[ "MIT" ]

null

"""hug/routing.py Defines the chainable classes responsible for defining the routing of Python functions for use with Falcon and CLIs Copyright (C) 2016 Timothy Edmund Crosley 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. """ from __future__ import absolute_import import os import re from collections import OrderedDict from functools import wraps from urllib.parse import urljoin import falcon import hug.api import hug.interface import hug.output_format from falcon import HTTP_METHODS from hug import introspect from hug.exceptions import InvalidTypeData class Router(object): """The base chainable router object""" __slots__ = ('route', ) def __init__(self, transform=None, output=None, validate=None, api=None, requires=(), **kwargs): self.route = {} if transform is not None: self.route['transform'] = transform if output: self.route['output'] = output if validate: self.route['validate'] = validate if api: self.route['api'] = api if requires: self.route['requires'] = (requires, ) if not isinstance(requires, (tuple, list)) else requires def output(self, formatter, **overrides): """Sets the output formatter that should be used to render this route""" return self.where(output=formatter, **overrides) def transform(self, function, **overrides): """Sets the function that should be used to transform the returned Python structure into something serializable by specified output format """ return self.where(transform=function, **overrides) def validate(self, validation_function, **overrides): """Sets the secondary validation fucntion to use for this handler""" return self.where(validate=validation_function, **overrides) def api(self, api, **overrides): """Sets the API that should contain this route""" return self.where(api=api, **overrides) def requires(self, requirements, **overrides): """Adds additional requirements to the specified route""" return self.where(requires=tuple(self.route.get('requires', ())) + tuple(requirements), **overrides) def doesnt_require(self, requirements, **overrides): """Removes individual requirements while keeping all other defined ones within a route""" return self.where(requires=tuple(set(self.route.get('requires', ())).difference(requirements if type(requirements) in (list, tuple) else (requirements, )))) def where(self, **overrides): """Creates a new route, based on the current route, with the specified overrided values""" route_data = self.route.copy() route_data.update(overrides) return self.__class__(**route_data) class CLIRouter(Router): """The CLIRouter provides a chainable router that can be used to route a CLI command to a Python function""" __slots__ = () def __init__(self, name=None, version=None, doc=None, **kwargs): super().__init__(**kwargs) if name is not None: self.route['name'] = name if version: self.route['version'] = version if doc: self.route['doc'] = doc def name(self, name, **overrides): """Sets the name for the CLI interface""" return self.where(name=name, **overrides) def version(self, version, **overrides): """Sets the version for the CLI interface""" return self.where(version=version, **overrides) def doc(self, documentation, **overrides): """Sets the documentation for the CLI interface""" return self.where(doc=documentation, **overrides) def __call__(self, api_function): """Enables exposing a Hug compatible function as a Command Line Interface""" hug.interface.CLI(self.route, api_function) return api_function class InternalValidation(Router): """Defines the base route for interfaces that define their own internal validation""" __slots__ = () def __init__(self, raise_on_invalid=False, on_invalid=None, output_invalid=None, **kwargs): super().__init__(**kwargs) if raise_on_invalid: self.route['raise_on_invalid'] = raise_on_invalid if on_invalid is not None: self.route['on_invalid'] = on_invalid if output_invalid is not None: self.route['output_invalid'] = output_invalid def raise_on_invalid(self, setting=True, **overrides): """Sets the route to raise validation errors instead of catching them""" return self.where(raise_on_invalid=setting, **overrides) def on_invalid(self, function, **overrides): """Sets a function to use to transform data on validation errors. Defaults to the transform function if one is set to ensure no special handling occurs for invalid data set to `False`. """ return self.where(on_invalid=function, **overrides) def output_invalid(self, output_handler, **overrides): """Sets an output handler to be used when handler validation fails. Defaults to the output formatter set globally for the route. """ return self.where(output_invalid=output_handler, **overrides) class LocalRouter(InternalValidation): """The LocalRouter defines how interfaces should be handled when accessed locally from within Python code""" __slots__ = () def __init__(self, directives=True, validate=True, version=None, **kwargs): super().__init__(**kwargs) if version is not None: self.route['version'] = version if not directives: self.route['skip_directives'] = True if not validate: self.route['skip_validation'] = True def directives(self, use=True, **kwargs): return self.where(directives=use) def validate(self, enforce=True, **kwargs): return self.where(validate=enforce) def version(self, supported, **kwargs): return self.where(version=supported) def __call__(self, api_function): """Enables exposing a hug compatible function locally""" return hug.interface.Local(self.route, api_function) class HTTPRouter(InternalValidation): """The HTTPRouter provides the base concept of a router from an HTTPRequest to a Python function""" __slots__ = () def __init__(self, versions=None, parse_body=False, parameters=None, defaults={}, status=None, response_headers=None, private=False, inputs=None, **kwargs): super().__init__(**kwargs) self.route['versions'] = (versions, ) if isinstance(versions, (int, float, None.__class__)) else versions if parse_body: self.route['parse_body'] = parse_body if parameters: self.route['parameters'] = parameters if defaults: self.route['defaults'] = defaults if status: self.route['status'] = status if response_headers: self.route['response_headers'] = response_headers if private: self.route['private'] = private if inputs: self.route['inputs'] = inputs def versions(self, supported, **overrides): """Sets the versions that this route should be compatiable with""" return self.where(versions=supported, **overrides) def parse_body(self, automatic=True, **overrides): """Tells hug to automatically parse the input body if it matches a registered input format""" return self.where(parse_body=automatic, **overrides) def set_status(self, status, **overrides): """Sets the status that will be returned by default""" return self.where(status=status, **overrides) def parameters(self, parameters, **overrides): """Sets the custom parameters that will be used instead of those found introspecting the decorated function""" return self.where(parameters=parameters, **overrides) def defaults(self, defaults, **overrides): """Sets the custom defaults that will be used for custom parameters""" return self.where(defaults=defaults, **overrides) def _create_interface(self, api, api_function, catch_exceptions=True): interface = hug.interface.HTTP(self.route, api_function, catch_exceptions) return (interface, api_function) def response_headers(self, headers, **overrides): """Sets the response headers automatically injected by the router""" return self.where(response_headers=headers, **overrides) def add_response_headers(self, headers, **overrides): """Adds the specified response headers while keeping existing ones in-tact""" response_headers = self.route.get('response_headers', {}).copy() response_headers.update(headers) return self.where(response_headers=response_headers, **overrides) def cache(self, private=False, max_age=31536000, s_maxage=None, no_cache=False, no_store=False, must_revalidate=False, **overrides): """Convience method for quickly adding cache header to route""" parts = ('private' if private else 'public', 'max-age={0}'.format(max_age), 's-maxage={0}'.format(s_maxage) if s_maxage is not None else None, no_cache and 'no-cache', no_store and 'no-store', must_revalidate and 'must-revalidate') return self.add_response_headers({'cache-control': ', '.join(filter(bool, parts))}, **overrides) def allow_origins(self, *origins, methods=None, **overrides): """Convience method for quickly allowing other resources to access this one""" headers = {'Access-Control-Allow-Origin': ', '.join(origins) if origins else '*'} if methods: headers['Access-Control-Allow-Methods'] = ', '.join(methods) return self.add_response_headers(headers, **overrides) class NotFoundRouter(HTTPRouter): """Provides a chainable router that can be used to route 404'd request to a Python function""" __slots__ = () def __init__(self, output=None, versions=None, status=falcon.HTTP_NOT_FOUND, **kwargs): super().__init__(output=output, versions=versions, status=status, **kwargs) def __call__(self, api_function): api = self.route.get('api', hug.api.from_object(api_function)) (interface, callable_method) = self._create_interface(api, api_function) for version in self.route['versions']: api.http.set_not_found_handler(interface, version) return callable_method class SinkRouter(HTTPRouter): """Provides a chainable router that can be used to route all routes pass a certain base URL (essentially route/*)""" __slots__ = () def __init__(self, urls=None, output=None, **kwargs): super().__init__(output=output, **kwargs) if urls: self.route['urls'] = (urls, ) if isinstance(urls, str) else urls def __call__(self, api_function): api = self.route.get('api', hug.api.from_object(api_function)) (interface, callable_method) = self._create_interface(api, api_function) for base_url in self.route.get('urls', ("/{0}".format(api_function.__name__), )): api.http.add_sink(interface, base_url) return callable_method class StaticRouter(SinkRouter): """Provides a chainable router that can be used to return static files automatically from a set of directories""" __slots__ = ('route', ) def __init__(self, urls=None, output=hug.output_format.file, cache=False, **kwargs): super().__init__(urls=urls, output=output, **kwargs) if cache is True: self.cache() elif cache is not False: self.cache(**cache) def __call__(self, api_function): directories = [] for directory in api_function(): path = os.path.abspath( directory ) directories.append(path) api = self.route.get('api', hug.api.from_object(api_function)) for base_url in self.route.get('urls', ("/{0}".format(api_function.__name__), )): def read_file(request=None, path=""): filename = path.lstrip("/") for directory in directories: path = os.path.abspath(os.path.join(directory, filename)) if not path.startswith(directory): hug.redirect.not_found() if os.path.isdir(path): new_path = os.path.join(path, "index.html") if os.path.exists(new_path) and os.path.isfile(new_path): path = new_path if os.path.exists(path) and os.path.isfile(path): return path hug.redirect.not_found() api.http.add_sink(self._create_interface(api, read_file)[0], base_url) return api_function class ExceptionRouter(HTTPRouter): """Provides a chainable router that can be used to route exceptions thrown during request handling""" __slots__ = () def __init__(self, exceptions=(Exception, ), exclude=(), output=None, **kwargs): super().__init__(output=output, **kwargs) self.route['exceptions'] = (exceptions, ) if not isinstance(exceptions, (list, tuple)) else exceptions self.route['exclude'] = (exclude, ) if not isinstance(exclude, (list, tuple)) else exclude def __call__(self, api_function): api = self.route.get('api', hug.api.from_object(api_function)) (interface, callable_method) = self._create_interface(api, api_function, catch_exceptions=False) for version in self.route['versions']: for exception in self.route['exceptions']: api.http.add_exception_handler(exception, interface, version) return callable_method def _create_interface(self, api, api_function, catch_exceptions=False): interface = hug.interface.ExceptionRaised(self.route, api_function, catch_exceptions) return (interface, api_function) class URLRouter(HTTPRouter): """Provides a chainable router that can be used to route a URL to a Python function""" __slots__ = () def __init__(self, urls=None, accept=HTTP_METHODS, output=None, examples=(), versions=None, suffixes=(), prefixes=(), response_headers=None, parse_body=True, **kwargs): super().__init__(output=output, versions=versions, parse_body=parse_body, response_headers=response_headers, **kwargs) if urls is not None: self.route['urls'] = (urls, ) if isinstance(urls, str) else urls if accept: self.route['accept'] = (accept, ) if isinstance(accept, str) else accept if examples: self.route['examples'] = (examples, ) if isinstance(examples, str) else examples if suffixes: self.route['suffixes'] = (suffixes, ) if isinstance(suffixes, str) else suffixes if prefixes: self.route['prefixes'] = (prefixes, ) if isinstance(prefixes, str) else prefixes def __call__(self, api_function): api = self.route.get('api', hug.api.from_object(api_function)) api.http.routes.setdefault(api.http.base_url, OrderedDict()) (interface, callable_method) = self._create_interface(api, api_function) use_examples = self.route.get('examples', ()) if not interface.required and not use_examples: use_examples = (True, ) for base_url in self.route.get('urls', ("/{0}".format(api_function.__name__), )): expose = [base_url, ] for suffix in self.route.get('suffixes', ()): if suffix.startswith('/'): expose.append(os.path.join(base_url, suffix.lstrip('/'))) else: expose.append(base_url + suffix) for prefix in self.route.get('prefixes', ()): expose.append(prefix + base_url) for url in expose: handlers = api.http.routes[api.http.base_url].setdefault(url, {}) for method in self.route.get('accept', ()): version_mapping = handlers.setdefault(method.upper(), {}) for version in self.route['versions']: version_mapping[version] = interface api.http.versioned.setdefault(version, {})[callable_method.__name__] = callable_method interface.examples = use_examples return callable_method def urls(self, *urls, **overrides): """Sets the URLs that will map to this API call""" return self.where(urls=urls, **overrides) def accept(self, *accept, **overrides): """Sets a list of HTTP methods this router should accept""" return self.where(accept=accept, **overrides) def get(self, urls=None, **overrides): """Sets the acceptable HTTP method to a GET""" if urls is not None: overrides['urls'] = urls return self.where(accept='GET', **overrides) def delete(self, urls=None, **overrides): """Sets the acceptable HTTP method to DELETE""" if urls is not None: overrides['urls'] = urls return self.where(accept='DELETE', **overrides) def post(self, urls=None, **overrides): """Sets the acceptable HTTP method to POST""" if urls is not None: overrides['urls'] = urls return self.where(accept='POST', **overrides) def put(self, urls=None, **overrides): """Sets the acceptable HTTP method to PUT""" if urls is not None: overrides['urls'] = urls return self.where(accept='PUT', **overrides) def trace(self, urls=None, **overrides): """Sets the acceptable HTTP method to TRACE""" if urls is not None: overrides['urls'] = urls return self.where(accept='TRACE', **overrides) def patch(self, urls=None, **overrides): """Sets the acceptable HTTP method to PATCH""" if urls is not None: overrides['urls'] = urls return self.where(accept='PATCH', **overrides) def options(self, urls=None, **overrides): """Sets the acceptable HTTP method to OPTIONS""" if urls is not None: overrides['urls'] = urls return self.where(accept='OPTIONS', **overrides) def head(self, urls=None, **overrides): """Sets the acceptable HTTP method to HEAD""" if urls is not None: overrides['urls'] = urls return self.where(accept='HEAD', **overrides) def connect(self, urls=None, **overrides): """Sets the acceptable HTTP method to CONNECT""" if urls is not None: overrides['urls'] = urls return self.where(accept='CONNECT', **overrides) def call(self, **overrides): """Sets the acceptable HTTP method to all known""" return self.where(accept=HTTP_METHODS, **overrides) def http(self, **overrides): """Sets the acceptable HTTP method to all known""" return self.where(accept=HTTP_METHODS, **overrides) def get_post(self, **overrides): """Exposes a Python method externally under both the HTTP POST and GET methods""" return self.where(accept=('GET', 'POST'), **overrides) def put_post(self, **overrides): """Exposes a Python method externally under both the HTTP POST and PUT methods""" return self.where(accept=('PUT', 'POST'), **overrides) def examples(self, *examples, **overrides): """Sets the examples that the route should use""" return self.where(examples=examples, **overrides) def suffixes(self, *suffixes, **overrides): """Sets the suffixes supported by the route""" return self.where(suffixes=suffixes, **overrides) def prefixes(self, *prefixes, **overrides): """Sets the prefixes supported by the route""" return self.where(prefixes=prefixes, **overrides) def where(self, **overrides): if 'urls' in overrides: existing_urls = self.route.get('urls', ()) use_urls = [] for url in (overrides['urls'], ) if isinstance(overrides['urls'], str) else overrides['urls']: if url.startswith('/') or not existing_urls: use_urls.append(url) else: for existing in existing_urls: use_urls.append(urljoin(existing.rstrip('/') + '/', url)) overrides['urls'] = tuple(use_urls) return super().where(**overrides)

43.393214

120

0.645814

69f81adbb32343db474b1b0c44099404db6431dd

1,017

py

Python

designing-restful-apis/Lesson_4/13_BargainMart/Starter Code/hungryclient.py

robinl3680/udacity-course

308daf62479f9bf6f4256eb19313631f1bb4c5da

[ "MIT" ]

68

2016-07-28T07:24:57.000Z

2021-10-09T19:28:48.000Z

designing-restful-apis/Lesson_4/13_BargainMart/Starter Code/hungryclient.py

robinl3680/udacity-course

308daf62479f9bf6f4256eb19313631f1bb4c5da

[ "MIT" ]

1

2022-03-12T01:01:42.000Z

designing-restful-apis/Lesson_4/13_BargainMart/Starter Code/hungryclient.py

robinl3680/udacity-course

308daf62479f9bf6f4256eb19313631f1bb4c5da

[ "MIT" ]

105

2016-10-19T03:56:33.000Z

2022-03-15T02:12:08.000Z

from __future__ import division from time import sleep import json import httplib2 h = httplib2.Http() url = raw_input("Please enter the uri you want to access, \n If left blank the connection will be set to 'http://localhost:5000/catalog': ") if url == '': url = 'http://localhost:5000/catalog' req_per_minute = float(raw_input("Please specify the number of requests per minute: ") ) interval = (60.0 / req_per_minute) def SendRequests(url, req_per_minute): requests = 0 while requests < req_per_minute: result = json.loads(h.request(url,'GET')[1]) #result = h.request(url,'GET')[1] #print result if result.get('error') is not None: print "Error #%s : %s" %(result.get('error'), result.get('data')) print "Hit rate limit. Waiting 5 seconds and trying again..." sleep(5) SendRequests(url, req_per_minute) else: print "Number of Requests: ", requests+1 print result requests = requests + 1 sleep(interval) print "Sending Requests..." SendRequests(url, req_per_minute)

27.486486

142

0.701082

48686733444b8b26da69768b70182d397dd5f102

329

py

Python

xd/build/core/data/num.py

esben/xd-build-core

b1f805d4a50167f4c54003ffa8f33d224d4c819d

[ "MIT" ]

1

2020-11-27T23:34:53.000Z

xd/build/core/data/num.py

esben/xd-build-core

b1f805d4a50167f4c54003ffa8f33d224d4c819d

[ "MIT" ]

6

2015-10-30T12:22:56.000Z

2016-08-25T09:38:48.000Z

xd/build/core/data/num.py

XD-embedded/xd-build-core

357e4d78d35456d6906aa30151ddc989781227ab

[ "MIT" ]

null

import logging log = logging.getLogger(__name__) log.setLevel(logging.INFO) from .var import * __all__ = ['Bool', 'Int', 'Float'] class Bool(Variable): __slots__ = [] basetype = bool class Int(Variable): __slots__ = [] basetype = int class Float(Variable): __slots__ = [] basetype = float

10.612903

34

0.632219

9d8773c7a917fd3a30407a178f62eaf15bb45aab

1,574

py

Python

setup.py

Galarzaa90/GuildWatcher

2ec08ebb7a500a332962e6d78b8d175b842959a2

[ "MIT" ]

5

2017-05-01T20:37:10.000Z

2020-06-04T20:41:38.000Z

setup.py

Galarzaa90/GuildWatcher

2ec08ebb7a500a332962e6d78b8d175b842959a2

[ "MIT" ]

7

2018-05-08T20:56:49.000Z

2021-02-22T20:38:06.000Z

setup.py

Galarzaa90/GuildWatcher

2ec08ebb7a500a332962e6d78b8d175b842959a2

[ "MIT" ]

4

2017-09-02T05:10:02.000Z

2018-02-22T01:41:24.000Z

import sys from setuptools import setup if sys.version_info < (3, 6): sys.exit('Sorry, Python < 3.6 is not supported') with open('requirements.txt') as f: requirements = f.read().splitlines() with open('README.md') as f: readme = f.read() setup( name='guildwatcher', version='2.0.0', author='Allan Galarza', author_email="allan.galarza@gmail.com", description='A discord webhook to track Tibia guild changes.', long_description=readme, long_description_content_type="text/markdown", license="MIT", url='https://github.com/Galarzaa90/GuildWatcher', py_modules=['guildwatcher'], install_requires=requirements, entry_points=''' [console_scripts] guildwatcher=guildwatcher:scan_guilds ''', project_urls={ "Coverage: Codecov": "https://codecov.io/gh/Galarzaa90/GuildWatcher/", }, python_requires=">=3.6", include_package_data=True, classifiers=[ 'Development Status :: 5 - Production/Stable', 'Environment :: Console', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Topic :: Communications :: Chat', 'Topic :: Games/Entertainment', 'Topic :: Games/Entertainment :: Role-Playing' ] )

31.48

78

0.628971

a6c60ee2e988ec4e7f6c0eb606c5bab98f0814dc

15,803

py

Python

mathics/builtin/attributes.py

ptjb/Mathics

c44678e73ce753e5ce3959cdca18ec983f05716d

[ "Apache-2.0" ]

null

mathics/builtin/attributes.py

ptjb/Mathics

c44678e73ce753e5ce3959cdca18ec983f05716d

[ "Apache-2.0" ]

null

mathics/builtin/attributes.py

ptjb/Mathics

c44678e73ce753e5ce3959cdca18ec983f05716d

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- r""" Attributes There are several builtin-attributes which have a predefined meaning in \Mathics. However, you can set any symbol as an attribute, in contrast to \Mathematica. """ from mathics.builtin.base import Predefined, Builtin from mathics.builtin.evaluation import Sequence from mathics.core.expression import Expression, Symbol, SymbolNull, String from mathics.builtin.assignment import get_symbol_list class Attributes(Builtin): """ <dl> <dt>'Attributes'[$symbol$] <dd>returns the attributes of $symbol$. <dt>'Attributes'[$symbol$] = {$attr1$, $attr2$} <dd>sets the attributes of $symbol$, replacing any existing attributes. </dl> >> Attributes[Plus] = {Flat, Listable, NumericFunction, OneIdentity, Orderless, Protected} 'Attributes' always considers the head of an expression: >> Attributes[a + b + c] = {Flat, Listable, NumericFunction, OneIdentity, Orderless, Protected} You can assign values to 'Attributes' to set attributes: >> Attributes[f] = {Flat, Orderless} = {Flat, Orderless} >> f[b, f[a, c]] = f[a, b, c] Attributes must be symbols: >> Attributes[f] := {a + b} : Argument a + b at position 1 is expected to be a symbol. = $Failed Use 'Symbol' to convert strings to symbols: >> Attributes[f] = Symbol["Listable"] = Listable >> Attributes[f] = {Listable} """ attributes = ("HoldAll", "Listable") def apply(self, expr, evaluation): "Attributes[expr_]" name = expr.get_lookup_name() attributes = list(evaluation.definitions.get_attributes(name)) attributes.sort() attr = [Symbol(attribute) for attribute in attributes] return Expression("List", *attr) class SetAttributes(Builtin): """ <dl> <dt>'SetAttributes'[$symbol$, $attrib$] <dd>adds $attrib$ to the list of $symbol$'s attributes. </dl> >> SetAttributes[f, Flat] >> Attributes[f] = {Flat} Multiple attributes can be set at the same time using lists: >> SetAttributes[{f, g}, {Flat, Orderless}] >> Attributes[g] = {Flat, Orderless} """ attributes = ("HoldFirst",) def apply(self, symbols, attributes, evaluation): "SetAttributes[symbols_, attributes_]" symbols = get_symbol_list( symbols, lambda item: evaluation.message("SetAttributes", "sym", item, 1) ) if symbols is None: return values = get_symbol_list( attributes, lambda item: evaluation.message("SetAttributes", "sym", item, 2) ) if values is None: return for symbol in symbols: if "System`Locked" in evaluation.definitions.get_attributes(symbol): evaluation.message("SetAttributes", "locked", Symbol(symbol)) else: for value in values: evaluation.definitions.set_attribute(symbol, value) return SymbolNull class ClearAttributes(Builtin): """ <dl> <dt>'ClearAttributes'[$symbol$, $attrib$] <dd>removes $attrib$ from $symbol$'s attributes. </dl> >> SetAttributes[f, Flat] >> Attributes[f] = {Flat} >> ClearAttributes[f, Flat] >> Attributes[f] = {} Attributes that are not even set are simply ignored: >> ClearAttributes[{f}, {Flat}] >> Attributes[f] = {} """ attributes = ("HoldFirst",) def apply(self, symbols, attributes, evaluation): "ClearAttributes[symbols_, attributes_]" symbols = get_symbol_list( symbols, lambda item: evaluation.message("ClearAttributes", "sym", item, 1) ) if symbols is None: return values = get_symbol_list( attributes, lambda item: evaluation.message("ClearAttributes", "sym", item, 2), ) if values is None: return for symbol in symbols: if "System`Locked" in evaluation.definitions.get_attributes(symbol): evaluation.message("ClearAttributes", "locked", Symbol(symbol)) else: for value in values: evaluation.definitions.clear_attribute(symbol, value) return SymbolNull class Protect(Builtin): """ <dl> <dt>'Protect'[$s1$, $s2$, ...] <dd>sets the attribute 'Protected' for the symbols $si$. <dt>'Protect'[$str1$, $str2$, ...] <dd>protects all symbols whose names textually match $stri$. </dl> >> A = {1, 2, 3}; >> Protect[A] >> A[[2]] = 4; : Symbol A is Protected. >> A = {1, 2, 3} """ attributes = ("HoldAll",) messages = { "ssym": "`1` is not a symbol or a string.", } def apply(self, symbols, evaluation): "Protect[symbols___]" protected = Symbol("System`Protected") items = [] if isinstance(symbols, Symbol): symbols = [symbols] elif isinstance(symbols, String): symbols = [symbols] elif isinstance(symbols, Expression): if symbols.get_head_name() in ("System`Sequence", "System`List"): symbols = symbols.get_leaves() else: evaluation.message("Protect", "ssym", symbols) return SymbolNull for symbol in symbols: if isinstance(symbol, Symbol): items.append(symbol) else: pattern = symbol.get_string_value() if not pattern or pattern == "": evaluation.message("Protect", "ssym", symbol) continue if pattern[0] == "`": pattern = evaluation.definitions.get_current_context() + pattern[1:] names = evaluation.definitions.get_matching_names(pattern) for defn in names: symbol = Symbol(defn) if not "System`Locked" in evaluation.definitions.get_attributes( defn ): items.append(symbol) Expression("SetAttributes", Expression("List", *items), protected).evaluate( evaluation ) return SymbolNull class Unprotect(Builtin): """ <dl> <dt>'Unprotect'[$s1$, $s2$, ...] <dd>removes the attribute 'Protected' for the symbols $si$. <dt>'Unprotect'[$str$] <dd>unprotects symbols whose names textually match $str$. </dl> """ attributes = ("HoldAll",) messages = { "ssym": "`1` is not a symbol or a string.", } def apply(self, symbols, evaluation): "Unprotect[symbols___]" protected = Symbol("System`Protected") items = [] if isinstance(symbols, Symbol): symbols = [symbols] elif isinstance(symbols, Expression): symbols = symbols.get_leaves() elif isinstance(symbols, String): symbols = [symbols] else: symbols = symbols.get_sequence() for symbol in symbols: if isinstance(symbol, Symbol): items.append(symbol) else: pattern = symbol.get_string_value() if not pattern or pattern == "": evaluation.message("Unprotect", "ssym", symbol) continue if pattern[0] == "`": pattern = evaluation.definitions.get_current_context() + pattern[1:] names = evaluation.definitions.get_matching_names(pattern) for defn in names: symbol = Symbol(defn) if not "System`Locked" in evaluation.definitions.get_attributes( defn ): items.append(symbol) Expression("ClearAttributes", Expression("List", *items), protected).evaluate( evaluation ) return SymbolNull class Protected(Predefined): """ <dl> <dt>'Protected' <dd>is an attribute that prevents values on a symbol from being modified. </dl> Values of 'Protected' symbols cannot be modified: >> Attributes[p] = {Protected}; >> p = 2; : Symbol p is Protected. >> f[p] ^= 3; : Tag p in f[p] is Protected. >> Format[p] = "text"; : Symbol p is Protected. However, attributes might still be set: >> SetAttributes[p, Flat] >> Attributes[p] = {Flat, Protected} Thus, you can easily remove the attribute 'Protected': >> Attributes[p] = {}; >> p = 2 = 2 You can also use 'Protect' or 'Unprotect', resp. >> Protect[p] >> Attributes[p] = {Protected} >> Unprotect[p] If a symbol is 'Protected' and 'Locked', it can never be changed again: >> SetAttributes[p, {Protected, Locked}] >> p = 2 : Symbol p is Protected. = 2 >> Unprotect[p] : Symbol p is locked. """ class ReadProtected(Predefined): """ <dl> <dt>'ReadProtected' <dd>is an attribute that prevents values on a symbol from being read. </dl> Values associated with 'ReadProtected' symbols cannot be seen in 'Definition': >> ClearAll[p] >> p = 3; >> Definition[p] = p = 3 >> SetAttributes[p, ReadProtected] >> Definition[p] = Attributes[p] = {ReadProtected} """ class Locked(Predefined): """ <dl> <dt>'Locked' <dd>is an attribute that prevents attributes on a symbol from being modified. </dl> The attributes of 'Locked' symbols cannot be modified: >> Attributes[lock] = {Flat, Locked}; >> SetAttributes[lock, {}] : Symbol lock is locked. >> ClearAttributes[lock, Flat] : Symbol lock is locked. >> Attributes[lock] = {} : Symbol lock is locked. = {} >> Attributes[lock] = {Flat, Locked} However, their values might be modified (as long as they are not 'Protected' too): >> lock = 3 = 3 """ class Flat(Predefined): """ <dl> <dt>'Flat' <dd>is an attribute that specifies that nested occurrences of a function should be automatically flattened. </dl> A symbol with the 'Flat' attribute represents an associative mathematical operation: >> SetAttributes[f, Flat] >> f[a, f[b, c]] = f[a, b, c] 'Flat' is taken into account in pattern matching: >> f[a, b, c] /. f[a, b] -> d = f[d, c] #> SetAttributes[{u, v}, Flat] #> u[x_] := {x} #> u[] = u[] #> u[a] = {a} #> u[a, b] : Iteration limit of 1000 exceeded. = $Aborted #> u[a, b, c] : Iteration limit of 1000 exceeded. = $Aborted #> v[x_] := x #> v[] = v[] #> v[a] = a #> v[a, b] (* in Mathematica: Iteration limit of 4096 exceeded. *) = v[a, b] #> v[a, b, c] (* in Mathematica: Iteration limit of 4096 exceeded. *) : Iteration limit of 1000 exceeded. = $Aborted """ class Orderless(Predefined): """<dl> <dt>'Orderless' <dd>is an attribute that can be assigned to a symbol $f$ to indicate that the elements $ei$ in expressions of the form $f$[$e1$, $e2$, ...] should automatically be sorted into canonical order. This property is accounted for in pattern matching. </dl> The leaves of an 'Orderless' function are automatically sorted: >> SetAttributes[f, Orderless] >> f[c, a, b, a + b, 3, 1.0] = f[1., 3, a, b, c, a + b] A symbol with the 'Orderless' attribute represents a commutative mathematical operation. >> f[a, b] == f[b, a] = True 'Orderless' affects pattern matching: >> SetAttributes[f, Flat] >> f[a, b, c] /. f[a, c] -> d = f[b, d] """ class OneIdentity(Predefined): """ <dl> <dt>'OneIdentity' <dd>is an attribute specifying that '$f$[$x$]' should be treated as equivalent to $x$ in pattern matching. </dl> 'OneIdentity' affects pattern matching: >> SetAttributes[f, OneIdentity] >> a /. f[args___] -> {args} = {a} It does not affect evaluation: >> f[a] = f[a] """ class SequenceHold(Predefined): """ <dl> <dt>'SequenceHold' <dd>is an attribute that prevents 'Sequence' objects from being spliced into a function's arguments. </dl> Normally, 'Sequence' will be spliced into a function: >> f[Sequence[a, b]] = f[a, b] It does not for 'SequenceHold' functions: >> SetAttributes[f, SequenceHold] >> f[Sequence[a, b]] = f[Sequence[a, b]] E.g., 'Set' has attribute 'SequenceHold' to allow assignment of sequences to variables: >> s = Sequence[a, b]; >> s = Sequence[a, b] >> Plus[s] = a + b """ class HoldFirst(Predefined): """ <dl> <dt>'HoldFirst' <dd>is an attribute specifying that the first argument of a function should be left unevaluated. </dl> >> Attributes[Set] = {HoldFirst, Protected, SequenceHold} """ class HoldRest(Predefined): """ <dl> <dt>'HoldRest' <dd>is an attribute specifying that all but the first argument of a function should be left unevaluated. </dl> >> Attributes[If] = {HoldRest, Protected} """ class HoldAll(Predefined): """ <dl> <dt>'HoldAll' <dd>is an attribute specifying that all arguments of a function should be left unevaluated. </dl> >> Attributes[Function] = {HoldAll, Protected} """ class HoldAllComplete(Predefined): """ <dl> <dt>'HoldAllComplete' <dd>is an attribute that includes the effects of 'HoldAll' and 'SequenceHold', and also protects the function from being affected by the upvalues of any arguments. </dl> 'HoldAllComplete' even prevents upvalues from being used, and includes 'SequenceHold'. >> SetAttributes[f, HoldAllComplete] >> f[a] ^= 3; >> f[a] = f[a] >> f[Sequence[a, b]] = f[Sequence[a, b]] """ class NHoldAll(Predefined): """ <dl> <dt>'NHoldAll' <dd>is an attribute that protects all arguments of a function from numeric evaluation. </dl> >> N[f[2, 3]] = f[2., 3.] >> SetAttributes[f, NHoldAll] >> N[f[2, 3]] = f[2, 3] """ class NHoldFirst(Predefined): """ <dl> <dt>'NHoldFirst' <dd>is an attribute that protects the first argument of a function from numeric evaluation. </dl> """ class NHoldRest(Predefined): """ <dl> <dt>'NHoldRest' <dd>is an attribute that protects all but the first argument of a function from numeric evaluation. </dl> """ class Listable(Predefined): """ <dl> <dt>'Listable' <dd>is an attribute specifying that a function should be automatically applied to each element of a list. </dl> >> SetAttributes[f, Listable] >> f[{1, 2, 3}, {4, 5, 6}] = {f[1, 4], f[2, 5], f[3, 6]} >> f[{1, 2, 3}, 4] = {f[1, 4], f[2, 4], f[3, 4]} >> {{1, 2}, {3, 4}} + {5, 6} = {{6, 7}, {9, 10}} """ class Constant(Predefined): """ <dl> <dt>'Constant' <dd>is an attribute that indicates that a symbol is a constant. </dl> Mathematical constants like 'E' have attribute 'Constant': >> Attributes[E] = {Constant, Protected, ReadProtected} Constant symbols cannot be used as variables in 'Solve' and related functions: >> Solve[x + E == 0, E] : E is not a valid variable. = Solve[E + x == 0, E] """

26.559664

91

0.563627

fc60677ffde2308d0c6acb29de3b5a3bd690cf71

4,235

py

Python

malaya/text/rake.py

DevconX/Malaya

a2e7030f0911d65c9c1c72d38bc3e7c53b8e06fc

[ "MIT" ]

39

2018-03-12T04:26:42.000Z

2018-12-05T03:53:45.000Z

malaya/text/rake.py

DevconX/Malaya

a2e7030f0911d65c9c1c72d38bc3e7c53b8e06fc

[ "MIT" ]

12

2018-10-01T07:28:23.000Z

2018-12-10T01:59:25.000Z

malaya/text/rake.py

DevconX/Malaya

a2e7030f0911d65c9c1c72d38bc3e7c53b8e06fc

[ "MIT" ]

16

2018-03-16T05:46:12.000Z

2018-12-10T04:15:07.000Z

# Implementation of RAKE - Rapid Automtic Keyword Exraction algorithm # as described in: # Rose, S., D. Engel, N. Cramer, and W. Cowley (2010). # Automatic keyword extraction from indi-vidual documents. # In M. W. Berry and J. Kogan (Eds.), Text Mining: Applications and # Theory.unknown: John Wiley and Sons, Ltd. import re debug = False test = True def is_number(s): try: float(s) if '.' in s else int(s) return True except ValueError: return False def load_stop_words(stop_word_file): """ Utility function to load stop words from a file and return as a list of words @param stop_word_file Path and file name of a file containing stop words. @return list A list of stop words. """ stop_words = [] for line in open(stop_word_file): if line.strip()[0:1] != '#': for word in line.split(): # in case more than one per line stop_words.append(word) return stop_words def separate_words(text, min_word_return_size): """ Utility function to return a list of all words that are have a length greater than a specified number of characters. @param text The text that must be split in to words. @param min_word_return_size The minimum no of characters a word must have to be included. """ splitter = re.compile('[^a-zA-Z0-9_\\+\\-/]') words = [] for single_word in splitter.split(text): current_word = single_word.strip() # leave numbers in phrase, but don't count as words, since they tend to # invalidate scores of their phrases if ( len(current_word) > min_word_return_size and current_word != '' and not is_number(current_word) ): words.append(current_word) return words def split_sentences(text): """ Utility function to return a list of sentences. @param text The text that must be split in to sentences. """ sentence_delimiters = re.compile( u'[.!?,;:\t\\\\"\$\$\\\'\u2019\u2013]|\\s\\-\\s' ) sentences = sentence_delimiters.split(text) return sentences def build_stop_word_regex(stop_word_file_path): stop_word_list = load_stop_words(stop_word_file_path) stop_word_regex_list = [] for word in stop_word_list: word_regex = r'\b' + word + r'(?![\w-])' stop_word_regex_list.append(word_regex) stop_word_pattern = re.compile( '|'.join(stop_word_regex_list), re.IGNORECASE ) return stop_word_pattern def generate_candidate_keywords(sentence_list, stopword_pattern): phrase_list = [] for s in sentence_list: tmp = re.sub(stopword_pattern, '|', s.strip()) phrases = tmp.split('|') for phrase in phrases: phrase = phrase.strip() if phrase != '': phrase_list.append(phrase) return phrase_list def calculate_word_scores(phraseList, attentions=None): word_frequency = {} word_degree = {} for phrase in phraseList: word_list = separate_words(phrase, 0) word_list_length = len(word_list) word_list_degree = word_list_length - 1 for word in word_list: word_frequency.setdefault(word, 0) if attentions: score = attentions.get(word, 0) else: score = 1 word_frequency[word] += score word_degree.setdefault(word, 0) word_degree[word] += word_list_degree for item in word_frequency: word_degree[item] = word_degree[item] + word_frequency[item] # Calculate Word scores = deg(w)/frew(w) word_score = {} for item in word_frequency: word_score.setdefault(item, 0) word_score[item] = word_degree[item] / (word_frequency[item] * 1.0) return word_score def generate_candidate_keyword_scores(phrase_list, word_score): keyword_candidates = {} for phrase in phrase_list: keyword_candidates.setdefault(phrase, 0) word_list = separate_words(phrase, 0) candidate_score = 0 for word in word_list: candidate_score += word_score[word] keyword_candidates[phrase] = candidate_score return keyword_candidates

32.576923

120

0.644392

417425e6e95b15315e52c1840f79246098008dc7

3,631

py

Python

examples/tf/rl2_ppo_metaworld_ml10.py

michahu/garage

c045a1e5e5088a18828ec48bfee0addb1943bfd4

[ "MIT" ]

null

examples/tf/rl2_ppo_metaworld_ml10.py

michahu/garage

c045a1e5e5088a18828ec48bfee0addb1943bfd4

[ "MIT" ]

null

examples/tf/rl2_ppo_metaworld_ml10.py

michahu/garage

c045a1e5e5088a18828ec48bfee0addb1943bfd4

[ "MIT" ]

null

#!/usr/bin/env python3 """Example script to run RL2 in ML10.""" # pylint: disable=no-value-for-parameter # yapf: disable import click import metaworld from garage import wrap_experiment from garage.envs import MetaWorldSetTaskEnv from garage.experiment import (MetaEvaluator, MetaWorldTaskSampler, SetTaskSampler) from garage.experiment.deterministic import set_seed from garage.np.baselines import LinearFeatureBaseline from garage.sampler import LocalSampler from garage.tf.algos import RL2PPO from garage.tf.algos.rl2 import RL2Env, RL2Worker from garage.tf.policies import GaussianGRUPolicy from garage.trainer import TFTrainer # yapf: enable @click.command() @click.option('--seed', default=1) @click.option('--meta_batch_size', default=10) @click.option('--n_epochs', default=10) @click.option('--episode_per_task', default=10) @wrap_experiment def rl2_ppo_metaworld_ml10(ctxt, seed, meta_batch_size, n_epochs, episode_per_task): """Train RL2 PPO with ML10 environment. Args: ctxt (garage.experiment.ExperimentContext): The experiment configuration used by Trainer to create the snapshotter. seed (int): Used to seed the random number generator to produce determinism. meta_batch_size (int): Meta batch size. n_epochs (int): Total number of epochs for training. episode_per_task (int): Number of training episode per task. """ set_seed(seed) with TFTrainer(snapshot_config=ctxt) as trainer: ml10 = metaworld.ML10() tasks = MetaWorldTaskSampler(ml10, 'train', lambda env, _: RL2Env(env)) test_task_sampler = SetTaskSampler(MetaWorldSetTaskEnv, env=MetaWorldSetTaskEnv( ml10, 'test'), wrapper=lambda env, _: RL2Env(env)) meta_evaluator = MetaEvaluator(test_task_sampler=test_task_sampler) env_updates = tasks.sample(10) env = env_updates[0]() env_spec = env.spec policy = GaussianGRUPolicy(name='policy', hidden_dim=64, env_spec=env_spec, state_include_action=False) baseline = LinearFeatureBaseline(env_spec=env_spec) algo = RL2PPO(meta_batch_size=meta_batch_size, task_sampler=tasks, env_spec=env_spec, policy=policy, baseline=baseline, discount=0.99, gae_lambda=0.95, lr_clip_range=0.2, optimizer_args=dict(batch_size=32, max_optimization_epochs=10), stop_entropy_gradient=True, entropy_method='max', policy_ent_coeff=0.02, center_adv=False, meta_evaluator=meta_evaluator, episodes_per_trial=episode_per_task) trainer.setup(algo, tasks.sample(meta_batch_size), sampler_cls=LocalSampler, n_workers=meta_batch_size, worker_class=RL2Worker, worker_args=dict(n_episodes_per_trial=episode_per_task)) trainer.train(n_epochs=n_epochs, batch_size=episode_per_task * env_spec.max_episode_length * meta_batch_size) rl2_ppo_metaworld_ml10()

38.62766

79

0.595153

5b6072163a85260bd478e300d75a5ebf011af9cf

256

py

Python

source/cornish/version.py

demitri/cornish

b1f37feac38edaab5543bcb8ccbd0aa26b87456d

[ "MIT" ]

1

2020-11-10T18:59:00.000Z

source/cornish/version.py

demitri/cornish

b1f37feac38edaab5543bcb8ccbd0aa26b87456d

[ "MIT" ]

2

2020-08-03T22:52:46.000Z

2020-08-13T19:09:48.000Z

source/cornish/version.py

demitri/cornish

b1f37feac38edaab5543bcb8ccbd0aa26b87456d

[ "MIT" ]

1

2020-02-23T19:17:20.000Z

# Store the version here so: # 1) we don't load dependencies by storing it in __init__.py # 2) we can import it in setup.py for the same reason # 3) we can import it into your module __version__ = '1.1' # Ref: https://stackoverflow.com/a/16084844/2712652

32

60

0.730469

9c5078ea6e6f0469763693f508e041144a64fbae

4,208

py

Python

users/views.py

oss2019/crisp.ai

95fe0f0e09386b97037db6d790623394c8a81dc8

[ "MIT" ]

7

2019-05-07T17:31:57.000Z

2021-07-06T15:08:14.000Z

users/views.py

oss2019/crisp.ai

95fe0f0e09386b97037db6d790623394c8a81dc8

[ "MIT" ]

60

2019-05-04T08:52:37.000Z

2022-03-11T23:53:25.000Z

users/views.py

oss2019/crisp.ai

95fe0f0e09386b97037db6d790623394c8a81dc8

[ "MIT" ]

21

2019-04-12T14:31:54.000Z

2019-09-29T09:51:20.000Z

from django.shortcuts import render, redirect from django.contrib.auth import authenticate, login from django.core.mail import send_mail from django.forms import ValidationError from django.contrib.auth.models import User from django.contrib.sites.shortcuts import get_current_site from django.template.loader import render_to_string from django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode from django.utils.encoding import force_bytes, force_text from .tokens import account_activation_token from crispy_ai.settings import EMAIL_HOST_USER from django.contrib.auth.decorators import login_required from .forms import UserRegisterForm, UserUpdateForm, ProfileUpdateForm from validate_email import validate_email from django.contrib import messages from admin.models import CourseModel, LectureModel @login_required(login_url="/users/login/") def home(request): if request.user.is_superuser: return redirect('admin_home') courses = CourseModel.objects.all() return render(request, './users/home.html', {'courses': courses, 'user': request.user}) def register_user(request): if request.method == 'POST': form = UserRegisterForm(request.POST) if form.is_valid(): email = form.cleaned_data.get('email') user = form.save(commit=False) user.is_active = False user.save() current_site = get_current_site(request) subject = 'Activate Your Crispy AI Account' message = render_to_string('registration/account_activation_email.html', { 'user': user, 'domain': current_site.domain, 'uid': urlsafe_base64_encode(force_bytes(user.pk)), 'token': account_activation_token.make_token(user), }) from_mail = EMAIL_HOST_USER to_mail = [user.email] send_mail(subject, message, from_mail, to_mail, fail_silently=False) return render(request, 'registration/email_sent.html') else: form = UserRegisterForm() return render(request, 'registration/register.html', {'form': form}) # when user click on email link then this function execute def activate(request, uidb64, token): try: uid = force_text(urlsafe_base64_decode(uidb64)) user = User.objects.get(pk=uid) except (TypeError, ValueError, OverflowError, User.DoesNotExist): user = None if user is not None and account_activation_token.check_token(user, token): user.is_active = True user.save() return render(request, 'registration/account_activated.html') else: return render(request, 'registration/account_activation_invalid.html') @login_required(login_url="/users/login/") def profile(request): if request.method == "POST": u_form = UserUpdateForm(request.POST, instance=request.user) p_form = ProfileUpdateForm(request.POST, request.FILES, instance=request.user.profilemodel) if u_form.is_valid() and p_form.is_valid(): u_form.save() p_form.save() messages.success(request, f'Profile updated successfully!') return redirect('profile') else: u_form = UserUpdateForm(instance=request.user) p_form = ProfileUpdateForm(instance=request.user.profilemodel) return render(request, 'users/profile.html', {'u_form': u_form, 'p_form': p_form, 'profile_pic_url': request.user.profilemodel.profile_image.url}) @login_required(login_url="/users/login/") def course_display(request): course = CourseModel.objects.first() if request.method == "POST": course_id = request.POST.get('course_id') course = CourseModel.objects.get(pk=course_id) lectures = LectureModel.objects.filter(course=course) return render(request, './users/course.html', {'course': course, 'lectures': lectures}) def lecture_display(request): lecture = LectureModel.objects.first() if request.method == "POST": lecture_id = request.POST.get('lecture_id') lecture = LectureModel.objects.get(pk=lecture_id) return render(request, './users/lecture.html', {'lecture': lecture})

40.461538

119

0.700095

49da520b3217dcc17773d251759759bdc542648a

669

py

Python

Symbol in Matrix.py

Lyubomir-Dakov/Python-Advanced

3b3b7181cc2bafc6f60329d6e42873d0f78b972f

[ "MIT" ]

null

Symbol in Matrix.py

Lyubomir-Dakov/Python-Advanced

3b3b7181cc2bafc6f60329d6e42873d0f78b972f

[ "MIT" ]

null

Symbol in Matrix.py

Lyubomir-Dakov/Python-Advanced

3b3b7181cc2bafc6f60329d6e42873d0f78b972f

[ "MIT" ]

null

# import sys # from io import StringIO # # input_1 = """3 # ABC # DEF # X!@ # ! # """ # input_2 = """4 # asdd # xczc # qwee # qefw # 4 # """ # # sys.stdin = StringIO(input_2) square_size = int(input()) matrix = [] for _ in range(square_size): row = input() matrix.append(row) symbol = input() found_symbol = False row = 0 for i in matrix: col = 0 for j in i: if matrix[row][col] == symbol: print(f"({row}, {col})") found_symbol = True col += 1 if found_symbol: break row += 1 if not found_symbol: print(f"{symbol} does not occur in the matrix")

15.204545

52

0.5142

560bdbf688bc946ada679732db81a14fce0669ea

2,461

py

Python

pingo/intel/intel.py

pingo-io/pingo-py

5d7081f99ff13973404dc6361560f30ce8f7009c

[ "MIT" ]

116

2015-05-06T17:49:22.000Z

2021-11-16T12:59:35.000Z

pingo/intel/intel.py

pingo-io/pingo-py

5d7081f99ff13973404dc6361560f30ce8f7009c

[ "MIT" ]

49

2015-05-08T23:18:05.000Z

2017-07-12T17:11:48.000Z

pingo/intel/intel.py

pingo-io/pingo-py

5d7081f99ff13973404dc6361560f30ce8f7009c

[ "MIT" ]

47

2015-05-04T07:42:04.000Z

2021-08-04T20:49:54.000Z

import pingo mraa = None class BaseMraa(pingo.Board, pingo.AnalogInputCapable, pingo.PwmOutputCapable): _import_error_msg = 'pingo.intel.BaseMraa requires mraa installed' def __init__(self): global mraa try: import mraa as mraa except ImportError: raise ImportError(self._import_error_msg) super(Galileo2, self).__init__() self.PIN_MODES = { pingo.IN: mraa.DIR_IN, pingo.OUT: mraa.DIR_OUT, } self.PIN_STATES = { pingo.HIGH: 1, pingo.LOW: 0, } pwm_pin_numbers = [3, 5, 6, 9, 10, 11, 13] digital_pin_numbers = [1, 2, 4, 7, 8, 12] self._add_pins( [pingo.PwmPin(self, location) for location in pwm_pin_numbers] + [pingo.DigitalPin(self, location) for location in digital_pin_numbers] + [pingo.AnalogPin(self, 'A' + location, 12) for location in '012345'] ) self.mraa_pins, self.mraa_analogs, self.mraa_pwms = {}, {}, {} def _set_digital_mode(self, pin, mode): if pin.mode == pingo.PWM: self.mraa_pwms[pin.location].enable(False) self.mraa_pins[pin.location] = mraa.Gpio(pin.location) self.mraa_pins[pin.location].dir(self.PIN_MODES[mode]) def _set_analog_mode(self, pin, mode): mraa_id = int(pin.location[1]) self.mraa_analogs[pin.location] = mraa.Aio(mraa_id) def _set_pwm_mode(self, pin, mode): if pin.mode == pingo.IN: self.mraa_pins[pin.location].dir(mraa.DIR_OUT) self.mraa_pwms[pin.location] = mraa.Pwm(pin.location) self.mraa_pwms[pin.location].enable(True) def _set_pin_state(self, pin, state): self.mraa_pins[pin.location].write(self.PIN_STATES[state]) def _get_pin_state(self, pin): value = self.mraa_pins[pin.location].read() return pingo.HIGH if value == 1 else pingo.LOW def _get_pin_value(self, pin): return self.mraa_analogs[pin.location].read() def _set_pwm_duty_cycle(self, pin, value): self.mraa_pwms[pin.location].write(value) def _set_pwm_frequency(self, pin, value): raise NotImplementedError class Galileo2(BaseMraa): _import_error_msg = 'pingo.intel.Galileo2 requires mraa installed' class Edison(BaseMraa): _import_error_msg = 'pingo.intel.Edison requires mraa installed'

29.297619

78

0.624137

8f25b3c9bfa38a3a07c72c28248c700fbdc95ada

30,285

py

Python

services/connectors/fronius-modbus-connector/source/connector/main.py

fzi-forschungszentrum-informatik/BEMCom

0a0c359d889c6d5975e4d4d3b17c24adb5bf883b

[ "MIT" ]

4

2021-09-10T09:46:18.000Z

2021-12-05T17:55:14.000Z

services/connectors/fronius-modbus-connector/source/connector/main.py

fzi-forschungszentrum-informatik/BEMCom

0a0c359d889c6d5975e4d4d3b17c24adb5bf883b

[ "MIT" ]

null

services/connectors/fronius-modbus-connector/source/connector/main.py

fzi-forschungszentrum-informatik/BEMCom

0a0c359d889c6d5975e4d4d3b17c24adb5bf883b

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ """ __version__="0.1.0" import os import json import struct import logging from time import sleep from fastnumbers import fast_real from dotenv import load_dotenv, find_dotenv from pymodbus.client.sync import ModbusTcpClient from pymodbus.exceptions import ModbusException from pymodbus.payload import BinaryPayloadBuilder from pymodbus.constants import Endian from pyconnector_template.pyconnector_template import SensorFlow as SFTemplate from pyconnector_template.pyconnector_template import ActuatorFlow as AFTemplate from pyconnector_template.pyconnector_template import Connector as CTemplate from pyconnector_template.dispatch import DispatchInInterval logger = logging.getLogger("pyconnector") class SensorFlow(SFTemplate): """ Bundles all functionality to handle sensor messages. This is a template for a SensorFlow class, i.e. one that holds all functions that are necessary to handle messages from the device(s) towards the message broker. The methods could also be implemented into the Connector class, but are seperated to support clarity. Overload these functions ------------------------ In order to transform this class into operational code you need to inherit from it and overload the following methods: - receive_raw_msg - parse_raw_msg Connector Methods ----------------- The connector must provide the following methods to allow correct operation of the methods in this class: - _update_available_datapoints Connector Attributes -------------------- The following attributes must be set up by the connector to allow these methods to run correctly: mqtt_client : class instance. Initialized Mqtt client library with signature of paho mqtt. SEND_RAW_MESSAGE_TO_DB : string if SEND_RAW_MESSAGE_TO_DB == "TRUE" will send raw message to designated DB via MQTT. MQTT_TOPIC_RAW_MESSAGE_TO_DB : string The topic which on which the raw messages will be published. datapoint_map : dict of dict. Mapping from datapoint key to topic. Is generated by the AdminUI. Looks e.e. like this: datapoint_map = { "sensor": { "Channel__P__value__0": "example-connector/msgs/0001", "Channel__P__unit__0": "example-connector/msgs/0002", }, "actuator": { "example-connector/msgs/0003": "Channel__P__setpoint__0", } } Note thereby that the keys "sensor" and "actuator"" must alaways be present, even if the child dicts are empty. """ def receive_raw_msg(self, raw_data=None): """ Receive raw data from Modbus maser device using. ModbusTCP. Poll the Modbus master device for the data specified in MODBUS_CONFIG. Parameters ---------- raw_data : TYPE, optional Not used, here as we poll for data. Kept for consistency. Returns ------- msg : dict The message object containing the raw unprocessed data. Should be formated like this: msg = { "payload": { "raw_message": <the raw data> } } E.g. msg = { "payload": { "raw_message": "device_1:{sensor_1:2.12,sensor_2:3.12}" } } """ if not hasattr(self, "modbus_connection"): # Establish connection to modbus master device. logger.debug( "Connecting to Modbus master %s:%s", *(self.modbus_master_ip, self.modbus_master_port) ) self.modbus_connection = ModbusTcpClient( host=self.modbus_master_ip, port=self.modbus_master_port ) if not self.modbus_connection.connect(): raise RuntimeError("Could not connect to Modbus master.") # Read all data requested in configuration. raw_message = {k: {} for k in self.modbus_read_method_names} for read_method_name in self.modbus_read_method_names: read_method = getattr(self.modbus_connection, read_method_name) requested_ranges = self.modbus_config[read_method_name] # requested_range is an entry like: # { # "address": 19000, # "count": 20, # "unit": 1, # "datatypes": ">ffffffffff", # }, for i, requested_range in enumerate(requested_ranges): logger.debug( "Using method %s to request data from address %s with " "count %s from unit %s.", *( read_method_name, requested_range["address"], requested_range["count"], requested_range["unit"], ) ) retry = 0 while True: response = read_method( address=requested_range["address"], count=requested_range["count"], unit=requested_range["unit"], ) if isinstance(response, BaseException) or response.isError(): # This track here is if the read failed. Then we wait # a bit and retry a few times before we finally fail. # If we retried to often we raise the execption and # exit. logger.info( "Reading from modbus device failed with function " " %s for address %s. Retrying in %s seconds. " "Error was: %s", *( read_method_name, requested_range["address"], self.retry_wait, str(response), ) ) retry += 1 if retry >= self.max_retries: raise RuntimeError( "Max number of retries exceeded.") sleep(self.retry_wait) continue # Pack the registers/coils into the raw message. elif "_registers" in read_method_name: raw_message[read_method_name][i] = response.registers else: raw_message[read_method_name][i] = response.bits break # Maybe wait a bit before next request. sleep(self.poll_break) if self.disconnect_between_polls: logger.debug("Disconnecting from Modbus Master.") self.modbus_connection.close() # This is required so we create a new connection next poll. delattr(self, "modbus_connection") msg = { "payload": { "raw_message": raw_message } } return msg def parse_raw_msg(self, raw_msg): """ Functionality to receive a raw message from device. Poll the device/gateway for data and transforms this raw data into the format epxected by run_sensor_flow. If the device/gateway uses some protocol that pushes data, the raw data should be passed as the raw_data argument to the function. Be aware: All keys in the output message should be strings. All values should be converted be strings, too. Parameters ---------- raw_msg : dict. Raw msg with data from device/gateway. Should be formated like: msg = { "payload": { "raw_message": <the raw data>, "timestamp": <milliseconds since epoch> } } Returns ------- msg : dict The message object containing the parsed data as python dicts from dicts strucuture. Should be formated like this: msg = { "payload": { "parsed_message": <the parsed data as object>, "timestamp": <milliseconds since epoch> } } E.g: msg = { "payload": { "parsed_message": { "device_1": { "sensor_1": "2.12", "sensor_2": "3.12" } }, "timestamp": 1573680749000 } } """ raw_message = raw_msg["payload"]["raw_message"] parsed_message = {} for read_method_name in raw_message: parsed_message[read_method_name] = {} modbus_config_for_method = self.modbus_config[read_method_name] mbas_for_method = self.modbus_addresses[read_method_name] for i in raw_message[read_method_name]: # Load modbus addresses as strings as other stuff is loaded # from JSON and expects strings too. mbas = [str(m) for m in mbas_for_method[i]] if "_registers" in read_method_name: registers = raw_message[read_method_name][i] datatypes = modbus_config_for_method[i]["datatypes"] # Now we going to to encode the registers (which are # currently represented as 16bit int values) to bytes so # we can decode the data back with the correct datatype. # This approach is taken from the pymodbus code, which # does the same but doesn't allow to decode all of the # values at once. values_b = b''.join(struct.pack('!H', x) for x in registers) try: values = struct.unpack(datatypes, values_b) except struct.error: logger.error( "Unpacking binary data with struct failed. " "The Modbus request returned %s registers, aka " "%s bytes. However, you may have configured " "datatypes in MODBUS_CONFIG corresponding to " "a different number of bytes, see struct.error " "below.", *(len(registers), len(registers)*2) ) raise else: values = [] for value in raw_message[read_method_name][i]: if value: values.append("1") else: values.append("0") # Store each value under it's Modbus address. # This may overwrite values if overlapping address # ranges have been specified by the user. # Also apply scaling factors while we are here, but only # to registers. Scaling bits doesn't make sense, even if the # user would request it. sfs = {} if ( "scaling_factors" in modbus_config_for_method[i] and "_registers" in read_method_name ): sfs = modbus_config_for_method[i]["scaling_factors"] for mba, value in zip(mbas, values): if mba in sfs: try: scaled_value = float(value) * sfs[mba] value = str(scaled_value) except ValueError: pass # All values are handled as strings in BEMCom. parsed_message[read_method_name][mba] = str(value) msg = { "payload": { "parsed_message": parsed_message, "timestamp": raw_msg["payload"]["timestamp"], } } return msg class ActuatorFlow(AFTemplate): """ Bundles all functionality to handle actuator messages. This is a template for a ActuatorFlow class, i.e. one that holds all functions that are necessary to handle messages from the message broker towards the devices/gateway. The methods could also be implemented into the Connector class, but are seperated to support clarity. Overload these functions ------------------------ In order to transform this class into operational code you need to inherit from it and overload the following methods: - send_command Connector Attributes -------------------- The following attributes must be set up by the connector to allow these methods to run correctly: datapoint_map : dict of dict. Mapping from datapoint key to topic. Is generated by the AdminUI. Looks e.e. like this: datapoint_map = { "sensor": { "Channel__P__value__0": "example-connector/msgs/0001", "Channel__P__unit__0": "example-connector/msgs/0002", }, "actuator": { "example-connector/msgs/0003": "Channel__P__setpoint__0", } } Note thereby that the keys "sensor" and "actuator"" must alaways be present, even if the child dicts are empty. """ def send_command(self, datapoint_key, datapoint_value): """ Send message to target device, via gateway if applicable. TODO: Extend for writing stuff. FC15: write_coils FC16: write_registers Parameters ---------- datapoint_key : string. The internal key that is used by device/gateway to identify the datapoint. value : string. The value that should be sent to the datapoint. """ # Examples: datapoint_key = "write_coil__10" if not hasattr(self, "modbus_connection"): # Establish connection to modbus master device. logger.debug( "Connecting to Modbus master %s:%s", *(self.modbus_master_ip, self.modbus_master_port) ) self.modbus_connection = ModbusTcpClient( host=self.modbus_master_ip, port=self.modbus_master_port ) if not self.modbus_connection.connect(): raise RuntimeError("Could not connect to Modbus master.") # Parse write_method_name and coil/register number from datapoint_key modbus_datatype = "" modbus_method = datapoint_key.split("__")[0] modbus_reg = int(datapoint_key.split("__")[1]) modbus_unit = int(datapoint_key.split("__")[2]) if "register" in modbus_method: modbus_datatype = self.method_range[modbus_method][str(modbus_reg)]["datatypes"] modbus_scaling_factor = self.method_range[modbus_method][str(modbus_reg)]["scaling_factor"] # Apply scaling_factor if provided. # Parse datatype for resgisters and pack value as binary. # Parse coil values ("0" and "1") as Bools (True/False) if "coil" in modbus_method: values = False if int(datapoint_value) == 0 else True else: builder = BinaryPayloadBuilder(byteorder=modbus_datatype[0], wordorder=Endian.Big) builder.reset() p_string = builder._pack_words(modbus_datatype[1:], fast_real(datapoint_value)*modbus_scaling_factor) builder._payload.append(p_string) values = builder.to_registers()[0] # Call write_method with parsed (aka. decoded) write_method = getattr(self.modbus_connection, modbus_method) try: response = write_method( address=modbus_reg, value=values, unit=modbus_unit, ) logger.debug( "Sent %s to register %s in unit %s", *(values, modbus_reg, modbus_unit ) ) logger.debug("Received response for send_command: %s", response) register_wmaxlim_pct_env = os.getenv('MODBUS_W_MAX_LIM_PCT') if register_wmaxlim_pct_env: register_wmaxlimpct = int(register_wmaxlim_pct_env) else: register_wmaxlimpct = 40242 register_wmaxlim_ena_env = os.getenv('MODBUS_W_MAX_LIM_ENA') if register_wmaxlim_ena_env: register_wmaxlim_ena = int(register_wmaxlim_ena_env) else: register_wmaxlim_ena = 40246 if modbus_reg == register_wmaxlimpct: logger.info("Power limit to change (WMaxLimPct). Writing additional register WMaxLim_Ena to ensure power control via Modbus is enabled.") builder = BinaryPayloadBuilder(byteorder=modbus_datatype[0], wordorder=Endian.Big) builder.reset() p_string = builder._pack_words(modbus_datatype[1:], 1) builder._payload.append(p_string) values = builder.to_registers()[0] response = write_method( address=register_wmaxlim_ena, value=values, unit=modbus_unit, ) logger.debug( "Sent %s to register %s in unit %s", *(values, modbus_reg, modbus_unit ) ) logger.debug("Received response for send_command: %s", response) finally: # Maybe wait a bit before next request. sleep(self.poll_break) class Connector(CTemplate, SensorFlow, ActuatorFlow): """ The generic logic of the connector. It should not be necessary to overload any of these methods nor to call any of those apart from __init__() and run(). Configuration Attributes ------------------------ Confiugration will be populated from environment variables on init. CONNECTOR_NAME : string The name of the connector instance as seen by the AdminUI. MQTT_TOPIC_LOGS : string The topics used by the log handler to publish log messages on. MQTT_TOPIC_HEARTBEAT : string The topics used by the connector to publish heartbeats on. MQTT_TOPIC_AVAILABLE_DATAPOINTS : string The topic on which the available datapoints will be published. MQTT_TOPIC_DATAPOINT_MAP : string The topic the connector will listen on for datapoint maps SEND_RAW_MESSAGE_TO_DB : string if SEND_RAW_MESSAGE_TO_DB == "TRUE" will send raw message to designated DB via MQTT. This is a string and not a bool as environment variables are always strings. MQTT_TOPIC_RAW_MESSAGE_TO_DB : string The topic which on which the raw messages will be published. DEBUG : string if DEBUG == "TRUE" will log debug message to, elso loglevel is info. MODBUS_MASTER_IP : string The ip adress or DNS name of the Modbus master device which we want to connect to. MODBUS_MASTER_PORT : string (as env variables are always strings) The port on which the master device awaits Modbus communication. Computed Attributes ------------------- These attriubutes are created by init and are then dynamically used by the Connector. mqtt_client : class instance. Initialized Mqtt client library with signature of paho mqtt. available_datapoints : dict of dict. Lists all datapoints known to the connector and is sent to the AdminUI. Actuator datapoints must be specified manually. Sensor datapoints are additionally automatically added once a value for a new datapoint is received. The object contains the connector internal key and a sample and value looks e.g. like this: available_datapoints = { "sensor": { "Channel__P__value__0": 0.122, "Channel__P__unit__0": "kW", }, "actuator": { "Channel__P__setpoint__0": 0.4, } } datapoint_map : dict of dict. Mapping from datapoint key to topic. Is generated by the AdminUI. Looks e.e. like this: datapoint_map = { "sensor": { "Channel__P__value__0": "example-connector/msgs/0001", "Channel__P__unit__0": "example-connector/msgs/0002", }, "actuator": { "example-connector/msgs/0003": "Channel__P__setpoint__0", } } Note thereby that the keys "sensor" and "actuator"" must alaways be present, even if the child dicts are empty. """ def __init__(self, *args, **kwargs): """ Init the inherited code from python_connector_template and add function to parse the special environment variable args to configure this connector. TODO: Add support for swapping ordering of bits? Seems like our code works if byte order and bit are identical, i.e. both Big endian or both little endian. Is there a use case for other scenarios? """ # dotenv allows us to load env variables from .env files which is # convient for developing. If you set override to True tests # may fail as the tests assume that the existing environ variables # have higher priority over ones defined in the .env file. load_dotenv(find_dotenv(), verbose=True, override=False) # We need to specify a dispatcher that triggers the connection with # the device or gateway. Here we want to poll the device with the # interval set in the POLL_SECONDS environment variable. kwargs["DeviceDispatcher"] = DispatchInInterval kwargs["device_dispatcher_kwargs"] = { "call_interval": float(os.getenv("POLL_SECONDS")) } # CTemplate.__init__(self, *args, **kwargs) self.modbus_master_ip = os.getenv("MODBUS_MASTER_IP") self.modbus_master_port = int(os.getenv("MODBUS_MASTER_PORT")) self.modbus_config = self.parse_modbus_config( config_json_str=os.getenv("MODBUS_CONFIG") ) self.modbus_addresses = self.compute_addresses( modbus_config=self.modbus_config ) self.modbus_read_method_names = [ k for k in self.modbus_config if "read_" in k ] # add write methods to connector self.modbus_write_method_names = [ k for k in self.modbus_config if "write_" in k ] self.method_range = self.compute_method_ranges() kwargs["available_datapoints"] = { "sensor": {}, "actuator": self.compute_actuator_datapoints() } CTemplate.__init__(self, *args, **kwargs) self.max_retries = int(os.getenv("MODBUS_MAX_RETRIES") or 3) self.retry_wait = int(os.getenv("MODBUS_RETRY_WAIT_SECONDS") or 15) self.poll_break = float(os.getenv("MODBUS_POLL_BREAK") or 0) self.disconnect_between_polls = False if os.getenv("MODBUS_DISCONNECT_BETWEEN_POLLS") == "TRUE": self.disconnect_between_polls = True def compute_actuator_datapoints(self): actuator_temp = {} for write_method in self.modbus_write_method_names: for requested_range in self.modbus_config[write_method]: if "_coil" in write_method: datapoint = write_method + "__" + \ str(requested_range["address"]) + \ "__" + str(requested_range["unit"]) actuator_temp.update({datapoint: "0"}) elif "_register" in write_method: datapoint = write_method + "__" + \ str(requested_range["address"]) + "__" + str(requested_range["unit"]) actuator_temp.update({datapoint: "0"}) return actuator_temp def compute_method_ranges(self): method_range_temp = {} for write_method in self.modbus_write_method_names: method_range_temp.update({write_method: {}}) for requested_range in self.modbus_config[write_method]: if "_register" in write_method: method_range_temp[write_method].update({str(requested_range["address"]): {}}) method_range_temp[write_method][str(requested_range["address"])].update( { "unit": requested_range["unit"], "scaling_factor": requested_range["scaling_factor"], "datatypes": requested_range["datatypes"] } ) elif "_coil" in write_method: method_range_temp[write_method].update({str(requested_range["address"]): {}}) method_range_temp[write_method][str(requested_range["address"])].update( { "unit": requested_range["unit"] } ) return method_range_temp @staticmethod def parse_modbus_config(config_json_str): """ Parse and verify the configuration JSON string. This also removes unexpected components of the config. Arguements: ----------- config_json_str : string The MODBUS_CONFIG JSON string as defined in the Readme. Returns: -------- config : dict The parsed version of the input. """ logger.info("Parsing MODBUS_CONFIG.") expected_config_keys = [ "read_coils", "read_discrete_inputs", "read_holding_registers", "read_input_registers", "write_register", "write_coil" ] config = json.loads(config_json_str) for config_key in list(config.keys()): if config_key not in expected_config_keys: logger.warning( "Found unexpected key in MODBUS_CONFIG: %s" "The corresponding values are:\n%s", *(config_key, json.dumps(config[config_key], indent=2)) ) del config[config_key] return config @staticmethod def compute_addresses(modbus_config): """ Compute the corresponding register and coil addresses to the address ranges specified by the user in MODBUS_CONFIG. Arguments: ---------- modbus_config : dict As returned by parse_modbus_config. """ # These are the Modbus functions (supported by the connector) # that interact with registers. method_names = [ "read_coils", "read_discrete_inputs", "read_holding_registers", "read_input_registers" ] # These is the mapping from the struct keys to the Modbus # register count, that is how many registers are filled with that # variable. See also: # https://docs.python.org/3/library/struct.html#format-characters char_register_size = { "c": 1, "b": 1, "B": 1, "?": 1, "h": 1, "H": 1, "i": 2, "I": 2, "l": 2, "L": 2, "q": 4, "Q": 4, "e": 1, "f": 2, "d": 4, } addresses = {} for method_name in method_names: if method_name not in modbus_config: continue addresses[method_name] = {} requested_ranges = modbus_config[method_name] if "register" in method_name: for i, requested_range in enumerate(requested_ranges): # The first value starts at the start of the # address range. range_addresses = [] current_address = requested_range["address"] for datatype_char in requested_range["datatypes"]: if datatype_char not in char_register_size: # Ignore chars defining endianess or padding. continue # Append the address this value starts and add it's # length so we get the starting address of the next # value. range_addresses.append(current_address) current_address += char_register_size[datatype_char] # Finally store the addresses of this range under the # index the range has in the config. addresses[method_name][i] = range_addresses else: # Handling for read_discrete_inputs and read_coils methods, # is acutally quite simple as every bit is exactly one bit # long :) for i, requested_range in enumerate(requested_ranges): start = requested_range["address"] count = requested_range["count"] addresses[method_name][i] = list(range(start, start+count)) return addresses if __name__ == "__main__": connector = Connector(version=__version__) connector.run()

40.542169

153

0.55473

3be00669e611d58e8f5dd89176fbc8e94b07bb3e

11,317

py

Python

docs/build/lib/generativepoetry/poemgen.py

coreybobco/generativepoetrypy

b4a5f117157d55c6314309ff6dfe9dbeb27a5ebf

[ "MIT" ]

25

2019-07-23T03:05:59.000Z

2022-03-21T20:56:16.000Z

docs/build/lib/generativepoetry/poemgen.py

coreybobco/generativepoetrypy

b4a5f117157d55c6314309ff6dfe9dbeb27a5ebf

[ "MIT" ]

3

2019-07-30T00:57:48.000Z

2020-04-08T17:17:37.000Z

docs/build/lib/generativepoetry/poemgen.py

coreybobco/procedural-poetry

b4a5f117157d55c6314309ff6dfe9dbeb27a5ebf

[ "MIT" ]

3

2020-10-10T18:52:36.000Z

2021-08-16T02:11:45.000Z

import itertools from typing import List, Optional from .lexigen import * from .markov import StochasticJolasticWordGenerator from .utils import too_similar class Poem: def __init__(self, input_words, words_for_sampling): self.input_words = input_words self.words_for_sampling = words_for_sampling self.title = "'".join(input_words) self.lines: List[str] = [] def __str__(self): return self.raw_text def update(self): self.raw_text = '\n'.join(self.lines) @property def previous_line(self): if len(self.lines): return self.lines[-1] return '' class PoemGenerator: def __init__(self): self.default_connectors = [' ', ' ', '... ', random.choice([' & ', ' and ']), ' or ', ' or '] self.line_enders = ['.', ', ', '!', '?', '', ' or', '...'] self.markov_line_enders = ['', '', ',', ',', '!', '.', '?'] self.line_indents = ['', ' ', ' '] self.poem = None def poem_line_from_markov(self, starting_word: str, num_words: int = 4, rhyme_with: Optional[str] = None, words_for_sampling: List[str] = [], max_line_length: Optional[int] = 35) -> str: """Generate a line of poetry using a markov chain that optionally tries to make a line rhyme with the last one Different algorithms handle the last word and all the other words: both algorithms use a mix of random probability and process stopwords differently to keep the generated text interesting and non-repetitive. :param starting_word: the input word for the Markov algorithm, which hence is also the poem line's first word :param num_words: the number of words to write in the poem line :param rhyme_with: an optional word to try to make the poem line rhyme with. The algorithm will try something else if this word is a common stopword or if it can't find a rhyme though. :param words_for_sampling: a list of other words to throw in to the poem. If you don't know what to pass here, phonetically related words to the starting word probably adds some sonority. :param max_line_length: an upper limit in characters for the line -- important for PDF generation to keep everything on the page. """ output_words, previous_word = [starting_word], starting_word markovgen = StochasticJolasticWordGenerator(previous_lines=self.poem.lines) for i in range(num_words - 1): if (i == num_words - 2) or (max_line_length and (max_line_length > 14 and len(' '.join(output_words)) >= max_line_length - 14)): # Checks if if it's the last word--the limit can be determined by either word count or character count max_word_length = 12 if max_line_length else None word = markovgen.last_word_of_markov_line(output_words, rhyme_with=rhyme_with, max_length=max_word_length) output_words.append(word) break else: word = markovgen.nonlast_word_of_markov_line(output_words, words_for_sampling=words_for_sampling) output_words.append(word) correct_a_vs_an(output_words) return " ".join(output_words) def poem_from_markov(self, input_words, num_lines=10, min_line_words: int = 5, max_line_words: int = 9, max_line_length: Optional[int] = 35) -> str: """Generate a line of poetry using a markov chain that optionally tries to make a line rhyme with the last one Different algorithms handle the last word and all the other words: both algorithms use a mix of random probability and process stopwords differently to keep the generated text interesting and non-repetitive. :param input words: the user provided words to try making a poem from :param num_lines: the number of lines the poem will have :param max_line_words: the maximum number of words a line may have :param words_for_sampling: a list of other words to throw in to the poem. If you don't know what to pass here, phonetically related words to the starting word probably adds some sonority. :param max_line_length: an upper limit in characters for the line -- important for PDF generation to keep everything on the page. """ self.poem = None words_for_sampling = input_words + phonetically_related_words(input_words, limit_results_per_input_word=20) # Check for undesirable similarity overlap in the words for sampling list similarity_checks = list(itertools.combinations(words_for_sampling, 2)) words_removed = [] for word_pair in similarity_checks: if not(word_pair[0] in words_removed or word_pair[1] in words_removed) and \ too_similar(word_pair[0], word_pair[1]): words_removed.append(random.choice([word_pair[0], word_pair[1]])) words_for_sampling.remove(words_removed[-1]) self.poem = Poem(input_words, words_for_sampling) last_line_last_word = '' random.shuffle(words_for_sampling) line_enders = [] print("\n") for i in range(num_lines): rhyme_with = last_line_last_word if i % 2 == 1 else None # 67.5 % chance the line starts with an input word or something relate, 32.5% with a common word line_starter = words_for_sampling.pop() if random.random() > .4 else \ random.choice(StochasticJolasticWordGenerator.common_words) while i >= 1 and too_similar(line_starter, self.poem.lines[i - 1].split(' ')[0]): # while statement prevents repetition of line starters line_starter = words_for_sampling.pop() if random.random() > .4 else \ random.choice(StochasticJolasticWordGenerator.common_words) line = self.poem_line_from_markov(line_starter, words_for_sampling=words_for_sampling, num_words=random.randint(min_line_words, max_line_words), rhyme_with=rhyme_with, max_line_length=max_line_length) self.poem.lines.append(line) last_line_last_word = line.split(' ')[-1] # Directly adding line ender to line now will screw up rhyme pairs so save it & add it in another iteration line_enders.append(random.choice(self.markov_line_enders)) print(line + line_enders[-1]) for i, line in enumerate(self.poem.lines): self.poem.lines[i] += line_enders[i] poem = self.poem return poem def poem_line_from_word_list(self, word_list: List[str], max_line_length=35, connectors: List[str] = []) -> str: """Generate a line of a visual poem from a list of words by gluing them together with random connectors (whitespace, conjunctions, punctuation, and symbols). :param word_list: the words that will be used (in order, not randomly) that will form a visual poem :param max_line_length: upper limit on the length of the return value in characters :param connectors (list): list of glue strings """ connectors = connectors if len(connectors) else self.default_connectors output, last_word = word_list[0], word_list[0] last_connector = '' for word in word_list[1:]: if random.random() < ( .2 + len(output) / 100): # Increasing probability of line termination as line gets longer break if too_similar(last_word, word): continue connector = random.choice(connectors) while connector == last_connector: connector = random.choice(connectors) if len(output + connector + word) <= max_line_length: output += connector + word last_word = word last_connector = connector return output def poem_from_word_list(self, input_word_list: List[str], num_lines: int = 6, max_line_length: int = 35, connectors: List[str] = [], limit_line_to_one_input_word: bool = False): """Generate a visual poem from a list of words by taking a given input word list, adding the phonetically related words to that word list, and then using those words to create a visual/concrete poem. :param input_word_list: the list of user-provided words that will be used, along with phonetically related words, to generate a poem :param max_line_length: upper limit on the length of the return value in characters :param max_line_length: upper limit on length of poem lines (excluding line ending punctuation) in characters :param connectors: list of glue strings :param limit_line_to_one_input_word: If true, when generating a line of poetry, only use words that are phonetically related to one input word. """ connectors = self.default_connectors if not len(connectors) else connectors output, line_indent = '', '' if limit_line_to_one_input_word: for i in range(num_lines - 1): linked_word = random.choice(input_word_list) output += self.poem_line_from_word_list(phonetically_related_words(linked_word), connectors=connectors, max_line_length=max_line_length) line_indent = random.choice(self.line_indents) if line_indent == '' else \ random.choice([li for li in self.line_indents if li is not line_indent]) # Don't repeat the same indent 2x output += random.choice(self.line_enders) + '\n' + line_indent else: word_list = input_word_list.copy() for word in input_word_list: word_list.extend(phonetically_related_words(word)) for i in range(num_lines - 1): random.shuffle(word_list) output += self.poem_line_from_word_list(word_list, connectors=connectors, max_line_length=max_line_length) # Don't repeat the same indent 2x line_indent = random.choice(self.line_indents) if line_indent == '' else \ random.choice([li for li in self.line_indents if li is not line_indent]) output += random.choice(self.line_enders) + '\n' + line_indent output += random.choice(input_word_list[:-1]) + ' ' + input_word_list[-1] return output def print_poem(poem: str): """Print the poem with a newline before and after so it's easy to take a screenshot of its 'recipe' and the poem in your terminal and share it. :) :param poem: the poem, as a string, to be printed """ print('\n') print(poem) print('\n')

57.156566

127

0.625873

02556791f4b2dd6aef7a9a5ab2a8387a25107f84

20,408

py

Python

ax/models/torch/tests/test_surrogate.py

lyhyl/Ax

44384a0cb1a622c9e395c95f683cfee25c7b61f6

[ "MIT" ]

null

ax/models/torch/tests/test_surrogate.py

lyhyl/Ax

44384a0cb1a622c9e395c95f683cfee25c7b61f6

[ "MIT" ]

null

ax/models/torch/tests/test_surrogate.py

lyhyl/Ax

44384a0cb1a622c9e395c95f683cfee25c7b61f6

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from unittest.mock import patch, MagicMock import torch from ax.core.search_space import SearchSpaceDigest from ax.models.torch.botorch_modular.acquisition import Acquisition from ax.models.torch.botorch_modular.surrogate import Surrogate from ax.utils.common.constants import Keys from ax.utils.common.testutils import TestCase from ax.utils.testing.torch_stubs import get_torch_test_data from botorch.acquisition.monte_carlo import qSimpleRegret from botorch.models import SaasFullyBayesianSingleTaskGP, SingleTaskGP from botorch.models.model import Model from botorch.sampling.samplers import SobolQMCNormalSampler from botorch.utils.containers import TrainingData from gpytorch.constraints import Interval from gpytorch.kernels import Kernel, RBFKernel, ScaleKernel # noqa: F401 from gpytorch.likelihoods import ( # noqa: F401 FixedNoiseGaussianLikelihood, GaussianLikelihood, Likelihood, ) from gpytorch.mlls import ExactMarginalLogLikelihood, LeaveOneOutPseudoLikelihood from torch import Tensor ACQUISITION_PATH = f"{Acquisition.__module__}" CURRENT_PATH = f"{__name__}" SURROGATE_PATH = f"{Surrogate.__module__}" class SingleTaskGPWithDifferentConstructor(SingleTaskGP): def __init__(self, train_X: Tensor, train_Y: Tensor): super().__init__(train_X=train_X, train_Y=train_Y) class SurrogateTest(TestCase): def setUp(self): self.device = torch.device("cpu") self.dtype = torch.float self.Xs, self.Ys, self.Yvars, self.bounds, _, _, _ = get_torch_test_data( dtype=self.dtype ) self.training_data = TrainingData.from_block_design( X=self.Xs[0], Y=self.Ys[0], Yvar=self.Yvars[0] ) self.mll_class = ExactMarginalLogLikelihood self.search_space_digest = SearchSpaceDigest( feature_names=["x1", "x2"], bounds=self.bounds, target_fidelities={1: 1.0}, ) self.metric_names = ["y"] self.fixed_features = {1: 2.0} self.refit = True self.objective_weights = torch.tensor( [-1.0, 1.0], dtype=self.dtype, device=self.device ) self.outcome_constraints = (torch.tensor([[1.0]]), torch.tensor([[0.5]])) self.linear_constraints = ( torch.tensor([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]), torch.tensor([[0.5], [1.0]]), ) self.options = {} def _get_surrogate(self, botorch_model_class): surrogate = Surrogate( botorch_model_class=botorch_model_class, mll_class=self.mll_class ) surrogate_kwargs = botorch_model_class.construct_inputs(self.training_data) return surrogate, surrogate_kwargs @patch(f"{CURRENT_PATH}.Kernel") @patch(f"{CURRENT_PATH}.Likelihood") def test_init(self, mock_Likelihood, mock_Kernel): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate, _ = self._get_surrogate(botorch_model_class=botorch_model_class) self.assertEqual(surrogate.botorch_model_class, botorch_model_class) self.assertEqual(surrogate.mll_class, self.mll_class) @patch(f"{SURROGATE_PATH}.fit_gpytorch_model") def test_mll_options(self, _): mock_mll = MagicMock(self.mll_class) surrogate = Surrogate( botorch_model_class=SingleTaskGP, mll_class=mock_mll, mll_options={"some_option": "some_value"}, ) surrogate.fit( training_data=self.training_data, search_space_digest=self.search_space_digest, metric_names=self.metric_names, refit=self.refit, ) self.assertEqual(mock_mll.call_args[1]["some_option"], "some_value") def test_model_property(self): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate, _ = self._get_surrogate(botorch_model_class=botorch_model_class) with self.assertRaisesRegex( ValueError, "BoTorch `Model` has not yet been constructed." ): surrogate.model def test_training_data_property(self): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate, _ = self._get_surrogate(botorch_model_class=botorch_model_class) with self.assertRaisesRegex( ValueError, "Underlying BoTorch `Model` has not yet received its training_data.", ): surrogate.training_data def test_dtype_property(self): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate, _ = self._get_surrogate(botorch_model_class=botorch_model_class) surrogate.construct( training_data=self.training_data, fidelity_features=self.search_space_digest.fidelity_features, ) self.assertEqual(self.dtype, surrogate.dtype) def test_device_property(self): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate, _ = self._get_surrogate(botorch_model_class=botorch_model_class) surrogate.construct( training_data=self.training_data, fidelity_features=self.search_space_digest.fidelity_features, ) self.assertEqual(self.device, surrogate.device) def test_from_botorch(self): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate_kwargs = botorch_model_class.construct_inputs(self.training_data) surrogate = Surrogate.from_botorch(botorch_model_class(**surrogate_kwargs)) self.assertIsInstance(surrogate.model, botorch_model_class) self.assertTrue(surrogate._constructed_manually) @patch(f"{CURRENT_PATH}.SaasFullyBayesianSingleTaskGP.__init__", return_value=None) @patch(f"{CURRENT_PATH}.SingleTaskGP.__init__", return_value=None) def test_construct(self, mock_GP, mock_SAAS): mock_GPs = [mock_SAAS, mock_GP] for i, botorch_model_class in enumerate( [SaasFullyBayesianSingleTaskGP, SingleTaskGP] ): surrogate, _ = self._get_surrogate(botorch_model_class=botorch_model_class) with self.assertRaises(NotImplementedError): # Base `Model` does not implement `construct_inputs`. Surrogate(botorch_model_class=Model).construct( training_data=self.training_data, fidelity_features=self.search_space_digest.fidelity_features, ) surrogate.construct( training_data=self.training_data, fidelity_features=self.search_space_digest.fidelity_features, ) mock_GPs[i].assert_called_once() call_kwargs = mock_GPs[i].call_args[1] self.assertTrue(torch.equal(call_kwargs["train_X"], self.Xs[0])) self.assertTrue(torch.equal(call_kwargs["train_Y"], self.Ys[0])) self.assertFalse(surrogate._constructed_manually) # Check that `model_options` passed to the `Surrogate` constructor are # properly propagated. with patch.object( botorch_model_class, "construct_inputs", wraps=botorch_model_class.construct_inputs, ) as mock_construct_inputs: surrogate = Surrogate( botorch_model_class=botorch_model_class, mll_class=self.mll_class, model_options={"some_option": "some_value"}, ) surrogate.construct(self.training_data) mock_construct_inputs.assert_called_with( training_data=self.training_data, some_option="some_value" ) def test_construct_custom_model(self): # Make sure covar_module and likelihood are filtered for a model that doesn't # support them. surrogate = Surrogate( botorch_model_class=SingleTaskGPWithDifferentConstructor, mll_class=self.mll_class, covar_module_class=RBFKernel, likelihood_class=FixedNoiseGaussianLikelihood, ) surrogate.construct(self.training_data) self.assertEqual(type(surrogate._model.covar_module), ScaleKernel) self.assertEqual(type(surrogate._model.likelihood), GaussianLikelihood) # Pass custom options to a SingleTaskGP and make sure they are used noise_constraint = Interval(1e-6, 1e-1) surrogate = Surrogate( botorch_model_class=SingleTaskGP, mll_class=LeaveOneOutPseudoLikelihood, covar_module_class=RBFKernel, covar_module_options={"ard_num_dims": 1}, likelihood_class=GaussianLikelihood, likelihood_options={"noise_constraint": noise_constraint}, ) surrogate.construct(self.training_data) self.assertEqual(type(surrogate._model.likelihood), GaussianLikelihood) self.assertEqual( surrogate._model.likelihood.noise_covar.raw_noise_constraint, noise_constraint, ) self.assertEqual(surrogate.mll_class, LeaveOneOutPseudoLikelihood) self.assertEqual(type(surrogate._model.covar_module), RBFKernel) self.assertEqual(surrogate._model.covar_module.ard_num_dims, 1) @patch(f"{CURRENT_PATH}.SingleTaskGP.load_state_dict", return_value=None) @patch(f"{SURROGATE_PATH}.fit_fully_bayesian_model_nuts") @patch(f"{SURROGATE_PATH}.fit_gpytorch_model") @patch(f"{CURRENT_PATH}.ExactMarginalLogLikelihood") def test_fit(self, mock_MLL, mock_fit_gpytorch, mock_fit_saas, mock_state_dict): for mock_fit, botorch_model_class in zip( [mock_fit_saas, mock_fit_gpytorch], [SaasFullyBayesianSingleTaskGP, SingleTaskGP], ): surrogate, surrogate_kwargs = self._get_surrogate( botorch_model_class=botorch_model_class ) # Checking that model is None before `fit` (and `construct`) calls. self.assertIsNone(surrogate._model) # Should instantiate mll and `fit_gpytorch_model` when `state_dict` # is `None`. surrogate.fit( training_data=self.training_data, search_space_digest=self.search_space_digest, metric_names=self.metric_names, refit=self.refit, ) # Check that training data is correctly passed through to the # BoTorch `Model`. self.assertTrue( torch.equal( surrogate.model.train_inputs[0], surrogate_kwargs.get("train_X"), ) ) self.assertTrue( torch.equal( surrogate.model.train_targets, surrogate_kwargs.get("train_Y").squeeze(1), ) ) mock_state_dict.assert_not_called() mock_fit.assert_called_once() mock_state_dict.reset_mock() mock_MLL.reset_mock() mock_fit.reset_mock() # Should `load_state_dict` when `state_dict` is not `None` # and `refit` is `False`. state_dict = {"state_attribute": "value"} surrogate.fit( training_data=self.training_data, search_space_digest=self.search_space_digest, metric_names=self.metric_names, refit=False, state_dict=state_dict, ) mock_state_dict.assert_called_once() mock_MLL.assert_not_called() mock_fit.assert_not_called() mock_state_dict.reset_mock() mock_MLL.reset_mock() @patch(f"{SURROGATE_PATH}.predict_from_model") def test_predict(self, mock_predict): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate, _ = self._get_surrogate(botorch_model_class=botorch_model_class) surrogate.construct( training_data=self.training_data, fidelity_features=self.search_space_digest.fidelity_features, ) surrogate.predict(X=self.Xs[0]) mock_predict.assert_called_with(model=surrogate.model, X=self.Xs[0]) def test_best_in_sample_point(self): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate, _ = self._get_surrogate(botorch_model_class=botorch_model_class) surrogate.construct( training_data=self.training_data, fidelity_features=self.search_space_digest.fidelity_features, ) # `best_in_sample_point` requires `objective_weights` with patch( f"{SURROGATE_PATH}.best_in_sample_point", return_value=None ) as mock_best_in_sample: with self.assertRaisesRegex(ValueError, "Could not obtain"): surrogate.best_in_sample_point( search_space_digest=self.search_space_digest, objective_weights=None, ) with patch( f"{SURROGATE_PATH}.best_in_sample_point", return_value=(self.Xs[0], 0.0) ) as mock_best_in_sample: best_point, observed_value = surrogate.best_in_sample_point( search_space_digest=self.search_space_digest, objective_weights=self.objective_weights, outcome_constraints=self.outcome_constraints, linear_constraints=self.linear_constraints, fixed_features=self.fixed_features, options=self.options, ) mock_best_in_sample.assert_called_with( Xs=[self.training_data.X], model=surrogate, bounds=self.search_space_digest.bounds, objective_weights=self.objective_weights, outcome_constraints=self.outcome_constraints, linear_constraints=self.linear_constraints, fixed_features=self.fixed_features, options=self.options, ) @patch(f"{ACQUISITION_PATH}.Acquisition.__init__", return_value=None) @patch( f"{ACQUISITION_PATH}.Acquisition.optimize", return_value=([torch.tensor([0.0])], [torch.tensor([1.0])]), ) @patch( f"{SURROGATE_PATH}.pick_best_out_of_sample_point_acqf_class", return_value=(qSimpleRegret, {Keys.SAMPLER: SobolQMCNormalSampler}), ) def test_best_out_of_sample_point( self, mock_best_point_util, mock_acqf_optimize, mock_acqf_init ): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate, _ = self._get_surrogate(botorch_model_class=botorch_model_class) surrogate.construct( training_data=self.training_data, fidelity_features=self.search_space_digest.fidelity_features, ) # currently cannot use function with fixed features with self.assertRaisesRegex(NotImplementedError, "Fixed features"): surrogate.best_out_of_sample_point( search_space_digest=self.search_space_digest, objective_weights=self.objective_weights, fixed_features=self.fixed_features, ) candidate, acqf_value = surrogate.best_out_of_sample_point( search_space_digest=self.search_space_digest, objective_weights=self.objective_weights, outcome_constraints=self.outcome_constraints, linear_constraints=self.linear_constraints, options=self.options, ) mock_acqf_init.assert_called_with( surrogate=surrogate, botorch_acqf_class=qSimpleRegret, search_space_digest=self.search_space_digest, objective_weights=self.objective_weights, outcome_constraints=self.outcome_constraints, linear_constraints=self.linear_constraints, fixed_features=None, options={Keys.SAMPLER: SobolQMCNormalSampler}, ) self.assertTrue(torch.equal(candidate, torch.tensor([0.0]))) self.assertTrue(torch.equal(acqf_value, torch.tensor([1.0]))) @patch(f"{CURRENT_PATH}.SingleTaskGP.load_state_dict", return_value=None) @patch(f"{SURROGATE_PATH}.fit_fully_bayesian_model_nuts") @patch(f"{SURROGATE_PATH}.fit_gpytorch_model") @patch(f"{CURRENT_PATH}.ExactMarginalLogLikelihood") def test_update(self, mock_MLL, mock_fit_gpytorch, mock_fit_saas, mock_state_dict): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate, surrogate_kwargs = self._get_surrogate( botorch_model_class=botorch_model_class ) surrogate.construct( training_data=self.training_data, fidelity_features=self.search_space_digest.fidelity_features, ) # Check that correct arguments are passed to `fit`. with patch(f"{SURROGATE_PATH}.Surrogate.fit") as mock_fit: # Call `fit` by default surrogate.update( training_data=self.training_data, search_space_digest=self.search_space_digest, metric_names=self.metric_names, refit=self.refit, state_dict={"key": "val"}, ) mock_fit.assert_called_with( training_data=self.training_data, search_space_digest=self.search_space_digest, metric_names=self.metric_names, candidate_metadata=None, refit=self.refit, state_dict={"key": "val"}, ) # Check that the training data is correctly passed through to the # BoTorch `Model`. Xs, Ys, Yvars, bounds, _, _, _ = get_torch_test_data( dtype=self.dtype, offset=1.0 ) training_data = TrainingData.from_block_design( X=Xs[0], Y=Ys[0], Yvar=Yvars[0] ) surrogate_kwargs = botorch_model_class.construct_inputs(training_data) surrogate.update( training_data=training_data, search_space_digest=self.search_space_digest, metric_names=self.metric_names, refit=self.refit, state_dict={"key": "val"}, ) self.assertTrue( torch.equal( surrogate.model.train_inputs[0], surrogate_kwargs.get("train_X"), ) ) self.assertTrue( torch.equal( surrogate.model.train_targets, surrogate_kwargs.get("train_Y").squeeze(1), ) ) # If should not be reconstructed, check that error is raised. surrogate._constructed_manually = True with self.assertRaisesRegex(NotImplementedError, ".* constructed manually"): surrogate.update( training_data=self.training_data, search_space_digest=self.search_space_digest, metric_names=self.metric_names, refit=self.refit, ) def test_serialize_attributes_as_kwargs(self): for botorch_model_class in [SaasFullyBayesianSingleTaskGP, SingleTaskGP]: surrogate, _ = self._get_surrogate(botorch_model_class=botorch_model_class) expected = surrogate.__dict__ self.assertEqual(surrogate._serialize_attributes_as_kwargs(), expected)

46.06772

88

0.636858

380358b87b6bad885697ed0ea4e7e3629b47e9c2

2,585

py

Python

examples/source_separation/utils/dist_utils.py

zkneupper/audio

1f136671b84071a2fe1d5b762df64f3a76310c31

[ "BSD-2-Clause" ]

4

2022-03-16T15:35:35.000Z

2022-03-22T23:55:41.000Z

examples/source_separation/utils/dist_utils.py

zkneupper/audio

1f136671b84071a2fe1d5b762df64f3a76310c31

[ "BSD-2-Clause" ]

6

2020-09-22T22:19:09.000Z

2021-06-21T17:37:32.000Z

examples/source_separation/utils/dist_utils.py

zkneupper/audio

1f136671b84071a2fe1d5b762df64f3a76310c31

[ "BSD-2-Clause" ]

1

2022-03-16T00:40:40.000Z

import os import csv import types import logging import torch import torch.distributed as dist def _info_on_master(self, *args, **kwargs): if dist.get_rank() == 0: self.info(*args, **kwargs) def getLogger(name): """Get logging.Logger module with additional ``info_on_master`` method.""" logger = logging.getLogger(name) logger.info_on_master = types.MethodType(_info_on_master, logger) return logger _LG = getLogger(__name__) def setup_distributed( world_size, rank, local_rank, backend="nccl", init_method="env://" ): """Perform env setup and initialization for distributed training""" if init_method == "env://": _set_env_vars(world_size, rank, local_rank) if world_size > 1 and "OMP_NUM_THREADS" not in os.environ: _LG.info("Setting OMP_NUM_THREADS == 1") os.environ["OMP_NUM_THREADS"] = "1" params = { "backend": backend, "init_method": init_method, "world_size": world_size, "rank": rank, } _LG.info("Initializing distributed process group with %s", params) dist.init_process_group(**params) _LG.info("Initialized distributed process group.") def _set_env_vars(world_size, rank, local_rank): for key, default in [("MASTER_ADDR", "127.0.0.1"), ("MASTER_PORT", "29500")]: if key not in os.environ: os.environ[key] = default os.environ["WORLD_SIZE"] = str(world_size) os.environ["RANK"] = str(rank) os.environ["LOCAL_RANK"] = str(local_rank) def save_on_master(path, obj): if dist.get_rank() == 0: _LG.info("Saving %s", path) torch.save(obj, path) def write_csv_on_master(path, *rows): if dist.get_rank() == 0: with open(path, "a", newline="") as fileobj: writer = csv.writer(fileobj) for row in rows: writer.writerow(row) def synchronize_params(path, device, *modules): if dist.get_world_size() < 2: return rank = dist.get_rank() if rank == 0: _LG.info("[Parameter Sync]: Saving parameters to a temp file...") torch.save({f"{i}": m.state_dict() for i, m in enumerate(modules)}, path) dist.barrier() if rank != 0: _LG.info("[Parameter Sync]: Loading parameters...") data = torch.load(path, map_location=device) for i, m in enumerate(modules): m.load_state_dict(data[f"{i}"]) dist.barrier() if rank == 0: _LG.info("[Parameter Sync]: Removing the temp file...") os.remove(path) _LG.info_on_master("[Parameter Sync]: Complete.")

29.712644

81

0.631721

05c30891dac6cc7720760ab683d3dadfed43ecc1

1,038

py

Python

make_pseudo_label.py

phamduyhk/signate_student_cup_2020

19e158b08a86f2df8e4ee45445169ae396c91409

[ "MIT" ]

null

make_pseudo_label.py

phamduyhk/signate_student_cup_2020

19e158b08a86f2df8e4ee45445169ae396c91409

[ "MIT" ]

null

make_pseudo_label.py

phamduyhk/signate_student_cup_2020

19e158b08a86f2df8e4ee45445169ae396c91409

[ "MIT" ]

null

import pandas as pd train = pd.read_csv("./data/data_augmentation_using_language_translation.csv") test = pd.read_csv("./data/test.csv") trained_output_file = "./output/pseudo_bert-large-cased_0-192_5cv_4ep.csv" trained_output = pd.read_csv(trained_output_file, header=None) pseudo_label = trained_output.iloc[:,1] test["jobflag"] = pseudo_label submit = pd.read_csv("./data/submit_sample.csv", header=None) all_prob = None for i, item in enumerate(trained_output.iloc[:,2]): arr = eval(item) for v in arr: if v>0.8: row = test.loc[i] row["jobflag"] = trained_output.iloc[i,1] # print(row) train.loc[len(train)+1]= row print(train) train.to_csv("data/pseudo_train.csv", index=False) # trained_output_file ="submission_gru_50ep.csv" # trained_output = pd.read_csv(trained_output_file, header=None) # pseudo_label = trained_output.iloc[:,1] # test["jobflag"] = pseudo_label # pseudo = pd.concat([train,test]) # pseudo.to_csv("data/pseudo_train_gru.csv", index=False)

29.657143

78

0.705202

53628893ba7d1c3644178d0a037d63e138d0bc49

670

py

Python

LeetCodeSolutions/python/42_Trapping_Rain_Water.py

ChuanleiGuo/AlgorithmsPlayground

90b6287b742c8bfd3797540c408d679be2821a40

[ "MIT" ]

1

2017-03-27T13:38:37.000Z

LeetCodeSolutions/python/42_Trapping_Rain_Water.py

ChuanleiGuo/AlgorithmsPlayground

90b6287b742c8bfd3797540c408d679be2821a40

[ "MIT" ]

null

LeetCodeSolutions/python/42_Trapping_Rain_Water.py

ChuanleiGuo/AlgorithmsPlayground

90b6287b742c8bfd3797540c408d679be2821a40

[ "MIT" ]

null

class Solution(object): def trap(self, height): """ :type height: List[int] :rtype: int """ area = 0 sec_height = 0 left, right = 0, len(height) - 1 while left < right: if height[left] < height[right]: sec_height = max(height[left], sec_height) area += sec_height - height[left] left += 1 else: sec_height = max(height[right], sec_height) area += sec_height - height[right] right -= 1 return area height = [0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1] print Solution().trap(height)

29.130435

59

0.471642

c580578d7f263ab79d26173e2dece5e2671bd134

1,714

py

Python

test/functional/feature_uacomment.py

patrykwnosuch/machinecoin-core

b6783c857f43f7f077de594d1e03d156f5295b9c

[ "MIT" ]

1

2019-05-27T11:12:53.000Z

test/functional/feature_uacomment.py

patrykwnosuch/machinecoin-core

b6783c857f43f7f077de594d1e03d156f5295b9c

[ "MIT" ]

null

test/functional/feature_uacomment.py

patrykwnosuch/machinecoin-core

b6783c857f43f7f077de594d1e03d156f5295b9c

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2017-2018 The Machinecoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test the -uacomment option.""" import re from test_framework.test_framework import MachinecoinTestFramework from test_framework.test_node import ErrorMatch from test_framework.util import assert_equal class UacommentTest(MachinecoinTestFramework): def set_test_params(self): self.num_nodes = 1 self.setup_clean_chain = True def run_test(self): self.log.info("test multiple -uacomment") test_uacomment = self.nodes[0].getnetworkinfo()["subversion"][-12:-1] assert_equal(test_uacomment, "(testnode0)") self.restart_node(0, ["-uacomment=foo"]) foo_uacomment = self.nodes[0].getnetworkinfo()["subversion"][-17:-1] assert_equal(foo_uacomment, "(testnode0; foo)") self.log.info("test -uacomment max length") self.stop_node(0) expected = "Error: Total length of network version string $[0-9]+$ exceeds maximum length $256$. Reduce the number or size of uacomments." self.nodes[0].assert_start_raises_init_error(["-uacomment=" + 'a' * 256], expected, match=ErrorMatch.FULL_REGEX) self.log.info("test -uacomment unsafe characters") for unsafe_char in ['/', ':', '(', ')']: expected = "Error: User Agent comment $" + re.escape(unsafe_char) + "$ contains unsafe characters." self.nodes[0].assert_start_raises_init_error(["-uacomment=" + unsafe_char], expected, match=ErrorMatch.FULL_REGEX) if __name__ == '__main__': UacommentTest().main()

41.804878

150

0.694866

5461fe612fc1d33d67bb4e1889f5f40786662632

1,347

py

Python

fm/models/enumeration.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

84

2017-10-22T11:01:39.000Z

2022-02-27T03:43:48.000Z

fm/models/enumeration.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

22

2017-12-11T07:21:56.000Z

2021-09-23T02:53:50.000Z

fm/models/enumeration.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

23

2017-12-06T06:59:52.000Z

2022-02-24T00:02:25.000Z

# --------------------------------------------------------------------- # Enumeration model # --------------------------------------------------------------------- # Copyright (C) 2007-2020 The NOC Project # See LICENSE for details # --------------------------------------------------------------------- # Third-party modules from mongoengine.document import Document from mongoengine.fields import StringField, DictField, UUIDField # Python modules from noc.core.text import quote_safe_path from noc.core.prettyjson import to_json class Enumeration(Document): meta = { "collection": "noc.enumerations", "strict": False, "auto_create_index": False, "json_collection": "fm.enumerations", "json_unique_fields": ["name"], } name = StringField(unique=True) uuid = UUIDField(binary=True) values = DictField() # value -> [possible combinations] def __str__(self): return self.name def get_json_path(self): return "%s.json" % quote_safe_path(self.name) def to_json(self): return to_json( { "name": self.name, "$collection": self._meta["json_collection"], "uuid": self.uuid, "values": self.values, }, order=["name", "$collection", "uuid"], )

29.282609

71

0.512992

4e736966fe4b4f3f84ee1d2f40f9848eebf8457a

24,872

py

Python

Skydipper/utils.py

Skydipper/LMIPy

4e3a5e474706df7272ffc11ef4e0bcc5d46ed36a

[ "MIT" ]

null

Skydipper/utils.py

Skydipper/LMIPy

4e3a5e474706df7272ffc11ef4e0bcc5d46ed36a

[ "MIT" ]

8

2019-12-20T12:32:27.000Z

2020-06-15T17:41:25.000Z

Skydipper/utils.py

Skydipper/LMIPy

4e3a5e474706df7272ffc11ef4e0bcc5d46ed36a

[ "MIT" ]

null

import json import math import ee from time import sleep from google.cloud import storage def html_box(item): """Returns an HTML block with template strings filled-in based on item attributes.""" is_layer = str(type(item)) == "<class 'Skydipper.layer.Layer'>" is_dataset = str(type(item)) == "<class 'Skydipper.dataset.Dataset'>" is_widget = str(type(item)) == "<class 'Skydipper.Skydipper.Widget'>" is_geometry = str(type(item)) == "<class 'Skydipper.geometry.Geometry'>" is_image = str(type(item)) == "<class 'Skydipper.image.Image'>" site_link = "<a href='https://skydipper.com/' target='_blank'>" site_logo = "<img class='itemThumbnail' src='https://skydipper.com/images/logo.png'>" if is_layer: kind_of_item = 'Layer' url_link = f'{item.server}/v1/layer/{item.id}?includes=metadata' elif is_dataset: kind_of_item = 'Dataset' url_link = f'{item.server}/v1/dataset/{item.id}?includes=metadata,layer' elif is_image: if item.type in ['Classified Image', 'Composite Image']: instrument = item.type else: instrument = item.instrument html_string = ("<div class='item_container' style='height: auto; overflow: hidden; border: 1px solid #2BA4A0;" "border-radius: 5px; background: #2BA4A0; line-height: 1.21429em; padding: 10px;''>" "<div class='item_left' style='width: 100px; height: 100px; float: left;''>" f"<a href='{item.thumb_url}' target='_blank'>" f"<img class='itemThumbnail' src='{item.thumb_url}'>" "</a></div><div class='item_right' style='float: none; width: auto; hidden;padding-left: 10px; overflow: hidden;''>" f"<b>Image Source</b>: {instrument} </br>" f"<b>Datetime</b>: {item.date_time} </br>" f"<b>Cloud score </b>: {item.cloud_score} </br>" " </div> </div>") return html_string elif is_geometry: kind_of_item = 'Geometry' url_link = f'{item.server}/v1/geostore/{item.id}' html_string = ("<div class='item_container' style='height: auto; overflow: hidden; border: 1px solid #2bA4A0;" "border-radius: 5px; background: #2bA4A0; line-height: 1.21429em; padding: 10px;''>" "<div class='item_left' style='width: 210px; float: left;''>" f"{site_link}" f"{site_logo}" "</a></div><div class='item_right' style='float: none; width: auto; hidden;padding-left: 10px; overflow: hidden;''>" f"<b>Geometry id</b>: <a href={url_link} target='_blank'>{item.id}</a></br>") for k,v in item.attributes.get('info').items(): if v and k != 'simplifyThresh': html_string += f"<br><i>{k}: {v}</i>" html_string += ("" " </div> </div>") return html_string else: kind_of_item = 'Unknown' url_link = None table_statement = f"Data source {item.attributes.get('provider')}" if item.attributes.get('connectorUrl') and item.attributes.get('provider') == "cartodb": table_statement = (f"Carto table: <a href={item.attributes.get('connectorUrl')}" " target='_blank'>" f"{item.attributes.get('tableName')}" "</a>" ) if item.attributes.get('provider') == 'gee': table_statement = (f"GEE asset: <a href='https://code.earthengine.google.com/asset='" f"{item.attributes.get('tableName')} target='_blank'>" f"{item.attributes.get('tableName')}" "</a>" ) html = ("<div class='item_container' style='height: auto; overflow: hidden; border: 1px solid #2BA4A0;" "border-radius: 2px; background: #2BA4A0; line-height: 1.21429em; padding: 10px;''>" "<div class='item_left' style='width: 210px; float: left;''>" f"{site_link}" f"{site_logo}" "</a></div><div class='item_right' style='float: none; width: auto; hidden;padding-left: 10px; overflow: hidden;''>" f"<a href={url_link} target='_blank'>" f"<b>{item.attributes.get('name')}</b>" "</a>" f"<br> {table_statement} | {kind_of_item} in {', '.join(item.attributes.get('application')).upper()}." f"<br>Last Modified: {item.attributes.get('updatedAt')}" f"<br><a href='{item.server}/v1/fields/{item.id}' target='_blank'>Fields</a>" f" Connector: {item.attributes.get('provider')}" f" | Published: {item.attributes.get('published')}" " </div> </div>") return html def show_image_collection(item, i): html_string = ("<div class='item_container' style='height: auto; overflow: hidden; border: 1px solid #2BA4A0;" "border-radius: 2px; background: #2BA4A0; line-height: 1.21429em; padding: 10px;''>" "<div class='item_left' style='width: 100px; height: 100px; hidden;padding-left: 10px; float: left''>" f"<a href='{item.get('thumb_url')}' target='_blank'>" f"<img class='itemThumbnail' src='{item.get('thumb_url')}'>" "</a></div><div class='item_right' style='float: none; hidden;padding-left: 10px; width: auto; overflow: hidden;''>" f"<b>Image Source</b>: {item.get('instrument')} </br>" f"<b>Datetime</b>: {item.get('date_time')} </br>" f"<b>Cloud score </b>: {item.get('cloud_score')} </br>" " </div> </div>") return html_string def show(item, i): """Returns an HTML block with template strings filled-in based on item attributes.""" is_layer = item.get('type') == 'Layer' is_dataset = item.get('type') == 'Dataset' server = item.get('server', "https://api.skydipper.com") item_id = item.get('id', None) attributes = item.get('attributes', None) if is_layer: kind_of_item = 'Layer' url_link = f'{server}/v1/layer/{item_id}?includes=metadata' elif is_dataset: kind_of_item = 'Dataset' url_link = f'{server}/v1/dataset/{item_id}?includes=metadata,layer' else: kind_of_item = 'Unknown' url_link = None table_statement = f"Data source {attributes.get('provider')}" if attributes.get('connectorUrl') and attributes.get('provider') == "cartodb": table_statement = (f"Carto table: <a href={attributes.get('connectorUrl')}" " target='_blank'>" f"{attributes.get('tableName')}" "</a>" ) if attributes.get('connectorUrl') and attributes.get('provider') == "csv": table_statement = (f"CSV Table: <a href={attributes.get('connectorUrl')}" " target='_blank'>" f"{attributes.get('tableName')}" "</a>" ) if attributes.get('provider') == 'gee': table_statement = (f"GEE asset: <a href='https://code.earthengine.google.com/asset='" f"{attributes.get('tableName')} target='_blank'>" f"{attributes.get('tableName')}" "</a>" ) site_link = "<a href='https://skydipper.com/' target='_blank'>" site_logo = "<img class='itemThumbnail' src='https://skydipper.com/images/logo.png'>" html = ("<div class='item_container' style='height: auto; overflow: hidden; border: 1px solid #2BA4A0;" "border-radius: 2px; background: #2BA4A0; line-height: 1.21429em; padding: 10px;''>" "<div class='item_left' style='width: 210px; float: left;''>" f"{site_link}" f"{site_logo}" "</a></div><div class='item_right' style='float: none; width: auto; hidden;padding-left: 10px; overflow: hidden;''>" f"<b>{i}. </b>" f"<a href={url_link} target='_blank'>" f"<b>{attributes.get('name')}</b>" "</a>" f"<br> {table_statement} | {kind_of_item} in {', '.join(attributes.get('application')).upper()}." f"<br>Last Modified: {attributes.get('updatedAt')}" f"<br><a href='{server}/v1/fields/{item_id}' target='_blank'>Fields</a>" f" | Connector: {attributes.get('provider')}" f" | Published: {attributes.get('published')}" " </div> </div>") return html def create_class(item): from .dataset import Dataset from .layer import Layer from .Skydipper import Widget from .image import Image if item['type'] == 'metadata': return Dataset(id_hash=item.get('attributes').get('dataset'), server = item.get('server')) if item['type'] == 'Dataset' : return Dataset(id_hash = item.get('id'), server = item.get('server')) elif item['type'] == 'Layer': return Layer(id_hash = item.get('id'), server = item.get('server')) elif item['type'] == 'Widget': return Widget(id_hash = item.get('id'), attributes=item.get('attributes'), server = item.get('server')) elif item['type'] == 'Image': return Image(**item) def flatten_list(nested_list): if len(nested_list) > 0: return [item for sublist in nested_list for item in sublist] else: return [] def get_geojson_string(geom): coords = geom.get('coordinates', None) if coords and not any(isinstance(i, list) for i in coords[0]): geom['coordinates'] = [coords] feat_col = {"type": "FeatureCollection", "features": [{"type": "Feature", "properties": {}, "geometry": geom}]} return json.dumps(feat_col) def parse_filters(filter_objects): filter_whitelist = ['connectorType', 'provider', 'status', 'published', 'protected', 'geoInfo'] return_string = '' error_string = '' if filter_objects: for k, v in filter_objects.items(): if k in filter_whitelist: return_string += f'{k}={v}&' else: error_string += f' {k},' if error_string: print(f'Unable to filter by{error_string[:-1]}.') return return_string return '' def sldDump(sldObj): """ Creates valid SldStyle string from an object. """ sld_type = sldObj.get('type', None) extended = str(sldObj.get('extended', 'false')).lower() items = sldObj.get('items', None) sld_attr = {'color': None, 'label': None, 'quantity': None, 'opacity': None} if sld_type not in ['linear', 'ramp', 'gradient', 'intervals', 'values']: print('Unable to create SldStyle. Type must be in "linear", "ramp", "gradient", "intervals", "values".') return None if items: sld_str = f'<RasterSymbolizer> <ColorMap type="{sld_type}" extended="{extended}"> ' for item in items: sld_str += f'<ColorMapEntry ' for k in sld_attr.keys(): if item.get(k, None): sld_str += f'{k}="{item[k]}" ' sld_str += '/> + ' return sld_str + "</ColorMap> </RasterSymbolizer>" def sldParse(sld_str): """ Builds a dictionary from an SldStyle string. """ sld_str = sld_str.replace("'", '"').replace('\"', '"') keys = ['color', 'label', 'quantity', 'opacity'] items = [el.strip() for el in sld_str.split('ColorMapEntry') if '<RasterSymbolizer>' not in el] sld_items = [] for i in items: tmp = {} for k in keys: v = find_between(i, f'{k}="', '"') if v: tmp[k] = v sld_items.append(tmp) return { 'type': find_between(sld_str, 'type="', '"'), 'extended': find_between(sld_str, 'extended="', '"'), 'items': sld_items } def find_between(s, first, last): try: start = s.index(first) + len(first) end = s.index(last, start) return s[start:end] except ValueError: return "" def nested_set(dic, keys, value): for key in keys[:-1]: dic = dic.setdefault(key, {}) dic[keys[-1]] = value def server_uses_widgets(server): """ Does the server currently set use Widget objects? Response gives True if it does, false if not. """ uses_widgets = ['https://api.resourcewatch.org','https://staging-api.globalforestwatch.org'] if any(server in s for s in uses_widgets): return True else: return False def tile_url(image, viz_params=None): """Create a target url for tiles from an EE image asset. e.g. im = ee.Image("LE7_TOA_1YEAR/" + year).select("B3","B2","B1") viz = {'opacity': 1, 'gain':3.5, 'bias':4, 'gamma':1.5} url = tile_url(image=im),viz_params=viz) """ if viz_params: d = image.getMapId(viz_params) else: d = image.getMapId() base_url = 'https://earthengine.googleapis.com' url = (f"https://earthengine.googleapis.com/v1alpha/{d['mapid']}/tiles/{{z}}/{{x}}/{{y}}") return url class EE_TILE_CALCS(object): """ Copyright (c) 2018 Gennadii Donchyts. All rights reserved. This work is licensed under the terms of the MIT license. For a copy, see <https://opensource.org/licenses/MIT>. Refactored to Python, Vizzuality, 2020. This code will help you calculate tile bounds, intersected with a given geom, at a specified z-level. """ def __init__(self, tileSize=256): ee.Initialize() self.tileSize = tileSize self.equatorial_circumference = 40075016.686 self.origin = 2 * math.pi * 6378137 / 2.0 def zoomToScale(self, zoom): tileWidth = self.equatorial_circumference / math.pow(2, zoom) pixelWidth = tileWidth / self.tileSize return pixelWidth def scaleToZoom(self, scale): tileWidth = scale * self.tileSize zoom = math.log(self.equatorial_circumference / tileWidth) / math.log(2) return math.ceil(zoom) def pixelsToMeters(self, px, py, zoom): resolution = self.zoomToScale(zoom) x = px * resolution - self.origin y = py * resolution - self.origin return [x, y] def metersToPixels(self, x, y, zoom): resolution = zoomToScale(zoom) px = (x + self.origin) / resolution py = (y + self.origin) / resolution return px, py def degreesToTiles(self, lon, lat, zoom): tx = math.floor((lon + 180) / 360 * math.pow(2, zoom)) ty = math.floor((1 - math.log(math.tan(self.toRadians(lat)) + 1 / math.cos(self.toRadians(lat))) / math.pi) / 2 * math.pow(2, zoom)) return [tx, ty] @staticmethod def tilesToDegrees(tx, ty, zoom): lon = tx / math.pow(2, zoom) * 360 - 180 n = math.pi - 2 * math.pi * ty / math.pow(2, zoom) lat = toDegrees(math.atan(0.5 * (math.exp(n) - math.exp(-n)))) return [lon, lat] def getTilesForGeometry(self, geometry, zoom): bounds = ee.List(geometry.bounds().coordinates().get(0)) ll = bounds.get(0).getInfo() # <-- Look at making this happen server-side ur = bounds.get(2).getInfo() # <-- Look at making this happen server-side tmin = self.degreesToTiles(ll[0], ll[1], zoom) tmax = self.degreesToTiles(ur[0], ur[1], zoom) tiles = [] for tx in range(tmin[0], tmax[0] + 1): for ty in range(tmax[1], tmin[1] + 1): bounds = self.getTileBounds(tx, ty, zoom) rect = ee.Geometry.Rectangle(bounds, 'EPSG:3857', False) tiles.append(ee.Feature(rect).set({'tx': tx, 'ty': ty, 'zoom': zoom })) return ee.FeatureCollection(tiles).filterBounds(geometry) def getTilesList(self, geometry, zoom): """Returns a list of individual features, where each feature element is a tile footprint.""" bounds = ee.List(geometry.bounds().coordinates().get(0)) ll = bounds.get(0).getInfo() # <-- Look at making this happen server-side ur = bounds.get(2).getInfo() # <-- Look at making this happen server-side tmin = self.degreesToTiles(ll[0], ll[1], zoom) tmax = self.degreesToTiles(ur[0], ur[1], zoom) tiles = [] for tx in range(tmin[0], tmax[0] + 1): for ty in range(tmax[1], tmin[1] + 1): bounds = self.getTileBounds(tx, ty, zoom) rect = ee.Geometry.Rectangle(bounds, 'EPSG:3857', False) tiles.append(ee.Feature(rect).set({'tx': tx, 'ty': ty, 'zoom': zoom })) return tiles def getTileBounds(self, tx, ty, zoom, tileSize=256): """Returns a FeatureCollection object, where each feature is a tile footprint""" ty = math.pow(2, zoom) - ty - 1 # TMS -> XYZ, flip y index tmp_min = self.pixelsToMeters(tx * tileSize, ty * tileSize, zoom) tmp_max = self.pixelsToMeters((tx + 1) * tileSize, (ty + 1) * tileSize, zoom) return [tmp_min, tmp_max] @staticmethod def toRadians(degrees): return degrees * math.pi / 180 @staticmethod def toDegrees(radians): return radians * 180 / math.pi class MovieMaker(object): """ Create movie tiles for a list of bounds, to go into a GCS bucket Parameters ---------- area: ee.Geometry.Polygon() A polygon that covers the area which you want to generate tiles within. zlist: list A list of integer values of z-levels to process, e.g. z=[3] or z=[3,4,5] privatekey_path: string A string specifying the direction of a json keyfile on your local filesystem e.g. "/Users/me/.privateKeys/key_with_bucket_permissions.json" bucket_name: string A string specifying a GCS bucket (to which your private key should have access) e.g. 'skydipper_materials' folder_path: string A string specifying a folder name to create within the target bucket. e.g. 'movie-tiles/DTEST' report_status: bool Set to true if you want the program to report on the files it is generating. Beware it can get long for high z-levels. """ def __init__(self, privatekey_path, bucket_name, folder_path, area=None, zlist=None, ic=None, report_status=False): self.storage_client = storage.Client.from_service_account_json(privatekey_path) self.privatekey_path = privatekey_path self.bucket = self.storage_client.get_bucket(bucket_name) self.bucket_name = bucket_name self.folder_path = folder_path self.tiler = EE_TILE_CALCS() self.area = area self.zlist = zlist self.ic = ic self.report_status = report_status ee.Initialize() def run(self): """Main worker method""" assert type(self.zlist) == list, "the zlist must be a list to run, e.g. zlist=[2]" assert type(self.area) == ee.geometry.Geometry, "An area of type ee.geometry.Geometry must be provided to run" for zlevel in self.zlist: print(f"🧨 Calculating Z-level {zlevel}") tileset = self.tiler.getTilesList(self.area, zlevel) d = self.initial_dic_creation(tileset=tileset) # Starting dic of whatever has been burned to the bucket to_do = self.get_items_by_state(d, 'WAITING') for unprocessed in to_do: z=unprocessed[0].split('/')[-3] x=unprocessed[0].split('/')[-2] y=unprocessed[0].split('/')[-1].split('.mp4')[0] if self.report_status: print(f'{z}/{x}/{y}') try: self.movie_maker(tile=unprocessed[1].get('tile'), z=z, x=x, y=y) except (ee.EEException) as err: sleep(60 * 5) # Simple - Wait 5 mins and try assigning tasks again (this assumes the only issue ) self.movie_maker(tile=unprocessed[1].get('tile'), z=z, x=x, y=y) print("Program ended normally. Note that after the files have been generated you should run MovieMaker().reNamer()") self.reNamer() return def movie_maker(self, tile, z, x, y): """Generates a single movie tile""" g = tile.geometry() filtered = self.ic.filterBounds(g) #print(f"🗺 Exporting movie-tile to {self.bucket_name}/{self.folder_path}/{z}/{x}/{y}.mp4") exp_task = ee.batch.Export.video.toCloudStorage( collection = filtered, description = f'{z}_{x}_{y}', bucket= self.bucket_name, fileNamePrefix = f"{self.folder_path}/{z}/{x}/{y}", dimensions = [256,256], framesPerSecond = 2, region = g) exp_task.start() def reNamer(self): """Renames source files to a clean target that removes jank added by EE.""" blob_gen = self.bucket.list_blobs(prefix=self.folder_path) blobs = [blob for blob in blob_gen] print(f'Scanning target {self.bucket_name}/{self.folder_path} for files that require renaming...') for blob in blobs: tmp_name = blob.name if tmp_name[-4:] == '.mp4' and ("ee-export-video" in tmp_name): target_name = f"{tmp_name.split('ee-export-video')[0]}.mp4" if self.report_status: print(f'renaming:{tmp_name}-->{target_name}') _ = self.bucket.rename_blob(blob, target_name) def getDoneFileList(self): """Returns list of file names in a bucket/path that have already been created""" blob_gen = self.bucket.list_blobs(prefix=self.folder_path) blobs = [blob for blob in blob_gen] completed_files = [] for blob in blobs: completed_files.append(blob.name) return completed_files def getFullTargetList(self, z): """ Return a list of all files we intend to create for a specified z (int) target to cover a given ee.Geometry input area, with a folder path (string) direction appended. """ target_list = [] tmp_fc = self.tiler.getTilesForGeometry(self.area, z) tmp_info = tmp_fc.getInfo() # <---this is the point where I have the properties, I should make sure to propagate them rather then call getinfo again target_list = [f"{self.folder_path}/{item.get('properties').get('zoom')}/{item.get('properties').get('tx')}/{item.get('properties').get('ty')}.mp4" for item in tmp_info.get('features')] return sorted(target_list) def get_current_status(self): """Consult the current EE Task list to see what's what""" batch_jobs = ee.batch.Task.list() processing_list = [] processing_status = [] badness_list = [] for job in batch_jobs: state = job.state try: tmp = job.config['description'].split("_") tmp_fname = f"{self.folder_path}/{tmp[0]}/{tmp[1]}/{tmp[2]}.mp4" processing_list.append(tmp_fname) processing_status.append(state) except: badness_list.append(job) return {'processing':processing_list, 'status': processing_status, 'badness': badness_list} def get_objective_list(self, tileset): """Returns a list of target files 1:1 for the target tiles""" tmp_list = [] for tile in tileset: tmp_str = tile.__str__() zoom = json.loads(tmp_str[11:-1]).get('arguments').get('value') ty = json.loads(tmp_str[11:-1]).get('arguments').get('object').get('arguments').get('value') tx = json.loads(tmp_str[11:-1]).get('arguments').get('object').get('arguments').get('object').get('arguments').get('value') tmp_list.append(f"{self.folder_path}/{zoom}/{tx}/{ty}.mp4") return tmp_list @staticmethod def generate_master_dic(objectives, tileset): d = {} for obj, tile in zip(objectives, tileset): d[obj] = {'tile': tile, 'status': "WAITING"} return d @staticmethod def get_items_by_state(d, state="WAITING"): result = [] for i in d.items(): if (i[1].get('status') == state): result.append(i) return result def initial_dic_creation(self, tileset): objectives = self.get_objective_list(tileset) # <--- calculate target files to keep track d = self.generate_master_dic(objectives=objectives, tileset=tileset) self.reNamer() done = self.getDoneFileList() # Consult the bucket and see what files have been completed for item in done: if d.get(item, None): d[item]['status']='COMPLETED' return d

46.059259

158

0.581296

bccbd751c2c4606ce707a9cc4299b8b83453bcfd

64,371

py

Python

salt/cloud/clouds/azurearm.py

wedge-jarrad/salt

7897d7922c70b039db8284976dcbf812eb3f2acf

[ "Apache-2.0" ]

null

salt/cloud/clouds/azurearm.py

wedge-jarrad/salt

7897d7922c70b039db8284976dcbf812eb3f2acf

[ "Apache-2.0" ]

null

salt/cloud/clouds/azurearm.py

wedge-jarrad/salt

7897d7922c70b039db8284976dcbf812eb3f2acf

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- ''' Azure ARM Cloud Module ====================== .. versionadded:: 2016.11.0 .. versionchanged:: Fluorine The Azure ARM cloud module is used to control access to Microsoft Azure Resource Manager :depends: * `Microsoft Azure SDK for Python <https://pypi.python.org/pypi/azure>`_ >= 2.0rc6 * `Microsoft Azure Storage SDK for Python <https://pypi.python.org/pypi/azure-storage>`_ >= 0.32 * `AutoRest swagger generator Python client runtime (Azure-specific module) <https://pypi.python.org/pypi/msrestazure>`_ >= 0.4 :configuration: Required provider parameters: if using username and password: * ``subscription_id`` * ``username`` * ``password`` if using a service principal: * ``subscription_id`` * ``tenant`` * ``client_id`` * ``secret`` Optional provider parameters: **cloud_environment**: Used to point the cloud driver to different API endpoints, such as Azure GovCloud. Possible values: * ``AZURE_PUBLIC_CLOUD`` (default) * ``AZURE_CHINA_CLOUD`` * ``AZURE_US_GOV_CLOUD`` * ``AZURE_GERMAN_CLOUD`` * HTTP base URL for a custom endpoint, such as Azure Stack. The ``/metadata/endpoints`` path will be added to the URL. **userdata** and **userdata_file**: Azure Resource Manager uses a separate VirtualMachineExtension object to pass userdata scripts to the virtual machine. Arbitrary shell commands can be passed via the ``userdata`` parameter, or via a file local to the Salt Cloud system using the ``userdata_file`` parameter. Note that the local file is not treated as a script by the extension, so "one-liners" probably work best. If greater functionality is desired, a web-hosted script file can be specified via ``userdata_file: https://raw.githubusercontent.com/account/repo/master/azure-script.py``, which will be executed on the system after VM creation. For Windows systems, script files ending in ``.ps1`` will be executed with ``powershell.exe``. The ``userdata`` parameter takes precedence over the ``userdata_file`` parameter when creating the custom script extension. **win_installer**: This parameter, which holds the local path to the Salt Minion installer package, is used to determine if the virtual machine type will be "Windows". Only set this parameter on profiles which install Windows operating systems. Example ``/etc/salt/cloud.providers`` or ``/etc/salt/cloud.providers.d/azure.conf`` configuration: .. code-block:: yaml my-azure-config with username and password: driver: azurearm subscription_id: 3287abc8-f98a-c678-3bde-326766fd3617 username: larry password: 123pass Or my-azure-config with service principal: driver: azurearm subscription_id: 3287abc8-f98a-c678-3bde-326766fd3617 tenant: ABCDEFAB-1234-ABCD-1234-ABCDEFABCDEF client_id: ABCDEFAB-1234-ABCD-1234-ABCDEFABCDEF secret: XXXXXXXXXXXXXXXXXXXXXXXX cloud_environment: AZURE_US_GOV_CLOUD The Service Principal can be created with the new Azure CLI (https://github.com/Azure/azure-cli) with: az ad sp create-for-rbac -n "http://<yourappname>" --role <role> --scopes <scope> For example, this creates a service principal with 'owner' role for the whole subscription: az ad sp create-for-rbac -n "http://mysaltapp" --role owner --scopes /subscriptions/3287abc8-f98a-c678-3bde-326766fd3617 *Note: review the details of Service Principals. Owner role is more than you normally need, and you can restrict scope to a resource group or individual resources. ''' # pylint: disable=wrong-import-position,wrong-import-order from __future__ import absolute_import, print_function, unicode_literals from multiprocessing import cpu_count from multiprocessing.pool import ThreadPool import importlib import logging import os import os.path import pprint import string import time # Salt libs import salt.cache import salt.config as config import salt.loader import salt.utils import salt.utils.cloud import salt.utils.yaml import salt.ext.six as six import salt.version from salt.exceptions import ( SaltCloudConfigError, SaltCloudSystemExit, SaltCloudExecutionFailure, SaltCloudExecutionTimeout, ) # Import 3rd-party libs HAS_LIBS = False try: import azure.mgmt.compute.models as compute_models import azure.mgmt.network.models as network_models from azure.storage.blob.blockblobservice import BlockBlobService from msrestazure.azure_exceptions import CloudError HAS_LIBS = True except ImportError: pass __opts__ = salt.config.minion_config('/etc/salt/minion') __utils__ = salt.loader.utils(__opts__) __virtualname__ = 'azurearm' if HAS_LIBS is True: _get_client = __utils__['azurearm.get_client'] # pylint: disable=invalid-name _log_cloud_error = __utils__['azurearm.log_cloud_error'] # pylint: disable=invalid-name _paged_object_to_list = __utils__['azurearm.paged_object_to_list'] # pylint: disable=invalid-name log = logging.getLogger(__name__) def __virtual__(): ''' Check for Azure configurations. ''' if get_configured_provider() is False: return False if get_dependencies() is False: return ( False, 'The following dependencies are required to use the AzureARM driver: ' 'Microsoft Azure SDK for Python >= 2.0rc6, ' 'Microsoft Azure Storage SDK for Python >= 0.32, ' 'MS REST Azure (msrestazure) >= 0.4' ) return __virtualname__ def get_api_versions(call=None, kwargs=None): # pylint: disable=unused-argument ''' Get a resource type api versions ''' if kwargs is None: kwargs = {} if 'resource_provider' not in kwargs: raise SaltCloudSystemExit( 'A resource_provider must be specified' ) if 'resource_type' not in kwargs: raise SaltCloudSystemExit( 'A resource_type must be specified' ) api_versions = [] try: resconn = get_conn(client_type='resource') provider_query = resconn.providers.get( resource_provider_namespace=kwargs['resource_provider'] ) for resource in provider_query.resource_types: if six.text_type(resource.resource_type) == kwargs['resource_type']: resource_dict = resource.as_dict() api_versions = resource_dict['api_versions'] except CloudError as exc: _log_cloud_error('resource', exc.message) return api_versions def get_resource_by_id(resource_id, api_version, extract_value=None): ''' Get an AzureARM resource by id ''' ret = {} try: resconn = get_conn(client_type='resource') resource_query = resconn.resources.get_by_id( resource_id=resource_id, api_version=api_version ) resource_dict = resource_query.as_dict() if extract_value is not None: ret = resource_dict[extract_value] else: ret = resource_dict except CloudError as exc: _log_cloud_error('resource', exc.message) ret = {'Error': exc.message} return ret def get_configured_provider(): ''' Return the first configured provider instance. ''' def __is_provider_configured(opts, provider, required_keys=()): ''' Check if the provider is configured. ''' if ':' in provider: alias, driver = provider.split(':') if alias not in opts['providers']: return False if driver not in opts['providers'][alias]: return False for key in required_keys: if opts['providers'][alias][driver].get(key, None) is None: return False return opts['providers'][alias][driver] for alias, drivers in six.iteritems(opts['providers']): for driver, provider_details in six.iteritems(drivers): if driver != provider: continue skip_provider = False for key in required_keys: if provider_details.get(key, None) is None: # This provider does not include all necessary keys, # continue to next one. skip_provider = True break if skip_provider: continue return provider_details return False provider = __is_provider_configured( __opts__, __active_provider_name__ or __virtualname__, ('subscription_id', 'tenant', 'client_id', 'secret') ) if provider is False: provider = __is_provider_configured( __opts__, __active_provider_name__ or __virtualname__, ('subscription_id', 'username', 'password') ) return provider def get_dependencies(): ''' Warn if dependencies aren't met. ''' return config.check_driver_dependencies( __virtualname__, {'azurearm': HAS_LIBS} ) def get_conn(client_type): ''' Return a connection object for a client type. ''' conn_kwargs = {} conn_kwargs['subscription_id'] = config.get_cloud_config_value( 'subscription_id', get_configured_provider(), __opts__, search_global=False ) cloud_env = config.get_cloud_config_value( 'cloud_environment', get_configured_provider(), __opts__, search_global=False ) if cloud_env is not None: conn_kwargs['cloud_environment'] = cloud_env tenant = config.get_cloud_config_value( 'tenant', get_configured_provider(), __opts__, search_global=False ) if tenant is not None: client_id = config.get_cloud_config_value( 'client_id', get_configured_provider(), __opts__, search_global=False ) secret = config.get_cloud_config_value( 'secret', get_configured_provider(), __opts__, search_global=False ) conn_kwargs.update({'client_id': client_id, 'secret': secret, 'tenant': tenant}) else: username = config.get_cloud_config_value( 'username', get_configured_provider(), __opts__, search_global=False ) password = config.get_cloud_config_value( 'password', get_configured_provider(), __opts__, search_global=False ) conn_kwargs.update({'username': username, 'password': password}) client = _get_client( client_type=client_type, **conn_kwargs ) return client def get_location(call=None): # pylint: disable=unused-argument ''' Return the location that is configured for this provider ''' return config.get_cloud_config_value( 'location', get_configured_provider(), __opts__, search_global=False ) def avail_locations(call=None): ''' Return a dict of all available regions. ''' if call == 'action': raise SaltCloudSystemExit( 'The avail_locations function must be called with ' '-f or --function, or with the --list-locations option' ) ret = {} ret['locations'] = [] try: resconn = get_conn(client_type='resource') provider_query = resconn.providers.get( resource_provider_namespace='Microsoft.Compute' ) locations = [] for resource in provider_query.resource_types: if six.text_type(resource.resource_type) == 'virtualMachines': resource_dict = resource.as_dict() locations = resource_dict['locations'] for location in locations: lowercase = location.lower().replace(' ', '') ret['locations'].append(lowercase) except CloudError as exc: _log_cloud_error('resource', exc.message) ret = {'Error': exc.message} return ret def avail_images(call=None): ''' Return a dict of all available images on the provider ''' if call == 'action': raise SaltCloudSystemExit( 'The avail_images function must be called with ' '-f or --function, or with the --list-images option' ) compconn = get_conn(client_type='compute') region = get_location() publishers = [] ret = {} def _get_publisher_images(publisher): ''' Get all images from a specific publisher ''' data = {} try: offers = compconn.virtual_machine_images.list_offers( location=region, publisher_name=publisher, ) for offer_obj in offers: offer = offer_obj.as_dict() skus = compconn.virtual_machine_images.list_skus( location=region, publisher_name=publisher, offer=offer['name'], ) for sku_obj in skus: sku = sku_obj.as_dict() results = compconn.virtual_machine_images.list( location=region, publisher_name=publisher, offer=offer['name'], skus=sku['name'], ) for version_obj in results: version = version_obj.as_dict() name = '|'.join(( publisher, offer['name'], sku['name'], version['name'], )) data[name] = { 'publisher': publisher, 'offer': offer['name'], 'sku': sku['name'], 'version': version['name'], } except CloudError as exc: _log_cloud_error('compute', exc.message) data = {publisher: exc.message} return data try: publishers_query = compconn.virtual_machine_images.list_publishers( location=region ) for publisher_obj in publishers_query: publisher = publisher_obj.as_dict() publishers.append(publisher['name']) except CloudError as exc: _log_cloud_error('compute', exc.message) pool = ThreadPool(cpu_count() * 6) results = pool.map_async(_get_publisher_images, publishers) results.wait() ret = {k: v for result in results.get() for k, v in six.iteritems(result)} return ret def avail_sizes(call=None): ''' Return a list of sizes available from the provider ''' if call == 'action': raise SaltCloudSystemExit( 'The avail_sizes function must be called with ' '-f or --function, or with the --list-sizes option' ) compconn = get_conn(client_type='compute') ret = {} location = get_location() try: sizes = compconn.virtual_machine_sizes.list( location=location ) for size_obj in sizes: size = size_obj.as_dict() ret[size['name']] = size except CloudError as exc: _log_cloud_error('compute', exc.message) ret = {'Error': exc.message} return ret def list_nodes(call=None): ''' List VMs on this Azure account ''' if call == 'action': raise SaltCloudSystemExit( 'The list_nodes function must be called with -f or --function.' ) ret = {} nodes = list_nodes_full() for node in nodes: ret[node] = {'name': node} for prop in ('id', 'image', 'size', 'state', 'private_ips', 'public_ips'): ret[node][prop] = nodes[node].get(prop) return ret def list_nodes_full(call=None): ''' List all VMs on the subscription with full information ''' if call == 'action': raise SaltCloudSystemExit( 'The list_nodes_full function must be called with -f or --function.' ) netapi_versions = get_api_versions(kwargs={ 'resource_provider': 'Microsoft.Network', 'resource_type': 'networkInterfaces' } ) netapi_version = netapi_versions[0] compconn = get_conn(client_type='compute') ret = {} def _get_node_info(node): ''' Get node info. ''' node_ret = {} node['id'] = node['vm_id'] node['size'] = node['hardware_profile']['vm_size'] node['state'] = node['provisioning_state'] node['public_ips'] = [] node['private_ips'] = [] node_ret[node['name']] = node try: image_ref = node['storage_profile']['image_reference'] node['image'] = '|'.join([ image_ref['publisher'], image_ref['offer'], image_ref['sku'], image_ref['version'], ]) except TypeError: try: node['image'] = node['storage_profile']['os_disk']['image']['uri'] except TypeError: node['image'] = None try: netifaces = node['network_profile']['network_interfaces'] for index, netiface in enumerate(netifaces): netiface_name = get_resource_by_id( netiface['id'], netapi_version, 'name' ) netiface, pubips, privips = _get_network_interface( netiface_name, node['resource_group'] ) node['network_profile']['network_interfaces'][index].update(netiface) node['public_ips'].extend(pubips) node['private_ips'].extend(privips) except Exception: pass node_ret[node['name']] = node return node_ret for group in list_resource_groups(): nodes = [] nodes_query = compconn.virtual_machines.list( resource_group_name=group ) for node_obj in nodes_query: node = node_obj.as_dict() node['resource_group'] = group nodes.append(node) pool = ThreadPool(cpu_count() * 6) results = pool.map_async(_get_node_info, nodes) results.wait() group_ret = {k: v for result in results.get() for k, v in six.iteritems(result)} ret.update(group_ret) return ret def list_resource_groups(call=None): ''' List resource groups associated with the subscription ''' if call == 'action': raise SaltCloudSystemExit( 'The list_hosted_services function must be called with ' '-f or --function' ) resconn = get_conn(client_type='resource') ret = {} try: groups = resconn.resource_groups.list() for group_obj in groups: group = group_obj.as_dict() ret[group['name']] = group except CloudError as exc: _log_cloud_error('resource', exc.message) ret = {'Error': exc.message} return ret def show_instance(name, call=None): ''' Show the details from AzureARM concerning an instance ''' if call != 'action': raise SaltCloudSystemExit( 'The show_instance action must be called with -a or --action.' ) try: node = list_nodes_full('function')[name] except KeyError: log.debug('Failed to get data for node \'%s\'', name) node = {} __utils__['cloud.cache_node'](node, __active_provider_name__, __opts__) return node def delete_interface(call=None, kwargs=None): # pylint: disable=unused-argument ''' Delete a network interface. ''' if kwargs is None: kwargs = {} netconn = get_conn(client_type='network') if kwargs.get('resource_group') is None: kwargs['resource_group'] = config.get_cloud_config_value( 'resource_group', {}, __opts__, search_global=True ) ips = [] iface = netconn.network_interfaces.get( kwargs['resource_group'], kwargs['iface_name'], ) iface_name = iface.name for ip_ in iface.ip_configurations: ips.append(ip_.name) poller = netconn.network_interfaces.delete( kwargs['resource_group'], kwargs['iface_name'], ) poller.wait() for ip_ in ips: poller = netconn.public_ip_addresses.delete(kwargs['resource_group'], ip_) poller.wait() return {iface_name: ips} def _get_public_ip(name, resource_group): ''' Get the public ip address details by name. ''' netconn = get_conn(client_type='network') try: pubip_query = netconn.public_ip_addresses.get( resource_group_name=resource_group, public_ip_address_name=name ) pubip = pubip_query.as_dict() except CloudError as exc: _log_cloud_error('network', exc.message) pubip = {'error': exc.message} return pubip def _get_network_interface(name, resource_group): ''' Get a network interface. ''' public_ips = [] private_ips = [] netapi_versions = get_api_versions(kwargs={ 'resource_provider': 'Microsoft.Network', 'resource_type': 'publicIPAddresses' } ) netapi_version = netapi_versions[0] netconn = get_conn(client_type='network') netiface_query = netconn.network_interfaces.get( resource_group_name=resource_group, network_interface_name=name ) netiface = netiface_query.as_dict() for index, ip_config in enumerate(netiface['ip_configurations']): if ip_config.get('private_ip_address') is not None: private_ips.append(ip_config['private_ip_address']) if 'id' in ip_config.get('public_ip_address', {}): public_ip_name = get_resource_by_id( ip_config['public_ip_address']['id'], netapi_version, 'name' ) public_ip = _get_public_ip(public_ip_name, resource_group) public_ips.append(public_ip['ip_address']) netiface['ip_configurations'][index]['public_ip_address'].update(public_ip) return netiface, public_ips, private_ips def create_network_interface(call=None, kwargs=None): ''' Create a network interface. ''' if call != 'action': raise SaltCloudSystemExit( 'The create_network_interface action must be called with -a or --action.' ) # pylint: disable=invalid-name IPAllocationMethod = getattr( network_models, 'IPAllocationMethod' ) # pylint: disable=invalid-name NetworkInterface = getattr( network_models, 'NetworkInterface' ) # pylint: disable=invalid-name NetworkInterfaceIPConfiguration = getattr( network_models, 'NetworkInterfaceIPConfiguration' ) # pylint: disable=invalid-name PublicIPAddress = getattr( network_models, 'PublicIPAddress' ) if not isinstance(kwargs, dict): kwargs = {} vm_ = kwargs netconn = get_conn(client_type='network') if kwargs.get('location') is None: kwargs['location'] = get_location() if kwargs.get('network') is None: kwargs['network'] = config.get_cloud_config_value( 'network', vm_, __opts__, search_global=False ) if kwargs.get('subnet') is None: kwargs['subnet'] = config.get_cloud_config_value( 'subnet', vm_, __opts__, search_global=False ) if kwargs.get('network_resource_group') is None: kwargs['resource_group'] = config.get_cloud_config_value( 'resource_group', vm_, __opts__, search_global=False ) else: kwargs['resource_group'] = kwargs['network_resource_group'] if kwargs.get('iface_name') is None: kwargs['iface_name'] = '{0}-iface0'.format(vm_['name']) subnet_obj = netconn.subnets.get( resource_group_name=kwargs['resource_group'], virtual_network_name=kwargs['network'], subnet_name=kwargs['subnet'], ) ip_kwargs = {} ip_configurations = None if 'private_ip_address' in kwargs.keys(): ip_kwargs['private_ip_address'] = kwargs['private_ip_address'] ip_kwargs['private_ip_allocation_method'] = IPAllocationMethod.static else: ip_kwargs['private_ip_allocation_method'] = IPAllocationMethod.dynamic if kwargs.get('allocate_public_ip') is True: pub_ip_name = '{0}-ip'.format(kwargs['iface_name']) poller = netconn.public_ip_addresses.create_or_update( resource_group_name=kwargs['resource_group'], public_ip_address_name=pub_ip_name, parameters=PublicIPAddress( location=kwargs['location'], public_ip_allocation_method=IPAllocationMethod.static, ), ) count = 0 poller.wait() while True: try: pub_ip_data = netconn.public_ip_addresses.get( kwargs['resource_group'], pub_ip_name, ) if pub_ip_data.ip_address: # pylint: disable=no-member ip_kwargs['public_ip_address'] = PublicIPAddress( six.text_type(pub_ip_data.id), # pylint: disable=no-member ) ip_configurations = [ NetworkInterfaceIPConfiguration( name='{0}-ip'.format(kwargs['iface_name']), subnet=subnet_obj, **ip_kwargs ) ] break except CloudError as exc: log.error('There was a cloud error: {0}'.format(exc)) count += 1 if count > 120: raise ValueError('Timed out waiting for public IP Address.') time.sleep(5) else: priv_ip_name = '{0}-ip'.format(kwargs['iface_name']) ip_configurations = [ NetworkInterfaceIPConfiguration( name=priv_ip_name, subnet=subnet_obj, **ip_kwargs ) ] network_security_group = None if kwargs.get('security_group') is not None: network_security_group = netconn.network_security_groups.get( resource_group_name=kwargs['resource_group'], network_security_group_name=kwargs['security_group'], ) iface_params = NetworkInterface( location=kwargs['location'], network_security_group=network_security_group, ip_configurations=ip_configurations, ) poller = netconn.network_interfaces.create_or_update( kwargs['resource_group'], kwargs['iface_name'], iface_params ) try: poller.wait() except Exception as exc: log.warn('Network interface creation could not be polled. ' 'It is likely that we are reusing an existing interface. (%s)', exc) count = 0 while True: try: return _get_network_interface(kwargs['iface_name'], kwargs['resource_group']) except CloudError: count += 1 if count > 120: raise ValueError('Timed out waiting for operation to complete.') time.sleep(5) def request_instance(vm_): ''' Request a VM from Azure. ''' compconn = get_conn(client_type='compute') # pylint: disable=invalid-name CachingTypes = getattr( compute_models, 'CachingTypes' ) # pylint: disable=invalid-name DataDisk = getattr( compute_models, 'DataDisk' ) # pylint: disable=invalid-name DiskCreateOptionTypes = getattr( compute_models, 'DiskCreateOptionTypes' ) # pylint: disable=invalid-name HardwareProfile = getattr( compute_models, 'HardwareProfile' ) # pylint: disable=invalid-name ImageReference = getattr( compute_models, 'ImageReference' ) # pylint: disable=invalid-name LinuxConfiguration = getattr( compute_models, 'LinuxConfiguration' ) # pylint: disable=invalid-name SshConfiguration = getattr( compute_models, 'SshConfiguration' ) # pylint: disable=invalid-name SshPublicKey = getattr( compute_models, 'SshPublicKey' ) # pylint: disable=invalid-name NetworkInterfaceReference = getattr( compute_models, 'NetworkInterfaceReference' ) # pylint: disable=invalid-name NetworkProfile = getattr( compute_models, 'NetworkProfile' ) # pylint: disable=invalid-name OSDisk = getattr( compute_models, 'OSDisk' ) # pylint: disable=invalid-name OSProfile = getattr( compute_models, 'OSProfile' ) # pylint: disable=invalid-name StorageProfile = getattr( compute_models, 'StorageProfile' ) # pylint: disable=invalid-name VirtualHardDisk = getattr( compute_models, 'VirtualHardDisk' ) # pylint: disable=invalid-name VirtualMachine = getattr( compute_models, 'VirtualMachine' ) # pylint: disable=invalid-name VirtualMachineSizeTypes = getattr( compute_models, 'VirtualMachineSizeTypes' ) if vm_.get('driver') is None: vm_['driver'] = 'azurearm' if vm_.get('location') is None: vm_['location'] = get_location() if vm_.get('resource_group') is None: vm_['resource_group'] = config.get_cloud_config_value( 'resource_group', vm_, __opts__, search_global=True ) if vm_.get('name') is None: vm_['name'] = config.get_cloud_config_value( 'name', vm_, __opts__, search_global=True ) # pylint: disable=unused-variable iface_data, public_ips, private_ips = create_network_interface( call='action', kwargs=vm_ ) vm_['iface_id'] = iface_data['id'] disk_name = '{0}-vol0'.format(vm_['name']) vm_username = config.get_cloud_config_value( 'ssh_username', vm_, __opts__, search_global=True, default=config.get_cloud_config_value( 'win_username', vm_, __opts__, search_global=True ) ) ssh_publickeyfile_contents = None ssh_publickeyfile = config.get_cloud_config_value( 'ssh_publickeyfile', vm_, __opts__, search_global=False, default=None ) if ssh_publickeyfile is not None: try: with salt.utils.fopen(ssh_publickeyfile, 'r') as spkc_: ssh_publickeyfile_contents = spkc_.read() except Exception as exc: raise SaltCloudConfigError( "Failed to read ssh publickey file '{0}': " "{1}".format(ssh_publickeyfile, exc.args[-1]) ) disable_password_authentication = config.get_cloud_config_value( 'disable_password_authentication', vm_, __opts__, search_global=False, default=False ) vm_password = config.get_cloud_config_value( 'ssh_password', vm_, __opts__, search_global=True, default=config.get_cloud_config_value( 'win_password', vm_, __opts__, search_global=True ) ) os_kwargs = {} win_installer = config.get_cloud_config_value( 'win_installer', vm_, __opts__, search_global=True ) if not win_installer and ssh_publickeyfile_contents is not None: sshpublickey = SshPublicKey( key_data=ssh_publickeyfile_contents, path='/home/{0}/.ssh/authorized_keys'.format(vm_username), ) sshconfiguration = SshConfiguration( public_keys=[sshpublickey], ) linuxconfiguration = LinuxConfiguration( disable_password_authentication=disable_password_authentication, ssh=sshconfiguration, ) os_kwargs['linux_configuration'] = linuxconfiguration if win_installer or (vm_password is not None and not disable_password_authentication): if not isinstance(vm_password, str): raise SaltCloudSystemExit( 'The admin password must be a string.' ) if len(vm_password) < 8 or len(vm_password) > 123: raise SaltCloudSystemExit( 'The admin password must be between 8-123 characters long.' ) complexity = 0 if any(char.isdigit() for char in vm_password): complexity += 1 if any(char.isupper() for char in vm_password): complexity += 1 if any(char.islower() for char in vm_password): complexity += 1 if any(char in string.punctuation for char in vm_password): complexity += 1 if complexity < 3: raise SaltCloudSystemExit( 'The admin password must contain at least 3 of the following types: ' 'upper, lower, digits, special characters' ) os_kwargs['admin_password'] = vm_password cloud_env = _get_cloud_environment() storage_endpoint_suffix = cloud_env.suffixes.storage_endpoint if isinstance(vm_.get('volumes'), six.string_types): volumes = salt.utils.yaml.safe_load(vm_['volumes']) else: volumes = vm_.get('volumes') data_disks = None if isinstance(volumes, list): data_disks = [] else: volumes = [] lun = 0 luns = [] for volume in volumes: if isinstance(volume, six.string_types): volume = {'name': volume} volume.setdefault( 'name', volume.get( 'name', volume.get( 'name', '{0}-datadisk{1}'.format(vm_['name'], six.text_type(lun)) ) ) ) volume.setdefault( 'disk_size_gb', volume.get( 'logical_disk_size_in_gb', volume.get('size', 100) ) ) # Old kwarg was host_caching, new name is caching volume.setdefault('caching', volume.get('host_caching', 'ReadOnly')) while lun in luns: lun += 1 if lun > 15: log.error('Maximum lun count has been reached') break volume.setdefault('lun', lun) lun += 1 # The default vhd is {vm_name}-datadisk{lun}.vhd if 'media_link' in volume: volume['vhd'] = VirtualHardDisk(volume['media_link']) del volume['media_link'] elif volume.get('vhd') == 'unmanaged': volume['vhd'] = VirtualHardDisk( 'https://{0}.blob.{1}/vhds/{2}-datadisk{3}.vhd'.format( vm_['storage_account'], storage_endpoint_suffix, vm_['name'], volume['lun'], ), ) elif 'vhd' in volume: volume['vhd'] = VirtualHardDisk(volume['vhd']) if 'image' in volume: volume['create_option'] = DiskCreateOptionTypes.from_image elif 'attach' in volume: volume['create_option'] = DiskCreateOptionTypes.attach else: volume['create_option'] = DiskCreateOptionTypes.empty data_disks.append(DataDisk(**volume)) if vm_['image'].startswith('http') or vm_.get('vhd') == 'unmanaged': if vm_['image'].startswith('http'): source_image = VirtualHardDisk(vm_['image']) img_ref = None else: source_image = None img_pub, img_off, img_sku, img_ver = vm_['image'].split('|') img_ref = ImageReference( publisher=img_pub, offer=img_off, sku=img_sku, version=img_ver, ) if win_installer: os_type = 'Windows' else: os_type = 'Linux' os_disk = OSDisk( caching=CachingTypes.none, create_option=DiskCreateOptionTypes.from_image, name=disk_name, vhd=VirtualHardDisk( 'https://{0}.blob.{1}/vhds/{2}.vhd'.format( vm_['storage_account'], storage_endpoint_suffix, disk_name, ), ), os_type=os_type, image=source_image, disk_size_gb=vm_.get('os_disk_size_gb') ) else: img_pub, img_off, img_sku, img_ver = vm_['image'].split('|') source_image = None os_type = None os_disk = None img_ref = ImageReference( publisher=img_pub, offer=img_off, sku=img_sku, version=img_ver, ) userdata_file = config.get_cloud_config_value( 'userdata_file', vm_, __opts__, search_global=False, default=None ) userdata = config.get_cloud_config_value( 'userdata', vm_, __opts__, search_global=False, default=None ) userdata_template = config.get_cloud_config_value( 'userdata_template', vm_, __opts__, search_global=False, default=None ) if userdata_file: if os.path.exists(userdata_file): with salt.utils.fopen(userdata_file, 'r') as fh_: userdata = fh_.read() if userdata and userdata_template: userdata = salt.utils.cloud.userdata_template(__opts__, vm_, userdata) custom_extension = None if userdata is not None or userdata_file is not None: try: if win_installer: publisher = 'Microsoft.Compute' virtual_machine_extension_type = 'CustomScriptExtension' type_handler_version = '1.8' if userdata_file and userdata_file.endswith('.ps1'): command_prefix = 'powershell -ExecutionPolicy Unrestricted -File ' else: command_prefix = '' else: publisher = 'Microsoft.Azure.Extensions' virtual_machine_extension_type = 'CustomScript' type_handler_version = '2.0' command_prefix = '' settings = {} if userdata: settings['commandToExecute'] = userdata elif userdata_file.startswith('http'): settings['fileUris'] = [userdata_file] settings['commandToExecute'] = command_prefix + './' + userdata_file[userdata_file.rfind('/')+1:] custom_extension = { 'resource_group': vm_['resource_group'], 'virtual_machine_name': vm_['name'], 'extension_name': vm_['name'] + '_custom_userdata_script', 'location': vm_['location'], 'publisher': publisher, 'virtual_machine_extension_type': virtual_machine_extension_type, 'type_handler_version': type_handler_version, 'auto_upgrade_minor_version': True, 'settings': settings, 'protected_settings': None } except Exception as exc: log.exception('Failed to encode userdata: %s', exc) params = VirtualMachine( location=vm_['location'], plan=None, hardware_profile=HardwareProfile( vm_size=getattr( VirtualMachineSizeTypes, vm_['size'].lower() ), ), storage_profile=StorageProfile( os_disk=os_disk, data_disks=data_disks, image_reference=img_ref, ), os_profile=OSProfile( admin_username=vm_username, computer_name=vm_['name'], **os_kwargs ), network_profile=NetworkProfile( network_interfaces=[ NetworkInterfaceReference(vm_['iface_id']), ], ), ) __utils__['cloud.fire_event']( 'event', 'requesting instance', 'salt/cloud/{0}/requesting'.format(vm_['name']), args=__utils__['cloud.filter_event']( 'requesting', vm_, ['name', 'profile', 'provider', 'driver'] ), sock_dir=__opts__['sock_dir'], transport=__opts__['transport'] ) try: vm_create = compconn.virtual_machines.create_or_update( resource_group_name=vm_['resource_group'], vm_name=vm_['name'], parameters=params ) vm_create.wait() if custom_extension: create_or_update_vmextension(kwargs=custom_extension) except CloudError as exc: _log_cloud_error('compute', exc.message) try: return show_instance(vm_['name'], call='action') except CloudError: return {} def create(vm_): ''' Create a single VM from a data dict. ''' try: if vm_['profile'] and config.is_profile_configured( __opts__, __active_provider_name__ or 'azurearm', vm_['profile'], vm_=vm_ ) is False: return False except AttributeError: pass if vm_.get('bootstrap_interface') is None: vm_['bootstrap_interface'] = 'public' __utils__['cloud.fire_event']( 'event', 'starting create', 'salt/cloud/{0}/creating'.format(vm_['name']), args=__utils__['cloud.filter_event']( 'creating', vm_, ['name', 'profile', 'provider', 'driver'] ), sock_dir=__opts__['sock_dir'], transport=__opts__['transport'] ) __utils__['cloud.cachedir_index_add']( vm_['name'], vm_['profile'], 'azurearm', vm_['driver'] ) location = get_location(vm_) vm_['location'] = location log.info('Creating Cloud VM %s in %s', vm_['name'], location) request_instance(vm_=vm_) def _query_node_data(name, bootstrap_interface): ''' Query node data. ''' data = show_instance(name, call='action') ip_address = None if len(data.keys()) == 0: return False if bootstrap_interface == 'public': ip_address = data['public_ips'][0] if bootstrap_interface == 'private': ip_address = data['private_ips'][0] if ip_address is None: return False return ip_address try: data = salt.utils.cloud.wait_for_ip( _query_node_data, update_args=(vm_['name'], vm_['bootstrap_interface'],), timeout=config.get_cloud_config_value( 'wait_for_ip_timeout', vm_, __opts__, default=10 * 60), interval=config.get_cloud_config_value( 'wait_for_ip_interval', vm_, __opts__, default=10), interval_multiplier=config.get_cloud_config_value( 'wait_for_ip_interval_multiplier', vm_, __opts__, default=1), ) except ( SaltCloudExecutionTimeout, SaltCloudExecutionFailure, SaltCloudSystemExit ) as exc: try: log.warning(exc) finally: raise SaltCloudSystemExit(six.text_type(exc)) vm_['ssh_host'] = data if not vm_.get('ssh_username'): vm_['ssh_username'] = config.get_cloud_config_value( 'ssh_username', vm_, __opts__ ) vm_['password'] = config.get_cloud_config_value( 'ssh_password', vm_, __opts__ ) ret = __utils__['cloud.bootstrap'](vm_, __opts__) data = show_instance(vm_['name'], call='action') log.info('Created Cloud VM \'%s\'', vm_['name']) log.debug( '\'%s\' VM creation details:\n%s', vm_['name'], pprint.pformat(data) ) ret.update(data) __utils__['cloud.fire_event']( 'event', 'created instance', 'salt/cloud/{0}/created'.format(vm_['name']), args=__utils__['cloud.filter_event']( 'created', vm_, ['name', 'profile', 'provider', 'driver'] ), sock_dir=__opts__['sock_dir'], transport=__opts__['transport'] ) return ret def destroy(name, call=None, kwargs=None): # pylint: disable=unused-argument ''' Destroy a VM. CLI Examples: .. code-block:: bash salt-cloud -d myminion salt-cloud -a destroy myminion service_name=myservice ''' if kwargs is None: kwargs = {} if call == 'function': raise SaltCloudSystemExit( 'The destroy action must be called with -d, --destroy, ' '-a or --action.' ) compconn = get_conn(client_type='compute') node_data = show_instance(name, call='action') if node_data['storage_profile']['os_disk'].get('managed_disk'): vhd = node_data['storage_profile']['os_disk']['managed_disk']['id'] else: vhd = node_data['storage_profile']['os_disk']['vhd']['uri'] ret = {name: {}} log.debug('Deleting VM') result = compconn.virtual_machines.delete(node_data['resource_group'], name) result.wait() if __opts__.get('update_cachedir', False) is True: __utils__['cloud.delete_minion_cachedir']( name, __active_provider_name__.split(':')[0], __opts__ ) cleanup_disks = config.get_cloud_config_value( 'cleanup_disks', get_configured_provider(), __opts__, search_global=False, default=False, ) if cleanup_disks: cleanup_vhds = kwargs.get( 'delete_vhd', config.get_cloud_config_value( 'cleanup_vhds', get_configured_provider(), __opts__, search_global=False, default=False ) ) if cleanup_vhds: log.debug('Deleting vhd') comps = vhd.split('/') container = comps[-2] blob = comps[-1] ret[name]['delete_disk'] = { 'delete_disks': cleanup_disks, 'delete_vhd': cleanup_vhds, 'container': container, 'blob': blob, } if vhd.startswith('http'): ret[name]['data'] = delete_blob( kwargs={'container': container, 'blob': blob}, call='function' ) else: ret[name]['data'] = delete_managed_disk( kwargs={'resource_group': node_data['resource_group'], 'container': container, 'blob': blob}, call='function' ) cleanup_data_disks = kwargs.get( 'delete_data_disks', config.get_cloud_config_value( 'cleanup_data_disks', get_configured_provider(), __opts__, search_global=False, default=False ) ) if cleanup_data_disks: log.debug('Deleting data_disks') ret[name]['data_disks'] = {} for disk in node_data['storage_profile']['data_disks']: datavhd = disk.get('managed_disk', {}).get('id') or disk.get('vhd', {}).get('uri') comps = datavhd.split('/') container = comps[-2] blob = comps[-1] ret[name]['data_disks'][disk['name']] = { 'delete_disks': cleanup_disks, 'delete_vhd': cleanup_vhds, 'container': container, 'blob': blob, } if datavhd.startswith('http'): ret[name]['data'] = delete_blob( kwargs={'container': container, 'blob': blob}, call='function' ) else: ret[name]['data'] = delete_managed_disk( kwargs={'resource_group': node_data['resource_group'], 'container': container, 'blob': blob}, call='function' ) cleanup_interfaces = config.get_cloud_config_value( 'cleanup_interfaces', get_configured_provider(), __opts__, search_global=False, default=False ) if cleanup_interfaces: ret[name]['cleanup_network'] = { 'cleanup_interfaces': cleanup_interfaces, 'resource_group': node_data['resource_group'], 'data': [], } ifaces = node_data['network_profile']['network_interfaces'] for iface in ifaces: resource_group = iface['id'].split('/')[4] ret[name]['cleanup_network']['data'].append( delete_interface( kwargs={ 'resource_group': resource_group, 'iface_name': iface['name'], }, call='function', ) ) return ret def list_storage_accounts(call=None): ''' List storage accounts within the subscription. ''' if call == 'action': raise SaltCloudSystemExit( 'The list_storage_accounts function must be called with ' '-f or --function' ) storconn = get_conn(client_type='storage') ret = {} try: accounts_query = storconn.storage_accounts.list() accounts = _paged_object_to_list(accounts_query) for account in accounts: ret[account['name']] = account except CloudError as exc: _log_cloud_error('storage', exc.message) ret = {'Error': exc.message} return ret def _get_cloud_environment(): ''' Get the cloud environment object. ''' cloud_environment = config.get_cloud_config_value( 'cloud_environment', get_configured_provider(), __opts__, search_global=False ) try: cloud_env_module = importlib.import_module('msrestazure.azure_cloud') cloud_env = getattr(cloud_env_module, cloud_environment or 'AZURE_PUBLIC_CLOUD') except (AttributeError, ImportError): raise SaltCloudSystemExit( 'The azure {0} cloud environment is not available.'.format(cloud_environment) ) return cloud_env def _get_block_blob_service(kwargs=None): ''' Get the block blob storage service. ''' resource_group = kwargs.get('resource_group') or config.get_cloud_config_value( 'resource_group', get_configured_provider(), __opts__, search_global=False ) sas_token = kwargs.get('sas_token') or config.get_cloud_config_value( 'sas_token', get_configured_provider(), __opts__, search_global=False ) storage_account = kwargs.get('storage_account') or config.get_cloud_config_value( 'storage_account', get_configured_provider(), __opts__, search_global=False ) storage_key = kwargs.get('storage_key') or config.get_cloud_config_value( 'storage_key', get_configured_provider(), __opts__, search_global=False ) if not resource_group: raise SaltCloudSystemExit( 'A resource group must be specified' ) if not storage_account: raise SaltCloudSystemExit( 'A storage account must be specified' ) if not storage_key: storconn = get_conn(client_type='storage') storage_keys = storconn.storage_accounts.list_keys(resource_group, storage_account) storage_keys = {v.key_name: v.value for v in storage_keys.keys} storage_key = next(six.itervalues(storage_keys)) cloud_env = _get_cloud_environment() endpoint_suffix = cloud_env.suffixes.storage_endpoint return BlockBlobService(storage_account, storage_key, sas_token=sas_token, endpoint_suffix=endpoint_suffix) def list_blobs(call=None, kwargs=None): # pylint: disable=unused-argument ''' List blobs. ''' if kwargs is None: kwargs = {} if 'container' not in kwargs: raise SaltCloudSystemExit( 'A container must be specified' ) storageservice = _get_block_blob_service(kwargs) ret = {} try: for blob in storageservice.list_blobs(kwargs['container']).items: ret[blob.name] = { 'blob_type': blob.properties.blob_type, 'last_modified': blob.properties.last_modified.isoformat(), 'server_encrypted': blob.properties.server_encrypted, } except Exception as exc: log.warn(six.text_type(exc)) return ret def delete_blob(call=None, kwargs=None): # pylint: disable=unused-argument ''' Delete a blob from a container. ''' if kwargs is None: kwargs = {} if 'container' not in kwargs: raise SaltCloudSystemExit( 'A container must be specified' ) if 'blob' not in kwargs: raise SaltCloudSystemExit( 'A blob must be specified' ) storageservice = _get_block_blob_service(kwargs) storageservice.delete_blob(kwargs['container'], kwargs['blob']) return True def delete_managed_disk(call=None, kwargs=None): # pylint: disable=unused-argument ''' Delete a managed disk from a resource group. ''' compconn = get_conn(client_type='compute') try: compconn.disks.delete(kwargs['resource_group'], kwargs['blob']) except Exception as exc: log.error('Error deleting managed disk %s - %s', kwargs.get('blob'), six.text_type(exc)) return False return True def list_virtual_networks(call=None, kwargs=None): ''' List virtual networks. ''' if kwargs is None: kwargs = {} if call == 'action': raise SaltCloudSystemExit( 'The avail_sizes function must be called with ' '-f or --function' ) netconn = get_conn(client_type='network') resource_groups = list_resource_groups() ret = {} for group in resource_groups: try: networks = netconn.virtual_networks.list( resource_group_name=group ) except CloudError: networks = {} for network_obj in networks: network = network_obj.as_dict() ret[network['name']] = network ret[network['name']]['subnets'] = list_subnets( kwargs={'resource_group': group, 'network': network['name']} ) return ret def list_subnets(call=None, kwargs=None): ''' List subnets in a virtual network. ''' if kwargs is None: kwargs = {} if call == 'action': raise SaltCloudSystemExit( 'The avail_sizes function must be called with ' '-f or --function' ) netconn = get_conn(client_type='network') resource_group = kwargs.get('resource_group') or config.get_cloud_config_value( 'resource_group', get_configured_provider(), __opts__, search_global=False ) if not resource_group and 'group' in kwargs and 'resource_group' not in kwargs: resource_group = kwargs['group'] if not resource_group: raise SaltCloudSystemExit( 'A resource group must be specified' ) if kwargs.get('network') is None: kwargs['network'] = config.get_cloud_config_value( 'network', get_configured_provider(), __opts__, search_global=False ) if 'network' not in kwargs or kwargs['network'] is None: raise SaltCloudSystemExit( 'A "network" must be specified' ) ret = {} subnets = netconn.subnets.list(resource_group, kwargs['network']) for subnet in subnets: ret[subnet.name] = subnet.as_dict() ret[subnet.name]['ip_configurations'] = {} for ip_ in subnet.ip_configurations: comps = ip_.id.split('/') name = comps[-1] ret[subnet.name]['ip_configurations'][name] = ip_.as_dict() ret[subnet.name]['ip_configurations'][name]['subnet'] = subnet.name ret[subnet.name]['resource_group'] = resource_group return ret def create_or_update_vmextension(call=None, kwargs=None): # pylint: disable=unused-argument ''' .. versionadded:: Fluorine Create or update a VM extension object "inside" of a VM object. required kwargs: .. code-block:: yaml extension_name: myvmextension virtual_machine_name: myvm settings: {"commandToExecute": "hostname"} optional kwargs: .. code-block:: yaml resource_group: < inferred from cloud configs > location: < inferred from cloud configs > publisher: < default: Microsoft.Azure.Extensions > virtual_machine_extension_type: < default: CustomScript > type_handler_version: < default: 2.0 > auto_upgrade_minor_version: < default: True > protected_settings: < default: None > ''' if kwargs is None: kwargs = {} if 'extension_name' not in kwargs: raise SaltCloudSystemExit( 'An extension name must be specified' ) if 'virtual_machine_name' not in kwargs: raise SaltCloudSystemExit( 'A virtual machine name must be specified' ) compconn = get_conn(client_type='compute') # pylint: disable=invalid-name VirtualMachineExtension = getattr( compute_models, 'VirtualMachineExtension' ) resource_group = kwargs.get('resource_group') or config.get_cloud_config_value( 'resource_group', get_configured_provider(), __opts__, search_global=False ) if not resource_group: raise SaltCloudSystemExit( 'A resource group must be specified' ) location = kwargs.get('location') or get_location() if not location: raise SaltCloudSystemExit( 'A location must be specified' ) publisher = kwargs.get('publisher', 'Microsoft.Azure.Extensions') virtual_machine_extension_type = kwargs.get('virtual_machine_extension_type', 'CustomScript') type_handler_version = kwargs.get('type_handler_version', '2.0') auto_upgrade_minor_version = kwargs.get('auto_upgrade_minor_version', True) settings = kwargs.get('settings', {}) protected_settings = kwargs.get('protected_settings') if not isinstance(settings, dict): raise SaltCloudSystemExit( 'VM extension settings are not valid' ) elif 'commandToExecute' not in settings and 'script' not in settings: raise SaltCloudSystemExit( 'VM extension settings are not valid. Either commandToExecute or script must be specified.' ) log.info('Creating VM extension %s', kwargs['extension_name']) ret = {} try: params = VirtualMachineExtension( location=location, publisher=publisher, virtual_machine_extension_type=virtual_machine_extension_type, type_handler_version=type_handler_version, auto_upgrade_minor_version=auto_upgrade_minor_version, settings=settings, protected_settings=protected_settings ) poller = compconn.virtual_machine_extensions.create_or_update( resource_group, kwargs['virtual_machine_name'], kwargs['extension_name'], params ) ret = poller.result() ret = ret.as_dict() except CloudError as exc: _log_cloud_error('compute', 'Error attempting to create the VM extension: {0}'.format(exc.message)) ret = {'error': exc.message} return ret def stop(name, call=None): ''' .. versionadded:: Fluorine Stop (deallocate) a VM CLI Examples: .. code-block:: bash salt-cloud -a stop myminion ''' if call == 'function': raise SaltCloudSystemExit( 'The stop action must be called with -a or --action.' ) compconn = get_conn(client_type='compute') resource_group = config.get_cloud_config_value( 'resource_group', get_configured_provider(), __opts__, search_global=False ) ret = {} if not resource_group: groups = list_resource_groups() for group in groups: try: instance = compconn.virtual_machines.deallocate( vm_name=name, resource_group_name=group ) instance.wait() vm_result = instance.result() ret = vm_result.as_dict() break except CloudError as exc: if 'was not found' in exc.message: continue else: ret = {'error': exc.message} if not ret: _log_cloud_error('compute', 'Unable to find virtual machine with name: {0}'.format(name)) ret = {'error': 'Unable to find virtual machine with name: {0}'.format(name)} else: try: instance = compconn.virtual_machines.deallocate( vm_name=name, resource_group_name=resource_group ) instance.wait() vm_result = instance.result() ret = vm_result.as_dict() except CloudError as exc: _log_cloud_error('compute', 'Error attempting to stop {0}: {1}'.format(name, exc.message)) ret = {'error': exc.message} return ret def start(name, call=None): ''' .. versionadded:: Fluorine Start a VM CLI Examples: .. code-block:: bash salt-cloud -a start myminion ''' if call == 'function': raise SaltCloudSystemExit( 'The start action must be called with -a or --action.' ) compconn = get_conn(client_type='compute') resource_group = config.get_cloud_config_value( 'resource_group', get_configured_provider(), __opts__, search_global=False ) ret = {} if not resource_group: groups = list_resource_groups() for group in groups: try: instance = compconn.virtual_machines.start( vm_name=name, resource_group_name=group ) instance.wait() vm_result = instance.result() ret = vm_result.as_dict() break except CloudError as exc: if 'was not found' in exc.message: continue else: ret = {'error': exc.message} if not ret: _log_cloud_error('compute', 'Unable to find virtual machine with name: {0}'.format(name)) ret = {'error': 'Unable to find virtual machine with name: {0}'.format(name)} else: try: instance = compconn.virtual_machines.start( vm_name=name, resource_group_name=resource_group ) instance.wait() vm_result = instance.result() ret = vm_result.as_dict() except CloudError as exc: _log_cloud_error('compute', 'Error attempting to start {0}: {1}'.format(name, exc.message)) ret = {'error': exc.message} return ret

31.914229

131

0.585403

0c08e9bd195aba4c06a7aed6eed7031cc932355e

1,856

py

Python

tests/test_persona.py

manuelfel12/MiProyectoUniAndes

187efb581fe6c5bb229110dad8f87b01b910476d

[ "MIT" ]

null

tests/test_persona.py

manuelfel12/MiProyectoUniAndes

187efb581fe6c5bb229110dad8f87b01b910476d

[ "MIT" ]

1

2021-03-13T13:13:37.000Z

tests/test_persona.py

manuelfel12/MiProyectoUniAndes

187efb581fe6c5bb229110dad8f87b01b910476d

[ "MIT" ]

null

import unittest import datetime from src.mi_proyecto.persona import Persona class PersonaTestCase(unittest.TestCase): def setUp(self): self.persona1 = Persona(nombre='Alejandra', edad=25) self.persona2 = Persona(nombre='Diego', edad=22) self.persona3 = Persona(nombre='Alejandra', edad=25) self.persona4 = Persona(nombre='Diana', edad=25) #Print() self.grupo = [self.persona1, self.persona2, self.persona3] def test_constructor(self): self.assertEqual(self.persona1.dar_nombre(), 'Alejandra') self.assertEqual(self.persona1.dar_edad(), 25) def test_anio_nacimiento(self): self.assertEqual(self.persona1.calcular_anio_nacimiento(True), datetime.datetime.now().year - 25) self.assertNotEqual(self.persona1.calcular_anio_nacimiento(False), datetime.datetime.now().year - 25) self.assertEqual(self.persona1.calcular_anio_nacimiento(False), datetime.datetime.now().year - 25 + 1) self.assertNotEqual(self.persona1.calcular_anio_nacimiento(True), datetime.datetime.now().year - 25 + 1) def test_asingacion(self): self.persona2.asignar_edad(28) self.persona2.asignar_nombre("Felipe") self.assertFalse(self.persona2.dar_nombre()=='Diego') self.assertFalse(self.persona2.dar_edad()==22) self.assertTrue(self.persona2.dar_nombre()=='Felipe') self.assertTrue(self.persona2.dar_edad()==28) def test_objetos_iguales(self): persona_nueva = self.persona1 self.assertIsNot(self.persona1, self.persona3) self.assertIs(self.persona1, persona_nueva) def test_elemento_en_conjunto(self): self.assertIn(self.persona3, self.grupo) self.assertNotIn(self.persona4, self.grupo) def test_instancia_clase(self): self.assertIsInstance(self.persona1, Persona) self.assertNotIsInstance(self.grupo, Persona)

41.244444

110

0.727371

330ce4edecd87038aefb13070645974d118c26c7

18,618

py

Python

jeditor/main.py

Yadkee/orphans

1208a9cbd95ca7608da9855b27d717beaff1711e

[ "MIT" ]

null

jeditor/main.py

Yadkee/orphans

1208a9cbd95ca7608da9855b27d717beaff1711e

[ "MIT" ]

null

jeditor/main.py

Yadkee/orphans

1208a9cbd95ca7608da9855b27d717beaff1711e

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import tkinter as tk from tkinter import ttk import widgets from struct import unpack_from, pack from collections import OrderedDict MONOSPACE = ("Consolas", 9) ARIAL = ("Arial, 8") OP_CODES = {} with open("opcodes") as f: for i in f.read().splitlines(): data = i.split(";") OP_CODES[data[0].upper()] = data[1:] def fancy_newlines(s, maxPerLine=128): s += " " out = [] while s: line, rest = s[:maxPerLine].rsplit(" ", 1) out.append(line) s = rest + s[maxPerLine:] return "\n".join(out) class App(tk.Frame): def __init__(self, master): tk.Frame.__init__(self, master, border=5) w, h = 700, 500 master.geometry("%dx%d+100+100" % (w, h)) master.minsize(w, h) self.entryVariable = tk.StringVar() self.current = [0, 0] self.filebutton = tk.Menubutton(self, text="File", bd=0, relief="flat", activebackground="blue", activeforeground="white") self.filemenu = tk.Menu(self.filebutton, tearoff=0) self.notebook = ttk.Notebook(self) self.constantPool = tk.Frame(self) self.cpList = widgets.ScrolledListbox( self.constantPool, font=MONOSPACE) self.cpXscrollbar = widgets.AutoScrollbar( self.constantPool, command=self.cpList.xview, orient="horizontal") self.methods = tk.Frame(self) self.methodFrame = tk.Frame(self.methods) self.methodList = widgets.ScrolledListbox( self.methodFrame, width=26, height=5) self.methodLabelFrame = ttk.Labelframe( self.methodFrame, text="Selected instruction") self.methodEntry = tk.Entry( self.methodLabelFrame, width=16, font=MONOSPACE, textvariable=self.entryVariable) self.methodLabel1 = tk.Label( self.methodLabelFrame, anchor="w", font=ARIAL) self.methodLabel2 = tk.Label( self.methodLabelFrame, anchor="w", font=ARIAL, justify="left") kw = {"width": 8, "font": MONOSPACE, "activestyle": "dotbox"} self.methodIndexes = tk.Listbox(self.methods, **kw) self.methodCode = tk.Listbox(self.methods, **kw) self.methodExplanation = tk.Listbox(self.methods, **kw) self.methodXscrollbar = widgets.AutoScrollbar( self.methods, command=self.methodExplanation.xview, orient="horizontal") self.methodYscrollbar = widgets.AutoScrollbar( self.methods, command=self.yview_methods) self.filebutton.config(menu=self.filemenu) self.filemenu.add_command(label="Open") self.filemenu.add_command(label="Save") self.notebook.add(self.constantPool, text="Constant Pool") self.notebook.add(self.methods, text="Methods") self.cpList.config(xscrollcommand=self.cpXscrollbar.set) self.methodIndexes.config(yscrollcommand=self.yscroll_methods) self.methodCode.config(yscrollcommand=self.yscroll_methods) self.methodExplanation.config( xscrollcommand=self.methodXscrollbar.set, yscrollcommand=self.yscroll_methods) # app self.columnconfigure(0, weight=1) self.rowconfigure(1, weight=1) self.filebutton.grid(column=0, row=0, sticky="NSW") self.notebook.grid(column=0, row=1, sticky="NSEW") # cp self.constantPool.columnconfigure(0, weight=1) self.constantPool.rowconfigure(0, weight=1) self.cpList.grid(column=0, row=0, sticky="NSEW") self.cpXscrollbar.grid(column=0, row=1, sticky="NSEW") # methods self.methods.columnconfigure(2, weight=1) self.methods.rowconfigure(1, weight=1) self.methodFrame.columnconfigure(1, weight=1) self.methodLabelFrame.columnconfigure(1, weight=1) self.methodFrame.grid(column=0, columnspan=3, row=0, sticky="NSEW") self.methodList.grid(column=0, row=0, sticky="NSEW") self.methodLabelFrame.grid(column=1, row=0, sticky="NSEW") self.methodEntry.grid(column=0, row=0, sticky="NSW") self.methodLabel1.grid(column=1, row=0, sticky="NSEW") self.methodLabel2.grid(column=0, columnspan=2, row=1, sticky="NSEW") self.methodIndexes.grid(column=0, row=1, sticky="NSEW", padx=2, pady=5) self.methodCode.grid(column=1, row=1, sticky="NSEW", padx=2, pady=5) self.methodExplanation.grid( column=2, row=1, sticky="NSEW", padx=2, pady=5) self.methodXscrollbar.grid( column=0, columnspan=3, row=2, sticky="NSEW") self.methodYscrollbar.grid(column=3, row=0, rowspan=3, sticky="NSEW") self.methodList.bind("<<ListboxSelect>>", self.select_method) self.methodIndexes.bind("<<ListboxSelect>>", self.select_index) self.methodCode.bind("<<ListboxSelect>>", self.select_index) self.methodExplanation.bind("<<ListboxSelect>>", self.select_index) self.methodEntry.bind("<Return>", self.update_code) # To open a file do: # self.f = File("CvcMinigame.xclass") # self.refresh() def yview_methods(self, *arg): self.methodIndexes.yview(*arg) self.methodCode.yview(*arg) self.methodExplanation.yview(*arg) def yscroll_methods(self, *arg): self.methodIndexes.yview_moveto(arg[0]) self.methodCode.yview_moveto(arg[0]) self.methodExplanation.yview_moveto(arg[0]) self.methodYscrollbar.set(*arg) def refresh(self): self.code = [] self.cpList.delete(0, "end") for a, i in enumerate(self.f.constantPool): if a == 0: continue short_name = i[0][9:] s = "{0:04X};{1}: {2}".format(a, short_name, self.f.format(a)) self.cpList.insert("end", s) for method in self.f.methods: name = self.f.cp(method[1])[1] for attribute in method[4]: if len(attribute) == 5: code = parse_code(attribute[3]) break self.methodList.insert("end", name) self.code.append(code) def select_method(self, _): self.methodIndexes.delete(0, "end") self.methodCode.delete(0, "end") self.methodExplanation.delete(0, "end") if self.methodList.curselection(): self.current[0] = self.methodList.curselection()[0] a = 0 for line in self.code[self.current[0]]: self.methodIndexes.insert("end", "%08X" % a) self.methodCode.insert("end", " ".join(line)) op = OP_CODES[line[0]] explanation = op[0].ljust(20, " ") if "indexbyte" in op[1]: explanation += " %s" % self.f.format( int(line[1] + line[2], 16)) elif "index" in op[1]: explanation += " %s" % self.f.format(int(line[1], 16)) elif "branchbyte" in op[1]: explanation += " pos.%08X" % (a + int(line[1] + line[2], 16)) self.methodExplanation.insert("end", explanation) a += len(line) def select_index(self, e): if e and e.widget.curselection(): self.current[1] = e.widget.curselection()[0] self.methodIndexes.selection_set(self.current[1]) line = self.code[self.current[0]][self.current[1]] op = OP_CODES[line[0]] self.entryVariable.set(" ".join(line)) self.methodLabel1.config(text="[%s] %s" % (op[0], op[2])) self.methodLabel2.config(text=fancy_newlines(op[3], 100)) def update_code(self, _): code = [] for a, line in enumerate(self.code[self.current[0]]): if a == self.current[1]: line = self.methodEntry.get().split(" ") code.extend(line) raw = bytes.fromhex("".join(code)) for attribute in self.f.methods[self.current[0]][4]: if len(attribute) == 5: attribute[3] = raw self.code[self.current[0]] = parse_code(raw) self.select_method(None) self.select_index(None) class File(): def __init__(self, path): self.path = path self.info = OrderedDict() with open(path, "rb") as f: self.data = f.read() self.parse() self.unparse() print(self.data == self.newData) def cp(self, number_or_index): try: return "#%d" % number_or_index except TypeError: return self.constantPool[int(number_or_index[1:])] def format(self, a): constant = self.constantPool[a] if constant[0] == "CONSTANT_String": return "'%s'" % self.cp(constant[1])[1] out = [] for j in constant[1:]: if type(j) is str and j.startswith("#"): j = self.format(int(j[1:])) out.append(str(j)) return ", ".join(out).replace("/", ".") def parse(self): d = self.data i = 0 self.info["magic_number"], self.info["minor_version"], self.info["major_version"], \ self.info["constant_pool_count"] = unpack_from(">IHHH", d, i) i += 10 # =========================================================================== self.constantPool = [None] n = 1 while n < self.info["constant_pool_count"]: tag = d[i] i += 1 if tag == 1: lenght = unpack_from(">H", d, i)[0] i += 2 text = d[i:i + lenght] i += lenght self.constantPool.append(("CONSTANT_Utf8", text.decode())) elif 3 <= tag <= 4: name = ("CONSTANT_Integer", "CONSTANT_Float")[tag - 3] value = unpack_from(">I", d, i)[0] i += 4 self.constantPool.append((name, value)) elif 5 <= tag <= 6: n += 1 name = ("CONSTANT_Long", "CONSTANT_Double")[tag - 5] value = unpack_from(">Q", d, i)[0] i += 8 self.constantPool.append((name, value)) elif tag == 7: index = unpack_from(">H", d, i)[0] i += 2 self.constantPool.append(("CONSTANT_Class", self.cp(index))) elif tag == 8: string_index = unpack_from(">H", d, i)[0] i += 2 self.constantPool.append( ("CONSTANT_String", self.cp(string_index))) elif 9 <= tag <= 11: name = ("CONSTANT_Fieldref", "CONSTANT_Methodref", "CONSTANT_InterfaceMethodref")[tag - 9] class_index, name_and_type_index = unpack_from(">HH", d, i) i += 4 self.constantPool.append( (name, self.cp(class_index), self.cp(name_and_type_index))) elif tag == 12: name_index, descriptor_index = unpack_from(">HH", d, i) i += 4 self.constantPool.append( ("CONSTANT_NameAndType", self.cp(name_index), self.cp(descriptor_index))) elif tag == 15: reference_kind = d[i] i += 1 reference_index = unpack_from(">H", d, i)[0] i += 2 self.constantPool.append( ("CONSTANT_MethodHandle", reference_kind, self.cp(reference_index))) elif tag == 16: descriptor_index = unpack_from(">H", d, i)[0] i += 2 self.constantPool.append( ("CONSTANT_MethodType", self.cp(descriptor_index))) elif tag == 18: bootstrap_method_attr_index, name_and_type_index = unpack_from(">HH", d, i)[ 0] i += 4 self.constantPool.append(("CONSTANT_InvokeDynamic", self.cp( bootstrap_method_attr_index), self.cp(name_and_type_index))) else: raise Exception("!cp error [%d]" % tag) n += 1 # =========================================================================== self.info["access_flags"], self.info["this_class"], self.info["super_class"], \ self.info["interfaces_count"] = unpack_from(">HHHH", d, i) i += 8 self.interfaces = [] for _ in range(self.info["interfaces_count"]): self.interfaces.append(unpack_from(">H", d, i)[0]) i += 2 self.info["fields_count"] = unpack_from(">H", d, i)[0] i += 2 self.fields, i = self.parse_fields(d, i, self.info["fields_count"]) self.info["methods_count"] = unpack_from(">H", d, i)[0] i += 2 self.methods, i = self.parse_fields(d, i, self.info["methods_count"]) self.info["attributes_count"] = unpack_from(">H", d, i)[0] i += 2 self.attributes, i = self.parse_attributes( d, i, self.info["attributes_count"]) def parse_fields(self, d, i, count): fields = [] for _ in range(count): access_flags, name_index, descriptor_index, attributes_count = unpack_from( ">HHHH", d, i) i += 8 attributes, i = self.parse_attributes(d, i, attributes_count) fields.append((access_flags, self.cp(name_index), self.cp( descriptor_index), attributes_count, attributes)) return fields, i def parse_attributes(self, d, i, count): attributes = [] for _ in range(count): attribute_name_index = unpack_from(">H", d, i)[0] i += 2 attribute_length = unpack_from(">I", d, i)[0] i += 4 info = d[i:i + attribute_length] if self.constantPool[attribute_name_index][1] == "Code": max_stack, max_locals, code_length = unpack_from(">HHI", d, i) code = d[i + 8:i + 8 + code_length] attributes.append([self.cp(attribute_name_index), attribute_length, (max_stack, max_locals), code, info[8 + code_length:]]) else: attributes.append( [self.cp(attribute_name_index), attribute_length, info]) i += attribute_length return attributes, i def unparse(self): d = [] d.append(pack(">IHHH", self.info["magic_number"], self.info["minor_version"], self.info["major_version"], self.info["constant_pool_count"])) d.append(unparse_constant_pool(self.constantPool)) d.append(pack(">HHHH", self.info["access_flags"], self.info["this_class"], self.info["super_class"], self.info["interfaces_count"])) for i in self.interfaces: d.append(pack(">H", i)) d.append(pack(">H", self.info["fields_count"])) d.append(unparse_fields(self.fields)) d.append(pack(">H", self.info["methods_count"])) d.append(unparse_fields(self.methods)) d.append(pack(">H", self.info["attributes_count"])) d.append(unparse_attributes(self.attributes)) self.newData = b"".join(d) def unparse_constant_pool(constantPool): d = [] for constant in constantPool: if constant is None: continue name = constant[0] if name == "CONSTANT_Utf8": d.append(b"\x01") d.append(pack(">H", len(constant[1]))) d.append(constant[1].encode()) elif name == "CONSTANT_Integer": d.append(b"\x03") d.append(pack(">I", constant[1])) elif name == "CONSTANT_Float": d.append(b"\x04") d.append(pack(">I", constant[1])) elif name == "CONSTANT_Long": d.append(b"\x05") d.append(pack(">Q", constant[1])) elif name == "CONSTANT_Double": d.append(b"\x06") d.append(pack(">Q", constant[1])) elif name == "CONSTANT_Class": d.append(b"\x07") d.append(pack(">H", int(constant[1][1:]))) elif name == "CONSTANT_String": d.append(b"\x08") d.append(pack(">H", int(constant[1][1:]))) elif name == "CONSTANT_Fieldref": d.append(b"\x09") d.append(pack(">HH", int(constant[1][1:]), int(constant[2][1:]))) elif name == "CONSTANT_Methodref": d.append(b"\x0A") d.append(pack(">HH", int(constant[1][1:]), int(constant[2][1:]))) elif name == "CONSTANT_InterfaceMethodref": d.append(b"\x0B") d.append(pack(">HH", int(constant[1][1:]), int(constant[2][1:]))) elif name == "CONSTANT_NameAndType": d.append(b"\x0C") d.append(pack(">HH", int(constant[1][1:]), int(constant[2][1:]))) elif name == "CONSTANT_MethodHandle": d.append(b"\x0F") d.append(bytes([constant[1]])) d.append(pack(">H", int(constant[2][1:]))) elif name == "CONSTANT_MethodType": d.append(b"\x10") d.append(pack(">H", int(constant[1][1:]))) elif name == "CONSTANT_InvokeDynamic": d.append(b"\x12") d.append(pack(">HH", int(constant[1][1:]), int(constant[2][1:]))) return b"".join(d) def unparse_attributes(attributes): d = [] for attribute in attributes: d.append(pack(">H", int(attribute[0][1:]))) if len(attribute) == 5: d.append(pack(">I", 8 + len(attribute[3]) + len(attribute[4]))) d.append(pack(">HHI", attribute[2][0], attribute[2][1], len(attribute[3]))) d.append(attribute[3]) d.append(attribute[4]) else: d.append(pack(">I", attribute[1])) d.append(attribute[2]) return b"".join(d) def unparse_fields(fields): d = [] for field in fields: d.append(pack(">HHHH", field[0], int( field[1][1:]), int(field[2][1:]), field[3])) d.append(unparse_attributes(field[4])) return b"".join(d) def parse_code(d): lines = [] i = 0 while i < len(d): h = "%02X" % d[i] i += 1 opc = OP_CODES[h] args = int(opc[1].split(":")[0]) lines.append([h] + list("%02X" % d[i + j] for j in range(args))) i += args return lines if __name__ == "__main__": root = tk.Tk() root.title("Bytecode editor") app = App(root) app.pack(expand=True, fill="both") root.mainloop()

40.650655

93

0.544903

dfb317554b6dd1a25819504f9792e43e357aa661

578

py

Python

arch_2.py

sushantjha78/complexity_and_performance

cc8a26241f462c671c352f33998f0d3facaf8a75

[ "MIT" ]

null

arch_2.py

sushantjha78/complexity_and_performance

cc8a26241f462c671c352f33998f0d3facaf8a75

[ "MIT" ]

null

arch_2.py

sushantjha78/complexity_and_performance

cc8a26241f462c671c352f33998f0d3facaf8a75

[ "MIT" ]

null

import torch import torch.nn as nn class model(nn.Module): def __init__(self): super(model, self).__init__() self.end = nn.Sequential( nn.LeakyReLU(inplace = True), nn.LazyLinear(32), nn.Dropout(0.5), nn.LeakyReLU(inplace = True), nn.LazyLinear(10)) #same block being concatenated def forward(self, X, num_blocks = 1): h = X.view(X.shape[0], -1) h = self.end(h) h = nn.functional.log_softmax(h, dim=1) return h

27.52381

48

0.512111

a93d2fbf8fb42add76c8b490c6e3d7a9a8532325

182

py

Python

Class 11/11-Programs/Palindrome.py

edwardmasih/Python-School-Level

545e8fcd87f540be2bbf01d3493bd84dd5504739

[ "MIT" ]

null

Class 11/11-Programs/Palindrome.py

edwardmasih/Python-School-Level

545e8fcd87f540be2bbf01d3493bd84dd5504739

[ "MIT" ]

null

Class 11/11-Programs/Palindrome.py

edwardmasih/Python-School-Level

545e8fcd87f540be2bbf01d3493bd84dd5504739

[ "MIT" ]

null

n=int(input("Enter a three digit Number :- ")) o=n%10 t=(n//10)%10 h=n//100 if o==h: print ("Your number is Palindrome") else: print("Your number is NOT Palindrome")

20.222222

47

0.60989

215c1cdddf0ed5a746191607ecbfaa986c2a7cc4

4,576

py

Python

datahub/company/admin/utils.py

reupen/data-hub-api

d854188f4c45da0e89075add132a15bb1227ff79

[ "MIT" ]

null

datahub/company/admin/utils.py

reupen/data-hub-api

d854188f4c45da0e89075add132a15bb1227ff79

[ "MIT" ]

16

2020-04-01T15:25:35.000Z

2020-04-14T14:07:30.000Z

datahub/company/admin/utils.py

cgsunkel/data-hub-api

a92faabf73fb93b5bfd94fd465eafc3e29aa6d8e

[ "MIT" ]

null

import functools from django.contrib import messages as django_messages from django.http import HttpResponseRedirect from datahub.metadata.models import Country def format_company_diff(dh_company, dnb_company): """ Format the Datahub and D&B companies for templates. """ def get_field(name): return dh_company._meta.get_field(name) def get_country(address): country = address.get('country') return None if country is None else Country.objects.get(id=country) address = dnb_company.get('address') or {} registered_address = dnb_company.get('registered_address') or {} return { get_field('name'): ( dh_company.name, dnb_company.get('name'), ), get_field('address_1'): ( dh_company.address_1, address.get('line_1'), ), get_field('address_2'): ( dh_company.address_2, address.get('line_2'), ), get_field('address_town'): ( dh_company.address_town, address.get('town'), ), get_field('address_county'): ( dh_company.address_county, address.get('county'), ), get_field('address_postcode'): ( dh_company.address_postcode, address.get('postcode'), ), get_field('address_country'): ( dh_company.address_country, get_country(address), ), get_field('registered_address_1'): ( dh_company.registered_address_1, registered_address.get('line_1'), ), get_field('registered_address_2'): ( dh_company.registered_address_2, registered_address.get('line_2'), ), get_field('registered_address_town'): ( dh_company.registered_address_town, registered_address.get('town'), ), get_field('registered_address_county'): ( dh_company.registered_address_county, registered_address.get('county'), ), get_field('registered_address_postcode'): ( dh_company.registered_address_postcode, registered_address.get('postcode'), ), get_field('registered_address_country'): ( dh_company.registered_address_country, get_country(registered_address), ), get_field('company_number'): ( dh_company.company_number, dnb_company.get('company_number'), ), get_field('trading_names'): ( ', '.join(dh_company.trading_names), ', '.join(dnb_company.get('trading_names', [])), ), get_field('website'): ( dh_company.website, dnb_company.get('website'), ), get_field('number_of_employees'): ( dh_company.number_of_employees, dnb_company.get('number_of_employees'), ), get_field('is_number_of_employees_estimated'): ( dh_company.is_number_of_employees_estimated, dnb_company.get('is_number_of_employees_estimated'), ), get_field('turnover'): ( dh_company.turnover, dnb_company.get('turnover'), ), get_field('is_turnover_estimated'): ( dh_company.is_turnover_estimated, dnb_company.get('is_turnover_estimated'), ), get_field('global_ultimate_duns_number'): ( dh_company.global_ultimate_duns_number, dnb_company.get('global_ultimate_duns_number'), ), } def redirect_with_messages(func): """ Decorator that redirects to a given URL with one or more messages for the user in case of an error. """ @functools.wraps(func) def wrapper(model_admin, request, *args, **kwargs): try: return func(model_admin, request, *args, **kwargs) except AdminException as exc: messages = exc.messages redirect_url = exc.redirect_url for message in messages: django_messages.add_message(request, django_messages.ERROR, message) return HttpResponseRedirect(redirect_url) return wrapper class AdminException(Exception): """ Exception in an admin view. Contains the message to be displayed to the user and the redirect_url. """ def __init__(self, messages, redirect_url): """ Initialise the AdminException. """ self.messages = messages self.redirect_url = redirect_url

32.453901

96

0.60118

7c4e61fc011730797c0c59a1ed42afe60dab5476

14,942

py

Python

sdb/commands/stacks.py

shartse/sdb

5ad91838ae869b7a72b6fc15c0b8ec2a0d0c6c77

[ "Apache-2.0" ]

null

sdb/commands/stacks.py

shartse/sdb

5ad91838ae869b7a72b6fc15c0b8ec2a0d0c6c77

[ "Apache-2.0" ]

null

sdb/commands/stacks.py

shartse/sdb

5ad91838ae869b7a72b6fc15c0b8ec2a0d0c6c77

[ "Apache-2.0" ]

null

# # Copyright 2019 Delphix # # 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. # # pylint: disable=missing-docstring import argparse from typing import Any, Dict, Iterable, List, Tuple from collections import defaultdict import drgn from drgn.helpers.linux.list import list_for_each_entry from drgn.helpers.linux.pid import for_each_task import sdb # # Note: This is a rudimentary version of what the command could/should be. # # On the high-level, it could be a `Locator`, or something similar, where # objects could be passed as input from the pipeline dispatching different # methods depending on the type of object. E.g. input could be a namespace # object and we print all the task structs within it, or it could be just # a list of task structs passed from a previous command for filtering. # Another option would be to decouple the stack listing, filtering, and # pretty-printing functionality to independent SDB commands. # # There are also other lower-level usability improvements like supporting # filtering by `function+offset` with the `-c` option, or by namespace ID # using `-n <ID>`. # # Finally, the command lacks any support for userland targets. # # SDB is still in its early stages and hasn't been used enough for us to # be clear which use cases really matter. In the meantime if we don't have # anything that provides this functionality it won't be easy to do this # exploration. The version below is a good enough for the time being # providing some basic functionality and being our tracer bullet for # future iterations. # class Stacks(sdb.Command): """ Print the stack traces for the active tasks / threads By default, the command will aggregate similar call stacks printing them in descending order of frequency. The output includes the `struct task_struct` address, thread state, and aggregation count. Optionally, the command can filter stacks, displaying only those that match a given thread state, containing a given function, or belonging to a given kernel module. EXAMPLES Print the call stacks for all tasks sdb> stacks TASK_STRUCT STATE COUNT ========================================== 0xffff9521bb3c3b80 IDLE 394 __schedule+0x24e schedule+0x2c worker_thread+0xba kthread+0x121 ret_from_fork+0x35 0xffff9521bb3cbb80 INTERRUPTIBLE 384 __schedule+0x24e schedule+0x2c smpboot_thread_fn+0x166 kthread+0x121 ret_from_fork+0x35 ... Print stacks containing functions from the zfs module sdb> stacks -m zfs TASK_STRUCT STATE COUNT ========================================== 0xffff952130515940 INTERRUPTIBLE 1 __schedule+0x24e schedule+0x2c cv_wait_common+0x11f __cv_wait_sig+0x15 zthr_procedure+0x51 thread_generic_wrapper+0x74 kthread+0x121 ret_from_fork+0x35 ... Print stacks containing the l2arc_feed_thread function sdb> stacks -c l2arc_feed_thread TASK_STRUCT STATE COUNT ========================================== 0xffff9521b3f43b80 INTERRUPTIBLE 1 __schedule+0x24e schedule+0x2c schedule_timeout+0x15d __cv_timedwait_common+0xdf __cv_timedwait_sig+0x16 l2arc_feed_thread+0x66 thread_generic_wrapper+0x74 kthread+0x121 ret_from_fork+0x35 Print stacks of threads in the RUNNING state sdb> stacks -t RUNNING TASK_STRUCT STATE COUNT ========================================== 0xffff95214ff31dc0 RUNNING 1 """ names = ["stacks"] @classmethod def _init_parser(cls, name: str) -> argparse.ArgumentParser: parser = super()._init_parser(name) parser.add_argument( "-a", "--all", action="store_true", help="list all threads for each unique stack trace" + " instead of printing a single representative thread") parser.add_argument( "-c", "--function", help="only print threads whose stacks contains FUNCTION") parser.add_argument( "-m", "--module", help="only print threads whose stacks contain functions from MODULE" ) parser.add_argument( "-t", "--tstate", help="only print threads which are in TSTATE thread state") parser.epilog = "TSTATE := [{:s}]".format(", ".join( Stacks.TASK_STATES.values())) return parser # # See include/linux/sched.h # TASK_STATES = { 0x00: "RUNNING", 0x01: "INTERRUPTIBLE", 0x02: "UNINTERRUPTIBLE", 0x04: "STOPPED", 0x08: "TRACED", 0x10: "DEAD", 0x20: "ZOMBIE", 0x40: "PARKED", 0x402: "IDLE", } # # See man page of ps(1) # TASK_STATE_SHORTCUTS = { "R": 0x00, "S": 0x01, "D": 0x02, "T": 0x04, "t": 0x08, "X": 0x10, "Z": 0x20, } @staticmethod def task_struct_get_state(task: drgn.Object) -> str: state = task.state.value_() if state == 0x402: return "IDLE" exit_state = task.exit_state.value_() return Stacks.TASK_STATES[(state | exit_state) & 0x7f] # # Unfortunately the drgn Symbol API does not specify the namelist # that a symbol came from. As a result, we created the following # function to implement the `-m` functionality. Whenever we filter # by module name, we find the segment in memory where this module # resides and do the matching based on the address of the function # of the current frame. # @staticmethod def find_module_memory_segment(mod_name: str) -> Tuple[int, int]: """ Looks for the segment in memory where `mod_name` is loaded. Returns: (<base_offset>, <size>) if `mod_name` is found. (-1, 0) otherwise. """ for mod in list_for_each_entry('struct module', sdb.get_object('modules').address_of_(), 'list'): if mod.name.string_().decode("utf-8") == mod_name: return (mod.core_layout.base.value_(), mod.core_layout.size.value_()) return (-1, 0) def validate_args(self, args: argparse.Namespace) -> None: if args.function: try: func = sdb.get_object(args.function) except KeyError: raise sdb.CommandError( self.name, "symbol '{:s}' does not exist".format(args.function)) if func.type_.kind != drgn.TypeKind.FUNCTION: raise sdb.CommandError( self.name, "'{:s}' is not a function".format(args.function)) task_states = Stacks.TASK_STATES.values() task_states_lowercase = list(map(lambda x: x.lower(), task_states)) state_shortcuts = Stacks.TASK_STATE_SHORTCUTS if args.tstate and not args.tstate.lower( ) in task_states_lowercase and not args.tstate in state_shortcuts: raise sdb.CommandError( self.name, "'{:s}' is not a valid task state (acceptable states: {:s})". format(args.tstate, ", ".join(task_states))) if args.module and Stacks.find_module_memory_segment( args.module)[0] == -1: raise sdb.CommandError( self.name, "module '{:s}' doesn't exist or isn't currently loaded".format( args.module)) def _call(self, objs: Iterable[drgn.Object]) -> Iterable[drgn.Object]: # pylint: disable=too-many-locals # pylint: disable=too-many-branches # pylint: disable=too-many-statements # # As the exception explains the code that follows this statement # only works for linux kernel targets (crash dumps or live systems). # When support for userland is added we can factor the kernel code # that follows into its own function and switch to the correct # codepath depending on the target. # if not sdb.get_target_flags() & drgn.ProgramFlags.IS_LINUX_KERNEL: raise sdb.CommandError(self.name, "userland targets are not supported yet") self.validate_args(self.args) # # Resolve TSTATE shortcut and/or sanitize it to standard uppercase # notation if it exists. # if self.args.tstate: if self.args.tstate in Stacks.TASK_STATE_SHORTCUTS: self.args.tstate = Stacks.TASK_STATES[ Stacks.TASK_STATE_SHORTCUTS[self.args.tstate]] else: self.args.tstate = self.args.tstate.upper() mod_start, mod_end = -1, -1 if self.args.module: mod_start, mod_size = Stacks.find_module_memory_segment( self.args.module) assert mod_start != -1 mod_end = mod_start + mod_size header = "{:<18} {:<16s}".format("TASK_STRUCT", "STATE") if not self.args.all: header += " {:>6s}".format("COUNT") print(header) print("=" * 42) # # We inspect and group the tasks by recording their state and # stack frames once in the following loop. We do this because # on live systems state can change under us, thus running # something like sdb.get_prog().stack_trace(task) twice (once for # grouping and once for printing) could yield different stack # traces resulting into misleading output. # stack_aggr: Dict[Any, List[drgn.Object]] = defaultdict(list) for task in for_each_task(sdb.get_prog()): stack_key = [Stacks.task_struct_get_state(task)] try: for frame in sdb.get_prog().stack_trace(task): stack_key.append(frame.pc) except ValueError: # # Unwinding the stack of a running/runnable task will # result in an exception. Since we expect some tasks to # be running, we silently ignore this case, and move on. # # Unfortunately, the exception thrown in this case is a # generic "ValueError" exception, so we may wind up # masking other "ValueError" exceptions that are not due # to unwinding the stack of a running task. # # We can't check the state of the task here, and verify # it's in the "R" state, since that state can change in # between the point where the "ValueError" exception was # originally raised, and here where we'd verify the # state of the task; i.e. it could have concurrently # transitioned from running to some other state. # pass stack_aggr[tuple(stack_key)].append(task) for stack_key, tasks in sorted(stack_aggr.items(), key=lambda x: len(x[1]), reverse=True): task_state = stack_key[0] if self.args.tstate and self.args.tstate != task_state: continue stacktrace_info = "" if self.args.all: for task in tasks: stacktrace_info += "{:<18s} {:<16s}\n".format( hex(task.value_()), task_state) else: stacktrace_info += "{:<18s} {:<16s} {:6d}\n".format( hex(tasks[0].value_()), task_state, len(tasks)) mod_match, func_match = False, False # # Note on the type-check being ignored: # The original `stack_key` type is a list where the first # element is a string and the rest of them are integers # but this is not easily expressed in mypy, thus we ignore # the assignment error below. # frame_pcs: List[int] = stack_key[1:] #type: ignore[assignment] for frame_pc in frame_pcs: if mod_start != -1 and mod_start <= frame_pc < mod_end: mod_match = True try: sym = sdb.get_symbol(frame_pc) func, offset = sym.name, frame_pc - sym.address if self.args.function and self.args.function == func: func_match = True except LookupError: func, offset = hex(frame_pc), 0x0 # # As a potential future item, we may want to print # the frame with the module where the pc/function # belongs to. For example: # txg_sync_thread+0x15e [zfs] # stacktrace_info += "{:18s}{}+{}\n".format("", func, hex(offset)) if mod_start != -1 and not mod_match: continue if self.args.function and not func_match: continue print(stacktrace_info) return []

39.633952

80

0.545643

9d9384d58037ab02899807e2babceb56c1c5c3e6

10,815

py

Python

exps/centernet_pandaset.py

joonielee832/mmdet

552c09c6f82cab41427754102c7b8d1c36929552

[ "Apache-2.0" ]

null

exps/centernet_pandaset.py

joonielee832/mmdet

552c09c6f82cab41427754102c7b8d1c36929552

[ "Apache-2.0" ]

null

exps/centernet_pandaset.py

joonielee832/mmdet

552c09c6f82cab41427754102c7b8d1c36929552

[ "Apache-2.0" ]

null

optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) lr_config = dict( policy='step', warmup='linear', warmup_iters=1000, warmup_ratio=0.001, step=[18, 24]) runner = dict(type='EpochBasedRunner', max_epochs=5) checkpoint_config = dict(interval=5) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) custom_hooks = [dict(type='NumClassCheckHook')] dist_params = dict(backend='nccl') log_level = 'INFO' load_from = 'checkpoints/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth' resume_from = None workflow = [('train', 1)] opencv_num_threads = 0 mp_start_method = 'fork' model = dict( type='CenterNet', backbone=dict( type='ResNet', depth=18, norm_eval=False, norm_cfg=dict(type='BN'), init_cfg=dict( type='Pretrained', checkpoint='checkpoints/resnet18-f37072fd.pth')), neck=dict( type='CTResNetNeck', in_channel=512, num_deconv_filters=(256, 128, 64), num_deconv_kernels=(4, 4, 4), use_dcn=True), bbox_head=dict( type='CenterNetHead', num_classes=27, in_channel=64, feat_channel=64, loss_center_heatmap=dict(type='GaussianFocalLoss', loss_weight=1.0), loss_wh=dict(type='L1Loss', loss_weight=0.1), loss_offset=dict(type='L1Loss', loss_weight=1.0)), train_cfg=None, test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100)) img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile', to_float32=True, color_type='color'), dict(type='LoadAnnotations', with_bbox=True), dict( type='PhotoMetricDistortion', brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18), dict( type='RandomCenterCropPad', crop_size=(512, 512), ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True, test_pad_mode=None), dict(type='Resize', img_scale=(512, 512), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] test_pipeline = [ dict(type='LoadImageFromFile', to_float32=True), dict( type='MultiScaleFlipAug', scale_factor=1.0, flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict( type='RandomCenterCropPad', ratios=None, border=None, mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True, test_mode=True, test_pad_mode=['logical_or', 31], test_pad_add_pix=1), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict( type='Collect', meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg', 'border'), keys=['img']) ]) ] dataset_type = 'PandaDataset' data_root = '../data/' data = dict( samples_per_gpu=8, workers_per_gpu=2, train=dict( type='PandaDataset', classes=('Animals - Bird', 'Semi-truck', 'Personal Mobility Device', 'Temporary Construction Barriers', 'Car', 'Signs', 'Emergency Vehicle', 'Other Vehicle - Pedicab', 'Pedestrian with Object', 'Train', 'Other Vehicle - Uncommon', 'Cones', 'Rolling Containers', 'Construction Signs', 'Other Vehicle - Construction Vehicle', 'Tram / Subway', 'Towed Object', 'Animals - Other', 'Bus', 'Pedestrian', 'Motorcycle', 'Bicycle', 'Road Barriers', 'Pickup Truck', 'Medium-sized Truck', 'Motorized Scooter', 'Pylons'), data_root='../data/', ann_file='train/labels.json', img_prefix='train/images', pipeline=[ dict( type='LoadImageFromFile', to_float32=True, color_type='color'), dict(type='LoadAnnotations', with_bbox=True), dict( type='PhotoMetricDistortion', brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18), dict( type='RandomCenterCropPad', crop_size=(512, 512), ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True, test_pad_mode=None), dict(type='Resize', img_scale=(512, 512), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ]), val=dict( type='PandaDataset', classes=('Animals - Bird', 'Semi-truck', 'Personal Mobility Device', 'Temporary Construction Barriers', 'Car', 'Signs', 'Emergency Vehicle', 'Other Vehicle - Pedicab', 'Pedestrian with Object', 'Train', 'Other Vehicle - Uncommon', 'Cones', 'Rolling Containers', 'Construction Signs', 'Other Vehicle - Construction Vehicle', 'Tram / Subway', 'Towed Object', 'Animals - Other', 'Bus', 'Pedestrian', 'Motorcycle', 'Bicycle', 'Road Barriers', 'Pickup Truck', 'Medium-sized Truck', 'Motorized Scooter', 'Pylons'), data_root='../data/', ann_file='val/labels.json', img_prefix='val/images', pipeline=[ dict(type='LoadImageFromFile', to_float32=True), dict( type='MultiScaleFlipAug', scale_factor=1.0, flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict( type='RandomCenterCropPad', ratios=None, border=None, mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True, test_mode=True, test_pad_mode=['logical_or', 31], test_pad_add_pix=1), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict( type='Collect', meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg', 'border'), keys=['img']) ]) ]), test=dict( type='PandaDataset', classes=('Animals - Bird', 'Semi-truck', 'Personal Mobility Device', 'Temporary Construction Barriers', 'Car', 'Signs', 'Emergency Vehicle', 'Other Vehicle - Pedicab', 'Pedestrian with Object', 'Train', 'Other Vehicle - Uncommon', 'Cones', 'Rolling Containers', 'Construction Signs', 'Other Vehicle - Construction Vehicle', 'Tram / Subway', 'Towed Object', 'Animals - Other', 'Bus', 'Pedestrian', 'Motorcycle', 'Bicycle', 'Road Barriers', 'Pickup Truck', 'Medium-sized Truck', 'Motorized Scooter', 'Pylons'), data_root='../data/', ann_file='val/labels.json', img_prefix='val/images', pipeline=[ dict(type='LoadImageFromFile', to_float32=True), dict( type='MultiScaleFlipAug', scale_factor=1.0, flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict( type='RandomCenterCropPad', ratios=None, border=None, mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True, test_mode=True, test_pad_mode=['logical_or', 31], test_pad_add_pix=1), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict( type='Collect', meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg', 'border'), keys=['img']) ]) ])) custom_imports = dict( imports=['mmdet.datasets.pandaset'], allow_failed_imports=False) classes = ('Animals - Bird', 'Semi-truck', 'Personal Mobility Device', 'Temporary Construction Barriers', 'Car', 'Signs', 'Emergency Vehicle', 'Other Vehicle - Pedicab', 'Pedestrian with Object', 'Train', 'Other Vehicle - Uncommon', 'Cones', 'Rolling Containers', 'Construction Signs', 'Other Vehicle - Construction Vehicle', 'Tram / Subway', 'Towed Object', 'Animals - Other', 'Bus', 'Pedestrian', 'Motorcycle', 'Bicycle', 'Road Barriers', 'Pickup Truck', 'Medium-sized Truck', 'Motorized Scooter', 'Pylons') evaluation = dict(metric='mAP') work_dir = './exps' seed = 0 auto_resume = False gpu_ids = [0]

40.811321

89

0.503282

3a406b9675782cbd0e2f704d383ef244fc25ad9b

1,566

py

Python

find.py

caspii/domainfinder

b4d74c6f9e2017d2856f1ab5437db5a6f84d0596

[ "Apache-2.0" ]

10

2015-09-16T19:12:40.000Z

2021-01-20T12:27:24.000Z

find.py

caspii/domainfinder

b4d74c6f9e2017d2856f1ab5437db5a6f84d0596

[ "Apache-2.0" ]

null

find.py

caspii/domainfinder

b4d74c6f9e2017d2856f1ab5437db5a6f84d0596

[ "Apache-2.0" ]

8

2015-07-28T13:33:18.000Z

2020-10-27T15:03:39.000Z

#!/usr/bin/env python from time import sleep import sys try: import whois except ImportError: print("ERROR: This script requires the python-whois module to run.") print(" You can install it via 'pip install python-whois'") sys.exit(0) # Change top-level domain to check here TLD = '.com' # 1. Get prefixes and suffixes from input.txt suffixes = [] prefixes = [] readingPrefixes = False f = open('input.txt') for l in f: line = l.strip() if line == '--prefixes': readingPrefixes = True continue elif line == '--suffixes': readingPrefixes = False continue elif not line: continue # Ignore empty lines if readingPrefixes: prefixes.append(line) else: suffixes.append(line) f.close() # 2. create list of domains from prefixes and suffixes domains =[] for pre in prefixes: for suff in suffixes: domains.append( pre + suff + TLD) # 3. Get list of domains that have aleady found to be free and removed them checkeddomains= [line.strip() for line in open('free-domains.txt')] # Strip out newlines too for remove in checkeddomains: try: domains.remove(remove) except ValueError: pass # Ignore exceptions # 4. Check list of domains and write to file for domain in domains: sleep(0.5) # Too many requests lead to incorrect responses print(' Checking: ' + domain), # Comma means no newline is printed try: w = whois.whois(domain) print('\tTAKEN') except whois.parser.PywhoisError: # Exception means that the domain is free print('\tFREE') f = open('free-domains.txt', 'a') f.write(domain + '\n') f.close() print("DONE!")

24.857143

92

0.709451

ea261b28eb1a028ec373a89221c0c296962e57ba

2,520

py

Python

saleor/graphql/middleware.py

sunilsrikumar/saleor

c9d147dba24ddef1def889852c229027bab767df

[ "BSD-3-Clause" ]

1

2020-06-16T19:49:54.000Z

saleor/graphql/middleware.py

gurupratap-matharu/saleor

c9d147dba24ddef1def889852c229027bab767df

[ "CC-BY-4.0" ]

null

saleor/graphql/middleware.py

gurupratap-matharu/saleor

c9d147dba24ddef1def889852c229027bab767df

[ "CC-BY-4.0" ]

1

2019-09-15T02:19:10.000Z

from typing import Optional import opentracing as ot import opentracing.tags as ot_tags from django.conf import settings from django.contrib.auth.models import AnonymousUser from django.utils.functional import SimpleLazyObject from graphql import ResolveInfo from graphql_jwt.middleware import JSONWebTokenMiddleware from ..app.models import App from ..core.tracing import should_trace from .views import API_PATH, GraphQLView class JWTMiddleware(JSONWebTokenMiddleware): def resolve(self, next, root, info, **kwargs): request = info.context if not hasattr(request, "user"): request.user = AnonymousUser() return super().resolve(next, root, info, **kwargs) class OpentracingGrapheneMiddleware: @staticmethod def resolve(next_, root, info: ResolveInfo, **kwargs): if not should_trace(info): return next_(root, info, **kwargs) operation = f"{info.parent_type.name}.{info.field_name}" with ot.global_tracer().start_active_span(operation_name=operation) as scope: span = scope.span span.set_tag(ot_tags.COMPONENT, "graphql") span.set_tag("graphql.parent_type", info.parent_type.name) span.set_tag("graphql.field_name", info.field_name) return next_(root, info, **kwargs) def get_app(auth_token) -> Optional[App]: qs = App.objects.filter(tokens__auth_token=auth_token, is_active=True) return qs.first() def app_middleware(next, root, info, **kwargs): app_auth_header = "HTTP_AUTHORIZATION" prefix = "bearer" request = info.context if request.path == API_PATH: if not hasattr(request, "app"): request.app = None auth = request.META.get(app_auth_header, "").split() if len(auth) == 2: auth_prefix, auth_token = auth if auth_prefix.lower() == prefix: request.app = SimpleLazyObject(lambda: get_app(auth_token)) return next(root, info, **kwargs) def process_view(self, request, view_func, *args): if hasattr(view_func, "view_class") and issubclass( view_func.view_class, GraphQLView ): request._graphql_view = True if settings.ENABLE_DEBUG_TOOLBAR: import warnings try: from graphiql_debug_toolbar.middleware import DebugToolbarMiddleware except ImportError: warnings.warn("The graphiql debug toolbar was not installed.") else: DebugToolbarMiddleware.process_view = process_view

32.727273

85

0.687302

51909c57cfc39e4485846a7835d0fd8a85621f6e

1,106

py

Python

azure/mgmt/network/v2016_12_01/models/application_gateway_ssl_policy.py

EnjoyLifeFund/Debian_py36_packages

1985d4c73fabd5f08f54b922e73a9306e09c77a5

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

1

2022-01-25T22:52:58.000Z

azure/mgmt/network/v2016_12_01/models/application_gateway_ssl_policy.py

EnjoyLifeFund/Debian_py36_packages

1985d4c73fabd5f08f54b922e73a9306e09c77a5

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

azure/mgmt/network/v2016_12_01/models/application_gateway_ssl_policy.py

EnjoyLifeFund/Debian_py36_packages

1985d4c73fabd5f08f54b922e73a9306e09c77a5

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class ApplicationGatewaySslPolicy(Model): """Application gateway SSL policy. :param disabled_ssl_protocols: SSL protocols to be disabled on application gateway. Possible values are: 'TLSv1_0', 'TLSv1_1', and 'TLSv1_2'. :type disabled_ssl_protocols: list[str or ~azure.mgmt.network.v2016_12_01.models.ApplicationGatewaySslProtocol] """ _attribute_map = { 'disabled_ssl_protocols': {'key': 'disabledSslProtocols', 'type': '[str]'}, } def __init__(self, disabled_ssl_protocols=None): self.disabled_ssl_protocols = disabled_ssl_protocols

36.866667

83

0.640145

452798a28c1de698467dde21b543e9dd2fd1282c

6,668

py

Python

software/scripts/programmer.py

eeproto/PiAVRProg

33914b3e30636d55c2985b684ae16c0c8e9bd2f4

[ "CC0-1.0" ]

null

software/scripts/programmer.py

eeproto/PiAVRProg

33914b3e30636d55c2985b684ae16c0c8e9bd2f4

[ "CC0-1.0" ]

null

software/scripts/programmer.py

eeproto/PiAVRProg

33914b3e30636d55c2985b684ae16c0c8e9bd2f4

[ "CC0-1.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ This file is part of the PiAVRProg project https://github.com/eeproto/PiAVRProg This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-sa/4.0/ Copyright (c) 2019 EE Proto LLC https://www.eeproto.com Author: christian@eeproto.com """ import RPi.GPIO as GPIO import time import json import os import logging import traceback import signal import sys import led import button import avrdude DEVICE_FILE_PATH = '/media/usb/device.json' FLASH_FILE_PATH = '/media/usb/firmware.hex' DEVICE_KEYS = ['H', 'L', 'E', 'type'] SPI_DEVICE = '/dev/spidev0.0' PROGRAMMER = 'linuxspi' BAUDRATE_SLOW = '19200' BITRATE_SLOW = '10' BAUDRATE_FAST = '200000' BITCLOCK_FAST = '1' LOG_LEVEL = logging.INFO VERSION = '0.2' class PiProgrammer(object): def __init__(self): self.init_gpio() self.leds = {} self.led_ready = led.LED(6) self.leds['programming'] = led.LED(22) self.leds['flash_pass'] = led.LED(17) self.leds['flash_fail'] = led.LED(18) self.leds['fuse_pass'] = led.LED(23) self.leds['fuse_fail'] = led.LED(24) self.buffer_switch = led.LED(5) self.go_button = button.Button(27, self.button_pressed) self.clear_leds() self._alive = True def init_gpio(self): GPIO.setwarnings(False) GPIO.setmode(GPIO.BCM) def led_cycle(self, *args): l = list(self.leds.keys()) l.sort() self.leds[l[self.x]].off() self.x = (self.x + 1) % len(self.leds) self.leds[l[self.x]].on() def clear_leds(self): for l in self.leds.values(): l.off() self.led_ready.off() def load_firmware(self): device = {} try: logging.debug('reading device configuration from %s', DEVICE_FILE_PATH) with open(DEVICE_FILE_PATH, 'r') as f: device = json.load(f) device_ok = (len(set(device.keys()) & set(DEVICE_KEYS)) == len(DEVICE_KEYS)) logging.debug('found %s keys in device file %s', len(DEVICE_KEYS), DEVICE_FILE_PATH) except Exception as ex: device_ok = False logging.error('error loading device file: %s', ex) try: logging.debug('opening flash file %s', FLASH_FILE_PATH) flash_size = os.path.getsize(FLASH_FILE_PATH) logging.debug('found %s byte flash file %s', flash_size, FLASH_FILE_PATH) flash_ok = (flash_size > 0) except Exception as ex: flash_ok = False logging.error('error loading flash file: %s', ex) return device, device_ok, flash_ok def button_pressed(self, *args): self.clear_leds() if self._ready_to_go: try: logging.info('begin programming') self.leds['programming'].on() self.buffer_switch.on() self.run_programming() except avrdude.AvrDeviceNotRespondingError as ex: logging.error('device not ready %s %s', type(ex), ex.args) logging.debug(traceback.format_exc()) self.leds['fuse_fail'].blink(500, 500) except avrdude.SignatureReadError as ex: logging.error('can not read sig %s %s', type(ex), ex.args) logging.debug(traceback.format_exc()) self.leds['flash_fail'].blink(500, 500) except avrdude.SignatureDoesNotMatchError as ex: logging.error('signature mismatch %s %s', type(ex), ex.args) logging.debug(traceback.format_exc()) self.leds['fuse_pass'].blink(500, 500) except Exception as ex: logging.error('programming error %s %s', type(ex), ex.args) logging.debug(traceback.format_exc()) finally: self.buffer_switch.off() self.leds['programming'].off() def run_programming(self): dude = avrdude.AvrDude( self.device['type'], SPI_DEVICE, PROGRAMMER, BAUDRATE_SLOW, BITRATE_SLOW, BAUDRATE_FAST, BITCLOCK_FAST) read_signature = dude.verify_signature_and_fuses() logging.info('device signature verified as %s', read_signature) time.sleep(0.1) read_fuses = dude.read_fuses() fuses_same = ( self.device['E'] == read_fuses['E'] and self.device['H'] == read_fuses['H'] and self.device['L'] == read_fuses['L'] ) logging.info('device fuses read %s, same as device settings? %s', read_fuses, fuses_same) if not fuses_same: time.sleep(0.1) fuses_ok = dude.write_fuses( E=self.device['E'], H=self.device['H'], L=self.device['L'], ) logging.info('fuse write done') if fuses_ok: self.leds['fuse_pass'].on() self.leds['fuse_fail'].off() else: self.leds['fuse_pass'].off() self.leds['fuse_fail'].on() time.sleep(0.1) flash_ok = dude.write_flash(FLASH_FILE_PATH) logging.info('flash write result %s', flash_ok) if flash_ok: self.leds['flash_pass'].on() self.leds['flash_fail'].off() else: self.leds['flash_pass'].off() self.leds['flash_fail'].on() def run(self): while self._alive: self.device, device_file_ok, flash_file_ok = self.load_firmware() self._ready_to_go = device_file_ok and flash_file_ok if self._ready_to_go: self.led_ready.on() else: self.led_ready.blink(500, 500) time.sleep(2) logging.info('shut down') def shutdown(self): self._alive = False self.clear_leds() sys.exit(0) def signal_received_handler(signum, frame): logging.info("signal %s received in frame %s", signum, frame) if signum in (signal.SIGINT, signal.SIGTERM): global prog prog.shutdown() def main(): signal.signal(signal.SIGINT, signal_received_handler) signal.signal(signal.SIGTERM, signal_received_handler) signal.signal(signal.SIGHUP, signal_received_handler) signal.signal(signal.SIGTSTP, signal_received_handler) logging.basicConfig(level=LOG_LEVEL) logging.info('PiAVR programmer version %s', VERSION) global prog prog = PiProgrammer() prog.run() if __name__ == '__main__': main()

33.34

97

0.591032

2acf7a4ec5b6adb1955c8ab1566f03cf3f35e7d0

2,755

py

Python

yatube/posts/tests/test_urls.py

DeffronMax/hw05_test

f3c271dfcb60a2a96fa8ab98ee3e88fa351ca8b5

[ "MIT" ]

null

yatube/posts/tests/test_urls.py

DeffronMax/hw05_test

f3c271dfcb60a2a96fa8ab98ee3e88fa351ca8b5

[ "MIT" ]

null

yatube/posts/tests/test_urls.py

DeffronMax/hw05_test

f3c271dfcb60a2a96fa8ab98ee3e88fa351ca8b5

[ "MIT" ]

null

from http import HTTPStatus from django.contrib.auth import get_user_model from django.test import TestCase, Client from django.urls import reverse from ..models import Post, Group User = get_user_model() class URLTests(TestCase): @classmethod def setUpClass(cls): super().setUpClass() cls.author = User.objects.create_user(username="Author") cls.not_author = User.objects.create_user(username="NotAuthor") cls.group = Group.objects.create(title="Group", slug='slug') cls.text = "test text" cls.post = Post.objects.create(text=cls.text, author=cls.author) cls.templates_url_names = { 'base.html': '/', 'posts/group_list.html': f'/group/{cls.group.slug}/', 'posts/create_post.html': '/create/', 'posts/profile.html': f'/profile/{cls.author.username}/', 'posts/post_detail.html': f'/posts/{cls.post.id}/' } cls.group_name = Group.objects.create( title="test_group" ) def setUp(self): self.guest_client = Client() self.authorized_client = Client() self.authorized_client.force_login(self.author) self.not_author_client = Client() self.not_author_client.force_login(self.not_author) def test_anon_user(self): """Гость ошибка редактирования""" for template, adress in self.templates_url_names.items(): with self.subTest(adress=adress): if adress == reverse('posts:post_create'): response = self.guest_client.get(adress) self.assertEqual(response.status_code, HTTPStatus.FOUND) else: response = self.guest_client.get(adress) self.assertEqual(response.status_code, HTTPStatus.OK) response = self.guest_client.get(f'/posts/{self.post.id}/edit/') self.assertEqual(response.status_code, HTTPStatus.FOUND) def test_author_user(self): """автор редактирование""" for template, adress in self.templates_url_names.items(): with self.subTest(adress=adress): response = self.authorized_client.get(adress) self.assertEqual(response.status_code, HTTPStatus.OK) response = self.authorized_client.get(f'/posts/{self.post.id}/edit/') self.assertEqual(response.status_code, HTTPStatus.OK) def test_wrong_url_returns_404(self): response = self.client.get('/non-exist-page/') self.assertEqual(response.status_code, 404) def test_404_page_right_template(self): response = self.client.get('/non-exist-page/') template = 'core/404.html' self.assertTemplateUsed(response, template)

39.927536

77

0.641379

694e86b5292a70226ebf67994825acec91064f3f

38,401

py

Python

plot/chi_msd.py

yketa/UBC---Spring-2018---code

b065544639a483dda48cda89bcbb11c1772232aa

[ "MIT" ]

1

2021-12-15T13:38:13.000Z

plot/chi_msd.py

yketa/UBC---Spring-2018---code

b065544639a483dda48cda89bcbb11c1772232aa

[ "MIT" ]

1

2019-05-25T20:00:17.000Z

plot/chi_msd.py

yketa/UBC---Spring-2018---code

b065544639a483dda48cda89bcbb11c1772232aa

[ "MIT" ]

1

2020-01-22T17:05:18.000Z

""" Module chi_msd plots cooperativities, maximum cooperativities, times of maximum cooperativities and mean square displacements for either different persistence times at fixed self-propelling velocity or different self-propelling velocities at fixed persistence time. Simulation directories must follow the active_particles.naming.AHB2D naming standard and input files in simulation directories must follow either the active_particles.naming.Cuu standard (for cooperativities from displacement correlations), or active_particles.naming.Cww standard (for cooperativities from displacement relative to centre of mass displacement correlations), or active_particles.naming.Cdd standard (for cooperativities from displacement norm correlations), or active_particles.naming.Cee (for cooperativities from displacement direction correlations), and the active_particles.naming.Msd (mean square displacements). Environment modes ----------------- VARIABLE : string Plot of maximum cooperativities and times of maximum cooperativity x-coordinate variable. _____________________________________________________________________ | Mode | Variable | x-coordinate if not(PECLET) | |_________|_____________________________|_____________________________| | 'dr' | Rotation diffusion constant | \\tau = 1/dr | |_________|_____________________________|_____________________________| | 'vzero' | self-propelling velocity | vzero | |_________|_____________________________|_____________________________| DEFAULT: dr PECLET : bool Plot maximum cooperativities and times of maximum cooperativity as functions of the Péclet number Pe = vzero/dr. DEFAULT: True CORRELATION : string Correlations from which to calculate cooperativities. _____________________________________________________________ | Mode | Correlations | |______|______________________________________________________| | Cuu | displacement | |______|______________________________________________________| | Cww | displacement relative to centre of mass displacement | |______|______________________________________________________| | Cdd | displacement norm | |______|______________________________________________________| | Cee | displacement direction | |______|______________________________________________________| DEFAULT: Cuu DRDT : bool Use the product of the rotation diffusion constant and lag time rather than the bare lag time. DEFAULT: True MSDDT : bool Divide mean square displacement by lag time. DEFAULT: True FIT : bool Fit maximum cooperativity and time of maximum cooperativity as power law of their x-coordinates, on both sides of the variabe transition value VAR_C. DEFAULT: False Environment parameters ---------------------- DATA_DIRECTORY : string Data directory. DEFAULT: active_particles.naming.sim_directory EXCLUDE : string Simulation directories in DATA_DIRECTORY to exclude from the plots. DEFAULT: PARAMETERS_FILE : string Simulation parameters file name. DEFAULT: active_particles.naming.parameters_file DENSITY : float Packing fraction of particles. DEFAULT: active_particles.plot.chi_msd._density N : int Number of particles. DEFAULT: active_particles.plot.chi_msd._N VZERO ['dr' mode] : float Self-propulsion velocity. DEFAULT: active_particles.plot.chi_msd._vzero DR_MIN ['dr' mode] : float Minimum rotation diffusion constant. NOTE: Rotation diffusion constants lesser than DR_MIN will appear in the mean square displacement figure but not in the maximum cooperativity figure. DEFAULT: active_particles.plot.chi_msd._dr_min DR_MAX ['dr' mode] : float Maximum rotation diffusion constant. NOTE: Rotation diffusion constants greater than DR_MAX will appear in the mean square displacement figure but not in the maximum cooperativity figure. DEFAULT: active_particles.plot.chi_msd._dr_max DR_C ['dr' and FIT mode] : float Transition rotation diffusion constant. DEFAULT: active_particles.plot.chi_msd._dr_c DR ['vzero' mode] : float Rotation diffusion constant. DEFAULT: active_particles.plot.chi_msd._dr VZERO_MIN ['vzero' mode] : float Minimum self-propulsion velocity. NOTE: Self-propelling velocities lesser than VZERO_MIN will appear in the mean square displacement figure but not in the maximum cooperativity figure. DEFAULT: active_particles.plot.chi_msd._vzero_min VZERO_MAX ['vzero' mode] : float Maximum self-propulsion velocity. NOTE: Self-propelling velocities greater than VZERO_MIN will appear in the mean square displacement figure but not in the maximum cooperativity figure. DEFAULT: active_particles.plot.chi_msd._vzero_max VZERO_C ['vzero' and FIT mode] : float Transition self-propelling velocity. DEFAULT: active_particles.plot.chi_msd._vzero_c BOX_SIZE : float Size of the square box which was considered. DEFAULT: simulation box size X_ZERO : float 1st coordinate of the centre of the square box to consider. DEFAULT: 0 Y_ZERO : float 2nd coordinate of the centre of the square box to consider. DEFAULT: 0 INITIAL_FRAME_COR : int Frame to consider as initial for correlations. DEFAULT: active_particles.plot.chi_msd._init_frame_cor INTERVAL_MAXIMUM_COR : int Maximum number of intervals of length dt considered for correlations. DEFAULT: active_particles.plot.chi_msd._int_max_cor N_CASES : int Number of boxes in each direction with which the displacement grid was computed. DEFAULT: active_particles.plot.chi_msd._Ncases_cor INITIAL_FRAME_MSD : int separated by ':' Display only mean square displacements calculated with initial frame from this list except if this list is empty. DEFAULT: [] INTERVAL_MAXIMUM_MSD : int Maximum number of intervals of length dt considered for mean square displacements calculation. DEFAULT: active_particles.plot.chi_msd._int_max_msd INTERVAL_PERIOD : int Period of dumps for which mean square displacements were calculated. DEFAULT: active_particles.plot.chi_msd._int_period_msd R_MIN : float Minimum radius for correlations integration. DEFAULT: active_particles.plot.chi_msd._r_min R_MAX : float Maximum radius for correlations integration. DEFAULT: active_particles.plot.chi_msd._r_max FONT_SIZE : int Plot font size. DEFAULT: active_particles.plot.chi_msd._font_size MARKER_SIZE : int Plot marker size. DEFAULT: active_particles.plot.chi_msd._marker_size COLORMAP : string Plot colormap. DEFAULT: active_particles.plot.chi_msd._colormap COLORMAP0 : string Plot colormap for variables out of variable window. DEFAULT: active_particles.plot.chi_msd._colormap0 RATIO_LEGEND : float Width ratio between legend and figure. DEFAULT: active_particles.plot.chi_msd._ratio_legend NCOL_LEGEND : int Number of columns for the legend. DEFAULT: active_particles.plot.chi_msd._ncol_legend WSPACE : float Plots width space. DEFAULT: active_particles.plot.chi_msd._wspace HSPACE : float Plots height space. DEFAULT: active_particles.plot.chi_msd._hspace CHI_XS : string Cooperativity plot x-scale. DEFAULT: active_particles.plot.chi_msd._chi_xs CHI_YS : string Cooperativity plot y-scale. DEFAULT: active_particles.plot.chi_msd._chi_ys X_SCALE : string Maximum cooperativity and time of maximum cooperativity plots x-scale. DEFAULT: active_particles.plot.chi_msd._x_scale DTMAX_YS : string Time of maximum cooperativity plot y-scale. DEFAULT: active_particles.plot.chi_msd._dtmax_ys CHIMAX_YS : string Maximum cooperativity plot y-scale. DEFAULT: active_particles.plot.chi_msd._chimax_ys MSD_XS : string Mean square displacement plot x-scale. DEFAULT: active_particles.plot.chi_msd._msd_xs MSD_YS : string Mean square displacement plot y-scale. DEFAULT: active_particles.plot.chi_msd._msd_ys """ import active_particles.naming as naming from active_particles.init import get_env, get_env_list, dir_list from os import environ as envvar if __name__ == '__main__': envvar['SHOW'] = 'True' from os.path import join as joinpath from active_particles.plot.plot import list_colormap, list_markers,\ list_linestyles from active_particles.analysis.cuu import c1Dtochi from collections import OrderedDict import pickle import numpy as np from scipy import stats as st import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec from matplotlib.lines import Line2D # DEFAULT VARIABLES _dr = 3e-4 # default rotation diffusion constant _dr_min = 1e-5 # default minimum diffusion rotation constant _dr_max = 1e-2 # default maximum diffusion rotation constant _dr_c = 3e-4 # default transition diffusion rotation constant _vzero = 1e-2 # default self-propelling velocity _vzero_min = 1e-2 # default minimum self-propelling velocity _vzero_max = 1e-1 # default maximum self-propelling velocity _vzero_c = 5e-1 # default transition self-propelling velocity _density = 0.8 # default packing fraction of particles _N = int(1e5) # default number of particles _init_frame_cor = 0 # default frame to consider as initial for correlations _int_max_cor = 1 # default maximum number of intervals of length dt considered for correlations _Ncases_cor = 500 # default number of boxes in each direction with which the displacement grid is computed _int_max_msd = 1 # default maximum number of intervals of length dt considered for correlations _int_period_msd = 1 # default period of dumps for which mean square displacements were calculated _r_min = 1 # default minimum radius for correlations integration _r_max = 20 # default maximum radius for correlations integration _font_size = 15 # default font size for the plot _marker_size = 5 # default plot marker size _colormap = 'jet' # default plot colormap _colormap0 = 'Greys' # default plot colormap for variables out of variable window _ncol_legend = 2 # default number of legend columns _ratio_legend = 10 # default width ratio between graph and legend _wspace = 0.4 # default plots width space _hspace = 0.05 # default plots height space _chi_xs = 'log' # default cooperativity plot x-scale _chi_ys = 'log' # default cooperativity plot y-scale _x_scale = 'log' # default x-scale _dtmax_ys = 'log' # default time of maximum cooperativity plot y-scale _chimax_ys = 'log' # default maximum cooperativity plot y-scale _msd_xs = 'log' # default mean square displacement plot x-scale _msd_ys = 'log' # default mean square displacement plot y-scale # FUNCTIONS AND CLASSES class ChiMsd: """ Search and read correlations files, calculate cooperativities. Also search and read mean square displacement files. """ def __init__(self, data_dir, dir_standard, dir_attributes, parameters_file, var, var_min, var_max, excluded_dir=''): """ Create list of directories to consider and compute plot variable values associated to them. Parameters ---------- data_dir : string Data directory. dir_standard : active_particles.naming._File standard Simulation directory naming object. dir_attributes : hash table Attributes to be displayed in directory names. parameters_file : string Simulations parameters file name. var : string Plot variable name. var_min : float Minimum plot variable value. var_max : float Maximum plot variable value. excluded_dir : string Names of directories to be ignored. (default: '') """ self.data_dir = data_dir self.dir_standard = dir_standard self.dir_attributes = dir_attributes self.excluded_dir = excluded_dir self.parameters_file = parameters_file self.var = var self.var_min = var_min self.var_max = var_max (self.dirs, self.var_hash, self.var_list, self.var0_list, self.isinvarinterval) = dir_list( self.data_dir, self.dir_standard, self.dir_attributes, self.var, self.var_min, self.var_max, self.parameters_file, excluded_dir=self.excluded_dir, include_out=True) self.calculate_msd = False # calculate mean square displacements with cooperativities def calculate(self, cor_standard, cor_attributes, r_min, r_max, box_size=None, multiply_with_dr=True): """ Calculate cooperativities, maximum cooperativities and times of maximum cooperativites. Also calculates mean square displacements. Parameters ---------- cor_standard : active_particles.naming._File standard Correlation files naming object. cor_attributes : hash table Attributes to be displayed in correlation file names. r_min : float Minimum radius for correlations integration. r_max : float Maximum radius for correlations integration. box_size : float or None Size of the square box which was considered. (default: None) NOTE: if None, then the size is taken as the simulation box size. multiply_with_dr : bool Consider the product of rotation diffusion constant and lag time rather than just lag time. (default: True) """ self.cor_standard = cor_standard self.cor_attributes = cor_attributes self.r_min = r_min self.r_max = r_max self.box_size = box_size self.multiply_with_dr = multiply_with_dr self.time_step = {} # hash table of directories' simulation time step self.chi = {} # hash table of list of lag times and corresponding cooperativities self.dtmax = {} # hash table of time of maximum cooperativities self.chimax = {} # hash table of maximum cooperativities self.islocalmax = {} # hash table of booleans indicating if the time of maximum cooperativity is a local maximum for dir in self.dirs: # COOPERATIVITY with open( joinpath(self.data_dir, dir, self.parameters_file), 'rb')\ as param_file: parameters = pickle.load(param_file) # simulation parameters hash table if self.box_size == None: L = parameters['box_size'] else: L = self.box_size # box size pdts = parameters['period_dump']*parameters['time_step'] # time corresponding to one dump length of time if self.multiply_with_dr: pdts *= parameters['dr'] # plot dr*dt rather than dt chidir = [] # list of lag times and corresponding cooperativities for current directory for cor_filename in self.cor_standard.get_files( directory=joinpath(self.data_dir, dir), **self.cor_attributes): # loop over correlations files in directory with open(joinpath(self.data_dir, dir, cor_filename), 'rb')\ as cor_file: c1D = pickle.load(cor_file)[1] # 1D correlation chidir += [[ pdts*self.cor_standard.get_data(cor_filename, 'dt')[0], c1Dtochi(c1D, L, r_min=self.r_min, r_max=self.r_max)]] # cooperativity if not(chidir): continue # no cooperativities files with requested attributes self.time_step[dir] = parameters['time_step'] # simulation time step self.dtmax[dir], self.chimax[dir] = max(chidir, key=lambda el: el[1]) # time of maximum cooperativity and maximum cooperativity self.chi[dir] = np.transpose(sorted(chidir, key=lambda el: el[0])) # cooperativity self.islocalmax[dir] =\ self.dtmax[dir] > min(self.chi[dir][:, 0])\ and self.dtmax[dir] < max(self.chi[dir][:, 0]) # is dtmax a local maximum if not(self.calculate_msd): continue # do not calculate mean square displacements # MEAN SQUARE DISPLACEMENT for msd_filename in self.msd_standard.get_files( directory=joinpath(self.data_dir, dir), **self.msd_attributes): # loop over mean square displacements files in directory init_frame = self.msd_standard.get_data( msd_filename, 'init_frame')[0] # initial frame if not(self.init_frame_msd)\ or init_frame in self.init_frame_msd: self.msd[(dir, init_frame)] = np.genfromtxt( fname=joinpath(self.data_dir, dir, msd_filename), delimiter=',', skip_header=True) # mean square displacement if self.divide_by_dt: self.msd[(dir, init_frame)][:, 1] /=\ self.msd[(dir, init_frame)][:, 0] self.msd[(dir, init_frame)][:, 2] /=\ self.msd[(dir, init_frame)][:, 0] if self.multiply_with_dr: self.msd[(dir, init_frame)][:, 0] *= parameters['dr'] self.time_step_list = sorted(OrderedDict.fromkeys( self.time_step.values())) # list of time steps def calculate_with_msd(self, cor_standard, cor_attributes, r_min, r_max, msd_standard, msd_attributes, init_frame_msd, box_size=None, multiply_with_dr=True, divide_by_dt=True): """ Calculate cooperativities, maximum cooperativities and times of maximum cooperativites. Also calculates mean square displacements. Parameters ---------- cor_standard : active_particles.naming._File standard Correlation files naming object. cor_attributes : hash table Attributes to be displayed in correlation file names. r_min : float Minimum radius for correlations integration. r_max : float Maximum radius for correlations integration. msd_standard : active_particles.naming._File standard Mean square displacement files naming object. msd_attributes : hash table Attributes to be displayed in mean square displacement file names. init_frame_msd : list of int Calculate only mean square displacements calculated with initial frame from this list except if this list is empty. box_size : float or None Size of the square box which was considered. (default: None) NOTE: if None, then the size is taken as the simulation box size. multiply_with_dr : bool Consider the product of rotation diffusion constant and lag time rather than just lag time. (default: True) divide_by_dt : bool Divide mean square displacement by lag time. (default: True) """ self.calculate_msd = True self.msd_standard = msd_standard self.msd_attributes = msd_attributes self.init_frame_msd = init_frame_msd self.divide_by_dt = divide_by_dt self.msd = {} # hash table of lists of lag times and corresponding mean square displacement and associated standard error self.calculate(cor_standard, cor_attributes, r_min, r_max, box_size=box_size, multiply_with_dr=multiply_with_dr) self.init_frame_list = sorted(OrderedDict.fromkeys( [init_frame for dir, init_frame in self.msd])) # list of mean square displacements initial frames # SCRIPT if __name__ == '__main__': # executing as script # VARIABLES DEFINITIONS mode = get_env('VARIABLE', default='dr') # plotting variable peclet = get_env('PECLET', default=True, vartype=bool) # display Péclet number rather than mode variable if mode == 'dr': vzero = get_env('VZERO', default=_vzero, vartype=float) # self-propelling velocity attributes = {'vzero': vzero} # attributes displayed in filenames var = 'dr' # plot variable var_min = get_env('DR_MIN', default=_dr_min, vartype=float) # minimum rotation diffusion constant var_max = get_env('DR_MAX', default=_dr_max, vartype=float) # maximum rotation diffusion constant var_c = get_env('DR_C', default=_dr_c, vartype=float) # transition rotation diffusion constant var_label = r'$\tilde{\nu}_r$' # variable label if peclet: x_func = lambda x: vzero/x # x-coordinate as function of plot variable else: x_label = r'$\tau_r \equiv \tilde{\nu}_r^{-1}$' # x-coordinate label x_func = lambda x: 1/x # x-coordinate as function of plot variable elif mode == 'vzero': dr = get_env('DR', default=_dr, vartype=float) # rotation diffusion constant attributes = {'dr': dr} # attributes displayed in filenames var = 'vzero' # plot variable var_min = get_env('VZERO_MIN', default=_vzero_min, vartype=float) # minimum self-propelling velocity var_max = get_env('VZERO_MAX', default=_vzero_max, vartype=float) # maximum self-propelling velocity var_c = get_env('VZERO_C', default=_vzero_c, vartype=float) # transition self-propelling velocity var_label = r'$\tilde{v}$' # variable label if peclet: x_func = lambda x: x/dr # x-coordinate as function of plot variable else: x_label = r'$\tilde{v}$' # x-coordinate label x_func = lambda x: x # x-coordinate as function of plot variable else: raise ValueError('Mode %s is not known.' % mode) # mode is not known if peclet: x_label = r'$Pe$' # x-coordinate label cor = get_env('CORRELATION', default='Cuu') # correlation variable if cor == 'Cuu': # cooperativities from naming_cor = naming.Cuu() # correlations naming object cor_name = 'C_{uu}' # correlations name elif cor == 'Cww': naming_cor = naming.Cww() # correlations naming object cor_name = 'C_{\\delta u \\delta u}' # correlations name elif cor == 'Cdd': naming_cor = naming.Cdd() # correlations naming object cor_name = 'C_{|u||u|}' # correlations name elif cor == 'Cee': naming_cor = naming.Cee() # correlations naming object cor_name = 'C_{\\hat{u}\\hat{u}}' # correlations name else: raise ValueError('Correlation %s is not known.' % cor) # correlation is not known data_dir = get_env('DATA_DIRECTORY', default=naming.sim_directory) # data directory excluded_directories = get_env('EXCLUDE', default='') # directories to exclude parameters_file = get_env('PARAMETERS_FILE', default=naming.parameters_file) # simulations parameters file name density = get_env('DENSITY', default=_density, vartype=float) # packing fraction of particles N = get_env('N', default=_N, vartype=int) # number of particles box_size = get_env('BOX_SIZE', vartype=float) # size of the square box which was considered centre = (get_env('X_ZERO', default=0, vartype=float), get_env('Y_ZERO', default=0, vartype=float)) # centre of the box init_frame_cor = get_env('INITIAL_FRAME_COR', default=_init_frame_cor, vartype=int) # frame to consider as initial for correlations int_max_cor = get_env('INTERVAL_MAXIMUM_COR', default=_int_max_cor, vartype=int) # maximum number of intervals of length dt considered for correlations Ncases_cor = get_env('N_CASES', default=_Ncases_cor, vartype=int) # number of boxes in each direction with which the displacement grid is computed init_frame_msd = get_env_list('INITIAL_FRAME_MSD', delimiter=':', vartype=int) # display only mean square displacements calculated with initial frame from this list except if this list is empty int_max_msd = get_env('INTERVAL_MAXIMUM_MSD', default=_int_max_msd, vartype=int) # maximum number of intervals of length dt considered for mean square displacements calculation int_period_msd = get_env('INTERVAL_PERIOD', default=_int_period_msd, vartype=int) # period of dumps for which mean square displacements were calculated r_min = get_env('R_MIN', default=_r_min, vartype=float) # minimum radius for correlations integration r_max = get_env('R_MAX', default=_r_max, vartype=float) # maximum radius for correlations integration # NAMING common_attributes = {**attributes, 'density': density, 'N': N} # attributes to be displayed in file names attributes_cor = {**common_attributes, 'init_frame': init_frame_cor, 'int_max': int_max_cor, 'Ncases': Ncases_cor, 'box_size': box_size, 'x_zero': centre[0], 'y_zero': centre[1]} # attributes displayed in file names specifically for correlations attributes_msd = {**common_attributes, 'int_max': int_max_msd, 'int_period': int_period_msd} # attributes displayed in file names specifically for mean square displacements naming_msd = naming.Msd() # mean square displacements naming object naming_simdir = naming.AHB2D() # simulation directory naming object # PLOT PARAMETERS font_size = get_env('FONT_SIZE', default=_font_size, vartype=float) # plot font size marker_size = get_env('MARKER_SIZE', default=_marker_size, vartype=int) # plot marker size mpl.rcParams.update({'font.size': font_size, 'lines.markersize': marker_size}) colormap = get_env('COLORMAP', default=_colormap) # plot colormap colormap0 = get_env('COLORMAP0', default=_colormap0) # plot colormap for variables out of variable window ratio_legend = get_env('RATIO_LEGEND', default=_ratio_legend, vartype=float) # width ratio between graph and legend ncol_legend = get_env('NCOL_LEGEND', default=_ncol_legend, vartype=int) # number of legend columns wspace = get_env('WSPACE', default=_wspace, vartype=float) # plots width space hspace = get_env('HSPACE', default=_hspace, vartype=float) # plots height space chi_xs = get_env('CHI_XS', default=_chi_xs) # cooperativity plot x-scale chi_ys = get_env('CHI_YS', default=_chi_ys) # cooperativity plot y-scale x_scale = get_env('X_SCALE', default=_x_scale) # maximum cooperativity and time of maximum cooperativity plots x-scale dtmax_ys = get_env('DTMAX_YS', default=_dtmax_ys) # time of maximum cooperativity plot y-scale chimax_ys = get_env('CHIMAX_YS', default=_chimax_ys) # maximum cooperativity plot y-scale msd_xs = get_env('MSD_XS', default=_msd_xs) # mean square displacement plot x-scale msd_ys = get_env('MSD_YS', default=_msd_ys) # mean square displacement plot y-scale multiply_with_dr = get_env('DRDT', default=True, vartype=bool) # plot dr*dt rather than dt if multiply_with_dr: dt_label = '\\tilde{\\nu}_r \\Delta t' # dt label else: dt_label = '\\Delta t' # dt label divide_by_dt = get_env('MSDDT', default=True, vartype=bool) # divide mean square displacement by lag time msd_label = r'$<|\Delta \vec{r}(t)|^2>$' # mean square displacement plot label if divide_by_dt: msd_label += r'$/\Delta t$' fit_max = get_env('FIT', default=False, vartype=bool) # fit maximum cooperativity and time of maximum cooperativity as power law of their x-coordinates on both sides of the variable transition value # CALCULATION chimsd = ChiMsd(data_dir, naming_simdir, common_attributes, parameters_file, var, var_min, var_max, excluded_dir=excluded_directories) # cooperativities and mean square displacements calculator chimsd.calculate_with_msd(naming_cor, attributes_cor, r_min, r_max, naming_msd, attributes_msd, init_frame_msd, box_size=box_size, multiply_with_dr=multiply_with_dr, divide_by_dt=divide_by_dt) # calculate cooperativities and mean square displacements # PLOT colors = {**list_colormap(chimsd.var_list, colormap=colormap), **list_colormap(chimsd.var0_list, colormap=colormap0)} # plot colors hash table markers = list_markers(chimsd.time_step_list) # plot markers hash table linestyles = list_linestyles(chimsd.init_frame_list) # plot linestyles hash table # CHI, CHIMAX, DTMAX fig_chi = plt.figure() fig_chi.subplots_adjust(wspace=wspace, hspace=hspace) fig_chi.suptitle( r'$N=%.1e, \phi=%1.2f,$' % (N, density) + (r'$\tilde{v} = %.2e$' % vzero if mode == 'dr' else r'$\tilde{\nu}_r = %.2e$' % dr) + r'$, r_{min} = %.2e, r_{max} = %.2e$' % (r_min, r_max)) gs = GridSpec(2, 3, width_ratios=[1, 1, 2/ratio_legend]) ax_chi = plt.subplot(gs[:, 0]) ax_chi.set_xlabel(r'$%s$' % dt_label) ax_chi.set_xscale(chi_xs) ax_chi.set_ylabel(r'$\chi(\Delta t) = \frac{1}{L^2}$' + r'$\int_{r=r_{min}}^{r=r_{max}} dr 2 \pi r %s(r, \Delta t)$' % cor_name) ax_chi.set_yscale(chi_ys) ax_chi.set_title( r'$S_{init} = %.1e, S_{max} = %.1e,$' % (init_frame_cor, int_max_cor) + r'$N_{cases} = %.1e$' % Ncases_cor) ax_dtmax = plt.subplot(gs[0, 1]) ax_dtmax.set_xscale(x_scale) ax_dtmax.set_ylabel(r'$%s^*$' % dt_label) ax_dtmax.set_yscale(dtmax_ys) ax_chimax = plt.subplot(gs[1, 1], sharex=ax_dtmax) plt.setp(ax_dtmax.get_xticklabels(), visible=False) ax_chimax.set_xlabel(x_label) ax_chimax.set_xscale(x_scale) ax_chimax.set_ylabel(r'$\chi(\Delta t^*) = \frac{1}{L^2}$' + r'$\int_{r=r_{min}}^{r=r_{max}} dr 2 \pi r %s(r, \Delta t^*)$' % cor_name) ax_chimax.set_yscale(chimax_ys) ax_legend = plt.subplot(gs[:, 2]) ax_legend.axis('off') lines_fig_chi = list(map( lambda var_value: Line2D([0], [0], color=colors[var_value], lw=2, label='%s = %.0e' % (var_label, var_value)), chimsd.var_list)) lines_fig_chi += [Line2D([0], [0], lw=0, label='')] lines_fig_chi += list(map( lambda time_step: Line2D([0], [0], marker=markers[time_step], color='black', label=r'$dt=%.0e$' % time_step), chimsd.time_step_list)) ax_legend.legend(handles=lines_fig_chi, loc='center', ncol=ncol_legend) for dir in chimsd.chi: if chimsd.isinvarinterval[dir]: time_step_value = chimsd.time_step[dir] var_value = chimsd.var_hash[dir] plot_parameters = {'color': colors[var_value], 'marker': markers[time_step_value]} ax_chi.plot(*chimsd.chi[dir], **plot_parameters) ax_dtmax.plot(x_func(var_value), chimsd.dtmax[dir], **plot_parameters) ax_chimax.plot(x_func(var_value), chimsd.chimax[dir], **plot_parameters) if fit_max: # fit maximum cooperativity and time of maximum cooperativity as power law of dt or dr*dt # VALUES dtmax_lt_varc, dtmax_gt_varc = [], [] chimax_lt_varc, chimax_gt_varc = [], [] for dir in chimsd.chi: if chimsd.isinvarinterval[dir]: var_value = chimsd.var_hash[dir] if var_value < var_c: dtmax_lt_varc += [[np.log(x_func(var_value)), np.log(chimsd.dtmax[dir])]] chimax_lt_varc += [[np.log(x_func(var_value)), np.log(chimsd.chimax[dir])]] elif var_value > var_c: dtmax_gt_varc += [[np.log(x_func(var_value)), np.log(chimsd.dtmax[dir])]] chimax_gt_varc += [[np.log(x_func(var_value)), np.log(chimsd.chimax[dir])]] dtmax_lt_varc = np.transpose(dtmax_lt_varc) dtmax_gt_varc = np.transpose(dtmax_gt_varc) chimax_lt_varc = np.transpose(chimax_lt_varc) chimax_gt_varc = np.transpose(chimax_gt_varc) # FIT dtmax_lt_varc_slo, dtmax_lt_varc_int, _, _, dtmax_lt_varc_std =\ st.linregress(*dtmax_lt_varc) dtmax_gt_varc_slo, dtmax_gt_varc_int, _, _, dtmax_gt_varc_std =\ st.linregress(*dtmax_gt_varc) chimax_lt_varc_slo, chimax_lt_varc_int, _, _, chimax_lt_varc_std =\ st.linregress(*chimax_lt_varc) chimax_gt_varc_slo, chimax_gt_varc_int, _, _, chimax_gt_varc_std =\ st.linregress(*chimax_gt_varc) # SORT FOR PLOTS dtmax_lt_varc[0].sort() dtmax_gt_varc[0].sort() chimax_lt_varc[0].sort() chimax_gt_varc[0].sort() # PLOT ax_dtmax.plot(np.exp(dtmax_lt_varc[0]), np.exp(dtmax_lt_varc_int) *(np.exp(dtmax_lt_varc[0])**dtmax_lt_varc_slo), color='black', linestyle='-.', label=r'$%s \propto (%s)^{%.1e \pm %.1e}$' % (ax_dtmax.get_ylabel().replace('$', ''), x_label.replace('$', ''), dtmax_lt_varc_slo, dtmax_lt_varc_std)) ax_dtmax.plot(np.exp(dtmax_gt_varc[0]), np.exp(dtmax_gt_varc_int) *(np.exp(dtmax_gt_varc[0])**dtmax_gt_varc_slo), color='black', linestyle='--', label=r'$%s \propto (%s)^{%.1e \pm %.1e}$' % (ax_dtmax.get_ylabel().replace('$', ''), x_label.replace('$', ''), dtmax_gt_varc_slo, dtmax_gt_varc_std)) ax_chimax.plot(np.exp(chimax_lt_varc[0]), np.exp(chimax_lt_varc_int) *(np.exp(chimax_lt_varc[0])**chimax_lt_varc_slo), color='black', linestyle='-.', label=r'$%s \propto (%s)^{%.1e \pm %.1e}$' % ('\\chi(\\Delta t^*)', x_label.replace('$', ''), chimax_lt_varc_slo, chimax_lt_varc_std)) ax_chimax.plot(np.exp(chimax_gt_varc[0]), np.exp(chimax_gt_varc_int) *(np.exp(chimax_gt_varc[0])**chimax_gt_varc_slo), color='black', linestyle='--', label=r'$%s \propto (%s)^{%.1e \pm %.1e}$' % ('\\chi(\\Delta t^*)', x_label.replace('$', ''), chimax_gt_varc_slo, chimax_gt_varc_std)) # LABEL ax_dtmax.legend() ax_chimax.legend() # CHI, MSD if chimsd.msd: # if there are mean square displacements to plot fig_msd = plt.figure() fig_msd.subplots_adjust(wspace=wspace, hspace=hspace) fig_msd.suptitle( r'$N=%.1e, \phi=%1.2f,$' % (N, density) + (r'$\tilde{v} = %.2e$' % vzero if mode == 'dr' else r'$\tilde{\nu}_r = %.2e$' % dr) + r'$, r_{min} = %.2e, r_{max} = %.2e$' % (r_min, r_max)) gs = GridSpec(1, 3, width_ratios=[1, 1, 2/ratio_legend]) ax_msd_chi = plt.subplot(gs[0]) ax_msd_chi.set_xlabel(r'$%s$' % dt_label) ax_msd_chi.set_xscale(chi_xs) ax_msd_chi.set_ylabel(r'$\chi(\Delta t) = \frac{1}{L^2}$' + r'$\int_{r=r_{min}}^{r=r_{max}} dr 2 \pi r %s(r, \Delta t)$' % cor_name) ax_msd_chi.set_yscale(chi_ys) ax_msd_chi.set_title( r'$S_{init} = %.1e$' % init_frame_cor + r'$, S_{max} = %.1e,$' % int_max_cor + r'$N_{cases} = %.1e$' % Ncases_cor) lines_ax_msd_chi = list(map( lambda time_step: Line2D([0], [0], marker=markers[time_step], color='black', label=r'$dt=%.0e$' % time_step), chimsd.time_step_list)) ax_msd_chi.legend(handles=lines_ax_msd_chi, loc='best') ax_msd = plt.subplot(gs[1]) ax_msd.set_xlabel(r'$%s$' % dt_label) ax_msd.set_xscale(msd_xs) ax_msd.set_ylabel(msd_label) ax_msd.set_yscale(msd_ys) ax_msd.set_title( r'$S_{max} = %.1e$' % int_max_msd + r'$, S_{period} = %.1e$' % int_period_msd) lines_ax_msd = list(map( lambda init_frame: Line2D([0], [0], linestyle=linestyles[init_frame], color='black', label=r'$S_{init}=%.1e$' % init_frame), chimsd.init_frame_list)) ax_msd.legend(handles=lines_ax_msd, loc='best') ax_legend_msd = plt.subplot(gs[2]) ax_legend_msd.axis('off') lines_fig_msd = list(map( lambda var_value: Line2D([0], [0], color=colors[var_value], lw=2, label='%s = %.0e' % (var_label, var_value)), sorted(chimsd.var_list + chimsd.var0_list))) ax_legend_msd.legend(handles=lines_fig_msd, loc='center', ncol=ncol_legend) for dir in chimsd.chi: ax_msd_chi.plot(*chimsd.chi[dir], color=colors[chimsd.var_hash[dir]], marker=markers[chimsd.time_step[dir]]) for dir, init_frame in chimsd.msd: ax_msd.errorbar( chimsd.msd[(dir, init_frame)][:, 0], chimsd.msd[(dir, init_frame)][:, 1], yerr=chimsd.msd[(dir, init_frame)][:, 2], color=colors[chimsd.var_hash[dir]], linestyle=linestyles[init_frame]) # SHOW plt.show()

44.75641

204

0.646988

8ead5d3c0fafa18f55f4f5107bc62847c162c836

7,324

py

Python

sdk/python/pulumi_azure_native/avs/v20210101preview/global_reach_connection.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/avs/v20210101preview/global_reach_connection.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/avs/v20210101preview/global_reach_connection.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables __all__ = ['GlobalReachConnection'] class GlobalReachConnection(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, authorization_key: Optional[pulumi.Input[str]] = None, global_reach_connection_name: Optional[pulumi.Input[str]] = None, peer_express_route_circuit: Optional[pulumi.Input[str]] = None, private_cloud_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, __props__=None, __name__=None, __opts__=None): """ A global reach connection resource :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] authorization_key: Authorization key from the peer express route used for the global reach connection :param pulumi.Input[str] global_reach_connection_name: Name of the global reach connection in the private cloud :param pulumi.Input[str] peer_express_route_circuit: Identifier of the ExpressRoute Circuit to peer with in the global reach connection :param pulumi.Input[str] private_cloud_name: The name of the private cloud. :param pulumi.Input[str] resource_group_name: The name of the resource group. The name is case insensitive. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['authorization_key'] = authorization_key __props__['global_reach_connection_name'] = global_reach_connection_name __props__['peer_express_route_circuit'] = peer_express_route_circuit if private_cloud_name is None and not opts.urn: raise TypeError("Missing required property 'private_cloud_name'") __props__['private_cloud_name'] = private_cloud_name if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name __props__['address_prefix'] = None __props__['circuit_connection_status'] = None __props__['name'] = None __props__['provisioning_state'] = None __props__['type'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:avs/v20210101preview:GlobalReachConnection"), pulumi.Alias(type_="azure-native:avs:GlobalReachConnection"), pulumi.Alias(type_="azure-nextgen:avs:GlobalReachConnection"), pulumi.Alias(type_="azure-native:avs/v20200717preview:GlobalReachConnection"), pulumi.Alias(type_="azure-nextgen:avs/v20200717preview:GlobalReachConnection")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(GlobalReachConnection, __self__).__init__( 'azure-native:avs/v20210101preview:GlobalReachConnection', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'GlobalReachConnection': """ Get an existing GlobalReachConnection resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["address_prefix"] = None __props__["authorization_key"] = None __props__["circuit_connection_status"] = None __props__["name"] = None __props__["peer_express_route_circuit"] = None __props__["provisioning_state"] = None __props__["type"] = None return GlobalReachConnection(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="addressPrefix") def address_prefix(self) -> pulumi.Output[str]: """ The network used for global reach carved out from the original network block provided for the private cloud """ return pulumi.get(self, "address_prefix") @property @pulumi.getter(name="authorizationKey") def authorization_key(self) -> pulumi.Output[Optional[str]]: """ Authorization key from the peer express route used for the global reach connection """ return pulumi.get(self, "authorization_key") @property @pulumi.getter(name="circuitConnectionStatus") def circuit_connection_status(self) -> pulumi.Output[str]: """ The connection status of the global reach connection """ return pulumi.get(self, "circuit_connection_status") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Resource name. """ return pulumi.get(self, "name") @property @pulumi.getter(name="peerExpressRouteCircuit") def peer_express_route_circuit(self) -> pulumi.Output[Optional[str]]: """ Identifier of the ExpressRoute Circuit to peer with in the global reach connection """ return pulumi.get(self, "peer_express_route_circuit") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[str]: """ The state of the ExpressRoute Circuit Authorization provisioning """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ Resource type. """ return pulumi.get(self, "type") def translate_output_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

44.932515

417

0.669306

02b7313efdffc3a160fba7abffbe993d5474b0f3

1,903

py

Python

actions/post_message.py

StackStorm-Exchange/stackstorm-irc

719f2b20952a97f44e237e2d3c96bf11ae602cad

[ "Apache-2.0" ]

null

actions/post_message.py

StackStorm-Exchange/stackstorm-irc

719f2b20952a97f44e237e2d3c96bf11ae602cad

[ "Apache-2.0" ]

4

2017-03-28T15:17:04.000Z

2021-05-09T05:11:44.000Z

actions/post_message.py

StackStorm-Exchange/stackstorm-irc

719f2b20952a97f44e237e2d3c96bf11ae602cad

[ "Apache-2.0" ]

1

2021-01-28T17:44:50.000Z

import random import eventlet from irc.bot import SingleServerIRCBot from st2common.runners.base_action import Action eventlet.monkey_patch( os=True, select=True, socket=True, thread=True, time=True) __all__ = [ 'PostMessageAction' ] class StackStormActionIRCBot(SingleServerIRCBot): def __init__(self, server_host, server_port, nickname, channel, message): server_list = [(server_host, server_port)] super(StackStormActionIRCBot, self).__init__(server_list=server_list, nickname=nickname, realname=nickname) self._channel = channel self._message = message def on_welcome(self, connection, event): try: connection.join(self._channel) self.connection.privmsg(self._channel, self._message) # pylint: disable=no-member finally: self.die(msg='Disconnecting') # pylint: disable=no-member def on_nicknameinuse(self, connection, event): new_nickname = '%s-%s' % (connection.get_nickname(), random.randint(1, 1000)) connection.nick(new_nickname) class PostMessageAction(Action): def run(self, channel, message): bot = self._get_bot(channel=channel, message=message) bot.start() # pylint: disable=no-member return True def _get_bot(self, channel, message): split = self.config['server'].split(':') server_host = split[0] server_port = int(split[1]) nickname = self.config['nickname'] bot = StackStormActionIRCBot(server_host=server_host, server_port=server_port, nickname=nickname, channel=channel, message=message) return bot

32.254237

94

0.593274

6a5eb6db117c948da70f5ff978206e73602d1698

5,595

py

Python

src/robotide/utils/eventhandler.py

chandrapola/RIDE

08e01b98026b618359e47689cb5e65a0a67795f5

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

775

2015-01-12T06:54:09.000Z

2022-03-25T05:18:05.000Z

src/robotide/utils/eventhandler.py

chandrapola/RIDE

08e01b98026b618359e47689cb5e65a0a67795f5

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

2,191

2015-05-19T16:49:09.000Z

2022-03-28T21:38:34.000Z

src/robotide/utils/eventhandler.py

chandrapola/RIDE

08e01b98026b618359e47689cb5e65a0a67795f5

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

382

2015-01-24T08:41:44.000Z

2022-03-13T10:14:20.000Z

# Copyright 2008-2015 Nokia Networks # Copyright 2016- Robot Framework Foundation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import wx class _RideFSWatcherHandler: def __init__(self): self._fs_watcher = None self._is_workspace_dirty = False self._initial_watched_path = None self._watched_path = set() def create_fs_watcher(self, path): if self._fs_watcher: return self._initial_watched_path = path self._fs_watcher = wx.FileSystemWatcher() self._fs_watcher.Bind(wx.EVT_FSWATCHER, self._on_fs_event) # print(f"DEBUG: FileSystemWatcher create_fs_watcher CREATED") def start_listening(self, path): self.stop_listening() if os.path.isdir(path): # only watch folders # MSW do not support watch single file path = os.path.join(path, '') result = self._fs_watcher.AddTree(path) #, filter="*.r*o*") # print(f"DEBUG: FileSystemWatcher start_listening DIR result is ={result}") # Add all files to the monitoring list from wx import FileSystem fs = FileSystem() fs.ChangePathTo(path, True) file_search = fs.FindFirst("*") while file_search: if self._is_valid_file_format(file_search): # print(f"DEBUG: FileSystemWatcher start_listening file_search={file_search}") self._watched_path.add(fs.URLToFileName(file_search)) file_search = fs.FindNext() self._watched_path.add(path) else: self._watched_path.add(path) # Here we add the file path path = os.path.join(os.path.dirname(path), '') result = self._fs_watcher.Add(path) # Here we only add the file parent directory # print(f"DEBUG: FileSystemWatcher start_listening FILE result is ={result}") # print(f"DEBUG: FileSystemWatcher start_listening self._watched_path={self._watched_path}") def stop_listening(self): # print(f"DEBUG: FileSystemWatcher stop_listening") self._is_workspace_dirty = False self._fs_watcher.RemoveAll() self._watched_path = set() def is_workspace_dirty(self): # print(f"DEBUG: is_workspace_dirty self._watched_path = {self._watched_path}") if self._watched_path: return self._is_workspace_dirty else: return False def is_watcher_created(self): return self._fs_watcher is not None def get_workspace_new_path(self): return self._initial_watched_path # self._watched_path.pop() # Returning file or directory name def _on_fs_event(self, event): if self._is_mark_dirty_needed(event): self._is_workspace_dirty = True def _is_mark_dirty_needed(self, event): new_path = event.GetNewPath() previous_path = event.GetPath() change_type = event.GetChangeType() if change_type == wx.FSW_EVENT_MODIFY: # DEBUG or change_type == wx.FSW_EVENT_ACCESS """ paths = list() count = self._fs_watcher.GetWatchedPaths(paths) # DEBUG This is always empty print(f"DEBUG: FSW_EVENT_MODIFY count={count} paths={paths}") for file in paths: # print(f"DEBUG: FileSystemWatcher count files {count} event wx.FSW_EVENT_MODIFY file = {file}") if file == previous_path: return True """ if previous_path in self._watched_path: return True return False if change_type == wx.FSW_EVENT_CREATE: if os.path.isdir(previous_path): return True elif os.path.isfile(previous_path): return self._is_valid_file_format(previous_path) elif change_type == wx.FSW_EVENT_DELETE: if previous_path in self._watched_path: # workspace root folder / suite file is deleted self._watched_path.remove(previous_path) return True if previous_path.endswith(os.sep): return True else: return self._is_valid_file_format(previous_path) elif change_type == wx.FSW_EVENT_RENAME: if previous_path in self._watched_path: # workspace root folder / suite file is renamed self._watched_path.remove(previous_path) self._watched_path.add(new_path) return True if os.path.isdir(new_path): return True elif os.path.isfile(new_path): return self._is_valid_file_format(new_path) else: return False @staticmethod def _is_valid_file_format(file_path): # only watch files with certain extensions suffixes = ('.robot', '.txt', '.resource', '.tsv') return os.path.splitext(file_path)[-1].lower() in suffixes RideFSWatcherHandler = _RideFSWatcherHandler()

39.680851

112

0.63092

16803ba4965525e0d1574560910fcbb179078581

189

py

Python

Instagram/admin.py

IsaacMurage-dev/Insta-lookalike

0b394a3e60c669dfb78e8d538e869cebbcec70b8

[ "MIT" ]

null

Instagram/admin.py

IsaacMurage-dev/Insta-lookalike

0b394a3e60c669dfb78e8d538e869cebbcec70b8

[ "MIT" ]

null

Instagram/admin.py

IsaacMurage-dev/Insta-lookalike

0b394a3e60c669dfb78e8d538e869cebbcec70b8

[ "MIT" ]

null

from django.contrib import admin from .models import Profile,Image,Comment # Register your models here. admin.site.register(Profile) admin.site.register(Image) admin.site.register(Comment)

27

41

0.820106

a4257e9fca84a4a5cfe3f469e0b80cc613fd2042

16,165

py

Python

osmtm/views/task.py

loggingroads/osm-tasking-manager2

cb482d667273adffbe27a905308aec579ed71cec

[ "BSD-2-Clause" ]

null

osmtm/views/task.py

loggingroads/osm-tasking-manager2

cb482d667273adffbe27a905308aec579ed71cec

[ "BSD-2-Clause" ]

null

osmtm/views/task.py

loggingroads/osm-tasking-manager2

cb482d667273adffbe27a905308aec579ed71cec

[ "BSD-2-Clause" ]

null

from pyramid.view import view_config from pyramid.httpexceptions import ( HTTPNotFound, HTTPBadRequest, HTTPUnauthorized, HTTPForbidden, ) from ..models import ( DBSession, Task, TaskState, TaskLock, TaskComment, PriorityArea, Project, User, Message, ) from geoalchemy2 import ( shape, ) from sqlalchemy.orm import ( joinedload, ) from sqlalchemy.orm.exc import NoResultFound from sqlalchemy.exc import OperationalError from sqlalchemy.sql.expression import ( not_, and_, or_, ) from pyramid.security import authenticated_userid import datetime import random import re import transaction from ..models import EXPIRATION_DELTA, ST_SetSRID from user import username_to_userid import logging log = logging.getLogger(__name__) def __get_user(request, allow_none=False): user_id = authenticated_userid(request) if not user_id: if allow_none: return None raise HTTPUnauthorized() user = DBSession.query(User).get(user_id) if not allow_none and not user: # pragma: no cover raise HTTPUnauthorized() return user def __get_task(request, lock_for_update=False): task_id = request.matchdict['task'] project_id = request.matchdict['project'] filter = and_(Task.project_id == project_id, Task.id == task_id) query = DBSession.query(Task) \ .options(joinedload(Task.cur_lock)) \ .filter(filter) if lock_for_update: query = query.with_for_update(nowait=True, of=Task) _ = request.translate try: task = query.one() except NoResultFound: task = None except OperationalError: # pragma: no cover raise HTTPBadRequest(_("Cannot update task. Record lock for update.")) if not task or \ task.cur_state and task.cur_state.state == TaskState.state_removed: # FIXME return translated text via JSON raise HTTPNotFound(_("This task doesn't exist.")) return task def __ensure_task_locked(request, task, user): _ = request.translate locked_task = get_locked_task(task.project_id, user) if locked_task != task: raise HTTPForbidden(_("You need to lock the task first.")) @view_config(route_name='task_xhr', renderer='task.mako', http_cache=0) def task_xhr(request): user = __get_user(request, allow_none=True) task = __get_task(request) locked_task = get_locked_task(task.project_id, user) task_id = request.matchdict['task'] project_id = request.matchdict['project'] filter = and_(TaskState.task_id == task_id, TaskState.project_id == project_id) states = DBSession.query(TaskState).filter(filter) \ .order_by(TaskState.date).all() # remove the first state (task creation with state==ready) states.pop(0) filter = and_(TaskLock.task_id == task_id, TaskLock.project_id == project_id) locks = DBSession.query(TaskLock).filter(filter) \ .order_by(TaskLock.date).all() # remove the first lock (task creation) locks.pop(0) filter = and_(TaskComment.task_id == task_id, TaskComment.project_id == project_id) comments = DBSession.query(TaskComment).filter(filter) \ .order_by(TaskComment.date).all() history = states + locks + comments history = sorted(history, key=lambda step: step.date, reverse=True) return dict(task=task, user=user, locked_task=locked_task, history=history) @view_config(route_name='task_empty', renderer='task.empty.mako', http_cache=0) def task_empty(request): user = __get_user(request, allow_none=True) project_id = request.matchdict['project'] locked_task = get_locked_task(project_id, user) assigned_tasks = get_assigned_tasks(project_id, user) return dict(locked_task=locked_task, project_id=project_id, assigned_tasks=assigned_tasks, user=user) @view_config(route_name='task_done', renderer='json') def done(request): user = __get_user(request) task = __get_task(request, lock_for_update=True) __ensure_task_locked(request, task, user) add_comment(request, task, user) task.states.append(TaskState(user=user, state=TaskState.state_done)) task.locks.append(TaskLock(user=None, lock=False)) DBSession.add(task) DBSession.flush() _ = request.translate return dict(success=True, msg=_("Task marked as done. Thanks for your contribution")) @view_config(route_name='task_lock', renderer="json") def lock(request): _ = request.translate user = __get_user(request) task = __get_task(request, lock_for_update=True) locked_task = get_locked_task(task.project_id, user) if locked_task is not None: raise HTTPBadRequest if task.cur_lock and task.cur_lock.lock: # FIXME use http errors return dict(success=False, task=dict(id=task.id), error_msg=_("Task already locked.")) if task.assigned_to is not None and task.assigned_to != user: request.response.status = 400 return dict(success=False, task=dict(id=task.id), error_msg=_("Task assigned to someone else.")) task.locks.append(TaskLock(user=user, lock=True)) DBSession.add(task) return dict(success=True, task=dict(id=task.id), msg=_("Task locked. You can start mapping.")) @view_config(route_name='task_unlock', renderer="json") def unlock(request): user = __get_user(request) task = __get_task(request, lock_for_update=True) __ensure_task_locked(request, task, user) add_comment(request, task, user) task.locks.append(TaskLock(user=None, lock=False)) DBSession.add(task) DBSession.flush() _ = request.translate return dict(success=True, task=dict(id=task.id), msg=_("Task unlocked.")) @view_config(route_name='task_comment', renderer="json") def comment(request): user = __get_user(request) task = __get_task(request) add_comment(request, task, user) _ = request.translate return dict(success=True, task=dict(id=task.id), msg=_("Comment added.")) def add_comment(request, task, user): if 'comment' in request.params and request.params.get('comment') != '': comment = request.params['comment'] # check for mentions in the comment p = re.compile(ur'((?<=@)\w+|\[.+?\])') def repl(var): username = var.group() username = re.sub('(\[|\])', '', username) return username_to_userid(username) # put ids instead of usernames in comment comment = re.sub(p, repl, comment) p = re.compile(ur'((?<=@)\d+)') for userid in p.findall(comment): to = DBSession.query(User).get(userid) if to: mention_user(request, user, to, comment) task.comments.append(TaskComment(comment, user)) DBSession.add(task) DBSession.flush() def mention_user(request, from_, to, comment): _ = request.translate project_id = request.matchdict['project'] task_id = request.matchdict['task'] href = request.route_path('project', project=project_id) href = href + '#task/%s' % task_id link = '<a href="%s">#%s</a>' % (href, task_id) subject = _('You were mentioned in a comment - Task ${link}', mapping={'link': link}) send_message(subject, from_, to, comment) def send_message(subject, from_, to_, msg): DBSession.add(Message(subject, from_, to_, msg)) def send_invalidation_message(request, task, user): """Sends message to contributors of invalidated tasks.""" comment = request.params.get('comment', '') states = sorted(task.states, key=lambda state: state.date, reverse=True) recipients = set() for state in states: if (state.state == TaskState.state_validated or state.state == TaskState.state_done): recipients.add(state.user) from_ = user while recipients: to = recipients.pop() if from_ != to: _ = request.translate href = request.route_path('project', project=task.project_id) href = href + '#task/%s' % task.id link = '<a href="%s">#%d</a>' % (href, task.id) subject = _('Task ${link} invalidated', mapping={'link': link}) send_message(subject, from_, to, comment) @view_config(route_name='task_validate', renderer="json") def validate(request): user = __get_user(request) task = __get_task(request, lock_for_update=True) __ensure_task_locked(request, task, user) task.user = None _ = request.translate if 'validate' in request.params: state = TaskState.state_validated msg = _("Task validated.") else: state = TaskState.state_invalidated msg = _("Task invalidated.") send_invalidation_message(request, task, user) add_comment(request, task, user) task.states.append(TaskState(user=user, state=state)) task.locks.append(TaskLock(user=None, lock=False)) DBSession.add(task) DBSession.flush() return dict(success=True, msg=msg) @view_config(route_name='task_split', renderer='json') def split(request): user = __get_user(request) task = __get_task(request, lock_for_update=True) __ensure_task_locked(request, task, user) if task.zoom is None or (task.zoom - task.project.zoom) > 1: raise HTTPBadRequest() for i in range(0, 2): for j in range(0, 2): t = Task(int(task.x) * 2 + i, int(task.y) * 2 + j, int(task.zoom) + 1) t.project = task.project t.update = datetime.datetime.utcnow() task.states.append(TaskState(user=user, state=TaskState.state_removed)) task.locks.append(TaskLock(user=None, lock=False)) DBSession.add(task) return dict() def get_locked_task(project_id, user): if user is None: return None try: query = DBSession.query(Task).options(joinedload(Task.cur_lock)) \ .filter(and_(Task.cur_lock.has(lock=True), Task.project_id == project_id, Task.cur_lock.has(user_id=user.id))) return query.one() except NoResultFound: return None def get_assigned_tasks(project_id, user): if user is None: return None query = DBSession.query(Task) \ .filter(Task.project_id == project_id, Task.assigned_to == user) \ .order_by(Task.assigned_date.desc()) return query.all() def find_matching_task(project_id, filter): state_filter = or_(Task.cur_state.has(state=TaskState.state_ready), Task.cur_state.has(state=TaskState.state_invalidated)) query = DBSession.query(Task) \ .filter_by(project_id=project_id) \ .filter(state_filter) \ .filter(not_(Task.cur_lock.has(lock=True))) query = query.filter(filter) count = query.count() if count != 0: # pragma: no cover atask = query.offset(random.randint(0, count - 1)).first() return atask return None @view_config(route_name='task_random', http_cache=0, renderer='json') def random_task(request): """Gets a random not-done task. First it tries to get one that does not border any in-progress tasks.""" project_id = request.matchdict['project'] # filter for tasks not bordering busy tasks locked = DBSession.query(Task.geometry.ST_Union()) \ .filter_by(project_id=project_id) \ .filter(Task.cur_lock.has(lock=True)) \ .scalar() locked_filter = None if locked is not None: locked_filter = Task.geometry.ST_Disjoint(ST_SetSRID(locked, 4326)) # filter for tasks within priority areas priority = DBSession.query(PriorityArea.geometry.ST_Union()) \ .join(Project.priority_areas) \ .filter(Project.id == project_id) \ .scalar() priority_filter = None if priority is not None: priority_filter = Task.geometry.ST_Intersects( ST_SetSRID(priority, 4326) ) # search attempts filters = [] if priority_filter is not None and locked_filter is not None: # tasks in priority areas and not bordering busy tasks filters.append(and_(locked_filter, priority_filter)) if priority_filter is not None: # tasks in priority areas filters.append(priority_filter) if locked_filter is not None: # tasks not bordering busy tasks filters.append(locked_filter) # any other available task filters.append(True) for filter in filters: atask = find_matching_task(project_id, filter) if atask: return dict(success=True, task=dict(id=atask.id)) _ = request.translate return dict(success=False, error_msg=_("Random task... none available! Sorry.")) @view_config(route_name='task_assign', renderer='json', permission='project_edit') def task_assign(request): """Assigns a taks to a given user""" task = __get_task(request) _ = request.translate if task.cur_lock and task.cur_lock.lock: request.response.status = 400 return dict(success=True, msg=_("You cannot assign an already locked task")) username = request.matchdict['user'] user = DBSession.query(User).filter(User.username == username).one() task.assigned_to_id = user.id task.assigned_date = datetime.datetime.utcnow() DBSession.add(task) return dict(success=True, msg=_("Task assigned.")) @view_config(route_name='task_assign_delete', renderer='json', permission='project_edit') def task_assign_delete(request): """Remove assignment""" task = __get_task(request) task.assigned_to_id = None task.assigned_date = None _ = request.translate return dict(success=True, msg=_("Task assignment removed")) @view_config(route_name='task_gpx', renderer='task.gpx.mako') def task_gpx(request): task = __get_task(request) request.response.headerlist.append(('Access-Control-Allow-Origin', 'http://www.openstreetmap.org')) return dict(multipolygon=shape.to_shape(task.geometry), project_id=task.project_id) @view_config(route_name='task_osm', renderer='task.osm.mako') def task_osm(request): task = __get_task(request) request.response.headerlist.append(('Access-Control-Allow-Origin', 'http://www.openstreetmap.org')) return dict(multipolygon=shape.to_shape(task.geometry), project_id=task.project_id) @view_config(route_name='task_difficulty', renderer='json', permission='project_edit') def task_difficulty(request): """Change task difficulty""" task = __get_task(request) difficulty = request.matchdict['difficulty'] task.difficulty = difficulty _ = request.translate return dict(success=True, msg=_("Task difficulty changed.")) @view_config(route_name='task_difficulty_delete', renderer='json', permission='project_edit') def task_difficulty_delete(request): """Remove assignment""" task = __get_task(request) task.difficulty = None _ = request.translate return dict(success=True, msg=_("Task difficulty removed")) # unlock any expired task def check_task_expiration(): # pragma: no cover query = DBSession.query(Task).filter( and_( Task.lock_date.__ne__(None), Task.lock_date < datetime.datetime.utcnow() - EXPIRATION_DELTA)) with transaction.manager: for task in query: new_lock = TaskLock() new_lock.task_id = task.id new_lock.project_id = task.project_id DBSession.add(new_lock) log.debug("found one task") transaction.commit()

29.935185

79

0.648995

3aaafe4e05bafd956771739d4e02826d833a9281

218

py

Python

classical_control_theory/steady_state_final_value_theorem.py

andreamunafo/automatic_control

dd1d89f732bfd8d95b0ebef6fe99df29b18a1fc2

[ "Apache-2.0" ]

null

classical_control_theory/steady_state_final_value_theorem.py

andreamunafo/automatic_control

dd1d89f732bfd8d95b0ebef6fe99df29b18a1fc2

[ "Apache-2.0" ]

null

classical_control_theory/steady_state_final_value_theorem.py

andreamunafo/automatic_control

dd1d89f732bfd8d95b0ebef6fe99df29b18a1fc2

[ "Apache-2.0" ]

null

# AUTOGENERATED! DO NOT EDIT! File to edit: 07_Final_value_theorem_and_steady_state_error.ipynb (unless otherwise specified). __all__ = [] # Cell import numpy as np import matplotlib.pyplot as plt import pandas as pd

27.25

125

0.802752

6ee7b2f3e1db8d3c5577bc083bf0a909608f987d

4,212

py

Python

paint.py

naemazam/Paint

9346cbbfd13e1556922f2e4377f08439ea227239

[ "MIT" ]

null

paint.py

naemazam/Paint

9346cbbfd13e1556922f2e4377f08439ea227239

[ "MIT" ]

null

paint.py

naemazam/Paint

9346cbbfd13e1556922f2e4377f08439ea227239

[ "MIT" ]

null

######################################################################## ## Naem Azam # YOUTUBE: (The Terminal Boy) # WEBSITE: naemazam.github.io ######################################################################## from tkinter import * from tkinter.colorchooser import askcolor from PIL import ImageTk, Image class Paint(object): DEFAULT_PEN_SIZE = 5.0 DEFAULT_COLOR = 'black' def __init__(self): self.root = Tk() self.root.title('NA Paint') self.root.geometry('500x300') self.root.maxsize(500,300) self.root.minsize(500,300) self.paint_tools = Frame(self.root,width=100,height=300,relief=RIDGE,borderwidth=2) self.paint_tools.place(x=0,y=0) self.pen_logo = ImageTk.PhotoImage(Image.open('pen.png')) self.p = Label(self.paint_tools, text="pen",borderwidth=0,font=('verdana',10,'bold')) self.p.place(x=5,y=11) self.pen_button = Button(self.paint_tools,padx=6,image=self.pen_logo,borderwidth=2,command=self.use_pen) self.pen_button.place(x=60,y=10) self.brush_logo = ImageTk.PhotoImage(Image.open('brush.png')) self.b = Label(self.paint_tools,borderwidth=0,text='brush',font=('verdana',10,'bold')) self.b.place(x=5,y=40) self.brush_button = Button(self.paint_tools,image = self.brush_logo,borderwidth=2,command=self.use_brush) self.brush_button.place(x=60,y=40) self.color_logo = ImageTk.PhotoImage(Image.open('color.png')) self.cl = Label(self.paint_tools, text='color',font=('verdana',10,'bold')) self.cl.place(x=5,y=70) self.color_button = Button(self.paint_tools,image = self.color_logo,borderwidth=2,command=self.choose_color) self.color_button.place(x=60,y=70) self.eraser_logo = ImageTk.PhotoImage(Image.open('eraser.png')) self.e = Label(self.paint_tools, text='eraser',font=('verdana',10,'bold')) self.e.place(x=5,y=100) self.eraser_button = Button(self.paint_tools,image = self.eraser_logo,borderwidth=2,command=self.use_eraser) self.eraser_button.place(x=60,y=100) self.pen_size = Label(self.paint_tools,text="Pen Size",font=('verdana',10,'bold')) self.pen_size.place(x=15,y=250) self.choose_size_button = Scale(self.paint_tools, from_=1, to=10, orient=VERTICAL) self.choose_size_button.place(x=20,y=150) self.c = Canvas(self.root, bg='white', width=600, height=600,relief=RIDGE,borderwidth=0) self.c.place(x=100,y=0) self.setup() self.root.mainloop() def setup(self): self.old_x = None self.old_y = None self.line_width = self.choose_size_button.get() self.color = self.DEFAULT_COLOR self.eraser_on = False self.active_button = self.pen_button self.c.bind('<B1-Motion>', self.paint) self.c.bind('<ButtonRelease-1>', self.reset) def use_pen(self): self.activate_button(self.pen_button) def use_brush(self): self.activate_button(self.brush_button) def choose_color(self): self.eraser_on = False self.color = askcolor(color=self.color)[1] def use_eraser(self): self.activate_button(self.eraser_button, eraser_mode=True) def activate_button(self, some_button, eraser_mode=False): self.active_button.config(relief=RAISED) some_button.config(relief=SUNKEN) self.active_button = some_button self.eraser_on = eraser_mode def paint(self, event): self.line_width = self.choose_size_button.get() paint_color = 'white' if self.eraser_on else self.color if self.old_x and self.old_y: self.c.create_line(self.old_x, self.old_y, event.x, event.y, width=self.line_width, fill=paint_color, capstyle=ROUND, smooth=TRUE, splinesteps=36) self.old_x = event.x self.old_y = event.y def reset(self, event): self.old_x, self.old_y = None, None if __name__ == '__main__': Paint()

39

117

0.609212

0750ba5cb5381ebd85f27d00a103f5726833b1af

2,188

py

Python

tests/test_enum.py

bannsec/py010parser

cba61cab345f9b7c19628904d7667153dbddf326

[ "BSD-3-Clause" ]

54

2015-04-08T22:54:17.000Z

2022-02-16T10:28:43.000Z

tests/test_enum.py

bannsec/py010parser

cba61cab345f9b7c19628904d7667153dbddf326

[ "BSD-3-Clause" ]

27

2015-08-16T11:45:21.000Z

2020-09-07T20:28:22.000Z

tests/test_enum.py

bannsec/py010parser

cba61cab345f9b7c19628904d7667153dbddf326

[ "BSD-3-Clause" ]

11

2015-04-12T04:24:44.000Z

2021-01-08T04:53:07.000Z

#!/usr/bin/env python # encoding: utf-8 import os import sys import unittest sys.path.insert(0, "..") from py010parser import parse_file, parse_string, c_ast class TestEnum(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_enum_types(self): # note that there have been problems using a built-in # type (int/float/etc) vs the typedefd ones, TYPEID vs res = parse_string(""" enum <ulong> COLORS { WHITE = 1 } var1; enum <int> COLORS { WHITE = 1 } var1; enum IFD_dirtype { IFD_TYPE_EXIF = 1, IFD_TYPE_GEOTAG, IFD_TYPE_CASIO_QV_R62, }; enum { TEST, TEST2 } blah; """, optimize=True) def test_untypedefd_enum_as_typeid(self): res = parse_string(""" enum <ulong> BLAH { BLAH1, BLAH2, BLAH3 }; local BLAH x; """, optimize=True) def test_c_keywords_in_enum(self): res = parse_string(""" enum <int> BLAH { goto, register, extern, goto, volatile, static }; local BLAH x; """, optimize=True) def test_untypedefd_enum_as_typeid(self): res = parse_string(""" enum <ulong> BLAH { BLAH1, BLAH2, BLAH3 }; local BLAH x; """, optimize=True) def test_enum_types(self): # note that there have been problems using a built-in # type (int/float/etc) vs the typedefd ones, TYPEID vs res = parse_string(""" enum <ulong> COLORS { WHITE = 1 } var1; enum <int> COLORS { WHITE = 1 } var1; enum IFD_dirtype { IFD_TYPE_EXIF = 1, IFD_TYPE_GEOTAG, IFD_TYPE_CASIO_QV_R62, }; enum { TEST, TEST2 } blah; """, optimize=True) if __name__ == "__main__": unittest.main()

21.45098

63

0.481718

df81eaef5adf4e020f49d11b770b26c14b844ade

7,818

py

Python

utils/exporters/blender/addons/io_three/exporter/api/material.py

lchl7890987/WebGL

f73593b00e3b2c927bb1a3236240cea5597166ec

[ "MIT" ]

10

2015-07-09T06:08:03.000Z

2021-11-08T13:00:01.000Z

utils/exporters/blender/addons/io_three/exporter/api/material.py

lchl7890987/WebGL

f73593b00e3b2c927bb1a3236240cea5597166ec

[ "MIT" ]

1

2015-06-25T16:42:04.000Z

2021-01-12T19:22:16.000Z

utils/exporters/blender/addons/io_three/exporter/api/material.py

lchl7890987/WebGL

f73593b00e3b2c927bb1a3236240cea5597166ec

[ "MIT" ]

3

2015-05-24T13:57:14.000Z

2018-01-17T09:49:08.000Z

from bpy import data, types from .. import constants, logger from .constants import MULTIPLY, WIRE, IMAGE def _material(func): """ :param func: """ def inner(name, *args, **kwargs): """ :param name: :param *args: :param **kwargs: """ if isinstance(name, types.Material): material = name else: material = data.materials[name] return func(material, *args, **kwargs) return inner @_material def ambient_color(material): """ :param material: :return: rgb value :rtype: tuple """ logger.debug("material.ambient_color(%s)", material) diffuse = diffuse_color(material) return (material.ambient * diffuse[0], material.ambient * diffuse[1], material.ambient * diffuse[2]) @_material def blending(material): """ :param material: :return: THREE_blending_type value """ logger.debug("material.blending(%s)", material) try: blend = material.THREE_blending_type except AttributeError: logger.debug("No THREE_blending_type attribute found") blend = constants.NORMAL_BLENDING return blend @_material def bump_map(material): """ :param material: :return: texture node for bump """ logger.debug("material.bump_map(%s)", material) for texture in _valid_textures(material): if texture.use_map_normal and not \ texture.texture.use_normal_map: return texture.texture @_material def bump_scale(material): """ :param material: :rtype: float """ return normal_scale(material) @_material def depth_test(material): """ :param material: :return: THREE_depth_test value :rtype: bool """ logger.debug("material.depth_test(%s)", material) try: test = material.THREE_depth_test except AttributeError: logger.debug("No THREE_depth_test attribute found") test = True return test @_material def depth_write(material): """ :param material: :return: THREE_depth_write value :rtype: bool """ logger.debug("material.depth_write(%s)", material) try: write = material.THREE_depth_write except AttributeError: logger.debug("No THREE_depth_write attribute found") write = True return write @_material def diffuse_color(material): """ :param material: :return: rgb value :rtype: tuple """ logger.debug("material.diffuse_color(%s)", material) return (material.diffuse_intensity * material.diffuse_color[0], material.diffuse_intensity * material.diffuse_color[1], material.diffuse_intensity * material.diffuse_color[2]) @_material def diffuse_map(material): """ :param material: :return: texture node for map """ logger.debug("material.diffuse_map(%s)", material) for texture in _valid_textures(material): if texture.use_map_color_diffuse and not \ texture.blend_type == MULTIPLY: return texture.texture @_material def emissive_color(material): """ :param material: :return: rgb value :rtype: tuple """ logger.debug("material.emissive_color(%s)", material) diffuse = diffuse_color(material) return (material.emit * diffuse[0], material.emit * diffuse[1], material.emit * diffuse[2]) @_material def light_map(material): """ :param material: :return: texture node for light maps """ logger.debug("material.light_map(%s)", material) for texture in _valid_textures(material, strict_use=False): if texture.use_map_color_diffuse and \ texture.blend_type == MULTIPLY: return texture.texture @_material def normal_scale(material): """ :param material: :rtype: float """ logger.debug("material.normal_scale(%s)", material) for texture in _valid_textures(material): if texture.use_map_normal: return texture.normal_factor @_material def normal_map(material): """ :param material: :return: texture node for normals """ logger.debug("material.normal_map(%s)", material) for texture in _valid_textures(material): if texture.use_map_normal and \ texture.texture.use_normal_map: return texture.texture @_material def opacity(material): """ :param material: :rtype: float """ logger.debug("material.opacity(%s)", material) return round(material.alpha, 2) @_material def shading(material): """ :param material: :return: shading type (phong or lambert) """ logger.debug("material.shading(%s)", material) dispatch = { True: constants.PHONG, False: constants.LAMBERT } return dispatch[material.specular_intensity > 0.0] @_material def specular_coef(material): """ :param material: :rtype: float """ logger.debug("material.specular_coef(%s)", material) return material.specular_hardness @_material def specular_color(material): """ :param material: :return: rgb value :rtype: tuple """ logger.debug("material.specular_color(%s)", material) return (material.specular_intensity * material.specular_color[0], material.specular_intensity * material.specular_color[1], material.specular_intensity * material.specular_color[2]) @_material def specular_map(material): """ :param material: :return: texture node for specular """ logger.debug("material.specular_map(%s)", material) for texture in _valid_textures(material): if texture.use_map_specular: return texture.texture @_material def transparent(material): """ :param material: :rtype: bool """ logger.debug("material.transparent(%s)", material) return material.use_transparency @_material def type(material): """ :param material: :return: THREE compatible shader type """ logger.debug("material.type(%s)", material) if material.diffuse_shader != 'LAMBERT': material_type = constants.BASIC elif material.specular_intensity > 0: material_type = constants.PHONG else: material_type = constants.LAMBERT return material_type @_material def use_vertex_colors(material): """ :param material: :rtype: bool """ logger.debug("material.use_vertex_colors(%s)", material) return material.use_vertex_color_paint def used_materials(): """ :return: list of materials that are in use :rtype: generator """ logger.debug("material.used_materials()") for material in data.materials: if material.users > 0: yield material.name @_material def visible(material): """ :param material: :return: THREE_visible value :rtype: bool """ logger.debug("material.visible(%s)", material) try: vis = material.THREE_visible except AttributeError: logger.debug("No THREE_visible attribute found") vis = True return vis @_material def wireframe(material): """ :param material: :rtype: bool """ logger.debug("material.wireframe(%s)", material) return material.type == WIRE def _valid_textures(material, strict_use=True): """ :param material: :rtype: generator """ for texture in material.texture_slots: if not texture: continue if strict_use: in_use = texture.use else: in_use = True if texture.texture.type != IMAGE or not in_use: continue logger.debug("Valid texture found %s", texture) yield texture

19.994885

69

0.634433

8d2e40ebdd6e5ae328594aa62d2168e2efb83cd3

455

py

Python

Symbol Patterns/symbolpattern135.py

vaidehisinha1/Python-PatternHouse

49f71bcc5319a838592e69b0e49ef1edba32bf7c

[ "MIT" ]

null

Symbol Patterns/symbolpattern135.py

vaidehisinha1/Python-PatternHouse

49f71bcc5319a838592e69b0e49ef1edba32bf7c

[ "MIT" ]

471

2022-01-15T07:07:18.000Z

2022-02-28T16:01:42.000Z

Symbol Patterns/symbolpattern135.py

vaidehisinha1/Python-PatternHouse

49f71bcc5319a838592e69b0e49ef1edba32bf7c

[ "MIT" ]

2

2022-01-17T09:43:16.000Z

2022-01-29T15:15:47.000Z

height = int(input()) for i in range(1,height+1): for j in range(1,height+1): if(i == 1 or j == 1 or i == height-j+1): print("*",end=" ") elif(i == j and i >= height//2 and j > height//2): print("*",end=" ") else: print(end=" ") print() # Sample Input :- 7 # Output :- # * * * * * * * # * * # * * # * * # * * * # * * * # * *

16.851852

58

0.32967

94bf1d9726bb8cb73141750666bd2999eda26087

1,798

py

Python

api/src/repository/SampleRepository.py

SamuelJansen/FeatureManager

6b259aeb073ff705273e7d1f283232272fd629c9

[ "MIT" ]

1

2021-11-21T21:18:32.000Z

api/src/repository/SampleRepository.py

SamuelJansen/FeatureManager

6b259aeb073ff705273e7d1f283232272fd629c9

[ "MIT" ]

null

api/src/repository/SampleRepository.py

SamuelJansen/FeatureManager

6b259aeb073ff705273e7d1f283232272fd629c9

[ "MIT" ]

null

from python_framework import Repository from Sample import Sample @Repository(model = Sample) class SampleRepository: def findAll(self) : return self.repository.findAllAndCommit(self.model) def existsByKey(self,key) : return self.repository.existsByKeyAndCommit(key, self.model) def findByKey(self,key) : if self.existsByKey(key) : return self.repository.findByKeyAndCommit(key, self.model) def notExistsByKey(self,key) : return not self.existsByKey(key) def save(self,model) : return self.repository.saveAndCommit(model) def deleteByKey(self,key): self.repository.deleteByKeyAndCommit(key, self.model) def findAllByFeatureKeyIn(self, featureKeyList): sampleList = [] for sample in self.repository.session.query(self.model) : keepIt = False for key in featureKeyList : for featureData in sample.featureDataList : if featureData.feature.key == key : keepIt = True sampleList.append(sample) break if keepIt : break return sampleList def findAllByAllFeatureKeyIn(self, featureKeyList): sampleList = [] for sample in self.repository.session.query(self.model) : keepIt = True for key in featureKeyList : keepIt = False for featureData in sample.featureDataList : if featureData.feature.key == key : keepIt = True break if not keepIt : break if keepIt : sampleList.append(sample) return sampleList

32.107143

70

0.576752

f1b4c7820b46b1625a2f9106deaa0d899cf386c6

13,526

py

Python

jumpcutter.py

marc-philipp-knechtle/jumpcutter

26bfe561e73e311ac160baebd362a337098c2853

[ "MIT" ]

null

jumpcutter.py

marc-philipp-knechtle/jumpcutter

26bfe561e73e311ac160baebd362a337098c2853

[ "MIT" ]

null

jumpcutter.py

marc-philipp-knechtle/jumpcutter

26bfe561e73e311ac160baebd362a337098c2853

[ "MIT" ]

null

import argparse import math import os import re import subprocess from shutil import copyfile, rmtree from time import sleep import numpy as np from audiotsm import phasevocoder from audiotsm.io.wav import WavReader, WavWriter from loguru import logger from pytube import YouTube from scipy.io import wavfile def download_file(url): name = YouTube(url).streams.first().download() newname = name.replace(' ', '_') os.rename(name, newname) return newname def get_max_volume(s): maxv = float(np.max(s)) minv = float(np.min(s)) return max(maxv, -minv) def copy_frame(input_frame, output_frame, temp_folder): # src = TEMP_FOLDER + "/frame{:06d}".format(inputFrame + 1) + ".jpg" frame_input: str = "frame{:06d}".format(input_frame + 1) + ".jpg" src = os.path.join(temp_folder, frame_input) # dst = TEMP_FOLDER + "/newFrame{:06d}".format(outputFrame + 1) + ".jpg" frame_output: str = "newFrame{:06d}".format(output_frame + 1) + ".jpg" dst = os.path.join(temp_folder, frame_output) # if not os.path.isfile(src): # return False try: copyfile(src, dst) except FileNotFoundError: raise FileNotFoundError("Special case for last frame in video!") if output_frame % 20 == 19: print(str(output_frame + 1) + " time-altered frames saved.") def input_to_output_filename(filename): dot_index = filename.rfind(".") return filename[:dot_index] + "_ALTERED" + filename[dot_index:] def create_path(s): # assert (not os.path.exists(s)), "The filepath "+s+" already exists. Don't want to overwrite it. Aborting." try: logger.info("attempting to create dir with path: " + s) os.mkdir(s) except OSError: assert False, "Creation of the directory %s failed. " \ "(The TEMP folder may already exist. Delete or rename it, and try again.)" def delete_path(s): try: rmtree(s, ignore_errors=False) except OSError: print("Deletion of the directory %s failed" % s) print(OSError) def parse_arguments() -> argparse.Namespace: parser = argparse.ArgumentParser( description='Modifies a video file to play at different speeds when there is sound vs. silence.') parser.add_argument('--input_file', type=str, help='the video file you want modified') parser.add_argument('--url', type=str, help='A youtube url to download and process') parser.add_argument('--output_file', type=str, default="", help="the output file. (optional. if not included, it'll just modify the input file name)") parser.add_argument('--silent_threshold', type=float, default=0.03, help="the volume amount that frames' audio needs to surpass to be consider \"sounded\". " "It ranges from 0 (silence) to 1 (max volume)") parser.add_argument('--sounded_speed', type=float, default=1.00, help="the speed that sounded (spoken) frames should be played at. Typically 1.") parser.add_argument('--silent_speed', type=float, default=5.00, help="the speed that silent frames should be played at. 999999 for jumpcutting.") parser.add_argument('--frame_margin', type=float, default=1, help="some silent frames adjacent to sounded frames are included to provide context. " "How many frames on either the side of speech should be included? That's this variable.") parser.add_argument('--sample_rate', type=float, default=44100, help="sample rate of the input and output videos") parser.add_argument('--frame_rate', type=float, default=30, help="frame rate of the input and output videos. optional... " "I try to find it out myself, but it doesn't always work.") parser.add_argument('--frame_quality', type=int, default=3, help="quality of frames to be extracted from input video. " "1 is highest, 31 is lowest, 3 is the default.") parser.add_argument('--tmp_working_dir', type=str, default="", help="Please specify a directory where all generated files will be temporarily stored. " "This may be be helpful considering the large storage space this script needs to run on.") parser.add_argument('--folder_watcher_mode', type=bool, default=False, help="Mark true if you want to run this script in watcher mode. " "This mode will process all files in the specified --watched_dir directory.") parser.add_argument('--watched_dir', type=str, help='The directory to listen for new files to process.') parser.add_argument('--processed_output_dir', type=str, default="", help="The processed files will be placed in this directory if specfied. " "It's otherwise not possible to specify the output directory. " "The default output location is the scripts location.") return parser.parse_args() def create_frames(input_file, frame_quality, temp_folder): command = "ffmpeg -i " + input_file + " -qscale:v " + str( frame_quality) + " \"" + temp_folder + "/frame%06d.jpg\" -hide_banner" logger.info("Executing: " + command) subprocess.call(command, shell=True) def create_audio(input_file, sample_rate, temp_folder): command = "ffmpeg -i " + input_file + " -ab 160k -ac 2 -ar " + str( sample_rate) + " -vn \"" + temp_folder + "/audio.wav\"" logger.info("Executing: " + command) subprocess.call(command, shell=True) def set_input_file(arguments: argparse.Namespace) -> str: if arguments.url is not None: return download_file(arguments.url) else: return arguments.input_file def set_output_file(arguments: argparse.Namespace) -> str: if len(arguments.output_file) >= 1: return arguments.output_file elif arguments.folder_watcher_mode: return "" else: return input_to_output_filename(arguments.input_file) def create_params(temp_folder): command = "ffmpeg -i " + temp_folder + "/input.mp4 2>&1" file = open(temp_folder + "/params.txt", "w") subprocess.call(command, shell=True, stdout=file) def write_to_file(temp_folder, frame_rate, output_file): """ outputFrame = math.ceil(outputPointer/samplesPerFrame) for endGap in range(outputFrame,audioFrameCount): copyFrame(int(audioSampleCount/samplesPerFrame)-1,endGap) """ logger.info("Writing processed file to: " + output_file) command_local = "ffmpeg -framerate " + str( frame_rate) + " -i " + temp_folder + "/newFrame%06d.jpg -i " + temp_folder + "/audioNew.wav -strict -2 " \ + output_file subprocess.call(command_local, shell=True) def create_jumpcutted_video(frame_rate, temp_folder: str, silent_threshold, frame_spreadage, sample_rate, new_speed, audio_fade_envelope_size): global output_audio_data sample_rate, audio_data = wavfile.read(os.path.join(temp_folder, "audio.wav")) audio_sample_count = audio_data.shape[0] max_audio_volume = get_max_volume(audio_data) f = open(temp_folder + "/params.txt", 'r+') pre_params = f.read() f.close() params = pre_params.split('\n') for line in params: m = re.search('Stream #.*Video.* ([0-9]*) fps', line) if m is not None: frame_rate = float(m.group(1)) samples_per_frame = sample_rate / frame_rate audio_frame_count = int(math.ceil(audio_sample_count / samples_per_frame)) has_loud_audio = np.zeros(audio_frame_count) for i in range(audio_frame_count): start = int(i * samples_per_frame) end = min(int((i + 1) * samples_per_frame), audio_sample_count) audiochunks = audio_data[start:end] maxchunks_volume = float(get_max_volume(audiochunks)) / max_audio_volume if maxchunks_volume >= silent_threshold: has_loud_audio[i] = 1 chunks = [[0, 0, 0]] should_include_frame = np.zeros(audio_frame_count) for i in range(audio_frame_count): start = int(max(0, i - frame_spreadage)) end = int(min(audio_frame_count, i + 1 + frame_spreadage)) should_include_frame[i] = np.max(has_loud_audio[start:end]) if i >= 1 and should_include_frame[i] != should_include_frame[i - 1]: # Did we flip? chunks.append([chunks[-1][1], i, should_include_frame[i - 1]]) chunks.append([chunks[-1][1], audio_frame_count, should_include_frame[i - 1]]) chunks = chunks[1:] output_audio_data = np.zeros((0, audio_data.shape[1])) output_pointer = 0 last_existing_frame = None for chunk in chunks: audio_chunk = audio_data[int(chunk[0] * samples_per_frame):int(chunk[1] * samples_per_frame)] s_file = temp_folder + "/tempStart.wav" e_file = temp_folder + "/tempEnd.wav" wavfile.write(s_file, sample_rate, audio_chunk) with WavReader(s_file) as reader: with WavWriter(e_file, reader.channels, reader.samplerate) as writer: tsm = phasevocoder(reader.channels, speed=new_speed[int(chunk[2])]) tsm.run(reader, writer) _, altered_audio_data = wavfile.read(e_file) leng = altered_audio_data.shape[0] end_pointer = output_pointer + leng output_audio_data = np.concatenate((output_audio_data, altered_audio_data / max_audio_volume)) # outputAudioData[output_pointer:end_pointer] = altered_audio_data/max_audio_volume # smooth out transitiion's audio by quickly fading in/out if leng < audio_fade_envelope_size: output_audio_data[output_pointer:end_pointer] = 0 # audio is less than 0.01 sec, let's just remove it. else: premask = np.arange(audio_fade_envelope_size) / audio_fade_envelope_size mask = np.repeat(premask[:, np.newaxis], 2, axis=1) # make the fade-envelope mask stereo output_audio_data[output_pointer:output_pointer + audio_fade_envelope_size] *= mask output_audio_data[end_pointer - audio_fade_envelope_size:end_pointer] *= 1 - mask start_output_frame = int(math.ceil(output_pointer / samples_per_frame)) end_output_frame = int(math.ceil(end_pointer / samples_per_frame)) for outputFrame in range(start_output_frame, end_output_frame): input_frame = int(chunk[0] + new_speed[int(chunk[2])] * (outputFrame - start_output_frame)) try: copy_frame(input_frame, outputFrame, temp_folder) last_existing_frame = input_frame except FileNotFoundError: copy_frame(last_existing_frame, outputFrame, temp_folder) output_pointer = end_pointer def process_single_file(audio_fade_envelope_size, frame_quality, frame_rate, frame_spreadage, input_file, new_speed, output_file, sample_rate, silent_threshold, temp_folder): create_path(temp_folder) create_frames(input_file, frame_quality, temp_folder) create_audio(input_file, sample_rate, temp_folder) create_params(temp_folder) create_jumpcutted_video(frame_rate, temp_folder, silent_threshold, frame_spreadage, sample_rate, new_speed, audio_fade_envelope_size) wavfile.write(temp_folder + "/audioNew.wav", sample_rate, output_audio_data) write_to_file(temp_folder, frame_rate, output_file) delete_path(temp_folder) def main(): args = parse_arguments() frame_rate = args.frame_rate sample_rate = args.sample_rate silent_threshold = args.silent_threshold frame_spreadage = args.frame_margin new_speed = [args.silent_speed, args.sounded_speed] watcher_mode: bool = args.folder_watcher_mode watched_dir: str = args.watched_dir tmp_working_dir: str = args.tmp_working_dir processed_output_dir: str = args.processed_output_dir input_file = set_input_file(args) output_file = set_output_file(args) url = args.url frame_quality = args.frame_quality temp_folder = os.path.join(tmp_working_dir, "tmp") # smooth out transition's audio by quickly fading in/out (arbitrary magic number whatever) audio_fade_envelope_size = 400 if not watcher_mode: process_single_file(audio_fade_envelope_size, frame_quality, frame_rate, frame_spreadage, input_file, new_speed, output_file, sample_rate, silent_threshold, temp_folder) else: logger.info("Started folder watcher mode on folder: " + watched_dir) try: while True: for filename in os.listdir(watched_dir): logger.info("Received file with name: " + filename) filepath = watched_dir + "/" + filename output_filename = input_to_output_filename(filename) process_single_file(audio_fade_envelope_size, frame_quality, frame_rate, frame_spreadage, filepath, new_speed, os.path.join(processed_output_dir, output_filename), sample_rate, silent_threshold, temp_folder) os.remove(filepath) logger.info("Saved processed file as: " + output_filename) sleep(2) except KeyboardInterrupt: exit(0) main()

46.321918

120

0.662724

494317434b33db4f523d57b81be2d7964a681d93

17,837

py

Python

pytorch3d/ops/subdivide_meshes.py

hangg7/pytorcg3d

f7f363eeb8efeba0927f674c83ab927ad8ce3e32

[ "BSD-3-Clause" ]

1

2020-07-13T12:40:42.000Z

pytorch3d/ops/subdivide_meshes.py

hangg7/pytorch3d

f7f363eeb8efeba0927f674c83ab927ad8ce3e32

[ "BSD-3-Clause" ]

null

pytorch3d/ops/subdivide_meshes.py

hangg7/pytorch3d

f7f363eeb8efeba0927f674c83ab927ad8ce3e32

[ "BSD-3-Clause" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import torch import torch.nn as nn from pytorch3d.structures import Meshes class SubdivideMeshes(nn.Module): """ Subdivide a triangle mesh by adding a new vertex at the center of each edge and dividing each face into four new faces. Vectors of vertex attributes can also be subdivided by averaging the values of the attributes at the two vertices which form each edge. This implementation preserves face orientation - if the vertices of a face are all ordered counter-clockwise, then the faces in the subdivided meshes will also have their vertices ordered counter-clockwise. If meshes is provided as an input, the initializer performs the relatively expensive computation of determining the new face indices. This one-time computation can be reused for all meshes with the same face topology but different vertex positions. """ def __init__(self, meshes=None): """ Args: meshes: Meshes object or None. If a meshes object is provided, the first mesh is used to compute the new faces of the subdivided topology which can be reused for meshes with the same input topology. """ super(SubdivideMeshes, self).__init__() self.precomputed = False self._N = -1 if meshes is not None: # This computation is on indices, so gradients do not need to be # tracked. mesh = meshes[0] with torch.no_grad(): subdivided_faces = self.subdivide_faces(mesh) if subdivided_faces.shape[1] != 3: raise ValueError('faces can only have three vertices') self.register_buffer('_subdivided_faces', subdivided_faces) self.precomputed = True def subdivide_faces(self, meshes): r""" Args: meshes: a Meshes object. Returns: subdivided_faces_packed: (4*sum(F_n), 3) shape LongTensor of original and new faces. Refer to pytorch3d.structures.meshes.py for more details on packed representations of faces. Each face is split into 4 faces e.g. Input face :: v0 /\ / \ / \ e1 / \ e0 / \ / \ / \ /______________\ v2 e2 v1 faces_packed = [[0, 1, 2]] faces_packed_to_edges_packed = [[2, 1, 0]] `faces_packed_to_edges_packed` is used to represent all the new vertex indices corresponding to the mid-points of edges in the mesh. The actual vertex coordinates will be computed in the forward function. To get the indices of the new vertices, offset `faces_packed_to_edges_packed` by the total number of vertices. :: faces_packed_to_edges_packed = [[2, 1, 0]] + 3 = [[5, 4, 3]] e.g. subdivided face :: v0 /\ / \ / f0 \ v4 /______\ v3 /\ /\ / \ f3 / \ / f2 \ / f1 \ /______\/______\ v2 v5 v1 f0 = [0, 3, 4] f1 = [1, 5, 3] f2 = [2, 4, 5] f3 = [5, 4, 3] """ verts_packed = meshes.verts_packed() with torch.no_grad(): faces_packed = meshes.faces_packed() faces_packed_to_edges_packed = meshes.faces_packed_to_edges_packed() faces_packed_to_edges_packed += verts_packed.shape[0] f0 = torch.stack( [ faces_packed[:, 0], faces_packed_to_edges_packed[:, 2], faces_packed_to_edges_packed[:, 1], ], dim=1, ) f1 = torch.stack( [ faces_packed[:, 1], faces_packed_to_edges_packed[:, 0], faces_packed_to_edges_packed[:, 2], ], dim=1, ) f2 = torch.stack( [ faces_packed[:, 2], faces_packed_to_edges_packed[:, 1], faces_packed_to_edges_packed[:, 0], ], dim=1, ) f3 = faces_packed_to_edges_packed subdivided_faces_packed = torch.cat( [f0, f1, f2, f3], dim=0 ) # (4*sum(F_n), 3) return subdivided_faces_packed def forward(self, meshes, feats=None): """ Subdivide a batch of meshes by adding a new vertex on each edge, and dividing each face into four new faces. New meshes contains two types of vertices: 1) Vertices that appear in the input meshes. Data for these vertices are copied from the input meshes. 2) New vertices at the midpoint of each edge. Data for these vertices is the average of the data for the two vertices that make up the edge. Args: meshes: Meshes object representing a batch of meshes. feats: Per-vertex features to be subdivided along with the verts. Should be parallel to the packed vert representation of the input meshes; so it should have shape (V, D) where V is the total number of verts in the input meshes. Default: None. Returns: 2-element tuple containing - **new_meshes**: Meshes object of a batch of subdivided meshes. - **new_feats**: (optional) Tensor of subdivided feats, parallel to the (packed) vertices of the subdivided meshes. Only returned if feats is not None. """ self._N = len(meshes) if self.precomputed: return self.subdivide_homogeneous(meshes, feats) else: return self.subdivide_heterogenerous(meshes, feats) def subdivide_homogeneous(self, meshes, feats=None): """ Subdivide verts (and optionally features) of a batch of meshes where each mesh has the same topology of faces. The subdivided faces are precomputed in the initializer. Args: meshes: Meshes object representing a batch of meshes. feats: Per-vertex features to be subdivided along with the verts. Returns: 2-element tuple containing - **new_meshes**: Meshes object of a batch of subdivided meshes. - **new_feats**: (optional) Tensor of subdivided feats, parallel to the (packed) vertices of the subdivided meshes. Only returned if feats is not None. """ verts = meshes.verts_padded() # (N, V, D) edges = meshes[0].edges_packed() # The set of faces is the same across the different meshes. new_faces = self._subdivided_faces.view(1, -1, 3).expand( self._N, -1, -1 ) # Add one new vertex at the midpoint of each edge by taking the average # of the vertices that form each edge. new_verts = verts[:, edges].mean(dim=2) new_verts = torch.cat( [verts, new_verts], dim=1 ) # (sum(V_n)+sum(E_n), 3) new_feats = None # Calculate features for new vertices. if feats is not None: if feats.dim() == 2: # feats is in packed format, transform it from packed to # padded, i.e. (N*V, D) to (N, V, D). feats = feats.view(verts.size(0), verts.size(1), feats.size(1)) if feats.dim() != 3: raise ValueError( 'features need to be of shape (N, V, D) or (N*V, D)' ) # Take average of the features at the vertices that form each edge. new_feats = feats[:, edges].mean(dim=2) new_feats = torch.cat( [feats, new_feats], dim=1 ) # (sum(V_n)+sum(E_n), 3) new_meshes = Meshes(verts=new_verts, faces=new_faces) if feats is None: return new_meshes else: return new_meshes, new_feats def subdivide_heterogenerous(self, meshes, feats=None): """ Subdivide faces, verts (and optionally features) of a batch of meshes where each mesh can have different face topologies. Args: meshes: Meshes object representing a batch of meshes. feats: Per-vertex features to be subdivided along with the verts. Returns: 2-element tuple containing - **new_meshes**: Meshes object of a batch of subdivided meshes. - **new_feats**: (optional) Tensor of subdivided feats, parallel to the (packed) vertices of the subdivided meshes. Only returned if feats is not None. """ # The computation of new faces is on face indices, so gradients do not # need to be tracked. verts = meshes.verts_packed() with torch.no_grad(): new_faces = self.subdivide_faces(meshes) edges = meshes.edges_packed() face_to_mesh_idx = meshes.faces_packed_to_mesh_idx() edge_to_mesh_idx = meshes.edges_packed_to_mesh_idx() num_edges_per_mesh = edge_to_mesh_idx.bincount(minlength=self._N) num_verts_per_mesh = meshes.num_verts_per_mesh() num_faces_per_mesh = meshes.num_faces_per_mesh() # Add one new vertex at the midpoint of each edge. new_verts_per_mesh = num_verts_per_mesh + num_edges_per_mesh # (N,) new_face_to_mesh_idx = torch.cat([face_to_mesh_idx] * 4, dim=0) # Calculate the indices needed to group the new and existing verts # for each mesh. verts_sort_idx = create_verts_index( num_verts_per_mesh, num_edges_per_mesh, meshes.device ) # (sum(V_n)+sum(E_n),) verts_ordered_idx_init = torch.zeros( new_verts_per_mesh.sum(), dtype=torch.int64, device=meshes.device, ) # (sum(V_n)+sum(E_n),) # Reassign vertex indices so that existing and new vertices for each # mesh are sequential. verts_ordered_idx = verts_ordered_idx_init.scatter_add( 0, verts_sort_idx, torch.arange(new_verts_per_mesh.sum(), device=meshes.device), ) # Retrieve vertex indices for each face. new_faces = verts_ordered_idx[new_faces] # Calculate the indices needed to group the existing and new faces # for each mesh. face_sort_idx = create_faces_index( num_faces_per_mesh, device=meshes.device ) # Reorder the faces to sequentially group existing and new faces # for each mesh. new_faces = new_faces[face_sort_idx] new_face_to_mesh_idx = new_face_to_mesh_idx[face_sort_idx] new_faces_per_mesh = new_face_to_mesh_idx.bincount( minlength=self._N ) # (sum(F_n)*4) # Add one new vertex at the midpoint of each edge by taking the average # of the verts that form each edge. new_verts = verts[edges].mean(dim=1) new_verts = torch.cat([verts, new_verts], dim=0) # Reorder the verts to sequentially group existing and new verts for # each mesh. new_verts = new_verts[verts_sort_idx] if feats is not None: new_feats = feats[edges].mean(dim=1) new_feats = torch.cat([feats, new_feats], dim=0) new_feats = new_feats[verts_sort_idx] verts_list = list(new_verts.split(new_verts_per_mesh.tolist(), 0)) faces_list = list(new_faces.split(new_faces_per_mesh.tolist(), 0)) new_verts_per_mesh_cumsum = torch.cat( [ new_verts_per_mesh.new_full(size=(1,), fill_value=0.0), new_verts_per_mesh.cumsum(0)[:-1], ], dim=0, ) faces_list = [ faces_list[n] - new_verts_per_mesh_cumsum[n] for n in range(self._N) ] if feats is not None: feats_list = new_feats.split(new_verts_per_mesh.tolist(), 0) new_meshes = Meshes(verts=verts_list, faces=faces_list) if feats is None: return new_meshes else: new_feats = torch.cat(feats_list, dim=0) return new_meshes, new_feats def create_verts_index(verts_per_mesh, edges_per_mesh, device=None): """ Helper function to group the vertex indices for each mesh. New vertices are stacked at the end of the original verts tensor, so in order to have sequential packing, the verts tensor needs to be reordered so that the vertices corresponding to each mesh are grouped together. Args: verts_per_mesh: Tensor of shape (N,) giving the number of vertices in each mesh in the batch where N is the batch size. edges_per_mesh: Tensor of shape (N,) giving the number of edges in each mesh in the batch Returns: verts_idx: A tensor with vert indices for each mesh ordered sequentially by mesh index. """ # e.g. verts_per_mesh = (4, 5, 6) # e.g. edges_per_mesh = (5, 7, 9) V = verts_per_mesh.sum() # e.g. 15 E = edges_per_mesh.sum() # e.g. 21 verts_per_mesh_cumsum = verts_per_mesh.cumsum(dim=0) # (N,) e.g. (4, 9, 15) edges_per_mesh_cumsum = edges_per_mesh.cumsum( dim=0 ) # (N,) e.g. (5, 12, 21) v_to_e_idx = verts_per_mesh_cumsum.clone() # vertex to edge index. v_to_e_idx[1:] += edges_per_mesh_cumsum[ :-1 ] # e.g. (4, 9, 15) + (0, 5, 12) = (4, 14, 27) # vertex to edge offset. v_to_e_offset = ( V - verts_per_mesh_cumsum ) # e.g. 15 - (4, 9, 15) = (11, 6, 0) v_to_e_offset[1:] += edges_per_mesh_cumsum[ :-1 ] # e.g. (11, 6, 0) + (0, 5, 12) = (11, 11, 12) e_to_v_idx = ( verts_per_mesh_cumsum[:-1] + edges_per_mesh_cumsum[:-1] ) # (4, 9) + (5, 12) = (9, 21) e_to_v_offset = ( verts_per_mesh_cumsum[:-1] - edges_per_mesh_cumsum[:-1] - V ) # (4, 9) - (5, 12) - 15 = (-16, -18) # Add one new vertex per edge. idx_diffs = torch.ones(V + E, device=device, dtype=torch.int64) # (36,) idx_diffs[v_to_e_idx] += v_to_e_offset idx_diffs[e_to_v_idx] += e_to_v_offset # e.g. # [ # 1, 1, 1, 1, 12, 1, 1, 1, 1, # -15, 1, 1, 1, 1, 12, 1, 1, 1, 1, 1, 1, # -17, 1, 1, 1, 1, 1, 13, 1, 1, 1, 1, 1, 1, 1 # ] verts_idx = idx_diffs.cumsum(dim=0) - 1 # e.g. # [ # 0, 1, 2, 3, 15, 16, 17, 18, 19, --> mesh 0 # 4, 5, 6, 7, 8, 20, 21, 22, 23, 24, 25, 26, --> mesh 1 # 9, 10, 11, 12, 13, 14, 27, 28, 29, 30, 31, 32, 33, 34, 35 --> mesh 2 # ] # where for mesh 0, [0, 1, 2, 3] are the indices of the existing verts, and # [15, 16, 17, 18, 19] are the indices of the new verts after subdivision. return verts_idx def create_faces_index(faces_per_mesh, device=None): """ Helper function to group the faces indices for each mesh. New faces are stacked at the end of the original faces tensor, so in order to have sequential packing, the faces tensor needs to be reordered to that faces corresponding to each mesh are grouped together. Args: faces_per_mesh: Tensor of shape (N,) giving the number of faces in each mesh in the batch where N is the batch size. Returns: faces_idx: A tensor with face indices for each mesh ordered sequentially by mesh index. """ # e.g. faces_per_mesh = [2, 5, 3] F = faces_per_mesh.sum() # e.g. 10 faces_per_mesh_cumsum = faces_per_mesh.cumsum(dim=0) # (N,) e.g. (2, 7, 10) switch1_idx = faces_per_mesh_cumsum.clone() switch1_idx[1:] += ( 3 * faces_per_mesh_cumsum[:-1] ) # e.g. (2, 7, 10) + (0, 6, 21) = (2, 13, 31) switch2_idx = 2 * faces_per_mesh_cumsum # e.g. (4, 14, 20) switch2_idx[1:] += ( 2 * faces_per_mesh_cumsum[:-1] ) # e.g. (4, 14, 20) + (0, 4, 14) = (4, 18, 34) switch3_idx = 3 * faces_per_mesh_cumsum # e.g. (6, 21, 30) switch3_idx[1:] += faces_per_mesh_cumsum[ :-1 ] # e.g. (6, 21, 30) + (0, 2, 7) = (6, 23, 37) switch4_idx = 4 * faces_per_mesh_cumsum[:-1] # e.g. (8, 28) switch123_offset = F - faces_per_mesh # e.g. (8, 5, 7) idx_diffs = torch.ones(4 * F, device=device, dtype=torch.int64) idx_diffs[switch1_idx] += switch123_offset idx_diffs[switch2_idx] += switch123_offset idx_diffs[switch3_idx] += switch123_offset idx_diffs[switch4_idx] -= 3 * F # e.g # [ # 1, 1, 9, 1, 9, 1, 9, 1, -> mesh 0 # -29, 1, 1, 1, 1, 6, 1, 1, 1, 1, 6, 1, 1, 1, 1, 6, 1, 1, 1, 1, -> mesh 1 # -29, 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1 -> mesh 2 # ] faces_idx = idx_diffs.cumsum(dim=0) - 1 # e.g. # [ # 0, 1, 10, 11, 20, 21, 30, 31, # 2, 3, 4, 5, 6, 12, 13, 14, 15, 16, 22, 23, 24, 25, 26, 32, 33, 34, 35, 36, # 7, 8, 9, 17, 18, 19, 27, 28, 29, 37, 38, 39 # ] # where for mesh 0, [0, 1] are the indices of the existing faces, and # [10, 11, 20, 21, 30, 31] are the indices of the new faces after subdivision. return faces_idx

37.3159

83

0.565734

6b8e21d87a95ad4cf3c8e97460c91ed05e1e383e

365

py

Python

library/library/migrations/0005_auto_20220505_1020.py

buckldav/merit-api

8a3851a9703f5a57549fd858c3e94d136083a15a

[ "MIT" ]

null

library/library/migrations/0005_auto_20220505_1020.py

buckldav/merit-api

8a3851a9703f5a57549fd858c3e94d136083a15a

[ "MIT" ]

1

2021-11-23T17:56:07.000Z

library/library/migrations/0005_auto_20220505_1020.py

buckldav/merit-api

8a3851a9703f5a57549fd858c3e94d136083a15a

[ "MIT" ]

2

2022-03-03T15:43:50.000Z

2022-03-31T15:08:29.000Z

# Generated by Django 3.1.13 on 2022-05-05 16:20 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('library', '0004_checkout_checkin_time'), ] operations = [ migrations.RenameField( model_name='checkout', old_name='book', new_name='isbn', ), ]

19.210526

50

0.586301

f61c8e02e89b10dc8003c8403fe81b340de7a758

246

py

Python

a3a_logster/utility/meta.py

Giddius/A3A_Logster_repo

d2417c783e6c5e5e0b07504b3d3d7ce5534673e6

[ "MIT" ]

null

a3a_logster/utility/meta.py

Giddius/A3A_Logster_repo

d2417c783e6c5e5e0b07504b3d3d7ce5534673e6

[ "MIT" ]

null

a3a_logster/utility/meta.py

Giddius/A3A_Logster_repo

d2417c783e6c5e5e0b07504b3d3d7ce5534673e6

[ "MIT" ]

null

class SingletonMeta(type): _instance = {} def __call__(cls, *args, **kwargs): if cls not in cls._instance: cls._instance[cls] = super(SingletonMeta, cls).__call__(*args, **kwargs) return cls._instance[cls]

22.363636

84

0.617886

29f64523710c7beea13b964cf82d8712ad9586c7

6,854

py

Python

teamcat_service/docker_build/target/one_step_build/teamcat/doraemon/api/ci/views/ci_task_flow_view.py

zhangyin2088/Teamcat

be9be8d7c1e58c8d2d22ab78d25783d9aee4de71

[ "Apache-2.0" ]

6

2018-11-26T08:42:52.000Z

2020-06-01T08:33:48.000Z

teamcat_service/doraemon/doraemon/api/ci/views/ci_task_flow_view.py

zhangyin2088/Teamcat

be9be8d7c1e58c8d2d22ab78d25783d9aee4de71

[ "Apache-2.0" ]

null

teamcat_service/doraemon/doraemon/api/ci/views/ci_task_flow_view.py

zhangyin2088/Teamcat

be9be8d7c1e58c8d2d22ab78d25783d9aee4de71

[ "Apache-2.0" ]

1

2019-01-22T06:45:36.000Z

#coding=utf-8 # coding=utf-8 ''' Created on 2014-1-5 @author: ETHAN ''' from rest_framework import generics,status from doraemon.api.ci.serializer import ci_taskflow_serializer from rest_framework.permissions import AllowAny from doraemon.ci.models import CITaskFlow,CITaskFlowSection from doraemon.api.ci.viewmodel.api_ci_taskflow import ApiCITaskFlow from rest_framework.response import Response from business.ci.ci_taskflow_service import CITaskFlowService from business.ci.ci_taskflow_section_service import CITaskFlowSectionService from doraemon.api.ci.filters.ci_pagination import CIPagination from doraemon.api.ci.filters.ci_taskflow_filter import CITaskFlowFilterSet from rest_framework.authentication import SessionAuthentication, BasicAuthentication from doraemon.api.project.views.CsrfExemptSessionAuthentication import CsrfExemptSessionAuthentication from gatesidelib.common.simplelogger import SimpleLogger class CITaskFlowView(generics.RetrieveUpdateDestroyAPIView): """ path:/api/ci/task_flow/<id>/ id:taskid """ serializer_class = ci_taskflow_serializer.CITaskFlowSerializer permission_classes=[AllowAny] authentication_classes = (CsrfExemptSessionAuthentication, BasicAuthentication) def get_object(self): taskflow_id = int(self.kwargs['id']) print(taskflow_id) task_flow=CITaskFlow.objects.get(taskflow_id) temp=ApiCITaskFlow(task_flow) task_flow.Sections=temp.Sections return task_flow def delete(self,request, *args, **kwargs): flow_id =int(kwargs['id']) task_flow=CITaskFlow.objects.get(flow_id) result = '任务流 ['+ task_flow.FlowName +'] 删除失败，请联系管理员或者重试！' try: result = CITaskFlowService.delete_taskflow(request.user,flow_id) except Exception as ex: SimpleLogger.exception(ex) return Response({'message': result}) class CITaskFlowOperationView(generics.RetrieveAPIView): """ path:/api/ci/task_flow/<id>/<operation> id:taskflow id operation:start,copy """ serializer_class = ci_taskflow_serializer.CITaskFlowSerializer permission_classes=[AllowAny] authentication_classes = (CsrfExemptSessionAuthentication, BasicAuthentication) def get_object(self): flow_id = int(self.kwargs['id']) task_flow=CITaskFlow.objects.get(flow_id) return task_flow def get(self,request, *args, **kwargs): flow_id =int(kwargs['id']) task_flow=CITaskFlow.objects.get(flow_id) operation = kwargs['operation'].strip() result = '任务流 ['+ task_flow.FlowName +'] 执行指令下发失败，请联系管理员或者重试！' try: if operation == 'start': result = CITaskFlowService.start_taskflow(request,flow_id) if operation == 'copy': result = CITaskFlowService.copy_taskflow(request.user,flow_id) except Exception as ex: SimpleLogger.exception(ex) return Response({'message': result}) class CITaskFlowListView(generics.ListCreateAPIView): """ /api/ci/task_flow/list get all ci taskflow and create new ci task FilterSet: id, Project FilterOperation:=,__in,__gt,__contains,__icontains,Range__in,__lt,!=,__isnull """ serializer_class = ci_taskflow_serializer.CITaskFlowListSerializer permission_classes=[AllowAny] authentication_classes = (CsrfExemptSessionAuthentication, BasicAuthentication) pagination_class = CIPagination def get_queryset(self): qs = CITaskFlow.objects.all().filter(IsActive=1) return CITaskFlowFilterSet(data=self.request.GET, queryset=qs).filter() def create(self, request, *args, **kwargs): task_flow = CITaskFlowService.create_taskflow(request.data,request.user) serializer = ci_taskflow_serializer.CITaskFlowListSerializer(instance=task_flow,data = request.data) serializer.is_valid(raise_exception=True) headers = self.get_success_headers(serializer.data) return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers) class CITaskFlowMyListView(generics.ListAPIView): """ /api/ci/task_flow/my get all my ci taskflow FilterSet:id,Project FilterOperation:=,__in,__gt,__contains,__icontains,Range__in,__lt,!= """ serializer_class = ci_taskflow_serializer.CITaskFlowListSerializer permission_classes=[AllowAny] authentication_classes = (CsrfExemptSessionAuthentication, BasicAuthentication) pagination_class = CIPagination def get_queryset(self): qs = CITaskFlowService.get_my_taskflows(self.request,'all') return CITaskFlowFilterSet(data=self.request.GET, queryset=qs).filter() class CITaskFlowSectionView(generics.RetrieveUpdateDestroyAPIView): """ /api/ci/task_flow/section/id get,update,delete section with section id """ serializer_class = ci_taskflow_serializer.CITaskFlowSectionSerializer permission_classes=[AllowAny] authentication_classes = (CsrfExemptSessionAuthentication, BasicAuthentication) pagination_class = CIPagination def get_object(self): section_id = int(self.kwargs.get('id', 0)) section = CITaskFlowSection.objects.get(section_id) return section class CIFlowSectionOperationView(generics.RetrieveAPIView): """ path:/api/ci/flow_section/<id>/<operation> id:section id operation:start """ serializer_class = ci_taskflow_serializer.CITaskFlowSectionSerializer permission_classes=[AllowAny] authentication_classes = (CsrfExemptSessionAuthentication, BasicAuthentication) pagination_class = CIPagination def get_object(self): section_id = int(self.kwargs.get('id', 0)) section = CITaskFlowSection.objects.get(section_id) return section def get(self,request, *args, **kwargs): section = self.get_object() operation = kwargs['operation'].strip() result = '任务流阶段 ['+ section.SectionName +'] 执行指令下发失败，请联系管理员或者重试！' try: if operation == 'start': result = CITaskFlowSectionService.start_flowsection(request,section.id) except Exception as ex: SimpleLogger.exception(ex) return Response({'message': result}) class CITaskFlowSectionListView(generics.ListCreateAPIView): """ /api/ci/task_flow_section/id get,update,delete section with section id """ serializer_class = ci_taskflow_serializer.CITaskFlowSectionSerializer permission_classes=[AllowAny] authentication_classes = (CsrfExemptSessionAuthentication, BasicAuthentication) pagination_class = CIPagination def get_queryset(self): flow_id = int(self.kwargs.get('flow_id', 0)) sections = CITaskFlowSection.objects.flow_sections(flow_id).order_by('SectionOrder') return sections

36.073684

137

0.730522

aa51c8350ed9222859efd6ec91a2b5aed98658c3

3,334

py

Python

create_segm_labels.py

Fkaneko/kaggle-hpa-single-cell-image-classification

52000cbf5c7eec6ace29274d9e85b5b24fac281b

[ "MIT" ]

1

2022-01-12T08:44:55.000Z

create_segm_labels.py

Fkaneko/kaggle-hpa-single-cell-image-classification

52000cbf5c7eec6ace29274d9e85b5b24fac281b

[ "MIT" ]

null

create_segm_labels.py

Fkaneko/kaggle-hpa-single-cell-image-classification

52000cbf5c7eec6ace29274d9e85b5b24fac281b

[ "MIT" ]

null

import argparse import copy import glob import os from typing import Tuple import numpy as np import pandas as pd import pycocotools.mask as coco_mask from tqdm import tqdm from run_cam_infer import decode_ascii_mask from src.dataset.utils import ErrorSample, save_segm_label from src.utils.util import print_argparse_arguments def convert_to_train_format(pred_df: pd.DataFrame) -> Tuple[dict, list]: samples = {} error_samples = [] for i, row_ in enumerate(tqdm(pred_df.itertuples(), total=len(pred_df))): try: pred = row_.PredictionString.split(" ") except AttributeError as e: print(i, row_.ID, e) error_samples.append(ErrorSample(ID=row_.ID, csv_idx=i)) pass w_size, h_size = row_.ImageWidth, row_.ImageHeight input_id = row_.ID class_ids = np.array(pred[0::3], dtype=np.int32) confs = np.array(pred[1::3], dtype=np.float32) rle_asciis = pred[2::3] last_ascii = "" rles: list = [] rles_idxs = [] rles_idx = -1 for ins_id, rle_ascii in enumerate(rle_asciis): if last_ascii == rle_ascii: rles.append(rles[-1].copy()) rles_idxs.append(rles_idx) continue else: rles_idx += 1 rles_idxs.append(rles_idx) last_ascii = copy.deepcopy(rle_ascii) mask_dict = decode_ascii_mask(rle_ascii, w_size, h_size) rles.append(mask_dict["rle"]) bboxes = coco_mask.toBbox(rles) bboxes[:, 2] += bboxes[:, 0] bboxes[:, 3] += bboxes[:, 1] input_sample = { "filename": input_id, "width": w_size, "height": h_size, "ann": { "bboxes": np.array(bboxes, dtype=np.float32), "labels": class_ids, "confs": confs, "masks": rles, "mask_idxs": rles_idxs, }, } samples[input_id] = input_sample return samples, error_samples if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--save_folder", default="./save_uni/version_15/", type=str, help="the path to save the sub-category label", ) parser.add_argument( "--sub_csv", type=str, default="./save_uni/version_15/submission_full_v15_0407.csv", help="submission style csv, or a directory which contains csvs", ) args = parser.parse_args() print_argparse_arguments(args) if os.path.isdir(args.sub_csv): sub_csvs = glob.glob(os.path.join(args.sub_csv, "*.csv")) dfs = [] for path_ in sub_csvs: dfs.append(pd.read_csv(path_)) print(f"load: {path_} \t len: {len(dfs[-1])}") pred_df = pd.concat(dfs, axis=0) print("csvs are merged:", len(pred_df)) assert pred_df.duplicated(["ID"]).sum() == 0 elif os.path.isfile(args.sub_csv): pred_df = pd.read_csv(args.sub_csv) else: raise NotImplementedError print(pred_df.head()) samples, error_samples = convert_to_train_format(pred_df) save_segm_label( samples=samples, error_samples=error_samples, save_folder=args.save_folder )

30.309091

82

0.589682

83012fd0373aa81ea02c81d79acdd2328f7fc856

16,325

py

Python

napari/components/viewer_model.py

zeroth/napari

c8d755b13716d1a60003e88f9d75dd6af8a346f9

[ "BSD-3-Clause" ]

null

napari/components/viewer_model.py

zeroth/napari

c8d755b13716d1a60003e88f9d75dd6af8a346f9

[ "BSD-3-Clause" ]

null

napari/components/viewer_model.py

zeroth/napari

c8d755b13716d1a60003e88f9d75dd6af8a346f9

[ "BSD-3-Clause" ]

null

import numpy as np from ..utils.events import EmitterGroup, Event from ..utils.key_bindings import KeymapHandler, KeymapProvider from ..utils.theme import palettes from ._viewer_mouse_bindings import dims_scroll from .add_layers_mixin import AddLayersMixin from .dims import Dims from .layerlist import LayerList class ViewerModel(AddLayersMixin, KeymapHandler, KeymapProvider): """Viewer containing the rendered scene, layers, and controlling elements including dimension sliders, and control bars for color limits. Parameters ---------- title : string The title of the viewer window. ndisplay : {2, 3} Number of displayed dimensions. order : tuple of int Order in which dimensions are displayed where the last two or last three dimensions correspond to row x column or plane x row x column if ndisplay is 2 or 3. axis_labels = list of str Dimension names. Attributes ---------- window : Window Parent window. layers : LayerList List of contained layers. dims : Dimensions Contains axes, indices, dimensions and sliders. themes : dict of str: dict of str: str Preset color palettes. """ themes = palettes def __init__( self, title='napari', ndisplay=2, order=None, axis_labels=None ): super().__init__() self.events = EmitterGroup( source=self, auto_connect=True, status=Event, help=Event, title=Event, interactive=Event, cursor=Event, reset_view=Event, active_layer=Event, palette=Event, grid=Event, layers_change=Event, ) self.dims = Dims( ndim=None, ndisplay=ndisplay, order=order, axis_labels=axis_labels ) self.layers = LayerList() self._status = 'Ready' self._help = '' self._title = title self._cursor = 'standard' self._cursor_size = None self._interactive = True self._active_layer = None self._grid_size = (1, 1) self.grid_stride = 1 self._palette = None self.theme = 'dark' self.dims.events.camera.connect(self.reset_view) self.dims.events.ndisplay.connect(self._update_layers) self.dims.events.order.connect(self._update_layers) self.dims.events.axis.connect(self._update_layers) self.layers.events.changed.connect(self._update_active_layer) self.layers.events.changed.connect(self._update_grid) self.layers.events.changed.connect(self._on_layers_change) self.keymap_providers = [self] # Hold callbacks for when mouse moves with nothing pressed self.mouse_move_callbacks = [] # Hold callbacks for when mouse is pressed, dragged, and released self.mouse_drag_callbacks = [] # Hold callbacks for when mouse wheel is scrolled self.mouse_wheel_callbacks = [dims_scroll] self._persisted_mouse_event = {} self._mouse_drag_gen = {} self._mouse_wheel_gen = {} @property def palette(self): """dict of str: str : Color palette with which to style the viewer. """ return self._palette @palette.setter def palette(self, palette): if palette == self.palette: return self._palette = palette self.events.palette() @property def theme(self): """string or None : Preset color palette. """ for theme, palette in self.themes.items(): if palette == self.palette: return theme @theme.setter def theme(self, theme): if theme == self.theme: return try: self.palette = self.themes[theme] except KeyError: raise ValueError( f"Theme '{theme}' not found; " f"options are {list(self.themes)}." ) @property def grid_size(self): """tuple: Size of grid """ return self._grid_size @grid_size.setter def grid_size(self, grid_size): if np.all(self.grid_size == grid_size): return self._grid_size = grid_size self.reset_view() self.events.grid() @property def status(self): """string: Status string """ return self._status @status.setter def status(self, status): if status == self.status: return self._status = status self.events.status(text=self._status) @property def help(self): """string: String that can be displayed to the user in the status bar with helpful usage tips. """ return self._help @help.setter def help(self, help): if help == self.help: return self._help = help self.events.help(text=self._help) @property def title(self): """string: String that is displayed in window title. """ return self._title @title.setter def title(self, title): if title == self.title: return self._title = title self.events.title(text=self._title) @property def interactive(self): """bool: Determines if canvas pan/zoom interactivity is enabled or not. """ return self._interactive @interactive.setter def interactive(self, interactive): if interactive == self.interactive: return self._interactive = interactive self.events.interactive() @property def cursor(self): """string: String identifying cursor displayed over canvas. """ return self._cursor @cursor.setter def cursor(self, cursor): if cursor == self.cursor: return self._cursor = cursor self.events.cursor() @property def cursor_size(self): """int | None: Size of cursor if custom. None is yields default size """ return self._cursor_size @cursor_size.setter def cursor_size(self, cursor_size): if cursor_size == self.cursor_size: return self._cursor_size = cursor_size self.events.cursor() @property def active_layer(self): """int: index of active_layer """ return self._active_layer @active_layer.setter def active_layer(self, active_layer): if active_layer == self.active_layer: return if self._active_layer is not None: self.keymap_providers.remove(self._active_layer) self._active_layer = active_layer if active_layer is not None: self.keymap_providers.insert(0, active_layer) self.events.active_layer(item=self._active_layer) @property def _sliced_extent_world(self) -> np.ndarray: """Extent of layers in world coordinates after slicing. D is either 2 or 3 depending on if the displayed data is 2D or 3D. Returns ------- sliced_extent_world : array, shape (2, D) """ if len(self.layers) == 0 and self.dims.ndim != 2: # If no data is present and dims model has not been reset to 0 # than someone has passed more than two axis labels which are # being saved and so default values are used. return np.vstack( [np.zeros(self.dims.ndim), np.repeat(512, self.dims.ndim)] ) else: return self.layers._extent_world[:, self.dims.displayed] def reset_view(self, event=None): """Resets the camera's view using `event.rect` a 4-tuple of the x, y corner position followed by width and height of the camera """ extent = self._sliced_extent_world scene_size = extent[1] - extent[0] corner = extent[0] grid_size = list(self.grid_size) if len(scene_size) > len(grid_size): grid_size = [1] * (len(scene_size) - len(grid_size)) + grid_size size = np.multiply(scene_size, grid_size) centroid = np.add(corner, np.divide(size, 2)) if self.dims.ndisplay == 2: # For a PanZoomCamera emit a 4-tuple of the rect corner = np.subtract(corner, np.multiply(0.05, size))[::-1] size = np.multiply(1.1, size)[::-1] rect = tuple(corner) + tuple(size) self.events.reset_view(rect=rect) else: # For an ArcballCamera emit the center and scale_factor center = centroid[::-1] scale_factor = 1.1 * np.max(size[-2:]) # set initial camera angle so that it matches top layer of 2D view # when transitioning to 3D view quaternion = [np.pi / 2, 1, 0, 0] self.events.reset_view( center=center, scale_factor=scale_factor, quaternion=quaternion ) def _new_labels(self): """Create new labels layer filling full world coordinates space.""" extent = self.layers._extent_world scale = self.layers._step_size scene_size = extent[1] - extent[0] corner = extent[0] shape = [ np.round(s / sc).astype('int') if s > 0 else 1 for s, sc in zip(scene_size, scale) ] empty_labels = np.zeros(shape, dtype=int) self.add_labels(empty_labels, translate=np.array(corner), scale=scale) def _update_layers(self, event=None, layers=None): """Updates the contained layers. Parameters ---------- layers : list of napari.layers.Layer, optional List of layers to update. If none provided updates all. """ layers = layers or self.layers for layer in layers: # adjust the order of the global dims based on the number of # dimensions that a layer has - for example a global order of # [2, 1, 0, 3] -> [0, 1] for a layer that only has two dimensions # or -> [1, 0, 2] for a layer with three as that corresponds to # the relative order of the last two and three dimensions # respectively offset = self.dims.ndim - layer.dims.ndim order = np.array(self.dims.order) if offset <= 0: order = list(range(-offset)) + list(order - offset) else: order = list(order[order >= offset] - offset) layer.dims.order = order layer.dims.ndisplay = self.dims.ndisplay # Update the point values of the layers for the dimensions that # the layer has for axis in range(layer.dims.ndim): point = self.dims.point[axis + offset] layer.dims.set_point(axis, point) def _toggle_theme(self): """Switch to next theme in list of themes """ theme_names = list(self.themes.keys()) cur_theme = theme_names.index(self.theme) self.theme = theme_names[(cur_theme + 1) % len(theme_names)] def _update_active_layer(self, event): """Set the active layer by iterating over the layers list and finding the first selected layer. If multiple layers are selected the iteration stops and the active layer is set to be None Parameters ---------- event : Event No Event parameters are used """ # iteration goes backwards to find top most selected layer if any # if multiple layers are selected sets the active layer to None active_layer = None for layer in self.layers: if active_layer is None and layer.selected: active_layer = layer elif active_layer is not None and layer.selected: active_layer = None break if active_layer is None: self.status = 'Ready' self.help = '' self.cursor = 'standard' self.interactive = True self.active_layer = None else: self.status = active_layer.status self.help = active_layer.help self.cursor = active_layer.cursor self.interactive = active_layer.interactive self.active_layer = active_layer def _on_layers_change(self, event): if len(self.layers) == 0: self.dims.ndim = 2 self.dims.reset() else: extent = self.layers._extent_world ss = self.layers._step_size ndim = extent.shape[1] self.dims.ndim = ndim for i in range(ndim): self.dims.set_range(i, (extent[0, i], extent[1, i], ss[i])) self.events.layers_change() def _update_status(self, event): """Set the viewer status with the `event.status` string.""" self.status = event.status def _update_help(self, event): """Set the viewer help with the `event.help` string.""" self.help = event.help def _update_interactive(self, event): """Set the viewer interactivity with the `event.interactive` bool.""" self.interactive = event.interactive def _update_cursor(self, event): """Set the viewer cursor with the `event.cursor` string.""" self.cursor = event.cursor def _update_cursor_size(self, event): """Set the viewer cursor_size with the `event.cursor_size` int.""" self.cursor_size = event.cursor_size def grid_view(self, n_row=None, n_column=None, stride=1): """Arrange the current layers is a 2D grid. Default behaviour is to make a square 2D grid. Parameters ---------- n_row : int, optional Number of rows in the grid. n_column : int, optional Number of column in the grid. stride : int, optional Number of layers to place in each grid square before moving on to the next square. The default ordering is to place the most visible layer in the top left corner of the grid. A negative stride will cause the order in which the layers are placed in the grid to be reversed. """ n_grid_squares = np.ceil(len(self.layers) / abs(stride)).astype(int) if n_row is None and n_column is None: n_row = np.ceil(np.sqrt(n_grid_squares)).astype(int) n_column = n_row elif n_row is None: n_row = np.ceil(n_grid_squares / n_column).astype(int) elif n_column is None: n_column = np.ceil(n_grid_squares / n_row).astype(int) n_row = max(1, n_row) n_column = max(1, n_column) self.grid_size = (n_row, n_column) self.grid_stride = stride for i, layer in enumerate(self.layers): if stride > 0: adj_i = len(self.layers) - i - 1 else: adj_i = i adj_i = adj_i // abs(stride) adj_i = adj_i % (n_row * n_column) i_row = adj_i // n_column i_column = adj_i % n_column self._subplot(layer, (i_row, i_column)) def stack_view(self): """Arrange the current layers is a stack. """ self.grid_view(n_row=1, n_column=1, stride=1) def _update_grid(self, event=None): """Update grid with current grid values. """ self.grid_view( n_row=self.grid_size[0], n_column=self.grid_size[1], stride=self.grid_stride, ) def _subplot(self, layer, position): """Shift a layer to a specified position in a 2D grid. Parameters ---------- layer : napari.layers.Layer Layer that is to be moved. position : 2-tuple of int New position of layer in grid. size : 2-tuple of int Size of the grid that is being used. """ extent = self._sliced_extent_world scene_size = extent[1] - extent[0] translate_2d = np.multiply(scene_size[-2:], position) translate = [0] * layer.ndim translate[-2:] = translate_2d layer.translate_grid = translate

32.913306

79

0.588668

1068297e50830302ed944f8bd43a501591bf5d9d

3,942

py

Python

testproject/testproject/app/tests/test_connection.py

maria-grigorieva/django_cassandra_engine

70918eeb6edd26c50a394a1ddcf6521b92ec429a

[ "BSD-2-Clause" ]

null

testproject/testproject/app/tests/test_connection.py

maria-grigorieva/django_cassandra_engine

70918eeb6edd26c50a394a1ddcf6521b92ec429a

[ "BSD-2-Clause" ]

null

testproject/testproject/app/tests/test_connection.py

maria-grigorieva/django_cassandra_engine

70918eeb6edd26c50a394a1ddcf6521b92ec429a

[ "BSD-2-Clause" ]

null

from cassandra.auth import PlainTextAuthProvider from cassandra.cluster import Cluster from mock import patch from django.test import TestCase from django_cassandra_engine.connection import CassandraConnection, Cursor from django_cassandra_engine.utils import get_cassandra_connection class CassandraConnectionTestCase(TestCase): def setUp(self): self.cassandra_connection = get_cassandra_connection() self.connection = CassandraConnection( **self.cassandra_connection.settings_dict) def test_cursor(self): self.assertIsInstance(self.connection.cursor(), Cursor) self.assertEqual(self.connection.cursor().connection, self.connection) def test_connected_to_db(self): from cqlengine import connection as cql_connection self.assertIsInstance(cql_connection.cluster, Cluster) self.assertIsNotNone(cql_connection.session) def test_session_property(self): from cqlengine import connection as cql_connection self.assertEqual(self.connection.session, cql_connection.session) def test_cluster_property(self): from cqlengine import connection as cql_connection self.assertEqual(self.connection.cluster, cql_connection.cluster) def test_connection_options(self): connection_options = \ self.cassandra_connection.settings_dict['OPTIONS']['connection'] self.assertEqual( self.connection.connection_options, connection_options) @patch("cqlengine.connection") def test_connection_setup_called_first_time_with_proper_options( self, connection_mock): settings = self.cassandra_connection.settings_dict connection_mock.cluster = None connection = CassandraConnection(**settings) connection_mock.setup.assert_called_once_with( connection.hosts, connection.keyspace, **settings['OPTIONS']['connection']) @patch("django_cassandra_engine.connection.connection") def test_connection_setup_called_second_time( self, connection_mock): settings = self.cassandra_connection.settings_dict connection_mock.cluster = Cluster() CassandraConnection(**settings) self.assertFalse(connection_mock.setup.called) def test_connection_auth_provider_added_to_connection_options(self): settings = self.cassandra_connection.settings_dict settings['USER'] = 'user' settings['PASSWORD'] = 'pass' connection = CassandraConnection(**settings) self.assertIsInstance(connection.connection_options['auth_provider'], PlainTextAuthProvider) def test_connection_auth_provider_not_changed(self): settings = self.cassandra_connection.settings_dict settings['USER'] = 'user' settings['PASSWORD'] = 'pass' settings['OPTIONS']['connection'] = {} settings['OPTIONS']['connection']['auth_provider'] = 'sth' connection = CassandraConnection(**settings) self.assertEqual(connection.connection_options['auth_provider'], settings['OPTIONS']['connection']['auth_provider']) def test_connection_session_options_default_timeout(self): session_opts = \ self.cassandra_connection.settings_dict['OPTIONS']['session'] self.assertEqual(self.connection.session_options, session_opts) self.assertEqual(self.connection.session.default_timeout, session_opts.get('default_timeout')) def test_raw_cql_cursor_queries(self): cursor = self.connection.cursor() self.assertEqual( cursor.execute("SELECT count(*) from example_model")[0]['count'], 0 ) cursor.execute("INSERT INTO example_model (id) VALUES (1)") self.assertEqual( cursor.execute("SELECT count(*) from example_model")[0]['count'], 1 )

35.513514

79

0.703957

952b6f650a35a1769a46d9337b0752082101bf29

241

py

Python

debug/testmondo.py

merlinxcy/iptcFirewallOS

5427cd9104aefcef9071b17f54ca6574837932d6

[ "MIT" ]

null

debug/testmondo.py

merlinxcy/iptcFirewallOS

5427cd9104aefcef9071b17f54ca6574837932d6

[ "MIT" ]

null

debug/testmondo.py

merlinxcy/iptcFirewallOS

5427cd9104aefcef9071b17f54ca6574837932d6

[ "MIT" ]

null

import mongolib class a(): def aa(self): a=mongolib.mongodb() a.log_collect(msg='1gaejiusfuadaifuagusuifhiau afdu gaudf uisg uagsi gaug asyaigasydg aug iug ') a.log_collect(msg='2') a.log_input() a.log_output() aaaa=a() aaaa.aa()

21.909091

98

0.721992

425c6c9d0129f41044428190f3b7cdda95e1d73d

82

py

Python

yc238/994-2.py

c-yan/yukicoder

cdbbd65402177225dd989df7fe01f67908484a69

[ "MIT" ]

null

yc238/994-2.py

c-yan/yukicoder

cdbbd65402177225dd989df7fe01f67908484a69

[ "MIT" ]

null

yc238/994-2.py

c-yan/yukicoder

cdbbd65402177225dd989df7fe01f67908484a69

[ "MIT" ]

null

N, K = map(int, input().split()) if N >= K: print(K - 1) else: print(-1)

11.714286

32

0.47561

dd6156d4fc4d4e17db1881fbb6b14aa4c4eec0a3

738

py

Python

setup.py

755/python_ndms2_client

b6b0c8642979faed8f6623359e4637b05134f838

[ "MIT" ]

null

setup.py

755/python_ndms2_client

b6b0c8642979faed8f6623359e4637b05134f838

[ "MIT" ]

null

setup.py

755/python_ndms2_client

b6b0c8642979faed8f6623359e4637b05134f838

[ "MIT" ]

null

import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="ndms2_client", version="0.0.8", author="Andrey F. Kupreychik", author_email="foxel@quickfox.ru", description="Keenetic NDMS2 client", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/foxel/python_ndms2_client", packages=setuptools.find_packages(exclude=['tests']), classifiers=( "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ), )

30.75

57

0.655827

40eb2babe15b5b4b6e0b25e28017501b2418f088

1,753

py

Python

PSET3/P1 & P2/matstri2.py

MonitSharma/Computational-Methods-in-Physics

e3b2db36c37dd5f64b9a37ba39e9bb267ba27d85

[ "MIT" ]

null

PSET3/P1 & P2/matstri2.py

MonitSharma/Computational-Methods-in-Physics

e3b2db36c37dd5f64b9a37ba39e9bb267ba27d85

[ "MIT" ]

null

PSET3/P1 & P2/matstri2.py

MonitSharma/Computational-Methods-in-Physics

e3b2db36c37dd5f64b9a37ba39e9bb267ba27d85

[ "MIT" ]

null

# EqStringAnimateMat.py: Animated leapfrog solution Vibrating string using MatPlotLib from numpy import * import numpy as np, matplotlib.pyplot as plt, matplotlib.animation as animation rho = 0.01; ten = 40.; c = sqrt(ten/rho) # density, tension c1 = c; ratio = c*c/(c1*c1) # CFL criterium = 1 xi = np.zeros((101,3), float) # Declaration k = range(0,101) def Initialize(): # Initial conditions for i in range(0, 81): xi[i, 0] = 0.00125*i for i in range (81, 101): xi[i, 0] = 0.1 - 0.005*(i - 80) # second part of string def animate(num): for i in range(1, 100): xi[i,2] = 2.*xi[i,1]-xi[i,0]+ratio*(xi[i+1,1]+xi[i-1,1]-2*xi[i,1]) line.set_data(k,xi[k,2]) # Data to plot ,x,y for m in range (0,101): xi[m, 0] = xi[m, 1] # Recycle array xi[m, 1] = xi[m, 2] return line Initialize() # Plot initial string fig = plt.figure() ax = fig.add_subplot(111, autoscale_on=False, xlim=(0, 101), ylim=(-0.15, 0.15)) ax.grid() # Plot grid plt.title("Vibrating String") line, = ax.plot(k, xi[k,0], lw=2) for i in range(1,100): xi[i,1] = xi[i,0] + 0.5*ratio*(xi[i+1,0] + xi[i-1,0] -2*xi[i,0]) ani = animation.FuncAnimation(fig, animate,1) # Dummy argument: 1 plt.show() print("finished")

51.558824

102

0.436395

4f6c77703d1f233fb07892ff042aa9ff2a4365fd

418

py

Python

spacenet/schemas/events.py

space-logistics-org/spacenet

fd004437ed7b27dd6dc41a374e1dedfcea92e37d

[ "MIT" ]

1

2022-02-17T18:01:41.000Z

spacenet/schemas/events.py

space-logistics-org/spacenet

fd004437ed7b27dd6dc41a374e1dedfcea92e37d

[ "MIT" ]

2

2021-06-19T19:41:15.000Z

2021-07-21T17:07:48.000Z

spacenet/schemas/events.py

space-logistics-org/spacenet

fd004437ed7b27dd6dc41a374e1dedfcea92e37d

[ "MIT" ]

3

2021-06-16T16:31:12.000Z

2022-02-17T18:02:57.000Z

""" A module re-exporting all events from one file so that `from spacenet.schemas.events import *` is usable. """ from .bases import * from .flight_transport import * from .space_transport import * from .surface_transport import * from .propulsive_burn import * from .element_events import * from .transfer_resources import * from .consume_resource import * from .crewed_eva import * from .crewed_exploration import *

27.866667

58

0.782297

3a44644bb3ea9fa59bfd5048376a7e03bc9281f1

389

py

Python

shorty/asgi.py

malakbenr/Shorty_URL-Shortner

1babc411acfc72e3d559a1a1b1aba3582d713c80

[ "MIT" ]

null

shorty/asgi.py

malakbenr/Shorty_URL-Shortner

1babc411acfc72e3d559a1a1b1aba3582d713c80

[ "MIT" ]

null

shorty/asgi.py

malakbenr/Shorty_URL-Shortner

1babc411acfc72e3d559a1a1b1aba3582d713c80

[ "MIT" ]

null

""" ASGI config for shorty project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/4.0/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'shorty.settings') application = get_asgi_application()

22.882353

78

0.784062

04a998e0af08a9aa1a96ea80490d4302de174e59

4,727

py

Python

pycls/core/plotting.py

feymanpriv/pymetric

f7c4f354f87969142263c87e1fb33499b7b2d62a

[ "MIT" ]

62

2020-08-26T11:06:37.000Z

2022-03-29T03:26:00.000Z

pycls/core/plotting.py

ym547559398/pycls

f7c4f354f87969142263c87e1fb33499b7b2d62a

[ "MIT" ]

2

2021-06-02T10:19:53.000Z

2021-12-06T05:41:23.000Z

pycls/core/plotting.py

ym547559398/pycls

f7c4f354f87969142263c87e1fb33499b7b2d62a

[ "MIT" ]

11

2020-09-14T12:26:17.000Z

2021-10-04T06:29:35.000Z

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """Plotting functions.""" import colorlover as cl import matplotlib.pyplot as plt import plotly.graph_objs as go import plotly.offline as offline import pycls.core.logging as logging def get_plot_colors(max_colors, color_format="pyplot"): """Generate colors for plotting.""" colors = cl.scales["11"]["qual"]["Paired"] if max_colors > len(colors): colors = cl.to_rgb(cl.interp(colors, max_colors)) if color_format == "pyplot": return [[j / 255.0 for j in c] for c in cl.to_numeric(colors)] return colors def prepare_plot_data(log_files, names, metric="top1_err"): """Load logs and extract data for plotting error curves.""" plot_data = [] for file, name in zip(log_files, names): d, data = {}, logging.sort_log_data(logging.load_log_data(file)) for phase in ["train", "test"]: x = data[phase + "_epoch"]["epoch"] y = data[phase + "_epoch"][metric] x = [int(e.split("/")[0]) for e in x] d["x_" + phase], d["y_" + phase] = x, y d[phase + "_label"] = "[{:5.2f}] ".format(min(y) if y else 0) + name plot_data.append(d) assert len(plot_data) > 0, "No data to plot" return plot_data def plot_error_curves_plotly(log_files, names, filename, metric="top1_err"): """Plot error curves using plotly and save to file.""" plot_data = prepare_plot_data(log_files, names, metric) colors = get_plot_colors(len(plot_data), "plotly") # Prepare data for plots (3 sets, train duplicated w and w/o legend) data = [] for i, d in enumerate(plot_data): s = str(i) line_train = {"color": colors[i], "dash": "dashdot", "width": 1.5} line_test = {"color": colors[i], "dash": "solid", "width": 1.5} data.append( go.Scatter( x=d["x_train"], y=d["y_train"], mode="lines", name=d["train_label"], line=line_train, legendgroup=s, visible=True, showlegend=False, ) ) data.append( go.Scatter( x=d["x_test"], y=d["y_test"], mode="lines", name=d["test_label"], line=line_test, legendgroup=s, visible=True, showlegend=True, ) ) data.append( go.Scatter( x=d["x_train"], y=d["y_train"], mode="lines", name=d["train_label"], line=line_train, legendgroup=s, visible=False, showlegend=True, ) ) # Prepare layout w ability to toggle 'all', 'train', 'test' titlefont = {"size": 18, "color": "#7f7f7f"} vis = [[True, True, False], [False, False, True], [False, True, False]] buttons = zip(["all", "train", "test"], [[{"visible": v}] for v in vis]) buttons = [{"label": b, "args": v, "method": "update"} for b, v in buttons] layout = go.Layout( title=metric + " vs. epoch<br>[dash=train, solid=test]", xaxis={"title": "epoch", "titlefont": titlefont}, yaxis={"title": metric, "titlefont": titlefont}, showlegend=True, hoverlabel={"namelength": -1}, updatemenus=[ { "buttons": buttons, "direction": "down", "showactive": True, "x": 1.02, "xanchor": "left", "y": 1.08, "yanchor": "top", } ], ) # Create plotly plot offline.plot({"data": data, "layout": layout}, filename=filename) def plot_error_curves_pyplot(log_files, names, filename=None, metric="top1_err"): """Plot error curves using matplotlib.pyplot and save to file.""" plot_data = prepare_plot_data(log_files, names, metric) colors = get_plot_colors(len(names)) for ind, d in enumerate(plot_data): c, lbl = colors[ind], d["test_label"] plt.plot(d["x_train"], d["y_train"], "--", c=c, alpha=0.8) plt.plot(d["x_test"], d["y_test"], "-", c=c, alpha=0.8, label=lbl) plt.title(metric + " vs. epoch\n[dash=train, solid=test]", fontsize=14) plt.xlabel("epoch", fontsize=14) plt.ylabel(metric, fontsize=14) plt.grid(alpha=0.4) plt.legend() if filename: plt.savefig(filename) plt.clf() else: plt.show()

35.276119

81

0.542204

20fb7b5b24de209d168958ec2035bb0ed7e7d948

12,386

py

Python

intersight/model/meta_display_name_definition.py

CiscoDevNet/intersight-python

04b721f37c3044646a91c185c7259edfb991557a

[ "Apache-2.0" ]

5

2021-12-16T15:13:32.000Z

2022-03-29T16:09:54.000Z

intersight/model/meta_display_name_definition.py

CiscoDevNet/intersight-python

04b721f37c3044646a91c185c7259edfb991557a

[ "Apache-2.0" ]

4

2022-01-25T19:05:51.000Z

2022-03-29T20:18:37.000Z

intersight/model/meta_display_name_definition.py

CiscoDevNet/intersight-python

04b721f37c3044646a91c185c7259edfb991557a

[ "Apache-2.0" ]

2

2020-07-07T15:01:08.000Z

2022-01-31T04:27:35.000Z

""" Cisco Intersight Cisco Intersight is a management platform delivered as a service with embedded analytics for your Cisco and 3rd party IT infrastructure. This platform offers an intelligent level of management that enables IT organizations to analyze, simplify, and automate their environments in more advanced ways than the prior generations of tools. Cisco Intersight provides an integrated and intuitive management experience for resources in the traditional data center as well as at the edge. With flexible deployment options to address complex security needs, getting started with Intersight is quick and easy. Cisco Intersight has deep integration with Cisco UCS and HyperFlex systems allowing for remote deployment, configuration, and ongoing maintenance. The model-based deployment works for a single system in a remote location or hundreds of systems in a data center and enables rapid, standardized configuration and deployment. It also streamlines maintaining those systems whether you are working with small or very large configurations. The Intersight OpenAPI document defines the complete set of properties that are returned in the HTTP response. From that perspective, a client can expect that no additional properties are returned, unless these properties are explicitly defined in the OpenAPI document. However, when a client uses an older version of the Intersight OpenAPI document, the server may send additional properties because the software is more recent than the client. In that case, the client may receive properties that it does not know about. Some generated SDKs perform a strict validation of the HTTP response body against the OpenAPI document. # noqa: E501 The version of the OpenAPI document: 1.0.9-4950 Contact: intersight@cisco.com Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from intersight.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) def lazy_import(): from intersight.model.meta_display_name_definition_all_of import MetaDisplayNameDefinitionAllOf from intersight.model.mo_base_complex_type import MoBaseComplexType globals()['MetaDisplayNameDefinitionAllOf'] = MetaDisplayNameDefinitionAllOf globals()['MoBaseComplexType'] = MoBaseComplexType class MetaDisplayNameDefinition(ModelComposed): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { ('class_id',): { 'META.DISPLAYNAMEDEFINITION': "meta.DisplayNameDefinition", }, ('object_type',): { 'META.DISPLAYNAMEDEFINITION': "meta.DisplayNameDefinition", }, } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ lazy_import() return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = True @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ lazy_import() return { 'class_id': (str,), # noqa: E501 'object_type': (str,), # noqa: E501 'format': (str,), # noqa: E501 'include_ancestor': (bool,), # noqa: E501 'name': (str,), # noqa: E501 } @cached_property def discriminator(): val = { } if not val: return None return {'class_id': val} attribute_map = { 'class_id': 'ClassId', # noqa: E501 'object_type': 'ObjectType', # noqa: E501 'format': 'Format', # noqa: E501 'include_ancestor': 'IncludeAncestor', # noqa: E501 'name': 'Name', # noqa: E501 } required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', '_composed_instances', '_var_name_to_model_instances', '_additional_properties_model_instances', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """MetaDisplayNameDefinition - a model defined in OpenAPI Args: Keyword Args: class_id (str): The fully-qualified name of the instantiated, concrete type. This property is used as a discriminator to identify the type of the payload when marshaling and unmarshaling data.. defaults to "meta.DisplayNameDefinition", must be one of ["meta.DisplayNameDefinition", ] # noqa: E501 object_type (str): The fully-qualified name of the instantiated, concrete type. The value should be the same as the 'ClassId' property.. defaults to "meta.DisplayNameDefinition", must be one of ["meta.DisplayNameDefinition", ] # noqa: E501 _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) format (str): A specification for constructing the displayname from the MO's properties.. [optional] # noqa: E501 include_ancestor (bool): An indication of whether the displayname should be contructed 'recursively' including the displayname of the first ancestor with a similarly named displayname.. [optional] # noqa: E501 name (str): The name of the displayname used as a key in the DisplayName map which is returned as part of an MO for a Rest request.. [optional] # noqa: E501 """ class_id = kwargs.get('class_id', "meta.DisplayNameDefinition") object_type = kwargs.get('object_type', "meta.DisplayNameDefinition") _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) constant_args = { '_check_type': _check_type, '_path_to_item': _path_to_item, '_spec_property_naming': _spec_property_naming, '_configuration': _configuration, '_visited_composed_classes': self._visited_composed_classes, } required_args = { 'class_id': class_id, 'object_type': object_type, } model_args = {} model_args.update(required_args) model_args.update(kwargs) composed_info = validate_get_composed_info( constant_args, model_args, self) self._composed_instances = composed_info[0] self._var_name_to_model_instances = composed_info[1] self._additional_properties_model_instances = composed_info[2] unused_args = composed_info[3] for var_name, var_value in required_args.items(): setattr(self, var_name, var_value) for var_name, var_value in kwargs.items(): if var_name in unused_args and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ not self._additional_properties_model_instances: # discard variable. continue setattr(self, var_name, var_value) @cached_property def _composed_schemas(): # we need this here to make our import statements work # we must store _composed_schemas in here so the code is only run # when we invoke this method. If we kept this at the class # level we would get an error beause the class level # code would be run when this module is imported, and these composed # classes don't exist yet because their module has not finished # loading lazy_import() return { 'anyOf': [ ], 'allOf': [ MetaDisplayNameDefinitionAllOf, MoBaseComplexType, ], 'oneOf': [ ], }

49.150794

1,678

0.637817

95872d38ad4d9304260e50509ad9311374158a46

551

py

Python

setup.py

wishful-project/tests

fc628bee6b6c495cff55ba3891ff9aa9ce969c98

[ "MIT" ]

null

setup.py

wishful-project/tests

fc628bee6b6c495cff55ba3891ff9aa9ce969c98

[ "MIT" ]

null

setup.py

wishful-project/tests

fc628bee6b6c495cff55ba3891ff9aa9ce969c98

[ "MIT" ]

null

from setuptools import setup, find_packages def readme(): with open('README.md') as f: return f.read() setup( name='wishful_testing_framework', version='0.1.0', packages=find_packages(), url='http://www.wishful-project.eu/software', license='', author='Piotr Gawlowicz', author_email='gawlowicz@tkn.tu-berlin.de', description='WiSHFUL Testing Framework', long_description='Test of WiSHFUL Control Framework', keywords='testing', install_requires=['pytest', 'sh', 'PyRIC', 'pyzmq', 'gevent'] )

27.55

65

0.673321

96e13148e96be3c7afef36adf635ed6ce6f53302

401

py

Python

interiorshop/wsgi.py

wilfex81/INTERIORSHOP

0b1859e240b47b956f366e8eab53cecdc2786bcd

[ "MIT" ]

null

interiorshop/wsgi.py

wilfex81/INTERIORSHOP

0b1859e240b47b956f366e8eab53cecdc2786bcd

[ "MIT" ]

1

2022-02-24T12:47:49.000Z

interiorshop/wsgi.py

wilfex81/INTERIORSHOP

0b1859e240b47b956f366e8eab53cecdc2786bcd

[ "MIT" ]

null

""" WSGI config for interiorshop project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/4.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'interiorshop.settings') application = get_wsgi_application()

23.588235

78

0.790524

2f1de2da16fa30175c565c03eb9949989f6a6942

4,475

py

Python

tests/conftest.py

alvistack/ionrock-cachecontrol

09992a140edca2602cf8230370fc8b28566a069b

[ "Apache-2.0" ]

353

2015-01-03T11:07:43.000Z

2022-03-01T10:24:29.000Z

tests/conftest.py

alvistack/ionrock-cachecontrol

09992a140edca2602cf8230370fc8b28566a069b

[ "Apache-2.0" ]

190

2015-01-07T10:00:49.000Z

2022-02-23T19:04:03.000Z

tests/conftest.py

alvistack/ionrock-cachecontrol

09992a140edca2602cf8230370fc8b28566a069b

[ "Apache-2.0" ]

108

2015-01-05T19:17:33.000Z

2022-02-27T20:02:18.000Z

# SPDX-FileCopyrightText: 2015 Eric Larson # # SPDX-License-Identifier: Apache-2.0 from pprint import pformat import os import socket import pytest import cherrypy class SimpleApp(object): def __init__(self): self.etag_count = 0 self.update_etag_string() def dispatch(self, env): path = env["PATH_INFO"][1:].split("/") segment = path.pop(0) if segment and hasattr(self, segment): return getattr(self, segment) return None def optional_cacheable_request(self, env, start_response): """A request with no hints as to whether it should be cached. Yet, we might still choose to cache it via a heuristic.""" headers = [ ("server", "nginx/1.2.6 (Ubuntu)"), ("last-modified", "Mon, 21 Jul 2014 17:45:39 GMT"), ("content-type", "text/html"), ] start_response("200 OK", headers) return [pformat(env).encode("utf8")] def vary_accept(self, env, start_response): response = pformat(env).encode("utf8") headers = [ ("Cache-Control", "max-age=5000"), ("Content-Type", "text/plain"), ("Vary", "Accept-Encoding, Accept"), ] start_response("200 OK", headers) return [response] def update_etag_string(self): self.etag_count += 1 self.etag_string = '"ETAG-{}"'.format(self.etag_count) def update_etag(self, env, start_response): self.update_etag_string() headers = [("Cache-Control", "max-age=5000"), ("Content-Type", "text/plain")] start_response("200 OK", headers) return [pformat(env).encode("utf8")] def conditional_get(self, env, start_response): return start_response("304 Not Modified", []) def etag(self, env, start_response): headers = [("Etag", self.etag_string)] if env.get("HTTP_IF_NONE_MATCH") == self.etag_string: start_response("304 Not Modified", headers) return [] else: start_response("200 OK", headers) return [pformat(env).encode("utf8")] def cache_60(self, env, start_response): headers = [("Cache-Control", "public, max-age=60")] start_response("200 OK", headers) return [pformat(env).encode("utf8")] def no_cache(self, env, start_response): headers = [("Cache-Control", "no-cache")] start_response("200 OK", headers) return [pformat(env).encode("utf8")] def permanent_redirect(self, env, start_response): headers = [("Location", "/permalink")] start_response("301 Moved Permanently", headers) return ["See: /permalink".encode("utf-8")] def permalink(self, env, start_response): start_response("200 OK", [("Content-Type", "text/plain")]) return ["The permanent resource".encode("utf-8")] def multiple_choices(self, env, start_response): headers = [("Link", "/permalink")] start_response("300 Multiple Choices", headers) return ["See: /permalink".encode("utf-8")] def stream(self, env, start_response): headers = [("Content-Type", "text/plain"), ("Cache-Control", "max-age=5000")] start_response("200 OK", headers) for i in range(10): yield pformat(i).encode("utf8") def __call__(self, env, start_response): func = self.dispatch(env) if func: return func(env, start_response) headers = [("Cache-Control", "max-age=5000"), ("Content-Type", "text/plain")] start_response("200 OK", headers) return [pformat(env).encode("utf8")] @pytest.fixture(scope="session") def server(): return cherrypy.server @pytest.fixture() def url(server): return "http://%s:%s/" % server.bind_addr def get_free_port(): s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.bind(("", 0)) ip, port = s.getsockname() s.close() ip = os.environ.get("WEBTEST_SERVER_BIND", "127.0.0.1") return ip, port def pytest_configure(config): cherrypy.tree.graft(SimpleApp(), "/") ip, port = get_free_port() cherrypy.config.update({"server.socket_host": ip, "server.socket_port": port}) # turn off logging logger = cherrypy.log.access_log logger.removeHandler(logger.handlers[0]) cherrypy.server.start() def pytest_unconfigure(config): try: cherrypy.server.stop() except: pass

28.685897

85

0.608268

6a5ec02889d3e5098dc3073d5c220a9693f85494

8,403

py

Python

dashboard/apps/app1.py

gamyers/solar-697

90ca38072456af385c98b1bdf3c3d563e2c71f15

[ "MIT" ]

1

2021-08-24T00:00:23.000Z

dashboard/apps/app1.py

gamyers/solar-697

90ca38072456af385c98b1bdf3c3d563e2c71f15

[ "MIT" ]

null

dashboard/apps/app1.py

gamyers/solar-697

90ca38072456af385c98b1bdf3c3d563e2c71f15

[ "MIT" ]

2

2021-08-30T20:36:36.000Z

2021-11-02T19:13:33.000Z

import sqlite3 import sys import dash import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html import logzero import numpy as np import pandas as pd import plotly.graph_objects as go import yaml from app import app from dash.dependencies import Input, Output from dash_table import DataTable from logzero import logger sys.path.append("../source") import queries import plot_tools import ts_tools # open and retrieve configuration data try: with open("../source/config.yml", "r") as config_in: cfg = yaml.load(config_in, Loader=yaml.SafeLoader) logger.info(f"{cfg}\n") except: logger.error(f"config file open failure.") exit(1) db_path = cfg["file_paths"]["db_path"] db_files = ts_tools.get_db_files(db_path) logger.info(f"DB Path: {db_path}\n{db_files}\n") # --------------------------begin layout--------------------------# layout_app1 = html.Div( [ # Dropdown row 0 dbc.Row( [ dbc.Col( dcc.Dropdown( id="dd-db-selection", options=[{"label": db, "value": db} for db in db_files], value=cfg["file_names"]["default_db"], placeholder="Select a database", persistence=True, persistence_type="session", ), width={"size": 2, "offset": 0}, ), dbc.Col( [ dcc.Dropdown( id="dd-zipcode-selection", placeholder="Select a Zip Code", persistence=True, ), html.H5(id="dd-zipcode-selection-locale"), ], width={"size": 2, "offset": 1}, ), ], ), # Plots row 1 dbc.Row( [ dbc.Col( [ html.H6( "Data View", style={"display": "inline-block", "textAlign": "center"}, ), dcc.Graph(id="graph-data-view"), ], width={"size": 6}, ), dbc.Col( [ html.H6( "Distribution View", style={"display": "inline-block", "textAlign": "center"}, ), dcc.Graph(id="graph-dist-view"), ], width={"size": 5}, ), ], ), # Plots row 2 dbc.Row( [ dbc.Col( [ html.H6( "Meteorological View", style={"display": "inline-block", "textAlign": "center"}, ), dcc.Graph( id="graph-meteoro-view", ), ], width={"size": 6, "offset": 0}, ), dbc.Col( [ html.H6( "Desciptive Statistics", style={ "display": "inline-block", "textAlign": "center", }, ), DataTable( id="table-desc-stats", style_table={ "height": "395px", }, style_cell={ "backgroundColor": "black", "forgroundColor": "white", }, style_header={ "backgroundColor": "black", "forgroundColor": "white", "fontWeight": "bold", "fontColor": "gold", }, ), ], width={"size": 5}, ), ], ), ], ) # --------------------------begin callbacks--------------------------# @app.callback( Output("dd-zipcode-selection", "options"), Input("dd-db-selection", "value"), ) def get_zipcodes(file_name): logger.info(f"get_zipcodes callback: {file_name}") conn = ts_tools.get_db_connection(db_path, file_name) zipcodes = ts_tools.get_db_zipcodes(conn) conn.close() logger.info(f"app1 zipcodes retrieved\n{zipcodes}") # return the list object to properly populate the dropdown! return [{"label": zipcode, "value": zipcode} for zipcode in zipcodes] # -------------------------------------------------------------------# # @app.callback( # Output("dd-zipcode-selection", "value"), # [ # Input("dd-zipcode-selection", "options"), # ], # ) # def set_zipcode_value(options): # logger.info(f"app1 zipcode selected: {options[0]['value']}") # return options[0]["value"] # -------------------------------------------------------------------# @app.callback( # [ Output("graph-data-view", "figure"), Output("graph-dist-view", "figure"), Output("graph-meteoro-view", "figure"), Output("table-desc-stats", "data"), Output("table-desc-stats", "columns"), Output("dd-zipcode-selection-locale", "children"), # ------------------------------------- Input("dd-db-selection", "value"), Input("dd-zipcode-selection", "value"), ) def graph_output(db_filename, zipcode): cntx = dash.callback_context context = cntx.triggered[0]["prop_id"].split(".")[0] logger.info(f"app1 graph_output #1 Context = {context}\n") # print(f"app1 graph_output #1 Context: {context}") if context == "dd-db-selection": conn = ts_tools.get_db_connection(db_path, db_filename) # zipcodes = ts_tools.get_db_zipcodes(conn) # zipcode = zipcodes[0] locale_data = ts_tools.get_locale_data(conn, zipcode) df = ts_tools.get_irr_data(conn, zipcode) logger.info(f"app1 Made if: {db_filename}, {zipcode}") elif context == "dd-zipcode-selection": conn = ts_tools.get_db_connection(db_path, db_filename) locale_data = ts_tools.get_locale_data(conn, zipcode) df = ts_tools.get_irr_data(conn, zipcode) logger.info(f"app1 Made elif: {db_filename}, {zipcode}") else: db_filename = cfg["file_names"]["default_db"] conn = ts_tools.get_db_connection(db_path, db_filename) zipcodes = ts_tools.get_db_zipcodes(conn) if not zipcode: zipcode = zipcodes[0] locale_data = ts_tools.get_locale_data(conn, zipcode) df = ts_tools.get_irr_data(conn, zipcode) logger.info(f"app1 Made else: {db_filename}, {zipcode}") logger.info(f"app1 passed if/elif/else") df_desc = df.describe().transpose().round(decimals=2).reset_index(drop=False) df_desc.rename(columns={"index": "feature"}, inplace=True) df_desc.insert(loc=1, column="unit", value=[value for value in cfg["data_units"].values()]) desc_columns = [{"id": col, "name": col} for col in df_desc.columns] logger.info(f"app1 passed df_desc") title1 = "Irradiance Data" fig1 = plot_tools.plot_irradiance( df, title=title1, zipcode=zipcode, irr_columns=cfg["irradiance_columns"], locale=locale_data ) logger.info(f"app1 passed {title1}") title2 = "Data Distributions" fig2 = plot_tools.plot_histograms( df, title=title2, zipcode=zipcode, ) logger.info(f"app1 passed {title2}") title3 = "Meteorological Conditions" fig3 = plot_tools.plot_multi_line( df, title=title3, locale=locale_data, columns=cfg["meteorological_fields"], ) logger.info(f"app1 passed {title3}") return ( fig1, fig2, fig3, df_desc.to_dict("records"), desc_columns, f"{locale_data[0]}, {locale_data[2]}", )

32.952941

100

0.471855

9f159ed4cbe2e34fae811c65f4ab2181e34c476a

1,377

py

Python

bloomfilter.py

jetz/algorithms

1cf3a0864b4de06442457ab4a171f2eabfc2bb66

[ "MIT" ]

null

bloomfilter.py

jetz/algorithms

1cf3a0864b4de06442457ab4a171f2eabfc2bb66

[ "MIT" ]

null

bloomfilter.py

jetz/algorithms

1cf3a0864b4de06442457ab4a171f2eabfc2bb66

[ "MIT" ]

null

#!/usr/bin/env python # coding=utf-8 from BitVector import BitVector class BloomFilter(object): """ 布隆过滤器 """ def __init__(self, n): super(BloomFilter, self).__init__() self.n = n self.bits = BitVector(size=n) def __hashcodes(self, val): """ 供布隆过滤器使用的哈希函数 seed: 种子 val : 被哈希的字符串 """ results = [] seeds = [3, 5, 7, 11, 13, 31, 37, 61] for seed in seeds: result = 0 for v in val: result = seed * result + ord(v) results.append((self.n - 1) & result) return results def has(self, val): """ 根据哈希函数进行过滤，返回True或False 使用不同种子构建8个不同的哈希函数 """ hash_pos = self.__hashcodes(val) has = True for i in hash_pos: if not self.bits[i]: self.bits[i] = 1 has = False return has if __name__ == '__main__': bloomFilter = BloomFilter(1000000) examples = ['http://www.baidu.com', 'http://s1.bdstatic.com/r/www/img/i-1.0.0.png', 'http://s1.bdstatic.com/r/www/img/i-1.0.0.png', 'http://www.baidu.com/gaoji/preferences.html', 'http://news.baidu.com', 'http://news.baidu.com'] for e in examples: print bloomFilter.has(e)

25.981132

63

0.499637

306c5e21b421427000a6e1be2af450f7b02b3e8c

43,900

py

Python

evennia/evennia/utils/evmenu.py

MarsZone/DreamLand

87455f421c1ba09cb6efd5fc0882fbc2a29ea1a5

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

null

evennia/evennia/utils/evmenu.py

MarsZone/DreamLand

87455f421c1ba09cb6efd5fc0882fbc2a29ea1a5

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

null

evennia/evennia/utils/evmenu.py

MarsZone/DreamLand

87455f421c1ba09cb6efd5fc0882fbc2a29ea1a5

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

null

""" EvMenu This implements a full menu system for Evennia. It is considerably more flexible than the older contrib/menusystem.py and also uses menu plugin modules. To start the menu, just import the EvMenu class from this module. Example usage: ```python from evennia.utils.evmenu import EvMenu EvMenu(caller, menu_module_path, startnode="node1", cmdset_mergetype="Replace", cmdset_priority=1, auto_quit=True, cmd_on_exit="look", persistent=True) ``` Where `caller` is the Object to use the menu on - it will get a new cmdset while using the Menu. The menu_module_path is the python path to a python module containing function defintions. By adjusting the keyword options of the Menu() initialization call you can start the menu at different places in the menu definition file, adjust if the menu command should overload the normal commands or not, etc. The `perstent` keyword will make the menu survive a server reboot. It is `False` by default. Note that if using persistent mode, every node and callback in the menu must be possible to be *pickled*, this excludes e.g. callables that are class methods or functions defined dynamically or as part of another function. In non-persistent mode no such restrictions exist. The menu is defined in a module (this can be the same module as the command definition too) with function defintions: ```python def node1(caller): # (this is the start node if called like above) # code return text, options def node_with_other_name(caller, input_string): # code return text, options ``` Where caller is the object using the menu and input_string is the command entered by the user on the *previous* node (the command entered to get to this node). The node function code will only be executed once per node-visit and the system will accept nodes with both one or two arguments interchangeably. The menu tree itself is available on the caller as `caller.ndb._menutree`. This makes it a convenient place to store temporary state variables between nodes, since this NAttribute is deleted when the menu is exited. The return values must be given in the above order, but each can be returned as None as well. If the options are returned as None, the menu is immediately exited and the default "look" command is called. text (str, tuple or None): Text shown at this node. If a tuple, the second element in the tuple is a help text to display at this node when the user enters the menu help command there. options (tuple, dict or None): ( {'key': name, # can also be a list of aliases. A special key is # "_default", which marks this option as the default # fallback when no other option matches the user input. 'desc': description, # optional description 'goto': nodekey, # node to go to when chosen 'exec': nodekey}, # node or callback to trigger as callback when chosen. This # will execute *before* going to the next node. Both node # and the explicit callback will be called as normal nodes # (with caller and/or raw_string args). If the callable/node # returns a single string (only), this will replace the current # goto location string in-place. Note that relying to # much on letting exec assign the goto location can make it # hard to debug your menu logic. {...}, ...) If key is not given, the option will automatically be identified by its number 1..N. Example: ```python # in menu_module.py def node1(caller): text = ("This is a node text", "This is help text for this node") options = ({"key": "testing", "desc": "Select this to go to node 2", "goto": "node2", "exec": "callback1"}, {"desc": "Go to node 3.", "goto": "node3"}) return text, options def callback1(caller): # this is called when choosing the "testing" option in node1 # (before going to node2). If it returned a string, say 'node3', # then the next node would be node3 instead of node2 as specified # by the normal 'goto' option key above. caller.msg("Callback called!") def node2(caller): text = ''' This is node 2. It only allows you to go back to the original node1. This extra indent will be stripped. We don't include a help text. ''' options = {"goto": "node1"} return text, options def node3(caller): text = "This ends the menu since there are no options." return text, None ``` When starting this menu with `Menu(caller, "path.to.menu_module")`, the first node will look something like this: This is a node text ______________________________________ testing: Select this to go to node 2 2: Go to node 3 Where you can both enter "testing" and "1" to select the first option. If the client supports MXP, they may also mouse-click on "testing" to do the same. When making this selection, a function "callback1" in the same Using `help` will show the help text, otherwise a list of available commands while in menu mode. The menu tree is exited either by using the in-menu quit command or by reaching a node without any options. For a menu demo, import CmdTestMenu from this module and add it to your default cmdset. Run it with this module, like `testmenu evennia.utils.evmenu`. """ from __future__ import print_function from builtins import object, range from textwrap import dedent from inspect import isfunction, getargspec from django.conf import settings from evennia import Command, CmdSet from evennia.utils import logger from evennia.utils.evtable import EvTable from evennia.utils.ansi import strip_ansi from evennia.utils.utils import mod_import, make_iter, pad, m_len from evennia.commands import cmdhandler # read from protocol NAWS later? _MAX_TEXT_WIDTH = settings.CLIENT_DEFAULT_WIDTH # we use cmdhandler instead of evennia.syscmdkeys to # avoid some cases of loading before evennia init'd _CMD_NOMATCH = cmdhandler.CMD_NOMATCH _CMD_NOINPUT = cmdhandler.CMD_NOINPUT # Return messages # i18n from django.utils.translation import ugettext as _ _ERR_NOT_IMPLEMENTED = _("Menu node '{nodename}' is not implemented. Make another choice.") _ERR_GENERAL = _("Error in menu node '{nodename}'.") _ERR_NO_OPTION_DESC = _("No description.") _HELP_FULL = _("Commands: <menu option>, help, quit") _HELP_NO_QUIT = _("Commands: <menu option>, help") _HELP_NO_OPTIONS = _("Commands: help, quit") _HELP_NO_OPTIONS_NO_QUIT = _("Commands: help") _HELP_NO_OPTION_MATCH = _("Choose an option or try 'help'.") _ERROR_PERSISTENT_SAVING = \ """ {error} |rThe menu state could not be saved for persistent mode. Switching to non-persistent mode (which means the menu session won't survive an eventual server reload).|n """ _TRACE_PERSISTENT_SAVING = \ "EvMenu persistent-mode error. Commonly, this is because one or " \ "more of the EvEditor callbacks could not be pickled, for example " \ "because it's a class method or is defined inside another function." class EvMenuError(RuntimeError): """ Error raised by menu when facing internal errors. """ pass #------------------------------------------------------------ # # Menu command and command set # #------------------------------------------------------------ class CmdEvMenuNode(Command): """ Menu options. """ key = _CMD_NOINPUT aliases = [_CMD_NOMATCH] locks = "cmd:all()" help_category = "Menu" def func(self): """ Implement all menu commands. """ def _restore(caller): # check if there is a saved menu available. # this will re-start a completely new evmenu call. saved_options = caller.attributes.get("_menutree_saved") if saved_options: startnode_tuple = caller.attributes.get("_menutree_saved_startnode") try: startnode, startnode_input = startnode_tuple except ValueError: # old form of startnode stor startnode, startnode_input = startnode_tuple, "" if startnode: saved_options[1]["startnode"] = startnode saved_options[1]["startnode_input"] = startnode_input # this will create a completely new menu call EvMenu(caller, *saved_options[0], **saved_options[1]) return True caller = self.caller # we store Session on the menu since this can be hard to # get in multisession environemtns if caller is a Player. menu = caller.ndb._menutree if not menu: if _restore(caller): return orig_caller = caller caller = caller.player if hasattr(caller, "player") else None menu = caller.ndb._menutree if caller else None if not menu: if caller and _restore(caller): return caller = self.session menu = caller.ndb._menutree if not menu: # can't restore from a session err = "Menu object not found as %s.ndb._menutree!" % (orig_caller) orig_caller.msg(err) # don't give the session as a kwarg here, direct to original raise EvMenuError(err) # we must do this after the caller with the menui has been correctly identified since it # can be either Player, Object or Session (in the latter case this info will be superfluous). caller.ndb._menutree._session = self.session # we have a menu, use it. menu._input_parser(menu, self.raw_string, caller) class EvMenuCmdSet(CmdSet): """ The Menu cmdset replaces the current cmdset. """ key = "menu_cmdset" priority = 1 mergetype = "Replace" no_objs = True no_exits = True no_channels = False def at_cmdset_creation(self): """ Called when creating the set. """ self.add(CmdEvMenuNode()) # These are default node formatters def dedent_strip_nodetext_formatter(nodetext, has_options, caller=None): """ Simple dedent formatter that also strips text """ return dedent(nodetext).strip() def dedent_nodetext_formatter(nodetext, has_options, caller=None): """ Just dedent text. """ return dedent(nodetext) def evtable_options_formatter(optionlist, caller=None): """ Formats the option list display. """ if not optionlist: return "" # column separation distance colsep = 4 nlist = len(optionlist) # get the widest option line in the table. table_width_max = -1 table = [] for key, desc in optionlist: if not (key or desc): continue table_width_max = max(table_width_max, max(m_len(p) for p in key.split("\n")) + max(m_len(p) for p in desc.split("\n")) + colsep) raw_key = strip_ansi(key) if raw_key != key: # already decorations in key definition table.append(" |lc%s|lt%s|le: %s" % (raw_key, key, desc)) else: # add a default white color to key table.append(" |lc%s|lt|w%s|n|le: %s" % (raw_key, raw_key, desc)) ncols = (_MAX_TEXT_WIDTH // table_width_max) + 1 # number of ncols nlastcol = nlist % ncols # number of elements left in last row # get the amount of rows needed (start with 4 rows) nrows = 4 while nrows * ncols < nlist: nrows += 1 ncols = nlist // nrows # number of full columns nlastcol = nlist % nrows # number of elements in last column # get the final column count ncols = ncols + 1 if nlastcol > 0 else ncols if ncols > 1: # only extend if longer than one column table.extend([" " for i in range(nrows - nlastcol)]) # build the actual table grid table = [table[icol * nrows : (icol * nrows) + nrows] for icol in range(0, ncols)] # adjust the width of each column for icol in range(len(table)): col_width = max(max(m_len(p) for p in part.split("\n")) for part in table[icol]) + colsep table[icol] = [pad(part, width=col_width + colsep, align="l") for part in table[icol]] # format the table into columns return unicode(EvTable(table=table, border="none")) def underline_node_formatter(nodetext, optionstext, caller=None): """ Draws a node with underlines '_____' around it. """ nodetext_width_max = max(m_len(line) for line in nodetext.split("\n")) options_width_max = max(m_len(line) for line in optionstext.split("\n")) total_width = max(options_width_max, nodetext_width_max) separator1 = "_" * total_width + "\n\n" if nodetext_width_max else "" separator2 = "\n" + "_" * total_width + "\n\n" if total_width else "" return separator1 + "|n" + nodetext + "|n" + separator2 + "|n" + optionstext def null_node_formatter(nodetext, optionstext, caller=None): """ A minimalistic node formatter, no lines or frames. """ return nodetext + "\n\n" + optionstext def evtable_parse_input(menuobject, raw_string, caller): """ Processes the user's node inputs. Args: menuobject (EvMenu): The EvMenu instance raw_string (str): The incoming raw_string from the menu command. caller (Object, Player or Session): The entity using the menu. """ cmd = raw_string.strip().lower() if cmd in menuobject.options: # this will take precedence over the default commands # below goto, callback = menuobject.options[cmd] menuobject.callback_goto(callback, goto, raw_string) elif menuobject.auto_look and cmd in ("look", "l"): menuobject.display_nodetext() elif menuobject.auto_help and cmd in ("help", "h"): menuobject.display_helptext() elif menuobject.auto_quit and cmd in ("quit", "q", "exit"): menuobject.close_menu() elif menuobject.default: goto, callback = menuobject.default menuobject.callback_goto(callback, goto, raw_string) else: caller.msg(_HELP_NO_OPTION_MATCH, session=menuobject._session) if not (menuobject.options or menuobject.default): # no options - we are at the end of the menu. menuobject.close_menu() #------------------------------------------------------------ # # Menu main class # #------------------------------------------------------------ class EvMenu(object): """ This object represents an operational menu. It is initialized from a menufile.py instruction. """ def __init__(self, caller, menudata, startnode="start", cmdset_mergetype="Replace", cmdset_priority=1, auto_quit=True, auto_look=True, auto_help=True, cmd_on_exit="look", nodetext_formatter=dedent_strip_nodetext_formatter, options_formatter=evtable_options_formatter, node_formatter=underline_node_formatter, input_parser=evtable_parse_input, persistent=False, startnode_input="", session=None, **kwargs): """ Initialize the menu tree and start the caller onto the first node. Args: caller (Object, Player or Session): The user of the menu. menudata (str, module or dict): The full or relative path to the module holding the menu tree data. All global functions in this module whose name doesn't start with '_ ' will be parsed as menu nodes. Also the module itself is accepted as input. Finally, a dictionary menu tree can be given directly. This must then be a mapping `{"nodekey":callable,...}` where `callable` must be called as and return the data expected of a menu node. This allows for dynamic menu creation. startnode (str, optional): The starting node name in the menufile. cmdset_mergetype (str, optional): 'Replace' (default) means the menu commands will be exclusive - no other normal commands will be usable while the user is in the menu. 'Union' means the menu commands will be integrated with the existing commands (it will merge with `merge_priority`), if so, make sure that the menu's command names don't collide with existing commands in an unexpected way. Also the CMD_NOMATCH and CMD_NOINPUT will be overloaded by the menu cmdset. Other cmdser mergetypes has little purpose for the menu. cmdset_priority (int, optional): The merge priority for the menu command set. The default (1) is usually enough for most types of menus. auto_quit (bool, optional): Allow user to use "q", "quit" or "exit" to leave the menu at any point. Recommended during development! auto_look (bool, optional): Automatically make "looK" or "l" to re-show the last node. Turning this off means you have to handle re-showing nodes yourself, but may be useful if you need to use "l" for some other purpose. auto_help (bool, optional): Automatically make "help" or "h" show the current help entry for the node. If turned off, eventual help must be handled manually, but it may be useful if you need 'h' for some other purpose, for example. cmd_on_exit (callable, str or None, optional): When exiting the menu (either by reaching a node with no options or by using the in-built quit command (activated with `allow_quit`), this callback function or command string will be executed. The callback function takes two parameters, the caller then the EvMenu object. This is called after cleanup is complete. Set to None to not call any command. nodetext_formatter (callable, optional): This callable should be on the form `function(nodetext, has_options, caller=None)`, where `nodetext` is the node text string and `has_options` a boolean specifying if there are options associated with this node. It must return a formatted string. `caller` is optionally a reference to the user of the menu. `caller` is optionally a reference to the user of the menu. options_formatter (callable, optional): This callable should be on the form `function(optionlist, caller=None)`, where ` optionlist is a list of option dictionaries, like [{"key":..., "desc",..., "goto": ..., "exec",...}, ...] Each dictionary describes each possible option. Note that this will also be called if there are no options, and so should be able to handle an empty list. This should be formatted into an options list and returned as a string, including the required separator to use between the node text and the options. If not given the default EvMenu style will be used. `caller` is optionally a reference to the user of the menu. node_formatter (callable, optional): This callable should be on the form `func(nodetext, optionstext, caller=None)` where the arguments are strings representing the node text and options respectively (possibly prepared by `nodetext_formatter`/`options_formatter` or by the default styles). It should return a string representing the final look of the node. This can e.g. be used to create line separators that take into account the dynamic width of the parts. `caller` is optionally a reference to the user of the menu. input_parser (callable, optional): This callable is responsible for parsing the options dict from a node and has the form `func(menuobject, raw_string, caller)`, where menuobject is the active `EvMenu` instance, `input_string` is the incoming text from the caller and `caller` is the user of the menu. It should use the helper method of the menuobject to goto new nodes, show help texts etc. See the default `evtable_parse_input` function for help with parsing. persistent (bool, optional): Make the Menu persistent (i.e. it will survive a reload. This will make the Menu cmdset persistent. Use with caution - if your menu is buggy you may end up in a state you can't get out of! Also note that persistent mode requires that all formatters, menu nodes and callables are possible to *pickle*. When the server is reloaded, the latest node shown will be completely re-run with the same input arguments - so be careful if you are counting up some persistent counter or similar - the counter may be run twice if reload happens on the node that does that. startnode_input (str, optional): Send an input text to `startnode` as if a user input text from a fictional previous node. When the server reloads, the latest visited node will be re-run using this kwarg. session (Session, optional): This is useful when calling EvMenu from a player in multisession mode > 2. Note that this session only really relevant for the very first display of the first node - after that, EvMenu itself will keep the session updated from the command input. So a persistent menu will *not* be using this same session anymore after a reload. Kwargs: any (any): All kwargs will become initialization variables on `caller.ndb._menutree`, to be available at run. Raises: EvMenuError: If the start/end node is not found in menu tree. Notes: While running, the menu is stored on the caller as `caller.ndb._menutree`. Also the current Session (from the Command, so this is still valid in multisession environments) is available through `caller.ndb._menutree._session`. The `_menutree` property is a good one for storing intermediary data on between nodes since it will be automatically deleted when the menu closes. In persistent mode, all nodes, formatters and callbacks in the menu must be possible to be *pickled*, this excludes e.g. callables that are class methods or functions defined dynamically or as part of another function. In non-persistent mode no such restrictions exist. """ self._startnode = startnode self._menutree = self._parse_menudata(menudata) self._nodetext_formatter = nodetext_formatter self._options_formatter = options_formatter self._node_formatter = node_formatter self._input_parser = input_parser self._persistent = persistent if startnode not in self._menutree: raise EvMenuError("Start node '%s' not in menu tree!" % startnode) # public variables made available to the command self.caller = caller self.auto_quit = auto_quit self.auto_look = auto_look self.auto_help = auto_help self._session = session if isinstance(cmd_on_exit, str): # At this point menu._session will have been replaced by the # menu command to the actual session calling. self.cmd_on_exit = lambda caller, menu: caller.execute_cmd(cmd_on_exit, session=menu._session) elif callable(cmd_on_exit): self.cmd_on_exit = cmd_on_exit else: self.cmd_on_exit = None self.default = None self.nodetext = None self.helptext = None self.options = None # assign kwargs as initialization vars on ourselves. if set(("_startnode", "_menutree", "_nodetext_formatter", "_options_formatter", "node_formatter", "_input_parser", "_peristent", "cmd_on_exit", "default", "nodetext", "helptext", "options")).intersection(set(kwargs.keys())): raise RuntimeError("One or more of the EvMenu `**kwargs` is reserved by EvMenu for internal use.") for key, val in kwargs.iteritems(): setattr(self, key, val) # store ourself on the object self.caller.ndb._menutree = self if persistent: # save the menu to the database try: caller.attributes.add("_menutree_saved", ((menudata, ), {"startnode": startnode, "cmdset_mergetype": cmdset_mergetype, "cmdset_priority": cmdset_priority, "auto_quit": auto_quit, "auto_look": auto_look, "auto_help": auto_help, "cmd_on_exit": cmd_on_exit, "nodetext_formatter": nodetext_formatter, "options_formatter": options_formatter, "node_formatter": node_formatter, "input_parser": input_parser, "persistent": persistent,})) caller.attributes.add("_menutree_saved_startnode", (startnode, startnode_input)) except Exception as err: caller.msg(_ERROR_PERSISTENT_SAVING.format(error=err), session=self._session) logger.log_trace(_TRACE_PERSISTENT_SAVING) persistent = False # set up the menu command on the caller menu_cmdset = EvMenuCmdSet() menu_cmdset.mergetype = str(cmdset_mergetype).lower().capitalize() or "Replace" menu_cmdset.priority = int(cmdset_priority) self.caller.cmdset.add(menu_cmdset, permanent=persistent) # start the menu self.goto(self._startnode, startnode_input) def _parse_menudata(self, menudata): """ Parse a menufile for node functions and store in dictionary map. Alternatively, accept a pre-made mapping dictionary of node functions. Args: menudata (str, module or dict): The python.path to the menufile, or the python module itself. If a dict, this should be a mapping nodename:callable, where the callable must match the criteria for a menu node. Returns: menutree (dict): A {nodekey: func} """ if isinstance(menudata, dict): # This is assumed to be a pre-loaded menu tree. return menudata else: # a python path of a module module = mod_import(menudata) return dict((key, func) for key, func in module.__dict__.items() if isfunction(func) and not key.startswith("_")) def _format_node(self, nodetext, optionlist): """ Format the node text + option section Args: nodetext (str): The node text optionlist (list): List of (key, desc) pairs. Returns: string (str): The options section, including all needed spaces. Notes: This will adjust the columns of the options, first to use a maxiumum of 4 rows (expanding in columns), then gradually growing to make use of the screen space. """ # handle the node text nodetext = self._nodetext_formatter(nodetext, len(optionlist), self.caller) # handle the options optionstext = self._options_formatter(optionlist, self.caller) # format the entire node return self._node_formatter(nodetext, optionstext, self.caller) def _execute_node(self, nodename, raw_string): """ Execute a node. Args: nodename (str): Name of node. raw_string (str): The raw default string entered on the previous node (only used if the node accepts it as an argument) Returns: nodetext, options (tuple): The node text (a string or a tuple and the options tuple, if any. """ try: node = self._menutree[nodename] except KeyError: self.caller.msg(_ERR_NOT_IMPLEMENTED.format(nodename=nodename), session=self._session) raise EvMenuError try: # the node should return data as (text, options) if len(getargspec(node).args) > 1: # a node accepting raw_string nodetext, options = node(self.caller, raw_string) else: # a normal node, only accepting caller nodetext, options = node(self.caller) except KeyError: self.caller.msg(_ERR_NOT_IMPLEMENTED.format(nodename=nodename), session=self._session) raise EvMenuError except Exception: self.caller.msg(_ERR_GENERAL.format(nodename=nodename), session=self._session) raise return nodetext, options def display_nodetext(self): self.caller.msg(self.nodetext, session=self._session) def display_helptext(self): self.caller.msg(self.helptext, session=self._session) def callback_goto(self, callback, goto, raw_string): """ Call callback and goto in sequence. Args: callback (callable or str): Callback to run before goto. If the callback returns a string, this is used to replace the `goto` string before going to the next node. goto (str): The target node to go to next (unless replaced by `callable`).. raw_string (str): The original user input. """ if callback: # replace goto only if callback returns goto = self.callback(callback, raw_string) or goto if goto: self.goto(goto, raw_string) def callback(self, nodename, raw_string): """ Run a function or node as a callback (with the 'exec' option key). Args: nodename (callable or str): A callable to run as `callable(caller, raw_string)`, or the Name of an existing node to run as a callable. This may or may not return a string. raw_string (str): The raw default string entered on the previous node (only used if the node accepts it as an argument) Returns: new_goto (str or None): A replacement goto location string or None (no replacement). Notes: Relying on exec callbacks to set the goto location is very powerful but will easily lead to spaghetti structure and hard-to-trace paths through the menu logic. So be careful with relying on this. """ if callable(nodename): # this is a direct callable - execute it directly try: if len(getargspec(nodename).args) > 1: # callable accepting raw_string ret = nodename(self.caller, raw_string) else: # normal callable, only the caller as arg ret = nodename(self.caller) except Exception: self.caller.msg(_ERR_GENERAL.format(nodename=nodename), self._session) raise else: # nodename is a string; lookup as node try: # execute the node ret = self._execute_node(nodename, raw_string) except EvMenuError: return if isinstance(ret, basestring): # only return a value if a string (a goto target), ignore all other returns return ret def goto(self, nodename, raw_string): """ Run a node by name Args: nodename (str): Name of node. raw_string (str): The raw default string entered on the previous node (only used if the node accepts it as an argument) """ try: # execute the node, make use of the returns. nodetext, options = self._execute_node(nodename, raw_string) except EvMenuError: return if self._persistent: self.caller.attributes.add("_menutree_saved_startnode", (nodename, raw_string)) # validation of the node return values helptext = "" if hasattr(nodetext, "__iter__"): if len(nodetext) > 1: nodetext, helptext = nodetext[:2] else: nodetext = nodetext[0] nodetext = "" if nodetext is None else str(nodetext) options = [options] if isinstance(options, dict) else options # this will be displayed in the given order display_options = [] # this is used for lookup self.options = {} self.default = None if options: for inum, dic in enumerate(options): # fix up the option dicts keys = make_iter(dic.get("key")) if "_default" in keys: keys = [key for key in keys if key != "_default"] desc = dic.get("desc", dic.get("text", _ERR_NO_OPTION_DESC).strip()) goto, execute = dic.get("goto", None), dic.get("exec", None) self.default = (goto, execute) else: keys = list(make_iter(dic.get("key", str(inum+1).strip()))) desc = dic.get("desc", dic.get("text", _ERR_NO_OPTION_DESC).strip()) goto, execute = dic.get("goto", None), dic.get("exec", None) if keys: display_options.append((keys[0], desc)) for key in keys: if goto or execute: self.options[strip_ansi(key).strip().lower()] = (goto, execute) self.nodetext = self._format_node(nodetext, display_options) # handle the helptext if helptext: self.helptext = helptext elif options: self.helptext = _HELP_FULL if self.auto_quit else _HELP_NO_QUIT else: self.helptext = _HELP_NO_OPTIONS if self.auto_quit else _HELP_NO_OPTIONS_NO_QUIT self.display_nodetext() def close_menu(self): """ Shutdown menu; occurs when reaching the end node or using the quit command. """ self.caller.cmdset.remove(EvMenuCmdSet) del self.caller.ndb._menutree if self._persistent: self.caller.attributes.remove("_menutree_saved") self.caller.attributes.remove("_menutree_saved_startnode") if self.cmd_on_exit is not None: self.cmd_on_exit(self.caller, self) # ------------------------------------------------------------------------------------------------- # # Simple input shortcuts # # ------------------------------------------------------------------------------------------------- class CmdGetInput(Command): """ Enter your data and press return. """ key = _CMD_NOMATCH aliases = _CMD_NOINPUT def func(self): "This is called when user enters anything." caller = self.caller callback = caller.ndb._getinput._callback if not callback: # this can be happen if called from a player-command when IC caller = self.player callback = caller.ndb._getinput._callback if not callback: raise RuntimeError("No input callback found.") caller.ndb._getinput._session = self.session prompt = caller.ndb._getinput._prompt result = self.raw_string.strip() # we strip the ending line break caused by sending ok = not callback(caller, prompt, result) if ok: # only clear the state if the callback does not return # anything del caller.ndb._getinput caller.cmdset.remove(InputCmdSet) class InputCmdSet(CmdSet): """ This stores the input command """ key = "input_cmdset" priority = 1 mergetype = "Replace" no_objs = True no_exits = True no_channels = False def at_cmdset_creation(self): "called once at creation" self.add(CmdGetInput()) class _Prompt(object): "Dummy holder" pass def get_input(caller, prompt, callback, session=None): """ This is a helper function for easily request input from the caller. Args: caller (Player or Object): The entity being asked the question. This should usually be an object controlled by a user. prompt (str): This text will be shown to the user, in order to let them know their input is needed. callback (callable): A function that will be called when the user enters a reply. It must take three arguments: the `caller`, the `prompt` text and the `result` of the input given by the user. If the callback doesn't return anything or return False, the input prompt will be cleaned up and exited. If returning True, the prompt will remain and continue to accept input. session (Session, optional): This allows to specify the session to send the prompt to. It's usually only needed if `caller` is a Player in multisession modes greater than 2. The session is then updated by the command and is available (for example in callbacks) through `caller.ndb.getinput._session`. Raises: RuntimeError: If the given callback is not callable. Notes: The result value sent to the callback is raw and not processed in any way. This means that you will get the ending line return character from most types of client inputs. So make sure to strip that before doing a comparison. When the prompt is running, a temporary object `caller.ndb._getinput` is stored; this will be removed when the prompt finishes. If you need the specific Session of the caller (which may not be easy to get if caller is a player in higher multisession modes), then it is available in the callback through `caller.ndb._getinput._session`. """ if not callable(callback): raise RuntimeError("get_input: input callback is not callable.") caller.ndb._getinput = _Prompt() caller.ndb._getinput._callback = callback caller.ndb._getinput._prompt = prompt caller.ndb._getinput._session = session caller.cmdset.add(InputCmdSet) caller.msg(prompt, session=session) #------------------------------------------------------------ # # test menu strucure and testing command # #------------------------------------------------------------ def test_start_node(caller): menu = caller.ndb._menutree text = """ This is an example menu. If you enter anything except the valid options, your input will be recorded and you will be brought to a menu entry showing your input. Select options or use 'quit' to exit the menu. The menu was initialized with two variables: %s and %s. """ % (menu.testval, menu.testval2) options = ({"key": ("{yS{net", "s"), "desc": "Set an attribute on yourself.", "exec": lambda caller: caller.attributes.add("menuattrtest", "Test value"), "goto": "test_set_node"}, {"key": ("{yL{nook", "l"), "desc": "Look and see a custom message.", "goto": "test_look_node"}, {"key": ("{yV{niew", "v"), "desc": "View your own name", "goto": "test_view_node"}, {"key": ("{yQ{nuit", "quit", "q", "Q"), "desc": "Quit this menu example.", "goto": "test_end_node"}, {"key": "_default", "goto": "test_displayinput_node"}) return text, options def test_look_node(caller): text = "" options = {"key": ("{yL{nook", "l"), "desc": "Go back to the previous menu.", "goto": "test_start_node"} return text, options def test_set_node(caller): text = (""" The attribute 'menuattrtest' was set to {w%s{n (check it with examine after quitting the menu). This node's has only one option, and one of its key aliases is the string "_default", meaning it will catch any input, in this case to return to the main menu. So you can e.g. press <return> to go back now. """ % caller.db.menuattrtest, # optional help text for this node """ This is the help entry for this node. It is created by returning the node text as a tuple - the second string in that tuple will be used as the help text. """) options = {"key": ("back (default)", "_default"), "desc": "back to main", "goto": "test_start_node"} return text, options def test_view_node(caller): text = """ Your name is {g%s{n! click |lclook|lthere|le to trigger a look command under MXP. This node's option has no explicit key (nor the "_default" key set), and so gets assigned a number automatically. You can infact -always- use numbers (1...N) to refer to listed options also if you don't see a string option key (try it!). """ % caller.key options = {"desc": "back to main", "goto": "test_start_node"} return text, options def test_displayinput_node(caller, raw_string): text = """ You entered the text: "{w%s{n" ... which could now be handled or stored here in some way if this was not just an example. This node has an option with a single alias "_default", which makes it hidden from view. It catches all input (except the in-menu help/quit commands) and will, in this case, bring you back to the start node. """ % raw_string options = {"key": "_default", "goto": "test_start_node"} return text, options def test_end_node(caller): text = """ This is the end of the menu and since it has no options the menu will exit here, followed by a call of the "look" command. """ return text, None class CmdTestMenu(Command): """ Test menu Usage: testmenu <menumodule> Starts a demo menu from a menu node definition module. """ key = "testmenu" def func(self): if not self.args: self.caller.msg("Usage: testmenu menumodule") return # start menu EvMenu(self.caller, self.args.strip(), startnode="test_start_node", persistent=True, cmdset_mergetype="Replace", testval="val", testval2="val2")

39.65673

120

0.611435

ebee1b0a7566e5214889d60be92f3600253130b4

7,951

py

Python

src/parsec/configs-mesi-two-level/run_parsec_mesi_two_level.py

my569/gem5-resources

5788f1394a1894efec8e4784d37f473a743aa9f6

[ "MIT" ]

1

2022-01-04T03:41:23.000Z

src/parsec/configs-mesi-two-level/run_parsec_mesi_two_level.py

my569/gem5-resources

5788f1394a1894efec8e4784d37f473a743aa9f6

[ "MIT" ]

null

src/parsec/configs-mesi-two-level/run_parsec_mesi_two_level.py

my569/gem5-resources

5788f1394a1894efec8e4784d37f473a743aa9f6

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2019 The Regents of the University of California. # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer; # redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution; # neither the name of the copyright holders 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. # """ Script to run PARSEC benchmarks with gem5. The memory model used in the experiments is Ruby and uses MESEI_Two_Level protocolx. The script expects kernel, diskimage, cpu (kvm or timing), benchmark, benchmark size, and number of cpu cores as arguments. This script is best used if your disk-image has workloads tha have ROI annotations compliant with m5 utility. You can use the script in ../disk-images/parsec/ with the parsec-benchmark repo at https://github.com/darchr/parsec-benchmark.git to create a working disk-image for this script. """ import errno import os import sys import time import m5 import m5.ticks from m5.objects import * sys.path.append('gem5/configs/common/') # For the next line... import SimpleOpts from system import * def writeBenchScript(dir, bench, size, num_cpus): """ This method creates a script in dir which will be eventually passed to the simulated system (to run a specific benchmark at bootup). """ file_name = '{}/run_{}'.format(dir, bench) bench_file = open(file_name, 'w+') bench_file.write('cd /home/gem5/parsec-benchmark\n') bench_file.write('source env.sh\n') bench_file.write('parsecmgmt -a run -p \ {} -c gcc-hooks -i {} -n {}\n'.format(bench, size, num_cpus)) # sleeping for sometime makes sure # that the benchmark's output has been # printed to the console bench_file.write('sleep 5 \n') bench_file.write('m5 exit \n') bench_file.close() return file_name if __name__ == "__m5_main__": (opts, args) = SimpleOpts.parse_args() kernel, disk, cpu, benchmark, size, num_cpus = args if not cpu in ['kvm', 'timing']: m5.fatal("cpu not supported") # create the system we are going to simulate system = MyRubySystem(kernel, disk, int(num_cpus), opts) # Exit from guest on workbegin/workend system.exit_on_work_items = True # Create and pass a script to the simulated system to run the reuired # benchmark system.readfile = writeBenchScript(m5.options.outdir, benchmark, size, num_cpus) # set up the root SimObject and start the simulation root = Root(full_system = True, system = system) if system.getHostParallel(): # Required for running kvm on multiple host cores. # Uses gem5's parallel event queue feature # Note: The simulator is quite picky about this number! root.sim_quantum = int(1e9) # 1 ms #needed for long running jobs m5.disableAllListeners() # instantiate all of the objects we've created above m5.instantiate() globalStart = time.time() print("Running the simulation") print("Using cpu: {}".format(cpu)) start_tick = m5.curTick() end_tick = m5.curTick() start_insts = system.totalInsts() end_insts = system.totalInsts() m5.stats.reset() exit_event = m5.simulate() if exit_event.getCause() == "workbegin": # Reached the start of ROI # start of ROI is marked by an # m5_work_begin() call print("Resetting stats at the start of ROI!") m5.stats.reset() start_tick = m5.curTick() start_insts = system.totalInsts() # switching to timing cpu if argument cpu == timing if cpu == 'timing': system.switchCpus(system.cpu, system.timingCpu) else: print("Unexpected termination of simulation!") print() m5.stats.dump() end_tick = m5.curTick() end_insts = system.totalInsts() m5.stats.reset() print("Performance statistics:") print("Simulated time: %.2fs" % ((end_tick-start_tick)/1e12)) print("Instructions executed: %d" % ((end_insts-start_insts))) print("Ran a total of", m5.curTick()/1e12, "simulated seconds") print("Total wallclock time: %.2fs, %.2f min" % \ (time.time()-globalStart, (time.time()-globalStart)/60)) exit() # Simulate the ROI exit_event = m5.simulate() # Reached the end of ROI # Finish executing the benchmark with kvm cpu if exit_event.getCause() == "workend": # Reached the end of ROI # end of ROI is marked by an # m5_work_end() call print("Dump stats at the end of the ROI!") m5.stats.dump() end_tick = m5.curTick() end_insts = system.totalInsts() m5.stats.reset() # switching to timing cpu if argument cpu == timing if cpu == 'timing': # This line is commented due to an unimplemented # flush request in MESI_Two_Level that results in # the crashing of simulation. There will be a patch # fixing this issue but the line is commented out # for now. # system.switchCpus(system.timingCpu, system.cpu) print("Performance statistics:") print("Simulated time: %.2fs" % ((end_tick-start_tick)/1e12)) print("Instructions executed: %d" % ((end_insts-start_insts))) print("Ran a total of", m5.curTick()/1e12, "simulated seconds") print("Total wallclock time: %.2fs, %.2f min" % \ (time.time()-globalStart, (time.time()-globalStart)/60)) exit() else: print("Unexpected termination of simulation!") print() m5.stats.dump() end_tick = m5.curTick() end_insts = system.totalInsts() m5.stats.reset() print("Performance statistics:") print("Simulated time: %.2fs" % ((end_tick-start_tick)/1e12)) print("Instructions executed: %d" % ((end_insts-start_insts))) print("Ran a total of", m5.curTick()/1e12, "simulated seconds") print("Total wallclock time: %.2fs, %.2f min" % \ (time.time()-globalStart, (time.time()-globalStart)/60)) exit() # Simulate the remaning part of the benchmark exit_event = m5.simulate() print("Done with the simulation") print() print("Performance statistics:") print("Simulated time in ROI: %.2fs" % ((end_tick-start_tick)/1e12)) print("Instructions executed in ROI: %d" % ((end_insts-start_insts))) print("Ran a total of", m5.curTick()/1e12, "simulated seconds") print("Total wallclock time: %.2fs, %.2f min" % \ (time.time()-globalStart, (time.time()-globalStart)/60))

38.97549

84

0.664319

9aab08fbd44542df04653a9aaebf08ab7e755cf4

666

py

Python

tests/test_ekey.py

shatgupt/django-encrypted-id

4fcbc66cd2b7d3489e73357efbf3db61feaf37a2

[ "BSD-2-Clause" ]

31

2016-05-30T19:28:25.000Z

2021-11-10T01:33:12.000Z

tests/test_ekey.py

shatgupt/django-encrypted-id

4fcbc66cd2b7d3489e73357efbf3db61feaf37a2

[ "BSD-2-Clause" ]

21

2016-05-30T11:44:24.000Z

2020-10-22T08:12:21.000Z

tests/test_ekey.py

wittfabian/django-encrypted-id

9c15b114a0d1459f6d68de3815e5f372e197078b

[ "BSD-2-Clause" ]

18

2016-05-11T17:57:35.000Z

2022-03-23T14:53:10.000Z

# -*- coding: utf-8 -*- from __future__ import division from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import pytest from django.http import Http404 from encrypted_id import ekey, get_object_or_404 from tapp.models import Foo def test_ekey(db): assert db is db foo = Foo.objects.create(text="asd") assert ekey(foo) == foo.ekey assert foo == get_object_or_404(Foo, foo.ekey) def test_allow_none_ekey(db): assert db is db with pytest.raises(Http404): get_object_or_404(Foo, None) with pytest.raises(Foo.DoesNotExist): Foo.objects.get(ekey=None)

22.2

50

0.738739

20abaf05368fe7f54f1946da0911b5baed5a7122

2,295

py

Python

tests/test_examples.py

brentyi/jaxlie

4dbe16f3c1d1cfda30e0418ef5d1e1772cf9f537

[ "MIT" ]

128

2020-11-28T19:43:31.000Z

2022-03-14T11:48:12.000Z

tests/test_examples.py

brentyi/jaxlie

4dbe16f3c1d1cfda30e0418ef5d1e1772cf9f537

[ "MIT" ]

4

2021-06-27T09:04:54.000Z

2022-01-07T07:23:52.000Z

tests/test_examples.py

brentyi/jaxlie

4dbe16f3c1d1cfda30e0418ef5d1e1772cf9f537

[ "MIT" ]

7

2021-01-17T10:04:39.000Z

2022-01-06T21:22:15.000Z

"""Tests with explicit examples. """ import numpy as onp from hypothesis import given, settings from hypothesis import strategies as st from utils import assert_arrays_close, assert_transforms_close, sample_transform import jaxlie @settings(deadline=None) @given(_random_module=st.random_module()) def test_se2_translation(_random_module): """Simple test for SE(2) translation terms.""" translation = onp.random.randn(2) T = jaxlie.SE2.from_xy_theta(*translation, theta=0.0) assert_arrays_close(T @ translation, translation * 2) @settings(deadline=None) @given(_random_module=st.random_module()) def test_se3_translation(_random_module): """Simple test for SE(3) translation terms.""" translation = onp.random.randn(3) T = jaxlie.SE3.from_rotation_and_translation( rotation=jaxlie.SO3.identity(), translation=translation, ) assert_arrays_close(T @ translation, translation * 2) def test_se2_rotation(): """Simple test for SE(2) rotation terms.""" T_w_b = jaxlie.SE2.from_rotation_and_translation( rotation=jaxlie.SO2.from_radians(onp.pi / 2.0), translation=onp.zeros(2), ) p_b = onp.array([1.0, 0.0]) p_w = onp.array([0.0, 1.0]) assert_arrays_close(T_w_b @ p_b, p_w) def test_se3_rotation(): """Simple test for SE(3) rotation terms.""" T_w_b = jaxlie.SE3.from_rotation_and_translation( rotation=jaxlie.SO3.from_rpy_radians(onp.pi / 2.0, 0.0, 0.0), translation=onp.zeros(3), ) p_b = onp.array([0.0, 1.0, 0.0]) p_w = onp.array([0.0, 0.0, 1.0]) assert_arrays_close(T_w_b @ p_b, p_w) def test_so3_xyzw_basic(): """Check that we can create an SO3 object from an xyzw quaternion.""" assert_transforms_close( jaxlie.SO3.from_quaternion_xyzw(onp.array([0, 0, 0, 1])), jaxlie.SO3.identity(), ) @settings(deadline=None) @given(_random_module=st.random_module()) def test_se3_compose(_random_module): """Compare SE3 composition in matrix form vs compact form.""" T1 = sample_transform(jaxlie.SE3) T2 = sample_transform(jaxlie.SE3) assert_arrays_close(T1.as_matrix() @ T2.as_matrix(), (T1 @ T2).as_matrix()) assert_transforms_close( jaxlie.SE3.from_matrix(T1.as_matrix() @ T2.as_matrix()), T1 @ T2 )

31.013514

80

0.695425

0b644d1d60f7b06b5e5b3416bfc00a3e0dacde3d

495

py

Python

Python/calendar_fun.py

ArmstrongYang/StudyShare

6dffcfba6811865589a8e11748ffc7e71a656f50

[ "Apache-2.0" ]

2

2017-08-10T13:41:19.000Z

2017-11-30T09:00:33.000Z

Python/calendar_fun.py

ArmstrongYang/StudyShare

6dffcfba6811865589a8e11748ffc7e71a656f50

[ "Apache-2.0" ]

null

Python/calendar_fun.py

ArmstrongYang/StudyShare

6dffcfba6811865589a8e11748ffc7e71a656f50

[ "Apache-2.0" ]

null

#!/usr/bin/python3 #! /usr/bin/python # coding=utf-8 import calendar """ 返回的某个月的日历返回类型是字符串型 """ cal = calendar.month(2011, 11) """ 返回一年的日历 """ cal = calendar.calendar(2011) cal = calendar.HTMLCalendar(calendar.MONDAY) """ 打印出一个月日历 """ cal.formatmonth(2011, 11) """ 打印出一年的日历 formatyearpage将生成完整的页面代码 """ print (cal.formatyear(2017)) cal.formatyearpage(2017) """ 默认每周的第一天是星期一，这里修改成星期天 """ calendar.setfirstweekday(calendar.SUNDAY) if __name__=='__main__': print(__file__) exit(0)

12.375

44

0.711111

ec783959fa263d5603428b8ed2e86b9f71992fdb

45,218

py

Python

xnu-2782.1.97/tools/lldbmacros/ipc.py

LeeWongSnail/SourceCode_ReadingNote

b6bbf99a5fef6a087e053f6dfcc7f12691961ba6

[ "MIT" ]

null

xnu-2782.1.97/tools/lldbmacros/ipc.py

LeeWongSnail/SourceCode_ReadingNote

b6bbf99a5fef6a087e053f6dfcc7f12691961ba6

[ "MIT" ]

null

xnu-2782.1.97/tools/lldbmacros/ipc.py

LeeWongSnail/SourceCode_ReadingNote

b6bbf99a5fef6a087e053f6dfcc7f12691961ba6

[ "MIT" ]

1

2021-03-28T02:56:16.000Z

""" Please make sure you read the README file COMPLETELY BEFORE reading anything below. It is very critical that you read coding guidelines in Section E in README file. """ from xnu import * import sys, shlex from utils import * from process import * from atm import * from bank import * import xnudefines @header("{0: <20s} {1: <6s} {2: <6s} {3: <10s} {4: <15s}".format("task", "pid", '#acts', "tablesize", "command")) def GetTaskIPCSummary(task): """ Display a task's ipc summary. params: task : core.value represeting a Task in kernel returns str - string of ipc info for the task """ out_string = '' format_string = "{0: <#020x} {1: <6d} {2: <6d} {3: <10d} {4: <15s}" pval = Cast(task.bsd_info, 'proc *') table_size = int(task.itk_space.is_table_size) proc_name = str(pval.p_comm) out_string += format_string.format(task, pval.p_pid, task.thread_count, table_size, proc_name) return out_string @header("{0: <20s} {1: <28s} {2: <12s} {3: <6s} {4: <4s} {5: <20s} {6: <4s}\n".format( "port", "mqueue", "recvname", "flags", "refs", "recvname", "dest")) def GetPortSummary(port, show_kmsg_summary=True, prefix=""): """ Display a port's summary params: port : core.value representing a port in the kernel returns str : string of ipc info for the given port """ out_string = "" portp = Cast(port, 'struct ipc_port *') destspacep = kern.GetValueFromAddress(0, 'struct ipc_space *') spacep = portp.data.receiver format_string = "{0: #019x} {1: #019x} {2: <8s} {3: #011x} {4: <5s} {5: #05x} {6: #019x} {7: <16s}\n" if portp.ip_object.io_bits & 0x80000000: out_string += prefix + format_string.format( unsigned(portp), addressof(portp.ip_messages), ' '*8, unsigned(portp.ip_messages.data.port.receiver_name), "APort", portp.ip_object.io_references, unsigned(portp.ip_messages.data.port.receiver_name), GetPortDestProc(portp)) else: out_string += prefix + format_string.format( unsigned(portp), addressof(portp.ip_messages), ' '*8, unsigned(portp.ip_messages.data.port.receiver_name), "DPort", portp.ip_object.io_references, unsigned(portp), "inactive-port") if show_kmsg_summary: kmsgp = Cast(portp.ip_messages.data.port.messages.ikmq_base, 'ipc_kmsg_t') out_string += prefix + GetKMsgSummary.header + prefix + GetKMsgSummary(kmsgp) kmsgheadp = kmsgp kmsgp = kmsgp.ikm_next while (kmsgp) != (kmsgheadp): out_string += prefix + GetKMsgSummary(kmsgp) kmsgp = kmsgp.ikm_next return out_string def GetPortDestProc(portp): """ Display the name and pid of a given port's receiver params: portp : core.value representing a pointer to a port in the kernel destspacep : core.value representing a pointer to an ipc_space returns: str : string containing receiver's name and pid """ spacep = portp.data.receiver out_str = "Not found" for tsk in kern.tasks: if tsk.itk_space == spacep: if tsk.bsd_info: destprocp = Cast(tsk.bsd_info, 'struct proc *') out_str = "{0:s}({1: <d})".format(destprocp.p_comm, destprocp.p_pid) else: out_str = "unknown" break return out_str @header("{0: <20s} {1: <28s} {2: <12s} {3: <6s} {4: <6s} {5: <19s} {6: <26s} {7: <26s}\n".format( "dest-port", "kmsg", "msgid", "disp", "size", "reply-port", "source", "destination")) def GetKMsgSummary(kmsgp): """ Display a summary for type ipc_kmsg_t params: kmsgp : core.value representing the given ipc_kmsg_t struct returns: str : string of summary info for the given ipc_kmsg_t instance """ kmsghp = kmsgp.ikm_header kmsgh = dereference(kmsghp) out_string = "" out_string += "{0: <19s} {1: <#019x} {2: <8s} {3: <#011x} ".format( ' '*19, unsigned(kmsgp), ' '*8, kmsgh.msgh_id) if (kmsgh.msgh_bits & 0xff) == 17: out_string += "{0: <2s}".format("rS") else: out_string += "{0: <2s}".format("rO") if (kmsgh.msgh_bits & 0xff00) == (17 << 8): out_string += "{0: <2s}".format("lS") else: if (kmsgh.msgh_bits & 0xff00) == (18 << 8): out_string += "{0: <2s}".format("lO") else: out_string += "{0: <2s}".format("l-") if kmsgh.msgh_bits & 0xf0000000: out_string += "{0: <2s}".format("c") else: out_string += "{0: <2s}".format("s") dest_proc_name = "" if kmsgp.ikm_header.msgh_remote_port: dest_proc_name = GetDestinationProcessFromPort(kmsgp.ikm_header.msgh_remote_port) out_string += "{0: ^6d} {1: <#019x} {2: <26s} {3: <26s}\n".format( unsigned(kmsgh.msgh_size), unsigned(kmsgh.msgh_local_port), GetKMsgSrc(kmsgp), dest_proc_name) return out_string def GetKMsgSrc(kmsgp): """ Routine that prints a kmsg's source process and pid details params: kmsgp : core.value representing the given ipc_kmsg_t struct returns: str : string containing the name and pid of the kmsg's source proc """ kmsgsrchp = Cast(kmsgp, 'ipc_kmsg_t').ikm_header kmsgpid = int(Cast(kern.GetValueFromAddress(unsigned(kmsgsrchp) + kmsgsrchp.msgh_size, 'uint *')[10], 'pid_t')) return "{0:s} ({1:d})".format(GetProcNameForPid(kmsgpid), kmsgpid) @header("{0: <20s} {1: <28s} {2: <12s} {3: <6s} {4: <6s} {5: <20s} {6: <7s}\n".format( "portset", "waitqueue", "recvname", "flags", "refs", "recvname", "process")) def GetPortSetSummary(pset): """ Display summary for a given struct ipc_pset * params: pset : core.value representing a pset in the kernel returns: str : string of summary information for the given pset """ out_str = "" if pset.ips_object.io_bits & 0x80000000: out_str += "{0: #019x} {1: #019x} {2: <7s} {3: #011x} {4: <4s} {5: >6d} {6: #019x} ".format( unsigned(pset), addressof(pset.ips_messages), ' '*7, pset.ips_messages.data.pset.local_name, "ASet", pset.ips_object.io_references, pset.ips_messages.data.pset.local_name) else: out_str += "{0: #019x} {1: #019x} {2: <7s} {3: #011x} {4: <4s} {5: >6d} {6: #019x} ".format( unsigned(pset), addressof(pset.ips_messages), ' '*7, pset.ips_messages.data.pset.local_name, "DSet", pset.ips_object.io_references, pset.ips_messages.data.pset.local_name) once = True setlinksp = addressof(pset.ips_messages.data.pset.set_queue.wqs_setlinks) wql = Cast(pset.ips_messages.data.pset.set_queue.wqs_setlinks.next, 'WaitQueueLink *') portoff = getfieldoffset('struct ipc_port', 'ip_messages') prefix_str = "{0:<21s}".format(' '*21) while unsigned(wql) != unsigned(Cast(setlinksp, 'void *')): portp = kern.GetValueFromAddress(unsigned(wql.wql_element.wqe_queue) - portoff, 'ipc_port *') if once: once = False out_str += "{0:s}\n{1:s}{2:s}".format(GetPortDestProc(portp), prefix_str, GetPortSummary.header) out_str += GetPortSummary(portp, False, prefix_str) wql = Cast(wql.wql_setlinks.next, 'WaitQueueLink *') return out_str # Macro: showipc @lldb_command('showipc') def ShowIPC(cmd_args=None): """ Routine to print data for the given IPC space Usage: showipc <address of ipc space> """ if not cmd_args: print "No arguments passed" print ShowIPC.__doc__ return False ipc = kern.GetValueFromAddress(cmd_args[0], 'ipc_space *') if not ipc: print "unknown arguments:", str(cmd_args) return False print GetIPCInformation.header print GetIPCInformation(ipc, False, False) # EndMacro: showipc # Macro: showtaskipc @lldb_command('showtaskipc') def ShowTaskIPC(cmd_args=None): """ Routine to print IPC summary of given task Usage: showtaskipc <address of task> """ if not cmd_args: print "No arguments passed" print ShowTaskIPC.__doc__ return False tval = kern.GetValueFromAddress(cmd_args[0], 'task *') if not tval: print "unknown arguments:", str(cmd_args) return False print GetTaskSummary.header + " " + GetProcSummary.header pval = Cast(tval.bsd_info, 'proc *') print GetTaskSummary(tval) + " " + GetProcSummary(pval) print GetTaskIPCSummary.header print GetTaskIPCSummary(tval) # EndMacro: showtaskipc # Macro: showallipc @lldb_command('showallipc') def ShowAllIPC(cmd_args=None): """ Routine to print IPC summary of all tasks Usage: showallipc """ for t in kern.tasks: print GetTaskSummary.header + " " + GetProcSummary.header pval = Cast(t.bsd_info, 'proc *') print GetTaskSummary(t) + " " + GetProcSummary(pval) print GetIPCInformation.header print GetIPCInformation(t.itk_space, False, False) + "\n\n" # EndMacro: showallipc @lldb_command('showipcsummary') def ShowIPCSummary(cmd_args=None): """ Summarizes the IPC state of all tasks. This is a convenient way to dump some basic clues about IPC messaging. You can use the output to determine tasks that are candidates for further investigation. """ print GetTaskIPCSummary.header for t in kern.tasks: print GetTaskIPCSummary(t) return def GetKObjectFromPort(portval): """ Get Kobject description from the port. params: portval - core.value representation of 'ipc_port *' object returns: str - string of kobject information """ kobject_str = "{0: <#020x}".format(portval.kdata.kobject) io_bits = unsigned(portval.ip_object.io_bits) objtype_index = io_bits & 0xfff if objtype_index < len(xnudefines.kobject_types) : desc_str = "kobject({0:s})".format(xnudefines.kobject_types[objtype_index]) if xnudefines.kobject_types[objtype_index] in ('TASK_RESUME', 'TASK'): desc_str += " " + GetProcNameForTask(Cast(portval.kdata.kobject, 'task *')) else: desc_str = "kobject(UNKNOWN) {:d}".format(objtype_index) return kobject_str + " " + desc_str @static_var('destcache', {}) def GetDestinationProcessFromPort(port): """ params: port - core.value representation of 'ipc_port *' object returns: str - name of process """ out_str = '' dest_space = port.data.receiver found_dest = False #update destcache if data is not found if hex(dest_space) not in GetDestinationProcessFromPort.destcache: for t in kern.tasks: if hex(t.itk_space) == hex(dest_space): pval = Cast(t.bsd_info, 'proc *') GetDestinationProcessFromPort.destcache[hex(dest_space)] = (t, pval) found_dest = True break #end of for loop else: found_dest = True if found_dest: (ftask , fproc) = GetDestinationProcessFromPort.destcache[hex(dest_space)] if fproc: out_str = "{0:s}({1:d})".format(fproc.p_comm, fproc.p_pid ) else: out_str = "task {0: <#020x}".format(ftask) return out_str @header("{0: <20s} {1: <20s}".format("destname", "destination") ) def GetPortDestinationSummary(port): """ Get destination information for a port. params: port - core.value representation of 'ipc_port *' object returns: str - string of info about ports destination """ out_str = '' format_string = "{0: <20s} {1: <20s}" destname_str = '' destination_str = '' ipc_space_kernel = unsigned(kern.globals.ipc_space_kernel) target_spaceval = port.data.receiver if unsigned(target_spaceval) == ipc_space_kernel : destname_str = GetKObjectFromPort(port) else: if int(port.ip_object.io_bits) & 0x80000000 : destname_str = "{0: <#020x}".format(port.ip_messages.data.port.receiver_name) destination_str = GetDestinationProcessFromPort(port) else: destname_str = "{0: <#020x}".format(port) destination_str = "inactive-port" out_str += format_string.format(destname_str, destination_str) return out_str @lldb_type_summary(['ipc_entry_t']) @header("{0: <20s} {1: <20s} {2: <8s} {3: <8s} {4: <20s} {5: <20s}".format("object", "name","rite", "urefs", "destname", "destination")) def GetIPCEntrySummary(entry, ipc_name=''): """ Get summary of a ipc entry. params: entry - core.value representing ipc_entry_t in the kernel ipc_name - str of format '0x0123' for display in summary. returns: str - string of ipc entry related information """ out_str = '' entry_ptr = int(hex(entry), 16) format_string = "{0: <#020x} {1: <12s} {2: <8s} {3: <8d} {4: <20s} {5: <20s}" right_str = '' destname_str = '' destination_str = '' ie_object = entry.ie_object ie_bits = int(entry.ie_bits) urefs = int(ie_bits & 0xffff) if ie_bits & 0x00100000 : right_str = 'Dead' elif ie_bits & 0x00080000: right_str = 'Set' else: if ie_bits & 0x00010000 : if ie_bits & 0x00020000 : right_str = 'SR' else: right_str = 'S' elif ie_bits & 0x00020000: right_str = 'R' elif ie_bits & 0x00040000 : right_str = 'O' portval = Cast(ie_object, 'ipc_port_t') if int(entry.index.request) != 0: requestsval = portval.ip_requests sorightval = requestsval[int(entry.index.request)].notify.port soright_ptr = unsigned(sorightval) if soright_ptr != 0: if soright_ptr & 0x1 : right_str +='s' elif soright_ptr & 0x2 : right_str +='d' else : right_str +='n' if ie_bits & 0x00800000 : right_str +='c' if portval.ip_nsrequest != 0: right_str +='x' # now show the port destination part destname_str = GetPortDestinationSummary(Cast(ie_object, 'ipc_port_t')) out_str = format_string.format(ie_object, ipc_name, right_str, urefs, destname_str, destination_str) return out_str @header("{0: >20s}".format("user bt") ) def GetPortUserStack(port, task): """ Get UserStack information for the given port & task. params: port - core.value representation of 'ipc_port *' object task - value representing 'task *' object returns: str - string information on port's userstack """ out_str = '' ie_port_callstack = port.ip_callstack ie_port_spares = port.ip_spares[0] proc_val = Cast(task.bsd_info, 'proc *') if ie_port_callstack[0]: out_str += "{: <10x}".format(ie_port_callstack[0]) count = 1 while count < 16 and ie_port_callstack[count]: out_str += ": <10x".format(ie_port_callstack[count]) count = count + 1 if ie_port_spares != proc_val.p_pid: out_str += " ({:<10d})".format(ie_port_spares) out_str += '\n' return out_str @lldb_type_summary(['ipc_space *']) @header("{0: <20s} {1: <20s} {2: <20s} {3: <8s} {4: <10s} {5: <16s} {6: <10s} {7: <7s}".format('ipc_space', 'is_task', 'is_table', 'flags', 'ports', 'table_next', 'low_mod', 'high_mod')) def GetIPCInformation(space, show_entries=False, show_userstack=False): """ Provide a summary of the ipc space """ out_str = '' format_string = "{0: <#020x} {1: <#020x} {2: <#020x} {3: <8s} {4: <10d} {5: <#01x} {6: >10d} {7: >10d}" is_tableval = space.is_table ports = int(space.is_table_size) flags ='' is_bits = int(space.is_bits) if (is_bits & 0x40000000) == 0: flags +='A' else: flags += ' ' if (is_bits & 0x20000000) != 0: flags +='G' out_str += format_string.format(space, space.is_task, space.is_table, flags, space.is_table_size, space.is_table_next, space.is_low_mod, space.is_high_mod) #should show the each individual entries if asked. if show_entries == True: out_str += "\n\t" + GetIPCEntrySummary.header + "\n" num_entries = ports index = 0 while index < num_entries: entryval = GetObjectAtIndexFromArray(is_tableval, index) entry_ie_bits = unsigned(entryval.ie_bits) if (int(entry_ie_bits) & 0x001f0000 ) != 0: entry_name = "{0: <#020x}".format( (index <<8 | entry_ie_bits >> 24) ) out_str += "\t" + GetIPCEntrySummary(entryval, entry_name) + "\n" if show_userstack == True: entryport = Cast(entryval.ie_object, 'ipc_port *') if entryval.ie_object and (int(entry_ie_bits) & 0x00070000) and entryport.ip_callstack[0]: out_str += GetPortUserStack.header out_str += GetPortUserStack(entryport, space.is_task) index +=1 #done with showing entries return out_str # Macro: showrights @lldb_command('showrights') def ShowRights(cmd_args=None): """ Routine to print rights information for the given IPC space Usage: showrights <address of ipc space> """ if not cmd_args: print "No arguments passed" print ShowRights.__doc__ return False ipc = kern.GetValueFromAddress(cmd_args[0], 'ipc_space *') if not ipc: print "unknown arguments:", str(cmd_args) return False print GetIPCInformation.header print GetIPCInformation(ipc, True, False) # EndMacro: showrights @lldb_command('showtaskrights') def ShowTaskRights(cmd_args=None): """ Routine to ipc rights information for a task Usage: showtaskrights <task address> """ if cmd_args == None: print "No arguments passed" print ShowTaskStacksCmdHelper.__doc__ return False tval = kern.GetValueFromAddress(cmd_args[0], 'task *') if not tval: print "unknown arguments:", str(cmd_args) return False print GetTaskSummary.header + " " + GetProcSummary.header pval = Cast(tval.bsd_info, 'proc *') print GetTaskSummary(tval) + " " + GetProcSummary(pval) print GetIPCInformation.header print GetIPCInformation(tval.itk_space, True, False) # Macro: showataskrightsbt @lldb_command('showtaskrightsbt') def ShowTaskRightsBt(cmd_args=None): """ Routine to ipc rights information with userstacks for a task Usage: showtaskrightsbt <task address> """ if cmd_args == None: print "No arguments passed" print ShowTaskRightsBt.__doc__ return False tval = kern.GetValueFromAddress(cmd_args[0], 'task *') if not tval: print "unknown arguments:", str(cmd_args) return False print GetTaskSummary.header + " " + GetProcSummary.header pval = Cast(tval.bsd_info, 'proc *') print GetTaskSummary(tval) + " " + GetProcSummary(pval) print GetIPCInformation.header print GetIPCInformation(tval.itk_space, True, True) # EndMacro: showtaskrightsbt # Macro: showallrights @lldb_command('showallrights') def ShowAllRights(cmd_args=None): """ Routine to print rights information for IPC space of all tasks Usage: showallrights """ for t in kern.tasks: print GetTaskSummary.header + " " + GetProcSummary.header pval = Cast(t.bsd_info, 'proc *') print GetTaskSummary(t) + " " + GetProcSummary(pval) try: print GetIPCInformation.header print GetIPCInformation(t.itk_space, True, False) + "\n\n" except (KeyboardInterrupt, SystemExit): raise except: print "Failed to get IPC information. Do individual showtaskrights <task> to find the error. \n\n" # EndMacro: showallrights # Macro: showpipestats @lldb_command('showpipestats') def ShowPipeStats(cmd_args=None): """ Display pipes usage information in the kernel """ print "Number of pipes: {: d}".format(kern.globals.amountpipes) print "Memory used by pipes: {:s}".format(sizeof_fmt(int(kern.globals.amountpipekva))) print "Max memory allowed for pipes: {:s}".format(sizeof_fmt(int(kern.globals.maxpipekva))) # EndMacro: showpipestats # Macro: showtaskbusyports @lldb_command('showtaskbusyports') def ShowTaskBusyPorts(cmd_args=None): """ Routine to print information about receive rights belonging to this task that have enqueued messages. This is oten a sign of a blocked or hung process Usage: showtaskbusyports <task address> """ if not cmd_args: print "No arguments passed. Please pass in the address of a task" print ShowTaskBusyPorts.__doc__ return task = kern.GetValueFromAddress(cmd_args[0], 'task_t') print GetTaskBusyPorts(task) return def GetTaskBusyPorts(task): """ Prints all busy ports for a given task. ie. all receive rights belonging to this task that have enqueued messages. params: task : core.value representing a task in kernel returns: str : String containing information about the given task's busy ports """ isp = task.itk_space i = 0 out_string = "" while i < isp.is_table_size: iep = addressof(isp.is_table[i]) if iep.ie_bits & 0x00020000: port = Cast(iep.ie_object, 'ipc_port_t') if port.ip_messages.data.port.msgcount > 0: out_string += GetPortSummary.header + GetPortSummary(port) i = i + 1 return out_string # EndMacro: showtaskbusyports # Macro: showallbusyports @lldb_command('showallbusyports') def ShowAllBusyPorts(cmd_args=None): """ Routine to print information about all receive rights on the system that have enqueued messages. """ task_queue_head = kern.globals.tasks for tsk in kern.tasks: print GetTaskBusyPorts(tsk) return # EndMacro: showallbusyports # Macro: showmqueue: @lldb_command('showmqueue') def ShowMQueue(cmd_args=None): """ Routine that lists details about a given mqueue Syntax: (lldb) showmqueue 0xaddr """ if not cmd_args: print "Please specify the address of the ipc_mqueue whose details you want to print" print ShowMQueue.__doc__ return mqueue = kern.GetValueFromAddress(cmd_args[0], 'struct ipc_mqueue *') wq_type = mqueue.data.pset.set_queue.wqs_wait_queue.wq_type if int(wq_type) == 3: psetoff = getfieldoffset('struct ipc_pset', 'ips_messages') pset = unsigned(ArgumentStringToInt(cmd_args[0])) - unsigned(psetoff) print GetPortSetSummary.header + GetPortSetSummary(kern.GetValueFromAddress(pset, 'struct ipc_pset *')) if int(wq_type) == 2: portoff = getfieldoffset('struct ipc_port', 'ip_messages') port = unsigned(ArgumentStringToInt(cmd_args[0])) - unsigned(portoff) print GetPortSummary.header + GetPortSummary(kern.GetValueFromAddress(port, 'struct ipc_port *')) # EndMacro: showmqueue # Macro: showkmsg: @lldb_command('showkmsg') def ShowKMSG(cmd_args=[]): """ Show detail information about a <ipc_kmsg_t> structure Usage: (lldb) showkmsg <ipc_kmsg_t> """ if not cmd_args: raise ArgumentError('Invalid arguments') kmsg = kern.GetValueFromAddress(cmd_args[0], 'ipc_kmsg_t') print GetKMsgSummary.header print GetKMsgSummary(kmsg) # EndMacro: showkmsg # Macro: showpset @lldb_command('showpset') def ShowPSet(cmd_args=None): """ Routine that prints details for a given ipc_pset * Syntax: (lldb) showpset 0xaddr """ if not cmd_args: print "Please specify the address of the pset whose details you want to print" print ShowPSet.__doc__ return print GetPortSetSummary.header + GetPortSetSummary(kern.GetValueFromAddress(cmd_args[0], 'ipc_pset *')) # EndMacro: showpset # IPC importance inheritance related macros. @lldb_command('showalliits') def ShowAllIITs(cmd_args=[], cmd_options={}): """ Development only macro. Show list of all iits allocated in the system. """ try: iit_queue = kern.globals.global_iit_alloc_queue except ValueError: print "This debug macro is only available in development or debug kernels" return print GetIPCImportantTaskSummary.header for iit in IterateQueue(iit_queue, 'struct ipc_importance_task *', 'iit_allocation'): print GetIPCImportantTaskSummary(iit) return @header("{: <18s} {: <3s} {: <18s} {: <20s} {: <18s} {: <8s}".format("ipc_imp_inherit", "don", "to_task", "proc_name", "from_elem", "depth")) @lldb_type_summary(['ipc_importance_inherit *', 'ipc_importance_inherit_t']) def GetIPCImportanceInheritSummary(iii): """ describes iii object of type ipc_importance_inherit_t * """ out_str = "" fmt = "{o: <#018x} {don: <3s} {o.iii_to_task.iit_task: <#018x} {task_name: <20s} {o.iii_from_elem: <#018x} {o.iii_depth: <#08x}" donating_str = "" if unsigned(iii.iii_donating): donating_str = "DON" taskname = GetProcNameForTask(iii.iii_to_task.iit_task) if hasattr(iii.iii_to_task, 'iit_bsd_pid'): taskname = "({:d}) {:s}".format(iii.iii_to_task.iit_bsd_pid, iii.iii_to_task.iit_procname) out_str += fmt.format(o=iii, task_name = taskname, don=donating_str) return out_str @static_var('recursion_count', 0) @header("{: <18s} {: <4s} {: <8s} {: <8s} {: <18s} {: <18s}".format("iie", "type", "refs", "made", "#kmsgs", "#inherits")) @lldb_type_summary(['ipc_importance_elem *']) def GetIPCImportanceElemSummary(iie): """ describes an ipc_importance_elem * object """ if GetIPCImportanceElemSummary.recursion_count > 500: GetIPCImportanceElemSummary.recursion_count = 0 return "Recursion of 500 reached" out_str = '' fmt = "{: <#018x} {: <4s} {: <8d} {: <8d} {: <#018x} {: <#018x}" type_str = 'TASK' if unsigned(iie.iie_bits) & 0x80000000: type_str = "INH" refs = unsigned(iie.iie_bits) & 0x7fffffff made_refs = unsigned(iie.iie_made) kmsg_count = sum(1 for i in IterateQueue(iie.iie_kmsgs, 'struct ipc_kmsg *', 'ikm_inheritance')) inherit_count = sum(1 for i in IterateQueue(iie.iie_inherits, 'struct ipc_importance_inherit *', 'iii_inheritance')) out_str += fmt.format(iie, type_str, refs, made_refs, kmsg_count, inherit_count) if config['verbosity'] > vHUMAN: if kmsg_count > 0: out_str += "\n\t"+ GetKMsgSummary.header for k in IterateQueue(iie.iie_kmsgs, 'struct ipc_kmsg *', 'ikm_inheritance'): out_str += "\t" + "{: <#018x}".format(k.ikm_header.msgh_remote_port) + ' ' + GetKMsgSummary(k).lstrip() out_str += "\n" if inherit_count > 0: out_str += "\n\t" + GetIPCImportanceInheritSummary.header + "\n" for i in IterateQueue(iie.iie_inherits, 'struct ipc_importance_inherit *', 'iii_inheritance'): out_str += "\t" + GetIPCImportanceInheritSummary(i) + "\n" out_str += "\n" if type_str == "INH": iii = Cast(iie, 'struct ipc_importance_inherit *') out_str += "Inherit from: " + GetIPCImportanceElemSummary(iii.iii_from_elem) return out_str @header("{: <18s} {: <18s} {: <20s}".format("iit", "task", "name")) @lldb_type_summary(['ipc_importance_task *']) def GetIPCImportantTaskSummary(iit): """ iit is a ipc_importance_task value object. """ fmt = "{: <#018x} {: <#018x} {: <20s}" out_str='' pname = GetProcNameForTask(iit.iit_task) if hasattr(iit, 'iit_bsd_pid'): pname = "({:d}) {:s}".format(iit.iit_bsd_pid, iit.iit_procname) out_str += fmt.format(iit, iit.iit_task, pname) return out_str @lldb_command('showallimportancetasks') def ShowIPCImportanceTasks(cmd_args=[], cmd_options={}): """ display a list of all tasks with ipc importance information. Usage: (lldb) showallimportancetasks Tip: add "-v" to see detailed information on each kmsg or inherit elems """ print ' ' + GetIPCImportantTaskSummary.header + ' ' + GetIPCImportanceElemSummary.header for t in kern.tasks: s = "" if unsigned(t.task_imp_base): s += ' ' + GetIPCImportantTaskSummary(t.task_imp_base) s += ' ' + GetIPCImportanceElemSummary(addressof(t.task_imp_base.iit_elem)) print s @lldb_command('showipcimportance', '') def ShowIPCImportance(cmd_args=[], cmd_options={}): """ Describe an importance from <ipc_importance_elem_t> argument. Usage: (lldb) showimportance <ipc_importance_elem_t> """ if not cmd_args: raise ArgumentError("Please provide valid argument") elem = kern.GetValueFromAddress(cmd_args[0], 'ipc_importance_elem_t') print GetIPCImportanceElemSummary.header print GetIPCImportanceElemSummary(elem) @header("{: <18s} {: <10s} {: <18s} {: <18s} {: <8s} {: <5s} {: <5s} {: <5s}".format("ivac", "refs", "port", "tbl", "tblsize", "index", "Grow", "freelist")) @lldb_type_summary(['ipc_voucher_attr_control *', 'ipc_voucher_attr_control_t']) def GetIPCVoucherAttrControlSummary(ivac): """ describes a voucher attribute control settings """ out_str = "" fmt = "{c: <#018x} {c.ivac_refs: <10d} {c.ivac_port: <#018x} {c.ivac_table: <#018x} {c.ivac_table_size: <8d} {c.ivac_key_index: <5d} {growing: <5s} {c.ivac_freelist: <5d}" growing_str = "" if unsigned(ivac) == 0: return "{: <#018x}".format(ivac) if unsigned(ivac.ivac_is_growing): growing_str = "Y" out_str += fmt.format(c=ivac, growing = growing_str) return out_str @lldb_command('showivac','') def ShowIPCVoucherAttributeControl(cmd_args=[], cmd_options={}): """ Show summary of voucher attribute contols. Usage: (lldb) showivac <ipc_voucher_attr_control_t> """ if not cmd_args: raise ArgumentError("Please provide correct arguments.") ivac = kern.GetValueFromAddress(cmd_args[0], 'ipc_voucher_attr_control_t') print GetIPCVoucherAttrControlSummary.header print GetIPCVoucherAttrControlSummary(ivac) if config['verbosity'] > vHUMAN: cur_entry_index = 0 last_entry_index = unsigned(ivac.ivac_table_size) print "index " + GetIPCVoucherAttributeEntrySummary.header while cur_entry_index < last_entry_index: print "{: <5d} ".format(cur_entry_index) + GetIPCVoucherAttributeEntrySummary(addressof(ivac.ivac_table[cur_entry_index])) cur_entry_index += 1 @header("{: <18s} {: <30s} {: <30s} {: <30s} {: <30s} {: <30s}".format("ivam", "get_value_fn", "extract_fn", "release_value_fn", "command_fn", "release_fn")) @lldb_type_summary(['ipc_voucher_attr_manager *', 'ipc_voucher_attr_manager_t']) def GetIPCVoucherAttrManagerSummary(ivam): """ describes a voucher attribute manager settings """ out_str = "" fmt = "{: <#018x} {: <30s} {: <30s} {: <30s} {: <30s} {: <30s}" if unsigned(ivam) == 0 : return "{: <#018x}".format(ivam) get_value_fn = kern.Symbolicate(unsigned(ivam.ivam_get_value)) extract_fn = kern.Symbolicate(unsigned(ivam.ivam_extract_content)) release_value_fn = kern.Symbolicate(unsigned(ivam.ivam_release_value)) command_fn = kern.Symbolicate(unsigned(ivam.ivam_command)) release_fn = kern.Symbolicate(unsigned(ivam.ivam_release)) out_str += fmt.format(ivam, get_value_fn, extract_fn, release_value_fn, command_fn, release_fn) return out_str @header("{: <18s} {: <10s} {:s} {:s}".format("ivgte", "key", GetIPCVoucherAttrControlSummary.header.strip(), GetIPCVoucherAttrManagerSummary.header.strip())) @lldb_type_summary(['ipc_voucher_global_table_element *', 'ipc_voucher_global_table_element_t']) def GetIPCVoucherGlobalTableElementSummary(ivgte): """ describes a ipc_voucher_global_table_element object """ out_str = "" fmt = "{g: <#018x} {g.ivgte_key: <10d} {ctrl_s:s} {mgr_s:s}" out_str += fmt.format(g=ivgte, ctrl_s=GetIPCVoucherAttrControlSummary(ivgte.ivgte_control), mgr_s=GetIPCVoucherAttrManagerSummary(ivgte.ivgte_manager)) return out_str @lldb_command('showglobalvouchertable', '') def ShowGlobalVoucherTable(cmd_args=[], cmd_options={}): """ show detailed information of all voucher attribute managers registered with vouchers system Usage: (lldb) showglobalvouchertable """ entry_size = sizeof(kern.globals.iv_global_table[0]) elems = sizeof(kern.globals.iv_global_table) / entry_size print GetIPCVoucherGlobalTableElementSummary.header for i in range(elems): elt = addressof(kern.globals.iv_global_table[i]) print GetIPCVoucherGlobalTableElementSummary(elt) # Type summaries for Bag of Bits. @lldb_type_summary(['user_data_value_element', 'user_data_element_t']) @header("{0: <20s} {1: <16s} {2: <20s} {3: <20s} {4: <16s} {5: <20s}".format("user_data_ve", "maderefs", "checksum", "hash value", "size", "data")) def GetBagofBitsElementSummary(data_element): """ Summarizes the Bag of Bits element params: data_element = value of the object of type user_data_value_element_t returns: String with summary of the type. """ format_str = "{0: <#020x} {1: <16d} {2: <#020x} {3: <#020x} {4: <16d}" out_string = format_str.format(data_element, unsigned(data_element.e_made), data_element.e_sum, data_element.e_hash, unsigned(data_element.e_size)) out_string += " 0x" for i in range(0, (unsigned(data_element.e_size) - 1)): out_string += "{:02x}".format(int(data_element.e_data[i])) return out_string def GetIPCHandleSummary(handle_ptr): """ converts a handle value inside a voucher attribute table to ipc element and returns appropriate summary. params: handle_ptr - uint64 number stored in handle of voucher. returns: str - string summary of the element held in internal structure """ elem = kern.GetValueFromAddress(handle_ptr, 'ipc_importance_elem_t') if elem.iie_bits & 0x80000000 : iie = Cast(elem, 'struct ipc_importance_inherit *') return GetIPCImportanceInheritSummary(iie) else: iit = Cast(elem, 'struct ipc_importance_task *') return GetIPCImportantTaskSummary(iit) def GetATMHandleSummary(handle_ptr): """ Convert a handle value to atm value and returns corresponding summary of its fields. params: handle_ptr - uint64 number stored in handle of voucher returns: str - summary of atm value """ elem = kern.GetValueFromAddress(handle_ptr, 'atm_value *') return GetATMValueSummary(elem) def GetBankHandleSummary(handle_ptr): """ converts a handle value inside a voucher attribute table to bank element and returns appropriate summary. params: handle_ptr - uint64 number stored in handle of voucher. returns: str - summary of bank element """ elem = kern.GetValueFromAddress(handle_ptr, 'bank_element_t') if elem.be_type & 1 : ba = Cast(elem, 'struct bank_account *') return GetBankAccountSummary(ba) else: bt = Cast(elem, 'struct bank_task *') return GetBankTaskSummary(bt) def GetBagofBitsHandleSummary(handle_ptr): """ Convert a handle value to bag of bits value and returns corresponding summary of its fields. params: handle_ptr - uint64 number stored in handle of voucher returns: str - summary of bag of bits element """ elem = kern.GetValueFromAddress(handle_ptr, 'user_data_element_t') return GetBagofBitsElementSummary(elem) @static_var('attr_managers',{1: GetATMHandleSummary, 2: GetIPCHandleSummary, 3: GetBankHandleSummary, 7: GetBagofBitsHandleSummary}) def GetHandleSummaryForKey(handle_ptr, key_num): """ Get a summary of handle pointer from the voucher attribute manager. For example key 1 -> ATM and it puts atm_value_t in the handle. So summary of it would be atm value and refs etc. key 2 -> ipc and it puts either ipc_importance_inherit_t or ipc_important_task_t. key 3 -> Bank and it puts either bank_task_t or bank_account_t. key 7 -> Bag of Bits and it puts user_data_element_t in handle. So summary of it would be Bag of Bits content and refs etc. """ key_num = int(key_num) if key_num not in GetHandleSummaryForKey.attr_managers: return "Unknown key %d" % key_num return GetHandleSummaryForKey.attr_managers[key_num](handle_ptr) @header("{: <18s} {: <18s} {: <10s} {: <4s} {: <18s} {: <18s}".format("ivace", "value_handle", "#refs", "rel?", "maderefs", "next_layer")) @lldb_type_summary(['ivac_entry *', 'ivac_entry_t']) def GetIPCVoucherAttributeEntrySummary(ivace, manager_key_num = 0): """ Get summary for voucher attribute entry. """ out_str = "" fmt = "{e: <#018x} {e.ivace_value: <#018x} {e.ivace_refs: <10d} {release: <4s} {made_refs: <18s} {next_layer: <18s}" release_str = "" free_str = "" made_refs = "" next_layer = "" if unsigned(ivace.ivace_releasing): release_str = "Y" if unsigned(ivace.ivace_free): free_str = 'F' if unsigned(ivace.ivace_layered): next_layer = "{: <#018x}".format(ivace.ivace_u.ivaceu_layer) else: made_refs = "{: <18d}".format(ivace.ivace_u.ivaceu_made) out_str += fmt.format(e=ivace, release=release_str, made_refs=made_refs, next_layer=next_layer) if config['verbosity'] > vHUMAN and manager_key_num > 0: out_str += " " + GetHandleSummaryForKey(unsigned(ivace.ivace_value), manager_key_num) if config['verbosity'] > vHUMAN : out_str += ' {: <2s} {: <4d} {: <4d}'.format(free_str, ivace.ivace_next, ivace.ivace_index) return out_str @lldb_command('showivacfreelist','') def ShowIVACFreeList(cmd_args=[], cmd_options={}): """ Walk the free list and print every entry in the list. usage: (lldb) showivacfreelist <ipc_voucher_attr_control_t> """ if not cmd_args: raise ArgumentError('Please provide <ipc_voucher_attr_control_t>') ivac = kern.GetValueFromAddress(cmd_args[0], 'ipc_voucher_attr_control_t') print GetIPCVoucherAttrControlSummary.header print GetIPCVoucherAttrControlSummary(ivac) if unsigned(ivac.ivac_freelist) == 0: print "ivac table is full" return print "index " + GetIPCVoucherAttributeEntrySummary.header next_free = unsigned(ivac.ivac_freelist) while next_free != 0: print "{: <5d} ".format(next_free) + GetIPCVoucherAttributeEntrySummary(addressof(ivac.ivac_table[next_free])) next_free = unsigned(ivac.ivac_table[next_free].ivace_next) @header('{: <18s} {: <8s} {: <18s} {: <18s} {: <18s} {: <18s} {: <18s}'.format("ipc_voucher", "refs", "checksum", "hash", "tbl_size", "table", "voucher_port")) @lldb_type_summary(['ipc_voucher *', 'ipc_voucher_t']) def GetIPCVoucherSummary(voucher, show_entries=False): """ describe a voucher from its ipc_voucher * object """ out_str = "" fmt = "{v: <#018x} {v.iv_refs: <8d} {v.iv_sum: <#018x} {v.iv_hash: <#018x} {v.iv_table_size: <#018x} {v.iv_table: <#018x} {v.iv_port: <#018x}" out_str += fmt.format(v = voucher) entries_str = '' if show_entries or config['verbosity'] > vHUMAN: elems = unsigned(voucher.iv_table_size) entries_header_str = "\n\t" + "{: <5s} {: <3s} {: <16s} {: <30s}".format("index", "key", "value_index", "manager") + " " + GetIPCVoucherAttributeEntrySummary.header fmt = "{: <5d} {: <3d} {: <16d} {: <30s}" for i in range(elems): voucher_entry_index = unsigned(voucher.iv_inline_table[i]) if voucher_entry_index: s = fmt.format(i, GetVoucherManagerKeyForIndex(i), voucher_entry_index, GetVoucherAttributeManagerNameForIndex(i)) e = GetVoucherValueHandleFromVoucherForIndex(voucher, i) if e is not None: s += " " + GetIPCVoucherAttributeEntrySummary(addressof(e), GetVoucherManagerKeyForIndex(i) ) if entries_header_str : entries_str = entries_header_str entries_header_str = '' entries_str += "\n\t" + s if not entries_header_str: entries_str += "\n\t" out_str += entries_str return out_str def GetVoucherManagerKeyForIndex(idx): """ Returns key number for index based on global table. Will raise index error if value is incorrect """ return unsigned(kern.globals.iv_global_table[idx].ivgte_key) def GetVoucherAttributeManagerForKey(k): """ Walks through the iv_global_table and finds the attribute manager name params: k - int key number of the manager return: cvalue - the attribute manager object. None - if not found """ retval = None entry_size = sizeof(kern.globals.iv_global_table[0]) elems = sizeof(kern.globals.iv_global_table) / entry_size for i in range(elems): elt = addressof(kern.globals.iv_global_table[i]) if k == unsigned(elt.ivgte_key): retval = elt.ivgte_manager break return retval def GetVoucherAttributeControllerForKey(k): """ Walks through the iv_global_table and finds the attribute controller params: k - int key number of the manager return: cvalue - the attribute controller object. None - if not found """ retval = None entry_size = sizeof(kern.globals.iv_global_table[0]) elems = sizeof(kern.globals.iv_global_table) / entry_size for i in range(elems): elt = addressof(kern.globals.iv_global_table[i]) if k == unsigned(elt.ivgte_key): retval = elt.ivgte_control break return retval def GetVoucherAttributeManagerName(ivam): """ find the name of the ivam object param: ivam - cvalue object of type ipc_voucher_attr_manager_t returns: str - name of the manager """ return kern.Symbolicate(unsigned(ivam)) def GetVoucherAttributeManagerNameForIndex(idx): """ get voucher attribute manager name for index return: str - name of the attribute manager object """ return GetVoucherAttributeManagerName(GetVoucherAttributeManagerForKey(GetVoucherManagerKeyForIndex(idx))) def GetVoucherValueHandleFromVoucherForIndex(voucher, idx): """ traverse the voucher attrs and get value_handle in the voucher attr controls table params: voucher - cvalue object of type ipc_voucher_t idx - int index in the entries for which you wish to get actual handle for returns: cvalue object of type ivac_entry_t None if no handle found. """ manager_key = GetVoucherManagerKeyForIndex(idx) voucher_num_elems = unsigned(voucher.iv_table_size) if idx >= voucher_num_elems: debuglog("idx %d is out of range max: %d" % (idx, voucher_num_elems)) return None voucher_entry_value = unsigned(voucher.iv_inline_table[idx]) debuglog("manager_key %d" % manager_key) ivac = GetVoucherAttributeControllerForKey(manager_key) if ivac is None or unsigned(ivac) == 0: debuglog("No voucher attribute controller for idx %d" % idx) return None ivac = kern.GetValueFromAddress(unsigned(ivac), 'ipc_voucher_attr_control_t') # ??? No idea why lldb does not addressof directly ivace_table = ivac.ivac_table if voucher_entry_value >= unsigned(ivac.ivac_table_size): print "Failed to get ivace for value %d in table of size %d" % (voucher_entry_value, unsigned(ivac.ivac_table_size)) return None return ivace_table[voucher_entry_value] @lldb_command('showallvouchers') def ShowAllVouchers(cmd_args=[], cmd_options={}): """ Display a list of all vouchers in the global voucher hash table Usage: (lldb) showallvouchers """ iv_hash_table = kern.globals.ivht_bucket num_buckets = sizeof(kern.globals.ivht_bucket) / sizeof(kern.globals.ivht_bucket[0]) print GetIPCVoucherSummary.header for i in range(num_buckets): for v in IterateQueue(iv_hash_table[i], 'ipc_voucher_t', 'iv_hash_link'): print GetIPCVoucherSummary(v) @lldb_command('showvoucher', '') def ShowVoucher(cmd_args=[], cmd_options={}): """ Describe a voucher from <ipc_voucher_t> argument. Usage: (lldb) showvoucher <ipc_voucher_t> """ if not cmd_args: raise ArgumentError("Please provide valid argument") voucher = kern.GetValueFromAddress(cmd_args[0], 'ipc_voucher_t') print GetIPCVoucherSummary.header print GetIPCVoucherSummary(voucher, show_entries=True)

42.618285

186

0.648835

be015dcb455504cf55a34eb613263c8aaf4a86f5

56,787

py

Python

jp.atcoder/abc011/abc011_4/16937650.py

kagemeka/atcoder-submissions

91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e

[ "MIT" ]

1

2022-02-09T03:06:25.000Z

jp.atcoder/abc011/abc011_4/16937650.py

kagemeka/atcoder-submissions

91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e

[ "MIT" ]

1

2022-02-05T22:53:18.000Z

2022-02-09T01:29:30.000Z

jp.atcoder/abc011/abc011_4/16937650.py

kagemeka/atcoder-submissions

91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e

[ "MIT" ]

null

import math import string import sys from bisect import bisect_left as bi_l from bisect import bisect_right as bi_r from collections import Counter, defaultdict, deque from heapq import heappop, heappush from itertools import accumulate, combinations, product # from numba import jit import networkx as nx import numpy as np from scipy import optimize from scipy.sparse import csr_matrix from scipy.sparse.csgraph import maximum_flow, shortest_path inf = float("inf") from functools import lru_cache, reduce sys.setrecursionlimit(10**6) MOD = 10**9 + 7 # MOD = 998244353 class NumberTheory: def __init__(self, n=2 * 10**6, numpy=True): self.n = n self.np_flg = numpy self.is_prime_number, self.prime_numbers = self.sieve_of_eratosthenes( n ) def sieve_of_eratosthenes(self, n): if self.np_flg: sieve = np.ones(n + 1, dtype=np.int64) sieve[:2] = 0 for i in range(2, int(n**0.5) + 1): if sieve[i]: sieve[i * 2 :: i] = 0 prime_numbers = np.flatnonzero(sieve) else: sieve = [1] * (n + 1) sieve[0] = sieve[1] = 0 for i in range(2, int(n**0.5) + 1): if not sieve[i]: continue for j in range(i * 2, n + 1, i): sieve[j] = 0 prime_numbers = [i for i in range(2, n + 1) if sieve[i]] return sieve, prime_numbers def prime_factorize(self, n): res = dict() if n < 2: return res border = int(n**0.5) for p in self.prime_numbers: if p > border: break while n % p == 0: res[p] = res.get(p, 0) + 1 n //= p if n == 1: return res res[n] = 1 return res def prime_factorize_factorial(self, n): res = dict() for i in range(2, n + 1): for p, c in self.prime_factorize(i).items(): res[p] = res.get(p, 0) + c return res @classmethod def gcd(cls, a, b): return cls.gcd(b, a % b) if b else abs(a) @classmethod def lcm(cls, a, b): return abs(a // cls.gcd(a, b) * b) @staticmethod def find_divisors(n): divisors = [] for i in range(1, int(n**0.5) + 1): if n % i: continue divisors.append(i) j = n // i if j != i: divisors.append(j) return divisors @staticmethod def base_convert(n, b): if not n: return [0] res = [] while n: n, r = divmod(n, b) if r < 0: n += 1 r -= b res.append(r) return res class UnionFind: def __init__(self, n=10**6): self.root = list(range(n)) self.height = [0] * n self.size = [1] * n def find_root(self, u): if self.root[u] == u: return u self.root[u] = self.find_root(self.root[u]) return self.root[u] def unite(self, u, v): ru = self.find_root(u) rv = self.find_root(v) if ru == rv: return hu = self.height[ru] hv = self.height[rv] if hu >= hv: self.root[rv] = ru self.size[ru] += self.size[rv] self.height[ru] = max(hu, hv + 1) else: self.root[ru] = rv self.size[rv] += self.size[ru] class Combinatorics: def __init__(self, N=10**9, n=10**6, mod=10**9 + 7, numpy=True): self.mod = mod self.nCr = dict() self.np_flg = numpy self.make_mod_tables(N, n) sys.setrecursionlimit(10**6) def choose(self, n, r, mod=None): # no mod, or mod ≠ prime if r > n or r < 0: return 0 if r == 0: return 1 if (n, r) in self.nCr: return self.nCr[(n, r)] if not mod: self.nCr[(n, r)] = self.choose(n - 1, r) + self.choose( n - 1, r - 1 ) else: self.nCr[(n, r)] = ( self.choose(n - 1, r, mod) + self.choose(n - 1, r - 1, mod) ) % mod return self.nCr[(n, r)] def cumprod(self, a): p = self.mod l = len(a) sql = int(np.sqrt(l) + 1) a = np.resize(a, sql**2).reshape(sql, sql) for i in range(sql - 1): a[:, i + 1] *= a[:, i] a[:, i + 1] %= p for i in range(sql - 1): a[i + 1] *= a[i, -1] a[i + 1] %= p return np.ravel(a)[:l] def make_mod_tables(self, N, n): p = self.mod if self.np_flg: fac = np.arange(n + 1) fac[0] = 1 fac = self.cumprod(fac) ifac = np.arange(n + 1, 0, -1) ifac[0] = pow(int(fac[-1]), p - 2, p) ifac = self.cumprod(ifac)[n::-1] n_choose = np.arange(N + 1, N - n, -1) n_choose[0] = 1 n_choose[1:] = self.cumprod(n_choose[1:]) * ifac[1 : n + 1] % p else: fac = [None] * (n + 1) fac[0] = 1 for i in range(n): fac[i + 1] = fac[i] * (i + 1) % p ifac = [None] * (n + 1) ifac[n] = pow(fac[n], p - 2, p) for i in range(n, 0, -1): ifac[i - 1] = ifac[i] * i % p n_choose = [None] * (n + 1) n_choose[0] = 1 for i in range(n): n_choose[i + 1] = n_choose[i] * (N - i) % p for i in range(n + 1): n_choose[i] = n_choose[i] * ifac[i] % p self.fac, self.ifac, self.mod_n_choose = fac, ifac, n_choose def mod_choose(self, n, r): return ( self.fac[n] * self.ifac[r] % self.mod * self.ifac[n - r] % self.mod ) def z_algorithm(s): n = len(s) a = [0] * n a[0] = n l = r = -1 for i in range(1, n): if r >= i: a[i] = min(a[i - l], r - i) while i + a[i] < n and s[i + a[i]] == s[a[i]]: a[i] += 1 if i + a[i] >= r: l, r = i, i + a[i] return a class GeometryTopology: class GraphTheory: class MaximumFlow: @staticmethod def dinic(graph, s, t): # s: source, t: sink def bfs(): level = defaultdict(int) level[s] = 0 q = deque([s]) while q: u = q.popleft() for v, cap in graph[u].items(): if cap == 0 or v in level: continue level[v] = level[u] + 1 q.append(v) return level def flow_to_sink(u, flow_in): if u == t: return flow_in flow = 0 for v, cap in graph[u].items(): if cap == 0 or level[v] <= level[u]: continue f = flow_to_sink(v, min(flow_in, cap)) if not f: continue graph[u][v] -= f graph[v][u] += f flow_in -= f flow += f return flow flow = 0 while True: level = bfs() if not t in level: return flow flow += flow_to_sink(s, float("inf")) @staticmethod def ford_fulkerson(): pass @staticmethod def push_relabel(): pass class ShortestPath: pass class AtCoder: class ABC001: @staticmethod def a(): h1, h2 = map(int, sys.stdin.read().split()) print(h1 - h2) class ABC002: @staticmethod def a(): print(max(map(int, sys.stdin.readline().split()))) @staticmethod def b(): vowels = set("aeiou") print( "".join( [ c for c in sys.stdin.readline().rstrip() if c not in vowels ] ) ) @staticmethod def c(): def triangle_area(x0, y0, x1, y1, x2, y2): x1 -= x0 x2 -= x0 y1 -= y0 y2 -= y0 return abs(x1 * y2 - x2 * y1) / 2 print(triangle_area(*map(int, sys.stdin.readline().split()))) @staticmethod def d(): n, m = map(int, sys.stdin.readline().split()) edges = set( (x - 1, y - 1) for x, y in zip(*[map(int, sys.stdin.read().split())] * 2) ) print( max( len(s) for i in range(1, 1 << n) for s in [[j for j in range(n) if i >> j & 1]] if all((x, y) in edges for x, y in combinations(s, 2)) ) ) @staticmethod def d_2(): n, m = map(int, sys.stdin.readline().split()) relations = [1 << i for i in range(n)] for x, y in zip(*[map(int, sys.stdin.read().split())] * 2): x -= 1 y -= 1 relations[x] |= 1 << y relations[y] |= 1 << x res = 0 for i in range(1 << n): cnt = 0 s = 0 t = (1 << n) - 1 for j in range(n): if i >> j & 1: s |= 1 << j t &= relations[j] cnt += 1 if t & s == s: res = max(res, cnt) print(res) class ABC003: @staticmethod def a(): print((int(sys.stdin.readline().rstrip()) + 1) * 5000) @staticmethod def b(): atcoder = set("atcoder") s, t = sys.stdin.read().split() print( all( s[i] == t[i] or s[i] == "@" and t[i] in atcoder or t[i] == "@" and s[i] in atcoder for i in range(len(s)) ) and "You can win" or "You will lose" ) @staticmethod def c(): n, k, *r = map(int, sys.stdin.read().split()) print(reduce(lambda x, y: (x + y) / 2, sorted(r)[-k:], 0)) class ABC004: @staticmethod def a(): print(int(sys.stdin.readline().rstrip()) * 2) @staticmethod def b(): for l in [sys.stdin.readline().rstrip() for _ in range(4)][::-1]: print(l[::-1]) @staticmethod def c(): n = int(sys.stdin.readline().rstrip()) % 30 res = list(range(1, 7)) for i in range(n): i %= 5 res[i], res[i + 1] = res[i + 1], res[i] print(*res, sep="") class ABC005: @staticmethod def a(): x, y = map(int, sys.stdin.readline().split()) print(y // x) @staticmethod def b(): n, *t = map(int, sys.stdin.read().split()) print(min(t)) @staticmethod def c(): t = int(sys.stdin.readline().rstrip()) n = int(sys.stdin.readline().rstrip()) a = [int(x) for x in sys.stdin.readline().split()] m = int(sys.stdin.readline().rstrip()) b = [int(x) for x in sys.stdin.readline().split()] i = 0 for p in b: if i == n: print("no") return while p - a[i] > t: i += 1 if i == n: print("no") return if a[i] > p: print("no") return i += 1 print("yes") @staticmethod def d(): n = int(sys.stdin.readline().rstrip()) d = np.array( [sys.stdin.readline().split() for _ in range(n)], np.int64 ) s = d.cumsum(axis=0).cumsum(axis=1) s = np.pad(s, 1) max_del = np.zeros((n + 1, n + 1), dtype=np.int64) for y in range(1, n + 1): for x in range(1, n + 1): max_del[y, x] = np.amax( s[y : n + 1, x : n + 1] - s[0 : n - y + 1, x : n + 1] - s[y : n + 1, 0 : n - x + 1] + s[0 : n - y + 1, 0 : n - x + 1] ) res = np.arange(n**2 + 1)[:, None] i = np.arange(1, n + 1) res = max_del[i, np.minimum(res // i, n)].max(axis=1) q = int(sys.stdin.readline().rstrip()) p = np.array(sys.stdin.read().split(), dtype=np.int64) print(*res[p], sep="\n") class ABC006: @staticmethod def a(): n = sys.stdin.readline().rstrip() if "3" in n: print("YES") elif int(n) % 3 == 0: print("YES") else: print("NO") @staticmethod def b(): mod = 10007 t = [0, 0, 1] for _ in range(1001001): t.append(t[-1] + t[-2] + t[-3]) t[-1] %= mod n = int(sys.stdin.readline().rstrip()) print(t[n - 1]) @staticmethod def c(): n, m = map(int, sys.stdin.readline().split()) cnt = [0, 0, 0] if m == 1: cnt = [-1, -1, -1] else: if m & 1: m -= 3 cnt[1] += 1 n -= 1 cnt[2] = m // 2 - n cnt[0] = n - cnt[2] if cnt[0] < 0 or cnt[1] < 0 or cnt[2] < 0: print(-1, -1, -1) else: print(*cnt, sep=" ") @staticmethod def d(): n, *c = map(int, sys.stdin.read().split()) lis = [inf] * n for x in c: lis[bi_l(lis, x)] = x print(n - bi_l(lis, inf)) class ABC007: @staticmethod def a(): n = int(sys.stdin.readline().rstrip()) print(n - 1) @staticmethod def b(): s = sys.stdin.readline().rstrip() if s == "a": print(-1) else: print("a") @staticmethod def c(): r, c = map(int, sys.stdin.readline().split()) sy, sx = map(int, sys.stdin.readline().split()) gy, gx = map(int, sys.stdin.readline().split()) sy -= 1 sx -= 1 gy -= 1 gx -= 1 maze = [sys.stdin.readline().rstrip() for _ in range(r)] queue = deque([(sy, sx)]) dist = np.full((r, c), np.inf) dist[sy, sx] = 0 while queue: y, x = queue.popleft() for i, j in [(-1, 0), (1, 0), (0, -1), (0, 1)]: i += y j += x if maze[i][j] == "#" or dist[i, j] != np.inf: continue dist[i, j] = dist[y, x] + 1 queue.append((i, j)) print(int(dist[gy, gx])) @staticmethod def d(): ng = set([4, 9]) def count(d): return d if d <= 4 else d - 1 def f(n): x = [int(d) for d in str(n)] flg = True dp = 0 for d in x: dp = dp * 8 + flg * count(d) if d in ng: flg = False return n - (dp + flg) a, b = map(int, sys.stdin.readline().split()) print(f(b) - f(a - 1)) class ABC008: @staticmethod def a(): s, t = map(int, sys.stdin.readline().split()) print(t - s + 1) @staticmethod def b(): n, *s = sys.stdin.read().split() res = defaultdict(int) for name in s: res[name] += 1 print(sorted(res.items(), key=lambda x: x[1])[-1][0]) @staticmethod def c(): n, *a = map(int, sys.stdin.read().split()) a = np.array(a) c = n - np.count_nonzero(a[:, None] % a, axis=1) print(np.sum((c + 1) // 2 / c)) @staticmethod def d(): w, h, n, *xy = map(int, sys.stdin.read().split()) (*xy,) = zip(*([iter(xy)] * 2)) @lru_cache(maxsize=None) def count(x1, y1, x2, y2): res = 0 for x, y in xy: if not (x1 <= x <= x2 and y1 <= y <= y2): continue cnt = (x2 - x1) + (y2 - y1) + 1 cnt += count(x1, y1, x - 1, y - 1) cnt += count(x1, y + 1, x - 1, y2) cnt += count(x + 1, y1, x2, y - 1) cnt += count(x + 1, y + 1, x2, y2) res = max(res, cnt) return res print(count(1, 1, w, h)) class ABC009: @staticmethod def a(): n = int(sys.stdin.readline().rstrip()) print((n + 1) // 2) @staticmethod def b(): n, *a = map(int, sys.stdin.read().split()) print(sorted(set(a))[-2]) @staticmethod def c(): n, k = map(int, sys.stdin.readline().split()) s = list(sys.stdin.readline().rstrip()) cost = [1] * n r = k for i in range(n - 1): q = [] for j in range(i + 1, n): if s[j] < s[i] and cost[i] + cost[j] <= r: heappush(q, (s[j], cost[i] + cost[j], -j)) if not q: continue _, c, j = heappop(q) j = -j s[i], s[j] = s[j], s[i] r -= c cost[i] = cost[j] = 0 print("".join(s)) @staticmethod def d(): k, m = map(int, sys.stdin.readline().split()) a = np.array([int(x) for x in sys.stdin.readline().split()]) c = np.array([int(x) for x in sys.stdin.readline().split()]) mask = (1 << 32) - 1 d = np.eye(k, k, -1, dtype=np.uint32) * mask d[0] = c def bitwise_dot(a, b): return np.bitwise_xor.reduce( a[:, None, :] & b.T[None, :, :], axis=-1 ) def bitwise_mat_pow(a, n): if n == 0: return np.eye(k, dtype=np.uint32) * mask res = bitwise_mat_pow(a, n // 2) res = bitwise_dot(res, res) return bitwise_dot(res, a) if n & 1 else res if m <= k: print(a[m - 1]) return print( bitwise_dot(bitwise_mat_pow(d, m - k), a[::-1].reshape(-1, 1))[ 0 ].item() ) class ABC010: @staticmethod def a(): print(sys.stdin.readline().rstrip() + "pp") @staticmethod def b(): n, *a = map(int, sys.stdin.read().split()) tot = 0 for x in a: c = 0 while x % 2 == 0 or x % 3 == 2: x -= 1 c += 1 tot += c print(tot) @staticmethod def c(): sx, sy, gx, gy, t, v, n, *xy = map(int, sys.stdin.read().split()) x, y = np.array(xy).reshape(-1, 2).T def dist(x1, y1, x2, y2): return np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) ans = ( "YES" if (dist(sx, sy, x, y) + dist(x, y, gx, gy) <= v * t).any() else "NO" ) print(ans) @staticmethod def d_1(): n, g, e = map(int, sys.stdin.readline().split()) p = [int(x) for x in sys.stdin.readline().split()] x, y = [], [] for _ in range(e): a, b = map(int, sys.stdin.readline().split()) x.append(a) y.append(b) x.append(b) y.append(a) for a in p: x.append(a) y.append(n) if not x: print(0) return c = [1] * len(x) min_cut = maximum_flow( csr_matrix((c, (x, y)), (n + 1, n + 1)), source=0, sink=n ).flow_value print(min_cut) @staticmethod def d_2(): n, g, e = map(int, sys.stdin.readline().split()) if g + e == 0: print(0) return graph = nx.DiGraph() graph.add_nodes_from(range(n + 1)) for p in [int(x) for x in sys.stdin.readline().split()]: graph.add_edge(p, n, capacity=1) for _ in range(e): a, b = map(int, sys.stdin.readline().split()) graph.add_edge(a, b, capacity=1) graph.add_edge(b, a, capacity=1) print(nx.minimum_cut_value(graph, 0, n)) @staticmethod def d_3(): n, g, e = map(int, sys.stdin.readline().split()) if g + e == 0: print(0) return graph = defaultdict(dict) for p in [int(x) for x in sys.stdin.readline().split()]: graph[p][n] = 1 graph[n][p] = 0 for a, b in zip(*[map(int, sys.stdin.read().split())] * 2): graph[a][b] = 1 graph[b][a] = 1 print(GeometryTopology.GraphTheory.MaximumFlow.dinic(graph, 0, n)) class ABC011: @staticmethod def a(): n = int(sys.stdin.readline().rstrip()) print(n % 12 + 1) @staticmethod def b(): s = sys.stdin.readline().rstrip() print(s[0].upper() + s[1:].lower()) @staticmethod def c(): n, *ng = map(int, sys.stdin.read().split()) ng = set(ng) if n in ng: print("NO") else: r = 100 while n > 0: if r == 0: print("NO") return for i in range(3, 0, -1): if (n - i) in ng: continue n -= i r -= 1 break else: print("NO") return print("YES") @staticmethod def d(): n, d, x, y = map(int, sys.stdin.read().split()) x, y = abs(x), abs(y) if x % d or y % d: print(0) return x, y = x // d, y // d r = n - (x + y) if r < 0 or r & 1: print(0) return combinatorics = Combinatorics() choose = combinatorics.choose res = 0 half_p = pow(1 / 2, n) for d in range(r // 2 + 1): # 0 <= d <= r//2, south south, north = d, y + d west = (r - 2 * d) // 2 res += ( half_p * choose(n, south) * choose(n - south, north) * choose(n - south - north, west) * half_p ) print(res) class ABC032: @staticmethod def a(): a, b, n = map(int, sys.stdin.read().split()) l = NumberTheory.lcm(a, b) print((n + l - 1) // l * l) @staticmethod def b(): s, k = sys.stdin.read().split() k = int(k) res = set() for i in range(len(s) - k + 1): res.add(s[i : i + k]) print(len(res)) @staticmethod def c(): n, k, *s = map(int, sys.stdin.read().split()) if 0 in s: print(n) return s += [inf] res = 0 l = r = 0 tmp = 1 while r <= n: tmp *= s[r] while tmp > k: res = max(res, r - l) tmp //= s[l] l += 1 r += 1 print(res) class ABC033: @staticmethod def a(): n = set(sys.stdin.readline().rstrip()) print("SAME" if len(n) == 1 else "DIFFERENT") @staticmethod def b(): n = int(sys.stdin.readline().rstrip()) res = dict() for _ in range(n): s, p = sys.stdin.readline().split() p = int(p) res[s] = p tot = sum(res.values()) for s, p in res.items(): if p > tot / 2: print(s) return print("atcoder") @staticmethod def c(): s = sys.stdin.readline().rstrip() res = sum(not "0" in f for f in s.split("+")) print(res) class ABC034: @staticmethod def a(): x, y = map(int, sys.stdin.readline().split()) print("Better" if y > x else "Worse") @staticmethod def b(): n = int(sys.stdin.readline().rstrip()) print(n + 1 if n & 1 else n - 1) @staticmethod def c(): h, w = map(int, sys.stdin.read().split()) combinatorics = Combinatorics(n=2 * 10**5, numpy=True, mod=MOD) print(combinatorics.mod_choose(h + w - 2, h - 1)) @staticmethod def d(): n, k, *wp = map(int, sys.stdin.read().split()) w, p = np.array(wp).reshape(-1, 2).T def f(x): return np.sort(w * (p - x))[-k:].sum() print(optimize.bisect(f, 0, 100)) class ABC035: @staticmethod def a(): w, h = map(int, sys.stdin.readline().split()) print("4:3" if 4 * h == 3 * w else "16:9") @staticmethod def b(): s, t = sys.stdin.read().split() y = 0 x = 0 z = 0 for c in s: if c == "?": z += 1 elif c == "L": x -= 1 elif c == "R": x += 1 elif c == "D": y -= 1 elif c == "U": y += 1 d = abs(y) + abs(x) if t == "1": print(d + z) else: print(max(d - z, (d - z) & 1)) @staticmethod def c(): n, q, *lr = map(int, sys.stdin.read().split()) l, r = np.array(lr).reshape(q, 2).T res = np.zeros(n + 1, dtype=int) np.add.at(res, l - 1, 1) np.subtract.at(res, r, 1) np.cumsum(res, out=res) res = res & 1 print("".join(map(str, res[:-1]))) @staticmethod def d(): n, m, t = map(int, sys.stdin.readline().split()) point = np.array(sys.stdin.readline().split(), dtype=int) a, b, c = ( np.array(sys.stdin.read().split(), dtype=np.int64) .reshape(m, 3) .T ) a -= 1 b -= 1 d_1 = shortest_path( csr_matrix((c, (a, b)), (n, n)), method="D", directed=True, indices=0, ) d_2 = shortest_path( csr_matrix((c, (b, a)), (n, n)), method="D", directed=True, indices=0, ) print(int(np.amax((t - (d_1 + d_2)) * point))) class ABC036: @staticmethod def a(): a, b = map(int, sys.stdin.readline().split()) print((b + a - 1) // a) @staticmethod def b(): n, *s = sys.stdin.read().split() n = int(n) for j in range(n): row = "" for i in range(n - 1, -1, -1): row += s[i][j] print(row) @staticmethod def c(): n, *a = map(int, sys.stdin.read().split()) b = [None] * n prev = None j = -1 for i, x in sorted(enumerate(a), key=lambda x: x[1]): if x != prev: j += 1 b[i] = j prev = x print(*b, sep="\n") @staticmethod def d(): n, *ab = map(int, sys.stdin.read().split()) edges = [[] for _ in range(n)] for a, b in zip(*[iter(ab)] * 2): a -= 1 b -= 1 edges[a].append(b) edges[b].append(a) parent = [None] * n def count(u): black, white = 1, 1 for v in edges[u]: if v == parent[u]: continue parent[v] = u b, w = count(v) black *= w black %= MOD white *= (b + w) % MOD white %= MOD return black, white print(sum(count(0)) % MOD) class ABC037: @staticmethod def a(): a, b, c = map(int, sys.stdin.readline().split()) print(c // min(a, b)) @staticmethod def b(): n, q, *lrt = map(int, sys.stdin.read().split()) a = np.zeros(n, dtype=int) for l, r, t in zip(*[iter(lrt)] * 3): a[l - 1 : r] = t print(*a, sep="\n") @staticmethod def c(): n, k, *a = map(int, sys.stdin.read().split()) a = np.array([0] + a) np.cumsum(a, out=a) s = (a[k:] - a[:-k]).sum() print(s) @staticmethod def d(): h, w = map(int, sys.stdin.readline().split()) a = [ [int(x) for x in sys.stdin.readline().split()] for _ in range(h) ] dyx = [(-1, 0), (0, -1), (1, 0), (0, 1)] path = [[None] * w for _ in range(h)] def paths(i, j): if path[i][j]: return path[i][j] val = a[i][j] cnt = 1 for dy, dx in dyx: y = i + dy x = j + dx if 0 <= y < h and 0 <= x < w and a[y][x] < val: cnt += paths(y, x) cnt %= MOD path[i][j] = cnt return cnt tot = 0 for i in range(h): for j in range(w): tot += paths(i, j) tot %= MOD print(tot) class ABC038: @staticmethod def a(): s = sys.stdin.readline().rstrip() print("YES" if s[-1] == "T" else "NO") @staticmethod def b(): a, b, c, d = map(int, sys.stdin.read().split()) print("YES" if a == c or b == c or a == d or b == d else "NO") @staticmethod def c(): n, *a = map(int, sys.stdin.read().split()) a += [-1] cnt = n tmp = 1 for i in range(n): if a[i + 1] > a[i]: tmp += 1 else: cnt += tmp * (tmp - 1) // 2 tmp = 1 print(cnt) @staticmethod def d(): n, *wh = map(int, sys.stdin.read().split()) wh = sorted(zip(*[iter(wh)] * 2), key=lambda x: (-x[0], x[1])) w = [x[1] for x in wh][::-1] res = [inf] * n for x in w: res[bi_l(res, x)] = x print(bi_l(res, inf)) class ABC039: @staticmethod def a(): a, b, c = map(int, sys.stdin.readline().split()) print((a * b + b * c + c * a) * 2) @staticmethod def b(): x = int(sys.stdin.readline().rstrip()) for n in range(1, int(x**0.5) + 1): if pow(n, 4) == x: print(n) return @staticmethod def c(): board = "WBWBWWBWBWBW" * 3 convert = "Do, *, Re, *, Mi, Fa, *, So, *, La, *, Si".split(", ") s = sys.stdin.readline().rstrip() print(convert[board.index(s)]) @staticmethod def d(): h, w = map(int, sys.stdin.readline().split()) s = sys.stdin.read().split() dyx = list(product((-1, 0, 1), repeat=2)) black_certain = set() black_before = set() for i in range(h): for j in range(w): black_cand = set() for dy, dx in dyx: y = i + dy x = j + dx if y < 0 or y >= h or x < 0 or x >= w: continue if s[y][x] == ".": break black_cand.add((y, x)) else: black_before.add((i, j)) black_certain |= black_cand for i in range(h): for j in range(w): if s[i][j] == "#" and not (i, j) in black_certain: print("impossible") return print("possible") for i in range(h): row = "" for j in range(w): row += "#" if (i, j) in black_before else "." print("".join(row)) class ABC040: @staticmethod def a(): n, x = map(int, sys.stdin.readline().split()) print(min(x - 1, n - x)) @staticmethod def b(): n = int(sys.stdin.readline().rstrip()) res = inf for i in range(1, int(n**0.5) + 1): res = min(res, n // i - i + n % i) print(res) @staticmethod def c(): n, *h = map(int, sys.stdin.read().split()) h = [h[0]] + h cost = [None] * (n + 1) cost[0] = cost[1] = 0 for i in range(2, n + 1): cost[i] = min( cost[i - 2] + abs(h[i] - h[i - 2]), cost[i - 1] + abs(h[i] - h[i - 1]), ) print(cost[n]) @staticmethod def d(): n, m = map(int, sys.stdin.readline().split()) uf = UnionFind(n=n) queue = [] for _ in range(m): a, b, y = map(int, sys.stdin.readline().split()) heappush(queue, (-(2 * y), a - 1, b - 1)) q = int(sys.stdin.readline().rstrip()) for i in range(q): v, y = map(int, sys.stdin.readline().split()) heappush(queue, (-(2 * y + 1), v - 1, i)) res = [None] * q while queue: y, i, j = heappop(queue) if y & 1: res[j] = uf.size[uf.find_root(i)] else: uf.unite(i, j) print(*res, sep="\n") class ABC041: @staticmethod def a(): s, i = sys.stdin.read().split() i = int(i) print(s[i - 1]) @staticmethod def b(): a, b, c = map(int, sys.stdin.readline().split()) ans = a * b % MOD * c % MOD print(ans) @staticmethod def c(): n, *a = map(int, sys.stdin.read().split()) for i, h in sorted(enumerate(a), key=lambda x: -x[1]): print(i + 1) @staticmethod def d(): n, m, *xy = map(int, sys.stdin.read().split()) (*xy,) = zip(*[iter(xy)] * 2) edges = [0] * n for x, y in xy: x -= 1 y -= 1 edges[x] |= 1 << y comb = [None] * (1 << n) comb[0] = 1 def count(edges, bit): if comb[bit] is not None: return comb[bit] comb[bit] = 0 for i in range(n): if (bit >> i) & 1 and not edges[i]: nxt_bit = bit & ~(1 << i) nxt_edges = edges.copy() for j in range(n): nxt_edges[j] &= ~(1 << i) cnt = count(nxt_edges, nxt_bit) comb[bit] += cnt return comb[bit] print(count(edges, (1 << n) - 1)) class ABC042: @staticmethod def a(): a = [int(x) for x in sys.stdin.readline().split()] c = Counter(a) print("YES" if c[5] == 2 and c[7] == 1 else "NO") @staticmethod def b(): n, l, *s = sys.stdin.read().split() print("".join(sorted(s))) @staticmethod def c(): n, k, *d = sys.stdin.read().split() l = len(n) ok = sorted(set(string.digits) - set(d)) cand = [int("".join(p)) for p in product(ok, repeat=l)] + [ int(min(x for x in ok if x > "0") + min(ok) * l) ] print(cand[bi_l(cand, int(n))]) @staticmethod def d(): h, w, a, b = map(int, sys.stdin.read().split()) combinatorics = Combinatorics(n=2 * 10**5, mod=MOD, numpy=True) tot = combinatorics.mod_choose(h + w - 2, h - 1) i = np.arange(h - a, h) ng = np.sum( combinatorics.mod_choose(i + b - 1, i) * combinatorics.mod_choose(h - i + w - b - 2, h - 1 - i) % MOD ) tot -= ng tot %= MOD print(tot) class ABC043: @staticmethod def a(): n = int(sys.stdin.readline().rstrip()) print((1 + n) * n // 2) @staticmethod def b(): s = sys.stdin.readline().rstrip() t = "" for c in s: if c == "B": t = t[:-1] else: t += c print(t) @staticmethod def c(): n, *a = map(int, sys.stdin.read().split()) a = np.array(a) x = np.around(a.sum() / n).astype(int) print(np.sum((a - x) ** 2)) @staticmethod def d(): s = sys.stdin.readline().rstrip() n = len(s) for i in range(n - 1): if s[i] == s[i + 1]: print(i + 1, i + 2) return for i in range(n - 2): if s[i] == s[i + 2]: print(i + 1, i + 3) return print(-1, -1) class ABC170: @staticmethod def a(): x = [int(x) for x in sys.stdin.readline().split()] for i in range(5): if x[i] != i + 1: print(i + 1) break @staticmethod def b(): x, y = map(int, sys.stdin.readline().split()) print("Yes" if 2 * x <= y <= 4 * x and y % 2 == 0 else "No") @staticmethod def c(): x, n, *p = map(int, sys.stdin.read().split()) a = list(set(range(102)) - set(p)) a = [(abs(y - x), y) for y in a] print(sorted(a)[0][1]) @staticmethod def d(): n, *a = map(int, sys.stdin.read().split()) cand = set(a) cnt = 0 for x, c in sorted(Counter(a).items()): cnt += c == 1 and x in cand cand -= set(range(x * 2, 10**6 + 1, x)) print(cnt) @staticmethod def e(): n, q = map(int, sys.stdin.readline().split()) queue = [] m = 2 * 10**5 infants = [[] for _ in range(m)] highest_rate = [None] * m where = [None] * n rate = [None] * n def entry(i, k): where[i] = k while infants[k]: r, j = heappop(infants[k]) if where[j] != k or j == i: continue if rate[i] >= -r: highest_rate[k] = rate[i] heappush(queue, (rate[i], k, i)) heappush(infants[k], (r, j)) break else: highest_rate[k] = rate[i] heappush(queue, (rate[i], k, i)) heappush(infants[k], (-rate[i], i)) def transfer(i, k): now = where[i] while infants[now]: r, j = heappop(infants[now]) if where[j] != now or j == i: continue if highest_rate[now] != -r: highest_rate[now] = -r heappush(queue, (-r, now, j)) heappush(infants[now], (r, j)) break else: highest_rate[now] = None entry(i, k) def inquire(): while True: r, k, i = heappop(queue) if where[i] != k or r != highest_rate[k]: continue heappush(queue, (r, k, i)) return r for i in range(n): a, b = map(int, sys.stdin.readline().split()) rate[i] = a entry(i, b - 1) for _ in range(q): c, d = map(int, sys.stdin.readline().split()) transfer(c - 1, d - 1) print(inquire()) class ABC171: @staticmethod def a(): c = sys.stdin.readline().rstrip() print("A" if c < "a" else "a") @staticmethod def b(): n, k, *p = map(int, sys.stdin.read().split()) print(sum(sorted(p)[:k])) @staticmethod def c(): n = int(sys.stdin.readline().rstrip()) n -= 1 l = 1 while True: if n < pow(26, l): break n -= pow(26, l) l += 1 res = "".join( [chr(ord("a") + d) for d in NumberTheory.base_convert(n, 26)][ ::-1 ] ) res = "a" * (l - len(res)) + res print(res) @staticmethod def d(): n = int(sys.stdin.readline().rstrip()) a = [int(x) for x in sys.stdin.readline().split()] s = sum(a) cnt = Counter(a) q = int(sys.stdin.readline().rstrip()) for _ in range(q): b, c = map(int, sys.stdin.readline().split()) s += (c - b) * cnt[b] print(s) cnt[c] += cnt[b] cnt[b] = 0 @staticmethod def e(): n, *a = map(int, sys.stdin.read().split()) s = 0 for x in a: s ^= x b = map(lambda x: x ^ s, a) print(*b, sep=" ") class ABC172: @staticmethod def a(): a = int(sys.stdin.readline().rstrip()) print(a * (1 + a + a**2)) @staticmethod def b(): s, t = sys.stdin.read().split() print(sum(s[i] != t[i] for i in range(len(s)))) @staticmethod def c(): n, m, k = map(int, sys.stdin.readline().split()) a = [0] + [int(x) for x in sys.stdin.readline().split()] b = [int(x) for x in sys.stdin.readline().split()] (*sa,) = accumulate(a) (*sb,) = accumulate(b) res = 0 for i in range(n + 1): r = k - sa[i] if r < 0: break res = max(res, i + bi_r(sb, r)) print(res) @staticmethod def d(): n = int(sys.stdin.readline().rstrip()) f = np.zeros(n + 1, dtype=np.int64) for i in range(1, n + 1): f[i::i] += 1 print((np.arange(1, n + 1) * f[1:]).sum()) class ABC173: @staticmethod def a(): n = int(sys.stdin.readline().rstrip()) charge = (n + 999) // 1000 * 1000 - n print(charge) @staticmethod def b(): n, *s = sys.stdin.read().split() c = Counter(s) for v in "AC, WA, TLE, RE".split(", "): print(f"{v} x {c[v]}") @staticmethod def c(): h, w, k = map(int, sys.stdin.readline().split()) c = [sys.stdin.readline().rstrip() for _ in range(h)] tot = 0 for i in range(1 << h): for j in range(1 << w): cnt = 0 for y in range(h): for x in range(w): if i >> y & 1 or j >> x & 1: continue cnt += c[y][x] == "#" tot += cnt == k print(tot) @staticmethod def d(): n, *a = map(int, sys.stdin.read().split()) a.sort(reverse=True) res = ( a[0] + sum(a[1 : 1 + (n - 2) // 2]) * 2 + a[1 + (n - 2) // 2] * (n & 1) ) print(res) @staticmethod def e(): MOD = 10**9 + 7 n, k, *a = map(int, sys.stdin.read().split()) minus = [x for x in a if x < 0] plus = [x for x in a if x > 0] if len(plus) + len(minus) // 2 * 2 >= k: # plus (*minus,) = map(abs, minus) minus.sort(reverse=True) plus.sort(reverse=True) cand = [] if len(minus) & 1: minus = minus[:-1] for i in range(0, len(minus) - 1, 2): cand.append(minus[i] * minus[i + 1] % MOD) if k & 1: res = plus[0] plus = plus[1:] else: res = 1 if len(plus) & 1: plus = plus[:-1] for i in range(0, len(plus) - 1, 2): cand.append(plus[i] * plus[i + 1] % MOD) cand.sort(reverse=True) for x in cand[: k // 2]: res *= x res %= MOD print(res) elif 0 in a: print(0) else: cand = sorted(map(abs, a)) res = 1 for i in range(k): res *= cand[i] res %= MOD res = MOD - res print(res) pass class ABC174: @staticmethod def a(): print("Yes" if int(sys.stdin.readline().rstrip()) >= 30 else "No") class ACL001: @staticmethod def a(): n, *xy = map(int, sys.stdin.read().split()) (*xy,) = zip(*[iter(xy)] * 2) print(xy) pass class MSolutions2020: @staticmethod def a(): x = int(sys.stdin.readline().rstrip()) x -= 400 print(8 - x // 200) @staticmethod def b(): r, g, b, k = map(int, sys.stdin.read().split()) while k and g <= r: g *= 2 k -= 1 while k and b <= g: b *= 2 k -= 1 print("Yes" if r < g < b else "No") @staticmethod def c(): n, k, *a = map(int, sys.stdin.read().split()) for i in range(k, n): print("Yes" if a[i] > a[i - k] else "No") @staticmethod def d(): n, *a = map(int, sys.stdin.read().split()) a += [-1] m = 1000 s = 0 for i in range(n): if a[i + 1] == a[i]: continue elif a[i + 1] > a[i]: cnt = m // a[i] m -= a[i] * cnt s += cnt else: m += a[i] * s s = 0 print(m) class Codeforces: pass class ProjectEuler: @staticmethod def p1(): def f(n, x): return (x + n // x * x) * (n // x) // 2 n = 1000 ans = f(n - 1, 3) + f(n - 1, 5) - f(n - 1, 15) print(ans) @staticmethod def p2(): fib = [1, 2] while fib[-1] < 4 * 10**6: fib.append(fib[-1] + fib[-2]) print(sum(fib[1:-1:3])) @staticmethod def p3(): number_theory = NumberTheory() res = number_theory.prime_factorize(600851475143) print(max(res.keys())) @staticmethod def p4(): def is_palindrome(n): n = str(n) return n == n[::-1] cand = [] for a in range(100, 1000): for b in range(a, 1000): n = a * b if is_palindrome(n): cand.append(n) print(max(cand)) @staticmethod def p5(): number_theory = NumberTheory() res = defaultdict(int) for i in range(1, 21): for p, c in number_theory.prime_factorize(i).items(): res[p] = max(res[p], c) ans = 1 for p, c in res.items(): ans *= pow(p, c) print(ans) @staticmethod def p6(): a = np.arange(101) b = np.cumsum(a**2) a = a.cumsum() print(a[100] ** 2 - b[100]) @staticmethod def p7(): number_theory = NumberTheory() print(sorted(number_theory.prime_numbers)[10000]) @staticmethod def p8(): n = "7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450" n = [int(d) for d in list(n)] res = 0 for i in range(988): x = 1 for j in range(13): x *= n[i + j] res = max(res, x) print(res) @staticmethod def p9(): for a in range(1, 997): for b in range(a, 998 - a): c = 1000 - a - b if a**2 + b**2 == c**2: print(a * b * c) return @staticmethod def p10(): number_theory = NumberTheory(2 * 10**6 - 1) print(sum(number_theory.prime_numbers)) @staticmethod def p11(): grid = "08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08 49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00 81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65 52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91 22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80 24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50 32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70 67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21 24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72 21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95 78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92 16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57 86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58 19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40 04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66 88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69 04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36 20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16 20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54 01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48" # grid = np.array(grid.split(), dtype=np.int64).reshape(20, -1) # cand = [] # for i in range(20): # bl1 = i+3 < 20 # for j in range(20): # bl2 = j+3 < 20 # if bl1: # np.prod # tmp = 1 # for d in range(4): # tmp *= grid[i+d, j] print(grid) pass class Yukicoder: pass if __name__ == "__main__": AtCoder.ABC011.d()

32.137521

1,217

0.374293

a438ef5c97e48703427243c06af3a37a0d3e6373

738

py

Python

src/infrastructure/database/connection.py

YegorMedvedev/python-onion-scaffold

16ca5eebe58b3aee7dd304cc9f7856de6309b5cc

[ "MIT" ]

1

2022-02-23T05:07:09.000Z

src/infrastructure/database/connection.py

YegorMedvedev/python-onion-scaffold

16ca5eebe58b3aee7dd304cc9f7856de6309b5cc

[ "MIT" ]

null

src/infrastructure/database/connection.py

YegorMedvedev/python-onion-scaffold

16ca5eebe58b3aee7dd304cc9f7856de6309b5cc

[ "MIT" ]

null

from os import getenv from sqlalchemy import create_engine from sqlalchemy.engine import Engine from sqlalchemy.ext.declarative import declarative_base, DeclarativeMeta from sqlalchemy.orm import sessionmaker, Session as SqlSession from infrastructure.utils.utils import Singleton class DatabaseConnection(metaclass=Singleton): engine: Engine session: SqlSession base: DeclarativeMeta = declarative_base() def establish_connection(self): postgresql_url = getenv("POSTGRESQL_URL") self.engine = create_engine(postgresql_url) def generate_database_schema(self): self.base.metadata.create_all(self.engine) def create_session(self): self.session = sessionmaker(bind=self.engine)

29.52

72

0.776423

10ffe8bd1c76095040531ba2a0758192085b9081

384

py

Python

bebras/2012/2012-IT-04/genImgs.py

davidnarum/bebras-tasks

57deb129451c0b95947e4ac0cac5022759a4095e

[ "MIT" ]

1

2021-05-20T12:01:21.000Z

bebras/2012/2012-IT-04/genImgs.py

davidnarum/bebras-tasks

57deb129451c0b95947e4ac0cac5022759a4095e

[ "MIT" ]

1

2019-09-21T02:22:24.000Z

2021-05-29T04:57:27.000Z

bebras/2012/2012-IT-04/genImgs.py

davidnarum/bebras-tasks

57deb129451c0b95947e4ac0cac5022759a4095e

[ "MIT" ]

5

2017-08-14T21:03:48.000Z

2020-11-01T14:29:59.000Z

#! /usr/bin/python3 -B conv = {} conv['2012-IT-04_carte_bouee-original.png'] = '-quality 50 -colors 50' conv['2012-IT-04_carte_bouee_V2-original.png'] = '-quality 50 -colors 50' conv['2012-IT-04_solution-original.png'] = '-quality 50 -colors 50' if __name__ == '__main__': import sys, os sys.path.append(os.path.join('..', 'scripts')) from common import * execute(conv)

29.538462

73

0.677083

4be6a81e32af429a9352eb6eb972c5c180fe0996

39,116

py

Python

yolov5/utils/datasets.py

kungfumas/American-Sign-Language

393ba1ba066a3e3e4a60076415fba902649121b3

[ "MIT" ]

null

yolov5/utils/datasets.py

kungfumas/American-Sign-Language

393ba1ba066a3e3e4a60076415fba902649121b3

[ "MIT" ]

null

yolov5/utils/datasets.py

kungfumas/American-Sign-Language

393ba1ba066a3e3e4a60076415fba902649121b3

[ "MIT" ]

null

import glob import math import os import random import shutil import time from pathlib import Path from threading import Thread import cv2 import numpy as np import torch from PIL import Image, ExifTags from torch.utils.data import Dataset from tqdm import tqdm from utils.general import xyxy2xywh, xywh2xyxy, torch_distributed_zero_first help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dng'] vid_formats = ['.mov', '.avi', '.mp4', '.mpg', '.mpeg', '.m4v', '.wmv', '.mkv'] # Get orientation exif tag for orientation in ExifTags.TAGS.keys(): if ExifTags.TAGS[orientation] == 'Orientation': break def get_hash(files): # Returns a single hash value of a list of files return sum(os.path.getsize(f) for f in files if os.path.isfile(f)) def exif_size(img): # Returns exif-corrected PIL size s = img.size # (width, height) try: rotation = dict(img._getexif().items())[orientation] if rotation == 6: # rotation 270 s = (s[1], s[0]) elif rotation == 8: # rotation 90 s = (s[1], s[0]) except: pass return s def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False, rank=-1, world_size=1, workers=8): # Make sure only the first process in DDP process the dataset first, and the following others can use the cache. with torch_distributed_zero_first(rank): dataset = LoadImagesAndLabels(path, imgsz, batch_size, augment=augment, # augment images hyp=hyp, # augmentation hyperparameters rect=rect, # rectangular training cache_images=cache, single_cls=opt.single_cls, stride=int(stride), pad=pad, rank=rank) batch_size = min(batch_size, len(dataset)) nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers]) # number of workers sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None dataloader = InfiniteDataLoader(dataset, batch_size=batch_size, num_workers=nw, sampler=sampler, pin_memory=True, collate_fn=LoadImagesAndLabels.collate_fn) # torch.utils.data.DataLoader() return dataloader, dataset class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader): """ Dataloader that reuses workers. Uses same syntax as vanilla DataLoader. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) self.iterator = super().__iter__() def __len__(self): return len(self.batch_sampler.sampler) def __iter__(self): for i in range(len(self)): yield next(self.iterator) class _RepeatSampler(object): """ Sampler that repeats forever. Args: sampler (Sampler) """ def __init__(self, sampler): self.sampler = sampler def __iter__(self): while True: yield from iter(self.sampler) class LoadImages: # for inference def __init__(self, path, img_size=640): p = str(Path(path)) # os-agnostic p = os.path.abspath(p) # absolute path if '*' in p: files = sorted(glob.glob(p, recursive=True)) # glob elif os.path.isdir(p): files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir elif os.path.isfile(p): files = [p] # files else: raise Exception('ERROR: %s does not exist' % p) images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats] videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats] ni, nv = len(images), len(videos) self.img_size = img_size self.files = images + videos self.nf = ni + nv # number of files self.video_flag = [False] * ni + [True] * nv self.mode = 'images' if any(videos): self.new_video(videos[0]) # new video else: self.cap = None assert self.nf > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \ (p, img_formats, vid_formats) def __iter__(self): self.count = 0 return self def __next__(self): if self.count == self.nf: raise StopIteration path = self.files[self.count] if self.video_flag[self.count]: # Read video self.mode = 'video' ret_val, img0 = self.cap.read() if not ret_val: self.count += 1 self.cap.release() if self.count == self.nf: # last video raise StopIteration else: path = self.files[self.count] self.new_video(path) ret_val, img0 = self.cap.read() self.frame += 1 print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nf, self.frame, self.nframes, path), end='') else: # Read image self.count += 1 img0 = cv2.imread(path) # BGR assert img0 is not None, 'Image Not Found ' + path print('image %g/%g %s: ' % (self.count, self.nf, path), end='') # Padded resize img = letterbox(img0, new_shape=self.img_size)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) # cv2.imwrite(path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image return path, img, img0, self.cap def new_video(self, path): self.frame = 0 self.cap = cv2.VideoCapture(path) self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) def __len__(self): return self.nf # number of files class LoadWebcam: # for inference def __init__(self, pipe=0, img_size=640): self.img_size = img_size if pipe == '0': pipe = 0 # local camera # pipe = 'rtsp://192.168.1.64/1' # IP camera # pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login # pipe = 'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa' # IP traffic camera # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera # https://answers.opencv.org/question/215996/changing-gstreamer-pipeline-to-opencv-in-pythonsolved/ # pipe = '"rtspsrc location="rtsp://username:password@192.168.1.64/1" latency=10 ! appsink' # GStreamer # https://answers.opencv.org/question/200787/video-acceleration-gstremer-pipeline-in-videocapture/ # https://stackoverflow.com/questions/54095699/install-gstreamer-support-for-opencv-python-package # install help # pipe = "rtspsrc location=rtsp://root:root@192.168.0.91:554/axis-media/media.amp?videocodec=h264&resolution=3840x2160 protocols=GST_RTSP_LOWER_TRANS_TCP ! rtph264depay ! queue ! vaapih264dec ! videoconvert ! appsink" # GStreamer self.pipe = pipe self.cap = cv2.VideoCapture(pipe) # video capture object self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size def __iter__(self): self.count = -1 return self def __next__(self): self.count += 1 if cv2.waitKey(1) == ord('q'): # q to quit self.cap.release() cv2.destroyAllWindows() raise StopIteration # Read frame if self.pipe == 0: # local camera ret_val, img0 = self.cap.read() img0 = cv2.flip(img0, 1) # flip left-right else: # IP camera n = 0 while True: n += 1 self.cap.grab() if n % 30 == 0: # skip frames ret_val, img0 = self.cap.retrieve() if ret_val: break # Print assert ret_val, 'Camera Error %s' % self.pipe img_path = 'webcam.jpg' print('webcam %g: ' % self.count, end='') # Padded resize img = letterbox(img0, new_shape=self.img_size)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) return img_path, img, img0, None def __len__(self): return 0 class LoadStreams: # multiple IP or RTSP cameras def __init__(self, sources='streams.txt', img_size=640): self.mode = 'images' self.img_size = img_size if os.path.isfile(sources): with open(sources, 'r') as f: sources = [x.strip() for x in f.read().splitlines() if len(x.strip())] else: sources = [sources] n = len(sources) self.imgs = [None] * n self.sources = sources for i, s in enumerate(sources): # Start the thread to read frames from the video stream print('%g/%g: %s... ' % (i + 1, n, s), end='') cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s) assert cap.isOpened(), 'Failed to open %s' % s w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = cap.get(cv2.CAP_PROP_FPS) % 100 _, self.imgs[i] = cap.read() # guarantee first frame thread = Thread(target=self.update, args=([i, cap]), daemon=True) print(' success (%gx%g at %.2f FPS).' % (w, h, fps)) thread.start() print('') # newline # check for common shapes s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0) # inference shapes self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal if not self.rect: print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') def update(self, index, cap): # Read next stream frame in a daemon thread n = 0 while cap.isOpened(): n += 1 # _, self.imgs[index] = cap.read() cap.grab() if n == 4: # read every 4th frame _, self.imgs[index] = cap.retrieve() n = 0 time.sleep(0.01) # wait time def __iter__(self): self.count = -1 return self def __next__(self): self.count += 1 img0 = self.imgs.copy() if cv2.waitKey(1) == ord('q'): # q to quit cv2.destroyAllWindows() raise StopIteration # Letterbox img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0] # Stack img = np.stack(img, 0) # Convert img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416 img = np.ascontiguousarray(img) return self.sources, img, img0, None def __len__(self): return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years class LoadImagesAndLabels(Dataset): # for training/testing def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, cache_images=False, single_cls=False, stride=32, pad=0.0, rank=-1): try: f = [] # image files for p in path if isinstance(path, list) else [path]: p = str(Path(p)) # os-agnostic parent = str(Path(p).parent) + os.sep if os.path.isfile(p): # file with open(p, 'r') as t: t = t.read().splitlines() f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path elif os.path.isdir(p): # folder f += glob.iglob(p + os.sep + '*.*') else: raise Exception('%s does not exist' % p) self.img_files = sorted( [x.replace('/', os.sep) for x in f if os.path.splitext(x)[-1].lower() in img_formats]) except Exception as e: raise Exception('Error loading data from %s: %s\nSee %s' % (path, e, help_url)) n = len(self.img_files) assert n > 0, 'No images found in %s. See %s' % (path, help_url) bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index nb = bi[-1] + 1 # number of batches self.n = n # number of images self.batch = bi # batch index of image self.img_size = img_size self.augment = augment self.hyp = hyp self.image_weights = image_weights self.rect = False if image_weights else rect self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) self.mosaic_border = [-img_size // 2, -img_size // 2] self.stride = stride # Define labels sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings self.label_files = [x.replace(sa, sb, 1).replace(os.path.splitext(x)[-1], '.txt') for x in self.img_files] # Check cache cache_path = str(Path(self.label_files[0]).parent) + '.cache' # cached labels if os.path.isfile(cache_path): cache = torch.load(cache_path) # load if cache['hash'] != get_hash(self.label_files + self.img_files): # dataset changed cache = self.cache_labels(cache_path) # re-cache else: cache = self.cache_labels(cache_path) # cache # Get labels labels, shapes = zip(*[cache[x] for x in self.img_files]) self.shapes = np.array(shapes, dtype=np.float64) self.labels = list(labels) # Rectangular Training https://github.com/ultralytics/yolov3/issues/232 if self.rect: # Sort by aspect ratio s = self.shapes # wh ar = s[:, 1] / s[:, 0] # aspect ratio irect = ar.argsort() self.img_files = [self.img_files[i] for i in irect] self.label_files = [self.label_files[i] for i in irect] self.labels = [self.labels[i] for i in irect] self.shapes = s[irect] # wh ar = ar[irect] # Set training image shapes shapes = [[1, 1]] * nb for i in range(nb): ari = ar[bi == i] mini, maxi = ari.min(), ari.max() if maxi < 1: shapes[i] = [maxi, 1] elif mini > 1: shapes[i] = [1, 1 / mini] self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride # Cache labels create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False nm, nf, ne, ns, nd = 0, 0, 0, 0, 0 # number missing, found, empty, datasubset, duplicate pbar = enumerate(self.label_files) if rank in [-1, 0]: pbar = tqdm(pbar) for i, file in pbar: l = self.labels[i] # label if l is not None and l.shape[0]: assert l.shape[1] == 5, '> 5 label columns: %s' % file assert (l >= 0).all(), 'negative labels: %s' % file assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file if np.unique(l, axis=0).shape[0] < l.shape[0]: # duplicate rows nd += 1 # print('WARNING: duplicate rows in %s' % self.label_files[i]) # duplicate rows if single_cls: l[:, 0] = 0 # force dataset into single-class mode self.labels[i] = l nf += 1 # file found # Create subdataset (a smaller dataset) if create_datasubset and ns < 1E4: if ns == 0: create_folder(path='./datasubset') os.makedirs('./datasubset/images') exclude_classes = 43 if exclude_classes not in l[:, 0]: ns += 1 # shutil.copy(src=self.img_files[i], dst='./datasubset/images/') # copy image with open('./datasubset/images.txt', 'a') as f: f.write(self.img_files[i] + '\n') # Extract object detection boxes for a second stage classifier if extract_bounding_boxes: p = Path(self.img_files[i]) img = cv2.imread(str(p)) h, w = img.shape[:2] for j, x in enumerate(l): f = '%s%sclassifier%s%g_%g_%s' % (p.parent.parent, os.sep, os.sep, x[0], j, p.name) if not os.path.exists(Path(f).parent): os.makedirs(Path(f).parent) # make new output folder b = x[1:] * [w, h, w, h] # box b[2:] = b[2:].max() # rectangle to square b[2:] = b[2:] * 1.3 + 30 # pad b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int) b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image b[[1, 3]] = np.clip(b[[1, 3]], 0, h) assert cv2.imwrite(f, img[b[1]:b[3], b[0]:b[2]]), 'Failure extracting classifier boxes' else: ne += 1 # print('empty labels for image %s' % self.img_files[i]) # file empty # os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i])) # remove if rank in [-1, 0]: pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % ( cache_path, nf, nm, ne, nd, n) if nf == 0: s = 'WARNING: No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url) print(s) assert not augment, '%s. Can not train without labels.' % s # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) self.imgs = [None] * n if cache_images: gb = 0 # Gigabytes of cached images pbar = tqdm(range(len(self.img_files)), desc='Caching images') self.img_hw0, self.img_hw = [None] * n, [None] * n for i in pbar: # max 10k images self.imgs[i], self.img_hw0[i], self.img_hw[i] = load_image(self, i) # img, hw_original, hw_resized gb += self.imgs[i].nbytes pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9) def cache_labels(self, path='labels.cache'): # Cache dataset labels, check images and read shapes x = {} # dict pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files)) for (img, label) in pbar: try: l = [] image = Image.open(img) image.verify() # PIL verify # _ = io.imread(img) # skimage verify (from skimage import io) shape = exif_size(image) # image size assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels' if os.path.isfile(label): with open(label, 'r') as f: l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32) # labels if len(l) == 0: l = np.zeros((0, 5), dtype=np.float32) x[img] = [l, shape] except Exception as e: x[img] = [None, None] print('WARNING: %s: %s' % (img, e)) x['hash'] = get_hash(self.label_files + self.img_files) torch.save(x, path) # save for next time return x def __len__(self): return len(self.img_files) # def __iter__(self): # self.count = -1 # print('ran dataset iter') # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) # return self def __getitem__(self, index): if self.image_weights: index = self.indices[index] hyp = self.hyp mosaic = self.mosaic and random.random() < hyp['mosaic'] if mosaic: # Load mosaic img, labels = load_mosaic(self, index) shapes = None # MixUp https://arxiv.org/pdf/1710.09412.pdf if random.random() < hyp['mixup']: img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1)) r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0 img = (img * r + img2 * (1 - r)).astype(np.uint8) labels = np.concatenate((labels, labels2), 0) else: # Load image img, (h0, w0), (h, w) = load_image(self, index) # Letterbox shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling # Load labels labels = [] x = self.labels[index] if x.size > 0: # Normalized xywh to pixel xyxy format labels = x.copy() labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0] # pad width labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1] # pad height labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0] labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1] if self.augment: # Augment imagespace if not mosaic: img, labels = random_perspective(img, labels, degrees=hyp['degrees'], translate=hyp['translate'], scale=hyp['scale'], shear=hyp['shear'], perspective=hyp['perspective']) # Augment colorspace augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) # Apply cutouts # if random.random() < 0.9: # labels = cutout(img, labels) nL = len(labels) # number of labels if nL: labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1 labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1 if self.augment: # flip up-down if random.random() < hyp['flipud']: img = np.flipud(img) if nL: labels[:, 2] = 1 - labels[:, 2] # flip left-right if random.random() < hyp['fliplr']: img = np.fliplr(img) if nL: labels[:, 1] = 1 - labels[:, 1] labels_out = torch.zeros((nL, 6)) if nL: labels_out[:, 1:] = torch.from_numpy(labels) # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) return torch.from_numpy(img), labels_out, self.img_files[index], shapes @staticmethod def collate_fn(batch): img, label, path, shapes = zip(*batch) # transposed for i, l in enumerate(label): l[:, 0] = i # add target image index for build_targets() return torch.stack(img, 0), torch.cat(label, 0), path, shapes # Ancillary functions -------------------------------------------------------------------------------------------------- def load_image(self, index): # loads 1 image from dataset, returns img, original hw, resized hw img = self.imgs[index] if img is None: # not cached path = self.img_files[index] img = cv2.imread(path) # BGR assert img is not None, 'Image Not Found ' + path h0, w0 = img.shape[:2] # orig hw r = self.img_size / max(h0, w0) # resize image to img_size if r != 1: # always resize down, only resize up if training with augmentation interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp) return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized else: return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) dtype = img.dtype # uint8 x = np.arange(0, 256, dtype=np.int16) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed # Histogram equalization # if random.random() < 0.2: # for i in range(3): # img[:, :, i] = cv2.equalizeHist(img[:, :, i]) def load_mosaic(self, index): # loads images in a mosaic labels4 = [] s = self.img_size yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices for i, index in enumerate(indices): # Load image img, _, (h, w) = load_image(self, index) # place img in img4 if i == 0: # top left img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) elif i == 1: # top right x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h elif i == 2: # bottom left x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, max(xc, w), min(y2a - y1a, h) elif i == 3: # bottom right x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] padw = x1a - x1b padh = y1a - y1b # Labels x = self.labels[index] labels = x.copy() if x.size > 0: # Normalized xywh to pixel xyxy format labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh labels4.append(labels) # Concat/clip labels if len(labels4): labels4 = np.concatenate(labels4, 0) np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_perspective # img4, labels4 = replicate(img4, labels4) # replicate # Augment img4, labels4 = random_perspective(img4, labels4, degrees=self.hyp['degrees'], translate=self.hyp['translate'], scale=self.hyp['scale'], shear=self.hyp['shear'], perspective=self.hyp['perspective'], border=self.mosaic_border) # border to remove return img4, labels4 def replicate(img, labels): # Replicate labels h, w = img.shape[:2] boxes = labels[:, 1:].astype(int) x1, y1, x2, y2 = boxes.T s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices x1b, y1b, x2b, y2b = boxes[i] bh, bw = y2b - y1b, x2b - x1b yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) return img, labels def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True): # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 shape = img.shape[:2] # current shape [height, width] if isinstance(new_shape, int): new_shape = (new_shape, new_shape) # Scale ratio (new / old) r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) if not scaleup: # only scale down, do not scale up (for better test mAP) r = min(r, 1.0) # Compute padding ratio = r, r # width, height ratios new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding if auto: # minimum rectangle dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding elif scaleFill: # stretch dw, dh = 0.0, 0.0 new_unpad = (new_shape[1], new_shape[0]) ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios dw /= 2 # divide padding into 2 sides dh /= 2 if shape[::-1] != new_unpad: # resize img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border return img, ratio, (dw, dh) def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)): # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) # targets = [cls, xyxy] height = img.shape[0] + border[0] * 2 # shape(h,w,c) width = img.shape[1] + border[1] * 2 # Center C = np.eye(3) C[0, 2] = -img.shape[1] / 2 # x translation (pixels) C[1, 2] = -img.shape[0] / 2 # y translation (pixels) # Perspective P = np.eye(3) P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) # Rotation and Scale R = np.eye(3) a = random.uniform(-degrees, degrees) # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations s = random.uniform(1 - scale, 1 + scale) # s = 2 ** random.uniform(-scale, scale) R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) # Shear S = np.eye(3) S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) # Translation T = np.eye(3) T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) # Combined rotation matrix M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed if perspective: img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) else: # affine img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) # Visualize # import matplotlib.pyplot as plt # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() # ax[0].imshow(img[:, :, ::-1]) # base # ax[1].imshow(img2[:, :, ::-1]) # warped # Transform label coordinates n = len(targets) if n: # warp points xy = np.ones((n * 4, 3)) xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 xy = xy @ M.T # transform if perspective: xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale else: # affine xy = xy[:, :2].reshape(n, 8) # create new boxes x = xy[:, [0, 2, 4, 6]] y = xy[:, [1, 3, 5, 7]] xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T # # apply angle-based reduction of bounding boxes # radians = a * math.pi / 180 # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5 # x = (xy[:, 2] + xy[:, 0]) / 2 # y = (xy[:, 3] + xy[:, 1]) / 2 # w = (xy[:, 2] - xy[:, 0]) * reduction # h = (xy[:, 3] - xy[:, 1]) * reduction # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T # clip boxes xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width) xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height) # filter candidates i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T) targets = targets[i] targets[:, 1:5] = xy[i] return img, targets def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1): # box1(4,n), box2(4,n) # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio w1, h1 = box1[2] - box1[0], box1[3] - box1[1] w2, h2 = box2[2] - box2[0], box2[3] - box2[1] ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16)) # aspect ratio return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr) & (ar < ar_thr) # candidates def cutout(image, labels): # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 h, w = image.shape[:2] def bbox_ioa(box1, box2): # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 box2 = box2.transpose() # Get the coordinates of bounding boxes b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] # Intersection area inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) # box2 area box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 # Intersection over box2 area return inter_area / box2_area # create random masks scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction for s in scales: mask_h = random.randint(1, int(h * s)) mask_w = random.randint(1, int(w * s)) # box xmin = max(0, random.randint(0, w) - mask_w // 2) ymin = max(0, random.randint(0, h) - mask_h // 2) xmax = min(w, xmin + mask_w) ymax = min(h, ymin + mask_h) # apply random color mask image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] # return unobscured labels if len(labels) and s > 0.03: box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area labels = labels[ioa < 0.60] # remove >60% obscured labels return labels def reduce_img_size(path='path/images', img_size=1024): # from utils.datasets import *; reduce_img_size() # creates a new ./images_reduced folder with reduced size images of maximum size img_size path_new = path + '_reduced' # reduced images path create_folder(path_new) for f in tqdm(glob.glob('%s/*.*' % path)): try: img = cv2.imread(f) h, w = img.shape[:2] r = img_size / max(h, w) # size ratio if r < 1.0: img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_AREA) # _LINEAR fastest fnew = f.replace(path, path_new) # .replace(Path(f).suffix, '.jpg') cv2.imwrite(fnew, img) except: print('WARNING: image failure %s' % f) def recursive_dataset2bmp(dataset='path/dataset_bmp'): # from utils.datasets import *; recursive_dataset2bmp() # Converts dataset to bmp (for faster training) formats = [x.lower() for x in img_formats] + [x.upper() for x in img_formats] for a, b, files in os.walk(dataset): for file in tqdm(files, desc=a): p = a + '/' + file s = Path(file).suffix if s == '.txt': # replace text with open(p, 'r') as f: lines = f.read() for f in formats: lines = lines.replace(f, '.bmp') with open(p, 'w') as f: f.write(lines) elif s in formats: # replace image cv2.imwrite(p.replace(s, '.bmp'), cv2.imread(p)) if s != '.bmp': os.system("rm '%s'" % p) def imagelist2folder(path='path/images.txt'): # from utils.datasets import *; imagelist2folder() # Copies all the images in a text file (list of images) into a folder create_folder(path[:-4]) with open(path, 'r') as f: for line in f.read().splitlines(): os.system('cp "%s" %s' % (line, path[:-4])) print(line) def create_folder(path='./new'): # Create folder if os.path.exists(path): shutil.rmtree(path) # delete output folder os.makedirs(path) # make new output folder

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